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Transcriptional response to an alternative diet on liver, muscle, and rumen of beef cattle.

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  • معلومة اضافية
    • المصدر:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • بيانات النشر:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • الموضوع:
    • نبذة مختصرة :
      Feed cost represents a major economic determinant within cattle production, amounting to an estimated 75% of the total variable costs. Consequently, comprehensive approaches such as optimizing feed utilization through alternative feed sources, alongside the selection of feed-efficient animals, are of great significance. Here, we investigate the effect of two diets, traditional corn-grain fed and alternative by-product based, on 14 phenotypes related to feed, methane emission and production efficiency and on multi-tissue transcriptomics data from liver, muscle, and rumen wall, derived from 52 Nellore bulls, 26 on each diet. To this end, diets were contrasted at the level of phenotype, gene expression, and gene-phenotype network connectivity. As regards the phenotypic level, at a P value < 0.05, significant differences were found in favour of the alternative diet for average daily weight gain at finishing, dry matter intake at finishing, methane emission, carcass yield and subcutaneous fat thickness at the rib-eye muscle area. In terms of the transcriptional level of the 14,776 genes expressed across the examined tissues, we found 487, 484, and 499 genes differentially expressed due to diet in liver, muscle, and rumen, respectively (P value < 0.01). To explore differentially connected phenotypes across both diet-based networks, we focused on the phenotypes with the largest change in average number of connections within diets and tissues, namely methane emission and carcass yield, highlighting, in particular, gene expression changes involving SREBF2, and revealing the largest differential connectivity in rumen and muscle, respectively. Similarly, from examination of differentially connected genes across diets, the top-ranked most differentially connected regulators within each tissue were MEOX1, PTTG1, and BASP1 in liver, muscle, and rumen, respectively. Changes in gene co-expression patterns suggest activation or suppression of specific biological processes and pathways in response to dietary interventions, consequently impacting the phenotype. The identification of genes that respond differently to diets and their associated phenotypic effects serves as a crucial stepping stone for further investigations, aiming to build upon our discoveries. Ultimately, such advancements hold the promise of improving animal welfare, productivity, and sustainability in livestock farming.
      (© 2024. The Author(s).)
    • References:
      Kenny, D. A., Fitzsimons, C., Waters, S. M. & McGee, M. Improving feed efficiency of beef cattle; current state of the art and future challenges. Animal 12(9), 1815–1826. https://doi.org/10.1017/S1751731118000976 (2018). (PMID: 10.1017/S175173111800097629779496)
      Terry, S. A., Basarab, J. A., Guan, L. L. & McAllister, T. A. Strategies to improve the efficiency of beef cattle production. Can. J. Anim. Sci. 101(1), 1–19. https://doi.org/10.1139/cjas-2020-0022 (2020). (PMID: 10.1139/cjas-2020-0022)
      Sandström, V. et al. Food system by-products upcycled in livestock and aquaculture feeds can increase global food supply. Nat. Food 3, 729–740. https://doi.org/10.1038/s43016-022-00589-6 (2022). (PMID: 10.1038/s43016-022-00589-637118146)
      Pörtner, H.-O. et al. Climate change 2022: Impacts, adaptation and vulnerability. In Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Pörtner, H.-O., et al.). 3056 (Cambridge University Press, 2022).
      Olijhoek, D. W., Difford, G. F., Lund, P. & Løvendahl, P. Phenotypic modeling of residual feed intake using physical activity and methane production as energy sinks. J. Dairy Sci. 103(8), 6967–6981. https://doi.org/10.3168/jds.2019-17489 (2020). (PMID: 10.3168/jds.2019-1748932475658)
      Te Pas, M. F. W., Veldkamp, T., de Haas, Y., Bannink, A. & Ellen, E. D. Adaptation of livestock to new diets using feed components without competition with human edible protein sources-A review of the possibilities and recommendations. Animals (Basel). 11(8), 2293. https://doi.org/10.3390/ani11082293 (2021). (PMID: 10.3390/ani11082293)
      Koch, R. M., Swiger, L. A., Chambers, D. & Gregory, K. E. Efficiency of feed use in beef cattle. J. Anim. Sci. 22(2), 486–494. https://doi.org/10.2527/jas1963.222486x (1963). (PMID: 10.2527/jas1963.222486x)
      Nkrumah, J. D. et al. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci. 84(1), 145–153. https://doi.org/10.2527/2006.841145x (2006). (PMID: 10.2527/2006.841145x16361501)
      Cantalapiedra-Hijar, G. et al. Review: Biological determinants of between-animal variation in feed efficiency of growing beef cattle. Animal 12(s2), s321–s335. https://doi.org/10.1017/S1751731118001489 (2018). (PMID: 10.1017/S175173111800148930139392)
      de Lima, A. O. et al. Potential biomarkers for feed efficiency-related traits in nelore cattle identified by co-expression network and integrative genomics analyses. Front. Genet. 11, 189. https://doi.org/10.3389/fgene.2020.00189 (2020). (PMID: 10.3389/fgene.2020.00189321946427064723)
      Taussat, S. et al. Gene networks for three feed efficiency criteria reveal shared and specific biological processes. Genet. Sel. Evol. 52(1), 67. https://doi.org/10.1186/s12711-020-00585-z (2020). (PMID: 10.1186/s12711-020-00585-z331678707653997)
      Chen, W. et al. Identification of predictor genes for feed efficiency in beef cattle by applying machine learning methods to multi-tissue transcriptome data. Front. Genet. 12, 619857. https://doi.org/10.3389/fgene.2021.619857 (2021). (PMID: 10.3389/fgene.2021.619857336647677921797)
      Lam, S. et al. Identification of functional candidate variants and genes for feed efficiency in Holstein and Jersey cattle breeds using RNA-sequencing. J. Dairy Sci. 104(2), 1928–1950. https://doi.org/10.3168/jds.2020-18241 (2021). (PMID: 10.3168/jds.2020-1824133358171)
      Manzanilla-Pech, C. I. V. et al. Genome-wide association study for methane emission traits in Danish Holstein cattle. J. Dairy Sci. 105(2), 1357–1368. https://doi.org/10.3168/jds.2021-20410 (2022). (PMID: 10.3168/jds.2021-2041034799107)
      Johnsson, M. Genomics in animal breeding from the perspectives of matrices and molecules. Hereditas 160(1), 20. https://doi.org/10.1186/s41065-023-00285-w (2023). (PMID: 10.1186/s41065-023-00285-w3714966310163706)
      Nowacka-Woszuk, J. Nutrigenomics in livestock-recent advances. J. Appl. Genet. 61(1), 93–103. https://doi.org/10.1007/s13353-019-00522-x (2020). (PMID: 10.1007/s13353-019-00522-x31673964)
      Sella, G. & Barton, N. H. Thinking about the evolution of complex traits in the era of genome-wide association studies. Annu. Rev. Genom. Hum. Genet. 20, 461–493. https://doi.org/10.1146/annurev-genom-083115-022316 (2019). (PMID: 10.1146/annurev-genom-083115-022316)
      Hudson, N. J., Dalrymple, B. P. & Reverter, A. Beyond differential expression: The quest for causal mutations and effector molecules. BMC Genom. 13, 356. https://doi.org/10.1186/1471-2164-13-356 (2012). (PMID: 10.1186/1471-2164-13-356)
      Gaiteri, C., Ding, Y., French, B., Tseng, G. C. & Sibille, E. Beyond modules and hubs: The potential of gene co-expression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 13(1), 13–24. https://doi.org/10.1111/gbb.12106 (2014). (PMID: 10.1111/gbb.1210624320616)
      van Dam, S., Vōsa, U., van der Graaf, A., Franke, L. & de Magalhães, J. P. Gene co-expression analysis for functional classification and gene–disease predictions. Brief Bioinform. 19(4), 575–592. https://doi.org/10.1093/bib/bbw139 (2018). (PMID: 10.1093/bib/bbw13928077403)
      Huhtanen, P., Cabezas-Garcia, E. H., Utsumi, S. & Zimmerman, S. Comparison of methods to determine methane emissions from dairy cows in farm conditions. J. Dairy Sci. 98(5), 3394–3409. https://doi.org/10.3168/jds.2014-9118 (2015). (PMID: 10.3168/jds.2014-911825771050)
      Hristov, A. N. et al. The use of an automated system (GreenFeed) to monitor enteric methane and carbon dioxide emissions from ruminant animals. J. Vis. Exp. 103, e52904. https://doi.org/10.3791/52904 (2015). (PMID: 10.3791/52904)
      Rosen, B. D. et al. De novo assembly of the cattle reference genome with single-molecule sequencing. Gigascience 9(3), 1–9. https://doi.org/10.1093/gigascience/giaa021 (2020). (PMID: 10.1093/gigascience/giaa021)
      Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35(4), 316–319. https://doi.org/10.1038/nbt.3820 (2017). (PMID: 10.1038/nbt.382028398311)
      Ewels, P. A. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat. Biotechnol. 38(3), 276–278. https://doi.org/10.1038/s41587-020-0439-x (2020). (PMID: 10.1038/s41587-020-0439-x32055031)
      Krueger, F. Trim Galore: A wrapper around cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. https://github.com/FelixKrueger/TrimGalore (2019).
      Dobin, A. et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 29(1), 15–21. https://doi.org/10.1093/bioinformatics/bts635 (2013). (PMID: 10.1093/bioinformatics/bts63523104886)
      Li, B. & Dewey, C. N. RSEM: Accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinform. 12, 323. https://doi.org/10.1186/1471-2105-12-323 (2011). (PMID: 10.1186/1471-2105-12-323)
      Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. https://doi.org/10.1093/bioinformatics/btp616 (2010). (PMID: 10.1093/bioinformatics/btp61619910308)
      R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021). https://www.R-project.org/ .
      Bolormaa, S. et al. A multi-trait, meta-analysis for detecting pleiotropic polymorphisms for stature, fatness and reproduction in beef cattle. PLoS Genet. 10, e1004198. https://doi.org/10.1371/journal.pgen.1004198 (2014). (PMID: 10.1371/journal.pgen.1004198246756183967938)
      Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Ser. B Methodol. 64, 479–498. https://doi.org/10.1111/1467-9868.00346 (2002). (PMID: 10.1111/1467-9868.00346)
      Reverter, A., Hudson, N. J., Nagaraj, S. H., Pérez-Enciso, M. & Dalrymple, B. P. Regulatory impact factors: Unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics 26, 896–904. https://doi.org/10.1093/bioinformatics/btq051 (2010). (PMID: 10.1093/bioinformatics/btq05120144946)
      Wen-Kang, S. et al. AnimalTFDB 4.0: A comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic Acids Res. 51(D1), D39–D45. https://doi.org/10.1093/nar/gkac907 (2023). (PMID: 10.1093/nar/gkac907)
      Mi, H. et al. PANTHER version 11: Expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45(D1), D183–D189. https://doi.org/10.1093/nar/gkw1138 (2017). (PMID: 10.1093/nar/gkw113827899595)
      Caraux, G. & Pinloche, S. PermutMatrix: A graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21(7), 1280–1281. https://doi.org/10.1093/bioinformatics/bti141 (2005). (PMID: 10.1093/bioinformatics/bti14115546938)
      Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12, 35. https://doi.org/10.1186/1471-2105-12-35 (2011). (PMID: 10.1186/1471-2105-12-35)
      Reverter, A. & Chan, E. Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics 24(21), 2491–2497. https://doi.org/10.1093/bioinformatics/btn482 (2008). (PMID: 10.1093/bioinformatics/btn48218784117)
      Shannon, P. et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303 (2003). (PMID: 10.1101/gr.123930314597658403769)
      Reverter, A. et al. Association analysis of loci implied in “buffering” epistasis. J. Anim. Sci. 98(3), skaa045. https://doi.org/10.1093/jas/skaa045 (2020). (PMID: 10.1093/jas/skaa045320479227067535)
      Purfield, D. C., Evans, R. D. & Berry, D. P. Reaffirmation of known major genes and the identification of novel candidate genes associated with carcass-related metrics based on whole genome sequence within a large multi-breed cattle population. BMC Genom. 20(1), 720. https://doi.org/10.1186/s12864-019-6071-9 (2019). (PMID: 10.1186/s12864-019-6071-9)
      Kenny, D. A., Sleator, R. D., Murphy, C. P., Evans, R. D. & Berry, D. P. Detection of genomic imprinting for carcass traits in cattle using imputed high-density genotype data. Front. Genet. 13, 951087. https://doi.org/10.3389/fgene.2022.951087 (2022). (PMID: 10.3389/fgene.2022.951087359102339334527)
      Cassar-Malek, I. et al. Transcriptome profiling reveals stress-responsive gene networks in cattle muscles. PeerJ 10, e13150. https://doi.org/10.7717/peerj.13150 (2022). (PMID: 10.7717/peerj.13150354112558994496)
      Matsumoto, H. et al. The non-synonymous mutation in bovine SPP1 gene influences carcass weight. Heliyon 5(12), e03006. https://doi.org/10.1016/j.heliyon.2019.e03006 (2019). (PMID: 10.1016/j.heliyon.2019.e03006318797116920195)
      Ibeagha-Awemu, E. M. et al. Regionally distinct immune and metabolic transcriptional responses in the bovine small intestine and draining lymph nodes during a subclinical Mycobacterium avium subsp. paratuberculosis infection. Front. Immunol. 12, 760931. https://doi.org/10.3389/fimmu.2021.760931 (2021). (PMID: 10.3389/fimmu.2021.760931349758528714790)
      Oxelius, V.-A. & Pandey, J. P. Human immunoglobulin constant heavy G chain (IGHG)(Fcγ)(GM) genes, defining innate variants of IgG molecules and B cells, have impact on disease and therapy. Clin. Immunol. 149(3), 475–486. https://doi.org/10.1016/j.clim.2013.10.003 (2013). (PMID: 10.1016/j.clim.2013.10.00324239836)
      Li, J. et al. Applying multi-omics data to study the genetic background of bovine respiratory disease infection in feedlot crossbred cattle. Front. Genet. 13, 1046192. https://doi.org/10.3389/fgene.2022.1046192 (2022). (PMID: 10.3389/fgene.2022.1046192365793349790935)
      Takeshima, S., Sarai, A., Saitou, N. & Aida, Y. MHC class II DR classification based on antigen-binding groove natural selection. Biochem. Biophys. Res. Commun. 385, 137–142. https://doi.org/10.1016/j.bbrc.2009.04.142 (2009). (PMID: 10.1016/j.bbrc.2009.04.14219422791)
      Zhang, Q., Koser, S. L. & Donkin, S. S. Identification of promoter response elements that mediate propionate induction of bovine cytosolic phosphoenolpyruvate carboxykinase (PCK1) gene transcription. J. Dairy Sci. 104(6), 7252–7261. https://doi.org/10.3168/jds.2020-18993 (2021). (PMID: 10.3168/jds.2020-1899333741163)
      Abo-Ismail, M. K. et al. Identification of single nucleotide polymorphisms in genes involved in digestive and metabolic processes associated with feed efficiency and performance traits in beef cattle. J. Anim. Sci. 91(6), 2512–2529. https://doi.org/10.2527/jas.2012-5756 (2013). (PMID: 10.2527/jas.2012-575623508024)
      Hussain, A. et al. TEC family kinases in health and disease–loss-of-function of BTK and ITK and the gain-of-function fusions ITK-SYK and BTK-SYK. FEBS J. 278, 2001–2010. https://doi.org/10.1111/j.1742-4658.2011.08134.x (2011). (PMID: 10.1111/j.1742-4658.2011.08134.x21518255)
      Li, J. et al. The mTOR kinase inhibitor everolimus synergistically enhances the anti-tumor effect of the Bruton’s tyrosine kinase (BTK) inhibitor PLS-123 on Mantle cell lymphoma. Int. J. Cancer 142(1), 202–213. https://doi.org/10.1002/ijc.31044 (2018). (PMID: 10.1002/ijc.3104428905990)
      Mohamed, A. R. et al. Leveraging transcriptome and epigenome landscapes to infer regulatory networks during the onset of sexual maturation. BMC Genom. 23(1), 413. https://doi.org/10.1186/s12864-022-08514-8 (2022). (PMID: 10.1186/s12864-022-08514-8)
      Greenwood, P. L. An overview of beef production from pasture and feedlot globally, as demand for beef and the need for sustainable practices increase. Animal 15(1), 100295. https://doi.org/10.1016/j.animal.2021.100295 (2021). (PMID: 10.1016/j.animal.2021.10029534274250)
      Deota, S. et al. Diurnal transcriptome landscape of a multi-tissue response to time-restricted feeding in mammals. Cell Metab. 35(1), 150–165. https://doi.org/10.1016/j.cmet.2022.12.006 (2023). (PMID: 10.1016/j.cmet.2022.12.0063659929910026518)
      Panigrahi, A. & O’Malley, B. W. Mechanisms of enhancer action: The known and the unknown. Genome Biol. 22(1), 108. https://doi.org/10.1186/s13059-021-02322-1 (2021). (PMID: 10.1186/s13059-021-02322-1338584808051032)
      Diniz, W. J. S. et al. Cerebrum, liver, and muscle regulatory networks uncover maternal nutrition effects in developmental programming of beef cattle during early pregnancy. Sci. Rep. 11(1), 2771. https://doi.org/10.1038/s41598-021-82156-w (2021). (PMID: 10.1038/s41598-021-82156-w335315527854659)
      Yu, B. et al. The dynamic alteration of transcriptional regulation by crucial TFs during tumorigenesis of gastric cancer. Mol. Med. 28(1), 41. https://doi.org/10.1186/s10020-022-00468-7 (2022). (PMID: 10.1186/s10020-022-00468-7354219239008954)
      Waku, T. et al. NRF3 upregulates gene expression in SREBP2-dependent mevalonate pathway with cholesterol uptake and lipogenesis inhibition. iScience 24(10), 103180. https://doi.org/10.1016/j.isci.2021.103180 (2021). (PMID: 10.1016/j.isci.2021.103180346679458506969)
      Karisa, B., Moore, S. & Plastow, G. Analysis of biological networks and biological pathways associated with residual feed intake in beef cattle. Anim. Sci. J. 85, 374–387. https://doi.org/10.1111/asj.12159 (2014). (PMID: 10.1111/asj.1215924373146)
      Alexandre, P. A. et al. Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC Genom. 16, 1073. https://doi.org/10.1186/s12864-015-2292-8 (2015). (PMID: 10.1186/s12864-015-2292-8)
      Bourgon, S. L., de Amorim, M. D., Miller, S. P. & Montanholi, Y. R. Associations of blood parameters with age, feed efficiency and sampling routine in young beef bulls. Livest. Sci. 195, 27–37. https://doi.org/10.1016/j.livsci.2016.11.003 (2017). (PMID: 10.1016/j.livsci.2016.11.003)
      Dong, K. et al. Mesenchyme homeobox 1 mediates transforming growth factor-β (TGF-β)-induced smooth muscle cell differentiation from mouse mesenchymal progenitors. J. Biol. Chem. 293(22), 8712–8719. https://doi.org/10.1074/jbc.RA118.002350 (2018). (PMID: 10.1074/jbc.RA118.002350296788825986210)
      Mankoo, B. S. et al. The concerted action of Meox homeobox genes is required upstream of genetic pathways essential for the formation, patterning and differentiation of somites. Development 130, 4655–4664. https://doi.org/10.1242/dev.00687 (2003). (PMID: 10.1242/dev.0068712925591)
      Espina, A. G. et al. Induction of Dlk1 by PTTG1 inhibits adipocyte differentiation and correlates with malignant transformation. Mol. Biol. Cell 20(14), 3353–3362. https://doi.org/10.1091/mbc.e08-09-0965 (2009). (PMID: 10.1091/mbc.e08-09-0965194779292710844)
      Loats, A. E. et al. Cholesterol is required for transcriptional repression by BASP1. Proc. Natl. Acad. Sci. USA 118(29), e2101671118. https://doi.org/10.1073/pnas.2101671118 (2021). (PMID: 10.1073/pnas.2101671118342669558307447)
      Reynolds, L. P., Ward, A. K. & Caton, J. S. Epigenetics and developmental programming in ruminants: Long-term impacts on growth and development. In Biology of Domestic Animals (eds. Reynolds, L. P., et al.) 370 (CRC Press, 2017).
    • Grant Information:
      2021/14321-0 Fundação de Amparo à Pesquisa do Estado de São Paulo; 2018/11953-2 Fundação de Amparo à Pesquisa do Estado de São Paulo; 2019/04089-2 Fundação de Amparo à Pesquisa do Estado de São Paulo
    • الموضوع:
      Date Created: 20240613 Date Completed: 20240613 Latest Revision: 20240616
    • الموضوع:
      20240617
    • الرقم المعرف:
      PMC11176196
    • الرقم المعرف:
      10.1038/s41598-024-63619-2
    • الرقم المعرف:
      38871745