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1. Analogous Metabolic Decoupling in Pseudomonas putida and Comamonas testosteroni Implies Energetic Bypass to Facilitate Gluconeogenic Growth

   https://doi.org/10.1128/mbio.03259-21  

   Wilkes, Rebecca A. ; Waldbauer, Jacob ; Aristilde, Ludmilla ( December 2021 , mBio)

   Dunn, Anne K. ; Ruby, Edward G. (Ed.)

   ABSTRACT Gluconeogenic carbon metabolism is not well understood, especially within the context of flux partitioning between energy generation and biomass production, despite the importance of gluconeogenic carbon substrates in natural and engineered carbon processing. Here, using multiple omics approaches, we elucidate the metabolic mechanisms that facilitate gluconeogenic fast-growth phenotypes in Pseudomonas putida and Comamonas testosteroni , two Proteobacteria species with distinct metabolic networks. In contrast to the genetic constraint of C. testosteroni , which lacks the enzymes required for both sugar uptake and a complete oxidative pentose phosphate (PP) pathway, sugar metabolism in P. putida is known to generate surplus NADPH by relying on the oxidative PP pathway within its characteristic cyclic connection between the Entner-Doudoroff (ED) and Embden-Meyerhoff-Parnas (EMP) pathways. Remarkably, similar to the genome-based metabolic decoupling in C. testosteroni , our 13 C-fluxomics reveals an inactive oxidative PP pathway and disconnected EMP and ED pathways in P. putida during gluconeogenic feeding, thus requiring transhydrogenase reactions to supply NADPH for anabolism in both species by leveraging the high tricarboxylic acid cycle flux during gluconeogenic growth. Furthermore, metabolomics and proteomics analyses of both species during gluconeogenic feeding, relative to glycolytic feeding, demonstrate a 5-fold depletion in phosphorylated metabolites and the absence of or up to a 17-fold decrease in proteins of the PP and ED pathways. Such metabolic remodeling, which is reportedly lacking in Escherichia coli exhibiting a gluconeogenic slow-growth phenotype, may serve to minimize futile carbon cycling while favoring the gluconeogenic metabolic regime in relevant proteobacterial species. IMPORTANCE Glycolytic metabolism of sugars is extensively studied in the Proteobacteria , but gluconeogenic carbon sources (e.g., organic acids, amino acids, aromatics) that feed into the tricarboxylic acid (TCA) cycle are widely reported to produce a fast-growth phenotype, particularly in species with biotechnological relevance. Much remains unknown about the importance of glycolysis-associated pathways in the metabolism of gluconeogenic carbon substrates. Here, we demonstrate that two distinct proteobacterial species, through genetic constraints or metabolic regulation at specific metabolic nodes, bypass the oxidative PP pathway during gluconeogenic growth and avoid unnecessary carbon fluxes by depleting protein investment into connected glycolysis pathways. Both species can leverage instead the high TCA cycle flux during gluconeogenic feeding to meet NADPH demand. Importantly, lack of a complete oxidative pentose phosphate pathway is a widespread metabolic trait in Proteobacteria with a gluconeogenic carbon preference, thus highlighting the important relevance of our findings toward elucidating the metabolic architecture in these bacteria. 

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2. SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

   https://doi.org/10.1186/s12859-021-04281-7  

   Lee, Justin Y. ; Nguyen, Britney ; Orosco, Carlos ; Styczynski, Mark P. ( July 2021 , BMC Bioinformatics)

   The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR.

   We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification.

   SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

    

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3. Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth

   https://doi.org/10.1038/s41467-020-17612-8  

   Zuñiga, Cristal ; Li, Tingting ; Guarnieri, Michael T. ; Jenkins, Jackson P. ; Li, Chien-Ting ; Bingol, Kerem ; Kim, Young-Mo ; Betenbaugh, Michael J. ; Zengler, Karsten ( July 2020 , Nature Communications)

   Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secretingSynechococcus elongatuswith heterotrophicEscherichia coliK-12,Escherichia coliW,Yarrowia lipolytica, orBacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing toS. elongatus-E. coliK-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.

    

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4. Model metabolic strategy for heterotrophic bacteria in the cold ocean based on Colwellia psychrerythraea 34H

   https://doi.org/10.1073/pnas.1807804115  

   Czajka, Jeffrey J. ; Abernathy, Mary H. ; Benites, Veronica T. ; Baidoo, Edward E. K. ; Deming, Jody W. ; Tang, Yinjie J. ( November 2018 , Proceedings of the National Academy of Sciences)

   Colwellia psychrerythraea34H is a model psychrophilic bacterium found in the cold ocean—polar sediments, sea ice, and the deep sea. Although the genomes of such psychrophiles have been sequenced, their metabolic strategies at low temperature have not been quantified. We measured the metabolic fluxes and gene expression of 34H at 4 °C (the mean global-ocean temperature and a normal-growth temperature for 34H), making comparative analyses at room temperature (above its upper-growth temperature of 18 °C) and with mesophilicEscherichia coli. When grown at 4 °C, 34H utilized multiple carbon substrates without catabolite repression or overflow byproducts; its anaplerotic pathways increased flux network flexibility and enabled CO₂fixation. In glucose-only medium, the Entner–Doudoroff (ED) pathway was the primary glycolytic route; in lactate-only medium, gluconeogenesis and the glyoxylate shunt became active. In comparison,E. coli, cold stressed at 4 °C, had rapid glycolytic fluxes but no biomass synthesis. At their respective normal-growth temperatures, intracellular concentrations of TCA cycle metabolites (α-ketoglutarate, succinate, malate) were 4–17 times higher in 34H than inE. coli, while levels of energy molecules (ATP, NADH, NADPH) were 10- to 100-fold lower. Experiments withE. colimutants supported the thermodynamic advantage of the ED pathway at cold temperature. Heat-stressed 34H at room temperature (2 hours) revealed significant down-regulation of genes associated with glycolytic enzymes and flagella, while 24 hours at room temperature caused irreversible cellular damage. We suggest that marine heterotrophic bacteria in general may rely upon simplified metabolic strategies to overcome thermodynamic constraints and thrive in the cold ocean.

    

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5. Designing Metabolic Division of Labor in Microbial Communities

   https://doi.org/10.1128/mSystems.00263-18  

   Thommes, Meghan ; Wang, Taiyao ; Zhao, Qi ; Paschalidis, Ioannis C. ; Segrè, Daniel ; Dutton, Rachel J. ( April 2019 , mSystems)

   ABSTRACT Microbes face a trade-off between being metabolically independent and relying on neighboring organisms for the supply of some essential metabolites. This balance of conflicting strategies affects microbial community structure and dynamics, with important implications for microbiome research and synthetic ecology. A “gedanken” (thought) experiment to investigate this trade-off would involve monitoring the rise of mutual dependence as the number of metabolic reactions allowed in an organism is increasingly constrained. The expectation is that below a certain number of reactions, no individual organism would be able to grow in isolation and cross-feeding partnerships and division of labor would emerge. We implemented this idealized experiment using in silico genome-scale models. In particular, we used mixed-integer linear programming to identify trade-off solutions in communities of Escherichia coli strains. The strategies that we found revealed a large space of opportunities in nuanced and nonintuitive metabolic division of labor, including, for example, splitting the tricarboxylic acid (TCA) cycle into two separate halves. The systematic computation of possible solutions in division of labor for 1-, 2-, and 3-strain consortia resulted in a rich and complex landscape. This landscape displayed a nonlinear boundary, indicating that the loss of an intracellular reaction was not necessarily compensated for by a single imported metabolite. Different regions in this landscape were associated with specific solutions and patterns of exchanged metabolites. Our approach also predicts the existence of regions in this landscape where independent bacteria are viable but are outcompeted by cross-feeding pairs, providing a possible incentive for the rise of division of labor. IMPORTANCE Understanding how microbes assemble into communities is a fundamental open issue in biology, relevant to human health, metabolic engineering, and environmental sustainability. A possible mechanism for interactions of microbes is through cross-feeding, i.e., the exchange of small molecules. These metabolic exchanges may allow different microbes to specialize in distinct tasks and evolve division of labor. To systematically explore the space of possible strategies for division of labor, we applied advanced optimization algorithms to computational models of cellular metabolism. Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species. We found that predicted consortia partition metabolic pathways in ways that would be difficult to identify manually, possibly providing a competitive advantage over individual organisms. In addition to helping understand diversity in natural microbial communities, our approach could assist in the design of synthetic consortia. 

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