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Delving deeper into the evolution of drug resistance

Á Chiner-Oms(1,2) M G López(3) S Mallawaarachchi(4,5,8) J Corander(4,6,7) I Comas(2,3)

1:FISABIO-Public Health; 2:CIBER Epidemiology and Public Health; 3:Biomedicine Institute of Valencia (IBV-CSIC); 4:University of Oslo; 5:Peter MacCallum Cancer Centre; 6:Wellcome Sanger Institute; 7:Helsinki Institute for Information Technology HIIT; 8:The University of Melbourne

Past and present selective presures acting on bacterial organisms result in genetic mutations that leave lasting imprints on their genomes. For obligate pathogenic bacteria like those belonging to the Mycobacterium tuberculosis complex, antibiotic treatments represent a significant selective force. In this work, we have made use of a comprehensive dataset generated by the Cryptic consortium, comprising ~11,000 genomes along with phenotypic data (minimum inhibitory concentrations (MIC)) for 13 antitubercular drugs. Our aim was to study the evolutionary factors that have influenced the development of drug-resistant (DR) phenotypes over time.

We utilized innovative phylogeny-based methods to investigate the genes and mutations linked to variations in the MIC values. Genes that were not previously linked to drug resistance, had sparse prior data, or were deemed uncertain in their associations through traditional statistical methods, emerged as potential candidates associated with these phenotypes.

Furthermore, we examined ancestral phylogenetic branches preceding the emergence of drug-resistant mutations to identify mutations that might facilitate the acquisition of these genomic traits. In this instance, we identified previously linked tolerance factors and uncovered novel ones, including regulators of metablic pathways (such as the methylcitrate pathway), genes related with growth and survival, and some others whose prior associations lacked definitive confirmation. Alongside nonsynonymous mutations, synonymous ones, substitutions in intergenic regions, generation of new regulatory regions and even reversions emerged as significant contributors to these phenomenons. We assert that our approach, when combined with other statistical analyses, has the capacity to unveil the determinants of complex DR phenotypes.

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