Back

Modulation of rob expression accelerates development of antibiotic resistance in Yersinia enterocolitica

Wang, X. H.; Chen, T.; Jonker, M. J.; de koster, A.; De Leeuw, W. C.; Dugar, G.; Ter Kuile, B. H.

2026-02-24 microbiology
10.64898/2026.02.23.707304 bioRxiv
Show abstract

Multidrug-resistant bacteria pose a severe threat to global health. Mutations in transcriptional regulators accelerate the emergence of multidrug resistance and may have a crucial impact on pathogen evolvability under antibiotic exposure. Here, we investigate these dynamics in the bacterial pathogen Yersinia enterocolitica. In this organism, we identified a high-frequency de novo mutation in the promoter of an AraC/XylS-family transcriptional regulator, Rob. This mutation arose independently during resistance evolution against three of six antibiotic classes. Sequence and structure alignments indicate that Rob is a previously uncharacterized, lineage-specific regulator in Y. enterocolitica, featuring a conserved promoter architecture. This promoter mutation resulted in robust rob overexpression, leading to the activation of multiple downstream efflux- and membrane-associated pathways. This regulatory mutation emerges early and shapes the acquisition of tetracycline resistance, while also enhancing high-level enrofloxacin resistance in combination with canonical mutations in gyrA and parC. Despite being favored under antibiotic selection, rob overexpression is costly, resulting in counter-selection of the -57 G>A rob allele in the absence of antibiotics. Together, these findings identify Rob as a Yersinia-specific efflux regulator and demonstrate how regulatory mutations can transiently accelerate antibiotic resistance. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/707304v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@107aa88org.highwire.dtl.DTLVardef@4ccd66org.highwire.dtl.DTLVardef@411e2aorg.highwire.dtl.DTLVardef@1235fbc_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.