PEDRO PERDIGUERO JIMENEZ
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UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE MONTES GENES IMPLICADOS EN LA RESPUESTA MOLECULAR AL ESTRÉS HÍDRICO EN Pinus pinaster Ait. TESIS DOCTORAL PEDRO PERDIGUERO JIMÉNEZ Licenciado en Ciencias Ambientales 2012 DEPARTAMENTO DE SILVOPASCICULTURA ESCUELA TÉCNICA SUPERIOR DE INGENIEROS DE MONTES GENES IMPLICADOS EN LA RESPUESTA MOLECULAR AL ESTRÉS HÍDRICO EN Pinus pinaster Ait. PEDRO PERDIGUERO JIMÉNEZ Licenciado en Ciencias Ambientales DIRECTORES: CARMEN COLLADA COLLADA ÁLVARO SOTO DE VIANA Doctora en Ciencias Químicas Doctor Ingeniero de Montes UNIVERSIDAD POLITÉCNICA DE MADRID Tribunal nombrado por el Magfco. Y Excmo. Sr. Rector de la Universidad Politécnica de Madrid, el día …. de ……………….. de 201… Presidente: .………………………………………………………………… Vocal: ……………………………………………………………………….. Vocal: ……………………………………………………………………….. Vocal: ……………………………………………………………………….. Secretario: ………………………………………………………………….. Suplente: ..………………………………………………………………….. Suplente: ..………………………………………………………………….. Realizado el acto de defensa y lectura de tesis el día …. de ……………….. de 201… en la E.T.S.I. Montes EL PRESIDENTE LOS VOCALES EL SECRETARIO MENCIÓN DE DOCTORADO INTERNACIONAL INTERNATIONAL DOCTORATE MENTION Esta Tesis ha sido informada positivamente para su defensa en exposición pública por los siguientes investigadores: This Ph.D. Thesis has been positively evaluated for its defense by the next external reviewers: Dr. Célia Maria Miguel Instituto de Tecnologia Química e Biológica (ITQB) Instituto de Biologia Experimental e Tecnológica (IBET) Universidade Nova de Lisboa, Portugal Dr. Pablo Fuentes Utrilla ARK- Genomics. The Roslin Institute University of Edinburg Agradecimientos Deseo expresar mi más sincero agradecimiento a todas las personas que de alguna manera han participado en el largo camino que ha supuesto, no solo la realización de este trabajo, sino cada paso que he dado hasta llegar al mismo. A mi familia, porque jamás cuestionaron ninguna de las decisiones que tomé en mi vida y dejaron que fuera yo el que eligiera mi propio camino. Creo que el ensayoerror es tan importante en la vida como en la ciencia, a ellos les debo la mayoría de mis aciertos. A mi Rous, mi pareja, mi mejor amiga, mi confidente; porque a su lado todo me parece más fácil y su compañía es un aliciente para rendir más en el trabajo y en la vida. Gracias por apoyarme desde el principio en mi decisión de “subirme al carro de la ciencia” aun sabiendo que tiene numerosas “averías” lo que se traduce en una constante economía de subsistencia… al final tendremos que ir a buscar “piezas” al extranjero. Gracias por escuchar mis despotriques de esa PCR que se resiste, esas plantas que no crecen, mi ataque frontal a la lengua inglesa… en resumen, gracias por estar ahí siempre. A mis directores Álvaro Soto y Carmen Collada, porque sin apenas conocernos me concedieron la oportunidad de entrar en un proyecto que me interesó desde que lo vi en la larga lista de FPIs. Gracias por vuestra entrega y apoyo durante todos estos años; esperemos seguir recogiendo frutos después de tantos esfuerzos. A toda la unidad de Anatomía al completo; a Luis Gil por su apoyo científico, técnico y logístico, gracias a este apoyo he podido acabar la tesis con relativa tranquilidad. A Unai, Pilar, Nikos, Mamen, Guille, Martin, Jesús, Rosa Ana, Javi, David Victoria, Chechu, Ricardo… por intercambiar inquietudes y dar ánimos en los momentos de agobio. Y por supuesto a mi queridísima red de la buhardilla; la niña del moño, Zaiduqui, el Rober, Paulita, la pies sucios, mi supuesto doble Víctor, la morocha, er Hose y mi añorado y querido calvito, “capag que” sin vosotros seguramente esta tesis sí hubiese sido posible pero seguro que no tan, tan, tan gratificante. Es difícil decir los por… gracias por todo. A Célia Miguel y todo su grupo del ITQB, por su calidad humana y por abrirme de par en par las puertas de su laboratorio. Gracias por darme la posibilidad de trabajar con nuevas y apasionantes técnicas que me han permitido “ponerle la guinda al pastel”. A Carmen Díaz-Sala y su grupo de la Universidad de Alcalá por sus consejos para la realización y análisis de las RT-PCRs, y una mención especial a Elena Carneros por compartir conmigo todo su conocimiento sobre embriogénesis somática de pinos, parte de los resultados obtenidos están directamente relacionados con sus buenos consejos. A mis compañeros de laboratorio en mis inicios en el laboratorio de Bioquímica; Ángela, Irene, Víctor, Raquel y Rosa, por facilitarme la adaptación, por los buenos consejos y por las largas conversaciones en comidas y cañeos. A toda mi gente de Villalba, por cada “miernes”, “juernes” y fines de semana intentando desconectar la neurona y por escuchar pacientemente mis desvaríos a pesar de no entender “por qué quiero poner pinos en el desierto”. A toda mi gente de Ávila, Elche y Salamanca, que aunque sea en la distancia siempre me han transmitido palabras de ánimo. A mis compañeros de “Rûa da Quintinha” Josep, Marcelo y Liliana, por esas cervecitas en “o quiosque” y por las cenas en la cocina rodeados de goteras. ÍNDICE RESUMEN……………………………………………………………………………………. i ABSTRACT…………………………………………………………………………………... ii 1. Introducción…………………………………………………………………………….... 1 1.1 Respuesta de las plantas al estrés hídrico...…………….………………………. 5 1.1.1 Proteínas reguladoras en respuesta a estrés hídrico…...……………… 6 1.1.2 Proteínas funcionales implicadas en la tolerancia al estrés hídrico…... 9 1.2 El estudio de la respuesta al estrés hídrico en coníferas………………………. 10 1.2.1 Las especies modelo utilizadas en este trabajo………………………… 13 2. Objetivos………………………………………………………………………………...... 19 3. Identificación de genes inducidos por estrés hídrico……………………………. 23 3.1 Material y métodos…………………………………………………………………. 26 3.1.1 Material vegetal y condiciones de cultivo………………………………… 26 3.1.2 Tratamiento de estrés hídrico……………………………………………... 27 3.1.3 Extracción de ARN y construcción de la genoteca sustractiva………... 27 3.1.4 Preselección de genes inducidos……………..………………………...... 28 3.1.5 Análisis de secuencias……………………………………………………... 28 3.2 Resultados y discusión…………………………………………………………….. 29 3.2.1 Construcción de la genoteca sustractiva………………………………… 29 3.2.2 Anotación y clasificación funcional de los genes obtenidos…………… 30 4. Análisis de expresión durante el estrés hídrico…………………………………… 37 4.1 Material y métodos…………………………………………………………………. 40 4.1.1 Material vegetal y condiciones de cultivo………………………………… 40 4.1.2 Tratamiento de estrés hídrico en sustrato……………………………...... 40 4.1.3 Tratamiento de estrés hídrico con PEG………………………………….. 40 4.1.4 Análisis de expresión con microarray…………………………………..... 40 4.1.5 PCR a tiempo real………………………………………………………….. 41 4.1.6 Análisis de datos……………………………………………………………. 42 4.1.6.1 Normalización e identificación de genes inducidos…………….. 42 4.1.6.2 Expresión diferencial entre órganos y especies……………....... 42 Análisis funcional…………………………………………………... 43 4.2 Resultados y discusión……………………………………………………………... 43 4.1.6.3 4.2.1 Análisis de la expresión de los genes durante el tratamiento de PEG.. 43 4.2.2 Tratamiento de sequía en sustrato sólido………………………………... 52 4.2.2.1 Genes inducidos por la suspensión de riego en P. pinaster y P. pinea……………………………………………………………… 4.2.2.2 52 Patrón de expresión en respuesta a sequía en P. pinaster y P. pinea……………………………………………………………… 59 5. Caracterización molecular de la familia de las deshidrinas en P. pinaster..…. 71 5.1 Material y métodos…………………………………………………………………. 75 5.1.1 Material vegetal y tratamiento de estrés hídrico………………………… 75 5.1.2 Análisis de secuencias……………………………………………………... 75 5.1.3 Extracción de ADN y ARN y amplificación de los genes completos….. 76 5.1.4 PCR a tiempo real………………………………………………………….. 76 5.1.5 Análisis estadístico…………………………………………………………. 76 5.2 Resultados y discusión…………………………………………………………….. 77 5.2.1 Búsqueda de deshidrinas in silico y amplificación de genes completos……………………………………………………………………. 77 5.2.2 Identificación de nuevos segmentos conservados……………………… 78 5.2.3 Análisis de la estructura de las deshidrinas de P. pinaster...………….. 79 5.2.4 Análisis de la expresión por RT-PCR…………………………………….. 82 6. Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster..……………………………………………………………………………….. 87 6.1 Material y métodos…………………………………………………………………. 92 6.1.1 Amplificación de los genes completos y de la su promotora…………... 92 6.1.2 Análisis de secuencias……………………………………………………... 92 6.1.3 Construcción del vector de sobreexpresión……………………………… 93 6.1.4 Material vegetal y condiciones de cultivo………………………………… 94 6.1.4.1 Arabidopsis thaliana……………………………………………….. 94 6.1.4.2 Células embrionárias de Pinus pinaster…………………………. 94 Transformación genética mediada por Agrobacterium tumefaciens….. 94 6.1.5 6.1.5.1 Transformación de Arabidopsis thaliana………………………… 94 6.1.5.2 Transformación de células embrionarias de Pinus pinaster…... 95 6.2 Resultados y discusión…………………………………………………………….. 95 6.2.1 Análisis de la secuencias………………………………………………….. 95 6.2.1.1 Ppter_dhn_ESK2 …………………………………………………... 95 6.2.1.2 Nodulina…………………………………………………………….. 97 6.2.1.3 Factor de transcripción tipo AP2………………………………..... 101 6.2.2 Transformación de Arabidopsis thaliana……………………………........ 105 6.2.3 Transformación de Pinus pinaster……………………………………....... 106 7. Conclusiones/Conclusions…………………………………………………………..... 113 8. Bibliografía……………………………………………………………………………….. 117 ANEXOS……………………………………………………………………………………… 137 I. Aranda I., Gil-Pelegrín E., Gascó A., Guevara M.A., Cano J., de Miguel M., Ramírez-Valiente J.A., Peguero-Pina J.J., Perdiguero P., Soto Á., Cervera M.T., Collada C. 2012. Drought response in forest trees: from the species to the gene. En Ricardo Aroca (Ed): Plant Responses to Drought Stress: From Morphological to Molecular Features (2012) 293-333. Springer. II. Fernández-Pozo N., Canales J., Guerrero-Fernández D., Villalobos D., DíazMoreno S., Bautista R., Flores-Monterroso A., Guevara M.A., Perdiguero P., Collada C., Cervera M.T., Soto Á., Ordás R., Cantón F., Ávila C., Cánovas F., Claros M.G., EuroPineDB: a high-coverage Web database for maritime pine transcriptome, BMC Genomics 12 (2011) 366. III. Perdiguero P., Collada C., Barbero M.C., García Casado G., Cervera M.T., Soto Á., Identification of water stress genes in Pinus pinaster Ait. by controlled progressive stress and suppression-subtractive hybridization, Plant Physiology and Biochemistry 50 (2012) 44-53. IV. Perdiguero P., Barbero M.C., García Casado G., Cervera M.T., Collada C., Soto Á., Molecular response to water stress in two contrasting Mediterranean pines (Pinus pinaster and Pinus pinea). Manuscrito V. Perdiguero P., Barbero M.C., Cervera M.T., Soto Á., Collada C., Novel conserved segments are associated with differential expression patterns for Pinaceae dehydrins, Planta 236 (2012) 1863-1874. ÍNDICE DE FIGURAS Figura 1.1 Predicciones climáticas en Europa para los años 2070-2100………….. 4 Figura 1.2 Respuestas de las plantas al estrés hídrico……………………………… 7 Figura 1.3 Área de distribución de Pinus Pinaster……………………………………. 14 Figura 1.4 Área de distribución de Pinus pinea………………………………………. 15 Figura 3.1 Banco clonal de P. pinaster del SERIDA...……………………………….. 26 Figura 3.2 Esquema del tratamiento de estrés hídrico aplicado……………………. 27 Figura 3.3 Amplificación de los clones de la genoteca sustractiva…………………. 29 Figura 3.4 Preselección de clones inducidos….……………………………………… 30 Figura 3.5 Clasificación funcional de los genes obtenidos en la genoteca sustractiva…………………………………………………………………………………. 31 Figura 4.1 Número de transcritos sobreexpresados de manera significativa en cada órgano estudiado durante el tratamiento de sequía inducida con PEG……… 44 Figura 4.2 Análisis de enriquecimiento de aquellos genes identificados específicamente durante el tratamiento de PEG……………………………………… 45 Figura 4.3 Patrones de expresión de los genes sobreexpresados a lo largo del tratamiento de PEG………………………………………………………………………. 46 Figura 4.4 RT-PCR de diez genes inducidos en el tratamiento de PEG…………... 50 Figura 4.5 Potencial hídrico medido al mediodía en acícula a lo largo del experimento de sequía con P. pinaster y P. pinea……………………………………. 52 Figura 4.6 Número de transcritos sobreexpresados de manera significativa en cada órgano estudiado durante el tratamiento de sequía en sustrato con P. pinea..…………………………………………………………………………………… 53 Figura 4.7 Número de transcritos sobreexpresados de manera significativa en cada órgano estudiado durante el tratamiento de sequía en sustrato con P. pinaster…….......................................................................................................... 54 Figura 4.8 Número de transcritos sobreexpresados de manera significativa en ambas especies para un mismo órgano ………………………………………………. 54 Figura 4.9 Análisis de enriquecimiento de los genes candidatos seleccionados…. 55 Figura 4.10 Clasificación funcional de los genes seleccionados como candidatos para estrés hídrico en pinos……………………………………………………………... 55 Figura 4.11 Número de transcritos sobreexpresados de manera significativa en cada tratamiento de sequía aplicado…………………………………………………… 59 Figura 4.12 Patrones de expresión de los genes sobreexpresados a lo largo del tratamiento de P. pinaster en sustrato sólido..………………………………………… 60 Figura 4.13 Patrones de expresión de los genes sobreexpresados a lo largo del tratamiento de P. pinea en sustrato sólido…..………………………………………… 63 Figura 4.14 RT-PCR de los 16 genes seleccionados para los tratamientos de estrés hídrico con P. pinaster y P. pinea.………………………………………………. 66 Figura 5.1 TCs correspondientes a posibles deshidrinas de Pinus sp. agrupadas de acuerdo con el número de segmentos conservados……………………………… 77 Figura 5.2 Alineamiento de la secuencia de aminoácidos deducida para las 8 deshidrinas identificadas en P. pinaster…..……………………………………………. 79 Figura 5.3 Productos de PCR correspondientes a los diferentes loci de Ppter_dhn_K2 y Ppter_dhn_SK3 amplificados mediante ADN genómico haploide de megagametofito……………………………………………………………………….. 80 Figura 5.4 Análisis de la expresión por RT-PCR de las ocho deshidrinas identificadas en P. pinaster…...…………………………………………………………. 83 Figura 6.1 Vector de sobreexpresión pK7WG2.0 utilizado en la transformación de Arabidopsis thaliana…………………………………………………………………... 93 Figura 6.2 Vector de sobreexpresión pMBb7Fm21GW-UBIL utilizado en la transformación de Pinus pinaster……………………………………………………….. 93 Figura 6.3 Estructura de la deshidrinas Ppter_dhn_ESK2…………………………... 96 Figura 6.4 Secuencia de nucleótidos correspondiente al gen completo y región promotora de la deshidrina Ppter_dhn_ESK2 y secuencia de aminoácidos deducida para la misma………………………………………………………………….. 96 Figura 6.5 Alineamiento de la secuencia de aminoácidos de la nodulina con genes homólogos en otras especies (transportadores de azucares tipo Sweets)… 98 Figura 6.6 Estructura del gen de la nodulina………………………………………….. 99 Figura 6.7 Secuencia de nucleótidos correspondiente al gen completo y región promotora de la nodulina y secuencia de aminoácidos deducida para el mismo…………………………………………………………………………………........ 100 Figura 6.8 Alineamiento de la secuencia de aminoácidos del factor de transcripción tipo AP2 frente a genes homólogos en otras especies, correspondientes a la familia ERF………………………………...……………………. 102 Figura 6.9 Secuencia de nucleótidos correspondiente al gen completo y región promotora del factor de transcripción tipo AP2 y secuencia de aminoácidos deducida para el mismo………………………………………………………………….. 104 Figura 6.10 Plantas de Arabidopsis transformadas con diferentes genes correspondientes a la primera generación t0…………………………………………... 105 Figura 6.11 Plantas de Arabidopsis correspondientes a la generación t2 y comprobación de la transformación por PCR …………………………………..…….. 106 Figura 6.12 Comprobación por PCR de la colonia de Agrobacterium empleada en la transformación……………………………………………………………............... 107 Figura 6.13 Selección de líneas transformantes con crecimiento visible en medio selectivo con PPT………………………………………………………………………… 108 Figura 6.14 Proliferación de líneas transformantes…………………………………... 108 Figura 6.15 Comprobación por PCR de las líneas transformantes………………… 109 Figura 6.16 Obtención de falsos positivos…………………………………………….. 110 Figura 6.17 Maduración y germinación de las líneas transformantes……………… 111 ÍNDICE DE TABLAS Tabla 5.1 Descripción de las deshidrinas de P. pinaster analizadas en la presente tesis……………………………………………………………………………... 81 Tabla 6.1 Selección de líneas transformantes con crecimiento en medio de selección con PPT………………………………………………………………………... 107 Tabla 6.2 Líneas transformantes que fueron transferidas a medio de proliferación……………………………………………………………………………….. 109 Tabla 6.3 Maduración de las líneas transformantes seleccionadas………………… 110 ÍNDICE MATERIAL SUPLEMENTARIO Tabla S1. Correspondencia entre los distintos identificadores para los 351 unigenes nucleares identificados en la genoteca sustractiva………………………... MS-3 Tabla S2. Clasificación funcional de los 351 unigenes nucleares identificado en la librería sustractiva en base a su homología con proteínas de Arabidopsis thaliana…………………………………………………………………………………….. MS-8 Tabla S3. Términos GO y EC asociados con los unigenes identificados………….. MS-14 Tabla S4. Valores de expresión del experimento de P. pinaster con PEG...……… MS-35 Tabla S5. Valores de expresión del experimento de P. pinea en sustrato sólido……………………………………………………………………………………….. MS-40 Tabla S6. Valores de expresión del experimento de P. pinaster en sustrato sólido……………………………………………………………………………………….. MS-45 Tabla S7. Selección de 113 genes candidatos sobreexpresados de manera significativa en ambas especies y para el mismo órgano/s………………………….. MS-51 Tabla S8: Combinaciones de cebadores empleados en las distintas actividades desarrolladas en la tesis doctoral……………………………………………………….. MS-53 Tabla S9. Motivos de unión para diferentes factores de transcripción identificados en las regiones promotoras de los genes de estudio…………………………………. MS-55 Tabla S10. Generación de líneas transformantes de Arabidopsis thaliana con el gen de la nodulina………………………………………………………………………... 偐MSMS-57 Tabla S11. Generación de líneas transformantes de Arabidopsis thaliana con el factor de transcripción AP2……………………………………………………………… MS-58 Tabla S12. Generación de líneas transformantes de Arabidopsis thaliana con Ppter_dhn_ESK2………………………………………………………………………..... MS-59 Figura S1. Alineamiento de las deshidrinas identificadas en P. pinaster frente a otras de gimnospermas y angiospermas disponibles en las bases de datos públicas…………………………………………………………………………………….. MS-60 RESUMEN La presente tesis doctoral se centra en el estudio de la respuesta molecular de las coníferas mediterráneas al estrés hídrico. Para ello se ha escogido como especie modelo Pinus pinaster Ait., la conífera más abundante en España, y que habita un amplio rango de situaciones ecológicas, especialmente en lo relativo a la disponibilidad de agua. En primer lugar, se ha aplicado un estrés hídrico controlado en cultivo hidropónico y se ha generando una genoteca sustractiva con objeto de identificar los genes inducidos por el estrés, analizando su expresión en raíces, tallos y acículas. A continuación, se ha analizado, la expresión de los genes anteriormente obtenidos así como de otros seleccionados de las bases de datos disponibles, durante una sequía prolongada en tierra, similar a las que las plantas deben afrontar en la naturaleza. Se ha utilizado en este caso, además de P. pinaster, P. pinea, otra conífera mediterránea adaptada a las sequías recurrentes. Este trabajo ha permitido identificar genes candidato expresionales, presumiblemente comunes en la respuesta molecular de las coníferas al déficit hídrico. Se han detectado diferencias notables en la expresión de determinados genes, que podrían ser los responsables de las diferencias exhibidas por ambas especies en el comportamiento frente a la sequía. Entre los genes identificados como inducidos por el estrés hídrico se encuentran varios miembros de la familia de las deshidrinas. Trabajos previos han utilizado deshidrinas como genes candidato; no obstante, la falta de especificidad de ciertos fragmentos y marcadores utilizados, debido a la complejidad estructural de esta familia, resta fiabilidad a algunos de los resultados publicados. Por este motivo, se ha estudiado en detalle esta familia en P. pinaster, se han identificado y caracterizado 8 miembros y se ha analizado su patrón de expresión frente a sequía. Este estudio ha permitido describir por primera vez unos segmentos conservados en la secuencia de aminoácidos de las deshidrinas de pináceas, cuya presencia y número de repeticiones parece estar relacionado con su especificidad. Por último, se han escogido tres genes implicados en distintas fases de la respuesta al estrés hídrico para su análisis exhaustivo: una deshidrina, una nodulina y un factor de transcripción tipo AP2. Se ha caracterizado su estructura exón/intrón y secuenciado su región promotora. Además, se han obtenido líneas transformadas que sobreexpresan estos genes tanto de forma heteróloga, en la especie modelo Arabidopsis thaliana, como en el propio P. pinaster. Este material facilitará la realización de futuros estudios sobre la función y el mecanismo de actuación de estos genes en la respuesta al estrés hídrico. i ABSTRACT This thesis focuses in the study of the molecular response to water stress in Mediterranean conifers. For this purpose, P. pinaster was selected as model species. It’s the most abundant conifer in Spain, living in a wide range of ecological conditions, especially regarding water availability. First, we have applied a controlled polyethylene glycol-induced water stress in hydroponic culture and obtained a suppression subtractive hybridization (SSH) library, with the aim of identifying genes induced by water stress, analysing their expression in roots, stems and needles. We have then analysed the expression patterns of the identified genes, together with other genes selected from public databases. This study was conducted throughout a prolonged drought stress in soil, similar to the ones plants have to face in nature. In this case not only P. pinaster was analysed but also P. pinea, another Mediterranean conifer well adapted to recurrent droughts. This work has enabled us to identify of reliable candidate genes, presumably shared with other conifers in the response to water stress. We observed remarkable differences in the expression of some genes, which could be involved in the differential behaviour that these species show in the water stress response. Within the genes induced by water stress, several members of the dehydrin gene family were identified. Due to the structural complexity of the family, certain ambiguities and inconsistencies have been detected in previous works that have used dehydrins as candidate genes. For this reason, we have analysed thoroughly this gene family in P. pinaster, and have identified and characterized eight different members, whose expression patterns during drought have also been assessed. This study has allowed us to identify for the first time novel conserved segments in the amino acids sequences of Pinaceae. The presence and number of repetitions of these segments could be associated with the functional specificity of these proteins. Finally, three genes involved in different steps of the water stress response were selected for an exhaustive analysis: a dehydrin, a nodulin and an AP2 transcription factor. For all of them, the exon/intron structure was established and their promoter region was sequenced. Also, transformed lines were obtained both in Arabidopsis thaliana and in P. pinaster for the constitutive overexpression of these genes. This material will facilitate the development of further studies to investigate the function of these genes during the water stress response. ii 1. INTRODUCCIÓN Introducción 1. INTRODUCCIÓN A lo largo de la evolución y diversificación de las plantas vasculares desde su aparición en el Silúrico (hace 420 millones de años) la adaptación a ambientes secos ha sido sin duda una de las condiciones que ha ejercido mayor presión selectiva. El paso del hábitat acuático al terrestre fue asociado al desarrollo de un complejo abanico de adaptaciones anatómicas y moleculares tanto para la adquisición y transporte del agua como para hacer frente a la pérdida de la misma hacia una atmósfera insaturada. Algunas de estas adaptaciones tienen un carácter constitutivo, mientras que otras se inducen como mecanismos de emergencia para hacer frente a los periodos de sequía. Estas adaptaciones presentan mayor relevancia en especies perennes, que tienen que hacer frente a la escasez de agua en numerosas ocasiones a lo largo de su ciclo de vida. En la actualidad la Tierra vuelve a enfrentarse a un importante cambio climático a nivel global. Durante el siglo pasado ya se observaron importantes modificaciones sobre la mayoría de parámetros meteorológicos, con el aumento de la temperatura media mundial del aire y del océano, la fusión incrementada de nieves y hielos y el aumento medio del nivel del mar. Este calentamiento se ve especialmente agravado por el aumento de los gases de efecto invernadero (CO2, CH4 y N2O) de origen antrópico así como por la deforestación de montes y bosques para convertirlos en tierras de cultivo y pastoreo. Según diferentes modelos climáticos este cambio global será especialmente intenso en regiones concretas del planeta, como el arco mediterráneo (IPCC, 2007). Según el informe Clivar España 2010 (Pérez et al., 2010), ya los registros instrumentales del siglo XX muestran un aumento progresivo de la temperatura que fue especialmente acusado en las tres últimas décadas cuando se registró una tasa media de calentamiento de ~0,5ºC/década (un 50% superior a la media continental en el Hemisferio Norte y casi el triple de la media global). Igualmente, la precipitación anual en las dos últimas décadas disminuyó de forma significativa en relación a las décadas de los 60 y 70, especialmente a finales de invierno. La mayoría de las predicciones muestran un incremento en las temperaturas medias diarias de aproximadamente 3º- 5.5ºC en invierno y verano respectivamente. Los cambios en la precipitación estacional mostrarían una estructura norte-sur en invierno, con aumentos 3 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. ligeros en la mitad norte y descensos en la parte sur, mientras que en verano pronostican un descenso significativo en toda la Península Ibérica (Figura 1.1). Figura 1.1 Predicciones climáticas en Europa para los años 2070-2100. Se muestra a la izquierda la predicción sobre el incremento de las temperaturas medias anuales. A la derecha los cambios en la precipitación media de los meses de verano en %. (Fuente; Plataforma europea de adaptación al clima) Las especies mediterráneas deben afrontar la dificultad añadida que supone la coincidencia del período más favorable para la actividad fisiológica en cuanto a luz y temperatura con el de máxima restricción hídrica. Por consiguiente, la creciente sequía que sufren los ecosistemas que integran el suroeste europeo limitará el crecimiento y probablemente la supervivencia de las actuales poblaciones naturales y reforestaciones pudiendo provocar una importante simplificación estructural de la vegetación acompañada de migraciones altitudinales de especies así como extinciones locales (Mestre y de Cara, 2009). Si el cambio del clima es tan rápido como se espera los seres vivos más longevos, entre ellos los árboles, estarán obligados a hacer frente a las condiciones adversas con sus estructuras genéticas actuales, sin tiempo disponible para que los procesos selectivos permitan la definición de otras estructuras adaptadas a las nuevas condiciones ambientales (Jump y Peñuelas, 2005). Ante estas predicciones y debido a la importancia de las especies forestales desde un punto de vista de conservación, uso sostenible y productividad, se están realizando grandes esfuerzos para conocer los mecanismos tanto moleculares como fisiológicos de adaptación a la sequía a todos los 4 Introducción niveles, desde las poblaciones en su conjunto hasta la función de un gen concreto (Anexo I). 1.1 Respuesta de las plantas al estrés hídrico El agua es el principal factor limitante para el crecimiento y la reproducción de las plantas, y el déficit hídrico puede suponer un grave riesgo para el equilibrio homeostático, alterando el metabolismo; aumenta la producción de radicales libres y especies reactivas del oxígeno (reactive oxygen species, ROS), tales como iones oxígeno, superóxido y peróxidos, que pueden dañar las membranas celulares, especialmente la maquinaria fotosintética, así como el ADN, llegando incluso a suponer un serio riesgo para la propia supervivencia de la planta. Las plantas a lo largo de la evolución han desarrollado tres diferentes estrategias que han permitido la supervivencia ante las sucesivas sequías (Valladares et al., 2004). Así una primera estrategia sería la elusiva o de escape, mediante la cual las plantas completarían su ciclo vital antes de la llegada del estrés hídrico, pasando el período desfavorable en forma de semilla. Otra estrategia sería la de tolerancia del estrés: un conjunto de modificaciones fisiológicas permiten a estos individuos soportar un notable grado de deshidratación de los tejidos, protegiendo de manera eficaz sus estructuras celulares o reconstruyéndolas una vez restablecidos los niveles hídricos apropiados. Es el caso de las conocidas como “plantas resurrección” (pertenecientes a géneros como Craterostigma, Eragrostis, Myrothamnus, Selaginella, Sporobolus, o Xerophyta), propias de territorios extremadamente áridos. Sin embargo, la estrategia más común, y a partir de la cual presumiblemente evolucionaron las otras dos (Levitt, 1980), es la denominada evitadora. En este caso las plantas previenen o minimizan la penetración del estrés en sus tejidos, maximizando la absorción de agua (por ejemplo mediante sistemas radicales profundos) y/o minimizando las pérdidas de agua (por ejemplo con un rápido cierre de estomas). Estas tres estrategias no son excluyentes las unas de las otras, y en la práctica las plantas suelen combinarlas (Ludlow, 1989); así, por ejemplo, los mecanismos de tolerancia complementan los mecanismos evitadores en las plantas resurrección, y se aprecian también mecanismos evitadores en plantas anuales que eludirán la peor fase del estrés hídrico. Las respuestas de las plantas al estrés son dinámicas y engloban una serie de complejos mecanismos interconectados para la regulación a diferentes niveles, incluyendo ajustes del metabolismo así como la expresión de genes implicados en la adaptación fisiológica y morfológica (Farooq et al., 2009) (Figura 1.2). La identificación 5 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. de los caracteres genéticos responsables de los mecanismos de resistencia a sequía en plantas resulta especialmente difícil debido a la complejidad de la variación de factores climáticos, la diversidad de ambientes hidrológicos, las relaciones establecidas entre el suelo y planta, la disponibilidad de nutrientes y las distintas interacciones que se generan en cada ambiente. Además, otros factores que inciden en la respuesta deben ser considerados, ya que la falta de agua puede estar asociada a otros tipos de estrés, como el térmico o el salino. Desde el punto de vista molecular, la respuesta al estrés hídrico incluye numerosos mecanismos de control a distintos niveles como la regulación posttranscripcional, post-traduccional o epigenética (Floris et al., 2009; Hirayama y Shinozaki, 2010; Vaahtera y Brosché, 2011). Los productos génicos asociados a la respuesta al estrés hídrico pueden clasificarse en dos grupos (Wang et al., 2003; Shinozaki y Yamaguchi-Shinozaki, 2007): el primero incluye proteínas implicadas en la regulación de la transducción de señales (proteínas reguladoras) y el segundo engloba proteínas con diferentes funciones biológicas implicadas en la tolerancia al estrés (proteínas funcionales). 1.1.1 Proteínas reguladoras en respuesta a estrés hídrico Las proteínas reguladoras controlan la expresión génica y la transducción de señal en respuesta al estrés; este grupo incluye factores de transcripción, quinasas y fosfatasas y enzimas implicadas en el metabolismo de los fosfolípidos. Los resultados de numerosos estudios desarrollados en especies modelo, todas ellas angiospermas, describen una serie de etapas sucesivas que formarían la ruta general de transducción en respuesta a un estrés abiótico. El primer paso corresponde a la percepción de la señal, el cual sigue siendo el menos claro dado que aún no se han identificado los sensores responsables de la misma. Posteriormente intervienen mensajeros secundarios como el Ca++, ROS e inositol fosfatasas. Estos mensajeros intermedios están involucrados en la regulación de los niveles de calcio en el citoplasma celular, cuya perturbación cambia la conformación de proteínas sensoras de calcio. Este cambio activa una cascada de fosforilación que finaliza con la activación directa de genes de respuesta o con la activación de factores de transcripción que a su vez regulan la expresión de genes de respuesta al estrés (Huang et al., 2012). 6 Introducción El estudio en especies modelo herbáceas ha permitido identificar al menos cuatro rutas independientes de transducción de señal inducida por la sequía, cada una de ellas mediada por un tipo de factor de transcripción (Figura 1.2); Figura 1.2 Respuestas de las plantas al estrés hídrico. La parte superior representa un esquema de la respuesta molecular de las plantas al estrés hídrico; se inicia con la percepción de la señal y posterior activación de las distintas rutas de señalización. Estas finalizan con la expresión de genes de respuesta al estrés implicados en las distintas respuestas metabólicas y fisiológicas. [Adaptado de Valladares et al. (2004) y García y Capiati (2011)]. 7 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Una primera ruta se caracteriza por la acción de los factores de transcripción tipo DREB (“Drought response element binding factor”) que regulan aquellos genes de respuesta poseen en sus promotores elementos DRE (“Dehydratation Responsive Element”). En la segunda ruta intervienen factores de transcripción con dominios tipo NAC (NAM, ATAF1, 2 y CUC2) así como los ZF-HD (Zinc finger homeodomain) que actúan de forma generalmente conjunta. Una tercera ruta estaría regulada por factores de transcripción tipo bZIP “ABRE binding protein” (AREB/ABF); se caracteriza por la presencia de genes que se sobreexpresan en condiciones de sequía y salinidad, los cuales contienen en sus promotores elementos de activación en cis ABRE (“ABA responsive Element”). La cuarta ruta estaría regulada por la acción combinada de varios factores de transcripción tipo MYC y MYB. Otros tipos de factor de transcripción también han sido relacionados con respuestas a estrés abiótico como los NF-Y (Nuclear factor Y) o los factores de transcripción tipo WRKY pero aún no se han determinado las posibles rutas que controlan (ver (Agarwal y Jha, 2009; Cramer et al., 2011; Fujita et al., 2011; Qin et al., 2011; Huang et al., 2012) y referencias citadas en ellos). Las dos primeras rutas descritas anteriormente son independientes de los niveles de ácido abcísico (ABA) en la célula, mientras que las otras dos parecen estar reguladas por dichos niveles. Las hormonas vegetales, especialmente el ABA, etileno y jasmonatos, varían sus niveles a lo largo de la sequía y su presencia puede amplificar la señal inicial e incluso activar nuevas rutas de respuesta (Vanková, 2010). La activación de las rutas podría ser secuencial, iniciándose la activación de las rutas independientes de ABA como respuesta rápida de emergencia; si el estrés hídrico se mantiene, la acumulación de ABA endógeno provoca la activación de las rutas dependientes de ABA, comenzando con el sistema bZIP/ABRE y por último la ruta que requiere la producción de las proteínas MYB y MYC en respuesta a ABA. En todo caso las rutas descritas no funcionan de forma independiente, sino que se han identificado numerosas interacciones no sólo entre rutas dependientes e independientes de ABA, sino entre rutas activadas por otros tipos de estrés tanto abiótico como biótico (Fujita et al., 2006). 8 Introducción 1.1.2 Proteínas funcionales implicadas en la tolerancia al estrés hídrico Encontramos en este grupo proteínas que actúan minimizando la deshidratación o protegiendo las estructuras celulares de los efectos de ésta, como por ejemplo enzimas implicadas en la biosíntesis y transporte de osmoprotectores, proteínas LEA, chaperonas y enzimas de detoxificación (Oliver et al., 2010; dos Reis et al., 2012). Osmoprotectores Las plantas se enfrentan a la deshidratación mediante la producción y acumulación de osmolitos lo que les permite realizar un ajuste osmótico y minimizar la pérdida de agua y con ello la deshidratación de los tejidos. Muchos de los genes activados durante la respuesta a estrés hídrico codifican enzimas que catalizan los diferentes pasos en la biosíntesis de solutos compatibles. Los osmoprotectores comúnmente acumulados durante la deshidratación corresponderían con aminoácidos como prolina y GABA, aminas como poliaminas y glicin-betaínas, y azúcares como fructanos, tri-rafinosa y almidón, mono y disacáridos (dos Reis et al., 2012; Krasensky y Jonak, 2012). Otro grupo de genes directamente relacionados con los osmoprotectores son aquéllos que codifican proteínas de membrana implicadas en el transporte de los compuestos generados. Proteínas LEA Las proteínas LEA (Late Embryogenesis Abundant proteins) forman un numeroso grupo identificado inicialmente durante los últimos pasos del desarrollo embrionario, aunque en los últimos años se ha descrito una importante acumulación de estos transcritos en tejidos vegetativos ante diferentes estreses ambientales. Generalmente presentan bajos pesos moleculares y diferentes motivos conservados, en muchas ocasiones con numerosas repeticiones a lo largo de la secuencia de aminoácidos. Aunque no se conoce claramente su modo de acción durante condiciones de sequía, está aceptado su papel como moléculas capaces de conferir cierto grado de tolerancia a la deshidratación en las células de la planta. Se ha demostrado su implicación en procesos como secuestro de iones y estabilización de membranas plasmáticas así como de otras proteínas (Shih et al., 2008; Olvera-Carrillo et al., 2011). 9 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Chaperonas Las chaperonas, dentro de las cuales encontramos las proteínas de choque térmico (heat shock proteins, HSP) colaboran naturalmente en el plegado de otras proteínas, uniéndose de forma reversible a las zonas desplegadas de los polipéptidos. Evitan de este modo interacciones que conllevarían la adquisición de estructuras tridimensionales incorrectas o incluso la formación de agregados insolubles. La síntesis y acumulación de proteínas HSP juega un papel central en la respuesta y tolerancia de las plantas ante diferentes estreses ambientales, ya que están implicadas en el restablecimiento de la conformación nativa de las proteínas y con ello de la homeostasis celular. (Wang et al., 2004; dos Reis et al., 2012). Enzimas de detoxificación La disminución en los niveles intracelulares de CO2 da lugar a una sobrereducción de los componentes de la cadena de transporte electrónico generando ROS. Estos compuestos, como se mencionó anteriormente, son una parte importante en la respuesta a estrés hídrico ya que pueden actuar como mensajeros secundarios en la transducción de la señal de respuesta. Sin embargo pueden llegar a niveles tóxicos para la planta y deben ser eliminados para evitar que afecten al correcto funcionamiento de algunas enzimas como por ejemplo las de la maquinaria fotosintética. Los niveles de transcrito de algunos antioxidantes como catalasas, superoxido dismutasas o del ciclo ascorbato-glutatión aumentan durante el déficit hídrico protegiendo la célula de la oxidación producida por ROS (Oliver et al., 2010). 1.2 El estudio de la respuesta al estrés hídrico en coníferas Desde el año 2000 se han logrado importantes avances en el conocimiento de los mecanismos por los cuales las plantas controlan distintos caracteres de interés, debido en gran parte a la mejora de las técnicas de secuenciación de DNA y la obtención a raíz de ello de genomas completos de diferentes plantas; la primera especie vegetal secuenciada fue Arabidopsis thaliana (Lin et al., 1999; Mayer et al., 1999; Salanoubat et al., 2000; Tabata et al., 2000; Theologis et al., 2000), seguida por Oryza sativa (Goff et al., 2002; Yu et al., 2002), y Vitis vinífera (Velasco et al., 2007), siendo Populus trichocarpa la primera especie forestal secuenciada por completo (Tuskan et al., 2006). 10 Introducción En los últimos años se ha producido una nueva revolución en las técnicas de secuenciación, tras la aparición de los secuenciadores de segunda generación y de las nuevas plataformas de secuenciación 454-FLX (Roche), SOLiD (Applied Byosistem), Illumina (Solexa) e Ion Torrent (Life Tecnologies). La mayor ventaja de estas técnicas es que a partir de una única muestra de ADNc se puede obtener una robusta colección de secuencias consenso para genes completos con una buena proporción de transcritos poco representados. Los primeros resultados de estas técnicas de secuenciación empiezan a incorporarse a la información obtenida durante la última década mediante métodos tradicionales de secuenciación. Recientemente se han desarrollado los denominados secuenciadores de tercera generación, basados en la secuenciación de una única molécula de ADN., con los que se prevé la obtención de secuencias altamente fiables con un menor coste, lo que se traducirá en la disponibilidad de numerosas secuencias para un número mayor de especies. Sin embargo, la complejidad del genoma de las coníferas, especialmente de las pináceas, con un elevado porcentaje de secuencias altamente repetitivas y con un gran tamaño (28.9 pg/C para P. pinaster y 28.6 pg/C para P. pinea (Zonneveld, 2012), que equivale a más de 100 veces el genoma de Arabidopsis y más de tres veces el humano) continúa dificultando la obtención del genoma completo de una especie modelo de esta división. Por este motivo, la mayor parte de los proyectos de genómica desarrollados hasta fechas recientes en coníferas se han encaminado a la caracterización del transcriptoma; la obtención de numerosas ESTs (Expressed Sequenced Tags) se presentó como una eficiente aproximación para la caracterización de parte del genoma, contribuyendo rápidamente al conocimiento de caracteres de interés en la especie estudiada, así como a la obtención de nuevas herramientas moleculares para la mejora genética (Kirst et al., 2003). El número de ESTs de gimnospermas ha ido aumentando de manera considerable hasta alcanzar 1.204.091 secuencias en las bases de datos del GenBank (3 de Diciembre de 2012) de las cuales casi el 89% pertenecen a la familia de las pináceas. Centrándonos en las especies estudiadas en esta tesis, el número de ESTs de P. pinaster alcanza 34.911 mientras que únicamente han sido depositadas 327 secuencias de P. pinea, lo que demuestra el escaso conocimiento genómico sobre esta especie. La gran cantidad de información redundante observada entre las secuencias hace necesario un procesado de las mismas; se han habilitado diferentes bases de 11 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. datos que emplean distintas técnicas de alineamiento para obtener una secuencia consenso que represente cada gen. Entre ellas PlantGDB, (Duvick et al., 2008) hace alineamientos individualizados por especie; en la última versión para Pinus pinaster (177a del 25 de mayo de 2010) obtiene 15.648 posibles unigenes a partir de 35.139 ESTs empleadas en el alineamiento. Llama la atención el número de transcritos únicos obtenidos en la última actualización de Pinus sylvestris (versión 187a, de 1 de febrero de 2012): 73.609 unigenes desde 76.256 ESTs, frente a Pinus taeda (157a, de 1 de febrero de 2007), para el que se obtuvo un número de unigenes ligeramente inferior, 72.829 pero a partir de 329.584 ESTs, lo que da una idea del alto grado de redundancia en las genotecas obtenidas para esta especie. Otra base de datos destacable es la PGI (Pine Gene Index database; http://compbio.dfci.harvard.edu/tgi/plant.html) la cual contiene un catálogo de posibles transcritos únicos para un género concreto a partir de una colección de ESTs de diferentes. En la última actualización para Pinus sp (Pine 9.0, de 26 de marzo de 2011) obtuvieron 77.195 genes únicos a partir de 452.256 ESTs, números muy similares a los obtenidos para P. taeda en PlantGDB. La base de datos EuroPineDB se centra en tres de las especies de mayor importancia en el ámbito mediterráneo (P. pinaster, P. sylvestris y P. pinea) (Anexo II). La especie con mayor peso en esta base de datos es P. pinaster que presenta secuencias procedentes de distintos experimentos, entre ellas las producidas en la presente tesis, e incluye los primeros resultados obtenidos mediante técnicas de secuenciación masiva. Gracias a ello el número de secuencias válidas asciende considerablemente hasta 877.523, de las que se obtienen 55.332 unigenes. Esta base de datos incluye también dos genotecas de P. sylvestris que contienen 2466 secuencias de las que se obtienen 679 unigenes. P. pinea únicamente aporta 266 unigenes, obtenidos a partir de 306 ESTs empleadas en el alineamiento. EuroPineDB ha evolucionado en los últimos años a una nueva base de datos, SustainPineDB, que incorpora los resultados del proyecto europeo Sustainpine dedicado exclusivamente al estudio de P. pinaster. La última versión de esta base de datos (SustainPineDB v.2.0, del 16 de abril de 2012) incorpora numerosos resultados procedentes de secuenciadores de segunda generación alcanzando 2.905.300 secuencias que dan como resultado 92.478 unigenes para esta especie. Esto supone casi 77.000 unigenes más que los obtenidos en PlantGDB a partir únicamente de las ESTs disponibles, lo que da una idea de la importancia que empiezan a tener las técnicas de secuenciación masiva en las investigaciones con coníferas. 12 Introducción El rápido avance de las técnicas de secuenciación convierte la caracterización funcional de los genes en el cuello de botella actual en la investigación molecular con gimnospermas. Entre el 35 y el 40% de las secuencias contenidas en las bases de datos no muestran homología con genes previamente descritos, y la anotación de aquellos que presentan homología generalmente se basa en la información descrita para angiospermas. Dado que angiospermas y gimnospermas divergieron hace 300 millones de años (Magallóan y Sanderson, 2005) muchos genes podrían estar implicados en procesos diferentes a los descritos en especies modelo. Es por tanto necesaria la utilización de gimnospermas como especies modelo para el estudio de la actividad de muchos genes identificados en esta división. 1.2.1 Las especies modelo utilizadas en este trabajo Con el objetivo de profundizar en el conocimiento de la respuesta molecular al estrés hídrico en coníferas hemos escogido como especies de estudio dos pinos mediterráneos, P. pinaster y P. pinea. El pino rodeno, marítimo o negral, Pinus pinaster Aiton, se distribuye por la cuenca occidental del Mediterráneo, costa atlántica de la Península Ibérica y suroeste de Francia. En España es la conífera más abundante, ocupando más de 1.500.000 ha debido, en gran medida, a su empleo en la reforestación de grandes superficies. De hecho, algo más del 50% de la superficie ocupada serían el resultado de repoblaciones llevadas a cabo entre los años 1940 y 1982 (Alía et al., 1995) mientras que el resto corresponderían a poblaciones naturales (Alía y Martín, 2009). (Figura 1.3). A pesar de presentar una distribución natural relativamente reducida, P. pinaster presenta una elevada diversidad genética. A finales de los años ochenta se identificaron hasta 18 razas geográficas en función de la composición en terpenos, que quedaban agrupadas en tres grandes grupos: el atlántico, el mediterráneo occidental y el magrebí (Baradat y Marpeau-Bezard, 1988). Estudios posteriores con marcadores moleculares establecieron seis grandes grupos dentro de la especie: zona francesa continental (Landas), Córcega, zona del noroeste y centro de España, zona sur de España, Marruecos y Túnez (Bucci et al., 2007; Eveno et al., 2008). 13 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 1.3 Área de distribución de Pinus Pinaster (fuente; Euforgen) Asociada a esta diversidad genética, P. pinaster muestra una gran valencia ecológica, desarrollándose en una gran diversidad de suelos y condiciones ambientales. Se encuentra en llanuras, regiones de costa y cadenas montañosas, alcanzando altitudes de aproximadamente 2000 m (Gil et al., 1990; Alía et al., 1990, 1991 y 1996) . Es capaz de sobrevivir bajo condiciones ambientales muy severas, mostrando una buena resistencia a la sequía y las heladas (Alía et al., 1996). Por el contrario, el pino piñonero, Pinus pinea L., ocupa una mayor área de distribución, a lo largo de toda la orla mediterránea (Figura 1.4). Sin embargo, presenta una casi nula variabilidad genética, siendo un caso excepcional entre las especies forestales. Así, no se han descrito razas geográficas, ecotipos o cultivares. Los estudios con marcadores de ADN de cloroplasto (Vendramin et al., 2008) o isoenzimas (Fallour et al., 1997) han confirmado esta virtual ausencia de diversidad. El hábitat de ambas especies solapa en gran medida, si bien P. pinea suele encontrarse en las zonas más cálidas, sometidas a sequías frecuentes, y sobre todo en los suelos con menor capacidad de retención de agua. De hecho, los pinares de P. pinea (puros o en mezcla con P. pinaster) están considerados como hábitat prioritario para su conservación por la Directiva Hábitat europea (1992/43/CEE), y la especie se utiliza ampliamente para la fijación de dunas, conservación de suelos y protección de cultivos agrícolas en zonas costeras. 14 Introducción Figura 1.4 Área de distribución de Pinus pinea (fuente; Euforgen) Numerosos trabajos han estudiado el comportamiento hídrico de Pinus pinaster en condiciones de sequía midiendo distintos parámetros ecosifiológicos, generalmente en ensayos con diferentes procedencias adaptadas a ambientes contrastados y en algunos casos en estudios intrapoblacionales (p. ej.(Hopkins, 1971; Alía et al., 1991; Correia et al., 2008). Un proceso común observado durante los periodos de sequía es la reducción de la tasa de crecimiento muchas veces acompañada de cambios en el balance entre el crecimiento radicular y aéreo. En un estudio con plántulas de cuatro meses de edad sometidas a una sequía prolongada se observó una clara correlación entre el diámetro, la altura y la distribución de biomasa de las plantas (Fernández et al., 2006). Se han descrito diferencias entre procedencias en cuanto al reparto de biomasa raíz-tallo, encontrando la mayor inversión en biomasa de raíz para procedencias del norte de África mientras que las procedencias españolas y francesas mostraron mayor inversión en biomasa aérea (Aranda et al., 2010). Este amplio grado de variación interpoblacional también se ha visto reflejado en otros caracteres como el contenido hídrico de la planta y el intercambio gaseoso (Fernandez et al., 1999, 2000), el ajuste osmótico (Nguyen-Queyrens y Bouchet-Lannat, 2003; Lopez et al., 2009), la discriminación isotópica (Guehl et al., 1995; Correia et al., 2008; Aranda et al., 2010; Corcuera et al., 2010) o el embolismo del xilema (Corcuera et al., 2011; Lamy et al., 2011). En algunos casos se han observado diferencias significativas en algunos de estos caracteres a escala intrapoblacional o incluso intrafamiliar (Sánchez-Gómez et al., 2010; de Miguel et al., 2012). 15 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. A nivel molecular, P. pinaster ha sido empleada como especie modelo en diversos estudios centrados en la respuesta al estrés hídrico (Costa et al., 1998; Dubos y Plomion, 2001, 2003; Dubos et al., 2003). Varios trabajos han empleado genes presumiblemente implicados en esta respuesta en estudios de diversidad relacionados con los niveles de sequía. Por ejemplo, se ha analizado la variabilidad en la secuencia de ADN de varios genes implicados en la respuesta a sequía en diferentes poblaciones de P. pinaster a lo largo de un gradiente tanto latitudinal como de precipitaciones (Eveno et al., 2008; Grivet et al., 2011). Igualmente se han empleado otras metodologías como la cuantificación del nivel de determinadas hormonas u osmolitos como la prolina, en la búsqueda de comportamientos diferenciales entre procedencias de ambientes contrastados (Pedranzani et al., 2007; Corcuera et al., 2012). Esta variación adaptativa hace de P. pinaster un material de gran interés para el estudio de la respuesta al estrés hídrico. Por el contrario, no conocemos ningún trabajo que aborde la respuesta de P. pinea al estrés hídrico a nivel molecular e incluso la información acerca de su comportamiento hídrico es bastante escasa. Estudios de germinación han concluido que inducción por polietilenglicol de un estrés hídrico leve reduce al 70% el porcentaje de germinación, inhibiéndose completamente a altas dosis de PEG (Muscolo et al., 2007; Sidari et al., 2008), sugiriendo que una sequía prolongada podría provocar serios problemas en la regeneración natural de la especie. Un estudio detallado del reparto de biomasa durante un tratamiento de sequía con diferentes procedencias de varios pinos mediterráneos, entre ellos P. pinea y P. pinaster, determinó una menor variabilidad debida al tratamiento en el piñonero, que en ningún caso presentó diferencias significativas entre procedencias a lo largo de la sequía, frente a P. pinaster que fue la especie que presentó mayor variabilidad durante el estrés hídrico con marcadas diferencias entre las procedencias estudiadas. A pesar de que P. pinea no vio afectado su crecimiento durante el tratamiento de estrés, todas las procedencias empleadas presentaron los valores más altos de plasticidad en el reparto de biomasa frente al resto de especies (Chambel et al., 2007). Recientemente, un estudio centrado en la variación entre clones de P. pinea en caracteres funcionales relacionados con la sequía también ha señalado la elevada plasticidad fenotípica de la especie en cuanto a eficacia intrínseca en el uso del agua, conductancia estomática y tasa neta de fotosíntesis (Sánchez-Gómez et al., 2011). 16 Introducción Lograr una adecuada comprensión de los mecanismos moleculares a través de los cuales las plantas perciben y responden al estrés es vital para lograr un mayor éxito en los programas de mejora genética de la resistencia frente a estas situaciones y para la gestión apropiada de los recursos genéticos. Los pinos mediterráneos constituyen un modelo especialmente relevante para el estudio de la respuesta al estrés hídrico, tanto desde el punto de vista científico, como de cara a la gestión ante unas condiciones ambientales de creciente xericidad. 17 2. OBJETIVOS Objetivos 2. OBJETIVOS El objetivo principal de este trabajo consiste en analizar la respuesta molecular a estrés hídrico en pinos mediterráneos. Este objetivo general se divide en los siguientes objetivos específicos; 1.- Identificación de genes inducidos en respuesta al estrés hídrico en plántulas de P. pinaster Ait. 2.- Estudio del patrón de expresión durante el estrés hídrico en P. pinaster y P. pinea de los genes previamente identificados 3.- Caracterización molecular de diferentes genes implicados en la respuesta a estrés hídrico en P. pinaster. 21 3. IDENTIFICACIÓN DE GENES INDUCIDOS POR ESTRÉS HÍDRICO Identificación de genes inducidos por estrés hídrico A lo largo de la última década diferentes trabajos se han centrado en la identificación de genes implicados en la respuesta a estrés hídrico en pinos, especialmente en P. taeda, P. halepensis y P. pinaster. Los primeros estudios con P. taeda (Chang et al., 1996; Lorenz et al., 2006) o con P. halepensis (Sathyan et al., 2005) se basaron en la secuenciación de fragmentos incluidos en genotecas de expresión (ESTs), a menudo obtenidas a partir de raíces de plantas estresadas. Los primeros trabajos de identificación de genes de respuesta a sequía en P. pinaster emplearon técnicas de proteómica: mediante electroforesis bidimensional con geles de poliacrilamida Costa et al. (1996) identificaron 38 spots diferenciales en plantas de dos años sometidas a sequía, obteniendo secuencias parciales para 11 de ellas. Posteriormente, Dubos et al. (2003) y Dubos y Plomion (2003) aplicaron la técnica cDNA-AFLP para identificar genes cuya expresión estuviese alterada en acículas y raíces de plántulas sometidas a un estrés hídrico suave. Este análisis reveló la existencia de 48 fragmentos con expresión diferencial al compararlos con plántulas control. Todos estos trabajos se limitaban a un único punto de muestreo y generalmente con un estrés leve, lo que podría simplificar en exceso los resultados teniendo en cuenta la cantidad de rutas de respuesta temprana y tardía que intervienen a lo largo de la respuesta a estrés hídrico. En la presente parte del trabajo se empleó polietilenglicol (PEG) como agente osmótico para inducir un estrés hídrico controlado en plantas de P. pinaster en cultivo hidropónico. Se recogió material desde las primeras fases del estrés, con la intención de identificar genes de respuesta rápida y con bajos niveles de inducción que pudieran no haber sido detectados en los trabajos previos. Para la identificación de genes se construyó una genoteca sustractiva, una metodología a priori más exhaustiva y fiable que las empleadas anteriormente, y que estaba dando buenos resultados en otras especies. 25 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 3.1 Material y métodos 3.1.1 Material vegetal y condiciones de cultivo Para el desarrollo del experimento se empleó material del banco clonal del SERIDA (Servicio Regional de Investigación y Desarrollo Agroalimentario del Principado de Asturias, Grado) (Figura 3.1) de la procedencia Oria (Almería). Dicha procedencia muestra un buen comportamiento en condiciones de déficit hídrico (Sánchez-Salguero et al., 2010), coherente con la adaptación a las bajas e irregulares precipitaciones, con frecuentes sequías, que se producen en esta zona del Sureste peninsular. Considerando la variabilidad intrapoblacional observada en el comportamiento frente a la sequía y con objeto de optimizar la detección de genes inducidos por el estrés hídrico, se emplearon diez genotipos diferentes para la construcción de la genoteca. Las plantas crecieron en invernadero en condiciones controladas (24ºC día /22ºC noche; 12/12 horas de fotoperiodo; humedad relativa del 60% de día y 80% de noche) en cultivo hidropónico con una solución nutritiva (30 litros para 45 plantas; NPK 90:41:72 pH~6.5) debidamente aireada y renovada dos veces por semana. Figura 3.1 Banco clonal de P. pinaster del SERIDA (Grado, Asturias) 26 Identificación de genes inducidos por estrés hídrico 3.1.2 Tratamiento de estrés hídrico El estrés hídrico fue aplicado mediante la adición de polietilenglicol (PEG, PM 8000) en el medio de cultivo. Durante el primer día del tratamiento se cambió la solución cada cuatro horas, disminuyendo cada vez en 0.4 MPa el potencial hídrico de la solución. Tras el cuarto cambio de solución se alcanzó un potencial hídrico de 1.6MPa, que se mantuvo hasta el final del tratamiento. Se cosechó una planta de cada genotipo una hora después de cada cambio de solución (muestras S1-S4) así como a las 24 horas (S5), 48 horas (S6), 10 días (S7) y 21 días (S8) desde el inicio del tratamiento (Figura 3.2) . Como control se recogieron plantas de cada genotipo mantenidas sin PEG. Las raíces, tallos y acículas de cada planta fueron procesadas de manera independiente, congeladas inmediatamente en nitrógeno líquido y Potencial hídrico (MPa) guardadas a -80ºC. Figura 3.2 Esquema del tratamiento de estrés hídrico aplicado. Se muestran los puntos de muestreo así como los cambios de solución realizados. 3.1.3 Extracción de ARN y construcción de la genoteca sustractiva La extracción de ARN total se realizó de manera independiente para cada órgano (raíz, tallo y acícula) y cada planta muestreada, siguiendo el protocolo descrito por Chang et al. (1993). Se mezclaron cantidades equivalentes de ARN de cada órgano y punto de muestreo y se obtuvo ADNc mediante el “Super SMARTTM PCR ADNc Synthesis Kit” (Clontech, CA, USA). Se construyeron dos colecciones de ADNc utilizando el “PCR-SelectTM Subtraction kit” (Clontech, CA, USA): una enriquecida en 27 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. genes que se inducen y otra enriquecida en genes que se reprimen durante el estrés. La muestra enriquecida en genes inducidos por el estrés fue amplificada mediante PCR, ligadas en el vector pGEM®-T easy (Promega, WI, USA) y transferidas a Escherichia coli DH5α. Después de crecer estas bacterias en medio selectivo se picaron de forma individualizada y se comprobó la presencia y tamaño de los insertos por PCR. 3.1.4 Preselección de genes inducidos Una vez construida la genoteca sustractiva se utilizó el “PCR-SelectTM differential screening kit” (Clontech, CA, USA) para realizar una selección inicial de los clones positivos. Los productos de PCR correspondientes a cada clon fueron fijados sobre membranas de nylon Hybond-N+ (GE Healthcare BioSciences, WI, USA). Se generaron cuatro réplicas y cada una fue hibridada con una de las cuatro muestras obtenidas durante la construcción de la genoteca (ADNc de plantas control, ADNc de plantas estresadas, muestra enriquecida en ADNc de plantas control y muestra enriquecida en ADNc de plantas estresadas). Se utilizó el “DIG DNA labelling and detection Kit” (Roche, Basilea, Suiza) para el marcaje de las sondas con digoxigeninadUTP y la posterior detección. Las intensidades de la señales de hibridación fueron cuantificadas mediante el analizador de imágenes “ChemiDocTMXRS System” (BioRad, CA, USA). Aquellos clones que presentaron una ratio de expresión mayor a 1.5 entre la muestra enriquecida en genes inducidos y el control fueron seleccionados como expresados de manera diferencial y posteriormente secuenciados empleando el analizador de DNA 3730 XL (Applied Biosystems; Life Technologies, CA, USA) de Macrogen (Seúl, Corea). 3.1.5 Análisis de secuencias Las secuencias obtenidas fueron procesadas eliminando posibles contaminaciones, artefactos y secuencias correspondientes al vector. Posteriormente se realizó un alineamiento para obtener los genes únicos, que fueron anotados según la información contenida en las bases de datos del NCBI mediante los programas BLASTN y BLASTX considerando significativos los e-valores inferiores a 10-05 para más de 100 nucleótidos. Igualmente se realizó una anotación según las bases de datos de Pinus y Picea del Gene Index Project (http://compbio.dfci.harvard.edu/cgibin/tgi/Blast/index.cgi). La clasificación funcional de los genes se realizó de acuerdo a las categorías establecidas para proteínas de Arabidopsis thaliana en la base de datos 28 Identificación de genes inducidos por estrés hídrico FunCat (http://mips.helmholtz-muenchen.de/proj/funcatDB/) (Ruepp et al., 2004). Asimismo, se empleó el programa Blast2GO (Conesa et al., 2005) para identificar los términos GO (Gene Ontology) asociados a los genes identificados. 3.2 Resultados y discusión 3.2.1 Construcción de la genoteca sustractiva Aunque los resultados previos de Watkinson et al. (2003) y Lorenz et al.(2006) muestran que la respuesta a sequía implica tanto la inducción como la inhibición de genes, nuestro estudio se centró en la identificación de genes candidato sobreexpresados en respuesta al estrés. Se cosecharon diferentes muestras a lo largo del tratamiento en el que se aumentó de forma progresiva el grado de estrés hasta alcanzar valores de severidad moderada que fueron mantenidos en el tiempo. Este diseño experimental, que no había sido empleado en ningún trabajo anterior, estaba dirigido a la identificación de genes implicados en diferentes patrones de respuesta que podrían están iniciadas por diferentes niveles de estrés (Watkinson et al., 2003). Empleando una mezcla de muestras cosechadas a lo largo del tratamiento se obtuvo la genoteca de ADNc enriquecida en genes inducidos por el estrés. Se picaron 4.940 colonias de E.coli que fueron transformadas con la muestra enriquecida en genes de estrés y se amplificaron los insertos por PCR para comprobar el tamaño y calidad de los mismos (Figura 3.3). Figura 3.3 Amplificación de los clones de la genoteca sustractiva. Se amplificaron por PCR los insertos incorporados en el vector pGEM-Teasy (Promega). En la línea central se muestra el marcador 1 Kb DNA ladder (Invitrogen) 29 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Posteriormente, se preseleccionaron 1718 clones presumiblemente inducidos por estrés hídrico (Figura 3.4). Exploración con ADNc sin enriquecimiento Exploración con ADNc enriquecido Figura 3.4 Preselección de clones inducidos. Se seleccionaron aquellos clones que presentaban en la muestra enriquecida una señal al menos 1.5 veces superior a la correspondiente al ADNc de las plantas control. 3.2.2 Anotación y clasificación funcional de los genes obtenidos Se secuenciaron los 1718 clones preseleccionados, de los que se obtuvieron 1099 secuencias válidas tras eliminar secuencias contaminantes o de baja calidad. Se obtuvieron 386 unigenes tras realizar el ensamblaje de las secuencias. Utilizando su secuencia de nucleótidos y la secuencia de aminoácidos obtenida en su traducción se llevó a cabo una búsqueda de homología en las bases de datos no redundantes de ácidos nucleicos (ADN) y de proteínas del NCBI y en las bases de datos de pino y pícea del Gene Index. De los 386 unigenes, 351 fueron considerados nucleares y 35 mostraron homología con secuencias procedentes de ADN de plastidios celulares. De los 351 genes nucleares, 124 aparecen en otras colecciones de EST obtenidas de pinos sometidos a estrés hídrico, mientras que 40 genes no mostraron homología con ninguna secuencia procedente de pináceas. Respecto a la homología 30 Identificación de genes inducidos por estrés hídrico con proteínas, las secuencias obtenidas para 170 genes no presentaron homología con ninguna secuencia de aminoácidos (48% de los unigenes). Para incrementar la anotación se realizó una asignación del posible gen completo (TC; Tentative consensus sequence) presente en las bases de datos de pino y pícea del Gene Index, identificador que se empleó durante las siguientes etapas de la tesis (Material suplementario Tabla S1). Esta asociación redujo el número de genes sin homología hasta el 17%, lo que demuestra la importancia de unificar la información depositada en diferentes bases de datos. La secuencia de cada clon de la genoteca fue incluida en la base de datos dedicada a pinos mediterráneos EuropineDB (Anexo II, Material suplementario Tabla S1). Las secuencias para las que se había obtenido una homología significativa fueron clasificadas según las categorías funcionales de la base de datos FunCatDB (Figura 3.5, Material suplementario Tabla S2) y se asociaron los términos GO (Gene ontology) utilizando el programa Blast2GO (Material suplementario Tabla S3). Figura 3.5 Clasificación funcional de los genes obtenidos en la genoteca sustractiva. En total, 351 unigenes fueron agrupados de acuerdo con las categorías funcionales establecidas en la base datos FunCatDB para Arabidopsis thaliana. Se muestra el valor porcentual para cada grupo. 31 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Genes implicados en metabolismo El grupo más representado corresponde a genes involucrados en metabolismo. Dentro de este grupo, el 42% corresponde a genes implicados en el metabolismo de hidratos de carbono (hasta un 42%). La acumulación de azúcares en las células de la planta durante periodos de sequía interviene en el ajuste osmótico, protegiendo a las estructuras celulares del estrés, tanto mecánico como metabólico, producido durante la deshidratación. Este mecanismo está relacionado con la adquisición de tolerancia a estrés hídrico (Oliver et al., 2010). Muchos de los genes incorporados en este subgrupo tienen homología con proteínas descritas previamente en respuesta a estrés hídrico como α-galactosidasas (Hara et al., 2008), malato sintasa (Micheletto et al., 2007), glicosiltransferasas (Wang y Hou, 2009) o quitinasas (Rabello et al., 2008). Otro gen incluido en este grupo presenta homología con la aldehído deshidrogenasa. Este enzima podría estar involucrado en la detoxificación de los aldehídos producidos durante el metabolismo del etanol, y su sobreexpresión resulta coherente con la acumulación de etanol en plántulas de coníferas sometidas a sequía descrita por Manter y Kelsey (2008). La expresión heteróloga en plantas de tabaco de la aldehídodeshidrogenasa de maíz ZmALDH22A1 confiere a las plantas transformadas una elevada tolerancia al estrés (Huang et al., 2008). El segundo subgrupo, con un 18%, corresponde al metabolismo de lípidos, ácidos grasos e isoprenoides. Estos genes podrían estar implicados en la síntesis de hormonas vegetales que se acumulan en los tejidos mediando o intensificando las rutas de señalización activadas durante el estrés hídrico. Los genes clasificados dentro del metabolismo de aminoácidos alcanzan el 16%. Dentro del cuarto subgrupo (9%), correspondiente al metabolismo secundario, aparecen algunos genes que presentan homología con enzimas que también han sido relacionadas en estudios previos con la respuesta a sequía. Así, la enzima ácido 12-oxofitodienoico reductasa (12-oxo-PDA reductasa, OPR) se induce durante estrés osmótico en maíz y su expresión heteróloga en plantas de Arabidopsis confiere resistencia al estrés osmótico y salino producido durante la germinación de la semilla (Gu et al., 2008). También dentro de este grupo se encuentra un gen homólogo a la ACC oxidasa, implicada en la respuesta mediada por etileno y jasmonatos. 32 Identificación de genes inducidos por estrés hídrico Genes implicados en la transducción de la señal Aunque este grupo representa tan sólo el 2% del total, contiene genes que presentan homología con proteínas de interés dentro de la transducción de la señal de estrés hídrico. Por ejemplo, aparecen varias quinasas que podrían intervenir en la activación de la cascada de fosforilación. Una de ellas tiene homología con una CBLinteracting quinasa relacionada con la regulación de la transducción de la señal mediada por calcio durante el estrés hídrico (Beck et al., 2007). Batistic y Kudla (2009) sugirieron que estas proteínas podrían constituir un grupo único de sensores de calcio característico de vegetales, interactuando de forma específica con un grupo de quinasas tipo CIPKs. Otro gen incorporado en este grupo, una calmodulina, podría estar igualmente implicada en la regulación de la transducción de la señal por calcio. La sobreexpresión de una calmodulina de arroz en Arabidopsis produjo un incremento de la tolerancia ante el estrés hídrico y salino, aumentando también la sensibilidad al ABA de las plantas transformadas (Xu et al., 2012). Factores de transcripción Al menos tres genes presentan dominios característicos de los factores de transcripción tipo AP2. Dos de ellos muestran homología con factores de transcripción DREB, implicados en la respuesta a deshidratación independiente de ABA (Agarwal et al., 2006) y cuya sobreexpresión puede incrementar la tolerancia al estrés hídrico (Chen et al., 2007). El tercer factor de transcripción con dominio AP2 presenta homología con la subfamilia ERF (Ethylene responsive factor). Aún se desconocen las rutas de señalización de estos factores de transcripción, aunque se ha propuesto que podrían participar en una ruta independiente y en otra dependiente de la presencia de etileno (Mizoi et al., 2012). Otro gen incluido en este grupo presenta homología con un factor de transcripción tipo NAC, una de las familias más numerosas de factores de transcripción específicos de plantas, que también están involucrados en tolerancia a la sequía (Tran et al., 2004) y podrían regular la segunda ruta independiente de ABA. También se incluyeron en este grupo varios factores de transcripción tipo bZIP que podrían estar involucrados en una de las rutas de señalización mediadas por ABA. En cambio, no se encontró en la genoteca sustractiva ningún factor de transcripción homólogo a un MYB o MYC, característicos de la otra ruta de señalización mediada por ABA descrita en herbáceas. Otros genes agrupados como factores de transcripción, como por ejemplo varios “Dead- box RNA helicasa” no se corresponden 33 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. con ninguna de las rutas previamente descritas pero sí han sido relacionados con la respuesta a varios estreses (Rocak y Linder, 2004). Genes implicados en transporte Los mecanismos de transporte, actuando de manera coordinada con genes relacionados con el metabolismo, pueden jugar un papel importante en la tolerancia a la sequía, ayudando al ajuste osmótico a través de la movilización y acumulación de hormonas y solutos compatibles, así como participando en mecanismos de detoxificación celular que se suceden a lo largo del estrés. Se encuentra dentro de este grupo un gen homólogo a un transportador de inositol que podría estar implicado en el transporte de azúcares a través de las membranas. También dentro de este grupo encontramos diferentes genes homólogos con transportadores tipo ABC que están involucrados en la respuesta a diferentes estreses tanto bióticos como abióticos (Wanke y Üner Kolukisaoglu, 2010). Otros dos genes incluidos dentro de este grupo presentan homología con proteínas de membrana del peroxisoma y podrían estar implicados en el establecimiento del mecanismo de limpieza de las ROS acumuladas en el citoplasma celular. Genes de defensa y rescate celular Dentro de este grupo se encuentran varios genes que presentan homología con proteínas LEA, chaperonas o enzimas de detoxificación. Las proteínas LEA forman un numeroso grupo con conocidas implicaciones en la tolerancia al estrés hídrico ((Battaglia et al., 2008) y referencias citadas). Una de las LEA identificadas presenta homología con AtLEA14, gen incluido dentro de un grupo de 10 genes de Arabidopsis thaliana que se presentan sobreexpresión por alta intensidad de luz, sequía, frio y salinidad, lo que parece indicar una importante función general durante la respuesta a estreses ambientales (Kimura et al., 2003). Al menos tres proteínas de choque térmico de diferentes pesos moleculares han sido identificadas en este grupo. Dos de ellos presentan homología con HSP90; este tipo de chaperonas podrían estar implicadas en la estabilización de quinasas y hormonas implicadas en las rutas de señalización de diferentes estreses abióticos (Wang et al., 2004). Dentro de los genes que presentan homología con enzimas de 34 Identificación de genes inducidos por estrés hídrico detoxificación encontramos como ejemplo una fenilalanina amonio-liasa, una catalasa y una glutatión S-transferasa; podrían estar implicadas en la regulación del estrés oxidativo producido por el aumento de ROS durante episodios de sequía. Las glutatión S-transferasas forman un grupo de genes que se inducen en respuesta a la sequía en Arabidopsis (Kumar et al., 2010). Dentro de la clasificación funcional, los genes sin homología, con homología pero sin funcionalidad asignada o de los que únicamente se conoce su probable localización celular alcanzan el 39%, lo que pone de manifiesto el importante desconocimiento aún existente sobre la genómica de gimnospermas. Los resultados presentados en este capítulo han dado origen a los siguientes artículos: EuroPineDB: a high-coverage Web database for maritime pine transcriptome, BMC Genomics 12 (2011) 366 (Anexo II) Identification of water stress genes in Pinus pinaster Ait. by controlled progressive stress and suppression-subtractive hybridization, Plant Physiology and Biochemistry 50 (2012) 44-53 (Anexo III) 35 4. ANÁLISIS DE EXPRESIÓN DURANTE EL ESTRÉS HÍDRICO Análisis de expresión durante el estrés hídrico Desde que fue descrita por Diatchenko et al. (1996) la construcción de genotecas sustractivas se mostró como una poderosa herramienta para la obtención de genotecas enriquecidas en genes expresados de manera diferencial. Sin embargo, con esta técnica se pueden obtener falsos positivos así como artefactos ocasionados en las secuencias durante la construcción de las genotecas. Por tanto, una vez identificados los genes presumiblemente implicados en la respuesta al estrés hídrico mediante el análisis de la genoteca sustractiva se procedió a estudiar su expresión en diferentes situaciones de déficit de agua, para lo que se diseñó una micromatriz o microarray que incluía dichos genes, así como otros genes candidato descritos en la literatura. La fiabilidad de los perfiles de expresión obtenidos con esta técnica se comprobó mediante PCR cuantitativa. En primera instancia se comprobó su perfil de expresión durante el estrés hídrico inducido por PEG en P. pinaster y utilizado para hacer la genoteca. El PEG, además de actuar como agente osmótico, presenta cierto grado de toxicidad para las plantas, pudiendo inducir una respuesta específica frente a este compuesto, por lo que a continuación se procedió a estudiar los patrones de expresión a lo largo de un tratamiento prolongado de falta de riego en sustrato sólido. Este estudio permite no sólo analizar la expresión durante un estrés más parecido a episodios de sequía que deben afrontar las plantas en el medio natural, sino también discriminar aquellos genes identificados en el capítulo anterior más relacionados con la respuesta específica al PEG que con el estrés hídrico. Para realizar un análisis más general de la respuesta al estrés hídrico en pinos mediterráneos, el tratamiento de sequía en sustrato se aplicó de forma paralela en P. pinaster y P. pinea. Ambas especies, a pesar de ser muy cercanas filogenéticamente, muestran ciertas diferencias en su comportamiento ante situaciones de estrés hídrico. Por tanto, este análisis permitiría identificar los patrones de respuesta comunes en los pinos mediterráneos, así como las bases moleculares de las diferencias de comportamiento observadas. 39 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 4.1 Material y métodos 4.1.1 Material vegetal y condiciones de cultivo Para llevar a cabo el experimento de estrés hídrico en sustrato se utilizaron plantas de P. pinaster de la procedencia Oria (Almería) y plantas de P. pinea de la procedencia Meseta Central (Tordesillas, Valladolid). Como sustrato se empleó turba: perlita:vermiculita (3:1:1 en peso). Las plantas de un año de edad fueron aclimatadas durante dos meses en cámara de cultivo. Se mantuvieron con un fotoperiodo 16/8 día/noche, con 24ºC de temperatura y 60% de humedad relativa durante el día y 20ºC de temperatura y 80% de humedad relativa durante la noche, realizando riegos hasta la capacidad de campo. 4.1.2 Tratamiento de estrés hídrico en sustrato Como control se recogió material sin estresar una hora después del riego. Las plantas sometidas a estrés permanecieron sin riego durante 50 días, tomando las muestras cada diez días. La recogida de material se realizó a mediodía, midiendo en ese momento el potencial hídrico de las plantas empleando una cámara de Scholander. Las raíces, tallos y acículas fueron recogidas de manera independiente, congeladas inmediatamente en nitrógeno líquido y guardadas a -80ºC. 4.1.3 Tratamiento de estrés hídrico con PEG El tratamiento de estrés hídrico inducido por polietilenglicol se describe en el apartado 3.1.2. En la hibridación del microarray se emplearon las muestras S1, S2, S4, S6 y S8 frente a las plantas sin estresar. 4.1.4 Análisis de expresión con microarray Se diseñó un microarray (Agilent 8x15K, Agilent Technologies, CA, USA) con 1124 genes conteniendo los unigenes obtenidos en la genoteca sustractiva y otros genes seleccionados de trabajos previos. Para cada unigen se diseñaron entre una y cuatro sondas de 60 pares de bases, que fueron fijadas sobre el cristal por triplicado. Igualmente se incorporaron en el diseño otros genes seleccionados de trabajos previos realizados en el grupo de investigación, así como genes depositados en las 40 Análisis de expresión durante el estrés hídrico bases de datos de pinos y otras especies, que fueron empleados como controles positivos y negativos. El ARN total fue purificado empleando el “Qiagen RNeasy kit” (QIAGEN, CA, USA) y posteriormente amplificado y marcado según el protocolo descrito por Adie et al. (2007). Las hibridaciones se hicieron de acuerdo a “The manual two-colour microarray based gene expression analysis” (Agilent Technologies, CA, USA). Las imágenes de ambos canales (Cy3 y Hyper5) fueron equilibradas y capturadas con un GenePix 4000B (Axon, CA, USA), cuantificando la intensidad de la señal mediante el programa GenePix (Axon, CA, USA). En todos los casos se emplearon cuatro réplicas biológicas 4.1.5 PCR a tiempo real El ARN fue tratado con DNAsa Turbo (Ambion; Applied Biosystems, Life Technologies, CA, USA). Se realizó la transcripción inversa a ADNc para cada muestra a partir de 2 µg de ARN total empleando la retrotranscriptasa PowerScriptIII (Invitrogen) y siguiendo el manual del fabricante. Como control se empleó el ribosómico 18S una vez comprobado que la intensidad de la señal permanece constante a lo largo del tratamiento. Se utilizó el programa Primer Express 3.0.0 (Applied Biosystems, Life Technologies, CA, USA) para el diseño de los cebadores. Las PCR se realizaron utilizando EvaGreen para monitorizar la síntesis de ADN en cada ciclo en placas ópticas de 96 pocillos, cuantificando la señal emitida mediante el sistema de detección CFX 96 (BIO-RAD). Las reacciones con 2x SsoFast EvaGreen Supermix (BIO-RAD, CA, USA), 12.5 ng de ADNc y 500 nM de cada cebador en un volumen final de 10 µl se llevaron a cabo siguiendo las siguientes condiciones: 3 minutos a 95ºC, 40 ciclos de 10 segundos a 95ºC y 10 segundos a 60ºC. Se realizaron tres réplicas técnicas para cada reacción. Los valores CT medios junto con la eficiencia de la reacción fueron utilizados para hallar la cantidad relativa de transcrito en cada muestra respecto al control. Los valores de expresión fueron obtenidos empleando el método ΔΔCT (Pfaffl, 2001) normalizando los datos en función de la cantidad relativa de transcrito obtenida para el 18S. 41 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 4.1.6 Análisis de datos 4.1.6.1 Normalización e identificación de genes inducidos La corrección de la señal de fondo y la normalización de los datos fue realizada empleando el programa LIMMA (Linear Models for Microarray Data) (Smyth, 2005). Se utilizaron los métodos “normexp” y “loess” para la corrección de la señal de fondo local y la normalización respectivamente. Posteriormente los logaritmos del ratio se corrigieron en función del valor absoluto de la mediana para obtener una distribución comparable para todos los arrays. La expresión de un gen en cada punto de estrés respecto al control fue calculada como la intensidad media de los cuatro genotipos empleados como réplicas. Para identificar aquellos genes diferencialmente expresados se utilizó “RankProd” (Hong et al., 2006). Así mismo, un gen fue considerado sobreexpresado de forma significativa cuando cumplía los siguientes criterios: 1) El nivel de expresión fue al menos 1.6 veces el del control en alguno de los puntos de muestreo y en algún órgano 2) El valor de FDR (False Discovery Rate) calculado con RankProd era inferior a 0.05 3) La tendencia se mantuvo para todas las réplicas de la prueba presentes en el array. Se agruparon los genes sobreexpresados con un patrón de expresión similar utilizando el programa MeV 4.4 (Saeed et al., 2006), según un agrupamiento jerárquico empleando el método de la distancia máxima y estableciendo un umbral basal de 2.5. 4.1.6.2 Expresión diferencial entre órganos y especies Se realizaron comparaciones multiclases para identificar aquellos genes que mostraban un comportamiento diferencial entre los órganos estudiados para cada especie, así como comparaciones entre dos clases para identificar aquellos genes que mostraban una expresión diferencial entre ambas especies para un órgano concreto. Para ello se utilizó la implementación de LIMMA (Smyth, 2005) incluida en www.babelomics.org (Al-Shahrour et al., 2006).Tras el análisis de la expresión en cada punto de muestreo se obtiene un p valor corregido mediante FDR para cada gen. 42 Análisis de expresión durante el estrés hídrico Aquellos genes que mostraban un p valor inferior a 0.05 fueron identificados como diferencialmente expresados entre las clases estudiadas. 4.1.6.3 Análisis funcional Se utilizó FatiGO (Al-Shahrour et al., 2004) en www.babelomics.org (Al-Shahrour et al., 2006) para identificar términos GO significativamente sobrerrepresentados en los genes seleccionados como sobreexpresados. Arabidopsis thaliana fue empleada como especie modelo para realizar la comparación entre los genes seleccionados respecto al resto del genoma anotado. Se realizó un test de corrección múltiple para analizar las hipótesis probadas, una por cada término funcional. Se consideraron enriquecidos de manera significativa aquellos GO con un p valor inferior a 0.05. 4.2 Resultados y discusión 4.2.1 Análisis de la expresión de los genes durante el tratamiento de PEG La hibridación de las muestras seleccionadas del tratamiento de PEG dio como resultado 192 genes significativamente sobrerrepresentados en al menos un punto de muestreo y órgano (Material Suplementario, Tabla S4). De estos genes, 67 forman parte de los 351 unigenes nucleares identificados previamente en la genoteca sustractiva, un porcentaje similar a los obtenidos en estudios previos con otras especies forestales, como por ejemplo Populus (Bae et al., 2009). De estos 192 unigenes sobreexpresados de manera significativa durante el tratamiento de estrés hídrico inducido por PEG, se identificaron 71 genes sobreexpresados en acícula, otros 101 genes inducidos en tallo y 106 en raíz, de los cuales 54 presentan inducción únicamente en este órgano; esta mayor respuesta en raíz no es sorprendente, teniendo en cuenta el papel principal de las raíces en la percepción de la señal de estrés. Se identificaron 22 genes con inducción significativa en los tres órganos estudiados (Figura 4.1). 43 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.1 Número de transcritos sobreexpresados de manera significativa en cada órgano estudiado durante el tratamiento de sequía inducida con PEG. En total se identificaron192 genes inducidos en algún punto de muestreo/órgano a lo largo del tratamiento. Los genes identificados como sobreexpresados en respuesta al tratamiento con PEG pertenecen mayoritariamente a las categorías principales descritas para la genoteca. La Figura 4.2 muestra los términos GO más representativos entre estos genes. Los genes identificados en cada órgano fueron agrupados en “clusters” en función del patrón de expresión a lo largo del tratamiento (Figuras 4.3 a, b, y c). 44 Análisis de expresión durante el estrés hídrico Figura 4.2 Análisis de enriquecimiento de genes identificados específicamente durante el tratamiento de PEG. Además de términos GO relacionados con respuesta a estreses abióticos se aprecia enriquecimiento en términos GO asociado a otros estímulos o procesos biológicos. 45 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.3a Patrones de expresión de los genes sobreexpresados en raíz a lo largo del tratamiento de PEG 46 Análisis de expresión durante el estrés hídrico Figura 4.3b Patrones de expresión de los genes sobreexpresados en tallo a lo largo del tratamiento de PEG 47 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.3c Patrones de expresión de los genes sobreexpresados en acícula a lo largo del tratamiento de PEG 48 Análisis de expresión durante el estrés hídrico Del análisis de estos perfiles de expresión se pueden extraer las siguientes pautas generales: La mayor parte de los genes inducidos por PEG alcanzan el máximo nivel de transcripción en el punto S6 (48 horas tras el comienzo del tratamiento y 36 horas a -1.6 MPa. Algunos de estos genes presentan una inducción más rápida en raíces, alcanzando el máximo en el punto S4 (1 hora a -1.6 MPa). Estos genes incluyen genes presumiblemente relacionados con procesos de defensa, como HSP o glutatión S-transferasas o factores de transcripción. Este patrón es coherente con el papel de las raíces en la percepción e inicio de la respuesta al estrés. Varios genes presentan una respuesta rápida, con un máximo en el punto S2 (1 hora a -0.8 MPa), como varias deshidrinas detectadas en raíces (cluster 12), así como otras LEA y varias taumatinas en acícula (cluster 12) y tallo (cluster 10). En este último órgano se observó una importante inducción de proteínas plastídicas en este punto del estrés (clusters 7, 8 Y 9). Por el contrario, el nivel de transcripción de otros genes aumenta de manera continua durante todo el tratamiento. En este grupo se incluyen fundamentalmente genes relacionados con el metabolismo de carbohidratos. Para validar los perfiles detectados con los microarrays, se analizó mediante RTPCR el patrón de expresión de 10 genes, representativos de los principales “clusters” de expresión y grupos funcionales (Figura 4.4). 49 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.4 RT-PCR de diez genes inducidos en el tratamiento de PEG. El grafico de barras representa el valor observado en la RT-PCR y la línea representa el valor observado en el microarray para el genotipo estudiado. 50 Análisis de expresión durante el estrés hídrico Figura 4.4 (continuación) RT-PCR de diez genes inducidos en el tratamiento de PEG. El grafico de barras representa el valor observado en la RT-PCR y la línea representa el valor observado en el microarray para el genotipo estudiado 51 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 4.2.2 Tratamiento de sequía en sustrato sólido Si bien es conocido que P. pinea y P. pinaster presentan diferencias en sus requerimientos hídricos, siendo la primera capaz de sobrevivir y reproducirse en condiciones incluso más xéricas que la segunda, el análisis del potencial hídrico durante el experimento no ha reflejado diferencias apreciables, como se muestra en la Figura 4.5. Figura 4.5 Potencial hídrico medido al mediodía en acícula a lo largo del experimento de sequía con P. pinaster y P. pinea. La línea continua muestra el potencial hídrico medio de las cuatro réplicas de P. pinea. La línea discontinua muestra el potencial hídrico medio de las cuatro réplicas de P. pinaster. Las barras de error representan el error estándar asociado a cada punto de muestreo. 4.2.2.1 Genes inducidos por la suspensión de riego en P. pinaster y P. pinea El alto grado de conservación que habitualmente presentan las regiones codificantes de los genes permite la utilización de los microarrays diseñados sobre una especie para el análisis de la expresión génica en otra especie cercana, como se ha descrito en pináceas y otras familias (van Zyl et al., 2002; Brinker et al., 2004; Davey et al., 2009). En este caso, el microarray de oligonucleótidos diseñado a partir de genes candidato de P. pinaster fue empleado de manera satisfactoria para el análisis de 52 Análisis de expresión durante el estrés hídrico expresión de los genes durante un tratamiento de sequía en P. pinea. Si bien en algunos casos la especificidad de las pruebas puede dificultar en la hibridación heteróloga la detección de determinados genes sobreexpresados (clasificados así como “falsos negativos”), la fiabilidad de los genes “positivos” es equivalente a la otorgada en la especie para la que fue diseñado el array. En este caso, se identificaron 218 genes sobreexpresados en P. pinea (Material Suplementario, Tabla S5). Entre los genes identificados 67 presentan sobreexpresión en los tres órganos estudiados. El número de genes inducidos en acícula y raíz es similar, 144 y 140, respectivamente. 40 genes se indujeron de manera exclusiva en acícula y 33 en raíz. Por su parte, en tallo se identificaron 123 genes inducidos, 23 de los cuales no fueron detectados en los otros órganos (Figura 4.6). Figura 4.6 Número de transcritos sobreexpresados de manera significativa en cada órgano estudiado durante el tratamiento de sequía en sustrato con P. pinea. En total se identificaron 218 genes inducidos en algún punto de muestreo/órgano a lo largo del tratamiento. Se identificaron 181 genes sobreexpresados significativamente en P. pinaster en algún punto del tratamiento y para alguno de los órganos estudiados (Material Suplementario, Tabla S6). Entre ellos, se han detectado 53 genes inducidos de manera significativa en los tres órganos. En tallo se encontró el mayor número de genes sobreexpresados, 116, de los cuales 41 se detectaron exclusivamente en este órgano. En acícula se identificaron 107 genes, 44 de ellos no detectados en los otros órganos. Por último, 88 genes se indujeron significativamente en raíz, y 19 de ellos de manera exclusiva (Figura 4.7). 53 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.7 Número de transcritos sobreexpresados de manera significativa en cada órgano estudiado durante el tratamiento de sequía en sustrato con P. pinaster. En total se identificaron181 genes inducidos en algún punto de muestreo/órgano a lo largo del tratamiento. En total 113 genes mostraron sobreexpresión significativa en ambas especies y en el mismo órgano (Figura 4.8), y se perfilan como un buen conjunto de genes candidatos para el análisis de la respuesta a estrés hídrico en pináceas (Material suplementario Tabla S7). Figura 4.8 Número de transcritos sobreexpresados de manera significativa en ambas especies para un mismo órgano. 54 Análisis de expresión durante el estrés hídrico Entre los sobreexpresados especies se genes en ambas aprecia un enriquecimiento en dos términos GO directamente relacionados con la respuesta a estrés hídrico: GO:0009415, respuesta a estímulos GO:0009725, por agua, respuesta y a estímulos hormonales, y en su mayoría pertenecen a las principales clases funcionales descritas para la genoteca sustractiva (Figuras 4.9 y 4.10). Figura 4.9 Análisis de enriquecimiento de los genes candidatos seleccionados Figura 4.10 Clasificación funcional de los genes seleccionados como candidatos para estrés hídrico en pinos. En total, 113 genes que presentaron inducción significativa en ambas especies fueron agrupados de acuerdo con las categorías funcionales establecidas en la base datos FunCatDB para Arabidopsis thaliana. Se muestra el valor porcentual para cada grupo. 55 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Genes implicados en metabolismo Un cuarto de los genes inducidos de forma significativa en ambas especies están presumiblemente implicados en metabolismo y, dentro de este grupo, hasta un tercio estaría relacionado con el metabolismo de carbohidratos. Entre ellos encontramos, por ejemplo, genes también inducidos por el tratamiento con PEG, como es el caso de la alfa-galactosidasa (TC181331), la malato sintasa (TC155104), la glicosiltransferasa (TC156369), la quitinasa (TC157851) y la aldehído deshidrogenasa(TC158839). Otros dos genes incluidos en este grupo presentan homología con diferentes sacarosas sintasas (TC188296 y TC176883), una beta-galactosidasa (TC156138) y la gliceraldehido-3-fosfato deshidrogenasa (TC177891). Todos estos genes podrían estar implicados en la acumulación de azúcares y prolina, solutos que favorecen el mantenimiento de la presión osmótica y la protección de estructuras celulares, como ya se ha comentado. Igualmente se seleccionaron varios genes implicados en el metabolismo de lípidos y metabolismo secundario, incluyendo algunos genes relacionados con la síntesis de etileno (TC182140, ACC sintasa y TC174045, ACC oxidasa) o jasmonatos (TC188679, 12-oxo PDA reductasa). Factores de transcripción Un 9% de los genes inducidos en ambas especies muestran homología con factores de transcripción de diferentes familias. Entre ellos, encontramos representantes de la mayoría de las rutas de señalización. Dentro de los factores de transcipción con dominios tipo AP2 se incluyen el TC157919, homólogo a un factor de transcripción correspondiente a la subfamilia DREB (Drought responsive element binding proteins) y los TC158167 y TC178542, presumiblemente miembros de la subfamilia ERF (Ethylene responsive factor). También se incluye en este grupo un factor de transcripción tipo WRKY (TC163430). Estos factores están muy conservados en plantas, y se ha propuesto para ellos un papel central en la respuesta regulada por ABA (Rushton et al., 2010); su sobreexpresión podría incrementar la tolerancia al estrés hídrico (Zhou et al., 2008). También se identificó un factor de transcripción tipo bZIP (TC161257), que mostró inducción significativa en todos los órganos estudiados. Dentro de este grupo se incluye un factor de transcripción con un homeodominio correspondiente a la familia BEL (TC170594) que podría cumplir un papel de señalización a larga distancia (Campbell et al., 2008). Resulta destacable que este tipo de factores de transcripción nunca ha sido relacionado con la respuesta a estrés 56 Análisis de expresión durante el estrés hídrico abiótico, mientras que en este trabajo ha sido detectado con inducción significativa en todos los órganos, tanto en P. pinea como en P. pinaster y tanto en el tratamiento con PEG como en la suspensión del riego. Genes implicados en transporte Otro 9% de los genes identificados fueron incluidos en el grupo de transportadores; la mayoría podrían estar relacionados con el transporte de azúcares, aniones y aminoácidos a través de membranas. Entre ellos se encuentran, por ejemplo, dos transportadores de hexosas (TC170434 y DR099938) así como dos transportadores de inositol (TC170498 y TC171882). Otro gen incluido en este grupo, el TC176635, presenta homología con una permeasa de aminoácidos que podría mediar en la acumulación de aminoácidos libres como la prolina (Bruria, 2010). Este aminoácido aporta resistencia a la desecación, como sugiere su presencia en altas concentraciones en procedencias de P. pinaster adaptadas a ambientes secos (Corcuera et al., 2012). Estos resultados son coherentes con la alta capacidad que muestra P. pinaster para realizar ajustes osmóticos (Lopez et al., 2009). Por otro lado, TC173812 y TC167700 muestran homología con proteínas de membrana del peroxisoma, posiblemente involucradas en el establecimiento del mecanismo de detoxificación de ROS (Mathur, 2009). Genes de defensa y rescate celular Los genes más relevantes dentro de esta categoría se corresponden con proteínas LEA y chaperonas. Por ejemplo, TC182917 y TC180126 son homólogos de HSP de bajo peso molecular, que posiblemente estén implicadas en el mantenimiento de la integridad de membrana plasmática (Nakamoto y Vígh, 2007). Aunque estos genes han sido habitualmente relacionados con otros estreses abióticos como calor o frio (Soto et al., 1999; Sun et al., 2002; Savić et al., 2012), algunos trabajos han mostrado que pueden conferir tolerancia a sequía y salinidad (Jiang et al., 2009). TC194781 tiene homología con un miembro de la familia HSP70, descritos previamente en respuesta a estrés hídrico (Cho y Choi, 2009; Vásquez-Robinet et al., 2010). Entre las proteínas LEA, cuatro genes (TC162509, TC179486, TC193003 y TC176703) presentan homología con diferentes deshidrinas, una familia multigénica muy compleja implicada en la respuesta a diferentes estreses abióticos así como 57 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. procesos ontogénicos (Ver capítulo 5). Por su parte, TC168999 es homólogo al gen AtLEA14, uno de los 10 genes que se sobreexpresan por luz, sequía, frio y salinidad en Arabidopsis (Kimura et al., 2003). Otras funciones y proteínas sin anotación Muchos genes incluidos en otras categorías funcionales han sido identificados en todos los órganos en ambos tratamientos y en muchos casos también en el tratamiento de PEG. En esta situación se encuentran otras proteínas LEA (TC174716 y TC156877) incluidas dentro del grupo de genes implicados en el desarrollo de la planta. En el grupo que incluye genes relacionados con una respuesta sistémica a estímulos ambientales aparecen dos proteínas homólogas a taumatinas (TC182741 y TC180421), que igualmente podrían regular la presión osmótica. También en este grupo aparecen varios genes que podrían estar regulados por diferentes hormonas como giberelinas (TC196029), auxinas (TC167447) y ácido indol-3-acético (AIA) (TC175790). La ralentización del crecimiento como consecuencia del estrés hídrico va asociada a una disminución de los niveles de estos reguladores del crecimiento, lo que podría desencadenar nuevas respuestas adaptativas. El 19% de los genes inducidos por sequía en ambas especies muestran homología con genes de función desconocida e incluso sin homología con las proteínas presentes en las bases de datos. Por ejemplo, TC188788 y TC163698, que presentaron sobreexpresión en los tres órganos en ambas especies y también en P. pinaster en respuesta al tratamiento con PEG, tienen homología con nodulinas de la familia MtN3. Estos genes no tienen asignada una función en la base de datos FunCat; sin embargo, estudios recientes en Arabidopsis y Oryza han identificado esta familia génica como un nuevo tipo de transportador de azúcares denominados “Sweet” (Chen et al., 2010). La sobreexpresión de TC188788, uno de los genes que presentó valores más altos de inducción en los tratamientos estudiados, ya había sido descrita en trabajos centrados en la respuesta a estrés hídrico en P. pinaster y P. taeda (Dubos et al., 2003; Dubos y Plomion, 2003; Lorenz et al., 2006), así como en respuesta al frio en Cupressus sempervirens (Pedron et al., 2009). Otro dos genes presentan marcada sobreexpresión en todos los tratamientos: TC154582 presenta homología con una proteína regulada por ABA y TC197470 fue anotada como una proteína rica en hidroxiprolina. La expresión de estas proteínas está estimulada por heridas y diferentes estreses ambientales. Saythan et al. (2005) identificaron otra PRP con alta inducción durante estrés hídrico en P.halepensis. 58 Análisis de expresión durante el estrés hídrico De los genes sobreexpresados durante el tratamiento con PEG, 86 no se indujeron significativamente durante el estrés por falta de riego (Figura 4.11). Este hecho podría deberse a las diferencias en el nivel de estrés y tiempos de muestreo utilizados en un experimento y otro, pero también a la respuesta a la toxicidad producida por el PEG, más allá de su actuación como agente osmótico. Las plantas pueden absorber el PEG por la raíces, especialmente las formas de bajo peso molecular (por debajo de 6000), desarrollando una respuesta específica y adicional a la causada por la bajada del potencial hídrico. Así, el análisis de enriquecimiento en términos GO realizado para este grupo de genes mostró una representación significativa tanto de términos asociados a estrés abiótico (por ejemplo; GO:0006970 Estrés osmótico, GO:0009414 Respuesta a privación de agua) como asociado a respuesta a estímulos químicos (GO:0010038 respuesta a iones metálicos) (Figura 4.2, Pagina 42). Este es el caso de una metionina sintasa (TC157740) o una pirofosfatasa inorgánica soluble (TC178028). El término GO:0006457 (Plegamiento de proteínas) también está sobrerrepresentado entre los genes inducidos por PEG, incluyendo 7 HSP no identificadas en la sequía en sustrato. Figura 4.11 Número de transcritos sobreexpresados de manera significativa en cada tratamiento de sequía aplicado. En total se identificaron 364 genes inducidos en algún punto de muestreo/órgano/tratamiento. 4.2.2.2 Patrón de expresión en respuesta a sequía en P. pinaster y P. pinea Las Figuras 4.13 (a,b y c) y Figuras 4.14 (a,b y c) muestran el agrupamiento de los genes sobreexpresados en cada especie y órgano, según la similitud en sus patrones de expresión. Los resultados obtenidos por microarray son comúnmente 59 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. validados por RT-PCR debido a la mayor precisión atribuida a esta técnica, especialmente para aquellos genes con bajos niveles de inducción. Se realizó RT-PCR para 16 genes cubriendo la mayoría de grupos funcionales y patrones de expresión. La expresión observada con ambas técnicas fue bastante consistente, con valores de correlación de Pearson superiores al 80% en la mayoría de los casos (Figura 4.15). Figura 4.12a Patrones de expresión de los genes sobreexpresados en raíz a lo largo del tratamiento de P. pinaster en sustrato sólido 60 Análisis de expresión durante el estrés hídrico Figura 4.12b Patrones de expresión de los genes sobreexpresados en tallo a lo largo del tratamiento de P. pinaster en sustrato sólido 61 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.12c Patrones de expresión de los genes sobreexpresados en acícula a lo largo del tratamiento de P .pinaster en sustrato sólido 62 Análisis de expresión durante el estrés hídrico Figura 4.13a Patrones de expresión de los genes sobreexpresados en raíz a lo largo del tratamiento de P. pinea en sustrato sólido 63 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.13b Patrones de expresión de los genes sobreexpresados en tallo a lo largo del tratamiento de P. pinea en sustrato sólido 64 Análisis de expresión durante el estrés hídrico Figura 4.13c Patrones de expresión de los genes sobreexpresados en acícula a lo largo del tratamiento de P. pinea en sustrato sólido 65 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 4.14 RT-PCR de los 16 genes seleccionados para los tratamientos de estrés hídrico con P. pinaster y P. pinea 66 Análisis de expresión durante el estrés hídrico Figura 4.14 (continuación) RT-PCR de los 16 genes seleccionados para los tratamientos de estrés hídrico con P. pinaster y P. pinea 67 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. En cuanto al momento de la inducción de los genes sobreexpresados en ambas especies, éstos se pueden agrupar en 3 clases: a) genes que se inducen en los primeros pasos de la sequía (10 y 20 días), descendiendo o manteniéndose constante su nivel de expresión durante el resto del tratamiento, b) genes que muestran unos niveles de inducción que aumentan constantemente a lo largo del tratamiento c) genes altamente inducidos en los últimos puntos del estrés, a los 30 días y especialmente a los 40 y 50 días. Como se ha comentado anteriormente, no es sorprendente que muchos de los genes sobreexpresados en ambas especies se induzcan más rápidamente en raíces que en las partes aéreas. Sin embargo, se identificaron ciertas diferencias entre P. pinaster y P. pinea: Se identificó un mayor número de genes con inducción significativa en P. pinea (218 genes) respecto a P. pinaster (181 genes), especialmente en raíz y acícula. Los valores de sobreexpresión observados en los genes inducidos en ambas especies fueron en general más altos en P. pinea que en P. pinaster. Así, se registraron valores de expresión más de 10 veces superiores a los de las plantas control en 83 genes en P. pinea con un máximo de 72 veces el valor del control para una proteína rica en hidroxiprolina (TC197470), mientras que sólo 43 genes presentaron una expresión más de 10 veces superior al control para P. pinaster, presentando el valor máximo (62x) una deshidrina (TC162509). P. pinea mostró una mayor proporción de genes inducidos en los primeros puntos del estrés, especialmente en el primer punto de muestreo a los 10 días sin riego. Por el contrario, algunos genes mostraron una respuesta más retrasada en P. pinea, con marcadas inducciones a los 40 y 50 días de estrés, mientras que en P. pinaster se observa una importante inducción a los 20 o 30 días sin riego. Este es el caso de los clusters 8, 9, 10, 11 y 12 de acícula, clusters 1 y 9 en tallo o clusters 4, 6 y 8 de raíz en P. pinea y los clusters 2,4 y 6 de acícula, clusters 4 68 Análisis de expresión durante el estrés hídrico y 8 de tallo y los clusters 1, 2, 3 y 4 de raíz en P. pinaster. Este resultado se confirmó en los genes seleccionados para RT-PCR, especialmente en tallo y acícula; es el caso de TC162509 (deshidrina), TC157851 (posible quitinasa), TC197470 (proteína rica en hidroxiprolina), TC188679 (posible 12-oxo-PDA reductasa) y TC156369 (posible glicosiltransferasa). Todos estos resultados indican que P. pinea es una especie con alta capacidad de respuesta, mostrada a nivel transcripcional de manera mucho más rápida e intensa que en P. pinaster. No obstante, el retraso en la inducción de ciertos genes en P. pinea podría ser coherente con los resultados expuestos recientemente por SánchezGómez et al. (2011), según los cuales durante las primeras etapas de un estrés hídrico moderado los mejores clones mostraron una estrategia de gasto de agua. Esta estrategia podría aportar una ventaja competitiva en ambientes secos, privando del recurso limitante a otros árboles competidores que siguen una estrategia de ahorro de agua (Cohen, 1970; Zhang et al., 1997; Nguyen-Queyrens et al., 1998), como es el caso de P. pinaster (Picon et al., 1996; Fernandez et al., 2000). Al persistir el estrés hídrico, las plantas de P. pinea pasarían a adoptar una estrategia de ahorro de agua, tal y como se ha descrito en otras especies “derrochadoras” (Levitt, 1980; Kozlowski y Pallardy, 1996). Se requerirán nuevos experimentos para la comprobación de esta hipótesis Por otra parte, varios genes presentaron un patrón de expresión opuesto entre ambas especies. El más claro ejemplo corresponde al TC177528, que muestra una fuerte inducción en P. pinea (59 veces superior al de las plantas control en tallo y 32 veces en acícula a los 40 días sin riego) mientras que en P. pinaster mostró una importante represión (15 y 5 veces inferior a los controles en los mismos puntos de muestreo). Este gen codifica un péptido posiblemente implicado en la asimilación del amonio en P. pinaster (Canales et al., 2011). La sequía puede reducir la disponibilidad de este compuesto en el suelo; ante esta situación las plantas podrían acidificar la rizosfera para solubilizar más amonio. Los cambios en el flujo de protones aumentan el efecto del ABA en la reducción de la conductancia estomática (Goodger y Schachtman, 2010). Un patrón opuesto se observó para el gen TC172144, de función desconocida, el cual mostró una sobreexpresión moderada en P. pinaster mientras que se reprimía en P. pinea. También es destacable el aumento detectado en la raíz de P. pinaster de posibles retrotransposones a los 50 días de sequía (Cluster 8; TC183415 y 69 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. TC194362). El estrés puede aumentar la transposición de estos elementos probablemente debido a modificaciones epigenéticas transitorias (Mirouze y Paszkowski, 2012). En este sentido, CDT-1, un gen inducido por sequía en la planta de resurrección Craterostigma plantagineum que muestra similitudes con retrotransposones confiere resistencia a la desecación posiblemente actuando como una molécula de ARN no codificante con funciones reguladoras (Phillips et al., 2007). Ninguno de estos elementos fue detectado como sobreexpresado en P. pinea, lo que podría deberse a la restrictividad de los criterios utilizados, a la especificidad de las sondas empleadas o a las propias diferencias en la respuesta entre ambas especies. En conclusión, se ha identificado una colección de genes inducidos significativamente en P. pinaster y P. pinea y que pueden considerarse como buenos genes candidato para el estudio de la respuesta al estrés hídrico en las coníferas. Mientras que los perfiles de expresión compartidos entre ambas especies pueden ser considerados generales para los pinos mediterráneos, aquellos genes con un patrón de expresión divergente probablemente estén relacionados con las diferentes pautas frente al estrés hídrico mostrados por estas especies. Parte de los resultados presentados en este capítulo están recogidos en los Anexos III y IV; Identification of water stress genes in Pinus pinaster Ait. by controlled progressive stress and suppression-subtractive hybridization, Plant Physiology and Biochemistry 50 (2012) 44-53 (Anexo III) Molecular response to water stress in two contrasting Mediterranean pines (Pinus pinaster and Pinus pinea). Manuscrito (Anexo IV). 70 5. CARACTERIZACIÓN MOLECULAR DE LA FAMILIA DE LAS DESHIDRINAS EN P. PINASTER Caracterización molecular de la familia de las deshidrinas en P. pinaster Las deshidrinas forman una familia multigénica implicada en la respuesta a diferentes estreses abióticos. En muchos casos están localizadas en QTL asociados con importantes rasgos fenotípicos como la respuesta a la vernalización, el tiempo de floración, la tolerancia a bajas temperaturas, la resistencia a la congelación o el crecimiento bajo estrés osmótico o en condiciones de sequía (Campbell y Close, 1997). Por estos motivos, varios miembros de esta familia génica han sido empleados como genes candidatos en distintos trabajos centrados en identificar patrones de diversidad genética asociada con adaptación local a diferentes factores ambientales en especies de pino (González-Martínez et al., 2006; Eveno et al., 2008; Grivet et al., 2009; Wachowiak et al., 2009; Grivet et al., 2011). Además han sido utilizadas para detectar evidencias de selección natural subyacentes a la filogenia del género Pinus (Palmé et al., 2009). Durante el análisis de los resultados obtenidos tras la hibridación del microarray se observaron algunas discrepancias entre sondas diseñadas para una misma deshidrina. En un análisis inicial de las secuencias mediante búsquedas en bases de datos así como en un estudio de aquéllas empleadas en trabajos previos se observó la notable complejidad que presenta esta familia génica, hecho que provoca indeterminaciones en la nomenclatura de cada miembro de la familia y que a su vez podría traducirse en errores en la interpretación de los resultados obtenidos en estos trabajos. Todo ello, unido a la escasa caracterización de esta familia génica en gimnospermas nos impulsó a realizar una caracterización de la misma, así como a estudiar su patrón de expresión a lo largo de la sequía. Las deshidrinas forman un grupo bien diferenciado dentro de las proteínas LEA. La cantidad de transcritos de estas proteínas aumenta considerablemente en los tejidos vegetativos de las plantas cuando desciende de forma considerable su contenido hídrico. Son proteínas muy hidrofílicas con alta proporción en glicina y otros pequeños residuos en su composición de aminoácidos. Debido a sus propiedades físico químicas se ha propuesto que podrían cumplir un papel central preservando y manteniendo las funciones celulares durante la deshidratación (Olvera-Carrillo et al., 73 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 2011). Las proteínas LEA fueron descritas por primera vez por su acumulación durante el desarrollo de la semilla en algodón (Dure III et al., 1981), formando en la actualidad un complejo y diverso grupo que incluye proteínas con baja homología global. Se han agrupado en diferentes familias atendiendo a su composición en aminoácidos (Dure 1993 y Bray 1993, Cuming (1999)). Una clasificación más reciente de las LEA de Arabidopsis thaliana realizada en función de la presencia de dominios conservados, incluidos en la base de datos Pfam (Wellcome Trust Sanger Institute), establece nueve grupos diferentes. Uno de esos grupos corresponde a las deshidrinas (grupo D-11 propuesto por Dure o grupo 2 propuesto por Bray). Las deshidrinas se caracterizan por la combinación de tres motivos conservados, los segmentos Y, S y K, que están separados por otros segmentos denominados Φ, menos conservados y ricos en glicina y aminoácidos polares (Close, 1997). Todas las deshidrinas contienen al menos una copia del segmento K, el cual consiste en un fragmento de 15 aminoácidos altamente conservados y ricos en lisina. En angiospermas presenta la secuencia consenso EKKGIMDKIKEKLPG (Close, 1996) y en gimnospermas (Q/E)K(P/A)G(M/L)LDKIK(A/Q)(K/M)(I/L)PG (Jarvis et al., 1996) y se encuentra habitualmente en la región C-terminal. Los segmentos K pueden estar involucrados en la formación de hélices anfipáticas alfa tipo A2 (Baker et al., 1988). El segmento S es un fragmento de residuos de serina contiguos que pueden ser fosforilados (Godoy et al., 1994; Campbell et al., 1998). Por su parte, el segmento Y [(V/T)DEYGNP] presenta homología con dominios de unión de nucleótidos presentes en chaperonas (Close, 1996 y 1997). Este segmento únicamente ha sido identificado en deshidrinas de angiospermas hasta el momento. En función de la combinación de segmentos que presentan en la secuencia de aminoácidos las deshidrinas han sido clasificadas en 5 grupos; YnSKn, YnKn, SKn, Kn y KnS. Las deshidrinas se consideran generalmente genes de expresión tardía en la respuesta al estrés (Mahajan y Tuteja, 2005) y se han descrito varias funciones en las que podrían estar implicadas. Una de ellas sería la de protección, estabilizando membranas mediante interacciones hidrofóbicas con los segmentos K (Campbell y Close, 1997; Danyluk et al., 1998; Koag et al., 2003). En algún caso se ha descrito actividad chaperona, impidiendo la agregación de proteínas (Kovacs et al., 2008a; 2008b). Las deshidrinas también podrían tener la capacidad de unión a moléculas de agua que a su vez podría influir en la protección de enzimas, como por ejemplo la alfa amilasa durante estrés por frío (Rinne et al., 1999), al mantener una adecuada concentración de agua a nivel local y reduciendo con ello los daños provocados por los 74 Caracterización molecular de la familia de las deshidrinas en P. pinaster cristales de hielo durante la congelación (Wisniewski et al., 1999). Algunas deshidrinas nucleares están involucradas en la protección de la maquinaria de transcripción durante la desecación asociada a la formación de la semilla (Castillo et al., 2002). Podrían tener un papel clave en este proceso y su abundancia estaría directamente relacionada con la viabilidad y longevidad de la semilla (Hundertmark et al., 2011). Otras deshidrinas podrían presentar una función como antioxidantes debido a su capacidad para unir iones metálicos libres. Esto previene de una excesiva formación de ROS (Hara et al., 2005; Sun y Lin, 2010). No obstante, a pesar de que sus funciones han sido frecuentemente investigadas y revisadas en los últimos años, especialmente en angiospermas (Allagulova et al., 2003; Rorat, 2006; Yuxiu et al., 2007; Kosová et al., 2010; Eriksson y Harryson, 2011) , el mecanismo por el cual las llevan a cabo aún está sin esclarecer. 5.1 Material y métodos 5.1.1 Material vegetal y tratamiento de estrés hídrico Para el análisis de expresión durante el estrés hídrico se utilizaron tres genotipos diferentes (F1P3, F2P2 y F4P4) de Pinus pinaster de la procedencia Oria, empleando cuatro ramets de cada uno en cada tiempo de muestreo. Las condiciones de cultivo y desarrollo del experimento son las descritas en los Capítulos 4.1.1 y 4.1.2. 5.1.2 Análisis de secuencias Se realizó una búsqueda de aquellos contigs tentativos (TCs) ensamblados a partir de ESTs que estaban anotados como posibles deshidrinas en la base de datos Pine Gene Index 9.0 (Versión marzo 2011; http://compbio.dfci.harvard.edu/tgi/cgibin/tgi/gimain.pl?gudb=pine). Se utilizó el programa MUSCLE (Edgar, 2004) para alinear las secuencias de aminoácidos deducidos a partir de cada TC y para obtener un dendrograma empleando el método “Neighbour Joining”. Aquellos TCs que se correspondían con deshidrinas de P. pinaster empleadas en trabajos previos fueron seleccionados para diseñar cebadores específicos empleados en la amplificación del ADN genómico así como del ADNc correspondiente a cada gen. 75 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 5.1.3 Extracción de ADN y ARN y amplificación de los genes completos Se extrajo ADN genómico de acículas así como de megagametofitos empleando el método propuesto por Doyle (Doyle, 1990). El ARN total fue extraído de manera independiente desde raíces, tallos y acículas siguiendo el protocolo descrito por (Chang et al., 1996). Se sintetizó ADNc a partir de 1µg de ARN total usando la retrotranscriptasa PowerScriptIII (Invitrogen). Utilizando cebadores específicos se amplificaron por PCR las secuencias completas de cada deshidrina estudiada empleando como molde ADN genómico y ADNc. Los productos de PCR fueron clonados en el vector pGEM®T-easy (Promega, WI, USA) y transferidos a Escherichia coli DH5α. Posteriormente se secuenciaron los clones obtenidos y se alinearon las secuencias para determinar su estructura de intrones y exones empleando el programa Spidey del NCBI (http://www.ncbi.nlm.nih.gov/spidey/). 5.1.4 PCR a tiempo real El estudio de la expresión de las deshidrinas identificadas se realizó para cada órgano mediante RT-PCR según el apartado 4.2.5 de la presente tesis. Se secuenciaron los fragmentos de RT-PCR, rediseñando primer siempre que fue necesario hasta verificar la especificidad de la amplificación. 5.1.5 Análisis estadístico Se empleó un modelo lineal para estudiar la significación del nivel de expresión respecto al de las plantas sin estresar para cada gen y para cada órgano estudiado (raíz, tallo y acícula); yijk = m + gi + sj + gsij + eijk Donde yijk es el nivel de expresión relativa de las k réplicas de los i genotipos en el punto de estrés j, m es la media global, gi representa el efecto del genotipo, sj representa el efecto del estrés, gsij es el efecto de la interacción y eijk representa el error residual. Todo el experimento se llevó a cabo en una única cámara de cultivo, por lo que no se consideró este factor en el modelo. La significación de los cambios en la expresión fue comprobada empleando el método de la diferencia mínima significativa (LSD), ajustado por el método de Bonferroni, con un nivel de significación del 95%. Para este análisis se utilizó el programa Statgraphis Centurion XVI. 76 Caracterización molecular de la familia de las deshidrinas en P. pinaster 5.2 Resultados y discusión 5.2.1 Búsqueda de deshidrinas in silico y amplificación de genes completos La búsqueda de deshidrinas en la última versión disponible de la base de datos del PGI dio como resultado 100 TCs/ESTs anotadas como miembros de esta familia génica. Tras deducir la secuencia de aminoácidos únicamente se obtuvieron 47 secuencias que presentaban la posible región codificante completa. El resto de secuencias o bien no estaban completas o, en muchos casos, presentaban codones de terminación a lo largo de la región codificante. En algunos casos se observó la presencia de contaminaciones con secuencias de ADN genómico en las bases de datos de ESTs. Las secuencias de aminoácidos completas fueron clasificadas en seis grupos de acuerdo con los segmentos conservados; K1, K2, SK2, SK3, SK4 y SK5. Posteriormente se diseñaron cebadores específicos para los siete TCs que estaban formados por ESTs de P. pinaster en algunos casos homólogos a deshidrinas que habían sido utilizadas en trabajos previos con esta especie. Las amplificaciones por PCR permitieron identificar 8 deshidrinas diferentes pertenecientes a los grupos Kn y SKn (Figura 5.1). Figura 5.1 TCs correspondientes a posibles deshidrinas de Pinus sp. agrupadas de acuerdo con el número de segmentos conservados empleados habitualmente en el estudio de deshidrinas. Se muestra la correspondencia con las ocho deshidrinas descritas en esta tesis y las secuencias empleadas en trabajos previos. 77 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 5.2.2 Identificación de nuevos segmentos conservados Una vez obtenidas las secuencias correspondientes a la región codificante completa se dedujo la secuencia de aminoácidos correspondiente a cada deshidrina. Éstas fueron alineadas con otras deshidrinas tanto de gimnospermas como de angiospermas disponibles en las bases de datos de proteínas, algunas de ellas deducidas a partir de ESTs (Figura 5.3). Los primeros 23 aminoácidos de la región N-terminal presentan en P. pinaster la secuencia consenso MAEEAPEHQDRGMFGLFGKKKED y están altamente conservados en las deshidrinas de otras pináceas. Igualmente, este fragmento está conservado en las deshidrinas de otras gimnospermas y en las deshidrinas tipo SKn de angiospermas, con distintas inserciones al inicio de la secuencia. Todas las deshidrinas que presentan esta región inicial conservada muestran un elevado porcentaje de residuos de glutamina (>13%) y prolina (>5.9%), frente a las deshidrinas tipo Kn o YnSKn de angiospermas, que presentan una región inicial más corta precediendo los segmentos K y S y una composición de aminoácidos enriquecida en residuos de glicina (>21%) y treonina (>12%). Ninguna de las proteínas identificadas contiene el segmento Y descrito para angiospermas. En cambio identificamos dos fragmentos que se encuentran altamente conservados y aparecen de forma repetitiva en diferentes deshidrinas de pináceas - Segmento A: presenta en P. pinaster la secuencia consenso EAASYYP (en negrita los residuos también conservados en Picea) y ocupa una posición parecida al segmento Y típico de angiospermas. Además de en numerosas deshidrinas de Pinus y Picea también está presente en otras de Larix o Pseudotsuga. Varios TCs obtenidos a partir de ESTs de Pinus taeda y P. banksiana presentan este segmento repetido 4 veces (TC183122), 5 veces (TC171685 y TC187956) y hasta 19 veces (TC191238). - Segmento E: se encuentra precediendo al segmento S, con una secuencia consenso en P. pinaster GHGHEGQLTPEEAEQQKH (se muestran en negrita aquellos residuos también conservados en Picea). Este segmento aparece en distintas deshidrinas de pináceas (Pinus, Picea, Larix y Pseudotsuga) así como en una deshidrina identificada en Cupressus sempervirens. 78 Caracterización molecular de la familia de las deshidrinas en P. pinaster Todos los segmentos conservados están identificados en la Figura 5.2 y la Figura Suplementaria S1. La presencia y número de repeticiones de estos segmentos y de los clásicos S y K se puede emplear en la clasificación de las deshidrinas de gimnospermas, como hemos hecho en este trabajo con las identificadas en P. pinaster. Figura 5.2 Alineamiento de la secuencia de aminoácidos deducida para las 8 deshidrinas identificadas en P. pinaster. Se muestran los residuos y segmentos conservados. Los segmentos S y K clásicos son indicados con cajas de línea sólida. Cajas de puntos o rayas indican los nuevos segmentos descritos así como la región N-terminal conservadas entre gimnospermas y las deshidrinas SKn de angiospermas 5.2.3 Análisis de la estructura de las deshidrinas de P. pinaster La comparación de las secuencias de ADN genómico y ADNc correspondientes a las distintas deshidrinas de Pinus pinaster permitió establecer la estructura de intrones y exones que presenta cada una de ellas. El análisis de los diferentes segmentos conservados en la secuencia de las proteínas se muestra en la Tabla 5.1. De las 8 deshidrinas identificadas dos pertenecen al grupo K2. Ppter_dhn_K2a y Ppter_dhn_K2b están formados por 306 nucleótidos con 18 sustituciones entre ambas que implican 10 cambios en la secuencia de aminoácidos. Ninguna de las dos contiene intrones. Un caso similar se da para otros tres genes que fueron identificados en el grupo AESK3. Todos ellos muestran una región codificante con 579 nucleótidos. 79 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Ppter_dhn_AESK3a y Ppter_dhn_AESK3a2 presentan mucha homología entre sí, cambiando únicamente 11 nucleótidos, que implican el cambio de 5 aminoácidos. La mayor diferencia entre ambas radica en que solamente Ppter_dhn_AESK3a2 presenta un intrón de 113 nucleótidos. El tercer gen presente en este grupo se corresponde con otro TC en la base de datos PGI. Contiene un intrón de 123 nucleótidos y fue nombrado como Ppter_dhn_AESK3b, pues presenta notables diferencias en la secuencia de aminoácidos: 24 aminoácidos son diferentes respecto a los otros dos miembros de este grupo, con cuatro cambios adicionales frente a Ppter_dhn_AESK3a y uno frente Figura 5.3 Productos de PCR correspondientes a los diferentes loci Ppter_dhn_AESK3a2. de Ppter_dhn_K2 y Ppter_dhn_SK3 El empleo de ADN haploide procedente amplificados mediante ADN genómico haploide de de megagametofitos permitió concluir que megagametofito. estas variantes corresponden a diferentes 1: Marcador de Peso Molecular duplicaciones en el genoma, descartando la 2: Ppter_dhn_K2a posibilidad de diferencias alélicas para un mismo loci propuestas por Velasco-Conde et 3: Ppter_dhn_K2b 4: Ppter_dhn_AESK3a y AESK3a2 5: Ppter_dhn_AESK3b al. (2012) (Figura 5.3). Las otras tres deshidrinas identificadas pertenecen a grupos diferentes; Ppter_dhn_ESK2 es la única deshidrina dentro las identificadas que presenta una inserción previa a la región de aminoácidos conservados en la zona N-terminal. La región codificante la forman 531 nucleótidos y presenta un intrón de 99 nucleótidos. Ppter_dhn_AESK4 tiene una región codificante de 714 nucleótidos así como un intrón de 108 pares de bases. Por último, Ppter_dhn_A2E2SK5 es la deshidrina de mayor tamaño, con una parte inicial muy larga comparada con el resto, donde aparecen dos repeticiones del segmento A y otras dos del segmento E. La secuencia de la región codificante contiene 978 nucleótidos y también presenta un intrón en este caso mayor, con 334 nucleótidos. Todos los intrones descritos están localizados en medio del segmento S. 80 Caracterización molecular de la familia de las deshidrinas en P. pinaster Tabla 5.1 Descripción de las deshidrinas de P. pinaster analizadas en la presente tesis Deshidrina Acc. genómico Acc. ARNm Descripción Ppter_dhn_K2a HE796685 HE716959 Ppter_dhn_K2b HE796686 HE716960 Estructura en intrones y exones Contiene dos segmentos K (KEPGLVDKIKEKIPG, KKPGVVDKIKEKLPG) Contiene dos segmentos K (KKPGLVDKIKEKLPG, KKPGMFDKIKEKLPG) Contiene dos segmentos K (KKKGLKDKIKEKLPG, Ppter_dhn_ESK2 HE796687 HE716961 KKGLVDKIKDKLPG), un segmento E (GHGHAGQFTAAEAEKQQHT) y un segmento S con 10 residuos de serina Contiene un segmento A (EASSYYP), un segmento E Ppter_dhn_AESK3a HE796688 HE716962 (GHGHEGQFAPEDAKQQKH), un segmento S con ocho residuos de serina y tres segmentos (KKKGSKDKTKEKLPG, KKTGLVGKIKEKIPG, KKTGMLDKIKEKLPG) Contiene un segmento A (EAASYYP), un segmento E Ppter_dhn_AESK3a2 HE796689 HE716963 (GHGHEGQFAPEEAKQQKH), un segmento S con ocho residuos de serina y tres segmentos K (KKKGSKDKTKEKLPG, KKTGLVGKIKEKIPG, KKMGMLDKIKEKLPG) Contiene un segmento A (EAASYYP), un segmento E Ppter_dhn_AESK3b HE796690 HE716964 (GHGYEGQFTPEEAEQQKH), un segmento S con ocho residuos de serina y tres segmentos K (KKKGSMEKTKEKLPG, KKTGLLDKIKEKIPG, KKTGLLDKIKEKLPG) Contiene un segmento A (EAASYYP), un segmento E Ppter_dhn_AESK4 HE796691 HE716965 (GHGHEEQPTPEEAEQQKH), un segmento S con ocho residuos de serina y cuatro segmentos K (KKKGSKDKSKEKLPG, KKTGLLDKIKEKIPG, KKTGLLDKIKEKIPG, KKLGVLGKIKEKLPG) Contiene dos segmentos A (EAASYYP, EAASYYP), dos segmentos E (GHGHEGQLTPEEAEQQKR, GHGHEGQLTPEEAEQQKH), un Ppter_dhn_A2E2SK5 HE796692 HE716966 segmento S con siete residuos de serina con una ileucina intercalada y cinco segmentos K (KKKEAKDKTKKKVPG, KKAGLLDKFKEKLPA, KKTGLLDKIKEKLPV, KKAGLLDKIKEKLPG, KKISLIDKIKEKLPG) 81 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 5.2.4 Análisis de la expresión por RT-PCR Se analizó la expresión de todas las deshidrinas identificadas para cada órgano de la planta (raíces, tallos y acículas) (Figura 5.4). Según el modelo lineal considerado para los niveles de expresión, el cual explica más del 97,5% de la variación observada para cada gen y órgano estudiado, tanto la duración de la sequía, como el genotipo y la interacción entre ambos son factores significativos en todos los casos (p valor<0.0005), salvo para Ppter_dhn_AESK3a2 en raíz, donde el genotipo no es significativo (p valor=0.3142). El nivel de estrés es el factor más determinante en la mayoría de los casos, contabilizando más del 50% de la variación observada, y alcanzando más del 80% para Ppter_dhn_K2a en raíz y para Ppter_dhn_AESK4 en acícula. A pesar de las diferencias detectadas entre genotipos se pueden reconocer tendencias generales en los patrones de expresión de las deshidrinas estudiadas. Los niveles de transcritos de Ppter_dhn_K2a y Ppter_dhn_K2b aumentan de forma significativa especialmente desde los 30 días sin riego, alcanzando en raíces niveles de expresión entre 4 y 9 veces superiores respecto las plantas sin estresar. En tallo se observa una inducción mucho menor, entre 1.5 y 2.5 veces mayores que los niveles de las plantas control. Las mayores diferencias entre genotipos se observan en las acículas, con valores de sobreexpresión entre 5 y 16 veces superiores al control a los 40 días de tratamiento. Ppter_dhn_A2E2SK5 muestra un patrón de expresión similar a los genes anteriores, con valores de inducción en acículas ligeramente inferiores. La mayor inducción la presenta Ppter_dhn_ESK2. Los niveles de transcritos de esta deshidrina aumentan desde el primer punto de muestreo y en los tres órganos estudiados durante todo el experimento alcanzando valores de expresión desde 1.000 hasta 10.000 veces superiores a los de las plantas control. Este resultado concuerda con el observado por Lorenz et al. (2011) para el ortólogo en Pinus taeda, deshidrina 2 (ACA51879.1), descrito como uno de los 25 genes más sobreexpresados durante el estrés hídrico. Este gen muestra la mayor divergencia en la secuencia de nucleótidos y aminoácidos respecto a las otras deshidrinas identificadas y no se ha detectado ninguna duplicación o parálogo en el genoma. Esto convierte a Ppter_dhn_ESK2 en un buen gen candidato para su empleo en estudios de variación poblacional, siendo fácil de desarrollar y aplicar marcadores específicos (como genotipado mediante SNP) o técnicas de resecuenciación, evitando interferencias con otros miembros de la familia. 82 Caracterización molecular de la familia de las deshidrinas en P. pinaster Figura 5.4 Análisis de la expresión por RT-PCR de las ocho deshidrinas identificadas en P. pinaster. Se analizó la expresión relativa al control a lo largo del tratamiento de estrés hídrico en raíz, tallo y acícula. Los cambios significativos en la expresión respecto a las plantas control (95% de nivel de significancia) se indican con un asterisco. 83 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Ppter_dhn_AESK4 presenta una inducción mucho menor, aunque significativa. Los mayores niveles de sobreexpresión se dan en raíz, con valores entre 2 y 4 veces superiores a los de las plantas sin estresar. Por otro lado, la sobreexpresión de las tres variantes tipo AESK3 es muy baja, aunque significativa y en algunos órganos y puntos de estrés la expresión del gen es incluso menor que en las plantas sin estresar. Solamente en raíz o en el último punto de estrés en acícula los niveles de expresión son aproximadamente el doble que en las plantas sin estresar. Los bajos niveles de inducción de estas deshidrinas tipo AESKn podrían correlacionarse con la presencia simultanea de una única copia de los segmentos conservados A y E, identificados en las secuencias de aminoácidos de pináceas. Los mayores niveles de inducción observados en este trabajo están presentes en el Ppter_dhn_ESK2, deshidrina que presenta segmento E pero sin segmento A. También muestran importante inducción las deshidrinas tipo Kn, que no presentan ninguno de estos fragmentos. La deshidrina Ppter_dhn_A2E2SK5, que muestra una duplicación de cada uno de estos fragmentos, vuelve a presentar unos niveles de inducción frente a la sequía similares a los observados en las deshidrinas Kn. Aunque no es descartable cierta funcionalidad de las deshidrinas tipo AESKn en respuesta a estrés hídrico, teniendo en cuenta las diferencias leves pero significativas observadas en los niveles de expresión, los resultados nos sugieren que podrían jugar un papel más importante en otros procesos. Serían necesarios nuevos análisis para determinar una posible implicación de los nuevos segmentos conservados descritos en la regulación de la expresión de las deshidrinas de pináceas. A pesar de su conocida implicación en la respuesta al estrés hídrico, los trabajos previos encaminados a la identificación de genes inducidos por estrés hídrico en P. pinaster no habían detectado deshidrinas (Dubos et al., 2003; Dubos y Plomion, 2003), hecho que se repitió en la construcción de la librería sustractiva obtenida durante la presente tesis, descrita en el capitulo 3. Únicamente se habían realizado estudios preliminares de su expresión durante diferentes estreses abióticos con Pinus taeda (Watkinson et al., 2003; Lorenz et al., 2011) o Pinus sylvestris (Joosen et al., 2006). Recientemente se ha publicado un estudio de la expresión de varias deshidrinas a lo largo de un tratamiento de sequía en varias procedencias de P. pinaster de regímenes hídricos contrastados (Velasco-Conde et al., 2012). No obstante, nuestros resultados difieren ligeramente de los obtenidos en dicho trabajo. La mayor diferencia reside en el patrón divergente descrito para SK5 en dos de los genotipos coincidentes con los que se han empleado en el presente capítulo, y en el perfil descrito para SK2, diferente 84 Caracterización molecular de la familia de las deshidrinas en P. pinaster entre genotipos y con una apreciable represión en la primera semana con disponibilidad de agua reducida en aquel trabajo. Distintos factores pueden haber contribuido a estas discrepancias. En primer lugar, el tipo de estrés aplicado en el trabajo de Velasco-Conde et al. (2012) es más corto y suave que el utilizado en el presente trabajo y muchos genes que muestran un leve descenso en los niveles de transcripción en las primeras etapas del tratamiento están en realidad notablemente sobreexpresados cuando aumentan los niveles de estrés. En segundo lugar, durante el desarrollo de esta tesis se ha analizado el patrón de expresión no sólo en acícula sino también en tallo y raíz, órgano este último especialmente relevante durante el estrés hídrico. Por último, Velasco-Conde et al. (2012) no aislaron y secuenciaron los genes completos en el estudio, lo que les llevó a una interpretación incorrecta de variación alélica, frente a la duplicación en el genoma que se ha verificado en el presente trabajo. Esto puede llevar asociado un diseño inespecífico de los cebadores utilizados en la RT-PCR, produciendo amplificaciones cruzadas, lo que resta fiabilidad a los resultados de expresión obtenidos. Por el contrario, en este trabajo se han diseñado cebadores específicos para cada forma evitando en lo posible la hibridación cruzada. Igualmente, la secuenciación de los amplicones confirmó la especificidad de la reacción. Los resultados presentados en este capítulo dieron origen al artículo: Novel conserved segments are associated with differential expression patterns for Pinaceae dehydrins, Planta 236 (2012) 1863-1874 (Anexo V) 85 6. CARACTERIZACIÓN MOLECULAR DE GENES INDUCIDOS POR ESTRÉS HÍDRICO EN P. PINASTER Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster Durante el desarrollo de la tesis se ha identificado un importante número de genes inducidos de forma significativa durante el estrés hídrico en pinos y cuya posible función ha sido establecida de acuerdo con su homología con otras secuencias anotadas depositadas en las bases de datos. No obstante, como se mencionó en el capítulo 1, aunque este procedimiento es de uso común, las funciones de los genes deben ser confirmadas experimentalmente. Esto es especialmente importante en el caso de las gimnospermas, ya que las anotaciones en muchos casos se basan en investigaciones previas realizadas con angiospermas, y dado el largo tiempo de divergencia entre ambas divisiones, es probable que se den notables diferencias en la actividad de genes homólogos. Por este motivo, se abrió una línea de investigación que pretende caracterizar de forma más profunda algunos de estos genes: una deshidrina y una nodulina putativa, ambas con una marcada sobreexpresión durante el estrés hídrico, y un posible factor de transcripción del tipo AP2. Los primeros pasos de esta línea de investigación incluyen la obtención y estudio de la secuencia completa de los genes seleccionados así como de su región promotora y la obtención de organismos modificados genéticamente con los genes de estudio para su empleo en futuros experimentos, actividad que se llevó a cabo en parte durante una estancia en el Instituto de Tecnología Química y Biológica de Oeiras (Lisboa) Existen dos estrategias complementarias para el estudio de la acción de genes in vivo mediante la utilización de organismos modificados genéticamente: Pérdida de función del gen: La función del gen estudiado es alterada por la inserción, deleción o sustitución en una zona específica, remplazando el gen funcional por un alelo inactivo. Los métodos convencionales introducen mutaciones de forma aleatoria en el genoma de la planta, inducidas por agentes físicos (rayos X, neutrones rápidos, etc…) o químicos (agentes metilantes o intercalantes) (Østergaard y Yanofsky, 2004). Otra técnica comúnmente empleada se basa en la inserción aleatoria de fragmentos de ADN como los transposones o T-DNA. Si la inserción se produce dentro de las regiones codificantes o reguladoras de un gen pueden dar lugar a la pérdida de su función (Krysan et al., 1999; Ramachandran y Sundaresan, 2001). Por 89 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. último, otras metodologías utilizan el ARN de interferencia (ARNi) para provocar el silenciamiento del gen. Ganancia de función del gen: Estas técnicas buscan un incremento de los niveles de expresión de un gen específico o bien la activación aleatoria de genes endógenos, al incrementar los niveles de potenciadores de la transcripción. En ambos casos el gen o el potenciador de la transcripción se expresan de forma constitutiva en las células de la planta al ser clonados bajo el control de un fuerte promotor (Nakazawa et al., 2003; Ichikawa et al., 2006). Mediante la ganancia de función se puede analizar de forma independiente el efecto del gen de interés cuando éste es miembro de una familia génica, mientras que el silenciamiento puede inhibir la actividad de varios miembros de la familia de forma simultánea. La embriogénesis somática y su uso asociado a la transformación de P. pinaster El estudio de individuos transformados genéticamente en especies alógamas, como es el caso de los pinos, requiere de un eficiente sistema de propagación vegetativa a partir de las células transformadas. La embriogénesis somática, a partir de estas células, se basa en el principio de totipotencialidad celular enunciado por Haberlandt por el cual todas las células de un organismo, al contener la misma información genética que la célula inicial o zigoto podrían volver a expresar un patrón de desarrollo embriogénico hasta formar auténticos embriones somáticos. La embriogénesis somática se consigue por primera vez en los años 50 a partir de células de parénquima de la raíz de zanahoria y ha ido ganando importancia hasta el punto de estar considerada como la técnica más adecuada para la micropropagación de especies forestales (Sutton, 2002). El empleo de las masas embrionarias como tejido diana en procesos de transformación genética permite la obtención de células modificadas genéticamente que una vez seleccionadas siguen los pasos normales de multiplicación y maduración hasta generar individuos completos con la modificación genética incorporada (Peña y Séguin, 2001). Las líneas transformadas pueden ser criopreservadas en nitrógeno líquido para mantener su potencial multiplicativo. En coníferas se consiguió por primera vez inducir embriogénesis somática en Picea abies (Chalupa, 1985; Hakman y Arnold, 1985) y Larix decidua (Nagmani y Bonga, 1985). La embriogésis somática en pinos ha sido ampliamente estudiada a lo 90 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster largo de las últimas dos décadas (Klimaszewska et al., 2007 y referencias citadas). Los primeros resultados con P. pinaster llegaron a finales de los años 80 (JarletHugues, 1989). Desde entonces la embriogénesis somática en esta especie ha sido ampliamente estudiada (Bercetche y Pâques, 1994; Lelu-Walter et al., 2002; LeluWalter et al., 2006), (Miguel et al., 2004), consiguiéndose importantes progresos en la iniciación y proliferación de las masas embrionarias, así como en pasos posteriores relativos a la maduración y conversión en planta. Igualmente, se han conseguido importantes progresos en la transformación genética de pinos. La mayoría de los trabajos publicados se centran en la optimización de los protocolos de transformación empleando tanto bombardeo de partículas como Agrobacterium tumefaciens (Trontin et al., 2007 y referencias citadas). Hasta la fecha son pocos los trabajos publicados con pinos que han empleado genes específicos en busca de un determinado carácter adaptativo. Se podrían destacar diferentes trabajos de Tang et al. con P. virginiana, P. strobus o P. taeda (Tang y Tian, 2003; Tang et al., 2005 y 2007). En el caso de P. pinaster se están realizado importantes esfuerzos dentro del proyecto europeo Sustainpine para obtener un protocolo eficiente que combine la embriogénesis somática con la transformación genética a fin de obtener transgénicos que permitan hacer una caracterización funcional profunda de un grupo de genes seleccionados por sus implicaciones en procesos concretos como absorción de nitrógeno, desarrollo de madera o alguno de los seleccionados en esta tesis de respuesta a estrés hídrico. Transformación genética de Arabidopsis thaliana como especie modelo Dadas las dificultades aún existentes en coníferas para la transformación y regeneración de las líneas transformadas, así como la larga duración de todo el proceso, hasta poder realizar con las plantas los experimentos pertinentes, a menudo se recurre a la expresión heteróloga del gen estudiado en una planta modelo, siendo Arabidopsis thaliana la más utilizada. Distintos trabajos han abordado con éxito la expresión heteróloga en esta especie de genes relacionados con la sequía. Así, por ejemplo, se ha descrito la mayor resistencia tanto a salinidad como a sequía de Arabidopsis que sobreexpresaban un factor de transcripción de tipo MYB de crisantemo (Shan et al., 91 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 2012) o una proteína con motivos estructurales del tipo “dedo de zinc” Cys2/His2, de la misma especie (Gao et al., 2012). Zhang et al. (2007) refieren la tolerancia a sequía conferida por la sobreexpresión en Arabidopsis de factores de transcripción tipo ERF de Medicago trunculata. Sin embargo, la expresión heteróloga, en un entorno molecular diferente, puede no tener el mismo efecto que en la especie de origen. 6.1 Material y métodos 6.1.1 Amplificación de los genes completos y de su región promotora Se empleó el ADN genómico y el ADNc, así como el procedimiento descrito en el capítulo 5.3.3 de la presente tesis. En aquellos casos en los que no existían secuencias en las bases de datos sobre las que diseñar cebadores específicos se realizó la búsqueda a partir de RNA total empleando el “SMARTTM RACE ADNc Amplification Kit” (Clontech, CA, USA). Una vez obtenida la secuencia del gen se diseñaron dos cebadores específicos y consecutivos hacia la región N-terminal. Se utilizó el “GenomeWalkerTM Universal Kit” (Clontech, CA, USA) para amplificar la región correspondiente al promotor. Aquellos productos de amplificación que mostraban tamaño y calidad suficiente fueron clonados en el vector pGEM®T-easy (Promega, WI, USA) y transferidos a Escherichia coli DH5α. Las secuencias obtenidas fueron alineadas con el gen para corroborar que correspondía a la región promotora. 6.1.2 Análisis de secuencias Una vez obtenida la secuencia completa del gen se realizó una nueva búsqueda en las bases de datos del NCBI para determinar la homología del gen completo e identificar posibles dominios o regiones conservadas mediante la base de datos CDD (Conserved domain database) (Marchler-Bauer et al., 2011). Se analizó la estructura de intrones y exones de los genes alineando las secuencias correspondientes al ADN genómico y el ADNc con el programa Spidey (http://www.ncbi.nlm.nih.gov/spidey/). Se identificaron y analizaron posibles regiones reguladoras presentes en la región promotora de los genes empleando la base de datos TransFac (Wingender et al., 1996). 92 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster 6.1.3 Construcción del vector de sobreexpresión La construcción de los vectores de sobreexpresión se llevó a cabo utilizando el kit “Gateway® Technology with clonaseTM II” (Invitrogen). Los vectores con los genes completos fueron empleados como molde para construir el producto de PCR flanqueado por los adaptadores attB. Como vector donador se utilizó el pDONRTM 201 (Invitrogen). Se utilizaron vectores de sobreexpresión diferentes en función de la especie a transformar. En el caso de Arabidopsis thaliana de utilizó el vector de sobreexpresión pK7WG2.0 (Figura 6.1) y para la transformación de P. pinaster se empleó el vector pMBb7Fm21GW-UBIL (Figura 6.2), ambos comercializados por el Departamento de Biología de Sistemas de Plantas de la Universidad de Gante (Bélgica). Las zonas de inserción de las construcciones finales fueron secuenciadas para corroborar la posición de los genes en el vector final de sobreexpresión. Figura 6.1 Vector de sobreexpresión pK7WG2.0 utilizado en la transformación de Arabidopsis thaliana. Contiene genes que confieren resistencia a espectinomicina y kanamicina. El gen de estudio se inserta entre las regiones attR1 y attR2, y se induce su expresión por la acción del promotor 35S del virus de la coliflor. La cepa de Agrobacterium contiene un plásmido sin TDNA pero con los genes Vir que actúan durante la infección de las células vegetales. Figura 6.2 Vector pMBb7Fm21GW-UBIL de sobreexpresión utilizado en la transformación de Pinus pinaster. Contiene genes que confieren resistencia a espectinomicina y fosfinotricina. El gen de estudio se inserta entre las regiones attR1 y attR2, y se induce su expresión por la acción del promotor de la ubiquitina del maíz. El vector también incorpora el gen GFP (Green Fluorescence Protein) que puede emplearse como marcador fluorescente de las células transformantes. La cepa de Agrobacterium contiene un plásmido sin T-DNA pero con los genes Vir que actúan durante la infección de las células vegetales. 93 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. 6.1.4 Material vegetal y condiciones de cultivo 6.1.4.1 Arabidopsis thaliana Se utilizaron semillas de Arabidopsis thaliana del ecotipo Columbia (Col-0) para generar plantas. La esterilización de las semillas se realizó mediante un lavado de 5 minutos con 1ml de hipoclorito sódico al 35%, seguido de tres lavados consecutivos con agua estéril. Tras el último lavado las semillas se dejaron en agua estéril y en oscuridad a 4ºC durante 4 días para su vernalización. Las plantas que iban a ser empleadas en la transformación fueron sembradas directamente en maceta con una mezcla de sustrato turba:vermiculita:perlita (4:3:2 en volumen). Las semillas de las líneas transformadas fueron sembradas en placas de Petri con medio Murashige and Skoog con kanamicina (50mg/L). A los 15 días las plántulas fueron trasplantadas a macetas con la mezcla de sustrato descrita anteriormente. Las plantas se mantuvieron en cámara de cultivo con fotoperiodo 16/8 día/noche y a una temperatura de 22ºC por el día y 18ºC por la noche. 6.1.4.2 Células embrionarias de Pinus pinaster Se empleó la línea celular del genotipo PN519 procedente de cruzamientos controlados entre arboles élite de familias no relacionadas, que fue proporcionada por el FCBA (Forêt Cellulose Bois-Construction Ameublement; Francia) dentro del proyecto europeo Sustainpine. La línea fue cultivada en oscuridad a 23ºC en placas de Petri con medio de proliferación mLV descrito por el FCBA (Francia) realizando subcultivo cada dos semanas seleccionando aquellas células que presentaban crecimiento activo. 6.1.5 Transformación genética mediada por Agrobacterium tumefaciens 6.1.5.1 Transformación de Arabidopsis thaliana Las construcciones con el plámido pK7WG2.0 fueron transferidas a la cepa de Agrobacterium tumefaciens LBA4404. La transformación de las plantas se llevo a cabo siguiendo el protocolo descrito por Clough y Bent (1998). Se dejaron de regar las plantas días antes de realizar la transformación, cuando presentaban mayoritariamente flores inmaduras y pocas silicuas fertilizadas que fueron eliminadas antes del tratamiento. Las plantas fueron sumergidas durante 30 segundos con 94 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster agitación constante en una solución fresca de sacarosa al 5%, 10mM de MgCl2 y 0.05% de Silwet L-77 con la que se resuspendieron las bacterias crecidas durante una noche hasta alcanzar una densidad óptica a 600nm próxima a 1. Las plantas impregnadas con la solución de Agrobacterium fueron envueltas y mantenidas en posición horizontal durante 24 horas y posteriormente continuaron su crecimiento en condiciones normales hasta completar el ciclo vital. 6.1.5.2 Transformación de células embrionarias de Pinus pinaster En el caso de la transformación de células embrionarias de P. pinaster las construcciones con el plásmido pMBb7Fm21GW-UBIL fueron transferidas a la cepa de Agrobacterium tumefaciens C58Pmp90. Se empleó el protocolo proporcionado por el FCBA (Nangis; Francia) dentro del proyecto europeo Sustainpine realizando ligeras modificaciones. En primer lugar, en el proceso de descontaminación en lugar de vacío se situó el filtro con la masa embrionaria tratada con Agrobacterium sobre una torre de toallas de papel y se realizaron tres lavados consecutivos de 1ml de DM líquido, según el protocolo del ITQB (Lisboa, Portugal). En segundo lugar, durante el proceso de selección se aumentó progresivamente la concentración de fosfinotricina (PPT) (0,1mg/L las primeras 2 semanas; 0,5 mg/L de la segunda a la sexta semana y 1 mg/L se la sexta a la décima semana) y se intercalaron los antibióticos en cada cambio de medio (Augmentine 300mg/L o timentine 400mg/l) para la eliminación de Agrobacterium. Las líneas transformadas fueron seleccionadas y subcultivadas cada dos semanas hasta completar un mínimo de 12 semanas en medio de selección. Posteriormente se cultivaron en medio de proliferación mLV para su multiplicación. La maduración y germinación de las líneas se realizó siguiendo el protocolo descrito por Lelu-Walter et al. (2006). 6.2 Resultados y discusión 6.2.1 Análisis de la secuencias 6.2.1.1 Ppter_dhn_ESK2 Ppter_dhn_ESK2 es la deshidrina que presentó, con diferencia, mayores niveles de sobreexpresión en todos los tratamientos estudiados. Como se describe en el capítulo 5.3.3 de la presente tesis, la región codificante de este gen la forman 531 nucleótidos y presenta un intrón de 99 nucleótidos (Figura 6.3). La secuencia 95 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. deducida para la proteína está compuesta por 177 aminoácidos, entre los que encontramos un segmento E (GHGHAGQFTAAEAEKQQHT), un segmento S con 10 residuos de serina y dos segmentos K (KKKGLKDKIKEKLPG, KKGLVDKIKDKLPG). Figura 6.3 Estructura de la deshidrina Ppter_dhn_ESK2. Los exones son identificados en color naranja; se señala el nucleótido de inicio y final de cada exón en la parte superior. Durante la búsqueda de la secuencia promotora se obtuvo un fragmento de 372pb en dirección 5´ desde el punto de inicio de la transcripción. A pesar de que este fragmento no se corresponde con el promotor completo del gen se han encontrando diferentes motivos reguladores para factores de transcripción implicados en estrés hídrico (Figura 6.4): En la posición -352(+) se encontró un elemento de unión para factores de transcripción tipo NAC. Se identificó un elemento de unión para factores de transcripción tipo DREB en la posición -219(+). Figura 6.4 Secuencia de nucleótidos correspondiente al gen completo y región promotora de la deshidrina Ppter_dhn_ESK2 y secuencia de aminoácidos deducida para la misma. Se diferencia la región codificante en mayúsculas y se identifica el codón de inicio __, el codón de terminación __ y la secuencia de aminoácidos correspondiente los segmentos E __, S __ y K __. En la región promotora se señalan posibles elementos de unión para factores de transcripción según código de colores descrito en la Tabla S9 del Material Suplementario. 96 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster En la posición -213(-) se identificó la secuencia CCACGT, descrita como un elemento de reconocimiento para el factor de transcripción ABI5, del tipo bZIP. En la posición -163(+) aparece otro motivo de reconocimiento para factores de transcripción tipo bZIP para el que se ha descrito regulación por luz y hormonas. 6.2.1.2 Nodulina Este gen presentó alta inducción en todos los tratamientos y órganos durante el análisis de la expresión mediante microarrays. En la genoteca sustractiva se habían obtenido dos fragmentos para este gen; uno de ellos con un solo clon de 351pb correspondiente a la región 3´ del gen que presentaba el poliA (SI_Ppin_R4_311) y otro fragmento de 547pb compuesto por dos clones (SI_Ppin_R1_59 y SI_Ppin_R8_711) que codificaba una secuencia parcial del gen. Se obtuvo la secuencia completa del mensajero mediante Smart RACE. La región codificante está compuesta por 756pb. La secuencia de 255 aminoácidos deducida contiene dos dominios conservados característicos de la superfamilia PQ-loop; los miembros de esta familia génica codifican proteínas de membrana que presentan dos hélices transmembrana unidas por un bucle. Los dominios encontrados en la secuencia de la nodulina se corresponden específicamente con la subclase MtN3_slv (pfam03083). Este dominio está presente en un tipo de transportadores de azúcares denominados Sweets descritos recientemente por Cheng et al. (Chen et al., 2010). Trabajos previos sugieren que estas proteínas podrían estar implicadas en el desarrollo del polen, resistencia a patógenos, senescencia, secreción de azúcar en nectarinas y en la absorción de glucosa en raíces de Arabidopsis (Ge et al., 2000; Yang et al., 2006; Chaudhuri et al., 2008; Guan et al., 2008). Los transportadores Sweets podrían estar localizados en la membrana plasmática y estar implicados en el transporte bidireccional a través de la membrana de glucosa y otros azúcares aún por determinar (Slewinski, 2011). (Figura 6.5). 97 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 6.5 Alineamiento de la secuencia de aminoácidos de la nodulina con genes homólogos en otras especies (transportadores de azucares tipo Sweets). Las dos regiones que presentan mayor homología corresponden a los dominios conservados tipo MtN3_slv. La región C-terminal presenta el menor grado de conservación entre los genes comparados. 98 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster La secuencia genómica presenta seis exones de 40, 37, 223, 162, 123 y 183 nucleótidos y cinco intrones de 196, 106, 165, 266 y 106 nucleótidos (Figura 6.6). Figura 6.6 Estructura del gen de la nodulina. Los exones son identificados en color naranja; se señala el nucleótido de inicio y final de cada exón en la parte superior. Durante la búsqueda de la secuencia promotora se obtuvo un fragmento de 1956 nucleótidos en dirección 5´ desde el codón de inicio de la transcripción. En esta región se identificaron numerosos motivos de unión para factores de transcripción y muchos de ellos han sido previamente descritos en regulación durante el estrés hídrico u otros estreses abióticos así como regulación por diferentes hormonas (Figura 6.7). En la posición -1654(-) y -939(+) se encontraron dos elementos de unión para factores de transcripción tipo NAC. Se identificó un elemento de unión para factores de transcripción tipo DREB en la posición -1614(-) y dos elementos más para CBF1 en las posiciones -1312 (-) y -324(+). En la posición -1459(+) existe un elemento de unión descrito para el factor de transcripción BCP1, el cual se induce por etileno. En las posiciones -1181(-) y -665(-) se identificaron dos elementos de unión para un factor de transcripción tipo bZIP regulado por luz y hormonas. Se identificaron tres elementos de unión en las posiciones -846(+), -761(-) y -157(+) para el factor de transcripción ASR1 (Abscisic stress ripening 1) identificado en tomate, con alta inducción en condiciones de sequía y durante la formación del fruto. Se identificó en la posición -606(+) un elemento de unión para un factor de transcripción tipo NAC involucrado en la represión de la respuesta mediada por calmodulina. 99 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. En la posición -566(-) se identificó un elemento de unión para un factor de transcripción tipo WRKY que está implicado en la activación rápida de proteínas PR (Pathogenesis related) en situaciones de estrés. Figura 6.7 Secuencia de nucleótidos correspondiente al gen completo y región promotora de la nodulina y secuencia de aminoácidos deducida para el mismo. Se diferencia la región codificante en mayúsculas y se identifica el codón de inicio __, el codón de terminación __ y la secuencia de aminoácidos correspondiente al dominio conservado __. En la región promotora se señalan posibles elementos de unión para factores de transcripción según código de colores descrito en la Tabla S9 del Material Suplementario 100 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster 6.3.1.3 Factor de transcripción tipo AP2 El factor de transcripción seleccionado presentó una inducción significativa con valores entre 2 y 3 veces los del control en los experimentos de sequía con P. pinaster y P. pinea. Aunque mediante microarrays no se detectó sobreexpresión significativa en el tratamiento con PEG, el gen sí se identificó en la genoteca sustractiva, en la que se obtuvo un fragmento de 189pb (clon SI_Ppin_R4-371). Tras realizar una búsqueda en la base de datos del PGI se le asignó el TC178542. Se diseñaron cebadores específicos para amplificar el gen completo a partir de ADN genómico y ADNc respectivamente. La región codificante del gen consta de 705pb, sin presencia de intrones. Su traducción da lugar a una proteína de 235 aminoácidos. Esta secuencia contiene un dominio de copia única característico de la superfamilia AP2 (Apetala 2; pfam00847), característica asociada a factores de transcripción involucrados en la respuesta al estrés. Este gen muestra mayor homología con factores de transcripción de la familia EREBP (Ethylene responsive element binding protein). Se realizó un alineamiento con genes homólogos de otras especies. El dominio más próximo a la zona N-terminal presenta un alto grado de homología entre especies e igualmente se encontró alto grado de conservación en dos regiones cercanas a la región C-terminal, una de ellas compuesta por varios residuos de serina, lo que podría estar relacionado con algún proceso de fosforilación (Figura 6.8). El dominio de estos factores de transcripción se une de manera específica a la secuencia de 11pb conocida como caja GCC de un elemento denominado ERE (Ethylene response element) (Fujimoto et al., 2000); la presencia de este elemento en la secuencia promotora de ciertos genes es determinante para regular la respuesta mediada por etileno. No obstante, se ha descrito inducción de varios miembros de esta familia ante diferentes estreses abióticos como sequía, salinidad o bajas temperaturas y en diferentes especies (Nakano et al., 2006; Zhuang et al., 2008; Sharma et al., 2010). En varios trabajos se ha observado que la sobreexpresión de factores de transcripción de este tipo puede conferir cierto grado de resistencia a la sequía (Park et al., 2001; Zhang et al., 2007; Zhang et al., 2009). Así, Tang et al. (2005; 2007) describieron un aumento en la resistencia a sequía y otros estreses abióticos tanto en Pinus strobus como en Pinus virginiana en aquellas plantas que sobreexpresaban un ERF identificado en pimiento. Recientemente se ha descrito en Arabidopsis la 101 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. implicación de la respuesta mediada por etileno en la detención del ciclo celular durante el estrés osmótico (Skirycz et al., 2011). Figura 6.8 Alineamiento de la secuencia de aminoácidos del factor de transcripción tipo AP2 frente a genes homólogos en otras especies, correspondientes a la familia ERF. Cerca de la región N-terminal se encuentra el dominio característico de la superfamilia AP2 con un alto grado de conservación entre especies. La región N-terminal presenta dos zonas conservadas, una compuesta por varios residuos de serina, por lo que podría corresponder a una zona de fosforilación y una segunda zona conservada más próxima la región C-terminal. 102 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster Aunque aún se desconocen los genes que podrían estar regulados por los ERF, todo parece indicar que podrían estar implicados en la respuesta a estrés, tanto de un modo dependiente como independiente de etileno, similar a la regulación descrita para las rutas de señalización mediadas por ABA (Mizoi et al., 2012). Durante la búsqueda de la secuencia promotora se obtuvo un fragmento de 2158 nucleótidos en dirección 5´ desde el punto de inicio de la transcripción. Se identificaron numerosos elementos de unión para diferentes factores de transcripción (Figura 6.9); En la posición -1887(-) se identificó un elemento de unión para un factor de transcripción tipo bZIP regulado por luz y hormonas. En la posición -1884(+) se encuentra un elemento de unión para factores de transcripción tipo NAC. Se identificó un elemento de unión para factores de transcripción tipo DREB en la posición -1775(-). Se identificaron dos elementos de unión para factores de transcripción tipo WRKY en las posiciones -1660(+) y -1076(+). Es remarcable la presencia repetitiva del elemento de unión para factor de transcripción ASR1, descrito en respuesta a estrés hídrico en tomate, repetida hasta en 8 regiones de la secuencia estudiada; -1650(+), -1239 (-), -1137(-), -1081(-), -1072(-), 939(-), -832(-) y -536(-). En la posición -1570(-) se sitúa un elemento de unión descrito para RAV1, un factor de transcripción tipo AP2/ERF que actúa como regulador negativo del crecimiento y desarrollo de la planta. También es llamativa la presencia de un elemento de reconocimiento tipo caja GCC, que como se indicó anteriormente, es el reconocido por factores transcripción de la familia del estudiado (-959(-) ERF1 o -958(-) PLT1). Esto podría indicar una regulación en cascada para este tipo de factores de transcripción o una autorregulación del gen de estudio durante la respuesta a estrés hídrico. 103 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 6.9 Secuencia de nucleótidos correspondiente al gen completo y región promotora del factor de transcripción tipo AP2 y secuencia de aminoácidos deducida para el mismo. Se identifica el codón de inicio __, el codón de terminación __ y la secuencia de aminoácidos correspondiente al dominio conservado __. En la región promotora se identifican posibles elementos de unión para factores de transcripción según código de colores descrito en la Tabla S9 del Material Suplementario 104 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster 6.3.2 Transformación de Arabidopsis thaliana Se plaquearon semillas cosechadas de las plantas tratadas con la solución de Agrobacterium. Una vez germinadas las plantas se seleccionaron aquéllas que presentaban un crecimiento normal en el medio selectivo con kanamicina (Figura 6.10). Figura 6.10 Plantas de Arabidopsis transformadas con diferentes genes correspondientes a la primera generación t0. Las plantas transformadas presentaban crecimiento normal en medio de cultivo con kanamicina. Estas líneas transformadas correspondientes a la primera generación o t0 fueron cultivadas en las condiciones habituales favoreciendo la autofecundación. Se recogieron las semillas de las plantas seleccionadas y se prosiguió con el procedimiento de selección analizando el porcentaje de germinación hasta alcanzar la tercera generación, t3, probablemente homocigota para el gen introducido (Material suplementario Tablas S10, S11 y S12). A partir de la t2 el porcentaje de plantas resistentes en todas las líneas transformantes superó el 85%; se comprobó la incorporación del gen en el genoma de la planta mediante PCR (Figura 6.11). En este momento se dispone de semillas de t3 de al menos tres líneas propagadas de forma independiente desde la t0 para cada gen. Igualmente, cada una de ellas presenta líneas independientes en la última generación (Material suplementario Tablas S10, S11 y S12). Este material está listo para la planificación y desarrollo de experimentos para estudiar la respuesta de las plantas transformantes a diferentes factores abióticos. 105 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 6.11 Plantas de Arabidopsis correspondientes a la generación t2 y comprobación de la transformación por PCR. A la izquierda se muestran plantas transformadas con el gen Ppter_dhn_ESK2 y con la nodulina. A la derecha se muestra la comprobación mediante amplificación por PCR de diferentes líneas transformantes para cada gen. 6.3.3 Transformación de Pinus pinaster Para los tres genes estudiados se realizó la construcción introduciendo el codón de terminación. En el caso de la deshidrina se realizó también una construcción sin codón de terminación, para comprobar el posible efecto provocado por la fusión entre del gen de estudio y el gen GFP. Una vez obtenidas las construcciones fueron transferidas a Agrobacterium. Se sembró en placa desde el reservorio de células conservado a -80º para cada una de las transformaciones (NOD, AP2, DH2_con stop y DH2_sin stop) así como los 106 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster controles positivos (pMb y pCbar). Se realizó una PCR para comprobar que la colonia seleccionada contenía la construcción (Figura 6.12). Figura 6.12 Comprobación por PCR de la colonia de Agrobacterium empleada en la transformación. Se realizaron PCR con un cebador específico del gen y otro diseñado sobre el promotor de la Ubiquitina del vector (Calles 1-4). También se realizaron amplificaciones del gen de resistencia a PPT para todas las construcciones y los controles (Calles 6-11). En la calle central se muestra el marcador de peso molecular. Se emplearon 550 mg de masa celular que se cocultivaron con una suspensión de Agrobacterium ajustado a una densidad óptica a 600nm de 0.6. Se realizó el mismo procedimiento de transformación sobre 550 mg de masa sin poner en contacto con Agrobacterium, que fue empleado como control negativo. Tras el proceso de transformación se realizó la selección agrupando las células en agregados (clumps) en el medio selectivo con PPT. Se aumentó progresivamente la concentración de PPT hasta alcanzar 1 mg/ml en la sexta semana. A partir de este punto se seleccionaron aquellas células que presentaban crecimiento evidente en el medio de selección (Figura 6.13) y se realizó el cálculo de líneas positivas en medio de selección por gramo de masa (Tabla 6.4). Tabla 6.1 Selección de líneas transformantes con crecimiento en medio de selección con PPT 29/3/12 Clumps + Líneas clumps 26/1/12 2/2/12 9/2/12 16/2/12 2/3/12 16/3/12 Final % P+/gr NOD 47 16 23 34 38 41 41 41 87.2 74.5 AP2 49 2 3 6 10 14 16 19 38.7 34.5 DH2_con_stop 53 3 6 12 12 16 25 37 69.8 67.2 DH2_sin_stop 49 3 6 10 18 21 24 26 53 47.2 pMb 67 13 24 33 37 41 42 42 62.7 76.3 pCbar 52 6 10 11 20 25 31 31 59.6 56.3 Control - Se obtuvieron unos porcentajes muy elevados de líneas positivas en medio selectivo con PPT con valores entre 35 y 75 líneas transformantes por gramo de masa empleado. Los mejores resultados se consiguieron para la nodulina, con un 87% de los clumps transformados. 107 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Transformación NOD Control (-) Figura 6.13 Selección de líneas transformantes con crecimiento visible en medio selectivo con PPT. Se muestra como ejemplo la transformación realizada para la nodulina que presento el mayor número de líneas transformantes. Cada dos semanas se realizó un cambio de medio de cultivo, transfiriendo las células más blancas con crecimiento activo a un medio selectivo fresco en el que se fue bajando progresivamente la concentración de PPT. Doce líneas de cada transformación que presentaban mejor proliferación en medio selectivo, fueron transferidas a medio de proliferación (Figura 6.14, Tabla 6.5). Proliferación DH2 con stop Proliferación NOD Figura 6.14 Proliferación de líneas transformantes. Se pasaron a medio de proliferación doce líneas seleccionadas de cada transformación. 108 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster Tabla 6.2 Líneas transformantes que fueron transferidas a medio de proliferación NOD 1 2 3 8 9 10 11 13 15 19 AP2 1 3 4 5 7 8 2 6 9 11 TB 1 2 3 4 5 6 7 9 10 11 DH2_con_stop 1 2 3 4 5 6 7 8 11 12 DH2_sin_stop 1 2 3 4 6 7 8 10 11 14 pMb 1 3 4 8 11 12 13 15 18 19 pCbar 1 2 3 4 5 6 7 8 9 10 Control - 29 12 12 14 16 20 18 - 31 15 13 22 18 23 20 - Se confirmó por PCR la presencia de la construcción en el 100% de las líneas seleccionadas. También se comprobó la persistencia de Agrobacterium en las líneas seleccionadas mediante PCR con cebadores diseñados sobre el gen VirVG (Figura 6.15). Figura 6.15 Comprobación por PCR de las líneas transformantes. Se realizaron PCRs empleando como molde ADN extraído de las células en proliferación. Se utilizó un cebador específico del gen y otro diseñado sobre el promotor de la Ubiquitina del vector (Figura superior). También se realizó la comprobación de la persistencia de Agrobacterium en las líneas transformadas (Figura inferior). En ambos casos se empleó un control positivo (plásmido o colonia de Agrobacterium), células sin transformar (WT) para descartar amplificación control posibles inespecífica negativo para y un descartar contaminaciones en la mezcla de PCR. Se utilizó el control negativo introducido durante la transformación para la generación de falsos positivos. Para ello se transfirieron los agregados de color pardo durante la selección a medio de proliferación. Las células que permanecían vivas proliferaron nuevamente en este medio (Figura 6.16). 109 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Figura 6.16 Obtención de falsos positivos. Se transfirió el control negativo a medio de proliferación para obtener una línea no transformada pero que había experimentado todo el proceso de selección Falsos Positivos Falsos positivos Finalmente, se realizó una maduración a partir de 300mg de masa celular de tres líneas seleccionadas de cada transformación. Tras tres meses en medio de maduración se seleccionaron los embriones maduros y se transfirieron a medio de germinación para la obtención de plantas (Tabla 6.3, Figura 6.17). Tabla 6.3 Maduración de las líneas transformantes seleccionadas Embriones maduros Embriones/gr masa NOD_1 44 146 NOD_3 28 93 NOD_15 36 120 AP2_1 0 AP2_4 111 370 AP2_5 55 183 DH2_con_stop_4 29 96 DH2_con_stop_6 23 76 DH2_con_stop_11 22 73 DH2_sin_stop_3 98 326 DH2_sin_stop_6 56 186 DH2_sin_stop_14 21 70 pMb_1 0 pMb_19 11 37 pMb_23 36 120 pCbar_1 27 90 pCbar_2 56 186 pCbar_3 35 116 Falsos positivos 59 196 Plantas iniciales 18 13 17 48 20 13 11 14 44 15 6 8 24 10 20 15 28 Hasta la fecha, los trabajos publicados sobre la transformación genética de P. pinaster se han centrado en la optimización de protocolos empleando transgenes marcadores. Aunque la transformación genética de esta especie es aún una tarea difícil se están logrando notables progresos en diferentes proyectos en curso. Por ejemplo, en el proyecto Sustainpine se han obtenido rendimientos de transformación de aproximadamente 90 líneas resistentes a PPT por gramo de masa embrionaria 110 Caracterización molecular de genes inducidos por estrés hídrico en P. pinaster utilizando el vector de referencia pCbar (Trontin et al., 2012). Estas eficiencias son notablemente superiores a las obtenidas en trabajos anteriores (Tereso et al., 2006; Trontin et al., 2007). Figura 6.17 Maduración y germinación de las líneas transformantes. En la parte superior se muestra una placa en medio de maduración y diferentes embriones en distinta fase del desarrollo. En la parte inferior los embriones transferidos a medio de germinación comienzan a diferenciarse en las distintas partes de la planta. 111 Genes implicados en la respuesta molecular a estrés hídrico en Pinus pinaster Ait. Las eficiencias de transformación obtenidas en la presente tesis son muy altas, no sólo cuando se utiliza el vector de referencia, sino también cuando se utiliza el vector de sobreexpresión con los transgenes integrados, lo que permite llegar a la conclusión de que la construcción que porta el gen de interés no afecta negativamente al proceso de transformación. Respecto a la regeneración de embriones maduros, el rendimiento de referencia para la línea PN519 no transformada es de 85 ± 16 embriones por gramo de masa utilizada (Trontin et al., 2012). Sin embargo, la pérdida progresiva de la capacidad embriogénica debida al envejecimiento de la línea (normalmente dentro de 6 meses de propagación) puede provocar una reducción del rendimiento. Los procedimientos de transformación someten a ciertas tensiones a los cultivos, y además implican el mantenimiento de los mismos durante largos periodos de tiempo, por lo que suelen afectar negativamente a la maduración de los embriones. Sin embargo en este trabajo se ha obtenido un alto rendimiento de embriones somáticos. La modificación en el proceso de selección de transformantes, aumentando progresivamente la concentración de PPT en el medio de cultivo, podría rebajar el estrés soportado por las células y favorecer la recuperación de las líneas seleccionadas una vez son transferidas al medio de proliferación. En algunos casos, como en las líneas que sobreexpresan el factor de trascripción tipo AP2 y Ppter_dhn_ESK2, este número fue aún mayor que en los controles. Esto puede sugerir que los genes tienen un efecto beneficioso sobre la maduración del embrión; la regulación positiva de estos genes de respuesta a la sequía puede aumentar la capacidad de maduración por jugar un papel fundamental en el proceso de desecación que sufren los embriones hasta que llegan a una etapa de madurez completa. Esta observación debe ser confirmada con experimentos adicionales. Los resultados obtenidos en esta tesis van un paso más allá respecto los trabajos anteriores con P. pinaster. Las plantas transformadas proporcionan una herramienta muy importante para caracterizar el papel de estos genes frente a diferentes situaciones de estrés, abriendo nuevas perspectivas para el análisis funcional de los mismos en coníferas. 112 7. CONCLUSIONES/CONCLUSION Conclusiones/Conclusions 7. CONCLUSIONS 1. We have applied a progressive, controlled PEG-induced water stress treatment on P. pinaster plantlets in hydroponic culture and obtained a SSH library enriched in genes overexpressed in response to stress. 2. We have analysed the expression patterns of the genes included in the library, as well as other genes available in the public databases, during the PEG treatment in P. pinaster and during a prolonged drought experiment in soil performed on P. pinaster and P. pinea. Based on these experiments we have selected a collection of reliable candidate genes for the study of molecular response to drought stress in conifers. 3. While gene expression patterns shared between both species can be reckoned as common for Mediterranean pines, genes with divergent expression patterns are probably related with the differences displayed by these species in their performance under water stress. 4. We have performed an in depth analysis of the dehydrin gene family, identifying eight different members in P. pinaster and correcting wrong allelic variation interpretations previously reported. 5. We have analysed the transcription patterns shown by these eight dehydrin genes during water stress, identifying highly induced members and other ones presumably involved in the response to other abiotic stresses and/or ontological processes. 6. We have described for the first time two amino acid motifs highly conserved in SKn dehydrins in Pinaceae and absent in angiosperm dehydrins, and whose presence and number is associated with the differential expression patterns described. 7. We have performed a thorough characterization of three genes induced by water stress in P. pinaster: a dehydrin, a putative nodulin, both of them highly inducible by water deficit, and a putative transcription factor, which could be involved in the regulation of the response to the stress. 8. We have obtained plants of Arabidopsis thaliana and P. pinaster genetically modified for the constitutive overexpression of each of these genes. This material will be highly valuable for further studies on the molecular response to water stress in plants and especially in conifers. 115 8. BIBLIOGRAFÍA Bibliografía Adie BAT, Pérez-Pérez J, Pérez-Pérez MM, Godoy M, Sánchez-Serrano JJ, Schmelz EA, Solano R. 2007. 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Collada Abstract Forest tree species, considering their long lifespan, symbolize one of the best biological examples of adaptation to a frequently changing harsh terrestrial environment. The adaptation to environments with water scarcity was the first challenge in the evolution of terrestrial photosynthetic organisms, and prompted the development of strategies and mechanisms to cope with drought. In this respect, the particular evolution and life history of forest tree species have brought about a plethora of specific adaptations to dry environments. The presence of a hydraulic system for long distance water transport and the need of maintaining functional tissues and organs for long periods of time are two important characteristics making forest tree species singular organisms within the plant kingdom. Selective pressure has prompted a variety of strategies in the control of water losses to maintain the functionality of the hydraulic system without compromising the carbon balance of the plant. These and other physiological responses focussed I. Aranda (&) A. Gascó M. A. Guevara J. F. Cano M. De Miguel J. A. Ramírez-Valiente M. T. Cervera Instituto Nacional de Investigaciones Agrarias (INIA), Centro de Investigaciones Forestales (CIFOR), Carretera de la Coruña km 7,5, 28040, Madrid, Spain e-mail: [email protected] E. Gil-Pelegrín Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA de Aragón), Av. Montañana 930, 50059, Zaragoza, Spain M. A. Guevara J. F. Cano P. Perdiguero A. Soto M. T. Cervera C. Collada Unidad Mixta de Genómica y Ecofisiología Forestal, INIA/UPM, Madrid, Spain J. F. Cano P. Perdiguero A. Soto C. Collada GENFOR Grupo de investigación en Genética y Fisiología Forestal, Universidad Politécnica de Madrid, E-28040, Madrid, Spain J. J. Peguero-Pina Department de Biologia, Universitat de les Illes Balears, Ctra. Valldemossa km 7.5, 07122, Palma de Mallorca Illes Balears, Spain R. Aroca (ed.), Plant Responses to Drought Stress, DOI: 10.1007/978-3-642-32653-0_12, Ó Springer-Verlag Berlin Heidelberg 2012 293 294 I. Aranda et al. to increase the dehydration tolerance of tissues (e.g., osmotic adjustment) have played an important role in the development of specific adaptations under water limiting conditions. The adaptive changes are observable at different scales: from the population to the species, from the individual to the gene. The advance of highthroughput technologies will enable to unveil the complex interplay between phenotype and genotype. Genomic, proteomic, transcriptomic, and metabolomic approaches are beginning to bring light to the molecular basis of adaptation to drought in forest tree species. These new technologies, combined with more traditional approaches, will improve our current knowledge of the functional and molecular basis underlying adaptation and evolution of forest tree species living under dry environments. In this respect, this chapter covers some aspects of adaptation to drought at different integrative levels, from an ecophysiological perspective to a molecular-based point of view. 12.1 Introduction Forest tree species are considered some of the most long-living organisms. During their long lifetime, and considering their substantial developmental plasticity, trees need to overcome the restrictions imposed by harsh dry land media. This is probably one of the most relevant properties that make them ideal models to understand the complexity of adaptive processes through the life cycle of plants. Within the plant kingdom, forest tree species cover a particular ample range of adaptive solutions to the challenges of terrestrial media. The mid Devonian marked the appearance of true trees with homoisohydric lifestyle. Since then, the maintenance of a proper water economy has been one of the main challenges for these plant organisms in order to thrive under environments highly limited by water availability. The importance of this factor shaping forest landscapes was already recognized by naturalists of the nineteenth century such as Alexander von Humboldt (von Humboldt and Bonpland 2009). Water availability was soon viewed as the most important factor eliciting segregation of forest tree species along gradients of soil moisture availability. The control of the hydration of tissues within ranges that are compatible with their functionality is important for any tree species. Though, various degrees of tolerance to the dehydration of leaf tissues have been developed within different phyla relying on common processes, such as osmotic adjustment or the regulation of water losses by an efficient stomatal control. In addition, forest tree species have required the evolutionary development of an efficient hydraulic system for transporting water from soil to leaves in order to control the hydration of aerial tissues. This system must fulfill the compromise of conductance efficiency versus security against the loss of its functionality in response to stressful conditions (Tyree and Sperry 1989; Aranda et al. 2005; Brodribb and Cochard 2009). The maintenance of the hydraulic function versus an optimum carbon balance could be the basis to 12 Drought Response in Forest Trees 295 understand survival and growth in most forest tree species (Breshears et al. 2009; McDowell 2011); but especially under dry conditions, where the optimum performance of both functions may be highly threatened and conditioned by biophysical compromises (McDowell et al. 2008). In the last decades, an increasing number of reports have pointed out that some forests might become more vulnerable to drought in the close future (Seager et al. 2007; Allen et al. 2010). An increased vulnerability may not be only a consequence of the direct effect of dryness intensification in some areas, but also the result of the interaction between dry periods and other climatic factors, such as higher temperatures (Adams et al. 2009). Drastic changes in forest species composition in response to intense drought events have been already reported in different forest ecosystems worldwide in the last decades (Breshears et al. 2005, 2009). In other cases, altitudinal displacements of drought sensitive forest tree species, from lower and drier altitudes to higher and wetter ones, have been observed at the retreatment area in Europe (Jump et al. 2006; Peñuelas et al. 2008; Lindner et al. 2010). Furthermore, the increase in the recurrence and intensity of droughts is being a motive of concern even in areas of the world where dry periods have been considered atypical, such as temperate or tropical rain forests (van Mantgem et al. 2009; Newbery and Lingenfelder 2009; Allen et al. 2010). Therefore, it is important to frame the study of water use by forest trees into a proper perspective, considering not only evolutionary and ecological consequences of drought in drier areas, but its importance for most forest ecosystems from the Tropics to Mediterranean environments. The analysis of drought response in forest trees may be considered at different scales, ranging from a species level to individuals (Aranda et al. 2000, 2010; Sánchez-Gómez et al. 2011). In this sense, forest tree species covering broad ranges of distribution are comprised of many local populations covering a high degree of genetic variability and adaptive solutions to cope with multiple environmental gradients (Zhang and Marshall 1995; Aspelmeier and Leuschner 2006; Ducrey et al. 2008). In addition, it is expected that a high degree of phenotypic plasticity will allow long-living sessile organisms withstand different environmental conditions varying from a seasonal to an annual basis (Bradshshaw 1965; Kremer 1995). Furthermore, forest tree species have to endure very adverse conditions such as singular extreme droughts, and throughout their life cycles spanning several centuries in many cases. These characteristics highlight the idiosyncrasy of forest tree species within the plant kingdom, and make them singular examples of adaptation to land media by a complex inter-play between mechanisms of local adaptation with a genetic basis (Linhart and Grant 1996; Savolainen et al. 2007), and phenotypic plasticity that partially relies on epigenetic mechanisms (Bossdorf et al. 2008; Nicotra et al. 2010). Both genetic variation and phenotypic plasticity are considered as fundamental to understand the future of forest tree species in their challenge to face the Global Change (Hamrick 2004; Kremer et al. 2010; Benito et al. 2011; Chmura et al. 2011). As a natural selection factor, drought may have modulated the relationship between genetic and epigenetic adaptive changes in forest tree species under water limiting conditions 296 I. Aranda et al. (Raj et al. 2011). This turns the analysis of drought effects and the underpinning physiological and molecular mechanisms into a task of prime importance (Neale and Kremer 2011). In this chapter, some basic mechanisms related to the capacity of forest tree species to cope with drought are summarized. The response of trees under water limiting conditions is briefly treated at different scales, from the species-specific performance to the molecular response. The fast development of genomic, proteomic, transcriptomic and metabolomic approaches is providing new insights into the molecular basis of adaptation to drought. The combination of these lines of research with more traditional approaches will open new perspectives in the understanding of the functional and molecular basis of adaptation and evolution of forest tree species in dry environments. This chapter ends with a brief overview of these new technological approaches applied to the study of drought responses in forest tree species. The ability to understand and advance the adaptive potential of forest tree species in response to the expected Climate Change requires the integration of the information gathered at different scales of study in the very close future. This chapter itself is an exercise of analyzing some aspects of drought response at different scales in forest tree species, but, furthermore, attempts to push forward the limits imposed by specific fields of research and investigation to the whole understanding of this topic. 12.2 Mechanisms of Ecophysiological Response to Drought in Forest Tree Species Water is one of the main environmental factors conditioning the segregation of species into different biomes across ranges of wetness. Composition of forest ecosystems reflects different strategies and sensitivities to water stress displayed by each species. Even in ecosystems such as tropical rainforest or temperate forests, which are characterized by a seasonally high rainfall over most of the growing season, water availability can modulate community structure and function in the long term by infrequent but intense dry periods (Ciais et al. 2005; Breda et al. 2006; Newbery and Lingenfelder 2009). The long-living nature of trees implies that their relationship with the environment changes throughout their life span. Environmentally induced developmental changes constitute an intrinsic process of permanent adjustment of plant performance (Day et al. 2002). Nevertheless, living for a long period of time, and the multiple contingencies trees must cope with in terms of extreme events, may not be enough to adequately be buffered by its phenotypic plasticity (Grether 2005). Therefore, extrinsic modifications in the habitat, due to perturbations or degradation (Camarero et al. 2011), changes in the silvicultural practices (Corcuera et al. 2006), or climate fluctuations (Foster et al. 2006), might lead forest tree species to their survival limits (Linares et al. 2009). Their capability to 12 Drought Response in Forest Trees 297 withstand these changes would ultimately establish the frontier between the survival of trees and their death, and relies in the adjustment of different functional and morphological traits, especially under water stressful conditions. These changes sum up in modifications of biomass investment in different plant organs, development of efficient hydraulic systems from roots to leaves, an efficient management of water losses, and the production of leaf tissues with a high degree of tolerance to dehydration. 12.2.1 Changes in Biomass Investment The architecture, stratification, and lateral and vertical extent of root systems are key factors for understanding water relations of plants. Trees and shrubs clearly have the potential for developing deeper root systems than grasses, although the depth to which soil water depletion occurs varies widely among species (Canadell et al. 1996; Eggemeyer et al. 2009) and sites (Meier and Leuschner 2008a). The understanding of root system structure and function in trees is based largely on highly controlled seedling studies (Pemán et al. 2006). However, changes in function and allocation to roots with ontogeny must be considered when scaling from seedlings to mature trees (Topa 2004; Poorter et al. 2012). Root depth influences capacity to extract water from different soil horizons, being lower at the juvenile phase. In this way, Esteso-Martínez et al. (2006) showed that the minimum seasonal water potential in a stand of Quercus faginea were much less negative in adult trees that in seedlings, results that could explain the high percentage of cavitation found in seedling stems. The investment of large amounts of reserves in the development of a large and deep root system may be considered crucial for trees in water-limited habitats according to the optimal partitioning theory (Bloom et al. 1985). A higher partitioning of biomass to belowground organs can be adaptive in relation to water stress, as observed from the inter-population variation within some forest tree species (Aranda et al. 2010). However, the investment in roots may condition the amount of reserves that can be used to produce shoots for the capture of aerial resources (Valladares and Pearcy 2002), as those under a Mediterranean type climate (Corcuera et al. 2005). The adjustment of the root to shoot ratio has been early recognized as a tradeoff between growth and survival under water limitations (Monk 1966). However, the production of a large root system does not ensure survival of trees under an extreme water deficit. Effectively, soil drought induces a loss of root hydraulic conductivity which can be due to changes in root anatomy (Nobel and Lee 1991), root xylem cavitation (Sperry and Ikeda 1997; MartínezVilalta and Piñol 2002), or changes in the expression of aquaporins (Secchi et al. 2007). The recovery from this lack of root conductivity usually implies the investment of new resources for reconstructing the root system (Lo Gullo et al. 1998), except for some woody plants that can recover the hydraulic conductivity by refilling xylem conduits through the generation of positive water pressures in 298 I. Aranda et al. the root (Tyree and Ewers 1991; Melcher et al. 2001). Recently, other mechanisms have also been proposed for recovering the hydraulic conductivity (see Zwieniecki and Holbrook 2009). In addition, the ability to survive during severe drought events by shedding expendable organs has been pointed as other mechanism to cope with water stress. This would be at last explained in terms of a segmented vulnerability to cavitation across plant organs. Petioles would be more vulnerable than stems, and stems more than roots, in forest tree species that develop a low whole-plant hydraulic resistance such as Acer saccharum Marsh. (Tyree et al. 1991), Junglans regia L. (Tyree et al. 1993) or Acer saccharinum L. (Tsuda and Tyree 1997). However, petioles of the strict riparian Betula occidentalis Hook showed lower vulnerability to cavitation than stems, and stems were less vulnerable than roots (Sperry and Saliendra 1994). On the other hand, no differences in vulnerability were reported for other forest tree species (e.g., three Quercus species in Cochard et al. 1992). 12.2.2 Hydraulic System and Response to Water Stress The movement of water according to a gradient of water potential through the xylem starts as water reaches the root stele. At this point, water moves through a path of conduits (Fig. 12.1) overcoming the hydraulic resistance imposed to water flow by very narrow elements, as explained by the Hagen–Poiseuille law (Tyree and Zimmermann 2002). According to Sperry et al. (2003), the restriction to flow through the nonvascular and vascular pathway can be of similar magnitude if expressed as conductances (volume flow rate per pressure difference) in wellwatered plants, whereas the radial barrier to water movement in the roots shows an intrinsically low conductivity (volume flow rate per pressure gradient per crosssectional area). As a consequence of tree height growth, the hydraulic path is lengthened (King 1990), so a decrease in leaf-specific hydraulic conductance results due to the increased path length (Barnard and Ryan 2003). Accordingly, it has been proposed that tree growth may be hydraulically limited due to the drop in water potential through the path (Ryan and Yoder 1997; Becker et al. 2000; Ryan et al. 2006; Sperry et al. 2008). The diameter of conduits has been reported to increase with tree height (Zach et al. 2010), branching order (Mayr et al. 2003), or tree age (Corcuera et al. 2004a) as a way to increase the efficiency of the xylem to transport water. Nevertheless, drought can affect negatively the average diameter of xylem conduits as a plastic response that induces an unbalance between demands and flow, which may result in damaging consequences for plant survival (Corcuera et al. 2004b). Significant differences in the vulnerability to xylem cavitation have been reported for a wide range of species widespread from tropical rain forests to deserts; resulting that those species showing higher drought tolerance generally exhibit hydraulic systems more resistant to cavitation (Maherali et al. 2004; 12 Drought Response in Forest Trees 299 Fig. 12.1 Water transport pathways through trees following a classical Ohm’s Law analogical representation. Resistances may be dynamically modified by cavitation (CAV) and refilling (REF) processes, ion-mediated changes of hydrogel configuration in inter-conduit pits (ION), or aquaporin gating (AQP) against environmental changes. In addition, some other components as the extra-vascular xylem resistance (Rextra-) and the resistance of the mesophyll (Rmesophyll) may be additionally decomposed into apoplastic (Rapo-), symplastic (Rsymp-) and trans-membrane (Rtrans-) components as it is represented for radial root resistance (Rradial). Water storage has been included as a series of capacitors (C) within different plant tissues (Cleaf and Cradial). Rest of abbreviators represent: gs (stomatal conductance to water vapor), gb (boundary layer conductance), E (transpiration), VPD (vapor pressure deficit between leaf and air), Wleaf (leaf water potential), Wsoil (soil water potential). The relative size of each element is not proportional to its real average magnitude Brodribb and Cochard 2009). In general, a functional tradeoff between hydraulic efficiency and safety, comprising support and water storage, results from considering the water transport through the xylem pipeline (Tyree and Zimmermann 300 I. Aranda et al. 2002), their size distribution (Vander Willigen and Pammenter 1998), configuration of pit membranes (Zwieniecki et al. 2001; Pittermann et al. 2006; Jansen et al. 2011), and the intrinsic mechanical reinforcement against lumen implosion (Hacke et al. 2001, 2005). In short, very small dimensional changes (e.g., vessel diameter, parenchyma cells volume, and pit structure) drive substantial changes in hydraulic conductivity, water storage capacity, and resistance to cavitation (Fig. 12.1). Bhaskar et al. (2007) found that closely related species may strongly differ in terms of hydraulic conductivity, being higher in those living under drier climates. Hence, the increase in the efficiency of the xylem would serve as a way to cope with an extreme water flow demand through the plant under very high vapor pressure deficit conditions. Nevertheless, very early studies on plant hydraulics already showed that wider conduits, such as those formed during the early annual growth in ring-porous tree species, are extremely vulnerable to freeze–thawinduced cavitation (e.g., Cochard and Tyree 1990; Lo Gullo and Salleo 1993). Recently, Peguero-Pina et al. (2011) compared tracheids dimension in two Mediterranean fir species with a strong phylogenetic link, resulting that the better adapted species to drier environments (Abies pinsapo) show wider tracheids than Abies alba, which is commonly found under wetter climatic regimes (PegueroPina et al. 2007). However, the size of A. pinsapo tracheids makes this species very sensitive to frosty winters imposing a high number of freeze–thaw cycles (Mayr et al. 2003), which is a typical characteristic of montane climatic regimes where its congeneric species live. On the other hand, some recent evidence indicates that these wider conduits are also more vulnerable to drought-induced cavitation (Pittermann et al. 2006); so, a tradeoff between safety and efficiency shapes tracheids when both drought and freeze are considered (Martínez-Vilalta et al. 2002; Peguero-Pina et al. 2011). The resistance of xylem to functionality loss is not the only important trait for understanding drought tolerance of forest tree species. Recovering after water stress is also relevant in the short and long terms. This may be mediated by development of new xylem, or by restoring the function of previously embolized vessels (Resco et al. 2009; Brodribb et al. 2010). Although the refilling of embolized vessels is still far from being completely understood, this mechanism should be considered at least as important as the runoff of cavitation. Experimental evidence shows that plants are able to repair embolized xylem conduits by exerting enough root pressure during the night and/or along rainy seasons (Sperry et al. 1987; Hacke and Sauter 1996), or by pushing water from living conduit-associated parenchyma cells into gas-filled lumina when the bulk of water-transporting xylem is still under tension (novel refilling, Hacke and Sperry 2003; Bucci et al. 2003). The specific mechanisms of refilling are beginning to be elucidated. It seems to require the hydraulic isolation of embolized conduits, and some changes in the sugar metabolism of vessel-associated cells (evidenced through an observed consumption of starch) to provide the necessary driving force for water. In fact, novel refilling might be simplified to a particular case of phloem unloading (Nardini et al. 2011). A different refilling rate has been reported across several forest trees species (e.g., Hacke and Sperry 2003); and it has been suggested that 12 Drought Response in Forest Trees 301 refilling under tension might be operated somehow different in conifers, given its markedly different wood anatomy from angiosperms (Borghetti et al. 1991). In short, embolism repair may be more likely to occur in organs where there is a closer contact between phloem, xylem and other involved living cells, and/or if conduits are narrower and shorter (Clearwater and Goldstein 2005). Although there is much research to be performed in order to fully address this hypothesis, refilling might be more likely to occur in conifers, monocots, and protoxylem conduits, than in dicots, angiosperms, and metaxylem; as well as in smaller distal organs or leaf veins where cavitation vulnerability is also higher. Considering this hypothesis, xylem structures developed under drought conditions might be also tested for higher refilling occurrence. Once water arrives to the leaves, it still flows through the xylem across their veins. As they are made up of xylem tissue, petioles (Bucci et al. 2003) and leaf veins (Nardini et al. 2001; Salleo et al. 2003) are also vulnerable to cavitation whether water flow to the atmosphere is high enough to generate critical water potential drops. Leaf hydraulic properties are receiving a special attention by plant physiologists that are unraveling the mechanism for preserving the integrity of other organs by limiting transpiration during water stress (Brodribb and Holbrook 2003; Zufferey et al. 2011). Considering the full hydraulic path, the hydraulics of the leaf lamina accounts for about the 25 % of the whole-plant resistance to water flow on average (Sack et al. 2003). This particular highlights the importance of any hydraulic dynamic change to whole-plant water balance, such as molecular mechanisms involved in aquaporin gating within the mesophyll of leaves (ShatilCohen et al. 2011). For instance, regulation of leaf hydraulic conductivity by light and mediated by aquaporin expression, would be a species-specific molecular mechanism allowing a fine tuning of water movement into the leaf lamina according to the light environment (Cochard et al. 2007; Baaziz et al. 2012). In addition, the resistance against water flowing out of the xylem to the nonvascular pathway of the leaf is also of major importance for understanding the overall contribution of leaves to whole-plant hydraulic resistance (Sack and Holbrook 2006; Johnson et al. 2009); especially, if the conclusions supported in Brodribb et al. (2005) relating leaf hydraulic conductivity and photosynthetic capacity are taken into consideration. In fact, leaf hydraulic conductance itself has been also addressed as highly coordinated with both stomatal conductance and net assimilation rate (e.g., Aasamaa et al. 2001 or Brodribb and Holbrook 2006). This coordination would be of major importance under water stress conditions (ShatilCohen et al. 2011). 12.2.3 Stomatal Control of Water Loss Plants lose at least 100 times more water than they are able to gain carbon by gas exchange through stomata. Thus, net carbon assimilation is a very expensive process in terms of water consumption. While most assimilated carbon dioxide is 302 I. Aranda et al. integrated within tree biomass, water has a short lifetime inside plant tissue and requires continuous replenishment. Though nonstomatal mechanisms such as the leaf cuticle resistance plays an important role in limiting leaf water losses, probably stomatal regulation is the main point control in water use. Leaf stomatal closure is a common plant mechanism for water saving in drought stress periods at the expense of reducing net CO2 assimilation (Chaves et al. 2003; Peguero-Pina et al. 2009). Moreover, stomata respond to very different environmental stimuli, and even within the same forest ecosystem it is possible to find a full range of strategies regarding water economy under drought conditions, varying from isohydric or ‘‘water saving’’, to anisohydric or ‘‘water spender’’ performance (Breda et al. 2006). However, in the last years, the idea that leaf stomatal control in forest tree species is constrained by the need to maintain the hydraulic function versus an optimum carbon balance has gained force (Campanello et al. 2008). In species with secondary growth, but especially in forest trees, maintenance of the hydraulic system within safety margins from water-stress-induced embolism is of prime importance (Tyree and Sperry 1989). Woody plants have shown their ability to avoid the partial cavitation of their xylem through a fine stomatal control of xylem pressures (Jones and Sutherland 1991). This regulation would be especially relevant under dry conditions where optimum in both functions is more threatened and conditioned by biophysical compromises (McDowell 2011). This has conducted to propose either hydraulic failure or carbon starvation (although see Sala et al. 2010), as the mechanistic basis explaining the phenomena of mortality after intense periods of drought (McDowell et al. 2008). In both processes, the particular strategy of forest tree species regarding stomatal regulation of water losses would be the keystone. A consequence of stomatal closure under water stress to prevent a catastrophic loss of hydraulic conductance, and to minimize dehydration of leaf tissues, is an overheating of leaves. In addition, an excess of excitation energy cannot be directed to the photosynthetic electron transport chain (Demmig-Adams and Adams 1996). Electrons not consumed in CO2 fixation may react with O2 generating reactive oxygen species and increasing the possibility of oxidative damage. Under these conditions, both the pH and the de-epoxidation state of the xanthophyll cycle pigments increase, protecting the photosynthetic apparatus through a mechanism that dissipates excess of light as heat (Demmig-Adams and Adams 1996; Li et al. 2000a; Morales et al. 2006). However, there is no evidence for major sustained photodamage in water-stressed plants, as judged by the lack of effects of drought on the maximum potential PSII efficiency (FV/FM) even for very stressed leaves (Morales et al. 2006 and references therein). On the other hand, recent studies show evidence of drought-mediated down regulation of FV/FM in some Mediterranean forest tree species (Peguero-Pina et al. 2009), which seems to be related to an additional photoprotective mechanism that may play an important role for survival of species living in sites with long and intense summer drought periods. 12 Drought Response in Forest Trees 303 12.2.4 Increase of Tolerance to Dehydration of Leaf Tissues Besides mechanisms to optimize water capture, transport and control of losses under drought, forest tree species, as other plants, have developed means to increase the tolerance to dehydration of their tissues. Differences in leaf osmotic adjustment capacity, a well-known mechanism to increase drought tolerance in plants, frequently reflect also the dryness of the species habitat (Abrams 1988; Corcuera et al. 2002). Even within the same stand, it is possible to find forest tree species with a marked differentiation in tolerance to dehydration of their leaf tissues, reflecting different functional strategies regarding water economy. In this regard, Lenz et al. (2006) found in a meta analysis a good correlation between the osmotic potential at full and zero turgor and the degree of tolerance to drought for the pool of species analyzed. They found a continuous range of responses, with species originating from xeric sites showing lower values of leaf osmotic potential than those from wet sites (Abrams 1988). Relevance of the osmotic properties in leaf tissues is present even for ecosystems where drought is not a permanent handicap for growth and survival, as temperate forests, but where species from the same functional group maintain a different degree of leaf tolerance to dehydration through the same range of water stress and according to the species drought resistance (Fig. 12.2). Differences in leaf osmotic potential at full turgor are underpinned by a complex molecular expression of different metabolites with osmotic activity. Important variations have been observed in the kind and amount of the metabolomic profiling according to the species and degree of water stress endured (Merchant et al. 2006; Warren et al. 2011, 2012), and with important consequences from the point of view of species strategy regarding drought adaptation (see Sect. 12.4.3). 12.3 Genetic and Phenotypic Variation in Response to Drought In the last decades, study of forest tree response to drought has focused mostly in the functional analysis at the specific level (i.e. Breda et al. 2006). Though, in the case of forest tree species characterised by a high intra-specific variability, analysis of the response of different populations deserves especial consideration. The wide geographical range of many forest tree species suggests maintenance of a large adaptive genetic variability (Aitken et al. 2008; Savolainen et al. 2007). Species-specific response to drought would be modulated by the differential capacity of local populations to cope with water stressful environments. Consistently, differential response to drought has been reported for different geographical origins (Burczyk and Giertych 1991; Zhang et al. 1993; Lauteri et al. 1997; Ares et al. 2000; Ramírez-Valiente et al. 2009), and even within the same local population offspring from different 304 I. Aranda et al. Fig. 12.2 The leaf osmotic potential at full turgor (p100- MPa) is a good trait indicative of the leaf dehydration tolerance, even for seedlings of temperate forest tree species not adapted to dry environments. The parameter can be used as a proxy of tolerance to drought by different forest tree species (Lenz et al. 2006). Each line depicts a decreasing trend of p100 with leaf predawn water potential (Wpd) as surrogate of the effective water stress endured by seedlings. Fagus sylvatica (continuous line) maintained a higher value of p100 whichever the Wpd compared to the two oak species: Quercus petraea (dotted line) and Quercus petraea (dashed line). Redrawn from data in Aranda et al. (2001, 2002, 2004), Robson et al. (2009), Rodríguez-Calcerrada et al. (2010) mother trees shows dissimilar performances (Major and Johnsenn 1996; Major and Johnsenn 1999; Aranda et al. 2010). Changes reported recently for some forestlands seem to have been abrupt as a consequence of sporadic, but very intense droughts that have modified the competitive relation among species in a few years (Ciais et al. 2005; Breshears et al. 2005; Allen et al. 2010). Reports of drastic changes in the composition of forest vegetation seem already to confirm current standing genetic variability would not be enough to suit to the new environments (Hamrick 2004; Kremer 2010). Widespread phenomena in the last decade of forest die-back, increase in mortality rates, and altitudinal displacement are examples that seem to confirm local populations could not keep enough adaptive potential to overcome the awkward situation brought about by an increase in the dryness at the local scale. The issue is especially relevant for populations close to the trailing edge of the species distribution, and where larger changes in thermal and moisture regimes are expected in the next decades (IPCC 2007). This is the case, for instance, in some countries surrounding the Mediterranean basin where some changes in forest systems are already beginning to be observed (Peñuelas et al. 2001; Martinez-Villalta and Piñol 2002b; Jump et al. 2006; Peñuelas et al. 2008; Linares et al. 2010). Therefore, knowledge of the intra-specific variation in drought response emerges as a need for a better understanding of the microevolutive changes that could affect the sustainability of forest tree species in new climate contexts. 12 Drought Response in Forest Trees 305 12.3.1 Within-Species Variability in Functional Traits Related to Drought Forest tree species maintain high degrees of intra-specific adaptive variability for traits related to growth or phenology (Jensen 1993; Meier and Leuschner 2008b; Chambel et al. 2007; Vitasse et al. 2010), and with a wealth of examples dealing with specific local adaptation and variability in quantitative traits responding to different stresses, particularly drought (Aranda et al. 2005; Savolainen et al. 2007; Kremer et al. 2010). Well known is the high within-species genetic variability in phenotypic traits responding to water stress, such as water-use efficiency in Populus sp (Ceulemans and Impens 1980; Bassman and Zwier 1991; Monclus et al. 2006), Eucalyptus sp (Li et al. 2000b), Pinus sp. (Cregg and Zhang 2001; Guy and Holowachuk 2001; Voltas et al. 2008; Aranda et al. 2010;), Pseudostuga sp. (Zhang et al. 1993) or Quercus sp. (Arend et al. 2011). This variability has been the point of attention for tree breeding programs from the middle of the twentieth century (Langlet 1971; König 2005). Although information related to the within-species functional response to water stress is scarcer than for growth or phenology, it is enough to conclude that it might be also under genetic control. Besides recognizing the expression of most functional traits is mostly conditioned by the environment, the genetic variation also underlies expression of different functional traits such as water use efficiency, stomatal control of water losses or net photosynthesis under a common environment (Table 12.1). Variation is observed at different genetic levels from population (Zhang and Marshall 1995; Benowicz et al. 2000; Ducrey et al. 2008) and open pollinated families within the same population (Prasolova et al. 2001), to clones (Aspelmeier and Leuschner 2006; De Miguel et al. 2012). Genetic variance is high, for instance, when analyzing the expression of traits such as carbon isotope discrimination (D13C) related with intrinsic water-use efficiency (Zhang et al. 1993; Flanagan and Johnsen 1995; Prasolova et al. 2001; Voltas et al. 2008). No matter the high phenotypic variance reported for drought response, the proportion of this variance attributable to genetic factors is usually just moderate. In general, moderate values are found for narrow sense heritability in D13C (the proportion of total variance attributable to additive genetic variance), as well as for other drought stress-related traits (Brendel et al. 2002). Analogously, relatively high QST values (genetic parameter used as surrogate of distance among populations in quantitative traits) have been also reported for morpho-functional traits. This indicates a moderate among-population genetic differentiation for drought tolerance in some forest tree species (Ramírez-Valiente et al. 2009). The relationship between the expression of morpho functional and growth traits and fitness is inferred from genetic correlations on most cases, but in general it is recognized functional traits could be submitted to strong selective pressure (Lamy et al. 2011). Traits such as water use efficiency, leaf size, or osmotic adjustment capacity, would maintain a putative adaptive value according to the environment, but especially in dry areas with a direct impact on plant fitness. The ecological and Thuja plicata (Western red cedar) Cedrus brevifolia, C. libani (Cedrus) Pinus pinaster (Maritime pine) Picea glauca (white spruce) Pseudotsuga menziesii (Douglas-fir) Picea mariana (Black spruce) Larix occidentalis (Western Larch) Fagus sylvatica (Beech) Pinus pinaster (Maritime pine) Populus deltoides (Poplar) Pseudotsuga menziesii (Douglas-fir) Picea abies (Norway spruce) Castanea sativa (Chesnut) Pinus sylvestris (Scotch pine) Alnus sinuata (Sitka alder) Betula papyrifera (Paper birch) Fagus sylvatica (Beech) Low Moderate Moderate Gas exchange, chlorophyll fluorescence Hydraulic system and xylem cavitation, growth parameters Carbon isotope discrimination Gas exchange Gas exchange, water use efficiency Hydraulic system and xylem embolism Osmotic adjustment Families Families Family Population/ family Clones Family Family Population Moderate Low Low Moderate Low Low Gas exchange, carbon isotope discrimination, and hydraulic traits Hydraulic system and xylem embolism Low Moderate Population Gas exchange, metabolites, and ABA Population Low Low Low Moderate Gas exchange, water use efficiency Growth parameters Gas exchange, water use efficiency Gas exchange Gas exchange, growth Populations Population Population Population Moderate Population Gas exchange, carbon isotope discrimination Population Gebre et al. (1994) Leonardi et al. (2006) Lamy et al. (2011) (continued) Flanagan and Johnsen (1995) Zhang et al. (1994) Bigras (2005) Anekonda et al. (2002) Corcuera et al. (2011) Ducrey et al. (2008) Peuke et al. (2002) and Robson et al. (2012) Grossnickle et al. (2005) Burczyk and Giertych (1991) Lauteri et al. (1997) Cregg and Zhang (2001) Benowicz et al. (2000) Zhang et al. (1993) Table 12.1 Variation within forest tree species of physiological and morphological traits related to drought response has been frequently reported at different genetic levels Species Genetic level Characters Level of Reference variation 306 I. Aranda et al. Gas exchange, xylem embolism Growth parameters Carbon Isotope discrimination Growth parameters, Gas exchange Gas exchange, intrinsic water use efficiency Clones Clones Clones Clones Characters Clones Genetic level Moderate High/ moderate Moderate High Moderate Level of variation De Miguel et al. (2012) Possen et al. (2011) Aspelmeier and Leuschner (2006) Monclus et al. (2006) Sangsing et al. (2004) Reference Although functional response is under high environmental influence, its control through experiments in common garden tests, or greenhouse and climatic chambers where environment is standardized, allows ascertaining different functional strategies by different genetic backgrounds. Level of variation for population was graded only as low or moderate Hevea brasiliensis (Rubber tree) Betula pendula (Silver birch) Populus deltoides 9 nigra (Poplar) Populus tremula (Common Aspen) Betula pendula (Silver birch) Pinus pinaster (Maritime pine) Table 12.1 (continued) Species 12 Drought Response in Forest Trees 307 308 I. Aranda et al. evolutionary importance of morpho-functional traits, spanning from changes in biomass partitioning to gas exchange or water parameters, and in response to water limiting environments, has been tested for a wide range of annual model species or crops. Results are expressed in terms of the expression of a specific phenotype jointly a better performance under drought, and increase in fitness by using growth, reproductive success, or harvest index in crops as biological success indexes (Dudley 1996; Donovan et al. 2009). However, there is currently still little information available regarding forest tree species (Brendel et al. 2008; Scotti et al. 2010; Ramírez-Valiente et al. 2011; Lamy et al. 2011). 12.3.2 Genetics and Phenotypic Plasticity in Tracking Future Environmental Changes The key point in the future maintenance of local populations coping with drier environments will be given by both the genetic variability and phenotypic plasticity facilitating adjustment to the new environmental conditions (Abrams 1994; Hamrick 2004). New expectations have emerged during the last years in relation to this issue in the face of climate change. Natural forest tree populations are advocated to the extirpation, migration (by tracking the environmental change), or adaptation in relation to the new environments (see Aitken et al. 2008 and Kremer et al. 2010 for a compressive review). In respect to the adaptive potential, it has been highlighted recently that adaptive differentiation between extant populations of forest tree species can be fast (Kremer et al. 2010). However, the speed of the expected change will likely be faster than generation turnover for most forest tree species. Such a high speed could override the potential to generate new recombinants better adapted to the future conditions. This leaves phenotypic plasticity within populations as one of the main evolutionary mechanisms to cope with the new climate contexts (Nicotra et al. 2010). Thriving under the new climatic contexts at the local scale will therefore rely on the degree of genetic polymorphism in adaptive traits, phenotypic plasticity or both within populations (Nicotra et al. 2010). Whichever the mechanisms acting first on the process of adaptation, both probably will play an important role for forest tree populations to adjust to more water stressed environments in the future. Albeit, it has been outlined that the extent of phenotypic plasticity blurs in some cases, the importance of local adaptation when considering the functional response to drought (Baquedano et al. 2008, Gimeno et al. 2009), highlighting the major effect of the environment in the phenotypic variance and, particularly, the strong genotype x environment interactions. Thus the capability of a genotype to acclimate to different environments, showing different phenotypes (plasticity), seems to be a noteworthy feature of forest tree species, in which epigenetic factors likely play a key role in adaptive terms further than the mere molecular basis of the acclimation response (see below and Bossdorf et al. 2008 or Raj et al. 2011). 12 Drought Response in Forest Trees 309 12.3.3 Interaction of Drought With Other Environmental Factors Differentiation among and within forest tree species in the response to drought is given by the expression of some of the aforementioned traits, which are mainly related to delaying or minimizing dehydration of tissues. The univariate point of view in approaching the water-stress response has been embraced in most studies analyzing drought response, in ecological (Ogaya and Peñuelas 2007; Vilagrosa et al. 2010), functional (Aranda et al. 2000; Brodribb and Holbrook 2003; David et al. 2007), or evolutionary contexts (Eveno et al. 2008; Grivet et al. 2011). This view avoids the biological and ecological reality of the multifactor world perceived by most biological organisms, and tree species in particular (Niinemets 2010). However, this response, as others related with the impact of limiting nutrients, light or salt, are commonly viewed in evolutionary and ecological terms from a single-factor perspective, relating the observed response to the capacity to cope with scarcity of the considered factor. Actually, from a functional and ecological point of view, we must keep in mind there is a complex interplay between different abiotic and biotic factors in shaping the response to drought. An example is given, for instance, by the interaction between light and water stress on some specific mechanisms related to drought tolerance as the osmotic adjustment capacity (Fig. 12.3). There are other examples showing a nonadditive action of factors such as light, water, nutrients, temperature or atmospheric CO2 concentration over the expected expression, and assemblages of physiological, morphological and even molecular traits (Ellsworth and Reich 1992; Valladares and Pearcy 1997; Mittler 2006; Eller et al. 2011). This adds a new point of complexity to the interpretation of functional and morphological traits responding to water stress. The interaction with biotic agents, indeed potentially relevant, is out of the scope of the present review. 12.4 Molecular Foundations of Drought Response in Forest Tree Species Drought response in forest tree species relies on changes at the molecular level that represent the lowest scale in the biological integration range. However, it is an additional step in the understanding of the overall response, maintaining important connections with the phenotypic and genetic levels previously described. The complexity of the interrelations between different molecular processes is now beginning to be specifically elucidated for some model forest tree species. However, thanks to the development of high-throughput technologies, and a higher degree of interaction among different fields of research, more knowledge is generated about the complex interplay between phenotype and genotype and water stress responses. In the next years, these advances probably will impact our understanding of the responses to drought by forest tree species in ecological and evolutionary contexts. 310 I. Aranda et al. Fig. 12.3 A decrease in osmotic potential at full turgor (p100) has been considered traditionally a mechanism of increasing tolerance of leaf tissues to dehydration. It is present even for temperate forest species such as beech. However, this mechanism of drought tolerance is hindered by light limiting conditions, eventually restraining the capacity to maintain leaf turgor under water stress conditions. In the graph it is shown the negative relationship between osmotic potential at full turgor versus predawn water potential, the last as a proxy of the water stress endured by beech seedlings. Black points represent seedlings growing with high light levels in a gap of a natural stand, and white points represent seedlings growing in the understory (modified from Robson et al. 2009) 12.4.1 Dissecting the Genetic Foundations of Response to Drought 12.4.1.1 Identification of Quantitative Trait Loci and Candidate Genes Involved in Drought Stress Response Using Association Analysis in Forest Tree Species Drought stress response is a complex biological process. Thus, combination of genomic and physiological studies is required to advance knowledge in this field (Fig. 12.4a, b). Two forward-genetic phenotype-based strategies have been used to start unraveling genetic control of this complex response (Fig. 12.4b). Family based quantitative trait loci (QTL) mapping can be applied to search for associations between markers and phenotypes in pedigrees from crosses among genotypes with a contrasting response to drought. Alternatively, we can use populationbased association mapping, with populations of unrelated individuals to examine associations between single nucleotide polymorphisms (SNPs) and phenotypes. 12 Drought Response in Forest Trees 311 Fig. 12.4 A comprehensive understanding of plant response to environmental modification (i.e., drought stress) will require integrating data from different levels of biological organization, whose study is addressed by different ‘‘omics’’ disciplines. a Schematic of the ‘‘omic’’ hierarchy and integration. b A flow sheet summarizing the most common strategies employed to identify and validate a candidate gene using forest tree models. Association analysis and genome-wide association studies GWAS; QTL analysis Detection of QTL depends on the size of the mapping progeny, genetic background, heritability of the trait under study, coverage, and saturation of the genetic map as well as genotype by environment interactions. QTL analysis has been applied to identify genome regions involved in the genetic control of drought response of different model forest tree species, evaluating different phenotypic traits of materials grown in different experimental layouts. QTL have been detected for, among other traits, carbon isotope discrimination in F1 individuals from intra-specific crosses of Castanea sativa (Cassasoli et al. 2004) and Quercus robur (Brendel et al. 2008), in three consecutive years. Comparative analysis between these two studies, based on a set of orthologous markers, revealed that no QTL for carbon isotope discrimination was conserved between both Fagaceae species (Casasoli et al. 2006). In Salicaceae, response to drought stress was also analyzed in a hybrid F2 progeny of Salix dasyclados 9 Salix viminalis (RönnbergWästljung et al. 2005). In poplar, two different studies used an F2 progeny of Populus trichocarpa 9 P. deltoides, to analyze QTL for osmotic potential (Tschaplinski et al. 2006), and leaf coloration (chlorophyll and carotenoid content), expansion, and abscission (Street et al. 2006). The latest study also integrated 312 I. Aranda et al. transcriptomics, searching for positional candidate genes (CGs)—genes co-localizing with QTL for the parameter evaluated, in many cases supported by previous functional description in other species. In Eucalyptus sp., Teixeira et al. (2011) have detected QTL related to drought response in a hybrid Eucalyptus grandis 9 E. urophylla F1 progeny. QTL analysis for drought response in Pinaceae has been approached in Pinus pinaster, species characterized by a significant genetic and adaptive diversity (Eveno et al. 2008). Brendel et al. (2002) analyzed an intraspecific F2 progeny whose grandparents showed a differential response to drought in terms of d13C on growth rings. A way to validate QTL is through comparative mapping. Most of the QTL analyses described above revealed QTL for different traits, which have been detected across different years. It is important to highlight that overlapping QTL among drought-related traits have been observed. Such co-locations indicate that the shared QTL clusters may indicate pleiotropic effects. Additionally, correlations between drought-related traits have been observed which may be attributable to either pleiotropic effects of single genes or to tight linkage of several genes that individually influence specific traits (Pelgas et al. 2011). However, up to now, comparison of QTL among different genetic backgrounds (families or species) has been difficult due to the limited number of orthologous markers among unrelated mapping families or species (Casasoli et al. 2006), as well as due to the different experimental procedures and phenotypic/physiological parameters used to evaluate drought response. The standardization of experimental procedures together with the use of high-resolution genetic maps will allow generalizing this approach (Neale and Kremer 2011). For instance, the work on Q. robur (Brendel et al. 2008) revealed a major QTL for water-use efficiency in the same linkage group where other analysis found a major linkage group for stomatal density (Gailing et al. 2008). Allelic variants, associated with mapped SNPs, may be analyzed in association population to unravel these two effects. Power for QTL detection depends on the number of generations. Most forest trees are highly heterozygous outbred plant species. Due to their long generation time, QTL analysis of forest trees has been mainly carried out using F1 progenies or, in the better cases, three generation populations, limiting accuracy of these QTL analyses. Different strategies have been developed to increase the power of QTL detection for forest tree species (Plomion et al. 2007). The relatively low number of individuals in several forest tree mapping progenies also negatively influences the accuracy of calculated QTL effects and the power to detect QTL with small effects. However, this may be, to some degree, compensated by the use of ramets of each individual that provide higher precision in the phenotypic screening. Precision of phenotypic measurements is an important factor for QTL mapping, because a high measurement error reduces the estimated heritability and decreases the detection power. Nowadays, the use of high-throughput genotyping techniques allows construction of highly dense genetic maps, most of them integrating high numbers of gene-based markers, including markers transferable across pedigrees. The use of highly dense genetic maps and more precise phenotyping protocols will increase precision of QTL detection by narrowing down 12 Drought Response in Forest Trees 313 the intervals to small regions. Additionally, integration of complementary information such as expression analysis (expressional QTL—eQTL), proteomics, metabolomics, and epigenomics (i.e., Drost et al. 2010; Morreel et al. 2006; Henery et al. 2007; Long et al. 2011), will provide a more comprehensive view of mechanisms underlying drought response in forest tree species, highlight putative CGs and corresponding alleles involved in such a response. Population-based association mapping searches for associations between genetic markers and adaptive traits in natural populations; thus, analyzing variation accumulated during evolution of the targeted germplasm used as a discovery population. Linkage disequilibrium (LD) usually extends over much shorter distances in association mapping populations than in family mapping population. Therefore, high numbers of genetic markers are needed to ensure adequate coverage to detect linkage between markers and a causal locus. As the cost of genotyping has dropped dramatically, association mapping has rapidly become a very promising approach for the genetic dissection of complex traits in plants (Ingvarsson and Street 2011). Association mapping has been applied to analyze Pinus taeda drought response (González-Martínez et al. 2008). It is important to differentiate association mapping based on analysis of CGs from the genome-wide association studies (GWAS). The first one is based on genetic markers genotyped at loci thought to be involved in trait expression, testing for associations between these genetic markers and the phenotype. In plants, this approach has been successful for CGs in relatively simple pathways and for CGs with extensive prior evidence of a role in the phenotype of interest (Thumma et al. 2005; Ingvarsson et al. 2008). However, drought response is a complex trait that involves genetic control of different pathways in different tissues. GWAS implies genotyping enough markers across the genome, so that functional alleles will likely be in LD with at least one of the genotyped markers. The number of markers and their density is defined by genome size and LD decay, and will therefore vary considerably among species, being mainly approached for those species whose genome sequence (or at least exome sequence) is already known. 12.4.1.2 Transcriptomics A complementary approach to the identification of drought-responsive genes is the analysis of transcriptomic response. As reviewed recently by Vaahtera and Brosché (2011), there are several processes contributing to the response to a specific abiotic stress: post-translational activation and selective nuclear import of transcription factors, regulation of DNA accessibility by chromatin modifying and remodeling enzymes, interplay between response elements, and so on. Most of these factors have been usually analyzed independently, due to the intricacy of the whole process. Analysis of the expression patterns under stress and recovery allows the identification of expressional CGs, induced by stress. This has been the most common approach followed to explore the molecular basis of the response to biotic 314 I. Aranda et al. and abiotic stresses, and when functional information is lacking, provides the most reliable CGs. Expression profiles reveal not just the genes induced by the stress, but also the genes whose transcription is inhibited. Inhibition of transcription shares importance with transcriptional up-regulation, as suggested by the results of Watkinson et al. (2003) and Lorenz et al. (2006). Nevertheless, first transcriptomic approaches to the analysis of the response to drought stress have focused on the identification of genes overexpressed during the stress. Prior to the development of current techniques of expression analysis and highthroughput sequencing, researchers could deal only with few genes at a time. Chang et al. (1996) published a pioneer work where four water deficit-induced cDNAs were identified and characterized in loblolly pine (Pinus taeda). In an attempt to discover a higher number of genes induced by water deficit, Dubos and Plomion (2003) used cDNA-AFLP and reverse Northern blot in maritime pine (Pinus pinaster), and identified 48 putative genes presumably involved in the response to drought stress in roots. Nowadays, accurate techniques such as suppression subtractive hybridization (SSH), together with the capacity to check the expression of high number of genes at a time using microarrays provide noticeable precision for the isolation of induced genes in stressed versus control material. Complementarily, massive sequencing technology is much more affordable, and analysis of the whole transcriptome under control, stress, or recovery conditions is currently feasible. Nevertheless, for many forest tree species (mainly for Gymnosperms) a good annotation is not available yet, and for many genes detected this way, even putative ones, homology-based function has not been proposed. Among angiosperm forest trees, poplars are the main model for molecular analysis, and even more since the release in 2006 of the complete genome of Populus trichocarpa (Tuskan et al. 2006). Poplar species show a comparatively fast growth and a noticeable capability for vegetative propagation, which allows undertaking experimental procedures not applicable to other tree species. Additionally, poplar was one of the first tree species successfully transformed, both using Agrobacterium (Fillatti et al. 1987) and gene gun (McCown et al. 1991). In the last years, many works referred to the transcriptomic response to drought stress of Populus species. Some of those studies have focused on the identification of genes induced by stress (Caruso et al. 2008; Bae et al. 2010), while others examined the expression pattern and function of specific genes (Bae et al. 2009, 2011; Chen et al. 2011). Exhaustive transcriptome studies using massive sequencing have also been published, describing the intra-specific variation (e.g., Hamanishi and Campbell 2011) or comparing specific genotypes (e.g., Cohen et al. 2010). The effect of drought stress on the transcriptome of the cambial region, where wood cells are developing, has been recently published by Berta et al. (2010). Although not in the same depth as in poplar, transcriptomic response to drought has also been studied in other angiosperm forest tree species. For instance, Gailing et al. (2009) have highlighted the role of Quercus sp. as a model for forest tree species, being one of the most important forest genera in the northern hemisphere. 12 Drought Response in Forest Trees 315 Knowledge is not so advanced for gymnosperms which display peculiar characteristics that make the study of their genetic adaptations difficult. They usually have huge genomes, with a high percentage of repeated sequences and pseudogenes whose functions are not well known. For instance, whereas the genomes of Arabidopsis thaliana and Populus trichocarpa are approximately 150 and 550 Mbp long, respectively, the Pinus pinaster genome is about 30,900 Mbp, 70–75 % of which is made up of highly repeated sequences (Morse et al. 2009), and no complete genome of a reference conifer species is yet available. Furthermore, angiosperms diverged from gymnosperms more than 300 million years ago. Thus, to a large extent, knowledge and molecular tools developed for the former are not readily applicable to the latter. For these reasons, the selection of CGs for the study of adaptation in gymnosperms based solely on their homology with angiosperm genes, without further confirmation of their participation in the stress response, is not fully reliable. Additionally, conifers are highly recalcitrant to transformation and vegetative propagation, limiting the sort of feasible experimental designs. Picea and Pinus are the most common model genera for conifers. In Picea, some studies have focused on the expression patterns of specific drought-related genes or on proteomic changes induced by drought (i.e. Blödner et al. 2007). In the case of Pinus, several genomic studies of the response to drought have been launched in different species, in an attempt to identify genes induced by water deficit. For instance, Heath et al. (2002) and Watkinson et al. (2003) performed a preliminary analysis of gene expression during drought-induced stress in P. taeda. These authors used microarrays that included cDNAs obtained from pre-existing libraries from the xylem, male cones and shoot tips, but did not attempt to exhaustively identify genes induced by water stress. Other noteworthy work is the one by Dubos and Plomion (2003), based on cDNA-AFLP in P. pinaster. More recently, several groups have used more precise techniques for the identification of drought-induced genes, such as comparison of EST libraries generated during drought-induced stress and drought recovery as well as from well-watered roots (Lorenz et al. 2006, in P. taeda), or SSH libraries, followed by microarray and RTPCR expression analysis (Perdiguero et al. 2012, in P. pinaster). Genes putatively involved in the response to water deficit, identified by these techniques or by their homology with other known genes, have been used to assess diversity and differentiation at different taxonomical and demographic levels regarding drought response. For instance, DNA sequence variability for drought stress CGs has been analyzed in different populations of Pinus pinaster across a latitudinal and precipitation gradient (Eveno et al. 2008). In the same way, different works have focused not on the genomic diversity but on the expressed response. Thus, Yang et al. (2010) performed a proteomic analysis of the response in Populus kangdingensis and Populus cathayana. Comparison of the transcriptomic response in different populations has also been performed in different species (e.g., Sathyan et al. 2005 in Pinus halepensis or Hamanishi and Campbell 2011 in Populus balsamifera). In poplar, comparisons at the individual level, 316 I. Aranda et al. among particular genotypes, have been published (Cohen et al. 2010) or even among ramets of the same genotype acclimated to different sites (Raj et al. 2011). The study of the transcriptomic profiles allows identification of different functional groups of genes related to drought response. A high proportion of genes induced by drought stress are related to metabolism, and mainly to carbohydrate metabolism. The accumulation of sugars has been correlated with the acquisition of desiccation tolerance in plants, probably because sugars protect the structures from mechanical and metabolic stresses during dehydration (Oliver et al. 2010). Within this group genes presumably involved in the detoxification of aldehydes generated by alcohol metabolism are found. This result is consistent with the accumulation of ethanol in conifer seedlings during drought (Manter and Kelsey 2008). This group also includes genes related to aminoacid (e.g., proline), lipid, fatty acid and isoprenoid metabolism, which could be involved in the synthesis and accumulation of compatible solutes and hormones during water stress. Another important group of genes comprises those related to defence and cell rescue. This category includes genes coding for late embryogenesis abundant (LEA) proteins, heat shock proteins (HSP) or dehydrins, among others. HSP are known to act as molecular chaperones, helping in the correct folding of other proteins and protecting them from unfolding and denaturation, and are involved in different abiotic stresses (Wang et al. 2004). LEA proteins seem to be involved in detoxification and may also act as chaperones, and their participation in desiccation tolerance is well known (Battaglia et al. 2008). Dehydrins are a complex family of proteins directly related to the response to water deficit and included among the LEA proteins. They are thought to act in protecting the cell metabolism during the stress, and have been the subject of different expression analysis in forest tree species (e.g., Bae et al. 2009; Vornam et al. 2011). Transport processes play an important role in the mobilisation and accumulation of solutes and hormones and in cell detoxification pathways during adaptation to water stress. Thus, sugar transporters, involved in the modification of osmotic pressure under stress, or ABC transporters, which are involved in the response to different biotic and abiotic stresses (Wanke and Kolukisaoglu 2010) have been detected in drought response analysis. Aquaporins deserve special mention. These are channel proteins located in cellular membranes and mediate water flux, maintaining proper water balance. They can be found both in the plasma membrane and in the vacuole membrane, and their expression is induced by water deficit. Several works have focused on this protein family in forest tree species and on its role during hydric stress recovery (e.g. Almeida-Rodriguez et al. 2010; Berta et al. 2010). Finally, there is a major group of proteins involved in drought stress response that corresponds to regulation processes. Two major pathways have been described in the plant response to abiotic stresses, including water deficit: an ABA-dependent pathway, and an ABA-independent one, with complex interactions (see, for review Shinozaki and Yamaguchi-Shinozaki 2007 or Hirayama and Shinozaki 2010). Several ABA-responsive transcription factors, such as bZIPs, NACs, MYBs and MYCs, have been described (e.g. Olsen et al. 2005). In the ABA-independent 12 Drought Response in Forest Trees 317 pathways other NAC and DREB transcription factors are involved (e.g. Chen et al. 2009, 2011). Other proteins involved in post-transcriptional (alternative splicing) and post-translational (e.g., kinases and phosphorylases) modifications are also included in this group. Nevertheless, there are still a number of genes responsive to drought stress whose function is unknown and even a putative function cannot be proposed, due to a lack of homology with annotated sequences; this situation is particularly relevant in conifers (see, for instance, Perdiguero et al. 2012). 12.4.2 Epigenetic Foundations of Response to Drought In addition to the genetic component, epigenetic variation has been suggested to contribute to the phenotypic plasticity and adaptive potential of individuals and populations (Bossdorf et al. 2008; Herrera and Bazaga 2010). Epigenetic mechanisms include heritable, but potentially reversible, changes in gene expression without changing the nucleotide sequence. Three epigenetic processes have been determined: insertion of methyl groups in cytosine bases of DNA, acetylation and methylation of N-terminal histone tails, and generation of regulatory noncoding RNA molecules. DNA methylation is the only epigenetic mark for which the mechanism of inheritance has been well established, so this mark can be considered the driving force for epiallelic variation (Mirouze and Paszkowski 2011). Regulation of gene expression is important to plant tolerance to stresses. For this reason, epigenetic variation is gaining interest, because it may regulate the expression of genes that have a key role in acclimation responses. Although the number of studies concerning epigenetic variation in plants subjected to stress is increasing, only few studies correspond to forest tree species growing under drought stress. In the first study of this type with a woody plant (Gourcilleau et al. 2010) six hybrid genotypes of the genus Populus were subjected to moderate water deficit. Significant genotype and treatment effects were detected for global DNA methylation and for morphological traits, such as height, stem biomass, leaf number and total leaf area. A positive correlation was demonstrated between DNA methylation percentage and productivity (stem biomass and height) under well-watered conditions but no correlation between DNA methylation and morphological traits was observed in response to water deficit. While there was a general decrease of growth for all genotypes in response to a water deficit, variations in DNA methylation were found, suggesting different responses among hybrids. Raj et al. (2011) studied the influence of recent individual history on the Populus transcriptome and global DNA methylation-level response to drought. Plants of the same genotype established in contrasting locations showed different transcript patterns and DNA methylation levels in response to drought, which suggests an epigenomic base for the clone history-dependent transcriptome divergence. 318 I. Aranda et al. Trees of the white mangrove (Laguncularia racemosa) can occur naturally in salt marsh and riverside, exhibiting significant differences in morphological traits, thus presenting an excellent system to study natural variation in genetic and epigenetic marks and their relation to phenotypic variation and plasticity in trees (Lira-Medeiros et al. 2010). Analysis of DNA methylation patterns and nucleotide sequences detected greater epigenetic than genetic variation within and between populations in contrasting environments, which indicates that epigenetic variation in natural populations plays an important role in long-term adaptation to different environments. 12.4.3 Metabolomics Modifications in the profiling of metabolites represent one of the first outcomes at the molecular level brought about by drought. Some molecular changes such as accumulation of compounds with osmotic effect, triggers as a consequence of a runway sensing system that tend to compensate the negatives effects of water stress on processes such as turgor maintenance or growth of plants. In this respect, the amount and kind of certain metabolites represent a by-product resulting from the direct impact of water stress at a higher level of organization (i.e., organ or individual). Probably, osmotic adjustment is one of the mechanisms affected in higher degree by the accumulation of different metabolites, as it involves the active accumulation of osmotic compounds acting directly on turgor maintenance (Chaves et al. 2003, and Fig. 12.2 on this chapter). Increase in metabolites such as nonstructural carbohydrates (NSC) is a classic example of response to drought related with the adaptation to water stress of forest tree species. Thus, the increase in NSC is a typical response for those species with a higher tolerance to water stress (Piper 2011). Cyclitol is other important group of compounds related with osmotic adjustment, and especially quercitol has found to increase in greater proportion for stressed plants in several species of Quercus sp. and Eucalyptus sp. (Merchant et al. 2006; Passarinho et al. 2006). However, important changes in other compounds lacking of osmotic role have been observed as well, and playing an important role in cellular processes further than the osmotic adjustment as those involved in photorespiration, stability of membranes, or changes in compounds of the cellular wall (Warren et al. 2011). Differences in the specific metabolites expressed, even under constitutive conditions without water stress, are related to ecological and evolutionary aspects of forest tree species such as tolerance to drought (Merchant et al. 2007a, b; Warren et al. 2012), or may result from different phylogenetic pathways (Warren et al. 2011). In this respect, even within the same genus, such as for example Eucalyptus sp., it is possible to find species with a trend to accumulate certain metabolites compared to others species that do not (Fig. 12.5). Although metabolic profiling depends on the species considered and the degree of water stress endured, not less important is the change in metabolic profiling in 12 Drought Response in Forest Trees 319 Fig. 12.5 Relative soluble organic compound to total polar organic metabolites in seven Eucalyptus species submitted to 2 months of water stress (Wpd * -2 MPa) ordered by annual potential water balance. Upper panel shows relative proportion of oligosaccharides (fructose, glucose, sucrose); middle panel shows polyols (agrupping myo inositol, scyllo inisitol, galactinol; proto quercitol and others cyclohexanepentols) and lower panel shows organic acids (shikimic acid, quinic acid, malic acid, gallic acid and citric acid). These main metabolites accounted for over 90 % of organic soluble metabolites in Eucalyptus leaves. Open and closed bars represent well watered and water stressed plants, respectively. Means and standard deviation (n = 5). Redrawn from data in Warren et al. (2011, 2012) the recovering phase after drought release, which shows different patterns according to the ecology of forest tree species. In a recent report, Warren et al. (2012) observed a different pattern in the osmotic potential and osmotically active solutes during the re-watering phase after a drought cycle for two Eucalyptus species. The mesic E. pauciflora showed an increase in osmotic potential at full turgor and decrease of osmotically active solutes, possibly related to a recovery of growth; the semi-arid E. dumosa maintained an increase in osmotic compounds such as some sugars, even after water stress relief, and that would allow plants to endure new water stress cycles. Until recently, assessment of metabolites involved in osmotic adjustment or other physiological processes related to water stress was limited to the analysis of a few specific sugars or amino acid compounds. With the fast development of more 320 I. Aranda et al. insightful techniques, a more detailed approach to metabolites profile is possible, with hundreds of different compounds analyzed with the same platform. In the last, modifications in biochemical products result from a complex regulation of molecular networks responding to an endogenous and exogenous signaling process in response to the water stress suffered by the plant, which will be responsible of the resulting phenotype (Fig. 12.4). 12.5 Facing the Future of the Study of Drought Response in Trees: ‘‘Omics’’ and Interplay with Higher Scales of Biological Integration High-throughput technologies have made it possible to see organisms as complex interactive systems. This new approach represents a shift toward a more holistic perspective. Integrative functional genomics combines the molecular components ((epi)genome, transcripts, proteins, metabolites, and their interactions) of an organism and incorporates them into functional networks or models designed to describe the dynamic activities of that organism which are responsible for the observed phenotype (Fig. 12.4a). The first studies of the response to drought stress at the transcriptomic level addressed simple experimental conditions, with a single stress level. However, an integrated knowledge of the response to water deficit needs experimental designs mimicking field conditions, in order to detect the various pathways initiated by different levels of stress, as suggested by Watkinson et al. (2003). Trees in nature often must face more than one stressful factor at a time. Interaction among stresses, both biotic and abiotic, must be elucidated at different scales of biological integration from ecophysiological (Aranda et al. 2005) to the molecular response (Mittler 2006). For instance, Duan et al. (2008) analyzed the combined effects of UV-B, ABA and water status in allometric patterns of Populus yunnanensis. In this sense, natural populations can offer a valuable (although with evident practical hindrances) resource for these experiments, as proven in the work of Brosché et al. (2005). Advances in the study of drought response in forest tree species using other ‘‘omics’’ have been recently reported. In this respect, the study of metabolomic profiling together with water relations, at the leaf or plant level, deserves new efforts in order to better integrate both scales of study in the knowledge of acclimation and adaptation to water stress (Sardans et al. 2011). Different responses can take place in the diverse organs and even tissues of the tree. Although organ-specific analysis has been performed, development of laser microdissection capture techniques will allow in the future the analysis of the response in differentiated tissues and cells. Knowledge of the role played by the adaptive genetic and epigenetic variability on the observed phenotypic variation in traits related with drought tolerance is still limited for most forest tree species, and not conclusive when framed in an 12 Drought Response in Forest Trees 321 evolutionary context. Hence, disentangling the potential adaptive value, and its molecular control, for those characters involved in adaptation to drought will be of prime importance in the future (Neale and Kremer 2011). Epigenetic and genetic studies will probably open new avenues to the understanding of the evolution of forest tree species, and the always complex interplay between genotype and environment as the main driver in the adaptation process under water stress (Raj et al. 2011). In this regard, and taking into account the rate of climate change, epigenetic modifications operating as molecular basis of phenotypic plasticity, which may be heritable, will likely play a key role that will be unraveled in the forthcoming years (see Sect. 12.3.2). In this game of scales, the integration of ecophysiological studies with the characterization of the molecular response will break the traditional oversimplification from the functional ecology approach focused on the species-specific response, as well as the reductionist view that emerges from most of molecular analysis. Probably, one clear example of oversimplification in ecophysiological studies is the frequent tendency to forget species comprises groups of local populations. Furthermore, it is well known that in many cases, one unique forest population embraces a high intraspecific genetic variability for quantitative traits related with growth, phenology, or morpho-functional traits. The genetic variability has its counterpart in a high degree of phenotypic alternatives to the same environmental challenge. In the future, and according to the expectations about the intensification of droughts in wide areas all round the world, the ecophysiological studies will have to center efforts in the analysis of those traits more related to the fitness and survival of forest tree species facing water stress. In the next years, more detailed studies will be necessary to improve our understanding of complex functional traits such as the stomatal control of water losses (Leonardi et al. 2006; Voltas et al. 2008; De Miguel et al. 2012), net photosynthesis (Major and Johnsenn 1996; Benowicz et al. 2000; Scotti et al. 2010), or hydraulic resistance to droughtinduced cavitation (Corcuera et al. 2011; Lamy et al. 2011). Acknowledgments This work was supported by funding from the Spanish projects ECOFISEPI AGL2011-25365, MAPINSEQ AGL2009-10496, SUM2008-62500004-C03-01, as well as the transnational project Plant-KBBE PLE2009-0016. We thank Drs. J. Voltas and F. Ewers for a previous critical reading of this work. Authors are grateful to the anonymous reviewer that improved a first version of this contribution. References Aasamaa K, Sober A, Rahi M (2001) Leaf anatomical characteristics associated with shoot hydraulic conductance, stomatal conductance and stomatal sensitivity to changes of leaf water status in temperate deciduous trees. Funct Plant Biol 28:765–774 Abrams MD (1988) Sources of variation in osmotic potentials with special reference to North American tree species. Forest Sci 34:1030–1046 Abrams MD (1994) Genotypic and phenotypic variation as stress adaptations in temperate tree species: a review of several case studies. Tree Physiol 14:833–842 322 I. Aranda et al. 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BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 DATABASE Open Access EuroPineDB: a high-coverage web database for maritime pine transcriptome Noé Fernández-Pozo1, Javier Canales1, Darío Guerrero-Fernández2, David P Villalobos1, Sara M Díaz-Moreno1, Rocío Bautista2, Arantxa Flores-Monterroso1, M Ángeles Guevara3, Pedro Perdiguero4, Carmen Collada3,4, M Teresa Cervera3,4, Álvaro Soto3,4, Ricardo Ordás5, Francisco R Cantón1, Concepción Avila1, Francisco M Cánovas1 and M Gonzalo Claros1,2* Abstract Background: Pinus pinaster is an economically and ecologically important species that is becoming a woody gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply. Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to be stored in dedicated databases. Description: EuroPineDB is the largest sequence collection available for a single pine species, Pinus pinaster (maritime pine), since it comprises 951 641 raw sequence reads obtained from non-normalised cDNA libraries and high-throughput sequencing from adult (xylem, phloem, roots, stem, needles, cones, strobili) and embryonic (germinated embryos, buds, callus) maritime pine tissues. Using open-source tools, sequences were optimally preprocessed, assembled, and extensively annotated (GO, EC and KEGG terms, descriptions, SNPs, SSRs, ORFs and InterPro codes). As a result, a 10.5× P. pinaster genome was covered and assembled in 55 322 UniGenes. A total of 32 919 (59.5%) of P. pinaster UniGenes were annotated with at least one description, revealing at least 18 466 different genes. The complete database, which is designed to be scalable, maintainable, and expandable, is freely available at: http://www.scbi.uma.es/pindb/. It can be retrieved by gene libraries, pine species, annotations, UniGenes and microarrays (i.e., the sequences are distributed in two-colour microarrays; this is the only conifer database that provides this information) and will be periodically updated. Small assemblies can be viewed using a dedicated visualisation tool that connects them with SNPs. Any sequence or annotation set shown on-screen can be downloaded. Retrieval mechanisms for sequences and gene annotations are provided. Conclusions: The EuroPineDB with its integrated information can be used to reveal new knowledge, offers an easy-to-use collection of information to directly support experimental work (including microarray hybridisation), and provides deeper knowledge on the maritime pine transcriptome. 1 Background Conifers (Coniferales), the most important group of gymnosperms, represent 650 species, some of which are the largest, tallest, and oldest non-clonal terrestrial organisms on Earth. They are of immense ecological importance, dominating many terrestrial landscapes and representing the largest terrestrial carbon sink. Currently present in a large number of ecosystems, they have evolved very efficient physiological adaptation systems. * Correspondence: [email protected] 1 Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Campus de Teatinos s/n, Universidad de Málaga, 29071 Málaga, Spain Full list of author information is available at the end of the article Given that trees are the great majority of conifers, they provide a different perspective on plant genome biology and evolution taking into account that conifers are separated from angiosperms by more than 300 million years of independent evolution. Studies on the conifer genome are revealing unique information which cannot be inferred from currently sequenced angiosperm genomes (such as poplar, Eucaliptus, Arabidopsis or rice): around 30% of conifer genes have little or no sequence similarity to plant genes of known function [1,2]. Unfortunately, conifer genomics is hindered by the very large genome (e.g. the pine genome is approximately 160 times larger than Arabidopsis and seven times larger © 2011 Fernández-Pozo et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 than the human genome; in fact, it is larger than any other genome sequenced to date) that is replete with highly repetitive, non-coding sequences [3]. Conifers include the economically and ecologically important species of spruces (Picea sp) and pines (Pinus), Pinus being the largest extant genus with approximately 115 species. The importance of pines is due to the fact that: (i) their timber and paper pulp are used for the construction of buildings and furniture; (ii) they are used in reforestation due to their rapid growth and drought tolerance as compared to other tree species; (iii) they help stabilise sandy soils and indirectly act as an atmospheric CO 2 sink, helping to reduce global warming; (iv) some pine nuts are widely used in Mediterranean cuisine. Consequently, the genus Pinus is becoming a woody gymnosperm model. The main pine model species in Europe are Pinus pinaster and Pinus sylvestris, whereas Pinus taeda and Pinus contorta are the equivalent in North America. Therefore, it is relevant to investigate and increase our knowledge of the content of the pine genome as this would allow the exploitation of natural genetic resources and the use of new forest reproductive material appropriate to adapt these trees to a changing climate. The application of genome-based science is playing an important role in understanding the genome content and structure of different organisms. Since whole-genome sequencing approaches are hard to apply to large genomes such as the pine genome, scientists have focused on the expressed portion of the genome using dedicated technologies. For example, the sequencing of clones obtained by suppression subtractive hybridisation (SSH) [4-6] provides gene-enriched sequences that are specific to a particular condition. However, the dominant approach to characterising the transcriptionally active portions of pine genomes has been expressed sequence tags (ESTs) [7,8] due to the absence of non-coding DNA (mainly introns and intergenic regions). Classic ESTs are subject to artefacts during cDNA library construction and are highly error prone during sequencing procedures. As a result, erroneous clustering and assembling occur during reconstruction of putative transcripts and may ultimately lead to inaccurate gene annotation [9]. However, next-generation sequencing technologies have removed many drawbacks and timeconsuming steps involved in classic ESTs and have facilitated transcriptome sequencing of many species at a fraction of the total time and cost previously required [10]. ESTs have also driven the development of pine microarrays [11-14], although there is no easy way to relate the data printed on these microarrays to the corresponding pine sequences. Sequencing projects should store, organise, and retrieve sequences by means of user-friendly databases. Since many sequences in EST databases are reported to Page 2 of 11 be highly contaminated or incorrectly pre-processed [9], there is a need for more reliable pre-processing, clustering, assembly and annotation pipelines to yield reliable information. ConiferEST [15] (now part of ConiferGDB http://www.conifergdb.org/coniferEST.php) was the first attempt to rationalise pine sequences by more precise pre-processing dedicated to Pinus taeda traces only. The DFCI Pine Gene Index http://compbio.dfci.harvard. edu/cgi-bin/tgi/gimain.pl?gudb=pine, a subset of the discontinued TGI Gene Indices [16], is a non-redundant database of all putative Pinus genes. This is a very large compilation of pine sequences, but only GO and KEGG annotations are available and no separation by species is provided, P. taeda is highly over-represented, and its interface only allows limited interaction. ForestTreeDB was created to centralise large-scale ESTs from diverse tissues of conifer and poplar trees [1], but it is no longer available. The TreeGenes database http://dendrome. ucdavis.edu/treegenes/ is composed of a wide range of forest tree species [17]. This effort to combine and inter-relate a great variety of different information should be acknowledged, even though EST pre-processing is not optimal. TreeSNPs [18] and PineSAP [19] are databases exclusively devoted to single nucleotide polymorphisms (SNPs) in Picea and Pinus species, respectively. Recently, Parchman and co-workers [2] described the first high-throughput analysis of a pine species, but no database was created for this. It should be noted that none of the above databases are linked to the pine microarrays described in literature. Our group has been working on pine genomics for many years (e.g., EMBL accession numbers AM982822AM983454, BX248593-BX255804, BX682240-BX683073, BX784033-BX784385, EC428477-EC428747, FM945441FM945999 or FN256437-FN257130) and wish to provide high-quality sequences and annotations of pine genomes by means of EuroPineDB. Taking advantage of next-generation sequencing methods, recently released pre-processors [20], reliable sequence annotators [21], and the bioinformatics infrastructure of the University of Málaga (Spain), EuroPineDB was designed to gather the most reliable re-pre-processed, assembled, and annotated P. pinaster sequences using different technologies. Retrieval systems based on sequence similarity, description matches or microarray positions are also included, as well as browsing by species, experimental process, and annotation. As a new feature, many of its sequences have been printed on a microarray for expression analysis [22] and can be freely browsed. 2 Construction and content 2.1 Pine sequences Although EuroPineDB is mainly devoted to the P. pinaster (maritime pine) genome, several sequences from Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 two other species (P. sylvestris and P. pinea) are also included since their sequences were printed in a pine microarray (see below). 2.1.1 Gene libraries Different gene libraries, all of which were constructed using different tissues and different strategies (described in Table 1) were included. All libraries were sequenced using Sanger’s dideoxynucleotide method except for the sequences generated from Pp-454, which were obtained with a GS-FLX pyrosequencer using Titanium technology. Pp-454 was the main contributor to the database (55 431 UniGenes and 844 737 curated reads). Frequency distributions of reads and contigs are shown in Figure 1. 2.1.2 EMBL sequences Gene library reads were completed with 13 206 sequences from the EMBL v. 102 database including the plant EST (Expressed Sequence Tag) and plant STD (Standard) sets for sequences whose ‘source organism’ field contained P. pinaster, P. sylvestris or P. pinea, provided that the sequence was not already included as a member of one of the gene libraries, and discarding any organellar sequence or sequences whose length was below 100 bp. The idea was to gather all sequence data on the three species, including their annotations. With P. pinaster as the main contributor (12 673 out of 13 206 EMBL entries), EMBL entries only provided 5667 different UniGenes. 2.1.3 Microarray Before EuroPineDB was constructed – based on the existing putative UniGenes http://cbi.labri.fr/outils/ Page 3 of 11 SAM/COMPLETE/index.php?ID=gemini – an ESTbased microarray was designed containing 3456 spots printed twice with clones taken from the Pin, Gemini and CK16 gene libraries only (Table 2) [22]. Spots were distributed on the chip into 16 blocks of 16 × 14 dots. The microarray also included some full-length cDNA sequences of genes related to nitrogen metabolism, such as aspartate aminotransferase, asparagine synthetase, Lasparaginase, glutamine synthetase a and b, NADP+ isocitrate dehydrogenase and ornithine aminotransferase, which can be found in the EMBL v102 set. The inclusion of microarray information in EuroPineDB facilitates accessing the most complete information on each sequence printed on it. In the near future, microarrays implemented with sequences contained in EuroPineDB will be also included. 2.2 Database architecture The EuroPineDB was built using Ruby On Rails 2.0 http://rubyonrails.org/, a web development framework that uses a model-view-controller pattern to maintain strict separation between the web interface (views) code, database tables (models), and all methods that handle interactions between views and database (controllers). It also maintains different environments for each development phase (development, production and testing). This enabled EuroPineDB to be developed and tested in a redundant Oracle RAC (Real Application Cluster) database. Bulk imports, updates, and database managements were automated by means of Ruby scripts. Table 1 Gene libraries providing sequences for EuroPineDB Gene library Tissue Species Experimental conditions Pp-454 Roots, stem, embryos, callus, cones, male and female strobili, buds, xylem, phloem. P. pinaster ESTs from several different tissues LG0BCA Buds P. pinaster ESTs, adult buds GEMINIa Xylem P. pinaster ESTs from normal, compression, opposite, early and late wood SSH Xylem Xylem P. pinaster SSH, compression vs. opposite, and juvenile vs. mature UPM Roots, stem, needles P. pinaster SSH, drought stress ARG Roots P. pinaster SSH, ammonium excess vs. ammonium deficiency SSH LacPine Roots P. pinaster SSH, inoculated with Laccaria bicolor vs. not inoculated SSH Mic Roots P. pinaster SSH, mycorrhizal vs. not mycorrhizal CK16b Cotyledons P. pinea SSH, adventitious shoot induction SSH Embryos Embryos P. sylvestris SSH, lack of N vs. normal N Pin EMBL v. 102 Cotyledons - P. sylvestris P. pinaster, P. pinea, P. sylvestris ESTs from photosynthetic tissues Miscellaneous a b GEMINI gene library was described in [8] CK16 gene library was described in [4] Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 Figure 1 A, Size distribution of pre-processed 454 and Sanger’s reads used for EuroPineDB. As expected, Sanger reads were longer than 454 reads in length. B, Contig size distribution within EuroPineDB. An automated pipeline that combines all the tools described here is executed on every EuroPineDB update. An update incorporates new pine sequence retrieval from dbEST and EMBL databases, new user reads, and the re-execution of bioinformatics tools with every new UniGene. 2.3 Pre-processing and assembling entries Most problems for automated sequence assembly resulted from chimerical clones in the plasmid libraries, bacterial DNA contamination, low-quality sequences, and low-complexity repeats. Since ESTs in databases are commonly inaccurately pre-processed, any data sequence contained in EuroPineDB whose quality values (QV) for each nucleotide are available was pre-processed using SeqTrim http://www.scbi.uma.es/seqtrim with parameter customisation for each type of library [20]. High-quality pre-processed EST and SSH sequences guarantee that only non-chimerical, good-quality sequences (i.e., reads devoid of vectors, adaptors, polyA/T tails, contaminants, and potential cloning artefacts) are included in the database, while sequences consisting of cloning vectors (AC BX676903) or containing poly-A (AC BX676940) were removed. The better the quality of the trimmed sequences, the more reliable the final assembly. It should be noted that 52.5% of nucleotides Page 4 of 11 from the Pp-454 gene library were discarded due to low quality. The EMBL v. 102 subset included in EuroPineDB was also pre-processed with SeqTrim in order to remove uninformative, contaminant or erroneous sequences, even though these sequences lack a QV. Curated sequences, i.e. those longer than 100 bp for Sanger’s reads and 60 bp for 454 reads that exceed SeqTrim pre-processing, were assembled to produce putative pine transcriptional units (UniGenes) as contigs and singletons. Sanger’s reads were de novo assembled with a web version of CAP3 [http://www.scbi.uma.es/cap3, [23]] since it has been described as a highly reliable sequence assembler for establishing UniGenes [24] with these types of reads. CAP3 assembly was conducted with default parameters using 85% as the cut-off for overlap percent identity to deal with the sequence variation due to the high heterozygosity of pine genes and genome heterogeneity between samples (Table 1). The 454 reads were de novo assembled with a web version of MIRA3 [http://www.scbi.uma.es/mira, [25]] using 454 settings for ESTs, which enormously reduces the number of misassembled contigs; moreover, this assembly contains 99.94% of curated reads and provides as few as 471 singletons (0.06%). Each library sequence set (Table 1) was assembled separately to obtain specific UniGene assemblies for every gene library and every pine species (Table 2); P. pinaster comprehensive assembly was also performed with MIRA3 with mixed 454-Sanger settings. A collection of all UniGenes is available for every assembly and the distribution of contig lengths is shown in Figure 1, where the longest reads correspond to Sanger reads and the mean length of GS-FLX Titanium reads are below the expected mean length due to the extremely high number of low quality nucleotides (52.5%, Table 2). SSH libraries account for the small shoulder on 200 bp in Figure 1A for Sanger reads. 2.4 Annotation The annotation of pine sequences is especially challenging since, phylogenetically, pine is distantly related to angiosperm plant models, for which significantly more data and tools are already available [26]. EuroPineDB contains detailed and reliable annotations on UniGenes. This was achieved by combining the results of several annotation processes (described below). The redundant bioinformatics approach makes the curation and annotation processes highly reliable. During every EuroPineDB update, new sequences and new contigs will be re-annotated to provide the correct link and annotation as knowledge increases. Each sequence and UniGene has a specific page to display its annotation together with the E value associated with it to enable the empirical assessment of annotation quality. The current version of Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 Page 5 of 11 Table 2 Statistics for the gene libraries shown in Table 1 Gene library Raw Curated Mean lengtha Singletons Contigs UniGenes (% annotated) Discarded nt (%) by QV Vector Artefactsb Pp-454 913 786 844 737 227 471 54 960 55 431 (59.5%) 52.5% NA 3.03% LG0BCA 8766 8766 608 3834 1363 5197 (68.2%) NA NA 0.24% GEMINI 13 057 7916 458 3066 1124 4190 (49.9%) 9.4% 10.4% 2.9% SSH Xylem 992 790 474 385 142 527 (49.5%) 5.35% 31.8% 2.5% UPM 2806 1115 465 258 157 415 (31.8%) 3.2% 15.9% 21.04% ARG 218 148 394 127 7 134 (47.8%) 22.5% 5.1% 5.3% SSH Lac-Pine 351 231 350 210 8 218 (34.4%) 18.5% 4.7% 2.64% SSH Mic CK16 294 358 194 282 314 575 149 221 13 24 162 (38.3%) 245 (65.3%) 15.3% NA 13.4% 0.05% 5.75% 6.6% SSH Embryos 96 57 437 34 6 40 (57.5%) 1.7% 20.6% 8.8% Pin 863 617 532 335 86 421 (68.9%) 10.2% 9% 2.9% 502 3704 1963 5667 (NA) NA 0.1% 0.58% EMBL v. 102 13 206 12 673 TOTAL 954 793 880 295 P. pinaster 951 641 877 523 597 684 54 648 55 332 (59.5%) P. sylvestris 2770 2466 730 476 203 679 (65.9%) P. pinea 382 306 574 239 27 266 (63.2%) QV, quality value. NA, not applicable. a Mean lengths are calculated with gene library reads. Nevertheless, they are calculated for contigs in the last three rows corresponding to the three species. b Artefacts include poly-A, poly-T, adaptors, contaminant sequences, and chimerical inserts. EuroPineDB includes annotations for 59.5% of pine UniGenes. 2.4.1 Putative description A sequence description is a user-friendly manner to offer information about putative functions. Every Sanger sequence in EuroPineDB is given a definition from up to four different sources, with the advantage that inconsistent descriptions are evidence of misannotation. Descriptions were obtained from: (i) the original description, if the sequence has one in the EMBL; (ii) the user definition, provided by the sequence owner when downloading sequences; (iii) the description retrieved by Blast2GO (see below); and (iv) the PGI definition obtained by the best hit in a low stringency BLAST (E < 10-3). Each UniGene contains only the definition provided by Blast2GO (see below). 2.4.2 GO terms, EC keys, KEGG maps, and interpro codes Every UniGene sequence was annotated using Blast2GO [21] using the best 10 sequences providing hits of at least 150 nt with a threshold E-value of 10-10 against the non-redundant GeneBank in order to remove spurious annotations. In addition, GO terms with experimental evidence codes were the most preferred, while computational evidence codes and codes inferred by curator were half-weighted for the final annotation; GO terms without biological evidence data or inferred from electronic annotation were discarded. This provided annotations with a high degree of confidence for UniGenes using an E-value for evidence codes of 10 -6 . A Ruby script was designed to assign the corresponding metabolic pathway (a KEGG map) to each EC key provided by Blast2GO. InterPro codes obtained from Blast2GO were included to add other high-valued annotations (such as functional sites, protein families or conserved domains) since it is an integrated documentation resource [27]. Entries without annotations are candidates for re-annotation with every database update. 2.4.3 SSRs, SNPs and ORFs Plant cDNAs contain a high frequency of polymorphisms whose main sources are single nucleotide polymorphisms (SNPs) and single sequence repeats (SSRs). They serve to build molecular markers that form an essential starting point for association studies and other genome scan applications such as comparative genomics. SNPs and SSRs can also be used as templates to design primers that amplify specific genomic DNA in diverse populations [28]. SSRs have been assessed with MREPS (http://bioinfo.lifl.fr/mreps/ [29]). SNPs have been calculated with an adapted version of AlignMiner [28]. The tentative (complete or incomplete) ORFs were inferred from the results of Full-Lengther [30]. 3 Utility and discussion Molecular sequence databases are fundamental resources for modern bioscientists. The development of such a genomic resource for Pinus pinaster should facilitate basic and applied research on the genetics and evolution of this species, its role in maintaining Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 forest health and ecosystem function, and the genetic traits that are desirable for the paper pulp and wood industry. 3.1 Web interface EuroPineDB has been designed with a user-friendly interface (Figure 2) that can be browsed anonymously, although an authentication process has been considered to grant sequence owners the necessary permissions to browse their sequences privately, or browse through other authorised-while-private unpublished data. It has five top tabs and a menu on the left that enable database mining from different entry points. Two types of search tabs have also been implemented to perform queries and to retrieve and browse the resulting Page 6 of 11 sequences. Information on tool versions and database releases used for obtaining the last update is shown on the right of the home page. There is an option to download files containing UniGene sequences or sequences displayed on a page in FASTA format (including their QV when available), which facilitates further analyses by laboratories. Crosslinks to the EMBL database are always provided by means of the accession numbers. 3.1.1 Navigation tabs By means of the ‘Gene Libraries’ tab, the user can see gene libraries, UniGenes and annotations for every gene library included in EuroPineDB. Each one contains a short description and some characteristics, including the statistical distribution of the relevant GO terms. The Figure 2 An example of microarray page in EuroPineDB Web. The upper part contains general information about the microarray as well as some statistical representation of the GO term distribution. The lower part is a representation of all sequences printed in a selected block. The colour codes are defined at the bottom of the Web page (not shown) and in the text. Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 Page 7 of 11 UniGene dataset consists of a consensus sequence of each contig and the singletons (see above). Gene library clones are stored in 96-well and/or 384well plates in the laboratory. Navigation using the ‘96Well plates’ and ‘384-Well plates’ tabs displays the plate organisation of the libraries. Users can download the sequences of all clones in a plate or browse the plate in which red clones are useless sequences, green clones are those that have successfully passed SeqTrim pre-processing, and black ones are printed controls. Currently, only one microarray (Pinarray1) has been designed with EuroPineDB sequences [22]. The ‘Microarray’ tab displays general and statistical information about Pinarray1 (Figure 2), whose printed sequences and annotations can be downloaded. Each microarray block organisation is displayed in the lower part of the page. Coordinates refer to a single sequence. The colours green, red and black have the same meaning as in the plates (see above). The graphic representation offers the possibility of retrieving information from specific clones after analysis of any experimental result using this microarray. Sequences in EuroPineDB have been assembled by gene library and pine species, and can be accessed using the ‘Assemblies’ tab. Each assembly can be inspected in detail, showing a paged list of UniGenes and a summary description. The detailed view of every UniGene includes the aligned sequences, their orientation, the contig alignment (as a simple-text), a description for the consensus sequence, and the putative description of each included Sanger sequence. Clicking on the name of a clone provides access to all the information about it (e.g. EMBL accession number, sequence length, the plate(s) in which it can be found, annotations, original and pre-processed sequences, gene library source, etc). From the sequence entry, users can return to any previously described browsing page (Figure 3). At the home page, a menu on the left enables filtered browsing by microarray, pine species, or annotation. Filtered browsing only displays entries sharing the same selected annotation. Each item in the list opens a new page with the EuroPineDB entries that share this specific annotation. For example, based on nitrogen metabolism (KEGG 00910), it is possible to know how many sequences are present in the database, since by clicking on 00910 every enzyme from this pathway can be seen, as well as the entries that are annotated as being one of these enzymes. As an additional example, all UniGenes involved in photosynthesis (GO:0015979) that belong to a particular library or pine species can be identified by means of GO term filtering. 3.1.2 Database retrieval In addition to a guided browsing, EuroPineDB contents can be retrieved by means of text search or sequence Home Search BLAST Gene libraries 96-Well plates 384-Well plates Microarrays Assemblies Each library Each 96w_plate Each 384w_plate Each microarray block List of assemblies All sequences Each clone/sequence External links Annotations Descriptions GO EC KEGG InterPro Each UniGene SNP SSR ORF Figure 3 Navigating through EuroPineDB. Arrowheads indicate the direction of navigation. Green boxes correspond to available views from all pages (thus, no incoming arrowhead is specified). Violet text indicates the option of downloading sequences in FASTA format. Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 similarity. A text search engine has been implemented. It can look for words in annotations (i.e. descriptions for sequences, GOs, ECs and InterPro) or for specific codes (i.e. accession numbers or EuroPineDB identifiers). Search results can be restricted to different database subsets (displayed on the Web page as checkboxes), and they are then grouped by common characteristics and displayed in tabs which show/hide the list of elements that match the request. Results are also linked to their respective description pages. A low-stringent (E < 10-3) BLAST-based search engine enables users to look for EuroPineDB entries similar to their amino acid or nucleotide sequence. The type of sequence (amino acid or nucleotides) is automatically detected, and either BLASTN or BLASTX will be used from the latest BLAST+ version [31]. BLAST searches may be conducted against different subsets of EuroPineDB: by species (P. pinaster, P. sylvestris and/or P. pinea, which can be chosen and combined as desired); and by single sequence or UniGenes. BLAST executions are queued and the results are accessible for up to 1 month with a custom URL that is sent to the user. 3.2 EuroPineDB is a large maritime pine sequence collection EuroPineDB is mainly devoted to P. pinaster since its 877 523 curated reads (99.7% of total reads) have produced approximately 5.24 × 108 nt in 55 332 UniGenes (Table 2), 24 937 being > 500 bp. Assuming that a similar number of genes occur in P. pinaster as in Arabidopsis thaliana (25 000, which is close to the number of UniGenes > 500 bp) and a similar average transcript length (2000 nt), average transcriptome coverage was estimated at 10.5×. This amount of data and the high coverage represent a substantial sequence resource for P. pinaster that will contribute significantly to its genomic analysis and make EuroPineDB one of the largest sequence collections available for a pine species. Further evidence of the high coverage is that the number of P. pinaster UniGenes is slightly lower than the number of UniGenes in the Pp-454 library (55 431, Table 2). This indicates that UniGenes from the other gene libraries, which are also longer in size (Figure 1), have served to gather together apparently independent contigs from the 454 sequencing, and that most ESTs revealed by capillary (Sanger) analyses of cDNA libraries (Table 1) were also encountered in the 454-sequenced pooled RNA. 3.3 EuroPineDB sequences include a low occurrence of repetitive and retrotransposon-like sequences The percentage of retrotransposon-like sequences in EuroPineDB is quite low (127 [0.0001%] reads and 20 [0.0003%] UniGenes), in contrast with the 6.2% found in P. contorta, indicating that mRNA isolation for cDNA Page 8 of 11 synthesis in all gene libraries guarantees the best approach for gene discovery, instead of using total RNA. The reduced amount of repetitive DNA found in the coding sequences and the relatively long reads obtained by the sequencing procedures have enabled an accurate de novo assembly and a reliable UniGene collection of maritime pine. A relative high proportion of reads (876 839 out of 877 523, 99.92%) was assembled into reliable contigs (containing less than 9% mismatches), which is in agreement with other high-coverage assemblies [32]. 3.4 EuroPineDB shed light on pine transcriptome Since estimating the number of genes and the level of transcript coverage represented in an EST collection is an important issue for transcriptome sequencing projects, functional information of EuroPineDB UniGenes was included by means of the widely-used Blast2GO annotator [21]. A wide range of GO terms was assigned to EuroPineDB UniGenes indicating that a wide diversity of transcripts is represented in the database (results not shown). Therefore, 32 919 (59.5%) out of 55 332 UniGenes of P. pinaster were annotated and corresponded to at least 18 466 different genes (which is the number of unique UniProt hits). Assuming that UniGenes inferred from contigs longer than 500 bp are a reliable view of a transcriptome and observing that the number of P. pinaster UniGenes longer than 500 bp (20 928, including annotated and unannotated UniGenes) is slightly greater than the number of different UniGenes regarding unique UniProt hits (18 466), it can be inferred that much of the P. pinaster genes have been identified with the gene libraries described in Table 1, since both numbers (18 466 and 20 928) are close to the 25 000 genes that are supposed to form the A. thaliana genome. The 59.5% of annotated maritime pine UniGenes is consistent with the 63.6% (20 928 out of 32 919 UniGenes) obtained when considering only UniGenes longer than 500 bp. Both percentages are only slightly lower than the 65% annotated in Eucawood [33] and the 67.8% in Melogen [34], but clearly more than the 32% of annotated sequences of P. contorta [2]. In total, 935 (1.7%) UniGenes were annotated with another pine sequence and 16 113 (29.1%) were annotated with a conifer (mainly Picea), which reflects the paucity of information on conifers in databases. This is further highlighted by the fact that the 12 057 (31%) annotated pine genes are qualified as “unknown” proteins, even though most of these unknown proteins correspond to non-annotated full-length transcripts from Picea glauca [35]. The predicted putative ORF could perhaps provide further support to any future functional annotation. The 40.5% of unannotated UniGenes may then correspond to one or more of the following possibilities: (i) putative Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 new pine genes that do not have an orthologue; (ii) noncoding RNAs (including pseudogenes, antisense transcripts, structured RNAs, microRNAs, etc) that have recently been found in abundance when deep transcriptome analysis is performed [36,37]; (iii) short sequences from the UTR part that are difficult to match, even though 36.4% (11 991) of annotated UniGenes are shorter than 500 bp; and (iv) artefactual assemblies that do not correspond to valid protein-coding sequences, as occurs in 15% of entries in the human gene catalogue [38]. Each UniGene also includes information about putative SSRs and SNPs, since the development of SSR and SNP markers in pine species could serve to dissect complex traits given that linkage disequilibrium is low or declines rapidly within the length of an average-sized gene [39]. A total of 4740 SSRs have been found in EuroPineDB for P. pinaster; tri-nucleotide repeats were found to be the most common SSRs in EuroPineDB (55.7%), with tetra-nucleotide (12.6%) and di-nucleotide (10.5%) repeats being present at much smaller frequencies. This contrasts with P. contorta in which di-nucleotide repeats were the most abundant. A total of 44 185 SNPs were also identified. Most SSRs and SNPs (3546 [74.8%] and 41 152 [93.1%], respectively) occur in UniGenes with a putative ORF (2966 and 10 756, respectively), and 1% occurs in start/end codons. These numbers are difficult to compare to other conifers because of the different algorithms used for detection [2,18,39]. Due to the enormous size of the pine genome, ORF-based SSRs and SNPs are advantageous since they will reduce the mapping efforts required for the development of high-density maps and association studies. The development of SSR/SNP molecular markers, as well as the ORF predictions contained in EuroPineDB, will facilitate comparative genomics with other wellknown conifers like P. taeda, P. contorta, P. glauca and P. sitchensis, and will be very useful to scientists interested in different aspects of pine genomics. In contrast to other plant databases, the SSRs and SNPs included in EuroPineDB are downloadable and can be used within any research project. 3.5 EuroPineDB differential features EuroPineDB is a dynamic structure since its content is re-assembled and re-annotated when new sequences are added. It is designed to include new tables that display other pine genomic features in the near future. Its purpose is to make UniGenes and their annotations available to the scientific community involved in pine genomics by means of a flexible interface for developing queries. Although overlaps exist with the content of the Pine Gene Index (PGI) [16] and TreeGenes [17], each Page 9 of 11 database offers distinct analytical approaches, enabling EuroPineDB to contain sequence relationships that cannot be found elsewhere. Moreover, EuroPineDB only includes sequences that have passed stringent quality filtering and only reliable annotations are assigned. Such a procedure provides a high level of confidence in the putative function and characteristics of P. pinaster UniGenes. Whereas the final aim of TreeGenes is to compare the different Pinaceae species, EuroPineDB, like ConiferEST [15], is more focused on deep information about a single species. In contrast to PGI and to ConiferEST, EuroPineDB differentiates pine species, contains the highest number of ESTs for a single conifer species, and provides insights on every gene library used to seed the database. ConiferEST attempted to provide reliable P. taeda EST pre-processing, anticipating the finding that at least 4.8% of ESTs in dbEST are contaminated by vectors, linkers, E. coli DNA and mitochondrial sequences [9]. TreeGenes does not fully pre-process chromatograms, since it was started using the high-quality sequence provided by PHRED; this has been described as a suboptimal strategy since it over-trims in relation to terminal structures, representing a loss of directional, positional and structural information on cDNA termini [20]. Since Pine Gene Index 7.0 extracted the sequences directly from databases, it contains untrimmed terminus parts, which has a detrimental impact on many downstream EST applications, thereby compromising the reliability of their resulting tentative contigs. Early pre-processing of some gene libraries considered in EuroPineDB [40] have proven to be incorrectly processed and annotated (e.g. accession numbers BX252344, BX255382, BX252627, BX252630 or BX251344), and this is mainly due to using the nowoutdated StackPack [41] workflows based on PHRED and PHRAP algorithms. EuroPineDB was pre-processed with SeqTrim [20] and assembled using CAP3 [23] and MIRA3 [25]. The use of SeqTrim was advantageous in obtaining reliable trimmed sequences, which is preferable to tailor-made scripts for every kind of sequence. An example of this improvement is indicated by the fact that EMBL v. 102 provided 12 673 out of 13 206 sequence entries (Table 2) devoid of any class of contaminant/artefactual sequences. Although this percentage could be considered too high for the EMBL database, it is clearly under the 4.8% reported for ESTs [9] because EMBL sequences have a more detailed curation process than ESTs. The use of SeqTrim is also devoid of the original reads of contaminating sequences from several bacteria and fungi genomes. Since contigs established with MIRA3 are highly reliable (the maximal mismatch percent in a contig is Fernández-Pozo et al. BMC Genomics 2011, 12:366 http://www.biomedcentral.com/1471-2164/12/366 below 9%), EuroPineDB assemblies for P. pinaster would provide highly reliable UniGenes that would reflect a realistic set of pine genes. 4 Conclusion EuroPineDB can be browsed intuitively (Figures 2 and 3) using several tabs, and data can be retrieved by text terms or sequence similarity using a stand-alone BLAST implementation. As a new feature, location information on sequences in microarrays is provided (Figure 2). UniGenes and its annotations can be browsed and downloaded (Figure 3) by pine species as well as by gene library, thus providing scientists with a comprehensive source of information on genomics and transcriptomics of P. pinaster. All this, together with the detailed sequence information and annotation, user-friendly Web-interface (Figure 2), regular updates, as well as connection to printed microarrays, make EuroPineDB extremely valuable to researchers using pine as a model organism, since its annotations and UniGenes cannot be found elsewhere. Finally, the current EuroPineDB assembly could also be used to design a new generation of pine microarrays comprising more UniGenes that would cover more transcriptome elements. Any scientists wishing to incorporate their sequences in EuroPineDB should contact the administrator to upload the data and obtain a private user account. Availability and requirements Project name: EuroPineDB; Web site: http://www.scbi. uma.es/pindb; Operating system(s): platform independent; Programming language: Ruby, HTML; Other requirements: Ruby on Rails; Licence: e.g. GNU Affero Public License; Any restrictions to use by non-academics: licence needed. Acknowledgements The authors gratefully acknowledge the computer resources and technical support provided by the Plataforma Andaluza de Bioinformática of the University of Málaga, Spain. This study was supported by the Spanish Ministerio de Ciencia e Innovación [AGL2009-12139-C02-02, BIO2009-07490], the European Union [PLE2009-0016] and the Junta de Andalucía [CVI-6075 and BIO-114]. Author details 1 Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Campus de Teatinos s/n, Universidad de Málaga, 29071 Málaga, Spain. 2 Plataforma Andaluza de Bioinformática, Edificio de Bioinnovación, C/Severo Ochoa 34, Universidad de Málaga, 29590 Málaga, Spain. 3Departamento de Ecología y Genética Forestal, CIFOR-UNIA, Carretera de La Coruña, km 7,5, 28040 Madrid, Spain. 4UM Genómica y Ecofisiología Forestal INIA-UPM, Universidad Politécnica de Madrid, Madrid, Spain. 5Área de Fisiología Vegetal, Departamento BOS, Instituto Universitario de Biotecnología de Asturias, Universidad de Oviedo, 33071 Oviedo, Spain. Authors’ contributions NFP analysed and annotated sequences, developed the database interface and put all information in the database. DGF designed and constructed the database and contributed to input/output scripting. JC, DPV, SDM, AFM, Page 10 of 11 MAG, PP, CC, MTC and AS and RO conceived, designed and constructed the different gene libraries. AFM performed sequencing reactions. RB tested the functioning and created different case studies. FRC conceived and designed libraries and the microarray. RO, CA and FMC conceived and designed libraries and contributed to the interpretation of data. FRC and FMC were involved in drafting the manuscript. MGC conceived and designed the database, checked its functioning and wrote the manuscript. All authors read and approved the final manuscript. Conflict of interests statement The authors declare that they have no competing interests. Received: 26 May 2011 Accepted: 15 July 2011 Published: 15 July 2011 References 1. Pavy N, Johnson JJ, Crow JA, Paule C, Kunau T, MacKay J, Retzel EF: ForestTreeDB: a database dedicated to the mining of tree transcriptomes. Nucleic Acids Res 2007, , 35 Database: D888-894. 2. Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA: Transcriptome sequencing in an ecologically important tree species: assembly, annotation, and marker discovery. 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Christoffels A, van Gelder A, Greyling G, Miller R, Hide T, Hide W: STACK: Sequence Tag Alignment and Consensus Knowledgebase. Nucleic Acids Res 2001, 29(1):234-238. doi:10.1186/1471-2164-12-366 Cite this article as: Fernández-Pozo et al.: EuroPineDB: a high-coverage web database for maritime pine transcriptome. BMC Genomics 2011 12:366. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit ANEXO III. Identification of water stress genes in Pinus pinaster Ait. by controlled progressive stress and suppression-subtractive hybridization Plant Physiology and Biochemistry 50 (2012) 44-53 Plant Physiology and Biochemistry 50 (2012) 44e53 Contents lists available at SciVerse ScienceDirect Plant Physiology and Biochemistry journal homepage: www.elsevier.com/locate/plaphy Research article Identification of water stress genes in Pinus pinaster Ait. by controlled progressive stress and suppression-subtractive hybridization Pedro Perdiguero a, b, Carmen Collada a, b, María del Carmen Barbero a, b, Gloria García Casado c, María Teresa Cervera a, b, d, Álvaro Soto a, b, * a GENFOR Grupo de investigación en Genética y Fisiología Forestal, Universidad Politécnica de Madrid, E-28040 Madrid, Spain Unidad Mixta de Genómica y Ecofisiología Forestal, INIA/UPM, Madrid, Spain Centro Nacional de Biotecnología, CNB, Madrid, Spain d Departamento de Ecología y Genética Forestal, CIFOR-INIA, Madrid, Spain b c a r t i c l e i n f o a b s t r a c t Article history: Received 18 July 2011 Accepted 30 September 2011 Available online 8 October 2011 Climate change is a major challenge particularly for forest tree species, which will have to face the severe alterations of environmental conditions with their current genetic pool. Thus, an understanding of their adaptive responses is of the utmost interest. In this work we have selected Pinus pinaster as a model species. This pine is one of the most important conifers (for which molecular tools and knowledge are far more scarce than for angiosperms) in the Mediterranean Basin, which is characterised in all foreseen scenarios as one of the regions most drastically affected by climate change, mainly because of increasing temperature and, particularly, by increasing drought. We have induced a controlled, increasing water stress by adding PEG to a hydroponic culture. We have generated a subtractive library, with the aim of identifying the genes induced by this stress and have searched for the most reliable expressional candidate genes, based on their overexpression during water stress, as revealed by microarray analysis and confirmed by RT-PCR. We have selected a set of 67 candidate genes belonging to different functional groups that will be useful molecular tools for further studies on drought stress responses, adaptation, and population genomics in conifers, as well as in breeding programs. ! 2011 Elsevier Masson SAS. All rights reserved. Keywords: Pinus pinaster Drought stress response SSH Microarray Polyethylene glycol 1. Introduction Climate change is a highly topical question nowadays. In view of the current climatic forecasts for the next hundred years, the need to study the adaptive responses of living organisms is broadly acknowledged among the scientific community. This is particularly relevant for tree species, which are sessile individuals and thus cannot flee from adverse conditions. Moreover, because of their longevity, similar to the predicted time-frame for climate change, they will have to face these perturbations with the same genetic makeup they currently possess. Conifers, which represent approximately 34% of the world’s forests (up to 61%, including mixed forest) and 60% of plantations for * Corresponding author. GENFOR, G.I. Genética y Fisiología Forestal, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, E-28040 Madrid, Spain. Tel.: þ34 91 336 63 92; fax: þ34 91 336 55 56. E-mail address: [email protected] (Á. Soto). 0981-9428/$ e see front matter ! 2011 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.plaphy.2011.09.022 wood production [1], display peculiar characteristics that make the study of their genetic adaptations difficult. For instance, they usually have huge genomes, with a high percentage of repeated sequences and pseudogenes whose functions are not well known. As an illustration, whereas the genomes of Arabidopsis thaliana and Populus trichocarpa are approximately 150 and 550 Mbp long, respectively, the Pinus pinaster genome is about 30,900 Mbp, 70e75% of which is made up of highly repeated sequences [2]. Furthermore, angiosperms diverged from gymnosperms more than 300 million years ago. Thus, to a large extent, the knowledge and molecular tools developed for the former are not fully applicable to the latter. For these reasons, the selection of candidate genes for the study of adaptation in gymnosperms based solely on their homology with angiosperm genes, without further confirmation of their participation in the stress response, is not fully reliable. The aim of this study was to identify and select for genes involved in the response to water stress in conifers. We used the maritime pine (P. pinaster Ait.), which is one of the most common conifer species in the western Mediterranean basin, as a model P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 species. According to current models, this region will be dramatically affected by climate change, with an increase of 3.0e3.5 " C in the annual average temperature and a decrease of 10e20% in the annual rainfall, with increasingly long and more intense periods of drought [3]. Mediterranean species face an additional difficulty because the most favourable physiological period in terms of light and temperature, coincides with the period of maximum water restriction. Notwithstanding its relatively small geographical range, the maritime pine has a large ecological amplitude, particularly in relation to rainfall regimes and water availability. Several works have revealed striking differences among maritime pine populations in growth and survival under drought-induced stress, and different strategies to face drought ([4,5] and references therein). These reasons make the maritime pine a uniquely suitable model species for the analysis of adaptation to drought-induced stress. Genomic studies of the response to drought have been launched in different pine species. For instance, Heath et al. [6] and Watkinson et al. [7] performed a preliminary analysis of gene expression during drought-induced stress in Pinus taeda. These authors used microarrays that included cDNAs obtained from pre-existing libraries from the xylem, male cones and shoot tips, but did not attempt to exhaustively identify genes induced by drought-induced stress. In addition, the comparison of EST libraries generated from the roots of loblolly pines during drought-induced stress and drought recovery as well as from well-watered roots led to the identification of 24 transcripts that were significantly induced by drought [8]. Very recently, this research group used a microarray to analyse the expression of 25,848 genes obtained from different libraries in the roots of P. taeda after seven days of withholding water and after two days of recovery. They identified up to 2445 genes that were responsive to drought-induced stress in this organism [9]. Sathyan et al. [10] generated an EST library from the roots of Pinus halepensis during drought-induced stress, and a preliminary 45 expression analysis using a macroarray technique detected 156 transcripts that were likely to be induced by drought. This result was confirmed by qRT-PCR for 14 genes. In P. pinaster, Dubos et al. [11,12] used the cDNAeAFLP technique to identify 48 differentially expressed fragments in the needles and roots of seedlings subjected to a mild water stress, checking their expression by reverse northern blot. Recently, 13 P. pinaster and P. taeda candidate genes selected from these works were evaluated for genetic variation in P. pinaster to detect patterns of selection and adaptation to local climatic conditions [13]. In this work we report the identification of 67 candidate genes (including 37 that had not been described in previous water stress studies), obtained from an SSH library from plants subjected to a progressive, controlled water deficit, and that were selected based on their expression patterns during this stress, analysed by microarrays and confirmed by RT-PCR. This set of genes constitutes a valuable molecular tool for further studies on adaptation to drought-induced stress in gymnosperms with important implications for breeding programs. 2. Results and discussion 2.1. Construction and differential screening of a subtracted cDNA library A cDNA library enriched in genes that are induced by water stress was constructed, with the aim of identifying and characterising genes involved in the drought response in P. pinaster Ait. Our work differs from previous analyses of the water stress response in pines in terms of experimental design and methodology. First, we applied a progressive and more severe stress that is more similar to the first steps of natural drought-induced stress, with the aim of detecting genes involved in the different response pathways initiated by different levels of stress, as suggested by Watkinson et al. (2003). Although the inhibition of transcription shares importance Fig. 1. Funtional distribution of genes obtained from the SSH library. In total, 351 unique ESTs were grouped according to MIPS functional categories for Arabidopsis thaliana. The percentage of gene transcripts in each group is listed. 46 P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 Table 1 Functional classification of the 67 selected candidate genes induced during the treatment. ID_sequence Accession No. 1. Metabolism 1.1. Amino Acid Metabolism Ppter_DR_61 FR846597 Annotation Prephenate Dehydrogenase Family Protein 1.4. Phosphate metabolism Ppter_DR_227 FR846763 Pyrophosphorylase 4 1.5. C-compound and carbohydrate metabolism Ppter_DR_4 FR846540 Aldehyde Dehydrogenase 7B4 Ppter_DR_33 FR846569 Seed Imbibition 2 Ppter_DR_52 FR846588 Quitinase IV Ppter_DR_99 FR846635 Glyceraldehyde-3-Phospate Dehydrogenase Ppter_DR_101 FR846637 Glycosyltransferase Family 14 Protein Ppter_DR_120 FR846656 Seed Imbibition 2 Ppter_DR_276 FR846812 Malate Synthase Ppter_DR_305 FR846841 Beta-Galactosidase 3 1.6. Lipid, fatty acid and isoprenoid metabolism Ppter_DR_48 FR846584 Lipase_3 Ppter_DR_90 FR846626 Myo-Inositol-1-Phostpate Synthase 2 Ppter_DR_244 FR846780 Sugar-Dependent 1 Ppter_DR_278 FR846814 Cytochrome P450 Like_TPB 1.7. Metabolism of intercellular mediators Ppter_DR_180 FR846716 Lesion Initiation 2; Copropophyrogen Oxidase 1.20. Secondary metabolism Ppter_DR_77 FR846613 Acc Oxidase 2 Ppter_DR_274 FR846810 12-Oxophytodienoate Reductase 11. Transcription Ppter_DR_213 FR846749 NAC Domain Containing Protein 28 Ppter_DR_254 FR846790 AP2 Domain Containing Transcription Factor Ppter_DR_296 FR846832 DEAD-Box Rna Helicase Ppter_DR_315 FR846851 BEL1-Like Homeodomain 1 Ppter_DR_319 FR846855 AP2 Domain Containing Transcription Factor 14. Protein fate Ppter_DR_136 FR846672 DNAJ Heat Shock N-Terminal Domain Containing Protein Ppter_DR_287 FR846823 DEGP Protease 9 16. Protein with binding function or cofactor requirement Ppter_DR_13 FR846549 Accelerated Cell Death 1 Ppter_DR_242 FR846778 Unusual Floral Organs Ppter_DR_275 FR846811 (R)-Mandelonitrile Lyase, Putative/(R)-Oxynitrilase, Putative 20. Transport Ppter_DR_123 FR846659 Inositol Transporter 2 Ppter_DR_162 FR846698 Pleiotropic Drug Resistance 12 Ppter_DR_187 FR846723 Farnesylated Protein 3 Ppter_DR_239 FR846775 Peroxisomal Adenine Nucleotide Carrier 1 Ppter_DR_329 FR846865 General Control Non-Repressible 1 30. Cellular communication/signal transduction Ppter_DR_137 FR846673 WD 40 Repeat Family Protein Ppter_DR_198 FR846734 CBL-Interacting Protein Kinase 20 32. Cell rescue, defence, cell death and ageing Ppter_DR_41 FR846577 Nonphotochemical Quenching Ppter_DR_72 FR846608 Late Embryogenesis Abundant 14 Ppter_DR_106 FR846642 Tau Class Glutatione S-Transferase Ppter_DR_152 FR846688 Heat Shock Protein 90.5 Ppter_DR_159 FR846695 Phenylalanine Ammonia-Lyase 2 Ppter_DR_235 FR846771 Heat Shock Protein 90 32. Systemic interaction with the environment Ppter_DR_262 FR846798 Aluminium Induced LP1 38. Transposables elements Ppter_DR_37 FR846573 Retrotransposon Protein 40. Cell fate Ppter_DR_22 FR846558 Tetraspanin 3 41. Plant development Ppter_DR_95 FR846631 Late Embryogenesis Abundant Protein, Putative 70. Subcellular localisation Ppter_DR_44 FR846580 Metallo-Beta-Lacmatasa Ppter_DR_80 FR846616 Unknown Ppter_DR_334 FR846870 Unknown Table 1 (continued) ID_sequence Accession No. Annotation 99. Unclassified or unknown proteins Ppter_DR_6 FR846542 Unknown Ppter_DR_51 FR846587 Plasmodesmata-Located Protein 8 Ppter_DR_115 FR846651 Nodulin MtN3 Family Protein Ppter_DR_122 FR846658 Unknown Ppter_DR_160 FR846696 Unknown Ppter_DR_163 FR846699 Nodulin MtN3 Family Protein Ppter_DR_194 FR846730 Unknown Ppter_DR_283 FR846819 Dormancy/Auxin Associated Family Protein Ppter_DR_303 FR846839 Acyl-Activating Enzyme 18 Ppter_DR_341 FR846877 Unknown Ppter_DR_342 FR846878 Oxidoreductase, 2OG-Fe(II) Oxigenase Family Protein Ppter_DR_349 FR846885 Unknown No hits found Ppter_DR_63 FR846599 No Hits Found Ppter_DR_107 FR846643 No Hits Found Ppter_DR_112 FR846648 No Hits Found Ppter_DR_200 FR846736 No Hits Found Ppter_DR_223 FR846759 No Hits Found Ppter_DR_284 FR846820 No Hits Found Ppter_DR_307 FR846843 No Hits Found Ppter_DR_327 FR846863 No Hits Found with transcriptional upregulation during acclimation to stress, as indicated by the results of Watkinson et al., (2003) and Lorenz et al., (2005), our goal in this study was to identify reliable candidate genes induced by water deficit. For this purpose, we generated an SSH library using clonal material, which is a more exhaustive technique for the isolation of such candidate genes than has been used in previous works. We picked 4940 colonies that were transformed with the forward-subtractive PCR and amplified the inserts using nested primers. In all cases, the amplified inserts were more than 200 bp long. Differential screening by macroarray led us to the identification of 1718 clones that were presumably induced by water stress. 2.2. Annotation and functional classification of ESTs We proceeded with the sequencing of the 1718 candidate clones. In total, 1099 good sequences were clustered into 386 unigenes. We used these nucleotide sequences and their translated amino acid sequences for annotation by performing homology searches with non-redundant databases from NCBI and the Gene Index (Pine and Spruce). Thirty-five unigenes showed a significant homology with plastid sequences. Of the 351 presumably nuclear sequences (included in the EST database of EMBL with accession numbers FR846537 to FR846887, as well as in EuroPineDB [14]; Supplementary Table S1), 124 have been reported in previous works that focused on water stress in Pinus, whereas other 40 have not been formerly described in Pinaceae. Based on the translated sequences, no significant homology was detected for up to 170 amino acid sequences (48% of the putative nuclear unigenes), which illustrates the current lack of genomic knowledge for gymnosperms. Putative functional categories were assigned to the other 181 amino acid sequences using FuncatDB [15] and Blast2GO [16] (Fig. 1, Supplementary Table S2, Supplementary Table S3). The largest group corresponded to genes involved in metabolism (16%). Up to 42% of these were related to carbohydrate metabolism, whereas lipid, fatty acid and isoprenoid metabolism related sequences, which could be related with the synthesis and accumulation of hormones during water stress, comprised the 18% of the metabolism group. Another important group contained proteins involved in the transcription process. Some ESTs showed P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 Fig. 2. Expression profiles obtained with microarray (histograms) and RT-PCR (lines) techniques for ten selected genes. 47 48 P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 Fig. 2. (continued). P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 a high homology with transcription factors linked to environmental stresses, such as DREB, bZIP and NAC. The library also includes ESTs putatively involved in cell rescue and defence, such as RD21 and heat shock proteins. 2.2.1. Metabolism related genes One third of the ESTs with a putative function according to FunCat correspond to this group. Half of them (8) are included in the carbohydrate metabolism group. The accumulation of sugars has been correlated with the acquisition of desiccation tolerance in plants, probably because the sugars protect the structures from mechanical and metabolic stresses during dehydration [17]. Several ESTs in this group share homology with genes that have been previously reported to be related to drought stress tolerance, such as alkaline a-galactosidases (Ppter_DR_33 and Ppter_DR_120; [18]), malate synthases (Ppter_DR_276; [19]), glycosyltransferases (Ppter_DR_101; [20]) or chitinases (Ppter_DR_52; [21]). This group also includes a putative aldehyde dehydrogenase (Ppter_DR_4), which is presumably involved in the detoxification of aldehydes generated by alcohol metabolism. This result is consistent with the accumulation of ethanol in conifer seedlings during drought [22]. Similarly, the heterologous expression of maize ZmALDH22A1 confers elevated stress tolerance in tobacco [23]. Ppter_DR_274, which was highly induced in the needles, stems and roots, is homologous to 12-oxo-phytodienoic acid reductase (OPR). This gene is induced by osmotic stress in Zea mays, and its heterologous expression in Arabidopsis improves the resistance to osmotic and salt-induced stress during seed germination [24]. 2.2.2. Cell rescue and defence genes This category includes putative late embryogenesis abundant (LEA) proteins (Ppter_DR_51, Ppter_DR_72 and Ppter_DR_95), which is a group of proteins whose involvement in desiccation tolerance is well known (f. i., Battaglia et al. (2008) [25] and references therein). Ppter_DR_136, Ppter_DR_152 and Ppter_DR_235 show homology with heat shock proteins of the HSP90 family, which are known as molecular chaperones and are related to different abiotic stresses (heat, cold, salt and heavy metals) [26]. Also in this group appeared an EST (Ppter_DR_106) putatively corresponding to a glutathione Stransferase, which are a group of genes induced by drought in Arabidopsis [27]. 49 factors, which are one of the largest families of plant specific factors that are also involved in drought tolerance [32]. Ppter_DR_296 has homology with a DEAD-box ATP-dependent RNA helicases, which have been reported to play a key role in stress responses in various organisms [33]. Nevertheless, almost one third (20) of the 67 selected ESTs (see below) were either homologous to sequences coding for unknown or unclassified proteins or lacked homologous sequences in the available databases. However, their high inducibility makes them interesting candidate genes. For instance, Ppter_DR_115 and Ppter_DR_163 are homologous to different members of the nodulin MtN3 family from Arabidopsis, which has not yet been assigned to any functional category. Nodulin-like proteins are plasmatic membrane aquoporins, and could be involved in the maintenance of water balance in plants. Both microarray and RT-PCR analyses showed a continuous accumulation of the Ppter_DR_115 transcript during the water stress experiment, with levels reaching 20-fold higher than in control samples. Another example is Ppter_DR_107, which was highly overexpressed in needles during long-term stress, and did not share homology with any angiosperm genes. Tentative annotation suggests it could represent a hydroxyproline-rich protein (PRP). Another proline-rich product was reported by Sathyan et al. (2005) to be induced by water stress in P. halepensis. Other upregulated ESTs include Ppter_DR_160, Ppter_DR334 and Ppter_DR_341, which were homologous to unknown proteins in Picea. 2.3. Microarray analysis and RT-PCR Since its description by Diatchenko et al. [34] the SSH approach has proved to be a powerful tool for enriching libraries with differentially expressed genes. However, this approach generates quite a few false positives, and ESTs are subject to artefacts during construction of the cDNA library. For this reason, a careful analysis of the fragments obtained is needed. In this study, the selection of candidate genes from among the 351 nuclear unigenes was based on their overexpression during PEG-induced water stress. For this purpose we examined the transcription levels during the treatment in the needles, stems and roots of four genotypes independently using a customised microarray. 2.2.3. Transport Transport processes play an important role in the mobilisation and accumulation of solutes and hormones and in cell detoxification pathways during adaptation to water stress. Among the selected ESTs, several putative transporters were also found. For instance, Ppter_DR_123 shows high homology with an inositol transporter, which could be involved in the transport of sugars through membranes to modify osmotic pressure under stress, a function that is consistent with the high capacity of maritime pine for osmotic adjustment as reported by López et al. [28]. Ppter_DR_162 shows homology with certain ABC transporters, which are involved in the response to different biotic and abiotic stresses [29]. 2.2.4. Transcription-related genes The expression of many of the genes mentioned above is likely controlled by different transcriptional regulatory pathways. Among the ESTs selected based on their expression patterns there were several putative transcription factors. For example, Ppter_DR_254 and Ppter_DR_319 show high homology with DREB2 factors, which are involved in an ABA-independent pathway induced by dehydration [30] and whose overexpression can increase water stress tolerance [31]. Ppter_DR_213 is homologous to NAC transcription Fig. 3. Transcripts that are significantly upregulated by PEG stress in needles, stems and roots by PEG stress. In total, 67 unique ESTs were significantly upregulated in response to treatment. 50 P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 Table 2 Fold change in the expression of the 67 selected candidate genes upregulated in different organs during treatment. The table shows data with a significant difference (FDR < 0.05 and fold change # 1.6) in relative level. ID_sequence Roots S1/US Ppter_DR_101 Ppter_DR_115 Ppter_DR_120 Ppter_DR_160 Ppter_DR_274 Ppter_DR_276 Ppter_DR_305 Ppter_DR_33 Ppter_DR_349 Ppter_DR_6 Ppter_DR_61 Ppter_DR_72 Ppter_DR_112 Ppter_DR_122 Ppter_DR_136 Ppter_DR_152 Ppter_DR_278 Ppter_DR_41 Ppter_DR_77 Ppter_DR_90 Ppter_DR_107 Ppter_DR_239 Ppter_DR_244 Ppter_DR_284 Ppter_DR_303 Ppter_DR_315 Ppter_DR_342 Ppter_DR_4 Ppter_DR_48 Ppter_DR_51 Ppter_DR_80 Ppter_DR_95 Ppter_DR_123 Ppter_DR_200 Ppter_DR_235 Ppter_DR_242 Ppter_DR_334 Ppter_DR_37 Ppter_DR_63 Ppter_DR_106 Ppter_DR_13 Ppter_DR_137 Ppter_DR_162 Ppter_DR_163 Ppter_DR_180 Ppter_DR_187 Ppter_DR_194 Ppter_DR_198 Ppter_DR_213 Ppter_DR_22 Ppter_DR_223 Ppter_DR_227 Ppter_DR_254 Ppter_DR_262 Ppter_DR_287 Ppter_DR_296 Ppter_DR_307 Ppter_DR_319 Ppter_DR_327 Ppter_DR_329 Ppter_DR_341 Ppter_DR_99 Ppter_DR_275 Ppter_DR_283 Ppter_DR_44 Ppter_DR_159 Ppter_DR_52 Stems S2/US S4/US S6/US S8/US S1/US Needles S2/US S4/US 1,96 S6/US 2,82 3,01 4,46 2,87 2,46 2,51 3,94 2,46 10,28 3,49 3,42 2,80 S8/US S1/US S2/US 6,24 3,99 3,09 1,97 2,27 2,92 2,75 3,35 7,97 2,36 4,16 2,63 1,95 3,55 3,75 2,71 3,40 S4/US 1,95 3,22 S6/US S8/US 2,54 10,34 7,84 6,07 8,10 4,03 2,97 5,50 45,22 10,16 3,94 3,19 4,92 4,04 5,08 2,52 2,61 3,36 3,62 2,32 4,27 8,67 2,61 2,45 2,41 2,18 2,15 2,90 2,25 $2,92 $4,65 1,61 $1,92 2,15 1,74 6,38 2,35 2,61 2,68 2,39 2,27 2,35 2,97 3,20 2,31 3,24 5,41 3,46 2,27 3,18 1,95 2,14 2,43 4,33 2,52 2,99 6,11 1,88 2,00 1,95 2,09 4,75 5,22 6,75 2,52 2,32 2,38 2,49 3,33 2,71 2,43 2,14 2,36 2,06 2,97 2,30 2,16 2,00 2,12 2,69 3,33 2,84 2,08 1,86 2,25 2,04 3,07 2,74 2,16 2,29 2,38 2,20 2,76 1,76 2,86 2,10 $2,63 2,01 2,64 3,58 3,16 3,43 2,95 2,74 5,44 2,90 3,72 3,67 2,66 3,20 4,56 4,10 P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 We selected 67 positive candidate genes whose average expression for the four genotypes, in at least one sampling point and in at least one organ, was more than 1.6 fold higher than in the control (Table 1). This represents 19% of the presumed 351 nuDNA ESTs identified, which is a proportion similar to that obtained in previous studies with forest trees, as reported by Bae et al. in Populus [35]. The largest functional groups among the 67 selected genes coincided with those for the complete 351 EST set. To validate the expression patterns detected by microarray, 10 candidate genes covering the main functional groups and expressional clusters (see below) were subjected to RT-PCR. This time, only a single genotype was used in the analysis. RT-PCR analysis confirmed the microarray expression patterns (Fig. 2). For most genes, both techniques had a Pearson correlation value higher than 90%, although lower values were found for a few genes. This kind of discrepancy has been previously reported, mainly for genes with low induction levels, and has been attributed to the different dynamic range of the two techniques, with a higher accuracy attributed to RT-PCR [36]. 51 3. Conclusion Here, we have identified a set of 67 reliable expressional candidate genes that may be useful for further studies on water stress response in conifers, which is a plant division for which reliable molecular tools are still scarce and for which transfer from angiosperms is difficult and very often impossible. Of 67 candidate genes, 37 correspond to new genes that were not reported in previous works on water stress in this genus. The expression patterns of these genes revealed by microarray analysis were confirmed by RT-PCR for a subset of genes, including the most abundant expression clusters and functional groups. As a follow-up to this study, the expression of these candidate genes under drought stress induced by soil water depletion will be examined, allowing us to discriminate between water stress effects and the effects derived specifically from the treatment with PEG. 4. Materials and methods 4.1. Plant materials and treatment conditions 2.4. Expression pattern of candidate genes Of the 67 unigenes that were significantly upregulated by PEGinduced water stress, 29 were identified in needles, 29 in stems and 45 (two thirds of the total) in roots (half of them, i. e., 23, exclusively in this organ), which is consistent with the key role played by roots in water stress (Fig. 3). Significant overexpression levels are reflected in Table 2. Hierarchical clustering of the expression patterns detected with microarrays (from the four genotypes) established six groups in the stems, and five groups in the roots and needles (Supplementary data Fig. 1). Differences among the genotypes regarding these profiles were also detected. Nevertheless, the expression profiles were fairly consistent between the microarray and qRT-PCR techniques within the same genotype. The following general considerations can be drawn from an analysis of the expression clusters: - Most PEG-induced genes reached a maximum transcription level at S6 (48 h from the beginning of the experiment, and after 36 h at $1.6 MPa). In this group, the most noticeable induction was seen for Ppter_DR_274 in the roots and stems. - Some of these genes were induced faster in the roots, with the maximum induction seen at S4 (1 h after reaching $1.6 MPa). These include genes putatively involved in cell rescue and defence based on their homology with heat shock proteins (Ppter_DR_235) or glutathione S-transferases (Ppter_DR_106), and the putative transcription factor Ppter_DR_254. This pattern is consistent with the role of roots in detecting and triggering the response to water stress. - Several genes showed a fast response, reaching their maximum induction at S2 (1 h after the second change of hydroponic solution, at $0.8 MPa). Among them, a putative transcription factor was detected in the roots (Ppter_DR_319) and several sequences had high homology with the HSP90 heat shock protein family (Ppter_DR_136, 152). - The induction of other genes continued to increase until the end of the experiment. This group mainly includes genes putatively related to carbohydrate metabolism, as well as some putative transcription factors in the roots (Ppter_DR_296, Ppter_DR_213). - The prolonged and even increasing induction of most of these genes during the water stress treatment is consistent with the conditions faced by Mediterranean trees during a real drought in nature. We have used clonal material from the Oria provenance (Almería, southeastern Spain). This provenance has been previously shown to have a good inducible response to water stress because it has adapted to low and irregular annual precipitations and frequent droughts (f. i., Sánchez-Gómez et al.,2010). Taking into account the individual variability found within populations, which is common for conifers (see, f. i., [37] and references therein), ten different genotypes were included in this study. The plants were grown in a greenhouse in hydroponic culture with an aerated nutrient solution under controlled conditions (24 " C day/22 " C night, 12 h photoperiod, relative humidity: 60% by day and 80% by night). The nutrient solution (30 l/45 plants; NPK 90:41:72; pHw6.5) was changed twice a week. Water stress was induced by adding polyethylene glycol (PEG, MW 8000) to the culture solution. For this purpose the hydroponic solution was changed every 4 h, with progressively increasing concentrations of PEG added and a 0.4 MPa decrease in the water potential of the solution each time until a final water potential of $1.6 MPa was reached. One plant per genotype was collected separately 1 h after every change of the hydroponic solution (sampling points S1-S4), and 24 h (S5), 48 h (S6), 10 days (S7) and 21 days (S8) after the beginning of the treatment (for a total of 8 sampling points). The needles, stem and roots from each plant were collected separately, immediately frozen in liquid nitrogen and stored at $80 " C. Plants of each genotype kept without PEG in the hydroponic solution were harvested as controls. 4.2. Isolation of RNA and the subtraction technique The total RNA for each sampling point was extracted separately from the roots, stem and needles of each sampled plant following the CTABeLiCl precipitation method [38]. Equal amounts of the total RNA from the roots, stems and needles of all of the plants were mixed to form an RNA pool. To identify sequences putatively regulated by water stress more efficiently, we constructed a subtractive cDNA library using a PCR-Select" cDNA Subtraction kit (Clontech, CA, USA) and following the manufacturer’s protocols. A mix of cDNA from plants treated with PEG for 1 h to 21 days was used as tester and the cDNA from control plants was used as driver. Subtracted PCR products were ligated into the pGEM# T-easy vector (Promega, WI, USA) and transformed into Escherichia coli DH5a cells. We then picked 4940 clones. The presence and size of the inserts were determined by direct amplification from crude bacterial lysates using the nested PCR primers 1 and 2R, which were provided in the PCR-selected cDNA subtraction kit. 52 P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 4.3. Differential screening of the subtracted library by cDNA macroarray As a first selection of positive clones, differential screening was performed using a PCR-Select" differential screening kit (Clontech, CA, USA). Amplified inserts from each clone were spotted onto Hybond-Nþ nylon membranes (GE Healthcare BioSciences, WI, USA). Membranes were screened with four probes labelled with dioxigenin-dUTP (Roche, Basel, Switzerland), i.e., two subtracted cDNA probes (forward and reverse subtractions) and two unsubtracted probes (PEG-stressed and unstressed). Detection was performed using the DIG DNA labelling and detection kit (Roche, Basel, Switzerland). The intensities of the hybridisation signals were visualised using a Molecular Imager#ChemiDoc"XRS System (BioRad, CA, USA). Clones with a mean expression ratio (forward subtracted probe intensity divided by reverse probe intensity) of more than 1.5 were selected as upregulated genes. All clones of differentially expressed genes were selected for sequencing. 4.4. Sequence analysis The selected positive clones were sequenced using a 3730 XL DNA analyser (Applied Biosystems; Life Technologies, CA, USA) at Macrogen (Seoul, Korea). All unique ESTs were annotated on the basis of the existing annotation of non-redundant databases at the NCBI using BLASTN and BLASTX. ESTs without significant protein homology were then compared with the pine and spruce databases included in the Gene Index Project (http:// compbio.dfci.harvard.edu/cgi-bin/tgi/Blast/index.cgi) and EuroPineDB (http://www.scbi.uma.es/pindb/). Homologies with evalues lower than 1e-05 for more than 100 nucleotides were considered significant. Functional classification of the ESTs was performed according to the functional categories of A. thaliana proteins (http://mips.helmholtz-muenchen.de/proj/funcatDB/) [15]. Blast2GO was used to identify gene ontology (GO) terms associated with identificated genes [16]. 4.5. Microarray design and hybridation In total, 351 unigenes selected from the SSH library were included in the microarray design (Agilent 8 % 15 K, Agilent Technologies, CA, USA). For each unigene, one to four 60-bp-long probes were designed and spotted at least three times on the slide. Probes designed for other pine, spruce and human ESTs available in public databases were included as negative controls. To select reliable candidate genes that would be widely useful for future studies, four unrelated genotypes from the Oria provenance were used as experimental biological replicates. RNA from sampling points S1, S2, S4, S6 and S8 and control plants was hybridised to the microarrays. RNA amplification and labelling were performed as described by Adie et al. [39]. RNA was purified by using the Qiagen RNeasy kit (QIAGEN, CA, USA). “The manual two-colour microarray based gene expression analysis” protocol (Agilent Technologies, CA, USA) was followed for hybridisations. Images from Cy3 and Hyper5 channels were equilibrated and captured with a GenePix 4000B (Axon, CA, USA), and spots were quantified using the GenPix software (Axon, CA, USA). 4.6. Data analysis Background correction and normalisation of expression data were performed using LIMMA (Linear Models for Microarray Data) [40,41]. LIMMA is part of “Bioconductor, an R language project” (www.bioconductor.org). For local background correction and normalisation, the methods “normexp” and loess in LIMMA were used, respectively. To achieve a similar distribution across arrays and consistency among arrays, log-ratio values were scaled using the median-absolute-value as scale estimator. Differentially expressed genes were evaluated by the non-parametric algorithm ‘Rank Products’ available as the “RankProd” package at “Bioconductor, an R language project” [42,43]. This method detects genes that are consistently high ranked in a number of replicated experiments independently of their numerical intensities. The results are provided in the form of p-values defined as the probability that a given gene is ranked in the observed position by chance. The expected false discovery rate was controlled to be less than 5%. Changes in the expression of a gene relative to control plants were estimated using the average signal intensity across the four data sets (four genotypes). Based on the statistical analysis, a gene was considered to be significantly upregulated if it met all three of the following criteria: (1) FDR RankProd < 0.05; (2) the fold change was #1.6 at any sampling point and in any organ and (3) the trend was consistent for all data. Hierarchical clustering of upregulated genes in the different organs was performed using the log-ratio data and the Euclidean distance (complete linkage and threshold 2.5) options of the MeV 4.4 software [44]. 4.7. Real-time quantitative PCR The expression pattern of several genes was confirmed by RT-PCR using RNA from one of the genotypes used as a biological replicate in the microarrays. For this purpose, the RNA was treated with DNAse Turbo (Ambion; Applied Biosystems, Life Technologies, CA, USA). First-strand cDNA was synthesised from 2 mg total RNA from each sample using PowerScriptIII reverse transcriptase (Invitrogen) according to the supplier’s manual. 18S rRNA was used as a control, after verifying that the signal intensity remained unchanged across all treatments. The primers for experimental genes were designed using Primer Express version 3.0.0 (Applied Biosystems Life Technologies, CA, USA) and are shown in Supplementary Table S4. Polymerase chain reactions were performed in an optical 96-well plate with a CFX 96 Detection system (BIO-RAD), using EvaGreen to monitor dsDNA synthesis. Reactions containing 2% SsoFast EvaGreen Supermix reagent (BIO-RAD, CA, USA), 12.5 ng cDNA and 500 nM of primers in a final volume of 10 ml were subjected to the following standard thermal profile: 95 " C for 3 min, 40 cycles of 95 " C for 10 s and 60 " C for 10 s. Three technical replicates were performed for each PCR run. To compare the data from different PCR runs or cDNA samples, the mean of the CT values of the three technical replicates was normalised to the mean CT value of Ri18S. The expression ratios were then obtained using the DDCT method corrected for the PCR efficiency for each gene [45]. Acknowledgements The authors would like to thank Dr. Luis Gil, for technical and scientific support, Dr. Juan Majada and Dr. Tania Velasco, from SERIDA for providing the plant material and helping us with the hydroponic treatments, and Dr. Carmen Díaz-Sala (UAH) for help with RT-PCR analysis. This work has been funded through the projects AGL2006-03242/FOR (Spanish Ministry of Education and Science), CCG07-UPM/AMB-1932 and CCG10-UPM/AMB-5038 (Madrid Regional Government e UPM). PP has a pre-doctoral fellowship from the Spanish Ministry of Education and Science. Appendix. Supplementary data Supplementary data related to this article can be found online at doi:10.1016/j.plaphy.2011.09.022. P. Perdiguero et al. / Plant Physiology and Biochemistry 50 (2012) 44e53 References [1] FAO, Situacion de los Bosques del Mundo 2007 (2007) Roma. [2] A.M. Morse, D.G. Peterson, M.N. Islam-Faridi, K.E. Smith, Z. Magbanua, S.A. Garcia, T.L. Kubisiak, H.V. Amerson, J.E. Carlson, C.D. Nelson, J.M. 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Solano, ABA is an essential signal for plant resistance to pathogens affecting JA biosynthesis and the activation of defenses in Arabidopsis, Plant Cell 19 (2007) 1665e1681. [40] G.K. Smyth, T. Speed, Normalization of cDNA microarray data, Methods 31 (2003) 265e273. [41] G.K. Smyth, Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments. The Berkeley Electronic Press, 2004. [42] R. Breitling, P. Armengaud, A. Amtmann, P. Herzyk, Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments, FEBS Letters 573 (2004) 83e92. [43] F. Hong, R. Breitling, C.W. McEntee, B.S. Wittner, J.L. Nemhauser, J. Chory, RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis, Bioinformatics 22 (2006) 2825e2827. [44] A.I. Saeed, N.K. Bhagabati, J.C. Braisted, W. Liang, V. Sharov, E.A. Howe, J. Li, M. Thiagarajan, J.A. White, J. Quackenbush, K. Alan, O. Brian, TM4 Microarray Software Suite, Methods in Enzymology, vol. 411, Academic Press, 2006, pp. 134e193. [45] M.W. Pfaffl, A new mathematical model for relative quantification in real-time RT-PCR, Nucleic Acids Research 29 (2001) e45. Further readings [46] http://compbio.dfci.harvard.edu/cgi-bin/tgi/Blast/index.cgi [47] http://www.scbi.uma.es/pindb/ [48] http://mips.helmholtz-muenchen.de/proj/funcatDB/ ANEXO IV. Molecular response to water stress in two contrasting Mediterranean pines (Pinus pinaster and Pinus pinea). Manuscrito Molecular response to water stress in two contrasting Mediterranean pines (Pinus pinaster and Pinus pinea) Pedro Perdiguero1, 2, María del Carmen Barbero1, 2, Gloria García Casado3, María Teresa Cervera1, 2, 4, Carmen Collada1, 2, †, Álvaro Soto1,2, †, * 1 GENFOR Grupo de investigación en Genética y Fisiología Forestal, Universidad Politécnica de Madrid. E-28040 Madrid, Spain. 2 Unidad Mixta de Genómica y Ecofisiología Forestal, INIA/UPM, Madrid, Spain 3 4 Centro Nacional de Biotecnología. CNB. Madrid, Spain. Departamento de Ecología y Genética Forestal. CIFOR-INIA, Madrid, Spain † : CC and AS co-directed this work * Address for correspondence: Álvaro Soto de Viana GENFOR, G.I. Genética y Fisiología Forestal Universidad Politécnica de Madrid Ciudad Universitaria s/n E-28040 Madrid, Spain T: 34 91 336 63 92 Fax: 34 91 336 55 56 Email: [email protected] Date of submission: Number of tables: Number of figures: 4 Short running title: Transcriptional response to water stress in pines Abstract Adaptation to water stress has determined the evolution and diversification of vascular plants. This stress is forecasted to increase drastically in the next decades in certain regions, such as the Mediterranean basin. Consequently, a proper knowledge of the response and adaptations to drought stress is essential for the correct management of plant genetic resources. However, most of the advances in the understanding of the molecular response to water stress have been attained in angiosperms, and are not always applicable to gymnosperms. In this work we analyze the transcriptional response of two emblematic Mediterranean pines, P. pinaster and P. pinea, which show noticeable differences in their performance under water stress. Up to 113 genes have been detected as significantly induced by drought in both species. While induced genes with similar profiles in both species can be considered as general candidate genes for the study of drought response in conifers, genes with diverging expression patterns can underpin the differences displayed by these species under water stress. Additionally, differences in the molecular response to drought and polyethylene-glycol-induced water stress are also discussed. 1. Introduction Undoubtedly, one of the major driving factors of evolution and diversification of vascular plants since the Silurian (~430 MYA) is the adaptation to dry land environments, involving the development of water uptake and transport mechanisms and minimization of water losses. Terrestrial plants display a whole panoply of constitutive and inducible anatomical and molecular adaptations to drought stress. These are particularly relevant in certain regions, and for perennial species such as trees, which most likely have to face water shortage several times during their lifespan. According to the current climatic forecast, drought stress will become even a more defining factor in great part of the planet in the near future. For instance, Mediterranean regions will suffer a decrease of 10-20% of annual precipitations, with more frequent and severe drought periods, together with an increase in 3-3.5ºC in the mean annual temperature by the end of this century [1]. Thus, an understanding of the adaptive responses with which tree species will have to face these situations is of the utmost interest. Drought stress can limit plant growth and reproduction, and can lead to serious and eventually insurmountable difficulties to keep the homeostatic equilibrium in cells: metabolism can be disrupted, leading to an increased production of free radicals and reactive oxygen species (ROS) that damage the membranes, especially the photosynthetic machinery. Higher order plants display protection mechanisms addressed to avoid desiccation and its deleterious effects. It is well known that, at the molecular level, inducible response to drought, as well as to other abiotic stresses, is controlled by several genes, comprising multiple signalling pathways [2]. Most of the advances in the comprehension of the molecular response to drought stress have been performed in angiosperms, which display peculiar characteristics that ease this kind of studies. For instance, they have smaller and simpler genomes than gymnosperms, so that the complete genome sequences of several herbaceous and woody angiosperm species are available, with a better annotation; there are also short-lived model species which allow the faster performance of repeated and serial experiments, etc. On the contrary, no such a model species is available among gymnosperms. Additionally, due to the long time elapsed since both groups diverged (300 MYA), different genes and mechanisms can be expected to be involved in the response of gymnosperms to drought. The aim of this work is to help covering the gap in the current knowledge of the molecular response of conifers to water stress. Over the last decade several works have reported on the identification of genes induced by drought stress in conifers, mainly Pinus, performing preliminary expression pattern analysis under moderate water stress [3-10], usually applying a moderate stress. Other works have focused in the analysis of certain gene families presumably involved in the response of water stress, such as dehydrins [11-14]. In this work we compare the transcriptional response of two closely related pine species, P. pinaster and P. pinea, which show, however, noticeable differences in their performance under water deficit.. Both species thrive under the drought-prone conditions of the Mediterranean basin, and can be found on sandy soils, with low water retention capacity, where they play a major role in a characteristic, priority conservation habitat of the European Union, the “wooded dunes with P. pinea and/or P. pinaster”. Their ecological requirements overlap to a great extent, and mixed stands of both species are frequent. Nevertheless, P. pinaster, although occupying a relatively small geographical range, in the Western Mediterranean basin, shows larger ecological amplitude, particularly in relation to water availability and has been used as model species for the study of the molecular response to drought stress in conifers in several works [3, 4, 8, 13, 15]. Differences among provenances have been detected for this species, regarding mass allocation, water use efficiency under water stress [16, 17] but, in general, it is considered as a droughtavoiding species which shows sensitive stomata and fast osmotic adjustment in response to water stress [18, 19]. On its side, stone pine, P. pinea, shows a wider distribution, all around the Mediterranean sea, although displaying an extremely low neutral diversity [20] . P. pinea is a more thermophilic and xerophytic species, and is usually found in poorer soils. Additionally, and contrarily to P. pinaster, tolerates also shade rather well [21] , being the combination of shade and drought one of the most restrictive conditions for plants in Mediterranean-type ecosystems [22]. In this work we have used microarray and RT-PCR techniques to analyse the expression pattern in both species of 1124 genes presumably involved in the response to water stress during a severe and prolonged drought stress, similar to the ones these trees have to face in nature, in order to identify the expression profiles associated to higher drought resistance in conifers. 2. Result and discussion 2.1 Water potential in leaves of P. pinaster and P. pinea during drought stress Fig. 1 shows the evolution of leaf water potential (Ψ) in P. pinaster and P. pinea plants throughout the drought experiment. Notwithstanding the known differences in hydric requirements shown by both species, the evolution of Ψ during the stress is noticeably similar in P. pinaster and P. pinea. 2.2 Genes induced by drought stress in P. pinaster and P. pinea A total of 181 genes were significantly upregulated in needles, stems and/or roots in P. pinaster in response to the non-irrigation treatment (Supplementary table S1). 53 out of these genes were upregulated in the three organs. The highest number of overexpressed genes was found in stems with 116 genes, and 41of they appeared significantly induced exclusively in this organ. 44 out of the 107 genes significantly overexpressed in needles were not detected as significantly induced in the other organs. Finally, 88 genes were significantly overexpressed in roots, and only 19 of them were identified exclusively for this organ (Supplementary Fig. S1). On its side, the drought treatment in P. pinea led to the significant induction of 218 genes (Supplementary table S2), and 67 out of them were upregulated in the three organs. 123 genes were significantly upregulated in stems, and 23 of them exclusively in this organ. 144 genes were significantly induced in needles, and 40 out of them were not significantly induced in stems nor in roots. Finally, up to 140 genes were significantly upregulated in roots, and 33 of them exclusively in this organ (Supplementary Fig. S2). An enrichment analysis of the 113 genes significantly induced for both species (Fig. 2, Supplementary Table S3) yielded overrepresented GO terms as response to water stimulus (GO:0009415) or response to hormone stimulus (GO:0009725). Most of them can be classified into four functional categories, according to FuncatDB [23]: i) metabolism, ii) cell rescue and defence, iii) transport and iv) transcription related genes. 2.2.1 Metabolism related genes A quarter of the genes significantly induced in both species corresponds to metabolism related genes, and up to one third of them are related to carbohydrate metabolism. Accumulation of sugars and other solutes is supposed to maintain turgor pressure and to protect structures from mechanical and metabolic stresses during dehydration, contributing to the acquisition of desiccation tolerance in plants. Some of these genes have also been reported to be induced by PEG-induced water stress in P. pinaster [8], as for example an alkaline α-galactosidase (TC181331), a malate synthase (TC155104), a glycoyltransferase (TC156369), a beta-galactosidase (TC156138), a chitinase (TC157851) or an aldehyde dehydrogenase (TC158839). Other abundant group of genes is related to secondary metabolism, including genes involved in the synthesis of hormones as ethylene (TCC182140, 1-aminocyclopropane-1-carboxylate (ACC) synthase; TC174045, ACC oxidase) or jasmonate (TC188679, 12-oxo-phytodienoic acid reductase). 2.2.2 Cell rescue and defence genes The most relevant genes within this category putatively encode late embryogenesis abundant (LEA) proteins and heat shock proteins (HSP). For instance, TC182917 and TC180126 are homologous to small HSP (sHSP), which are presumably involved in the maintenance of membrane integrity [24]. Although these genes have usually been related to other abiotic stresses such as heat or cold stress [25-27],, it has also been reported that they can confer tolerance to drought and salt stress [28]. On its side, TC194781 corresponds to a HSP70, a type of HSP related to water stress resistance [29, 30]. Among LEA genes, four different dehydrin genes (TC162509, TC179486, TC193003 and TC176703) have been detected as significantly upregulated in both species. Dehydrins are a complex, multigenic family involved in different stress response and ontogenic processes. An analysis of their structure and expression under drought stress in P. pinaster has recently been published [13].. TC168999 is homologous to AtLEA14, one of 10 genes that is upregulated by high light, drought, cold, and salt stresses in Arabidopsis [31]. Also in this category can be included TC176662, a putative U-box containing protein. This family, very abundant and diversified in plants, are supposed to be involved in ubiquitination under different conditions [32]. Thus, TC176662 could play a role in the degradation of proteins damaged under water stress. Interestingly, a recent rangewide study of P. pinaster populations has revealed a strong association of SNP allele frequencies for this gene (as well as for a putative heat shock factor, TC171120, see below) with temperature variables, suggesting the existence of variants adapted to local climatic conditions (González-Martínez et al., pers. comm.). 2.2.3 Transport Approximately 9% of the genes induced by drought stress in both species are presumably related to the transport of sugars, anions and amino acid. These genes could act co-ordinately with inducible genes involved in metabolism, helping in adjusting osmotic pressure. For instance, two different hexose transporters (TC170434 and DR099938) and two inositol transporters (TC170498 and TC171882) can be found in this group. TC176635 corresponds to an amino acid permease, which could mediate the accumulation of free amino acids as proline, reported to confer resistance to desiccation[33]. Consistently, higher proline content in P. pinaster from xeric provenances has recently been reported [34]. On their side, TC173812 and TC167700 show homology with peroxisomal membrane proteins, which could be involved in the establishment of a ROS scavenging mechanism [35]. 2.2.4 Transcription related genes Another 9% of the genes induced both in P. pinea and P. pinaster show homology with transcription factors from different families, and could therefore be considered to play a key role in the transcriptional response of pines to drought. Thus, TC157919 is homologous to the DREB (drought responsive element binding proteins) subfamily. TC158167 and TC178542 correspond to the ERF (ethylene response factor) subfamily, which is supposed to be involved in gene regulation in both ethylene dependent and independent pathways [36]. A putative WRKY factor has also been detected (TC163430). These conserved plant transcription factors have been shown to play a critical role in ABA response [37] and their overexpression can increase water stress tolerance [38]. On its side, TC161257, significantly induced in the three organs of both species, has been identified as a bZIP transcription factor. It is noteworthy the detection of a putative BEL1-like homeodomain transcription factor (TC170594) also induced in roots, stems and needles of both species. These regulatory elements, with a potential role as long distance signals [39], had never been described before as involved in the response to abiotic stress; however, TC170594 was also significantly upregulated in response to PEG-induced water stress in P. pinaster [8]. 2.2.5 Other functions, unclassified or unknown proteins Up to a 19% of the genes induced by drought in both species show homology with genes of unknown function or even lack homologous in the databases. For instance, TC188788 and TC163698, which are overexpressed in the three organs both by drought and by PEG-induced water stress [8], are homologous to genes of the MtN3 nodulin family. No functional classification has been assigned to this family; however, recent studies on Arabidpsis and rice have reported their activity as sugar transporter, supporting import and efflux of sugars from the cells [40]. Overexpression of TC188788 has already been reported in response to drought in P. pinaster and P. taeda [3, 4, 6], as well as in response to cold in Cupressus sempervirens [41]. Therefore, this family could play an important role in the capacity of osmotic adjustment shown by many conifers, as P. pinaster [18]. On the contrary, this gene family never has been reported as drought responsive genes in angiosperms. Another remarkable example is the overexpression of TC197470, also induced by PEG treatment in P. pinaster [8] and corresponding to a tentatively annotated hydroxyproline-rich protein. The expression of proline rich proteins is stimulated by wounding and environmental stresses [42]; consistently, overexpression of a PRP under drought stress has been reported in P. halepensis [9]. These results are consistent with the ones reported previously for a water stress induced in P. pinaster by the addition of polyethylene glycol (PEG) to a hydroponic culture [8]. However, several differences have also been detected. 24 out of the 67 genes reported as significantly induced by PEG in that work have not been detected here. A feasible explanation to this point could be related to the toxicity of PEG. This substance, especially in its low molecular weight forms, can be absorbed by the roots, eliciting a specific response, in addition to the effect related to the decrease in water potential in the substrate. For example, significant overexpression of a putative soluble inorganic pyrophosphatase (Ppter_DR_227 ~ TC178028), gene associated to GO term GO:0010038 “response to metallic ions” was detected in roots during PEG-induced water stress but not in the experiments reported here, neither in P. pinaster nor in P. pinea. Similar results were observed for a gene (Ppter_DR_162 ~ TC160632) homologous to PDR ABC transporters, which are presumably involved in the response to different stresses [43]. In the same way, a significant enrichment in the GO term “binding unfolded ER proteins” (GO:0051082) has been detected among those 24 genes induced by PEG treatment. This term is related to chaperone activity, probably involved in the response to toxicity. Within this category, three high molecular weight heat shock proteins induced by PEG treatment (Ppter_DR_136 ~ spruceTC171037, Ppter_DR_152 ~ TC187027 and Ppter_DR_235 ~TC183705) were not detected here. Nevertheless, the failure to detect some of these 24 genes as significantly induced by the drought treatment could be also related to their time of response and to the different sampling schemes used in both works, since the PEG treatment lasted for three weeks, with a more intensive sampling in the first 48 h. This could be the case of a putative heat-shock factor (TC171120), not detected as significantly induced by the drought treatment in any organ of P. pinaster, but in roots and stems of P. pinea, as well as in roots of P. pinaster under PEG treatment. In the same way, an embryo-abundant protein (Ppter_DR_51 ~ TC178394) showed overexpression in needles during the first steps of PEG-induced water stress, whereas remarkable repression has been detected in this work, especially in roots (-25 to -45-fold the values of the unstressed plants). This gene could be involved in signal reception and modulation of changes in needles during the very first stages of water deficit. 2.3 Expression patterns in P. pinaster and P. pinea Hierarchical clustering of the expression levels detected with microarray led to the identification of 10 clusters in roots, 14 in stems and 9 in needles of P. pinaster, and of 15 clusters in roots, 19 in stems and up to 22 in needles of P. pinea (Supplementary Fig. S3 y S4). Microarray data are commonly validated by RT-PCR due to the higher accuracy attributed to this tool, especially for genes with low induction levels [44]. We have performed RT-PCR analysis for 16 genes, covering the main functional groups and expression patterns, and including some genes responsive to PEG-induced water stress [8] but not detected as significantly induced here, using microarrays (TC183705; HSP90 and TC166071; unknown) (Fig. 3). Expression patterns detected with both techniques were fairly consistent, with Pearson correlation values higher than 80% for 35 (P. pinaster) and 33 (P. pinea) of the 48 gene-organ combinations. As expected, lower correlations were obtained for the genes-organs with lower induction levels, including those not identified as significantly overexpressed according to microarray analysis. Genes overexpressed in both species can be grouped in three categories, according to their induction patterns detected by microarrays: a) genes induced in the first steps of the drought, (S1 and S2), whose expression level can decrease in further steps or can be kept stable during the rest of the treatment, b) genes whose expression constantly increases throughout the treatment and c) genes highly induced in later steps, S3 and, particularly, S4 and S5. In both species many genes are induced earlier in the roots than in the aerial parts, which is consistent with the role of roots in detecting and triggering the response to water stress. However, several differences can be observed between P. pinaster and P. pinea: - A higher number of genes are significantly induced by drought stress in P. pinea, compared to P. pinaster, especially for roots and needles. - Stronger inductions levels are detected in overexpressed genes in P. pinea, compared to P. pinaster. Thus, 83 inductions higher than 10-fold were detected for P. pinea, with a miximum of 72-fold for a putative hydroxyproline-rich protein (TC197470), whereas 43 inductions higher than 10-fold were detected for P. pinaster, with a maximum of 62-fold for a dehydrin (TC162509). - A higher proportion of genes are induced in the first steps of the drought in P. pinea compared to P. pinaster, particularly for S1 (10 days without watering). - On the contrary, certain genes show a delayed response in P. pinea, with higher inductions in S4-S5, compared to P. pinaster, with higher inductions in S2-S3. This is the case, for instance, of genes included in clusters 8, 9, 10, 11 and 12 in needles, cluster 1 and 9 in stems or clusters 4, 6 and 8 in roots of P. pinea and clusters 2, 4 and 6 in needles, clusters 4 and 8 in stems, and clusters 1, 2, 3 and 4 of roots in P. pinaster. These observations were confirmed, especially stems and needles, in some genes studied with RTPCR. This is the case of TC162509 (Pper_dhn_ESK2 [13]), TC156369 (putative glycosiltransferase), TC197470 (putative hydroxyproline richprotein), TC157851 (putative quitinase) y TC188679 (putative OPR) Altogether, these results suggest P. pinea is a highly responsive species, displaying a faster and more intense transcriptional response to drought, compared to P. pinaster. Notwithstanding, several genes show a delayed induction in P. pinea. This result could be is consistent with the ones reported recently by Sánchez-Gómez et al. (2011)[45], according to which, during the first steps of a moderate drought stress, the best performing clones of P. pinea showed a water-spending strategy, which could provide functional advantage in dry environments, out-competing other water-saving trees [4648] as P. pinaster [19, 49]. As the stress situation persists, P. pinea plants would turn out swiftly to a water-saving behaviour, as reported for other water-spending species [50, 51]. Further studies will be required to confirm this hypothesis. It is also noteworthy the opposite expression pattern shown by some genes in both species. TC177528 is strongly upregulated in P. pinea (59-fold in stems and 32-fold in needles at S4), while repressed in P. pinaster (-15-fold and -5-fold for the same points). This gene encodes a peptide presumably involved in ammonium nutrition in P. pinaster [52], which can have an indirect effect in the ABA-mediated reduction of stomatal conductance during drought [53]. On contrary, TC172144, a gene of unknown function, shows a moderate overexpression in P. pinaster in response to drought stress, whereas it seems repressed in P. pinea. Increase in transcription of putative retrotransposons elements detected in roots of P. pinaster at 50 days of drought (Cluster 8; TC194362 and TC183415) is also remarkable. Enhanced transposition of such elements induced by stress, probably due to epigenetic transient modifications, is a well known phenomenon ([54] and references therein). Interestingly, CDT-1, a dehydration-inducible gene of resurrection plant Craterostigma plantagineum showing similarities with retrotransposons, has been reported to confere dessication tolerance, putatively acting as regulatory non-coding RNA molecule [55]. None of these elements was detected as overexpressed in P. pinea, which could be due to the stringency of the criteria used, the specificity of the probes employed or own differences in response between the two species. 3. Conclusion Since its employ by Schema et al. [56] microarray technology has been used for the analysis of gene expression in different processes. Due to the high degree of conservation frequently found in coding regions, heterologous array analysis has often been applied in Pinaceae and other species [57-59]While failure to detect significant overexpressions due to specificity of the probes, which would not hybridize properly with the heterologous cDNA, cannot be discarded, significant inductions revealed by microarray are as reliable as for homologous samples. In this study, the oligo-array designed with drought candidate genes from maritime pine was successfully employed to study and compare the response to water stress displayed by P. pinaster and P. pinea. No matter both P. pinaster and P. pinea are closely related and well adapted to drought, they display different patterns in the response to water stress, with P. pinea thriving even in more xeric conditions than P. pinaster. P. pinea appears as a more sensitive species, displaying a faster and stronger transcriptional response. Notwithstanding this fact, several genes show strong delayed induction compared to P. pinaster, or even show opposite expression patterns in both species. While induced genes with similar profiles between both species can be considered as general candidate genes for the study of drought response in conifers, genes with diverging expression patterns can underpin the differences displayed by these species in their performance under water stress Further research must focus in the regulation of the expression of inducible genes, as well as in the epigenetic modifications surely involved in such regulation as well as in the intraspecific variability in the response. We can expect that fast adaptation to an increasingly drought-prone environment in the following decades will rely to a major extent in these epigenetic modifications, underlying plastic responses. 4. Material and methods 4.1. Plant materials and treatment conditions Plant material from Oria provenance (37º30’30”N 2º20’20”W, south eastern Spain) and Tordesillas (41° 30′ 6″ N, 4° 59′ 57″ W, central Spain)) provenances was used for P. pinaster and P. pinea, respectively. Both species were grown using containers with peat:perlite:vermiculite (3:1:1 by weight). One year old plants were kept in a growth chamber for two months prior to the drought experiment, with a photoperiod of 16/8 (day/night), with a temperature of 24ºC and 60% of relative humidity during the day and 20ºC and 80% of relative humidity during the night, and watered at field capacity. Unstressed plants were harvested one hour after the last watering. The remaining plants were maintained without irrigation and collected at midday every ten days (five sampling points, S1-S5). Water potential in needles was measured at each sampling point (at midday) using a Scholander pressure chamber. Needles, stem and roots from each plant were collected separately, immediately frozen in liquid nitrogen and stored at -80 ºC. 4.2. Microarray design and hybridisation A total of 1124 unigenes, 351 from water stress SSH library reported by Perdiguero et al. (2012) [8] and 773 genes selected from other libraries, were included in the microarray design (Agilent 8 x 15K, Agilent Technologies, CA, USA). Genes are identified in this work with the code of the most homologous TC (Tentative Consensus Sequence) from Pine Gene Index. For each unigene, one to four 60-bp-long probes were designed and spotted at least three times on the slide. Probes designed for other pine, spruce and human ESTs available in public databases were included as negative controls. Four different genotypes for P.pinaster or full-sib families for P.pinea collected at each sampling point were used as biological replicates during the experiment. RNA from sampling points S1-S5 and control plants was hybridised to the microarrays. RNA amplifications, labelling and hybridisations, as well as data analysis was carried out as described elsewhere [8] RNA amplification and labelling were performed as described by Adie et al. [60]. RNA was purified by using the Qiagen RNAeasy kit (QIAGEN, CA, USA). “The manual two-color microarray based gene expression analysis” protocol (Agilent Technologies, CA, USA) was followed for hybridisations. Images from Cy3 and Hyper5 channels were equilibrated and captured with a GenePix 4000B (Axon, CA, USA), and spots were quantified using the GenPix software (Axon, CA, USA). 4.3. Data analysis Background correction and normalisation of expression data were performed using LIMMA (Linear Models for Microarray Data) [61, 62]. LIMMA is part of “Bioconductor, an R language project” (www.bioconductor.org). For local background correction and normalisation, the methods "normexp" and loess in LIMMA were used, respectively. To achieve a similar distribution across arrays and consistency among arrays, log-ratio values were scaled using the median-absolute-value as scale estimator. Differentially expressed genes were evaluated by the non-parametric algorithm 'Rank Products' available as the "RankProd" package at “Bioconductor, an R language project” [63, 64]. This method detects genes that are consistently high ranked in a number of replicated experiments independently of their numerical intensities. The results are provided in the form of p-values defined as the probability that a given gene is ranked in the observed position by chance. The expected false discovery rate was controlled to be less than 5%. Changes in the expression of a gene relative to control plants were estimated using the average signal intensity across the four data sets (four genotypes). Based on the statistical analysis, a gene was considered to be significantly up-regulated if it met all three of the following criteria: (1) FDR Rank Prod <0.05; (2) the fold change was ≥1.6 at any sampling point and in any organ and (3) the trend was consistent for all data. Hierarchical clustering of upregulated genes in the different organs was performed using the log ratio data and the Euclidean distance (complete linkage and threshold 2.5) options of the MeV 4.4 software [65]. 4.3 Statistical analysis 4.3.1 Differential gene expression Differential expression was performed to find the difference in the mean expression among the three organs (multi-class) or among species (two class) by using limma package [66] implemented in Babelomics suite [67]. The gene expression pattern for each sample point was analysed, obtaining P values for each gene in the experiment. To account for multiple testing effects, P values were corrected using the false discovery rate. Significant differential expression was considered for P values < 0.05. 4.3.2 Functional analysis GO term enrichment for upregulated genes was analysed by using FatiGO software [68] implemented in Babelomics suite [67]. This program executes a Fisher's exact test for 2×2 contingency tables is used to check for significant over-representation of GO annotations. Arabidopsis thalina was used as model species in order to identified overrepresentation of GO term from upregulated genes respect the rest of annotated genome. Multiple test correction to account for the multiple hypotheses tested (one for each functional term) is applied. Significant enrichment of GO terms was considered for P values < 0.01 4.4. Real-time quantitative PCR The expression pattern of several genes was confirmed by RT-PCR using RNA from one of the genotypes used as a biological replicate in the microarrays. For this purpose, the RNA was treated with DNAse Turbo (Ambion; Applied Biosystems, Life Technologies, CA, USA). First-strand cDNA was synthesised from 2 μg total RNA from each sample using PowerScriptIII reverse transcriptase (Invitrogen) according to the supplier’s manual. 18S rRNA was used as a control, after verifying that the signal intensity remained unchanged across all treatments. The primers for experimental genes were designed using Primer Express version 3.0.0 (Applied Biosystems Life Technologies, CA, USA) and are shown in Supplementary Table S4. For those genes that showed low efficiency for P.pinea new pair of primers was designed with specific sequences. Polymerase chain reactions were performed in an optical 96-well plate with a CFX 96 Detection system (BIO-RAD), using EvaGreen to monitor dsDNA synthesis. Reactions containing 2x SsoFast EvaGreen Supermix reagent (BIO-RAD, CA, USA), 12.5 ng cDNA and 500 nM of primers in a final volume of 10 μl were subjected to the following standard thermal profile: 95 °C for 3 min, 40 cycles of 95 °C for 10 s and 60 °C for 10 s. Three technical replicates were performed for each PCR run. To compare the data from different PCR runs or cDNA samples, the mean of the CT values of the three technical replicates was normalised to the mean CT value of Ri18S. The expression ratios were then obtained using the ΔΔCT method corrected for the PCR efficiency for each gene [69]. SUPPLEMENTARY DATA Table S1: Fold change in the expression of the 181 genes upregulated in different organs during P.pinaster treatment. Table S2: Fold change in the expression of the 219 genes upregulated in different organs during P.pinea treatment Table S3: Selection of 113 water stress candidate genes in conifers. Table S4: Primer pairs used in RT-PCR Figure S1: Transcripts significantly up-regulated shared between needles, stems and roots in Pinus pinaster. A total of 181 genes significantly overexpressed were identified Figure S2: Transcripts significantly up-regulated shared between needles, stems and roots in Pinus pinea. A total of 219 genes significantly upregulated were identified Figure S3a, S3b and S3c: Hierarchical cluster analysis of the expression patterns of water stress induced genes for each organ in P.pinaster Figure S4a, S4b and S4c: Hierarchical cluster analysis of the expression patterns of water stress induced genes for each organ in Pinus pinea ACKNOWLEDGEMENTS The authors would like to thank Dr. Luis Gil and Dr Santiago Gonzalez-Martinez for technical support. This work has been funded through the projects AGL200603242/FOR (Spanish Ministry of Education and Science), CCG07-UPM/AMB-1932 and CCG10-UPM/AMB-5038 (Madrid Regional Government– UPM). 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[69] Pfaffl M.W., A new mathematical model for relative quantification in real-time RT-PCR, Nucleic Acids Research 29 (2001) e45. Figure legends Fig 1. Average midday water potential in needles along the drought treatment. P. pinaster: dashed line; P. pinea: continuous line. Bars represent standard errors. Fig. 2. Transcripts significantly up-regulated shared between needles, stems and roots for both species. A total of 113 genes significantly upregulated were selected. Fig. 3. Verification of microarray results by RT-PCR. Expression profiles along the drought stress treatment in roots, stems and needles from 16 genes involved in different functionalities. Fig. 4. Functional distribution of selected candidate genes obtained. A total of 113 genes were grouped according MIPS functional categories of Arabidopsis thaliana. The percentage of gene transcripts in each group is listed. Fig 1 Fig 2 Fig 2(cont.) Fig 3 Fig 4 ANEXO V. Novel conserved segments are associated with differential expression patterns for Pinaceae dehydrins Planta 236 (2012) 1863-1874. Author's personal copy Planta (2012) 236:1863–1874 DOI 10.1007/s00425-012-1737-4 ORIGINAL ARTICLE Novel conserved segments are associated with differential expression patterns for Pinaceae dehydrins Pedro Perdiguero • M. Carmen Barbero • M. Teresa Cervera • Álvaro Soto • Carmen Collada Received: 17 April 2012 / Accepted: 3 August 2012 / Published online: 26 August 2012 Ó Springer-Verlag 2012 Abstract Dehydrins are thought to play an essential role in the response, acclimation and tolerance to different abiotic stresses, such as cold and drought. These proteins have been classified into five groups according to the presence of conserved and repeated motifs in their amino acid sequence. Due to their putative functions in the response to stress, dehydrins have been often used as candidate genes in studies on population variability and local adaptation to environmental conditions. However, little is still known regarding the differential role played by such groups or the mechanism underlying their function. Based on the sequences corresponding to dehydrins available in public databases we have isolated eight different dehydrins from cDNA of Pinus pinaster. We have obtained also their genomic sequences and identified their intron/exon structure. Quantitative RT-PCR analysis of their expression pattern in needles, stems and roots during a severe and prolonged drought stress, similar to the ones trees must face in nature, is also reported. Additionally, we have identified Electronic supplementary material The online version of this article (doi:10.1007/s00425-012-1737-4) contains supplementary material, which is available to authorized users. P. Perdiguero M. C. Barbero Á. Soto (&) C. Collada GENFOR Grupo de investigación en Genética y Fisiologı́a Forestal, Universidad Politécnica de Madrid, 28040 Madrid, Spain e-mail: [email protected] P. Perdiguero M. C. Barbero M. T. Cervera Á. Soto C. Collada Unidad Mixta de Genómica y Ecofisiologı́a Forestal, INIA/UPM, Madrid, Spain M. T. Cervera Departamento de Genética y Fisiologı́a Forestal, CIFOR-INIA, Madrid, Spain two amino acid motifs highly conserved and repeated in Pinaceae dehydrins and absent in angiosperms, presumably related to the divergent expression profiles observed. Keywords Dehydrin Drought Gene expression Pinus qRT-PCR Sequence analysis Abbreviations EST Expressed sequence tag LEA proteins Late embryogenesis abundant proteins qRT-PCR Quantitative reverse transcription polymerase chain reaction TC Tentative contig Introduction Terrestrial plant species have developed a complex array of strategies and responses to surmount the restrictive conditions of dry land. Particularly, anatomical and physiological adaptations and molecular responses allow them to endure drought stresses, which can affect plant growth, regeneration and even survival. This is especially important in forest trees, which most likely, have to face such situations more than once during their long lifespan. Within molecular responses, many genes as transcription factors, late embryogenesis abundant (LEA) proteins or enzymes for the biosynthesis of hormones and sugars have been described as principal components conferring dehydration tolerance in plants (Sunkar et al. 2010). In particular, LEA proteins accumulate in vegetative plant tissues when a marked decrease in water content takes place. They are highly hydrophilic proteins with a high proportion of Gly and other small residues in their 123 Author's personal copy 1864 amino acid composition. Due to their physiochemical properties, they have been proposed to play a key role in preserving and maintaining cell functions during dehydration (Olvera-Carrillo et al. 2011). LEA proteins constitute a complex, diverse group, first described by their accumulation during seed formation (Dure et al. 1981), and include proteins with low global homology. Different classifications based on their amino acid sequence and composition have been attempted, establishing four (Bray 1993) to ten (Dure 1993) groups. The most recent classification was proposed by Hundertmark and Hincha (2008) according to the conserved domains included in Pfam database (Wellcome Trust Sanger Institute). Dehydrins (name kept in this last classification), corresponding to Dure’s D-11 group and to Bray’s group 2, are characterized by the combination of three different conserved motifs described by Close (1997), and traditionally used to classify them: the Y, S, and K-segments, separated by the less conserved U-segments, rich in polar amino acids, mainly glycine. All the dehydrins contain one to multiple K-segments, which consist in highly conserved lysine rich 15-mers [EKKGIMDKIKEKLPG in angiosperms (Close 1996), and (Q/E)K(P/A)G(M/L)LDKIK(A/Q)(K/M)(I/L)PG in gymnosperms (Jarvis et al. 1996)]; they usually locate near the C-terminal region and may be involved in the formation of class A2 amphipathic a-helix (Baker et al. 1988). The S-segment is a stretch of contiguous Ser residues, which may be phosphorylated (Godoy et al. 1994; Campbell et al. 1998). The last feature are the Y-segments [(V/T)DEYGNP], which have similarities with nucleotide-binding domains described in plant chaperones (Close 1996, 1997). Interestingly, this segment has only been described so far in angiosperm species. According to the different combination of segments, dehydrins have been classified into five groups; YnSKn, YnKn, SKn, Kn and KnS. Dehydrins are considered as late expressed genes in the stress-signalling pathway (Mahajan and Tuteja 2005), and different functions have been reported for them. It has been proposed that they play important protective roles via stabilization of membranes due to hydrophobic interactions with the K-segments (Campbell and Close 1997; Danyluk et al. 1998; Koag et al. 2003). Chaperone activity has also been reported for these proteins (Kovacs et al. 2008a, 2008b). Water-binding capacity of dehydrins may account for their role in protecting enzymes such as alpha-amylase during cold stress (Rinne et al. 1999), maintaining an adequate local water concentration and decreasing the damages done by ice crystals during freeze (Wisniewski et al. 1999). Nuclear dehydrins protect transcription machinery from the desiccation associated with seed formation (Castillo et al. 2002). Dehydrins play a key role in this process, and their abundance is directly related to seed viability and longevity (Hundertmark et al. 2011). Some dehydrins could also 123 Planta (2012) 236:1863–1874 function as antioxidants due to their capacity to bind free metal ions, thus preventing excessive ROS (reactive oxygen species) formation (Hara et al. 2005; Sun and Lin 2010). Although their functions have been frequently investigated and revised in the last years, mostly in angiosperm species (Allagulova et al. 2003; Rorat 2006; Yuxiu et al. 2007; Kosová et al. 2010; Eriksson and Harryson 2011), the mechanism underlying these roles remain unclear. Dehydrins have been located within QTL intervals for important phenotypic traits related with drought tolerance (growth under osmotic stress, vernalization response, flowering time, low temperature tolerance, frost resistance, yield under drought stress) (Campbell and Close 1997). For this reason, as well as for their putative roles described above, different dehydrins have been used as candidate genes in several works aimed to identify nucleotide diversity patterns associated with local adaptation to different abiotic factors in Pinus species (González-Martı́nez et al. 2006; Eveno et al. 2008; Wachowiak et al. 2009; Grivet et al. 2009, 2011). Additionally, dehydrins have been proposed as nuclear genes to investigate natural selection in a pine phylogeny (Palmé et al. 2009). However, in depth characterization of these genes in gymnosperms has not been performed yet. Furthermore, previous works focused in the identification of genes induced by water deficit in Pinus pinaster Ait. have not detected dehydrins (Dubos et al. 2003; Dubos and Plomion 2003; Perdiguero et al. 2012); notwithstanding, preliminary results referred to their induction under different abiotic stresses were obtained by their inclusion in microarray studies in Pinus taeda (Watkinson et al. 2003; Lorenz et al. 2011) or Pinus sylvestris (Joosen et al. 2006), and very recently Velasco-Conde et al. (2012) have published a paper focused in the expression of dehydrins during a drought treatment in Pinus pinaster. Here we report the identification and structural characterization of eight dehydrin genes in Pinus pinaster, a species well adapted to water deficit. We have also analysed by quantitative RT-PCR their expression pattern during a severe and prolonged drought stress, similar to the one trees of this Mediterranean species have to face in nature. Additionally, we describe for the first time two motifs highly conserved in Pinaceae SKn dehydrins, whose presence and number could be related with the different transcription patterns observed. Materials and methods Plant material and treatment conditions Three genotypes (F1P3, F2P2 and F4P4) from the Pinus pinaster Oria provenance (37° 300 3000 N 2° 200 2000 W, southeastern Spain) have been used in this study. Each genotype comes from a different open-pollinated mother Author's personal copy ATTCTCATCCATGCTCCATGC TTGGAACCCCGTTCATGAA A2E2SK5_ORF_RV CTTGTAACACAACACATTACTAATC AESK4_ORF_FW A2E2SK5_ORF_FW Ppter_dhn_AESK4 Ppter_dhn_A2E2SK5 ATCTCTGCCGATTATCGTTTCA AESK4_ORF_RV AESK3b_ORF_RV AESK3b_ORF_FW Ppter_dhn_AESK3b CTGTTCATATCTGCACTCGCTC AESK3a2_ORF_FW Ppter_dhn_AESK3a2 CTATATCGTCTGCAAATTTTACC CAAAACCCCACAACACGCA AESK3a2_ORF_RV AESK3a_ORF_FW Ppter_dhn_AESK3a ATTAGTATTAAGATGGCGGAAGA CTTGACAAAATCAAAACAATAGCGA CAAAACCCCACAACACGCA AESK3a_ORF_RV ESK2_ORF_FW Ppter_dhn_ESK2 ATTAGTATTAAGATGGCGGAAGA ESK2_ORF_RV K2b_ORF_FW Ppter_dhn_K2b TCATTCTGCAATTTGTTGTTTGAGA K2b_ORF_RV K2a_ORF_RV K2a_ORF_FW Ppter_dhn_K2a Total RNA from roots, stem and needles of each plant was treated with DNAse Turbo (Ambion). First-strand cDNA Forward Real-time quantitative PCR Dehydrin Genomic DNA was extracted from needles and megagametophytes following Doyle (1990) with slight modifications. Total RNA was isolated separately from roots, stem and needles following a CTAB–LiCl precipitation method (Chang et al. 1993). cDNA was synthesised from 1 lg of total RNA using PowerScriptIII reverse transcriptase (Invitrogen). Complete sequences for each studied dehydrin were amplified by PCR, using cDNA and genomic DNA as templates and specific primers (Table 1). PCR conditions are provided in Supplementary Table S1. The PCR products were cloned into pGEMÒT-easy vector (Promega) and transformed into Escherichia coli DH5a cells. The obtained clones were sequenced and aligned using Spidey mRNAto-genomic program at the NCBI (http://www.ncbi.nlm.nih. gov/spidey/) to reveal the exon–intron structure of the genes. Table 1 Specific primers used to amplify the full ORF for each dehydrin DNA and RNA isolation and gene searching Sequence (50 –30 ) Tentative contigs (TCs) assembled from ESTs corresponding to putative dehydrins were searched in the Pine Gene Index 9.0 (http://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/ gimain.pl?gudb=pine; released March 2011). MUSCLE software (Edgar 2004) was used to align deduced amino acid sequences and to obtain a dendrogram using the neighbour joining method. TCs corresponding to P. pinaster dehydrins reported in the literature were selected to design primers for PCR amplification in gDNA and cDNA in the following steps. TGGTTATTAAGATGGCGGAAGA Sequence analysis TGGTTATTAAGATGGCGGAAGA Reverse Sequence (50 –30 ) tree. Clonal material (cuttings), kindly provided by Dr. J. Majada (SERIDA, Spain), was grown at nursery for a year using containers with peat:perlite:vermiculite (3:1:1, by weight). One-year-old cuttings were kept in a growth chamber for two months prior to the drought experiment, with a photoperiod of 16/8 (day/night), with a temperature of 24 °C and 60 % of relative humidity during the day and 20 °C and 80 % of relative humidity during the night, and watered at field capacity. Four ramets per genotype were collected at each sampling point. Unstressed plants were harvested 1 h after the last watering. The remaining plants were maintained without irrigation and collected at midday every 10 days along 50 days. Water potential in needles was measured at each sampling point (at midday) using a Scholander bomb. Needles, stem and roots from each plant were collected separately, immediately frozen in liquid nitrogen and stored at -80 °C. CAGACGCTCAGGTACGCTTGTA 1865 AAATACCGACCTCACCATC Planta (2012) 236:1863–1874 123 Author's personal copy 123 76 TTGGAACCCCGTTCATGAA A2E2SK5_RT_RV AESK3b_RT_FW AESK4_RT_FW A2E2SK5_RT_FW Ppter_dhn_AESK3b Ppter_dhn_AESK4 Ppter_dhn_A2E2SK5 AATGTGAGCCGACGGATCC 209 157 TTTCCGCCTTCCTCCTCTTC CGGTGAAGCTTTCCCATCAAA AESK4_RT_RV AESK3b_RT_RV AESK3a2_RT_FW Ppter_dhn_AESK3a2 GGTATGTTCGGCTTATTCGGC AESK3a_RT_FW Ppter_dhn_AESK3a CTGCTCCTACGTATGGAGC 113 CGGGAATACTGCTTTTCATGC AESK3a2_RT_RV ESK2_RT_FW Ppter_dhn_ESK2 GGTAAGAAGACGGGACTGGTA 108 76 CAAACATAGAGACGAACACAAAGACTT CGGGAATACTGCTTTTCATGC AESK3a_RT_RV ESK2_RT_RV K2b_RT_FW Ppter_dhn_K2b GAAGACGGGACTGGTGGGTA CAGTCCGAGGAGGAGCCTG AGAGGAGGAGCCTGATCACA K2a_RT_FW Ppter_dhn_K2a A total of 47 full amino acid sequences were deduced from 100 TC/ESTs sequences corresponding to putative Forward In silico searching and isolation of Pinus pinaster dehydrin genes Dehydrin Results Table 2 Specific primers used in RT-PCR where yijk is the relative expression level of the kth replicate of the ith genotype at the jth sampling point (stress point), m is the global mean, gi represents the effect of the genotype, sj stands for the effect of the stress (sampling point), gsij is the effect of their interaction and eijk represents the residual error. Since the whole experiment was performed in the same growth chamber, its effect has not been included in the model. Significance of expression changes has been tested using LSD and Bonferroni tests at 95 % confidence level. Statgraphis Centurion XVI software was used for statistical analysis. Sequence (50 –30 ) yijk ¼ m þ gi þ sj þ gsij þ eijk CGGTTTGTTCGGCTTAGGA A general lineal model has been considered for the expression level (relative to unstressed plants) of each gene at each organ (root, stem, needle): CGCGGTATGTTCGGCTTATTA Statistical analysis CCATGATAAGGAATAGAAGGGAAGAG The sequences of P. pinaster dehydrin genes obtained in this study were submitted to GenBank with the following accessions numbers; HE716959–HE716966 for mRNA and HE796685–HE796692 for genomic DNA. K2a_RT_RV Reverse Sequences deposition K2b_RT_RV Sequence (50 –30 ) Length (bp) was synthesised from 2 lg of total RNA from each sample using PowerScriptIII reverse transcriptase (Invitrogen) according to the supplier’s manual. 18S rRNA was used as a control, after verifying that the signal intensity remained unchanged across all treatments. Primer Express v. 3.0.0 (Applied Biosystems) software was used to design PCR primers. Amplified fragments were sequenced to check reaction specificity, and primers were modified when needed in order to avoid cross amplification. Final primers are shown in Table 2. Polymerase chain reactions were performed in an optical 96-well plate with a CFX 96 Detection system (Bio-Rad), using EvaGreen to monitor dsDNA synthesis. Reactions containing 29 SsoFast EvaGreen Supermix reagent (Bio-Rad), 12.5 ng cDNA and 500 nM of primers in a final volume of 10 ll were subjected to the specific thermal profile. PCR conditions are provided in Supplementary Table S1. Three technical replicates were performed for each PCR run. The expression ratios were then obtained using the DDCT method corrected for the PCR efficiency for each gene (Pfaffl 2001). 76 Planta (2012) 236:1863–1874 69 1866 Author's personal copy Planta (2012) 236:1863–1874 1867 Fig. 1 Tentative contigs (TC) corresponding to putative dehydrins from Pinus spp., grouped according to the presence and number of classical conserved motifs. Correspondence with the eight P. pinaster dehydrins described in this work, and with the sequences used in previous works is shown dehydrins from pines available in the last version of Pine Gene Index. According to the presence of the conserved segments usually considered, sequences were classified in six different groups: K1, K2, SK2, SK3, SK4 and SK5. We designed specific PCR primers for the seven TCs constructed with P. pinaster ESTs and/or homologous to P. pinaster dehydrins used as candidate genes in previous works (Fig. 1). PCR amplification from genomic DNA and cDNA from water stressed P. pinaster plants have led to the identification of 8 different dehydrins, described in Table 3 and aligned in Fig. 2. Two different sequences were found within the K2 group. Haploid DNA from several megagametophytes, processed individually, was used for amplification and sequencing, confirming these sequences correspond to a duplication of the locus in the genome (Fig. 3). No intron has been detected for these genes, and both ORFs are 306-nucleotide long, with 18 nucleotide substitutions, entailing 10 changes in the deduced amino acid sequence, differentiating them. In the same way, three different genes have been identified in the group SK3, all of them with a 579-nucleotide long ORF. Ppter_dhn_AESK3a has no intron in its genomic sequence, while Ppter_dhn_AESK3a2 has an intron of 113 nucleotides, and 11 nucleotide substitutions, producing five changes in the deduced amino acid sequence. Also in this case amplification and sequencing from haploid megagametophyte DNA suggest these two sequences correspond to a duplication of the locus in the genome (Fig. 3). Ppter_dhn_AESK3b, corresponding to another TC in the Pine Gene Index database, has an intron of 123 nt and shows considerable differences in the deduced amino acid sequence: 24 aa are different from both of the other two sequences of the group, and additional four aa are different from the deduced sequence for Ppter_dhn_AESK3a and one aa from Ppter_dhn_AESK3a2. Comparison of the genomic and the cDNA sequences of Ppter_dhn_ESK2 shows the presence of an intron of 99 nucleotides, while the ORF is 531-nucleotide long. Ppter_dhn_AESK4 has an ORF of 714 nucleotides. One 123 Author's personal copy 1868 Planta (2012) 236:1863–1874 Table 3 Description of Pinus pinaster dehydrins genes analysed in this study Dehydrin Acc. genomic seq. Acc. mRNA seq. Description Ppter_dhn_K2a HE796685 HE716959 Contains two K-segments (KEPGLVDKIKEKIPG, KKPGVVDKIKEKLPG) Ppter_dhn_K2b HE796686 HE716960 Ppter_dhn_ESK2 HE796687 HE716961 Contains two K-segments (KKPGLVDKIKEKLPG, KKPGMFDKIKEKLPG) Contains one E-segment (GHGHAGQFTAAEAEKQQHT), one S-segment with ten serine residues, and two K-segments (KKKGLKDKIKEKLPG, KKGLVDKIKDKLPG). Ppter_dhn_AESK3a HE796688 HE716962 Contains one A-segment (EASSYYP), one E-segment (GHGHEGQFAPEDAKQQKH), one S-segment with eight serine residues and three K-segments (KKKGSKDKTKEKLPG, KKTGLVGKIKEKIPG, KKTGMLDKIKEKLPG) Ppter_dhn_AESK3a2 HE796689 HE716963 Ppter_dhn_AESK3b HE796690 HE716964 Contains one A-segment (EAASYYP), one E-segment (GHGHEGQFAPEEAKQQKH), one S-segment with eight serine residues and three K-segments (KKKGSKDKTKEKLPG, KKTGLVGKIKEKIPG, KKMGMLDKIKEKLPG) Contains one A-segment (EAASYYP), one E-segment (GHGYEGQFTPEEAEQQKH), one S-segment with eight serine residues and three K-segments (KKKGSMEKTKEKLPG, KKTGLLDKIKEKIPG, KKTGLLDKIKEKLPG) Ppter_dhn_AESK4 HE796691 HE716965 Contains one A-segment (EAASYYP), one E-segment (GHGHEEQPTPEEAEQQKH), one S-segment with eight serine residues and four K-segments (KKKGSKDKSKEKLPG, KKTGLLDKIKEKIPG, KKTGLLDKIKEKIPG, KKLGVLGKIKEKLPG) Ppter_dhn_A2E2SK5 HE796692 HE716966 Contains two A-segments (EAASYYP, EAASYYP), two E-segments (GHGHEGQLTPEEAEQQKR, GHGHEGQLTPEEAEQQKH), one S-segment with seven serine residues and one interleaved ileucine and five K-segments (KKKEAKDKTKKKVPG, KKAGLLDKFKEKLPA, KKTGLLDKIKEKLPV, KKAGLLDKIKEKLPG, KKISLIDKIKEKLPG) intron of 108 bp was identified aligning the genomic and cDNA sequences. The longest sequence corresponds to Ppter_dhn_A2E2SK5, with an ORF of 978 nucleotides and a very long initial part, compared with the others. Analysis of the genomic sequence also reveals a large intron, of 334 nucleotides. All the introns described are located in the middle of the S segment. 123 Intron–exon structure Structure analysis of dehydrin genes from Pinus pinaster and other gymnosperms. Identification of novel conserved segments Complete amino acid sequences of these 8 P. pinaster dehydrins were aligned with the ones from gymnosperms and angiosperms available in public databases (Supplementary Figure S1). The first 23 aa in the N-terminal Author's personal copy Planta (2012) 236:1863–1874 Fig. 2 Optimized alignment of the eight deduced amino acid sequences of Pinus pinaster dehydrin, performed with MUSCLE software. Conserved residues and segments are shown up. Solid line boxes indicate K and S-segments. Dashed line box indicate Fig. 3 PCR products corresponding to different Ppter_dhn_K2 and Ppter_dhn_AESK3 loci amplified from haploid genomic DNA (isolated from megagametophytes). 1: 100 bp DNA Ladder, 2: Ppter_dhn_K2a, 3: Ppter_dhn_K2b, 4: Ppter_dhn_AESK3a and Ppter_dhn_AESK3a2, 5: Ppter_dhn_AESK3b region are highly conserved in Pinaceae dehydrins, with MAEEAPEHQDRGMFGLFGKKKED as consensus sequence in P. pinaster. This fragment is also conserved in the other gymnosperm dehydrins and, interestingly, SKn angiosperm dehydrins, although with diverse indels at its beginning. Gymnosperm dehydrins lack the Y-segment described for angiosperm dehydrins. On the contrary, two fragments have been found to be highly conserved and repeated among Pinaceae dehydrins but absent in angiosperms. The first one, which we have called A-segment, appears in dehydrins from groups SKn and its consensus sequence in P. pinaster is EAASYYP (in bold the residues also conserved in Picea). The other fragment, named E-segment, appears also in dehydrins from groups SKn, very close upstream the S-segments, with GHGHEGQLTPEEAE 1869 E-segment. Dotted line box indicate A-segment. Dotted-dashed line box indicates the conserved fragment at the N-terminal region of gymnosperm and SKn angiosperm dehydrins Fig. 4 Midday water potential in needles along the drought treatment. Genotype F1P3 dashed line, genotype F2P2 dotted line, genotype F4P4 thin continuous line, average thick continuous line. At each point water potential has been measured in four replicates of each genotype. Bars represent standard deviation QQKH as consensus sequence in P. pinaster (in bold, conserved residues in Picea). These conserved segments are highlighted in Fig. 2 and Supplementary Figure S1. The 8 dehydrins identified in this work in P. pinaster have been named according to the number of repeats of conserved A, E, S and K-fragments. Expression of dehydrin genes of Pinus pinaster A drought experiment was performed, withholding watering for up to 50 days (Fig. 4) Quantitative RT-PCR analysis of the expression patterns of the eight P. pinaster dehydrin genes were carried out independently in roots, stems and needles of three genotypes from Oria (Fig. 5). This provenance, in southeastern Spain, has previously 123 Author's personal copy 1870 123 Planta (2012) 236:1863–1874 Author's personal copy Planta (2012) 236:1863–1874 b Fig. 5 qRT-PCR expression profiles of the eight Pinus pinaster dehydrin genes in roots, stems and needles of three different genotypes along a drought stress treatment, relative to unstressed (control) plants. Four ramets per genotype where processed at each sampling point, and three technical replicates of each qRT-PCR reaction were performed. Significant changes in expression relative to unstressed plants (95 % significance level) are indicated with an asterisk. Standard errors are shown been shown to have a good inducible response to water stress (Sánchez-Gómez et al. 2010) and has been used for the selection of candidate genes involved in the response to water deficit (Perdiguero et al. 2012). According to the general lineal models considered for gene expression levels (which explain more than 97.5 % of the observed variation for each gene and organ studied), drought duration (stress level), genotype and their interaction are significant factors (p \ 0.0005) in all the cases, but for Ppter_dhn_AESK3a2 in roots, for which genotype is not significant (p = 0.3142). Stress level is the strongest factor in most cases, accounting for more than 50 % of the observed variation (even more than 80 % for Ppter_dhn_K2a in roots and for Ppter_dhn_AESK4 in needles). Notwithstanding the differences detected among genotypes, general trends in expression patterns can be acknowledged: a significant increase of Ppter_dhn_K2a and Ppter_dhn_K2b transcripts is detected especially from the third sampling point (30 days without watering) onwards in roots of the three genotypes (4- to 9-fold higher levels, compared to unstressed plants). Induction is much lower in stems, ranging between 1.5- and 2.5-fold higher levels than in control plants. Higher differences among genotypes have been detected in needles, with overexpression ranging from 5- to 16-fold transcription levels among genotypes in the fourth sampling point (40 days without watering). Ppter_dhn_A2E2SK5 shows a similar induction pattern, although overexpression in needles is slightly lower than for the previous genes. Much lower induction has been detected for Ppter_dhn_AESK4, mainly in roots, with levels around 2- and 4-fold higher than in unstressed plants. The highest induction has been detected for Ppter_dhn_ ESK2. Transcription levels of this dehydrin increases from the first sampling point onwards in the three organs all throughout the experiment, reaching values 1,000 to 10,000fold higher than in control plants. On the contrary, overexpression of Ppter_dhn_AESK3a, Ppter_dhn_AESK3a2, Ppter_dhn_AESK3b is very low, although significant, and even in certain organs and sampling points expression of these genes is lower than in unstressed plants. Only in roots or in the last sampling points in needles transcription levels reach values approximately 2-fold higher than in unstressed plants. 1871 Discussion Dehydrins have been reported to play a crucial role in the response and acclimation to adverse abiotic conditions, such as cold or water deficit (Rorat 2006; Kosová et al. 2010); consistently, their expression is also related to ontogenic processes linked to environmental conditions, such as bud burst (Yakovlev et al. 2008) or desiccation associated with seed development (Castillo et al. 2002). In pines, certain dehydrins have been often used as candidate genes in studies on variability and local adaptation to water stress (Eveno et al. 2008; Palmé et al. 2009; Grivet et al. 2009, 2011). However, an in depth characterization of this complex and multigenic family has not been performed yet in the genus, and only very recently their expression during a drought treatment has been reported (Velasco-Conde et al. 2012). Additionally, in a previous work we did not detect dehydrins among the genes overexpressed during a polyethyleneglycol-induced water stress in Pinus pinaster (Perdiguero et al. 2012). These facts pushed us to perform a more complete search of dehydrins and to characterize their expression pattern during a real and severe drought stress. Forty-seven out of the 100 TCs/ESTs corresponding to putative dehydrin genes present in the Pine Gene Index database provide a complete ORF. The other sequences are incomplete or include a stop codon in the coding region. Contamination with genomic DNA cannot be discarded for some of these sequences, for which the stop codon is located in regions corresponding to putative introns, or the typical dehydrin amino acid motifs appear in different reading frames along the sequence (f. i., TC178971 or TC188683). Using specific primers designed for the TCs constructed with P. pinaster ESTs and/or homologous to P. pinaster dehydrins used as candidate genes in previous works, we have identified 8 different dehydrins in the species, belonging to the classical groups Kn and SKn, and have determined their intron/exon structure. We have detected two variants within the Kn group, Ppter_dhn_ K2a and Ppter_dhn_K2b, and three variants of SK3 dehydrins, Ppter_dhn_AESK3a, Ppter_dhn_AESK3a2 and Ppter_dhn_AESK3b. The presence of these variants in haploid tissues (megagametophytes) has led us to conclude they are actually duplications of the genes in the genome, discarding they correspond to different alleles of the same loci, as proposed by Velasco-Conde et al. (2012). Comparison of the deduced amino acid sequences with the ones corresponding to angiosperm and gymnosperm dehydrins has revealed that the N-terminal region is highly conserved among the latter, with a fragment of 23 residues also conserved in angiosperm SKn dehydrins. Additionally, both groups show a comparatively high percentage of glutamine and proline residues, while angiosperm Kn and 123 Author's personal copy 1872 YnSKn dehydrins show a much shorter N-terminal region preceding the classical conserved motifs and an amino acid composition enriched in glycine and threonine residues. We have also confirmed that the classical Y-segment, typical of angiosperm dehydrins, is absent in gymnosperm dehydrins. Conversely, we have identified two motifs highly conserved and repeated in SKn dehydrins in Pinaceae, and absent in angiosperm dehydrins. The so-called A-segment is similar in length and position to the angiosperm Y-segment; we have detected it in many Pinus and Picea dehydrins as well as in dehydrins from Larix and Pseudotsuga. TCs assembled from P. taeda and P. banksiana ESTs show this segment repeated 4 (TC183122), 5 (TC171685, TC187956) and up to 19 times (TC191238). The other, which we have named E-segment, locates very close to the S-segment; we have detected it in Cupressus sempervirens sequences, as well as in Pinaceae (Pinus, Picea, Larix, Pseudotsuga). Both segments appear in the N-terminal half of the sequence, before the S-segment, and their presence and number could be used to classify the gymnosperm dehydrins, similarly as the Y-segment is used in angiosperm dehydrins, together with the S and K-segments. We have applied a severe drought treatment, withholding watering for up to 50 days and reaching water potential values in needles at midday of around -3.86 MPa, in order to examine the expression pattern of dehydrin genes in roots, stems and needles. No noticeable induction and even slight repression of some dehydrin genes have been detected for the first sampling point (10 days without watering), when no strong differences in the midday water potential in needles respect control plants have been measured. We have detected a clear increase in the transcription levels of Ppter_dhn_K2a, Ppter_dhn_K2b and Ppter_dhn_A2E2SK5 in roots, as the water potential goes under -2.0 to -2.5 MPa (20–30 days without watering). Induction is lower and/or belated in stems and needles, highlighting the role played by roots in detecting and triggering the response to water stress. The highest induction in response to water depletion has been detected for Ppter_dhn_ESK2 in the three organs. This result is consistent with the ones reported by Lorenz et al. (2011) for the orthologous in Pinus taeda, dehydrin 2 (ACA51879.1), which appears among the 25 most-upregulated genes during water stress. Additionally, this gene shows the highest divergence in nucleotide and amino acid sequence with the other ones reported here, and we have not detected any duplication or paralog in the genome. These facts make Ppter_dhn_ESK2 a very suitable candidate gene for further studies on population variation, etc., being easier to develop and apply specific markers (such as SNP genotyping) or re-sequencing approaches avoiding interferences from other loci. 123 Planta (2012) 236:1863–1874 On the contrary, no remarkable induction has been detected for AESKn dehydrins neither in stems nor in needles. Only Ppter_dhn_AESK4 transcription increases in roots as the water depletion persists, and always at much lower levels than for the other induced dehydrins. This pattern seems to be linked to the simultaneous presence of the newly described A and E-segments in the amino acid sequence. Nevertheless, when these segments are duplicated, as in the case of Ppter_dhn_A2E2SK5, transcription increases with drought, with a pattern similar to that of Kn genes, and the highest induction has been found for Ppter_dhn_ESK2, in which E-segment appears without A-segment. Given the role and mechanisms usually suggested for dehydrin activity, we believe AESKn genes could have a major role in other processes different from drought stress. Nevertheless, their implication in the response to drought stress cannot be discarded, accordingly to the significant differences in their expression levels reported here. These dehydrin transcripts could be required but effective in protection against drought stress at lower concentrations than the other ones. Further studies in this and other species are needed to confirm this point. Our results differ slightly from the ones reported by Velasco-Conde et al. (2012). These authors perform RTPCR to describe the expression pattern of five dehydrin genes in needles of maritime pine during a drought experiment and discuss the possible role of dehydrins in drought response. The most noticeable divergences are found in the contrasting patterns described for SK5 in two of the genotypes used also here, and the profile described for SK2, divergent among genotypes an with an appreciable repression in the first week of reduced water availability. Several factors can account for these discrepancies. First, the stress induced in the work of Velasco-Conde et al. is shorter and milder than the one applied here, and some genes showing a slight decrease in transcription levels in the first stages of the treatment are actually noticeably overexpressed as the stress increases. Secondly, we have analysed the expression patterns not only in needles but also in stems and, particularly, roots, taking into account the key role played by this organ in the response to drought. Furthermore, Velasco-Conde et al. do not isolate and sequence the genes, so that incorrect interpretation of allelic variation instead of gene duplications in the genome leads them to design primer pairs that can produce cross amplification, yielding not reliable RT-PCR results. Conversely, we have identified such duplications by sequencing in haploid material (megagametophytes), and have designed specific primers for each form, avoiding cross amplification. Even more, sequencing of amplicons have confirmed specificity of the reaction. In conclusion, we have identified and characterized eight different dehydrin genes in Pinus pinaster, and Author's personal copy Planta (2012) 236:1863–1874 analysed their expression in roots, stems and needles under a severe and prolonged drought treatment, pinpointing the genes most likely involved in water stress response. Additionally, we have identified for the first time two motifs in the deduced amino acid sequence, highly conserved in SKn dehydrins in Pinaceae and absent in angiosperm dehydrins, and whose presence and number is associated with the differential expression patterns described. Our results support the idea that the different members of this complex gene family play different and specialized roles in response to environmental or endogenous stimuli. 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