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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Bacterial pathogens pose a major risk to human health, leading to tens of millions of deaths annually and significant global economic losses. While bacterial infections are typically treated with antibiotic regimens, there has been a rapid emergence of antimicrobial resistant (AMR) bacterial strains due to antibiotic overuse. Because of this, treatment of infections with traditional antimicrobials has become increasingly difficult, necessitating the development of innovative approaches for deeply understanding pathogen function. To combat issues presented by broad- spectrum antibiotics, the idea of narrow-spectrum antibiotics has been previously proposed and explored. Rather than interrupting universal bacterial cellular processes, narrow-spectrum antibiotics work by targeting specific functions or essential genes in certain species or subgroups of bacteria. Here, we generate a collection of genome-scale metabolic network reconstructions (GENREs) of pathogens through an automated computational pipeline. We used these GENREs to identify subgroups of pathogens that share unique metabolic phenotypes and determined that pathogen physiological niche plays a role in the development of unique metabolic function. For example, we identified several unique metabolic phenotypes specific to stomach pathogens. We identified essential genes unique to stomach pathogens in silico and a corresponding inhibitory compound for a uniquely essential gene. We then validated our in silico predictions with an in vitro microbial growth assay. We demonstrated that the inhibition of a uniquely essential gene, thyX, inhibited growth of stomach-specific pathogens exclusively, indicating possible physiological location-specific targeting. This pioneering computational approach could lead to the identification of unique metabolic signatures to inform future targeted, physiological location-specific, antimicrobial therapies, reducing the need for broad-spectrum antibiotics. The emergence of resistance to traditional antimicrobials increases the need to develop innovative approaches to understanding pathogen function. This study uses a computational data-driven approach to drug target discovery by identifying metabolic signatures of diverse pathogens conserved within body sites. In vitro validation shows that such site-targeted antimicrobials can effectively inhibit stomach-specific pathogens. [ABSTRACT FROM AUTHOR]

Respiratory infections are a leading cause of disease and death worldwide (1,2), especially among young children and older adults. However, the adverse effects of respiratory infections on pregnant women and [...]

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment. Author summary: Microbes secrete and respond to environmental metabolite signals, resulting in the spatial organization of heterogeneous physiological metabolic states within infectious microbial communities. Despite experimental advances, it is difficult to measure the connected, dynamic processes that control microbial community organization across multiple spatial and time scales. Thus, we developed an extendable multi-scale computational framework to simulate metabolic processes spanning intracellular to extracellular scales. We used this framework to simulate a Pseudomonas aeruginosa, a bacterium that causes lung infections, 3D biofilm community containing tens of thousands of single-cells. This is the first framework to integrate spatially resolved gene expression data. This data integration was advantageous to simulate cells regulating anaerobic, toxic metabolite byproduct secretion, and antioxidant metabolic states in response to a heterogeneous oxygen and toxic byproduct biofilm microenvironment. These cellular metabolic states were predicted to co-exist in biofilm spatial niches due to microscale variations in extracellular oxygen and toxic metabolite signals. Difficult to measure in experiments, this framework revealed multi-scale mechanisms underlying the emergent dynamics of each metabolic state, which may help to inform P. aeruginosa biofilm treatment strategies. We believe this multi-scale framework is valuable to integrate microbial data to uncover the multi-scale mechanisms regulating heterogenous metabolic processes in microbial communities. [ABSTRACT FROM AUTHOR]