Thomas R. Ioerger

This paper identifies thienothiazolocarboxamides as rapidly cidal antituberculosis agents that cause thiol collapse in Mycobacterium tuberculosis through EthA-mediated activation.

This study identifies Rv2061c as a critical F420-dependent oxidoreductase that protects Mycobacterium tuberculosis from antimicrobials and oxidative stress.

This study shows that interstitial macrophages prevent tuberculosis relapse by restricting Mtb immune evasion during latent infection.

The paper identifies and evaluates dibasic piperidines as novel cell wall inhibitors against Mycobacterium tuberculosis, showing activity across multiple media and differential sensitivity in resistant strains.

This study identifies LdtB as essential for Mycobacterium tuberculosis acid resistance by linking peptidoglycan cross-linking to intrabacterial pH homeostasis and antibiotic susceptibility.

The paper evaluates positive selection in M. tuberculosis genes at three scales, finding that intermediate-scale analysis identifies the most credible candidate genes under selection.

This study identifies Mtb genes essential for survival in vaccinated and previously infected hosts using TnSeq, revealing differences in genetic requirements across immunization strategies.

This paper introduces CRISPRi-DR, a dose-response model to analyze chemical-genetic interactions in CRISPRi screens, showing improved precision over existing methods.

This study identifies novel anti-TB pro-drugs based on α-aminooxyacetic acid that are effective against M. tuberculosis and show low cytotoxicity.

This study defines the role of RNase E in mRNA degradation in Mycolicibacterium smegmatis, showing it limits degradation of 89% of protein-coding genes and has distinct sequence preferences compared to gram-negative bacteria.

This study uses machine learning to identify de novo genes in plant genomes, showing high accuracy within species and reduced false positives compared to traditional methods.

This paper introduces a statistical method using Zero-Inflated Negative Binomial regression to identify genes with significant variability in insertion counts across multiple conditions in TnSeq data.