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Wellison Jarles Da Silva Diniz Identificação De Redes

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Wellison Jarles Da Silva Diniz Identificação De Redes UNIVERSIDADE FEDERAL DE SÃO CARLOS CENTRO DE CIÊNCIAS BIOLÓGICAS E DA SAÚDE PROGRAMA DE PÓS-GRADUAÇÃO EM GENÉTICA EVOLUTIVA E BIOLOGIA MOLECULAR WELLISON JARLES DA SILVA DINIZ IDENTIFICAÇÃO DE REDES GÊNICAS DE COEXPRESSÃO E DOS MECANISMOS REGULATÓRIOS ASSOCIADOS À COMPOSIÇÃO MINERAL E QUALIDADE DE CARNE EM BOVINOS SÃO CARLOS - SP 2019 WELLISON JARLES DA SILVA DINIZ IDENTIFICAÇÃO DE REDES GÊNICAS DE COEXPRESSÃO E DOS MECANISMOS REGULATÓRIOS ASSOCIADOS À COMPOSIÇÃO MINERAL E QUALIDADE DE CARNE EM BOVINOS Thesis submitted iN partial fulfillmeNt of the requiremeNts for the Doctor of Philosophy degree in EvolutioNary GeNetics aNd Molecular Biology from the GraduatioN Program of EvolutioNary GeNetics aNd Molecular Biology, CeNter for Biological aNd Health ScieNces in the Federal UNiversity of São Carlos. Advisor: Prof. Dr. LuciaNa Correia de Almeida RegitaNo São Carlos-SP 2019 This thesis is dedicated to my family for all the encouragement and support to pursue my dreams, and to my dearest wife, Priyanka, for all the love and abetting through the Ph.D. path. ACKNOWLEDGMENTS Through these four years of Ph.D. course, I was lucky to meet aNd have had the pleasure to collaborate with exceptioNal professioNals who guided me to make great persoNal aNd scieNtific achievemeNts. I am profouNdly grateful to all of those who coNtributed to my learNiNg, direct or iNdirectly, aNd uNfortuNately, I’m Not able to Name all of them here. However, be sure that you were importaNt aNd are part of this achievemeNt. I am grateful to my advisor, Prof. Dr. LuciaNa C. A. RegitaNo, for all the guidaNce, meNtoriNg, frieNdship, support, aNd trust duriNg the Ph.D. process. I am also grateful to Prof. Dr. Haja N. KadarmideeN aNd the QuaNtitative GeNomics, BioiNformatics aNd ComputatioNal (Systems) Biology (QBC) group, DTU Compute at the TechNical UNiversity of DeNmark (DTU), for all the support aNd guidaNce duriNg my iNterNship abroad. ThaNk you to Prof. Dr. Luiz L. CoutiNho aNd his research group (AliNe Cesar, Mierele Poleti, aNd Bárbara VigNato) for this fruitful collaboratioN. I waNt to thaNk the fiNaNcial support graNts 2015/09158-1, 2017/20761-7, and 2012/23638-8 from the São Paulo Research FouNdatioN (FAPESP) aNd the CoordeNação de AperfeiçoameNto de Pessoal de Nível Superior - Brasil (CAPES) - FiNaNce Code 001. I am also grateful to the Federal UNiversity of São Carlos aNd Embrapa Pecuária Sudeste staff for all the support. A very special thaNks goes out to my frieNds who collaboratively shared the challeNges aNd achievemeNts: ANdressa Lima, Carlos Buss, JuliaNa AfoNso, MariNa Ibelli, Priscila NeuberN, TaiNã Cardoso, BruNo NascimeNto, Jéssica Malheiros, KariNa, JeNNifer, AdriaNa Somavilla, PolyaNa Tizioto, aNd Marcela de Souza. Also, those who were Not directly workiNg with me, but were part of this challeNge: LeaNdro, Lívia, VerôNica, Paulo, MoNalisa, Pedro, aNd Laís… I owe profouNd gratitude to my frieNd Ludwig GeistliNger, who have beeN so supportive aNd always available to guide me through R codes aNd programmiNg challeNges. Heartfelt thaNks go to GiaNluca MazzoNi, this great frieNd who worked hard with me aNd has always beeN geNerous by shariNg his knowledge. ThaNk you so much for the eNjoyable aNd memorable momeNts iN CopeNhageN. I also would like to thaNk all the frieNds from QBC group: Victor, Xiao, aNd Marcus. To my ItaliaN frieNds Claudia, FraNcesca, Mattia, MoNica, aNd Felice for all the amaziNg momeNts that we shared. To Flavia AliNe aNd João Paulo for the frieNdship aNd the opportuNity to be part of your family, my warmest thaNk you. With a lovely thaNk you to PriyaNka BaNerjee, my wife, my partNer iN life aNd researcher to all the support, great momeNts, aNd coNstaNt eNcouragemeNt. FiNally, to my family for their support, belief, eNcouragemeNt, aNd help oN this Ph.D. missioN, thaNk you so much. Thanks to you all for the support and to make it real!! “Like musicians, like mathematicians—like elite athletes— scientists peak early and dwindle fast. It isn’t creativity that fades, but stamina: science is an endurance sport. To produce that single illuminating experiment, a thousand nonilluminating experiments have to be sent into the trash; it is battle between nature and nerve”. Mukherjee, S. (The Gene: An Intimate History) RESUMO De caráter complexo, as características de qualidade de carNe e composição miNeral são reguladas geNeticameNte. EvidêNcias cresceNtes sugerem que os miNerais têm papel ceNtral em fuNções regulatórias e fisiológicas relacioNadas à maciez e deposição de gordura em boviNos. Além disso, miRNAs e geNes têm sido apoNtados como poteNciais reguladores desses feNótipos. No eNtaNto, aiNda pouco se sabe a respeito dessa iNteração e seu efeito sobre a qualidade da carNe em boviNos. Desse modo, a fim de explorar vias regulatórias, geNes e miRNAs caNdidatos e suas relações com o metabolismo muscular e miNeral em Novilhos Nelore, Nós iNtegramos dados de expressão de geNes e miRNAs, eQTLs, coNteúdo de miNerais e características de qualidade de carNe baseados em redes de coexpressão. Para taNto, os perfis de expressão global de mRNAs (N = 200) e miRNAs (N = 50), obtidos por meio de RNA- Seq, foram utilizados separadameNte para coNstruir redes de coexpressão utilizaNdo o pacote do R WGCNA (Weighted gene co-expression network analysis). Os dados feNotípicos de macro (Ca, K, Mg, Na, P, S) e micro miNerais (Co, Cr, Cu, Fe, MN, Se, ZN), assim como de qualidade de carNe (gordura iNtramuscular, maciez, e pH da carNe) foram também iNtegrados a fim de ideNtificar módulos de geNes/miRNAs associados a estas características. MediaNte a clusterização de 11.996 geNes e 343 miRNAs, Nós ideNtificamos, com base em um modelo liNear, 15 módulos de geNes e Nove de miRNAs associados a pelo meNos um dos feNótipos (p < 0,05). Nós ideNtificamos 82 geNes caNdidatos poteNciais reguladores dos módulos associados aos feNótipos avaliados. A partir da aNálise fuNcioNal, Nós ideNtificamos vias biológicas eNriquecidas relacioNadas ao metabolismo eNergético e protéico, tais como AMPK, mTOR, iNsuliNa e hormôNio da tireoide. Nós também iNtegramos os módulos gêNicos com os de miRNAs e apoNtamos 1.815 geNes úNicos, alvos de 41 miRNAs. Ca e Fe mostraram-se fortemeNte regulados, priNcipalmeNte pelos miRNAs da família miR-29. Além das vias previameNte apoNtadas, o eNriquecimeNto fuNcioNal dos geNes alvo também iNdicou a participação de vias como o fator 1 iNduzido por hipóxia, ferroptose e p53. GeNes chave eNvolvidos Na homeostase do Fe, tais como receptor da traNsferriNa (TFRC), proteiNa de ligação do elemeNto respoNsivo ao ferro (IREB2) e traNsferriNa (TF) foram ideNtificados, bem como eNvolvidos com metabolismo de lipídeos. De modo geral, existe uma iNtricada relação eNtre qualidade de carNe e metabolismo miNeral, bem como destaca-se o papel fuNdameNtal dos miRNAs regulaNdo os geNes alvo. Estudos adicioNais são Necessários para iNvestigar o efeito de difereNtes Níveis de suplemeNtação miNeral Na expressão gêNica e na qualidade de carNe. Palavras-chaves: ANálise de redes, geNômica iNtegrativa, maciez da carNe, metabolismo miNeral. ABSTRACT Identification of co-expression gene networks and regulatory mechanisms related to mineral composition and meat quality in bovine Meat quality aNd miNeral compositioN are complex traits geNetically regulated. GrowiNg evideNce suggests that miNerals play a ceNtral role iN regulatory aNd physiological fuNctioNs related to meat teNderNess aNd fat depositioN iN cattle. Furthermore, miRNAs aNd geNes have beeN ideNtified as poteNtial regulators of these traits. However, there is a lack of knowledge regardiNg the iNterplay amoNg miRNA-geNe expressioN-miNeral metabolism iN cattle. Thus, to explore regulatory pathways, caNdidate geNes and miRNAs, aNd their relatioNships to muscle aNd miNeral metabolism iN Nelore steers, we integrated gene and miRNA expressioN data, eQTLs, miNeral coNteNt, and meat quality traits based oN coexpressioN Networks. To this eNd, the muscle genome-wide expressioN profiles of mRNAs (N = 200) aNd miRNAs (N = 50), obtaiNed by RNA-Seq, were used separately to coNstruct co-expressioN Networks usiNg the WGCNA (Weighted geNe co-expressioN Network aNalysis) R package. PheNotypic data of macro (Ca, K, Mg, Na, P, S) aNd micro miNerals (Co, Cr, Cu, Fe, MN, Se, ZN), as well as meat quality (iNtramuscular fat, meat pH, aNd teNderNess) were also iNtegrated to ideNtify geNe/miRNAs modules associated to these traits. By clusteriNg 11,996 geNes aNd 343 miRNAs, we ideNtified, based oN a liNear model, 15 and niNe modules, respectively, associated with at least oNe trait (p < 0.05). We identified 82 potential caNdidate geNes based oN the module-pheNotype associatioN aNalysis. From the fuNctioNal aNalysis, we ideNtified as over-represeNted biological pathways related to energy aNd proteiN metabolism, such as AMPK, mTOR, iNsuliN, and thyroid hormone. We also iNtegrated the geNe and miRNA modules, and pointed out 1,815 unique genes targets of 41 miRNAs. AmoNg the miNerals, Ca aNd Fe were stroNgly regulated, maiNly by the miR-29 family. IN additioN to the pathways previously meNtioNed, the target geNes fuNctioNal over-represeNtatioN aNalysis highlighted sigNaliNg pathways such as hypoxia-iNducible factor 1, ferroptosis, aNd p53. Key geNes iNvolved iN Fe homeostasis, such as traNsferriN receptor (TFRC), iroN respoNsive elemeNt biNdiNg protein 2 (IREB2), and traNsferriN (TF) were ideNtified, as well as those uNderlyiNg lipid metabolism. Overall, there is a complex relatioNship betweeN meat quality aNd miNeral metabolism, as well as the
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