Back

CRISPRi-mediated validation of candidate Myocobacterium abscessus drug targets during host infection

Gupta, R.; Simcox, B.; Rohde, K. H.

2025-12-17 microbiology
10.64898/2025.12.16.694790 bioRxiv
Show abstract

Mycobacterium abscessus (Mab) is a multidrug-resistant nontuberculous mycobacterium that causes debilitating TB-like pulmonary infections for which effective treatment options are lacking. Poor in vivo drug efficacy may stem from altered vulnerability of drug targets driven by host-specific environmental conditions. To enable validation and prioritization of candidate drug targets in vivo, we exploited CRISPRi (CRi) gene silencing in multiple mouse infection models. Inducible silencing of ftsZMab, a previously validated target, and three predicted targets (leuSMab, folPMab, fusAMab) confirmed their essentiality in vitro. We then assessed the in vivo vulnerability of these targets in both immunocompetent C57BL/6N and immunodeficient NSG mice by assessing the impact of CRi silencing on pulmonary mycobacterial burden. In NSG mice, silencing of all four genes led to comparable decreases in Mab burden. However, in C57BL/6N mice, the degree of Mab clearance varied among targets, suggesting that immune pressure may influence the outcome of CRi-mediated gene silencing. Notably, repression of fusAMab yielded a larger decline in mycobacterial burden in C57BL/6N mice despite a lower level of gene silencing in vitro, consistent with enhanced vulnerability of this target. Overall, this study demonstrated that ftsZMab, leuSMab, folPMab, and fusAMab are essential for Mab growth in vitro and, for the first time, validated their vulnerability to inhibition by CRi during infection. These data also identified potential context-dependent target vulnerabilities, which could inform the prioritization of bacterial drug targets and accelerate the development of effective therapeutics for Mab infections.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.