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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
LUCIANA MARA FÉ GONÇALVES
Manaus, Amazonas
Dezembro, 2019
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
ii
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)
*Pesquisa autorizada pelo CEUA/INPA em 07 de novembro de 2016 (n°. de aprovação 032/2016).
†Projeto sob Anotação de Responsabilidade Técnica (ART) n°. 2016/00090 – CRBio 90936/06-D.
Manaus, Amazonas Dezembro, 2019
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
Dezembro/2011
iii
iv
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
v
Ao meu amor, André. Porque família é tudo.
vi
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.
vii
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!
viii
‘‘Deus está contigo em tudo o que fazes’’. Gn 21,22
ix
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.
x
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.
xi
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
xii
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
xiii
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
xiv
LISTA DE ABREVIAÇÕES E SIGLAS
AM
A1B
A2
B1
Amazonas
Cenário moderado
Cenário extremo
Cenário brando
CEUA Comissão de Ética em Pesquisa no Uso de Animais
cm Centímetro
CO2
CO3-2
Dióxido de carbono
Íon carbonato
CONCEA
CTTPA
Conselho Nacional de Controle de Experimentação Animal
Centro de Tecnologia, Treinamento e Produção em Aquicultura
°C Graus Celsius
DEG
DNA
cDNA
Gene Diferencialmente Expresso (do inglês, Differentially
Expressed Gene)
Ácido desoxirribonucleico (do inglês, Deoxyribonucleic Acid)
DNA complementar
FDR Taxa de Falsa Descoberta (do inglês, False Discovery Rate)
g
GEE
Grama
Gases de Efeito Estufa
GO Ontologia Gênica (do inglês, Gene Ontology)
GTA
H+
IBAMA
IBGE
Guia de Trânsito Animal
Próton hidrogênio
Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais
Renováveis
Instituto Brasileiro de Geografia e Estatística
INPA
INPE
Instituto Nacional de Pesquisas da Amazônia
Instituto Nacional de Pesquisas Espaciais
IPCC Painel Intergovernamental sobre Mudanças Climáticas (do inglês:
Intergovernmental Panel on Climate Change)
KEGG
Kg
Enciclopédia de Genes e Genoma de Kyoto (do inglês, Kyoto
Encyclopedia of Genes and Genomes)
Quilograma
L
m
Litro
Metro
MAPA
mg
mg.L-1
Ministério da Agricultura, Pecuária e Abastecimento
Miligrama
Miligrama por litro
mgO2.L-1 Miligrama de oxigênio por litro
mM
MPA
NH4Cl
NGS
Milimolar
Ministério da Pesca e Aquicultura
Cloreto de amônio
Sequenciamento de nova geração (do inglês, Next Generation
xv
NO2-
OD
Sequencing)
Íon nitrito
Oxigênio dissolvido
pb
PCR
Par de bases
Reação de Polimerização em Cadeia (do inglês, Polymerase
Chain Reaction)
pH Potencial hidrogeniônico
pM Picomolar
PPI
ppm
Interação Proteína-Proteína (do inglês, Protein-Protein Interaction)
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-Seq
SEPA
RNA ribossomal
Sequenciamento de RNA (do inglês, RNA Sequencing)
Secretaria Executiva de Pesca e Aquicultura SEPROR Secretaria de Estado da Produção Rural
SP São Paulo
μg
μL
Micrograma
Microlitro
16
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
estufa (GEE1) 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
17
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
18
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
19
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
de íons carbonato (CO3-2) 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).
20
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
21
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).
22
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).
23
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
24
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
25
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
concentração de oxigênio (4 mgO2.L-1) 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
26
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
27
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
28
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
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).
29
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),
30
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
(~25,7 °C; 7,0 mgO2.L-1; 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
31
alimentados uma vez por dia (15h) com ração pelletilizada com 32% de proteína
bruta (Nutripeixe, Purina).
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
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.
32
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).
33
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.
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
Temperatura
(°C)
CO2
(ppm)
OD
(mgO2.L-1)
pH Amônia total
(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
34
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).
35
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
36
(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
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
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).
37
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.
38
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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)
52
Status atual da submissão em 10/12/2019.
53
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]
54
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.
55
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).
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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
57
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
58
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
59
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).
60
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.
61
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
62
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
63
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
64
(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
65
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.
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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
72
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
DN15136_c0_g1_i1 12.54 IDI1 Isopentenyl-diphosphate
delta isomerase 1
GO:0006629 lipid metabolic
process
GO:0016787 hydrolase
activity
DN16258_c2_g1_i3 10.71 creb3l3b
Cyclic AMP-responsive
element-binding protein
3-like protein 3-B
GO:0006355 regulation of
transcription, DNA-templated
GO:0001071 nucleic acid
binding transcription factor
activity
DN15150_c1_g1_i17 9.98 AP1G1 AP-1 complex subunit
gamma-1
GO:0016192 vesicle-mediated
transport
DN13446_c7_g1_i1 9.94 CNNM3 Metal transporter CNNM3 GO:0022857 transmembrane
transporter activity
DN15804_c1_g1_i17 9.65 SLMAP Sarcolemmal membrane-
associated protein
GO:0008104 protein
localization
DN13528_c0_g1_i2 9.11 ACIN1 Apoptotic chromatin
condensation inducer 1
GO:0042981 regulation of
apoptotic process
GO:0003676 nucleic acid
binding
DN13489_c2_g4_i1 8.31 Pphln1 Periphilin-1 GO:0006355 regulation of
transcription, DNA-templated
DN14576_c3_g2_i5 8.30 Golt1a Golgi transport 1A GO:0016192 vesicle-mediated
transport
DN15916_c5_g1_i3 8.28 NUFIP2 Nuclear FMR1-interacting
protein 2 GO:0003723 RNA binding
DN13598_c6_g1_i5 8.18 HHIP Hedgehog-interacting
protein
GO:0009966 regulation of
signal transduction
GO:0008270 zinc ion binding
DN15473_c4_g1_i8 8.09 ADAMTSL5 ADAMTS-like protein 5 GO:1901681 sulfur compound
binding
DN13646_c3_g1_i1 8.02 ECI2 Enoyl-CoA delta
isomerase 2
GO:0006629 lipid metabolic
process
GO:0000062 fatty-acyl-CoA
binding
DN13372_c3_g2_i5 7.92 maea E3 ubiquitin-protein
transferase MAEA
GO:0070646 protein
modification by small protein
removal
GO:0004842 ubiquitin-protein
transferase activity
DN12837_c0_g1_i1 7.77 MRPL9 39S ribosomal protein L9
GO:0006412 translation
GO:0016072 rRNA metabolic
process
DN16263_c2_g1_i3 7.64 PROC Vitamin K-dependent
protein C
GO:0050727 regulation of
inflammatory response
GO:0004252 serine-type
endopeptidase activity
DN15506_c4_g2_i1 7.55 Cd7 T-cell antigen CD7 GO:0038023 signaling
receptor activity
73
GO:0016021 integral
component of membrane
DN14966_c2_g1_i12 7.53 Igf2bp2
Insulin-like growth factor
2 mRNA-binding
protein 2
GO:0006810 transport
GO:0003723 RNA binding
DN15289_c7_g1_i12 7.46 KANK1
KN motif and ankyrin
repeat domain-
containing protein 1
GO:0006355 regulation of
transcription, DNA-templated
GO:0005515 protein binding
DN15264_c5_g2_i9 7.43 LY75 Lymphocyte antigen 75
GO:0006954 inflammatory
response
GO:0004888 transmembrane
signaling receptor activity
DN15198_c0_g1_i4 7.38 LRRC41 Leucine-rich repeat-
containing protein 41
GO:0070646 protein
modification by small protein
removal
DN14189_c1_g1_i5 7.26 CYP2J2 Cytochrome P450 2J2
GO:0006629 lipid metabolic
process
GO:0016705 cytochrome
p450 activity
DN14494_c1_g2_i8 7.13 PDIA3 Protein disulfide-
isomerase
GO:0006457 protein folding
GO:0016853 isomerase
activity
DN15995_c4_g1_i11 7.10 THRAP3
Thyroid hormone
receptor-associated
protein 3
GO:0048511 rhythmic
process
GO:0003713 transcription
coactivator activity
DN14712_c2_g1_i1 7.08 NLRC3
NLR family CARD
domain-containing
protein 3
GO:0035556 intracellular
signal transduction
GO:0005524 ATP binding
DN15785_c1_g2_i10 6.99 glyr1 Putative oxidoreductase
GLYR1
GO:0016491 oxidoreductase
activity
DN15537_c3_g1_i2 6.92 MTSS1 Metastasis suppressor
protein 1
GO:0007009 plasma
membrane organization
GO:0003779 actin binding
DN16282_c0_g2_i1 6.78 cgn Cingulin GO:0003774 motor activity
DN14613_c2_g2_i4 6.62 impad1 Inositol monophosphatase
3
GO:0016791 phosphatase
activity
DN15794_c0_g1_i5 6.61 Sh3d19 SH3 domain-containing
protein 19
GO:0007010 cytoskeleton
organization
DN16271_c6_g1_i11 6.18 slc29a1 Solute carrier family 29
member 1a
GO:0015858 nucleoside
transport
GO:0005337 nucleoside
transmembrane transporter
activity
DN13894_c1_g1_i10 5.48 apmap
Adipocyte plasma
membrane-associated
protein
GO:0009058 biosynthetic
process
GO:0016844 strictosidine
synthase activity
DN13396_c1_g2_i5 5.44 cyp2k1 Cytochrome P450 2K1
GO:0030258 lipid
modification
GO:0016705 cytochrome
p450 activity
74
DN14963_c0_g1_i20 5.35 Nlrc3 Protein NLRC3
GO:0035556 intracellular
signal transduction
GO:0005524 ATP binding
DN15981_c2_g5_i2 5.08 SUGCT
Succinate--
hydroxymethylglutarate
CoA-transferase
GO:0016782 transferase
activity, transferring sulfur-
containing groups
DN16498_c5_g1_i9 4.97 l-2 Lactose-binding lectin l-2
GO:0006952 defense response
GO:0030246 carbohydrate
binding
DN13568_c0_g2_i7 4.91 AFDN Afadin
GO:0007155 cell adhesion
GO:0050839 cell adhesion
molecule binding
DN16570_c8_g1_i10 4.81 Cyp27a1 Sterol 26-hydroxylase
GO:0042632 cholesterol
homeostasis
GO:0004497 monooxygenase
activity
DN14390_c2_g4_i1 4.77 CPT1A Carnitine O-
palmitoyltransferase 1
GO:0006629 lipid metabolic
process
GO:0016746 transferase
activity, transferring acyl
groups
DN16417_c2_g10_i1 4.77 CXCL8 Interleukin-8
GO:0006955 immune
response
GO:0008009 chemokine
activity
DN13592_c4_g1_i17 4.59 riox1 Ribosomal oxygenase 1
GO:0016570 histone
modification
GO:0051213 dioxygenase
activity
DN13897_c0_g1_i12 4.21 epd Ependymin
GO:0007160 cell-matrix
adhesion
GO:0005509 calcium ion
binding
DN16042_c2_g3_i3 4.00 Ermap Erythroid membrane-
associated protein
GO:0050776 regulation of
immune response
GO:0005102 signaling
receptor binding
DN15608_c1_g1_i4 3.82 DNAJC13 DnaJ homolog subfamily
C member 13 GO:0015031 protein transport
DN13357_c1_g1_i2 3.53 Tpk1 Thiamin
pyrophosphokinase 1
GO:0009229 thiamine
diphosphate biosynthetic
process
GO:0004788 thiamine
diphosphokinase activity
DN15595_c0_g1_i1 3.41 PC Pyruvate carboxylase
GO:0005975 carbohydrate
metabolic process
GO:0016874 ligase activity
DN13635_c1_g1_i7 3.35 AKR1B1 Aldo-keto reductase
family 1 member B1
GO:0005975 carbohydrate
metabolic process
GO:0016491 oxidoreductase
activity
DN16392_c0_g3_i1 3.28 PDLIM2 PDZ and LIM domain GO:0005856 cytoskeleton
75
protein 2 GO:0003779 actin binding
DN15624_c0_g1_i16 3.13 Srsf5 Serine/arginine-rich
splicing factor 5
GO:0006397 mRNA
processing
GO:0003723 RNA binding
DN13615_c0_g9_i2 3.06 Nop53 Ribosome biogenesis
protein NOP53
GO:0006364 rRNA
processing
GO:0042802 identical protein
binding
76
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
Lipid metabolic process 19 Regulation of biological
quality 28
Organic acid metabolic process 16 Response to stress 27
Carboxylic acid metabolic
process 15
Regulation of response to
stimulus 24
Cellular lipid metabolic process 14 Programmed cell death 22
Carbohydrate metabolic process 13 Apoptotic process 21
Generation of precursor
metabolites and energy 13
Regulation of multicellular
organismal process 20
Lipid biosynthetic process 12 Immune system process 20
Nucleotide metabolic process 12 Regulation of signal
transduction 19
Response to endogenous
stimulus 12 Cellular localization 19
Energy derivation by oxidation
of organic compounds 11 Catabolic process 18
Response to hormone stimulus 10 Cellular catabolic process 17
Coenzyme metabolic process 9 Establishment of localization
in cell 17
Cofactor metabolic process 9 Regulation of apoptotic
process 16
Purine nucleotide metabolic
process 9
Regulation of programmed cell
death 16
Alcohol metabolic process 8 Cellular component assembly 14
Intracellular protein transport 8 Intracellular transport 13
Peptidyl_amino acid
modification 8 Response to external stimulus 13
Purine ribonucleotide metabolic
process 8 Cellular response to stress 13
Ribonucleotide metabolic
process 8 Tissue development 13
Cellular amino acid metabolic
process 7 Homeostatic process 12
Cellular respiration 7 Cell migration 11
Monocarboxylic acid metabolic
process 7
Enzyme linked receptor
protein signaling pathway 10
Regulation of body fluid levels 7 Regulation of immune system
process 10
Steroid metabolic process 7 Carbohydrate metabolic
process 10
77
Coenzyme biosynthetic process 6 Hemopoiesis 9
Cofactor biosynthetic process 6 Hematopoietic or lymphoid
organ development 9
Leukocyte migration 6 Immune system development 9
Protein folding 6 Purine ribonucleotide
metabolic process 9
Steroid biosynthetic process 6 Ribonucleotide metabolic
process 9
Aging 5 Purine nucleotide metabolic
process 9
Glucose metabolic process 5 Cellular homeostasis 9
Negative regulation of
phosphate metabolic process 5 Nucleotide metabolic process 9
Nucleotide biosynthetic process 5
Negative regulation of
multicellular organismal
process
8
Protein oligomerization 5 Regulation of cell migration 8
Carbohydrate biosynthetic
process 4
Regulation of catabolic
process 8
Cellular modified amino acid
metabolic process 4 Regulation of body fluid levels 8
Energy reserve metabolic
process 4
Positive regulation of immune
system process 8
Isoprenoid metabolic process 4 Cell_substrate adhesion 7
Regulation of lipid metabolic
process 4 Regulation of cell adhesion 7
Response to steroid hormone
stimulus 4
Regulation of small GTPase
mediated signal transduction 7
Triglyceride metabolic process 4 Regulation of response to
external stimulus 7
Glutamine family amino acid
metabolic process 3 Blood coagulation 7
Leukocyte chemotaxis 3 Coagulation 7
Protein N_linked glycosylation 3 Hemostasis 7
Protein targeting to membrane 3 Behavior 7
Response to carbohydrate
stimulus 3 Vasculature development 7
Response to toxin 3 Wound healing 7
Aerobic respiration 2 Regulation of anatomical
structure morphogenesis 7
Cellular modified amino acid
biosynthetic process 2 Tissue remodeling 6
Excretion 2 Regulation of Rho protein
signal transduction 6
78
Positive regulation of cell
migration 6
Intracellular receptor mediated
signaling pathway 6
Regulation of Ras protein
signal transduction 6
Cellular component
disassembly 6
RNA splicing 6
Regulation of cell
morphogenesis 6
Response to drug 6
Muscle cell differentiation 6
Nucleocytoplasmic transport 6
Nuclear transport 6
Leukocyte differentiation 6
RNA splicing 6
Positive regulation of
hydrolase activity 6
Actin cytoskeleton
organization 6
MRNA processing 6
Positive regulation of cellular
component organization 6
Actin filament_based process 6
Positive regulation of cell
adhesion 5
Rhythmic process 5
Response to hypoxia 5
Myeloid cell differentiation 5
Leukocyte migration 5
Cellular protein complex
assembly 5
Circadian rhythm 4
Intracellular steroid hormone
receptor signaling pathway 4
Rho protein signal
transduction 4
Intrinsic apoptotic signaling
pathway 4
Response to carbohydrate
stimulus 4
Protein maturation 4
Maintenance of location 4
79
Post_translational protein
modification 4
Transforming growth factor
beta receptor signaling
pathway
4
Protein import into nucleus 4
Nuclear import 4
Protein folding 4
Extracellular structure
organization 4
Regulation of GTPase activity 4
Lymphocyte differentiation 4
Apoptotic signaling pathway 4
Protein import 4
Regulation of MAP kinase
activity 4
Ras protein signal transduction 4
Myoblast differentiation 3
Androgen receptor signaling
pathway 3
Regulation of JUN kinase
activity 3
B cell differentiation 3
Regulation of Rho GTPase
activity 3
Maintenance of protein
location in cell 3
Regulation of cell shape 3
Regulation of transforming
growth factor beta receptor
signaling pathway
3
Maintenance of location in cell 3
Maintenance of protein
location 3
Cell maturation 3
Protein N_linked glycosylation 3
Protein processing 3
Leukocyte chemotaxis 3
Regulation of JNK cascade 3
Cyclic nucleotide metabolic
process 3
Epidermal growth factor
receptor signaling pathway 3
Protein polymerization 3
80
Regulation of Ras GTPase
activity 3
Developmental maturation 3
Focal adhesion assembly 2
Bone remodeling 2
Positive regulation of JUN
kinase activity 2
Vacuole organization 2
Regulation of cell_cell
adhesion 2
Cytoplasm organization 1
81
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).
82
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.
Mo
lecular F
unctio
nGO:0005488 Binding
GO:0003824 Catalytic activity
GO:0043167 Ion binding
GO:1901363 Heterocyclic compound binding
GO:0097159 Organic cyclic compound binding
GO:0005515 Protein binding
GO:0016491 Oxidoreductase activity
GO:0016787 Hydrolase activity
GO:0036094 Small molecule binding
GO:0043168 Anion binding
3.1%
2.9%
1.6%
1.5%
1.5%
1.0%
1.0%
0.9%
0.9%
0.9%
Number of contigs
0 10 20 30 40 50 60 70 80 90 100 110
Bio
log
ical Pro
cess
GO:0008152 Metabolic process
GO:0044699 Single-organism process
GO:0071704 Organic substance metabolic process
GO:0044710 Single-organism metabolic process
GO:0044238 Primary metabolic process
GO:0009987 Cellular process
GO:0044237 Cellular metabolic process
GO:0006807 Nitrogen compound metabolic process
GO:1901564 Organonitrogen compound metabolism
GO:0055114 Oxidation-reduction process
2.8%
1.9%
1.5%
1.5%
1.5%
1.4%
1.1%
1.0%
1.5%
0.8%
Cellu
lar Co
mp
onen
t
GO:0044464 Cell part
GO:0044424 Intracellular part
GO:0044425 Membrane part
GO:0016021 Integral component of membrane
GO:0031224 Intrinsic component of membrane
GO:0032991 Macromolecular complex
GO:0044444 Cytoplasmic part
GO:0016020 Membrane
GO:0043229 Intracellular organelle
GO:0043226 Organelle
0.8%
0.7%
0.7%
0.5%
0.5%
0.5%
0.4%
0.3%
0.3%
0.3%
83
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.
Number of contigs
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160
Bio
logical P
rocess
GO:0008152 Metabolic process
GO:0009987 Cellular process
GO:0044699 Single-organism process
GO:0071704 Organic substance metabolic process
GO:0044238 Primary metabolic process
GO:0044710 Single-organism metabolic process
GO:0006807 Nitrogen compound metabolic process
GO:0065007 Biological regulation
GO:0043170 Macromolecule metabolic process
GO:0050789 Regulation of biological process
2.3%
1.4%
1.4%
1.2%
1.2%
1.0%
0.9%
0.9%
0.9%
0.9%
3.5%
2.6%
1.8%
1.8%
1.8%
1.5%
1.0%
1.1%
0.9%
0.8%
Mo
lecular F
unctio
nGO:0005488 Binding
GO:0003824 Catalytic activity
GO:0043167 Ion binding
GO:1901363 Heterocyclic compound binding
GO:0097159 Organic cyclic compound binding
GO:0005515 Protein binding
GO:0043169 Cation binding
GO:0046872 Metal ion binding
GO:0016491 Oxidoreductase activity
GO:0016787 Hydrolase activity
Cellu
lar Co
mpo
nen
t
GO:0044464 Cell part
GO:0044424 Intracellular part
GO:0044425 Membrane part
GO:0016021 Integral component of membrane
GO:0031224 Intrinsic component of membrane
GO:0016020 Membrane
GO:0043226 Organelle
GO:0043229 Intracellular organelle
GO:0043227 Membrane-bounded organelle
GO:0044444 Cytoplasmic part
0.7%
0.7%
0.5%
0.5%
0.5%
0.4%
0.4%
0.4%
0.3%
0.3%
84
Figure 4. Functional representation based on KEGG pathways for differentially expressed
gene-sets in the Balbina (upper) and Brumado (lower) populations.
85
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.
86
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.
87
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
PPP2R3B 8.95 Serine/threonine-protein phosphatase 2A regulatory subunit B''
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
88
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
RPN2 2.94 Dolichyl-diphosphooligosaccharide--protein glycosyltransferase
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
NDUFB8 2.24 NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 8,
mitochondrial
NDUFS2 2.22 NADH dehydrogenase [ubiquinone] iron-sulfur protein 2,
mitochondrial
ENO1 2.17 Alpha-enolase
89
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-
90
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
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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)
92
Status atual da submissão em 10/12/2019.
93
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
94
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
95
in dealing with upcoming climate changes.
96
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
97
(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
98
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
99
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,
respectively, 25.7°C, 7.0 mgO2.L-1, 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
100
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,
101
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
105
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
107
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
108
(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).
109
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.
110
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).
112
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.
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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
(°C)
Water CO2
(ppm)
Water O2
(mg.L-1)
pH Total
ammonia
(mM)
Northern population
Current 26.1±0.2
(24.0-28.0)
11.7±1.9
(6-29)
7.3±0.1
(6.2-8.6)
6.9±0.09
(5.6-7.3)
0.17±0.01
(0.008-0.269)
Extreme 29.0±0.2*
(26.7-30.7)
17.7±2.2*
(12-36)
6.5±0.1*
(5.3-8.1)
6.5±0.09*
(5.4-7.5)
0.21±0.01*
(0.02-0.254)
Southeastern population
Current 26.3±0.2
(23.9-28.2)
11.5±1.4
(7-25)
7.2±0.1
(6.2-8.7)
6.8±0.06
(6.3-6.9)
0.15±0.01
(0.006-0.183)
Extreme 29.1±0.2*
(26.8-30.9)
18.4±2.0*
(14-30)
6.8±0.1*
(5.9-8.2)
6.6±0.07
(5.8-6.9)
0.18±0.01
(0.014-0.140)
126
Table 2. Hub genes enrichment analysis of the northern population exposed to the extreme
climate scenario.
Pathway Extreme scenario
effect Gene LogFC Protein
GO:0034605 Cellular response to
heat Up YWHAE 9.30 14-3-3 protein epsilon
GO:0019538 Protein metabolic
process Up
SSH1 8.84 Protein phosphatase Slingshot homolog 1
KHDRBS1 7.71 KH domain-containing, RNA-binding, signal
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
GO:0016567 Protein ubiquitination Up UBE3A 7.65 Ubiquitin-protein ligase E3A
UBE2J2 6.84 Ubiquitin-conjugating enzyme E2 J2
GO:0000165 MAPK cascade Up
MAP3K7 7.34 Mitogen-activated protein kinase kinase
kinase 7
PAQR3 6.64 Progestin and adipoQ receptor family
member 3
GO:0006979 Response to oxidative
stress Up DUSP1 6.94 Dual specificity protein phosphatase 1
GO:0006974 DNA damage response Up ERCC6 6.85 DNA excision repair protein ERCC-6
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
GO:0008283 Cell proliferation Down TXNRD1 -7.76 Thioredoxin reductase 1, cytoplasmic
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
127
Table 3. Hub genes enrichment analysis of the southeastern population exposed to the
extreme climate scenario.
Pathway Extreme scenario
effect Gene LogFC Protein
GO:0030949 Positive regulation of
VEGF receptor signalling pathway Up GRB10 8.21 Growth factor receptor-bound protein 10
GO:0006950 Response to stress Up ULK1 8.12 Serine/threonine-protein kinase ULK1
RB1CC1 6.67 RB1-inducible coiled-coil protein 1
GO:0050801 Ion homeostasis Up ATP6V1E1 7.81 V-type proton ATPase subunit E 1
TFRC 7.35 Transferrin receptor protein 1
GO:0042632 Cholesterol homeostasis Up LDLR 7.61 Low-density lipoprotein receptor
GO:0050729 Positive regulation of
inflammatory response Up IL6ST 7.49 Interleukin 6 signal transducer
GO:0000165 MAPK cascade Up
AKT2 7.33 RAC-beta serine/threonine-protein kinase
MAP3K1 6.70 Mitogen-activated protein kinase kinase
kinase 1
PTPN6 6.67 Tyrosine-protein phosphatase non-receptor
type 6
GO:0010628 Positive regulation of
gene expression Up KDM6A 6.63 Lysine-specific demethylase 6A
GO:0051726 Regulation of cell cycle Up CCNG1 6.62 Cyclin-G1
GO:0030036 Actin cytoskeleton
organisation Down
FLII -8.95 Protein flightless-1 homolog
ARHGEF5 -6.95 Rho guanine nucleotide exchange factor 5
GO:0003779 Actin binding Down SPTAN1 -8.05 Spectrin alpha chain, non-erythrocytic 1
MPRIP -7.49 Myosin phosphatase Rho-interacting protein
GO:0006096 Glycolysis Down GCK -7.36 Glucokinase
GO:0006914 Autophagy Down ATG5 -7.29 Autophagy protein 5
ULK2 -6.84 Serine/threonine-protein kinase ULK2
GO:0006629 Lipid metabolism Down APOA1 -7.25 Apolipoprotein A-I
POR -7.04 NADPH--cytochrome P450 reductase
GO:0042127 Regulation of cell
proliferation Down PIAS1 -6.90 E3 SUMO-protein ligase PIAS1
GO:0050790 Regulation of catalytic
activity Down CAPN1 -6.90 Calpain-1 catalytic subunit
GO:0006357 Regulation of
transcription Down
SMARCC1 -6.59 SWI/SNF complex subunit SMARCC1
BCL3 -6.47 B-cell lymphoma 3 protein
GO:0032922 Circadian regulation of
gene expression Down OGT -6.49
UDP-N-acetylglucosamine--peptide N-
acetylglucosaminyltransferase 110 kDa
subunit
128
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.
129
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.
130
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.
131
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.
132
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.
133
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.
