#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Genes associated with body weight gain and feed intake identified by meta-analysis of the mesenteric fat from crossbred beef steers


Autoři: Amanda K. Lindholm-Perry aff001;  Harvey C. Freetly aff001;  William T. Oliver aff001;  Lea A. Rempel aff001;  Brittney N. Keel aff001
Působiště autorů: Agricultural Research Service, United States Department of Agriculture, United States Meat Animal Research Center, Clay Center, Nebraska, United States of America aff001
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0227154

Souhrn

Mesenteric fat is a visceral fat depot that increases with cattle maturity and can be influenced by diet. There may be a relationship between the accumulation of mesenteric fat and feed efficiency in beef cattle. The purpose of this study was to identify genes that may be differentially expressed in steers with high and low BW gain and feed intake. RNA-Seq was used to evaluate the transcript abundance of genes in the mesenteric fat from a total of 78 steers collected over 5 different cohorts. A meta-analysis was used to identify genes involved with gain, feed intake or the interaction of both phenotypes. The interaction analysis identified 11 genes as differentially expressed. For the main effect of gain, a total of 87 differentially expressed genes (DEG) were identified (PADJ<0.05), and 24 were identified in the analysis for feed intake. Genes identified for gain were involved in functions and pathways including lipid metabolism, stress response/protein folding, cell proliferation/growth, axon guidance and inflammation. The genes for feed intake did not cluster into pathways, but some of the DEG for intake had functions related to inflammation, immunity, and/or signal transduction (JCHAIN, RIPK1, LY86, SPP1, LYZ, CD5, CD53, SRPX, and NF2). At PADJ<0.1, only 4 genes (OLFML3, LOC100300716, MRPL15, and PUS10) were identified as differentially expressed in two or more cohorts, highlighting the importance of evaluating the transcriptome of more than one group of animals and incorporating a meta-analysis. This meta-analysis has produced many mesenteric fat DEG that may be contributing to gain and feed intake in cattle.

Klíčová slova:

Gene expression – Metaanalysis – Inflammation – Fats – Cattle – Livestock – MAPK signaling cascades – Heat shock response


Zdroje

1. Hill RA, Ebooks Corporation. Feed efficiency in the beef industry. Ames, Iowa: Wiley-Blackwell; 2012.

2. Martins AP, Lopes PA, Costa ASH, Martins SV, Santos NC, Prates JAM, et al. Differential mesenteric fat deposition in bovines fed on silage or concentrate is independent of glycerol membrane permeability. Animal 2011; 5:12.

3. Zhou Z, Lamont SJ, Lee WR, Abasht B. RNA-Seq analysis of abdominal fat reveals differences between modern commercial broiler chickens with high and low feed efficiencies. PLoS ONE 2015; 10:e0135810. doi: 10.1371/journal.pone.0135810 26295149

4. Lindholm-Perry AK, Cunningham HC, Kuehn LA, Vallet JL, Keele JW, Cammack KM, et al. Relationships between the genes expressed in the mesenteric adipose tissue of beef cattle and feed intake and gain. Anim Genet. 2017; 48:386–94. doi: 10.1111/age.12565 28568315

5. Keel BN, Zarek CM, Keele JW, Kuehn LA, Snelling WM, Oliver WT, et al. RNA-Seq meta-analysis identifies genes in skeletal muscle associated with gain and intake across a multi-season study of crossbred beef steers. BMC Genom. 2018; 19:430.

6. Li MD, Burns TC, Morgan AA, Khatri P. Integrated multi-cohort transcriptional meta-analysis of neurodegenerative diseases. Acta Neuropathol Commun. 2014; 2:93. doi: 10.1186/s40478-014-0093-y 25187168

7. Andres-Terre M, McGuire HM, Pouliot Y, Bongen E, Sweeney TE, Tato CM, et al. Integrated, multi-cohort analysis identifies conserved transcriptional signatures across multiple respiratory viruses. Immunity. 2015; 43:1199–1211. doi: 10.1016/j.immuni.2015.11.003 26682989

8. Sweeney TE, Braviak L, Tato CM, Khatri P. Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis. Lancet Respir Med. 2016; 4:213–224. doi: 10.1016/S2213-2600(16)00048-5 26907218

9. FASS: Guide for Care and Use of Agricultural Animals in Agricultural Research and Teaching. Fed. Animal Science Society, Savoy, IL; 1999.

10. Schiermiester LN, Thallman RM, Kuehn LA, Kachman SD, and Spangler ML. Estimation of breed-specific heterosis effects for birth, weaning, and yearling weight in cattle. J Anim Sci. 2015; 93:46–52. doi: 10.2527/jas.2014-8493 25568356

11. National Research Council. 2000. Nutrient Requirements of Beef Cattle: Seventh Revised Edition: Update 2000. Washington, DC: The National Academies Press. https://doi.org/10.17226/9791.

12. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30:2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

13. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015; 12:357–360. doi: 10.1038/nmeth.3317 25751142

14. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotech. 2015; 33:290–295.

15. Love M, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15:1.

16. Fisher RA. Statistical Methods for Research Workers. 1932. Edinburgh: Oliver and Boyd.

17. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B. 1995; 57:289–300.

18. Miller RG. The jackknife:a review. Biometrika. 1974; 61:1–15.

19. Mi H, Poudel S, Muruganujan A, Casagrande JT, Thomas PD. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 2016; 44(D1):D336–42. doi: 10.1093/nar/gkv1194 26578592

20. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009; 4(1):44–57.

21. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37(1):1–13. doi: 10.1093/nar/gkn923 19033363

22. Crawford RR, Prescott ET, Sylvester CF, Higdon AN, Shan J, Kilberg MS, et al. Human CHAC1 protein degrades glutathione and mRNA induction is regulated by the transcription factors ATF4 and ATF3 and a bipartite ATF/CRE element. J Biol Chem. 2015; 290:15878–15891. doi: 10.1074/jbc.M114.635144 25931127

23. Kern RJ, Lindholm-Perry AK, Freetly HC, Snelling WM, Kern JW, Keele JW, et al. Transcriptome differences in the rumen of beef steers with variation in feed intake and gain. Gene. 2016; 586:12–26. doi: 10.1016/j.gene.2016.03.034 27033587

24. Lindholm-Perry AK, Butler AR, Kern RJ, Hill R, Kuehn LA, Wells JE, et al. Differential gene expression in the duodenum, jejunum and ileum among crossbred steers with divergent gain and feed intake phenotypes. Anim Genet. 2016; 47:408–27. doi: 10.1111/age.12440 27226174

25. Chen Y, Gondro C, Quinn K, Herd RM, Parnell PF,& Vanselow B. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Anim. Genet. 2011; 42(5): 475–490. doi: 10.1111/j.1365-2052.2011.02182.x 21906099

26. Bottje WG, Kong B-W, Song JJ, Lee JY, Hargis BM, Lassiter K, et al. Gene expression in breast muscle associated in a single male broiler line using 44K microarray. II. Differentially expressed focus genes. Poultry Sci. 2012; 91:2576–87.

27. Grubbs JK, Fritchen AN, Huff-Lonergan E, Gabler NK, Lonergan SM. Selection for residual feed intake alters the mitochondrial protein profile in pigs. J Proteomics. 2013; 80:334–345. doi: 10.1016/j.jprot.2013.01.017 23403255

28. Tizioto PC, Coutinho LL, Oliveira SN, Cesar ASM, Diniz WJS, Lima AO, et al. Gene expression differences in Longissimus muscle of Nelore steers genetically divergent for residual feed intake. Sci. Rep. 2016; 6:39493. doi: 10.1038/srep39493 28004777

29. Gondret F, Vincent A, Houee-Bigot M, Siegel A, Lagarringue S, Causeur D. et al. A transcriptome multi-tissue analysis identifies biological pathways and genes associated with variations in feed efficiency of growing pigs. BMC Genom. 2017; 18:244.

30. Higgins MG, Kenny DA, Fitzsimons C, Blackshields G, Coyle S, McKenna C, et al. The effect of breed and diet type on the global transcriptome of hepatic tissue in beef cattle divergent for feed efficiency. BMC Genom. 2019; 20: 525. https://doi.org/10.1186/s12864-019-5906-8.

31. Locker F, Vidali S, Holub BS, Stockinger J, Brunner SM, Ebner S, et al. Lack of galanin receptor 3 alleviates psoriasis by altering vascularization, immune cell infiltration, and cytokine expression. J Invest Dermatol. 2018; 138:199–207. doi: 10.1016/j.jid.2017.08.015 28844939

32. Shattuck-Heidorn H, Reiches MW, Prentice AM, Moore SE, Ellison PT. Energetics and the immune system. Trade-offs associated with non-acute levels of CRP in adolescent Gambian girls. Evol Med Public Health. 2016; 2017:27–38.

33. Johnson RW. Immune and endocrine regulation of food intake in sick animals. Domest Anim Endocrinol. 1998; 15:309–319. doi: 10.1016/s0739-7240(98)00031-9 9785035

34. Zuo D., Subjeck J, Wang X-Y. Unfolding the role of large heat shock proteins: New insights and therapeutic implications. Front Immunol. 2016; 7:75. doi: 10.3389/fimmu.2016.00075 26973652

35. Arai C, Nomura Y, Matsuzawa M, Hanada N, Nakamura Y. Extracellular HSP72 induces proinflammatory cytokines in human periodontal ligament fibroblast cells through the TLR4/NFkB pathway in vitro. ArchOral Biol. 2017; 83:181–186.

36. Lindholm-Perry AK, Kern RJ, Keel BN, Snelling WM, Kuehn LA, Freetly HC. Profile of the spleen transcriptome in beef steers with variation in gain and feed intake. Front. Genet. 2016; 7:127. doi: 10.3389/fgene.2016.00127 27504120

37. Ramayo-Caldas Y, Ballester M, Sanchez JP, Gonzalez-Rodriguez O, Revilla M, Reyer H, et al. Integrative approach using liver and duodenum RNA-Seq data identifies candidate genes and pathways associated with feed efficiency in pigs. Sci Rep. 2018; 8:558. doi: 10.1038/s41598-017-19072-5 29323241

38. Prins JM, Chao C-K, Jacobson SM, Thompson CM, George KM. Oxidative stress resulting from exposure of a human salivary gland cells to paraoxon: An in vitro model for organophosphate oral exposure. Toxicol in Vitro 2014; 28:715–721. doi: 10.1016/j.tiv.2014.01.009 24486155

39. Grant RW, Vester Boler BM, Ridge TK, Graves TK, Swanson KS. Adipose tissue transcriptome changes during obesity development in female dogs. Physiol Genom. 2011; 43:295–307.

40. Anderson J. Adipose tissue as an active organ: blood flow regulation and tissue-specific glucocorticoid metabolism. 2011; Umeå, Sweden. http://umu.diva-portal.org/

41. Chen W, Wilson JL, Khaksari M, Cowley MA, Enriori PJ. Abdominal fat analyzed by DEXA scan reflects visceral body fat and improves the phenotype description and the assessment of metabolic risk in mice. Am J Physiol Endocrinol Metab. 2012; 303:E635–E643. doi: 10.1152/ajpendo.00078.2012 22761161

42. Kershaw EE, Flier JS. Adipose tissue as an endocrine organ. J Clin Endocrinol Metab. 2004; 89:2548–2556. doi: 10.1210/jc.2004-0395 15181022

43. Fantuzzi G, Mazzone T, Pond CM. Interactions of adipose and lymphoid tissues. In: Fantuzzi G, Mazzone T, editors. Adipose Tissue and Adipokines in Health and Disease, 2007. pp. 133–150.

44. Symonds ME & Pond CM.The evolution of mammalian adipose tissues. In: Adipose Tissue BiologySpringer, New York, 2017. pp. 1–59.

45. West-Eberhard MJ. Nutrition, the visceral immune system, and the evolutionary origins of pathogenic obesity. PNAS, 2019; 116(3):723–731. doi: 10.1073/pnas.1809046116 30598443

46. Da Silva Junior IA, de Sousa Andrade LN, Jancar S, Chammas R. Platelet activating factor receptor antagonists improve the efficacy of experimental chemo- and radiotherapy. Clinics (Sao Paulo). 2018; 73:e792s.

47. Karisa BK, Thomson J, Wang Z, Stothard P, Moore SS, Plastow GS. Candidate genes and single nucleotide polymorphisms associated with variation in residual feed intake in beef cattle. J Anim Sci. 2013; 91:3502–3513. doi: 10.2527/jas.2012-6170 23736061

48. Richardson EC. Herd RM. Biological basis for variation in residual feed intake in beef cattle. 2. Synthesis of results following divergent selection. Aust J Exp Agric. 2004; 44:431–440.

49. Naik M. Identification and characterization of genetic markers and metabolic pathways controlling net feed efficiency in beef cattle. PhD thesis, University of Adelaide University, Australia. 2008. Available from: http://hdl.handle.net/2440/59201

50. Hasegawa S, Yamasaki M, Inage T, Takahashi N, Fukui T. 2008. Transcriptional regulation of ketone body-utilizing enzyme, acetoacetyl-CoA synthetase, by C/EBPα during adipocyte differentiation. Biochim Biophys Acta. 2008; 1779:414–419. doi: 10.1016/j.bbagrm.2008.05.001 18514076

51. Gabory A., Ripoche M-A, Le Digarcher A, Watrin F, Ziyyat A, Forne T, et al. H19 acts as a trans regulator of the imprinted gene network controlling growth in mice. Development 2009; 136:3413–3421. doi: 10.1242/dev.036061 19762426

52. Zhou Z, Toh SY, Chen Z, Guo K, Ng CP, Ponniah S, et al. Cidea-deficient mice have lean phenotype and are resistant to obesity. Nat Genet. 2003; 35:49–56.

53. Arai S, Miyake K, Voit R, Nemoto S, Wakeland EK, Grummt I, et al. Death-effector domain-containing protein DEDD is an inhibitor of mitotic Cdk1/cyclin B1. PNAS 2007; 104:2289–2294. doi: 10.1073/pnas.0611167104 17283331

54. Mukiibi R, Vinsky M, Keogh KA, Fitzsimmons C, Stothard P, Waters SM, et al. Transcriptome analyses reveal reduced hepatic lipid synthesis and accumulation in more feed efficient beef cattle. Sci Rep. 2018; 8:7303. doi: 10.1038/s41598-018-25605-3 29740082

55. Graugnard DE, Piantoni P, Bionaz M, Berger LL, Faulkner DB, Loor JJ. Adipogenic and energy metabolism gene networks in longissimus luborum during rapid post-weaning growth in Angus and Angus x Simmental cattle fed high-starch or low-starch diets. BMC Genom. 2009; 10:142.

56. Bottje W, Kong B-W, Reverter A, Waardenberg AJ, Lassiter K, Hudson NJ. Progesterone signaling in broiler skeletal muscle is associated with divergent feed efficiency. BMC Systems Biol. 2017; 11:29.

57. Li J, Takaishi K, Cook W, McCorkle SK, Unger RH. Insig-1 “brakes” lipogenesis in adipocytes and inhibits differentiation of preadipocytes. PNAS 2013; 100:9476–81.


Článok vyšiel v časopise

PLOS One


2020 Číslo 1
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#