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Metabolites
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Metabolomics and Bioinformatics Approaches to Studying Human Gut Microbiota-Derived Metabolites
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Metabolomics and Bioinformatics Approaches to Studying Human Gut Microbiota-Derived Metabolites

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Microbiology and Ecological Metabolomics".

Deadline for manuscript submissions: 15 March 2025 | Viewed by 2219

Special Issue Editors

Dr. Zhiwei Zhou

E-Mail Website
Guest Editor
Department of Pathology, Stanford University, Stanford, CA, USA
Interests: metabolomics; bioinformatics; microbial metabolism
Dr. Shipei Xing

E-Mail Website
Guest Editor
Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
Interests: mass spectrometry informatics; metabolomics
Dr. Xiaotao Shen

E-Mail Website
Guest Editor
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
Interests: multi-omics data integration methods development; host-microbe inter-action; aging and neurodegenerative disease; environment health

Special Issue Information

Dear Colleagues,

The human gastrointestinal tract harbors trillions of microbes that influence human health and physiology. Dysbiosis of the gut microbiome may contribute to the pathogenesis of various diseases, including obesity, type 2 diabetes (T2D), cardiovascular disease (CVD), inflammatory bowel disease (IBD), metabolic-associated fatty liver disease, and so on. One of the key ways that the gut microbiota affects the host’s health is by producing bioactive metabolites, namely gut microbiota-derived metabolites. These metabolites can be further absorbed and enter the host circulation system, where they then affect human health. Liquid chromatography–mass spectrometry-based metabolomics, together with advances in bioinformatics, enables the capture of thousands of metabolites, facilitating the identification of potential mechanistic links between the gut microbiome, the metabolome, and host phenotypes.

This Special Issue aims to facilitate the development and applications of metabolomics and bioinformatics approaches in gut microbiome research to elucidate the impact of gut microbiota-derived metabolites on human health and diseases. It welcomes original research articles, short communications, protocols, reviews, and perspectives on topics including, but not limited to, the following: 1) the development of analytical methodologies to qualify and quantify gut microbiota-derived metabolites; 2) advanced metabolomics and bioinformatics approaches (algorithms, tools, databases) to accelerate data analysis of the microbiome and metabolomics; 3) metabolomics and multiomics studies of bacterial cultures, animal models, and human cohort samples to deepen our understanding of the impact of the gut microbiome; 4) the roles and impacts of the gut microbiome and its derived metabolites on host health and diseases.

Dr. Zhiwei Zhou
Dr. Shipei Xing
Dr. Xiaotao Shen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metabolomics
  • bioinformatics
  • databases
  • gut microbiota
  • microbiome
  • host–microbe interaction
  • microbial metabolism
  • multiomics

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Published Papers (2 papers)

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15 pages, 4682 KiB  
Article
Integrative Metagenomic Analyses Reveal Gut Microbiota-Derived Multiple Hits Connected to Development of Diabetes Mellitus
by Sehad N. Alarifi, Essam Jamil Alyamani, Mohammed Alarawi, Azzam A. Alquait, Mohammed A. Alolayan, Ahmad M. Aldossary, Randa A. Abd EL-Rahman and Rashid Mir
Metabolites 2024, 14(12), 720; https://doi.org/10.3390/metabo14120720 - 21 Dec 2024
Viewed by 721
Abstract
Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with gut dysbiosis. To investigate the association between gut microbiota and T2DM in a Saudi Arabian population. Methods: We conducted a comparative analysis of fecal microbiota from 35 individuals, including both [...] Read more.
Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with gut dysbiosis. To investigate the association between gut microbiota and T2DM in a Saudi Arabian population. Methods: We conducted a comparative analysis of fecal microbiota from 35 individuals, including both T2DM patients and healthy controls. 16S rRNA gene sequencing was employed to characterize the microbial community structure. Results: Our findings revealed significant differences in microbial composition between the two groups. The T2DM group exhibited a higher abundance of Firmicutes and lower levels of Bacteroidetes compared to the healthy control group. At the genus level, T2DM patients showed a decrease in butyrate-producing bacteria such as Bacteroides and Akkermansia, while an increase in Ruminococcus and Prevotella was observed. Additionally, the T2DM group had a higher abundance of Faecalibacterium, Agathobacter, and Lachnospiraceae, along with a lower abundance of Bacteroides. Conclusions: These results suggest that alterations in gut microbiota composition may contribute to the development of T2DM in the Saudi Arabian population. Further large-scale studies are needed to validate these findings and explore potential therapeutic interventions targeting the gut microbiome. Full article
(This article belongs to the Special Issue Metabolomics and Bioinformatics Approaches to Studying Human Gut Microbiota-Derived Metabolites)
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16 pages, 2250 KiB  
Article
MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data
by Nami Sakamoto, Takaki Oka, Yuki Matsuzawa, Kozo Nishida, Jayashankar Jayaprakash, Aya Hori, Makoto Arita and Hiroshi Tsugawa
Metabolites 2024, 14(11), 602; https://doi.org/10.3390/metabo14110602 - 7 Nov 2024
Viewed by 1167
Abstract
Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based [...] Read more.
Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in determining the lipid subclass of ion features. We designed a new descriptor, MCH (mode of carbon and hydrogen), to increase the specificity of lipid subclass prediction in nominal mass resolution MS data. Results: The model, trained with 6760 and 6862 manually curated MS/MS spectra for the positive and negative ion modes, respectively, classified queries into one or several of 97 lipid subclasses, achieving an accuracy of 97.4% in the test set. The program was further validated using various datasets from different instruments and curators, with the average accuracy exceeding 87.2%. Using an integrated approach with molecular spectral networking, we demonstrated the utility of MS2Lipid by annotating microbiota-derived esterified bile acids, whose abundance was significantly increased in fecal samples of obese patients in a human cohort study. This suggests that the machine learning model provides an independent criterion for lipid subclass classification, enhancing the annotation of lipid metabolites within known lipid classes. Conclusions: MS2Lipid is a highly accurate machine learning model that enhances lipid subclass annotation from MS/MS data and provides an independent criterion. Full article
(This article belongs to the Special Issue Metabolomics and Bioinformatics Approaches to Studying Human Gut Microbiota-Derived Metabolites)
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