Adaptation to the coupling of glycolysis to toxic methylglyoxal production in tpiA deletion strains of Escherichia coli requires synchronized and counterintuitive genetic changes

Abstract

Methylglyoxal is a highly toxic metabolite that can be produced in all living organisms. Methylglyoxal was artificially elevated by removal of the tpiA gene from a growth optimized Escherichia coli strain. The initial response to elevated methylglyoxal and its toxicity was characterized, and detoxification mechanisms were studied using adaptive laboratory evolution. We found that: 1) Multi-omics analysis revealed biological consequences of methylglyoxal toxicity, which included attack on macromolecules including DNA and RNA and perturbation of nucleotide levels; 2) Counter-intuitive cross-talk between carbon starvation and inorganic phosphate signalling was revealed in the tpiA deletion strain that required mutations in inorganic phosphate signalling mechanisms to alleviate; and 3) The split flux through lower glycolysis depleted glycolytic intermediates requiring a host of synchronized and coordinated mutations in non-intuitive network locations in order to re-adjust the metabolic flux map to achieve optimal growth. Such mutations included a systematic inactivation of the Phosphotransferase System (PTS) and alterations in cell wall biosynthesis enzyme activity. This study demonstrated that deletion of major metabolic genes followed by ALE was a productive approach to gain novel insight into the systems biology underlying optimal phenotypic states.

Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers

“…In order to achieve strong genetic perturbations of enzyme usage, we used gene knockout (KO) strains for the PTS system (ptsHIcrr 6 ), the phosphoglucose isomerase (pgi 8 ), triosephosphate isomerase (tpiA 7 ), and succinate dehydrogenase (sdhCB 9 ). As kapp increases with growth rate 4 , we used KO strains that were optimized for growth on glucose minimal medium via adaptive laboratory evolution (ALE) [6][7][8][9] experiments. In addition to these KO strains, we utilized a wild type MG1655 strain that was subjected to ALE [6][7][8][9][10] .…”

“…As kapp increases with growth rate 4 , we used KO strains that were optimized for growth on glucose minimal medium via adaptive laboratory evolution (ALE) [6][7][8][9] experiments. In addition to these KO strains, we utilized a wild type MG1655 strain that was subjected to ALE [6][7][8][9][10] . As evolution is not a deterministic process, ALE endpoints differ in genotype, and we included a total of 21 strains that resulted from replicates of ALE experiments (i.e., four endpoint strains for ptsHIcrr, eight for pgi, four for tpiA, three for sdhCB, and two WT controls) and that were representative for the respective endpoint population.…”

“…Measured protein abundances show a median R 2 of 0.91 between biological replicates, and a median number of 2076 proteins were detected per strain ( Supplementary Table 1). The obtained protein abundance vectors cluster by the genetic background of the strain used for ALE ( Supplementary Figure 1), indicating that protein levels have adjusted in a specific pattern to compensate for the respective gene KO (see [6][7][8][9] for details on the transcript level).…”

“…To obtain strains with high growth rates for which kapp approaches kapp,max, adaptive laboratory evolution 5 was used on the metabolic knockout strains. We profiled 21 strains, representing metabolic specialists with diverse flux profiles that are able to obtain high growth rates [6][7][8][9] . With this data-driven approach, we show that in vivo kcats are stable and robust to genetic perturbations, and that they can be used in genome-scale models to obtain a high predictive performance for unseen protein abundance data.…”

“…With this data-driven approach, we show that in vivo kcats are stable and robust to genetic perturbations, and that they can be used in genome-scale models to obtain a high predictive performance for unseen protein abundance data. Figure 1: Approach for obtaining kcat in vivo from metabolic specialists: Knock out of enzymes in central metabolism was followed by adaptive laboratory evolution (ALE) to obtain 21 strains that had diverse flux profiles, while achieving high growth rates [6][7][8][9] . Fluxomics and proteomics data was then integrated for the evolved strains to obtain the maximum kapp across the 21 strains (kapp,max) for each enzyme that could be mapped uniquely.…”