Back

Utilizing natural competence to genetically manipulate Lactobacillus iners

Cao, K. Y.; Serrador, D.; Campbell, J. R.; Kaul, R.; Navarre, W. W.

2026-03-03 microbiology
10.64898/2026.03.03.709335 bioRxiv
Show abstract

The healthy human vaginal microbiota is typically dominated by one species of Lactobacillus: L. iners, L. crispatus, L. jensenii, or L. gasseri. L. iners, the most prevalent vaginal microbe globally, is the most fastidious of the vaginal lactobacilli, has the smallest genome, and produces less lactic acid (only the L-isoform). L. iners is also less protective against bacterial vaginosis, and uniquely encodes a cholesterol-dependent cytolysin, inerolysin, suggesting it may be a pathobiont. Despite its central role in the health of over one billion females, L. iners biology remains poorly understood in part due to a lack of genetic editing tools. Here, we present findings that L. iners is naturally competent and can be transformed easily by exogenous DNA. Natural competence was leveraged to disrupt the iny gene encoding inerolysin, and comGA, encoding the ATPase component of the competence pilus. Both gene disruptions were accomplished using PCR assembled DNA fragments comprising a drug resistance gene cassette (tetM or ermB) flanked by [~]2 kb regions of homology to the L. iners chromosome. We further demonstrate that comGA is essential for L. iners transformation. The ability to rapidly perform targeted deletions in L. iners with in vitro generated DNA templates provides a straightforward and much needed method to probe the genetics and physiology of these important vaginal bacteria. IMPORTANCEThis study describes, to our knowledge, the first method for genetically manipulating L. iners, the most prevalent bacteria of the human vaginal microbiota. This work paves the way for the rapid development of genetic tools to explore L. iners physiology in the context of the vaginal microbiome and potentially alter its properties as a probiotic.

Published in Applied and Environmental Microbiology (predicted rank #1) · training set

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.