Genome-wide association studies builds a predictive model and reveals novel resistance features for macrolide-resistant Bordetella pertussis.

OBJECTIVES

The macrolide resistance in Bordetella pertussis cannot be fully explained by 23S rRNA mutations, underscoring the need for comprehensive methods to detect resistant isolates and clarify mechanisms.

METHODS

Whole-genome sequencing data from 556 isolates with macrolide resistance information, including 398 resistant and 158 sensitive strains, were retrieved from the National Center for Biotechnology Information (NCBI). A k-mer-based genome-wide k-mer-based association studies using Pyseer identified 1322 resistance-associated k-mers. Refinement with Scoary2, least absolute shrinkage and selection operator (LASSO) and variable selection using random forests (VSURF) yielded six key k-mers, enabling the construction of a simplified model for predicting resistance.

RESULTS

A total of 1322 different k-mers were involved in resistance. In the further simplified model, only six k-mers were included, in which a DHCW motif cupin fold protein (odds ratio [OR]: 25.84, 95% confidence interval [CI]: 15.58-44.03) and an IS481 insertion sequence located near infA (OR: 28.96, 95% CI: 7.64-243.86) showed strong associations with resistance. Despite the reduced feature set, the simplified model achieved classification performance comparable to the initial model, with similar sensitivity (97.7% versus 98.7%), specificity (92.1% versus 99.5%), and accuracy (93.4% versus 99.3%). Notably, it maintained a robust area under the receiver operating characteristic curve (area under the curve = 0.98), indicating strong predictive capability.

CONCLUSIONS

This study developed a simplified k-mer-based model for accurately identifying macrolide-resistant B. pertussis isolates and uncovered novel resistance features.