Clustering MIC data through Bayesian mixture models: an application to detect M. Tuberculosis resistance mutations

TL;DR

This paper introduces a Bayesian mixture model approach to analyze MIC data for clustering M. tuberculosis strains by resistance level, aiding in identifying resistance-associated mutations and improving genomic resistance prediction.

Contribution

It presents a novel Bayesian clustering method for MIC data that enhances detection of resistance patterns and mutation associations in tuberculosis.

Findings

Effective clustering of MIC data into resistance levels.

Identification of mutations linked to resistance.

Improved statistical power in mutation-resistance association.

Abstract

Antimicrobial resistance is becoming a major threat to public health throughout the world. Researchers are attempting to contrast it by developing both new antibiotics and patient-specific treatments. In the second case, whole-genome sequencing has had a huge impact in two ways: first, it is becoming cheaper and faster to perform whole-genome sequencing, and this makes it competitive with respect to standard phenotypic tests; second, it is possible to statistically associate the phenotypic patterns of resistance to specific mutations in the genome. Therefore, it is now possible to develop catalogues of genomic variants associated with resistance to specific antibiotics, in order to improve prediction of resistance and suggest treatments. It is essential to have robust methods for identifying mutations associated to resistance and continuously updating the available catalogues. This work proposes a general method to study minimal inhibitory concentration (MIC) distributions and to identify clusters of strains showing different levels of resistance to antimicrobials. Once the clusters are identified and strains allocated to each of them, it is possible to perform regression method to identify with high statistical power the mutations associated with resistance. The method is applied to a new 96-well microtiter plate used for testing M. Tuberculosis.