DNA methylation, microRNA expression profiles and their relationships with transcriptome in grass-fed and grain-fed Angus cattle rumen tissue


Autoři: Yaokun Li aff001;  José A. Carrillo aff002;  Yi Ding aff002;  Yanghua He aff002;  Chunping Zhao aff003;  Jianan Liu aff002;  Linsen Zan aff003;  Jiuzhou Song aff002
Působiště autorů: College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, P.R. China aff001;  Department of Animal & Avian Sciences, University of Maryland, College Park, Maryland, United States of America aff002;  College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, P.R. China aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0214559

Souhrn

Rumen is an organ for supplying nutrients for the growth and production of bovine, which might function differently under grass-fed and grain-fed regimens considering the association of gene expression, DNA methylation, and microRNA expression. The objective of this study was to explore the potential mechanism influencing rumen function of grass-fed and grain-fed animals. Methylated DNA binding domain sequencing (MBD-Seq) and microRNA-Seq were respectively utilized to detect the DNA methylation and microRNA expression in rumen tissue of grass-fed and grain-fed Angus cattle. Combined analysis revealed that the expression of the differentially expressed genes ADAMTS3 and ENPP3 was correlated with the methylation abundance of the corresponding differentially methylated regions (DMRs) inside these two genes, and these two genes were reported to be respectively involved in biosynthesis and regulation of glycosyltransferase activity; the differentially expressed microRNA bta-mir-122 was predicted to possibly target the differentially expressed genes OCLN and RBM47, potentially affecting the rumen function; the microRNA bta-mir-655 was exclusively detected in grain-fed group; its targets were significantly enriched in insulin and TGF-beta signaling pathways, which might worked together to regulate the function of rumen, resulting in different characteristics between grass-fed and grain-fed cattle. Collectively, our results provided insights into understanding the mechanisms determining rumen function and unraveled the biological basis underlying the economic traits to improve the productivity of animals.

Klíčová slova:

Beef – Cattle – DNA methylation – Gene expression – Gene ontologies – Gene regulation – MicroRNAs – Polymerase chain reaction


Zdroje

1. Wood JD, Richardson RI, Nute GR, Fisher AV, Campo MM, Kasapidou E, et al. Effects of fatty acids on meat quality: a review. Meat Sci 2004,66:21–32. doi: 10.1016/S0309-1740(03)00022-6 22063928

2. Daley CA, Abbott A, Doyle PS, Nader GA, Larson S. A review of fatty acid profiles and antioxidant content in grass-fed and grain-fed beef. Nutr J 2010,9:10. doi: 10.1186/1475-2891-9-10 20219103

3. French P, Stanton C, Lawless F, O’Riordan EG, Monahan FJ, Caffrey PJ, et al. Fatty acid composition, including conjugated linoleic acid, of intramuscular fat from steers offered grazed grass, grass silage, or concentrate-based diets. J Anim Sci 2000,78:2849–2855. doi: 10.2527/2000.78112849x 11063308

4. Wood JD, Enser M. Factors influencing fatty acids in meat and the role of antioxidants in improving meat quality. Br J Nutr 1997,78 Suppl 1:S49–60.

5. Descalzo AM, Insani EM, Biolatto A, Sancho AM, Garcia PT, Pensel NA, et al. Influence of pasture or grain-based diets supplemented with vitamin E on antioxidant/oxidative balance of Argentine beef. Meat Sci 2005,70:35–44. doi: 10.1016/j.meatsci.2004.11.018 22063278

6. Beauchesne-Rondeau E, Gascon A, Bergeron J, Jacques H. Plasma lipids and lipoproteins in hypercholesterolemic men fed a lipid-lowering diet containing lean beef, lean fish, or poultry. Am J Clin Nutr 2003,77:587–593. doi: 10.1093/ajcn/77.3.587 12600847

7. Smith DR, Wood R, Tseng S, Smith SB. Increased beef consumption increases apolipoprotein A-I but not serum cholesterol of mildly hypercholesterolemic men with different levels of habitual beef intake. Exp Biol Med (Maywood) 2002,227:266–275.

8. Hunninghake DB, Maki KC, Kwiterovich PO, Davidson MH, Dicklin MR, Kafonek SD. Incorporation of lean red meat into a National Cholesterol Education Program Step I diet: A long-term, randomized clinical trial in free-living persons with hypercholesterolemia. Journal of the American College of Nutrition 2000,19:351–360. doi: 10.1080/07315724.2000.10718931 10872897

9. Elmore JS, Warren HE, Mottram DS, Scollan ND, Enser M, Richardson RI, et al. A comparison of the aroma volatiles and fatty acid compositions of grilled beef muscle from Aberdeen Angus and Holstein-Friesian steers fed diets based on silage or concentrates. Meat Sci 2004,68:27–33. doi: 10.1016/j.meatsci.2004.01.010 22062004

10. De la Fuente J, Diaz MT, Alvarez I, Oliver MA, Font IFM, Sanudo C, et al. Fatty acid and vitamin E composition of intramuscular fat in cattle reared in different production systems. Meat Sci 2009,82:331–337. doi: 10.1016/j.meatsci.2009.02.002 20416720

11. Bondzio A, Gabler C, Badewien-Rentzsch B, Schulze P, Martens H, Einspanier R. Identification of differentially expressed proteins in ruminal epithelium in response to a concentrate-supplemented diet. Am J Physiol Gastrointest Liver Physiol 2011,301:G260–268. doi: 10.1152/ajpgi.00304.2010 21566014

12. Naeem A, Drackley JK, Lanier JS, Everts RE, Rodriguez-Zas SL, Loor JJ. Ruminal epithelium transcriptome dynamics in response to plane of nutrition and age in young Holstein calves. Funct Integr Genomics 2014,14:261–273. doi: 10.1007/s10142-013-0351-2 24318765

13. Egger G, Liang G, Aparicio A, Jones PA. Epigenetics in human disease and prospects for epigenetic therapy. Nature 2004,429:457–463. doi: 10.1038/nature02625 15164071

14. Su ZX, Xia JF, Zhao ZM. Functional complementation between transcriptional methylation regulation and post-transcriptional microRNA regulation in the human genome. Bmc Genomics 2011,12.

15. Li E, Bestor TH, Jaenisch R. Targeted mutation of the DNA methyltransferase gene results in embryonic lethality. Cell 1992,69:915–926. doi: 10.1016/0092-8674(92)90611-f 1606615

16. Li E, Beard C, Jaenisch R. Role for DNA methylation in genomic imprinting. Nature 1993,366:362–365. doi: 10.1038/366362a0 8247133

17. Boyes J, Bird A. DNA methylation inhibits transcription indirectly via a methyl-CpG binding protein. Cell 1991,64:1123–1134. doi: 10.1016/0092-8674(91)90267-3 2004419

18. Lorincz MC, Dickerson DR, Schmitt M, Groudine M. Intragenic DNA methylation alters chromatin structure and elongation efficiency in mammalian cells. Nat Struct Mol Biol 2004,11:1068–1075. doi: 10.1038/nsmb840 15467727

19. Eden A, Gaudet F, Waghmare A, Jaenisch R. Chromosomal instability and tumors promoted by DNA hypomethylation. Science 2003,300:455. doi: 10.1126/science.1083557 12702868

20. Klose RJ, Bird AP. Genomic DNA methylation: the mark and its mediators. Trends Biochem Sci 2006,31:89–97. doi: 10.1016/j.tibs.2005.12.008 16403636

21. Li M, Wu H, Luo Z, Xia Y, Guan J, Wang T, et al. An atlas of DNA methylomes in porcine adipose and muscle tissues. Nat Commun 2012,3:850. doi: 10.1038/ncomms1854 22617290

22. Li Q, Li N, Hu X, Li J, Du Z, Chen L, et al. Genome-wide mapping of DNA methylation in chicken. PLoS One 2011,6:e19428. doi: 10.1371/journal.pone.0019428 21573164

23. Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SW, Chen H, et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in arabidopsis. Cell 2006,126:1189–1201. doi: 10.1016/j.cell.2006.08.003 16949657

24. Eckhardt F, Lewin J, Cortese R, Rakyan VK, Attwood J, Burger M, et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat Genet 2006,38:1378–1385. doi: 10.1038/ng1909 17072317

25. Diederich M, Hansmann T, Heinzmann J, Barg-Kues B, Herrmann D, Aldag P, et al. DNA methylation and mRNA expression profiles in bovine oocytes derived from prepubertal and adult donors. Reproduction 2012,144:319–330. doi: 10.1530/REP-12-0134 22733804

26. Ponsuksili S, Murani E, Schwerin M, Schellander K, Tesfaye D, Wimmers K. Gene expression and DNA-methylation of bovine pretransfer endometrium depending on its receptivity after in vitro-produced embryo transfer. PLoS One 2012,7:e42402. doi: 10.1371/journal.pone.0042402 22952593

27. Yang BF, Lu YJ, Wang ZG. MicroRNAs and apoptosis: implications in the molecular therapy of human disease. Clinical and Experimental Pharmacology & Physiology 2009,36:951–960.

28. Filipowicz W, Bhattacharyya SN, Sonenberg N. Mechanisms of post-transcriptional regulation by microRNAs: are the answers in sight? Nat Rev Genet 2008,9:102–114. doi: 10.1038/nrg2290 18197166

29. van Kouwenhove M, Kedde M, Agami R. MicroRNA regulation by RNA-binding proteins and its implications for cancer. Nat Rev Cancer 2011,11:644–656. doi: 10.1038/nrc3107 21822212

30. Langmead B. Aligning short sequencing reads with Bowtie. Current Protocols in Bioinformatics 2010,Chapter 11:Unit 11 17.

31. Liu T. Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells. Methods in Molecular Biology 2014,1150:81–95 doi: 10.1007/978-1-4939-0512-6_4 24743991

32. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014,15:550. doi: 10.1186/s13059-014-0550-8 25516281

33. Zhu LJ, Gazin C, Lawson ND, Pages H, Lin SM, Lapointe DS, et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010,11:237. doi: 10.1186/1471-2105-11-237 20459804

34. Li Y, Carrillo JA, Ding Y, He Y, Zhao C, Zan L, et al. Ruminal Transcriptomic Analysis of Grass-Fed and Grain-Fed Angus Beef Cattle. PLoS One 2015,10:e0116437. doi: 10.1371/journal.pone.0116437 26090810

35. Li RW, Li C. Butyrate induces profound changes in gene expression related to multiple signal pathways in bovine kidney epithelial cells. BMC Genomics 2006,7:234. doi: 10.1186/1471-2164-7-234 16972989

36. Li RW, Schroeder SG. Cytoskeleton remodeling and alterations in smooth muscle contractility in the bovine jejunum during nematode infection. Funct Integr Genomics 2012,12:35–44. doi: 10.1007/s10142-011-0259-7 22203460

37. An J, Lai J, Lehman ML, Nelson CC. miRDeep*: an integrated application tool for miRNA identification from RNA sequencing data. Nucleic Acids Res 2013,41:727–737. doi: 10.1093/nar/gks1187 23221645

38. Wang HJ, Wang BZ, Zhang PJ, Deng J, Zhao ZR, Zhang X, et al. Identification of four novel serum protein biomarkers in sepsis patients encoded by target genes of sepsis-related miRNAs. Clinical Science 2014,126:857–867. doi: 10.1042/CS20130301 24303815

39. Sun J, Zhang B, Lan X, Zhang C, Lei C, Chen H. Comparative transcriptome analysis reveals significant differences in MicroRNA expression and their target genes between adipose and muscular tissues in cattle. PLoS One 2014,9:e102142. doi: 10.1371/journal.pone.0102142 25006962

40. De Smet S, Raes K, Demeyer D. Meat fatty acid composition as affected by fatness and genetic factors: a review. Animal Research 2004,53:81–98.

41. Garcia PT, Pensel NA, Sancho AM, Latimori NJ, Kloster AM, Amigone MA, et al. Beef lipids in relation to animal breed and nutrition in Argentina. Meat Science 2008,79:500–508. doi: 10.1016/j.meatsci.2007.10.019 22062910

42. Ball MP, Li JB, Gao Y, Lee JH, LeProust EM, Park IH, et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nature Biotechnology 2009,27:361–368. doi: 10.1038/nbt.1533 19329998

43. Clark IM, Parker AE. Metalloproteinases: their role in arthritis and potential as therapeutic targets. Expert Opinion on Therapeutic Targets 2003,7:19–34. doi: 10.1517/14728222.7.1.19 12556200

44. Fernandes RJ, Hirohata S, Engle JM, Colige A, Cohn DH, Eyre DR, et al. Procollagen II amino propeptide processing by ADAMTS-3. Insights on dermatosparaxis. J Biol Chem 2001,276:31502–31509. doi: 10.1074/jbc.M103466200 11408482

45. Stefan C, Jansen S, Bollen M. NPP-type ectophosphodiesterases: unity in diversity. Trends Biochem Sci 2005,30:542–550. doi: 10.1016/j.tibs.2005.08.005 16125936

46. Stefan C, Jansen S, Bollen M. Modulation of purinergic signaling by NPP-type ectophosphodiesterases. Purinergic Signal 2006,2:361–370. doi: 10.1007/s11302-005-5303-4 18404476

47. Zimmermann H. Nucleotide signaling in nervous system development. Pflugers Arch 2006,452:573–588. doi: 10.1007/s00424-006-0067-4 16639549

48. Korekane H, Park JY, Matsumoto A, Nakajima K, Takamatsu S, Ohtsubo K, et al. Identification of Ectonucleotide Pyrophosphatase/Phosphodiesterase 3 (ENPP3) as a Regulator of N-Acetylglucosaminyltransferase GnT-IX (GnT-Vb). Journal of Biological Chemistry 2013,288:27912–27926. doi: 10.1074/jbc.M113.474304 23960081

49. Lubke T, Marquardt T, Etzioni A, Hartmann E, von Figura K, Korner C. Complementation cloning identifies CDG-IIc, a new type of congenital disorders of glycosylation, as a GDP-fucose transporter deficiency. Nat Genet 2001,28:73–76. doi: 10.1038/88299 11326280

50. Noda K, Miyoshi E, Gu JG, Gao CX, Nakahara S, Kitada T, et al. Relationship between elevated FX expression and increased production of GDP-L-fucose, a common donor substrate for fucosylation in human hepatocellular carcinoma and hepatoma cell lines. Cancer Research 2003,63:6282–6289. 14559815

51. Taniguchi N. A sugar-coated switch for cellular growth and arrest. Nature Chemical Biology 2007,3:307–309. doi: 10.1038/nchembio0607-307 17510646

52. Lee YH, Na HS, Jeong SY, Jeong SH, Park HR, Chung J. Comparison of inflammatory microRNA expression in healthy and periodontitis tissues. Biocell 2011,35: 43–49. 22128589

53. Liu FJ, Jin LJ, Ma XG, Zhang YL, Zhai XW, Chen JJ, et al. Differentially expressed microRNAs and affected signaling pathways in placentae of transgenic cloned cattle. Theriogenology 2014,82:338–346. doi: 10.1016/j.theriogenology.2014.04.010 24853279

54. Hsu SH, Wang B, Kota J, Yu JH, Costinean S, Kutay H, et al. Essential metabolic, anti-inflammatory, and anti-tumorigenic functions of miR-122 in liver. Journal of Clinical Investigation 2012,122:2871–2883. doi: 10.1172/JCI63539 22820288

55. Hou QK, Huang YQ, Zhu SL, Li PW, Chen XL, Hou ZK, et al. MiR-144 Increases Intestinal Permeability in IBS-D Rats by Targeting OCLN and ZO1. Cellular Physiology and Biochemistry 2017,44:2256–2268. doi: 10.1159/000486059 29258088

56. Blanc V, Xie Y, Kennedy S, Riordan JD, Rubin DC, Madison BB, et al. Apobec1 complementation factor (A1CF) and RBM47 interact in tissue-specific regulation of C to U RNA editing in mouse intestine and liver. Rna 2019,25:70–81. doi: 10.1261/rna.068395.118 30309881

57. Kobayashi T, Masaki T, Sugiyama M, Atomi Y, Furukawa Y, Nakamura Y. A gene encoding a family with sequence similarity 84, member A (FAM84A) enhanced migration of human colon cancer cells. International Journal of Oncology 2006,29:341–347. 16820875

58. Rimkus C, Martini M, Friederichs J, Rosenberg R, Doll D, Siewert J, et al. Prognostic significance of downregulated expression of the candidate tumour suppressor gene SASH1 in colon cancer. British Journal of Cancer 2006,95:1419–1423. doi: 10.1038/sj.bjc.6603452 17088907

59. Sun G, Zhao G, Lu Y, Wang Y, Yang C. Association of EMP1 with gastric carcinoma invasion, survival and prognosis. Int J Oncol 2014,45:1091–1098. doi: 10.3892/ijo.2014.2488 24920167

60. Chen YJ, Chang JT, Lee L, Wang HM, Liao CT, Chiu CC, et al. DSG3 is overexpressed in head neck cancer and is a potential molecular target for inhibition of oncogenesis. Oncogene 2007,26:467–476. doi: 10.1038/sj.onc.1209802 16878157


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