GSE131992
Systematic identification of metabolites controlling gene expression in E. coli
Cellular metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. Methods to detect these regulatory interactions are mostly based on in vitro binding assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that are potential effectors of transcription factors in E. coli. By switching the culture conditions between starvation and growth for 20 hours, we induced strong metabolite concentration changes and accompanying gene expression changes, which were measured by LC-MS/MS and RNA sequencing. From the transcriptome data we calculated the activity of 209 transcriptional regulators with Network Component Analysis, and then tested which metabolites correlated with these activities. This approach captured, for instance, the in vivo Hill-kinetics of CRP regulation by cyclic-AMP, a canonical example of allosteric transcription factor regulation in E. coli. By testing correlations between all pairs of transcription factors and metabolites, we predicted putative effectors of 65 transcription factors, and validated five of them in vitro. These results show that the combination of transcriptomics and metabolomics can generate hypotheses about metabolism-transcription interactions that are relevant in vivo and drive transitions between physiological states. Overall design: Expression profiles of E. coli at 29 timepoints in duplicates during a perturbation experiment