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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. 

ABSTRACT Pseudomonas aeruginosais considered one of the most challenging, drug-resistant, opportunistic pathogens partly due to its ability to synthesize robust biofilms. Biofilm is a mixture of extracellular polymeric substances (EPS) that encapsulates microbial cells, leading to immune evasion, antibiotic resistance, and thus higher risk of infection. In the cystic fibrosis lung environment,P. aeruginosaundergoes a mucoid transition, defined by overproduction of the exopolysaccharide alginate. Alginate encapsulation results in bacterial resistance to antibiotics and the host immune system. Given its role in airway inflammation and chronic infection, alginate is an obvious target to improve treatment forP. aeruginosainfection. Previously, we demonstrated polysaccharide lyase Smlt1473 fromStenotrophomonas maltophiliastrain k279a can catalyze the degradation of multiple polyuronidesin vitro, including D-mannuronic acid (poly-ManA). Poly-ManA is a major constituent ofP. aeruginosaalginate, suggesting that Smlt1473 could have potential application against multidrug-resistantP. aeruginosaand perhaps other microbes with related biofilm composition. In this study, we demonstrate that Smlt1473 can inhibit and degrade alginate fromP. aeruginosa. Additionally, we show that testedP. aeruginosastrains are dominant in acetylated alginate and that all but one have similar M-to-G ratios. These results indicate that variation in enzyme efficacy among the isolates is not primarily due to differences in total EPS or alginate chemical composition. Overall, these results demonstrate Smlt1473 can inhibit and degradeP. aeruginosaalginate and suggest that other factors including rate of EPS production, alginate sequence/chain length, or non-EPS components may explain differences in enzyme efficacy. IMPORTANCEPseudomonas aeruginosais a major opportunistic human pathogen in part due to its ability to synthesize biofilms that confer antibiotic resistance. Biofilm is a mixture of polysaccharides, DNA, and proteins that encapsulate cells, protecting them from antibiotics, disinfectants, and other cleaning agents. Due to its ability to increase antibiotic and immune resistance, the exopolysaccharide alginate plays a large role in airway inflammation and chronicP. aeruginosainfection. As a result, colonization withP. aeruginosais the leading cause of morbidity and mortality in CF patients. Thus, it is an obvious target to improve the treatment regimen forP. aeruginosainfection. In this study, we demonstrate that polysaccharide lyase, Smlt1473, inhibits alginate secretion and degrades established alginate from a variety of mucoidP. aeruginosaclinical isolates. Additionally, Smlt1473 differs from other alginate lyases in that it is active against acetylated alginate, which is secreted during chronic lung infection. These results suggest that Smlt1473 may be useful in treating infections associated with alginate-producingP. aeruginosa, as well as have the potential to reduceP. aeruginosaEPS in non-clinical settings. 

Computational models are complex scientific constructs that have become essential for us to better understand the world. Many models are valuable for peers within and beyond disciplinary boundaries. However, there are no widely agreed-upon standards for sharing models. This paper suggests 10 simple rules for you to both (i) ensure you share models in a way that is at least “good enough,” and (ii) enable others to lead the change towards better model-sharing practices. 

Bacterial pathogens adapt their metabolism to the plant environment to successfully colonize their hosts. In our efforts to uncover the metabolic pathways that contribute to the colonization ofArabidopsis thalianaleaves byPseudomonas syringaepvtomatoDC3000 (PstDC3000), we created iPst19, an ensemble of 100 genome-scale network reconstructions ofPstDC3000 metabolism. We developed a novel approach for gene essentiality screens, leveraging the predictive power of iPst19 to identify core and ancillary condition-specific essential genes. Constraining the metabolic flux of iPst19 withPstDC3000 gene expression data obtained from naïve-infected or pre-immunized-infected plants, revealed changes in bacterial metabolism imposed by plant immunity. Machine learning analysis revealed that among other amino acids, branched-chain amino acids (BCAAs) metabolism significantly contributed to the overall metabolic status of each gene-expression-contextualized iPst19 simulation. These predictions were tested and confirmed experimentally.PstDC3000 growth and gene expression analysis showed that BCAAs suppress virulence gene expressionin vitrowithout affecting bacterial growth.In planta, however, an excess of BCAAs suppress the expression of virulence genes at the early stages of infection and significantly impair the colonization of Arabidopsis leaves. Our findings suggesting that BCAAs catabolism is necessary to express virulence and colonize the host. Overall, this study provides valuable insights into how plant immunity impactsPstDC3000 metabolism, and how bacterial metabolism impacts the expression of virulence.