DOI QR코드

DOI QR Code

Protein levels alter yak rumen microbiota profiles, meat properties, and longissimus dorsi metabolites

  • Jiyuan Zhang (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Shuxiang Wang (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Shatuo Chai (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Shengchun Xu (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Ziming Zeng (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Zhilong Wang (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Xun Wang (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Yingkui Yang (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Shujie Liu (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Jiaying Lv (Qinghai Academy of Animal Husbandry and Veterinary Sciences in Qinghai University) ;
  • Mingliang Wang (Huangzhong District Xibao Town Ecological Dairy Farming Base of Qinghai Province) ;
  • Xinjun Zhang (Qinghai Xia Hua Halal Meat Products Co., Ltd.)
  • Received : 2025.01.12
  • Accepted : 2025.07.02
  • Published : 2026.05.01

Abstract

Objective: The study investigated how varying protein levels in low-energy diets affected the microbiota, meat quality, and metabolomics of the longissimus dorsi muscle in yaks. The aim was to determine the optimal yak diet for growth and meat quality under low-energy conditions. Methods: Twenty-four adult male yaks were divided into two groups of 12: the low-energy, medium-protein (LM) group and the low-energy, high-protein (LH) group. The study analysed rumen microbiota and longissimus dorsi muscle metabolites using 16S rDNA gene sequencing and untargeted metabolomic analysis. The effects of the diets on growth performance, meat quality and microbial community composition were evaluated. Results: There were no significant differences in growth performance between the LH and LM groups. However, the LH group had a lower pH value at 45 minutes after death and was better for meat colour and tenderness. There were no significant differences in average daily gain, cooking loss, hardness, elasticity, adhesiveness, chewiness, or the pH at 24 hours after death in the longissimus dorsi muscle between the groups. Microbial community analysis revealed no significant differences in diversity indices; however, it did indicate distinct bacterial composition between the groups. Predictions of function suggested the LM group had a higher level of enrichment and a greater number of unique operational taxonomic units compared to the LH group. Metabolomic analysis revealed differences in muscle metabolites and metabolic pathways, with the LM group having a higher capacity for fatty acid and selenocompound metabolism, implying greater energy utilisation efficiency and antioxidant function. Conclusion: The study suggests that a diet with 14% protein, as part of low-energy diets, is best for increasing yak fattening. This is because it improves energy use and antioxidant function, without affecting growth.

Keywords

Acknowledgement

We express our gratitude for the assistance provided by experts and students during the experiment, and we also thank the staff at the Jinyintan Beef and Sheep Standardized Breeding Demonstration Ranch in Haiyan County, Qinghai Province, for their help.

References

  1. Shah AM, Bano I, Qazi IH, Matra M, Wanapat M. "The yak": a remarkable animal living in a harsh environment: an overview of its feeding, growth, production performance, and contribution to food security. Front Vet Sci 2023;10:1086985. https://doi.org/10.3389/fvets.2023.1086985
  2. Zhu Q, Chen H, Peng C, et al. An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau. Nat Commun 2023;14:6406. https://doi.org/10.1038/s41467-023-42099-4
  3. Luo G, Cui J. Exploring high quality development of animal husbandry in Qinghai province from the perspective of the Tibetan sheep industry. Sci Rep 2024;14:21500. https://doi.org/10.1038/s41598-024-72462-4
  4. Ren Y, Zhu Y, Baldan D, et al. Optimizing livestock carrying capacity for wild ungulate-livestock coexistence in a Qinghai-Tibet Plateau grassland. Sci Rep 2021;11:3635. https://doi.org/10.1038/s41598-021-83207-y
  5. Yáñez-Ruiz DR, Abecia L, Newbold CJ. Manipulating rumen microbiome and fermentation through interventions during early life: a review. Front Microbiol 2015;6:1133. https://doi.org/10.3389/fmicb.2015.01133
  6. Hao LZ, Liu SJ, Hu LH, Chai ST. Research progress on nutrient requirements of yak and nutritional value evaluation of forage. Chin J Anim Nutr 2020;32:4725-32. https://doi.org/10.3969/j.issn.1006-267x.2020.10.024
  7. Association of Official Analytical Chemists (AOAC) International. Protein (crude) in animal feed: combustion method (Dumas). 15th ed. AOAC International; 1990.
  8. Van Soest PJ, Robertson JB, Lewis BA. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci 1991;74:3583-97. https://doi.org/10.3168/jds.S0022-0302(91)78551-2
  9. Gawel NJ, Jarret RL. A modified CTAB DNA extraction procedure for Musa and Ipomoea. Plant Mol Biol Rep 1991;9:262-6. https://doi.org/10.1007/BF02672076
  10. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 2013;10:996-8. https://doi.org/10.1038/nmeth.2604
  11. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40. https://doi.org/10.1093/bioinformatics/btp616
  12. Boonsaen P, Soe NW, Maitreejet W, Majarune S, Reungprim T, Sawanon S. Effects of protein levels and energy sources in total mixed ration on feedlot performance and carcass quality of kamphaeng saen steers. Agric Nat Resour 2017;51:57-61. https://doi.org/10.1016/j.anres.2017.02.003
  13. Li L, Zhu Y, Wang X, He Y, Cao B. Effects of different dietary energy and protein levels and sex on growth performance, carcass characteristics and meat quality of F1 angus × Chinese xiangxi yellow cattle. J Anim Sci Biotechnol 2014;5:21. https://doi.org/10.1186/2049-1891-5-21
  14. Matthews C, Crispie F, Lewis E, Reid M, O'Toole PW, Cotter PD. The rumen microbiome: a crucial consideration when optimising milk and meat production and nitrogen utilisation efficiency. Gut Microbes 2019;10:115-32. https://doi.org/10.1080/19490976.2018.1505176
  15. Chaucheyras-Durand F, Ossa F. Review: the rumen microbiome: composition, abundance, diversity, and new investigative tools. Prof Anim Sci 2014;30:1-12. https://doi.org/10.15232/S1080-7446(15)30076-0
  16. Kim WS, Ghassemi Nejad J, Peng DQ, Jo YH, Kim J, Lee HG. Effects of different protein levels on growth performance and stress parameters in beef calves under heat stress. Sci Rep 2022;12:8113. https://doi.org/10.1038/s41598-022-09982-4
  17. Carvalho EB, Gionbelli MP, Rodrigues RTS, et al. Differentially expressed mRNAs, proteins and miRNAs associated to energy metabolism in skeletal muscle of beef cattle identified for low and high residual feed intake. BMC Genomics 2019;20:501. https://doi.org/10.1186/s12864-019-5890-z
  18. Reddy PRK, Hyder I. Ruminant digestion. In: Das PK, Sejian V, Mukherjee J, Banerjee D, editors. Textbook of veterinary physiology. Springer Nature; 2023. pp. 353-66.
  19. Zhang X, Xu T, Wang X, et al. Effect of dietary protein levels on dynamic changes and interactions of ruminal microbiota and metabolites in yaks on the Qinghai-Tibetan Plateau. Front Microbiol 2021;12:684340. https://doi.org/10.3389/fmicb.2021.684340
  20. Kong F, Gao Y, Tang M, et al. Effects of dietary rumen-protected Lys levels on rumen fermentation and bacterial community composition in Holstein heifers. Appl Microbiol Biotechnol 2020;104:6623-34. https://doi.org/10.1007/s00253-020-10684-y
  21. Diether NE, Willing BP. Microbial fermentation of dietary protein: an important factor in diet-microbe-host interaction. Microorganisms 2019;7:19. https://doi.org/10.3390/microorganisms7010019
  22. Ibarguren M, López DJ, Escribá PV. The effect of natural and synthetic fatty acids on membrane structure, microdomain organization, cellular functions and human health. Biochim Biophys Acta Biomembr 2014;1838:1518-28. https://doi.org/10.1016/j.bbamem.2013.12.021
  23. Liu Q, Wang C, Guo G, et al. Effects of branched-chain volatile fatty acids supplementation on growth performance, ruminal fermentation, nutrient digestibility, hepatic lipid content and gene expression of dairy calves. Anim Feed Sci Technol 2018;237:27-34. https://doi.org/10.1016/j.anifeedsci.2018.01.006
  24. Zhang ZD, Wang C, Du HS, et al. Effects of sodium selenite and coated sodium selenite on lactation performance, total tract nutrient digestion and rumen fermentation in Holstein dairy cows. Animal 2020;14:2091-9. https://doi.org/10.1017/S1751731120000804
  25. Morgavi DP, Kelly WJ, Janssen PH, Attwood GT. Rumen microbial (meta)genomics and its application to ruminant production. Animal 2013;7:184-201. https://doi.org/10.1017/S1751731112000419
  26. Qu Q, Zeng F, Liu X, Wang QJ, Deng F. Fatty acid oxidation and carnitine palmitoyltransferase I: emerging therapeutic targets in cancer. Cell Death Dis 2016;7:e2226. https://doi.org/10.1038/cddis.2016.132
  27. Longo N, Frigeni M, Pasquali M. Carnitine transport and fatty acid oxidation. Biochim Biophys Acta Mol Cell Res 2016;1863:2422-35. https://doi.org/10.1016/j.bbamcr.2016.01.023
  28. Betancur-Murillo CL, Aguilar-Marín SB, Jovel J. Prevotella: a key player in ruminal metabolism. Microorganisms 2023;11:1. https://doi.org/10.3390/microorganisms11010001
  29. Li JM, Li LY, Qin X, et al. Systemic regulation of L-carnitine in nutritional metabolism in zebrafish, Danio rerio. Sci Rep 2017;7:40815. https://doi.org/10.1038/srep40815
  30. Xu S, Feng X, Zhao W, Bi Y, Diao Q, Tu Y. Rumen and hindgut microbiome regulate average daily gain of preweaning Holstein heifer calves in different ways. Microbiome 2024;12:131. https://doi.org/10.1186/s40168-024-01844-7
  31. Wu G, Yan Q, Jones JA, Tang YJ, Fong SS, Koffas MAG. Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications. Trends Biotechnol 2016;34:652-64. https://doi.org/10.1016/j.tibtech.2016.02.010