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

Prevalence of bedaquiline resistance in patients with drug-resistant tuberculosis: a systematic review and meta-analysis

BACKGROUND

Drug-resistant tuberculosis (TB) remains a major global public health challenge. While bedaquiline (BDQ) offers improved treatment outcomes for patients with multi-drug resistant TB (MDR-TB), its widespread use has led to the emergence of BDQ resistance.

METHODS

This systematic review evaluated the prevalence of BDQ resistance among adult patients through searches of PubMed, Web of Science, and Embase databases. Sensitivity and subgroup analyses were performed to explore sources of heterogeneity and compare prevalence estimates across groups. The Joanna Briggs Institute's quality assessment checklist was used to evaluate the methodological quality of the included studies. Heterogeneity between studies was evaluated using Cochran's Q and I2 tests.This study is registered with PROSPERO, CRD42024620791.

RESULTS

The weighted average prevalence of BDQ resistance was 5.7% (95% CI: 3.6-8.3), with acquired resistance reported at 5.4%. Geographic differences were observed, with South Africa showing a higher prevalence (10.4%) compared to China (2.4%).High-quality studies reported a prevalence of 5.2%, while fair-quality studies reported 7.7%. Mutations in the Rv0678 gene represented a significant proportion, reaching as high as 65.6%.

CONCLUSIONS

Our findings highlight an increasing trend in acquired resistance to BDQ, offering critical insights for managing MDR-TB. The application of whole-genome sequencing shows promise for advancing understanding of drug resistance mechanisms in Mycobacterium tuberculosis.

Clinical value of serum miRNA-206 in pulmonary tuberculosis

OBJECTIVE

This study sought to investigate the diagnostic value and the effect of microRNA (miR)-206 on drug resistance in pulmonary tuberculosis (TB) patients.

METHODS

This study included 88 TB patients (TB group) as study subjects, 80 healthy subjects as control 1 (Control group), and 85 latent TB infection (LTBI) patients as control 2 (LTBI group). The drug resistance of TB patients after standard anti-TB treatment was recorded. TB patients were assigned into the pan-sensitive and drug-resistance groups, with miR-206 level in drug-resistant TB patients analyzed. The correlation coefficients between inflammatory indicators (TNF-α, IgG, IL-4, IFN-γ, IP-10) and drug resistance in TB patients were analyzed, and the independent correlation between miR-206 levels and drug resistance was analyzed.

RESULTS

Compared to the Control and LTBI groups, serum miR-206 and IP-10 were highly-expressed in TB group. The miR-206 levels positively correlated with IP-10 levels. miR-206 had potential diagnostic value for TB. Levels of TNF-α, IgG, IFN-γ, and IL-4 were elevated in TB group and positively-correlated with miR-206 levels. Moreover, miR-206 levels were higher in the Drug-resistance group than the pan-sensitive group. The low-expression group had a lower incidence of drug resistance than the high-expression group (χ2 = 16.84, P < 0.0001). miR-206 was the most significant indicator affecting drug resistance in TB patients (β = 0.516, P = 0.013). miR-206 was an independent risk factor for drug resistance.

CONCLUSION

High miR-206 expression helps TB diagnosis and may predict drug-resistance incidence. miR-206 may be an independent risk factor for drug resistance in TB patients.

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning

New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Model (GAM) that accurately identifies genetic variants linked to drug resistance and mitigates false-positive cross-resistance artifacts without prior knowledge. GAM analysis of 7,179 Mycobacterium tuberculosis (Mtb) isolates identifies gene targets for all analyzed drugs, revealing comparable performance but fewer cross-resistance artifacts than World Health Organization (WHO) mutation catalogue approach, which requires expert rules and precedents. GAM also reveals generalizability, demonstrating high predictive accuracy with 3,942 S. aureus isolates. GAM refinement by machine learning (ML) improves predictive accuracy with small or incomplete datasets. These findings were validated using 427 Mtb isolates from three sites, where GAM inputs are also found to be more suitable in ML prediction models than WHO inputs. GAM + ML could thus address the limitations of current drug resistance prediction methods to improve treatment decisions for drug-resistant microbial infections.

aurora: a machine learning gwas tool for analyzing microbial habitat adaptation

A primary goal of microbial genome-wide association studies is identifying genomic variants associated with a particular habitat. Existing tools fail to identify known causal variants if the analyzed trait shaped the phylogeny. Furthermore, due to inclusion of allochthonous strains or metadata errors, the stated sources of strains in public databases are often incorrect, and strains may not be adapted to the habitat from which they were isolated. We describe a new tool, aurora, that identifies autochthonous strains and the genes associated with habitats while acknowledging the potential role of the habitat adaptation trait in shaping phylogeny.

Machine learning-based approach for identification of new resistance associated mutations from whole genome sequences of Mycobacterium tuberculosis

Motivation

Currently available methods for the prediction of genotypic drug resistance in Mycobacterium tuberculosis utilize information on known markers of drug resistance. Hence, machine learning approaches are needed that can discover new resistance markers.

Results

Whole genome sequences with known phenotypic drug resistance profiles have been utilized to train XGBoost and ANN classifiers for 5 first-line and 8 second-line tuberculosis drugs. Benchmarking on a completely independent dataset from CRyPTIC database revealed that our method has high sensitivity (90%-95%) and specificity (94%-99%) for five first-line drugs and robust performance for six second-line drugs with a sensitivity of 77%-89% at over 95% specificity. An explainable AI method, SHapley Additive exPlanations, has successfully identified resistance mutations for each drug in a completely automated way. This approach could not only identify known resistance associated mutations in agreement with the WHO mutation catalogue, but also predicted >100 other potential resistance associated mutations for 13 antibiotics in new genes outside the known resistance loci. Identification of new resistance markers opens up the opportunity for the discovery of novel mechanisms of drug resistance.

Availability and implementation

Our prediction method has been implemented as TB-AMRpred webserver and command line tool, available freely at http://www.nii.ac.in/TB-AMRpred.html and https://github.com/Ankitapal1995/TB-AMRprd.

Whole genomic analysis uncovers high genetic diversity of rifampicin-resistant Mycobacterium tuberculosis strains in Botswana

Background

The emergence of drug-resistant Mycobacterium tuberculosis (M. tb) strains remains a threat to tuberculosis (TB) prevention and care. Understanding the drug resistance profiles of circulating strains is crucial for effective TB control. This study aimed to describe the genetic diversity of rifampicin-resistant M. tb strains circulating in Botswana using whole genome sequencing (WGS).

Methods

This study included 202 stored M. tb isolates from people diagnosed with rifampicin-resistant TB (RR-TB) between January 2016 and June 2023. Genomic DNA was extracted using the cetyltrimethylammonium bromide (CTAB) method. Library preparation was performed using the Illumina DNA prep kit following the manufacturer's instructions. Sequencing was done on Illumina NextSeq2000. TBProfiler software was used to identify known M. tb lineages and drug resistance profiles. Statistical analyses were performed on STATA version 18.

Results

WGS analysis revealed multidrug resistance (57.9%: 95% CI; 50.7-64.8), Pre-XDR (16.8%, 95% CI: 11.9-22.7), RR-TB (20.2%: 95% CI: 14.98-26.5), and HR-TB (0.5%, 95% CI; 0.01-2.7). We identified a high genetic diversity with three predominant lineages: lineage 4 (60.9%, 95% CI; 53.8-67.7), lineage 1 (22.8%: 95% CI; 17.2-29.2), and lineage 2 (13.9%, 95% CI: 9.4-19.4). The most frequently observed drug resistance mutations for rifampicin, isoniazid, ethambutol, streptomycin, pyrazinamide, and fluoroquinolones were rpoB S450L (28.6%), katG S315T (60.5%), embA_c.-29_-28delCT, embB Q497R (31.7%), rrs_n.517C>T (47.1%), pncA_c.375_389delCGATGAGGTCGATGT (36.0%) and gyrA A90V (79.4%), respectively. No bedaquiline and delamanid resistance-associated mutations were detected.

Conclusions

This study highlights the high genetic diversity of M. tb strains, with a predominance of lineage 4 among people with RR-TB in Botswana. It provides valuable insights into the genetic diversity of rifampicin-resistant M. tb strains circulating in Botswana.

The Role of the Hfq Protein in Bacterial Resistance to Antibiotics: A Narrative Review

The antibiotic resistance of pathogenic microorganisms is currently one of most major medical problems, causing a few million deaths every year worldwide due to untreatable bacterial infections. Unfortunately, the prognosis is even worse, as over 8 million deaths associated with antibiotic resistance are expected to occur in 2050 if no new effective antibacterial treatments are discovered. The Hfq protein has been discovered as a bacterial RNA chaperone. However, subsequent studies have indicated that this small protein (composed of 102 amino acid residues in Escherichia coli) has more activities, including binding to DNA and influencing its compaction, interaction with biological membranes, formation of amyloid-like structures, and others. Although Hfq is known to participate in many cellular processes, perhaps surprisingly, only reports from recent years have demonstrated its role in bacterial antibiotic resistance. The aim of this narrative review is to discuss how can Hfq affects antibiotic resistance in bacteria and propose how this knowledge may facilitate developing new therapeutic strategies against pathogenic bacteria. We indicate that the mechanisms by which the Hfq protein modulates the response of bacterial cells to antibiotics are quite different, from the regulation of the expression of genes coding for proteins directly involved in antibiotic transportation or action, through direct effects on membranes, to controlling the replication or transposition of mobile genetic elements bearing antibiotic resistance genes. Therefore, we suggest that Hfq could be considered a potential target for novel antimicrobial compounds. We also discuss difficulties in developing such drugs, but since Hfq appears to be a promising target for drugs that may enhance the efficacy of antibiotics, we propose that works on such potential therapeutics are encouraged.

Exploring natural products library to identify potential inhibitors targeting isoniazid-resistant tuberculosis

Mycobacterium tuberculosis (MTB) causing tuberculosis (TB) infection is a leading source of illness and death in developing nations, and the emergence of drug-resistant TB remains a significant global threat and a challenge in treating the disease. Mutations in the inhA and katG genes are connected to the principal molecular mechanism of isoniazid (INH) resistance, and continuous treatment of INH for more than a decade led to the evolution of INH resistant-TB (inhR-TB). Structure-based drug discovery approaches on traditional natural compounds are the contemporary source to identify significant lead molecules. This work focuses on discovering effective small compounds from natural compound libraries and applying pharmacophore-based virtual screening to filter out the molecules. The best-identified hit complexes were used for molecular dynamics simulations (MDS) to observe their stability and compactness. A three-dimensional e-pharmacophore hypothesis and screening generated 62 hits based on phase fitness scores from the pharmacophore-based virtual screening. Molecular docking experiments in Maestro's GLIDE module indicated that ZINC000002383126 and ASN22022 may be potential inhibitors of inhA and katG (native, inhA mutants S94A, Y158A, Y158F and Y158S and D137S, Y229F, S315T, W321F, and R418L mutants of katG). In addition, MDS analysis indicated that the native and mutant docked complexes of inhA and katG had good stability and remained compact in the binding pocket of the targets. In vitro studies can further validate the compounds that can act as INH competitive inhibitors.Communicated by Ramaswamy H. Sarma.

Comprehensive genomic surveillance reveals transmission profiles of extensively drug-resistant tuberculosis cases in Pará, Brazil

Bedaquiline, an antimicrobial used to treat drug-resistant tuberculosis (DR-TB), was introduced in Brazil in October 2021. Monitoring the emergence and transmission of DR-TB is crucial for implementing public health to control the spread of DR strains of Mycobacterium tuberculosis. To measure its impact on the multi-drug treatment scheme in the state of Pará, we aimed to conduct genomic surveillance of DR-TB after bedaquiline was introduced in Brazil. Individuals treated for DR-TB between October 2021 and December 2022, in the reference hospital to treat DR-TB cases from the state of Pará, were included in the study. Clinical and bacteriological information was obtained from the National Laboratory Management Environment and the Special TB Treatment Information System. Genomic DNA was extracted from bacterial cultures performed at the Pará Central Laboratory (LACEN-PA). Whole-genome sequencing (WGS) was obtained using Illumina Nextera-XT and NextSeq 550 and genomes were analyzed using the MAGMA and TB-Profiler pipelines interpreted according to the World Health Organization (WHO) mutations catalog 2nd edition. Geoprocessing was performed based on the patient's residences. Cutoffs of 5-12 single nucleotide polymorphisms (SNPs) were used for transmission analysis. From the 103 patients reported as DR-TB, viable cultures were obtained from 67. Forty isolates were selected randomly for WGS. Among these, a mixed infection of M. tuberculosis L1 and L4 and a co-infection of M. tuberculosis and Mycobacterium chelonae were observed. The genotypic drug susceptibility profile of TB stains (39/40) was as follows: sensitive (1/2, 5%), rifampicin mono-resistant (RR) (4/10%), isoniazid mono-resistant (1/2%), multidrug-resistant (MDR) (21/52%), extensively drug-resistant (XDR) (3/7%), pre-XDR (8/20%), and other (1/2%). Among the 38 isolates of M. tuberculosis strains without mixed infection, using a cutoff of 12 SNPs and suggestive of recent TB transmission, 14 (37%) were grouped into five clusters (C1-C5) and included RR (C5), MDR (C3, C4, C5), pre-XDR, and XDR (C2) strains. We recommend greater attention from the regional public health authorities to detect and track resistance to new drugs, especially in areas with pre-XDR and XDR cases. This is the first report on the detection and transmission of XDR-TB in Pará, Brazil, after the recent re-definition of XDR-TB by the WHO in 2021.

Targeted sputum sequencing for rapid and broad drug resistance of Mycobacterium tuberculosis

PURPOSE

Rapid detection of drug resistance in Mycobacterium tuberculosis (Mtb) from clinical samples facilitates the timely provision of optimal treatment regimens for tuberculosis (TB) patients.

METHODS

In November, 2023, the WHO released its second catalogue of resistance-conferring mutations in Mtb. Utilizing this information, we developed a single 17-plex PCR assay covering 16 key resistance genes and modified thermo-protection buffer to amplify 30 kbp DNA directly from sputum samples for nanopore sequencing. We implemented our protocol using rapid barcoding for sequencing with both a Flongle and a MinION flow cell.

RESULTS

The single multiplex PCR assay was successfully validated on clinical sputum samples using the thermo-protection buffer. The protocol was applied to both Flongle and MinION flow cells, analyzing 12 and 40 samples, respectively. Data analysis suggested that optimal performance could be achieved by processing 6 and 12 samples with similar microscope staining scores on these two platforms. This approach facilitated rapid antimicrobial resistance (AMR) predictions directly from sputum on the day of collection or the following day, with a cost of less than $35 per sample. Compared to AMR predictions based on whole-genome sequencing (WGS) using Mykrobe and TBProfiler, our amplicon-based analysis tool, ARapidTb, demonstrated superior resistance detection capabilities. When analyzing publicly available nanopore WGS datasets for 442 isolates, ARapidTb achieved agreement rates of 95.8% and 98.0%, outperforming Mykrobe (89.4% and 98.3%) and TBProfiler (75.6% and 89.8%).

CONCLUSIONS

Our study significantly reduces the time required for drug resistance detection, enabling quicker initiation of appropriate treatments and potentially improving patient outcomes and TB management.

Tiled Amplicon Sequencing Enables Culture-free Whole-Genome Sequencing of Pathogenic Bacteria From Clinical Specimens

Pathogen sequencing is an important tool for disease surveillance and demonstrated its high value during the COVID-19 pandemic. Viral sequencing during the pandemic allowed us to track disease spread, quickly identify new variants, and guide the development of vaccines. Tiled amplicon sequencing, in which a panel of primers is used for multiplex amplification of fragments across an entire genome, was the cornerstone of SARS-CoV-2 sequencing. The speed, reliability, and cost-effectiveness of this method led to its implementation in academic and public health laboratories across the world and adaptation to a broad range of viral pathogens. However, similar methods are not available for larger bacterial genomes, for which whole-genome sequencing typically requires in vitro culture. This increases costs, error rates and turnaround times. The need to culture poses particular problems for medically important bacteria such as Mycobacterium tuberculosis, which are slow to grow and challenging to culture. As a proof of concept, we developed two novel whole-genome amplicon panels for M. tuberculosis and Streptococcus pneumoniae. Applying our amplicon panels to clinical samples, we show the ability to classify pathogen subgroups and to reliably identify markers of drug resistance without culturing. Development of this work in clinical settings has the potential to dramatically reduce the time of diagnosis of drug resistance for multiple drugs in parallel, enabling earlier intervention for high priority pathogens.

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.

Holistic understanding of trimethoprim resistance in Streptococcus pneumoniae using an integrative approach of genome-wide association study, resistance reconstruction, and machine learning

Antimicrobial resistance (AMR) is a public health threat worldwide. Next-generation sequencing (NGS) has opened unprecedented opportunities to accelerate AMR mechanism discovery and diagnostics. Here, we present an integrative approach to investigate trimethoprim (TMP) resistance in the key pathogen Streptococcus pneumoniae. We explored a collection of 662 S. pneumoniae genomes by conducting a genome-wide association study (GWAS), followed by functional validation using resistance reconstruction experiments, combined with machine learning (ML) approaches to predict TMP minimum inhibitory concentration (MIC). Our study showed that multiple additive mutations in the folA and sulA loci are responsible for TMP non-susceptibility in S. pneumoniae and can be used as key features to build ML models for digital MIC prediction, reaching an average accuracy within ±1 twofold dilution factor of 86.3%. Our roadmap of in silico analysis-wet-lab validation-diagnostic tool building could be adapted to explore AMR in other combinations of bacteria-antibiotic.

IMPORTANCE

In the age of next-generation sequencing (NGS), while data-driven methods such as genome-wide association study (GWAS) and machine learning (ML) excel at finding patterns, functional validation can be challenging due to the high numbers of candidate variants. We designed an integrative approach combining a GWAS on S. pneumoniae clinical isolates, followed by whole-genome transformation coupled with NGS to functionally characterize a large set of GWAS candidates. Our study validated several phenotypic folA mutations beyond the standard Ile100Leu mutation, and showed that the overexpression of the sulA locus produces trimethoprim (TMP) resistance in Streptococcus pneumoniae. These validated loci, when used to build ML models, were found to be the best inputs for predicting TMP minimal inhibitory concentrations. Integrative approaches can bridge the genotype-phenotype gap by biological insights that can be incorporated in ML models for accurate prediction of drug susceptibility.

MmpR5 protein truncation and bedaquiline resistance in Mycobacterium tuberculosis isolates from South Africa: a genomic analysis

BACKGROUND

The antibiotic bedaquiline is a key component of new WHO regimens for drug-resistant tuberculosis; however, predicting bedaquiline resistance from bacterial genotypes remains challenging. We aimed to understand the genetic mechanisms of bedaquiline resistance by analysing Mycobacterium tuberculosis isolates from South Africa.

METHODS

For this genomic analysis, we conducted whole-genome sequencing of Mycobacterium tuberculosis samples collected at two referral laboratories in Cape Town and Johannesburg, covering regions of South Africa with a high prevalence of tuberculosis. We used the tool ARIBA to measure the status of predefined genes that are associated with bedaquiline resistance. To produce a broad genetic landscape of M tuberculosis in South Africa, we extended our analysis to include all publicly available isolates from the European Nucleotide Archive, including isolates obtained by the CRyPTIC consortium, for which minimum inhibitory concentrations of bedaquiline were available.

FINDINGS

Between Jan 10, 2019, and July, 22, 2020, we sequenced 505 M tuberculosis isolates from 461 patients. Of the 64 isolates with mutations within the mmpR5 regulatory gene, we found 53 (83%) had independent acquisition of 31 different mutations, with a particular enrichment of truncated MmpR5 in bedaquiline-resistant isolates resulting from either frameshift mutations or the introduction of an insertion element. Truncation occurred across three M tuberculosis lineages, and were present in 66% of bedaquiline-resistant isolates. Although the distributions overlapped, the median minimum inhibitory concentration of bedaquiline was 0·25 mg/L (IQR 0·12-0·25) in mmpR5-disrupted isolates, compared with 0·06 mg/L (0·03-0·06) in wild-type M tuberculosis.

INTERPRETATION

Reduction in the susceptibility of M tuberculosis to bedaquiline has evolved repeatedly across the phylogeny. In our data, we see no evidence that this reduction has led to the spread of a successful strain in South Africa. Binary phenotyping based on the bedaquiline breakpoint might be inappropriate to monitor resistance to this drug. We recommend the use of minimum inhibitory concentrations in addition to MmpR5 truncation screening to identify moderate increases in resistance to bedaquiline.

FUNDING

US Centers for Disease Control and Prevention.

Surveillance of fluoroquinolones resistance in rifampicin-susceptible tuberculosis in eastern China with whole-genome sequencing-based approach

Background

Leveraging well-established DNA-level drug resistance mechanisms, whole-genome sequencing (WGS) has emerged as a valuable methodology for predicting drug resistance. As the most effective second-line anti-tuberculosis (anti-TB) drugs, fluoroquinoloness (FQs) are generally used to treat multidrug-resistant tuberculosis (MDR-TB, defined as being resistant to resistant to rifampicin and isoniazid) or rifampicin-resistant tuberculosis (RR-TB). However, FQs are also commonly used in the management of other bacterial infections. There are few published data on the rates of FQs resistance among rifampicin-susceptible TB. The prevalence of FQs resistance among TB patients who are rifampicin-susceptible has not been studied in Zhejiang Province, China. The goal of this study was to provide a baseline characterization of the prevalence of FQs resistance, particularly among rifampicin-susceptible TB in Zhejiang Province, China.

Methods

Based on WGS, we have investigated the prevalence of FQs resistance among rifampicin-susceptible TB in Zhejiang Province. All pulmonary TB patients with positive cultures who were identified in Zhejiang area during TB drug resistance surveillance from 2018 to 2019 have enrolled in this population-based retrospective study.

Results

The rate of FQs resistance was 4.6% (32/698) among TB, 4.0% (27/676) among rifampicin-susceptible TB, and 22.7% (5/22) among RR-TB. According to WGS, strains that differ within 12 single-nucleotide polymorphisms (SNPs) were considered to be transmission of FQ-resistant strains. Specifically, 3.7% (1/27) of FQs resistance was caused by the transmission of FQs-resistant strains among the rifampicin-susceptible TB and 40.7% (11/27) of FQs resistance was identified as hetero-resistance.

Conclusion

The prevalence of FQs resistance among TB patients who were rifampicin-susceptible was severe in Zhejiang. The emergence of FQs resistance in TB isolates that are rifampicin-susceptible was mainly caused by the selection of drug-resistant strains. In order to prevent the emergence of FQs resistance, the WGS-based surveillance system for TB should be urgently established, and clinical awareness of the responsible use of FQs for respiratory infections should be enhanced.

Decoding drug resistance in Mycobacterium tuberculosis complex: genetic insights and future challenges

INTRODUCTION

Tuberculosis (TB), particularly its drug-resistant forms (MDR-TB and XDR-TB), continues to pose a significant global health challenge. Despite advances in treatment and diagnosis, the evolving nature of drug resistance in Mycobacterium tuberculosis (MTB) complicates TB eradication efforts. This review delves into the complexities of anti-TB drug resistance, its mechanisms, and implications on healthcare strategies globally.

AREAS COVERED

We explore the genetic underpinnings of resistance to both first-line and second-line anti-TB drugs, highlighting the role of mutations in key genes. The discussion extends to advanced diagnostic techniques, such as Whole-Genome Sequencing (WGS), CRISPR-based diagnostics and their impact on identifying and managing drug-resistant TB. Additionally, we discuss artificial intelligence applications, current treatment strategies, challenges in managing MDR-TB and XDR-TB, and the global disparities in TB treatment and control, translating to different therapeutic outcomes and have the potential to revolutionize our understanding and management of drug-resistant tuberculosis.

EXPERT OPINION

The current landscape of anti-TB drug resistance demands an integrated approach combining advanced diagnostics, novel therapeutic strategies, and global collaborative efforts. Future research should focus on understanding polygenic resistance and developing personalized medicine approaches. Policymakers must prioritize equitable access to diagnosis and treatment, enhancing TB control strategies, and support ongoing research and augmented government funding to address this critical public health issue effectively.

Universal Lineage-Independent Markers of Multidrug Resistance in Mycobacterium tuberculosis

(1) Background: This study was aimed to identify universal genetic markers of multidrug resistance (MDR) in Mycobacterium tuberculosis (Mtb) and establish statistical associations among identified mutations to enhance understanding of MDR in Mtb and inform diagnostic and treatment development. (2) Methods: GWAS analysis and the statistical evaluation of identified polymorphic sites within protein-coding genes of Mtb were performed. Statistical associations between specific mutations and antibiotic resistance were established using attributable risk statistics. (3) Results: Sixty-four polymorphic sites were identified as universal markers of drug resistance, with forty-seven in PE/PPE regions and seventeen in functional genes. Mutations in genes such as cyp123, fadE36, gidB, and ethA showed significant associations with resistance to various antibiotics. Notably, mutations in cyp123 at codon position 279 were linked to resistance to ten antibiotics. The study highlighted the role of PE/PPE and PE_PGRS genes in Mtb's evolution towards a 'mutator phenotype'. The pathways of acquisition of mutations forming the epistatic landscape of MDR were discussed. (4) Conclusions: This research identifies marker mutations across the Mtb genome associated with MDR. The findings provide new insights into the molecular basis of MDR acquisition in Mtb, aiding in the development of more effective diagnostics and treatments targeting these mutations to combat MDR tuberculosis.

Novel strategies based on natural products and synthetic derivatives to overcome resistance in Mycobacterium tuberculosis

One of the biggest health challenges of today's world is the emergence of antimicrobial resistance (AMR), which renders conventional therapeutics insufficient and urgently demands the generation of novel antimicrobial strategies. Mycobacterium tuberculosis (M. tuberculosis), the pathogen causing tuberculosis (TB), is among the most successful bacteria producing drug-resistant infections. The versatility of M. tuberculosis allows it to evade traditional anti-TB agents through various acquired and intrinsic mechanisms, rendering TB among the leading causes of infectious disease-related mortality. In this context, researchers worldwide focused on establishing novel approaches to address drug resistance in M. tuberculosis, developing diverse alternative treatments with varying effectiveness and in different testing phases. Overviewing the current progress, this paper aims to briefly present the mechanisms involved in M. tuberculosis drug-resistance, further reviewing in more detail the under-development antibiotics, nanotechnological approaches, and natural therapeutic solutions that promise to overcome current treatment limitations.

Genetic mechanisms of co-emergence of INH-resistant Mycobacterium tuberculosis strains during the standard course of antituberculosis therapy

The incidence of isoniazid (INH) resistant Mycobacterium tuberculosis is increasing globally. This study aimed to identify the molecular mechanisms behind the development of INH resistance in M. tuberculosis strains collected from the same patients during the standard course of treatment. Three M. tuberculosis strains were collected from a patient before and during antituberculosis (anti-TB) therapy. The strains were characterized using phenotypic drug susceptibility tests, Mycobacterial Interspersed Repeated Unit-Variable-Number Tandem Repeats (MIRU-VNTR), and whole-genome sequencing (WGS) to identify mutations associated with INH resistance. To validate the role of the novel mutations in INH resistance, the mutated katG genes were electroporated into a KatG-deleted M. tuberculosis strain (GA03). Three-dimensional structures of mutated KatG were modeled to predict their impact on INH binding. The pre-treatment strain was susceptible to INH. However, two INH-resistant strains were isolated from the patient after anti-TB therapy. MIRU-VNTR and WGS revealed that the three strains were clonally identical. A missense mutation (P232L) and a nonsense mutation (Q461Stop) were identified in the katG of the two post-treatment strains, respectively. Transformation experiments showed that katG of the pre-treatment strain restored INH susceptibility in GA03, whereas the mutated katG genes from the post-treatment strains rendered negative catalase activity and INH resistance. The protein model indicated that P232L reduced INH-KatG binding affinity while Q461Stop truncated gene transcription. Our results showed that the two katG mutations, P232L and Q461Stop, accounted for the co-emergence of INH-resistant clones during anti-TB therapy. The inclusion of these mutations in the design of molecular assays could increase the diagnostic performance.IMPORTANCEThe evolution of drug-resistant strains of Mycobacterium tuberculosis within the lung lesions of a patient has a detrimental impact on treatment outcomes. This is particularly concerning for isoniazid (INH), which is the most potent first-line antimycobacterial drug. However, the precise genetic factors responsible for drug resistance in patients have not been fully elucidated, with approximately 15% of INH-resistant strains harboring unknown genetic factors. This raises concerns about the emergence of drug-resistant clones within patients, further contributing to the global epidemic of resistance. In this study, we revealed the presence of two novel katG mutations, which emerged independently due to the stress exerted by antituberculosis (anti-TB) treatment on a parental strain. Importantly, we experimentally demonstrated the functional significance of both mutations in conferring resistance to INH. Overall, this research sheds light on the genetic mechanisms underlying the evolution of INH resistance within patients and provides valuable insights for improving diagnostic performance by targeting specific mutations.

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.

Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research

Antimicrobial resistance (AMR) is a growing global concern with significant implications for infectious disease control and therapeutics development. This chapter presents a comprehensive overview of computational methods in the study of AMR. We explore the prevalence and statistics of AMR, underscoring its alarming impact on public health. The role of AMR in infectious disease outbreaks and its impact on therapeutics development are discussed, emphasizing the need for novel strategies. Resistance mutations are pivotal in AMR, enabling pathogens to evade antimicrobial treatments. We delve into their importance and contribution to the spread of AMR. Experimental methods for quantitatively evaluating resistance mutations are described, along with their limitations. To address these challenges, computational methods provide promising solutions. We highlight the advantages of computational approaches, including rapid analysis of large datasets and prediction of resistance profiles. A comprehensive overview of computational methods for studying AMR is presented, encompassing genomics, proteomics, structural bioinformatics, network analysis, and machine learning algorithms. The strengths and limitations of each method are briefly outlined. Additionally, we introduce ResScan-design, our own computational method, which employs a protein (re)design protocol to identify potential resistance mutations and adaptation signatures in pathogens. Case studies are discussed to showcase the application of ResScan in elucidating hotspot residues, understanding underlying mechanisms, and guiding the design of effective therapies. In conclusion, we emphasize the value of computational methods in understanding and combating AMR. Integration of experimental and computational approaches can expedite the discovery of innovative antimicrobial treatments and mitigate the threat posed by AMR.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

BACKGROUND

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature.

RESULTS

In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics.

CONCLUSIONS

Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Trends and Progress on Antibiotic-Resistant Mycobacterium tuberculosis and Genes in relation to Human Immunodeficiency Virus

Human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) and tuberculosis (TB) are among the infectious diseases that cause high rates of mortality worldwide. The epidemiology of antibiotic resistance in correlation to people that live with TB and HIV has not been thoroughly investigated particularly in South Africa. Numerous cases of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) have been announced immensely worldwide. The spread and control of the MDR-TB pandemic due to unsuccessful treatment is one of the most serious public issues of concern, and this challenge is of international interest. Despite all measures that have been executed to overcome the challenge of MDR-TB in recent decades, the global MDR-TB trends have kept on accelerating with more and more people becoming victims. This is attributed to the abuse, misuse, and overuse of different antibacterial agents in human medicine, animal farms, and agricultural activities which serve as a wellspring for the evolution of antimicrobial resistance within the population. Over and above, the impetuous evolution, mutation, and the transfer of resistant genes via horizontal gene transfer are well-known contributive factors towards the antimicrobial resistance problem. Among the public health concerns in the world currently is the ever-increasing problem of antibiotic resistance which outpaces the progress of newly developed antimicrobials. The propagation of antimicrobial resistance (AMR) is even more amplified in areas where the pressure of antimicrobial resistant pathogens is elevated, and hence the population with ubiquitous HIV and AIDS is considered the hotspot. This review therefore aims to give in-depth coverage on the trends and the progress on the development of TB and HIV-resistant strains, highlight strategies to solve the problem, and accentuate the repercussions of the COVID-19 epidemic on the AMR.

Pediatric Drug-Resistant Tuberculosis: The Current and Future Prospects for Management and Prevention

In the continued battle against one of the oldest enemies known to mankind, Mycobacterium tuberculosis (MTB), the emergence of drug resistance to antituberculosis drugs among children poses multiple challenges for early detection and treatment. Molecular diagnostics and newer drugs like bedaquiline and delamanid have strengthened the armamentarium and helped design convenient, safe, and child-friendly therapeutic regimens against drug-resistant tuberculosis (TB). Preventive strategies like treatment of TB infection among children living in close contact with patients with drug-resistant TB and effective vaccines against TB are currently in the investigative stages of development and implementation. In addition to the implementation of recent novel diagnostics and treatment modalities, effective psychosocial and nutritional support, as well as dedicated monitoring for compliance and adverse effects, are crucial determinants for successful treatment outcomes in these children.

Implementation of national whole-genome sequencing of Mycobacterium tuberculosis, National Public Health Laboratory, Singapore, 2019-2022

The National Tuberculosis Programme (NTBP) monitors the occurrence and spread of tuberculosis (TB) and multidrug-resistant TB (MDR-TB) in Singapore. Since 2020, whole-genome sequencing (WGS) of Mycobacterium tuberculosis isolates has been performed at the National Public Health Laboratory (NPHL) for genomic surveillance, replacing spoligotyping and mycobacterial interspersed repetitive unit-variable number tandem repeats analysis (MIRU-VNTR). Four thousand three hundred and seven samples were sequenced from 2014 to January 2023, initially as research projects and later developed into a comprehensive public health surveillance programme. Currently, all newly diagnosed culture-positive cases of TB in Singapore are prospectively sent for WGS, which is used to perform lineage classification, predict drug resistance profiles and infer genetic relationships between TB isolates. This paper describes NPHL's operational and technical experiences with implementing the WGS service in an urban TB-endemic setting, focusing on cluster detection and genomic drug susceptibility testing (DST). Cluster detection: WGS has been used to guide contact tracing by detecting clusters and discovering unknown transmission networks. Examples have been clusters in a daycare centre, housing apartment blocks and a horse-racing betting centre. Genomic DST: genomic DST prediction (gDST) identifies mutations in core genes known to be associated with TB drug resistance catalogued in the TBProfiler drug resistance mutation database. Mutations are reported with confidence scores according to a standardized approach referencing NPHL's internal gDST confidence database, which is adapted from the World Health Organization (WHO) TB drug mutation catalogue. Phenotypic-genomic concordance was observed for the first-line drugs ranging from 2959/2998 (98.7 %) (ethambutol) to 2983/2996 (99.6 %) (rifampicin). Aspects of internal database management, reporting standards and caveats in results interpretation are discussed.

Learning Individual Survival Models from PanCancer Whole Transcriptome Data

PURPOSE

Personalized medicine attempts to predict survival time for each patient, based on their individual tumor molecular profile. We investigate whether our survival learner in combination with a dimension reduction method can produce useful survival estimates for a variety of patients with cancer.

EXPERIMENTAL DESIGN

This article provides a method that learns a model for predicting the survival time for individual patients with cancer from the PanCancer Atlas: given the (16,335 dimensional) gene expression profiles from 10,173 patients, each having one of 33 cancers, this method uses unsupervised nonnegative matrix factorization (NMF) to reexpress the gene expression data for each patient in terms of 100 learned NMF factors. It then feeds these 100 factors into the Multi-Task Logistic Regression (MTLR) learner to produce cancer-specific models for each of 20 cancers (with >50 uncensored instances); this produces "individual survival distributions" (ISD), which provide survival probabilities at each future time for each individual patient, which provides a patient's risk score and estimated survival time.

RESULTS

Our NMF-MTLR concordance indices outperformed the VAECox benchmark by 14.9% overall. We achieved optimal survival prediction using pan-cancer NMF in combination with cancer-specific MTLR models. We provide biological interpretation of the NMF model and clinical implications of ISDs for prognosis and therapeutic response prediction.

CONCLUSIONS

NMF-MTLR provides many benefits over other models: superior model discrimination, superior calibration, meaningful survival time estimates, and accurate probabilistic estimates of survival over time for each individual patient. We advocate for the adoption of these cancer survival models in clinical and research settings.

The Mycobacterium tuberculosis genome at 25 years: lessons and lingering questions

First achieved in 1998 by Cole et al., the complete genome sequence of Mycobacterium tuberculosis continues to provide an invaluable resource to understand tuberculosis (TB), the leading cause of global infectious disease mortality. At the 25-year anniversary of this accomplishment, we describe how insights gleaned from the M. tuberculosis genome have led to vital tools for TB research, epidemiology, and clinical practice. The increasing accessibility of whole-genome sequencing across research and clinical settings has improved our ability to predict antibacterial susceptibility, to track epidemics at the level of individual outbreaks and wider historical trends, to query the efficacy of the bacille Calmette-Guérin (BCG) vaccine, and to uncover targets for novel antitubercular therapeutics. Likewise, we discuss several recent efforts to extract further discoveries from this powerful resource.

Multi-epitope vaccine against drug-resistant strains of Mycobacterium tuberculosis: a proteome-wide subtraction and immunoinformatics approach

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the most deadly infections in humans. The emergence of multidrug-resistant and extensively drug-resistant Mtb strains presents a global challenge. Mtb has shown resistance to many frontline antibiotics, including rifampicin, kanamycin, isoniazid, and capreomycin. The only licensed vaccine, Bacille Calmette-Guerin, does not efficiently protect against adult pulmonary tuberculosis. Therefore, it is urgently necessary to develop new vaccines to prevent infections caused by these strains. We used a subtractive proteomics approach on 23 virulent Mtb strains and identified a conserved membrane protein (MmpL4, NP_214964.1) as both a potential drug target and vaccine candidate. MmpL4 is a non-homologous essential protein in the host and is involved in the pathogen-specific pathway. Furthermore, MmpL4 shows no homology with anti-targets and has limited homology to human gut microflora, potentially reducing the likelihood of adverse effects and cross-reactivity if therapeutics specific to this protein are developed. Subsequently, we constructed a highly soluble, safe, antigenic, and stable multi-subunit vaccine from the MmpL4 protein using immunoinformatics. Molecular dynamics simulations revealed the stability of the vaccine-bound Toll-like receptor-4 complex on a nanosecond scale, and immune simulations indicated strong primary and secondary immune responses in the host. Therefore, our study identifies a new target that could expedite the design of effective therapeutics, and the designed vaccine should be validated. Future directions include an extensive molecular interaction analysis, in silico cloning, wet-lab experiments, and evaluation and comparison of the designed candidate as both a DNA vaccine and protein vaccine.

Identification of Hub Genes and Typing of Tuberculosis Infections Based on Autophagy-Related Genes

Tuberculosis (TB) caused by Mycobacterium tuberculosis is one of the leading causes of morbidity and death in humans worldwide. Some autophagy genes associated with TB and some miRNAs regulating TB have been found, but the identification of autophagy-related genes in M. tuberculosis remains to be explored. Forty-seven autophagy-related genes differentially expressed in TB were identified in this study by analysis of TB-related datasets in the Gene Expression Omnibus (GEO) and autophagy-related genes in the Human Autophagy Database. The potential crucial genes affecting TB were found through the protein-protein interaction (PPI) network, and the possible pathways affected by these genes were verified. Analysis of the PPI network of miRNAs associated with M. tuberculosis infection and their target genes revealed that hsa-let-7, hsa-mir-155, hsa-mir-206, hsa-mir-26a, hsa-mir-30a, and hsa-mir-32 may regulate the expression of multiple autophagy-related genes (MAPK8, UVRAG, UKL2, and GABARAPL1) alone or in combination. Subsequently, Cytoscape was utilized to screen the differentially expressed genes related to autophagy. The hub genes (GABARAPL1 and ULK2) affecting TB were identified. Combined with Gene Set Enrichment Analysis (GSEA), the signaling pathways affected by the hub genes were verified. Finally, we divided TB patients into two subgroups based on autophagy-related genes, and the immune microenvironment of patients in different subgroups was significantly different. Our study found two autophagy-related hub genes that could affect TB and divide TB samples into two subgroups. This finding is of great significance for TB treatment and provides new ideas for exploring the pathogenesis of M. tuberculosis.

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.

Targeted Chromosomal Barcoding Establishes Direct Genotype-Phenotype Associations for Antibiotic Resistance in Mycobacterium abscessus

A bedaquiline-resistant Mycobacterium abscessus isolate was sequenced, and a candidate mutation in the atpE gene was identified as responsible for the antibiotic resistance phenotype. To establish a direct genotype-phenotype relationship of this mutation which results in a Asp-to-Ala change at position 29 (D29A), we developed a recombineering-based method consisting of the specific replacement of the desired mutation in the bacterial chromosome. As surrogate bacteria, we used two M. abscessus bedaquiline-susceptible strains: ATCC 19977 and the SL541 clinical isolate. The allelic exchange substrates used in recombineering carried either the sole D29A mutation or a genetic barcode of silent mutations in codons flanking the D29A mutation. After selection of bedaquiline-resistant M. abscessus colonies transformed with both substrates, we obtained equivalent numbers of recombinants. These resistant colonies were analyzed by allele-specific PCR and Sanger sequencing, and we demonstrated that the presence of the genetic barcode was linked to the targeted incorporation of the desired mutation in its chromosomal location. All recombinants displayed the same MIC to bedaquiline as the original isolate, from which the D29A mutation was identified. Finally, to demonstrate the broad applicability of this method, we confirmed the association of bedaquiline resistance with the atpE A64P mutation in analysis performed in independent M. abscessus strains and by independent researchers. IMPORTANCE Antimicrobial resistance (AMR) threatens the effective prevention and treatment of an ever-increasing range of infections caused by microorganisms. On the other hand, infections caused by Mycobacterium abscessus affect people with chronic lung diseases, and their incidence has grown alarmingly in recent years. Further, these bacteria are known to easily develop AMR to the few therapeutic options available, making their treatment long-lasting and challenging. The recent introduction of new antibiotics against M. abscessus, such as bedaquiline, makes us anticipate a future when a plethora of antibiotic-resistant strains will be isolated and sequenced. However, in the era of whole-genome sequencing, one of the challenges is to unequivocally assign a biological function to each identified polymorphism. Thus, in this study, we developed a fast, robust, and reliable method to assign genotype-phenotype associations for putative antibiotic-resistant polymorphisms in M. abscessus.

Oxford nanopore long-read sequencing enables the generation of complete bacterial and plasmid genomes without short-read sequencing

Introduction

Genome-based analysis is crucial in monitoring antibiotic-resistant bacteria (ARB)and antibiotic-resistance genes (ARGs). Short-read sequencing is typically used to obtain incomplete draft genomes, while long-read sequencing can obtain genomes of multidrug resistance (MDR) plasmids and track the transmission of plasmid-borne antimicrobial resistance genes in bacteria. However, long-read sequencing suffers from low-accuracy base calling, and short-read sequencing is often required to improve genome accuracy. This increases costs and turnaround time.

Methods

In this study, a novel ONT sequencing method is described, which uses the latest ONT chemistry with improved accuracy to assemble genomes of MDR strains and plasmids from long-read sequencing data only. Three strains of Salmonella carrying MDR plasmids were sequenced using the ONT SQK-LSK114 kit with flow cell R10.4.1, and de novo genome assembly was performed with average read accuracy (Q > 10) of 98.9%.

Results and Discussion

For a 5-Mb-long bacterial genome, finished genome sequences with accuracy of >99.99% could be obtained at 75× sequencing coverage depth using Flye and Medaka software. Thus, this new ONT method greatly improves base-calling accuracy, allowing for the de novo assembly of high-quality finished bacterial or plasmid genomes without the need for short-read sequencing. This saves both money and time and supports the application of ONT data in critical genome-based epidemiological analyses. The novel ONT approach described in this study can take the place of traditional combination genome assembly based on short- and long-read sequencing, enabling pangenomic analyses based on high-quality complete bacterial and plasmid genomes to monitor the spread of antibiotic-resistant bacteria and antibiotic resistance genes.

Evolution and epidemic success of Mycobacterium tuberculosis in eastern China: evidence from a prospective study

BACKGROUND

Lineage distribution of Mycobacterium tuberculosis (Mtb) isolates is strongly associated with geographically distinct human populations, and its transmission can be further impacted by the bacterial genome. However, the epidemic success of Mtb isolates at an individual level was unknown in eastern China. Knowledge regarding the emergence and transmission of Mtb isolates as well as relevant factors may offer a new solution to curb the spread of the disease. Thus, this study aims to reveal the evolution and epidemic success of Mtb isolates in eastern China.

RESULTS

Of initial 1040 isolates, 997 were retained after removing duplicates and those with insufficient sequencing depth. Of the final samples, 733 (73.52%) were from Zhejiang Province, and 264 (26.48%) were from Shanghai City. Lineage 2 and lineage 4 accounted for 80.44% and 19.56%, with common ancestors dating around 7017 years ago and 6882 years ago, respectively. Sub-lineage L2.2 (80.34%) contributed the majority of total isolates, followed by L4.4 (8.93%) and L4.5 (8.43%). Additionally, 51 (5.12%) isolates were identified to be multidrug-resistant (MDR), of which 21 (29.17%) were pre-extensively drug-resistant (pre-XDR). One clade harboring katG S315T mutation may date back to 65 years ago and subsequently acquired mutations conferring resistance to another five antibiotic drugs. The prevalence of compensatory mutation was the highest in pre-XDR isolates (76.19%), followed by MDR isolates (47.06%) and other drug-resistant isolates (20.60%). Time-scaled haplotypic density analyses suggested comparable success indices between lineage 2 and lineage 4 (P = 0.306), and drug resistance did not significantly promote the transmission of Mtb isolates (P = 0.340). But for pre-XDR isolates, we found a higher success index in those with compensatory mutations (P = 0.025). Mutations under positive selection were found in genes associated with resistance to second-line injectables (whiB6) and drug tolerance (prpR) in both lineage 2 and lineage 4.

CONCLUSIONS

Our study demonstrates the population expansion of lineage 2 and lineage 4 in eastern China, with comparable transmission capacity, while accumulation of resistance mutations does not necessarily facilitate the success of Mtb isolates. Compensatory mutations usually accompany drug resistance and significantly contribute to the epidemiological transmission of pre-XDR strains. Prospective molecular surveillance is required to further monitor the emergence and spread of pre-XDR/XDR strains in eastern China.

Immunotherapies against human bacterial and fungal infectious diseases: A review

Nations' ongoing struggles with a number of novel and reemerging infectious diseases, including the ongoing global health issue, the SARS-Co-V2 (severe acute respiratory syndrome coronavirus 2) outbreak, serve as proof that infectious diseases constitute a serious threat to the global public health. Moreover, the fatality rate in humans is rising as a result of the development of severe infectious diseases brought about by multiple drug-tolerant pathogenic microorganisms. The widespread use of traditional antimicrobial drugs, immunosuppressive medications, and other related factors led to the establishment of such drug resistant pathogenic microbial species. To overcome the difficulties commonly encountered by current infectious disease management and control processes, like inadequate effectiveness, toxicities, and the evolution of drug tolerance, new treatment solutions are required. Fortunately, immunotherapies already hold great potential for reducing these restrictions while simultaneously expanding the boundaries of healthcare and medicine, as shown by the latest discoveries and the success of drugs including monoclonal antibodies (MAbs), vaccinations, etc. Immunotherapies comprise methods for treating diseases that specifically target or affect the body's immune system and such immunological procedures/therapies strengthen the host's defenses to fight those infections. The immunotherapy-based treatments control the host's innate and adaptive immune responses, which are effective in treating different pathogenic microbial infections. As a result, diverse immunotherapeutic strategies are being researched more and more as alternative treatments for infectious diseases, leading to substantial improvements in our comprehension of the associations between pathogens and host immune system. In this review we will explore different immunotherapies and their usage for the assistance of a broad spectrum of infectious ailments caused by various human bacterial and fungal pathogenic microbes. We will discuss about the recent developments in the therapeutics against the growing human pathogenic microbial diseases and focus on the present and future of using immunotherapies to overcome these diseases. Graphical AbstractThe graphical abstract shows the therapeutic potential of different types of immunotherapies like vaccines, monoclonal antibodies-based therapies, etc., against different kinds of human Bacterial and Fungal microbial infections.

Updating the WHO target product profile for next-generation Mycobacterium tuberculosis drug susceptibility testing at peripheral centres

There were approximately 10 million tuberculosis (TB) cases in 2020, of which 500,000 were drug-resistant. Only one third of drug-resistant TB cases were diagnosed and enrolled on appropriate treatment, an issue partly driven by a lack of rapid, accurate drug-susceptibility testing (DST) tools deployable in peripheral settings. In 2014, World Health Organization (WHO) published target product profiles (TPPs) which detailed minimal and optimal criteria to address high-priority TB diagnostic needs, including DST. Since then, the TB community's needs have evolved; new treatment regimens, changes in TB definitions, further emergence of drug resistance, technological advances, and changing end-users requirements have necessitated an update. The DST TPP's revision was therefore undertaken by WHO with the Stop TB Partnership New Diagnostics Working Group. We describe the process of updating the TPP for next-generation TB DST for use at peripheral centres, highlight key updates, and discuss guidance regarding technical and operational specifications.

Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

Strain diversity and gene mutations associated with presumptive multidrug-resistant Mycobacterium tuberculosis complex isolates in Northwest Ethiopia

OBJECTIVES

In this study, we assessed the genetic diversity and gene mutations that confer resistance to rifampicin (RIF), isoniazid (INH), fluoroquinolone (FQ), and second-line injectable (SLI) drugs in RIF-resistant (RR)/multidrug-resistant tuberculosis (MDR-TB) isolates in Northwest Ethiopia.

METHODS

Spoligotyping was used to assign isolates to TB lineages (Ls), and Hain line probe assays were used to detect resistance to RIF, INH, and FQs, and SLIs.

RESULTS

Among 130 analyzed strains, 68.5% were RR, and four major Mycobacterium tuberculosis complex lineages (L1, L3, L4, and L7) were identified with a predominance of the Euro-American L4 (72, 54.7%), while L7 genotypes were less common (3, 2.3%). Overall, the L4-T3-ETH (41, 32.0%), L3-CAS1-Delhi (29, 22.7%), and L3-CAS1-Killi (19, 14.8%) families were most common. Line probe analysis showed that among rpoB mutants, 65.2% were S450L, while 87.8% of katG mutants were S315T. Only three isolates showed mutation (c-15t) at the inhA gene, and no double mutation with katG and inhA genes was found. Six strains, two each of L1, L3, and L4, were resistant to FQs, having gyrA mutations (D94G, S91P), of which three isolates had additional resistance to SLI (rrs A1401G or C1402T mutations) including one isolate with low-level kanamycin (KAN) resistance.

CONCLUSIONS

This study showed a predominance of L4-T3-ETH, L3-CAS1-Delhi, and L3-CAS1-Killi families, with a high rate of rpoB_S450L and katG_S315T mutations and a low proportion of gyrA and rrs mutations. L7 was less frequently observed in this study. Further investigations are, therefore, needed to understand L7 and other lineages with undefined mutations.

Whole-genome sequencing-based analyses of drug-resistant Mycobacterium tuberculosis from Taiwan

Drug-resistant tuberculosis (DR-TB) posed challenges to global TB control. Whole-genome sequencing (WGS) is recommended for predicting drug resistance to guide DR-TB treatment and management. Nevertheless, data are lacking in Taiwan. Phenotypic drug susceptibility testing (DST) of 12 anti-TB drugs was performed for 200 Mycobacterium tuberculosis isolates. WGS was performed using the Illumina platform. Drug resistance profiles and lineages were predicted in silico using the Total Genotyping Solution for TB (TGS-TB). Using the phenotypic DST results as a reference, WGS-based prediction demonstrated high concordance rates of isoniazid (95.0%), rifampicin (RIF) (98.0%), pyrazinamide (98.5%) and fluoroquinolones (FQs) (99.5%) and 96.0% to 99.5% for second-line injectable drugs (SLIDs); whereas, lower concordance rates of ethambutol (87.5%), streptomycin (88.0%) and ethionamide (84.0%). Furthermore, minimum inhibitory concentrations confirmed that RIF rpoB S450L, FQs gyrA D94G and SLIDs rrs a1401g conferred high resistance levels. Besides, we identified lineage-associated mutations in lineage 1 (rpoB H445Y and fabG1 c-15t) and predominant lineage 2 (rpoB S450L and rpsL K43R). The WGS-based prediction of drug resistance is highly concordant with phenotypic DST results and can provide comprehensive genetic information to guide DR-TB precision therapies in Taiwan.

Mycobacterium smegmatis: The Vanguard of Mycobacterial Research

The genus Mycobacterium contains several slow-growing human pathogens, including Mycobacterium tuberculosis, Mycobacterium leprae, and Mycobacterium avium. Mycobacterium smegmatis is a nonpathogenic and fast growing species within this genus. In 1990, a mutant of M. smegmatis, designated mc2155, that could be transformed with episomal plasmids was isolated, elevating M. smegmatis to model status as the ideal surrogate for mycobacterial research. Classical bacterial models, such as Escherichia coli, were inadequate for mycobacteria research because they have low genetic conservation, different physiology, and lack the novel envelope structure that distinguishes the Mycobacterium genus. By contrast, M. smegmatis encodes thousands of conserved mycobacterial gene orthologs and has the same cell architecture and physiology. Dissection and characterization of conserved genes, structures, and processes in genetically tractable M. smegmatis mc2155 have since provided previously unattainable insights on these same features in its slow-growing relatives. Notably, tuberculosis (TB) drugs, including the first-line drugs isoniazid and ethambutol, are active against M. smegmatis, but not against E. coli, allowing the identification of their physiological targets. Furthermore, Bedaquiline, the first new TB drug in 40 years, was discovered through an M. smegmatis screen. M. smegmatis has become a model bacterium, not only for M. tuberculosis, but for all other Mycobacterium species and related genera. With a repertoire of bioinformatic and physical resources, including the recently established Mycobacterial Systems Resource, M. smegmatis will continue to accelerate mycobacterial research and advance the field of microbiology.

GWAS and functional studies suggest a role for altered DNA repair in the evolution of drug resistance in Mycobacterium tuberculosis

The emergence of drug resistance in Mycobacterium tuberculosis (Mtb) is alarming and demands in-depth knowledge for timely diagnosis. We performed genome-wide association analysis using 2237 clinical strains of Mtb to identify novel genetic factors that evoke drug resistance. In addition to the known direct targets, we identified for the first time, a strong association between mutations in DNA repair genes and the multidrug-resistant phenotype. To evaluate the impact of variants identified in the clinical samples in the evolution of drug resistance, we utilized knockouts and complemented strains in Mycobacterium smegmatis and Mtb. Results show that variant mutations compromised the functions of MutY and UvrB. MutY variant showed enhanced survival compared with wild-type (Rv) when the Mtb strains were subjected to multiple rounds of ex vivo antibiotic stress. In an in vivo guinea pig infection model, the MutY variant outcompeted the wild-type strain. We show that novel variant mutations in the DNA repair genes collectively compromise their functions and contribute to better survival under antibiotic/host stress conditions.

Drug Resistance and Molecular Characteristics of Mycobacterium tuberculosis: A Single Center Experience

In recent years, the incidence of tuberculosis (TB) and mortality caused by the disease have been decreasing. However, the number of drug-resistant tuberculosis patients is increasing rapidly year by year. Here, a total of 380 Mycobacterium tuberculosis (MTB)-positive formalin-fixed and paraffin-embedded tissue (FFPE) specimens diagnosed in the Department of Pathology of the Eighth Medical Center, Chinese PLA General Hospital were collected. Among 380 cases of MTB, 85 (22.37%) were susceptible to four anti-TB drugs and the remaining 295 (77.63%) were resistant to one or more drugs. The rate of MDR-TB was higher in previously treated cases (52.53%) than in new cases [(36.65%), p  <  0.05]. Of previously treated cases, the rate of drug resistance was higher in females than in males (p  <  0.05). Among specimens obtained from males, the rate of drug resistance was higher in new cases than in previously treated cases (p  <  0.05). Of mutation in drug resistance-related genes, the majority (53/380, 13.95%) of rpoB gene carried the D516V mutation, and 13.42% (51/380) featured mutations in both the katG and inhA genes. Among the total specimens, 18.68% (71/380) carried the 88 M mutation in the rpsL gene, and the embB gene focused on the 306 M2 mutation with a mutation rate of 19.74%. Among the resistant INH, the mutation rate of −15 M was higher in resistance to more than one drug than in monodrug-resistant (p  <  0.05). In conclusion, the drug resistance of MTB is still very severe and the timely detection of drug resistance is conducive to the precise treatment of TB.

Therapeutic Potentials of Immunometabolomic Modulations Induced by Tuberculosis Vaccination

Metabolomics is emerging as a promising tool to understand the effect of immunometabolism for the development of novel host-directed alternative therapies. Immunometabolism can modulate both innate and adaptive immunity in response to pathogens and vaccinations. For instance, infections can affect lipid and amino acid metabolism while vaccines can trigger bile acid and carbohydrate pathways. Metabolomics as a vaccinomics tool, can provide a broader picture of vaccine-induced biochemical changes and pave a path to potentiate the vaccine efficacy. Its integration with other systems biology tools or treatment modes can enhance the cure, response rate, and control over the emergence of drug-resistant strains. Mycobacterium tuberculosis (Mtb) infection can remodel the host metabolism for its survival, while there are many biochemical pathways that the host adjusts to combat the infection. Similarly, the anti-TB vaccine, Bacillus Calmette-Guerin (BCG), was also found to affect the host metabolic pathways thus modulating immune responses. In this review, we highlight the metabolomic schema of the anti-TB vaccine and its therapeutic applications. Rewiring of immune metabolism upon BCG vaccination induces different signaling pathways which lead to epigenetic modifications underlying trained immunity. Metabolic pathways such as glycolysis, central carbon metabolism, and cholesterol synthesis play an important role in these aspects of immunity. Trained immunity and its applications are increasing day by day and it can be used to develop the next generation of vaccines to treat various other infections and orphan diseases. Our goal is to provide fresh insight into this direction and connect various dots to develop a conceptual framework.

Pretomanid development and its clinical roles in treating tuberculosis

Tuberculosis (TB) is the leading infectious cause of mortality worldwide. Despite the development of different antituberculosis drugs, managing resistant mycobacteria is still challenging. The discovery of novel drugs and new methods of targeted drug delivery have the potential to improve treatment outcomes, lower the duration of treatment, and reduce adverse events. Following bedaquiline and delamanid, pretomanid is the third medicine approved as part of a novel drug regimen for treating drug-resistant TB. It is a promising drug that has the capacity to shape TB treatment and achieve the End TB strategy set by the World Health Organization. The effectiveness of pretomanid has been reported in different observational and clinical studies. However, long-term safety data in humans are not yet available and the pretomanid-based regimen is recommended under an operational research framework that prohibits its wider and programmatic use. Further research is needed before pretomanid can be celebrated as a promising candidate for the treatment of different categories of TB and specific patients. This review covers the update on pretomanid development and its clinical roles in treating Mycobacterium tuberculosis.

Transmission and Drug Resistance Genotype of Multidrug-Resistant or Rifampicin-Resistant Mycobacterium tuberculosis in Chongqing, China

Multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) is a global barrier for the Stop TB plan. To identify risk factors for treatment outcome and cluster transmission of MDR/RR-TB, whole-genome sequencing (WGS) data of isolates from patients of the Chongqing Tuberculosis Control Institute were used for phylogenetic classifications, resistance predictions, and cluster analysis. A total of 223 MDR/RR-TB cases were recorded between 1 January 2018 and 31 December 2020. Elderly patients and those with lung cavitation are at increased risk of death due to MDR/RR-TB. A total of 187 MDR/RR strains were obtained from WGS data; 152 were classified as lineage 2 strains. Eighty (42.8%) strains differing by a distance of 12 or fewer single nucleotide polymorphisms were classified as 20 genomic clusters, indicating recent transmission. Patients infected with lineage 2 strains or those with occupations listed as "other" are significantly associated with a transmission cluster of MDR/RR-TB. Analysis of resistant mutations against first-line tuberculosis drugs found that 76 (95.0%) of all 80 strains had the same mutations within each cluster. A total of 55.0% (44 of 80) of the MDR/RR-TB strains accumulated additional drug resistance mutations along the transmission chain, especially against fluoroquinolones (63.6% [28 of 44]). Recent transmission of MDR/RR strains is driving the MDR/RR-TB epidemics, leading to the accumulation of more serious resistance along the transmission chains. IMPORTANCE The drug resistance molecular characteristics of MDR/RR-TB were elucidated by genome-wide analysis, and risk factors for death by MDR/RR-TB were identified in combination with patient information. Cluster characteristics of MDR/RR-TB in the region were analyzed by genome-wide analysis, and risk factors for cluster transmission (recent transmission) were analyzed. These analyses provide reference for the prevention and treatment of MDR/RR-TB in Chongqing.

The evolving biology of Mycobacterium tuberculosis drug resistance

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb) is an ancient disease that has remained a leading cause of infectious death. Mtb has evolved drug resistance to every antibiotic regimen ever introduced, greatly complicating treatment, lowering rates of cure and menacing TB control in parts of the world. As technology has advanced, our understanding of antimicrobial resistance has improved, and our models of the phenomenon have evolved. In this review, we focus on recent research progress that supports an updated model for the evolution of drug resistance in Mtb. We highlight the contribution of drug tolerance on the path to resistance, and the influence of heterogeneity on tolerance. Resistance is likely to remain an issue for as long as drugs are needed to treat TB. However, with technology driving new insights and careful management of newly developed resources, antimicrobial resistance need not continue to threaten global progress against TB, as it has done for decades.

Mycobacterium tuberculosis functional genetic diversity, altered drug sensitivity, and precision medicine

In the face of the unrelenting global burden of tuberculosis (TB), antibiotics remain our most effective tools to save lives and control the spread of Mycobacterium tuberculosis (Mtb). However, we confront a dual challenge in our use of antibiotics: simplifying and shortening the TB drug regimen while also limiting the emergence and propagation of antibiotic resistance. This task is now more feasible due to the increasing availability of bacterial genomic data at or near the point of care. These resources create an opportunity to envision how integration of bacterial genetic determinants of antibiotic response into treatment algorithms might transform TB care. Historically, Mtb drug resistance studies focused on mutations in genes encoding antibiotic targets and the resulting increases in the minimal inhibitory concentrations (MICs) above a breakpoint value. But recent progress in elucidating the effects of functional genetic diversity in Mtb has revealed various genetic loci that are associated with drug phenotypes such as low-level MIC increases and tolerance which predict the development of resistance and treatment failure. As a result, we are now poised to advance precision medicine approaches in TB treatment. By incorporating information regarding Mtb genetic characteristics into the development of drug regimens, clinical care which tailors antibiotic treatment to maximize the likelihood of success has come into reach.

Tuberculosis Treatment Monitoring and Outcome Measures: New Interest and New Strategies

Despite the advent of new diagnostics, drugs and regimens, tuberculosis (TB) remains a global public health threat. A significant challenge for TB control efforts has been the monitoring of TB therapy and determination of TB treatment success. Current recommendations for TB treatment monitoring rely on sputum and culture conversion, which have low sensitivity and long turnaround times, present biohazard risk, and are prone to contamination, undermining their usefulness as clinical treatment monitoring tools and for drug development. We review the pipeline of molecular technologies and assays that serve as suitable substitutes for current culture-based readouts for treatment response and outcome with the potential to change TB therapy monitoring and accelerate drug development.

Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective

Antimicrobial resistance (AMR) is a global health crisis that poses a great threat to modern medicine. Effective prevention strategies are urgently required to slow the emergence and further dissemination of AMR. Given the availability of data sets encompassing hundreds or thousands of pathogen genomes, machine learning (ML) is increasingly being used to predict resistance to different antibiotics in pathogens based on gene content and genome composition. A key objective of this work is to advocate for the incorporation of ML into front-line settings but also highlight the further refinements that are necessary to safely and confidently incorporate these methods. The question of what to predict is not trivial given the existence of different quantitative and qualitative laboratory measures of AMR. ML models typically treat genes as independent predictors, with no consideration of structural and functional linkages; they also may not be accurate when new mutational variants of known AMR genes emerge. Finally, to have the technology trusted by end users in public health settings, ML models need to be transparent and explainable to ensure that the basis for prediction is clear. We strongly advocate that the next set of AMR-ML studies should focus on the refinement of these limitations to be able to bridge the gap to diagnostic implementation.

Evaluation of whole-genome sequence to predict drug resistance of nine anti-tuberculosis drugs and characterize resistance genes in clinical rifampicin-resistant Mycobacterium tuberculosis isolates from Ningbo, China

Setting

Controlling drug-resistant tuberculosis in Ningbo, China.

Objective

Whole-genome sequencing (WGS) has not been employed to comprehensively study Mycobacterium tuberculosis isolates, especially rifampicin-resistant tuberculosis, in Ningbo, China. Here, we aim to characterize genes involved in drug resistance in RR-TB and create a prognostic tool for successfully predicting drug resistance in patients with TB.

Design

Drug resistance was predicted by WGS in a "TB-Profiler" web service after phenotypic drug susceptibility tests (DSTs) against nine anti-TB drugs among 59 clinical isolates. A comparison of consistency, sensitivity, specificity, and positive and negative predictive values between WGS and DST were carried out for each drug.

Results

The sensitivities and specificities for WGS were 95.92 and 90% for isoniazid (INH), 100 and 64.1% for ethambutol (EMB), 97.37 and 100% for streptomycin (SM), 75 and 100% for amikacin (AM), 80 and 96.3%for capreomycin (CAP), 100 and 97.22% for levofloxacin (LFX), 93.33 and 90.91% for prothionamide (PTO), and 70 and 97.96% for para-aminosalicylic acid (PAS). Around 53 (89.83%) and 6 (10.17%) of the isolates belonged to lineage two (East-Asian) and lineage four (Euro-American), respectively.

Conclusion

Whole-genome sequencing is a reliable method for predicting resistance to INH, RIF, EMB, SM, AM, CAP, LFX, PTO, and PAS with high consistency, sensitivity, and specificity. There was no transmission that occurred among the patients with RR-TB in Ningbo, China.

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.