Genetic Determinants of Drug Resistance in Mycobacterium tuberculosis and Their Diagnostic Value

Drug-resistant tuberculosis: a persistent global health concern

Drug-resistant tuberculosis (TB) is estimated to cause 13% of all antimicrobial resistance-attributable deaths worldwide and is driven by both ongoing resistance acquisition and person-to-person transmission. Poor outcomes are exacerbated by late diagnosis and inadequate access to effective treatment. Advances in rapid molecular testing have recently improved the diagnosis of TB and drug resistance. Next-generation sequencing of Mycobacterium tuberculosis has increased our understanding of genetic resistance mechanisms and can now detect mutations associated with resistance phenotypes. All-oral, shorter drug regimens that can achieve high cure rates of drug-resistant TB within 6-9 months are now available and recommended but have yet to be scaled to global clinical use. Promising regimens for the prevention of drug-resistant TB among high-risk contacts are supported by early clinical trial data but final results are pending. A person-centred approach is crucial in managing drug-resistant TB to reduce the risk of poor treatment outcomes, side effects, stigma and mental health burden associated with the diagnosis. In this Review, we describe current surveillance of drug-resistant TB and the causes, risk factors and determinants of drug resistance as well as the stigma and mental health considerations associated with it. We discuss recent advances in diagnostics and drug-susceptibility testing and outline the progress in developing better treatment and preventive therapies.

Mycobacterial biofilms: Understanding the genetic factors playing significant role in pathogenesis, resistance and diagnosis

Even though the genus Mycobacterium is a diverse group consisting of a majority of environmental bacteria known as non-tuberculous mycobacteria (NTM), it also contains some of the deadliest pathogens (Mycobacterium tuberculosis) in history associated with chronic disease called tuberculosis (TB). Formation of biofilm is one of the unique strategies employed by mycobacteria to enhance their ability to survive in hostile conditions. Biofilm formation by Mycobacterium species is an emerging area of research with significant implications for understanding its pathogenesis and treatment of related infections, specifically TB. This review provides an overview of the biofilm-forming abilities of different species of Mycobacterium and the genetic factors influencing biofilm formation with a detailed focus on M. tuberculosis. Biofilm-mediated resistance is a significant challenge as it can limit antibiotic penetration and promote the survival of dormant mycobacterial cells. Key genetic factors promoting biofilm formation have been explored such as the mmpL genes involved in lipid transport and cell wall integrity as well as the groEL gene essential for mature biofilm formation. Additionally, biofilm-mediated antibiotic resistance and pathogenesis highlighting the specific niches, sites of infection along with the possible mechanisms of biofilm dissemination have been discussed. Furthermore, drug targets within mycobacterial biofilm and their role as potential biomarkers in the development of rapid diagnostic tools have been highlighted. The review summarises the current understanding of the complex nature of Mycobacterium biofilm and its clinical implications, paving the way for advancements in the field of disease diagnosis, management and treatment against its multi-drug resistant species.

Pan-lineage Mycobacterium tuberculosis reference genome for enhanced molecular diagnosis

In Mycobacterium tuberculosis (MTB) control, whole genome sequencing-based molecular drug susceptibility testing (molDST-WGS) has emerged as a pivotal tool. However, the current reliance on a single-strain reference limits molDST-WGS's true potential. To address this, we introduce a new pan-lineage reference genome, 'MtbRf'. We assembled 'unmapped' reads from 3,614 MTB genomes (751 L1; 881 L2; 1,700 L3; and 282 L4) into 35 shared, annotated contigs (54 coding sequences [CDSs]). We constructed MtbRf through: (1) searching for contig homologues among genome database that precipitate results uniquely within Mycobacteria genus; (2) comparing genomes with H37Rv ('lift-over') to define 18 insertions; and (3) filling gaps in H37Rv with insertions. MtbRf adds 1.18% sequences to H37rv, salvaging >60% of previously unmapped reads. Transcriptomics confirmed gene expression of new CDSs. The new variants provided a moderate DST predictive value (AUROC 0.60-0.75). MtbRf thus unveils previously hidden genomic information and lays the foundation for lineage-specific molDST-WGS.

Drug resistance and epidemiological success of modern Mycobacterium tuberculosis lineages in western India

BACKGROUND

Transmission is contributing to the slow decline of tuberculosis (TB) incidence globally. Drivers of TB transmission in India, the country estimated to carry a quarter of the World's burden, are not well studied. We conducted a genomic epidemiology study to compare epidemiological success, host factors and drug resistance (DR) among the four major Mycobacterium tuberculosis (Mtb) lineages (L1-4) circulating in Pune, India.

METHODS

We performed whole-genome sequencing (WGS) of Mtb sputum culture-positive isolates from participants in two prospective cohort studies and predicted genotypic susceptibility using a validated random forest model. We used maximum likelihood estimation to build phylogenies. We compared lineage specific phylogenetic and time-scaled metrics to assess epidemiological success.

RESULTS

Of the 642 isolates that underwent WGS, 612 met sequence quality criteria. Most isolates belonged to L3 (44.6%). The majority (61.1%) of multidrug-resistant isolates belonged to L2 (P < 0.001). In molecular dating, L2 demonstrated a higher rate and more recent resistance acquisition. We measured higher clustering, and time-scaled haplotypic density (THD) for L4 and L2 compared to L3 and/or L1 suggesting higher epidemiological success. L4 demonstrated higher THD and clustering (OR 5.1 (95% CI 2.3-12.3) in multivariate models controlling for host factors and DR.

CONCLUSION

L2 shows a higher frequency of DR and both L2 and L4 demonstrate evidence of higher epidemiological success than L3 or L1 in the study setting. Our findings highlight the need for contact tracing around TB cases, and heightened surveillance of TB DR in India.

Genetic factors associated with acquired phenotypic drug resistance and its compensatory evolution during tuberculosis treatment

OBJECTIVES

We elucidated the factors, evolution, and compensation of antimicrobial resistance (AMR) in Mycobacterium tuberculosis (MTB) isolates under dual pressure from the intra-host environment and anti-tuberculosis (anti-TB) drugs.

METHODS

This retrospective case-control study included 337 patients with pulmonary tuberculosis from 15 clinics in Tianjin, China, with phenotypic drug susceptibility testing results available for at least two time points between January 1, 2009 and December 31, 2016. Patients in the case group exhibited acquired AMR to isoniazid (INH) or rifampicin (RIF), while those in the control group lacked acquired AMR. The whole-genome sequencing (WGS) was conducted on 149 serial longitudinal MTB isolates from 46 patients who acquired or reversed phenotypic INH/RIF-resistance during treatment. The genetic basis, associated factors, and intra-host evolution of acquired phenotypic INH/RIF-resistance were elucidated using a combined analysis.

RESULTS

Anti-TB interruption duration of ≥30 days showed association with acquired phenotypic INH/RIF resistance (aOR = 2·2, 95% CI, 1·0-5·1) and new rpoB mutations (p = 0·024). The MTB evolution was 1·2 (95% CI, 1·02-1·38) single nucleotide polymorphisms per genome per year under dual pressure from the intra-host environment and anti-TB drugs. AMR-associated mutations occurred before phenotypic AMR appearance in cases with acquired phenotypic INH (10 of 16) and RIF (9 of 22) resistances.

DISCUSSION

Compensatory evolution may promote the fixation of INH/RIF-resistance mutations and affect phenotypic AMR. The TB treatment should be adjusted based on gene sequencing results, especially in persistent culture positivity during treatment, which highlights the clinical importance of WGS in identifying reinfection and AMR acquisition before phenotypic drug susceptibility testing.

Development of a multiplex droplet digital PCR method for detection and monitoring of Mycobacterium tuberculosis and drug-resistant tuberculosis

BACKGROUND

The prevalence of multidrug-resistant tuberculosis (MDR-TB) among Korean tuberculosis patients is about 4.1%, which is higher than the OECD average of 2.6%. Inadequate drug use and poor patient compliance increase MDR-TB prevalence through selective pressure. Therefore, prompt detection of drug resistance in tuberculosis patients at the time of diagnosis and quantitative monitoring of these resistant strains during treatment are crucial.

METHODS

A multiplex droplet digital PCR (ddPCR) assay was developed and assessed using DNA material of nine Mycobacterium tuberculosis strains with known mutation status that were purchased from the Korean National Tuberculosis Association. We collected a total of 18 MDR-TB residual samples referred for PCR analysis. Total DNA was extracted from the samples and subjected to the quadruplex ddPCR assay. Their results were compared to those of known resistance phenotypes.

RESULTS

The analytical sensitivity and specificity of the multiplex ddPCR assay for detecting INH, RIF, EMB, FQ, and SM resistance-causing mutations ranged from 71.43 to 100% and 94.12-100%, respectively. Follow-up sample results showed that the quadruplex ddPCR assay was sensitive enough to detect IS6110 and other mutations even after onset of treatment.

CONCLUSIONS

We developed a sensitive and accurate multiplex ddPCR assay that can detect the presence of tuberculosis quantitatively and resistance-conveying mutations concurrently. This tool could aid clinicians in the diagnosis and treatment monitoring of tuberculosis.

Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning

BACKGROUND

Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.

METHODS

We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa.

RESULTS

Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotypic resistance among mono-resistant TB estimates overlapped with the South African DR survey, except for isoniazid, ethionamide and second-line injectables, which were underestimated (n=3134). Among multidrug resistant (MDR) TB (n=268), estimates overlapped for the fluoroquinolones but overestimated other drugs. Globally pooled mono-resistance to isoniazid was 10.9% (95% CI: 10.2-11.7%, n=14 012). Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% (0.1-11%), n=111 and India 2.8% (0.08-9.4%), n=114). Given the recent interest in drugs enhancing ethionamide activity and their expected activity against isolates with resistance discordance between isoniazid and ethionamide, we measured this rate and found it to be high at 74.4% (IQR: 64.5-79.7%) of isoniazid-resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3964).

CONCLUSIONS

This is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics, but public WGS data demonstrates oversampling of isolates with higher resistance levels than MDR. Nevertheless, our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug-susceptible TB in South Asia and indicate underutilisation of ethionamide in MDR treatment.

Deep learning for precise diagnosis and subtype triage of drug-resistant tuberculosis on chest computed tomography

Deep learning, transforming input data into target prediction through intricate network structures, has inspired novel exploration in automated diagnosis based on medical images. The distinct morphological characteristics of chest abnormalities between drug-resistant tuberculosis (DR-TB) and drug-sensitive tuberculosis (DS-TB) on chest computed tomography (CT) are of potential value in differential diagnosis, which is challenging in the clinic. Hence, based on 1176 chest CT volumes from the equal number of patients with tuberculosis (TB), we presented a Deep learning-based system for TB drug resistance identification and subtype classification (DeepTB), which could automatically diagnose DR-TB and classify crucial subtypes, including rifampicin-resistant tuberculosis, multidrug-resistant tuberculosis, and extensively drug-resistant tuberculosis. Moreover, chest lesions were manually annotated to endow the model with robust power to assist radiologists in image interpretation and the Circos revealed the relationship between chest abnormalities and specific types of DR-TB. Finally, DeepTB achieved an area under the curve (AUC) up to 0.930 for thoracic abnormality detection and 0.943 for DR-TB diagnosis. Notably, the system demonstrated instructive value in DR-TB subtype classification with AUCs ranging from 0.880 to 0.928. Meanwhile, class activation maps were generated to express a human-understandable visual concept. Together, showing a prominent performance, DeepTB would be impactful in clinical decision-making for DR-TB.

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP ), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

Two-stage tuberculosis diagnostics: combining centrifugal microfluidics to detect TB infection and Inh and Rif resistance at the point of care with subsequent antibiotic resistance profiling by targeted NGS

Globally, tuberculosis (TB) remains the deadliest bacterial infectious disease, and spreading antibiotic resistances is the biggest challenge for combatting the disease. Rapid and comprehensive diagnostics including drug susceptibility testing (DST) would assure early treatment, reduction of morbidity and the interruption of transmission chains. To date, rapid genetic resistance testing addresses only one to four drug groups while complete DST is done phenotypically and takes several weeks. To overcome these limitations, we developed a two-stage workflow for rapid TB diagnostics including DST from a single sputum sample that can be completed within three days. The first stage is qPCR detection of M. tuberculosis complex (MTBC) including antibiotic resistance testing against the first-line antibiotics, isoniazid (Inh) and rifampicin (Rif). The test is automated by centrifugal microfluidics and designed for point of care (PoC). Furthermore, enriched MTBC DNA is provided in a detachable sample tube to enable the second stage: if the PCR detects MTBC and resistance to either Inh or Rif, the MTBC DNA is shipped to specialized facilities and analyzed by targeted next generation sequencing (tNGS) to assess the complete resistance profile. Proof-of-concept testing of the PoC test revealed an analytical sensitivity of 44.2 CFU ml-1, a diagnostic sensitivity of 96%, and a diagnostic specificity of 100% for MTBC detection. Coupled tNGS successfully provided resistance profiles, demonstrated for samples from 17 patients. To the best of our knowledge, the presented combination of PoC qPCR with tNGS allows for the fastest comprehensive TB diagnostics comprising decentralized pathogen detection with subsequent resistance profiling in a facility specialized in tNGS.

Phenotypic and genotypic drug susceptibility patterns of Mycobacterium tuberculosis isolates from pulmonary tuberculosis patients in Central and Southern Ethiopia

INTRODUCTION

The persistence of tuberculosis (TB) infection in some patients after treatment has highlighted the importance of drug susceptibility testing (DST). This study aimed to determine the drug susceptibility patterns of Mycobacterium tuberculosis (M. tuberculosis) isolates from pulmonary TB (PTB) patients in Central and Southern Ethiopia.

METHODS

A health institution-based cross-sectional study was conducted between July 2021 and April 2022. Sputum samples were collected from newly diagnosed smear microscopy and/or Xpert MTB/RIF-positive PTB patients. The samples were processed and cultivated in Lowenstein-Jensen (LJ) pyruvate and glycerol medium. M. tuberculosis isolates were identified using polymerase chain reaction (PCR) based region of difference 9 (RD9) deletion typing. Phenotypic DST patterns of the isolates were characterized using the BACTEC MGIT™ 960 instrument with SIRE kit. Isoniazid (INH) and Rifampicin (RIF) resistant M. tuberculosis isolates were identified using the GenoType® MTBDRplus assay.

RESULTS

Sputum samples were collected from 350 PTB patients, 315 (90%) of which were culture-positive, and phenotypic and genotypic DST were determined for 266 and 261 isolates, respectively. Due to invalid results and missing data, 6% (16/266) of the isolates were excluded, while 94% (250/266) were included in the paired analysis. According to the findings, 14.4% (36/250) of the isolates tested positive for resistance to at least one anti-TB drug. Gene mutations were observed only in the rpoB and katG gene loci, indicating RIF and high-level INH resistance. The GenoType® MTBDRplus assay has a sensitivity of 42% and a specificity of 100% in detecting INH-resistant M. tuberculosis isolates, with a kappa value of 0.56 (95%CI: 0.36-0.76) compared to the BACTEC MGIT™ DST. The overall discordance between the two methods was 5.6% (14/250) for INH alone and 0% for RIF resistance and MDR-TB (resistance to both INH and RIF) detection.

CONCLUSION

This study reveals a higher prevalence of phenotypic and genotypic discordant INH-resistant M. tuberculosis isolates in the study area. The use of whole-genome sequencing (WGS) is essential for gaining a comprehensive understanding of these discrepancies within INH-resistant M. tuberculosis strains.

Mycobacterium tuberculosis complex whole-genome sequencing in New York State: Implementation of a reduced phenotypic drug susceptibility testing algorithm

Whole-genome sequencing (WGS) can predict drug resistance and antimicrobial susceptibility in Mycobacterium tuberculosis complex (MTBC) and has shown promise in partially replacing culture-based phenotypic drug susceptibility testing (pDST). We performed a two-year side by side study comparing the prediction of drug resistance and antimicrobial susceptibility by WGS molecular DST (mDST) to pDST to determine resistance at the critical concentration by Mycobacterial Growth Indicator Tube (MGIT) and agar proportion testing. Negative predictive values of WGS results were consistently high for the first-line drugs: rifampin (99.9%), isoniazid (99.0%), pyrazinamide (98.5%), and ethambutol (99.8%); the rates of resistance to these drugs, among strains in our population, are 2.9%, 10.4%, 46.3%, and 2.3%, respectively. WGS results were available an average 8 days earlier than first-line MGIT pDST. Based on these findings, we implemented a new testing algorithm with an updated WGS workflow in which strains predicted pan-susceptible were no longer tested by pDST. This algorithm was applied to 1177 isolates between October 2018 and September 2020, eliminating pDST for 66.6% of samples and reducing pDST for an additional 22.0%. This algorithm change resulted in faster turnaround times and decreased cost while maintaining comprehensive antimicrobial susceptibility profiles of all culture-positive MTBC cases in New York.

Omics analysis of Mycobacterium tuberculosis isolates uncovers Rv3094c, an ethionamide metabolism-associated gene

Global control of the tuberculosis epidemic is threatened by increasing prevalence of drug resistant M. tuberculosis isolates. Many genome-wide studies focus on SNP-associated drug resistance mechanisms, but drug resistance in 5-30% of M. tuberculosis isolates (varying with antibiotic) appears unrelated to reported SNPs, and alternative drug resistance mechanisms involving variation in gene/protein expression are not well-studied. Here, using an omics approach, we identify 388 genes with lineage-related differential expression and 68 candidate drug resistance-associated gene pairs/clusters in 11 M. tuberculosis isolates (variable lineage/drug resistance profiles). Structural, mutagenesis, biochemical and bioinformatic studies on Rv3094c from the Rv3093c-Rv3095 gene cluster, a gene cluster selected for further investigation as it contains a putative monooxygenase/repressor pair and is associated with ethionamide resistance, provide insights on its involvement in ethionamide sulfoxidation, the initial step in its activation. Analysis of the structure of Rv3094c and its complex with ethionamide and flavin mononucleotide, to the best of our knowledge the first structures of an enzyme involved in ethionamide activation, identify key residues in the flavin mononucleotide and ethionamide binding pockets of Rv3094c, and F221, a gate between flavin mononucleotide and ethionamide allowing their interaction to complete the sulfoxidation reaction. Our work broadens understanding of both lineage- and drug resistance-associated gene/protein expression perturbations and identifies another player in mycobacterial ethionamide metabolism.

Investigating resistance in clinical Mycobacterium tuberculosis complex isolates with genomic and phenotypic antimicrobial susceptibility testing: a multicentre observational study

BACKGROUND

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis complex has become an important tool in diagnosis and management of drug-resistant tuberculosis. However, data correlating resistance genotype with quantitative phenotypic antimicrobial susceptibility testing (AST) are scarce.

METHODS

In a prospective multicentre observational study, 900 clinical M tuberculosis complex isolates were collected from adults with drug-resistant tuberculosis in five high-endemic tuberculosis settings around the world (Georgia, Moldova, Peru, South Africa, and Viet Nam) between Dec 5, 2014, and Dec 12, 2017. Minimum inhibitory concentrations (MICs) and resulting binary phenotypic AST results for up to nine antituberculosis drugs were determined and correlated with resistance-conferring mutations identified by WGS.

FINDINGS

Considering WHO-endorsed critical concentrations as reference, WGS had high accuracy for prediction of resistance to isoniazid (sensitivity 98·8% [95% CI 98·5-99·0]; specificity 96·6% [95% CI 95·2-97·9]), levofloxacin (sensitivity 94·8% [93·3-97·6]; specificity 97·1% [96·7-97·6]), kanamycin (sensitivity 96·1% [95·4-96·8]; specificity 95·0% [94·4-95·7]), amikacin (sensitivity 97·2% [96·4-98·1]; specificity 98·6% [98·3-98·9]), and capreomycin (sensitivity 93·1% [90·0-96·3]; specificity 98·3% [98·0-98·7]). For rifampicin, pyrazinamide, and ethambutol, the specificity of resistance prediction was suboptimal (64·0% [61·0-67·1], 83·8% [81·0-86·5], and 40·1% [37·4-42·9], respectively). Specificity for rifampicin increased to 83·9% when borderline mutations with MICs overlapping with the critical concentration were excluded. Consequently, we highlighted mutations in M tuberculosis complex isolates that are often falsely identified as susceptible by phenotypic AST, and we identified potential novel resistance-conferring mutations.

INTERPRETATION

The combined analysis of mutations and quantitative phenotypes shows the potential of WGS to produce a refined interpretation of resistance, which is needed for individualised therapy, and eventually could allow differential drug dosing. However, variability of MIC data for some M tuberculosis complex isolates carrying identical mutations also reveals limitations of our understanding of the genotype and phenotype relationships (eg, including epistasis and strain genetic background).

FUNDING

Bill & Melinda Gates Foundation, German Centre for Infection Research, German Research Foundation, Excellence Cluster Precision Medicine of Inflammation (EXC 2167), and Leibniz ScienceCampus EvoLUNG.

Characterization of Genetic Variants Associated with Rifampicin Resistance Level in Mycobacterium tuberculosis Clinical Isolates Collected in Guangzhou Chest Hospital, China

Objective

Rifampicin (RIF)-resistance, a surrogate marker for multidrug-resistant tuberculosis (TB), is mediated by mutations in the rpoB gene. We aimed to investigate the prevalence of mutations pattern in the entire rpoB gene of Mycobacterium tuberculosis clinical isolates and their association with resistance level to RIF.

Methods

Among 465 clinical isolates collected from the Guangzhou Chest Hospital, drug-susceptibility of 175 confirmed Mtb strains was performed via the proportion method and Bactec MGIT 960 system. GeneXpert MTB/RIF and sanger sequencing facilitated in genetic characterization, whereas the MICs of RIF were determined by Alamar blue assay.

Results

We found 150/175 (85.71%) RIF-resistant strains (MIC: 4 to >64 µg/mL) of which 57 were MDR and 81 pre-XDR TB. Genetic analysis identified 17 types of mutations 146/150 (97.33%) within RRDR (codons 426–452) of rpoB, mainly at L430 (P), D435 (V, E, G, N), H445 (N, D, Y, R, L), S450 (L, F) and L452 (P). D435V 12/146 (8.2%), H445N 16/146 (10.9%), and S450L 70/146 (47.94%) were the most frequently encountered mutations. Mutations Q432K, M434V, and N437D are rarely identified in RRDR. Deletions at (1284–1289 CCAGCT), (1295–1303 AATTCATGG), and insertion at (1300–1302 TTC) were detected within RRDR of three RIF^R strains for the first time. We detected 47 types of mutations and insertions/deletions (indels) outside the RRDR. Four RIF^R strains were detected with only novel mutations/indels outside the RRDR. Two of the four had (K274Q + C897 del + I491M) and (A286V + L494P), respectively. The other two had (G1687del + P454L) and (TT1835-6 ins + I491L) individually. Compared with phenotypic characterization, diagnostic sensitivities of GeneXpert MTB/RIF and sequencing analysis were 95.33% (143/150), and 100% (150/150) respectively.

Conclusion

Our findings underscore the key role of RRDR mutations and the contribution of non-RRDR mutations in rapid molecular diagnosis of RIF^R clinical isolates. Such insights will support early detection of disease and recommend the appropriate anti-TB regimens in high-burden settings.

Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms

The emergence of drug-resistant tuberculosis is a major global public health concern that threatens the ability to control the disease. Whole-genome sequencing as a tool to rapidly diagnose resistant infections can transform patient treatment and clinical practice. While resistance mechanisms are well understood for some drugs, there are likely many mechanisms yet to be uncovered, particularly for new and repurposed drugs. We sequenced 10,228 Mycobacterium tuberculosis (MTB) isolates worldwide and determined the minimum inhibitory concentration (MIC) on a grid of 2-fold concentration dilutions for 13 antimicrobials using quantitative microtiter plate assays. We performed oligopeptide- and oligonucleotide-based genome-wide association studies using linear mixed models to discover resistance-conferring mechanisms not currently catalogued. Use of MIC over binary resistance phenotypes increased sample heritability for the new and repurposed drugs by 26% to 37%, increasing our ability to detect novel associations. For all drugs, we discovered uncatalogued variants associated with MIC, including in the Rv1218c promoter binding site of the transcriptional repressor Rv1219c (isoniazid), upstream of the vapBC20 operon that cleaves 23S rRNA (linezolid) and in the region encoding an α-helix lining the active site of Cyp142 (clofazimine, all p < 10-7.7). We observed that artefactual signals of cross-resistance could be unravelled based on the relative effect size on MIC. Our study demonstrates the ability of very large-scale studies to substantially improve our knowledge of genetic variants associated with antimicrobial resistance in M. tuberculosis.

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis

With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

A convolutional neural network highlights mutations relevant to antimicrobial resistance in Mycobacterium tuberculosis

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability.

Using machine learning techniques to predict antimicrobial resistance in stone disease patients

PURPOSE

Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predicting bacterial resistance in a urology department.

METHODS

Data were retrieved from laboratory information system (LIS) concerning 239 patients with urolithiasis hospitalized in the urology department of a tertiary hospital over a 1-year period (2019): age, gender, Gram stain (positive, negative), bacterial species, sample type, antibiotics and antimicrobial susceptibility. In our experiments, we compared several classifiers following a tenfold cross-validation approach on 2 different versions of our dataset; the first contained only information of Gram stain, while the second had knowledge of bacterial species.

RESULTS

The best results in the balanced dataset containing Gram stain, achieve a weighted average receiver operator curve (ROC) area of 0.768 and F-measure of 0.708, using a multinomial logistic regression model with a ridge estimator. The corresponding results of the balanced dataset, that contained bacterial species, achieve a weighted average ROC area of 0.874 and F-measure of 0.783, with a bagging classifier.

CONCLUSIONS

Artificial intelligence technology can be used for making predictions on antibiotic resistance patterns when knowing Gram staining with an accuracy of 77% and nearly 87% when identifying specific microorganisms. This knowledge can aid urologists prescribing the appropriate antibiotic 24-48 h before test results are known.

Molecular Determinants of Ethionamide Resistance in Clinical Isolates of Mycobacterium tuberculosis

Background: Ethionamide and prothionamide are now included in group C of the WHO recommended drugs for the treatment of tuberculosis resistant to rifampicin and multidrug-resistant tuberculosis. The clinical relevance of ethionamide and prothionamide has increased with the wide spread of resistant tuberculosis. Methods: We retrospectively analyzed 349 clinical isolates obtained between 2016 and 2020. The susceptibility to ethionamide was tested using both the Bactec^TM MGIT^TM 960 system and the Sensititre^TM MYCOTB plate. Results: The MIC of ethionamide increases with the total resistance of the isolates in a row from susceptible to XDR strains. A significant part of the isolates have a MIC below the breakpoint: 25%, 36%, and 50% for XDR, pre-XDR, and MDR strains. Sensitivity and specificity of detection of mutations were 96% and 86% using MGIT resistance as a reference. Conclusions: Phenotypic methods for testing ethionamide are imperfectly correlated, and the isolates with MIC of 5 mg/L have the intermediate resistance. A significant proportion of resistant TB cases are susceptible and eligible for ethionamide treatment. Resistance could be explained using only analysis of loci ethA, P_fabG1, and inhA for most isolates in the Moscow region. The promoter mutation P_fabG1 c(-15)t predicts resistance to ethionamide with high specificity but low sensitivity.

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis

BACKGROUND

Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling.

RESULTS

We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%).

CONCLUSION

Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Whole-Genome Sequencing to Identify Missed Rifampicin and Isoniazid Resistance Among Tuberculosis Isolates-Chennai, India, 2013-2016

India has a high burden of drug-resistant tuberculosis (DR TB) and many cases go undetected by current drug susceptibility tests (DSTs). This study was conducted to identify rifampicin (RIF) and isoniazid (INH) resistance associated genetic mutations undetected by current clinical diagnostics amongst persons with DR TB in Chennai, India. Retrospectively stored 166 DR TB isolates during 2013–2016 were retrieved and cultured in Löwenstein-Jensen medium. Whole genome sequencing (WGS) and MGIT DST for RIF and INH were performed. Discordant genotypic and phenotypic sensitivity results were repeated for confirmation and the discrepant results considered final. Further, drug resistance-conferring mutations identified through WGS were analyzed for their presence as targets in current WHO-recommended molecular diagnostics. WGS detected additional mutations for rifampicin and isoniazid resistance than WHO-endorsed line probe assays. For RIF, WGS was able to identify an additional 10% (15/146) of rpoB mutant isolates associated with borderline rifampicin resistance compared to MGIT DST. WGS could detect additional DR TB cases than commercially available and WHO-endorsed molecular DST tests. WGS results reiterate the importance of the recent WHO revised critical concentrations of current MGIT DST to detect low-level resistance to rifampicin. WGS may help inform effective treatment selection for persons at risk of, or diagnosed with, DR TB.

Wastewater-Based Surveillance of Antibiotic Resistance Genes Associated with Tuberculosis Treatment Regimen in KwaZulu Natal, South Africa

Essential components of public health include strengthening the surveillance of infectious diseases and developing early detection and prevention policies. This is particularly important for drug-resistant tuberculosis (DR-TB), which can be explored by using wastewater-based surveillance. This study aimed to use molecular techniques to determine the occurrence and concentration of antibiotic-resistance genes (ARGs) associated with tuberculosis (TB) resistance in untreated and treated wastewater. Raw/untreated and treated (post-chlorination) wastewater samples were taken from three wastewater treatment plants (WWTPs) in South Africa. The ARGs were selected to target drugs used for first- and second-line TB treatment. Both conventional polymerase chain reaction (PCR) and the more advanced droplet digital PCR (ddPCR) were evaluated as surveillance strategies to determine the distribution and concentration of the selected ARGs. The most abundant ARG in the untreated wastewater was the rrs gene, associated with resistance to the aminoglycosides, specifically streptomycin, with median concentration ranges of 4.69–5.19 log copies/mL. In contrast, pncA gene, associated with resistance to the TB drug pyrazinamide, was the least detected (1.59 to 2.27 log copies/mL). Resistance genes associated with bedaquiline was detected, which is a significant finding because this is a new drug introduced in South Africa for the treatment of multi-drug resistant TB. This study, therefore, establishes the potential of molecular surveillance of wastewater for monitoring antibiotic resistance to TB treatment in communities.

Application of Machine Learning Classifier to Candida auris Drug Resistance Analysis

Candida auris (C. auris) is an emerging fungus associated with high morbidity. It has a unique transmission ability and is often resistant to multiple drugs. In this study, we evaluated the ability of different machine learning models to classify the drug resistance and predicted and ranked the drug resistance mutations of C. auris. Two C. auris strains were obtained. Combined with other 356 strains collected from the European Bioinformatics Institute (EBI) databases, the whole genome sequencing (WGS) data were analyzed by bioinformatics. Machine learning classifiers were used to build drug resistance models, which were evaluated and compared by various evaluation methods based on AUC value. Briefly, two strains were assigned to Clade III in the phylogenetic tree, which was consistent with previous studies; nevertheless, the phylogenetic tree was not completely consistent with the conclusion of clustering according to the geographical location discovered earlier. The clustering results of C. auris were related to its drug resistance. The resistance genes of C. auris were not under additional strong selection pressure, and the performance of different models varied greatly for different drugs. For drugs such as azoles and echinocandins, the models performed relatively well. In addition, two machine learning algorithms, based on the balanced test and imbalanced test, were designed and evaluated; for most drugs, the evaluation results on the balanced test set were better than on the imbalanced test set. The mutations strongly be associated with drug resistance of C. auris were predicted and ranked by Recursive Feature Elimination with Cross-Validation (RFECV) combined with a machine learning classifier. In addition to known drug resistance mutations, some new resistance mutations were predicted, such as Y501H and I466M mutation in the ERG11 gene and R278H mutation in the ERG10 gene, which may be associated with fluconazole (FCZ), micafungin (MCF), and amphotericin B (AmB) resistance, respectively; these mutations were in the “hot spot” regions of the ergosterol pathway. To sum up, this study suggested that machine learning classifiers are a useful and cost-effective method to identify fungal drug resistance-related mutations, which is of great significance for the research on the resistance mechanism of C. auris.

Mycobacterial Lineages Associated with Drug Resistance in Patients with Extrapulmonary Tuberculosis in Addis Ababa, Ethiopia

BACKGROUND

In Ethiopia, tuberculosis (TB) is one of the most common causes of illness and death. However, there is limited information available on lineages associated with drug resistance among extrapulmonary tuberculosis patients in Ethiopia. In this study, researchers looked into Mycobacterium tuberculosis lineages linked to drug resistance in patients with extrapulmonary tuberculosis in Addis Ababa, Ethiopia.

METHODS

On 151 Mycobacterium tuberculosis isolates, a cross-sectional analysis was performed. Spoligotyping was used to characterize mycobacterial lineages, while a phenotypic drug susceptibility test was performed to determine the drug resistance pattern. Data were analyzed using SPSS version 23.

RESULTS

Among 151 Mycobacterium tuberculosis complex (MTBC) genotyped isolates, four lineages (L1-L4), and Mycobacterium bovis were identified. The predominantly identified lineage was Euro-American (73.5%) followed by East-African-Indian (19.2%). Any drug resistance (RR) and multidrug-resistant (MDR) tuberculosis was identified among 16.2% and 7.2% of the Euro-American lineage, respectively, while it was 30.8% and 15.4% among the East-African-Indian lineages. Among all three preextensively drug-resistance (pre-XDR) cases identified, two isolates belong to T3-ETH, and the other one strain was not defined by the database. There was no statistically significant association between any type of drug resistance and either lineage or sublineages of Mycobacterium tuberculosis.

CONCLUSION

A higher proportion of any type of drug resistance and MDR was detected among the East-African-Indian lineage compared to others. However, there was no statistically significant association between any type of drug resistance and either lineages or sublineages. Thus, the authors recommend a large-scale study.

Prevalence and Molecular Characteristics Based on Whole Genome Sequencing of Mycobacterium tuberculosis Resistant to Four Anti-Tuberculosis Drugs from Southern Xinjiang, China

Objective

Drug-resistant tuberculosis is a major public health problem, especially in the southern region of Xinjiang, China; however, there is little information regarding drug resistance profiles and mechanism of Mycobacterium tuberculosis in this area. The aim of this study was to determine the prevalence and molecular characteristics of M. tuberculosis resistant to four anti-tuberculosis drugs from this area.

Methods

Three hundred and forty-six isolates from the southern region of Xinjiang, China were included and used to perform phenotypic drug susceptibility testing and whole genome sequencing (WGS). Mutations in seven loci associated with drug resistance, including rpoB for rifampicin (RMP), katG, inhA promoter and oxyR-ahpC for isoniazid (INH), rrs 530 and 912 loops and rpsL for streptomycin (STR), and embB for ethambutol (EMB), were characterized.

Results

Among 346 isolates, 106, 60, 70 and 29 were resistant to INH, RMP, STR and EMB, respectively; 132 were resistant to at least one of the four anti-tuberculosis drugs and 51 were multi-drug resistant (MDR). Beijing genotype and retreated patients showed a significantly increased risk for developing MDR tuberculosis. Compared with the phenotypic data, the sensitivity and specificity for WGS to predict resistance were 96.7% and 98.6% for RMP, 75.5% and 97.1% for INH, 68.6% and 99.6% for STR, 93.1% and 93.7% for EMB, respectively. The most common mutations conferring RMP, INH, STR and EMB resistance were Ser450Leu (51.7%) in rpoB, Ser315Thr (44.3%) in katG, Lys43Arg (35.7%) in rpsL and Met306Val (24.1%) in embB.

Conclusion

This study provides the first information on the prevalence and molecular characters of drug resistant M. tuberculosis in the southern region of Xinjiang, China, which will be helpful for choosing early detection methods for drug resistance (ig, molecular methods) and subsequently initiation of proper therapy of tuberculosis in this area.

GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning

BACKGROUND

Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens.

RESULTS

We present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB's predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6-78.5%) and 75.4% (95% CI 74.5-76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4-75.3%) and Mykrobe at 71.9% (95% CI 70.9-72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5-97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants CONCLUSIONS: GenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu , and the source code is available at https://github.com/farhat-lab/gentb-site .

The within-host evolution of antimicrobial resistance in Mycobacterium tuberculosis

Tuberculosis (TB) has been responsible for the greatest number of human deaths due to an infectious disease in general, and due to antimicrobial resistance (AMR) in particular. The etiological agents of human TB are a closely-related group of human-adapted bacteria that belong to the Mycobacterium tuberculosis complex (MTBC). Understanding how MTBC populations evolve within-host may allow for improved TB treatment and control strategies. In this review, we highlight recent works that have shed light on how AMR evolves in MTBC populations within individual patients. We discuss the role of heteroresistance in AMR evolution, and review the bacterial, patient and environmental factors that likely modulate the magnitude of heteroresistance within-host. We further highlight recent works on the dynamics of MTBC genetic diversity within-host, and discuss how spatial substructures in patients' lungs, spatiotemporal heterogeneity in antimicrobial concentrations and phenotypic drug tolerance likely modulates the dynamics of MTBC genetic diversity in patients during treatment. We note the general characteristics that are shared between how the MTBC and other bacterial pathogens evolve in humans, and highlight the characteristics unique to the MTBC.

Performance of QuantaMatrix Microfluidic Agarose Channel system integrated with mycobacteria growth indicator tube liquid culture

The QuantaMatrix Microfluidic Agarose Channel (QMAC) system was used for rapid drug susceptibility testing (DST). Here, we performed DST using QMAC integrated with the mycobacteria growth indicator tube (MGIT) liquid culture employing a specially designed cross agarose channel for the tuberculosis chip. MGIT-, QMAC-, and Löwenstein-Jensen (LJ)-DSTs were performed using 13 drugs. The protocol for QMAC-DST was optimized using the inoculum obtained after the disaggregation of Mycobacterium tuberculosis clumps in MGIT culture. The completion times of QMAC-DST and MGIT-DST were analyzed, and the results of all three DSTs were compared. Discrepant results were analyzed using line probe assays and DNA sequencing. Nontuberculous mycobacteria were distinguished using the ρ-nitrobenzoic acid inhibition test. The overall agreement rate of QMAT-DST and LJ-DST was 97.0% and that of QMAT-DST and MGIT-DST was 86.3%. An average turnaround time for DST was 5.4 days, which was considerably less than the time required for MGIT-DST. The overall time required to obtain DST results using QMAC-DST integrated with MGIT culture was an average of 18.6 days: 13.2 days for culture and identification and 5.4 days for DST. Hence, QMAC-DST integrated with liquid culture can be used to perform DSTs with short turnaround times and effective detection. KEY POINTS: • QMAC system can simultaneously perform phenotypic DST with 13 anti-TB drugs and PNB. • An optimized DST protocol led to a marked decrease in clumping in MGIT culture. • QMAC system integrated with MGIT liquid culture system reduced the turnaround time.

Systematic review of mutations associated with resistance to the new and repurposed Mycobacterium tuberculosis drugs bedaquiline, clofazimine, linezolid, delamanid and pretomanid

BACKGROUND

Improved genetic understanding of Mycobacterium tuberculosis (MTB) resistance to novel and repurposed anti-tubercular agents can aid the development of rapid molecular diagnostics.

METHODS

Adhering to PRISMA guidelines, in March 2018, we performed a systematic review of studies implicating mutations in resistance through sequencing and phenotyping before and/or after spontaneous resistance evolution, as well as allelic exchange experiments. We focused on the novel drugs bedaquiline, delamanid, pretomanid and the repurposed drugs clofazimine and linezolid. A database of 1373 diverse control MTB whole genomes, isolated from patients not exposed to these drugs, was used to further assess genotype-phenotype associations.

RESULTS

Of 2112 papers, 54 met the inclusion criteria. These studies characterized 277 mutations in the genes atpE, mmpR, pepQ, Rv1979c, fgd1, fbiABC and ddn and their association with resistance to one or more of the five drugs. The most frequent mutations for bedaquiline, clofazimine, linezolid, delamanid and pretomanid resistance were atpE A63P, mmpR frameshifts at nucleotides 192-198, rplC C154R, ddn W88* and ddn S11*, respectively. Frameshifts in the mmpR homopolymer region nucleotides 192-198 were identified in 52/1373 (4%) of the control isolates without prior exposure to bedaquiline or clofazimine. Of isolates resistant to one or more of the five drugs, 59/519 (11%) lacked a mutation explaining phenotypic resistance.

CONCLUSIONS

This systematic review supports the use of molecular methods for linezolid resistance detection. Resistance mechanisms involving non-essential genes show a diversity of mutations that will challenge molecular diagnosis of bedaquiline and nitroimidazole resistance. Combined phenotypic and genotypic surveillance is needed for these drugs in the short term.

Resistance-Conferring Mutations on Whole-Genome Sequencing of Fluoroquinolone-resistant and -Susceptible Mycobacterium tuberculosis Isolates: A Proposed Threshold for Identifying Resistance

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.

First description of the katG gene deletion in a Mycobacterium tuberculosis clinical isolate and its impact on the mycobacterial fitness

Isoniazid (INH) is the cornerstone of the anti-tuberculosis regimens and emergence of Mycobacterium tuberculosis (Mtb) resistant strains is a major threat to our ability to control tuberculosis (TB) at global level. Mutations in the gene coding the catalase KatG confer resistance to high level of INH. In this paper, we describe for the first time a complete deletion of the genomic region containing the katG gene in an Mtb clinical strain isolated in Italy in a patient with HIV infection that previously completed INH preventive therapy. We genotypically characterized the Mtb strain and showed that katG deletion confers high-level resistance to INH (MIC > 25.6 μg/mL). The katG deletion did not impact significantly on Mtb fitness as we did not detect enhanced susceptibility to H₂O₂ compared to the wild type Mtb strains nor impaired growth in in vitro infection models. These findings highlight the ability of Mtb to acquire resistance to INH while maintaining fitness and pathogenic potential.

Microbial Genomics as a Catalyst for Targeted Antivirulence Therapeutics

Virulence arresting drugs (VAD) are an expanding class of antimicrobial treatment that act to “disarm” rather than kill bacteria. Despite an increasing number of VAD being registered for clinical use, uptake is hampered by the lack of methods that can identify patients who are most likely to benefit from these new agents. The application of pathogen genomics can facilitate the rational utilization of advanced therapeutics for infectious diseases. The development of genomic assessment of VAD targets is essential to support the early stages of VAD diffusion into infectious disease management. Genomic identification and characterization of VAD targets in clinical isolates can augment antimicrobial stewardship and pharmacovigilance. Personalized genomics guided use of VAD will provide crucial policy guidance to regulating agencies, assist hospitals to optimize the use of these expensive medicines and create market opportunities for biotech companies and diagnostic laboratories.

Tuberculosis Drug Susceptibility, Treatment, and Outcomes for Belarusian HIV-Positive Patients with Tuberculosis: Results from a National and International Laboratory

BACKGROUND

To cure drug-resistant (DR) tuberculosis (TB), the antituberculous treatment should be guided by Mycobacterium tuberculosis drug-susceptibility testing (DST). In this study, we compared conventional DST performed in Minsk, Belarus, a TB DR high-burden country, with extensive geno- and phenotypic analyses performed at the WHO TB Supranational Reference Laboratory in Copenhagen, Denmark, for TB/HIV coinfected patients. Subsequently, DST results were related to treatment regimen and outcome.

METHODS

Thirty TB/HIV coinfected patients from Minsk were included and descriptive statistics applied.

RESULTS

Based on results from Minsk, 10 (33%) TB/HIV patients had drug-sensitive TB. Two (7%) had isoniazid monoresistant TB, 8 (27%) had multidrug-resistant (MDR) TB, 5 (17%) preextensive drug-resistant (preXDR) TB, and 5 (17%) had extensive drug-resistant (XDR) TB. For the first-line drugs rifampicin and isoniazid, there was DST agreement between Minsk and Copenhagen for 90% patients. For the second-line anti-TB drugs, discrepancies were more pronounced. For 14 (47%) patients, there were disagreements for at least one drug, and 4 (13%) patients were classified as having MDR-TB in Minsk but were classified as having preXDR-TB based on DST results in Copenhagen. Initially, all patients received standard anti-TB treatment with rifampicin, isoniazid, pyrazinamide, and ethambutol. However, this was only suitable for 40% of the patients based on DST. On average, DR-TB patients were changed to 4 (IQR 3-5) active drugs after 1.5 months (IQR 1-2). After treatment adjustment, the treatment duration was 8 months (IQR 2-11). Four (22%) patients with DR-TB received treatment for >18 months. In total, sixteen (53%) patients died during 24 months of follow-up.

CONCLUSIONS

We found high concordance for rifampicin and isoniazid DST between the Minsk and Copenhagen laboratories, whereas discrepancies for second-line drugs were more pronounced. For patients with DR-TB, treatment was often insufficient and relevant adjustments delayed. This example from Minsk, Belarus, underlines two crucial points in the management of DR-TB: the urgent need for implementation of rapid molecular DSTs and availability of second-line drugs in all DR-TB high-burden settings. Carefully designed individualized treatment regimens in accordance with DST patterns will likely improve patients' outcome and reduce transmission with drug-resistant Mycobacterium tuberculosis strains.

Exploratory development of PCR-fluorescent probes in rapid detection of mutations associated with extensively drug-resistant tuberculosis

This study aims to evaluate the clinical value of PCR-fluorescent probes for detecting the mutation gene associated with extensively drug-resistant tuberculosis (XDR-TB). The molecular species identification of 900 sputum specimens was performed using polymerase chain reaction (PCR)-fluorescent probe. The mutations of the drug resistance genes rpoB, katG, inhA, embB, rpsL, rrs, and gyrA were detected. The conventional drug susceptibility testing (DST) and PCR-directed sequencing (PCR-DS) were carried out as control. DST demonstrated that there were 501 strains of rifampicin resistance, 451 strains of isoniazid resistance, 293 strains of quinolone resistance, 425 strains of streptomycin resistance, 235 strains of ethambutol resistance, and 204 strains of amikacin resistance. Furthermore, 427 (47.44%) or 146 (16.22%) strains were MDR-TB or XDR-TB, respectively. The mutations of the rpoB, katG, inhA, embB, rpsL, rrs, and gyrA genes were detected in 751 of 900 TB patients by PCR-fluorescent probe method, and the rate of drug resistance was 751/900 (83.44%). No mutant genes were detected in the other 149 patients. Compared with DST, the mutant rates of rpoB, katG/inhA, rpsL, rrs, embB, and gyrA of six drugs were higher than 88%; five of six drugs were higher than 90% except for SM (88.11%). The MDR and XDR mutant gene types were found in 398 (42.22%) and 137 (15.22%) samples. PCR-DS was also employed and confirmed the PCR-fluorescent probe method with the accordance rate of 100%. The PCR-fluorescent probe method is rapid and straightforward in detecting XDR-TB genotypes and is worthy of being applied in hospitals.

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

In-host population dynamics of Mycobacterium tuberculosis complex during active disease

Tuberculosis (TB) is a leading cause of death globally. Understanding the population dynamics of TB’s causative agent Mycobacterium tuberculosis complex (Mtbc) in-host is vital for understanding the efficacy of antibiotic treatment. We use longitudinally collected clinical Mtbc isolates that underwent Whole-Genome Sequencing from the sputa of 200 patients to investigate Mtbc diversity during the course of active TB disease after excluding 107 cases suspected of reinfection, mixed infection or contamination. Of the 178/200 patients with persistent clonal infection >2 months, 27 developed new resistance mutations between sampling with 20/27 occurring in patients with pre-existing resistance. Low abundance resistance variants at a purity of ≥19% in the first isolate predict fixation in the subsequent sample. We identify significant in-host variation in 27 genes, including antibiotic resistance genes, metabolic genes and genes known to modulate host innate immunity and confirm several to be under positive selection by assessing phylogenetic convergence across a genetically diverse sample of 20,352 isolates.

Globally diverse Mycobacterium tuberculosis resistance acquisition: a retrospective geographical and temporal analysis of whole genome sequences

Background

Mycobacterium tuberculosis whole genome sequencing (WGS) data can provide insights into temporal and geographical trends in resistance acquisition and inform public health interventions. We aimed to use a large clinical collection of M tuberculosis WGS and resistance phenotype data to study how, when, and where resistance was acquired on a global scale.

Methods

We did a retrospective analysis of WGS data. We curated a set of clinical M tuberculosis isolates with high-quality sequencing and culture-based drug susceptibility data (spanning four lineages and 52 countries in Africa, Asia, the Americas, and Europe) using public databases and literature curation. For inclusion, sequence quality criteria and country of origin data were required. We constructed geographical and lineage specific M tuberculosis phylogenies and used Bayesian molecular dating with BEAST, version 1.10.4, to infer the most recent common susceptible ancestor age for 4869 instances of resistance to ten drugs.

Findings

Between Jan 1, 1987, and Sept 12, 2014, of 10 299 M tuberculosis clinical isolates, 8550 were curated, of which 6099 (71%) from 15 countries met criteria for molecular dating. The number of independent resistance acquisition events was lower than the number of resistant isolates across all countries, suggesting ongoing transmission of drug resistance. Ancestral age distributions supported the presence of old resistance, 20 years or more before, in most countries. A consistent order of resistance acquisition was observed globally starting with resistance to isoniazid, but resistance ancestral age varied by country. We found a direct correlation between gross domestic product per capita and resistance age (r ²=0·47; p=0·014). Amplification of fluoroquinolone and second-line injectable resistance among multidrug-resistant isolates is estimated to have occurred very recently (median ancestral age 4·7 years [IQR 1·9-9·8] before sample collection). We found the sensitivity of commercial molecular diagnostics for second-line resistance to vary significantly by country (p<0·0003).

Interpretation

Our results highlight that both resistance transmission and amplification are contributing to disease burden globally but vary by country. The observation that wealthier nations are more likely to have old resistance (most recent common susceptible ancestor >20 years before isolation) suggests that programmatic improvements can reduce resistance amplification, but that fit resistant strains can circulate for decades subsequently implies the need for continued surveillance.

Abattoir survey of bovine tuberculosis in tanta, centre of the Nile delta, with in silico analysis of gene mutations and protein-protein interactions of the involved mycobacteria

Bovine tuberculosis is a transboundary disease of high economic and public health burden worldwide. In this study, post-mortem examination of 750 cattle and buffalo in Tanta abattoir, Centre of the Nile Delta, revealed visible TB in 4% of animals and a true prevalence of 6.85% (95% CI: 5.3%-8.9%). Mycobacterial culture, histopathology and RT-PCR targeting all members of M. tuberculosis complex were performed, upon which 85%, 80% and 100% of each tested lesions were confirmed as TB, respectively. Mpb70-targeting PCR was conducted on ten RT-PCR positive samples for sequencing and identified nine Mycobacterium (M.) bovis strains and, interestingly, one M. tuberculosis (Mtb) strain from a buffalo. Bioinformatics tools were used for prediction of mutations, nucleotide polymorphisms, lineages, drug resistance and protein-protein interactions (PPI) of the sequenced strains. The Mtb strain was resistant to rifampicin, isoniazid and streptomycin, and to the best of our knowledge, this is the first report of multidrug resistant (MDR)-Mtb originating from buffaloes. Seven M. bovis strains were resistant to ethambutol and ethionamide. Such resistances were associated with KatG, rpoB, rpsL, embB and ethA genes mutations. Other mutations and nucleotide polymorphisms were also predicted, some are reported for the first time and require experimental work for validation. PPI revealed more interactions than what would be expected for a random set of proteins of similar size and had dense interactions between nodes that are biologically connected, as a group. Two M. bovis strains belonged to BOV AFRI lineage (Spoligotypes BOV 1; BOV 2) and eight strains belonged to East-Asian (Beijing) lineage. In conclusion, visible TB was prevalent in the study area, RT-PCR is the best to confirm the disease, MDR-Mtb is associated with buffalo TB, and mycobacteria of different lineages carry many resistance genes to chemotherapeutic agents used in treatment of human TB constituting a major public health risk.

The recent trend in mycobacterial strain diversity among extra pulmonary lymph node tuberculosis and their association with drug resistance and the host immunological response in South India

Background

Tuberculosis (TB) though primarily affects the lungs it may also affect the other parts of the body and referred as extra pulmonary (EPTB). This study is focused on understanding the genetic diversity and molecular epidemiology of Mycobacterium tuberculosis (M.tb) among tuberculous lymphadenitis (TBL), a form of EPTB patients identified in Chennai, Tamil Nadu.

Methods

The genetic diversity was identified by performing spoligotyping on the M.tb clinical isolates that were recovered from lymph node samples. A total of 71 M.tb isolates were recovered from extra pulmonary lymph node samples and subjected to Drug susceptibility testing and spoligotyping was carried out. In addition, immunological characterization from blood of same individuals from whom M.tb was isolated was carried out between the two major lineages groups East African Indian 3 (EAI3) and non-EAI3 strains by ELISA. The results of spoligotyping patterns were compared with the world Spoligotyping Database of Institute Pasteur de Guadeloupe (SpolDB4).

Results

We found 41 spoligotype patterns and their associated lineages. Out of 41 spoligotype pattern, only 22 patterns are available in the spoldB4 database with Spoligotype international Type (SIT) number and remaining patterns were orphan strains without SIT number. The most predominant spoligotype lineage that was found in lymph node sample in this region of India was EAI (36), followed by central Asian strain (CAS) (6), T1 (5), Beijing (3), Latin American & Mediterranean (LAM) (2), U (1), X2 (1) and orphan (22). In addition to EAI, CAS and Beijing, our study identified the presence of orphan and unique spoligotyping patterns in Chennai region. We observed six drug resistant isolates. Out of six drug resistant isolates, four were resistant to isoniazid drug and associated with EAI family. Moreover, we observed increased levels of type 2 and type 17 cytokine profiles between EAI3 and non-EAI family, infected individuals.

Conclusions

The study confirms that EAI lineage to be the most predominant lineages in EPTB patients with lymphadenitis and were found to have increased type 1 and type 17 proinflammatory cytokine profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-020-05597-0.

Rapid and Simple Detection of Isoniazid-Resistant Mycobacterium tuberculosis Utilizing a DNA Chromatography-Based Technique

Despite the availability of anti-tuberculosis drugs, the treatment of tuberculosis has been complicated by drug-resistant tuberculosis. The early detection of drug resistance makes early treatment possible. However, the available tools are mainly for rifampicin resistance detection, and the existing isoniazid resistance detection method is expensive, highly technical, and complicated, making it unsustainable for use in developing nations. This study aimed to develop a simple, rapid, and low-cost diagnostic kit for isoniazid-resistant tuberculosis using the single-stranded tag hybridization method to target an isoniazid resistance-conferring mutation. Specificity and sensitivity were assessed using DNA extracted from 49 isoniazid-resistant and 41 isoniazid-susceptible Mycobacterium tuberculosis clinical isolates cultured in mycobacterial growth indicator tubes. Positive signals were observed on mutant and wild-type lines with 100% sensitivity and specificity compared with Sanger sequencing results. In contrast, no positive signal was observed for non-tuberculosis mycobacteria. The detection limit of this method was 10³ CFU or less. The STH-PAS system for isoniazid-resistant M. tuberculosis detection developed in this study offers a better alternative to conventional phenotypic isoniazid resistance determination, which will be of both clinical and epidemiological significance in resource-limited nations.

Interpreting k-mer-based signatures for antibiotic resistance prediction

BACKGROUND

Recent years have witnessed the development of several k-mer-based approaches aiming to predict phenotypic traits of bacteria on the basis of their whole-genome sequences. While often convincing in terms of predictive performance, the underlying models are in general not straightforward to interpret, the interplay between the actual genetic determinant and its translation as k-mers being generally hard to decipher.

RESULTS

We propose a simple and computationally efficient strategy allowing one to cope with the high correlation inherent to k-mer-based representations in supervised machine learning models, leading to concise and easily interpretable signatures. We demonstrate the benefit of this approach on the task of predicting the antibiotic resistance profile of a Klebsiella pneumoniae strain from its genome, where our method leads to signatures defined as weighted linear combinations of genetic elements that can easily be identified as genuine antibiotic resistance determinants, with state-of-the-art predictive performance.

CONCLUSIONS

By enhancing the interpretability of genomic k-mer-based antibiotic resistance prediction models, our approach improves their clinical utility and hence will facilitate their adoption in routine diagnostics by clinicians and microbiologists. While antibiotic resistance was the motivating application, the method is generic and can be transposed to any other bacterial trait. An R package implementing our method is available at https://gitlab.com/biomerieux-data-science/clustlasso.

How To Optimally Combine Genotypic and Phenotypic Drug Susceptibility Testing Methods for Pyrazinamide

False-susceptible phenotypic drug-susceptibility testing (DST) results for pyrazinamide due to mutations with MICs close to the critical concentration (CC) confound the classification of pncA resistance mutations, leading to an underestimate of the specificity of genotypic DST. This could be minimized by basing treatment decisions on well-understood mutations and by adopting an area of technical uncertainty for phenotypic DST rather than only testing the CC, as is current practice for the Mycobacterium tuberculosis complex.

Rifampicin and rifabutin resistance in 1003 Mycobacterium tuberculosis clinical isolates

OBJECTIVES

Drug-resistant TB remains a public health challenge. Rifamycins are among the most potent anti-TB drugs. They are known to target the RpoB subunit of RNA polymerase; however, our understanding of how rifamycin resistance is genetically encoded remains incomplete. Here we investigated rpoB genetic diversity and cross-resistance between the two rifamycin drugs rifampicin and rifabutin.

METHODS

We performed WGS of 1003 Mycobacterium tuberculosis clinical isolates and determined MICs of both rifamycin agents on 7H10 agar using the indirect proportion method. We generated rpoB mutants in a laboratory strain and measured their antibiotic susceptibility using the alamarBlue reduction assay.

RESULTS

Of the 1003 isolates, 766 were rifampicin resistant and 210 (27%) of these were rifabutin susceptible; 102/210 isolates had the rpoB mutation D435V (Escherichia coli D516V). Isolates with discordant resistance were 17.2 times more likely to harbour a D435V mutation than those resistant to both agents (OR 17.2, 95% CI 10.5-27.9, P value <10-40). Compared with WT, the D435V in vitro mutant had an increased IC50 of both rifamycins; however, in both cases to a lesser degree than the S450L (E. coli S531L) mutation.

CONCLUSIONS

The observation that the rpoB D435V mutation produces an increase in the IC50 of both drugs contrasts with findings from previous smaller studies that suggested that isolates with the D435V mutation remain rifabutin susceptible despite being rifampicin resistant. Our finding thus suggests that the recommended critical testing concentration for rifabutin should be revised.

Genomic Analysis Identifies Mutations Concerning Drug-Resistance and Beijing Genotype in Multidrug-Resistant Mycobacterium tuberculosis Isolated From China

Development of modern genomics provides us an effective method to understand the molecular mechanism of drug resistance and diagnose drug-resistant Mycobacterium tuberculosis. In this study, mutations in 18 genes or intergenic regions acquired by whole-genome sequencing (WGS) of 183 clinical M. tuberculosis strains, including 137 multidrug-resistant and 46 pan-susceptible isolates from China, were identified and used to analyze their associations with resistance of isoniazid, rifampin, ethambutol, and streptomycin. Using the proportional method as the gold standard method, the accuracy values of WGS to predict resistance were calculated. The association between synonymous or lineage definition mutations with different genotypes were also analyzed. The results show that, compared to the phenotypic proportional method, the sensitivity and specificity of WGS for resistance detection were 94.2 and 100.0% for rifampicin (based on mutations in rpoB), 90.5 and 97.8% for isoniazid (katG), 83.0 and 97.8% for streptomycin (rpsL combined with rrs 530 loop and 912 loop), and 90.9 and 65.1% for ethambutol (embB), respectively. WGS data also showed that mutations in the inhA promoter increased only 2.2% sensitivity for INH based on mutations in katG. Synonymous mutation rpoB A1075A was confirmed to be associated with the Beijing genotype. This study confirmed that mutations in rpoB, katG, rrs 530 loop and 912 loop, and rpsL were excellent biomarkers for predicting rifampicin, isoniazid, and streptomycin resistance, respectively, and provided clues in clarifying the drug-resistance mechanism of M. tuberculosis isolates from China.

A novel risk factor for predicting anti-tuberculosis drug resistance in patients with tuberculosis complicated with type 2 diabetes mellitus

OBJECTIVES

This study aimed to explore the relationship between glycosylated hemoglobin (HbA1c) and the risk of anti-tuberculosis (TB) drug resistance for TB-type 2 diabetes mellitus (T2DM) patients.

METHODS

From March 2014 to June 2019, medical records from multiple centers were searched. Logistic regression analyses were performed. A predictive model for multidrug-resistance (MDR) was developed and validated. Calibration and discrimination of the model were assessed.

RESULTS

Inconsistent results were found in the systemic review. A multicenter chart review with 657 records was thus conducted. The HbA1c <7% group and HbA1c ≥7% group had 390 and 267 patients, respectively. The HbA1c<7% group had a lower risk of developing rifampicin resistance, isoniazid resistance and MDR, with odd ratios (ORs) of 1.904 (p=0.001), 2.896 (p<0.001) and 3.228 (p<0.001), respectively. The between-group differences in the risk of anti-TB drug resistance were analyzed based on data from three provinces in China. After adding HbA1c grading, the predictive model for MDR (https://mengyuan.shinyapps.io/Shinyapp/) showed excellent capacity with an AUC of 75.4% in the training set (Sichuan and Gansu) and 73.9% in the internal validation set (Henan). The performances in calibration, prediction probabilities and net clinical benefit were significantly improved by HbA1c grading.

CONCLUSIONS

HbA1c grading was an independent risk factor for isoniazid resistance and MDR in TB-T2DM patients.

Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking

Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 Mycobacterium tuberculosis isolates. Results confirmed that MLRFs can improve performance compared to conventional clinical methods (by 18.10%) and SLRFs (by 0.91%). In addition, we identified a list of candidate mutations that are important for resistance prediction or that are related to resistance co-occurrence. Moreover, we found that retraining our analysis to a subset of top-ranked mutations was sufficient to achieve satisfactory performance. The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php.

Accuracy of a reverse dot blot hybridization assay for simultaneous detection of the resistance of four anti-tuberculosis drugs in Mycobacterium tuberculosis isolated from China

Background

Drug resistant tuberculosis poses a great challenge for tuberculosis control worldwide. Timely determination of drug resistance and effective individual treatment are essential for blocking the transmission of drug resistant Mycobacterium tuberculosis. We aimed to establish and evaluate the accuracy of a reverse dot blot hybridization (RDBH) assay to simultaneously detect the resistance of four anti-tuberculosis drugs in M. tuberculosis isolated in China.

Methods

In this study, we applied a RDBH assay to simultaneously detect the resistance of rifampicin (RIF), isoniazid (INH), streptomycin (SM) and ethambutol (EMB) in 320 clinical M. tuberculosis isolates and compared the results to that from phenotypic drug susceptibility testing (DST) and sequencing. The RDBH assay was designed to test up to 42 samples at a time. Pearson’s chi-square test was used to compute the statistical measures of the RDBH assay using the phenotypic DST or sequencing as the gold standard method, and Kappa identity test was used to determine the consistency between the RDBH assay and the phenotypic DST or sequencing.

Results

The results showed that the concordances between phenotypic DST and RDBH assay were 95% for RIF, 92.8% for INH, 84.7% for SM, 77.2% for EMB and the concordances between sequencing and RDBH assay were 97.8% for RIF, 98.8% for INH, 99.1% for SM, 93.4% for EMB. Compared to the phenotypic DST results, the sensitivity and specificity of the RDBH assay for resistance detection were 92.4 and 98.5% for RIF, 90.3 and 97.3% for INH, 77.4 and 91.5% for SM, 61.4 and 85.7% for EMB, respectively; compared to sequencing, the sensitivity and specificity of the RDBH assay were 97.7 and 97.9% for RIF, 97.9 and 100.0% for INH, 97.8 and 100.0% for SM, 82.6 and 99.1% for EMB, respectively. The turnaround time of the RDBH assay was 7 h for testing 42 samples.

Conclusions

Our data suggested that the RDBH assay could serve as a rapid and efficient method for testing the resistance of M. tuberculosis against RIF, INH, SM and EMB, enabling early administration of appropriate treatment regimens to the affected drug resistant tuberculosis patients.

Performance of Genotype MTBDRsl V2.0 over the Genotype MTBDRsl V1 for detection of second line drug resistance: An Indian perspective

Genotype MTBDRsl Version 1 (V1.0) was recommended as an initial test for rapid detection of pre-extensively drug resistant (pre-XDR) and extensively drug resistant tuberculosis (XDR-TB). However, in recent years a number of novel mutations are identified that confer resistance. Thus, Genotype MTBDRsl V2.0 was endorsed by WHO. Though, Genotype MTBDRsl V2.0 has been rolled out in national TB programme in 2018, there is dearth of data from India on its performance for second line drug susceptibility testing (DST). For this, performance of new version was evaluated on 113 MDR-TB isolates. The results showed that 39 (34.5%) of these isolates were resistant to FQ and 7 (6.2%) were XDR by Genotype MTBDRsl V2.0. Amongst the FQ resistant isolates most prevalent mutation was ΔWT3-D94G (17; 38.6%) and N538D (12; 85.7%). Among the AG/CP and KAN resistant isolates most common mutation in the rrs region was ΔWT1-A1401G (5; 71.4%) and C-14T (2; 28.5%) in eis gene. Second line Bactec MGIT-960 detected 40 (35.4%) isolates as resistant to FQ and 6 (5.3%) as XDR isolates, whereas Genotype MTBDRsl V1.0 also detected 39 (34.5%) as resistant to FQ but missed 2 isolates in correctly identifying as XDR (5; 4.4%). Thus, concordance of second line Bactec MGIT-960 with Genotype MTBDRsl V2.0 was similar (100%) for FQ detection but it has improvised the diagnostic sensitivity for correctly identifying XDR isolates. Nevertheless, the cost of Genotype MTBDRsl V2.0 remains an issue for screening of second line drug (SLDs) resistance from countries with high burden of MDR-TB.