interações bióticas e abióticas em feijão-caupi vigna ... · para a vida, as portas não são...

Universidade Federal de Pernambuco Centro de Ciências Biológicas

Programa de Pós-Graduação em Ciências Biológicas

Interações Bióticas e Abióticas em Feijão-Caupi (Vigna unguiculata) pela técnica de SAGE

(Serial Analysis of Gene Expression)

Pedranne Kelle de Araújo Barbosa

Recife, 2010

Pedranne Kelle de Araújo Barbosa

Interações Bióticas e Abióticas em Feijão-Caupi (Vigna unguiculata) pela técnica de SAGE

(Serial Analysis of Gene Expression)

Tese apresentada como requisito para obtenção de título de Doutor em Ciências Biológicas, junto ao Programa de Pós-Graduação em Ciências Biológicas, área de concentração em Biotecnologia da Universidade Federal de Pernambuco.

Orientadora: Profa. Dra. Ana Maria Benko-Iseppon

Co-orientador: Prof. Dr. Éderson Akio Kido

Recife, 2010

Barbosa, Pedranne Kelle de Araújo

Interações bióticas e abióticas em feijão- c aupi ( Vigna unguiculata) pela técnica de SAGE (Serial Analysis of Gene Expression) / Pedranne Kelle de Araújo Barbosa. – Recife: O Autor, 2010.

152 folhas : il., fig., tab.

Orientadora: Ana Maria Benko-Iseppon. Co-orientador: Éderson Akio Kido

Tese (doutorado) – Universidade Federal de Pernambuco. CCB. Ciências Biológicas. Biologia Vegetal, 2010.

Inclui bibliografia e anexos.

1. Feijão Caupi 2. Estresse abiótico 3. Genética vegetal 4. Biotecnologia . I. Título.

581.38 CDD (22.ed.) UFPE/CCB-2010-123

Para a vida, as portas não são obstáculos,

mas diferentes passagens. (Içami Tiba)

Dedico

Dedico

Agradecimentos

Agradeço a minha mãe, Maria Araújo, a quem dedico esta tese. Mulher guerreira que se

faz presente todos os dias na minha vida, incentivando, orientando, acalmando,

instruindo e dando todo o suporte que sempre precisei para que mais esta etapa

pudesse ser concluída. A ela, meu amor incondicional;

Às minhas irmãs, Arianne Barbosa e Anne Barbosa, que em todos os momentos e

decisões se fizeram presentes, me apoiando e me incentivando, fazendo com que a

distância física entre nós sempre fosse pequena comparada à vontade de “chegar lá”. Ao

meu pai, Edmar Barbosa, fonte de inspiração na busca deste objetivo;

Agradeço a oportunidade de ter encontrado essa pessoa maravilhosa, Ricardo Castro,

que hoje muito mais que meu “namorido”, é um companheiro que me ajudou dando todo

o suporte necessário, abdicando horas do seu trabalho, para que eu pudesse concluir

essa etapa da minha vida. E que com ele compartilho o maior tesouro da minha vida,

nosso filho, Caio Barbosa e Castro;

À Profa. Dra. Ana M. Benko-Iseppon pela oportunidade de trabalhar em seu grupo de

pesquisa e pela confiança ofertada a mim para o desenvolvimento desse projeto;

Ao Prof. Dr. Éderson A. Kido, que muito me surpreendeu nessa fase final do trabalho, e

que sem dúvida, sem seu empenho tudo seria muito mais difícil e demorado. Agradeço a

acolhida nesse momento;

À Fofi (Dra. Valesca Pandolfi), uma grande companheira a quem tenho muito respeito.

Uma pessoa que sempre se colocou prontamente a me ajudar para que nossos objetivos

finais fossem alcançados;

Ao Prof. Dr. Paulo Andrade, a quem tenho grande admiração por toda sua genialidade,

presteza, criatividade e alegria. Obrigada pela paciência, os conselhos e as ajudas.

Tirando o “Fau”, você é o “cara”!

Agradeço a Amanda Martins, pela dedicação nas formatações finais, bem como o

companheirismo nos trabalhos extra-laboratório (personal promoter); Ao João Pacífico

pela disponibilidade e paciência ofertada nos momentos de “socorro” auxiliando com os

programas da bioinfo;

A Nina Mota, por toda boa vontade e até mesmo paciência na análise inicial dos dados e

por conseguir fazer dos momentos estressantes, momentos até divertidos;

Ao Prof. Dr. Tercilio Calsa que se disponibilizou na orientação da construção das

bibliotecas SAGE;

Aos colegas e amigos de laboratório conquistados, tanto do LGM quanto do LGBV, e que

hoje mesmo não fazendo mais parte do grupo (alguns), ainda fazem parte dessa minha

história: “quarteto” fantástico (Thiago Souza, Rodrigo Assunção, Bruno Ribeiro),

Celuza Castro, Riba (Neto Costa Ferreira), Renata Castro, Nayara Vieira, Marcelo

Oliva, Marcelo Lucena, Michely Diniz, Rodrigo Gazzaneo, à Elite (Hayana Azevedo,

Kênia Lucena, Mario Correia, Geyner Alves, Diego Sotero, Lidiane Amorim, Alberto

Vinicius), Santelmo Vasconcelos, Ebénezer Bernardes, Claudete Marques;

Aos amigos que fiz “No Recife” e com certeza me deram suporte psicológico ajudando no

andamento dos trabalhos e deixando meus dias sempre mais alegres: as flores (Fátima

Alves, Vanessa Oliveira), aos tios (Mércia Melo, Amaro Castro, Leila Martins), aos

malinhas (Alexandre Campos, Tony Brito), as sem noção (Lidiane Freire, Marcella

Oliveira), ao trio do Renault (Flávio Beltrão, Pedro Neto, Priscilla Belo), a docinho

(Hayana Azevedo, Kenia Lucena, Joana Araújo);

Aos amigos conquistados na vida acadêmica e familiares que me acompanham (mesmo à

distância), sempre torcendo e me incentivando: Profa. Dra. Ana Brito (minha eterna

orientadora e a quem tenho uma eterna admiração), Patrese Calheiros, Adriana Lima,

Laura Souza, Luiz Fabiano, Maria Eugênia (Tia Gê), Luiz Araújo (tio Lula), Stanley

Gonçalves, Genival Costa, Juliana Costa.

Ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) pela

concessão da bolsa de Doutorado e pelo suporte financeiro necessário à realização desta

pesquisa;

À Universidade Federal de Pernambuco, por meio do Departamento de Ciências

Biológicas, pela oportunidade de realização do curso;

A DEUS, causa primária de todas as coisas.

SUMÁRIO LISTA DE FIGURAS .................................................................................................................................... viii LISTA DE TABELAS ...................................................................................................................................... ix RESUMO .............................................................................................................................................................. x ABSTRACT ........................................................................................................................................................ xi 1. INTRODUÇÃO ........................................................................................................................................... 12 2. REVISÃO BIBLIOGRÁFICA ................................................................................................................. 13 2.1. A Cultura do Feijão-Caupi e sua importância econômica ............................................. 13 2.2. Taxonomia e Características Botânicas ................................................................................. 14 2.3. Estresses Bióticos e Abióticos e suas consequências ....................................................... 15 2.4. Interação Planta-Patógeno .......................................................................................................... 17 2.5. Interação Planta-Vírus ................................................................................................................. 18 2.6. Melhoramento Genético .............................................................................................................. 22 2.7. Técnicas de Avaliação da Expressão Gênica ....................................................................... 24 2.8. Aplicações da SAGE em Plantas ................................................................................................ 26 2.9. Transcriptômica do Feijão-Caupi ............................................................................................ 28 2.10. Bioinformática .............................................................................................................................. 31 2.10.1. Bancos de Dados e Ferramentas de Bioinformática ............................................. 32 3. REFERÊNCIAS BIBLIOGRÁFICAS ................................................................................................... 36 4. CAPÍTULO 1 Transcriptional profiling of wound stress response in Vigna unguiculata (L.)

Walp. revealed by SuperSAGE 4.1. Abstract ............................................................................................................................................... 59 4.2. Background ....................................................................................................................................... 60 4.3. Material and Methods ................................................................................................................... 62 4.4. Results and discussion ................................................................................................................. 64 4.5. References ......................................................................................................................................... 81 4.6. Additional file ................................................................................................................................... 94 5. CAPÍTULO 2 The analysis of differential expression in Vigna unguiculata (L.) Walp. to the

severe mosaic virus (CPSMV) revealed by SuperSAGE 5.1. Abstract ............................................................................................................................................ 111 5.2. Background .................................................................................................................................... 112 5.3. Material and Methods ................................................................................................................ 113 5.4. Results and discussion .............................................................................................................. 115 5.5. References ....................................................................................................................................... 130 6. CONSIDERAÇÕES FINAIS ................................................................................................................. 142 7. Instruções para autores da revista BMC Genomics ........................................................ 143

viii

LISTA DE FIGURAS

REVISÃO BIBLIOGRÁFICA

Figura 1. Principais mecanismos do reconhecimento do patógeno e da resposta de

defesa em plantas superiores ................................................................................................................... 18

Figura 2. Folhas de Vigna unguiculata (Cultivar IT85F) apresentando sintomas severos

após 23 dias da inoculação com vírus do Mosaico Severo (CPSMV) ....................................... 21

CAPÍTULO 1

Figure 1. Distribution of the 30 most represented GO terms in the category “Cellular

Component”, including absolute values and percentage ............................................................ 65

Figure 2. Distribution of 30 most represented GO terms in the category “Biological

Process”, including absolute values and percentage ...................................................................... 66

Figure 3. Table with the representative sequenced tag number …......................................... 67

Figure 4. Quantitative distribution of SuperSAGE tags ................................................................ 68

Figure 5. Best matches (in %) regarding differentially expressed tags that could not be

annotated with the cowpea EST database .......................................................................................... 71

Figure 6. Distribution of the differentially expressed transcripts in absolute numbers

within the three principal Gene Ontology categories ..................................................................... 72

Figure 7. Functional categorization of Vigna unguiculata unitags .......................................... 74

CAPÍTULO 2

Figure 1. Distribution of unique tags (axis Y) in relation to tag copy number (axis X).

Only tags with a copy number ≥ 2 were plotted on the graph ………………………………. 116

Figure 2. Diagram Venn showed distribution of tags among the three SuperSAGE

libraries for each stress treatment (1) BMCT123; (2) BMCT4; (3) BRC1 …………….…. 117

Figure 3. Functional categorization of Vigna unguiculata unitags ………………………….. 121

Figure 4. Response to stress category in SuperSAGE libraries from V. unguiculata … 122

Figure 5. Fold change in Vigna unguiculata tags showing significant changes in

expression following BMCT123 and BMCT4 infestation of CPSMV ……………………..… 123

Figure 6. Heat map representing expression perfiles in subcategory response to stress

of Vigna unguiculata ………………………………………………………………………………………..…. 129

ix

LISTA DE TABELAS

CAPÍTULO 1

Table 1. Differentially expressed tags after comparison of the control versus

stressed libraries ..................................................................................................................................... 69

Table 2. Sequences of SuperTags (26 pb) differentially expressed ………….…..………. 95

Table 3. Functional classification of the differentially expressed genes …..….……..…. 97

CAPÍTULO 2

Table 1. Summary of SuperSAGE libraries of Vigna unguiculata …………….………… 116

Table 2. Annotation primary of tags SuperSAGE ……………………………………………… 118

Table 3. Summary of 30 most abundant antisense tags ……………………………..…….. 119

x

RESUMO

Danos provocados por estresses bióticos e/ou abióticos são fatores limitantes na

produção do feijão-caupi (Vigna unguiculata), favorecendo a redução no crescimento e

na produtividade desta cultura. Uma alternativa a estes fatores limitantes é o uso de

cultivares com características genéticas competitivas e eficientes contribuindo para

alcançar um padrão de agricultura mais sustentável e com melhores condições de

produção. Assim, pesquisas que possibilitem compreender funções específicas de genes

preditos de plantas e seus perfis de expressão em resposta a uma dada condição, são de

extrema importância. Com base nisso, uma das metas do projeto NordEST

(http://www.vigna.ufpe.br) consistiu na análise funcional de genes de feijão-caupi

associados a estes tipos de estresses. Neste âmbito, o presente trabalho teve como

objetivo analisar o perfil de expressão diferencial de genes através da técnica de

SuperSAGE a partir de transcritos de folhas de feijão-caupi submetidas a injúria

mecânica (biblioteca C2) e ao estresse causado pelo vírus do mosaico severo do feijão-

caupi (CPSMV) (biblioteca BRM), com o intuito de obter um melhor entendimento com

relação à resposta específica a este tipo de estresse, comparativamente a um controle

negativo (ausência de injúria; biblioteca C1). As tags que apresentaram 100% de

identidade com sequências de EST do banco privado de Vigna (banco NordEST), foram

analisadas quanto à sua expressão diferencial e os transcritos que tiveram seus genes

superexpressados e/ou reprimidos, dentro dos parâmetros requeridos (escore ≥42)

foram anotados em categorias funcionais, de acordo com os termos de ontologia gênica

(Gene Ontology) relativos a processos biológicos, função molecular e componente

celular. Os resultados demonstraram que muitas sequências, tanto das bibliotecas

submetidas à injúria, quanto as bibliotecas inoculadas com CPSMV estão relacionadas à

categorias associadas a estresse, seguido de categorias relacionadas ao processos de

tradução, ligação de proteínas, regulação da transcrição, redução de oxidação,

transporte, proteólise, entre outros. Estas categorias estão relacionadas a rotas

metabólicas importantes na resposta a estresses bióticos, indicando que estas tags

representam um potencial real para descobertas de novos genes responsivos à injúria

ou à resistência ao CPSMV, talvez ainda não descritos e/ou caracterizados.

Palavras-chave: Vigna unguiculata, SuperSAGE, perfil transcricional, anotação

funcional.

xi

ABSTRACT

Damage caused by biotic and / or abiotic factors are limiting factors in the production of

cowpea (Vigna unguiculata), favoring a reduction in growth and productivity of this

crop. An alternative to these limiting factors is the use of cultivars with genetic

competitive and efficient helping to achieve a pattern of more sustainable agriculture

and better production conditions. Thus, studies that allow for understanding specific

functions of predicted genes of plants and their expression profiles in response to a

given condition are of extreme importance. On this basis, one of the goals of the project

NordEST (http://www.vigna.ufpe.br) was the functional analysis of genes of cowpea

associated with these types of stress. In this context, this study aimed to analyze the

profile of differential gene expression using the technique of SuperSAGE transcripts

from cowpea leaves subjected to mechanical injury (C2 library) and the stress caused by

severe mosaic virus cowpea (CPSMV) library (BRM), in order to gain a better

understanding regarding the specific response to this type of stress, compared to a

negative control (no injury; Library C1). The tags that showed 100% identity with EST

sequences of the private bank of Vigna (NordEST bank), were analyzed for their

differential expression and the transcripts whose genes were up and / or down

regulated within the required parameters (score ≥ 42) were noted in functional

categories, according to the terms of gene ontology (Gene Ontology) related to biological

processes, molecular function and cellular component. The results showed that many

sequences, both of libraries subjected to injury, as the libraries are inoculated with

CPSMV related categories associated with stress, followed by categories related to the

processes of translation, protein binding, transcription regulation, oxidation reduction,

transport, proteolysis, among others. These categories are related to important

metabolic pathways in response to biotic stresses, indicating that these tags represent a

real potential for discovery of new genes responsive to injury or resistance CPSMV may

not yet described and / or characterized.

Keywords: Vigna unguiculata, SuperSAGE, transcriptional profile, functional annotation.

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1. INTRODUÇÃO

Nas regiões Norte e Nordeste, o feijão-caupi [Vigna unguiculata (L.) Walp.] é a

leguminosa com maior propagação, representando aproximadamente 80% da produção

total de grãos para alimentação humana, sejam verdes ou secos, constituindo uma fonte

importante de proteínas (23-30%) e carboidratos (56-68%) (Bressani, 1993; Hall et al.,

2003). Entretanto, um dos fatores limitantes desta leguminosa são os estresses causados

por fatores bióticos e abióticos, acarretando grandes perdas na sua produtividade.

Qualquer uma destas condições pode retardar o crescimento e o desenvolvimento,

reduzir a produtividade e, em casos extremos, levar a planta à morte (Qiang et al., 2000;

Jiang e Zhang, 2002; Ozturk et al., 2002; Xiong et al., 2002).

Além disso, os métodos de cultivo adotados, na maioria das vezes utilizando

pouca tecnologia, reduzem a produtividade e qualidade do grão. Desta forma, o uso de

cultivares com características genéticas competitivas e eficientes contribuem para

alcançar um padrão de agricultura mais sustentável e com maior produtividade.

As pesquisas que possibilitem compreender funções específicas de genes

preditos de plantas e seus perfis de expressão em resposta a uma dada condição podem

contribuir decisivamente no melhoramento de plantas. Neste contexto, os projetos de

sequenciamento aumentaram não somente o conhecimento de sequências genômicas

para muitos organismos, mas igualmente para sequências de ESTs/cDNA para muitas

espécies vegetais, criando novas oportunidades para usar estas informações na

compreensão de mecanismos genéticos e desenvolvimento do controle da planta e suas

respostas aos estímulos ambientais.

As técnicas utilizando a análise de expressão de genes em indivíduos com

características diferenciais (por exemplo, resistência/suscetibilidade a doenças) vêm

sendo adotadas com grande sucesso em várias culturas vegetais. Dentre elas enquadra-

se a SAGE (Serial Analysis of Gene Expression, Análise Serial da Expressão de Genes), que

simultaneamente identifica e estuda genes expressos sob diferentes situações, sendo

baseada no sequenciamento e quantificação de um grande número de regiões

específicas (tags), obtidas de populações contrastantes de transcritos (Velculescu et al.,

2000).

Neste trabalho, o estudo do perfil de expressão diferencial de genes através da

técnica de SuperSAGE em V. unguiculata, submetido a injúria mecânica e ao ataque pelo

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vírus do mosaico severo do caupi (CPSMV), foi aplicado com o intuito de obter um maior

entendimento a respeito da relação planta-estresse e/ou planta-patógeno,

representando uma fonte de informações que poderá ser utilizada em estudos de genes

de resistência.

2. REVISÃO

2.1. A CULTURA DO FEIJÃO-CAUPI E SUA IMPORTÂNCIA ECONÔMICA

O feijão-caupi [Vigna unguiculata (L.) Walp.], também conhecido como feijão-de-

corda, feijão-vigna ou feijão-macassar (Freire-Filho et al., 2002), além de saboroso,

apresenta alto valor nutricional, com baixos teores de fatores anti-nutricionais e outras

toxinas (Kay, 1979; Quass, 1995). O grão do feijão-caupi possui baixos índices de

gordura, sendo rico nos aminoácidos lisina e triptofano, apresentando teores protéicos

de duas a quatro vezes maior que outros cereais (Viera, 1983; Fall et al., 2003). Seus

grãos também são ricos em minerais e vitaminas (Hall et al., 2003), apresentando um

dos mais elevados níveis de ácido fólico e vitamina B1, ajudando a prevenir defeitos no

tubo neural (DTN) em fetos (http://www.cdc.gov/; Toriello, 2005).

A semente ou “grão seco” (como é referida às vezes) do feijão-caupi compreende

um dos produtos mais importantes da planta para consumo humano, embora os grãos

frescos e vagens verdes frescas também sejam importantes em alguns locais (Nielson et

al., 1997; Ahenkora et al., 1998).

Seu cultivo é em grande parte praticado por pequenos produtores,

desempenhando importante papel econômico-social na região Nordeste, onde constitui

o feijão mais consumido (Frota e Pereira, 2000), gerando cerca de 2,4 milhões de

empregos diretos e abastecendo a mesa de 27,5 milhões de nordestinos (Benevenutti,

1996; Maia et al., 2000).

O feijão-caupi também possui uma resposta de crescimento favorável em

condições de estresse como seca, temperaturas elevadas e outros estresses abióticos

(Ehlers e Hall, 1997; Oliveira, 2006). Com base nisso, no período de seca, o feijão-caupi

desenvolve particularmente um papel crítico na alimentação animal em muitas partes

do oeste da África (Singh e Tarawali, 1997; Tarawali et al., 1997, 2002).

14

Além disso, devido à sua tolerância em solos com baixa fertilidade - em

decorrência da sua capacidade de fixação de nitrogênio (Martins et al., 1997), bem como

de realizar simbiose efetiva com micorrizas e habilidade para tolerar solos com grandes

variações de pH, o feijão-caupi é um dos componentes mais valiosos em sistemas

agrícolas, restaurando a fertilidade dos solos para sucessão de outras culturas (Carsky et

al., 2002; Tarawali et al, 2002; Sanginga et al, 2003).

Segundo estimativas da FAO a produção mundial da cultura de feijão-caupi é de

aproximadamente 3,7 milhões de toneladas, em uma área cultivada de cerca de 8,7

milhões de hectares. A Nigéria é o maior produtor, com aproximadamente 57% do total

da produção mundial, seguida pelo Brasil, que contribui com 17% da produção mundial

(Pereira et al., 2001).

O feijão-caupi é um componente importante nos sistemas de produção em

especial no Norte e Nordeste do Brasil, no entanto, a produtividade é relativamente

baixa (entre 300 a 400 kg/ha), sendo decorrente, principalmente, dos sistemas de

produção usados, onde na maioria não são adotadas práticas adequadas de manejo do

solo, de pragas e doenças (Freire-Filho et al., 1999; Pio-Ribeiro, 2005).

2.2. TAXONOMIA E CARACTERÍSTICAS BOTÂNICAS

O nome “caupi” advém do inglês “cowpea” e se deve, provavelmente, à sua

importância na produção de feno para alimentação bovina no sudeste dos Estados

Unidos e em outras partes do mundo. Ainda nos Estados Unidos, outros nomes usados

para descrever os grãos incluem “southernpeas” “blackeyed peas”, “field peas,” “pinkeyes”

e “crowders”. Estes nomes refletem a semente tradicional e algumas novas classes

desenvolvidas no sul dos Estados Unidos (Timko et al., 2007). Na África ocidental são

atribuídas as denominações “niebe”, “wake” e “ewa”. No Brasil, sua denominação varia

conforme a região, sendo mais conhecido como “feijão-de-corda” e “feijão-macassar” na

região Nordeste, “feijão-de-praia” e “feijão-de-estrada”, na região Norte, bem como

“feijão-miúdo”, na região Sul. É também chamado de “feijão-catador” e “feijão-gerutuba”,

em algumas regiões do estado da Bahia e norte de Minas Gerais. Já no estado do Rio de

Janeiro é conhecido como “feijão-fradinho” (Freire Filho et al., 1983).

O feijão-caupi é classificado dentre as Dicotyledoneae, na ordem Fabales, família

Fabaceae, subfamília Faboideae, tribo Phaseoleae, subtribo Phaseolinea, gênero Vigna,

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secção Catiang e espécie Vigna unguiculata (L.) Walp.) (Verdecourt, 1970; Marechal et

al., 1978; Padulosi e Ng, 1997). Trata-se de planta herbácea, autógama, anual (Singh et

al., 2002) que apresenta dois tipos de ramificações. No primeiro tipo, o caule produz um

número limitado de nós e para de crescer quando emite uma inflorescência. No segundo

tipo (o mais cultivado no Brasil), o caule continua crescendo e emitindo novas ramas

secundárias e gemas florais. Apresenta inflorescências simples, embora tenham sido

identificados genes recessivos que condicionam a produção de inflorescências

compostas (Araújo et al., 1981; Machado et al., 2007). As vagens apresentam entre oito e

dezoito sementes, cujo formato pode ser cilíndrico, curvado ou em linha reta. As

sementes das cultivares pesam entre 80 e 320 mg, podendo seu revestimento

apresentar variações como textura (por exemplo, liso, áspero ou enrugado) e cor

(branco, creme, verde, vermelho, marrom, preto, entre outros) (Timko e Singh, 2008).

2.3. ESTRESSES BIÓTICOS E ABIÓTICOS E SUAS CONSEQUÊNCIAS

O desenvolvimento geral das plantas pode ser afetado por diferentes tipos de

estresses, caracterizados por condições externas que adversamente afetam o

crescimento, o desenvolvimento e/ou a produtividade. Estes podem ser bióticos,

impostos por organismos, como vírus, bactérias, fungos, nematóides e insetos (Santos et

al., 1999; Korth, 2003) ou abióticos, incluindo excesso ou deficiência de fatores do

ambiente físico ou químico (Agrios, 1997; Sticher et al., 1997; Dias e Rangel, 2007;

Soares e Machado, 2007).

Dentre as condições ambientais que podem causar alguns desses tipos de danos

estão o excesso ou a falta de água (estresse hídrico), variações na temperatura (frio ou

calor), excesso de salinidade, deficiência mineral no solo, o excesso ou falta de luz, além

da chuva e vento. Compostos fitotóxicos como o O3 (ozônio) também podem causar

danos nos tecidos das plantas (Eckey-Kaltenback, et al., 1997; Sanz et al., 2002; Krupa et

al., 2003).

O dano ocasionado devido a esses fatores pode ter como consequência a redução

da qualidade fisiológica da planta após a injúria (efeito imediato) e/ou após

determinado período de armazenamento (efeito latente), no caso de sementes e frutos.

O ferimento representa uma ameaça constante à sobrevivência da planta porque não

16

somente destrói fisicamente os tecidos, mas fornece um caminho para a invasão pelo

patógeno (Cheong et al, 2002).

As plantas são organismos sésseis e obtêm nutrientes e água através de suas

raízes, e são assim desprovidos de mecanismos que impedem os ferimentos, sejam

mecânicos ou causados por patógenos. No entanto, as plantas são dotadas de barreiras

pré-existentes que limitam o dano, tal como a cutícula, número e disposição dos

estômatos que podem com sucesso suportar a agressão de pequenos herbívoros, ou

então os tricomas, os espinhos e outros órgãos especializados que podem restringir o

acesso da praga às partes mais nutritivas da planta (Leon et al, 2001; Korth, 2003).

Diante da situação de estresse, embora não há possibilidade de mobilizar células

especializadas, as plantas evoluíram desenvolvendo células competentes para a ativação

das respostas de defesa que dependem da ativação transcricional de genes específicos.

Estas respostas são dirigidas a recuperação dos tecidos danificados e a ativação de

mecanismos de defesa que impeçam danos adicionais. A maioria das respostas induzidas

ocorre em um curto período de tempo entre alguns minutos a diversas horas após o

ferimento e incluem a geração/liberação, percepção e transdução de sinais específicos

para a ativação de genes de defesa relacionados à injúria (Leon et al, 2001).

Em resposta aos danos causados pela injúria as plantas se defendem da mesma

forma como se estivessem sendo atacada por patógenos, consequentemente supõe-se

que o mecanismo de defesa das plantas nestas duas situações evoluiu integradamente.

Na sustentação desta ideia, os estudos mostraram que a injúria utiliza um número de

genes que são regulados igualmente e/ou que possuem um mesmo papel em resposta ao

patógeno (Durrant et al., 2000; Reymond et al., 2000). Por exemplo, estudos mostraram

que diversos hormônios de plantas são importantes nesta resposta, dentre eles, ácido

jasmônico, ácido salicílico e o etileno (Dong, 1998, Thomma et al., 1998). Além destes,

algumas horas após o ferimento, as plantas produzem espécies reativas de oxigênio

(ROS), incluindo o ânion superóxido no tecido danificado e água oxigenada (H2O2)

ambos local ou sistemicamente. A produção do superóxido é máxima alguns minutos

após o ferimento e 4-6 horas para a H2O2, declinando em seguida (Orozco-Cárdenas e

Ryan, 1999).

Na situação de ataque por patógenos, em geral, as plantas respondem a estes

tipos de estresses através de uma cascata de respostas envolvendo desde a alteração da

expressão gênica e do metabolismo celular; até a alteração da taxa de crescimento e

17

mudanças na produtividade (Staskawicz et al., 1995; Moraes, 1998). Entretanto, estas

respostas (resistência, tolerância ou suscetibilidade) dependem não somente da

duração, severidade, número de exposições e da combinação desses fatores de estresse,

mas também do tipo de órgão e tecido, idade de desenvolvimento, genótipo e espécie ou

variedade das plantas (Staskawicz et al., 1995).

As plantas possuem mecanismos que, dependendo da virulência do patógeno e do

efeito sinérgico entre o patógeno e a cultivar (McDowell e Dangl, 2000, Brioso, 2006)

podem impedir ou minimizar os danos causados. Esses mecanismos são chamados de

“resistência induzida” envolvendo a construção de barreiras histológicas para evitar a

entrada ou progressão dos patógenos, principalmente reforçando a parede celular

(Durrant e Dong, 2004).

2.4. INTERAÇÃO PLANTA-PATÓGENO

As plantas, diferentemente dos animais, não possuem sistemas imunológicos para

enfrentar determinadas situações adversas. Esse fato, associado à sua imobilidade

(condição séssil) fez com que elas aperfeiçoassem, ao longo da evolução, mecanismos de

defesa, tanto pré-formados, como induzidos (Hammond-Kosack e Jones, 2000; Benko-

Iseppon et al., 2010). A defesa pré-formada constitui-se no principal mecanismo de

resistência não específica, em que as plantas formam barreiras estruturais (estômatos,

cutículas, vasos condutores e tricomas) ou bioquímicas (fenóis, alcalóides, glicosídeos

fenólicos e fitotoxinas). Já na defesa induzida ou pós-formada também podem ser

encontradas barreiras estruturais como, cortiça, halos, lignificação, calose, etc; e

bioquímicas: a síntese de peptídeos, proteínas e metabólitos secundários, no combate à

infecção por patógenos (Pascholati e Leite, 1995; Taiz e Zeiger, 1998; Heath, 2000).

Quando ocorre uma interação planta-patógeno, uma série de sinais moleculares

coordenados, que ativam regiões do genoma da planta são desencadeados, interferindo

na severidade da doença causada pelo patógeno. Muitas dessas respostas requerem

ativação transcricional de genes por enzimas que produzem uma forma de barreira

físico-fisiológica (por exemplo, lignina) ou por enzimas que participam da rota

biossintética que conduz à síntese de compostos de defesa, como por exemplo, as

fitoalexinas (metabólitos secundários - como derivados fenólicos) (Kahn et al., 2002;

Woolhouse et al., 2002; Wink, 2003; Benko-Iseppon et al., 2010).

18

A percepção inicial do patógeno pela planta ocorre a partir da síntese de um fator

de avirulência (avr) pelo patógeno que pode ser percebida pela planta a partir dos

chamados genes de resistência (genes R), em uma interação compatível ou não,

denominada interação gene-a-gene. Havendo interação compatível (patógeno virulento

e hospedeiro suscetível), os genes R induzem a ativação de uma cascata de sinais,

incluindo proteínas relacionadas à patogênese, ou proteínas PR (Pathogenesis-Related)

(Van Loon et al., 1994; Heath, 2000; Durrant e Dong, 2004). As proteínas PR, por sua vez,

além de serem produzidas no local da infecção, são também induzidas sistemicamente

(Dixon e Harry, 1990), tomando parte ativa na eliminação do agente patogênico e no

desenvolvimento da Resistência Sistêmica Adquirida – SAR (Systemic Aquired

Resistance), contra futuros ataques desse patógeno (Nimchuk et al, 2003; Vallad e

Goodman, 2004). O amplo espectro de compostos da SAR promove uma imunidade

integrada e de longa memória contra o patógeno indutor no local da infecção, bem como

em tecidos não infectados. Infecções experimentais algumas vezes resultam nesta

resistência patógeno-específica, embora a proteção induzida também possa ser

inespecífica (Scherer, 2002; Vallad e Goodman, 2004; Benko-Iseppon et al., 2010).

Nas interações incompatíveis (patógeno avirulento e hospedeiro resistente), o

sistema de defesa da planta é eficientemente ativado, conduzindo à resistência

(Nimchuk et al, 2003) .

A Figura 1 sintetiza as principais etapas e mecanismos do reconhecimento do

patógeno e da resposta de defesa em plantas superiores, incluindo uma ou mais das 17

categorias de genes PR (Pathogen Related), dentre os quais se incluem proteínas

antimicrobianas (Antimicrobial Proteins, AMPs), compreendendo pequenos peptídeos

ricos em cisteína (Benko-Iseppon et al., 2010).

2.5. INTERAÇÃO PLANTA-VÍRUS

Durante a evolução, plantas e vírus desenvolveram mecanismos complementares

de ataque e defesa, onde o fenótipo de resistência ou suscetibilidade das plantas à

infecção por vírus irá depender do balanço entre estes mecanismos (Zerbini et al.,

2005). Os vírus de plantas interferem nos processos normais da célula hospedeira,

provocando modificações histológicas e fisiológicas, ruptura do balanço energético,

alteração na síntese de proteínas, ácidos nucléicos e clorofila, supressão do

silenciamento gênico pós-transcricional, além de alteração nas taxas respiratórias (Flint

et al., 2000; Voinnet, 2005; Soosaar

Figura 1. Principais mecanismos do reconhecimento do patógeno e da resposta de defesa em plantas

superiores. Organismos patogênicos (principalmente vírus, bactérias e fungos) sintetizam produtos

(avirulence) que podem ser compatíveis com produtos de genes

compatíveis levam à ativação de uma cascata de sinais induzindo fatores da resistência sistêmica (como

etileno e ácido jasmônico), bem como fatores da resistência adquirida, compreendendo uma ou mais das

17 categorias de genes PR (Pathogen Related

(Antimicrobial Proteins, AMPs). Fonte: Benko

A entrada dos vírus nas células vegetais é realizada por meio de um vetor

biológico, como no caso de insetos, fungos, nematóides, ácaros ou após danos mecânicos


transcricional, além de alteração nas taxas respiratórias (Flint

2000; Voinnet, 2005; Soosaar et al., 2005).

Principais mecanismos do reconhecimento do patógeno e da resposta de defesa em plantas

superiores. Organismos patogênicos (principalmente vírus, bactérias e fungos) sintetizam produtos

) que podem ser compatíveis com produtos de genes R secretados pela planta. Interações



Pathogen Related), dentre os quais se incluem proteínas antimicrobianas

, AMPs). Fonte: Benko-Iseppon et al. (2010).



19


transcricional, além de alteração nas taxas respiratórias (Flint

Principais mecanismos do reconhecimento do patógeno e da resposta de defesa em plantas

superiores. Organismos patogênicos (principalmente vírus, bactérias e fungos) sintetizam produtos avr

retados pela planta. Interações



), dentre os quais se incluem proteínas antimicrobianas



20

(Matthews, 1991; Medeiros et al., 2003; Ng e Falk, 2006 ). Estes utilizam dois processos

para a invasão na planta; um decorrente do movimento de célula-a-célula pelos

plasmodesmos e o outro pelo transporte em longa distância (sistêmico) pelos tecidos

vasculares do floema. No movimento célula-a-célula, os vírus de plantas são

dependentes, para sua sobrevivência, da transmissão eficiente por proteínas de

movimento (MP) codificadas pelos vírus, bem como componentes codificados pelo

hospedeiro (Atabekov e Taliansky, 1990; Lucas et al., 2001). Esta propagação assegura a

sobrevivência do vírus, resultando muitas vezes em ocorrência de doença. Os primeiros

estudos sobre transmissão de vírus de plantas por vetores demonstraram tanto a

complexidade como a especificidade da interação vírus-vetor (Ng e Falk, 2006).

Os patossistemas virais apresentam maior complexidade para o diagnóstico

quando comparados àqueles causados por outros agentes etiológicos, pois de certa

forma, há uma maior dificuldade em identificar precisamente os sintomas de viroses,

uma vez que existem múltiplas doenças, pragas e deficiências nutricionais que causam

sintomas semelhantes àqueles de vírus. Esta complexidade aumenta ainda mais quando

se considera a existência da interação entre o vírus, a planta hospedeira e o vetor

(Nutter, 1997; Zhang et al., 2000).

Dentre os vírus que infectam plantas, mais de 70 gêneros foram descritos

(Walkey e Payne, 1990), dos quais acredita-se que mais de 200 sejam disseminados por

sementes em uma ou mais espécies de hospedeiros (Mandahar, 1981).

No feijão-caupi, cerca de 30 gêneros virais envolvendo mais de 119 espécies

foram citadas em diferentes partes do mundo (Thottappilly e Rossel, 1985; Brioso,

2006). Estima-se que a perda da cultura devido à infecção por vírus varie entre 10 e

100% (Shoyinka, 1974; Rachie, 1985; Shoyinka et al., 1988), dependendo da relação

entre vírus-hospedeiro, assim como a prevalência de fatores epidemiológicos

(Thottappilly e Rossel, 1988).

No Brasil, os principais vírus que infectam o feijão-caupi são: o Cucumber mosaic

virus (CMV) (família Bromoviridae), o Cowpea severe mosaic virus (CPSMV) (família

Comoviridae), o cowpea golden mosaic virus (BGMV) (família Geminiviridae), o Bean

common mosaic virus (BCMV) (família Potyviridae), o Cowpea aphid-borne mosaic virus

(CABMV) (família Potyviridae), o Cowpea green vein banding virus (CGVBV) (família

Potyviridae), o Cowpea rugose mosaic virus (CPRMV) (família Potyviridae), o Cowpea

21

severe mottle virus (CPSMoV) (família Potyviridae) (Kitajima, 1986; Kitajima, 1995; Lima

et al., 1998), e o Blackeye cowpea mosaic virus (BICMV) (Lin et al., 1981).

Dentre as espécies virais que possuem importância econômica, o gênero

Potyvírus, com mais de 100 espécies, corresponde a 16% de todas as viroses de plantas.

Sua transmissão se dá por meio de várias espécies de pulgões ou afídeos, através de

picadas de prova (transmissão não persistente); portanto, a transmissão do vírus ocorre

em segundos (Pirone, 1991; Gray, 1996).

Outra virose que constitui um dos principais fatores limitantes na produção de

feijão-caupi é a do Mosaico Severo, causada pelo CPSMV, família Comoviridae, gênero

Comovirus (Chen e Bruening, 1992; Assunção et al., 2005). Estes vírus possuem forma

isomérica com aproximadamente 28 nm de diâmetro, possui um genoma bipartido e é

constituído de duas moléculas de RNA de fita simples que codifica proteínas para o

movimento célula a célula e a longa distância (Hull, 2002; Pio-Ribeiro et al., 2005).

Em condições naturais, no Nordeste brasileiro, os CPSMV são transmitidos por

espécies do gênero Diabrotica e Cerotoma (Costa et al., 1978) embora o vírus se

encontre disseminado em praticamente todas as regiões produtoras do país. Essa ampla

distribuição geográfica do vírus é decorrente da numerosa gama de hospedeiros

(espécies cultivadas e silvestres da família Fabaceae) e das dificuldades encontradas no

manejo da doença (Paz et al., 1999).

As plantas infectadas pelo CPSMV apresentam sintomas severos, como

modificações da cor e no hábito das plantas, subdesenvolvimento e clareamento das

nervuras principais, bolhosidade, manchas cloróticas, além de mosqueado e distorção

foliar (Zerbini 2002; Lima et al., 2005). Tais características podem ser vistas na figura 2,

em experimento realizado em casa de vegetação do Departamento de Genética da UFPE.

As folhas da cultivar IT85F-2687 de V. unguiculata foi inoculada com CPSMV em vários

tempos diferentes (30, 60 e 90 min e 16 h) para posterior utilização em bibliotecas de

SuperSAGE utilizadas neste trabalho.

Algumas fontes de resistência ao CPSMV no germoplasma de caupi já foram

relatados por diversos pesquisadores (Umaharan et al., 1996, Paz et al., 1999), porém

continua sendo um dos fatores limitantes da produção em se tratando de cultivares

suscetíveis. Uma das alternativas como medidas de controle de doenças causadas por

vírus fundamenta-se na introdução de resistência genética em cultivares comerciais

através do melhoramento genético.

Figura 2. Folhas de Vigna unguiculata

da inoculação com o vírus do Mosaico Severo (

com feijão-caupi sob estresses bióticos (

2.6. MELHORAMENTO GENÉTICO

Uma das principais finalidades do melhoramento genético de plantas é descobrir

e testar novos acessos de germoplasma, desenvolver novas cultivares e variedades de

culturas economicamente importantes, com vistas a garantir o suprimento de alimentos

(Borém, 1998).

Para alcançar seus objetivos, os melhoristas têm contado com o auxílio de

algumas ferramentas valiosas. O uso dos sistemas de incompatibilidade nas plantas,

para a criação de variedades híbridas e os cruzamentos interespecíficos, para a

aquisição de novos genes, também têm sido efetivos em algumas espécies (De

Nettancourt, 1997). Dois dos principais fatores da evolução, a recombinação e a seleção,

também têm sido intensivamente utilizados pelos melhoristas, com o emprego de

métodos refinados desenvo

terceiro grande fator da evolução

capazes de auxiliar os métodos convencionais de melhoramento, aumentando a

variabilidade genética das espécies (Duvic

Dentre as técnicas mais usadas estão a mutagênese e a transgenia. Com grande

repercussão os transgênicos crescem de importância a cada ano e embora a área com

Vigna unguiculata (Cultivar IT85F-2687) apresentando sintomas severos após 23 dias

vírus do Mosaico Severo (CPSMV). Foto cedida por Pandolfi (2007) em experimentos

caupi sob estresses bióticos (CPSMV) em casa de vegetação da UFPE.

2.6. MELHORAMENTO GENÉTICO







e novos genes, também têm sido efetivos em algumas espécies (De



métodos refinados desenvolvidos na primeira metade deste século. As mutações

terceiro grande fator da evolução – têm sido consideradas instrumentos adicionais,


variabilidade genética das espécies (Duvick, 1986).



22

2687) apresentando sintomas severos após 23 dias

cedida por Pandolfi (2007) em experimentos







e novos genes, também têm sido efetivos em algumas espécies (De



lvidos na primeira metade deste século. As mutações – o

têm sido consideradas instrumentos adicionais,




23

transgênicos esteja em constante crescimento em vários países, o uso desta ferramenta

biotecnológica é alvo de discussão, devendo-se salientar que do ponto de vista do

melhoramento genético, as técnicas convencionais e a transgenia não são mutuamente

excludentes, ao contrário: são complementares (Paterniani, 2006).

O feijão-caupi possui uma ampla variabilidade genética para praticamente todos

os caracteres de interesse agronômico (Embrapa, 1990; Teófilo et al., 1990) sendo alvo

importante em programas de melhoramento genético. Entretanto, embora os estudos

para a seleção de feijão-caupi para a região Nordeste tenham se iniciado na década de 40

(Krutman et al., 1973), comparativamente a outras culturas, são poucas as cultivares

recomendadas e lançadas comercialmente, devido principalmente aos múltiplos

objetivos adotados pelos agricultores, visando não só a produtividade.

No entanto, nos últimos anos a qualidade do grão e a arquitetura da planta

também têm sido enfatizado devido as exigências do mercado quanto a qualidade para

cozimento do grão comercializado (Carbonell et al., 2003), além das características

relacionadas à produtividade e à resistência a patógenos, principalmente viroses

(Miranda et al., 1996, Freire-Filho 2005).

Algumas cultivares de feijão-caupi já foram relatadas como completamente

resistentes aos vírus do mosaico amarelo, BICMV e CABMV. Destas, as cultivares IT96D-

659, IT96D-660, IT97K-1068-7 e IT95K-52-34 foram as que apresentaram melhores

características de resistência e rendimento (Singh e Hughes, 1998; 1999).

Van-Boxtel e colaboradores (2000) selecionaram 14 variedades de feijão-caupi,

três isolados de BICMV e 10 isolados de CABMV com o intuito de identificar cultivares de

caupi com resistência múltipla as viroses. Foi observado que as cultivares IT86D-880 e

IT86D-1010 foram resistentes a três isolados de BICMV e cinco isolados de CABMV. As

cultivares IT82D-889, IT90K-277-2 e TVu 201 se mostraram resistentes aos outros

cinco isolados de CABMV. Esses resultados evidenciaram que é possível produzir novas

variedades de caupi com resistência combinada aos 13 isolados virais.

No Brasil, a resistência ao CPSMV, ao CABMV e ao BGMV também já foi relatada

em algumas cultivares: BR 10-Piauí (Santos et al. 1987), BR 12-Canindé (Cardoso et al.,

1988), BR 14-Mulato (Cardoso et al., 1990), BR 17-Gurguéia (Freire Filho et al., 1994),

EPACE 10 (Barreto et al,. 1988), Setentão (Paiva et al., 1988), IPA 206 (IPA, 1989). Além

destas, a BR 16-Chapéu-de-couro (Fernandes et al. 1990), BRS Paraguaçu (Alcântara et

al., 2002) e BRS Guariba (Vilarinho, 2007) se mostraram resistentes ao CABMV. A

24

cultivar BRS Guariba se mostrou resistente também ao CGMV, moderadamente

resistente ao oídio (Erysiphe polygoni DC.) e a mancha-café (Colletotrichum truncatum

(Schw. Andrus & Moore)) e moderadamente tolerante à seca e a altas temperaturas

(Vilarinho, 2007).

Outra cultivar recentemente lançada foi a BRS Pujante, que submetida a cultivos

em áreas de sequeiro ou sob irrigação no sertão nordestino, apresenta elevada

produtividade sem adubações: 705 kg/ha e 1586 kg/ha, respectivamente. São

quantidades que superam às obtidas por cultivos tradicionais na região e, além disso,

apresentram valores próximos de 1,0 (sem sintomas) para as viroses do mosaico

dourado (MDO), CPSMV e Potyvírus (Santos et al., 2008).

2.7. TÉCNICAS DE AVALIAÇÃO DA EXPRESSÃO GÊNICA

Diferentes metodologias têm sido empregadas com a finalidade de medir a

expressão global de genes em nível celular, tecidual, órgãos ou organismos em

diferentes estágios de desenvolvimento e/ou sob várias condições ambientais

(Velculescu et al., 1995), incluindo fatores bióticos e abióticos. Estas tecnologias estão

divididas em duas categorias: técnicas “abertas” e técnicas “fechadas” (Matsumura et al.,

2003).

Na tecnologia fechada tal como arranjos de DNA (microarrays) as análises são

baseadas em hibridização usando sequências completas ou parciais de DNA (cDNAs,

produtos de PCR, plasmídeos ou bactérias contendo plasmídeos), previamente

conhecidas e disponíveis em bancos de dados. Nesta tecnologia, sondas de cDNAs (a

partir da transcrição reversa dos RNA mensageiros obtidos de células sob condições

específicas) são submetidas à hibridização com o DNA fixado na membrana. A indução

ou repressão de cada gene é medida em função desta intensidade de sinal emitido em

cada condição testada, refletindo o nível de expressão de cada gene (Freeman et al.,

2000). Assim, o “padrão de expressão” de milhares de genes pode ser comparado

simultaneamente; entretanto, o espaço de análise é finito e o nível de análise da

expressão do gene é limitado à sequência previamente caracterizada do transcrito para

os quais corresponde a prova que foi colocada no microarranjo (Schena et al., 1995).

Por outro lado, na tecnologia aberta, os métodos mais usados para análise do

transcriptoma são baseados no sequenciamento do seu cDNA (ESTs – Expressed

25

Sequence Tags) ou de etiquetas representativas de transcritos denominadas “tags”

(“etiquetas” em inglês). Dentre as estratégias desenvolvidas destacam-se a Differential

Display - DDRT (Liang e Pardee, 1992), Serial Analysis of Gene Expression - SAGE

(Velculescu et al., 1995) e algumas variantes, o cDNA-AFLP (Bachem et al., 1996);

GeneCalling (Shimkets et al., 1999), Total Gene Expression Analysis - TOGA (Sutcliffe et al.,

2000), e o Massively Parallel Signature Sequencing - MPSS (Brenner et al., 2000)

(Matsumura et al., 2005; Hanriott et al., 2008).

A SAGE ou Análise Serial da Expressão Gênica destaca-se por ser um método

rápido e abrangente, estabelecido como uma técnica para análise quantitativa de um

grande número de transcritos (Velculescu et al., 1995). Esta tecnologia é baseada em

dois princípios: primeiro, uma sequência curta ou tag (9-11 pb) contém informação

suficiente para identificar um transcrito único. Segundo, várias tags podem ser

concatenadas em uma única molécula formando longos clones que, após

sequenciamento resultam na identificação simultânea de muitas tags diferencialmente

expressas (Saha et al., 2002; White, et al., 2008). Consequentemente, o padrão de

expressão de qualquer população de transcritos pode ser quantitativamente avaliado

pela abundância dos transcritos e pela identificação do gene correspondente a cada tag

(Velculescu et al., 1997). Outra vantagem da Técnica SAGE, é que ela permite que as tags

sejam utilizadas como primers ou sondas para identificar genes desconhecidos, como foi

demonstrado na banana (Musa acuminata L.) por Coemans et al. (2005).

A SAGE também apresenta grandes vantagens sobre os microarranjos, uma vez

que possui maior potencial para discriminar entre transcritos homólogos e parálogos,

revelando valores absolutos na expressão do transcriptoma e propiciando uma

comparação direta entre os genes (Lu et al., 2004, Poole et al., 2008). Entretanto, um dos

problemas da SAGE, quando comparada ao microarranjo, é que a SAGE é usada para

poucas amostras ao mesmo tempo, existindo às vezes necessidade de comparar o perfil

de expressão dos genes de múltiplas amostras (Matsumura et al., 2005).

Outra deficiência na utilização da SAGE é o tamanho da tag de 15 pb, considerada

demasiadamente curta para permitir a identificação inequívoca do gene de origem. Além

disso, em organismos não modelos, isto é, com limitações ou sem sequências de DNA ou

cDNA/ESTs disponíveis, a SAGE clássica de 15 pb não é devidamente prática devido à

baixa casualidade e confiança da anotação das sequências. Isso é observado quando se

realiza o alinhamento de sequências (BLAST). Usando uma sequência de entrada (query),

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a mesma tag frequentemente combina dois ou mais genes, podendo confundir a análise

(Matsumura et al., 2005).

Diante disso, para aumentar a fidelidade do tag mapping, vários esforços foram

feitos para aumentar o tamanho da tag, facilitando, desse modo, uma anotação mais

acurada. Dentre estes aperfeiçoamentos estão as modificações da SAGE de 15 pb para 21

pb (LongSAGE) (Saha et al., 2002) e SuperSAGE (26pb) (Matsumura et al., 2003).

Uma técnica desenvolvida comparável aos princípios da SAGE é a MPSS. No

entanto, a MPSS utiliza a clonagem in vitro de fragmentos de cDNA em microgrânulos

(microbeads), gerando pequenas etiquetas a partir desses cDNAs onde então é realizado

o sequenciamento em larga escala dessas partículas sem a necessidade de separação

física desses fragmentos. O resultado final da MPSS é uma abundância de milhares de

tags de 17 ou 20 bases, a maioria das quais identifica um transcrito. Esta tecnologia

permite a produção de um numero 100 vezes maior de tags em relação a SAGE,

entretanto, esta técnica requer equipamento especializado e possui custos

extremamente elevados (Brenner et al., 2000; Christensen et al., 2003; Meyers et al.,

2004).

2.8. APLICAÇÕES DA SAGE EM PLANTAS

A análise através da SAGE passou a ser aplicada com sucesso em um grande

número de espécies eucariotas incluindo Saccharomyces cerevisiae (Velculescu et al.,

1997), Homo sapiens (Zhang et al., 1997), Caenorhabditis elegans (Jones et al., 2001) e

Drosophila melanogaster (Gorski et al., 2003).

Em plantas, a técnica foi descrita pela primeira vez por Matsumura et al. (1999),

ao analisar a expressão de genes de arroz (Oryza sativa L.) sob diferentes condições de

germinação. Em seguida, Lorenz e Dean (2002) aplicaram a SAGE em pinheiro (Pinus

taeda L.) para identificação dos genes envolvidos na formação da madeira e na

caracterização das funções em relação à qualidade da madeira. Esses trabalhos

representaram avanços por permitir a utilização da SAGE em plantas.

Diversos trabalhos utilizando a planta modelo Arabidopsis thaliana também

foram realizados. Jung et al. (2003) utilizaram a SAGE para comparar a expressão de

genes sob diferentes estados fisiológicos e identificar genes que possuem papel

importante na tolerância ao frio. Essa característica (tolerância ao frio) tem sido alvo de

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diversos estudos em plantas. Com este objetivo, a tecnologia LongSAGE foi

eficientemente aplicada na caracterização de genes associados ao estresse causado pelo

frio em Arabidopsis (Byun et al., 2009). Os resultados revelaram que diversas estratégias

são adotadas pela planta na regulação da transcrição em resposta à exposição ao frio.

Ainda em Arabidopsis, seis bibliotecas SAGE contrastantes de raízes em crescimento

hidropônico foram construídas para a identificação de genes expressos em grande

escala, fornecendo novos conhecimentos das especificidades funcionais do sistema

radicular (Fizames et al., 2004)

Em cevada (Hordeum vulgare L.) a LongSAGE foi empregada para identificar a

variação dos transcritos de RNAm desde o grão seco até a germinação, totalizando um

período de 120 h. Os resultados forneceram dados na compreensão de como taxas

relativas de modificação de proteínas e carboidratos contribuem na malteação (processo

empregado para preparar o malte através da germinação sob condições controladas),

exibida por alguns genes-chave para a germinação da semente da cevada (White et al.,

2006; White et al., 2008).

A SuperSAGE foi aplicada pela primeira vez em folhas de arroz infectado pelo

fungo Magnaporthe grisea (T.T. Hebert) M.E. Barr. Os perfis de expressão dos genes do

arroz e do fungo foram monitorados simultaneamente e foi visto que o gene da

hidrofobina de M. grisea é o gene ativamente mais expresso no arroz, demonstrando o

poder da SuperSAGE para a identificação simultânea da expressão de genes na interação

entre dois ou mais organismos tais como patógeno-hospedeiro. Ainda nesse trabalho, a

SuperSAGE foi aplicada na análise da mudança da expressão do gene elicitor IFN1 de

Nicotiana benthamiana Domin. A técnica permitiu a identificação de genes

superexpressos e reprimidos pelo elicitor em um organismo não-modelo, onde os genes

mais reprimidos estavam envolvidos na fotossíntese (Matsumura et al., 2003).

Outro organismo não modelo a qual a SuperSAGE foi aplicada foi em folhas de

bananeira para caracterizar a expressão global de genes. As tags de SuperSAGE foram

usadas como primers em 3’ RACE permitindo a identificação de transcritos

desconhecidos e fornecendo uma ferramenta poderosa para a genômica funcional em

organismos não modelo (Coemans et al., 2005).

Um recente trabalho utilizando a SuperSAGE foi feito com grão de bico (Cicer

arietinum L.). A técnica foi aplicada para analisar a expressão de genes de raízes de grão

de bico em resposta a seca. Este estudo demonstrou que a transdução de sinais, a

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regulação da transcrição, a acumulação de osmólitos e as espécies reativas a oxigênio

estão sob remodelamento transcricional após 6h de estresse hídrico e que algumas

isoformas destes transcritos que caracterizam estes processos são alvos em potenciais

de tolerância a seca (Molina et al., 2008).

Embora a aplicação mais importante da SAGE seja a identificação da expressão

diferencial de genes, ela também tem possibilitado a identificação da ocorrência de

regulação anti-senso, como foi demonstrado em arroz (Gibbings et al., 2003; Gowda et

al., 2007), em Arabidopsis (Robinson et al., 2004) e na cana-de-açúcar (Saccharum L.)

(Calsa e Figueira, 2007).

2.9. TRANSCRIPTÔMICA DO FEIJÃO-CAUPI

A caracterização de genomas foi uma das forças motrizes da ciência nos anos 90.

Desde o sequenciamento completo do primeiro organismo de vida livre (Haemophilus

influenzae) em 1995 (Fleishmann et al., 1995), a consolidação e a inovação das técnicas

de análise genômica tornaram possível o sequenciamento do genoma de seres

complexos como plantas, animais e seres humanos. Aliado a isso, os crescentes

investimentos na área fizeram com que a lista de sequências de genomas completos

tenha crescido a uma velocidade cada vez maior, contribuindo com um volume de dados

disponíveis para acesso público (Binneck, 2004).

Em plantas, embora o número de genomas completos disponíveis ainda seja

limitado, quando comparado a outros organismos, um crescente número de sequências

expressas como ESTs e tags têm sido disponibilizadas à comunidade científica,

auxiliando no entendimento de processos genéticos em organismos com grandes

genomas, como vegetais (Benko-Iseppon, 2001). Em função disso, em 2004, surgiu uma

proposta de efetuar uma análise genômica funcional e estrutural no feijão-caupi (V.

unguiculata), incluindo uma rede de laboratórios da região Nordeste do Brasil,

denominada rede NordEST, visando identificar genes candidatos potencialmente úteis

para fins de melhoramento desta cultura. Este projeto, coordenado pela Universidade

Federal de Pernambuco (Profs. Ana M. Benko Iseppon e Ederson A. Kido), conta com a

colaboração das Universidades Federais do Piauí, do Ceará, da Paraíba, das estações da

Embrapa (Recursos Genéticos, Brasília-DF; CAPTSA, Meio-Norte) e da Universidade de

São Paulo (ESALQ; CENA), tendo seu início oficial em junho/2005. O projeto contou

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ainda com o auxílio de dois grupos consultores: da Universidade de Frankfurt (Johann

Wolfgang Goethe Universität) na Alemanha e da Universidade da Califórnia, em

Riverside (EUA).

As principais metas do citado projeto incluíram (a) a geração de no mínimo 100

mil transcritos diferencialmente expressos em tecidos e condições importantes pra o

entendimento de processos de tolerância ou resistência a estresses bióticos e abióticos;

(b) o desenvolvimento de um mapa genético de alta resolução e (c) o desenvolvimento

de estratégias eficientes de cultivo, transformação e regeneração in vitro, de modo a

propiciar a rápida conversão dos dados gerados em benefício da cultura vegetal em

questão. Desta forma, técnicas modernas de mapeamento genético assistido por

marcadores moleculares (incluindo CAPs, dCAPs, DAF, SSR, AFLP, ISSR e RGAs) vêm

sendo utilizadas a fim de mapear as regiões responsáveis a resistência a viroses (CPSMV;

família Comoviridae e CABMV; família Potyviridae), bem como estresse abiótico como

salinidade e seca (incluindo QTLs) que envolvam a produtividade e a arquitetura da

planta (Benko-Iseppon et al., 2005; Amorim et al., 2009).

No âmbito do citado projeto, ferramentas de genômica expressa (EST e SAGE)

relacionadas a estresses bióticos (Potyvírus e Mosaico Severo) e abióticos (salinidade)

do feijão-caupi também foram utilizadas, incluindo a construção de 10 bibliotecas de

EST submetidas a estresse biótico (vírus do mosaico severo) e abiótico (salinidade),

nove bibliotecas de SuperSAGE submetidas ao estresse salino e ao Vírus do Mosaico

Severo e quatro bibliotecas de LongSAGE submetidas ao estresse por Potyvírus (Benko-

Iseppon et al., 2008) (Tabela 1). Atualmente o projeto envolve atividades referentes à

categorização das bibliotecas de ESTs de V. unguiculata (sequências do banco NordEST,

HarvEST e NCBI) compiladas em um banco local e a análise das bibliotecas controles

(injúriado e não injúriado) de SuperSAGE infectadas pelo Mosaico Severo, desenvolvidas

junto à empresa GenXPro (Frankfurt am Main, Alemanha) no âmbito do projeto. Em

setembro/2009 o referido projeto contava com mais de cinco milhões de transcritos

para análise (Kido et al., 2009), número que atualmente excede 20 milhões de

transcritos, incluindo SuperSAGE-tags, Long-SAGE-tags e ESTs, ultrapassando

largamente o número de 100 mil transcritos inicialmente planejado (Kido e Benko-

Iseppon, com. pess.).

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Tabela 1. Descrição das bibliotecas de ESTs de feijão-caupi sob estresse biótico (Vírus do Mosaico Severo

do Caupi) e abiótico (estresse salino, NaCl 200mM) e das bibliotecas de LongSAGE sob estresse biótico

(Potivírus) e biliotecas de SuperSAGE sob estresse biótico (CPSMV e Potyvírus) e abiótico (estresse salino,

NaCl 200mM) – Projeto NordEST- RENORBIO.

I- Bibliotecas de EST (Expressed Sequence Tags) Biblioteca Descrição

VUSS00 Canapu Controle (2+8h s/ estresse) VUSS02 Canapu (2h após estresse) VUSS08 Canapu (8h após estresse) VUST00 Pitiuba Controle (2+8h s/ estresse) VUST02 Pitiuba (2h após estresse) VUST08 Pitiuba (8 h após estresse) VUBM90 BR-14 mulato - Mosaico (30+60+90 min após estresse) VUBM01 BR-14 mulato - Mosaico controle 01 (30+60+90 s/ estresse) VUIM90 IT- 85F - Mosaico (30+60+90 min após estresse) VUIM01 IT – 85F - Mosaico controle 01 (30+60+90 s/ estresse)

II - Bibliotecas de LongSAGE (Serial Analysis of Gene Expression) Biblioteca Descrição

IP+ IP- BR+ BR-

IT – 85F - Potyvirus (30+60+90 min e 16h após estresse) IT – 85F - Potyvirus (30+60+90 min e 16h sem estresse) BR14-mulato- Potyvirus (30+60+90 min e 16h após estresse) BR14-mulato- Potyvirus (30+60+90 min e 16h sem estresse)

III- Bibliotecas de SuperSAGE (Serial Analysis of Gene Expression)

Biblioteca Descrição

BRC1 BR14-mulato - controle absoluto BRC2T123 BR14-mulato, controle injuriado (30+60+90 min após estresse) BRC2T6 BR14-mulato, controle injuriado (16h após estresse) BRMT123 BR-mulato - Mosaico (30+60+90 min após estresse) BRMT6 BR14-mulato - Mosaico (16h após estresse) PTS3T Pitiuba, stress salino, (30+60+90 min, 2h, 8h após estresse) BRS3T BR14-mulato, stress salino (30+60+90 min, 2h, 8h após estresse) PTCtr Pitiuba controle (30+60+90 min, 2h, 8h) BR14Ctr BR14-mulato (30+60+90 min, 2h, 8h) L1_CMV-2_16 L2_CMV-2_BRC1 L3_CMV-2_3090

BR14-mulato, controle injuriado (16h após estresse) BR14-mulato - controle absoluto BR14-mulato, controle injuriado (30+60+90 min)

L1_CPV1-3 IT – 85F (30+60+90 min após estresse) L2_CPV_4 IT – 85F (16h após estresse) L3_CPV_5 IT – 85F (30+60+90 min e 16h sem estresse) L4_CPV_6

IT – 85F (16h após o estesse)

Além da iniciativa que integra a rede brasileira de genômica do feijão-caupi (Rede

NordEST), destaca-se uma iniciativa desenvolvida pelas Universidades de Virgínia e da

Califórnia (USA), com sequências já disponibilizadas em bancos de dados públicos,

31

integrada no projeto HarvEST, tendo gerado cerca de 180.000 ESTs (Close, 2007;

HarvEST, 2008). Atualmente estas sequências encontram-se integradas no servidor

NordEST, onde foram clusterizadas com as sequências de EST do citado projeto,

perfazendo 248.500 ESTs disponíveis para ancoragem das tags de SuperSAGE e

LongSAGE, bem como para outras análises in silico.

Adicionalmente, destaca-se um banco de dados genômico, o Cowpea Genespace /

Genomics Knowledge Base (CGKB) derivado de análise e sequenciamento de porções

ativas do genoma do feijão-caupi, a partir da filtração do DNA genômico metilado (Chen

et al., 2007). O “Cowpea-Genespace” tem se mostrado como uma excelente ferramenta

para anotação, caracterização e análise de SAGE e EST-tags, colocando projetos com

feijão-caupi em posição de vantagem face a outras leguminosas de importância nacional,

como o feijão-comum (Phaseolus vulgaris L.) e o amendoim (Arachis hypogea L.) (Benko-

Iseppon, 2009).

2.10. BIOINFORMÁTICA

A bioinformática tem sido referida como um campo interdisciplinar, agindo como

interface entre o campo científico e tecnológico. É caracterizada por prover métodos

computadorizados para interpretar os dados gerados em estudos de sequenciamento de

genomas, gerando grande volume de informação, de forma a trazer novos avanços para

a biologia molecular. A bioinformática representa um dos grandes desafios para se

tentar decifrar o genoma (Lengauer, 2001), consistindo na criação, desenvolvimento e

operação de bancos de dados associados a ferramentas computacionais que permitam

coletar, organizar e interpretar dados (Ouzounis, 2002).

Devido ao grande volume de informação gerado pelos projetos de análise de

genomas e transcriptomas, tem se tornado cada vez mais complexo o armazenamento,

acesso e a análise dos dados gerados. Para contornar tal dificuldade, bancos para

armazenamento e processamento de dados, através de ferramentas de análise, têm sido

implementados e disponibilizados (bancos abertos), aumentando ainda mais a

aplicabilidade da pesquisa (Félix, 2002; Wheeler et al., 2002).

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2.10.1. Bancos de Dados e Ferramentas de Bioinformática

Existem dois tipos de bancos de dados envolvendo sequências de genes e

proteínas: os bancos de dados primários e os secundários. Os bancos de dados primários

são aqueles derivados diretamente dos dados obtidos a partir do sequenciamento de

ácidos nucléicos ou proteínas. Estes bancos podem conter, além da sequência em si,

outros dados como uma tradução de uma sequência de um clone de DNA, sequências

padrão (como sítios de fosforilação), promotores e outras anotações semelhantes. Entre

os principais bancos primários destacam-se o GenBank (Benson et al., 2000), EBI-EMBL

(European Molecular Biology Laboratory; http://www.ebi.ac.uk/embl/, Emmert et al.,

1994), DDBJ (DNA Database of Japan, http://www.ddbj.nig.ac.jp/, Tateno et al., 2002) e

PDB (Protein Data Base; http://www.rcsb.org/pdb/home/home.do) (Westbrook et al.,

2002)

Os bancos de dados secundários são derivados dos primários, tais como o Blocks,

sequências sem "gaps" alinhadas contendo as regiões mais conservadas em proteínas

(Henikoff e Henikoff, 1991), o SWISS-PROT e TrEMBL (Bairoch e Apweiller, 1998), o

PROSITE (banco de dados de famílias e domínios de proteínas) (Sigrist et al., 2002) e o

REBASE, banco de dados com informações sobre enzimas de restrição, metilases,

microorganismos de origem, sequências de reconhecimento, sítios de clivagem,

especificidade de metilação, disponibilidade comercial e referências (Roberts et al.,

2007).

Inúmeros outros bancos de dados têm surgido nos últimos anos, os quais, assim

como novas ferramentas de bioinformática, visam auxiliar o pesquisador na aquisição de

informação biológica, na identificação de significado e na associação de tal informação

com determinada categoria ou processo, para que a mesma possa ser utilizada de forma

mais abrangente. Dentre as ferramentas de bioinformática utilizadas para este projeto,

podemos citar algumas comentadas a seguir.

a) PHRED

Programa utilizado para análise da qualidade das sequências de DNA (Ewing e

Green, 1998; Ewing et al., 1998).

b) PHRAP E CAP3

O Phrap e o CAP3 são exemplos de programas que fazem a montagem das reads

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(transcritos) com a finalidade de alinhá-las entre si, produzindo sequências maiores,

dando origem aos contigs (sequenciamento genômico) ou clusters (sequenciamento de

cDNA). Esses programas utilizam os valores de qualidade de bases produzidos na

comparação das regiões de sobreposição das reads, na construção de alinhamento

múltiplo das sequências e na geração das sequências consenso (Huang e Madan, 1999).

Após a inserção das informações de montagem dos clusters no banco de dados, a

próxima etapa é a análise por métodos comparativos contra um banco de dados público

para dedução das funções por regiões similares das sequências comparadas (Peruski e

Peruski, 1997).

c) BLAST

Um dos programas mais utilizados para buscas por similaridades é o BLAST

(Basic Local Alignment Search Tool), disponível no site do NCBI (National Center

Biotechnology Information - http://www.ncbi.nlm.nih.gov), o qual calcula o nível de

similaridade que pode existir entre uma região da sequência do cluster e outra que

esteja disponível em um banco de dados, como o GenBank, realizando um alinhamento

local (Altschul et al., 1990).

O BLAST é subdividido de acordo com o tipo de sequência de entrada

(nucleotídeo ou aminoácido) e com o tipo de resultado esperado (Altschul et al., 1990).

Assim, pode-se escolher entre: BLASTn - compara sequências de nucleotídeos com o

banco de dados de nucleotídeos; BLASTp - compara sequência de aminoácidos com

banco de dados de proteínas; BLASTx - sequência de nucleotídeos traduzida nos seis

possíveis quadros de leitura em um banco de dados de proteínas; tBLASTn, sequência de

aminoácidos em um banco de dados de nucleotídeos traduzido dinamicamente nos seis

quadros de leitura e tBLASTx - sequência de nucleotídeos em um banco de dados de

nucleotídeos traduzido por computador (Gibas e Jambeck, 2001).

O programa BLAST permite ainda alinhar sequências, através da ferramenta

BLAST2Seq. Quando o programa padrão for usado para procurar por sequências

homólogas em bases de dados de nucleotídeos e de proteínas, frequentemente existe a

necessidade de comparar somente duas sequências que já são sabidamente homólogas,

ou que venham de espécies relacionadas, ou ainda foram isoladas do mesmo organismo.

Nesse caso, procurar no banco de dados completo consumiria um tempo e esforços

desnecessários. O BLAST de duas sequências utiliza o algoritmo do BLAST para

comparar sequências de DNA-DNA, DNA-proteína ou sequências de proteína-proteína

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(Tatusova e Madden, 1999).

Algumas sequências incluem regiões com baixa complexidade, apresentando uma

composição incomum, o que pode criar problemas quando se procuram sequências com

similaridades. Os filtros de baixa complexidade são usados para se remover a sequência

de baixa complexidade que pode causar problemas, mostrando um resultado que nem

sempre se refere a sequências verdadeiramente relacionadas. Nas buscas no BLAST

executadas sem um filtro podem ser relatados índices de similaridade elevados somente

por causa da presença de uma região de baixa complexidade (Wootton e Federhen,

1996).

d) DiscoverySpace 4.01

DiscoverySpace é um software gráfico que integra banco de dados contendo

informações funcionais de sequências de expressão gênica e mapeamento de tags. Essas

informações são reunidas em um único banco de dados, onde é possível realizar análises

comparativas, aplicando o teste estatístico de Audic e Claverie (1997), visualizando os

resultados em um gráfico de dispersão ou gerando conjuntos de tags específicas (Wang

et al., 2005).

Sua aplicação permite que o usuário utilize bases de dados biológicos múltiplas

sem exigir o conhecimento detalhado da fonte das bases de dados, além de fornecer

ferramentas domínio-especificas (Robertson et al., 2007).

e) Blast2GO

É uma ferramenta para a anotação funcional de sequências novas, com

simultânea análise de dados da anotação. A principal aplicação se caracteriza pela

anotação de milhares de sequências em uma sessão; pela possibilidade de modificação

no processo de anotação em todas as etapas; pela geração de significado biológico dos

dados com funções gráficas e estatísticas diferentes. O banco do Gene Ontology, os

mapas do KEGG e o InterPro são suportados pelo Blast2GO (Conesa et al., 2005).

O Blast2GO otimiza a função de transferir sequências homólogas através de um

algoritmo elaborado que considera a similaridade, a extensão da homologia, a base de

dados de escolha, a hierarquia do GO e a qualidade das anotações originais (Conesa e

Götz, 2008).

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f) Cluster 3.0

O Cluster (Eisen et al., 1998) é um programa que fornece um ambiente

computacional e gráfico para análise de experimentos de microarranjos e outros dados,

como exemplo, de SAGE. O programa inclui várias ferramentas de clusterização, dentre

eles: o método de clusterização hierárquica, que organiza os genes em uma arvore

estrutural baseada na suas similaridades; o método de clusterização de medias K, onde

os genes são organizados nos clusters; a auto-organização de mapas, onde são montados

os clusters dos genes em uma grade bidimensional retangular e os clusters vizinhos são

similares. Para cada um desses métodos diferentes, as distâncias mensuradas podem ser

usadas (Hoon et al., 2004).

Essas ferramentas foram utilizadas no estudo do perfil de expressão diferencial

de genes através da técnica de SuperSAGE em Vigna unguiculata, como observado nos

capítulos 1 (submetido a injúria mecânica) e o capítulo 2 (ataque pelo vírus do Mosaico

Severo do Caupi), com o intuito de se obter um maior entendimento a respeito da

relação planta-estresse e/ou planta-patógeno, representando informações a serem

utilizadas em programas de melhoramento da cultura.

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Amorim LLB, Onofre AVC, Ferreira Neto JRC, Silva RLO, Correia CN, Silva MD, Carvalho R,

Horres R, Moretzsohn MC, Sittolin IM, Rocha MM, Freire-Filho FR, Monte SJH,

Pandolfi V, Andrad, GP, Pio-Ribeiro G, Kido EA, Benko-Iseppon AM (2009) Mapa

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58

CAPÍTULO 1

Transcriptional profiling of wound stress response in Vigna unguiculata (L.) Walp. revealed by SuperSAGE

To be submitted to the journal BMC Genomics

59

4.1. ABSTRACT

Background: Despite its importance, the overall transcriptional response after

wounding in higher plants is still scarcely known. Evidences have shown that plants

tend to defend themselves against a pathogen attack, immediately after wounding

perception, but similar responses have been recognized after some abiotic stress

conditions. Legumes are among the most important crops, but little knowledge is

available regarding their response profile after wounding. Cowpea (Vigna unguiculata)

is among the most tolerant crops against biotic and abiotic conditions in semi-arid areas,

with as in tropical regions of Africa and South America, being a natural candidate to

deliver important genes for biotechnological approaches regarding other crops,

especially legume pulses. Results: Considering this demand, the NordEST network

(www.vigna.ufpe.br) generated two SuperSAGE libraries from mRNA isolated from

leaves submitted to mechanical injury (C2), as compared with a negative control (C1,

leaves without injury). A total of 113.828 tags have been generated for C1 and 110.686

for C2. From these, 5.481 tags presented 100% identity with the NordEST EST bank,

from a total of 10.907 unique tags analyzed. A total of 1.503 was unequivocally

recognized as differentially expressed, from which 60% were super-expressed and 40%

were repressed. From 3.851 tags without known function, 1.692 presented similarities

with available ESTs from V. unguiculata, uncovering the high potential of this approach

for the discovery of unknown genes. The remaining 2.159 tags presented similarity

neither with cowpea ESTs nor with other known plant sequences, also representing a

source for novelty. Considering the differential expression (up- and down-regulated) the

functional GO (Gene Ontology) categories have been annotated regarding biological

processes, molecular functions and cellular components. The results revealed

considerable amounts of sequences related to reduction of oxidative processes (20%),

transduction (14%), transcription regulation (10%), transport (9%), and proteolysis

(6%), among other. Conclusion: The genes corresponding to such categories are known

to be involved in pathways especially regarding response to abiotic stress, indicating

their potential use as targets for RTqPCR essays using duplicates of the mRNA samples

used to generate the here analyzed libraries.

Keywords: Vigna unguiculata; tolerance to stress; Super Serial analysis of Genes

Expression (SuperSAGE); gene expression.

60

4.2. BACKGROUND

Higher plants are faced with the difficulty that they must survive and propagate

while being immobilized within their local environment. There, they can be exposed to

all types of abiotic stresses and to the attack of living enemies that to use plant tissue as

a home or food [1]. Among different kinds of biotic and abiotic threats, wounding is an

important stress factor that may affect plants. Many factors can contribute to wounding,

such as insect predation, wind, rain, and chilling. Despite of being considered an abiotic

stress factor, mechanical wounding is a very important biotic process, since it provides

pathways for pathogen invasion.

To respond efficiently, plants have to defend themselves a priori against

pathogen attack, immediately after wounding perception. Hence, an integrated response

considering the pathogen and wounding pathways has been hypothesized, with

evidences showing that wounding regulates a number of genes that are also regulated

by or play a role in pathogen response [2, 3, 4, 5].

To date, the overall response to wounding in higher plants is known especially

for model plants as Arabidopsis thaliana [4, 6, 7, 8] and to a lesser extent in rice (Oryza

sativa) [9], and tomato (Lycopersicon esculentum) plants [10]; with few available

information regarding important crops, such as soybean (Cicer arietinum) [11, 12].

Besides physiological evaluations, no expressions assays have been found so far for

important tropical legumes as common bean (Phaseolus vulgaris) and cowpea (Vigna

unguiculata).

Cowpea (Vigna unguiculata (L.) Walp.) is an essential element of the tropical

cropping systems, especially considering dry areas of Africa, South America and Asia,

but also some temperate (especially semi-arid) areas of other regions as the

Mediterranean region and the southern region of the United States [13].

In stress situations, plants respond with drastic changes in expression profiling,

with induction of signal cascades immediately after stress perception at cellular level

[14]. Previous works have revealed an interconnection among wounding and other

biotic and abiotic stresses, as pathogen attack and also other abiotic stresses, as drought

and salinity [4, 7, 8]. Responses to such stress categories are known to present both

constitutive and inducible resistance mechanisms, with defensive weapons including

morphological barriers, secondary metabolites, and antimicrobial proteins that in

combination impair pathogen invasion. Countless expression assays have shown, in the

61

past decade that a single plant/pathogen interaction (either compatible or

incompatible) is able to recruit or silence hundreds of genes, many of them already

known, while others remain to be described [15, 16].

Expression profiling methods are essential to understand questions regarding

not only the identity of recruited genes in a given situation, but also to recognize the

modulation of such responses, since the surveillance may depend not only on the

‘quality’ of the activated genes, in many cases it may associated with differences in the

timing and magnitude of the expression, but also on the contemporary expression of

different sets of genes [15, 16].

Among important methods the SAGE (Serial Analysis of Gene Expression)

approach allows the simultaneous identification of expressed genes under contrasting

situations, allowing the quantification of tags representing different sets of genes [17].

The annotation of these tags has been a concern, since it depends on the availability of

annotated cDNA (EST) libraries. Therefore, efforts have been carried out in order to

increase the size of SAGE tags, generating technical improvements with emphasis on the

methods of LongSAGE (21 pb tags) [18] and SuperSAGE (26 pb tags) [19]. This last

method, besides providing better tag-gene annotation, presents the advantage of

potential use of the tag as primers for cDNA amplification, its use in RNA interference

(RNAi) essays, the exportation of tags for use in DNA arrays (chips) and the use in essays

regarding co-expression of two eukaryotic models, in the case of host-pathogen

evaluations [20].

The combination of the SuperSAGE approach using high throughput sequencing

methods (as 454 Plattaform/Roche; Solexa/Illumina and Applied Biosystems) permitted

an increase in the sensitivity of the method for the identification of rare transcripts and

isoforms under contrasting stress conditions in legumes [21].

The present work brings evidences regarding the transcriptional profile of

cowpea against mechanical wounding using SuperSAGE approach, aiming to bring some

light to the processes involved in such response in cowpea, as compared with the up to

date knowledge in A. thaliana and other studied higher plants.

62

4. 3. MATERIALS AND METHODS

4.3.1. Mechanical Injury Essay

Cowpea plants (cultivar BR-14 Mulato, developed by EMBRAPA-CPMN, Teresina,

Brazil) were cultivated in pods with two kg capacity in a greenhouse under anti-aphid

net. The substrate was composed by two parts of organic soil to three parts of river

sand. The experiment included 45 pods with five seeds per pod, grown under 12/12 h

photoperiod and temperature varying from 28 to 32°C. The stress experiment was

conducted 20 days after the plantlet emergence, when all plants contained the two first

true leaves emerged after the cotyledons.

The injury was carried out on five plants per treatment, which were submitted

to mechanical stress by rubbing with Carborundum® on the abaxial surface of all true

leaves. The treatments consisted of different groups collected 30, 60, 90 minutes and 16

hours after the stress treatment.

Plant groups for each treatment and for control (leaves collected without stress)

were maintained isolated from the plants grown for each stress experiment, but under

the same environmental conditions, aiming to avoid the influence of volatile compounds

emitted during the stress treatments. The control group was constituted by the same

number and tissues, without injury. Collected materials were properly identified and

immediately frozen in liquid N2, being maintained in a deep freezer (-80ºC) until RNA

extraction.

4.3.2. Extraction of total RNA and isolation of messenger RNA (mRNA)

Total RNA was extracted from cowpea leaves using a CTAB extraction followed

by precipitation in LiCl solution, as described by Chang et al. (1993)[22], followed by

DNAse treatment and checking the RNA quality and amount in 1,5% (p/v) agarose gel as

well as in the Qubit (INVITROGEN®, USA) fluorometer.

Each total RNA sample was purified using the RNAeasy (CLONTECH®) kit,

being quantified again in fluorometer. Equimolar amounts of the four control treatments

(not injured) were combined in order to compose the C1 library. The second library

(C2T123) included an equimolar mixture of the injured plants, including the three initial

times (30, 60 e 90). The remaining injured sample (16 h) was used to compose the

63

library C2T6. The mRNA isolation was carried out using 1 mg of RNA according to

Oligotex-dT (QIAGEN®) batch protocol.

4.3.3. Construction of SuperSage libraries

SuperSAGE libraries were developed under supervision of Prof. Dr. Günter Kahl

(Frankfurt University) in collaboration with the GenXPro GmbH company (Frankfurt am

Main, Germany). Procedures followed the protocol described by [19] with

improvements described by [21], and minor modifications. Basically the Poli(A)-RNA

was used for the cDNA synthesis using the cDNA Synthesis System (INVITROGEN®, USA)

with reverse transcription using a biotinilated oligo-dT containing the restriction site for

EcoP15I (CAGCAG). The product was converted to double stranded cDNA, being

subsequently fragmented with the enzyme NlaIII (New England Biolabs, NEB®, Beverly,

MA). cDNA fragments were ligated to magnetic beads with addition of streptavidin

(Promega®, Madison, WI, USA).

After washing, the cDNA was divided into two parts, and each was ligated to a

different adaptor containing the EcoP15I restriction site using T4 DNA ligase (NEB®).

After ligation, both parts were mixed and digested using EcoP15I. The digested products

(tags + adapters) were separated in acrilamide gel 10% (p/v) and visualized with

ethidium bromide. Fragments with the expected size were isolated from the gel, eluted

and purified, with subsequent sticky ends converted to blunt ends using the KOD DNA

polymerase (line Thermococcus kodakaraensis KOD1, TOYOBO®, Osaka) and subsequent

tag ligation to form ditags. The ditags were PCR amplified (for details see [21]) and

directly sequenced by 454 Life Sciences sequencer (Branford, CT, USA).

4.3.4. Analysis of SuperSAGE products

From each sequence, 26 bp long tags were extracted using the ‘GXP-Tag Sorter’

software provided by GenXPro (Frankfurt am Main, Germany). Library comparison and

primary statistical treatment was carried out using the DiscoverySpace 4.01 software

(Canada's Michael Smith Genome Sciences Centre, available at

http://www.bcgsc.ca/discoveryspace), after exclusion of singlets (tags appearing a

single time).

The program also allowed the identification of tags appearing exclusively in a

given library (here regarded as unique tags or unigenes), and that differentially

64

transcribed (p-value; p>0.05). Scatter plots of the distribution of the expression ratios

(R(ln)) and significance of the results were calculated according to Audic and Claverie

(1997) [23]. The frequency ratio was calculated the counted tags of injured library C2

(C2T123+C2T6) in relation to the control C1. In the case of exclusive tags in a given

library, the zero frequency in the other library was modified to 0.5 following the

recommendations given by the developers of the program DiscoverySpace. The R ratio

was considered the modulation value of the transcriptional expression (FC; Fold Change)

when R > 1 when super expressed and 1/R when repressed.

4.3.5. Bioinformatics and Annotation of SuperSAGE Tags

The tags were first annotated using BLASTn (score ≥ 42) local, against

nucleotide sequences of following data banks: (a) NCBI (http://www.ncbi.nlm.nih.gov):

only plant ESTs (January 2009); Refseq (plant ESTs; June 2009); (b) TIGR

(www.tigr.org/db.shtml), compiled plant sequences (2009); (c) Vigna unguiculata ESTs.

This last bank included 202,076 ESTs comprising the private NordEST bank

(http:/www.vigna.ufpe.br) with 23,000 ESTs as well as sequences from two public data

banks: NCBI (4,000 ESTs) and HarvEST [http://harvest.ucr.edu; (103,923 ESTS). The

clusterization was carried out using the program Cap3 [24], via EGassembler

(http://egassembler.hgc.jp/) [25].

The unigenes were also annotated using a local BLASTx tool (e-value ≤ 10-10)

against the UNIPROT-Swiss-Prot/TrEMBL (http://www.uniprot.org/; release 15.7)

database. Best scores were taken considering the BLAST evaluations against the various

data banks cited above. In the case of identical scores/e-values the best described

sequence was chosen, giving priority to cowpea sequences or taxonomic most related

organisms. The functional annotation was carried out using the Blast2GO tool

(http://www.blast2go.org) [26], with default parameters and terms according to the

Gene Ontology classification [27].

4.4. Results and Discussion

4.4.1. Functional annotation

For an efficient annotation of the SuperSAGE tags the best comparative source is

a well annotated EST data bank of the own species, previously compared against the

Uniprot/Swiss-Prot, since the tag

recognition, also permitting the development of primers and probes for validation

purposes. Therefore the comparison with 202,076 ESTs from three cowpea data banks

(NordEST, HarvEST and NCBI) permitted the identification of 36.715 u

BLASTx from which 15.865 (43%) Uniprot/Swiss

with significant e-values (

successfully annotated using Blast2GO

Process” - BP, “Molecular Function”

30.659 GO terms. Considering that the GO uses a hierarchical structure to describe

function and localization of a given gene in the cell, the genes may be associated

than one function or process, being therefore included in more than one GO category or

subcategory [28, 29].

In the present approach the unigenes classified in more than one category were

included separately in each of them, with 100% corresponding

occurrences in each category. The 30 most represented subcategories in PB, FM and CC,

totalized 14,425 (47%) of the

Considering the category

“Intracellular Components”,

second subcategory was “Cell Wall Components” with 1,494 unigenes (26%). The

subcategories “Extracellular Region” and “Cell Wall” were represented with 3.5% and

1.8%, respectively (Figure 1).

Figure 1. Distribution of the 30 most represented GO

terms in the category “Cellular Component”, including

absolute values and percentage.

1.494

, since the tag-gene availability will facilitate the function



NordEST, HarvEST and NCBI) permitted the identification of 36.715 u

BLASTx from which 15.865 (43%) Uniprot/Swiss-Prot matched with known proteins

values (≤ 10-10). From these, 15.141 unigenes (95%) could be

successfully annotated using Blast2GO [27] for the three main GO categories (“Biolog

BP, “Molecular Function” - MF and “Cellular Component”

. Considering that the GO uses a hierarchical structure to describe

function and localization of a given gene in the cell, the genes may be associated



included separately in each of them, with 100% corresponding to the total number of


totalized 14,425 (47%) of the 30,659 GO terms.

Considering the category Cellular Component 3,129 terms (69%) regarded

represented by cytoplasmatic and nuclear organelles. The



1.8%, respectively (Figure 1).

Distribution of the 30 most represented GO


absolute values and percentage.

65

facilitate the function



NordEST, HarvEST and NCBI) permitted the identification of 36.715 unigenes after

Prot matched with known proteins

). From these, 15.141 unigenes (95%) could be

27] for the three main GO categories (“Biological

MF and “Cellular Component” - CC) resulting in

. Considering that the GO uses a hierarchical structure to describe

function and localization of a given gene in the cell, the genes may be associated to more



to the total number of


3,129 terms (69%) regarded

represented by cytoplasmatic and nuclear organelles. The



Distribution of the 30 most represented GO


For the ontology for the

related “Ligation Activities”

Activity”, while “Transporter Activity” and “Structural Molecular Activity” represented

each 6% of the occurrences (data not shown).

For the ontological category

terms, the most frequent was “Metabolism” with 1,809 unigenes (50%) in the

biosynthetic and catabolic processes

considering the “Metabolism” category (1,809 unigenes), the subcategories “Messeng

RNA Processing”, “Translation”, ”Fosforilation” and “Protein Folding” presented 700

occurrences (39%), followed by “Oxidation Reduction” with 543 occurrences (30%) and

“Carbohydrate Metabolism” with 196 hits (10.83%).

The second most frequent GO term i

590 matches (16%), from which 344

stresses, as “salt stress” (18%), water deficit (10%) or response against “other chemical

stimuli” (31%). The biotic stress cate

remaining hits regarded “response to stress in general”. The third most frequent GO

term was “biological process regulation” with 417 matches (12%), including the

subcategories “transcription regulation mechan

“signaling cascades” (signal transduction), with 177 occurrences (42%).

Figure 2. Distribution of 30 most represented GO terms in the category

“Biological Process”, including absolute values and per

Considering the annotated amount and diversified categories of the identified

sequences, it is clear that the cowpea ESTs data bank from different sources

For the ontology for the Molecular Function 3,792 unigenes (75%) were

es” followed by 589 unigenes (12%) related to “Catalitic


each 6% of the occurrences (data not shown).

For the ontological category Biological Process (Figure 2), from


biosynthetic and catabolic processes, both at cellular as at organismal level. Still

considering the “Metabolism” category (1,809 unigenes), the subcategories “Messeng



“Carbohydrate Metabolism” with 196 hits (10.83%).

The second most frequent GO term in the BP category was “Stress Response” with

590 matches (16%), from which 344 transcripts regarded terms associated to abiotic


stimuli” (31%). The biotic stress category comprised 88 occurrences (15%), while the



“transcription regulation mechanism” (58%), followed by terms related to


Distribution of 30 most represented GO terms in the category

“Biological Process”, including absolute values and percentage.


sequences, it is clear that the cowpea ESTs data bank from different sources

66

3,792 unigenes (75%) were

related to “Catalitic


(Figure 2), from 3,607 related


, both at cellular as at organismal level. Still

considering the “Metabolism” category (1,809 unigenes), the subcategories “Messenger



“Stress Response” with

transcripts regarded terms associated to abiotic


gory comprised 88 occurrences (15%), while the



ism” (58%), followed by terms related to


Distribution of 30 most represented GO terms in the category


sequences, it is clear that the cowpea ESTs data bank from different sources (leaves,

67

roots, meristems, axillary buds, root nodules, seeds, etc.) with or without stress

induction is adequate for anchoring 26 bp tags generated by SuperSAGE from V.

unguiculata, allowing the annotation of 65% of the 10,907 unique tags.

4.4.2. Distribution of the differentially expressed SuperSAGE tags

A total of 270,894 tags (26 pb) were sequenced, including 132,743 tags from the

mechanical injured leaves (C2, bulk of four treatments with different times after stress)

and 138,151 tags from the control experiment (C1). After exclusion of the tags with one

or more undefined sequences (n) and also those considered singletons (appearing a

single time in one of the both treatments) a total of 113,828 tags remained in the control

library (C1) and 110,686 from the bulked injured treatments (C2). The tags were

validated by the program DiscoverySpace (Figure 3A), that identified also 10,907

distinct unique tags, with 2,009 exclusive of the C2 library, 1,872 corresponding to the

control (C1) and 7,026, common to both (Figure 3B).

Figure 3. (A) Table with the representative sequenced tag number regarding the no stressed control (C1) and mix of mechanically injured leaves (probes from 30, 60, 90 minutes and 16 h), showing number of unique tags exclusive of each library. (B) Venn diagram showing the distribution of unique tags in both C1 and C2 libraries, as well as common tags for both.

The absolute and relative amounts regarding the diverse categories of

transcripts considering their abundance in the normalized libraries (100,000

tags•library-1), are represented in Figure 4. In both libraries, only about 1 to 1.44% of

the tags presented frequencies higher than 100, while more than 90% presented

frequencies of up to 20 times. Such observation is in accordance to other similar

approaches [30, 31, 32] using the SAGE method in plants. The results reveal the

68

advantage of this method in the comparative and simultaneous detection transcripts

with low expression levels.

Tag Abundance

Library C1 Library C2 Total % Total %

≥ 100 128 1.44 90 1.00 21-99 593 6.66 793 8.78 6-20 2,293 25.77 2,490 27.56 2-5 5,884 66.13 5,662 62.67

Total 8,898 100.00 9,035 100.00

Figure 4. Quantitative distribution of SuperSAGE tags. Tag frequencies in relation

number of copies per library (in %).

Considering the transcription pattern comparing the absolute frequencies of the

tags in the injured in relation to the control, it was possible to observe that of the 10,907

tags, 8,501 (77.49% of the unique tags) were expressed constitutively (without

significant difference among both treatments (p < 0.05), probably regarding

housekeeping genes involved in the basal physiological processes of the plant (Table 1).

The differentially expressed transcripts represented 22.51% (2,406 tags) from which

1,404 tags (12.80% of the total) were activated after stress perception, while other

1,002 tags (9.13%) were repressed (Table 1).

Taking the total of transcripts in account, 681 (48.5%) modulated their

expression five times or more (FC ≥ 5) after stress, from which 486 beard increased

expression already in the first hours after injury (up to 90 minutes), while 918

presented higher expression only 16 h after injury, indicating different sets of genes

activated by the mechanical injury. By the other hand, 510 tags were detected

exclusively in the injured library (being absent in the control), while 315 tags were

exclusive of the control, indicating their silencing after the stress.

69

Table 1. Differentially expressed tags after comparison of the control versus

stressed libraries.

Differential Expression tags Total (%)

Number of unique tags 10,907 (100) Constitutive Tags 8,501 (77.5)

Differentially expressed 2,406 (22.5) Up regulated 1,404 (12.8)

Down regulated 1,002 (9.13)

Number of unique tags 10,907 (100)

Tags annotated (score ≥42) 7,056 (65) 100% identity against cowpea 5,481 (78)

Differentially expressed 1,503 (27)

4.4.3. Tag annotation and potential new genes

The V. unguiculata EST data bank was previously annotated against the

Uniprot/Swiss-Prot /TrEMBL data bank (as described in the item 3.1.) and was used for

individual annotation of SuperSAGE tags, considering the previously mentioned criteria

(best score/e-value, best description and taxonomic proximity), resulting in the

identification of 10,907 unique tags and 7,056 tags (65%) with similarity (score ≥42)

with previously annotated V. unguiculata ESTs.

These results were similar to those obtained by Calsa and Figueira [33]

analyzing sugarcane SAGE tags (14 pb) from sugarcane (Saccharum spp.). From 5,227

unique tags analyzed, 70% (3,659 tags) had the corresponding gene annotated against

GenBank and the TIGR database. It is important to emphasize that such significant

number of annotated tags was only possible due to the small number of tags and the

high number of ESTs generated by the Sugarcane Transcriptome project (SUCEST). Our

results were also superior to the obtained by Molina et al [34] that used SuperSAGE for

expression profiling of abiotic stress in chickpea, annotating 22% of the 17,493 available

tags against the Fabaceae ESTs available in public databanks. Another example regards

the essay conducted by [35] that annotated 46% of 11,089 unique LongSAGE tags from

Arabidopsis thaliana, using the Arabidopsis UniGene (NCBI) bank.

From 7,056 tags (score ≥42), 5,485 tags presented 100% identity with V.

unguiculata sequences from our annotated bank, being 1,503 differentially expressed (p

< 0.05). From these, 909 were considered super-expressed (up-regulated) in the injured

70

library (C2), while 594 were repressed (down-regulated) in the same situation. Those

tags are potential targets for RT-qPCR validation using cDNA from the same extractions

used for the generation of the libraries, minimizing the need to use other approaches as

3’or 5’RACE with posterior sequencing [36].

For 3,851 tags no significant matches with V. unguiculata EST bank were

observed. From these 1,692 presented alignments (score ≥42) with sequences from

other annotated EST data banks (EST/NCBI; TIGR, etc.), with 416 classified under the

super expressed and 321 under the repressed tags (p < 0.05); despite of that, many

lacked the needed functional description for a good annotation. This high proportion of

non annotated tags may be explained by the lack of differentially expressed but less

abundant transcripts deposited in EST databases [37, 38] revealing a potential source

for the discovery of new genes involved in the response against mechanical injury. This

is also the case of the 2,159 tags without similarity to any sequence previously deposited

in public databases, representing a source for novelty related to the here analyzed

stress.

4.4.4. Categorization of the DNA sequences associated to the tags

The functional annotation of the 1,137 differentially expressed transcripts (909

up regulated and 594 down regulated), via Blast2GO [39] was carried out considering

the similarity to ESTs of cowpea related species and/or model plants. During this

analysis a higher similarity was expected to unitags from leguminous plants considering

their taxonomic proximity to cowpea [40, 41]. However, a higher similarity was

observed with the model plant A. thaliana (37%), followed by Ricinus communis (10%),

Vitis vinifera (7%), Populus trichocarpa (6%), Medicago truncatula and Glycine max

(5%), Pisum sativa and Oryza sativa (4%), among other (12%)(Figure 5).

This result may be explained by the abundance of Arabidopsis transcripts and

also the availability of the whole genome sequence of Arabidopsis (important for the

functional annotation of its own transcriptome), despite of its phylogenetic distance

when compared with other Fabaceae species, many of them bearing only EST species.

Similar results were obtained by Varshney et al [42] during the analysis of Chickpea

unigenes (ESTs) revealing higher similarity to soybean (65.8%) as compared with the

near related species Lotus tenuis (53.3%).

Figure 5. Best matches (in %) regarding differentially expressed

tags that could not be annotated with the cowpea EST database,

including leguminous and other far related species.

The transcripts significantly

categories [Biological Process (

(CC)] resulting in 3,595 GO hits with 1,255 (35%) categorized transcripts under B

1,307 (36%) under MF and 1,033 (29%) a

Regarding the “Cellular Component” category in Figure 6, the repression of

some housekeeping GO categories may be observed, as chloroplast and thylakoid;

besides other categories presented higher expression, especially those associated

transport as it is the case of the membrane category that is generally demanded in stress

situations.

Among the 20 differentially expressed transcripts in the category “Molecular

Function”, similar terms were observed for both up and down regulated t

ligation (7% up and 6% down); structural constituent (5% up and 3% down); ATP

ligation (3% up and 4% down), zinc ion ligation (both 3%), transcription factors (3% up

and 2% down). Despite of the frequent focus on the u

down-regulated genes may lead to important processes regarding stress tolerance [43].

Especially repressed groups included electron carriers, magnesium ion ligation and

ligation to chlorophyll. This last category (CAB

proteins associated to the thylakoid membrane which expression is associated to light

intensity [44], suggesting the repression of photosynthetic processes and electron

transport after mechanical injury.

Best matches (in %) regarding differentially expressed


including leguminous and other far related species.

The transcripts significantly aligned via BLASTx were annotated to the three GO

categories [Biological Process (BP), Molecular Function (MF) and Cellular Component

3,595 GO hits with 1,255 (35%) categorized transcripts under B

(36%) under MF and 1,033 (29%) among CC (Figure 6).



besides other categories presented higher expression, especially those associated



Function”, similar terms were observed for both up and down regulated t



and 2% down). Despite of the frequent focus on the up-regulated stress respo

regulated genes may lead to important processes regarding stress tolerance [43].


ligation to chlorophyll. This last category (CAB - chlorophyll a/b binding) re



transport after mechanical injury.

71

Best matches (in %) regarding differentially expressed


aligned via BLASTx were annotated to the three GO

), Molecular Function (MF) and Cellular Component

3,595 GO hits with 1,255 (35%) categorized transcripts under BP,



besides other categories presented higher expression, especially those associated with



Function”, similar terms were observed for both up and down regulated terms: protein



regulated stress response, the

regulated genes may lead to important processes regarding stress tolerance [43].


chlorophyll a/b binding) regard



Figure 6. Distribution of the differentially expressed transcripts

in absolute numbers within the three principal

categories considering the cellular component (CC) subcategory,

the bars represent the number of GO terms relative to the up and

down regulated tags after comparison of both libraries (C1xC2).

Numbers in the vertical regard the categories: (1)

biogenesis, (2) nucleus, (3) plasma membrane, (4) integral to

membrane, (5) cytoplasm, (6) chloroplast and (7) thylakoid.

Figure 7 presents the first 20 subcategories super expressed and repressed tags

with respective GO terms regarding “Biological Process” (410

represented five subcategories were: “response to any stress

tags); “protein processing a

“transcription regulation” (48 tags), and “transport” (45 tags).

Category “response to any stress

In this group were included those tags differentially expressed in the process of

“oxidation reduction” (117 tags: 33 up regulated and 84 down regulated), as well as

during the “response to any stress

down regulated) (Table 3) (See additional file).

Distribution of the differentially expressed transcripts

in absolute numbers within the three principal Gene Ontology

considering the cellular component (CC) subcategory,


ulated tags after comparison of both libraries (C1xC2).

Numbers in the vertical regard the categories: (1) ribosome



s the first 20 subcategories super expressed and repressed tags

with respective GO terms regarding “Biological Process” (410

represented five subcategories were: “response to any stress – biotic or abiotic” (136

tags); “protein processing and/or degradation” (132 tags); “photosynthesis” (49 tags);

“transcription regulation” (48 tags), and “transport” (45 tags).

Category “response to any stress – biotic or abiotic”



response to any stress – biotic or abiotic” (19 tags: 10 up regulated and 9

(Table 3) (See additional file).

72

Distribution of the differentially expressed transcripts

Gene Ontology

considering the cellular component (CC) subcategory,


ulated tags after comparison of both libraries (C1xC2).

ribosome



s the first 20 subcategories super expressed and repressed tags

with respective GO terms regarding “Biological Process” (410 tags). The most

biotic or abiotic” (136

nd/or degradation” (132 tags); “photosynthesis” (49 tags);



(19 tags: 10 up regulated and 9

73

In the subcategory “oxidation reduction” five tags were annotated as

dehydrogenases (alcohol, aldehyde, acid, glycols and retinol dehydrogenases) (FC>2< 7).

Such dehydrogenases have been associated to the survival of some plants submitted to

hypoxia [45], due to the necessity to suppress the energy deficit when the plant redirect

the metabolic pathways to guarantee extra ATP production [46, 47]. In such situations,

processes regarding catalysis of dehydrogenases (especially alcoholic and lactic

dehydrogenases) are selectively induced [48, 49]. Alcohol dehydrogenases, among other

transcripts, were also over expressed in a previous SAGE assay in Arabidopsis leaves

submitted to cold stress [49].

Other transcripts presenting super expression regarded hydrophenilpyruvat

dioxygenase – Hppd (FC>2) and a leucoanthocyanidin dioxygenase - Ldox (FC > 5), two

enzymes that participate in the flavonoid biosynthesis, a function associated to

pigmentation of flowers and fruits, but also active in the plant-bacteria symbiotic

interaction during the process of nitrogen fixation [50], as well as in the plant defense

against abiotic and abiotic stress, for example after wounding and ultraviolet light [51,

52]. The Hppd gene is also classified as a senescence gene, since it is associated to

photosynthesis decay [53]. The analysis of this gene in barley leaves showed low levels

in non senescent leaves and super expression in senescent leaves [54], an observation

also reproduced in experiments with rice [55]. This observation may suggest the

existence of a specific senescence mechanism regarding the injured leaves in cowpea,

leading to their elimination and subsequent protection against pathogen invasion in the

wounded sites.

Figure 7. Functional categorization of Vigna unguiculata

“Biological Process” and “Molecular Function” after Gene Ontology evaluation of C1 (control) x C2 (mechanically injured) libr

Vigna unguiculata unitags. 20 most differentially expressed transcripts

“Biological Process” and “Molecular Function” after Gene Ontology evaluation of C1 (control) x C2 (mechanically injured) libr

74

unitags. 20 most differentially expressed transcripts (up and down) in both categories

“Biological Process” and “Molecular Function” after Gene Ontology evaluation of C1 (control) x C2 (mechanically injured) libraries.

75

Other over expressed gene (FC>4) regarded the enzyme lipoxigenase-3 (Lox3)

of the PR (Pathogen Related) category. Lipoxygenases have been found in various plant

parts, associated with different processes as wounding response [56], insect resistance

and pathogen resistance [57]. One of the pathways regarding lipoxygenase

hydroperoxide involves the fatty acid formation that are precursors of jasmonic acid, an

important plant signal molecule in response to wounding, herbivory and pathogen

attack.

Two tags related to a superoxide dismutase (sodf) were also among the injury

over expressed transcripts (FC>2<5) in the second time of injury (16 hours after stress).

The frequent damages caused by stress (biotic or abiotic) lead to the production of

oxygen reactive species (ROS) and may explain the sodf abundance, constituting a lasting

response after stress perception [58]. Most intense ROS generation in plants has been

associated to mechanical injury and insect feeding [59], as well as pathogen attack [60,

61]. In such situations superoxide dismutases (DOS) are among the key molecules in

response to different stress situations [62, 63].

An additional important transcript was the cytochrome p450 (p450). This

transcript was repressed considering the initial times (FC>0.2<0.5), but over expressed

in the cowpea leaves 16 hours after injury (FC>5<11). p450 comprises a well conserved

protein family, with more than 2,000 described sequences in plants. In Arabidopsis they

are grouped in 44 families, comprising about 458 members in rice [64, 65]. The

accumulation of p450 enzymes has been associated to many stress types (e.g. osmotic)

including many high organisms types, including plants, occupying an essential role in the

stress tolerance [65, 66].

The p450 transcripts were also activated in other wounding essays in plants

[67, 68] as well as after pathogen attack [65, 70]. For example, Kong et al [71] related

the accumulation of p450 transcripts in wheat (cultivar Ning7840) after infection with

the phytopathogen Fusarium graminearum, with increased levels up to 14 times, as

compared with the negative control.

Among the super expressed transcripts in the subcategory “stress response” the

glutathione S-transferase (GST) achieved significant expression (FC >35) in the injured

library in the first 90 minutes (C2T123) after stress. Expression essays in plants have

revealed that some members of this gene family respond to a variety of stimuli,

including pathogen attack, herbicide application [62], drought [72] and ROS with H2O2

76

[73]. Evaluating the transcriptional response in chickpea after heat, cold and salinity

stress Mantri et al [74] observed a GST super expression in the susceptible material and

a repression in the tolerant. By the other hand, a micro array analysis in Arabidopsis

permitted the identification of GSTs among wound induced genes [75]. This study

permitted the identification of a clear association among different kind of stresses,

including pathogen attack, abiotic stresses and hormonal changes.

According to Sappl et al [76] the expression pattern observed for GST suggests

their participation in different signaling pathways, a proposition supported also by

Fujita et al [77] that emphasize the clear interaction of the signaling pathways regarding

biotic and abiotic stresses. Another similar tag, the protein phosphatase (pp1), was also

over expressed (FC>1<2). According to Luan [78] this protein represents an important

role on the cell signaling after pathogen attack, among other stress types.

Concerning the suppressed transcripts from the group “stress response”

regarding “oxidative stress or in function of biotic and abiotic stimuli”, 46 tags deserve

special mentioning among the 84 tags classified under “oxidative stress”, with similar

behavior as the ribulose biphosphate carboxilase (rbs1) (FC≥1 ≤5). From this group nine

regarded dehydrogenases (glyceraldehyde-3-phosphate dehydrogenase: four tags;

glycerate dehydrogenase, glycine dehydrogenase mitochondrial; retinol dehydrogenase

one tag each, and mannitol dehydrogenase: two tags) and to three citochrome p450.

Environmental conditions as water deficit, temperature changes and salinity, affecting

mechanisms associated to plant growth and other processes as the photosystem II

repair and rbs, decreasing the photosynthetic efficiency [79]. The immediate decreasing

of growth and the possible activation of senescence processes, previously discussed may

justify the suppression of genes of the rbs family.

The reductases (14 tags) figured also among the repressed genes, with emphasis

on the thioredoxin reductase, a protein family those catalyses oxidoreductase reactions

by using a dithiol-disulphide, aiming to reduce disulfide bridges in target proteins [80],

as it is the case of the ribonucleotide reductases and phosphatases reductases (PAPs).

This gene participate on acetate to the glyoxylate cycle being also identified as repressed

by a methionin sulphoxide reductase (MSR), an important target regarding primary

oxidation processes, including DNA repair after damaging due to the action of oxidant

agents [81].

77

Among the nine transcripts classified under “stress response” four have been

related to heat shock proteins – HSP: one chaperone and three tags classified as

drought-induced stress protein. The HSPs are part of the mechanisms involved in

protein conformation, translocation and degradation, acting also in the plant protection

against stress, reinforcing the cellular homeostasis by reestablishing the normal protein

conformation [82]. The presence of these proteins reinforces the activation of common

pathways regarding other stress types.

Category: “Protein processing and/or degradation”

This was the second largest category in GO terms, including 132 associated

tags, including mechanisms as “translation” (85 tags), “protein folding” (19 tags) and

“proteolysis” (28 tags) (Table 3) (See additional file).

Regarding the “translation” subcategory, most tags (73%, or 62 out of 85) were

associated to ribossomal proteins (30, 40 and 60S) with 47 super expressed tags and 15

repressed. Other nine tags were related to translation initiation factors (eIF1, eIF3, eIF4

and eIF5), with one of them (eIF6) being repressed. Among the observed eIFs, the eIF4

deserves special mentioning due to its special role in the resistance to viral infections in

mutant lines of Arabidopsis [83, 84] and also in pepper (Capsicum annuum) [85] while

the eIF5 has been shown to be essential to growth and differentiation by the regulation

of cell divisions, growth and death [86]. Still in this subcategory, five EF-Tu elongation

factors were found. The EF-Tu are proteins with 45-46 kD that participate of the

polypeptide elongation during protein synthesis [87]; besides this function, its

importance was also recognized in maize in association with heat tolerance (Zea mays)

[88] with increased expression also observed in wheat (Triticum aestivum) mature

leaves [89].

In the subcategory proteolysis 13 transcripts were super expressed, with

differences reaching up to ca. 12 times. Among this group the cysteine-proteases figure

as prevailing transcripts, being known to assume many complex physiological and

metabolic roles, with emphasis on regulatory processes, justifying their presence in all

eukaryotes previously analyzed. In plants and microorganisms the main activities

regard gene expression regulation, programmed cell death and resistance against

invading agents [90, 91]. It is interesting that proteins from this group bear distinct roles

in plant defense, acting at the perception, signaling and execution levels [92]. One

78

example is the protein p34, represented among super expressed transcripts in the

present evaluation. The p34 is a cysteine protease that binds to an elicitor probably

conferring the capacity of recognizing this elicitor in resistant plants [93], being

probably over expressed in cowpea as a preventive mean to avoid possible pathogen

attack after wounding. Another type of expressed tag in the proteolysis category was

also a cysteine proteinase named Oryzain alpha chain from rice (Oryza sativa) that was

23 times suppressed when compared with the negative control.

Among the repressed transcripts two aspartic proteinases (AP) were observed,

constituting a non expected situation, since they are normally over expressed and

accumulate in the intracellular spaces under biotic or abiotic stresses. They are also

considered important in the reuse of PR (Pathogen Related) proteins, also preventing

their superaccumulation, regulating their biological function during stress [94].

Another well represented subcategory regarding up regulated and down

regulated transcripts was the “protein folding” (38 tags). This subcategory included

transcripts related to heat shock proteins (HSPs) and chaperones (six repressed and

three super expressed). Such proteins are activated not only after exposition to heat but

also during other types of stress, since many different agents lead to miss conformation

of the proteins after stress, activating heat shock factors, what can be the case of the

present cowpea injured libraries.

“Photosynthesis” Category

Most transcripts of this category were up regulated (with exception of only five

out of 49 tags) after mechanical injury (Table 3). A previous work observing the

relationships among the photosynthesis genes and the hypersensitive response (HR) in

higher plants [95] reported the suppression of the chloroplast gene ftSH in the course of

the HR triggered by the tobacco mosaic virus (TMV) in a resistant tobacco plant, being

considered a remnant of an overall suppression of photosynthetic genes in Nicotiana

benthamiana. In the present work the repression of photosynthetic genes after injury

also reinforce the decrease in growth and activation of local senescence processes in the

injured cowpea leaves.

79

Transcription Regulation

This category included 48 differentially expressed tags including transcription

factors, from which 31 (65%) were up and 17 (35%) down (Table 2) (See additional

file). In this subcategory 12 tags deserve special mentioning due to their over expression

higher than five times (FC>5<24), indicating the important paper of such factors under

stress. Table 2 presents three tags (10%) regarding transcription factors. The first was

the ethylene response factor (ERF), a facto associated to many kinds of stress, including

pathogen and insect attack, exposition to toxic substances, low temperatures, and water

deficit, leading to ethylene production above the basal level [96, 97], also justifying the

ERF expression in cowpea injured leaves.

Also considering the transcription factors, three repressed tags (InjC1C2_8108,

InjC1C2_1573 and InjC1C2_3722) were identified, being similar to the apetala 2

(AP2/ERF) factor from Arabidopsis, also members of the ERF family. These sequences

belong to a large family of conserved genes that act in central points of a regulatory

network in plants being responsive to many biotic and abiotic stress types [98].

Another suppressed tag (InjC1C2_9656) is associated to the Dof (DNA-binding

with One Finger) transcription factor that act in the regulation of important processes in

higher plants, with emphasis on photosynthesis and carbohydrate metabolism. In the

literature Dof factors were associated with drought stress, as well as lack or excess of

light [99]. Therefore, its repression indicates a relationship among mechanical injury

and water deficit, confirming observations from previous essays.

Transport Category

Among the differentially expressed transcripts 45 tags regarded the transport

subcategory (Table 3), with emphasis on transcripts related to Photosystem II (9%),

suggesting that injury stress may have affected electron transference system. In the case

of both, Photosynthesis I and II, significant changes may occur under stress, fact justified

by the light absorbance by excited pigments that transfer energy to photosynthesis

reaction centers [100, 101]. Also the proteins associated to phosphatidylinositol are

among important phospholipids, acting as membrane components and in the growth

regulation especially under stress [102, 103] being recruited to mediate different

mechanisms in such situations [104].

80

The super expression of the tag InjC1C2_77 classified under the aquaporines

(FC≥6) suggests a response aiming to redirect the water balance to specific plant organs

during stress situations [105], also in consonance with the here studied injury stress, a

situation that leads to water loss through increased evapo-transpiration.

Other six transcripts were related to “pinta auxin”, being three up and three

downregulated. Auxins are phytormone that have an essential role in coordination of

many growth and behavioral processes in the plant life cycle. On the cellular level,

auxins are essential for cell growth, affecting both cell division and cellular expansion

[106], probably indicating reprogramming in the tissue growth and cell expansion in

cowpea injured plants.

81

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Additional file

95

Table 2. Sequences of SuperTags (26 pb) differentially expressed after annotation using the annotated V. unguiculata EST database

against transcription factors from Uniprot/Swiss-Prot/TrEMBL. Legend for abbreviations: Sc = Score; FC = fold change; Reg =

regulation.

TAG

UNIPROT

DESCRIPTION

FC

p-value

Reg

InjC1C2_7249 Q9H501 chromosome 20 open reading frame 6 3,09 1,14E-02 UP

InjC1C2_3500 Q6P2Z0 bzw1_basic leucine zipper and w2 domain-containing protein 1 4,52 2,81E-02 UP

InjC1C2_4421 Q9ZWL6 etr1_ethylene receptor 3,53 1,51E-03 UP

InjC1C2_7461 O24542 ax22d_auxin-induced protein 22d 5,42 1,38E-02 UP

InjC1C2_10167 Q8BT14 ccr4-not transcription subunit 4 isoform 2 3,50 8,33E-03 UP

InjC1C2_4559 P24068 octopine synthase binding factor1 5,83 1,13E-03 UP

InjC1C2_5852 P24068 octopine synthase binding factor1 3,09 2,16E-02 UP

InjC1C2_8522 Q99090 cprf2_light-inducible protein cprf2 10,84 1,94E-04 UP

InjC1C2_5398 Q99090 cprf2_light-inducible protein cprf2 10,54 2,58E-09 UP

InjC1C2_5077 O14270 fork head transcription factor fhl1 7,23 3,33E-03 UP

InjC1C2_1437 Q9FWQ5 hac12_histone acetyltransferase of the cbp family 12 5,42 1,38E-02 UP

InjC1C2_4115 Q8GTE5 transcription factor erebp-like protein 3,95 0,00E+00 UP

InjC1C2_5945 Q9LFY2 athb54 (A. thaliana homeobox protein 54) nucleic acid binding

transcription factor 6,32 6,78E-03 UP

InjC1C2_4769 Q9LYD3 tiny2 dna binding transcription factor 4,52 2,81E-02 UP

InjC1C2_2239 Q8LJS2 hdt1_histone deacetylase 4,46 1,04E-02 UP

InjC1C2_8406 Q61502 e2f transcription factor 5 4,52 2,81E-02 UP

InjC1C2_7758 A0JP85 ccr4-not transcription subunit 1 4,63 3,32E-02 UP

InjC1C2_5962 A9P8K1 predicted protein [Populus trichocarpa] 4,11 5,66E-03 UP

InjC1C2_6839 O24606 ein3 (ethylene-insensitive3) transcription factor 4,32 9,49E-09 UP

InjC1C2_7045 O82199 ccch-type zinc finger protein 2,19 1,80E-04 UP

InjC1C2_7112 O82307 atcth transcription factor 23,49 9,22E-09 UP

InjC1C2_10797 Q66GR3 basic helix-loop-helix family protein 6,43 4,97E-13 UP

InjC1C2_9552 Q9FH37 ilr3 (iaa-leucine resistant3) dna binding transcription factor 5,14 1,90E-02 UP

96

InjC1C2_7475 Q9SDQ3 scl1 (scarecrow-like 1) transcription factor 6,32 6,78E-03 UP

InjC1C2_7835 Q9SQI2 gigan_protein gigantea 4,46 1,04E-02 UP

InjC1C2_10096 P46604 homeobox protein hat22 6,17 6,39E-04 UP

InjC1C2_1178 Q9FKG2 ethylene responsive element binding factor 7,88 3,37E-05 UP

InjC1C2_4048 P46668 athb6_dna binding transcription activator transcription factor 4,52 6,56E-04 UP

InjC1C2_7367 Q42808 tbp_tata-box-binding protein 3,23 3,87E-03 UP

InjC1C2_191 Q02283 Homeobox-leucine zipper protein HAT5 7,23 3,33E-03 UP

InjC1C2_7615 Q02283 Homeobox-leucine zipper protein HAT5 1,56 6,16E-03 UP

InjC1C2_9780 Q02283 Homeobox-leucine zipper protein HAT5 0,00 4,57E-03 Down

InjC1C2_124 Q00423 hmgya_hmg-y-related protein 0,00 1,77E-02 Down

InjC1C2_9656 Q0GLC9 Dof22 [Glycine max] 0,17 8,99E-03 Down

InjC1C2_5830 Q41109 regulator of mat2 0,00 1,77E-02 Down

InjC1C2_8108 A7PLE5 transcription factor apetala2 0,00 6,02E-04 Down

InjC1C2_3722 Q56XP9 ap2 domain-containing transcription factor family protein 0,00 3,47E-02 Down

InjC1C2_9758 Q9FY69 transcription factor transcription regulator 0,21 2,64E-02 Down

InjC1C2_10547 Q01085 tia-1 related protein isoform 1 0,04 2,09E-13 Down

InjC1C2_4255 Q7Y1B6 gai_gibberellic acid-insensitive mutant protein 0,53 2,78E-02 Down

InjC1C2_4198 P42499 phyb_phytochrome b 0,07 4,33E-11 Down

InjC1C2_2712 Q700D2 jkd_ transcription factor zinc ion binding 0,23 4,46E-02 Down

InjC1C2_9694 Q47894 glnb_nitrogen regulatory protein p 0,00 6,02E-04 Down

InjC1C2_5376 Q9SUP6 wrky53_transcription activator transcription factor 0,00 6,02E-04 Down

InjC1C2_9325 Q39266 zinc finger protein zfp7 0,00 3,47E-02 Down

InjC1C2_9780 Q02283 Homeobox-leucine zipper protein HAT5 0,00 4,57E-03 Down

InjC1C2_5318 A7PME6 hypothetical protein [Vitis vinifera] 0,00 3,47E-02 Down

InjC1C2_1573 P47927 apetala2 protein 0,35 2,30E-04 Down

97

Table 3. Functional classification of the differentially expressed genes from the “Biological Process” (BP) and the most represented subcategories.

UNIPROT DESCRIPTION SC p-value FC Reg

Translation

Q6UNT2 rl5_60s ribosomal protein l5 52 2,76E-02 0,58 Down

Q68VN6 30s ribosomal protein s16 52 4,46E-02 0,23 Down

Q6UNT2 rl5_60s ribosomal protein l5 52 2,76E-02 0,58 Down

Q8RXX5 ribosomal protein l19 family protein 52 1,01E-03 0,31 Down

Q8VWX5 small ribosomal subunit 30S 52 9,36E-03 0,43 Down

Q8VY91 plastid ribosomal protein 52 4,78E-09 0,11 Down

Q9ASV6 chloroplast 30s ribosomal protein 52 8,99E-03 0,17 Down

Q9FJP3 50s ribosomal protein l29 52 1,86E-02 0,44 Down

Q9FWS4 emb2184_ structural constituent of ribosome 52 2,81E-09 0,06 Down

Q9M4Y3 rr10_30s ribosomal protein 52 1,33E-02 0,47 Down

Q9SPB3 rl10_60s ribosomal protein l10 52 1,01E-02 0,70 Down

Q9XJ27 ribosomal protein s9 52 1,86E-02 0,34 Down

A4GGF8 ribosomal protein l2 52 1,69E-09 0,09 Down

O22795 chloroplast 50s ribosomal protein l28 52 2,35E-02 0,29 Down

O22795 chloroplast 50s ribosomal protein l28 52 6,99E-04 0,28 Down

O55135 eukaryotic translation initiation factor 6 52 3,47E-02 0,00 Down

O80439 30s ribosomal protein s31 52 4,51E-06 0,31 Down

P24929 ribosomal protein l12-1a 52 2,93E-09 0,21 Down

P34811 efgc_elongation factor c 52 1,85E-36 0,02 Down

P49163 rk22_50s ribosomal protein 52 1,26E-03 0,23 Down

P72749 elongation factor ef-g 52 2,96E-03 0,15 Down

Q43467 eftu1_elongation factor 52 2,58E-26 0,05 Down

P56331 if1a_eukaryotic translation initiation factor 52 2,52E-03 2,10 Up

Q9SGA6 40s ribosomal protein s19 52 5,10E-03 3,70 Up

98

Q9SGA6 40s ribosomal protein s19 52 1,38E-02 5,42 Up

P49637 at1g70600 f5a18_22 52 1,09E-09 3,15 Up

Q9FKC0 60s ribosomal protein l13a 52 9,45E-03 3,86 Up

Q9SCM3 40s ribosomal protein s2 homolog 52 8,67E-04 1,76 Up

Q8VZB9 ribosomal protein l10 52 4,95E-05 1,64 Up

Q9LZ57 60s ribosomal 52 1,26E-05 2,84 Up

P51430 at5g10360 f12b17_290 52 1,17E-03 2,40 Up

O82204 60s ribosomal protein l28 52 1,63E-03 8,13 Up

P41127 60s ribosomal protein bbc1 protein 52 5,90E-07 5,73 Up

O22518 rssa_40s ribosomal protein 52 3,63E-03 1,98 Up

O22518 rssa_40s ribosomal protein 52 1,38E-02 5,42 Up

O65731 rs5_40s ribosomal protein s5 52 4,93E-02 3,43 Up

Q9M2F1 ribosomal protein s27 52 4,88E-02 1,77 Up

Q9C912 ribosomal protein 52 4,06E-06 3,27 Up

P81795 eukaryotic translation initiation factor 52 6,78E-03 6,32 Up

P81795 eukaryotic translation initiation factor 52 1,90E-02 5,14 Up

P24922 if5a2_eukaryotic translation initiation factor 5a-2 52 1,48E-05 2,88 Up

Q94JV4 eukaryotic translation initiation factor 52 6,76E-10 5,31 Up

Q94JV4 eukaryotic translation initiation factor 52 1,20E-11 2,48 Up

P35614 erf1-3 _translation release factor 52 7,62E-05 3,34 Up

O23755 ef2_elongation factor 2 52 1,26E-02 1,55 Up

P25698 ef1a_elongation factor 1-alpha 52 1,26E-02 1,55 Up

A7RWP6 eif3e_eukaryotic translation initiation factor 3 52 4,93E-02 3,43 Up

Q9FLF0 40s ribosomal protein s9 52 3,42E-02 2,88 Up

Q9FY64 ribosomal protein s15-like 52 2,87E-02 1,70 Up

P49689 40s ribosomal protein s30 52 1,88E-02 2,57 Up

Q9SS17 at3g04920 t9j14_13 52 3,71E-04 4,28 Up

P49690 60s ribosomal protein l17 52 1,25E-02 2,47 Up

P49211 ribosomal protein l32-like protein 52 2,50E-05 10,80 Up


Q6UNT2 rl5_60s ribosomal protein l5 52 2,57E-06 6,58 Up

B6IPJ8 ribosomal protein l20 52 2,97E-02 3,77 Up

Q5I7K3 rs29_40s ribosomal protein s29 52 8,31E-03 2,40 Up

B7FH86 unknown [Medicago truncatula] 52 4,71E-02 1,45 Up

B7FMI2 unknown [Medicago truncatula] 52 1,90E-02 5,14 Up

99



O65743 rl24_60s ribosomal protein l24 52 3,15E-03 1,81 Up


P34091 rl6_60s ribosomal protein l6 52 4,32E-02 2,57 Up

P34091 rl6_60s ribosomal protein l6 52 3,42E-02 2,88 Up

P35685 60s ribosomal protein l7a 52 2,81E-02 4,52 Up

P35685 60s ribosomal protein l7a 52 1,56E-02 3,60 Up

P46302 ribosomal protein s28 46,1 6,78E-03 6,32 Up

P46302 ribosomal protein s28 52 3,29E-02 1,88 Up


P62302 ribosomal protein s13 52 2,13E-06 7,71 Up

Q05462 rl27_60s ribosomal protein l27 52 2,05E-02 2,14 Up

Q9M573 rl31_60s ribosomal protein l31 52 2,92E-02 2,06 Up

Q9M5L0 rl35_60s ribosomal protein l35 52 1,07E-04 1,99 Up

Q9ZNS1 rs7_40s ribosomal protein s7 52 5,66E-03 4,11 Up


P17093 rs11_40s ribosomal protein s11 52 4,35E-10 4,73 Up

P62981 ubiquitin extension protein 52 4,89E-02 1,62 Up

Q940B0 60s ribosomal protein 52 6,78E-03 6,32 Up

P60040 ribosomal protein l7 52 4,54E-02 2,06 Up

Q93VI3 60s ribosomal protein l17 52 1,08E-02 5,66 Up

Q42064 ribosomal protein l8 52 6,10E-03 1,93 Up

Q42064 ribosomal protein l8 52 4,35E-03 2,86 Up

Q40465 if411_eukaryotic initiation factor 4a-11 52 3,32E-02 4,63 Up

Q6UNT2 rl5_60s ribosomal protein l5 52 2,57E-06 6,58 Up

Reduction Oxidation

P00865 rbs1_ribulose bisphosphate carboxylase small chain 52 7,93E-05 0,00 Down

P00865 rbs1_ribulose bisphosphate carboxylase small chain 44,1 2,83E-02 0,36 Down





100









P00865 rbs1_ribulose bisphosphate carboxylase small chain 52 0,00E+00 0,17 Down








O48927 c78a3_cytochrome p450 52 3,16E-02 0,45 Down

O65012 c78a4_cytochrome p450 52 4,26E-02 0,39 Down

O65837 lcye_lycopene epsilon chloroplastic 52 1,23E-03 0,21 Down

O81360 aba2_zeaxanthin chloroplastic 52 2,35E-02 0,29 Down

O82515 mtdh_probable mannitol dehydrogenase 52 1,77E-02 0,00 Down

P00865 rbs1_ribulose bisphosphate carboxylase 44,1 2,35E-02 0,29 Down






P00865 rbs1_ribulose bisphosphate carboxylase 52 0,00E+00 0,17 Down





P00865 rbs1_ribulose bisphosphate carboxylase 52 2,19E-04 0,15 Down



101










P12858 g3pa_glyceraldehyde-3-phosphate dehydrogenase 52 4,06E-24 0,14 Down

P12858 g3pa_glyceraldehyde-3-phosphate dehydrogenase 52 7,74E-56 0,09 Down

P12858 g3pa_glyceraldehyde-3-phosphate dehydrogenase 44,1 1,05E-05 0,00 Down

P12859 g3pb_glyceraldehyde-3-phosphate dehydrogenase 52 5,59E-03 0,21 Down

P13284 gilt_gamma-interferon-inducible lysosomal thiol reductase 52 2,96E-03 0,15 Down

P13443 dhgy_glycerate dehydrogenase 52 1,37E-13 0,13 Down

P24465 c71a1_cytochrome p450 52 1,55E-02 0,24 Down

P25861 g3pc_glyceraldehyde-3-phosphate cytosolic 52 5,58E-04 0,56 Down

P26969 gcsp_glycine dehydrogenase mitochondrial 52 2,29E-22 0,13 Down

P28553 crti_phytoene chloroplastic chromoplastic 52 4,15E-02 0,28 Down

P31023 dldh_dihydrolipoyl mitochondrial 52 4,66E-11 0,08 Down

P39866 nia2_nitrate reductase 2 52 1,29E-02 0,42 Down

P51104 dfra_dihydroflavonol-4-reductase 52 1,91E-19 0,19 Down

P51104 dfra_dihydroflavonol-4-reductase 52 9,69E-07 0,16 Down

P51978 thioredoxin reductase 52 2,19E-04 0,15 Down

P72854 sulfite reductase subunit beta 52 7,35E-04 0,20 Down

Q01289 por_protochlorophyllide chloroplastic 52 2,81E-05 0,09 Down

Q01289 por_protochlorophyllide chloroplastic 52 3,31E-03 0,19 Down

Q42807 stad_acyl- chloroplastic 52 3,50E-04 0,44 Down

Q42822 rbs_ribulose bisphosphate carboxylase 52 1,70E-05 0,15 Down

Q43155 gltb_ferredoxin-dependent glutamate 52 5,78E-09 0,08 Down

Q4PGW7 ncb5r_nadh-cytochrome b5 reductase 52 1,57E-05 0,22 Down

Q55087 chlp_geranylgeranyl diphosphate reductase 52 6,02E-04 0,00 Down

Q6MD85 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase 52 1,78E-02 0,72 Down

Q8DJK9 methionine sulfoxide reductase b 52 1,62E-02 0,36 Down

Q8TC12 retinol dehydrogenase 11 (all-trans 9-cis 11-cis) 52 2,71E-06 0,00 Down

102

Q945B7 crd1_magnesium-protoporphyrin ix monomethyl ester 52 3,31E-03 0,19 Down

Q948P6 fri3_ferritin- chloroplastic 52 2,32E-12 0,09 Down

Q9SEC2 msra_peptide methionine sulfoxide reductase 52 6,76E-16 0,23 Down

Q9XG54 opr1_12-oxophytodienoate reductase 52 1,77E-02 0,00 Down



Q9ZRF1 mtdh_probable mannitol dehydrogenase 52 4,66E-11 0,08 Down

Q9ZRF1 mtdh_probable mannitol dehydrogenase 52 2,28E-05 0,12 Down

P17817 p5cr- pyrroline-5-carboxylate reductase 52 6,78E-03 6,32 Up

P13603 adh1_alcohol dehydrogenase 1 52 5,38E-04 3,09 Up

P09186 lox3_ lipoxygenase-3 52 2,81E-02 4,52 Up

O23920 hppd_4-hydroxyphenylpyruvate dioxygenase 52 1,43E-02 2,21 Up

P32291 fad3e_3 fatty acid endoplasmic reticulum 52 1,11E-03 4,32 Up

B1WTZ2 4-hydroxy-3-methylbut-2-enyl diphosphate reductase 52 5,83E-12 5,51 Up

P35738 acid dehydrogenase e1 52 7,69E-05 4,80 Up

Q06215 ppo_polyphenol oxidase chloroplastic 52 1,13E-05 14,46 Up

P37115 tcmo_trans-cinnamate 4-monooxygenase 52 1,09E-03 2,83 Up

O81974 c71d8_cytochrome p450 52 6,78E-03 6,32 Up

Q2MJ15 cytochrome p450 monooxygenase 52 2,50E-05 10,80 Up

A6TF98 udp_glucuronic acid decarboxylase 52 1,08E-02 2,12 Up

P37221 maom_dependent malic enzyme 62 kda mitochondrial 52 1,38E-02 5,42 Up

P51615 maox_nadp-dependent malic enzyme 52 4,25E-02 1,62 Up

A7PN93 hypothetical protein [Vitis vinifera] 52 3,36E-03 4,37 Up

Q06652 gpx4_phospholipid hydroperoxide glutathione peroxidase 52 0,00E+00 2,64 Up

Q06652 gpx4_phospholipid hydroperoxide glutathione peroxidase 44,1 2,30E-05 13,55 Up

Q05047 c72a1_secologanin synthase 52 6,98E-08 5,78 Up

Q503L9 nxn_nucleoredoxin 52 0,00E+00 10,17 Up

Q9FR99 acco_1-aminocyclopropane-1-carboxylate oxidase 52 1,53E-03 2,98 Up

P48621 fad3c_3 fatty acid chloroplastic 52 3,84E-04 5,40 Up

P51091 ldox_leucoanthocyanidin dioxygenase 52 1,38E-02 5,42 Up

O04892 cytochrome p450 52 1,83E-11 5,14 Up

B7FIB3 unknown [Medicago truncatula] 52 1,85E-02 2,37 Up

Q2HVL4 rna-binding region rnp-1 52 2,81E-02 2,74 Up

Q96558 ugdh_udp-glucose 6-dehydrogenase 52 3,33E-03 7,23 Up

P25795 al7a1_aldehyde dehydrogenase 52 3,65E-07 5,40 Up

103

P28759 sodf_superoxide dismutase chloroplastic 52 1,42E-05 2,63 Up

P28759 sodf_superoxide dismutase chloroplastic 52 1,97E-03 4,63 Up

P00865 rbs1_ribulose bisphosphate carboxylase small chain 52 2,43E-05 4,11 Up

O62964 rbl_ribulose bisphosphate carboxylase large chain 52 2,97E-02 3,77 Up

Q9HBH5 retinol dehydrogenase 14 52 1,88E-02 2,57 Up

P00865 rbs1_ribulose bisphosphate carboxylase 52 2,43E-05 4,11 Up

Regulation transcription

Q9H501 chromosome 20 open reading frame 6 52 1,14E-02 3,09 Up

Q6P2Z0 basic leucine zipper and w2 domain-containing protein 52 2,81E-02 4,52 Up

Q9ZWL6 etr1_ethylene receptor 52 1,51E-03 3,53 Up

O24542 ax22d_auxin-induced protein 22d 52 1,38E-02 5,42 Up

Q8BT14 ccr4-not transcription subunit 4 isoform 2 52 8,33E-03 3,50 Up

P24068 octopine synthase binding factor1 52 1,13E-03 5,83 Up

P24068 octopine synthase binding factor1 52 2,16E-02 3,09 Up

Q99090 cprf2_light-inducible protein cprf2 52 1,94E-04 10,84 Up

Q99090 cprf2_light-inducible protein cprf2 52 2,58E-09 10,54 Up

O14270 fork head transcription factor fhl1 52 3,33E-03 7,23 Up

Q9FWQ5 hac12 (histone acetyltransferase of the cbp family 12) h3 h4_ histone acetyltransferase transcription cofactor

52 1,38E-02 5,42 Up

Q8GTE5 transcription factor erebp-like protein 52 0,00E+00 3,95 Up

Q9LFY2 athb54 (arabidopsis thaliana protein 54) nucleic acid binding transcription factor

52 6,78E-03 6,32 Up

Q9LYD3 tiny2 dna binding transcription factor 52 2,81E-02 4,52 Up

Q8LJS2 hdt1_histone deacetylase 52 1,04E-02 4,46 Up

Q61502 e2f transcription factor 5 52 2,81E-02 4,52 Up

A0JP85 ccr4-not transcription subunit 1 52 3,32E-02 4,63 Up

A9P8K1 predicted protein [Populus trichocarpa] 52 5,66E-03 4,11 Up

O24606 ein3 (ethylene-insensitive3) transcription factor 52 9,49E-09 4,32 Up

O82199 ccch-type zinc finger protein 52 1,80E-04 2,19 Up

O82307 atcth transcription factor 52 9,22E-09 23,49 Up

Q66GR3 basic helix-loop-helix family protein 52 4,97E-13 6,43 Up

Q9FH37 ilr3_dna binding transcription factor 52 1,90E-02 5,14 Up

Q9SDQ3 scl1 (scarecrow-like 1) transcription factor 52 6,78E-03 6,32 Up

104

Q9SQI2 gigan_protein gigantea 52 1,04E-02 4,46 Up

P46604 homeobox protein hat22 52 6,39E-04 6,17 Up

Q9FKG2 ethylene responsive element binding factor 52 3,37E-05 7,88 Up

P46668 athb6_transcription activator transcription factor 52 6,56E-04 4,52 Up

Q42808 tbp_tata-box-binding protein 52 3,87E-03 3,23 Up

Q02283 at3g01470 f4p13_2 52 3,33E-03 7,23 Up

Q02283 at3g01470 f4p13_2 52 6,16E-03 1,56 Up

Q02283 at3g01470 f4p13_2 52 4,57E-03 0,00 Down

Q00423 hmgya_hmg-y-related protein 52 1,77E-02 0,00 Down

Q0GLC9 Dof22 [Glycine max] 52 8,99E-03 0,17 Down

Q41109 regulator of mat2 52 1,77E-02 0,00 Down

A7PLE5 transcription factor apetala2 52 6,02E-04 0,00 Down

Q56XP9 ap2 domain-containing transcription factor family protein 52 3,47E-02 0,00 Down

Q9FY69 transcription factor transcription regulator 52 2,64E-02 0,21 Down

Q01085 tia-1 related protein isoform 1 52 2,09E-13 0,04 Down

Q7Y1B6 gai_gibberellic acid-insensitive mutant protein 52 2,78E-02 0,53 Down

P42499 phyb_phytochrome b 52 4,33E-11 0,07 Down

Q700D2 jkd_ transcription factor zinc ion binding 52 4,46E-02 0,23 Down

Q47894 glnb_nitrogen regulatory protein p 52 6,02E-04 0,00 Down

Q9SUP6 wrky53_transcription activator transcription factor 52 6,02E-04 0,00 Down

Q39266 zinc finger protein zfp7 52 3,47E-02 0,00 Down

Q02283 at3g01470 f4p13_2 52 4,57E-03 0,00 Down

A7PME6 hypothetical protein [Vitis vinifera] 52 3,47E-02 0,00 Down

P47927 apetala2 protein 52 2,30E-04 0,35 Down

Photosynthesis

P10933 fenr1_ferredoxin--nadp 52 2,17E-36 0,08 Down

P10933 fenr1_ferredoxin--nadp 48,1 2,28E-05 0,12 Down

P12357 g chain improved model of plant photosystem i 52 7,00E-07 0,00 Down

P12357 g chain improved model of plant photosystem i 52 8,74E-33 0,05 Down

P16059 psbp_protein evolving system of photosystem 52 1,12E-67 0,05 Down

P22179 h chain improved model of plant photosystem i 52 2,37E-20 0,23 Down

P22179 h chain improved model of plant photosystem i 52 3,47E-02 0,00 Down

P27489 cb23_chlorophyll a-b binding protein 52 1,08E-15 0,18 Down

105



P27524 cb4a_chlorophyll a-b binding protein cp24 52 8,99E-03 0,00 Down




P32869 psad_photosystem i reaction center subunit 52 4,72E-20 0,19 Down

P46486 psaf_photosystem i reaction center 52 3,23E-39 0,21 Down

P46486 psaf_photosystem i reaction center 52 1,04E-09 0,29 Down

P54773 psbs_photosystem ii 22 kda 52 1,51E-32 0,08 Down

P72580 solanesyl diphosphate synthase 52 1,25E-04 0,14 Down

P72580 solanesyl diphosphate synthase 52 6,35E-14 0,00 Down

P80470 psby_photosystem ii core complex proteins 52 2,82E-10 0,09 Down

P80470 psby_photosystem ii core complex proteins 52 4,95E-23 0,30 Down

Q01289 por_protochlorophyllide oxidoreductase 52 2,81E-05 0,09 Down

Q01289 por_protochlorophyllide oxidoreductase 52 3,31E-03 0,19 Down

Q07473 chlorophyll a b-binding protein cp29 52 3,29E-47 0,15 Down

Q40519 psbr_photosystem ii 10 kda 52 2,55E-02 0,83 Down

Q40519 psbr_photosystem ii 10 kda 52 5,48E-18 0,22 Down

Q41387 psbw_photosystem ii reaction center w 52 1,73E-26 0,30 Down

Q41387 psbw_photosystem ii reaction center w 52 2,12E-14 0,45 Down

Q55087 chlp_geranylgeranyl diphosphate reductase 52 6,02E-04 0,00 Down

Q945B7 crd1_magnesium-protoporphyrin ix monomethyl ester 52 3,31E-03 0,19 Down

Q9RFD5 magnesium chelatase subunit h 52 1,14E-03 0,17 Down

Q9S7W1 chlorophyll a b binding protein 52 5,67E-19 0,06 Down

Q9SDM1 cb121_chlorophyll a-b binding protein 1b 44,1 2,55E-02 0,26 Down

Q9SDM1 cb121_chlorophyll a-b binding protein 1b 44,1 1,77E-02 0,00 Down

Q9SDM1 cb121_chlorophyll a-b binding protein 1b 52 2,31E-70 0,34 Down

Q9XF89 chlorophyll a b-binding 52 7,81E-15 0,37 Down

Q9XF89 chlorophyll a b-binding 42,1 8,99E-03 0,17 Down





Q9ZT05 psak_photosystem i reaction center subunit 52 2,57E-34 0,23 Down

106

Q9ZT05 psak_photosystem i reaction center subunit 50,1 3,47E-02 0,00 Down

Q9XF89 chlorophyll a b-binding 44,1 2,81E-02 4,52 Up

Q9XF89 chlorophyll a b-binding 52 0,00E+00 2,67 Up




Transport

Q9FY14 tip1_probable aquaporin 1 52 4,81E-03 0,57 Down

Q46036 outer membrane lipoprotein blc 52 5,73E-03 0,44 Down

Q2LAM0 fatty acid 2-hydroxylase 52 3,47E-02 0,00 Down

Q2LAM0 fatty acid 2-hydroxylase 52 2,64E-02 0,21 Down

P21727 tpt_triose phosphate phosphate 52 3,16E-02 0,45 Down

P52178 tpt2_triose phosphate phosphate non-green 52 3,47E-02 0,00 Down

Q6J163 5ng4_pintaauxin-induced protein 5ng4 52 6,25E-03 0,54 Down

Q6J163 5ng4_pintaauxin-induced protein 5ng4 52 3,40E-03 0,23 Down

O82316 tip4 1 (tonoplast intrinsic protein 4 1) water channel 52 2,33E-03 0,00 Down

Q9ZVX8 plasma membrane intrinsic protein 52 2,28E-05 0,12 Down

O05519 abc transporter (atp-binding protein) 52 2,64E-02 0,21 Down

P07030 plas_chloroplastic 52 4,14E-65 0,12 Down

P07030 plas_chloroplastic 50,1 1,77E-02 0,00 Down



P07030 plas_chloroplastic 52 4,35E-04 0,19 Down

P10933 fenr1_ferredoxin--nadp 52 2,17E-36 0,08 Down

P10933 fenr1_ferredoxin--nadp 48,1 2,28E-05 0,12 Down

P29450 thioredoxin f 52 5,96E-14 0,16 Down

P29450 thioredoxin f 50,1 3,47E-02 0,00 Down

P29450 thioredoxin f 44,1 2,89E-22 0,19 Down

Q9ZR41 glrx_glutaredoxin 52 3,07E-14 0,53 Down

P52232 thioredoxin m 52 1,21E-02 0,32 Down

P0A3C7 ferredoxin i 52 4,15E-02 0,28 Down

P0A3C7 ferredoxin i 52 4,57E-03 0,00 Down

A4GYQ4 photosystem ii protein d2 52 7,50E-05 4,41 Up

107

Q5XIF3 nadh dehydrogenase fe-s protein 4 52 4,93E-02 3,43 Up

P49098 cytochrome b5 52 1,86E-03 7,20 Up

Q6J163 5ng4_pintaauxin-induced protein 5ng4 52 3,36E-03 6,68 Up

Q6J163 5ng4_pintaauxin-induced protein 5ng4 52 1,71E-13 5,14 Up

Q94FN1 phosphatidylinositol transfer-like protein iii 52 2,97E-02 3,77 Up

Q9SV31 aquaporin mip-like protein 52 6,78E-03 6,32 Up

Q1KUQ8 hypothetical protein [Cleome spinosa] 52 1,13E-03 5,83 Up

O22342 adt1_adp atp translocase 1 52 1,90E-02 5,14 Up

Q29RM1 solute carrier family member 19 52 9,45E-03 3,86 Up

P51132 ucri2_ubiquinol-cytochrome c reductase 52 3,42E-02 2,88 Up

O04066 acbp_acyl- -binding protein 52 2,97E-02 3,77 Up

O04066 acbp_acyl- -binding protein 52 1,38E-02 5,42 Up

Q9UG63 atp-binding sub-family member 2 isoform b 52 5,10E-03 3,70 Up

P27572 nu4m_nadh-ubiquinone oxidoreductase 52 3,94E-04 9,94 Up

P35721 succinate dehydrogenase subunit 3 52 1,13E-03 5,83 Up

P29449 trxh1_thioredoxin h-type 1 52 0,00E+00 18,36 Up

A4GYQ4 photosystem ii protein d2 52 7,50E-05 4,41 Up

Q01366 photosystem ii protein d1 52 4,69E-05 10,28 Up

Q01366 photosystem ii protein d1 52 1,90E-02 5,14 Up

Proteolysis

O04057 aspr_aspartic proteinase 52 2,71E-02 0,56 Down

O24326 vpe2_vacuolar-processing enzyme 52 2,17E-14 0,15 Down

O65351 cucumisin-like serine protease 52 2,60E-02 0,42 Down

P04825 aminopeptidase n 52 1,96E-02 0,53 Down

P25776 orya_oryzain alpha chain 52 1,38E-13 0,53 Down

P25776 orya_oryzain alpha chain 42,1 3,06E-04 0,00 Down

P42211 asprx_aspartic proteinase 52 6,59E-06 0,41 Down

P43508 cathepsin b-like cysteine proteinase 52 3,61E-04 0,66 Down

Q766C3 nep1_aspartic proteinase nepenthesin-1 52 4,99E-02 0,37 Down

Q766C3 nep1_aspartic proteinase nepenthesin-1 52 2,35E-02 0,29 Down

O24325 vpe1_legumain-like proteinase 52 2,16E-02 3,09 Up

Q8YV57 wd-40 repeat-containing protein 52 2,56E-02 3,34 Up

A0YSJ1 hypothetical protein L8106_22426 52 3,36E-03 4,37 Up

108

A0YSJ1 hypothetical protein L8106_22426 52 1,58E-07 19,88 Up

Q8RY22 Protease Do-like 7 52 1,38E-02 5,42 Up

Q42384 prl1_Protein pleiotropic regulatory locus 1 52 6,04E-03 4,80 Up

Q42290 Probable mitochondrial-processing peptidase 52 1,38E-02 5,42 Up

P12412 cysep_cysteine proteinase 52 3,87E-03 3,23 Up

P22895 p34_p34 probable thiol protease 52 1,04E-02 4,46 Up

Q93Z89 matrix metalloproteinase mmp2 52 0,00E+00 66,59 Up

Q40983 metalloendopeptidase [Pisum sativum] 52 1,38E-02 5,42 Up



P13917 7sb1_basic 7s globulin 52 2,28E-10 5,48 Up

P13917 7sb1_basic 7s globulin 52 2,10E-03 3,21 Up

Q9M9Z2 tpp2_probable thylakoidal processing peptidase 52 1,56E-02 3,60 Up

O73944 pyrrolidone-carboxylate peptidase 52 1,63E-03 8,13 Up

P25776 orya_oryzain alpha chain 44,1 3,42E-02 2,88 Up

Response to stress

B7FH14 unknown [Medicago truncatula] 52 6,98E-03 0,33 Down

P27322 hsp72_heat shock cognate 70 kda protein 2 52 8,17E-09 0,26 Down

P36181 hsp80_heat shock cognate protein 80 52 2,35E-02 0,29 Down

P80471 lipc_drought-induced stress protein 52 1,49E-07 0,64 Down

P80471 lipc_drought-induced stress protein 44,1 2,33E-03 0,00 Down

P80471 lipc_drought-induced stress protein 50,1 1,18E-03 0,00 Down

Q02028 hsp7s_stromal 70 kda heat shock-related 52 1,82E-02 0,64 Down

Q4UKR8 small heat shock protein 52 6,96E-15 0,26 Down

Q9FVL0 hbl1_non-symbiotic hemoglobin 52 2,55E-02 0,26 Down

Q8YM56 clpb2_chaperone protein clpb 2 52 3,36E-03 6,68 Up

P48490 pp1_serine threonine-protein phosphatase pp1 52 3,68E-02 1,92 Up

P25795 al7a1_aldehyde dehydrogenase family 7 52 3,65E-07 5,40 Up

Q01899 hsp7m_heat shock 70 kda mitochondrial 52 1,90E-02 5,14 Up

P32292 arg2_indole-3-acetic acid-induced protein 52 7,79E-08 15,43 Up

P32292 arg2_indole-3-acetic acid-induced protein 52 1,01E-12 6,01 Up

Q07A28 usp-like protein 52 1,85E-02 2,37 Up

Q94G23 af281656_1 transcription factor 52 9,50E-12 3,22 Up

109

Q94G23 af281656_1 transcription factor 44,1 1,63E-03 8,13 Up

P32110 gstx6_probable glutathione s-transferase 52 0,00E+00 35,99 Up

Protein Folding

P22954 heat shock cognate 70 kda protein 2 52 8,17E-09 0,26 Down

A9PH85 predicted protein [Populus trichocarpa] 52 2,81E-05 0,09 Down

O49886 cyph_peptidyl-prolyl cis-trans isomerase 52 2,92E-12 0,26 Down

P35016 enpl_endoplasmin homolog 52 2,78E-158 0,08 Down

P35016 enpl_endoplasmin homolog 50,1 8,99E-03 0,00 Down

P36181 hsp80_heat shock cognate protein 80 52 2,35E-02 0,29 Down

Q02028 hsp7s_stromal 70 kda heat shock-related 52 1,82E-02 0,64 Down

Q5WZN0 sura_chaperone sura 52 4,57E-03 0,00 Down

Q75VW3 dnaj_chaperone protein dnaj 52 1,77E-02 0,00 Down

Q8RB67 molecular chaperone 52 1,47E-02 0,27 Down

Q9ASS6 peptidyl-prolyl cis-trans isomerase cyclophilin 52 2,86E-08 0,20 Down

Q9SDN0 Chaperone protein dnaJ 20 52 5,10E-03 3,70 Up

P08926 ruba_60 kda chaperonin subunit alpha 52 1,73E-03 2,44 Up

P22954 heat shock cognate 70 kda protein 2 52 8,31E-03 2,40 Up

P42824 dnjh2_protein homolog 2 52 5,91E-06 2,12 Up

P42824 dnjh2_protein homolog 2 52 6,78E-03 6,32 Up

Q01899 hsp7m_heat shock 70 kda mitochondrial 52 1,90E-02 5,14 Up

Q38867 peptidylprolyl isomerase 52 8,03E-04 9,03 Up

Q39817 calx_calnexin homolog 52 2,97E-02 3,77 Up

110

CAPÍTULO 2

The analysis of differential expression in Vigna

unguiculata (L.) Walp. to the severe mosaic virus (CPSMV) revealed by SuperSAGE

To be submitted to the journal BMC Genomics

111

ABSTRACT

Background: Cowpea (Vigna unguiculata L. Walp.) is a widely adapted, stress tolerant

grain legume, vegetable, and fodder crop grown on about 7 million ha in warm to hot

regions of Africa, Asia, and the Americas. However, biotic stress such as virus stress

limits plant growth and crop productivity, including those of legumes. We anticipate that

studies on Vigna unguiculata will shed light on other economically important legumes

across the world and innovative molecular tools such as transcriptome analyses

providing insight into stress-related gene activity, which combined with molecular

markers and expression QTL mapping may contributed knowledge-based breeding. In

this report, we describe the genes identified by SuperSAGE that are up or down

regulated during the early resistance response to CPSMV in cowpea. Results: Gene

ontologies of the differentially expressed genes revealed a wide range of functions and

processes. In addition, differentially expressed genes were identified that were involved

in numerous biological pathways and functions including transcription regulation and

response to defense, including response to biotic stress. Among the stress inducible

genes identified, we found 356 distinct tags corresponding the regulatory auxin genes,

transcription factor, involved pathway to jasmonate, genes involved in antioxidant

activities, heat shock, and oxidative stress, suggesting that various transcriptional

regulatory mechanisms function in the stress signal transduction pathways. Conclusion:

This work significantly contributes to our understanding of the molecular mechanisms

of genes response to stress and, to our knowledge, this is the first essay to analyze

differential gene expression of the Vigna unguiculata.

Keywords: Vigna unguiculata, viral diseases, SuperSAGE, transcriptome, expression

profile.

112

1. INTRODUCTION

Cowpea [Vigna unguiculata (L.) Walp.], is an important legume grown as a grain,

vegetable, fiber, or fodder crop in the tropical and subtropical world [1]. In the Brazil,

the culture of cowpea represents an important alternative in the supplement of the

proteins necessities of small agriculturists in the North and Northeast regions. The crop

has a considerable ability to adapt to high temperatures and drought compared to other

crop species [2]. However, like most crop plants, cowpea production is limited by

numerous biotic and abiotic factors and, among several diseases, those caused by

viruses are considered of great importance, becoming one of the most important

problem for the production this crop [3]. The Cowpea severe mosaic vírus (CPSMV) - a

comovirus [4] transmitted by more than ten species of beetles [5] stands out as most

important virus affecting cowpea. Significant yield losses associated with CPSMV

infections can vary from as little as 2% to as much as 85%, depending on the time of

inoculation, season, and cultivar [1].

Despite its economic and social importance, cowpea improvement programs have

directed efforts in the screening of sources of resistance genes in wild and cultivated

germoplasm, to development of desirable agronomic traits cultivars, such as those

governing the abiotic and biotic stresses [6]. Although progress had been made in

cowpea breeding for CPSMV resistance, the generation of resistant varieties is a difficult

and time consuming task. In addition, CPSMV presents a large biological variability with

a wide host range in the leguminous family [5] and/ or genotypes with higher and lower

degree of resistance and susceptibility to each isolate, suggesting evidence of new

strains of CPSMV developed over the years by genetic mutation, rearrangement of

genome components and adaptation to new cowpea cultivars or leguminous species [7].

Understanding of molecular mechanisms underlying host-pathogen interactions

is of primary importance in the definition of strategies to control diseases [8]. Until

recently, few evaluations regarding the genus Vigna have appeared in the literature

examining differential gene regulation during growth and development or in response to

several stresses [6]. Consequently, the developing of innovative biotechnology for

cowpea improvement requires not only an understanding of its genome organization

and complexity, but also of its gene structure and function. The most significant studies

in legume genomics have been made for model species as M. truncatula and L. japonicus,

113

[9, 10] and for soybean (G. max), the economically most important legume crop species

[11].

In this context, progress in the development of genome-scale data sets for several

legume species offers important possibilities for crop improvement, allowing more

rapidly and precisely access to target genes associated to a series of abiotic and biotic

stresses [12]. For this purpose, a number of methods have been used to isolate

differentially expressed plant genes. One of the most powerful gene expression analysis

techniques is the serial analysis of gene expression (SAGE) as developed by Vesculescu

et al [13]. Although SAGE is a useful technique for transcriptomics, the size of the SAGE

tag (15 bp) is frequently too short to unequivocally identify the corresponding gene. To

circumvent this problem, a novel method called SuperSAGE was introduced as a

modification of the conventional SAGE procedure, whereby the tag size of 15 bp of the

latter is increased to 26 bp [8, 14]. Its tag length is advantageous in tag-to-gene

annotation with higher specificity, thereby allowing the application of the technique for

expression profiling in organisms in which little genome information is available [15,

16].

The present work reports genes identified by SuperSAGE that are up or down

regulated during the early resistance response to CPSMV in resistant and susceptible

cowpea cultivars, bringing some new insights regarding the response to pathogen attack

in this species as compared with other legumes and higher plants. The importance of the

here identified genes in the resistance response is discussed.

2. MATERIAL AND METHODS

2. 1. Plants, virus inoculation, RNA extraction

Cowpea plants (cultivar BR-14 Mulato, developed by EMBRAPA-CPMN, Teresina,

Brazil) were cultivated in a greenhouse under anti-aphid net. The substrate was

composed by two parts of organic soil to three parts of river sand. The experiment

included 45 pods with five seeds per pod, grown under 12/12 h photoperiod and

temperature varying from 28 to 32°C. The virus isolate used in this procedure (CPSMV-

Cowpea severe mosaic virus) was obtained from the plant viruses collection of the

Department of Plant Pathology at the Federal Rural University of Pernambuco - UFRPE,

114

Brazil (under the coordination of Dr. Gilvan Pio-Ribeiro). Cowpea plants were inoculated

with CPSMV 20 days after the plantlet emergence, when all plants contained the two

first true leaves emerged after the cotyledons. Leaves were harvested 30, 60, 90 min and

16 h after mechanical wounding with Carborundum™ and virus inoculation. Negative

controls consisted of plants both neither infected nor mechanically injured. All leaves

from each treatment were harvested, immediately frozen in liquid nitrogen and stored

at -80ºC until RNA extraction.

2.2. RNA isolation and construction of SuperSAGE libraries

Total RNA was extracted from cowpea leaves using a CTAB extraction followed

by precipitation in LiCl solution, as described by Chang et al [17], followed by DNAse

treatment and checking of the RNA quality and amount in 1,5% (p/v) agarose gel as well

as in the Qubit (INVITROGEN®, USA) fluorometer. From approximately 1 mg of total

RNA, poly (A) RNA was purified using the Oligotex mRNA Mini Kit (QIAGEN®) according

to the manufacturer's batch protocol. Subsequent steps for construction of SuperSAGE

libraries were performed as detailed by [8, 18]. However, instead of concatenation of

ditags and subsequent cloning and sequencing, amplified ditags were directly sequenced

by 454 Life Sciences, Branford, CT, USA.

2.3. SuperSAGE data analysis

The statistical tests were used to determine tags with significant temporal

changes in abundance from the Two SuperSAGE libraries. The statistical analysis of

SAGE data for identification of genes differentially expressed was carried out using the

DiscoverySpace 4.01 software (Canada's Michael Smith Genome Sciences Centre,

available at http://www.bcgsc.ca/discoveryspace), using a procedure of Audic and

Claverie [19] for identification of tags appearing exclusively in a given library and that

differentially transcribed (p-value; p>0.05). The frequency ratio was calculated the

counted tags of inoculated library BRM (BRMT123+BRMT4) in relation to the control

C1. The R ratio was considered the modulation value of the transcriptional expression

(FC; Fold Change) when R > 1 when super expressed and 1/R when repressed. Each

library was normalized to 100,000 total counts per library prior to loading into Cluster

3.0.

115

2.4. Annotation of SuperSAGE Tags

The unigenes were annotated using a local BLASTx tool (e-value ≤ 10-10) against

the UNIPROT-Swiss-Prot/TrEMBL (http://www.uniprot.org/; release 15.7) database.

Best scores were taken considering the BLAST evaluations against the various data

banks cited above. In the case of identical scores/e-values the best described sequence

was chosen, giving priority to cowpea sequences or taxonomic most related organisms.

The functional annotation was carried out using the Blast2GO tool

(http://www.blast2go.org;) [20], with default parameters and terms according to the

Gene Ontology classification [21].

2.5. Cluster analysis and functional category distribution analysis

To generate an overall picture of genes involved in category response to stress

expression patterns in Vigna, a hierarchical clustering approach was applied using

normalized data (100,000 total counts per library ) and a graphic representation

constructed with the aid of the software package Cluster 3.0 (http://rana.lbl.gov/Eisen

Software.htm). A distance matrix for the R (ln) was calculated with Pearson's correlation

distance method. Dendrograms including both axes (using the weighted pair-group for

each gene class and library) were generated by the TreeView program [22]. In the

diagrams (figure 7, see Results), black means no expression and red all degrees of

expression.

3. RESULTS AND DISCUSSION

A total of 6,801,062 26-bp tags were generated, from which 1,011,380 tags

regarded the BRC1 library while 5,789,682 tags belonged the BRM (BRMT123+BRMT4).

Regarding BRM library, 2,974,661 tags regarded the initial times regarded the three

initial virus stress times (BRMT123) while 2,815,021 tags regarded the later collections

after inoculation (BRMT4) (Table 1).

The number of singletons (tags appearing only once) regarded 353,175,

representing approximately 6% of the total generated in all three libraries. This number

is slightly lower than that observed by McIntosh et al [23] in wheat (Triticum aestivum)

libraries of various development stages (8 to 40 dpa; days post

96,441 LongSAGE tags from which

Table 1. Summary of SuperSAGE libraries of

Library

Tags

Total sequenced

Total analyzed

Singletons

Unique to a given library

The most abundant tags with 100 copies or more corresponded to about 6%

(11,951) of the total tag number, while most transcripts occurred in 2

representing about 55% (100,491) of the tags

Figure 1. Distribution of unique tags (axis Y) in relation to tag copy number (axis X). Only tags with a copy number plotted on the graph.

A total of 107,161 unique (non redundant) tags were available

the program DiscoverySpace, from which 24,026 were exclusive to the library

BRMT123, whilst 24,304 tags were exclusive to the library and 9,333 tags appeared only

in the BRC1 (control) library, against 28,468 tags that were occurred in

libraries. Considering only inoculated libraries (BRMT123 and BRMT4) a total of 14,853

were shared by both treatments (Figure 2).

libraries of various development stages (8 to 40 dpa; days post anthesis

LongSAGE tags from which 29,261 were singletons.

Summary of SuperSAGE libraries of Vigna unguiculata

BRC1 BMCT123

1,101,845 3,099,384

1,011,380 2,974,661

90,466 124,723

43,978 70,523


(11,951) of the total tag number, while most transcripts occurred in 2

representing about 55% (100,491) of the tags exclusive of a given library (Figure 1).

Distribution of unique tags (axis Y) in relation to tag copy number (axis X). Only tags with a copy number ≥ 2 were plotted on the graph.

A total of 107,161 unique (non redundant) tags were available


whilst 24,304 tags were exclusive to the library and 9,333 tags appeared only

in the BRC1 (control) library, against 28,468 tags that were occurred in


were shared by both treatments (Figure 2).

116

anthesis) and sequenced

BMCT4

2,953,007

2,815,021

137,986

70,626


(11,951) of the total tag number, while most transcripts occurred in 2-5 copies,

exclusive of a given library (Figure 1).

Distribution of unique tags (axis Y) in relation to tag ≥ 2 were

A total of 107,161 unique (non redundant) tags were available for analysis using


whilst 24,304 tags were exclusive to the library and 9,333 tags appeared only

in the BRC1 (control) library, against 28,468 tags that were occurred in all three


Figure 2. distribution among the three SuperSAGE libraries BMCT123 (h); (3) BRC1 (control).

Primary annotation

The first annotation routine was against the cowpea EST data bank previously

annotated Uniprot-Swiss-Prot/TrEMBL. The tags that could be not annotated against

cowpea were evaluated against other plants considering the adopted (higher score/e

value, best description and taxonomic proximity). From the 107,161 unique tags

analyzed, 27,514 could be annotated (score

presented 100% identity (score=52) with cowpea sequences available in the database

(Table 2). These tags will be

validation via RT-qPCR using the cDNAs used to generate the libraries, probably without

need of further approaches as 3’ or 5’RACE and sequencing

A group of 3,368 tags presented alignments wi

(score ≥40), including 2,249 tags with 100% identity (score=52) with

Prot/TrEMBL sequences; despite of that, they did not present informative descriptions,

being annotated but not categorized (Table 2). Regarding

identity), 684 tags were differentially expressed, being 347 super expressed and 337

repressed.

Figure 2. Venn diagram showing the tag istribution among the three SuperSAGE

libraries for each stress treatment (1) BMCT123 (30, 60, 90 min); (2) BMCT4 (16

); (3) BRC1 (control).


Prot/TrEMBL. The tags that could be not annotated against

cowpea were evaluated against other plants considering the adopted (higher score/e

scription and taxonomic proximity). From the 107,161 unique tags

analyzed, 27,514 could be annotated (score ≥40), from which 17,928 (65%) tags

100% identity (score=52) with cowpea sequences available in the database

(Table 2). These tags will be potentially useful to develop primers and probes for gene

qPCR using the cDNAs used to generate the libraries, probably without

need of further approaches as 3’ or 5’RACE and sequencing [24].

A group of 3,368 tags presented alignments with the established parameters

2,249 tags with 100% identity (score=52) with


being annotated but not categorized (Table 2). Regarding the same 2,249 tags (100%


117


Prot/TrEMBL. The tags that could be not annotated against

cowpea were evaluated against other plants considering the adopted (higher score/e-

scription and taxonomic proximity). From the 107,161 unique tags

17,928 (65%) tags

100% identity (score=52) with cowpea sequences available in the database

potentially useful to develop primers and probes for gene

qPCR using the cDNAs used to generate the libraries, probably without

th the established parameters

2,249 tags with 100% identity (score=52) with Uniprot-Swiss-


the same 2,249 tags (100%


118

Another group of 31,600 SuperSAGE tags presented no functional annotation

against Uniprot-SwissProt/TrEMBL, despite of that they presented alignments (score

≥40) with sequences from other databases (EST/NCBI; TIGR, etc.). From these, 8,904

tags presented 100% identity (score=52) (Table 2). This high proportion may be due to

a significant fraction of low expression level transcripts that could not be detected by

previous approaches [25, 26] indicating the potential the SuperSAGE method for gene

discovery.

Similarly, 44,680 tags did not present the required similarity (no hit) with

sequences available in public data banks (Table 2), bearing also an important for new

gene discovery regarding virus resistance.

Table 2. Annotation primary of tags SuperSAGE

Score=52 Score ≥40 Total

Annotation Uniprot 17,928 9,586 27,514 Others databases 8,904 22,696 36,488

No description Uniprot 2,249 1,119 3,368

No hits -- 44,681 4,681

Antisense Transcripts

The orientation of each SuperSAGE tag is generally in the sense orientation, an

assumption also consistent with other SAGE-related methods [23, 27, 28]. Despite of

that, the present work revealed some reverse tags (reverse perfect or fuzzy) that aligned

in the antisense direction of the DNA transcript. The antisense transcripts normally

regard about 25-30% of all identified gene products [29, 30] being typically associated

to gene silencing, transcription occlusion and direction of methylation that may result in

the reduction of sense transcripts. Additionally, the antisense transcription may be

associated with alternative splicing processes and polyadenylation, what may have an

effect also regarding the sense transcripts [31, 32, 33].

Based on the parameters adopted to note antisense tags, this work annotated

potential antisense 4,776 transcripts, corresponding to 4% of the total (107,161) unique

tags analyzed. For 530 antisense tags, despite of being not differentially expressed

(p>0.05), it was possible to identify the putative functions, revealing that 91 tags

presented Fold Change ≥2. Among the 30 most abundant antisense tags (FC ≥2; Table 3)

are proteins known for their involvement with gene regulation as the H4 histone, the

119

histone deacetylase 19, ubiquitin-conjugating enzyme, and an X-linked inhibitor of

apoptosis protein. Other additionally important proteins regarded cellular transport,

structure, function and signaling.

Table 3. Summary of 30 most abundant antisense tags, including Uniprot/Swiss-

Prot/TrEMBL identification, protein description, FC and p-value.

Tag

Uniprot ID

Protein description

FC

p-value

AllCMV_60948 sp|O23969|SF21_HELAN Pollen-specific protein SF21 6,9 6,8E-02

AllCMV_9600 sp|Q76H85|H4_SILLA Histone H4 6,2 9,4E-02

AllCMV_29198 sp|Q9LRR9|GOX2_ARATH Probable peroxisomal (S)-2-hydroxy-acid oxidase 2 5,9 1,1E-01

AllCMV_82696 sp|P17067|CAHC_PEA Carbonic anhydrase 5,9 1,1E-01

AllCMV_16739 sp|Q9LVM5|TTHL_ARATH Uric acid degradation bifunctional protein 5,5 1,3E-01

AllCMV_15013 sp|P52232|THIO1_SYNY3 Thioredoxin-like protein 5,5 1,3E-01

AllCMV_29527 sp|Q93VI3|RL171_ARATH 60S ribosomal protein 5,5 1,3E-01

AllCMV_20119 sp|O22446|HDA19_ARATH Histone deacetylase 19 5,2 1,5E-01

AllCMV_15014 sp|P52232|THIO1_SYNY3 Thioredoxin-like protein 4,8 1,8E-01

AllCMV_48855 sp|P28759|SODF_SOYBN Superoxide dismutase [Fe] 4,8 1,8E-01

AllCMV_42904 sp|Q43681|NLTP_VIGUN Probable non-specific lipid-transfer protein 4,8 1,8E-01

AllCMV_61541 sp|Q9LYN8|EXS_ARATH Leucine-rich repeat receptor protein kinase 4,8 1,8E-01

AllCMV_25317 sp|P27489|CB23_SOLLC Chlorophyll a-b binding protein 13 4,8 1,8E-01

AllCMV_106025 sp|P00551|KKA1_ECOLX Aminoglycoside 3'-phosphotransferase 4,8 1,8E-01

AllCMV_91998 sp|Q96451|1433B_SOYBN 14-3-3-like protein B 4,8 1,8E-01

AllCMV_79098 tr|B9RE37|B9RE37_RICCO X-linked inhibitor of apoptosis protein 4,5 2,1E-01

AllCMV_68159 sp|P81898|PNAA_PRUDU Peptide N4 asparagine amidase A 4,5 2,1E-01

AllCMV_80644 sp|Q9LY00|WRK70_ARATH Probable WRKY transcription factor 70 4,1 2,5E-01

AllCMV_68042 sp|Q39459|MT2_CICAR Metallothionein-like protein 4,1 2,5E-01

AllCMV_51261 sp|Q42521|DCE1_ARATH Glutamate decarboxylase 1 4,1 2,5E-01

AllCMV_26923 sp|P35135|UBC4_SOLLC Ubiquitin-conjugating enzyme 3,8 2,9E-01

AllCMV_7803 sp|Q6I581|GH35_ORYSJ Probable indole-3-acetic acid-amido synthetase 3,8 2,9E-01

AllCMV_29229 sp|O04834|SAR1A_ARATH GTP-binding protein SAR1A 3,8 2,9E-01

AllCMV_67987 sp|A6Q0K5|CP12_CHLRE Calvin cycle protein CP12 3,8 2,9E-01

AllCMV_83554 sp|Q8GYB8|OPR2_ARATH 12-oxophytodienoate reductase 2 3,8 2,9E-01

AllCMV_93732 sp|P62313|LSM6_MOUSE U6 snRNA-associated Sm-like protein 3,5 3,4E-01

AllCMV_18314 sp|Q01289|POR_PEA Protochlorophyllide reductase 3,5 3,4E-01

AllCMV_41589 sp|P55880|THIJ_SALTY Protein thiJ 3,5 3,4E-01

AllCMV_7802 sp|Q6I581|GH35_ORYSJ Probable indole-3-acetic acid-amido synthetase 3,5 3,4E-01

AllCMV_55702 sp|P43309|PPO_MALDO Polyphenol oxidase 3,5 3,4E-01

A transcript associated to a peroxisomal (S)-2-hydroxy-acid oxidase 2 (Hao 2)

(FC=5,9) was also found among the antisense tags. The Hao2 belongs to an enzyme

120

family that acts in the glyoxilat cycle with putative contribution to fatty acids α-

oxidation catalyzing the oxidation of glycolat in glyoxilat [34]. Transcripts related to

oxyreduction activities were also found (AllCMV_15014; AllCMV_48855;

AllCMV_83554), as well as ribossomal proteins (AllCMV_29527) and a putative WRKY

transcription factor 70 (AllCMV_80644).

Considering the important role of the antisense transcripts in the gene regulation,

an accurated analysis of such gene products is necessary aiming to explain the processes

associated to their expression, a scenario where the SuperSAGE approach may

contribute significantly.

Functional categorization of SuperSAGE tags

The functional categorization was carried out with 8,268 differentially expressed

tags (p>0.05), corresponding to 30% of the 27,514 annotated tags against the

Uniprot/Swiss-Prot/TrEMBL. From these, 3,182 were considered up-regulated and

5,086 down-regulated, as shown comparatively in Figure 3.

The differentially expressed transcripts were annotated using the program

BLAST2GO [21], with automatic annotation regarding the Gene Ontology (GO)

categories: [Biological Process (BP), Molecular Function (MF) and Cellular Component

(CC)] generating 10,933 annotations with 5,700 tags characterized in at least one

category.

Regarding the CC category, most over expressed transcripts regarded chloroplast

compartments (582), plasma membrane (504), cytoplasm (409) and nucleus (376)

(data not shown). In the MF category the 20 most represented subcategories included

tags associated to ligation proteins, as for example zinc ion, magnesium, DNA, RNA, GTP,

and calcium ligation, among other, within 440 upregulated tags. Other well represented

terms in this category were tags associated to serine/threonine proteins (75) and to

electron carrier activity (73).

Considering 20 most represented upregulated tags in the BP category, most

depicted subcategories included translation (213), oxidation reduction (204),

transcription regulation (144), defense response (114) and transport (84) (Figure 4).

Figure 3. Functional categorization of Vigna unguiculata

classified in the Gene Ontology categories “biological processes” and “molecular function”, considering the comparison of the(BRC1) and Virus-Inoculated (BRM).

Vigna unguiculata unitags. 20 most differentially expressed tags (up and downregulated) classified in the Gene Ontology categories “biological processes” and “molecular function”, considering the comparison of the

121

20 most differentially expressed tags (up and downregulated) classified in the Gene Ontology categories “biological processes” and “molecular function”, considering the comparison of the Control

Many of the upregulated subcategories also appeared as downregulated,

sometimes with downregulated in higher proportion. This was also the case, for

example, of Cytokinin (CK) in rice (

regulating gene (OsRR6) that in transgenic plants was able to change the morphology

and the metabolism of CK. Such regulator genes play a particular role in the plant

response to hormones, being sometimes activated

sometimes due to abiotic stress, with a parallel up and down regulation in the same

category [35].

Considering the overall subcategories presented in Figure 4, it is noteworthy that

a high number of tags are associated t

according to GO in distinct subcategories.

Considering the need to understand the GO subcategories, they were grouped in a

new category named “Stress

subcategories (Figure 5). From these, the most represented subgroups regarded stress

response (47), response to bacteria (41), response to cold (39), response to injury (33),

response to saline stress (29) and defense response (28).

Figure 4. Response to strunguiculata, including 14 subcategories according to the Gene Ontology classification. Numbers represent the amount of 26 bp tags annotated to each subcategory.



example, of Cytokinin (CK) in rice (Oryza sativa), probably due to the association with a



response to hormones, being sometimes activated in response to pathogen attack and



a high number of tags are associated to stress responsive genes, being grouped

according to GO in distinct subcategories.


new category named “Stress Response”, which included 356 distinct tags divided in 14

egories (Figure 5). From these, the most represented subgroups regarded stress


response to saline stress (29) and defense response (28).

Response to stress category in SuperSAGE libraries from , including 14 subcategories according to the Gene Ontology

Numbers represent the amount of 26 bp tags annotated to

122



), probably due to the association with a



in response to pathogen attack and



o stress responsive genes, being grouped


Response”, which included 356 distinct tags divided in 14

egories (Figure 5). From these, the most represented subgroups regarded stress


ess category in SuperSAGE libraries from V.

, including 14 subcategories according to the Gene Ontology Numbers represent the amount of 26 bp tags annotated to

For this category modulation values (Fold Change; F

FC≥2 to FC≥100. Additionally, for the discussion of this new category the tags with

modulation value (FC) higher than 10 were used to generate a differential expression

graphical analysis (Figure 6).

Figure 5. showing significant changes in expression following BMCT123 and BMCT4 inoculation with Significant changes in expression were determined by BMCT4/BMCT123 of six independent replicates.

A total of 11 tags associated to the TIFY protein from

distributed among the following subcategories: wounding (1), response to bacterium (5)

and response to jasmonic acid (5). The tag associated to “response to wounding”

(AllCMV_59591) was ca. 40 times over expressed (FC=38.71) in the BRM library as

compared to the control (BRC1), also corresponding to TIFY10a from

remaining 10 tags corresponded to TIFY10b from

values varying from ≥11 to ≥50.

The TIFY gene family is plant

being considered a regulatory factor of the phytormone category of the auxins,

associated with plant growth and root development

belongs to a TIFY subfamily known as JAZ

molecule in the regulation of the hormone jasmonate in

studies in rice have revealed a role for the

including jasmonic acid treatment, mechanical injury associated to the TIFY

transcriptional modulation showing that this gene varies not only in response to abiotic

For this category modulation values (Fold Change; FC) observed varied from

Additionally, for the discussion of this new category the tags with


).

Figure 5. Fold change in Vigna unguiculata tags, showing significant changes in expression following BMCT123 and BMCT4 inoculation with CPSMVSignificant changes in expression were determined by BMCT4/BMCT123 of six independent replicates.

A total of 11 tags associated to the TIFY protein from Arabidopsis



) was ca. 40 times over expressed (FC=38.71) in the BRM library as

compared to the control (BRC1), also corresponding to TIFY10a from

remaining 10 tags corresponded to TIFY10b from Arabidopsis and presented modulation

≥50.

The TIFY gene family is plant-specific and was first described in Arabidopsis,


associated with plant growth and root development [36]. Additionally the TIFY10a

belongs to a TIFY subfamily known as JAZ (Jasmonate Zim-domain) which is a key

molecule in the regulation of the hormone jasmonate in Arabidopsis

studies in rice have revealed a role for the TIFY10a family in response to abiotic stress,

cluding jasmonic acid treatment, mechanical injury associated to the TIFY


123

C) observed varied from

Additionally, for the discussion of this new category the tags with


tags, showing significant changes in expression following

CPSMV. Significant changes in expression were determined by

Arabidopsis appeared



) was ca. 40 times over expressed (FC=38.71) in the BRM library as

compared to the control (BRC1), also corresponding to TIFY10a from Arabidopsis. The

and presented modulation

specific and was first described in Arabidopsis,


Additionally the TIFY10a

domain) which is a key

Arabidopsis [37, 38]. Recent

family in response to abiotic stress,

cluding jasmonic acid treatment, mechanical injury associated to the TIFY


124

stresses but also during jasmonate expression modulation different developmental

stages [38]. In Arabidopsis the over expression of TIFY motifs lead to the repression of

the jasmonate signaling pathway through an alternative splicing of the JAS domain [39].

However, in the present work, the subcategories response to jasmonic acid and

response to water, four tags were associated to lox2 (lipoxygenase 2). The lipoxygenases

are related to the jasmonate (JA) pathway that in turn is induced in plants exposed to

biotic and abiotic stresses. One of the best characterized functions of the jasmonate

signalization pathway is the protective response against damages caused by herbivory.

JA levels increase rapidly in response to herbivory and mechanical [40]. Additionally, the

JA signaling cascade is also important to activate genes associated to pathogen invasion

[41, 42], as it is the case of virus diseases. Therefore, the modulation of the expression

regarding the above mentioned gene families is perfectly in consonance with the

expectations in cowpea, indicating their role in response to mosaic virus infection.

The tag AllCMV_2055 presented FC=170.99 and was identified as an

endochitinase, a protein from the PR-3 category (that included chitinases class I, II, IV).

Such enzymes degrade and hydrolyze β(1,4) chitin bonds, occurring in a variety of

organisms including virus, bacteria, fungi, insects, plants and animals. In plants such

enzymes are associated to defense and development while in virus they are associated

to the pathogenesis [43, 44, 45]. The activation of this gene in the present essay may be

associated to the feeding of the insect vectors, responsible for the CPSMV infection.

Associations with transcription factors (TFs) were found for 23 tags that

presented modulation values varying from 10 to 27. Such tags were distributed within

the subcategories response to salt stress, response to fungus, response to bacterium,

response to cold, response to wounding, response to chitin and response to jasmonic

acid. From the 23 tags, 14 were related to the WRKY transcription factors (WRKY 33;

WRKY 40; WRKY 70 and WRKY 11), a large TF gene family described for more than 10

plant species. They have been associated to defense against bacteria, fungi, virus and

oomycetes [46, 47, 48, 49], being also active during the response against abiotic stress,

including mechanical injury [50, 51], drought [52] and cold [19, 53, 54].

Furthermore, some members of this family play an important role in the

regulation of morphogenesis and embryogenesis of trichomes, in senescence, dormancy,

and pathways associated to plant growth [55, 56, 57].

125

Moreover, four tags (AllCMV_88878, AllCMV_52405, AllCMV_88840,

AllCMV_100174) were related with NAC, another TF family belonging to subfamily

ATAF1. NAC transcription factors (NAM, ATAF and CUC) belong to a plant specific gene

family that play an important role in the plant development and stress response [58, 59].

Members of the subfamily ATAF (ATAF1 and ATAF2) were described by the first time in

the negative response to drought and injury in fungi [60] suggesting that this subfamily

is associated to the response to abiotic stress. However, studying these genes in the

plant pathogen infection of Arabidopsis with the fungus Blumeria graminis f. sp. hordei,

Jansen et al. [61] observed a co-regulation of its expression in situations as injury,

infection, levels of methyl jasmonate, abscisic aid, hydrogen peroxide, cold, drought,

salinity and osmotic stress, indicating that this gene family responds collectively to

biotic and abiotic stimuli.

Another transcription factor of the MYB category (AllCMV_10553) was found in

the category “response to salt stress” in the subcategory “response to jasmonic acid”.

The TF MYB family is one of the most abundant in plants, being essential especially

under abiotic stress [62]. The expression of MYB32 was already reported in many

tissues, with emphasis on anther tapetum, stigma papillae, and lateral root primordia,

uncovering its tissue specific action [63]. Besides their importance in the response to

environmental stresses, a correlation to cell death was also reported in association with

the hypersensitive response after pathogen attack [64]. The presence of this tag in

different subcategories may be explained by the redundancy regarding BLAST2GO

outputs, since the same tag can be associated to different ontological terms.

The gene-specific transcription regulation is fundamental for the understanding

the integration of extracellular and intracellular signals to elicit an appropriate gene

expression response [65], a system known as combinatorial control [66]. Both genetic

and physical interactions have shown that MYB and bHLH (basic helix-loop-helix)

proteins are associated [66, 67]. A bHLH-like protein (AllCMV_51098) was found in the

subcategory response to wounding suggesting the relation among both regulators also

in cowpea.

Another important TF, the “Ethylene responsive factor transcription” (ERF) was

here represented by two tags in the subcategory response to chitin. The ERF family

belongs to a TFs superfamily named AP2/ERF, including two subfamilies as: CBF/DREB

and ERF [68]. Previous essays have shown that ERF members are responsible for the

126

response against biotic stress. Berrocal-Lobo et al. [69] demonstrated that the over

expression of ERF1 induced the expression of PDF1.2, b_CHI and Thi2.1, resulting in

increased resistance against Botrytis cinerea and Pseudomonas syringae in tomato. Other

works have shown that different members of the ERF family assume different functions

in the biotic and abiotic stress response [68, 70, 71]. In tomato (Solanum lycopersicum [f.

sp. Lycopersicon esculentum]) and tobacco (N. tabacum) a co-expression of both factors

TERF2/LeERF2 in the ethylene pathway has lead to an increased cold tolerance [72, 73].

Two tags [AllCMV_88380 (FC=24,18) and AllCMV_8274 (FC=20,73)] have shown

similarity to patatins, proteins known by their lipolytic activity similar to phospholipase

A2 [74, 75]. The patatins present approximately 40-45 kDa constituting the main

protein storage factor in potato (Solanum tuberosum L.) [76, 77]. Among the main roles

attributed to patatins some activities stand out, as acyltransferase, lipid acyl hydrolase

and antioxidant action [76, 78].

Five tags of the subcategory “response to stress” were similar to heat shock

proteins (HSPs), two regarding the HSP11 (11 kDa heat shock protein), one similar to

HSP70 (70 kDa heat shock protein) and one to “small heat shock proteins” (smHSPs).

The HSPs, also known as chaperones, are present under normal circumstances in basal

levels, being over expressed under stress situations in order to assure the maintenance

of the functional protein conformation and for prevention protein degradation [79, 80].

HSPs, including HSP70, are fundamental for the plant protection under biotic and abiotic

stresses, reestablishing the cellular homeostase while interacting with a large number of

co-chaperones and proteases [32]. In Arabidopsis an analysis of the transcriptional

profile under oxidative stress revealed an increased HSP activity, including HSP70,

HSP17.6 and smHSPs. Besides, transcription factors associated to heat shock (HSf4A and

HsfA2) were also co-expressed, being important regulators of the stress response [81,

82]. Considering that the virus inoculation in cowpea depends on the injury of the leaves

to permit the virus penetration, the activation of HSPs fits perfectly under the expected

transcripts within the here analyzed stress.

Still in the subcategory “response to stress” three tags were associated to a

“universal stress protein A” (UspA), a class of phosphoproteins responsible by the

autophosphorilation of Escherichia coli, a conserved protein family of bacteria (Usp

family) [83, 84]. UspA coding genes have been also observed in multiple copies in

Arabidopsis (data extracted from The Sanger Centre) [85]. In E. coli the UspA was

127

described in the resistance to DNA degrading agents [85], but its function is still

uncovered in plants.

Three proteins observed in the subcategories “response to fungus” and “response

to bacterium” belonged to the “heat stable proteins”, also known as “late-

embryogenesis-abundant” (LEA) – one of them with modulation value of 102.06. Their

super expression has been described in response to drought, and also saline and cold

stress [86, 87]. The presence of these high modulated proteins in inoculated cowpea

plants indicates their participation in the process of injury and possibly also response to

pathogen in this species.

During stress, a common feature in plants is the activation of genes associated to

oxidative stress. In the present work a super expression of five tags similar to a reticulin

oxidase was observed in the subcategory “response to oxidative stress”, presenting

modulation values from 11 to 27. The reticulin oxidase is a key component in the

alkaloid pathway, being essential for the formation of benzophenanthridine alkaloid

during the defense against pathogen attack [88, 89] Higher plants produce a variety of

secondary metabolites including terpenoids, phenolic compounds and alkaloids [90]

that may be exploited in agriculture to produce cultivars with increased resistance

against pathogens, besides the exploitation of enzymes, especially those stereospecific

as the reticulin [91] important for the alkaloid regulation and accumulation in plants

[92]. The increased expression of this alkaloid in cowpea may be justified by its role in

the prevention against insect feeding, a step intimately associated with virus infections,

including the here studied cowpea mosaic virus.

The present evaluation represents the first high through output evaluation of a

leguminous genome using an open transcription platform, as it is the case of SuperSAGE

analysis. It is evident that the transcriptional modulation in such a complex situation –

as the primary reaction of the plant to injury associated to virus infection – will demand

efforts not only in the annotation of the modulated genes, but also in their differential

structural and functional features. However, this first insight permitted the

identification of a huge amount of genetic factors, associated to different pathways,

many related to biotic and abiotic stress responses, as observed in other higher plants.

Among the most interesting candidates are those genes especially activated during the

first hours after virus inoculation, probably responsible not only to the ‘quality’ of the

defense genes subsequently activated, but also probably regarding the differences in the

128

timing and magnitude of their expression or, still, the contemporary expression of

different sets of genes comparing resistant and susceptible plants during future qRT-

PCR essays.

129

Figure 6. Heat map representing expression profiles in the subcategory stress response in Vigna unguiculata. The map shows experimental treatments along the horizontal axis (BRC1; BMCT123; BMCT6) and hierarchical clustering of SuperSAGE tags along the vertical axis of 92 up regulated genes. Colored bars represent the expression profile reflecting the magnitude of the log2 expression ratio (Cy5/Cy3) for each transcript at each time point (see color scale).

130

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pathways. Mol Plant Microbe Interact 2003, 16: 295–305.

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regulatory networks in rice and Arabidopsis. BMC Plant Biol 2009, 9: 120.

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encoding a tobacco WRKY transcription factor upon wounding. Mol Gen Genet 2000,

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reveals novel interactions betweenwounding, pathogen, abiotic stress, and

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shock on gene expression in tobacco. Plant Physiol 2002, 130: 1143-1151.

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related genes in Arabidopsis. Plant J 2005, 43: 745-757.

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Lyngkjaer MF: Transcriptional regulation by an NAC (NAM–ATAF1, 2–CUC2)

transcription factor attenuates ABA signalling for efficient basal defence towards

Blumeria graminis f. sp. hordei in Arabidopsis. Plant J 2008, 56: 867-880.

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thaliana. Curr Opin Plant Biol 2001, 4(5):447-456.

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normal pollen development in Arabidopsis thaliana. Plant J 2004, 40:979-995.

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program in plants in response to pathogen attach. Proc Natl Acad Sci USA. 2002, 99:

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Biochemistry 2009, 74(1): 1-11.

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transcription factors can regulate the transactivation strength. Planta 2008, 227:

707-715.

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family in Arabidopsis and rice. Plant Physiol 2006, 140: 411-432.

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response-factor 1 in arabidopsis confers resistance to several necrotrophic fungi.

The Plant Journal 2002, 29:23-32.

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Tsi1 gene encoding an EREBP/AP2-type transcription factor enhances resistance

against pathogen attack and osmotic stress in tobacco. Plant Cell 2001, 13: 1035-

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AP2/EREBP transcription factor gene family. Plant Mol Biol 2005, 59: 853-868.

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ethylene response factor LeERF2 in the expression of ethylene biosynthesis genes

controls ethylene production in tomato and tobacco. Plant Physiol 2009, 150(1):

365-77.

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heat shock proteins and transcription factors reveals extensive overlap between

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6. CONSIDERAÇÕES FINAIS

Considerando o esclarecimento dos mecanismos regulatórios da expressão

gênica e elucidação das funções de genes em diferentes tecidos e/ou situações, este

trabalho evidencia o potencial da técnica de SAGE em gerar perfis transcricionais

complexos e caracterizar funcionalmente diferentes processos celulares. Além disso, a

utilização da técnica permitiu o acesso a transcritos de baixa abundância, identificando

genes exclusivamente expressos em determinado tratamento e permitindo a

diferenciação de possíveis isoformas de localização celular específica.

A comparação dos perfis transcricionais permitiu a amostragem de uma lista de

genes potenciais, caracterizados ou não, ligados a diferentes processos metabólicos e

fisiológicos, como síntese de proteínas, resposta a estresse, resposta a defesa, estresse

oxidativo, regulação transcricional, entre outros, que podem ser grandes alvos usados

em programas de melhoramento da cultura do feijão-caupi.

Como esperado, o stresse provocado pela inoculação do isolado do CPSMV

(Cowpea Severo Mosaic Virus; Vírus do Mosaico Severo do Feijão-Caupi) desencadeou

uma série de respostas específicas de estresses bióticos, bem como outras

tradicionalmente associadas a estresses abióticos. Da mesma forma, a simples injúria

mecânica induziu respostas típicas associadas a fatores classificados como abióticos,

ativando também genes reconhecidos por conferirem resistência a patógenos em

modelos previamente testados. No conjunto, tais constatações confirmam a íntima

relação das respostas de vegetais a estes dois tipos de estresses, já propostas

principalmente com base em estudos prévios envolvendo organismos-modelo.

Outra contribuição deste trabalho, refere-se à disponibilização das sequências

tags para a comunidade cientifica, possibilitando não só o conhecimento sobre o padrão

de expressão gênica, como a ampliação em números de genes identificados e que podem

ser comparados a outras espécies vegetais, em especial, as leguminosas. Além disso, a

identificação e a posterior validação de transcritos potencialmente antisenso, podem

favorecer o entendimento nos mecanismos da regulação pós transcricional da expressão

dos genes fornecendo respostas na interação planta-patógeno.

Não obstante, a seleção de tags úteis identificadas neste trabalho para validação

por RT-qPCR, poderá não só confirmar os resultados obtidos pela técnica de superSAGE,

como identificar diferentes categorias ligadas as processos metabólicos de interesse.

7. 1. INSTRUÇÕES PARA AUTORES DA REVISTA BMC GENOMICS

General information

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The following word processor file formats are acceptable for the main manuscript document:

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Note that figures must be submitted as separate image files, not as part of the submitted DOC/ PDF/TEX/DVI file.

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Please read the descriptions of each of the article types, choose which is appropriate for your article and structure it accordingly. If in doubt, your manuscript should be classified as a Research article, the structure for which is described below.

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Microsoft Word (version 2 and above)

Portable document format (PDF) BioMed Central's TeX template)

DeVice Independent format (DVI)

Users of other word processing packages should save or convert their files to RTF before uploading. Many ls are available which ease this process.

TeX/LaTeX users: We recommend using BioMed Central's TeX template and BibTeX stylefilethis standard format, you can submit your manuscript in TeX format (after you submit your TEX file, you will be prompted to submit your BBL file). If you have used another template for your manuscript, or if you do not wish to use BibTeX, then please submit your manuscript as a DVI file. We do not recommend

must be submitted as separate image files, not as part of the submitted DOC/

When submitting your manuscript, you will be asked to assign one of the following types to

Please read the descriptions of each of the article types, choose which is appropriate for your article and structure it accordingly. If in doubt, your manuscript should be classified as a Research article, the structure for which is described below.

Manuscript sections for Research articles Manuscripts for Research articles submitted to BMC Genomics should be divided into the following

(can also be placed after Background) List of abbreviations used(if any)

(if any)

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Users of other word processing packages should save or convert their files to RTF before uploading. Many

BioMed Central's TeX template and BibTeX stylefile. If you use eX format (after you submit your TEX file, you

will be prompted to submit your BBL file). If you have used another template for your manuscript, or if you do not wish to use BibTeX, then please submit your manuscript as a DVI file. We do not recommend

must be submitted as separate image files, not as part of the submitted DOC/

u will be asked to assign one of the following types to

Please read the descriptions of each of the article types, choose which is appropriate for your article and structure it accordingly. If in doubt, your manuscript should be classified as a Research article, the

should be divided into the following

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• Figure legends (if any) • Tables and captions (if any) • Description of additional data files (if any)

You can download a template (Mac and Windows compatible; Microsoft Word 98/2000) for your article. For instructions on use, see below.

The Accession Numbers of any nucleic acid sequences, protein sequences or atomic coordinates cited in the manuscript should be provided, in square brackets and include the corresponding database name; for example, [EMBL:AB026295, EMBL:AC137000, DDBJ:AE000812, GenBank:U49845, PDB:1BFM, Swiss-Prot:Q96KQ7, PIR:S66116].

The databases for which we can provide direct links are: EMBL Nucleotide Sequence Database (EMBL), DNA Data Bank of Japan (DDBJ ), GenBank at the NCBI (GenBank), Protein Data Bank (PDB), Protein Information Resource (PIR) and the Swiss-Prot Protein Database (Swiss-Prot).

Title page This should list: the title of the article, which should include an accurate, clear and concise description of the reported work, avoiding abbreviations; and the full names, institutional addresses, and e-mail addresses for all authors. The corresponding author should also be indicated.

Abstract The abstract of the manuscript should not exceed 350 words and must be structured into separate sections: Background, the context and purpose of the study; Results, the main findings; Conclusions, brief summary and potential implications. Please minimize the use of abbreviations and do not cite references in the abstract.

Background The background section should be written from the standpoint of researchers without specialist knowledge in that area and must clearly state - and, if helpful, illustrate - the background to the research and its aims. The section should end with a very brief statement of what is being reported in the article.

Results and Discussion The Results and Discussion may be combined into a single section or presented separately. They may also be broken into subsections with short, informative headings.

Conclusions This should state clearly the main conclusions of the research and give a clear explanation of their importance and relevance. Summary illustrations may be included.

Methods This should be divided into subsections if several methods are described.

List of abbreviations If abbreviations are used in the text, either they should be defined in the text where first used, or a list of abbreviations can be provided, which should precede the authors' contributions and acknowledgements.

Authors' contributions In order to give appropriate credit to each author of a paper, the individual contributions of authors to the manuscript should be specified in this section.

An "author" is generally considered to be someone who has made substantive intellectual contributions to a published study. To qualify as an author one should 1) have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data; 2) have been involved in drafting the manuscript or revising it critically for important intellectual content; and 3) have given final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content. Acquisition of funding, collection of data, or general supervision of the research group, alone, does not justify authorship.

We suggest the following kind of format (please use initials to refer to each author's contribution): AB carried out the molecular genetic studies, participated in the sequence alignment and drafted the manuscript. JY carried out the immunoassays. MT participated in the sequence alignment. ES participated in the design of the study and performed the statistical analysis. FG conceived of the study, and

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participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

All contributors who do not meet the criteria for authorship should be listed in an acknowledgements section. Examples of those who might be acknowledged include a person who provided purely technical help, writing assistance, or a department chair who provided only general support.

Authors' information You may choose to use this section to include any relevant information about the author(s) that may aid the reader’s interpretation of the article, and understand the standpoint of the author(s). This may include details about the authors' qualifications, current positions they hold at institutions or societies, or any other relevant background information. Please refer to authors using their initials. Note this section should not be used to describe any competing interests.

Acknowledgements Please acknowledge anyone who contributed towards the study by making substantial contributions to conception, design, acquisition of data, or analysis and interpretation of data, or who was involved in drafting the manuscript or revising it critically for important intellectual content, but who does not meet the criteria for authorship. Please also include their source(s) of funding. Please also acknowledge anyone who contributed materials essential for the study.

Authors should obtain permission to acknowledge from all those mentioned in the Acknowledgements.

Please list the source(s) of funding for the study, for each author, and for the manuscript preparation in the acknowledgements section. Authors must describe the role of the funding body, if any, in study design; in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.

References All references must be numbered consecutively, in square brackets, in the order in which they are cited in the text, followed by any in tables or legends. Reference citations should not appear in titles or headings. Each reference must have an individual reference number. Please avoid excessive referencing. If automatic numbering systems are used, the reference numbers must be finalized and the bibliography must be fully formatted before submission.

Only articles and abstracts that have been published or are in press, or are available through public e-print/preprint servers, may be cited; unpublished abstracts, unpublished data and personal communications should not be included in the reference list, but may be included in the text and referred to as "unpublished data", "unpublished observations", or "personal communications" giving the names of the involved researchers. Notes/footnotes are not allowed. Obtaining permission to quote personal communications and unpublished data from the cited author(s) is the responsibility of the author. Journal abbreviations follow Index Medicus/MEDLINE. Citations in the reference list should contain all named authors, regardless of how many there are.

Examples of the BMC Genomics reference style are shown below. Please take care to follow the reference style precisely; references not in the correct style may be retyped, necessitating tedious proofreading.

Links Web links and URLs should be included in the reference list. They should be provided in full, including both the title of the site and the URL, in the following format: The Mouse Tumor Biology Database [http://tumor.informatics.jax.org/mtbwi/index.do]

BMC Genomics reference style Style files are available for use with popular bibliographic management software:

• BibTeX • EndNote style file • Reference Manager

Article within a journal

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1. Koonin EV, Altschul SF, Bork P: BRCA1 protein products: functional motifs. Nat Genet 1996, 13:266-267.

Article within a journal supplement

2. Orengo CA, Bray JE, Hubbard T, LoConte L, Sillitoe I: Analysis and assessment of ab initio three-dimensional prediction, secondary structure, and contacts prediction. Proteins 1999, 43(Suppl 3):149-170.

In press article 3. Kharitonov SA, Barnes PJ: Clinical aspects of exhaled nitric oxide. Eur Respir J, in press.

Published abstract 4. Zvaifler NJ, Burger JA, Marinova-Mutafchieva L, Taylor P, Maini RN: Mesenchymal cells, stromal derived factor-1 and rheumatoid arthritis [abstract]. Arthritis Rheum 1999, 42:s250.

Article within conference proceedings 5. Jones X: Zeolites and synthetic mechanisms. In Proceedings of the First National Conference on Porous

Sieves: 27-30 June 1996; Baltimore. Edited by Smith Y. Stoneham: Butterworth-Heinemann; 1996:16-27.

Book chapter, or article within a book 6. Schnepf E: From prey via endosymbiont to plastids: comparative studies in dinoflagellates. In Origins of

Plastids. Volume 2. 2nd edition. Edited by Lewin RA. New York: Chapman and Hall; 1993:53-76.

Whole issue of journal 7. Ponder B, Johnston S, Chodosh L (Eds): Innovative oncology. In Breast Cancer Res 1998, 10:1-72.

Whole conference proceedings 8. Smith Y (Ed): Proceedings of the First National Conference on Porous Sieves: 27-30 June 1996; Baltimore. Stoneham: Butterworth-Heinemann; 1996.

Complete book 9. Margulis L: Origin of Eukaryotic Cells. New Haven: Yale University Press; 1970.

Monograph or book in a series 10. Hunninghake GW, Gadek JE: The alveolar macrophage. In Cultured Human Cells and Tissues. Edited by Harris TJR. New York: Academic Press; 1995:54-56. [Stoner G (Series Editor): Methods and Perspectives in

Cell Biology, vol 1.]

Book with institutional author 11. Advisory Committee on Genetic Modification: Annual Report. London; 1999.

PhD thesis 12. Kohavi R: Wrappers for performance enhancement and oblivious decision graphs. PhD thesis. Stanford University, Computer Science Department; 1995.

Link / URL 13. The Mouse Tumor Biology Database [http://tumor.informatics.jax.org/mtbwi/index.do]

Microsoft Word template Although we can accept manuscripts prepared as Microsoft Word, RTF or PDF files, we have designed a Microsoft Word template that can be used to generate a standard style and format for your article. It can be used if you have not yet started to write your paper, or if it is already written and needs to be put into BMC Genomics style.

Download the template (compatible with Mac and Windows Word 97/98/2000/2003/2007) from our site, and save it to your hard drive. Double click the template to open it.

How to use the BMC Genomics template The template consists of a standard set of headings that make up a BMC Genomics Research article manuscript, along with dummy fragments of body text. Follow these steps to create your manuscript in the standard format:

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• Replace the dummy text for Title, Author details, Institutional affiliations, and the other sections of the manuscript with your own text (either by entering the text directly or by cutting and pasting from your own manuscript document).

• If there are sections which you do not need, delete them (but check the rest of the Instructions for Authors to see which sections are compulsory).

• If you need an additional copy of a heading (e.g. for additional figure legends) just copy and paste. • For the references, you may either manually enter the references using the reference style given,

or use bibliographic software to insert them automatically. We provide style files for EndNote and Reference Manager.

For extra convenience, you can use the template as one of your standard Word templates. To do this, put a copy of the template file in Word's 'Templates' folder, normally C:\Program Files\Microsoft Office\Templates on a PC. The next time you create a new document in Word using the File menu, the template will appear as one of the available choices for a new document.

Preparing illustrations and figures Figures should be provided as separate files. Each figure should comprise only a single file. There is no charge for the use of color. Please read our figure preparation guidelines for detailed instructions on maximising the quality of your figures, Formats The following file formats can be accepted:

• EPS (preferred format for diagrams) • PDF (also especially suitable for diagrams) • PNG (preferred format for photos or images) • Microsoft Word (figures must be a single page) • PowerPoint (figures must be a single page) • TIFF • JPEG • BMP • CDX (ChemDraw) • TGF (ISIS/Draw)

Figure legends The legends should be included in the main manuscript text file immediately following the references, rather than being a part of the figure file. For each figure, the following information should be provided: Figure number (in sequence, using Arabic numerals - i.e. Figure 1, 2, 3 etc); short title of figure (maximum 15 words); detailed legend, up to 300 words.

Please note that it is the responsibility of the author(s) to obtain permission from the copyright holder to reproduce figures or tables that have previously been published elsewhere.

Preparing tables Each table should be numbered in sequence using Arabic numerals (i.e. Table 1, 2, 3 etc.). Tables should also have a title that summarizes the whole table, maximum 15 words. Detailed legends may then follow, but should be concise.

Smaller tables considered to be integral to the manuscript can be pasted into the end of the document text file, in portrait format (note that tables on a landscape page must be reformatted onto a portrait page or submitted as additional files). These will be typeset and displayed in the final published form of the article. Such tables should be formatted using the 'Table object' in a word processing program to ensure that columns of data are kept aligned when the file is sent electronically for review; this will not always be the case if columns are generated by simply using tabs to separate text. Commas should not be used to indicate numerical values. Color and shading should not be used.

Larger datasets can be uploaded separately as additional files. Additional files will not be displayed in the final, published form of the article, but a link will be provided to the files as supplied by the author.

Tabular data provided as additional files can be uploaded as an Excel spreadsheet (.xls) or comma separated values (.csv). As with all files, please use the standard file extensions.

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Preparing additional files Although BMC Genomics does not restrict the length and quantity of data in a paper, there may still be occasions where an author wishes to provide data sets, tables, movie files, or other information as additional information. These files can be uploaded using the 'Additional Material files' button in the manuscript submission process.

The maximum file size for additional files is 20 MB each, and files will be virus-scanned on submission.

Any additional files will be linked into the final published article in the form supplied by the author, but will not be displayed within the paper. They will be made available in exactly the same form as originally provided.

If additional material is provided, please list the following information in a separate section of the manuscript text, immediately following the tables (if any):

• File name • File format (including name and a URL of an appropriate viewer if format is unusual) • Title of data • Description of data

Additional datafiles should be referenced explicitly by file name within the body of the article, e.g. 'See additional file 1: Movie1 for the original data used to perform this analysis'.

Formats and uploading Ideally, file formats for additional files should not be platform-specific, and should be viewable using free or widely available tools. The following are examples of suitable formats.

• Additional documentation o PDF (Adobe Acrobat)

• Animations o SWF (Shockwave Flash)

• Movies o MOV (QuickTime) o MPG (MPEG)

• Tabular data o XLS (Excel spreadsheet) o CSV (Comma separated values)

As with figure files, files should be given the standard file extensions. This is especially important for Macintosh users, since the Mac OS does not enforce the use of standard extensions. Please also make sure that each additional file is a single table, figure or movie (please do not upload linked worksheets or PDF files larger than one sheet).

Mini-websites Small self-contained websites can be submitted as additional files, in such a way that they will be browsable from within the full text HTML version of the article. In order to do this, please follow these instructions:

1. Create a folder containing a starting file called index.html (or index.htm) in the root 2. Put all files necessary for viewing the mini-website within the folder, or sub-folders 3. Ensure that all links are relative (ie "images/picture.jpg" rather than "/images/picture.jpg" or

"http://yourdomain.net/images/picture.jpg" or "C:\Documents and Settings\username\My Documents\mini-website\images\picture.jpg") and no link is longer than 255 characters

4. Access the index.html file and browse around the mini-website, to ensure that the most commonly used browsers (Internet Explorer and Firefox) are able to view all parts of the mini-website without problems, it is ideal to check this on a different machine

5. Compress the folder into a ZIP, check the file size is under 20 MB, ensure that index.html is in the root of the ZIP, and that the file has .zip extension, then submit as an additional file with your article

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Style and language Currently, BMC Genomics can only accept manuscripts written in English. Spelling should be US English or British English, but not a mixture.

Gene names should be in italic, but protein products should be in plain type.

There is no explicit limit on the length of articles submitted, but authors are encouraged to be concise. There is no restriction on the number of figures, tables or additional files that can be included with each article online. Figures and tables should be sequentially referenced. Authors should include all relevant supporting data with each article.

BMC Genomics will not edit submitted manuscripts for style or language; reviewers may advise rejection of a manuscript if it is compromised by grammatical errors. Authors are advised to write clearly and simply, and to have their article checked by colleagues before submission. In-house copyediting will be minimal. Non-native speakers of English may choose to make use of a copyediting service.

Help and advice on scientific writing The abstract is one of the most important parts of a manuscript. For guidance, please visit our page on "Writing titles and abstracts for scientific articles"

Tim Albert has produced for BioMed Central a list of tips for writing a scientific manuscript. MedBioWorld also provides a list of resources for science writing.

Abbreviations Abbreviations should be used as sparingly as possible. They can be defined when first used or a list of abbreviations can be provided preceding the acknowledgements and references.

Typography

• Please use double line spacing. • Type the text unjustified, without hyphenating words at line breaks. • Use hard returns only to end headings and paragraphs, not to rearrange lines. • Capitalize only the first word, and proper nouns, in the title. • All pages should be numbered. • Use the BMC Genomics reference format. • Footnotes to text should not be used. • Greek and other special characters may be included. If you are unable to reproduce a particular

special character, please type out the name of the symbol in full. • Please ensure that all special characters used are embedded in the text, otherwise they will be lost

during conversion to PDF.

Units SI Units should be used throughout (liter and molar are permitted, however).