The funders had changing over time in response to growth conditions. We characterized this phenomenon using bulk liquid culture experiments, colony growth tracking, flow cytometry, single-cell timelapse microscopy, transcriptomics, and genome resequencing. Finally, we used mathematical modeling to better understand the processes by which cells change phenotype, and found evidence for both stochastic, bidirectional phenotypic diversification and responsive, directed phenotypic shifts, depending on the growth substrate and the presence of toxin.

Historically, cellular metabolism has been described in the context of discrete metabolic units (e.g., biosynthetic or degradative pathways), a perspective that facilitated efforts to understand metabolic components such as enzymes and metabolites. In reality, these units are integrated to form a complex metabolic network whose connectivity is mediated extensively by metabolites. Under appropriate circumstances (i.e., growth conditions or strain background), dynamic changes of intracellular metabolite levels can have significant impacts on cellular fitness, by providing unexpected mechanisms for survival (1, 2) or compromising growth. Dissecting metabolic integration in bacteria and understanding the diverse roles of metabolites can reveal paradigms that are conserved across phylogenetic kingdoms. Defining these paradigms provides insights into the consequences of perturbing the metabolic network that can be exploited for desired outcomes.

Metabolism consists of biochemical reactions that are combined to generate a robust metabolic network that can respond to perturbations and also adapt to changing environmental conditions. Escherichia coli and Salmonella enterica are closely related enterobacteria that share metabolic components, pathway structures, and regulatory strategies. The synthesis of thiamine in S. enterica has been used to define a node of the metabolic network by analyzing alternative inputs to thiamine synthesis from diverse metabolic pathways. To assess the conservation of metabolic networks in organisms with highly conserved components, metabolic contributions to thiamine synthesis in E. coli were investigated. Unexpectedly, we found that, unlike S. enterica, E. coli does not use the phosphoribosylpyrophosphate (PRPP) amidotransferase (PurF) as the primary enzyme for synthesis of phosphoribosylamine (PRA). In fact, our data showed that up to 50% of the PRA used by E. coli to make thiamine requires the activities of threonine dehydratase (IlvA) and anthranilate synthase component II (TrpD). Significantly, the IlvA- and TrpD-dependent pathway to PRA functions in S. enterica only in the absence of a functional reactive intermediate deaminase (RidA) enzyme, bringing into focus how these closely related bacteria have distinct metabolic networks.

In Salmonella enterica, aminoimidazole carboxamide ribotide (AICAR) is a purine biosynthetic intermediate and a substrate of the AICAR transformylase/IMP cyclohydrolase (PurH) enzyme. When purH is eliminated in an otherwise wild-type strain, AICAR accumulates and indirectly inhibits synthesis of the essential coenzyme thiamine pyrophosphate (TPP). In this study, untargeted metabolomics approaches were used to i) corroborate previously defined metabolite changes, ii) define the global consequences of AICAR accumulation and iii) investigate the metabolic effects of mutations that restore thiamine prototrophy to a purH mutant. The data showed that AICAR accumulation led to an increase in the global regulator cyclic AMP (cAMP) and that disrupting central carbon metabolism could decrease AICAR and/or cAMP to restore thiamine synthesis. A mutant (icc) blocked in cAMP degradation that accumulated cAMP but had wild-type levels of AICAR was used to identify changes in the purH metabolome that were a direct result of elevated cAMP. Data herein describe the use of metabolomics to identify the metabolic state of mutant strains and probe the underlying mechanisms used by AICAR to inhibit thiamine synthesis. The results obtained provide a cautionary tale of using metabolite concentrations as the only data to define the physiological state of a bacterial cell.