Pedranne Kelle de Araújo Barbosa

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 PósGraduação em Ciências Biológicas, área de
concentração
em
Biotecnologia
da
Universidade Federal de Pernambuco.
Orientadora: Profa. Dra. Ana Maria BenkoIseppon
Co-orientador: Prof. Dr. Éderson Akio Kido
Recife, 2010
Barbosa, Pedranne Kelle de Araújo
Interações bióticas e abióticas em feijão- caupi (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
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
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
viii
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
ix
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 feijãocaupi (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.
x
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.
xi
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 enquadrase 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
12
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-decorda, 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).
13
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,
14
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
15
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
16
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; BenkoIseppon 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).
17
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,
18
alteração na síntese de proteínas, ácidos nucléicos e clorofila, supressão do
silenciamento gênico pós-transcricional,
transcricional, além de alteração nas taxas respiratórias (Flint
et al., 2000; Voinnet, 2005; Soosaar et al., 2005).
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 avr
(avirulence)) que podem ser compatíveis com produtos de genes R secretados
retados pela planta. Interações
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
Pathogen Related),
Related), dentre os quais se incluem proteínas antimicrobianas
(Antimicrobial Proteins,, AMPs). Fonte: Benko-Iseppon
Benko
et al. (2010).
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
19
(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
20
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.
21
Figura 2. Folhas de Vigna unguiculata (Cultivar IT85F-2687)
2687) apresentando sintomas severos após 23 dias
da inoculação com o vírus do Mosaico Severo (CPSMV).
(
Foto cedida por Pandolfi (2007) em experimentos
com feijão-caupi
caupi sob estresses bióticos (CPSMV)
(
em casa de vegetação da UFPE.
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 dee 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 desenvolvidos
desenvolvidos na primeira metade deste século. As mutações – o
terceiro grande fator da evolução – têm sido consideradas instrumentos adicionais,
capazes de auxiliar os métodos convencionais de melhoramento, aumentando a
variabilidade genética das espécies (Duvick,
(Duvic 1986).
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
22
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 IT96D659, 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
23
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
24
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),
25
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
26
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
27
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
28
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 (BenkoIseppon 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 BenkoIseppon, com. pess.).
29
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
VUSS02
VUSS08
VUST00
VUST02
VUST08
VUBM90
VUBM01
VUIM90
VUIM01
Canapu Controle (2+8h s/ estresse)
Canapu (2h após estresse)
Canapu (8h após estresse)
Pitiuba Controle (2+8h s/ estresse)
Pitiuba (2h após estresse)
Pitiuba (8 h após estresse)
BR-14 mulato - Mosaico (30+60+90 min após estresse)
BR-14 mulato - Mosaico controle 01 (30+60+90 s/ estresse)
IT- 85F - Mosaico (30+60+90 min após estresse)
IT – 85F - Mosaico controle 01 (30+60+90 s/ estresse)
II - Bibliotecas de LongSAGE (Serial Analysis of Gene Expression)
Biblioteca
Descrição
IP+
IPBR+
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
BRC2T123
BRC2T6
BRMT123
BRMT6
PTS3T
BRS3T
PTCtr
BR14Ctr
L1_CMV-2_16
L2_CMV-2_BRC1
L3_CMV-2_3090
L1_CPV1-3
L2_CPV_4
L3_CPV_5
L4_CPV_6
BR14-mulato - controle absoluto
BR14-mulato, controle injuriado (30+60+90 min após estresse)
BR14-mulato, controle injuriado (16h após estresse)
BR-mulato - Mosaico (30+60+90 min após estresse)
BR14-mulato - Mosaico (16h após estresse)
Pitiuba, stress salino, (30+60+90 min, 2h, 8h após estresse)
BR14-mulato, stress salino (30+60+90 min, 2h, 8h após estresse)
Pitiuba controle (30+60+90 min, 2h, 8h)
BR14-mulato (30+60+90 min, 2h, 8h)
BR14-mulato, controle injuriado (16h após estresse)
BR14-mulato - controle absoluto
BR14-mulato, controle injuriado (30+60+90 min)
IT – 85F (30+60+90 min após estresse)
IT – 85F (16h após estresse)
IT – 85F (30+60+90 min e 16h sem estresse)
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,
30
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.) (BenkoIseppon, 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).
31
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
32
(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
33
(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).
34
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.
35
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57
CAPÍTULO 1
Transcriptional profiling of wound stress response in
Vigna unguiculata (L.) Walp. revealed by SuperSAGE
To be submitted to the journal
BMC Genomics
58
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.
59
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
60
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.
61
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
62
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
63
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
64
Uniprot/Swiss-Prot,, since the tag-gene
tag
availability will facilitate the function
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,
NordEST, HarvEST and NCBI) permitted the identification of 36.715 unigenes
u
after
BLASTx from which 15.865 (43%) Uniprot/Swiss-Prot
Uniprot/Swiss Prot matched with known proteins
with significant e-values
values (≤
( 10-10). From these, 15.141 unigenes (95%) could be
successfully annotated using Blast2GO [27]
27] for the three main GO categories (“Biological
(“Biolog
Process” - BP, “Molecular Function” - MF and “Cellular Component” - CC) resulting in
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 to more
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 to the total number of
occurrences in each category. The 30 most represented subcategories in PB, FM and CC,
totalized 14,425 (47%) of the 30,659 GO terms.
Considering the category Cellular Component 3,129 terms (69%) regarded
“Intracellular Components”, represented by cytoplasmatic and nuclear organelles. The
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).
1.494
Figure 1. Distribution of the 30 most represented GO
terms in the category “Cellular Component”, including
absolute values and percentage.
65
For the ontology for the Molecular Function 3,792 unigenes (75%) were
related “Ligation Activities”
es” followed by 589 unigenes (12%) related to “Catalitic
Activity”, while “Transporter Activity” and “Structural Molecular Activity” represented
each 6% of the occurrences (data not shown).
For the ontological category Biological Process (Figure 2), from 3,607 related
terms, the most frequent was “Metabolism” with 1,809 unigenes (50%) in the
biosynthetic and catabolic processes,
processes, both at cellular as at organismal level. Still
considering the “Metabolism” category (1,809 unigenes), the subcategories “Messenger
“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 in
i the BP category was “Stress Response” with
590 matches (16%), from which 344 transcripts regarded terms associated to abiotic
stresses, as “salt stress” (18%), water deficit (10%) or response against “other chemical
stimuli” (31%). The biotic stress category
category comprised 88 occurrences (15%), while the
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 mechanism”
mechanism” (58%), followed by terms related to
“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 percentage.
per
Considering the annotated amount and diversified categories of the identified
sequences, it is clear that the cowpea ESTs data bank from different sources (leaves,
66
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
67
advantage of this method in the comparative and simultaneous detection transcripts
with low expression levels.
Tag
Abundance
≥ 100
21-99
6-20
2-5
Total
Library C1
Total
%
128
1.44
593
6.66
2,293
25.77
5,884
66.13
8,898
100.00
Library C2
Total
%
90
1.00
793
8.78
2,490
27.56
5,662
62.67
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.
68
Table 1. Differentially expressed tags after comparison of the control versus
stressed libraries.
Differential Expression tags
Total (%)
Number of unique tags
Constitutive Tags
Differentially expressed
Up regulated
Down regulated
10,907 (100)
8,501 (77.5)
2,406 (22.5)
1,404 (12.8)
1,002 (9.13)
Number of unique tags
Tags annotated (score ≥42)
100% identity against cowpea
Differentially expressed
10,907 (100)
7,056 (65)
5,481 (78)
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
69
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%).
70
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 aligned via BLASTx were annotated to the three GO
categories [Biological Process (BP),
( ), Molecular Function (MF) and Cellular Component
(CC)] resulting in 3,595 GO hits with 1,255 (35%) categorized transcripts under BP,
B
1,307 (36%) under MF and 1,033 (29%) among
a
CC (Figure 6).
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 with
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 terms:
t
protein
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 up-regulated
u regulated stress response,
respo
the
down-regulated
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 - chlorophyll a/b binding) regard
re
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.
71
Figure 6. Distribution of the differentially expressed transcripts
in absolute numbers within the three principal Gene Ontology
categories considering the cellular component (CC) subcategory,
the bars represent the number of GO terms relative to the up and
down regulated
ulated tags after comparison of both libraries (C1xC2).
Numbers in the vertical regard the categories: (1) ribosome
biogenesis, (2) nucleus, (3) plasma membrane, (4) integral to
membrane, (5) cytoplasm, (6) chloroplast and (7) thylakoid.
Figure 7 presentss the first 20 subcategories super expressed and repressed tags
with respective GO terms regarding “Biological Process” (410 tags). The most
represented five subcategories were: “response to any stress – biotic or abiotic” (136
tags); “protein processing and/or
and/or degradation” (132 tags); “photosynthesis” (49 tags);
“transcription regulation” (48 tags), and “transport” (45 tags).
Category “response to any stress – biotic or abiotic”
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
response to any stress – biotic or abiotic” (19 tags: 10 up regulated and 9
down regulated) (Table 3) (See additional file).
72
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.
73
Figure 7. Functional categorization of Vigna unguiculata 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.
libr
74
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
75
[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].
76
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
77
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.
78
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].
79
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.
80
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Additional file
94
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
3,09
1,14E-02
UP
InjC1C2_3500
Q6P2Z0
4,52
2,81E-02
UP
InjC1C2_4421
Q9ZWL6
3,53
1,51E-03
UP
InjC1C2_7461
O24542
5,42
1,38E-02
UP
InjC1C2_10167
Q8BT14
3,50
8,33E-03
UP
InjC1C2_4559
P24068
5,83
1,13E-03
UP
InjC1C2_5852
P24068
3,09
2,16E-02
UP
InjC1C2_8522
Q99090
10,84
1,94E-04
UP
InjC1C2_5398
Q99090
10,54
2,58E-09
UP
InjC1C2_5077
O14270
7,23
3,33E-03
UP
InjC1C2_1437
Q9FWQ5
5,42
1,38E-02
UP
InjC1C2_4115
Q8GTE5
3,95
0,00E+00
UP
InjC1C2_5945
Q9LFY2
6,32
6,78E-03
UP
InjC1C2_4769
Q9LYD3
chromosome 20 open reading frame 6
bzw1_basic leucine zipper and w2 domain-containing protein 1
etr1_ethylene receptor
ax22d_auxin-induced protein 22d
ccr4-not transcription subunit 4 isoform 2
octopine synthase binding factor1
octopine synthase binding factor1
cprf2_light-inducible protein cprf2
cprf2_light-inducible protein cprf2
fork head transcription factor fhl1
hac12_histone acetyltransferase of the cbp family 12
transcription factor erebp-like protein
athb54 (A. thaliana homeobox protein 54) nucleic acid binding
transcription factor
tiny2 dna binding transcription factor
hdt1_histone deacetylase
e2f transcription factor 5
ccr4-not transcription subunit 1
predicted protein [Populus trichocarpa]
ein3 (ethylene-insensitive3) transcription factor
ccch-type zinc finger protein
atcth transcription factor
basic helix-loop-helix family protein
ilr3 (iaa-leucine resistant3) dna binding transcription factor
4,52
2,81E-02
UP
4,46
1,04E-02
UP
4,52
2,81E-02
UP
4,63
3,32E-02
UP
4,11
5,66E-03
UP
4,32
9,49E-09
UP
2,19
1,80E-04
UP
23,49
9,22E-09
UP
6,43
4,97E-13
UP
5,14
1,90E-02
UP
InjC1C2_2239
Q8LJS2
InjC1C2_8406
Q61502
InjC1C2_7758
A0JP85
InjC1C2_5962
A9P8K1
InjC1C2_6839
O24606
InjC1C2_7045
O82199
InjC1C2_7112
O82307
InjC1C2_10797
Q66GR3
InjC1C2_9552
Q9FH37
95
InjC1C2_7475
Q9SDQ3
InjC1C2_7835
Q9SQI2
InjC1C2_10096
P46604
InjC1C2_1178
Q9FKG2
InjC1C2_4048
P46668
InjC1C2_7367
Q42808
InjC1C2_191
Q02283
InjC1C2_7615
Q02283
InjC1C2_9780
Q02283
InjC1C2_124
Q00423
InjC1C2_9656
Q0GLC9
InjC1C2_5830
Q41109
InjC1C2_8108
A7PLE5
InjC1C2_3722
Q56XP9
InjC1C2_9758
Q9FY69
InjC1C2_10547
Q01085
InjC1C2_4255
Q7Y1B6
InjC1C2_4198
P42499
InjC1C2_2712
Q700D2
InjC1C2_9694
Q47894
InjC1C2_5376
Q9SUP6
InjC1C2_9325
Q39266
InjC1C2_9780
Q02283
InjC1C2_5318
A7PME6
InjC1C2_1573
P47927
scl1 (scarecrow-like 1) transcription factor
gigan_protein gigantea
homeobox protein hat22
ethylene responsive element binding factor
athb6_dna binding transcription activator transcription factor
tbp_tata-box-binding protein
Homeobox-leucine zipper protein HAT5
Homeobox-leucine zipper protein HAT5
Homeobox-leucine zipper protein HAT5
hmgya_hmg-y-related protein
Dof22 [Glycine max]
regulator of mat2
transcription factor apetala2
ap2 domain-containing transcription factor family protein
transcription factor transcription regulator
tia-1 related protein isoform 1
gai_gibberellic acid-insensitive mutant protein
phyb_phytochrome b
jkd_ transcription factor zinc ion binding
glnb_nitrogen regulatory protein p
wrky53_transcription activator transcription factor
zinc finger protein zfp7
Homeobox-leucine zipper protein HAT5
hypothetical protein [Vitis vinifera]
apetala2 protein
6,32
6,78E-03
UP
4,46
1,04E-02
UP
6,17
6,39E-04
UP
7,88
3,37E-05
UP
4,52
6,56E-04
UP
3,23
3,87E-03
UP
7,23
3,33E-03
UP
1,56
6,16E-03
UP
0,00
4,57E-03
Down
0,00
1,77E-02
Down
0,17
8,99E-03
Down
0,00
1,77E-02
Down
0,00
6,02E-04
Down
0,00
3,47E-02
Down
0,21
2,64E-02
Down
0,04
2,09E-13
Down
0,53
2,78E-02
Down
0,07
4,33E-11
Down
0,23
4,46E-02
Down
0,00
6,02E-04
Down
0,00
6,02E-04
Down
0,00
3,47E-02
Down
0,00
4,57E-03
Down
0,00
3,47E-02
Down
0,35
2,30E-04
Down
96
Table 3. Functional classification of the differentially expressed genes from the “Biological Process” (BP) and the most represented
subcategories.
UNIPROT
Translation
Q6UNT2
Q68VN6
Q6UNT2
Q8RXX5
Q8VWX5
Q8VY91
Q9ASV6
Q9FJP3
Q9FWS4
Q9M4Y3
Q9SPB3
Q9XJ27
A4GGF8
O22795
O22795
O55135
O80439
P24929
P34811
P49163
P72749
Q43467
P56331
Q9SGA6
DESCRIPTION
rl5_60s ribosomal protein l5
30s ribosomal protein s16
rl5_60s ribosomal protein l5
ribosomal protein l19 family protein
small ribosomal subunit 30S
plastid ribosomal protein
chloroplast 30s ribosomal protein
50s ribosomal protein l29
emb2184_ structural constituent of ribosome
rr10_30s ribosomal protein
rl10_60s ribosomal protein l10
ribosomal protein s9
ribosomal protein l2
chloroplast 50s ribosomal protein l28
chloroplast 50s ribosomal protein l28
eukaryotic translation initiation factor 6
30s ribosomal protein s31
ribosomal protein l12-1a
efgc_elongation factor c
rk22_50s ribosomal protein
elongation factor ef-g
eftu1_elongation factor
if1a_eukaryotic translation initiation factor
40s ribosomal protein s19
SC
p-value
FC
Reg
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
2,76E-02
4,46E-02
2,76E-02
1,01E-03
9,36E-03
4,78E-09
8,99E-03
1,86E-02
2,81E-09
1,33E-02
1,01E-02
1,86E-02
1,69E-09
2,35E-02
6,99E-04
3,47E-02
4,51E-06
2,93E-09
1,85E-36
1,26E-03
2,96E-03
2,58E-26
2,52E-03
5,10E-03
0,58
0,23
0,58
0,31
0,43
0,11
0,17
0,44
0,06
0,47
0,70
0,34
0,09
0,29
0,28
0,00
0,31
0,21
0,02
0,23
0,15
0,05
2,10
3,70
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Up
Up
97
Q9SGA6
P49637
Q9FKC0
Q9SCM3
Q8VZB9
Q9LZ57
P51430
O82204
P41127
O22518
O22518
O65731
Q9M2F1
Q9C912
P81795
P81795
P24922
Q94JV4
Q94JV4
P35614
O23755
P25698
A7RWP6
Q9FLF0
Q9FY64
P49689
Q9SS17
P49690
P49211
P49204
Q6UNT2
B6IPJ8
Q5I7K3
B7FH86
B7FMI2
40s ribosomal protein s19
at1g70600 f5a18_22
60s ribosomal protein l13a
40s ribosomal protein s2 homolog
ribosomal protein l10
60s ribosomal
at5g10360 f12b17_290
60s ribosomal protein l28
60s ribosomal protein bbc1 protein
rssa_40s ribosomal protein
rssa_40s ribosomal protein
rs5_40s ribosomal protein s5
ribosomal protein s27
ribosomal protein
eukaryotic translation initiation factor
eukaryotic translation initiation factor
if5a2_eukaryotic translation initiation factor 5a-2
eukaryotic translation initiation factor
eukaryotic translation initiation factor
erf1-3 _translation release factor
ef2_elongation factor 2
ef1a_elongation factor 1-alpha
eif3e_eukaryotic translation initiation factor 3
40s ribosomal protein s9
ribosomal protein s15-like
40s ribosomal protein s30
at3g04920 t9j14_13
60s ribosomal protein l17
ribosomal protein l32-like protein
40s ribosomal protein s4
rl5_60s ribosomal protein l5
ribosomal protein l20
rs29_40s ribosomal protein s29
unknown [Medicago truncatula]
unknown [Medicago truncatula]
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
1,38E-02
1,09E-09
9,45E-03
8,67E-04
4,95E-05
1,26E-05
1,17E-03
1,63E-03
5,90E-07
3,63E-03
1,38E-02
4,93E-02
4,88E-02
4,06E-06
6,78E-03
1,90E-02
1,48E-05
6,76E-10
1,20E-11
7,62E-05
1,26E-02
1,26E-02
4,93E-02
3,42E-02
2,87E-02
1,88E-02
3,71E-04
1,25E-02
2,50E-05
2,24E-05
2,57E-06
2,97E-02
8,31E-03
4,71E-02
1,90E-02
5,42
3,15
3,86
1,76
1,64
2,84
2,40
8,13
5,73
1,98
5,42
3,43
1,77
3,27
6,32
5,14
2,88
5,31
2,48
3,34
1,55
1,55
3,43
2,88
1,70
2,57
4,28
2,47
10,80
6,68
6,58
3,77
2,40
1,45
5,14
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
98
O22584
O22584
O65743
O65743
P34091
P34091
P35685
P35685
P46302
P46302
P49199
P62302
Q05462
Q9M573
Q9M5L0
Q9ZNS1
O50003
P17093
P62981
Q940B0
P60040
Q93VI3
Q42064
Q42064
Q40465
Q6UNT2
rs14_40s ribosomal protein s14
rs14_40s ribosomal protein s14
rl24_60s ribosomal protein l24
rl24_60s ribosomal protein l24
rl6_60s ribosomal protein l6
rl6_60s ribosomal protein l6
60s ribosomal protein l7a
60s ribosomal protein l7a
ribosomal protein s28
ribosomal protein s28
40s ribosomal protein s8
ribosomal protein s13
rl27_60s ribosomal protein l27
rl31_60s ribosomal protein l31
rl35_60s ribosomal protein l35
rs7_40s ribosomal protein s7
rl12_60s ribosomal protein l12
rs11_40s ribosomal protein s11
ubiquitin extension protein
60s ribosomal protein
ribosomal protein l7
60s ribosomal protein l17
ribosomal protein l8
ribosomal protein l8
if411_eukaryotic initiation factor 4a-11
rl5_60s ribosomal protein l5
52
52
52
52
52
52
52
52
46,1
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
1,68E-07
2,81E-02
3,15E-03
1,04E-02
4,32E-02
3,42E-02
2,81E-02
1,56E-02
6,78E-03
3,29E-02
1,94E-04
2,13E-06
2,05E-02
2,92E-02
1,07E-04
5,66E-03
9,18E-05
4,35E-10
4,89E-02
6,78E-03
4,54E-02
1,08E-02
6,10E-03
4,35E-03
3,32E-02
2,57E-06
6,68
4,52
1,81
4,46
2,57
2,88
4,52
3,60
6,32
1,88
10,84
7,71
2,14
2,06
1,99
4,11
2,71
4,73
1,62
6,32
2,06
5,66
1,93
2,86
4,63
6,58
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
52
44,1
44,1
44,1
44,1
42,1
7,93E-05
2,83E-02
5,18E-03
2,81E-05
2,33E-03
9,35E-03
0,00
0,36
0,16
0,09
0,00
0,22
Down
Down
Down
Down
Down
Down
Reduction Oxidation
P00865
P00865
P00865
P00865
P00865
P00865
99
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
O48927
O65012
O65837
O81360
O82515
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
rbs1_ribulose bisphosphate carboxylase small chain
c78a3_cytochrome p450
c78a4_cytochrome p450
lcye_lycopene epsilon chloroplastic
aba2_zeaxanthin chloroplastic
mtdh_probable mannitol dehydrogenase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
44,1
48,1
44,1
44,1
44,1
44,1
50,1
50,1
52
44,1
44,1
44,1
44,1
52
52
50,1
52
52
52
52
52
44,1
44,1
44,1
44,1
50,1
50,1
52
44,1
44,1
44,1
44,1
52
52
50,1
4,64E-04
3,02E-07
2,35E-02
5,34E-04
2,33E-03
4,57E-03
2,64E-02
7,22E-06
0,00E+00
1,55E-02
1,26E-03
2,33E-03
1,55E-02
2,19E-04
2,36E-125
3,47E-02
3,16E-02
4,26E-02
1,23E-03
2,35E-02
1,77E-02
2,35E-02
5,34E-04
2,33E-03
4,57E-03
2,64E-02
7,22E-06
0,00E+00
1,55E-02
1,26E-03
2,33E-03
1,55E-02
2,19E-04
2,36E-125
3,47E-02
0,21
0,20
0,29
0,12
0,00
0,00
0,21
0,11
0,17
0,19
0,23
0,00
0,24
0,15
0,08
0,00
0,45
0,39
0,21
0,29
0,00
0,29
0,12
0,00
0,00
0,21
0,11
0,17
0,19
0,23
0,00
0,24
0,15
0,08
0,00
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
100
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P00865
P08706
P12858
P12858
P12858
P12859
P13284
P13443
P24465
P25861
P26969
P28553
P31023
P39866
P51104
P51104
P51978
P72854
Q01289
Q01289
Q42807
Q42822
Q43155
Q4PGW7
Q55087
Q6MD85
Q8DJK9
Q8TC12
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
rbs1_ribulose bisphosphate carboxylase
g3pa_glyceraldehyde-3-phosphate dehydrogenase
g3pa_glyceraldehyde-3-phosphate dehydrogenase
g3pa_glyceraldehyde-3-phosphate dehydrogenase
g3pb_glyceraldehyde-3-phosphate dehydrogenase
gilt_gamma-interferon-inducible lysosomal thiol reductase
dhgy_glycerate dehydrogenase
c71a1_cytochrome p450
g3pc_glyceraldehyde-3-phosphate cytosolic
gcsp_glycine dehydrogenase mitochondrial
crti_phytoene chloroplastic chromoplastic
dldh_dihydrolipoyl mitochondrial
nia2_nitrate reductase 2
dfra_dihydroflavonol-4-reductase
dfra_dihydroflavonol-4-reductase
thioredoxin reductase
sulfite reductase subunit beta
por_protochlorophyllide chloroplastic
por_protochlorophyllide chloroplastic
stad_acyl- chloroplastic
rbs_ribulose bisphosphate carboxylase
gltb_ferredoxin-dependent glutamate
ncb5r_nadh-cytochrome b5 reductase
chlp_geranylgeranyl diphosphate reductase
4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase
methionine sulfoxide reductase b
retinol dehydrogenase 11 (all-trans 9-cis 11-cis)
52
44,1
44,1
44,1
44,1
42,1
44,1
48,1
52
52
52
44,1
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
7,93E-05
2,83E-02
5,18E-03
2,81E-05
2,33E-03
9,35E-03
4,64E-04
3,02E-07
7,93E-05
4,06E-24
7,74E-56
1,05E-05
5,59E-03
2,96E-03
1,37E-13
1,55E-02
5,58E-04
2,29E-22
4,15E-02
4,66E-11
1,29E-02
1,91E-19
9,69E-07
2,19E-04
7,35E-04
2,81E-05
3,31E-03
3,50E-04
1,70E-05
5,78E-09
1,57E-05
6,02E-04
1,78E-02
1,62E-02
2,71E-06
0,00
0,36
0,16
0,09
0,00
0,22
0,21
0,20
0,00
0,14
0,09
0,00
0,21
0,15
0,13
0,24
0,56
0,13
0,28
0,08
0,42
0,19
0,16
0,15
0,20
0,09
0,19
0,44
0,15
0,08
0,22
0,00
0,72
0,36
0,00
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
101
Q945B7
Q948P6
Q9SEC2
Q9XG54
Q9XG54
Q9XG54
Q9ZRF1
Q9ZRF1
P17817
P13603
P09186
O23920
P32291
B1WTZ2
P35738
Q06215
P37115
O81974
Q2MJ15
A6TF98
P37221
P51615
A7PN93
Q06652
Q06652
Q05047
Q503L9
Q9FR99
P48621
P51091
O04892
B7FIB3
Q2HVL4
Q96558
P25795
crd1_magnesium-protoporphyrin ix monomethyl ester
fri3_ferritin- chloroplastic
msra_peptide methionine sulfoxide reductase
opr1_12-oxophytodienoate reductase
opr1_12-oxophytodienoate reductase
opr1_12-oxophytodienoate reductase
mtdh_probable mannitol dehydrogenase
mtdh_probable mannitol dehydrogenase
p5cr- pyrroline-5-carboxylate reductase
adh1_alcohol dehydrogenase 1
lox3_ lipoxygenase-3
hppd_4-hydroxyphenylpyruvate dioxygenase
fad3e_3 fatty acid endoplasmic reticulum
4-hydroxy-3-methylbut-2-enyl diphosphate reductase
acid dehydrogenase e1
ppo_polyphenol oxidase chloroplastic
tcmo_trans-cinnamate 4-monooxygenase
c71d8_cytochrome p450
cytochrome p450 monooxygenase
udp_glucuronic acid decarboxylase
maom_dependent malic enzyme 62 kda mitochondrial
maox_nadp-dependent malic enzyme
hypothetical protein [Vitis vinifera]
gpx4_phospholipid hydroperoxide glutathione peroxidase
gpx4_phospholipid hydroperoxide glutathione peroxidase
c72a1_secologanin synthase
nxn_nucleoredoxin
acco_1-aminocyclopropane-1-carboxylate oxidase
fad3c_3 fatty acid chloroplastic
ldox_leucoanthocyanidin dioxygenase
cytochrome p450
unknown [Medicago truncatula]
rna-binding region rnp-1
ugdh_udp-glucose 6-dehydrogenase
al7a1_aldehyde dehydrogenase
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
44,1
52
52
52
52
52
52
52
52
52
52
3,31E-03
2,32E-12
6,76E-16
1,77E-02
3,47E-02
7,35E-04
4,66E-11
2,28E-05
6,78E-03
5,38E-04
2,81E-02
1,43E-02
1,11E-03
5,83E-12
7,69E-05
1,13E-05
1,09E-03
6,78E-03
2,50E-05
1,08E-02
1,38E-02
4,25E-02
3,36E-03
0,00E+00
2,30E-05
6,98E-08
0,00E+00
1,53E-03
3,84E-04
1,38E-02
1,83E-11
1,85E-02
2,81E-02
3,33E-03
3,65E-07
0,19
0,09
0,23
0,00
0,00
0,34
0,08
0,12
6,32
3,09
4,52
2,21
4,32
5,51
4,80
14,46
2,83
6,32
10,80
2,12
5,42
1,62
4,37
2,64
13,55
5,78
10,17
2,98
5,40
5,42
5,14
2,37
2,74
7,23
5,40
Down
Down
Down
Down
Down
Down
Down
Down
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
102
P28759
P28759
P00865
O62964
Q9HBH5
P00865
Regulation
transcription
Q9H501
Q6P2Z0
Q9ZWL6
O24542
Q8BT14
P24068
P24068
Q99090
Q99090
O14270
Q9FWQ5
Q8GTE5
Q9LFY2
Q9LYD3
Q8LJS2
Q61502
A0JP85
A9P8K1
O24606
O82199
O82307
Q66GR3
Q9FH37
Q9SDQ3
sodf_superoxide dismutase chloroplastic
sodf_superoxide dismutase chloroplastic
rbs1_ribulose bisphosphate carboxylase small chain
rbl_ribulose bisphosphate carboxylase large chain
retinol dehydrogenase 14
rbs1_ribulose bisphosphate carboxylase
52
52
52
52
52
52
1,42E-05
1,97E-03
2,43E-05
2,97E-02
1,88E-02
2,43E-05
2,63
4,63
4,11
3,77
2,57
4,11
Up
Up
Up
Up
Up
Up
chromosome 20 open reading frame 6
basic leucine zipper and w2 domain-containing protein
etr1_ethylene receptor
ax22d_auxin-induced protein 22d
ccr4-not transcription subunit 4 isoform 2
octopine synthase binding factor1
octopine synthase binding factor1
cprf2_light-inducible protein cprf2
cprf2_light-inducible protein cprf2
fork head transcription factor fhl1
hac12 (histone acetyltransferase of the cbp family 12) h3 h4_
histone acetyltransferase transcription cofactor
transcription factor erebp-like protein
athb54 (arabidopsis thaliana protein 54) nucleic acid binding
transcription factor
tiny2 dna binding transcription factor
hdt1_histone deacetylase
e2f transcription factor 5
ccr4-not transcription subunit 1
predicted protein [Populus trichocarpa]
ein3 (ethylene-insensitive3) transcription factor
ccch-type zinc finger protein
atcth transcription factor
basic helix-loop-helix family protein
ilr3_dna binding transcription factor
scl1 (scarecrow-like 1) transcription factor
52
52
52
52
52
52
52
52
52
52
52
1,14E-02
2,81E-02
1,51E-03
1,38E-02
8,33E-03
1,13E-03
2,16E-02
1,94E-04
2,58E-09
3,33E-03
1,38E-02
3,09
4,52
3,53
5,42
3,50
5,83
3,09
10,84
10,54
7,23
5,42
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
52
52
0,00E+00
6,78E-03
3,95
6,32
Up
Up
52
52
52
52
52
52
52
52
52
52
52
2,81E-02
1,04E-02
2,81E-02
3,32E-02
5,66E-03
9,49E-09
1,80E-04
9,22E-09
4,97E-13
1,90E-02
6,78E-03
4,52
4,46
4,52
4,63
4,11
4,32
2,19
23,49
6,43
5,14
6,32
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
103
Q9SQI2
P46604
Q9FKG2
P46668
Q42808
Q02283
Q02283
Q02283
Q00423
Q0GLC9
Q41109
A7PLE5
Q56XP9
Q9FY69
Q01085
Q7Y1B6
P42499
Q700D2
Q47894
Q9SUP6
Q39266
Q02283
A7PME6
P47927
gigan_protein gigantea
homeobox protein hat22
ethylene responsive element binding factor
athb6_transcription activator transcription factor
tbp_tata-box-binding protein
at3g01470 f4p13_2
at3g01470 f4p13_2
at3g01470 f4p13_2
hmgya_hmg-y-related protein
Dof22 [Glycine max]
regulator of mat2
transcription factor apetala2
ap2 domain-containing transcription factor family protein
transcription factor transcription regulator
tia-1 related protein isoform 1
gai_gibberellic acid-insensitive mutant protein
phyb_phytochrome b
jkd_ transcription factor zinc ion binding
glnb_nitrogen regulatory protein p
wrky53_transcription activator transcription factor
zinc finger protein zfp7
at3g01470 f4p13_2
hypothetical protein [Vitis vinifera]
apetala2 protein
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
1,04E-02
6,39E-04
3,37E-05
6,56E-04
3,87E-03
3,33E-03
6,16E-03
4,57E-03
1,77E-02
8,99E-03
1,77E-02
6,02E-04
3,47E-02
2,64E-02
2,09E-13
2,78E-02
4,33E-11
4,46E-02
6,02E-04
6,02E-04
3,47E-02
4,57E-03
3,47E-02
2,30E-04
4,46
6,17
7,88
4,52
3,23
7,23
1,56
0,00
0,00
0,17
0,00
0,00
0,00
0,21
0,04
0,53
0,07
0,23
0,00
0,00
0,00
0,00
0,00
0,35
Up
Up
Up
Up
Up
Up
Up
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
fenr1_ferredoxin--nadp
fenr1_ferredoxin--nadp
g chain improved model of plant photosystem i
g chain improved model of plant photosystem i
psbp_protein evolving system of photosystem
h chain improved model of plant photosystem i
h chain improved model of plant photosystem i
cb23_chlorophyll a-b binding protein
52
48,1
52
52
52
52
52
52
2,17E-36
2,28E-05
7,00E-07
8,74E-33
1,12E-67
2,37E-20
3,47E-02
1,08E-15
0,08
0,12
0,00
0,05
0,05
0,23
0,00
0,18
Down
Down
Down
Down
Down
Down
Down
Down
Photosynthesis
P10933
P10933
P12357
P12357
P16059
P22179
P22179
P27489
104
P27522
P27522
P27524
P27524
P27524
P27524
P32869
P46486
P46486
P54773
P72580
P72580
P80470
P80470
Q01289
Q01289
Q07473
Q40519
Q40519
Q41387
Q41387
Q55087
Q945B7
Q9RFD5
Q9S7W1
Q9SDM1
Q9SDM1
Q9SDM1
Q9XF89
Q9XF89
Q9XF89
Q9XF89
Q9XF89
Q9XF89
Q9ZT05
cb13_chlorophyll a-b binding protein
cb13_chlorophyll a-b binding protein
cb4a_chlorophyll a-b binding protein cp24
cb4a_chlorophyll a-b binding protein cp24
cb4a_chlorophyll a-b binding protein cp24
cb4a_chlorophyll a-b binding protein cp24
psad_photosystem i reaction center subunit
psaf_photosystem i reaction center
psaf_photosystem i reaction center
psbs_photosystem ii 22 kda
solanesyl diphosphate synthase
solanesyl diphosphate synthase
psby_photosystem ii core complex proteins
psby_photosystem ii core complex proteins
por_protochlorophyllide oxidoreductase
por_protochlorophyllide oxidoreductase
chlorophyll a b-binding protein cp29
psbr_photosystem ii 10 kda
psbr_photosystem ii 10 kda
psbw_photosystem ii reaction center w
psbw_photosystem ii reaction center w
chlp_geranylgeranyl diphosphate reductase
crd1_magnesium-protoporphyrin ix monomethyl ester
magnesium chelatase subunit h
chlorophyll a b binding protein
cb121_chlorophyll a-b binding protein 1b
cb121_chlorophyll a-b binding protein 1b
cb121_chlorophyll a-b binding protein 1b
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
psak_photosystem i reaction center subunit
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
44,1
44,1
52
52
42,1
50,1
44,1
52
52
52
6,46E-07
2,91E-03
8,99E-03
1,20E-07
6,89E-12
4,37E-02
4,72E-20
3,23E-39
1,04E-09
1,51E-32
1,25E-04
6,35E-14
2,82E-10
4,95E-23
2,81E-05
3,31E-03
3,29E-47
2,55E-02
5,48E-18
1,73E-26
2,12E-14
6,02E-04
3,31E-03
1,14E-03
5,67E-19
2,55E-02
1,77E-02
2,31E-70
7,81E-15
8,99E-03
1,77E-02
1,02E-06
3,21E-10
1,66E-09
2,57E-34
0,31
0,30
0,00
0,20
0,14
0,49
0,19
0,21
0,29
0,08
0,14
0,00
0,09
0,30
0,09
0,19
0,15
0,83
0,22
0,30
0,45
0,00
0,19
0,17
0,06
0,26
0,00
0,34
0,37
0,17
0,00
0,12
0,18
0,20
0,23
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
105
Q9ZT05
Q9XF89
Q9XF89
Q9XF89
Q9XF89
Q9XF89
psak_photosystem i reaction center subunit
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
chlorophyll a b-binding
50,1
44,1
52
44,1
44,1
50,1
3,47E-02
2,81E-02
0,00E+00
3,94E-04
3,94E-04
2,81E-02
0,00
4,52
2,67
9,94
9,94
4,52
Down
Up
Up
Up
Up
Up
Q9FY14
Q46036
Q2LAM0
Q2LAM0
P21727
P52178
Q6J163
Q6J163
O82316
Q9ZVX8
O05519
P07030
P07030
P07030
P07030
P07030
P10933
P10933
P29450
P29450
P29450
Q9ZR41
P52232
P0A3C7
P0A3C7
A4GYQ4
tip1_probable aquaporin 1
outer membrane lipoprotein blc
fatty acid 2-hydroxylase
fatty acid 2-hydroxylase
tpt_triose phosphate phosphate
tpt2_triose phosphate phosphate non-green
5ng4_pintaauxin-induced protein 5ng4
5ng4_pintaauxin-induced protein 5ng4
tip4 1 (tonoplast intrinsic protein 4 1) water channel
plasma membrane intrinsic protein
abc transporter (atp-binding protein)
plas_chloroplastic
plas_chloroplastic
plas_chloroplastic
plas_chloroplastic
plas_chloroplastic
fenr1_ferredoxin--nadp
fenr1_ferredoxin--nadp
thioredoxin f
thioredoxin f
thioredoxin f
glrx_glutaredoxin
thioredoxin m
ferredoxin i
ferredoxin i
photosystem ii protein d2
52
52
52
52
52
52
52
52
52
52
52
52
50,1
50,1
50,1
52
52
48,1
52
50,1
44,1
52
52
52
52
52
4,81E-03
5,73E-03
3,47E-02
2,64E-02
3,16E-02
3,47E-02
6,25E-03
3,40E-03
2,33E-03
2,28E-05
2,64E-02
4,14E-65
1,77E-02
8,99E-03
1,05E-05
4,35E-04
2,17E-36
2,28E-05
5,96E-14
3,47E-02
2,89E-22
3,07E-14
1,21E-02
4,15E-02
4,57E-03
7,50E-05
0,57
0,44
0,00
0,21
0,45
0,00
0,54
0,23
0,00
0,12
0,21
0,12
0,00
0,00
0,00
0,19
0,08
0,12
0,16
0,00
0,19
0,53
0,32
0,28
0,00
4,41
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Up
Transport
106
Q5XIF3
P49098
Q6J163
Q6J163
Q94FN1
Q9SV31
Q1KUQ8
O22342
Q29RM1
P51132
O04066
O04066
Q9UG63
P27572
P35721
P29449
A4GYQ4
Q01366
Q01366
nadh dehydrogenase fe-s protein 4
cytochrome b5
5ng4_pintaauxin-induced protein 5ng4
5ng4_pintaauxin-induced protein 5ng4
phosphatidylinositol transfer-like protein iii
aquaporin mip-like protein
hypothetical protein [Cleome spinosa]
adt1_adp atp translocase 1
solute carrier family member 19
ucri2_ubiquinol-cytochrome c reductase
acbp_acyl- -binding protein
acbp_acyl- -binding protein
atp-binding sub-family member 2 isoform b
nu4m_nadh-ubiquinone oxidoreductase
succinate dehydrogenase subunit 3
trxh1_thioredoxin h-type 1
photosystem ii protein d2
photosystem ii protein d1
photosystem ii protein d1
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
52
4,93E-02
1,86E-03
3,36E-03
1,71E-13
2,97E-02
6,78E-03
1,13E-03
1,90E-02
9,45E-03
3,42E-02
2,97E-02
1,38E-02
5,10E-03
3,94E-04
1,13E-03
0,00E+00
7,50E-05
4,69E-05
1,90E-02
3,43
7,20
6,68
5,14
3,77
6,32
5,83
5,14
3,86
2,88
3,77
5,42
3,70
9,94
5,83
18,36
4,41
10,28
5,14
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
O04057
O24326
O65351
P04825
P25776
P25776
P42211
P43508
Q766C3
Q766C3
O24325
Q8YV57
A0YSJ1
aspr_aspartic proteinase
vpe2_vacuolar-processing enzyme
cucumisin-like serine protease
aminopeptidase n
orya_oryzain alpha chain
orya_oryzain alpha chain
asprx_aspartic proteinase
cathepsin b-like cysteine proteinase
nep1_aspartic proteinase nepenthesin-1
nep1_aspartic proteinase nepenthesin-1
vpe1_legumain-like proteinase
wd-40 repeat-containing protein
hypothetical protein L8106_22426
52
52
52
52
52
42,1
52
52
52
52
52
52
52
2,71E-02
2,17E-14
2,60E-02
1,96E-02
1,38E-13
3,06E-04
6,59E-06
3,61E-04
4,99E-02
2,35E-02
2,16E-02
2,56E-02
3,36E-03
0,56
0,15
0,42
0,53
0,53
0,00
0,41
0,66
0,37
0,29
3,09
3,34
4,37
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Up
Up
Up
Proteolysis
107
A0YSJ1
Q8RY22
Q42384
Q42290
P12412
P22895
Q93Z89
Q40983
Q40983
Q40983
P13917
P13917
Q9M9Z2
O73944
P25776
hypothetical protein L8106_22426
Protease Do-like 7
prl1_Protein pleiotropic regulatory locus 1
Probable mitochondrial-processing peptidase
cysep_cysteine proteinase
p34_p34 probable thiol protease
matrix metalloproteinase mmp2
metalloendopeptidase [Pisum sativum]
metalloendopeptidase [Pisum sativum]
metalloendopeptidase [Pisum sativum]
7sb1_basic 7s globulin
7sb1_basic 7s globulin
tpp2_probable thylakoidal processing peptidase
pyrrolidone-carboxylate peptidase
orya_oryzain alpha chain
52
52
52
52
52
52
52
52
52
52
52
52
52
52
44,1
1,58E-07
1,38E-02
6,04E-03
1,38E-02
3,87E-03
1,04E-02
0,00E+00
1,38E-02
3,33E-03
6,87E-04
2,28E-10
2,10E-03
1,56E-02
1,63E-03
3,42E-02
19,88
5,42
4,80
5,42
3,23
4,46
66,59
5,42
7,23
2,06
5,48
3,21
3,60
8,13
2,88
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
Up
unknown [Medicago truncatula]
hsp72_heat shock cognate 70 kda protein 2
hsp80_heat shock cognate protein 80
lipc_drought-induced stress protein
lipc_drought-induced stress protein
lipc_drought-induced stress protein
hsp7s_stromal 70 kda heat shock-related
small heat shock protein
hbl1_non-symbiotic hemoglobin
clpb2_chaperone protein clpb 2
pp1_serine threonine-protein phosphatase pp1
al7a1_aldehyde dehydrogenase family 7
hsp7m_heat shock 70 kda mitochondrial
arg2_indole-3-acetic acid-induced protein
arg2_indole-3-acetic acid-induced protein
usp-like protein
af281656_1 transcription factor
52
52
52
52
44,1
50,1
52
52
52
52
52
52
52
52
52
52
52
6,98E-03
8,17E-09
2,35E-02
1,49E-07
2,33E-03
1,18E-03
1,82E-02
6,96E-15
2,55E-02
3,36E-03
3,68E-02
3,65E-07
1,90E-02
7,79E-08
1,01E-12
1,85E-02
9,50E-12
0,33
0,26
0,29
0,64
0,00
0,00
0,64
0,26
0,26
6,68
1,92
5,40
5,14
15,43
6,01
2,37
3,22
Down
Down
Down
Down
Down
Down
Down
Down
Down
Up
Up
Up
Up
Up
Up
Up
Up
Response to stress
B7FH14
P27322
P36181
P80471
P80471
P80471
Q02028
Q4UKR8
Q9FVL0
Q8YM56
P48490
P25795
Q01899
P32292
P32292
Q07A28
Q94G23
108
Q94G23
P32110
af281656_1 transcription factor
gstx6_probable glutathione s-transferase
44,1
52
1,63E-03
0,00E+00
8,13
35,99
Up
Up
heat shock cognate 70 kda protein 2
predicted protein [Populus trichocarpa]
cyph_peptidyl-prolyl cis-trans isomerase
enpl_endoplasmin homolog
enpl_endoplasmin homolog
hsp80_heat shock cognate protein 80
hsp7s_stromal 70 kda heat shock-related
sura_chaperone sura
dnaj_chaperone protein dnaj
molecular chaperone
peptidyl-prolyl cis-trans isomerase cyclophilin
Chaperone protein dnaJ 20
ruba_60 kda chaperonin subunit alpha
heat shock cognate 70 kda protein 2
dnjh2_protein homolog 2
dnjh2_protein homolog 2
hsp7m_heat shock 70 kda mitochondrial
peptidylprolyl isomerase
calx_calnexin homolog
52
52
52
52
50,1
52
52
52
52
52
52
52
52
52
52
52
52
52
52
8,17E-09
2,81E-05
2,92E-12
2,78E-158
8,99E-03
2,35E-02
1,82E-02
4,57E-03
1,77E-02
1,47E-02
2,86E-08
5,10E-03
1,73E-03
8,31E-03
5,91E-06
6,78E-03
1,90E-02
8,03E-04
2,97E-02
0,26
0,09
0,26
0,08
0,00
0,29
0,64
0,00
0,00
0,27
0,20
3,70
2,44
2,40
2,12
6,32
5,14
9,03
3,77
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Down
Up
Up
Up
Up
Up
Up
Up
Up
Protein Folding
P22954
A9PH85
O49886
P35016
P35016
P36181
Q02028
Q5WZN0
Q75VW3
Q8RB67
Q9ASS6
Q9SDN0
P08926
P22954
P42824
P42824
Q01899
Q38867
Q39817
109
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
110
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.
111
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,
112
[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 (CPSMVCowpea severe mosaic virus) was obtained from the plant viruses collection of the
Department of Plant Pathology at the Federal Rural University of Pernambuco - UFRPE,
113
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.
114
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)
115
libraries of various development stages (8 to 40 dpa; days post anthesis)
anthesis and sequenced
96,441 LongSAGE tags from which 29,261 were singletons.
Table 1. Summary of SuperSAGE libraries of Vigna unguiculata
Library
BRC1
BMCT123
BMCT4
Total sequenced
1,101,845
3,099,384
2,953,007
Total analyzed
1,011,380
2,974,661
2,815,021
Singletons
90,466
124,723
137,986
Unique to a given library
43,978
70,523
70,626
Tags
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 2-5
2 copies,
representing about 55% (100,491) of the tags exclusive of a given library (Figure 1).
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 for analysis using
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 all three
libraries. Considering only inoculated libraries (BRMT123 and BRMT4) a total of 14,853
were shared by both treatments (Figure 2).
116
Figure 2. Venn diagram showing the tag
distribution
istribution among the three SuperSAGE
libraries for each stress treatment (1)
BMCT123 (30,
(
60, 90 min); (2) BMCT4 (16
h);
); (3) BRC1 (control).
Primary annotation
The first annotation routine was against the cowpea EST data bank previously
annotated Uniprot-Swiss-Prot/TrEMBL.
Prot/TrEMBL. The tags that could be not annotated against
cowpea were evaluated against other plants considering the adopted (higher score/escore/e
value, best description
scription and taxonomic proximity). From the 107,161 unique tags
analyzed, 27,514 could be annotated (score ≥40), from which 17,928 (65%) tags
presented 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
validation via RT-qPCR
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
with the established parameters
(score ≥40), including 2,249 tags with 100% identity (score=52) with Uniprot-SwissProt/TrEMBL sequences; despite of that, they did not present informative descriptions,
being annotated but not categorized (Table 2). Regarding the same 2,249 tags (100%
identity), 684 tags were differentially expressed, being 347 super expressed and 337
repressed.
117
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
Annotation Uniprot
Others databases
No description Uniprot
No hits
Score=52
Score ≥40
Total
17,928
8,904
2,249
--
9,586
22,696
1,119
44,681
27,514
36,488
3,368
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
118
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/SwissProt/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
1,1E-01
AllCMV_82696
sp|P17067|CAHC_PEA
Probable peroxisomal (S)-2-hydroxy-acid oxidase 2 5,9
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
119
family that acts in the glyoxilat cycle with putative contribution to fatty acids αoxidation 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).
120
Figure 3. Functional categorization of 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 Control
(BRC1) and Virus-Inoculated (BRM).
121
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 (Oryza
(
sativa),
), probably due to the association with a
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 in response to pathogen attack and
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 to
to stress responsive genes, being grouped
according to GO in distinct subcategories.
Considering the need to understand the GO subcategories, they were grouped in a
new category named “Stress Response”, which included 356 distinct tags divided in 14
subcategories
egories (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 stress
stress category in SuperSAGE libraries from V.
unguiculata,, including 14 subcategories according to the Gene Ontology
classification. Numbers represent the amount of 26 bp tags annotated to
each subcategory.
122
For this category modulation values (Fold Change; FC)
FC) observed varied from
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. Fold change in Vigna unguiculata tags,
showing significant changes in expression following
BMCT123 and BMCT4 inoculation with CPSMV.
CPSMV
Significant changes in expression were determined by
BMCT4/BMCT123 of six independent replicates.
A total of 11 tags associated to the TIFY protein from Arabidopsis appeared
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 Arabidopsis. The
remaining 10 tags corresponded to TIFY10b from Arabidopsis and presented modulation
values varying from ≥11 to ≥50.
The TIFY gene family is plant-specific
plant specific and was first described in Arabidopsis,
being considered a regulatory factor of the phytormone category of the auxins,
associated with plant growth and root development [36]. Additionally the TIFY10a
belongs to a TIFY subfamily known as JAZ (Jasmonate Zim-domain)
domain) which is a key
molecule in the regulation of the hormone jasmonate in Arabidopsis [37, 38]. Recent
studies in rice have revealed a role for the TIFY10a family in response to abiotic stress,
including
cluding jasmonic acid treatment, mechanical injury associated to the TIFY
transcriptional modulation showing that this gene varies not only in response to abiotic
123
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].
124
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
125
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
126
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 “lateembryogenesis-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
127
timing and magnitude of their expression or, still, the contemporary expression of
different sets of genes comparing resistant and susceptible plants during future qRTPCR essays.
128
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).
129
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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.
142
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ls are available which ease this process.
TeX/LaTeX users: We recommend using BioMed Central's TeX template and BibTeX stylefile.
stylefile If you use
this standard format, you can submit your manuscript in TeX
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
converting to RTF.
Note that figures must be submitted as separate image files, not as part of the submitted DOC/
PDF/TEX/DVI file.
Article types When submitting your manuscript, you
you will be asked to assign one of the following types to
your article:
Research article
Database
Methodology article
Software
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
sections:
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Title page
Abstract
Background
Results
Discussion
Conclusions
Methods (can also be placed after Background)
List of abbreviations used(if
used any)
Authors' contributions
Authors' information (if any)
Acknowledgements
References
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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, SwissProt: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 eprint/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:
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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 threedimensional prediction, secondary structure, and contacts prediction. Proteins 1999, 43(Suppl 3):149170.
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):
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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.
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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:
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2.
3.
4.
5.
Create a folder containing a starting file called index.html (or index.htm) in the root
Put all files necessary for viewing the mini-website within the folder, or sub-folders
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
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 miniwebsite without problems, it is ideal to check this on a different machine
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
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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).
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