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. 2024 Jan 12;15(1):488.
doi: 10.1038/s41467-023-44325-5.

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach

CRyPTIC Consortium
Collaborators

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach

CRyPTIC Consortium. Nat Commun. .

Abstract

The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.

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Conflict of interest statement

E.R. is employed by Public Health England and holds an honorary contract with Imperial College London. I.F.L. is Director of the Scottish Mycobacteria Reference Laboratory. S.N. receives funding from German Center for Infection Research, Excellenz Cluster Precision Medicine in Chronic Inflammation, Leibniz Science Campus Evolutionary Medicine of the LUNG (EvoLUNG)tion EXC 2167. P.S. is a consultant at Genoscreen. T.R. is funded by NIH and DoD and receives salary support from the non-profit organization FIND. T.R. is a co-founder, board member and shareholder of Verus Diagnostics Inc, a company that was founded with the intent of developing diagnostic assays. Verus Diagnostics was not involved in any way with data collection, analysis or publication of the results. T.R. has not received any financial support from Verus Diagnostics. UCSD Conflict of Interest office has reviewed and approved T.R.’s role in Verus Diagnostics Inc. T.R. is a co-inventor of a provisional patent for a TB diagnostic assay (provisional patent #: 63/048.989). T.R. is a co-inventor on a patent associated with the processing of TB sequencing data (European Patent Application No. 14840432.0 and USSN 14/912,918). T.R. has agreed to “donate all present and future interest in and rights to royalties from this patent” to UCSD to ensure that he does not receive any financial benefits from this patent. S.S. is working and holding ESOPs at HaystackAnalytics Pvt. Ltd. (Product: Using whole genome sequencing for drug susceptibility testing for Mycobacterium tuberculosis). The remaining authors declare no competing interest.

Figures

Fig. 1
Fig. 1. Data flow and sample sizes for CRyPTIC MIC models.
Numbers in brackets represent the number of variables (mutations plus lineage and site effects) included in the final model.
Fig. 2
Fig. 2. Variation in effect size by mutation type and drug.
A Effects on log2MIC for the 540 variants significant after false discovery rate correction using the Benjamini-Hochberg method. Mutation types are delineated by color. Homoplastic mutations are shown as solid circles. ECOFF (minus baseline MIC) is shown as tan line. Common resistance mutations are highlighted. B Comparison of effects on log2MIC for promoter and corresponding gene body variants for ethambutol (EMB) and isoniazid (INH). ECOFF (minus baseline MIC) is shown as a tan line. Points are the mean effect in the interval regression model with error bar representing the 95% confidence interval. Exact effects, sample sizes and p values are provided in Supplementary Data 3.
Fig. 3
Fig. 3. Heterogenous effects of rpoB mutations on rifampicin resistance.
A Mean effects of target gene variants on rifampicin (blue) and rifabutin (gray) log2MIC. ECOFFs (minus baseline MICs) are highlighted as lines. B Rifampicin (blue) and rifabutin (gray) bound to rpoB with resistance-associated variants highlighted (red-high, orange-variable, yellow-low). C Mean effects on rifampicin MIC of mutations in rpoB with error bars representing 95% confidence interval. Exact sample size for each mutation is shown at the bottom of panel B. Colored shading highlights “borderline” variants. P-threshold is the value reaching significance after Bejamini-Hochberg correction for multiple testing. Sample sizes and p-values for each mutation effect are provided in Supplementary Data 3.
Fig. 4
Fig. 4. Resistance to isoniazid and ethambutol is a multi-gene phenomenon.
A Independent effects of variants in target genes on isoniazid log2MIC. ECOFF (minus baseline MIC) is denoted in red. B KatG dimer with isoniazid (blue) modeled and resistance-associated positions highlighted in orange. C Independent effects of variants in target genes on ethambutol log2MIC. ECOFF (minus baseline MIC) is denoted in red. D EmbA-embB complex bound to ethambutol (blue) with resistant mutations highlighted in orange. Sample sizes and p values for all effects are provided in Supplementary Data 3.
Fig. 5
Fig. 5. Resistance to second line drugs.
A Effects of mutations in gyrA and gyrB on levofloxacin (pink) and moxifloxacin (green) log2MIC. B Structural mapping of fluoroquinolone resistance-associated variants reveal that majority lie within 10 Å of the drug binding site. Positions gyrB R446 and S447 are not shown. C Effects of mutations in aminoglycoside target genes on amikacin and kanamycin log2MIC. ECOFFs (minus baseline MIC) are shown for comparison. Sample sizes and p values for all effects are provided in Supplementary Data 3.
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