Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation
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Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA-protein binding, protein-metabolite interaction, and protein-protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots.
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Zhu explored Bayesian networks to understand cell regulation in yeast using metabolomics and transcriptomics data.
“Similarly, Zhu (Zhu et al., 2012) has also explored BN (Bayesian network) to understand cell regulation in yeast using metabolomics and transcriptomics data.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zhu et al., 2012
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- Authors
- Zhu, J., Sova, P., Xu, Q., Dombek, K. M., Xu, E. Y., Vu, H., Tu, Z., Brem, R. B., Bumgarner, R. E., & Schadt, E. E.
- Affiliations
- Sage Bionetworks (US)
- University of Washington (US)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- University of California, Berkeley (US)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Zhu, Jun
- Schadt, Eric E.
- ORCID
- Venue
- PLoS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-04-03
- Volume
- 10
- Issue
- 4
- Pages
- e1001301
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC3317911
- OpenAlex
- W2044124287
- Cited by
- 205
- References
- 75
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 6 · 2023: 9 · 2022: 12 · 2021: 19 · 2020: 15 · 2019: 14 · 2018: 15 · 2017: 15 · 2016: 17 · 2015: 22 · 2014: 16 · 2013: 21 · 2012: 11
- FWCI
- 8.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- metabolomics, transcriptomics, multi-omics integration, gene expression regulation, expression quantitative trait loci, protein-DNA interactions, protein-metabolite interactions, protein-protein interaction, genetic variation
- MeSH terms
- Genes, Fungal, Models, Genetic, Saccharomyces cerevisiae, Stress, Physiological, Chromosomes, Fungal, Gene Expression Regulation, Fungal, Protein Interaction Mapping, Quantitative Trait Loci, Gene Regulatory Networks, Biosynthetic Pathways, Metabolome, Transcriptome
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