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Combined transcriptome and proteome analyses reveal differences in the longissimus dorsi muscle between Kazakh cattle and Xinjiang brown cattle

  • Yan, XiangMin (Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry) ;
  • Wang, Jia (College of Geographic Science, Shanxi Normal University) ;
  • Li, Hongbo (Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry) ;
  • Gao, Liang (Yili Vocational and Technical College) ;
  • Geng, Juan (Xinjiang Animal Husbandry General Station) ;
  • Ma, Zhen (Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry) ;
  • Liu, Jianming (Yili Animal Husbandry General Station) ;
  • Zhang, Jinshan (Institute of Animal Husbandry, Xinjiang Academy of Animal Husbandry) ;
  • Xie, Penggui (Yili Vocational and Technical College) ;
  • Chen, Lei (College of Animal Science and Technology, Shihezi University)
  • Received : 2020.10.31
  • Accepted : 2021.01.29
  • Published : 2021.09.01

Abstract

Objective: With the rapid development of proteomics sequencing and RNA sequencing technology, multi-omics analysis has become a current research hotspot. Our previous study indicated that Xinjiang brown cattle have better meat quality than Kazakh cattle. In this study, Xinjiang brown cattle and Kazakh cattle were used as the research objects. Methods: Proteome sequencing and RNA sequencing technology were used to analyze the proteome and transcriptome of the longissimus dorsi muscle of the two breeds of adult steers (n = 3). Results: In this project, 22,677 transcripts and 1,874 proteins were identified through quantitative analysis of the transcriptome and proteome. By comparing the identified transcriptome and proteome, we found that 1,737 genes were identified at both the transcriptome and proteome levels. The results of the study revealed 12 differentially expressed genes and proteins: troponin I1, crystallin alpha B, cysteine, and glycine rich protein 3, phosphotriesterase-related, myosin-binding protein H, glutathione s-transferase mu 3, myosin light chain 3, nidogen 2, dihydropyrimidinase like 2, glutamate-oxaloacetic transaminase 1, receptor accessory protein 5, and aspartoacylase. We performed functional enrichment of these differentially expressed genes and proteins. The Kyoto encyclopedia of genes and genomes results showed that these differentially expressed genes and proteins are enriched in the fatty acid degradation and histidine metabolism signaling pathways. We performed parallel reaction monitoring (PRM) verification of the differentially expressed proteins, and the PRM results were consistent with the sequencing results. Conclusion: Our study provided and identified the differentially expressed genes and proteins. In addition, identifying functional genes and proteins with important breeding value will provide genetic resources and technical support for the breeding and industrialization of new genetically modified beef cattle breeds.

Keywords

Acknowledgement

This study was supported by the Xinjiang autonomous region basic research fee in 2021, the autonomous region modern livestock and poultry seed industry promotion special fund in 2021 (2021XJHN), the Xinjiang autonomous region basic research fee in 2019 (KY2019117), the modern agricultural industrial technology system (CARS-37) and the study on school-level project (high-level talent research start-up project, 20200162).

References

  1. Zhao C, Raza SHA, Khan R, et al. Genetic variants in MYF5 affected growth traits and beef quality traits in Chinese Qinchuan cattle. Genomics 2020;112:2804-12. https://doi.org/10.1016/j.ygeno.2020.03.018
  2. Li N, Yu QL, Yan XM, Li HB, Zhang Y. Sequencing and characterization of miRNAs and mRNAs from the longissimus dorsi of Xinjiang brown cattle and Kazakh cattle. Gene 2020; 741:144537. https://doi.org/10.1016/j.gene.2020.144537
  3. Yan XM, Zhang Z, Meng Y, et al. Genome-wide identification and analysis of circular RNAs differentially expressed in the longissimus dorsi between Kazakh cattle and Xinjiang brown cattle. PeerJ 2020;8:e8646. https://doi.org/10.7717/peerj.8646
  4. Li XZ, Park BK, Shin JS, et al. Effects of dietary linseed oil and propionate precursors on ruminal microbial community, composition, and diversity in Yanbian yellow cattle. PLoS One 2015;10:e0126473. https://doi.org/10.1371/journal.pone.0126473
  5. Zhou J, Liu L, Chen CJ, et al. Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle. BMC Genomics 2019;20:827. https://doi.org/10.1186/s12864-019-6224-x
  6. Yan XM, Zhang Z, Liu JB, et al. Genome-wide identification and analysis of long noncoding RNAs in longissimus muscle tissue from Kazakh cattle and Xinjiang brown cattle. Asian-Australas J Anim Sci 2020 Sept 20 [Epub]. https://doi.org/10.5713/ajas.20.0317
  7. Li N, Zhang Y, Li HP, et al. Differential expression of mRNA-miRNAs related to intramuscular fat content in the longissimus dorsi in Xinjiang brown cattle. PLoS One 2018;13:e0206757. https://doi.org/10.1371/journal.pone.0206757
  8. Ju X, Huang X, Zhang M, et al. Effects of eight InDel variants in FHIT on milk traits in Xinjiang brown cattle. Anim Biotechnol 2020 May 13 [Epub]. https://doi.org/10.1080/10495398.2020.1724124
  9. Qian W, Li Z, Ao W, Zhao G, Li G, Wu JP. Bacterial community composition and fermentation in the rumen of Xinjiang brown cattle (Bos taurus), Tarim red deer (Cervus elaphus yarkandensis), and Karakul sheep (Ovis aries). Can J Microbiol 2017;63:375-83. https://doi.org/10.1139/cjm-2016-0596
  10. Bai J, Lin J, Li W, Liu M. Association of toll-like receptor 2 polymorphisms with somatic cell score in Xinjiang Brown cattle. Anim Sci J 2012;83:23-30. https://doi.org/10.1111/j.1740-0929.2011.00909.x
  11. Kolder ICRM, van der Plas-Duivesteijn SJ, Tan G, et al. A full-body transcriptome and proteome resource for the European common carp. BMC Genomics 2016;17:701. https://doi.org/10.1186/s12864-016-3038-y
  12. Bathke J, Konzer A, Remes B, McIntosh M, Klug G. Comparative analyses of the variation of the transcriptome and proteome of Rhodobacter sphaeroides throughout growth. BMC Genomics 2019;20:358. https://doi.org/10.1186/s12864-019-5749-3
  13. Schenk S, Bannister SC, Sedlazeck FJ, et al. Combined transcriptome and proteome profiling reveals specific molecular brain signatures for sex, maturation and circalunar clock phase. eLife 2019;8:e41556. https://doi.org/10.7554/eLife.41556
  14. Chen X, Tao Y, Ali A, et al. Transcriptome and proteome profiling of different colored rice reveals physiological dynamics involved in the flavonoid pathway. Int J Mol Sci 2019;20:2463. https://doi.org/10.3390/ijms20102463
  15. Ceciliani F, Lecchi C, Urh C, Sauerwein H. Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows. J Proteomics 2018;178:92-106. https://doi.org/10.1016/j.jprot.2017.10.010
  16. Hou S, Hao Q, Zhu Z, et al. Unraveling proteome changes and potential regulatory proteins of bovine follicular Granulosa cells by mass spectrometry and multi-omics analysis. Proteome Sci 2019;17:4. https://doi.org/10.1186/s12953-019-0152-1
  17. Pawlowski K, Pires JAA, Faulconnier Y, et al. Mammary gland transcriptome and proteome modifications by nutrient restriction in early lactation Holstein cows challenged with intra-mammary lipopolysaccharide. Int J Mol Sci 2019;20:1156. https://doi.org/10.3390/ijms20051156
  18. Ladeira MM, Schoonmaker JP, Gionbelli MP, et al. Nutrigenomics and beef quality: a review about lipogenesis. Int J Mol Sci 2016;17:918. https://doi.org/10.3390/ijms17060918
  19. Rosa AF, Moncau CT, Poleti MD, et al. Proteome changes of beef in Nellore cattle with different genotypes for tenderness. Meat Sci 2018;138:1-9. https://doi.org/10.1016/j.meatsci.2017.12.006
  20. Oh H, Lee HJ, Lee J, Jo C, Yoon Y. Identification of microorganisms associated with the quality improvement of dry-aged beef through micro-biome analysis and DNA sequencing, and evaluation of their effects on beef quality. J Food Sci 2019;84:2944-54. https://doi.org/10.1111/1750-3841.14813
  21. Raza SHA, Khan R, Abdelnour SA, et al. Advances of molecular markers and their application for body variables and carcass traits in Qinchuan cattle. Genes 2019;10:717. https://doi.org/10.3390/genes10090717
  22. Taye M, Kim J, Yoon SH, et al. Whole genome scan reveals the genetic signature of African Ankole cattle breed and potential for higher quality beef. BMC Genet 2017;18:11. https://doi.org/10.1186/s12863-016-0467-1
  23. Cassar-Malek I, Picard B. Expression marker-based strategy to improve beef quality. Sci World J 2016;2016:Article ID 2185323. https://doi.org/10.1155/2016/2185323
  24. Favero R, Menezes GRO, Torres RAA, et al. Crossbreeding applied to systems of beef cattle production to improve performance traits and carcass quality. Animal 2019;13:2679-86. https://doi.org/10.1017/s1751731119000855
  25. Chang T, Xia J, Xu L, et al. A genome-wide association study suggests several novel candidate genes for carcass traits in Chinese Simmental beef cattle. Anim Genet 2018;49:312-6. https://doi.org/10.1111/age.12667
  26. Mao Y, Hopkins DL, Zhang Y, et al. Beef quality with different intramuscular fat content and proteomic analysis using isobaric tag for relative and absolute quantitation of differentially expressed proteins. Meat Sci 2016;118:96-102. https://doi.org/10.1016/j.meatsci.2016.03.028
  27. Fu W, Chen N, Han S, et al. Tissue expression and variation analysis of three bovine adipokine genes revealed their effect on growth traits in native Chinese cattle. Reprod Domest Anim 2018;53:1227-34. https://doi.org/10.1111/rda.13244
  28. Scollan ND, Price EM, Morgan SA, Huws SA, Shingfield KJ. Can we improve the nutritional quality of meat? Proc Nutr Soc 2017;76:603-18. https://doi.org/10.1017/s0029665117001112
  29. Ji GG, Shu JT, Zhang M, et al. Transcriptional regulatory region and DNA methylation analysis of TNNI1 gene promoters in Gaoyou duck skeletal muscle (Anas platyrhynchos domestica). Br Poult Sci 2019;60:202-8. https://doi.org/10.1080/00071668.2019.1602250
  30. He H, Hu ZG, Tserennadmid S, Chen S, Liu XL. Novel muscle-specific genes TCAP, TNNI1, and FHL1 in cattle: SNVs, linkage disequilibrium, combined genotypes, association analysis of growth performance, and carcass quality traits and expression studies. Anim Biotechnol 2018;29:259-68. https://doi.org/10.1080/10495398.2017.1377084
  31. Shu J, Ji G, Zhang M, et al. Molecular cloning, characterization, and temporal expression profile of troponin i type 1 (TNNI1) gene in skeletal muscle during early development of Gaoyou duck (Anas Platyrhynchos Domestica). Anim Biotechnol 2019;30:118-28. https://doi.org/10.1080/10495398.2018.1444620
  32. Picard B, Gagaoua M, Al-Jammas M, De Koning L, Valais A, Bonnet M. Beef tenderness and intramuscular fat proteomic biomarkers: muscle type effect. PeerJ 2018;6:e4891. https://doi.org/10.7717/peerj.4891
  33. Yin B, Tang S, Xu J, et al. CRYAB protects cardiomyocytes against heat stress by preventing caspase-mediated apoptosis and reducing F-actin aggregation. Cell Stress Chaperones 2019;24:59-68. https://doi.org/10.1007/s12192-018-0941-y
  34. Hernandez-Carretero A, Weber N, LaBarge SA, et al. Cysteine-and glycine-rich protein 3 regulates glucose homeostasis in skeletal muscle. Am J Physiol Endocrinol Metab 2018;315: E267-78. https://doi.org/10.1152/ajpendo.00435.2017
  35. Neu R, Adams S, Munz B. Differential expression of entactin-1/ nidogen-1 and entactin-2/nidogen-2 in myogenic differentiation. Differentiation 2006;74:573-82. https://doi.org/10.1111/j.1432-0436.2006.00100.x
  36. Gan S, Qiu S, Feng Y, et al. Identification of genes associated with the effect of inflammation on the neurotransmission of vascular smooth muscle cell. Exp Ther Med 2017;13:1303-12. https://doi.org/10.3892/etm.2017.4138
  37. Boudon S, Ounaissi D, Viala D, Monteils V, Picard B, Cassar-Malek I. Label free shotgun proteomics for the identification of protein biomarkers for beef tenderness in muscle and plasma of heifers. J Proteomics 2020;217:103685. https://doi.org/10.1016/j.jprot.2020.103685
  38. Zhang C, Wang J, Wang G, et al. Molecular cloning and mRNA expression analysis of sheep MYL3 and MYL4 genes. Gene 2016;577:209-14. https://doi.org/10.1016/j.gene.2015.11.041
  39. Lee SH, Hadipour-Lakmehsari S, Murthy HR, et al. REEP5 depletion causes sarco-endoplasmic reticulum vacuolization and cardiac functional defects. Nat Commun 2020;11:965. https://doi.org/10.1038/s41467-019-14143-9
  40. Hussain R, Daud S, Kakar N, et al. A missense mutation (p.G274R) in gene ASPA causes Canavan disease in a Pakistani family. Mol Biol Rep 2012;39:6197-201. https://doi.org/10.1007/s11033-011-1438-2
  41. Gutierrez-Aguilar R, Kim DH, Woods SC, Seeley RJ. Expression of new loci associated with obesity in diet-induced obese rats: from genetics to physiology. Obesity 2012;20:306-12. https://doi.org/10.1038/oby.2011.236
  42. Zhu T, He Y, Yang J, Fu W, Xu X, Si Y. MYBPH inhibits vascular smooth muscle cell migration and attenuates neointimal hyperplasia in a rat carotid balloon-injury model. Exp Cell Res 2017;359:154-62. https://doi.org/10.1016/j.yexcr.2017.07.036
  43. Anttila S, Hirvonen A, Vainio H, Husgafvel-Pursiainen K, Hayes JD, Ketterer B. Immunohistochemical localization of glutathione S-transferases in human lung. Cancer Res 1993; 53:5643-8.
  44. Zhang L, Keung W, Samokhvalov V, Wang W, Lopaschuk GD. Role of fatty acid uptake and fatty acid beta-oxidation in mediating insulin resistance in heart and skeletal muscle. Biochim Biophys Acta Mol Cell Biol Lipids 2010;1801:1-22. https://doi.org/10.1016/j.bbalip.2009.09.014
  45. Chen Y, Chen J, Zhang C, et al. Deficiency in the short-chain acyl-CoA dehydrogenase protects mice against diet-induced obesity and insulin resistance. FASEB J 2019;33:13722-33. https://doi.org/10.1096/fj.201901474RR
  46. Lee HC, Shiou YL, Jhuo SJ, et al. The sodium-glucose cotransporter 2 inhibitor empagliflozin attenuates cardiac fibrosis and improves ventricular hemodynamics in hyper-tensive heart failure rats. Cardiovasc Diabetol 2019;18:45. https://doi.org/10.1186/s12933-019-0849-6
  47. Li T, Li X, Meng H, Chen L, Meng F. ACSL1 affects triglyceride levels through the PPARγ pathway. Int J Med Sci 2020;17: 720-7. https://doi.org/10.7150/ijms.42248
  48. Bakshi I, Brown SHJ, Brandon AE, et al. Increasing Acyl CoA thioesterase activity alters phospholipid profile without effect on insulin action in skeletal muscle of rats. Sci Rep 2018;8:13967. https://doi.org/10.1038/s41598-018-32354-w