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Segatella copri and gut microbial ammonia metabolism contribute to chronic kidney disease pathogenesis

Nature Microbiology volume 10, pages 1684–1697 (2025)Cite this article

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Abstract

Alterations in gut microbiota have been linked to chronic kidney disease (CKD), but large-scale studies and mechanistic insights are limited. Here we analysed gut metagenome data from 1,550 older individuals (aged 65–93 years) with comprehensive kidney function measurements. Segatella copri was positively associated with kidney function through microbial ammonia metabolism-related pathways and the asnA gene, which encodes an ammonia-assimilating enzyme. These associations were replicated in two external studies. In mice, ammonia supplementation increased serum levels of creatinine and blood urea nitrogen, accelerating CKD progression. In vitro cultures of S. copri or asnA-overexpressing Escherichia coli reduced ammonia concentrations, which was markedly attenuated in asnA-knockout S. copri. Gavage of either S. copri or asnA-overexpressing E. coli, but not asnA-knockout S. copri, mitigated ammonia-induced CKD progression in mice. These findings highlight the role of gut microbial ammonia metabolism in CKD pathogenesis and underscore the therapeutic potential of microbial-based interventions.

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Fig. 1: Study schematic flowchart.
Fig. 2: Association of microbial composition and species with kidney function.
Fig. 3: Associations between microbial functional features related to ammonia metabolism and kidney function.
Fig. 4: Alleviation of ammonia-accelerated CKD progression by S. copri transplantation.
Fig. 5: The crucial role of asnA in S. copri-mediated alleviation of ammonia-accelerated CKD progression.

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Data availability

Metagenomic data of the current study have been deposited to the Genome Sequence Archive under the accession codes PRJCA039229 (RLAS cohort) and PRJCA040203 (HS study). All other data supporting the findings of this study are provided as source data files accompanying this paper. The UniProt database is accessible at https://www.uniprot.org/. Source data are provided with this paper.

Code availability

Codes associated with the data analysis and visualization are available at https://github.com/LinscStef/CKDGutMicrobiome (ref. 73).

References

  1. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 395, 709–733 (2020).

    Article  Google Scholar 

  2. Kalantar-Zadeh, K., Jafar, T. H., Nitsch, D., Neuen, B. L. & Perkovic, V. Chronic kidney disease. Lancet 398, 786–802 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Eckardt, K. U. et al. Evolving importance of kidney disease: from subspecialty to global health burden. Lancet 382, 158–169 (2013).

    Article  PubMed  Google Scholar 

  4. Yang, T., Richards, E. M., Pepine, C. J. & Raizada, M. K. The gut microbiota and the brain–gut–kidney axis in hypertension and chronic kidney disease. Nat. Rev. Nephrol. 14, 442–456 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Meijers, B., Evenepoel, P. & Anders, H. J. Intestinal microbiome and fitness in kidney disease. Nat. Rev. Nephrol. 15, 531–545 (2019).

    Article  PubMed  Google Scholar 

  6. Wang, X. et al. Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. Gut 69, 2131–2142 (2020).

    Article  CAS  PubMed  Google Scholar 

  7. Wu, I. W. et al. Integrative metagenomic and metabolomic analyses reveal severity-specific signatures of gut microbiota in chronic kidney disease. Theranostics 10, 5398–5411 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Wang, H. et al. Perturbed gut microbiome and fecal and serum metabolomes are associated with chronic kidney disease severity. Microbiome 11, 3 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sato, N. et al. Metagenomic profiling of gut microbiome in early chronic kidney disease. Nephrol. Dial. Transplant. 36, 1675–1684 (2021).

    Article  CAS  PubMed  Google Scholar 

  10. Rysz, J. et al. The impact of CKD on uremic toxins and gut microbiota. Toxins 13, 252 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Krukowski, H. et al. Gut microbiome studies in CKD: opportunities, pitfalls and therapeutic potential. Nat. Rev. Nephrol. 19, 87–101 (2023).

    Article  PubMed  Google Scholar 

  12. Weiner, I. D., Mitch, W. E. & Sands, J. M. Urea and ammonia metabolism and the control of renal nitrogen excretion. Clin. J. Am. Soc. Nephrol. 10, 1444–1458 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Davila, A. M. et al. Intestinal luminal nitrogen metabolism: role of the gut microbiota and consequences for the host. Pharmacol. Res. 68, 95–107 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. Leigh, J. A. & Dodsworth, J. A. Nitrogen regulation in bacteria and archaea. Annu. Rev. Microbiol. 61, 349–377 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Carrero, J. J., Hecking, M., Chesnaye, N. C. & Jager, K. J. Sex and gender disparities in the epidemiology and outcomes of chronic kidney disease. Nat. Rev. Nephrol. 14, 151–164 (2018).

    Article  PubMed  Google Scholar 

  16. Stengel, B. Chronic kidney disease and cancer: a troubling connection. J. Nephrol. 23, 253–262 (2010).

    PubMed  PubMed Central  Google Scholar 

  17. Fabrizi, F. et al. Decreased serum aminotransferase activity in patients with chronic renal failure: impact on the detection of viral hepatitis. Am. J. Kidney Dis. 38, 1009–1015 (2001).

    Article  CAS  PubMed  Google Scholar 

  18. Louis, P. & Flint, H. J. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 19, 29–41 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Li, W. et al. Ecological and network analyses identify four microbial species with potential significance for the diagnosis/treatment of ulcerative colitis (UC). BMC Microbiol. 21, 138 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Nath, K. A., Hostetter, M. K. & Hostetter, T. H. Pathophysiology of chronic tubulo-interstitial disease in rats. Interactions of dietary acid load, ammonia, and complement component C3. J. Clin. Invest. 76, 667–675 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tolins, J. P., Hostetter, M. K. & Hostetter, T. H. Hypokalemic nephropathy in the rat. Role of ammonia in chronic tubular injury. J. Clin. Invest. 79, 1447–1458 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Vince, A. J. & Burridge, S. M. Ammonia production by intestinal bacteria: the effects of lactose, lactulose and glucose. J. Med. Microbiol. 13, 177–191 (1980).

    Article  CAS  PubMed  Google Scholar 

  23. Richardson, A. J., McKain, N. & Wallace, R. J. Ammonia production by human faecal bacteria, and the enumeration, isolation and characterization of bacteria capable of growth on peptides and amino acids. BMC Microbiol. 13, 6 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Chen, Z. et al. The role of intestinal bacteria and gut–brain axis in hepatic encephalopathy. Front. Cell. Infect. Microbiol. 10, 595759 (2020).

    Article  PubMed  Google Scholar 

  25. Lau, W. L., Kalantar-Zadeh, K. & Vaziri, N. D. The gut as a source of inflammation in chronic kidney disease. Nephron 130, 92–98 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Bartochowski, P. et al. Gut–kidney axis investigations in animal models of chronic kidney disease. Toxins 14, 626 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Diwan, V., Brown, L. & Gobe, G. C. Adenine-induced chronic kidney disease in rats. Nephrology 23, 5–11 (2018).

    Article  CAS  PubMed  Google Scholar 

  28. Lun, H. et al. Altered gut microbiota and microbial biomarkers associated with chronic kidney disease. MicrobiologyOpen 8, e00678 (2019).

    Article  PubMed  Google Scholar 

  29. Yang, R. et al. Coprococcus eutactus, a potent probiotic, alleviates colitis via acetate-mediated IgA response and microbiota restoration. J. Agric. Food Chem. https://doi.org/10.1021/acs.jafc.2c06697 (2023).

  30. Fan, Y. et al. The gut microbiota contributes to the pathogenesis of anorexia nervosa in humans and mice. Nat. Microbiol. 8, 787–802 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chaves, L. D. et al. Chronic kidney disease, uremic milieu, and its effects on gut bacterial microbiota dysbiosis. Am. J. Physiol. Renal Physiol. 315, F487–F502 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Andrianova, N. V. et al. Microbiome–metabolome signature of acute kidney injury. Metabolites 10, 142 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Verbrugghe, P. et al. Evaluation of hypoglycemic effect, safety and immunomodulation of Prevotella copri in mice. Sci. Rep. 11, 21279 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Abdelsalam, N. A., Hegazy, S. M. & Aziz, R. K. The curious case of Prevotella copri. Gut Microbes 15, 2249152 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Scher, J. U. et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wen, C. et al. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis. Genome Biol. 18, 142 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  38. De Vadder, F. et al. Microbiota-produced succinate improves glucose homeostasis via intestinal gluconeogenesis. Cell Metab. 24, 151–157 (2016).

    Article  PubMed  Google Scholar 

  39. Metwaly, A. & Haller, D. Strain-level diversity in the gut: the P. copri case. Cell Host Microbe 25, 349–350 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. De Filippis, F. et al. Distinct genetic and functional traits of human intestinal Prevotella copri strains are associated with different habitual diets. Cell Host Microbe 25, 444–453.e3 (2019).

    Article  PubMed  Google Scholar 

  41. Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in westernized populations. Cell Host Microbe 26, 666–679.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ramezani, A. et al. Role of the gut microbiome in uremia: a potential therapeutic target. Am. J. Kidney Dis. 67, 483–498 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. Hinchman, S. K., Henikoff, S. & Schuster, S. M. A relationship between asparagine synthetase A and aspartyl tRNA synthetase. J. Biol. Chem. 267, 144–149 (1992).

    Article  CAS  PubMed  Google Scholar 

  44. Chan, M. J. et al. Breath ammonia is a useful biomarker predicting kidney function in chronic kidney disease patients. Biomedicines 8, 468 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Vallet, M. et al. Urinary ammonia and long-term outcomes in chronic kidney disease. Kidney Int. 88, 137–145 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Andrade, S. L. & Einsle, O. The Amt/Mep/Rh family of ammonium transport proteins. Mol. Membr. Biol. 24, 357–365 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. van Heeswijk, W. C., Westerhoff, H. V. & Boogerd, F. C. Nitrogen assimilation in Escherichia coli: putting molecular data into a systems perspective. Microbiol. Mol. Biol. Rev. 77, 628–695 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Joshi, S., McMacken, M. & Kalantar-Zadeh, K. Plant-based diets for kidney disease: a guide for clinicians. Am. J. Kidney Dis. 77, 287–296 (2021).

    Article  PubMed  Google Scholar 

  49. Kalantar-Zadeh, K. et al. Plant-dominant low-protein diet for conservative management of chronic kidney disease. Nutrients 12, 1931 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Precup, G. & Vodnar, D. C. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: a comprehensive literature review. Br. J. Nutr. 122, 131–140 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Goldberg, M. R. et al. Microbial signature in IgE-mediated food allergies. Genome Med. 12, 92 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Trefflich, I., Dietrich, S., Braune, A., Abraham, K. & Weikert, C. Short- and branched-chain fatty acids as fecal markers for microbiota activity in vegans and omnivores. Nutrients 13, 1808 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Su, G. et al. Fiber intake and health in people with chronic kidney disease. Clin. Kidney J. 15, 213–225 (2022).

    Article  CAS  PubMed  Google Scholar 

  54. Chou, Y. H. & Chen, Y. M. Aging and renal disease: old questions for new challenges. Aging Dis. 12, 515–528 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Nishijima, S. et al. Fecal microbial load is a major determinant of gut microbiome variation and a confounder for disease associations. Cell 188, 222–236.e15 (2025).

    Article  CAS  PubMed  Google Scholar 

  56. Liu, Z. et al. Cohort profile: the Rugao Longevity and Ageing Study (RuLAS). Int. J. Epidemiol. 45, 1064–1073 (2016).

    PubMed  Google Scholar 

  57. Guan, H. et al. Comparison of fecal collection methods on variation in gut metagenomics and untargeted metabolomics. mSphere 6, e0063621 (2021).

    Article  PubMed  Google Scholar 

  58. Inker, L. A. et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N. Engl. J. Med. 367, 20–29 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. van Anken, H. C. & Schiphorst, M. E. A kinetic determination of ammonia in plasma. Clin. Chim. Acta 56, 151–157 (1974).

    Article  PubMed  Google Scholar 

  60. Wang, Y. et al. Sample collection, DNA extraction, and library construction protocols of the human microbiome studies in the International Human Phenome Project. Phenomics 3, 300–308 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Pu, Y. et al. Impact of DNA extraction methods on gut microbiome profiles: a comparative metagenomic study. Phenomics 5, 76–90 (2025).

  62. Sun, Z. et al. Gut microbiome alterations and gut barrier dysfunction are associated with host immune homeostasis in COVID-19 patients. BMC Med. 20, 24 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Blanco-Miguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat. Biotechnol. 41, 1633–1644 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Holmes, I., Harris, K. & Quince, C. Dirichlet multinomial mixtures: generative models for microbial metagenomics. PLoS ONE 7, e30126 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Ali, B. H. et al. New model for adenine-induced chronic renal failure in mice, and the effect of gum acacia treatment thereon: comparison with rats. J. Pharmacol. Toxicol. Methods 68, 384–393 (2013).

    Article  CAS  PubMed  Google Scholar 

  67. Li, J. et al. A versatile genetic toolbox for Prevotella copri enables studying polysaccharide utilization systems. EMBO J. 40, e108287 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wang, P. et al. Development of an efficient conjugation-based genetic manipulation system for Pseudoalteromonas. Microb. Cell Fact. 14, 11 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Chen, B. et al. Gut bacteria alleviate smoking-related NASH by degrading gut nicotine. Nature 610, 562–568 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Li, X. et al. Ketogenic diet-induced bile acids protect against obesity through reduced calorie absorption. Nat. Metab. 6, 1397–1414 (2024).

    Article  CAS  PubMed  Google Scholar 

  71. Wu, L. L. et al. Transforming growth factor beta 1 and renal injury following subtotal nephrectomy in the rat: role of the renin-angiotensin system. Kidney Int. 51, 1553–1567 (1997).

    Article  CAS  PubMed  Google Scholar 

  72. Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lin, S. CKDGutMicrobiome. GitHub https://github.com/LinscStef/CKDGutMicrobiome (2025).

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Acknowledgements

We thank the participants for their participation in this research and the research staff in the Rugao Longitudinal Ageing Study. This work was jointly supported by the National Key R&D Program of China (2021YFA1301000, 2020YFC2005000 and 2021YFC2500202 to Y.Z. and J.C.) and the Shanghai Municipal Science and Technology Major Project (Grant No. 2023SHZDZX02 to Y.Z.). The computations in this research were performed using the CFFF platform and the Human Phenome Data Center at Fudan University, and we thank the centre staff for their support.

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Author notes

  1. These authors contributed equally: Shuchun Lin, Zhonghan Sun, Xinna Zhu.

Authors and Affiliations

  1. Division of Nephrology, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China

    Shuchun Lin, Mengjing Wang, Qian Zhang, Jing Qian, Jing Chen & Xiaofeng Wang

  2. State Key Laboratory of Genetics and Development of Complex Phenotypes, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China

    Shuchun Lin, Zhonghan Sun, Hui Zhang, Yanni Pu, Mengmeng Kong, Peifeng Guo, Xiaofeng Zhou, Jin Li, Xuehui Sun, Shangyu Hong, Xiaofeng Wang, Xiao Li & Yan Zheng

  3. Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

    Xinna Zhu & Xueli Zhang

  4. Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Zhendong Mei

  5. Department of Nephrology, Institute of Kidney Diseases, West China Hospital of Sichuan University, Chengdu, China

    Liang Ma

  6. Institute of Zoology, Chinese Academy of Sciences, Beijing, China

    Fangqing Zhao

  7. The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Key Laboratory of Organ Failure Research (Ministry of Education), Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People’s Republic of China

    Jing Nie

  8. Fudan University- the People’s Hospital of Rugao Joint Research Institute of Longevity and Aging, Rugao, China

    Xiaofeng Wang

  9. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai, China

    Yan Zheng

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  1. Shuchun Lin
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Contributions

Y.Z., X.L., and S.L. conceived of and designed the study. Y.Z., J.N., X.W. and J.C. were involved in the funding acquisition. X.W., Y.Z. and X.S. designed the RLAS cohort. S.L., Z.S., H.Z. and X.W. recruited the study participants and collected faecal and blood samples. S.L., M.W., Q.Z., J.Q. and J.C. recruited the HS study participants and collected faecal and blood samples. S.L., Z.S., M.K. and X. Zhou prepared the samples for sequencing and analysed the data. X. Zhu, X. Zhang and X.L. performed the in vitro and in vivo experiments. S.L., Z.S. and X.L. drafted the paper. Z.M., Y.P., P.G., J.L., L.M., F.Z. and S.H. contributed to the revision of the paper.

Corresponding authors

Correspondence to Jing Chen, Xiaofeng Wang, Xiao Li or Yan Zheng.

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The authors declare no competing interests.

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Nature Microbiology thanks Andrew Holmes, Brandilyn Peters-Samuelson, Till Strowig and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 The variation in kidney function explained by host characteristics.

The bars represented the proportion of variation in eGFR (a) and impaired kidney function (b) explained by host characteristics and gut microbiome. The R2 was calculated using a univariate linear regression, with each explanatory variable analyzed separately in individual models. P values were not adjusted for multiple comparisons. All statistical tests were two-sided. The eGFR was calculated by using the 2012 Chronic Kidney Disease Epidemiology Collaboration equation based on creatinine and cystatin C. Impaired kidney function was defined as eGFR less than 60 mL/min/1.73 m2. Participants with impaired kidney function, n = 328; Participants with normal kidney function, n = 1,222. eGFR, estimated glomerular filtration rate. BMI, body mass index. Medication Use, whether people have had at least five types of drugs. TG, triglyceride. HDL-c, high-density lipoprotein cholesterol. LDL-c, low-density lipoprotein cholesterol. PCo, principal coordinates.

Source data

Extended Data Fig. 2 Associations of host characteristics with the gut microbial composition.

(a–d) Species-level and (e–h) pathway-level alpha diversity across participants with different kidney function status. P values for group differences were determined using multivariate linear regression, with adjustments for age, sex, BMI, diabetes, hypertension, cancer, medication use, HDL-c, and LDL-c. The boxes represented the median and interquartile range (IQR) of the distributions, and top and bottom whiskers marked the point at 1.5×IQR. Participants of impaired kidney function, n = 328; Participants of normal kidney function, n = 1,222; Participants of eGFR ≥ 90 mL/min/1.73 m2, n = 187; Participants of eGFR ≥ 60 & < 90 mL/min/1.73 m2, n = 1035; Participants of eGFR < 60 mL/min/1.73 m2, n = 328. (i) The microbial composition among participants with impaired kidney function or normal kidney function at the pathway-level. Group difference was calculated by permutational multivariate analysis of variance (PERMANOVA) using Bray-Curtis dissimilarity. Participants with impaired kidney function, n = 328; Participants with normal kidney function, n = 1,222. (j–l) Proportion of the variance in microbiome taxonomic composition explained by each measured kidney function parameters and other host characteristics. (m–p) Proportion of the variance in microbiome functional composition explained by each measured kidney function parameters and other host characteristics. The explained variance (R2) for (j–p) was calculated by PERMANOVA using Bray-Curtis dissimilarity, and all variables were jointly fitted in one model for each panel. ns, not significant (P > 0.05). eGFR, estimated glomerular filtration rate. BMI, body mass index. Medication Use, whether people have had at least five types of drugs. HDL-c, high-density lipoprotein cholesterol. LDL-c, low-density lipoprotein cholesterol. P values were not adjusted for multiple comparisons. All statistical tests were two-sided.

Source data

Extended Data Fig. 3 Association of Phocaeicola vulgatus with kidney function.

The data represented the AST-transformed relative abundance of P. vulgatus across participants with different kidney disease severity in the RLAS cohort, HS study, and published dataset. P values for the group difference were tested by MaAsLin2 (two-sided test) with the adjustment of age, sex, body mass index, diabetes, hypertension, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol in the HS study, and further adjusted for cancer and medication use in the RLAS cohort. The Benjamini-Hochberg method was used to calculate FDR-adjusted P values only in the RLAS cohort. The boxes represented the median and interquartile range (IQR) of the distributions, and top and bottom whiskers marked the point at 1.5×IQR. Participants of eGFR ≥ 90 mL/min/1.73 m2, n = 187; Participants of eGFR ≥ 60 & < 90 mL/min/1.73 m2, n = 1,035; Participants of eGFR < 60 mL/min/1.73 m2, n = 328. Participants in HS study: control, n = 15; CKD, n = 47; KF, n = 52. Participants in the published dataset: control, n = 69; KF, n = 223. ns, not significant (P > 0.05). eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease; KF, kidney failure.

Source data

Extended Data Fig. 4 Associations of plasma ammonia with eGFR (a) and the relative abundance of S. copri (b) in HS study.

The associations were estimated by multivariable linear regression (a) and MaAsLin2 (b), with adjustment for age, sex, body mass index, diabetes, hypertension, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and abnormal liver function. All statistical tests were two-sided. Abnormal liver function was defined as any of the following: alanine aminotransferase > 40 U/L, aspartate aminotransferase > 35 U/L, or total bilirubin > 21 μmol/l, or direct Bilirubin > 8 μmol/l, or alkaline phosphatase > 100 U/L, or gamma-glutamyl transferase > 45 U/L. Participants with plasma ammonia data: control, n = 15; CKD, n = 39; KF = 50. eGFR, estimated glomerular filtration rate; CKD, chronic kidney disease; KF, kidney failure.

Source data

Extended Data Fig. 5 Correlation between microbial species and functional features.

Data represented the correlations of 7 pathways and 6 enzymes related to ammonia production (x-axis) with 16 microbial species related to kidney function and the enzyme/gene related to ammonia assimilation (y-axis) in the RLAS cohort. We included the 15 replicated species associated with eGFR, and the species that showed the strongest inverse association with eGFR, that is, P. vulgatus. Color and numbers indicated the Spearman’s correlation coefficient. *P < 0.05, **P < 0.01, ***P < 0.001. P values were not adjusted for multiple comparisons. All statistical tests were two-sided.

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Extended Data Fig. 6 Ammonia accumulation exacerbated kidney disease severity.

(a) Experimental design. ACD-fed SPF mice were treated with NaCl or NH4Cl water for 4 weeks. The diagram was created using BioRender. (b, c) Daily water intake (b) and food intake (c) per mouse basis in NaCl and NH4Cl water treated groups (n = 6 replicates/treatment). (d) Body weight of ACD-fed mice after 4 weeks of NaCl or NH4Cl water treatment. (e–g) Plasma ammonia (e), serum creatinine (f), and BUN (g) levels in CKD mice after 4 weeks of NaCl or NH4Cl water treatment. (h–l) Representative images (h) and quantification (i–l) of kidney sections from mice in (a), from top to bottom: PAS staining (400×, red arrow indicates glomerular atrophy and black arrow indicates tubule dilation) and quantification of the glomerulosclerotic index in renal glomeruli (i); Masson’s trichrome staining (400×) and quantification of the proportion of the fibrotic area in renal cortex (j); Immunohistochemistry of TGF-β1 (200×) and quantification of the anti-inflammatory cytokine TGF-β1 positive area in renal cortex (k); Immunohistochemistry for 8-OHdG (200×) and quantification of the DNA damage marker 8-OHdG positive area in renal cortex (l). n = 6 mice per group. Data are presented as the mean ± s.e.m. ns, not significant (P > 0.05). *P < 0.05, **P < 0.01, ***P < 0.001 determined by two-tailed Student’s t-test (b-g, and i-l). ACD, adenine-containing diet; SPF, specific-pathogen-free; CKD, chronic kidney disease; BUN, blood urea nitrogen; PAS, Periodic Acid-Schiff; TGF-β1, transforming growth factor-β1; 8-OHdG, 8-hydroxy-2′-deoxyguanosine.

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Extended Data Fig. 7 S. copri transplantation delayed kidney damage.

(a–c) The relative abundance of S. copri (a), asnA gene (b), and P. vulgatus (c) in fecal samples from mice gavaged with S. copri, P. vulgatus, or vehicle, determined by qPCR. (d, e) Daily water intake (d) and food intake (e) per mouse basis in vehicle or bacteria strain gavaged groups (n = 6 replicates/treatment). (f) Body weight of CKD mice after 4 weeks gavage-feeding vehicle or bacteria strain. (g, h) Representative immunohistochemistry for TGF-β1 (200×, g) and 8-OHdG (200×, h) staining of kidney sections from S. copri, P. vulgatus, or vehicle gavaged CKD mice. (i) Quantification of the anti-inflammatory cytokine TGF-β1 positive area in renal cortex based on TGF-β1 immunohistochemical sections. (j) Quantification of the DNA damage marker 8-OHdG positive area in renal cortex based on 8-OHdG immunohistochemical sections. n = 6 mice per group. Data are presented as the mean ± s.e.m. ns, not significant (P > 0.05). *P < 0.05, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test (a-f and i-j). CKD, chronic kidney disease; TGF-β1, transforming growth factor-β1; 8-OHdG, 8-hydroxy-2′-deoxyguanosine.

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Extended Data Fig. 8 Knocking out of asnA in S. copri abolished its protection against ammonia-induced kidney damage.

(a) Schematic representation of the workflow for knocking out the asnA gene in S. copri by allelic exchange via a suicide plasmid (left). The workflow includes the construction of donor E. coli carrying the suicide plasmid, the conjugation between donor E. coli and recipient S. copri, homologous recombination, and screening and identification of asnA-knockout S. copri. The diagram was created using BioRender. The picture shows the screening of erythromycin-sensitive colonies using blood plates, and the electropherogram shows the confirmation of the asnA gene deletion by PCR validation (right). (b, c) The relative abundance of S. copri (b) and asnA gene (c) in fecal samples from mice gavaged with vehicle, WT S. copri, or asnA-KO S. copri, determined by qPCR. (d, e) Daily water intake (d) and food intake (e) per mouse basis in vehicle, WT S. copri, or asnA-KO S. copri gavaged groups (n = 6 replicates/treatment). (f) Body weight of CKD mice after four weeks gavage-feeding vehicle, WT S. copri, or asnA-KO S. copri. (g, h) Representative immunohistochemistry for TGF-β1 (200×, g) and 8-OHdG (200×, h) staining of kidney sections from vehicle, WT S. copri, or asnA-KO S. copri gavaged CKD mice. (i) Quantification of the anti-inflammatory cytokine TGF-β1 positive area in renal cortex based on TGF-β1 immunohistochemical sections. (j) Quantification of the DNA damage marker 8-OHdG positive area in renal cortex based on 8-OHdG immunohistochemical sections. n = 6 mice per group. Data are presented as the mean ± s.e.m. ns, not significant (P > 0.05). *P < 0.05, **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test (b-f and i-j). asnA-KO S. copri, asnA-knockout S. copri; WT S. copri, wild type S. copri; CKD, chronic kidney disease; TGF-β1, transforming growth factor-β1; 8-OHdG, 8-hydroxy-2′-deoxyguanosine.

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Extended Data Fig. 9 The transplantation of asnA-overexpressing E. coli delayed kidney damage.

(a) Schematic diagram illustrating the experimental workflow for the introduction of the asnA gene into E. coli, and the transfection of the control E. coli with an unloaded plasmid (left). The diagram was created using BioRender. Western blot analysis depicting the expression of His-tagged asnA protein in the engineered E. coli strains (right), indicating successful protein expression at specific molecular weights. (b) The relative abundance of the asnA gene in fecal samples from mice gavaged with vehicle or engineered E. coli, determined by qPCR. (c, d) Daily water intake (c) and food intake (d) per mouse basis in vehicle or engineered E. coli gavaged groups (n = 6 replicates/treatment). (e) Body weight of CKD mice after 4 weeks gavage-feeding vehicle or engineered E. coli. (f–g) Representative immunohistochemistry for TGF-β1 (200×, f) and 8-OHdG (200×, g) staining of kidney sections from vehicle, asnA-OE E. coli, or control E. coli gavaged CKD mice. (h) Quantification of the anti-inflammatory cytokine TGF-β1 positive area in renal cortex based on TGF-β1 immunohistochemical sections. (i) Quantification of the DNA damage marker 8-OHdG positive area in renal cortex based on 8-OHdG immunohistochemical sections. n = 6 mice per group. Data are presented as the mean ± s.e.m. ns, not significant (P > 0.05). *P < 0.05, **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test (b-e and h-i). asnA-OE E. coli, asnA-overexpressing E. coli; CKD, chronic kidney disease; TGF-β1, transforming growth factor-β1; 8-OHdG, 8-hydroxy-2′-deoxyguanosine.

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Extended Data Fig. 10 Graphical abstract of the study workflow and findings.

Workflow was created with Biorender.com.

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Lin, S., Sun, Z., Zhu, X. et al. Segatella copri and gut microbial ammonia metabolism contribute to chronic kidney disease pathogenesis. Nat Microbiol 10, 1684–1697 (2025). https://doi.org/10.1038/s41564-025-02039-y

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