ESKAPE pathogens rapidly develop resistance against antibiotics in development in vitro.

Despite ongoing antibiotic development, evolution of resistance may render candidate antibiotics ineffective. Here we studied in vitro emergence of resistance to 13 antibiotics introduced after 2017 or currently in development, compared with in-use antibiotics. Laboratory evolution showed that clinically relevant resistance arises within 60 days of antibiotic exposure in Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii and Pseudomonas aeruginosa, priority Gram-negative ESKAPE pathogens.

Antibiotic candidates for Gram-positive bacterial infections induce multidrug resistance.

Several antibiotic candidates are in development against Gram-positive bacterial pathogens, but their long-term utility is unclear. To investigate this issue, we studied the laboratory evolution of resistance to antibiotics that have not yet reached the market. We found that, with the exception of compound SCH79797, antibiotic resistance generally readily evolves in .

A systematic quantitative approach comprehensively defines domain-specific functional pathways linked to Schizosaccharomyces pombe heterochromatin regulation.

Heterochromatin plays a critical role in regulating gene expression and maintaining genome integrity. While structural and enzymatic components have been linked to heterochromatin establishment, a comprehensive view of the underlying pathways at diverse heterochromatin domains remains elusive. Here, we developed a systematic approach to identify factors involved in heterochromatin silencing at pericentromeres, subtelomeres and the silent mating type locus in Schizosaccharomyces pombe.

mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate.

Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenges, we introduce mulea, an R package offering comprehensive overrepresentation and functional enrichment analysis. mulea employs a progressive empirical false discovery rate (eFDR) method, specifically designed for interconnected biological data, to accurately identify significant terms within diverse ontologies.