Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation

• Published in: PLoS biology 2012-04, Vol.10 (4), p.e1001301-e1001301
• Main Authors: Zhu, Jun, Sova, Pavel, Xu, Qiuwei, Dombek, Kenneth M, Xu, Ethan Y, Vu, Heather, Tu, Zhidong, Brem, Rachel B, Bumgarner, Roger E, Schadt, Eric E
• Format: Article
• Language: English
• Subjects: Biology; Biosynthetic Pathways - genetics; Cellular control mechanisms; Chromosomes, Fungal - genetics; Gene Expression Regulation, Fungal; Gene Regulatory Networks; Genes, Fungal; Genetic transcription; Genetics; Genomics; Metabolites; Metabolome - genetics; Metabolomics; Models, Genetic; NMR; Nuclear magnetic resonance; Protein Interaction Mapping; Proteins; Quantitative Trait Loci; Saccharomyces cerevisiae - genetics; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae - physiology; Stress, Physiological; Transcriptome; Yeast
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.1001301

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.