{"doi":"10.1101/2022.06.02.494585","title":"Community interactions drive the evolution of antibiotic tolerance in bacteria","abstract":"Abstract The emergence of antibiotic tolerance (prolonged survival against exposure) in natural bacterial populations is a major concern. Since it has been studied primarily in isogenic populations, we do not yet understand how ecological interactions in a diverse community impact the evolution of tolerance. To address this, we studied the evolutionary dynamics of a synthetic bacterial community composed of two interacting strains. In this community, an antibiotic-resistant strain protected the other, susceptible strain by degrading the antibiotic ampicillin in the medium. Surprisingly, we found that in the presence of antibiotics, the susceptible strain evolved tolerance. Tolerance was typified by an increase in survival as well as an accompanying decrease in growth rate, highlighting a trade-off between the two. A simple mathematical model explained that the observed decrease in death rate, even when coupled with a decreased growth rate, is beneficial in a community with weak protective interactions. In the presence of strong interactions, the model predicted that the trade-off would instead be detrimental and tolerance would not emerge, which we experimentally verified. By whole genome sequencing the evolved tolerant isolates, we identified three genetic hotspots which accumulated mutations in parallel lines, suggesting their association with tolerance. Our work highlights that ecological interactions can promote antibiotic tolerance in bacterial communities, which has remained understudied. Significance Bacteria evolve to evade antibiotic pressure, leading to adverse infection outcomes. Understanding the evolutionary dynamics which lead to different antibiotic responses has thus far focused on single-strain bacterial populations, with limited attention to multi-strain communities which are more common in nature. Here, we experimentally evolved a simple two-strain community, comprising an antibiotic-resistant strain protecting a susceptible one, and found that susceptible populations evolve tolerance, helping them better survive long antibiotic exposure. Using the interplay between community interactions, antibiotic dynamics, and resource availability, we explain this finding with a simple mathematical model, and predict and experimentally verify that an increased resistant strain carrying capacity would render tolerance detrimental. Our results highlight that community interactions can alter bacterial evolutionary responses to antibiotics.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":299278,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9513,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":554619,"name":"Akshit Goyal","orcid":"0000-0002-9425-8269","position":1,"is_corresponding":false},{"id":318439,"name":"Jeff Gore","orcid":"0000-0003-4583-8555","position":2,"is_corresponding":false},{"id":989107,"name":"Sivan Pearl Mizrahi","orcid":"0000-0001-5093-8361","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T00:31:44.904250Z","pmid":null,"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":[]}