This manuscript addresses a central and broad interest topic in environmental microbiology, i.e. the effect of growth temperature on microbial cell physiology. We investigated if and how metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely and that match measured changes on the field.

Multidrug-resistant pathogens represent a serious threat to human health. The inefficacy of traditional antibiotic drugs could be surmounted through the exploitation of natural bioactive compounds of which medicinal plants are a great reservoir. The finding that bacteria living inside plant tissues, (i.e., the endophytic bacterial microbiome) can influence the synthesis of the aforementioned compounds leads to the necessity of unraveling the mechanisms involved in the determination of this symbiotic relationship. Here, we report the genome sequence of four endophytic bacterial strains isolated from the medicinal plant Origanum vulgare L. and able to antagonize the growth of opportunistic pathogens of cystic fibrosis patients. The in silico analysis revealed the presence of gene clusters involved in the production of antimicrobial compounds, such as paeninodin, paenilarvins, polymyxin, and paenicidin A. Endophytes’ adaptation to the plant microenvironment was evaluated through the analysis of the presence of antibiotic resistance genes in the four genomes. The diesel fuel degrading potential was also tested. Strains grew in minimum media supplemented with diesel fuel, but no n-alkanes degradation genes were found in their genomes, suggesting that diesel fuel degradation might occur through other steps involving enzymes catalyzing the oxidation of aromatic compounds.

The urgent necessity to fight antimicrobial resistance is universally recognized. In the search of new targets and strategies to face this global challenge, a promising approach resides in the study of the cellular response to antimicrobial exposure and on the impact of global cellular reprogramming on antimicrobial drugs’ efficacy. The metabolic state of microbial cells has been shown to undergo several antimicrobial-induced modifications and, at the same time, to be a good predictor of the outcome of an antimicrobial treatment. Metabolism is a promising reservoir of potential drug targets/adjuvants that has not been fully exploited to date. One of the main problems in unraveling the metabolic response of cells to the environment resides in the complexity of such metabolic networks. To solve this problem, modeling approaches have been developed, and they are progressively gaining in popularity due to the huge availability of genomic information and the ease at which a genome sequence can be converted into models to run basic phenotype predictions. Here, we review the use of computational modeling to study the relationship between microbial metabolism and antimicrobials and the recent advances in the application of genome-scale metabolic modeling to the study of microbial responses to antimicrobial exposure.

Horizontal gene transfer (HGT) is a major contributor to the spread of antimicrobial resistance (AMR) in microbial communities, particularly through conjugative plasmids. The dynamics of the spread of these plasmids in a susceptible microbial community requires a defined and controllable in vitro system, which is currently lacking from existing laboratory evolution systems. Here we describe the development of TriComm, an engineered microbial community of three strains with complementary amino acid auxotrophies, maintained in continuous culture under real-time optical and fluorescence monitoring. We show that two conjugative plasmids carrying resistance genes ( bla _OXA-48 and mcr-1 ) generally rescued TriComm from collapse under antimicrobial treatment, and that this protection was dependent on conjugative transfer. Community composition remained stable across most conditions, including after the AMR plasmid had spread through the population. Through mathematical modeling we predict that modulating both drug efficacy and conjugation rate can lead to the collapse of the community, suggesting potential strategies to limit the spread of AMR via HGT.

Besides genetic mutations, the metabolic state of bacterial cells represents another driving factor in the emergence of antimicrobial resistance and in the actual efficacy of treatments. In this direction, studying how bacteria reprogram their metabolism when facing antimicrobial exposure is crucial to enhance our ability to limit the development and spread of antibiotic resistance. Here we have studied the metabolic consequences of antimicrobial exposure in bacteria using an integrated approach that exploits transcriptomics and computational modelling. Specifically, we asked whether common metabolic strategies emerge during the exposure to antimicrobials, regardless of the kind of antimicrobial used or, on the contrary, antimicrobial-specific pathways exist. To this purpose, we have used an heterogeneous dataset from six published studies on Escherichia coli exposed to different concentrations/types of compounds. We show that experimental condition, not antimicrobial exposure, is the factor that influences the most the resulting metabolic networks. However, despite condition-dependent metabolic signatures being evident, specific changes in flux distributions by antimicrobial exposed cells could be identified. In particular, purine and pyrimidine biosynthesis, and cofactor and prosthetic group biosynthesis were commonly affected by all considered antimicrobials. This suggests the presence of general metabolic strategies to face the stress posed by antimicrobial exposure and that, in turn, may represent an untapped resource for the fight against microbial infections. Finally, our analysis predicted an overall metabolic rewiring following bacteriostatic vs. bactericidal drug exposure that is in line with the current knowledge about the effects of these two classes of compounds on microbial metabolic phenotypes. IMPORTANCE: A mechanistic understanding of microbial metabolic reprogramming during antimicrobial exposure is key to facilitate the discovery of new resistance mechanisms and to identify novel areas of intervention to face microbial infections. This study shows how the integration of transcriptomic data and genome-scale metabolic modelling can be used to address this critical issue, and to trace general metabolic strategies exploited by bacteria to face the stress posed by antimicrobial drugs.

Quorum sensing (QS) is a mechanism of gene regulation in which bacteria coordinate collective behaviours via the release and detection of extracellular chemical signals. It is one of the most studied mechanisms of bacterial cell-cell communication. The architecture of QS networks can vary in complexity, from single LuxI/LuxR pairs to large interconnected systems with multiple signal synthases, receptors, and regulators. These multi-layered networks allow bacteria to process environmental and physiological cues, control the timing of gene expression, and adapt to different ecological conditions. In this study we developed a stochastic model of QS in Burkholderia thailandensis, starting from the chemical network of the three N-acyl homoserine lactones (AHL) pathways (BtaI1-BtaR1, BtaI2-BtaR2, BtaI3-BtaR3). We derived the corresponding chemical master equation that describes the probabilistic dynamics of each molecular species and obtained the deterministic system. The analytical predictions for the second-order moments, calculated using the van Kampen expansion, were verified using stochastic simulations based on the Langevin description. This confirmed the approximation's accuracy in capturing the stochastic behaviour. We then compared two network architectures: the complete model, which included all three QS interconnected circuits, and a reduced model that omitted the third one. Our results showed that the complete model is more robust to random fluctuations, in line with previous studies highlighting how the presence of multiple QS modules reduces cellular density variability. Moreover, by scanning a range of activation and inhibition rates, we identified a set of optimal kinetic parameters that minimized fluctuations in the complete model. These optimized parameter values reduced stochastic variability, suggesting that correct calibration of AHL inhibition and activation rates can stabilize the collective behaviour of the population. Overall, our findings highlight that including intrinsic noise in QS models is essential for accurately predicting the dynamics and robustness of bacterial communities.