{"doi":"10.1101/2025.07.08.663783","title":"Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance","abstract":"The rapid evolution of extended-spectrum β-lactamases (ESBLs) represents a global health threat, undermining the efficacy of β-lactams, the most extensively used antibiotic class. To elucidate the evolutionary dynamics underlying β-lactam resistance, we constructed a comprehensive combinatorial mutant library comprising all 55,296 possible TEM-1 β-lactamase variants integrating 18 clinically observed mutations across 13 key residues. Over eight million empirical fitness measurements were obtained under selection pressure with both a native antibiotic substrate (ampicillin) and a novel antibiotic (aztreonam), establishing the largest experimentally determined fitness landscape for antibiotic resistance to date. Through graph-theoretic and epistatic analyses, we discovered that selection with ampicillin resulted in weak epistasis, with mutants rarely surpassing the fitness of the wild-type enzyme. Conversely, aztreonam selection elicited extensive higher-order epistasis, generating a rugged fitness landscape characterized by increased phenotypic unpredictability. Interpretable machine-learning analyses identified context-dependent epistatic interactions necessary for achieving high-level aztreonam resistance. Further evolutionary statistical analyses, including direct coupling analysis and latent generative landscapes, showed that top-performing TEM-1 variants consistently adhered to conserved epistatic patterns found in naturally occurring β-lactamases. Our findings demonstrate that higher-order epistasis critically shapes fitness landscape ruggedness when enzymes adapt to novel substrates, whereas adaptations to native substrates exhibit predictably smoother landscapes. This integrated experimental and computational framework provides a foundation for predictive evolutionary pharmacology, enabling assessments of newly developed β-lactams or emerging β-lactamase variants for their potential contribution to ESBL evolution. Importantly, incorporating graph-theoretically informed evolutionary constraints can strategically disrupt evolutionary pathways, presenting a viable approach to mitigate the rise of antibiotic resistance.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":555657,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9471,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1180005,"name":"Muhammed Sadik Yildiz","orcid":"0000-0002-1473-0819","position":1,"is_corresponding":false},{"id":1454902,"name":"Devin Meng","orcid":null,"position":2,"is_corresponding":false},{"id":315623,"name":"Jose Alberto de la Paz","orcid":"0000-0002-5053-4807","position":3,"is_corresponding":false},{"id":1169135,"name":"Sophia Alvarez","orcid":"0009-0007-3112-2632","position":4,"is_corresponding":false},{"id":315625,"name":"Faruck Morcos","orcid":"0000-0001-6208-1561","position":5,"is_corresponding":false},{"id":444015,"name":"Milo M. Lin","orcid":"0000-0001-8680-2685","position":6,"is_corresponding":false},{"id":637037,"name":"Erdal Toprak","orcid":"0000-0002-4883-7681","position":7,"is_corresponding":false},{"id":637033,"name":"Ilona K. Gaszek","orcid":"0000-0002-3397-7294","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-19T02:55:03.976486Z","pmid":"40672211","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}