Evolution of high-level ethambutol-resistant tuberculosis through interacting mutations in decaprenylphosphoryl-β-D-arabinose biosynthetic and utilization pathway genes

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.

Age-Dependent Pleomorphism in Mycobacterium monacense Cultures

Changes in cell shape have been shown to be an integral part of the mycobacterial life cycle; however, systematic investigations into its patterns of pleomorphic behaviour in connection with stages or conditions of growth are scarce. We have studied the complete growth cycle of Mycobacterium monacense cultures, a Non-Tuberculous Mycobacterium (NTM), in solid as well as in liquid media. We provide data showing changes in cell shape from rod to coccoid and occurrence of refractive cells ranging from Phase Grey to phase Bright (PGB) in appearance upon ageing. Changes in cell shape could be correlated to the bi-phasic nature of the growth curves for M. monacense (and the NTM Mycobacterium boenickei) as measured by the absorbance of liquid cultures while growth measured by colony-forming units (CFU) on solid media showed a uniform exponential growth. Based on the complete M. monacense genome we identified genes involved in cell morphology, and analyses of their mRNA levels revealed changes at different stages of growth. One gene, dnaK_3 (encoding a chaperone), showed significantly increased transcript levels in stationary phase cells relative to exponentially growing cells. Based on protein domain architecture, we identified that the DnaK_3 N-terminus domain is an MreB-like homolog. Endogenous overexpression of M. monacense dnaK_3 in M. monacense was unsuccessful (appears to be lethal) while exogenous overexpression in Mycobacterium marinum resulted in morphological changes with an impact on the frequency of appearance of PGB cells. However, the introduction of an anti-sense "gene" targeting the M. marinum dnaK_3 did not show significant effects. Using dnaK_3-lacZ reporter constructs we also provide data suggesting that the morphological differences could be due to differences in the regulation of dnaK_3 in the two species. Together these data suggest that, although its regulation may vary between mycobacterial species, the dnaK_3 might have a direct or indirect role in the processes influencing mycobacterial cell shape.

Predicting Drug Resistance in Mycobacterium tuberculosis: A Machine Learning Approach to Genomic Mutation Analysis

Background/Objectives: Tuberculosis (TB), caused by Mycobacterium tuberculosis (M. tuberculosis), remains a leading cause of death from infectious diseases globally. The treatment of active TB relies on first- and second-line drugs, however, the emergence of drug resistance poses a significant challenge to global TB control efforts. Recent advances in whole-genome sequencing combined with machine learning have shown promise in predicting drug resistance. This study aimed to evaluate the performance of four machine learning models in classifying resistance to ethambutol, isoniazid, and rifampicin in M. tuberculosis isolates. Methods: Four machine learning models-Extreme Gradient Boosting Classifier (XGBC), Logistic Gradient Boosting Classifier (LGBC), Gradient Boosting Classifier (GBC), and an Artificial Neural Network (ANN)-were trained using a Variant Call Format (VCF) dataset preprocessed by the CRyPTIC consortium. Three datasets were used: the original dataset, a principal component analysis (PCA)-reduced dataset, and a dataset prioritizing significant mutations identified by the XGBC model. The models were trained and tested across these datasets, and their performance was compared using sensitivity, specificity, Precision, F1-scores and Accuracy. Results: All models were applied to the PCA-reduced dataset, while the XGBC model was also evaluated using the mutation-prioritized dataset. The XGBC model trained on the original dataset outperformed the others, achieving sensitivity values of 0.97, 0.90, and 0.94; specificity values of 0.97, 0.99, and 0.96; and F1-scores of 0.93, 0.94, and 0.92 for ethambutol, isoniazid, and rifampicin, respectively. These results demonstrate the superior accuracy of the XGBC model in classifying drug resistance. Conclusions: The study highlights the effectiveness of using a binary representation of mutations to train the XGBC model for predicting resistance and susceptibility to key TB drugs. The XGBC model trained on the original dataset demonstrated the highest performance among the evaluated models, suggesting its potential for clinical application in combating drug-resistant tuberculosis. Further research is needed to validate and expand these findings for broader implementation in TB diagnostics.

Identification of potential molecular targets and repurposed drugs for tuberculosis using network-based screening approach, molecular docking, and simulation

The spread of drug-resistant strains of tuberculosis has hampered efforts to control the disease worldwide. The Mycobacterium tuberculosis cell wall envelope is dynamic, with complex features that protect it from the host immunological response. As a result, the bacterial cell wall components represent a potential target for drug discovery. Protein-protein interaction networks (PPIN) are critical for understanding disease conditions and identifying precise therapeutic targets. We used a rational theoretical approach by constructing a PPIN with the proteins involved in cell wall biosynthesis. The PPIN was constructed through the STRING database and embB was identified as a key protein by using four topological measures, betweenness, closeness, degree, and eigenvector, in the CytoNCA tool in Cytoscape. The 'Drug repurposing' approach was employed to find suitable inhibitors against embB. We used the Schrödinger suites for molecular docking, molecular dynamics simulation, and binding free energy calculations to validate the binding of protein with the ligand. FDA-approved drugs from the ZINC database and DrugBank were screened against embB (PDB ID: 7BVF) using high-throughput virtual screening, standard precision, and extra precision docking. The drugs were screened based on the XP docking score of the standard drug ethambutol. Accordingly, from the top five hits, azilsartan and dihydroergotamine were selected based on the binding free energy values and were further subjected to Molecular Dynamics Simulation studies for 100 ns. Our study confirms that Azilsartan and Dihydroergotamine form stable complexes with embB and can be used as potential lead molecules based on further in vitro and in vivo experimental validation.Communicated by Ramaswamy H. Sarma.

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.

Different Contributions of embB and ubiA Mutations to Variable Level of Ethambutol Resistance in Mycobacterium tuberculosis Isolates

Objective

To explore the association between the variant mutations within embB and ubiA, and the degree of ethambutol (EMB) resistance of Mycobacterium tuberculosis (M. tuberculosis) isolates.

Methods

A total of 146 M. tuberculosis isolates were used to determine the minimum inhibitory concentrations (MICs) of EMB with a 96-well microplate-based assay. The mutations within embB and ubiA among these isolates were identified with DNA sequencing. Moreover, a multivariate regression model and a computer model were established to assess the effects of mutations on EMB resistance.

Results

Our data showed that overall 100 isolates exhibited 28 mutated patterns within the sequenced embB and ubiA. Statistical analysis indicated that embB mutations Met306Val, Met306Ile, Gly406Ala, and Gln497Arg, were strongly associated with EMB resistance. Of these mutations, Met306Val and Gln497Arg were significantly associated with high-level EMB resistance. Almost all multiple mutations occurred in high-level EMB-resistant isolates. Although the mutation within ubiA accompanied with embB mutation presented exclusively in EMB-resistant isolates, four single ubiA mutations (Ala39Glu, Ser173Ala, Trp175Cys, and Val283Leu) leading to protein instability were observed in EMB-susceptible isolates.

Conclusion

This study highlighted the complexity of EMB resistance. Some individual mutations and multiple mutations within embB and ubiA contributed to the different levels of EMB resistance.

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.

Structural analysis of phosphoribosyltransferase-mediated cell wall precursor synthesis in Mycobacterium tuberculosis

In Mycobacterium tuberculosis, Rv3806c is a membrane-bound phosphoribosyltransferase (PRTase) involved in cell wall precursor production. It catalyses pentosyl phosphate transfer from phosphoribosyl pyrophosphate to decaprenyl phosphate, to generate 5-phospho-β-ribosyl-1-phosphoryldecaprenol. Despite Rv3806c being an attractive drug target, structural and molecular mechanistic insight into this PRTase is lacking. Here we report cryogenic electron microscopy structures for Rv3806c in the donor- and acceptor-bound states. In a lipidic environment, Rv3806c is trimeric, creating a UbiA-like fold. Each protomer forms two helical bundles, which, alongside the bound lipids, are required for PRTase activity in vitro. Mutational and functional analyses reveal that decaprenyl phosphate and phosphoribosyl pyrophosphate bind the intramembrane and extramembrane cavities of Rv3806c, respectively, in a distinct manner to that of UbiA superfamily enzymes. Our data suggest a model for Rv3806c-catalysed phosphoribose transfer through an inverting mechanism. These findings provide a structural basis for cell wall precursor biosynthesis that could have potential for anti-tuberculosis drug development.

Antitubercular drugs: possible role of natural products acting as antituberculosis medication in overcoming drug resistance and drug-induced hepatotoxicity

Mycobacterium tuberculosis (Mtb) is a pathogenic bacterium which causes tuberculosis (TB). TB control programmes are facing threats from drug resistance. Multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mtb strains need longer and more expensive treatment with many medications resulting in more adverse effects and decreased chances of treatment outcomes. The World Health Organization (WHO) has emphasised the development of not just new individual anti-TB drugs, but also novel medication regimens as an alternative treatment option for the drug-resistant Mtb strains. Many plants, as well as marine creatures (sponge; Haliclona sp.) and fungi, have been continuously used to treat TB in various traditional treatment systems around the world, providing an almost limitless supply of active components. Natural products, in addition to their anti-mycobacterial action, can be used as adjuvant therapy to increase the efficacy of conventional anti-mycobacterial medications, reduce their side effects, and reverse MDR Mtb strain due to Mycobacterium's genetic flexibility and environmental adaptation. Several natural compounds such as quercetin, ursolic acid, berberine, thymoquinone, curcumin, phloretin, and propolis have shown potential anti-mycobacterial efficacy and are still being explored in preclinical and clinical investigations for confirmation of their efficacy and safety as anti-TB medication. However, more high-level randomized clinical trials are desperately required. The current review provides an overview of drug-resistant TB along with the latest anti-TB medications, drug-induced hepatotoxicity and oxidative stress. Further, the role and mechanisms of action of first and second-line anti-TB drugs and new drugs have been highlighted. Finally, the role of natural compounds as anti-TB medication and hepatoprotectants have been described and their mechanisms discussed.

Evolution of Laboratory Diagnosis of Tuberculosis

Tuberculosis (TB) is an infectious disease of global public health importance caused by the Mycobacterium tuberculosis complex. Despite advances in diagnosis and treatment, this disease has worsened with the emergence of multidrug-resistant strains of tuberculosis. We aim to present and review the history, progress, and future directions in the diagnosis of tuberculosis by evaluating the current methods of laboratory diagnosis of tuberculosis, with a special emphasis on microscopic examination and cultivation on solid and liquid media, as well as an approach to molecular assays. The microscopic method, although widely used, has its limitations, and the use and evaluation of other techniques are essential for a complete and accurate diagnosis. Bacterial cultures, both in solid and liquid media, are essential methods in the diagnosis of TB. Culture on a solid medium provides specificity and accuracy, while culture on a liquid medium brings rapidity and increased sensitivity. Molecular tests such as LPA and Xpert MTB/RIF have been found to offer significant benefits in the rapid and accurate diagnosis of TB, including drug-resistant forms. These tests allow the identification of resistance mutations and provide essential information for choosing the right treatment. We conclude that combined diagnostic methods, using several techniques and approaches, provide the best result in the laboratory diagnosis of TB. Improving the quality and accessibility of tests, as well as the implementation of advanced technologies, is essential to help improve the sensitivity, efficiency, and accuracy of TB diagnosis.

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

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

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

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

Analysis of Genome-Wide Mutational Dependence in Naturally Evolving Mycobacterium tuberculosis Populations

Pathogenic microorganisms are in a perpetual struggle for survival in changing host environments, where host pressures necessitate changes in pathogen virulence, antibiotic resistance, or transmissibility. The genetic basis of phenotypic adaptation by pathogens is difficult to study in vivo. In this work, we develop a phylogenetic method to detect genetic dependencies that promote pathogen adaptation using 31,428 in vivo sampled Mycobacterium tuberculosis genomes, a globally prevalent bacterial pathogen with increasing levels of antibiotic resistance. We find that dependencies between mutations are enriched in antigenic and antibiotic resistance functions and discover 23 mutations that potentiate the development of antibiotic resistance. Between 11% and 92% of resistant strains harbor a dependent mutation acquired after a resistance-conferring variant. We demonstrate the pervasiveness of genetic dependency in adaptation of naturally evolving populations and the utility of the proposed computational approach.

Heterologous production of the D-cycloserine intermediate O-acetyl-L-serine in a human type II pulmonary cell model

Tuberculosis (TB) is the second leading cause of death by a single infectious disease behind COVID-19. Despite a century of effort, the current TB vaccine does not effectively prevent pulmonary TB, promote herd immunity, or prevent transmission. Therefore, alternative approaches are needed. We seek to develop a cell therapy that produces an effective antibiotic in response to TB infection. D-cycloserine (D-CS) is a second-line antibiotic for TB that inhibits bacterial cell wall synthesis. We have determined D-CS to be the optimal candidate for anti-TB cell therapy due to its effectiveness against TB, relatively short biosynthetic pathway, and its low-resistance incidence. The first committed step towards D-CS synthesis is catalyzed by the L-serine-O-acetyltransferase (DcsE) which converts L-serine and acetyl-CoA to O-acetyl-L-serine (L-OAS). To test if the D-CS pathway could be an effective prophylaxis for TB, we endeavored to express functional DcsE in A549 cells as a human pulmonary model. We observed DcsE-FLAG-GFP expression using fluorescence microscopy. DcsE purified from A549 cells catalyzed the synthesis of L-OAS as observed by HPLC-MS. Therefore, human cells synthesize functional DcsE capable of converting L-serine and acetyl-CoA to L-OAS demonstrating the first step towards D-CS production in human cells.

Designing New Magic Bullets to Penetrate the Mycobacterial Shield: An Arduous Quest for Promising Therapeutic Candidates

Mycobacterium spp. intimidated mankind since time immemorial. The triumph over this organism was anticipated with the introduction of potent antimicrobials in the mid-20th century. However, the emergence of drug resistance in mycobacteria, Mycobacterium tuberculosis, in particular, caused great concern for the treatment. With the enemy growing stronger, there is an immediate need to equip the therapeutic arsenal with novel and potent chemotherapeutic agents. The task seems intricating as our understanding of the dynamic nature of the mycobacteria requires intense experimentation and research. Targeting the mycobacterial cell envelope appears promising, but its versatility allows it to escape the lethal effect of the molecules acting on it. The unique ability of hiding (inactivity during latency) also assists the bacterium to survive in a drug-rich environment. The drug delivery systems also require upgradation to allow better bioavailability and tolerance in patients. Although the resistance to the novel drugs is inevitable, our commitment to the research in this area will ensure the discovery of effective weapons against this formidable opponent.

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.

Marine Macrolides to Tackle Antimicrobial Resistance of Mycobacterium tuberculosis

Tuberculosis has become a major health problem globally. This is worsened by the emergence of resistant strains of Mycobacterium tuberculosis showing ability to evade the effectiveness of the current antimycobacterial therapies. Therefore, the efforts carried out to explore new entities from many sources, including marine, are critical. This review summarizes several marine-derived macrolides that show promising activity against M. tuberculosis. We also provide information regarding the biosynthetic processes of marine macrolides, including the challenges that are usually experienced in this process. As most of the studies reporting the antimycobacterial activities of the listed marine macrolides are based on in vitro studies, the future direction should consider expanding the trials to in vivo and clinical trials. In addition, in silico studies should also be explored for a quick screening on marine macrolides with potent activities against mycobacterial infection. To sum up, macrolides derived from marine organisms might become therapeutical options for tackling antimycobacterial resistance of M. tuberculosis.

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.

mbtD and celA1 association with ethambutol resistance in Mycobacterium tuberculosis: A multiomics analysis

Ethambutol (EMB) is a first-line antituberculosis drug currently being used clinically to treat tuberculosis. Mutations in the embCAB operon are responsible for EMB resistance. However, the discrepancies between genotypic and phenotypic EMB resistance have attracted much attention. We induced EMB resistance in Mycobacterium tuberculosis in vitro and used an integrated genome–methylome–transcriptome–proteome approach to study the microevolutionary mechanism of EMB resistance. We identified 509 aberrantly methylated genes (313 hypermethylated genes and 196 hypomethylated genes). Moreover, some hypermethylated and hypomethylated genes were identified using RNA-seq profiling. Correlation analysis revealed that the differential methylation of genes was negatively correlated with transcription levels in EMB-resistant strains. Additionally, two hypermethylated candidate genes (mbtD and celA1) were screened by iTRAQ-based quantitative proteomics analysis, verified by qPCR, and corresponded with DNA methylation differences. This is the first report that identifies EMB resistance-related genes in laboratory-induced mono-EMB-resistant M. tuberculosis using multi-omics profiling. Understanding the epigenetic features associated with EMB resistance may provide new insights into the underlying molecular mechanisms.

Are all antibiotic persisters created equal?

Antibiotic persisters are a sub-population of bacteria able to survive in the presence of bactericidal antibiotic despite the lack of heritable drug resistance mechanisms. This phenomenon exists across many bacterial species and is observed for many different antibiotics. Though these bacteria are often described as “multidrug persisters” very few experiments have been carried out to determine the homogeneity of a persister population to different drugs. Further, there is much debate in the field as to the origins of a persister cell. Is it formed spontaneously? Does it form in response to stress? These questions are particularly pressing in the field of Mycobacterium tuberculosis, where persisters may play a crucial role in the required length of treatment and the development of multidrug resistant organisms. Here we aim to interpret the known mechanisms of antibiotic persistence and how they may relate to improving treatments for M. tuberculosis, exposing the gaps in knowledge that prevent us from answering the question: Are all antibiotic persisters created equal?

Resistance and tolerance of Mycobacterium tuberculosis to antimicrobial agents-How M. tuberculosis can escape antibiotics

Tuberculosis (TB) poses a serious threat to public health worldwide since it was discovered. Until now, TB has been one of the top 10 causes of death from a single infectious disease globally. The treatment of active TB cases majorly relies on various anti-tuberculosis drugs. However, under the selection pressure by drugs, the continuous evolution of Mycobacterium tuberculosis (Mtb) facilitates the emergence of drug-resistant strains, further resulting in the accumulation of tubercle bacilli with multiple drug resistance, especially deadly multidrug-resistant TB and extensively drug-resistant TB. Researches on the mechanism of drug action and drug resistance of Mtb provide a new scheme for clinical management of TB patients, and prevention of drug resistance. In this review, we summarized the molecular mechanisms of drug resistance of existing anti-TB drugs to better understand the evolution of drug resistance of Mtb, which will provide more effective strategies against drug-resistant TB, and accelerate the achievement of the EndTB Strategy by 2035. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology.

Genome-scale metabolic modeling in antimicrobial pharmacology

The increasing antimicrobial resistance has seriously threatened human health worldwide over the last three decades. This severe medical crisis and the dwindling antibiotic discovery pipeline require the development of novel antimicrobial treatments to combat life-threatening infections caused by multidrug-resistant microbial pathogens. However, the detailed mechanisms of action, resistance, and toxicity of many antimicrobials remain uncertain, significantly hampering the development of novel antimicrobials. Genome-scale metabolic model (GSMM) has been increasingly employed to investigate microbial metabolism. In this review, we discuss the latest progress of GSMM in antimicrobial pharmacology, particularly in elucidating the complex interplays of multiple metabolic pathways involved in antimicrobial activity, resistance, and toxicity. We also highlight the emerging areas of GSMM applications in modeling non-metabolic cellular activities (e.g., gene expression), identification of potential drug targets, and integration with machine learning and pharmacokinetic/pharmacodynamic modeling. Overall, GSMM has significant potential in elucidating the critical role of metabolic changes in antimicrobial pharmacology, providing mechanistic insights that will guide the optimization of dosing regimens for the treatment of antimicrobial-resistant infections.

An Overview of Antimicrobial Stewardship Optimization: The Use of Antibiotics in Humans and Animals to Prevent Resistance

Antimicrobials are a type of agent widely used to prevent various microbial infections in humans and animals. Antimicrobial resistance is a major cause of clinical antimicrobial therapy failure, and it has become a major public health concern around the world. Increasing the development of multiple antimicrobials has become available for humans and animals with no appropriate guidance. As a result, inappropriate use of antimicrobials has significantly produced antimicrobial resistance. However, an increasing number of infections such as sepsis are untreatable due to this antimicrobial resistance. In either case, life-saving drugs are rendered ineffective in most cases. The actual causes of antimicrobial resistance are complex and versatile. A lack of adequate health services, unoptimized use of antimicrobials in humans and animals, poor water and sanitation systems, wide gaps in access and research and development in healthcare technologies, and environmental pollution have vital impacts on antimicrobial resistance. This current review will highlight the natural history and basics of the development of antimicrobials, the relationship between antimicrobial use in humans and antimicrobial use in animals, the simplistic pathways, and mechanisms of antimicrobial resistance, and how to control the spread of this resistance.

Identification of mutations in rpoB, pncA, embB, and ubiA genes among drug-resistant Mycobacterium tuberculosis clinical isolates from Iran

Mycobacterium tuberculosis resistant to effective first-line drugs (FLDs) has challenged national and global tuberculosis control programs. This study aimed to identify mutations in 4 genes related to rifampin, pyrazinamide, and ethambutol resistance among clinical isolates of M. tuberculosis from southwestern Iran. After drug susceptibility testing of 6620 M. tuberculosis clinical isolates by proportional method, a total of 24 FLD-resistant strains were included in the study. Fragments of rpoB, pncA, embB, and ubiA genes were amplified and sequenced to mine the mutations by pairwise alignment with the corresponding M. tuberculosis H37Rv genes. Phenotypic resistance to rifampin, isoniazid, and ethambutol was detected in 67, 54, and 33% (n = 16, 13, and 8) of the isolates, respectively. Of rifampin-resistant isolates, 31% (5/16) were mono-resistant, and 56% (9/16) were multidrug-resistant (MDR). In 100% of rifampin-resistant isolates, mutations were found in the rifampin resistance-determining region (RRDR) of the rpoB, with S450L substitution being the most common, especially in MDRs (77.8%, 7/9). Resistance-conferring mutations in pncA were present in 12.5% (3/24) of FLD-resistant isolates. The embB and ubiA mutations were found in 62.5 and 12.5% (5/8 and 1/8) of ethambutol-resistant isolates, respectively, of which the embB D354A was the most common substitution (37.5%, 3/8). Sixteen distinct mutations were identified, one of which was novel. The sequence analysis of the RRDR segment was the best way to detect rifampin resistance. The rpoB S450L substitution could be a helpful molecular marker to predict MDR. In other genes, no mutation was identified as a reliable marker.

Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis

Combination therapy has, to some extent, been successful in limiting the emergence of drug-resistant tuberculosis. Drug combinations achieve this advantage by simultaneously acting on different targets and metabolic pathways. Additionally, drug combination therapies are shown to shorten the duration of therapy for tuberculosis. As new drugs are being developed, to overcome the challenge of finding new and effective drug combinations, systems biology commonly uses approaches that analyse mycobacterial cellular processes. These approaches identify the regulatory networks, metabolic pathways, and signaling programs associated with M. tuberculosis infection and survival. Different preclinical models that assess anti-tuberculosis drug activity are available, but the combination of models that is most predictive of clinical treatment efficacy remains unclear. In this structured literature review, we appraise the options to accelerate the TB drug development pipeline through the evaluation of preclinical testing assays of drug combinations.

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

BACKGROUND

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

RESULTS

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

CONCLUSION

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

Silent Genes: Antimicrobial Resistance and Antibiotic Production

Silent genes are DNA sequences that are generally not expressed or expressed at a very low level. These genes become active as a result of mutation, recombination, or insertion. Silent genes can also be activated in laboratory conditions using pleiotropic, targeted genome-wide, or biosynthetic gene cluster approaches. Like every other gene, silent genes can spread through horizontal gene transfer. Most studies have focused on strains with phenotypic resistance, which is the most common subject. However, to fully understand the mechanism behind the spreading of antibiotic resistance, it is reasonable to study the whole resistome, including silent genes.

Retrospective evaluation of routine whole genome sequencing of Mycobacterium tuberculosis at the Belgian National Reference Center, 2019

OBJECTIVES

Since January 2019, the Belgian National Reference Center for Mycobacteria (NRC) has switched from conventional typing to prospective whole-genome sequencing (WGS) of all submitted Mycobacterium tuberculosis complex (MTB) isolates. The ISO17025 validated procedure starts with semi-automated extraction and purification of gDNA directly from the submitted MGIT tubes, without preceding subculturing. All samples are then sequenced on an Illumina MiSeq sequencer and analyzed using an in-house developed and validated bioinformatics workflow to determine the species and antimicrobial resistance. In this study, we retrospectively compare results obtained via WGS to conventional phenotypic and genotypic testing, for all Belgian MTB strains analyzed in 2019 (n = 306).

RESULTS

In all cases, the WGS-based procedure was able to identify correctly the MTB species. Compared to MGIT drug susceptibility testing (DST), the sensitivity and specificity of genetic prediction of resistance to first-line antibiotics were respectively 100 and 99% (rifampicin, RIF), 90.5 and 100% (isoniazid, INH), 100 and 98% (ethambutol, EMB) and 61.1 and 100% (pyrazinamide, PZA). The negative predictive value was above 95% for these four first-line drugs. A positive predictive value of 100% was calculated for INH and PZA, 80% for RIF and 45% for EMB.

CONCLUSIONS

Our study confirms the effectiveness of WGS for the rapid detection of M. tuberculosis complex and its drug resistance profiles for first-line drugs even when working directly on MGIT tubes, and supports the introduction of this test into the routine workflow of laboratories performing tuberculosis diagnosis.

Role of Epistasis in Amikacin, Kanamycin, Bedaquiline, and Clofazimine Resistance in Mycobacterium tuberculosis Complex

Antibiotic resistance among bacterial pathogens poses a major global health threat. Mycobacterium tuberculosis complex (MTBC) is estimated to have the highest resistance rates of any pathogen globally. Given the low growth rate and the need for a biosafety level 3 laboratory, the only realistic avenue to scale up drug susceptibility testing (DST) for this pathogen is to rely on genotypic techniques. This raises the fundamental question of whether a mutation is a reliable surrogate for phenotypic resistance or whether the presence of a second mutation can completely counteract its effect, resulting in major diagnostic errors (i.e., systematic false resistance results). To date, such epistatic interactions have only been reported for streptomycin that is now rarely used. By analyzing more than 31,000 MTBC genomes, we demonstrated that the eis C-14T promoter mutation, which is interrogated by several genotypic DST assays endorsed by the World Health Organization, cannot confer resistance to amikacin and kanamycin if it coincides with loss-of-function (LoF) mutations in the coding region of eis. To our knowledge, this represents the first definitive example of antibiotic reversion in MTBC. Moreover, we raise the possibility that mmpR (Rv0678) mutations are not valid markers of resistance to bedaquiline and clofazimine if these coincide with an LoF mutation in the efflux pump encoded by mmpS5 (Rv0677c) and mmpL5 (Rv0676c).

Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms

Antimicrobial resistance (AMR) is becoming one of the largest threats to public health worldwide, with the opportunistic pathogen Escherichia coli playing a major role in the AMR global health crisis. Unravelling the complex interplay between drug resistance and metabolic rewiring is key to understand the ability of bacteria to adapt to new treatments and to the development of new effective solutions to combat resistant infections. We developed a computational pipeline that combines machine learning with genome-scale metabolic models (GSMs) to elucidate the systemic relationships between genetic determinants of resistance and metabolism beyond annotated drug resistance genes. Our approach was used to identify genetic determinants of 12 AMR profiles for the opportunistic pathogenic bacterium E. coli. Then, to interpret the large number of identified genetic determinants, we applied a constraint-based approach using the GSM to predict the effects of genetic changes on growth, metabolite yields, and reaction fluxes. Our computational platform leads to multiple results. First, our approach corroborates 225 known AMR-conferring genes, 35 of which are known for the specific antibiotic. Second, integration with the GSM predicted 20 top-ranked genetic determinants (including accA, metK, fabD, fabG, murG, lptG, mraY, folP, and glmM) essential for growth, while a further 17 top-ranked genetic determinants linked AMR to auxotrophic behavior. Third, clusters of AMR-conferring genes affecting similar metabolic processes are revealed, which strongly suggested that metabolic adaptations in cell wall, energy, iron and nucleotide metabolism are associated with AMR. The computational solution can be used to study other human and animal pathogens.

IMPORTANCEEscherichia coli is a major public health concern given its increasing level of antibiotic resistance worldwide and extraordinary capacity to acquire and spread resistance via horizontal gene transfer with surrounding species and via mutations in its existing genome. E. coli also exhibits a large amount of metabolic pathway redundancy, which promotes resistance via metabolic adaptability. In this study, we developed a computational approach that integrates machine learning with metabolic modeling to understand the correlation between AMR and metabolic adaptation mechanisms in this model bacterium. Using our approach, we identified AMR genetic determinants associated with cell wall modifications for increased permeability, virulence factor manipulation of host immunity, reduction of oxidative stress toxicity, and changes to energy metabolism. Unravelling the complex interplay between antibiotic resistance and metabolic rewiring may open new opportunities to understand the ability of E. coli, and potentially of other human and animal pathogens, to adapt to new treatments.

The Epistatic Landscape of Antibiotic Resistance of Different Clades of Mycobacterium tuberculosis

Drug resistance (DR) remains a global challenge in tuberculosis (TB) control. In order to develop molecular-based diagnostic methods to replace the traditional culture-based diagnostics, there is a need for a thorough understanding of the processes that govern TB drug resistance. The use of whole-genome sequencing coupled with statistical and computational methods has shown great potential in unraveling the complexity of the evolution of DR-TB. In this study, we took an innovative approach that sought to determine nonrandom associations between polymorphic sites in Mycobacterium tuberculosis (Mtb) genomes. Attributable risk statistics were applied to identify the epistatic determinants of DR in different clades of Mtb and the possible evolutionary pathways of DR development. It was found that different lineages of Mtb exploited different evolutionary trajectories towards multidrug resistance and compensatory evolution to reduce the DR-associated fitness cost. Epistasis of DR acquisition is a new area of research that will aid in the better understanding of evolutionary biological processes and allow predicting upcoming multidrug-resistant pathogens before a new outbreak strikes humanity.

Design of multidrug-resistant tuberculosis treatment regimens based on DNA sequencing

BACKGROUND

Comprehensive and reliable drug susceptibility testing (DST) is urgently needed to provide adequate treatment regimens for patients with multidrug-resistant/rifampicin-resistant tuberculosis (MDR/RR-TB). We investigated if next generation sequencing (NGS) analysis of Mycobacterium tuberculosis complex isolates and genes implicated in drug resistance can guide the design of effective MDR/RR-TB treatment regimens.

METHODS

NGS-based genomic DST predictions of M. tuberculosis complex isolates from MDR/RR-TB patients admitted to a TB reference center in Germany between 01/01/2015 and 04/30/2019 were compared with phenotypic DST results of Mycobacteria growth indicator tubes (MGIT). Standardized treatment algorithms were applied to design individualized therapies based on either genomic or phenotypic DST results, and discrepancies were further evaluated by determination of minimum inhibitory drug concentrations (MIC) using Sensititre MYCOTBI and UKMYC microtiter plates.

RESULTS

In 70 patients with MDR/RR-TB, agreement among 1048 pairwise comparisons of genomic and phenotypic DST was 86.3%; 76 (7.2%) results were discordant, and 68 (6.5%) could not be evaluated due to presence of polymorphisms with yet unknown implications for drug resistance. Importantly, 549/561 (97.9%) predictions of drug susceptibility were phenotypically confirmed in MGIT, and 27/64 (42.2%) false positive results were linked to previously described mutations mediating a low or moderate MIC increase. Virtually all drugs (99.0%) used in combination therapies that were inferred from genomic DST, were confirmed to be susceptible by pDST.

CONCLUSIONS

NGS-based genomic DST can reliably guide the design of effective MDR/RR-TB treatment regimens.

Compromised base excision repair pathway in Mycobacterium tuberculosis imparts superior adaptability in the host

Tuberculosis caused by Mycobacterium tuberculosis (Mtb) is a significant public health concern, exacerbated by the emergence of drug-resistant TB. To combat the host’s dynamic environment, Mtb encodes multiple DNA repair enzymes that play a critical role in maintaining genomic integrity. Mtb possesses a GC-rich genome, rendering it highly susceptible to cytosine deaminations, resulting in the occurrence of uracils in the DNA. UDGs encoded by ung and udgB initiate the repair; hence we investigated the biological impact of deleting UDGs in the adaptation of pathogen. We generated gene replacement mutants of uracil DNA glycosylases, individually (RvΔung, RvΔudgB) or together (RvΔdKO). The double KO mutant, RvΔdKO exhibited remarkably higher spontaneous mutation rate, in the presence of antibiotics. Interestingly, RvΔdKO showed higher survival rates in guinea pigs and accumulated large number of SNPs as revealed by whole-genome sequence analysis. Competition assays revealed the superior fitness of RvΔdKO over Rv, both in ex vivo and in vivo conditions. We propose that compromised DNA repair results in the accumulation of mutations, and a subset of these drives adaptation in the host. Importantly, this property allowed us to utilize RvΔdKO for the facile identification of drug targets.

Mutation in the genome of bacteria contributes to the acquisition of drug resistance. Mutations in bacteria can arise due to exposures to antibiotics, oxidative, reductive, and many other stresses that bacteria encounter in the host. Mtb has multiple DNA repair mechanisms, including a base excision repair pathway to restore the damaged genome. Here we set out to determine the impact of deleting the Uracil DNA base excision pathway on pathogen adaptability to both antibiotic and host induced stresses. Combinatorial mutant of Mtb UDGs showed higher spontaneous rates of mutations when subjected to antibiotic stress and showed higher survival levels in the guinea pig model of infection. Whole-genome sequence analysis showed significant accumulation of SNPs, suggesting that mutations providing survival advantage may have been positively selected. We also showed that double mutant of Mtb UDGs would be an excellent means to identify antibiotic targets in the bacteria. Competition experiments wherein we pitted wild type and double mutant against each other demonstrated that double mutant has a decisive edge over the wild type. Together, data suggest that the absence of a base excision repair pathway leads to higher mutations and provides a survival advantage under stress. They could be an invaluable tool for identifying targets of new antibiotics.

Comprehensive review on mechanism of action, resistance and evolution of antimycobacterial drugs

Tuberculosis is one of the deadliest infectious diseases existing in the world since ancient times and still possesses serious threat across the globe. Each year the number of cases increases due to high drug resistance shown by Mycobacterium tuberculosis (Mtb). Available antimycobacterial drugs have been classified as First line, Second line and Third line antibiotics depending on the time of their discoveries and their effectiveness in the treatment. These antibiotics have a broad range of targets ranging from cell wall to metabolic processes and their non-judicious and uncontrolled usage in the treatment for years has created a significant problem called multi-drug resistant (MDR) tuberculosis. In this review, we have summarized the mechanism of action of all the classified antibiotics currently in use along with the resistance mechanisms acquired by Mtb. We have focused on the new drug candidates/repurposed drugs, and drug in combinations, which are in clinical trials for either treating the MDR tuberculosis more effectively or involved in reducing the time required for the chemotherapy of drug sensitive TB. This information is not discussed very adequately on a single platform. Additionally, we have discussed the recent technologies that are being used to discover novel resistance mechanisms acquired by Mtb and for exploring novel drugs. The story of intrinsic resistance mechanisms and evolution in Mtb is far from complete. Therefore, we have also discussed intrinsic resistance mechanisms of Mtb and their evolution with time, emphasizing the hope for the development of novel antimycobacterial drugs for effective therapy of tuberculosis.

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

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

Sources of Multidrug Resistance in Patients With Previous Isoniazid-Resistant Tuberculosis Identified Using Whole Genome Sequencing: A Longitudinal Cohort Study

BACKGROUND

Meta-analysis of patients with isoniazid-resistant tuberculosis (TB) given standard first-line anti-TB treatment indicated an increased risk of multidrug-resistant TB (MDR-TB) emerging (8%), compared to drug-sensitive TB (0.3%). Here we use whole genome sequencing (WGS) to investigate whether treatment of patients with preexisting isoniazid-resistant disease with first-line anti-TB therapy risks selecting for rifampicin resistance, and hence MDR-TB.

METHODS

Patients with isoniazid-resistant pulmonary TB were recruited and followed up for 24 months. Drug susceptibility testing was performed by microscopic observation drug susceptibility assay, mycobacterial growth indicator tube, and by WGS on isolates at first presentation and in the case of re-presentation. Where MDR-TB was diagnosed, WGS was used to determine the genomic relatedness between initial and subsequent isolates. De novo emergence of MDR-TB was assumed where the genomic distance was 5 or fewer single-nucleotide polymorphisms (SNPs), whereas reinfection with a different MDR-TB strain was assumed where the distance was 10 or more SNPs.

RESULTS

Two hundred thirty-nine patients with isoniazid-resistant pulmonary TB were recruited. Fourteen (14/239 [5.9%]) patients were diagnosed with a second episode of TB that was multidrug resistant. Six (6/239 [2.5%]) were identified as having evolved MDR-TB de novo and 6 as having been reinfected with a different strain. In 2 cases, the genomic distance was between 5 and 10 SNPs and therefore indeterminate.

CONCLUSIONS

In isoniazid-resistant TB, de novo emergence and reinfection of MDR-TB strains equally contributed to MDR development. Early diagnosis and optimal treatment of isoniazid-resistant TB are urgently needed to avert the de novo emergence of MDR-TB during treatment.

Mycobacterial ethambutol responsive genes and implications in antibiotics resistance

Mycobacterium tuberculosis (M. tuberculosis), the causative agent of tuberculosis (TB), remains a formidable threat in mortality and morbidity worldwide. Ethambutol (EMB) is one of the first-line drugs regimens for TB treatment. Arabinosyl transferases are established targets of EMB, which is involved in the biosynthesis of arabinogalactan (AG) and lipoarabinomannan (LAM). Mutations among embCAB operon are responsible for around 70% clinical EMB resistant M. tuberculosis. In this review, we summarised other potential factors associated with EMB resistance via analysing whole genome, proteome and transcriptome of M. tuberculosis exposed to EMB. This will help to design better diagnosis of EMB resistance.

Genome-Scale Metabolic Modeling for Unraveling Molecular Mechanisms of High Threat Pathogens

Pathogens give rise to a wide range of diseases threatening global health and hence drawing public health agencies' attention to establish preventative and curative solutions. Genome-scale metabolic modeling is ever increasingly used tool for biomedical applications including the elucidation of antibiotic resistance, virulence, single pathogen mechanisms and pathogen-host interaction systems. With this approach, the sophisticated cellular system of metabolic reactions inside the pathogens as well as between pathogen and host cells are represented in conjunction with their corresponding genes and enzymes. Along with essential metabolic reactions, alternate pathways and fluxes are predicted by performing computational flux analyses for the growth of pathogens in a very short time. The genes or enzymes responsible for the essential metabolic reactions in pathogen growth are regarded as potential drug targets, as a priori guide to researchers in the pharmaceutical field. Pathogens alter the key metabolic processes in infected host, ultimately the objective of these integrative constraint-based context-specific metabolic models is to provide novel insights toward understanding the metabolic basis of the acute and chronic processes of infection, revealing cellular mechanisms of pathogenesis, identifying strain-specific biomarkers and developing new therapeutic approaches including the combination drugs. The reaction rates predicted during different time points of pathogen development enable us to predict active pathways and those that only occur during certain stages of infection, and thus point out the putative drug targets. Among others, fatty acid and lipid syntheses reactions are recent targets of new antimicrobial drugs. Genome-scale metabolic models provide an improved understanding of how intracellular pathogens utilize the existing microenvironment of the host. Here, we reviewed the current knowledge of genome-scale metabolic modeling in pathogen cells as well as pathogen host interaction systems and the promising applications in the extension of curative strategies against pathogens for global preventative healthcare.

Dynamics of Extensive Drug Resistance Evolution of Mycobacterium tuberculosis in a Single Patient During 9 Years of Disease and Treatment

Mycobacterium tuberculosis is one of the hardest to treat bacterial pathogens with a high capacity to develop antibiotic resistance by mutations. Here we have performed whole-genome sequencing of consecutive M. tuberculosis isolates obtained during 9 years from a patient with pulmonary tuberculosis. The infecting strain was isoniazid resistant and during treatment it stepwise accumulated resistance mutations to 8 additional antibiotics. Heteroresistance was common and subpopulations with up to 3 different resistance mutations to the same drug coexisted. Sweeps of different resistant clones dominated the population at different time points, always coupled to resistance mutations coinciding with changes in the treatment regimens. Resistance mutations were predominant and no hitch-hiking, compensatory, or virulence-increasing mutations were detected, showing that the dominant selection pressure was antibiotic treatment. The results highlight the dynamic nature of M. tuberculosis infection, population structure, and resistance evolution and the importance of rapid antibiotic susceptibility tests to battle this pathogen.

Rapidly Correcting Frameshift Mutations in the Mycobacterium tuberculosis orn Gene Produce Reversible Ethambutol Resistance and Small-Colony-Variant Morphology

We have identified a previously unknown mechanism of reversible high-level ethambutol (EMB) resistance in Mycobacterium tuberculosis that is caused by a reversible frameshift mutation in the M. tuberculosisorn gene. A frameshift mutation in orn produces the small-colony-variant (SCV) phenotype, but this mutation does not change the MICs of any drug for wild-type M. tuberculosis However, the same orn mutation in a low-level EMB-resistant double embB-aftA mutant (MIC = 8 μg/ml) produces an SCV with an EMB MIC of 32 μg/ml. Reversible resistance is indistinguishable from a drug-persistent phenotype, because further culture of these orn-embB-aftA SCV mutants results in rapid reversion of the orn frameshifts, reestablishing the correct orn open reading frame, returning the culture to normal colony size, and reversing the EMB MIC back to that (8 μg/ml) of the parental strain. Transcriptomic analysis of orn-embB-aftA mutants compared to wild-type M. tuberculosis identified a 27-fold relative increase in the expression of embC, which is a cellular target for EMB. Expression of embC in orn-embB-aftA mutants was also increased 5-fold compared to that in the parental embB-aftA mutant, whereas large-colony orn frameshift revertants of the orn-embB-aftA mutant had levels of embC expression similar to that of the parental embB-aftA strain. Reversible frameshift mutants may contribute to a reversible form of microbiological drug resistance in human tuberculosis.

Exploiting Homoplasy in Genome-Wide Association Studies to Enhance Identification of Antibiotic-Resistance Mutations in Bacterial Genomes

Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

Rifampicin and rifabutin resistance in 1003 Mycobacterium tuberculosis clinical isolates

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Next-Generation Sequencing in Clinical Microbiology: Are We There Yet?

Next-generation sequencing (NGS) applications have been transitioning from research tools to diagnostic methods and are becoming more commonplace in clinical microbiology laboratories. These applications include (1) whole-genome sequencing, (2) targeted next-generation sequencing methods, and (3) metagenomic next-generation sequencing. The introduction of these methods into the clinical microbiology laboratory has led to the theoretic question of "Will NGS-based methods supplant traditional methods for strain typing, identification, and antimicrobial susceptibility prediction?" The authors address this question and discuss where we are at now with clinical NGS applications for infectious diseases, what does the future hold, and at what cost?

Cryo-EM structure of arabinosyltransferase EmbB from Mycobacterium smegmatis

Arabinosyltransferase B (EmbB) belongs to a family of membrane-bound glycosyltransferases that build the lipidated polysaccharides of the mycobacterial cell envelope, and are targets of anti-tuberculosis drug ethambutol. We present the 3.3 Å resolution single-particle cryo-electron microscopy structure of Mycobacterium smegmatis EmbB, providing insights on substrate binding and reaction mechanism. Mutations that confer ethambutol resistance map mostly around the putative active site, suggesting this to be the location of drug binding.

A biochemically-interpretable machine learning classifier for microbial GWAS

Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machine learning classifier, named Metabolic Allele Classifier (MAC), that uses flux balance analysis to estimate the biochemical effects of alleles. We apply the MAC to a dataset of 1595 drug-tested Mycobacterium tuberculosis strains and show that MACs predict AMR phenotypes with accuracy on par with mechanism-agnostic machine learning models (isoniazid AUC = 0.93) while enabling a biochemical interpretation of the genotype-phenotype map. Interpretation of MACs for three antibiotics (pyrazinamide, para-aminosalicylic acid, and isoniazid) recapitulates known AMR mechanisms and suggest a biochemical basis for how the identified alleles cause AMR. Extending flux balance analysis to identify accurate sequence classifiers thus contributes mechanistic insights to GWAS, a field thus far dominated by mechanism-agnostic results.

Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses flux balance analysis to estimate the biochemical effects of alleles.

Detecting Ethambutol Resistance in Mycobacterium tuberculosis Isolates in China: A Comparison Between Phenotypic Drug Susceptibility Testing Methods and DNA Sequencing of embAB

With the increasing incidence of drug-resistant tuberculosis (DR-TB), determining a rapid and accurate drug susceptibility testing (DST) method to identify ethambutol (EMB) resistance in Mycobacterium tuberculosis has become essential for patient management in China. Herein, we evaluated the correlation between three phenotypic DST methods, namely, proportion method (PM), MGIT 960 system, and microplate alamar Blue assay (MABA), and DNA sequencing of embAB in 118 M. tuberculosis isolates from China. When the results of the phenotypic DST methods were compared with those of DNA sequencing, the overall agreement and kappa values of the PM, MGIT 960 system, and MABA were 81.4% and 0.61, 77.1% and 0.55, and 84.7% and 0.67, respectively. The agreement for EMB resistance between MABA and PM was significantly higher than that between the MGIT 960 system and PM (P = 0.02). Moreover, among the isolates with detectable embAB mutations, 97.2% (70/72 isolates) harbored mutations in embB. The analysis of embB mutations predicted EMB resistance with 81.3% sensitivity, 86.8% specificity, and 83.1% accuracy. Thus, MABA may be a better phenotypic DST method for detecting EMB resistance. DNA sequencing of embB may be useful for the early identification of EMB resistance and the consequent optimization of the treatment regimen.

Mycobacterial Cell Wall: A Source of Successful Targets for Old and New Drugs

Eighty years after the introduction of the first antituberculosis (TB) drug, the treatment of drug-susceptible TB remains very cumbersome, requiring the use of four drugs (isoniazid, rifampicin, ethambutol and pyrazinamide) for two months followed by four months on isoniazid and rifampicin. Two of the drugs used in this “short”-course, six-month chemotherapy, isoniazid and ethambutol, target the mycobacterial cell wall. Disruption of the cell wall structure can enhance the entry of other TB drugs, resulting in a more potent chemotherapy. More importantly, inhibition of cell wall components can lead to mycobacterial cell death. The complexity of the mycobacterial cell wall offers numerous opportunities to develop drugs to eradicate Mycobacterium tuberculosis, the causative agent of TB. In the past 20 years, researchers from industrial and academic laboratories have tested new molecules to find the best candidates that will change the face of TB treatment: drugs that will shorten TB treatment and be efficacious against active and latent, as well as drug-resistant TB. Two of these new TB drugs block components of the mycobacterial cell wall and have reached phase 3 clinical trial. This article reviews TB drugs targeting the mycobacterial cell wall in use clinically and those in clinical development.