The role of the gut microbiome in the regulation of high-altitude adaptation

Abstract

Hypoxia and cold temperatures are major limiting factors for animals reared at high altitudes. Previous adaptation studies have primarily focused on genetic and genomic aspects, while the mechanisms by which the gut microbiome contributes to this adaptation are still not fully understood. We used ruminants as both naturally adapted (yaks) and non-adapted (Holstein cows) models to investigate the role of gut microbiome in high-altitude adaptation by applying multi-omics approaches. First, 20 yaks and 20 Holstein cows that had been reared at approximately 4000 m altitude since birth were fed the same diet for 44 days prior to sampling to eliminate the short-term effects of nutrition and altitude adaptation. The yak rumen microbiome showed significant enrichment in carbon metabolism, particularly central carbon metabolism pathways, such as glycolysis/gluconeogenesis, pyruvate metabolism, and the pentose phosphate pathway, whereas that of Holstein cows was enriched in starch, sucrose, pentose, and glucuronate interconversions. Compared with those of Holstein cows kept at high altitudes for their entire life, the yak rumen epithelial cells, as determined by single-nucleus RNA sequencing, exhibited higher elevated scores for ketone body biosynthesis and fatty acid beta-oxidation. Second, mixed rumen fluid was transplanted from 10 yaks to 10 Holstein cows. Holstein cows then showed better milk production performance. A progressive decline in carbon metabolism activity from 6 h to 7 and 28 days post-transplantation was verified. In conclusion, the rumen microbiome and host epithelial function appear to support high-altitude adaptation by improving the energy supply of the host.

Keywords

high altitude adaptation / rumen epithelial cell / rumen microbiome / yak

References

[1] Peng, Xuefeng, St. Elmo Wilken, Thomas S. Lankiewicz, Sean P. Gilmore, Jennifer L. Brown, and John K. Henske. 2021. “Genomic and Functional Analyses of Fungal and Bacterial Consortia That Enable Lignocellulose Breakdown in Goat Gut Microbiomes.” Nature Microbiology 6(4): 499–511. https://doi.org/10.1038/s41564-020-00861-0

[2] Groussin, Mathieu, Florent Mazel, Jon G. Sanders, Chris S. Smillie, Sébastien Lavergne, Wilfried Thuiller, and Eric J. Alm. 2017. “Unraveling the Processes Shaping Mammalian Gut Microbiomes Over Evolutionary Time.” Nature Communications 8(1): 14319. https://doi.org/10.1038/ncomms14319

[3] . Flint Harry, J. 2004. “Polysaccharide Breakdown by Anaerobic Microorganisms Inhabiting the Mammalian Gut, in Advances in Applied Microbiology 56, pp. 89–120. Elsevier

[4] Qiao, G. H., T. Shao, C. Q. Yu, X. L. Wang, X. Yang, X. Q. Zhu, and Y. Lu. 2013. “A Comparative Study at Two Different Altitudes With Two Dietary Nutrition Levels on Rumen Fermentation and Energy Metabolism in Chinese Holstein Cows.” Journal of Animal Physiology and Animal Nutrition 97(5): 933–41. https://doi.org/10.1111/j.1439-0396.2012.01339.x

[5] Kulyar, Md. F., Quan Mo, Shah Nawaz, and Jiakui Li. 2025. “High Altitude Microbiome: Insight into Yak Gut Microbiota and Its Nutritional and Functional Involvement for Food Systems.” Trends in Food Science & Technology 159: 104897. https://doi.org/10.1016/j.tifs.2025.104897

[6] Xinyu, Zhang, Michael Kreuzer, Mutian Niu, and Shengli Li. 2025. “Differences in Milk Production Efficiency and Physiology Between Lactating Holstein Cows and Yaks Born and Kept at About 4000 m of Altitude When Fed the Same Diet.” Journal of Animal Physiology and Animal Nutrition jpn 109: 14122. https://doi.org/10.1111/jpn.14122

[7] Qiu, Qiang, Guojie Zhang, Tao Ma, Wubin Qian, Junyi Wang, Zhiqiang Ye, Changchang Cao, et al. 2012. “The Yak Genome and Adaptation to Life at High Altitude.” Nature Genetics 44(8): 946–9. https://doi.org/10.1038/ng.2343

[8] Liu, Xinfeng, Wenyu Liu, Johannes A. Lenstra, Zeyu Zheng, Xiaoyun Wu, Jiao Yang, Bowen Li, et al. 2023. “Evolutionary Origin of Genomic Structural Variations in Domestic Yaks.” Nature Communications 14(1): 5617. https://doi.org/10.1038/s41467-023-41220-x

[9] Xin, Jinwei, Zhixin Chai, Chengfu Zhang, Qiang Zhang, Yong Zhu, Hanwen Cao, Cidan Yangji, et al. 2020. “Methylome and Transcriptome Profiles in Three Yak Tissues Revealed That DNA Methylation and the Transcription Factor ZGPAT Co-Regulate Milk Production.” BMC Genomics 21(1): 731. https://doi.org/10.1186/s12864-020-07151-3

[10] Zhang, Zhigang, Dongming Xu, Li Wang, Junjun Hao, Jinfeng Wang, Xin Zhou, Weiwei Wang, et al. 2016. “Convergent Evolution of Rumen Microbiomes in High-Altitude Mammals.” Current Biology 26(14): 1873–9. https://doi.org/10.1016/j.cub.2016.05.012

[11] Li, Bin, Yuangang Yang, Bo Xu, Pengfei Song, Feng Jiang, Hongmei Gao, Zhenyuan Cai, Haifeng Gu, and Tongzuo Zhang. 2025. “Comparative Macrogenomics Reveal Plateau Adaptation of Gut Microbiome in Cervids.” BMC Biology 23(1): 154. https://doi.org/10.1186/s12915-025-02269-w

[12] Zhao, Fangfang, Lili Yang, Tao Zhang, Daohua Zhuang, Qunfu Wu, Jiangkun Yu, Chen Tian, and Zhigang Zhang. 2023. “Gut Microbiome Signatures of Extreme Environment Adaption in Tibetan Pig.” npj Biofilms and Microbiomes 9(1): 27. https://doi.org/10.1038/s41522-023-00395-3

[13] Su, Qian, Dao-Hua Zhuang, Yu-Chun Li, Yu Chen, Xia-Yan Wang, Ming-Xia Ge, Ting-Yue Xue, et al. 2024. “Gut Microbiota Contributes to High-Altitude Hypoxia Acclimatization of Human Populations.” Genome Biology 25(1): 232. https://doi.org/10.1186/s13059-024-03373-w

[14] Wu, Jia-Jin, Senlin Zhu, Fengfei Gu, Teresa G. Valencak, Jian-Xin Liu, and Hui-Zeng Sun. 2022. “Cross-Tissue Single-Cell Transcriptomic Landscape Reveals the Key Cell Subtypes and Their Potential Roles in the Nutrient Absorption and Metabolism in Dairy Cattle.” Journal of Advanced Research 37: 1–18. https://doi.org/10.1016/j.jare.2021.11.009

[15] Chu, Hiutung, Arya Khosravi, Indah P. Kusumawardhani, Alice H. K. Kwon, Anilton C. Vasconcelos, Larissa D. Cunha, Anne E. Mayer, et al. 2016. “Gene-Microbiota Interactions Contribute to the Pathogenesis of Inflammatory Bowel Disease.” Science 352(6289): 1116–20. https://doi.org/10.1126/science.aad9948

[16] Xue, Ming-Yuan, Hui-Zeng Sun, Xue-Hui Wu, Jian-Xin Liu, and Le Luo Guan. 2020. “Multi-Omics Reveals That the Rumen Microbiome and Its Metabolome Together With the Host Metabolome Contribute to Individualized Dairy Cow Performance.” Microbiome 8(1): 64. https://doi.org/10.1186/s40168-020-00819-8

[17] Kamke, Janine, Sandra Kittelmann, Priya Soni, Yang Li, Michael Tavendale, Siva Ganesh, Peter H. Janssen, et al. 2016. “Rumen Metagenome and Metatranscriptome Analyses of Low Methane Yield Sheep Reveals a Sharpea-Enriched Microbiome Characterised by Lactic Acid Formation and Utilisation.” Microbiome 4(1): 56. https://doi.org/10.1186/s40168-016-0201-2

[18] Li, Fuyong, Thomas C. A. Hitch, Yanhong Chen, Christopher J. Creevey, and Le Luo Guan. 2019. “Comparative Metagenomic and Metatranscriptomic Analyses Reveal the Breed Effect on the Rumen Microbiome and Its Associations With Feed Efficiency in Beef Cattle.” Microbiome 7(1): 6. https://doi.org/10.1186/s40168-019-0618-5

[19] Henry A, Paz, Christopher L. Anderson, Makala J. Muller, Paul J., Kononoff, and Samodha C. Fernando. 2016. “Rumen Bacterial Community Composition in Holstein and Jersey Cows Is Different Under Same Dietary Condition and Is Not Affected by Sampling Method.” Frontiers in Microbiology 7: 01206. https://doi.org/10.3389/fmicb.2016.01206

[20] Zhang, Meng, Chengbo Liang, Bin Li, Feng Jiang, Pengfei Song, Haifeng Gu, Hongmei Gao, Zhenyuan Cai, and Tongzuo Zhang. 2025. “Gut Microbiome and Diet Contribute to Ecological Niche Differentiation Between Argali (Ovis Ammon Hodgsoni) and Blue Sheep (Pseudois Nayaur) on the Qinghai-Tibet Plateau.” Communications Biology 8(1): 930. https://doi.org/10.1038/s42003-025-08185-7

[21] Zhao, Hongwen, Longjie Sun, Jiali Liu, Bin Shi, Yaopeng Zhang, Ci-Ren Qu-Zong, Tsechoe Dorji, et al. 2024. “Meta-Analysis Identifying Gut Microbial Biomarkers of Qinghai-Tibet Plateau Populations and the Functionality of Microbiota-Derived Butyrate in High-Altitude Adaptation.” Gut Microbes 16(1): 2350151. https://doi.org/10.1080/19490976.2024.2350151

[22] Xin, Jinwei, Zhixin Chai, Chengfu Zhang, Qiang Zhang, Yong Zhu, Hanwen Cao, Jincheng Zhong, and Qiumei Ji. 2019. “Comparing the Microbial Community in Four Stomach of Dairy Cattle, Yellow Cattle and Three Yak Herds in Qinghai-Tibetan Plateau.” Frontiers in Microbiology 10: 1547. https://doi.org/10.3389/fmicb.2019.01547

[23] Shabat, Sheerli Kruger Ben, Goor Sasson, Adi Doron-Faigenboim, Thomer Durman, Shamay Yaacoby, Margret E., Berg Miller, Bryan A. White, Naama Shterzer, and Itzhak Mizrahi. 2016. “Specific Microbiome-Dependent Mechanisms Underlie the Energy Harvest Efficiency of Ruminants.” The ISME Journal 10(12): 2958–72. https://doi.org/10.1038/ismej.2016.62

[24] Neu, Alexander T., Eric E. Allen, and Kaustuv Roy. 2021. “Defining and Quantifying the Core Microbiome: Challenges and Prospects.” Proceedings of the National Academy of Sciences 118(51): e2104429118. https://doi.org/10.1073/pnas.2104429118

[25] Wang, Dangdang, Luyu Chen, Guangfu Tang, Junjian Yu, Jie Chen, Zongjun Li, Yangchun Cao, et al. 2023. “Multi-Omics Revealed the Long-Term Effect of Ruminal Keystone Bacteria and the Microbial Metabolome on Lactation Performance in Adult Dairy Goats.” Microbiome 11(1): 215. https://doi.org/10.1186/s40168-023-01652-5

[26] Qi, Keke, Xiaoming Men, Jie Wu, Bo Deng, and Ziwei Xu. 2022. “Effects of Growth Stage and Rearing Pattern on Pig Gut Microbiota.” Current Microbiology 79(5): 136. https://doi.org/10.1007/s00284-022-02828-2

[27] Wardman, Jacob F., Rajneesh K. Bains, Peter Rahfeld, and Stephen G. Withers. 2022. “Carbohydrate-Active Enzymes (CAZymes) in the Gut Microbiome.” Nature Reviews Microbiology 20(9): 542–56. https://doi.org/10.1038/s41579-022-00712-1

[28] Gharechahi, Javad, Sajjad Sarikhan, Jian-Lin Han, Xue-Zhi Ding, and Ghasem Hosseini Salekdeh. 2022. “Functional and Phylogenetic Analyses of Camel Rumen Microbiota Associated With Different Lignocellulosic Substrates.” npj Biofilms and Microbiomes 8(1): 46. https://doi.org/10.1038/s41522-022-00309-9

[29] Gharechahi, Javad, Mohammad Farhad Vahidi, Xue-Zhi Ding, Jian-Lin Han, and Ghasem Hosseini Salekdeh. 2020. “Temporal Changes in Microbial Communities Attached to Forages With Different Lignocellulosic Compositions in Cattle Rumen.” FEMS Microbiology Ecology 96(6): fiaa069. https://doi.org/10.1093/femsec/fiaa069

[30] Guo, Jingling, Runzhou Zhao, Kun Li, Yafang Tan, Likun Wang, Hui Ling, Huan Zhang, et al. 2025. “Altitude Adaptation: The Unseen Work of Gut Microbiota.” hLife 3(1): 5–20. https://doi.org/10.1016/j.hlife.2024.11.004

[31] Cumming, Bridgette M., and Adrie J. C. Steyn. 2015. “Metabolic Plasticity of Central Carbon Metabolism Protects Mycobacteria.” Proceedings of the National Academy of Sciences 112(43): 13135–6. https://doi.org/10.1073/pnas.1518171112

[32] Noor, Elad, Eran Eden, Ron Milo, and Uri Alon. 2010. “Central Carbon Metabolism as a Minimal Biochemical Walk Between Precursors for Biomass and Energy.” Molecular Cell 39(5): 809–20. https://doi.org/10.1016/j.molcel.2010.08.031

[33] TeSlaa, Tara, Markus Ralser, Jing Fan, and Joshua D. Rabinowitz. 2023. “The Pentose Phosphate Pathway in Health and Disease.” Nature Metabolism 5(8): 1275–89. https://doi.org/10.1038/s42255-023-00863-2

[34] Midha, Ayush D., Yuyin Zhou, Bruno B. Queliconi, Alec M. Barrios, Augustinus G. Haribowo, Brandon T. L. Chew, Cyril O. Y. Fong, et al. 2023. “Organ-Specific Fuel Rewiring in Acute and Chronic Hypoxia Redistributes Glucose and Fatty Acid Metabolism.” Cell Metabolism 35(3): 504–16.e5. https://doi.org/10.1016/j.cmet.2023.02.007

[35] Zhao, Congcong, Lamei Wang, Shanlin Ke, Xinhua Chen, Ákos Kenéz, Wei Xu, Dangdang Wang, et al. 2022. “Yak Rumen Microbiome Elevates Fiber Degradation Ability and Alters Rumen Fermentation Pattern to Increase Feed Efficiency.” Animal Nutrition 11: 201–14. https://doi.org/10.1016/j.aninu.2022.07.014

[36] Brosh, A., Y. Aharoni, E. Shargal, B. Sharir, M. Gutman, and I. Choshniak. 2004. “Energy Balance of Grazing Beef Cattle in Mediterranean Pasture, the Effects of Stocking Rate and Season.” Livestock Production Science 90(2–3): 101–15. https://doi.org/10.1016/j.livprodsci.2004.03.008

[37] Wang, Weiwei, Yuntao Dong, Wei Guo, Xiao Zhang, A. Allan Degen, Sisi Bi, Luming Ding, Xiang Chen, and Ruijun Long. 2024. “Linkages Between Rumen Microbiome, Host, and Environment in Yaks, and Their Implications for Understanding Animal Production and Management.” Frontiers in Microbiology 15: 1301258. https://doi.org/10.3389/fmicb.2024.1301258

[38] Zhang, Jun, Haitao Shi, Yajing Wang, Shengli Li, Zhijun Cao, Shoukun Ji, Yuan He, and Hongtao Zhang. 2017. “Effect of Dietary Forage to Concentrate Ratios on Dynamic Profile Changes and Interactions of Ruminal Microbiota and Metabolites in Holstein Heifers.” Frontiers in Microbiology 8: 2206. https://doi.org/10.3389/fmicb.2017.02206

[39] Ding, X. Z., R. J. Long, M. Kreuzer, J. D. Mi, and B. Yang. 2010. “Methane Emissions From Yak (Bos Grunniens) Steers Grazing or Kept Indoors and Fed Diets With Varying Forage:Concentrate Ratio During the Cold Season on the Qinghai-Tibetan Plateau.” Animal Feed Science and Technology 162(3–4): 91–8. https://doi.org/10.1016/j.anifeedsci.2010.09.007

[40] Han, Yang, Xiaoshuang Liu, Qian Jia, Jiayu Xu, Jinlong Shi, Xiang Li, Guotong Xie, Xiaojing Zhao, and Kunlun He. 2024. “Longitudinal Multi-Omics Analysis Uncovers the Altered Landscape of Gut Microbiota and Plasma Metabolome in Response to High Altitude.” Microbiome 12(1): 70. https://doi.org/10.1186/s40168-024-01781-5

[41] Machmüller, Andrea, Carla R. Soliva, and Michael Kreuzer. 2003. “Effect of Coconut Oil and Defaunation Treatment on Methanogenesis in Sheep.” Reproduction Nutrition Development 43(1): 41–55. https://doi.org/10.1051/rnd:2003005

[42] Na, Sang Weon, and Le Luo Guan. 2022. “Understanding the Role of Rumen Epithelial Host-Microbe Interactions in Cattle Feed Efficiency.” Animal Nutrition 10: 41–53. https://doi.org/10.1016/j.aninu.2022.04.002

[43] Lane, M. A., R. L. Baldwin, and B. W. Jesse. 2002. “Developmental Changes in Ketogenic Enzyme Gene Expression During Sheep Rumen Development.” Journal of Animal Science 80(6): 1538–44. https://doi.org/10.2527/2002.8061538x

[44] Xiang, Ruidong, Victor Hutton Oddy, Alan L. Archibald, Phillip E. Vercoe, and Brian P. Dalrymple. 2016. “Epithelial, Metabolic and Innate Immunity Transcriptomic Signatures Differentiating the Rumen From Other Sheep and Mammalian Gastrointestinal Tract Tissues.” PeerJ 4: e1762. https://doi.org/10.7717/peerj.1762

[45] Xue, Ming-Yuan, Jia-Jin Wu, Yun-Yi Xie, Sen-Lin Zhu, Yi-Fan Zhong, Jian-Xin Liu, and Hui-Zeng Sun. 2022. “Investigation of Fiber Utilization in the Rumen of Dairy Cows Based on Metagenome-Assembled Genomes and Single-Cell RNA Sequencing.” Microbiome 10(1): 11. https://doi.org/10.1186/s40168-021-01211-w

[46] Depommier, Clara, Amandine Everard, Céline Druart, Hubert Plovier, Matthias Van Hul, Sara Vieira-Silva, Gwen Falony, et al. 2019. “Supplementation with Akkermansia Muciniphila in Overweight and Obese Human Volunteers: A Proof-of-Concept Exploratory Study.” Nature Medicine 25(7): 1096–103. https://doi.org/10.1038/s41591-019-0495-2

[47] Gu, Yu, Can Liu, Ningning Zheng, Wei Jia, Weidong Zhang, and Houkai Li. 2019. “Metabolic and Gut Microbial Characterization of Obesity-Prone Mice Under a High-Fat Diet.” Journal of Proteome Research 18(4): 1703–14. https://doi.org/10.1021/acs.jproteome.8b00945

[48] Ridaura, Vanessa K., Jeremiah J. Faith, Federico E. Rey, Jiye Cheng, Alexis E. Duncan, Andrew L. Kau, Nicholas W. Griffin, et al. 2013. “Gut Microbiota From Twins Discordant for Obesity Modulate Metabolism in Mice.” Science 341(6150): 1241214. https://doi.org/10.1126/science.1241214

[49] Xue, Ming-Yuan, Yun-Yi Xie, Yifan Zhong, Xiao-Jiao Ma, Hui-Zeng Sun, and Jian-Xin Liu. 2022. “Integrated Meta-Omics Reveals New Ruminal Microbial Features Associated With Feed Efficiency in Dairy Cattle.” Microbiome 10(1): 32. https://doi.org/10.1186/s40168-022-01228-9

[50] Clemmons, Brooke A., Brynn H. Voy, and Phillip R. Myer. 2019. “Altering the Gut Microbiome of Cattle: Considerations of Host-Microbiome Interactions for Persistent Microbiome Manipulation.” Microbial Ecology 77(2): 523–36. https://doi.org/10.1007/s00248-018-1234-9

[51] Li, Yan, Yingkui Yang, Shatuo Chai, Kaiyue Pang, Xun Wang, Linpeng Xu, Zheng Chen, et al. 2023. “Ruminal Fluid Transplantation Accelerates Rumen Microbial Remodeling and Improves Feed Efficiency in Yaks.” Microorganisms 11(8): 1964. https://doi.org/10.3390/microorganisms11081964

[52] DePeters, E. J., and L. W. George. 2014. “Rumen Transfaunation.” Immunology Letters 162(2): 69–76. https://doi.org/10.1016/j.imlet.2014.05.009

[53] Chen, Shifu, Yanqing Zhou, Yaru Chen, and Jia Gu. 2018. “fastp: An Ultra-Fast All-In-One FASTQ Preprocessor.” Bioinformatics 34(17): i884–90. https://doi.org/10.1093/bioinformatics/bty560

[54] Li, Dinghua, Chi-Man Liu, Ruibang Luo, Tak-Wah Lam, and Sadakane. 2015. “MEGAHIT: An Ultra-Fast Single-Node Solution for Large and Complex Metagenomics Assembly Via Succinct de bruijn Graph.” Bioinformatics 31(10): 1674–6. https://doi.org/10.1093/bioinformatics/btv033

[55] Fu, Limin, Beifang Niu, Zhengwei Zhu, Sitao Wu, and Weizhong Li. 2012. “CD-HIT: Accelerated for Clustering the Next-Generation Sequencing Data.” Bioinformatics 28(23): 3150–2. https://doi.org/10.1093/bioinformatics/bts565

[56] Li, Ruiqiang, Yingrui Li, Karsten Kristiansen, and Jun Wang. 2008. “SOAP: Short Oligonucleotide Alignment Program.” Bioinformatics 24(5): 713–4. https://doi.org/10.1093/bioinformatics/btn025

[57] Haas, Brian J., Alexie Papanicolaou, Moran Yassour, Manfred Grabherr, Philip D. Blood, Joshua Bowden, Matthew Brian Couger, et al. 2013. “De Novo Transcript Sequence Reconstruction From RNA-seq Using the Trinity Platform for Reference Generation and Analysis.” Nature Protocols 8(8): 1494–512. https://doi.org/10.1038/nprot.2013.084.

[58] Buchfink, Benjamin, Chao Xie, and Daniel H. Huson. 2015. “Fast and Sensitive Protein Alignment Using DIAMOND.” Nature Methods 12(1): 59–60. https://doi.org/10.1038/nmeth.3176

[59] Benjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society Series B: Statistical Methodology 57(1): 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

[60] Butler, Andrew, Paul Hoffman, Peter Smibert, Efthymia Papalexi, and Rahul Satija. 2018. “Integrating Single-Cell Transcriptomic Data Across Different Conditions, Technologies, and Species.” Nature Biotechnology 36(5): 411–20. https://doi.org/10.1038/nbt.4096

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