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:

Targeting symbionts by apolipoprotein L proteins modulates gut immunity

Nature volume 643, pages 210–218 (2025)Cite this article

Subjects

Abstract

The mammalian gut harbours trillions of commensal bacteria that interact with their hosts through various bioactive molecules1,2. However, the mutualistic strategies that hosts evolve to benefit from these symbiotic relationships are largely unexplored. Here we report that mouse enterocytes secrete apolipoprotein L9a and b (APOL9a/b) in the presence of microbiota. By integrating flow cytometry sorting of APOL9-binding bacterial taxa with 16S ribosomal RNA gene sequencing (APOL9-seq), we identify that APOL9a/b, as well as their human equivalent APOL2, coat gut bacteria belonging to the order of Bacteroidales with a high degree of specificity through commensal ceramide-1-phosphate (Cer1P) lipids. Genetic abolition of ceramide-1-phosphate synthesis pathways in gut-dominant symbiote Bacteroides thetaiotaomicron significantly decreases the binding of APOL9a/b to the bacterium. Instead of lysing the bacterial cells, coating of APOL9a/b induces the production of outer membrane vesicles (OMVs) from the target bacteria. Subsequently, the Bacteroides-elicited outer membrane vesicles enhance the host’s interferon-γ signalling to promote major histocompatibility complex class II expression in the intestinal epithelial cells. In mice, the loss of Apol9a/b compromises the gut major histocompatibility complex class II-instructed immune barrier function, leading to early mortality from infection by intestinal pathogens. Our data show how a host-elicited factor benefits gut immunological homeostasis by selectively targeting commensal ceramide molecules.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Identification of Bacteroidales as dominant APOL protein-binding symbionts.
Fig. 2: APOL proteins bind to gut commensal Bacteroidales through Cer1Ps.
Fig. 3: Targeting gut Bacteroidales by APOL proteins induces commensal OMV release.
Fig. 4: Bacteroidales-derived OMVs promote MHC-II gene expression in ileal ECs.
Fig. 5: APOL9a/b control intraepithelial CD4+CD8αα+ cells and mucosal infection.

Similar content being viewed by others

Data availability

The 16S rRNA-seq, bulk RNA-seq and scRNA-seq data generated in this study are available in the NCBI database under BioProject IDs PRJNA1107192, PRJNA1107183, PRJNA1107279, PRJNA1108888 and PRJNA1198673. The public datasets used in this study are available in the NCBI database under the GEO accession numbers GSE92332 and GSE125970 (refs. 27,28). All other data supporting the findings of this study are included in this manuscript. Source data are provided with this paper.

Code availability

No custom code was created for this paper.

References

  1. Donia, M. S. & Fischbach, M. A. Small molecules from the human microbiota. Science 349, 1254766 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Krautkramer, K. A., Fan, J. & Backhed, F. Gut microbial metabolites as multi-kingdom intermediates. Nat. Rev. Microbiol. 19, 77–94 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Arpaia, N. et al. Metabolites produced by commensal bacteria promote peripheral regulatory T-cell generation. Nature 504, 451–455 (2013).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  4. Smith, P. M. et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).

    Article  CAS  PubMed  ADS  Google Scholar 

  5. Furusawa, Y. et al. Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature 504, 446–450 (2013).

    Article  CAS  PubMed  ADS  Google Scholar 

  6. Hang, S. et al. Bile acid metabolites control TH17 and Treg cell differentiation. Nature 576, 143–148 (2019).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  7. Song, X. et al. Microbial bile acid metabolites modulate gut RORγ+ regulatory T cell homeostasis. Nature 577, 410–415 (2020).

    Article  CAS  PubMed  Google Scholar 

  8. Campbell, C. et al. Bacterial metabolism of bile acids promotes generation of peripheral regulatory T cells. Nature 581, 475–479 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  9. Cervantes-Barragan, L. et al. Lactobacillus reuteri induces gut intraepithelial CD4+CD8αα+ T cells. Science 357, 806–810 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Song, X. et al. Gut microbial fatty acid isomerization modulates intraepithelial T cells. Nature 619, 837–843 (2023).

    Article  CAS  PubMed  ADS  Google Scholar 

  11. Mazmanian, S. K., Round, J. L. & Kasper, D. L. A microbial symbiosis factor prevents intestinal inflammatory disease. Nature 453, 620–625 (2008).

    Article  CAS  PubMed  ADS  Google Scholar 

  12. Oh, S. F. et al. Host immunomodulatory lipids created by symbionts from dietary amino acids. Nature 600, 302–307 (2021).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  13. Peterson, L. W. & Artis, D. Intestinal epithelial cells: regulators of barrier function and immune homeostasis. Nat. Rev. Immunol. 14, 141–153 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Mukherjee, S. & Hooper, L. V. Antimicrobial defense of the intestine. Immunity 42, 28–39 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Wu, M. et al. Gut complement induced by the microbiota combats pathogens and spares commensals. Cell https://doi.org/10.1016/j.cell.2023.12.036 (2024).

  16. Cash, H. L., Whitham, C. V., Behrendt, C. L. & Hooper, L. V. Symbiotic bacteria direct expression of an intestinal bactericidal lectin. Science 313, 1126–1130 (2006).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  17. Vaishnava, S. et al. The antibacterial lectin RegIIIγ promotes the spatial segregation of microbiota and host in the intestine. Science 334, 255–258 (2011).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  18. Bergstrom, J. H. et al. Gram-positive bacteria are held at a distance in the colon mucus by the lectin-like protein ZG16. Proc. Natl Acad. Sci. USA 113, 13833–13838 (2016).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  19. McPherson, R. L. et al. Lectin-Seq: a method to profile lectin-microbe interactions in native communities. Sci. Adv. 9, eadd8766 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Palm, N. W. et al. Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease. Cell 158, 1000–1010 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kau, A. L. et al. Functional characterization of IgA-targeted bacterial taxa from undernourished Malawian children that produce diet-dependent enteropathy. Sci. Transl. Med. 7, 276ra224 (2015).

    Article  Google Scholar 

  22. Shapiro, J. M. et al. Immunoglobulin A targets a unique subset of the microbiota in inflammatory bowel disease. Cell Host Microbe 29, 83–93 e83 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hooper, L. V. et al. Molecular analysis of commensal host-microbial relationships in the intestine. Science 291, 881–884 (2001).

    Article  CAS  PubMed  ADS  Google Scholar 

  24. Geva-Zatorsky, N. et al. Mining the human gut microbiota for immunomodulatory organisms. Cell 168, 928–943 e911 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Sun, L. et al. Type I interferons link viral infection to enhanced epithelial turnover and repair. Cell Host Microbe 17, 85–97 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Kreit, M., Vertommen, D., Gillet, L. & Michiels, T. The interferon-inducible mouse apolipoprotein L9 and prohibitins cooperate to restrict Theiler’s virus replication. PLoS ONE 10, e0133190 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  28. Wang, Y. et al. Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J. Exp. Med. https://doi.org/10.1084/jem.20191130 (2020).

  29. Wexler, A. G. & Goodman, A. L. An insider’s perspective: Bacteroides as a window into the microbiome. Nat. Microbiol. 2, 17026 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Uzureau, S. et al. APOL1 C-terminal variants may trigger kidney disease through interference with APOL3 control of actomyosin. Cell Rep. 30, 3821–3836 e3813 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Brown, E. M. et al. Bacteroides-derived sphingolipids are critical for maintaining intestinal homeostasis and symbiosis. Cell Host Microbe 25, 668–680 e667 (2019).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  32. Johnson, E. L. et al. Sphingolipids produced by gut bacteria enter host metabolic pathways impacting ceramide levels. Nat. Commun. 11, 2471 (2020).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  33. Stankeviciute, G. et al. Convergent evolution of bacterial ceramide synthesis. Nat. Chem. Biol. 18, 305–312 (2022).

    Article  CAS  PubMed  Google Scholar 

  34. Dhakephalkar, T., Stukey, G. J., Guan, Z., Carman, G. M. & Klein, E. A. Characterization of an evolutionarily distinct bacterial ceramide kinase from Caulobacter crescentus. J. Biol. Chem. 299, 104894 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Heaver, S. L. et al. Characterization of inositol lipid metabolism in gut-associated Bacteroidetes. Nat. Microbiol. 7, 986–1000 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  37. Perez-Morga, D. et al. Apolipoprotein L-I promotes trypanosome lysis by forming pores in lysosomal membranes. Science 309, 469–472 (2005).

    Article  CAS  PubMed  ADS  Google Scholar 

  38. Thomson, R. & Finkelstein, A. Human trypanolytic factor APOL1 forms pH-gated cation-selective channels in planar lipid bilayers: relevance to trypanosome lysis. Proc. Natl Acad. Sci. USA 112, 2894–2899 (2015).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  39. Gaudet, R. G. et al. A human apolipoprotein L with detergent-like activity kills intracellular pathogens. Science https://doi.org/10.1126/science.abf8113 (2021).

  40. Stefan, K. L., Kim, M. V., Iwasaki, A. & Kasper, D. L. Commensal microbiota modulation of natural resistance to virus infection. Cell 183, 1312–1324 e1310 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Yang, D. et al. Dysregulated lung commensal bacteria drive interleukin-17B production to promote pulmonary fibrosis through their outer membrane vesicles. Immunity 50, 692–706 e697 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Shen, Y. et al. Outer membrane vesicles of a human commensal mediate immune regulation and disease protection. Cell Host Microbe 12, 509–520 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Heuberger, C., Pott, J. & Maloy, K. J. Why do intestinal epithelial cells express MHC class II? Immunology 162, 357–367 (2021).

    Article  CAS  PubMed  Google Scholar 

  44. Moon, S. et al. Niche-specific MHC II and PD-L1 regulate CD4+CD8αα+ intraepithelial lymphocyte differentiation. J. Exp. Med. https://doi.org/10.1084/jem.20201665 (2021).

  45. Lockhart, A., Mucida, D. & Bilate, A. M. Intraepithelial lymphocytes of the intestine. Annu. Rev. Immunol. https://doi.org/10.1146/annurev-immunol-090222-100246 (2024).

  46. Cullen, T. W. et al. Antimicrobial peptide resistance mediates resilience of prominent gut commensals during inflammation. Science 347, 170–175 (2015).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  47. Garcia-Bayona, L. & Comstock, L. E. Streamlined genetic manipulation of diverse Bacteroides and Parabacteroides isolates from the human gut microbiota. mBio https://doi.org/10.1128/mBio.01762-19 (2019).

  48. Sugiura, M. et al. Ceramide kinase, a novel lipid kinase. Molecular cloning and functional characterization. J. Biol. Chem. 277, 23294–23300 (2002).

    Article  CAS  PubMed  Google Scholar 

  49. Clarke, E. L. et al. Sunbeam: an extensible pipeline for analyzing metagenomic sequencing experiments. Microbiome 7, 46 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lam, S. M. et al. A multi-omics investigation of the composition and function of extracellular vesicles along the temporal trajectory of COVID-19. Nat. Metab. 3, 909–922 (2021).

    Article  CAS  PubMed  Google Scholar 

  53. Yuan, X. et al. Identification of an endogenous ligand bound to a native orphan nuclear receptor. PLoS ONE 4, e5609 (2009).

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  54. Han, X., Yang, Y., Zhao, F., Zhang, T. & Yu, X. An improved protein lipid overlay assay for studying lipid-protein interactions. Plant Methods 16, 33 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Couter, C. J. & Surana, N. K. Isolation and flow cytometric characterization of murine small intestinal lymphocytes. J. Vis. Exp. https://doi.org/10.3791/54114 (2016).

  56. Ivanov, I. I. et al. Induction of intestinal Th17 cells by segmented filamentous bacteria. Cell 139, 485–498 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Hsu, C. C., Okumura, R. & Takeda, K. Human LYPD8 protein inhibits motility of flagellated bacteria. Inflamm. Regen. 37, 23 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Wang, Y. C. et al. Intestinal cell type-specific communication networks underlie homeostasis and response to Western diet. J. Exp. Med. https://doi.org/10.1084/jem.20221437 (2023).

  59. Gilmore, W. J. et al. Bacteroides fragilis outer membrane vesicles preferentially activate innate immune receptors compared to their parent bacteria. Front. Immunol. 13, 970725 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Bousbaine, D. et al. A conserved Bacteroidetes antigen induces anti-inflammatory intestinal T lymphocytes. Science 377, 660–666 (2022).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  61. Jiang, L. et al. Salmonella typhimurium reprograms macrophage metabolism via T3SS effector SopE2 to promote intracellular replication and virulence. Nat. Commun. 12, 879 (2021).

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  62. Athman, R. et al. Shigella flexneri infection is dependent on villin in the mouse intestine and in primary cultures of intestinal epithelial cells. Cell. Microbiol. 7, 1109–1116 (2005).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The work received support from the National Key R&D Program of China grant no. 2020YFA0509100, and the NSF of China grant nos. 81830018 and 32030039 (to Y.Q.); the National Key R&D Program of China grant nos. 2022YFA0807300 and 2023YFA1800200, the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDB0990000), the International Partnership Program of the Chinese Academy of Sciences for Future Network (grant no. 318GJHZ2022030FN), the NSF of China grant nos. 32270945 and 92474105, and STCSM grant nos. 22ZR1468700 and 22140902400 (to X.S.); the NSF of China grant nos. 32200036 and 82341116 (to G.L.); and the Department of Defense grant no. HT9425-23-0226 (to D.L.K.). We thank L. Qiu, X. Miao, Y. Bu, Y. Zhai and J. Wu (Institutional Center for Shared Technologies and Facilities of SINH, CAS) for ultracentrifugation and FACS support. We thank Y. Qin, Z. Li and S. Yan (Institutional Center for Shared Technologies and Facilities of SINH, CAS) for technical assistance in microscopy. We appreciate the technical support for lipidomics or lipid structure analysis from LipidALL Technologies (Changzhou, China), Z. Li (Institutional Center for Shared Technologies and Facilities of SINH, CAS) and Z. Yang (Chemical Biology Core Facility of SIBCB, CAS). We also appreciate the technical support for proteomics analysis from PANOMIX Biomedical Tech Co. (Suzhou, China). We thank the staff of the Bioinformatics Core Facility of SIBCB for the scRNA-seq analysis, Y. Kong and L. Pan (Electron Microscopy Core Facilities of ION, CAS) for assistance with EM sample preparation and EM image analysis, and H. Jiang (Core Facility and Technical Service Center for SLSB of SJTU) for technical assistance with FACS analysis of intestinal bacteria. We also thank S. Lin for helping with STm infection experiments (Shanghai Institute of Immunity and Infection, CAS).

Author information

Author notes

  1. These authors contributed equally: Tao Yang, Xiaohu Hu, Fei Cao

Authors and Affiliations

  1. Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China

    Tao Yang, Xiaohu Hu, Fenglin Yun, Gaohui Kong, Biyu Nie, Yuexing Liu, Guohong Hu & Youcun Qian

  2. Key Laboratory of Multi-Cell Systems, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Fei Cao, Haohao Zhang & Xinyang Song

  3. Key Laboratory of RNA Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Kaiwen Jia & Jiantao Shi

  4. School of Life Science and Technology, ShanghaiTech University, Shanghai, China

    Mingxiang Zhang & Youcun Qian

  5. Center for Inflammatory Bowel Disease Research and Department of Gastroenterology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China

    Xiaoyu Li

  6. Center for Infectious Disease Research, School of Medicine, Tsinghua University, Beijing, China

    Hongyan Gao & Guanxiang Liang

  7. Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA, USA

    Dennis L. Kasper

Authors
  1. Tao Yang
    View author publications

    Search author on:PubMed Google Scholar

  2. Xiaohu Hu
    View author publications

    Search author on:PubMed Google Scholar

  3. Fei Cao
    View author publications

    Search author on:PubMed Google Scholar

  4. Fenglin Yun
    View author publications

    Search author on:PubMed Google Scholar

  5. Kaiwen Jia
    View author publications

    Search author on:PubMed Google Scholar

  6. Mingxiang Zhang
    View author publications

    Search author on:PubMed Google Scholar

  7. Gaohui Kong
    View author publications

    Search author on:PubMed Google Scholar

  8. Biyu Nie
    View author publications

    Search author on:PubMed Google Scholar

  9. Yuexing Liu
    View author publications

    Search author on:PubMed Google Scholar

  10. Haohao Zhang
    View author publications

    Search author on:PubMed Google Scholar

  11. Xiaoyu Li
    View author publications

    Search author on:PubMed Google Scholar

  12. Hongyan Gao
    View author publications

    Search author on:PubMed Google Scholar

  13. Jiantao Shi
    View author publications

    Search author on:PubMed Google Scholar

  14. Guanxiang Liang
    View author publications

    Search author on:PubMed Google Scholar

  15. Guohong Hu
    View author publications

    Search author on:PubMed Google Scholar

  16. Dennis L. Kasper
    View author publications

    Search author on:PubMed Google Scholar

  17. Xinyang Song
    View author publications

    Search author on:PubMed Google Scholar

  18. Youcun Qian
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.Q. and X.S. supervised the research. X.S., T.Y., D.L.K. and Y.Q. wrote the manuscript. T.Y. performed proteomics and lipidomics analysis, immunoblotting, immunofluorescence, H&E staining, ceramide kinase assays and bacterial killing assays. T.Y., X.H., F.C. and F.Y. performed flow cytometric sorting of APOL9-binding faecal bacterial taxa and flow cytometric analysis of APOL binding with human gut microbiota. X.H. performed TEM analysis of OMVs and in vitro DC–IEL–IEC co-culture experiments. T.Y. and X.H. performed all sequencing studies and STm infection experiments. F.C. cultured commensal bacterial strains and performed genetic manipulation of B. thet. F.Y., M.Z. and G.K. purified recombinant APOL proteins and B. thet ceramide kinase. B.N. assisted with immunofluorescence and H&E staining. T.Y., H.Z. and F.C. contributed to the flow cytometry of IELs and LPLs. G.L. and H.G. performed the phylogenetic analysis of the Cer1P biosynthesis genes. K.J., Y.L. and J.S. assisted in the analysis of scRNA-seq data. X.L. performed pathological scoring. T.Y. and M.Z. created all the graphical illustrations for the figures and Extended data figures using Adobe Illustrator CC 2018. D.L.K. and G.H. provided technical assistance and suggestions.

Corresponding authors

Correspondence to Xinyang Song or Youcun Qian.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Kenya Honda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Microbiota-dependent regulation of ileal APOL9a/b.

a, Q-PCR analysis of Apol9a and Apol9b tissue expression in various organs of WT C57BL/6 J mice (n = 3). b, Q-PCR analysis of Apol9a and Apol9b expression in ileal ECs, IELs, and LPLs (n = 4). c and d, Q-PCR (c) or immunoblot (d) analysis of Apol9 expression in ileal ECs from GF, EX-GF (obtained by co-housing of 4-week-old GF mice with age- and gender-matched SPF mice), or SPF C57BL/6 J mice (n = 3). e, Immunoblot analysis of Apol9 expression in ileal ECs from water or antibiotics (ABX)-treated SPF C57BL/6 J mice for 4 weeks (n = 3). f and g, Q-PCR (f, n = 6) or immunoblot (g, n = 3) analysis of Apol9 expression in ileal ECs from Ifnar1+/+ or Ifnar1–/– mice. h and i, Q-PCR (h, n = 6) or immunoblot (i, n = 3) analysis of Apol9 expression in ileal ECs from Ifngr1+/+ or Ifngr1–/– mice. Data are representative of two (c,d,e) or three independent experiments (a,b,f,g,h,i). n represents biologically independent animals (a-i). Data are presented as mean ± SEM. Statistical analysis was performed using a two-tailed Student’s t-test (f,h) or one-way ANOVA followed by Bonferroni post hoc test (c). For gel source data, see Supplementary Fig. 2.

Source data

Extended Data Fig. 2 Generation of Apol9a/b–/– mice by CRISPR/Cas9 genomic targeting.

a, Schematic diagram of the Apol9a and Apol9b locus with the sgRNA target sequences depicted for generating the Apol9a and Apol9b double-deficient (Apol9a/b–/–) mice. b, Genotyping of Apol9a and Apol9b exon locus by DNA sequencing after CRISPR-CAS9 targeting. c, Schematic diagram showing the nonsense mutations in Apol9a and Apol9b locus that terminate the protein translation. d, Validation of knock-out efficiency of Apol9a and Apol9b in ileal ECs from Apol9a/b+/+ and Apol9a/b–/– mice by immunoblot analysis (n = 3). e, t-SNE plots showing Apol9a and Apol9b expression (left) in the indicated epithelial cell types (right) from a previously published single-cell RNA sequencing study on mouse small intestine (GSE92332). The heatmap represents the expression level. f, Immunostaining of ileal sections of WT SPF, WT GF, or Apol9a/b–/– mice. Blue, DAPI; green, UEA1 (mucus); red, APOL9. Scale bar, 50 μm. Data are representative of three independent experiments (d,f). n represents biologically independent animals (d). For gel source data, see Supplementary Fig. 2.

Source data

Extended Data Fig. 3 Purification of recombinant mouse APOL9a/b and human APOL1-APOL4 proteins.

a, Coomassie blue staining or immunoblot analysis of purified recombinant his-tagged APOL9a protein. b, Coomassie blue staining or immunoblot analysis of purified recombinant his-tagged APOL9b protein. c, Phylogenetic tree of mouse APOL9a and APOL9b with human APOL1-APOL6 proteins. d, Predicted domain structure of human APOL1, APOL2, APOL3, and APOL4. e, Coomassie blue staining or immunoblot analysis of purified recombinant flag-tagged human APOL1, APOL2, APOL3, and APOL4 proteins. f, t-SNE plots showing human APOL1, APOL2, APOL3, and APOL4 expression in the indicated epithelial cell types from a previously published single-cell RNA sequencing study on human small intestine (GSE125970). The Heatmap represents the expression level. Data are representative of two independent experiments (a,b,e). For gel source data, see Supplementary Fig. 2.

Source data

Extended Data Fig. 4 Phylogenetic analysis of Cer1P biosynthesis genes in human gut microbiota.

a and b, The distribution of key gene orthologues involved in Cer1P biosynthesis, including serine palmitoyltransferase (BT_0870), keto-reductase (BT_3075), ceramide synthase (BT_3032), and ceramide kinase (BT_0871), across fifty-two human commensal bacterial strains (a) used in this study or gut microbiota species (b) from the NIH Human Microbiome Project (HMP) database. c, Coomassie blue staining of purified recombinant his-tagged WT or kinase-dead B. thetaiotaomicron ceramide kinase (BT_0871) protein. Data are representative of two independent experiments (c). For gel source data, see Supplementary Fig. 2.

Source data

Extended Data Fig. 5 APOL9a and APOL2 induce bacterial membrane defense and biogenesis responses in B. thetaiotaomicron.

a, Different concentrations of rmAPOL9a or rmAPOL9b were added to ~104 CFU of log phase B. thetaiotaomicron for two hours, and surviving bacteria were enumerated by propidium iodide (PI) intake. BSA was used as a negative control (n = 3). b and c, rmAPOL9a (5 μg/mL) or rmAPOL9b (5 μg/mL) was added to log phase B. thetaiotaomicron under different pH conditions (c) or NaCl concentrations (d), and surviving bacteria were quantified by propidium iodide (PI) intake (n = 3). d, B. fragilis, B. vulgatus, P. distasonis, and P. goldsteinii were individually incubated with BSA, rmAPOL9a, or rhAPOL2 for three hours under anaerobic conditions, followed by transmission electron microscopy (TEM) observation. Representative images of a single bacteria producing OMVs are shown. Scale bar, 500 nm. e, E. coli was incubated with BSA or rmAPOL9a, rmAPOL9b, or rhAPOL2 for three hours under anaerobic conditions, followed by TEM observation. Representative TEM images of a single E. coli are shown. Scale bar, 500 nm. f, B. fragilis, B. vulgatus, P. distasonis, and P. goldsteinii were individually incubated with BSA, rmAPOL9a, or rhAPOL2 for six hours under anaerobic conditions. The OMVs produced in the bacterial culture supernatant were collected by ultracentrifuge and counted under TEM. The representative TEM images or OMV numbers per view (n = 8) are shown. Scale bar, 200 nm. g and h, Volcano plot of differentially expressed genes when WT or BT_0871-deficient B. thetaiotaomicron were exposed to rmAPOL9a (g), or rhAPOL2 (h) for four hours under anaerobic conditions, compared to an untreated control condition (|log2[FC] | > 2; FDR < 0.05) (n = 3). i and j, Gene ontology analysis of up-regulated differentially expressed genes when WT B. thetaiotaomicron were exposed to rmAPOL9a (i), or rhAPOL2 (j) for four hours under anaerobic conditions, compared to an untreated control condition (log2[FC] > 2; FDR < 0.05) (n = 3). Data are representative of two independent experiments (a-i). n represents biologically independent samples (a-c,f,g-j). Data are presented as mean ± SEM. Statistical analysis was performed using one-way ANOVA followed by Bonferroni post hoc test (a-c,f) or one-way ANOVA followed by False Discovery Rate (FDR) procedure (g,h).

Source data

Extended Data Fig. 6 Characterization of the size and composition of steady-state or APOL9a-induced B. thetaiotaomicron OMVs.

a, Size distribution of steady-state or APOL9a-induced B. thetaiotaomicron OMVs was determined using ZetaView Nanoparticle Tracking Analysis (NTA, n = 3). b, Quantification of OMV-associated protein, DNA, RNA, peptidoglycan, or LPS from per 109 steady-state or APOL9a-induced B. thetaiotaomicron OMVs (n = 3). c, Relative abundance of the most represented lipid species as a percentage of the total lipid composition for each fraction from pooled steady-state or APOL9a-induced B. thetaiotaomicron OMVs (n = 2). d and e, ELISA analysis of supernatant IFNγ production (d) or Q-PCR analysis of MHC-II gene expression in ileal ECs (e) in a Transwell coculture of DC-IELs under steady-state or APOL9a-induced B. thetaiotaomicron-derived OMVs incubation (n = 3). Data are representative of two independent experiments (a-e). n represents biologically independent samples (a-e). Data are presented as mean ± SEM. Statistical analysis was performed using one-way ANOVA followed by Bonferroni post hoc test (d,e) or two-tailed Student’s t-test (b).

Source data

Extended Data Fig. 7 Single-cell RNA sequencing on ileal intraepithelial CD45+ immune cells.

a, Dot plots showing the top DEGs for the populations depicted in ileal intraepithelial CD45+ immune cells in Fig. 5a. Color saturation indicates the strength of average gene expression, whereas the dot size reflects the percentage of each cell cluster expressing the gene. b, Violin plots showing the selected maker gene expression among all ileal intraepithelial CD45+ immune cells in Fig. 5a. c, Gene Set Enrichment Analysis (GSEA) analysis of lymphocyte differentiation, T cell activation, immune response regulation, positive regulation of cytokine production, or positive regulation of epithelial proliferation pathways in the downregulated genes of ileal CD4+CD8αα+ IELs from Apol9a/b–/– mice. d and e, The intraepithelial lymphocytes (n = 5) and lamina propria lymphocytes (n = 4) were isolated from ilea of Apol9a/b+/+ and Apol9a/b–/– littermates and were analyzed by flow cytometry. The percentages of ileal intraepithelial lymphocyte subsets (d) or the percentages of ileal lamina propria lymphocyte subsets (e) from Apol9a/b+/+ or Apol9a/b–/– mice are shown. f, Intraepithelial CD4+CD8αα+ cells in different small intestine segments (duodenum, jejunum, ileum) from Apol9a/b+/+ or Apol9a/b–/– mice (n = 6) were analyzed by flow cytometry. The representative flow cytometry plots and CD4+CD8αα+ cell percentages are shown. Data are representative of two independent experiments (d,e,f). n represents biologically independent animals (d,e,f). Data are presented as mean ± SEM. Statistical analysis was performed using a two-tailed Student’s t-test (d,e,f).

Source data

Extended Data Fig. 8 The ileal immune phenotypes of Apol9a/b-deficient mice are associated with Bacteroidales.

a, Schematic diagram of FMT of ABX-treated Apol9a/b+/+ or Apol9a/b–/– mice with ileal microbiota from a WT C57BL/6 strain largely devoid of gut Bacteroidales. After 4 weeks, indicated ileal tissues or cells were analyzed. b, 16S rRNA gene sequencing of the microbial composition of the ileal contents from a WT C57BL/6 strain largely devoid of gut Bacteroidales (n = 3). c-e, Q-PCR (c, ABX: n = 4, FMT: n = 6) and flow cytometry (d,e, ABX: n = 5, FMT: n = 6) analysis of representative MHC-II molecule expression in ileal ECs from mice as described in (a). f and g, Ileal intraepithelial CD4+CD8αα+ cells from mice as described in (a) were analyzed by flow cytometry. The representative flow cytometry plots (f) and CD4+CD8αα+ cell percentages (g, ABX: n = 5, FMT: n = 6) are shown. Data are representative from two independent experiments (a-g). n represents biologically independent samples (b) or biologically independent animals (c,e,g). Data are presented as mean ± SEM. Statistical analysis was performed using one-way ANOVA followed by Bonferroni post hoc test (c,e,g).

Source data

Extended Data Fig. 9 Ileal immunomodulatory functions of APOL9 require Bacteroides-APOL9 interaction in vivo.

a, Q-PCR analysis of the relative abundance of B. thetaiotaomicron in the feces from ABX-treated Apol9a/b+/+ or Apol9a/b–/– mice transferred with a gut Bacteroidales-minus ileal microbiota plus WT or BT_0871-deficient B. thetaiotaomicron strain (n = 8). b, ELISA analysis (n = 8) of ileal IFNγ production in the mice as described in (a). c-d, Q-PCR (c, n = 8), and flow cytometry (d, n = 8) analysis of representative MHC-II molecule expression in ileal ECs from mice as described in (a). e and f, Ileal intraepithelial CD4+CD8αα+ cells from mice as described in (a) were analyzed by flow cytometry. The representative flow cytometry plots (e) and CD4+CD8αα+ cell percentages (f, n = 8) are shown. Data are pooled from two independent experiments (a-f). n represents biologically independent animals (a-d,f). Data are presented as mean ± SEM. Statistical analysis was performed using one-way ANOVA followed by Bonferroni post hoc test (a-d,f).

Source data

Extended Data Fig. 10 APOL9a/b control mucosal infection in a microbiota context-dependent manner.

a and b, The representative flow cytometry plots (a) and binding efficiencies (b) of rmAPOL9a (5 μg/mL) or rmAPOL9b (5 μg/mL) bound with STm. Transferrin binding as a positive control (n = 3). c, Propidium iodide (PI) staining of STm after incubation with BSA (5 μg/mL), rmAPOL9a (5 μg/mL), or rmAPOL9b (5 μg/mL) for 3 h. EDTA (20 μM) plus Polymyxin B (25 μg/mL) treatment as a positive control (n = 3). d, The dynamics of CFU within the Apol9a/b+/+ or Apol9a/b–/– ileal ECs after STm in vitro infection (n = 3). e, STm quantification in the ileal lumen of Apol9a/b+/+ or Apol9a/b–/– mice after STm infection in the early or late time point (n = 6). f-h, FMT of ABX-treated Apol9a/b+/+ or Apol9a/b–/– mice with ileal microbiota from a WT C57BL/6 strain largely devoid of gut Bacteroidales, followed by STm infection. Survival curve (f, n = 8), ileal histology (day 5) (g), or bacteria quantitation (day 5) in the spleen and liver (h, n = 6) are shown. Scale bar, 10 μm. i and j, Apol9a/b+/+ or Apol9a/b–/– mice were infected intraperitoneally with STm. Survival curve (i, Apol9a/b+/+: n = 9; Apol9a/b–/–: n = 8) or bacteria quantitation (day 5) in the spleen and liver (j, n = 6) are shown. Data are representative of two independent experiments (a-j). n represents biologically independent samples (b-d) or biologically independent animals (e-j). Data are presented as mean ± SEM. Statistical analysis was performed using one-way ANOVA followed by Bonferroni post hoc test (b,c), two-tailed Student’s t-test (d,e,g,h,j), or log-rank (Mantel-Cox) test (f,i).

Source data

Supplementary information

Supplementary Fig. 1

Flow cytometry gating strategy.

Reporting Summary

Supplementary Fig. 2

Uncropped western blots.

Supplementary Tables

Supplementary Tables 1–9.

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

Yang, T., Hu, X., Cao, F. et al. Targeting symbionts by apolipoprotein L proteins modulates gut immunity. Nature 643, 210–218 (2025). https://doi.org/10.1038/s41586-025-08990-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41586-025-08990-4

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