Wakeman, C. | Search bioRxiv

bioRxiv2026

Proton motive force mediated efflux mismatch drives gentamycin-novobiocin collateral sensitivity in Pseudomonas aeruginosa

Resistance to a particular antibiotic can make bacteria sensitive to others, a phenomenon known as collateral sensitivity (CS). This study explored potential CS in clinical and experimentally evolved drug-resistant Pseudomonas aeruginosa (PA) and investigated underlying mechanisms. Whole-genome sequencing and RNA-seq were analyzed to identify genetic and transcriptional correlations. In vitro efficacies were assessed with co-and sequential-exposure regimens. Multiple CF isolates and experimentally evolved gentamycin (GEN) resistant strains consistently exhibited strong CS to novobiocin (NOV). Comparative genomics revealed pmrB gain-of-function mutations, which was further supported by transcriptomic signatures of pmrAB activation. Transcriptomic data suggests potential outer-membrane remodeling characterized by polyamine accumulation and compromised porin channel expression. Additionally, the reduction in proton motive force (PMF) further explains the possible mechanism underlying GEN resistance. As NOV efflux is PMF-dependent, this energetic deficit created a PMF-efflux mismatch, leading to hypersensitivity to NOV. Notably, sequential GEN[->]NOV treatment effectively restricted the emergence of GEN resistant subpopulations. Overall, our data suggest GEN resistance in PA may arises through envelope remodeling and reduced PMF, which impairs efflux pumps and creates hypersensitivity to NOV. Exploiting this PMF-efflux mismatch with sequential treatment effectively restricted the emergence of GEN resistance.

microbiology↗

bioRxiv2020

Analyzing Genomic Data Using Tensor-Based Orthogonal Polynomials

An important goal in molecular biology is to quantify both the patterns across a genomic sequence and the relationship between phenotype and underlying sequence. We propose a multivariate tensor-based orthogonal polynomial approach to characterize nucleotides or amino acids in a given sequence and map corresponding phenotypes onto the sequence space. We have applied this method to a previously published case of small transcription activating RNAs (STARs). Covariance patterns along the sequence showcased strong correlations between nucleotides at the ends of the sequence. However, when the phenotype is projected onto the sequence space, this pattern doesnt emerge. When doing second order analysis and quantifying the functional relationship between the phenotype and pairs of sites along the sequence, we identified sites with high regressions spread across the sequence, indicating potential intramolecular binding. In addition to quantifying interactions between different parts of a sequence, the method quantifies sequence-phenotype interactions at first and higher order levels. We discuss the strengths and constraints of the method and compare it to computational methods such as machine learning approaches. An accompanying command line tool to compute these polynomials is provided. We show proof of concept of this approach and demonstrate its potential application to other biological systems.

bioinformatics↗