Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease
Abstract Background The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-o...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s40168-024-01990-y |
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| author | Emmanouil Nychas Andrea Marfil-Sánchez Xiuqiang Chen Mohammad Mirhakkak Huating Li Weiping Jia Aimin Xu Henrik Bjørn Nielsen Max Nieuwdorp Rohit Loomba Yueqiong Ni Gianni Panagiotou |
| author_facet | Emmanouil Nychas Andrea Marfil-Sánchez Xiuqiang Chen Mohammad Mirhakkak Huating Li Weiping Jia Aimin Xu Henrik Bjørn Nielsen Max Nieuwdorp Rohit Loomba Yueqiong Ni Gianni Panagiotou |
| author_sort | Emmanouil Nychas |
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| description | Abstract Background The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases. Results Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845–0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively. Conclusion Our study reveals robust and highly specific NAFLD signatures and offers a more realistic microbiome-therapeutics approach over individual species for this complex disease. Video Abstract |
| format | Article |
| id | doaj-art-77a65af8460b4114a1d47c547d9043af |
| institution | Kabale University |
| issn | 2049-2618 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Microbiome |
| spelling | doaj-art-77a65af8460b4114a1d47c547d9043af2025-01-26T12:43:13ZengBMCMicrobiome2049-26182025-01-0113112110.1186/s40168-024-01990-yDiscovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver diseaseEmmanouil Nychas0Andrea Marfil-Sánchez1Xiuqiang Chen2Mohammad Mirhakkak3Huating Li4Weiping Jia5Aimin Xu6Henrik Bjørn Nielsen7Max Nieuwdorp8Rohit Loomba9Yueqiong Ni10Gianni Panagiotou11Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll InstituteDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll InstituteDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll InstituteDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll InstituteDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Diabetes InstituteDepartment of Endocrinology and Metabolism, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Diabetes InstituteThe State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong KongClinical MicrobiomicsAmsterdam UMC, Location AMC, Department of Vascular Medicine, University of AmsterdamDepartment of Medicine, MASLD Research Center, University of CaliforniaDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll InstituteDepartment of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll InstituteAbstract Background The pathogenesis of non-alcoholic fatty liver disease (NAFLD) with a global prevalence of 30% is multifactorial and the involvement of gut bacteria has been recently proposed. However, finding robust bacterial signatures of NAFLD has been a great challenge, mainly due to its co-occurrence with other metabolic diseases. Results Here, we collected public metagenomic data and integrated the taxonomy profiles with in silico generated community metabolic outputs, and detailed clinical data, of 1206 Chinese subjects w/wo metabolic diseases, including NAFLD (obese and lean), obesity, T2D, hypertension, and atherosclerosis. We identified highly specific microbiome signatures through building accurate machine learning models (accuracy = 0.845–0.917) for NAFLD with high portability (generalizable) and low prediction rate (specific) when applied to other metabolic diseases, as well as through a community approach involving differential co-abundance ecological networks. Moreover, using these signatures coupled with further mediation analysis and metabolic dependency modeling, we propose synergistic defined microbial consortia associated with NAFLD phenotype in overweight and lean individuals, respectively. Conclusion Our study reveals robust and highly specific NAFLD signatures and offers a more realistic microbiome-therapeutics approach over individual species for this complex disease. Video Abstracthttps://doi.org/10.1186/s40168-024-01990-yNAFLDGut microbiotaMetabolic diseasesMachine learningNetwork analysisMetabolomics |
| spellingShingle | Emmanouil Nychas Andrea Marfil-Sánchez Xiuqiang Chen Mohammad Mirhakkak Huating Li Weiping Jia Aimin Xu Henrik Bjørn Nielsen Max Nieuwdorp Rohit Loomba Yueqiong Ni Gianni Panagiotou Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease Microbiome NAFLD Gut microbiota Metabolic diseases Machine learning Network analysis Metabolomics |
| title | Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease |
| title_full | Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease |
| title_fullStr | Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease |
| title_full_unstemmed | Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease |
| title_short | Discovery of robust and highly specific microbiome signatures of non-alcoholic fatty liver disease |
| title_sort | discovery of robust and highly specific microbiome signatures of non alcoholic fatty liver disease |
| topic | NAFLD Gut microbiota Metabolic diseases Machine learning Network analysis Metabolomics |
| url | https://doi.org/10.1186/s40168-024-01990-y |
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