Raikwar, P. S. | bioRxiv Science

bioRxiv2026

Genetic context drives evolution of divergent antibiotic survival phenotypes in Staphylococcus epidermidis

Effective treatment of infections is a global challenge, complicated by bacterias capacity to endure antibiotics. Survival under drug pressure is often driven by interactions between genetic factors rather than single genes. To investigate the genomics underlying antibiotic survival, we analysed Staphylococcus epidermidis isolates from clinical infections and carriage using high-throughput phenotyping, population genomics, and directed evolution. We observed widespread multidrug resistance, with strong links between specific genomic elements and resistance. All isolates harbouring mecA were resistant to oxacillin, though minimum inhibitory concentrations varied significantly, suggesting modulation by additional genetic factors. Directed evolution revealed potentiating mutations that enhanced oxacillin resistance in mecA+ strains. In mecA-isolates, however, evolution of mutations in the same genes conferred increased survival to oxacillin through antibiotic tolerance. These findings show that antibiotic resistance and tolerance can be genetically connected yet phenotypically distinct, and suggest a complex epistatic genetic landscape that shapes antibiotic survival phenotypes in S. epidermidis.

microbiology↗

bioRxiv2025

GeneScanner: profiling genetic variation across bacterial populations

Rapid, low-cost genome sequencing has transformed microbiology, advancing efforts to link genetic and phenotypic variation. In laboratory settings, genome-wide functional screens of reference strains are revealing genes and mechanisms underlying important phenotypes. Simultaneously, population-scale comparative genomics describes the breadth of natural genetic and phenotypic diversity across lineages. These approaches provide complementary but contrasting information. Whereas laboratory studies offer causal understanding within simplified systems, population-level analyses capture ecological realism but are largely limited to detecting associations rather than cause-and-effect relationships. Linking laboratory-derived mutations to natural population variation remains challenging, particularly for researchers lacking bioinformatics expertise. Here, we present GeneScanner, a user-friendly tool that facilitates analysis of gene- and protein-level variation across large bacterial genome collections. GeneScanner detects genetic variation and amino acid substitutions in homologous sequence and supports genotype-phenotype association studies. By bridging laboratory functional genomics data with genomic diversity across natural populations, GeneScanner enhances functional interpretation of microbial variation in the real world, supporting research in antimicrobial resistance, virulence, and pathogen surveillance.

bioinformatics↗