Back

In vitro and in vivo validation of cwlM and pbpB essentiality for viability and resistance to imipenem in Mycobacterium abscessus

Nuermberger, E. L.; Lee, J. I.; Li, S.-Y.; Rifat, D.; Kurepina, N.; Chen, L.; Kreiswirth, B. N.

2024-03-09 microbiology
10.1101/2024.03.08.584109 bioRxiv
Show abstract

Mycobacterium abscessus lung infection is notoriously difficult to treat due, in part, to the intrinsic resistance of this pathogen to most marketed antibiotics. {beta}-Lactams, namely imipenem and cefoxitin, are first-line drugs in combination regimens used to treat this infection; and there is growing interest in dual-{beta}-lactam-based regimens. Better understanding of the molecular basis of {beta}-lactam activity through study of the genetic determinants of {beta}-lactam susceptibility and tolerance would enable more rational drug combinations and guide discovery of novel drug targets to complement {beta}-lactams. We recently used an inducible CRISPR interference (CRISPRi) system to silence cwlM and pbpB and confirm their essentiality for in vitro growth and resistance to sub-MIC concentrations of imipenem. Here, we extend those findings to show that silencing either gene alone is bactericidal and augments the bactericidal activity of imipenem in vitro. Furthermore, using CRISPRi in a mouse model of M. abscessus lung infection for the first time, we confirm the essentiality of each gene for in vivo survival. These results validate cwlM and pbpB as essential genes and promising drug targets in this pathogen, including for potentiation of carbapenem activity. The results further establish CRISPRi as a powerful method for validating drug targets and studying gene-gene and gene-drug interactions in vitro and in vivo.

Matching journals

The top 1 journal accounts 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.