Pathogen-based precision medicine for drug-resistant tuberculosis

Pharmacogenetic Study of Drugs Affecting Mycobacterium tuberculosis

BACKGROUND

Pharmacogenetic research has led to significant progress in understanding how genetic factors influence drug response in tuberculosis (TB) treatment. One ongoing challenge is the variable occurrence of adverse drug reactions in some TB patients. Previous studies have indicated that genetic variations in the N-acetyltransferase 2 (NAT2) and solute carrier organic anion transporter family member 1B1 (SLCO1B1) genes can impact the blood concentrations of the first-line anti-TB drugs isoniazid (INH) and rifampicin (RIF), respectively. This study aimed to investigate the influence of pharmacogenetic markers in the NAT2 and SLCO1B1 genes on TB treatment outcomes using whole-exome sequencing (WES) analysis.

METHODS

DNA samples were collected from 30 healthy Iranian adults aged 18-40 years. The allelic frequencies of single-nucleotide polymorphisms (SNPs) in the NAT2 and SLCO1B1 genes were determined through WES.

RESULTS

Seven frequent SNPs were identified in the NAT2 gene (rs1041983, rs1801280, rs1799929, rs1799930, rs1208, rs1799931, rs2552), along with 16 frequent SNPs in the SLCO1B1 gene (rs2306283, rs11045818, rs11045819, rs4149056, rs4149057, rs2291075, rs201722521, rs11045852, rs11045854, rs756393362, rs11045859, rs74064211, rs201556175, rs34671512, rs71581985, rs4149085).

CONCLUSION

Genetic variations in NAT2 and SLCO1B1 can affect the metabolism of INH and RIF, respectively. A better understanding of the pharmacogenetic profile in the study population may facilitate the design of more personalized and effective TB treatment strategies. Further research is needed to directly correlate these genetic markers with clinical outcomes in TB patients.

Molecular determinants of multidrug-resistant tuberculosis in Sierra Leone

Multidrug-resistant tuberculosis (MDR-TB) management has become a serious global health challenge. Understanding its epidemic determinants on the regional level is crucial for developing effective control measures. We used whole genome sequencing data of 238 of Mycobacterium tuberculosis complex (MTBC) strains to determine drug resistance profiles, phylogeny, and transmission dynamics of MDR/rifampicin-resistant (RR) MTBC strains from Sierra Leone. Forty-two strains were classified as RR, 196 as MDR, 5 were resistant to bedaquiline (BDQ) and clofazimine (CFZ), but none was found to be resistant to fluoroquinolones. Sixty-one (26%) strains were resistant to all first-line drugs, three of which had additional resistance to BDQ/CFZ. The strains were classified into six major MTBC lineages (L), with strains of L4 being the most prevalent, 62% (n = 147), followed by L6 (Mycobacterium africanum) strains, (21%, n = 50). The overall clustering rate (using ≤d12 single-nucleotide polymorphism threshold) was 44%, stratified into 31 clusters ranging from 2 to 16 strains. The largest cluster (n = 16) was formed by sublineage 2.2.1 Beijing Ancestral 3 strains, which developed MDR several times. Meanwhile, 10 of the L6 strains had a primary MDR transmission. We observed a high diversity of drug resistance mutations, including borderline resistance mutations to isoniazid and rifampicin, and mutations were not detected by commercial assays. In conclusion, one in five strains investigated was resistant to all first-line drugs, three of which had evidence of BDQ/CFZ resistance. Implementation of interventions such as rapid diagnostics that prevent further resistance development and stop MDR-TB transmission chains in the country is urgently needed.

IMPORTANCE

A substantial proportion of MDR-TB strains in Sierra Leone were resistant against all first line drugs; however this makes the all-oral-six-month BPaLM regimen or other 6-9 months all oral regimens still viable, mainly because there was no FQ resistance.Resistance to BDQ was detected, as well as RR, due to mutations outside of the hotspot region. While the prevalence of those resistances was low, it is still cause for concern and needs to be closely monitored.

Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.

Advancing tuberculosis management: the role of predictive, preventive, and personalized medicine

Tuberculosis is a major global health issue, with approximately 10 million people falling ill and 1.4 million dying yearly. One of the most significant challenges to public health is the emergence of drug-resistant tuberculosis. For the last half-century, treating tuberculosis has adhered to a uniform management strategy in most patients. However, treatment ineffectiveness in some individuals with pulmonary tuberculosis presents a major challenge to the global tuberculosis control initiative. Unfavorable outcomes of tuberculosis treatment (including mortality, treatment failure, loss of follow-up, and unevaluated cases) may result in increased transmission of tuberculosis and the emergence of drug-resistant strains. Treatment failure may occur due to drug-resistant strains, non-adherence to medication, inadequate absorption of drugs, or low-quality healthcare. Identifying the underlying cause and adjusting the treatment accordingly to address treatment failure is important. This is where approaches such as artificial intelligence, genetic screening, and whole genome sequencing can play a critical role. In this review, we suggest a set of particular clinical applications of these approaches, which might have the potential to influence decisions regarding the clinical management of tuberculosis patients.

Lineage classification and antitubercular drug resistance surveillance of Mycobacterium tuberculosis by whole-genome sequencing in Southern India

Whole-genome sequencing has created a revolution in tuberculosis management by providing a comprehensive picture of the various genetic polymorphisms with unprecedented accuracy. Studies mapping genomic heterogeneity in clinical isolates of Mycobacterium tuberculosis using a whole-genome sequencing approach from high tuberculosis burden countries are underrepresented. We report whole-genome sequencing results of 242 clinical isolates of culture-confirmed M. tuberculosis isolates from tuberculosis patients referred to a tertiary care hospital in Southern India. Phylogenetic analysis revealed that the isolates in our study belonged to five different lineages, with Indo-Oceanic (lineage 1, n = 122) and East-African Indian (lineage 3, n = 80) being the most prevalent. We report several mutations in genes conferring resistance to first and second line antitubercular drugs including the genes rpoB, katG, ahpC, inhA, fabG1, embB, pncA, rpsL, rrs, and gyrA. The majority of these mutations were identified in relatively high proportions in lineage 1. Our study highlights the utility of whole-genome sequencing as a potential supplemental tool to the existing genotypic and phenotypic methods, in providing expedited comprehensive surveillance of mutations that may be associated with antitubercular drug resistance as well as lineage characterization of M. tuberculosis isolates. Further larger-scale whole-genome datasets with linked minimum inhibition concentration testing are imperative for resolving the discrepancies between whole-genome sequencing and phenotypic drug sensitivity testing results and quantifying the level of the resistance associated with the mutations for optimization of antitubercular drug and precise dose selection in clinics. IMPORTANCE Studies mapping genetic heterogeneity of clinical isolates of M. tuberculosis for determining their strain lineage and drug resistance by whole-genome sequencing are limited in high tuberculosis burden settings. We carried out whole-genome sequencing of 242 M. tuberculosis isolates from drug-sensitive and drug-resistant tuberculosis patients, identified and collected as part of the TB Portals Program, to have a comprehensive insight into the genetic diversity of M. tuberculosis in Southern India. We report several genetic variations in M. tuberculosis that may confer resistance to antitubercular drugs. Further wide-scale efforts are required to fully characterize M. tuberculosis genetic diversity at a population level in high tuberculosis burden settings for providing precise tuberculosis treatment.

Implementation of targeted next-generation sequencing for the diagnosis of drug-resistant tuberculosis in low-resource settings: a programmatic model, challenges, and initial outcomes

Targeted next-generation sequencing (tNGS) from clinical specimens has the potential to become a comprehensive tool for routine drug-resistance (DR) prediction of Mycobacterium tuberculosis complex strains (MTBC), the causative agent of tuberculosis (TB). However, TB mainly affects low- and middle-income countries, in which the implementation of new technologies have specific needs and challenges. We propose a model for programmatic implementation of tNGS in settings with no or low previous sequencing capacity/experience. We highlight the major challenges and considerations for a successful implementation. This model has been applied to build NGS capacity in Namibia, an upper middle-income country located in Southern Africa and suffering from a high-burden of TB and TB-HIV, and we describe herein the outcomes of this process.

Clinical implications of molecular drug resistance testing for Mycobacterium tuberculosis: a 2023 TBnet/RESIST-TB consensus statement

Drug-resistant tuberculosis is a substantial health-care concern worldwide. Despite culture-based methods being considered the gold standard for drug susceptibility testing, molecular methods provide rapid information about the Mycobacterium tuberculosis mutations associated with resistance to anti-tuberculosis drugs. This consensus document was developed on the basis of a comprehensive literature search, by the TBnet and RESIST-TB networks, about reporting standards for the clinical use of molecular drug susceptibility testing. Review and the search for evidence included hand-searching journals and searching electronic databases. The panel identified studies that linked mutations in genomic regions of M tuberculosis with treatment outcome data. Implementation of molecular testing for the prediction of drug resistance in M tuberculosis is key. Detection of mutations in clinical isolates has implications for the clinical management of patients with multidrug-resistant or rifampicin-resistant tuberculosis, especially in situations when phenotypic drug susceptibility testing is not available. A multidisciplinary team including clinicians, microbiologists, and laboratory scientists reached a consensus on key questions relevant to molecular prediction of drug susceptibility or resistance to M tuberculosis, and their implications for clinical practice. This consensus document should help clinicians in the management of patients with tuberculosis, providing guidance for the design of treatment regimens and optimising outcomes.

Whole-Genome Sequencing for Resistance Prediction and Transmission Analysis of Mycobacterium tuberculosis Complex Strains from Namibia

Namibia is among 30 countries with a high burden of tuberculosis (TB), with an estimated incidence of 460 per 100,000 population and around 800 new multidrug-resistant (MDR) TB cases per year. Still, data on the transmission and evolution of drug-resistant Mycobacterium tuberculosis complex (Mtbc) strains are not available. Whole-genome sequencing data of 136 rifampicin-resistant (RIFr) Mtbc strains obtained from 2016 to 2018 were used for phylogenetic classification, resistance prediction, and cluster analysis and linked with phenotypic drug susceptibility testing (pDST) data. Roughly 50% of the strains investigated were resistant to all first-line drugs. Furthermore, 13% of the MDR Mtbc strains were already pre-extensively drug resistant (pre-XDR). The cluster rates were high, at 74.6% among MDR and 85% among pre-XDR strains. A significant proportion of strains had borderline resistance-conferring mutations, e.g., inhA promoter mutations or rpoB L430P. Accordingly, 25% of the RIFr strains tested susceptible by pDST. Finally, we determined a potentially new bedaquiline resistance mutation (Rv0678 D88G) occurring in two independent clusters. High rates of resistance to first-line drugs in line with emerging pre-XDR and likely bedaquiline resistance linked with the ongoing recent transmission of MDR Mtbc clones underline the urgent need for the implementation of interventions that allow rapid diagnostics to break MDR TB transmission chains in the country. A borderline RIFr mutation in the dominant outbreak strain causing discrepancies between phenotypic and genotypic resistance testing results may require breakpoint adjustments but also may allow individualized regimens with high-dose treatment. IMPORTANCE The transmission of drug-resistant tuberculosis (TB) is a major problem for global TB control. Using genome sequencing, we showed that 13% of the multidrug-resistant (MDR) M. tuberculosis complex strains from Namibia are already pre-extensively drug resistant (pre-XDR), which is substantial in an African setting. Our data also indicate that the ongoing transmission of MDR and pre-XDR strains contributes significantly to the problem. In contrast to other settings with higher rates of drug resistance, we found a high proportion of strains having so-called borderline low-level resistance mutations, e.g., inhA promoter mutations or rpoB L430P. This led to the misclassification of 25% of the rifampicin-resistant strains as susceptible by phenotypic drug susceptibility testing. This observation potentially allows individualized regimens with high-dose treatment as a potential option for patients with few treatment options. We also found a potentially new bedaquiline resistance mutation in rv0678.

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis

Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.

Metaomics in Clinical Laboratory: Potential Driving Force for Innovative Disease Diagnosis

Currently, more and more studies suggested that reductionism was lack of holistic and integrative view of biological processes, leading to limited understanding of complex systems like microbiota and the associated diseases. In fact, microbes are rarely present in individuals but normally live in complex multispecies communities. With the recent development of a variety of metaomics techniques, microbes could be dissected dynamically in both temporal and spatial scales. Therefore, in-depth understanding of human microbiome from different aspects such as genomes, transcriptomes, proteomes, and metabolomes could provide novel insights into their functional roles, which also holds the potential in making them diagnostic biomarkers in many human diseases, though there is still a huge gap to fill for the purpose. In this mini-review, we went through the frontlines of the metaomics techniques and explored their potential applications in clinical diagnoses of human diseases, e.g., infectious diseases, through which we concluded that novel diagnostic methods based on human microbiomes shall be achieved in the near future, while the limitations of these techniques such as standard procedures and computational challenges for rapid and accurate analysis of metaomics data in clinical settings were also examined.

Whole-Genome Sequencing for Resistance Level Prediction in Multidrug-Resistant Tuberculosis

Defining the precise relationship between resistance mutations and quantitative phenotypic drug susceptibility testing will increase the value of whole-genome sequencing (WGS) for predicting tuberculosis drug resistance. However, a large number of WGS data sets currently lack corresponding quantitative phenotypic data—the MICs. Using MYCOTBI plates, we determined the MICs to nine antituberculosis drugs for 154 clinical multidrug-resistant tuberculosis isolates from the Shenzhen Center for Chronic Disease Control in Shenzhen, China. Comparing MICs with predicted drug-resistance profiles inferred by WGS showed that WGS could predict the levels of resistance to isoniazid, rifampicin, streptomycin, fluoroquinolones, and aminoglycosides. We also found some mutations that may not be associated with drug resistance, such as EmbB D328G, mutations in the gid gene, and C−12T in the eis promoter. However, some strains carrying the same mutations showed different levels of resistance to the corresponding drugs. The MICs of different strains with the RpsL K88R, fabG1 C−15T mutations and some with mutations in embB and rpoB, had MICs to the corresponding drugs that varied by 8-fold or more. This variation is unexplained but could be influenced by the bacterial genetic background. Additionally, we found that 32.3% of rifampicin-resistant isolates were rifabutin-susceptible, particularly those with rpoB mutations H445D, H445L, H445S, D435V, D435F, L452P, S441Q, and S441V. Studying the influence of bacterial genetic background on the MIC and the relationship between rifampicin-resistant mutations and rifabutin resistance levels should improve the ability of WGS to guide the selection of medical treatment regimens.

IMPORTANCE Whole-genome sequencing (WGS) has excellent potential in drug-resistance prediction. The MICs are essential indications of adding a particular antituberculosis drug dosage or changing the entire treatment regimen. However, the relationship between many known drug-resistant mutations and MICs is unclear, especially for rarer ones. The results showed that WGS could predict resistance levels to isoniazid, rifampicin, streptomycin, fluoroquinolones, and aminoglycosides. However, some mutations may not be associated with drug resistance, and some others may confer various MICs to strains carrying them. Also, 32.3% of rifampicin (RIF)-resistant strains were classified as sensitive to rifabutin (RFB), and some mutations in the rpoB gene may be associated with this phenotype. Our data on the MIC distribution of strains with some rarer mutations add to the accumulated data on the resistance level associated with such mutations to help guide further research and draw meaningful conclusions.

Emergence of bedaquiline resistance in a high tuberculosis burden country

RATIONALE

Bedaquiline has been classified as a group A drug for the treatment of multidrug-resistant tuberculosis (MDR-TB) by the World Health Organization; however, globally emerging resistance threatens the effectivity of novel MDR-TB treatment regimens.

OBJECTIVES

We analysed pre-existing and emerging bedaquiline resistance in bedaquiline-based MDR-TB therapies, and risk factors associated with treatment failure and death.

METHODS

In a cross-sectional cohort study, we employed patient data, whole-genome sequencing (WGS) and phenotyping of Mycobacterium tuberculosis complex (MTBC) isolates. We could retrieve baseline isolates from 30.5% (62 out of 203) of all MDR-TB patients who received bedaquiline between 2016 and 2018 in the Republic of Moldova. This includes 26 patients for whom we could also retrieve a follow-up isolate.

MEASUREMENTS AND MAIN RESULTS

At baseline, all MTBC isolates were susceptible to bedaquiline. Among 26 patients with available baseline and follow-up isolates, four (15.3%) patients harboured strains which acquired bedaquiline resistance under therapy, while one (3.8%) patient was re-infected with a second bedaquiline-resistant strain. Treatment failure and death were associated with cavitary disease (p=0.011), and any additional drug prescribed in the bedaquiline-containing regimen with WGS-predicted resistance at baseline (OR 1.92 per unit increase, 95% CI 1.15-3.21; p=0.012).

CONCLUSIONS

MDR-TB treatments based on bedaquiline require a functional background regimen to achieve high cure rates and to prevent the evolution of bedaquiline resistance. Novel MDR-TB therapies with bedaquiline require timely and comprehensive drug resistance monitoring.

SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission

Whole genome sequencing (WGS) can provide insight into drug-resistance, transmission chains and the identification of outbreaks, but data analysis remains an obstacle to its routine clinical use. Although several drug-resistance prediction tools have appeared, until now no website integrates drug-resistance prediction with strain genetic relationships and species identification of nontuberculous mycobacteria (NTM). We have established a free, function-rich, user-friendly online platform for MTB WGS data analysis (SAM-TB, http://samtb.szmbzx.com) that integrates drug-resistance prediction for 17 antituberculosis drugs, detection of variants, analysis of genetic relationships and NTM species identification. The accuracy of SAM-TB in predicting drug-resistance was assessed using 3177 sequenced clinical isolates with results of phenotypic drug-susceptibility tests (pDST). Compared to pDST, the sensitivity of SAM-TB for detecting multidrug-resistant tuberculosis was 93.9% [95% confidence interval (CI) 92.6–95.1%] with specificity of 96.2% (95% CI 95.2–97.1%). SAM-TB also analyzes the genetic relationships between multiple strains by reconstructing phylogenetic trees and calculating pairwise single nucleotide polymorphism (SNP) distances to identify genomic clusters. The incorporated mlstverse software identifies NTM species with an accuracy of 98.2% and Kraken2 software can detect mixed MTB and NTM samples. SAM-TB also has the capacity to share both sequence data and analysis between users. SAM-TB is a multifunctional integrated website that uses WGS raw data to accurately predict antituberculosis drug-resistance profiles, analyze genetic relationships between multiple strains and identify NTM species and mixed samples containing both NTM and MTB. SAM-TB is a useful tool for guiding both treatment and epidemiological investigation.

Whole genome sequencing-based classification of human-related Haemophilus species and detection of antimicrobial resistance genes

Background

Bacteria belonging to the genus Haemophilus cause a wide range of diseases in humans. Recently, H. influenzae was classified by the WHO as priority pathogen due to the wide spread of ampicillin resistant strains. However, other Haemophilus spp. are often misclassified as H. influenzae. Therefore, we established an accurate and rapid whole genome sequencing (WGS) based classification and serotyping algorithm and combined it with the detection of resistance genes.

Methods

A gene presence/absence-based classification algorithm was developed, which employs the open-source gene-detection tool SRST2 and a new classification database comprising 36 genes, including capsule loci for serotyping. These genes were identified using a comparative genome analysis of 215 strains belonging to ten human-related Haemophilus (sub)species (training dataset). The algorithm was evaluated on 1329 public short read datasets (evaluation dataset) and used to reclassify 262 clinical Haemophilus spp. isolates from 250 patients (German cohort). In addition, the presence of antibiotic resistance genes within the German dataset was evaluated with SRST2 and correlated with results of traditional phenotyping assays.

Results

The newly developed algorithm can differentiate between clinically relevant Haemophilus species including, but not limited to, H. influenzae, H. haemolyticus, and H. parainfluenzae. It can also identify putative haemin-independent H. haemolyticus strains and determine the serotype of typeable Haemophilus strains. The algorithm performed excellently in the evaluation dataset (99.6% concordance with reported species classification and 99.5% with reported serotype) and revealed several misclassifications. Additionally, 83 out of 262 (31.7%) suspected H. influenzae strains from the German cohort were in fact H. haemolyticus strains, some of which associated with mouth abscesses and lower respiratory tract infections.

Resistance genes were detected in 16 out of 262 datasets from the German cohort. Prediction of ampicillin resistance, associated with bla_TEM-1D, and tetracycline resistance, associated with tetB, correlated well with available phenotypic data.

Conclusions

Our new classification database and algorithm have the potential to improve diagnosis and surveillance of Haemophilus spp. and can easily be coupled with other public genotyping and antimicrobial resistance databases. Our data also point towards a possible pathogenic role of H. haemolyticus strains, which needs to be further investigated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01017-x.

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.

Personalised Medicine for Tuberculosis and Non-Tuberculous Mycobacterial Pulmonary Disease

Personalised medicine, in which clinical management is individualised to the genotypic and phenotypic data of patients, offers a promising means by which to enhance outcomes in the management of mycobacterial pulmonary infections. In this review, we provide an overview of how personalised medicine approaches may be utilised to identify patients at risk of developing tuberculosis (TB) or non-tuberculous mycobacterial pulmonary disease (NTM-PD), diagnose these conditions and guide effective treatment strategies. Despite recent technological and therapeutic advances, TB and NTM-PD remain challenging conditions to diagnose and treat. Studies have identified a range of genetic and immune factors that predispose patients to pulmonary mycobacterial infections. Molecular tests such as nucleic acid amplification assays and next generation sequencing provide a rapid means by which to identify mycobacterial isolates and their antibiotic resistance profiles, thus guiding selection of appropriate antimicrobials. Host-directed therapies and therapeutic drug monitoring offer ways of tailoring management to the clinical needs of patients at an individualised level. Biomarkers may hold promise in differentiating between latent and active TB, as well as in predicting mycobacterial disease progression and response to treatment.

Use of next generation sequencing technologies for the diagnosis and epidemiology of infectious diseases

For the first time, next generation sequencing technologies provide access to genomic information at a price and scale that allow their implementation in routine clinical practice and epidemiology. While there are still many obstacles to their implementation, there are also multiple examples of their major advantages compared with previous methods. Their main advantage is that a single determination allows epidemiological information on the causative microorganism to be obtained simultaneously, as well as its resistance profile, although these advantages vary according to the pathogen under study. This review discusses several examples of the clinical and epidemiological use of next generation sequencing applied to complete genomes and microbiomes and reflects on its future in clinical practice.

Implementation of whole genome sequencing for tuberculosis diagnostics in a low-middle income, high MDR-TB burden country

Whole genome sequencing (WGS) is revolutionary for diagnostics of TB and its mutations associated with drug-resistances, but its uptake in low- and middle-income countries is hindered by concerns of implementation feasibility. Here, we provide a proof of concept for its successful implementation in such a setting. WGS was implemented in the Kyrgyz Republic. We estimated needs of up to 55 TB-WGS per week and chose the MiSeq platform (Illumina, USA) because of its capacity of up to 60 TB-WGS per week. The project's timeline was completed in 93-weeks. Costs of large equipment and accompanying costs were 222,065 USD and 8462 USD, respectively. The first 174 WGS costed 277 USD per sequence, but this was skewed by training inefficiencies. Based on real prices and presuming optimal utilization of WGS capacities, WGS costs could drop to 167 and 141 USD per WGS using MiSeq Reagent Kits v2 (500-cycles) and v3 (600-cycles), respectively. Five trainings were required to prepare the staff for autonomous WGS which cost 48,250 USD. External assessment confirmed excellent performance of WGS by the Kyrgyz laboratory in an interlaboratory comparison of 30 M. tuberculosis genomes showing complete agreeance of results.

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.

Microevolution of Mycobacterium tuberculosis Subpopulations and Heteroresistance in a Patient Receiving 27 Years of Tuberculosis Treatment in Germany

Preexisting and newly emerging resistant pathogen subpopulations (heteroresistance) are potential risk factors for treatment failure of multi/extensively drug resistant (MDR/XDR) tuberculosis (TB). Intrapatient evolutionary dynamics of Mycobacterium tuberculosis complex (Mtbc) strains and their implications on treatment outcomes are still not completely understood. To elucidate how Mtbc strains escape therapy, we analyzed 13 serial isolates from a German patient by whole-genome sequencing. Sequencing data were compared with phenotypic drug susceptibility profiles and the patient's collective 27-year treatment history to further elucidate factors fostering intrapatient resistance evolution. The patient endured five distinct TB episodes, ending in resistance to 16 drugs and a nearly untreatable XDR-TB infection. The first isolate obtained, during the patient's 5th TB episode, presented fixed resistance mutations to 7 anti-TB drugs, including isoniazid, rifampin, streptomycin, pyrazinamide, prothionamide, para-aminosalicylic acid, and cycloserine-terizidone. Over the next 13 years, a dynamic evolution with coexisting, heterogeneous subpopulations was observed in 6 out of 13 sequential bacterial isolates. The emergence of drug-resistant subpopulations coincided with frequent changes in treatment regimens, which often included two or fewer active compounds. This evolutionary arms race between competing subpopulations ultimately resulted in the fixation of a single XDR variant. Our data demonstrate the complex intrapatient microevolution of Mtbc subpopulations during failing MDR/XDR-TB treatment. Designing effective treatment regimens based on rapid detection of (hetero) resistance is key to avoid resistance development and treatment failure.

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.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

Purpose: Coronavirus disease–2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.

Methods: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.

Findings: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).

Implications: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

Mycobacterium tuberculosis Complex Lineage 3 as Causative Agent of Pulmonary Tuberculosis, Eastern Sudan1

Pathogen-based factors associated with tuberculosis (TB) in eastern Sudan are not well defined. We investigated genetic diversity, drug resistance, and possible transmission clusters of Mycobacterium tuberculosis complex (MTBC) strains by using a genomic epidemiology approach. We collected 383 sputum specimens at 3 hospitals in 2014 and 2016 from patients with symptoms suggestive of TB; of these, 171 grew MTBC strains. Whole-genome sequencing could be performed on 166 MTBC strains; phylogenetic classification revealed that most (73.4%; n = 122) belonged to lineage 3 (L3). Genome-based cluster analysis showed that 76 strains (45.9%) were grouped into 29 molecular clusters, comprising 2–8 strains/patients. Of the strains investigated, 9.0% (15/166) were multidrug resistant (MDR); 10 MDR MTBC strains were linked to 1 large MDR transmission network. Our findings indicate that L3 strains are the main causative agent of TB in eastern Sudan; MDR TB is caused mainly by transmission of MDR L3 strains.

Combatting the Rising Tide of Antimicrobial Resistance: Pharmacokinetic/Pharmacodynamic Dosing Strategies for Maximal Precision

OBJECTIVE

Antimicrobial pharmacokinetics/pharmacodynamics (PK/PD) principles and PK/PD models have been essential in characterizing the mechanism of antibiotic bacterial killing and determining the most optimal dosing regimen that maximizes clinical outcomes. This review summarized the fundamentals of antimicrobial PK/PD and the various types of PK/PD experiments that shaped the utilization and dosing strategies of antibiotics today.

METHODS

Multiple databases - including PubMed, Scopus, and EMBASE - were searched for published articles that involved PK/PD modelling and precision dosing. Data from in vitro, in vivo and mechanistic PK/PD models were reviewed as a basis for compiling studies that guide dosing regimens used in clinical trials.

RESULTS

Literature regarding the utilization of exposure-response analyses, mathematical modelling and simulations that were summarized are able to provide a better understanding of antibiotic pharmacodynamics that influence translational drug development. Optimal pharmacokinetic sampling of antibiotics from patients can lead to personalized dosing regimens that attain target concentrations while minimizing toxicity. Thus the development of a fully integrated mechanistic model based on systems pharmacology can continually adapt to data generated from clinical responses, which can provide the framework for individualized dosing regimens.

CONCLUSIONS

The promise of what PK/PD can provide through precision dosing for antibiotics has not been fully realized in the clinical setting. Antimicrobial resistance, which has emerged as a significant public health threat, has forced clinicians to empirically utilize therapies. Future research focused on implementation and translation of PK/PD-based approaches integrating novel approaches that combine knowledge of combination therapies, systems pharmacology and resistance mechanisms are necessary. To fully realize maximally precise therapeutics, optimal PK/PD strategies are critical to maximize antimicrobial efficacy against extremely-drug-resistant organisms, while minimizing toxicity.

Isoniazid and Rifampin-Resistance Mutations Associated With Resistance to Second-Line Drugs and With Sputum Culture Conversion

BACKGROUND

Mutations in the genes inhA, katG, and rpoB confer resistance to anti-tuberculosis (TB) drugs isoniazid and rifampin. We questioned whether specific mutations in these genes were associated with different clinical and microbiological characteristics.

METHODS

In a multicountry prospective cohort study of multidrug-resistant TB, we identified inhA, katG, and rpoB mutations in sputum isolates using the Hain MTBDRplus line probe assay. For specific mutations, we performed bivariate analysis to determine relative risk of baseline or acquired resistance to other TB drugs. We compared time to sputum culture conversion (TSCC) using Kaplan-Meier curves and stratified Cox regression.

RESULTS

In total, 447 participants enrolled from January 2005 to December 2008 from 7 countries were included. Relative to rpoB S531L, isolates with rpoB D516V had less cross-resistance to rifabutin, increased baseline resistance to other drugs, and increased acquired fluoroquinolone resistance. Relative to mutation of katG only, mutation of inhA promoter and katG was associated with baseline extensively drug resistant (XDR) TB, increased acquired fluoroquinolone resistance, and slower TSCC (125.5 vs 89.0 days).

CONCLUSIONS

Specific mutations in inhA and katG are associated with differences in resistance to other drugs and TSCC. Molecular testing may make it possible to tailor treatment and assess additional drug resistance risk according to specific mutation profile.

Evolution of Drug-Resistant Mycobacterium tuberculosis Strains and Their Adaptation to the Human Lung Environment

In the last two decades, multi (MDR), extensively (XDR), extremely (XXDR) and total (TDR) drug-resistant Mycobacterium tuberculosis (M.tb) strains have emerged as a threat to public health worldwide, stressing the need to develop new tuberculosis (TB) prevention and treatment strategies. It is estimated that in the next 35 years, drug-resistant TB will kill around 75 million people and cost the global economy $16.7 trillion. Indeed, the COVID-19 pandemic alone may contribute with the development of 6.3 million new TB cases due to lack of resources and enforced confinement in TB endemic areas. Evolution of drug-resistant M.tb depends on numerous factors, such as bacterial fitness, strain's genetic background and its capacity to adapt to the surrounding environment, as well as host-specific and environmental factors. Whole-genome transcriptomics and genome-wide association studies in recent years have shed some insights into the complexity of M.tb drug resistance and have provided a better understanding of its underlying molecular mechanisms. In this review, we will discuss M.tb phenotypic and genotypic changes driving resistance, including changes in cell envelope components, as well as recently described intrinsic and extrinsic factors promoting resistance emergence and transmission. We will further explore how drug-resistant M.tb adapts differently than drug-susceptible strains to the lung environment at the cellular level, modulating M.tb-host interactions and disease outcome, and novel next generation sequencing (NGS) strategies to study drug-resistant TB.

From the Pipeline to the Bedside: Advances and Challenges in Clinical Metagenomics

Next-generation sequencing (NGS) technologies have revolutionized multiple areas in the field of infectious diseases, from pathogen discovery to characterization of genes mediating drug resistance. Consequently, there is much anticipation that NGS technologies may be harnessed in the realm of diagnostic methods to complement or replace current culture-based and molecular microbiologic techniques. In this context, much consideration has been given to hypothesis-free, culture-independent tests that can be performed directly on primary clinical samples. The closest realizations of such universal diagnostic methods achieved to date are based on targeted amplicon and unbiased metagenomic shotgun NGS approaches. Depending on the exact details of implementation and analysis, these approaches have the potential to detect viruses, bacteria, fungi, parasites, and archaea, including organisms that were previously undiscovered and those that are uncultivatable. Shotgun metagenomics approaches additionally can provide information on the presence of virulence and resistance genetic elements. While many limitations to the use of NGS in clinical microbiology laboratories are being overcome with decreasing technology costs, expanding curated pathogen sequence databases, and better data analysis tools, there remain many challenges to the routine use and implementation of these methods. This review summarizes recent advances in applications of targeted amplicon and shotgun-based metagenomics approaches to infectious disease diagnostic methods. Technical and conceptual challenges are considered, along with expectations for future applications of these techniques.

Use of a whole genome sequencing-based approach for Mycobacterium tuberculosis surveillance in Europe in 2017-2019: an ECDC pilot study

Whole genome sequencing (WGS) can be used for molecular typing and characterisation of Mycobacterium tuberculosis complex (MTBC) strains. We evaluated the systematic use of a WGS-based approach for MTBC surveillance involving all European Union/European Economic Area (EU/EEA) countries and highlight the challenges and lessons learnt to be considered for the future development of a WGS-based surveillance system.

WGS and epidemiological data of patients with rifampicin-resistant (RR) and multidrug-resistant (MDR) tuberculosis (TB) were collected from EU/EEA countries between January 2017 and December 2019. WGS-based genetic relatedness analysis was performed using a standardised approach including both core genome multilocus sequence typing (cgMLST) and single nucleotide polymorphism (SNP)-based calculation of distances on all WGS data that fulfilled minimum quality criteria to ensure data comparability.

A total of 2218 RR/MDR-MTBC isolates were collected from 25 countries. Among these, 56 cross-border clusters with increased likelihood of recent transmission (≤5 SNPs distance) comprising 316 RR/MDR-MTBC isolates were identified. The cross-border clusters included between two and 30 resistant isolates from two to six countries, demonstrating different RR/MDR-TB transmission patterns in Western and Eastern EU countries.

This pilot study shows that a WGS-based surveillance system is not only feasible but can efficiently elucidate the dynamics of in-country and cross-border RR/MDR-TB transmission across EU/EEA countries. Lessons learnt from this study highlight that the establishment of an EU/EEA centralised WGS-based surveillance system for TB will require strengthening of national integrated systems performing prospective WGS surveillance and the development of clear procedures to facilitate international collaboration for the investigation of cross-border clusters.

The implementation of a WGS-based surveillance system for monitoring the emergence of MDR-TB outbreaks in Europe is feasible and has the potential to provide supporting evidence to better elucidate cross-border transmission patternshttps://bit.ly/2ZTnPjk

MDR M. tuberculosis outbreak clone in Eswatini missed by Xpert has elevated bedaquiline resistance dated to the pre-treatment era

Background

Multidrug-resistant (MDR) Mycobacterium tuberculosis complex strains not detected by commercial molecular drug susceptibility testing (mDST) assays due to the RpoB I491F resistance mutation are threatening the control of MDR tuberculosis (MDR-TB) in Eswatini.

Methods

We investigate the evolution and spread of MDR strains in Eswatini with a focus on bedaquiline (BDQ) and clofazimine (CFZ) resistance using whole-genome sequencing in two collections ((1) national drug resistance survey, 2009–2010; (2) MDR strains from the Nhlangano region, 2014–2017).

Results

MDR strains in collection 1 had a high cluster rate (95%, 117/123 MDR strains) with 55% grouped into the two largest clusters (gCL3, n = 28; gCL10, n = 40). All gCL10 isolates, which likely emerged around 1993 (95% highest posterior density 1987–1998), carried the mutation RpoB I491F that is missed by commercial mDST assays. In addition, 21 (53%) gCL10 isolates shared a Rv0678 M146T mutation that correlated with elevated minimum inhibitory concentrations (MICs) to BDQ and CFZ compared to wild type isolates. gCL10 isolates with the Rv0678 M146T mutation were also detected in collection 2.

Conclusion

The high clustering rate suggests that transmission has been driving the MDR-TB epidemic in Eswatini for three decades. The presence of MDR strains in Eswatini that are not detected by commercial mDST assays and have elevated MICs to BDQ and CFZ potentially jeopardizes the successful implementation of new MDR-TB treatment guidelines. Measures to limit the spread of these outbreak isolates need to be implemented urgently.

Application of Targeted Next-Generation Sequencing Assay on a Portable Sequencing Platform for Culture-Free Detection of Drug-Resistant Tuberculosis from Clinical Samples

Targeted next-generation sequencing (tNGS) has emerged as a comprehensive alternative to existing methods for drug susceptibility testing (DST) of Mycobacterium tuberculosis from patient sputum samples for clinical diagnosis of drug-resistant tuberculosis (DR-TB). However, the complexity of sequencing platforms has limited their uptake in low-resource settings. The goal of this study was to evaluate the use of the tNGS-based DST solution Genoscreen Deeplex Myc-TB, for use on the compact, low-cost Oxford Nanopore Technologies MinION sequencer. One hundred four DNA samples extracted from smear-positive sputum sediments, previously sequenced using the Deeplex assay on an Illumina MiniSeq, were resequenced on MinION after applying a custom library preparation. MinION read quality, mapping statistics, and variant calling were computed using an in-house pipeline and compared to the reference MiniSeq data. The average percentage of MinION reads mapped to an H37RV reference genome was 90.8%, versus 99.5% on MiniSeq. The mean depths of coverage were 4,151× and 4,177× on MinION and MiniSeq, respectively, with heterogeneous distribution across targeted genes. Composite reference coverage breadth was >99% for both platforms. We observed full concordance between technologies in reporting the clinically relevant drug-resistant markers, including full gene deletions. In conclusion, we demonstrated that the workflow and sequencing data obtained from Deeplex on MinION are comparable to those for the MiniSeq, despite the higher raw error rates on MinION, with the added advantage of MinION's portability, versatility, and low capital costs. Targeted NGS on MinION is a promising DST solution for rapidly providing clinically relevant data to manage complex DR-TB cases.

Two Pandemics, One Challenge-Leveraging Molecular Test Capacity of Tuberculosis Laboratories for Rapid COVID-19 Case-Finding

In many settings, the ongoing coronavirus disease (COVID-19) pandemic coincides with other major public health threats, in particular tuberculosis. Using tuberculosis (TB) molecular diagnostic infrastructure, which has substantially expanded worldwide in recent years, for COVID-19 case-finding might be warranted. We analyze the potential of using TB diagnostic and research infrastructures for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing. We focused on quality control by adapting the 12 Quality System Essentials framework to the COVID-19 and TB context. We conclude that diagnostic infrastructures for TB can in principle be leveraged to scale-up SARS-CoV-2 testing, in particular in resource-poor settings. TB research infrastructures also can support sequencing of SARS-CoV-2 to study virus evolution and diversity globally. However, fundamental principles of quality management must be followed for both TB and SARS-CoV-2 testing to ensure valid results and to minimize biosafety hazards, and the continuity of TB diagnostic services must be guaranteed at all times.

Comparison of metagenomic next-generation sequencing technology, culture and GeneXpert MTB/RIF assay in the diagnosis of tuberculosis

Background

With a high incidence rate and mortality rate, tuberculosis (TB) is a major public health concern worldwide. There is an urgent need to develop the rapid, reliable and affordable diagnostic test for the detection of Mycobacterium tuberculosis (Mtb). Metagenomic next-generation sequencing (mNGS) showed promising values in the rapid diagnosis of clinical TB, while studies on the application of mNGS in pulmonary and extrapulmonary TB remain scarce.

Methods

In this study, we collected the results of both mNGS and culture of 70 specimens from suspected TB patients at the Shanghai Pulmonary Hospital of Tongji University. Results of GeneXpert MTB/RIF assay (Xpert) were obtained from 19 patients. We also assessed the diagnostic performance, relationship and consistency between mNGS, traditional culture method, and Xpert.

Results

Overall, 36 of 70 suspected patients were finally diagnosed with TB. The sensitivity, specificity, positive predictive value, negative predictive value and the Youden index of mNGS in all clinical TB specimens were calculated as 66.7% (95% CI: 0.489–0.809), 97.1% (95% CI: 0.829–0.998), 96.0% (95% CI: 0.777–0.998), 73.3% (95% CI: 0.578–0.849), 63.8%, respectively. The sensitivity of mNGS was much superior to that of conventional culture method (66.7%, 95% CI: 0.489–0.809 vs. 36.1%, 95% CI: 0.213–0.538) and Xpert (76.9%, 95% CI: 0.460–0.938 vs. 61.5%, 95% CI: 0.323–0.849). In pulmonary TB cases, mNGS, culture, and Xpert all demonstrated better diagnostic capacity for TB when compared with extrapulmonary TB cases. Among all methods and sample classifications, the Youden index of parallel diagnostic test in pulmonary samples was outstanding (mNGS/culture 88.2%; mNGS/Xpert 100%). Furthermore, the correlation and concordance between mNGS and culture method in all types of specimens was statistically significant (both P<0.001).

Conclusions

With high sensitivity and specificity, mNGS showed remarkable diagnostic performance in various samples from suspected TB patients. Combining mNGS and culture or Xpert method had the potential for clinical application in TB.

Detection of low-frequency resistance-mediating SNPs in next-generation sequencing data of Mycobacterium tuberculosis complex strains with binoSNP

Accurate drug resistance detection is key for guiding effective tuberculosis treatment. While genotypic resistance can be rapidly detected by molecular methods, their application is challenged by mixed mycobacterial populations comprising both susceptible and resistant cells (heteroresistance). For this, next-generation sequencing (NGS) based approaches promise the determination of variants even at low frequencies. However, accurate methods for a valid detection of low-frequency variants in NGS data are currently lacking. To tackle this problem, we developed the variant detection tool binoSNP which allows the determination of low-frequency single nucleotide polymorphisms (SNPs) in NGS datasets from Mycobacterium tuberculosis complex (MTBC) strains. By taking a reference-mapped file as input, binoSNP evaluates each genomic position of interest using a binomial test procedure. binoSNP was validated using in-silico, in-vitro, and serial patient isolates datasets comprising varying genomic coverage depths (100-500×) and SNP allele frequencies (1-30%). Overall, the detection limit for low-frequency SNPs depends on the combination of coverage depth and allele frequency of the resistance-associated mutation. binoSNP allows for valid detection of resistance associated SNPs at a 1% frequency with a coverage ≥400×. In conclusion, binoSNP provides a valid approach to detect low-frequency resistance-mediating SNPs in NGS data from clinical MTBC strains. It can be implemented in automated, end-user friendly analysis tools for NGS data and is a step forward towards individualized TB therapy.

Pharmacologic management of Mycobacterium ulcerans infection

INTRODUCTION

Pharmacological treatment of Buruli ulcer (Mycobacterium ulcerans infection; BU) is highly effective, as shown in two randomized trials in Africa.

AREAS COVERED

We review BU drug treatment - in vitro, in vivo and clinical trials (PubMed: '(Buruli OR (Mycobacterium AND ulcerans)) AND (treatment OR therapy).' We also highlight the pathogenesis of M. ulcerans infection that is dominated by mycolactone, a secreted exotoxin, that causes skin and soft tissue necrosis, and impaired immune response and tissue repair. Healing is slow, due to the delayed wash-out of mycolactone. An array of repurposed tuberculosis and leprosy drugs appears effective in vitro and in animal models. In clinical trials and observational studies, only rifamycins (notably, rifampicin), macrolides (notably, clarithromycin), aminoglycosides (notably, streptomycin) and fluoroquinolones (notably, moxifloxacin, and ciprofloxacin) have been tested.

EXPERT OPINION

A combination of rifampicin and clarithromycin is highly effective but lesions still take a long time to heal. Novel drugs like telacebec have the potential to reduce treatment duration but this drug may remain unaffordable in low-resourced settings. Research should address ulcer treatment in general; essays to measure mycolactone over time hold promise to use as a readout for studies to compare drug treatment schedules for larger lesions of Buruli ulcer.

Challenges of Immune Response Diversity in the Human Population Concerning New Tuberculosis Diagnostics, Therapies, and Vaccines

Universal approaches to the prevention and treatment of human diseases fail to take into account profound immune diversity resulting from genetic variations across populations. Personalized or precision medicine takes into account individual lifestyle, environment, and biology (genetics and immune status) and is being adopted in several disease intervention strategies such as cancer and heart disease. However, its application in infectious diseases, particularly global diseases such as tuberculosis (TB), is far more complex and in a state of infancy. Here, we discuss the impact of human genetic variations on immune responses and how they relate to failures seen in current TB diagnostic, therapy, and vaccine approaches across populations. We offer our perspective on the challenges and potential for more refined approaches going forward.

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

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

Improving treatment outcomes for MDR-TB - Novel host-directed therapies and personalised medicine of the future

Multidrug-resistant TB (MDR-TB) is a major threat to global health security. In 2017, only 50% of patients with MDR-TB who received WHO-recommended treatment were cured. Most MDR-TB patients who recover continue to suffer from functional disability due to long-term lung damage. Whilst new MDR-TB treatment regimens are becoming available, conventional drug therapies need to be complemented with host-directed therapies (HDTs) to reduce tissue damage and improve functional treatment outcomes. This viewpoint highlights recent data on biomarkers, immune cells, circulating effector molecules and genetics which could be utilised for developing personalised HDTs. Novel technologies currently used for cancer therapy which could facilitate in-depth understanding of host genetics and the microbiome in patients with MDR-TB are discussed. Against this background, personalised cell-based HDTs for adjunct MDR-TB treatment to improve clinical outcomes are proposed as a possibility for complementing standard therapy and other HDT agents. Insights into the molecular biology of the mechanisms of action of cellular HDTs may also aid to devise non-cell-based therapies targeting defined inflammatory pathway(s) in Mtb-driven immunopathology.