Proton Nuclear Magnetic Resonance Metabolomics Corroborates Serine Hydroxymethyltransferase as the Primary Target of 2-Aminoacrylate in a ridA Mutant of Salmonella enterica

Abstract

The reactive intermediate deaminase RidA (EC 3.5.99.10) is conserved across all domains of life and deaminates reactive enamine species. When Salmonella enterica ridA mutants are grown in minimal medium, 2-aminoacrylate (2AA) accumulates, damages several pyridoxal 5′-phosphate (PLP)-dependent enzymes, and elicits an observable growth defect. Genetic studies suggested that damage to serine hydroxymethyltransferase (GlyA), and the resultant depletion of 5,10-methelenetetrahydrofolate (5,10-mTHF), was responsible for the observed growth defect. However, the downstream metabolic consequence from GlyA damage by 2AA remains relatively unexplored. This study sought to use untargeted proton nuclear magnetic resonance (1H NMR) metabolomics to determine whether the metabolic state of an S. enterica ridA mutant was accurately reflected by characterizing growth phenotypes. The data supported the conclusion that metabolic changes in a ridA mutant were due to the IlvA-dependent generation of 2AA, and that the majority of these changes were a consequence of damage to GlyA. While many of the metabolic differences for a ridA mutant could be explained, changes in some metabolites were not easily modeled, suggesting that additional levels of metabolic complexity remain to be unraveled. IMPORTANCE The accumulation of the reactive enamine intermediate 2-aminoacrylate (2AA) elicits global metabolic stress in many prokaryotes and eukaryotes by simultaneously damaging multiple pyridoxal 5′-phosphate (PLP)-dependent enzymes. This work employed 1H NMR to expand our understanding of the consequence(s) of 2AA stress on metabolite pools and effectively identify the metabolic changes stemming from one damaged target: GlyA. This study shows that nutrient supplementation during 1H NMR metabolomics experiments can disentangle complex metabolic outcomes stemming from a general metabolic stress. Metabolomics shows great potential to complement classical reductionist approaches to cost-effectively accelerate the rate of progress in expanding our global understanding of metabolic network structure and physiology. To that end, this work demonstrates the utility in implementing nutrient supplementation and genetic perturbation into metabolomics workflows as a means to connect metabolic outputs to physiological phenomena and establish causal relationships.

Metabolomics Insights into Chemical Convergence in Xanthomonas perforans and Metabolic Changes Following Treatment with the Small Molecule Carvacrol

“…The relative number of energy biosynthesis and degradation pathways affected by carvacrol in this study suggests that, perhaps, carvacrol acted largely on the energy biosynthesis pathways, thereby eliciting the global changes in the metabolome of X. perforans. A recent study has shown that global metabolic changes may be a consequence of a cascade of responses initiated by a unique metabolite [157,158]. It is, therefore, likely that the global changes identified in X. perforans were due to the activities of carvacrol on specific pathways.…”

NMR: Unique Strengths That Enhance Modern Metabolomics Research

“…NMR measurements of chemically defined spent minimal media, spent minimal media supplemented with isoleucine, and the resulting cell pellets demonstrated that key metabolite changes between ridA and wild-type strains were due to the IlvA-dependent generation of 2AA. Further media supplementations circumvented the downstream pathway damage to GlyA, largely restoring those same endogenous and exogenous metabolites to wild-type levels . This classical genetic perturbation and metabolome rescue approach, and the agreement between media analysis and bacterial endometabolome, disentangled the complex metabolic relationships between enzymes and further expanded the interacting edges of the known ridA biochemical model to previously unexplored pathways. , …”

A Pilot Study For Fragment Identification Using 2D NMR and Deep Learning

“…The role that metabolomics play together with the other 'omics' technologies in the understanding of complex biological systems is broadly accepted. Many Life Science projects have implemented untargeted metabolomics within their pipeline to help them characterize phenotypes for different applications [5,6,7,8,9,10,11,12]. However, one of the major bottlenecks to be overcome in this approach is compound identification, since important features with no identification lead to no meaningful understanding of a biological system [13,14].…”