Higher-order epistasis drives evolutionary unpredictability toward new antibiotic resistance.
Gaszek Ilona Kamila IK, Yildiz Muhammed Sadik MS, Meng Zhaoxu Z, de la Paz Jose Alberto JA et al.
Antibiotic resistance has become a critical public health problem, rendering many antibiotics ineffective. In particular, the evolution of extended-spectrum β-lactamases (ESBLs) threatens β-lactams, the cornerstone of bacterial infection treatment. We investigated the evolution of Escherichia coli TEM-1 β-lactamase into ESBLs by constructing a combinatorially complete library of all 55,296 TEM-1 variants from 18 clinical mutations across 13 residues. We obtained over 9,000,000 fitness measurements under native (ampicillin) and non-native (aztreonam) selection. Graph-theoretic and epistatic analyses revealed that ampicillin selection produced weak epistasis and predictable evolutionary trajectories, whereas aztreonam selection induced extensive higher-order epistasis, increasing phenotypic unpredictability. Machine learning identified interpretable epistatic rules shaping these landscapes. Evolutionary statistics, including direct coupling analysis and latent generative landscapes, showed that top-performing ESBL variants followed conserved epistatic patterns observed in natural β-lactamases. Our integrated experimental-computational framework provides a foundation for predicting ESBL evolution and quantifying mutational contributions to ESBL variants.