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A gut microbial odd-chain fatty acid alleviates atherosclerosis in mice

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Data availability

All data supporting the findings of this study are available in the Article, its Supplementary Information and the public repositories described below. The RNA-seq data generated for this study are available at the NCBI under the accession number PRJNA1213157. Metagenomic datasets and human gut metagenomes and metagenome-assembled genomes from publicly available databases were used in this study, and raw data were accessed under the BioProjects PRJNA615842 (‘Alteration in gut microbiota composition and functional relevance in subclinical carotid atherosclerosis in the general population’), PRJEB21528 (‘The gut microbiome in atherosclerotic cardiovascular disease’) and in the European Nucleotide Archive under accession number ERP116715 for the UHGG catalogue. The human HMGCR structure used for molecular docking is available from the RCSB PDB under accession 1HW8. Source data are provided with this paper.

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Acknowledgements

We are grateful to C. Wu for his guidance and help with animal experimental designs; S. Li for assistance with the purification of HMGCR protein; S. Liu and X. Li for discussions and support; staff at the Multi-Omics Mass Spectrometry Core of the Biomedical Research Core Facilities at Shenzhen Bay Laboratory for assistance with the MS and NMR experiments; staff at the Shenzhen Bay Laboratory Supercomputing Center for providing the platform for meta-omic data analyses; and staff at OE Biotech Co., Ltd. for technical support with scRNA-seq. Part of the computational analysis work was supported by the High-performance Computing Public Platform (Shenzhen Campus) of Sun Yat-sen University.

Funding

This work has been supported by the National Key Research and Development Program of China (grant no. 2020YFA0907800 to W.Z. and X.M.), the Shenzhen Medical Research Fund (B2502007 to W.Z. and X.T.), the Major Program of Shenzhen Bay Laboratory (C1012523006 to X.T. and W.Z.), the Shenzhen Science and Technology Programs (RCJC20231211085944057 to W.Z., and ZDSYS20220606100803007 to W.Z. and X.M.), the Shenzhen Science and Technology Programs (KQTD20200820145822023 to W.Z. and X.M.), Shenzhen Bay Laboratory Start-up Funds (21230051 to X.T.), the Shenzhen Bay Scholar Fellowship (to X.T.), and the Guangdong Basic and Applied Basic Research Fund (2514050002740 to W.Z.).

Author information

Author notes

  1. These authors contributed equally: Chao Yin, Youzhe Chen, Gan Lin, Mingwei Cai

Authors and Affiliations

  1. Shenzhen Key Laboratory for Systems Medicine in Inflammatory Diseases, Zhongshan School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China

    Chao Yin, Gan Lin, Xianzun Xiao, Kaining Han, Chaoxiong Mei, Peizhi Fan, Yibo Zhao, Lihong Du, Yanqin Xie, Yudan Mao, Xiangting Zhou, Xue Gao, Li Jin, Peijie Li, Xiangyu Mou & Wenjing Zhao

  2. Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen, China

    Youzhe Chen, Mingwei Cai, Miaomiao Qin, Jiang Wang, Ruolan Sun & Xiaoyu Tang

  3. CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Cuiping Pang, Zepeng Qu & Lei Dai

  4. Department of Clinical Laboratory, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China

    Zhipeng Tan & Zhaofan Luo

  5. Shenzhen Medical Academy of Research and Translation (SMART), Shenzhen, China

    Ruolan Sun, Yang Zhang & Xiaoyu Tang

  6. Department of Geriatrics, Shenzhen Key Laboratory of Bone Tissue Repair and Translational Research, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China

    Xiahong Lin

  7. Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China

    Yang Zhang

Authors

  1. Chao Yin
  2. Youzhe Chen
  3. Gan Lin
  4. Mingwei Cai
  5. Cuiping Pang
  6. Xianzun Xiao
  7. Kaining Han
  8. Chaoxiong Mei
  9. Miaomiao Qin
  10. Peizhi Fan
  11. Yibo Zhao
  12. Lihong Du
  13. Yanqin Xie
  14. Jiang Wang
  15. Yudan Mao
  16. Xiangting Zhou
  17. Xue Gao
  18. Li Jin
  19. Peijie Li
  20. Zepeng Qu
  21. Zhipeng Tan
  22. Ruolan Sun
  23. Xiahong Lin
  24. Yang Zhang
  25. Lei Dai
  26. Zhaofan Luo
  27. Xiangyu Mou
  28. Xiaoyu Tang
  29. Wenjing Zhao

Contributions

X.M., X.T. and W.Z. conceived, designed and supervised the project. C.Y., G.L., X.X., K.H., C.M., M.Q., P.F., L. Du, Y.X., Y.M., X.Z., X.G., L.J., P.L. and R.S. performed the mouse experiments. C.Y., Y.C. and G.L. participated in the bioassay tests and compound isolation and identification. M.C. performed the bioinformatic analyses. C.Y., Y.C., G.L. and J.W. participated in culturing bacteria and LC–MS analyses. C.Y., Y.C., G.L., M.C., C.P. and C.M. performed molecular, biochemical and cellular experiments. Y. Zhang performed the computational simulation. C.Y., G.L., X.X. and Z.Q. participated in the gene editing of B. uniformis under the supervision of L. Dai, X.M., X.T. and W.Z., and C.Y., G.L., P.F., Y. Zhao, L. Du, Y.X. and Z.T. were responsible for patient recruitment and the collection of clinical samples, performed under the supervision of X.L., Z.L., X.M. and W.Z. X.M., X.T. and W.Z. provided primary financial support for the study. The manuscript was prepared by C.Y., Y.C., G.L., M.C., X.M., X.T. and W.Z. with contributions from all authors. All authors reviewed and provided their approval for the final manuscript.

Corresponding authors

Correspondence to Xiangyu Mou, Xiaoyu Tang or Wenjing Zhao.

Ethics declarations

Competing interests

X.M. is an inventor on a pending patent application (CN202510500448.8) covering the use of PA for the prevention or treatment of atherosclerosis. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature thanks Arash Haghikia, Robert Quinn, Federico Rey, Soraya Taleb and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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Extended data figures and tables

Extended Data Fig. 1 B. uniformis attenuates atherosclerosis and systemic inflammation in HFD-fed SPF Apoe−/− mice.

a. Genus-level aggregation of the top ten healthy-enriched bacterial species from Fig. 1a. (n = 223 for case; n = 189 for control). Data are presented as box plots of relative abundance (%). Boxes indicate the 25th and 75th percentiles, central lines indicate the median, whiskers extend to the most extreme data points within 1.5 × the interquartile range calculated on the log10-transformed relative-abundance scale, and points beyond the whiskers represent outliers. A pseudocount of 0.00001% was added to zero relative-abundance values before plotting on a log scale. Statistical significance was determined by two-sided Wilcoxon rank-sum tests with Benjamini–Hochberg correction for multiple comparisons across the 7 aggregated genera: **P < 0.01; ****P < 0.0001. The adjusted P values (left to right): <0.0001, <0.0001, 0.0011, 0.0013, <0.0001, <0.0001, <0.0001. b, g and h. Representative images of Oil Red O-stained plaques in whole aorta with quantifications (n = 5 for b; n = 7 for h). For b, P values (left to right): 0.0281, 0.0003. For h, P values (left to right): 0.0065. c, d and i. Representative images of Oil Red O-stained plaques in the sections of aortic roots with quantifications (n = 5 for d; n = 7 for i). For d, P values (left to right): 0.0056. For i, P values (left to right): 0.0013. Scale bar: 200 μm. e. Absolute quantification of B. uniformis abundance in mouse faeces collected at multiple time points after a single oral gavage, determined by TaqMan qPCR based on the copy numbers of the 16S rRNA-encoding gene (n = 6). f. Absolute quantification of B. uniformis abundance in mouse faeces collected at multiple time points during PBS or BU treatment, determined by TaqMan qPCR based on the copy numbers of the 16S rRNA-encoding gene (n = 6). P = 0.0011. j and k. OGTT and ITT indexes (n  =  7). l. Body weight changes (n  =  7). m. Ratios of liver mass to body mass (n = 7). P < 0.0001. n and p. Representative flow cytometric plots (n) and quantification (p, n = 8) of blood CD11B+ Ly6C+ monocytes (total, and separately as Ly6Chi and Ly6Clo). P values (left to right): 0.0189, <0.0001, 0.0376. o and q. Representative flow cytometric plots (o) and quantification (q, n = 8) of blood CD11B+ F4/80+ macrophages. P = 0.0067. r. Relative quantification of Bacteroides in faeces (n = 7). P = 0.0017. b-d and f-r. HFD-fed SPF Apoe−/− mice were gavaged with PBS and BU (Low dose: 108 CFU/mouse; High dose: 109 CFU/mouse) three times per week for BU − 1 month or BU − 3 months (12 weeks). Data are presented as mean ± SEM (b, d-f, h-m and r), or the median with the first and third quartiles in box and whiskers (p and q). Statistical significance was determined using one-way ANOVA with Dunnett’s test (b, d, h and i), two-tailed t-tests with Welch’s correction (f), or two-tailed Student’s t test (j-m and p-r): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 2 B. uniformis increases plasma LDL-C clearance in a SREBP2/LDLR axis-dependent manner.

Related to Fig. 2. a. Gene ontology (GO) biological process analysis in HFD-fed SPF Apoe−/− mice treated with PBS (n = 4) and BU (n = 4) three times per week for 12 weeks. b. Diagram depicting the negative feedback regulation of cholesterol biosynthesis and its impact on hepatic Ldlr expression and plasma LDL-C levels. c. Schematic illustration of the SaCas9-sgRNA strategy and experimental design. HFD-fed SPF Apoe−/− mice were administered a single intravenous injection of adeno-associated virus (AAV8-sgNTC or AAV8-sgLdlr). After 7 days, the mice were gavaged with PBS or BU for 12 weeks. d and e. Protein levels (d) and quantification analysis (e) of Ldlr in liver tissues, with n = 3 per group. P values (up to down, left to right): 0.0354, 0.0019, 0.0002. f-i. Analysis of plasma levels of triglycerides (f, TG), total cholesterol (g, TC), high-density lipoprotein cholesterol (h, HDL-C), and low-density lipoprotein cholesterol (i, LDL-C) was performed (n = 7 mice). P values (left to right): f, 0.0002, 0.0025; g, 0.0011, 0.0020; i, 0.0002, 0.0005. j. Left: Representative images of CD68 immunofluorescence staining used to detect macrophages in the sections of aortic roots. Right: Quantification of CD68-positive area (n = 7 mice). P values (left to right): 0.0015, <0.0001. Scale bar: 200 μm. k and l. Representative flow cytometric plots (k) and quantification (l) of blood CD11B+ Ly6C+ monocytes (total, and separately as Ly6Chi and Ly6Clo) (n = 7 mice). P values (left to right): Ly6Chi, <0.0001, <0.0001. m and n. Representative flow cytometric plots (m) and quantification (n) of blood CD11B+ F4/80+ macrophages (n = 7 mice). P values (left to right): 0.0004, 0.0072. Data are presented as mean ± SEM (e-j), or the median with the first and third quartiles in box and whiskers (l and n). Statistical significance was determined using one-way ANOVA with Tukey’s post hoc test (e-j, l and n): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Schematics in b and c created in BioRender; Mou, X. https://biorender.com/1x3qxol (2026).

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Extended Data Fig. 3 Screening of organic solvents for the extraction of bioactive metabolites from B. uniformis.

a. Schematic illustration of the B. uniformis crude extract screening platform. b and c. Representative images (b) of DiI–LDL uptake in HepG2 cells incubated with the extracts (1 mg/ml) prepared from fresh medium and BU culture supernatant using three solvents of varying polarity (dichloromethane, ethyl acetate, or n-butanol), with the quantification (c, n = 3 independent experiments) of the mean intensity of DiI–LDL. P values (left to right): 0.0005, 0.0268, 0.0073, 0.0334, 0.0311, 0.0070. Yellow, DiI–LDL; blue, nuclei stained with Hoechst. Scale bar, 100 μm. d. Relative mRNA (d) of genes involved in LDL-C uptake in HepG2 cells incubated with the indicated extracts (1 mg/ml) from fresh medium and BU culture supernatant (n = 3 independent experiments). P values (left to right): SREBF2, 0.0011, 0.0003, 0.0004, 0.0002, 0.0359; LDLR, 0.0014, 0.0011, 0.0446, 0.0086, 0.0111, 0.0117. e and f. Protein levels (e) and quantification analysis (f) of genes involved in LDL-C uptake in HepG2 cells incubated with the indicated extracts (1 mg/ml) from fresh medium and BU culture supernatant (n = 3 independent experiments). P values (left to right): LDLR, 0.0455, 0.0173, 0.0005, 0.0039; pre-SREBP2, <0.0001, 0.0060, 0.0045, 0.0482; nSREBP2, 0.0043, 0.0128, 0.0418. Data are presented as mean ± SEM (c, d and f). Statistical significance was determined using two-tailed t-tests with Welch’s correction (c, d and f): *P < 0.05; **P < 0.01; ***P < 0.001, ****P < 0.0001. Schematic in a created in BioRender; Mou, X. https://biorender.com/1x3qxol (2026).

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Extended Data Fig. 4 Live B. uniformis attenuates atherosclerosis progression.

a. Free total cholesterol in HepG2 cells treated with Control 3 or Extract 3 (1 mg/ml; n = 3 independent experiments). P = 0.0052. b. Representative images of Oil Red O-stained plaques in whole aorta with quantifications (n = 8 for PBS and BU; n = 7 for HKBU and Extract 3; BU = Live B. uniformis; HKBU = Heat-killed B. uniformis; Extract 3 = ethyl acetate extract of B. uniformis culture). P values (left to right): 0.0039, 0.0207. c. Representative images of Oil Red O-stained plaques in the sections of aortic roots with quantifications (n = 6). P values (left to right): 0.0269, 0.0212. Scale bar: 200 μm. d-g. Analysis of plasma levels of triglycerides (d, TG), total cholesterol (e, TC), high-density lipoprotein cholesterol (f, HDL-C), and low-density lipoprotein cholesterol (g, LDL-C) was performed (n = 7). P values (left to right): d, 0.0007, 0.0486; e, 0.0406, 0.0025; g, 0.0002, 0.0169. h. Left: Representative images of CD68 immunofluorescence staining used to detect macrophages in the sections of aortic roots. Right: Quantification of CD68-positive area (n = 7). P values (left to right): <0.0001, <0.0001. Scale bar: 200 μm. i and j. Representative flow cytometric plots (i) and quantification (j, n = 7) of blood CD11B+ Ly6C+ monocytes (total, and separately as Ly6Chi and Ly6Clo). P values (left to right): total Ly6C+, 0.0330, 0.0218; Ly6Chi, 0.0016, 0.0062. k and l. Representative flow cytometric plots (k) and quantification (l, n = 7) of blood CD11B+ F4/80+ macrophages. P values (left to right): 0.0123, 0.0086. m. Relative mRNA levels of genes involved in hepatic cholesterol metabolism (n = 6). P values (left to right): Ldlr, 0.0417, <0.0001; Srebf2, 0.0267, 0.0004; Hmgcr, 0.0044, <0.0001; Mvk, 0.0006; Pcsk9, 0.0360, <0.0001. n and o. Protein levels (n, n = 3 per group, representative data of three independently repeated experiments) and quantification analysis (o) of genes involved in hepatic cholesterol metabolism. P values (left to right): LDLR, 0.0292, 0.0003; pre-SREBP2, 0.0012, 0.0007; nSREBP2, 0.0068, 0.0002. b-o. HFD-fed SPF Apoe−/− mice were gavaged with PBS, live BU, heat-killed BU (HKBU) and Extract 3 three times per week for 12 weeks. Data are presented as mean ± SEM (a-h, m and o), or the median with the first and third quartiles in box and whiskers (j and l). Statistical significance was determined using two-tailed Student’s t test (a), one-way ANOVA with Dunnett’s test (b, f-h, j, l, m and o), or Kruskal-Wallis test followed by Dunn’s post hoc test (c-e): *P < 0.05; **P < 0.01; ***P < 0.001, ****P < 0.0001.

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Extended Data Fig. 5 Screening of bioactive fractions from B. uniformis.

Related to Fig. 3. a and b. Representative images (a) of DiI–LDL uptake in HepG2 cells incubated with the indicated BU fractions (100 μg/ml), with the quantification (b, n = 3 independent experiments) of the mean intensity of DiI–LDL. P values (left to right): <0.0001, 0.0004, <0.0001, <0.0001, 0.0006, <0.0001. Yellow, DiI–LDL; blue, nuclei stained with Hoechst. Scale bar, 100 μm. c. Relative mRNA levels of genes involved in LDL-C uptake in HepG2 cells incubated with the indicated BU fractions (100 μg/ml; n = 3 independent experiments). P values (left to right): SREBF2, 0.0062, 0.0018, 0.0091; LDLR, 0.0134, 0.0118, 0.0013, 0.0280. d, e and f. Protein levels (d) and quantification analysis (e and f) of genes involved in LDL-C uptake in HepG2 cells incubated with the indicated BU fractions (100 μg/ml; n = 3 independent experiments). For e, P values for DMSO versus F1, F2 and F5-F8 were <0.0001, 0.0409, <0.0001, <0.0001, <0.0001 and <0.0001, respectively. For f, P values for pre-SREBP2 (DMSO versus F1-F3 and F5-F9) were <0.0001, 0.0042, 0.0176, 0.0005, 0.0060, 0.0006, <0.0001 and 0.0065, respectively; P values for nSREBP2 (DMSO versus F1-F8) were <0.0001, <0.0001, 0.0002, 0.0468, 0.0008, 0.0010, 0.0068 and <0.0001, respectively. Data are presented as mean ± SEM (b, c, e and f). Statistical significance was determined using one-way ANOVA with Dunnett’s test (b, e and f), or Kruskal-Wallis test followed by Dunn’s post hoc test (c): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 6 Screening of bioactive compounds from B. uniformis.

a. Relative mRNA expression levels of the key genes involved in LDL-C uptake (80 μM; n = 3 independent experiments). P values (left to right): SREBF2, <0.0001, 0.0035, <0.0001, <0.0001; LDLR, <0.0001, <0.0001, <0.0001, 0.0009, <0.0001. b-d. Protein levels (b and c) and quantification analysis (d) of genes involved in LDL-C uptake in HepG2 cells incubated with the indicated compounds (n = 3 independent experiments). For d, P values for LDLR (DMSO versus F1-1, Cpd 2, Cpd 3 and Cpd 6-Cpd 8) were 0.0469, 0.0393, 0.0018, <0.0001, <0.0001 and 0.0007, respectively. P values for pre-SREBP2 (DMSO versus Cpd 2, Cpd 3, Cpd 7, Cpd 9 and Cpd 10) were <0.0001, <0.0001, 0.0497, 0.0036 and 0.0007, respectively. P values for nSREBP2 (DMSO versus Cpd 2, Cpd 3, Cpd 6 and Cpd 7) were 0.0197, 0.0035, 0.0286 and 0.0009, respectively. e. HRMS of Cpd 3 in negative mode produces a reporter ion at m/z 241.2188. f. HPLC-MS assays of BHIS, BU culture, and PA standards: TIC chromatograms are under negative ion mode. Data are presented as mean ± SEM (a and d). Statistical significance was determined using one-way ANOVA with Dunnett’s test (a and d): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 7 PA reduces plasma lipids and systemic inflammation with activation of hepatic SREBP2-LDLR pathway.

a. Experimental design. HFD-fed SPF Apoe−/− mice were gavaged with corn oil, PA (dissolved in corn oil) and ATO for 8 weeks. b-e. Analysis of plasma levels of triglycerides (b, TG), total cholesterol (c, TC), high-density lipoprotein cholesterol (d, HDL-C), and low-density lipoprotein cholesterol (e, LDL-C) was performed (n = 7). P values (left to right): c, 0.0035, 0.0294; d, 0.0266; e, 0.0002, 0.0004. f. Left: Representative images of CD68 immunofluorescence staining used to detect macrophages in the sections of aortic roots. Right: Quantification of CD68-positive area (n = 8). P values (left to right): 0.0002, <0.0001. Scale bar: 200 μm. g and i. Representative flow cytometric plots (g) and quantification (i, n = 8 mice for corn oil and ATO; n = 7 mice for PA) of blood CD11B+ Ly6C+ monocytes (total, and separately as Ly6Chi and Ly6Clo). P values for i (left to right): total, 0.0077, 0.0010; Ly6Chi, 0.0383, 0.0133; Ly6Clo, 0.0225, 0.0029. h and j. Representative flow cytometric plots (h) and quantification (j, n = 8 mice for corn oil and ATO; n = 7 mice for PA) of blood CD11B+ F4/80+ macrophages. P values (left to right): 0.0145, 0.0162. k. Relative mRNA levels of genes involved in hepatic cholesterol metabolism (n = 6). P values (left to right): 0.0004, 0.0019, <0.0001, 0.0002, 0.0004. l and m. Protein levels (l, n = 3 per group, representative data of three experiments) and quantification analysis (m) of genes involved in hepatic cholesterol metabolism. P values (left to right): 0.0499, 0.0003, 0.0241. b-m. HFD-fed SPF Apoe−/− mice were gavaged with Corn oil, PA (25 mg/kg) or ATO (25 mg/kg) three times per week for 8 weeks. Data are presented as mean ± SEM (b-f, k and m), or the median with the first and third quartiles in box and whiskers (i and j). Statistical analysis was performed using one-way ANOVA with Dunnett’s test (b-f and j), two-sided Welch ANOVA with Dunnett’s T3 test (i), or two-tailed t-tests with Welch’s correction (k and m): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Schematic in a created in BioRender; Mou, X. https://biorender.com/1x3qxol (2026).

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Extended Data Fig. 8 PA promotes plasma LDL-C clearance to attenuate atherosclerosis progression by compensatory upregulation of hepatic Ldlr expression.

HFD-fed SPF Apoe−/− mice were administered a single intravenous injection of adeno-associated virus (AAV8-sgNTC or AAV8-sgLdlr). After 7 days, the mice were gavaged with corn oil or PA for 12 weeks. a and b. Protein levels (a) and quantification analysis (b) of Ldlr in liver tissues, with n = 3 per group. P values (left to right): <0.0001, <0.0001. c. Representative images of Oil Red O-stained plaques in whole aorta with quantifications (n = 7). P values (left to right): 0.0005, <0.0001. d. Representative images of Oil Red O-stained plaques in the sections of aortic roots with quantifications (n = 7). P values (left to right): 0.0374, 0.0017. Scale bar: 200 μm. e-h. Analysis of plasma levels of triglycerides (e, TG), total cholesterol (f, TC), high-density lipoprotein cholesterol (g, HDL-C), and low-density lipoprotein cholesterol (h, LDL-C) was performed (n = 7). P values (left to right): e, 0.0304, 0.0006; f, 0.0004, 0.0006; h, <0.0001, <0.0001. i. Left: Representative images of CD68 immunofluorescence staining used to detect macrophages in the sections of aortic roots. Right: Quantification of CD68-positive area (n = 7). P values (up to down, left to right): 0.0095, 0.0474, <0.0001. Scale bar: 200 μm. j and l. Representative flow cytometric plots (j) and quantification (l, n = 7) of blood CD11B+ Ly6C+ monocytes (total, and separately as Ly6Chi and Ly6Clo). P values (left to right): total, 0.0187, 0.0011; Ly6Chi, <0.0001, 0.0150; Ly6Clo, 0.0099. k and m. Representative flow cytometric plots (k) and quantification (m, n = 7) of blood CD11B+ F4/80+ macrophages. P values (left to right): 0.0001, <0.0001. n. PA binding to HMGCR was illustrated by SPR assay of purified HMGCR (catalytic portion) with PA. a-m. HFD-fed SPF Apoe−/− mice, with adeno-associated virus injection (AAV8-sgNTC or AAV8-sgLdlr), were gavaged with Corn oil or PA (25 mg/kg) three times per week for 12 weeks. Data are presented as mean ± SEM (b-i), the median with the first and third quartiles in box and whiskers (l and m). Statistical significance was determined using one-way ANOVA with Tukey’s post hoc test (b, c, e-i and m), Kruskal-Wallis test followed by Dunn’s post hoc test (d), or two-sided Welch ANOVA with Dunnett’s T3 test (l): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 9 PA is primarily derived from the Bacteroidota phylum.

Related to Fig. 5. a and b. Isotopologue distributions of PA from [1-13C] acetate (a) and [1-13C] propionate (b). c. Schematic representation of the biosynthetic pathway of PA in Bacteroidota. d. Putative gene operon containing ack/pta in C. glutamicum and mutA/scpA and pta/ack in B. thetaiotaomicron were highly conserved in BU DSM6597. e. Phylogenetic tree of 100 gut microbial isolates with corresponding PA production levels. f and g. Prevalence and presence of PA synthesis genes in the human gut microbiome. Prevalence and presence of ack/pta and mutA/scpA/mce/mcd genes (f) phylum level and (g) genus level. Given that the mutA and mce genes are most abundant in Bacteroidota, only the genus-level data for phylum Bacteroidota are displayed in (g). The presence of genes for PA biosynthesis is detailed in Supplementary Table 7.

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Extended Data Fig. 10 B. uniformis attenuates atherosclerosis progression in a PA-dependent manner.

a. PCR analysis of colonies counter-selected for the deletion of pta or mutA genes in B. uniformis, showing data from individual PCR reactions. b. SEM visualization of WT, ΔmutA, and Δpta. SEM images captured at a magnification of 30,000×. Scale bar: 300 nm. The experiments in a and b were independently repeated three times with similar results. c. Quantification of growth for WT and mutant strains in BHIS medium under anaerobic conditions (n = 3 independent experiments, each from a separate colony). OD600 were determined at the indicated time points. d-g. Analysis of plasma levels of triglycerides (d, TG), total cholesterol (e, TC), high-density lipoprotein cholesterol (f, HDL-C), and low-density lipoprotein cholesterol (g, LDL-C) was performed (n = 7). P values (left to right): e, 0.0011, 0.0172, 0.0036; g, <0.0001, 0.0005, 0.0021. h. Left: Representative images of CD68 immunofluorescence staining used to detect macrophages in the sections of aortic roots. Right: Quantification of CD68-positive area (n = 7). P values (left to right): 0.0029, 0.0344. Scale bar: 200 μm. i and j. Representative flow cytometric plots (i) and quantification (j, n = 7) of blood CD11B+ Ly6C+ monocytes (total, and separately as Ly6Chi and Ly6Clo). P values (up to down, left to right): total Ly6C+, 0.0307, 0.0233; Ly6Chi, 0.0123, 0.0319, 0.0207, <0.0001, 0.0078. k and l. Representative flow cytometric plots (k) and quantification (l, n = 7) of blood CD11B+ F4/80+ macrophages. P values (left to right): 0.0006, 0.0282, 0.0420. m. Relative mRNA levels of genes involved in hepatic cholesterol metabolism (n = 6). P values (up to down, left to right): Ldlr, 0.0020, 0.0019, 0.0030; Srebf2, 0.0242, 0.0025, 0.0028; Hmgcr, 0.0140, 0.0005, 0.0312; Mvk, 0.0297, 0.0129, <0.0001, 0.0068; Pcsk9, 0.0081, 0.0004, 0.0081. n and o. Protein levels (n, n = 3 per group, representative data of three experiments) and quantification analysis (o) of genes involved in hepatic cholesterol metabolism. P values (left to right): LDLR, <0.0001, <0.0001, <0.0001; pre-SREBP2, 0.0008, 0.0263, 0.0064; nSREBP2, 0.0003, 0.0003, 0.0007. d-o. HFD-fed SPF Apoe−/− mice were gavaged with PBS, WT, Δpta and ΔmutA three times per week for 12 weeks. Data are presented as mean ± SEM (c-h, m and o), or the median with the first and third quartiles in box and whiskers (j and l). Statistical significance was determined using one-way ANOVA with Dunnett’s test (c), one-way ANOVA with Tukey’s post hoc test (d-h, j, m and o), or Kruskal-Wallis test followed by Dunn’s post hoc test (l): *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

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Extended Data Fig. 11 Association of PA with CVD.

a. Correlation of serum PA with parameters associated with CVD. Q represents the quartile of PA proportions (replotted from published data) b. Relative abundance of bacterial genes involved in PA synthesis among case and control individuals (n = 223 for case; n = 189 for control). RPKM values for each gene were calculated using CoverM (v0.6.1). Data are presented as violin plots with embedded box plots; violin width indicates the distribution density, boxes indicate the 25th and 75th percentiles, central lines indicate the median, whiskers indicate the minimum and maximum values within 1.5 × the interquartile range, and points beyond the whiskers represent outliers. Statistical significance was determined using a two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction for multiple comparisons. c. Summary diagram depicting the role of PA in the modulation of hepatic cholesterol metabolism and atherosclerosis progression. d. The levels of C17:0 in faecal samples (n = 90 per group). P = 0.0004. e. HPLC-MS analysis of BHIS, BU culture, and C17:0 standards. Extracted ion chromatograms (EICs) were obtained at m/z 269.2461 ±  0.01 [M − H]− for C17:0. f. HRMS of C17:0 BU fermentation supplemented with 13C-labeled acetate (Left) and propionate (Right). g. C17:0 levels in faeces, liver tissues and serum. HFD-fed SPF Apoe−/− mice after 12-week gavage with PBS or BU (n = 7). h. FTC in HepG2 cells treated with DMSO and long chain fatty acids (C15:0, C16:0 or C17:0; 80 μM; n = 3 independent experiments). P values (left to right): 0.0127, <0.0001, 0.0004. i. Inhibition curve of C16:0, C17:0 and ATO against HMGCR (representative data of two independent experiments). Data are presented as mean ± SEM (d, g and h). Statistical significance was determined using two-tailed Mann-Whitney U test (d), two-tailed t-tests with Welch’s correction (g), or two-sided Welch ANOVA with Dunnett’s T3 test (h): *P < 0.05; ***P < 0.001; ****P < 0.0001. Schematic in c created in BioRender; Mou, X. https://biorender.com/s4a1t8m (2026).

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Yin, C., Chen, Y., Lin, G. et al. A gut microbial odd-chain fatty acid alleviates atherosclerosis in mice. Nature (2026). https://doi.org/10.1038/s41586-026-11142-x

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