In silico method for modelling metabolism and gene product expression at genome scale

Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem; Portnoy, Vasiliy A.; Lewis, Nathan E.; Orth, Jeffrey D.; Schrimpe‐Rutledge, Alexandra C.; Smith, Richard D.; Adkins, Joshua N.; Zengler, Karsten; Palsson, Bernhard Ø.

doi:10.1038/ncomms1928

Cited by 264 publications

(254 citation statements)

References 57 publications

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“…However, they do not appear to be "activated" as part of a general stress response in this case. These insights and the dataset presented here should help advance predictive metabolic modeling (35)(36)(37). Overall, these results add to our understanding of adaptive evolution by elucidating how challenges to specific cellular subsystems-that is, central carbon metabolism and glycolysis-are overcome.…”

Section: Discussionmentioning

confidence: 91%

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

Long

Gonzalez

Feist

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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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 high-resolution 13C-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.

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Section: Discussionmentioning

confidence: 91%

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

Long

Gonzalez

Feist

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…M-Models, although powerful tools for predicting and analyzing physiology, do not quantitatively predict gene expression, which can in certain circumstances lead to inaccurate predictions (36). Monte Carlo sampling is one way to skirt this issue, but genome-scale models of metabolism that factor in gene expression and its concomitant energy costs, dubbed ME-Models, have recently been developed (37).…”

Section: Resultsmentioning

confidence: 99%

Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies

Sandberg

Lloyd

Palsson

et al. 2017

Appl Environ Microbiol

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Adaptive laboratory evolution (ALE) experiments are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the observed distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.IMPORTANCE Evolution and natural selection inexorably lead to an organism's improved fitness in a given environment, whether in a laboratory or natural setting. However, despite the frequent natural occurrence of complex and dynamic growth environments, laboratory evolution experiments typically maintain simple, static culturing environments so as to reduce selection pressure complexity. In this study, we investigated the adaptive strategies underlying evolution to fluctuating environments by evolving Escherichia coli to conditions of frequently switching growth substrate. Characterization of evolved strains via a number of different data types revealed the various genetic and phenotypic changes implemented in pursuit of growth optimality and how these differed across the different growth substrates and switching protocols. This work not only helps to establish general principles of adaptation to complex environments but also suggests strategies for experimental design to achieve desired evolutionary outcomes.KEYWORDS adaptive laboratory evolution, Escherichia coli, adaptive mutations, phenotypic variation I n heterotrophs such as Escherichia coli, catabolism of carbon substrates is the driving force behind the energy generation and chemical synthesis necessary for homeostasis and anabolism (1). Although glucose is the most readily metabolized carbohydrate

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“…Metabolic networks, for a start, can be described using flux balance analysis and have been successfully merged with regulatory networks (Covert et al ., 2004). Additionally, global allocation of transcriptional and translational resources could be implemented with little computational cost (Lerman et al ., 2012). Finally, it is now becoming widely accepted that bacterial cells are not mere containers of homogenously distributed chemical species.…”

mentioning

confidence: 99%

Shedding light on the black box models of the cell

Jiménez

2017

Microbial Biotechnology

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In silico method for modelling metabolism and gene product expression at genome scale

Cited by 264 publications

References 57 publications

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

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

Laboratory Evolution to Alternating Substrate Environments Yields Distinct Phenotypic and Genetic Adaptive Strategies

Shedding light on the black box models of the cell

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