LUCIANA MARA FÉ GONÇALVES Manaus, Amazonas Dezembro

MINISTÉRIO DA CIÊNCIA, TECNOLOGIA, INOVAÇÕES E COMUNICAÇÕES – MCTIC INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA – INPA PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA, CONSERVAÇÃO E BIOLOGIA EVOLUTIVA – PPG GCBEv

Perfil transcriptômico do tambaqui Colossoma macropomum (Cuvier, 1818) revela adaptação local de duas populações artificialmente criadas e

plasticidade regional sobre um cenário extremo de mudança climática

LUCIANA MARA FÉ GONÇALVES

Manaus, Amazonas Dezembro, 2019

LUCIANA MARA FÉ GONÇALVES

Perfil transcriptômico do tambaqui Colossoma macropomum (Cuvier, 1818) revela adaptação local de duas populações artificialmente criadas e plasticidade regional sobre um cenário extremo de mudança climática*†

Orientadora: VERA MARIA FONSECA DE ALMEIDA E VAL Agências Financiadoras: CAPES (n°. 39/2012, Projeto Pró-Amazônia); CNPq (n°. 465540/2014-7, INCT-ADAPTA II e n°. 424468/2016-6, Chamada Universal); FAPEAM (n°. 0621187/2017, INCT-ADAPTA II)

Tese apresentada ao Programa de Pós-Graduação em Genética, Conservação e Biologia Evolutiva como parte dos requisitos para obtenção do título de Doutor em Genética, Conservação e Biologia Evolutiva.

Manaus, Amazonas *Pesquisa autorizada pelo CEUA/INPA em 07 de novembro de 2016 (n°. de aprovação 032/2016). Dezembro/2011 †Projeto sob Anotação de Responsabilidade Técnica (ART) n°. 2016/00090 – CRBio 90936/06-D.

Manaus, Amazonas Dezembro, 2019

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Sinopse Este estudo teve como objetivo avaliar comparativamente o transcriptoma diferencial de duas populações de tambaqui de cativeiro tanto aclimatizadas em diferentes latitudes quanto sobre a influência de um cenário extremo de mudança climática. Palavras-chave: adaptação local; mudança climática; plasticidade fenotípica; RNA-Seq; tambaqui

Ao meu amor, André. Porque família é tudo.

AGRADECIMENTOS

Esta Tese encerra um período de 30 anos dedicados ao estudo. Tantos Mestres cruzaram o meu caminho que seria impossível mensurar a contribuição de cada um deles na minha formação acadêmica e pessoal. Ainda assim, é possível reconhecer que a educação é uma força transformadora da vida em todos os sentidos. Na minha, não foi diferente. Graças a Deus e ao esforço dos meus pais, concluo esta etapa com o coração repleto de orgulho, alegria e gratidão. Obrigada, Senhor Jesus e Nossa Senhora, por me manterem de pé até aqui e por cuidarem tão bem dos meus sonhos. Aos meus amados pais, Flávio e Lúcia Fé, pelo amor, carinho e dedicação incondicionais na minha criação e na do meu irmão, Fabrício. Vocês são minha referência de companheirismo e cumplicidade familiar. Amo muito vocês! Ao meu amado esposo, André Luis Gonçalves, por tudo o que você representa na minha vida ao longo desses 11 anos de relacionamento. Além de um sobrenome, você me presenteia diariamente com seu amor, carinho, amizade, companheirismo e respeito. Você é a minha família; os braços que quero abraçar, a boca que quero beijar, as metas com quem planejo alcançar, as vitórias com quem quero festejar, a trajetória de vida com quem desejo dividir... amo muito você! À carinhosa Leona, nossa gatinha preta, pela companhia durante as viagens do André, e por despertar meu coração à empatia, atenção e cuidado para com os animais. Aos membros e agregados das famílias Fé, Gonçalves, Lima, Lopes e Sousa pela união, alegria e boa convivência. Em especial, a Zenaide Fé, minha adorada vó, por sempre acreditar no meu potencial. Aos amigos-de-toda-vida pelos fortes laços de amizade, amor e sintonia que nos unem mesmo na distância que nos separam. Especialmente, à Grazy e Vivi pelas deliciosas sessões de ‘‘terapia’’ com café. À querida orientadora, Dra. Vera Val, pela oportunidade, confiança, carinho, amizade e generosidade. Muitas são as suas virtudes e ensinamentos que me inspiram a fazer Ciência com excelência e a crescer como mulher. Você é incrível! Ao estimado Dr. Adalberto Val pela oportunidade, respeito e confiança ao longo desses 10 anos de parceria. À MSc. Nazaré Silva pela sempre disponibilidade em ajudar e apoio moral e emocional em qualquer situação. À Dra. Alzira Miranda pela amizade, carinho, torcida e excelente assessoria técnica e intelectual na condução do Projeto Pró-Amazônia – Tambaqui. À Fer, agora Dra. Fernanda Dragan, pela parceria, apoio incondicional e companheirismo durante os experimentos e coletas. À Dra. Carolina Sá-Leitão, a quem carinhosamente chamo de Carolzona, pela alegria contagiante, cumplicidade, disposição e por aceitar tão bem meu temperamento de nuances meigo e reativo.

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Aos colegas do LEEM pela boa convivência e amizade. Especialmente, à Carolzona, Érica e Kátia pelos momentos de alegria e descontração no lanche da tarde, e à Jaque pelos agradáveis momentos de conversa com chá nos intervalos. Agradecimento especial também à Naza, Zizi, Renan, Karina, Carolzinha, Bia e Cadu por toda ajuda nas coletas experimentais. Ao Doutorando Deney Araújo pela primazia técnica nas análises dos dados de Bioinformática. Ao Dr. Carlos Henrique dos Santos pela colaboração na redação dos manuscritos. Às Professoras Maria do Carmo Fialho (UFAM) e Carolina Sá-Leitão (CIESA) por gentilmente abrirem as portas de suas salas para o meu Estágio Docência. Às integrantes do Conselho e da Comissão de Bolsa do PPG GCBEv, onde tive a honra de participar como Representante Discente, juntamente com Francy, no período de 09/2017 a 02/2019. Professoras Jacqueline, Vera, Eliana, Gislene e Francy, vocês são meus exemplos das geneticistas bem-sucedidas, da ética profissional e do valor feminino na Ciência. Às secretárias do LEEM e do PPG GCBEv pela disponibilidade, colaboração e gentileza em atender a qualquer demanda. Aos servidores do Departamento de Vigilância Ambiental e Controle de Doenças (DVA/FVS-AM/SUSAM) pelo acolhimento e ensinamentos no novo universo de estudo em Saúde Pública. Em especial, aos amigos, Raiane Aila e Walter, pela cumplicidade e bons momentos na hora do almoço. Aos colaboradores da CTTPA – SEPA/SEPROR em Balbina/AM (José Baracho e Ronan Freitas) e da Fazenda Santo Antônio Brumado em Mogi Mirim/SP (Paulo Longhi, Selmo e Ederson) pelo apoio logístico na aquisição dos lotes de juvenis de tambaqui. Ao suporte técnico da Illumina Brasil. Em especial, à Juliana Gamba, Carolina Marcano e ao Eng. Antônio Brugnollo. Aos apoios financeiros da CAPES, CNPq e FAPEAM que contribuíram significativamente para a realização desta pesquisa. Especialmente, à CAPES pela concessão da bolsa de estudo. Enfim, agradeço antecipadamente à banca de avaliação pela disponibilidade na leitura, participação e valiosas contribuições para a melhoria deste estudo. Muito obrigada!

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‘‘Deus está contigo em tudo o que fazes’’. Gn 21,22

RESUMO

Mudanças climáticas de influência antrópica representam atualmente um grave problema de ordem ambiental, econômica e social, intensificando seus agravos em todos os níveis da escala biológica. Em face das mudanças ambientais em curso, são esperados dois mecanismos de resposta adaptativa pelos organismos: plasticidade fenotípica e evolutiva. Mudanças plásticas envolvem a habilidade de um genótipo expressar diferentes fenótipos em curto prazo, enquanto evolutivas ocorrem a longo prazo, dependendo de mutações e seleção natural. Ajustes nos diferentes níveis da organização biológica em organismos expostos experimentalmente a condições abióticas variáveis têm sido caracterizados nos últimos anos, revelando a plasticidade fenotípica de várias espécies. Com objetivo de compreender, em nível molecular, os ajustes de uma espécie de peixe nativa comercialmente valiosa como o tambaqui Colossoma macropomum (Cuvier, 1818), o presente estudo apresenta duas abordagens inéditas: i) a avaliação comparativa do perfil transcriptômico de duas populações de tambaqui criadas em regiões termicamente distintas (Capítulo I); e ii) a avaliação da influência de um cenário climático extremo sobre o transcriptoma de peixes de cativeiro (Capítulo II). No primeiro momento, um total de 20 juvenis de tambaqui foi coletado ex-situ em duas estações de piscicultura brasileiras localizadas nas regiões Norte (Centro de Tecnologia, Treinamento e Produção em Aquicultura, CTTPA – SEPA/SEPROR, Balbina/AM) e Sudeste (Piscicultura Brumado, Mogi Mirim/SP). No segundo momento, 200 juvenis de tambaqui provenientes das mesmas populações foram adquiridos, transportados para Manaus/AM e aclimatados às condições laboratoriais. Um total de 36 juvenis de tambaqui de ambas as populações foram artificialmente expostos durante 30 dias aos cenários climáticos atual (condição controle) e extremo, tal como o RCP8.5 previsto em recente relatório do IPCC (2014). A análise de sequenciamento de RNA (RNA-Seq) de 18 bibliotecas de fígado de tambaqui revelou 2.765 genes diferencialmente expressos (DEGs), cujos termos foram classificados em uma gama de funções moleculares (5.311), processos biológicos (5.610) e componentes celulares (1.202). No geral, genes responsivos ao estresse celular foram regulados positivamente tanto nos indivíduos provenientes de diferentes latitudes (Capítulo I) quanto expostos a um cenário climático extremo, em relação ao cenário controle (Capítulo II), tais como: YWHAE, MAPKAPK2, ATXN3 (resposta celular ao calor); KCNMA, nrp1a, Ireb2, Pink1, Slc29a1, LONP1, Nop53, ldha (resposta à hipóxia); e sod, Gpx4, IDH1, LONP1, NDUFS2, TXN2, ATOX1 (resposta celular ao estresse oxidativo). A regulação dos DEGs revela diferentes estratégias para a adaptação local das populações aos distintos locais de criação bem como plasticidade regional em lidar com as mudanças climáticas previstas para o final do século XXI.

ABSTRACT Transcriptomic profile of tambaqui Colossoma macropomum (Cuvier, 1818) reveals local adaptation of two farmed populations and regional plasticity under extreme climate change scenario Human-induced climate change is considered a severe threat to environmental, economic, and social sustainability framework, inducing disturbances at all levels of biodiversity. Overall, two mechanisms of adaptive responses by organisms are expected in the face of ongoing environmental changes: phenotypic plasticity and evolutionary change. Plasticity shifts short-term responses producing different phenotypes, whereas long-term responses through mutations and natural selection are expected from the evolutionary point of view. Phenotypic plasticity in organisms experimentally exposed to variable abiotic conditions has been investigated over the last few years. Herein, the molecular plasticity of tambaqui Colossoma macropomum (Cuvier, 1818), an economically important species for Brazilian aquaculture, was assessed through two unprecedented approaches: i) a comparative transcriptome profile evaluation in two farmed tambaqui populations (Chapter 1); and ii) the investigation of the effects of an extreme climate change scenario on transcripts differentially expressed compared to the current scenario of the same two farmed populations (Chapter 2). Firstly, a total of 20 tambaqui juveniles were ex-situ sampled in two fish farms located in the Northern (Balbina Center of Technology, Training and Production in Aquaculture, CTTPA – SEPA/SEPROR, Balbina/AM) and Southeast (Brumado Fish Farming, Mogi Mirim/SP) regions from Brazil. Secondly, 200 tambaqui juveniles from the same populations were purchased, transported to Manaus/AM, and acclimated to laboratory conditions. Thirty-six tambaqui juveniles of both populations were artificially exposed during 30 days in simulated climate scenarios: current (baseline condition) and extreme such as foreseen in RCP8.5, according to a recent IPCC report (2014). RNA sequencing analysis (RNA-Seq) from 18 libraries of the liver tissue revealed 2,765 differentially expressed genes, whose terms were assigned into a range of molecular functions (5,311), biological processes (5,610) and cellular components (1,202). Overall, responsive genes to cellular stress were upregulated in tambaquis of both different latitudes and after exposure to an extreme climate scenario, such as YWHAE, MAPKAPK2, ATXN3 (cellular responses to heat); KCNMA, nrp1a, Ireb2, Pink1, Slc29a1, LONP1, Nop53, ldha (hypoxia responses); and sod, Gpx4, IDH1, LONP1, NDUFS2, TXN2, ATOX1 (oxidative stress responses). The regulation of DEGs suggests different strategies for local adaptation of populations raised in climatically variable regions (different latitudes), as well as regional plasticity to deal with climate changes projected for the end of the century by IPCC. Key words: local adaptation; climate changes; phenotypic plasticity; RNA-Seq; tambaqui.

SUMÁRIO

INTRODUÇÃO GERAL ...... 16 1.1 MUDANÇA CLIMÁTICA GLOBAL: IMPACTOS NA AMAZÔNIA ...... 16 1.2 EFEITOS DAS MUDANÇAS CLIMÁTICAS SOBRE OS PEIXES ...... 18 1.3 A ESPÉCIE EM ESTUDO, Colossoma macropomum (Cuvier, 1818) ...... 21 1.4 INVESTIGAÇÃO DA PLASTICIDADE FENOTÍPICA POR RNA-Seq ...... 24 2 OBJETIVOS ...... 27 2.1 OBJETIVO GERAL ...... 27 2.2 OBJETIVOS ESPECÍFICOS ...... 27 2.2.1 Capítulo I ...... 27 3.2.2 Capítulo II ...... 27 3 MATERIAL E MÉTODOS ...... 27 3.1 LICENÇAS E AUTORIZAÇÕES ...... 27 3.2 DELINEAMENTO EXPERIMENTAL ...... 28 3.2.1 Capítulo I ...... 28 3.2.1.1 Coleta ex-situ ...... 28 3.2.1.2 Obtenção das amostras ...... 29 3.2.2 Capítulo II ...... 30 3.2.2.1 Aquisição e aclimatação dos animais ...... 30 3.2.2.2 Exposição artificial em salas climáticas ...... 30 3.2.2.3 Acompanhamento das variávies amabientais ...... 31 3.2.2.4 Obtenção das amostras ...... 31 3.3 SEQUENCIAMENTO POR RNA-Seq ...... 33 3.3.1 Extração do RNA total, eletroforese e quantificação ...... 33 3.3.2 Sequenciamento dos transcritos ...... 35 3.3.3 Análises de Bioinformática ...... 36 4 RESULTADOS E DISCUSSÃO ...... 37 5 REFERÊNCIAS BIBLIOGRÁFICAS ...... 38

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LISTA DE TABELAS

Tabela 1. Parâmetros físico-químicos da água dos aquários mantidos por 30 dias nas salas climáticas atual e extrema. Os dados são apresentados como média ± erro padrão da média (N= 30). *Indica diferenças significativas em relação ao cenário atual (teste-t de Student, P< 0,05), mostrando a eficiência na variação artificial entre as salas climáticas...... 33

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LISTA DE FIGURAS

Figura 1. Exemplar juvenil de tambaqui Colossoma macropomum (Cuvier, 1818), evidenciando seu formato corporal romboidal, nadadeira adiposa com raios e linha lateral destacada (adaptado de Santos et al. 2006)...... 28 Figura 2. (A) Modelo das salas climáticas que reproduzem os cenários atual e extremo, e seus respectivos valores (média ± erro padrão da média) de temperatura e níveis de CO2 durante 30 dias de experimento (outubro a novembro de 2016). (B) Exposição artificial de juvenis de tambaqui provenientes de populações criadas na região Norte, em verde (n=18) e Sudeste, em azul (n=18) aos dois ambientes controlados...... 31

Figura 3. Variações diárias na temperatura e concentração de CO2 do ar das salas climáticas medidas ao longo de 30 dias de experimento, às 15h (25 de outubro a 26 de novembro de 2016)...... 32 Figura 4. Eletroforese microfluídica de RNA total extraído do fígado de juvenis de tambaqui provenientes das populações de cativeiro de Balbina (Norte) e Brumado (Sudeste) (acima), bem como expostas às salas climáticas atual e extrema (abaixo). Os valores médios do RIN foram, respectivamente: 9,35 (Balbina), 9,32 (Brumado), 9,74 (população do Norte) e 10,0 (população do Sudeste)...... 34 Figura 5. Quantificação absoluta das 18 bibliotecas de RNA-Seq. Curvas de diluição seriada (20 a 0,0002 pM de DNA) e de amplificação, respectivamente, das bibliotecas de DNA das populações de cativeiro de Balbina (Norte) e Brumado (Sudeste) (acima), bem como expostas às salas climáticas atual e extrema (abaixo)...... 36

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LISTA DE ABREVIAÇÕES E SIGLAS

AM Amazonas A1B Cenário moderado A2 Cenário extremo B1 Cenário brando CEUA Comissão de Ética em Pesquisa no Uso de Animais cm Centímetro CO2 Dióxido de carbono -2 CO3 Íon carbonato CONCEA Conselho Nacional de Controle de Experimentação Animal CTTPA Centro de Tecnologia, Treinamento e Produção em Aquicultura °C Graus Celsius DEG Gene Diferencialmente Expresso (do inglês, Differentially Expressed Gene) DNA Ácido desoxirribonucleico (do inglês, Deoxyribonucleic Acid) cDNA DNA complementar FDR Taxa de Falsa Descoberta (do inglês, False Discovery Rate) g Grama GEE Gases de Efeito Estufa GO Ontologia Gênica (do inglês, Gene Ontology) GTA Guia de Trânsito Animal H+ Próton hidrogênio IBAMA Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis IBGE Instituto Brasileiro de Geografia e Estatística INPA Instituto Nacional de Pesquisas da Amazônia INPE Instituto Nacional de Pesquisas Espaciais IPCC Painel Intergovernamental sobre Mudanças Climáticas (do inglês: Intergovernmental Panel on Climate Change) KEGG Enciclopédia de Genes e Genoma de Kyoto (do inglês, Kyoto Encyclopedia of Genes and Genomes) Kg Quilograma L Litro m Metro MAPA Ministério da Agricultura, Pecuária e Abastecimento mg Miligrama mg.L-1 Miligrama por litro -1 mgO2.L Miligrama de oxigênio por litro mM Milimolar MPA Ministério da Pesca e Aquicultura NH4Cl Cloreto de amônio NGS Sequenciamento de nova geração (do inglês, Next Generation

Sequencing) - NO2 Íon nitrito OD Oxigênio dissolvido pb Par de bases PCR Reação de Polimerização em Cadeia (do inglês, Polymerase Chain Reaction) pH Potencial hidrogeniônico pM Picomolar PPI Interação Proteína-Proteína (do inglês, Protein-Protein Interaction) ppm Partes por milhão RCP Via de Concentração Representativa (do inglês, Representative Concentration Pathway) RIN Número de Integridade do RNA (do inglês, RNA Integrity Number) RNA Ácido ribonucleico (do inglês, Ribonucleic Acid) mRNA RNA mensageiro rRNA RNA ribossomal RNA-Seq Sequenciamento de RNA (do inglês, RNA Sequencing) SEPA Secretaria Executiva de Pesca e Aquicultura SEPROR Secretaria de Estado da Produção Rural SP São Paulo μg Micrograma μL Microlitro

INTRODUÇÃO GERAL

1.1 MUDANÇA CLIMÁTICA GLOBAL: IMPACTOS NA AMAZÔNIA

As influências antrópicas sobre o meio ambiente têm causado profundas alterações no clima global (IPCC 2014). As recentes emissões dos gases de efeito

1 estufa (GEE ) atingiram valores sem precedentes; a quantidade de CO2 subiu dos

280 ppm (partes por milhão), durante o período pré-industrial, para os atuais 390 ppm (IPCC 2014), podendo atingir 540-970 ppm em 2100 (IPCC 2001). Além da intensa queima de combustíveis fósseis nas indústrias, as mudanças no uso da terra e a expansão da agricultura representam as principais atividades humanas que contribuíram com o aumento da concentração de GEE na atmosfera e, consequentemente, com o aquecimento do planeta (IPCC 2007).

A preocupação acerca do impacto dos fatores naturais e humanos sobrepondo a variação natural do clima mobilizou os governantes mundiais e a comunidade científica desde o início da década de 80. Em 1988, o Painel

Intergovernamental sobre Mudanças Climáticas (IPCC2) foi criado com o objetivo de elaborar relatórios com rigor científico que descrevessem os cenários ambientais futuros do planeta (Griggs e Noguer 2002). No geral, os modelos climáticos foram desenhados em função do aumento populacional e desenvolvimentos socioeconômico e tecnológico, bem como dos cenários de emissões dos gases causadores do efeito estufa (Justino e Amorim 2007). Uma síntese dos últimos relatórios divulgados pelo IPCC (2007, 2014) prevê um aumento global na temperatura e concentração de CO2, sendo mais preocupante em cenário extremo, tal como o RCP8.5 (IPCC 2014), o que corresponderia a um aumento de 6 ºC na

1 GEE: dióxido de carbono (CO2), gás metano (CH4) e dióxido de nitrogênio (NO2)

2 IPCC, da sigla em inglês: Intergovernmental Panel on Climate Change

temperatura e 1.250 ppm de CO2 até o ano de 2100. De fato, até os cenários considerados otimista (B1) e intermediário (A1B) mostram tendência de aquecimento; e uma série de mudanças nos sistemas geofísicos, biológicos e socioeconômicos mundial é prevista (Schneider et al. 2007).

Em 2007, o Instituto Nacional de Pesquisas Espaciais (INPE) publicou o

Relatório de Clima que destacou os principais impactos das mudanças climáticas no

Brasil, experimentados de forma diferenciada em nível regional (Ambrizzi et al. 2007,

Marengo 2007). De acordo com este estudo, as áreas negativamente afetadas compreendem a Amazônia e a região Nordeste, cujas previsões climáticas extremas indicam aumento de 2 a 8 ºC na temperatura, reduções no volume das chuvas e nível dos açudes, além de risco de desertificação em ambas as regiões.

Situada em sua maior abrangência na região Norte do Brasil, a Amazônia exibe uma ampla heterogeneidade ambiental, abrigando grande parte da biodiversidade de espécies da flora e da fauna do mundo (Sioli 1990). Por seu relevante papel na ciclagem do carbono do planeta (Salati 1983), a região amazônica é considerada uma área crítica às mudanças climáticas (Nobre et al.

2007, 2008). Segundo esses autores, os impactos causados pelo aquecimento do clima global sobre os ecossistemas terrestre e aquático amazônicos são preocupantes, podendo alterar a precipitação pluviométrica, a cobertura da vegetação e os regimes hidrológicos da bacia.

Modelos regionalizados de mudanças climáticas apontam aumento da temperatura de 2 a 4 ºC na América do Sul, além da diminuição da precipitação no leste da Amazônia para o final do século XXI (Li et al. 2006, Ambrizzi et al. 2007,

Salazar et al. 2007). A partir de modelagem climática, é previsto que o aumento da temperatura do ar combinado com alterações no regime de chuvas resultem na

substituição da grande área de floresta amazônica por uma vegetação típica de savana (Cândido et al. 2007, Nobre et al. 2007, 2008, Fearnside 2009). Além disso, o iminente cenário de mudanças no clima já representa um potencial risco para o ciclo hidrológico na Amazônia (Nobre et al. 2007), cujos eventos extremos ultimamente registrados, como as secas de 2005 e 2010, e as cheias de 2009, 2012 e 2015, podem se tornar mais frequentes. Portanto, a magnitude do aquecimento do clima global pode afetar não somente os ecossistemas amazônicos, mas também toda sua biodiversidade. Diante disso, a necessidade de estudos detalhados é especialmente válida para os animais tropicais em face à particular vulnerabilidade desses organismos às mudanças climáticas que já estão acontecendo (Tewksbury et al. 2008).

1.2 EFEITOS DAS MUDANÇAS CLIMÁTICAS SOBRE OS PEIXES

Variações espaciais, diárias e sazonais nos parâmetros físico-químicos dos ambientes aquáticos influenciam a fisiologia dos animais ectotérmicos que ali vivem, tais como a maioria dos peixes (Barton 2002, Pörtner et al. 2006). Dentre esses parâmetros, a temperatura atua como o principal fator abiótico que afeta diretamente a vida desses organismos, visto que são fisiologicamente dependentes da temperatura ambiental (Beitinger et al. 2000). Variações na temperatura da água podem induzir uma gama de respostas fisiológicas (Barton 2002) que incluem expressão diferenciada de genes relacionados à adaptação térmica (Schulte 2001,

2004, Wang et al. 2009a), mudanças na demanda metabólica (Enzor et al. 2013), nas atividades enzimáticas (Almeida‐Val et al. 2006, Braz-Mota et al. 2017), no comportamento e desempenho natatório (Quigley and Hinch 2006), na hierarquia social (Kochhann et al. 2015) e no crescimento e taxa de reprodução (Pörtner e

Peck 2010); podendo até mesmo agir como um fator letal, uma vez que cada

espécie apresenta uma faixa de temperatura ideal de sobrevivência (ver Apêndice de Pörtner e Peck 2010).

Além da elevação da temperatura média do planeta, a acidificação aquática constitui outra perturbação ambiental que vem causando mudanças complexas na biogeoquímica dos oceanos (Harley et al. 2006). O aumento da concentração de H+, devido à alta solubilidade do CO2 atmosférico na água, desequilibra a concentração

-2 de íons carbonato (CO3 ) e reduz o pH (Feely et al. 2004, Guinotte e Fabry 2008).

Segundo Kita et al. (2003), altas concentrações de CO2 induzem uma série de efeitos adversos em peixes, independente do estágio de desenvolvimento. Efeitos a curto-prazo incluem hipercapnia (aumento de CO2 no sangue), perturbações osmorregulatórias e no equilíbrio ácido-base, dificuldades respiratórias, mudanças nos parâmetros hematológicos; enquanto efeitos a longo-prazo incluem atraso no crescimento e redução da taxa de reprodução (Ishimatsu et al. 2004). Além disso, aqueles autores observaram que elevadas concentrações de CO2 saturado na água salgada resultam em alta mortalidade de ovos, larvas e adultos de peixes marinhos.

Os efeitos das variações ambientais relacionadas às mudanças climáticas sobre o desempenho fisiológico dos organismos ectotérmicos estão bem documentados na literatura, com uma série de estudos que mostra a variabilidade na tolerância térmica em peixes, especialmente para as espécies de clima temperado (Elliott 1991, Ford e Beitinger 2005, Fangue et al. 2006, Fivelstad et al.

2007, Nowicki et al. 2012, Schulte 2014, Jesus et al. 2018). Entretanto, estudos que descrevam os efeitos sinérgicos do aumento de temperatura e CO2 em teleósteos tropicais, conforme os modelos climáticos preconizados pelo IPCC, são incipientes

(Prado-Lima e Val 2016, Oliveira e Val 2017, Fé-Gonçalves et al. 2018, Lapointe et al. 2018, Lopes et al. 2018, Campos et al. 2019).

Considerando os recursos pesqueiros em geral, as alterações climáticas em curso representam uma realidade preocupante que afeta todo o quadro social e econômico do setor aquícola (Roessig et al. 2004). Diante desse cenário, Brander

(2010) revisou os impactos das mudanças climáticas sobre a atividade pesqueira, reunindo evidências de efeitos negativos sobre a distribuição, produtividade e capacidade de resistência das populações de peixes comerciais de ambientes marinhos e de água doce.

Os impactos do aquecimento global sobre os organismos ectotérmicos dependem não apenas da magnitude das mudanças de temperatura, mas também do comportamento, fisiologia e ecologia do organismo em questão (Tewksbury et al.

2008). Segundo Pörtner e Peck (2010), tal como a maioria dos animais, os peixes tendem a se deslocar para locais onde há uma temperatura desejada quando se encontram em faixas de temperatura que não lhes são adequadas. Diante disso, o recente deslocamento de peixes (Golani et al. 2002, Perry et al. 2005), moluscos

(Lima et al. 2006) e crustáceos (Beaugrand et al. 2003) para locais termicamente favoráveis foi observado. Sem dúvida, a complexa interação entre o clima e os ecossistemas afeta direta e indiretamente espécies econômica e ecologicamente importantes (Brander 2010).

Como demonstrado acima, em ambiente natural, os peixes evitam áreas onde a temperatura está fora de sua faixa ideal. Entretanto, para peixes artificialmente criados, esta possibilidade não existe. De acordo com van Maaren et al. (2000), como os peixes em cativeiro não têm a opção da busca de outros ambientes quando a temperatura aumenta, estes reduzem o consumo de oxigênio na tentativa de manter o metabolismo por meio da aclimatação térmica. Em revisão, Beitinger et al.

(2000) afirmaram que a aclimatação é o mecanismo pelo qual os organismos podem

mudar sua janela térmica de desempenho, promovida por uma série de ajustes que se apresentam em seus vários níveis de organização biológica. Portanto, diante desses exemplos, é importante reconhecer a influência das mudanças do clima nos ambientes naturais e artificias sobre as respostas espécie-específicas, revelando, sob o ponto de vista da plasticidade fenotípica, seus mecanismos de adaptação aos desafios ambientais resultantes do aquecimento global.

1.3 A ESPÉCIE EM ESTUDO, Colossoma macropomum (Cuvier, 1818)

A produção de peixes em cativeiro representa uma prática de relevância social e econômica em crescente expansão no Brasil (Sidonio et al. 2012). É uma atividade realizada nas cinco regiões do país, que se diferenciam em relação às espécies, volumes produzidos e sistemas artificiais de criação (MPA 2011). Ainda de acordo com Sidonio et al. (2012), a piscicultura figura como uma alternativa sustentável e economicamente viável para a produção de pescado destinada a atender a demanda do mercado nacional e mundial que busca alimentos mais saudáveis.

Na Amazônia, a pesca é considerada uma das atividades intensamente praticadas pela população regional (Cerdeira et al. 1997), sendo destinada basicamente à alimentação dos ribeirinhos, bem como à comercialização do pescado em feiras e mercados locais (Santos et al. 2006). A fauna de peixes amplamente distribuída na maior bacia de água doce do planeta (Sioli 1984) é a mais rica quando comparada à de outros ambientes aquáticos continentais (Val e

Almeida-Val 1995). Embora existam cerca de três mil espécies de peixes na bacia amazônica (Lévêque et al. 2008, Dagosta e De Pinna 2019), poucas são utilizadas como alimento ou fonte de rendimentos dos produtores locais (Santos e Santos

2005).

Dentre as espécies nativas, Colossoma macropomum (Cuvier, 1818), conhecido popularmente como tambaqui, figura como o pescado mais consumido na região Norte (Santos et al. 2006, IBAMA 2007, MPA 2011), a espécie nativa mais criada no Brasil (IBGE 2016) e em outros países (FAO 2018), e desponta como espécie potencial em programas de melhoramento genético voltados à Aquicultura

(Hilsdorf e Orfão 2011, Perazza et al. 2015, 2017, Nunes et al. 2017, Gonçalves et al. 2018). Pertencente à ordem Characiformes e família Serrasalmidae (Mirande

2010), o tambaqui ocorre naturalmente nas bacias do Amazonas e Orinoco, onde se alimenta de frutos, sementes e zooplânctons na fase jovem, tornando-se exclusivamente frugívoro quando adulto. Esta espécie apresenta maturação sexual a partir dos três anos de vida e pode atingir 30 kg e 1 m de comprimento (Saint-Paul

1986). Além da região Norte, o tambaqui é artificialmente criado no Nordeste,

Centro-Oeste e Sudeste (Ostrensky et al. 2008), cujas populações estão aclimatizadas a ambientes de criação localizados em diferentes latitudes e sobre influência de fatores climáticos peculiares.

Devido à sua importância ecológica e econômica, estudos sobre as estratégias adaptativas do tambaqui em face a condições abióticas variáveis têm revelado seu potencial altamente adaptável. Por exemplo, é uma espécie que apresenta alta tolerância à hipóxia (Saint-Paul 1984); quer seja pela expansão labial com o objetivo de otimizar a captação de oxigênio na camada superficial da água, quer seja pela ativação do metabolismo anaeróbico, seguido de depressão metabólica (Almeida-Val et al. 1990, Almeida-Val e Farias 1996). O tambaqui também é tolerante a mudanças de pH (Costa 1995, Wood et al. 1998, 2018), nitrito

- (NO2 ) (Paula-Silva 1999), amônia (NH4Cl) (Souza-Bastos et al. 2017) e à variação térmica, sobrevivendo de 12 a 43,4 ºC (Dragan 2015).

Estudos recentes vêm reforçando as mudanças na plasticidade genotípica e fenotípica do tambaqui de cativeiro, as quais envolvem ajustes nos diferentes níveis da hierarquia biológica, permitindo-lhe sobreviver às adversidades impostas pelas mudanças climáticas. Oliveira e Val (2017), submetendo cronicamente juvenis de tambaqui aos cenários climáticos (brando – B1, moderado – A1B e extremo – A2) do

IPCC (2007), observaram alterações significativas nos parâmetros hematológicos e diminuição no crescimento devido ao deslocamento de energia para atender ao aumento da demanda metabólica em condições severas (A1B e A2). Segundo

Baldisseroto (2009), qualquer fator ambiental que afete o balanço de energia, como o consumo de alimento e o gasto com o metabolismo tende a influenciar o crescimento dos peixes, o que pode comprometer até mesmo sua produção em escala comercial (Santos et al. 2008). Oliveira et al. (in prep)3 também observaram expressão diferencial do gene estearoil-CoA dessaturase-1 (scd-1) no fígado de tambaqui exposto por 150 dias àqueles mesmos cenários, resultando em mudanças no metabolismo dos ácidos graxos e regulação da fluidez da membrana celular.

Ainda, experimentos que simularam o aumento sinérgico da temperatura e concentrações de CO2 induziram a expressão do gene hsp70 (Heat Shock Protein) em fígado de tambaqui (Sakuragui et al. 2012), agindo como um mecanismo de proteção celular ao estresse térmico (Feder e Hofmann 1999, Wegele et al. 2001), bem como um aumento significativo na frequência de danos no DNA de eritrócitos

(Souza-Netto 2012).

Do ponto de vista genético, estudos desenvolvidos com marcadores microssatélites em populações de tambaqui provenientes do ambiente natural e de cativeiro revelam que a prática artificial de criação está contribuindo

3 Oliveira, A.M.; Fé-Gonçalves, L.M.; Val, A.L., in prep

significativamente para a perda de variabilidade genética e estruturação das populações produzidas (Santana et al. 2012, Santos et al. 2012, 2016). É importante salientar que uma redução na variabilidade genética pode comprometer a adaptabilidade das diferentes populações de tambaqui criadas tanto no Amazonas quanto em outros estados do Brasil (Gonçalves et al. 2018). A capacidade de regular e expressar genes responsivos à sobrevivência em ambientes de variações extremas pode diferir em peixes provenientes de populações geneticamente diferentes (Hilsdorf e Orfão 2011).

1.4 INVESTIGAÇÃO DA PLASTICIDADE FENOTÍPICA POR RNA-Seq

Diante da recente mudança nos padrões climáticos globais, sobrepondo a variabilidade natural, ajustes nos diferentes níveis da organização biológica (desde molecular até populacional) são resultantes de algum grau de plasticidade fenotípica e evolutiva, ambos considerados mecanismos-chave da resposta adaptativa pelos organismos (Bellard et al. 2014). Mudanças plásticas envolvem a habilidade de um genótipo em expressar diferentes fenótipos em curto prazo (Ghalambor et al. 2007,

Salamin et al. 2010), enquanto evolutivas ocorrem a longo prazo, dependendo de mutações e seleção natural (Parmesan 2006). Assim, ampliar o entendimento e caracterização molecular das respostas plásticas e evolutivas das espécies em face dos desafios relacionados às mudanças climáticas é especialmente válida frente aos avanços tecnológicos na abordagem dos seus efeitos sob a biodiversidade global

(Oomen e Hutchings 2017).

Os avanços nos estudos genéticos aliados às análises bioinformáticas têm possibilitado a caracterização de genomas e transcriptomas de uma ampla variedade de organismos por meio de sequenciamentos em larga-escala em

plataformas de nova geração4. O emprego da metodologia de Sequenciamento de

RNA (RNA-Seq) permite o mapeamento e quantificação precisa e diferencial nos níveis de expressão gênica de cada transcrito sob diferentes condições avaliadas, bem como sua caracterização em perfis metabólicos (Mardis 2008, Wang et al.

2009b).

Com o uso dos sequenciadores de NGS, as análises do transcriptoma de teleósteos, tanto de espécies-modelo quanto não-modelo, têm avançado nos últimos anos (Qian et al. 2014), com o objetivo de predizer em nível molecular suas respostas plásticas e evolutivas às mudanças ambientais (ver revisões de Crozier e

Hutchings 2014, Oomen e Hutchings 2017). Por exemplo, em 2013, Liu et al. (2013) realizaram o primeiro estudo transcriptômico em híbridos de bagre-de-canal (fêmea de Ictalurus punctatus R. x macho de I. furcatus Valenciennes, 1840) e identificaram genes induzidos em resposta ao estresse térmico, utilizando plataforma Illumina

HiSeq 2000. Um total de 2.260 genes foram diferencialmente expressos nas brânquias e no fígado do grupo mantido a 36 ºC, temperatura 12 graus superior à do grupo controle (24 ºC). De acordo com os autores, genes envolvidos no transporte de oxigênio e íons, na dobradura e na degradação de proteínas, no metabolismo energético e na reorganização do citoesqueleto podem ser candidatos valiosos para o desenvolvimento de linhagens de bagres de interesse comercial termicamente resistentes. A expressão gênica diferencial do fígado de adultos de salmão do

Atlântico (Salmo salar Linnaeus, 1758) expostos à alta temperatura (19 ºC) e à baixa

-1 concentração de oxigênio (4 mgO2.L ) foi avaliada por Olsvik et al. (2013) com o uso do sequenciador 454 FLX da Roche. No geral, apenas 19 genes, que exercem funções regulatórias transcricionais e metabólicas, foram comumente expressos em

4 NGS, da sigla em inglês: Next Generation Sequencing

ambos os tratamentos; entretanto, a exposição crônica reprimiu a síntese de proteínas, indicando uma depressão do metabolismo que resultou na redução do crescimento nesses animais, o que pode ser prejudicial para a atividade aquícola da espécie em um cenário de mudança climática.

Embora seja uma ferramenta moderna e de custo relativamente baixo (Wang et al. 2009b), seu uso ainda é incipiente no estudo de genes potencialmente envolvidos na adaptação dos peixes amazônicos às adversidades dos seus ambientes (Lemgruber et al. 2013, Prado-Lima e Val 2016, Araújo et al. 2017,

Fagundes et al. in prep5). O primeiro estudo baseado na caracterização do transcriptoma do músculo branco de tambaqui exposto cronicamente a cenários climáticos foi conduzido por Prado-Lima e Val (2016). Dos 32.512 transcritos obtidos em plataforma SOLiDTM (ABI), os autores identificaram 445 genes diferencialmente expressos (DEGs6) responsivos aos diferentes cenários testados, que incluem chaperonas envolvidas no dobramento de proteínas, genes relacionados ao metabolismo energético, biossíntese de macromoléculas, organização celular, manutenção da homeostase e desenvolvimento.

Portanto, o potencial emprego de ferramentas moleculares modernas possibilita primariamente a compreensão dos mecanismos plásticos desencadeados tanto em ambientes naturais quanto artificiais. O impacto de tais mudanças sobre a ictiofauna tropical precisa ser investigado no presente para melhor entendimento dos processos de adaptação molecular e bioquímica, ambos necessários para o processo de aclimatização e sobrevivência frente a ambientes desafiadores.

5 Fagundes, D.B.; Lemgruber, R.S.P; Araújo, J.D.A.; Fé-Gonçalves, L.M.; Val, A.L., in prep

6 DEG, da sigla em inglês: Differentially Expressed Gene

2 OBJETIVOS

2.1 OBJETIVO GERAL

O presente estudo objetivou avaliar comparativamente o transcriptoma diferencial de duas populações de tambaqui de cativeiro tanto aclimatizadas em diferentes latitudes quanto sobre a influência de um cenário extremo de mudança climática.

2.2 OBJETIVOS ESPECÍFICOS

2.2.1 Capítulo I

Descrever o perfil funcional dos transcritos diferencialmente expressos em duas populações de tambaqui criadas em regiões de diferentes latitudes e relacioná- los à adaptação local.

3.2.2 Capítulo II

Descrever o perfil funcional dos transcritos diferencialmente expressos em duas populações de tambaqui de cativeiro submetidas a uma condição climática extrema e identificar os principais genes relacionados à plasticidade regional.

3 MATERIAL E MÉTODOS

3.1 LICENÇAS E AUTORIZAÇÕES

As técnicas de manejo e eutanásia dos juvenis de tambaqui (Figura 1) foram realizadas de acordo com as Diretrizes Brasileiras de Ética no Uso de Animais, como sugeridas pelo Conselho Nacional de Controle de Experimentação Animal (CONCEA

2013). A Guia de Trânsito Animal (GTA), que permitiu o transporte aéreo dos juvenis de tambaqui provenientes de Mogi Mirim/SP, foi atestada junto ao Ministério da

Agricultura, Pecuária e Abastecimento (MAPA), sob processo de n°. 003856. Este

estudo foi aprovado pela Comissão de Ética em Pesquisa no Uso de Animais

(CEUA) do Instituto Nacional de Pesquisas da Amazônia (INPA), sob processo de n°. 032/2016.

3.2 DELINEAMENTO EXPERIMENTAL

3.2.1 Capítulo I

3.2.1.1 Coleta ex-situ

Juvenis de tambaqui foram coletados em duas estações de piscicultura brasileiras localizadas nas regiões Norte e Sudeste, respectivamente: i) Centro de

Tecnologia, Treinamento e Produção em Aquicultura (CTTPA – SEPA/SEPROR em

Balbina/AM – 1°55'54.4"S; 59°24'39.1"O) e ii) Piscicultura Brumado (Mogi Mirim/SP

– 22°31'16.00"S; 46°53'5.71"O). Ambas as pisciculturas também foram escolhidas em estudos anteriores que objetivaram aplicar programas de melhoramento genético em populações de tambaqui de cativeiro (Nunes et al. 2017, Gonçalves et al. 2018).

Além disso, as populações de tambaqui do Norte e do Sudeste são criadas em diferentes latitudes que exibem a variação climática típica do Brasil, de acordo com a classificação de Köppen (Alvares et al. 2013). A população do Norte vive em uma

região climática caracterizada pelo clima tropical úmido (clima Af) com temperatura média anual de 27,1 °C (variando de 22,3 a 32,6 °C). A população do Sudeste vive em uma região de clima temperado úmido com inverno seco e verão quente (clima

Cwa) com uma temperatura média anual de 20,1 °C (variando de 9,4 a 28,0 °C), respectivamente.

Cada lote de tambaqui foi proveniente de diferentes matrizes, com o manejo reprodutivo realizado de acordo com seus respectivos locais de criação. Por exemplo, o cruzamento das matrizes de Brumado foi realizado no período de reprodução de dezembro de 2015, enquanto o de Balbina em maio de 2016. Assim, a população de Brumado (~60 g e 13 cm) foi coletada em fevereiro de 2016, período do verão nesta região cuja temperatura varia de 18,8 a 28 °C (CPTEC/INPE 2018).

A população de Balbina (~26 g e 10 cm) foi coletada em junho de 2016, marcando o início da estação seca – o ‘‘verão amazônico’’ (Fisch et al. 1998), com a temperatura variando entre 23 a 31 °C (Climatempo 2018). No momento da coleta dos espécimes, a temperatura da água dos tanques de criação registrou 21 °C em

Brumado e 29,5 °C em Balbina; a concentração de oxigênio dissolvido (OD) variou entre 5 a 7 mg.L-1 em ambos os locais.

3.2.1.2 Obtenção das amostras

Em cada piscicultura, dez juvenis de tambaqui foram sacrificados por secção medular e dissecados para a coleta de tecidos com o uso de material cirúrgico esterilizado. Amostras de fígado foram imediatamente armazenadas em RNAlater®

Stabilization Solution (Thermo Fisher Scientific) para assegurar a preservação do

ácido ribonucleico (RNA) durante o transporte para o Laboratório de Ecofisiologia e

Evolução Molecular (LEEM/COBio/INPA) em Manaus/AM. No laboratório, as 20 amostras foram removidas do RNAlater®, lavadas em água livre de RNase (Qiagen),

blotadas em papel-filtro (Whatman®) para retirar o excesso de tampão, e então congeladas em freezer -80 ºC até a extração de RNA. O tecido hepático foi escolhido neste estudo por desempenhar reações metabolicamente importantes relacionadas ao estresse térmico (Logan e Buckley 2015).

3.2.2 Capítulo II

3.2.2.1 Aquisição e aclimatação dos animais

Lotes de juvenis de tambaqui provenientes das pisciculturas Balbina e

Brumado, acima descritas, foram transportados ao LEEM (COBio/INPA) via terrestre e via aérea, respectivamente. No laboratório, as populações foram aclimatadas separadamente em tanques de polietileno de 310 L em condições controladas

-1 (~25,7 °C; 7,0 mgO2.L ; pH 6,5 e 0,13 mM de amônia total). Durante os períodos pré-experimental e experimental, os peixes foram alimentados com ração pelletilizada comercial constituída por 32% de proteína bruta (Nutripeixe, Purina), fornecida uma vez ao dia até a saciedade aparente.

3.2.2.2 Exposição artificial em salas climáticas

Exemplares de tambaqui de ambas as populações foram artificialmente expostos aos cenários climáticos atual (condição controle) e extremo (RCP8.5) como descritos no 5°. Relatório de Avaliação do IPCC (IPCC 2014). O cenário atual simulou as variações, em tempo real, na temperatura e concentração de CO2 iguais

às atuais. O cenário extremo reproduziu um aumento de 4,5 ºC na temperatura e

850 ppm de CO2 no ar, tendo seus parâmetros variando em relação ao cenário atual.

Um total de 36 indivíduos das populações de Norte e Sudeste foram expostos por 30 dias a estes ambientes controlados, durante a estação seca da Amazônia

(Figura 2). Em cada cenário, foram acondicionados 18 aquários de plástico

(Sanremo) contendo 20 L de água sob aeração constante, onde os animais foram

alimentados uma vez por dia (15h) com ração pelletilizada com 32% de proteína bruta (Nutripeixe, Purina).

Figura 2. (A) Modelo das salas climáticas que reproduzem os cenários atual e extremo, e seus respectivos valores (média ± erro padrão da média) de temperatura e níveis de CO2 durante 30 dias de experimento (outubro a novembro de 2016). (B) Exposição artificial de juvenis de tambaqui provenientes de populações criadas na região Norte, em verde (N= 18) e Sudeste, em azul (N= 18) aos dois ambientes controlados.

3.2.2.3 Acompanhamento das variáveis ambientais

A temperatura, concentração de CO2, umidade relativa do ar e ciclo circadiano

12L:12D foram monitorados por um sistema de controle computacional integrado que coleta informações sobre todos os parâmetros, a cada dois minutos e os armazena em um computador exclusivo para esta finalidade. As oscilações diárias na temperatura e na quantidade de CO2 nas duas salas climáticas são ilustradas na

Figura 3. Como esperado, os valores de umidade relativa não variaram entre os cenários atual (69,5 ± 0,04) e extremo (68,7 ± 0,03).

Os parâmetros físico-químicos da água dos aquários foram também medidos diariamente (15h) durante a execução dos experimentos (Tabela 1). Os valores de pH foram obtidos com o auxílio de um pHmetro digital UltraBasic UB-10 (Denver

Instrument), e as medidas de temperatura e concentrações de OD foram tomadas

com o auxílio de um multianalisador YSI-85 (Yellow Springs Instruments). Ensaios colorimétricos determinaram os níveis de CO2 por meio de titulação em seringa descartável (Boyd e Tucker 1992), bem como a dosagem total de amônia usando um leitor de microplaca SpectraMax Plus 384 (Molecular Devices) (Verdouw et al.

1978). Para evitar o acúmulo tóxico de amônia, a renovação parcial da água foi realizada diariamente.

Figura 3. Variações diárias na temperatura e concentração de CO2 do ar das salas climáticas medidas ao longo de 30 dias de experimento, às 15h (25 de outubro a 26 de novembro de 2016).

Temperatura CO2 OD pH Amônia total -1 (°C) (ppm) (mgO2.L ) (mM) População do Norte

Atual 26,1±0,2 11,7±1,9 7,3±0,1 6,9±0,09 0,17±0,01

Extremo 29,0±0,2* 17,7±2,2* 6,5±0,1* 6,5±0,09* 0,21±0,01*

População do Sudeste

Atual 26,3±0,2 11,5±1,4 7,2±0,1 6,8±0,06 0,15±0,01

Extremo 29,1±0,2* 18,4±2,0* 6,8±0,1* 6,6±0,07 0,18±0,01

3.2.2.4 Obtenção das amostras

Os peixes do Norte pesavam 52,4 g ± 3,0 e mediam 11,9 cm ± 0,2, e os do

Sudeste, 67,9 g ± 6,5 e 13,0 cm ± 0,5. Conforme procedimento padrão de eutanásia do CONCEA, os peixes foram sacrificados por secção medular para a coleta de tecido, com uso de pinça e tesoura estéreis. As 36 amostras de fígado foram imediatamente preservadas em RNAlater®, e conservadas em freezer -20 ºC até a fase de extração de RNA.

3.3 SEQUENCIAMENTO POR RNA-Seq

A metodologia descrita a seguir foi empregada para resolver os objetivos específicos dos Capítulos I e II do presente estudo.

3.3.1 Extração do RNA total, eletroforese e quantificação

O RNA total das 56 amostras de fígado de tambaqui (n= 20, Capítulo I e n=36, Capítulo II) foi extraído conforme instruções do kit RNeasy® Mini (Qiagen) com o uso da estação robótica QIACube (Qiagen). Ao final da extração

automatizada, pellets de RNA total foram ressuspendidos em 30 μL de água livre de

RNase.

A integridade do RNA total foi primeiramente verificada em gel de agarose 1%

(UltraPure™ Agarose, Thermo Fisher Scientific) por meio da visualização dos RNAs ribossômicos (rRNAs) 28S e 18S. Posteriormente, a qualidade do RNA foi avaliada por meio de eletroforese microfluídica no equipamento Agilent 2100 BioAnalyzer

(Agilent Technologies), seguindo as instruções do kit Agilent RNA 6000 Nano

(Agilent Technologies). Amostras de boa qualidade apresentaram valor de RIN (RNA

Integrity Number) ≥ 8 (Figura 4).

Figura 4. Eletroforese microfluídica de RNA total extraído do fígado de juvenis de tambaqui provenientes das populações de cativeiro de Balbina (Norte) e Brumado (Sudeste) (acima), bem como expostas às salas climáticas atual e extrema (abaixo). Os valores médios do RIN foram, respectivamente: 9,35 (Balbina), 9,32 (Brumado), 9,74 (população do Norte) e 10,0 (população do Sudeste).

A concentração e pureza do RNA total foram verificadas no NanoDrop® 2000

Espectrophotometer (Thermo Fisher Scientific) e, mais tarde, confirmadas por fluorimetria no Qubit® 2.0 (Thermo Fisher Scientific), conforme o manual do kit Qubit

RNA BR Assay (Thermo Fisher Scientific). Todas as amostras de RNA apresentaram rendimento considerável, como seguem: 0,97 μg ± 0,08 (Balbina), 0,46 μg ± 0,16

(Brumado), 0,96 μg ± 0,09 (população do Norte) e 1,04 μg ± 0,1 (população do

Sudeste).

3.3.2 Sequenciamento dos transcritos

Antes do início do protocolo para a construção das bibliotecas de RNA-Seq, foram formados pools de RNA total com a finalidade de garantir uma quantidade adequada de RNA para os procedimentos subsequentes. Para tal, foram formados três (03) pools de RNA para cada condição aqui estudada, totalizando seis (06) réplicas biológicas das pisciculturas de Balbina e Brumado (Capítulo I) e 12 das populações do Norte e Sudeste submetidas às condições experimentais em salas climáticas (Capítulo II).

Os 18 pools obtidos foram sequenciados em plataforma de nova geração

MiSeq® da Illumina. As bibliotecas de DNA foram construídas de acordo com as instruções do kit TruSeq RNA Sample Preparation (Illumina). Os reagentes fornecidos nesse kit possibilitaram (i) a purificação e a fragmentação do mRNA isolado a partir de 0,25 µg de RNA total; (ii) a síntese de cDNA fita simples e fita dupla; (iii) a adenilação da extremidade 3’ do cDNA de fita dupla; e (iv) a ligação dos adaptadores em ambas as extremidades dos fragmentos. Após o enriquecimento dos fragmentos de DNA por Reação de Polimerização em Cadeia (PCR), as bibliotecas foram validadas no ViiA 7 Real-Time PCR System (Thermo Fisher

Scientific), seguindo as instruções do kit KAPA SYBR® FAST qPCR Master Mix

(Kapa Biosystems) (Figura 5). Em seguida, as bibliotecas normalizadas foram denaturadas e diluídas para o sequenciamento, utilizando o MiSeq Reagent Kit v2

(Illumina) que possibilita a formação dos clusters de sequenciamento e geração de até 30 milhões de reads com tamanho aproximado de 250 pares de base (pb) de

Figura 5. Quantificação absoluta das 18 bibliotecas de RNA-Seq. Curvas de diluição seriada (20 a 0,0002 pM de DNA) e de amplificação, respectivamente, das bibliotecas de DNA das populações de cativeiro de Balbina (Norte) e Brumado (Sudeste) (acima), bem como expostas às salas climáticas atual e extrema (abaixo). comprimento.

3.3.3 Análises de Bioinformática

O processamento dos dados brutos gerados do sequenciamento de RNA foi realizado no Laboratório de Bioinformática do LEEM (COBio/INPA). Cada etapa de análise dos dados de RNA-Seq é descrita a seguir: i) análises dos parâmetros de qualidade das reads no FastQC v.0.11.6 (Andrews 2010); ii) trimagem das leituras de baixa qualidade (Q-score <20 e comprimento <50 pb) e remoção das sequências adaptadoras das reads no Trimmomatic v.0.36 (Bolger et al. 2014); iii) montagem e alinhamento do transcriptoma com o Trinity v.2.5.1 (Grabherr et al. 2011) e Bowtie2

v.2.3.3.1 (Langmead and Salzberg 2012); iv) cálculo de abundância dos transcritos com o RSEM (Li and Dewey 2011) e pacotes do R/Bioconductor (Bates et al. 2004); v) quantificação dos genes diferencialmente expressos (DEGs) usando o pacote

R/Bioconductor, edgeR (Robinson et al. 2009), considerando FDR ≤ 0,05 e fold change ≥ 2; e vi) anotação dos DEGs com o BLASTx (Altschul et al. 1997), comparado com o banco de dados de proteínas Uniprot/TrEMBL (classe

Actinopterygii) e proteínas Swiss-Prot não redundante, com valor e-value 1e-5. O programa Trinotate v.3.1.1 (https://trinotate.github.io/) foi usado para classificar os

DEGs de acordo com as três categorias da Ontologia Gênica (GO – Gene Ontology): i) Componente Celular, ii) Função Molecular e iii) Processo Biológico.

No NetworkAnalyst (https://www.networkanalyst.ca/), foi realizada a construção redes biológicas baseadas na interação proteína-proteína (PPI – protein- protein interaction) a partir dos DEGs. Esse programa também permitiu a análise de enriquecimento dos termos do GO, de acordo com os grupos KEGG (Kyoto

Encyclopedia of Genes and Genomes) (Xia et al. 2014).

4. RESULTADOS E DISCUSSÃO

O transcriptoma comparativo das populações de tambaqui criadas em duas pisciculturas brasileiras bem como cronicamente expostas a um cenário extremo, que mimetiza mudanças climáticas, é descrito a seguir. A identificação e caracterização funcional dos principais genes responsáveis pela adaptação das populações de tambaqui às condições distintas são discutidas separadamente em dois manuscritos que compõem os Capítulos I e II desta Tese.

5 REFERÊNCIAS BIBLIOGRÁFICAS

Almeida-Val, V.M.F.; Farias, I.P. 1996. Respiration in fish of the Amazon: Metabolic adjustments to chronic hypoxia. In: Val, A.L.; Almeida-Val, V.M.F.; Randall, D.J. (Eds.), Physiology and Biochemistry of the Fishes of the Amazon, INPA, Manaus, BR, p.257–271.

Almeida-Val, V.M.F.; Schwantes, M.L.B.; Val, A.L. 1990. LDH isozymes in Amazon fish – I. Electroforetic studies on two species from Serrasalmidae family: Mylossoma duriventris and Colossoma macropomum. Comparative Biochemistry and Physiology (B) – Biochemistry and Molecular Biology 98: 77–84.

Almeida-Val, V.M.F.; Chippari-Gomes, A.R.; Lopes, N.P. 2006. Metabolic and physiological adjustments to low oxygen and high temperature in fishes of the Amazon. In: Val, A.L.; Almeida-Val, V.M.F.; Randall, D.J. (Eds.), The Physiology of Tropical Fishes, Elsevier, Lodon, UK, p.443–500.

Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; et al. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25: 3389–3402.

Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.L.M.; Sparovek, G. 2013. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 22: 711–728.

Ambrizzi, T.; Rocha, R.P.; Marengo, J.A.; Pisnitchenco, I.; Nunes, L.A.; Fernandez, J.P.R. 2007. Cenários regionalizados de clima no Brasil e América do Sul para o século XXI: Projeções de clima futuro usando três modelos regionais. Relatório 3. São Paulo, BR, 180p.

Andrews, S. 2010. FASTQC. A quality control tool for high throughput sequence data. 2010. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed on 16 Feb. 2019.

Araújo, J.D.A.; Ghelfi, A.; Val, A.L. 2017. Triportheus albus Cope, 1872 in the blackwater, clearwater, and whitewater of the amazon: A case of phenotypic plasticity? Frontiers in Genetics 8: 1–12.

Baldisseroto, B. 2009. Crescimento. In: Baldisseroto, B. (Ed.), Fisiologia de Peixes

Aplicada à Piscicultura, Ed. UFSM, Santa Maria, BR, p.231–286.

Barton, B.A. 2002. Stress in fishes: A diversity of responses with particular reference to changes in circulating corticosteroids. Integrative and Comparative Biology 42: 517–525.

Bates, D.M.; Ellis, B.; Smith, C.; Irizarry, R.; Tierney, L.; Huber, W.; et al. 2004. Bioconductor: open software development for computational biology and bioinformatics. Genome Biology 5: 1–16.

Beaugrand, G.; Brander, K.M.; Lindley, J.A.; Souissi, S.; Reid, P.C. 2003. Plankton effect on cod recruitment in the North Sea. Nature 426: 661–664.

Beitinger, T.L.; Bennett, W.A.; Mccauley, R.W. 2000. Temperature tolerances of North American freshwater fishes exposed to dynamic changes in temperature. Environmental Biology of Fishes 58: 237–275.

Bellard, C.; Bertelsmeier, C.; Leadley, P.; Thuiller, W.; Courchamp, F. 2014. Impacts of climate change on the future of biodiversity. Ecology letters 15: 365–377.

Bolger, A.M.; Lohse, M.; Usadel, B. 2014. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114–2120.

Boyd, C.E.; Tucker, C.S. 1992. Water quality and pond soil analyses for aquaculture. Auburn University, Opelika, USA, 183p.

Brander, K. 2010. Impact of climate change on fisheries. Journal of Marine Systems journal 79: 389–402.

Braz-Mota, S.; Fé, L.M.L.; Delunardo, F.A.C.; Sadauskas-Henrique, H.; de Almeida- Val, V.M.F.; Val, A.L. 2017. Exposure to waterborne copper and high temperature induces the formation of reactive oxygen species and causes mortality in the Amazonian fish Hoplosternum littorale. Hydrobiologia 789: 157– 166.

Campos, D.F.; Braz-Mota, S.; Val, A.L.; Almeida-Val, V.M.F. 2019. Predicting thermal sensitivity of three Amazon fishes exposed to climate change scenarios. Ecological Indicators 101: 533–540.

Cândido, L.A.; Manzi, A.O.; Tota, J.; Silva, P.R.T.; Silva, F.S.M.; Santos, R.M.N.; et

al. 2007. O clima atual e futuro da Amazônia nos cenários do IPCC: A questão da savanização. Ciência e Cultura 59: 44–47.

Cerdeira, R.G.P.; Ruffino, M.L.; Isaac, V.J. 1997. Consumo de pescado e outros alimentos pela população ribeirinha do Lago Grande de Monte Alegre, PA – Brasil. Acta Amazonica 27: 213–228.

Climatempo. 2018. Climatempo - O melhor site de meteorologia do Brasil. (https://www.climatempo.com.br/). Accessed on 10 Jan. 2019.

CONCEA. 2013. Diretrizes da prática de eutanásia do CONCEA. Brasília, BR, 1– 54p.

Costa, O.T.F. 1995. Efeito do pH da água sobre os parâmetros hematológicos, eletrólitos e equilíbrio ácido-base do sangue arterial de Colossoma macropomum (Characiformes, Serrasalmidae). Instituto Nacional de Pesquisas da Amazônia (INPA)/ Universidade do Amazonas (UA), 108p.

CPTEC/INPE. 2018. Centro de Previsão de Tempo e Estudos Climáticos - CPTEC/INPE. (https://www.cptec.inpe.br/). Accessed on 10 Jan. 2019.

Crozier, L.G.; Hutchings, J.A. 2014. Plastic and evolutionary responses to climate change in fish. Evolutionary Applications 7: 68–87.

Dagosta, F.C.; De Pinna, M. 2019. The fishes of the Amazon: Distribution and biogeographical patterns, with a comprehensive list of species. Bulletin of the American Museum of Natural History, 431: 1–163.

Dragan, F.G. 2015. Respostas fisiológicas e metabólicas do tambaqui (Colossoma macropomum, Cuvier 1818) às alterações climáticas previstas pelos diferentes cenários do IPCC para o ano de 2100. Instituto Nacional de Pesquisa da Amazônia (INPA), 81p.

Elliott, J. 1991. Tolerance and resistance to thermal stress in juvenile Atlantic salmon, Salmo salar. Freshwater Biology 25: 61–70.

Enzor, L.A.; Zippay, M.L.; Place, S.P. 2013. High latitude fish in a high CO2 world: Synergistic effects of elevated temperature and carbon dioxide on the metabolic rates of Antarctic notothenioids. Comparative Biochemistry and Physiology - Part A: Molecular and Integrative Physiology 164: 154–161.

Fangue, N.; Hofmeister, M.; Schulte, P. 2006. Intraspecific variation in thermal tolerance and heat shock protein gene expression in common killifish, Fundulus heteroclitus. Journal of Experimental Biology 209: 2859–2872.

Fé-Gonçalves, L.M.; Paula-Silva, M. de N.; Val, A.L.; Almeida-Val, V.M.F. 2018. Differential survivorship of congeneric ornamental fishes under forecasted climate changes are related to anaerobic potential. Genetics and Molecular Biology 41: 107–118.

Fearnside, P.M. 2009. A vulnerabilidade da floresta amazônica perante as mudanças climáticas. Oecologia Australis 13: 609–618.

Feder, M.E.; Hofmann, G.E. 1999. Heat-shock proteins, molecular chaperones, and the stress response: evolutionary and ecological physiology. Annual Review of Physiology 61: 243–282.

Feely, R.A.; Sabine, C.L.; Lee, K.; Berelson, W.; Kleypas, J.; Fabry, V.J.; et al. 2004.

Impact of anthropogenic CO2 on the CaCO3 system in the oceans. Science 305: 362–366.

Fisch, G.; Marengo, J.A.; Nobre, C.A. 1998. The climate of Amazonia - a review. Acta Amazônica 28: 101–126.

Fivelstad, S.; Waagbø, R.; Stefansson, S.; Olsen, A.B. 2007. Impacts of elevated water carbon dioxide partial pressure at two temperatures on Atlantic salmon (Salmo salar L.) parr growth and haematology. Aquaculture 269: 241–249.

Ford, T.; Beitinger, T.L. 2005. Temperature tolerance in the goldfish, Carassius auratus. Journal of Thermal Biology 30: 147–152.

Ghalambor, C.K.; McKay, J.K.; Carroll, S.P.; Reznick, D.N. 2007. Adaptive versus non-adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments. Functional Ecology 21: 394–407.

Golani, D.; Orsi-Relini, L.; Massut, E.; Quignard, J.P. 2002. CIESM Atlas of exotic fishes in the Mediterranean Sea. (http://www.ciesm.org/atlas/appendix1.html). Accessed on 25 May 2019.

Gonçalves, R.A.; Santos, C.H.A.; Sá-Leitão, C.S.; Souza, É.M.S.; Almeida-Val, V.M.F. 2018. Genetic basis of Colossoma macropomum broodstock:

Perspectives for an improvement program. Journal of the World Aquaculture Society: 633–644.

Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; et al. 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology 29: 644–652.

Griggs, D.J.; Noguer, M. 2002. Climate change 2001: The scientific basis. Contribution of working group I to the third assessment report of the Intergovernmental Panel on Climate Change. Weather 57: 267–269.

Guinotte, J.M.; Fabry, V.J. 2008. Ocean acidification and its potential effects on marine ecosystems. Annals of the New York Academy of Sciences 1134: 320– 342.

Harley, C.D.G.; Randall Hughes, A.; Hultgren, K.M.; Miner, B.G.; Sorte, C.J.B.; Thornber, C.S.; et al. 2006. The impacts of climate change in coastal marine systems. Ecology Letters 9: 228–241.

Hilsdorf, A.W.S.; Orfão, L.H. 2011. General aspects of genetic improvement of fish in Brazil. Revista Brasileira Zootecnia 40: 317–324.

IBAMA. 2007. Estatística da pesca. Grandes regiões e unidades da federação. Distrito Federal, BR, 113p.

IBGE. 2016. Produção da Pecuária Municipal. Rio de Janeiro, BR, 51p.

IPCC. 2001. Climate Change 2001, Synthesis Report. A Contribution of Working Groups I, II, and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK and New York, USA, 151p.

IPCC. 2007. Summary for Policymakers. In: Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.B.; et al. (Eds.), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, USA, p.1–18.

IPCC. 2014. Summary for policymakers. In: Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J, Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L, Estrada, Y.O.,

Genova, R.C, Girma, B., Kissel, E.S., Levy, A.N, MacCracken, S., Mastrandrea, P.R., White, L.L. (Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, USA, p.1–32.

Ishimatsu, A.; Kikkawa, T.; Hayashi, M.; Lee, K.S.; Kita, J. 2004. Effects of CO2 on marine fish: Larvae and adults. Journal of Oceanography 60: 731–741.

Jesus, T.F.; Rosa, I.C.; Repolho, T.; Lopes, A.R.; Pimentel, M.S.; Almeida-Val, V.M.F.; et al. 2018. Different ecophysiological responses of freshwater fish to warming and acidification. Comparative Biochemistry and Physiology - Part A: Molecular and Integrative Physiology 216: 34–41.

Justino, F.; Amorim, M. 2007. Projeções do clima da América do Sul segundo o cenário “B1” do IPCC adotando um modelo acoplado oceano-atmosfera- vegetação-gelo marinho. Boletim da Sociedade Brasileira de Meteorologia 31: 29–35.

Kita, J.; Ishimatsu, A.; Kikkawa, T.; Hayashi, M. 2003. Effects of CO2 on marine fish. Greenhouse Gas Control Technologies 2: 1695–1698.

Kochhann, D.; Campos, D.F.; Val, A.L. 2015. Experimentally increased temperature and hypoxia affect stability of social hierarchy and metabolism of the Amazonian cichlid Apistogramma agassizii. Comparative Biochemistry and Physiology - Part A: Molecular and Integrative Physiology 190: 54–60.

Langmead, B.; Salzberg, S.L. 2012. Fast gapped-read alignment with Bowtie 2. Nature methods 9: 357–359.

Lapointe, D.; Cooperman, M.S.; Chapman, L.J.; Clark, T.D.; Val, A.L.; Ferreira, M.S.; et al. 2018. Predicted impacts of climate warming on aerobic performance and upper thermal tolerance of six tropical freshwater fishes spanning three continents. Conservation Physiology 6: coy056.

Lemgruber, R.S..; Marshall, N.A.A.; Ghelfi, A.; Fagundes, D.B.; Val, A.L. 2013. Functional categorization of transcriptome in the species Symphysodon aequifasciatus Pellegrin 1904 (Perciformes: Cichlidae) exposed to

benzo[a]pyrene and phenanthrene. PLoS ONE 8: e81083.

Lévêque, C.; Oberdorff, T.; Paugy, D.; Stiassny, M.L.J.; Tedesco, P.A. 2008. Global diversity of fish (Pisces) in freshwater. Hydrobiologia 595: 545–567.

Li, B.; Dewey, C.N. 2011. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12: 41–74.

Li, W.; Fu, R.; Dickinson, R.E. 2006. Rainfall and its seasonality over the Amazon in the 21st century as assessed by the coupled models for the IPCC AR4. Journal of Geophysical Research Atmospheres 111: 1–14.

Lima, F.P.; Queiroz, N.; Ribeiro, P.A.; Hawkins, S.J.; Santos, A.M. 2006. Recent changes in the distribution of a marine gastropod, Patella rustica Linnaeus, 1758, and their relationship to unusual climatic events. Journal of Biogeography 33: 812–822.

Liu, S.; Wang, X.; Sun, F.; Zhang, J.; Feng, J.; Liu, H.; et al. 2013. RNA-Seq reveals expression signatures of genes involved in oxygen transport, protein synthesis, folding, and degradation in response to heat stress in catfish. Physiological Genomics 45: 462–476.

Logan, C.A.; Buckley, B.A. 2015. Transcriptomic responses to environmental temperature in eurythermal and stenothermal fishes. Journal of Experimental Biology 218: 1915–1924.

Lopes, I.G.; Araújo-Dairiki, T.B.; Kojima, J.T.; Val, A.L.; Portella, M.C. 2018. Predicted 2100 climate scenarios affects growth and skeletal development of tambaqui (Colossoma macropomum) larvae. Ecology and Evolution 8: 10039– 10048. van Maaren, C.C.; Kita, J.; Daniels, H.V. 2000. Temperature tolerance and oxygen consumption rates for juvenile southern flounder Paralichthys lethostigma acclimated for five different temperature. UJNR Technical Report 28: 135–140.

Mardis, E.R. 2008. Next-Generation DNA Sequencing Methods. Annual Review of Genomics and Human Genetics 9: 387–402.

Marengo, J.A. 2007. Caracterização do clima no Século XX e Cenários Climáticos no Brasil e na América do Sul para o Século XXI derivados dos Modelos Globais de

Clima do IPCC. Relatório 1. São Paulo, BR, 185p.

Mirande, J.M. 2010. Phylogeny of the family Characidae (Teleostei: Characiformes): from characters to taxonomy. Neotropical Ichthyology 8: 385–568.

MPA. 2011. Boletim estatístico da pesca e aquicultura. Distrito Federal, BR, 60p.

Nobre, A.C.; Sampaio, G.; Salazar, L. 2007. Mudanças climáticas e Amazônia. Ciência & Cultura 59: 22–27.

Nobre, C.A.; Sampaio, G.; Salazar, L. 2008. Cenários de mudança climática para a América do Sul para o final do século 21. Parcerias Estratégicas 3: 19–43.

Nowicki, J.P.; Miller, G.M.; Munday, P.L. 2012. Interactive effects of elevated

temperature and CO2 on foraging behavior of juvenile coral reef fish. Journal of Experimental Marine Biology and Ecology 412: 46–51.

Nunes, J.R.S.; Liu, S.; Pértille, F.; Perazza, C.A.; Villela, P.M.S.; Almeida-Val, V.M.F.; et al. 2017. Large-scale SNP discovery and construction of a high- density genetic map of Colossoma macropomum through genotyping-by- sequencing. Scientific Reports 7: 1–11.

Oliveira, A.M.; Val, A.L. 2017. Effects of climate scenarios on the growth and physiology of the Amazonian fish tambaqui (Colossoma macropomum) (Characiformes: Serrasalmidae). Hydrobiologia 789: 167–178.

Olsvik, P.A.; Vikeså, V.; Lie, K.K.; Hevrøy, E.M. 2013. Transcriptional responses to temperature and low oxygen stress in Atlantic salmon studied with next- generation sequencing technology. BMC Genomics 14.

Oomen, R.A.; Hutchings, J.A. 2017. Transcriptomic responses to environmental change in fishes: Insights from RNA sequencing. Facets 2: 610–641.

Ostrensky, A.; Borguetti, J.R.; Soto, D. 2008. Aquicultura no Brasil. O desafio é crescer. Secretaria Especial de Aquicultura e Pescas, Distrito Federal, BR, 276p.

Parmesan, C. 2006. Ecological and evolutionary responses to recent climate change. Annual Review of Ecology, Evolution, and Systematics 37: 637–669.

Paula-Silva, M. de N. 1999. Influência do nitrito sobre aspectos fisiológicos do

tambaqui - Colossoma macropomum (Cuvier 1818). Instituto Nacional de Pesquisa da Amazônia (INPA)/ Universidade Federal do Amazonas (UFAM), 61p.

Perazza, C.A.; Pinaffi, F.L.V.; Silva, L.A. 2015. Evaluation of ultrasound imaging to predict loin eye area in tambaqui. Boletim do Instituto de Pesca 41: 803–809.

Perazza, C.A.; de Menezes, J.T.B.; Ferraz, J.B.S.; Pinaffi, F.L.V.; Silva, L.A.; Hilsdorf, A.W.S. 2017. Lack of intermuscular bones in specimens of Colossoma macropomum: An unusual phenotype to be incorporated into genetic improvement programs. Aquaculture 472: 57–60.

Perry, A.L.; Low, P.J.; Ellis, J.R.; Reynolds, J.D. 2005. Climate change and distribution shifts in marine fishes. Science 308: 1912–1915.

Pörtner, H.O.; Peck, M.A. 2010. Climate change effects on fishes and fisheries: Towards a cause-and-effect understanding. Journal of Fish Biology 77: 1745– 1779.

Pörtner, H.O.; Bennett, A.F.; Bozinovic, F.; Clarke, A.; Lardies, M.A.; Lucassen, M.; et al. 2006. Trade-offs in thermal adaptation: The need for a molecular to ecological integration. Physiological and Biochemical Zoology 79: 295–313.

Prado-Lima, M.; Val, A.L. 2016. Transcriptomic characterization of tambaqui (Colossoma macropomum, Cuvier, 1818) exposed to three climate change scenarios. PLoS ONE 11: 1–21.

Qian, X.; Ba, Y.; Zhuang, Q.; Zhong, G. 2014. RNA-Seq technology and its application in fish transcriptomics. OMICS: A Journal of Integrative Biology 18: 98–110.

Quigley, J.T.; Hinch, S.G. 2006. Effects of rapid experimental temperature increases on acute physiological stress and behaviour of stream dwelling juvenile chinook salmon. Ournal of Thermal Biology 31: 429–441.

Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. 2009. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139–140.

Roessig, J.; Woodley, C.; Cech, J.; Hansen, L. 2004. Effects of global climate change

on marine and estuarine fishes and fisheries. Reviews in Fish Biology and Fisheries 14: 251–275.

Saint-Paul, U. 1984. Physiological adaptation to hypoxia of a neotropical characoid fish Colossoma macropomum, Serrasalmidae. Environmental Biology of Fishes 11: 53–62.

Saint-Paul, U. 1986. Potential for aqualculture of South American freshwater fishes: A review. Aquaculture 54: 205–240.

Sakuragui, M.M.; Caterina, F.E.G.; Vasquez, K.L.; Fagundes, D.B.; Nozawa, S.R.; Val, A.L. 2012. Heat shock expression in Colossoma macropomum exposed to

increased temperature and CO2. ADAPTA Short Note 1: 34–36.

Salamin, N.; Wüest, R.O.; Lavergne, S.; Thuiller, W.; Pearman, P.B. 2010. Assessing rapid evolution in a changing environment. Trends in Ecology and Evolution 25: 692–698.

Salati, E. 1983. O clima atual depende da floresta. In: Salati, E.; Junk, W.J.; Shubart, H.O.R.; Oliveira, A.E. (Eds.), Amazônia: Desenvolvimento, Integração e Ecologia, Brasiliense, São Paulo, BR, p.15–44.

Salazar, L.F.; Nobre, C.A.; Oyama, M.D. 2007. Climate change consequences on the biome distribution in tropical South America. Geophysical Research Letters 34.

Santana, G.X.; Santos, C.H.A.; Sousa, C.F.S.; Nascimento, P.R.M.; Paula-Silva, M.N.; Sousa, A.C.B.; et al. 2012. Isolation of novel microsatellite markers for tambaqui (Colossoma macropomum, Cuvier 1818), an important freshwater fish of the Amazon. Conservation Genetics Resources 4: 197–200.

Santos, C.H.A.; Santana, G.X.; Sá-Leitão, C.S.; Paula-Silva, M.N.; Almeida-Val, V.M.F. 2016. Loss of genetic diversity in farmed populations of Colossoma macropomum estimated by microsatellites. Animal Genetics 47: 373–376.

Santos, C.H.A.; Leitão, M.A. de B.; Sousa, C.F.S.; Santana, G.X.; Paula-Silva, M. de N.; Almeida-Val, V.M.F. 2012. Genetic variability of wild and captivity populations of Colossoma macropomum (Cuvier, 1818). Acta Scientiarum - Biological Sciences 34: 191–197.

Santos, G.M.; Santos, A.C.M. 2005. Sustentabilidade da pesca na Amazônia.

Estudos Avançados 19: 165–182.

Santos, G.M.; Ferreira, E.J.G.; Zuanon, J.A.S. 2006. Peixes comerciais de Manaus. IBAMA/AM, ProVárzea, Manaus, BR, 144p.

Santos, V.B.; Firetti, R.; Sales, D.S. 2008. Peixe quer água boa. In: FNP, I.E. (Ed.), Anuário Da Pecuária Brasileira, Instituto FNP, São Paulo, BR, p.294–296.

Schneider, S.H.; Semenov., S.; Patwardhan, A.; Burton, I.; Magadza, C.H.D.; Oppenheimer, M.; et al. 2007. Assessing key vulnerabilities and the risk from climate change. In: Parry, M.L., Canziani, O.F., Palutikof, J.P., van der Linder, P.J., Hanson, C.E. (Ed.), Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK, p.779–810.

Schulte, P.M. 2001. Environmental adaptations as windows on molecular evolution. Comparative Biochemistry and Physiology - B Biochemistry and Molecular Biology 128: 597–611.

Schulte, P.M. 2004. Changes in gene expression as biochemical adaptations to environmental change: A tribute to Peter Hochachka. Comparative Biochemistry and Physiology - B Biochemistry and Molecular Biology 139: 519–529.

Schulte, P.M. 2014. What is environmental stress? Insights from fish living in a variable environment. Journal of Experimental Biology 217: 23–34.

Sidonio, L.; Cavalcanti, I.; Capanema, L.; Morch, R.; Magalhães, G.; Lima, J.; et al. 2012. Panorama da aquicultura no Brasil : desafios e oportunidades. Agroindústria, BNDES Setorial 35: 421–463.

Sioli, H. 1984. The Amazon and its main affluents: Hydrography, morphology of the river course, and river types. In: Sioli, H. (Ed.), The Amazon: Limnology and Landscape Ecology of a Mighty Tropical River and Its Basin, Junk Publishers, Dordrecht, NE, p.127–165.

Sioli, H. 1990. Amazônia. Fundamentos da ecologia da maior região de florestas tropicais. Vozes, Petropólis, RJ, 72p.

Souza-Bastos, L.R.; Val, A.L.; Wood, C.M. 2017. Are Amazonian fish more sensitive

to ammonia? Toxicity of ammonia to eleven native species. Hydrobiologia 789: 143–155.

Souza-Netto, J.G. 2012. Respostas fisiológicas de Colossoma macropomum (Cuvier

1818) ao aumento de temperatura e CO2. Instituto Nacional de Pesquisa da Amazônia (INPA), 54p.

Tewksbury, J.; Huey, R.; Deutsch, C. 2008. Putting heat on tropical animals. Science 320: 1296–1297.

Val, A..; Almeida-Val, V.M.F. 1995. The amazon ichthyofauna. In: Val, A.L.; Almeida- Val, V.M.F. (Eds.), Fishes of the Amazon and Their Environment: Physiological and Biochemical Aspects, Springer - Verlang, Heidelbeg, Berlin, DE, p.29–30.

Verdouw, H.; van Echteld, C.J.A.; Dekkers, E.M.J. 1978. Ammonia determination based on indophenol formation with sodium salicylate. Water Research 12: 339– 402.

Wang, P.; Bouwman, F.G.; Mariman, E.C.M. 2009a. Generally detected proteins in comparative proteomics - A matter of cellular stress response? Proteomics 9: 2955–2966.

Wang, Z.; Gerstein, M.; Snyder, M. 2009b. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57–63.

Wegele, H.; Müller, L.; Buchner, J. 2001. Hsp70 and Hsp90 - a relay team for protein folding. In: Portland, S.G. Amara, Frankfurt, E.B, Innsbruck, H.G., Gottingen, R.J., Baltimore, W.J. Lederer, Tokyo, A. Miyajima, Zurich, H. Murer, Heidelberg, Offermanns, S., Berlin, G. Schultz, Berlin, M.S. (Ed.), Reviews of Physiology, Bochemistry and Pharmacology, Springer, Berlin, Heidelberg, DE, p.1–44.

Wood, C.M.; Gonzalez, R.J.; Ferreira, M.S.; Braz-Mota, S.; Val, A.L. 2018. The physiology of the tambaqui (Colossoma macropomum) at pH 8.0. Journal of Comparative Physiology B 188: 393–408.

Wood, C.M.; Wilson, R.W.; Gonzalez, R.J.; Patrick, M.L.; Bergman, H.L.; Nahara, A.; et al. 1998. Responses of an Amazonian teleost, the tambaqui (Colossoma macropomum) to low pH in extremely soft water. Physiological Zoology 71: 658– 670.

Xia, J.; Benner, M.J.; Hancock, R.E.W. 2014. NetworkAnalyst - Integrative approaches for protein-protein interaction network analysis and visual exploration. Nucleic Acids Research 42: 167–174.

Capítulo I

Transcriptomic evidences of local thermal adaptation for the native fish Colossoma macropomum (Cuvier, 1818)

Publicação relacionada: Fé-Gonçalves, L.M., Araújo J.D.A., Santos, C.H.A., Almeida-Val, V.M.F. 2019. Transcriptomic evidences of local thermal adaptation for the native fish Colossoma macropomum (Cuvier, 1818) Manuscrito submetido à revista Genetics and Molecular Biology (IF= 2,12)

Status atual da submissão em 10/12/2019.

Transcriptomic evidences of local thermal adaptation for the native fish Colossoma macropomum (Cuvier, 1818)

Luciana Mara Fé-Gonçalves1, José Deney Alves Araújo2, Carlos Henrique dos Anjos dos

Santos1, Vera Maria Fonseca de Almeida-Val1

1Laboratory of Ecophysiology and Molecular Evolution, National Institute for Amazonian

Research, Av. André Araújo, 2936, 69067-375, Petrópolis, Manaus, AM, Brazil

2Computational Systems Biology Laboratory, University of São Paulo. Professor Lúcio

Martins Rodrigues Avenue, 370, 05508020, Butantã, São Paulo, SP, Brazil

Local adaptation of captive tambaqui

Key words: transcriptome; tambaqui; population; temperature; thermal adaptation

Corresponding author: Luciana Mara Fé-Gonçalves

Postal address: Brazilian National Institute for Research in the Amazon, 2936 André Araújo

Avenue, Petrópolis 69067-375, Manaus, AM, Brazil

E-mail address: [email protected]

Abstract

Brazil has five climatically distinct regions, with an annual average temperature difference up to 14ºC between the northern and southern extremes. Environmental variations of this magnitude can lead to new genetic patterns among farmed fish populations. Genetically differentiated populations of tambaqui (Colossoma macropomum Cuvier, 1818), an important freshwater fish for Brazilian continental aquaculture, may be associated with regional adaptation. In this study, we selected tambaquis raised in two thermally distinct regions, belonging to different latitudes, to test this hypothesis. De novo transcriptome analysis was performed to compare the significant differences of genes expressed in the liver of juvenile tambaqui from a northern population (Balbina) and a southeastern population (Brumado). In total, 2,410 genes were differentially expressed (1,196 in Balbina and 1,214 in Brumado).

Many of the genes are involved in a multitude of biological functions such as biosynthetic processes, homeostasis, biorhythm, immunity, cell signaling, ribosome biogenesis, modification of proteins, intracellular transport, structure/cytoskeleton, and catalytic activity.

Enrichment analysis based on biological networks showed a different protein interaction profile for each population, whose encoding genes may play potential functions in local thermal adaptation of fish to their respective farming environments.

Introduction

The large teleost fish, Colossoma macropomum (Cuvier, 1818) (popularly called

“tambaqui” or “cachama negra”) is a native species found in the Amazonas and Orinoco rivers (Araújo-Lima and Goulding 1998), being economically important for Brazilian continental aquaculture (IBGE 2016). Belonging to the Characiformes order and the

Serrasalmidae family (Mirande 2010), an adult tambaqui may reach a weight of 30 kg and a length of 1 m (Saint-Paul 1986). Due to these traits, the tambaqui has become the primary commercial resource in Amazonian aquaculture and fisheries for its good zootechnical aspects: high level of adaptability to different culture systems, easy manipulation and reproduction in captivity by hormonal induction, high growth rate, and, of course, consumer market acceptance due to the quality of its meat (Moro et al. 2013; Morais and O´Sullivan

2017). As a result, the intensification of its production has been spread by fish farming, which is located in four distinct geographic regions of Brazil (Ostrensky et al. 2008).

Brazil displays a climatic variability which can be divided into five regions; Northern,

Northeastern, Central-West, Southeastern, and Southern (Alvares et al. 2013). However, the most climatically distinct Northern and Southeastern regions are highlighted in our study.

According to Köppen’s classification of climates, the Northern region is naturally dominated by a humid equatorial climate (Af climate), with an annual average temperature of 27.1ºC

(ranging from 22.3 to 32.6ºC), while the Southeastern region presents a humid, temperate climate (Cwa climate), with an annual average temperature of 20.1ºC (varying from 9.4 to

28.0ºC). In winter, cold fronts originating from the Atlantic polar mass may cause frost

(Alvares et al. 2013).

Considering seasonal temperature variations between climatic zones, recent studies have investigated the environmental adaptations of species based on genomic approaches, which reflect biological processes that are important in adaptive evolution (Yi et al. 2016).

Genetic variations within populations have suggested that captive tambaquis already show signs of local adaptation to regions with different climatic conditions (Santos et al. 2016;

Nunes et al. 2017; Gonçalves et al. 2018). Moreover, specific thermal adaptations of these populations have revealed differential expression of genes, displaying critical roles in metabolic process for fish homeostasis, such as circadian rhythm, cell proliferation, energy metabolism and protein modification (Dragan 2019).

Transcriptome analysis of non-model organisms is one the most important approaches for providing insights into the adaptive evolution of species in response to their living environments (Yi et al. 2016). However, under the current perspective of global climate change, such molecular informations may be particularly valuable in the conservation of species which are threatened by extreme environmental challenges (Bellard et al. 2014). In general, fishes are highly able to respond plastically to a myriad of environmental changes, but whether their plastic responses are beneficial seems to depend on the environmental variable that they are being subjected to (Schulte 2001). Climate changes may negatively affect fish populations living close to their thermal comfort zone (Pörtner and Peck 2010), and fish, particularly in the Amazon region, will be those most threatened (Fé-Gonçalves et al.

2018; Campos et al. 2019).

The genetic basis for the tambaqui fish has been developed in recent years. Thus, the present study provides a novel investigation regarding the regional adaptation of tambaqui populations raised in two thermally distinct regions of Brazil based on a comparison of transcriptome profiles.

Material and Methods

Liver sampling

Twenty juvenile tambaquis were collected ex-situ from two fish farms located in the northern and southeastern regions of Brazil (Figure 1). Sampling was carried out during the

dry season when regional climate variables were similar between both sites. The population from Balbina (n= 10; ~ 26 g and 10 cm) was collected in June 2016, at the beginning of the

Amazonian “summer” period (Fisch et al. 1998), with temperatures varying between 23 to

31ºC (Climatempo 2018). The population from Brumado (n= 10; ~ 60 g and 13 cm) was collected during the summer of February 2016, when temperature varied from 18.8 to 28ºC

(CPTEC/INPE 2018). At the time, the water temperature of the rearing tanks was 29.5ºC in

Balbina and 21ºC in Brumado; the level of dissolved oxygen ranged from 5 to 7 mg.L-1.

For tissue sampling from each population, fish (42 g ± 4.7 and 11 cm ± 0.4) were anesthetized and euthanized by cervical sectioning according to Brazilian Guidelines from the

National Board of Control and Care for Ethics in the use of Experimental Animals (CONCEA

2013). Twenty liver samples were immediately stored in RNAlater® Stabilization Solution

(Thermo Fisher Scientific, Massachusetts, USA) to ensure the preservation of the ribonucleic acid (RNA) during transport to the Laboratory of Ecophysiology and Molecular Evolution

(LEEM/COBio/INPA), Manaus, Amazonas state, Brazil. In the laboratory, samples were removed from RNAlater®, washed in RNase-free water (Qiagen, Hilden, DE), dapped dry on an absorbent paper tissue (Whatman®, GE Healthcare Life Sciences, Maidstone, UK), and then stored at -80 ºC until extraction of the RNA. Herein, the liver was analyzed tissue due to its essential metabolically responses under environmental stress (Lemgruber et al. 2013;

Logan and Buckley 2015).

Library construction for RNA sequencing

Total RNA was extracted from the tambaqui livers using RNeasy® Mini Kit (Qiagen,

Hilden, DE) protocol. Approximately 20 mg tissue was homogenized in lysis buffer in a

TissueLyser II (Qiagen, Hilden, DE) for 2x2 minutes at 20 Hz. Automated purification of

RNA was performed on a QIACube robotic workstation (Qiagen, Hilden, DE) using silica- membrane technology. The quality and quantity of extracted RNA were accurately checked

using both an RNA 6000 Nano Bioanalyzer chip (Agilent Technologies, Santa Clara, USA) and a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Massachusetts, USA).

All the RNA samples were free of gDNA and had a suitable RNA yield (~ 0.7μg) and optimal purity (average RIN = 9.3, A260:A280 and A260:A230 ratios = 2.0). Before library construction, three samples of total RNA were pooled, totaling six RNA-Seq libraries, with three biological replicates for each tambaqui population (Balbina and Brumado).

All procedures for constructing and sequencing of RNA-Seq libraries were carried out in the Molecular Biology Laboratory of LEEM/INPA following the Illumina protocols. The mRNA was isolated from the total RNA (0.72μg eluted in 50 μL) using oligo d(T)25 magnetic beads bound to the poly (A) tail of the mRNA. Then, the first and second strands of complementary DNA (cDNA) were synthesized, and a single adenine (A) nucleotide was added to the end 3' of the fragments. Adapters were ligated to the cDNA fragments and a

Polymerase Chain Reaction (PCR) was performed to enrich these fragments. cDNA libraries were prepared using the reagents provided in the TruSeq RNA Library Sample Preparation

Kit v2 (Illumina, San Diego, USA).

The absolute quantification of cDNA libraries was measured on a ViiA 7 Real-Time

PCR System (Thermo Fisher Scientific, Massachusetts, USA) using the KAPA SYBR® FAST qPCR Master Mix (Kapa Biosystems, Wilmington, USA). Normalized cDNA libraries were clustered using the MiSeq Reagent Kit v2 (500-cycles) and sequenced on an Illumina MiSeq platform in three sequencing paired-end runs (2×250 cycles). These sequence data have been submitted to the GenBank databases under accession number PRJNA547332

(www.ncbi.nlm.nih.gov/genbank).

Bioinformatic analysis

Analyses of the high-throughput RNA sequencing were performed at the

Bioinformatics Laboratory of LEEM/INPA. The quality of sequenced reads was checked

using the FastQC v.0.11.6 program (Andrews 2010). The low-quality reads (Q-score ≤ 20) were trimmed by removing the adaptor sequences, and filtering the reads with less than 50 base pairs (bp) were performed using the Trimmomatic v.0.36 program (Bolger et al. 2014).

Due to the absence of the complete genome for Colossoma macropomum species, we choose to use the de novo transcriptome assembly using the Trinity v.2.5.1 program (Grabherr et al.

2011). In addition, programs that assisted Trinity were used to assemble the transcriptome with the Bowtie2 v.2.3.3.1 (Langmead and Salzberg 2012), and calculate the abundance of transcripts using the RSEM v.1.3.0 program (Li and Dewey 2011) and R/Bioconductor packages v.3.3.2 (Bates et al. 2004), respectively.

Differential expression was quantified into up- and downregulated genes using the edgeR v.3.16.5 program (Robinson et al. 2009) of R/Bioconductor package. The assumed

False Discovery Rate (FDR) was ≤0.05 in order to correct P values, and the data generated by the RSEM were used to calculate the fold change values of ≥ 2. The differentially expressed genes (DEGs) were annotated with the BLASTx v.2.7.1+ program (Altschul et al. 1997), against the database of Uniprot/TrEMBL proteins (class Actinopterygii) and Swiss-Prot for non-redundant proteins, with e-value 1e-5. The Trinotate tool v.3.1.1

(https://trinotate.github.io/) was used to classify the DEGs according to the three general categories of Gene Ontology (GO) annotation: i) Biological Process (BP); ii) Cellular

Component (CC); and iii) Molecular Function (MF).

Further analysis on Network Analyst (https://www.networkanalyst.ca/) was performed to construct relevant biological networks based on Protein-Protein Interaction (PPI) starting from a list of DEGs, using their official names and fold change values. NetworkAnalyst also allows performing functional enrichment analysis of significantly expressed GO terms according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Xia et al.

2014).

Results

Six cDNA libraries were constructed from the liver of juvenile tambaquis raised on the

Balbina and Brumado fish farms. Three RNA-Seq runs performed on the Illumina MiSeq platform yielded 106,161,098 million (M) raw reads, with an average of 8,846,758 M reads per library. After quality trimming (Q-score < 20 and removal of reads of length < 50 bp),

100,945,530 M filtered reads were saved. About 95% of the total reads sequenced were assembled for de novo analysis and aligned; 166,819 contigs were assembled, and the average length was 912 bp, with an N50 value of 1,777 bp. The assembled bases totaled 152,281,627

M. Considering only those genes with a FDR < 0.05 and fold change > 2, a total of 2,410 genes showed significant differential expression between the two populations (Balbina versus

Brumado). Of these, 1,196 (49.6%) genes were found in the Balbina population, whereas

1,214 (50.4%) genes were differentially expressed in the Brumado population. The overview of the de novo transcriptome statistics for the two populations of Colossoma macropomum is described in Table 1.

Regarding the functional classification of the DEGs, only the upregulated genes were annotated through GO terms: BP – Biological Process, CC – Cell Component, and MF –

Molecular Function. In the population from the Balbina farm, 3,443 terms were successfully assigned into 703 GO subcategories: BP, 1,684; CC, 318 and MF, 1,441. For the population from the Brumado farm, 4,260 terms were categorized into 851 GO subcategories: BP, 1,854;

CC, 442 and MF, 1,964. GO representation of the top 30 upregulated terms identified in each population is shown in Figures 2 and 3, respectively. Forty-nine upregulated terms were shared in the two populations of tambaqui (Table 2). Overall, the genes commonly expressed between populations were related to several biosynthetic processes, homeostasis, biorhythm, immunity, cell signaling, ribosome biogenesis, metabolism of proteins, protein folding/modification, intracellular transport, structure/cytoskeleton and catalytic activity.

The two biological networks were constructed from the DE genes upregulated in the liver of both populations. A fully correlated seed node (or hubs) list is given in Tables S1 and

S2. Each generated PPI network was composed for a suitable number of nodes (proteins) and edges (interactions between nodes); the Balbina population’s PPI presented 752 nodes and

948 edges, whereas the one of Brumado population contained 671 nodes and 818 edges.

Enrichment analysis of the PPI network from each population showed a total of 36

KEGG pathways (Figure 4). Furthermore, enrichment categories based on GO terms for

Biological Process were identified in both populations, as listed in Table 3. Seventy-four seed nodes were highlighted in the protein interaction network of the Balbina population (Figure

5). Proteins biologically involved in the metabolism of carbohydrates and lipids, reproduction, protein folding, and transport were represented in enriched hubs. However, the PPI network containing 70 seeds from the Brumado population showed another metabolic profile, with hub genes encoding proteins that participate in cellular homeostasis, response to external stimulus

(oxygen radical, hypoxia and heat), RNA processing, signal transduction and protein import

(Figure 6). Taken together, four putative functional categories involved in local adaptation of tambaqui to their respective farming sites are related to: i) energy metabolism; ii) protein folding; iii) cellular homeostasis; and iv) circadian rhythm.

Discussion

In order to investigate the candidate genes potentially involved in the adaptation of fishes to new or constantly changing environments, the introduction of deep-sequencing technologies has provided a revolutionary tool for the precise measurement of transcript levels (Oomen and Hutchings 2017). In the present study, we employed an RNA sequencing approach to compare the transcriptomic profile of two populations of artificially farmed tambaqui from tropical and subtropical zones in Brazil. In total, 2,410 differentially expressed genes (1,196 in Balbina and 1,214 in Brumado) which are involved in a multitude of

biological functions may assign valuable information into the particular metabolic processes of each population related to regional adaptation.

It is well known that temperature drives a physical influence on the environmental adaptation of natural fish populations which live in distinct climate regions (Schulte 2001).

Based on an RNA-seq analysis, evidences for local adaptation were identified in three loaches from different climatic zones in China (Yi et al. 2016). In these species of Misgurnus, population-specific adaptations were linked to 59 candidate genes playing functions in energy metabolism, signal transduction, membrane, and cell proliferation or apoptosis. Also, for broodstocks reared in several farming systems, among them, the two herein analyzed, regional adaptation correlated with environmental variables were first report by Nunes (2017) when comparing the eight broodstocks of tambaqui from three different climatic regions in Brazil.

Eighteen candidate genes under positive selection were identified through genotyping-by- sequencing (GBS) and were related to the immune system, metabolism, biorhythm, and growth. According to the Nunes (2017), the climatic contrast of Brazilian region may impose selective forces on the locally adapted populations.

Herein, studying juveniles of the two mentioned fish facilities, the upregulation of a set of transcripts revealed the potential genes that are directly involved in the regional adaptation of each population to their living environment. After detailed functional annotation, many genes were assigned to several overlapping pathways (energy metabolism, protein folding, cellular homeostasis, and circadian rhythm), which somewhat corroborated the results of Nunes (2017).

According to Beitinger et al. (2000), temperature affects virtually all fish physiology.

Under thermal stress, metabolic adjustments, including lipid and carbohydrate catabolism, are modulated due to the higher metabolic demand (Wang et al. 2009). Compared to Brumado, at least 14 genes assigned to energy metabolism were enriched in the Balbinas’s biological

network (Figure 5). The overexpressed genes APOB and ACLY encode proteins that participate in the lipid metabolism, indicating this may be considered the preferential energy fuel under farming climate conditions in the northern region. Likewise, we found the FADS2

(or scd) upregulated gene only in this population, which assures the fluidity and flexibility of cellular membranes by increasing the level of unsaturated fatty acids (Ntambi and Miyazaki

2004). Remarkably, Oliveira (2014) reported that higher relative transcript levels from liver

SCD-1 of tambaqui juveniles from farm cages and streams are modulated according to the daily abiotic oscillations in their breeding environment.

Besides energy metabolism, cytoskeleton organization, growth and cell death, and molecular chaperones are the main pathways of generally detected proteins in cellular stress response (Wang et al. 2009). Differentially expressed proteins in the Brumado network were associated with some aspects of the responses to external stimulus (Figure 6). Particularly, heat- (ATXN3) and hypoxia-responsive genes (TXN2, ldha, BAD, EPAS1, Slc29a1, AGTRAP,

PTK2B, rest, and Adam8) were enriched in this population, suggesting that their breeding environment might periodically undergo oscillations in the abiotic parameters. Moreover, in order to maintain homeostasis under variable farming conditions, fish from Brumado expressed PDIA3, KIF5B, PLG, and PTH1R genes whose proteins are responsible for cellular homeostasis. In the Balbina population, protein folding was a biologically enriched category that might be related to protein homeostasis against environmental stress (Sherman and

Goldberg 2004). Induced expression of co-chaperones such as FKBP3, FKBP8, SLMAP,

PPIB, PDIA3, and GANAB genes play an essential role in assisting the proper folding of nascent or stress-damaged proteins (Lee et al., 2011; Wegele et al., 2001). According to

Tomalty et al. (2015), the upregulation of chaperones (HSP90 and HSP70) and associated co- chaperone genes (CDC37, AHSA1, FKBP4, CHORDC1, HSP5A,and STIP1) was strongly related to the management of denatured protein in thermally stressed juvenile Chinook salmon

(Oncorhynchus tshawytscha). Taken together, those enriched functional categories in each population represent a relevant picture of the phenotypic plasticity that ensures the maintenance of the homeostatic state when facing the abiotic variables of their farming sites.

Biological clocks play a crucial role in controlling the many functions of organisms, ranging from subcellular processes to behaviour. The basic feature of circadian rhythm involves transcriptional feedback loop regulation being strongly associated with environmental conditions (Prokkola and Nikinmaa 2018). Both populations of tambaqui differentially expressed genes encoding proteins involved in the positive and negative feedback loops: PER1 in Balbina population, and CRY1, ARNTL, ATXN3 and FBXL3 in

Brumado (Figure 6). According to Mohawk et al. (2012), the expression of PER and CRY transcripts drives the generating of the circadian rhythm by repressing the activity of CLOCK-

ARNTL transcription factors. Notably, the upregulation of other clock-controlling genes in

Brumado suggests that the seasonal changes in photoperiod in the subtropical region govern the plasticity of the rhythmicity of this population. Indeed, differential expression of circadian clock genes in response to hypoxia and temperature were observed in a cold-adapted salmonid

Arctic char (Salvelinus alpinus) providing new insights into rhythmic regulation in fish

(Prokkola et al. 2018).

Thus, the suite of genes that were differentially expressed revealed the signatures of local thermal adaptation of each fish population to their environments. For the aquaculture production, the identified candidate genes can be further applied in improvement programs for the creation of more heat-tolerant tambaqui fish in the face of forecasted global climate changes.

Acknowledgements

This research was supported by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) through Pro-Amazon Project #047/2012, CNPq (Conselho Nacional de

Desenvolvimento Científico e Tecnológico) through INCT-ADAPTA II Project

#465540/2014-7 and Universal Calls #424468/2016-6, and with funding from FAPEAM

(Fundação de Amparo à Pesquisa do Estado do Amazonas) through INCT-ADAPTA II

Project #0621187/2017.CAPES also funded a Ph.D. scholarship to L.M.F.G. C.H.A.S and

V.M.F.A.V. are the recipients of research fellowships from CNPq. Special thanks go to

Adalberto Luis Val, Alzira Miranda de Oliveira, Maria de Nazaré Paula-Silva and Fernanda

Garcia Dragan for their excellent logistical and technical support.

Conflict of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicial to the impartiality of the reported research.

Authors contributions

LMFG and VMFAV conceived and designed the experiments. LMFG conducted the experiments, collected the samples and performed the molecular protocols. LMFG and JDAA analyzed the data. LMFG and CHAA wrote the paper. All authors read, revised and approved the final version.

References

Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W and Lipman DJ (1997)

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Nucleic Acids Res 25:3389–3402. doi: 10.1093/nar/25.17.3389

Alvares CA, Stape JL, Sentelhas PC, Gonçalves JLM and Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorol Zeitschrift 22:711–728. doi: 10.1127/0941-

2948/2013/0507

Andrews S (2010) FastQC - A quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/Projects/Fastqc/. Accessed 16 Feb 2019

Araújo-Lima C and Goulding M (1998) Os frutos do tambaqui: ecologia, conservação e

cultivo na Amazônia. Sociedade Civil Mamirauá/CNPq, Tefé, AM

Bates DM, Ellis B, Smith C, Irizarry R, Tierney L, Huber W, Leisch F, Iacus S, Maechler M,

Hornik K et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:1–16. doi: 10.1186/gb-2004-5-10-r80

Beitinger TL, Bennett WA and Mccauley RW (2000) Temperature tolerances of North

American freshwater fishes exposed to dynamic changes in temperature. Environ Biol Fishes

58:237–275. doi: 10.1023/A:1007676325825

Bellard C, Bertelsmeier C, Leadley P, Thuiller W and Courchamp F (2014) Impacts of climate change on the future of biodiversity. Ecol Lett 15:365–377. doi: 10.1111/j.1461-

0248.2011.01736.x.

Bolger AM, Lohse M and Usadel B (2014) Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. doi: 10.1093/bioinformatics/btu170

Campos DF, Braz-Mota S, Val AL and Almeida-Val VMF (2019) Predicting thermal sensitivity of three Amazon fishes exposed to climate change scenarios. Ecol Indic 101:533–

540. doi: 10.1016/j.ecolind.2019.01.051

Climatempo (2018) Climatempo - O melhor site de meteorologia do Brasil. https://www.climatempo.com.br/. Accessed 10 Jan 2019

CONCEA (2013) Diretrizes da prática de eutanásia do CONCEA. Brasília, BR

CPTEC/INPE (2018) Centro de Previsão de Tempo e Estudos Climáticos - CPTEC/INPE. https://www.cptec.inpe.br/. Accessed 10 Jan 2019

Dragan FG (2019) Influência das mudanças climáticas sobre as respostas gênicas adaptativas do tambaqui (Colossoma macropomum, Cuvier 1818) aclimatizado em regiões climáticas distintas. Universidade Nilton Lins

Fé-Gonçalves LM, Paula-Silva M de N, Val AL and Almeida-Val VMF (2018) Differential survivorship of congeneric ornamental fishes under forecasted climate changes are related to

anaerobic potential. Genet Mol Biol 41:107–118. doi: 10.1590/1678-4685-gmb-2017-0016

Fisch G, Marengo JA and Nobre CA (1998) The climate of Amazonia - a review. Acta Amaz

28:101–126.

Gonçalves RA, Santos CHA, Sá-Leitão CS, Souza ÉMS and Almeida-Val VMF (2018)

Genetic basis of Colossoma macropomum broodstock: Perspectives for an improvement program. J World Aquac Soc 1–12. doi: https://doi.org/10.1111/jwas.12564

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L,

Raychowdhury R, Zeng Q et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29:644–652. doi: 10.1038/nbt.1883

IBGE (2016) Produção da Pecuária Municipal. Rio de Janeiro, BR

Langmead B and Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat

Methods 9:357–359. doi: 10.1038/nmeth.1923

Lee YS, Smith RS, Jordan W, King BL, Won J, Valpuesta JM, Naggert JK, Nishina PM

(2011) Prefoldin 5 is required for normal sensory and neuronal development in a murine model. J Biol Chem 286:726–736. https://doi.org/10.1074/jbc.M110.177352

Lemgruber RS., Marshall NAA, Ghelfi A, Fagundes DB and Val AL (2013) Functional categorization of transcriptome in the species Symphysodon aequifasciatus Pellegrin 1904

(Perciformes: Cichlidae) exposed to benzo[a]pyrene and phenanthrene. PLoS One 8:e81083. doi: 10.1371/journal.pone.0081083

Li B and Dewey CN (2011) RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12:41–74. doi: 10.1201/b16589

Logan CA and Buckley BA (2015) Transcriptomic responses to environmental temperature in eurythermal and stenothermal fishes. J Exp Biol 218:1915–1924. doi: 10.1242/jeb.114397

Mirande JM (2010) Phylogeny of the family Characidae (Teleostei: Characiformes): from characters to taxonomy. Neotrop Ichthyol 8:385–568. doi: 10.1590/S1679-

62252010000300001

Mohawk J, Green C and Takahashi J (2012) Central and peripheral circadian clocks in mammals. Annu Rev Neurosci 35:445–462. doi: 10.1146/annurev-neuro-060909-153128

Morais IDS and O´Sullivan FLA (2017) Biologia, habitat e cultivo do tambaqui Colossoma macropomum (CUVIER, 1816). Sci Amaz 1:81–93.

Moro GV, Rezende F, Alves AL, Hashimoto DT, Varela ES and Torati LS (2013) Espécies de peixe para piscicultura. In: Rodrigues APO, Lima AF, Alves AL, Rosa DK, Torati LS and

Santos VRM (eds) Piscicultura de água doce: Multiplicando conhecimento. EMBRAPA,

Brasília, BR, pp 29–70

Ntambi J and Miyazaki M (2004) Regulation of stearoyl-CoA desaturases and role in metabolism. Prog Lipid Res 43:91–104. doi: 10.1016/S0163-7827(03)00039-0

Nunes JRS (2017) SNP discovery, high-density genetic map construction, and identification of genes associated with climate adaptation, and lack of intermuscular bone in tambaqui

(Colossoma macropomum). University of São Paulo

Nunes JRS, Liu S, Pértille F, Perazza CA, Villela PMS, Almeida-Val VMF, Hilsdorf AWS,

Liu Z and Coutinho LL (2017) Large-scale SNP discovery and construction of a high-density genetic map of Colossoma macropomum through genotyping-by-sequencing. Sci Rep 7:1–11. doi: 10.1038/srep46112

Oliveira AM (2014) Influência da temperatura ambiental e dos cenários climáticos futuros sobre os ácidos graxos ω-3 e ω-6 e desempenho zootécnico do tambaqui (Colossoma macropomum). Instituto Nacional de Pesquisa da Amazônia (INPA)

Oomen RA and Hutchings JA (2017) Transcriptomic responses to environmental change in fishes: Insights from RNA sequencing. Facets 2:610–641. doi: 10.1139/facets-2017-0015

Ostrensky A, Borguetti JR and Soto D (2008) Aquicultura no Brasil. O desafio é crescer.

Secretaria Especial de Aquicultura e Pescas, Distrito Federal, BR

Pörtner HO and Peck MA (2010) Climate change effects on fishes and fisheries: Towards a cause-and-effect understanding. J Fish Biol 77:1745–1779. doi: 10.1111/j.1095-

8649.2010.02783.x

Prokkola JM and Nikinmaa M (2018) Circadian rhythms and environmental disturbances – underexplored interactions. J Exp Biol 221:jeb179267. doi: 10.1242/jeb.179267

Prokkola JM, Nikinmaa M, Lewis M, Anttila K, Kanerva M, Ikkala K, Seppänen E, Kolari I and Leder EH (2018) Cold temperature represses daily rhythms in the liver transcriptome of a stenothermal teleost under decreasing day length. J Exp Biol jeb.170670. doi:

10.1242/jeb.170670

Robinson MD, McCarthy DJ and Smyth GK (2009) edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. doi: 10.1093/bioinformatics/btp616

Saint-Paul U (1986) Potential for aqualculture of South American freshwater fishes: A review. Aquaculture 54:205–240.

Santos CHA, Santana GX, Sá-Leitão CS, Paula-Silva MN and Almeida-Val VMF (2016)

Loss of genetic diversity in farmed populations of Colossoma macropomum estimated by microsatellites. Anim Genet 47:373–376. doi: 10.1111/age.12422

Schulte PM (2001) Environmental adaptations as windows on molecular evolution. Comp

Biochem Physiol - B Biochem Mol Biol 128:597–611. doi: 10.1016/S1096-4959(00)00357-2

Sherman MY and Goldberg AL (2004) Cellular defenses against unfolded proteins. Neuron

29:15–32. doi: 10.1016/s0896-6273(01)00177-5

Tomalty KMH, Meek MH, Stephens MR, Rincón G, Fangue NA, May BP and Baerwald MR

(2015) Transcriptional response to acute thermal exposure in juvenile Chinook salmon determined by RNAseq. Genes, Genomes, Genet 5:1335–1349. doi: 10.1534/g3.115.017699

Wang P, Bouwman FG and Mariman ECM (2009) Generally detected proteins in comparative

proteomics - A matter of cellular stress response? Proteomics 9:2955–2966. doi:

10.1002/pmic.200800826

Wegele H, Müller L, Buchner J (2001) Hsp70 and Hsp90 - a relay team for protein folding.

In: Portland, SGA, Frankfurt EB, Innsbruck HG, Gottingen RJ, Baltimore WJL, Tokyo AM,

Zurich HM, Heidelberg OS, Berlin GS, Berlin M.S. (eds) Reviews of Physiology,

Bochemistry and Pharmacology. Springer, Berlin, Heidelberg, pp 1–44.

Xia J, Benner MJ and Hancock REW (2014) NetworkAnalyst - Integrative approaches for protein-protein interaction network analysis and visual exploration. Nucleic Acids Res

42:167–174. doi: 10.1093/nar/gku443

Yi S, Wang S, Zhong J and Wang W (2016) Comprehensive transcriptome analysis provides evidence of local thermal adaptation in three loaches (Genus: Misgurnus). Int J Mol Sci 17:1–

13. doi: 10.3390/ijms17121943

Tables

Table 1. Summary of the Illumina sequencing statistics.

Balbina Brumado Raw reads 57,361,634 48,799,464 Min. raw reads 8,873,256 7,426,829 Max. raw reads 9,963,942 8,570,267 Average raw reads 9,560,272 8,133,244 Trimmed reads 54,363,724 46,581,806 Min. trimmed reads 8,295,129 6,990,030 Max. trimmed reads 9,465,852 8,173,557 Average trimmed reads 9,060,621 7,763,634 DE genes upregulated 622 616 Upregulated genes annotated by BLASTx 413 468 Upregulated terms assigned GO terms 3,443 4,260 DE genes downregulated 574 598 Downregulated genes annotated by BLASTx 426 389 Downregulated terms assigned GO terms 4,734 3,821

Table 2. Common terms identified between populations of tambaqui sourced from the Balbina and Brumado fish farms.

Contig ID LogFC Gene symbol Gene product GO annotation GO:0006629 lipid metabolic Isopentenyl-diphosphate process DN15136_c0_g1_i1 12.54 IDI1 delta isomerase 1 GO:0016787 hydrolase activity GO:0006355 regulation of Cyclic AMP-responsive transcription, DNA-templated DN16258_c2_g1_i3 10.71 creb3l3b element-binding protein GO:0001071 nucleic acid 3-like protein 3-B binding transcription factor activity AP-1 complex subunit GO:0016192 vesicle-mediated DN15150_c1_g1_i17 9.98 AP1G1 gamma-1 transport GO:0022857 transmembrane DN13446_c7_g1_i1 9.94 CNNM3 Metal transporter CNNM3 transporter activity Sarcolemmal membrane- GO:0008104 protein DN15804_c1_g1_i17 9.65 SLMAP associated protein localization GO:0042981 regulation of Apoptotic chromatin apoptotic process DN13528_c0_g1_i2 9.11 ACIN1 condensation inducer 1 GO:0003676 nucleic acid binding GO:0006355 regulation of DN13489_c2_g4_i1 8.31 Pphln1 Periphilin-1 transcription, DNA-templated GO:0016192 vesicle-mediated DN14576_c3_g2_i5 8.30 Golt1a Golgi transport 1A transport Nuclear FMR1-interacting DN15916_c5_g1_i3 8.28 NUFIP2 GO:0003723 RNA binding protein 2 GO:0009966 regulation of Hedgehog-interacting DN13598_c6_g1_i5 8.18 HHIP signal transduction protein GO:0008270 zinc ion binding GO:1901681 sulfur compound DN15473_c4_g1_i8 8.09 ADAMTSL5 ADAMTS-like protein 5 binding GO:0006629 lipid metabolic Enoyl-CoA delta process DN13646_c3_g1_i1 8.02 ECI2 isomerase 2 GO:0000062 fatty-acyl-CoA binding GO:0070646 protein modification by small protein E3 ubiquitin-protein DN13372_c3_g2_i5 7.92 maea removal transferase MAEA GO:0004842 ubiquitin-protein transferase activity GO:0006412 translation DN12837_c0_g1_i1 7.77 MRPL9 39S ribosomal protein L9 GO:0016072 rRNA metabolic process GO:0050727 regulation of Vitamin K-dependent inflammatory response DN16263_c2_g1_i3 7.64 PROC protein C GO:0004252 serine-type endopeptidase activity GO:0038023 signaling DN15506_c4_g2_i1 7.55 Cd7 T-cell antigen CD7 receptor activity

GO:0016021 integral component of membrane Insulin-like growth factor GO:0006810 transport DN14966_c2_g1_i12 7.53 Igf2bp2 2 mRNA-binding GO:0003723 RNA binding protein 2 KN motif and ankyrin GO:0006355 regulation of DN15289_c7_g1_i12 7.46 KANK1 repeat domain- transcription, DNA-templated containing protein 1 GO:0005515 protein binding GO:0006954 inflammatory response DN15264_c5_g2_i9 7.43 LY75 Lymphocyte antigen 75 GO:0004888 transmembrane signaling receptor activity GO:0070646 protein Leucine-rich repeat- DN15198_c0_g1_i4 7.38 LRRC41 modification by small protein containing protein 41 removal GO:0006629 lipid metabolic process DN14189_c1_g1_i5 7.26 CYP2J2 Cytochrome P450 2J2 GO:0016705 cytochrome p450 activity GO:0006457 protein folding Protein disulfide- DN14494_c1_g2_i8 7.13 PDIA3 GO:0016853 isomerase isomerase activity GO:0048511 rhythmic Thyroid hormone process DN15995_c4_g1_i11 7.10 THRAP3 receptor-associated GO:0003713 transcription protein 3 coactivator activity NLR family CARD GO:0035556 intracellular DN14712_c2_g1_i1 7.08 NLRC3 domain-containing signal transduction protein 3 GO:0005524 ATP binding Putative oxidoreductase GO:0016491 oxidoreductase DN15785_c1_g2_i10 6.99 glyr1 GLYR1 activity GO:0007009 plasma Metastasis suppressor DN15537_c3_g1_i2 6.92 MTSS1 membrane organization protein 1 GO:0003779 actin binding DN16282_c0_g2_i1 6.78 cgn Cingulin GO:0003774 motor activity Inositol monophosphatase GO:0016791 phosphatase DN14613_c2_g2_i4 6.62 impad1 3 activity SH3 domain-containing GO:0007010 cytoskeleton DN15794_c0_g1_i5 6.61 Sh3d19 protein 19 organization GO:0015858 nucleoside transport Solute carrier family 29 DN16271_c6_g1_i11 6.18 slc29a1 GO:0005337 nucleoside member 1a transmembrane transporter activity GO:0009058 biosynthetic Adipocyte plasma process DN13894_c1_g1_i10 5.48 apmap membrane-associated GO:0016844 strictosidine protein synthase activity GO:0030258 lipid modification DN13396_c1_g2_i5 5.44 cyp2k1 Cytochrome P450 2K1 GO:0016705 cytochrome p450 activity

GO:0035556 intracellular DN14963_c0_g1_i20 5.35 Nlrc3 Protein NLRC3 signal transduction GO:0005524 ATP binding Succinate-- GO:0016782 transferase DN15981_c2_g5_i2 5.08 SUGCT hydroxymethylglutarate activity, transferring sulfur- CoA-transferase containing groups GO:0006952 defense response DN16498_c5_g1_i9 4.97 l-2 Lactose-binding lectin l-2 GO:0030246 carbohydrate binding GO:0007155 cell adhesion DN13568_c0_g2_i7 4.91 AFDN Afadin GO:0050839 cell adhesion molecule binding GO:0042632 cholesterol homeostasis DN16570_c8_g1_i10 4.81 Cyp27a1 Sterol 26-hydroxylase GO:0004497 monooxygenase activity GO:0006629 lipid metabolic process Carnitine O- DN14390_c2_g4_i1 4.77 CPT1A GO:0016746 transferase palmitoyltransferase 1 activity, transferring acyl groups GO:0006955 immune response DN16417_c2_g10_i1 4.77 CXCL8 Interleukin-8 GO:0008009 chemokine activity GO:0016570 histone modification DN13592_c4_g1_i17 4.59 riox1 Ribosomal oxygenase 1 GO:0051213 dioxygenase activity GO:0007160 cell-matrix adhesion DN13897_c0_g1_i12 4.21 epd Ependymin GO:0005509 calcium ion binding GO:0050776 regulation of Erythroid membrane- immune response DN16042_c2_g3_i3 4.00 Ermap associated protein GO:0005102 signaling receptor binding DnaJ homolog subfamily DN15608_c1_g1_i4 3.82 DNAJC13 GO:0015031 protein transport C member 13 GO:0009229 thiamine diphosphate biosynthetic Thiamin DN13357_c1_g1_i2 3.53 Tpk1 process pyrophosphokinase 1 GO:0004788 thiamine diphosphokinase activity GO:0005975 carbohydrate DN15595_c0_g1_i1 3.41 PC Pyruvate carboxylase metabolic process GO:0016874 ligase activity GO:0005975 carbohydrate Aldo-keto reductase metabolic process DN13635_c1_g1_i7 3.35 AKR1B1 family 1 member B1 GO:0016491 oxidoreductase activity DN16392_c0_g3_i1 3.28 PDLIM2 PDZ and LIM domain GO:0005856 cytoskeleton

protein 2 GO:0003779 actin binding GO:0006397 mRNA Serine/arginine-rich DN15624_c0_g1_i16 3.13 Srsf5 processing splicing factor 5 GO:0003723 RNA binding GO:0006364 rRNA Ribosome biogenesis processing DN13615_c0_g9_i2 3.06 Nop53 protein NOP53 GO:0042802 identical protein binding

Table 3. List of enriched biological processes represented in the protein-protein interactions (PPI) networks of both the Balbina and Brumado populations.

BP Pathways at Balbina # Proteins BP Pathways at Brumado # Proteins Regulation of biological Lipid metabolic process 19 28 quality Organic acid metabolic process 16 Response to stress 27 Carboxylic acid metabolic Regulation of response to 15 24 process stimulus Cellular lipid metabolic process 14 Programmed cell death 22 Carbohydrate metabolic process 13 Apoptotic process 21 Generation of precursor Regulation of multicellular 13 20 metabolites and energy organismal process Lipid biosynthetic process 12 Immune system process 20 Regulation of signal Nucleotide metabolic process 12 19 transduction Response to endogenous 12 Cellular localization 19 stimulus Energy derivation by oxidation 11 Catabolic process 18 of organic compounds Response to hormone stimulus 10 Cellular catabolic process 17 Establishment of localization Coenzyme metabolic process 9 17 in cell Regulation of apoptotic Cofactor metabolic process 9 16 process Purine nucleotide metabolic Regulation of programmed cell 9 16 process death Alcohol metabolic process 8 Cellular component assembly 14 Intracellular protein transport 8 Intracellular transport 13 Peptidyl_amino acid 8 Response to external stimulus 13 modification Purine ribonucleotide metabolic 8 Cellular response to stress 13 process Ribonucleotide metabolic 8 Tissue development 13 process Cellular amino acid metabolic 7 Homeostatic process 12 process Cellular respiration 7 Cell migration 11 Monocarboxylic acid metabolic Enzyme linked receptor 7 10 process protein signaling pathway Regulation of immune system Regulation of body fluid levels 7 10 process Carbohydrate metabolic Steroid metabolic process 7 10 process

Coenzyme biosynthetic process 6 Hemopoiesis 9 Hematopoietic or lymphoid Cofactor biosynthetic process 6 9 organ development Leukocyte migration 6 Immune system development 9 Purine ribonucleotide Protein folding 6 9 metabolic process Ribonucleotide metabolic Steroid biosynthetic process 6 9 process Purine nucleotide metabolic Aging 5 9 process Glucose metabolic process 5 Cellular homeostasis 9 Negative regulation of 5 Nucleotide metabolic process 9 phosphate metabolic process Negative regulation of Nucleotide biosynthetic process 5 multicellular organismal 8 process Protein oligomerization 5 Regulation of cell migration 8 Carbohydrate biosynthetic Regulation of catabolic 4 8 process process Cellular modified amino acid 4 Regulation of body fluid levels 8 metabolic process Energy reserve metabolic Positive regulation of immune 4 8 process system process Isoprenoid metabolic process 4 Cell_substrate adhesion 7 Regulation of lipid metabolic 4 Regulation of cell adhesion 7 process Response to steroid hormone Regulation of small GTPase 4 7 stimulus mediated signal transduction Regulation of response to Triglyceride metabolic process 4 7 external stimulus Glutamine family amino acid 3 Blood coagulation 7 metabolic process Leukocyte chemotaxis 3 Coagulation 7 Protein N_linked glycosylation 3 Hemostasis 7 Protein targeting to membrane 3 Behavior 7 Response to carbohydrate 3 Vasculature development 7 stimulus Response to toxin 3 Wound healing 7 Regulation of anatomical Aerobic respiration 2 7 structure morphogenesis Cellular modified amino acid 2 Tissue remodeling 6 biosynthetic process Regulation of Rho protein Excretion 2 6 signal transduction

Positive regulation of cell 6 migration Intracellular receptor mediated 6 signaling pathway Regulation of Ras protein 6 signal transduction Cellular component 6 disassembly RNA splicing 6 Regulation of cell 6 morphogenesis Response to drug 6 Muscle cell differentiation 6 Nucleocytoplasmic transport 6 Nuclear transport 6 Leukocyte differentiation 6 RNA splicing 6 Positive regulation of 6 hydrolase activity Actin cytoskeleton 6 organization MRNA processing 6 Positive regulation of cellular 6 component organization Actin filament_based process 6 Positive regulation of cell 5 adhesion Rhythmic process 5 Response to hypoxia 5 Myeloid cell differentiation 5 Leukocyte migration 5 Cellular protein complex 5 assembly Circadian rhythm 4 Intracellular steroid hormone 4 receptor signaling pathway Rho protein signal 4 transduction Intrinsic apoptotic signaling 4 pathway Response to carbohydrate 4 stimulus Protein maturation 4 Maintenance of location 4

Post_translational protein 4 modification Transforming growth factor beta receptor signaling 4 pathway Protein import into nucleus 4 Nuclear import 4 Protein folding 4 Extracellular structure 4 organization Regulation of GTPase activity 4 Lymphocyte differentiation 4 Apoptotic signaling pathway 4 Protein import 4 Regulation of MAP kinase 4 activity Ras protein signal transduction 4 Myoblast differentiation 3 Androgen receptor signaling 3 pathway Regulation of JUN kinase 3 activity B cell differentiation 3 Regulation of Rho GTPase 3 activity Maintenance of protein 3 location in cell Regulation of cell shape 3 Regulation of transforming growth factor beta receptor 3 signaling pathway Maintenance of location in cell 3 Maintenance of protein 3 location Cell maturation 3 Protein N_linked glycosylation 3 Protein processing 3 Leukocyte chemotaxis 3 Regulation of JNK cascade 3 Cyclic nucleotide metabolic 3 process Epidermal growth factor 3 receptor signaling pathway Protein polymerization 3

Regulation of Ras GTPase 3 activity Developmental maturation 3 Focal adhesion assembly 2 Bone remodeling 2 Positive regulation of JUN 2 kinase activity Vacuole organization 2 Regulation of cell_cell 2 adhesion Cytoplasm organization 1

Figures

Figure 1. Map of the sampling sites of two tambaqui populations from different regions of Brazil. The northern (Balbina Experimental Station, Balbina, Amazonas state – 1°55'54.4"S; 59°24'39.1"W) and southeastern (Brumado Fish Farming, Mogi Mirim, São Paulo state – 22°31'16.00"S; 46°53'5.71"W) populations are raised in regions that display climate variability typically found in Brazil, according to Köppen’s climate classification (Alvares et al. 2013).

Number of contigs

0 10 20 30 40 50 60 70 80 90 100 110

GO:0008152 Metabolic process 2.8%

GO:0044699 Single-organism process 1.9%

GO:0071704 Organic substance metabolic process 1.5% BiologicalProcess GO:0044710 Single-organism metabolic process 1.5%

GO:0044238 Primary metabolic process 1.5%

GO:0009987 Cellular process 1.4%

GO:0044237 Cellular metabolic process 1.1%

GO:0006807 Nitrogen compound metabolic process 1.0%

GO:1901564 Organonitrogen compound metabolism 1.5%

GO:0055114 Oxidation-reduction process 0.8%

GO:0044464 Cell part 0.8%

GO:0044424 Intracellular part 0.7%

GO:0044425 Membrane part 0.7% CellularComponent

GO:0016021 Integral component of membrane 0.5%

GO:0031224 Intrinsic component of membrane 0.5%

GO:0032991 Macromolecular complex 0.5%

GO:0044444 Cytoplasmic part 0.4%

GO:0016020 Membrane 0.3%

GO:0043229 Intracellular organelle 0.3%

GO:0043226 Organelle 0.3%

GO:0005488 Binding 3.1%

GO:0003824 Catalytic activity 2.9%

GO:0043167 Ion binding 1.6% MolecularFunction GO:1901363 Heterocyclic compound binding 1.5%

GO:0097159 Organic cyclic compound binding 1.5%

GO:0005515 Protein binding 1.0%

GO:0016491 Oxidoreductase activity 1.0%

GO:0016787 Hydrolase activity 0.9%

GO:0036094 Small molecule binding 0.9%

GO:0043168 Anion binding 0.9%

Figure 2. The top 30 terms classification of the contigs significantly upregulated in Balbina population and separated into three functional Gene Ontology (GO) categories: Biological Process (green bars), Cell Component (gray bars) and Molecular Function (blue bars). The percentages indicate the representation of genes that belong to each category.

Number of contigs

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160

GO:0008152 Metabolic process 2.3%

GO:0009987 Cellular process 1.4%

GO:0044699 Single-organism process 1.4% BiologicalProcess GO:0071704 Organic substance metabolic process 1.2%

GO:0044238 Primary metabolic process 1.2%

GO:0044710 Single-organism metabolic process 1.0%

GO:0006807 Nitrogen compound metabolic process 0.9%

GO:0065007 Biological regulation 0.9%

GO:0043170 Macromolecule metabolic process 0.9%

GO:0050789 Regulation of biological process 0.9%

GO:0044464 Cell part 0.7%

GO:0044424 Intracellular part 0.7%

GO:0044425 Membrane part 0.5% CellularComponent

GO:0016021 Integral component of membrane 0.5%

GO:0031224 Intrinsic component of membrane 0.5%

GO:0016020 Membrane 0.4%

GO:0043226 Organelle 0.4%

GO:0043229 Intracellular organelle 0.4%

GO:0043227 Membrane-bounded organelle 0.3%

GO:0044444 Cytoplasmic part 0.3%

GO:0005488 Binding 3.5%

GO:0003824 Catalytic activity 2.6%

GO:0043167 Ion binding 1.8% MolecularFunction

GO:1901363 Heterocyclic compound binding 1.8%

GO:0097159 Organic cyclic compound binding 1.8%

GO:0005515 Protein binding 1.5%

GO:0043169 Cation binding 1.0%

GO:0046872 Metal ion binding 1.1%

GO:0016491 Oxidoreductase activity 0.9%

GO:0016787 Hydrolase activity 0.8%

Figure 3. The top 30 terms classification of the contigs significantly upregulated in Brumado population and separated into three functional Gene Ontology (GO) categories: Biological Process (green bars), Cell Component (gray bars) and Molecular Function (blue bars). The percentages indicate the representation of genes that belong to each category.

Figure 4. Functional representation based on KEGG pathways for differentially expressed gene-sets in the Balbina (upper) and Brumado (lower) populations.

Figure 5. Enriched hubs highlighting the main biological processes in the protein interaction network of the Balbina population. Hubs with different colors represent prior pathways; orange – energy metabolism, dark blue – lipid metabolism, lemon green – reproductive process, light blue – RNA metabolic process, pink – protein folding, and red – intracellular protein transport. Smaller grey hubs reflect interacting non-differentially expressed genes.

Figure 6. Enriched hubs highlighting the main biological processes in the protein interaction network of the Brumado population. Hubs with different colors represent prior pathways; orange – cellular response to stress, dark blue – circadian rhythm, lemon green – cellular homeostasis, light blue – mRNA processing, pink – cell signaling, and red – intracellular transport. Smaller grey hubs reflect interacting non-differentially expressed genes.

Supplementary material Table S1. List of prior hubs that formed the biological network of the Balbina population.

Gene logFC Protein PPIB 10.91 Peptidyl-prolyl cis-trans isomerase B KCNMA1 10.81 Calcium-activated potassium channel subunit alpha-1 PDIA3 10.71 Protein disulfide-isomerase A3 SMARCC1 10.21 SWI/SNF complex subunit SMARCC1 PNP 10.21 Purine nucleoside phosphorylase MRPL9 9.98 39S ribosomal protein L9, mitochondrial ALDH16A1 9.90 Aldehyde dehydrogenase family 16 member A1 ABCC4 9.79 Multidrug resistance-associated protein 4 GANAB 9.59 Neutral alpha-glucosidase AB CTNNA1 9.53 Catenin alpha-1 CNNM3 9.50 Metal transporter CNNM3 AP1G1 8.97 AP-1 complex subunit gamma-1 Serine/threonine-protein phosphatase 2A regulatory subunit B'' PPP2R3B 8.95 subunit beta TTC31 8.85 Tetratricopeptide repeat protein 31 GOT1 8.83 Aspartate aminotransferase, cytoplasmic HMGCR 8.65 3-hydroxy-3-methylglutaryl-coenzyme A reductase ARF6 8.64 ADP-ribosylation factor 6 PDLIM2 8.59 PDZ and LIM domain protein 2 PIKFYVE 8.59 1-phosphatidylinositol 3-phosphate 5-kinase SLMAP 8.73 Sarcolemmal membrane-associated protein RIOK3 8.41 Serine/threonine-protein kinase RIO3 TFPI 8.40 Tissue fator pathway inhibitor EDNRB 8.27 Endothelin receptor type B APOB 8.25 Apolipoprotein B-100 IQGAP1 8.21 Ras GTPase-activating-like protein IQGAP1 ADSS 8.19 Adenylosuccinate synthetase isozyme 2 MTSS1 8.09 Metastasis supressor protein 1 ACIN1 8.09 Apoptotic chromatin condensation inducer in the nucleus FKBP8 8.06 Peptidyl-prolyl cis-trans isomerase FKBP8 NOP2 7.71 Probable 28S rRNA (cytosine(4447)-C(5))-methyltransferase RPL29 7.66 60S ribosomal protein L29 IDH3G 7.64 Isocitrate dehydrogenase [NAD] subunit gamma, mitochondrial TOM1 7.58 Target of Myb protein 1 THRAP3 7.53 Thyroid hormone receptor-associated protein 3 SQLE 7.41 Squalene monooxygenase MRPL19 7.19 39S ribosomal protein L19, mitochondrial RPL3 7.04 60S ribosomal protein L3 STRADA 6.88 STE20-related kinase adapter protein alpha

CCL2 6.66 C-C motif chemokine 2 ME1 6.47 NADP-dependent malic enzyme FDPS 6.36 Farnesyl pyrophosphate synthase CCL13 6.14 C-C motif chemokine 13 RBM39 6.03 RNA-binding protein 39 PTPN6 5.37 Tyrosine-protein phosphatase non-receptor type 6 IDH1 5.18 Isocitrate dehydrogenase [NADP] cytoplasmic FASN 5.08 Fatty acid synthase SLC7A11 4.99 Cystine/glutamate transporter NLRP12 4.90 NACHT, LRR and PYD domains-containing protein 12 ALDH18A1 4.84 Delta-1-pyrroline-5-carboxylate synthase CPT1A 4.69 Carnitine O-palmitoyltransferase 1, liver isoform AKR1B1 4.55 Aldose reductase ACLY 4.49 ATP-citrate synthase ASNS 4.43 Asparagine synthetase [glutamine-hydrolyzing] FDFT1 4.00 Squalene synthase PER1 3.95 Period circadian protein homolog 1 PC 3.70 Pyruvate carboxylase, mitochondrial RDH12 3.66 Retinol dehydrogenase 12 FADS2 3.49 Fatty acid desaturase 2 PISD 3.42 Phosphatidyl serine decarboxylase proenzyme, mitochondrial TKTL2 3.36 Transketolase-like protein 2 MAT2A 3.32 S-adenosylmethionine synthase isoform type-2 TKT 3.22 Transketolase Dolichyl-diphosphooligosaccharide--protein glycosyltransferase RPN2 2.94 subunit 2 ACSS2 2.86 Acetyl-coenzyme A synthetase, cytoplasmic NDUFB6 2.80 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 6 NME4 2.71 Nucleoside diphosphate kinase, mitochondrial FKBP3 2.68 Peptidyl-prolyl cis-trans isomerase FKBP3 COX6B1 2.60 Cytochrome c oxidase subunit 6B1 HINT1 2.43 Histidine triad nucleotide-binding protein 1 ACP1 2.39 Low molecular weight phosphotyrosine protein phosphatase TAF5 2.33 Transcription initiation factor TFIID subunit 5 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 8, NDUFB8 2.24 mitochondrial NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, NDUFS2 2.22 mitochondrial ENO1 2.17 Alpha-enolase

Table S2. List of prior hubs that formed the biological network of the Brumado population.

Gene logFC Protein SGPL1 10.66 Sphingosine-1-phosphate lyase 1 CNNM3 9.94 Metal transporter CNNM3 MTMR4 9.54 Myotubularin-related protein 4 SART1 9.47 U4/U6.U5 tri-snRNP-associated protein 1 LMTK2 9.42 Serine/threonine-protein kinase LMTK2 DDX5 9.22 Probable ATP-dependent RNA helicase DDX5 ARHGAP5 9.17 Rho GTPase-activating protein 5 AP1G1 9.12 AP-1 complex subunit gamma-1 ACIN1 9.11 Apoptotic chromatin condensation inducer in the nucleus ZC3H11A 9.05 Zinc finger CCCH domain-containing protein 11A CANX 9.04 Calnexin EHMT1 8.74 Histone-lysine N-methyltransferase EHMT1 FGG 8.67 Fibrinogen gamma chain GATAD2A 8.62 Transcriptional repressor p66-alpha TTC31 8.39 Tetratricopeptide repeat protein 31 KIF5B 8.31 Kinesin-1 heavy chain VDAC2 8.22 Voltage-dependent anion-selective channel protein 2 PEG10 8.21 Retrotransposon-derived protein PEG10 ATG5 7.97 Autophagy protein 5 SRSF11 7.64 Serine/arginine-rich splicing factor 11 RBM19 7.57 Probable RNA-binding protein 19 WIPI2 7.47 WD repeat domain phosphoinositide-interacting protein 2 PAQR3 7.28 Progestin and adipoQ receptor family member 3 PLG 7.26 Plasminogen SLC29A1 7.20 Equilibrative nucleoside transporter 1 ATXN3 7.18 Ataxin-3 PDIA3 7.13 Protein disulfide-isomerase A3 GNG7 7.11 Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-7 THRAP3 7.10 Thyroid hormone receptor-associated protein 3 CYB5R1 7.07 NADH-cytochrome b5 reductase 1 KLC4 7.02 Kinesin light chain 4 SPATA13 6.93 Spermatogenesis-associated protein 13 BAD 6.89 Bcl2-associated agonist of cell death TIAL1 6.89 Nucleolysin TIAR EPAS1 6.84 Endothelial PAS domain-containing protein 1 HNRNPR 6.77 Heterogeneous nuclear ribonucleoprotein R IRF3 6.68 Interferon regulatory factor 3 SLC3A2 6.66 4F2 cell-surface antigen heavy chain NOP58 6.63 Nucleolar protein 58 PRPF4B 6.41 Serine/threonine-protein kinase PRP4 homolog MAGI1 6.30 Membrane-associated guanylate kinase, WW and PDZ domain-

containing protein 1 DAB2 6.24 Disabled homolog 2 APOH 6.23 Beta-2-glycoprotein 1 NKTR 6.20 NK-tumor recognition protein ARHGEF18 6.19 Rho guanine nucleotide exchange factor 18 A2M 5.80 Alpha-2-macroglobulin MMP9 5.75 Matrix metalloproteinase-9 LGALS1 5.60 Galectin-1 MMP13 5.43 Collagenase 3 EZR 5.16 Ezrin SAR1A 5.16 GTP-binding protein SAR1a NGEF 5.04 Ephexin-1 CPT1A 4.77 Carnitine O-palmitoyltransferase 1, liver isoform NFE2L1 4.74 Endoplasmic reticulum membrane sensor NFE2L1 PTK2B 4.55 Protein-tyrosine kinase 2-beta RALBP1 4.42 RalA-binding protein 1 NLRX1 4.40 NLR Family member X1 PTPRJ 4.31 Receptor-type tyrosine-protein phosphatase eta KLKB1 4.26 Plasma kallikrein CRY1 4.03 Cryptochrome-1 PTH1R 4.02 Parathyroid hormone/parathyroid hormone-related peptide receptor CBR1 3.67 Carbonyl reductase [NADPH] 1 FLNB 3.65 Filamin-B ARNTL 3.45 Aryl hydrocarbon receptor nuclear translocator-like protein 1 AKR1B1 3.35 Aldose reductase PDLIM2 3.28 PDZ and LIM domain protein 2 ARRDC3 3.25 Arrestin domain-containing protein 3 CALR 2.63 Calreticulin CBS 2.49 Cystathionine beta-synthase FBXL3 2.32 F-box/LRR-repeat protein 3

Capítulo II

How will farmed populations of freshwater fish deal with the extreme climate scenario in 2100? Transcriptional responses of Colossoma macropomum from two Brazilian climate regions

Publicação relacionada: Fé-Gonçalves, L.M., Araújo J.D.A., Santos, C.H.A., Val, A.L., Almeida-Val, V.M.F. 2019. How will farmed populations of freshwater fish deal with the extreme climate scenario in 2100? Transcriptional responses of Colossoma macropomum from two Brazilian climate regions Manuscrito submetido à revista Journal of Thermal Biology (IF: 1,90)

Status atual da submissão em 10/12/2019.

How will farmed populations of freshwater fish deal with the extreme climate scenario in 2100? Transcriptional responses of Colossoma macropomum from two Brazilian climate regions

Luciana Mara Fé-Gonçalves1, José Deney Alves Araújo2, Carlos Henrique dos Anjos dos

Santos1, Adalberto Luis Val1 and Vera Maria Fonseca de Almeida-Val1

1 Laboratory of Ecophysiology and Molecular Evolution, Brazilian National Institute for

Research of the Amazon. André Araújo Avenue, 2936, 69067-375, Petrópolis, Manaus, AM,

Brazil

2 Computational Systems Biology Laboratory, University of São Paulo. Professor Lúcio

Martins Rodrigues Avenue, 370, 05508020, Butantã, São Paulo, SP, Brazil

Corresponding author:

Luciana Mara Fé-Gonçalves, Laboratory of Ecophysiology and Molecular Evolution,

Brazilian National Institute for Research of the Amazon, 69067-375, Manaus, AM, Brazil

E-mail address: [email protected]

Telephone and fax number: +55 92 3643 3186

Keywords: tambaqui; climate change; RNA-seq; differential expression; thermal adaptation

Abstract

Tambaqui (Colossoma macropomum Cuvier, 1818) is an endemic fish of the Amazon and

Orinoco basins, and it is the most economically important native species in Brazil being raised in five climatically distinct regions. In the face of current global warming, environmental variations in farm ponds represent additional challenges that may drive new adaptive regional genetic variations among broodstocks of tambaqui. In an experimental context based on the high-emission scenario of the 5th Intergovernmental Panel on Climate

Change (IPCC) report, we used two farmed tambaqui populations to test this hypothesis.

RNA-seq transcriptome analysis was performed in the liver of juvenile tambaqui from northern (Balbina Experimental Station, Balbina, AM) and southeastern (Brumado Fish

Farming, Mogi Mirim, SP) Brazilian regions kept for 30 days in artificial environmental rooms mimicking the current and extreme climate scenarios. Three Illumina MiSeq runs produced close to 120 million 500 bp paired-end reads; 191,139 contigs were assembled with

N50= 1,595. 355 genes were differentially expressed for both populations in response to the extreme scenario. After enrichment analysis, each population presented a core set of genes to cope with climate change. Northern fish induced genes related to the cellular response to stress, activation of MAPK activity, response to unfolded protein, protein metabolism and cellular response to DNA damage stimuli. Genes biologically involved in regulating cell proliferation, protein stabilisation and protein ubiquitination for degradation through the ubiquitin-proteasome system were downregulated. Genes associated with biological processes, including the cellular response to stress, MAPK cascade activation, homeostatic processes and positive regulation of immune responses were upregulated in southeastern fish.

The downregulated genes were related to cytoskeleton organisation, energy metabolism, and the regulation of transcription and biological rhythms. Our findings reveal the signatures of promising candidate genes involved in the regional plasticity of each population of tambaqui

in dealing with upcoming climate changes.

1 Introduction

Overlapping with natural climate variability, global climate warming has been rising since the mid-20th century (Hansen et al., 2012). Anthropogenic climate change due to intensive deforestation, fossil fuel burning and other sources of greenhouse gas emissions has driven changes in the temperature patterns in both hemispheres (Caldeira and Wickett, 2003).

A significant increase in mean air temperature by about 0.5°C has been predicted since the

Intergovernmental Panel on Climate Change’s first report in 1990 (IPCC, 1990); nevertheless, a further increase in Earth’s temperature by around 6.0ºC is now expected to the year 2100

(IPCC, 2014). Based on thousands of relevant scientific publications, the latest Assessment

Report of Working Group II (WGII AR5) predicted, in two models, the potential climate- related future risk on social, economic, environmental and biological scales (IPCC, 2014).

Additional warming to terrestrial and aquatic ecosystems threaten worldwide biodiversity at all levels (Schneider et al., 2007), leading to altered species distribution and population structure, and, in the worst-case scenarios, the extinction of endemic species

(Bellard et al., 2014). Freshwater biota are particularly vulnerable to the expected global warming, and many resident species have a limited ability to disperse as the environment changes (Woodward et al., 2010). Thus, empirical evidence suggests that freshwater species may have started to display some adaptive responses to climate change in the last millennia, centuries or decades (Brander, 2010).

In the face of recent climatic conditions, evolutionary processes can substantially influence the patterns and rates of responses by individuals, populations or species (Walther et al., 2002). Thus, the two contrasting, but non-exclusive, mechanisms that could improve adaptive responses of species to climate change are: (i) a microevolutionary response to natural selection and (ii) phenotypic plasticity (Bellard et al., 2014). However, at the species level, for evolution to occur, either appropriate novel mutations or a novel genetic architecture

(new gene complexes) would have to emerge to allow a response to selection (Parmesan,

2006). Thus, phenotypic plasticity, which enables organisms to respond rapidly and effectively to new environmental changes, may occur at a lower energy cost and in a shorter period of time (Salamin et al., 2010).

Although we are only at an early stage in understanding global warming trends and their impacts on future biodiversity, shifts in species fitness components (behaviour, survival, growth and reproduction) to current climate change are already clearly visible (Walther et al.,

2002; Bellard et al., 2014). In light of previous findings showing the plasticity of Amazonian fish species to cope with environmental changes (Almeida-Val and Val, 1990; Almeida‐Val et al., 2006; Araújo et al., 2017), the tambaqui (Colossoma macropomum Cuvier, 1818) has been used as a model freshwater species for climate change-related studies (Prado-Lima and

Val, 2016; Oliveira and Val, 2017; Lapointe et al., 2018). Tambaqui is endemic in the

Amazon and Orinoco basins (Araújo-Lima and Goulding, 1998) and is an economically important species for Brazilian continental aquaculture (IBGE, 2016) and in several other countries (FAO, 2018). In Brazil, its artificial farming has expanded into five climatically distinct regions, with an average annual temperature difference ranging up to 14ºC between northern and southern cities (Ostrensky et al., 2008). Consequently, recent studies have suggested that captive tambaqui already shows signs of local adaptation to regions with different climatic conditions (Gonçalves et al., 2018; Nunes et al., 2017; Santos et al., 2016).

With the development of next-generation sequencing (NGS) technology, RNA sequencing (RNA-seq) has been widely used as an efficient and accessible approach to determine variations in gene expression by organisms in response to new challenges (Wang et al., 2009a). Transcriptomic studies often rely on partial reference transcriptomes or de novo expression (without a reference genome) (Martin and Wang, 2011). In this context, NGS has the potential to dramatically accelerate biological research by enabling comprehensive

analysis in an inexpensive, routine and widespread manner, rather than requiring significant production-scale efforts (Shendure and Ji, 2008).

The present study aimed to address variations in the transcriptional plasticity of two tambaqui populations, acclimatised to the climatic conditions of the northern and southeastern regions of Brazil following experimental exposure to an extreme climate scenario proposed by the 5th IPCC report. Based on the findings of Gonçalves et al. (2018), we hypothesised that each population of tambaqui would differentially express a core set of genes in response to ongoing climate change.

2 Methods

2.1 Ethical statement

All experimental protocols employed in the present study were performed in accordance with Brazilian Guidelines from the National Board of Control and Care for Ethics in the use of Experimental Animals (CONCEA, 2013) and approved by the Committee of

Ethics on Animal Care (CEUA) at the Brazilian National Institute for Research of the

Amazon (INPA) with protocol number 032/2016.

2.2 Acquisition of tambaqui populations and acclimation

A total of 200 juveniles of tambaqui were acquired from Brazilian fish farms located in Amazonas state (Balbina Experimental Station, Balbina – 1°55’54.4"S; 59°24’39.1"W) and

São Paulo state (Brumado Fish Farming, Mogi Mirim – 22°31’16.00"S; 46°53’5.71"W). Each batch of tambaqui was sourced from different broodstocks, with crossbreeding of Brumado stocks performed during the December 2015 breeding season, while Balbina ones on May

2016. Then, sampling was carried out during the summer season when the water temperature of the rearing tanks recorded 29.5°C in Balbina and 21°C in Brumado; the dissolved oxygen concentration ranged from 5 to 7 mg.L-1. Fish were carefully collected using mesh hand nets and held in 50 L aerated containers for transportation to the Laboratory of Ecophysiology and

Molecular Evolution (LEEM) of INPA, Manaus, Amazonas state, Brazil. These two fish farms were also selected in previous studies based on aspects of genetic improvement in populations of tambaqui obtained from different natural and artificial stocks (Gonçalves et al.,

2018; Nunes et al., 2017). Furthermore, the northern (Balbina) and southeastern (Brumado) populations were raised in two different regions that display the typical climate variability of

Brazil, according to Köppen’s climate classification (Alvares et al., 2013). The northern population lives in a climatic region classified as a humid tropical climate (Af climate) with an annual average temperature of 27.1°C (ranging from 22.3 to 32.6°C). The southeastern population lives in a region of humid temperate with a dry winter and hot summer (Cwa climate) with an annual average temperature of 20.1°C (varying from 9.4 to 28.0°C).

In the laboratory, each population was kept separately outdoors in 310 litre (L) polyethylene tanks under controlled conditions to recover from transport stress and to acclimate to the local conditions. During the first month of fish acclimation, average water temperature, dissolved oxygen, pH and total ammonia in both holding tanks were,

-1 respectively, 25.7°C, 7.0 mgO2.L , 6.5 and 0.13 mM. Fish weighing 42 g ± 4.7 and measuring 11 cm ± 0.4 were held in two tanks physicochemically stables, where they were fed daily until the start of the experiment.

2.3 Experimental design: artificial chronic exposure to climate scenarios

After acclimation, thirty-six specimens of farmed tambaqui were placed in an experimental facility that consisted of two real time-controlled environmental rooms with

25m3, as described by Fé-Gonçalves et al. (2018). Each room reproduced current (baseline condition) and extreme (RCP8.5) scenarios according to the 5th IPCC report for the year 2100

(IPCC, 2014). The current condition mimics real-time changes in temperature and CO2 levels occurring in an Amazonian forested area without human influence, whereas the extreme climate room is based on the IPCC Representative Concentration Pathway (RCP8.5) that

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reproduces increases of 4.5°C and 850 ppm CO2 above the current scenario values at real- time. For 30 days, daily variations in the air temperature and CO2 of both experimental rooms were recorded every two minutes on a 24h-cycle (Figure 1). The artificial light-dark cycle was

12:12, and humidity was set as a derived condition.

Juveniles from the northern (N = 18; 52.4 g ± 3.0; 11.9 cm ± 0.2) and southeastern (N

= 18; 67.9 g ± 6.5; 13.0 cm ± 0.5) stocks were individually transferred to 20 L aerated PVC tanks (Sanremo, Esteio, BRA) being nine tanks per population in both scenarios. They were chronically exposed to each environmental room during the Amazon dry season 2016

(October 25 to November 26). The physicochemical parameters of the water were measured every day using well-established protocols (Table 1). Toxic ammonia accumulation was avoided by partial water renewal throughout the experiment. All animals were fed commercial dry food pellets, with a 32% crude protein content (Purina, Missouri, EUA), once a day (3:00 pm). After 30 days of exposure to the current and extreme scenarios, fish were anaesthetised, weighed, measured and euthanised by cervical sectioning. Liver samples were collected using sterile tweezers and scissors and immediately preserved in RNAlater® stabilization solution

(Thermo Fisher Scientific, Massachusetts, USA) until the isolation of ribonucleic acid (RNA).

Hepatic tissue was chosen due to its important metabolic role in response to most kind of environmental stress, including heat (Logan and Buckley, 2015).

2.4 RNA extraction, RNA-seq library construction and sequencing

Total RNA was extracted from 36 samples of tambaqui liver using the RNeasy® Mini

Kit (Qiagen, Hilden, DE) protocol. Approximately 20 mg tissue was homogenised in lysis buffer into TissueLyser II (Qiagen, Hilden, DE) for 2x2 minutes at 20 Hz. Automated purification of RNA was performed on QIACube robotic workstation (Qiagen, Hilden, DE) using silica-membrane technology. The quality and quantity of extracted RNA were accurately checked using both an RNA 6000 Nano Bioanalyzer chip (Agilent Technologies,

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Santa Clara, USA) and a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific,

Massachusetts, USA). All RNA samples were free of gDNA and had the optimal RNA yield

(1.06 μg ± 0.06) and purity (average RIN = 8.6, A260:A280 and A260:A230 ratios = 2.0), respectively. Before library construction, three samples of total RNA were pooled, totalling

12 RNA-seq libraries, with three biological replicates for each tambaqui population (northern and southeastern) and climatic scenarios (current and extreme).

All procedures for constructing and sequencing the RNA-seq libraries were carried out in the Molecular Biology Laboratory of LEEM/INPA following Illumina’s protocols. The mRNA was isolated from the total RNA (0.95 μg eluted in 50 μL) using oligo d(T)25 magnetic beads bound the poly(A) tail of the mRNA. Then, the first and second strands of complementary DNA (cDNA) were synthesised, and a single adenine (A) nucleotide was added to the 3’ end of the fragments. Adapters were ligated to the cDNA fragments and polymerase chain reaction (PCR) was performed to enrich for those fragments. cDNA libraries were prepared using the reagents provided in the TruSeq RNA Library Sample

Preparation Kit v2 (Illumina, San Diego, USA).

The absolute quantification of cDNA libraries was measured on a ViiA 7 Real-Time

PCR System (Thermo Fisher Scientific, Massachusetts, USA) using the KAPA SYBR® FAST qPCR Master Mix (Kapa Biosystems, Wilmington, USA). Normalised cDNA libraries were clustered using the MiSeq Reagent Kit v2 (500-cycles) and sequenced on an Illumina MiSeq platform in three sequencing paired-end runs (2×250 cycles). These sequence data have been submitted to the GenBank database under accession number PRJNA521052

(www.ncbi.nlm.nih.gov/genbank).

2.5 Bioinformatics analysis

Analyses of the high-throughput RNA sequencing were performed on the premises of

Bioinformatics Laboratory of LEEM/INPA. The quality of sequenced reads was checked

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using the FastQC v.0.11.6 program (Andrews, 2010). The low-quality reads (Q-score ≤ 20) were trimmed by removing adaptor sequences and filtering out reads less than 50 bases pairs

(bp) was performed using the Trimmomatic v.0.36 program (Bolger et al., 2014). With the absence of the complete genome for Colossoma macropomum species, we chose de novo transcriptome assembly using the Trinity v.2.5.1 program (Grabherr et al., 2011). In addition, programs were used to assist Trinity to assemble the transcriptome with Bowtie2 v.2.3.3.1

(Langmead and Salzberg, 2012) and to calculate the abundance of transcripts using RSEM v.1.3.0 (Li and Dewey, 2011) and the R/Bioconductor package v.3.3.2 (Bates et al., 2004).

Differential expression was quantified into up- and downregulated genes using the edgeR v.3.16.5 program (Robinson et al., 2009) of the R/Bioconductor package. The assumed false discovery rate (FDR) was ≤ 0.05 to corrected P values, and the data generated by RSEM were used to calculate the fold change values of ≥ 2. The differentially expressed genes

(DEGs) were annotated with the BLASTx v.2.7.1+ program (Altschul et al., 1997) against the

Uniprot/TrEMBL protein database (class Actinopterygii) and Swiss-Prot non-redundant proteins, with an e-value of 1e-5. The Trinotate tool v.3.1.1 (https://trinotate.github.io/) was used to classify the DEGs according to the three general categories of Gene Ontology (GO) annotation: Biological Process (BP), Cellular Component (CC) and Molecular Function (MF).

Further analysis on NetworkAnalyst (https://www.networkanalyst.ca/) was performed to construct relevant biological networks based on protein-protein interactions (PPI) starting from a list of DEGs, using their official names and fold change values. NetworkAnalyst also allows for performing functional enrichment analysis of significantly expressed GO terms according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Xia et al.,

2014).

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3. Results

RNA sequencing was carried out on 12 liver cDNA libraries of juvenile tambaqui from the northern and southeastern regions of Brazil with 30 days of artificial exposure to current and extreme climate scenarios. Three Illumina MiSeq runs sequenced about 120 million (M) of paired-end reads, with an average of 4,991,486 M reads per library (minimum

3,988,488 M and maximum 6,108,990 M). After quality control analysis (Q-score ≤ 20 and exclusion of sequenced reads smaller than 50 bp), about 91% trimmed reads (108,013,754 M) were saved for de novo assembly and alignment. Thus, 191,139 contigs were assembled with an average length of 851 bp and N50 value of 1,595 bp, respectively. The assembled bases totalled 162,713,280 M.

Differential expression analyses between tambaqui populations under climatic scenarios (extreme versus current) identified a total of 355 expressed transcripts (FDR ≤ 0.05 and fold change ≥ 2). Out of those, 158 DEGs were found in juvenile tambaqui from the northern population, with 97 upregulated genes (61.4%) and 61 downregulated genes

(38.6%). In the southeastern population, 197 genes were differentially expressed in response to the extreme condition, with 74 upregulated genes (37.6%), and 123 downregulated genes

(62.4%).

Considering the transcriptome annotation of tambaqui, we identified 107,924 contigs, whereas unannotated genes totalled 32,656 contigs. Thus, DEGs were grouped into the three

GO terms: Biological Process (BP), Cell Component (CC) and Molecular Function (MF). In the northern tambaqui population, 1,923 terms were annotated, of which 1,307 terms were assigned to upregulated genes (387 functional groups: BP, 636; CC, 147 and MF, 524) and

616 terms were assigned to downregulated genes (264 functional groups: BP, 293; CC, 67 and

MF, 256). For the southeastern tambaqui population, 2,497 terms were successfully annotated. Out of those, 932 terms were upregulated genes (327 functional groups: BP, 389;

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CC, 72 and MF, 471) and 1,565 terms were downregulated genes (445 functional groups: BP,

754; CC, 156 and MF, 655). GO classification of top ten up- and downregulated terms in both tambaqui populations exposed to extreme scenario are shown in Figures 2 and 3, respectively.

Two PPI networks were generated from the DEGs (seed nodes) of the northern and southeastern populations submitted to an extreme climate scenario. Each constructed network was composed for a suitable number of nodes (proteins) and edges (interactions between nodes). The biological network of northern tambaqui presented 317 nodes and 361 edges, whereas the one of southeastern population contained 360 nodes and 413 edges.

Nineteen important seeds (also called hubs) were highlighted in the PPI network of the northern population (Figure 4). The red hubs are essentially related to upregulated genes encoding proteins involved in cellular response to stress, activation of MAPK activity, response to unfolded protein, protein metabolism and cellular response to DNA damage stimuli. Contrarily, the green hubs represent downregulated genes biologically involved in cell proliferation, maintenance of protein location and protein ubiquitination (Table 2).

The southeastern population showed a different PPI network profile, with 26 seeds

(Figure 5). The upregulated genes highlighted in red hubs encoded proteins that also participate in cellular response to stress, MAPK cascade activation, homeostatic process and positive regulation of immune responses. Most of the green hubs represented downregulated genes responsible for cytoskeleton organisation, energy metabolism, regulation of transcription and biological rhythms, as listed in Table 3. The metabolic functions of these

DEGs and their putative interactions related to a population response to the forecasted climate change scenario are discussed below.

Discussion

Human-induced climate change is causing profound alterations on the Earth’s natural climate system (Schneider et al., 2007). Important components of physical and chemical

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changes, rising temperature and elevated carbon dioxide concentrations have driven many environmental disturbances, with complex implications for all scales of living systems

(Woodward et al., 2010; Doney et al., 2012). Over the past decades, a growing body of study has predicted a myriad of biological responses of organisms to climate change impacts and their potential to adapt (or evolve) (Bellard et al., 2014; Fé-Gonçalves et al., 2018; Lapointe et al., 2018; Campos et al., 2019).

In this study, those abiotic parameters associated with climate warming were synergistically tested. Special rooms reproduced both current climate conditions and the

IPCC’s RCP8.5 model, a high-emission scenario. The perspective of increased air temperature has made us wonder how a tropical fish species living near its thermal limit will respond to this challenge. Coupled with a warming trend, acidification of sea and inland waters due to higher uptake of atmospheric CO2 is also a threat to fish at all response levels

(Ishimatsu et al., 2008). At the molecular level, transcriptomic approaches have built a genome-wide transcriptional landscape for further research into the plasticity and evolution of tropical freshwater fish in the face of global climate change (Oomen and Hutchings, 2017).

Herein, we used RNA-seq analysis to assemble a de novo transcriptome for the

Neotropical fish C. macropomum sampled from states of Amazonas and São Paulo fish farms, and experimentally submitted to the current and extreme scenarios in climate rooms. Based on statistical treatment, we verified that half of all assembled bases were found in contigs of at least 1,595 bp for N50 value, with the N50 to the isoform of 1,041, respectively. The high numbers of isoforms in long transcripts is an important metric because low N50 values can indicate poor quality assembly. While some rare and low abundance transcripts were excluded, it is important to present a more conservative and reliable approach to differential expression testing by emphasising the accuracy of the assembly rather than the identification of low abundance transcripts (Smith et al., 2013).

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Predicting the impacts of climate warming on inland fisheries, Lapointe et al. (2018) compared the thermal tolerance of six species of freshwater fish based on critical thermal maxima (CTmax) values. These authors observed that tambaqui, when acclimated above 4ºC of temperature, presented one of the highest values in CTmax. As postulated, tambaqui have developed several adaptation mechanisms to preserve survival ability in order to cope with adverse environmental conditions (Almeida-Val and Val, 1990; Almeida‐Val et al., 2006).

The first RNA-seq-based study by Prado-Lima & Val (2016) in tambaqui juveniles elucidated how the plasticity of responses is crucial to their adaptation to different scenarios of global climate change. Although phenotypic plasticity and adaptation might buffer species against habitat degradation associated with global climate change, few studies making such claims also possess the necessary and sufficient data to support them (Mccairns et al., 2016).

Comparing the transcriptional responses of two tambaqui populations under exposure to an extreme scenario, we identified promising candidate genes involved in these plasticity changes. A total of 355 transcripts were differentially expressed, but the enrichment analyses grouped gene sets into two differentiated network configurations based on common functional categories (Figures 4 and 5). In this context, a novel array of genes and pathways is proposed here, including those involved in some aspect of the cellular response to stress.

Heat- (YWHAE) and stress-response genes (DUSP1 and MAP3K7) were upregulated in the liver of tambaqui from the northern population. The 14-3-3 protein epsilon, encoded by the YWHAE gene, is part of a conserved protein family whose isoforms interact with over one hundred targets, such as members of the protein kinase and phosphatase families (Fu et al.,

2002). This small adaptor protein plays a significant role in many biological processes, including cell-signalling pathways involved in responses to a changing environment (Fu et al.,

2002; Koskinen et al., 2004). The DUSP1 (dual specificity phosphatase 1) gene encodes for serine/threonine and tyrosine protein phosphatases, with narrow substrate specificity for

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members of the mitogen-activated protein kinase (MAPK) family (Camps et al., 2000). DUSP genes regulate intracellular signalling events, and participate in cellular growth, proliferation, cell cycling and cell death (Schweikl et al., 2008). According to Liu et al. (2008) overexpression of DUSP1 happens in response to growth factor stimulation and environmental stressors, increasing cellular susceptibility to oxidative damage by hydrogen peroxide (H2O2) or other oxidative compounds. Under thermal stress, the up-regulation of

DUSP1 and DUSP2 genes was related to immune signalling in the gills of Chinook salmon juveniles (Oncorhynchus tshawytscha) acutely exposed to a lethal temperature of 25ºC

(Tomalty et al., 2015). As a member of the MAPK superfamily, MAP3K7 gene (mitogen- activated protein kinase kinase kinase 7, or TAK1) plays key role in the cascades of cellular response to diverse stimuli (growth factors, cytokines or environmental stresses) (Camps et al., 2000). Coordinated functions of stress-activated MAPK pathways virtually control cell metabolism, regulating transcription factors responsible for cell survival, differentiation and inflammatory responses, protein biosynthesis, cell cycle control, apoptosis and differentiation

(Landström, 2010; Kyriakis and Avruch, 2012). Jiang et al. (2018) observed activation of the

MAPK signalling cascade involved in the immune defence response in the gills of Yesso scallop (Patinopecten yessoensis) following exposure to water temperature fluctuations.

Taken together, the up-regulation of these genes suggests the induction of alternative cell signalling molecules to promoting new plasticity changes in response to a future warming scenario.

PFDN (prefoldin), a ubiquitously expressed heterohexameric co-chaperone, is necessary for proper folding of nascent proteins, in particular tubulin and actin. The

Hsp70/Hsp90 chaperone and co-chaperone machinery is crucial for cellular development and maintenance as these proteins assist in protein folding and the stabilisation of unfolded or misfolded proteins (Wegele et al., 2001; Lee et al., 2011). It has been clearly shown that Hsp

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(heat shock protein) overexpression is correlated with thermal stress (Feder and Hofmann,

1999; Logan and Somero, 2011; Jesus et al., 2013, 2017). Prado-Lima & Val (2016) identified heat-induced genes involved in heat shock response and protein folding in the white muscle of tambaqui chronically exposed to climate change scenarios: Dnaja2, Dnajc7, hsp90aa1.1, hsp90aa1.2, hmgb1a, and pfdn2. Herein, the up-regulation of FKBP5 (peptidyl- prolyl cis-trans isomerase), a prolyl isomerase that interacts with the chaperone network, indicates the folding or renaturation processing of stress-damaged proteins in this population.

Despite the classical protein rescue machinery, thermal stress can generate non-native proteins that molecular chaperones cannot repair (Todgham et al., 2017). Thus, the accumulation of unrepaired proteins interferes with proper maintaining cell homeostasis

(Sherman and Goldberg, 2004). To avoid cytotoxic aggregates, misfolded or damaged protein will be a target for degradation through the ubiquitin-proteasome system (Zhang and Ye,

2014). Here, induced expression of UBE3A (ubiquitin-protein ligase E3A) and UBE2J2

(ubiquitin-conjugating enzyme E2 J2) genes involved in the ubiquitin (Ub)-mediated modification of proteins play an important role in tagging numerous substrates for regulation by multiple cellular processes, e.g. cell cycle, components of signal transduction pathways, enzymes involved in metabolism and degradation of abnormal proteins (Herrmann et al.,

2007). Genes encoding for proteins involved in proteolysis via the ubiquitin-proteasome route were significantly upregulated in the eurythermal fish Gillichthys mirabilis after exposure to acute heat stress (Logan and Somero, 2011) as well as in Atlantic salmon kept in a chronic low oxygen concentration (Olsvik et al., 2013). Briefly, high expression of genes related to proteolysis assures the maintenance of protein homeostasis (Todgham et al., 2017), reflecting the dynamic balance in energy reallocation for the organism’s activities such as growth, reproduction and foraging, with an impact on fitness (Sherman and Goldberg, 2004).

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Interestingly, juvenile tambaqui from the northern region suppressed genes associated with the regulation of cell growth and proliferation as a compensatory mechanism to save energy to deal with an extreme climate scenario. Heat stress resulted in the underexpression of genes responsible for cell division and growth in Squalius torgalensis, suggesting a strategy to re-allocating energy towards the repair of proteins and membranes (Jesus et al., 2016).

Corroborating the growth performance of tambaqui in various climate scenarios, Oliveira &

Val (2017) observed that more extreme conditions affected the food conversion efficiency for growth due an increase in cellular metabolic demand under physiological stress. Thus, predicted climate change might impair the growth of tambaqui farmed in this region, leading to higher feeding costs during the first growth phase.

The southeastern population of tambaqui differentially expressed other genes responsive to climate change when compared to the northern stock. However, genes encoding for protein kinases involved in the MAPK cascade were also upregulated during the stress response. AKT occurs in three isoforms, of which AKT2 (RAC-beta serine/threonine-protein kinase) is predominantly expressed in insulin-responsive tissues (Wolf et al., 2013). Activated

AKT prevents apoptosis via increasing glucose uptake by mediating the insulin-induced translocation of the SLC2A4/GLUT4 glucose transporter to the cell surface, and regulates the storage of glucose in the form of glycogen (Weiss and Refetoff, 2015). AKT isoforms have cell- and tissue-specific functions, but most prominently, AKT activation can promote cell survival, proliferation, growth and changes in cellular metabolic pathways through its numerous downstream targets (Manning and Toker, 2017). Jiang et al. (2018) identified some enriched pathways involved in signal transduction in the Patinopecten yessoensis transcriptome after exposure to warmer water, with the PI3K-AKT signalling pathway associated with immune function.

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DEGs implicated in immune responses were found through KEGG enrichment. The proinflammatory gene IL6ST (interleukin 6 signal transducer) that encodes for gp130 protein

(Kondaurova et al., 2011) was upregulated in the liver cells of tambaqui from the southeastern region. Expression of gp130-related cytokines such as IL-6 (interleukin-6) is primarily related to the regulation of immunity, inflammation, haematopoiesis and oncogenesis (Naka et al.,

2002; Landström, 2010). These tissue stress responses are also mediated by changing external conditions such as mineral deficiencies, hypoxia and temperature fluctuations (Thorne et al.,

2010; Chovatiya and Medzhitov, 2014). Likewise, in the inflammatory state, autophagy is considered a survival mechanism to preserve cellular homeostasis through the lysosomal degradation of damaged or harmful cytosolic components (Kroemer et al., 2010). Here, the expression of genes related to autophagy were induced: ULK1 (serine/threonine-protein kinase ULK1) and RB1CC1 (RB1-inducible coiled-coil protein 1, or FIP200). ULK1 plays a role in the regulation of autophagy by interacting directly with RB1CC1. Thus, both genes are essential for autophagosome assembly (Kroemer et al., 2010). Other authors also found the up-regulation of genes involved in inflammatory and immune signalling in heat-stressed O. tshawytscha (Tomalty et al., 2015) and Harpagifer antarcticus (Thorne et al., 2010). Thus, inflammatory processes seem to be a common response when a fish is under thermal stress, which may affect immune signalling in fish tissues. Moreover, these tambaqui juveniles were acclimatised under a broad thermal range in the southeastern region of Brazil within a different temperature interval, i.e. temperatures much lower than higher, as prior mentioned in this work.

The southeastern population also showed a down-regulation in energy metabolism- associated genes under extreme scenario exposure. Apolipoprotein A-I (APOA1) is a crucial component of the high-density lipoproteins (HDLs) in plasma, and is a cofactor for lecithin∶cholesterol acyltransferase (LCAT), playing a key role in lipid metabolism and the

111

reverse transport of lipids (Lewis and Rader, 2005). In teleost fish, APOA1 is also involved in many biological processes (Xu et al., 2013), including anti-inflammatory function (Tabet and

Rye, 2009). Cunha et al. (2015) suggested that apolipoprotein genes might be related to lipid trafficking for other purposes than energy production, e.g. for incorporating lipids into membranes of newly formed or differentiating cells, which does not modify the total lipid content or lipid classes. Under thermally variable conditions, changes in the cell membrane structure and a metabolic shift toward lipid synthesis or transport processes are expected

(Ribeiro, 2010). Smith et al. (2013) identified transcripts involved in lipid metabolism in rainbowfish (Melanotaenia duboulayi) as being a plasticity response to cope with temperature stress. However, under the same conditions, lower expression of the APOA1 gene in the liver of hybrid catfish (Ictalurus sp.) suggested the return of energy to the basal level in response to thermal acclimation (Liu et al., 2013). In fact, the response of each species to thermal stress will be related to its life history and environmental acclimatisation history, revealing mechanisms of phenotypic plasticity ability facing global warming (Bellard et al., 2014).

Energy metabolism (including lipid and carbohydrate catabolism) is one of the key mechanisms of the cellular stress response (Wang et al., 2009b). The decrease in the expression of glucokinase (GCK), an enzyme that catalyses the initial step in the utilisation of glucose, indicates a down-regulation of glycolytic process under the extreme climate scenario exposure. Compared to the northern population, suppression of oxidative metabolism followed by the activation of anaerobic glycolysis may constitute differential capacity of this population to enhanced environmental disturbance tolerance. The transcript profiles in tambaqui liver from the southeastern population showed an overall trend to undergo metabolic depression, which was confirmed by the number of downregulated genes in the heat map (Figure 6).

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The two populations of tambaqui displayed different phenotypic plasticity strategies in response to chronic exposure to an extreme scenario of climate change. Differentially expressed genes are part of a wide metabolic route, exhibiting plastic regulatory mechanisms to deal with a warmer climate. In this context, we identified excellent candidate genes for further investigations of population adaptation in the ongoing climate change scenario.

Additionally, our results corroborate those of Santos et al. (2016), showing that populations of tambaqui from Brazilian fish farms are locally adapted. Thus, resilient species with a modest evolutionary potential may possess an eco-evolutionary strategy that enables them to persist over macroevolutionary time and rapidly respond to novel challenges, which may allow for further genetic adaptations to take place over time (Lighten et al., 2016).

Conclusion

According to Crozier & Hutchings (2014), many species will likely adapt to long-term warming trends overlaid on natural climate oscillations. Thus, field and laboratory experiments on both model and non-model fish have already provided some insight into the potential for individuals to respond plastically to short- and long-term environmental stress and for populations and species to evolve with changes in environmental regimes (Oomen and

Hutchings, 2017). Herein, the RNA-seq results provide evidence for the local adaptation of captive tambaqui populations nurtured in different regional temperature regimes and indicate new target genes that will help elucidate processes and pathways enabling which adaptation will occur to future warmer climates. These novel insights into the adaptive evolution in tambaqui from different temperature zones in Brazil will be valuable to clarify the genetic basis of climate change adaptation in Neotropical fishes.

Acknowledgements

The authors are grateful for financial support from the Coordination for the Improvement of Higher Level Personnel (CAPES) to the Pro-Amazon Project (n°

113

047/2012), Brazilian National Research Council (CNPq) to the INCT-ADAPTA II Project (n°

465540/2014-7) and Universal Calls (n° 424468/2016-6), and Amazonas Research

Foundation (FAPEAM) to the INCT-ADAPTA II Project (n° 0621187/2017). L.M.F.G. was a recipient of a Ph.D. fellowship from CAPES. C.H.A.S. was a recipient of a research fellowship from PCI-INPA/CNPq. A.L.V. and V.M.F.A.V. are the recipients of research fellowships from CNPq. We thank Fernanda Garcia Dragan and Maria de Nazaré Paula-Silva for their excellent technical support in the experiments. Special thanks go to Dra. Alzira

Miranda de Oliveira for the logistical support in obtaining the experimental fish.

Conflict of interest

The authors declare that they have no conflict of interest.

References

Almeida-Val, V.M.F., Val, A.L., 1990. Adaptação bioquímica em peixes da Amazônia.

Ciência Hoje 11, 62–67.

Almeida‐Val, V.M.F., Chippari-Gomes, A.R., Lopes, N.P., 2006. Metabolic and

physiological adjustments to low oxygen and high temperature in fishes of the Amazon,

in: Val, A.L., Almeida-Val, V.M.F., Randall, D.J. (Eds.), The Physiology of Tropical

Fishes. Elsevier, Lodon, UK, pp. 443–500. https://doi.org/10.1016/S1546-

5098(05)21010-5

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.,

1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search

programs. Nucleic Acids Res. 25, 3389–3402. https://doi.org/10.1093/nar/25.17.3389

Alvares, C.A., Stape, J.L., Sentelhas, P.C., Gonçalves, J.L.M., Sparovek, G., 2013. Köppen’s

climate classification map for Brazil. Meteorol. Zeitschrift 22, 711–728.

https://doi.org/10.1127/0941-2948/2013/0507

Andrews, S., 2010. FastQC - A quality control tool for high throughput sequence data.

114

http://www.bioinformatics.babraham.ac.uk/Projects/Fastqc/ (accessed 2.16.19).

Araújo-Lima, C., Goulding, M., 1998. Os frutos do tambaqui: ecologia, conservação e cultivo

na Amazônia. Sociedade Civil Mamirauá/CNPq, Tefé, AM.

Araújo, J.D.A., Ghelfi, A., Val, A.L., 2017. Triportheus albus Cope, 1872 in the blackwater,

clearwater, and whitewater of the amazon: A case of phenotypic plasticity? Front. Genet.

8, 1–12. https://doi.org/10.3389/fgene.2017.00114

Bates, D.M., Ellis, B., Smith, C., Irizarry, R., Tierney, L., Huber, W., Leisch, F., Iacus, S.,

Maechler, M., Hornik, K., Zhang, J., Gentry, J., Carey, V.J., Yang, J.Y.H., Sawitzki, G.,

Dudoit, S., Gautier, L., Li, C., Gentleman, R.C., Bolstad, B., Rossini, A.J., Hothorn, T.,

Dettling, M., Ge, Y., Smyth, G., 2004. Bioconductor: open software development for

computational biology and bioinformatics. Genome Biol. 5, 1–16.

https://doi.org/10.1186/gb-2004-5-10-r80

Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W., Courchamp, F., 2014. Impacts of

climate change on the future of biodiversity. Ecol. Lett. 15, 365–377.

https://doi.org/10.1111/j.1461-0248.2011.01736.x.

Bolger, A.M., Lohse, M., Usadel, B., 2014. Trimmomatic: A flexible trimmer for Illumina

sequence data. Bioinformatics 30, 2114–2120.

https://doi.org/10.1093/bioinformatics/btu170

Brander, K., 2010. Impact of climate change on fisheries. J. Mar. Syst. J. 79, 389–402.

https://doi.org/10.1016/B978-0-12-812160-3.00009-X

Caldeira, K., Wickett, M.E., 2003. Anthropogenic carbon and ocean pH. Nature 425, 365–

365. https://doi.org/10.1038/425365a

Campos, D.F., Braz-Mota, S., Val, A.L., Almeida-Val, V.M.F., 2019. Predicting thermal

sensitivity of three Amazon fishes exposed to climate change scenarios. Ecol. Indic. 101,

533–540. https://doi.org/10.1016/j.ecolind.2019.01.051

115

Camps, M., Nichols, A., Arkinstall, S., 2000. Dual specificity phosphatases: a gene family for

control of MAP kinase function. FASEB J. 14, 6–16.

https://doi.org/10.1096/fasebj.14.1.6

Chovatiya, R., Medzhitov, R., 2014. Stress, inflammation, and defense of homeostasis. Mol.

Cell 54, 281–288. https://doi.org/10.1016/j.molcel.2014.03.030

CONCEA, 2013. Diretrizes da prática de eutanásia do CONCEA. Brasília, BR.

Crozier, L.G., Hutchings, J.A., 2014. Plastic and evolutionary responses to climate change in

fish. Evol. Appl. 7, 68–87. https://doi.org/10.1111/eva.12135

Cunha, I., Galante-Oliveira, S., Rocha, E., Urbatzka, R., Castro, L.F.C., 2015. Expression of

intercellular lipid transport and cholesterol metabolism genes in eggs and early larvae

stages of turbot, Scophthalmus maximus, a marine aquaculture species. Mar. Biol. 162,

1673–1683. https://doi.org/10.1007/s00227-015-2706-9

Doney, S.C., Ruckelshaus, M., Emmett Duffy, J., Barry, J.P., Chan, F., English, C.A.,

Galindo, H.M., Grebmeier, J.M., Hollowed, A.B., Knowlton, N., Polovina, J., Rabalais,

N.N., Sydeman, W.J., Talley, L.D., 2012. Climate change impacts on marine

ecosystems. Ann. Rev. Mar. Sci. 4, 11–37. https://doi.org/10.1146/annurev-marine-

041911-111611

FAO, 2018. The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable

development goals. Rome, Italy.

Fé-Gonçalves, L.M., Paula-Silva, M. de N., Val, A.L., Almeida-Val, V.M.F., 2018.

Differential survivorship of congeneric ornamental fishes under forecasted climate

changes are related to anaerobic potential. Genet. Mol. Biol. 41, 107–118.

https://doi.org/10.1590/1678-4685-gmb-2017-0016

Feder, M.E., Hofmann, G.E., 1999. Heat-shock proteins, molecular chaperones, and the stress

response: evolutionary and ecological physiology. Annu. Rev. Physiol. 61, 243–282.

116

https://doi.org/10.1146/annurev.physiol.61.1.243

Fu, H., Subramanian, R.R., Masters, S.C., 2002. 14-3-3 Proteins: Structure, Function, and

Regulation. Annu. Rev. Pharmacol. Toxicol. 40, 617–647.

https://doi.org/10.1146/annurev.pharmtox.40.1.617

Gonçalves, R.A., Santos, C.H.A., Sá-Leitão, C.S., Souza, É.M.S., Almeida-Val, V.M.F.,

2018. Genetic basis of Colossoma macropomum broodstock: Perspectives for an

improvement program. J. World Aquac. Soc. 633–644.

https://doi.org/10.1111/jwas.12564

Grabherr, M.G., Haas, B.J., Yassour, M., Levin, J.Z., Thompson, D.A., Amit, I., Adiconis, X.,

Fan, L., Raychowdhury, R., Zeng, Q., Chen, Z., Mauceli, E., Hacohen, N., Gnirke, A.,

Rhind, N., Di Palma, F., Birren, B.W., Nusbaum, C., Lindblad-Toh, K., Friedman, N.,

Regev, A., 2011. Full-length transcriptome assembly from RNA-Seq data without a

reference genome. Nat. Biotechnol. 29, 644–652. https://doi.org/10.1038/nbt.1883

Hansen, J., Sato, M., Ruedy, R., 2012. Perception of climate change. Proc. Natl. Acad. Sci.

109, E2415–E2423. www.pnas.org/cgi/doi/10.1073/pnas.1205276109

Herrmann, J., Lerman, L.O., Lerman, A., 2007. Ubiquitin and ubiquitin-like proteins in

protein regulation. Circ. Res. 100, 1276–1291.

https://doi.org/10.1161/01.RES.0000264500.11888.f0

IBGE, 2016. Produção da Pecuária Municipal. Rio de Janeiro, BR.

IPCC, 2014. Summary for policymakers, in: Field, C.B., Barros, V.R., Dokken, D.J., Mach,

K.J, Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L, Estrada, Y.O., Genova,

R.C, Girma, B., Kissel, E.S., Levy, A.N, MacCracken, S., Mastrandrea, P.R., White, L.L.

(Ed.), Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and

Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of

the Intergovernmental Panel on Climate Change. Cambridge University Press,

117

Cambridge, UK and New York, USA, pp. 1–32.

https://doi.org/10.1017/CBO9781107415324

IPCC, 1990. Climate change: the Intergovernmental Panel on Climate Change (IPCC)

scientific assessment. CUP, Cambridge, UK.

Ishimatsu, A., Hayashi, M., Kikkawa, T., 2008. Fishes in high-CO2, acidified oceans. Mar.

Ecol. Prog. Ser. 373, 295–302. https://doi.org/10.3354/meps07823

Jesus, T.F., Grosso, A.R., Almeida-Val, V.M.F., Coelho, M.M., 2016. Transcriptome

profiling of two Iberian freshwater fish exposed to thermal stress. J. Therm. Biol. 55, 54–

61. https://doi.org/10.1016/j.jtherbio.2015.11.009

Jesus, T.F., Inácio, Â., Coelho, M.M., 2013. Different levels of hsp70 and hsc70 mRNA

expression in Iberian fish exposed to distinct river conditions. Genet. Mol. Biol. 36, 61–

69. https://doi.org/http://dx.doi.org/10.1590/S1415-47572013000100009

Jesus, T.F., Moreno, J.M., Repolho, T., Athanasiadis, A., Rosa, R., Almeida-Val, V.M.F.,

Coelho, M.M., 2017. Protein analysis and gene expression indicate differential

vulnerability of Iberian fish species under a climate change scenario. PLoS One 12, 1–

18. https://doi.org/10.1371/journal.pone.0181325

Jiang, W., Lin, F., Fang, Jianguang, Gao, Y., Du, M., Fang, Jinghui, Li, W., Jiang, Z., 2018.

Transcriptome analysis of the Yesso scallop, Patinopecten yessoensis gills in response to

water temperature fluctuations. Fish Shellfish Immunol. 80, 133–140.

https://doi.org/10.1016/j.fsi.2018.05.038

Kondaurova, E.M., Naumenko, V.S., Sinyakova, N.A., Kulikov, A. V, 2011. Map3k1, Il6st,

Gzmk, and Hspb3 gene coexpression network in the mechanism of freezing reaction in

mice. J. Neurosci. Res. 89, 267–273. https://doi.org/10.1002/jnr.22545

Koskinen, H., Krasnov, A., Rexroad, C., Gorodilov, Y., Afanasyev, S., Mölsä, H., 2004. The

14-3-3 proteins in the teleost fish rainbow trout (Oncorhynchus mykiss). J. Exp. Biol.

118

207, 3361–3368. https://doi.org/10.1242/jeb.01165

Kroemer, G., Mariño, G., Levine, B., 2010. Autophagy and the integrated stress response.

Mol. Cell 40, 280–293. https://doi.org/10.1016/j.molcel.2010.09.023

Kyriakis, J.M., Avruch, J., 2012. Mammalian MAPK signal transduction pathways activated

by stress and inflammation: A 10-year update. Physiol. Rev. 92, 689–737.

https://doi.org/10.1152/physrev.00028.2011

Landström, M., 2010. The TAK1-TRAF6 signalling pathway. Int. J. Biochem. Cell Biol. 42,

585–589. https://doi.org/10.1016/j.biocel.2009.12.023

Langmead, B., Salzberg, S.L., 2012. Fast gapped-read alignment with Bowtie 2. Nat. Methods

9, 357–359. https://doi.org/10.1038/nmeth.1923

Lapointe, D., Cooperman, M.S., Chapman, L.J., Clark, T.D., Val, A.L., Ferreira, M.S.,

Balirwa, J.S., Mbabazi, D., Mwanja, M., Chhom, L., Hannah, L., Kaufman, L., Farrell,

A.P., Cooke, S.J., 2018. Predicted impacts of climate warming on aerobic performance

and upper thermal tolerance of six tropical freshwater fishes spanning three continents.

Conserv. Physiol. 6, coy056. https://doi.org/10.1093/conphys/coy056

Lee, Y.S., Smith, R.S., Jordan, W., King, B.L., Won, J., Valpuesta, J.M., Naggert, J.K.,

Nishina, P.M., 2011. Prefoldin 5 is required for normal sensory and neuronal

development in a murine model. J. Biol. Chem. 286, 726–736.

https://doi.org/10.1074/jbc.M110.177352

Lewis, G.F., Rader, D.J., 2005. New insights into the regulation of HDL metabolism and

reverse cholesterol transport. Circ. Res. 96, 1221–1232.

https://doi.org/10.1161/01.RES.0000170946.56981.5c

Li, B., Dewey, C.N., 2011. RSEM: accurate transcript quantification from RNA-seq data with

or without a reference genome. BMC Bioinformatics 12, 41–74.

https://doi.org/10.1201/b16589

119

Lighten, J., Incarnato, D., Ward, B.J., van Oosterhout, C., Bradbury, I., Hanson, M., Bentzen,

P., 2016. Adaptive phenotypic response to climate enabled by epigenetics in a K-strategy

species, the fish Leucoraja ocellata (Rajidae). R. Soc. Open Sci. 3, 160299.

https://doi.org/10.1098/rsos.160299

Liu, S., Wang, X., Sun, F., Zhang, J., Feng, J., Liu, H., Rajendran, K. V., Sun, L., Zhang, Y.,

Jiang, Y., Peatman, E., Kaltenboeck, L., Kucuktas, H., Liu, Z., 2013. RNA-Seq reveals

expression signatures of genes involved in oxygen transport, protein synthesis, folding,

and degradation in response to heat stress in catfish. Physiol. Genomics 45, 462–476.

https://doi.org/10.1152/physiolgenomics.00026.2013

Liu, Y.-X., Wang, J., Guo, J., Wu, J., Lieberman, H.B., Yin, Y., 2008. DUSP1 is controlled

by p53 during the cellular response to oxidative stress. Mol. Cancer Res. 6, 624–633.

https://doi.org/10.1158/1541-7786.mcr-07-2019

Logan, C.A., Buckley, B.A., 2015. Transcriptomic responses to environmental temperature in

eurythermal and stenothermal fishes. J. Exp. Biol. 218, 1915–1924.

https://doi.org/10.1242/jeb.114397

Logan, C.A., Somero, G.N., 2011. Effects of thermal acclimation on transcriptional responses

to acute heat stress in the eurythermal fish Gillichthys mirabilis (Cooper). Am. J.

Physiol. Integr. Comp. Physiol. 300, R1373–R1383.

https://doi.org/10.1152/ajpregu.00689.2010

Manning, B.D., Toker, A., 2017. AKT/PKB Signaling: Navigating the Network. Cell 169,

381–405. https://doi.org/10.1016/j.cell.2017.04.001

Martin, J.A., Wang, Z., 2011. Next-generation transcriptome assembly. Nat. Rev. Genet. 12,

671–682. https://doi.org/10.1038/nrg3068

Mccairns, R.J.S., Smith, S., Sasaki, M., Bernatchez, L., Beheregaray, L.B., 2016. The

adaptive potential of subtropical rainbowfish in the face of climate change: Heritability

120

and heritable plasticity for the expression of candidate genes. Evol. Appl. 9, 531–545.

https://doi.org/10.1111/eva.12363

Naka, T., Nishimoto, N., Kishimoto, T., 2002. The paradigm of IL-6: from basic science to

medicine. Arthritis Res. 4, S233–S242. https://doi.org/10.1186/ar565

Nunes, J.R.S., Liu, S., Pértille, F., Perazza, C.A., Villela, P.M.S., Almeida-Val, V.M.F.,

Hilsdorf, A.W.S., Liu, Z., Coutinho, L.L., 2017. Large-scale SNP discovery and

construction of a high-density genetic map of Colossoma macropomum through

genotyping-by-sequencing. Sci. Rep. 7, 1–11. https://doi.org/10.1038/srep46112

Oliveira, A.M., Val, A.L., 2017. Effects of climate scenarios on the growth and physiology of

the Amazonian fish tambaqui (Colossoma macropomum) (Characiformes:

Serrasalmidae). Hydrobiologia 789, 167–178. https://doi.org/10.1007/s10750-016-2926-

Olsvik, P.A., Vikeså, V., Lie, K.K., Hevrøy, E.M., 2013. Transcriptional responses to

temperature and low oxygen stress in Atlantic salmon studied with next-generation

sequencing technology. BMC Genomics 14. https://doi.org/10.1186/1471-2164-14-817

Oomen, R.A., Hutchings, J.A., 2017. Transcriptomic responses to environmental change in

fishes: Insights from RNA sequencing. Facets 2, 610–641. https://doi.org/10.1139/facets-

2017-0015

Ostrensky, A., Borguetti, J.R., Soto, D., 2008. Aquicultura no Brasil. O desafio é crescer.

Secretaria Especial de Aquicultura e Pescas, Distrito Federal, BR.

Parmesan, C., 2006. Ecological and evolutionary responses to recent climate change. Annu.

Rev. Ecol. Evol. Syst. 37, 637–669.

https://doi.org/10.1146/annurev.ecolsys.37.091305.110100

Prado-Lima, M., Val, A.L., 2016. Transcriptomic characterization of tambaqui (Colossoma

macropomum, Cuvier, 1818) exposed to three climate change scenarios. PLoS One 11,

121

1–21. https://doi.org/10.1371/journal.pone.0152366

Ribeiro, C., 2010. A influência térmica na regulação da expressão gênica e sua ação na

dinâmica das membranas celulares. Rev. da Biol. 4, 6–9.

https://doi.org/10.7594/revbio.04.02

Robinson, M.D., McCarthy, D.J., Smyth, G.K., 2009. edgeR: A Bioconductor package for

differential expression analysis of digital gene expression data. Bioinformatics 26, 139–

140. https://doi.org/10.1093/bioinformatics/btp616

Salamin, N., Wüest, R.O., Lavergne, S., Thuiller, W., Pearman, P.B., 2010. Assessing rapid

evolution in a changing environment. Trends Ecol. Evol.

https://doi.org/10.1016/j.tree.2010.09.009

Santos, C.H.A., Santana, G.X., Sá-Leitão, C.S., Paula-Silva, M.N., Almeida-Val, V.M.F.,

2016. Loss of genetic diversity in farmed populations of Colossoma macropomum

estimated by microsatellites. Anim. Genet. 47, 373–376.

https://doi.org/10.1111/age.12422

Schneider, S.H., Semenov., S., Patwardhan, A., Burton, I., Magadza, C.H.D., Oppenheimer,

M., Pittock, A.B., Rahman, A., Smith, J.B., Suarez, A., Yamin, F., 2007. Assessing key

vulnerabilities and the risk from climate change., in: Parry, M.L., Canziani, O.F.,

Palutikof, J.P., van der Linder, P.J., Hanson, C.E. (Ed.), Climate Change 2007: Impacts,

Adaptation and Vulnerability. Contribution of Working Group II to the Fourth

Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge

University Press, Cambridge, UK, pp. 779–810. https://doi.org/10.2134/jeq2008.0015br

Schweikl, H., Hiller, K.-A., Eckhardt, A., Bolay, C., Spagnuolo, G., Stempfl, T., Schmalz, G.,

2008. Differential gene expression involved in oxidative stress response caused by

triethylene glycol dimethacrylate. Biomaterials 29, 1377–1387.

https://doi.org/10.1016/J.BIOMATERIALS.2007.11.049

122

Shendure, J., Ji, H., 2008. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–45.

https://doi.org/10.1038/nbt1486

Sherman, M.Y., Goldberg, A.L., 2004. Cellular defenses against unfolded proteins. Neuron

29, 15–32. https://doi.org/10.1016/s0896-6273(01)00177-5

Smith, S., Bernatchez, L., Beheregaray, L.B., 2013. RNA-seq analysis reveals extensive

transcriptional plasticity to temperature stress in a freshwater fish species. BMC

Genomics 14, 1. https://doi.org/10.1186/1471-2164-14-375

Tabet, F., Rye, K.-A., 2009. High-density lipoproteins, inflammation and oxidative stress.

Clin. Sci. 116, 87–98. https://doi.org/10.1042/CS20080106

Thorne, M.A.S., Burns, G., Fraser, K.P.P., Hillyard, G., Clark, M.S., 2010. Transcription

profiling of acute temperature stress in the Antarctic plunderfish Harpagifer antarcticus.

Mar. Genomics 3, 35–44. https://doi.org/10.1016/j.margen.2010.02.002

Todgham, A.E., Crombie, T.A., Hofmann, G.E., 2017. The effect of temperature adaptation

on the ubiquitin-proteasome pathway in notothenioid fishes. J. Exp. Biol. 220, 369–378.

https://doi.org/10.1242/jeb.145946

Tomalty, K.M.H., Meek, M.H., Stephens, M.R., Rincón, G., Fangue, N.A., May, B.P.,

Baerwald, M.R., 2015. Transcriptional response to acute thermal exposure in juvenile

Chinook salmon determined by RNAseq. Genes, Genomes, Genet. 5, 1335–1349.

https://doi.org/10.1534/g3.115.017699

Walther, G.-R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J.C., Fromentin, J.-

M., Hoegh-Guldberg, O., Bairlein, F., 2002. Ecological responses to recent climate

change. Nature 416, 389–395. https://doi.org/10.1038/416389a

Wang, P., Bouwman, F.G., Mariman, E.C.M., 2009. Generally detected proteins in

comparative proteomics - A matter of cellular stress response? Proteomics 9, 2955–2966.

https://doi.org/10.1002/pmic.200800826

123

Wang, Z., Gerstein, M., Snyder, M., 2009. RNA-Seq: a revolutionary tool for transcriptomics.

Nat. Rev. Genet. 10, 57–63. https://doi.org/10.1038/nrg2484

Wegele, H., Müller, L., Buchner, J., 2001. Hsp70 and Hsp90 - a relay team for protein

folding, in: Portland, S.G. Amara, Frankfurt, E.B, Innsbruck, H.G., Gottingen, R.J.,

Baltimore, W.J. Lederer, Tokyo, A. Miyajima, Zurich, H. Murer, Heidelberg,

Offermanns, S., Berlin, G. Schultz, Berlin, M.S. (Ed.), Reviews of Physiology,

Bochemistry and Pharmacology. Springer, Berlin, Heidelberg, DE, pp. 1–44.

Weiss, R.E., Refetoff, S., 2015. Genetic diagnosis of endocrine disorders. Academic Press,

San Diego, USA.

Wolf, A., Rietscher, K., Glass, M., Huttelmaier, S., Schutkowski, M., Ihling, C., Sinz, A.,

Wingenfeld, A., Mun, A., Hatzfeld, M., 2013. Insulin signaling via Akt2 switches

plakophilin 1 function from stabilizing cell adhesion to promoting cell proliferation. J.

Cell Sci. 126, 1832–1844. https://doi.org/10.1242/jcs.118992

Woodward, G., Perkins, D.M., Brown, L.E., 2010. Climate change and freshwater

ecosystems: Impacts across multiple levels of organization. Philos. Trans. R. Soc. B

Biol. Sci. https://doi.org/10.1098/rstb.2010.0055

Xia, J., Benner, M.J., Hancock, R.E.W., 2014. NetworkAnalyst - Integrative approaches for

protein-protein interaction network analysis and visual exploration. Nucleic Acids Res.

42, 167–174. https://doi.org/10.1093/nar/gku443

Xu, Q., Feng, C.Y., Hori, T.S., Plouffe, D.A., Buchanan, J.T., Rise, M.L., 2013. Family-

specific differences in growth rate and hepatic gene expression in juvenile triploid

growth hormone (GH) transgenic Atlantic salmon (Salmo salar). Comp. Biochem.

Physiol. - Part D Genomics Proteomics 8, 317–333.

https://doi.org/10.1016/j.cbd.2013.09.002

Zhang, T., Ye, Y., 2014. The final moments of misfolded proteins en route to the proteasome.

124

DNA Cell Biol. 33, 477–483. https://doi.org/10.1089/dna.2014.2452

125

Tables

Table 1. Physicochemical parameters of tank water maintained for 30 days in environmental rooms. Data are shown as mean ± standard error of the mean (N = 30); minimum and maximum values are in parentheses. *Significant differences from the current scenario (Student’s t-test, P < 0.05), showing the effectiveness of the artificial variation between the rooms.

Water temperature Water CO2 Water O2 pH Total (°C) (ppm) (mg.L-1) ammonia (mM) Northern population

Current 26.1±0.2 11.7±1.9 7.3±0.1 6.9±0.09 0.17±0.01 (24.0-28.0) (6-29) (6.2-8.6) (5.6-7.3) (0.008-0.269)

Extreme 29.0±0.2* 17.7±2.2* 6.5±0.1* 6.5±0.09* 0.21±0.01* (26.7-30.7) (12-36) (5.3-8.1) (5.4-7.5) (0.02-0.254)

Southeastern population

Current 26.3±0.2 11.5±1.4 7.2±0.1 6.8±0.06 0.15±0.01 (23.9-28.2) (7-25) (6.2-8.7) (6.3-6.9) (0.006-0.183)

Extreme 29.1±0.2* 18.4±2.0* 6.8±0.1* 6.6±0.07 0.18±0.01 (26.8-30.9) (14-30) (5.9-8.2) (5.8-6.9) (0.014-0.140)

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Table 2. Hub genes enrichment analysis of the northern population exposed to the extreme climate scenario.

Extreme scenario Pathway Gene LogFC Protein effect GO:0034605 Cellular response to Up YWHAE 9.30 14-3-3 protein epsilon heat SSH1 8.84 Protein phosphatase Slingshot homolog 1 GO:0019538 Protein metabolic Up KH domain-containing, RNA-binding, signal process KHDRBS1 7.71 transduction-associated protein 1 GO:0006457 Protein folding Up PFDN2 8.54 Prefoldin subunit 2 GO:0015833 Peptide transport Up NUP62 7.89 Nuclear pore glycoprotein p62 UBE3A 7.65 Ubiquitin-protein ligase E3A GO:0016567 Protein ubiquitination Up UBE2J2 6.84 Ubiquitin-conjugating enzyme E2 J2 Mitogen-activated protein kinase kinase MAP3K7 7.34 kinase 7 GO:0000165 MAPK cascade Up Progestin and adipoQ receptor family PAQR3 6.64 member 3 GO:0006979 Response to oxidative Up DUSP1 6.94 Dual specificity protein phosphatase 1 stress ERCC6 6.85 DNA excision repair protein ERCC-6 GO:0006974 DNA damage response Up PLK2 6.68 Serine/threonine-protein kinase PLK2 GO:0001525 Angiogenesis Up HIPK1 6.87 Homeodomain-interacting protein kinase 1 GO:0042113 B cell activation Up CD22 4.79 B-cell receptor CD22 GO:0030154 Cell differentiation Down TBCB -7.83 Tubulin-folding cofactor B TXNRD1 -7.76 Thioredoxin reductase 1, cytoplasmic GO:0008283 Cell proliferation Down SGK2 -7.39 Serine/threonine-protein kinase Sgk2 GO:0050821 Protein stabilisation Down MORC3 -7.74 MORC family CW-type zinc finger protein 3 GO:0016567 Protein ubiquitination Down UBA1 -7.38 Ubiquitin-like modifier-activating enzyme 1

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Table 3. Hub genes enrichment analysis of the southeastern population exposed to the extreme climate scenario.

Extreme scenario Pathway Gene LogFC Protein effect GO:0030949 Positive regulation of Up GRB10 8.21 Growth factor receptor-bound protein 10 VEGF receptor signalling pathway ULK1 8.12 Serine/threonine-protein kinase ULK1 GO:0006950 Response to stress Up RB1CC1 6.67 RB1-inducible coiled-coil protein 1 ATP6V1E1 7.81 V-type proton ATPase subunit E 1 GO:0050801 Ion homeostasis Up TFRC 7.35 Transferrin receptor protein 1 GO:0042632 Cholesterol homeostasis Up LDLR 7.61 Low-density lipoprotein receptor GO:0050729 Positive regulation of Up IL6ST 7.49 Interleukin 6 signal transducer inflammatory response AKT2 7.33 RAC-beta serine/threonine-protein kinase Mitogen-activated protein kinase kinase MAP3K1 6.70 GO:0000165 MAPK cascade Up kinase 1 Tyrosine-protein phosphatase non-receptor PTPN6 6.67 type 6 GO:0010628 Positive regulation of Up KDM6A 6.63 Lysine-specific demethylase 6A gene expression GO:0051726 Regulation of cell cycle Up CCNG1 6.62 Cyclin-G1 GO:0030036 Actin cytoskeleton FLII -8.95 Protein flightless-1 homolog Down organisation ARHGEF5 -6.95 Rho guanine nucleotide exchange factor 5 SPTAN1 -8.05 Spectrin alpha chain, non-erythrocytic 1 GO:0003779 Actin binding Down MPRIP -7.49 Myosin phosphatase Rho-interacting protein GO:0006096 Glycolysis Down GCK -7.36 Glucokinase ATG5 -7.29 Autophagy protein 5 GO:0006914 Autophagy Down ULK2 -6.84 Serine/threonine-protein kinase ULK2 APOA1 -7.25 Apolipoprotein A-I GO:0006629 Lipid metabolism Down POR -7.04 NADPH--cytochrome P450 reductase GO:0042127 Regulation of cell Down PIAS1 -6.90 E3 SUMO-protein ligase PIAS1 proliferation GO:0050790 Regulation of catalytic Down CAPN1 -6.90 Calpain-1 catalytic subunit activity GO:0006357 Regulation of SMARCC1 -6.59 SWI/SNF complex subunit SMARCC1 Down transcription BCL3 -6.47 B-cell lymphoma 3 protein UDP-N-acetylglucosamine--peptide N- GO:0032922 Circadian regulation of Down OGT -6.49 acetylglucosaminyltransferase 110 kDa gene expression subunit

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Figures

Figure 1. Daily variations in air temperature (upper) and CO2 concentration (bottom) in the two environmental room facilities over the 30-day experimental period.

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Figure 2. Representation of the functional categories involved in the response to an extreme climate scenario, in relation to the current scenario, for northern tambaqui (Colossoma macropomum). The top 10 upregulated (upper) and top 10 downregulated (bottom) terms are based on three general Gene Ontology (GO) categories: Biological Process (BP), Cell Component (CC) and Molecular Function (MF). The percentages below each term are related to the differentially expressed genes (DEGs) that belong to each category.

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Figure 3. Representation of the functional categories involved in the response to an extreme climate scenario, in relation to the current scenario, for southeastern tambaqui (Colossoma macropomum). The top 10 upregulated (upper) and top 10 downregulated (bottom) terms are based on three general Gene Ontology (GO) categories: Biological Process (BP), Cell Component (CC) and Molecular Function (MF). The percentages below each term are related to the differentially expressed genes (DEGs) that belong to each category.

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Figure 4. Protein-protein interaction (PPI) network of DEGs in northern tambaqui (Colossoma macropomum) after 30 days of exposure to an extreme scenario. Size of the circles represents the relative amount of expression. Each hub (red or green) represents prior genes that correlate and form a PPI network. Red hubs represent upregulated genes and green hubs represent downregulated genes, in relation to the current scenario. Smaller grey nodes reflect interacting non-differentially expressed genes.

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Figure 5. Protein-protein interaction (PPI) network of DEGs in southeastern tambaqui (Colossoma macropomum) after 30 days of exposure to an extreme scenario. Size of the circles represents the relative amount of expression. Each hub (red or green) represents prior genes that correlate and form a PPI network. Red hubs represent upregulated genes and green hubs represent downregulated genes, in relation to the current scenario. Smaller grey nodes reflect interacting non-differentially expressed genes.

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Figure 6. Expression profiles (heat map) of DEGs in the liver of northern (upper) and southeastern (bottom) tambaqui (Colossoma macropomum) after 30 days of exposure to current and extreme climate scenarios. Bar colours intensity represents at least two-fold changes in expression levels; yellow indicates higher expression, whereas purple shows lower expression.