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Varik, V.

Publications and source records attributed to Varik, V..

2 recordsLinked to original sources

Uncovering nitroxoline activity spectrum, mode of action and resistance across Gram-negative bacteria

Nitroxoline is a bacteriostatic quinoline antibiotic, considered a metal chelator inhibiting the activity of RNA-polymerase1. Its clinical indications are limited to uncomplicated urinary tract infections (UTIs), with a clinical susceptibility breakpoint only available for Escherichia coli2. By testing > 1,000 clinical isolates, here we demonstrate a much broader activity spectrum and species-specific bactericidal activity, including multidrug-resistant Gram-negative bacteria for which therapeutic options are limited due to resistance. By combining systematic genetic and proteomic approaches with direct measurement of intracellular metals, we dissect nitroxoline perturbation of metal homeostasis and unveil additional effects on bacterial physiology. We show that nitroxoline affects outer membrane integrity, synergizing with large-scaffold antibiotics and resensitizing colistin-resistant Enterobacteriaceae in vitro and in vivo. We further characterise resistance mechanisms across E. coli, Acinetobacter baumannii and Klebsiella pneumoniae, recapitulating known E. coli resistance determinants and uncovering novel and conserved mechanisms across species, demonstrating their common effect on nitroxoline efflux.

microbiology↗

Systematic mapping of antibiotic cross-resistance and collateral sensitivity with chemical genetics

By acquiring or evolving resistance to one antibiotic, bacteria can become resistant to a second one, due to shared underlying mechanisms. This is called cross-resistance (XR) and further limits therapeutic choices. The opposite scenario, in which initial resistance leads to sensitivity to a second antibiotic, is termed collateral sensitivity (CS) and can inform cycling or combinatorial treatments. Despite their clinical relevance, our current knowledge of such interactions is limited, mostly due to experimental constraints in their assessment and lack of understanding of the underlying mechanisms. To fill this gap, we used published chemical genetic data on the impact of all Escherichia coli non-essential genes on resistance/sensitivity to 40 antibiotics, and devised a metric that robustly discriminates between known XR and CS antibiotic interactions. This metric, based on chemical genetic profile (dis)similarity between two drugs, allowed us to infer 404 XR and 267 CS interactions, thereby expanding the number of known interactions by more than 3-fold - including reclassifying 116 previously reported interactions. We benchmarked our results by validating 55 out of 59 inferred interactions via experimental evolution. By identifying mutants driving XR and CS interactions in chemical genetics, we recapitulated known and uncovered previously unknown mechanisms, and demonstrated that a given drug pair can exhibit both interactions depending on the resistance mechanism. Finally, we applied CS drug pairs in combination to reduce antibiotic resistance development in vitro. Altogether, our approach provides a systematic framework to map XR/CS interactions and their mechanisms, paving the way for the development of rationally-designed antibiotic combination treatments.

systems biology↗