{"doi":"10.1093/cid/ciaa496","title":"Resistance-Conferring Mutations on Whole-Genome Sequencing of Fluoroquinolone-resistant and -Susceptible <i>Mycobacterium tuberculosis</i> Isolates: A Proposed Threshold for Identifying Resistance","abstract":"BACKGROUND: Fluoroquinolone resistance in Mycobacterium tuberculosis (Mtb) is conferred by DNA gyrase mutations, but not all fluoroquinolone-resistant Mtb isolates have mutations detected. The optimal allele frequency threshold to identify resistance-conferring mutations by whole-genome sequencing is unknown. METHODS: Phenotypically ofloxacin-resistant and lineage-matched ofloxacin-susceptible Mtb isolates underwent whole-genome sequencing at an average coverage depth of 868 reads. Polymorphisms within the quinolone-resistance-determining region (QRDR) of gyrA and gyrB were identified. The allele frequency threshold using the Genome Analysis Toolkit pipeline was ~8%; allele-level data identified the predominant variant allele frequency and mutational burden (ie, sum of all variant allele frequencies in the QRDR) in gyrA, gyrB, and gyrA + gyrB for each isolate. Receiver operating characteristic (ROC) curves assessed the optimal measure of allele frequency and potential thresholds for identifying phenotypically resistant isolates. RESULTS: Of 42 ofloxacin-resistant Mtb isolates, area under the ROC curve (AUC) was highest for predominant variant allele frequency, so that measure was used to evaluate optimal mutation detection thresholds. AUCs for 8%, 2.5%, and 0.8% thresholds were 0.8452, 0.9286, and 0.9069, respectively. Sensitivity and specificity were 69% and 100% for 8%, 86% and 100% for 2.5%, 91% and 91% for 0.8%. The sensitivity of the 2.5% and 0.8% thresholds were significantly higher than the 8% threshold (P = .016 and .004, respectively) but not significantly different between one another (P = .5). CONCLUSIONS: A predominant mutation allele frequency threshold of 2.5% had the highest AUC for detecting DNA gyrase mutations that confer ofloxacin resistance, and was therefore the optimal threshold.","journal":"Clinical Infectious Diseases","year":2020,"id":83406,"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":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9609,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":115114,"name":"Yan Guo","orcid":"0000-0001-5252-3960","position":1,"is_corresponding":false},{"id":428985,"name":"Amondrea Blackman","orcid":"0000-0003-0785-938X","position":2,"is_corresponding":false},{"id":428986,"name":"Yuri F. van der Heijden","orcid":"0000-0001-7631-9796","position":3,"is_corresponding":false},{"id":240239,"name":"Peter F. Rebeiro","orcid":"0000-0003-1951-9104","position":4,"is_corresponding":false},{"id":240257,"name":"Timothy R. Sterling","orcid":"0000-0002-4822-6979","position":5,"is_corresponding":false},{"id":311221,"name":"Fernanda Maruri","orcid":"0000-0001-9533-8239","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":null,"created_at":"2026-07-18T21:54:09.383533Z","pmid":"32348473","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":[]}