Study reveals how targeting unique metabolic pathways in specific pathogens could lead to precision antibiotics, offering a solution to antimicrobial resistance.
Study: Niche-specific metabolic phenotypes can be used to identify antimicrobial targets in pathogens. Image Credit: Fahroni/Shutterstock.com
In a recent study published in PLoS Biology, a group of researchers identified niche-specific metabolic phenotypes and essential genes in pathogens using genome-scale metabolic reconstructions (GENREs), demonstrating their potential as targets for developing targeted antimicrobial therapies.
Background
Bacterial pathogens are responsible for significant global mortality, accounting for 16% of deaths worldwide and 44% in low-resource settings. With over 500 known human-associated pathogens, growing antimicrobial resistance has made treatment increasingly challenging.
Targeting metabolic pathways unique to specific physiological niches offers a promising alternative to broad-spectrum antibiotics, potentially reducing resistance development. Evolutionary phenomena such as natural selection and convergent evolution likely influence pathogen metabolic phenotypes in distinct niches, yet these connections remain underexplored.
High-throughput GENREs can uncover niche-specific metabolic signatures, paving the way for novel, targeted antimicrobial therapies. Further research is needed for validation.
About the study
Bacterial genome sequences from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) version 3.6.12 database were filtered based on quality, completeness, and human host origin. Criteria for inclusion required genomes to be at least 80% complete, have contamination levels under 10%, and exhibit high consistency with known protein sequences.
Metadata-driven selection prioritized sequences with comprehensive annotations, ensuring accurate downstream analyses. This process yielded 914 unique genome sequences, which were annotated using the Rapid Annotation using Subsystem Technology (RAST) version 2.0 and reconstructed into GENREs via the Reconstructor algorithm. Benchmarking with the Metabolic Model Testing (MEMOTE) tool confirmed the quality of the reconstructed models.
A reaction presence matrix was generated to analyze metabolic variability, classifying reactions into core, accessory, and unique categories. A histogram revealed 232 reactions uniquely present in a single strain, underscoring the diversity of metabolic functions across pathogens.
Flux Balance Analysis (FBA) was performed for all GENREs, followed by dimensionality reduction using t-distributed Stochastic Neighbor Embedding (t-SNE) for visualization. This approach highlighted taxonomic and niche-specific clustering, validating the use of 10 flux samples per GENRE for effective analysis.
Essential genes were identified through FBA-based single-gene knockouts, isolating niche-specific genes. The gene thymidylate synthase X (thyX), uniquely important to stomach isolates, was targeted with the compound lawsone.
Experimental validation using microbial growth assays confirmed its efficacy, supporting computational predictions and demonstrating the potential of niche-specific antimicrobial strategies.
Study results
To capture the diversity of functional metabolic phenotypes across bacterial pathogens, 914 in silico GENREs were created, encompassing 345 species across nine bacterial phyla. These reconstructions, generated through an automated pipeline, include over one million combined reactions, genes, and metabolites.
On average, each model contains approximately 1,500 genes, reactions, and metabolites. The GENRE collection, termed PATHogen GENome-scale Network reconstruction (PATHGENN), is the first high-quality compilation of metabolic reconstructions for all known human-associated bacterial pathogens.
Models were constructed using publicly available genome sequences from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), and their quality was validated using MEMOTE benchmarking, which confirmed an average score of 84%, signifying high biological relevance.
PATHGENN offers valuable insights into pathogen metabolism by categorizing metabolic reactions as core (present in >75% of GENREs), accessory (25%-75%), or unique (<25%). Reaction annotation revealed that unique reactions frequently involve terpenoid, polyketide, and xenobiotic metabolism, which are linked to drug metabolism and antimicrobial resistance.
The analysis also showed that the clustering of metabolic phenotypes aligns with both taxonomic class and physiological niche, highlighting the impact of evolutionary history and environmental pressures on metabolic function.
Focusing on niche-specific targets, the study identified seven uniquely essential genes in stomach-associated pathogens, including thyX, which encodes thymidylate synthase. This enzyme, crucial for Deoxyribonucleic Acid (DNA) synthesis, is absent in humans, making it a promising target for antimicrobial development.
Lawsone, a known inhibitor of thyX, was tested for its ability to inhibit the growth of stomach pathogens selectively. Experimental validation demonstrated that lawsone effectively inhibited the growth of stomach-associated pathogens without affecting non-stomach-associated isolates, supporting the computational predictions and the potential for targeted antimicrobial therapies.
The findings underscore the potential of leveraging physiological niches to develop site-specific antimicrobial strategies. Targeting uniquely essential genes shared by pathogens in a specific environment could reduce reliance on broad-spectrum antibiotics and combat antimicrobial resistance.
Conclusions
To summarize, the antimicrobial resistance crisis demands innovative strategies for identifying new or repurposed therapies. Using genomic data and metabolic network modeling, this study identified thyX, a niche-specific essential gene, as a promising antimicrobial target in stomach pathogens.
A collection of 914 GENREs provided valuable insights into pathogen metabolism. Validation experiments confirmed that lawsone, a thyX inhibitor, selectively inhibited stomach-specific pathogens without affecting non-stomach isolates.
This approach highlights the potential for targeted, site-specific antimicrobial therapies to address resistance challenges.
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