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Record W4396514780 · doi:10.1128/spectrum.03930-23

Identification of novel resistance-associated mutations and discrimination within whole-genome sequences of fluoroquinolone-resistant <i>Mycobacterium tuberculosis</i> isolates

2024· article· en· W4396514780 on OpenAlexaff
Yingzhi Chong, Xueying Li, Yifei Long, Shengfei Pei, Q. Ren, Fumin Feng, Haibo Zhang

Bibliographic record

VenueMicrobiology Spectrum · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsSt. Michael's Hospital
FundersHebei Province Graduate Innovation Funding ProjectHebei Provincial Department of Bureau of Science and TechnologyNatural Science Foundation of Hebei ProvinceDepartment of Education of Hebei Province
KeywordsMycobacterium tuberculosisBiologyIdentification (biology)GeneticsTuberculosisGenomeWhole genome sequencingComputational biologyMicrobiologyGeneMedicine

Abstract

fetched live from OpenAlex

ABSTRACT This study aims to elucidate additional mutation loci associated with fluoroquinolone (FQ) resistance and evaluate the discriminatory capacity of mutation loci and allele mutation frequencies in identifying FQ-resistant Mycobacterium tuberculosis (MTB) isolates. A random selection of isolates was extracted from an ongoing collection. Drug resistance was determined using the resazurin microtiter assay (REMA) as the gold standard. Mutation loci and the burden of mutations in the quinolone resistance-determining region (QRDR) were elucidated through whole-genome sequencing (WGS). Novel amino acid mutations, namely, G520D and G520T, were identified in the gyrB and associated with FQ resistance. In the context of distinguishing FQ-resistant isolates, the AUC for the QRDR mutation frequency burden (0.969) surpassed that of the mutation locus (0.929), and this difference was statistically significant ( P = 0.03). Furthermore, using the resistance mutation locus as a reference, setting the QRDR mutation frequency burden threshold at 1.31% resulted in a 3.60% increase in the accuracy of classifying FQ-resistant isolates (NRI = 3.60%, P < 0.001). The QRDR mutation frequency burden appears to offer superior diagnostic efficacy in discriminating FQ-resistant isolates compared to qualitative detection of mutant loci. IMPORTANCE Fluoroquinolone (FQ) drugs are recommended as second-line drugs for the treatment of multidrug-resistant tuberculosis. With the massive use of FQ drugs in the clinical treatment of tuberculosis (TB), there is an increasing rate of drug resistance to FQ drugs. In this study, we identified and demonstrated novel amino acid mutations associated with FQ resistance in Mycobacterium tuberculosis (MTB), and we quantified the mutation sites and identified the quinolone resistance-determining region (QRDR) mutation frequency burden as a novel diagnostic method for FQ resistance. We hope that the results of this study will provide data support and a theoretical basis for the rapid diagnosis of FQ-resistant MTB.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.285
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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