Modification of E. coli central metabolism to optimize the biotransformation of L-isoleucine into 4-hydroxyisoleucine by enzymatic hydroxylation

2012 ◽  
Vol 48 (7) ◽  
pp. 639-644 ◽  
Author(s):  
A. D. Kivero ◽  
A. E. Novikova ◽  
S. V. Smirnov
Keyword(s):  
PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0139507 ◽  
Author(s):  
Ahmad A. Mannan ◽  
Yoshihiro Toya ◽  
Kazuyuki Shimizu ◽  
Johnjoe McFadden ◽  
Andrzej M. Kierzek ◽  
...  

2008 ◽  
Vol 2 (1) ◽  
pp. 41 ◽  
Author(s):  
Jiao Zhao ◽  
Douglas Ridgway ◽  
Gordon Broderick ◽  
Andriy Kovalenko ◽  
Michael Ellison

2021 ◽  
Author(s):  
Camillo Iacometti ◽  
Katharina Marx ◽  
Maria Hoenick ◽  
Viktoria Biletskaia ◽  
Helena Schulz-Mirbach ◽  
...  

All living organisms share similar reactions within their central metabolism to provide precursors for all essential building blocks and reducing power. To identify whether alternative metabolic routes of glycolysis can operate in E. coli, we complementarily employed in silico design, rational engineering, and adaptive laboratory evolution. First, we used a genome-scale model and identified two potential pathways within the metabolic network of this organism replacing canonical Embden-Meyerhof-Parnas (EMP) glycolysis to convert phosphosugars into organic acids. One of these glycolytic routes proceeds via methylglyoxal, the other via serine biosynthesis and degradation. Then, we implemented both pathways in E. coli strains harboring defective EMP glycolysis. Surprisingly, the pathway via methylglyoxal immediately operated in a triosephosphate isomerase deletion strain cultivated on glycerol. By contrast, in a phosphoglycerate kinase deletion strain, the overexpression of methylglyoxal synthase was necessary for implementing a functional methylglyoxal pathway. Furthermore, we engineered the serine shunt which converts 3-phosphoglycerate via serine biosynthesis and degradation to pyruvate, bypassing an enolase deletion. Finally, to explore which of these alternatives would emerge by natural selection we performed an adaptive laboratory evolution study using an enolase deletion strain. The evolved mutants were shown to use the serine shunt. Our study reveals the flexible redesignation of metabolic pathways to create new metabolite links and rewire central metabolism.


PLoS ONE ◽  
2012 ◽  
Vol 7 (4) ◽  
pp. e34533 ◽  
Author(s):  
Guido Santos ◽  
José A. Hormiga ◽  
Paula Arense ◽  
Manuel Cánovas ◽  
Néstor V. Torres

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Pierre Millard ◽  
Brice Enjalbert ◽  
Sandrine Uttenweiler-Joseph ◽  
Jean-Charles Portais ◽  
Fabien Létisse

Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.


2013 ◽  
Vol 1833 (12) ◽  
pp. 2879-2889 ◽  
Author(s):  
Susan Jahn ◽  
Bart R. Haverkorn van Rijsewijk ◽  
Uwe Sauer ◽  
Katja Bettenbrock
Keyword(s):  

mBio ◽  
2020 ◽  
Vol 11 (5) ◽  
Author(s):  
Madeline Tong ◽  
Shawn French ◽  
Sara S. El Zahed ◽  
Wai kit Ong ◽  
Peter D. Karp ◽  
...  

ABSTRACT Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux. IMPORTANCE While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3–5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.


Metabolites ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 749
Author(s):  
Wolfram Liebermeister ◽  
Elad Noor

Enzyme kinetic constants in vivo are largely unknown, which limits the construction of large metabolic models. Given measured metabolic fluxes, metabolite concentrations, and enzyme concentrations, these constants may be inferred by model fitting, but the estimation problems are hard to solve if models are large. Here we show how consistent kinetic constants, metabolite concentrations, and enzyme concentrations can be determined from data if metabolic fluxes are known. The estimation method, called model balancing, can handle models with a wide range of rate laws and accounts for thermodynamic constraints between fluxes, kinetic constants, and metabolite concentrations. It can be used to estimate in-vivo kinetic constants, to complete and adjust available data, and to construct plausible metabolic states with predefined flux distributions. By omitting one term from the log posterior—a term for penalising low enzyme concentrations—we obtain a convex optimality problem with a unique local optimum. As a demonstrative case, we balance a model of E. coli central metabolism with artificial or experimental data and obtain a physically and biologically plausible parameterisation of reaction kinetics in E. coli central metabolism. The example shows what information about kinetic constants can be obtained from omics data and reveals practical limits to estimating in-vivo kinetic constants. While noise-free omics data allow for a reasonable reconstruction of in-vivo kcat and KM values, prediction from noisy omics data are worse. Hence, adjusting kinetic constants and omics data to obtain consistent metabolic models is the main application of model balancing.


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