cycling dof factors: molecular and …oa.upm.es/35207/1/alba_rocio_corrales_ducuara.pdf ·...

UNIVERSIDAD POLITÉCNICA DE MADRID

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS AGRÓNOMOS

CENTRO DE BIOTECNOLOGÍA Y GENÓMICA DE PLANTAS

CYCLING DOF FACTORS: MOLECULAR AND FUNCTIONAL CHARACTERIZATION OF Arabidopsis thaliana AtCDF3 AND TOMATO

(Solanum lycopersicum L.) SlCDF3 IN RESPONSE TO ABIOTIC STRESS.

TESIS DOCTORAL

ALBA ROCÍO CORRALES DUCUARA

Licenciada en Biología

Madrid, 2014

UNIVERSIDAD POLITÉCNICA DE MADRID (UPM)

DEPARTAMENTO DE BIOTECNOLOGÍA

CENTRO DE BIOTECNOLOGÍA Y GENÓMICA DE PLANTAS, CBGP (UPM-INIA)

ESCUELA TÉCNICA SUPERIOR DE INGENIEROS AGRÓNOMOS

CYCLING DOF FACTORS: MOLECULAR AND FUNCTIONAL CHARACTERIZATION OF Arabidopsis thaliana AtCDF3 AND TOMATO

(Solanum lycopersicum L.) SlCDF3 IN RESPONSE TO ABIOTIC STRESS.

Memoria presentada por Alba Rocío Corrales Ducuara

para optar al grado de Doctor en el programa de doctorado

“Biotecnología y Recursos Genéticos de Plantas y Microorganismos Asociados”

Departamento de Biotecnología (UPM)

Trabajo realizado en el CBGP (grupo consolidado “Respuestas a condiciones de estrés abiótico y señalización energética en plantas”), bajo la dirección de Dr. Joaquín Medina Alcázar y el Profesor Jesús

Vicente-Carbajosa.

Madrid, Noviembre 2014

V°B° LOS DIRECTORES LA DOCTORANDA

Dr. Joaquín MedinaAlcázar Alba Rocío Corrales Ducuara Profesor Jesús Vicente-Carbajosa

UNIVERSIDAD POLITÉCNICA DE MADRID

Tribunal nombrado por el Magfco. y Excmo. Sr. Rector de la Universidad Politécnica de Madrid el día de Noviembre de 2014

Presidente:

Secretario:

Vocal:

Vocal:

Vocal:

Suplente:

Suplente:

Realizada la defensa y lectura de Tesis el día de 2015 en la Escuela Técnica Superior de Ingenieros Agrónomos.

EL PRESIDENTE LOS VOCALES

EL SECRETARIO

RECONOCIMIENTOS

Este trabajo ha sido realizado en el Centro de Biotecnología y Genómica de Plantas (CBGP),

Departamento de Biotecnología de la E.T.S.I Agrónomos de la Universidad Politécnica de Madrid (UPM),

en el marco del proyecto de investigación dirigido por el Doctor Joaquín Medina Alcázar, financiado por el

Instituto Nacional de Investigaciones Agrarias (INIA) proyecto (INIA, RTA 2009-00042-C02-02) de cuya

Beca de Formación de Personal Investigador (FPI-INIA) he sido beneficiaria.

En primer lugar quiero agradecer a mis directores de tesis, Dr. Joaquín Medina Alcázar y al

Profesor Jesús Vicente Carbajosa, por brindarme la oportunidad de formar parte de su grupo de

investigación, así como su orientación científica y enseñanza.

A la profesora Rosa Victoria Molina, al Doctor Sergio Gonzalez Nebauer y a todo su grupo de

investigación de la Universidad Politecnica de Valencia (UPM), por permitirme realizar una estancia en su

laboratorio y aprender a realizar ensayos de tolerancia a estrés abiótico con las líneas transgenicas de

tomate, también por su acogida y ayuda durante esta tesis.

A la Doctora Laura Carrillo Gil, por su continua supervisión, orientación y apoyo durante el

desarrollo de esta tesis.

Al Doctor Jan Zouhar, por haber leído y aportado sugerencias a esta tesis.

Al profesor Stephan Pollmann y a su grupo de investigación, por haberme permitido aprender a

realizar los distintos análisis metabolomicos realizados en este trabajo.

Al Doctor Abdelhafid Bendahmane y a todo su equipo de la Unidad de Investigación de Genomica

Vegetal (URGV) del Instituto Nacional de Investigación Agronómica de Francia (INRA), por su gran

acogida y por haberme permitido tabajar con la población mutagenizada de tomate (M82) de la plataforma

de Tilling para la identificación de líneas de tomate mutagenizadas.

A Mar González Ceballos, por su colaboración en la obtención de las líneas transgenicas de

35S::AtCDF3 y 35S::AtCDF3-stop de Arabidopsis.

i

ÍNDICE

RESUMEN............................................................................................................................. .......................iv

SUMMARY…………………………………………………………………………………………………...….…..vi

ABREVIATURAS………………………………………………………………………………………………......viii

1.- INTRODUCCIÓN GENERAL ............................................................................................................ 11

1.1.- EL TOMATE, Solanum lycopersicum L. ........................................................................................1 1.1.1.- Origen, taxonomía y morfología. ............................................................................................1

1.1.2.- El tomate como planta modelo. ..............................................................................................4

1.2.- IMPORTANCIA ECONÓMICA DEL TOMATE. ..............................................................................5 1.2.1.- Importancia económica en el mundo. .....................................................................................5 1.2.2.- Importancia económica del tomate en España. ......................................................................6 1.2.3.- Impacto del estrés abiótico en el cultivo de tomate. ................................................................7

1.3.- ESTRÉS ABIÓTICO. .................................................................................................................. 11 1.3.1.- Generalidades del estrés abiótico en plantas. ...................................................................... 11

1.3.2.- Efectos de la salinidad, las bajas temperaturas y la sequía sobre el crecimiento de la planta. ...................................................................................................................................................... 12 1.3.3.- Respuesta de la planta a la salinidad, las bajas temperaturas y la sequía. ........................... 15 1.3.4.- Respuesta metabólica al estrés por salinidad, bajas temperaturas y sequía. ........................ 18 1.3.5.- Respuesta metabólica al estrés por salinidad, bajas temperaturas y sequía en tomate. .... 19

1.4.- REGULACIÓN DE LA EXPRESIÓN GÉNICA.................................................................................. 20

1.4.1.- Regulación de la transcripción. ............................................................................................ 20

1.5.- FACTORES DE TRANSCRIPCIÓN EN PLANTAS. ..................................................................... 21

1.6.- EXPRESIÓN GÉNICA Y REGULACIÓN BAJO ESTRÉS ABIÓTICO. .......................................... 25 1.6.1.- Complejidad de la expresión génica y regulación. ................................................................ 25 1.6.2.- Factores de transcripción involucrados en la respuesta a estrés abiótico. ............................ 27 1.6.2.1.- Regulon CBF/DREB. ........................................................................................................ 27 1.6.2.2.- Regulon NAC y ZF-HD. .................................................................................................... 29 1.6.2.3.- Regulon AREB / ABF. ....................................................................................................... 29

1.6.2.4.- Regulon MYB / MYC. ........................................................................................................ 30 1.6.3.- Expresión génica en respuesta a estrés abiótico en tomate. ................................................ 32 1.6.4.- Factores de transcripción involucrados en la respuesta estrés abiótico en tomate. ............... 34 1.6.4.1.- Regulon CBF/DREB. ........................................................................................................ 34 1.6.4.2.- Regulon NAC y ZF-HD. .................................................................................................... 35 1.6.4.3.- Regulon AREB / ABF. ....................................................................................................... 35 1.6.4.4.- Regulon MYB / MYC. ........................................................................................................ 36

1.7.- FACTORES DE TRANSCRIPCIÓN DE TIPO DOF. .................................................................... 38 1.7.1.- Diferenciación de tejido........................................................................................................ 40 1.7.2.- Desarrollo de semilla. .......................................................................................................... 41 1.7.3.- Regulación del metabolismo. ............................................................................................... 42

1.8.- CYCLING DOF FACTORS (CDFS). ........................................................................................... 44

ii

2.- OBJETIVOS ...................................................................................................................................... 47

3.- Arabidopsis CYCLING DOF FACTOR 3 CDF3 REGULATE DROUGHT AND LOW TEMPERATURE

STRESS RESPONSE AND FLOWERING TIME IN Arabidopsis thaliana ............................................. 50

3.1.- INTRODUCTION. ....................................................................................................................... 50

3.2.- MATERIAL AND METHODS. ...................................................................................................... 54 3.2.1.- Plant material and growth conditions. ................................................................................... 54

3.2.2.- Plasmid constructs and Arabidopsis transformation.............................................................. 55 3.2.3.- Tomato transformation. ........................................................................................................ 55 3.2.4.- Histochemical GUS staining and subcellular localization of AtCDF3 protein. ........................ 56 3.2.5.- Protoplast transformation and GUS assays. ......................................................................... 57 3.2.6.- RNA isolation and expression analysis by real-time RT-qPCR.............................................. 57 3.2.7.- Germination and post-germinative growth assay. ................................................................. 58 3.2.8.- Photosynthesis and leaf fluorescence measurement. ........................................................... 59 3.2.9.- Drought and cold stress tolerance assay. ............................................................................. 60 3.2.10.- Microarray analysis. ........................................................................................................... 60

3.2.11.- Metabolomic analyses. ...................................................................................................... 61

3.3.- RESULTS. .................................................................................................................................. 62 3.3.1.- Abiotic stress response and expression pattern of AtCDF3. ................................................. 62 3.3.2.- AtCDF3 protein localize to the cell nucleus and display specific DNA-binding and activation properties ....................................................................................................................................... 65 3.3.3.- Overexpression of AtCDF3 enhanced drought and low temperature tolerance in Arabidopsis. ...................................................................................................................................................... 68 ...................................................................................................................................................... 71 3.3.4.- AtCDF3 overexpression increased photosynthesis and stomatal aperture. ........................... 72

3.3.5.- The effect of AtCDF3 on drought tolerance is related to its transcriptional activity. ................ 73 3.3.6.- Transcriptome analysis of transgenic Arabidopsis overexpressing AtCDF3. ......................... 75 3.3.7.- The overexpression of AtCDF3 promotes important metabolic changes in vegetative tissues. ...................................................................................................................................................... 80 3.3.8.- Overexpression of AtCDF3 in tomato enhance osmotic and low temperature tolerance. ....... 82

3.4.- DISCUSSION. ............................................................................................................................ 85 3.4.1.- AtCDF3 involvement in abiotic stress responses. ................................................................. 85 3.4.2.- AtCDF3 as a regulatory link between carbon and nitrogen metabolism. ................................ 87 3.4.3.- AtCDF3 is involved in the cross-talk of abiotic stress responses and flowering time. ............. 88

4.- CHARACTERIZATION OF TOMATO CYCLING DOF FACTORS REVEALS CONSERVED AND NEW FUNCTIONS IN THE CONTROL OF FLOWERINGTIME AND ABIOTIC STRESS RESPONSES………………………………………………………………………………………………………..93

4.1.- INTRODUCTION. ....................................................................................................................... 93

4.2.- MATERIAL AND METHODS. ...................................................................................................... 96 4.2.1.- Database searches for the identification of DOF family members in ..................................... 96 Solanum lycopersicum L. ................................................................................................................ 96 4.2.2.- Subcellular localization of tomato CDF proteins.................................................................... 97

4.2.3.- DNA-binding specificity of CDF proteins using the yeast one-hybrid assay. .......................... 97 4.2.4.- Protoplast transformation and GUS assays. ......................................................................... 98 4.2.5.- Plant growth conditions and quantification of CDF gene expression in tomato. ..................... 98 4.2.6.- Plasmid constructs and plant transformation. ....................................................................... 99 4.2.7.- RNA measurements by RT-qPCR in Arabidopsis. .............................................................. 100 4.2.8.- Salt and drought stress tolerance tests. ............................................................................. 100

iii

4.2.9.- Metabolomic analyses. ...................................................................................................... 101

4.3.- RESULTS. ................................................................................................................................ 101 4.3.1.- Identification of CDF proteins in tomato plants. .................................................................. 101

4.3.2.- Tomato SlCDF1-5 proteins localize to the cell nucleus and display distinct DNA- binding and activation properties. .................................................................................................................... 105 4.3.3.- Expression of tomato SlCDFs follows a circadian rhythm. .................................................. 107 4.3.4.- Expression of tomato SlCDF1-5 genes is differentially regulated during development. ...... 109

4.3.5.- SLCDF1-5 GENES ARE DIFFERENTIALLY INDUCED IN RESPONSE TO ABIOTIC STRESS CONDITIONS. .............................................................................................................................. 110 4.3.6.- Overexpression of tomato SlCDF3 promotes late flowering in transgenic Arabidopsis plants. .................................................................................................................................................... 110 4.3.7.- Overexpression of SlCDF1 and SlCDF3 has an impact in drought and salt tolerance in transgenic Arabidopsis plants. ...................................................................................................... 113 4.3.8.- Overexpression of SlCDF3 in transgenic Arabidopsis plants induces metabolic changes and accumulation of specific compounds. ............................................................................................ 117

4.4.- DISCUSSION. .......................................................................................................................... 120 4.4.1.- SlCDFs share a high degree of sequence similarity but display different DNA-binding affinities and diverse transcriptional activation capabilities. ......................................................................... 120 4.4.2.- Expression of SlCDFs follows a circadian rhythm with two different patterns. ..................... 121 4.4.3.- Expression of tomato SICDF genes in Arabidopsis unveils a conserved function in the control of flowering time. .......................................................................................................................... 122

4.4.4.-SlCDFs involvement in abiotic stress responses. ................................................................ 123 4.4.5.- Impact of SlCDFs expression on C/N metabolism. ............................................................. 124 4.4.6.- CDFs at the interplay between environmental conditions and flowering time. ..................... 125

5.- CONCLUSIONES ............................................................................................................................ 127

6.- BIBLIOGRAFÍA ............................................................................................................................... 131

SUPPLEMENTARY .............................................................................................................................. 155

Resumen

iv

RESUMEN

El tomate (Solanum lycopersicum L.) es considerado uno de los cultivos hortícolas de mayor

importancia económica en el territorio Español. Sin embargo, su producción está seriamente afectada por

condiciones ambientales adversas como, salinidad, sequía y temperaturas extremas. Para resolver los

problemas que se presentan en condiciones de estrés, se han empleado una serie de técnicas culturales

que disminuyen sus efectos negativos, siendo de gran interés el desarrollo de variedades tolerantes. En

este sentido la obtención y análisis de plantas transgénicas, ha supuesto un avance tecnológico, que ha

facilitado el estudio y la evaluación de genes seleccionados en relación con la tolerancia al estrés.

Estudios recientes han mostrado que el uso de genes reguladores como factores de transcripción (FTs)

es una gran herramienta para obtener nuevas variedades de tomate con mayor tolerancia a estreses

abióticos. Las proteínas DOF (DNA binding with One Finger) son una familia de FTs específica de plantas

(Yangisawa, 2002), que están involucrados en procesos fisiológicos exclusivos de plantas como:

asimilación del nitrógeno y fijación del carbono fotosintético, germinación de semilla, metabolismo

secundario y respuesta al fotoperiodo pero su preciso rol en la tolerancia a estrés abiótico se desconoce

en gran parte.

El trabajo descrito en esta tesis tiene como objetivo estudiar genes reguladores tipo DOF para

incrementar la tolerancia a estrés abiotico tanto en especies modelo como en tomate. En el primer

capítulo de esta tesis se muestra la caracterización funcional del gen CDF3 de Arabidopsis, así como su

papel en la respuesta a estrés abiótico y otros procesos del desarrollo. La expresión del gen AtCDF3 es

altamente inducido por sequía, temperaturas extremas, salinidad y tratamientos con ácido abscísico

(ABA). La línea de inserción T-DNA cdf3-1 es más sensible al estrés por sequía y bajas temperaturas,

mientras que líneas transgénicas de Arabidopsis 35S::AtCDF3 aumentan la tolerancia al estrés por

sequía, osmótico y bajas temperaturas en comparación con plantas wild-type (WT). Además, estas

plantas presentan un incremento en la tasa fotosintética y apertura estomática. El gen AtCDF3 se localiza

Resumen

v

en el núcleo y que muestran una unión específica al ADN con diferente afinidad a secuencias diana y

presentan diversas capacidades de activación transcripcional en ensayos de protoplastos de Arabidopsis.

El dominio C-terminal de AtCDF3 es esencial para esta localización y su capacidad activación, la

delección de este dominio reduce la tolerancia a sequía en plantas transgénicas 35S::AtCDF3. Análisis

por microarray revelan que el AtCDF3 regula un set de genes involucrados en el metabolismo del carbono

y nitrógeno. Nuestros resultados demuestran que el gen AtCDF3 juega un doble papel en la regulación de

la respuesta a estrés por sequía y bajas temperaturas y en el control del tiempo de floración.

En el segundo capítulo de este trabajo se lleva a cabo la identificación de 34 genes Dof en tomate

que se pueden clasificar en base a homología de secuencia en cuatro grupos A-D, similares a los

descritos en Arabidopsis. Dentro del grupo D se han identificado cinco genes DOF que presentan

características similares a los Cycling Dof Factors (CDFs) de Arabidopsis. Estos genes son considerados

ortólogos de Arabidopsis CDF1-5, y han sido nombrados como Solanum lycopersicum CDFs o SlCDFs.

Los SlCDF1-5 son proteínas nucleares que muestran una unión específica al ADN con diferente afinidad

a secuencias diana y presentan diversas capacidades de activación transcripcional in vivo. Análisis de

expresión de los genes SlCDF1-5 muestran diferentes patrones de expresión durante el día y son

inducidos de forma diferente en respuesta a estrés osmótico, salino, y de altas y bajas temperaturas.

Plantas de Arabidopsis que sobre-expresan SlCDF1 y SlCDF3 muestran un incremento de la tolerancia a

la sequía y salinidad. Además, de la expresión de varios genes de respuesta estrés como AtCOR15,

AtRD29A y AtERD10, son expresados de forma diferente en estas líneas. La sobre-expresión de SlCDF3

en Arabidopsis promueve un retardo en el tiempo de floración a través de la modulación de la expresión

de genes que controlan la floración como CONSTANS (CO) y FLOWERING LOCUS T (FT). En general,

nuestros datos demuestran que los SlCDFs están asociados a funciones aun no descritas, relacionadas

con la tolerancia a estrés abiótico y el control del tiempo de floración a través de la regulación de genes

específicos y a un aumento de metabolitos particulares.

Summary

vi

SUMMARY

Tomato (Solanum lycopersicum L.) is one of the horticultural crops of major economic importance

in the Spanish territory. However, its production is being affected by adverse environmental conditions

such as salinity, drought and extreme temperatures. To resolve the problems triggered by stress

conditions, a number of agricultural techniques that reduce the negative effects of stress are being

frequently applied. However, the development of stress tolerant varieties is of a great interest. In this

direction, the technological progress in obtaining and analysis of transgenic plants facilitated the study and

evaluation of selected genes in relation to stress tolerance. Recent studies have shown that a use of

regulatory genes such as transcription factors (TFs) is a great tool to obtain new tomato varieties with

greater tolerance to abiotic stresses. The DOF (DNA binding with One Finger) proteins form a family of

plant-specific TFs (Yangisawa, 2002) that are involved in the regulation of particular plant processes such

as nitrogen assimilation, photosynthetic carbon fixation, seed germination, secondary metabolism and

flowering time bur their precise roles in abiotic stress tolerance are largely unknown.

The work described in this thesis aims at the study of the DOF type regulatory genes to increase

tolerance to abiotic stress in both model species and the tomato. In the first chapter of this thesis, we

present molecular characterization of the Arabidopsis CDF3 gene as well as its role in the response to

abiotic stress and in other developmental processes. AtCDF3 is highly induced by drought, extreme

temperatures, salt and abscisic acid (ABA) treatments. The cdf3-1 T-DNA insertion mutant was more

sensitive to drought and low temperature stresses, whereas the AtCDF3 overexpression enhanced the

tolerance of transgenic plants to drought, cold and osmotic stress comparing to the wild-type (WT) plants.

In addition, these plants exhibit increased photosynthesis rates and stomatal aperture. AtCDF3 is localized

in the nuclear region, displays specific binding to the canonical DNA target sequences and has a

transcriptional activation activity in Arabidopsis protoplast assays. In addition, the C-terminal domain of

AtCDF3 is essential for its localization and activation capabilities and the deletion of this domain

significantly reduces the tolerance to drought in transgenic 35S::AtCDF3 overexpressing plants.

Microarray analysis revealed that AtCDF3 regulated a set of genes involved in nitrogen and carbon

Summary

vii

metabolism. Our results demonstrate that AtCDF3 plays dual roles in regulating plant responses to

drought and low temperature stress and in control of flowering time in vegetative tissues.

In the second chapter this work, we carried out to identification of 34 tomato DOF genes that were

classified by sequence similarity into four groups A-D, similar to the situation in Arabidopsis. In the D

group we have identified five DOF genes that show similar characteristics to the Cycling Dof Factors

(CDFs) of Arabidopsis. These genes were considered orthologous to the Arabidopsis CDF1 - 5 and were

named Solanum lycopersicum CDFs or SlCDFs. SlCDF1-5 are nuclear proteins that display specific

binding to canonical DNA target sequences and have transcriptional activation capacities in vivo.

Expression analysis of SlCDF1-5 genes showed distinct diurnal expression patterns and were differentially

induced in response to osmotic, salt and low and high temperature stresses. Arabidopsis plants

overexpressing SlCDF1 and SlCDF3 showed increased drought and salt tolerance. In addition, various

stress-responsive genes, such as AtCOR15, AtRD29A and AtERD10, were expressed differently in these

lines. The overexpression of SlCDF3 in Arabidopsis also results in the late flowering phenotype through

the modulation of the expression of flowering control genes such CONSTANS (CO) and FLOWERING

LOCUS T (FT). Overall, our data connet SlCDFs to undescribed functions related to abiotic stress

tolerance and flowering time through the regulation of specific target genes and an increase in particular

metabolites.

Director. Director.

Dr. Joaquín MedinaAlcázar Profesor. Jesús Vicente-Carbajosa

Alba Rocío Corrales Ducuara

Abreviaturas

viii

ABREVIATURAS

%: Porcentaje

3-AT: 3-aminotriazol.

ABA: Ácido abscísico.

aa: Amino acidos

ABRE: ABA responsive element - Elemento de respuesta a ABA.

bp: Pares de bases.

bZip: Basic Leucine Zipper

CaMV: Cauliflower mosaic virus – Virus del mosaico de la coliflor.

CDF1: Cyclin Dof Factor 1

CO: Constans

COR: COLD REGULATED

CRE: cis-regulatory element- Elemento regulador en cis.

DL: Día largo

DC: Día corto

DOF: DNA binding with one finger- Unión a DNA con un dedo de Zinc.

DRE: Dehydration-responsive element- Elemento de respuesta a deshidratación

ELF3: EARLY FLOWERING 3

FAO: Food and Agriculture Organization- Organización para la Alimentación y la Agricultura.

FKF1: (Flavin-binding, Kelch repeat, F-box-1)

GABA: γ-amino butírico

GI: GIGANTEA

DAG: DOF Affecting Germination

genes R: Genes de resistencia

GUS: β-Glucoronidasa

HMG: High Mobility Group

Ha: Hectáreas

LI: Línea de Introgresión

OBP1: OBF- binding factor-1

PSII: Fotosistema II

PEPC: C4 Fosfo-Enol-Piruvato-Carboxilasa

Abreviaturas

ix

PK: Piruvato quinas

QTLs: Quantitative trait loci.

ROS: Especies reactivas de oxígeno.

RT-qPCR: Real Time Quantitative PCR-PCR cuantitativa a tiempo real.

Rubisco: (Ribulosa-1,5-bifosfato)

RD29A: RESPONSIVE TO DEHYDRATION 29A

TF: Transcription Factor- Factor de Transcripción

Tn: Toneladas

WT: Wild type-Fenotipo Silvestre

Y1H: Hibrido de levadura

1

INTRODUCCIÓN GENERAL

Introducción General

1

1.1.- EL TOMATE, Solanum lycopersicum L.

1.1.1.- Origen, taxonomía y morfología.

El tomate (Solanum lycopersicum L.) es una especie perteneciente a la familia de las solanáceas

al igual que la patata y el pimiento. El centro de origen del género Solanum, es la región andina que

incluye partes de Colombia, Ecuador, Perú, Bolivia y Chile (Nuez y Prohens, 2008). El lugar donde se

produjo su domesticación ha sido motivo de controversia. Sin embargo, antes de su introducción en

Europa y Asia ya presentaba un cierto grado de domesticación caracterizado por la forma, acostillado,

tamaño y color de los frutos (Nuez, 1995).

La introducción y difusión del tomate en Europa data del siglo XVI, pero hasta el siglo XVII no

presentó un incremento en su producción y consumo en Italia y la Península Ibérica (Nuez y Prohens,

2008). Comerciantes españoles y portugueses difundieron el tomate por Oriente Medio, África y Filipinas

a través de sus colonias ultramarinas (Nuez, 1995), y a partir del comercio en estos países el tomate llego

a establecerse en China, Japón e India, permitiendo así difusión por Asia. El tomate, es una planta

dicotiledónea perteneciente a la familia Solanaceae. Esta gran familia está compuesta por 96 géneros y

más de 2800 especies distribuidas en tres subfamilias Solanoideae (en la cual se encuentra el género

Solanum), Cestroideae y Solanineae (Foolad, 2007) su taxonomía aceptada es:

REINO: Plantae

Subreino: Traqueobinta

Superdivisión: Spermatophyta

Clase: Magnoliopsida

Subclase: Asteridae

Orden: Solanales

Suborden: Solanineae

Familia: Solanaceae

Subfamilia: Solanoideae

Género: Solanum

Especie: lycopersicum


2

En la primera clasificación taxonómica, el tomate cultivado fue llamado Solanum lycopersicum por

Linnaeus (1953). En 1754, Miller designo el género Lycopersicon y la especie esculentum para el tomate

cultivado, esto ayudó a que el tomate fuera aceptado como alimento de consumo. La distinción entre el

género Lycopersicon y el Solanum, se basó inicialmente en caracteres diferenciales en hojas y anteras.

Así, en el género Lycopersicon su dehiscencia se produce en el lateral de las anteras y sus hojas son

generalmente pinadas. Mientras que en el género Solanum su dehiscencia se produce en el extremo de

las anteras y sus hojas son simples. Relaciones filogenéticas entre los géneros Lycopersicon y Solanum

han sido tema de debate a lo largo del tiempo algunos investigadores sugieren que Lycopersicon es un

género distinto, mientras que otros argumentan que este debía estar unido al Solanum. Así, basados en

estudios moleculares y morfológicos, se ha re adoptado el nombre Solanum lycopersicum para el tomate

cultivado (Foolad, 2007). Así mismo, otras especies del género Lycopersicon han sido asignadas al

Solanum.

El tomate es una planta perenne de porte arbustivo que se cultiva como anual, puede

desarrollarse de forma rastrera, semierecta o erecta, su crecimiento puede ser limitado en algunas

variedades determinadas e ilimitado en variedades indeterminadas (Figura 1.1A). Su sistema radicular,

está constituido por la raíz principal, las raíces segundarias y las raíces adventicias. El tallo principal es

un eje de 2-4 cm de grosor en su base, sobre el que se desarrollan los tallos secundarios (ramificación

simpodial). La hoja es compuesta e imparipinnada con foliolos peciolados y lobulados con bordes

dentados y recubierta de pelos glandulares. Una hoja típica de tomate cultivado tiene aproximadamente

0,5 m de largo con un gran foliolo terminal y varios foliolos laterales (Figura 1.1B). La flor de tomate es

regular e hipógina y consta de 5 ó más sépalos y pétalos dispuestos de forma helicoidal. Los estambres

que se alternan con los pétalos forman un cono estaminal que envuelve el gineceo (con un ovario bi o

plurilocular) (Figura 1.1C). Las flores se agrupan en inflorescencias de tipo racimoso unidas al eje floral

por un pedicelo articulado que contiene la zona de abscisión (Nuez, 1995) (Figura 1.1D). El número de

flores en cada inflorescencia depende de factores ambientales como la temperatura (Rost, 1996).


3

Figura 1.1. Características generales de la planta de tomate (Solanum lycopersicum L.) A) Planta entera, B) Hoja, C) Flor, D) Inflorescencia, E) Fruto bilocular, F) Fruto plurilocular, G) Fruto adulto. Adaptado de C.M Jones http://tgrc.ucdavis.edu/Data/Acc/taxon_images.aspx.

El fruto de tomate es una baya bi o plurilocular que se desarrolla a partir de un ovario

aproximadamente de unos 5-10mg (Figura 1.1 E-F). Un fruto adulto está constituido por: pericarpo, que

se compone de pared externa, pared interna y paredes radiales, tejido placentario y semillas (Figura

1.1G). El desarrollo del fruto tarda aproximadamente de 7 a 9 semanas en función del cultivar, la posición

en el racimo y las condiciones ambientales. El crecimiento del fruto se ajusta a una curva sigmoide simple

que se divide en tres periodos (Nuez, 1995; Cuartero y Muñoz., 1999):

i. El primer periodo, denominado crecimiento lento, puede durar 2 ó 3 semanas y se

produce fundamentalmente por división celular. La transición del primer periodo al

segundo requiere una estimulación hormonal que es normalmente provista por el

crecimiento del tubo polínico y la fertilización del ovulo.

ii. El segundo periodo, de crecimiento rápido, dura entre 3-5 semanas prolongándose hasta

el inicio de la maduración. En esta etapa el crecimiento de fruto se produce por aumento

del tamaño de las células preformadas. El tamaño de las vacuolas también aumenta y se

A B C D

E F G


4

produce una acumulación de almidón, ácidos orgánicos y otros componentes que darán

características al fruto maduro.

iii. Por último, un periodo de crecimiento lento que se extiende durante 2 semanas y en el

que no hay un aumento significativo del tamaño del fruto pero por el contrario se

producen los cambios metabólicos propios de la maduración. El tamaño del fruto está

estrechamente correlacionado con el número de semillas y el número de lóculos.

1.1.2.- El tomate como planta modelo.

El tomate es considerado un buen sistema modelo para estudios genómicos y de desarrollo del

fruto, por su facilidad de cultivo bajo un amplio rango de condiciones medioambientales, ciclo de vida

corto, elevado potencial reproductivo, fácil polinización y propagación vegetativa (Foolad, 2007). Además,

su uso en investigación está ampliamente aceptado por ser una especie diploide, con 2n=2x=24

cromosomas, con un genoma relativamente pequeño (950 Mbp) del cual un 75% corresponde a

heterocromatina, en cambio una gran proporción de genes se agrupan en grandes regiones eucromáticas

que se localizan en las regiones distales de los brazos cromosómicos (220 Mb). Esta característica

permitió la estrategia de secuenciamiento solo de regiones eucromáticas, para cubrir la mayor parte del

genoma (Mueller y col., 2005). En el año 2012, la iniciativa de secuenciación del genoma de tomate

completo la secuenciación de la línea LA1589 de Solanum pimpinellifolium L. dentro del Proyecto

Internacional del Secuenciamiento del Genoma del Tomate (SOL) (The tomato genome consortium 2012).

Estudios recientes han demostrado que la base para obtener el tomate moderno ha sido a partir

de los diferentes procesos de domesticación. Análisis evolutivo de 333 accesiones de tomate (S.

pimpinellifolium, S. lycopersium var. cerasiforme y S. lycopersicum), que representan varios orígenes

geografícos, tipos de consumo y especies mejoradas. Al igual que 10 accesiones de especies wild-type

(WT), incluyendo algunas donadoras de genes de resistencia (genes R) a enfermedades y 17 híbridos

comerciales modernos (F1). Demuestran que la domesticación y la mejora del cultivo de tomate, se ha


5

enfocado sobre dos grupos independientes de QTLs (Quantitative trait locus), que han permitido obtener

un cultivar ~100 veces más grande en comparción con su ancestro (Lin y col., 2014). De esta manera, la

domesticación y mejora ha incrementado la productividad del cultivo de tomate, pero ha disminuido su

base genetica. En la actualidad, se han realizado introgresiones de genes R de especies WT en cultivares

comerciales. Un ejemplo de ello es la introgresión del gen Tm-2a de Solanum peruvianum L. (PI 128650)

sobre el cromosoma 9 (51.7-54.7 Mb) de tomate, este gen confiere resistencia al Virus del mosaico del

tomate (Tomato mosaic virus; ToMV) (Tanksley y col., 1998). Por otra parte, otras dos grandes

introgresiones sobre el cromosoma 6 de tomate han sido importantes para la tolerancia a patógenos. La

introgresión del gen Mi-1 de S.peruvianum L. (PI 128657) que confiere resistencia a nematodos, y el gen

Ty-1 de Solanum chilense L. (LA1969) que conlleva resistencia al Virus del rizado amarillo del tomate

(Tomato yellow leaf curl virus; TYLCV) han sido útiles para aumentar la resistencia a plagas y

enfermedades. Ambas introgresiones ocupan la misma región genómica, impidiendo la recombinación de

ambos genes en un mismo cultivar. Otros estudios con diferentes líneas de introgresión (LIs) de Solanum

pennelli y Solanum lycopersicum cv. M82 han identificado genes candidatos para la tolerancia a estrés

abiótico y han proporcionado evidencia que son elementos transportables y presentan un rol en la

evolución de estas características (Bolger y col., 2014). Sin embargo, son pocas las variedades

comerciales que se conocen hasta en momento que responden eficazmente en la tolerancia a estrés

abiótico.

1.2.- Importancia económica del tomate.

1.2.1.- Importancia económica en el mundo.

El tomate, es el segundo cultivo vegetal más importante en el mundo después de la patata

(Solanum tuberosum L.) en términos de consumo per cápita (Pandey y col., 2011). En las últimas

décadas, la producción de tomate (fresco y cultivado) se ha incrementado alrededor de un 300% (Costa y

Heuvelink, 2005).


6

Según datos de la FAO (Food and Agriculture Organization of the United Nations, www.fao.org),

en el año 2012 la producción mundial de tomate alcanzó 161,7 millones de toneladas (Tn) con un

superficie cultivada de más de 4,8 millones de ha. Los países con mayor producción en Tn a nivel

mundial fueron: China (50 millones) seguido por India (17 millones), Estados Unidos (13 millones),

Turquía (11 millones) y Egipto (8 millones). España ocupa el noveno lugar a nivel mundial con 4,0

millones de Tn al año y un área de 48.800 ha (FAOSTAT, 2012; Figura 1.2).

Figura 1.2. Producción mundial de tomate. Ranking de los principales países productores en el mundo en millones de (Tn) (FAOSTAT, 2012).

En Europa, la producción de tomate está dividida en dos grandes sistemas. En el norte el sistema

de producción es intensivo bajo invernadero para obtener grandes cosechas de tomate fresco. Por otra

parte, en el Mediterráneo el sistema de producción está enfocado en zonas abiertas para el tomate

procesado y bajo estructuras cubiertas para el tomate fresco (Harvey y col, 2002).

1.2.2.- Importancia económica del tomate en España.

El tomate es un producto básico de la horticultura Española, ocupa el 14% de la superficie de

cultivo y aporta un 23% del valor de la producción del sector (Nuez, 1995). Entre los países productores

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7

europeos, España es el segundo después de Italia (Tabla 1.1). Sin embargo, a diferencia de la situación

italiana, gran parte de la producción española se dedica al mercado fresco (Pazos, 2003). En el territorio

Español, el tomate es uno de los cultivos hortícolas de mayor producción. Según el Anuario de

Estadística Agraria (2011), el 65 % de la superficie cultivada es de cosecha al aire libre y el resto bajo

invernadero.

Tabla 1.1 Producción, superficie y rendimiento de los principales países productores de la Unión Europea

en 2012 (FAOSTAT, 2012).

En la actualidad, las provincias con mayor producción son: Extremadura (1.275.368 Tn), Almería

(892.510 Tn) y Murcia (311.065 Tn), que en los últimos años han emergido como una competitiva e

importante área en la producción y exportación de tomate fresco bajo invernadero (Costa y Heuvelink.,

2005).

1.2.3.- Impacto del estrés abiótico en el cultivo de tomate.

Como se ha mencionado anteriormente, el tomate (S. lycopersicum L.) es el segundo cultivo

hortícola más importante del mundo después de la patata (S. tuberosum L.). Sin embargo, su

productividad está influenciada por diferentes tipos de estrés abiótico (Pandey y col., 2011). El tomate

puede adaptarse a casi a todos los climas y regiones del mundo, su crecimiento y desarrollo es sensible

Países Producción (Tn) Superficie (ha) Rto (Kg/ha)

España 4.007.000 48.800 821,106.56

Italia 5.131.977 91.850 558,734.57

Francia 588.660 6.369 924,258.13

Grecia 979.600 16.000 612,250

Portugal 1.392.700 15.400 904,350.65

Total UE 15.133.447 506.583 408.493.57


8

a diferentes estreses medioambientales como: salinidad, sequía, temperaturas extremas y polución

ambiental (Foolad, 2005).

Entre los diferentes tipos de estrés abiótico que afectan la producción de tomate, el déficit hídrico o

estrés por sequía es el factor más devastador (Boyer, 1982). Las pérdidas en la producción agrícola a

causa de la sequía exceden a las producidas por otras causas, creando graves problemas a la tercera

parte de las áreas cultivables en el mundo (Blum, 1998; Kramer, 1980; Schonfeld y col., 1988; Foolad

2005; Bruce y col., 2002). España es uno de los países afectados por el fenómeno de la sequía; durante

el periodo de 1880-2000 más de la mitad de los años se han calificado como secos o muy secos. El

estrés por sequía afecta a todo el territorio español, aunque las zonas donde las precipitaciones anuales

no superan los 600mm como Andalucía, son los que sufren en mayor medida sus efectos.

La necesidad de agua en el cultivo de tomate varía en función de su estado de desarrollo. Al

principio del cultivo, la masa vegetal es muy pequeña y el consumo de agua es mínimo. Sin embargo,

este incrementa paulatinamente hasta el inicio del cuajado. Durante el cuajado del tomate las

necesidades hídricas aumentan considerablemente debido que la planta sigue produciendo hojas y tallos

nuevos, a la vez que van creciendo los frutos. En el periodo de maduración, las necesidades de agua

disminuyen (en variedades con floración y fructificación agrupadas) (Cuartero y col., 1995). Así, el tomate

de industria puede dejarse de regar cuando se tiene un 29-30% del fruto maduro sin que la cosecha

disminuya (Cuartero y col., 1995).

El crecimiento y desarrollo de la planta depende en gran parte de la absorción de agua que realice

el sistema radicular. La planta de tomate responde al estrés hídrico aumentando la relación raíz/parte

aérea; plantas cultivadas con déficit hídrico reducen su sistema radicular en comparación con plantas

cultivadas en condiciones control, pero en un grado menor que la reducción del desarrollo de la parte

aérea, (Brouwer, 1981). Una de las causas de este crecimiento diferencial reside en que la distribución de

agua en el suelo no es homogénea. El contenido de agua en el suelo repercute en la distribución del


9

sistema radicular haciéndolo más profundo en el caso de presentarse déficit o más superficial cuando el

potencial hídrico del suelo es cercano a cero (Bierhuizen, 1981; Cuartero y col., 1995). Un bajo potencial

hídrico afecta a la expansión y división celular (Kirkham y col., 1991) afectando directamente al desarrollo

del fruto. Además, el déficit hídrico provoca cambios en el tamaño del fruto a corto plazo. Estudios

realizados por Johnson y colaboradores (1992), han demostrado que el diámetro del fruto puede

aumentar o disminuir según el sentido del gradiente de déficit hídrico entre el tallo y el fruto. Teniendo en

cuenta que el potencial hídrico del fruto permanece relativamente constante, las variaciones en el

potencial hídrico del tallo durante el día explican el aumento o disminución del diámetro del fruto.

Por otra parte, la salinidad es otro de los mayores estreses abióticos que afectan a la

productividad de cultivos en el mundo (Munns y Tester 2008; Peleg y col., 2012; Apse y Blumwald, 2002;

Foolad, 2004). Diferentes cultivos de interés agronómico incluido el tomate, S. lycopersicum son sensibles

a la salinidad (Foolad, 2004; Passam y col., 2007). Una alta salinidad en la zona radicular de la planta

impide un adecuado crecimiento y desarrollo, dando lugar a una reducción o pérdida total de la cosecha.

Según Foolad (2004), de los 14 billones de ha disponibles sobre la tierra, se estima que 6,5 billones de ha

pertenecen a regiones áridas y semiáridas y a su vez 1 billón son suelos salinos. Aproximadamente, el

22% de la tierra agrícola es salina (FAO, 2004) con un incremento de la tasa salinización del 10% por

año (Foolad, 2005). El problema de la salinidad en España es especialmente importante en sistemas

cerrados del sureste peninsular. La escasa disponibilidad de agua de óptima calidad y la búsqueda de la

misma origina altos costes de producción que afectan a la rentabilidad del cultivo. Algunos cultivares de

tomate son moderadamente sensibles a la salinidad en todos los estados de desarrollo de la planta

incluyendo: germinación de semilla, crecimiento vegetativo y crecimiento reproductivo (Jones y col., 1988;

Maas, 1986; Mass, 1990; Bolarin y col., 1993).

La salinidad del suelo se relaciona con un exceso de entre 0.20 y 0.25% de sodio, calcio,

magnesio, cloratos, sulfatos o carbonatos (Pandey y col., 2011). Se considera que un suelo es salino

cuando la conductividad eléctrica (CE) del extracto de saturación en la zona radicular excede 4 dSm -1 a


10

25ºC (Foolad, 2004; FAO: Rome 2005). En la raíz, el estrés salino genera cambios en el crecimiento,

morfología y fisiología modificando así la absorción de agua e iones y la producción de moléculas

señalizadoras (hormonas). La salinidad produce un efecto negativo en la cantidad de biomasa. Una de las

razones por la que el crecimiento de la raíz se reduce en condiciones de estrés salino es que presenta

una disminución del crecimiento celular causada por: el bajo potencial de agua del medio externo, la

interferencia de iones salinos con los nutrientes esenciales o la toxicidad debida a la acumulación de

iones que conducen a la muerte celular. Además, la salinidad disminuye el crecimiento de los brotes de

tomate y causa una disminución del área foliar. La reducción en la tasa de crecimiento de la hoja está

relacionada con una reducción de la turgencia celular y de la tasa fotosintética (Cuartero y Muñoz., 1999).

Niveles de salinidad iguales o superiores de 8ds/m afectan el número de frutos por planta. La disminución

del número de frutos podría ser debido a la reducción del número de flores y/o del cuajado de las mismas,

que a su vez puede deberse a una disminución de la cantidad y/o fertilidad del polen (Cuartero y col.,

1995). El tamaño del fruto también se ve afectado por la salinidad ya que esto limita el transporte de agua

hacia el fruto produciendo un aumento de la tasa de acumulación de materia seca (Johnson y col., 1992).

En condiciones de salinidad (CE= 3-9 dSm-1) la reducción del tamaño del fruto es la principal causa de la

disminución de la producción (Van-Ieperen, 1996).

Por otra parte, el tomate es una planta termo-periódica (Went, 1994), requiere fluctuaciones de

temperatura entre el día y la noche. Se estiman como óptimas las diferencias térmicas noche/día de 6-

7ºC (Verter, 1957). A su vez, las temperaturas óptimas están relacionadas con la iluminación (Calvert y

Slack, 1975). En condiciones mediterráneas, temperaturas diurnas de 21ºC a 27ºC (según radiación) y

nocturnas de 12ºC -15ºC se consideran las adecuadas para el cultivo (Brun y Lagier, 1984). El tomate es

altamente sensible al estrés por bajas temperaturas o chilling ya que en esas condiciones se produce una

inhibición de la germinación de semillas y del desarrollo reproductivo causando transformaciones

homeóticas florales (Lozano, 1998). Las bajas temperaturas afectan al desarrollo del fruto, pero la

temperatura mínima a la que planta sufre daño es difícil de precisar, debido a que depende del estado de

desarrollo de la planta, de su potencial hídrico y de la condición hídrica del suelo. Temperaturas alrededor


11

de 1ºC producen síntomas de heladas en hojas (Cuartero, 1995), sin embargo, no es el principal

problema. Las fases más sensibles al frío son la germinación de la semilla y el cuajado del fruto, esta

última debida principalmente a una disminución de la fertilidad del polen y a una dificultad en la

dehiscencia de las anteras (Cuartero y col., 1995; Cuartero y Fernández-Muñoz, 1999).

1.3.- ESTRÉS ABIÓTICO.

1.3.1.- Generalidades del estrés abiótico en plantas.

Las plantas durante su desarrollo se enfrentan a condiciones ambientales adversas que afectan

negativamente a su crecimiento y productividad (Screenivasulu y col., 2007; Seki y col., 2007). El estrés

abiótico es la principal causa de las pérdidas de cultivos en el mundo, reduciendo su rendimiento en más

de un 50% (Bray y col., 2000), lo que supone pérdidas de cientos de millones de dólares cada año. En

términos generales los estreses abióticos más comunes son: salinidad, sequía, bajas y altas

temperaturas. La disminución de la disponibilidad de agua y las condiciones ambientales adversas,

sumado al incremento de la población humana están produciendo serios cambios en la agricultura

mundial (Mittler y Blumwald, 2010; Peleg y col., 2012). Recientes estudios han mostrado que para el año

2050 la población mundial necesitara un aumento de entre 70 a 100% más de alimento (World

Development Report 2008) principalmente en cultivos como: arroz (Oryza sativa L.), trigo (Triticum

aestivum L.) y maíz (Zea mays L.) (Godfray y col., 2010). Este incremento de la población mundial

aumenta la necesidad de producir estos cultivos en áreas donde las condiciones climáticas adversas son

un factor limitante (Pennisi, 2008; Nakashima y col., 2014).

Las plantas responden y se adaptan al estrés abiótico a través de mecanismos bioquímicos,

moleculares y fisiológicos que permiten su desarrollo y supervivencia (Munns, 2002; Chaves y col., 2003;

Osakabe y col., 2013; Yamaguchi-Shinozaki y Shinozaki, 2005). Las respuestas a uno o más estreses

varían dependiendo de la especie y el genotipo. Sin embargo, la respuesta al estrés también depende de


12

la duración y severidad del evento, así como de la edad y del estado de desarrollo de la planta cuando se

impone el estrés (Bray, 1997). En plantas de cultivo, la sensibilidad/tolerancia al estrés abiótico es

determinada por la pérdida de la cosecha y la tasa de supervivencia (Peleg y col., 2012). Además, en

respuesta a estos estreses un número determinado de genes son altamente regulados, mitigando los

efectos del estrés y ajustando el entorno celular y la tolerancia de la planta (Mahajan y col., 2005).

Entre los diferentes tipos de estrés abiótico, la salinidad, la sequía y las bajas temperaturas son de

las principales causas que limitan la productividad de cultivos en el mundo (Boyer, 1982; Araus y col.,

2008; Munns y Tester, 2008). Investigaciones previas, han estudiado los diferentes mecanismos de

respuesta a estrés tanto en sistemas modelo, particularmente Arabidopsis thaliana, como en cultivos de

interés agronómico. Esto ha sido un punto de partida para el desarrollo de nuevas aproximaciones

biotecnológicas de mejora genética, que permitan desarrollar nuevas variedades con mayor producción

bajo condiciones de estrés abiótico.

1.3.2.- Efectos de la salinidad, las bajas temperaturas y la sequía sobre el crecimiento de la planta.

La exposición de las plantas a estreses medioambientales como salinidad, sequía y bajas

temperaturas causan efectos adversos sobre el crecimiento de plantas y la productividad de cultivos.

Estos efectos adversos se deben a alteraciones morfológicas y fisiológicas, a partir de cambios en

procesos como la división celular y el metabolismo incluyendo fotosíntesis (Saibo y col., 2009). Las

condiciones medioambientales pueden afectar a mecanismos específicos. La salinidad afecta a procesos

como el crecimiento, la fotosíntesis, la síntesis de proteínas, y el metabolismo de lípidos. Además, causa

efectos hiperosmóticos e hiperiónicos que producen la muerte de la planta. La fase inicial del estrés

salino es atribuida a un choque osmótico, similar al estrés causado por exceso de agua, y que es

probablemente constituida por un fuerte ajuste osmótico. La segunda fase presenta un periodo más


13

extenso en la que se produce toxicidad por iones debido a su acumulación. Además, la salinidad causa

problemas a nivel celular: i) alteración del equilibrio iónico, influenciado por la dispersión de Na+; ii)

toxicidad de Na+ sobre el metabolismo celular, ya que tiene un efecto deletéreo sobre el funcionamiento

de algunas enzimas (Niu y col., 1995); iii) alta concentración de Na+, causa desbalance osmótico,

desorganización de membrana, reducción en el crecimiento e inhibición de la expansión celular,

reducción de la fotosíntesis y producción de especies reactivas de oxigeno (ROS) (Peleg y col., 2012;

Yeo, 1998). Por último, la salinidad causa efectos significativos sobre el desarrollo, reducción en el

tamaño de brotes, altura de la planta, número de hojas por planta y longitud de la raíz (Mohammad y col.,

1998).

Muy diferentes son los efectos observados en respuesta a bajas temperaturas como por ejemplo:

reducción en la expansión de la hoja, marchitamiento, clorosis y necrosis. El estrés por bajas

temperaturas afecta al desarrollo reproductivo en el momento de la antesis, produciendo esterilidad en las

flores. Además, produce daños a nivel de membrana (Steponkus y col., 1984, 1993), causa principal de

una fuerte deshidratación asociada al frío (Mahajan y Tuteja, 2005). Por otra parte, el estrés por bajas

temperaturas induce un número de alteraciones en los componentes celulares incluyendo, ácidos grasos

insaturados (Crossins, 1994), glicerolípidos (Lynch y Thompsom, 1982), cambios en la composición de

carbohidratos y proteínas, activación de canales de iones (Knight, 1996) y acumulación de sacarosa y

otros azúcares simples que se generan con la aclimatación al frío y también contribuyen a la

estabilización de la membrana (Mahajan y Tuteja, 2005).

En cuanto a los efectos causados por déficit hídrico o estrés por sequía, se presentan cambios

fisiológicos y bioquímicos a nivel celular que incluyen, perdida de turgencia, cambios en la composición y

fluidez de membrana y cambios en la concentración de solutos (Chaves y col., 2003). El estrés por sequía

causa reducción en la actividad fotosintética por la disminución en la actividad de las enzimas

fotosintéticas, acumulación de ácidos orgánicos, osmolitos y cambios en el metabolismo de

carbohidratos. Estas moléculas que regulan el balance osmótico se acumulan en las células de la planta

en respuesta al estrés y posteriormente son degradadas (Valliyodan y Nguyen, 2006; Tabaeizadeh,


14

1998). Otros efectos fisiológicos de la sequía sobre la planta es la reducción del crecimiento, en

particular, el crecimiento de brotes, debido a una disminución de la actividad de las quinasas

dependientes de ciclinas (CDK) que causa reducción de la división celular (Shuppler y col., 1998). Por

otra parte, el crecimiento de hojas es más sensible que el crecimiento de raíz. La reducción de la

expansión de la hoja es beneficiosa para la planta bajo condiciones de déficit hídrico, debido a que se

produce una reducción de la transpiración.

En último término, la sequía, la salinidad y las bajas temperaturas son limitaciones

medioambientales que disminuyen la eficiencia fotosintética y afectan al crecimiento y a la productividad

de plantas. Estos estreses interfieren con la fotosíntesis en diferentes puntos: difusión de CO2, eficiencia

del PSII, transporte electrónico, formación de ROS, contenido de ribulosa-1,5-bifosfato (RuBP)

(dependiente de ATP y NADPH), actividad de la ribulosa-1,5-bifosfato carboxilasa/oxigenasa (Rubisco) y

foto respiración. También inducen el cierre de estomas (Wilkinson y col., 2001; Zhu, 2002; Chaves y col.,

2003), disminuyen la asimilación de CO2 y consecuentemente reducen la tasa fotosintética (Figura 1.3).

El control de la apertura estomatica es un proceso mediado por ABA y posiblemente por otras señales

generadas en respuesta a estrés abiotico. Tanto las bajas temperaturas como el deficit hidrico pueden,

por diferentes razones, disminuir la sintesis de RuBP que depende de las concentraciones de ATP y

NADPH y la actividad de las enzimas del ciclo de Calvin, reduciendo la fotosíntesis y la tasa de

asimilación de CO2 (Saibo y col., 2009). Cuando la planta es expuesta a estos estreses medioambientales

la disponibilidad de CO2 dentro de la hoja (Ci) es limitado y/o la síntesis de ATP disminuye, la actividad

del ciclo de Calvin es reducido pero el PSII permanece activo, por lo que la concentración del aceptor final

NADP+ es generalmente baja (Figura 1.3). Por otra parte, la inhibición de la fotosintesis, disfunción

metabolica y daño en las estructuras celulares, desencadena una inhibición en la expansión celular que

inhibe el crecimiento de la planta, acelerando el desarrollo y senescencia (Chinnusamy y col., 2006;

Krasensky y Jonak, 2012; Mahajan y Tuteja, 2005). Bajo estos efectos, una disminución del cultivo es

debido a una senscencia prematura en los diferentes tejidos de la planta ya que reducen el crecimiento y

así el número los frutos (Albacete y col., 2014).


15

1.3.3.- Respuesta de la planta a la salinidad, las bajas temperaturas y la sequía.

Las plantas desarrollan una amplia variedad de mecanismos de respuesta y adaptación a los

cambios medioambientales. Estos desencadenan múltiples mecanismos a nivel fisiológico, bioquímico,

metabólico como molecular, que ayudan a mitigar los efectos impuestos por el estrés. Entender los

mecanismos de respuesta a estrés es de vital importancia para diseñar nuevas estrategias que permitan

mejorar la tolerancia de cultivos al estrés abiótico.

En respuesta a la salinidad las plantas desencadenan un ajuste osmótico crucial en la adaptación

a este estrés, debido a que permite mantener la turgencia y bajar el potencial hídrico, manteniendo la

actividad metabólica y activando el crecimiento cuando los niveles de sal disminuyen. La tolerancia a la

salinidad se produce cuando la planta tiene la habilidad de recuperarse del estrés osmótico y mantiene el

crecimiento y la fotosíntesis durante periodos prolongados. Las plantas toleran la salinidad del suelo a

partir de tres grandes mecanismos: la exclusión de Na+, tolerancia del tejido al Na+ y tolerancia osmótica

(Munns y Tester, 2008; Plett y Moller, 2010). Sin embargo, el impacto de cada uno de los mecanismos

varía dentro de cada especie y durante el ciclo de vida de la planta (Peleg y col., 2012). Por otra parte,

diferentes estudios han mostrado que Ca2+ juega un rol importante en la tolerancia a la salinidad. La

aplicación de Ca2+ de forma externa reduce los efectos tóxicos del NaCl. Una alta salinidad incrementa

los niveles de Ca2+ en el citosol que es transportado desde el apoplasto a los compartimentos

intracelulares (Knight y col., 1997).

Este incremento transitorio de Ca2+ en el citosol inicia la señal por el estrés y la transducción para

la adaptación al estrés. Una de las mayores consecuencias del estrés por NaCl es la perdida intracelular

de agua. Para contrarrestar esta pérdida de agua y proteger las proteínas celulares, las plantas acumulan

metabolitos conocidos como “solutos compatibles”. Estos no inhiben las reacciones metabólicas normales

(Ford y col., 1984). Entre los metabolitos observados que facilitan el ajuste osmótico se encuentran:

azúcares como fructosa, sacarosa, alcoholes y otros azúcares como trehalosa (Delauney y col., 1993).


16

Figura 1.3. Esquema de los mecanismos relacionados con la fotosíntesis que pueden ser afectados por la salinidad, la

sequía y las bajas temperaturas. Las flechas azules y marrónes corresponden a las señales por sequía, salinidad y bajas

temperaturas respectivamente. Condiciones ambientales adversas que causa estrés osmótico inducen cierre estomático,

limitando la tasa de asimilación de CO2. El ácido abscísico (ABA) está involucrado en la respuesta a salinidad y sequía y puede

mediar algunos efectos por bajas temperaturas. Las flechas gruesas representan la señal a la que contribuye. Las líneas

discontinuas representan las posibles interacciones. Fotosistemas I (PSI), Fotosistema (PSII), especies reactivas al oxigeno

(ROS), 3-fosofoglicerato (3PGA), ribulosa-1,5bifosfato (RuBP), fosfoglicolato (PG), factor de acoplamiento (FC), concentración

interna de CO2 (Ci), concentración de CO2 en el cloroplasto (Cc). Adaptado de Saibo y col., 2009.

La exposición de las plantas a las bajas temperaturas resulta en una serie de cambios fisiológicos

y bioquímicos. El primer cambio influye en la fluidez de la membrana celular y en la composición de

ácidos grasos (Murata y Los, 1997; Suzuki y col., 2001). Estos cambios producen un incremento del

contenido de lípidos poliinsaturados esenciales para la supervivencia de la planta cuando es sometida al

estrés, debido a los efectos negativos de las bajas temperaturas sobre los procesos metabólicos de la

membrana así como la respiración y fotosíntesis (Cossins, 1994). La exposición al frío también induce la

acumulación de otras proteínas que no están localizadas en las membranas celulares, y que en algunos

casos tienen un rol de protección (Bae y col., 2003; Gao y col., 2009). El contenido de azúcares también


17

es alterado en la planta en respuesta a bajas temperaturas. Diferentes estudios han encontrado una alta

correlación entre los niveles de azúcares y la tolerancia al frío en diferentes especies (Guy y col., 1992;

Sasaki y col., 1996; Sundblad y col., 2001). Finalmente, la exposición a bajas temperaturas desencadena

cambios estructurales en la planta, como modificaciones en la composición de la pared celular (Wei y col.,

2006)

La exposición de las plantas a una limitación de agua durante varios estadios del desarrollo

desencadena diferentes cambios fisiológicos durante su crecimiento. Estudios recientes han permitido

dilucidar los mecanismos de tolerancia a sequía en plantas, a través de aproximaciones moleculares y

genómicas con las que se han identificado un número determinado de genes que responden a la sequía a

nivel transcripcional (Seki y col., 2002; Guo y col., 2009). Las plantas responden rápidamente para

prevenir que la maquinaria fotosintética sufra daños irreversibles. La primera respuesta al déficit hídrico

es el cierre estomático para prevenir la perdida de agua por transpiración (Mansfield y col., 2005;

Osakabe y col, 2014). El ABA se acumula en el tejido de las plantas sometidas a estrés hídrico y

promueve la reducción de la transpiración vía cierre estomático. A través de estos mecanismos, las

plantas minimizan la perdida de agua y disminuyen el daño causado por el estrés (Mahajan y Tuteja,

2005). Bajo condiciones severas de sequía también se detecta una disminución de la actividad de la

enzima Rubisco. La actividad fotosintética mediada por la cadena de transporte electrónico es ajustada

por la disponibilidad de CO2 en la planta y en el fotosistema II (PSII), que disminuye en paralelo bajo

condiciones de sequía. Esto indica que la disminución en la tasa fotosintética bajo estrés por sequía es

debida principalmente por la deficiencia de CO2 (Loreto y col., 1995; Mahajan y Tuteja, 2005). Por otra

parte, las plantas superan el déficit hídrico a través del ajuste osmótico que producen determinados

procesos metabólicos. La acumulación de solutos en la célula derivado de estos procesos disminuyen el

potencial osmótico facilitando la resistencia a la deshidratación celular y manteniendo la turgencia de la

hoja (Ramanjulu y col., 2002; Mahajan y Tuteja, 2005). Estudios recientes han mostrado que la

acumulación de azúcares simples como glucosa y fructosa permiten un aumento en la actividad invertasa

en hojas de plantas sometidas al estrés por déficit hídrico (Pinhero y col., 2011).


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1.3.4.- Respuesta metabólica al estrés por salinidad, bajas temperaturas y sequía.

La amplia diversidad metabólica en plantas es producto de los continuos procesos de evolución.

En la actualidad, se conocen más de 200.000 metabolitos secundarios que participan en gran número de

funciones. Las condiciones ambientales afectan el crecimiento de la planta, y el metabolismo está

profundamente involucrado en la señalización y en la regulación fisiológica. El estrés abiótico afecta a la

biosíntesis, acumulación, transporte y almacenamiento de los metabolitos primarios y secundarios (Fraire

y Balderas, 2013). Uno de los mecanismos de defensa de las plantas frente al estrés abiótico es la

producción y acumulación de solutos compatibles. Entre los osmoprotectores de bajo peso molecular

producidos encontramos amino ácidos (asparagina, prolina y serina), aminas (poliaminas y glicinbetaina),

y ácido γ-amino butírico (GABA). Además, otros azúcares son producidos como: fructosa, sacarosa,

trehalosa, rafinosa, y polioles (myo-inositol, D-pinitol) (Krasensky y Jonak, 2012; Banu y col., 2010) y

otros grupos de antioxidantes como glutatión (GSH) y ascorbato que son acumulados en respuesta a

estrés oxidativo (Shabrawi y col., 2010; Phang y col., 2008).

Recientes estudios han mostrado que algunos solutos compatibles han sido conservados a lo largo de la

evolución (bacterias, plantas y algas) en respuesta a sequía. Análisis metabólicos con cromatografía de

gases-espectrometría de masas (CG-MS) confirman que el musgo Physcomitrella patens presenta una

acumulación de solutos compatibles en respuesta a sequía. En este estudio un grupo determinado de

metabolitos (maltitol, L-prolina, maltosa, isomaltosa y acido butírico) son diferencialmente afectados, y

presentan características similares a los reportados previamente en plantas de Arabidopsis sometidas a

déficit hídrico (Erxleben y col., 2012).

En respuesta a salinidad importantes rutas metabólicas están implicadas. Perfiles metabólicos

realizados en plantas de tabaco sometidas a varios tratamientos de salinidad (50mM de NaCl) han

mostrado una acumulación de sacarosa y fructosa vía gluconeogénesis en periodos cortos de

tratamiento. Sin embargo, a mayor concentración de sal (500mM de NaCl) durante periodos más largos


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(días) los niveles de prolina, sacarosa y a su vez de glucosa y fructosa son elevados demostrando, que la

ruta de biosíntesis de azúcares y prolina son mecanismos metabólicos producidos en la respuesta a

salinidad en periodos de tiempo (cortos-lagos) (Zhang y col., 2001; Fraire y Balderas, 2013). Estos

estudios demuestran que la respuesta metabólica al estrés salino es variable y depende del género,

especie y cultivar.

1.3.5.- Respuesta metabólica al estrés por salinidad, bajas temperaturas y sequía en tomate.

En tomate, la respuesta metabólica frente al estrés abiótico involucra una serie de adaptaciones

bioquímicas y fisiológicas, que ayudan a mitigar el efecto causado por el estrés. Diferentes compuestos

(prolina, azúcares solubles, azúcares alcoholes y compuestos de amonio cuaternario) son acumulados en

respuesta a sequía, salinidad y bajas temperaturas (Parvanova y col 2004; Gong y col., 2010). La

mayoría de estos, están involucrados en el metabolismo secundario y presentan algunas diferencias y

conexiones entre el los diferentes tipos de estrés abiótico (Gong y col., 2010).

Estudios realizados en plantas transgénicas de tabaco que sobreexpresan el gen ERD15 de S.

pinennelli han mostrado un incremento en la tolerancia a bajas temperaturas y sequía. Estas plantas

presentan un aumento en los contenidos de prolina y azúcares solubles. Además, muestran cambios en

el contenido de malondialdehido (MDA) un biomarcador del estrés oxidativo indicando que la sobre-

expresión del SpERD15 en tabaco confiere una acumulación de solutos compatibles y aumenta la

estabilidad de membrana bajos diferentes tipos de estrés abiótico (Ziaf y col., 2011). Por otra parte, la

sobre-expresión de un FT zinc finger de tomate ZF2 incrementa los niveles de expresión de un grupo de

metabolitos secundarios involucrados en la biosíntesis de poliaminas, alcaloides y compuestos fenólicos

son cruciales para la adaptación a condiciones medioambientales, ya que mantienen la actividad

fotosintéticas y biosíntesis/señalización de hormonas (Hichri y col., 2014). Por último, Albacete y col.,

(2014), ha descrito que la interacción entre el metabolismo de la sacarosa y los factores hormonales


20

(CKs, ABA, GA3 y etileno) es un factor clave en el esfuerzo fisiológico de la planta para mantener la

producción en condiciones de salinidad.

1.4.- REGULACIÓN DE LA EXPRESIÓN GÉNICA.

En organismos multicelulares, todas las células contienen en su secuencia de ADN la información

necesaria para hacer miles de proteínas diferentes y moléculas de ARN. Sin embargo, el patrón de

expresión de cada una de estas células puede ser muy distinto, debido a que pueden cambiar su modelo

de expresión en respuesta a cambios medioambientales, así como a señales de otras células. En

eucariotas, la expresión génica puede ser controlada a diferentes niveles o estados (Slater y col., 2008;

Alberts y col., 2008). Estos son clasificados en: i) remodelación de la cromatina, ii) regulación

transcripcional, en la que intervienen proteínas específicas denominadas factores de transcripción

generales, específicos y cofactores, iii) modificación nuclear de RNA, splicing y transporte, iv) traducción,

v) modificaciones post-traduccionales que afectan a la estructura y función de la proteína, vi) localización

de la proteína, vii) proteínas de recambio, y viii) recambio del RNA citoplasmático.

1.4.1.- Regulación de la transcripción.

El control transcripcional es uno de los mecanismos de regulación más importantes en todos los

organismos. La regulación de la expresión génica a nivel transcripcional controla muchos de los procesos

biológicos como: progresión a través del ciclo celular, balance metabólico y fisiológico y respuesta al

medio ambiente (Riechmann y col., 2000).

La transcripción de un gen inicia con la formación de un complejo de iniciación de la transcripción,

llamado complejo de pre-iniciación PIC (Pre-Initiation Complex). Este complejo se forma tras la unión de

la RNA- polimerasa II y el complejo FTIID (Factor de transcripción II D). El complejo FTIID está

compuesto por la proteína de unión a la caja TATA (TBP: TATA-box Binding Protein) y un número de


21

factores asociados y a este complejo de pre-iniciación se unen además otros factores transcripcionales

que interaccionan con secuencias específicas del promotor, llamados elementos reguladores en cis -

(CRE: Cis- Regulatory Elements). Estos elementos son secuencias cortas (5-10 pb) moduladas por otros

factores de transcripción (FTs) denominados co-reguladores ó co-factores que regulan la transcripción

interaccionando con el PIC o con los reguladores específicos que se unen al DNA, y pueden funcionar

como remodeladores de la cromatina (Jones y col., 2013)-

1.5.- FACTORES DE TRANSCRIPCIÓN EN PLANTAS.

Los FTs son proteínas que muestran secuencias específicas de unión al ADN, capaces de activar

y/o reprimir la transcripción de genes específicos y por lo tanto responsables de controlar los patrones

temporales y espaciales de la transcripción (Riechmann y col., 2000; Udvardi y col., 2007). En plantas, los

FTs son componentes esenciales en la regulación de algunos procesos de importancia agronómica tales

como rendimiento y respuesta a estrés biótico o abiótico (Hernando-Amado y col., 2012).

Un factor de transcripción típico de plantas contiene, con alguna excepción, una región de unión al

DNA, un sitio de oligomerización, un dominio de regulación de la transcripción y un dominio de

localización nuclear. Estos cuatro dominios estructurales son esenciales para su función (Liu y col., 1999;

Hernando-Amado y col., 2012):

i) Dominio de unión al DNA. Encargado de interactuar con los elementos reguladores en cis de

los promotores de genes diana y puede activar o reprimir su expresión. Este tipo de dominios

están muy conservados entre los FTs de una misma familia, hecho que sirve como criterio para

establecer la clasificación de los FTs.

ii) Dominio de localización nuclear (NLS: Nuclear Localization Signal). Este dominio es

necesario para el transporte de los FTs que se sintetizan en el citoplasma hacía el núcleo donde

llevan a cabo su función biológica.


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iii) Dominio de regulación transcripcional. Involucra tanto activadores transcripcionales como

represores, integra señales y resultados de expresión génica.

iv) Dominio de oligomerización. Este dominio interacciona con otros FTs para formar complejos

regulatorios. Las variaciones de oligomerización incrementan la versatilidad de la maquinaria

transcripcional y tiene la capacidad de modular la expresión génica en plantas.

En plantas, se estima que entre un 5-10% del genoma codifica FTs, lo que indica la complejidad

de la regulación transcripcional en estos organismos (Udvardi y col., 2007). El genoma de A. thaliana

codifica alrededor de 1500 FTs lo que corresponde a un 5% de todo su genoma, porcentaje algo mayor

en comparación con otros organismos como: Caenorhabditis elegans y Drosophila melanogaster donde

representan un 4.5 y 3.5% respectivamente (Riechmann y col., 2000). En cuanto al genoma del tomate

(Solanum pimpinellifolium L. línea LA1589), se estima que codifica al menos 998 FTs de 62 familias

diferentes (Cai y col., 2010), lo que corresponde a un 2.8% del genoma y aproximadamente 34.727 genes

(The Tomato Genome Consortium 2012). Los FTs se pueden clasificar en diferentes familias de acuerdo

con su domino de unión a ADN, tanto el número como los miembros de cada familia han ido aumentado

en el curso de la evolución (Pabo y Sauer, 1992; Riechmann y col., 2000). Análisis filogenéticos en

eucariotas han mostrado que algunas familias de FTs presentan mayor expansión en plantas que en

otros eucariotas (Shiu y col., 2005). Existen también algunas familias de FTs exclusivas de plantas como:

AP2/EREBP, NAC, WRKY y DOF (Riechmann y col., 2000; Yanagisawa, 2002a; Moreno-Risueño y col.,

2007).

En la tabla 1.2 y 1.3 se presentan las principales familias de FTs en plantas al igual que en tomate

(S. pimpinellifolium). Las familias de FTs han sido identificadas a partir de análisis de secuencias de FTs

de otras especies previamente identificadas y disponibles en la base de datos de plantas PlantTFDB;

Tomato Transcription Factor Database (Zhang y col., 2010).


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Tabla 1.2. Principales familias de FTs en plantas. Adaptado de Riechmann y Ratcliffe, 2000, Riechmann y col., 2000;

Rueda, 2011.

Familia Nº de genes estimados

en Arabidopsis Funciones de la Familia Dominio de unión al ADN

DOF 41 Germinación de semilla, expresión específica en endospermo y metabolismo del carbono.

Dedo de Zinc estructurado por cuatro cisteínas con un ion Zn2+.

AP2 150 Desarrollo floral, proliferación celular, metabolismo secundario, respuesta a estrés biótico y abiótico, respuesta a ABA y respuesta a etileno.

Región de 68 amino ácidos (aa)con un dominio conservado que forma una posible α-hélice anfipática.

NAC 105 Desarrollo y respuesta a estrés abiótico. Región en el extremo N‐terminal dividido en cinco subdominios.

bHLH 100 Biosíntesis de antocianinas, respuesta la luz, desarrollo floral y estrés abiótico.

Región rica en residuos básicos, próxima a un motivo hélice-lazo-hélice.

bZIP 100 Expresión génica de almacenamiento de semilla, fotomorfogénesis, desarrollo de hoja, desarrollo floral, respuesta a ABA y biosíntesis de giberelinas.

Dos hélices básicas que interaccionan mediante una cremallera de leucinas.

Z-C2H2 85 Desarrollo floral, tiempo de floración, desarrollo de semilla y desarrollo de nódulos de la raíz.

Dedo de zinc mantenido por el enlace de coordinación entre 2 cisteínas y 2 histidinas con un ión Zn2+.

MADS 80 Desarrollo floral, desarrollo de fruto, tiempo de floración y desarrollo de raíz.

Motivo de 57 residuos aproximadamente

que adoptan una conformación de una α‐hélice larga y dos láminas β.

WRKY 75 Respuesta a defensa. Dedo de zinc con la secuencia amino acídica WRKYGQK conservada en el

extremo N‐terminal.

MYB 180 Metabolismo secundario, respuesta a estrés biótico y abiótico, ritmo circadiano.

De una a cuatro repeticiones imperfectas de 52 aminoácidos que adquieren una conformación hélice-giro-hélice.

B3

29 Participan en el desarrollo de plantas, crecimiento y maduración de semilla. Respuesta a fitohormonas, ABA y auxinas.

Secuencia conservada de 120 aa en el extremo C-terminal.

GRAS 47 Desarrollo y crecimiento de la planta, señalización de GA, formación de meristemos y transducción de señales por luz.

Motivo pentapartito formado por un heptámero de leucinas, el motivo VHIID, otro heptámero de leucinas, el motivo PFYRE y el motivo SAW.

TCP 21 Involucrados en el control del desarrollo de la planta, formación órganos.

Región de 59 aa que adopta la estructura hélice-lazo-hélice.

YABBY 8 Involucrados en el desarrollo, formación de meristemo apical y floral.

Dedo de Zinc mantenido por el enlace entre 2 cisteínas y un ión Zn2+, seguido de una estructura hélice-lazo-hélice.

GATA 29 Control del desarrollo de diversos tejidos, controlan la maduración celular, implicados en la respuesta a cáncer.

Dedo de Zinc estructurado por cuatro cisteínas con un ión Zn2+ seguido de una región básica.

RING finger 469 Reparación del AND y en la reorganización génica de inmunoglobulinas.

Dedo de zinc mantenido por el enlace de coordinación entre 7 cisteínas y una histidina con dos iones de Zn2+.

NF‐Y 29 Involucrados en la activación de varios genes asociados a enfermedades humanas y apoptosis.

Dominio de unión al motivo CCAAT similar a histonas.

Homeobox 74 Reguladores del desarrollo, respuesta a estímulos ambientales.

Tres o cuatro hélices y un brazo N terminal con 60 residuos.


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Tabla 1.3. Principales familias FTs en tomate. Adaptado de Tomato Transcription Factor Database;

http://planttfdb_v1.cbi.pku.edu.cn:9010/web/index.php?sp=le y Zhang y col., 2010.

Familia Nº de FTs estimados en Tomate

Funciones de la Familia Dominio de unión al ADN Referencia

DOF 23

Participan en la regulación de síntesis de proteínas de reserva, desarrollo de endospermo, metabolismo de carbohidratos mecanismos de defensa , germinación y respuesta a auxinas

Dedo de Zinc estructurado por cuatro cisteínas con un ión Zn2+.

Yanagisawa, 2002b.

AP2 EREBP

84 Respuesta a varios tipos de estrés biótico y abiótico. Procesos del desarrollo y órganos florales.

Región de 70 aa con un dominio conservado que forma una posible α-hélice anfipática.

Riechmann JL y Meyerowitz EM, 1998.

ABI3-VP1 27 Regulación de la respuesta a ABA durante el desarrollo de semilla, tolerancia a la desecación.

Lazarova G y col., 2003.

HB 36 Involucrados en la diferenciación celular y crcimiento.

Interaccionan con el ADN como homodimeros, reconocen dos distintas secuencias pseudopalindromicas de 9 pb.

Sessa G y col., 1997.

ZF-HD 10 Involucrados en el metabolismo del carbono, gen C4 PEPCase.

Dominio de 54 aa en la región N-terminal. Windhovel A y col., 2001.

AS2 13

Participan en el sistema de vernalización de la hoja, así como en el desarrollo de la simetría de la lámina.

Dominio AS2 en el motivo C-terminal, un residuo de glicina conservado y un motivo leucine-zipper-like.

Iwakawa H y col., 2002.

bHLH 58 Involucrados en la respuesta la luz, desarrollo floral y estrés abiótico.

Dominio de 60 aa con dos regiones funcionales, la región básica está localizada en el dominio N-terminal. Región rica en residuos básicos, próxima a un motivo hélice-lazo-hélice.

Toledo-Ortiz y col., 2003.

MYB

48 Regulan el metabolismo secundario, respuesta a estrés biótico y abiótico, ritmo circadiano.

Dominio de 53 aa que adquieren una conformación hélice-giro-hélice.

Stracke R y col., 2001.

ZIM 20 Involucrados en el desarrollo de flor e inflorescencias.

Contiene una estructura modular con un único dedo de Zinc C2-C2.

Nishii A y col., 2000.

AUX-IAA 21 Involucrada en la actividad de las proteínas Aux/IAA.

Presenta cuatro motivos de secuencias de aa conservados I, II, III, IV que presentan distintas funciones.

Reed JW, 2001.

bZIP 47

Expresión génica de almacenamiento de semilla, fotomorfogénesis, desarrollo de hoja, desarrollo floral, respuesta a ABA y biosíntesis de giberelinas.

Dos hélices básicas que interaccionan mediante una cremallera de leucinas.

Schindler U y col., 1992

MYB 36 Responden en el metabolismo secundario, respuesta a estrés biótico y abiótico, ritmo circadiano.

De una a cuatro repeticiones imperfectas de 52 aa que adquieren una conformación hélice-giro-hélice.

Kirik V y Baumlein H, 1996.

NAC 41 Desarrollo y respuesta a estrés abiótico.

Región en el extremo N-terminal dividido en cinco subdominios.

Duval M y col., 2002.

WRKY 52 Defensa de patógenos, senescencia, desarrollo de tricomas.

Dedo de zinc con la secuencia amino acídica WRKYGQK conservada en el

extremo N‐terminal. Eulgem T y col., 2000.

http://planttfdb_v1.cbi.pku.edu.cn:9010/web/index.php?sp=le


25

1.6.- EXPRESIÓN GÉNICA Y REGULACIÓN BAJO ESTRÉS ABIÓTICO.

1.6.1.- Complejidad de la expresión génica y regulación.

Las plantas han desarrollado una serie de mecanismos complejos a múltiples niveles que

incrementan la adaptación a condiciones ambientales adversas. La exposición de plantas a estreses

medioambientales como, la salinidad, la sequía y las bajas temperaturas causan efectos adversos sobre

el crecimiento de la planta y la productividad de los cultivos. Cuando una planta es sometida a un estrés

abiótico, se observa que un gran número de genes cambian sus niveles de expresión como respuesta, lo

que provoca de forma general, cambios a nivel de proteínas y metabolitos (Fernie y col., 2009; Vij y Tyagi,

2007). Análisis transcriptómicos usando tecnología de microarray en plantas modelo como A. thaliana,

Brachypodium distachyon y Medicago truncata (Hirayama y Shinozaki, 2010; Seki y col., 2001; Shinozaki

y col., 2003; Bohert y col., 2006), y otras de interés agronómico como arroz (Rabbani y col 2003), cebada

(Guo y col., 2009), pimiento (Hwang y col., 2005), chopo (Brosche y col., 2005), sorgo (Pratt y col., 2005),

maíz (Hayano-Kanashiro y col., 2009), trigo (Gulick y col., 2005), patata (Vasquez-Robinet y col., 2008) y

tomate (Gong y col., 2010; Ouyang y col., 2007; Sun y col., 2010), han permitido identificar un

determinado grupo de genes inducibles por estrés, al igual que ha facilitado comprender la regulación de

la expresión génica en respuesta a estrés abiótico (Hirayama y Shinozaki, 2010; Shinozaki y col., 2003;

Yamaguchi-Shinozaki y Shinozaki, 2006).

Muchos de los genes que cambian sus niveles de expresión en respuesta a diferentes tipos de

estrés, no solo protegen a la célula frente al estrés por producción de importantes proteínas metabólicas,

también regulan genes para la transducción de señales en respuesta al estrés (Yamaguchi-Shinozaki y

Shinozaki, 2006; Seki y col., 2001). Así, los productos de estos genes han sido clasificados en dos

grupos. El primero incluye genes que codifican proteínas implicadas en el desarrollo de tolerancia a

estrés como; chaperonas, proteínas LEA (Late Embryogenesis Abundant), enzimas clave para la

biosíntesis de osmolitos como: prolina, proteínas antifreezen, proteínas de canales de agua,


26

transportadores de azúcares y prolina, enzimas de detoxificación, enzimas para el metabolismo de ácidos

grasos y proteínas de transferencia de lípidos (Yamaguchi-Shinozaki y Shinozaki, 2006). El segundo

grupo contiene genes que codifican proteínas con función reguladora de la expresión génica y

transducción de señales en respuesta a estrés. Estas incluyen MAP quinasas, fosfatasas, enzimas del

metabolismo de fosfolípidos, varios tipos de factores de transcripción (FTs) y otras moléculas de

señalización. Esto sugiere, que varios mecanismos de regulación transcripcional, funcionan en las vías de

transducción de señales en respuesta a estrés (Hirayama y Shinozaki, 2010; Seki y col., 2001;

Yamaguchi-Shinozaki y Shinozaki, 2006).

El grado de complejidad de la respuesta a estrés abiótico a nivel de control de la expresión génica,

se ha estudiado a partir de los distintos análisis de expresión global realizados tanto en Arabidopsis como

en arroz, en respuesta a sequía, salinidad y bajas temperaturas (Yamaguchi-Shinozaki y Shinozaki,

2006). Estos análisis han permitido identificar más de 300 genes inducibles por estrés. Entre estos más

de la mitad de los genes inducibles por sequía también son inducidos por alta salinidad y/o tratamientos

con ABA indicando un “cross-talk” entre la respuesta a sequía, salinidad y ABA (Seki y col., 2001; Seki y

col., 2002). Por el contrario, alrededor de un 10% de los genes inducidos por sequía lo son por bajas

temperaturas (Yamaguchi-Shinozaki y Shinozaki, 2003). Muchos de estos genes codifican FTs que tienen

la capacidad de controlar grandes grupos de genes implicados directamente en la respuesta a estrés, o

actúan en la biosíntesis de moléculas reguladoras como la fitohormona ácido abscísico ABA (Yamaguchi-

Shinozaki y Shinozaki, 2006; Osakabe y col., 2013).

Estos FTs inducibles por estrés incluyen miembros de las principales familias de FTs (Tabla 2), y

pueden regular varios genes inducibles por estrés de forma conjunta o separada constituyendo redes.

(Shinozaki y col., 2003). Así, estos trabajos indican que existe un alto grado de solapamiento en la

respuesta a estrés, puesto que muchos de los genes están simultáneamente controlados por uno o varios

estreses, poniendo en evidencia que existe un mecanismo regulador común, o que existe una intensa


27

intercomunicación entre las vías que controlan la expresión de la respuesta a deshidratación, bajas

temperaturas y ABA (Shinozaki y col., 2003).

1.6.2.- Factores de transcripción involucrados en la respuesta a estrés abiótico.

La respuesta a estrés abiótico requiere la producción de importantes proteínas metabólicas así

como la síntesis de osmoprotectores y proteínas de regulación que operan en las vías de transducción de

señales como quinasas o FTs (Saibó y col., 2009; Chaves y Oliveira, 2004). Un grupo de genes

controlado por cierto tipo de FTs se conoce como regulon, ya que controlan la respuesta a estrés abiótico

en plantas. Los elementos de regulación de algunos genes que actúan en cis que tienen un perfil de

expresión inducible por estrés y los FTs que afectan a la expresión de estos genes, se ha estudiado

recientemente para conocer más a fondo los mecanismos de regulación en respuesta a estrés abiótico.

Análisis de promotores de genes inducibles por sequía y/o frío han facilitado la identificación de cuatro

sistemas independientes de regulación para la expresión génica en respuesta a estrés (Valliyodan y

Nguyen, 2006; Shinozaki y Yamaguchi-Shinozaki, 2000; Saibo y col., 2009). Dos pertenecen a la vía

dependiente-ABA y los otros dos a la vía independiente-ABA.

1.6.2.1.- Regulon CBF/DREB.

CBF/DREB es un regulon principalmente involucrado en respuesta a bajas temperaturas. Esta

conservado en plantas a lo largo de la evolución, incluyendo plantas no aclimatadas como: tomate y

arroz) (Dobouzet y col., 2003). En Arabidopsis, los genes RD29/COR78/LT78 son inducidos por sequía,

bajas temperaturas y ABA. Análisis de los promotores de estos genes han mostrado que una secuencia

conservada de 9-pb (TACCGACAT) llamada DRE, es un elemento esencial en cis, para regular la

inducción del gen RD29A (RESPONSIVE TO DEHYDRATION 29A) de Arabidopsis en la respuesta

independiente-ABA por deshidratación y bajas temperaturas (Yamaguchi-Shinozaki y Shinozaki, 1994). El

elemento DRE también se encuentra en las regiones promotoras de algunos genes inducibles por sequía


28

y frío. Elementos similares que actúan en cis, llamados C-repeat (CRT) y elementos de respuesta a bajas

temperaturas (LTRE), contienen el motivo A/GCCGAC, que regula los promotores inducibles por frío

(Baker y col., 1994; Thomashow, 1999; Stockinger y col., 1997). Los cDNAs que codifican proteínas DRE-

/CRT-binding, CBF/DREB1 (C-repeat Binding Factor/DRE Binding protein1), y DREB2 fueron aislados

usando el sistema de un hibrido de levadura (Y1H) (Liu y col., 1998). Estas proteínas contienen un

dominio de unión al DNA conservado en las proteínas ERF (ethylene-responsive element-binding factor) y

AP2 (Apetala 2). ERF/AP2 se unen específicamente a la secuencia DRE/CRT y activan la transcripción

de genes inducidos por esta secuencia (Yamaguchi-Shinozaki y Shinozaki, 2006). La expresión de los

genes CBF/DREB1 es inducida por bajas temperaturas y la expresión de los genes DREB2 es inducida

por sequía y alta salinidad. Tanto las proteínas DREB2 y CBF/DREB1 se unen al elemento de respuesta

DRE, CBF/DREB1 a través de la expresión génica en respuesta a bajas temperaturas, mientras que

DREB2 está involucrada en la expresión génica en respuesta a sequía, lo que indica la existencia de un

entrecruzamiento entre la expresión de genes inducidos por frío y sequía a través del elemento DRE/CRT

(Yamaguchi-Shinozaki y Shinozaki, 2005). La sobre-expresión del gen CBF3/DREB1A en Arabidopsis

promueve un incremento en la tolerancia a bajas temperaturas, alta salinidad y sequía (Gilmor y col.,

2000; Kasuga y col., 1999; Liu y col., 1998). Sin embargo, plantas transgénicas de patata, tabaco, trigo y

arroz que sobre-expresan el gen CBF3/DREB1A exhiben un incremento en la tolerancia a sequía (Oh y

col., 2005, 2007; Behnam y col., 2006) (Figura 1.5).

Por otra parte, los genes RD29A y RD29B de Arabidopsis son diferencialmente inducidos bajo

condiciones de estrés y tratamientos por ABA. La región promotora de rd29A contiene como mínimo dos

elementos que actúan en cis, dos elementos DREs y uno ABRE involucrados en la expresión génica

dependiente e independiente-ABA (Yamaguchi-Shinozaki y Shinozaki, 1994). Estudios previos, han

mostrado que el elemento que actúa en cis DRE funciona en la respuesta inicial de AtRD29 a salinidad,

deshidratación y bajas temperaturas y otra región que contiene ABRE es necesaria para la inducción por

ABA (Yamaguchi-Shinozaki y Shinozaki, 1994). Muchos de los genes inducibles por bajas temperaturas

también responden a estrés inducido por ABA, sequía y altas temperaturas. Las regiones promotoras de


29

estos genes contienen los motivos DRE y ABRE, necesarios para la expresión de genes inducibles por

estrés abiótico (Yamaguchi-Shinozaki y Shinozaki, 1994).

1.6.2.2.- Regulon NAC y ZF-HD.

Las proteínas NAC son FTs específicos de plantas cuya función está involucrada en el desarrollo

de la planta y en la respuesta a estrés biótico y/o abiótico (Nakashima y col., 2012). Estas proteínas

regulan la expresión génica del gen ERD1 (Early Responsive to Dehydration 1) en Arabidopsis (Tran y

col., 2007; Kiyosue y col., 1993). Los genes de la familia ERD fueron identificados colectivamente como

genes que se inducen rápidamente por deshidratación (Kyosue y col., 1994). Análisis del promotor del

gen ERD1 revelan que los FTs pertenecientes a la familia NAC y ZF-HD (zinc finger homeodomain) son

activadores esenciales del gen ERD1 (Tran y col., 2007). La sobre-expresión de los genes NAC en

Arabidopsis aumenta la tolerancia a sequía sin activación del gen ERD1, sugiriendo que la interacción de

otros factores pueden ser necesarios para el control de la expresión de ERD1 bajo condiciones de estrés

(Tan y col., 2004). Estudios realizados por Hu y colaboradores (2006) han reportado que la sobre-

expresión del gen de respuesta a estrés SNAC1 incrementa la tolerancia a sequía y salinidad en arroz.

SNAC1 aumenta la tolerancia a sequía en plantas transgénicas de arroz durante la etapa reproductiva

bajo condiciones severas de sequía sin afectar a la producción. Por otra parte, la sobre-expresión del gen

de respuesta a estrés SNAC2 (OsNAC6) en arroz incrementa la tolerancia a sequía y salinidad (Hu y col.,

2008). SNAC1, también induce la expresión de genes que codifican proteínas relacionadas con el ajuste

osmótico y la estabilidad de la membrana celular, regulando así la respuesta a estrés (Hu y col., 2006).

1.6.2.3.- Regulon AREB / ABF.

Los genes inducibles por ABA tienen una secuencia conservada en las regiones promotoras, un

elemento que actúa en cis llamado ABRE (ABA-responsive element; PyACGTGGC) responsable de la

expresión génica en respuesta a ABA (Yamaguchi-Shinozaki y Shinozaki, 2005; Yamaguchi-Shinozaki y


30

Shinozaki, 2006). El elemento de respuesta ABRE, es reconocido por los FTs AREB/ABF, considerados

los principales reguladores de la expresión de genes dependientes de ABA (Figura 1.5). Las proteínas

AREB/ABF son activadas por fosforilación gracias a dos tipos de enzimas quinasas: SnRK2s (SNF-related

protein kinase subfamily 2) y CDPKs (calcium-dependent protein kinases) (Zhao y col., 2011). Por otra

parte, la sobre-expresión del factor ABF3 en Arabidopsis produce un aumento de la tolerancia a la

deshidratación, debido que se produce un incremento en la expresión de diferentes tipos de genes

relacionados con la ruta de respuesta a estrés mediada por ABA, como los genes que codifican

fosfatasas tipo ABI1, ABI2 y genes de tipo LEA (Kang y col., 2002). Asimismo, siguiendo una estrategia

similar, la sobre-expresión del gen ABF3 en una planta de interés agronómico como arroz, produce un

incremento significativo de la tolerancia a estrés por déficit hídrico (Oh y col., 2005).

1.6.2.4.- Regulon MYB / MYC.

Las proteínas MYC (Myelocytomatosis oncogene) y MYB (Myoblastosis oncogene) son otros

importantes reguladores transcripcionales, ya que son activadores de uno de los sistemas de regulación

de la vía dependiente-ABA (Abe y col., 2003). La expresión del gen inducible por sequía RD22

(RESPONSIVE TO DEHYDRATION 22) de Arabidopsis es mediado por ABA (Abe y col., 1997) (Figura

1.5), y su región promotora contiene los sitios de reconocimiento en cis MYC (CANNTG) y MYB

(C/TAACNA/G) (Abe y col., 1997). Los FTs, AtMYC2 (rd22BP1) y AtMYB2 se unen a los elementos en cis

del promotor AtRD22 y activan cooperativamente su expresión. Estos dos FTs son sintetizados por

acumulación endógena de ABA. Plantas transgénicas que sobre-expresan AtMYC2 y AtMYB2 presentan

alta sensibilidad a ABA y tolerancia a estrés osmótico (Abe y col., 2003). Además, la sobreexpresión de

AtMYB15 aumenta la tolerancia a estrés hídrico en Arabidopsis. Los genes MYC/MYB también han sido

sobre-expresados en especies de interés agronómico. Un ejemplo es el caso del gen StMYB1R-1 en

patata que produce un aumento en la tolerancia a sequía sin afectar su productividad (Shin y col., 2011).

Igualmente, la sobre-expresión en Solanaceas del gen MYB4 de arroz produce aumento de la tolerancia


31

al déficit hídrico (Vannini y col., 2007). Estudios transcriptómicos de plantas transgénicas que sobre-

expresan genes de tipo MYB/MYC revelan que no solo los genes relacionados al estrés por ABA están

diferencialmente regulados, también los relacionados con el ácido jasmonico (Figura 1.5). Estos datos

indican que existe un “cross talk” entre las rutas de respuesta a estrés biótico y abiótico (Abe y col.,

2003).

Figura 1.5. Esquema que representa la red transcripcional de respuesta a estrés abiótico. Los FTs se muestran en

óvalos. Los triángulos pequeños corresponden a modificaciones post-traduccionales. Los cuadrados azules con interrogante

representan FTs MYC ICE-1 putativos que pueden activar CBF1/DREB1B y CBF2/DRE1C. Las cajas verdes representan

elementos reguladores en cis presentes en los genes de respuesta a estrés. Las cajas verdes con el interrogante

representan los elementos en cis putativos sobre los promotores de los genes de respuesta-estrés. Las líneas discontinuas

negras de SIZ1 a HOS1 representan la competición por el sitio sobre el TF ICE 1. CBF4/DREB1D es un factor que se une al

elemento-cis DRE dependiente de ABA. Adaptado de Saibo y col., 2009.


32

1.6.3.- Expresión génica en respuesta a estrés abiótico en tomate.

De acuerdo con lo descrito anteriormente diferentes análisis transcriptómicos en especies modelo

como Arabidopsis y en cultivos de interés agronómico como arroz, han dado a conocer los distintos

mecanismos de respuesta a estrés. El uso de aproximaciones de microarray y recientemente las nuevas

metodologías de secuenciación (NGS) han proporcionado nuevos mecanismos de regulación, que

permiten comprender más a fondo los distintos mecanismos de respuesta a estrés abiótico en el cultivo

de tomate (Duque y col., 2013; Gong y col., 2010). Estudios transcriptómicos en tomate, con LIs de una

población de Solanum pennelli tolerante a sequía y S. lycopersicum cultivar “M82” sensible, han sido

estudiados bajo condiciones de déficit hídrico. En este estudio se han identificado alrededor de 1400

genes de respuesta a sequía, la mayoría de estos pertenecen a genotipos tolerantes que codifican FTs

(Gong y col., 2010).

Figura 1.6. Vías bioquímicas afectadas por el estrés por sequía en genotipos de tomate. Las flechas marrones y negras

corresponden a las vías responsables de la biosíntesis o degradación de diversos metabolitos incluyendo: secundarios,

transporte de electrones, amino ácidos, hormonas, compuestos aromáticos, componentes de la estructura celular, azúcares,

ácidos grasos y lípidos. Adaptado de Gong y col., 2010.


33

Miembros de las principales familias de FTs en Arabidopsis como: AP2/EREBP, bZIP y NAC,

bHLH, HSF, MYB y reguladores GRAS (Tabla 2), también han sido identificados en tomate (Tabla 3).

Estos FTs juegan un papel importante en la tolerancia a diferentes estreses medioambientales (Zuo y

col., 2007; Zhang y col., 2009; Trujillo y col. 2008; Zheng y col., 2009) lo que evidencia la complicada red

de regulación transcripcional en respuesta al déficit hídrico en tomate (Gong y col., 2010).

Estudios de expresión en tomate, han permitido identificar un grupo de genes que aumentan sus

niveles de expresión en respuesta a salinidad. Alrededor de 201 genes no redundantes han sido

identificados en condiciones severas de salinidad en S. lycopersicum (Ouyang y col., 2007). Estos

mismos genes se han observado en análisis transcriptómicos previos realizados en Arabidopsis en

respuesta a este estrés. Diferentes FTs entre los que se encuentran miembros de la familia de proteínas

NAC, que juegan un papel importante de “cross-linking” en diferentes vías de señalización, de las familias

de FTs EREBP, zinc-finger, WRKY y HSF que son de factores asociados a la tolerancia a salinidad y han

sido observados en distintos estudios transcriptómicos tanto en tomate como Arabidopsis. Por último, se

ha descrito genes involucrados en las vías metabólicas de la reducción y fijación de nitrógeno y la

biosíntesis de metionina que son significativamente afectados por este estrés. Esto indica que, la

salinidad tiene un impacto significativo en la reducción y fijación del nitrógeno en el cultivo de tomate

(Ouyang y col., 2007).

Otros análisis de expresión entre LIs de Solanum habrochaites que es una especie tolerante a

bajas temperaturas, y el cultivar S. lycopersicum que es sensible, han permitido la identificación de

alrededor de 1500 genes que aumentan su expresión en respuesta a bajas temperaturas, en los dos

genotipos. Esto sugiere la existencia de un mecanismo de respuesta común a las bajas temperaturas

entre genotipos tolerantes y sensibles de tomate (Liu y col., 2012). Además, gran número de FTs son

regulados en respuesta a frío en al menos un genotipo de tomate. Dentro de estos FTs se encuentran

miembros de las familias MYB, NAC, WRKY, AP2/ERBP, HSF, bHLH, bZIP y Zinc finger que presentan


34

expresión diferencial de sus transcritos entre genotipos tolerantes y sensibles. Esto indica que la

regulación de estos FTs podría ser una respuesta común a varios estreses abióticos en tomate (Liu y col.,

2012).

1.6.4.- Factores de transcripción involucrados en la respuesta estrés abiótico en tomate.

Durante los últimos años se han realizado diferentes estudios para identificar y caracterizar la

función que realizan los distintos tipos de FTs en respuesta a salinidad, sequía y bajas temperaturas.

Anteriormente se ha mencionado que en Arabidopsis existen distintos FTs así como elementos en cis que

participan en la respuesta a estrés abiótico. Estos, han sido divididos en dos grupos: aquellos que

participan en la vía de respuesta controlada por ABA o los que participan en rutas independientes

(Yamaguchi-Shinozaki y Shinozaki, 2006). Estudios transcriptómicos realizados en tomate bajo diferentes

tipos de estrés abiótico como: salinidad, sequía y bajas temperaturas, han permitido la identificación de

un gran número de genes inducidos o reprimidos por los mismos. Sin embargo, se desconocen muchas

de sus funciones en el control de la expresión génica en respuesta a estrés abiótico en tomate.

1.6.4.1.- Regulon CBF/DREB.

La expresión del FT DREB1A aumenta la expresión de genes inducibles por estrés. Por tanto

aumenta la tolerancia a la salinidad y al estrés hídrico en Arabidopsis y tomate (Liu y Zhu, 1998;

Nakashima y Yamaguchi-Shinozaki, 2006) (Figura 1.7). Por otra parte, la sobre-expresión del CBF1

induce la expresión del gen COR (COLD REGULATED) e incrementa la tolerancia a la congelación en

Arabidopsis (Jaglo-Ottosen y col., 1998). En tomate, plantas que sobre-expresan el gen CBF1, presentan

mayor tolerancia al déficit hídrico que plantas WT (Hsieh y col., 2002). Sin embargo, estas plantas

exhiben un retardo en el crecimiento, reducción del tamaño del fruto, número de semillas y peso fresco.

Además, contienen altos niveles de prolina en comparación con plantas WT bajo condiciones normales y


35

de déficit hídrico (Hsieh y col., 2002). Un efecto similar es observado cuando se sobre-expresa el gen

CBF1 de Arabidopsis bajo el control del promotor ABRC1 de cebada inducible por estrés en tomate.

Plantas que sobre-expresan el gen AtCBF1 presentan un incremento en la tolerancia al estrés por chilling,

déficit hídrico y salinidad cuando estas plantas son comparadas con plantas WT (Lee y col., 2003).

1.6.4.2.- Regulon NAC y ZF-HD.

Trabajos realizados por Hichri y col., 2014, reportaron el primer represor tipo zinc finger en tomate

SlZF2. El ortólogo en Arabidopsis aumenta la sensibilidad a salinidad, pero en tomate SlZF2 retarda la

senescencia y aumenta la tolerancia a estrés salino ya que mantiene la actividad fotosintética e

incrementa la biosíntesis de poliaminas. SlZF2 está involucrado en la biosíntesis y señalización de ABA

debido a que es rápidamente inducido y como consecuencia tomate transgénicos 35S::SlZF2 acumulan

más ABA que tomates WT. Además, análisis transcriptómicos han mostrado que SlZF2 incrementa y

reduce la expresión de involucrados en el metabolismo secundario que son cruciales para la adaptación

de la planta a condiciones medioambientales (Figura 1.7).

1.6.4.3.- Regulon AREB / ABF.

Estudios recientes han mostrado que la clase de FTs bZIP juega un rol importante en la respuesta

a estrés abiótico en el género Solanum. Las proteínas bZIP son un grupo específico de FTs que se unen

al elemento de respuesta-ABA (AREBs) (Uno y col., 2000) o ABFs (Choi y col., 2000). El cDNA que

codifica estos bZIP ha sido aislado de especies cultivadas y silvestres de tomate y se ha demostrado que

su expresión es inducida por ABA y diferentes estreses abióticos incluyendo salinidad, sequía y frío

(Yañez y col., 2009). Además, la expresión del cDNA de SlAREB1 en tabaco y tomate, ha mostrado que

regula la transcripción de genes de respuesta a estrés como: RD29A, LEA, ERD10B y TAS14, el factor de

transcripción PHI-2 y el gen que codifica la enzima trehalosa-6-fosfato fosfatasa (Yañez y col., 2009).

Recientes trabajos han mostrado la posibilidad de desarrollar especies de tomate tolerantes al déficit


36

hídrico usando el factor ABRE-binding, ABF4 de Arabidopsis. Plantas transgénicas de tomate que sobre-

expresan el gen ABF4/AREB2 exhiben tolerancia a la sequía (Na, 2005), efecto que se atribuye a una

disminución de la pérdida de agua por unidad de área de la hoja. Otros estudios han mostrado que la

sobre-expresión de SIAREB incrementa la tolerancia a salinidad y sequía en tomate (Hsieh y col., 2010;

Orellana y col., 2010) (Figura 1.7). Plantas transgénicas de tomate que sobre-expresan el gen SIAREB

regulan la expresión de genes de respuesta a estrés como AtRD29A, AtCOR47 y SlC17 dehidrin bajo

diferentes tipos de estrés abiótico (Pandey y col., 2011).

1.6.4.4.- Regulon MYB / MYC.

En cuanto a las proteínas MYB/MYC, la sobre-expresión del gen Osmyb4 un factor transcripcional

de la familia MYB aumenta la tolerancia a déficit hídrico y a enfermedades virales en tomate (Vannini y

col., 2007) (Figura 1.7). Estudios realizados por Pan y colaboradores (2010) han mostraron que la sobre-

expresión en tomate transgénico de un FTs de respuesta a etileno (ERFs) que presenta delecciones en el

dominio de represión anfifilic ERF-asociado (SlERF3ΔRD), conducen la reducción de los niveles de

peroxidación de lípidos de membrana y aumentan la tolerancia a estrés salino. El crecimiento de tomates

transgénicos SlERF3ΔRD bajo estrés salino produce un aumento en el número flores, frutos y semillas

en comparación con plantas WT.


37

Figura 1.7. Esquema que representa los factores de transcripción (FTs) y elementos en cis implicados en la

respuesta a la sequía, la salinidad y las bajas temperaturas en tomate. Varios FTs como AREB, MYB, NAC y bZIP en

respuesta a la sequía, la salinidad y las bajas temperaturas median la influencia sobre genes en la vía dependiente de ABA

en tomate. FTs como NAC, ZFHD y AP2 en la vía independiente de ABA permiten la expresión de genes involucrados en la

respuesta a salinidad y sequía en tomate. Los óvalos gris/lila corresponden a FTs que han sido identificados en la ruta

dependiente e independiente de ABA. Las cajas azules representan elementos reguladores en cis presentes en los genes de

respuesta a estrés. Adaptado de Gong y col., 2010.


38

1.7.- FACTORES DE TRANSCRIPCIÓN DE TIPO DOF.

Las proteínas DOF (DNA binding with One Finger) son una familia de FTs especifica de plantas

(Yangisawa, 2002). Se caracterizan por tener un dominio de unión al ADN altamente conservado de 52

aa en la región N-terminal. Este dominio contiene cuatro residuos cisteínas que unen un átomo de zinc

(Zn2+), estructurando un único dedo Zn2+ C2/C2 (Yanagisawa y Schmidt, 1999) (Figura 1.8).

Figura 1.8. Representación esquemática de la estructura del domino DOF. Dominio de 52 aa altamente conservado

(Rojo), cuatro residuos de cisteína coordinan un átomo de Zn2+. Adaptado de Noguero y col., 2013.

Este dominio de unión a ADN es esencial para reconocer los motivos en cis que contienen la

secuencias 5´-(T/A) AAAG-3´ en los promotores de los genes diana (Yanagisawa y Schmidt, 1999). Las

proteínas DOF pueden unirse a promotores que contienen un único motivo 5´-AAAG-3´ ó más

frecuentemente a repeticiones del mismo motivo (Yanagisawa, 2000). Además, contienen un dominio

conservado de localización nuclear bipartito (Krebs y col., 2010), sin embargo, las regiones N- y C-

terminal son muy variables y su función se desconoce. En algunos casos concretos, el dominio C-terminal

se ha visto implicado en la interacción con otras proteínas y con otros elementos regulatorios (Lijavetzky y

col., 2003; Moreno-Risueño y col., 2007; Yanagisawa, 2001).


39

Los FTs de tipo DOF se originaron a partir de un ancestro común en el alga unicelular

Chlamydomonas reinhardtii (CrDof1) y a través de recurrentes eventos de duplicación en el curso de la

evolución se fueron formando los diferentes grupos taxonómicos de plantas vasculares (Moreno-Risueño

y col., 2007). Los genes DOF se clasifican en familias de diferentes tamaños dentro de especies. En

gimnospermas y plantas inferiores como: Selaginella moellendorffii y P. patens se han encontrado 8 y 9

genes DOF respectivamente (Moreno-Risueño y col., 2007). Entre las angiospermas, se han identificado

36 genes DOF en A. thaliana (Yanagisawa y Schmidt, 1999), 27 genes en Brachypodium distachyon

(Hernando-Amado y col., 2012), 30 en arroz (Oryza sativa L.) (Gaur y col., 2011) y 41 en chopo (Populus

trichocarpa) (Yang y Tuskan, 2006), indicando recientes eventos de duplicación en plantas superiores

(Moreno-Risueño y col., 2007).

Figura 1.9. Árbol filogenético de los FTs de la familia DOF en Arabidopsis. El árbol fue realizado por el método neighbor-

joining después del alineamiento de secuencias aa de los 36 genes Arabidopsis. Los MCOGs (Major Clusters of Orthologous

Genes) deducidos se muestran en diferentes colores: A=naranja, B=azul, C=rojo, D=verde. La barra de escala corresponde a

una estimación de 0.05 sustituciones de aa por sitio (Lijavetzky y col., 2003)


40

Diferentes estrategias se han usado para estudiar las relaciones filogenéticas entre miembros de

la familia de genes DOF. Análisis filogenéticos realizados con los 36 FTs de Arabidopsis, han mostrado

que se organizan en cuatro grupos o subfamilias (A, B, C, D) (Lijavetzky y col., 2003) (Figura 1.9).

Los FTs de la familia DOF presentan diversidad de funciones y juegan un rol importante en

muchos procesos fisiológicos exclusivos de plantas como: asimilación del nitrógeno y fijación del carbono

fotosintético (Yanagisawa y Sheen, 1998), germinación de semilla (Papi y col., 2000), metabolismo

secundario (Skirycz y col., 2006), desarrollo vascular (Hir y Bellini, 2013; Guo y col., 2009), control de la

floración y respuesta al fotoperiodo (Imaizumi y col., 2005; Iwamoto y col., 2009) (Figura 1.10). Los genes

DOF se encuentran involucrados en la regulación/ajuste del metabolismo bajo diferentes señales

medioambientales que aún no han sido descritas. Dentro de los procesos que regulan los FTs DOF, el

desarrollo de semilla, la diferenciación de tejido y la regulación del metabolismo son considerados los

más relevantes y se describen a continuación:

1.7.1.- Diferenciación de tejido.

Anteriormente, hemos mencionado que los FTs de tipo DOF desempeñan un importante papel en

diversos procesos fisiológicos de la planta. En cuanto a la diferenciación de tejido, un ejemplo es el gen

ZmDOF1 que regula el desarrollo de polen por represión de genes que controlan este proceso (Chen y

col., 2012; Yanagisawa y col., 2000). Otro factor de tipo DOF como OBP1 (OBF- binding factor-1) de A.

thaliana, está implicado en el control de la división celular y promueve la reentrada del ciclo celular

(Skirycz y col., 2008). Además, el gen AtOBP2, juega un importante rol en la respuesta a estrés biótico,

posiblemente como parte de la vía de regulación en el metabolismo de GS en Arabidopsis (Skirycz y col.,

2006) (Figura 1.10). Así mismo, los FTs de tipo DOF juegan un papel importante en la regulación del

desarrollo del tejido vascular. AtDOF5.6/HCA2, actúa como un regulador positivo en la formación del

cambium interfascicular durante el desarrollo del tejido vascular en A.thaliana (Guo y col., 2009). El

desarrollo vascular en Arabidopsis también es susceptible a la regulación por dos FTs de tipo DOF


41

AtDOF2.4 y AtDOF5.8, la cual son diferencialmente expresados. El gen AtDof2.4 es detectado en las

células del procambium y AtDOF5.8 en las células de la hoja y en el tejido vascular de los capullos

florales (Konishi y col., 2007). Numerosos estudios han mostrado que los FTs de tipo DOF están

implicados en la regulación del desarrollo de células de guarda. Trabajos realizados por Cominelli y col,

(2011), han mostrado que tan solo un promotor mínimo del gen Myb60 es suficiente para conferir una

actividad específica de las células de guarda, además de ser considerado como un gen candidato para la

expresión génica de las células de guarda. StDof1 también ha sido identificado como candidato para la

activación de la expresión especifica de células de las guarda e interacciona y activa la expresión del gen

especifico de células de la guarda KST1 (Plesch y col., 2000).

1.7.2.- Desarrollo de semilla.

En relación con el desarrollo de semilla varios FTs de tipo DOF están implicados en la regulación y

síntesis de proteínas de reserva y proteínas expresadas durante el desarrollo del endospermo en

cereales (Noguero y col., 2013). El primer FT de la familia DOF implicado en la regulación de los genes

que codifican SSPs en semillas de maíz fue PBF (Prolamin Binding Factor) (Vicente-Carbajosa y col.,

1997). Los PBFs de maíz comparten un alto grado de similaridad de secuencia con los descritos en arroz

(Yamamoto y col., 2006), trigo (WPBF; Dong y col., 2007), cebada (BPBF; Mena y col., 1998) mijo

(FMPBF). Todos estos presentan perfiles similares de expresión y están restringidos para el desarrollo del

endospermo apareciendo al principio de este proceso. Otros genes DOF tienen un rol principal en el

control de la germinación de semilla (Figura 1.10). En Arabidopsis, dos genes parálogos DAG1 y DAG2

(DOF Affecting Germination) tienen papeles opuestos durante la germinación. El mutante dga1 reduce la

dormacia (Papi y col., 2000), sin embargo, el mutante dga2 incrementa el periodo de dormancia y

disminuye su respuesta a giberelinas cuando es mutado (Gualberti y col., 2002; Papi y col., 2000; 2002).

Otro regulador negativo de la germinación de Arabidopsis perteneciente a esta familia de FTs es AtDOF6.

La sobre-expresión de AtDOF6 retarda la germinación e induce genes de la ruta biosintética de ABA y


42

aumenta la sensibilidad de las semillas durmientes a esta hormona. Además, se ha demostrado que

interacciona con TCP14 un activador de la germinación (Rueda-Romero y col., 2012).

1.7.3.- Regulación del metabolismo.

Los FTs de la familia DOF intervienen en muchos aspectos metabólicos en respuesta a distintos

cambios medioambientales. Algunos genes DOF se encuentran involucrados en la regulación/ajuste del

metabolismo bajo diferentes señales medioambientales que aún no han sido descritas. El primer gen

DOF identificado en maíz (ZmDOF1), actúa como un regulador transcripcional en el metabolismo del

carbono del gen C4 Fosfo-Enol-Piruvato-Carboxilasa (PEPC) en repuesta a la luz (Yanagisawa y Sheen,

1998; Yanagisawa, 2002). Además, ZmDOF1 interacciona con la proteína Dof2 de maíz y con el grupo de

proteínas HMG (High Mobility Group), que pueden actuar como chaperonas que facilitan la unión del

factor DOF al ADN (Yanagisawa y col., 1997). El efecto positivo de DOF1 sobre la producción de

esqueletos de carbono se ha estado explorando para estudios de ingeniería metabólica en A.thaliana

(Yanagisawa y col., 2004). La sobre-expresión de FT, en Arabidopsis y arroz, promueve un aumento de la

expresión de genes que codifican enzimas implicadas en la biosíntesis de carbohidratos y amino ácidos

(Yanagisava et al. 2004; Kurai et al. 2011). Este hecho sugiere que este gen modifica los metabolismos

del C y del N. Por otra parte la sobre-expresión de OsDOF25 en Arabidopsis altera el metabolismo del

carbono y nitrógeno y resulta en un incremento de la concentración de aa (Santos y col., 2012). Además,

aumenta la expresión de transportadores de amonio de baja y alta afinidad (AMTs). Un incremento en los

contenidos totales de amino-N y piruvato quinasa (PK1 y PK2), fosfoenol piruvato carboxilasa (PEPC1 y

PEPC2), NADP-dependiente y NAD-dependiente de isocitrato deshidrogenasa se han observado.

Además, se ha detectado un aumento en los niveles de expresión y actividad de la enzima glutamato

deshidrogenasa (GDH) (Figura 1.10).

Los genes DOF también son importantes moduladores de las respuesta de plantas tanto a

estreses bióticos como abióticos. En este contexto muchos son inducidos por fitohormonas y/o patógenos


43

y participan en la regulación de fitoalexinas (Skirycz y col., 2007; Nakano y col., 2006), como es el caso

del gen OBP2, este juega un rol importante en la respuesta a estrés biótico, posiblemente como parte de

la ruta que regula el metabolismo del indol glucosinato (GS) en Arabidopsis (Skirycz y col., 2006) (Figura

1.10). Por todo lo anterior, las proteínas DOF actúan como activadores o represores en el control de la

expresión génica de numerosos genes y diferentes procesos fisiológicos de plantas (Yanagisawa y

Sheen., 1998; Díaz y col., 2002).

Figura1.10. Ejemplos de algunos procesos regulados por FTs de tipo DOF. Representación de la regulación de la expresión

génica de las proteínas DOF, durante el desarrollo de semilla, diferenciación del tejido y regulación del metabolismo. Las líneas

con un punto en el extremo (─●) conectan los procesos. El papel de los TFs en la regulación de la transcripción se indica con

flechas rojas ( ) si es de activación o con flechas truncadas en rojo ( ) si es de represión. Se indican las interacciones

descritas con otros TFs implicados en la regulación transcripcional en semillas con líneas negras con puntos en los extremos

(●―●). Los símbolos y indican regulación por luz u oscuridad. Las flechas negras con punta en diamante ( ) indican el

gen responsable de la señal. Las flechas negras ( ) indican los diferentes procesos. Adaptado de Noguero y col., (2013).


44

1.8.- CYCLING DOF FACTORS (CDFs).

De acuerdo con lo descrito anteriomente, en Arabidopsis existen 36 FTs tipo DOF que han sido

clasificados en cuatro grupos (A-D) (Lijavetzky y col., 2003). En el grupo D, se encuentra un set de

factores tipo DOF cuyos niveles de expression osicilan en condiciones de luz continua y son conocidos

como CYCLING DOF FACTORS (CDFs) (Imaizumi y col., 2005; Fornara y col., 2009). Los CDFs cumplen

un papel importante en la floración y respuesta al fotoperiodo en Arabidopsis, a través de la modulación

de la expresión de CO (CONSTANS) (Fornara y col., 2009).

El primer gen CYCLING DOF FACTOR 1 (CDF1) fue inicialmente identificado en un screening de

levadura en el que se buscaban proteínas que interaccionan con la proteína FLAVIN BIDING, KELCH

REPEAT (F-box-1, FKF1) que es considerado, además, un receptor de luz azul. CDF1 es una proteína

nuclear que se expresa principalmente en el tejido vascular (Imaizumi y col., 2005). La expresión ectópica

de CDF1 produce un desarrollo más tardío en el tiempo de floración bajo condiciones de día largo (DL),

ya que reprime la transcripción de CO (Imaizumi y col., 2005). Además, otros factores como CDF2, CDF3

y CDF5 presentan una función redundante, por lo que también retrasan la floración, mediante la

regulación negativa de CO (Fornara y col., 2009). Por otro lado, CDF1 y CDF2 juegan un papel central

como integradores de la señal de luz y participan en el ajuste metabólico apropiado a las condiciones del

luz/oscuridad del ambiente (Imaizumi y col., 2005; Fornara y col., 2009). De esta forma, los CDFs están

implicados en el control del tiempo de floración en Arabidopsis (Fornara y col., 2009).

En Arabidopsis, la floración se induce durante periodos prolongados de luz (día largo) que coincide

con las estaciónes de primavera y principios de verano, pero se retarda cuando el periodo de oscuridad

es más prolongado (día corto), que coincide con el final del otoño y el invierno. Estudios moleculares

definen que la ruta del fotoperiodo engloba importantes genes como GIGANTEA (GI), CONSTANS (CO) y

FLOWERING LOCUS T (FT) (Kobayashi y Weigel, 2007; Turck y col., 2008), cuyas funciones son

conservadas en especies lejanamente relacionadas (Hayama y col., 2003). La estimulación de la


45

transcripción de CO en condiciones de DL es esencial para la inducción de la floración, dado que los

largos periodos de iluminación promueven la estabilidad de la proteína CO (Jang y col., 2008; Valverde y

col., 2004), y desencadenan la transcripción de FT en hojas (An y col., 2004, Takada y Goto, 2003, Wigge

y col., 2005; Yoo y col., 2005), y una posterior traslocación de la proteína FT a los meristemos apicales

(Corbesier y col., 2007, Jaeger y Wigge, 2007, Mathieu y col., 2007).

Como se ha descrito anteriormente, múltiples FTs tipo DOF, homólogos de CDF1 están

implicados en la represión de CO en la primera parte del día (Fornara y col., 2009). En condiciones de DL

las proteínas CDF1 y CDF2 son degradas por el complejo FKF1 y GI dependiente-luz-azul que es

requerido para la medición de la duración del día (Figura 1.11). Este complejo regula directamente la

estabilidad de CDF1 en la tarde, permitiendo la activación de la transcripción de mRNA de CO, debido a

la degradación de las proteínas CDF1 y CDF2 (Figura 1.11) (Fornara y col., 2009; Imaizumi y col., 2005;

Sawa y col., 2007; Gómez, 2013; Jarillo y col., 2008). En la noche, GI es degradada vía E3 ubiquitin-

ligasa CONSTITUTIVE PHOTOMORPHOGENESIS 1 (COP1). La interacción COP1 y GI requiere ELF3

(EARLY FLOWERING 3) un factor circadiano que actúa como un adaptador entre proteínas (Yu y col.,

2008; McWatters y col., 2000) (Figura 1.11). De esta manera, cuatro puntos de regulación durante el día,

aseguran que CO se active específicamente en condiciones de DL.

Por otro lado, recientes estudios han demostrado que los CDFs a parte de estar implicados en la

floración y respuesta al fotoperido, también juega un rol importante en el desarrollo de tuberculo. Trabajos

realizados por Kloosterman y col (2013) han demostrado que el gen CDF1 de S. tuberosum esta

implicado en la formación de tubérculo en patata. La sobre-expresión de una variante alélica de StCDF1

(StCDF1.2), produce un desarrollo temprano del tubérculo y no afecta el tiempo de floración en patata,

indicando que las vías de trasducción de señal para el desarrollo de tubérculo y tiempo de floración están

separadas. Sin embargo, la sobre-expresión de StCDF1.2 en A. thaliana causa un retardo en el tiempo de

floración, sugiriendo que el StCDF1 presenta una función similar conservada sobre la expresión de CO,

como se ha visto con el CDF1 de Arabidopsis (Imaizumi y col., 2005). Además, se ha demostrado que la


46

pérdida de la región C-terminal en las variantes alélicas StCDF1.2 y StCDF1.3, afecta la interacción de

estas proteínas con el complejo FKF1-GI, causando defectos en la madurez de la planta y en el desarrollo

del turberculo. Todos estos trabajos demuestran una vez más la importancia del estudiar a los CDFs

como herramienta para mejorar la producción y calidad del cultivo de tomate y otras Solanáceas

Figura 1.11. Representación esquemática de la regulación diurna de la expresión de CO. En la mañana los niveles de

expresión de los genes AtCDF1, AtCDF2, AtCDF3 y AtCDF5 son elevados. Las proteínas AtCDFs se unen al promotor de CO

reprimiendo su transcripción. La transcripción de GI y FKF1 es reprimida por CCA1 y LHY en la tarde. Además la expresión de

AtCDF1 es reducida por la participación de PRR5, PRR7 y PRR9. Una vez FKF1 percibe la luz azul, forma un complejo con GI.

Este complejo está involucrado en la degradación de AtCDF1 y AtCDF2 y posiblemente de los otros AtCDFs. En la noche GI es

degradado por el complejo COP1 que contiene ELF3. COP1 forma un complejo con GI vía ELF3 y también regula la estabilidad

de GI. Adaptado de Imaizumi y col., 2010.

2

OBJETIVOS

Objetivos

48

El tomate (Solanum lycopersicum L.) es el cultivo hortícola más importante de España. Sin

embargo, su producción se ve afectada a causa de diferentes condiciones medioambientales, tales como:

salinidad, sequía y temperaturas extremas. Estudios realizados en los últimos años señalan la posibilidad

de utilizar FTs para mejorar la fotosíntesis y la producción de biomasa, así como la tolerancia al estrés

abiótico. Los FTs de tipo DOF participan en la regulación de la fisiología y metabolismo de la planta. Sin

embargo, su función exacta en el desarrollo de la tolerancia a distintos tipos de estrés abiótico y su

impacto en el desarrollo del fruto es, en gran parte, desconocida.

El objetivo general de este trabajo es la identificación y caracterización de nuevos factores

reguladores implicados en el control del ajuste metabólico en respuesta a condiciones de estrés abiótico

en tomate.

Se pretende ampliar nuestro conocimiento de los procesos de control genético de la respuesta a

condiciones ambientales adversas, y cómo se integran con procesos del desarrollo como la fructificación.

Esta información puede ser una herramienta para nuevos programas biotecnológicos de mejora de la

tolerancia a estrés en Solanaceas.

Para alcanzar este objetivo general nos planteamos los siguientes objetivos específicos:

Primero. Caracterización molecular y estudio de la función del factor de transcripción de tipo DOF, CDF3

de Arabidopsis thaliana durante el desarrollo y en condiciones de estrés abiótico.

Segundo. Estudio de la respuesta fisiológica de la sobre-expresión del gen AtCDF3 en tomate.

Tercero. Identificación y caracterización molecular y funcional de factores de transcripción de la familia

DOF de tomate (Solanum lycopersicum L.) con un papel regulador en la respuesta a estrés abiótico.

3

Arabidopsis CYCLING DOF FACTOR 3 CDF3 REGULATE DROUGHT AND LOW

TEMPERATURE STRESS RESPONSE AND FLOWERING TIME IN

Arabidopsis thaliana

Arabidopsis Cycling Dof Factor 3 CDF3

50

3.1.- INTRODUCTION.

Abiotic stresses such as drought, cold and high salinity are among the major important

environmental factors that limit crop growth and productivity. These effects are counteracted by altered

morphology and physiology, resulting in changes in cellular processes such as, inhibition of cell division

and alterations of photosynthetic metabolism (Saibo et al., 2009; Chaves et al., 2009; Lawlor and Cornic,

2002). Transcriptome analysis has identified many genes that are inducible by abiotic stresses

(Yamaguchi-Shinozaki and Shinozaki, 2006; Seki et al., 2002; Shinozaki et al., 2003). These genes might

be classified in two major groups according to their functions. The first group includes genes encoding

proteins with function in stress tolerance, mainly downstream effectors in the stress response pathway

including osmoregulatory genes, antioxidant proteins, chaperones, detoxification enzymes and LEA (Late

embryogenesis abundant) proteins (Gong et al ., 2010; Yamaguchi-Shinozaki and Shinozaki, 2006;

Shinozaki, K. and Yamaguchi-Shinozaki, K, 2004 ). The other group controls gene expression and signal

transduction in abiotic stress response, such as protein kinases, protein phosphatases, enzymes involved

in phospholipids metabolism (Yamaguchi-Shinozaki and Shinozaki, 2006; Seki et al., 2003; Shinozaki, K.

and Yamaguchi-Shinozaki, K, 2004 ) and various transcription factors (TFs) that regulate diverse stress-

inducible genes cooperatively o separately, and may constitute gene networks. These stress-inducible

transcription factors are members of the DRE-binding protein (DREB) family, ethylene-responsive element

binding factor (ERF), the zinc-finger family, members WRKY family, MYB, basic helix-loop-helix (bHLH),

basic-domain lucine zipper (bZIP) and NAM, ATAF and CUC (NAC) family (Shinozaki et al., 2003).

Several transcription factors interact with cis-acting elements in the promoter regions of stress-inducible

genes (Yamaguchi-Shinozaki and Shinozaki, 2005). The dehydration-responsive element (CTR/DRE) and

ABA-responsive element (ABRE) are two major cis-elements that play an important role in the abiotic

stress response (Maruyama et al., 2012). In silico studies of the complete genome sequences of

Arabidopsis, rice (Oryza sativa) and soybean (Glycine max) have predict novel cis-acting promoter

elements involved in cold-inducible gene expression (Maruyama et al., 2012). Nevertheless new cis-acting


51

elements and transcription factors have been identified recently, but their function in abiotic stress

tolerance is still unknown.

The DOF (DNA binding with One Finger) family of transcription factors is a group of plant-specific

TFs that contain the DNA-binding domain usually located close to the N-terminal region of the protein. The

DOF domain is a highly conserved region of 52 amino acid residues containing the string CX2 CX21

CX2C, which binds zinc (Zn2+) forming a structure called a “zinc finger” that binds specifically to cis

regulatory elements containing the common core 5´-T/AAAG-3´ (Yanagisawa and Schmidt, 1999; Noguero

et al., 2013). DOF proteins can bind to promoters containing either a unique AAAG motif o more

repetitions of this motif (Yanagisawa, 2000).

DOF genes are important modulators of plants response to both biotic and abiotic stresses and

could be induced by different phytohormones and/or pathogens (Skirycz et al., 2007; Nakano et al., 2006).

Besides, DOF TFs are involved in the regulation of plant specific processes such as carbon assimilation

(Yanagisawa, 2001), light signalling (Park et al., 2003), seed germination or development (Papi et al.,

2000; Gualberti et al., 2002; Rueda-Romero et al., 2012), flowering (Sawa et al., 2007, Fornara et al.,

2009), stomata functioning (Gardner et al ., 2009; Negi et al ., 2013), response to phytohormones (Kang et

al ., 2003; Nakano et al .,2006), carbon fixation and nitrogen assimilation (Yanagisawa and Sheen, 1998;

Rueda-Lopéz et al., 2008), secondary metabolism (Skirycz et al., 2006) and development of vascular

system (Guo et al., 2009; Konishi and Yanagisawa, 2007; Gardiner et al., 2010; Kim et al., 2010; Hir et al.,

2013).

Several reports have shown that DOF proteins can be acting both as activators or repressors in the

control of the expression of numerous plant genes (Mena et al., 1998; Yanagisawa and Sheen, 1998). In

vivo assays in Arabidopsis protoplast have shown that both AtDOF4.2 and AtDOF4.4 have transcriptional

activity and the TMD (Thr-Met-Asp) motif present in the C-terminal region of the two proteins is essential

for the transactivation ability (Zou et al., 2013). Moreover, it has been described that DOF proteins interact

with other transcription factors as basic leucine zipper bZIP (Zhang et al., 1995; Vicente-Carbajosa et al.,


52

1997) and High Mobility Group (HMG) proteins (Yanagisawa et al., 1997). Other DOF protein from

Solanaceae, StDOF1 regulates guard cell specific gene expression (Plesch et al., 2001), while StSRF1

modulates the carbohydrate metabolism in the storage roots (Tanaka et al., 2009).

The DOF transcription factors originated from a common ancestor, likely represented by the single

Chlamydomonas reinhardtii (CrDof1) gene, and then expanded in the different taxonomic groups of

vascular plants through recurrent duplication events (Moreno-Risueño et al., 2007). The first DOF protein

has been identified in maize (ZmDOF1) and enhances transcription from C4 Phospho-Enol-Pyruvate

Carboxylase (PEPC) promoter (Yanagisawa and Sheen, 1998; Yanagisawa, 2000). Later on many

different DOF genes have been identified in plants but their number of DOF genes varies depending on

the species. Different bioinformatic analysis of Arabidopsis and rice genome predicts 36 and 30 DOF

genes, respectively (Lijavetzky et al., 2003), whereas 27 are found in Brachypodium (Hernando-Amado et

al., 2012), 30 in rice (Oryza sativa) (Gaur et al., 2011), 34 in tomato (Solanum lycopersicum) (Cai et al.,

2013; Corrales et al., 2014), 41 in poplar (Populus trichocarpa) (Yang and Tuskan, 2006), 28 in sorghum

(Sorghum bicolor) (Kushwaha et al., 2010), 31 wheat (Shaw et al., 2009) and 26 in barley (Moreno-

Risueño et al., 2007). Phylogenetic studies of amino acid sequences of DOF proteins using MEME

software revealed the presence of homologous motifs that are conserved among monocot and dicot

species. Analysis of the DOF gene family in Arabidopsis, rice, tomato and Brachypodium indicated that

they could be classified into four major clusters of orthologous genes or subfamilies (A, B, C and D)

(Lijavetzky et al., 2003; Hernando-Amado et al., 2012). Interestingly comparative analyses of the deduced

amino acid sequences of DOF proteins of group D of different plant species indicate the existence of 3

homologous motifs located in the C-terminal region, conserved among their sequences which are different

of the DOF binding domain and represent common signatures of type D.

In Arabidopsis, the D group contains a set of DOF factors whose transcripts oscillate under

constant light conditions and are hence known as Cycling Dof Factors (CDF1-5) (Imaizumi et al., 2005;

Fornara et al., 2009). AtCDF1 is a nuclear protein expressed mainly in vascular tissues (Imaizumi et al.,


53

2005) and has been identified to regulate CONSTANS (CO) expression. The CDF transcription factors

have been therefore implicated in flowering-time control in Arabidopsis. The overexpression of AtCDF1-5

genes represses CO transcription causing strong delay of flowering under long-day (LD) conditions

(Fornara et al., 2009; Imaizumi et al., 2005). Moreover, characterization of the flowering time phenotype of

35S::AtCDF1-RNAi line showed a slight acceleration of flowering with unaltered diurnal pattern of CO

expression. FLAVIN-BINDING KELCH REPEAT F-BOX PROTEIN (FKF1) and GIGANTEA (GI) (Sawa et

al., 2007) complex directly regulate the CDF1 stability in the afternoon, allowing mRNA CO activation

transcription by CDF1 and CDF2 degradation mediated by FKF1 in LDs conditions (Fornara et al., 2009;

Imaizumi et al., 2005; Sawa et al., 2007; Gomez, 2013; Jarillo et al., 2008). Interestingly, the single cdf3-1

mutant did not show any alteration in the flowering phenotype under LDs and SDs conditions (Fornara et

al., 2009).

Here, we report the molecular and functional characterization of Arabidopsis CDF3 gene in

response to drought, low temperatures and osmotic stress. Subcellular localization analysis revealed that

AtCDF3 was localized in the nucleus and therefore could act as transcriptional activator. Transgenic plants

overexpressing AtCDF3 showed improved tolerance under low temperatures, oxidative and drought

stresses. Transcriptome comparison of AtCDF3 overexpressing line versus wild-type plants showed up-

regulation of nitrogen metabolism and stress-responsive genes (RD29, COR15 and ERD10), which might

indicate an important role of CDFs transcription factors in abiotic stress responses. Our results suggest

that CDF3 likely defines a cross-talk point of abiotic stress responses and flowering time signal

transduction pathways, and plays a multifaceted role in regulating Arabidopsis development and abiotic

stress tolerance.


54

3.2.- MATERIAL AND METHODS.

3.2.1.- Plant material and growth conditions.

The Arabidopsis thaliana ecotype Columbia (Col-0) was used as the WT. The cdf3-1 T-DNA

insertion knockout mutant was obtained from the GABI-Kat (GK-808605; Rosso et al., 2003). Seeds were

surface-sterilized with 70% ETOH and 20% sodium hypochlorite, 20% SDS and washed with sterile water.

Stratification was performed by planting seeds on MS/2 medium (Murashige and Skoog, 1962) containing

0.5% (w/v) sucrose and 0.8% (w/v) agar and incubating them at 4 °C for 2 d. The plates were then

transferred to a growth chamber at 22 °C and 60% relative humidity (RH) under long-day growth

conditions (16/8 h light/dark). After 15 d, the seedlings were transferred to plastic pots containing a mixture

of substrate and vermiculite (3:1). Controlled environmental conditions were provided in growth chambers

at 22 °C and 60% RH under long-day growth conditions.

The tomato (Solanum lycopersicum L.) cultivar Moneymaker was used in this study. Tomato seeds

were surface sterilized in 4% sodium hypochlorite and washed with sterile purified water for 3 times. The

sterilized seeds were germinated on one-half strength MS (Murashige and Skoog, 1962) solid medium

containing 50 mg/L kanamycin under controlled conditions (25 ± 2 °C, 16/8 h light/dark cycle and 100

-

cultured hydroponically in aerated one-half strength Hoagland solution (Hoagland and Arnon, 1950) under

controlled conditions in growth chambers (25/18 ºC, 16/8 h light/dark cycle, 100 mol/m2 s1 light intensity

and 70%–80% relative humidity. The nutrient solution was renewed twice a week. Thirty-days-old plants

(three to four leaves) were used for stress assays. Sodium chloride was added at 75 mM in the nutrient

solution for saline stress. Plants were transferred to growth chambers at 10/5 ºC or 15/8 ºC for low

temperature stress. Control plants were maintained at 25/18 ºC in half strength nutrient solution. Gas

exchange and fluorescence measurements were performed after 14 days under stress conditions, and

biomass was assessed after 15 days (Renau-Morata et al., 2014).

https://correo.inia.es/exchweb/bin/redir.asp?URL=http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905202/%23bib20


55

3.2.2.- Plasmid constructs and Arabidopsis transformation.

The open reading frame (ORF) of the AtCDF3 gene and the truncated version CDF3-stop (lacking

1035 to 1717bp of the C-terminal region) were cloned into pGWB2 binary vector under the control of the

35S promoter of Cauliflower Mosaic Virus (CaMV; Karimi et al., 2007) followed by the Nopaline synthase

gene (NOS) 3´terminator. The resultant plasmid was used to transform Arabidopsis thaliana (Col-0) plants

by the Agrobacterium tumefaciens-mediated floral dip method (Clough and Bent, 1998).Transformed

plants were selected on MS medium (Murashige and Skoog, 1962) containing 50 μg ml–1 of kanamycin.

For GUS histochemical staining experiments the promoter regions of AtCDF3 from - 1060 bp to the ATG

translation initiation codon) and CRUCIFERIN (from – 1200bp to the ATG translation initiation codon)

genes were cloned into a binary vector containing a β-glucuronidase (GUS; uidA gene) reporter gene,

producing an in-phase fusion with this reporter gene constructs pCDF3::GUS and pCRU::GUS,

respectively. The resultant plasmids were used to transform Arabidopsis plants.

3.2.3.- Tomato transformation.

The previously obtained construct 35S::AtCDF3 was used to transformed tomato plants var.

Moneymaker following the method described by Ellul et al. (2003). About 130 cotyledonary explants were

used for each construction. Explants were excise from plants were incubated 2 days in the dark on

preculture medium (CCM) and then were carefully submerged in Agrobacterium tumefaciens (LBA 4404

strain) inoculum in a Petri dish with gentle swinging. They were blotted dry on sterile filter paper and

transferred to the co-culture medium. After 72 h in the dark at 26ºC, the explants were washed twice with

liquid MS+2%sucrose medium for 15 min and after being blotted dry on sterile filter paper they were put

on shoot induction medium without selective pressure. After 48 h on 16:8h photoperiodic conditions,

explants were transferred to shoot-induction medium with 50 mg/L kanamycin. Every two weeks explants

were transferred to fresh medium. Individual shoots were excised and transferred to root-induction

medium. When the radicular system was fully developed, plants were transplanted to soil. Seeds from the


56

transformed plants were harvested and plated on selective medium and kanamycin-resistant seedlings

were transplanted to soil.

3.2.4.- Histochemical GUS staining and subcellular localization of AtCDF3 protein.

Full length ORF of the AtCDF3 gene and the truncated version AtCDF3-stop were cloned into the

pK7WGF2.0 plasmid using the Gateway recombination system (Invitrogen) to generate C-terminal green

fluorescent protein (GFP) fusions driven by the cauliflower mosaic virus (CaMV) 35S promoter (Karimi et

al., 2007). The resultant plasmids were used in transient transformation experiments of onion cells and to

transform Arabidopsis plants by Agrobacterium tumefaciens-mediated floral dip method (Clough and Bent,

1998). Onion (Allium cepa L.) epidermal cells were transiently transform using particle bombardment with

a biolistic helium gun device (DuPont PDS-1000; Bio-Rad) as described by Diaz et al, (2002). The GFP

gene expressed under the control of 35S promoter was used as a control. Fluorescence images were

acquired after 40 h of incubation at 22 °C in the dark using a confocal microscope (LEICA Sp2 AOBS UV).

GUS histochemical staining of pCDF3::GUS and pCRU::GUS transgenic plants was performed as

described by Jefferson et al., (1987). Plant tissues were incubated in 100mM NaPO4, 2.5mM X-GlcA (5-

Bromo-4-Chloro-3-Indolyl-β-D-Glucuronic acid cyclohexyl-ammonium salt), 0.5mM K3Fe(CN)6, 0.5mM

K4Fe(CN)6 and 0.25% Triton X-100 at 37ªC overnight. Analyses were verified using at least eight

independent T1 lines. The images were taken using LEICA DFC 280 digital camera. For stress treatments

three-week-old transgenic pCDF3::GUS Arabidopsis plants were grown under control conditions (control)

or exposed to low (4°C) or high temperature (40ºC), for 24 h, dried on the bench (drought) for 24 h, or

treated with 100mM ABA for 24 h or 150mM NaCl for 24 h before staining.


57

3.2.5.- Protoplast transformation and GUS assays.

Mesophyll protoplasts were isolated from rosette leaves of 3-week-old Arabidopsis plants grown in

soil (21/18 ºC, 8/16 h light/dark). Protoplast isolation and transfection was performed according to the

method described by Alonso et al. (2009). Plasmid DNA was prepared using a Genopure Plamid Maxi Kit

(Roche), and 5 µg of each pBT10-2xDOF-GUS (a dimer of the DOF binding element) and pCOR15::GUS

reporter plasmids and 14 µg of 35S::AtCDF3, 35S::AtCDF3-stop and 35S::SlCDF3 effector plasmids were

used for transfections. For normalization purposes, 1 µg of Pro35S::NAN plasmid (Kirby and Kavanagh,

2002) was added. Then, 20 µl of plasmid mixture (20 µg) and 200 µl of protoplasts were transferred to 2

ml microcentrifuge tubes following the procedure described in Weltmeier et al. (2006). GUS and NAN

enzyme assays were performed according to Kirby and Kavanagh (2002). The ratio of GUS and NAN

activities are represented as relative GUS/NAN units. For stress assays, protoplast were re-suspended in

250µl WI solution (control) or WI solution containing 25mM NaCl and ABA (100µM) or incubated in low

(4ºC) and high (37ºC) temperatures for 12h under controlled conditions (16 h/8 h light/dark cycle) in a

growth chamber except in the cold and heat treatments.

3.2.6.- RNA isolation and expression analysis by real-time RT-qPCR.

The expression of AtCDF3 gene, abiotic stress responsive genes (COR15, RD29A and ERD10),

and nitrogen metabolism genes (PEPC1, PEPC2, GS2, GLU1 and PK1) in AtCDF3 35S::AtCDF3 and

control lines (Col-0) was determined by RT-qPCR. Plants were maintained in growth chambers (21/18 ºC,

16/8 h photoperiod). Leaves of Arabidopsis of 3-week-old were collected and frozen into N2 and stored at -

80°C until use for RNA extraction. Total RNA was extracted following Oñate-Sanchez and Vicente-

Carbajosa (2008) protocol and treated with DNase (Roche). For cDNAs synthesis, 2 µg of total RNA was

primed with oligo(dT)15 primers (Promega) using the Avian Myeloblastosis Virus Reverse Trascriptase

(AMV RT) (Promega) according to the manufacturer´s instructions. Arabidopsis UBIQUITIN10

(Czechowski et al., 2005) mRNA level (At5g25760) was used as control. A LightCycler®480 System


58

(Roche) was used for real-time PCR (10 min at 95ºC, and 40 cycles of 95ºC for 15 s, 55ºc for 1min, 72ºC

for 10 s) using LightCycler®480 SYBR Green I Master (Roche). A final dissociation step was added to

provide the denaturing curve of the amplified products. In all treatments and conditions, three independent

samples from different extracts were used and each reaction was performed in triplicate. The primer pairs

used for amplification are described in Supplementary Table S3.1. Relative expression levels of the target

genes were calculated using the 2−ΔΔCT method (Livak and Schmittgen, 2001). Positive and negative

controls were included in the RT-qPCR analyses.

The expression levels of AtCDF3 gene in transgenic hetero- and homozygous tomato plants were

determined by RT-qPCR. Total RNA was extracted from 3 week-old plants following the protocol of Oñate-

Sanchez and Vicente-Carbajosa (2008) and treated with DNase (Roche). cDNA was synthesized from 2

μg of DNA-free RNA using the avian myeloblastosis virus reverse transcriptase and oligo(dT)15 primers

(Promega) according the manufacturer´s instructions. A LightCycler®480 System (Roche) was used for

real-time PCR (10 min at 95 °C, 40 cycles of 95 °C for 15 s, 55 °C for 1 min and 72 °C for 10 s) using

LightCycler® 480 SYBR Green I Master (Roche). In all the assays, three independent samples from

different extracts were used and each reaction was performed in triplicate. The primer pairs used for

amplification are described in Table S1. The UBIQUITIN3 gene from S. lycopersicum (Hoffman et al.,

1991) was used as reference gene. Relative expression levels of the target genes were calculated using

the 2-ΔΔCT method (Livak and Schmitten, 2001).

3.2.7.- Germination and post-germinative growth assay.

Germination, post germinative and root growth assays were carried out using control plants (Col-0),

cdf3-1 and 35S::AtCDF3 transgenic lines. Seeds were collected at the same time and obtained from

plants grown in the same conditions. For germination studies seeds were surface-sterilized as describe

above, laid on MS medium or MS containing various concentrations of Mannitol (200 and 250 mM) and

held 4ºC for 2d in dark before being transferred to 22ºC and 60% RH under long-day growth conditions.


59

Germination was scored as radicle emergence through the endosperm and testa every 24 h; assess the

green cotyledons and true leaves. All the assays were carried out in triplicate with at least two

independent seed batches and expressed as a percentage of the total number of seeds plated. Statistical

analysis was carried out by one-way analysis of variance (ANOVA) followed by a Student Newman-Keuls

test (P<0.05). For osmotic root length assays, seeds were sterilized and plated onto Petri dishes

containing MS medium. After 6 d, the seedlings were transferred to vertical plates containing MS medium

(control) and MS medium supplemented with 200 mM mannitol (Lakhssassi et al., 2012). About 20

seedlings were used per replicate and three replicates were made for each treatment. Primary root

elongation was measured after 10 d using ImageJ software (Abràmoff et al., 2004, Corrales et al., 2014).

To evaluate growth differences between control and saline stress, data were represented as the

percentage of root growth reduction relative to standard conditions. Statistical analyses were carried out

by one-way analysis of variance (ANOVA) followed by a Student–Newman–Keuls test (P<0.01).

3.2.8.- Photosynthesis and leaf fluorescence measurement.

Net photosynthesis and related gas exchange variables, stomatal conductance and substomatal

CO2 were determined using an LI-6400 infrared gas analyser (LICOR Biosciences, Lincoln, USA).

-2 s-1), 400 ppm

CO2, ambient temperature and a vapour pressure difference (vpd) between 1 and 2 kPa. Maximum

photochemical efficiency (Fv/Fm) on dark-adapted leaves was measured using a portable pulse amplitude

modulation fluorometer (MINI PAM, Walz, Effeltrich Germany). Responses to osmotic stress were

performed using three-week-old Arabidopsis plants that were transplanted to hydroponic culture, and

photosynthesis parameters were measured after 7 days of growth by adding 5% PEG-8000 (24h). The

analyses of ABA response were performed using four-week-old plants grown in soil by spraying with 0.5

µM ABA solution in the underside of the leaves and measurements were made after 1, 2 and 3.5 hours

after treatment.


60

For tomato analyses thirty-days-old plants (three to four leaves) were used for stress assays. Sodium

chloride was added at 75 mM in the nutrient solution for saline stress. Plants were transferred to growth

chambers at 10/5 ºC or 15/8 ºC for low temperature stress. Control plants were maintained at 25/18 ºC in

half strength nutrient solution. Gas exchange and fluorescence measurements were performed after 14

days under stress conditions, and biomass was assessed after 15 days. One measurement per plant was

taken on the third or fourth leaf from the apex. Eight to ten different plants were used. Fresh weight of

shoots and roots, stem height, number of leaves and total leaf surface were determined. Shoot and root

dry weights were measured after drying at 60 ºC for 48 h. For each genotype and treatment, eight to ten

plants were measured.

3.2.9.- Drought and cold stress tolerance assay.

Cold and drought stress assay were carried out using control plants (Col-0) and cdf3-1,

35S::AtCDF3 and 35S::AtCDF3-stop transgenic lines. Drought stress tolerance tests were performed on

plants grown in soil in individual pots. After 2 weeks, the water supply was cut off for 15 days and then

watering was resumed during 10 d. Plant survival rates were calculated afterwards and fresh weight was

measured 10 d after re-watering period. Statistical analysis was carried out by one-way analysis of

variance (ANOVA) followed by a Student Newman-Keuls test (P<0.05). Freezing tolerance was analyzed

by exposing non-acclimated or cold-acclimated (7 days at 4ºC) 2-week-old plants to -5 and -6ºC or -9 and-

10ºC for 6 h, respectively. Tolerance was determined as the capacity of plants to resume growth 2 weeks

after returning to control conditions.

3.2.10.- Microarray analysis.

Genome-wide expression studies with ATH1 array (Affymetrix) were performed using 3-week-old

35S::AtCDF3 and Col-0 plants, growth in chambers (21/18 ºC, 16/8 h photoperiod). Leaves of Arabidopsis

were collected and frozen into N2 and stored at -80°C until use for RNA extraction. Total RNA was


61

extracted following Oñate-Sanchez and Vicente-Carbajosa (2008). Three arrays for the different plant

materials were hybridized according the affymetrix GeneChip Expression Analysis manual

(www.affymetrix.com). Differentially expressed Arabidopsis genes in 35S::AtCDF3 compared to WT plants

(1.5 fold; P value<0,05) were selected. They were functionally annotated by search in the TAIR

Arabidopsis database, classified using the e-northern expression browser tool

(http://bar.utoronto.ca/affydb/cgi-bin/affy_db_exprss_browser_in.cgi) (Toufighi et al., 2005) and listed in

Supplementary Table S3.2. Venn diagrams were performed using venny tools

(http://bioinfogp.cnb.csic.es/tools/venny; Oliveros, 2007). Gene Ontology analyses of the differentially

regulate genes (fold change > 1.5 ) in 35S::AtCDF3 transgenic plants compared to Col-0 were performed

using agriGO (http://bioinfo.cau.edu.cn/agriGO/) (Du et al., 2010) and REVIGO (http://revigo.irb.hr/)

(Supek et al., 2011) software.

3.2.11.- Metabolomic analyses.

Non-targeted and targeted metabolomics analyses were performed on 12-d-old control plants (Col-

0) and two independent 35S::AtCDF3 lines. Extraction, manipulation and mass spectrometric analysis of

samples followed an adapted protocol, detailed in Supplementary File 4.1, which is based on previously

described methods (Fiehn et al., 2000; Gullberg et al., 2004; Gaquerel et al., 2010).

http://www.affymetrix.com/

http://bioinfogp.cnb.csic.es/tools/venny


62

3.3.- RESULTS.

3.3.1.- Abiotic stress response and expression pattern of AtCDF3.

To identify new DOF factors that might be involved in harmonizing abiotic stress responses with the

developmental program in Arabidopsis, in silico expression analyses using the complete set of DOF genes

encoded in the Arabidopsis genome (Lijavetzky et al., 2003) were performed. Thus, we first examined the

expression patterns of all Arabidopsis DOF members using the available data on the database

(http://bar.utoronto.ca/efp/cgi-bin/efpWeb.cgi) and found that the ones of group D are differentially

expressed in response to different abiotic stresses such as drought, salinity and extreme temperatures in

vegetative tissues (Fig. 3.1). Interestingly, inside the D group the set Cycling Dof Factors (AtCDF1–5)

exhibited higher levels of induction under some of those stresses. Among the genes belonging to group D,

AtCDF3 was further characterized.

To further confirm that AtCDF3 expression is controlled by different environmental cues, we

performed a detailed RT-qPCR expression analyses using RNA isolated from 3-week-old Arabidopsis

plants that had been subjected to different abiotic stresses such as salinity, high and low temperatures,

dehydration and exogenous ABA treatments for different periods of time. As it is shown in Figure 3.2, in

leaf tissues transcripts levels of AtCDF3 are significantly increased in response to temperature stress,

dehydration, high salt, and exogenous ABA treatment although with different dynamics and extents. In

fact, AtCDF3 transcript levels increased at higher levels in response to extreme temperatures, dehydration

and ABA treatment, although an earlier induction at was observed in response to dehydration treatments

reaching maximum levels at 4h. Moreover induction of AtCDF3 was also observed in leaf tissues under

salt treatment but later reaching maximum levels at 24h.


63

Figure 3.1. (A) Phylogenetic tree Arabidopsis DOF proteins. The tree was inferred by the neighbor-joining method after the

alignment of DOF domain amino acid sequences of the Arabidopsis DOFs. (B) Expression Profiles of the AtCDFs genes from

Arabidopsis in shoot and root under cold, osmotic, salt, drought, oxidative, UV and heat stress in

(http://bar.utoronto.ca/affydb/cgi-bin/affy_db_exprss_browser_in.cgi).

In order to perform a deeper analyses of the spatial expression patterns of AtCDF3 in different

plant tissues in response to abiotic stress, a promoter sequence covering 1-kb region upstream of the

AtCDF3 transcription start site was fused to a GUS-coding sequence, and the promoterAtCDF3::GUS

reporter was transformed into wild-type plants (WT). First, we studied the spatial expression patterns

during plant development. Overall It was observed low levels of GUS expression under control conditions

in all tissues of adult plants, however it was detected significantly staining in vascular systems of leafs and

stems, and guard cells (Fig. 3.3A). Moreover, flowers showed also GUS activity in pollen, petals, anthers

and stigmatic papillae with pollen (Fig. 3.3A).

A B


64

3.2. Transcription analysis of AtCDF3 gene by RT-qPCR in Arabidopsis plants exposed to different abiotic stress

conditions. Total RNA was isolated from leaves of 3-week-old Arabidopsis plants grown under control conditions (control),

treated with 150mM NaCl, low temperatures (4ºC), 100µM ABA (ABA), heat (40ºC) (A) or dried on the bench (drought) for the

indicated periods of time (B). Expression of the Arabidopsis UBIQUITIN10 gene (Czechowski et al., 2005) was used as a

reference gene. All data are expressed as means ± SE of three independent pools of extracts. Three technical replicates were

performed for each extract.

Interestingly, AtCDF3 promoter also provides a strong GUS staining in mature seeds, showing

maximum levels of expression in later maturation stage as compared to the GUS staining pattern

observed of the well characterized seed Cruciferin gene (pCRU::GUS, Fig. 3.3B, Suzuki et al., 2001).

When 2 weeks old transgenic plants containing the AtCDF3::GUS transgene were exposed to different

abiotic stresses such us low and high temperatures, dehydration and ABA or high salt treatments, GUS

expression increased but with very similar patterns in all cases, regardless of the treatment or the

transgenic line analyzed (Fig. 3.3D). Thus GUS staining was detected in leaves, both lateral and main

roots and stems, being especially strong in vascular bundles (Fig. 3.3C). Taken together, all these results

indicate that the expression of AtCDF3 is regulated during plant development and also in response to

different abiotic stresses and that this regulation occurs at least partially through transcriptional level.


65

Figure 3.3. Histochemical localization of GUS activity in adult pCDF3::GUS transgenic Arabidopsis plants grown under

control conditions or exposed to different abiotic stresses. (A) GUS activity in plants grown under control conditions. I, in

flower and pollen, II, young leave and III stomata; IV, secondary root and radicle and V, stem. (B) GUS activity in pCDF3::GUS

and pCRU::GUS during seed development and germination. I and IV staining of pCDF3::GUS and pCRU::GUS in mature seeds,

respectively. II and V staining of pCDF3::GUS and pCRU::GUS seeds in phase maturation of development, respectively. III and IV

staining of pCDF3::GUS and pCRU::GUS seeds after 7 days of germination, respectively. (C) Expression analyses of CDF3 in

different organs of Arabidopsis. Northern blot analysis were performed with total RNA isolated from the indicated organs of adults

Arabidopsis plants and hybridized with a CDF3 specific probe.(D) Histochemical localization of GUS activity in three-week-old

transgenic pCDF3::GUS Arabidopsis plants grown under control conditions (control) or exposed to low (4°C) or high temperature

(40ºC), for 24 h, dried on the bench (drought) for 24 h, or treated with 100mM ABA (ABA) for 24 h or 150mM NaCl for 24h.

3.3.2.- AtCDF3 protein localize to the cell nucleus and display specific DNA-binding and activation properties

To investigate the subcellular localization of AtCDF3 protein, translational fusions of their

corresponding ORFs to the C- terminus of GFP were made. These constructs, driven by the control of

35S promoter, were used for both transient assays of onion epidermal cells by particle bombardment and

for transformation Arabidopsis plants. Figure 3.4A, shows that the GFP-AtCDF3 fusion protein was

localized in the nuclei of onion epidermal cells, in contrast GFP control was observed in both nuclei and

promoAtCDF3

promoCRU

GUS

GUS

Control 4ºC Drought NaCl ABA 40ºC

AtCDF3

A B

C

D

I II

III IV V

I II III

IV V VI


66

cytoplasm of these cells. Similar results were obtained in when analyzed transgenic plants were analized

(Fig. 3.4B).

Figure 3.4. Subcellular localization and transcriptional activation properties of AtCDF3 protein. (A) Subcellular localization

of the AtCDF3 protein in onion epidermal cells. GFP alone (35S::GFP) or GFP-AtCDF3 (35S::GFP-AtCDF3) fusion proteins were

expressed transiently under the control of the CMV35S promoter in onion epidermal cells. After 36h of incubation, tissues were

observed with a confocal microscope for the emission spectrum of the GFP o by Nomaski imaging. Arrows indicate cell nuclei (B)

Confocal images of roots of transgenic Arabidopsis 35S::GFP-AtCDF3 and 35S::GFP-AtCDF3-stop plants. Arrows indicate cell

nuclei. (C) Transcriptional activation assays of AtCDF3 and AtCDF3-stop gene in transient expression experiments. Arabidopsis

protoplasts were transfect with the 35S::AtCDF3 and 35S::AtCDF3-stop effectors plasmids and reporter plasmid pBT10-2xDOF-

GUS. Tomato homologous gene (SlCDF3) was used as a positive control (Corrales et al., 2014). (D) Transient expression

analyses, co-transfection of protoplasts with pBT10-2xDOF-GUS reporter plasmid and empty effector plasmid and exposed to

different abiotic stresses such as NaCl (25mM), ABA (10µM), extreme temperatures (4ºC and 37ºC) for 15h. Empty pK7WGF2.0

plasmid was used as a negative control. Data are expressed as means ± standard error (SE) of three independent experiment


67

We further analyzed whether the conserved C-terminal region is important for the localization and

translational fusion to the AtCDF3 cDNA lacking the C-terminal region (amino acids 221-448; 35S::GFP-

AtCDF3-stop) were made and analyzed as previously indicated. The results obtained shown in Figure

3.4B, indicates that GFP-AtCDF3-stop protein change the localization pattern, and green fluorescence

was detected barely in the nucleus but also in the cytoplasm as multiple bright small spots. These results

indicate that AtCDF3 is a nuclear protein and that C-terminal domain also might play important roles in the

stability and its nuclear localization.

To gain deeper understanding of AtCDF3 function we analyzed the transcriptional activities of

AtCDF3 and transient expression analyses in Arabidopsis protoplasts were performed. Thus, effector

plasmids containing Arabidopsis CDF3 driven by the 35S promoter (35S::AtCDF3), and the previously

characterized tomato SlCDF3 (35S::SlCDF3) as positive control, were co-transfected with reporter plasmid

harboring a GUS gene under control of a minimal promoter containing a 2×DOF cis-DNA element (pBT10-

2×DOF::GUS). The results obtained confirmed that AtCDF3 could binds to the 5′-AAAG-3′ cis-DNA

element, and also that can moderate activate the GUS reporter gene in a similar way than the tomato

homologous gene SlCDF3. We further studied whether the conserved C-terminal region is important for

transcriptional activities and effector plasmid that contains AtCDF3 cDNA lacking the C-terminus region

(35S::AtCDF3-stop) were used and analyzed as previously indicated. The results obtained shown in

Figure 3.4C, indicates that the truncated AtCDF3 was not able to promote the transcription of the reporter

plasmid. These data indicate that C-terminal domain might be also essential for its transcription

capabilities.


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3.3.3.- Overexpression of AtCDF3 enhanced drought and low temperature tolerance in Arabidopsis.

Our expression analyses indicated that AtCDF3 might play an important role in the plant response

to different abiotic stresses. In order to further explore the AtCDF3 role in plant, and phenotypic

characterization of gain- and loss- function plants was performed by analyzing their response under abiotic

stresses, such as dehydration and low temperatures. Thus Arabidopsis plants overexpressing full length

AtCDF3 under the control of the CaMV 35S promoter were generated, and two homozygous lines with

relatively high expression of AtCDF3 were selected for further analyses (Fig. 3.5B). Moreover a T-DNA

insertion mutant cdf3-1 (GK808G05) without cdf3 expression was also identified and the insertion site was

localized 309 bp from the ATG (Supplementary Fig S3.1). When cultured in soil under greenhouse

conditions, cdf3-1 plants do not presented significant developmental differences relative to WT plants (Col-

0). Nevertheless, all transgenic 35S::AtCDF3 lines (L2.1 and L5.4) exhibit several developmental

differences relative to cdf3-1 and WT (Supplementary Fig S3.2).


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Figure 3.5. Drought and osmotic tolerance of 35S::AtCDF3, cdf3-1 and Col-0 plants. Drought stress tolerance was estimated

by scoring fresh weight (A) and survival rates (B) of 3-week-old 35S::AtCDF3 (L2.1 and L5.4), 35S::AtCDF3-stop (L1.7 and L7.2),

control (Col-0) and cdf3-1 plants, which were maintained for 15 days without irrigation and then 10 days of re-watering. Fresh

weight is expressed as means ± SE of two independent experiment with eight plants. Different letters indicate significant

differences between Col-0, cdf3-1 35S::AtCDF3 and 35S::AtCDF3-stop overexpressing lines (P<0.05; ANOVA Student-Newman-

Keuls tests). C) mRNA levels of AtCDF3 gene was analyzed by RT-qPCR in different T3 independent 35S::AtCDF3 (L2.1 and

L5.4) and 35S::AtCDF3-stop (L1.7 and L7.2) transgenic lines. Letters indicate significant differences compared to Col-0 (P<0.05;

ANOVA Student-Newman-Keuls tests). Osmotic stress tolerance was estimates by scoring germination and primary root length of

reduction in 35S::AtCDF3 (L2.1), Col-0 and cdf3-1 plants under different osmotic stress conditions. (D) Germination rates of

35S::AtCDF3, Col-0 and cdf3-1 plants under osmotic stress treatment.35S::AtCDF3 (L2.1), Col-0 and cdf3-1 seed were

germinated under different a concentration of mannitol. The germination rates were calculated after 4 days after radicle

emergence and appearance of green cotyledons was also scored at 5 days. All the assays were carried out in triplicate and

expressed as a percentage of the total number of seeds plated. Statistical analysis was carried out by one-way analysis of

variance (ANOVA) followed by a Student Newman-Keuls test (P<0.05). (E) Root elongation assays. Six-day-old seedlings were

transferred MS agar plates or supplemented with 200mM mannitol and incubated vertically for 10 d before primary root length

were estimated. Results are represented as percentage of reduction relative to standard conditions Data are expressed as means

± standard errors of three independent experiments with at least 20 plants each. Letters indicate significant differences between

Col-0, cdf3-1 and 35S::AtCDF3 overexpressing lines (P<0.05; ANOVA Student-Newman-Keuls tests. Representative images of

Col-0, cdf3-1 and 35S::AtCDF3 plants after the 10 d of treatment.


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Plants overexpressing AtCDF3 flowered slightly later than control plants under LD conditions.

Interestingly, similar results were obtained previously by Fornara et al, (2009) when overexpress AtCDF3

in companion cells using SUCROSE TRANSPORTER 2 (SUC2) promoter. However, when those lines

were subjected to water deprivation for 15 days and allowed to recover for 10 d during which they were

watered, important differences were found. Interestingly, WT and cdf3-1 plants exhibited similar severe

symptoms of water loss and significant wilting; only a little green color was visible in some leaves (Fig.

3.5B). In contrast, most of the 35S::AtCDF3 transgenic plants were less affected, keeping healthy greener

leaves. In fact after 10 d recovery period, most 35S:: AtCDF3 transgenic plants exhibited better survival

rates and fresh weight than WT and cdf3-1 plants, respectively (Fig. 3.5A,B).

To get insight and confirm the tolerance phenotypes observed in response to drought we also

further study osmotic stress tolerance, and then different germination and roots elongation studies were

conducted. First, we studied germination and the appearance of green cotyledons of 35S::AtCDF3, cdf3-1

and WT seeds when germinated on MS (control) or MS supplemented with 200 or 250mM mannitol and

scored after 4 and 5 days, respectively. When sown on control MS medium all genotypes 35S::AtCDF3,

cdf3-1 and WT seeds germinated equally well. However, when the lines where grown in 200 or 250mM

mannitol, germination and the appearance if green cotyledons rates in both treatments where clearly

higher in 35S::AtCDF3 than WT plants (Fig. 3.5D). By contrast compared to WT control, percentage of

germination of cdf3-1 seeds was significantly and consistently lowers. Similar results were obtained when

appearance of green cotyledons were scored (Fig. 3.5D). In a second experiment, primary elongation

assays were conducted and 35S::AtCDF3, cdf3-1, and WT plants were grown either on MS medium

(control) or on MS medium supplemented with 200 mM mannitol for 10 d (Fig. 3.5E). Under control

conditions, there was no difference between the gain and loss of function lines and WT plants. In contrast

on osmotic stress medium, 35S::AtCDF3 lines showed slight but significant lower values of primary growth

inhibition than the WT. In contrast, in the case of the cdf3-1 mutant exhibited lower values of root primary

growth inhibition than WT plants under similar stress conditions (Fig. 3.5E).


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In a further step, as low temperatures rapidly induced the expression of AtCDF3 we decide studied

the freezing tolerance of AtCDF3 gain- and loss-of- function plants. Freezing tolerance was determined in

non-acclimated and cold-acclimated (7 d at 4º C) and 35S::AtCDF3, cdf3-1 and WT plants as their

capacity to resume growth after being exposed for 6 h to different freezing temperatures when returned to

control conditions. Figure 3.6A shows that while the AtCDF3 overexpressor plants exhibit higher levels of

freezing tolerance than WT plants when non-acclimated, cdf3-1 exhibited significant lower freezing

tolerance to the WT plants. Moreover, 35S::AtCDF3 lines were also significantly more freezing tolerant

than the WT line after cold acclimation (Fig. 3.6B) but the cdf3-1 plants were significantly impaired in their

capacity tolerate freezing. The freezing tolerance phenotypes of non-acclimated and cold-acclimated WT,

cdf3-1 and 335S::AtCDF3 plants are displayed in Figure 3.6, respectively, as a representative example.

Collectively, these data suggested that AtCDF3 may be involved in plant responses and tolerance to

drought, osmotic and low temperature stresses.

Figure 3.6. Freezing tolerance of 35S::AtCDF3, cdf3-1 and Col-0 plants. (A) Non acclimated two-week-old 35S::AtCDF3

transgenic, Col-0 and cdf3-1 and plants were exposed to indicated freezing temperatures for 6h. (B) Two-week-old Arabidopsis

plants were exposed to the indicated freezing temperatures for 6 h after being acclimated 7 d at 4ºC. Freezing tolerance was

estimated as the percentage of plants surviving each specific temperature after 7d of recovery under control conditions. Data are

expressed as means ±SE of the three independent experiments with 50 plants each. On each time point, different letters indicates

significant differences (P<0.05 ANOVA followed by Student-Newman Keuls test).


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3.3.4.- AtCDF3 overexpression increased photosynthesis and stomatal aperture.

To investigate the underlying mechanisms involved the response to 35S::AtCDF3 and cdf3-1 plants

to drought stress we examined different physiological parameters such as net photosynthesis and related

gas exchange variables, stomatal conductance and sub stomatal CO2 using an LI-6400 infrared gas

analyzer (LICOR Biosciences, Lincoln, USA). Thus, three-week-old 35S::AtCDF3 (L2.1), cdf3-1 and

control (Col-0) plants were transplanted to hydroponic culture, and photosynthesis parameters were

measured 24 hours after treatment with 5% PEG-8000 and represented as percentage to control

conditions. The results shown in Figure 3.7 reveal that net photosynthesis (An) of 35S::AtCDF3 plants,

under control conditions, are similar to those of WT and cdf3-1 plants. Interestingly, when the same lines

where grown in osmotic stress conditions different reduction of photosynthetic rate among genotypes was

observed when compared to control conditions (Fig. 3.7A). In fact, while AtCDF3 overexpressing plants

exhibit significant higher values that WT plants, cdf3-1 show lower values than the control (Fig. 3.7A). As

photosynthetic efficiency is correlated with the rate of stomatal conductance, a considerably higher rate of

stomatal conductance (Gs) were also observed in 35S::AtCDF3 overexpression lines compared with the

WT, by contrast cdf3-1 show lower values than WT. Furthermore, the higher increase in the substomatal

CO2 concentration in the control plants (292 to 315 µmol/mol) compared to cdf3-1 (297 to 316 µmol/mol)

under osmotic stress suggests higher biochemical limitations to photosynthesis. Accordingly, it was

observed a reduction in the maximum quantum yield of PSII (Fv/Fm) in cdf3-1 and WT plants which

indicates the existence of photo inhibition events, whereas this parameter was not affected by PEG

treatment in 35S::AtCDF3 plants (Fig. 3.7B).

Since stomatal conductance in greatly affect by ABA, we decided to investigate the possible role of

ABA in the different responses of the stomatal conductance observed in the analyzed lines. Thus, four-

week-old 35S::AtCDF3 (L2.1), cdf3-1, and control (Col-0) plants grown in soil were analyze by spraying

with 0.5 µM ABA solution in the underside of the leaves and measurements photosynthesis parameters

were made 1, 2 and 3.5 h after treatment. The results obtained revealed that there lines analyzed show


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significant differences photosynthesis parameters with different dynamics and extents (Fig. 3.7C). While

control and cdf3-1 plants exhibit a similar significant reduction of stomatal conductance values after 1 hour

spraying with ABA (to a 60% of the non-treated values), 35S::AtCDF3 overexpressions plants exhibit a

delayed response with almost no effect after 1 hour of the treatment. However at longer times after

treatment (2-3h), 35S::AtCDF3 plants finally reach similar values of stomatal conductance than WT and

cdf3-1 plants. Correspondingly photosynthetic rate followed a similar response (Fig. 3.7D), showing an

earlier decrease (0-2 h) in control and loss- of- function plants, and delayed in 35S::AtCDF3 plants,

although equaled to the former ones after 3.5 hours. Collectively, these data suggested that AtCDF3 might

be involved in plant responses to abiotic stress by maintaining higher rates of photosynthesis under

unfavorable environmental conditions.

3.3.5.- The effect of AtCDF3 on drought tolerance is related to its transcriptional activity.

To examine whether AtCDF3 exerts effect in abiotic stress tolerance such us drought though its

transactivation activity a truncated form of AtCDF3 cDNA lacking the conserved C-terminal region, driven

by 35S promoter (35S::AtCDF3-stop), were used to transform Arabidopsis plants. Two homozygous

transgenic lines harboring the mutated gene with relatively high expression of truncated form AtCDF3-stop

were selected for further analyses (Fig. 3.5C). A phenotypic characterization 35S::AtCDF3-stop plants was

performed by analyzing their response to drought conditions as described previously. When cultured in

soil under greenhouse conditions, all overexpressing truncated 35S::AtCDF3-stop lines (L1.7 and L7.2)

plants do not presented significant developmental differences relative to WT plants. Notably recovering a

flowering time similar to the one of WT plants (Supplementary Figure S3.3). Interestingly, when those lines

were subjected to water deprivation for 15 days and allowed to recover for 10 d during which they were

watered, slight but significant differences were found. In fact all 35S::AtCDF3-stop transgenic plants were

less affected than WT, after 10 d recovery period, and exhibited better survival rates and fresh weight

values than WT, but significant lower (~2 fold) than the ones exhibited in transgenic plant overexpressing

full length AtCDF3 (Fig. 3.5A,B). All this data suggest that the AtCDF3 C-terminal domain might be


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essential for its functional capabilities, but the fact that the truncated form still promotes a significant effect

indicate that additional domain(s) in AtCDF3 protein might be also important for the function.

Figure 3.7. Effect of osmotic stress and ABA treatments on the reduction of stomatal conductance and photosynthetic

rate in 35S::AtCDF3, cdf3-1 and Col-0 plants. (A) Photosynthetic (An) rate and the maximum quantum yield (Gs) and (B)

chlorophyll florescence were estimated in three-week-old Col-0, cdf3-1 and 35S::AtCDF3 Arabidopsis plants that were

transplanted to hydroponic culture, and photosynthesis parameters were measured after 7 days of growth (control) or by adding

5% PEG-8000 (24h). Measurements were performed at steady state under saturating light (PAR 1000 mmol m-2 s-1), 400 ppm

CO2, ambient temperature and a vapour pressure difference (vpd) between 1 and 2 kPa. An and Gs were referred to the values at

control conditions. Each value is means ±SE of eight different measurements after 24 h under stress conditions. On each

parameter, different letters indicate significant differences (P<0.05). Effect of 0.5 µM ABA on the reduction of photosynthetic rate

(D) and stomatal conductance (E) in Col-0, cdf3-1 and 35S::AtCDF3 Arabidopsis plants. The analyses of ABA response were

performed using four-week-old plants grown in soil by spraying with 0.5 µM ABA solution in the underside of the leaves and

measurements were made after 1, 2 and 4 hours after treatment. For each genotype, values are referred to the parameter at t=0.

Each value is mean (±SE) of six different measurements. On each time point, different letters indicates significant differences

(P<0.05).


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3.3.6.- Transcriptome analysis of transgenic Arabidopsis overexpressing AtCDF3.

To clarify the AtCDF3 effects in regulation of the expression of abiotic stress-responsive genes and to

further understand the molecular mechanisms involved in the higher tolerance to drought and low

temperatures, a transcriptome of 3 week-old 35S::AtCDF3 (Line 2,1) and control (Col-0) plants was

performed using the affymetrix Arabidopsis oligo microarray. The analysis of transcriptome reveals that

among ~24,000 Arabidopsis genes the transcription levels of 641 was differentially expressed (with more

than two-fold change P>0,05) in AtCDF3 overexpressing plants compared with WT plants (Supplementary

Table S3.2; Fig. 3.8). About two-thirds (409) were up-regulated, whereas 122 were down-regulated.

Moreover detailed classification of the identified genes using the e-northern expression browser tool

(Toufighi et al., 2005) indicated that among the up-regulated genes 337, 109, 147 and 76 were

significantly misregulated (>1.5 fold) in at least one time point during drought, low temperature, salinity

and osmotic stresses, respectively (Fig. 3.8A). In the other hand, among the down-regulated in the

35S::AtCDF3 plants 22, 48, 52 and 30 genes were significantly misregulated in response to those

treatments, respectively. Overall these data indicated that many drought-, low temperature-, salinity- or

osmotic- inducible genes are potentially downstream of AtCDF3, including RD29A, COR15, ERD10 and

many others. Interestingly, we observed a significant overlap between the AtCDF3 regulated genes in

response to different abiotic stresses such as osmotic, drought, cold and salinity, indicating that a common

regulatory mechanism conferred by AtCDF3 is present in response to different abiotic stresses, especially

in the regulation of biosynthesis of protective compounds and/or control of primary metabolism (Fig. 3.9).


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Figure 3.8. Classification and Gene Ontology (GO) analyses of the genes differentially expressed in AtCDF3 overpresing

lines compared with WT plants. A-B) Venn diagrams showing overlap of up-regulated and down-regulated genes expressed in

35S::AtCDF3 transgenic plants compared with wild-type plants in response to different stresses. In silico expression analyses and

classification of 35S::AtCDF3 up-regulated (left) and down regulated (right) genes in response to cold, osmotic, salt and drought

stresses, by using e-Northern Expression Browser (http://bar.utoronto.ca). C-D) The scatter plot shows the cluster representatives

(terms remaining after reducing redundancy) in a two-dimensional space derived by applying multi-dimensional scaling to a matrix

of GO terms semantic similarities. The scatter plot was performed using http://revigo.irb.hr/revigo.jsp with list produced by the

analysis of functional categories defined by the Gene Ontology (GO) AgriGO (http://bioinfo.cau.edu.cn/agriGO/). Bubble color

indicates the p-value for the false discovery rates derived from the AgriGO analysis as well as biological processes.


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Quantitative RT-qPCR was performed to confirm some of the identified differentially regulated genes in

the 35S::AtCDF3 plants. Thus we analyzed the expression levels different classical abiotic stress-

responsive genes such as COR15A, RD29A and ERD10 in 35S::AtCDF3 lines (L2.1 and L5.4) and WT

plants under control conditions. Figure 3.9A shows the expression levels of the analyzed genes in

transgenic lines, where they exhibited higher values (from two- to fourfold) than in WT plants. These data

also confirmed the validity of the chip experiment and indicated that AtCDF3 might be upstream activator

in drought and low temperature stress pathways, acting directly or indirectly on the expression of different

stress-regulated target genes.

To elucidate whether AtCDF3 might directly regulated abiotic stress responsive genes, we first

searched for a common cis- acting elements present in the promoters of the AtCDF3 misegulated genes

using the promoter tool (Toufighi et al., 2005). We found enrichment in ABRE and DRE motifs like

(ACGTG and CCGAC, respectively) that have been identified as cis acting elements regulating gene

expression in response to drought, salt and cold stresses in Arabidopsis (Hao et al., 2002; Sakuma et al.,

2002). Interestingly we also found that many of them contain the DOF DNA-binding motifs 5´-T/AAAG-3´

in their promoter regions and among them COR15 gene was selected as a potential target of AtCDF3 for

further studies (Supplementary Figure 3.4S). In order to analyze the transcriptional activities properties of

AtCDF3, transient expression analyses in Arabidopsis protoplast were performed. Thus 35S::AtCDF3

effector plasmid was cotransfected with a reporter plasmid harboring GUS reporter gene under control of

1kb promoter region of COR15 containing 17 DOF cis-DNA binding elements (Supplementary Figure

S3.5). As shown in Figure 3.9, AtCDF3 could activate the expression of the reporter gene likely though

one of the DOF binding sites present in the COR15 promoter.


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All the previous expression data point to DOF cis-acting element might be play a role as an abiotic

stress response element. In order to verify this idea transient expression analyses were made in

Arabidopsis and effector plasmids with reporter plasmid harboring a GUS gene under control of a minimal

promoter containing a 2×DOF cis-DNA element (pBT10 2×DOF-GUS) were used to transform Arabidopsis

protoplast and then incubated under different stress conditions such as extreme temperatures (4ºC and

37ºC) or treated with NaCl (25mM) and ABA (100µM) for 12h. The results obtained indicated all tested

conditions activated the reporter gene but to different extents (Fig. 3.4D). Notably, higher levels of GUS

activity were observed in protoplasts exposed to low and high temperatures (>1,5 fold), whereas slightly

higher levels than the control untreated were detected in those protoplasts that were treated with salt and

ABA. Overall, the data obtained indicated 5′-AAAG-3′ DNA DOF binding site is a new cis-acting abiotic

stress response element and that the CDFs might be the nuclear trans-acting factors candidates that

control its activity.

In order to better understand the regulatory mechanism of AtCDF3 mediated regulation of stress

responses we performed gene ontology (GO) analyses using agriGO tool

(http://bioinfo.cau.edu.cn/agriGO/) (Du et al., 2010) of genes that are differentially expressed in

35S::AtCDF3 overexpressing line compared to WT plants. As shown in Figure 3.8C-D, GO terms like

response to stimulus (abiotic and biotic) and primary metabolism was overrepresented, but especially

those related to protein metabolism terms (Fig. 3.8C). Interestingly among them, some are involved in

nitrogen assimilation which could indicate that AtCDF3 might function as upstream regulators in nitrogen

assimilation pathways. Thus we decide to analyze the expression levels of several key genes involved in

nitrogen assimilation such us glutamine synthetase 2 (GS2) and glutamate synthase (GLU) in

35S::AtCDF3 lines and WT plants. Figure 3.9B shows the expression levels of the analyzed genes in

transgenic lines exhibited higher values (two- to four fold) than in WT plants. These results might indicate

that AtCDF3 might function as upstream regulator in nitrogen assimilation pathways. Since an important

amount of assimilated nitrogen should require and higher amount of carbon skeleton production we also


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analyze the expression levels genes involved in anapleurotic pathway for the TCA cycle such as

Phosphoenolpyruvate Carboxylase 1 (PEPC1), PEPC2 and Pyruvate kinase (PK1). The data obtained

revealed that these genes were coordinately activated in the overexpression lines (Fig. 3.9B) which

indicated that AtCDF3 could be involved in the production carbon skeletons for amino acid biosynthesis as

well.

Figure 3.9. Transcription analysis abiotic stress-responsive and nitrogen assimilation genes in 35S::AtCDF3 lines. (A)

Transcription analysis by RT-qPCR of COR15, RD29 and EDR10 stress-responsive genes in 35S::AtCDF3 (L2.1 and L5.4) and

control (Col-0) plants. (B) Expression levels nitrogen assimilation genes PK1, PEC1, PEPC2, GS2 and GLU in 35S::AtCDF3 (L2.1

and L5.4) and control (Col-0) plants. (C) Transient expression analyses. Co-transfection of Arabidopsis protoplasts with the

35S::AtCDF3 effector plasmid and promCOR15::GUS reporter plasmid, arrows indicate DOF binding sites. Empty effector plasmid

was used as negative control. Data are expressed as means ± standard error (SE) of three independent experiments. Asterisks

indicate significant differences (P<0.05; ANOVA Student-Newman-Keuls tests.


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3.3.7.- The overexpression of AtCDF3 promotes important metabolic changes in vegetative tissues.

It is well documented that drought and extreme temperatures stresses are conditions that promote

a large impact in many features of plant metabolism and physiology (Rizhsky et al., 2004; Seki et al.,

2007, Chaves et al., 2009). Thus, we carried different metabolomic analyses to investigate the weather

AtCDF3 overexpression in Arabidopsis promotes significant changes in plant’s metabolome. In a first step,

we performed a non-targeted metabolite analyses of 35S::SCDF3 (lines L2.1 and L5.4) and WT plants.

Then, we performed a principal component analysis (PCA) to compare about 1000 molecular features per

sample with each other. The results revealed that both 35S::AtCDF3 overexpressor lines exhibit a

significant alteration of the metabolome, as indicated by split-up clustering of the datasets (Fig. 3.10A,B).

Thus, we decided to further dissect this alteration tin the metabolome and performed a targeted

metabolomic profiling by gas chromatography-mass spectrometry to study the relative levels of different

polar compounds, including proteinogenic amino acids as well as four other amino acids and distinct

sugars, extracted from 12-d-old WT and 35S::AtCDF3 (L2.1 and L5.4) transgenic plants, grown under

non-stress conditions.


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3.10. Metabolic analyses of 35S::AtCDF3 and WT plants. A-B) PCAs of recorded, non-targeted metabolic profiles using Profile

Analysis (Bruker Daltonics, Bremen, Germany). Projection plots are shown for principal component 1 (PC1, 55.3% variance

explained) and PC2 (28%) of the L2.1line and principal component 1 (PC1, 72.7% variance explained) and PC2 (15%) of the L.4

line. Distinct grouping supports the different genotypes analyzed: WT control samples or overexpression Lines 2.1 and 5.4,

respectively. C) Relative quantities (% of WT) of selected metabolites analyzed by Gas chromatography-selected ion monitoring-

mass spectrometry. Results are shown as means ±SE (n=15). Similar results were obtained in five independent experiments

P>0.01; ANOVA, followed by a Student-Newman Keuls test).


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As shown in Figure 3.10B and Supplementary Table S3.3, comparison of gas chromatography

profiles revealed a number of clear differences between the control and overexpressing lines.

Overexpression of AtCDF3 in Arabidopsis significantly induced the accumulation of sugars like sucrose

(1.1 fold) , fructose (1.68 fold) and glucose (2.0 fold) and amino acids like γ-aminobutyric acid GABA (1.3

fold), L-valine (4.0 fold), L-proline (2.2 fold), L-asparagine (1.82 fold) and L-glutamine (1.53 fold), while the

amount of glycine decrease by up to 40%, relative to the control. It is remarkable that both AtCDF3

overexpressor lines exhibited important increase in sucrose content compared with the WT while glucose

and fructose, the two monomeric building blocks of sucrose, showed no reductions. This result are

consistent with a possible role of AtCDF3 in increasing CO2 fixation rates or/and carbon partitioning

altering the balance to production of sucrose instead of starch.

3.3.8.- Overexpression of AtCDF3 in tomato enhance osmotic and low temperature tolerance.

Considering globally all data, suggested that AtCDF3 may be involved in plant responses to

different environmental conditions. So, we decided to evaluate whether AtCDF3 could be used to improve

abiotic stress tolerance in a crop like tomato. Thus tomato plants were transformed with the previously

obtained 35S::AtCDF3 construct, and three homozygous lines with relatively high expression of AtCDF3

were selected for further analyses (Fig. 3.11A). A phenotypic characterization of tomato (T2) 35S::AtCDF3

plants was performed by analyzing their response under abiotic stresses, such as dehydration and low

temperature. Then, 30-day-old 35S::AtCDF3 and WT tomato plants were grown hydroponically under

control conditions (control) or subjected during 14 days to saline (75mM) or low temperature stress

(15/8ªC; day/night) and the biomass production and different photosynthesis parameters were evaluated.

As it shown in Figure 3.11B, when cultured in control conditions, all AtCDF3 overexpressing lines (L2, L5

and L10) presented developmental differences relative to WT tomato plants (M). In fact exhibit slight but

significant higher photosynthesis rate and total dry weight than WT plants (Fig. 3.11B). However, when the

same lines where grown in both stress conditions different photosynthetic rate and biomass among


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genotypes was observed when compared to control conditions (Fig. 3.11C). In fact, on salt stress

conditions all transgenic lines exhibit significant higher values of An and biomass that WT plants. In

contrast under low temperature stress treatment only line L5 exhibit higher rate of photosynthesis but all

lines exhibited significant higher values of biomass. These results indicated that overexpression AtCDF3

also increase to tolerance to salt stress and improve photosynthetic rate and biomass production in crop

tomato.


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Figure 3.11. Photosynthetic rate, biomass production and stress tolerance on 35S::AtCDF3 transgenic tomato plants

and WT exposed to salinity (75mM NaCl) and low temperatures during 14 days. A). Esquematic representation of the

construction used for tomato transformation and transcription analysis of the AtCDF3 gene in overexpressing lines. The

construction contains the ORF of the corresponding AtCDF3 gene flanked by the CaMV 35S promoter and the nopaline synthase

gene (NOS) 3´terminator. NPTII gene under the control of the NOS promoter was used as selective marker. Expression levels of

the corresponding AtCDF3 gene analyzed by RT-qPCR in heterozygous tomato lines. Expression of the UBIQUITIN3 (Hoffman et

al., 1991) gene was used as reference gene. Data are expressed as means ± SE of three independent extractions. Three

technical replicates were performed for each extraction. B). Salt and low temperatures tolerance of 35S::AtCDF3 tomato lines and

WT plants. Photosynthetic and dry weight parameters of 35S::AtCDF3 transgenic plants and WT growing under control, salt

stress (75 mM) or low temperature (10/5°C) conditions during 14 days. Biomass of plants growing under control, salt stress (75

mM) or low temperature (15/8°C) conditions. (C) Biomass production of 35S::AtCDF3 transgenic tomato plants and WT growing

under control or salt stress conditions after 14 days of treatment.


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3.4.- DISCUSSION.

During the last decade different reports have suggested that plant DOF factors regulated various

biological processes related to plant growth and development. In this work we have identified a group of

DOF Arabidopsis genes belonging to subfamily D are regulated by different abiotic stresses like salt,

drought, and extreme temperatures but with different expression patterns suggesting that they might

participate in abiotic stress responses. Furthermore, we provide functional evidences that support that one

them, AtCDF3, display a significant role in responses to drought and low temperature stress and also may

participate as a link between flowering signaling to responses to abiotic stimuli.

3.4.1.- AtCDF3 involvement in abiotic stress responses.

We analyzed the expression patterns of the complete set of 34 genes encoding DOF proteins of

Arabidopsis (Lijavetzky et al., 2003), and found that the ones included in the group D are notably highly

expressed in response to different abiotic stress conditions such as extreme temperatures, drought or

osmotic stress (Figure 3.1). Interestingly, among them the CDFs, seem to be regulated by drought, salinity

and extreme temperatures but with diverse timing and spatial expression patterns in roots and shoots,

suggesting that CDFs might display different function in responses to changes in environmental

conditions. Further detailed expression analyses revealed that AtCDF3, responded rapidly to different

abiotic stresses such us low and high temperatures, dehydration and high salt treatments with different

timing although with similar spatial patterns in adult plants, implying that it might participate in abiotic

stress responses. Moreover the fact that AtCDF3 show similar spatial expression patterns in response to

different stress treatments indicates that their roles may be related to a common effect caused by the

treatments. However the observation that AtCDF3 is also expressed in several tissues such as vascular

systems of leafs and stems guard cells but also in pollen and seeds during Arabidopsis development

probably implies that its product should play additional roles under normal growth conditions.


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Phenotypical analyses demonstrated that CDF3 T-DNA insertion mutant display reduced tolerance to

osmotic and low temperatures, in contrast AtCDF3 overexpressing lines were more tolerant to drought,

osmotic and low temperatures by survival rates and root length assays (Fig. 3.5). Moreover transcriptomic

analyses of 35S::AtCDF3 plants revealed that under non-stress conditions alter the expression of a large

number of genes (641) and about half of the AtCDF3 missregulated genes were responsive to osmotic,

drought or extreme temperatures (Fig. 3.8). Further transient expression analyses confirmed this

experiments and indicated that AtCDF3 could activate directly the expression of an abiotic stress

regulated genes such as COR15 (Fig. 3.9, Supplementary Figure S3.6) likely though the multiple DOF

binding sites localized in its promoter region, suggesting that CDFs might function as upstream regulators

of plant responses to abiotic stress. Moreover metabolomic analyses of AtCDF3 overexpressing plants

indicated the accumulation of amino acids like proline, GABA and sugars like sucrose and glucose which

usuallly are accumulated at higher levels in plant tissues when exposed to extreme temperatures, osmotic

stress or drought (Gill and Tuteja, 2010; Hussain et al., 2011; Rizhsky et al., 2004), displaying functions

related to osmotic adjustment, protection of membranes and ROS scavenging (Farrant et al., 2010,

Rajasekaran et al., 2000; Claussen, 2005; Munns and Tester, 2008). Thus, the increased amounts of free

aa such as proline and sugars observed in 35S::AtCDF3 plants are factors that would aid the tolerance to

low temperature and drought stresses.

Physiological studies have demonstrated that 35S::AtCDF3 plants exhibited higher rates of

photosynthesis and biomass under osmotic stress conditions than control plants. Drought, salinity and low

temperatures are environmental constrains decreasing photosynthetic efficiency and adversely affecting

plant growth and productivity. Generally theses unfavorable conditions force a water deficit that are

preceded by stomatal closure and a lower are CO2 diffusion which are the earlier responses (Tezara et al.,

1999, Flexas and Medrano 2002). Furthermore, membranes became unstable and disorganized, proteins

may undergo denaturation and loss of their activities and habitually increasing amount of reactive oxygen

species are generated, preceding oxidative stress. A general consequence of these phenomenon, it is

produced an inhibition of photosynthesis, metabolic unbalance and damage of cellular structures


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(Krasensky and Jonak, 2012). The significantly increased photosynthetic efficiency and biomass promoted

by the overexpression of AtCDF3 in Arabidopsis, seemingly contribute to improved drought and low

temperature tolerance, as it has been correlated with growth and stress tolerance (Kerepesi and Galiba,

2000; Farrant and Moore, 2011; Pinheiro and Chaves, 2011; Less et al., 2011). Overall, our results

strongly support that AtCDF3 play a significant role in plant responses and tolerance to changing

environmental conditions.

3.4.2.- AtCDF3 as a regulatory link between carbon and nitrogen metabolism.

The analyses of the expression of AtCDF3 reveal a very complex pattern been highly expressed in

very different tissues during development. Particularly, tissues with very different sink/ source dynamics

such as vascular tissues of shouts and roots, and reproductive tissues like flowers and seeds. This data

may undercover precise tissue-specific functions for the CDFs in controlling the expression levels of

particular group of genes that might be involved in particular metabolic processes. Metabolic analyses of

35S::CDF3 plants revealed that under control conditions the transgenic lines exhibit important changes,

specifically high levels of sugars like sucrose and glucose, and the accumulation of different amino acids

such as glutamine, asparagine, proline and GABA. Interestingly the levels GABA and glutamine are

reliable indicators nitrogen utilization efficiency (Stitt and Krapp, 1999; Foyer et al., 2006; Yanagisawa et

al., 2004). Remarkably GABA has been reported to be involved among other roles in nitrogen storage and

the pathway that converts glutamate to succinate via GABA (GABA shunt), which might be a great impact

in nitrogen economy of plants (Shelp et al., 1999). Overall the observed higher amino acid content in the

overexpression plants might be related to an improvement of nitrogen assimilation or alternatively by

imbibition of protein synthesis or a higher rate of protein degradation. However the observed higher rates

of photosynthesis rate and biomass of 35S::CDF3 plants together with the close link between nitrogen and

carbon metabolism might support the hypothesis of increased nitrogen assimilation. Moreover expression

analyses of the 35S::CDF3 lines showed that expression levels of several genes encoding enzymes

involved in nitrogen assimilation such as GS2 and GLU1 are increased under non stress conditions, which


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indicate that CDF3 might function as upstream regulators in nitrogen assimilation pathways. In the other

hand, an important amount of assimilated nitrogen should also require and higher amount of carbon

skeleton production and 2 oxogluturate is an intermediate between C and N metabolism and its levels are

important for N assimilation and it is produced though sequential reactions from photoassimilate

carbohydrates (Yuan et al., 2007; Kurai et al., 2011; Hodges, 2002). Interestingly, we also observed that

the expression of PEPC1, PEPC2 and PK1 genes involved in anapleurotic pathway for the TCA cycle was

coordinately activated in the overpression lines (Fig. 3.9B) which indicate that AtCDF3 might be involved

in the production carbon skeleton for amino acid biosynthesis. Similar results have been reported

previously with maize ZmDOF1 gene when overexpressed in Arabidopsis or rice plants (Yanagisawa et

al., 2004; Kurai et al., 2011), suggesting the participation of CDF factors and specially AtCDF3 in the

modulation of carbon a nitrogen metabolites, increase nitrogen assimilation and growth under abiotic

stress conditions and/or in particular tissues.

3.4.3.- AtCDF3 is involved in the cross-talk of abiotic stress responses and flowering time.

Plants have conquered many diverse environments, and most species that grow at higher latitudes

synchronize their developmental program with seasonal changes in day length (or photoperiod). In

Arabidopsis, flowering is induced during long days typical of spring and early summer but is delayed

during short winter days. Molecular genetic studies defined the photoperiodic flowering pathway,

comprising at its core the GIGANTEA, CONSTANS, and FLOWERING LOCUS T genes (Kobayashi and

Weigel, 2007; Turck et al., 2008), whose functions are highly conserved in distantly related species

(Hayama et al., 2003). The stimulation of CO transcription under long days is essential for the induction of

flowering since longer periods of exposure to light promote stabilization of CO protein (Jang et al., 2008

and Valverde et al., 2004), trigger FT transcription in the leaves ( An et al., 2004, Takada and Goto, 2003,

Wigge et al., 2005; Yoo et al., 2005), and posterior translocation of the FT protein to the shoot apical


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meristem (Corbesier et al., 2007, Jaeger and Wigge, 2007, Mathieu et al., 2007). In Arabidopsis

coordinately CDFs repress the transcript levels of CO to adjust the diurnal expression rhythm (Imauzami

et al., 2005, Fornara et al., 2009). The data obtained in this work further confirm the previously reported

aspects of CDFs function in the control of flowering time. Especially the overexpression of CDFs in phloem

companion cells in Arabidopsis promote a delay in flowering time under LD conditions but with a very

different extent (Imauzami et al., 2005, Fornara et al., 2009,). Here we could show that AtCDF3

overexpression not only promotes an impact flowering time but also in plant responses to different abiotic

stresses. Besides our results indicate that AtCDF3 functions mostly relays on a conserved C-terminal

domain. In fact though deletion experiments we have also shown that the C-terminal region of AtDF3 was

essential for its transcriptional capabilities and localization as determined by protoplast assays (Fig. 3.4B).

Moreover the role of this domain was further investigated trough transgenic analyses. Interestingly

transgenic plants that overexpresing a truncated form of AtCDF3 cDNA lacking the conserved C-terminal

region, (35S::AtCDF3-stop), not only recover a flowering time similar to the one of WT plants

(Supplemental figure S3.3), But also showed significant reduced tolerance to drought stress that one’s

exhibited in transgenic plant overexpressing full length AtCDF3 (Fig. 3.4A,B). Notably previous protein

sequence analyses of DOF proteins of group D from different plant species including Arabidopsis and

tomato reveled the existence of 3 homolog motifs of about 21, 22, and 33 aa are well conserved within

their C-terminal region (Yang et al., 2011, Hernando-Amado et al., 2012; Kloosterman et al., 2013).

Interestingly in Arabidopsis the C-terminal domain have been reported to be are essential for different

protein-protein interaction with the kelch repeat domain of the F-box proteins FKF1 and LKP2 and also

with GIGANTEA (GI) (Imauzumi et al., 2005, Sawa et al., 2007, Klossterman et al., 2013). Moreover it has

been reported that two different alleles (SlCDF1.2 and SlCDF1.3) of potato SlCDF1 gene that have lost

the C-terminal domain, are incapable to interact with the FKF1-GI complex, which results in important

alterations in tuber development and plant maturity (Klossterman et al., 2013). On this regard our data

might indicate that the C-terminal of CDFs is key domain though it exerts its action in flowering and abiotic

stress responses likely though the interaction with the signaling FKF1-GI complex. However the precise

regulatory mechanisms controlled by this complex in response to abiotic stress reposes are still unknown.


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The timing of flowering transition has significant consequences for the reproductive success in

plants and therefore their adaptability to changing environmental conditions. Consequently plants must

check closely changes in the environmental conditions to determine the onset of flowering to ensure

reproductive success. Plasticity in flowering time in response to change environmental conditions has

been reported in several plant species (Xu et al., 2005, Lafitte et al., 2006, Sherrard and Maherali 2006,

Franks et al., 2011, Ivey and Carr, 2012). Remarkably very contrasting effects on flowering time have

been reported for a variety of external cues. For instance, warm temperatures (28ºC) considerably

accelerate flowering in contrast with the opposite effect of low temperatures (16ºC) in Arabidopsis

(Balasubramanian et al., 2006; Blázquez et al., 2003). Moreover abiotic stresses such us UV-C exposure

accelerate flowering (Martínez et al., 2004). In the other hand intermittent salt stress or cold treatments

promotes a strong effect inhibiting flowering (Kaht et al., 2011). During the last years different reports also

point out the substantial effect of nutrient availability in flowering. For example, plants flowered earlier

when grown under low NO3- compared to high NO3- conditions. In contrast, growth with low Pi delayed

flowering compared to high Pi, in Arabidopsis. Further, the interaction between these two nutrients had a

cumulative effect given that the plants grown at 3 mM NO3 2-10 mM Pi flowered significantly earlier than

plants at 10 mM NO3 2-3 mM Pi (Kant et al., 2011). Our work shows that AtCDF3 not only play an

important role in flowering control by photoperiodic pathway but also display additional functions in plant

responses to adverse environmental conditions. It is remarkable that AtCDF3 control de expression of in

important set of genes including a number of genes that are involved in plant response to extreme temps,

drought and osmotic stress but also involved in primary metabolism. Consistently metabolomic analyses

reveal that CDFs overexpresssion promotes important changes in plant metabolome, altering the levels of

specific compounds with protective functions but also that alleviates detrimental effects of abiotic stresses

conditions. Moreover is also observed the accumulation of others such as sugars (sucrose and glucose)

and specific aa which resemble the profiles that usually are associated with specific energetic and

physiological status plant. Especially the ones involved in mobilization of nutrients from source to sink

tissues and nutrient recycling during aging and senescence programs (Jones, 2013, Bieker and Zentgraf,

2013), which would us allow to hypothesize that CDFs might be involved remobilization of valuable


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nutrients from various organs of the plant depending on stage of plant development and environmental

cues. On this regard, the present study provide of new notions regarding of the DOF transcription factor

and contributes to our understanding of the molecular mechanisms that takes integrates plant response to

adverse environmental conditions with the developmental program involved in the transition from

vegetative to reproductive phase.

4

CHARACTERIZATION OF TOMATO CYCLING DOF FACTORS REVEALS CONSERVED AND

NEW FUNCTIONS IN THE CONTROL OF FLOWERINGTIME AND ABIOTIC

STRESS RESPONSES

Characterization of tomato Cycling Dof Factors

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4.1.- INTRODUCTION.

DNA binding with One Finger (DOF) proteins are a group of plant-specific transcription factors (TFs) that

contain a 50 amino acid conserved domain in the N-terminal region. This DOF domain corresponds to a

C2-C2 configured zinc finger that binds specifically to the 5'-T/AAAAG-3' sequence motif in the promoters

of direct target genes (Yanagisawa and Schmidt, 1999). In contrast, the C-terminal protein region has a

highly variable structure, containing specific protein-protein interaction domains and other regulatory

elements. For instance, the Thr-Met-Asp motif present in Arabidopsis AtDOF4.2 and AtDOF4.4, (Zou et

al., 2013) and a 48 aa C-terminal domain of maize ZmDOF1 are responsible for their activation capacity

(Yanagisawa and Sheen, 1998; Yanagisawa, 2001). Consequently, DOF TFs exhibit a complex modular

structure, which allows them to display multiple regulatory functions, acting both as activators or

repressors in the control of the expression of numerous plant genes (Mena et al., 1998; Yanagisawa and

Sheen, 1998; Diaz et al., 2002; Yamamoto et al., 2006). The regulatory activity mediated by DOF proteins

involves not only DNA binding to target sequences, but also specific protein-protein interactions with other

regulatory proteins including bZIP and MYB TFs (Zhang et al., 1995; Vicente-Carbajosa et al., 1997;

Washio, 2001; Diaz et al., 2002) and nuclear high-mobility group (HMG) proteins (Yanagisawa, 1997;

Krohn et al., 2002).

Over the last years, DOF proteins have been reported to contribute to the control of very different

biological processes, as diverse as seed maturation and germination, tissue specific gene expression,

light responses and plant hormone signalling (Yanagisawa, 2002a, 2004; Moreno-Risueño et al., 2007a,

2007b). DOFs participate in the control of genes involved in carbon fixation and nitrogen assimilation

(Yanagisawa and Sheen, 1998; Rueda-Lopez et al., 2008), secondary metabolism (Skirycz et al., 2006,

2007), vascular development (Konishi and Yanagisawa, 2007; Guo et al., 2009; Gardiner et al., 2010),

lipid metabolism in the seed (Wang et al., 2007), seed germination (Papi et al., 2000, 2002; Gualberti et

al., 2002), photoperiodic flowering (Imaizami et al., 2005; Iwamoto et al., 2009) and flower abscission (Wei


94

et al., 2010). Nevertheless, DOF genes involvement in the regulation/adjustment of the metabolism under

different environmental cues has not been described.

The family of DOF TFs evolved from a common ancestor in green unicellular algae such as

Chlamydomonas reinhardtii, where only one gene has been found, and rapidly expanded in mosses, ferns

and vascular plants (Moreno-Risueño et al., 2007a). DOF genes are classified into families of different

size within species. In-silico analyses of the complete genome sequences of Arabidopsis, rice and

Brachypodium predicted 36, 30 and 27 DOF genes, respectively (Lijavetzky et al., 2003; Hernando-Amado

et al., 2012), whereas 31 members have been found in wheat (Shaw et al., 2009), 26 in barley (Moreno-

Risueño et al., 2007a) and 28 in sorghum (Kushwaha et al., 2011). Different phylogenetic analyses using

Arabidopsis, rice, barley and Brachypodium sets of predicted DOF genes indicate that they can be

classified into four major clusters of orthologous genes or subfamilies, A-D (Lijavetzky et al., 2003;

Hernando-Amado., 2012). In Arabidopsis, the D group contains a set of DOF factors whose transcripts

oscillate under constant light conditions and are hence known as Cycling Dof Factors, CDF1-5 (Imaizumi

et al., 2005; Fornara et al., 2009). CDFs display an important role in photoperiodic flowering in Arabidopsis

through the establishment of a diurnal rhythm in CONSTANS (CO) transcript levels by repressing its

expression. When overexpressed, CDF1-5 repress CO transcription,causing a strong delay of flowering

under long-day (LD). Consistently, combining loss-of-function alleles in four ofthese genes (CDF1, 2, 3,

and 5) causes photoperiod-insensitive early flowering (Fornara et al., 2009). In vivo, CDF1 and CDF2

degradation depends of the action of a protein complex that includes FLAVIN-BINDING KELCH REPEAT

F-BOX PORTEIN (FKF1) and GIGANTEA (GI) (Sawa et al., 2007). Light is required to stabilize their

interaction, so longer photoperiods cause enhanced accumulation of GI-FKF complexes and consequently

decreased CDF protein levels (Imaizumi et al., 2005; Fornara et al., 2009).

The Solanaceae family includes several horticultural crops of major economic importance, such as

tomato, potato, tobacco, and pepper. Although wide tolerance levels to abiotic stresses can be found in

their wild relative species, only moderate tolerance is conserved among their cultured varieties (Shannon


95

and Grieve, 1999; Nuez and Prohens, 2008). In the case of tomato, most cultivars show negative effects

under drought and salinity, resulting in growth inhibition, decreased seed germination and reduction of fruit

quality and production (Cuartero et al., 1995; Cuartero and Fernández-Muñoz, 1999). At the molecular

level, abiotic stresses induce changes in the expression of a large number of genes leading to

physiological and biochemical alterations. Drought and salinity significantly affect photosynthesis, which

impacts the function of other important metabolic pathways such as nitrogen assimilation (Chaves et al.,

2009). Moreover, respiration is enhanced to provide energy to maintain plant growth and development

(Haupt-Herting et al., 2001). Other protection systems are also affected by drought and salt stress, such

as the antioxidant and osmoregulation pathways that reinforce plant cells by the biosynthesis of

compatible solutes and reactive oxygen species (ROS) scavengers (Blumwald et al., 2000; Apel and Hirt,

2004; Zhu 2001, 2003; Munns and Tester, 2008).

Some efforts in the identification of genes responsible for salt and drought tolerance have been

made for both wild and cultivated tomato plants. Recent global expression analyses showed that more

than 2000 and 1300 genes are induced or repressed in response to drought and salinity, respectively

(Gong et al., 2010; Sun et al., 2010), suggesting that responses to these stresses are mediated by

multiple signal transduction pathways. Moreover, a number of the identified genes are commonly affected

by both stresses and by different stress conditions like low and high temperatures (Gong et al., 2010; Sun

et al., 2010) indicating an overlap of plant responses to abiotic stress. Despite these efforts, only a small

number of transcriptional regulators have been demonstrated to participate in abiotic stress responses in

Solanaceae, like LebZIP2 (Seong et al., 2008), SlAREB1 (Yañez et al., 2009), SlAREB1 (Orellana et al.,

2010) StERBEP1 (Lee et al., 2007), AIM1 (Abuqamar et al., 2009), TERF1 (Huang et al., 2004) and

JERF1 (Wu et al., 2007).

Expression levels of certain DOF genes are regulated by several environmental conditions.

Nevertheless, especially in crop plants like tomato, their exact roles in abiotic stress tolerance are not

known. In this work, we have identified 34 DOFs in tomato and performed phylogenetic analyses and


96

comparisons with their Arabidopsis counterparts. Based on sequence similarity and domain analyses we

have identified five genes homologous to Arabidopsis CDFs. We explored their expression patterns during

plant development, in response to abiotic stresses and under different light conditions. Among them,

SlCDF1 and SlCDF3 were investigated in more detail, focusing particularly on their roles in photoperiodic

flowering response and abiotic stress tolerance. Arabidopsis plants overexpressing SlCDF1 and SlCDF3

genes show improved tolerance to drought and salt when compared with the wild type (WT). Combined

studies of putative downstream target genes and metabolite-profiling shed light on the molecular basis of

the uncovered new roles of CDF proteins in response to environmental stresses.

4.2.- MATERIAL AND METHODS.

4.2.1.- Database searches for the identification of DOF family members in

Solanum lycopersicum L.

The nucleotide DOF domain sequences of Arabidopsis CDF genes (Lijavetzky et al., 2003) were

used to search for potential DOF genes in the tomato genome using the BLAST program (Altschul et al.,

1997) at the Sol Genomics Network website (Bombarely et al., 2011) and Phytozome database

(Goodstein et al., 2012). The amino acid sequences of the DOF genes were deduced through the

“Translate tool” at ExPASy Proteomics Server (Artimo et al., 2012). Alignments of protein sequences were

performed by CLUSTALW (Thompson et al., 1997). Phylogenetic and molecular evolutionary analyses

were conducted using the MEGA program software version 5.0 (Guindon and Gascuel, 2003; Tamura et

al., 2011) obtaining the phylogenetic trees from Neighbour-Joining analysis. The deduced protein

sequences of CDFs proteins from tomato and Arabidopsis have been further analysed by means of the

MEME program (Bailey et al., 2009; http://meme.sdsc.edu/meme4_6_0/intro.html).


97

4.2.2.- Subcellular localization of tomato CDF proteins.

Open reading frames (ORFs) of the tomato SlCDF genes were cloned into the pK7WGF2.0

plasmid using the Gateway recombination system (Invitrogen) to generate C-terminal GFP fusions driven

by the cauliflower mosaic virus 35S promoter (Karimi et al., 2007). As a control, the GFP gene expressed

under the control of 35S promoter was used. Transient transformations of onion (Allium cepa L.) epidermal

cells were performed by particle bombardment with a biolistic helium gun device (DuPont PDS-1000; Bio-

Rad) as described by Diaz et al, (2002). Fluorescence images were acquired after 40 h of incubation at 22

ºC in the dark using a confocal microscope (LEICA-Sp2-AOBS-UV) with appropriate filters.

4.2.3.- DNA-binding specificity of CDF proteins using the yeast one-hybrid assay.

Two copies of the DOF cis-DNA element were produced by annealing complementary single-

stranded oligonucleotides pTUYDOF-S (5´-

CGTGACATGTAAAGTGAATAACGTGACATGTAAAGTGAATAA-3´) and pTUYDOF-AS (5´-

CTAGTTATTCACTTTACATGTCACGTTATTCACTTTACATGTCACGAGCT-3´) which generate Xmal and

Xbal cohesive ends. This fragment was cloned into the Xmal and Xbal sites of the reporter plasmid

pTUY1H (Clontech) containing the HIS3 nutritional reporter gene. Entry clones containing the ORFs of the

SlCDF1-5 genes, were recombined into the pDEST22 plasmid (Invitrogen) using the LR reaction to

generate GAL4AD-ORF fusions. The resultant constructs and pTUY1H-2xDOF were co-transfected into

HF7c yeast cells. As negative control, an empty pDEST22 and pTUY1H-2xDOF vectors were used.

Transformed yeast cells were plated onto Skirtycz medium and incubated at 28 ºC. Single colonies were

then streaked on SD/-Trp-Leu selection medium with 30 mM of 3-AT (3-Amino-1, 2, 4-triazole). The plates

were subsequently incubated at 28 ºC.Single colonies were then streaked on SD/Trp-Leu-His selection

medium with 30mM 3-amino.1,2,4-triazole (3-AT). The plates were subsequently incubated at 28ºC for 2d

and yeast growth was determined.


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4.2.4.- Protoplast transformation and GUS assays.

Mesophyll protoplasts were isolated from rosette leaves of 3-week-old Arabidopsis plants ecotype

Columbia (Col-0) grown in soil (21/18ºC, 8/16 h light/dark). Protoplast isolation and transfection was

performed according to the method described by Alonso et al. (2009). Plasmid DNA was prepared using a

Genopure Plamid Maxi Kit (Roche), and 5 µg of pBT10-2xDOF-GUS (a dimer of the DOF binding

element) and 14 µg of each SlCDF1-5 effector plasmid were used for transfections. For normalization

purposes, 1 µg of Pro35S::NAN plasmid (Kirby and Kavanagh, 2002) was added. Then, 20 µl of plasmid

mixture (20 µg) and 200 µl protoplast were transferred to 2 ml microcentrifuge tubes following the

procedure described in Weltmeier et al. (2006). β-Glucoronidase (GUS) and NAN enzyme assays were

performed according to Kirby and Kavanagh, (2002). The ratio of GUS and NAN activities are represented

as relative GUS/NAN units.

4.2.5.- Plant growth conditions and quantification of CDF gene expression in tomato.

Characterization of the expression of CDF genes in tomato was performed in the Marmande RAF

cultivar. Seeds were germinated on a moistened mixture of peat moss and sand in growth chambers

(25/20 ºC, 16/8 h photoperiod) and irrigated regularly alternating water and nutrient solution (Hoagland

and Arnon, 1950). To study the expression profiling of SlCDF genes during vegetative and reproductive

development we collected plant material at different developmental stages: imbibed seeds, radicles and

cotyledons from 3-d-old seedlings, roots and leaves from 30 day-old plants, roots, leaves and flowers (in

anthesis) from 60 day-old plants, and green (30 days after anthesis) and red (60 days after anthesis) fruit

mesocarp. Three different pools of each plant material were harvested at any developmental stage. To

study the effect of abiotic stress and light regulation on the expression of SlCDFs, 3-week-old uniform

plantlets, bearing three leaves, were transferred to 1 L plastic pots containing half strength Hoagland

solution. Solutions were aerated and replaced every 4 d, and plants maintained during 4 weeks in growth

chambers (25/20 ºC; 16/8 h photoperiod). Salt stress was assayed by adding NaCl at 50 mM in the


99

nutrient solution. Polyethylene glycol (PEG 8000; Sigma) at 5% was used for water stress. Plants were

transferred for 24 h to growth chambers at 35/30 ºC and 10/5 ºC, for high and low temperature stresses,

respectively. Three different pools of roots and leaves were harvested (four plants per pool) after 6, 12 and

24 h of initiating the stress. Control plants were maintained at 25/20 ºC in half-strength nutrient solution.

To study the diurnal changes in the expression of SlCDF genes, leaves were harvested at 6 h intervals for

a total of 24. For continuous light experiment (LL), plants were shifted to continuous light at dawn. After 24

h, leaves were harvested every 4 h during 24 h (0, 4, 8, 12, 16, 20 and 24 h). Three independent extracts,

obtained from 12 plants (two leaves per plant and four plants per extract) were assayed at the different

time points in both experiments. Plant material was collected and stored at -80 ºC until analyzed. Total

RNA was extracted and purified using the RNeasy Mini Kit (Qiagen) and treated with Turbo DNase

(Ambion) following the manufacturer’s protocol. cDNA was synthesized from 2 µg of DNA-free RNA with

the use of Superscript II reverse transcriptase (Invitrogen) and random hexamers. The ABI Prism 7000

sequence detection system (Applied Biosystems) was used for the real-time PCR with programs

recommended by the manufacturer (2 min at 50 ºC, 10 min at 95 ºC, and 40 cycles of 95 ºC for 15 s and

60 ºC for 1 min) using Power SYBR Green PCR master mix (Applied Biosystems). In all treatments and

conditions, three independent samples from different extracts were used and each reaction was performed

in triplicate. The primer pairs used for amplification are described in Supplementary Table S4.3.

UBIQUITIN3 gene from S. lycopersicum (Hoffman et al., 1991) was used as reference gene. Relative

expression levels of the target genes were calculated using the 2−ΔΔCT method (Livak and Schmittgen,

2001). Positive and negative controls were included in the quantitative real-time PCR (RT-qPCR)

analyses.

4.2.6.- Plasmid constructs and plant transformation.

The ORF of SlCDF1 and SlCDF3 were cloned into the Gateway binary vector pGWB2 (Nakagawa

et al.,2007) under control of the 35S promoter. The resultant plasmid was used to transform Arabidopsis

thaliana plants, ecotype Columbia (Col-0) by the Agrobacterium tumefaciens-mediated floral dip method


100

(Clough and Bent, 1998). Transformed plants were selected on MS medium containing 50 µg/ml

kanamycin.

4.2.7.- RNA measurements by RT-qPCR in Arabidopsis.

The expression of SlCDF genes (SlCDF1 and SlCDF3), abiotic stress responsive genes (COR15,

RD29A and ERD10), and flowering control genes (CO and FT) in overexpression (35S::SlCDF1 and

35S::SlCDF3) and control lines (Col-0) was determined by RT-qPCR. Plants were maintained in growth

chambers (21/18 ºC, 16/8 h photoperiod). Total RNA was extracted from 10 day-old seedlings to study CO

and FT expression and from leaves of 3-week-old plants to study SlCDF1-3, COR15, RD29A and ERD10

following the protocol of Onate-Sanchez and Vicente-Carbajosa, (2008). For cDNA synthesis 2 µg of total

RNA were primed with oligo dT15 primers (Promega) using the AMV Reverse Transcriptase according to

the manufacturer’s instructions. Arabidopsis UBIQUITIN mRNA level (At5g25760) was used as control.

The reaction, PCR program and the analysis of the data were performed as mentioned above to analyze

the expression of CDF genes in tomato. The primers pairs used for PCR amplification are presented in

Supplementary Table S 4.3.

4.2.8.- Salt and drought stress tolerance tests.

Salinity and drought stress assay were carried out using control plants (Col-0), 35S::SlCDF1 and

35S::SlCDF3 transgenic lines. For salinity assays, seeds were sterilized and plated onto Petri dishes

containing MS medium (Murashige and Skoog, 1962). After 6 days, seedlings were transferred to vertical

plates containing MS medium (control) and MS medium supplemented with 80 mM NaCl (Lakhssassi et

al., 2012). About 20 seedlings were used per replicate and three replicates were made for each treatment.

Primary and lateral root elongation was measured after 10 days using ImageJ software (Abramoff et al.,

2004). To evaluate growth differences between control and saline stress, data were represented as

percentage of root growth reduction relative to standard conditions and statistical analyses were carried

out by one-way ANOVA followed by Student-Newman-Keuls test (P<0.01). Drought stress tolerance tests


101

were performed on plants grown in soil in individual pots. After 2 weeks, the water supply was cut off for

15 days and then watering was resumed during 10 d. Plant survival rates were calculated afterwards and

fresh weight was measured 10 d after re-watering period.

4.2.9.- Metabolomic analyses.

Non-targeted and targeted metabolomics analyses were performed on 12-day-old control plants

(Col-0) and two independent 35S::SlCDF3 lines. Extraction, manipulation and mass spectrometric analysis

of samples followed an adapted protocol, detailed in Supplementary File S4.1, which is based on

previously described methods (Fiehn et al., 2000; Gullberg et al., 2004; Gaquerel et al., 2010).

4.3.- RESULTS.

4.3.1.- Identification of CDF proteins in tomato plants.

In order to identify CDF proteins encoded by the tomato genome, the amino acid sequence of the

DNA-binding domain of Arabidopsis CDF1-5 proteins (Imaizumi et al., 2005; Fornara et al., 2009) was

used to perform a BLAST survey against the tomato whole-genome database (http://solgenomics.net/;

Bombarely et al., 2011). A total of 34 predicted DOF tomato transcription factor genes were identified,

annotated and named SlDOF1-34 (S. lycopersicum DOFs, Supplementary Table S4.1). Nucleotide

sequence comparisons between genomic and cDNA clones allowed the identification of precise exon-

intron structures (Supplementary Table S4.2). All encoded DOF proteins contain a unique DNA binding

domain of 50 aa encompassing a C2-C2 zinc finger (DOF). In a previous study, Lijavetzky et al. (2003)

identified 36 DOF proteins in Arabidopsis and classified them into four groups: A-D. In order to evaluate

the evolutionary relationships among the tomato and Arabidopsis DOFs, specific and combined

phylogenetic analysis based on their DNA binding domain sequences were performed. The resulting trees


102

were obtained by the neighbour-joining algorithm and supported by comparisons with the Arabidopsis tree

(Fig. 3.1A and B, Supplementary Figure S3.1). In both species, DOFs are clustered into four mayor

groups: A, B, C and D. Three of them were further divided into subgroups based on bootstrapping values.

Arabidopsis group D1 contains the Arabidopsis CDFs, i.e At5g62430, At5g39660, At3g47500, At1g26790

and At1g69570. Interestingly, sequence analyses also identified a D-type group in tomato, containing five

genes encoding proteins with high level of sequence similarity to the Arabidopsis CDFs. These tomato

genes were considered as putative CDF orthologues from tomato and were renamed as S. lycopersicum

CDF1-5, respectively (Supplementary Table S4.1). This tentative assignation was further supported by

comparative analyses of the deduced amino acid sequences of the whole Arabidopsis and tomato CDFs

proteins by the MEME software. As shown in Fig. 4.1C the analyses revealed the existence of

homologous motifs, conserved among their sequences and different from the DOF binding domain

characteristic of this family (motif 1, Lijavetzky et al., 2003; Yanagisawa 2004a; Moreno-Risueño et al.,

2007a). Two additional conserved domains are also found in all of the proteins: motifs 2 and 4 spanning

21 and 22 aa, respectively; and another 33 aa motif (motif 3) conserved in nine of 10 sequences. These

three associated motifs seem to represent a common signature of type-D group of CDF proteins of

Arabidopsis and tomato.


103

Figure 4.1. Phylogenetic trees and conserved motifs of Arabidopsis and tomato DOF protein families. (A, B) The

Arabidopsis (left) and tomato (right) trees were inferred by the neighbour-joining method after the alignment of the DOF

domain amino acid sequences of the 36 Arabidopsis (Lijavetzky et al., 2003) and 34 tomato DOF proteins (listed in

Supplementary Table S4.1), respectively. The resulting groups are shown as A, B, C or D and subscript numbers indicate

defined subgroups. The scale bar corresponds to 0.05 estimated amino acid substitutions per site. (C) Schematic distribution

of conserved motifs among Arabidopsis and tomato CDF proteins. Motifs were identified by means by MEME software using

the complete amino acid sequences of the 10 CDF proteins clustered in groups D of the phylogenetic trees. Position of the

identified motifs is relative to the DOF domain. Multilevel consensus sequences for the MEME defined motifs are listed.

D1

D2

A

B2

C1

C2.2

C2.1

C3

0.05

B1

At4g21030

A

D

B1

A1

C1.1

B2

C1.2

C2

C3

B

A2

0.05

D1

D2

A

B2

C1

C2.2

C2.1

C3

0.05

B1

At4g21030

A

D

B1

A

C1.1

B2

C1.2

C2

C3

B

A2

0.05

1: DOF domain

2, 3 and 4: CDFsdomains

1

4

4

4 4

4

4

4

CPRCNS[MAl][ED]TKFCY[FY]NN[YN]N[VA][NS]QPR[HY]FC[KR][NSAK]CQRYWTAGG[TS]MRN[VL]PVG[AS]GRRK

At5g62430 At5g39660 At3g47500 At1g69570 At1g26790 SlCDF2

SlCDF4 SlCDF3

SlCDF1

SlCDF5

1

1

1

1

1

1

1

1

1

2

2

2

2

2

2

2

2

2

2

3

3

3

3

3

3

3

3

3

4

4

4

4

4

4

4

4

1

4

4

1

FYPA[PA]PYWGCT[VI]PG[PS]W[NT][VL]P[WT][LMS]


[GK][CS][LV][LW]VPKTLRIDDP[GN]EAAKSSIW[AT]TLGIK[HN][DE]V[VM]

ETS[PL][SV]LQANPAA[LM]SRSMNF[HR]E[SQ]

2


3

FYPA[PA]PYWGCT[VI]PG[PS]W[NT][VL]P[WT][LMS]4


C C

1: DOF domain2, 3 and 4: CDFs domains


104

Supplementary Figure S1.- Phylogenetic tree of Arabidopsis and tomato DOF proteins.The tree was inferred by the

neighbor-joining method after the alignment of DOF domain amino acid sequences of the Arabidopsis and tomato DOFs. The

resulting Major Clusters of Orthologous Genes (MCOG) A, B, C and D are indicated (Figure 4.1A and 4.1B). The scale bar

corresponds to and 0.01 estimated amino acid substitution per site. Arabidopsis and Tomato CDFs clustered in group D are

marked in blue.

MCOG D

MCOG A

MCOG B

MCOG C


105

4.3.2.- Tomato SlCDF1-5 proteins localize to the cell nucleus and display distinct DNA- binding and activation properties.

To investigate the subcellular localization of SlCDF proteins, translational fusions of their

corresponding ORFs to the C-terminus of GFP were made. These constructs, driven by the 35S promoter,

were used in transient assays with onion epidermal cells by particle bombardment. As shown in Fig. 4.2A,

fluorescence corresponding to the emission spectrum of GFP was restricted to the nuclei of transformed

cells that carried the 35S::GFP::SlCDF: constructs (Fig. 4.2A, panels 8-12). When cells were transiently

transformed with 35S::GFP, the GFP fluorescence spread throughout the cell, indicating a cytoplasmic

localization (Fig. 4.2A panel 7). Nomarski pictures (Fig. 4.2A, panels 1-6) and the merged pictures of those

and the fluorescence images are also shown (Fig. 4.2A, panels 13-18). We examined the capacity of the

tomato SlCDF proteins for binding to the 5’-AAAG-3’ cis-DNA element using the yeast one-hybrid system.

Fig. 4.2B shows the results of an experiment where the different SlCDFs were expressed as fusion

proteins to the GAL4 activation domain in yeast cells harbouring a HIS3 reporter gene under control of a

minimal promoter containing a 2x DOF cis-DNA element. Yeast growth on His-depleted medium results

from the activation of the HIS3 gene through binding of the SlCDF proteins to the cis-DNA element.

Addition of 3-AT as an inhibitor of the HIS3 product was used to measure the strength of the protein-DNA

mediated activation. In all cases, effective yeast growth demonstrated that SlCDF-DNA binding was

sufficiently strong to overcome 3-AT inhibition. However, yeast cells expressing SlCDF1, SlCDF2 and

SlCDF5 grew much better on medium containing 30 mM of 3-AT than those expressing SlCDF3 and

SlCDF4, indicating their higher binding affinity to the 5´-AAAG-3´ motif than the latter.

In order to test the transcriptional activation properties of SlCDFs in planta, transient expression analyses

in Arabidopsis protoplasts were performed (Fig. 4.2C). The 35S::SlCDF1-5 effector plasmids were co-

transfected with reporter plasmid pBT10-GUS-2xDOF. The results confirmed that all of the tested CDFs

can bind to the 5´-AAAG-3´ cis-DNA element to different extents, and activate the reporter gene. This

showed that the previously detected DNA-binding capacity is fully functional in leaf protoplasts.

Interestingly, high levels of GUS activity were observed in protoplasts transformed with SlCDF3, 4 and 5,


106

whereas low levels were detected in those protoplasts that were transformed with SlCDF1 and SlCDF2.

Overall, the data obtained indicate that the identified tomato SlCDFs are functional nuclear factors that,

despite their high sequence similarity, bind the DOF element with different affinities and display distinct

transcriptional activation capacities.

Figure 4.2.- Subcellular localization, transcriptional activation and DNA binding specificity of tomato SlCDF1-5

proteins. (A) Subcellular localization of the SlCDF proteins in onion epidermal cells. GFP alone (35S::GFP) or GFP-SlCDF

(35S::GFP-SlCDF1-5) fusion proteins were expressed transiently under the control of the CaMV 35S promoter in onion

epidermal cells. After 36 h of incubation tissues were observed with a confocal microscope for the emission spectrum of the

GFP (panels 7-12) or by Nomarski (1-6). Merged Nomarski and fluorescence images are also shown (panels 13-18). Arrows

point to cell nuclei.

100 µm35S::GFP

A G M

35S::GFP-SlCDF1

B H N

100 µm35S::GFP-SlCDF4

E K Q

100 µm100 µm35S::GFP-SlCDF2

C I O


F L R


D J P

100 µm

35S::GFP-SlCDF2


107

Figure 4.2.- Continued. Subcellular localization, transcriptional activation and DNA binding specificity of tomato

SlCDF1-5 proteins. (B) The DNA binding specificity of SlCDF1-5 proteins was assayed using the yeast one-hybrid

system.Yeast HF7c cells were transfected with the genes encoding SlCDF proteins and pTUY1H driving HIS3 expression

under the control of 2xDOF binding element. The transformed yeast cells were plated onto the SD/−His/−Trp/−Leu medium

including the indicated amounts 3-AT. Empty pDEST22 plasmid was used as negative control. (C) Transcriptional activation

assays of SlCDFs in Arabidopsis protoplasts. Arabidopsis protoplasts were transfected with the 35S::SlCDF1-5 effector

plasmids (pK7WGF2.0) and pBT10-2XDOF-GUS reporter plasmid, containing 2X DOF cis-DNA element. Empty pK7WGF2.0

plasmid was used as negative control. Data are expressed as means ± standard errors of three independent experiments.

4.3.3.- Expression of tomato SlCDFs follows a circadian rhythm.

To investigate whether the identified SlCDF1-5 genes from tomato are controlled by the circadian

clock like in Arabidopsis (Imaizumi et al., 2005; Fornara et al., 2009), we performed RT-qPCR analyses

using RNA from tomato plants grown under a LD diurnal cycle of 16 h light/ 8 h dark and under continuous

light (LL), respectively. The results revealed that, under LD conditions, the expression levels of tomato

SlCDF1-5 oscillated during the day, although they display quite different patterns, which could be

classified in two groups (Fig.4.3A, B). The expression levels of SlCDF1 and SlCDF3 followed a similar

pattern that consisted of upregulated levels during the second half of the night and the first part of the day,

reaching its maximum level at approximately midday. The, expression then levels rapidly decreased to

SD/-L/-W/-H (3AT)

pTUY1H Ǿ

0mM 5mM 30mM 5mM

pTUY1H-2xDOF

pDEST22-SlCDF1

pDEST22Ǿ

2XDOF: 2xGCGTGACATGTAAAGTGAATAAA

pDEST22-SlCDF2

pDEST22-SlCDF3

pDEST22-SlCDF4

pDEST22-SlCDF5

10 pmoles MU/min 0,5

35S::SlCDF2

35S::SlCDF4

35S::SlCDF3

35S::SlCDF5

35S::SlCDF1

pK7WGF2.0 Ø

AAAG AAAG

SlCDF35S

GUS

2xDOF GCGTGACATGTAAAGTGAATAAA


108

lower levels in the middle of the night (Fig. 4.3A). In contrast SlCDF2, SlCDF4 and SlCDF5 transcript

levels dropped during the first part of the light period.

Figure 4.3. Transcription analyses of tomato SlCDF1-5 genesduring development and in response to different light

conditions. (A, B) SlCDF1-5 gene expression analyzed by RT-qPCR in 7-week-old tomato plants grown under diurnal cycle

of 16h light/ 8 h dark or under continuous light. White and black bars along the horizontal axis represent light and dark

periods, respectively. (C, D) Expression profiling of SlCDFs genes. SlCDF1-5 gene expression was analyzed by RT-qPCR

using RNA extracted from vegetative and reproductive tissues of tomato: radicles (root) and cotyledons from 3-d-old

seedlings, root and leaves from 30- and 60-day-old plants imbibed seeds, flowers from 60-day-old plants, and green and red

fruit 30 and 60 days after anthesis, as indicated. Expression of tomato UBIQUITIN3 gene (Hoffman et al., 1991) was used as

reference gene. All date are expressed as means ± SE of three independent pools of extracts. Three technical replicates

were performed for each extract.


109

Minimum expression levels were maintained during the second half of the day and the beginning of

the night and increased to reach its maximum at the beginning of the light period (Fig. 4.3A). However,

when the analyses were performed with plants grown under continuous LL conditions, the expression of

tomato SlCDF1-5 genes exhibited a 24 h period oscillation pattern, which is similar to the one observed

under LD (Fig. 4.3B). Moreover, the expression patterns of SlCDF1-5 could still be classified into the same

two groups. Taken together, these data indicate that the expression of SlCDF1-5 is light responsive and

follows a circadian pattern, which strongly supports that the identified tomato CDF genes are true

orthologues of the Arabidopsis CDFs.

4.3.4.- Expression of tomato SlCDF1-5 genes is differentially regulated during development.

We analysed the expression patterns of tomato SlCDF1-5 genes during plant development using

RT-qPCR (Fig. 4.3C and D) and found that SlCDF1-5 genes have distinct patterns of expression. SlCDF1

and SlCDF2 show higher expression levels in vegetative compared to reproductive organs, while SlCDF4

and SlCDF5 are expressed at significant levels in both types. In addition, SlCDF3 exhibited low expression

in all organs analysed. The difference in expression patterns became more evident when the expression

was analysed in closer detail during plant development (Fig. 4.3C). SlCDF1, SlCDF2, SlCDF4 and

SlCDF5 transcripts accumulated at high levels in cotyledons, but all of them showed minor levels of

expression in mature leaves of 4-week-old plants. In contrast, a significant increment of SlCDF1, SlCDF2

and SlCDF4 transcripts was detected in leaves of 8-week-old plants, while SlCDF3 and SlCDF5 showed a

slight reduction. In addition, a progressive enhancement of SlCDF1 expression was observed in roots

during plant development. SlCDF2, SlCDF4 and SlCDF5 expression was, however, reduced in roots of

older plants, and no changes were detected for SlCDF3. In the reproductive tissues analysed, the

expression of SlCDF1 and SlCDF3 was negligible when compared with the other SlCDFs (Fig. 4.3D).

Higher levels of SlCDF2, SlCDF4 and SlCDF5 transcripts were detected in flowers, fruits and seeds. It is


110

noteworthy that, during fruit ripening a considerable increment of SlCDF4 was detected, whereas SlCDF5

transcripts were abundant only in green fruit and SlCDF2 showed similar expression in green and red fruit.

4.3.5.- SlCDF1-5 genes are differentially induced in response to abiotic stress conditions.

To address the question whether the expression of SlCDFs is also regulated by environmental

cues other than light/photoperiod, SlCDF1-5 mRNAs levels were measured in leaves and roots of three-

week-old tomato plants that had been subjected to different abiotic stresses: salinity (50Mm NaCl),

osmotic (5% PEG), heat (35/30 ºC) and cold (10/5 ºC) treatments for 6, 12 and 24 h. In leaf tissues,

transcript levels of all SlCDFs increased under salt and osmotic stress, in particular those of SlCDF2 and

SlCDF4 after 24 h (Fig. 3.4A and B). In response to high temperatures, an earlier induction at 12 h was

observed for SlCDF4 and SlCDF5 with higher increases at 24 h together with SlCDF2 (Fig. 4.4C).

However, maximum induction was observed under cold treatment at 12 h for SlCDF1, SlCDF3, SlCDF4

and SlCDF5, with decay at 24 h (Fig. 4.4D). Induction of SlCDFs was also observed in root tissues

following different patterns. All SlCDF genes were regulated by salt and drought. Most importantly,

SlCDF4 and SlCDF5 showed induction after 24 h of salt treatment, whereas SlCDF1, SlCDF2 and SlCDF3

increased at early times (6 h) after osmotic treatment (Fig. 4.4A and B). Regarding to temperature

treatments, maximum increase was observed for SlCDF3 and SlCDF5 at 24 h after heat treatment (Fig.

4.4C), and for SlCDF1, SlCDF3 and SlCDF4 at 12 h after the exposure to low temperatures (Fig. 4.4D).

4.3.6.- Overexpression of tomato SlCDF3 promotes late flowering in transgenic Arabidopsis plants.

Tomato SlCDF1 and SlCDF3 were selected for further characterization because they responded to

various abiotic stresses and encode proteins that show highest sequence similarity to the functionally well-

characterized Arabidopsis CDF1 (Imazumi et al., 2005; Fornara et al., 2009). Transgenic Arabidopsis

plants overexpressing SlCDF1 and SlCDF3 under the control of CaMV 35S promoter were generated and

three homozygous lines with relatively high expression of SlCDF1 and SlCDF3 were selected for further

analyses (see Fig. 4.7A). When cultured in soil under greenhouse conditions, all the overexpressing


111

SlCDF3 lines (L2.10, L10.4, and L10.7) presented several developmental differences relative to WT plants

(Col-0). Plants overexpressing SlCDF3 flowered later than control plants under LD conditions but not in

short day (Fig. 4.5A, B, C and J), suggesting that these plants are impaired in the photoperiodic flowering

pathway. In addition, transgenic lines also displayed other pleiotropic alterations that became more

evident in adult plants both during vegetative and reproductive development. Fig. 4.5D-H shows

representative pictures of 4-week-old WT and 35S::SlCDF3 (line 10.7 as an example) plants showing that

leaves were bigger and petals and carpels of the mature flowers were larger than those of the WT.

Furthermore, the siliques of the overexpressing lines were bigger than WT (Fig.4.5I). In contrast, we did

not observe significantly different phenotypes in the SlCDF1 overexpressing plants (data not shown). To

assess whether the late flowering phenotype observed in the SlCDF3 overexpressing plants is due to

changes in the expression of reported key regulatory genes like CO and FT, we tested diurnal expression

profiles of these genes by RT-qPCR, comparing 35S::SlCDF3 (L2.10 and L10.7) and WT plants. Fig. 4.6A

shows that CO transcript levels decreased in the transgenic plants compared to the Col-0 and the

rhythmic cycling of the mRNA was dampened. Moreover, a reduction in the levels of FT expression was

detected in 35S::SlCDF3 plants (Fig. 4.6A). Altogether, these data support the assumption that the tomato

SlCDF3 exerts a similar mode of action as the Arabidopsis CDFs in the control of flowering time.


112

Figure 4.4. Transcription analysis of tomato SlCDF1-5 genes analyzed by RT-qPCR in plants exposed to different

abiotic stress conditions. Total RNA was extracted from 7-week-old tomato plants grown in nutrient solution (control) or

supplemented with 50 mM NaCl for salt stress (A), 5% PEG 8000 for drought stress (B) exposed to 35/30 °C for high

temperature stress (C) or exposed 10/5 °C for low temperatures stress, for the indicated times (D). Expression of tomato

UBIQUITIN3 gene (Hoffman et al., 1991) was used as reference gene. Results are presented as relative expression of

SlCDF1-5 under stress conditions compared to the expression under control conditions All data are expressed as means ±

SE of three independent pools of extracts. Three technical replicates were performed for each extract.


113

Figure 4.5. Phenotypic differences of Col-0 and 35S::SlCDF3 plants during vegetative and reproductive

development.(A) Representative images of four-week-old plants WT and 35S::SlCDF3 (L 10.7 as an example) grown under

LD. (B, C) Flowering-time phenotype under LD and short day (SD) conditions, respectively. (D) Rossete leaves of Col-0 and

35::SlCDF3 plants grown under LD conditions. All leaves, including cotyledons, are shown in order of production from the first

true leaf. (E) Cauline leaves of Col-0 and 35S::SlCDF3 plants grown under LD conditions. (F, G) Detached flowers and

detached petals of Col-0 and 35S::SlCDF3 plants grown under LD conditions. (H) WT and 35S::SlCDF3 flower gynoecium. (I)

Col-0 and 35S::SlCDF3 siliques.(J) Flowering-time analyses of Col-0 and 35S::SlCDF3 (L2.10, L10.4, L10.7) lines estimated

as rosette leaf number formed under LD conditions. Data are expressed as means ± SE of 20 homozygous plants. Asterisks

indicate significant differences (P<0.05; one-way ANOVA followed by Student-Newman-Keuls test).

4.3.7.- Overexpression of SlCDF1 and SlCDF3 has an impact in drought and salt tolerance in transgenic Arabidopsis plants.

As our expression analyses indicated that tomato SlCDF1 and SlCDF3 might play an important role

in the plant response to different abiotic stresses, we decided to further explore the function of SlCDF1

and SlCDF3. A phenotypic characterization of 35S::SlCDF1 and 35S::SlCDF3 plants was performed by

analysing their response under abiotic stresses, such as dehydration and high-salt treatment

Col-0 35S::SlCDF3

A

Col-0

35S::SlCDF3

D

C

B

Col-0 35S::SlCDF3

LD

SD

E F

G H I

35S::SlCDF3 35S::SlCDF3

35S::SlCDF3 35S::SlCDF3 35S::SlCDF3

Col-0 Col-0

Col-0Col-0Col-0

J

Col-02.10 10.4 10.7

35S::AtCDF3

Col-0 35S::SlCDF3

0

2

4

6

8

10

12

14

16

18

Ro

se

tte

Le

af

Nu

mb

er

** *


114

First, we studied the capacity of soil-grown 35S::SlCDF1 and 35S::SlCDF3 transgenic plants to

tolerate water deprivation compared to WT plants. After 15 d of drought, plants were allowed to recover for

10 d during which they were watered. As shown in Fig. 4.7B and C, when cultured in soil under non-stress

(control) conditions, both WT and transgenic lines performed equally well. After the drought treatment, all

WT plants exhibited severe symptoms of water loss and substantial wilting. In contrast, most of the

35S::SlCDF1 and 35S::SlCDF3 transgenic plants were less affected, retaining greener leaves. Only slight

wilting was observed in some of the 35S::SlCDF1 transgenic leaves. After the 10-days recovery period,

the 35S::SlCDF1 and 35S::SlCDF3 transgenic plants exhibited better survival and growth than the WT, as

judged by their survival rates and fresh weight (Fig. 4.7B and 4.7C). To assess tolerance to salt stress,

primary (PR) and lateral (LR) root elongation assays were conducted. 35S::SlCDF1, 35S::SlCDF3 and WT

plants were grown either on control medium (no NaCl) or salt stress medium, containing 80 mM NaCl for

10 days (Fig. 7D and E). Under control conditions there was no difference between the transgenic and the

WT plants. Only two transgenic 35S::SlCDF3 lines (10.4 and 10.7) did exhibit slightly longer roots. On salt

stress medium, 35S::SlCDF1 and 35S::SlCDF3 lines showed slight but significant lower values of root

primary growth inhibition than the WT. Moreover the effect was more evident on root lateral growth, as all

35S::SlCDF1 and 35S::SlCDF3 transgenic plants exhibited much lower values of root lateral growth

inhibition than WT plants under similar stress conditions (Fig. 4.7D and E). Collectively, these data

suggest that SlCDF1 and SlCDF3 may be involved in plant responses to drought and salt stress.

To investigate the molecular mechanisms underlying the enhanced tolerance to drought and salt

tolerance by SlCDF1 and SlCDF3, we tested the expression levels of different abiotic stress-responsive

genes such as COR15A, RD29A and ERD10 in 35S::SlCDF1 and35S::SlCDF3 and WT plants under

control conditions. Fig. 4.6B shows the expression levels of the analysed genes in transgenic lines, where

they exhibited higher values (from two to fourfold) than in WT plants. These data indicate that SlCDF1 and

SlCDF3 might be upstream activators in drought and salt stress pathways, acting directly or indirectly on

the expression of different stress-regulated target genes.


115

Figure 4.6. Transcription analysis of flowering time and abiotic stress-responsive genes in 35S::SlCDF1 and

35S::SlCDF3 lines. (A) mRNA levels of CO and FT genes were analyzed by RT-qPCR in 35S::SlCDF3 (L2.10, L10.7) and

control plants (Col-0). Total RNA was extracted from 10-day-old seedlings and harvested, at the indicated times, throughout a

LD. White and black bars along the horizontal axis represent light and dark period, respectively. (B) The expression of

COR15, RD29A and ERD10 genes was analyzed by RT-qPCR on three-week-old 35S::SlCDF1 (L1.2, L1.4, L2.6),

35S::SlCDF3 (L2.10, L10.4, L10.7) and control (Col-0) plants. Expression of Arabidopsis UBIQUITIN10 gene (Czechowski et

al., 2005) was used as reference gene. All data are expressed as means ± SE of three independent pools of extracts. Three

technical replicates were performed for each extract.

A

Col-0

35S::SlCDF3 (L2.10)35S::SlCDF3 (L10.7)

0 4 8 12 16 20 24

CO

/UB

I

Hours

0 4 8 12 16 20 24

FT

/UB

I

Hours

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

0.5

0.4

0.3

0.2

0.1

0

Col-0

35S::SlCDF3 (L2.10)35S::SlCDF3 (L10.7)

0

2

4

6

8

10

12

COR15 RD29 ERD10

Re

lati

ve

exp

ress

ion

L1.2Col-0

L1.4 L2.6

35S::SlCDF1

L2.10Col-0

L10.4 L10.7

35S::SlCDF3

0

2

4

6

8

10

12

COR15 RD29A ERD10

Re

lati

ve

exp

res

sio

nB


116

Figure 4.7. Drought stress tolerance of 35S::SlCDF1 and 35S::SlCDF3 plants.(A) Transcription analysis of tomato

SlCDF1 and SlCDF3 genes in different T3 independent 35S::SlCDF1 (L1.2, L1.4, L2.6) and 35S::SlCDF3 (L2.10, L10.4,

L10.7) transgenic lines. SlCDF1-3 expression was analysed by RT-qPCR in Arabidopsis plants. Expression of Arabidopsis

UBIQUITIN10 gene (Czechowski et al., 2005) was used as reference gene. Data are expressed as means ± SE of three

independent extractions. Three technical replicates were performed for each extraction. (B) Drought stress tolerance was

estimated by scoring fresh weight and survival rates of two-week-old 35S::SlCDF1 (L1.2, L1.4, L2.6), 35S::SlCDF3 (L2.10,

L10.4, L10.7), and control (Col-0) plants, that were maintained 15 days without irrigation and then 10 days of re-watering.

Representative images of plants before and after the treatment. Survival rates are indicated under the photographs. (C) Fresh

weight data are expressed as means ± SE of three independent experiments with five plants each. Asterisks indicate

significant differences between Col-0 and 35S::SlCDF1 or 35S::SlCDF3 overexpressing lines (P<0.01; ANOVA Student-

Newman-Keul

35S::SlCDF335S::SlCDF1

A

52% 100% 93% 86%

Control

Droughtstress

Survivalrate (%) 34% 100% 92% 50%

0

1

2

3

Fre

sh

we

igh

t(g

)

**

*

Col-0L2.10 L10.4 L10.7

C

0

1

2

3

Fre

sh

we

igh

t(g)

Col-0L1.2 L1.4 L2.6

**

0

0,2

0,4

0,6

0,8

1

Re

lati

ve

Ex

pre

ssio

n

Col-035S::SlCDF3

L2.10 L10.4 L10.70

0,2

0,4

0,6

0,8

1

Rel

ativ

eE

xpre

ssio

n

35S::SlCDF1Col-0

L1.2 L1.4 L2.6

B


117

Figure 4.7 Continued. Salt stress tolerance of 35S::SlCDF1 and 35S::SlCDF3 plants. (D) Salt stress tolerance estimated

by determining the reduction of primary and lateral growth of 35S::SlCDF1 (L1.2, L1.4, L2.6), 35S::SlCDF3 (L2.10, L10.4,

L10.7) and control (Col-0) plants after 10d in MS supplemented with 80mM NaCl and represented as percentage of reduction

relative to standard conditions. Data are expressed as means ± SE of three independent experiments with at least 20 plants

each. Asterisks indicate significant differences between Col-0 and 35S::SlCDF1 or 35S::SlCDF3 overexpressing lines

(P<0.05; ANOVA Student-Newman-Keuls test). (E) Representative images of Col-0,35S::SlCDF1 (L2.6) and 35S::SlCDF3

(L2.10) after the treatments.

4.3.8.- Overexpression of SlCDF3 in transgenic Arabidopsis plants induces metabolic changes and accumulation of specific compounds.

As drought and salt stress are known determinants that promote substantial physiological and

metabolic rearrangements in plants (Rizhsky et al., 2004; Seki et al., 2007), we carried out non-targeted

metabolite profiling to address the question whether the ectopic expression of SlCDF3 in Arabidopsis

translates into a detectable alteration of the plant´s metabolome. Principal component analysis (PCA) of

35S::SlCDF3 (2.10)Col-0

80 mM NaCl

35S::SlCDF1 (2.6)Col-0

80 mM NaCl

35S::SlCDF1

80mM NaCl

1.2 1.4 2.6Col-0

80mM NaCl

35S::SlCDF3

2.10 10.4 10.7Col-0

D

Rel

ativ

eIn

hibi

tion

of r

ootl

engt

h(%

)

*

*

*

0

5

10

15

20

25

30

35

40

*

*

*

*

*

*

*

Primary root

Lateral root

E

0

10

20

30

40

50

60

70


118

the retention time, intensity, and accurate mass identity matrices, carried to compare approximately 1000

molecular features per sample with each other, revealed that the overexpression of SlCDF3 results in a

distinguishable alteration of the metabolome, as indicated by the clear clustering of the datasets (Fig.

4.8A). When we tried to identify the differentially abundant components causing the grouping in the PCA,

we discovered that a great part of the differences were found among the group of small and polar

compounds, containing for example sugars, amino acids, and small acids. As an example, the increased

abundance of glutamine in the overexpressing lines compared to the WT is shown in Fig. 4.8B and C.

Hence, we focused our analyses on those polar compounds and performed a targeted metabolomic

profiling by gas chromatography-mass spectrometry (GC-MS) to study the relative levels of different polar

compounds, including proteinogenic amino acids as well as four other amino acids, eight distinct sugars

plus two sugar alcohols, and eight small acids, extracted from 12-day-old WT and 35S::SlCDF3 (L2.10

and L10.7 lines) transgenic plants, grown under non-stress conditions. As shown in Fig. 4.8 D and

Supplementary Table S4.4, the comparison of GC profiles revealed a number of clear differences between

control and overexpressing lines. Overexpression of SlCDF3 in Arabidopsis significantly induced the

accumulation of sugars like sucrose (2.5-fold), and amino acids like GABA (2-fold), L-proline (2.2-fold) and

L-glutamine (1.8-fold), and succinate (1.3-fold), while the amount of malate and gluconate decrease by up

to 24 and 34.9.%, respectively, relative to the control. Consistent with the expected similar effects in both

SlCDF3 overexpressing lines, most sugars appeared at comparable levels. Interestingly, these lines

showed an important increase in sucrose compared to the WT. Since glucose and fructose, the two

monomeric building blocks of sucrose, showed no considerable reductions, it may be concluded that

SlCDF3 overexpression either causes a change in carbon partitioning favoring the production of sucrose

over that of starch, or that CO2 fixation rates are generally increased. Finally, overexpression of SlCDF3

did not trigger the accumulation of organic acids, except succinate, as reflected by its increased

concentration in both transgenic lines grown under control conditions (Fig. 4.8D).


119

Figure. 4.8. Metabolic analyses of 35S::SlCDF3 and WT plants. (A) PCA of recorded, non-targeted metabolic profiles using

Profile Analysis (Bruker Daltonics, Bremen, Germany). Projection plots obtained for principal component 1 (PC1, 19%

variance explained) and PC2 (15%). Distinct grouping supports the different genotypes analysed: WT control samples or

overexpression lines 2.10 and 10.7, respectively. (B) Extracted ion chromatograms (EICs) for mass m/z 130.05 at 0.81 min

reveal induction of the compound in the overexpression lines. (C) The accurate mass of the parent ion and its isotopic pattern

led to the identification of L-glutamine. (D) Relative quantities (% of WT) of selected metabolites analysed by Gas

chromatography-selected ion monitoring-mass spectrometry. Results are shown as means ± SE (n = 15). Similar results were

obtained in five independent experiments. [Student’s t test; * P < 0.05, ** P < 0.01, *** P < 0.001].


120

4.4.- DISCUSSION.

DOF proteins are plant-specific FTs that participate in different developmental and physiological

processes (Lijavetsky et al., 2003; Moreno-Risueño et al., 2007a). In this work we have identified and

characterized tomato DOF genes, homologous to Arabidopsis CDFs, and found that the encoded proteins

possessed transcriptional activation ability. Furthermore, we provide evidence for their participation in the

control of flowering time and abiotic stress responses.

4.4.1.- SlCDFs share a high degree of sequence similarity but display different DNA-binding affinities and diverse transcriptional activation capabilities.

We searched the complete tomato genome sequence and identified 34 genes encoding DOF

proteins. In accordance with previous studies in Arabidopsis (Lijavetzky et al., 2003), these 34 genes

were divided into four groups (A-D) on the basis of similarities in their DNA binding domains. Within group

D, we found five tomato genes with high level of sequence similarity to Arabidopsis CDFs. The encoded

proteins showed conservation not only in their DNA-binding domain but also in their C-terminal region,

which contained three conserved motifs of 21, 22 and 33 aa, respectively, which were reported to be

essential for the protein-protein interaction with the C-terminal kelch repeat domain of the F-box proteins

FKF1 and LKP2 (Imaizumi, 2005; Sawa et al., 2007). In addition, these three motifs are also conserved in

homologous proteins from other species, such as Jatropha curcas (JcDOF3; Yang et al., 2011a),

Brachypodium distachyon (BdDOF4, -11, -16, -20 and -22; Hernando-Amado et al., 2012) and Solanum

tuberosum (StCDF1, Kloosterman et al., 2013). Interestingly, two allelic variants of potato StCDF1

(StCDF1.2 and StCDF1.3) lacking the C-terminal end have been reported to be impaired in their

interaction with the FKF1-GI complex. As a consequence, this results in major defects in plant maturity

and tuber development (Kloosterman et al., 2013). Consistent with these data, it may be concluded that

the three identified C-terminal motifs are common features of CDF proteins, through which the regulatory

mechanisms controlled by CDFs are determined.


121

Subcellular localization and yeast one-hybrid assays conducted in this study showed that the identified

tomato SlCDFs are nuclear factors that bind to the core 5’-TAAAG-3’ DOF cis-DNA element (Yanagisawa

and Schmidt, 1999) with different binding affinities. Transactivation assays confirmed these results and

indicated that SlCDFs can act as transcriptional activators, again to different extents. While SlCDF1 and

SlCDF2 exhibit only little transcriptional activation capabilities, SlCDF3, SlCDF4, and SlCDF5 display

higher transcriptional activation capacity. Consistent with these data, the overexpression of SlCDF1 and

SlCDF3 in Arabidopsis promote the expression of COR15, RD29A and ERD10. Whether they act directly

or indirectly as upstream activators remain to be elucidated. In contrast, we found that the overexpression

of SlCDF3 results in reduced expression of both CO and FT genes, most likely acting as a target

repressor, as reported for the Arabidopsis CDF1 protein (Imauzumi et al., 2005; Fornara et al., 2009). It

should be noted that the DOF domain was at first identified as a DNA-binding domain, but also reported as

a bifunctional domain for DNA-binding and protein-protein interactions (Mackay and Crossley, 1998).

Differences in the activities of DOF TFs have been associated to the core DOF domain (Yanagisawa,

2004) as well as their protein-protein interactions with other TFs. In fact, the DOF domain participates in

the interaction with other classes of TFs like bZIP proteins or HMG proteins, which in turn modify their

transcription capabilities (Vicente-Carbajosa et al., 1997; Yanagisawa, 1997; Zhang et al., 1995; Krohn et

al., 2002). For example, the Arabidopsis DOF protein OBP1 was identified as a protein interacting with

bZIP proteins OBF4 and OBF5 associated with stress responses (Zhang et al., 1995). Altogether, these

data suggest that the identified SlCDFs could display different transcription activities depending on target

gene promoters and the combinatorial interactions with other transcription factors present in a particular

tissue or under different environmental conditions.

4.4.2.- Expression of SlCDFs follows a circadian rhythm with two different patterns.

Diurnal oscillation of transcript levels of CDFs has been reported for Arabidopsis and other species

under day/night and constant light conditions (Imaizumi et al., 2005; Fornara et al., 2009; Iwamoto et al.,

http://pcp.oxfordjournals.org/content/45/4/386.full#ref-19

http://pcp.oxfordjournals.org/content/45/4/386.full#ref-43


122

2009; Yang et al., 2011). CDFs exhibit different diurnal expression patterns that can be classified in two

different groups: CDF1, CDF2, CDF3 and CDF5 show maximum expression at the beginning of the light

period, decreasing progressively thereafter to a minimum between 16-20h, then rising again during dawn;

and the group comprising CDF4, whose transcript levels rise progressively from dawn and decrease at the

end of the night (Fornara et al., 2009). In the present study, the identified tomato SlCDFs that exhibit

similar diurnal expression patterns under LD and LL conditions, supporting the assumption that they are

true homologues of the Arabidopsis CDFs. Interestingly, their gene expression patterns could be also

classified in two groups, the group of SlCDF1 and SlCDF3 exhibit a maximum at the beginning of the day

and SlCDF2, SlCDF4 and SlCDF5 that exhibit maximum levels during the night period, suggesting that the

family of CDFs might display different function (at least two conserved functions) and regulate specific

target genes at different periods of the day.

4.4.3.- Expression of tomato SICDF genes in Arabidopsis unveils a conserved function in the control of flowering time.

It is well established that regulation of the temporal expression of the transcription factor CO is

crucial to control the photoperiodic flowering in Arabidopsis and other photoperiod-sensitive species

(Suarez-López et al., 2001; Mizoguchi et al., 2005). The induction of CO mRNA by light under LD

conditions, but not in short-day conditions, is a key element for the triggering of flowering, as light

treatment is necessary for the stabilization of CO protein (Valverde et al., 2004; Jang et al., 2008) and the

subsequent activation of FT transcription (Takada and Goto, 2003; An et al., 2004; Wigge et al., 2005; Yoo

et al., 2005). In addition, the Arabidopsis CDFs act redundantly in repressing CO transcription to modulate

the diurnal expression rhythm (Imazumi et al., 2005; Fornara et al., 2009). Our results show that the

overexpression of tomato SlCDF3, in analogy to Arabidopsis CDF1, promoted late flowering in

Arabidopsis. Interestingly, SlCDF3 overexpression also leads to a reduction in the mRNA levels of CO and

FT, the natural direct targets of the Arabidopsis counterpart (Fig. 4.6), which is in support of a conserved

functionality. Nevertheless, it should be noted that tomato plants are photoperiod-insensitive in their native

habitats and there is no single environmental factor known to be critical for flower induction in this species


123

(Heuvelink and Dorais, 2005). Several factors such as light intensity, temperature and number of leaves

affect the time of flowering in tomato (Calvert, 1959; Hussey, 1963; Kinet, 1977; Uzun, 2006), a process

considered to be controlled by intraplant competition for assimilates (Sachs and Hackett, 1969; Atherton

and Harris, 1986; Dieleman and Heuvelink, 1992). Notably, key regulatory genes like CO and the CDFs,

implicated in the photoperiodic flowering pathway are also present in tomato (Pnueli et al., 1998, 2001;

Carmel-Goren et al., 2003; Ben-Naim et al., 2006). Our results suggest that some of the identified tomato

SlCDFs, like SlCDF3, might retain some functions in the control of flowering time through similar molecular

mechanisms to those observed when expressed in Arabidopsis, but also that they might have additional

functions in tomato.

4.4.4.-SlCDFs involvement in abiotic stress responses.

As revealed by RT-qPCR expression analyses all SlCDFs respond to different abiotic stresses like

salt, drought and extreme temperatures with different timing and spatial expression patterns in roots and

shoots, suggesting that they might participate in abiotic stress responses. This observation led us to the

generation and analyses of 35S::SlCDF1 and 35S::SlCDF3 transgenic Arabidopsis plants. We could

confirm that the overexpression of SlCDF1 and SlCDF3 resulted in increased tolerance to both salt and

drought stress, as shown by survival rates and root length assays. Moreover, both overexpressing lines

exhibit higher expression levels of abiotic stress-responsive genes, like COR15, RD29A and ERD10,

under non-stress conditions, which indicate that SlCDFs might function as upstream regulators in drought

and salt stress response pathways. Metabolic profiling of 35S::SlCDF3 plants showed increased levels of

proline, glutamine, GABA and sucrose. These compounds are normally accumulated under water stress

and salinity (Hoekstra et al., 2001; Rizhsky et al., 2004) aiding stress tolerance through osmotic

adjustment, detoxification of reactive oxygen species and intracellular pH regulation (Rajasekaran et al.,

2000; Claussen, 2005; Munns and Tester, 2008; Bressan et al., 2009; Chaves et al., 2009). Their

significant increased levels, promoted by the overexpression of SlCDF3 in Arabidopsis, seemingly

contribute to improved drought and salt tolerance since its content has been correlated with the stress


124

tolerance (Kerepesi and Galiba, 2000; Farrant and Moore, 2011; Pinheiro and Chaves, 2011). Altogether,

our results strongly support the participation of SlCDFs in plant responses and tolerance to abiotic stress

conditions.

4.4.5.- Impact of SlCDFs expression on C/N metabolism.

SlCDFs exhibit different expression patterns during development. However, with the exception of

SlCDF3, all of them are expressed during vegetative development at high levels, especially in young

tissues like cotyledons. In organs with contrasting sink and source activities like mature vegetative tissues

of shoots and roots, and reproductive tissues, such as flowers and fruits, they are also differentially

expressed. This may highlight precise tissue-specific functions for the SlCDFs in controlling the expression

levels of particular subsets of genes and consequently specific metabolic processes.In this regard, the

metabolic analyses of 35S::SlCDF3 plants show that the overexpression of SlCDF3 transcription factor in

Arabidopsis results in significant metabolic alterations. Specifically, we observed higher levels of sucrose

and of certain amino acids, indicative of increased nitrogen assimilation, as previously reported for other

DOF TFs (Yanagisawa et al., 2004b). In this line, our studies also revealed a higher content of succinate

and GABA. The hypothesis that GABA acts as a temporary nitrogen storage pool could explain the

increased concentration of this non-proteinogenic amino acid (Beuve et al., 2004). On the other hand, up-

regulation of the pathway that converts glutamate to succinate via GABA would explain the rise in

succinate content (Rhodes et al., 1999). Glutamic acid metabolism via the GABA shunt could be of

considerable importance in the nitrogen economy of plants (Shelp et al., 1999; 2006). As carbon and

nitrogen metabolites mutually influence each other in a fine balance between carbon and nitrogen

metabolism (Yanagisawa et al., 2004b; Kurai et al., 2011), the higher content of sucrose in 35S::SlCDF3

transgenic plants suggests that CO2 fixation could be also stimulated to maintain the carbon/nitrogen

balance. Hence, we hypothesize that SlCDFs genes could be involved in the regulation of the primary

metabolism in different tissues and under precise developmental and stress conditions.


125

4.4.6.- CDFs at the interplay between environmental conditions and flowering time.

The results of our study confirmed a previously reported and salient feature of CDFs in the control

of flowering time. Specifically, the overexpression of AtCDFs in phloem companion cells leads to a delay

in flowering in LDs although with a different impact in Arabidopsis (Imazumi et al., 2005; Fornara et al.,

2009). Here, we could demonstrate conservation in this function for specific tomato CDFs, which are able

to reproduce the same phenotype when expressed in Arabidopsis. Flowering time is critical in the plant life

cycle, yet plants must closely monitor the environmental state to determine the onset of flowering for

reproductive success. Intriguingly, data presented here reveal that, besides the participation of some

SlCDF genes in the control of flowering in photoperiod-sensitive species, they also display additional

functions. Notably, SlCDFs regulate the expression of genes involved in abiotic stress responses.

Moreover, metabolic analyses of SlCDF-overexpressing plants showed accumulation of precise

compounds that mitigate abiotic stress conditions. They also showed important changes in particular

metabolites, like increased levels of sucrose and certain amino acids, typically associated to physiological

states like the nutrient salvage and recycling under senescence programs (Jones, 2013) or the

mobilization and relocation of resources from source to sink organs. This information opens the possibility

of further investigating the links of CDF function in the adaptation to environmental conditions and the

progression from vegetative to reproductive phases. Additional research and in-depth physiological

characterization of transgenic plants for the different SlCDF genes, currently underway, will clarify the

precise role of these genes.

5

CONCLUSIONES

Conclusiones

127

El estudio de la implicación de diferentes factores de transcripción de la familia DOF en el

desarrollo y la tolerancia a estreses abióticos nos ha permitido obtener las siguientes conclusiones:

1. El análisis de expresión “in silico” de los genes que codifican factores de tipo DOF de

Arabidopsis indica que los pertenecientes al grupo D, y especialmente los AtCDFs, cambian su

niveles de expresión en respuesta a distintos tipos de estrés abiótico en tejidos vegetativos.

2. El gen CDF3 de Arabidopsis thaliana codifica un factor de transcripción de tipo DOF cuya

expresión aumenta en respuesta a salinidad, temperaturas extremas, deshidratación y

tratamientos con la fitohormona ABA en tejidos vegetativos.

3. El factor de transcripción AtCDF3 se localiza en el núcleo y se une al elemento cis-DNA 5´-

AAAG-3´. Además funciona como un activador transcripcional. El dominio C - terminal del factor

AtCDF3 es esencial para su localización nuclear y actividad transcripcional.

4. La sobre-expresión del gen AtCDF3 en Arabidopsis provoca un aumento de la tolerancia a las

bajas temperaturas y a la sequía, mientras que el mutante knock-out cdf3-1 es más sensible a

estos estreses. Además, la sobre-expresión de una forma truncada de AtCDF3 sin el dominio C-

terminal, limita el incremento de la tolerancia a sequía, lo que sugiere un papel importante de

este dominio en la función de la respuesta a estrés abiotico.

5. Análisis de expresión global de líneas transgénicas 35S::AtCDF3 indica que AtCDF3 regula la

expresión de un número importante de genes, implicados en la respuesta a estrés abiótico y en

la asimilación de nitrógeno. Además, análisis metabolómicos señalan la acumulación de

distintos metabolitos, como azúcares y aminoácidos. Estos datos confirman que los AtCDFs

desempeñan un papel importante en la regulación del metabolismo primario durante el

desarrollo y bajos distintas condiciones de estrés.

Conclusiones

128

6. La sobre-expresión del gen AtCDF3 en tomate promueve un aumento de la fotosíntesis y la

biomasa, tanto en condiciones habituales de cultivo, como bajo estrés salino y bajas

temperaturas.

7. El análisis filogenético comparado entre las proteínas SlDOF y sus ortólogas en Arabidopsis

establece cuatro grupos principales de genes ortólogos (MCOG: Major Clusters of Orthologous

Genes).

8. Se han identificado los ortólogos putativos de los genes “Cycling Dof Factors” CDF1-5 de

Arabidopsis en tomate. Estos genes presentan características estructurales similares a los del

grupo D de Arabidopsis y han sido denominados S. lycopersicum CDF1-5. Los SlCDF1-5 son

proteínas nucleares que se unen al motivo cis-DNA 5´-AAAG-3´ con diferentes afinidades y

actúan como activadores transcripcionales.

9. Los genes SlCDF1-5 exhiben distintos patrones de expresión en los órganos de la planta y

están regulados por ritmo circadiano. Además, sus niveles de expresión aumentan en respuesta

a estrés osmótico, salino y por temperaturas extremas.

10. La sobre-expresión del gen SlCDF3, pero no del SlCDF1, en Arabidopsis promueve un retardo

en el tiempo de floración a través de la modulación de la expresión de genes como AtCO y

AtFT. Estos datos indican que el gen SlCDF3 actúa de forma similar a los AtCDFs en el control

de la floración.

11. Líneas transgénicas de Arabidopsis que sobre-expresan los genes de tomate SlCDF1 y SlCDF3

muestran un aumento de la tolerancia a sequía y salinidad, asociado de un incremento en la

expresión de varios genes de respuesta a estrés como AtCOR15, AtRD29A y AtERD10.

Conclusiones

129

12. Análisis metabolomico de las líneas transgénicas de Arabidopsis 35S::SlCDF3 muestran una

acumulación de azúcares como sacarosa y determinados aminoácidos, sugiriendo que los

SlCDFs pueden estar involucrados en la regulación del metabolismo primario.

6

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SUPPLEMENTARY

Supplementary

156

3.-. Arabidopsis Cycling Dof Factor 3 CDF3 regulate drought and low temperature stress response and flowering time in Arabidopsis thaliana

Supplementary Figure S3.1.- Characterization of cdf3-1 mutant. A) Scheme of the T-DNA insertion

mutant cdf3-1 GABI-Kat (GK-808605). The insertion T-DNA is located to 400 pb from ATG. B) AtCDF3

gene expression analysis. Expression of AtCDF3 gene was analysed by RT-PCR in Col-0 and cdf3-1

mutant plants. Total RNA was extracted from 3-week-old plants grown under control conditions (20ºC)

and exposed to low temperatures (4ºC at 24h) stress. Expression of Arabidopsis UBIQUITIN10 gene

(Czechowski et al., 2005) was used as control.

C 4ºC C 4ºC

Col-0 cdf3-1

ATG STOP

GK-808605

AtCDF3

UBQ

Supplementary

157

Supplementary Figure S3.2. Phenotypic differences of Col-0 and 35S::AtCDF3 plants during vegetative

and reproductive development. A) Representative images of four-week-old plants WT and 35S::AtCDF3 (L

2.1) grown under LD. B) Flowering-time phenotype under long day (LD). C) Cauline leaves of Col-0 and

35S::AtCDF3 plants grown under LD conditions. D) Rossete leaves of Col-0 and 35S::AtCDF3 plants

grown under LD conditions. All leaves, including cotyledons, are shown in order of production from the first

true leaf. (E, F) Detached flowers and detached petals of Col-0 and 35S::AtCDF3 plants grown under LD

conditions. G) Wild type and 35S::AtCDF3 flower gynoecium. H) Col-0 and 35S::AtCDF3 siliques.

Col-0

Col-0

Col-0

Col-0

Col-0

Col-0

Col-0

Col-0 35S::AtCDF3 35S::AtCDF3

35S::AtCDF3 35S::AtCDF3

35S::AtCDF3

35S::AtCDF3

F G

A B

C

D

E

H

Supplementary

158

Supplementary Figure S3.3. Phenotypic differences of Col-0 and 35S::AtCDF3-stop plants during

vegetative and reproductive development. A) Representative images of four-week-old plants WT and

35S::AtCDF3-stop grown under LD. B) Flowering-time phenotype under long day (LD). C) Cauline leaves

of Col-0 and 35S::AtCDF3-stop plants grown under LD conditions. D) Rossete leaves of Col-0 and

35::CDF3-stop plants grown under LD conditions. All leaves, including cotyledons, are shown in order of

production from the first true leaf. (E, F) Detached flowers and detached petals of Col-0 and 35S::AtCDF3

plants grown under LD conditions. G) Wild type and 35S::AtCDF3-stop flower gynoecium. H) Col-0 and

35S::AtCDF3-stop siliques.

Col-0

Col-0

Col-0

Col-0

Col-0

Col-0 Col-0 35S::AtCDF3-stop 35S::AtCDF3stop

35S::AtCDF3-stop 35S::AtCDF3-stop

35S::AtCDF3-stop

35S::AtCDF3-stop 35S::AtCDF3-stop

A B

C

D

E

F G

H

Col-0

35S::AtCDF3-stop

Supplementary

159

Supplementary Figure S3.4. Identification of 5´- AAAG-3´ cis-DNA binding elements statiscally over-

represented in the promotor regions of responsive-genes stress using tool Promoter

http://bar.utoronto.ca/ntools/cgi-bin/BAR_Promomer.cgi (Toufighi et al., 2005).

http://bar.utoronto.ca/ntools/cgi-bin/BAR_Promomer.cgi

Supplementary

160

1100bp

AAACTTTGGTAGCTTTAGGGTTTTTTCAAGTCTGTGACTTATAAACGTCGAGAGAGTGTAAGCACGCT

CCATAATCTACCGCTGTTCACAAGTAATAATAGATCTTGTCCGTTGAATTTATTTTAGACTTTTTTTTTA

ATGGACTTCATTTTAAATTTTTACAAAATTAAATTATTGCATTTTCTATTTCATATTGAATTAGGAGATGT

TACTGTCCGTCAGATTCTCTAGACTTTTTTTTTTAAAGACTGATCTATGATCAGAATTCCAATTTTTTTT

TTCTTTAAGGAAATACATCAGAGAGAAAAATTATTACGAAACGATTCTATTACAAGTAATGATTTTAACC

TTTTTTTTTTTACAATTGACAATCTTTTCACAACAAAAATCCACAAGAAACGTTAGACAATGGCATAAAT

TTATTTAAATTAATCCGTATATATTCGCCTTCTATGAGAATTGAATTCTATACCACTGTAAAATTCTTAAA

CGAGATAAGATTATTTTCAGCATGTAAAAAATGGTTTGTGGTTTCAACTCATTTGGGCTATTAGTTTTA

CATTTAGGCTTGCAACCTTGTCGGTTTATTTTGTGTAGGCTTTTGGTAGATTTGGGCTTGCAAACCCAA

ATTAACTTGTTGGCCGACATACATTTGTTTCTATTACAAATTTAACAACAAACGTCAATAAATACACGTG

AAGGAAATGAGAACGACCCTCTTAAGTAGTACTGGAAATTGAAAAAAAGAAATCTAGAAATGCTAACA

TGTAAGTTTTTGTTACCAAAAATGCAATTTGTATGTAGCCACAATTTCATGGCCGACCTGCTTTTTTTTT

CTTCTTCTTTCTGAAAACCACAAATATGATTACACGTGGCCTGAAAAGAACGAACAGAAACTCGGTAA

TGTGCAAAAAATATCTTACTCTTAATACGTGTAATTTTGGAGTGTAATAGGTCTATCGATCTATAAAAC

GATACTATTGGAGATTAGATTCTTCTCATCTCACTTTGTTCATCTAAAAACTCCTCCTTTCATTTCCAAA

CAAAAACTTCTTTTTATTCTCACATCTTAAAGATCTCTCTCATGGCGATGTCTTTCTATG

Supplementary Figure S3.5. Sequence analysis of COR15 promoter. Identification of the DOF cis-DNA

binding elements in COR15 promoter (underline) and ATG (red) using AtPAN tool

(http://atpan.itps.ncku.edu.tw/promoter_analysis.php?sequence) (Chen et al., 2012)

http://atpan.itps.ncku.edu.tw/promoter_analysis.php?sequence

http://atpan.itps.ncku.edu.tw/promoter_analysis.php?sequence

Supplementary

161

Supplementary Table. S3.1.- Primers designed for Real-time PCR, expected size, and concentration used.

Gene Primer sequence Amplicon

length (pb)

Concentration

(µM)

AtCDF3

AtCDF3-stop

PEPC1

PEPC2

PK1

GLU1

GS2

AtCOR15

AtRD29A

AtERD10

AtUBQ10

5’-AGAAGGCCGGGTGCGTTCTG-3’

5’-ACCGGCTTTGCACATCGCCT-3’

5’-TTGGTCTCGAAGCTCAGCAG-3’

5’- TCAACAACACTGCCGCTTTG -3’

5’-CTTTGAATCTCTCTTTCTCTCTC-3’

5’- CTCGAAGTACTCGTACACG -3’

5’-CCATTCCCTAAATCTACGC-3’

5’-GGTAACATAACAACAATAG-3’

5’-CGTCAGGCCTCCTCCTTCATTC-3’

5’-GTTCATGTGTCAGAAGG-3’

5’-CTATGGAGAGCAGATTAATGG-3’

5’-GTACAGTTATAGCAACCATGG-3’

5´-GAATCTGATGAACACACGTGTC-3

5´-GGACATGCTCTAACAGTC-3´

5’-CAGCGGAGCCAAGCAGAGCAG-3’

5’-CATCGAGGATGTTGCCGTCACC-3’

5’-GGTTCGGTGGTGGTGCGACT-3’

5’-TGGCTTCACCTCCTCCGTCG-3’

5’-CGTTTGTGGCCAAGCACGAAG -3’

5’-AGAGCTGTTGGATCGGTGGAGT-3’

5’-GCTCTTATCAAAGGACCTTCG -3’

5’-CGAACTTGAGGAGGTTGCAAA-3’

93

67

66

72

80

72

90

125

113

128

61

0.200

0.300

0.200

0.300

0.300

0.300

0.200

0.200

0.300

0.200

0.375

0.125

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

Supplementary

162

Supplementay Table S3.2. Genes significantly (adjusted P-value ≤0.05) up- or down- regulated in

35S::AtCDF3 Arabidopsis plants compared with wild-type (Col-0) plants.

Order Gene Fold

Change p-value Description

1 At3g47500 2,16 0,000 Dof type zinc finger domain containing protein

2 At5g45650 1,85 0,000 subtilase family protein

3 At5g11070 1,9 0,000 expressed protein


5 At2g04240 1,84 0,000 zinc finger (C3HC4 type RING finger) family protein

6 At4g23200 2,76 0,000 protein kinase family protein

7 At2g21650 1,98 0,000 myb family transcription factor

8 At1g56150 2,7 0,000 auxin responsive family protein

9 At5g15160 1,65 0,000 bHLH family protein

10 At1g74940 1,87 0,000 senescence associated protein related



13 At4g37610 2,3 0,000 TAZ zinc finger family protein / BTB/POZ domain containing protein

14 At1g66090 2,42 0,000 disease resistance protein (TIR NBS class), putative

15 At1g69490 2,35 0,000 no apical meristem (NAM) family protein

16 At2g18660 3,48 0,000 expansin family protein (EXPR3)

17 At4g30110 1,86 0,000 ATPase E1 E2 type family protein / haloacid dehalogenase like hydrolase

family protein

18 At2g17040 3 0,000 no apical meristem (NAM) family protein


20 At1g24140 3,14 0,000 matrixin family protein

21 At4g23180 2,46 0,000 receptor like protein kinase 4, putative (RLK4)


23 At1g28370 3,03 0,001 ERF11 ERF domain protein 11 (ERF11)


25 At1g80920 1,89 0,001 DNAJ heat shock N terminal domain containing protein

26 At3g16530 1,96 0,001 legume lectin family protein

27 At5g02230 1,77 0,001 haloacid dehalogenase like hydrolase family protein

28 At1g75040 2,64 0,001 PR 5 pathogenesis related protein 5 (PR 5)

29 At4g21850 2,12 0,001 methionine sulfoxide reductase domain containing protein / SeIR domain

containing protein

30 At3g04640 2,38 0,001 glycine rich protein

31 At2g43010 1,68 0,001 PIF4/SRL2/bHLH9 phytochrome interacting factor 4 (PIF4) / basic helix loop

helix protein 9 (bHLH9) /

32 At1g56010 1,58 0,001 transcription activator NAC1 (NAC1)

33 At1g20450 2,08 0,001 dehydrin (ERD10)

34 At4g34150 1,85 0,001 C2 domain containing protein


36 At5g01870 1,87 0,001 lipid transfer protein, putative


38 At1g12160 1,75 0,001 flavin containing monooxygenase family protein / FMO family protein

39 At2g17840 2,45 0,001 senescence/dehydration associated protein related (ERD7)

40 At5g23020 2,39 0,001 IMS2 2 isopropylmalate synthase 2 (IMS2)

41 At1g14370 1,88 0,001 protein kinase (APK2a)

42 At1g25560 2,44 0,001 AP2 domain containing transcription factor, putative

43 At1g68840 2,69 0,001 DNA binding protein RAV2 (RAV2) / AP2 domain containing protein RAP2.8

44 At2g32150 2,35 0,001 haloacid dehalogenase like hydrolase family protein 45 At5g45340 2,7 0,001 cytochrome P450 family protein

Supplementary

163

Continued Supplementay Table S3.2. Genes significantly (adjusted P-value ≤0.05) up- or

down- regulated in 35S::AtCDF3 Arabidopsis plants compared with wild-type (Col-0) plants.

Order Gene Fold


46 At4g12720 1,73 0,001 MutT/nudix family protein

47 At4g16780 2,16 0,001 homeobox leucine zipper protein 4 (HAT4) / HD ZIP protein 4

48 At4g18880 2,81 0,001 heat shock transcription factor 21 (HSF21)

49 At2g32030 1,82 0,001 GCN5 related N acetyltransferase (GNAT) family protein

50 At2g41430 1,71 0,001 dehydration induced protein (ERD15)

51 At3g03520 1,87 0,001 phosphoesterase family protein



54 At2g38470 2,58 0,001 WRKY family transcription factor

55 At4g17460 1,53 0,001 homeobox leucine zipper protein 1 (HAT1) / HD ZIP protein 1

56 At2g06530 1,53 0,001 SNF7 family protein

57 At4g16260 1,59 0,001 glycosyl hydrolase family 17 protein

58 At3g55430 1,62 0,001 glycosyl hydrolase family 17 protein / beta 1,3 glucanase, putative

59 At3g19580 2,47 0,001 zinc finger (C2H2 type) protein 2 (AZF2)

60 At2g42540 2,35 0,001 cor15a cold responsive protein / cold regulated protein (cor15a)

61 At4g14365 2,89 0,002 zinc finger (C3HC4 type RING finger) family protein / ankyrin repeat family

protein

62 At2g31880 2 0,002 leucine rich repeat transmembrane protein kinase, putative

63 At1g51090 2,24 0,002 heavy metal associated domain containing protein



66 At5g15960 1,6 0,002 stress responsive protein (KIN1) / stress induced protein (KIN1) /// stress

responsive protein (KIN1) /

67 At1g33560 1,81 0,002 disease resistance protein (CC NBS LRR class), putative



70 At3g50770 2,2 0,002 calmodulin related protein, putative

71 At3g16860 2,22 0,002 phytochelatin synthetase related



74 At3g10980 1,57 0,002 wound responsive protein related


76 At2g41100 1,61 0,002 touch responsive protein / calmodulin related protein 3, touch induced (TCH3)

77 At1g73330 3,39 0,002 Dr4 protease inhibitor, putative (DR4)




81 At4g22710 1,64 0,002 cytochrome P450 family protein /// cytochrome P450 family protein




85 At1g51700 1,51 0,002 Dof type zinc finger domain containing protein (ADOF1)

86 At1g20510 2,11 0,002 4 coumarate CoA ligase family protein / 4 coumaroyl CoA synthase family protein


88 At3g56880 1,8 0,003 VQ motif containing protein 89 At3g50950 1,95 0,003 disease resistance protein (CC NBS LRR class), putative

Supplementary

164

Continued Supplementay Table S3.2. Genes significantly (adjusted P-value ≤0.05) up- or down- regulated in 35S::AtCDF3 Arabidopsis plants compared with wild-type (Col-0) plants.

Order Gene Fold


90 At5g46780 1,65 0,003 VQ motif containing protein

91 At1g65400 2,16 0,003 disease resistance protein (TIR class), putative

92 At2g26020 1,9 0,003 plant defensin fusion protein, putative (PDF1.2b)

93 At1g67360 1,95 0,003 rubber elongation factor (REF) family protein

94 At4g28240 1,77 0,003 wound responsive protein related

95 At5g47220 2,25 0,003 ethylene responsive element binding factor 2 (ERF2)


97 At2g01180 2,06 0,003 phosphatidic acid phosphatase family protein / PAP2 family protein

98 At5g44070 1,65 0,003 phytochelatin synthase 1 (PCS1)

99 At1g64280 1,65 0,003 regulatory protein (NPR1)

100 At3g26200 1,55 0,003 cytochrome P450 71B22, putative (CYP71B22)

101 At1g09070 1,66 0,003 C2 domain containing protein / src2 like protein, putative

102 At1g64710 1,57 0,003 ADH alcohol dehydrogenase, putative

103 At5g06320 1,72 0,003 harpin induced family protein / HIN1 family protein / harpin responsive family protein / NDR1/HIN1 like protein 3

104 At3g44260 1,84 0,003 CCR4 NOT transcription complex protein, putative

105 At2g24160 1,95 0,003 pseudogene, leucine rich repeat protein family

106 At5g57220 1,89 0,003 cytochrome P450, putative

107 At3g45860 2,13 0,003 receptor like protein kinase, putative




111 At5g10760 2,65 0,003 aspartyl protease family protein

112 At2g24600 2,21 0,003 ankyrin repeat family protein

113 At4g01700 1,57 0,003 chitinase, putative

114 At3g11820 1,65 0,003 SYP121 syntaxin 121 (SYP121) / syntaxin related protein (SYR1)

115 At4g25490 3,04 0,003 DRE binding protein (DREB1B) / CRT/CRE binding factor 1 (CBF1) /

transcriptional activator CBF1



118 At3g50930 2,97 0,003 AAA type ATPase family protein

119 At5g61160 2,7 0,004 transferase family protein

120 At3g17130 1,68 0,004 invertase/pectin methylesterase inhibitor family protein

121 At5g52640 2,49 0,004

HSP81 1 heat shock protein 81 1 (HSP81 1) / heat shock protein 83

(HSP83)


123 At1g73600 1,52 0,004 NMT3 phosphoethanolamine N methyltransferase 3, putative (NMT3)

124 At3g50760 1,61 0,004 glycosyl transferase family 8 protein

125 At3g25010 1,87 0,004 disease resistance family protein

126 At4g30140 1,55 0,004 GDSL motif lipase/hydrolase family protein

127 At4g17770 1,52 0,004 glycosyl transferase family 20 protein / trehalose phosphatase family protein

128 At1g27730 2,07 0,004 zinc finger (C2H2 type) family protein (ZAT10) / salt tolerance zinc finger protein (STZ)



131 At1g01140 1,51 0,004 CBL interacting protein kinase 9 (CIPK9)

132 At5g13190 1,68 0,004 expressed protein 133 At4g36040 1,51 0,004 DNAJ heat shock N terminal domain containing protein (J11) 134 At1g09740 1,77 0,004 ethylene responsive protein, putative

Supplementary

165


Order Gene Fold



136 At5g54940 1,59 0,004 eukaryotic translation initiation factor SUI1, putative

137 At4g23140 3,05 0,004 receptor like protein kinase 5 (RLK5) /// receptor like protein kinase 5 (RLK5)




141 At1g27770 1,75 0,004 ACA1/PEA1 calcium transporting ATPase 1, plasma membrane type / Ca(2+)

ATPase isoform 1


143 At5g13200 2,25 0,005 GRAM domain containing protein / ABA responsive protein related

144 At3g05030 1,57 0,005 sodium proton exchanger, putative (NHX2)

145 At4g34390 1,64 0,005 extra large guanine nucleotide binding protein, putative / G protein, putative

146 At2g20560 1,88 0,005 DNAJ heat shock family protein

147 At1g17420 2,04 0,005 lipoxygenase, putative

148 At1g80440 1,92 0,005 kelch repeat containing F box family protein



151 At4g09760 1,55 0,005 choline kinase, putative

152 At5g12200 1,51 0,005 PYD2 dihydropyrimidinase / DHPase / dihydropyrimidine amidohydrolase /

hydantoinase (PYD2)

153 At1g71030 1,87 0,005 myb family transcription factor


155 At1g17340 1,54 0,005 phosphoinositide phosphatase family protein


157 At4g01390 2,96 0,005 meprin and TRAF homology domain containing protein / MATH domain

containing protein


159 At3g23110 2,23 0,005 disease resistance family protein /// disease resistance family protein

160 At3g55980 1,99 0,005 zinc finger (CCCH type) family protein



163 At3g16150 1,52 0,006 L asparaginase, putative / L asparagine amidohydrolase, putative


165 At5g47230 1,79 0,006 AtERF5 ethylene responsive element binding factor 5 (ERF5)



168 At4g25470 2,1 0,006 DRE binding protein (DREB1C) / CRT/DRE binding factor 2 (CBF2)

169 At1g54130 1,57 0,006 RelA/SpoT protein, putative (RSH3)


171 At1g35230 1,82 0,006 AGP5 arabinogalactan protein (AGP5)

172 At1g35710 2,55 0,006 leucine rich repeat transmembrane protein kinase, putative

173 At5g11670 2,15 0,006 malate oxidoreductase, putative

174 At4g17500 2,12 0,006 ethylene responsive element binding protein 1 (ERF1) / EREBP 2 protein

175 At5g22570 1,76 0,006 WRKY family transcription factor 176 At1g72520 2,82 0,006 LOX lipoxygenase, putative

177 At1g74210 1,59 0,007 glycerophosphoryl diester phosphodiesterase family protein 178 At2g40435 1,84 0,007 expressed protein

Supplementary

166


Order Gene Fold


179 At3g12580 2,13 0,007 heat shock protein 70, putative / HSP70, putative


181 At1g73480 1,6 0,007 hydrolase, alpha/beta fold family protein

182 At1g01560 2,23 0,007 mitogen activated protein kinase, putative / MAPK, putative (MPK11)


184 At5g52310 1,8 0,007 low temperature responsive protein 78 (LTI78) / desiccation responsive protein

29A (RD29A)

185 At5g55450 2,26 0,007 protease inhibitor/seed storage/lipid transfer protein (LTP) family protein

186 At2g41180 1,57 0,007 sigA binding protein related

187 At2g42750 1,51 0,007 DNAJ heat shock N terminal domain containing protein

188 At3g21230 1,51 0,007 4 coumarate CoA ligase, putative / 4 coumaroyl CoA synthase, putative (4CL)

189 At1g74430 1,64 0,007 myb family transcription factor (MYB95)

190 At5g18470 2,78 0,007 curculin like (mannose binding) lectin family protein

191 At3g25760 2,51 0,008 ERD12 early responsive to dehydration stress protein (ERD12) /// early

responsive to dehydration stress protein (ERD12)



194 At5g16200 1,64 0,008 50S ribosomal protein related

195 At1g56600 1,79 0,008 galactinol synthase, putative

196 At3g52400 2,16 0,008 SYP122 syntaxin, putative (SYP122)

197 At1g12200 1,62 0,008 flavin containing monooxygenase family protein / FMO family protein


199 At3g50480 1,94 0,008 broad spectrum mildew resistance RPW8 family protein

200 At2g26440 1,55 0,008 pectinesterase family protein

201 At4g12470 1,68 0,008 protease inhibitor/seed storage/lipid transfer protein (LTP) family protein


203 At2g32680 2,04 0,008 disease resistance family protein

204 At5g20230 2,16 0,008 plastocyanin like domain containing protein

205 At5g47070 2,05 0,009 protein kinase, putative

206 At2g30870 1,69 0,009 glutathione S transferase, putative

207 At2g26190 1,56 0,009 calmodulin binding family protein



210 At2g27080 1,96 0,009 harpin induced protein related / HIN1 related / harpin responsive protein related



213 At4g02410 1,83 0,009 lectin protein kinase family protein

214 At1g13260 1,79 0,009 DNA binding protein RAV1 (RAV1)

215 At5g59820 2,15 0,009 zinc finger (C2H2 type) family protein (ZAT12)


217 At1g70300 1,64 0,009 potassium transporter, putative

218 At4g38340 1,95 0,009 RWP RK domain containing protein

219 At4g24380 1,87 0,010 expressed protein 220 At3g12500 2,04 0,010 basic endochitinase 221 At4g33050 1,82 0,010 calmodulin binding family protein

Supplementary

167


Order Gene Fold


222 At5g61600 1,94 0,010 ethylene responsive element binding family protein


224 At3g59940 1,63 0,010 kelch repeat containing F box family protein

225 At1g76650 2,23 0,010 calcium binding EF hand family protein



228 At5g52050 2,82 0,011 MATE efflux protein related

229 At5g61590 1,78 0,011 AP2 domain containing transcription factor family protein

230 At1g12520 1,59 0,011 superoxide dismutase copper chaperone, putative

231 At5g66210 1,58 0,011 calcium dependent protein kinase family protein / CDPK family protein

232 At5g05410 1,7 0,011 DRE binding protein (DREB2A)

233 At4g11360 1,74 0,011 zinc finger (C3HC4 type RING finger) family protein (RHA1b)



236 At2g22240 1,68 0,011 inositol 3 phosphate synthase isozyme 2 / myo inositol 1 phosphate synthase 2 / MI 1 P synthase 2 / IPS 2

237 At5g01540 2,35 0,011 lectin protein kinase, putative

238 At3g46600 1,65 0,011 scarecrow transcription factor family protein


240 At3g04720 2,01 0,011 HEL hevein like protein (HEL)

241 At3g14050 1,83 0,011 RSH2 RelA/SpoT protein, putative (RSH2)


243 At4g38460 1,56 0,012 GGPS geranylgeranyl pyrophosphate synthase, putative / GGPP synthetase, putative


245 At2g33580 1,95 0,012 protein kinase family protein / peptidoglycan binding LysM domain containing

protein

246 At4g26090 1,51 0,012 disease resistance protein RPS2 (CC NBS LRR class), putative


248 At5g11930 2,29 0,012 glutaredoxin family protein

249 At5g41740 3,43 0,012 disease resistance protein (TIR NBS LRR class), putative /// disease resistance

protein (TIR NBS LRR class), putative

250 At5g61010 1,66 0,012 exocyst subunit EXO70 family protein


252 At5g20830 1,77 0,012 SUS1 sucrose synthase / sucrose UDP glucosyltransferase (SUS1)

253 At1g02400 1,76 0,012 gibberellin 2 oxidase, putative / GA2 oxidase, putative



256 At1g73800 2,13 0,013 calmodulin binding protein

257 At5g49890 1,52 0,013 chloride channel protein (CLC c) 258 At1g02660 1,9 0,013 lipase class 3 family protein

259 At5g47910 1,65 0,013 RbohD respiratory burst oxidase protein D (RbohD) / NADPH oxidase


261 At2g24850 3,85 0,013 aminotransferase, putative 262 At1g59590 2,11 0,013 expressed protein

263 At5g45110 1,62 0,013 ankyrin repeat family protein / BTB/POZ domain containing protein

264 At4g21960 1,55 0,013 peroxidase 42 (PER42) (P42) (PRXR1) 265 At3g03870 1,53 0,014 expressed protein

Supplementary

168


Order Gene Fold





269 At5g62070 1,63 0,014 calmodulin binding family protein

270 At1g60190 2,23 0,014 armadillo/beta catenin repeat family protein / U box domain containing protein

271 At2g35930 2,43 0,014 U box domain containing protein




275 At1g51140 1,65 0,015 basic helix loop helix (bHLH) family protein

276 At3g50740 1,82 0,015 UDP glucoronosyl/UDP glucosyl transferase family protein

277 At2g17880 1,54 0,015 DNAJ heat shock protein, putative

278 At3g02840 3,33 0,015 immediate early fungal elicitor family protein





283 At5g22250 2,95 0,016 CCR4 NOT transcription complex protein, putative


285 At1g75020 1,51 0,016 phospholipid/glycerol acyltransferase family protein

286 At3g48650 3,14 0,016 pseudogene, At14a related protein


288 At3g16360 2,69 0,016 phosphotransfer family protein

289 At1g02930 2,26 0,017 glutathione S transferase, putative /// glutathione S transferase, putative



292 At4g15620 1,53 0,017 integral membrane family protein

293 At3g14440 1,51 0,017 NC1 9 cis epoxycarotenoid dioxygenase, putative / neoxanthin cleavage enzyme, putative /

294 At5g60950 1,55 0,017 phytochelatin synthetase related

295 At3g53230 1,53 0,017 cell division cycle protein 48, putative / CDC48, putative


297 At5g53870 1,52 0,017 plastocyanin like domain containing protein


299 At4g17490 2,01 0,018 ethylene responsive element binding protein, putative


301 At3g45640 1,66 0,018 mitogen activated protein kinase, putative / MAPK, putative (MPK3)

302 At1g33960 1,91 0,018 avirulence responsive protein / avirulence induced gene (AIG1)

303 At4g35985 1,8 0,018 senescence/dehydration associated protein related

304 At4g39210 2,62 0,018 APL3 glucose 1 phosphate adenylyltransferase large subunit 3 (APL3) / ADP glucose pyrophosphorylase

305 At5g07460 1,76 0,019 peptide methionine sulfoxide reductase, putative

306 At3g15350 1,51 0,019 glycosyltransferase family 14 protein / core 2/I branching enzyme family protein 307 At5g60900 1,87 0,019 lectin protein kinase family protein

308 At5g44420 1,8 0,019 plant defensin protein, putative (PDF1.2a) 309 At2g23810 1,55 0,019 senescence associated family protein

Supplementary

169


Order Gene Fold


310 At5g26030 1,68 0,020 ferrochelatase I 311 At5g35735 2,02 0,020 auxin responsive family protein

312 At4g33040 1,6 0,021 glutaredoxin family protein


314 At3g51450 1,56 0,021 strictosidine synthase family protein

315 At3g52430 1,72 0,022 pad4 phytoalexin deficient 4 protein (PAD4)


317 At1g29395 2 0,022 stress responsive protein, putative

318 At2g14080 1,59 0,022 disease resistance protein (TIR NBS LRR class), putative

319 At1g09080 1,7 0,022 BiP 3/BP3 luminal binding protein 3 (BiP 3) (BP3)


321 At1g72930 1,68 0,023 disease resistance protein (TIR NBS class), putative /// disease resistance

protein (TIR NBS class), putative

322 At1g61340 1,73 0,023 F box family protein

323 At2g37130 1,74 0,024 (ATP2a) peroxidase 21 (PER21) (P21) (PRXR5)

324 At4g11280 1,78 0,024 ACS6 1 aminocyclopropane 1 carboxylate synthase 6 / ACC synthase 6 (ACS6)

325 At1g62290 1,56 0,024 aspartyl protease family protein



328 At5g45380 2,09 0,024 sodium:solute symporter family protein

329 At3g09020 2,02 0,024 alpha 1,4 glycosyltransferase family protein / glycosyltransferase sugar binding

DXD motif containing protein

330 At4g32980 1,58 0,024 homeobox protein (ATH1)


332 At2g39420 1,66 0,025 esterase/lipase/thioesterase family protein


334 At4g17230 1,67 0,026 scarecrow like transcription factor 13 (SCL13)

335 At1g02390 2,07 0,026 phospholipid/glycerol acyltransferase family protein

336 At3g48520 1,72 0,026 cytochrome P450 family protein

337 At3g08870 1,58 0,026 lectin protein kinase, putative

338 At3g56710 1,83 0,027 sigA binding protein

339 At1g01470 1,55 0,027 late embryogenesis abundant protein, putative / LEA protein, putative




343 At3g51860 2,25 0,028 cation exchanger, putative (CAX3)

344 At4g27410 2,15 0,028 no apical meristem (NAM) family protein (RD26)

345 At3g57540 2,05 0,028 remorin family protein


347 At1g21270 1,53 0,028 WAK2 wall associated kinase 2 (WAK2)


349 At4g15530 1,63 0,029 pyruvate phosphate dikinase family protein

350 At4g39030 1,85 0,029 EDS5/SID1 enhanced disease susceptibility 5 (EDS5) / salicylic acid induction deficient 1 (SID1)

351 At1g63750 1,55 0,029 disease resistance protein (TIR NBS LRR class), putative

352 At3g15210 1,62 0,029 AtERF4 ethylene responsive element binding factor 4 (ERF4)

353 At5g13320 1,78 0,029 auxin responsive GH3 family protein 354 At4g04500 1,9 0,030 protein kinase family protein

Supplementary

170


Order Gene Fold





358 At4g10500 2,19 0,031 oxidoreductase, 2OG Fe(II) oxygenase family protein




362 At5g03630 1,53 0,033 monodehydroascorbate reductase, putative



365 At1g21250 1,84 0,034 WAK1 wall associated kinase 1 (WAK1)

366 At2g43570 2,32 0,034 chitinase, putative

367 At4g24570 1,94 0,034 mitochondrial substrate carrier family protein

368 At1g78460 1,64 0,035 SOUL heme binding family protein



371 At1g22770 1,71 0,035 gigantea protein (GI)

372 At2g29990 1,54 0,036 pyridine nucleotide disulphide oxidoreductase family protein

373 At4g23600 2,26 0,036 coronatine responsive tyrosine aminotransferase / tyrosine transaminase

374 At4g21830 1,77 0,036 methionine sulfoxide reductase domain containing protein / SeIR domain containing protein

375 At3g52180 1,81 0,036 PTPKIS1 protein tyrosine phosphatase/kinase interaction sequence protein

(PTPKIS1)


377 At1g30900 1,84 0,036 AtELP6 vacuolar sorting receptor, putative



380 At1g12610 1,7 0,038 DRE binding protein, putative / CRT/DRE binding factor, putative



383 At1g07440 1,72 0,039 tropinone reductase, putative / tropine dehydrogenase, putative

384 At5g04340 1,88 0,039 zinc finger (C2H2 type) family protein



387 At3g25610 1,53 0,041 haloacid dehalogenase like hydrolase family protein

388 At2g06050 1,62 0,041 OPR3 12 oxophytodienoate reductase (OPR3) / delayed dehiscence1 (DDE1)


390 At1g14870 2,25 0,041 expressed protein /// expressed protein


392 At1g04770 1,54 0,041 male sterility MS5 family protein

393 At2g29350 1,74 0,041 tropinone reductase, putative / tropine dehydrogenase, putative

394 At3g28340 1,58 0,042 galactinol synthase, putative

395 At3g13672 1,53 0,042 seven in absentia (SINA) family protein



398 At5g59680 1,71 0,043 leucine rich repeat protein kinase, putative 399 At5g12050 1,69 0,043 expressed protein

Supplementary

171


Order Gene Fold


400 At3g09940 1,57 0,043 monodehydroascorbate reductase, putative

401 At3g23550 1,51 0,044 MATE efflux family protein

402 At1g17430 1,54 0,045 hydrolase, alpha/beta fold family protein

403 At4g14020 2,13 0,045 rapid alkalinization factor (RALF) family protein



406 At3g25600 1,66 0,048 calmodulin, putative




Supplementary

172


Order Gene Fold Change p-value Description

1 At1g06350 -1,96 0,000 fatty acid desaturase family protein /// fatty acid desaturase family protein

2 At5g10140 -2,34 0,000 MADS-box protein flowering locus F (FLF)

3 At5g62430 -1,65 0,000 Dof-type zinc finger domain-containing protein

4 At4g25100 -3,8 0,000 SODB/FSD1 superoxide dismutase (Fe), chloroplast (SODB) / iron superoxide dismutase (FSD1)

5 At5g15310 -1,82 0,000 myb family transcription factor

6 At3g47340 -2,23 0,000 ASN1 asparagine synthetase 1 (glutamine-hydrolyzing) / glutamine-dependent asparagine synthetase 1 (ASN1)

7 At5g49100 -1,88 0,000 expressed protein

8 rpoA -1,55 0,000

9 At1g12860 -1,59 0,000 basic helix-loop-helix (bHLH) family protein / F-box family protein

10 At4g26150 -2,06 0,000 zinc finger (GATA type) family protein

11 At4g28780 -1,74 0,000 GDSL-motif lipase/hydrolase family protein

12 At1g74670 -1,97 0,001 gibberellin-responsive protein, putative

13 At1g32540 -1,56 0,001 zinc finger protein, putative


15 At4g26110 -1,6 0,001 nucleosome assembly protein (NAP), putative

16 At5g28060 -1,53 0,001 40S ribosomal protein S24 (RPS24B)

17 At5g23820 -1,79 0,001 MD-2-related lipid recognition domain-containing protein / ML domain-containing

protein

18 At5g02450 -1,55 0,001 60S ribosomal protein L36 (RPL36C)

19 At1g10470 -1,54 0,001 two-component responsive regulator / response regulator 4 (ARR4)

20 At2g39390 -1,64 0,001 60S ribosomal protein L35 (RPL35B)


22 At5g08180 -1,6 0,001 ribosomal protein L7Ae/L30e/S12e/Gadd45 family protein

23 rpoC2 -2,09 0,001

24 At2g40670 -2,35 0,001 two-component responsive regulator / response regulator 16 (ARR16)

25 At1g57660 -1,55 0,001 60S ribosomal protein L21 (RPL21E) /// 60S ribosomal protein L21 (RPL21E)

26 At5g35490 -1,71 0,001 expressed protein (MRU1)

27 At5g20150 -2,16 0,001 SPX (SYG1/Pho81/XPR1) domain-containing protein

28 At5g04790 -1,76 0,002 hypothetical protein

29 At3g11120 -2,15 0,002 60S ribosomal protein L41 (RPL41C) /// 60S ribosomal protein L41 (RPL41C) /

30 At3g47380 -1,59 0,002 invertase/pectin methylesterase inhibitor family protein

31 At5g42950 -1,61 0,002 GYF domain-containing protein

32 At2g22980 -1,63 0,002 serine carboxypeptidase S10 family protein


34 At3g02040 -1,71 0,002 glycerophosphoryl diester phosphodiesterase family protein

35 At5g67240 -1,69 0,002 exonuclease family protein


37 At1g11260 -1,61 0,002 STP1 glucose transporter (STP1)

38 At5g49700 -1,53 0,002 DNA-binding protein-related 39 At2g46310 -1,89 0,002 AP2 domain-containing transcription factor, putative

40 At5g24380 -1,59 0,003 transporter, putative 41 At5g37260 -1,79 0,003 myb family transcription factor

Supplementary

173



42 At5g63140 -1,51 0,003 calcineurin-like phosphoesterase family protein

43 At3g24080 -1,57 0,003 KRR1 family protein

44 At2g16660 -1,65 0,003 nodulin family protein


46 At1g68560 -1,75 0,003 alpha-xylosidase (XYL1)

47 At2g18740 -1,72 0,003 small nuclear ribonucleoprotein E, putative / snRNP-E, putative / Sm

protein E, putative

48 At3g13750 -1,7 0,004 beta-galactosidase, putative / lactase, putative




52 At5g04530 -1,52 0,004 beta-ketoacyl-CoA synthase family protein

53 At5g49360 -1,61 0,004 glycosyl hydrolase family 3 protein

54 At2g36590 -1,84 0,004 proline transporter, putative


56 At1g79410 -1,52 0,005 transporter-related

57 At1g26790 -1,56 0,005 Dof-type zinc finger domain-containing protein

58 At5g18600 -1,79 0,006 glutaredoxin family protein

59 At3g44735 -1,62 0,006 phytosulfokines-related

60 atpH -1,57 0,006

61 atpF -1,53 0,006

62 At1g69530 -1,67 0,007 expansin, putative (EXP1)

63 At1g02640 -1,59 0,007 glycosyl hydrolase family 3 protein


65 At5g44530 -1,53 0,007 subtilase family protein

66 At3g07050 -1,75 0,007 GTP-binding family protein

67 At1g80280 -1,89 0,007 hydrolase, alpha/beta fold family protein


69 At5g24420 -1,68 0,007 glucosamine/galactosamine-6-phosphate isomerase-related

70 petG -1,56 0,007



73 At1g05560 -1,72 0,008 UDP-glucose transferase (UGT75B2)

74 At3g28500 -1,56 0,008 60S acidic ribosomal protein P2 (RPP2C)

75 At3g58120 -1,69 0,009 bZIP transcription factor family protein

76 At5g45950 -1,55 0,009 GDSL-motif lipase/hydrolase family protein

77 At3g45930 -1,53 0,009 histone H4 /// histone H4

78 ndhJ -1,77 0,009

79 psbG -1,64 0,010

80 At1g30820 -1,59 0,010 CTP synthase, putative / UTP--ammonia ligase, putative

81 At2g29420 -1,88 0,011 glutathione S-transferase, putative

82 At2g47880 -1,53 0,012 glutaredoxin family protein 83 At2g30540 -1,83 0,012 glutaredoxin family protein

Supplementary

174

Continued Supplementay Table S3.2. Genes significantly (adjusted P-value ≤0.05) up- or down- regulated in 35S::AtCDF3 plants Arabidopsis plants compared with wild-type (Col-0) plants.



85 At1g68780 -1,69 0,013 leucine-rich repeat family protein

86 At4g37530 -1,52 0,013 peroxidase 50 (PER50) (P50) (PRXR2) /// peroxidase 50 (PER50) (P50)

(PRXR2)

87 At3g14990 -1,71 0,013 4-methyl-5(b-hydroxyethyl)-thiazole monophosphate biosynthesis protein,

putative

88 At4g38330 -2,17 0,014 expressed protein /// expressed protein


90 At4g23750 -1,71 0,015 AP2 domain-containing transcription factor, putative


92 psbL -1,54 0,017

93 At5g48240 -1,54 0,017 hypothetical protein


95 rps3

chloroplast -1,73 0,018

96 rpl14 -1,52 0,019

97 At5g25980 -1,7 0,019 glycosyl hydrolase family 1 protein /// glycosyl hydrolase family 1 protein

98 At5g65080 -1,6 0,019 MADS-box family protein

99 At3g08730 -1,6 0,020 serine/threonine protein kinase (PK1) (PK6)


101 At4g16380 -1,52 0,022

102 At4g15550 -1,53 0,022 IAGLU UDP-glucose:indole-3-acetate beta-D-glucosyltransferase (IAGLU)

103 At4g38620 -1,55 0,023 myb family transcription factor (MYB4)

104 atpI -2,34 0,025

105 rps7

1_chloroplast -1,61 0,026

106 At2g30610 -1,55 0,027 BTB/POZ domain-containing protein

107 At3g04770 -1,77 0,028 40S ribosomal protein SA (RPSaB)

108 At4g16140 -1,52 0,028 proline-rich family protein


110 At1g76810 -1,52 0,030 eukaryotic translation initiation factor 2 family protein / eIF-2 family protein

111 At5g40340 -1,53 0,030 PWWP domain-containing protein

112 At5g54470 -1,53 0,030 zinc finger (B-box type) family protein


114 At3g48720 -1,52 0,033 transferase family protein


116 At3g28740 -1,57 0,035 cytochrome P450 family protein

117 rps18 -1,91 0,035

118 At4g30350 -1,56 0,036 heat shock protein-related


120 At3g53460 -1,53 0,040 29 kDa ribonucleoprotein, chloroplast / RNA-binding protein cp 29

121 rpl32 -1,96 0,041


Supplementary

175

SupplementaryTable S3.3 Metabolite analyses of WT and 35S::AtCDF3 plants. Given are means ± SE

(n=15). Similar results were obtained in three independent experiments

Col-0 35S::AtCDF3 (L2.1) 35S::AtCDF3 (5.4)

Meatabolite

Relative amount

compound (%WT)

Relative amount compound (%WT)

P-value Relative amount

compound (%WT) P-value

Glucose 100 ± 1.84

199,93 ±19,58

0,0254

186,49 ± 20,37

0,0013

Fructose 100 ± 5.92 168,72 ± 23

0,6527

79,07 ± 6,16

0,0224

Sucrose 100 ± 0,35

111,67 ± 2.30

0,00005914 109,58 ± 40.09

0,0853736

GABA 100 ± 56 135,65 ± 43.0

0,0287085

101,14 ± 53.0

0,02870888

L- Leucine 100 ± 66.6 116,27 ± 32.87

0,00305188

116,79 ± 39,29

0,00305187

L- Valine 100 ± 10.11

414,78 ± 142,80

0,0888665

275,20 ± 208,31

0,0888665

L- Asparagine 100 ± 36.2 182,20 ± 39,42

0,0991184 153,63 ± 75,89

0,0991181

L- Alanine 100 ± 17.6 89,58 ± 26,56

0,0609482

134,90 ± 119,13

0,138921064

L- Glycine 100 ± 16.6 57,74 ± 17,90

0,0835558 44,27 ± 45,04

0,08355702

L-Proline 100 ± 16.8 219,03 ± 40,49

0,0119931 109,24 ± 7,62

0,01199322

L- Glutamine 100 ± 53.6

153,17 ± 59,77

0,115238

119,53 ± 45

0,1153

Supplementary

176

4. Characterization of tomato Cycling Dof Factors reveals conserved and new functions in the control of flowering time and abiotic stress responses.

Table S4.1.- S. lycopersicum DOF transcription factors sequence. DOF domain is underlined.

>SlDOF-1

MQDIHPIGGGGGRFLGGAGDRRLRPNNHQNHQALKCPRCDSLNTKFCYYNNYNLSQPRHFCKSCRRYWTKG

GVLRNVPVGGGCRKSKRSKPKSTTADDTPEEPKSDTNSSSESSSLTATTTAAAAATANTPGAATTEDVSATSS

NSASTYLNFPDSNFFIPHSTNQTFDDQPLMENSVEDQFQDIGNFTNMMTSSNDPFNMVDIPAYRLPENQNSNE

QWNTETKMVETLPTSGEMKMEQMSTDFLNQTGRVDEYPGLHQSNSELTPLNWQTGGDHGLYDLTGTVDHQS

YWSQTQWGENDNSLNFLP*

>SlDOF-2

MQDPSIYSQIKPQFPEQEHLKCPRCDSPNTKFCYYNNYNLSQPRHYCKSCRRYWTKGGTLRNIPVGGGSRKST

KRSSSSSSSSKKSSSTSTTSPATPPLTSSSSSTNPKPEPFGIPAIPSFDVMTTSTGPFSSLLASTEPQFGNFLEAL

NPNNNNGSTLQLGNPVSSSGHQNGNTSYLGVQNGGESNNCWNGGNNGWPDLAIFTPGSNFQ*

>SlDOF-3

MQDPSSIYSQINPQFPDQQVLKCPRCDSINTKFCYYNNYNLSQPRHFCKNCKRYWTKGGILRNIPVGGSSRKNT

KRSSSNSCKRSSTMTISSSTSSEQNSKTEHFDTPVVRNSPIVDANGPFGSLLASNGPEIGNFLNVLNPNGPDSG

SDAAAAQSGNSNNNHEFLGEDSNCWNGTNGWADLAIYTPGNVQLFQTKLKLPSSDLKLSSLQELHLEESFLDE

QLLQYLRTTCNHLQVLSFKRFLLLNTMKISCDCLERLAIVACDNLDVDAPNLMLFTYHVHYGTTLKLKGSHSLEA

HLTLIPETIESHWYSKLTKSLGFRCFVVYIQITEMHQIRMRDLVVHAYLYLLSVGKHKLERVKFQNFTSALLEKLKN

YIYIFTNADDLEIIEVPPECRL*

>SlDOF-4

MAFSSIPFYQDPPNWHHEQGNHHQQQQHLGITNENSSELSPTVLPPPGAAPGGGGGPVGSTRSGSITERARL

PKITQPDVALKCPRCESTNTKFCYFNNYNLSQPRHFCKTCRRYWTIGGTLRNVPVGGGCRRNNKRSSSKRSRK

SPIRSERSRNVPISTSNSTNTITTPSHFPPSSTHLSFFNTPFHNFNNFNSTQNCLNFGEIQPHEGDPTFVDQFRH

QQMEKFSFFSPLEQPSNLYPIYSEFRINHHDLENVKVEENKSSTNSTQGMNLQRNNNLGVNQFWTNYNISSTST

SQLL*

>SlDOF-5

MAFSSFPIYLDHPNLHHLQQQPDHHQQGNPGLDNPQLLPPPTQVGGGPGSIRPGSMVNRARIAKLPLPEAGLK

CPRCDSSNTKFCYFNNYSLSQPRHFCKNCRRYWTRGGALRNVPVGGGCRRNKRNKSSSNNNSAKSGGGGG

LMGNSNNASTSGIPSINCSMEMIGHHFSQSSTQFTSLMGAFQNLNNYGGGLIQPPTHQLVGEMGFQIGSNNLLP

SLVASNNFEHPTNLYPNFQNEGTTTTTTTTIEASNGVTQQVKMEDNNRQGMNSSTKQFLGTLENNNQYWDVA

NANANANVNSWIGFSSDLNNNSLSTTNHLL*

http://expasy.org/cgi-bin/dna_sequences?/work/expasy/tmp/http/seqdna.1256,6,173











Supplementary

177

>SlDOF-6

MVFSSIQAYLDSSNWQQAPPSNYNHDGTGASANGGHVLRPQLQPQQQPHPNGSGGGGGGGGGSIRAGSMV

DRARQANVALPEAALKCPRCESTNTKFCYFNNYSLTQPRHFCKTCRRYWTRGGALRNVPVGGGCRR

>SlDOF-7

MVFSSFPVYLDHPNLHQLQQADGHQQGNTGLELPTVQPPPPMQVGASPGSIRPGSMVDRARLAKIPLPEAGLK

CPRCDSTNTKFCYFNNYNLSQPRHFCKTCRRYWTRGGALRSVPVGGGCRRNKRSKSSSNNNNNNSSKSTGG

SNVNTNKTIASGTSTSASPSSCSTEIMNGRHHFPHEQPTQLTPLMAAFQNLNHHYGGFQPNLVSTHHGNGSAL

SSHHHEMGFQIGNSTNTNNTNNLPVPSGGGSDHQWRLPSLAANTNLYPFQHGSDQGIHESSAGNNNNINGHH

DEQGLNSTKQFLGTMENNSNQYWGGNAWTGGFSGLNSSSSASHLL*

>SlDOF-8

MVFSSISAYLDPSNWQQQVGYSIPNPQLPSGLSQPTPPRPLASTPPPPPPPPPPPQPHHVGGGGSIRPGSMAD

RARLANIPMSEATQKCPRCESTNTKFCYFNNYSLSQPRHFCKTCRRYWTRGGALRSVPVGGGCRRNKRSSTS

NSSTSAKSSNNNTKSQGSSQTTNSGSTSNNNSSPSSAASLLGLMNPPIHPLRFMSPLGPLTDQHFTPNEMNYT

SISSPSPAPIVMGTNENMNFQLGMGSNLEQWRLHQQLVNQFPYNLYGGLDSSPASGSGSASASGLYPFHQAH

YDASGGGVISQIRPKVSNPMLTQLALMKMEDDQDHVASMPRQFLGNENWTSNANWNELSASFSSSSTSNNVL

*

>SlDOF-9

MNFSSIPAYLDPANWQQQSGSTIQNHHHHQQQQLTSAPPPPVLPPGPPVVAPLQPHGGGGAGSIRPGSMADR

ARMANITMPETALKCPRCDSTNTKFCYFNNYSLSQPRHFCKACKRYWTRGGALRSVPVGGGCRRNKRSNNNN

KNSTNNNNNNNSSKSPASSTSTDGRQGTNNSGSSTTISSHSNSFSGPTSAASLLGLMSPQIPPLRFMSPLGQF

SSDHHHHHHFTPSNHMNLNFSTSSCGNILGGTTEGMMVSNNNLLGTGTGAGVGGHVASLLSSGNLEHWRMQ

QQFPNFLGGFDPSNSPSSYPFQGGVHEAVQYLGGESTSQISRPKISTSMLNQMASVKMEDNNNNNSNQDQSA

LSRQLLGIQGNNENWNTSASAWSDLSASFSSSSTSNAL*

>SlDOF-10

MIQELFAGNTTLIGDDNISNITPSSSPISCTTSNSNIAPASANANSENLRCPRCDSPNTKFCYYNNYNLTQPRHFC

KTCRRYWTKGGALRNVPIGGGCRKNKSIATSKSTAAKFKNSLPFEFIGKSGIFGGFEQEIIPSNYDNNNPFLFSS

PHQNHNPILSLLKGNLHKSIGVNQFPSNNGIWKNNYEENVGEVQNSRGFQELYQRLKASTNRCYTDNMHGPSS

SSMILDSAPVTGGELGCWNPTLSTWLDLPTANGAYL*

>SlDOF-11

MSQDNKGESQSSGGGDDGGGGPMGARPKEQALNCPRCDSPNTKFCYYNNYSLSQPRHFCKTCRRYWTKG

GALRNVPIGGGCRKNKKMKTSSSSTRFSGDSKDTISGSSDIGGLKFFHGLSPAMDFQLGGLNFPRLSNNSSTST

http://www.expasy.ch/cgi-bin/dna_sequences?/work/expasy/tmp/http/seqdna.32096,2,142



Supplementary

178

VGGNIFNQFSSFGENSTPNIGSTSCFSLDPSGSSSLLGFNNNFPFSSSMLKQGNEGVQEMGSMGVHHGTMAS

SIESLSSINQDLHWKLQQQRLSMLFGGENQKENIISSSIPLHDDQNQNQNQIQIQKPQPILFQNLEISSSKQQEDH

QETFGNNNVDSRKDCSTTIGNHHGNNLSTEWFFDNSFGLNSNSTHSNNNNNNGNGNANDDQNVNNWNSTIQ

AWSNLNQYSTLP*

>SlDOF-12

MMDSNGTNAATSNNMEKPIQDPSQQQQQQPPPHLKCPRCDSSNTKFCYYNNYSLSQPRHFCKACKRYWTRG

GTLRNVPVGGGCRKNKRIKRPSVNSSSSTTTTSHDIITTSTPNIVNPSHHHQLHHGVVHNNIDHLSTTNSQNHLN

PLFYGLTHERSDLNIPFARLFNSRVSSHAGGVDPEGQVYSLTDNIPGLMDRRMGLGFSNSSGGVVNMGHENN

NNNNNSSSSNYGHGGFNPIKQIQDVHVMSTSNCTTSTTSLLSSYPNMFGSSSSTSTMASLIASSLQQQKFMSNI

NGNSFHSLLTPNNYEELQMSRGENNNTNINNVHEGGGNGITMLKEEKMDLSNHQIHEQIINSSDPSLSWNGAW

LDPSNMGSNSVPSLI*

>SlDOF-13

MEDQLGGRSSGENDRNQQQRRMKMPENNSASPAQPPPQKCPRCDSNNTKFCYYNNYSLTQPRYFCKTCRR

YWTQGGTLRNVPVGGGCRKGKRTMKGGGSSSSAGESSSSRSHHQVLYPPQIPNLSAAAAAFFSGNNSRSQP

PPLPSMSSLYTGAAGGGGFLSSLAAMQSMSQLSQGINNQSQLGVISGTNNNNNQFGNFNIPTPPPKVQIDQQM

ESTGFYQKPNLESSFFPSDQTLQFQPARPLGSWTQRFINNNNNNIWPNNSASNTSSGAASSSAANASLGPNDH

QWPDLPGFGPSP*

>SlDOF-14

MDPSSAQHQHHQELSSQTLESMLVSTKPQQQDPKKPKPPEQAINCPRCDSSNTKFCYYNNYSLSQPRYFCKS

CRRYWTKGGTLRNVPVGGGCRKNKRSSSSRSSSISSQDQHSIVNTPNNPFPYDSSDLSLAFARLQKQGNGQL

GFENHGNLSMMCNENPSGIFLDALRGNTGFLENNNPMNGLIHQQNLYYGVGNIINGDIGLHNVENGGLGVNNN

DQEVGLMHNYDQEISSGVTTSTTMTTVKQEMCNMAKDQGDHNRVLWGFPWQINGEGINMADFDSTRRMWN

GVGGSSWHGLLNSPLM*

>SlDOF-15

MCIMDHHQQEMTSQTLESMLVCAKPDQDQKKPRPAAAEQQPQKCPRCDSANTKFCYYNNYSLTQPRYFCKS

CRRYWTKGGTLRNVPVGGGCRKNRKLSSAKRSSQDNISPNSSNSSTDLSLAFARLQKQTNAIDQEQDTNNNM

SMMYNTNNDNTSTTFLDALRGGFLENHHGLFQHNMYNYANMGQLVENGEMGLSYDQDQMSIGTMMTTTMKQ

EMCNVARSTEGHDLNDNNKVLWGFPWQQMSGDHHVNNNMNTNDFEYSTNKQSWNGFGGSSNWHGLINSPL

M*

>SlDOF-16

MSSQTLESMLACTKPQQEKKPRPQDEVQKCPRCDSSNTKFCYYNNYSLTQPRYFCKSCRRYWTKGGTLRNV

PVGGGCRKKQRSSSKRSSPDNQSLMTTTTYPNNQISPLTQFSYDPNDLSLAFSRLQNQESGQLVCPKLQRTVR























































Supplementary

179

TESIFSDSLYLTNFGNSVCHDVFENRDMCTSSSEAFSRAGFLDTLSDGLLDASNGFLHHNLYYYGSNGNNGNN

MGHVESASGEMITNFDQEVRSGALKEEIMCSDNKILLGYPWQINGDGNVADFEYSNRQNWNGLGVPSGHGLL

NSPLM*

>SlDOF-17

MGITSLQVCMDSSNWLQDTIPEENEFDSSSSPSGGDIFTCSRPLMERKLRPQHDKPINCPRCDSTHTKFCYYNN

YSLSQPRYFCKSCRRYWTKGGTLRNIPVGGGCRKNKKVSSKNSKLPNDNITTPHVESSNNYPEMSFSHFGNF

MGNNNMNHNFMHHAPIDFMDSSKYQALVGTTSTRNQDFFGNVNVGTTGLINGYGEMDISRIGPHYCSSAFGLP

NMDGNIINYEGQNITMDVKPNPKILSLEWHDQGYFNGGLGTWSGLMNNGYGSTATTNSLV*

>SlDOF-18

MGITSLQVCMDSSDWLQGTIHEDCAGGMDSSSSPSGGDHINNLMTCSRPIIVDQRRLRPPHDHSIKCPRCDST

HTKFCYYNNYSLTQPRYFCKTCRRYWTKGGTLRNIPVGGGCRKNKKVSSKKSSTNTNESIPLSTTTTTNNNNIP

EMPFPHHFMSSTNFGHHGNFMLENQAPIIDFMESKYEALVGSSSSTTTNSRLLNQDLFLGNGDNNNNIGMMMM

SGSTSTNGFGHDHDNIVATNYPFGITSIMDSSNNGNSFGMLLPYENHHEEVQNINAVEMKPNPKILSLEWHDDQ

LGSNKESSFGYYSGNGGLGSWTGLMNGNCYGSSATNPLV*

>SlDOF-19

MLPYHPRPMIMMERTRKSNIEQAPNCPRCASTNTKFCYYNNYSLSQPRYFCKACRRYWTKGGSLRNVPVGGG

CRKSRRSRSTRKDDNTLQTSSPALEGAPGAHDIDLADVFAKYLNQGTNNDHDDNIIIQESQDYSSIGASLSNSPS

SDSLVNNPTSFENESLLDNFQDYPCGNFLQEEQGAQINQDFLDFNASFLEMQAILGQEEDQFDHYNTSNFEWQ

PMMQFQDFGSILELDDHQLTKNSTTNLASHDNNNNNNYSSFDLSNYI*

>SlDOF-20

MESTQWSTHEEIGVVKSSMGAEIMNKKVRPVKDGAINCPRCNSINTKFCYYNNYSLTQPRYFCKTCRRYWTEG

GTLRNVPVGGGSRKNKRLSSSSSSSQKLPDLNPNPTSHHQNPNNIVIGSNQDLTLGFRTVPQDHHTSFHGVIP

QFLEFPKMDGSNNHLGSRTGIASRGFTSFISSASTPDLNALYNSGFPFQEIKPSAGNADHAASLSNSNYSSGGP

GGLENGSGARIMFPLGGLKQLSSTNQVDHLTKGQENNNSTAGLYWSGMISGTGGSW*

>SlDOF-21

MATQIAPPNVTCSRTSTTMEKKVRPQKDQVVNCPRCNSTNTKFCYYNNYSLTQPRYFCKTCRRYWTEGGTLR

NVPVGGGSRKNRRCSISSSSLSSISSSQKLLDLNPNPSLSSLQNPNYNLNLGSNQDLNLGFPSFNIHNHNNYFR

GMPQFLDFPKMDKGNNGINHFSTSTSNTSPVSALDLLQKGIASRGLTASISSSSSPSTPDLNALYTSSEVQENG

AKMMLPFGCLNNHNNSESKGQENSSSVGFWNDGMLGGGGTW*

>SlDOF-22



























Supplementary

180

MDTSQWPQEIVMKPMEDIIGNTSKPTNCVERKLVRPQKDQVVNCPRCNSTNTKFCYYNNYSLSQPRYFCKTCR

RYWTEGGSLRNIPVGGGSRKNKKSSSPYNHHVIVNNKKLPDLVVPPPQPDNIEEYPERHFGNRPHDQNPSKIIL

EGSQDLNLGFSSDFKTITDLIQVPNYDGSNKDNNNISTILPPPSSSSSASSPSQLSVMELINGITNNNNNNNNNSF

MTMPNSVYNSSGFSLMPSLNFSLDHGIGNDVHHSSSYGNNNNNLQDTNANGRFLFPFVGLKQVSNNTSDGGA

NEPSLGDQSTNGYWNGMLGGGSW*

>SlDOF-23

MDTSPQWPQGIGIVKGVDEAKLDQRKPRPQKEQAVNCPRCNSTNTKFCYYNNYSLTQPRYFCKTCRRYWTEG

GSLRNVPVGGGSRKNKRSNSISISSSSTTSSLSLSSSSKKLFTDLANPNDLNLTYNPIPSGGTTTTTATNFSNFSD

FMALPLLHPSANSASTFMTSSNLYPSSTTGISNLHDLKSSNGINFSLDGFENGYGSLPSHQEAKLFFPMDDLKIN

VSTVGDEQFEENRGQAADQSNGFWNGMLGGGGTSW*

>SlDOF-24

MDTSQWPQGIVVKTMEEMKIPKNTNTRKIRPQQQQNDEALKNCPRCNSTNTKFCYYNNYSLSQPRFFCKNCR

RYWTDGGSLRNIPIGGVSRKSKKSSSINIMKNNIISPKVQDIINNNNNKGVNQDLNLDFSSDFKIISELIQVPNNNS

FMPIMPNISDPNSIYLFSSNLDHGLIGSSSISDGGYECNNIIQDLQVCTSTTTSGGILFPFEDLKQVSNTSEQSRDG

ESSTNGYWDVILGGN*

>SlDOF-25 (SlCDF1)

MREVKEPEIKLFGKKIVLPENGMILPVIVTGEDSDVGKSMSASEVVTADESSTGSDRDPCLVDKEGNSSQQDES

DDGSEYEKDEADKDRMTRELSEAKLEEKDQNLMMEESENLKSPSENKTKTHTIDDDSPTVKSSKTEDDQNDAS

NSQQKTLKKPDKILPCPRCNSMDTKFCYYNNYNVNQPRHFCKSCQRYWTAGGTMRNVPVGAGRRKNKNSAS

HCRHIMISEALEAARIDPPNGFSHPVFKPNGTVLSFGPDLPLCDSMASVLNLAENKTPNGIRNGLYRPENPSGIG

GENGDDCSSGSSVTTSNSMAEGVKNCPPEAVMQTINAFPSPVPCLPGVPWPIPYAAVPFPAISPAGYPMPFCP

PPYWNCNVPGPWSLPWLTPPSPTANQKGSVSAPNSPLGKHSRDGELLKPNNPEGQKNSEGSVLVPKTLRIDD

PDEAAKSSIWSTLGIKYDSVSRGGLFKSLQPKNSEKDHPTTTSPSLQANPAAFSRSLSFQERV*

>SlDOF-26 (SlCDF3)

MTCDSEIKLFGKILPVVVSGVGRGLSGSDGVIYDGNRNGSDLDRCLEGSKASSVEKDEGSEYEKQEAEKDNITG

ELSEAKSEEGDQNQMIEESENPKTPSESESSPKSSTEEDPQAVKSSKTENEPTNVTNSEQNNLKKPDKILPCPR

CNSLDTKFCYYNNNNVNQPRHFCRSCQRYWTAGGTMRNLPVGAGRRKNKNLASQYRNISIPEGLLAAGIESPN

GLIHHPLFKPNGTILSFGPDLPLCEPMASALNQAEKRVSTGIQNGSHKSEVKNSSCKGGDSGDECCRGINIPTPN

MMVEEGKGEPHKAVMHSINGIPSPVPCLHGVPWPFPWNAAVPVSAICPIPFPMPFFPTPYWNCSVPPWSNPW

LSPPLRAANEKTSGSDPTSSLGKHSREGDLLKPSNPGGKEQSEQKYSEGSILVPKTLRIDDPDEAAKSSIWSTL

GIKYDSTNRGEFFKALQPKSNDKHNKANTFPVLHTNPAALSRSITFQQGA*




























Supplementary

181

>SlDOF-27 (SLCDF4)

MTDPAIKLFGRTIQLPDIPDSSGAQGDDSLPGDNNGEEDEEADKDDFGGNLDDDEEEMEILTGKELQDQNSEPT

KTDSIKELPVDNDCSTRPSKSEEEQGEASNSQEKILKKPDKIIPCPRCNSMETKFCYFNNYNVNQPRHFCKSCQ

RYWTAGGTMRNVPVGAGRRKNKNSIPHYRQISVSETLSNAQTDYPNGIQQPILAFGSPTPLCESMASVLNIADK

TMHNCSQNGFHKPQEPGVPVSYVVGDNGDDHSRRSSVTSANSEDEVNKTVPDLLKKNCHNFPPYMTYYPGA

PWPYPCSPVPWNSAIPPPGYCPPGFPMPFYPAASYWGYTVAGSWNVPWMSPATVSLIQTPTTSGPISPALGK

HSRDENIQKPLSSMEEPSNESNPEKCLWVPKTLRIDDPGEAAKSSIWATLGIKHDTVDSVGGSPFSAFQPKNDD

NNRVSENSTVLQANPAALSRSVNFNESL*

>SlDOF-28 (SlCDF5)

MSEAIAIKDPAIKLFGWTIQLPDFPAPAPEDSSFLAGEVEQELKGLYDDCIDDNEHLTTEDSQDQNPIQQRCDIIN

YYESSTAKTSKSKEEHGETSNSHERNLKKPEKTLPCPRCNSMETKFCYFNNYNASQPRHFCKNCQRYWTAGG

TMRNVPVGAGRRKHKNSVLHYSHVSVSEALSNVRTNFPTETQHPPLTLNGTILSFDTDKPVSESMVSVLNVADK

GMQNCSGNGFQKYKELRIQAGDNGDDHSDGSSVTAISSKDSDNGLPNTPRKNYNSFPTHLPCFTGAPWPYIW

SSVHCRNAVPPPGYSLPGIPMSFIPATTYWGCTIPGSWNVPWMSPPTASHNQMPLTPDPNSPTSRKHSRDEN

VLKSTGTEEEQRKESDPGKRLWFPKTLRIDDPGEAAKSPIWATLGIKHEVVNSVGGGLLSDFLPKNDERSCVSE

NSTLLQVNPAAMSRSLNFNESS*

>SlDOF-29 (SlCDF2)

MSEVRDPSIKLFGKTIGMTQQETNCVYLHDDHTTSSPLSIDDEKINLEGEVTQSKQVDELVDPAADSSIEPETSS

GISDDIKMQDADKETLSSKSVEEEDSSEEKALKKPDKLIPCPRCNSMETKFCYYNNYNVNQPRYFCKNCQRYW

TAGGTMRNVPVGSGRRKNKSSSISNYPLQAGRVEAAAHGMHLPASRTNGTVLTFGSDKPLCDSMVSALNLAE

NSHNMHRNEYHGSEQRMPTIGNDQSNGSCSTASSVTDKESSAGTHDLANWSNFQPFPPQVPYFQGAPWPYS

GFPVSFYPAAPYWGCTVSNPWNVPWLSSNQSVHNNSPTSPTLGKHSRDESKLDPSQSRRRDTTLQDREGER

CVLIPKTLRIHDPNEAAKSSIWSTLGIRNEKIDSTRGTMLFSAFNPKADHRNRELDTSFALQANPAALSRSLHFRE

STR*

>SlDOF-30

MAEVQESPISQGIKLFGATIEIQEKQAKATHQPTNKVVVDDDDDNDQEKRPDKIIPCPRCKSMETKFCYFNNYNV

NQPRHFCKGCQRYWTAGGALRNVPVGAGRRKAKPPCGPGPHGDLADGCNLFDVANQLDFDGSVVAHEDQW

HLFPAAKRRRSTSDSQSY*

>SlDOF-31

MGLSTKLVSIDDDGLDNWTTTTHNTRPEPPSIRRQLPSKSESLKCPRCDSINTKFCYYNNYNKSQPRHYCKGCK

RHWTEGGTLRNVPVGGGRKNKRVRPTDPVDHINGRKHVRLEVNDQRCPLITTTSMTNSIITSSILPSVTLIRGNS

TITSAIDEDIKNLTSSSLPYDIFSNISQDHGNTHFSLIPNSSTNTQLSSNVYYNYEHMGKFDSTILEESTITTIMPITS

NNDLHSYEPWKVPETSNNDLIIDENMSNNYWNWNEFETLSNAADLNISWDDLEIKP*








Supplementary

182

>SlDOF-32

MSSEIGDRRPARLPAPVNGTRPSEPENLPCPRCDSTNTKFCYYNNYNLSQPRHFCKSCRRYWTRGGTLRNVP

VGGGTRKNSSHKRPRINSGAGTVQEQTNPITMMGSGSGHVSGSGSMSLMGCEVNLNESVHEGGNGTSSFTS

LLTAPVGVGVGGFVPLGGFGLGLGGFGLGNLDWPMEQVSGGGNGGDGGENDKWQLSGGEMEGGGGGGGS

GGGGIGGDDDCFGWPDLAISAPGTSLK*

>SlDOF-33

MTLESSEKLVTKQQTGGVQAPPTQEPDHHLPCPRCDSINTKFCYYNNYNLSQPRHFCKSCRRYWTQGGTLRDI

PIGGGSRKNAKRSRIYTNTPFSSTIASVSSHAVPGNSPFMLPLPAANQLLFGTDVKPINNFTSLLSSHGPGVLAL

GGIEDMGFGIGRGNVWPFTGAPDSYSRNYNNGGGAGMWQFSGGEGGFVGSGDYFN*

>SlDOF-34

MPSDVRATKQQQGGAPAPEPEHLPCPRCDSTNTKFCYYNNYNFSQPRHFCKSCRRYWTHGGTLRDIPIGGGS

RKNAKRSRTITTNSMNSSCLSSTLSPRDYHHAPHPSHVSPFLVPLTADHGGSLPFDVKPSVNMCGSFTSLLSSA

QGPGGLLALGGFGLGVEDMGFGLGRPIWPFPGVSHNTSVDNNSNGAGASMYGSTWQLASGGEGGFVGAGG

EIFNFPDLAISTHGNVFNCTFLASGVLALEFRTAVPSIDHQL*












Supplementary

183

Table S4.2.- S. lycopersicum DOF transcription factors structure.

Sequence

namea Gen model nameb Solyc numberc

Predicted gene

structurec Groupd

SlDOF-1

SlDOF-2

SlDOF-3

SlDOF-4

SlDOF-5

SlDOF-6

SlDOF-7

SlDOF-8

SlDOF-9

SlDOF-10

SlDOF-11

SlDOF-12

SlDOF-13

SlDOF-14

SlDOF-15

SlDOF-16

SlDOF-17

SlDOF-18

SlDOF-19

SlDOF-20

SlDOF-21

SlDOF-22

SlDOF-23

SlDOF-24

SlDOF-25

SlDOF-26

SlDOF-27

SlDOF-28

SlDOF-29

SlDOF-30

SlDOF-31

SlDOF-32

SlDOF-33

SlDOF-34

SL1.00sc02597_315.1.1

SL1.00sc04007_502.1.1

SL1.00sc05575_495.1.1

SL1.00sc04337_438.1.1

SL1.00sc04765_101.1.1

SL1.00sc01795_2.1.1

SL1.00sc03759_1.1.1

SL1.00sc01435_36.1.1

SL1.00sc06070_132.1.1

SL1.00sc01656_196.1.1

SL1.00sc07184_188.1.1

SL1.00sc00226_375.1.1

SL1.00sc00226_376.1.1

SL1.00sc06019_225.1.1

SL1.00sc06004_311.1.1

SL1.00sc05805_285.1.1

SL1.00sc06255_2.1.1

SL1.00sc02749_314.1.1

SL1.00sc03736_1.1.1

SL1.00sc05858_346.1.1

SL1.00sc02642_1.1.1

SL1.00sc00777_49.1.1

SL1.00sc06118_114.1.1

SL1.00sc02257_183.1.1

SL1.00sc02749_33.1.1

SL1.00sc02597_358.1.1

SL1.00sc03187_39.1.1

SL1.00sc00530_73.1.1

SL1.00sc00395_292.1.1

SL1.00sc00226_263.1.1

SL1.00sc00226_436.1.1

SL1.00sc02164_222.1.1

SL1.00sc02606_70.1.1

SL1.00sc01656_203.1.1

-

Solyc03g121400.1

Solyc06g062520.1

Solyc05g054510.1

Solyc11g066050.1

Solyc00g024680.1

Solyc06g076030.2

Solyc10g086440.1

Solyc09g010680.2

Solyc02g090310.1

Solyc11g010940.1

Solyc02g077950.1

Solyc02g077960.1

Solyc06g075370.2

Solyc11g072500.1

Solyc06g005130.2

Solyc06g071480.2

Solyc03g112930.2

Solyc04g070960.1

Solyc01g096120.2

Solyc10g009360.2

Solyc08g082910.1

Solyc03g082840.2

Solyc08g008500.2

Solyc03g115940.2

Solyc06g069760.2

Solyc02g067230.2

Solyc02g088070.2

Solyc05g007880.2

Solyc02g076850.1

Solyc02g078620.1

Solyc04g079570.1

Solyc02g065290.1

Solyc02g090220.2

___Dof ___

_▼_Dof ___

__Dof _▼3_

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_D▼of ___

___Dof ___

__D▼of _▼_

___Dof ___

___Dof ___

_▼_Dof ___

___Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

___Dof ___

_▼_Dof _▼_

_▼_D▼of ___

_▼_Dof _▼_

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

_▼_Dof ___

___Dof ___

___Dof ___

___Dof ___

___Dof ___

___Dof _▼_

A

A

A

B

B

B

B

B

B

B

B

A

C

C

C

C

C

C

C

C

C

C

C

C

D

D

D

D

D

D

A

A

A

A

aSequence name designation is arbitrary. bS. lycopersicum gen model name and Solycnumber at SolGenomics.net. cIntron (▼) relative position respect to the DOF domain. Sub-index indicates number of introns. dGroup designation after the phylogenetic analysis displayed in Fig. S1.

Supplementary

184

Table S4.3.- Primers designed for Real-time PCR, expected size, and concentration used.

Gene Primer sequence Amplicon length

(pb)

Concentration

(µM)

SlCDF1

SlCDF2

SlCDF3

SlCDF4

SlCDF5

SlUBI3

AtCOR15

AtRD29A

AtERD10

AtCO

AtFT

AtUBQ10

5’-ACAGTTCACAGCAGGATGAATCA-3’

5’-GCTTCACTGAGCTCTCTTGTCATTC-3’

5’-AAAATCCTAAGACTCCATCAGAATCAG-3’

5’- GCTTGAGGGTCTTCCTCAGTTGA -3’

5’-AAAATCCTAAGACTCCATCAGAATCAG-3’

5’- GCTTGAGGGTCTTCCTCAGTTGA -3’

5’-CCGGGAGACAATAACGGAGA-3’

5’-ATCCAGGTTTCCTCCAAAGTCA-3’

5’-CTGGAGCTCCTTGGCCATAC-3’

5’-CCAGGTGGAGGTACTGCATTTC-3’

5’-AAGCAATGGATGCTGAGGCT-3’

5’-GAAGGTGCCGTTGAATGACA-3’

5’-CAGCGGAGCCAAGCAGAGCAG-3’

5’-CATCGAGGATGTTGCCGTCACC-3’

5’-GGTTCGGTGGTGGTGCGACT-3’

5’-TGGCTTCACCTCCTCCGTCG-3’

5’-CGTTTGTGGCCAAGCACGAAG -3’

5’-AGAGCTGTTGGATCGGTGGAGT-3’

5’-GAGAAATCGAAGCCCGAGGAGCA -3’

5’-TCAGAATGAAGGAACAATCCCATA-3’

5’-CTGGAACAACCTTTGGCAAT -3’

5’-AGCCACTCTCCCTCTGACAA -3’

5’-GCTCTTATCAAAGGACCTTCG -3’

5’-CGAACTTGAGGAGGTTGCAAA-3’

93

67

66

72

80

72

125

113

128

80

219

61

0.200

0.300

0.200

0.300

0.300

0.300

0.200

0.200

0.300

0.200

0.375

0.125

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

0.150

Supplementary

185

Table. S4.4.- Metabolite analyses of WT and 35S::SlCDF3 plants.

Col-0 35S::SlCDF3 (10.7) 35S::SlCDF3 (2.10)

Relative amountof compound(%WT)



Glucose 100 3.2 108.1 4.0 130.9 4.9

Fructose 100 3.2 79.1 4.0 103.3 2.9

Threalose 100 5.3 102.3 7.0 64.3 3.1

Sucrose 100 8.3 279.8 22.3 264.5 8.4

GABA 100 32.5 190.3 46.3 200.7 47.0

L-Proline 100 19.0 191.2 32.8 233.8 47.7

L-Glutamine 100 13.6 164.2 22.2 183.3 24.4

L-Asparagine

100 12.1 106.0 15.2 89.1 9.2

Succinate 100 4.6 128.8 9.2 122.2 7.2

Fumarate

100 4.7 124.9 5.7 81.6 5.6

Malate

100 3.6 74.1 3.3 84.3 5.9

Gluconate 100 5.1 83.7 5.0 73.6 6.2

Supplementary

186

Supplementary File 4.1. Methods for metabolite analyses.

Reagents and solvents. Most of the stand ard compounds used for this study and the oxidation reagent

methoxy amine hydrochloride were obtained from Sigma(St. Louis, USA).N,O-bis (trimethylsilyl) trifluoro

acetamide (BSTFA) containing 1% trimethyl chlorosilane(TMCS) was purchased from Supelco

(Bellefonte,USA).The six stable isotope reference compounds[13

C6]-glucose,[13

C12]-sucrose,[2

H3]-proline,

[13

C5]-glutamic acid, and [13

C4]-succinic acid were purchased from Campro (Veenendaal, The

Netherlands),[2

H4]-2-hydroxybenzoicacid from C/D/N Isotopes (Pointe-Claire, Canada).

Plant extraction. A total of 100 mg of pooled seedlings from A. thaliana (Col-0, 35S::SlCDF3 and

3S::AtCDF3 overexpressing lines) sterilely grown under long-day conditions on plates were harvested

12days after germination, immediately froze in liquid nitrogen, and thenstoredat-80ºC until further use.

When we were ready to commence the extraction, the frozen plant material was transferred into 2mL

screw-cap tubes filled with ceramic beads (Mag NALyser Green Beads), 1ml of a chloroform:

methanol: H2O(20:60:20)mixture including the internal standards([13

C6]-glucose, [13

C12]-sucrose, [2

H3]-

proline, [13

C5]-glutamic acid,[13

C4]-succinic, [2

H4]-2- hydroxy benzoic acid) was added, and the plant

material crushedina MagNALyser Instrument(Roche, Mannheim, Germany)at6,500rpmfor1min. The final

concentration of each reference compound in the solvent mixturewas15ngmL-1

. Next, the samples were

centrifuged (10 min, 14,000rpm), and 200 µL aliquots of the supernatant transfer red to fresh tubes.

Samples were either directly analyzed by LC- MS or taken to dryness in a speed-vac concentrator for

subsequent derivatization.

UHPLC/ESI-qTOF-MS. Five microliters of the extract were separate dusing an Ultimate 3000 RSLC

system(Dionex, Sunnyvale, USA).The column used was a 50mm x 2.1mm i.d.,1.7 µm, Acquity UPLC

BEH C18 column with a 5mmx2.1mm i.d. Acquity UPLCBEHC 18 Van Guardpre-column and a 0.2µm x

2.1mm i.d.in-line filter (Waters, Milford, USA). The following binary gradient was applied: 0 to 2min

isocratic 98% solvent A(water with 0.1%[v/v] formica cid), 2% B (acetonitrile with

Supplementary

187

0.1%[v/v] formic acid); 2 to 25 min linear gradient to 5% A, 95% B; at 25 min step gradient to 100% B;

isocratic for 1 min. Thereafter, the column was set to 98% A, 2% Band conditioned for 2min before the

next injection. The flow ratewas400µL/min. All solvents were of LC-MS grade.

Eluted compounds were detected by a micro TOF-QII mass spectrometer (Bruker Daltonics, Bremen,

Germany) operated in electro spray positive mode. Typical settings were as

follows:capillaryvoltage,4500V;dry gastemperature,200°C;dry gasflow,10L/min; funnel, RF200Vpp.Ions

were detected from m/z 50 to 1000 at a repetition rate of 5Hz.The instrument was operate deitherin MS

orauto MS/MS mode. Mass calibration was performed using sodium for mate clusters (10 mM solution of

NaOH in 50/50% v/v isopropanol/water containing 0.2%formic acid).

For data processing the Data Analysis 4.0 software (Bruker Daltonics, Bremen, Germany) was used.

Principle component analysis was conducted using Profile Analysis 2.0 (Bruker Daltonik, Bremen,

Germany).Data sets were evaluated from 0.25 to1500 sin the mass range between m/z 50 to1000.

Compound finding was performed prior to data processing using the find molecular feature (FMF)

algorithm (Krug et al., 2008 Anal Chim Acta). On the basisofline mass spectra, mass peak clusters,

which consist of a minimum number of 8 consecutive scans, were generated. From these clusters, local

extracted ion chromatograms (EIC) traces were calculated. Finally, chromatographic peak

detection(S/N= 3,no smoothing)was performed on these EIC traces allowing the subsequent correlation

analysis of neighboring isotopes(correlation coefficient threshold=0.7,maximumvalue=1.0). To correct for

anon-constant signal variance, the data set was scaled using the PARETO method. The two first

principal components (PCs) extracted accounted for approx. 34% of the total variance existing in the

sample population.

Derivatization. Prior to GC-MS analysis, samples were derivatized according to the two-step protocol

given in the following: At first, carbonyl moieties were protected by methoximation, using

30µLofa15mgmL-1

solution of methoxy amine hydrochloride in pyridin eat 22°C (room temperature) for

16h. Afterwards, acidic protons were derivatized by the addition of 30 µL of N, O-bis (trimethylsilyl)

trifluoro acetamide (BSTFA+1%TMCS) for 1h at room temperature (22°C). All samples were analyze in

randomized order and the first sample was analyzed by GC-MS 30 min after the silylation regents were

added.

GC-MS. One µL of the derivatized sample is injected split less by an Combi Palau to sampler (CTC,

Zwingen, Switzerland) into a BRUKER Daltonics 451 gas chromatograph equipped with a 30m x0.25 mm

i.d. fused silica capillary column with a chemically bonded 0.25µm DB5-MS stationary phase (BRUKER

Daltonics). The injector temperature is set to 270°C. After1 min, the split is opened (1:100). During the

first 1.10 min a pressure pulse (30psi) supports sample application onto the column. The gas flow rate

through the column is adjusted to 1mL min-1

, the column temperature is held at 70°C for 2min, then

Supplementary

188

increased by 20°C min-1 to 325°C, and held there for 5min. The column effluent is introduced into the

ion source of a Scion-TQ triple quadrupole mass spectrometer, GC-QQQ-MS (BRUKER Daltonics).

The transfer line and the ion source temperatures are maintained at 250°C. Ion sare generated by a 70e

Velectron beam at an ionization current of 80 µA, and 30spectra s-1

are recorded in the mass range 50 to

600m/z. The acceleration voltage was turned on after a solvent delay of 390s.

All data were processed by MS Workstation8 (rev2) software (Bruker Daltonics, Bremen, Germany).

Automatic peak detection and mass spectrum deconvolution was performed with a peak width setto 1.0s.

Peak areas were calculated using selected quantification masses for each metabolite and internal

standard. Mass spectra of all detected compounds were compared with spectra in the NIST11 mass

spectral library (as of May, 2011), and an in-house reference database

cycling dof factors: molecular and …oa.upm.es/35207/1/alba_rocio_corrales_ducuara.pdf ·...

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