New preprint: Deciphering bedaquiline and clofazimine resistance in tuberculosis Philip Fowler, 22nd March 202122nd March 2021 In this preprint we examine 14,151 clinical isolates drawn from the CRyPTIC dataset. Each isolate had its minimum inhibitory concentration (MIC) to bedaquiline and clofazimine measured and hence we were able to identify the transcription regulator Rv0678, as the current main source of elevated MICs to both these drugs. Lindsay Sonnenkalb, who is studying for her PhD with Stefan Niemann, then evolved Mycobacterium tuberculosis strains under sub-lethal concentrations of both compounds and was able to identify 189 different Rv0678 genetic variants that confer elevated MICs to bedaquiline and clofazimine. Detailed modelling of the protein structure allowed us to posit four main resistance mechanisms: impairment of DNA binding, reduction in protein stability, disruption of protein dimerization, and reduction in affinity for its fatty acid ligand. Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Related antimicrobial resistance clinical microbiology publication
GPAS stopover on the ORACLE road trip 1st February 20221st February 2022 You can listen to Philip Fowler talk about the Global Pathogen Analysis System (GPAS) as… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
antimicrobial resistance I’ve moved… 14th March 20165th August 2018 Today is my first day as a Senior Researcher in Modernising Medical Microbiology in the… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More
antimicrobial resistance New preprint: Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network 29th October 202530th October 2025 In previous work we’ve used “traditional” machine-learning approaches, like XGBoost, to learn and therefore predict… Share this: Share on X (Opens in new window) X Share on Bluesky (Opens in new window) Bluesky Email a link to a friend (Opens in new window) Email Share on LinkedIn (Opens in new window) LinkedIn Share on Mastodon (Opens in new window) Mastodon Read More