Abstract
An interplay between gene expression, mineral concentration, and beef quality traits in Bos indicus muscle has been reported previously under a network approach. However, growing evidence suggested that miRNAs not only modulate gene expression but are also involved with mineral homeostasis. To our knowledge, understanding of the miRNA–gene expression-mineral concentration relationship in mammals is still minimal. Therefore, we carried out a miRNA co-expression and multi-level miRNA–mRNA integration analyses to predict the putative drivers (miRNAs and genes) associated with muscle mineral concentration in Nelore steers. In this study, we identified calcium and iron to be the pivotal minerals associated with miRNAs and gene targets. Furthermore, we identified the miR-29 family (miR-29a, -29b, -29c, -29d-3p, and -29e) as the putative key regulators modulating mineral homeostasis. The miR-29 family targets genes involved with AMPK, insulin, mTOR, and thyroid hormone signaling pathways. Finally, we reported an interplay between miRNAs and minerals acting cooperatively to modulate co-expressed genes and signaling pathways both involved with mineral and energy homeostasis in Nelore muscle. Although we provided some evidence to understand this complex relationship, future work should determine the functional implications of minerals for miRNA levels and their feedback regulation system.\\An interplay between gene expression, mineral concentration, and beef quality traits in Bos indicus muscle has been reported previously under a network approach. However, growing evidence suggested that miRNAs not only modulate gene expression but are also involved with mineral homeostasis. To our knowledge, understanding of the miRNA–gene expression-mineral concentration relationship in mammals is still minimal. Therefore, we carried out a miRNA co-expression and multi-level miRNA–mRNA integration analyses to predict the putative drivers (miRNAs and genes) associated with muscle mineral concentration in Nelore steers. In this study, we identified calcium and iron to be the pivotal minerals associated with miRNAs and gene targets. Furthermore, we identified the miR-29 family (miR-29a, -29b, -29c, -29d-3p, and -29e) as the putative key regulators modulating mineral homeostasis. The miR-29 family targets genes involved with AMPK, insulin, mTOR, and thyroid hormone signaling pathways. Finally, we reported an interplay between miRNAs and minerals acting cooperatively to modulate co-expressed genes and signaling pathways both involved with mineral and energy homeostasis in Nelore muscle. Although we provided some evidence to understand this complex relationship, future work should determine the functional implications of minerals for miRNA levels and their feedback regulation system.
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All relevant data are within the paper and its Supporting Information files. All sequencing data is available in the European Nucleotide Archive (ENA) repository (EMBL-EBI), under accession PRJEB13188, PRJEB10898, and PRJEB19421 (https://www.ebi.ac.uk/ena/submit/sra/). All additional datasets generated and analyzed during this study are available from the corresponding author on reasonable request.
References
Afonso J, Coutinho LL, Tizioto PC, da Silva Diniz WJ, de Lima AO, Rocha MIP, Buss CE, Andrade BGN, Piaya O, da Silva JV, Lins LA, Gromboni CF, Nogueira ARA, Fortes MRS, Mourao GB, de Almeida Regitano LC (2019) Muscle transcriptome analysis reveals genes and metabolic pathways related to mineral concentration in Bos indicus. Sci Rep 9:12715. https://doi.org/10.1038/s41598-019-49089-x
Agarwal V, Bell GW, Nam JW, Bartel DP (2015) Predicting effective microRNA target sites in mammalian mRNAs. Elife 4:1–38. https://doi.org/10.7554/eLife.05005
Ahlberg CM, Schiermiester LN, Howard TJ, Calkins CR, Spangler ML (2014) Genome wide association study of cholesterol and poly- and monounsaturated fatty acids, protein, and mineral content of beef from crossbred cattle. Meat Sci 98:804–814. https://doi.org/10.1016/j.meatsci.2014.07.030
Amodio N, Rossi M, Raimondi L, Pitari MR, Botta C, Tagliaferri P, Tassone P, Amodio N, Rossi M, Raimondi L, Pitari MR, Botta C, Tagliaferri P, Tassone P (2015) miR-29s: a family of epi-miRNAs with therapeutic implications in hematologic malignancies. Oncotarget 6:12837–12861. https://doi.org/10.18632/oncotarget.3805
An JH, Ohn JH, Song JA, Yang JY, Park H, Choi HJ, Kim SW, Kim SY, Park WY, Shin CS (2014) Changes of microRNA profile and microRNA–mRNA regulatory network in bones of ovariectomized mice. J Bone Miner Res 29:644–656. https://doi.org/10.1002/jbmr.2060
Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11:R106. https://doi.org/10.1186/gb-2010-11-10-r106
Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 2 Apr 2018
Beckett EL, Yates Z, Veysey M, Duesing K, Lucock M (2014) The role of vitamins and minerals in modulating the expression of microRNA. Nutr Res Rev 27:94–106. https://doi.org/10.1017/S0954422414000043
Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093. https://doi.org/10.1093/bioinformatics/btp101
Brini M, Ottolini D, Calì T, Carafoli E (2013) Calcium in health and disease. In: Astrid S, Helmut Sigel RKOS (eds) Interrelations between essential metal ions and human diseases. Springer, Dordrecht, pp 81–137
Cao JY, Dixon SJ (2016) Mechanisms of ferroptosis. Cell Mol Life Sci 73:2195–2209. https://doi.org/10.1007/s00018-016-2194-1
Casas E, Duan Q, Schneider MJ, Shackelford SD, Wheeler TL, Cundiff LV, Reecy JM (2014) Polymorphisms in calpastatin and mu-calpain genes are associated with beef iron content. Anim Genet 45:283–284. https://doi.org/10.1111/age.12108
Chen J, Long F (2018) mTOR signaling in skeletal development and disease. Bone Res 6:1. https://doi.org/10.1038/s41413-017-0004-5
Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR, Vailaya A, Wang P-L, Adler A, Conklin BR, Hood L, Kuiper M, Sander C, Schmulevich I, Schwikowski B, Warner GJ, Ideker T, Bader GD (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2:2366–2382. https://doi.org/10.1038/nprot.2007.324
Davis M, Clarke S (2013) Influence of microRNA on the maintenance of human iron metabolism. Nutrients 5:2611–2628. https://doi.org/10.3390/nu5072611
Davis MR, Hester KK, Shawron KM, Lucas Smith EBJ, Clarke SL (2012a) Comparisons of the iron deficient metabolic response in rats fed either an AIN-76 or AIN-93 based diet. Nutr Metab (Lond) 9:95. https://doi.org/10.1186/1743-7075-9-95
Davis MR, Rendina E, Peterson SK, Lucas EA, Smith BJ, Clarke SL (2012b) Enhanced expression of lipogenic genes may contribute to hyperglycemia and alterations in plasma lipids in response to dietary iron deficiency. Genes Nutr 7:415–425. https://doi.org/10.1007/s12263-011-0278-y
Diniz WJS, Coutinho LL, Tizioto PC, Cesar ASM, Gromboni CF, Nogueira ARA, de Oliveira PSN, de Souza MM, de Regitano LCA (2016) Iron content affects lipogenic gene expression in the muscle of Nelore Beef Cattle. PLoS ONE 11:e0161160. https://doi.org/10.1371/journal.pone.0161160
Diniz WJS, Mazzoni G, Coutinho LL, Banerjee P, Geistlinger L, Cesar ASM, Bertolini F, Afonso J, de Oliveira PSN, Tizioto PC, Kadarmideen HN, de Regitano LCAA (2019) Detection of co-expressed pathway modules associated with mineral concentration and meat quality in Nelore Cattle. Front Genet 10:210. https://doi.org/10.3389/FGENE.2019.00210
de Oliveira PSN, Coutinho LL, Tizioto PC, Cesar ASM, de Oliveira GB, da Diniz WJ, S, De Lima AO, Reecy JM, Mourão GB, Zerlotini A, Regitano LCA, (2018) An integrative transcriptome analysis indicates regulatory mRNA–miRNA networks for residual feed intake in Nelore cattle. Sci Rep 8:17072. https://doi.org/10.1038/s41598-018-35315-5
de Souza MM, Zerlotini A, Geistlinger L, Tizioto PC, Taylor JF, Rocha MIP, Diniz WJS, Coutinho LL, Regitano LCA (2018) A comprehensive manually-curated compendium of bovine transcription factors. Sci Rep 8:13747. https://doi.org/10.1038/s41598-018-32146-2
Dengler VL, Galbraith M, Espinosa JM (2014) Transcriptional regulation by hypoxia inducible factors. Crit Rev Biochem Mol Biol 49:1–15. https://doi.org/10.3109/10409238.2013.838205
El AH, Leptidis S, Dirkx E, Hoeks J, van Bree B, Brand K, McClellan EA, Poels E, Sluimer JC, van den Hoogenhof MMG, Armand A-S, Yin X, Langley S, Bourajjaj M, Olieslagers S, Krishnan J, Vooijs M, Kurihara H, Stubbs A, Pinto YM, Krek W, Mayr M, da Martins PA, C, Schrauwen P, De Windt LJ, (2013) The hypoxia-inducible microRNA cluster miR-199a∼214 targets myocardial PPARδ and impairs mitochondrial fatty acid oxidation. Cell Metab 18:341–354. https://doi.org/10.1016/j.cmet.2013.08.009
FASTX-Toolkit (2009) FASTX-Toolkit. https://hannonlab.cshl.edu/fastx_toolkit/. Accessed 2 Apr 2018
Feng Y, Xing Y, Liu Z, Yang G, Niu X, Gao D (2018) Integrated analysis of microRNA and mRNA expression profiles in rats with selenium deficiency and identification of associated miRNA–mRNA network. Sci Rep 8:1–9. https://doi.org/10.1038/s41598-018-24826-w
Fischer D, Sironen A (2016) An Introduction to hoardeR. https://cran.r-project.org/web/packages/hoardeR/vignettes/hoardeR-vignette.pdf. Accessed 2 Apr 2018
Fleet JC, Replogle R, Salt DE (2011) Systems genetics of mineral metabolism. J Nutr 141:520–525. https://doi.org/10.3945/jn.110.128736
Friedländer MR, Chen W, Adamidi C, Maaskola J, Einspanier R, Knespel S, Rajewsky N (2008) Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol 26:407–415. https://doi.org/10.1038/nbt1394
Friedman RC, Farh KK-H, Burge CB, Bartel DP (2008) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105. https://doi.org/10.1101/gr.082701.108
Frost RJA, Olson EN (2011) Control of glucose homeostasis and insulin sensitivity by the Let-7 family of microRNAs. Proc Natl Acad Sci 108:21075–21080. https://doi.org/10.1073/pnas.1118922109
Gambacciani C, Kusmic C, Chiavacci E, Meghini F, Rizzo M, Mariani L, Pitto L (2014) miR-29a and miR-30c negatively regulate DNMT 3a in cardiac ischemic tissues: implications for cardiac remodelling. microRNA Diagn Ther. https://doi.org/10.2478/micrnat-2013-0004
Garmyn AJ, Hilton GG, Mateescu RG, Morgan JB, Reecy JM, Tait RG, Beitz C, Duan Q, Schoonmaker JP, Mayes MS, Drewnoski ME, Liu Q (2011) Estimation of relationships between mineral concentration and fatty acid composition of longissimus muscle and beef palatability traits. J Anim Sci 89:2849–2858. https://doi.org/10.2527/jas.2010-3497
Hao R, Hu X, Wu C, Li N (2014) Hypoxia-induced miR-15a promotes mesenchymal ablation and adaptation to hypoxia during lung development in chicken. PLoS ONE 9:e98868. https://doi.org/10.1371/journal.pone.0098868
He J, Jiang B-H (2016) Interplay between reactive oxygen species and microRNAs in cancer. Curr Pharmacol Rep 2:82–90. https://doi.org/10.1007/s40495-016-0051-4
Kaczmarek M, Cachau RE, Topol IA, Kasprzak KS, Ghio A, Salnikow K (2009) Metal ions-stimulated iron oxidation in hydroxylases facilitates stabilization of HIF-1 alpha protein. Toxicol Sci 107:394–403. https://doi.org/10.1093/toxsci/kfn251
Kappeler BIG, Regitano LCA, Poleti MD, Cesar ASM, Moreira GCM, Gasparin G, Coutinho LL (2019) MiRNAs differentially expressed in skeletal muscle of animals with divergent estimated breeding values for beef tenderness. BMC Mol Biol 20:1. https://doi.org/10.1186/s12867-018-0118-3
Kozomara A, Griffiths-Jones S (2014) miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 42:D68–D73. https://doi.org/10.1093/nar/gkt1181
Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 1:54. https://doi.org/10.1186/1752-0509-1-54
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. https://doi.org/10.1186/1471-2105-9-559
Langfelder P, Zhang B, Horvath S (2008) Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24:719–720. https://doi.org/10.1093/bioinformatics/btm563
Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25. https://doi.org/10.1186/gb-2009-10-3-r25
Li Q, Chen H, Huang X, Costa M (2006) Effects of 12 metal ions on iron regulatory protein 1 (IRP-1) and hypoxia-inducible factor-1 alpha (HIF-1α) and HIF-regulated genes. Toxicol Appl Pharmacol 213:245–255. https://doi.org/10.1016/j.taap.2005.11.006
Li Y, Lin L, Li Z, Ye X, Xiong K, Aryal B, Xu Z, Paroo Z, Liu Q, He C, Jin P (2012) Iron Homeostasis regulates the activity of the microRNA pathway through Poly(C)-binding protein 2. Cell Metab 15:895–904. https://doi.org/10.1016/j.cmet.2012.04.021
Magenta A, Dellambra E, Ciarapica R, Capogrossi MC (2016) Oxidative stress, microRNAs and cytosolic calcium homeostasis. Cell Calcium 60:207–217. https://doi.org/10.1016/J.CECA.2016.04.002
Mamdani M, Williamson V, McMichael GO, Blevins T, Aliev F, Adkins A, Hack L, Bigdeli T, van der Vaart DA, Web BT, Bacanu S-A, Kalsi G, Kendler KS, Miles MF, Dick D, Riley BP, Dumur C, Vladimirov VI (2015) Integrating mRNA and miRNA weighted gene co-expression networks with eQTLs in the nucleus accumbens of subjects with alcohol dependence. PLoS ONE 10:e0137671. https://doi.org/10.1371/journal.pone.0137671
Massart J, Sjögren RJO, Lundell LS, Mudry JM, Franck N, O’Gorman DJ, Egan B, Zierath JR, Krook A (2017) Altered miR-29 expression in type 2 diabetes influences glucose and lipid metabolism in skeletal muscle. Diabetes 66:1807–1818. https://doi.org/10.2337/db17-0141
Mateescu RG, Garrick DJ, Reecy JM (2017) Network analysis reveals putative genes affecting meat quality in Angus cattle. Front Genet. https://doi.org/10.3389/fgene.2017.00171
Oh K-J, Han H-S, Kim M-J, Koo S-H (2013) CREB and FoxO1: two transcription factors for the regulation of hepatic gluconeogenesis. BMB Rep 46:567–574. https://doi.org/10.5483/BMBRep.2013.46.12.248
Oliveira GB, Regitano LCA, Cesar ASM, Reecy JM, Degaki KY, Poleti MD, Felício AM, Koltes JE, Coutinho LL (2018) Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle. BMC Genom 19:126. https://doi.org/10.1186/s12864-018-4514-3
Otto T, Fandrey J (2008) Thyroid hormone induces hypoxia-inducible factor 1α gene expression through thyroid hormone receptor β/retinoid X receptor α-dependent activation of hepatic leukemia factor. Endocrinology 149:2241–2250. https://doi.org/10.1210/en.2007-1238
Peña KA, Kiselyov K (2015) Transition metals activate TFEB in overexpressing cells. Biochem J 470:65–76. https://doi.org/10.1042/BJ20140645
Qiu R, Li W, Liu Y (2018) MicroRNA-204 protects H9C2 cells against hypoxia/reoxygenation-induced injury through regulating SIRT1-mediated autophagy. Biomed Pharmacother 100:15–19. https://doi.org/10.1016/J.BIOPHA.2018.01.165
Ripa R, Dolfi L, Terrigno M, Pandolfini L, Savino A, Arcucci V, Groth M, Terzibasi Tozzini E, Baumgart M, Cellerino A (2017) MicroRNA miR-29 controls a compensatory response to limit neuronal iron accumulation during adult life and aging. BMC Biol 15:9. https://doi.org/10.1186/s12915-017-0354-x
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47–e47. https://doi.org/10.1093/nar/gkv007
Ritchie W, Rajasekhar M, Flamant S, Rasko JEJ (2009) Conserved expression patterns predict microRNA targets. PLoS Comput Biol 5:1000513. https://doi.org/10.1371/journal.pcbi.1000513
Ritchie H, Roser M (2018) Micronutrient deficiency. https://ourworldindata.org/micronutrient-deficiency. Accessed 22 Aug 2018
Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140. https://doi.org/10.1093/bioinformatics/btp616
Roczniak-Ferguson A, Petit CS, Froehlich F, Qian S, Ky J, Angarola B, Walther TC, Ferguson SM (2012) The transcription factor TFEB links mTORC1 signaling to transcriptional control of lysosome homeostasis. Sci Signal 5:ra42. https://doi.org/10.1126/scisignal.2002790
Sengar GS, Deb R, Singh U, Junghare V, Hazra S, Raja TV, Alex R, Kumar A, Alyethodi RR, Kant R, Jakshara S, Joshi CG (2018) Identification of differentially expressed microRNAs in Sahiwal (Bos indicus) breed of cattle during thermal stress. Cell Stress Chaperones 23:1019–1032. https://doi.org/10.1007/s12192-018-0911-4
Shalgi R, Lieber D, Oren M, Pilpel Y (2007) Global and local architecture of the mammalian microRNA–transcription factor regulatory network. PLoS Comput Biol 3:e131. https://doi.org/10.1371/journal.pcbi.0030131
Shen J, Sheng X, Chang Z, Wu Q, Wang S, Xuan Z, Li D, Wu Y, Shang Y, Kong X, Yu L, Li L, Ruan K, Hu H, Huang Y, Hui L, Xie D, Wang F, Hu R (2014) Iron metabolism regulates p53 signaling through direct Heme-p53 interaction and modulation of p53 localization, stability, and function. Cell Rep 7:180–193. https://doi.org/10.1016/j.celrep.2014.02.042
Speer RE, Karuppagounder SS, Basso M, Sleiman SF, Kumar A, Brand D, Smirnova N, Gazaryan I, Khim SJ, Ratan RR (2013) Hypoxia-inducible factor prolyl hydroxylases as targets for neuroprotection by “antioxidant” metal chelators: From ferroptosis to stroke. Free Radic Biol Med 62:26–36. https://doi.org/10.1016/J.FREERADBIOMED.2013.01.026
Su W-L, Kleinhanz RR, Schadt EE (2014) Characterizing the role of miRNAs within gene regulatory networks using integrative genomics techniques. Mol Syst Biol 7:490–490. https://doi.org/10.1038/msb.2011.23
Suttle N (2010) Mineral nutrition of livestock, 4a. CABI, Wallingford
Tarazona S, Furió-Tarí P, Turrà D, Di PA, Nueda MJ, Ferrer A, Conesa A (2015) Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv711
Tizioto PC, Gromboni CF, Nogueira ARDA, de Souza MM, Mudadu MDA, Tholon P, Rosa ADN, Tullio RR, Medeiros SR, Nassu RT, Regitano LCDA (2014) Calcium and potassium content in beef: influences on tenderness and associations with molecular markers in Nellore cattle. Meat Sci 96:436–440. https://doi.org/10.1016/j.meatsci.2013.08.001
Tizioto PC, Taylor JF, Decker JE, Gromboni CF, Mudadu MA, Schnabel RD, Coutinho LL, Mourão GB, Oliveira P, Souza MM, Reecy JM, Nassu RT, Bressani FA, Tholon P, Sonstegard TS, Alencar MM, Tullio RR, Nogueira A, Regitano L (2015) Detection of quantitative trait loci for mineral content of Nelore longissimus dorsi muscle. Genet Sel Evol 47:15. https://doi.org/10.1186/s12711-014-0083-3
Watson A, Lipina C, McArdle HJ, Taylor PM, Hundal HS (2016) Iron depletion suppresses mTORC1-directed signalling in intestinal Caco-2 cells via induction of REDD1. Cell Signal 28:412–424. https://doi.org/10.1016/J.CELLSIG.2016.01.014
Xu J, Ji J, Yan X (2012) Cross-talk between AMPK and mTOR in regulating energy balance. Crit Rev Food Sci Nutr 52:373–381. https://doi.org/10.1080/10408398.2010.500245
Xu Z, Shi Z, Li Y (2013) The crosstalk between micro RNA and iron homeostasis. Int J Genom Med 01:1–8. https://doi.org/10.4172/2332-0672.1000112
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. https://doi.org/10.2202/1544-6115.1128
Acknowledgements
We are thankful to the EMBRAPA Multiuser Bioinformatics Laboratory (Laboratório Multiusuário de Bioinformática da Embrapa—LMB) for providing high-performance computational infrastructure; Dr. Bruno G. N. Andrade for the server management and support in the EMBRAPA Pecuária Sudeste; and the Technical University of Denmark (DTU) for accepting the first author as a visiting scholar.
Funding
This study was conducted with funding from EMBRAPA (Macroprograma 1, 01/2005), São Paulo Research Foundation (FAPESP) (Grant #2012/23638-8) and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001. ARAN, LCAR, and LLC were granted CNPq fellowships. JA was granted CAPES fellowship. WJSD was granted FAPESP (Grant #2015/09158-1 and #2017/20761-7) scholarship.
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WJSD, LCAR, LLC, and HNK conceived the idea of this research. ARAN and CFG carried out the mineral measurement; WJSD, PB, and GM carried out the bioinformatics and data analysis. ASMC carried out miRNA data analysis (quality control, mapping, and counting). WJSD, PB, GM, ASMC, HNK, JA, and LCAR collaborated with the interpretation of results, discussion and review of the manuscript. WJSD and PB drafted the manuscript. All authors have reviewed, discussed, and approved the final version of the manuscript.
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Experimental procedures involving the animals used in this study were carried out in accordance with the Institutional Animal Care and Use Committee Guidelines of the Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA—Pecuária Sudeste) (approval code CEUA 01/2013).
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Supplementary Fig. S1
Module-trait association analysis. Modules are labeled by color on the y-axis and traits on the x-axis. For significantly associated modules, the coefficient from the linear model is given within the cell. Only associations with p < 0.05 are shown (PDF 6 kb)
Supplementary Fig. S2
Regulatory network of negative miRNA-mRNA pairs in Nelore muscle. The edges are colored according to miRNA module (miR.MEbrown, miR.MEcyan, miR.MEgreen, miR.MElightyellow, miR.MEmagenta, miR.MEmidnightblue, miR.MEred, and miR.MEtan) Transcription factors are represented by lightpurple diamond shape (TIFF 10471 kb)
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da Silva Diniz, W.J., Banerjee, P., Mazzoni, G. et al. Interplay among miR-29 family, mineral metabolism, and gene regulation in Bos indicus muscle. Mol Genet Genomics 295, 1113–1127 (2020). https://doi.org/10.1007/s00438-020-01683-9
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DOI: https://doi.org/10.1007/s00438-020-01683-9