MiMeNet: Exploring microbiome-metabolome relationships using neural networks
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Abstract
The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.
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Many functional metabolomics studies do not measure functional properties of the metabolites themselves but determine function using correlation and guilt by association with known bioactive metabolites, transcription, or protein expression.
“Many functional metabolomics studies do not measure functional properties of the metabolites themselves, but rather determine function using correlation and “guilt by association” with other known bioactive metabolites, transcription, or protein expression”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [17]
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Microbiome-metabolome interactions predict host phenotypePreprint
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- Authors
- Reiman, D., Layden, B. T., & Dai, Y.
- Affiliations
- University of Illinois Chicago (US)
- Corresponding author
- Dai, Yang
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-05-17
- Volume
- 17
- Issue
- 5
- Pages
- e1009021
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC8158931
- OpenAlex
- W3163924330
- Cited by
- 80
- References
- 69
- Citations by year
- 2026: 14 · 2025: 10 · 2024: 17 · 2023: 21 · 2022: 13 · 2021: 5
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Update notices
- new_version (2021-05-27)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (R01DK104927, P30DK020595)
- U.S. Department of Veterans Affairs (I01BX003382)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- microbiome-metabolome interactions, neural network modeling, guilt by association, host-microbe metabolic interactions
- MeSH terms
- Humans, Inflammatory Bowel Diseases, Neural Networks, Computer, Metabolome, Microbiota
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