Clustering minimal inhibitory concentration data through Bayesian mixture models: An application to detect Mycobacterium tuberculosis resistance mutations

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 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 Mycobacterium tuberculosis.

Keywords: Antimicrobial resistance; censored data; genome-wide association study; minimal inhibitory concentration distributions; mixture models.

Figure 1.

Barplots of the minimal inhibitory concentration (MIC) distributions of each drug under plate design UKMYC5. The $y$ -axis represents the density of each class/dilution and the $x$ -axis represents the ${\log }_2(\text{MIC})$ values.

Figure 2.

Manhattan plots resulting from a genome-wide regression with outcomes given by level of resistance identified by a Gaussian mixture model (a) and the censored Gaussian mixture model proposed in this work (b).