universidade federal do cearÁ centro de ciÊncias … · mantimento; todavia eu me alegrarei no...

UNIVERSIDADE FEDERAL DO CEARÁ CENTRO DE CIÊNCIAS AGRÁRIAS DEPARTAMENTO DE ZOOTECNIA

PROGRAMA DE PÓS-GRADUAÇÃO EM BIOTECNOLOGIA-PPGB

ANTÔNIA MOÊMIA LÚCIA RODRIGUES PORTELA

APLICAÇÕES DE TÉCNICAS MOLECULARES DE NOVA GERAÇÃO (NGS) PARA O ESTUDO DO DESEMPENHO REPRODUTIVO DE TOUROS E VACAS

LEITEIRAS

FORTALEZA

2018


APLICAÇÕES DE TÉCNICAS MOLECULARES DE NOVA GERAÇÃO (NGS) PARA O

ESTUDO DO DESEMPENHO REPRODUTIVO DE TOUROS E VACAS LEITEIRAS

Tese apresentada ao Curso de Doutorado em Biotecnologia da Rede Nordeste de Biotecnologia – RENORBIO da Universidade Federal do Ceará, como parte dos requisitos para obtenção do título de Doutora em Biotecnologia. Área de concentração: Biotecnologia em Agropecuária. Orientador: Prof. Dr. Arlindo de Alencar Araripe Noronha Moura Coorientador: Prof. Dr. Jorge André Matias Martins

FORTALEZA

2018

Dados Internacionais de Catalogação na Publicação Universidade Federal do Ceará

Biblioteca UniversitáriaGerada automaticamente pelo módulo Catalog, mediante os dados fornecidos pelo(a) autor(a)

P877a Portela, Antonia Moemia Lucia Rodrigues. Aplicações de técnicas moleculares de nova geração (NGS) para o estudo do desempenho reprodutivo detouros e vacas leiteiras / Antonia Moemia Lucia Rodrigues Portela. – 2018. 138 f. : il. color.

Tese (doutorado) – Universidade Federal do Ceará, Pró-Reitoria de Pesquisa e Pós-Graduação, Programade Pós-Graduação em Biotecnologia (Rede Nordeste de Biotecnologia), Fortaleza, 2018. Orientação: Prof. Dr. Arlindo de Alencar Araripe Noronha Moura. Coorientação: Prof. Dr. Jorge André Matias Martins.

1. Metilação. 2. Epigenética. 3. RNA sequencing. 4. Pós-parto. 5. Gene. I. Título. CDD 660.6


APLICAÇÕES DE TÉCNICAS MOLECULARES DE NOVA GERAÇÃO (NGS) PARA O ESTUDO DO DESEMPENHO REPRODUTIVO DE TOUROS E VACAS LEITEIRAS

Tese apresentada ao Programa de Pós-Graduação em Biotecnologia da Rede Nordeste de Biotecnologia – RENORBIO da Universidade Federal do Ceará, como requisito parcial à obtenção do título de Doutora em Biotecnologia. Área de concentração: Biotecnologia em Agropecuária.

Aprovada em: ___/___/______.

BANCA EXAMINADORA

______________________________________________________ Prof. Dr. Arlindo de Alencar Araripe Noronha Moura (Orientador)

Universidade Federal do Ceará (UFC)

______________________________________________________ Prof. Dr. Fábio Roger de Vasconcelos Universidade Federal do Ceará (UFC)

______________________________________________________ Prof. Dra. Lays Debora Silva Mariz

Universidade Federal do Ceará (UFC)

_______________________________________________________ Prof. Dr. Stefano Biffani

Italian National Research Council (CNR)

_______________________________________________________ Prof. Dr. Vicente José de Figueiredo Freitas

Universidade Estadual do Ceará (UECE)

A Deus, pela minha existência, força, coragem e determinação que me foi dada para alcançar mais esse objetivo, porque nada nos é possível se não for de Sua vontade.

Dedico

AGRADECIMENTOS

A Deus, pelo seu amor e pela sua infinita misericórdia manifestados a cada dia em

minha vida. Pela proteção, força e coragem para enfrentar todas as dificuldades da vida

pessoal e profissional. Senhor, a minha confiança descansa nas Tuas mãos. Sempre espero e

confio em ti. Obrigado por mais essa vitória.

Aos meus pais Maria Lia Neta Portela e Manoel Raimundo Portela, por terem me

dado a vida e por todo amor e dedicação fundamentais em todos os momentos da minha vida.

Às minhas irmãs Luana Portela e Nalda Portela e ao meu irmão Fábio Portela, com os quais

dividi momentos de alegrias e tristezas, e que sempre estarão me incentivando e torcendo pelo

meu sucesso.

À família Marques de Oliveira (minha segunda família), em especial à dona Maria

das Graças, Sr. Francisco e Jorge Luis, por fazerem me sentir parte da família, dividindo

comigo momentos de tristezas, mas principalmente momentos de muitas alegrias. Vocês

moram no meu coração.

Ao orientador Prof. Dr. Arlindo Moura, pela orientação deste trabalho, paciência,

pela dedicação dispensada, confiança e profissionalismo demonstrado no decorrer de nossa

convivência. Agradeço pela contribuição decisiva na minha formação e pelo muito que

aprendi durante os anos de doutorado.

Ao Co-orientador Prof. Dr. Jorge Martins, pela dedicação constante e por sempre

estar disposto a ajudar em todos os momentos.

Aos queridos amigos integrantes do grupo de pesquisa de Fisiologia Animal,

obrigada pelo apoio de todos.

À querida amiga e irmã Jordania Freire e toda a família Freire, Clayrtiano Freire,

Dona Celina, Sr. Raimundo e Rafael Freire, em especial meu afilhado João Arthur, agradeço

pela amizade, companheirismo, apoio e incentivo desde o início e por me tratarem sempre

com muito carinho, como se eu fosse uma irmã, obrigada pelo apoio e pelas palavras de

carinho nos momentos mais difíceis.

Aos amigos Anderson Weiny Silva, Regislane Pinto Ribeiro, Juliane Passos, Amélia

Soares e Jackson Costa agradeço pela amizade, pelos conselhos, pelos momentos de trabalho

e pelos momentos de descontração. Admiro a competência de vocês.

Ao meu amigo Rony Barroso pelo apoio incondicional e por ser meu companheiro de

momentos bons e ruins durante essa fase.

À Solange Damasceno, que esteve presente do início ao fim do trabalho, que me

apoia, que foi em muitos momentos meu porto seguro, pessoa muito especial na minha vida,

uma das melhores pessoas que conheci e que quero para a vida... Amo tu e serei sempre grata

por seu grande apoio.

À Danuza Leão e Denise Azevedo, obrigada pelo apoio, companheirismo, risadas,

por sempre se preocuparem comigo e fazer eu me sentir tão especial para vocês, nos

encontramos, um achado que deu certo.

Ao meu amigo Aderson Viana, que apesar de sermos muito diferentes é alguém em

quem confio, e que me passa confiança. Obrigada, nobre amigo.

Aos meus amigos Taciane Alves, Mayra Vetorazzi, Antônio Carlos, Mónica

Ramirez, Arabela Guedes, Nielyson Batista por todo o apoio nos momentos de desânimo,

obrigada a todos e amo muito vocês.

À Kamila Sousa e Renato Passos, pelo apoio e por terem sido presentes nessa

caminhada, por toda ajuda, agradeço.

A minha amiga Gisvani Lopes, por toda vibração positiva, pela força e apoio nos

momentos difícies, te amo amiga.

Aos integrantes do grupo de pesquisa do Consiglio Nazionale della Ricerche: Dra.

Flavia Pizzi, Emanuele Capra, Dra. Stefania Chessa, Dra. Paola cremonesi e em especial ao

Dr. Stefano Biffani por ter sido muito presente nos meus trabalhos desenvovlvidos, pela

paciência e por ter sido um amigo.

À Elisabety Mendes, por ter me recebido como membro da família, pelas conversas e

conselhos, por ter sido meu apoio durante o ano do meu doutorado sanduíche, muita gratidão

por você.

Aos integrantes da banca examinadora, Prof. Dr. Arlindo de Alencar Araripe

Noronha Moura (Orientador), Prof. Dr. Stefano Biffani, Profa. Dra. Lays Débora Silva Mariz,

Dr. Fábio Vasconcelos, Prof. Dr. José Roberto Viana Silva e Prof. Dr. Vicente José de

Figueiredo Freitas, por terem gentilmente aceito o convite para participar da banca de defesa

desta tese, e pela solicitude em contribuir no engrandecimento deste trabalho.

Aos funcionários da Universidade Federal do Ceará pela convivência, atenção e

disponibilidade durante todos esses anos de convívio.

A todos que de alguma forma me deram força e incentivo na realização do meu

doutoramento, seja profissionalmente ou sentimentalmente e por participarem da minha vida.

Por fim, agradeço a todos que contribuíram, de alguma forma, ou torceram para que

eu chegasse até aqui, compartilhando comigo um momento tão importante. A todos vocês, de

coração, o meu

Muito obrigada!!!

“Porquanto, ainda que a figueira não floresça, nem haja fruto na vide; o produto da oliveira minta, e os campos não produzam mantimento; Todavia eu me alegrarei no Senhor, exultarei no Deus da minha salvação. O Senhor é minha força e me fará andar sobre as minhas alturas.”

(HABACUQUE 3:17-19)

RESUMO O estudo 1 teve como objetivo produzir um perfil de metilação em todo o genoma e

identificar assinaturas epigenéticas diferenciais entre espermatozoides de alta motilidade

(AM) e baixa motilidade (BM). O estudo 2 teve como objetivo caracterizar as vias

metabólicas associadas ao início da lactação em vacas Holandesas utilizando dados de RNA-

seq obtidos de amostras de tecido adiposo subcutâneo coletadas em três momentos: em 2

(T0), 30 (T1) e 90 (T3) dias após o parto. No estudo 1 foi explorado a metilação de

dinucleotídeos citosina-guanina (CpGs) em populações de espermatozoides de AM e BM de

Bos taurus separados por Percoll. Padrões de metilação de espermatozoides de alta e baixa

motilidade foram investigados por sequenciamento de bissulfito. A comparação entre as

populações desses espermatozoides revelou que a variação da metilação afeta os genes

envolvidos na organização da cromatina, no qual houve metilação em genes associados à

remodelação da estrutura do DNA, bem como em um elemento repetitivo BTSAT4 em

regiões pericentroméricas. Desta forma, sugere-se que a manutenção da estrutura

cromossômica através da regulação epigenética seja crucial para a funcionalidade correta do

espermatozoide. Para o estudo 2, o RNA total foi extraído a partir do tecido adiposo

subcutâneo no dia do parto (T0), 30 dias após o parto (T1) e noventa dias após o parto (T3), e

comparações foram feitas entre os grupos mencionados. Um total de 12.294 genomas foram

identificados e submetidos a uma filtragem, identificando um total de 405.435.505 genes, nos

quais os genes diferencialmente expressos foram analisados através do False Discovery Rate

(FDR = 0,05). As vias metabólicas associadas ao início da lactação em vacas holandesas

foram caracterizadas utilizando dados de RNA-seq obtidos de amostras de tecido adiposo

subcutâneo coletadas em três momentos: aos 2 (T0), 30 (T1) e 90 (T3) dias pós-parto. A

análise de enriquecimento identificou 142 vias metabólicas. Os mais significativos foram:

secreção de insulina, sinalização da ocitocina, glicólise / gliconeogênese, metabolismo do

piruvato, resistência à insulina, sinalização de cálcio, GnRH (hormônio liberador de

gonadotropina), MAPK (proteína quinase mitogênica), sinalização de adipocitocinas e o

sistema renina-angiotensina. Todas essas vias representam importantes rotas metabólicas em

bovinos leiteiros em lactação.

Palavras-chave: Espermatozoide. metilação. epigenética. RNA sequencing. pós-parto. gene.

ABSTRACT Study 1 aimed to produce a methylation profile in genome in both populations, and to identify

differential epigenetic signatures between high motility (HM) and low motility (LM) sperm.

Study 2 aimed to characterize the metabolic pathways associated to early lactation in Holstein

cows using RNA-seq data obtained from subcutaneous fat tissue samples collected at three

time points: at 2 (T0), 30 (T1) and 90 (T3) days postpartum. In study 1, we explored the

methylation of cytosine-guanine dinucleotides (CpGs) in HM and LM sperm populations in

Bos taurus separated by Percoll. Methylation patterns of high and low motility sperm were

investigated by bisulphite sequencing. The comparison between the populations of HM and

LM sperm revealed that the variation of methylation affects the genes involved in the

organization of chromatin and that methylation occurred in genes associated with the

remodeling of the DNA structure, as well as in a repetitive element BTSAT4 in

pericentromeric regions. Thus, it is suggested that the maintenance of chromosome structure

through epigenetic regulation is crucial for the correct functionality of sperm. For study 2,

total RNA was extracted from subcutaneous adipose tissue on the day of birth (D0), 30 days

postpartum (D30) and ninety days postpartum (D90) and comparisons were made between the

groups mentioned. A total of 12.294 genomes were identified and subjected to a filtering,

identifying a total of 405.435.505 genes, in which differentially expressed genes were

analyzed using the False Discovery Rate (FDR = 0.05). Metabolic pathways associated to the

early lactation in Holstein cows were characterized using RNA-seq data obtained from

subcutaneous fat tissue samples collected at three time points: at 2 (T0), 30 (T1) and 90 (T3)

days postpartum. The enrichment analysis identified 142 metabolic pathways. The most

significative were insulin secretion, oxytocin signaling, glycolysis/gluconeogenesis, pyruvate

metabolism, insulin resistance, calcium signalling, GnRH (Gonadotropin releasing hormone),

MAPK (mitogen-activated protein kinase), adipocytokine signaling, and the

renin−angiotensin system. All these pathways are important metabolic routes in lactating

dairy cattle.

Keywords: Sperm. methylation. epigenetics. RNA sequencing. postpartum. gene.

LISTA DE ILUSTRAÇÕES

Figura 1 – Controle da expressão gênica por mecanismos epigenéticos

........................ 22

Figura 2 – Condensação e compactação da cromatina do espermatozoide

.................... 26

Figure 3 – Distribution of CpG methylation levels across the gene bodies, 5’UTR,

3’UTR and CGI ………………………….………………………………… 63

Figure 4 – Hierarchical clustering for DMRs present in CGIs, gene bodies, 5’ UTRs

and 3’ UTRs

…………................…………………………………………… 64

Figure 5 – Distribution of CGIs length in HM and LM (20-60 CpG methylated) and

HM and LM (80-100 CpG methylated)

........................................................... 65

Figure 6 – CpG methylation levels in MRs and DMRs of HM and LM sperm

populations

...................................................................................................... 66

Figure 7 – Top metabolic pathways (from KEGG) enriched in the genes associated

with the lactation period. These pathways were detected from genes

identified in subcutaneous adipose tissue

........................................................ 103

LISTA DE TABELAS

Table 1 – GO terms identified for the differentially methylated genes (DMGs) found

to differ between high motile (HM) and low motile (LM) sperm populations

in gene bodies (GENE), 5’ untranslated regions (5’UTRs), 3’ untranslated

regions (3’UTRs) and CpG islands (CGIs)

…………………………………… 54

Table 2 – Frequency of occurrence for Repetitive Elements (REs) overlapping CGIs

with different methylation levels (20-60 % methylation and 80-100%

methylation) in high motile (HM) and low motile (LM) sperm populations.

Frequency of occurrence for REs is also reported for Bos taurus genome

……. 56

Table 3 – Top 5 pathways associated at different time points comparison (at calving

(T0), at 30 days post-calving (T1) and at 90 days post-calving (T3)

.................. 103

LISTA DE ABREVIATURAS E SIGLAS Português Inglês

3’UTRs Regiões 3’ não traduzidas 3’ untranslated regions

5’UTR Regiões 5’ não traduzidas 5’ untranslated regions

ALH Amplitude do deslocamento lateral da

cabeça

Amplitude of lateral head

displacement

BCF Frequência de batimentos Frequency of head displacement

BTLTR1 Fragmentos de repetições

interrompidas Registrado por

RepeatMasker ID

Fragments of Interrupted Repeats

Joined by RepeatMasker ID

BTSAT3 Satélite/Centromérico Satellite/Centromeric

BTSAT4 Satélite/Centromérico Satellite/Centromeric

CASA Sistemas automáticos de análise de

sêmen

Computer-Assisted Semen Analysis

CATSPER1 Canal catiônico 1 do espermatozoide Cation Channel Sperm Associated 1

CGIs Ilhas CpGs CpG islands

CH3 Radical metil Methyl radical

CpGs Dinucleotídeos citosina-guanina Cytosine-guanine dinucleotides

DMGs Genes dierencialmente expressos Differentially methylated genes

DNA Ácido desoxirribonucleico Deoxyribonucleic acid

Dnmt3b DNA (citosina-5-)-metiltransferase 3

beta

DNA (cytosine-5-)-methyltransferase

3 beta

DNMTs DNA metiltransferases Deoxyribonucleic acid

methyltransferases

ELISA Ensaio de imunoabsorção enzimática Enzyme-linked immunosorbent assay

GO Ontologia gênica Gene ontology

HDM Histona demetilase histone demethylase

HM Alta motilidade High motile

HMT Histona metiltransferase Histone methyltransferase

HMTases Histona metiltransferases Histone methyltransferases

ICR1 Região 1 controladora de imprinting imprinting control region 1

IGF2 Fator de crescimento semelhante à

insulina-2

insulin-like growth factor 2

IVF Fertilização in vitro In vitro fertilization

KDM1 Lysine (K)-specific demethylase 1A

KDMs Lysine (K)-specific demethylase

KMT2A Histone lysine methyltransferases 2A

KMTs Histone lysine methyltransferases

LIN Linearidade Linearity

LINE Long interspersed elements

LM Baixa motilidade Low motile

LSD1 Lysine-specific histone demethylase 1

LSM Least squares means

MBD Methyl-binding domain

MEST Mesoderm-specific transcript

miRNAs micro RNAs MicroRNAs

MLL1 Mixed-lineage leukemia 1

MLL2 Mixed-lineage leukemia 2

MMSET Multiple myeloma SET domain

MRs Methylated regions

NCBI National center for biotechnology

information

NSD1 Nuclear receptor-binding SET

Domain 1


Domain 2


Domain 3

OSSAT2 Fragmentos de repetições

interrompidas Registrado por

RepeatMasker ID

Fragments of Interrupted Repeats

Joined by RepeatMasker ID

PCR Reação em cadeia da polimerase Polymerase chain reaction

Res Elementos repetidos Repetitive elements

RNA Ácido ribonucléico Ribonucleic acid

RNAm Ácido ribonucléico mensageiro Messenger ribonucleic acid

RRBS Reduced representation bisulfite

sequencing

SCNT Transferência nuclear de células

somáticas

Somatic cell nuclear transfer

SRA Sequence Reads Archive

STR Retilinearidade Straightness

Suv39h Suppressor of variegation 3-9

homolog

TALP Tyrode’s albumine lactate pyruvate

VAP Velocidade de Trajeto Average path velocity

VCL Velocidade Curvilinear Curvilinear velocity

VSL Velocidade Progressiva Straight line velocity

WHSC1 Wolf-Hirschhorn syndrome candidate-

1

LISTA DE SÍMBOLOS Português Inglês

% Percentagem Percentage

~ Aproximadamente Aproximately

± SEM Erro padrão da média Standard error of the mean

°C Graus Celsius Degrees Celsius

µg Micrograma Microgram

µL Microlitro Microliter

µm Micrômetro Micrometer

µM Micromolar Micromolar

CO2 Dióxido de carbono Carbon dioxide

H Hora Hour

IU/mL Unidades internacionais por mL International units per mL

Min Minuto Minute

Mg Miligrama Milligram

mL Mililitro Milliliter

mM Milimolar Millimolar

Mm Milímetro Millimeter

Ng Nanograma Nanogram

Nm Nanômetro Nanometer

P < 0,05 Probabilidade de erro menor do que 5% Error probabilities is less than 5%

P > 0,05 Probabilidade de erro maior do que 5% Error probabilities is more than 5%

SUMÁRIO

1 INTRODUÇÃO............................................................................................. 19

2 REVISÃO DE LITERATURA.................................................................... 21

2.1 Tecnologias ômicas na reprodução animal................................................. 21

2.1.1 Genômica....................................................................................................... 21

2.1.2 Epigenética..................................................................................................... 26

2.1.2.1 Epigenética e fertilidade................................................................................. 24

2.1.2.2 Estrutura do DNA espermático...................................................................... 25

2.1.2.3 Motilidade espermática.................................................................................. 27

2.1.3 Transcriptômica............................................................................................ 29

2.1.4 Proteômica..................................................................................................... 32

2.1.5 Metabolômica................................................................................................ 33

3 PROBLEMA.................................................................................................. 35

4 JUSTIFICATIVA.......................................................................................... 36

5 HIPÓTESES CIENTÍFICAS....................................................................... 38

6 OBJETIVOS.................................................................................................. 39

6.1 Objetivos gerais............................................................................................. 39

6.2 Objetivos específicos..................................................................................... 39

7 EPIGENETIC VARIATION IN PERICENTROMERIC REGIONS

BETWEEN HIGH AND LOW MOTILE SPERM POPULATIONS IN

BOS TAURUS …………......................................................

40

8 DYNAMIC PROFILE OF ACTIVE METABOLIC PATHWAYS IN

THE SUBCUTANEOUS FAT TISSUE OF HOLSTEIN COWS

DURING EARLY LACTATION …...............…………………………

88

9 CONCLUSÕES……………………………………………………………. 122

10 PERSPECTIVAS………………………………………………………….. 123

REFERÊNCIAS…………………………………………………………… 124

19

1 INTRODUÇÃO

A maioria dos estudos reprodutivos em bovinos está voltada para a fertilidade da

vaca, enquanto a fertilidade do macho recebeu menos importância. No entanto, estudos

relataram que um percentual significativo de falhas reprodutivas em bovinos de leite é

atribuído à subfertilidade dos machos (DEJARNETTE et al., 2004). Consequentemente, a

fertilidade de touros não deve ser considerada menos importante em esquemas de reprodução

destinados a melhorar o desempenho reprodutivo do gado leiteiro (BRAUNDMEIER;

MILLER, 2001).

A avaliação da fertilidade de touros consiste na avaliação seminal. Assim, os

parâmetros tais como concentração, motilidade e morfologia espermática podem não ser

suficientes para uma análise completa do potencial de fertilidade do sêmen. A análise do

sêmen não informa como o espermatozoide, particularmente o DNA espermático ajudará e

influenciará o desenvolvimento do embrião. Em termos de função espermática, a análise do

sêmen não informa realmente sobre eventos como a quimiotaxia, que é o processo no qual o

espermatozoide poderá encontrar o oócito, além de também não informar sobre a penetração,

o processo de obter o espermatozoide e seu DNA no oócito para que o embrião possa começar

a se formar. Dessa forma, o surgimento das tecnologias ômicas, como a genômica,

transcriptômica, proteômica, e metabolômica tornaram-se ferramentas valiosas para o estudo

do sêmen, fornecendo a detecção de possíveis biomarcadores da fertilidade (THUNDATHIL

et al., 2016).

Estudos recentes no campo da genômica comparativa e análise de expressão gênica

forneceram novas ferramentas de detecção molecular que permitem que vários parâmetros

sejam efetivamente integrados na avaliação do potencial fertilizante do sêmen

(BISSONNETTE et al., 2009). Recentemente, estudos baseados na expressão gênica e

epigenética foram usados para analisar características de fertilidade. Em humanos foi

demonstrado que os padrões de metilação do DNA de espermatozoides diferem

significativamente entre homens inférteis e férteis. Além disso, o padrão de metilação do

DNA pode ser preditivo da qualidade do embrião durante a fertilização in vitro (ASTON et

al., 2015). A metilação desordenada em genes em “loci” promotores e impressos está

fortemente associada a várias formas de infertilidade e defeitos de espermatozoides em

homens (WU et al., 2010). Da mesma forma, a hipometilação global do DNA espermático foi

relacionada a resultados insatisfatórios da gestação em pacientes através de fertilização in

vitro (BENCHAIB et al., 2005). Há muitos candidatos prováveis que podem causar alterações

20

epigenéticas nos espermatozoides e podem levar à embriogênese anormal (STUPPIA et al.,

2015). Assim, o epigenoma espermatico oferece interessantes oportunidades de estudo e

grandes esforços têm sido empregados para entender o papel dos padrões epigenéticos do

espermatozoide no seu desenvolvimento e funcionalidade, bem como no desenvolvimento

embrionário e na prole.

Em vacas leiteiras no momento de transição do período seco para o início da

lactação, o risco de doenças metabólicas é particularmente alta (HAMMON et al., 2006;

McART et al., 2013). Após o parto, o gado leiteiro requer um aumento acentuado dos

requisitos de nutrientes para propiciar produção de leite (DRACKLEY, 1999). Desta forma,

impedir ou contornar o balanço energético no período pós-parto pode reduzir a incidência de

doenças e diminuir a mobilização de reservas corporais (DUFFIELD et al., 2009).

O melhoramento genético no âmbito da produção de leite requer uma visão

abrangente da biologia do processo de lactação, desde um único estágio até a curva total da

lactação (CUI et al., 2014). Além de dados genéticos, perfis de expressão gênica e análise de

alterações em vias metabólicas oferecem novas oportunidades para elucidar os mecanismos

subjacentes de traços complexos em humanos e animais de produção, e atualmente ocorre o

rápido desenvolvimento e redução de custos do sequenciamento de próxima geração (NGS)

(REINERT et al., 2015). Assim, as novas tecnologias de sequenciamento de nova geração

(NGS), agora avaliado como uma ferramenta abrangente e precisa para analisar complexos

sistemas ômicos subjacentes a processos biológicos, oferecendo grandes oportunidades para

elucidar os mecanismos subjacentes de características complexas, como o processo de

lactação e da fertilidade em touros. Desta forma, ensaios baseados em sequências de

transcriptomas e sequenciamento de RNA e DNA (RNA-seq; DNA-seq), tornou-se uma

abrangente e precisa ferramenta para análise de padrões de expressão gênica.

21

2 REVISÃO DE LITERATURA

2.1 TECNOLOGIAS ÔMICAS NA REPRODUÇÃO ANIMAL

As tecnologias “ômicas” adotam uma visão geral das moléculas que compõem uma

célula, tecido ou organismo. Elas são voltadas principalmente para a detecção universal de

genes (genômica), RNAm (transcriptômica), proteínas (proteômica) e metabólitos

(metabolômica) em meio biológico específico. As abordagens “ômicas” propõem uma

caracterização global de classes específicas de biomoléculas-alvo em sistemas uni ou

multicelulares como uma estratégia para alcançar uma compreensão abrangente das funções

biológicas (ALEXANDRI et al., 2010). As tecnologias genômica, transcriptômica,

proteômica e metabolômica podem ser aplicadas não apenas para a maior compreensão dos

processos fisiológicos normais, mas também nos processos de doença, que desempenham um

papel na triagem, diagnóstico e prognóstico, bem como auxiliam na compreensão da etiologia

das doenças. Estratégias exclusivas se prestam à descoberta de biomarcadores à medida que

investigam múltiplas moléculas simultaneamente (HORGAN; KENNY, 2011).

Nos últimos anos, tem havido um notável desenvolvimento nestes campos. O grande

desafio, no entanto, é integrar várias formas de dados “ômicos” para fornecer informações

sobre os complexos sistemas biológicos dentro dos organismos vivos (SURAVAJHALA et

al., 2016). A maioria das características biológicas é controlada por um grande número de

genes que, além de seus efeitos aditivos, podem interagir entre si e sua expressão pode ser

alterada com base em uma variedade de efeitos ambientais. Desta forma, o rápido

desenvolvimento em tecnologias “omics” fornece oportunidades de investigar o genoma e o

epigenoma, bem como, a possibilidade de sua posterior implementação em métodos de

melhoramento.

2.1.1 Genômica

O efeito gênico nas características de produção e na fisiologia animal tem sido

estudado principalmente ao vincular esses parâmetros de resultado à variação em sequências

genéticas. A maioria das pesquisas em animais de produçãoé feita via observação de um

grande número de Polimorfismos de nucleotídeo Único (SNPs – mutações únicas em posições

específicas no genoma) ou sequenciando genes-alvo que devem estar envolvidos no resultado

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de interesse. Porém recentemente, o sequenciamento do genoma inteiro tornou-se mais

popular (CROUCHER et al., 2010).

Embora alguns genes isolados com efeito sobre características econômicas ocorram,

a maior parte da variação genética no ganho econômico se deve a características complexas

controladas por muitos genes. De fato, evidências recentes indicam que a maioria das

características quantitativas é controlada por milhares de polimorfismos (BOYLE et al.,

2017). Os primeiros estudos de reprodução na produção de gado leiteiro foram baseados em

observações fenotípicas e na habilidade de alguns criadores. Com o tempo, a pecuária leiteira

evoluiu para uma ciência com melhor compreensão e apreciação da herança de vários traços

importantes e assim, o desenvolvimento de conjuntos de dados para estimativa de mérito e

melhoria genética de raças leiteiras. Desta forma, ganhos genéticos significativos foram

obtidos usando essas estratégias de melhoramento em muitas áreas, incluindo características

de produção de leite. Embora a combinação de extensos dados genealógicos e fenótipos tenha

melhorado os programas de seleção, as características de baixa herdabilidade, como

fertilidade e saúde, e outras características difíceis de fenotipar, obteve a exploração de novas

metodologias para alcançar um aumento dos ganhos genéticos (FLEMING et al., 2018).

Assim, a genômica realiza o sequenciamento genético dos organismos, sendo

essencial para a compreensão dos complexos eventos que orquestram a função de todos os

organismos ou os defeitos que levam a doenças (SHULDINER; POLLIN, 2010). Portanto, o

desenvolvimento de técnicas como o sequenciamento de DNA tornou-se uma ferramenta

essencial para decifrar genes completos e, mais adiante, genomas inteiros.

Assim, diante do importante papel do macho na determinação da fertilidade do

rebanho bovino e o ganho genético possível com o advento da seleção genômica, múltiplas

abordagens são necessárias para desvendar a complexidade da fertilidade de touros. Apesar do

pequeno tamanho efetivo da população na maioria das raças bovinas e da natureza altamente

selecionada de touros para a inseminação artificial, Whiston et al. (2017) demonstraram o

primeiro catálogo abrangente de variação genética em genes de β-defensina em bovino e o

primeiro sequenciamento de todo exoma de touros divergentes de fertilidade. Essa abordagem

identificou novas variantes nos genes β-defensina e FOXJ3, potencialmente regulando a

função reprodutiva, e esses biomarcadores podem contribuir para futuras estratégias de

reprodução a fim de melhorar a fertilidade em machos

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2.1.2 Epigenética

A epigenética é usada para descrever as mudanças herdáveis que controlam a

expressão gênica, sem que a sequência original do DNA seja alterada (RUSSO et al., 1996).

Entre os mecanismos epigenéticos conhecidos, destacam-se as modificações covalentes nas

histonas, tais como: acetilação, metilação, ubiquitinação e fosforilação, que ocorrem nas

caudas N-terminais de proteínas histonas, as quais o DNA se enrola formando o nucleossomo,

estrutura fundamental da cromatina. O processo da metilação do DNA é descrito como a

introdução de radical metil (CH3) no carbono 5 de citosinas, seguidas de guaninas (ilhas

GpGs), na moléculas de DNA e RNAs não codificantes considerados pequenos ou micro

RNAs (miRNAs) espalhados ao longo do genoma com função regulatória de controle da

expressão gênica (JONES; TAKAI, 2001) conforme demonstrado na figura 1.

Figura 1. Controle da expressão gênica por mecanismos epigenéticos. Fonte: Adaptado de Hagood (2014).

A acetilação de lisinas é altamente dinâmica regulada pela ação oposta de duas

famílias de enzimas: histonas acetiltransferases e histonas deacetilases. Na acetilação há a

transferência de um grupo acetil ao grupo Ɛ-amino de cadeias laterais de lisina, levando a

neutralização da carga positiva da lisina e assim, enfraquecendo as interações entre histonas e

DNA. Há dois grupos de acetilases: A e B. O grupo A é uma família de enzimas mais

diversificada, e de modo geral modificam múltiplos sítios dentro das caudas N-terminais das

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histonas, devido à sua capacidade de interromper a estabilidade de interações eletrostáticas,

onde essas enzimas funcionam como coativadores transcricionais (YANG; SETO, 2008). O

grupo B é predominantemente citoplasmático [acetilando histonas livres, mas não aquelas que

já se depositaram na cromatina] (PARTHUN, 2007). No entanto, não são apenas as caudas

das histonas que estão envolvidas neste regulamento, sítios adicionais de acetilação presentes

dentro do núcleo globular da histona também são encontrados.

A metilação do DNA em ilhas GpGs é a única modificação epigenética que afeta

diretamente o DNA. Na mesma, um grupo metil é adicionado a uma base de citosina, onde

essa alteração não afeta a forma como a citosina é transcrita em RNA mensageiro, porém

promove localmente a compactação da cromatina, afetando o fator de ligação de transcrição

(BIRD, 2002). No processo de metilação do DNA, a introdução do radical metil é catalisada e

mantida por enzimas denominadas DNA metiltransferases (DNMTs), que obtêm e transferem

o radical metil a partir do composto S-adenosyl-L-metionina, que é o doador de metil, para o

carbono 5 da citosina (FERNANDES et al., 2007).

As ilhas CpGs estão em grande parte das regiões promotoras dos genes, sendo essa

região pelo menos 10 vezes mais metilada do que outras regiões do genoma com CpGs

(RODRIGUEZ et al., 2016). Porém, a atuação da metilação do DNA no controle da expressão

gênica em regiões promotoras é considerada um processo simples, visto que este evento pode

afetar a função gênica quando ocorre em regiões diferentes, como o que acontece em regiões

intrônicas, ilhas CpGs distantes da região promotora (shore CpG island) ou em elementos de

repetição em tandem (FERNANDES et al., 2007). Desta forma, o importante papel exercido

pela metilação do DNA, assim como pelas modificações de histonas, no controle da função

gênica assume-se que o epigenoma, ou seja, a programação epigenética total do DNA seja um

fenômeno dinâmico e diferente entre os tipos celulares (BOYES; BIRD, 1992).

2.1.2.1 Epigenética e fertilidade

Durante as últimas décadas, numerosos testes de avaliação da capacidade fertilizante

dos espermatozoides foram desenvolvidos, destacando-se as análises morfológicas (BONDE

et al., 1998), de motilidade (BUDWORTH et al., 1998), de penetração do muco cervical

(AITKEN et al., 1985), análise das membranas plasmática e acrossomal e a interação

espermatozoide-zona pelúcida em testes de fertilização in vitro com oócitos homólogos

(GUIENNE et al., 1990). Entretanto, é necessário um método preciso e amplamente aplicável

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para a avaliação de sêmen no diagnóstico de infertilidade do macho que permita melhorar os

padrões de avaliação seminal. Estudos genômicos e/ou proteômicos sistematizados das

características seminais, assim como, chips de microarranjos poderiam contribuir na

identificação de novas e melhores técnicas de avaliação (CORNER et al., 2006).

A metilação do DNA tem sido intimamente associada à infertilidade masculina, onde

o padrão de metilação em espermatozoides maduros reflete mudanças no padrão de expressão

gênica que ocorre durante a espermatogênese. A metilação do DNA controla a atividade

transcricional dos genes e está envolvida no estabelecimento de uma estrutura de cromatina de

ordem superior. Um padrão normal de metilação nas células germinativas contribui para a

progressão da meiose, culminando na produção de espermatozoides funcionais. Assim,

anormalidades na metilação do DNA podem afetar a produção de espermatozoides e explicar

alguns casos de infertilidade masculina (ROUSSEAUX et al., 2008).

A aplicação de tecnologias da genômica para estudo da célula espermática, em

conjunto com uma avaliação detalhada da competência funcional deve fornecer perspectivas

para as bases bioquímica, fisiológica e genética da qualidade do sêmen de baixo potencial

fertilizante (SAKKAS et al., 2004). Pesquisas têm investigado o papel da integridade do DNA

espermático na infertilidade masculina, e sugere-se que a integridade do DNA do

espermatozoide possa ser um bom preditor da fertilidade masculina, pois, evidências

demonstram que espermatozoides de homens inférteis apresentam mais danos no DNA em

comparação com homens férteis, e esse dano pode ter um efeito negativo no potencial da

fertilidade masculina (ZINI et al., 2001).

Vale ressaltar que os touros são capazes de gerar mais descendentes do que as vacas;

consequentemente, a seleção do macho é mais eficaz do que a seleção da fêmea para melhorar

qualquer característica (McDANIELD; KUEHN, 2014). Diante disso, quando se utilizam

touros com valores genético e reprodutivo superiores em um rebanho, pode-se reduzir o

número de reprodutores em serviço e acelerar o ganho genético (FORDYCE et al., 2002).

Entretanto, existem diferenças na capacidade fertilizante entre os touros (DEJARNETTE et

al., 1992). A estimativa da fertilidade do reprodutor é uma ferramenta importante na escolha

do macho, não só porque reflete o estado individual do reprodutor, mas também porque dito

resultado influencia o futuro do rebanho.

O espermatozoide possui uma natureza inerente quiescente. Contudo, existem vários

vestígios epigenéticas importantes que criam uma paisagem especializada e única, como a

composição da proteína nuclear, metilação do DNA e RNAs dos espermatozoides

(CARRELL, 2012). Desta forma, estudos desenvolvidos na investigação de sinais

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epigenéticos do espermatozoide entre touros de alta e baixa fertilidade demonstraram que 76

regiões são diferencialmente metiladas entre touros de diferentes estados de fertilidade

(KROPP et al., 2017). Verma et al. (2014) relataram análise de metilação por microarray de

espermatozoides de búfalo considerado alto e subfértil, dos quais 73 genes em alta fertilidade

e 78 genes em espermatozoides subférteis foram hipermetilados, logo após a análise da via

caracterizaram esses genes por desempenharem papeis na transcrição regulação e proliferação

celular.

2.1.2.2 Estrutura do DNA espermático

A cromatina espermática se destaca por ser extremamente condensada. Nas células

somáticas o DNA é enrolado ao redor de proteínas denominadas histonas e organizados em

estruturas solenoides (McGHEE et al., 1983). No entanto, o núcleo espermático não possui

volume suficiente para este tipo de organização (WARD, 2011), consequentemente a

cromatina do espermatozoide deve ser organizada de maneira única, altamente condensada e

compactada (SHARMA; AGARWAL, 2011) (FIGURA 2). A compactação da cromatina

espermática ocorre durante a espermatogênese, onde a célula germinativa, geneticamente

ativa, se transforma em um espermatozoide inativo ou quiescente até a fertilização do oócito.

Estas mudanças são induzidas pela modificação de proteínas que se ligam e compactam o

DNA, promovendo a desprogramação temporária do genoma paterno (BALHORN, 2011).

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Figura 2. Condensação e compactação da cromatina do espermatozoide. Fonte: Adaptado de Seli et al. (2004).

Antes da meiose, a cromatina dos espermatócitos é estruturalmente semelhante à das

células somáticas, onde as proteínas predominantes são as histonas. Com o progresso da

meiose e nos primeiros estágios da espermiogênese uma série de proteínas variantes das

histonas são sintetizadas; algumas dessas proteínas irão ser mantidas em pequenas proporções

no espermatozoide maduro, e o restante delas é substituída por proteínas de transição

(BALHORN, 2011). Com o aparecimento dessas proteínas de transição nas espermátides, é

iniciada a condensação da cromatina, que acontece no sentido da região apical para a caudal

(OKO et al., 1996). Então, as proteínas de transição são substituídas por proteínas de carga

positiva, denominadas protaminas (BALHORN, 2011).

A integridade do DNA de espermatozoides de mamíferos é de importância vital para

a contribuição paterna de um descendente normal, uma vez que danos de DNA podem resultar

em morte celular e na indução de mutações que podem ser transportadas para a próxima

geração ou resultar em infertilidade do macho(ANDRABI, 2007; SHIBAHARA et al., 2003).

Assim, a integridade do DNA tornou-se importante indicativo da qualidade do

espermatozoide (HUGHES et al., 1999). Consequentemente, os distúrbios na integridade da

cromatina são caracterizados pela presença de fraturas na banda simples ou dupla da molécula

de DNA que leva à formação de segmentos desnaturados (RYBAR et al., 2004). Uma elevada

suscetibilidade à desnaturação demonstra heterogeneidade da estrutura da cromatina e tem

sido relacionada a distúrbios na espermatogênese, morfologia anormal (ENCISO et al., 2011),

concentração e motilidade espermática diminuídas (BENCHAIB et al., 2003), danos ao

desenvolvimento embrionário e consequente fertilidade reduzida (HALLAP, 2005). Os

espermatozoides afetados possuem a capacidade de fertilizar oócitos, porém o consequente

desenvolvimento embrionário depende do grau de alteração do DNA (AHMADI; NG, 1999).

2.1.2.3 Motilidade espermática

A motilidade é um importante fator a ser considerado na análise da qualidade

espermática. Durante o processo de maturação dos espermatozoides no epidídimo ocorre

modificação da membrana plasmática, mitocôndrias, fibras e componentes microtubulares da

peça intermediária, resultando em motilidade progressiva (AMANN et al., 1993). A

motilidade espermática é o parâmetro mais comumente utilizado a fim de analisar o potencial

de fertilidade do sêmen, uma vez que a célula espermática necessita estar móvel para migrar

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ao longo do trato reprodutivo feminino e promover a fertilização do oócito (TALWAR, 2015).

Portanto, a motilidade apresenta correlação significativa (0,34) com a fertilidade, sendo

utilizada em centros de processamento de sêmen como parâmetro limitante para definir a

viabilidade de uma amostra de sêmen (SEVERO, 2009).

A motilidade é classificada de acordo com a porcentagem de células espermáticas

com movimento progressivo e qualidade do movimento, que envolve a velocidade de

movimento linear, distância total e progressão (JANUSKAUSKAS; ZILINSKAS, 2002).

Usualmente, a motilidade espermática é estimada em analisar o sêmen entre lâmina e

lamínula, assim, determinando subjetivamente o percentual de células móveis em uma

amostra com uso da microscopia óptica. Esse método é uma forma indireta de avaliação

simples e de baixo custo, no entanto, demonstra grande variabilidade (VERSTEGEN et al.,

2002). Esta variável, ocorre em função a experiência do avaliador, ocasionando diferenças

nos valores para um mesmo parâmetro. Assim, a avaliação pelo CASA mostra maior

padronização, precisão, confiabilidade e velocidade na obtenção de resultados nas análises

(COX et al., 2006). Esse tipo de análise permite uma avaliação mais exata e objetiva da

motilidade, fornecendo informações precisas e significativas da cinética celular espermática,

determinando não somente a percentagem de células móveis na amostra, mas também

quantifica características específicas do movimento espermático (GARNER, 1997).

Diferenças significativas foram observadas nos parâmetros de motilidade entre os

espermatozoides que resultaram em uma alta taxa de fertilização e àqueles que não

fertilizaram oócitos in vitro (HIRANO et al., 2001). Em bovinos, estudos demonstraram que

ejaculados com maior frequência de espermatozoides, gota citoplasmática proximal e

problemas de motilidade progressiva, não desenvolveram na fertilização in vitro em embriões

além da clivagem (THUNDATHIL et al., 2001a). Nagy et al., (2015) analisando a motilidade

de espermatozoides bovinos através de sistemas automáticos de análise de sêmen (CASA)

detectaram que o parâmetro velocidade de trajeto é algo característico de motilidade do

sêmen, útil e que possui relevância clínica na predição de fertilidade. Assim, o exame e

determinação da motilidade espermática é parte significativa da avaliação da qualidade do

sêmen.

De acordo com Verstegen et al. (2002) os parâmetros gerados da motilidade

espermática pelo CASA são: Total (%), que é a soma de todas as células contadas móveis e

não móveis; Motilidade (%),população de células que estão se movendo com uma velocidade

mínima determinada no “setup”, proporção de células móveis do total; Motilidade

Progressiva (%),porcentagem de células movendo-se progressivamente; Velocidade de

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Trajeto (VAP, µm/s), velocidade média ininterrupta do trajeto da célula; Velocidade

Progressiva (VSL, µm/s), velocidade média percorrida em linha reta entre os pontos inicial e

final do trajeto; Velocidade Curvilinear (VCL, µm/s), a velocidade média mensurada de ponto

a ponto do trajeto percorrido pela célula; Amplitude do Deslocamento Lateral da Cabeça

(ALH, µm), largura média da oscilação da cabeça conforme a célula se move; Frequência de

Batimentos (BCF, Hz), frequência com que a cabeça do espermatozóide move-se para trás e

para frente durante um trajeto percorrido; Retilinearidade (STR, %), valor médio da

proporção entre VSL/VAP; Linearidade (LIN, %), valor médio da proporção entre VSL/VCL;

e Velocidade Rápida (%). No entanto, estes parâmetros podem não ser suficientes para uma

avaliação completa do potencial da fertilidade.

O desenvolvimento das tecnologias ômicas tem fornecido novas ferramentas de

detecção molecular que permitem que vários parâmetros estejam efetivamente integrados na

avaliação do potencial fertilizante do sêmen. Por meio da abordagem proteômica, Zhao et al.

(2006) identificaram várias proteínas expressas diferencialmente em amostras de

espermatozoides de baixa motilidade, em comparação com amostras de espermatozoides que

apresentavam motilidade normal. Bissonnette et al. (2009) demonstram que alguns transcritos

previamente identificados em associação com a fertilidade também estão associados com

motilidade in vitro de espermatozoides bovinos. Hering et al. (2014) identificaram genes

associados à baixa motilidade espermática de touros, dentre eles o gene da proteína associada

ao canal catiônico 1 do espermatozoide (CATSPER1) que contribui para a patogênese da

astenozoospermia. Tais informações, podem contribuir para elucidar mecanismos moleculares

subjacentes à motilidade espermática.

2.1.3 Transcriptômica

Os estudos transcriptômicos analisam a transcrição de genes a partir RNAm. Antes

da formação de proteínas/enzimas, a transcrição dos genes é o primeiro passo para a

expressão da atividade dos genes (WANG et al., 2009). A transcriptômica tem como objetivo

realizar o estudo de todos os conjuntos de moléculas de RNA (RNAm, RNAr, RNAt e RNA

não-codificante) em uma única célula ou organismo. Como a transcriptômica reflete os genes

que estão ativos expressos em qualquer momento da célula, também é referida como perfil de

expressão. A principal técnica usada para abordar essa abordagem “omica” é o microarranjo

de RNA e o DNA (BLOW, 2009c).

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Estudos transcriptômicos frequentemente demonstram a abundância de RNAms e de

suas proteínas correspondentes e se as mesmas estão bem correlacionadas. O destino de um

RNAm é rigidamente regulado por uma interação complexa de modificação, processamento,

armazenamento, decaimento e tradução, todos envolvendo interações proteína-RNA através

de complexos de ribonucleoproteína mensageiro (RNPm). Alguns desses complexos

montados são conduzidos diretamente para a tradução, enquanto outros são desviados para a

repressão de armazenamento e translacional (MUELLER-MCNICOLL; NEUGEBAUER,

2013).

A transcriptômica é amplamente utilizada em estudos com animais de produção.

Diferenças transcriptômicas em embriões derivados de touros de diferente status de fertilidade

no estágio de pré-implantação do desenvolvimento, demonstrando que o gameta masculino

contribui não apenas com DNA, mas também com RNA e fatores de sinalização para o oócito

na fertilização (KROPP et al. 2017).

O RNA-seq é a atual tecnologia usada para a análise de alto rendimento dos perfis de

transcriptoma, o que é essencial para entender a base molecular dos fenótipos. Além disso,

tem a capacidade de sequenciar diretamente toda a população de transcritos através da

amplificação de cDNA curto e, em seguida, marcação fluorescente de uma única base de cada

vez gerando dezenas de milhões de leituras curtas, em torno de 30-400 pb de comprimento

(WANG et al. 2009). Este método foi aplicado para mapear o transcriptoma de diversas

espécies e tecidos, incluindo espermatozoides de humanos (SENDLER et al., 2013),

camundongos (FANG et al., 2014), bovinos (CARD et al., 2013; SELVARAJU et al., 2017) e

cavalo (DAS et al., 2013). Embora essas células sejam consideradas transcricionalmente e

translacionalmente inativas, as quais contêm uma ampla população de moléculas de RNA

codificadoras e não-codificadoras (JODAR et al. 2013), com funções que têm sido

relacionadas à espermatogênese (OSTERMEIER et al., 2002), reorganização da cromatina

espermática (HAMATANI, 2012), potencial de fertilidade (JODAR et al., 2015),

desenvolvimento embrionário precoce (SENDLER et al., 2013) e herança epigenética

transgeracional (RANDO, 2016). Assim, o estudo do transcriptoma do espermatozoide é

fundamental para entender sua biologia e seu papel na fertilidade. Contudo, um dos principais

desafios para o estudo do transcriptoma dos espermatozoides é o baixo rendimento de RNA e

a alta fragmentação dos transcritos tipicamente presente nestas células, visto que a química

padrão de RNA-seq normalmente requer uma grande quantidade (1 mg) de RNA de boa

qualidade.

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A técnica de RNA-Seq tem a capacidade de detectar polimorfismos de nucleotídeo

único (SNPs), bem como os limites de exon-exon para todas as transcrições expressas na

amostra (COSTA et al., 2010). Na reprodução de mamíferos, o RNA-Seq tem sido usado

recentemente para revelar a função e importância dos transcritos em vários estágios

reprodutivos com foco principal na aquisição da função reprodutiva em adultos e na qualidade

do embrião, demonstrando a importância desta técnica para reprodução de um modo geral.

Em bovinos, a análise de RNA-Seq do sêmen sequenciou o transcriptoma do espermatozoide

bovino, consistindo de 6.166 transcritos, incluindo várias transcrições previamente

identificadas e novas para posteriores estudos funcionais (SELVARAJU et al., 2017). O

sequenciamento direto é uma vantagem sobre a sequência curta de sondas de cDNA usadas

para chips de microarray que não cobrem transcritos inteiros potencialmente resultando em

deturpação de transcritos truncados e isoformas transcritas. Huang et al. (2012) utilizaram o

RNA-seq para caracterizar e comparar os padrões de splicing alternativo em blastocistos em

desenvolvimento ou em degeração usando embriões de bovinos fertilizados in vitro,

detectando novos genes que podem desempenhar importantes papéis no início do

desenvolvimento embrionário, demonstrando claramente o poder de RNA-seq e forneceu

novos conhecimentos sobre desenvolvimento embrionário inicial de bovinos, fornecendo uma

compreensão sistemática adicional desenvolvimento embrionário de mamíferos em grande

escala.

Em vacas leiteiras a análise da expressão gênica foi realizada pelo sequenciamento

de RNA da Illumina® e obtiveram um total de 16.892 genes expressos no período de

transição, 19.094 genes foram expressos no pico de lactação e 18.070 genes foram expressos

no final da lactação. Independentemente do estágio de lactação, aproximadamente 9.000

genes mostraram expressão em todos os períodos. A maioria dos genes na via do metabolismo

da gordura apresentou alta expressão no leite no período de transição e no pico de lactação.

Este foi o primeiro estudo a descrever o transcriptoma de forma abrangente do leite bovino

em vacas Holandesas (WICKRAMASINGHE et al., 2011).

Estudo analisando perfis de expressão de genes em amostras de tecido adiposo

subcutâneo em diferentes idades e sexos, identificou um total de 12.233 genes expressos, que

foram detectados pelo método RNA-Seq para mostrar os genes diferencialmente expressos

fornecendo uma nova compreensão do tecido adiposo a um nível molecular (ZHOU et al.,

2014).

A tecnologia RNA-seq foi também empregada em estudo para detectar a genes

diferencialmente expresos em glóbulo de gordura do leite com 10 dias e 70 dias após o parto

32

entre dois grupos de vacas com produção alta e baixa de leite após 305 dias, análise do

rendimento de gordura e rendimento de proteína do leite. No total, 1232, 81, 429 e 178 estes

resultados demonstraram alguns genes considerados promissores para características de

produção de leite em bovinos leiteiros (YANG et al., 2016).

2.1.4 Proteômica

O próximo passo após a transcrição de genes em RNAm é a tradução do RNAm

resultante em proteínas. Este processo pode ser influenciado por fatores como o RNAmi.

Nesta etapa, proteínas altamente abundantes podem ser produzidas, bem como proteínas de

baixa abundância que possuem uma ampla gama de funções em toda a fisiologia do animal de

produção. Assim, a proteômica é considerada uma das mais conhecidas abordagem “ômica”

(CRAVATT et al., 2007). A proteômica, consiste na identificação, quantificação e no estudo

das modificações pós-traducionais do proteoma, ou seja, o conjunto de proteínas expressas em

um genoma ou tecido; e no estudo das interações proteicas e mecanismos regulatórios

(BLACKSTOCK; WEIR, 1999).

Assim, a proteômica baseia-se em princípios bioquímicos, biofísicos e de

bioinformática para quantificar e identificar as proteínas expressas, pois se modificam de

acordo com o desenvolvimento de um organismo bem como em resposta aos fatores

ambientais (ANDERSON; ANDERSON, 1996; WILKINS et al., 1996). A proteômica surgiu

na década de 1970 quando pesquisadores começaram a criar as bases de dados de proteínas

utilizando a técnica de eletroforese bidimensional em gel de poliacrilamida (O’FARREL,

1975). A pesquisa proteômica permite identificar e caracterizar marcadores biológicos, ou

seja, moléculas endógenas ou exógenas específicas de um determinado estado patológico.

Assim, a capacidade de identificação dessas moléculas é útil no diagnóstico precoce de

doenças e no acompanhamento da evolução do tratamento (CASH, 2002).

No que concerne à reprodução animal, essa técnica tem sido empregada

principalmente para a detecção de marcadores bioquímicos da fertilidade, como também da

congelabilidade do sêmen. A análise proteômica dos espermatozoides proporciona maior

entendimento das interações proteicas do plasma seminal com estas células. Portanto, a

proteômica do sêmen é imprescindível para identificação de propriedades e funções das

proteínas envolvidas nos mecanismos de regulação das funções do trato reprodutivo

masculino (STRZEZK et al., 2005).

33

A abordagem proteômica clássica é baseada especialmente, na separação das

proteínas por eletroforese em gel bidimensional (2D), que separa as proteínas por dois

parâmetros independentes: na primeira dimensão pelo ponto isoelétrico e na segunda

dimensão pela massa molecular. A eletroforese bidimensional é um método fundamental, pois

possibilita a visualização de um grande número de proteínas simultaneamente e suas distintas

isoformas (HAYNES et al., 2000; LOW et al., 2002). No entanto, o método mais utilizado

para identificação das proteínas é a espectrometria de massas que consiste na mensuração do

peso molecular de átomos e moléculas. Primeiramente, as proteínas de interesse são

recortadas do gel, fragmentadas (obtenção dos peptídeos), geralmente por digestão tríptica, e

os fragmentos são analisados no espectrômetro de massa que, por sua vez, determina a massa

da molécula mesurando a razão massa/carga do íon da molécula (SIUZDAK, 2006).

2.1.5 Metabolômica

Os componentes do proteoma (proteínas/enzimas) são envolvidos no metabolismo do

animal. Além de analisar as enzimas utilizando a tecnologia proteômica, a pesquisa também

podese concentrar nos metabólitos produzidos pelas enzimas de interesse. Metabólitos são

produtos intermediários ou finais do metabolismo em uma amostra biológica. O conjunto de

todos os metabólitos de baixa massa molecular (até 1500 Da), presentes ou alterados em um

sistema biológico, é chamado de metaboloma (do inglês, metabolome) (WITTENBURG et

al., 2013). Existem muitas categorias diferentes de metabólitos que podem ser estudadas,

incluindo lípidos, metabólitos solúveis em água e metabólitos voláteis. Essas diferentes

categorias de metabólitos requerem a sua própria abordagem analítica para detecção (WANG

et al., 2009).

O desenvolvimento da metabolômica tem como principal objetivo avaliar as

mudanças do maior número possível de diferentes metabólitos de pequenas moléculas em

uma célula, tecido, órgão ou organismo. Assim, diferentemente da transcriptômica e

proteômica, o perfil metabolômico pode dar um panorama da fisiologia da célula e tem sido

amplamente utilizado (CARROLL et al., 2010; ZHAO et al., 2014). A metabolômica é um

método promissor para identificar possíveis biomarcadores de fertilidade e infertilidade

masculina (DEEPINDER et al., 2007; KOVAC et al., 2013). A presença, ausência e/ou

alterações de metabólitos específicos podem estar relacionadas à fisiologia dos

espermatozoides, e tais informações podem permitir um diagnóstico precoce associado a um

34

melhor tratamento da infertilidade (AITKEN, 2010). No estudo de Bender et al., 2010,

análises do perfil metabolômico de fluidos foliculares de vacas e novilhas em lactação, por

cromatografia gasosa associada à espectrometria de massa (CG-EM), identificaram níveis

mais elevados de ácidos graxos saturados no fluido de vacas em comparação ao fluido das

novilhas. Os autores desse estudo sugerem que elevados níveis de ácidos graxos no fluido

folicular podem ter efeitos negativos sobre a fertilidade de vacas (BENDER et al., 2010).

Velho et al. (2018) determinaram o perfil metabólico do plasma seminal de touros de

alta e baixa fertilidade e identificaram potenciais biomarcadores de fertilidade. Além disso,

foi utilizado ferramentas de bioinformática para revelar as redes e reações nas quais os

metabólitos do plasma seminal do touro podem estar envolvidos. Neste estudo foi

demonstrado que frutose, ácido cítrico, ácido lático, uréia e ácido fosfórico são os metabólitos

predominantes no plasma seminal de touros, ralatando uma clara separação dos perfis

metabólicos entre os touros de alta e baixa fertilidade, sendo a frutose e o ácido 2-

oxoglutárico potenciais candidatos a biomarcadores de fertilidade de touros. Os resultados do

deste estudo ajudarão a avançar nosso entendimento atual dos processos multifatoriais e

complexos relacionadas com a fisiologia da fertilidade em machos.

35

3 PROBLEMA

Conforme descrito na revisão de literatura, vários estudos têm demonstrado os

avanços das tecnologias “ômicas” na reprodução, bem como a importância de apronfundar os

conhecimentos relacionados a expressão de genes ligados a fertilidade de bovinos. Diante

disto, levantou-se os seguintes questionamentos:

1. Será que o padrão de metilação do DNA espermático pode ser um parâmetro importante

para a análise da fertilidade do sêmen de reprodutores?

2. Será que vacas leiteiras holandesas durantre o período da lactação apresentam alterações

na expressão gênica e consequentemente nas vias metabólicas?

36

4 JUSTIFICATIVA

O uso das tecnologias ômicas proporcionam o entendimento do funcionamento

celular dos organismos e suas alterações biológicas. Estas tecnologias tornaram-se de uso

corrente em estudo com animais de produção para melhor entender a fisiologia do animal e a

qualidade dos produtos produzidos. Objetivando descobrir potenciais biomarcadores,

inúmeros estudos têm sido realizados empregando as abordagens “ômicas”, incluindo

proteômica, metabolômica e lipidômica. Sabe-se que um biomarcador ou marcador biológico

é uma característica quantificável e/ou mensurável, que pode representar um fenótipo

funcional ou uma determinada patologia em um organismo vivo. Esses biomarcadores se

estabelecem de acordo com as mudanças nos níveis de genes, microRNAs, proteínas,

metabólitos ou outras moléculas que possam influenciar um determinado processo biológico.

Um aspecto importante de se fazer tais estudos “ômicos” é o entendimento da variação. Por

exemplo, em relação à paridade, lactação, estado alimentar e saúde animal, a variação pode

ocorrer em transcritos, proteínas ou metabólitos encontrados em animais de produção e nos

produtos produzidos. Essa variação pode ajudar a entender melhor a fisiologia do animal

(HETTINGA; ZHANG 2018).

As tecnologias ômicas fornecem informações sobre os benefícios da combinação de

conjunto de dados coletados em diferentes períodos. Isso pode ser ainda mais estendido para

maiores perspectivas, como compreender que os genes não funcionam isoladamente, mas sim

em conjuntos de genes que codificam, através de conjuntos de transcritos, conjuntos de

proteínas que estão envolvidas em processos metabólicos específicos. Uma vez que o genoma

de um animal é sequenciado e anotado, esta informação pode ser utilizada para construir vias

metabólicas, que descrevem o quadro integrado de como diferentes processos em uma espécie

funcionam juntos. Além disso, a construção de visões gerais das vias metabólicas é um

primeiro passo necessário para a pesquisa visando uma melhor compreensão do metabolismo

de um animal. Esta construção de vias metabólicas pode ser feita usando conhecimento de

reações enzimáticas conhecidas e caminhos para os quais os genes podem ser ligados. Como

exemplo, as redes de genes envolvidos na síntese de lipídeos (BIONAZ; LOOR 2008) e

proteínas (BIONAZ; LOOR 2011) de vacas leiteiras.

Portanto, estudar animais de produção em diversos períodos usando simultaneamente

tecnologias “ômicas” pode ser muito útil para entender melhor a fisiologia subjacente, bem

37

como as modificações que ocorrem. Uma vez identificadas tais vias metabólicas, novas linhas

de pesquisas serão propostas no sentido de melhor compreender seus mecanismos de ação. As

funções de alguns genes ainda são desconhecidos e, portanto, ainda precisam ser melhor

elucidados, especialmente aqueles associados à lactação de vacas de alto rendimento. Desta

forma, uma melhor compreensão desses mecanismos auxiliará a entender e identificar mais

precocemente animais com problemas metabólicos, aumentando dessa forma a eficiência na

seleção de animais de alta produção.

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5 HIPÓTESES CIENTÍFICAS

1) O enriquecimento de sequências metil-CpG altera o padrão de metilação do DNA

e estão associadas à motilidade dos espermatozoides.

2) A variação de metilação pode afetar os genes envolvidos na organização da

cromatina espermática.

3) Durante o período da lactação em vacas ocorre mudança na expressão de genes.

4) Com o uso das tecnologias ômicas é possível identificar os genes diferencialmente

expressos e revelar variações de sequência nas regiões transcritas.

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6 OBJETIVOS

6.1 OBJETIVOS GERAIS

Elaborar um perfil de metilação em todo o genoma nas duas populações de

espermatozoides e identificar assinaturas epigenéticas diferenciais entre espermatozóides

de alta (HM) e baixa motilidade (LM).

Identificar o padrão de expressão de genes do tecido adiposo subcutâneo de vacas no

parto e com 30 e 90 dias pós-parto.

6.2 OBJETIVOS ESPECÍFICOS

Avaliar a metilação de dinucleotídeos citosina-guanina (CpGs) em populações de

espermatozoides de Bos taurus de alta (HM) e baixa mobilidade (LM) separadas por

gradiente de Percoll.

Identificar os padrões de metilação em espermatozoides HM e LM investigados por

sequenciamento de bissulfito.

Comparar o perfil de expressão gênica no tecido adiposo subcutâneo de vacas leiteiras no

dia do parto (D2), 30 dias e 90 dias pós-parto utilizando o método RNAseq.

40

7 ARTIGO I:

Epigenetic variation in pericentromeric regions between high and low motile sperm

populations in Bos taurus

(Variação epigenética em regiões pericentroméricas entre populações de espermatozoides

de alta e baixa motilidade em Bos taurus)

Artigo aceito para publicação no periódico Scientific Reports

(Qualis A1 – Biotecnologia)

41

Epigenetic variation in pericentromeric regions between high and low motile sperm

populations in Bos taurus.

Capra E.1, Lazzari B. 1-2, Turri F.1, Cremonesi P.1, Portela AMLR.3, Ajmone-Marsan P.4,5

Stella A. 1-2, Pizzi F. 1Istituto di Biologia e Biotecnologia Agraria, Consiglio Nazionale delle Ricerche, Lodi, Italy. 2Parco Tecnologico Padano, Lodi, Italy. 3Department of Animal Science, Federal University of Ceará, Fortaleza, Brazil 4Istituto di Zootecnica, Università Cattolica del Sacro Cuore, Piacenza, Italy 5Centro di Ricerca Nutrigenomica e Proteomica – PRONUTRIGEN, Università Cattolica del Sacro Cuore, Piacenza, Italy Corresponding Author: aIstituto di Biologia e Biotecnologia Agraria, Consiglio Nazionale delle Ricerche, via Einstein, 26900 Lodi, Italy Tel.: +39 0371 4662505; fax: +39 0371 4662501. E-mail address: [email protected] (F. Pizzi). Email addresses: EC: [email protected] BL: [email protected] FT: [email protected] PC: [email protected] AMLRP: [email protected] PAM: [email protected] AS: [email protected] FP: [email protected]

Abstract: Sperm epigenetics is an emerging area of study supported by observations

reporting that abnormal sperm DNA methylation patterns are associated with infertility. Here,

we explore cytosine-guanine dinucleotides (CpGs) methylation in high (HM) and low motile

(LM) Bos taurus sperm populations separated by Percoll gradient. HM and LM methylation

patterns were investigated by bisulfite sequencing. The average level of methylated cytosine

was about 94%. Comparison between HM and LM sperm populations revealed that

methylation variation affects genes involved in chromatin organization. CpG Islands (CGIs),

were highly remodelled. A high proportion of CGIs was found to be methylated at low-

intermediated level (20-60%) and associated to the repetitive element BTSAT4 satellite. The

low-intermediate level of methylation in BTSAT4 was stably maintained in pericentromeric

regions of chromosomes. BTSAT4 was hypomethylated in HM sperm populations. The

characterization of the epigenome in HM and LM Bos taurus sperm populations provides a

42

first step towards the understanding of the effect of methylation on sperm fertility.

Methylation variation observed in HM and LM populations in genes associated to DNA

structure remodelling as well as in a repetitive element in pericentromeric regions suggests

that maintenance of chromosome structure through epigenetic regulation is probably crucial

for correct sperm functionality.

Keywords: sperm, motility, methylation, epigenetic, satellite

Introduction

Male infertility is a complex disorder affecting humans as well as other animals.

Infertility is partially explained by physiological and biochemical factors, such as low sperm

counts and poor sperm quality. The genetic basis of male infertility accounts for about 15% of

infertile cases [1, 2]. The etiology of this disorder remains unclear both in human and other

species. For example, bulls considered of high-merit based on different sperm traits such as

spermatozoa motility and morphology, are sometimes unable to produce successful full-term

pregnancies [3, 4]. Different molecular parameters related to sperm nuclear and mitochondrial

DNA, plasma membrane and lipid composition affect the ability of spermatozoa to fertilize

oocytes and contribute to normal embryo development [5-7]. Therefore, much remains to be

understood and novel molecular approaches may help to unravel the molecular basis of

infertility.

Among the known epigenetic processes in mammalian cells, DNA methylation has

been identified as an important regulatory mechanism of genome function in normal

embryonic development, X-chromosome inactivation and genomic imprinting [8, 9]. DNA

methylation of the 5-carbon position in cytosine residues was reported to be predominantly

present at cytosine-guanine dinucleotides (CpG) and especially in GC rich regions called CpG

islands (CGIs) [10]. CGIs methylation in different genomic features impacts gene expression

i.e. promoter hypomethylation is associated with gene expression, while methylation in gene

bodies influences splicing [11]. Methylation is also observed in Repetitive Elements (RE) of

adult cells playing a role in the maintenance of chromosome structure and genome integrity

[12].

Sperm epigenetic marks are unique, thus the factors that determine the patterns of

DNA methylation differ between male germ cells and somatic cells. Although RE are highly

methylated in both germ and somatic cells, elements from several subfamilies show different

43

levels of methylation in the two cell types [13]. Centromeric regions in spermatogonia are

known to be less methylated compared to somatic tissues [14]. This methylation pattern is

supposed to play a role in germ-cell chromatin organization, rather than in the control of gene

expression [15]. Most of the epigenetic signatures in germ cells are erased after conception

from the morula stage to the blastocyst stage in the inner cell mass (ICM), then a sharp

increase in the level of methylation in embryo is observed following implantation [16, 17].

However, a proper regulation of epigenetic processes during spermatogenesis is necessary to

ensure embryonic development in addition to sperm function. It has been reported that the

level of DNA methylation of round spermatid is different from that of mature spermatozoa.

Round spermatid rather than mature spermatozoa microinsemination was also observed to

profoundly influence epigenetic marks in the embryo, thus affecting embryonic development

and male fertility [18].

Aberrant locus specific or global methylation has been associated to abnormal semen

parameters, as well as male infertility. A study reported that oligospermic patients presented a

hypomethylation or unmethylation pattern at the H19/ gene encoding insulin-like growth

factor 2 (IGF2) imprinting control region 1 (ICR1) and hypermethylation at the Mesoderm-

specific transcript (MEST) imprinted locus as well as a reduced sperm quality, as compared

with normozoospermic men [19]. Broad DNA hypermethylation across many loci, including

also the Satellite 2 repetitive element, was associated to poor sperm concentration and

motility and to morphology alterations in abnormal human sperm [20]. The level of DNA

methylation in human sperm, determined by an ELISA-like method, was correlated to

conventional sperm parameters, e.g. concentration and motility, as well as sperm chromatin

and DNA integrity, but not to sperm viability and morphology [21]. DNA methylation in

human spermatozoa was higher in low quality spermatozoa [22]. Pyrosequencing analysis of

human long interspersed elements (LINE) after bisulfite conversion estimated an overall

global methylation of about 75% that increase with age. At the same time, targeted bisulfite

sequencing of different selected genes showed a lower methylation level with a strong trend

toward age associated hypomethylation in some genomic regions [23]. Targeted bisulfite

sequencing, also revealed different levels of methylation in the promoter regions between

high and low motile human sperm [24].

In farm animals, several studies showed altered sperm methylation to be associated

with male infertility. A different DNA methylation pattern was observed between

spermatozoa from high-fertile and sub-fertile buffalo bulls [25]. Recently, assessment of the

epigenetic signature of bull spermatozoa using a human DNA methylation microarray [26]

44

and Methyl-Binding Domain (MBD) Sequencing [27] revealed differentially methylated CpG

sites and regions associated to bull fertility rate.

In the present study, the 5-methyl cytosine variations in CpGs was evaluated in high

and low motility bull sperm populations following methyl enrichment and bisulfite

sequencing approach. The objective is to produce a genome-wide methylation profile in the

two populations, and to identify differential epigenetic signatures between high (HM) and low

motile (LM) sperm.

Results

Isolation of spermatozoa and evaluation of sperm characteristics

Sperm cells were successfully fractionated in HM and LM populations; a significant

(P<0.05) improvement of several sperm quality parameters was observed in HM population in

comparison to semen at thawing considering the following parameters: straight-line velocity

VSL, curvilinear velocity VCL, average path velocity VAP and amplitude of lateral head

displacement ALH variables [VSL (μm/s): 46.08±4.11, 61.24±2.91; VCL (μm/s): 76.35±6.02,

110.37±4.25; VAP (μm/s): 55.38±4.27, 74.02±3.01; ALH 2.53±0.14,3.72±0.10; respectively

in semen at thawing and HM population] (Supplementary Info 1).

Sequencing statistic and CpG methylation distribution

The average number of reads per sample was 28.1M (ranging from 13.2M to 37.5M).

Mapping efficiency was high for all samples (range between 83.1% - 90.6%). After

calculating cytosine methylation conversion, a high percentage (93.7%) of the cytosines in the

CpG enriched regions was methylated in both sperm populations (see Supplementary Info 2

for statistics). After applying a threshold of at least 5X coverage per cytosine, a total of 26.6M

methylated regions (MR) (100 bp tiles with sliding window size of 100 bp) were identified

spanning across the whole bovine genome. Among these, 1,086,748 methylated regions

(MRs), observed at least in three samples in both HM and LM sperm population, were

selected to compare the DNA cytosine methylation profile.

Among these, a total of 423,673 MRs mapped in 14,071 out of 23,970 annotated

genes. Furthermore 12,744 MRs mapped upstream (-2Kb) and 19,475 MRs downstream

(+2Kb) of gene regions. A total of 9,397 MRs were located within the 23,431 annotated CpG

45

islands (CGIs) Supplementary Dataset 1, 2, 3, 4). Gene bodies, 5’ and 3’ UTRs were

prevalently hyper-methylated in both sperm populations. Intriguingly, probes overlapping

CGIs showed a peculiar distribution, with a relevant proportion of cytosines having an

intermediate level of methylation (between 30 and 60%) (Figure 1).

Differentially Methylated Regions between HM and LM sperm populations

A genome-wide analysis that included genes and regulatory elements revealed that a

small percentage of CpGs showed a significant variation in the methylation level

(differentially methylated regions (DMRs)/MRs percentage) between HM and LM sperm

populations in gene bodies (1.45%), 5′ untranslated regions (5’UTRs) (3.12%) and 3’UTRs

regions (2.72%). Considering CGIs, a higher proportion of the methylome (9.77%) was

remodelled in HM vs LM sperm populations (Supplementary Info 3). Hierarchical analysis of

the 20 most hyper and hypo methylated DMRs found in CGIs, in gene bodies, 5’UTR and

3’UTR well discriminated HM from LM samples (Figure 2). A base resolution vision of some

of the differentially methylated regions in HM and LM sperm populations overlapping gene

bodies, 5’UTR, 3’UTR and CGIs is shown in Supplementary Info 4. Annotation of 6,131

DMRs that overlapped gene bodies resulted in 3,278 differentially methylated genes (DMGs)

(Supplementary Dataset 5). In addition, 398, 538 and 918 DMRs located near 5'UTR, 3'UTRs

and CGIs, were close (± 2Kb) to 355, 484 and 297 DMGs, respectively (Supplementary

Dataset 6, 7, 8). Gene ontology (GO) analysis was performed on genes found to be

differentially methylated in 5'UTR, 3'UTRs and CGIs, and on a selection of 423 genes

differentially methylated in gene bodies (468 DMRs with false discovery rate (FDR) <10exp-

10) (Supplementary Dataset 9). Variation in CpG methylation in different gene features and

CGIs affected GO terms related to DNA replication, repair, organization and maintenance. In

addition, GO terms related to hindbrain function, epithelia and endothelia migration metabolic

processes were also observed to differ between HM and LM sperm population. Unexpectedly,

3’UTR showed the highest number of significant gene ontology terms, whereas only few

terms were affected by CpG variation in 5’UTR (Table1).

Methylation distribution in CpG Islands

To further explore bovine sperm CpG methylation in CGIs, the global level of

cytosine methylation was calculated in each CGI. Out of 23,431 CGIs annotated in the bovine

46

genome, 3,869 were detected (at least 3 out of 4 samples for HM and LM) in our dataset.

Based on CpG methylation level in CGIs (Figure 1), profiles were grouped in two classes (20-

60% and 80-100%), and distribution of CGIs length was calculated in each class in HM and

LM sperm populations (Figure 3). Although CGIs length decrease exponentially, low-

intermediate methylated CGIs showed a peak at about 1.4 Kb (Figure 3A). In addition, larger

CGIs (10-240 Kb) were prevalently methylated at 20-60% (Figure 3B). These results were

consistent with the observation of genomic repetitive element motifs methylated at low-

intermediate levels.

Methylation distribution in BTSAT4

Analysis of CGIs size distribution in low-intermediated methylated regions

suggested that the atypical methylation profile observed is likely associated to repetitive DNA

elements.

To further test this hypothesis, low-intermediate and highly methylated sequences

were used as a query to the Database of repetitive rDNA element Repbase. Database

interrogation returned BTSAT4 for about 75% of intermediate methylated sequences, whereas

the percentage of BTSAT4 in hypermethylated region was close to zero. BTSAT3, OSSAT2,

BTLTR1 and ERV2-1-LTR were also methylated prevalently at intermediate level (20-60%)

(Table 2). Out of 2,434 BTSAT4 elements annotated in the bovine genome, 720 were detected

(at least 3 out of 4 samples for HM and LM) in our dataset. Analysis of CpG methylation

outlined an overall low level of BTSAT4 methylation in the HM sperm population.

Considering 159 DMRs in the BTSAT4 regions, 122 were more methylated in LM sperm

populations (Supplementary Dataset 10) (Figure 4).

Discussion

In this work the pattern of methylation in high and low motile bull sperm populations

was determined using an enrichment step of methyl-CpG sequences combined with bisulfite

sequencing.

Our data reveal an overall higher level of CpG methylation (about 94%) of bull

sperm, similar in HM and LM sperm populations that may be explained by the technical

approach here used to enrich samples for methylated sequences before bisulphite treatment

and sequencing. The distribution of CpG methylation observed across the genome was in fact

47

different from those previously described in mouse sperm following Reduced Representation

Bisulfite Sequencing RRBS [28]. RRBS method reduces the representation of repeats in the

data set [29] whereas methyl-Seq enrich for CpG methylated repeat. Accordingly, the

methylation level of CGIs observed in our study was higher than previously reported [28],

and highlight a portion of CpG rich regions methylated at low-intermediate level.

The comparison of different genomic features in HM and LM sperm populations

revealed several differentially methylated regions flanking genes with a role in chromatin

organization and maintenance. In particular, differential methylation in 3’UTR was found in

genes (histone lysine methyltransferases 2A (KMT2A), histone lysine demethylases 2A

(KDM2A) and nuclear receptor-binding SET Domain 2 (NSD2)/ multiple myeloma SET

domain (MMSET)/ Wolf-Hirschhorn syndrome candidate-1(WHSC1)) influencing chromatin

structure by epigenetic mechanisms, such as the regulation of histone H3-K4 methylation.

Previous studies reported a strict association between sperm DNA methylation levels and both

sperm chromatin condensation and DNA integrity, suggesting that the formation of a compact

chromatin and proper DNA methylation are closely related events during spermatogenesis

[21].

The NSD family of histone methyltransferase (HMT) comprises three members

(NSD1, NSD2/ MMSET/ WHSC1, and NSD3/WHSC1L) that recognise lysine residue of

histones H3 and H4 and mediate their methylation [30]. KMT2A (also known as mixed-

lineage leukemia 1 (MLL1)) catalyzes the methylation of H3K4 [31, 32]. KDM2A, a

Jumonji-C (JmjC)-domain containing histone demethylase (HDM), is a heterochromatin-

associated protein that is required to maintain the heterochromatic state, it represses

transcription of small non-coding RNAs that are encoded by clusters of satellite repeats at the

centromere [33].

Histone Lysine methylation is tightly regulated by distinct families of conserved

enzymes, KMTs and KDMs, which add and remove methyl groups at histone lysine [34].

They play a role in orchestrating methylation of H3K9 and H3K27 in sperm. The methylation

increases during meiosis, but the removal of H3K9me at the end of meiosis is essential for the

onset of spermiogenesis [35]. In mice, the reduction of MLL2 activity results in a dramatic

decrease of the number of spermatocytes by an apoptotic process and prevents spermatogenic

differentiation [36]. Lysine-specific histone demethylase 1 (LSD1)/KDM1 is required for

spermatogonial differentiation, as well as germ cell survival, in the developing testis [37]. An

evolutionarily conserved pathway between histone H3-K9 methylation and DNA methylation

exists in mammals, that is likely to be important to reinforce heterochromatic subdomains

48

stability and to protect genome integrity. Suppressor of variegation 3-9 homolog (Suv39h)

HMTases (also called KMT1A/B) are required to direct H3-K9 trimethylation and DNA

(cytosine-5-)-methyltransferase 3 beta (Dnmt3b)-dependent DNA methylation to major

satellite repeats at pericentric region [38].

A concurring variation in methylation of satellite repeats at pericentromeric region

was observed in our dataset. A group of CGIs methylated at intermediated level (20-60%),

located within genomic satellite repeats and in particular BTSAT4 Bovine satellite I [39] was

observed to be less methylated in HM sperm populations. In the bovine genome BTSAT4 is

likely to be the counterpart of human alpha satellites, because both present in high-copy

tandem repeat at centromeric position. Comparative analysis in hundreds of species found a

high variability in size for alpha satellites centromere repeats, e.g. approximately 171-bp in

human and 1,400-bp in Bovidae [40]. in agreement with the size of repetitive element that we

found to be methylated at intermediated level in bovine sperm. The bovine satellite I was

observed to be located in all pericentromeric regions of Bos taurus autosomes by fluorescence

in situ hybridization [41].

As observed in our study, a lower level of methylation of satellite DNA within

pericentromeric regions was previously observed in primate sperm profiling [13]. Bovine

alpha satellite I was observed to have low-intermediated methylation levels in sperm.

Embryos obtained by somatic cell nuclear transfer (SCNT) presented a hyper-methylation in

the bovine alpha satellite I, expected to cause higher chromatin condensation compared to

embryos generated by in vitro sperm fertilization (IVF). This may in turn contribute, either

immediately, or later in development, to the inefficiency of producing live offspring by SCNT

[42, 43]. Low methylation levels have also been correlated with the ability to bind cohesin

complexes that regulate the separation of chromatids at mytosis [44], suggesting a model in

which selective hypomethylation of centromeric satellites might be critical for accurate

chromosome segregation during meiosis. Recently, methylation at satellite repeats throughout

the genome has been observed to be increased in obese rat offspring [45]. Although obesity in

human is associated with infertility by numerous studies [46], a direct link between satellite

repeats methylation and sperm infertility is not yet described.

Conclusion

Methylation profiling in bovine semen revealed differential methylation of the

BTSAT4 repetitive element in pericentromeric regions between HM and LM sperm

49

populations. In addition, many DMRs were enriched in genes often functionally related to

sperm DNA organization and maintenance. Together, alteration of methylation in

pericentromeric regions and in genes associated to lysine histone methylation highlights that

the complex mechanism that regulates DNA condensation during chromosomal packaging in

sperm may affect sperm motility.

Methods

Isolation of spermatozoa and evaluation of sperm motility

Frozen semen straws from four mature progeny tested Holstein bulls with

satisfactory semen quality were purchased from an Artificial Insemination AI center

(INSEME S.P.A., Modena, Italy).

High Motile (HM) and Low Motile (LM) sperm populations were isolated through

Percoll gradient as previously described [47]. Total motility and sperm kinetics parameters

were assessed by CASA (Computer-Assisted Semen Analysis) system (ISAS® v1). Five μl of

semen pellet obtained after Percoll density gradient centrifugation were diluted in 5 μl

Tyrode’s albumine lactate pyruvate (TALP) sperm medium [48] pre-warmed at 37°C. Ten μl

of diluted semen was placed on a pre-warmed (37 °C) Makler chamber. During the analysis,

the microscope heating stage was maintained at 37 °C. Using a 10× objective in phase

contrast, the image was relayed, digitized and analyzed by the ISAS® software with user-

defined settings as follows: frames acquired, 25; frame rate, 20Hz; minimum particles area 20

microns2; maximum particles areas 70 microns2. Spermatozoa speed was assigned to 3 broad

categories: rapid (50 μm/s), medium (25 μm/s) and slow (10 μm/s). CASA kinetics

parameters were: total motility (MOT TOT, %), progressive motility (PRG, %), curvilinear

velocity (VCL, µm⁄s), straight-line velocity (VSL, µm⁄s), average path velocity (VAP, µm⁄s),

linearity coefficient (LIN, %, = VSL/VCL x 100), amplitude of lateral head displacement

(ALH, µm), straightness coefficient (STR, % = VSL/VAP x 100), wobble coefficient (WOB,

% = VAP/VCL x 100) and beat cross frequency (BCF, Hz).

DNA extraction, library preparation and sequencing

Four HM and four LM sperm samples extracted in previous step were used for DNA

extraction. DNA was isolated by NucleoSpin® Tissue (Macherey-Nagel) following

manufacturer instruction. One μg of genomic DNA was sonicated to produce DNA fragments

50

of about 350 bp lengths. Methyl-binding domain (MBD) enrichment was performed using the

MethylMiner™ Methylated DNA Enrichment Kit (Thermo Fisher Scientific), following

manufacture instruction. Libraries were generated using the TruSeq® DNA PCR-Free Library

Preparation Kit (Illumina) including a step of bisulfite treatment. After adapters ligation,

samples were converted with EpiTectBisulfite Kits (Qiagen) and finally PCR amplified with

KAPA HiFi Uracil+ (Kapa Biosystems) to obtain methyl enriched bisulfite libraries. The

eight libraries were used for cluster generation and subsequently sequenced on a single lane of

Illumina Hiseq2000.

Statistical analysis and bionformatics

Data obtained from CASA were analyzed using the SASTM package v 9.4 (SAS

Institute Inc.). The General Linear Model procedure (PROC GLM) was used to evaluate the

efficiency of the sperm separation comparing semen quality parameters at thawing and in the

HM population. The model included the fixed effect of the sperm population, and bull as

random. Results are given as adjusted least squares means ± standard error means (LSM ±

SEM).

Preliminary quality control of raw reads was carried out with FastQC

(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). Illumina raw sequences were

then filtered with Trimmomatic [49] to remove adapters and low quality bases at the ends of

sequence, using a sliding window approach. Data are available in the Sequence Reads

Archive (SRA), (Accession Number SRP119411). Bismark software v.0.17.0

(https://www.bioinformatics.babraham.ac.uk/projects/bismark/) was used to align each readto

a bisulfite-converted Bos taurus genome UMD311 with option -N 1, and methylation calls

were extracted using the Bismark methylation_extractor function. Seqmonk software (version

0.34.1) was used for visualization and analysis of the Bismark output

(http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/). Only position with at least 5

cytosine were recorded in all samples, others were discarded from the data set. Methylated

regions (MRs) were detected genome wide by dividing the genome in 100 bp tiles and

analyzing average methylation in a sliding window of 100 bp. MRs were considered if present

at least in 3 out of 4 samples in both HM and LM sperm populations. Methylation was

calculated independently for different features: 5’ UTR (-2Kb), 3’ UTR (+2Kb), gene bodies

and CpG islands (CGIs). MRs were also determined per CGI length classes and overlapping

BTSAT4 REs. Differentially methylated regions (DMRs) between HM and LM populations

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

http://www.bioinformatics.babraham.ac.uk/projects/seqmonk/

51

were calculated using the logistic regression filter in R to assess differential methylation

(FDR< 0.05, absolute cut-off of 5%). Hierarchical clustering was produced for DMRs present

in CGIs, gene bodies, 5’ UTRs and 3’ UTRs. The level of methylation was normalized across

samples and methylation percentage from a selection of DMRs showing the highest

differences in methylation was used for clustering using the Genesis software [50].

Genes included in DMRs at CGIs and different genomic features were submitted to

GO analysis. GO classification of the DMRs was performed according to canonical GO

categories, using the Cytoscape plug-in ClueGO which integrates GO [51] and enhances

biological interpretation of large lists of genes. Evaluation of REs in CGIs was performed by

intersecting genomic positions of both features by Bedtools intersect

(http://bedtools.readthedocs.io), thus frequencies for each RE category were calculated for

low-intermediate methylation CGIs (20-60% methylation) and high methylation CGIs (80-

100% methylation), in both HM and LM sperm populations and in Bos taurus genome.

Availability of data and material

All sequence data are deposited at the NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra) (Accession Number SRP119411).

Authors' contributions

EC, FP, and FT conceived the study. FT Isolated the spermatozoa fractions through

Percoll gradient and evaluated sperm characteristics after separation. EC, AMLRP performed

the DNA extraction, libraries preparation and sequencing. BL carried out the bioinformatic

analysis. EC carried out GO analysis. EC wrote the manuscript and generated the figures. AS,

PAM, PC and FP reviewed and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Consent to publish

Not applicable

Competing interest

https://www.ncbi.nlm.nih.gov/sra

52

The authors declare that they have no competing interests

Funding

The research was supported by the Italian Ministry of Education, Universities and Research

MIUR GenHome project “Technological Resort for the advancement of animal genomic

research” and “Progetto Bandiera INTEROMICS - Sottoprogetto 1: Sviluppo di Infrastrutture

di Bioinformatiche per le applicazioni OMICS in Biomedicina”, and by the European Union's

Horizon 2020 Research and Innovation Programme under the grant agreement n° 677353

IMAGE.”

53

References

1. GIANOTTEN, J.; LOMBARDI, M. P.; ZWINDERMAN, A. H.; LILFORD, R. J. & VAN DER VEEN, F. Idiopathic impaired spermatogenesis: genetic epidemiology is unlikely to provide a short-cut to better understanding. Human Reproduction Update, Oxford, v. 10, p. 533-539, 2004.

2. TAHMASBPOUR, E.; BALASUBRAMANIAN, D; & AGARWAL, A. A. multi-faceted approach to understanding male infertility: gene mutations, molecular defects and assisted reproductive techniques (ART). The Journal of Assisted Reproduction and Genetics, London, v. 3, p. 1115-1137, 2014.

3. CHENOWETH, P. J. Influence of the male on embryo quality. Theriogenology, USA, v. 68, p. 308-315, 2007.

4. PARKINSON, T. J. Evaluation of fertility and infertility in natural service bulls. The Veterinary Journal, London, v. 68, p. 215-229, 2004.

5. DE JONGE, C. Attributes of fertility spermatozoa: an update. Journal of Andrology, Chicago, v. 20, p. 463-473, 1999.

6. DeJARNETTE J. M; SAACKE, R. G; BAME, J; VOGLER, C. Accessory sperm: their importance to fertility and embryo quality, and attempts to alter their numbers in artificially inseminated cattle. Journal of Animal Science, London, v. 70, n. 2, p. 484-491, 1992.

7. LEWIS, S. E. Is sperm evaluation useful in predicting human fertility? Reproduction, New York, v. 134, p. 31-40, 2007.

8. RAZIN, A.; SHEMER, R. DNA methylation in early development. Human Molecular Genetics, Oxford, v. 4, p. 1751-1755, 1995.

9. HEARD, E.; CLERC, P.; AVNER, P. X-Chromosome inactivation in mammals. Annual Reviews of Genetics, USA, v. 31, p. 571-610, 1997.

10. GARDINER-GARDEN, M.; FROMMER, M. CpG islands in vertebrate genomes. Journal of Molecular Biology, London, v. 196, p. 261-282, 1987.

11. JONES, B. H; STANDRIDGE, M. K; MOUSTAID, N. Angiotensin II increases lipogenesis in 3T3-L1 and human adipose cells. Endocrinology, Oxford, v. 138, p. 1512-1519, 1997.

12. KATO, Y., KANEDA, M., HATA, K., KUMAKI, K., HISANO, M., KOHARA, Y., OKANO, M., LI, E., NOZAKI, M. & SASAKI, H. Role of the Dnmt3 family in de novo methylation of imprinted and repetitive sequences during male germ cell development in the mouse. Human Molecular Genetics, Oxford, v. 16, p. 2272-2280, 2007.

54

13. MOLARO, A., HODGES, E., FANG, F., SONG, Q., McCOMBIE, W. R., HANNON, G. J.; SMITH, A. D. Sperm methylation profiles reveal features of epigenetic inheritance and evolution in primates. Cell, Reino Unido, v. 146, p. 1029-1041, 2011.

14. MARCHAL, R., CHICHEPORTICHE, A., DUTRILLAUX, B.; BERNARDINO-SGHERRI, J. DNA methylation in mouse gametogenesis. Cytogenetic and Genome Research, Philadelphia, v. 105, p. 316-324, 2004.

15. OAKES, C. C.; LA SALLE, S.; SMIRAGLIA, D. J.; ROBAIRE, B.; TRASLER, J. M. Developmental acquisition of genome-wide DNA methylation occurs prior to meiosis in male germ cells. Developmental Biology, v. 307, p. 368-379, 2007.

16. SMITH, Z. D.; CHAN, M. M.; MIKKELSEN, T. S.; GU, H.; GNIRKE, A.; REGEV, A.; MEISSNER, A. A. unique regulatory phase of DNA methylation in the early mammalian embryo. Nature, London, v. 484, p. 339-344, 2012.

17. GUO, H., ZHU, P., YAN, L., LI, R., HU, B., LIAN, Y., YAN, J., REN, X., LIN, S., LI, J., JIN, X., SHI, X., LIU, P., WANG, X., WANG, W., WEI, Y., LI, X., GUO, F., WU, X., FAN, X., YONG, J., WEN, L., XIE, S.X., TANG, F., QIAO, J. The DNA methylation landscape of human early embryos. Nature, London, v. 511, p. 606-610, 2014.

18. KISHIGAMI, S., VAN THUAN, N HIKICHI, T., OHTA, H., WAKAYAMA, S., MIZUTANI, E. & WAKAYAMA, T. Epigenetic abnormalities of the mouse paternal zygotic genome associated with microinsemination of round spermatids. Developmental Biology, London, v. 289, p. 195-205, 2006.

19. POPLINSKI, A., TUTTELMANN, F., KANBER, D., HORSTHEMKE, B., GROMOLL, J. Idiopathic male infertility is strongly associated with aberrant methylation of MEST and IGF2/H19 ICR1. International Journal of Andrology, Nova Jersey, v. 33, p. 642-649, 2010.

20. HOUSHDARAN, S., CORTESSIS, V. K., SIEGMUND, K., YANG, A., LAIRD, P. W., SOKOL, R. Z. Widespread epigenetic abnormalities suggest a broad DNA methylation erasure defect in abnormal human sperm. PloS One, California, v. 2, e1289, 2007.

21. MONTJEAN, D., ZINI, A., RAVEL, C., BELLOC, S., DALLEAC, A., COPIN, H., BOYER, P., MCELREAVEY, K., BENKHALIFA, M. Sperm global DNA methylation level: association with semen parameters and genome integrity. Andrology, Australian, v. 3, p. 235-240, 2015.

22. CASSUTO, N.G., MONTJEAN, D., SIFFROI, J.P., BOURET, D., MARZOUK, F., COPIN, H. &BENKHALIFA, M. Different Levels of DNA Methylation Detected in Human Sperms after Morphological Selection Using High Magnification Microscopy. BioMed Research International, London, 2016, 6372171, 2016.

23. JENKINS, T.G., ASTON, K.I, PFLUEGER, C., CAIRNS, B.R., CARRELL, D.T. Age-associated sperm DNA methylation alterations: possible implications in offspring disease susceptibility. PLoS Genetics, Reino Unido, v. 10, e1004458, 2014.

55

24. DU, Y., LI, M., CHEN, J., DUAN, Y., WANG, X., QIU, Y., CAI, Z., GUI, Y., JIANG, H. Promoter targeted bisulfite sequencing reveals DNA methylation profiles associated with low sperm motility in asthenozoospermia. Human Reproduction, Oxford, v. 31, p. 24-33, 2016.

25. VERMA, A; RAJPUT, S; DE, S; KUMAR, R; CHAKRAVARTY, A. K; DATTA, T. K. Genome-wide profiling of sperm DNA methylation in relation to buffalo (Bubalus bubalis) bull fertility. Theriogenology, London, v. 82, n. 5, p. 750–759, e1, 2014.

26. TAKEDA, K., KOBAYASHI, E., AKAGI. S., NISHINO, K., KANEDA, M., WATANABE, S. Differentially methylated CpG sites in bull spermatozoa revealed by human DNA methylation arrays and bisulfite analysis. Journal of Reproduction and Developmental, Tokyo, v. 63, p. 279-287, 2017.

27. KROPP, J; CARRILLO, J. A; NAMOUS, H; DANIELS, A; SALIH, S. M; SONG, J; KHATIB, H. Male fertility status is associated with DNA methylation signatures in sperm and transcriptomic profiles of bovine preimplantation embryos. BMC Genomics, London, v. 18, n. 1, p. 218, 2017.

28. SMALLWOOD, S.A., TOMIZAWA, S., KRUEGER, F., RUF, N., CARLI, N., SEGONDS-PICHON, A., SATO, S., HATA, K., ANDREWS, S.R., KELSEY, G. Dynamic CpG island methylation landscape in oocytes and preimplantation embryos. Nature Genetics, London, v. 43, p. 811-814, 2011.

29. MEISSNER, A., GNIRKE, A., BELL, G.W., RAMSAHOYE, B., LANDER, E.S., JAENISCH, R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Research, Oxford, v. 33, p. 5868-5877, 2005.

30. MORISHITA, M., MEVIUS, D., DI LUCCIO, E. In vitro histone lysine methylation by NSD1, NSD2/MMSET/WHSC1 and NSD3/WHSC1L. BMC Structural Biology, London, v. 14, p. 25, 2014.

31. MILNE, T. A., BRIGGS, S. D., BROCK, H. W., MARTIN, M. E., GIBBS, D., ALLIS, C. D., HESS, J. L. MLL targets SET domain methyltransferase activity to Hox gene promoters. Molecular Cell, Philadelphia, v. 10, p. 1107-1117, 2002.

32. DOU, Y., MILNE, T. A., RUTHENBURG, A. J., LEE, S., LEE, J. W., VERDINE, G. L., ALLIS, C. D., ROEDER, R. G. Regulation of MLL1 H3K4 methyltransferase activity by its core components. Nature Structural & Molecular Biology, London, v. 13, p. 713-719, 2006.

33. FRESCAS, D., GUARDAVACCARO, D., KUCHAY, S.M., KATO, H., POLESHKO, A., BASRUR, V., ELENITOBA-JOHNSON, K.S., KATZ, R.A., PAGANO, M. KDM2A represses transcription of centromeric satellite repeats and maintains the heterochromatic state. Cell Cycle, v. 7, p. 3539-3547, 2008.

34. BLACK, J. C., VAN RECHEM, C., WHETSTINE, J. R. Histone lysine methylation dynamics: establishment, regulation, and biological impact. Molecular Cell, London, v. 48, p. 491-507, 2012.

56

35. BOISSONNAS, C. C., JOUANNET, P., JAMMES, H. Epigenetic disorders and male subfertility. Fertility and Sterility, Netherlands, v. 99, p. 624-631, 2013.

36. GLASER, S., LUBITZ, S., LOVELAND, K.L., OHBO, K., ROBB, L., SCHWENK, F., SEIBLER, J., ROELLIG, D., KRANZ, A., ANASTASSIADIS, K., STEWART A.F. The histone 3 lysine 4 methyltransferase, Mll2, is only required briefly in development and spermatogenesis. Epigenetics & Chromatin, London, v. 2, p. 5, 2009.

37. MYRICK, D.A., CHRISTOPHER, M.A., SCOTT, A.M., SIMON, A.K., DONLIN-ASP, P.G., KELLY, W.G., KATZ, D.J. KDM1A/LSD1 regulates the differentiation and maintenance of spermatogonia in mice. PLoS One, California, v. 12, e0177473, 2017.

38. LEHNERTZ, B., UEDA, Y., DERIJCK, A.A., BRAUNSCHWEIG, U., PEREZ-BURGOS, L., KUBICEK, S., CHEN, T., LI, E., JENUWEIN, T. & PETERS, A.H. Suv39h-mediated histone H3 lysine 9 methylation directs DNA methylation to major satellite repeats at pericentric heterochromatin. Current Biology, Reino Unido, v. 13, p. 1192-1200, 2003.

39. GAILLARD, C., DOLY, J., CORTADAS, J., BERNARDI, G. The primary structure of bovine satellite 1.715. Nucleic Acids Research, Oxford, v. 9, p. 6069-6082, 1981.

40. MELTERS, D.P., BRADNAM, K.R., YOUNG, H.A., TELIS, N., MAY, M.R., RUBY, J.G., SEBRA, R., PELUSO, P., EID, J., RANK, D., GARCIA, J.F., DERISI, J.L., SMITH, T., TOBIAS, C., ROSS-IBARRA, J., KORF, I., CHAN, S.W. Comparative analysis of tandem repeats from hundreds of species reveals unique insights into centromere evolution. Genome Biology, London, v. 14, R10, 2013.

41. NIEDDU, M., MEZZANOTTE, R., PICHIRI, G., CONI, P.P., DEDOLA, G.L., DETTORI, M.L., PAZZOLA, M., VACCA, G.M., ROBLEDO, R. Evolution of satellite DNA sequences in two tribes of Bovidae: A cautionary tale. Genetics and Molecular Biology, Brazil, v. 38, p. 513-518, 2015.

42. COULDREY, C., WELLS, D.N. DNA methylation at a bovine alpha satellite I repeat CpG site during development following fertilization and somatic cell nuclear transfer. PLoS One, California, v. 8, e55153, 2013.

43. ZHANG, S., CHEN, X., WANG, F., AN, X., TANG, B., ZHANG, X., SUN, L., LI, Z. Aberrant DNA methylation reprogramming in bovine SCNT preimplantation embryos. Scientific Reports, London, v. 6, p. 30345, 2016.

44. PARELHO, V., HADJUR, S., SPIVAKOV, M., LELEU, M., SAUER, S., GREGSON, H.C., JARMUZ, A., CANZONETTA, C., WEBSTER, Z., NESTEROVA, T., COBB, B.S., YOKOMORI, K., DILLON, N., ARAGON, L., FISHER, A.G., MERKENSCHLAGER, M. Cohesins functionally associate with CTCF on mammalian chromosome arms. Cell, London, v. 132, p. 422-433, 2008.

45. YOUNGSON, N.A., LECOMTE, V., MALONEY, C.A., LEUNG, P., LIU, J., HESSON, L.B., LUCIANI, F., KRAUSE, L., MORRIS, M.J. Obesity-induced sperm

57

DNA methylation changes at satellite repeats are reprogrammed in rat offspring. Asian Journal of Andrology, China, v. 18, p. 930-936, 2016.

46. KATIB, A. Mechanisms linking obesity to male infertility. Central European Journal of Urology, Poland, v. 68, p. 79-85, 2015.

47. CAPRA, E., TURRI, F., LAZZARI, B., CREMONESI, P., GLIOZZI, T.M., FOJADELLI, I., STELLA, A., PIZZI, F. Small RNA sequencing of cryopreserved semen from single bull revealed altered miRNAs and piRNAs expression between High- and Low-motile sperm populations. BMC Genomics, London, v. 18, p. 14, 2017.

48. BAVISTER, B. D., LEIBFRIED, M.L., LIEBERMAN, G. Development of preimplantation embryos of the golden hamster in a defined culture medium. Biology of Reproduction, Oxford, v. 28, p. 235-247, 1983.

49. BOLGER, A. M., LOHSE, M., USADEL, B. Trimmomatic: a flexible trimmer for Illumina Sequence Data. Bioinformatics, Oxford, v. 30, p. 2114-2120, 2014.

50. STURN, A., QUACKENBUSH, J., TRAJANOSKI, Z. Genesis: cluster analysis of microarray data. Bioinformatics, Oxford, v. 18, p. 207-208, 2002.

51. BINDEA, G., MLECNIK, B., HACKL, H., CHAROENTONG, P., TOSOLINI, M., KIRILOVSKY, A., FRIDMAN, W.H., PAGÈS, F., TRAJANOSKI, Z., GALON, J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, Oxford, v, 25, p. 1091-1093, 2009.

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Table 1. GO terms identified for the differentially methylated genes (DMGs) found to differ between high motile (HM) and low motile (LM)

sperm populations in gene bodies (GENE), 5' untranslated regions (5’UTRs), 3' untranslated regions (3’UTRs) and CpG islands (CGIs).

GO-ID GO-Term Associated Genes Found P-Value*

GENE

GO:0000723 Telomere maintenance [ERCC4, LRIG1, PRKDC, TEP1, WRN] 1.88E-02

GO:0032200 Telomere organization [ERCC4, LRIG1, PRKDC, TEP1, WRN] 1.92E-02

GO:0019722

Calcium, mediated multicellular organism

signaling [ASPH, HDAC4, ITPR1, KSR2, P2RX3, PLCE1] 2.13E-02

GO:0033555 Response to stress [CACNA1B, GRIN2B, P2RX3] 2.46E-02

GO:0043954 cellular component maintenance [ABL2, MTMR2, MTSS1] 2.81E-02

GO:0021575 Hindbrain morphogenesis [ABL2, ATP2B2, DLC1, LDB2] 3.48E-02

GO:0031623 Receptor internalization [CAV3, MTMR2, PICALM] 4.76E-02

5'UTR

GO:0010634 Positive regulation of epithelial cell migration [BCAR1, BCAS3, ENPP2, WNT7A] 3.70E-03

GO:0010595 Positive regulation of endothelial cell migration [BCAR1, BCAS3, WNT7A] 5.16E-03

3'UTR

GO:0035162 Embryonic haemopoiesis [GATA3, KAT6A, KMT2A, PBX1, STK4] 1.31E-04

GO:0006516 Glycoprotein catabolic process [FBXO6, GPC1, NEU4] 4.45E-03

GO:0046470 Phosphatidylcholine metabolic process [LIPC, LPCAT3, PLA2G2E, SLC44A2, SLC44A4] 1.11E-02

GO:0051569 Regulation of histone H3-K4 methylation [AUTS2, GATA3, KMT2A] 2.37E-02

GO:1901616 Organic hydroxy compound catabolic process [IMPA1, LIPC, NUDT3] 2.83E-02

59

GO:0042439

Ethanolamine, containing compound metabolic

process [LIPC, LPCAT3, PLA2G2E, SLC44A2, SLC44A4] 3.05E-02

GO:0006303

Double, strand break repair via nonhomologous

end joining [KDM2A, PRPF19, WHSC1] 3.85E-02

GO:0071353 Cellular response tointerleukin-4 [GATA3, MCM2, MCM7] 4.24E-02

GO:0032508 DNA duplex unwinding [FBXO18, MCM2, MCM7, MRPL36] 4.29E-02

GO:0032392 DNA geometric change [FBXO18, MCM2, MCM7, MRPL36] 4.31E-02

GO:0000726 non-recombinational repair [KDM2A, PRPF19, WHSC1] 4.97E-02

CGI

GO:0035637 Multicellular organismal signaling

[CACNA1C, DMRT3, DPP6, KCNQ1, NFASC,

P2RX3] 8.53E-04

GO:0032288 Myelin assembly [GPC1, NFASC, TENM4] 1.30E-03

GO:0006942 regulation of striated muscle contraction [CACNA1C, KCNQ1, PDE5A, TNNT3] 3.22E-03

GO:0019226 transmission of nerve impulse [DMRT3, DPP6, NFASC, P2RX3] 3.62E-03

GO:0032200 Telomere organization [ERCC4, LRIG1, TERT, WRN] 3.80E-03

GO:0000723 Telomere maintenance [ERCC4, LRIG1, TERT, WRN] 5.45E-03

Indicated are gene ontology IDs (GO-ID), gene ontology terms (GO-term), associated genes found and corrected p-values as determined by ClueGO (http://apps.cytoscape.org/apps/cluego). * Term P-Value Corrected with Bonferroni step down

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Table 2. Frequency of occurrence for Repetitive Elements (REs) overlapping CGIs with different methylation levels (20-60 % methylation and

80-100% methylation) in high motile (HM) and low motile (LM) sperm populations. Frequency of occurrence for REs is also reported for Bos

taurus genome.

HM LM GENOME

CGIs methyl.

20-60%

CGIs methyl.

80-100%

CGIs methyl.

20-60%

CGIs methyl.

80-100% Methyl. ref. genome

RE type % RE type % RE type % RE type % RE type %

BTSAT4 75.3 GC-rich 24 BTSAT4 75.9 GC-rich 23.5 Bov-tA2 8.6

SSU-rRNA 4.5 Bov-tA2 3.8 SSU-rRNA 4.6 Bov-tA2 4.1 ART2A 8.3

BTSAT2 3.8 MIRb 3.4 BTSAT2 3.9 MIRb 3.3 BovB 6.7

GC-rich 3 ART2A 2.6 GC-rich 3.3 C-rich 2.7 BOV-A2 5.1

OSSAT2 1.7 (TG)n 2.6 OSSAT2 1.7 ART2A 2.7 AT-rich 4.9

BTLTR1 1.2 C-rich 2.6 BTLTR1 1.2 (TG)n 2.6 Bov-tA1 3.7

ERV2-1-LTR 0.9 (CA)n 2.2 ERV2-1-LTR 0.9 (CA)n 2.2 MIRb 3.3

5S-rRNA 0.9 Bov-tA1 1.9 BTSAT3 0.8 Bov-tA1 2 MIR 2.5

BTSAT3 0.7 MIR 1.9 5S-rRNA 0.7 CHR-2A 1.8 L2a 2.2

LSU-rRNA 0.6 CHR-2A 1.8 LSU-rRNA 0.5 BovB 1.8 L1-2 2.1

G-rich 0.4 G-rich 1.7 BovB 0.4 MIR 1.7 L1 1.8

(CA)n 0.4 BovB 1.6 G-rich 0.4 BOV-A2 1.7 L2c 1.6

(CGGGG)n 0.4 BOV-A2 1.6 (CGGGG)n 0.4 G-rich 1.6 Bov-tA3 1.6

Bov-tA2 0.3 L2b 1.5 (CA)n 0.4 L2b 1.5 BTLTR1 1.5

BovB 0.3 L1-2 1.5 C-rich 0.3 L1-2 1.5 L2b 1.3

(TG)n 0.3 MIR3 1.4 (TG)n 0.2 CHR-2B 1.4 MIRc 1.2

(CGTG)n 0.3 CHR-2B 1.3 (CCCCAG)n 0.2 MIR3 1.4 MIR3 1.2

Bov-tA3 0.3 MLT1B 1.1 (CGG)n 0.2 MIRc 1.1 L1 1.2

C-rich 0.3 MIRc 1.1 L1-3 0.2 MLT1B 1.1 L1-3 1.1

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Additional File1. Bismark sequencing statistics for the four replicates (1-4) of high motile HM and LM low motile sperm populations.

HM1 HM2 HM3 HM4 LM1 LM2 LM3 LM4 Sequence pairs analysed in total: 2,73E+07 2,25E+07 3,55E+07 1,32E+07 3,04E+07 2,78E+07 3,06E+07 3,75E+07

Number of paired-end alignments with a unique best hit: 2,34E+07 1,95E+07 3,09E+07 1,10E+07 2,71E+07 2,42E+07 2,66E+07 3,39E+07

Mapping efficiency: 85,60% 86,60% 87,10% 83,10% 89,10% 87,10% 86,80% 90,60%

Sequence pairs with no alignments under any condition: 3,31E+06 2,26E+06 3,90E+06 1,88E+06 2,68E+06 2,84E+06 3,44E+06 2,28E+06

Sequence pairs did not map uniquely: 6,31E+05 7,56E+05 6,81E+05 3,47E+05 6,27E+05 7,60E+05 6,06E+05 1,26E+06

Total number of C's analysed: 9,63E+08 7,92E+08 1,23E+09 4,65E+08 1,17E+09 9,98E+08 1,10E+09 1,44E+09

Total methylated C's in CpG context: 1,01E+08 7,71E+07 1,04E+08 5,19E+07 1,26E+08 9,86E+07 1,10E+08 1,65E+08

Total methylated C's in CHG context: 2,16E+06 1,25E+06 1,97E+06 6,72E+05 1,26E+06 1,03E+06 1,54E+06 2,80E+06

Total methylated C's in CHH context: 4,86E+06 2,99E+06 5,12E+06 1,55E+06 2,88E+06 2,52E+06 3,64E+06 6,28E+06

Total methylated C's in Unknown context: 2,67E+02 1,79E+02 2,19E+02 1,34E+02 2,91E+02 2,18E+02 2,65E+02 4,46E+02

Total unmethylated C's in CpG context: 6,76E+06 5,56E+06 7,12E+06 3,75E+06 7,90E+06 6,49E+06 7,40E+06 1,08E+07

Total unmethylated C's in CHG context: 2,50E+08 2,03E+08 3,09E+08 1,21E+08 3,05E+08 2,56E+08 2,83E+08 3,76E+08

Total unmethylated C's in CHH context: 5,98E+08 5,02E+08 8,00E+08 2,86E+08 7,22E+08 6,33E+08 6,92E+08 8,79E+08

Total unmethylated C's in Unknown context: 3,81E+03 2,86E+03 4,16E+03 1,56E+03 4,38E+03 4,05E+03 3,93E+03 5,07E+03

C methylated in CpG context: 93,70% 93,30% 93,60% 93,30% 94,10% 93,80% 93,70% 93,90%

C methylated in CHG context: 0,90% 0,60% 0,60% 0,60% 0,40% 0,40% 0,50% 0,70%

C methylated in CHH context: 0,80% 0,60% 0,60% 0,50% 0,40% 0,40% 0,50% 0,70%

C methylated in unknown context (CN or CHN): 6,50% 5,90% 5,00% 7,90% 6,20% 5,10% 6,30% 8,10%

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Additional File2. Number of Methylated Regions (MRs), Diferentially methyalted Regions (DMRs) and their relative ratio percentage (%) found in different genomic features: Gene body, 5'UTR, 3'UTR and CpG islands CGIs.

MRs DMRs % Gene body 423673 6131 1,45

5'UTR 12744 398 3,12 3'UTR 19745 538 2,72 CGIs 9397 918 9,77

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Figure 1. Distribution of CpG methylation levels across the gene bodies, 5’UTR, 3’UTR and

CGI.

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Figure 2. Hierarchical clustering for DMRs present in CGIs, gene bodies, 5’ UTRs and 3’ UTRs.

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Figure 3. Distribution of CGIs length in HM and LM (20-60 CpG methylated) and HM and LM

(80-100 CpG methylated)

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Figure 4. CpG methylation levels in MRs and DMRs of HM and LM sperm populations.

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Supplementary Info Supplementary Dataset Legend: Supplementary Dataset 1. Methylated Regions (MRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations overlapping gene bodies. Columns report: Probe name, position (Chromosome, Start, End), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 2. Methylated Regions (MRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations upstream of genes (5’UTR). In each column are reported: Probe name, position (Chromosome, Start, End), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 3. Methylated Regions (MRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations downstream of genes (3’UTR). In each column are reported: Probe name, position (Chromosome, Start, End), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 4. Methylated Regions (MRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations overlapping CpG islands (CGIs). In each column are reported: Probe name, position (Chromosome, Start, End), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 5. Differentially Methylated Regions (DMRs) found at least in three samples in both high motile HM and LM low motile sperm populations overlapping gene bodies. In each column are reported: Probe name, position (Chromosome, Start, End), False Discovery Rate (FDR), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 6. Differentially Methylated Regions (DMRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations upstream of genes (5’UTR). In each column are reported: Probe name, position (Chromosome, Start, End), False Discovery Rate (FDR), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 7. Differentially Methylated Regions (DMRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations downstream of genes (3’UTR). In each column are reported: Probe name, position (Chromosome, Start, End), False Discovery Rate (FDR), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 8. Differentially Methylated Regions (DMRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations overlapping CpG islands (CGIs). In each column are reported: Probe name, position (Chromosome, Start, End),

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False Discovery Rate (FDR), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM). Supplementary Dataset 9. List of differentially Methylated Genes (DMGs) found in different genomic features: Gene body, 5'UTR, 3'UTR and CpG islands (CGIs). Supplementary Dataset 10. Differentially Methylated Regions (DMRs) found at least in three samples in both high motile (HM) and low motile (LM) sperm populations overlapping BTSAT4 satellite. In each column are reported: Probe name, position (Chromosome, Start, End), False Discovery Rate (FDR), Feature, protein ID, Description, MRs percentage for each sample (1HM, 2HM, 3HM, 4HM, 1LM, 2LM, 3LM, 4LM, average HM, average LM and differences between averages Δ HM-LM).

69

TITLE: Epigenetic variation in pericentromeric regions between high and low motile sperm populations in Bos taurus. Authors: Capra E., Lazzari B., Turri F., Cremonesi P., Portela AMLR., Ajmone-Marsan P., Stella A., Pizzi F. Supplementary Info 1. Kinetics parameters evaluated on semen at thawing and in High Motile population: MOT TOT total motility, PRG cells progressive motility, VSL straight-line velocity, VCL curvilinear velocity, VAP average path velocity, LIN linear coefficient, STR straightness coefficient, WOB wobble coefficient, ALH amplitude of lateral head displacement, BCF beat cross-frequency. a,b values within a row with different superscripts differ significantly at P <0.05.

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Supplementary Info 2. Bismark sequencing statistics for the four replicates (1-4) of high motile HM and LM low motile sperm populations.

71

Supplementary Info 3. Number of Methylated Regions (MRs), Diferentially methyalted Regions (DMRs) and their relative ratio percentage (%) found in different genomic features: Gene body, 5'UTR, 3'UTR and CpG islands CGIs.

72

Ovelapping gene bodies: Position Chr25:3880101-3880200 hyper-methylated in LM sperm population

73

Ovelapping gene bodies: Position Chr27:27773301-27773400 hyper-methylated in LM sperm population

74

Ovelapping gene bodies: Position Chr10:15110501-15110600 hyper-methylated in HM sperm population

75

Ovelapping gene bodies: Position Chr22:55144601-55144601 hyper-methylated in HM sperm population

76

Ovelapping CGIs: Position Chr26:24469801-24469900 hyper-methylated in LM sperm population

77

Ovelapping CGIs: Position Chr3:82832701-82832800 hyper-methylated in LM sperm population

78

Ovelapping CGIs: Position Chr15:71061401-71061500 hyper-methylated in HM sperm population

79

Ovelapping CGIs: Position Chr5:119435201-119435300 hyper-methylated in HM sperm population

80

Ovelapping 5’UTR: Position Chr5:488201-488300 hyper-methylated in LM sperm population

81


82

Ovelapping 5’UTR: Position Chr5:5613501-5613600 hyper-methylated in HM sperm population

83


84


85


86


87


Supplementary Info 4. Seqmonk base resolution vision of some of the differentially methylated regions (DMRs) in HM and LM sperm populations overlapping gene bodies, 5’UTR, 3’UTR and CGIs. For each DMR the chromosome position is reported. In red the methylated cytosines, in blue the un-methylated cytosines.

88

8 ARTIGO II

Dynamic profile of active metabolic pathways in the subcutaneous fat tissue of Holstein

cows during early lactation

(Perfil dinâmico de vias metabólicas ativas no tecido adiposo subcutâneo de vacas holandesas

no início da lactação)

Artigo submetido para publicação no periódico Animal Genetics

(Qualis B1 – Biotecnologia)

89

SHORT COMUNICATION

Dynamic profile of active metabolic pathways in the subcutaneous fat tissue of Holstein cows during early lactation A. M. L. R. Portela£, S. Chessa*, A. Boccardo¥, D. Pravettoni¥; A. A. Moura£, S. Biffani* and F. Biscarini* *Institute of Agricultural Biology and Biotechnology CNR, Italy. ¥Department of Veterinary Science, University of Milan, Via Celoria, 10 – 16, 20133 Milan, Italy. £Department of Animal Science, Laboratory of Animal Physiology, Federal University of Ceará, Campus do Pici – Bloco 810 – CEP 60.356-000 – Fortaleza – CE, Brazil. Sumary

Metabolic pathways associated to the early lactation in Holstein cows were characterized using

RNA-seq data obtained from subcutaneous fat tissue samples collected at three time points: at 2

(T0), 30 (T1) and 90 (T3) days postpartum. Enrichment analysis identified 142 metabolic

pathways. The most significative were: insulin secretion, oxytocin signaling,

glycolysis/gluconeogenesis, pyruvate metabolism, insulin resistance, calcium signalling, GnRH

(Gonadotropin releasing hormone), MAPK (mitogen-activated protein kinase), adipocytokine

signaling, and the renin−angiotensin system. All these pathways are important metabolic routes

in lactating dairy cattle. Some pathways were common between different time-point comparisons,

while others were unique to specific time-point comparisons.

Keywords Negative energy balance, fat tissue, metabolic pathways, rna-seq, dairy cattle

Eartly lactation is a challenging time for dairy cows, who have to simultaneously cope with milk

production and body maintenance. The rapid increase in energy requirements is only partially

met by feed intake which generally decreases around this period. Consequently, cows enter in a

90

state of negative energy balance (NEB) and the only way to counterbalance it is by mobilizing

body reserves, mainly represented by the fat tissue (Contreras et al. 2011; Nayeri & Stothard

2016). Indeed, the fat tissue acts as a caloric reservoir that in conditions of nutritional

overbalance stores surplus nutrients in the form of neutral lipids by stimulating lipogenesis,

whereas in case of nutrient deficit supplies nutrients to other tissues through lipolysis (Birsoy et

al. 2013). NEB is frequently associated with several metabolic disease as fatty liver (Ohtsuka et

al. 2001), ketosis (Sakai et al. 1993), left displacement of the abomasum (Biffani et al. 2014),

cystic ovarian disease (Opsomer et al. 1999), lipid mobilization and laminitis (Hendry et al.

1999). Additionally, NEB may be a relevant mediator of reduced fertility by negatively affecting

the follicular and luteal development and the quality of the oocyte (Wathes et al. 2007). The

complex interplay between fat tissue and NEB still is an unresolved conundrum in the physiology

of lactation, whose deeper understanding can be facilitated by the use of a technology like RNA

sequencing (RNA-seq). RNA-seq experiments provide a comprehensive understanding of the

expression of tissue-specific genes as well as of targeted metabolic pathways (Nayeri & Stothard

2016; Aguet & Ardlie 2016). The aim of this study was to characterize the metabolic pathways

associated to early lactation in Holstein cows using RNA-seq data obtained from subcutaneous

fat tissue samples collected at three time points: at 2 (T0), 30 (T1) and 90 (T3) days postpartum.

Seven healthy multiparous Holstein Friesian cows from a single commercial dairy farm in Lodi,

Italy were used in the present study. All procedures were approved by the Italian Ministry of

Health (approved protocol n° 480/2016-PR). Subcutaneous fat tissue was sampled from the tail of

the cows. RNA was extracted and sequenced, and reads were trimmed for quality (Phred > 15).

Additional details can be found in File S1. The read counts obtained were used to estimate gene

expression and identify differentially expressed (DE) genes. This was achieved using the R

packages edgeR version 3.10.0 (Robinson et al. 2010) and limma version 3.24.5 (Ritchie et al.

91

2015). Before performing statistical analysis, read counts were normalized using the trimmed

mean of M-values method implemented in edgeR. Finally, differential expression (DE) analysis

was performed comparing the log-fold differences in gene counts at different time points: calving

(T0), 30 days (T1) and 90 days after calving (T3). All genes with a FDR-adjusted p-value < 0.05

were considered significantly different and were retained for gene functional analysis. The

identified differentially expressed genes (DEG) were thus further analysed by querying biological

databases for related annotated functions and pathways. For each comparison between

timepoints, Ensembl gene IDs were obtained and used to search for corresponding gene

ontologies (GO) and metabolic pathways. Based on genes annotated to metabolic pathways in the

KEGG database (http://www.kegg.jp), an enrichment analysis was conducted to detect pathways

significantly associated with the identified genes, using a hypergeometric model (Zhang et al.

2015). We found 113, 324 and 17 genes differentially expressed (FDR < 0.05) in T0 versus T1,

T0 versus T3, and T1 versus T3, respectively (Supplementary Table 1). These genes are involved

in 142 metabolic pathways, which are shown, ordered by significance, in Figure 1. The top 5

pathways for each time comparison are reported in Table 1. These include insulin secretion,

oxytocin signaling, glycolysis/gluconeogenesis, pyruvate metabolism, insulin resistance, calcium

signalling, GnRH (Gonadotropin releasing hormone), MAPK (mitogen-activated protein kinase)

and adipocytokine signaling, and the renin−angiotensin system. All these pathways are important

metabolic routes in lactating dairy cattle. Some pathways are common between different time-

point comparisons (e.g. insulin secretion and resistance at T0 vs both T1 and T3), while others

are unique to specific time-point comparisons (e.g. cell proliferation pathways at T1 vs T3).

Among changes in hormonal regulation which take place early in the lactation, major variation

involves the insulin metabolism. Especially at the end of pregnancy and early lactation, dairy

cows show both a decrease in responsiveness of skeletal muscle and adipose tissue to insulin and

92

a low blood insulin concentration. This causes reduction of lipogenic pathways (Sadri et al. 2010)

and the redirection of glucose from peripheral tissues toward the uterus and mammary gland

(Bossaert et al. 2008; Nayeri & Stothard, 2016). The oxytocin signaling pathway differed at T0

vs T1 and T0 vs T3. The oxytocin receptor gene is highly expressed in fat tissue where stimulates

hormone-sensitive lipase and metabolic pathways of fatty acid oxidation (Yi et al. 2015). The

glycolysis/gluconeogenesis pathway was significant in all three time-point comparisons. NEB

causes a large increase of free fatty acids in the bloodstream which are then transferred to the

liver resulting in a low rate of glycolysis (Dębskil et al. 2017). Gluconeogenesis and the pyruvate

metabolism together are key pathways for energy metabolism during the mid-lactation of dairy

cows (Sun et al. 2014). Pyruvate is the initial point of gluconeogenesis and the final product for

glycolysis (Denton & Halestrap 1978). Pyruvate is also an intermediate metabolite for the

production of propionate from the succinic or lactate pathways (Jeyanathan et al. 2014). Calcium

signaling pathways participates in diverse biological processes, including lipid metabolism. In

particular, regulation of cellular calcium ion transport triggers nuclear transcription factors

related to the control of fat storage (Baumbach et al. 2015). The mobilization of calcium from

intracellular stores is influenced by GnRH, which binds to its receptor initiating many

intracellular signaling cascades, including calcium influx (Duran-Pasten & Fiordelisio 2013). The

GnRH signaling pathway was significantly different between T1 vs T3. The rapid elevation of

intracellular calcium induced by GnRH is necessary for the rapid secretion of gonadotropins

(Naor 2009). Therefore, the increase of calcium mobilization is an important event during this

phase of lactation. MAPK-signaling pathway is related to cell proliferation, differentiation,

migration, and apoptosis (Fata et al. 2007). This pathway can be involved in the continued

differentiation of mammary secretory cells which supports high milk secretion at peak lactation.

After peak, the mammary gland undergoes progressive regression through cell death by apoptosis

93

(Capuco et al. 2001), and the non-esterified fatty acids utilized by dairy cows during NEB

activate the MAPK pathway (Grethe et al. 2004). Thus, the presence of this pathway in our study

might be related to the metabolic changes that occur during lactation, especially the process of

cellular proliferation. Insulin resistance is significantly different at T0 vs T3 and T0 vs T9.

Excessive lipid mobilization can cause an exaggerated insulin resistance with consequent

increase in plasma non-esterified fatty acids and ketones (Bossaert et. al. 2008). The renin-

angiotensin pathway was significantly different between T1 and T3. The renin-angiotensin

system (RAS) plays a role on the regulation of blood pressure, fluid homeostasis,

vasoconstriction, hormone secretion, cellular growth (Yvan-Charvet & Quignard-Boulange 2011;

Kalupahana & Moustaid-Moussa 2012) and lipid metabolism (Jones et al. 1997; Saint-Marc et al.

2001). The main molecule of RAS is angiotensin II, which is synthetized by the fat tissue and

whose overexpression was found to induce an increase of fat mass and cell size as well as insulin

resistance in mice (Xu et al. 2003). Th1/Th2 cell differentiation is associated to the immune

response. Th1 cells protect the organism from intracellular pathogens providing cell-mediated

immune responses. Th2 cells represent essential mediators of the humoral immune response

thereby protecting organism against extracellular pathogens (Rodriguez-Manzanet et al. 2009). In

conclusion, the present study used RNA-seq to evaluate metabolic pathways associated with how

fat tissue of dairy cows go through the early lactation stage. These pathways are mainly

associated with cellular processes, inflammatory response and energy production, which

contribute to milk synthesis, fetal growth and homeostatic mechanisms. For a detailed

understanding of early-stage physiological changes and metabolic diseases (e.g. displacement of

abomasum, ketosis, NEB) and for validation of results, further studies will be necessary, e.g. with

higher sample size or different breeds. This knowledge could be potentially applied to provide

better farming conditions reducing the negative impact on the health and economics of the herds.

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Competing interests

Authors declare they have no competing interests.

Acknowledgements

This research was financially supported by the Italian national research project “GenHome”, by

the Brazilian Research Councils (CAPES and CNPq) and by the Ceara State Research

Foundation (FUNCAP).

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References AGUET, F. K; ARDLIE, G. Tissue Specificity of Gene Expression. Current Genetic Medicine Reports, London, Cambridge, v.4, n, 4, p. 163-169, 2016. BIFFANI, S; MORANDI, N; LOCATELLI, V; PRAVETTONI, D; BOCCARDO, A; STELLA, A; NICOLAZZI, E. L; BISCARINI, F. Adding evidence for a role of the SLITRK gene family in the pathogenesis of left displacement of the abomasum in Holstein-Friesian dairy cows. Livestock Science, London, v. 167, p. 104–109, 2014. BIRSOY, K; FESTUCCIA, W. T; LAPLANTE, M. A comparative perspective on lipid storage in animals. Journal of Cell Science, London, v. 126, p. 1541–1552, 2013. BOSSAERT, P; LEROY, J. L; DE VLIEGHER, S; OPSOMER, G. Interrelation between glucose-induced insulin response, metabolic indicators, and the time of first ovulation in high-yielding dairy cows. Journal of Dairy Sciences, London, v. 91, p. 3363–3371, 2008.. CAPUCO, A. V; WOOD, D. L; BALDWIN, R; MCLEOD, K; PAAPE, M. J. Mammary cell number, proliferation, and apoptosis during a bovine lactation: Relation to milk production and effect of bST. Journal of Dairy Sciences, London, v. 84, n. 10, p. 2177–87, 2001. CONTRERAS, G. A; SORDILLO, L. M. Lipid mobilization and inflammatory responses during the transition period of dairy cows. Comparative Immunology, Microbiology and Infectious Diseases, London, v. 34, n. 3, p. 281–89, 2011. DEBSKIL, B; NOWICKI, T; ZALEWSKI, W; BARTOSZEWICZ, A; TWARDON, J. Effect of pregnancy and stage of lactation on energy processes in isolated blood cells of dairy cows. Journal of Veterinary Research, Poland, v. 61, n. 2, p. 215-211, 2017. DENTON, R; HALESTRAP, A. Regulation of pyruvate metabolism in mammalian tissues. Essays in Biochemistry, London, v. 15, p. 37–77, 1978. DURAN-PASTEN, M. L; FIORDELISIO, T. GnRH-Induced Ca2+ Signaling Patterns and Gonadotropin Secretion in Pituitary Gonadotrophs. Functional Adaptations to Both Ordinary and Extraordinary Physiological Demands. Frontiers in Endocrinology (Lausanne), Rome, Italy, v. 4, p. 127, 2013. FATA, J. E; MORI, H. A; EWALD, J; ZHANG, H; YAO, E; WERB, Z; BISSELL, M. J. The MAPK (ERK-1,2) pathway integrates distinct and antagonistic signals from TGFalpha and FGF7 in morphogenesis of mouse mammary epithelium. Developmental Biology, London, v. 306, p. 193–207, 2007. GRETHE S., ARES M. P. S., ANDERSSON T. & PÖRN-ARES M. I. (p38MAPK mediates TNF-induced apoptosis in endothelial cells via phosphorylation and downregulation of Bcl-xL. Experimental Cell Research, Oxford, v.298, p. 632–42, 2004.

96

HENDRY, K. A; MacCALLUM, A. J; KNIGHT, C. H; WILDE, C. J. J. Effect of endocrine and paracrine factors on protein synthesis and cell proliferation in bovine hoof tissue culture. Journal of Dairy Research, London, v.66, p. 23–33, 1999. JEYANATHAN, J; MARTIN, C; MORGAVI, D. The use of direct-fed microbials for mitigation of ruminant methane emissions: a review. Animal, Reino Unido, v. 8, p. 250–61, 2014. JONES, B. H; STANDRIDGE, M. K; MOUSTAID, N. Angiotensin II increases lipogenesis in 3T3-L1 and human adipose cells. Endocrinology, Oxford, v. 138, p. 1512-1519, 1997. KALUPAHANA, N. S; MOUSTAID-MOUSSA, N. The adipose tissue renin-angiotensin system and metabolic disorders: a review of molecular mechanisms. Critical Reviews in Biochemestry and Molecular Biology, Reino Unido, v. 47, p. 379-390, 2012. NAOR, Z. Signaling by G-protein-coupled receptor (GPCR): studies on the GnRH receptor. Frontiers in Neuroendocrinology, London, v. 30, p. 10-29, 2009. NAYERI, S; STOTHARD, P. Tissues, Metabolic Pathways and Genes of Key Importance in Lactating Dairy Cattle. Springer Science Reviews, Germany, v. 4, p. 49–77, 2016. OHTSUKA, H; KOIWA, M; HATSUGAYA, A; KUDO, K; HOSHI, F; ITOH, N; YOKOTA, H; OKADA, H; KAWAMURA, S. Relationship between serum tumor necrosis factor-alpha activity and insulin resistance in dairy cows affected with naturally occurring fatty liver. Journal of Veterinary Medical Science, v. 63, p. 1021-1025, 2001. OPSOMER, G; WENSING, T; LAEVENS, H; CORYN, M; DE KRUIF, A. Insulin resistance: the link between metabolic disorders and cystic ovarian disease in high yielding dairy cows. Animal Reproduction Science, London, v. 56, p. 211–222, 1999. RITCHIE, M. E; PHIPSON, B; WU, D; HU, Y; LAW, C. W; SHI, W; SMYTH, G. K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, Oxford, v. 43, n. 7, e47, 2015. ROBINSON, M. D; McCARTHY, D. J; SMYTH, G. K. EdgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, Oxford, v. 26, p. 139–40, 2010. RODRIGUEZ-MANZANET, R; DEKRUYFF, R; KUCHROO, V. K; UMETSU, D. T. The costimulatory role of TIM molecules. Immunological Reviews, USA, v. 229, p. 259- 270, 2009. SADRI, H; BRUCKMAIER, R. M; RAHMANI, H. R; GHORBANI, G. R; MOREL, I; VAN DORLAND, H. A. Gene expression of tumour necrosis factor and insulin signalling‐related factors in subcutaneous adipose tissue during the dry period and in early lactation in dairy cows. Journal of animal physiology and animal nutrition, Oxford, v. 94, e194-e02, 2010.

97

SAINT-MARC, P; KOZAK, L. P; AILHAUD, G; DARIMONT, C; NEGREL, R. Angiotensin II as a trophic factor of white adipose tissue: Stimulation of adipose cell formation. Endocrinology, Oxford, v. 142, p. 487-92, 2001.. SAKAI, T; HAYAKAWA, T; HAMAKAWA, M; OGURA, K; KUBO, S. Therapeutic effects of simultaneous use of glucose and insulin in ketotic dairy cows. Journal of Dairy Science, London, v. 76, p. 109–114, 1993. SUN, L. W; ZHANG, H. Y; WU, L; SHU, S; XIA, C; XU, C; ZHENG, J. S. H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis. Journal of Dairy Science, London, v. 97, p. 1552-1562, 2014.. WATHES, D. C; FENWICK, M; CHENG, Z; BOURNE, N; LLEWELLYN, S; MORRIS, D. G; KENNY, D; MURPHY, J; RICK, F. Influence of negative energy balance on cyclicity and fertility in the high producing dairy cow. Theriogenology, London, v. 68, p. 232–241, 2007. XU, H; BARNES, G. T; YANG, Q; TAN, G; YANG, D; CHOU, C. J; SOLE, J; NICHOLS, A; ROSS, J. S; TARTAGLIA, L. A; CHEN, H. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. Journal of Clinical Investigation, Florida, v. 112, p. 1821, 2003. YVAN-CHARVET, L; QUIGNARD-BOULANGE, A. Role of adipose tissue renin-angiotensin system in metabolic and inflammatory diseases associated with obesity. Kidney International, London, v. 79, p. 162-168, 2011. ZHANG, L; XIA, Y; TANG, F; LI, S. J; YANG, L; WANG, B. The regulation of intrauterine immune cytokines and chemokines during early pregnancy in the bovine. Large Animals Review, Italy, v. 21, 23-31, 2015.

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File S1

Sampling, sequencing and processing

Seven healthy multiparous Holstein Friesian cows from a single commercial dairy farm in Lodi,

Italy were used in the present study. All procedures were approved by the Italian Ministry of

Health (approved protocol n ° 480/2016-PR). Cows underwent subcutaneous fat tissue biopsy in

T0, T1 and T3 from alternate sites of the tail-head. Briefly, each cow was gently allocated in a

standing hoof-trimming chute, the skin of the tail-head was aseptically prepared, 5 mL of

procaine hydrochloride was subcutaneously injected and a 1-cm stab incision was made. A

Gillies dissecting forceps was passed through the skin to grasp and expose the subcutaneous

adipose tissue flap that was cut with dissecting scissors. The incision was not closed and an

antibiotic solution was sprayed on the site.

After collection, adipose tissue was immediately transported at 4°C and preserved in Qiagen

AllPrep solution (IPA®, QIAGEN, Redwood City) for RNA extraction. Total RNA was extracted

from 80 mg of subcutaneous fat tissue using the Qiazol AllPrep method (Kit Qiagen RNeasy®

Lipid Tissue). RNA integrity was evaluated through the RNA Nano 6000 Assay Kit of the

Bioanalyzer 2100 system (Agilent Technologies, CA, USA) to check the requirements for library

preparation. Sequencing libraries were generated using the IlluminaTruSeq™ RNA Sample

Preparation Kit (Illumina, San Diego, CA, USA), following manufacturer’s instructions.

Messenger RNA (mRNA) was isolated and purified using the Dynabeads mRNA (SPRI AMPure

Beads). mRNA was then fragmented and cDNA synthesized. Then, after adapter ligation (400 to

500-bp fragment size), samples were sequenced on the Illumina Hiseq 2500 platform.

99

Preliminary quality control of raw reads and trimming (minimum Phred quality score > 15) for

low quality bases was carried out with FastQC and Trimmomatic, respectively. Trimmed reads

were mapped to the Bos taurus reference genome (Bos_taurus-UMD3.1, release 84).

100

Table 1. Top 5 pathways associated at different time points comparison (at calving (T0), at 30 days post-calving (T1) and at 90 days post-calving (T3).

Description pvalue Exp

Insulin secretion 0,0003 T0T1

Glutathione metabolismo 0,0017 T0T1

Adrenergic signaling in cardiomyocytes 0,0023 T0T1

Arrhythmogenic right ventricular cardiomyopathy (ARVC) 0,0024 T0T1

Gastric acid secretion 0,0030 T0T1

Insulin secretion 0,0000 T0T3

Adrenergic signaling in cardiomyocytes 0,0000 T0T3

Proximal tubule bicarbonate reclamation 0,0003 T0T3

Glucagon signaling pathway 0,0006 T0T3

cGMP-PKG signaling pathway 0,0007 T0T3

Hematopoietic cell lineage 0,0035 T1T3

HTLV-I infection 0,0195 T1T3

Renin-angiotensin system 0,0217 T1T3

Glyoxylate and dicarboxylate metabolism 0,0250 T1T3

Propanoate metabolism 0,0258 T1T3

101

Figure 1. Top metabolic pathways (from KEGG) enriched in the genes associated with the lactation period. These pathways were detected from genes identified in subcutaneous adipose tissue.

102

Top metabolic pathways (from KEGG) enriched in the genes associated with the candidate biomarkers for Graves’ disease and orbitopathy detected using blood proteins, blood miRNAs or both. File Supplementary: Table 1

n genes logFC logCPM LR PValue FDR name symbol entrezgene

1 ENSBTAG00000013303 -2,70404 5,310536 21,87785 2,91E-06 0,018705 acyl-CoA synthetase short chain

family member 2 ACSS2 506459

2 ENSBTAG00000038384 10,67051 5,511327 21,42152 3,69E-06 0,018705 keratin 5 KRT5 281268

3 ENSBTAG00000030257 2,645956 2,628976 21,28342 3,96E-06 0,018705 SH3 domain binding protein 1 SH3BP1 1E+08

4 ENSBTAG00000006689 -7,35356 -0,68732 18,60657 1,61E-05 0,037516 tectorin beta TECTB 538466

5 ENSBTAG00000027246 2,906444 3,674423 18,44409 1,75E-05 0,037516 ubiquitin D UBD 512938

6 ENSBTAG00000011887 3,322 3,524707 18,35461 1,83E-05 0,037516 CD22 molecule CD22

7 ENSBTAG00000039764 3,470445 4,241491 18,33376 1,85E-05 0,037516 immediate early response 5 IER5 523618

8 ENSBTAG00000000715 3,779209 2,48587 17,99941 2,21E-05 0,039126 GTPase IMAP family member 5 LOC530077 530077

9 ENSBTAG00000016648 -2,04256 6,008007 17,40543 3,02E-05 0,045691 basigin BSG 508716

10 ENSBTAG00000011481 3,695377 1,068157 17,16843 3,42E-05 0,045691 interleukin 12 receptor subunit

beta 1 IL12RB1 524299

11 ENSBTAG00000013730 3,429646 2,344363 17,09897 3,55E-05 0,045691 CD5 molecule CD5 280745

12 ENSBTAG00000010069 4,175072 4,608056 16,37624 5,19E-05 0,047356 early growth response 1 EGR1 407125

13 ENSBTAG00000000177 -3,18535 1,181068 16,29916 5,41E-05 0,047356 mesothelin MSLN 516237

14 ENSBTAG00000021145 2,655487 2,909291 16,27597 5,48E-05 0,047356 mitogen-activated protein

kinase kinase kinase kinase 1 MAP4K1 522002

15 ENSBTAG00000014400 4,730102 1,130545 16,26967 5,49E-05 0,047356 E2F transcription factor 2 E2F2 617024

16 ENSBTAG00000002075 -3,13199 1,337002 16,22051 5,64E-05 0,047356 membrane

metalloendopeptidase MME 536741

17 ENSBTAG00000009014 -3,14415 1,078186 16,20535 5,68E-05 0,047356 uroplakin 1B UPK1B 282113

103

File Supplementary: Table 2

n genes logFC logCPM LR PValue FDR Name symbol entrezgene

1 ENSBTAG00000019244 -4,71249 2,326983 29,61512 5,27E-08 0,000746 purinergic receptor P2X 6 P2RX6

2 ENSBTAG00000014465 3,010993 3,938036 26,89944 2,14E-07 0,001518 serpin family E member 1 SERPINE1 281375

3 ENSBTAG00000009617 -4,29634 1,747586 26,07803 3,28E-07 0,001548 solute carrier family 2

member 1 SLC2A1 282356

4 ENSBTAG00000008920 -8,88853 4,261534 25,20105 5,17E-07 0,001829 ATPase Na+/K+ transporting

family member beta 4 ATP1B4 510026

5 ENSBTAG00000038865 -6,71492 4,58145 23,41435 1,31E-06 0,0037 transcription elongation factor

A3 TCEA3 533803

6 ENSBTAG00000001842 -4,65073 1,423332 22,20241 2,45E-06 0,004964 glutathione S-transferase mu 3 GSTM3 615507

7 ENSBTAG00000019807 -2,94865 1,680982 22,19965 2,46E-06 0,004964 collagen type XXVII alpha 1

chain COL27A1 513668

8 ENSBTAG00000046177 -9,66348 4,111837 21,94646 2,8E-06 0,004964

9 ENSBTAG00000012307 -4,86726 4,479782 21,53447 3,48E-06 0,005423 dystrobrevin alpha DTNA 541153

10 ENSBTAG00000013303 -2,67325 5,310536 21,34903 3,83E-06 0,005423 acyl-CoA synthetase short

chain family member 2 ACSS2 506459

11 ENSBTAG00000009190 -4,95671 2,352072 20,72519 5,3E-06 0,006297 solute carrier family 2


12 ENSBTAG00000023806 -7,27783 2,218329 20,71316 5,33E-06 0,006297

13 ENSBTAG00000006354 3,288871 3,280718 20,39292 6,31E-06 0,006871 haptoglobin HP 280692

14 ENSBTAG00000031888 3,789565 3,449339 20,13683 7,21E-06 0,007022

15 ENSBTAG00000015980 -4,36368 8,481587 19,98364 7,81E-06 0,007022 fatty acid synthase FASN 281152

16 ENSBTAG00000019585 -7,42062 7,975513 19,9456 7,97E-06 0,007022 myomesin 1 MYOM1 538404

17 ENSBTAG00000011667 -3,32196 1,898905 19,81874 8,51E-06 0,007022 peptidase M20 domain

containing 2 PM20D2 521179

18 ENSBTAG00000012931 -5,13316 5,544736 19,58913 9,6E-06 0,007022 phospholamban PLN 1E+08

19 ENSBTAG00000032140 -6,74766 -0,80876 19,41419 1,05E-05 0,007022 SH3 domain binding kinase 1 SBK1

20 ENSBTAG00000045699 -6,18249 2,688334 19,37931 1,07E-05 0,007022 catenin alpha 3 CTNNA3 780777

21 ENSBTAG00000007109 -6,00704 5,295642 19,3469 1,09E-05 0,007022 ankyrin repeat and SOCS box

containing 2 ASB2 539244

22 ENSBTAG00000046333 -9,92129 5,265354 19,3459 1,09E-05 0,007022 chromosome 6 C4orf54

homolog C6H4orf54 1,02E+08

104

23 ENSBTAG00000008150 -5,44108 5,235774 19,17143 1,19E-05 0,007359 cAMP-dependent protein

kinase inhibitor alpha PKIA 613524

24 ENSBTAG00000031788 -5,88639 1,879974 19,04918 1,27E-05 0,007519 glutathione S-transferase mu

1-like LOC615514 615514

25 ENSBTAG00000027629 -7,39204 5,014539 18,94767 1,34E-05 0,007612 ankyrin 1 ANK1 353108

26 ENSBTAG00000047766 2,945773 4,874091 18,83521 1,43E-05 0,007764 G0/G1 switch 2 G0S2 507436

27 ENSBTAG00000005946 -5,26254 6,127103 18,6905 1,54E-05 0,008066 ubiquitin specific peptidase 13 USP13 1E+08

28 ENSBTAG00000017183 -6,70863 7,781903 18,53926 1,66E-05 0,00842 PDZ and LIM domain 3 PDLIM3 538516

29 ENSBTAG00000014400 5,094705 1,130545 18,46478 1,73E-05 0,008454 E2F transcription factor 2 E2F2 617024

30 ENSBTAG00000001003 -10,3184 3,521641 18,09042 2,11E-05 0,009947 creatine kinase, mitochondrial

2 CKMT2 538944

31 ENSBTAG00000018838 -4,78163 1,72365 17,92079 2,3E-05 0,010043 IQ motif containing with

AAA domain 1 like IQCA1L 1,02E+08

32 ENSBTAG00000015273 -5,04785 3,837391 17,88271 2,35E-05 0,010043

cullin associated and

neddylation dissociated 2

(putative)

CAND2 533826

33 ENSBTAG00000046467 -4,83916 3,704292 17,85303 2,39E-05 0,010043 protein tyrosine phosphatase

type IVA, member 3 PTP4A3 1E+08

34 ENSBTAG00000017616 -4,74699 3,806377 17,83375 2,41E-05 0,010043 adenylosuccinate synthase like

1 ADSSL1 784089

35 ENSBTAG00000044126 -3,26933 3,525528 17,66688 2,63E-05 0,010651 syntrophin beta 1 SNTB1

36 ENSBTAG00000046838 -6,51463 5,303351 17,50528 2,87E-05 0,011023

37 ENSBTAG00000000644 4,227697 1,77157 17,49587 2,88E-05 0,011023 S100 calcium binding protein

A5 S100A5 509251


39 ENSBTAG00000012653 -8,34017 3,196554 17,26908 3,24E-05 0,011211 calcium/calmodulin dependent

protein kinase II beta CAMK2B 525416

40 ENSBTAG00000001120 -6,24902 4,820735 17,25833 3,26E-05 0,011211 coronin 6 CORO6 614661

41 ENSBTAG00000000731 -6,35675 0,907019 17,22772 3,32E-05 0,011211

WAP, follistatin/kazal,

immunoglobulin, kunitz and

netrin domain containing 2

WFIKKN2 531979

42 ENSBTAG00000033835 -7,16085 2,032705 17,22279 3,32E-05 0,011211 myelin protein zero MPZ 539462

43 ENSBTAG00000015743 -4,31943 3,664486 17,17799 3,4E-05 0,011212 guanosine monophosphate

reductase GMPR 533000

44 ENSBTAG00000002006 2,859539 7,272473 17,08323 3,58E-05 0,011518 thrombospondin 1 THBS1 281530

45 ENSBTAG00000002964 -5,9441 5,009943 16,90625 3,93E-05 0,012065 taxilin beta TXLNB 518778

105

46 ENSBTAG00000020605 -6,41058 4,198606 16,86959 4E-05 0,012065 smoothelin like 2 SMTNL2 532143

47 ENSBTAG00000039356 -4,75598 0,540246 16,83547 4,08E-05 0,012065

48 ENSBTAG00000020080 -8,02338 8,193539 16,79548 4,16E-05 0,012065 myosin binding protein C, fast

type MYBPC2

49 ENSBTAG00000006305 -5,72867 4,77026 16,74162 4,28E-05 0,012065 adenylate kinase 1 AK1 280715

50 ENSBTAG00000002444 3,11443 4,273968 16,72268 4,33E-05 0,012065

51 ENSBTAG00000001517 -3,95595 0,263952 16,71492 4,34E-05 0,012065 keratin 18 KRT18 506480

52 ENSBTAG00000011530 -6,27116 0,959735 16,66872 4,45E-05 0,01209 cadherin 15 CDH15 522202

53 ENSBTAG00000021120 -9,19821 4,126165 16,63798 4,52E-05 0,01209 SET and MYND domain

containing 1 SMYD1 537404

54 ENSBTAG00000000177 -3,24663 1,181068 16,48789 4,9E-05 0,012843 mesothelin MSLN 516237

55 ENSBTAG00000010716 -5,91823 2,343902 16,43602 5,03E-05 0,012913 Fraser extracellular matrix

complex subunit 1 FRAS1

56 ENSBTAG00000014960 -5,45434 3,198204 16,38788 5,16E-05 0,012913 schwannomin interacting

protein 1 SCHIP1 535716

57 ENSBTAG00000021132 -6,31281 4,78174 16,34207 5,29E-05 0,012913 synaptopodin 2 like SYNPO2L 1,02E+08

58 ENSBTAG00000019554 -7,22666 4,554098 16,27409 5,48E-05 0,012913 fructose-bisphosphatase 2 FBP2 514066

59 ENSBTAG00000033367 -4,36759 0,860483 16,26936 5,49E-05 0,012913 D-aspartate oxidase DDO 280763

60 ENSBTAG00000018071 -6,52286 6,104024 16,25467 5,54E-05 0,012913 tropomodulin 4 TMOD4 505645

61 ENSBTAG00000012818 -4,25527 6,619854 16,24666 5,56E-05 0,012913 PDZ and LIM domain 5 PDLIM5 503621

62 ENSBTAG00000004770 -7,48368 4,429201 16,21385 5,66E-05 0,012926 sodium voltage-gated channel

alpha subunit 4 SCN4A 517426



65 ENSBTAG00000016924 -5,03263 5,379862 16,02805 6,24E-05 0,013432

cyclase associated actin

cytoskeleton regulatory

protein 2

CAP2 515190

66 ENSBTAG00000006505 3,634434 4,676701 16,02282 6,26E-05 0,013432 S100 calcium binding protein

A9 S100A9 532569

67 ENSBTAG00000006999 -7,67126 9,703428 15,94296 6,53E-05 0,013691 ryanodine receptor 1 RYR1 1E+08

68 ENSBTAG00000014930 -8,51385 6,106191 15,93009 6,57E-05 0,013691 myosin light chain kinase 2 MYLK2 533378

69 ENSBTAG00000009182 -8,80916 3,6067 15,86311 6,81E-05 0,013979 chloride voltage-gated channel

1 CLCN1 514597

70 ENSBTAG00000011730 -6,31864 7,133549 15,82169 6,96E-05 0,014084 titin-cap TCAP 513257

106

71 ENSBTAG00000016170 -8,54603 2,099571 15,78098 7,11E-05 0,014092

potassium voltage-gated

channel subfamily J member

11

KCNJ11 532060

72 ENSBTAG00000021581 -5,06982 4,603875 15,75082 7,23E-05 0,014092 formin homology 2 domain

containing 3 FHOD3 785433

73 ENSBTAG00000014508 -5,83922 4,128569 15,74123 7,26E-05 0,014092 F-box protein 40 FBXO40 613597

74 ENSBTAG00000046176 -4,4916 5,962274 15,52326 8,15E-05 0,0156 SPEG complex locus SPEG 523490

75 ENSBTAG00000023039 -9,81153 2,254751 15,39729 8,71E-05 0,016146 dual specificity phosphatase

13 DUSP13 616048

76 ENSBTAG00000020643 -9,17858 1,639349 15,38947 8,75E-05 0,016146 BARX homeobox 2 BARX2 617465

77 ENSBTAG00000005534 -6,07566 7,203073 15,38307 8,78E-05 0,016146 enolase 3 ENO3 540303

78 ENSBTAG00000003938 -2,32547 3,364627 15,32962 9,03E-05 0,01619 fibronectin type III domain

containing 1 FNDC1

79 ENSBTAG00000016648 -1,91083 6,008007 15,32246 9,06E-05 0,01619 Basigin BSG 508716

80 ENSBTAG00000009533 -7,736 3,882281 15,29434 9,2E-05 0,01619 ribosomal protein L3 like RPL3L 618440

81 ENSBTAG00000008471 3,298652 3,712697 15,27542 9,29E-05 0,01619 MX dynamin like GTPase 2 MX2 280873

82 ENSBTAG00000004237 -4,10213 2,950883 15,25912 9,37E-05 0,01619 betacellulin BTC 280737

83 ENSBTAG00000020928 -10,3093 2,788285 15,17267 9,81E-05 0,016712 ADP-ribosylhydrolase like 1 ADPRHL1 519627

84 ENSBTAG00000006823 -8,65557 10,25265 15,15369 9,91E-05 0,016712 cardiomyopathy associated 5 CMYA5 533581

85 ENSBTAG00000021508 -8,97583 7,075952 15,05617 0,000104 0,017283 leiomodin 3 LMOD3 509978

86 ENSBTAG00000014143 -5,25606 4,551759 15,0459 0,000105 0,017283 ankyrin repeat and SOCS box


87 ENSBTAG00000009570 -3,8567 0,18961 14,86134 0,000116 0,018567 angiopoietin like 8 ANGPTL8 1,02E+08

88 ENSBTAG00000022158 -6,9227 7,347342 14,85176 0,000116 0,018567 troponin T3, fast skeletal type TNNT3 282096

89 ENSBTAG00000010849 -6,03454 7,110506 14,82352 0,000118 0,018567 ankyrin repeat domain 23 ANKRD23 1E+08

90 ENSBTAG00000037693 -3,20943 0,996277 14,80666 0,000119 0,018567

91 ENSBTAG00000002098 -2,42584 3,359517 14,80404 0,000119 0,018567 cell division cycle 34 CDC34 616156

92 ENSBTAG00000007210 -6,02596 1,817155 14,73654 0,000124 0,018768 RNA binding fox-1 homolog

1 RBFOX1 521304

93 ENSBTAG00000010907 -6,98302 4,900824 14,73452 0,000124 0,018768

protein phosphatase 1

regulatory inhibitor subunit

1A

PPP1R1A 767949

94 ENSBTAG00000000286 -5,1558 6,910438 14,71273 0,000125 0,018768 phosphofructokinase, muscle PFKM 506544

95 ENSBTAG00000010880 -7,83486 7,025636 14,70267 0,000126 0,018768 troponin I2, fast skeletal type TNNI2 506434

107

96 ENSBTAG00000014581 -8,08497 4,762794 14,68262 0,000127 0,018771 muscular LMNA interacting

protein MLIP

97 ENSBTAG00000011644 -3,47209 0,650912 14,66141 0,000129 0,018787 thrombospondin type 1

domain containing 4 THSD4

98 ENSBTAG00000003476 -3,50163 5,897636 14,58045 0,000134 0,019412 fem-1 homolog A FEM1A 516550

99 ENSBTAG00000003275 -3,63715 3,296933 14,55014 0,000136 0,019527 ankyrin 1 ANK1

100 ENSBTAG00000013208 -4,00147 5,427628 14,48746 0,000141 0,019889 solute carrier family 25


101 ENSBTAG00000009012 3,261058 0,752524 14,4779 0,000142 0,019889 pentraxin 3 PTX3 541148

102 ENSBTAG00000047491 -7,87601 6,05184 14,43551 0,000145 0,020096 calcium voltage-gated channel

subunit alpha1 S CACNA1S

103 ENSBTAG00000047000 -4,15388 0,67059 14,39595 0,000148 0,020096

104 ENSBTAG00000000078 1,812596 3,274261 14,37936 0,000149 0,020096 GLI pathogenesis related 2 GLIPR2 538565

105 ENSBTAG00000012771 2,122067 5,022217 14,37707 0,00015 0,020096 colony stimulating factor 1

receptor CSF1R 509418

106 ENSBTAG00000003512 -6,19224 5,134729 14,36742 0,00015 0,020096 myosin heavy chain 7B MYH7B 521764

107 ENSBTAG00000001473 -2,68781 2,333706 14,31131 0,000155 0,020121 ARVCF, delta catenin family

member ARVCF 517045

108 ENSBTAG00000005259 -6,97097 2,532463 14,30746 0,000155 0,020121 uncoupling protein 3 UCP3 281563

109 ENSBTAG00000008842 -6,97909 6,352035 14,30342 0,000156 0,020121 junctophilin 1 JPH1 536811

110 ENSBTAG00000008061 -2,99465 4,663826 14,28399 0,000157 0,020121 Rab interacting lysosomal

protein like 1 RILPL1 505840

111 ENSBTAG00000010551 -7,54978 6,158551 14,27827 0,000158 0,020121 ATPase Na+/K+ transporting

subunit alpha 2 ATP1A2 515161

112 ENSBTAG00000008301 -3,69919 4,79944 14,2356 0,000161 0,020347 WNK lysine deficient protein

kinase 2 WNK2 506520

113 ENSBTAG00000002983 -8,15168 0,483838 14,21394 0,000163 0,020347 5'-nucleotidase, cytosolic IA NT5C1A 504454

114 ENSBTAG00000024187 3,31103 2,275495 14,20711 0,000164 0,020347 histone H2A type 1 LOC529277 529277

114 ENSBTAG00000024187 3,31103 2,275495 14,20711 0,000164 0,020347 histone H2A type 1 LOC104975683 1,05E+08

114 ENSBTAG00000024187 3,31103 2,275495 14,20711 0,000164 0,020347 histone cluster 1, H2am HIST1H2AM 618824

114 ENSBTAG00000024187 3,31103 2,275495 14,20711 0,000164 0,020347 histone H2A type 1 LOC104968446 1,05E+08

115 ENSBTAG00000007547 -10,2349 2,414635 14,15165 0,000169 0,020773 calcium voltage-gated channel

auxiliary subunit gamma 1 CACNG1 781184

116 ENSBTAG00000005048 -10,3235 2,683854 14,12199 0,000171 0,020921 dehydrogenase/reductase 7C DHRS7C 511943

117 ENSBTAG00000002450 2,758522 2,447994 14,02922 0,00018 0,021791

108

118 ENSBTAG00000015467 -4,48075 1,953828 14,01115 0,000182 0,021815 family with sequence

similarity 184 member A FAM184A 541122

119 ENSBTAG00000038748 4,479461 5,294035 13,99108 0,000184 0,021864 hemoglobin, beta HBB 280813

120 ENSBTAG00000001032 -8,72852 8,109922 13,94059 0,000189 0,022272 glycogen phosphorylase,

muscle associated PYGM 327664


containing 18 ASB18 1E+08

122 ENSBTAG00000004732 -4,80202 5,5275 13,88469 0,000194 0,022337 spectrin beta, erythrocytic SPTB 527711

123 ENSBTAG00000018859 -3,20291 3,30778 13,88145 0,000195 0,022337 chromosome 18 C19orf47

homolog C18H19orf47 531855

124 ENSBTAG00000027524 -7,4217 4,466169 13,86861 0,000196 0,022337 smoothelin like 1 SMTNL1

125 ENSBTAG00000032558 1,915662 3,60085 13,85842 0,000197 0,022337 tetratricopeptide repeat

domain 7A TTC7A 616640

126 ENSBTAG00000031573 -8,85159 3,409262 13,83777 0,000199 0,022404 nicotinamide riboside kinase 2 NMRK2 780788

127 ENSBTAG00000035844 -5,80174 2,848644 13,81159 0,000202 0,02246 HRAS like suppressor HRASLS 540539

128 ENSBTAG00000018358 -5,02193 5,176025 13,80351 0,000203 0,02246 SH3 and cysteine rich domain

3 STAC3 518234

129 ENSBTAG00000047155 -8,29906 6,957778 13,77991 0,000206 0,022566 chromosome 28 C10orf71

homolog C28H10orf71 540418

130 ENSBTAG00000019210 -7,15937 4,504201 13,76555 0,000207 0,022566 adenylate cyclase 2 ADCY2 510708

131 ENSBTAG00000002574 -8,15459 6,142971 13,7382 0,00021 0,022722 myozenin 2 MYOZ2 540487

132 ENSBTAG00000030845 2,402387 4,559455 13,69872 0,000215 0,022752 macrophage expressed 1 MPEG1 539997

133 ENSBTAG00000011548 -8,32747 5,184212 13,69371 0,000215 0,022752 adenosine monophosphate

deaminase 1 AMPD1 512748


135 ENSBTAG00000002951 3,297831 2,509992 13,6604 0,000219 0,022981 CD244 molecule CD244 513468

136 ENSBTAG00000010360 -1,74088 4,001996 13,62218 0,000224 0,023262

leucine rich repeats and

immunoglobulin like domains

1

LRIG1 505750

137 ENSBTAG00000015520 2,837898 3,788158 13,61 0,000225 0,023262 solute carrier family 11


138 ENSBTAG00000005353 -7,28009 9,468526 13,58761 0,000228 0,023371 Desmin DES 280765

139 ENSBTAG00000013319 -5,61322 1,920252 13,50816 0,000238 0,023997 single-pass membrane protein

with coiled-coil domains 1 SMCO1 788754

140 ENSBTAG00000013347 -2,90685 4,425585 13,47227 0,000242 0,023997 DM1 protein kinase DMPK 513675

109

141 ENSBTAG00000007732 -5,80302 3,341013 13,47001 0,000242 0,023997 cAMP regulated

phosphoprotein 21 ARPP21 618648

142 ENSBTAG00000038849 -8,48388 6,092793 13,46566 0,000243 0,023997

143 ENSBTAG00000007068 -4,25445 5,428251 13,46115 0,000244 0,023997 SH3 domain binding

glutamate rich protein SH3BGR 617797

144 ENSBTAG00000017181 -3,64232 5,297085 13,45812 0,000244 0,023997 MACRO domain containing 1 MACROD1 613568

145 ENSBTAG00000008868 -5,67146 5,536309 13,42026 0,000249 0,024211 calpain 3 CAPN3 281663

146 ENSBTAG00000001936 -2,43407 3,3104 13,41289 0,00025 0,024211 phosphoenolpyruvate

carboxykinase 1 PCK1 282855

147 ENSBTAG00000045889 -7,80462 2,617464 13,40063 0,000252 0,024211

148 ENSBTAG00000011752 -3,59691 6,899562 13,39001 0,000253 0,024211 Synemin SYNM 514186

149 ENSBTAG00000009014 -2,86983 1,078186 13,35185 0,000258 0,024224 uroplakin 1B UPK1B 282113

150 ENSBTAG00000046046 -9,75437 1,598061 13,35068 0,000258 0,024224 leucine rich repeat containing

30 LRRC30 527341

151 ENSBTAG00000047174 -4,05146 6,846384 13,35049 0,000258 0,024224

152 ENSBTAG00000022058 -5,13178 3,269657 13,33875 0,00026 0,024224 acetyl-CoA carboxylase beta ACACB 515338

153 ENSBTAG00000040028 -8,74422 5,248708 13,32675 0,000262 0,024224

obscurin, cytoskeletal

calmodulin and titin-

interacting RhoGEF

OBSCN 537193

154 ENSBTAG00000014956 -8,27041 4,755461 13,30312 0,000265 0,024268 junctional sarcoplasmic

reticulum protein 1 JSRP1 509378

155 ENSBTAG00000014466 -7,38871 -0,05359 13,28721 0,000267 0,024268 V-set domain containing T

cell activation inhibitor 1 VTCN1 539919

156 ENSBTAG00000009048 1,985503 5,713289 13,28183 0,000268 0,024268 EF-hand domain family

member D2 EFHD2 514259

157 ENSBTAG00000020454 -2,19086 2,748649 13,26308 0,000271 0,024268 leucine zipper tumor

suppressor family member 3 LZTS3 512397

158 ENSBTAG00000038630 -9,65279 3,509116 13,26306 0,000271 0,024268 kelch like family member 34 KLHL34 519605


20 LRRC20 521721

160 ENSBTAG00000006253 -6,48361 9,251174 13,13679 0,00029 0,025392 filamin C FLNC 528415

161 ENSBTAG00000018249 -3,1259 3,014032 13,13564 0,00029 0,025392

potassium calcium-activated

channel subfamily N member

3

KCNN3 534180

162 ENSBTAG00000048155 2,866163 4,352391 13,12374 0,000292 0,025392 ficolin 1 FCN1 497016

163 ENSBTAG00000021745 -9,29659 1,75722 13,10704 0,000294 0,025392 receptor associated protein of RAPSN 541061

110

the synapse

164 ENSBTAG00000012119 -4,3339 -0,609 13,09225 0,000297 0,025392 protein tyrosine phosphatase,

receptor type Z1 PTPRZ1 317725

165 ENSBTAG00000006998 2,652378 2,747911 13,09051 0,000297 0,025392 RAS guanyl releasing protein

4 RASGRP4 529375

166 ENSBTAG00000001085 2,479594 2,339007 13,0856 0,000298 0,025392

FAM20A, golgi associated

secretory pathway

pseudokinase

FAM20A 521099

167 ENSBTAG00000001503 -8,0015 3,397459 12,99529 0,000312 0,026487 tripartite motif containing 72 TRIM72

168 ENSBTAG00000006491 -3,74886 6,371594 12,96473 0,000317 0,026763 amylo-alpha-1, 6-glucosidase,

4-alpha-glucanotransferase AGL 517397

169 ENSBTAG00000026344 -8,58033 0,647537 12,94483 0,000321 0,026889 MAF bZIP transcription factor

A MAFA 618421

170 ENSBTAG00000019093 -2,88295 4,137678 12,86621 0,000335 0,027877 amylase, alpha 2B (pancreatic) AMY2B 505049

171 ENSBTAG00000009579 -3,00894 4,37656 12,84608 0,000338 0,028014 centrosomal protein 85 CEP85 517520

172 ENSBTAG00000012991 -2,42099 5,065972 12,81711 0,000343 0,028141 prune homolog 2 PRUNE2 518308

173 ENSBTAG00000002931 -9,18363 1,633686 12,80707 0,000345 0,028141 bestrophin 3 BEST3

174 ENSBTAG00000019037 -7,34064 2,029366 12,80506 0,000346 0,028141 aquaporin 4 AQP4 281008

175 ENSBTAG00000007172 -2,99101 5,953836 12,78734 0,000349 0,028247 glutamic-oxaloacetic

transaminase 2 GOT2 286886

176 ENSBTAG00000043553 1,936885 6,059685 12,74139 0,000358 0,028785 glutathione peroxidase 3 GPX3 281210

177 ENSBTAG00000018253 -2,91313 3,194324 12,71401 0,000363 0,028802 cholinergic receptor nicotinic

alpha 1 subunit CHRNA1 338070

178 ENSBTAG00000006541 -8,27107 8,507205 12,71377 0,000363 0,028802

ATPase

sarcoplasmic/endoplasmic

reticulum Ca2+ transporting 1

ATP2A1 518117

179 ENSBTAG00000021145 2,336688 2,909291 12,70469 0,000365 0,028802 mitogen-activated protein

kinase kinase kinase kinase 1 MAP4K1 522002

180 ENSBTAG00000006689 -5,39576 -0,68732 12,69823 0,000366 0,028802 tectorin beta TECTB 538466

181 ENSBTAG00000002319 -4,69196 2,844226 12,67764 0,00037 0,028868 hemicentin 2 HMCN2

182 ENSBTAG00000021992 -5,88252 3,919301 12,65064 0,000375 0,028868 caveolae associated protein 4 CAVIN4 528386

183 ENSBTAG00000001604 -9,1106 5,814724 12,63756 0,000378 0,028868

184 ENSBTAG00000011917 -2,10336 6,496721 12,63489 0,000379 0,028868 glycerol-3-phosphate

acyltransferase, mitochondrial GPAM 497202

185 ENSBTAG00000048264 -4,43003 0,325565 12,63008 0,00038 0,028868 fibronectin leucine rich

transmembrane protein 1 FLRT1 788007

111

186 ENSBTAG00000015470 -6,641 3,828448 12,62317 0,000381 0,028868 synaptophysin like 2 SYPL2 514665

187 ENSBTAG00000039340 -6,10757 2,916399 12,62267 0,000381 0,028868 sodium voltage-gated channel

beta subunit 4 SCN4B 538100


2 LRRC2 541113



190 ENSBTAG00000011869 -7,24591 8,012678 12,53109 0,0004 0,029645 cysteine and glycine rich

protein 3 CSRP3 540407

191 ENSBTAG00000002853 -6,85488 4,843655 12,51353 0,000404 0,029645 histidine rich calcium binding

protein HRC 617610

192 ENSBTAG00000023891 -5,86766 1,126069 12,51039 0,000405 0,029645 RNA binding motif protein 20 RBM20 525419

193 ENSBTAG00000007661 2,283533 5,355277 12,49595 0,000408 0,029645 myosin IF MYO1F 532964

194 ENSBTAG00000007103 2,463662 5,395028 12,49513 0,000408 0,029645 integrin subunit alpha L ITGAL 281874

195 ENSBTAG00000018598 -4,68027 5,965713 12,48535 0,00041 0,029645 heat shock protein family B

(small) member 6 HSPB6 534551

196 ENSBTAG00000017642 -4,32646 0,071483 12,48519 0,00041 0,029645 bone morphogenetic protein 3 BMP3 539527

197 ENSBTAG00000015129 -3,135 0,535762 12,40555 0,000428 0,030779 kallikrein related peptidase 10 KLK10 526736

198 ENSBTAG00000001564 -5,00388 9,199917 12,38536 0,000433 0,030957 phosphodiesterase 4D

interacting protein PDE4DIP 508547

199 ENSBTAG00000020223 -6,96934 6,996091 12,32256 0,000448 0,031724 calsequestrin 1 CASQ1 508394

200 ENSBTAG00000038462 -7,37218 2,579587 12,32089 0,000448 0,031724 transmembrane protein 182 TMEM182 618298

201 ENSBTAG00000017670 2,03081 3,312486 12,30307 0,000452 0,031801 interferon-induced guanylate-

binding protein 1 LOC512486 512486

202 ENSBTAG00000018707 -8,81368 7,400419 12,2854 0,000457 0,031801 LIM domain binding 3 LDB3 536781

203 ENSBTAG00000014906 2,290501 7,347291 12,28138 0,000457 0,031801 Versican VCAN 282662

204 ENSBTAG00000010940 -10,7151 3,602137 12,26982 0,00046 0,031801 heat shock protein family B

(small) member 7 HSPB7 512251

205 ENSBTAG00000016726 3,386752 1,51012 12,26569 0,000461 0,031801 kinesin family member 15 KIF15 541135

206 ENSBTAG00000004824 -3,21322 2,571961 12,26117 0,000462 0,031801 receptor accessory protein 1 REEP1 616916

207 ENSBTAG00000001609 -2,96169 2,662747 12,22754 0,000471 0,032223 mitogen-activated protein

kinase kinase 6 MAP2K6 286883

208 ENSBTAG00000011173 -4,26361 2,599629 12,2091 0,000476 0,032387 family with sequence

similarity 189 member A2 FAM189A2 509420

209 ENSBTAG00000020000 3,606433 2,65794 12,19057 0,00048 0,032554 RAP1 GTPase activating

protein 2 RAP1GAP2 786649

112

210 ENSBTAG00000018644 -3,01396 4,15393 12,16112 0,000488 0,032914 PDZ domain containing ring

finger 3 PDZRN3 509083

211 ENSBTAG00000014284 -4,57417 4,452849 12,1482 0,000491 0,032972 alpha kinase 2 ALPK2 510218

212 ENSBTAG00000008539 -4,13876 0,289646 12,14015 0,000493 0,032972 energy homeostasis associated ENHO 783487

213 ENSBTAG00000019327 -7,84848 9,739876 12,05234 0,000517 0,0344 nebulin related anchoring

protein NRAP 532640



215 ENSBTAG00000010954 -6,54921 2,309661 12,0111 0,000529 0,034784 ADP-ribosyltransferase 3 ART3 407147

216 ENSBTAG00000002323 -2,33309 5,338765 12,00007 0,000532 0,034784 ubiquitin specific peptidase 28 USP28 508902


218 ENSBTAG00000015848 -2,88684 4,354058 11,97623 0,000539 0,035007 phosphorylase kinase

regulatory subunit alpha 1 PHKA1 1E+08

219 ENSBTAG00000027425 -3,05756 0,29775 11,96743 0,000541 0,035007 coiled-coil domain containing

190 CCDC190 617478

220 ENSBTAG00000000053 -2,12237 5,346438 11,9595 0,000544 0,035007 filamin A interacting protein 1 FILIP1 514193

221 ENSBTAG00000009035 2,681267 2,158931 11,92843 0,000553 0,035329 centromere protein E CENPE

222 ENSBTAG00000013744 -3,68852 5,96294 11,92558 0,000554 0,035329 synaptopodin SYNPO 533531

223 ENSBTAG00000016819 -4,79104 3,512798 11,9153 0,000557 0,035365 fatty acid binding protein 3 FABP3 281758

224 ENSBTAG00000017722 2,312217 4,202988 11,87312 0,000569 0,035813 coagulation factor V F5 280687

225 ENSBTAG00000019686 2,130055 4,273507 11,86185 0,000573 0,035813 NCK associated protein 1 like NCKAP1L 513641

226 ENSBTAG00000001618 -3,71167 6,335233 11,85939 0,000574 0,035813 alpha kinase 3 ALPK3

227 ENSBTAG00000021673 4,258831 0,641797 11,85682 0,000575 0,035813 NDC80, kinetochore complex

component NDC80 538789

228 ENSBTAG00000000698 -8,27528 6,30741 11,85055 0,000576 0,035813 myosin XVIIIB MYO18B

229 ENSBTAG00000019164 -2,40823 3,849704 11,82385 0,000585 0,03596 Rho related BTB domain

containing 1 RHOBTB1 540513

230 ENSBTAG00000005085 -8,81808 3,612384 11,81505 0,000588 0,03596 tripartite motif containing 63 TRIM63 528912

231 ENSBTAG00000012443 2,546036 1,162501 11,81119 0,000589 0,03596 diaphanous related formin 3 DIAPH3 525628

232 ENSBTAG00000024929 -6,52958 2,473199 11,81054 0,000589 0,03596 protein phosphatase 1

regulatory subunit 27 PPP1R27 616223

233 ENSBTAG00000001183 -3,74384 3,649623 11,77928 0,000599 0,036412 kelch like family member 33 KLHL33 1E+08

234 ENSBTAG00000038523 -6,17706 0,85569 11,75034 0,000608 0,036824 ATPase Na+/K+ transporting

subunit alpha 4 ATP1A4 537960

113

235 ENSBTAG00000008185 -5,27369 2,418075 11,71819 0,000619 0,037272 reticulon 2 RTN2 359720


237 ENSBTAG00000034501 -2,23472 3,090885 11,70413 0,000624 0,037272 complement factor I CFI 513197

238 ENSBTAG00000022244 -7,90095 7,340362 11,69617 0,000626 0,037275 actinin alpha 3 ACTN3 539375

239 ENSBTAG00000017875 2,244385 4,913107 11,67952 0,000632 0,037337 Rho GTPase activating protein

30 ARHGAP30 538835

240 ENSBTAG00000013614 -5,86962 3,671402 11,67328 0,000634 0,037337 transmembrane protein 38A TMEM38A 532775

241 ENSBTAG00000027320 -6,35316 1,848393 11,66366 0,000637 0,037337


channel subfamily B member

1

KCNB1 539528


243 ENSBTAG00000031441 1,759937 4,084852 11,65213 0,000641 0,037382 FXYD domain containing ion

transport regulator 5 FXYD5 505584

244 ENSBTAG00000014762 3,076092 1,653012 11,64287 0,000644 0,037415 interferon stimulated

exonuclease gene 20 ISG20 506604

245 ENSBTAG00000008330 1,160736 3,837526 11,58529 0,000665 0,038333 ring finger protein 19B RNF19B 509774

246 ENSBTAG00000010630 -1,35333 2,864872 11,58262 0,000666 0,038333 abhydrolase domain

containing 18 ABHD18 530484

247 ENSBTAG00000014417 -4,51035 4,50516 11,5369 0,000682 0,038891 cytosolic arginine sensor for

mTORC1 subunit 2 CASTOR2 524593

248 ENSBTAG00000019177 -3,51905 6,70056 11,52943 0,000685 0,038891 bridging integrator 1 BIN1 614576


250 ENSBTAG00000011424 -6,87841 10,83662 11,52576 0,000686 0,038891 tropomyosin 2 TPM2 497015

251 ENSBTAG00000014016 2,447838 3,667218 11,5033 0,000695 0,039207 IKAROS family zinc finger 1 IKZF1 541154

252 ENSBTAG00000003455 -2,94954 2,57021 11,4916 0,000699 0,039297 ankyrin repeat domain 6 ANKRD6 516065

253 ENSBTAG00000001078 -4,46096 4,160596 11,45309 0,000714 0,039725 sarcalumenin SRL

254 ENSBTAG00000026586 -6,23272 0,346116 11,45019 0,000715 0,039725

protein phosphatase 1

regulatory inhibitor subunit

14C

PPP1R14C 617148

255 ENSBTAG00000021685 -8,40659 7,480028 11,43695 0,00072 0,039725 eukaryotic translation

elongation factor 1 alpha 2 EEF1A2 515233

256 ENSBTAG00000000328 -5,86177 0,718688 11,43573 0,00072 0,039725

tubulin polymerization

promoting protein family

member 2

TPPP2 507212

257 ENSBTAG00000006907 -8,68565 12,82033 11,43496 0,000721 0,039725 Nebulin NEB

114

258 ENSBTAG00000025778 -3,17657 1,615668 11,42124 0,000726 0,039865 ER membrane protein

complex subunit 9 EMC9 509858

259 ENSBTAG00000021922 1,612797 4,597982 11,40295 0,000733 0,040033 small ArfGAP2 SMAP2 514465

260 ENSBTAG00000012509 -1,65829 4,220305 11,39908 0,000735 0,040033

dual specificity tyrosine

phosphorylation regulated

kinase 1B

DYRK1B 507571

261 ENSBTAG00000020294 2,182957 4,504614 11,37962 0,000743 0,040166 protein tyrosine phosphatase,

non-receptor type 6 PTPN6 512312

262 ENSBTAG00000010085 -2,13854 1,400406 11,37867 0,000743 0,040166 solute carrier family 7


263 ENSBTAG00000018073 1,932952 2,256012 11,3449 0,000757 0,04026 translocator protein TSPO 281033

264 ENSBTAG00000031217 -9,27323 6,519731 11,3446 0,000757 0,04026 myosin light chain 6B MYL6B 515421

265 ENSBTAG00000047238 2,456243 5,158455 11,33389 0,000761 0,04026 integrin subunit alpha M ITGAM 407124

266 ENSBTAG00000021394 -2,60963 2,506038 11,33348 0,000761 0,04026 fat storage inducing

transmembrane protein 1 FITM1 510040

267 ENSBTAG00000030520 -3,92127 2,662801 11,33272 0,000762 0,04026 proline rich basic protein 1 PROB1 785007


269 ENSBTAG00000015204 -9,26872 4,411415 11,32454 0,000765 0,04026 small muscle protein X-linked SMPX 615975

270 ENSBTAG00000034182 1,679574 3,281803 11,30748 0,000772 0,04026

271 ENSBTAG00000048184 2,863171 2,328496 11,30484 0,000773 0,04026

leukocyte immunoglobulin-

like receptor subfamily A

member 6

LOC100336589 1E+08

272 ENSBTAG00000011500 -5,97477 2,890962 11,29713 0,000776 0,04026 calsequestrin 2 CASQ2 528555

273 ENSBTAG00000018438 -2,98946 4,110832 11,2937 0,000778 0,04026 Ras related GTP binding D RRAGD 541106

274 ENSBTAG00000008271 2,071383 4,308898 11,27965 0,000784 0,04026 mesenteric estrogen dependent

adipogenesis MEDAG 510187

275 ENSBTAG00000018267 -9,22791 4,59721 11,2735 0,000786 0,04026 tripartite motif containing 54 TRIM54 535320

276 ENSBTAG00000018650 -2,9122 1,506796 11,27196 0,000787 0,04026 hepatic and glial cell adhesion

molecule HEPACAM 521015

277 ENSBTAG00000024449 2,661424 3,536905 11,27095 0,000787 0,04026 centromere protein F CENPF 533089

278 ENSBTAG00000019708 2,06242 3,75988 11,25691 0,000793 0,040303 acyl-CoA synthetase long

chain family member 6 ACSL6 506059

279 ENSBTAG00000016457 -2,87141 6,816244 11,25263 0,000795 0,040303 FMR1 autosomal homolog 1 FXR1 536793

280 ENSBTAG00000000347 1,824221 4,376248 11,24897 0,000797 0,040303 ras homolog family member G RHOG 538559

281 ENSBTAG00000001398 -3,98759 8,503197 11,2272 0,000806 0,040564 ATPase ATP2A2 540568

115

sarcoplasmic/endoplasmic

reticulum Ca2+ transporting 2

282 ENSBTAG00000018530 -5,61239 3,286758 11,22378 0,000808 0,040564 tubulin alpha 8 TUBA8 768036

283 ENSBTAG00000007378 -2,70128 3,661696 11,19844 0,000819 0,040976

CAP-Gly domain containing

linker protein family member

4

CLIP4 515213

284 ENSBTAG00000046555 2,32608 3,660268 11,1702 0,000831 0,04118

285 ENSBTAG00000017593 3,214869 1,971585 11,16697 0,000833 0,04118 triggering receptor expressed

on myeloid cells 1 TREM1 404547

286 ENSBTAG00000011145 -2,55676 3,639695 11,16284 0,000835 0,04118 NDUFA4, mitochondrial

complex associated NDUFA4 327704

287 ENSBTAG00000010786 -3,46079 6,129979 11,15788 0,000837 0,04118 transforming acidic coiled-coil

containing protein 2 TACC2 533768

288 ENSBTAG00000016194 -3,18963 5,025762 11,14901 0,000841 0,04118 F-box protein 32 FBXO32 513776

289 ENSBTAG00000013761 1,909526 4,73113 11,14346 0,000843 0,04118 stathmin 1 STMN1 616317

290 ENSBTAG00000006161 -1,92553 3,893154 11,14307 0,000843 0,04118 MET proto-oncogene,

receptor tyrosine kinase MET 280855

291 ENSBTAG00000006860 -2,96109 0,608474 11,13755 0,000846 0,04118 von Willebrand factor A

domain containing 2 VWA2 530237


293 ENSBTAG00000004989 2,208726 4,351187 11,11323 0,000857 0,041426 interferon regulatory factor 5 IRF5 615340

294 ENSBTAG00000047268 -3,02089 1,331895 11,09878 0,000864 0,041426 Wilms tumor 1 WT1

295 ENSBTAG00000017512 -3,31148 2,58809 11,0987 0,000864 0,041426 microtubule associated protein

tau MAPT 281296

296 ENSBTAG00000019927 -2,95248 4,272282 11,09489 0,000866 0,041426 cytochrome b5 reductase 1 CYB5R1 516287

297 ENSBTAG00000018214 -8,6301 1,932727 11,05915 0,000883 0,041923 shisa family member 2 SHISA2 617336

298 ENSBTAG00000009950 -6,165 -0,63463 11,05632 0,000884 0,041923 paired box 3 PAX3 540951

299 ENSBTAG00000020061 -6,13411 1,525326 11,05405 0,000885 0,041923


channel subfamily J member

12

KCNJ12 538479

300 ENSBTAG00000002898 -8,41116 4,392058 11,01801 0,000902 0,042604 unc-45 myosin chaperone B UNC45B 535385

301 ENSBTAG00000039462 6,496187 -0,76519 10,99069 0,000916 0,043093 PCNA clamp associated factor PCLAF 540737

302 ENSBTAG00000002953 1,592838 4,254167 10,98327 0,000919 0,043123 thioredoxin TXN 280950

303 ENSBTAG00000021407 -6,55565 -0,69092 10,95008 0,000936 0,043757 caspase-14 LOC788915 788915

304 ENSBTAG00000014005 -1,54014 4,379536 10,93692 0,000943 0,043924 diphosphoinositol PPIP5K1 510684

116

pentakisphosphate kinase 1

305 ENSBTAG00000044202 -3,94574 1,076442 10,90694 0,000958 0,044396 connector enhancer of kinase

suppressor of Ras 2 CNKSR2 534112

306 ENSBTAG00000016704 2,354647 2,572736 10,90498 0,000959 0,044396 solute carrier family 37


307 ENSBTAG00000005359 -1,3708 3,195122 10,88684 0,000969 0,044687 transforming growth factor

beta 2 TGFB2 534069

308 ENSBTAG00000014046 4,150991 0,837155 10,8443 0,000991 0,045577 bactericidal permeability

increasing protein BPI 280734

309 ENSBTAG00000002015 -1,93214 1,917388 10,83528 0,000996 0,045651 ribosomal protein S6 kinase

A6 RPS6KA6 526227

310 ENSBTAG00000016413 -6,55021 4,333559 10,82909 0,000999 0,045656 dual specificity phosphatase

26 DUSP26 533896

311 ENSBTAG00000040053 -4,94558 2,771019 10,81396 0,001007 0,045883 myosin heavy chain 6 MYH6

312 ENSBTAG00000025853 -2,54305 4,336312 10,80206 0,001014 0,04603 homer scaffold protein 1 HOMER1 535311

313 ENSBTAG00000003014 2,195244 3,216947 10,76906 0,001032 0,046709

transient receptor potential

cation channel subfamily V

member 2

TRPV2 507664

314 ENSBTAG00000017765 -1,90422 1,975315 10,76287 0,001036 0,046716 glutathione S-transferase M1 GSTM1 327709

315 ENSBTAG00000032881 -4,76829 0,677892 10,73778 0,00105 0,047203

solute carrier organic anion

transporter family member

5A1

SLCO5A1 535202

316 ENSBTAG00000021516 -4,57793 -0,35654 10,72721 0,001056 0,047324 glutathione S-transferase

alpha 1 GSTA1 777644

317 ENSBTAG00000014540 -6,5224 4,070722 10,71148 0,001065 0,047577 PPARGC1 and ESRR induced

regulator, muscle 1 PERM1 520080

318 ENSBTAG00000008579 1,611603 3,396804 10,69025 0,001077 0,047975 regulator of chromosome

condensation 2 RCC2 509120

319 ENSBTAG00000006572 1,992009 3,492817 10,6717 0,001088 0,048306 caspase recruitment domain

family member 9 CARD9 768054

320 ENSBTAG00000019242 -3,20488 1,929587 10,64271 0,001105 0,048916 cholinergic receptor nicotinic

beta 1 subunit CHRNB1 282179

321 ENSBTAG00000021310 -4,08236 0,631553 10,62277 0,001117 0,049292 collagen type IV alpha 4 chain COL4A4

322 ENSBTAG00000010408 2,190215 2,248637 10,61523 0,001122 0,04934

inhibitor of nuclear factor

kappa B kinase subunit

epsilon

IKBKE 533216

323 ENSBTAG00000000546 -2,48636 4,330228 10,60945 0,001125 0,049341 transducer of ERBB2, 1 TOB1 768016

117

324 ENSBTAG00000046383 3,167453 2,155284 10,59615 0,001133 0,049544

118

File Supplementary: Table 3

n genes logFC logCPM LR PValue FDR name symbol entrezgene

1 ENSBTAG00000034644 -8,13372 0,392131 32,98889 9,27E-09 0,000131

2 ENSBTAG00000019244 -4,71334 2,326983 30,6172 3,14E-08 0,000223 purinergic receptor P2X 6 P2RX6

3 ENSBTAG00000000644 4,982116 1,77157 27,56762 1,52E-07 0,000716 S100 calcium binding protein A5 S100A5 509251

4 ENSBTAG00000009617 -4,09038 1,747586 24,98329 5,78E-07 0,002048 solute carrier family 2 member 1 SLC2A1 282356

5 ENSBTAG00000047752 -6,13732 5,072112 23,97988 9,73E-07 0,002352 OTU deubiquitinase 1 OTUD1

6 ENSBTAG00000002576 7,34414 -0,16948 23,60708 1,18E-06 0,002352 gliomedin GLDN 781681

7 ENSBTAG00000006354 3,273132 3,280718 23,57122 1,2E-06 0,002352 haptoglobin HP 280692

8 ENSBTAG00000008920 -8,3333 4,261534 23,38156 1,33E-06 0,002352 ATPase Na+/K+ transporting family

member beta 4 ATP1B4 510026

9 ENSBTAG00000043553 2,438857 6,059685 21,82768 2,98E-06 0,004694 glutathione peroxidase 3 GPX3 281210

10 ENSBTAG00000038865 -6,10216 4,58145 20,77484 5,17E-06 0,007317 transcription elongation factor A3 TCEA3 533803

11 ENSBTAG00000034182 2,138748 3,281803 20,33833 6,49E-06 0,008356

12 ENSBTAG00000018914 -7,38622 1,159724 19,83404 8,45E-06 0,00997 RAB25, member RAS oncogene

family RAB25 506482


14 ENSBTAG00000046177 -8,682 4,111837 19,25949 1,14E-05 0,011545


16 ENSBTAG00000039483 -8,04367 0,736896 18,91305 1,37E-05 0,012112 desmoglein 3 DSG3 529902

17 ENSBTAG00000016411 -3,17079 1,32266 18,18971 2E-05 0,016661 ring finger protein 122 RNF122 510037

18 ENSBTAG00000048264 -5,2818 0,325565 17,9663 2,25E-05 0,017694 fibronectin leucine rich

transmembrane protein 1 FLRT1 788007

19 ENSBTAG00000046333 -9,19664 5,265354 17,71742 2,56E-05 0,019106 chromosome 6 C4orf54 homolog C6H4orf54 1,02E+08

20 ENSBTAG00000047283 -6,85683 -0,66727 17,60648 2,72E-05 0,01924

RF00001


22 ENSBTAG00000008271 2,44417 4,308898 17,16241 3,43E-05 0,022095 mesenteric estrogen dependent

adipogenesis MEDAG 510187

23 ENSBTAG00000023806 -6,30884 2,218329 17,01481 3,71E-05 0,022842

24 ENSBTAG00000001882 -3,42955 1,030458 16,89897 3,94E-05 0,023268 CD79a molecule CD79A 281674



27 ENSBTAG00000009190 -4,20877 2,352072 16,48379 4,91E-05 0,025328 solute carrier family 2 member 4 SLC2A4 282359

119

28 ENSBTAG00000020080 -7,8267 8,193539 16,44558 5,01E-05 0,025328 myosin binding protein C, fast type MYBPC2

29 ENSBTAG00000047766 2,508195 4,874091 16,16665 5,8E-05 0,028001 G0/G1 switch 2 G0S2 507436

30 ENSBTAG00000002953 1,824192 4,254167 16,12476 5,93E-05 0,028001 thioredoxin TXN 280950

31 ENSBTAG00000040053 -6,23977 2,771019 15,93856 6,54E-05 0,029296 myosin heavy chain 6 MYH6

32 ENSBTAG00000013662 2,481848 4,618636 15,91695 6,62E-05 0,029296 collagen type VIII alpha 1 chain COL8A1 538564


34 ENSBTAG00000017690 -2,88641 1,928735 15,76693 7,16E-05 0,029848 carnosine synthase 1 CARNS1

35 ENSBTAG00000008539 -4,502 0,289646 15,69207 7,45E-05 0,030166 energy homeostasis associated ENHO 783487

36 ENSBTAG00000012653 -7,66525 3,196554 15,58744 7,88E-05 0,030463 calcium/calmodulin dependent protein

kinase II beta CAMK2B 525416


38 ENSBTAG00000015106 -5,0266 6,503862 15,39636 8,72E-05 0,031198 desmoplakin DSP 514360

39 ENSBTAG00000032140 -5,64522 -0,80876 15,39564 8,72E-05 0,031198 SH3 domain binding kinase 1 SBK1

40 ENSBTAG00000001003 -9,03441 3,521641 15,37169 8,83E-05 0,031198 creatine kinase, mitochondrial 2 CKMT2 538944

41 ENSBTAG00000031788 -5,03035 1,879974 15,32514 9,05E-05 0,031198 glutathione S-transferase mu 1-like LOC615514 615514

42 ENSBTAG00000015273 -4,49548 3,837391 15,28389 9,25E-05 0,031198 cullin associated and neddylation

dissociated 2 (putative) CAND2 533826

43 ENSBTAG00000002430 -5,06654 3,786729 15,2228 9,55E-05 0,031474 collagen type XVII alpha 1 chain COL17A1 513804



45 ENSBTAG00000005946 -4,46743 6,127103 14,82382 0,000118 0,034862 ubiquitin specific peptidase 13 USP13 1E+08

46 ENSBTAG00000010032 4,8093 -0,96004 14,8006 0,000119 0,034862 neurotrimin NTM 534414

47 ENSBTAG00000010849 -5,95442 7,110506 14,79231 0,00012 0,034862 ankyrin repeat domain 23 ANKRD23 1E+08

48 ENSBTAG00000004770 -6,92951 4,429201 14,7776 0,000121 0,034862 sodium voltage-gated channel alpha

subunit 4 SCN4A 517426

49 ENSBTAG00000019554 -6,66841 4,554098 14,76248 0,000122 0,034862 fructose-bisphosphatase 2 FBP2 514066

50 ENSBTAG00000016170 -8,04924 2,099571 14,74524 0,000123 0,034862 potassium voltage-gated channel

subfamily J member 11 KCNJ11 532060

51 ENSBTAG00000011530 -5,64557 0,959735 14,68151 0,000127 0,035354 cadherin 15 CDH15 522202

52 ENSBTAG00000018223 2,040561 4,581453 14,42918 0,000146 0,035847 chitinase 3 like 1 CHI3L1 286869

53 ENSBTAG00000015129 -3,32448 0,535762 14,41811 0,000146 0,035847 kallikrein related peptidase 10 KLK10 526736

54 ENSBTAG00000009902 -3,62878 1,52799 14,41632 0,000147 0,035847 aldo-keto reductase family 1, member

B1 (aldose reductase) AKR1B1 317748

55 ENSBTAG00000017183 -5,56317 7,781903 14,38964 0,000149 0,035847 PDZ and LIM domain 3 PDLIM3 538516

120

56 ENSBTAG00000011730 -5,84731 7,133549 14,38573 0,000149 0,035847 titin-cap TCAP 513257

57 ENSBTAG00000021120 -8,13617 4,126165 14,31722 0,000154 0,035847 SET and MYND domain containing 1 SMYD1 537404

58 ENSBTAG00000009705 2,278243 3,568098 14,30891 0,000155 0,035847 serpin family F member 1 SERPINF1 281386

59 ENSBTAG00000020605 -5,65568 4,198606 14,30258 0,000156 0,035847 smoothelin like 2 SMTNL2 532143

60 ENSBTAG00000020643 -8,55117 1,639349 14,26072 0,000159 0,035847 BARX homeobox 2 BARX2 617465

61 ENSBTAG00000023891 -6,22063 1,126069 14,21522 0,000163 0,035847 RNA binding motif protein 20 RBM20 525419

62 ENSBTAG00000020928 -9,68317 2,788285 14,21498 0,000163 0,035847 ADP-ribosylhydrolase like 1 ADPRHL1 519627

63 ENSBTAG00000022715 3,60823 2,299018 14,18199 0,000166 0,035847

64 ENSBTAG00000045699 -4,95213 2,688334 14,18142 0,000166 0,035847 catenin alpha 3 CTNNA3 780777

65 ENSBTAG00000001303 -4,30482 5,164952 14,14674 0,000169 0,035847 heat shock protein family B (small)

member 8 HSPB8 539524

66 ENSBTAG00000001842 -3,51147 1,423332 14,13462 0,00017 0,035847 glutathione S-transferase mu 3 GSTM3 615507

67 ENSBTAG00000001120 -5,39742 4,820735 14,133 0,00017 0,035847 coronin 6 CORO6 614661

68 ENSBTAG00000023039 -9,05891 2,254751 14,1137 0,000172 0,035847 dual specificity phosphatase 13 DUSP13 616048

69 ENSBTAG00000046467 -4,09701 3,704292 14,0463 0,000178 0,036139 protein tyrosine phosphatase type

IVA, member 3 PTP4A3 1E+08

70 ENSBTAG00000021132 -5,61958 4,78174 14,03519 0,000179 0,036139 synaptopodin 2 like SYNPO2L 1,02E+08

71 ENSBTAG00000003512 -6,01078 5,134729 14,00346 0,000182 0,036139 myosin heavy chain 7B MYH7B 521764

72 ENSBTAG00000009182 -7,95159 3,6067 13,99093 0,000184 0,036139 chloride voltage-gated channel 1 CLCN1 514597

73 ENSBTAG00000018352 -6,87099 2,780236 13,88866 0,000194 0,037433 actin binding Rho activating protein ABRA 539379

74 ENSBTAG00000018071 -5,77834 6,104024 13,87331 0,000196 0,037433 tropomodulin 4 TMOD4 505645

75 ENSBTAG00000011667 -2,63779 1,898905 13,79022 0,000204 0,038247 peptidase M20 domain containing 2 PM20D2 521179

76 ENSBTAG00000005534 -5,56158 7,203073 13,75746 0,000208 0,038247 enolase 3 ENO3 540303

77 ENSBTAG00000010880 -7,38552 7,025636 13,74703 0,000209 0,038247 troponin I2, fast skeletal type TNNI2 506434

78 ENSBTAG00000046512 -8,04652 6,801669 13,71716 0,000213 0,038247 xin actin binding repeat containing 1 XIRP1 509670

79 ENSBTAG00000014930 -7,54361 6,106191 13,71005 0,000213 0,038247 myosin light chain kinase 2 MYLK2 533378

80 ENSBTAG00000047491 -7,47574 6,05184 13,62526 0,000223 0,03904 calcium voltage-gated channel subunit

alpha1 S CACNA1S

81 ENSBTAG00000021581 -4,54952 4,603875 13,61945 0,000224 0,03904 formin homology 2 domain containing

3 FHOD3 785433

82 ENSBTAG00000032821 -4,74028 2,227442 13,60153 0,000226 0,03904 sciellin SCEL 784362

83 ENSBTAG00000011869 -7,60509 8,012678 13,56006 0,000231 0,039043 cysteine and glycine rich protein 3 CSRP3 540407

84 ENSBTAG00000018691 2,659473 1,601725 13,53959 0,000234 0,039043 ras homolog family member U RHOU 781044

85 ENSBTAG00000018981 4,146716 -0,19826 13,51558 0,000237 0,039043 transmembrane protein 236 TMEM236 520412

121

86 ENSBTAG00000022158 -6,40317 7,347342 13,512 0,000237 0,039043 troponin T3, fast skeletal type TNNT3 282096

87 ENSBTAG00000017616 -3,90904 3,806377 13,4142 0,00025 0,040562 adenylosuccinate synthase like 1 ADSSL1 784089

88 ENSBTAG00000044126 -2,72382 3,525528 13,39726 0,000252 0,040562 syntrophin beta 1 SNTB1


90 ENSBTAG00000008709 1,594924 2,245727 13,33087 0,000261 0,04109 KDEL motif containing 2 KDELC2 1E+08

91 ENSBTAG00000039764 -2,6651 4,241491 13,30364 0,000265 0,041233 immediate early response 5 IER5 523618

92 ENSBTAG00000039356 -4,05209 0,540246 13,27625 0,000269 0,041313

93 ENSBTAG00000006999 -6,64147 9,703428 13,25925 0,000271 0,041313 ryanodine receptor 1 RYR1 1E+08

94 ENSBTAG00000005666 -2,8761 1,985718 13,13787 0,000289 0,042715 leucine rich repeat containing 20 LRRC20 521721

95 ENSBTAG00000027524 -7,06063 4,466169 13,12538 0,000291 0,042715 smoothelin like 1 SMTNL1

96 ENSBTAG00000021508 -8,0122 7,075952 13,11786 0,000292 0,042715 leiomodin 3 LMOD3 509978

97 ENSBTAG00000012931 -3,92692 5,544736 13,11777 0,000293 0,042715 phospholamban PLN 1E+08

98 ENSBTAG00000005048 -9,5826 2,683854 13,06495 0,000301 0,043179 dehydrogenase/reductase 7C DHRS7C 511943

99 ENSBTAG00000012307 -3,52591 4,479782 13,05933 0,000302 0,043179 dystrobrevin alpha DTNA 541153

100 ENSBTAG00000006253 -6,37314 9,251174 13,02222 0,000308 0,043468 filamin C FLNC 528415

101 ENSBTAG00000006305 -4,7845 4,77026 13,00935 0,00031 0,043468 adenylate kinase 1 AK1 280715

102 ENSBTAG00000008150 -4,15858 5,235774 12,88424 0,000331 0,045964 cAMP-dependent protein kinase

inhibitor alpha PKIA 613524

103 ENSBTAG00000010907 -6,27423 4,900824 12,86815 0,000334 0,045964 protein phosphatase 1 regulatory

inhibitor subunit 1A PPP1R1A 767949

104 ENSBTAG00000046838 -5,19807 5,303351 12,78422 0,00035 0,04761

105 ENSBTAG00000005353 -6,86536 9,468526 12,70766 0,000364 0,048732 desmin DES 280765

106 ENSBTAG00000007210 -5,37914 1,817155 12,67231 0,000371 0,048732 RNA binding fox-1 homolog 1 RBFOX1 521304

107 ENSBTAG00000001032 -8,04078 8,109922 12,65732 0,000374 0,048732 glycogen phosphorylase, muscle

associated PYGM 327664




110 ENSBTAG00000016924 -4,25929 5,379862 12,61948 0,000382 0,048732 cyclase associated actin cytoskeleton

regulatory protein 2 CAP2 515190

111 ENSBTAG00000046176 -3,88784 5,962274 12,61885 0,000382 0,048732 SPEG complex locus SPEG 523490

112 ENSBTAG00000021035 1,724598 5,216359 12,60045 0,000386 0,048775 cathepsin K CTSK 513038


containing 18 ASB18 1E+08

122

9 CONCLUSÕES

O perfil de metilação no sêmen bovino revelou metilação diferencial do elemento

repetitivo BTSAT4 nas regiões pericentroméricas entre as populações de espermatozoides

HM e LM. Além disso, muitos DMRs foram enriquecidos em genes frequentemente

relacionados funcionalmente com a organização e manutenção do DNA do espermatozoide.

Juntos, alteração de metilação em regiões pericentroméricas e em genes associados à

metilação da histona lisina destaca o complexo mecanismo que regula a condensação do DNA

durante o acondicionamento cromossômico no espermatozóide, podendo afetar a motilidade

espermática.

O presente estudo utilizou o RNA-seq para avaliar as vias metabólicas associadas ao

modo como o tecido adiposo das vacas leiteiras passa pelo estágio inicial da lactação. Essas

vias estão associadas principalmente aos processos celulares, resposta inflamatória e produção

de energia, que contribuem para a síntese do leite, crescimento fetal e mecanismos

homeostáticos. Para uma compreensão detalhada das alterações fisiológicas da fase inicial e

doenças metabólicas (por exemplo, deslocamento do abomaso, cetose, NEB) e para validação

dos resultados, serão necessários mais estudos, p. com maior tamanho de amostra ou raças

diferentes. Esse conhecimento poderia ser potencialmente aplicado para proporcionar

melhores condições de manejoo, reduzindo o impacto negativo na saúde e economia dos

rebanhos.

123

10 PERSPECTIVAS

Os resultados obtidos no presente trabalho poderão ser utilizados para o

desenvolvimento de novas tecnologias que podem identificar animais com algum tipo de

anormalidade espermática de alto padrão zootécnico e na identificação de doenças

metabólicas relacionadas ao período da lactação.

A epigenética é um sistema de informação que fica no topo do DNA para controlar

quais genes são acessíveis, ativos e inativos. Assim, a epigenética é importante na fertilidade é

que possivelmente pode fornecer um biomarcador para problemas potenciais com a função

espermática e o desenvolvimento inicial do embrião. A questão mais fundamental em relação

ao transcriptoma, cromatina e metilação do DNA dos espermatozoides é se eles podem

transmitir informações sobre a exposição ambiental do macho à prole. Existem atualmente

muitos casos relatados de herança epigenética via espermatozoides.

Nos últimos anos, espermatozoides “Epigenomes” de diferentes espécies foram

descritos usando sequenciamento de alto rendimento. O espermatozoide está deixando de ser

um dos menos estudado para um dos o tipo de célula mais intensamente perfilado. Com base

na disponibilidade dessas novas tecnologias, os resultados deste trabalho poderão contribuir

para a solução dos problemas relacionados com a infertilidade no futuro.

124

REFERÊNCIAS

AGUET, F. K; ARDLIE, G. Tissue Specificity of Gene Expression. Current Genetic Medicine Reports, London, Cambridge, v.4, n, 4, p. 163-169, 2016. AHMADI, A; NG, S. C. Fertilizing ability of DNA-damaged spermatozoa. The Journal of Experimental Zoology, USA, v. 284, n. 6, p. 696-704, 1999. AITKEN, R. J; SUTTON, M; RICHARDSON, D. W; WARNER, P. Relationship between the movement characteristics of human spermatozoa and their ability to penetrate cervical mucus and zona-free hamster oocytes. Joural of Reproduction and Fertility, Teddington, v. 73, n. 2, p. 441-449. 1985. ALBALAT, A; HUSI, H; SIWI, J; NALLY, J; ECKERSALL, P. D; MULLEN, W. Capillary electrophoresis interfaced with a mass spectrometer (CE-MS): technical considerations and applicability for biomarker studies in animals. Current Protein & Peptide Science, Florida, USA, v. 15, n. 1, p. 23–35, 2014. ALILOO, H; PRYCE, J. E; GONZALEZ-RECIO, O; COCKS, B. G; AND HAYES, B. J. Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits. Genetic Selection Evolution, London, v. 48, p. 8–19, 2016. AMANN, R. P; HAMMERSTEDT, H. R; VEERAMACHANENI, R. D. N. The epididymis and sperm maturation: A perspective. Reproduction Fertility Development, Australian, v. 5, n. 4, p. 361-381, 1993. ANDRABI, S. M. H. Mammalian sperm chromatin structure and assessment of DNA fragmentation. Journal of Assisted Reproduction Genetics, London, v. 24, p.561–569, 2007.

ASTON, K. I.; UREN, P. J.; JENKINS, T. G.; HORSAGER, A.; CAIRNS, B. R.; SMITH A. D.; CARRELL, D. T. Aberrant sperm DNA methylation predicts male fertility status and embryo quality. Fertility and Sterility, London, v. 104, n. 6, p. 1388-1397, e1-5, 2015. BAI, X; ZHENG, Z; LIU, B; JI, X; BAI, Y; ZHANG, W. Whole blood transcriptional profiling comparison between different milk yield of Chinese Holstein cows using RNA-seq data. BMC Genomics, London, v. 17 (Suppl 7), p. 512, 2016. BALHORN, R. Sperm Chromatin: an overview. In: ZINI, A.; AGARWAL, A. (editores) Sperm Chromatin: Biological and Clinical Applications in Male Infertility and Assisted Reproduction. USA: Springer, London, p.3-18, 2011. BAVISTER, B. D., LEIBFRIED, M.L., LIEBERMAN, G. Development of preimplantation embryos of the golden hamster in a defined culture medium. Biology of Reproduction, Oxford, v. 28, p. 235-247, 1983.

125

BENCHAIB, M, BRAUN, V, RESSNIKOF, D, LORNAGE, J, DURAND, P, NIVELEAU, A, GUERIN, J. F. Influence of global sperm DNA methylation on IVF results. Human Reproduction, Oxford, v. 20, n. 3, p. 768-773, 2005. BENCHAIB, M; BRAUN, V; LORNAGE, J; HADJ, S; SALLE, B; LEJEUNE, H; GUERIN, J. F. Sperm DNA fragmentation decreases pregnancy rate in an assisted reproductive technique. Human Reproduction, Oxford, v. 18, n. 5, p. 1023-1028, 2003. BIFFANI, S; MORANDI, N; LOCATELLI, V; PRAVETTONI, D; BOCCARDO, A; STELLA, A; NICOLAZZI, E. L; BISCARINI, F. Adding evidence for a role of the SLITRK gene family in the pathogenesis of left displacement of the abomasum in Holstein-Friesian dairy cows. Livestock Science, London, v. 167, p. 104–109, 2014. BINDEA, G., MLECNIK, B., HACKL, H., CHAROENTONG, P., TOSOLINI, M., KIRILOVSKY, A., FRIDMAN, W.H., PAGÈS, F., TRAJANOSKI, Z., GALON, J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics, Oxford, v, 25, p. 1091-1093, 2009. BIONAZ, M; LOOR, J. J. Gene networks driving bovine mammary protein synthesis during the lactation cycle. Bioinformatics and Biology Insights, Germany, v. 5, p. 83–98, 2011. BIONAZ, M; LOOR, J. J. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics, v. 9, p. 366, 2008. BIRD, A. DNA methylation patterns and epigenetic memory. Genes & Development, USA, v. 16, n. 1, p. 6–21, 2002. BIRSOY, K; FESTUCCIA, W. T; LAPLANTE, M. A comparative perspective on lipid storage in animals. Journal of Cell Science, London, v. 126, p. 1541–1552, 2013. BISSONNETTE, N; LEVESQUE-SERGERIE, J. P; THIBAULT, C; BOISSONNEAULT. G. Spermatozoal transcriptome profiling for bull sperm motility: a potential tool to evaluate semen quality. Reproduction, New York, v. 138, n. 1, p. 65-80, 2009. BLACK, J. C., VAN RECHEM, C., WHETSTINE, J. R. Histone lysine methylation dynamics: establishment, regulation, and biological impact. Molecular Cell, London, v. 48, p. 491-507, 2012. BLOW, N. Transcriptomics: the digital generation. Nature, London, v. 458, n. 7235, p. 239-242, 2009. BOISSONNAS, C. C., JOUANNET, P., JAMMES, H. Epigenetic disorders and male subfertility. Fertility and Sterility, Netherlands, v. 99, p. 624-631, 2013. BOLGER, A. M., LOHSE, M., USADEL, B. Trimmomatic: a flexible trimmer for Illumina Sequence Data. Bioinformatics, Oxford, v. 30, p. 2114-2120, 2014. BONDE, J. P; ERNST, E; JENSEN, T. K; HJOLLUND, N. H; KOLSTAD, H; HENRIKSEN, T. B, SCHEIKE, T; GIWERCMAN, A; OLSEN, J; SKAKKEBAEK, N. E Relation between

126

semen quality and fertility: a population-based study of 4first- pregnancy planners. Lancet, Philadelphia, v. 352, n. 9135, p. 1172-1177, 1998. BOSSAERT, P; LEROY, J. L; DE VLIEGHER, S; OPSOMER, G. Interrelation between glucose-induced insulin response, metabolic indicators, and the time of first ovulation in high-yielding dairy cows. Journal of Dairy Sciences, London, v. 91, p. 3363–3371, 2008. BOYES, J; BIRD, A. Repression of genes by DNA methylation depends on CpG density and promoter strength: evidence for involvement of a methyl-CpG binding protein. The EMBO Journal, Germany, v. 11, n. 1, p. 327-333,1992. BOYLE, E. A; LI, Y. I; PRITCHARD, J. K. An expanded view of complex traits: From polygenic to omnigenic. Cell, London, v. 169, n. 7, p. 1177-1186, 2017. BRAUNDMEIER, A. G.; AND MILLER, D. J. The search is on: finding accurate molecular markers of male fertility. Journal of Dairy Science, London, v. 84, p. 1915–1925, 2001. BUDWORTH, P. R, AMANN, R. P; CHAPMAN, P. L. Relationships between computerized measurements of motion of frozen-thawed bull spermatozoa and fertility. Journal of Andrology, Chicago, v. 9, n. 1, p. 41-54, 1998. BUITENHUIS, B; POULSEN, N. A; GEBREYESUS, G; LARSEN, L. B. Estimation of genetic parameters and detection of chromosomal regions affecting the major milk proteins and their post translational modifications in Danish Holstein and Danish Jersey cattle. BMC Genetics, London, v. 17, p. 114, 2016. CAPRA, E., TURRI, F., LAZZARI, B., CREMONESI, P., GLIOZZI, T.M., FOJADELLI, I., STELLA, A., PIZZI, F. Small RNA sequencing of cryopreserved semen from single bull revealed altered miRNAs and piRNAs expression between High- and Low-motile sperm populations. BMC Genomics, London, v. 18, p. 14, 2017. CAPUCO, A. V; WOOD, D. L; BALDWIN, R; MCLEOD, K; PAAPE, M. J. Mammary cell number, proliferation, and apoptosis during a bovine lactation: Relation to milk production and effect of bST. Journal of Dairy Sciences, London, v. 84, n. 10, p. 2177–87, 2001. CARD, C. J; ANDERSON, E. J; ZAMBERLAN, S; KRIEGER, K. E; KAPROTH, M; SARTINI, B. L. Cryopreserved bovine spermatozoal transcript profile as revealed by high-throughput ribonucleic acid sequencing. Biology of Reproduction, Oxford, v. 88, n. 2, p. 49, 2013. CARRELL, D.T. Epigenetics of the male gamete. Fertility and Sterility, Netherlands, v. 97, n. 2, p. 267–274, 2012. CARROLL, A. J; BADGER, M. R; HARVEY MILLAR, A. The Metabolome Express Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets. BMC Bioinformatics, Oxford, v. 11, p. 376, 2010. CASSUTO, N.G., MONTJEAN, D., SIFFROI, J.P., BOURET, D., MARZOUK, F., COPIN, H. &BENKHALIFA, M. Different Levels of DNA Methylation Detected in Human Sperms

127

after Morphological Selection Using High Magnification Microscopy. BioMed Research International, London, 2016, 6372171, 2016. CHENOWETH, P. J. Influence of the male on embryo quality. Theriogenology, USA, v. 68, p. 308-315, 2007. CONTRERAS, G. A; SORDILLO, L. M. Lipid mobilization and inflammatory responses during the transition period of dairy cows. Comparative Immunology, Microbiology and Infectious Diseases, London, v. 34, n. 3, p. 281–89, 2011. CORNER, J. S; BARRATT, C. R. Genomic and proteomic approaches to defining sperm. Em: De Jonge, C; Barrat, C.The Sperm Cell. Cambridge University Press. Cambridge, p. 49-71, 2006. COSTA, V; ANGELINI, C; DE FEIS, I; CICCODICOLA, A. Uncovering the complexity oftranscriptomes with RNA-Seq. Journal of Biomedicine and Biotechnology, Egito, v. 2010, Article ID 853916, 19 pages http://dx.doi.org/10.1155/2010/853916, 2010. COUGHLIN, S. S. Toward a road map for global –omics: a primer on omic technologies. American Journal of Epidemiology, Oxford, v. 181, n. 3, p. 1188–1195, 2014. COULDREY, C., WELLS, D.N. DNA methylation at a bovine alpha satellite I repeat CpG site during development following fertilization and somatic cell nuclear transfer. PLoS One, California, v. 8, e55153, 2013. COX, J. F; ALFARO, V; MONTENEGRO, V; RODRIGUEZ-MARTINEZ, H. Computer-assisted analysis of sperm motion in goats and its relationship with sperm migration in cervical mucus. Theriogenology, v.66, n. 4, p. 860-867, 2006. CRAVATT, B. F; SIMON, G. M; YATES, J. R III. The biological impact of mass-spectrometry-based proteomics. Nature, London, 450:991-1000, 2007. CROUCHER, N. J; THOMSON, N. R. Studying bacterial transcriptomes using RNA-seq. Current Opinion in Microbiology, v. 13, n. 5, p. 619–624, 2010. CUI, X; HOU, Y; YANG, S; XIE, Y; ZHANG, S; ZHANG, Y; ZHANG, Q; LU, X; LIU, G. E; SUN, D. Transcriptional profiling of mammary gland in Holstein cows with extremely different milk protein and fat percentage using RNA sequencing. BMC Genomics, London, v. 15, p. 226, 2014. DAS, P. J; McCARTHY, F; VISHNOI, M; PARIA, N; GRESHAM, C; LI, G; KACHROO, P; SUDDERTH, A. K; TEAGUE, S; LOVE, C. C; VARNER, D. D; CHOWDHARY, B. P; RAUDSEPP, T. Stallion sperm transcriptome comprises functionally coherent coding and regulatory RNAs as revealed by microarray analysis and RNA-seq. PLoS One, California, v. 8, n. 2, e56535, 2013. DE JONGE, C. Attributes of fertility spermatozoa: an update. Journal of Andrology, Chicago, v. 20, p. 463-473, 1999.

128

DEBSKIL, B; NOWICKI, T; ZALEWSKI, W; BARTOSZEWICZ, A; TWARDON, J. Effect of pregnancy and stage of lactation on energy processes in isolated blood cells of dairy cows. Journal of Veterinary Research, Poland, v. 61, n. 2, p. 215-211, 2017.

DeJARNETTE J. M; SAACKE, R. G; BAME, J; VOGLER, C. Accessory sperm: their importance to fertility and embryo quality, and attempts to alter their numbers in artificially inseminated cattle. Journal of Animal Science, London, v. 70, n. 2, p. 484-491, 1992. DEJARNETTE, J. M. The effect of semen quality on reproductive efficiency. The Veterinary Clinics of North America: Food Animal Practice, London, v. 21, 409-418, 2005. DeJARNETTE, J. M.; MARSHALL, C. E.; LENZ, R. W.; MONKE, D. R.; AYARS, W. H.; SATTLER, C. G. Sustaining the fertility of artificially inseminated dairy cattle: the role of the artificial insemination industry. Journal of Dairy Science, London, v. 87, p. E93–E104, 2004. DENTON, R; HALESTRAP, A. Regulation of pyruvate metabolism in mammalian tissues. Essays in Biochemistry, London, v. 15, p. 37–77, 1978. DOU, Y., MILNE, T. A., RUTHENBURG, A. J., LEE, S., LEE, J. W., VERDINE, G. L., ALLIS, C. D., ROEDER, R. G. Regulation of MLL1 H3K4 methyltransferase activity by its core components. Nature Structural & Molecular Biology, London, v. 13, p. 713-719, 2006. DRACKLEY, J. K. ADSA Foundation Scholar Award. Biology of dairy cows during the transition period: the final frontier? Journal of Dairy Science, London, v. 82, n. 11, p. 2259–2273, 1999. DU, Y., LI, M., CHEN, J., DUAN, Y., WANG, X., QIU, Y., CAI, Z., GUI, Y., JIANG, H. Promoter targeted bisulfite sequencing reveals DNA methylation profiles associated with low sperm motility in asthenozoospermia. Human Reproduction, Oxford, v. 31, p. 24-33, 2016. DURAN-PASTEN, M. L; FIORDELISIO, T. GnRH-Induced Ca2+ Signaling Patterns and Gonadotropin Secretion in Pituitary Gonadotrophs. Functional Adaptations to Both Ordinary and Extraordinary Physiological Demands. Frontiers in Endocrinology (Lausanne), Rome, Italy, v. 4, p. 127, 2013. ENCISO, M; CISALE, H; JOHNSTON, S. D; SARASA, J; FERNÁNDEZ, J. L; GONSÁLVEZ, J. Major morphological abnormalities in the Bull are related to sperm DNA damage. Theriogenology, London, v. 26, p. 23-32, 2011. FANG, P; ZENG, P; WANG, Z; LIU, M; XU, W; DAI, J; ZHAO, X; ZHANG, D; LIANG, D; CHEN, X; SHI, S; ZHANG, M; WANG, L; QIAO, Z; SHI, H. Estimated diversity of messenger RNAs in each murine spermatozoa and their potential function during early zygotic development. Biology of Reproduction, Oxford, v. 90, n. 5, p. 94, 2014. FATA, J. E; MORI, H. A; EWALD, J; ZHANG, H; YAO, E; WERB, Z; BISSELL, M. J. The MAPK (ERK-1,2) pathway integrates distinct and antagonistic signals from TGFalpha and

129

FGF7 in morphogenesis of mouse mammary epithelium. Developmental Biology, London, v. 306, p. 193–207, 2007. FERNANDES, G.S; ARENA, A. C; FERNANDEZ, C. D; MERCADANTE, A; BARBISAN, L. F; KEMPINAS, W. G. Reproductive effects in male rats exposed to diuron. Reproductive Toxicology, London, v. 23, n. 1, p. 106–112, 2007. FLEMING, A; ABDALLA, E. A; MALTECCA, C;AND; BAES, C. F. Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle. Archives Animal Breeding, Germany, v. 61, p. 43-57, 2018. FORDYCE, G; FITZPATRICK, L; COOPER, N; DOOGAN, V. J; DE FAVERI, J; HOLROYD, R. Bull selection and use in Northem Australia. Social Behaviour and management. Animal Reproduction Science, London, v. 71, n. 1-2, p. 81-99, 2002. FORTES, M. R., REVERTER, A., HAWKEN, R. J., BOLORMAA, S., AND LEHNERT, S. A. Candidate genes associated with testicular development, sperm quality, and hormone levels of inhibin, luteinizing hormone, and insulin-like growth factor 1 in brahman bulls. Biology of Reproduction, Oxford, v. 87, 58, 2012a. FRESCAS, D., GUARDAVACCARO, D., KUCHAY, S.M., KATO, H., POLESHKO, A., BASRUR, V., ELENITOBA-JOHNSON, K.S., KATZ, R.A., PAGANO, M. KDM2A represses transcription of centromeric satellite repeats and maintains the heterochromatic state. Cell Cycle, v. 7, p. 3539-3547, 2008. GAILLARD, C., DOLY, J., CORTADAS, J., BERNARDI, G. The primary structure of bovine satellite 1.715. Nucleic Acids Research, Oxford, v. 9, p. 6069-6082, 1981. GARDINER-GARDEN, M.; FROMMER, M. CpG islands in vertebrate genomes. Journal of Molecular Biology, London, v. 196, p. 261-282, 1987. GARNER, D. L. Ancillary tests of bull semen quality. Veterinary Clinics of North America: Food Animal Practice, London, v. 13, n. 2, p. 313-330, 1997. GIANOTTEN, J.; LOMBARDI, M. P.; ZWINDERMAN, A. H.; LILFORD, R. J. & VAN DER VEEN, F. Idiopathic impaired spermatogenesis: genetic epidemiology is unlikely to provide a short-cut to better understanding. Human Reproduction Update, Oxford, v. 10, p. 533-539, 2004. GLASER, S., LUBITZ, S., LOVELAND, K.L., OHBO, K., ROBB, L., SCHWENK, F., SEIBLER, J., ROELLIG, D., KRANZ, A., ANASTASSIADIS, K., STEWART A.F. The histone 3 lysine 4 methyltransferase, Mll2, is only required briefly in development and spermatogenesis. Epigenetics & Chromatin, London, v. 2, p. 5, 2009. GRETHE S., ARES M. P. S., ANDERSSON T. & PÖRN-ARES M. I. (p38MAPK mediates TNF-induced apoptosis in endothelial cells via phosphorylation and downregulation of Bcl-xL. Experimental Cell Research, Oxford, v.298, p. 632–42, 2004.

130

GUIENNE, B; HUMBOLT, P; THIBIER, M; THIBAULT, C. Evaluation of bull semen fertility by homologous in vitro fertilization tests. Reproduction, Nutrition, Development, France, v. 30, n. 3, p. 259-266, 1990. GUO, H., ZHU, P., YAN, L., LI, R., HU, B., LIAN, Y., YAN, J., REN, X., LIN, S., LI, J., JIN, X., SHI, X., LIU, P., WANG, X., WANG, W., WEI, Y., LI, X., GUO, F., WU, X., FAN, X., YONG, J., WEN, L., XIE, S.X., TANG, F., QIAO, J. The DNA methylation landscape of human early embryos. Nature, London, v. 511, p. 606-610, 2014. HAGOOD, J. S. Beyond the Genome: Epigenetic mechanisms in lung remodeling. Physiology, London, v. 29, n. 3, p. 177-185, 2014. HALLAP, T. Assessment of sperm atributes of frozen-thawed AI doses from Swedish and Estonian dairy bulls sires. Tese de doutorado (Divison of comparative Reproduction, Obstetrics and Udder Health) – Department of Clinical Sciences - Swedish University of Agricultural Sciences. Uppsala. p.34, 2005. HAMATANI, T. Human spermatozoal RNAs. Fertility and Sterility, Netherlands, v. 97, n. 2, p. 275–281, 2012. HAMMON, D. S; EVJEN, I. M; DHIMAN, T. R; GOFF, J. P; WALTERS, J. L. Neutrophil function and energy status in Holstein cows with uterine health disorders. Veterinary Immunology and Immunopathology, London, v. 113, n. 1-2, p. 21–29, 2006. HEARD, E.; CLERC, P.; AVNER, P. X-Chromosome inactivation in mammals. Annual Reviews of Genetics, USA, v. 31, p. 571-610, 1997. HECK, J. M; SCHENNINK, A; van VALENBERG, H. J; BOVENHUIS, H; VISKER, M. H; van ARENDONK, J. A; van, HOOIJDONK, A. C. Effects of milk protein variants on the protein composition of bovine milk. Journal of Dairy Science, London, v. 92, n. 3, p. 1192–1202, 2009. HENDRY, K. A; MacCALLUM, A. J; KNIGHT, C. H; WILDE, C. J. J. Effect of endocrine and paracrine factors on protein synthesis and cell proliferation in bovine hoof tissue culture. Journal of Dairy Research, London, v.66, p. 23–33, 1999. HERING, D. M; OLENSKI, K; KAMINSKI, S. Genome-wide association study for poor sperm motility in Holstein-Friesian bulls. Animal Reproduction Science, London, v. 146, n. 3-4, p. 89-97, 2014. HETTINGA, K; ZHANG, L. Omics and Systems Biology: Integration of Production and Omics Data in Systems Biology. In: de Almeida A., Eckersall D., Miller I. (eds) Proteomics in Domestic Animals: from Farm to Systems Biology. Springer, Germany, Cham, 2018. HIRANO, Y; SHIBAHARA, H; OBARA, H; SUZUKI, T; TAKAMIZAWA, S; YAMAGUCHI, C; TSUNODA, H; SATO, I. ANDROLOGY: Relationships between sperm motility characteristics assessed by the computer-aided sperm analysis (CASA) and fertilization rates in vitro. Journal of Assisted Reproduction and Genetics, Germany, v. 18, n. 4, p. 215–218, 2001.

131

HORGAN, R. P; KENNY, L. C. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. The Obstetrician & Gynaecologist, London, v. 13, p. 189–195, 2011. HOUSHDARAN, S., CORTESSIS, V. K., SIEGMUND, K., YANG, A., LAIRD, P. W., SOKOL, R. Z. Widespread epigenetic abnormalities suggest a broad DNA methylation erasure defect in abnormal human sperm. PloS One, California, v. 2, e1289, 2007. HUGHES, C. M, McKELVEY-MARTIN, V. J; LEWIS, S. E. M. Human sperm DNA integrity assessed by Comet and ELISA assays. Mutagenesis, Oxford, v. 4, p. 71-75, 1999. THUNDATHIL, J. C., DANCE, A. L., KASTELIC, J.N .P.. Fertility management of bulls to improve beef cattle productivity. Theriogenology, London, v. p.1–9,2016. JANUSKAUSKAS, A; ZILINSKAS, H. Bull semen evaluation post-thaw and relation of semen characteristics to bull's fertility. Veterinarijair Zootechnika, Kaunas, v. 17, n. 39, p. 1-8, 2002. JENKINS, T.G., ASTON, K.I, PFLUEGER, C., CAIRNS, B.R., CARRELL, D.T. Age-associated sperm DNA methylation alterations: possible implications in offspring disease susceptibility. PLoS Genetics, Reino Unido, v. 10, e1004458, 2014. JEYANATHAN, J; MARTIN, C; MORGAVI, D. The use of direct-fed microbials for mitigation of ruminant methane emissions: a review. Animal, Reino Unido, v. 8, p. 250–61, 2014. JODAR, M; SELVARAJU, S; SENDLER, E; DIAMOND, M. P; KRAWETZ, S. A. The presence, role and clinical use of spermatozoal RNAs. Human Reproduction Update, Oxford, v. 19, n. 6, p. 604–624, 2013. JODAR, M; SENDLER, E; MOSKOVTSEV, S.I; LIBRACH, C. L; GOODRICH, R; SWANSON, S; HAUSER, R; DIAMOND, M. P; KRAWETZ, S. A. Absence of sperm RNA elements correlates with idiopathic male infertility. Science Translational Medicine, New York, v. 7, n. 295, 295re6, 2015. JONES, B. H; STANDRIDGE, M. K; MOUSTAID, N. Angiotensin II increases lipogenesis in 3T3-L1 and human adipose cells. Endocrinology, Oxford, v. 138, p. 1512-1519, 1997. JONES, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nature Reviews Genetics, London, v. 13, p. 484-492, 2012. JONES, P. A; TAKAI, D. The role of DNA methylation in mammalian epigenetics. Science, Washington, v. 293, n. 5532, p. 1068–1070, 2001. KALUPAHANA, N. S; MOUSTAID-MOUSSA, N. The adipose tissue renin-angiotensin system and metabolic disorders: a review of molecular mechanisms. Critical Reviews in Biochemestry and Molecular Biology, Reino Unido, v. 47, p. 379-390, 2012. KATIB, A. Mechanisms linking obesity to male infertility. Central European Journal of Urology, Poland, v. 68, p. 79-85, 2015.

132

KATO, Y., KANEDA, M., HATA, K., KUMAKI, K., HISANO, M., KOHARA, Y., OKANO, M., LI, E., NOZAKI, M. & SASAKI, H. Role of the Dnmt3 family in de novo methylation of imprinted and repetitive sequences during male germ cell development in the mouse. Human Molecular Genetics, Oxford, v. 16, p. 2272-2280, 2007. KISHIGAMI, S., VAN THUAN, N HIKICHI, T., OHTA, H., WAKAYAMA, S., MIZUTANI, E. & WAKAYAMA, T. Epigenetic abnormalities of the mouse paternal zygotic genome associated with microinsemination of round spermatids. Developmental Biology, London, v. 289, p. 195-205, 2006. KROPP, J; CARRILLO, J. A; NAMOUS, H; DANIELS, A; SALIH, S. M; SONG, J; KHATIB, H. Male fertility status is associated with DNA methylation signatures in sperm and transcriptomic profiles of bovine preimplantation embryos. BMC Genomics, London, v. 18, n. 1, p. 218, 2017. LEHNERTZ, B., UEDA, Y., DERIJCK, A.A., BRAUNSCHWEIG, U., PEREZ-BURGOS, L., KUBICEK, S., CHEN, T., LI, E., JENUWEIN, T. & PETERS, A.H. Suv39h-mediated histone H3 lysine 9 methylation directs DNA methylation to major satellite repeats at pericentric heterochromatin. Current Biology, Reino Unido, v. 13, p. 1192-1200, 2003. LEWIS, S. E. Is sperm evaluation useful in predicting human fertility? Reproduction, New York, v. 134, p. 31-40, 2007. MARCHAL, R., CHICHEPORTICHE, A., DUTRILLAUX, B.; BERNARDINO-SGHERRI, J. DNA methylation in mouse gametogenesis. Cytogenetic and Genome Research, Philadelphia, v. 105, p. 316-324, 2004. McART, J. A; NYDAM, D. V; OETZEL, G. R; OVERTON, T. R; OSPINA, P. A. Elevated non-esterified fatty acids and β-hydroxybutyrate and their association with transition dairy cow performance. Veterinary Journal, London, v. 198, n. 3, p. 560–570, 2013. McCOARD, S. A; HAYASHI, A. A; SCIASCIA; Q, ROUNCE, J; SINCLAIR, B; McNABB, W. C, ROY, N. C. Mammary transcriptome analysis of lactating dairy cows following administration of bovine growth hormone. Animal, Reino Unido, v. 10, n. 12, p. 2008–2017, 2016. McDANIELD T, KUEHN L. Male Chromosome Hinders Female Cattle Reproduction. Agricultural Research, USA, v. 62, n. 4, p. 12-13, 2014. McGHEE, J. D; NICKEL, J. M; FELSENFELD, G; FTAU, D. C. Higher Order Structure of Chromatin: Orientation of Nucleosomes within the 30 nm Chromatin Solenoid Is Independent of Species and Spacer Length. Cell, Reino Unido, v. 33, p. 831-841, 1983. MEISSNER, A., GNIRKE, A., BELL, G.W., RAMSAHOYE, B., LANDER, E.S., JAENISCH, R. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Research, Oxford, v. 33, p. 5868-5877, 2005. MELTERS, D.P., BRADNAM, K.R., YOUNG, H.A., TELIS, N., MAY, M.R., RUBY, J.G., SEBRA, R., PELUSO, P., EID, J., RANK, D., GARCIA, J.F., DERISI, J.L., SMITH, T., TOBIAS, C., ROSS-IBARRA, J., KORF, I., CHAN, S.W. Comparative analysis of tandem

133

repeats from hundreds of species reveals unique insights into centromere evolution. Genome Biology, London, v. 14, R10, 2013. MILNE, T. A., BRIGGS, S. D., BROCK, H. W., MARTIN, M. E., GIBBS, D., ALLIS, C. D., HESS, J. L. MLL targets SET domain methyltransferase activity to Hox gene promoters. Molecular Cell, Philadelphia, v. 10, p. 1107-1117, 2002. MOLARO, A., HODGES, E., FANG, F., SONG, Q., McCOMBIE, W. R., HANNON, G. J.; SMITH, A. D. Sperm methylation profiles reveal features of epigenetic inheritance and evolution in primates. Cell, Reino Unido, v. 146, p. 1029-1041, 2011. MONTJEAN, D., ZINI, A., RAVEL, C., BELLOC, S., DALLEAC, A., COPIN, H., BOYER, P., MCELREAVEY, K., BENKHALIFA, M. Sperm global DNA methylation level: association with semen parameters and genome integrity. Andrology, Australian, v. 3, p. 235-240, 2015. MORAN, B; CUMMINS, S. B; CREEVEY, C. J, BUTLER, S. T. Transcriptomics of liver and muscle in Holstein cows genetically divergent for fertility highlight differences in nutrient partitioning and inflammation processes. BMC Genomics, London, v. 17, n. 1, p. 603, 2016. MORISHITA, M., MEVIUS, D., DI LUCCIO, E. In vitro histone lysine methylation by NSD1, NSD2/MMSET/WHSC1 and NSD3/WHSC1L. BMC Structural Biology, London, v. 14, p. 25, 2014. MULLER-McNICOLL, M & NEUGEBAUER, K. M. How cells get the message: dynamic assembly and function of mRNA-protein complexes. Nature Reviews Genetics, London, v. 14, n. 4, p. 275–287, 2013. MYRICK, D.A., CHRISTOPHER, M.A., SCOTT, A.M., SIMON, A.K., DONLIN-ASP, P.G., KELLY, W.G., KATZ, D.J. KDM1A/LSD1 regulates the differentiation and maintenance of spermatogonia in mice. PLoS One, California, v. 12, e0177473, 2017. NAGY, Á; POLICHRONOPOULOS, T; GASPARDY A; SOLTI L; CSEH, S. Correlation between bull fertility and sperm cell velocity parameters generated by computer-assisted semen analysis Acta Veterinaria Hungarica, Hungary, v. 63, n. 3, p. 370-381, 2015. NAOR, Z. Signaling by G-protein-coupled receptor (GPCR): studies on the GnRH receptor. Frontiers in Neuroendocrinology, London, v. 30, p. 10-29, 2009. NAYERI, S; STOTHARD, P. Tissues, Metabolic Pathways and Genes of Key Importance in Lactating Dairy Cattle. Springer Science Reviews, Germany, v. 4, p. 49–77, 2016. NIEDDU, M., MEZZANOTTE, R., PICHIRI, G., CONI, P.P., DEDOLA, G.L., DETTORI, M.L., PAZZOLA, M., VACCA, G.M., ROBLEDO, R. Evolution of satellite DNA sequences in two tribes of Bovidae: A cautionary tale. Genetics and Molecular Biology, Brazil, v. 38, p. 513-518, 2015. OAKES, C. C.; LA SALLE, S.; SMIRAGLIA, D. J.; ROBAIRE, B.; TRASLER, J. M. Developmental acquisition of genome-wide DNA methylation occurs prior to meiosis in male germ cells. Developmental Biology, v. 307, p. 368-379, 2007.

134

OHTSUKA, H; KOIWA, M; HATSUGAYA, A; KUDO, K; HOSHI, F; ITOH, N; YOKOTA, H; OKADA, H; KAWAMURA, S. Relationship between serum tumor necrosis factor-alpha activity and insulin resistance in dairy cows affected with naturally occurring fatty liver. Journal of Veterinary Medical Science, v. 63, p. 1021-1025, 2001. OKO, R. J; JANDO, V; WAGNER, C. L; KISTLER, W. S; HERMO, L. S. Chromatin reorganization in rat spermatids during the disappearance of testis-specific histone, H1t, and the appearance of transition proteins TP1 and TP2. Biology of Reproduction, Oxford, v. 54, p. 1141-1157, 1996. OPSOMER, G; WENSING, T; LAEVENS, H; CORYN, M; DE KRUIF, A. Insulin resistance: the link between metabolic disorders and cystic ovarian disease in high yielding dairy cows. Animal Reproduction Science, London, v. 56, p. 211–222, 1999. OSTERMEIER, G. C; DIX, D. J; MILLER, D; KHATRI, P; KRAWETZ, S. A. Spermatozoal RNA profiles of normal fertile men. Lancet, New York, v. 360, n. 9335, p. 772–777, 2002. PAN, S; AEBERSOLD, R; CHEN, R; RUSH, J; GOODLETT, D. R; McINTOSH, M, W; ZHANG, J; BRENTNALL, TA. Mass spectrometry based targeted protein quantification: methods and applications. Journal of Proteome Research, London, v. 8, n. 2, p. 787-79, 2009. PARELHO, V., HADJUR, S., SPIVAKOV, M., LELEU, M., SAUER, S., GREGSON, H.C., JARMUZ, A., CANZONETTA, C., WEBSTER, Z., NESTEROVA, T., COBB, B.S., YOKOMORI, K., DILLON, N., ARAGON, L., FISHER, A.G., MERKENSCHLAGER, M. Cohesins functionally associate with CTCF on mammalian chromosome arms. Cell, London, v. 132, p. 422-433, 2008. PARKINSON, T. J. Evaluation of fertility and infertility in natural service bulls. The Veterinary Journal, London, v. 68, p. 215-229, 2004. PARTHUN, M. R. Hat1: the emerging cellular roles of a type B histone acetyltransferase. Oncogene, New York, v. 26, n. 37, p. 5319-5328, 2007. POPLINSKI, A., TUTTELMANN, F., KANBER, D., HORSTHEMKE, B., GROMOLL, J. Idiopathic male infertility is strongly associated with aberrant methylation of MEST and IGF2/H19 ICR1. International Journal of Andrology, Nova Jersey, v. 33, p. 642-649, 2010. RANDO, O. J. Intergenerational transfer of epigenetic information in sperm. Cold Spring Harbor Perspectives in Medicine, London, v. 6, n. 5, a022988, 2016. RAZIN, A.; SHEMER, R. DNA methylation in early development. Human Molecular Genetics, Oxford, v. 4, p. 1751-1755, 1995. REINERT, K; LANGMEAD, B; WEESE, D; EVERS, D. J. Alignment of Next-Generation Sequencing Reads. Annual Review of Genomics and Human Genetics, United States, v. 16, p. 133-151, 2015.

135

RITCHIE, M. E; PHIPSON, B; WU, D; HU, Y; LAW, C. W; SHI, W; SMYTH, G. K. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, Oxford, v. 43, n. 7, e47, 2015. ROBINSON, M. D; McCARTHY, D. J; SMYTH, G. K. EdgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, Oxford, v. 26, p. 139–40, 2010. RODRIGUEZ, I; DIAZ, A; VAAMONDE, D. Assessment of the effect of prolonged forced swimming on CD-1 mice sperm morphology with and without antioxidant supplementation. Andrology, Australian, v. 48, n. 3, p. 277-281, 2016. RODRIGUEZ-MANZANET, R; DEKRUYFF, R; KUCHROO, V. K; UMETSU, D. T. The costimulatory role of TIM molecules. Immunological Reviews, USA, v. 229, p. 259- 270, 2009. ROUSSEAUX, S; REYNOIRD, N; ESCOFFIER, E; THEVENON, J; CARON, C; KHOCHBIN, S. Epigenetic reprogramming of the male genome during gametogenesis and in the zygote. Reproductive Biomedicine Online, China, v. 16, n. 4, p. 492–503, 2008. RUSSO, V. E. A; MARTIENSSEN, R. A; RIGGS, A. D. Epigenetic mechanisms of gene regulation. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, New York, 1996. RYBAR, R; FALDIKOVA, L; FALDYNA, M; MACHATKOVA, M; RUBES, J. Bull and boar sperm DNA integrity evaluated by sperm chromatin structure assay in the Czech Republic. Veterinary Medicína, London, v. 49, p. 1-8, 2004. SADRI, H; BRUCKMAIER, R. M; RAHMANI, H. R; GHORBANI, G. R; MOREL, I; VAN DORLAND, H. A. Gene expression of tumour necrosis factor and insulin signalling‐related factors in subcutaneous adipose tissue during the dry period and in early lactation in dairy cows. Journal of animal physiology and animal nutrition, Oxford, v. 94, e194-e02, 2010. SAINT-MARC, P; KOZAK, L. P; AILHAUD, G; DARIMONT, C; NEGREL, R. Angiotensin II as a trophic factor of white adipose tissue: Stimulation of adipose cell formation. Endocrinology, Oxford, v. 142, p. 487-92, 2001. SAKAI, T; HAYAKAWA, T; HAMAKAWA, M; OGURA, K; KUBO, S. Therapeutic effects of simultaneous use of glucose and insulin in ketotic dairy cows. Journal of Dairy Science, London, v. 76, p. 109–114, 1993. SAKKAS, D; ALVAREZ, J. G. Sperm DNA fragmentation: mechanisms of origin, impact on reproductive outcome, and analysis. Fertility and Sterility, Netherlands, v. 93, n. 4, p. 1027-1036, 2010. SELI, E.; GARDNER, D.K.; SCHOOLCRAFT, W.B.; MOFFATT, O.; SAKKAS, D. Extent of nuclear DNA damage in ejaculated spermatozoa impacts on blastocyst development after in vitro fertilization. Fertility and Sterility, Netherlands, v. 82, p. 378-83, 2004. SELVARAJU, S; PARTHIPAN, S; SOMASHEKAR, L; KOLTE, A. P; KRISHNAN BINSILA, B; ARANGASAMY, A; RAVINDRA, J. P. Occurrence and functional

136

significance of the transcriptome in bovine (Bos taurus) spermatozoa. Scientific Reports, London, v. 7, 42392, 2017. . SENDLER, E; JOHNSON, G. D; MAO, S; GOODRICH, R. J; DIAMOND, M. P; HAUSER, R; KRAWETZ, S. A. Stability, delivery and functions of human sperm RNAs at fertilization. Nucleic Acids Research, Oxford, v. 41, n, 7, p. 4104–4117, 2013. SEO, S; LARKIN, D. M; LOOR, J. J. Cattle genomics and its implications for future nutritional strategies for dairy cattle. Animal, Reino Unido, v. 7, (Suppl 1), p. 172–183, 2013. SEVERO, N. C. Influência da qualidade do sêmen bovino congelado sobre a fertilidade. A Hora Veterinária, Brasil, v. 28, n. 167, p. 36-39, 2009. SHARMA, R; AGARWAL, A. Spermatogenesis: an overview. In: ZINI, A.; AGARWAL, A. Sperm Chromatin: Biological and Clinical Applications in Male Infertility and Assisted Reproduction. 1ª ed., USA: Springer, 2011. Cap. 2, p. 19-44. SHEN, B; ZHANG, L; LIAN, C; LU, C; ZHANG, Y; PAN, Q; YANG, R; ZHAO, Z. Deep sequencing and screening of differentially expressed MicroRNAs related to milk fat metabolism in bovine primary mammary epithelial cells. International Journal of Molecular Sciences, Switzerland, v. 17, n. 2, p. 200, 2016. SHIBAHARA, H; ONAGAWA, T; AYUSTAWATI, JORSARAEI, S; HIRANO, Y; SUZUKI, T; TAKAMIZAWA, S; SUZUKI, M. Clinical significance of the Acridine Orange test performed as a routine examination: comparison with the CASA estimates and strict criteria. International journal of andrology, Nova Jersey, v. 26, p. 236–241, 2003. SHULDINER, A. R; POLLIN, T. I. Genomics: variations in blood lipids. Nature, London, 466:703-704, 2010. SMALLWOOD, S.A., TOMIZAWA, S., KRUEGER, F., RUF, N., CARLI, N., SEGONDS-PICHON, A., SATO, S., HATA, K., ANDREWS, S.R., KELSEY, G. Dynamic CpG island methylation landscape in oocytes and preimplantation embryos. Nature Genetics, London, v. 43, p. 811-814, 2011. SMITH, Z. D.; CHAN, M. M.; MIKKELSEN, T. S.; GU, H.; GNIRKE, A.; REGEV, A.; MEISSNER, A. A. unique regulatory phase of DNA methylation in the early mammalian embryo. Nature, London, v. 484, p. 339-344, 2012. STUPPIA, L.; FRANZAGO, M.; BALLERINI, P.; GATTA, V.; ANTONUCCI, I. Epigenetics and male reproduction: the consequences of paternal lifestyle on fertility, embryo development, and children lifetime health. Clinical Epigenetics, London, v. 7, n. 120, 2015. STURN, A., QUACKENBUSH, J., TRAJANOSKI, Z. Genesis: cluster analysis of microarray data. Bioinformatics, Oxford, v. 18, p. 207-208, 2002. SUN, L. W; ZHANG, H. Y; WU, L; SHU, S; XIA, C; XU, C; ZHENG, J. S. H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis. Journal of Dairy Science, London, v. 97, p. 1552-1562, 2014.

137

SURAVAJHALA, P; KOGELMAN, L. J; KADARMIDEEN, H. N. Multiomic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare. Genetics Selection Evolution, London, v. 48, p. 38–52, 2016. TAHMASBPOUR, E.; BALASUBRAMANIAN, D; & AGARWAL, A. A. multi-faceted approach to understanding male infertility: gene mutations, molecular defects and assisted reproductive techniques (ART). The Journal of Assisted Reproduction and Genetics, London, v. 3, p. 1115-1137, 2014. TAKEDA, K., KOBAYASHI, E., AKAGI. S., NISHINO, K., KANEDA, M., WATANABE, S. Differentially methylated CpG sites in bull spermatozoa revealed by human DNA methylation arrays and bisulfite analysis. Journal of Reproduction and Developmental, Tokyo, v. 63, p. 279-287, 2017. TALWAR, P; HAYATNAGARKAR, S. Sperm function test. Journal of Human Reproduction Sciences, India, v. 8, n. 2, p. 61-69, 2015. THUNDATHIL, J; PALASZ, A. T; BARTH, A. D; MAPLETOFT, R. J. The use of in vitro fertilization techniques to investigate the fertilizing ability of bovine sperm with proximal cytoplasmic droplets. Animal Reproduction Science, London, v. 65, n. 3-4, p. 181-192, 2001a. VELHO, A. L.C; MENEZES, E; DINH, T; KAYA, A; TOPPER, E; MOURA, A. A; MEMILI, E. Metabolomic markers of fertility in bull seminal plasma. PLoS ONE, California, v. 13, n. 4, e0195279, 2018. VERMA, A; RAJPUT, S; DE, S; KUMAR, R; CHAKRAVARTY, A. K; DATTA, T. K. Genome-wide profiling of sperm DNA methylation in relation to buffalo (Bubalus bubalis) bull fertility. Theriogenology, London, v. 82, n. 5, p. 750–759, e1, 2014. VERSTEGEN, J.; IGUER-OUADA, M.; ONCLIN, K. Computer assisted semen analyzers in andrology research and veterinary practice. Theriogenology, London, v. 57, p. 149-179, 2002. WANG, Y; CAO, X; ZHAO, Y; FEI, J; HU, X; LI, N. Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens. Plos One, California, v. 12, n. 6, 2017. WANG, Z; GERSTEIN, M; SNYDER, M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Review. Genetics, New York, v. 10, n. 1, p. 57–63, 2009. WARD, W. S. Regulating DNA Supercoiling: Sperm Points the Way. Biology of Reproduction, Oxford, v. 84, p. 841-843, 2011. WATHES, D. C; FENWICK, M; CHENG, Z; BOURNE, N; LLEWELLYN, S; MORRIS, D. G; KENNY, D; MURPHY, J; RICK, F. Influence of negative energy balance on cyclicity and fertility in the high producing dairy cow. Theriogenology, London, v. 68, p. 232–241, 2007. WU, W.; SHEN, O.; QIN, Y.; NIU, X.;LU, C.; XIA, Y.; SONG, L.; WANG, S.; WANG, X. Idiopathic male infertility is strongly associated with aberrant promoter methylation of

138

methylenetetrahydrofolate reductase (MTHFR). PLoS One, California, v. 5, n, 11, p. e13884, 2010. XU, H; BARNES, G. T; YANG, Q; TAN, G; YANG, D; CHOU, C. J; SOLE, J; NICHOLS, A; ROSS, J. S; TARTAGLIA, L. A; CHEN, H. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. Journal of Clinical Investigation, Florida, v. 112, p. 1821, 2003. YANG, J; JIANG, L; LIU, X; WANG, H; GUO, G; ZHANG, Q; JIANG, L. Differential expression of genes in milk of dairy cattle during lactation. Animal Genetics, Reino Unido, v. 47, n. 2, p. 174-180, 2016. YANG, X. J; SETO, E. Lysine acetylation: codified crosstalk with other posttranslational modifications. Molecular Cell, Inglaterra, v. 31, n. 4, p. 449-461, 2008. YOUNGSON, N.A., LECOMTE, V., MALONEY, C.A., LEUNG, P., LIU, J., HESSON, L.B., LUCIANI, F., KRAUSE, L., MORRIS, M.J. Obesity-induced sperm DNA methylation changes at satellite repeats are reprogrammed in rat offspring. Asian Journal of Andrology, China, v. 18, p. 930-936, 2016. YVAN-CHARVET, L; QUIGNARD-BOULANGE, A. Role of adipose tissue renin-angiotensin system in metabolic and inflammatory diseases associated with obesity. Kidney International, London, v. 79, p. 162-168, 2011. ZHANG, L; XIA, Y; TANG, F; LI, S. J; YANG, L; WANG, B. The regulation of intrauterine immune cytokines and chemokines during early pregnancy in the bovine. Large Animals Review, Italy, v. 21, 23-31, 2015. ZHANG, S., CHEN, X., WANG, F., AN, X., TANG, B., ZHANG, X., SUN, L., LI, Z. Aberrant DNA methylation reprogramming in bovine SCNT preimplantation embryos. Scientific Reports, London, v. 6, p. 30345, 2016. ZHAO, C; HUO, R; WANG, F. Q; LIN, M; ZHOU, Z. M; SHA, J. H. Identification of several proteins involved in regulation of sperm motility by proteomic analysis. Fertility and Sterility, Netherlands, v. 87, n. 2, p. 436-438, 2006. ZHAO, S; ZHAO, J; BU, D; SUN, P; WANG, J; DONG, Z. Metabolomics analysis reveals large effect of roughage types on rumen microbial metabolic profile in dairy cows. Letters in Applied Microbiology, Dundee, UK, v. 59, n. 1, p. 79–85, 2014. ZHOU, Y; SUN, J; LI, C; WANG, Y; LI, L; CAI, H; LAN, X; LEI, C; ZHAO, X; CHEN, H. Characterization of Transcriptional Complexity during Adipose Tissue Development in Bovines of Different Ages and Sexes. PLoS ONE, California, v. 9, n. 7, e101261, 2014. ZIMIN, A. V; DELCHER, A. L; FLOREA, L; KELLEY, D. R; SCHATZ, M. C; PUIU, D; HANRAHAN, F; PERTEA, G; VAN TASSELL, C. P; SONSTEGARD, T. S; MARÇAIS, G; ROBERTS, M; SUBRAMANIAN, P; YORKE. J. A; SALZBERG, S. L. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biology, United States of America,v. 10, R42, 2009.

139

ZINI, A; BIELECKI, R; PHANG, D; ZENZES, M. T. Correlações entre dois marcadores de integridade do DNA espermático, desnaturação do DNA e fragmentação do DNA em homens férteis e inférteis. Fertility and Sterility, London, v. 75, p. 674–677, 2001. ALEXANDRI, F.L. D’., SCOLARI, S., FERREIRA, C.R. Reproductive biology in the “omics” era: what can be done?. Anim. Reprod, Netherlands, v.7, n.3, p.177-186, Jul./Sept. 2010 BISSONNETTE N., LÉVESQUE-SERGERIE J.P., THIBAULT C., BOISSONNEAULT G. Spermatozoal transcriptome profiling for bull sperm motility: a potential tool to evaluate semen quality. Reproduction, New York, v.138, p.65-80,2009. BLACKSTOCK, W. P.; WEIR, M. P. Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnology, Netherlands, v. 17, p. 121-127, 1999. HUANG W, LONG N, KHATIB H. Genome-wide identification and initial characterization of bovine long non-coding RNAs from EST data. Anim Genet, Reino Unido, v. 43(6), p.674–82, 2012. ANDERSON, N. G.; ANDERSON, N. L. Twenty years of two-dimensional electrophoresis: past, present and future. Electrophoresis, Weinheim, v. 17, p. 443-453, 1996. WILKINS, M.R., PASQUALI, C., APPEL, R.D., OU K., GOLAZ, O., SANCHEZ, J.C., YAN, J.X., GOOLEY, A.A., HUGHES, G., HUMPHERY-SMITH, I., WILLIAMS, K.L., HOCHSTRASSER, D.F. From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis. Nature Biotechnology, United Kingdom, v. 14, p. 61-65, 1996. O’FARRELL, P. H. High resolution two dimensional of proteins. Journal of Biological Chemistry, Bethesda, USA, v. 250, p. 4007-4021, 1975. CASH, P. Proteomics: the protein revolution. Biologist, London, v. 2, p. 49, 2002. STRZEZK, J., WYSOCKI, P., KORDAN, W., KUKLINSKA, M., MOGIELNICKA, M., SOLIWODA D., FRASER, L. Proteomics of boar seminal plasma – current studies possibility of their application in biotechnology of animal reproduction. Reproductive Biology, Poland, v. 5, p. 279-290, 2005. HAYNES, P. A.; YATES, J. R. Proteome profiling-pitfalls and progress.Yeast, San Diego, v. 17, p. 81-87, 2000. LOW, T.Y., SEOW, T.K., CHUNG, M.C. Separation of human erythrocyte membrane associated proteins with one-dimensional and two-dimensional gel electrophoresis followed by identification with matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Proteomics, Singapore, v. 2, p. 1229-1239, 2002. SIUZDAK,G. The expanding role of mass spectrometry in biotechnology. MCC press,San Diego, 2006. WHISTON, R., FINLAY, E. K., MCCABE, M. S., CORMICAN, P., FLYNN, P., CROMIE, A., HANSEN, P.J., LYONS, A., FAIR, S., LONERGAN, P., O’ FARRELLY, C. & MEADE, K. G. A dual targeted β-defensin and exome sequencing approach to identify, validate and

140

functionally characterise genes associated with bull fertility. Scientific Reports, Ireland, v.7, 2017. DEEPINDER, F., CHOWDARY, H.T., AGARWAL, A. Role of metabolomic analysis of biomarkers in the management of male infertility. Expert Rev Mol Diagn., London, v. 7(4), p.351–8, 2007. KOVAC, J.R., PASTUSZAK, A.W., LAMB, D.J. The use of genomics, proteomics, and metabolomics in identifying biomarkers of male infertility. Fertil Steril.; Texas, v. 99(4), p.998–1007, 2013. WICKRAMASINGHE, S., HUA, S., RINCON, G., ISLAS-TREJO, A., GERMAN, J.B., LEBRILLA, C.B., MEDRANO, J.F. Transcriptome Profiling of Bovine Milk Oligosaccharide Metabolism Genes Using RNA-Sequencing. PLoS One, California, v.6, 2011. DUFFIELD, T.F., LISSEMORE, K.D., MCBRIDE, B.W. AND LESLIE, K.E. Impact of hyperketonemia in early lactation dairy cows on health and production. J. Dairy Sci., Canada, v.92(2), p. 571-580, 2009. AITKEN, R.J., DE IULIIS, G.N. On the possible origins of DNA damage in human spermatozoa. Molecular Human Reproduction, Cambridge, v. 16, n. 1, p. 3-13, 2010. BENDER, K., WALSH, S., EVANS, A.C., FAIR, T., BRENNAN, L. Metabolite concentrations in follicular fluid may explain differences in fertility between heifers and lactating cows. Reproduction, Dublin, v.139(6), p.1047-55, 2010. WITTENBURG, D., MELZER, N. , WILLMITZER, L., LISEC , J., KESTING , U., REINSCH, N. AND REPSILBER, D. Milk metabolites and their genetic variability. J. Dairy Sci., Germany, v.96, p.2557–2569, 2013. MÜLLER-MCNICOLL, M., NEUGEBAUER, K.M. How cells get the message: dynamic assembly and function of mRNA-protein complexes. Nat Rev Genet., Germany, v.14(4), p. 275-87, 2013.

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