What do cells actually want?

Genome-scale models require an objective function representing what an organism strives for. A method has been developed to infer this fundamental biological function from data.Please see related Research article: www.dx.doi.org/10.1186/s13059-016-0968-2

Enhanced Metabolite Productivity of Escherichia coli Adapted to Glucose M9 Minimal Medium

High productivity of biotechnological strains is important to industrial fermentation processes and can be constrained by precursor availability and substrate uptake rate. Adaptive laboratory evolution (ALE) of Escherichia coli MG1655 to glucose minimal M9 medium has been shown to increase strain fitness, mainly through a key mutation in the transcriptional regulator rpoB, which increases flux through central carbon metabolism and the glucose uptake rate. We wanted to test the hypothesis that a substrate uptake enhancing rpoB mutation can translate to increased productivity in a strain possessing a heterologous metabolite pathway. When engineered for heterologous mevalonate production, we found that E. coli rpoB E672K strains displayed 114–167% higher glucose uptake rates and 48–77% higher mevalonate productivities in glucose minimal M9 medium. This improvement in heterologous mevalonate productivity of the rpoB E672K strain is likely mediated by the elevated glucose uptake rate of such strains, which favors overflow metabolism toward acetate production and availability of acetyl-CoA as precursor. These results demonstrate the utility of adaptive laboratory evolution (ALE) to generate a platform strain for an increased production rate for a heterologous product

Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli.

The genome-scale model (GEM) of metabolism in the bacterium Escherichia coli K-12 has been in development for over a decade and is now in wide use. GEM-enabled studies of E. coli have been primarily focused on six applications: (1) metabolic engineering, (2) model-driven discovery, (3) prediction of cellular phenotypes, (4) analysis of biological network properties, (5) studies of evolutionary processes, and (6) models of interspecies interactions. In this review, we provide an overview of these applications along with a critical assessment of their successes and limitations, and a perspective on likely future developments in the field. Taken together, the studies performed over the past decade have established a genome-scale mechanistic understanding of genotype–phenotype relationships in E. coli metabolism that forms the basis for similar efforts for other microbial species. Future challenges include the expansion of GEMs by integrating additional cellular processes beyond metabolism, the identification of key constraints based on emerging data types, and the development of computational methods able to handle such large-scale network models with sufficient accuracy

High-quality genome-scale metabolic modelling of \u3ci\u3ePseudomonas putida\u3c/i\u3e highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas

Dissecting the genetic and metabolic mechanisms of adaptation to the knockout of a major metabolic enzyme in Escherichia coli

Unraveling the mechanisms of microbial adaptive evolution following genetic or environmental challenges is of fundamental interest in biological science and engineering. When the challenge is the loss of a metabolic enzyme, adaptive responses can also shed significant insight into metabolic robustness, regulation, and areas of kinetic limitation. In this study, whole-genome sequencing and highresolution C-13-metabolic flux analysis were performed on 10 adaptively evolved pgi knockouts of Escherichia coli. Pgi catalyzes the first reaction in glycolysis, and its loss results in major physiological and carbon catabolism pathway changes, including an 80% reduction in growth rate. Following adaptive laboratory evolution (ALE), the knockouts increase their growth rate by up to 3.6-fold. Through combined genomic-fluxomic analysis, we characterized the mutations and resulting metabolic fluxes that enabled this fitness recovery. Large increases in pyridine cofactor transhydrogenase flux, correcting imbalanced production of NADPH and NADH, were enabled by direct mutations to the transhydrogenase genes sthA and pntAB. The phosphotransferase system component crr was also found to be frequently mutated, which corresponded to elevated flux from pyruvate to phosphoenolpyruvate. The overall energy metabolism was found to be strikingly robust, and what have been previously described as latently activated Entner-Doudoroff and glyoxylate shunt pathways are shown here to represent no real increases in absolute flux relative to the wild type. These results indicate that the dominant mechanism of adaptation was to relieve the rate-limiting steps in cofactor metabolism and substrate uptake and to modulate global transcriptional regulation from stress response to catabolism