Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Gut microbiota strain richness is species specific and affects engraftment

Nature volume 637, pages 422–429 (2025)Cite this article

Subjects

An Author Correction to this article was published on 28 January 2025

This article has been updated

Abstract

Despite the fundamental role of bacterial strain variation in gut microbiota function1,2,3,4,5,6, the number of unique strains of a species that can stably colonize the human intestine is still unknown for almost all species. Here we determine the strain richness (SR) of common gut species using thousands of sequenced bacterial isolates with paired metagenomes. We show that SR varies across species, is transferable by faecal microbiota transplantation, and is uniquely low in the gut compared with soil and lake environments. Active therapeutic administration of supraphysiologic numbers of strains per species increases recipient SR, which then converges back to the population average after dosing is ceased. Stratifying engraftment outcomes by high or low SR shows that SR predicts microbial addition or replacement in faecal transplants. Together, these results indicate that properties of the gut ecosystem govern the number of strains of each species colonizing the gut and thereby influence strain addition and replacement in faecal microbiota transplantation and defined live biotherapeutic products.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SRj of 92 human gut species.
Fig. 2: Factors influencing SRj .
Fig. 3: FMT durably transmits healthy donor SRj to rCDI patients.
Fig. 4: Supraphysiologic manipulation of SRj in FMT recipients converges to the population baseline observed in untransplanted people.

Similar content being viewed by others

Data availability

Sequence data files (FASTQ) for all cultured and whole-genome assembled sequences are stored in the SRA under project number PRJNA880610. Previously published whole-genome assembled sequenced can be found under project number PRJNA637878. Sequence data files (FASTQ) for all metagenomic sequencing samples from the US FMT study for rCDI patients can be found under project number PRJNA637878. Sequence data files (FASTQ) for all metagenomic sequencing samples from the Leiden FMT validation cohort for rCDI patients can be found under project number PRJEB44737. Sequence data files for metagenomic sequencing samples from the pooled donor FMT trial for UC patients can be found at PRJEB26357. B. fragilis isolate whole genomes7 that were used for validation can be accessed at project number PRJNA524913. Isolate whole genomes from ref. 46 can be accessed at project number PRJNA544527. Source data for Extended Data Fig. 1g,h are based on source data from Extended Data Fig. 1d–f. Source data for Fig. 2b and Extended Data Fig. 1a,b are available at Zenodo (https://doi.org/10.5281/zenodo.13942097)84. Source data are provided with this paper.

Code availability

The strain-tracking algorithm, Strainer, was published previously8. Code for Strainer can be accessed at https://bitbucket.org/faithj02/strainer-metagenomics/.

Change history

References

  1. Yang, C. et al. Immunoglobulin A antibody composition is sculpted to bind the self gut microbiome. Sci. Immunol. 7, eabg3208 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Parida, S. et al. A procarcinogenic colon microbe promotes breast tumorigenesis and metastatic progression and concomitantly activates notch and β-catenin axes. Cancer Discov. 11, 1138–1157 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Arthur, J. C. et al. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338, 120–123 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  4. Britton, G. J. et al. Defined microbiota transplant restores Th17/RORγt+ regulatory T cell balance in mice colonized with inflammatory bowel disease microbiotas. Proc Natl Acad. Sci. USA 117, 21536–21545 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  5. Yang, C. et al. Fecal IgA levels are determined by strain-level differences in bacteroides ovatus and are modifiable by gut microbiota manipulation. Cell Host Microbe 27, 467–475.e6 (2020).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  6. Spindler, M. P. et al. Human gut microbiota stimulate defined innate immune responses that vary from phylum to strain. Cell Host Microbe 30, 1481–1498.e5 (2022).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  7. Zhao, S. et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe 25, 656–667.e8 (2019).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  8. Aggarwala, V. et al. Precise quantification of bacterial strains after fecal microbiota transplantation delineates long-term engraftment and explains outcomes. Nat. Microbiol. 6, 1309–1318 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Conwill, A. et al. Anatomy promotes neutral coexistence of strains in the human skin microbiome. Cell Host Microbe 30, 171–182.e7 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat. Biotechnol. 39, 727–736 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Drewes, J. L. et al. Transmission and clearance of potential procarcinogenic bacteria during fecal microbiota transplantation for recurrent Clostridioides difficile. JCI Insight 4, 130848 (2019).

    Article  PubMed  MATH  Google Scholar 

  12. Vatanen, T. et al. Genomic variation and strain-specific functional adaptation in the human gut microbiome during early life. Nat. Microbiol. 4, 470–479 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Zheng, W. et al. High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome. Science 376, eabm1483 (2022).

    Article  CAS  PubMed  Google Scholar 

  14. Gupta, S. et al. LB973 Cutaneous surgical wounds have distinct microbiomes from intact skin. J. Invest. Dermatol. 142, B24 (2022).

    Article  Google Scholar 

  15. Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Valles-Colomer, M. et al. The person-to-person transmission landscape of the gut and oral microbiomes. Nature 614, 125–135 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schmidt, T. S. B. et al. Drivers and determinants of strain dynamics following fecal microbiota transplantation. Nat. Med. 28, 1902–1912 (2022).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  19. Schloss, P. D., Iverson, K. D., Petrosino, J. F. & Schloss, S. J. The dynamics of a family’s gut microbiota reveal variations on a theme. Microbiome 2, 25 (2014).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  20. Tamburini, F. B. et al. Precision identification of diverse bloodstream pathogens in the gut microbiome. Nat. Med. 24, 1809–1814 (2018).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  21. Dsouza, M. et al. Colonization of the live biotherapeutic product VE303 and modulation of the microbiota and metabolites in healthy volunteers. Cell Host Microbe 30, 583–598.e8 (2022).

    Article  CAS  PubMed  MATH  Google Scholar 

  22. Siranosian, B. A. et al. Rare transmission of commensal and pathogenic bacteria in the gut microbiome of hospitalized adults. Nat. Commun. 13, 586 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  23. Garud, N. R., Good, B. H., Hallatschek, O. & Pollard, K. S. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 17, e3000102 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Blaser, M. J. Missing Microbes: How the Overuse of Antibiotics Is Fueling Our Modern Plagues (Henry Holt and Co., 2014).

  25. Ni, J., Wu, G. D., Albenberg, L. & Tomov, V. T. Gut microbiota and IBD: causation or correlation? Nat. Rev. Gastroenterol. Hepatol. 14, 573–584 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Raffals, L. E. et al. The development and initial findings of a study of a prospective adult research cohort with inflammatory bowel disease (SPARC IBD). Inflamm. Bowel Dis. 28, 192–199 (2022).

    Article  PubMed  Google Scholar 

  27. Schnorr, S. L. et al. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5, 3654 (2014).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  28. Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  29. Lee, S. M. et al. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 501, 426–431 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  30. Verster, A. J. et al. The landscape of type VI secretion across human gut microbiomes reveals its role in community composition. Cell Host Microbe 22, 411–419.e4 (2017).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  31. Han, S. R. et al. Helicobacter pylori: clonal population structure and restricted transmission within families revealed by molecular typing. J. Clin. Microbiol. 38, 3646–3651 (2000).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  32. Saheb Kashaf, S. et al. Staphylococcal diversity in atopic dermatitis from an individual to a global scale. Cell Host Microbe 31, 578–592.e6 (2023).

    Article  CAS  PubMed  MATH  Google Scholar 

  33. Paramsothy, S. et al. Multidonor intensive faecal microbiota transplantation for active ulcerative colitis: a randomised placebo-controlled trial. Lancet 389, 1218–1228 (2017).

    Article  PubMed  MATH  Google Scholar 

  34. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  35. Faith, J. J., Colombel, J.-F. & Gordon, J. I. Identifying strains that contribute to complex diseases through the study of microbial inheritance. Proc. Natl Acad. Sci. USA 112, 633–640 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  36. Faith, J. J. et al. Strain population structure varies widely across bacterial species and predicts strain colonization in unrelated individuals. Preprint at bioRxiv https://doi.org/10.1101/2020.10.17.343640 (2020).

  37. Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  38. Rodriguez-R, L. M. et al. An ANI gap within bacterial species that advances the definitions of intra-species units. mBio 15, e02696-23 (2024).

  39. Seekatz, A. M., Rao, K., Santhosh, K. & Young, V. B. Dynamics of the fecal microbiome in patients with recurrent and nonrecurrent Clostridium difficile infection. Genome Med. 8, 47 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Contijoch, E. J. et al. Gut microbiota density influences host physiology and is shaped by host and microbial factors. eLife 8, e40553 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Weingarden, A. et al. Dynamic changes in short- and long-term bacterial composition following fecal microbiota transplantation for recurrent Clostridium difficile infection. Microbiome 3, 10 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333, 101–104 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  43. Bahtiyar Yilmaz, A. et al. Long-term evolution and short-term adaptation of microbiota strains and sub-strains in mice. Cell Host Microbe 29, 650–663.e9 (2021).

    Article  PubMed  MATH  Google Scholar 

  44. McNulty, N. P. et al. Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome. PLoS Biol. 11, e1001637 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Walker, A. W. et al. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J 5, 220–230 (2011).

    Article  CAS  PubMed  MATH  Google Scholar 

  46. Poyet, M. et al. A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nat. Med. 25, 1442–1452 (2019).

    Article  CAS  PubMed  MATH  Google Scholar 

  47. Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Page, A. J. et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  49. Brynildsrud, O., Bohlin, J., Scheffer, L. & Eldholm, V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol 17, 238 (2016).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  50. Hernández-Plaza, A. et al. eggNOG 6.0: enabling comparative genomics across 12 535 organisms. Nucleic Acids Res. 51, D389–D394 (2023).

    Article  PubMed  Google Scholar 

  51. Forbes, J. D. et al. A comparative study of the gut microbiota in immune-mediated inflammatory diseases—does a common dysbiosis exist? Microbiome 6, 221 (2018).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  52. Pascal, V. et al. A microbial signature for Crohn’s disease. Gut 66, 813–822 (2017).

    Article  CAS  PubMed  MATH  Google Scholar 

  53. Michail, S. et al. Alterations in the gut microbiome of children with severe ulcerative colitis. Inflamm. Bowel Dis. 18, 1799–1808 (2012).

    Article  PubMed  MATH  Google Scholar 

  54. Antharam, V. C. et al. Intestinal dysbiosis and depletion of butyrogenic bacteria in Clostridium difficile infection and nosocomial diarrhea. J. Clin. Microbiol. 51, 2884–2892 (2013).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  55. Hirten, R. P. et al. Microbial engraftment and efficacy of fecal microbiota transplant for Clostridium difficile in patients with and without inflammatory bowel disease. Inflamm. Bowel Dis. 25, 969–979 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Terveer, E. M. et al. Faecal microbiota transplantation for Clostridioides difficile infection: four years’ experience of the Netherlands Donor Feces Bank. United Eur. Gastroenterol. J. 8, 1236–1247 (2020).

    Article  CAS  MATH  Google Scholar 

  57. Ianiro, G. et al. Variability of strain engraftment and predictability of microbiome composition after fecal microbiota transplantation across different diseases. Nat. Med. 28, 1913–1923 (2022).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  58. Smith, V. H. Microbial diversity-productivity relationships in aquatic ecosystems. FEMS Microbiol. Ecol. 62, 181–186 (2007).

    Article  CAS  PubMed  MATH  Google Scholar 

  59. Graham, J. H. & Duda, J. J. The humpbacked species richness-curve: a contingent rule for community ecology. Int. J. Ecol. 2011, e868426 (2011).

    Article  MATH  Google Scholar 

  60. Sassone-Corsi, M. et al. Microcins mediate competition among Enterobacteriaceae in the inflamed gut. Nature 540, 280–283 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  61. Becattini, S. et al. Commensal microbes provide first line defense against Listeria monocytogenes infection. J. Exp. Med. 214, 1973–1989 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Caballero, S. et al. Cooperating commensals restore colonization resistance to vancomycin-resistant Enterococcus faecium. Cell Host Microbe 21, 592–602.e4 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ducarmon, Q. R. et al. Gut microbiota and colonization resistance against bacterial enteric infection. Microbiol Mol Biol Rev 83, e00007–e00019 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Donia, M. S. et al. A systematic analysis of biosynthetic gene clusters in the human microbiome reveals a common family of antibiotics. Cell 158, 1402–1414 (2014).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  65. Chatzidaki-Livanis, M., Coyne, M. J. & Comstock, L. E. An antimicrobial protein of the gut symbiont Bacteroides fragilis with a MACPF domain of host immune proteins. Mol. Microbiol. 94, 1361–1374 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Roelofs, K. G., Coyne, M. J., Gentyala, R. R., Chatzidaki-Livanis, M. & Comstock, L. E. Bacteroidales secreted antimicrobial proteins target surface molecules necessary for gut colonization and mediate competition in vivo. mBio 7, e01055–16 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Cohan, F. M. Transmission in the origins of bacterial diversity, from ecotypes to phyla. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.mtbp-0014-2016 (2017).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  68. Leslie, J. L. et al. Protection from lethal Clostridioides difficile infection via intraspecies competition for cogerminant. mBio 12, e00522–21 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Fung, C. et al. High-resolution mapping reveals that microniches in the gastric glands control Helicobacter pylori colonization of the stomach. PLoS Biol. 17, e3000231 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Balato, A. et al. Human microbiome: composition and role in inflammatory skin diseases. Arch. Immunol. Ther. Exp. (Warsz.). 67, 1–18 (2019).

    Article  PubMed  Google Scholar 

  71. Browne, H. P. et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature 533, 543–546 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  72. Lagier, J. C. et al. Culturing the human microbiota and culturomics. Nat. Rev. Microbiol. 16, 540–550 (2018).

    Article  CAS  PubMed  MATH  Google Scholar 

  73. 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 

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

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  75. Fehlner-Peach, H. et al. Distinct polysaccharide utilization profiles of human intestinal Prevotella copri isolates. Cell Host Microbe 26, 680–690.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Lopez-Siles, M. et al. Alterations in the abundance and co-occurrence of Akkermansia muciniphila and Faecalibacterium prausnitzii in the colonic mucosa of inflammatory bowel disease subjects. Front. Cell. Infect. Microbiol. 8, 281 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Louie, T. et al. VE303, a defined bacterial consortium, for prevention of recurrent clostridioides difficile infection: a randomized clinical trial. JAMA 329, 1356–1366 (2023).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  78. Nooij, S. et al. Fecal microbiota transplantation influences procarcinogenic Escherichia coli in recipient recurrent Clostridioides difficile patients. Gastroenterology 161, 1218–1228.e5 (2021).

    Article  CAS  PubMed  Google Scholar 

  79. Nooij, S. et al. Long-term beneficial effect of faecal microbiota transplantation on colonisation of multidrug-resistant bacteria and resistome abundance in patients with recurrent Clostridioides difficile infection. Genome Med. 16, 37 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Britton, G. J. et al. Microbiotas from humans with inflammatory bowel disease alter the balance of gut Th17 and RORγt+ regulatory T cells and exacerbate colitis in mice. Immunity 50, 212–224 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Llewellyn, S. R. et al. Interactions between diet and the intestinal microbiota alter intestinal permeability and colitis severity in mice. Gastroenterology 154, 1037–1046.e2 (2018).

    Article  PubMed  Google Scholar 

  83. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).

    Article  MathSciNet  MATH  Google Scholar 

  84. Chen-Liaw, A. Source Data. Zenodo https://doi.org/10.5281/zenodo.13942097 (2024).

Download references

Acknowledgements

We thank C. Fermin, E. Vazquez and G. N. Escano for gnotobiotic husbandry. This work was supported in part by the staff and resources of the Mount Sinai Gnotobiotic Facility and the Scientific Computing Division at the Icahn School of Medicine at Mount Sinai. This work was supported by the National Institutes of Health grants (nos. NIDDK DK112978, NIDDK DK124133, NIDDK DK123749), an NIH F30 to A.C.-L. (NIDDK DK131862), Crohn’s and Colitis Foundation awards (no. 650451 to V.A.; no. 651867 to J.J.F.; no. 988415 to N.O.K.) and Janssen Research & Development.

Author information

Authors and Affiliations

  1. Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Alice Chen-Liaw, Varun Aggarwala, Ilaria Mogno, Zhihua Li & Jeremiah J. Faith

  2. Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Varun Aggarwala, Ilaria Mogno, Zhihua Li, Joseph Eggers & Jeremiah J. Faith

  3. Reliance Foundation Institution of Education and Research, Jio Institute, Navi Mumbai, India

    Varun Aggarwala

  4. Concord Clinical School, University of Sydney, Sydney, New South Wales, Australia

    Craig Haifer & Sudarshan Paramsothy

  5. School of Clinical Medicine, University of New South Wales, Sydney, New South Wales, Australia

    Craig Haifer

  6. Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Drew Helmus, Jean Frédéric Colombel, Ari Grinspan & Marla C. Dubinsky

  7. Janssen R&D, Spring House, PA, USA

    Amy Hart, Jan Wehkamp & Esi S. N. Lamousé-Smith

  8. Department of Bacteriology, University of Wisconsin—Madison, Madison, WI, USA

    Robert L. Kerby & Federico E. Rey

  9. Department of Gastroenterology and Medicine, St. Vincent’s Hospital, Melbourne, Victoria, Australia

    Michael A. Kamm

  10. Vedanta Biosciences, Cambridge, MA, USA

    Bernat Olle, Jason M. Norman, Rajita Menon, Andrea R. Watson & Emily Crossette

  11. Netherlands Donor Feces Bank, Leiden University Medical Center, Leiden, The Netherlands

    Elisabeth M. Terveer & Josbert J. Keller

  12. Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands

    Elisabeth M. Terveer

  13. Department of Gastroenterology, Haaglanden Medical Center, The Hague, The Netherlands

    Josbert J. Keller

  14. Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands

    Josbert J. Keller

  15. Centre for Digestive Diseases, Sydney, New South Wales, Australia

    Thomas J. Borody

  16. Department of Gastroenterology and Hepatology, Macquarie University Hospital, Sydney, New South Wales, Australia

    Sudarshan Paramsothy

  17. School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia

    Nadeem O. Kaakoush

Authors
  1. Alice Chen-Liaw
    View author publications

    Search author on:PubMed Google Scholar

  2. Varun Aggarwala
    View author publications

    Search author on:PubMed Google Scholar

  3. Ilaria Mogno
    View author publications

    Search author on:PubMed Google Scholar

  4. Craig Haifer
    View author publications

    Search author on:PubMed Google Scholar

  5. Zhihua Li
    View author publications

    Search author on:PubMed Google Scholar

  6. Joseph Eggers
    View author publications

    Search author on:PubMed Google Scholar

  7. Drew Helmus
    View author publications

    Search author on:PubMed Google Scholar

  8. Amy Hart
    View author publications

    Search author on:PubMed Google Scholar

  9. Jan Wehkamp
    View author publications

    Search author on:PubMed Google Scholar

  10. Esi S. N. Lamousé-Smith
    View author publications

    Search author on:PubMed Google Scholar

  11. Robert L. Kerby
    View author publications

    Search author on:PubMed Google Scholar

  12. Federico E. Rey
    View author publications

    Search author on:PubMed Google Scholar

  13. Jean Frédéric Colombel
    View author publications

    Search author on:PubMed Google Scholar

  14. Michael A. Kamm
    View author publications

    Search author on:PubMed Google Scholar

  15. Bernat Olle
    View author publications

    Search author on:PubMed Google Scholar

  16. Jason M. Norman
    View author publications

    Search author on:PubMed Google Scholar

  17. Rajita Menon
    View author publications

    Search author on:PubMed Google Scholar

  18. Andrea R. Watson
    View author publications

    Search author on:PubMed Google Scholar

  19. Emily Crossette
    View author publications

    Search author on:PubMed Google Scholar

  20. Elisabeth M. Terveer
    View author publications

    Search author on:PubMed Google Scholar

  21. Josbert J. Keller
    View author publications

    Search author on:PubMed Google Scholar

  22. Thomas J. Borody
    View author publications

    Search author on:PubMed Google Scholar

  23. Ari Grinspan
    View author publications

    Search author on:PubMed Google Scholar

  24. Sudarshan Paramsothy
    View author publications

    Search author on:PubMed Google Scholar

  25. Nadeem O. Kaakoush
    View author publications

    Search author on:PubMed Google Scholar

  26. Marla C. Dubinsky
    View author publications

    Search author on:PubMed Google Scholar

  27. Jeremiah J. Faith
    View author publications

    Search author on:PubMed Google Scholar

Contributions

A.C.-L. and J.J.F. wrote the manuscript. A.C.-L., I.M., Z.L. and J.E. performed the microbiology, mouse model and molecular biology experiments on human bacterial isolates. R.L.K., F.E.R., I.M., Z.L., A.C.-L. and J.J.F performed the microbiology and molecular biology experiments on environmental microbes. A.G., A.C.-L., J.J.F. and V.A. performed the US rCDI clinical study and analysis. E.M.T., J.J.K., B.O., J.M.N., R.M., A.R.W., E.C., A.C.-L. and J.J.F. performed the Netherlands rCDI clinical study, data generation and analysis. S.P., N.O.K., C.H., M.A.K., T.J.B., V.A., A.C.-L. and J.J.F. performed the UC FMT clinical study, data generation and analysis. M.C.D., D.H., J.F.C., A.H., J.W., E.S.N.L-S., J.J.F. and A.C.-L., performed the non-FMT human clinical collections, data generation and analysis. All authors read and provided critical feedback and approved the final manuscript.

Corresponding author

Correspondence to Jeremiah J. Faith.

Ethics declarations

Competing interests

J.J.F. is a scientific advisory board member and consultant to Vedanta Biosciences, Inc. A.H., J.W., E.S.N.L.-S. are employees of Janssen Research & Development. B.O., J.M.N., R.M., A.R.W. and E.C. are employees of Vedanta Biosciences. J.K. and E.T. received research grants from Vedanta Biosciences. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Determination of SRj strain threshold and validation of SRj with deeper sampling of a subset of cultured gut bacterial species.

A, K-mer overlap was calculated for all pairwise combinations of isolates from a species cultured from two unrelated individuals (purple) with no direct microbial transfer between them or an individual’s own microbes from a single timepoint (green). Dotted line shows the threshold of 0.96 k-mer similarity. B, fastANI versus pairwise k-mer overlap for isolates of the same species. Dotted line represents k-mer overlap threshold of 0.96 (k-mer distance of <0.04). C, Comparison of B. fragilis SRij for the individuals in this study and in the study by Zhao et al. D, Preliminary calculation of SRj using genomes isolated from the Broad pipeline (standard pipeline used to create libraries of cultured gut bacteria). For deeper sampling estimates of SRj, we isolated additional genomes from the same species from E, the original human stool sample and F, mouse stool samples from gnotobiotic mice colonized with the same human stool (N = 2-3 mice per microbiota/diet combination) and given unique diets for strain enrichment. Error bars in D–F represent SEM. Comparison of SRj calculated with genomes from the broad pipeline or the broad pipeline plus additional genomes from G, human stool samples or H, human and mouse stool samples. I–L, Rarefaction curves for validation species. Dotted line shows the mean isolates/species for each species (overall mean isolates/species across the dataset was 4.7 isolates as demonstrated in Fig. 1b). M,N, Rarefaction curves for a soil species (M) and lake species (N). **p < 0.01, paired Wilcoxon test, each point represents the average SRj measured from microbiomes of a healthy or disease state. ns: not significant by paired Wilcoxon test. Grey regions on rarefaction curves indicate 95% confidence intervals.

Source Data

Extended Data Fig. 2 The influence of core genome size and disease state on strain richness.

A, Spearman rank correlation for SRj versus core genome fraction for several genera. B, SRj was compared for 59 species present in both healthy and CD microbiomes. C, SRj was compared for 51 species present in both healthy and UC microbiomes. Each point represents the average SRj of a species as calculated using isolates cultured from healthy microbiomes or isolates cultured from disease microbiomes. Lines in B,C connect the SRj for each shared species between a healthy subject and a subject with IBD. ns: not significant by paired Wilcoxon test.

Source Data

Extended Data Fig. 3 Metagenomics-quantified SRj correlates with the cultured SRj measured across our cohort.

A, Spearman correlation between donor SRj as measured by metagenomics and SRj from our original cultured cohort (first panel) and differences in SRj between the two groups (second panel). B, Spearman correlation between recipient SRj as measured by metagenomics at week 8 post-FMT and SRj from our original cohort (first panel) and differences in SRj between the two groups (second panel). A,B, Each point represents the average SRj for species. C, Heatmap representing the remaining six donors who donated stool to six different recipients. D, Correlation of SRj across all recipients and all donors in an independent FMT validation cohort (Leiden).

Source Data

Extended Data Fig. 4 An ecological framework for strain persistence, replacement, or addition based on strain richness.

A, Schematic for FMT experimental design with multi-donor stool batches administered to each recipient. B, Within a species, bacterial strains vary in their engraftment frequency when administered in the context of a multi-donor FMT product. C, Spearman rank correlation between the metagenomics SRj of the individual FOCUS donors with the overall cultured SRj measured across our cohort. D, Expected outcomes for donor strain engraftment based on species SRj.

Source Data

Extended Data Table 1 SRj for each species sampled in our cultured cohort
Full size table
Extended Data Table 2 Species with significantly different SRj based on Dunn test with Benjamini-Hochberg correction
Full size table
Extended Data Table 3 Impact of deeper sampling and ex-germ-free mouse enrichment on SRj
Full size table
Extended Data Table 4 SRj changes over time across the FOCUS FMT trial for UC
Full size table
Extended Data Table 5 Patient characteristics from Leiden FMT validation cohort
Full size table

Supplementary information

Supplementary Tables

Table 1. Characteristics of participants used in this study, including health status and recent antibiotic use. Table 2. Strain genomes, accessions, and donor health status. Table 3. Species frequency and SRj. Table S4 Enrichment of COG categories and COGs in genes associated with SR.

Reporting Summary

Peer Review File

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen-Liaw, A., Aggarwala, V., Mogno, I. et al. Gut microbiota strain richness is species specific and affects engraftment. Nature 637, 422–429 (2025). https://doi.org/10.1038/s41586-024-08242-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-024-08242-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing