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.

  • Letter
  • Published:

Gut microbiota–bile acid–interleukin-22 axis orchestrates polycystic ovary syndrome

Nature Medicine volume 25, pages 1225–1233 (2019)Cite this article

Subjects

A Publisher Correction to this article was published on 30 August 2019

This article has been updated

Abstract

Polycystic ovary syndrome (PCOS) is characterized by androgen excess, ovulatory dysfunction and polycystic ovaries1, and is often accompanied by insulin resistance2. The mechanism of ovulatory dysfunction and insulin resistance in PCOS remains elusive, thus limiting the development of therapeutics. Improved metabolic health is associated with a relatively high microbiota gene content and increased microbial diversity3,4. This study aimed to investigate the impact of the gut microbiota and its metabolites on the regulation of PCOS-associated ovarian dysfunction and insulin resistance. Here, we report that Bacteroides vulgatus was markedly elevated in the gut microbiota of individuals with PCOS, accompanied by reduced glycodeoxycholic acid and tauroursodeoxycholic acid levels. Transplantation of fecal microbiota from women with PCOS or B. vulgatus-colonized recipient mice resulted in increased disruption of ovarian functions, insulin resistance, altered bile acid metabolism, reduced interleukin-22 secretion and infertility. Mechanistically, glycodeoxycholic acid induced intestinal group 3 innate lymphoid cell IL-22 secretion through GATA binding protein 3, and IL-22 in turn improved the PCOS phenotype. This finding is consistent with the reduced levels of IL-22 in individuals with PCOS. This study suggests that modifying the gut microbiota, altering bile acid metabolism and/or increasing IL-22 levels may be of value for the treatment of PCOS.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Profiling of the gut microbiota and bile acids in individuals with PCOS.
Fig. 2: Effects of PCOS fecal microbiota or B. vulgatus transplantation disrupted insulin sensitivity, ovarian function, bile acid profiling and IL-22 production.
Fig. 3: IL-22 or bile acid administration improved insulin resistance, ovarian dysfunction and infertility in mice with PCOS.
Fig. 4: Bile acid–GATA3 pathway-regulated IL-22 improved IR and ovarian dysfunction in PCOS-like mice.

Similar content being viewed by others

Data availability

Data relating to the metagenomic sequencing that support the findings of this study have been uploaded to the Sequence Read Archive database (https://trace.ncbi.nlm.nih.gov/Traces/home/) and are available for download via accession number PRJNA530971.

Change history

  • 30 August 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Norman, R. J., Dewailly, D., Legro, R. S. & Hickey, T. E. Polycystic ovary syndrome. Lancet 370, 685–697 (2007).

    Article  CAS  Google Scholar 

  2. Dumesic, D. A. et al. Scientific statement on the diagnostic criteria, epidemiology, pathophysiology, and molecular genetics of polycystic ovary syndrome. Endocr. Rev. 36, 487–525 (2015).

    Article  CAS  Google Scholar 

  3. Sonnenburg, J. L. & Backhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).

    Article  CAS  Google Scholar 

  4. Turnbaugh, P. J., Backhed, F., Fulton, L. & Gordon, J. I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008).

    Article  CAS  Google Scholar 

  5. Lindheim, L. et al. Alterations in gut microbiome composition and barrier function are associated with reproductive and metabolic defects in women with polycystic ovary syndrome (PCOS): a pilot study. PLoS ONE 12, e0168390 (2017).

    Article  Google Scholar 

  6. Liu, R. et al. Dysbiosis of gut microbiota associated with clinical parameters in polycystic ovary syndrome. Front. Microbiol. 8, 324 (2017).

    PubMed  PubMed Central  Google Scholar 

  7. Torres, P. J. et al. Gut microbial diversity in women with polycystic ovary syndrome correlates with hyperandrogenism. J. Clin. Endocrinol. Metab. 103, 1502–1511 (2018).

    Article  Google Scholar 

  8. Martin, F. P. et al. A top-down systems biology view of microbiome–mammalian metabolic interactions in a mouse model. Mol. Syst. Biol. 3, 112 (2007).

    Article  Google Scholar 

  9. Claus, S. P. et al. Systemic multicompartmental effects of the gut microbiome on mouse metabolic phenotypes. Mol. Syst. Biol. 4, 219 (2008).

    Article  Google Scholar 

  10. Shindo, K., Machida, M., Fukumura, M., Koide, K. & Yamazaki, R. Omeprazole induces altered bile acid metabolism. Gut 42, 266–271 (1998).

    Article  CAS  Google Scholar 

  11. Van Houten, E. L. & Visser, J. A. Mouse models to study polycystic ovary syndrome: a possible link between metabolism and ovarian function? Reprod. Biol. 14, 32–43 (2014).

    Article  Google Scholar 

  12. Hand, T. W., Vujkovic-Cvijin, I., Ridaura, V. K. & Belkaid, Y. Linking the microbiota, chronic disease, and the immune system. Trends Endocrinol. Metab. 27, 831–843 (2016).

    Article  CAS  Google Scholar 

  13. Tata, B. et al. Elevated prenatal anti-Mullerian hormone reprograms the fetus and induces polycystic ovary syndrome in adulthood. Nat. Med. 24, 834–846 (2018).

    Article  CAS  Google Scholar 

  14. Zhong, C. et al. Group 3 innate lymphoid cells continuously require the transcription factor GATA-3 after commitment. Nat. Immunol. 17, 169–178 (2016).

    Article  CAS  Google Scholar 

  15. Bouchard, M. F., Taniguchi, H. & Viger, R. S. Protein kinase A-dependent synergism between GATA factors and the nuclear receptor, liver receptor homolog-1, regulates human aromatase (CYP19) PII promoter activity in breast cancer cells. Endocrinology 146, 4905–4916 (2005).

    Article  CAS  Google Scholar 

  16. Brestoff, J. R. & Artis, D. Immune regulation of metabolic homeostasis in health and disease. Cell 161, 146–160 (2015).

    Article  CAS  Google Scholar 

  17. Bartelt, A. & Heeren, J. Adipose tissue browning and metabolic health. Nat. Rev. Endocrinol. 10, 24–36 (2014).

    Article  CAS  Google Scholar 

  18. Yuan, X. et al. Brown adipose tissue transplantation ameliorates polycystic ovary syndrome. Proc. Natl Acad. Sci. USA 113, 2708–2713 (2016).

    Article  CAS  Google Scholar 

  19. Zhao, Y. et al. Up-regulated expression of WNT5a increases inflammation and oxidative stress via PI3K/AKT/NF-κB signaling in the granulosa cells of PCOS patients. J. Clin. Endocrinol. Metab. 100, 201–211 (2015).

    Article  CAS  Google Scholar 

  20. Dudakov, J. A., Hanash, A. M. & van den Brink, M. R. Interleukin-22: immunobiology and pathology. Annu. Rev. Immunol. 33, 747–785 (2015).

    Article  CAS  Google Scholar 

  21. Pedersen, B. K. & Febbraio, M. A. Muscle as an endocrine organ: focus on muscle-derived interleukin-6. Physiol. Rev. 88, 1379–1406 (2008).

    Article  CAS  Google Scholar 

  22. Sag, D., Carling, D., Stout, R. D. & Suttles, J. Adenosine 5′-monophosphate-activated protein kinase promotes macrophage polarization to an anti-inflammatory functional phenotype. J. Immunol. 181, 8633–8641 (2008).

    Article  CAS  Google Scholar 

  23. Pang, Y. et al. Intermedin restores hyperhomocysteinemia-induced macrophage polarization and improves insulin resistance in mice. J. Biol. Chem. 291, 12336–12345 (2016).

    Article  CAS  Google Scholar 

  24. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  Google Scholar 

  25. Zhu, W., Lomsadze, A. & Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 38, e132 (2010).

    Article  Google Scholar 

  26. Perez-Enciso, M. & Tenenhaus, M. Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Hum. Genet. 112, 581–592 (2003).

    PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFC1003200 and 2018YFC1003900 to Y.P.), National Natural Science Foundation of China (91857115 to C.J.; 81521002 and 81730038 to J.Q.; and 81771650 to R.L.), Clinical Medicine Plus X—Young Scholars Project of Peking University (PKU2019LCXQ025 and PKU2018LCXQ013 to Y.P. and C.J.) and the National Key Research and Development Program of China (2016YFC1000201 to R.L.).

Author information

Author notes

  1. These authors contributed equally: X. Qi, C. Yun.

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Center for Obesity and Metabolic Disease Research, School of Basic Medical Sciences, Third Hospital, Peking University, Beijing, China

    Xinyu Qi, Chuyu Yun, Lulu Sun, Jialin Xia, Qing Wu, Ying Wang, Lina Wang, Yangming Zhang, Xianyi Liang, Liying Wang, Huiying Liu, Liangshan Mu, Zehong Zhou, Yue Zhao, Rong Li, Ping Liu, Yanli Pang, Changtao Jiang & Jie Qiao

  2. Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Peking University, and the Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Beijing, China

    Chuyu Yun, Lulu Sun, Jialin Xia, Qing Wu, Yangming Zhang, Xianyi Liang, Huiying Liu & Changtao Jiang

  3. Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, China

    Ying Wang, Lina Wang, Liying Wang, Liangshan Mu, Zehong Zhou, Yue Zhao, Rong Li, Ping Liu, Yanli Pang & Jie Qiao

  4. National Clinical Research Center for Obstetrics and Gynecology, Beijing, China

    Ying Wang, Lina Wang, Liying Wang, Rong Li, Ping Liu & Jie Qiao

  5. Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Frank J. Gonzalez

  6. Center for Molecular Toxicology and Carcinogenesis, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, USA

    Andrew D. Patterson

  7. Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China

    Yue Zhao, Rong Li, Ping Liu, Yanli Pang & Jie Qiao

  8. Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing Key Laboratory of Tumor Systems Biology, Beijing, China

    Chao Zhong

  9. Beijing Advanced Innovation Center for Genomics, Beijing, China

    Jie Qiao

  10. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China

    Jie Qiao

Authors
  1. Xinyu Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuyu Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lulu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jialin Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lina Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yangming Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Xianyi Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Liying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Frank J. Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Andrew D. Patterson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Huiying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Liangshan Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Zehong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yue Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Rong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Ping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Chao Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Yanli Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Changtao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Jie Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.P., C.J. and J.Q. designed the study and supervised the analyses. X.Q., C.Y., J.X., Y.W., Lina.W., Liying.W., L.M. and Z.Z. enrolled patients and collected patient samples. X.Q., C.Y., L.S., J.X., Q.W., Y. Zhang, X.L. and Y.P. performed the animal experiments. H.L., Y. Zhao, R.L., P.L. and C.Z. helped with the data analyses. F.J.G. and A.D.P. discussed the results. X.Q., C.Y., Y.P., C.J. and J.Q. wrote the manuscript with input from all authors. All authors edited the manuscript and approved the final manuscript.

Corresponding authors

Correspondence to Yanli Pang, Changtao Jiang or Jie Qiao.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer Review Information: Randy Levinson and Joao Monteiro were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Gut microbial alterations in women with PCOS.

a,b, Alpha diversity (Shannon index) (a) and beta diversity (Bray–Curtis distance) (b) of the control and PCOS groups at the gene level. For box plots, the midline represents the median, the box represents the interquartile range (IQR) between the first and third quartiles, and whiskers represent the lowest or highest values within 1.5× the IQR from the first or third quartiles. Statistical significance was determined by two-tailed Mann–Whitney U-test. c,d, Top ten bacterial family (c) and bacterial genus (d) abundances in PCOS and control subjects. The q value represents the FDR-adjusted P value. The P value was determined by two-tailed Wilcoxon rank-sum test. In c, for Bacteroidaceae, q = 2.53 × 10−8. In d, for Bacteroides, q = 5.55 × 10−8. Data are presented as means ± s.e.m. Control: n = 43; PCOS: n = 50.

Source Data

Extended Data Fig. 2 Insulin, hormone, bile acid and IL-22 levels of mice transplanted with PCOS gut microbiota or B. vulgatus.

a, Timeline for the recipient mice transplanted with PCOS or control fecal microbiota. b, Area under the curve (AUC) of GTT (n = 6 mice per group; t10 = −3.032). c, AUC of ITT (n = 6 mice per group; t10 = −2.498). d, Fasting glucose levels (n = 6 mice per group). e, Insulin levels (n = 6 mice per group; t10 = 0.134). f, HOMA-IR (n = 6 mice per group; t10 = −2.669). g, Number of cystic follicles (n = 6 mice per group; t10 = −5.966). h, Number of CLs (n = 6 mice per group; t10 = 7.720; P = 0.000016). i, Levels of testosterone (T) (n = 6 mice per group; t10 = −2.335). j, Levels of luteinizing hormone (n = 6 mice per group; t10 = −5.675). k, Levels of estradiol for the trans-PCOS and trans-control mice (n = 6 mice per group; t10 = 1.322). l, AUC of GTT (n = 8 mice per group; F2,21 = 12.095). m, AUC of ITT (n = 8 mice per group; F2,21 = 5.609). n, Fasting glucose levels (n = 8 mice per group; F2,21 = 6.190). o, Insulin levels (n = 8 mice per group; F2,21 = 4.254). p, HOMA-IR (n = 8 mice per group; F2,21 = 13.559). q, Number of cystic follicles (n = 8 mice per group; F2,21 = 91.000; control versus B. vulgatus, P = 3.17 × 10−9; control versus DHEA, P = 1.74 × 10−11). r, Number of CLs (n = 8 mice per group). s, Levels of T (n = 8 mice per group; control versus DHEA, P = 0.000007). t, Levels of luteinizing hormone (n = 8 mice per group). u, Levels of estradiol for the mice in the control, B. vulgatus gavage or DHEA injection groups (n = 8 mice per group; F2,21 = 18.890; control versus DHEA, P = 0.000039). v, Levels of intestine bile acids (n = 6 mice per group). w, Il22 mRNA expression levels in the intestines, as measured by qPCR (n = 6 mice per group). x, IL-22 levels in the serum of mice transplanted with PCOS or control individual’s fecal microbiota (n = 6 mice per group; t10 = 7.518; P = 0.00002). For d, v and w, P values were determined by two-tailed Mann–Whitney U-test and the data are presented as medians with interquartile ranges. For b, c, e–k and x, P values were determined by two-tailed Student’s t-test and the data are presented as means ± s.e.m. For l–q and u, P values were determined by one-way ANOVA with Tukey’s multiple comparison post-hoc test and data are presented as means ± s.e.m. For r, s and t, P values were determined by Kruskal–Wallis test followed by Dunn’s post-hoc test and data are presented as medians with interquartile ranges.

Source Data

Extended Data Fig. 3 Improvement of insulin resistance and ovarian dysfunction by administration of IL-22 or bile acid in PCOS mice.

a, Timeline for the mice subjected to IL-22 or GDCA administration after gavage with B. vulgatus. b, Timeline for the PAMH administration PCOS-like mouse model. c, AUC of GTT (n = 6 mice per group; F3,20 = 8.170). d, AUC of ITT (n = 6 mice per group; F3,20 = 10.798; control versus B. vulgatus, P = 0.000035). e, Fasting glucose levels (n = 6 mice per group; F3,20 = 24.524; control versus B. vulgatus, P = 0.000003; B. vulgatus versus B. vulgatus + GDCA, P = 2.18 × 10−7; B. vulgatus versus B. vulgatus + IL-22, P = 0.000002). f, Insulin levels (n = 6 mice per group). g, HOMA-IR (n = 6 mice per group; F3,20 = 48.560; control versus B. vulgatus, P = 1.66 × 10−8; B. vulgatus versus B. vulgatus + GDCA, P = 8.55 × 10−10; B. vulgatus versus B. vulgatus + IL-22, P = 9.47 × 10−9). h, Levels of estradiol for the mice in the control, B. vulgatus, B. vulgatus + GDCA and B. vulgatus + IL-22 groups (n = 6 mice per group; F3,20 = 0.553). i, Levels of estradiol for the mice in the control, PAMH and PAMH + IL-22 groups (n = 6 mice per group; F2,15 = 0.498). For c–e and g–i, P values were determined by one-way ANOVA with Tukey’s multiple comparison post-hoc test and data are presented as means ± s.e.m. For f, the P values were determined by Kruskal–Wallis test followed by Dunn’s post-hoc test and data are presented as medians with interquartile ranges.

Source Data

Extended Data Fig. 4 Administration of IL-22 prevented insulin resistance and ovarian dysfunction in B. vulgatus-treated mice.

The mice were divided into three groups (control, B. vulgatus and B. vulgatus + IL-22). The animals were gavaged with B. vulgatus or heat-killed B. vulgatus as a control for 3 weeks. At the same time, the mice received IL-22 at a dose of 100 µg kg−1 d−1, intraperitoneally for the B. vulgatus + IL-22 group, or PBS as a parallel control for a–l. a, GTT (n = 8 mice per group). b, AUC of GTT (n = 8 mice per group; F2,21 = 4.076). c, ITT (n = 8 mice per group). d, AUC of ITT (n = 8 mice per group; F2,21 = 23.560; control versus B. vulgatus, P = 0.000036; B. vulgatus versus B. vulgatus + IL-22, P = 0.000002). e, Representative estrous cycles. f, Quantitative analysis of estrous cycles (n = 8 mice per group). g, Number of MII oocytes (n = 8 mice for the control and B. vulgatus + IL-22; n = 10 mice for B. vulgatus; F2,23 = 40.993; control versus B. vulgatus, P = 2.71 × 10−8; B. vulgatus versus B. vulgatus + IL-22, P = 3.40 × 10−7). h, Number of embryos (n = 6 mice per group). i, Immunofluorescence staining for tubulin (green) and DNA (blue) in MII oocytes (MII oocyte number = 150; n = 8 mice for the control and B. vulgatus + IL-22; n = 10 mice for B. vulgatus). Scale bars: 20 μm. Images are representative of two independent experiments with similar results. j, H&E staining of representative ovaries. Scale bars: 200 μm. Images are representative of three independent experiments with similar results. k, Quantitative analysis of cystic follicles (n = 8 mice per group; F2,21 = 37.800; control versus B. vulgatus, P = 2.14 × 10−7; B. vulgatus versus B. vulgatus + IL-22, P = 2.14 × 10−7). l, Quantitative analysis of CLs (n = 8 mice per group; control versus B. vulgatus, P = 0.000045). For a–d, g and k, P values were determined by one-way ANOVA with Tukey’s multiple comparison post-hoc test and data are presented as means ± s.e.m. For f, h and l, P values were determined by the Kruskal–Wallis test followed by Dunn’s post-hoc test and data are presented as medians with interquartile ranges. *P < 0.05; **P < 0.01 versus the control. #P < 0.05; ##P < 0.01 versus B. vulgatus.

Source Data

Extended Data Fig. 5 Role and mechanisms of bile acid in improving insulin sensitivity and ovarian function.

a, Percentage of IL-22+ cells in CD45+ lineage− RORγT+ ILC3s from siLP of mice with or without TUDCA treatment (n = 4 mice per group; t6 = −5.767). b, mRNA expression of Il22 in the intestines (n = 6 mice per group; t10 = −4.175). c, Serum IL-22 levels of the DHEA or DHEA + GDCA mice (n = 6 mice per group; t10 = −6.067). The mice were divided into three groups (control, GDCA and Il22r−/− + GDCA) for d–i. For the control, wild-type mice were injected daily with DHEA subcutaneously. For GDCA, wild-type mice were injected daily with DHEA subcutaneously and gavaged with 30 mg kg−1 d−1 GDCA. For Il22r−/− + GDCA, Il22r−/− mice were injected daily with DHEA and gavaged with GDCA. d, GTT (n = 6 mice per group). e, AUC of GTT (n = 6 mice per group; F2,15 = 6.293). f, ITT (n = 6 mice per group). g, AUC of ITT (n = 6 mice per group; F2,15 = 14.899). h, Representative estrous cycles for mice from the three groups. i, Quantitative analysis of estrous cycles for the mice from the control, GDCA and Il22r−/− + GDCA groups (n = 6 mice per group). j, IL-22 levels in the cell culture supernatant (n = 4 mice per group; F3,12 = 49.221; DMSO versus GDCA, P = 0.000002; GDCA versus triamterene + GDCA, P = 3.60 × 10−7). k, Il22 mRNA expression levels (n = 4 mice per group; F3,12 = 17.708; GDCA versus triamterene + GDCA, P = 0.000047). l, Gata3 mRNA expression levels in ILC3s with or without GDCA or triamterene treatment in vitro (n = 4 mice per group; F3,12 = 24.044; GDCA versus triamterene + GDCA, P = 0.000012). m, cAMP levels in the intestines of mice treated with DHEA or B. vulgatus (n = 8 mice per group; F2,21 = 29.246; control versus B. vulgatus, P = 3.52 × 10−7; control versus DHEA, P = 0.000013). n, Representative flow cytometry plots of IL-22+ cells in CD45+ lineage− RORγT+ ILC3s from siLP. Three independent experiments were performed with similar results. o, Percentage of IL-22+ cells in CD45+ lineage− RORγT+ ILC3s from siLP of the mice (n = 4 mice per group; F2,9 = 23.175). For a–c, P values were determined by two-tailed Student’s t-test and data are presented as means ± s.e.m. For d–g, j–m and o, P values were determined by one-way ANOVA with Tukey’s multiple comparison post-hoc test and data are presented as means ± s.e.m. For i, P values were determined by Kruskal–Wallis test followed by Dunn’s post-hoc test and data are presented as medians with interquartile ranges.

Source Data

Extended Data Fig. 6 Adipose tissue browning in PCOS-like mice with or without IL-22 or GDCA treatment.

a–c, Ucp1, Pgc1α, Cited1 and Cox8b mRNA expression levels in brown adipose tissue from: (a) trans-control and trans-PCOS mice (n = 6 mice per group; Ucp1: t10 = 4.789; Pgc1α: t10 = 4.427; Cited1: t10 = 3.846; Cox8b: t10 = 3.153); (b) control, B. vulgatus and DHEA mice (n = 8 mice per group; Ucp1: F2,21 = 14.107; Pgc1α: F2,21 = 8.638, Cited1: F2,21 = 2.612; Cox8b: F2,21 = 9.528); and (c) GDCA or IL-22 therapeutic administration in B. vulgatus-treated mice (control, B. vulgatus, B. vulgatus + GDCA and B. vulgatus + IL-22 mice) (n = 6 mice per group; Ucp1: F3,20 = 19.769, B. vulgatus versus B. vulgatus + IL-22, P = 3.41 × 10−7; Pgc1α: F3,20 = 53.487; control versus B. vulgatus, P = 8.83 × 10−8; B. vulgatus versus B. vulgatus + IL-22, P = 2.39 × 10−10; Cited1: F3,20 = 5.853; Cox8b: F3,20 = 9.242). d,e, Ucp1, Pgc1α, Cited1, Cox8b, Nr2f6 and Prdm16 mRNA expression levels in the subcutaneous fat (d; n = 8 mice per group), and Ucp1, Pgc1α, Cited1 and Cox8b mRNA expression levels in brown adipose tissue (e) after IL-22 preventive administration in B. vulgatus-treated mice (control, B. vulgatus and B. vulgatus + IL-22). For a–e, all of the mice were placed at 4 °C for 12 h before killing. For a, P values were determined by two-tailed Student’s t-test and data are presented as means ± s.e.m. For b and c, P values were determined by one-way ANOVA with Tukey’s multiple comparison post-hoc test and data are presented as means ± s.e.m. For d and e, P values were determined by Kruskal–Wallis test followed by Dunn’s post-hoc test and data are presented as medians with interquartile ranges.

Source Data

Supplementary information

Source data

Source Data Fig. 1

Statistical Source Data

Source Data Fig. 2

Statistical Source Data

Source Data Fig. 3

Statistical Source Data

Source Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 1

Statistical Source Data

Source Data Extended Data Fig. 2

Statistical Source Data

Source Data Extended Data Fig. 3

Statistical Source Data

Source Data Extended Data Fig. 4

Statistical Source Data

Source Data Extended Data Fig. 5

Statistical Source Data

Source Data Extended Data Fig. 6

Statistical Source Data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, X., Yun, C., Sun, L. et al. Gut microbiota–bile acid–interleukin-22 axis orchestrates polycystic ovary syndrome. Nat Med 25, 1225–1233 (2019). https://doi.org/10.1038/s41591-019-0509-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-019-0509-0

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