PubMed · 183412
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A Z Abou-Zeid, M Abd-el-Hamid, A I Hassan. 1976. Vancomycin.. https://doi.org/10.1016/s0044-4057(76)80002-0
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Bacteria exhibit an evolutionary trade-off through their development of collateral sensitivity (CS) which allows them to resist one antibiotic while becoming more vulnerable to another. This vulnerability offers a compelling therapeutic opportunity by selecting against resistant isolates. Laboratory evolution studies, genome sequencing, deep mutagenesis and use of artificial intelligence and machine learning can design the bespoke strategy against multi-drug-resistant bacteria. This review discusses about recent studies that are rationally designed to harness this evolutionary trade-off for the development of alternative antimicrobial strategies. The translational barriers to the clinical implementation of CS are addressed and evidence-based design principles for optimization of CS-guided therapy are discussed.
Although the study of microbes started with type strains and reference genomes, advances in sequencing technology and new interest in mixed microbial communities have made us aware that a single genome cannot and does not reflect the diversity of a given bacterial species. Bacteria rarely occupy an environmental or host niche alone and quickly diversify into strains upon colonization of a new niche. The genetic diversity present within a phylogenetically related set of bacterial strains (the 'pangenome') is influenced by the niche that they occupy and how they interact with the other microorganisms that they share that niche with. In this review, I examine how the social lives of bacteria can affect their genetic diversity and the bioinformatic techniques that we use to detect that diversity.
Microbiologically influenced corrosion (MIC) threatens global infrastructure, causing billions of dollars in annual losses. Its persistence stems from unresolved mechanisms─particularly the metabolites produced by microorganisms that drive or inhibit corrosion─and the microbial community structures. Progress has been hindered by the absence of systematic workflows to rapidly and accurately identify MIC-relevant microorganisms and their functions. Here, we present DURABLE (Detection of Unique Corrosion Resistant or Accelerating Biologics in a Laboratory Environment), a pipeline that couples high-throughput microbial screening with genomic and metabolic workflows. We applied the DURABLE workflow to six diesel tank samples and revealed fuel-dependent microbial community structures, which showed greater diversity and evenness in bacterial communities than their fungal counterparts. The workflow used carbon steel beads to rapidly screen over 80 bacterial isolates for corrosive activity, reducing assay time to approximately 2 days compared with the conventional 30-day metal coupon test. More than 40 isolates were identified as corrosive. Further testing using mass spectrometry analysis revealed corrosion-associated metabolites, which were further validated using electrochemical assays. Thus, DURABLE achieved a ∼15-fold increase in screening speed and provided a scalable and mechanistic framework for dissecting MIC dynamics. We expect this advance will enable the development of precision mitigation strategies in hydrocarbon fuel infrastructure.