instituto nacional de pesquisas d a amazÔnia - inpa ... · iii ficha catalogrÁfica sinopse o...

INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA - INPA PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA, CONSERVAÇÃO E

BIOLOGIA EVOLUTIVA - GCBEv

Marcos Prado Lima

Manaus - AM Maio de 2016

EFEITOS DAS MUDANÇAS CLIMÁTICAS SOBRE A EXPRESSÃO GÊNICA E

A FISIOLOGIA DO TAMBAQUI (Colossoma macropomum, Cuvier, 1818)

i

MARCOS PRADO LIMA

Orientador: Adalberto Luis Val

Tese apresentada ao Programa de

Pós-Graduação em Genética,

Conservação e Biologia Evolutiva do

Instituto Nacional de Pesquisas da

Amazônia, como requisito para a

obtenção do título de Doutor em Genética,

Conservação e Biologia Evolutiva.

Manaus - AM Maio de 2016

EFEITOS DAS MUDANÇAS CLIMÁTICAS SOBRE A EXPRESSÃO GÊNICA E

A FISIOLOGIA DO TAMBAQUI (Colossoma macropomum, Cuvier, 1818)

iii

FICHA CATALOGRÁFICA

SINOPSE O tambaqui, Colossoma macropomum, foi utilizado para investigar o efeito

das mudanças climáticas durante 150 dias de exposição aos cenários climáticos

B1, A1B e A2 previstos pelo IPCC para o ano 2100. Foram avaliados o perfil

transcricional, a ocorrência de danos ao DNA dos eritrócitos, diversos

parâmetros hematológicos e aspectos de regulação iônica. Os resultados

indicaram que as mudanças climáticas provocarão alterações na expressão de

diversos genes, assim como desequilíbrio em processos metabólicos vitais que

alteram a homeostase celular do tambaqui e podem colocar em risco a

sobrevivência da espécie.

Palavras-chave: Amazônia, aquecimento global, expressão diferencial, ensaio

L732e Prado-Lima, Marcos Efeitos das mudanças climáticas sobre a expressão gênica e a

fisiologia do tambaqui (Colossoma macropomum, Cuvier, 1818) / Marcos Prado Lima --- Manaus: [s.n.], 2016.

xvii, 152 f.: il., color. Tese (Doutorado) --- INPA, Manaus, 2016. Orientador: Adalberto Luis Val. Área de concentração : Genética, Conservação e Biologia Evolutiva.

1. Amazônia 2.Adaptação 3. Expressão diferencial. I.Título

CDD 591.15

iv

Dedico à minha esposa

Gilcideya Silva Prado e à minha

filha, Maria Clara Prado. Por tudo

que representam na minha vida e

pelo incondicional amor, apoio e

incentivo na busca do meu objetivo.

v

A realização deste projeto foi possível devido

Ao Programa de Pós-graduação em Genética, Conservação e Biologia

Evolutiva do INPA.

Ao Laboratório de Ecofisiologia e Evolução Molecular (LEEM) do INPA, ligado

a Coordenação de Biodiversidade, onde este trabalho foi desenvolvido com

financiamento proporcionado pelo Conselho Nacional de Desenvolvimento Científico

e Tecnológico (CNPq - Proc. Nº 573976/2008-2) e pela Fundação de Amparo à

Pesquisa do Estado do Amazonas (FAPEAM - Proc. No 3159/08), por meio do INCT-

ADAPTA (Centro de Adaptações da Biota Aquática da Amazônia).

À Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

pela concessão da bolsa de estudos durante a realização deste trabalho.

À Universidade Federal do Oeste do Pará (UFOPA) pela liberação das

atividades como docente da instituição para cursar o doutorado.

vi

AGRADECIMENTOS

A Deus por ter me dado forças para superar os incontáveis obstáculos

enfrentados nessa longa caminhada. À Nossa Senhora Aparecida pelo amparo e

proteção durante os anos longe de casa.

À minha querida esposa, Gilcideya Prado, que me incentivou e me apoiou em

todos os momentos. Obrigado pela paciência, compreensão e ombro amigo desde o

mestrado! Sua presença ao meu lado foi fundamental para a concretização deste

sonho. Te amo!

À minha filha, Maria Clara, que veio ao mundo no momento mais crítico dessa

caminhada, mas trouxe um alento imensurável ao meu coração. Obrigado pelo seu

sorriso! Era dele que eu lembrava nos momentos mais difíceis que enfrentei nessa

fase final do doutorado.

Às minhas mães Fátima, Vicência e Maria Portela (in memorian) por todo o

apoio na busca dos meus sonhos. Durante todos esses anos longe de Santarém

vocês foram e continuam sendo minha referência, meu porto seguro.

Ao meu orientador, Adalberto Val, pela confiança no meu trabalho, pelos

conselhos e orientações recebidas ao longo dos últimos nove anos, desde o início

do mestrado. Sua disponibilidade em ler e reler meus textos, inclusive em finais de

semana e feriados, é um exemplo que demostra sua dedicação e amor pelo que faz.

Obrigado também por ter se tornado um exemplo, uma referência de quais valores

devem ser seguidos no mundo da ciência. Serei eternamente grato a você,

Adalberto!

À Dra. Vera Val pelo apoio, amizade e incentivo em diversos momentos dessa

jornada.

À Alzira Miranda e Fernanda Dragan pela colaboração e companheirismo

durante os vários meses de experimento nas futurísticas salas dos microcosmos. A

Daniel Fagundes e Renato Lemgruber pelo auxílio no sequenciamento.

À Lucas Canesin do Laboratório de Bioinformática e Biologia de Sistemas da

UNICAMP pelo auxílio com as análises de bioinformática.

Aos colegas da sala de doutorandos, especialmente Daiani Kochhann, Rafael

Duarte, Helen Sadauskas, Ramon Baptista, Jorge Sá e Derek Campos pelas

discussões científicas, conselhos e dicas em diversos momentos.

vii

Às minhas PIBICs preferidas, Jéssica Rany e Aldiane Passos, pela ajuda em

diversas etapas. À Susana Mota e Deney Araújo pela disponibilidade e auxílio em

vários momentos.

À Nazaré Paula (Naza) pela amizade, apoio e eficiência na resolução de

incontáveis problemas. Obrigado também pelos muitos conselhos diante dos

obstáculos enfrentados para se fazer ciência no meio da Amazônia.

À Raimunda Brandão, Claudinha Silva e Raquel Abecassiz pelo apoio no

setor administrativo do laboratório. Aos técnicos Reginaldo Oliveira e Rogério

Pereira pela disponibilidade em ajudar.

Aos colegas do Laboratório de Ecofisiologia e Evolução Molecular (LEEM) do

INPA pelo companheirismo, amizade e pelas inúmeras discussões científicas (e não

científicas também) ao longo desses últimos nove anos. Aprendi muito com vocês!

A todas as pessoas que direta ou indiretamente colaboraram para a

concretização deste sonho, inclusive aquelas que, infelizmente, tenha esquecido de

citar. Muito obrigado por tudo!

viii

“Por vezes sentimos que aquilo que fazemos não é senão uma gota de água no mar. Mas o mar seria menor se lhe faltasse uma gota”

(Madre Teresa de Calcutá)

ix

RESUMO

Nos últimos anos o equilíbrio dos ecossistemas naturais do planeta tem sido afetado pelas mudanças climáticas resultantes do aumento de gases do efeito estufa, especialmente dióxido de carbono. De acordo com o Painel Internacional sobre Mudanças no Clima, o IPCC, até o ano 2100 haverá elevação na temperatura média em todo o planeta, o que aumenta o risco de perda de biodiversidade em regiões mais vulneráveis como a Amazônia. Os peixes, assim como outros animais ectotérmicos, não possuem a capacidade de controlar sua temperatura corporal, ficando então sujeitos a variações ambientais que podem inviabilizar processos metabólicos vitais, levando o organismo à morte. Assim, para compreender como o tambaqui e outros peixes amazônicos poderão reagir ao aumento nos níveis de CO2 e de temperatura, o presente estudo teve como objetivo avaliar os efeitos biológicos da exposição do tambaqui, uma espécie símbolo da Amazônia, aos principais cenários de mudanças climáticas previstos pelo IPCC para o ano 2100. Exemplares de tambaqui foram expostos durante 150 dias ao cenário atual e a três cenários climáticos B1, A1B e A2. Na primeira parte do estudo foram investigadas alterações na expressão gênica após 5 e 15 dias de exposição por meio da construção de oito bibliotecas de RNA-Seq, que foram sequenciadas em um sequenciador de nova geração SOLID 4. Os resultados obtidos possibilitaram a identificação de 32.512 genes do transcriptoma do tambaqui, sendo 236 e 209 diferencialmente expressos após 5 e 15 dias de exposição, respectivamente. O grande número de genes diferencialmente expressos identificados sugere que uma complexa combinação de genes está envolvida na resposta do tambaqui às mudanças climáticas, especialmente genes relacionados ao metabolismo energético, chaperonas, fatores de iniciação da transcrição e tradução e genes ribossomais. Na segunda parte do estudo foram investigadas a ocorrência de danos ao DNA de eritrócitos, diversos parâmetros hematológicos e aspectos de regulação iônica do tambaqui durante 150 dias de exposição. Os resultados revelaram aumento no número de quebras no DNA de eritrócitos após 30 dias de exposição, alterações em diversos parâmetros hematológicos, especialmente aqueles relacionados ao aumento no número de eritrócitos e um severo desbalanceamento na concentração de íons plasmáticos e na atividade das principais enzimas branquiais de regulação iônica. Dessa forma, concluímos que os três cenários de mudanças climáticas avaliados podem provocar graves desequilíbrios em processos metabólicos vitais que alteram a homeostase celular do tambaqui e podem colocar em risco a sobrevivência da espécie.

Palavras-chave: Amazônia, aquecimento global, expressão diferencial, ensaio cometa, hematologia, regulação iônica.

x

ABSTRACT In recent years, the balance of the natural ecosystems has been affected by

climate change resulting from a continuous increase of greenhouse gases, mainly carbon dioxide. According to the Intergovernmental Panel on Climate Change, the IPCC, there will be an increase in the temperature around the planet, which enhances the risk of biodiversity loss in vulnerable regions such as the Amazon. Fish, like other ectothermic animals, do not control their body temperature and will then be subject to environmental variations that can compromise vital metabolic processes, causing to death. Thus, to understand how Amazonian fish can respond to global climate change, this study aimed to evaluate the biological effects of exposure of tambaqui, an Amazonian important fish species, to major climate change scenarios foreseen by IPCC for the year 2100. Juveniles of tambaqui were exposed during 150 days to the current environmental situation and B1, A1B and A2 climate change scenarios. Firstly were investigated changes in gene expression profile after 5 and 15 days of exposure through the construction of eight RNA-Seq libraries, which were sequenced using a next generation sequencer SOLID 4. The obtained results enabled to identify 32,512 genes on the transcriptome of tambaqui, being 236 and 209 differentially expressed after 5 and 15 days of exposure, respectively. A large number of differentially expressed genes identified indicates that a complex combination of genes is involved in tambaqui's response to climate change, especially genes related to energy metabolism, chaperones, transcription initiation factors and translation and ribosomal genes. Secondly, we investigated the occurrence of DNA damage in erythrocytes, several hematological parameters and ion regulation of tambaqui during 150 days of exposure to the various climate change driven environmental scenarios. The results revealed the existence of DNA strand breaks in erythrocytes after 30 days of exposure, changes in several hematological parameters, particularly those related to increase of red blood cells to compensate the increased demand of oxygen to tissues, as well as a severe imbalance in the plasma ion concentrations and the activity of key ion regulation enzymes of the gills. Thus, we concluded that the exposure of tambaqui to the analyzed climate change driven environmental scenarios can result in severe imbalances of vital metabolic processes that impair cellular homeostasis and pose a greater health risk to the analyzed species. Keywords: Amazonia, global warming, differential expression, comet assay, hematology, ionic regulation.

xi

SUMÁRIO

1 – INTRODUÇÃO GERAL....................................................................................... 18

1.1 - Mudanças climáticas globais.................................................................. 18

1.2 - Mudanças climáticas na Amazônia......................................................... 21

1.3 - Peixes expostos às mudanças climáticas.............................................. 22

1.4 - O tambaqui............................................................................................. 24

2 – OBJETIVOS........................................................................................................ 26

2.1 - Objetivo geral.......................................................................................... 26

2.2 - Objetivos específicos.............................................................................. 26

3 – RESULTADOS E DISCUSSÃO.......................................................................... 27

3.1 - Capítulo I – Efeitos de três cenários de mudanças climáticas sobre

danos ao DNA, hematologia e enzimas branquiais Na+/K+-ATPase e H+-

ATPase no peixe amazônico tambaqui (Colossoma macropomum).............. 28

3.1.1 - Introdução................................................................................. 30

3.1.2 - Materiais e métodos.................................................................. 32

3.1.3 - Resultados................................................................................ 36

3.1.4 - Discussão................................................................................. 45

3.1.5 - Referências bibliográficas......................................................... 51

3.2 - Capítulo II – Caracterização transcriptômica do tambaqui (Colossoma

macropomum, Cuvier, 1818) exposto a três cenários de mudanças

climáticas........................................................................................................ 59

3.2.1 - Introdução................................................................................. 61

3.2.2 - Materiais e métodos.................................................................. 63

3.2.3 - Resultados................................................................................ 66

3.2.4 - Discussão................................................................................. 80

xii

3.2.5 - Referências bibliográficas......................................................... 86

3.3 - Capítulo III: Da Genética à Fisiologia: uma abordagem integrativa sobre

a exposição do tambaqui (Colossoma macropomum) aos principais cenários de

mudanças climáticas previstos pelo IPCC.............................................................. 119

3.2.1 - Introdução............................................................................... 121

3.2.2 - Materiais e métodos................................................................ 123

3.2.3 - Resultados............................................................................... 123

3.2.4 - Discussão................................................................................ 125

3.2.5 - Referências bibliográficas....................................................... 136

4 – CONCLUSÕES GERAIS.................................................................................. 144

5 – PERSPECTIVAS............................................................................................... 145

6 – REFERÊNCIAS BIBLIOGRÁFICAS................................................................. 146

xiii

LISTA DE TABELAS

Capítulo I

Tabela 1. Parâmetros físico-químicos da água e do ar dos ambientes climáticos

controlados, onde os espécimes de tambaqui foram expostos por 150 dias . Os

dados são apresentados como média ± erro padrão da média (n= 6)...................... 33

Tabela 2. Parâmetros hematológicos do tambaqui durante exposição aos cenários

climáticos controle, B1, A1B e A2. Os dados são apresentados como média ± erro

padrão da média. t Indica diferença significante em comparação ao cenário controle

no mesmo tempo de exposição (P<0.05).* Indica diferença significante em

comparação ao tempo zero (P<0.05)........................................................................ 40

Tabela 3. Concentração de íons no plasma (mEq l-1) de tambaqui durante exposição

aos cenários climáticos atual, B1, A1B e A2. t Indica diferença significante em

comparação ao cenário controle no mesmo tempo de exposição (P<0.05).* Indica

diferença significante em comparação ao tempo zero (P<0.05)............................... 41

Capítulo II

Tabela 1. Parâmetros físico-químicos da água e do ar do ambiente onde os

espécimes de tambaqui foram expostos durante cinco e quinze dias aos cenários

climáticos atual, B1, A1B e A2. Os dados são apresentados como média ± erro

padrão da média........................................................................................................ 63

Tabela 2. Visão geral das sequências de RNA-Seq de tambaqui obtidas após cinco

e quinze dias de exposição aos cenários climáticos B1, A1B e A2.......................... 67

Tabela 3. Principais genes do tambaqui afetados pela exposição aos cenários

climáticos................................................................................................................... 69

Tabela S1. Lista de genes diferencialmente expressos (Log2FC) de tambaqui após 5

dias de exposição aos cenários climáticos B1, A1B e A2. Genes regulados

positivamente são mostrados como valores positivos e genes regulados

negativamente são mostrados como valores negativos............................................ 99

xiv

Tabela S2. Lista de genes diferencialmente expressos (Log2FC) de tambaqui após

15 dias de exposição aos cenários climáticos, B1, A1B e A2. Genes regulados

positivamente são mostrados como valores positivos e genes regulados

negativamente são mostrados como valores negativos.......................................... 105

Tabela S3. Genes de tambaqui após 5 e 15 dias de exposição aos cenários

climáticos B1, A1B e A2, agrupados em cinco clusters utilizando o programa

STRING (v. 10)........................................................................................................ 110

Tabela S4. Oligonucleotideos iniciadores e tipos de microssatélites encontrados nas

sequências de tambaqui.......................................................................................... 116

Capítulo III

Tabela 1. Principais genes diferencialmente expressos que responderam a

exposição do tambaqui aos cenários climáticos B1, A1B e A2............................... 124

xv

LISTA DE FIGURAS

Introdução

Figura 1. Diferentes cenários climáticos previstos pelo IPCC para o ano 2100. As

linhas sólidas representam tendências globais de aquecimento da superfície para os

cenários B1, A1B e A2, mostrados como continuações das simulações do século

XX. Os sombreamentos representam intervalos de desvio padrão das médias

anuais. A linha laranja simula a tendência de aquecimento caso as concentrações de

gás carbônico se mantenham constantes aos valores do ano 2000. As barras cinzas

a direita indicam a melhor estimativa (linha sólida dentro de cada barra) e a faixa

provável avaliada para seis cenários climáticos. Fonte: AR4, IPCC (2007)............. 19

Figura 2: Exemplar de Colossoma macropomum.................................................... 24

Capítulo I

Figura 1. Indicador de Dano Genético (GDI) em eritrócitos de tambaqui ao longo do

período experimental de exposição aos cenários climáticos controle, B1, A1B e A2. t Indica diferença significante em comparação ao cenário controle no mesmo tempo

de exposição (P<0.05). * Indica diferença significante em comparação ao tempo zero

(P<0.05)..................................................................................................................... 37

Figura 2. Integridade do DNA Celular (CDI) em eritrócitos de tambaqui ao longo do

período experimental de exposição aos cenários climáticos controle, B1, A1B e

A2.............................................................................................................................. 38

Figura 3. Atividade da Na+/K+-ATPase nas brânquias de tambaqui ao longo do

período experimental de exposição aos cenários climáticos controle, B1, A1B e A2. t Indica diferença significante em comparação ao cenário controle no mesmo tempo

de exposição (P<0.05). * Indica diferença significante em comparação ao tempo zero

(P<0.05)..................................................................................................................... 43

Figura 4. Atividade da H+-ATPase nas brânquias de tambaqui ao longo do período

experimental de exposição aos cenários climáticos controle, B1, A1B e A2. t Indica

diferença significante em comparação ao cenário controle no mesmo tempo de

xvi

exposição (P<0.05). * Indica diferença significante em comparação ao tempo zero

(P<0.05)..................................................................................................................... 44

Capítulo II

Figura 1. Número e direção dos transcritos diferencialmente expressos de tambaqui

identificados após cinco e quinze dias de exposição aos diferentes cenários

climáticos B1, A1B e A2. O gráfico de barras mostra o número de transcritos

diferencialmente expressos identificados após cinco dias (barras à esquerda) e

quinze dias (barras à direita) de exposição aos cenários climáticos B1, A1B e A2. Os

valores positivos e negativos do eixo Y representam o número de transcritos com

regulação positiva e negativa, respectivamente. ...................................................... 68

Figura 2. Representação dos 20 principais termos do Gene Ontology relacionados

aos genes regulados positivamente no tambaqui após cinco dias de exposição aos

cenários climáticos B1, A1B e A2, sendo função molecular (A), processo biológico

(B) e componente celular (C). .................................................................................. 71


aos genes regulados negativamente no tambaqui após cinco dias de exposição aos


(B) e componente celular (C).................................................................................... 73


aos genes regulados positivamente no tambaqui após 15 dias de exposição aos




aos genes regulados negativamente no tambaqui após 15 dias de exposição aos



Figura 6. Rede de interação gênica gerada pelo programa STRING (v.10). As

interações de genes (ou proteínas) foram expressas no músculo branco de tambaqui

xvii

após cinco e quinze dias de exposição aos cenários climáticos B1, A1B e A2. Os

círculos com cores diferentes representam genes diferentes (ou proteínas). As linhas

azuis representam fortes interações entre esses genes (ou proteínas). Os círculos

sem nenhuma interação foram ocultados................................................................. 79

Figura 7. Distribuição dos microssatélites encontrados nas sequências de tambaqui

................................................................................................................................... 80

Figura S1. Genes identificados no tambaqui após 5 e 15 dias de exposição aos

cenários climáticos B1, A1B e A2 agrupados no cluster 1 utilizando o programa

STRING (v.10)........................................................................................................... 94



STRING (v.10)........................................................................................................... 95



STRING (v.10)........................................................................................................... 96



STRING (v.10)........................................................................................................... 97



STRING (v.10)........................................................................................................... 98

18

1 - INTRODUÇÃO GERAL

1.1 – Mudanças climáticas globais

O aquecimento global, resultante principalmente do acúmulo de gases de

efeito estufa (GEE) na atmosfera, especialmente dióxido de carbono (CO2), metano

(CH4) e os óxidos nitrosos (N2O), representa uma grande ameaça ao planeta (IPCC,

2014). Diversos estudos comprovam que as mudanças climáticas já em curso

resultam em alterações nos padrões globais da vegetação e mudanças no uso da

terra (Hirota et al. 2011; Lapola et al. 2014), redução da biodiversidade mundial

(Ficke et al. 2007; Chown et al. 2010) e mudanças na composição atmosférica

(Walker e Steffen1997) em diversas regiões do planeta, inclusive no Brasil (Lapola et

al. 2014).

As primeiras evidências de modificações no clima do planeta surgiram ainda

na década de 1980 e, desde então, vêm despertando de forma crescente maior

interesse e preocupação tanto da comunidade científica quanto da população em

geral (Lima et al. 2011). Devido ao desconhecimento sobre os impactos de

alterações na estabilidade climática mundial, em 1988 a Organização das Nações

Unidas, por meio do Programa das Nações Unidas para o Meio Ambiente, e a

Organização Meteorológica Mundial, criaram o Painel Intergovernamental sobre

Mudanças Climáticas (Intergovernamental Panel on Climate Change - IPCC) com o

objetivo de avaliar as informações científicas, técnicas e socioeconômicas relevantes

para entender os riscos induzidos pelas mudanças climáticas na população humana,

bem como propor cenários de mudanças climáticas para o futuro (Marengo 2007).

A organização do IPCC, tal como seus cinco relatórios publicados em 1990,

1995, 2001, 2007 e 2014, baseia-se em três eixos principais: 1) compreensão dos

fatores humanos e naturais que causam as mudanças do clima; 2) impacto social e

ambiental das mudanças do clima, adaptação e vulnerabilidade; 3) busca de

medidas de mitigação e adaptação às mudanças climáticas. Os cientistas do IPCC

utilizam modelos matemáticos para simular os impactos das mudanças climáticas no

clima e nos ecossistemas em nível global e regional, especialmente relacionados a

alterações na concentração de GEE na atmosfera (Stéfanon et al. 2015). Dessa

forma, coube ao IPCC o importante papel de criar diferentes cenários climáticos

cientificamente embasados, utilizando indicadores de áreas como meteorologia,

climatologia, paleontologia, hidrologia, biologia e áreas afins para prever possíveis

19

trajetórias de desenvolvimento global a partir da emissão de diferentes níveis de

GEE na atmosfera (Viner et al. 1995; Moss et al. 2010).

Em seus últimos relatórios, publicados em 2007 (AR4) e 2014 (AR5), o IPCC

descreve vários avanços obtidos a partir da utilização de projeções de possíveis

cenários climáticos baseando-se na avaliação do impacto dos fatores humanos e

naturais nas mudanças climáticas, considerando diferentes níveis de emissão de

GEE (IPCC 2007; 2014). Cenários são estórias relevantes e cientificamente

embasadas sobre como o futuro pode se desdobrar, expressas na forma de palavras

e números, evidenciando incertezas e caminhos futuros. Portanto, não podem ser

considerados previsões, projeções ou recomendações (Raskinet al. 2005; IPCC

2007; 2014). Todos os 40 cenários previstos pelo AR4 para o ano 2100 têm origem

a partir de quatro grandes famílias de cenários (A1, A2, B1 e B2) caracterizadas

qualitativamente a partir de diferentes caminhos de desenvolvimento

socioeconômico e uso de combustíveis fósseis ou fontes de energia renováveis e

devem ser vistos como matéria-prima para o aprofundamento de estudos que visem

a elaboração de ações de mitigação de impactos e adaptação às mudanças

climáticas (IPCC 2007).

Figura 1. Diferentes cenários climáticos previstos pelo IPCC para o ano 2100. As linhas sólidas representam tendências globais de aquecimento da superfície para os cenários B1, A1B e A2, mostrados como continuações das simulações do século XX. Os sombreamentos representam intervalos de desvio padrão das médias anuais. A linha laranja simula a tendência de aquecimento caso as concentrações de gás carbônico se mantenham constantes aos valores do ano 2000. As barras cinzas a direita indicam a melhor estimativa (linha sólida dentro de cada barra) e a faixa provável avaliada para seis cenários climáticos. Fonte: AR4, IPCC (2007).

20

O crescente aumento nas emissões de GEE, principalmente CO2, registrado

nas últimas décadas, relacionado à queima de combustíveis fósseis e mudanças no

uso da terra, tem sido o fator decisivo para o aquecimento global. Entre os anos de

1906 e 2005 houve um aumento de 0,74oC na temperatura média do planeta, esse

aumento pode, no entanto, chegar a 4,5oC até o ano de 2100. Outras consequências

decorrentes do aquecimento global têm sido registradas com maior frequência nos

últimos anos, como mudanças nos padrões de vento e chuvas, alterações de

correntes marítimas e intensificação da desertificação em determinadas regiões

(IPCC 2007).

Desde a revolução industrial até o ano de 2005, a concentração de CO2 na

atmosfera cresceu 33%, passando de 280 para 379 ppm, ritmo muito acima do que

foi registrado nos últimos 800 mil anos (180 a 300 ppm) (Brundtland et al. 2012;

IPCC 2007). De acordo com AR4, entre 1995 e 2005 o aumento médio de CO2 na

atmosfera foi de 1,9 ppm, podendo chegar entre 730 a 1020 ppm até 2100 (IPCC

2007). Caso os níveis atuais de emissões de CO2 continuem a crescer, a

temperatura no planeta pode ficar 4oC acima da registrada antes da revolução

industrial (Rockström et al. 2014).

Diversos estudos que analisaram os efeitos das mudanças climáticas indicam

que seus efeitos não serão homogêneos em diferentes regiões do planeta (Döll e

Zhang 2010; Stéfanon et al. 2015). Em decorrência da Cordilheira dos Andes e de

uma grande heterogeneidade climática, a América do Sul é uma das regiões mais

vulneráveis às mudanças climáticas (Grimm e Natori 2006; Hirota et al. 2011). De

acordo com o IPCC, depois da Polinésia, África, parte da Ásia e Caribe, a América

do Sul é considerada a parte do planeta que mais sofrerá com os efeitos das

mudanças no clima, que poderão resultar no aumento de eventos climáticos

extremos como alterações significativas da disponibilidade hídrica, salinização e

desertificação de áreas utilizadas para agricultura, riscos de inundação em áreas

costeiras baixas e deslocamento dos estoques pesqueiros (IPCC 2007). A

temperatura média da América do Sul sofrerá um aumento que poderá chegar a 6ºC,

produzindo efeitos catastróficos em diversas regiões do continente, porém o Brasil

sofrerá as alterações mais severas, especialmente nas regiões mais sensíveis às

mudanças climáticas como a Amazônia e a região Nordeste (Nobre et al. 2007;

Hirota et al. 2010).

21

1.2 – Mudanças climáticas na Amazônia

A bacia amazônica, formada pelo rio Amazonas e seus afluentes, escoa para

o mar entre 15 e 20% de toda a água doce do planeta (Salati e Vose 1984). No

período chuvoso estima-se que até 29% da área da bacia fique coberta por água em

razão de seus muitos rios, lagos e igarapés que atuam como uma fonte geradora de

calor e umidade que regula o regime de chuvas em ambos os hemisférios,

possuindo um papel vital no clima global (Castello et al. 2013; Cheng et al. 2013).

Dessa forma, as mudanças climáticas podem afetar substancialmente a região

amazônica e, consequentemente, alterar o clima global (Nobre et al. 2005; Castello

et al. 2013).

A região amazônica vem sofrendo nas últimas décadas uma crescente

pressão antrópica que tem provocado redução na sua cobertura vegetal como

consequência do desmatamento e de incêndios florestais que, somados ao

aquecimento global, ameaçam a estabilidade climática, ecológica e ambiental da

região (Nobre et al. 2007). A crescente fragmentação de habitats e ecossistemas,

como consequência do desmatamento, consiste não apenas em um fator aditivo ao

impacto das mudanças climáticas, mas sim em um fator multiplicador quando se

consideram também o estresse associado a essas mudanças (Marengo 2007).

Sendo assim, é difícil estimar a vulnerabilidade das espécies às mudanças

climáticas devido à escassez de informações cientificamente embasadas (Canhos et

al. 2007).

De acordo com vários cenários do IPCC, até o ano de 2100 a temperatura

média na Amazônia terá um aumento acima de 3ºC, o que aumentaria a chance de

perda da cobertura florestal em 40% em algumas regiões, elevando o risco de perda

de biodiversidade e aumentando a ocorrência de eventos climáticos extremos, como

a seca de 2005 (IPCC 2007; Nobre et al. 2007). A assombrosa velocidade com que

as mudanças no clima do planeta vêm ocorrendo nas últimas décadas, quando

comparadas àquelas dos processos naturais, consiste em uma ameaça real para

espécies da flora e da fauna amazônicas, colocando em perigo a região que possui

a maior biodiversidade do mundo, tendo como consequência um provável

“empobrecimento biológico” na região (Nobre et al. 2005; 2007).

Além das florestas, o clima mais seco que o atual em parte da Amazônia

afetará também o ambiente aquático que sofrerá profundas alterações, tanto pelas

mudanças na dinâmica do sistema hidrológico, quanto por alterações pontuais que

22

podem ter profundos efeitos sobre os ecossistemas e as comunidades de

organismos aquáticos (Portner e Farrell 2008; Gamito et al. 2013). Essas alterações

tornarão o solo e a atmosfera da Amazônia menos úmidos, aumentado a

evapotranspiração das plantas que terá efeitos tanto na redução do escoamento

superficial quanto na vazão dos rios, modificando ecossistemas e dificultando a

navegação em alguns períodos do ano (Döll e Zhang 2010; Liberato e Brito 2010).

1.3 – Peixes expostos às mudanças climáticas

Diversas alterações em ambientes aquáticos relacionadas com variações

climáticas têm sido descritas nas últimas décadas, indicando que esses

ecossistemas são extremamente sensíveis e vulneráveis às mudanças climáticas

globais (Richardson e Poloczanska 2008). Vários impactos têm sido documentados

em todos os níveis da organização biológica que vão desde alterações na expressão

de genes (Clark et al. 2011); passando por modificações fisiológicas (Del Toro-Silva

et al. 2008), esqueléticas (Booth et al. 2014; Lopes 2016) e comportamentais

(Abrahams et al. 2007); até alterações na composição de assembleias (Ter Hofstede

e Rijnsdorp 2011) e na distribuição geográfica de peixes (Perry et al. 2005).

A elevação da temperatura atmosférica gera efeitos também no ambiente

aquático por meio do aumento na temperatura dos corpos d’água. De uma maneira

geral, em virtude de uma menor capacidade de armazenamento de calor, os corpos

d’água mais rasos tendem a apresentar temperaturas máximas e mínimas mais

acentuadas em comparação com aqueles mais profundos (Talling2001).

Considerando que a solubilidade do oxigênio é inversamente proporcional a

temperatura, ou seja, quando a temperatura da água aumenta, a concentração de

oxigênio dissolvido diminui, podendo resultar em um estresse adicional causado pela

hipóxia, justamente no momento em que as demandas metabólicas dos peixes se

elevam para superar o desafio ambiental (Talling 2001; Abrahams et al. 2007).

Consequentemente, a interação desses dois parâmetros físico-químicos

(temperatura e concentração de oxigênio) pode colocar em risco a sobrevivência de

um organismo devido ao comprometimento de suas atividades biológicas vitais (Del

Toro-Silva et al. 2008; Blair et al. 2013).

Apesar dos peixes de água doce serem ectotérmicos, ou seja, não

conseguirem regular fisiologicamente sua temperatura corporal, que geralmente é

muito próxima a temperatura do ambiente onde vivem, os peixes podem se deslocar

23

vertical ou horizontalmente na coluna d’água para zonas com maior conforto térmico

(Clarke 2003; Ficke et al. 2007). Essa estratégia comportamental permite superar

variações térmicas em seus habitats decorrentes de mudanças rápidas na energia

solar, movimentos anormais da água e eventos de precipitação rápida (Long et al.

2012). Porém, essa estratégia acaba sendo limitada à gama de temperaturas

disponíveis no ambiente aquático e, consequentemente, devido as reações

fisiológicas variarem de acordo com a temperatura corporal, as mudanças climáticas

podem comprometer processos básicos como alimentação, crescimento,

comportamento, reprodução e a capacidade de manter a homeostase interna (Ficke

et al. 2007; Somero 2010).

Para a compreensão dos efeitos das mudanças climáticas sobre os peixes da

Amazônia é necessário considerar diversos fatores que vão além das características

da espécie em estudo, como a interação com outras espécies, a capacidade

adaptativa, distribuição geográfica e ocorrência nos diferentes ecossistemas. Por

exemplo, espécies com distribuição restrita a algumas regiões da bacia são mais

vulneráveis às mudanças climáticas do que aquelas com distribuição mais ampla

(Val e Almeida-Val 2008). Outra característica importante que deve ser considerada

é a aclimatação térmica, que consiste numa estratégia de ajuste fisiológico em que o

organismo é capaz de sintetizar determinadas proteínas que conferem maior

resistência a variações de temperatura como as chaperonas ou HSPs (heat shock

proteins) (Basu et al. 2002; Hofmann 2005; Somero 2010).

Contudo, para responder as modificações ambientais geradas a partir do

aumento na temperatura da água e nas alterações de gases dissolvidos,

especialmente CO2 e oxigênio, os peixes podem adotar diversas estratégias como

nadar para regiões com temperaturas mais baixas ou com maior disponibilidade de

oxigênio, manter-se na coluna d’água onde for metabolicamente mais conveniente

ou, até mesmo, fazer uso de algum tipo de respiração acessória (Oliveira 2003;

Nowickiet al. 2012). Porém, quando essas estratégias não forem suficientes, os

organismos podem desencadear um conjunto de respostas bioquímicas e

moleculares com o objetivo de compensar esses efeitos e garantir a sobrevivência

mesmo sob estresse.

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1.4 – O tambaqui

O tambaqui (Colossoma macropomum, Cuvier 1818), figura 2, é o maior

caraciforme da bacia amazônica (Goulding 1980, 1993), sendo endêmico desta

região e amplamente distribuído nos principais rios da bacia, onde pode alcançar

mais de um metro de comprimento e 30 kg de peso (Goulding 1993; Araújo-Lima e

Goulding 1998). Na natureza o tambaqui é onívoro, alimentando-se basicamente de

frutas e sementes durante o período chuvoso e zooplancton e insetos na estação

seca (Araújo-Lima e Goulding 1998). Apresenta comportamento migratório quando

se desloca para os rios de águas brancas em períodos de chuvas intensas em

busca de ambientes de várzea, considerados ideais para reprodução devido a sua

grande produtividade e disponibilidade de habitats que se tornam áreas de refúgio e

proteção para a espécie (Lima e Araujo-Lima 2004).

Figura 2: Exemplar de Colossoma macropomum. Foto: Maria de Nazaré Paula da Silva.

O sabor agradável e as características nutricionais de sua carne são

responsáveis pela ampla aceitação do tambaqui como fonte alimentar para a

população amazônica e de outras regiões do país, levando a um crescente esforço

de pesca das populações naturais (Araújo-Lima e Goulding 1998). Na década de

1970 o tambaqui correspondia a 45% da quantidade de peixe desembarcado no

mercado municipal de Manaus (Graef 1996), o que contribuiu fortemente para a

inclusão da espécie na lista oficial de espécies protegidas durante o defeso (IBAMA

2003).

Do ponto de vista fisiológico, diversos estudos mostram que o tambaqui é

uma espécie extremamente plástica, podendo adaptar-se a mudanças ambientais

por meio de ajustes metabólicos que combinam mudanças anatômicas com ajustes

hematológicos e de regulação da expressão gênica (Saint-Paul 1984; Val e Almeida-

2 cm

25

Val 1995). A principal modificação anatômica que ocorre no tambaqui, quando

submetido a baixas concentrações de oxigênio dissolvido, é a expansão do lábio

inferior que, apesar de não estar envolvido diretamente na troca gasosa, auxilia na

tomada da água próximo a superfície, aumentando assim o aporte de oxigênio para

as brânquias, ao mesmo tempo em que reduz os níveis intraeritrocitários de ATP e

GTP para amenizar os efeitos negativos provocados pela baixa concentração de

oxigênio dissolvido na água (Val e Almeida-Val 1995).

Devido à importância econômica do tambaqui para a região amazônica, os

programas para sua conservação e seu potencial para o desenvolvimento da

aquicultura, a espécie se tornou o peixe símbolo da Amazônia (Araújo-Lima e

Goulding 1998). Entretanto, a falta de informações relacionadas à adaptação de

peixes da Amazônia às mudanças climáticas globais, especialmente ao aumento da

temperatura e de CO2, deixa esses organismos e a Amazônia como um todo

altamente vulneráveis às variações ambientais. Tal fato pode ter consequências

graves para o meio ambiente, pois informações cientificamente embasadas são

fundamentais para a implementação de medidas mitigadoras por parte do poder

público.

26

2 – OBJETIVOS

2.1 – Objetivo geral

Avaliar os efeitos biológicos da exposição do tambaqui (Colossoma

macropomum) aos principais cenários de mudanças climáticas previstos pelo IPCC

para o ano 2100.

2.2 – Objetivos específicos

a) Avaliar o efeito da exposição do tambaqui aos cenários climáticos B1, A1B

e A2 previstos pelo IPCC para o ano 2100 sobre quinze parâmetros metabólicos e

fisiológicos.

b) Identificar alterações no transcriptoma do tambaqui relacionadas a

exposição aos cenários climáticos B1, A1B e A2 previstos pelo IPCC para o ano

2100.

c) Identificar e caracterizar potenciais genes relacionados aos ajustes

metabólicos e fisiológicos do tambaqui submetido aos cenários climáticos B1, A1B e

A2 previstos pelo IPCC para o ano 2100.

27

3 – RESULTADOS E DISCUSSÃO

O presente trabalho buscou investigar os principais efeitos biológicos da

exposição do tambaqui aos principais cenários de mudanças climáticas previstos

pelo IPCC para o ano 2100. Os resultados obtidos estão apresentados na forma de

artigos científicos, conforme descrito abaixo:

Capítulo I: Efeitos de três cenários de mudanças climáticas sobre danos ao DNA,

hematologia e enzimas branquiais Na+/K+-ATPase e H+-ATPase no

peixe amazônico tambaqui (Colossoma macropomum). Este capítulo

refere-se ao objetivo específico a.

Capítulo II: Caracterização transcriptômica do tambaqui (Colossoma macropomum,

Cuvier, 1818) exposto a três cenários de mudanças climáticas. Este

capítulo refere-se aos objetivos específicos b e c.

Capítulo III: Da Genética à Fisiologia: uma abordagem integrativa sobre a exposição

do tambaqui (Colossoma macropomum) aos principais cenários de

mudanças climáticas previstos pelo IPCC. Este capítulo faz uma

integração entre os objetivos específicos a, b e c.

28

Capítulo 1 ____________________________________________________________________ Prado-Lima, M.; Duarte, R.M. & Val, A.L. Effects of three climate change scenarios on DNA damage, hematology, and branchial Na+/K+-ATPase and H+-ATPase in the Amazonian fish tambaqui (Colossoma macropomum). Manuscrito formatado para Comparative Biochemistry and Physiology - Part A.

29

Abstract

Climate change-driven effects are likely to disturb all ecosystems around the world, especially those more sensible as Amazonia. Although Amazonia basin harbors a significant portion of Earth’s biodiversity, as well as the highest diversity of freshwater fishes in the world, there have been relatively few studies of Amazonian organisms, such as tambaqui, related to climate change. Thus, the aim of this study was to investigate the effects of long-term exposure (150 days) to B1, A1B and A2 climate scenarios foreseen by the IPCC on DNA damage of erythrocytes, hematology, plasma ion composition and Na+/K+-ATPase and H+-ATPase branchial ion regulation enzymes in the freshwater fish tambaqui. The results showed a significant increase on DNA strand breaks on erythrocytes of tambaqui after 30 days of exposure to the selected experimental scenarios. Increases in hematocrit, hemoglobin and red blood cells between 30 to 150 days of exposure, indicating a swelling of red blood cells of fish, were also observed. Plasma levels of monovalent ions Na+, K+, and Cl- was slightly affected, whereas the concentration of divalent ions Ca2+ and Mg2+ were reduced in animals exposed to all three analyzed climate scenarios. An apparent inhibition of both Na+/K+-ATPase and H+-ATPase activity, which may affect the internal ionic homeostasis of fish, was observed. Overall, the exposure of tambaqui to the three selected climate change scenarios promote an increase of DNA strand breaks in erythrocytes and severe changes in hematological parameters, plasmatic ion composition and branchial activity of Na+/K+-ATPase and H+-ATPase of tambaqui.

Keywords: global warming, Amazonia, DNA damage, ion composition

30

Introduction

The human society and the equilibrium of natural ecosystems have been

affected by climate changes caused especially by man (IPCC, 2014). To understand

the regional and global effects elicited by climate change and to provide information

for the development of adaptation and mitigation strategies, the scientists create

various climate scenarios based on the intensity of human activities causing

environmental degradation (Moss et al., 2010). Scenarios are plausible and relevant

descriptions about how the future might be, based on potential discharges of

greenhouse gases and aerosols into Earth’s atmosphere (Raskin et al., 2005).

According to the Fourth Assessment Report of the Intergovernmental Panel on

Climate Change (IPCC), three main scenarios of climate are foreseen for the year

2100: B1 (mild), A1B (intermediate) and A2 (extreme). These situations may vary

according to population growth, socioeconomic development and the use of fossil

fuels or renewable energy (IPCC, 2007).

Several studies have investigated the impact of climate change on water

ecosystems, specially increases of water temperatures (Brian et al., 2008; Booth et

al., 2014), changes in levels of dissolved gases (Miller et al., 2013; Dennis et al.,

2015), alterations of water flow regimes (Dall et al., 2010), and changes in

assemblage composition of fish (Last et al., 2011; Ter Hofstede et al., 2011;

Pletterbauer et al., 2014). The climate scenarios foreseen by the IPCC have been

used to preview responses of the marine and freshwater ecosystems to

environmental changes. The obtained results suggest that climate change will cause

severe disturbances such as impairing the distribution of species and fishery in

various countries (Brander, 2007; Last et al., 2011; Sharma et al., 2011).

Amazonia harbors the richest biodiversity on Earth with species that are still

unknown to the scientific community (Castello et al., 2013; Cheng et al., 2013). It is

also a massive source of heat and moisture in the tropics and has a crucial role in the

global climate system (Salazar and Nobre, 2010). Despite this, in last century it has

been subject to increasing anthropogenic environmental pressures, such as fires,

deforestation and growth of urban centers, occurring in connection with those

changes resulting from global warming (Nobre et al., 2007; Cheng et al., 2013).

Climate change substantially affects the Amazon region, which in turn is expected to

increase the risk of biodiversity loss considerably. Additionally, the hydrological cycle

of the Amazon rivers that is a major driving force of biological interactions have

31

experienced severe perturbations likely due climate change-driven processes, such

as the increase in temperature, changes in solar heat, and higher evapotranspiration

of plants (Roessig et al., 2004; Castello et al., 2013).

Climate change is considered foremost threats to aquatic ecosystems (Sala et

al., 2000). In both, marine and freshwater ecosystems, the climate change is

expected to modify various vital traits of aquatic organisms, such as growth,

metabolism, reproduction, species distribution, and even ecosystem structure (Ficke

et al., 2007; Brierley and Kingsford, 2009). The fishes are sensitive to environmental

modifications triggered by climate change due mainly to increases in water

temperature and variations in the concentrations of dissolved gases, especially

oxygen and carbon dioxide (CO2) (Ficke et al., 2007). Water temperature above of

thermal tolerance limit is known to cause many physiological changes in several fish

species, such as increased Na+/K+-ATPase activity in gills of Atlantic cod (Gadus

morhua) (Kreiss et al., 2015), DNA damage in goldfish (Carassius auratus) (Anitha et

al., 2000), modifications on gene expression in channel catfish (Ictalurus punctatus)

(Liu et al., 2013), metabolic alterations in Paralichthys lethostigma (Del Toro-Silva et

al., 2008), decreased growth rate in rainbow trout (Oncorhynchus mykiss) (Blair et

al., 2013) and increased mortality of cardinal tetra (Paracheirodon axelrodi) (Fé et al.

2013). The short-term exposure to increased PCO2 in water have also been shown to

cause physiological disturbances in salmonids, as hyperventilation (Janson and

Randall, 1975), reduced blood oxygen content (Eddy et al., 1977), lowered branchial

chloride influx rates (Goss et al., 1994) and chloride plasma concentration (Eddy et

al., 1977). Furthermore, at long-term exposure to high carbon dioxide levels in

freshwater, rainbow trout exhibited adverse sublethal effects on ion regulation,

lowered growth, reduced oxygen consumption and nephrocalcinosis (Smart et al.,

1979; Smart et al., 1981; Fivelstad et al., 1998).

Tambaqui (Colossoma macropomum) is a native fish species of the Amazon

basin found in rivers, lakes and floodplains (Junk et al., 2007). It is crucial to the local

economy, being the second most commonly raised fish in aquaculture in Brazil

(Brazil, 2012). It is widely known that tambaqui is one of most remarkable acidophilic

and hypoxia-tolerant fish species, displaying exceptional strategies on behavior and

morphology, as well as in their physiological and biochemical system, for living at

acidic and hypoxic waters of the Amazon (Val and Almeida-Val, 1995; Val and

Kapoor, 2003). In addition, tambaqui have also been considered tolerant to

32

hypercapnia conditions, exhibiting the typical responses of teleost fish to elevated

CO2 levels in the water (i.e. increased ventilatory frequencies and ventilation

amplitude, associated with pronounced bradycardia); however, the levels of CO2

required to trigger these physiological responses in tambaqui are quite higher than

those seen for salmonids (Gilmour and Perry, 1994; Reid et al., 2000; Florindo et al.,

2004).

To date, although tambaqui shows unique strategies to maintain their internal

homeostasis over a wide range of environmental challenges, there has been no

information regarding the effects of combined high temperature and CO2 levels in

water on their internal physiological responses. Therefore, the aim of the present

study was to investigate both the consequences of the exposure to three climate

change scenarios on hematology, plasma ionic composition and branchial ion

regulation enzymes (Na+/K+-ATPase and H+-ATPase) in Amazonian fish tambaqui,

and the potential effects of the simultaneous increment in temperature and CO2 to

cause DNA damage in erythrocytes of fish.

Materials and methods

Animals and rearing conditions

Juveniles of tambaqui weighting 15.5±1.9 g and measuring 8.3±0.3 cm (total

length) (mean ± SEM) were obtained from a local fish farm (Fazenda Tajá,

Amazonas, Brazil), and held in outdoors 500 L fiberglass tanks in the Laboratory of

Ecophysiology and Molecular Evolution (LEEM/INPA). They were acclimatized to

local well water ([Na+] = 31 µmol l-1, [K+] = 10 µmol l-1, [Cl-]= 30 µmol l-1, [Ca2+] = 9

µmol l-1, [Mg2+] = 4 µmol l-1, pH = 6.0, O2= 6.78 mg l-1, CO2= 8.25 ppm, temperature =

27oC) for at least one month prior the exposure to the selected experimental climate

change scenarios. No mortalities were observed during the acclimatization period.

The fishes were fed dry food pellets (Nutripeixe, Purina; 51% of total protein content)

ad libitum once a day during all experimental period, but feeding was suspended for

at least 48 h before starting the experiments. All experimental procedures followed

INPA’s animal care guidelines and were previously approved by the INPA’s Ethics

Committee on Animal Use (CEUA-INPA 056/2012).

Exposure to different climate change scenarios

Animals were exposed to four different environmental situations in

experimental rooms (microcosms), measuring 25m3 each. The characteristics of the

33

microcosms are described in details in Dragan et al. (in prep). Briefly, temperature,

carbon dioxide levels and humidity were measured every two minutes in a pristine

forest nearby the laboratory and radio-transmitted to the lab computers that set the

conditions to the control room (Table 1). The other three microcosms were designed

to simulate three future climate scenarios as foreseen by the Fourth Assessment

Report of the IPCC for the year 2100: mild scenario (B1) having a real-time

temperature adjusted to +1.5°C and +200 ppm CO2; intermediate scenario (A1B) with

+2.5°C and +400 ppm CO2; and the extreme scenario (A2) with +4.5°C and +850

ppm CO2 , all relative to control room.

Six PVC tanks (60 l each) containing 10 juveniles of tambaqui per tank, were

maintained in each of the microcosms for 150 days. Along the experiment, 30% of

the water was replaced every other day using environmentally stabilized water. The

fishes were transferred to each experimental microcosm (i.e. control and B1, A1B

and A2 scenarios) two days before to beginning the experiments. The pH, levels of

O2 and CO2 and water temperature were measured daily (Table 1).

Table 1. Physicochemical parameters of water and air in the microcosms (control,

B1, A1B, and A2) where the specimens of tambaqui were maintained for 150 days.

The data are reported as mean ± SEM (n= 6).

pH Water O2 (mg l-1)

Water CO2

(ppm)

Water temperature

(oC)

Enviromental CO2 (ppm)

Enviromental temperature

(oC) Control 5,8±0,1 7,0±0,1 15,5±0,4 26,0±0,1 478,6±0,2 28,0±0,01

B1 scenario

5,5±0,1 6,5±0,1 18,2±0,5 27,2±0,1 690,1±0,2 29,2±0,01

A1B scenario

5,5±0,1 6,2±0,1 20,0±0,3 28,3±0,2 898,3±0,2 30,4±0,01

A2 scenario

5,4±0,1 6,0±0,1 23,7±0,4 30,2±0,1 1325,9±0,2 32,4±0,01

One fish was removed from each tank at 0, 5, 15, 30, 60, 90, 120, and 150

days totally six fishes per scenario on each exposure time. After blood sampling, the

fishes were euthanized by rapidly severing their spinal cord with a scalpel, and a

sample of gills was collected for the experimental procedures determinations

described below.

34

Evaluation of genetic damage in erythrocytes

Comet assay. The assay was performed according to Singh et al. 1988 with

modifications. Firstly, a heparinized syringe was used to collect a blood sample from

the caudal vein from fishes. The blood was immediately transferred to an Eppendorf

tube with RPMI 1640 medium. Briefly, 5 µL of diluted blood were mixed with 95 µL of

0,75% (w/v) low melting agarose at 37oC and layered onto microscope slides

precoated with 1.5% (w/v) normal melting agarose and immediately covered with a

coverslip. After waiting 10 minutes to agarose solidify, the coverslips were removed

and slides were immersed in cold freshly made lysing solution (2.5 M NaCl, 10 mM

Tris, 100 mM EDTA, pH 10.2, 10% DMSO and 1% Triton X-100) and refrigerated at

4ºC for 2-24 hours. Slides were subsequently incubated in alkaline buffer (300 mM

NaOH and 1 mM EDTA, pH 13) for 20 min to allow DNA unwinding and horizontal

electrophoresis (300 mA and 25 V at 4oC) were performed. The steps described

above were carried out under indirect red light to avoid DNA damage. After

electrophoresis, the slides were washed three times with neutralizing buffer (0.4M

Tris, pH 7.5) during 5 min each time, then rinsed in distilled water, and left to dry

overnight at room temperature. The following step was the fixation of slides for 10

min (trichloroacetic acid 15% w/v, zinc sulfate 5% w/v, and glycerol 5% v/v), rinsing

three times with distilled water, and drying overnight at room temperature. Dried

slides were re-hydrated for 5 min in distilled water, and then submerged in silver

solution (5% sodium carbonate, 0.1% silver nitrate, 0.1% ammonia nitrate, 0.25%

acid tungstosilicic and 0.15% formaldehyde) to stain during 15 min at 37oC in water

bath. After stain, slides were cleaned with stop solution (acetic acid 1%) and rinsed

again in distilled water. After dried, slides were analyzed using an optical microscope

(Leica DM205) selecting randomly 100 cells from two replicate slides from each fish.

The cells were scored visually according to tail length into five classes, being class 0

undamaged and class 4 complete damage (comets with no heads), according to

Kobayashi et al. (1995). The DNA damage was evaluated according to two

parameters: Genetic Damage Indicator (GDI) and Cell DNA Integrity (CDI). The GDI

from each fish were calculated based on number of cells observed in each damage

class multiplied by arbitrary units in a scale of 0–400, according to respective

damage class: from zero (100 x 0 [100 undamaged cells]) to 400 (100 x 4 [100 cells

with maximum damage]). The CDI was calculated as mean of the number of cells

scored as class 0 in each group.

35

Physiological analyses

Blood parameters. From the total blood, both the hematocrit (Hct) and the total

content of haemoglobin in erythrocytes were measured immediately after collection.

Haemoglobin concentration (Hb) was measured spectrophotometrically in duplicate

using the Drabkin reagent. The red blood cell count (RBC) was performed in an

optical microscope (Leica DM205) with a conventional Neubauer hemocytometer,

and hematocrit (Hct) was determined after centrifugation of microhematocrit tubes

containing blood for 5 min at 10,000 g and percentage of red cell sedimentation was

determined using a microhematocrit reader card. The mean corpuscular volume

(MCV), mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin

concentration (MCHC) were determined according to the equations described by

Brown et al. (1976).

Plasma ion concentrations. After determination of blood parameters, plasma

samples were separate by centrifugation for 5 min at 10,000 g. A portion of obtained

plasma was diluted 1000 times to measure Na+ and Cl- levels, and another portion

diluted 200 times in 0.2% lanthanum chloride was used to determine Ca2+, K+ and

Mg2+. The concentrations of Na+, K+, Ca2+ and Mg2+ were determined in duplicate

using a flame atomic absorption spectrophotometer (PerkinElmer, AAnalyst-800,

Singapore), while Cl- concentration was determined spectrophotometrically

(Spectramax Plus 384; Molecular Devices, Sunnyvale, CA) by the mercuric

thiocyanate method described by Zall et al. (1956).

Branchial Na+/K+-ATPase and H+-ATPase activity. The activities of both

Na+/K+-ATPase and H+-ATPase in gills of tambaqui were determined using the assay

described by Kültz and Somero (1995). The assay is based on the oxidation of

reduced NADH by the enzyme reaction coupled to the hydrolysis of ATP. Briefly,

frozen gill were homogenate in ice-cold SEID buffer (150 mM sucrose, 50 mM

imidazol, 10 mM EDTA, 0,5% Na-desoxycholate, pH 7.5) at 1:10 wet sample mass to

buffer volume. Crude homogenates were then centrifuged (4oC, 2000 g) for 10 min

and the supernatant was collected for enzymatic assay. The supernatant (5 µl) were

added to 12 wells of 96 well microplate and incubated in salt solution (30 mM

imidazol, 45 mM NaCl, 15 mM KCl, 3 mM MgCl2.6H2O, 0.4 mM KCN) on microplate

reader (Spectramax Plus 384; Molecular Devices, Sunnyvale, CA) at 27 and 33oC.

After 5 min, 200 µl of the reaction solution (1.0 mM ATP, 0.2 mM NADH, 0.1 mM

fructose 1,6 difosfate, 2 mM PEP, 3 IU ml-1 PK and 2 IU ml-1 LDH) were added to the

36

wells to start the assay. Four of twelve wells received reaction solution with 2 mM

ouabain, while crude homeganate in other four wells received reaction solution with 2

mM N-ethylmaleimide. The rate of NADH oxidation was monitored every 10 s over 10

min at 340 nm in each experimental temperature and pH conditions. The difference

in slope of NADH oxidation versus time reaction between reaction solution free of

inhibitors and containing the inhibitors (ouabain and N-ethylmaleimide) were used to

determine Na+/K+-ATPase and H+-ATPase activity, respectively. Both enzyme

activities were presented as µmol h-1 mgprotein-1. Protein concentration in gills crude

homogenates was determined using the Bradford method (Bradford, 1976).

Statistical analysis

All data are expressed as mean ± standard error of the mean (N=6; mean ±

SEM). Two-way ANOVA followed by Holm-Sidak multiple comparison test was used

to test significant differences in genetic damage of erythrocytes and physiological

endpoints between fish exposed to each climate change scenario, and also

throughout the exposure time. The two-way ANOVA was performed using Sigma Stat

software (Systat Software Inc., USA). Results were considered statistically significant

at P<0.05.

Results

Comet assay

Tambaqui exposed for 30 days to both intermediate (A1B) and extreme (A2)

climate change scenarios revealed a significantly higher amount of DNA damage in

erythrocytes, evidenced by an average of 1.8 times increase of GDI values, in

relation to fish in the control scenario at the same time of exposure (P<0.007) (Figure

1). Following 30 days of exposure, tambaqui exposed to the mild (B1), intermediate

(A1B) and extreme (A2) climate change scenarios presented 2.5, 2.1 and 1.9 times

higher GDI (P<0.005), respectively, than seen in fish at the respective treatment on

the beginning of the exposure period (T0) (Figure 1). No significant changes in the

erythrocytic DNA integrity were observed in tambaqui exposed to different climate

change scenario, throughout the entire time of exposure (Figure 2).

37

Fig. 1. Genetic Damage Indicator (GDI) in erythrocytes of tambaqui over the

experimental period of exposure to control, B1, A1B and A2 climate scenarios. The

data are reported as the mean ± SEM (n= 6). t Indicates significant difference

compared to control scenario on same exposure time (P<0.05). * Indicates significant

difference compared to time zero (P<0.05).

Time exposure (days)

0 5 15 30 60 90 120 150

GD

I va

lues

(0-4

00

)

0

50

100

150

200

250

300

Control B1 scenario A1B scenario A2 scenario

* * t t

t t

38

Fig. 2. Cell DNA Integrity (CDI) in erythrocytes of tambaqui over the experimental

period of exposure to control, B1, A1B and A2 climate scenarios. The data are

reported as the mean ± SEM (n= 6).

Blood parameters

A significant effect of exposure time at each climate change scenario on Hct,

Hb and RBC levels of tambaqui (P<0.001). Following 30 to 150 days of exposure,

tambaqui revealed a significantly increase in Hct (P<0.046) at control, intermediate

(A1B) and extreme (A2) climate change scenario (Table 2). Under mild scenario (B1),

Hct of tambaqui was significantly increased on average 1.3 times (P<0.028), but only

after 60, 120 and 150 days of exposure in relation to T0 (Table 2). Also, the Hb

concentration of fish exposed for 15 and 60 days to the control, B1, A1B and A2

scenarios was significantly increased (P<0.037). Following 90 days of exposure,

control tambaqui showed 1.5 times (P<0.04) increase in Hb concentration, whereas

fish at the extreme scenario (A2) displayed 1.6 times (P<0.021) higher content of Hb,

in relation to fish at T0 (Table 2).


0 5 15 30 60 90 120 150

CD

I va

lue

s (%

)

0

20

40

60

80

100


39

The RBC levels in tambaqui were significantly increased at an average of 1.4

times after 60 and 120 days of exposure to the control (P<0.033), and at an average

of 1.3 times in fish at intermediate scenario (A1B) after 30 and 120 days of exposure

(P<0.021), and at 90 days of exposure to A2 scenario (P<0.045) (Table 2).

Plasma ion concentrations

Tambaqui exposed for 5 days to the mild scenario (B1) exhibited Na+

concentration in plasma 1.2 times higher (P=0.02) than seen in fish at the same time

of exposure at the control scenario (Table 3). Within the analyzed climate change

scenario, plasma concentration of Na+ was significantly increased by 1.3 times

(P=0.001) and 1.2 times (P=0.006) only in tambaqui exposed to A1B scenario, after

15 and 150 days of exposure, respectively (Table 3). Regarding to plasma Cl- levels,

only minor effects were observed in fish as at 5 days under A2 scenario a significant

(P=0.015) increase of 1.2 times of Cl-, compared to control at the same time of

exposure (Table 3).

Fish exposed for 30 days to the mild scenario (B1) also showed 1.5 times

(P=0.005) increased K+ concentration in plasma in relation to animals under control

and 2.1 times after 30 days and 1.6 times after 60 days of exposure, in comparison

with animals at T0. Similarly, fish showed an increase at an average of 2 times of

plasma K+ following exposure to A1B scenario after 15 and 120 days (P<0.045),

which was also seen in tambaqui exposed to the extreme scenario (A2) after 30 and

90 days of exposure (P<0.01) (Table 3).

.

40

Table 2. Hematological parameters in tambaqui over the experimental exposure to the control, B1, A1B and A2 climate scenarios. The

data are reported as mean ± SEM (n= 6). t Indicates significant difference compared to control scenario on same exposure time

(P<0.05). * Indicates significant difference compared to time zero (P<0.05).

Hct (%) Hb (g dl-1) RBC (106mm-3) MCV (mm3) MCH (pg) MCHC (%)

Control scenario

0 day 20,33±3,27 5,56±1,41 1,72±0,19 121,52±18,89 37,00±12,62 28,05±7,37 5 days 21,33±2,32 5,76±1,04 1,70±0,26 131,56±10,51 40,94±13,12 29,14±6,75 15 days 23,67±1,38 8,29±1,34t 1,87±0,26 137,06±17,90 52,10±12,17 36,35±6,58 30 days 25,50±1,12t 7,46±0,96 2,19±0,16 118,01±4,91 35,30±5,19 29,50±3,86 60 days 30,67±1,80t 8,40±0,55t 2,52±0,13t 124,20±10,94 33,62±2,29 27,71±2,17 90 days 26,25±0,96t 8,12±0,70t 2,20±0,08 120,58±7,40 36,79±2,17 31,14±2,96 120 days 28,83±1,35t 6,36±0,21 2,29±0,15t 128,13±8,02 28,24±1,38 22,17±0,61 150 days 27,58±1,21t 6,25±0,60 1,98±0,17 145,06±15,40 31,58±1,69 23,07±2,86

B1 scenario

0 day 21,67±1,26 5,22±1,35 2,01±0,18 110,86±9,21 28,09±8,40 23,91±6,13 5 days 21,83±1,51 5,95±1,21 1,28±0,18t 178,61±12,08t* 49,14±10,17 27,17±5,12 15 days 23,83±0,48 8,43±1,23t 1,62±0,17 153,31±12,27 55,65±10,40 35,48±5,23 30 days 24,50±1,18 7,22±1,09 2,03±0,21 125,72±10,22 36,37±4,63 28,97±3,53 60 days 29,33±1,73t 8,14±0,56t 2,11±0,14 140,35±6,70 38,75±1,64 28,07±2,25 90 days 24,83±0,95 7,75±0,63 2,15±0,16 118,83±9,93 36,45±2,66 31,66±3,60 120 days 26,25±0,87t 5,87±0,43 2,28±0,16 116,80±5,80 25,80±1,14 22,34±1,46 150 days 28,50±0,87t 6,51±0,86 1,88±0,16 159,47±19,24t 34,38±2,95 22,85±2,70

A1B scenario

0 day 18,17±1,82 4,98±1,13 1,83±0,36 154,06±60,33 80,79±11,14 32,03±11,14 5 days 21,50±0,89 5,50±1,03 1,54±0,17 146,01±12,02 37,83±4,15 25,21±4,15 15 days 23,50±1,15t 8,30±1,10t 1,66±0,18 146,19±8,59 55,43±5,47 36,42±5,47 30 days 28,83±0,98t 6,82±0,98 2,54±0,26t 118,75±10,68 29,01±3,66 23,86±3,66 60 days 28,00±1,67t 8,11±1,67t 2,02±0,10 139,71±7,62 40,15±2,73 29,47±2,73 90 days 28,42±0,84t 8,65±0,84t 2,24±0,10 128,18±5,87 38,81±1,70 30,39±1,70 120 days 28,25±1,03t 6,96±1,03 2,44±0,18t 117,62±5,29 29,37±1,08 24,78±1,08 150 days 26,67±1,30t 6,87±1,30 1,99±0,21 138,64±9,82 34,67±2,11 25,63±2,11

A2 scenario

0 day 22,67±1,87 5,61±1,45 1,88±0,14 122,07±9,51 31,15±8,35 24,91±6,23 5 days 20,83±1,30 5,35±1,01 1,66±0,29 146,54±25,21 34,45±5,19 25,23±4,24 15 days 23,83±1,51 8,58±1,16t 1,78±0,24 141,31±12,35 53,96±10,45 36,38±5,05 30 days 26,83±1,51t 6,54±1,07 2,20±0,11 123,36±9,70 30,44±5,56 25,20±4,84 60 days 30,17±2,20t 8,56±0,66t 2,38±9,70 127,25±9,42 36,24±3,07 29,12±3,20 90 days 28,50±1,34t 8,65±0,40t 2,41±9,42t 121,45±9,99 37,15±3,61 30,76±2,16 120 days 28,75±0,72t 6,16±0,32 2,35±9,99 124,52±5,89 26,44±0,69 21,39±0,80 150 days 27,08±1,21t 6,75±0,69 1,97±5,89 138,67±6,73 34,46±3,58 24,82±2,19

41

Table 3. Ion plasma concentration (mEq l-1) in tambaqui over the experimental exposure to control, B1, A1B and A2 climate scenarios.

The data are reported as the mean ± SEM (n= 6). t Indicates significant difference compared to control scenario on same exposure time

(P<0.05). * Indicates significant difference compared to time zero (P<0.05).

[Na+] [K+] [Cl-] [Ca2+] [Mg2+]

Control scenario

0 day 169,62±6,01 4,25±0,28 142,35±2,09 1,22±0,04 0,79±0,03 5 days 162,98±8,45 4,70±0,51 140,30±3,81 1,00±0,14 0,39±0,02t 15 days 176,27±4,81 7,50±1,34t 155,08±3,00 1,30±0,14 0,49±0,03t 30 days 170,23±7,03 7,14±0,90t 145,36±6,48 1,24±0,08 0,43±0,03t 60 days 157,65±4,68 8,83±0,94t 139,51±3,36 1,15±0,08 0,47±0,03t 90 days 168,02±3,78 5,80±0,38 145,19±2,10 1,07±0,13 0,44±0,04t 120 days 184,37±5,16 6,42±0,60 147,63±3,99 1,46±0,13 0,50±0,04t 150 days 159,55±13,35 5,32±0,80 126,67±7,22 1,15±0,09 0,48±0,04t

B1 scenario

0 day 158,13±6,00 5,12±0,52 146,26±6,65 1,48±0,08 0,91±0,04* 5 days 190,65±17,42t* 6,52±2,78 157,85±11,85 1,38±0,44* 0,37±0,04t 15 days 179,73±6,03 6,17±0,99 161,31±4,94 1,19±0,04 0,37±0,03t* 30 days 169,08±8,60 10,58±0,79t* 139,51±5,65 1,15±0,06 0,41±0,03t 60 days 157,72±11,02 8,37±0,39t 137,23±8,00 0,98±0,09t 0,37±0,03t 90 days 169,80±5,25 7,31±0,36 143,40±3,10 0,98±0,13t 0,37±0,03t 120 days 170,08±5,95 6,23±1.36 137,55±4,68 1,22±0,20 0,46±0,05t 150 days 166,23±15,23 4,32±0,36 140,56±11,38 1,03±0,07t 0,45±0,02t

A1B scenario

0 day 148,53±7,80 3,74±0,74 134,77±6,27 1,38±0,19 0,77±0,06 5 days 96,67±20,94 5,93±0,46 151,31±5,27 1.24±0,10 0,51±0,04t* 15 days 189,72±9,31t 6,24±0,98t 158,35±5,87t 1,30±0,17 0,47±0,09t 30 days 169,83±2,67 9,38±0,85t 140,65±2,34 1,08±0,12 0,38±0,04t 60 days 155,72±7,11 8,20±0,79t 125,21±14,61 0,77±0,09t* 0,39±0,03t 90 days 157,19±12,12 6,95±0,84t 134,47±9,66 1,07±0,11 0,45±0,03t 120 days 162,88±7,68 6,19±0,61t 132,03±6,06 1,03±0,08* 0,43±0,02t 150 days 181,60±8,17t 4,29±0,62 141,13±5,06 1,13±0,12 0,45±0,03t

A2 scenario

0 day 169,57±11,14 3,72±0,37 149,75±8,02 1,90±0,15* 0,42±0,04* 5 days 174,58±8,68 4,12±0,52 162,58±3,49* 1,16±0,09t 0,46±0,04 15 days 192,22±8,23 5,82±0,61 158,19±4,51 1,22±0,06t 0,43±0,03 30 days 159,82±3,06 7,92±0,70t 137,23±1,53 1,19±0,12t 0,36±0,03 60 days 153,47±8,09 7,09±0,65t 133,98±5,89 0,95±0,09t 0,47±0,01 90 days 173,80±2,81 6,86±0,22t 138,53±2,13 0,94±0,09t 0,46±0,03 120 days 165,63±5,02 5,77±0,35 134,15±2,84 1,27±0,10t 0,47±0,04 150 days 161,82±7,39 4,95±0,24 136,75±8,56 1,38±0,05t 0,52±0,01

42

Interestingly, there was a significant imbalance in the concentration of divalent

cations in plasma of tambaqui exposed to the different climate change scenarios. At

both mild (B1) and extreme (A2) scenarios, tambaqui exhibited plasma Ca2+

concentration increased by 1.4 times after 5 days (P=0.046), and 1.6 times at the T0

(P<0.001), respectively; whereas in fish exposed for 60 and 120 days to the

intermediate scenario (A1B), the Ca2+ concentration was at an average of 0.7 times

reduced (P<0.048), in comparison with animals at the same time of exposure at the

control scenario. In fish exposed to the control climate scenario, B1 and A1B the

concentration of Mg2+ in plasma was significantly lowered by an average of 0.7 times,

1.3 time and 0.8 times, respectively (P<0.001), in relation to T0 under the respective

analyzed scenario (Table 3). In addition, plasma Mg2+ concentration was also

significantly reduced by 0.8 times (P<0.001) in tambaqui exposed to the A2 scenario

at T0, and by 0.3 times (P=0.025) at B1 scenario after 15 days, when compared with

animals under the control scenario at the same time of exposure (Table 3).

Branchial Na+/K+-ATPase and H+-ATPase activity

In comparison with fish at the control scenario, tambaqui revealed a

stimulation in the activity of Na+/K+-ATPase (NKA) in gills by 1.7 times (P=0.014) and

3.2 times (P<0.001) at B1 scenario after 5 and 30 days, respectively, while an

increase in an average of 1.3 times (P<0.016) in NKA activity was seen in fish

exposed for 30 and 150 days to A1B scenario (Figure 3). In contrast, fish exposed to

A1B scenario showed inhibition of NKA activity by 1.2 times at T0 (P=0.039) and 1.9

times after 15 days of exposure (P=0.031) (Figure 3).

43

Fig. 3. Na+/K+-ATPase activity in the gills of tambaqui over the experimental period of

exposure to the control, B1, A1B and A2 climate scenarios. The data are reported as

the mean ± SEM (n= 6). t Indicates significant difference compared to control

scenario on same exposure time (P<0.05). * Indicates significant difference

compared to time zero (P<0.05).

Overall, NKA activity in gills of tambaqui was significantly increased over the

exposure period to each climate change scenarios (B1, A1B and A2). Fish exposed

to both B1 and A1B scenarios exhibited NKA activity increased at an average of 3.0

times and 2.9 times (P<0.001) following 30, 90, 120 and 150 days of exposure,

whereas NKA activity was also significantly increased in gills of fish after 5 days of

exposure to mild scenario (B1). Similarly, at the extreme scenario (A2), the activity of

NKA was also increased, but only at an average of 2.2 times (P<0.016) after 5, 15,

90 and 120 days of exposure (Figure 3).

Instead, fish exposed to both A1B and A2 scenarios showed inhibition of H+-

ATPase activity in gills by 4.9 times and 2.9 times after 5 days (P<0.001), by 5.9

times and 7.1 times after 15 days (P<0.005), by 3.2 times and 4.7 times after 120


0 5 15 30 60 90 120 150

AT

Pase

act

ivity

(µ

mo

l AT

P h-1

mg p

rote

in-1

)

0.0

0.5

1.0

1.5

2.0

2.5

3.0


* * t

t

t

t

t

t

t

t

t t

t

t

t

t

t

t

t

*

*

*

*

*

44

days (P<0.001) and by 1.6 times and 2.8 times after 150 days of exposure

(P<0.032), respectively (Figure 4). Similarly, in tambaqui at mild scenario (B1), the

activity of H+-ATPase was significantly inhibited by 2.3 times and 2.6 times (P<0.005)

after 120 and 150 days of exposure (Figure 4). Interestingly, 60 days of exposure to

both B1 and A1B scenario markedly inhibit H+-ATPase in gills of tambaqui, as seen

by 20.3 times and 29.9 times inhibition of H+-ATPase activity in comparison with fish

at the control scenario (Figure 4).

Fig. 4. H+-ATPase activity in the gills of tambaqui over the experimental period of

exposure to the control, B1, A1B and A2 climate scenarios. The data are reported as

the mean ± SEM (n= 6). t Indicates significant difference compared to control

scenario on same exposure time (P<0.05). * Indicates significant difference

compared to time zero (P<0.05).


0 5 15 30 60 90 120 150

AT

Pase

act

ivity

(µ

mol A

TP

h-1 m

g p

rote

in-1)

0

1

2

3

4

5

6


*

t

t

t

t t

t

t

t t

t

t

t

t

t

t

t t

t

t

t t

t

t t

*

*

*

*

*

*

*

* *

*

*

*

* *

45

The comparison within each analyzed climate change scenario evidenced that

the time of exposure significantly affects the activity of H+-ATPase in gills of

tambaqui. At mild scenario (B1), a significant inhibition of 4.5 times, 8.3 times and 2.2

times in H+-ATPase activity (P<0.001) was seen in gills of fish following 15 days, 30

days and 120 days, respectively (Figure 4). Interestingly, 60 days of exposure to both

B1 and A1B scenarios promote an almost complete inhibition of branchial H+-ATPase

activity in tambaqui (P<0.001). In addition, fish exposed to intermediate scenario

(A1B) also showed H+-ATPase activity inhibited by 6.5 times, 14.3 times, 4.8 times,

2.1 times, 4.1 times and 3.4 times after 5, 15, 30, 90, 120 and 150 days of exposure

(P<0.001), respectively. At the extreme scenario (A2), H+-ATPase activity in gills of

tambaqui was inhibited at an average of 11.9 times over the exposure time, in

comparison with fish at T0 (Figure 4).

Discussion

To understand how organisms are responding to climate change is important

to protect the planet against consequences triggered by global warming (Cheng et

al., 2013). Climate change imperils all environments, being the greatest threat to the

aquatic biodiversity (IPCC, 2007; 2014), once water temperature is one of the major

abiotic factors that influences most activities of aquatic organisms, such as growth,

reproduction, migration, behavior and feeding (Portner and Farrell, 2008). Indeed,

increased temperature is being considered a significant threat to the aquatic fauna,

as well as to the ecological balance of ecosystems, once the risk of loss of genetic

variability in wild populations might have significant consequences for the long-term

survival of exposed populations (Buschini et al., 2003; Long et al., 2012).

In the present study, we evaluate physiological responses of tambaqui to three

climate change scenarios foreseen by the IPCC for 2100. Since it is well established

that marked changes in water temperature and CO2 levels will directly affect the

integrity and functions of erythrocytes in fish, comet assay was used to investigate

the potential genotoxic effect of the climates scenarios tested on the erythrocytes of

tambaqui. Our results showed a significant increase of DNA strand breaks after 30

days of exposure to all experimental climate scenarios. In erythrocytes of goldfish

(Carassius aurata), the higher the water temperature the higher the DNA damage,

evidencing the high mutagenic effect of increased temperature (Anitha et al., 2000;

Buschini et al., 2003). Similarly, DNA damage has been related to increased CO2

46

levels, as seen by Montalto et al. (2013). Usually, increased DNA damage,

evidenced by the comet assay, is positively correlated with a pro-oxidant state inside

the cells, which is achieved when the increment in the generation of reactive oxygen

species (ROS) overload the intracellular antioxidant defenses (Abele and Puntarulo,

2004). If this DNA lesion is not repaired rapidly, it can start a cascade of biological

events that might compromise the survival of the cell, resulting in apoptosis and cell

death. DNA damage in a number of aquatic organisms has triggered abnormal

development, reduced growth, and committed survival of embryos, larvae and adults

(Simoniello et al., 2009; Kienzler et al., 2013).

However, the decrease in DNA damage, particularly after 60 days of

exposure, might indicate the ability of repair mechanisms of DNA lesions, loss of

severely damaged cells, or both mechanisms (Mitchelmore and Chipman, 1998;

Kienzler et al., 2013). Another plausible explanation for the reduction in formation of

DNA strand breaks in erythrocytes of tambaqui might be the activation of genes

related to temperature acclimation, such as heat shock proteins (HSPs), which are

known to mediate the repair and degradation of altered proteins, including cellular

structures damaged by high temperatures (Anitha et al., 2000; Basu et al., 2002;

Roessig et al., 2004). These effects correlate well with the data from a parallel study

performed in the same facility, where the expression of genes from both Hsp 40 and

Hsp 90 families were up-regulated in tambaqui exposed to same climate change

scenarios (Prado-Lima and Val, 2016).

In the present study, the hematological parameters of tambaqui were slightly

affected by climate change exposure. However, within the various analyzed climate

change scenarios, tambaqui exhibited a marked trend to increase the Hct, Hb and

RBC levels, especially between 30 and 150 days of exposure. Alterations in

hematological parameters of freshwater fish are recognized to have strong effects on

oxygen transfer, being continuously adjusted under both physiological and

environmental constraints (Val, 1995; Val and Almeida-Val, 1995; Tavares-Dias and

Moraes, 2004). In the Atlantic salmon parr, long-term exposure to increased CO2

levels promoted a significant decrease in Hct and MCV, and also increased Hb

concentration inside the erythrocytes (MCHC), suggesting a strong erythrocyte

shrinkage in fish (Fivelstad et al., 2007). However, in this fish species acclimated to

freshwater condition, the effects of increased CO2 levels were lowered at higher

temperature condition (Fivelstad et al., 2007). In contrast, the enhanced levels of Hct,

47

Hb and RBC of tambaqui exposed to the various climate change scenarios were

similar to those responses previously reported by Val (1995), when tambaqui was

exposed to hypoxic, or even anoxic, conditions. In elevated temperature conditions,

as simulated in this study, the metabolism of ectothermic organisms is up-regulated

to face the increased demand for oxygen in tissues. Thus, it is likely that under

simultaneous increases in temperature and CO2 levels the number of erythrocytes is

increased to compensate for impairment in oxygen delivery to tissues.

The concentration of monovalent ions in plasma of tambaqui was slightly

affected by the simultaneous exposure to increased temperature and CO2 levels. In

general, there is a strong relationship between the concentration of the primary

plasma ions and the magnitude of the branchial and extra-branchial ion losses in fish

(Wood, 1989; Wood et al., 1998). The short-term exposure to the gradual increment

in water temperature resulted in enhanced net losses of Na+ and Cl- in Metynnis

hypsauchen, an Amazonian fish species from the Serrasalmidae family, which was

associated with an increase of branchial permeability to ions mediated by

temperature. In contrast, in the Amazonian fish Pterygoplychthys pardalis, the

exposure to high PCO2 levels in water resulted in significant reduction in net losses of

Na+ and Cl- (Brauner et al., 2004), suggesting that under hypercapnia conditions the

permeability of branchial membrane might be regulated to reduce ionoregulatory

disturbances associated with Na+ and Cl- homeostasis.

Surprisingly, plasma Cl- concentration in tambaqui was slightly, but

significantly, increased following 5 days of exposure to the extreme scenario (A2),

and also after 15 days to the intermediate scenario (A1B) in comparison with fish at

T0. In contrast, many previous studies have demonstrated a decline of plasma Cl-

levels of freshwater fish after short- and long-term exposure to high PCO2 levels,

which is strongly related to an equivalent increase in plasma bicarbonate

concentration, as a physiological response to compensate the elevation in the blood

PCO2 (Heisler, 1984; Fivelstad et al., 1998; Fivelstad et al., 2003; Fivelstad et al.,

2015). Thus, adjustments in both bicarbonate and Cl- levels seems to be the

predominant mechanism for the extracellular pH regulation in fish, particularly in

those environmental conditions, as increased temperature and hypercapnia, which

are expected to promote acid-base disturbances in animals (Heisler, 1984). Although

most of the studied fish species show a preferential extracellular regulation to

maintain their internal acid-base homeostasis under internal acidosis promoted by

48

hypercapnia (Brauner et al., 2004), the Amazonian fish P. pardalis exhibited a

preferential intracellular regulation of pH to compensate marked acid-base

disturbances (Harter et al., 2014). In this fish species, both the extracellular pH and

blood levels of HCO3- were reduced after internal acidosis induced by the exposure

to anoxic condition, which was tightly associate with the reduction in intracellular pH

of RBC. Indeed, it suggests that blood bicarbonate is intracellularly transported,

perhaps by a Cl-/HCO3- exchange, to buffer the elevation in intracellular H+ levels,

resulting in higher intracellular PCO2 and, consequently, increased levels of plasma

Cl-. Although our data suggest that tambaqui exposed to the analyzed climate

change scenarios might maintain their plasma Cl- levels to preferentially regulate the

intracellular pH, in detriment of the extracellular pH, as a compensatory response to

acid-base disturbances, these mechanisms requires further investigation.

Interestingly, plasma K+ levels were also increased after 30 days of exposure

to the mild scenario (B1), while within the analyzed scenarios, the concentration of

plasma K+ was also enhanced in tambaqui exposed to B1 (30 to 60 days), A1B (15 to

120 days) and A2 (30 to 90 days), in comparison with fish at T0. Increased plasma

K+ levels of fish have been related to disturbances of the membrane integrity of

erythrocytes, implying in a marked red blood cell disruption. Thus, we suggest that

such an increase in plasma K+ levels of tambaqui is related to red blood cells swelling

induced by climate change exposure (see discussion above). Although tambaqui

exhibited a slight increase in plasma Ca2+ levels after 5 days of exposure to mild

scenario (B1), and at T0 in the extreme scenario (A2), a general trend of reduction in

Ca2+ concentration in plasma was observed within the analyzed scenarios. Similarly,

at B1 and A1B scenarios a significant reduction in plasma Mg2+ levels of tambaqui

was seen over the exposure period. Reduction of plasma levels of divalent cations

(Ca2+ and Mg2+) have been associated with depletions in several fundamental

biological process as transmembrane ion leakage, electrical activity of cells and

protein synthesis, as well as to decreased buffering capacity of extracellular fluids in

aquatic organism, as turtles (Johnson et al., 2000) and fish (Heisler, 1984)

experiencing internal acidosis. One possible explanation for the reduction of plasma

Ca2+ and Mg2+levels of tambaqui exposed to climates change scenarios is its

complexation with lactate molecules, which have been shown to be enhanced in

blood in fish under short term and prolonged conditions of acidosis (Heisler, 1984;

Harter et al., 2014). Although our data suggest that the concentration of plasma

49

divalent ions of tambaqui is down-regulated as a consequence of uncompensated

extracellular acidosis induced by the simultaneous increase in temperature and CO2

levels, additional evidence regarding these exact mechanisms certainly needs further

investigations.

Short- and long-term changes in water quality properties have been

recognized to modulate the entire functional capacity of gills of freshwater fish,

particularly through alterations of active transport of ions from the surrounding water

to blood, to maintain their internal ion homeostasis (Hwang et al., 2011). In gills of the

freshwater teleost, the mechanisms for Na+ uptake are tightly coupled to excretion of

acid products from the intermediate metabolism (e.g. H+ and NH4+), and a central role

of both Na+/K+-ATPase and H+-ATPase in the maintenance of the required gradient

that drives Na+ uptake in fish have been proposed (Kirschner, 2004; Evans et al.,

2005). However, changes in the activity of both enzymes have been found in gills of

freshwater fish under short- and long-term exposures to increased temperature and

hypercapnia conditions (Metz et al., 2003; Fivelstad et al., 2007). In our study,

significant increases in the activity of Na+/K+-ATPase were seen in tambaqui after 5

and 30 days of exposure to the mild scenario (B1), as well as in fish exposed for 30

and 150 days to the intermediate scenario (A1B). Also, increased H+-ATPase activity

was only found in gills of tambaqui exposed to both B1 (5 and 90 days) and A1B (T0)

scenarios. Enhanced activity of ATPases in gills of fish exposed to increased

temperature has been reported for rainbow trout (Pfeiler, 1978) and goldfish (Murphy

and Houston, 1974), as a compensatory response to increased branchial

permeability to Na+ mediated by temperature. Furthermore, at elevated CO2

conditions, the hydration of CO2 in the intracellular compartment is thought to provide

acid products (H+) that would be the primary source of the electrogenic proton pump

activity (Randall et al., 1996). However, inhibition of both enzymes was markedly

more evident in gills of tambaqui exposed to the three analyzed climate change

scenarios. In these animals, the activity of Na+/K+-ATPase had a tendency to

decrease within the first 30 days of exposure to both B1 and A1B scenarios, while

only after 150 days under the extreme scenario (A2) the activity of Na+/K+-ATPase

was significantly reduced. In contrast, short-term (5 to 15 days) exposure to

intermediate (A1B) and extreme scenarios (A2) causes a marked inhibition of H+-

ATPase in gills of tambaqui. Note that long-term reduction in H+-ATPase was found

in all three analyzed scenarios, particularly after 120 and 150 days of exposure to

50

both A1B and A2 scenario. Increases in environmental temperature could affect

cellular lipid composition of fish gills, inhibiting the activity of membrane-bound

ATPase as a result of thermo-instability of proteins of the branchial membrane (Hazel

and Prosser, 1974; Metz et al., 2003). It has also been demonstrated that a moderate

to high hypercapnia conditions decrease the activity of Na+/K+-ATPase, but not H+-

ATPase in gills of the Atlantic cod (Gadus morhua) at different temperature regimes.

Overall, our data indicate that climate change exposure might promote an imbalance

in the activity of key enzymes for ion regulation in gills of tambaqui, adversely

affecting the generation of the electrochemical gradients that serves as the driving

force for Na+ uptake in the gills of this fish species that otherwise thrives poor ion

waters.

In conclusion, the exposure for 150 days to the three analyzed climate change

scenarios promote severe changes in hematological parameters, plasma ionic

composition and branchial activity of Na+/K+-ATPase and H+-ATPase of tambaqui.

However, a relationship between the degree of alteration and analyzed scenarios

(B1, A1B, and A2) was not evident. Tambaqui exhibited a marked trend to increase

the Hct, Hb and RBC levels in order to compensate for an impairment in oxygen

delivery to tissues. While only minor effects on plasma levels of both Na+ and Cl-

were seen in tambaqui, the exposure to the climate scenarios tends to increase K+

concentration and strongly reduce the plasma levels of Ca2+ and Mg2+. The severity

of the analyzed climate scenarios caused an apparent inhibition of both Na+/K+-

ATPase and H+-ATPase activity in tambaqui, which affect ion homeostasis of fish

under a longer time of exposure. Furthermore, it is noteworthy that the analyzed

climate change scenarios were genotoxic to fish, suggesting that climate change

might adversely act as inducers of DNA damage in erythrocytes of tambaqui.

Funding

This work was supported by Brazilian National Research Council (CNPq:

573976/2008-2) and the Amazon State Research Foundation (FAPEAM: 3159/08)

through the INCT-ADAPTA; M.P.L. was a recipient of a Ph.D. fellowship from

CAPES; A.L.V. is a recipient of a research fellowship from CNPq.

51

Acknowledgements

We thank Dra. Alzira Miranda de Oliveira, MSc. Fernanda Dragan and MSc.

Maria de Nazaré Paula da Silva for all the contributions during the experiments and

lab assistances, and also to Jéssica Lima and Aldiane Passos.

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Capítulo 2

_____________________________________________________________________ Prado-Lima, M. & Val, A.L. 2016. Transcriptomic characterization of tambaqui (Colossoma macropomum, Cuvier, 1818) exposed to three climate change scenarios. PLoS ONE, 11(3): e0152366.

61

Introduction

Climate change, resulting mainly from increases in the concentration of

greenhouse gases (GHG) in the atmosphere, will affect all human activities and

different ecosystems [1–3]. Various climate change scenarios have been proposed

based on the intensity of human activities causing environmental degradation. These

scenarios provide plausible predictions in several key areas, such as the emissions

of GHG and aerosols and environmental and socioeconomic conditions [2,3].

When applied to climate change research, the various climate scenarios help

provide a preview of how Earth’s systems will respond to different levels of

greenhouse emissions as well as aid the design of strategies to reduce the resulting

impacts on organisms [4]. According to the Fourth Assessment Report of the

Intergovernmental Panel on Climate Change (IPCC), three main scenarios of climate

are foreseen for the year 2100: B1 (soft), A1B (intermediate) and A2 (extreme).

These scenarios may vary according to population growth, socioeconomic

development and the use of fossil fuels or renewable energy [2].

Several studies have used climate change scenarios foreseen by the IPCC to

predict the qualitative and quantitative responses of marine and freshwater

ecosystems to environmental changes associated with the accumulation of GHG in

the atmosphere [5,6]. These studies suggest that climate change will cause severe

disturbances to both marine and freshwater ecosystems, impairing the distribution of

species and fishery in various countries [7,8].

The Amazon basin harbors a significant portion of the world’s biodiversity and

exhibits the highest diversity of freshwater fishes in the world, with approximately

3,000 fish species [9–11]. The Amazon River and its tributaries are home to species

that are still unknown to the scientific community. Despite this, the Amazon region

has been subjected to environmental pressures in recent decades, including

pressures of anthropogenic origin, such as deforestation, fires and growth of urban

centers, and those resulting from global warming [11,12].

Climate change may affect many organisms, ranging from bacteria to

mammals [3]. Fishes are especially susceptible to climate change, due mainly to

increases in water temperature and variations in the concentrations of dissolved

gases, particularly oxygen and carbon dioxide (CO2) [6]. Poikilothermic aquatic

organisms, such as fishes, can face a new challenge in warm water because it holds

less oxygen, resulting in a hypoxic environment. Because each fish species has a

62

specific thermal tolerance, a species may face death and extinction if the water

temperature exceeds its thermal tolerance [13–15]. This is an issue of increasing

concern because temperatures may continue to increase as a result of global climate

change [16].

Tambaqui (Colossoma macropomum) is a native fish species of the Amazon

basin and member of the family Serrasalmidae [9] that plays an important economic

role in the Amazon region [17]. In Brazil, it is the second most commonly raised fish

in aquaculture [17]. This species exhibits a high tolerance for environmental changes

in dissolved oxygen, temperature and pH [18,19]. Therefore, in the face of global

climate change, it is important to understand the molecular mechanisms that confer

tolerance in this species.

Molecular methods such as high-throughput RNA sequencing (RNA-Seq)

provide the opportunity to investigate the transcriptional response of various

organisms, including fishes, to climate change, allowing the identification of

mechanisms underlying adaptation to environmental stress [20,21]. Furthermore,

RNA-Seq enables the correlation of the number of times that a transcript is

sequenced with its abundance in a tissue or organism, which provides evidence of

the quantitative gene expression profile [21,22].

To combat the adverse effects elicited by fluctuations in temperature and

dissolved gases, fishes have developed the ability to adapt to a wide range of

environmental challenges to maintain normal cellular functions [19,23,24]. The

biochemical changes induced by stress are attributed to modulation of gene

expression, including controlling the cell cycle, DNA and chromatin stabilization,

protein folding and repair, the removal of damaged proteins, and energy metabolism

[25–27]. While stress response genes have been well characterized in many species

[28], there have been relatively few studies of Amazonian organisms, such as

tambaqui, particularly in relation to issues of ecological and evolutionary significance,

such as climate change [29]. Thus, RNA-Seq provides the possibility to investigate

differential expression and identify biological pathways involved in the response of

fishes to climate change.

Therefore, the present study aimed to analyze alterations of gene expression

in tambaqui when exposed to the B1, A1B and A2 future climate scenarios foreseen

by the IPCC for the year 2100.

63

Materials and methods

Ethics statement

All experimental procedures were carried out in accordance with Brazilian

legislation and approved by the Ethics Committee on Animal Use of the Brazilian

National Institute for Research in the Amazon (CEUA-INPA) (Protocol 056/2012).

Climate change exposure

Tambaqui juveniles (15.5±1.9 g and 8.3±0.3 cm) were obtained from a local

fish farm (Fazenda Tajá, Amazonas, Brazil) and transported to the Laboratory of

Ecophysiology and Molecular Evolution of the Brazilian Institute for Amazonian

Research (INPA), Manaus, Amazonas state, Brazil. The fish were maintained in 500

L fiberglass outdoor tanks for one month, for local acclimatization.

Three future climate scenarios provided by the Fourth Assessment Report of

the IPCC for the year 2100 [2] were simulated: B1 (soft scenario): 1.5°C and 200

ppm CO2 above current levels; A1B (intermediate scenario): 2.5°C and 400 ppm CO2

above current levels; and A2 (extreme scenario): 4.5°C and 850 ppm CO2 above

current levels. The control scenario was the current real-time temperatures and CO2

levels (Table 1) in a near-forested area. Sensors measure these parameters every

other minute and transmit the data to laboratory computers that control

environmental rooms according to the above scenarios.

Table 1. Physicochemical parameters of water and air in the control climate

environment where the specimens of tambaqui were kept for five and fifteen days

while being exposed to the B1, A1B and A2 climate scenarios. The data are reported

as the mean ± standard error of the mean.

Scenario pH

Water O2

(mg.L-1)

Water CO2

(ppm)

Water temperature

(oC)

Environment CO2 (ppm)

Environment temperature

(oC)

5 days

Control 6.2±0.3 7.4±0.1 8.8±0.9 24.7±0.2 416±11.7 30.2±0.7 B1 5.4±0.2 7.3±0.1 11±0.8 25.7±0.2 622.1±17.5 32±0.8

A1B 5.3±0.2 7.3±0.2 12.3±0.7 26±0.2 830.6±23.4 33±0.8 A2 5.0±0.1 7.4±0.1 13.8±0.8 27.3±0.3 1257.1±35.4 34.3±0.9

15 days

Control 6.1±0.2 7.4±0.1 8.9±0.8 24.9±0.3 441.1±10.4 30.1±0.7 B1 5.5±0.2 7.4±0.1 11.9±0.9 25.8±0.2 641.3±15.1 31.7±0.8

A1B 5.3±0.1 7.5±0.1 15.3±0.6 26.1±0.1 856.6±20.2 32.8±0.8 A2 5.1±0.1 7.5±0.1 19±0.9 27.6±0.3 1280.9±30.2 34.2±0.9

64

Tambaqui juveniles were transferred to the conditions of the above climate

scenarios two days prior to beginning the experiments. Six 60-L PVC tanks

containing four fish per tank were maintained in each climate room. To avoid

ammonia accumulation, 30% of the water was replaced every other day using

environmentally stabilized water. The pH, O2 and CO2 levels and temperature of the

water were measured daily (Table 1). The fish were fed ad libitum once a day using

commercial pelleted feed (Nutripeixe, Purina). After each exposure period, one fish

was removed from each tank, with a total of six fish per scenario being collected after

five days and another six after fifteen days. The fish were subsequently euthanized

by rapidly severing their spinal cord with a scalpel, and white muscle samples were

collected and immediately stored in liquid nitrogen until RNA isolation.

RNA purification

Total RNA was isolated from the white muscle specimens from 48 tambaqui

using TRIzol (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s

instructions. Total RNA samples were then digested with DNase I to remove

potentially remaining genomic DNA, and ribosomal RNA was depleted using the

RiboMinusTM Eukaryote Kit for RNA-Seq (Invitrogen, Carlsbad, CA, USA). RNA

yields and quality were checked using both an Agilent 2100 Bioanalyzer (Agilent

Technologies, Waldbronn, Germany) and a Qubit 2.0 Fluorometer (Invitrogen,

Carlsbad, CA, USA). None of the samples showed signs of degradation or impurities

(260/280 and 260/230 >1.8, RIN >8.5). Using the total RNA (gDNA-free) from each

sample, mRNA was isolated with the Poly(A)PuristTM Kit (Ambion, Austin, TX, USA)

according to the manufacturer's protocol.

For each group, equal amounts of mRNA from six fish were pooled for RNA-

Seq library construction, with a total of eight RNA-Seq libraries being generated.

Using this method, we aimed to increase transcript diversity and to detect rare

transcripts in both the control and experimental libraries

RNA-Seq library construction and sequencing

Approximately 500 ng of mRNA from each pool was used to construct eight

RNA-Seq libraries [four for animals after five days and four for animals after fifteen

days of exposure to the climate scenarios (Control, B1, A1B and A2)] with the

SOLiD™ Total RNA-Seq Kit for Whole Transcriptome Libraries (ABI, Foster, CA,

65

USA), following the manufacturer’s instructions. To improve the statistical analyses,

one replicate of each of the eight libraries was constructed, resulting in a total of 16

libraries. Barcodes were used to identify each library individually. The libraries were

deposited on two full slides and sequenced using a SOLiD (v.4) sequencer (ABI,

Foster, CA, USA) to generate fifty-base sequences.

Processing of sequence data

The raw reads of 50 bp generated by the SOLiD sequencer from each sample

were trimmed and analyzed in CLC Genomics Workbench (CLC Bio, v. 7.5.1). First,

adapter sequences and low-quality reads were removed based on default

parameters. High-quality reads were then mapped to the D. rerio genome (the

closest species with an available genome). The D. rerio gene annotations were then

used for expression analyses. The expression profiles for each library were analyzed

using the EdgeR method implemented in CLC Genomics Workbench, with default

parameters. Statistical tests were conducted to identify the differentially expressed

genes between the control and experimental samples, subjected to five or fifteen

days of exposure to the B1, A1B and A2 climate scenarios. Genes with false

discovery rate (FDR)-corrected P values ≤0.05 and absolute fold change values ≥2.0

were included in the analysis as differentially expressed genes. Contigs with gene

annotations were used for further analysis.

Gene ontology

All of the differentially expressed genes identified only under the A2 scenario

after both five and fifteen days of exposure were split into two groups: up-regulated

and down-regulated genes. Then, each group was mapped separately to gene

ontology terms using the AmiGO (v. 2.0) program (http://www.geneontology.org/),

against the database of annotated genes for D. rerio. The Gene Ontology (GO)

program employs three general categories: molecular function (MF), biological

process (BP), and cellular component (CC). For each category, there is a structure of

terms and/or more specific levels for categorizing genes.

Protein interactions

To generate the protein functional interaction network, all of the differentially

expressed genes were submitted together, using their gene symbols, to STRING

66

(Search Tool for the Retrieval of Interacting Genes) software (v.10) and subjected to

searches against the D. rerio database to look for known interactions among the

genes. The STRING database (http://string-db.org/) is a collection of known and

predicted protein associations obtained from the mining of databases and the

literature, high-throughput experimental data, and predictions based on genomic

analyses of more than 2000 organisms [30]. From the STRING interaction data, we

extracted conserved genomic neighborhoods, gene fusion events, phylogenetic

profiles or gene co-occurrences across multiple genomes, text mining, experiments,

and other databases with the highest confidence scores (0.9) allowed by the STRING

schemes.

Identification of SSR motifs

To identify all repetitive elements in the assembled contigs from tambaqui, all

sequences were searched for SSR motifs using the Msatcommander software (v.

0.8.2, with default settings). To be considered a dinucleotide SSR, the sequence

must have a minimum of six repeats. All other types of SSRs should have a minimum

of five repetitions. To be considered a compound SSR, the interruption between two

neighboring SSRs cannot surpass 100 nucleotides. The Primer3 software [31] was

used to design PCR primers for flanking all identified SSR motifs.

Results

To characterize the transcriptional responses of genes affected by climate

change in tambaqui, eight RNA-Seq libraries were built after five and fifteen days of

exposure to the B1, A1B and A2 climate scenarios. A total of 776,301,503 reads

were sequenced, with an average of 97 million of reads per library (Table 2). Each

raw read was sequenced from one end, and the length was 50 bp. After filtering to

remove adaptors and low-quality sequences, we obtained a total of 116,176,912

trimmed reads. After clustering and de novo assembly, 54,206 contigs of high quality,

showing lengths ranging from 74 to 1,094 bp were generated. The redundant and

ribosomal protein sequences were excluded, and 32,512 genes were identified

based on comparison with the D. rerio database. The raw reads generated in this

study have been deposited in the National Center for Biotechnology Sequence Read

Archive (NCBI-SRA) database (SRP062336).

67

Table 2. Overview of the RNA-Seq reads acquired from tambaqui after five and

fifteen days of exposure to the B1, A1B and A2 climate scenarios.

Scenario Total

Reads Trimmed

reads

All uniquely mapped reads to Danio rerio genes

Uniquely mapped reads to

Danio rerio exons

5 days

Control 83.673.651 16.996.336 4.280.731 1.031.276

B1 116.529.116 16.594.828 4.543.412 752.684

A1B 96.692.812 16.531.446 4.082.882 1.035.552

A2 79.983.888 17.909.708 4.344.590 1.179.861

15 days

Control 93.479.183 19.706.164 4.785.282 1.301.147

B1 98.635.208 16.328.332 3.946.647 1.090.509

A1B 112.023.018 13.701.912 3.659.181 723.738

A2 95.284.627 15.404.522 3.931.801 953.959

Total 776.301.503 133.173.248 33.574.526 8.068.726

Considering only those genes with a p-value ≤0.05 and a ≥2-fold change in

expression across the control and treated libraries, we identified a total of 236 and

209 differentially expressed genes after five and fifteen days, respectively, of

exposure to the B1, A1B and A2 climate scenarios (Fig 1). After five days, 104, 114

and 116 up-regulated genes were observed in animals exposed to the B1, A1B and

A2 climate scenarios, respectively. The corresponding numbers of down-regulated

genes were 132, 124 and 120, respectively. After fifteen days, 61, 78 and 69 up-

regulated genes were identified in animals exposed to B1, A1B and A2, respectively.

The corresponding numbers of down-regulated genes were 149, 133 and 138,

respectively. A complete list of the differentially expressed genes, including the level

of expression, can be found in the Supplementary material (S1 and S2 Tables).

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Figure 1. Comparison of the number and direction of differentially expressed

transcripts identified in tambaqui after five and fifteen days of exposure to different

climate scenarios. The bar chart shows the number of differentially expressed

transcripts identified after five days (left bars) and fifteen days (right bars) under the

B1, A1B and A2 climate scenarios. Positive and negative values on the Y-axis

represent the number of up-regulated and down-regulated transcripts, respectively.

The list of differentially expressed genes showed several genes that play roles

in energy production, protein folding and maintaining cellular homeostasis, among

other functions. Genes encoding aldolases (aldoaa, aldoab and aldocb), enolases

(eno1, eno3), lactate dehydrogenase (ldha), pyruvate kinase (pkmb), cytochrome c

oxidase (cox5bs, cox4i2, cox8b) and chaperones such as Hsp40 (Dnaja2, Dnajc7)

and Hsp90 (hsp90aa1.1 and hsp90aa1.2) and genes responsible for protein folding

(such as hmgb1a and pfdn2) were identified. The main genes responsible for the

adaptation of tambaqui to climate change were grouped according to the current

literature, metabolic routes and the GO database (Table 3).

69

Table 3. Tambaqui genes1 affected by exposure to climate change scenarios.

Gene symbol Gene name Gene Ontology2

dnaja23 DnaJ (Hsp40) homolog, subfamily A, member 2

response to temperature stimulus (BP) heat shock protein binding (MF)

dnajc73 DnaJ (Hsp40) homolog, subfamily C, member 7

response to temperature stimulus (BP) heat shock protein binding (MF)

hmgb1a5 high mobility group box 1a response to temperature stimulus (BP)

chromatin remodeling (BP)

hsp90aa1.13 heat shock protein 90, alpha (cytosolic), class A member

1, tandem duplicate 1

chaperone-mediated protein (BP) complex assembly (BP)

hsp90aa1.24 heat shock protein 90, alpha (cytosolic), class A member

1, tandem duplicate 2

chaperone-mediated protein (BP) complex assembly (BP)

pfdn25 prefoldin subunit 2 protein folding (BP)

unfolded protein binding (MF)

1Genes selected based on the stress response using Blast X and the scientific literature. 2Functional annotation associated with the Danio rerio database. Gene ontology (GO) categories: biological process (BP) and molecular function (MF). 3Gene showing differential expression only after five days of exposure to the B1, A1B and A2 climate scenarios. 4Gene showing differential expression only after fifteen days of exposure to the B1, A1B and A2 climate scenarios. 5Gene showing differential expression after both five and fifteen days of exposure to the B1, A1B and A2 climate scenarios.

The GO database was used to categorize the differentially expressed genes

identified only under the A2 scenario after five and fifteen days of exposure. The A2

scenario was chosen because it was the most extreme scenario considered in the

present study. Among all the genes identified, 241 were successfully categorized, 82

of which were up-regulated, while 159 were down-regulated. Analysis of the GO term

distribution of the up-regulated genes at five days revealed 235 terms (MF: 48, BP:

138 and CC: 49) (Fig 2), while there were 263 terms associated with down-regulated

genes (MF: 118, BP: 91 and CC: 54) (Fig 3). The GO analysis using the fifteen-day

library revealed 257 terms for the up-regulated genes (MF: 25, BP: 194 and CC: 38)

(Fig 4) and 180 terms for the down-regulated genes (MF: 83, BP: 23 and CC: 74)

(Fig 5). For three broad categories (MF, BP and CC), four pie chart graphs were

generated: two containing the corresponding top 20 terms linked to up-regulated

genes after five and fifteen days and another two containing the top 20 terms linked

to down-regulated genes identified after five and fifteen days under the A2 climate

70

scenario. In both cases, the top 20 terms were those with the highest percentage of

representation within each of the three general categories. The terms other than the

top 20, with low percentages, usually ranging from 1 to 2% of the total

representation, were grouped and are also represented in the pie chart.

72

Figure 2. Pie chart of the top 20 GO terms associated with up-regulated genes

identified in tambaqui after five days of exposure to the B1, A1B and A2 climate

scenarios. Representation of the top 20 GO terms from the molecular function (A),

biological process (B) and cellular component (C) categories in the experimental

groups compared with the control. The terms other than the top 20 are represented

together.

74

Figure 3. Pie chart of the top 20 GO terms associated with down-regulated genes

identified in tambaqui after five days of exposure to the B1, A1B and A2 climate




together.

76

Figure 4. Pie chart of the top 20 GO terms associated with up-regulated genes

identified in tambaqui after fifteen days of exposure to the B1, A1B and A2 climate




together.

78

Figure 5. Pie chart of the top 20 GO terms associated with down-regulated genes

identified in tambaqui after fifteen days of exposure to the B1, A1B and A2 climate




together.

STRING software was used to generate a confidence gene interaction network

merging all of the differentially expressed genes identified after five and fifteen days

in animals exposed to the B1, A1B and A2 climate scenarios (Fig 6). The proteins are

represented with nodes. Direct protein-protein interactions are represented with

continuous blue lines. Due to the high number of interactions observed, the settings

were changed to only identify gene expression with the “highest confidence” (score

>0.9). Nodes with no interactions were hidden. A total of 985 interactions (S3 Table)

were obtained using 296 nodes among the analyzed genes. The gene network

showed five major clusters. Cluster 1, Cluster 2, Cluster 3 and Cluster 4 exhibited

interconnected interactions and sparsely connected sub-networks. Only Cluster 5

showed no connections with the other clusters. The lists of genes grouped in each of

the five clusters are highlighted in S1-S5 Figs.

79

Figure 6. Gene network interactions identified using STRING software (v.10).

Interactions of genes (or proteins) expressed in the white muscle of tambaqui after

five and fifteen days of exposure to the B1, A1B and A2 climate scenarios. Circles

with different colors represent different genes (or proteins). Blue lines represent

strong interactions between genes (or proteins). Nodes with no interactions have

been hidden.

A total of 1,201 SSRs, comprising 93% simple and 7% compound motifs, were

detected among the tambaqui contigs (Fig 7). These SSRs included the following

80

types of repeats: 81% dinucleotides, 16% trinucleotides and 3%

tetra/pentanucleotides. Among the dinucleotide repeats, the GT (33.5%) and CT

types (32.8%) were most abundant, while GAT was the most frequent trinucleotide

repeat (15.5%). Based on the detected SSRs, 88 primer sets were successfully

designed and will be tested in natural or captive populations (S4 Table).

Figure 7: Distribution of simple sequence repeats found in tambaqui sequences.

Discussion

Climate change is likely to endanger regions with high biodiversity, such as the

Amazon. Although several studies have sought to predict the impacts of climate

change in the Amazon, they are generally based on computer models that do not

always take into account all of the hidden characteristics of the rainforest. Thus, we

used the second most commonly farmed species in Brazil, and the most commonly

farmed species in the Amazon, to investigate the main impacts of climate change on

the regulation of gene expression in fish exposed to the B1, A1B and A2 climate

scenarios foreseen by the IPCC for the year 2100.

In the present study, a transcriptome-level analysis was conducted to

determine how tambaqui will respond to exposure to climate change using the NGS

81

and RNA-Seq methods. Two critical factors for fish survival were tested at different

levels during five and fifteen days of exposure to the B1, A1B and A2 climate

scenarios: temperature and CO2 levels. Because thermal stress and CO2 elevation

have broad biological effects on organisms, the transcriptional responses to these

parameters are expected to be highly diverse across a number of genes found in

ectothermic species such as tambaqui. This study confirms that numerous genes are

differentially expressed in tambaqui under the B1, A1B and A2 climate scenarios,

and several pathways are involved (S1 and S2 Tables). However, according to the

GO tool, a few key pathways contained a high proportion of differentially expressed

transcripts, including the mitochondrion, protein binding, protein metabolic process,

metabolic processes, gene expression, structural constituent of ribosome and

translation categories.

In the present study, SOLiD sequencing allowed the identification of 32,512

genes. Considering that no sequenced genome currently exists for tambaqui,

comparison with the D. rerio transcriptome was performed. Because D. rerio has a

total of 34,460 annotated genes [32], if we consider that the two species have the

same number of genes, the identified genes in the present work would represent

approximately 94% of the genes of the tambaqui transcriptome and can therefore be

employed in various approaches to evaluate information on the transcriptome,

genome and genetic breeding of tambaqui. The tool used in the present study

allowed us to identify 388 genes that showed different levels of expression according

to the time of exposure and the climate scenario. Evidence of acclimation was

indicated by the 11.4% decrease in the number of differentially expressed transcripts

from five to fifteen days (Fig 1). Despite the short period of time, this result is in

accordance with theories on acclimation to heat stress under which organisms can

regulate their heat shock response over time [15], as opposed to the theory in which

immediate survival is a priority [33]. However, the current results demonstrate that

from five to fifteen days, acclimation occurs throughout much of the transcriptome

and is not limited to genes related to heat shock, such as chaperones.

Climate change may introduce a number of environmental challenges for

fishes due to increased temperatures above the optimal range, leading to altered

transcription of genes involved in protein folding and heat-shock responses

[29,34,35], ribosomal and metabolism-related genes [27,36,37] and genes

associated with cell cycle arrest and apoptosis [23,29,34]. Ectothermic animals, such

82

as fishes, generally show temperature-dependent oxygen consumption [38].

Considering that increases in temperature may induce low-oxygen stress because

oxygen solubility is reduced in warmer water [18], fishes may also experience

hypoxia at elevated temperatures due to a reduced binding capacity of hemoglobin

for oxygen transport [19,39].

A sufficient oxygen supply is fundamental to ensuring optimal growth, foraging

and reproduction performance in fishes [13]. Under these conditions, the tambaqui

have developed several adaptation mechanisms to safeguard survival ability. For

example, tambaqui are able to expand their lower lip to direct the first film of the

water column, which is richer in oxygen, over their gills [18]. Simultaneously, the

species can reduce erythrocytic ATP and GTP to increase Hb-O2 affinity [18,40]. All

of these biochemical and physiological adaptations are important for increasing the

thermal tolerance of tambaqui and buffering the negative effects of low oxygen

concentrations in water.

In the present study, at least four gene groups were identified among all of the

differentially expressed genes. The first group comprised several types of heat shock

proteins (Hsps), including Hsp90 and Hsp40 (dnaja2, dnajc7). The expression levels

of these genes changed after five and fifteen days under the B1, A1B and A2 climate

scenarios. Hsps are a well-studied group of highly conserved, ubiquitously distributed

genes that are expressed upon exposure to various stress factors, including elevated

temperatures [41]. Hsps are part of an important mechanism that helps organisms

survive under different environmental conditions, such as thermal stress [41]. Hsp90

is a highly conserved molecular chaperone that has been proposed to act as a hub

for the signaling network and protein homeostasis associated with diverse

physiological processes, including the heat shock response, signal transduction and

stress responses, in eukaryotic cells [42]. Our findings suggest that Hsps are directly

involved in the thermal tolerance of tambaqui.

The second group of differentially expressed genes were involved in ATP-

derived energy production. Genes related to glycolysis, such as aldoaa, aldoab,

aldocb, eno1a, eno3, ldha, and pkmb, were down-regulated under all three tested

climate scenarios. Similarly, genes such fh, ndufs4, cox5b2, and mdh2, which play

important functions in the tricarboxylic acid cycle and electron transport chain, were

down-regulated in the white muscle of tambaqui under all three tested climate

scenarios. These results suggest an impairment of several ATP-generating enzymes,

83

which can cause an increase in the expression of transcription factors and target

genes as well changes in the fatty acid composition of membranes to equilibrate the

cellular energy balance and to maintain cellular homeostasis [43].

The third group consisted of genes that act as translation initiation factors.

These genes were repressed in tambaqui after fifteen days of exposure to all three

climate scenarios (eef1a1a, eef1a1l1, eef1a2, eif2s1b, eif4a3, eif4eb, and eif4ebp3l).

These genes encode proteins that are components of the eukaryotic translation

initiation factor complex, which is fundamental for several steps in the initiation of

protein synthesis through association with the 40S ribosome, and facilitate the

recruitment of eif-1, eif-1a, eif-2, and eif-5 to form a complex known as the 43S pre-

initiation complex (43S PIC), which is necessary for scanning mRNA for the

translation-initiation codon AUG [44,45]. Another important translation initiation factor

that was identified was the high-mobility group b1 protein, encoded by hmgb1. This

protein plays key roles in the immune system, transcription initiation and the

assembly of numerous nucleoprotein complexes that are critical to cell function and

the formation of enhanceosome complexes. Through these functions, hmgb1a acts

as a compensatory modulator of transcription in response to increased temperature,

making it a global temperature sensor that reduces the expression of key genes in

response to elevated temperature [46]. Hmgb1a may be also involved in the

modulation of the immune system of tambaqui in response to diverse climate

changes scenarios. However, additional studies are needed to confirm this

hypothesis.

The fourth and last group was composed of genes that are related to the

production of constituents of ribosomes or are important in the assembly and stability

of ribosomal subunits. Some of these genes were significantly repressed in

tambaqui, especially after fifteen days (rpl3, rpl7, rpl13, rpl18a, rpl35, rps10, rps27.1

and rpsa), while others (rpl5a, rpl14, rpl23, rpl32, rplp2 and rps26) showed varying

expression levels. Similar results have been obtained following thermal stress in

Arctic char [27]. These results, together with the translation initiation factors that were

found to be repressed, indicate that commitment of the translational machinery after

fifteen days of exposure preserves the synthesis of specific stress-responsive

proteins to enhance cell survival under climate changes, which can cause

disequilibrium of vital functions, such as metabolism, reproduction, growth and

competitiveness [13,14].

84

As explained above, the list of differentially expressed genes indicates that the

cellular responses to climate change in tambaqui are complex, involving a number of

genes, and may be controlled by different cues and transcription/translation

regulation mechanisms, as observed in other fishes [29,34,47] and various other

types of organisms [48,49], including humans [50].

When exposed to thermal stress, fishes need to increase their oxygen supply

in response to an elevated metabolic rate, resulting in behavioral changes [47].

Consequently, food consumption and foraging activities may be increased at higher

temperatures, increasing the energy requirement of the fishes. However, if a constant

search for food is not successful, fishes will be unable to maintain their growth rates

[51]. Furthermore, under conditions of increased CO2, the effects caused by thermal

stress are potentiated, impairing feeding and growth [52]. Another consequence is

that increases in temperature have a significant impact on predator–prey interactions,

potentially leading to ecosystem imbalances with consequences at various food

chain levels [53,51].

Gene ontology is commonly used to categorize gene products and to

standardize their representation across different species or environmental stress

situations [54–56]. To characterize transcriptional events over time, only the

differentially expressed genes related to the A2 climate scenario were subjected to

GO analysis (Figs 2-5). The results showed that the up-regulated genes were related

to the following categories: protein binding (MF), protein dimerization activity (MF),

protein heterodimerization activity (MF), protein homodimerization activity (MF), and

identical protein binding (MF). This is expected under thermal stress given that

proteins such as HSPs are involved in protein folding and unfolding, providing

thermo-tolerance in cells upon exposure to heat stress [41]. In the opposite situation,

the down-regulated genes were related to the following categories: cellular metabolic

process (BP), phosphorus metabolic process (BP), organic substance metabolic

process (BP), primary metabolic process (BP), protein metabolic process (BP),

macromolecule metabolic process (BP) and cellular protein metabolic process (BP).

These results, together with those for the gene expression (BP) and translation (BP)

categories, indicate suppression of various metabolic processes to ensure cellular

homeostasis and, thus, overcome the environmental challenge. The categories

mentioned above appeared at low percentages after five days, but their percentages

almost doubled after fifteen days of exposure to the A2 climate scenario, indicating

85

that these mechanisms that enable organisms to minimize some temperature-related

effects were already present at five days and increased after fifteen days. The results

of GO analysis followed a similar pattern to those observed in transcriptomic

analyses of other fish species [34,46,57].

Protein functional interaction networks, such as the one we generated using

the STRING database, are commonly employed to predict protein–protein

associations derived from high-throughput experimental data or from the mining of

databases and the literature as well as predictions based on genomic analyses of

more than 2,000 organisms [30]. We searched the interactions among all of the

differentially expressed genes obtained after five and fifteen days of exposure to the

three climate scenarios and used STRING to construct an interaction network (Fig 6).

The 296 nodes allowed the identification of five clusters (S1-S5 Figs). A central

interplay cluster was not detected, although cluster 1 appeared to start a chain of

interactions that extended to cluster 4. Cluster 1 included genes related to the folding

and unfolding of proteins, such as pdfn2, dnaja2, hsp90aa1.1 and hsp90aa1.2, which

are known to control gene expression in a number of organisms under thermal stress

[41,35,48]. Hsp90 (also known as endoplasmin) is a molecular chaperone that

functions in the processing and transport of secreted proteins [58]. The up-regulation

of Hsp90 is often used as a hallmark of endoplasmic reticulum stress [29]. Hsp90

interacts with more than 100 proteins, and some of its notable partners include

kinases, nuclear hormone receptors, transcription factors, and ion channels [58].

Clusters 2 and 3 comprised genes related to glycolysis and the mitochondrial

respiratory chain, including pkmb, ldha, aldoab, eno2, atp5d, and cox5b2. Cluster 5

mainly consisted of translation factors (eif2s1, eif4eb, eif3f, and eif1a1a) and genes

related to rRNA processing and ribosomal biogenesis (rpl11, rpl7, rps 3, and rps29)

that play key roles in the regulation of gene expression. Ribosomal proteins are

potential biomarkers of tolerance to thermal stress in fishes [27].

The 1201 SSRs identified and 88 primer sets designed in the current study

(S4 Table) provide a valuable resource for the development of molecular tools for

tambaqui and other closely related species. Given that only 41 SSR loci had

previously been developed for tambaqui [59–61], the 88 primer sets designed in the

present study more than double the number of SSR loci currently available. The high

number of dinucleotide repeats (Fig 7) observed in tambaqui (approximately 81%) is

consistent with previous studies in fishes and other aquatic organisms [62,63]. SSR

86

markers have been efficiently used for the identification of individuals, population

diversity and quantitative trait loci (QTL) analyses, and genetic breeding of stocks

[64,65]. The SSR loci developed here must be validated before use in future

applications.

Conclusions

This is the first RNA-Seq-based study of the transcriptional response of

tambaqui exposed to climate change. Several candidate genes that can be employed

to screen for genetic markers for climate change were identified. Using GO

enrichment analysis and STRING software, we identified diverse biological processes

and intracellular pathways related to the functional impacts of the observed changes

in transcript expression. The obtained results provide clues toward elucidating the

molecular mechanisms underlying the regulatory networks of gene expression during

climate change exposure. In addition, an analysis of repetitive elements was

conducted, and SSRs were identified for future marker development and linkage

analysis. The large number of differentially expressed genes recorded in this study

suggests that a complex combination of genes has evolved in tambaqui as a

response to climate change scenarios; in particular, these genes include chaperones,

energetic metabolism-related genes, translation initiation factors and ribosomal

genes. Overall, the results of this study on the tambaqui transcriptome should serve

as a valuable resource for future genetic or genomic studies.

Acknowledgments

We thank Dr. Alzira Miranda, Fernanda Dragan, MSc., and Maria de Nazaré

Paula da Silva, MSc., for their contributions to the fish exposure experiments and

Lucas Canesin, MSc., for the help provided with the bioinformatics analysis. This

work forms part of INCT-ADAPTA (CNPq/FAPEAM). MPL was a recipient of a Ph.D.

fellowship from CAPES, and ALV is a recipient of a research fellowship from CNPq.

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Supplementary material

Figures

S1 Figure: Tambaqui genes identified after five and fifteen days of exposure to the

B1, A1B and A2 climate scenarios grouped in cluster 1 using STRING software (v.

10).

95



10).

96



10).

97



10).

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10).

99

Tables

S1 Table: The list of differentially expressed genes (Log2FC) of tambaqui after five

days of exposure to B1, A1B and A2 climate scenarios. Up-regulated genes are

shown with positive values and down-regulated genes are shown with negative

values.

Five days of exposition

Gene symbol B1 Scenario A1B Scenario A2 Scenario

A2ML1 (5 of 12) 2.174 1.288 1.153

AC024175.1 -1.415 -4.791 -1.642

AC024175.11 -1.428 -1.764 -2.903

AC024175.13 -1.237 -1.477 -2.283

AC024175.14 -3.587 -93.539 -93.539

AC024175.17 -1.328 -1.727 -1.98

AC024175.6 -1.112 -2.221 -2.282

AC024175.7 4.638 4.605 -31.24

AC024175.8 2.303 -97.023 -1.131

AC024175.9 2.637 1.029 1.208

acta1b -2.208 -1.014 -1.339

actc1b -1.591 1.095 -1.184

ACVR2B (1 of 2) 2.616 4.631 7.597

Adss -5.739 1.096 -1.008

AL929108.1 5.227 -21.065 -21.065

AL935186.3 -1.797 -5.291 -2.062

Aldoaa -3.935 -2.237 -1.306

Aldoab -4.239 -1.361 -2.124

anp32b 1.847 -1.259 -1.349

arhgef9b_2 -4.073 -4.64 -4.688

arl8bb -2.233 2.305 2.611

arpc3 -1.198 -2.673 1.197

atg4a 2.055 5.596 -1.414

atp2a1l -3.825 -1.1 -1.265

atp2a2b -4.553 1.79 -1.161

atp5a1 -2.81 -1.112 -1.089

ATP5B -2.994 -1.191 1.026

atp5c1 -2.585 -1.231 -1.684

atp5d -1.67 -1.662 -1.92

atp5h 1.016 -1.21 -1.697

Bhmt -3.513 -2.274 -1.077

btf3 -1.598 1.325 2.251

BX470224.1 1.147 -1.627 3.412

BX548011.3_1 2.364 1.135 1.333

CABZ01055869.1 -1.278 -3.28 -148.539

100

CABZ01061343.1 93.445 1 1

CABZ01076182.1 -32.265 -32.265 -32.265

CABZ01077555.1 6.606 27.918 15.989

capn1a -8.046 -2.627 -4.7

ccng1 -1.115 2.39 3.601

cdh23_2 -1.394 -1.677 -2.194

cep89 -2.004 -1.293 2.085

Ckma -1.733 -1.475 -1.518

Ckmb -2.322 -1.358 -1.318

ckmt1 -2.52 -1.109 -1.11

cmc4 -1.249 -5.794 -4.401

cox4i2 -11.376 -1.51 -3.53

cox7c -1.282 -3.077 1.315

cox8b -2.699 -1.772 -2.402

cpn1_2 3.051 2.051 -1.097

CR354374.1 -1.54 1.041 -2.245

CR356235.1 48.411 1 1

CR381646.1 -1.402 -45.81 -2.241

CR450736.2 -3.646 1.22 1.482

CR854899.1 255.709 355.202 1

CR936200.1_1 2.612 1.146 -1.216

creb3l3l -2.466 1.11 7.259

cstf2_1 3.568 1.012 1.209

CU459159.1 1 38.331 1

CU570683.1 10.145 19.967 23.423

CU633479.5_1 -3.952 -1.253 2.269

CU929274.1 1.002 -81.378 -81.378

cxcr3.1_1 2.076 1.398 1.433

Dbi 11.457 12.846 5.819

Ddost 2.451 2.01 3.198

Desma 1.289 2.062 1.941

dicp1.1 -1.347 -3.469 -1.259

dlg3_2 3.646 1.598 1.654

dnaja2 -2.864 -1.376 -1.415

dnajc7_2 5.065 5.969 2.234

dre-let-7a-1 1.712 -103.825 -1.925

dre-let-7a-4 -1.427 -61.669 2.254

dre-let-7c-2 2.817 3.721 5.532

dre-let-7d-1 100.024 1 49.639

dre-let-7d-2 84.961 64.599 57.037

dre-let-7f 16.173 1 28.652

dre-mir-125a-2 42.165 1 1

dre-mir-193a-2 45.976 1 1

dre-mir-206-2 1.734 -1.104 1.751

dre-mir-20b -60.971 -60.971 -3.199

dre-mir-21-2 1 15.671 53.275

101

dre-mir-301c 1 1 36.96

dre-mir-363 -3.988 -1.794 -59.262

dre-mir-731 58.59 1 1

edil3 3.538 10.618 5.203

eef2l2 -1.878 -1.369 1.107

efr3a_2 2.388 1.21 1.001

eif3f 2.113 1.264 1.135

eif3m -1.883 1.092 1.115

eif4ebp3l -1.335 -1.432 -2.751

eno1a -5.643 -7.741 -1.309

eno3 -3.388 -2.06 -1.611

ENSDARG00000041884 3.247 2.64 1.9

ENSDARG00000071292 -2.688 -1.329 2.461

ENSDARG00000088545 95.301 14.619 5.694

ENSDARG00000091099 1.436 5.715 5.346

FBXL6 2.491 -1.74 -2.416

FER1L6 -15.823 -3.65 -8.985

Fh -3.469 -1.119 -1.194

FP102784.1 6.397 3.556 -4.975

Gapdh -3.189 -1.321 -1.466

Gapdhs -3.272 -1.856 -1.463

Gcdhl -5.069 -1.45 1.68

Gpib -10.688 -1.203 -1.835

her9 -1.074 2.041 1.606

hip1ra -8.784 -2.096 -3.488

hmgb1a -1.722 -2.413 -1.931

HSCB 1.76 2.209 1.844

hsp90aa1.1 1.124 2.389 3.465

ical1_1 1.382 2.116 1.047

IDH2 -1.85 -1.265 -1.647

IFT43_2 35.882 1 19.915

kcnab1_1 1.989 -1.455 -1.349

kif22 19.187 -4.436 5.065

lama2_1 1.755 -1.105 -1.176

lhfpl2a 1.406 -1.661 -2.369

lhfpl2b_2 15.019 2.976 4.816

lsm12b -5.189 -1.54 -1.35

map4k2l_2 3.271 -1.055 -1.501

me2_1 1.581 -1.146 -1.641

Metazoa_SRP_2 59.604 14.79 6.115

Metazoa_SRP_56 1 29.74 1

mhc1zaa_2 -1.089 -1.14 -3.138

mospd1 -9.36 -8.567 -3.124

MTHFS 7.442 4.056 24.842

MUSTN1 5.369 14.364 38.182

MYH13 (5 of 11) -2.561 1.158 1.808

102

myhz1.3 -3.906 1.23 1.332

myl1 -1.134 1.901 1.448

myl10 -2.568 1.361 8.015

MYL2 (1 of 2) -4.99 2.818 6.899

MYL3 -3.177 2.62 11.621

Mylpfb -3.199 1.843 3.341

mylz3 -2.075 -1.106 -1.051

ndufs4 -4.021 -3.862 -1.405

ngfrb_1 -1.212 1.242 -2.358

nipsnap3 -5.069 -1.767 -1.729

nmrk2 5.359 7.937 5.455

Nnt 1.698 3.589 1.205

nphp3 -2.687 -2.743 -4.803

Nudc 1.23 -2.24 1.031

OTTDARG00000019147_1 -2.5 -10.402 -2.181

pabpc4 -1.928 -1.312 1.077

PET100 1.68 1.199 2.893

pfdn2 -1.378 1.621 3.999

pgk1 -3.3 -1.019 -1.234

pgm1 -3.206 -1.725 -1.509

Pkmb -5.666 -1.415 -1.197

Pomp -2.064 -2.324 -2.348

psma1 -2.726 -1.704 -4.381

psma2 1.424 -1.368 -4.261

psma3 -3.814 -2.145 -2.055

psmb3 -3.859 -2.404 -2.806

pthlhb_2 -3.219 -2.061 -7.976

ptpn9b -5.287 1.078 -1.453

pvalb1 1.039 1.106 -1.381

rab10 -2.238 -5.857 -2.148

rbm19 -3.354 1.194 -1.393

rn7sk -1.474 -1.728 -2.217

rpl11 -1.605 1.118 -1.449

rpl22 1.583 2.034 1.426

rpl35 -1.181 -1.555 -1.083

rpl6 3.207 7.783 35.03

rpl7 -2.356 -1.515 -1.314

rps10 2.208 1.956 2.97

rps27.2_1 -1.288 -1.015 -1.63

rps29 10.681 4.009 6.317

rps3 -1.629 -1.168 1.403

Rpsa -1.584 -1.487 1.097

rtn2a -3.465 -2.398 -1.914

scn1a_2 -4.251 -3.138 -3.262

Sdhb -5.083 -2.757 -1.641

si:ch1073-140o9.2 -2.207 -1.129 3.627

103

si:ch211-122a17.5 1 47.534 65.228

si:ch211-122l24.5 -5.539 2.846 6.512

si:ch211-130l20.1 52.716 1 1

si:ch211-238p8.23 4.216 -1.276 1.783

si:ch211-241d24.2 1.048 -32.529 -3.761

si:ch211-242m24.8 1 1 26.102

si:ch211-37e10.1 4.32 1.485 1.301

si:ch211-39k3.2 1.087 -3.194 -1.596

si:ch211-59c24.1 -1.313 -8.559 -3.319

si:ch73-191k20.4 34.213 9.716 4.468

si:ch73-346o4.2 6.973 28.429 7.737

si:dkey-15h8.10 -5.996 -1.016 1.325

si:dkey-17m8.1 2.131 -1.224 1.208

si:dkey-186k21.2 37.868 26.195 7.779

si:dkey-234n3.3 -23.253 -23.253 -23.253

si:dkey-267i17.3 5.092 36.106 6.167

si:dkey-34e4.1 12.263 5.353 1.909

si:dkey-52l6.2 26.459 10.276 12.651

si:dkey-86e18.1 1.433 1.914 1.194

slc25a4 -1.991 -2.015 -1.596

smarcal1 -3.323 -2.32 -6.086

smyhc1 -1.886 1.282 2.772

SNORA17 38.671 1 1

SNORA9_2 -2.294 -78.718 -78.718

snoZ178 37.465 19.204 198.783

SPTBN4 (1 of 2) 10.132 1.757 -1.384

ssr3_2 -1.897 -33.199 -3.507

stxbp1a -1.917 2.271 -1.065

tank_1 -1.127 -1.982 -2.373

TARS2 (1 of 2) -5.619 -3.116 -2.525

thoc7 4.993 2.051 3.19

tmem167b_1 2.547 1.59 1.113

tmem55a 710.93 -3.561 -3.561

TNFRSF9 (2 of 2) -4.796 5.832 56.282

tnnc1a 1.565 2.592 30.773

tnnc1b -4.037 1.666 8.398

TNNC2 (2 of 2) 5.244 17.074 10.416

tnni2a.1 -1.439 -1.703 -1.465

tnni2a.4 -1.034 2.789 1.482

tnnt1 4.144 4.107 3.938

tomm20b -3.047 -2.328 -2.057

tpi1a -5.416 -1.344 -1.155

tpm2 -1.133 2.089 6.497

tpm3 -1.308 3.024 3.663

Tpma -2.409 1.374 1.275

txndc12 -5.319 1.195 -1.248

104

U1_101 24.934 1 1

U3_15 -1.449 -9.113 -22.542

U6_2 -3.86 -65.905 -65.905

ubxn6 -2.715 1.023 -1.272

ugt2b6_1 1.85 -1.174 -1.158

Ungb 8.259 22.533 1.342

v2rh25p 29.068 4.745 11.195

Vault_4 38.671 1 1

Vcp -5.996 -1.65 -1.757

WDR70 (1 of 2) 36.128 8.439 4.019

xrcc5_1 -1.881 -1.81 -3.714

zak_1 1.664 4.728 1.176

zgc:153129 -27.503 -1.784 -4.617

zgc:165409_2 -1.435 -5.547 -14.411

zgc:65894 1.448 3.02 11.202

zgc:86598 -4.076 -1.197 -1.06

zgc:91860 1.325 -1.437 -1.855

105

S2 Table: The list of differentially expressed genes (Log2FC) of tambaqui after fifteen

days of exposure to B1, A1B and A2 climate scenarios. Up-regulated genes are

shown with positive values and down-regulated genes are shown with negative

values.

Fifteen days of exposition

Gene symbol B1 Scenario A1B Scenario A2 Scenario

abca12 3.537 -1.935 1.567 abce1 -1.69 -11.541 -1.912 AC024175.1 -15.542 1.065 -2.27 AC024175.11 -2.292 1.608 -1.847 AC024175.13 -3.738 -1.179 -2.237

AC024175.17 -3.653 -1.44 -2.323

AC024175.4 -1.909 -1.07 -1.259

AC024175.6 -3.31 1.44 -2.019

AC024175.9 -4.043 2.935 2.081

actc1a -1.994 -3.548 -1.63

actn2 -1.031 -5.925 -1.459

actr3b 1.056 -5.167 -1.749

ACVR2B (1 of 2) -1.118 -7.484 -3.065

AL845481.1 -2.623 -1.028 -58.662

AL929192.1 -7.423 -42.884 -42.884

AL935186.3 -1.314 1.367 -5.339

AL935186.6 -1.33 1.919 1.234

aldocb -2.517 -6.145 -5.47

ampd3b -1.358 -2.252 -3.125

arpc3 1.037 -2.695 -1.643

atp5l -1.855 1.084 -1.148

atpif1b 2.46 18.894 14.318

bcl2_2 -1.227 1.658 1.123

bin3 31.527 1 34.876

BIN3 (2 of 2) 33.668 1 4.962

btf3 -1.77 -10.563 -2.192

BX005423.2 -37.367 -37.367 -1.534

BX322605.3 2.886 25.793 3.364

BX548011.3_1 -1.591 -1.122 -1.171

BX890616.1 -1.189 1.521 3.769

CABZ01025931.1 -5.754 1.713 2.5

CABZ01041280.1 27.687 11.276 25.724

calm2a -1.418 -2.376 -2.507

calm3a -1.919 -2.874 -1.567

casq1b 1.184 -2.355 -1.571

ccng1 -2.152 -3.466 -8.178

chchd10 -2.673 -6.581 1.006

106

clasrp 38.746 10.065 -1.545

clk4a 1.051 -1.673 3.154

col5a3a_1 -1.94 3.168 2.329

cops3 -1.927 -5.353 -1.653

cox5b2 -5.997 -2.881 -48.526

CR318660.2 49.585 1 1

CR354374.1 -4.112 1.175 -1.227

CR354382.1 1 1 45.405

CR354430.2 -65.91 -2.337 -65.91

CR790365.1 18.206 72.803 60.316

CR847944.3 1 159.101 1

CR854899.1 191.493 1 25.381

creb3l3l -8.451 -6.314 -1.816

ctnnd1_2 -1.109 2.237 1.592

CU633479.5_1 3.09 -1.05 1.361

CU984584.1 4.135 6.537 2.41

cxcl-c5c 5.163 24.858 5.659

ddx56 -6.631 -17.802 -2.089

desma -1.975 -3.991 -1.797

desmb 1.198 -3.035 1.199

dicp1.1 -2.982 -2.414 -4.124

dre-let-7a-1 -1.093 -1.015 -89.706

dre-let-7a-2 1.349 2.924 -76.928

dre-let-7a-3 -2.215 -2.946 -1.645

dre-let-7a-4 -3.621 -4.957 -139.727

dre-let-7a-5 1.154 -39.845 -1.159

dre-let-7d-1 -34.74 -1.293 -34.74

dre-mir-125b-2 -2.519 -56.941 -2.22

dre-mir-130b 20.493 69.759 99.54

dre-mir-142a 1 64.428 1

dre-mir-15a-1 1.129 -37.736 -37.736

dre-mir-181a-1 -39.98 1.483 -39.98

dre-mir-206-2 -1.754 -2.802 -1.241

dre-mir-210 -1.186 -88.136 -88.136

dre-mir-2188 34.612 1 20.637

dre-mir-218b 1 65.136 1

dre-mir-222b -34.746 -1.156 -34.746

dre-mir-363 21.539 -1.706 2.082

dre-mir-454b -2.149 -66.921 114.024

eef1a1a -1.265 -2.33 -3.688

eef1a1l1 -1.441 -2.164 -1.488

eef1a2 -1.647 -8.341 -1.48

efr3a_2 1.649 2.905 1.509

eif2s1b -5.704 -1.48 -1.299

eif4a3 -1.229 -4.222 -1.423

eif4eb -1.364 -26.474 -4.209

107

eif4ebp3l -1.466 -3.262 -1.615

ENSDARG00000073911 -1.029 -29.118 1.558

ENSDARG00000086253 -6.631 9.189 -6.631

ENSDARG00000088545 -6.245 9.845 -6.245

ENSDARG00000091099 1.546 -7.45 -2.004

ENSDARG00000094466 -1.522 -23.501 -2.571

ENSDARG00000096531 -1.257 -4.726 1.246

FER1L6 -1.46 -5.087 -2.849

FIS1 -3.74 -6.596 -5.249

GABARAP (2 of 2) 1.375 -3.316 -1.028

heatr5a_1 -1.889 3.461 1.054

her9 2.119 1.177 3.539

hmgb1a -1.842 -4.908 -1.181

hoxd3a 55.467 -1.749 -1.749

hsp90aa1.2 1.34 -18.356 -1.648

Hypk -2.63 -2.581 -2.02

Iars -2.312 -5.766 -1.64

kdm7ab 23.681 -1.296 -1.077

KLHDC8A 57.146 -2.482 13.435

lamtor2 12.788 4.297 29.943

Ldha -1.126 -3.705 -1.721

map4k2l_2 -3.067 -3.051 -2.419

mdh2 -1.564 -1.499 -1.359

Metazoa_SRP_23 -12.267 -4.251 -1.049

Metazoa_SRP_24 -23.171 -2.379 -23.171

Metazoa_SRP_38 3.863 9.774 1.193

mob4 1.008 -25.959 -2.522

mpc1 1.177 -4.901 -2.446

MTBP -1.828 3.661 1.793

MYH13 (10 of 11) 1.039 -5.111 -3.712

myl10 -9.559 -3.748 -2.687

MYL3 -8.388 -1.732 -2.259

mylpfb -1.385 -5.255 -2.677

naa10 -2.792 -5.044 -1.239

ndufa12 -5.742 1.231 -1.911

ndufaf6 -1.094 4.101 1.47

NDUFC2 -1.417 2.064 1.591

ndufv1 -1.741 -2.859 -1.543

nwd1_2 -3.272 -3.055 -1.616

Ostc -4.648 -2.55 -1.561

OXR1 (2 of 2) -1.689 -40.804 -2.167

parvb -1.038 -3.599 -1.242

pdap1b -1.609 -4.138 -1.99

pdlim7 1.861 -4.67 1.109

pfdn2 -2.897 -9.682 -2.246

prkag3b 4.991 -1.416 7.286

108

prmt1 -2.216 -24.793 -2.541

rbm8a -1.403 -3.084 -1.533

Rheb -1.805 -2.829 -2.171

rn7sk -4.028 -1.173 -2.461

rock2a 11.765 1.726 1.192

rpl13 -3.677 -2.207 -1.304

rpl14 -1.774 1.474 1.28

rpl18a -2.18 -2.574 -1.858

rpl19 -1.934 1.039 -1.398

rpl23 -2.051 1.253 1.163

rpl28 -1.897 1.05 -1.12

rpl3 -2.332 -1.16 -1.229

rpl32 -1.71 1.888 1.152

rpl35 -4.35 -1.042 -1.935

rpl36a -1.792 1.62 -1.017

rpl5a -1.909 1.749 1.143

rpl7 -1.993 -1.662 -1.163

rplp2 -1.488 1.702 1.172

rps10 -2.453 -1.032 -1.289

rps16 -1.286 1.642 -1.098

rps23 -1.254 1.468 -1.041

rps24 -3.95 2.206 -1.015

rps26 -1.936 1.071 1.04

rps26l -2.053 1.146 -1.511

rps27.1 -1.497 -1.127 -1.404

rps3a -1.777 1.184 -1.136

Rpsa -1.73 -1.187 -1.177

rtn2b -1.062 -9.541 -1.558

scn1a_2 1.89 3.587 1.951

SHISA7 (1 of 2) -1.343 -3.898 -1.645

si:ch1073-140o9.2 3.025 -1.568 -1.657

si:ch211-37e10.1 1.001 3.72 1.363

si:ch211-39k3.2 -1.559 -3.201 -2.088

si:ch211-59c24.1 -8.472 1.457 2.249

si:ch73-106n3.1 -5.941 -1.072 2.229

si:dkey-111b14.2 -5.253 -1.36 -2.48

si:dkey-151g10.6 -2.099 1.001 -1.713

si:dkey-153m14.1 -1.786 1.146 -1.255

si:dkey-16m19.1_2 -1.739 -51.973 -1.092

si:dkey-1b17.10 5.22 -2.277 2.319

si:dkey-240k8.2 31.051 1 1

si:dkey-41e15.4 -4.831 -2.098 1.263

si:dkey-86e18.1 1.852 2.387 2.311

si:dkeyp-84g1.2 22.863 1 9.819

slc25a4 -1.292 -2.418 -2.101

slc4a4b -2.735 1.476 -1.077

109

SMIM4 5.317 -14.296 -1.511

SNORA3_4 43.599 1 1

SNORD31_2 1.428 3.404 -91.533

snR56 -564.829 -7.481 -564.829

snrpb -4.447 -2.482 -1.307

soul4_1 2.382 -1.406 7.944

sparc -1.79 -15.228 -1.666

spcs1 -2.833 -2.171 -5.309

sssca1 -8.74 -6.637 -1.885

tcp1 -1.859 -3.275 -1.738

thoc7 -2.564 -13.11 -1.101

tmem38a -1.324 -5.143 -2.999

tmsb4x -1.058 -2.424 1.02

tnnc1a -10.433 -2.413 1.055

tnnc1b -4.96 -2.945 -1.066

tnnt3b -2.307 -1.8 -1.26

tomm20b -1.877 -4.951 -2.17

TSG101 (3 of 3) 13.702 10.623 28.24

U1_25 1 1 26.211

U2_9 1.302 2.822 1.493

u2af1 -2.012 -5.379 -1.996

U3_27 1.08 1.551 6.55

U6_234 -150.018 2.364 -2.515

U8_8 -1.279 3.026 -1.573

ugt2b6_1 -1.053 2.456 1.431

uqcrc1 -1.442 -6.095 -2.971

utp18 -3.263 -7.462 -1.941

vbp1 -2.723 -3.968 -6.275

WWP1 1.43 -5.171 -2.085

ybx1 -2.909 -5.797 -1.78

zgc:113295_1 -1.519 1.851 1.006

zgc:171759_4 -1.307 3.092 1.413

zgc:65894 -1.613 -12.707 -3.023

ZP2 (1 of 4) -1.974 -1.873 -2.536

110

S3 Table: Genes of tambaqui after five and fifteen days of B1, A1B and A2 climate scenarios exposure grouped in five clusters

using STRING software (v. 10).

STRING protein name

STRING id Gene name and description

Cluster 1

mob4 7955.ENSDARP00000072975 MOB family member 4, phocein tcp1 7955.ENSDARP00000104575 t-complex polypeptide 1

vbp1 7955.ENSDARP00000111810 von Hippel-Lindau binding protein 1

pfdn2 7955.ENSDARP00000035261 prefoldin subunit 2

zgc:65894 7955.ENSDARP00000002175 zgc:65894; Tubulin is the major constituent of microtubules. It binds two moles of GTP, one at an exchangeable site on the beta chain and one at a non-exchangeable site on the alpha chain (By similarity)

hsp90aa1.1 7955.ENSDARP00000022302

heat shock protein 90, alpha (cytosolic), class A member 1, tandem duplicate 1; Molecular chaperone that promotes the maturation, structural maintenance and proper regulation of specific target proteins involved for instance in cell cycle control and signal transduction. Undergoes a functional cycle that is linked to its ATPase activity. This cycle probably induces conformational changes in the client proteins, thereby causing their activation. Interacts dynamically with various co-chaperones that modulate its substrate recognition, ATPase cycle and chaperone function (By similarity). [...]

dnaja2 7955.ENSDARP00000028641 DnaJ (Hsp40) homolog, subfamily A, member 2

hsp90aa1.2 7955.ENSDARP00000026065 heat shock protein 90, alpha (cytosolic), class A member 1, tandem duplicate 2

zgc:86598 7955.ENSDARP00000024748 zgc:86598

Cluster 2

ATP5B 7955.ENSDARP00000060309 ATP synthase, H+ transporting, mitochondrial F1 complex, beta polypeptide; Produces ATP from ADP in the presence of a proton gradient across the membrane (By similarity)

atp5a1 7955.ENSDARP00000027947 ATP synthase, H+ transporting, mitochondrial F1 complex, alpha subunit 1, cardiac muscle; Produces ATP from ADP in the presence of a proton gradient across the membrane (By similarity)

atp5h 7955.ENSDARP00000038799 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit d

atp5l 7955.ENSDARP00000007716 ATP synthase, H+ transporting, mitochondrial F0 complex, subunit g

111

atp5d 7955.ENSDARP00000022528 ATP synthase, H+ transporting, mitochondrial F1 complex, delta subunit

atp5c1 7955.ENSDARP00000066929

ATP synthase, H+ transporting, mitochondrial F1 complex, gamma polypeptide 1; Mitochondrial membrane ATP synthase (F(1)F(0) ATP synthase or Complex V) produces ATP from ADP in the presence of a proton gradient across the membrane which is generated by electron transport complexes of the respiratory chain. F-type ATPases consist of two structural domains, F(1) - containing the extramembraneous catalytic core, and F(0) - containing the membrane proton channel, linked together by a central stalk and a peripheral stalk. During catalysis, ATP synthesis in the catalytic domain of F(1) is cou [...]

ndufs4 7955.ENSDARP00000051054 NADH dehydrogenase (ubiquinone) Fe-S protein 4, (NADH-coenzyme Q reductase) ndufv1 7955.ENSDARP00000052929 NADH dehydrogenase (ubiquinone) flavoprotein 1

ndufa12 7955.ENSDARP00000062277 NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 12

NDUFC2 7955.ENSDARP00000110392

NADH dehydrogenase (ubiquinone) 1, subcomplex unknown, 2, 14.5kDa; Accessory subunit of the mitochondrial membrane respiratory chain NADH dehydrogenase (Complex I), that is believed not to be involved in catalysis. Complex I functions in the transfer of electrons from NADH to the respiratory chain. The immediate electron acceptor for the enzyme is believed to be ubiquinone (By similarity)

uqcrc1 7955.ENSDARP00000108798 ubiquinol-cytochrome c reductase core protein I

cox7c 7955.ENSDARP00000069896 cytochrome c oxidase, subunit VIIc

cox4i2 7955.ENSDARP00000095260 cytochrome c oxidase subunit IV isoform 2

cox5b2 7955.ENSDARP00000105206 cytochrome c oxidase subunit Vb 2

Cluster 3 aldoaa 7955.ENSDARP00000119413 aldolase a, fructose-bisphosphate, a

aldocb 7955.ENSDARP00000024492 aldolase C, fructose-bisphosphate, b

pgm1 7955.ENSDARP00000006510 phosphoglucomutase 1

gapdhs 7955.ENSDARP00000058383

glyceraldehyde-3-phosphate dehydrogenase, spermatogenic; Glyceraldehyde-3-phosphate dehydrogenase is a key enzyme in glycolysis that catalyzes the first step of the pathway by converting D-glyceraldehyde 3-phosphate (G3P) into 3-phospho-D- glyceroyl phosphate (By similarity)

eno3 7955.ENSDARP00000120742 enolase 3, (beta, muscle)

gpib 7955.ENSDARP00000014578 glucose phosphate isomerase b

112

Gapdh 7955.ENSDARP00000063799

glyceraldehyde-3-phosphate dehydrogenase; Has both glyceraldehyde-3-phosphate dehydrogenase and nitrosylase activities, thereby playing a role in glycolysis and nuclear functions, respectively. Glyceraldehyde-3-phosphate dehydrogenase is a key enzyme in glycolysis that catalyzes the first step of the pathway by converting D-glyceraldehyde 3- phosphate (G3P) into 3-phospho-D-glyceroyl phosphate. Modulates the organization and assembly of the cytoskeleton. Also participates in nuclear events including transcription, RNA transport, DNA replication and apoptosis. Nuclear functions are prob [...]

pgk1 7955.ENSDARP00000070807 phosphoglycerate kinase 1

eno1a 7955.ENSDARP00000003738 enolase 1a, (alpha)

ldha 7955.ENSDARP00000059885 lactate dehydrogenase A4

eno2 7955.ENSDARP00000032456 enolase 2

tpi1a 7955.ENSDARP00000033907 triosephosphate isomerase 1a

pkmb 7955.ENSDARP00000122764 pyruvate kinase, muscle, b

aldoab 7955.ENSDARP00000042199 aldolase a, fructose-bisphosphate, b

Cluster 4 ns:zf-e68 7955.ENSDARP00000111326 myosin, heavy polypeptide 1.3, skeletal muscle

myl10 7955.ENSDARP00000050204 myosin, light chain 10, regulatory

actc1a 7955.ENSDARP00000100434 hm:zewp0073

tpma 7955.ENSDARP00000039656

alpha-tropomyosin; Binds to actin filaments in muscle and non-muscle cells. Plays a central role, in association with the troponin complex, in the calcium dependent regulation of vertebrate striated muscle contraction. Smooth muscle contraction is regulated by interaction with caldesmon. In non-muscle cells is implicated in stabilizing cytoskeleton actin filaments

actc1b 7955.ENSDARP00000055135 actin, alpha, cardiac muscle 1b

ckma 7955.ENSDARP00000037871 creatine kinase, muscle a

ckmb 7955.ENSDARP00000059365 creatine kinase, muscle b

pdlim7 7955.ENSDARP00000044908 PDZ and LIM domain 7

tnnc2 7955.ENSDARP00000095111 troponin C type 2 (fast)

myl1 7955.ENSDARP00000004932 myosin, light chain 1, alkali; skeletal, fast

mylz3 7955.ENSDARP00000018197 myosin, light polypeptide 3, skeletal muscle

smyhc1 7955.ENSDARP00000056852 slow myosin heavy chain 1

tpm3 7955.ENSDARP00000004352 tropomyosin 3

113

slc25a4 7955.ENSDARP00000030881 solute carrier family 25 (mitochondrial carrier; adenine nucleotide translocator), member 4 atp2a1l 7955.ENSDARP00000096674 ATPase, Ca++ transporting, cardiac muscle, fast twitch 1 like

desma 7955.ENSDARP00000075994 desmin a

tnnt3b 7955.ENSDARP00000105443 troponin T3b, skeletal, fast

mylpfb 7955.ENSDARP00000023063 myosin light chain, phosphorylatable, fast skeletal muscle b

rock2a 7955.ENSDARP00000122621 rho-associated, coiled-coil containing protein kinase 2a

myl2 7955.ENSDARP00000116241 myosin, light polypeptide 2, regulatory, cardiac, slow

LOC567740 7955.ENSDARP00000122502 Uncharacterized protein

tnni2a.4 7955.ENSDARP00000037759 troponin I, skeletal, fast 2a.4

MYL3 7955.ENSDARP00000066500 myosin, light chain 3, alkali; ventricular, skeletal, slow

pvalb1 7955.ENSDARP00000055061 parvalbumin 1

tmem38a 7955.ENSDARP00000037150 transmembrane protein 38A; Monovalent cation channel required for maintenance of rapid intracellular calcium release. May act as a potassium counter-ion channel that functions in synchronization with calcium release from intracellular stores (By similarity)

tnnc1b 7955.ENSDARP00000054663 troponin C type 1b (slow)

actn2 7955.ENSDARP00000095652 actinin, alpha 2

desmb 7955.ENSDARP00000065355 desmin b

tnnc1a 7955.ENSDARP00000025541 troponin C type 1a (slow)

tnni2a.1 7955.ENSDARP00000031650 troponin I, skeletal, fast 2a.1

calm1a 7955.ENSDARP00000092307 calmodulin 1b; Calmodulin mediates the control of a large number of enzymes, ion channels and other proteins by Ca(2+). Among the enzymes to be stimulated by the calmodulin-Ca(2+) complex are a number of protein kinases and phosphatases

tnnt1 7955.ENSDARP00000044153 troponin T2c, cardiac Cluster 5

eif4ebp3l 7955.ENSDARP00000060989 eukaryotic translation initiation factor 4E binding protein 3, like; Regulates eif4e1a activity by preventing its assembly into the eIF4F complex (By similarity)

eif2s1 7955.ENSDARP00000068470 eukaryotic translation initiation factor 2, subunit 1 alpha

EIF3F 7955.ENSDARP00000099664

eukaryotic translation initiation factor 3, subunit F; Component of the eukaryotic translation initiation factor 3 (eIF-3) complex, which is involved in protein synthesis and, together with other initiation factors, stimulates binding of mRNA and methionyl-tRNAi to the 40S ribosome (By similarity)

114

eef1a1a 7955.ENSDARP00000104468 eukaryotic translation elongation factor 1 alpha 1a; This protein promotes the GTP-dependent binding of aminoacyl-tRNA to the A-site of ribosomes during protein biosynthesis (By similarity)

rpl11 7955.ENSDARP00000063869 ribosomal protein L11

rpl5a 7955.ENSDARP00000006085 ribosomal protein L5a


rps26 7955.ENSDARP00000105328 ribosomal protein S26

rps3a 7955.ENSDARP00000051762 ribosomal protein S3A

rpsa 7955.ENSDARP00000123183

ribosomal protein SA; Required for the assembly and/or stability of the 40S ribosomal subunit. Required for the processing of the 20S rRNA- precursor to mature 18S rRNA in a late step of the maturation of 40S ribosomal subunits. Also functions as a cell surface receptor for laminin. Plays a role in cell adhesion to the basement membrane and in the consequent activation of signaling transduction pathways. May play a role in cell fate determination and tissue morphogenesis (By similarity)


rps26l 7955.ENSDARP00000111782 ribosomal protein S26, like

eif4a3 7955.ENSDARP00000027276

eukaryotic translation initiation factor 4A, isoform 3; ATP-dependent RNA helicase. Component of a splicing- dependent multiprotein exon junction complex (EJC) deposited at splice junction on mRNAs. The EJC is a dynamic structure consisting of a few core proteins and several more peripheral nuclear and cytoplasmic associated factors that join the complex only transiently either during EJC assembly or during subsequent mRNA metabolism. Core components of the EJC, that remains bound to spliced mRNAs throughout all stages of mRNA metabolism, functions to mark the position of the exon-exon [...]


eef2l2 7955.ENSDARP00000051080 eukaryotic translation elongation factor 2, like 2

rps27.1 7955.ENSDARP00000029079 ribosomal protein S27, isoform 1


rpl18a 7955.ENSDARP00000038658 ribosomal protein L18a

rps27.2 7955.ENSDARP00000072300 ribosomal protein S27, isoform 2

rpl35 7955.ENSDARP00000018594 ribosomal protein L35; Plays an essential role in early embryonic development. May act as a haploinsufficient tumor supressor

115



eef1a1l1 7955.ENSDARP00000111742 eukaryotic translation elongation factor 1 alpha 1, like 1; This protein promotes the GTP-dependent binding of aminoacyl-tRNA to the A-site of ribosomes during protein biosynthesis (By similarity)



ddost 7955.ENSDARP00000054289

dolichyl-diphosphooligosaccharide-protein glycosyltransferase; Essential subunit of the N-oligosaccharyl transferase (OST) complex which catalyzes the transfer of a high mannose oligosaccharide from a lipid-linked oligosaccharide donor to an asparagine residue within an Asn-X-Ser/Thr consensus motif in nascent polypeptide chains (By similarity)

rplp2 7955.ENSDARP00000025616 ribosomal protein, large P2

spcs1 7955.ENSDARP00000076814 signal peptidase complex subunit 1 homolog (S. cerevisiae)

rbm8a 7955.ENSDARP00000026575

RNA binding motif protein 8A; Component of a splicing-dependent multiprotein exon junction complex (EJC) deposited at splice junction on mRNAs. The EJC is a dynamic structure consisting of a few core proteins and several more peripheral nuclear and cytoplasmic associated factors that join the complex only transiently either during EJC assembly or during subsequent mRNA metabolism. Core components of the EJC, that remains bound to spliced mRNAs throughout all stages of mRNA metabolism, functions to mark the position of the exon-exon junction in the mature mRNA and thereby influences dow [...]

rpl36a 7955.ENSDARP00000075363 ribosomal protein L36A









116

S4 Table: Primers and types of SSR motifs found in tambaqui sequences.

Contig name Primer left Left_Tm Primer_right Right_Tm Motif

contig_117 AGGCTGGGTGGGAATTGTC 60 CAGCTTTACTGCAGATTTGGC 58,873 (CT)8 contig_133 CTTTGTCTAAGCCTGTTTACCTC 57,894 CGACCGTTCAGGGAAATGC 59,939 (CT)6...(CT)6 contig_175 ATGAGTGAAGCTAGTCTGGAG 57,316 ACGGCACATTATTGCAGGAC 59,655 (CT)6...(CT)6 contig_442 GGAGGGACGTGAAGAAGCC 60,828 GCTGATTCTCAGACATGGAGG 58,92 (CT)6 contig_652 AATGAATGGCGCGTTCTGG 59,937 CACTGTGATAGATCAGGTGGC 58,924 (GGT)5 contig_923 TGACCTGCATTAACTCTGCC 58,643 TCTTACTACATATACAAGTGTGCAGG 59,288 (AG)6 contig_1824 GAGACCGATGACCTGTGAAC 58,745 TCTGCTCACAACTACCTATCCG 60,076 (AG)6 contig_2593 TCAGGCCAGTTTCTTTGGC 59,024 GGGTGAACAGCTCATTTCTAGC 60,077 (GT)10 contig_2837 AAGCTTTGCAAGGTCAGCG 60,084 AAGGGATTCTGGGACAGGG 59,065 (AC)6 contig_3417 GAGAAGTAGCCGCTTGCAC 59,648 GCAGCGTGAATCTGGTTGG 60,231 (CT)8 contig_3664 GGCAGTAAACAGCAGGTGG 59,488 TGTGTTTGGCCGGTTTGAC 59,935 (AG)7 contig_3778 TGCCTTCCTTCATAAATGTGC 57,609 ACATGAACAAAGTCTCAACCC 57,123 (CT)7 contig_4294 AGGCCTGATCCTCGACAAC 59,856 CTGCCCTTCCAGCATTCAG 59,259 (CT)6 contig_5697 ACTGATTTCCAAAGATGACGG 57 GACAGAACCAAACTCGGGAC 59,235 (GT)9 contig_5857 AGCACACAAGCAAAGGTTCC 60,009 AGGCGATCAGAGTGTCAGC 60,232 (CT)6 contig_5979 AGCAAGTTTGAGGCCATGC 59,782 CCTCATCGCTGGAGGCG 60,659 (AG)6 contig_6299 AAGGCACCGTGAAATCTCC 58,502 CGTAACAGGCATTTATCATCGC 58,94 (AG)6 contig_6507 TGTATATTATCGCTGCTCTGTAGAC 58,923 CTGAAGCACACTGCCAAGC 60,451 (GT)6 contig_7058 AAGCCAAGGTGCAGAAGGG 60,995 TCGGAGTTCATCACTGCGG 60,528 (GAT)6 contig_7092 GTTTAGGATCATTTCTACAGTGGTATC 58,512 AGGCAGTGACATGATTGTGC 59,582 (AC)7 contig_7457 CGTTTCCTGCCTCAAGTCC 59,196 TGGTGTCTGCTCCAACGTC 60,678 (CCT)5 contig_7514 CAGGCTGTTTCTAATCTGCAC 57,992 TAAGGCGCCAGGTCAACAG 60,756 (AG)8 contig_7662 TTCCTGATGAATGCACCAATAC 57,511 ACAGCTGGCATATCAATGTCTTAC 59,953 (AG)9 contig_7842 GGACGCTTGGATAACACTGG 59,376 CCGGCAGTTGGCTTCTTTG 60,452 (GT)11 contig_7949 AGGATTGGAGCAGAGCCAC 60,157 AGCTTTGCGAATGCTAGAAGG 59,741 (GT)6 contig_7989 AGAGCCACCTGTGACTGAAG 60,079 GGGAAAGAATCCCAGTAGTAAGTTC 59,664 (CT)6 contig_8910 GGGACCTTAGACGAGCGG 59,931 CAGTGCATGAACGTTGTCTTCAG 61,353 (AG)6

117

contig_9621 ATTCATGGGCAACAGCTCC 58,867 CAATTGGCCCTGAGGTTGG 59,477 (AAC)5 contig_9628 TCACGATGAGTGAAGGCCG 60,528 AACTGTCTCCAGTCGGTGG 59,705 (CT)6 contig_9836 TAGCCTGCTACCCAAAGCC 60,157 ACTAACAGCCAGCACACTTG 59,155 (GCT)5 contig_10005 GTGGTGAAGGACTCTGTGG 58,133 TGATGTGGTGGTCCGCTC 60,085 (AAG)5 contig_10270 AGGCACACCAATGCTTCAC 59,407 GCTGCTCGTTTCACTGGTC 59,867 (GTT)5 contig_10556 TGTCAGGGAGATCTGAGTATGG 59 TTGGGACATGGGTTTCTGG 58,003 (GT)7 contig_10804 GAGAGAATGCCACTGATCTCG 59 AAGGAGTCCACAGTTTGCG 58,739 (CT)6 contig_11599 ACCGAGAGTTTACGAGCTG 57,643 AGGCACTCCCAGCTACAAG 59,777 (ATT)5 contig_11819 AGTGATGGAGGGTCGTTCG 60 TGGGTGATTCTTGCACAGC 59,106 (CT)7 contig_12219 AAGAAACTGTCCTTATTGTGTCC 57 AAATGGCCCACCAAAGCTG 59,701 (ATTTT)6 contig_12543 TCATGATTGTGGCCTTCTAGTATTG 59,721 TGCGCACGCGTAAACAG 59,776 (GT)6 contig_12583 GGCTATGGTCAAGGTAATGGAG 59,023 AGTCAGACCCTTGCTGACC 59,701 (AGC)7 contig_12782 GGCATGGTCCACCACAATTC 60,223 ACCCTAGCGTCACAATCAG 57,604 (CT)6 contig_13525 ACGTTAACTGTTGTGACCGC 59,804 AGTGAAATTAGCACAGCGATGG 60,077 (CT)6 contig_14183 TGGACCATGCCCTCAACG 60,402 GCAGTGCAGAAGTCAATGG 57,65 (GT)8 contig_14300 ACAACTATGGGTGTACATGGC 58,767 GTACTGTGATCTGTAACCAGACG 59,339 (AC)6 contig_14669 CGCTCTCAGCTTTGGGTTG 59,864 CAGGCATGTGCAAGACACC 60,158 (GT)6 contig_14961 TCTTGTCAGTTTCAGCTCTGG 58,642 GTGGCACTCAAGCTCATTC 57,352 (GTTT)5 contig_14981 CTCGTCCTCTGTGGAGCTG 60,232 GGATCCAGGCAGAGCTTTAG 58,503 (CCT)7 contig_15075 TGCAGAGGAATCTCCTTGTGG 60,492 GACCGTCCACTGTGTCATC 58,612 (GGT)5 contig_15957 CGTGGCGATCTCACCAAAG 59,648 TGCAACTTCAGAACAAGGCAC 60,35 (GGT)6 contig_16842 TAGGACTTGAGCGAATGTTTACTTG 60,13 GCAGTCGGAGGGAACACG 61,163 (CT)6 contig_17081 GCTAAAGGCGTAAGGTATGTC 57,256 AGGAGCAGCGACATGGG 59,753 (CT)8 contig_17305 TACATGGGACAGGGAAGTACTCG 61,969 AAATAGCTTGGATTAGCACCTG 57,238 (ATC)7 contig_19077 TGCAGCAGAGCTTTCTGATTG 59,944 AAAGCAGGCGTGGCATTAG 59,562 (GT)9 contig_20535 AGAGCATCAAAGTGTCCCTG 58,274 GGACTGGGAGGTGCTCTTG 60,459 (AGC)5 contig_21230 AACGTTAGCGTGGTGGCTC 61,407 TCATGAGTCCAAACTGCACC 58,861 (AC)6 contig_21296 GAATTCCAAAGAGGGCAGGTC 59,667 TGCCAGTCTCTGCTGGTAG 59,477 (AG)6 contig_21606 ACACCGAACATGGAGGGAC 60,082 ACGAACTGGAGCACTTTATGG 59,057 (CCT)5 contig_21771 GAGTTCCAGAGTCCTGCCC 60,157 CATCGTTGTGCCGCCAG 59,851 (CT)8 contig_22559 GAAGCTTGGAGCTCATCCAC 59,372 ACACATCTCCACAAGATATTTCTCC 59,428 (AAG)5

118

contig_22894 CGGCTACATCAATGCCCTC 59,12 AGGCTCCATCAGGAACTGTC 59,861 (GCT)7 contig_23572 CAGGGTTCTATTCCAGCAGTC 58,848 CAGGGAGCCAATGAAACGG 59,562 (CCT)6 contig_24414 GTTTGACACCTTTGACTGACG 58,418 ACTGACCAGTCCACTTACAG 57,259 (AG)6 contig_24661 ACACTCTTTGATGACCTGGTTG 59,287 GATGGAGTGGCCTCCCG 59,837 (GT)7 contig_24694 TCAGAGCCCATACCTGGAC 58,851 GTATCTGCGGGCGTGAAG 59,017 (AC)6 contig_25297 AAGTACTCGGTTGCCCGTG 60,751 TGATGTGCTGTAGATCCCGAG 60,078 (CTT)5 contig_25982 TCCATCCTGAGCCCAAACG 60,46 TCTGCAGCCTAAACTCAGC 58,208 (AG)6 contig_26248 CCCTGCCCTTGACAATCC 58,408 TTGATAAGCGCCACGTTCC 59,274 (GTT)5 contig_26320 CGACGAGTCCACAAACCTG 59,211 CTGAGCACTGACCACATCTTC 59,405 (GT)7 contig_26841 AGCCCTCACCAGTATTCACC 59,859 CTGAGGCACGTTGAAGCAG 59,867 (CT)7 contig_26880 TGGATGTTCAGGGTTAATGTCAG 59,186 CCACTGGCTCTTCCAGGTC 60,459 (GT)7 contig_27711 TGTAGTGCCCTTTGATCGTC 58,371 ACAGATACGCGCAATATGACC 59,342 (CT)7 contig_28070 AGTTGGAATCAGGACCCGC 60,46 ACGCTGTTCTTTGCACCAG 59,715 (CCT)6 contig_28755 GAAAGTACTGCACCTCTGCC 59,303 ACCACTGTTTAGGTCGAAGTATC 58,696 (GTTT)8 contig_28913 CCAATGTTCAGCTCCAGCC 59,561 CCTCAACTGCCAAAGTCCC 59,106 (AGC)6 contig_33077 GCAAGATCAGTTCGCAGGC 60,304 ACTTGGCAATCTGCTGTGG 59,103 (GTTT)5 contig_34226 CGCACTCCGAAAGCAACC 60,165 GGTCCTGTGGGCTTGTAAAC 59,51 (GCT)5 contig_34496 CTGTAAAGGTGTGACGCGG 59,577 GAGCACTATGATGGAGTGCG 59,243 (AC)6 contig_34868 TTCCACGCCTGCCTTCAAC 61,648 AGTCTAGTCATCAAGCGTTTAGC 59,332 (CCT)5 contig_34991 GACTTTGACGGTTCCACCTG 59,518 GCGCTCTTTGGATTTGGAGC 60,92 (AGC)5 contig_35837 CCCTTCCTCAATACTCTGACCC 60,34 TGTACGGCGGTGCATAGAC 60,6 (CTGT)5 contig_37007 TTGTCACGGGCTTCTTCCG 61,047 AGCAGTATCCAGAGCCAACC 60,224 (CT)10 contig_37854 ACTTCTGTGCTCGTTTCGG 58,835 TACCCGTGGACTCAGATCG 58,959 (CTT)7 contig_39773 CCCTGTTGTTTCCTTTCAGTC 57,763 CGTCATACTGCGTCCCGTG 61,597 (CTGT)6 contig_41091 AGACAGTGGCACGCATCG 61,165 ACATAGCACCCAGATAGGTTGG 60,341 (AG)8 contig_46762 GCTGAGCAATGAGCGTTCG 60,66 TGGATTCCGTAGTCGGAGG 58,951 (GAT)5 contig_48037 GAAGCTGGTGACATGGTACG 59,381 GACAACATGTAGACGGCATTTC 58,74 (CT)7 contig_48388 ACTGTAATTATACGGTCAGAACCC 58,862 TGTCACAGCGCCATAACTTG 59,589 (AC)6 contig_48788 TGAAAGAAGTAACATTACAGAGTCAGG 59,907 AAGCTGAGCACTGGCATTC 59,185 (GT)11 contig_48799 TGTGCCCAGTTGCTCCAC 60,956 TCTGTTCGGTCAGTAGAATGG 57,889 (CT)6

119

Capítulo 3 ___________________________________________________________________ Prado-Lima, M. & Val, A.L. Da Genética à Fisiologia: uma abordagem integrativa sobre a exposição do tambaqui (Colossoma macropomum) aos principais cenários de mudanças climáticas previstos pelo IPCC. Manuscrito formatado de acordo com as regras da Acta Amazonica.

120

Resumo As mudanças climáticas têm causado alterações físicas e biológicas nas

diversas partes do planeta e impactado o clima, os ecossistemas e seus organismos. As consequências geradas pelo aquecimento global ameaçam todos os níveis da organização biológica, desde os mais abrangentes até o molecular. No caso dos peixes, que estão sujeitos a variações ambientais, a elevação na temperatura da água e as modificações na concentração de gases dissolvidos colocam em risco a sobrevivência de diversas espécies, especialmente aquelas mais sensíveis. A capacidade de se adaptar às variações na temperatura ambiental depende de alterações em diversos processos metabólicos e bioquímicos que têm origem na regulação da expressão gênica em resposta ao agente estressor. Dessa forma, o objetivo do presente estudo foi investigar como o tambaqui (Colossoma macropomum), uma espécie amazônica de grande importância ecológica e econômica, pode reagir à exposição às mudanças climáticas globais. Os peixes foram expostos durante 150 dias aos cenários climáticos atual, B1, A1B e A2, previstos pelo IPCC para o ano 2100, e tiveram investigadas as alterações no transcriptoma, na integridade do DNA de eritrócitos, em parâmetros hematológicos e enzimas de regulação iônica. Os resultados obtidos indicam que os principais genes que responderam à exposição foram: atp2a1, atp2a2b, calm2a, calm3a, cox4i2, cox5b2, cox7c, cox8b, dnaja2, dnajc7, hmgb1a, hsp90aa1.1, hsp90aa1.2, pfdn2 e slc4a4b. As análises de ontologia indicaram que esses genes atuam em importantes processos metabólicos responsáveis pela homeostase celular e sobrevivência do organismo em situações de estresse térmico, principalmente, síntese, dobramento e transporte de proteínas, remodelamento da membrana plasmática, mecanismos de defesa antioxidante, osmoregulação e atividade das principais enzimas de regulação iônica. Assim, a análise integrativa de parâmetros genéticos e fisiológicos possibilitou um importante avanço na compreensão dos mecanismos moleculares relacionados à exposição do tambaqui às mudanças climáticas globais previstas pelo IPCC.

Palavras-chave: aquecimento global, Amazônia, adaptação, transcriptoma, fisiologia, ensaio cometa

121

Introdução

A temperatura corporal dos animais ectotérmicos está sujeita a variações

decorrentes de alterações na temperatura do ambiente onde vivem (Huey e

Kingsolver 1993). Dessa forma, muitos organismos aquáticos, como os peixes, não

conseguem controlar sua temperatura corporal, mas podem nadar para áreas com

temperaturas mais adequadas ao seu metabolismo (López-Olmeda e Sánchez-

Vázquez 2011). Ainda assim, os peixes podem se deparar com uma série de

variações diárias e sazonais na temperatura da água devido a variações rápidas na

irradiação solar, movimentos anormais na coluna d’água e ocorrência de

precipitações rápidas (Long et al. 2012; 2013). Em decorrência disso, as espécies

fazem uso de diversas estratégias adaptativas para superar esse estresse ambiental

(Ficke et al. 2007; Long et al. 2012).

A capacidade de se adaptar às variações de temperatura do ambiente é

dependente da alteração de diversos processos metabólicos e bioquímicos, à fim de

manter a homeostase celular e garantir a sobrevivência do organismo (Pörtner e

Farrell 2008; Clark et al. 2010; Logan e Buckley 2015). As taxas metabólicas de

animais ectotérmicos normalmente aumentam com o incremento na temperatura

ambiental, o que leva ao aumento nas exigências de combustíveis metabólicos

preciosos (Iftikar e Hickey 2013). Porém, se o aumento na taxa metabólica for

insuficiente, as reservas metabólicas devem ser direcionadas para a geração de

energia, comprometendo, consequentemente, o equilíbrio de processos fisiológicos

vitais como crescimento, reprodução, alimentação e, em última instância, a

sobrevivência do organismo (Iftikar e Hickey 2013; Strobel et al. 2013)

Em situações de estresse térmico, os peixes podem enfrentar também

modificações nas propriedades e no funcionamento de biomoléculas, alterações em

componentes estruturais das células, como o funcionamento, dobramento e

estabilidade de proteínas, modificações na composição, permeabilidade e fluidez

das membranas celulares (Hazel e Williams 1990; Los e Murata 2004; Long et al.

2012). Adicionalmente, devido a água aquecida possuir menor capacidade de

retenção de oxigênio dissolvido, os peixes podem enfrentar um estresse adicional

provocado pela hipóxia que, consequentemente, gera uma série de modificações

como aumento na taxa respiratória para lidar com esse novo desafio (Jeppesen et

al. 2012).

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As consequências geradas pelas mudanças climáticas, especialmente

aumento na temperatura e na concentração de CO2, ameaçam todos os níveis da

organização biológica, desde o comportamental até o molecular (Podrabsky e

Somero 2004; Hofmann 2005). As estratégias utilizadas pelos organismos para

superar as modificações ambientais têm início a partir de alterações na expressão

de genes, passando por modificações pós-transcricionais até chegar à síntese de

proteínas (Sterky e Lundeberg 2000; Oshlack et al. 2010). Diversos estudos

mostram que o estresse térmico gerado pelo aumento na temperatura resulta em

severas modificações nos padrões considerados normais de expressão gênica em

diversos peixes como zebrafish (Long et al. 2013), truta (Oncorhynchus mykiss

gairdneri) (Narum e Campbell 2015), bagre de canal (Ictalurus punctatus) (Liu et al.

2013), Pagothenia borchgrevinki (Bilyk e Cheng 2014), truta do ártico (Salvelinus

alpinus) (Quinn et al. 2011) e bacalhau do Atlântico (Gadus morhua) (Hori et al.

2010). Embora a expressão de vários genes seja alterada devido ao estresse

desencadeado pelo aumento na temperatura ambiental, poucos estudos têm

conseguido estabelecer uma correlação segura entre o nível de expressão gênica,

obtido a partir de diferentes abordagens de análise de transcriptomas, como RNA-

Seq, e os processos metabólicos celulares e proteínas resultantes da regulação

positiva ou negativa desses genes (Narum e Campbell 2015).

Contudo, o correto funcionamento das proteínas é dependente do seu

formato, ou seja, apenas as proteínas que tenham sido devidamente dobradas

podem manter-se estáveis para atuar em processos biológicos fundamentais ao

organismo (Basu et al. 2002; Hofmann 2005; Long et al. 2013). O dobramento das

proteínas, tanto aquelas recém-sintetizadas pelos ribossomos quanto a estabilidade

funcional das que foram sintetizadas há mais tempo, é um processo especializado e

altamente dependente da temperatura, tendo em vista que esse processo é

realizado, principalmente, por proteínas termossensíveis como as chaperonas,

também conhecidas como HSPs (heat shock proteins) (Basu et al. 2002; Liu et al.

2013; Narum e Campbell 2015).

A espécie escolhida para esse estudo foi o tambaqui, Colossoma

macropomum, um peixe nativo da bacia Amazônica que habita rios, lagos e áreas

alagáveis (Junk et al. 2007), onde se alimenta de frutos e sementes na idade adulta

(Araújo-Lima e Goulding 1998). É amplamente criado em diferentes regiões do Brasil

devido ao seu alto valor comercial e facilidade no cultivo (Araújo-Lima e Goulding

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1998; Brasil 2012). O tambaqui apresenta estratégias fisiológicas, morfológicas,

comportamentais e genéticas que lhe permite sobreviver em ambientes que seriam

desfavoráveis para muitas espécies devido a variações nas propriedades físicas e

químicas como a baixa concentração de oxigênio dissolvido e pH (Val e Almeida-Val

1995; Wood et al. 1998; Val e Kapoor 2003; Aride et al. 2007).

Considerando o crescente aumento na temperatura média do planeta e na

concentração de gases do efeito estufa, é importante compreender como o tambaqui

pode lidar com esse novo desafio ambiental e quais os principais impactos

resultantes dessa exposição. Assim, o objetivo do presente estudo foi realizar uma

análise integrativa envolvendo o perfil de expressão gênica e informações obtidas a

partir da análise de diversas variáveis fisiológicas para compreender como o

tambaqui poderá reagir à exposição aos principais cenários de mudanças climáticas

previstos pelo IPCC para o ano 2100.

Materiais e métodos

As variáveis fisiológicas analisadas foram: avaliação de danos ao DNA de

eritrócitos, análise de parâmetros hematológicos, concentração de íons plasmáticos

e atividade enzimática da Na+/K+-ATPase e H+-ATPase após exposição do tambaqui

durante 150 dias aos cenários climáticos B1, A1B e A2. Os métodos utilizados para

essas análises encontram-se descritos detalhadamente no Capítulo 1 desta tese.

Para investigar alterações no transcriptoma do tambaqui e identificar genes

diferencialmente expressos após 5 e 15 dias de exposição aos cenários climáticos

B1, A1B e A2, foi utilizado o sequenciamento de RNA (RNA-Seq). Os métodos

utilizados encontram-se descritos detalhadamente no Capítulo 2 desta tese.

Resultados

A partir da análise funcional e da ontologia de todos os genes

diferencialmente expressos identificados após a exposição do tambaqui aos

cenários climáticos B1, A1B e A2, foram selecionados os genes com rotas

metabólicas diretamente relacionadas às variáveis fisiológicas investigadas no

Capítulo 1. Esses genes foram agrupados na Tabela 1, que é composta

principalmente por vários tipos de chaperonas como Hsp90, DnaJ e Pfdn2 e genes

que estão envolvidos em modificações na composição e no funcionamento da

membrana plasmática como Citocromo oxidase, Calmodulina e Solute carrier.

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Tabela 1: Principais genes diferencialmente expressos que responderam a exposição do tambaqui aos cenários climáticos B1, A1B e A2.

SÍMBOLO GENE ONTOLOGIA1 Bibloteca RNA-seq2

Atp2a1 ATPase, Ca++

Transporting, Cardiac Muscle, Fast Twitch 1

Calcium-transporting ATPase activity Calcium ion binding Nucleotide binding Protein binding

5 dias

Atp2a2b ATPase, Ca++

Transporting, Cardiac Muscle, Slow Twitch 2

Calcium-transporting ATPase activity Calcium ion binding Nucleotide binding Protein binding

5 dias

Calm2a Calmodulin 2

(Phosphorylase Kinase, Delta)

Calcium ion binding Membrane organization Detection of calcium ion Ion channel binding

15 dias

Calm3a Calmodulin 3

(Phosphorylase Kinase, Delta)

Calcium ion binding Membrane organization Detection of calcium ion Ion channel binding

15 dias

Cox4i2 Cytochrome C Oxidase Subunit IV Isoform 2 (Lung)

Cytochrome-c oxidase activity Cellular respiration Oxidation-reduction process Hydrogen ion transmembrane transport

5 dias

Cox5b2 Cytochrome C Oxidase Subunit Vb

Cytochrome-c oxidase activity Hydrogen ion transmembrane transport Metal ion binding Protein binding

15 dias

Cox7c Cytochrome C Oxidase Subunit VIIc

Cytochrome-c oxidase activity Hydrogen ion transmembrane transport Integral component of membrane Respiratory electron transport chain

5 dias

Cox8b Cytochrome C Oxidase Subunit VIIIB, Pseudogene

Cytochrome-c oxidase activity Integral component of membrane Respiratory electron transport chain Transmembrane transport

5 dias

Dnaja2 DnaJ (Hsp40) Homolog, Subfamily A, Member 2

Cellular response to heat Hsp70 protein binding Heat shock protein binding Protein binding

5 dias

Dnajc7 DnaJ (Hsp40) Homolog, Subfamily C, Member 7

Cellular response to heat Heat shock protein binding Protein binding Regulation of cellular response to heat

5 dias

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Hmgb1a High Mobility Group Box 1

Chromatin remodeling DNA ligation involved in DNA repair Negative regulation of transcription from RNA polymerase II promoter Transcriptional repressor complex

5 dias 15 dias

Hsp90aa1.1 Heat Shock Protein 90kDa Alpha (Cytosolic), Class A

Member 1

Chaperone-mediated protein complex assembly Protein binding Response to heat Regulation of cellular response to heat

5 dias

Hsp90aa1.2 Heat Shock Protein 90kDa Alpha (Cytosolic), Class A

Member 1

Chaperone-mediated protein complex assembly Protein binding Response to heat Regulation of cellular response to heat

15 dias

Pfdn2 Prefoldin Subunit 2

Protein folding Protein binding involved in protein folding Positive regulation of cytoskeleton organization Unfolded protein binding

5 dias 15 dias

Slc4a4b Solute Carrier Family 4 (Sodium Bicarbonate

Cotransporter), Member 4

Inorganic anion exchanger activity Regulation of intracellular pH Sodium ion transmembrane transport Sodium: bicarbonate symporter activity

15 dias

1 Anotação funcional obtida a partir da base de dados do Gene Ontology (http://www.geneontology.org/), que inclui as categorias: processo biológico, função molecular e componente celular. 2 Gene diferencialmente expresso identificado após 5 e/ou 15 dias de exposição aos cenários climáticos.

Discussão

Entre os genes diferencialmente expressos identificados no tambaqui após

exposição aos cenários climáticos B1, A1B e A2, destacam-se genes ou famílias

gênicas (Tabela 1) que atuam individualmente ou em grupo para garantir a

homeostase celular. Nesse sentido, esses genes sintetizam enzimas e proteínas que

desempenham importantes funções no dobramento de proteínas, na regulação da

transcrição, como canais iônicos e reguladores do potencial da membrana

plasmática.

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São genes como Atp2a1 e Atp2a2b que atuam no armazenamento e controle

dos níveis intracelulares de cálcio a partir da ação coordenada de diversos

mecanismos de armazenamento de cálcio no retículo endoplasmático e ligação de

íons Ca2+ no citoplasma (Korosec et al. 2006; Pegoraro et al. 2011). A manutenção

dos níveis de cálcio e sua mobilização a partir do retículo endoplasmático para o

citoplasma celular é um fator fundamental para o controle de diversos padrões de

sinalização celular que são responsáveis, dentre outras funções, pela proliferação e

diferenciação celular, assim como pela apoptose, a morte programada da célula

(Korosec et al. 2008). O cálcio desempenha muitas funções fisiológicas

fundamentais para a sobrevivência dos organismos, como a coagulação sanguínea,

batimento cardíaco, contração muscular e transmissão de impulso nervoso (Heyen

et al. 2000; Korosec et al. 2008).

A concentração dos níveis plasmáticos de Ca2+ é regulada por diversos

receptores de membrana e bombas de cálcio (Ca2+-ATPase) (Varga-Szabo et al.

2009). Devido a atividade da Ca2+-ATPase, localizada na membrana plasmática da

célula e em organelas intracelulares como as mitocôndrias, os níveis de cálcio são

mantidos baixos dentro da célula na ausência de sinalização celular (Pegoraro et al.

2011). A Ca2+-ATPase atua no controle da homeostase celular de cálcio por meio da

hidrólise de um ATP que possibilita a captura de dois íons Ca2+ para dentro do

retículo endoplasmático (Periasamy e Kalyanasundaram 2007). Dentro da célula

podem estar ligadas ao retículo endoplasmático ou sarcoplasmático, (Atp2a /

SERCA) sendo transportadores de cálcio altamente conservados, encontrados

desde leveduras até mamíferos, e codificados por três genes: Atp2a1 (SERCA1),

Atp2a2 (SERCA 2) e Atp2a3 (SERCA3) (Korosec et al. 2008).

A estrutura molecular da família de genes Atp2a tem sido estudada em

abelhas (Magyar et al. 1995), galinhas (Machuca et al. 1999), mamíferos (Schleef et

al. 1996) e peixes (Lai et al. 2011). Em zebrafish (Danio rerio), peixe onde a família

gênica foi mais estudada, são conhecidas até o momento quatro formas

relacionadas a Atp2a: atp2a1, atp2a2a, atp2a2b e atp2a3 (Hirata et al. 2004; Ebert

et al. 2005). No tambaqui os genes da família Atp2a foram expressos negativamente

após 5 dias de exposição (Tabela S1 do Cap. 2), sendo Atp2a1 (B1: -3,825, A1B: -

1,1 e A2: -1,265) e Atp2a2b (B1: -4,553, A1B: 1,79 e A2: -1,161). Analisando os

níveis de Ca2+ detectados durante os 150 dias de exposição (Tabela 3 do Cap. 1),

fica evidente uma redução de Ca2+ plasmático já a partir de 5 dias de exposição em

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todos os cenários experimentais (B1, A1B e A2), indicando uma correlação entre a

expressão negativa dos genes Atp2a1 e Atp2a2b e os níveis de Ca2+ no plasma de

tambaqui.

Outra família gênica que tem seu mecanismo de ação relacionado aos níveis

celulares de Ca2+ é a Calmodulina (CaM). A pequena proteína acídica sintetizada

por essa família de genes é conservada estrutural e funcionalmente, sendo

encontrada nas células de todos os eucariotos (Friedberg e Taliaferro 2005). A CaM

é considerada o principal sensor dos níveis de cálcio dentro da célula e seu

mecanismo de ação está diretamente relacionado à disponibilidade de Ca2+, atuando

principalmente em mecanismos de divisão celular, sobrevivência e apoptose, sendo

fundamental para viabilidade de células e tecidos, assim como para o sistema

nervoso central (Simão e Gomes 2001; Huo e Wong 2009). Quando a CaM se liga

aos íons Ca2+ torna-se ativa e, consequentemente, ativa muitas outras enzimas,

sendo considerada o ponto central de uma grande variedade de proteínas cálcio-

dependentes (Shimoda et al. 2002). Diversos estudos mostram que o complexo

CaM-Ca2+ atua de forma marcante na homeostase iônica e na modulação de

diversas proteínas celulares que incluem fosfodiesterase, Ca2+-ATPase,

calcineurina, óxido nítrico sintase, Ser/Tre quinases e fosfatases (Simão e Gomes

2001).

Nos vertebrados a estrutura molecular da CaM é conservada durante o

processo evolutivo, sendo 100% idêntica em todos os vertebrados, inclusive nos

peixes (Friedberg e Taliaferro 2005, Huo e Wong 2009). Enquanto nos invertebrados

foi encontrado apenas um gene, entre os vertebrados e mamíferos são encontrados

três genes (Calm1, Calm2 e Calm3) que codificam proteínas homólogas (Mototani et

al. 2010). No tambaqui duas das três formas gênicas foram identificadas após 15

dias de exposição aos cenários climáticos analisados (B1, A1B e A2). Os genes

Calm2a (B1: -1,418, A1B: -2,376 e A2: -2,507) e Calm3a (B1: -1,919, A1B: -2,874 e

A2: -1,567) tiveram sua expressão reduzida quando comparados a sala controle

(Tabela S2 do Cap. 2). Da mesma forma como ocorreu com o gene Atp2a, fica

evidente uma relação entre a expressão dos genes Calm2a e Calm3a e os níveis de

Ca2+ no plasma de tambaqui, pois tanto a expressão gênica quanto os níveis de

Ca2+ foram reduzidos nos três cenários climáticos analisados desde o início do

experimento até 150 dias de exposição.

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Além dos genes relatados acima, a Citocromo C oxidase (Cox) foi identificada

no tambaqui entre os genes diferencialmente expressos após 5 e 15 dias de

exposição aos cenários climáticos. Em eucariotos os genes da Cox sintetizam um

complexo enzimático que está localizado na membrana interna das mitocôndrias, o

principal local onde ocorre a fosforilação oxidativa, fundamental para geração de

ATP (Zaslavsky e Gennis 2000; Popovic 2013). A Cox compreende uma família de

genes que sintetiza enzimas que atuam na etapa final da cadeia de transporte de

elétrons, onde catalisa a transferência de elétrons do Citocromo C para o oxigênio,

desempenhando, também, funções relacionadas a apoptose (Diaz 2010; Abumourad

2011). A Cox é composta por 13 subunidades, sendo as três maiores codificadas

pelo genoma mitocondrial (Cox1, Cox2 e Cox3) e dez pequenas subunidades

codificadas pelo genoma nuclear (Cox4, Cox5a, Cox5b, Cox6a, Cox6b, Cox6c,

Cox7a, Cox7b, Cox7c e Cox8). Além disso, qualquer limitação na síntese ou

atividade desse complexo enzimático implica na produção de espécies reativas de

oxigênio (ROS) sob condições de estresse oxidativo (Diaz 2010; Grim et al. 2010).

De uma maneira geral, os organismos aeróbicos enfrentam desafios

relacionados com a formação de ROS, incluindo superóxido (O2•–), radicais hidroxil

(–OH) e radicais peroxil (ROO•), que são capazes de danificar moléculas biológicas,

incluindo lipídios, proteínas e DNA (Dröge 2002). As células são protegidas dos

danos causados pela ROS utilizando dois mecanismos de defesa antioxidantes: não

enzimáticos, onde destacam-se a glutationa e as vitaminas E e C, e enzimáticos,

principalmente, superóxido dismutase e catalase. Em condições fisiológicas normais

a produção de ROS é similar à das defesas antioxidantes, mas devido à

interferência de agentes estressores diversos, os organismos podem se deparar

com situações de estresse oxidativo (Grim et al. 2010).

Modificações na temperatura ambiental onde vivem animais ectotérmicos

podem provocar uma reestruturação das membranas biológicas e modificações em

vários processos metabólicos que impactam as membranas biológicas, tornando-as

suscetíveis a processos potencialmente danosos desencadeados pelas ROS (Dröge

2002), alterações na permeabilidade da membrana a diversos íons como os prótons

(H+) (Hazel e Williams 1990; Zaslavsky e Gennis 2000) e até resultar em uma

completa perda de funcionamento da mitocôndria (Lee 2004). Em temperaturas mais

elevadas os organismos aumentam a demanda por oxigênio para a cadeia de

transporte de elétrons, com o objetivo de aumentar a produção de ATP (Strobel et al.

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2013). Por isso em animais ectotérmicos as mitocôndrias desempenham um papel

central nas respostas térmicas desencadeadas pelo metabolismo aeróbico (Blier e

Lemieux 2001).

Para compreender as propriedades das enzimas responsáveis pela utilização

do oxigênio em nível celular, especialmente aquelas relacionadas à cadeia de

transporte de elétrons e a Cox, diversos estudos têm utilizado os peixes como

modelo para investigar a expressão da Cox e de suas subunidades em várias

espécies (Bremer e Moyes 2011) como goldfish (Carassius auratus) (LeMoine et al.

2008), zebrafish (D. rerio) (McClelland et al. 2006), Gasterosteus aculeatus

(Orczewska et al. 2010) e tilápia do nilo (Oreochromis niloticus) (Abumourad 2011),

assim como a relação entre a Cox e taxa respiratória na truta do ártico (Salvelinus

alpinus) (Blier e Lemieux 2001).

No tambaqui foram identificadas quatro das 13 subunidades da Cox entre os

genes diferencialmente expressos, sendo Cox4i2, Cox7c e Cox8b após 5 dias

(Tabela S1 do Cap. 2) e Cox5b2 após 15 dias de exposição (Tabela S2 do Cap. 2)

aos cenários climáticos experimentais (B1, A1B e A2). Devido às limitações na

síntese de subunidades de Cox contribuírem de forma decisiva para a produção de

ROS, é possível que as quatro subunidades que tiveram sua expressão reduzida no

tambaqui tenham contribuído para o estresse oxidativo identificado no tambaqui. De

acordo com os resultados obtidos a partir do ensaio cometa, fica evidente que os

valores correspondentes ao índice de danos genéticos (GDI) começaram a

aumentar a partir de 5 dias, chegando aos valores mais elevados após 30 dias de

exposição (Tabela 3 do Cap. 1). Apesar dos resultados não mostrarem o nível de

expressão das subunidades de Cox a partir de 30 dias de exposição, os resultados

sugerem uma relação entre a redução dos níveis de expressão das subunidades de

Cox e o aumento de danos no DNA de eritrócitos de tambaqui, possivelmente em

decorrência do aumento na produção de ROS.

A família gênica mais frequente entre os genes diferencialmente foi, sem

dúvida, a das chaperonas, também conhecidas como heat shock proteins (Hsps).

Seus genes dão origem à síntese de proteínas altamente conservadas que já foram

encontradas em diversos organismos, inclusive em peixes. Nos vertebrados, a

expressão das Hsps é regulada pelos fatores de transcrição de choque térmico 1

(Hsf1), que se ligam às regiões promotoras das Hsps, chamadas de elementos de

choque térmico (Hse) (Adám et al. 2000). Com base em seu peso molecular, as

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Hsps são divididas em três principais grupos: Hsp90 (com 85 a 90 kDa), Hsp70 (com

68 a 73 kDa), Hsps de baixo peso molecular (com 16 a 47 kDa) (Basu et al. 2002).

As Hsps desempenham importantes funções na fisiologia celular dos organismos,

tanto em células em condições normais (sem estresse), em que são expressas

constitutivamente para manter a forma e a função das proteínas sintetizadas pela

célula, quanto em situações de estresse físico e metabólico, incluindo estresse

oxidativo e, principalmente, estresse térmico (Tine et al. 2010; Oksala et al. 2014).

De uma maneira geral, as Hsps atuam no dobramento de polipeptídeos recém-

sintetizados, no reparo e degradação de proteínas alteradas ou desnaturadas, na

formação e manutenção de componentes do citoesqueleto e de receptores de

hormônios esteroides, na manutenção das membranas lipoproteicas das

mitocôndrias e do núcleo celular, no transporte de proteínas através da membrana e

no controle de mudanças no formato e na função de diversas proteínas (Kiang and

Tsokos 1998; Basu et al. 2002).

Estudos realizados em peixes e outros organismos mostram que cada uma

das três categorias de Hsps apresenta diferentes funções em situações de estresse

térmico (Basu et al. 2002). A Hsp90 atua na redução dos níveis de ROS e da

peroxidação lipídica, além de aumentar as defesas antioxidantes por meio da

indução de glutationa. Assim, a Hsp90, juntamente com os mecanismos de defesas

antioxidantes, atua no reparo de células danificadas pela exposição aguda ou

crônica a variações de temperatura, sendo considerada um importante mecanismo

que evita a apoptose (Cui et al. 2014; Oksala et al. 2014). Em larvas de zebrafish,

várias Hsps, incluindo Hsp90aa1 (heat shock protein 90-alpha 1) e Hsp90aa.2 (heat

shock protein 90-alpha 2), foram expressas após exposição ao calor e ao frio, porém

essa expressão foi mais rápida quando as larvas foram expostas ao calor do que ao

frio (Long et al. 2012). As Hsp90 são as únicas chaperonas moleculares que não

atuam no dobramento de proteínas recém-sintetizadas, porém são fundamentais em

processos de sinalização celular, devido seus principais substratos serem proteínas

de transdução de sinal como receptores de hormônios esteroides e diversas

enzimas kinases (Basu et al. 2002; Chen et al. 2005).

A Hsp70 compreende o maior e mais estudado grupo de Hsps, sua expressão

varia em diferentes condições fisiológicas. Existem três formas de Hsp70: a Hsc70

(heat shock cognate) que é expressa constitutivamente, a Hsp70 que é expressa em

condições de estresse resultante de um estímulo externo e algumas subunidades

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que são expressas em ambas as condições (Jesus et al. 2013). Diversos padrões de

expressão da Hsp70 têm sido encontrados em vários peixes, incluindo zebrafish

(Malek et al. 2004), carpa (Ctenopharyngodon idellus) (Cui et al. 2014) e bacalhau

do Atlântico (Gadus morhua) (Hori et al. 2010). Um estudo recente comparou a

expressão da Hsp70 e da Hsc70 em duas espécies de peixe do gênero Squalius (S.

torgalensis e S. carolitertii) submetidos a diferentes temperaturas; os resultados

mostraram que apesar de serem evolutivamente próximas, o nível de expressão e a

tolerância térmica foram bastante diferentes entre as duas espécies do mesmo

gênero, pois enquanto S. torgalensis aumentou o nível de expressão de Hsp70 e

Hsc70 em temperaturas mais elevadas, S. carolitertii não evidenciou alterações na

expressão das Hsps, o que pode ter levado, de acordo com os autores, aos altos

níveis de mortalidade detectados em S. carolitertii quando submetido a temperaturas

mais elevadas (Jesus et al. 2013).

O terceiro e último grupo de Hsps, denominado de HSP40 ou DnaJ, atua de

forma muito semelhante a Hsp70, promovendo o dobramento, desdobramento, a

montagem, o transporte e a degradação de proteínas dentro da célula (Qiu et al.

2006; Li et al. 2011). A Hsp40 é capaz de regular o funcionamento da Hsp70

estimulando a atividade ATPase por meio do domínio J, normalmente localizado na

extremidade N-terminal da proteína (Li et al. 2011). Devido a hidrólise de ATP ser

essencial para a atividade da Hsp70, a Hsp40 consegue determinar a atividade da

Hsp70 promovendo a estabilização da sua interação com o substrato (Qiu et al.

2006; Liu et al. 2013). No bagre de canal diversas formas gênicas de Hsp40 foram

identificadas após construção de bibliotecas de RNA-seq de peixes submetidos a

diferentes níveis de estresse térmico (Liu et al. 2013).

Dois dos três grupos de Hsps foram identificados no tambaqui, sendo Hsp90

(Hsp90aa1.1 e Hsp90aa1.2) e Hsp40 (Dnaja3 e Dnajc7) (Tabelas S1 e S2 do Cap.

2). Com base na ontologia dos genes identificados e na amplitude da atividade das

Hsps, é possível que esses genes sejam os principais responsáveis pela

sobrevivência do tambaqui aos três cenários experimentais analisados (B1, A1B e

A2). A Hsp90, que é sintetizada apenas em situações de estresse, pode ter

contribuído para redução dos níveis de ROS e aumento das defesas antioxidantes

nos eritrócitos de tambaqui, particularmente após 30 dias de exposição. A Hsp40,

por atuar no controle do funcionamento da Hsp70 e na ligação a diversas enzimas

celulares, pode ter atuado no controle da atividade das principais enzimas de

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regulação iônica, Na+/K+-ATPase e H+-ATPase, contribuindo assim para a

manutenção do pH celular e para a concentração de íons monovalentes como Na+,

K+ e Cl- do tambaqui nos três cenários experimentais investigados.

Outra chaperona identificada entre os genes diferencialmente expressos foi a

Prefoldin (Pfdn) que sintetiza uma proteína com o mesmo nome. A Pfdn é, assim

como as outras Hsps descritas acima, uma chaperona molecular presente em todos

os eucariontes, onde atua no dobramento de cadeias polipeptídicas recém-

sintetizadas por meio da ligação às proteínas sintetizadas pelos ribossomos e

também no transporte até outras chaperonas, como Hsp70 e Hsp40 (Abe et al.

2013). Seu correto funcionamento nos eucariotos é influenciado por variações na

temperatura ambiental (Zako et al. 2010). Por não depender da hidrólise de ATP, ela

sozinha não teria condições de realizar o correto dobramento de proteínas

desnaturadas, por isso atua de forma acessória às outras chaperonas, contribuindo

assim para atribuições de forma e função às proteínas recém-sintetizadas pelos

ribossomos (Zako et al. 2010; Abe et al. 2013; Mirón-García et al. 2014). Além

dessas funções, a Pdfn, in vitro, também consegue se aderir à outras proteínas

celulares (não recém-sintetizadas) e transportá-las até as Hsps, porém in vivo essa

função ainda não foi comprovada (Zako et al. 2010).

A Pfdn é composta por seis subunidades (Pfdn1 a 6), sendo duas

subunidades alfa (pfdn3 e pfdn5) e quatro beta (Pfdn1, Pfdn2, Pfdn4, Pfdn6) (Abe et

al. 2013). No tambaqui apenas a subunidade 2 (Pfdn2) foi identificada, tanto após 5

dias (B1: -1,378, A1B: 1,621 e A2: 3,999) (Tabela S1 do Cap. 2), quanto após 15

dias (B1: -2,897, A1B: -9,682 e A2: -2,246) (Tabela S2 do Cap. 2) de exposição aos

cenários climáticos experimentais (B1, A1B e A2). Assim, acreditamos que a Pfdn2

seja responsável pela captura e transporte das proteínas recém-sintetizadas pelo

tambaqui para manter a homeostase celular. Porém, a expressão negativa da Pfdn2

no tambaqui pode indicar um comprometimento de suas funções devido a exposição

aos cenários climáticos, especialmente após 15 dias de exposição, podendo assim

dar início a uma cadeia de eventos que resultaria no incorreto dobramento de

proteínas recém-sintetizadas pelas células.

Outro gene identificado no tambaqui após 5 e 15 dias de exposição aos

cenários climáticos foi o Hmgb (High-Mobility Group Box), que sintetiza um grupo de

proteínas não histônicas que compõe a cromatina, atuando na montagem de

complexos nucleoproteicos de enhanceossomos (Stemmer et al. 2002; Xie et al.

133

2014). As Hmgbs são consideradas compensadoras gerais da transcrição,

auxiliando tanto no processo de transcrição em si, quanto na abertura da molécula

de DNA para possibilitar a atuação das polimerases (Stemmer et al. 2002).

As Hmgbs são altamente móveis, podendo ser encontradas no núcleo, no

citoplasma e até mesmo fora da célula, desempenhando uma grande variedade de

funções que incluem recombinação e reparo do DNA (Moleri et al. 2011; Xie et al.

2014). Em mamíferos e outros vertebrados, as Hmgbs compreendem três grupos

principais: Hmgb1, Hmgb2 e Hmgb3. Hmgb1 é uma proteína nuclear amplamente

distribuída que apresenta variações no padrão de expressão e localização dentro da

célula, sendo altamente expressa tanto na embriogênese, quanto em animais

adultos. O oposto ocorre com a Hmgb2, que apresenta uma distribuição mais restrita

e limitada a animais adultos. A Hmgb3 é considerada uma forma intermediária com

distribuição mais ampla que Hmgb2, porém muito frequente durante o

desenvolvimento embrionário (Moleri et al. 2011).

Uma das características marcantes da Hmgb1 é sua ação como modulador

compensatório em resposta a elevação da temperatura, sendo seu gene

considerado um sensor global de temperatura, inclusive em peixes (Podrabsky e

Somero 2004; Somero 2005; Logan e Buckley 2015). Diversos trabalhos mostram

que a abundância dos transcritos de Hmgb1 em peixes é reduzida quando são

expostos a temperaturas elevadas (Podrabsky e Somero 2004; Jayasundara et al.

2013; Logan e Buckley 2015). Podrabsky e Somero (2004) utilizando o teleósteo

killifish (Austrofundulus limnaeus), primeiramente aclimataram um grupo de

indivíduos a 20ºC (controle) e outro a 37ºC e, em seguida, fizeram alterações de

temperatura nos dois grupos para simular as variações diárias de temperatura que

ocorrem durante o dia e a noite. Os resultados mostraram que a expressão dos

transcritos de Hmgb1 foi elevada e mais estável em peixes aclimatados a 20ºC,

enquanto que a 37ºC a expressão foi negativa e bastante variável. Segundo os

autores, a expressão de Hmgb1 é negativamente correlacionada com aumento da

temperatura, influenciando a síntese de enzimas envolvidas em alterações na

composição lipídica da membrana e a expressão de genes característicos de

variações de temperatura, como as Hsps.

A Hmgb1 atua também por meio da ligação a importantes fatores de

transcrição, como p53, HoxD9 e receptores de hormônios esteroides através de

domínios específicos de interação. Além disso, a Hmgb1 permite a ligação a uma

134

grande variedade de outras proteínas, incluindo diversos elementos que formam o

citoesqueleto, proteínas da matriz extracelular, várias classes de lipídeos e proteínas

da membrana plasmática (Podrabsky e Somero 2004). Ela também eleva em pelo

menos 20 vezes a afinidade de várias proteínas à região TATA box, controlando

assim a transcrição de centenas de genes que possuem TATA box na região

promotora (Das e Scovell 2001). Adicionalmente, a estabilidade térmica e as

propriedades bioquímicas de Hmgb1 indicam que a proteína é muito sensível à

variações de temperatura in vivo, o que poderia interferir na afinidade de complexos

nucleoproteicos com fatores de iniciação da transcrição, resultando em mudanças

na taxa de expressão de genes, especialmente aqueles que possuem TATA box

(Podrabsky e Somero 2004).

Tendo em vista que a expressão de Hmgb1a no tambaqui foi negativa após 5

(B1: -1,722, A1B: -2,413 e A2: -1,931) (Tabela S1 do Cap. 2) e 15 dias (B1: -1,842,

A1B: -4,908 e A2: -1,181) (Tabela S2 do Cap. 2) de exposição aos cenários

climáticos, é possível que, assim como ocorreu em killifish, o Hmgb1a atue no

tambaqui como modulador compensatório da transcrição, controlando a síntese de

vários genes em resposta ao aumento na temperatura, especialmente genes que

codificam enzimas que atuam na membrana plasmática, como Na+/K+-ATPase e H+-

ATPase.

Com função marcante na regulação iônica e no equilíbrio ácido-base dos

peixes, a família gênica conhecida como solute carrier (SLC) compreende cerca de

338 genes já identificados em teleósteos, incluindo zebrafish, salmão do Atlântico

(Salmo salar), truta arco-íris (Oncorhynchus mykiss) e Anguilla japônica (Romano et

al. 2014). Os genes da família SLC são divididos em 12 grupos com base no tipo de

substrato que utilizam. Seus genes codificam proteínas que desempenham uma

variedade de funções nos peixes, principalmente transportadores de diversas

substâncias como aminoácidos, oligopeptídeos, cátions e ânions inorgânicos,

glicose e outros açúcares, íons metálicos e ureia (He et al. 2009; Romano et al.

2014). A ampla maioria dos estudos funcionais relacionados aos mecanismos de

ação dos genes da família SLC está relacionada aos mecanismos de transporte de

substâncias envolvendo peixes expostos a diferentes salinidades, temperaturas,

níveis de oxigênio e absorção de íons metálicos, com o objetivo de compreender

como esses desafios ambientais afetam a sobrevivência e o desenvolvimento dos

peixes desde a fase embrionária até a fase adulta (Romano et al. 2014).

135

Entre os membros da família SLC, o gene Slc4a4b é considerado decisivo na

determinação de padrões ionoregulatórios dos peixes, com atuação marcante nas

células da pele e do tecido branquial, agindo como principal transportador das

células basolaterais por meio da captação de Na+ e excreção de produtos acídicos

(H+), especialmente em situações de acidose ou hipercapnia (Parks et al. 2007; Lee

et al. 2011). Em zebrafish, o RNAm de Slc4a4b já foi sequenciado na pele e nas

brânquias, sempre associado à células ricas em H+-ATPase atuando como co-

transportador Na+/HCO3, por meio da captura de um Na+ e excreção de três HCO3-

(Lee et al. 2011). Utilizando anticorpos homólogos, o gene Slc4a4b foi encontrado

também associado a Na+/K+-ATPase em Tribolodon hakonensis, um ciprinídeo que

vive tanto na água doce quanto em água salgada (Hirata et al. 2003).

Em tambaqui o gene Slc4a4b foi identificado entre os genes diferencialmente

expressos após 15 dias de exposição (B1: -2,735, A1B: 1,476 e A2: -1,077) aos

cenários climáticos (Tabela S2 do Cap. 2). No entanto, as variações observadas na

atividade das enzimas Na+/K+-ATPase (Figura 3 do Cap. 1) e H+-ATPase (Figura 4

do Cap. 1) durante os 150 dias de exposição dificultam uma correlação direta com o

nível de expressão do gene nos três cenários experimentais (Tabela S2 do Cap. 2).

Apesar disso, com base na ontologia gênica e em resultados obtidos utilizando

outras espécies como modelo, é possível que o gene Slc4a4b exerça funções

importantes na regulação iônica e no controle do pH celular do tambaqui, tanto por

meio da captura de Na+ e excreção de HCO3-, quanto associado às enzimas Na+/K+-

ATPase e H+-ATPase. Mais estudos serão necessários para investigar a localização

do gene Slc4a4b no tambaqui e compreender melhor seu funcionamento.

O presente estudo confirma que numerosos genes estão envolvidos na

resposta do tambaqui aos cenários climáticos, especialmente Hsps e genes que

atuam no remodelamento das membranas celulares. A partir da análise integrativa

dos genes diferencialmente expressos com diversas variáveis fisiológicas

analisadas, fica evidente que a exposição do tambaqui aos principais cenários de

mudanças climáticas previstos pelo IPCC, pode comprometer processos biológicos e

vias de sinalização celular fundamentais para a sobrevivência do tambaqui,

particularmente a síntese, dobramento e transporte de proteínas recém-sintetizadas,

a biogênese mitocondrial, as defesas celulares antioxidantes, a osmoregulação, a

atividade de enzimas de regulação iônica e a manutenção de gradiente

eletroquímico das membranas celulares. Os resultados obtidos possibilitaram, por

136

meio da análise mais aprofundada de alterações no transcriptoma, conjuntamente

com a avaliação de características fisiológicas, a compreensão de importantes

mecanismos envolvidos em processos de adaptação do tambaqui às mudanças

climáticas globais.

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4 – CONCLUSÕES GERAIS

A exposição do tambaqui aos cenários climáticos comprometeu as defesas

antioxidantes celulares, resultando em danos ao DNA de eritrócitos, especialmente

após 30 dias de exposição.

Foram identificadas alterações em parâmetros hematológicos do tambaqui,

especialmente relacionadas ao aumento no número de eritrócitos circulantes,

possivelmente para compensar o aumento na demanda de oxigênio para os tecidos.

Os cenários climáticos provocaram desequilíbrio em diversos processos de

regulação iônica, tanto em relação à concentração de íons plasmáticos, quanto à

atividade das principais enzimas responsáveis pela regulação iônica nas brânquias

de tambaqui.

O grande número de genes diferencialmente expressos identificados no

transcriptoma do tambaqui, indica que uma complexa interação de genes está

envolvida na adaptação às mudanças climáticas.

As variações observadas no transcriptoma do tambaqui sugerem que vários

processos básicos foram comprometidos para garantir a sobrevivência do tambaqui

frente ao estresse causado pelos cenários climáticos, especialmente a síntese de

proteínas, a biogênese mitocondrial, as defesas celulares antioxidantes, a

osmoregulação, a atividade de enzimas de regulação iônica e a manutenção do

gradiente eletroquímico das membranas celulares.

Dessa forma, concluímos que os três cenários de mudanças climáticas

avaliados provocaram, de acordo com suas intensidades, graves desequilíbrios em

processos metabólicos vitais que alteraram a homeostase celular do tambaqui e

podem colocar em risco a sobrevivência da espécie.

145

5 – PERSPECTIVAS

Os resultados obtidos no presente estudo contribuíram de forma marcante

para a elucidação dos principais impactos das mudanças climáticas sobre o

tambaqui, bem como indicaram possíveis impactos que podem ocorrer em outros

peixes da Amazônia. Porém, devido à robustez e complexidade dos resultados

obtidos, surgiram questionamentos que necessitam ser esclarecidos em estudos

posteriores:

· O perfil de expressão gênica identificado entre os indivíduos jovens seria

semelhante em animais adultos?

· As diversas estratégias adaptativas adquiridas pelo tambaqui durante o

processo evolutivo seriam suficientes para manter a homeostase do

organismo até quantos graus de aumento de temperatura? Qual possível

cenário climático?

· Quais consequências seriam causadas pelo silenciamento dos principais

genes que responderam a exposição do tambaqui às mudanças climáticas?

· Em períodos de exposição prolongados, os resultados das análises do

transcriptoma seriam semelhantes aos obtidos após 5 e 15 dias de

exposição?

· As alterações identificados no DNA de eritrócitos do tambaqui, nos

parâmetros hematológicos e nos processos de regulação iônica podem

contribuir para o comprometimento do sistema imune, facilitando assim o

surgimento de doenças como o câncer, por exemplo?

· Os cenários de mudanças climáticas utilizados no presente estudo poderiam

levar à morte outras espécies amazônicas mais sensíveis à modificações

ambientais?

146

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