The transition from growth to stationary phase is a natural response of bacteria to starvation and stress. When stress is alleviated and more favorable growth conditions return, bacteria resume proliferation without a significant loss in fitness. Although specific adaptations that enhance the persistence and survival of bacteria in stationary phase have been identified, mechanisms that help maintain the competitive fitness potential of nondividing bacterial populations have remained obscure. Here, we demonstrate that staphylococci that enter stationary phase following growth in media supplemented with excess glucose, undergo regulated cell death to maintain the competitive fitness potential of the population. Upon a decrease in extracellular pH, the acetate generated as a byproduct of glucose metabolism induces cytoplasmic acidification and extensive protein damage in nondividing cells. Although cell death ensues, it does not occur as a passive consequence of protein damage. Instead, we demonstrate that the expression and activity of the ClpXP protease is induced, resulting in the degeneration of cellular antioxidant capacity and, ultimately, cell death. Under these conditions, inactivation of either clpX or clpP resulted in the extended survival of unfit cells in stationary phase, but at the cost of maintaining population fitness. Finally, we show that cell death from antibiotics that interfere with bacterial protein synthesis can also be partly ascribed to the corresponding increase in clpP expression and activity. The functional conservation of ClpP in eukaryotes and bacteria suggests that ClpP-dependent cell death and fitness maintenance may be a widespread phenomenon in these domains of life.

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Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understand its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 nonessential mutants from the Nebraska Transposon Mutant Library was performed, and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 863 metabolic genes, 1379 metabolites (including 1159 unique metabolites), and 1545 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data, and therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.npj Systems Biology and Applications (2020) 6:3 ; https://doi.

28Background. The role of methane in global warming has become paramount to the environment 29 and the human society, especially in the past few decades. Methane cycling microbial 30 communities play an important role in the global methane cycle, which is why the 31 characterization of these communities is critical to understand and manipulate their behavior. 32 Methanotrophs are a major player in these communities and are able to oxidize methane as their 33 primary carbon source. 34Results. Lake Washington is a freshwater lake characterized by a methane-oxygen 35 countergradient that contains a methane cycling microbial community. The major microbial 36 members include methanotrophs such as Methylobacter Tundripaludum 37 21/22 and Methylomonas sp. LW13. In this work, these methanotrophs are studied via developing 38 highly curated genome-scale metabolic models. Each model was then integrated to form a 39 community model with a multi-level optimization framework. The metabolic interactions for the 40 community were also characterized. While both organisms are competitors for methane, 41 Methylobacter was found to display altruistic behavior in consuming formaldehyde produced 42 by Methylomonas that inhibits its growth. The community was next tested under carbon, oxygen, 43 and nitrogen limited conditions to observe the systematic shifts in the internal metabolic 44 pathways and extracellular metabolite exchanges. Each condition showed variable differences 45 within the methane oxidation pathway, serine cycle, pyruvate metabolism, and the TCA cycle as 46 well as the excretion of formaldehyde and carbon di-oxide from the community. Finally, the 47 community model was simulated under fixed ratios of these two members to reflect the opposing 48 behavior of the community in synthetic and natural communities. The simulated community 49 demonstrated a noticeable switch in intracellular carbon metabolism and formaldehyde transfer 50 between community members in natural vs. synthetic condition. 51 Conclusion. In this work, we attempted to reveal the response of a simplified methane recycling 52 microbial community from Lake Washington to varying environments and also provide an 53 insight into the difference of dynamics in natural community and synthetic co-cultures. Overall, 54this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, 55 which can drive future efforts of understanding, engineering, and modifying these communities 56 for dealing with global warming issues. 57 58

Pancreatic ductal adenocarcinoma (PDAC) is a major research focus due to its poor therapy response and dismal prognosis. PDAC cells adapt their metabolism efficiently to the environment to which they are exposed, often relying on diverse fuel sources depending on availability. Since traditional experimental techniques appear exhaustive in the search for a viable therapeutic strategy against PDAC, in this study, a highly curated and omics-informed genome-scale metabolic model of PDAC was reconstructed using patient-specific transcriptomic data. From the analysis of the model-predicted metabolic changes, several new metabolic functions were explored as potential therapeutic targets against PDAC in addition to the already known metabolic hallmarks of pancreatic cancer. Significant downregulation in the peroxisomal fatty acid beta oxidation pathway reactions, flux modulation in the carnitine shuttle system, and upregulation in the reactive oxygen species detoxification pathway reactions were observed. These unique metabolic traits of PDAC were then correlated with potential drug combinations that can be repurposed for targeting genes with poor prognosis in PDAC. Overall, these studies provide a better understanding of the metabolic vulnerabilities in PDAC and will lead to novel effective therapeutic strategies.