IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-36079-x.html
   My bibliography  Save this article

Microbially produced vitamin B12 contributes to the lipid-lowering effect of silymarin

Author

Listed:
  • Wen-Long Sun

    (Shandong University of Technology)

  • Sha Hua

    (Shanghai Jiao Tong University School of Medicine)

  • Xin-Yu Li

    (Shandong University of Technology)

  • Liang Shen

    (Shandong University of Technology)

  • Hao Wu

    (Fudan University
    Fudan University)

  • Hong-Fang Ji

    (Shandong University of Technology
    Ludong University)

Abstract

Silymarin has been used for improving hepatic damage and lipid disorders, but its action mechanism remains to be clarified. Here, we investigate the contributions of the gut microbiota to the improvement of liver lipid metabolism by silymarin. We find i) strong and significant microbial shifts upon silymarin but not silibinin treatment; ii) over 60% variations of liver fat are explained by silymarin-induced bacterial B12 production in male rats but not in male germ-free mice; iii) fecal microbiota transplantation confirms their protective roles against liver fat accumulation; iv) upregulation of one-carbon metabolism and fatty acid degradation pathways are observed based on the liver transcriptome analyses; and v) in humans the delta changes of serum B12 associate negatively with the fluctuations of serum triglycerides. Overall, we reveal a mechanism of action underpinning the lipid-lowering effect of silymarin via the gut microbiota and its vitamin B12 producing capabilities.

Suggested Citation

  • Wen-Long Sun & Sha Hua & Xin-Yu Li & Liang Shen & Hao Wu & Hong-Fang Ji, 2023. "Microbially produced vitamin B12 contributes to the lipid-lowering effect of silymarin," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36079-x
    DOI: 10.1038/s41467-023-36079-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-36079-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-023-36079-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lawrence A. David & Corinne F. Maurice & Rachel N. Carmody & David B. Gootenberg & Julie E. Button & Benjamin E. Wolfe & Alisha V. Ling & A. Sloan Devlin & Yug Varma & Michael A. Fischbach & Sudha B. , 2014. "Diet rapidly and reproducibly alters the human gut microbiome," Nature, Nature, vol. 505(7484), pages 559-563, January.
    2. Victoria E. Ruiz & Thomas Battaglia & Zachary D. Kurtz & Luc Bijnens & Amy Ou & Isak Engstrand & Xuhui Zheng & Tadasu Iizumi & Briana J. Mullins & Christian L. Müller & Ken Cadwell & Richard Bonneau &, 2017. "A single early-in-life macrolide course has lasting effects on murine microbial network topology and immunity," Nature Communications, Nature, vol. 8(1), pages 1-14, December.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Jane M. Natividad & Bruno Lamas & Hang Phuong Pham & Marie-Laure Michel & Dominique Rainteau & Chantal Bridonneau & Gregory da Costa & Johan van Hylckama Vlieg & Bruno Sovran & Celia Chamignon & Julie, 2018. "Bilophila wadsworthia aggravates high fat diet induced metabolic dysfunctions in mice," Nature Communications, Nature, vol. 9(1), pages 1-15, December.
    5. Michael Zimmermann & Maria Zimmermann-Kogadeeva & Rebekka Wegmann & Andrew L. Goodman, 2019. "Mapping human microbiome drug metabolism by gut bacteria and their genes," Nature, Nature, vol. 570(7762), pages 462-467, June.
    6. Chih-Jung Chang & Chuan-Sheng Lin & Chia-Chen Lu & Jan Martel & Yun-Fei Ko & David M. Ojcius & Shun-Fu Tseng & Tsung-Ru Wu & Yi-Yuan Margaret Chen & John D. Young & Hsin-Chih Lai, 2015. "Ganoderma lucidum reduces obesity in mice by modulating the composition of the gut microbiota," Nature Communications, Nature, vol. 6(1), pages 1-19, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huimin Ye & Sabrina Borusak & Claudia Eberl & Julia Krasenbrink & Anna S. Weiss & Song-Can Chen & Buck T. Hanson & Bela Hausmann & Craig W. Herbold & Manuel Pristner & Benjamin Zwirzitz & Benedikt War, 2023. "Ecophysiology and interactions of a taurine-respiring bacterium in the mouse gut," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Efrat Muller & Itamar Shiryan & Elhanan Borenstein, 2024. "Multi-omic integration of microbiome data for identifying disease-associated modules," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    4. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    5. Ephrem Habyarimana & Faheem S Baloch, 2021. "Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-23, March.
    6. Alexander Wettstein & Gabriel Jenni & Ida Schneider & Fabienne Kühne & Martin grosse Holtforth & Roberto La Marca, 2023. "Predictors of Psychological Strain and Allostatic Load in Teachers: Examining the Long-Term Effects of Biopsychosocial Risk and Protective Factors Using a LASSO Regression Approach," IJERPH, MDPI, vol. 20(10), pages 1-20, May.
    7. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    8. Daifeng Xiang & Gangsheng Wang & Jing Tian & Wanyu Li, 2023. "Global patterns and edaphic-climatic controls of soil carbon decomposition kinetics predicted from incubation experiments," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
    10. Štefan Lyócsa & Petra Vašaničová & Branka Hadji Misheva & Marko Dávid Vateha, 2022. "Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    11. Marcos Rodrigues & Fermín Alcasena & Pere Gelabert & Cristina Vega‐García, 2020. "Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1762-1779, September.
    12. Giovanny Pillajo-Quijia & Blanca Arenas-Ramírez & Camino González-Fernández & Francisco Aparicio-Izquierdo, 2020. "Influential Factors on Injury Severity for Drivers of Light Trucks and Vans with Machine Learning Methods," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    13. Zander S. Venter & Adam Sadilek & Charlotte Stanton & David N. Barton & Kristin Aunan & Sourangsu Chowdhury & Aaron Schneider & Stefano Maria Iacus, 2021. "Mobility in Blue-Green Spaces Does Not Predict COVID-19 Transmission: A Global Analysis," IJERPH, MDPI, vol. 18(23), pages 1-12, November.
    14. G. Brooke Anderson & Keith W. Oleson & Bryan Jones & Roger D. Peng, 2018. "Classifying heatwaves: developing health-based models to predict high-mortality versus moderate United States heatwaves," Climatic Change, Springer, vol. 146(3), pages 439-453, February.
    15. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
    16. Jun Wang & Jinyong Huang & Yunlong Hu & Qianwen Guo & Shasha Zhang & Jinglin Tian & Yanqin Niu & Ling Ji & Yuzhong Xu & Peijun Tang & Yaqin He & Yuna Wang & Shuya Zhang & Hao Yang & Kang Kang & Xinchu, 2024. "Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    17. Ali Al-Ramini & Mohammad A Takallou & Daniel P Piatkowski & Fadi Alsaleem, 2022. "Quantifying changes in bicycle volumes using crowdsourced data," Environment and Planning B, , vol. 49(6), pages 1612-1630, July.
    18. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    19. Faisal Alsayegh & Moh A Alkhamis & Fatima Ali & Sreeja Attur & Nicholas M Fountain-Jones & Mohammad Zubaid, 2022. "Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-15, January.
    20. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36079-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.