Rapid Identification of Drug Resistance and Phylogeny in M. tuberculosis, Directly from Sputum Samples

Targeted next-generation sequencing to diagnose drug-resistant tuberculosis: a systematic review and meta-analysis

BACKGROUND

Targeted next-generation sequencing (NGS) can rapidly and simultaneously detect mutations associated with resistance to tuberculosis drugs across multiple gene targets. The use of targeted NGS to diagnose drug-resistant tuberculosis, as described in publicly available data, has not been comprehensively reviewed. We aimed to identify targeted NGS assays that diagnose drug-resistant tuberculosis, determine how widely this technology has been used, and assess the diagnostic accuracy of these assays.

METHODS

In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Library, Web of Science Core Collection, Global Index Medicus, Google Scholar, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform for published and unpublished reports on targeted NGS for drug-resistant tuberculosis from Jan 1, 2005, to Oct 14, 2022, with updates to our search in Embase and Google Scholar until Feb 13, 2024. Studies eligible for the systematic review described targeted NGS approaches to predict drug resistance in Mycobacterium tuberculosis infections using primary samples, reference strain collections, or cultured isolates from individuals with presumed or confirmed tuberculosis. Our search had no limitations on study type or language, although only reports in English, German, and French were screened for eligibility. For the meta-analysis, we included test accuracy studies that used any reference standard, and we assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes for the meta-analysis were sensitivity and specificity of targeted NGS to diagnose drug-resistant tuberculosis compared to phenotypic and genotypic drug susceptibility testing. We used a Bayesian bivariate model to generate summary receiver operating characteristic plots and diagnostic accuracy measures, overall and stratified by drug and sample type. This study is registered with PROSPERO, CRD42022368707.

FINDINGS

We identified and screened 2920 reports, of which 124 were eligible for our systematic review, including 37 review articles and 87 reports of studies collecting samples for targeted NGS. Sequencing was mainly done in the USA (14 [16%] of 87), western Europe (ten [11%]), India (ten [11%]), and China (nine [10%]). We included 24 test accuracy studies in the meta-analysis, in which 23 different tuberculosis drugs or drug groups were assessed, covering first-line drugs, injectable drugs, and fluoroquinolones and predominantly comparing targeted NGS with phenotypic drug susceptibility testing. The combined sensitivity of targeted NGS across all drugs was 94·1% (95% credible interval [CrI] 90·9-96·3) and specificity was 98·1% (97·0-98·9). Sensitivity for individual drugs ranged from 76·5% (52·5-92·3) for capreomycin to 99·1% (98·3-99·7) for rifampicin; specificity ranged from 93·1% (88·0-96·3) for ethambutol to 99·4% (98·3-99·8) for amikacin. Diagnostic accuracy was similar for primary clinical samples and culture isolates overall and for rifampicin, isoniazid, ethambutol, streptomycin, and fluoroquinolones, and similar after excluding studies at high risk of bias (overall sensitivity 95·2% [95% CrI 91·7-97·1] and specificity 98·6% [97·4-99·3]).

INTERPRETATION

Targeted NGS is highly sensitive and specific for detecting drug resistance across panels of tuberculosis drugs and can be performed directly on clinical samples. There is a paucity of data on performance for some currently recommended drugs. The barriers preventing the use of targeted NGS to diagnose drug-resistant tuberculosis in high-burden countries need to be addressed.

FUNDING

National Institutes of Allergy and Infectious Diseases and Swiss National Science Foundation.

Tuberculosis-targeted next-generation sequencing and machine learning: An ultrasensitive diagnostic strategy for paucibacillary pulmonary tuberculosis and tuberculous meningitis

BACKGROUND

Existing diagnostic approaches for paucibacillary tuberculosis (TB) are limited by the low sensitivity of testing methods and difficulty in obtaining suitable samples.

METHODS

An ultrasensitive TB diagnostic strategy was established, integrating efficient and specific TB targeted next-generation sequencing and machine learning models, and validated in clinical cohorts to test plasma cfDNA, cerebrospinal fluid (CSF) DNA collected from tuberculous meningitis (TBM) and pediatric pulmonary TB (PPTB) patients.

RESULTS

In the detection of 227 samples, application of the specific thresholds of CSF DNA (AUC = 0.974) and plasma cfDNA (AUC = 0.908) yielded sensitivity of 97.01 % and the specificity of 95.65 % in CSF samples and sensitivity of 82.61 % and specificity of 86.36 % in plasma samples, respectively. In the analysis of 44 paired samples from TBM patients, our strategy had a high concordance of 90.91 % (40/44) in plasma cfDNA and CSF DNA with both sensitivity of 95.45 % (42/44). In the PPTB patient, the sensitivity of the TB diagnostic strategy yielded higher sensitivity on plasma specimen than Xpert assay on gastric lavage (28.57 % VS. 15.38 %).

CONCLUSIONS

Our TB diagnostic strategy provides greater detection sensitivity for paucibacillary TB, while plasma cfDNA as an easily collected specimen, could be an appropriate sample type for PTB and TBM diagnosis.

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.

The MAGMA pipeline for comprehensive genomic analyses of clinical Mycobacterium tuberculosis samples

BACKGROUND

Whole genome sequencing (WGS) holds great potential for the management and control of tuberculosis. Accurate analysis of samples with low mycobacterial burden, which are characterized by low (<20x) coverage and high (>40%) levels of contamination, is challenging. We created the MAGMA (Maximum Accessible Genome for Mtb Analysis) bioinformatics pipeline for analysis of clinical Mtb samples.

METHODS AND RESULTS

High accuracy variant calling is achieved by using a long seedlength during read mapping to filter out contaminants, variant quality score recalibration with machine learning to identify genuine genomic variants, and joint variant calling for low Mtb coverage genomes. MAGMA automatically generates a standardized and comprehensive output of drug resistance information and resistance classification based on the WHO catalogue of Mtb mutations. MAGMA automatically generates phylogenetic trees with drug resistance annotations and trees that visualize the presence of clusters. Drug resistance and phylogeny outputs from sequencing data of 79 primary liquid cultures were compared between the MAGMA and MTBseq pipelines. The MTBseq pipeline reported only a proportion of the variants in candidate drug resistance genes that were reported by MAGMA. Notable differences were in structural variants, variants in highly conserved rrs and rrl genes, and variants in candidate resistance genes for bedaquiline, clofazmine, and delamanid. Phylogeny results were similar between pipelines but only MAGMA visualized clusters.

CONCLUSION

The MAGMA pipeline could facilitate the integration of WGS into clinical care as it generates clinically relevant data on drug resistance and phylogeny in an automated, standardized, and reproducible manner.

A hybridization target enrichment approach for pathogen genomics

IMPORTANCE

Emerging infectious diseases require continuous pathogen monitoring. Rapid clinical diagnosis by nucleic acid amplification is limited to a small number of targets and may miss target detection due to new mutations in clinical isolates. Whole-genome sequencing (WGS) identifies genome-wide variations that may be used to determine a pathogen's drug resistance patterns and phylogenetically characterize isolates to track disease origin and transmission. WGS is typically performed using DNA isolated from cultured clinical isolates. Culturing clinical specimens increases turn-around time and may not be possible for fastidious bacteria. To overcome some of these limitations, direct sequencing of clinical specimens has been attempted using expensive capture probes to enrich the entire genomes of target pathogens. We present a method to produce a cost-effective, time-efficient, and large-scale synthesis of probes for whole-genome enrichment. We envision that our method can be used for direct clinical sequencing of a wide range of microbial pathogens for genomic epidemiology.

Direct detection of drug-resistant Mycobacterium tuberculosis using targeted next generation sequencing

Mycobacterium tuberculosis complex (MTBC) infections are treated with combinations of antibiotics; however, these regimens are not as efficacious against multidrug and extensively drug resistant MTBC. Phenotypic (growth-based) drug susceptibility testing on slow growing bacteria like MTBC requires many weeks to months to complete, whereas sequencing-based approaches can predict drug resistance (DR) with reduced turnaround time. We sought to develop a multiplexed, targeted next generation sequencing (tNGS) assay that can predict DR and can be performed directly on clinical respiratory specimens. A multiplex PCR was designed to amplify a group of thirteen full-length genes and promoter regions with mutations known to be involved in resistance to first- and second-line MTBC drugs. Long-read amplicon libraries were sequenced with Oxford Nanopore Technologies platforms and high-confidence resistance mutations were identified in real-time using an in-house developed bioinformatics pipeline. Sensitivity, specificity, reproducibility, and accuracy of the tNGS assay was assessed as part of a clinical validation study. In total, tNGS was performed on 72 primary specimens and 55 MTBC-positive cultures and results were compared to clinical whole genome sequencing (WGS) performed on paired patient cultures. Complete or partial susceptibility profiles were generated from 82% of smear positive primary specimens and the resistance mutations identified by tNGS were 100% concordant with WGS. In addition to performing tNGS on primary clinical samples, this assay can be used to sequence MTBC cultures mixed with other mycobacterial species that would not yield WGS results. The assay can be effectively implemented in a clinical/diagnostic laboratory with a two to three day turnaround time and, even if batched weekly, tNGS results are available on average 15 days earlier than culture-derived WGS results. This study demonstrates that tNGS can reliably predict MTBC drug resistance directly from clinical specimens or cultures and provide critical information in a timely manner for the appropriate treatment of patients with DR tuberculosis.

Spotlight on mycobacterial lipid exploitation using nanotechnology for diagnosis, vaccines, and treatments

Tuberculosis (TB), historically the most significant cause of human morbidity and mortality, has returned as the top infectious disease worldwide, under circumstances worsened by the COVID-19 pandemic's devastating effects on public health. Although Mycobacterium tuberculosis, the causal agent, has been known of for more than a century, the development of tools to control it has been largely neglected. With the advancement of nanotechnology, the possibility of engineering tools at the nanoscale creates unique opportunities to exploit any molecular type. However, little attention has been paid to one of the major attributes of the pathogen, represented by the atypical coat and its abundant lipids. In this review, an overview of the lipids encountered in M. tuberculosis and interest in exploiting them for the development of TB control tools are presented. Then, the amalgamation of nanotechnology with mycobacterial lipids from both reported and future works are discussed.