Dissecting whole-genome sequencing-based online tools for predicting resistance in Mycobacterium tuberculosis: can we use them for clinical decision guidance?

Rifampicin tolerance and growth fitness among isoniazid-resistant clinical Mycobacterium tuberculosis isolates: an in-vitro longitudinal study

Antibiotic tolerance in Mycobacterium tuberculosis leads to less effective bacterial killing, poor treatment responses and resistant emergence. Therefore, we investigated the rifampicin tolerance of M. tuberculosis isolates, with or without pre-existing isoniazid-resistance. We determined the in-vitro rifampicin survival fraction by minimum duration of killing assay in isoniazid susceptible (IS, n=119) and resistant (IR, n=84) M. tuberculosis isolates. Then we correlated the rifampicin tolerance with bacterial growth, rifampicin minimum inhibitory concentrations (MICs) and isoniazid-resistant mutations. The longitudinal IR isolates collected from patients were analyzed for changes in rifampicin tolerance and associated emergence of genetic variants. The median duration of rifampicin exposure reducing the M. tuberculosis surviving fraction by 90% (minimum duration of killing-MDK90) increased from 1.23 (95%CI 1.11; 1.37) and 1.31 (95%CI 1.14; 1.48) to 2.55 (95%CI 2.04; 2.97) and 1.98 (95%CI 1.69; 2.56) days, for IS and IR respectively, during 15 to 60 days of incubation. This indicated the presence of fast and slow growing tolerant sub-populations. A range of 6 log 10 -fold survival fraction enabled classification of tolerance as low, medium or high and revealed IR association with increased tolerance with faster growth (OR=2.68 for low vs. medium, OR=4.42 for low vs. high, P -trend=0.0003). The high tolerance in IR isolates was specific to those collected during rifampicin treatment in patients and associated with bacterial genetic microvariants. Furthermore, the high rifampicin tolerant IR isolates have survival potential similar to multi-drug resistant isolates. These findings suggest that IR tuberculosis needs to be evaluated for high rifampicin tolerance to improve treatment regimen and prevent the risk of MDR-TB emergence.

Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam

IMPORTANCE

Drug-resistant tuberculosis (TB) infection is a growing and potent concern, and combating it will be necessary to achieve the WHO's goal of a 95% reduction in TB deaths by 2035. While prior studies have explored the evolution and spread of drug resistance, we still lack a clear understanding of the fitness costs (if any) imposed by resistance-conferring mutations and the role that Mtb genetic lineage plays in determining the likelihood of resistance evolution. This study offers insight into these questions by assessing the dynamics of resistance evolution in a high-burden Southeast Asian setting with a diverse lineage composition. It demonstrates that there are clear lineage-specific differences in the dynamics of resistance acquisition and transmission and shows that different lineages evolve resistance via characteristic mutational pathways.

A comparison of phenotypic and WGS drug susceptibility testing in Mycobacterium tuberculosis isolates from the Republic of Korea

Background

WGS has significant potential to help tackle the major public health problem of TB. The Republic of Korea has the third highest rates of TB of all Organisation for Economic Cooperation and Development countries but there has been very limited use of WGS in TB to date.

Objectives

A retrospective comparison of Mycobacterium tuberculosis (MTB) clinical isolates from 2015 to 2017 from two centres in the Republic of Korea using WGS to compare phenotypic drug susceptibility testing (pDST) and WGS drug susceptibility predictions (WGS-DSP).

Methods

Fifty-seven MTB isolates had DNA extracted and were sequenced using the Illumina HiSeq platform. The WGS analysis was performed using bwa mem, bcftools and IQ-Tree; resistance markers were identified using TB profiler. Phenotypic susceptibilities were carried out at the Supranational TB reference laboratory (Korean Institute of Tuberculosis).

Results

For first-line antituberculous drugs concordance for rifampicin, isoniazid, pyrazinamide and ethambutol was 98.25%, 92.98%, 87.72% and 85.96%, respectively. The sensitivity of WGS-DSP compared with pDST for rifampicin, isoniazid, pyrazinamide and ethambutol was 97.30%, 92.11%, 78.95% and 95.65%, respectively. The specificity for these first-line antituberculous drugs was 100%, 94.74%, 92.11% and 79.41%, respectively. The sensitivity and specificity for second-line drugs ranged from 66.67% to 100%, and from 82.98% to 100%, respectively.

Conclusions

This study confirms the potential role for WGS in drug susceptibility prediction, which would reduce turnaround times. However, further larger studies are needed to ensure current databases of drug resistance mutations are reflective of the TB present in the Republic of Korea.

Bioinformatics toolbox for exploring target mutation-induced drug resistance

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

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.

Molecular Capture of Mycobacterium tuberculosis Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences

The application of whole genome sequencing of Mycobacterium tuberculosis directly on clinical samples has been investigated as a means to avoid the time-consuming need for culture isolation that can lead to a potential prolonged suboptimal antibiotic treatment. We aimed to provide a proof-of-concept regarding the application of the molecular capture of M. tuberculosis genomes directly from positive sputum samples as an approach for epidemiological and drug susceptibility predictions. Smear-positive sputum samples (n = 100) were subjected to the SureSelectXT HS Target Enrichment protocol (Agilent Technologies, Santa Clara, CA, USA) and whole-genome sequencing analysis. A higher number of reads on target were obtained for higher smear grades samples (i.e., 3+ followed by 2+). Moreover, 37 out of 100 samples showed ≥90% of the reference genome covered with at least 10-fold depth of coverage (27, 9, and 1 samples were 3+, 2+, and 1+, respectively). Regarding drug-resistance/susceptibility prediction, for 42 samples, ≥90% of the >9000 hits that are surveyed by TB-profiler were detected. Our results demonstrated that M. tuberculosis genome capture and sequencing directly from clinical samples constitute a potential valid backup approach for phylogenetic inferences and resistance prediction, essentially in settings when culture is not routinely performed or for samples that fail to grow.

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

Setting

Controlling drug-resistant tuberculosis in Ningbo, China.

Objective

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

Design

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

Results

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

Conclusion

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

Mycobacterium tuberculosis Lineages Associated with Mutations and Drug Resistance in Isolates from India

Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. In this study, we performed whole-genome sequencing of 498 MDR M. tuberculosis isolates from India to determine the prevalence of drug resistance mutations and to understand the genomic diversity. A retrospective collection of 498 M. tuberculosis isolates submitted to the National Institute for Research in Tuberculosis for phenotypic susceptibility testing between 2014 to 2016 were sequenced. Genotypic resistance prediction was performed using known resistance-conferring determinants. Genotypic and phenotypic results for 12 antituberculosis drugs were compared, and sequence data were explored to characterize lineages and their association with drug resistance. Four lineages were identified although lineage 1 predominated (43%). The sensitivity of prediction for isoniazid and rifampicin was 92% and 98%, respectively. We observed lineage-specific variations in the proportion of isolates with resistance-conferring mutations, with drug resistance more common in lineages 2 and 3. Disputed mutations (codons 430, 435, 445, and 452) in the rpoB gene were more common in isolates other than lineage 2. Phylogenetic analysis and pairwise SNP difference revealed high genetic relatedness of lineage 2 isolates. WGS based resistance prediction has huge potential, but knowledge of regional and national diversity is essential to achieve high accuracy for resistance prediction. IMPORTANCE Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. We performed whole-genome sequencing of 498 MDR M. tuberculosis isolates from India to determine the prevalence of drug resistance mutations and to understand genomic diversity. Four lineages were identified although lineage 1 predominated (43%). The sensitivity of prediction for isoniazid and rifampicin was 92% and 98%, respectively. We observed lineage-specific variations in the proportion of isolates with resistance-conferring mutations, with drug resistance more common in lineages 2 and 3. Disputed mutations (codons 430, 435, 445, and 452) in the rpoB gene were more common in isolates other than lineage 2. Phylogenetic analysis and pairwise SNP difference revealed high genetic relatedness of lineage 2 isolates. WGS based resistance prediction has huge potential, but knowledge of regional and national diversity is essential to achieve high accuracy for resistance prediction.

The Neglected Contribution of Streptomycin to the Tuberculosis Drug Resistance Problem

The airborne pathogen Mycobacterium tuberculosis is responsible for a present major public health problem worsened by the emergence of drug resistance. M. tuberculosis has acquired and developed streptomycin (STR) resistance mechanisms that have been maintained and transmitted in the population over the last decades. Indeed, STR resistant mutations are frequently identified across the main M. tuberculosis lineages that cause tuberculosis outbreaks worldwide. The spread of STR resistance is likely related to the low impact of the most frequent underlying mutations on the fitness of the bacteria. The withdrawal of STR from the first-line treatment of tuberculosis potentially lowered the importance of studying STR resistance. However, the prevalence of STR resistance remains very high, could be underestimated by current genotypic methods, and was found in outbreaks of multi-drug (MDR) and extensively drug (XDR) strains in different geographic regions. Therefore, the contribution of STR resistance to the problem of tuberculosis drug resistance should not be neglected. Here, we review the impact of STR resistance and detail well-known and novel candidate STR resistance mechanisms, genes, and mutations. In addition, we aim to provide insights into the possible role of STR resistance in the development of multi-drug resistant tuberculosis.

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

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

Heterogeneous Streptomycin Resistance Level Among Mycobacterium tuberculosis Strains From the Same Transmission Cluster

Widespread and frequent resistance to the second-line tuberculosis (TB) medicine streptomycin, suggests ongoing transmission of low fitness cost streptomycin resistance mutations. To investigate this hypothesis, we studied a cohort of 681 individuals from a TB epidemic in Portugal. Whole-genome sequencing (WGS) analyses were combined with phenotypic growth studies in culture media and in mouse bone marrow derived macrophages. Streptomycin resistance was the most frequent resistance in the cohort accounting for 82.7% (n = 67) of the resistant Mycobacterium tuberculosis isolates. WGS of 149 clinical isolates identified 13 transmission clusters, including three clusters containing only streptomycin resistant isolates. The biggest cluster was formed by eight streptomycin resistant isolates with a maximum of five pairwise single nucleotide polymorphisms of difference. Interestingly, despite their genetic similarity, these isolates displayed different resistance levels to streptomycin, as measured both in culture media and in infected mouse bone marrow derived macrophages. The genetic bases underlying this phenotype are a combination of mutations in gid and other genes. This study suggests that specific streptomycin resistance mutations were transmitted in the cohort, with the resistant isolates evolving at the cluster level to allow low-to-high streptomycin resistance levels without a significative fitness cost. This is relevant not only to better understand transmission of streptomycin resistance in a clinical setting dominated by Lineage 4 M. tuberculosis infections, but mainly because it opens new prospects for the investigation of selection and spread of drug resistance in general.

Whole-genome sequencing analysis of multidrug-resistant Mycobacterium tuberculosis from Java, Indonesia

Introduction. Multidrug-resistant tuberculosis (MDR-TB) is a major public health problem globally, including in Indonesia. Whole-genome sequencing (WGS) analysis has rarely been used for the study of TB and MDR-TB in Indonesia.Aim. We evaluated the use of WGS for drug-susceptibility testing (DST) and to investigate the population structure of drug-resistant Mycobacterium tuberculosis in Java, Indonesia.Methodology. Thirty suspected MDR-TB isolates were subjected to MGIT 960 system (MGIT)-based DST and to WGS. Phylogenetic analysis was done using the WGS data. Results obtained using MGIT-based DST and WGS-based DST were compared.Results. Agreement between WGS and MGIT was 93.33 % for rifampicin, 83.33 % for isoniazid and 76.67 % for streptomycin but only 63.33 % for ethambutol. Moderate WGS-MGIT agreement was found for second-line drugs including amikacin, kanamycin and fluoroquinolone (73.33-76.67 %). MDR-TB was more common in isolates of the East Asian Lineage (63.3%). No evidence of clonal transmission of DR-TB was found among members of the tested population.Conclusion. Our study demonstrated the applicability of WGS for DST and molecular epidemiology of DR-TB in Java, Indonesia. We found no transmission of DR-TB in Indonesia.

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.

A nationwide study of multidrug-resistant tuberculosis in Portugal 2014-2017 using epidemiological and molecular clustering analyses

Background

Increasing multidrug-resistant tuberculosis (MDR-TB) incidence is a major threat against TB eradication worldwide. We aim to conduct a detailed MDR-TB study in Portugal, an European country with endemic TB, combining genetic analysis and epidemiological data, in order to assess the efficiency of public health containment of MRD-TB in the country.

Methods

We used published MIRU-VNTR data, that we reanalysed using a phylogenetic analysis to better describe MDR-TB cases transmission occurring in Portugal from 2014 to 2017, further enriched with epidemiological data of these cases.

Results

We show an MDR-TB transmission scenario, where MDR strains likely arose and are transmitted within local chains. 63% of strains were clustered, suggesting high primary transmission (estimated as 50% using MIRU-VNTR data and 15% considering epidemiological links). These values are higher than those observed across Europe and even for sensitive strains in Portugal using similar methodologies. MDR-TB cases are associated with individuals born in Portugal and evolutionary analysis suggests a local evolution of strains. Consistently the sublineage LAM, the most common in sensitive strains in Europe, is the more frequent in Portugal in contrast with the remaining European MDR-TB picture where immigrant-associated Beijing strains are more common.

Conclusions

Despite efforts to track and contain MDR-TB strains in Portugal, their transmission patterns are still as uncontrolled as that of sensitive strains, stressing the need to reinforce surveillance and containment strategies.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4189-7) contains supplementary material, which is available to authorized users.

A large scale evaluation of TBProfiler and Mykrobe for antibiotic resistance prediction in Mycobacterium tuberculosis

Recent years saw a growing interest in predicting antibiotic resistance from whole-genome sequencing data, with promising results obtained for Staphylococcus aureus and Mycobacterium tuberculosis. In this work, we gathered 6,574 sequencing read datasets of M. tuberculosis public genomes with associated antibiotic resistance profiles for both first and second-line antibiotics. We performed a systematic evaluation of TBProfiler and Mykrobe, two widely recognized softwares allowing to predict resistance in M. tuberculosis. The size of the dataset allowed us to obtain confident estimations of their overall predictive performance, to assess precisely the individual predictive power of the markers they rely on, and to study in addition how these softwares behave across the major M. tuberculosis lineages. While this study confirmed the overall good performance of these tools, it revealed that an important fraction of the catalog of mutations they embed is of limited predictive power. It also revealed that these tools offer different sensitivity/specificity trade-offs, which is mainly due to the different sets of mutation they embed but also to their underlying genotyping pipelines. More importantly, it showed that their level of predictive performance varies greatly across lineages for some antibiotics, therefore suggesting that the predictions made by these softwares should be deemed more or less confident depending on the lineage inferred and the predictive performance of the marker(s) actually detected. Finally, we evaluated the relevance of machine learning approaches operating from the set of markers detected by these softwares and show that they present an attractive alternative strategy, allowing to reach better performance for several drugs while significantly reducing the number of candidate mutations to consider.

Role and value of whole genome sequencing in studying tuberculosis transmission

BACKGROUND

Tuberculosis (TB) remains a serious public health threat worldwide. Theoretically ultimate resolution of whole genome sequencing (WGS) for Mycobacterium tuberculosis complex (MTBC) strain classification makes this technology very attractive for epidemiological investigations.

OBJECTIVES

To summarize the evidence available in peer-reviewed publications on the role and place of WGS in detection of TB transmission.

SOURCES

A total of 69 peer-reviewed publications identified in Pubmed database.

CONTENT

Evidence from >30 publications suggests that a cut-off value of fewer than six single nucleotide polymorphisms between strains efficiently excludes cases that are not the result of recent transmission and could be used for the identification of drug-sensitive isolates involved in direct human-to-human TB transmission. Sensitivity of WGS to identify epidemiologically linked isolates is high, reaching 100% in eight studies with specificity (17%-95%) highly dependent on the settings. Drug resistance and specific phylogenetic lineages may be associated with accelerated mutation rates affecting genetic distances. WGS can be potentially used to distinguish between true relapses and re-infections but in high-incidence low-diversity settings this would require consideration of epidemiological links and minority alleles. Data from four studies looking into within-host diversity highlight a need for developing criteria for acceptance or rejection of WGS relatedness results depending on the proportion of minority alleles.

IMPLICATIONS

WGS will potentially allow for more targeted public health actions preventing unnecessary investigations of false clusters. Consensus on standardization of raw data quality control processing criteria, analytical pipelines and reporting language is yet to be reached.

Overcoming the pitfalls of automatic interpretation of whole genome sequencing data by online tools for the prediction of pyrazinamide resistance in Mycobacterium tuberculosis

OBJECTIVES

Automated online software tools that analyse whole genome sequencing (WGS) data without the need for bioinformatics expertise can motivate the implementation of WGS-based molecular drug susceptibility testing (DST) in routine diagnostic settings for tuberculosis (TB). Pyrazinamide (PZA) is a key drug for current and future TB treatment regimens; however, it was reported that predictive power for PZA resistance by the available tools is low. Therefore, this low predictive power may make users hesitant to use the tools. This study aimed to elucidate why and to uncover the real performance of the tools when taking into account their variation calling lists (manual inspection), not just their automated reporting system (default setting) that was evaluated by previous studies.

METHODS

WGS data from 191 datasets comprising 108 PZA-resistant and 83 susceptible strains were used to evaluate the potential performance of the available online tools (TB Profiler, TGS-TB, PhyResSE, and CASTB) for predicting phenotypic PZA resistance.

RESULTS

When taking into consideration the variation calling lists, 73 variants in total (47 non-synonymous mutations and 26 indels) in pncA were detected by TGS-TB and PhyResSE, covering all mutations for the 108 PZA-resistant strains. The 73 variants were confirmed by Sanger sequencing. TB Profiler also detected all but three complete loss, two large deletion at the 3'-end, and one relatively large insertion of pncA. On the other hand, many of the 73 variants were lacking in the automated reporting systems except by TGS-TB; of these variants, CASTB detected only 20. By applying the 'non-wild type sequence' approach for predicting PZA resistance, accuracy of the results significantly improved compared with that of the automated results obtained by each tool.

CONCLUSION

Users can obtain more accurate predictions for PZA resistance than previously reported by manually checking the results and applying the 'non-wild type sequence' approach.

Genome-Based Prediction of Bacterial Antibiotic Resistance

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences.

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.

Evaluation of a gene-by-gene approach for prospective whole-genome sequencing-based surveillance of multidrug resistant Mycobacterium tuberculosis

Whole-genome sequencing (WGS) offers unprecedented resolution for tracking Mycobacterium tuberculosis transmission and antibiotic-resistance spread. Still, the establishment of standardized WGS-based pipelines and the definition of epidemiological clusters based on genetic relatedness are under discussion. We aimed to implement a dynamic gene-by-gene approach, fully relying on freely available software, for prospective WGS-based tuberculosis surveillance, demonstrating its application for detecting transmission chains by retrospectively analysing all M/XDR strains isolated in 2013-2017 in Portugal. We observed a good correlation between genetic relatedness and epidemiological links, with strongly epilinked clusters displaying mean pairwise allele differences (AD) always below 0.3% (ratio of mean AD over the total number of shared loci between same-cluster strains). This data parallels the genetic distances acquired by the core-SNV analysis, while providing higher resolution and epidemiological concordance than MIRU-VNTR genotyping. The dynamic analysis of strain sub-sets (i.e., increasing the number of shared loci within each sub-set) also strengthens the confidence in detecting epilinked clusters. This gene-by-gene strategy also offers several practical benefits (e.g., reliance on freely-available software, scalability and low computational requirements) that further consolidated its suitability for a timely and robust prospective WGS-based laboratory surveillance of M/XDR-TB cases.