Animal Bioscience

  • 제37권7호
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  • Pages.1141-1155
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  • 2024
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  • 2765-0189(pISSN)
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  • 2765-0235(eISSN)
아세아태평양축산학회 (Asian Australasian Association of Animal Production Societies)
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The role of RNA epigenetic modification-related genes in the immune response of cattle to mastitis induced by Staphylococcus aureus

  • Yue Xing (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University) ;
  • Yongjie Tang (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University) ;
  • Quanzhen Chen (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University) ;
  • Siqian Chen (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University) ;
  • Wenlong Li (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University) ;
  • Siyuan Mi (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University) ;
  • Ying Yu (Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture & National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University)
  • 투고 : 2023.08.25
  • 심사 : 2023.11.28
  • 발행 : 2024.07.01
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초록

Objective: RNA epigenetic modifications play an important role in regulating immune response of mammals. Bovine mastitis induced by Staphylococcus aureus (S. aureus) is a threat to the health of dairy cattle. There are numerous RNA modifications, and how these modification-associated enzymes systematically coordinate their immunomodulatory effects during bovine mastitis is not well reported. Therefore, the role of common RNA modification-related genes (RMRGs) in bovine S. aureus mastitis was investigated in this study. Methods: In total, 80 RMRGs were selected for this study. Four public RNA-seq data sets about bovine S. aureus mastitis were collected and one additional RNA-seq data set was generated by this study. Firstly, quantitative trait locus (QTL) database, transcriptome-wide association studies (TWAS) database and differential expression analyses were employed to characterize the potential functions of selected enzyme genes in bovine S. aureus mastitis. Correlation analysis and weighted gene co-expression network analysis (WGCNA) were used to further investigate the relationships of RMRGs from different types at the mRNA expression level. Interference experiments targeting the m6 A demethylase FTO and utilizing public MeRIP-seq dataset from bovine Mac-T cells were used to investigate the potential interaction mechanisms among various RNA modifications. Results: Bovine QTL and TWAS database in cattle revealed associations between RMRGs and immune-related complex traits. S. aureus challenged and control groups were effectively distinguished by principal component analysis based on the expression of selected RMRGs. WGCNA and correlation analysis identified modules grouping different RMRGs, with highly correlated mRNA expression. The m6 A modification gene FTO showed significant effects on the expression of m6 A and other RMRGs (such as NSUN2, CPSF2, and METTLE), indicating complex co-expression relationships among different RNA modifications in the regulation of bovine S. aureus mastitis. Conclusion: RNA epigenetic modification genes play important immunoregulatory roles in bovine S. aureus mastitis, and there are extensive interactions of mRNA expression among different RMRGs. It is necessary to investigate the interactions between RNA modification genes regulating complex traits in the future.

키워드

과제정보

The authors gratefully acknowledge the support of High-performance Computing Platform of China Agricultural University.

참고문헌

  1. Becker K, Schaumburg F, Kearns A, et al. Implications of identifying the recently defined members of the Staphylococcus aureus complex S. argenteus and S. schweitzeri: a position paper of members of the ESCMID Study Group for Staphylococci and Staphylococcal Diseases (ESGS). Clin Microbiol Infect 2019;25:1064-70. https://doi.org/10.1016/j.cmi.2019.02.028 
  2. Chan CTY, Dyavaiah M, DeMott MS, Taghizadeh K, Dedon PC, Begley TJ. A quantitative systems approach reveals dynamic control of tRNA modifications during cellular stress. PLoS Genet 2010;6:e1001247. https://doi.org/10.1371/journal.pgen.1001247 
  3. Mi S, Tang Y, Dari G, et al. Transcriptome sequencing analysis for the identification of stable lncRNAs associated with bovine Staphylococcus aureus mastitis. J Anim Sci Biotechnol 2021;12:120. https://doi.org/10.1186/s40104-021-00639-2 
  4. Chen Y, Yang J, Huang Z, et al. Exosomal lnc-AFTR as a novel translation regulator of FAS ameliorates Staphylococcus aureus-induced mastitis. Biofactors 2022;48:148-63. https://doi.org/10.1002/biof.1806 
  5. Huynh HT, Robitaille G, Turner JD. Establishment of bovine mammary epithelial cells (MAC-T): an in vitro model for bovine lactation. Exp Cell Res 1991;197:191-9. https://doi. org/10.1016/0014-4827(91)90422-q 
  6. Ogunnaike M, Wang H, Zempleni J. Bovine mammary alveolar MAC-T cells afford a tool for studies of bovine milk exosomes in drug delivery. Int J Pharm 2021;610:121263. https://doi.org/10.1016/j.ijpharm.2021.121263 
  7. Gunther J, Koy M, Berthold A, Schuberth HJ, Seyfert HM. Comparison of the pathogen species-specific immune response in udder derived cell types and their models. Vet Res 2016;47:22. https://doi.org/10.1186/s13567-016-0307-3 
  8. Gilbert WV, Bell TA, Schaening C. Messenger RNA modifications: form, distribution, and function. Science 2016;352: 1408-12. https://doi.org/10.1126/science.aad8711 
  9. Roundtree IA, Evans ME, Pan T, He C. Dynamic RNA modifications in gene expression regulation. Cell 2017;169:1187-200. https://doi.org/10.1016/j.cell.2017.05.045 
  10. Zhang M, Song J, Yuan W, Zhang W, Sun Z. Roles of RNA methylation on tumor immunity and clinical implications. Front Immunol 2021;12:641507. https://doi.org/10.3389/fimmu.2021.641507 
  11. Song P, Tayier S, Cai Z, Jia G. RNA methylation in mammalian development and cancer. Cell Biol Toxicol 2021;37:811-31. https://doi.org/10.1007/s10565-021-09627-8 
  12. Chen H, Gu L, Orellana EA, et al. METTL4 is an snRNA m(6)Am methyltransferase that regulates RNA splicing. Cell Res 2020;30:544-7. https://doi.org/10.1038/s41422-019-0270-4 
  13. Elkon R, Ugalde AP, Agami R. Alternative cleavage and polyadenylation: extent, regulation and function. Nat Rev Genet 2013;14:496-506. https://doi.org/10.1038/nrg3482 
  14. He C, Bozler J, Janssen KA, et al. TET2 chemically modifies tRNAs and regulates tRNA fragment levels. Nat Struct Mol Biol 2021;28:62-70. https://doi.org/10.1038/s41594-020-00526-w 
  15. He PC, He C. mRNA acetylation: a new addition to the epitranscriptome. Cell Res 2019;29:91-2. https://doi.org/10.1038/s41422-018-0135-2 
  16. Nishikura K. A-to-I editing of coding and non-coding RNAs by ADARs. Nat Rev Mol Cell Biol 2016;17:83-96. https://doi.org/10.1038/nrm.2015.4 
  17. Nombela P, Miguel-Lopez B, Blanco S. The role of m(6)A, m(5)C and Ψ RNA modifications in cancer: novel therapeutic opportunities. Mol Cancer 2021;20:18. https://doi.org/10.1186/s12943-020-01263-w 
  18. Safra M, Sas-Chen A, Nir R, et al. The m1A landscape on cytosolic and mitochondrial mRNA at single-base resolution. Nature 2017;551:251-5. https://doi.org/10.1038/nature24456 
  19. Selmi T, Hussain S, Dietmann S, et al. Sequence- and structure-specific cytosine-5 mRNA methylation by NSUN6. Nucleic Acids Res 2021;49:1006-22. https://doi.org/10.1093/nar/gkaa1193 
  20. Yang X, Yang Y, Sun BF, et al. 5-methylcytosine promotes mRNA export - NSUN2 as the methyltransferase and ALYREF as an m(5)C reader. Cell Res 2017;27:606-25. https://doi.org/10.1038/cr.2017.55 
  21. Zhang LS, Liu C, Ma H, et al. Transcriptome-wide mapping of internal N(7)-Methylguanosine methylome in mammalian mRNA. Mol Cell 2019;74:1304-16.e8. https://doi.org/10.1016/j.molcel.2019.03.036 
  22. Li W, Zhang X, Lu X, et al. 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res 2017;27:1243-57. https://doi.org/10.1038/cr.2017.121 
  23. Patel RK, Jain M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One 2012;7:e30619. https://doi.org/10.1371/journal.pone.0030619 
  24. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 2015;12:357-60. https://doi.org/10.1038/nmeth.3317 
  25. Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012;28:882-3. https://doi.org/10.1093/bioinformatics/bts034 
  26. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001;25:402-8. https://doi.org/10.1006/meth.2001.1262 
  27. Zhernakova A, van Diemen CC, Wijmenga C. Detecting shared pathogenesis from the shared genetics of immunerelated diseases. Nat Rev Genet 2009;10:43-55. https://doi. org/10.1038/nrg2489 
  28. Delatte B, Wang F, Ngoc LV, et al. Transcriptome-wide distribution and function of RNA hydroxymethylcytosine. Science 2016;351:282-5. https://doi.org/10.1126/science.aac5253 
  29. Gatsiou A, Stellos K. RNA modifications in cardiovascular health and disease. Nat Rev Cardiol 2023;20:325-46. https://doi.org/10.1038/s41569-022-00804-8 
  30. Esteve-Puig R, Climent F, Pineyro D, et al. Epigenetic loss of m1A RNA demethylase ALKBH3 in Hodgkin lymphoma targets collagen, conferring poor clinical outcome. Blood 2021;137:994-9. https://doi.org/10.1182/blood.2020005823 
  31. Yang H, Wang Y, Xiang Y, et al. FMRP promotes transcription-coupled homologous recombination via facilitating TET1-mediated m5C RNA modification demethylation. Proc Natl Acad Sci USA 2022;119:e2116251119. https://doi.org/10.1073/pnas.2116251119 
  32. Luo J, Wang F, Sun F, et al. Targeted inhibition of FTO demethylase protects mice against LPS-induced septic shock by suppressing NLRP3 inflammasome. Front Immunol 2021;12:663295. https://doi.org/10.3389/fimmu.2021.663295 
  33. Li T, Lin C, Zhu Y, et al. Transcriptome profiling of m(6)A mRNA modification in bovine mammary epithelial cells treated with Escherichia coli. Int J Mol Sci 2021;22:6254. https://doi.org/10.3390/ijms22126254 
  34. Ke S, Pandya-Jones A, Saito Y, et al. m(6)A mRNA modifications are deposited in nascent pre-mRNA and are not required for splicing but do specify cytoplasmic turnover. Genes Dev 2017;31:990-1006. https://doi.org/10.1101/gad.301036.117 
  35. Bai Q, Shi M, Sun X, et al. Comprehensive analysis of the m6A-related molecular patterns and diagnostic biomarkers in osteoporosis. Front Endocrinol (Lausanne) 2022;13:957742. https://doi.org/10.3389/fendo.2022.957742 
  36. Ke S, Alemu EA, Mertens C, et al. A majority of m6A residues are in the last exons, allowing the potential for 3' UTR regulation. Genes Dev 2015;29:2037-53. https://doi.org/10.1101/gad.269415.115 
  37. Hinske LC, Galante PA, Limbeck E, et al. Alternative polyadenylation allows differential negative feedback of human miRNA miR-579 on its host gene ZFR. PLoS One 2015;10:e0121507. https://doi.org/10.1371/journal.pone.0121507 
  38. Nilubol N, Boufraqech M, Zhang L, Kebebew E. Loss of CPSF2 expression is associated with increased thyroid cancer cellular invasion and cancer stem cell population, and more aggressive disease. J Clin Endocrinol Metab 2014;99:E1173-82. https://doi.org/10.1210/jc.2013-4140 
  39. Li W, Li X, Ma X, Xiao W, Zhang J. Mapping the m1A, m5C, m6A and m7G methylation atlas in zebrafish brain under hypoxic conditions by MeRIP-seq. BMC Genomics 2022;23:105. https://doi.org/10.1186/s12864-022-08350-w 
  40. Chen H, Yao J, Bao R, et al. Cross-talk of four types of RNA modification writers defines tumor microenvironment and pharmacogenomic landscape in colorectal cancer. Mol Cancer 2021;20:29. https://doi.org/10.1186/s12943-021-01322-w 
  41. Li D, Li K, Zhang W, et al. The m6A/m5C/m1A regulated gene signature predicts the prognosis and correlates with the immune status of hepatocellular carcinoma. Front Immunol 2022;13:918140. https://doi.org/10.3389/fimmu.2022.918140 
  42. Li Q, Li X, Tang H, et al. NSUN2-mediated m5C methylation and METTL3/METTL14-mediated m6A methylation cooperatively enhance p21 translation. J Cell Biochem 2017;118:2587-98. https://doi.org/10.1002/jcb.25957