David R. Sherman

This study shows that ESX-5 deletions in Mtb reduce cytokine secretion in macrophages via a post-transcriptional mechanism and alter heavy metal response genes.

This study identifies lineage-specific transcriptional network rewiring in Mtb by analyzing proteomic and transcriptomic data from L1 and L2 strains, revealing key regulators like Rv1985c that drive phenotypic differences.

This paper introduces Transcript-Capture Seq to enrich bacterial mRNA from host samples, enabling analysis of the complete Mtb transcriptome in clinical samples and revealing gene expression patterns associated with in vivo bacterial activity.

This study identifies niclosamide as a compound that induces drug tolerance in Mycobacterium tuberculosis by disrupting the pH gradient, revealing a new mechanism for persister cell formation.

This study identifies netupitant as a compound that enhances anti-TB drug activity and eliminates Mtb persisters, showing potent bacteriostatic and tuberculocidal effects in combination with standard drugs.

The paper predicts Mtb fitness using a transcription factor activity-based machine learning model trained on RNA-seq data and regulatory network analysis.

The paper develops a computational model to identify synergistic anti-TB drug combinations using transcriptomic data and validates its predictions with clinical trial data.

The paper identifies novel drug potentiators in Mycobacterium tuberculosis using a network-based genetic screening approach to uncover stress response adaptations to isoniazid.

The paper shows that Rv1258c is required for rifampin tolerance in Mtb Lineage 4 strains but not isoniazid tolerance, and that other lineages develop rifampin tolerance except Lineage 2 Beijing strains.