Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance.

Unlabelled: The erythromycin resistance RNA methyltransferase () confers cross-resistance to all therapeutically important macrolides, lincosamides, and streptogramins (MLS phenotype). The expression of is often induced by the macrolide-mediated ribosome stalling in the upstream co-transcribed leader sequence, thereby triggering a conformational switch of the intergenic RNA hairpins to allow the translational initiation of . We investigated the evolutionary emergence of the upstream regulatory elements and the impact of allelic variation on erm expression and the MLS phenotype. Through systematic profiling of the upstream regulatory sequences across all known operons, we observed that specific subfamilies, such as and , have independently evolved distinct configurations of small upstream ORFs and palindromic repeats. A population-wide genomic analysis of the upstream regions revealed substantial non-random allelic variation at numerous positions. Utilizing machine learning-based classification coupled with RNA structure modeling, we found that many alleles cooperatively influence the stability of alternative RNA hairpin structures formed by the palindromic repeats, which, in turn, affects the inducibility of expression and MLS phenotypes. Subsequent experimental validation of 11 randomly selected variants demonstrated an impressive 91% accuracy in predicting MLS phenotypes. Furthermore, we uncovered a mixed distribution of MLS-sensitive and MLS-resistant loci within the evolutionary tree, indicating repeated and independent evolution of MLS resistance. Taken together, this study not only elucidates the evolutionary processes driving the emergence and development of MLS resistance but also highlights the potential of using non-coding genomic allele data to predict antibiotic resistance phenotypes.

Importance: Antibiotic resistance (AR) poses a global health threat as the efficacy of available antibiotics has rapidly eroded due to the widespread transmission of AR genes. Using Erm-dependent MLS resistance as a model, this study highlights the significance of non-coding genomic allelic variations. Through a comprehensive analysis of upstream regulatory elements within the family, we elucidated the evolutionary emergence and development of AR mechanisms. Leveraging population-wide machine learning (ML)-based genomic analysis, we transformed substantial non-random allelic variations into discernible clusters of elements, enabling precise prediction of MLS phenotypes from non-coding regions. These findings offer deeper insight into AR evolution and demonstrate the potential of harnessing non-coding genomic allele data for accurately predicting AR phenotypes.