Background: Gut microbiota exerts a crucial role in gastrointestinal (GI) and extra-intestinal (EI) disorders. In this context, Akkermansia muciniphila is pivotal for the maintenance of host health and has been correlated with several disorders. Aim: To explore the potential role of A. muciniphila as common dysbiotic marker linked to the disease status. Methods: A cohort of patients affected by GI and EI disorders was enrolled and compared to healthy controls (CTRLs). A targeted-metagenomics approach combined to unsupervised cluster and machine learning (ML) analyses provided microbiota signatures. Results: Microbiota composition was associated to disease phenotype, therapies, diet and anthropometric features, identifying phenotype and therapies as the most impacting variables on microbiota ecology. Unsupervised cluster analyses identified one cluster composed by the majority of patients. DESeq2 algorithm identified ten microbial discriminatory features of patients and CTRLs clusters. Among these microbes, Akkermansia muciniphila resulted the discriminating ML node between patients and CTRLs, independently of specific GI/EI disease or confounding effects. A. muciniphila decrease represented a transversal signature of gut microbiota alteration, showing also an inverse correlation with α-diversity. Conclusion: Overall, A. muciniphila decline may have a crucial role in affecting microbial ecology and in discriminating patients from healthy subjects. Its grading may be considered as a gut dysbiosis feature associated to disease-related microbiota profile. © 2020

Towards a disease-associated common trait of gut microbiota dysbiosis: The pivotal role of Akkermansia muciniphila

Lopetuso, L. R.;Neri, M.;
2020

Abstract

Background: Gut microbiota exerts a crucial role in gastrointestinal (GI) and extra-intestinal (EI) disorders. In this context, Akkermansia muciniphila is pivotal for the maintenance of host health and has been correlated with several disorders. Aim: To explore the potential role of A. muciniphila as common dysbiotic marker linked to the disease status. Methods: A cohort of patients affected by GI and EI disorders was enrolled and compared to healthy controls (CTRLs). A targeted-metagenomics approach combined to unsupervised cluster and machine learning (ML) analyses provided microbiota signatures. Results: Microbiota composition was associated to disease phenotype, therapies, diet and anthropometric features, identifying phenotype and therapies as the most impacting variables on microbiota ecology. Unsupervised cluster analyses identified one cluster composed by the majority of patients. DESeq2 algorithm identified ten microbial discriminatory features of patients and CTRLs clusters. Among these microbes, Akkermansia muciniphila resulted the discriminating ML node between patients and CTRLs, independently of specific GI/EI disease or confounding effects. A. muciniphila decrease represented a transversal signature of gut microbiota alteration, showing also an inverse correlation with α-diversity. Conclusion: Overall, A. muciniphila decline may have a crucial role in affecting microbial ecology and in discriminating patients from healthy subjects. Its grading may be considered as a gut dysbiosis feature associated to disease-related microbiota profile. © 2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/725975
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