Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms

Whole genome sequencing for the prediction of resistant tuberculosis strains from northern India

PURPOSE

Tuberculosis is an important public health problem among infectious diseases. The problem becomes more concerning with the emergence of MDR-TB and pre-XDR-TB. Whole genome sequencing (WGS) detection of resistance has recently gained popularity as it has advantages over other commercial techniques.

METHODS

We performed in-house WGS followed by detailed analysis by an in-house pipeline to identify the resistance markers. This was accompanied by Phenotypic DST, and Sanger sequencing on all the 12 XDR, 06 pre-XDR, and 06 susceptible M. tb isolates. These results were collated with online M. tb WGS pipelines (TB profiler, PhyResSE, Mykrobe predictor) for comparative analysis.

RESULTS

Following our in-house analysis, we observed 64 non-synonymous SNPs, fifteen synonymous SNPs, and five INDELs in 25 drug resistance-associated genes/intergenic regions (IGRs) in M. tb isolates. Sensitivity for detecting XDR is 33%, 58%, 83%, and 83%, respectively, using Mykrobe predictor, PhyResSE, TB-profiler, and in-house pipeline for WGS analysis, respectively. TB-profiler detected a rare mutation H70R in the gyrA gene in one pre-XDR isolate. Lineage 2.2.1 East-Asian (Beijing sublineage type) predominated (60%) in WGS data analysis of the XDR isolates.

CONCLUSIONS

Our findings suggest that in-house analysis of WGS data and TB-profiler sensitivity was better for the detection of second-line resistance as compared to other automated tested tools. Frequent upgradation of newer mutations associated with resistance needs to be updated, as it potentiates tailored treatment for patients.

Mutational Dynamics Related to Antibiotic Resistance in M. tuberculosis Isolates from Serial Samples of Patients with Tuberculosis and Type 2 Diabetes Mellitus

Genetic variation in tuberculosis is influenced by the host environment, patients with comorbidity, and tuberculosis-type 2 diabetes mellitus (TB-T2DM) and implies a higher risk of treatment failure and development of drug resistance. Considering the above, this study aimed to evaluate the influence of T2DM on the dynamic of polymorphisms related to antibiotic resistance in TB. Fifty individuals with TB-T2DM and TB were initially characterized, and serial isolates of 29 of these individuals were recovered on day 0 (diagnosis), 30, and 60. Genomes were sequenced, variants related to phylogeny and drug resistance analyzed, and mutation rates calculated and compared between groups. Lineage X was predominant. At day 0 (collection), almost all isolates from the TB group were sensitive, apart from four isolates from the TB-T2DM group showing the mutation katG S315T, from which one isolate had the mutations rpoB S450L, gyrA A90G, and gyrA D94G. This pattern was observed in a second isolate at day 30. The results provide a first overview of the dynamics of mutations in resistance genes from individuals with TB-T2DM, describing an early development of resistance to isoniazid and a rapid evolution of resistance to other drugs. Although preliminary, these results help to explain the increased risk of drug resistance in individuals with TB and T2DM.

Genomic, phenotypic and demographic characterization of Mycobacterium tuberculosis in Israel in 2021

According to World Health Organization WHO, Tuberculosis (TB) is the second cause of death from infectious disease worldwide. During 2021, 10.6 million people were infected with TB, and 1.6 million people died. TB is caused by pathogens belonging to the Mycobacterium tuberculosis complex (MTBC), mainly by Mycobacterium tuberculosis (M.tb). Members of this complex are acid-fast bacilli, which can cause intrapulmonary and extra pulmonary TB, and can be divided into various lineages, based on genomic markers. The main public health threat comes from drug resistant M.tb strains, which are responsible for about 25% of TB death and treatment failure worldwide. Treating drug resistant TB patients significantly raises the costs of TB treatment. This study provides an overview of the demographic and drug susceptibility characteristics of newly diagnosed TB patients in Israel in 2021. The State of Israel has a very low level of TB endemicity and is at a pre-elimination phase. Notably, only 11.7% of the newly diagnosed TB patients were born in Israel. In this report, of the 154 new laboratory-confirmed TB patients, 66.7% had pulmonary TB, while 16% had extrapulmonary TB. Males accounted for 52% of the patients, with the most prevalent age group being 21-40. Most patients were citizens of Israel (53.9%), while 37.7% had no Israeli citizenship. Among non-citizens, there was a predominance of males and patients aged 21-40. The susceptibility profile showed a high resistance rate to streptomycin (18.2%) and to a lower extent to isoniazid (13.6%), pyrazinamide (8.4%), rifampicin (7.8%), and ethambutol (3.2%). Only 2 cases of XDR-TB and 10 MDR-TB strains were detected in Israel in 2021, with both XDR strains and 5 out of 10 MDR strains belonging to the Beijing lineage. Most of Beijing isolates were resistant to at least one tested drug. Genomic sequencing of 134 out of 156 strains and bioinformatics analysis using the MTBseq program and WHO mutation catalogue shows a good match with only 9 discrepancies between phenotypic and genotypic susceptibility profiles in first line drugs. The most common lineage is Delhi-Cas (23%) followed by the Beijing lineage (17%). Most patients from the Delhi-Cas lineage were born in Africa, while patients with Beijing isolates were born in different countries. Minimum spanning tree analysis identified 15 clusters. The study highlights the need for ongoing surveillance of TB using molecular and phenotypic tools to further decreasing the spreading level of the disease and develop effective treatment strategies.

Intramolecular cyclization of N-cyano sulfoximines by N-CN bond activation

Metal-free halogenated anhydrides promote the intramolecular cyclization of N-cyano sulfoximines. Trifluoro- or trichloroacetic anhydride (TFAA or TCAA, respectively) activate the N-cyano groups of N-cyano sulfoximines, leading to the intramolecular cyclization of 2-benzamide-N-cyano sulfoximines 1. This method results in excellent yields of thiadiazinone 1-oxides 2. A full intramolecular cyclization pattern was suggested by (i) labeling experiments with 13C, (ii) isolating of N-trifluoroacetyl sulfoximine 1ac, and (iii) confirming the generation of the intermediate 1ad by LC/MS analysis.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Population-based sequencing of Mycobacterium tuberculosis reveals how current population dynamics are shaped by past epidemics

Transmission is a driver of tuberculosis (TB) epidemics in high-burden regions, with assumed negligible impact in low-burden areas. However, we still lack a full characterization of transmission dynamics in settings with similar and different burdens. Genomic epidemiology can greatly help to quantify transmission, but the lack of whole genome sequencing population-based studies has hampered its application. Here, we generate a population-based dataset from Valencia region and compare it with available datasets from different TB-burden settings to reveal transmission dynamics heterogeneity and its public health implications. We sequenced the whole genome of 785 Mycobacterium tuberculosis strains and linked genomes to patient epidemiological data. We use a pairwise distance clustering approach and phylodynamic methods to characterize transmission events over the last 150 years, in different TB-burden regions. Our results underscore significant differences in transmission between low-burden TB settings, i.e., clustering in Valencia region is higher (47.4%) than in Oxfordshire (27%), and similar to a high-burden area as Malawi (49.8%). By modeling times of the transmission links, we observed that settings with high transmission rate are associated with decades of uninterrupted transmission, irrespective of burden. Together, our results reveal that burden and transmission are not necessarily linked due to the role of past epidemics in the ongoing TB incidence, and highlight the need for in-depth characterization of transmission dynamics and specifically tailored TB control strategies.

OUP accepted manuscript

Background

Rifampicin- or multidrug-resistant (RR/MDR) Mycobacterium tuberculosis complex (MTBC) strains account for considerable morbidity and mortality globally. WGS-based prediction of drug resistance may guide clinical decisions, especially for the design of RR/MDR-TB therapies.

Methods

We compared WGS-based drug resistance-predictive mutations for 42 MTBC isolates from MDR-TB patients in Tanzania with the MICs of 14 antibiotics measured in the Sensititre™ MycoTB assay. An isolate was phenotypically categorized as resistant if it had an MIC above the epidemiological-cut-off (ECOFF) value, or as susceptible if it had an MIC below or equal to the ECOFF.

Results

Overall, genotypically non-wild-type MTBC isolates with high-level resistance mutations (gNWT-R) correlated with isolates with MIC values above the ECOFF. For instance, the median MIC value (mg/L) for rifampicin-gNWT-R strains was >4.0 (IQR 4.0–4.0) compared with 0.5 (IQR 0.38–0.50) in genotypically wild-type (gWT-S, P < 0.001); isoniazid-gNWT-R >4.0 (IQR 2.0–4.0) compared with 0.25 (IQR 0.12–1.00) among gWT-S (P = 0.001); ethionamide-gNWT-R 15.0 (IQR 10.0–20.0) compared with 2.50 (IQR; 2.50–5.00) among gWT-S (P < 0.001). WGS correctly predicted resistance in 95% (36/38) and 100% (38/38) of the rifampicin-resistant isolates with ECOFFs >0.5 and >0.125 mg/L, respectively. No known resistance-conferring mutations were present in genes associated with resistance to fluoroquinolones, aminoglycosides, capreomycin, bedaquiline, delamanid, linezolid, clofazimine, cycloserine, or p-amino salicylic acid.

Conclusions

WGS-based drug resistance prediction worked well to rule-in phenotypic drug resistance and the absence of second-line drug resistance-mediating mutations has the potential to guide the design of RR/MDR-TB regimens in the future.

Analysis of Factors Influencing Multidrug-Resistant Tuberculosis and Validation of Whole-Genome Sequencing in Children with Drug-Resistant Tuberculosis

Objective

Pediatric tuberculosis (TB) is one of the top ten causes of death in children. Our study was to analyze influencing factors of multidrug-resistant tuberculosis (MDR-TB) and validation of whole-genome sequencing (WGS) used in children with drug-resistant TB (DR-TB).

Methods

All Mycobacterium tuberculosis (Mtb) strains were isolated from patients aged below 18 years old of Children’s Hospital of Chongqing Medical University, China. A total of 208 Mtb isolates were tested for eight anti-TB drugs with phenotypic drug susceptibility test (DST) and for genetic prediction of the susceptible profile with WGS. The patients corresponding to each strain were grouped according to drug resistance and genotype. Influencing factors of MDR-TB and DR-TB were analyzed.

Results

According to the phenotypic DST and WGS, 82.2% of Mtb strains were susceptible to all eight drugs, and 6.3% were MDR-TB. Using the phenotypic DSTs as the gold standard, the kappa value of WGS to predict isoniazid, rifampin, ethambutol, rifapentine, prothionamide, levofloxacin, moxifloxacin and amikacin was 0.84, 0.89, 0.59, 0.86, 0.89, 0.82, 0.88 and 1.00, respectively. There was significant difference in the distribution of severe TB, diagnosis, treatment and outcome between MDR and drug-susceptible group (P<0.05). The distribution of severe TB and treatment between DR and drug-susceptible group was statistically different (P<0.05). The results of binary logistic regression showed that Calmette–Guérin bacillus (BCG) vaccine is the protective factor for MDR-TB (OR=0.19), and MDR-TB is the risk factor for PTB and EPTB (OR=17.98).

Conclusion

The BCG vaccine is a protective factor for MDR-TB, and MDR-TB might not be confined to pulmonary infection, spreading to extrapulmonary organs in children. MDR-TB had more severe cases and a lower recovery rate than drug-susceptible TB. WGS could provide an accurate prediction of drug susceptibility test results for anti-TB drugs, which are needed for the diagnosis and precise treatment of TB in children.

Evaluation of whole-genome sequence data analysis approaches for short- and long-read sequencing of Mycobacterium tuberculosis

Whole-genome sequencing (WGS) of Mycobacterium tuberculosis (MTB) isolates can be used to get an accurate diagnosis, to guide clinical decision making, to control tuberculosis (TB) and for outbreak investigations. We evaluated the performance of long-read (LR) and/or short-read (SR) sequencing for anti-TB drug-resistance prediction using the TBProfiler and Mykrobe tools, the fraction of genome recovery, assembly accuracies and the robustness of two typing approaches based on core-genome SNP (cgSNP) typing and core-genome multi-locus sequence typing (cgMLST). Most of the discrepancies between phenotypic drug-susceptibility testing (DST) and drug-resistance prediction were observed for the first-line drugs rifampicin, isoniazid, pyrazinamide and ethambutol, mainly with LR sequence data. Resistance prediction to second-line drugs made by both TBProfiler and Mykrobe tools with SR- and LR-sequence data were in complete agreement with phenotypic DST except for one isolate. The SR assemblies were more accurate than the LR assemblies, having significantly (P <0.05) fewer indels and mismatches per 100 kbp. However, the hybrid and LR assemblies had slightly higher genome fractions. For LR assemblies, Canu followed by Racon, and Medaka polishing was the most accurate approach. The cgSNP approach, based on either reads or assemblies, was more robust than the cgMLST approach, especially for LR sequence data. In conclusion, anti-TB drug-resistance prediction, particularly with only LR sequence data, remains challenging, especially for first-line drugs. In addition, SR assemblies appear more accurate than LR ones, and reproducible phylogeny can be achieved using cgSNP approaches.

INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

Motivation

Prediction of drug resistance and identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Solving this problem requires a transparent, accurate, and flexible predictive model. The methods currently used for this purpose rarely satisfy all of these criteria. On the one hand, approaches based on testing strains against a catalogue of previously identified mutations often yield poor predictive performance; on the other hand, machine learning techniques typically have higher predictive accuracy, but often lack interpretability and may learn patterns that produce accurate predictions for the wrong reasons. Current interpretable methods may either exhibit a lower accuracy or lack the flexibility needed to generalize them to previously unseen data.

Contribution

In this paper we propose a novel technique, inspired by group testing and Boolean compressed sensing, which yields highly accurate predictions, interpretable results, and is flexible enough to be optimized for various evaluation metrics at the same time.

Results

We test the predictive accuracy of our approach on five first-line and seven second-line antibiotics used for treating tuberculosis. We find that it has a higher or comparable accuracy to that of commonly used machine learning models, and is able to identify variants in genes with previously reported association to drug resistance. Our method is intrinsically interpretable, and can be customized for different evaluation metrics. Our implementation is available at github.com/hoomanzabeti/INGOT_DR and can be installed via The Python Package Index (Pypi) under ingotdr. This package is also compatible with most of the tools in the Scikit-learn machine learning library.

Resistance Sniffer: An online tool for prediction of drug resistance patterns of Mycobacterium tuberculosis isolates using next generation sequencing data

The effective control of multidrug resistant tuberculosis (MDR-TB) relies upon the timely diagnosis and correct treatment of all tuberculosis cases. Whole genome sequencing (WGS) has great potential as a method for the rapid diagnosis of drug resistant Mycobacterium tuberculosis (Mtb) isolates. This method overcomes most of the problems that are associated with current phenotypic drug susceptibility testing. However, the application of WGS in the clinical setting has been deterred by data complexities and skill requirements for implementing the technologies as well as clinical interpretation of the next generation sequencing (NGS) data. The proposed diagnostic application was drawn upon recent discoveries of patterns of Mtb clade-specific genetic polymorphisms associated with antibiotic resistance. A catalogue of genetic determinants of resistance to thirteen anti-TB drugs for each phylogenetic clade was created. A computational algorithm for the identification of states of diagnostic polymorphisms was implemented as an online software tool, Resistance Sniffer (http://resistance-sniffer.bi.up.ac.za/), and as a stand-alone software tool to predict drug resistance in Mtb isolates using complete or partial genome datasets in different file formats including raw Illumina fastq read files. The program was validated on sequenced Mtb isolates with data on antibiotic resistance trials available from GMTV database and from the TB Platform of South African Medical Research Council (SAMRC), Pretoria. The program proved to be suitable for probabilistic prediction of drug resistance profiles of individual strains and large sequence data sets.

Tuberculosis in Liberia: high multidrug-resistance burden, transmission and diversity modelled by multiple importation events

Tuberculosis (TB) surveillance is scarce in most African countries, even though it is the continent with the greatest disease incidence according to the World Health Organization. Liberia is within the 30 countries with the highest TB burden, probably as a consequence of the long civil war and the recent Ebola outbreak, both crippling the health system and depreciating the TB prevention and control programmes. Due to difficulties working in the country, there is a lack of resistance surveys and bacillus characterization. Here, we use genome sequencing of Mycobacteriumtuberculosis clinical isolates to fill this gap. Our results highlight that the bacillus population structure is dominated by lineage 4 strains that harbour an outstanding genetic diversity, higher than in the rest of Africa as a whole. Coalescent analyses demonstrate that strains currently circulating in Liberia were introduced several times beginning in the early year 600 CE until very recently coinciding with migratory movements associated with the civil war and Ebola epidemics. A higher multidrug-resistant (MDR)-TB frequency (23.5 %) than current estimates was obtained together with non-catalogued drug-resistance mutations. Additionally, 39 % of strains were in genomic clusters revealing that ongoing transmission is a major contribution to the TB burden in the country. Our report emphasizes the importance of TB surveillance and control in African countries where bacillus diversity, MDR-TB prevalence and transmission are coalescing to jeopardize TB control programmes.

Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: progress, promise, and challenges

Tuberculosis (TB) is a global infectious threat that is intensified by an increasing incidence of highly drug-resistant disease. Whole-genome sequencing (WGS) studies of Mycobacterium tuberculosis, the causative agent of TB, have greatly increased our understanding of this pathogen. Since the first M. tuberculosis genome was published in 1998, WGS has provided a more complete account of the genomic features that cause resistance in populations of M. tuberculosis, has helped to fill gaps in our knowledge of how both classical and new antitubercular drugs work, and has identified specific mutations that allow M. tuberculosis to escape the effects of these drugs. WGS studies have also revealed how resistance evolves both within an individual patient and within patient populations, including the important roles of de novo acquisition of resistance and clonal spread. These findings have informed decisions about which drug-resistance mutations should be included on extended diagnostic panels. From its origins as a basic science technique, WGS of M. tuberculosis is becoming part of the modern clinical microbiology laboratory, promising rapid and improved detection of drug resistance, and detailed and real-time epidemiology of TB outbreaks. We review the successes and highlight the challenges that remain in applying WGS to improve the control of drug-resistant TB through monitoring its evolution and spread, and to inform more rapid and effective diagnostic and therapeutic strategies.

Recent developments in the diagnosis of drug-resistant tuberculosis

Urgent steps are required to control the drug-resistant tuberculosis (TB) epidemic worldwide. Individualised treatment, using detailed drug-susceptibility test results to guide choice of antibiotics, improves patient outcomes and minimises adverse effects. Recent years have seen substantial advances in our ability to provide rapid, detailed drug-resistance profiles using genotypic methods for detection of mutations conferring drug-resistance. Rapid testing using real-time PCR to target the most important drug-resistance mutations allows the diagnosis of drug resistance to be made with the first diagnostic test, even in low resource settings. The use of whole genome sequencing to infer resistance to a range of different drugs facilitates earlier tailoring of therapy and detection of resistant subpopulations in mixed infections. Low burden countries, such as Australia are well positioned to lead the development and refinement of these new methods, to accelerate the incorporation of these new tools into TB control programs in high burden countries.