Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe

Genome sequencing analysis of the pncA, rpsA and panD genes responsible for pyrazinamide resistance of Mycobacterium tuberculosis from Indonesian isolates

BACKGROUND

Developing the most suitable treatment against tuberculosis based on resistance profiles is imperative to effectively cure tuberculosis patients. Whole-genome sequencing is a molecular method that allows for the rapid and cost-effective detection of mutations in multiple genes associated with anti-tuberculosis drug resistance. This sequencing approach addresses the limitations of culture-based methods, which may not apply to certain anti-TB drugs, such as pyrazinamide, because of their specific culture medium requirements, potentially leading to biased resistance culture results.

METHODS

Thirty-four M. tuberculosis isolates were subcultured on a Lowenstein-Jensen medium. The genome of these bacteria was subsequently isolated using cetyltrimethylammonium bromide. Genome sequencing was performed with Novaseq Illumina 6000 (Illumina), and the data were analysed using the GenTB and Mykrobe applications. We also conducted a de novo analysis to compare the two methods and performed mutation analysis of other genes encoding pyrazinamide resistance, namely rpsA and panD.

RESULTS

The results revealed mutations in the pncA gene, which were identified based on the databases accessed through GenTB and Mykrobe. Two discrepancies between the drug susceptibility testing and sequencing results may suggest potential instability in the drug susceptibility testing culture, specifically concerning PZA. Meanwhile, the results of the de novo analysis showed the same result of pncA mutation to the GenTB or Mykrobe; meanwhile, there were silent mutations in rpsA in several isolates and a point mutation; no mutations were found in the panD gene. However, the mutations in the genes encoding pyrazinamide require further and in-depth study to understand their relationship to the phenotypic profile.

CONCLUSIONS

Compared to the conventional culture method, the whole-genome sequencing method has advantages in determining anti-tuberculosis resistance profiles, especially in reduced time and bias.

Draft genome sequences of clinical Mycobacterium canettii strains in Canada

Mycobacterium canettii is a rare pathogen causing tuberculosis in humans and presents a risk to public health. Here, we report the genome sequences of two M. canettii strains. The genomes will assist in creating sequence-based tools for M. canettii and serve as references for identification, surveillance, and epidemiological investigations.

Drug resistance and genomic variations among Mycobacterium tuberculosis isolates from The Nile Delta, Egypt

Tuberculosis is a global public health concern. Earlier reports suggested the emergence of high rates of drug resistant tuberculosis in Egypt. This study included 102 isolates of Mycobacterium tuberculosis collected from two reference laboratories in Cairo and Alexandria. All clinical isolates were sub-cultured on Löwenstein-Jensen medium and analyzed using both BD BACTEC MGIT 960 SIRE Kit and standard diffusion disk assays to identify the antibiotic sensitivity profile. Extracted genomic DNA was subjected to whole genome sequencing (WGS) using Illumina platform. Isolates that belong to lineage 4 represented > 80%, while lineage 3 represented only 11% of the isolates. The percentage of drug resistance for the streptomycin, isoniazid, rifampicin and ethambutol were 31.0, 17.2, 19.5 and 20.7, respectively. Nearly 47.1% of the isolates were sensitive to the four anti-tuberculous drugs, while only one isolate was resistant to all four drugs. In addition, several new and known mutations were identified by WGS. High rates of drug resistance and new mutations were identified in our isolates. Tuberculosis control measures should focus on the spread of mono (S, I, R, E)- and double (S, E)-drug resistant strains present at higher rates throughout the whole Nile Delta, Egypt.

Comparisons of genome assembly tools for characterization of Mycobacterium tuberculosis genomes using hybrid sequencing technologies

Background

Next-generation sequencing of Mycobacterium tuberculosis, the infectious agent causing tuberculosis, is improving the understanding of genomic diversity of circulating lineages and strain-types, and informing knowledge of drug resistance mutations. An increasingly popular approach to characterizing M. tuberculosis genomes (size: 4.4 Mbp) and variants (e.g., single nucleotide polymorphisms (SNPs)) involves the de novo assembly of sequence data.

Methods

We compared the performance of genome assembly tools (Unicycler, RagOut, and RagTag) on sequence data from nine drug resistant M. tuberculosis isolates (multi-drug (MDR) n = 1; pre-extensively-drug (pre-XDR) n = 8) generated using Illumina HiSeq, Oxford Nanopore Technology (ONT) PromethION, and PacBio platforms.

Results

Our investigation found that Unicycler-based assemblies had significantly higher genome completeness (~98.7%; p values = 0.01) compared to other assembler tools (RagOut = 98.6%, and RagTag = 98.6%). The genome assembly sizes (bp) across isolates and sequencers based on RagOut was significantly longer (p values < 0.001) (4,418,574 ± 8,824 bp) than Unicycler and RagTag assemblies (Unicycler = 4,377,642 ± 55,257 bp, and RagTag = 4,380,711 ± 51,164 bp). RagOut-based assemblies had the fewest contigs (~32) and the longest genome size (4,418,574 bp; vs. H37Rv reference size 4,411,532 bp) and therefore were chosen for downstream analysis. Pan-genome analysis of Illumina and PacBio hybrid assemblies revealed the greatest number of detected genes (4,639 genes; H37Rv reference contains 3,976 genes), while Illumina and ONT hybrid assemblies produced the highest number of SNPs. The number of genes from hybrid assemblies with ONT and PacBio long-reads (mean: 4,620 genes) was greater than short-read assembly alone (4,478 genes). All nine RagOut hybrid genome assemblies detected known mutations in genes associated with MDR-TB and pre-XDR-TB.

Conclusions

Unicycler software performed the best in terms of achieving contiguous genomes, whereas RagOut improved the quality of Unicycler's genome assemblies by providing a longer genome size. Overall, our approach has demonstrated that short-read and long-read hybrid assembly can provide a more complete genome assembly than short-read assembly alone by detecting pan-genomes and more genes, including IS6110, and SNPs.

Whole-genome sequencing-based genetic diversity, transmission dynamics, and drug-resistant mutations in Mycobacterium tuberculosis isolated from extrapulmonary tuberculosis patients in western Ethiopia

Background

Extrapulmonary tuberculosis (EPTB) refers to a form of Tuberculosis (TB) where the infection occurs outside the lungs. Despite EPTB being a devastating disease of public health concern, it is frequently overlooked as a public health problem. This study aimed to investigate genetic diversity, identify drug-resistance mutations, and trace ongoing transmission chains.

Methods

A cross-sectional study was undertaken on individuals with EPTB in western Ethiopia. In this study, whole-genome sequencing (WGS) was employed to analyze Mycobacterium tuberculosis (MTB) samples obtained from EPTB patients. Out of the 96 genomes initially sequenced, 89 met the required quality standards for genetic diversity, and drug-resistant mutations analysis. The data were processed using robust bioinformatics tools.

Results

Our analysis reveals that the majority (87.64%) of the isolates can be attributed to Lineage-4 (L4), with L4.6.3 and L4.2.2.2 emerging as the predominant sub-lineages, constituting 34.62% and 26.92%, respectively. The overall clustering rate and recent transmission index (RTI) were 30 and 17.24%, respectively. Notably, 7.87% of the isolates demonstrated resistance to at least one anti-TB drug, although multi-drug resistance (MDR) was observed in only 1.12% of the isolates.

Conclusions

The genetic diversity of MTBC strains in western Ethiopia was found to have low inter-lineage diversity, with L4 predominating and exhibiting high intra-lineage diversity. The notably high clustering rate in the region implies a pressing need for enhanced TB infection control measures to effectively disrupt the transmission chain. It's noteworthy that 68.75% of resistance-conferring mutations went undetected by both GeneXpert MTB/RIF and the line probe assay (LPA) in western Ethiopia. The identification of resistance mutations undetected by both GeneXpert and LPA, along with the detection of mixed infections through WGS, emphasizes the value of adopting WGS as a high-resolution approach for TB diagnosis and molecular epidemiological surveillance.

Evaluation of WHO catalog of mutations and five WGS analysis tools for drug resistance prediction of Mycobacterium tuberculosis isolates from China

The continuous advancement of molecular diagnostic techniques, particularly whole-genome sequencing (WGS), has greatly facilitated the early diagnosis of drug-resistant tuberculosis patients. Nonetheless, the interpretation of results from various types of mutations in drug-resistant-associated genes has become the primary challenge in the field of molecular drug-resistance diagnostics. In this study, our primary objective is to evaluate the diagnosis accuracy of the World Health Organization (WHO) catalog of mutations and five WGS analysis tools (PhyResSE, Mykrobe, TB Profiler, Gen-TB, and SAM-TB) in drug resistance to 10 anti-Mycobacterium tuberculosis (MTB) drugs. We utilized the data of WGS collected between 2014 and 2017 in Zhejiang Province, consisting of 110 MTB isolates as detailed in our previous study. Based on phenotypic drug susceptibility testing (DST) results using the proportion method on Löwenstein-Jensen medium with antibiotics, we evaluated the predictive accuracy of genotypic DST obtained by these tools. The results revealed that the WHO catalog of mutations and five WGS analysis tools exhibit robust predictive capabilities concerning resistance to isoniazid, rifampicin, ethambutol, streptomycin, amikacin, kanamycin, and capreomycin. Notably, Mykrobe, SAM-TB, and TB Profiler demonstrate the most accurate predictions for resistance to pyrazinamide, prothionamide, and para-aminosalicylic acid, respectively. These findings are poised to significantly guide and influence future clinical treatment strategies and resistance monitoring protocols.IMPORTANCEWhole-genome sequencing (WGS) has the potential for the early diagnosis of drug-resistant tuberculosis. However, the interpretation of mutations of drug-resistant-associated genes represents a significant challenge as the amount and complexity of WGS data. We evaluated the accuracy of the World Health Organization catalog of mutations and five WGS analysis tools in predicting drug resistance to first-line and second-line anti-TB drugs. Our results offer clinicians guidance on selecting appropriate WGS analysis tools for predicting resistance to specific anti-TB drugs.

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

US Centers for Disease Control and Prevention.

Molecular Epidemiology of Isoniazid-resistant M tuberculosis in Port-au-Prince, Haiti

Background

Isoniazid-resistant, rifampin-susceptible tuberculosis (Hr-TB) is associated with poor treatment outcomes and higher rates of acquisition of further drug resistance during treatment. Due to a lack of widespread diagnostics, Hr-TB is frequently undetected and its epidemiology is incompletely understood.

Methods

We studied the molecular epidemiology of Hr-TB among all patients diagnosed with culture-positive pulmonary tuberculosis between January 1 and June 30, 2017, at an urban referral tuberculosis clinic in Port-au-Prince, Haiti. Demographic and clinical data were extracted from the electronic medical record. Archived diagnostic Mycobacterium tuberculosis isolates were tested for genotypic and phenotypic isoniazid resistance using the Genotype MTBDRplus assay (Hain, Nehren, Germany) and culture-based testing, respectively. All isoniazid-resistant isolates and a randomly selected subset of isoniazid-susceptible isolates underwent whole-genome sequencing to confirm the presence of mutations associated with isoniazid resistance, to validate use of Genotype MTBDRplus in this population, and to identify potential transmission links between isoniazid-resistant isolates.

Results and Conclusions

Among 845 patients with culture-positive pulmonary tuberculosis in Haiti, 65 (7.7%) had Hr-TB based on the Genotype MTBDRplus molecular assay. Age < 20 years was significantly associated with Hr-TB (odds ratio, 2.39; 95% confidence interval, 1.14, 4.70; P = .015). Thirteen (20%) isoniazid-resistant isolates were found in 5 putative transmission clusters based on a single nucleotide polymorphism distance of ≤ 5. No patients in these transmission clusters were members of the same household. Adolescents are at higher risk for Hr-TB. Strains of isoniazid-resistant M tuberculosis are actively circulating in Haiti and transmission is likely occurring in community settings.

Genomic characteristics of prospectively sequenced Mycobacterium tuberculosis from respiratory and non-respiratory sources

Understanding the differences between Mycobacterium tuberculosis strains isolated from respiratory and non-respiratory sources may inform clinical care and control strategies. We examined demographic and genomic characteristics of all culture-confirmed M. tuberculosis cultures isolated from respiratory and non-respiratory sources in New South Wales, Australia, from January 2017 to December 2021, using logistic regression models. M. tuberculosis strains from 1,831 patients were sequenced; 64.7% were from respiratory, 32.1% from non-respiratory, and 2.2% from both sources. Female patients had more frequent isolation from a non-respiratory source (p = 0.03), and older adults (≧65 years) from a respiratory source (p < 0.0001). Lineage 2 strains were relatively over-represented among respiratory isolates (p = 0.01). Among 39 cases with sequenced isolates from both sources, 43.6% had 1-10 single nucleotide polymorphism differences. The finding that older adults were more likely to have M. tuberculosis isolated from respiratory sources has relevance for TB control given the expected rise of TB among older adults.

An overview of next generation sequencing strategies and genomics tools used for tuberculosis research

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

Evaluation of AAICare®-TB sequence analysis tool for accurate diagnosis of drug-resistant tuberculosis: A comparative study with TB-Profiler and Mykrobe

A rapid and comprehensive drug susceptibility test is essential for eliminating drug resistant tuberculosis. Next generation sequencing (NGS) based susceptibility testing is being explored as a potential substitute for the conventional phenotypic and genotypic testing methods. However, the adoption of NGS based genotypic susceptibility testing depends on the availability of simple, accurate and efficient analysis tools. This preliminary study aimed to evaluate the performance of a Mycobacterium tuberculosis (Mtb) genome analysis pipeline, AAICare®-TB, for susceptibility prediction, in comparison to two widely used gDST prediction tools, TB-Profiler and Mykrobe. This study was performed in a National Reference Laboratory in India on presumptive drug-resistant tuberculosis (DR-TB) isolates. Whole genome sequences of the 120 cultured isolates were obtained through Illumina sequencing on a MiSeq platform. Raw sequences were simultaneously analysed using the three tools. Susceptibility prediction reports thus generated, were compared to estimate the total concordance and discordance. WHO mutation catalogue (1st edition, 2021) was used as the reference standard for categorizing the mutations. In this study, AAICare®-TB was able to predict drug resistance status for First Line (Streptomycin, Isoniazid, Rifampicin, Ethambutol and Pyrazinamide) and Second Line drugs (Fluoroquinolones, Second Line Injectables and Ethionamide) in 93 samples along with lineage and hetero-resistance as per the WHO guidelines.

Scalable de novo classification of antibiotic resistance of Mycobacterium tuberculosis

MOTIVATION

World Health Organization estimates that there were over 10 million cases of tuberculosis (TB) worldwide in 2019, resulting in over 1.4 million deaths, with a worrisome increasing trend yearly. The disease is caused by Mycobacterium tuberculosis (MTB) through airborne transmission. Treatment of TB is estimated to be 85% successful, however, this drops to 57% if MTB exhibits multiple antimicrobial resistance (AMR), for which fewer treatment options are available.

RESULTS

We develop a robust machine-learning classifier using both linear and nonlinear models (i.e. LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M[i, j] is equal to the number of times the ith 31-mer occurs in the jth genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 >80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had an F-1 score >75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.

AVAILABILITY

The models and source code are publicly available on Github at https://github.com/M-Serajian/MTB-Pipeline.

Evaluation of Whole Genome Sequencing-Based Predictions of Antimicrobial Resistance to TB First Line Agents: A Lesson from 5 Years of Data

Phenotypic susceptibility testing of the Mycobacterium tuberculosis complex (MTBC) isolate requires culture growth, which can delay rapid detection of resistant cases. Whole genome sequencing (WGS) and data analysis pipelines can assist in predicting resistance to antimicrobials used in the treatment of tuberculosis (TB). This study compared phenotypic susceptibility testing results and WGS-based predictions of antimicrobial resistance (AMR) to four first-line antimicrobials-isoniazid, rifampin, ethambutol, and pyrazinamide-for MTBC isolates tested between the years 2018-2022. For this 5-year retrospective analysis, the WGS sensitivity for predicting resistance for isoniazid, rifampin, ethambutol, and pyrazinamide using Mykrobe was 86.7%, 100.0%, 100.0%, and 47.8%, respectively, and the specificity was 99.4%, 99.5%, 98.7%, and 99.9%, respectively. The predictive values improved slightly using Mykrobe corrections applied using TB Profiler, i.e., the WGS sensitivity for isoniazid, rifampin, ethambutol, and pyrazinamide was 92.31%, 100%, 100%, and 57.78%, respectively, and the specificity was 99.63%. 99.45%, 98.93%, and 99.93%, respectively. The utilization of WGS-based testing addresses concerns regarding test turnaround time and enables analysis for MTBC member identification, antimicrobial resistance prediction, detection of mixed cultures, and strain genotyping, all through a single laboratory test. WGS enables rapid resistance detection compared to traditional phenotypic susceptibility testing methods using the WHO TB mutation catalog, providing an insight into lesser-known mutations, which should be added to prediction databases as high-confidence mutations are recognized. The WGS-based methods can support TB elimination efforts in Canada and globally by ensuring the early start of appropriate treatment, rapidly limiting the spread of TB outbreaks.

Concordance of targeted and whole genome sequencing for Mycobacterium tuberculosis genotypic drug susceptibility testing

Targeted Next Generation Sequencing (tNGS) and Whole Genome Sequencing (WGS) are increasingly used for genotypic drug susceptibility testing (gDST) of Mycobacterium tuberculosis. Thirty-two multi-drugs resistant and 40 drug susceptible isolates from Madagascar were tested with Deeplex® Myc-TB and WGS using the Mykrobe analysis pipeline. Sixty-four of 72 (89 %) yielded concordant categorical gDST results for drugs tested by both assays. Mykrobe didn't detect pncA K96T, pncA Q141P, pncA H51P, pncA H82R, rrs C517T and rpsL K43R mutations, which were identified as minority variants in corresponding isolates by tNGS. One discrepancy (rrs C517T) was associated with insufficient sequencing depth on WGS. Deeplex® Myc-TB didn't detect inhA G-154A which isn't covered by the assay's amplification targets. Despite those targets being included in the Deeplex® Myc-TB assay, a pncA T47A and a deletion in gid were not identified in one isolate respectively. The evaluated WGS and tNGS gDST assays show high but imperfect concordance.

First whole genome sequencing data of a Mycobacterium tuberculosis STB-T1A strain isolated from a spinal tuberculosis patient in Sabah, Malaysia

Spinal tuberculosis, also referred to as Pott's disease, presents a significant risk of severe paralysis if not promptly detected and treated, owing to complications such as spinal cord compression and deformity. This article presents the genetic analysis of a Mycobacterium tuberculosis STB-T1A strain, isolated from the spine of a 29-year-old female diagnosed with spinal tuberculosis. Genomic DNA was extracted from pure culture and subjected to sequencing using the Illumina NovaSeq 6000 sequencing system. The genome of the M. tuberculosis STB-T1A strain spans 4,367,616 base pairs with a G+C content of 65.56 % and 4174 protein-coding genes. Comparative genomic analysis, conducted via single nucleotide polymorphism (SNP)-based phylogenetic analysis using the Maximum Likelihood method, revealed that the strain falls within the Indo-Oceanic lineage (Lineage 1). It clusters with the M. tuberculosis 43-16836 strain, which was isolated from the cerebrospinal fluid of a patient with tuberculous meningitis in Thailand. The complete genome sequence has been deposited at the National Center for Biotechnology Information (NCBI) GenBank database with the accession number JBBMVZ000000000.

Toward Characterizing Environmental Sources of Non-tuberculous Mycobacteria (NTM) at the Species Level: A Tutorial Review of NTM Phylogeny and Phylogenetic Classification

Nontuberculous mycobacteria (NTM) are any mycobacteria that do not cause tuberculosis or leprosy. While the majority of NTM are harmless and some of them are considered probiotic, a growing number of people are being diagnosed with NTM infections. Therefore, their detection in the environment is of interest to clinicians, environmental microbiologists, and water quality researchers alike. This review provides a tutorial on the foundational approaches for taxonomic classifications, with a focus on the phylogenetic relationships among NTM revealed by the 16S rRNA gene, rpoB gene, and hsp65 gene, and by genome-based approaches. Recent updates on the Mycobacterium genus taxonomy are also provided. A synthesis on the habitats of 189 mycobacterial species in a genome-based taxonomy framework was performed, with attention paid to environmental sources (e.g., drinking water, aquatic environments, and soil). The 16S rRNA gene-based classification accuracy for various regions was evaluated (V3, V3-V4, V3-V5, V4, V4-V5, and V1-V9), revealing overall excellent genus-level classification (up to 100% accuracy) yet only modest performance (up to 63.5% accuracy) at the species level. Future research quantifying NTM species in water systems, determining the effects of water treatment and plumbing conditions on their variations, developing high throughput species-level characterization tools for use in the environment, and incorporating the characterization of functions in a phylogenetic framework will likely fill critical knowledge gaps. We believe this tutorial will be useful for researchers new to the field of molecular or genome-based taxonomic profiling of environmental microbiomes. Experts may also find this review useful in terms of the selected key findings of the past 30 years, recent updates on phylogenomic analyses, as well as a synthesis of the ecology of NTM in a phylogenetic framework.

GenoMycAnalyzer: a web-based tool for species and drug resistance prediction for Mycobacterium genomes

BACKGROUND

Drug-resistant tuberculosis (TB) is a major threat to global public health. Whole-genome sequencing (WGS) is a useful tool for species identification and drug resistance prediction, and many clinical laboratories are transitioning to WGS as a routine diagnostic tool. However, user-friendly and high-confidence automated bioinformatics tools are needed to rapidly identify M. tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), detect drug resistance, and further guide treatment options.

RESULTS

We developed GenoMycAnalyzer, a web-based software that integrates functions for identifying MTBC and NTM species, lineage and spoligotype prediction, variant calling, annotation, drug-resistance determination, and data visualization. The accuracy of GenoMycAnalyzer for genotypic drug susceptibility testing (gDST) was evaluated using 5,473 MTBC isolates that underwent phenotypic DST (pDST). The GenoMycAnalyzer database was built to predict the gDST for 15 antituberculosis drugs using the World Health Organization mutational catalogue. Compared to pDST, the sensitivity of drug susceptibilities by the GenoMycAnalyzer for first-line drugs ranged from 95.9% for rifampicin (95% CI 94.8-96.7%) to 79.6% for pyrazinamide (95% CI 76.9-82.2%), whereas those for second-line drugs ranged from 98.2% for levofloxacin (95% CI 90.1-100.0%) to 74.9% for capreomycin (95% CI 69.3-80.0%). Notably, the integration of large deletions of the four resistance-conferring genes increased gDST sensitivity. The specificity of drug susceptibilities by the GenoMycAnalyzer ranged from 98.7% for amikacin (95% CI 97.8-99.3%) to 79.5% for ethionamide (95% CI 76.4-82.3%). The incorporated Kraken2 software identified 1,284 mycobacterial species with an accuracy of 98.8%. GenoMycAnalyzer also perfectly predicted lineages for 1,935 MTBC and spoligotypes for 54 MTBC.

CONCLUSIONS

GenoMycAnalyzer offers both web-based and graphical user interfaces, which can help biologists with limited access to high-performance computing systems or limited bioinformatics skills. By streamlining the interpretation of WGS data, the GenoMycAnalyzer has the potential to significantly impact TB management and contribute to global efforts to combat this infectious disease. GenoMycAnalyzer is available at http://www.mycochase.org .

Mycobacterium tuberculosis Lineage Distribution Using Whole-Genome Sequencing and Bedaquiline, Clofazimine, and Linezolid Phenotypic Profiles among Rifampicin-Resistant Isolates from West Java, Indonesia

Tuberculosis (TB) is caused by Mycobacterium tuberculosis infection. Indonesia is ranked second in the world for TB cases. New anti-TB drugs from groups A and B, such as bedaquiline, clofazimine, and linezolid, have been shown to be effective in curing drug resistance in TB patients, and Indonesia is already using these drugs to treat patients. However, studies comparing the TB strain types with anti-TB resistance profiles are still relevant to understanding the prevalent strains in the country and their phenotypic characteristics. This study aimed to determine the association between the TB lineage distribution using whole-genome sequencing and bedaquiline, clofazimine, and linezolid phenotypic profile resistance among M. tuberculosisrifampicin-resistant isolates from West Java. M. tuberculosis isolates stock of the Department of Microbiology, Faculty of Medicine, Universitas Indonesia, was tested against bedaquiline, clofazimine, and linezolid using a mycobacteria growth indicator tube liquid culture. All isolates were tested for M. tuberculosis and rifampicin resistance using Xpert MTB/RIF. The DNA genome of M. tuberculosis was freshly extracted from a Löwenstein-Jensen medium culture and then sequenced. The isolates showed phenotypically resistance to bedaquiline, clofazimine, and linezolid at 5%, 0%, and 0%, respectively. We identified gene mutations on phenotypically bedaquiline-resistant strains (2/3), and other mutations also found in phenotypically drug-sensitive strains. Mykrobe analysis showed that most (88.33%) of the isolates could be classified as rifampicin-resistant TB. Using Mykrobe and TB-Profiler to determine the lineage distribution, the isolates were found to belong to lineage 4 (Euro-American; 48.33%), lineage 2 (East Asian/Beijing; 46.67%), and lineage 1 (Indo-Oceanic; 5%). This work underlines the requirement to increase the representation of genotype-phenotype TB data while also highlighting the importance and efficacy of WGS in predicting medication resistance and inferring disease transmission.

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

Minimum Inhibitory Concentrations (MICs) are the gold standard for quantitatively measuring antibiotic resistance. However, lab-based MIC determination can be time-consuming and suffers from low reproducibility, and interpretation as sensitive or resistant relies on guidelines which change over time. Genome sequencing and machine learning promise to allow in silico MIC prediction as an alternative approach which overcomes some of these difficulties, albeit the interpretation of MIC is still needed. Nevertheless, precisely how we should handle MIC data when dealing with predictive models remains unclear, since they are measured semi-quantitatively, with varying resolution, and are typically also left- and right-censored within varying ranges. We therefore investigated genome-based prediction of MICs in the pathogen Klebsiella pneumoniae using 4367 genomes with both simulated semi-quantitative traits and real MICs. As we were focused on clinical interpretation, we used interpretable rather than black-box machine learning models, namely, Elastic Net, Random Forests, and linear mixed models. Simulated traits were generated accounting for oligogenic, polygenic, and homoplastic genetic effects with different levels of heritability. Then we assessed how model prediction accuracy was affected when MICs were framed as regression and classification. Our results showed that treating the MICs differently depending on the number of concentration levels of antibiotic available was the most promising learning strategy. Specifically, to optimise both prediction accuracy and inference of the correct causal variants, we recommend considering the MICs as continuous and framing the learning problem as a regression when the number of observed antibiotic concentration levels is large, whereas with a smaller number of concentration levels they should be treated as a categorical variable and the learning problem should be framed as a classification. Our findings also underline how predictive models can be improved when prior biological knowledge is taken into account, due to the varying genetic architecture of each antibiotic resistance trait. Finally, we emphasise that incrementing the population database is pivotal for the future clinical implementation of these models to support routine machine-learning based diagnostics.

Genotype-Phenotype Characterization of Serial Mycobacterium tuberculosis Isolates in Bedaquiline-Resistant Tuberculosis

BACKGROUND

Emerging resistance to bedaquiline (BDQ) threatens to undermine advances in the treatment of drug-resistant tuberculosis (DRTB). Characterizing serial Mycobacterium tuberculosis (Mtb) isolates collected during BDQ-based treatment can provide insights into the etiologies of BDQ resistance in this important group of DRTB patients.

METHODS

We measured mycobacteria growth indicator tube (MGIT)-based BDQ minimum inhibitory concentrations (MICs) of Mtb isolates collected from 195 individuals with no prior BDQ exposure who were receiving BDQ-based treatment for DRTB. We conducted whole-genome sequencing on serial Mtb isolates from all participants who had any isolate with a BDQ MIC >1 collected before or after starting treatment (95 total Mtb isolates from 24 participants).

RESULTS

Sixteen of 24 participants had BDQ-resistant TB (MGIT MIC ≥4 µg/mL) and 8 had BDQ-intermediate infections (MGIT MIC = 2 µg/mL). Participants with pre-existing resistance outnumbered those with resistance acquired during treatment, and 8 of 24 participants had polyclonal infections. BDQ resistance was observed across multiple Mtb strain types and involved a diverse catalog of mmpR5 (Rv0678) mutations, but no mutations in atpE or pepQ. Nine pairs of participants shared genetically similar isolates separated by <5 single nucleotide polymorphisms, concerning for potential transmitted BDQ resistance.

CONCLUSIONS

BDQ-resistant TB can arise via multiple, overlapping processes, including transmission of strains with pre-existing resistance. Capturing the within-host diversity of these infections could potentially improve clinical diagnosis, population-level surveillance, and molecular diagnostic test development.

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

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

Tuberculosis research: Quo vadis

Despite 142 years of ongoing research, since Robert Koch discovered the tuberculosis (TB) bacillus, TB continues to flourish in the most vulnerable parts of the globe in Asia, Africa and South America. Indeed, progressive socio-economic measures (nutrition, housing and environment) have shown to be more effective than research in disease elimination in affluent areas of the globe. Undoubtedly, however, areas undertaken in recent research studies underscore new knowledge that may yield far-reaching impact on disease control, if not elimination. This editorial aims to highlight such specific studies and their impact.

Role of the first WHO mutation catalogue in the diagnosis of antibiotic resistance in Mycobacterium tuberculosis in the Valencia Region, Spain: a retrospective genomic analysis

BACKGROUND

In June, 2021, WHO published the most complete catalogue to date of resistance-conferring mutations in Mycobacterium tuberculosis. Here, we aimed to assess the performance of genome-based antimicrobial resistance prediction using the catalogue and its potential for improving diagnostics in a real low-burden setting.

METHODS

In this retrospective population-based genomic study M tuberculosis isolates were collected from 25 clinical laboratories in the low-burden setting of the Valencia Region, Spain. Culture-positive tuberculosis cases reported by regional public health authorities between Jan 1, 2014, and Dec 31, 2016, were included. The drug resistance profiles of these isolates were predicted by the genomic identification, via whole-genome sequencing (WGS), of the high-confidence resistance-causing variants included in the catalogue and compared with the phenotype. We determined the minimum inhibitory concentration (MIC) of the isolates with discordant resistance profiles using the resazurin microtitre assay.

FINDINGS

WGS was performed on 785 M tuberculosis complex culture-positive isolates, and the WGS resistance prediction sensitivities were: 85·4% (95% CI 70·8-94·4) for isoniazid, 73·3% (44·9-92·2) for rifampicin, 50·0% (21·1-78·9) for ethambutol, and 57·1% (34·0-78·2) for pyrazinamide; all specificities were more than 99·6%. Sensitivity values were lower than previously reported, but the overall pan-susceptibility accuracy was 96·4%. Genotypic analysis revealed that four phenotypically susceptible isolates carried mutations (rpoB Leu430Pro and rpoB Ile491Phe for rifampicin and fabG1 Leu203Leu for isoniazid) known to give borderline resistance in standard phenotypic tests. Additionally, we identified three putative resistance-associated mutations (inhA Ser94Ala, katG Leu48Pro, and katG Gly273Arg for isoniazid) in samples with substantially higher MICs than those of susceptible isolates. Combining both genomic and phenotypic data, in accordance with the WHO diagnostic guidelines, we could detect two new multidrug-resistant cases. Additionally, we detected 11 (1·6%) of 706 isolates to be monoresistant to fluoroquinolone, which had been previously undetected.

INTERPRETATION

We showed that the WHO catalogue enables the detection of resistant cases missed in phenotypic testing in a low-burden region, thus allowing for better patient-tailored treatment. We also identified mutations not included in the catalogue, relevant at the local level. Evidence from this study, together with future updates of the catalogue, will probably lead in the future to the partial replacement of culture testing with WGS-based drug susceptibility testing in our setting.

FUNDING

European Research Council and the Spanish Ministerio de Ciencia.

Rapid Drug Susceptibility Testing to Preserve Antibiotics

Antibiotic resistance is a global challenge likely to cost trillions of dollars in excess costs in the health system and more importantly, millions of lives every year. A major driver of resistance is the absence of susceptibility testing at the time a healthcare worker needs to prescribe an antimicrobial. The effect is that many prescriptions are unintentionally wasted and expose mutable organisms to antibiotics increasing the risk of resistance emerging. Often simplistic solutions are applied to this growing issue, such as a naïve drive to increase the speed of drug susceptibility testing. This puts a spotlight on a technological solution and there is a multiplicity of such candidate DST tests in development. Yet, if we do not define the necessary information and the speed at which it needs to be available in the clinical decision-making progress as well as the necessary integration into clinical pathways, then little progress will be made. In this chapter, we place the technological challenge in a clinical and systems context. Further, we will review the landscape of some promising technologies that are emerging and attempt to place them in the clinic where they will have to succeed.

Tuberculosis in Lemurs and a Fossa at National Zoo, Madagascar, 2022

We diagnosed Mycobacterium tuberculosis in captive lemurs and a fossa in Antananarivo, Madagascar. We noted clinical signs in the animals and found characteristic lesions during necropsy. The source of infection remains unknown. Our results illustrate the potential for reverse zoonotic infections and intraspecies transmission of tuberculosis in captive wildlife.

Exploring programmatic indicators of tuberculosis control that incorporate routine Mycobacterium tuberculosis sequencing in low incidence settings: a comprehensive (2017-2021) patient cohort analysis

Background

Routine whole genome sequencing of Mycobacterium tuberculosis has been implemented with increasing frequency. However, its value for tuberculosis (TB) control programs beyond individual case management and enhanced drug resistance detection has not yet been explored.

Methods

We analysed routine sequencing data of culture-confirmed TB cases notified between 1st January 2017 and 31st December 2021 in New South Wales (NSW), Australia. Genomic surveillance included evidence of local TB transmission, defined by single nucleotide polymorphism (SNP) clustering over a variable (0-25) SNP threshold, and drug resistance conferring mutations.

Findings

M. tuberculosis sequences from 1831 patients were examined, representing 64.8% of all notified TB cases and 96.2% of culture-confirmed cases. Applying a traditional 5-SNP cluster threshold identified 62 transmission clusters with 183 clustered cases; 101/183 (55.2%) had 0 SNP differences. Cluster assessment over a 5-year period, using a 5-SNP threshold, provided a comprehensive overview of likely recent transmission within NSW, Australia, as an indicator of local TB control. Genotypic drug susceptibility testing (DST) was highly concordant with phenotypic DST and provided a 6.8% increase in antimycobacterial resistance detection. Importantly, it detected mutations missed by routine molecular tests. Lineage 2 strains were more likely to be drug resistant (p < 0.0001) and locally transmitted if drug resistant (p < 0.0001).

Interpretation

Performing routine prospective WGS in a low incidence country like Australia, provides genomically informed programmatic indicators of local TB control. A rolling 5-year cluster assessment reflects epidemic containment and progress towards 'zero TB transmission'. Genomic DST also provides valuable information for clinical care and drug resistance surveillance.

Funding

NHMRC Centre for Research Excellence in Tuberculosis (www.tbcre.org.au) and NSW Health Prevention Research Support Program.

Drug resistance and vaccine target surveillance of Plasmodium falciparum using nanopore sequencing in Ghana

Malaria results in over 600,000 deaths annually, with the highest burden of deaths in young children living in sub-Saharan Africa. Molecular surveillance can provide important information for malaria control policies, including detection of antimalarial drug resistance. However, genome sequencing capacity in malaria-endemic countries is limited. We designed and implemented an end-to-end workflow to detect Plasmodium falciparum antimalarial resistance markers and diversity in the vaccine target circumsporozoite protein (csp) using nanopore sequencing in Ghana. We analysed 196 clinical samples and showed that our method is rapid, robust, accurate and straightforward to implement. Importantly, our method could be applied to dried blood spot samples, which are readily collected in endemic settings. We report that P. falciparum parasites in Ghana are mostly susceptible to chloroquine, with persistent sulfadoxine-pyrimethamine resistance and no evidence of artemisinin resistance. Multiple single nucleotide polymorphisms were identified in csp, but their significance is uncertain. Our study demonstrates the feasibility of nanopore sequencing for malaria genomic surveillance in endemic countries.

Global pathogenomic analysis identifies known and candidate genetic antimicrobial resistance determinants in twelve species

Surveillance programs for managing antimicrobial resistance (AMR) have yielded thousands of genomes suited for data-driven mechanism discovery. We present a workflow integrating pangenomics, gene annotation, and machine learning to identify AMR genes at scale. When applied to 12 species, 27,155 genomes, and 69 drugs, we 1) find AMR gene transfer mostly confined within related species, with 925 genes in multiple species but just eight in multiple phylogenetic classes, 2) demonstrate that discovery-oriented support vector machines outperform contemporary methods at recovering known AMR genes, recovering 263 genes compared to 145 by Pyseer, and 3) identify 142 AMR gene candidates. Validation of two candidates in E. coli BW25113 reveals cases of conditional resistance: ΔcycA confers ciprofloxacin resistance in minimal media with D-serine, and frdD V111D confers ampicillin resistance in the presence of ampC by modifying the overlapping promoter. We expect this approach to be adaptable to other species and phenotypes.

Whole genome sequencing-based identification of human tuberculosis caused by animal-lineage Mycobacterium orygis

A recently described member of the Mycobacterium tuberculosis complex (MTBC) is Mycobacterium orygis, which can cause disease primarily in animals but also in humans. Although M. orygis has been reported from different geographic regions around the world, due to a lack of proper identification techniques, the contribution of this emerging pathogen to the global burden of zoonotic tuberculosis is not fully understood. In the present work, we report single nucleotide polymorphism (SNP) analysis using whole genome sequencing (WGS) that can accurately identify M. orygis and differentiate it from other members of the MTBC species. WGS-based SNP analysis was performed for 61 isolates from different provinces in Canada that were identified as M. orygis. A total of 56 M. orygis sequences from the public databases were also included in the analysis. Several unique SNPs in the gyrB, PPE55, Rv2042c, leuS, mmpL6, and mmpS6 genes were used to determine their effectiveness as genetic markers for the identification of M. orygis. To the best of our knowledge, five of these SNPs, viz., gyrB 277 (A→G), gyrB 1478 (T→C), leuS 1064 (A→T), mmpL6 486 (T→C), and mmpS6 334 (C→G), are reported for the first time in this study. Our results also revealed several SNPs specific to other species within MTBC. The phylogenetic analysis shows that the studied genomes were genetically diverse and clustered with M. orygis sequences of human and animal origin reported from different geographic locations. Therefore, the present study provides a new insight into the high-confidence identification of M. orygis from MTBC species based on WGS data, which can be useful for reference and diagnostic laboratories.

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

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

simpiTB - a pipeline designed to extract meaningful information from whole genome sequencing data of Mycobacterium tuberculosis complex, allows to combine genomic, phylogenetic and clustering analyses in existing SITVIT databases

Data obtained from new sequencing technologies are evolving rapidly, leading to the development of specific bioinformatic tools, pipelines and softwares. Several algorithms and tools are today available allowing a better identification and description of Mycobacterium tuberculosis complex (MTBC) isolates worldwide. Our approach consists in applying existing methods to analyze DNA sequencing data (from FASTA or FASTQ files), and tentatively extract meaningful information that would facilitate identification as well as a better understanding and management of MTBC isolates (taking into account whole genome sequencing and classical genotyping data). The aim of this study is to propose a pipeline analysis allowing to potentially simplify MTBC data analysis by providing different ways to interpret genomic or genotyping information based on existing tools. Furthermore, we propose a "reconciledTB" list making a link with results directly obtained from whole genome sequencing (WGS) data and results obtained from classical genotyping analysis (data inferred from SpoTyping and MIRUReader). Data visualization graphics and trees generated provide additional elements to better understand and confer associations among information overlap analyses. Additionally, comparison between data entered in an international genotyping database (SITVITEXTEND) and ensuing data obtained from the pipeline not only provide meaningful information, but further suggest that simpiTB could also be suitable for new data integration in specific TB genotyping databases.

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.

Whole-Genome Sequence-Based Characterization of Pre-XDR M. tuberculosis Clinical Isolates Collected in Kazakhstan

BACKGROUND

Kazakhstan has a high burden of multidrug-resistant tuberculosis in the Central Asian region. This study aimed to perform genomic characterization of Mycobacterium tuberculosis strains obtained from Kazakhstani patients with pre-extensively drug-resistant tuberculosis diagnosed in Kazakhstan.

METHODS

Whole-genome sequencing was performed on 10 pre-extensively drug-resistant M. tuberculosis strains from different regions of Kazakhstan. All strains had high-confidence resistance mutations according to the resistance grading system previously established by the World Health Organization. The genome analysis was performed using TB-Profiler, Mykrobe, CASTB, and ResFinder.

RESULTS

Valuable information for understanding the genetic diversity of tuberculosis in Kazakhstan can also be obtained from whole-genome sequencing. The results from the Phenotypic Drug Susceptibility Testing (DST) of bacterial strains were found to be consistent with the drug resistance information obtained from genomic data that characterized all isolates as pre-XDR. This information can help in developing targeted prevention and control strategies based on the local epidemiology of tuberculosis. Furthermore, the data obtained from whole-genome sequencing can help in tracing the transmission pathways of tuberculosis and facilitating early detection of outbreaks.

CONCLUSIONS

The results from whole-genome sequencing of tuberculosis clinical samples in Kazakhstan provide important insights into the drug resistance patterns and genetic diversity of tuberculosis in the country. These results can contribute to the improvement of tuberculosis control and management programs in Kazakhstan.

The evolution and international spread of extensively drug resistant Shigella sonnei

Shigella sonnei causes shigellosis, a severe gastrointestinal illness that is sexually transmissible among men who have sex with men (MSM). Multidrug resistance in S. sonnei is common including against World Health Organisation recommended treatment options, azithromycin, and ciprofloxacin. Recently, an MSM-associated outbreak of extended-spectrum β-lactamase producing, extensively drug resistant S. sonnei was reported in the United Kingdom. Here, we aimed to identify the genetic basis, evolutionary history, and international dissemination of the outbreak strain. Our genomic epidemiological analyses of 3,304 isolates from the United Kingdom, Australia, Belgium, France, and the United States of America revealed an internationally connected outbreak with a most recent common ancestor in 2018 carrying a low-fitness cost resistance plasmid, previously observed in travel associated sublineages of S. flexneri. Our results highlight the persistent threat of horizontally transmitted antimicrobial resistance and the value of continuing to work towards early and open international sharing of genomic surveillance data.

Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination, and Drug Susceptibility Prediction

Universal access to drug susceptibility testing for newly diagnosed tuberculosis patients is recommended. Access to culture-based diagnostics remains limited, and targeted molecular assays are vulnerable to emerging resistance mutations. Improved protocols for direct-from-sputum Mycobacterium tuberculosis sequencing would accelerate access to comprehensive drug susceptibility testing and molecular typing. We assessed a thermo-protection buffer-based direct-from-sample M. tuberculosis whole-genome sequencing protocol. We prospectively analyzed 60 acid-fast bacilli smear-positive clinical sputum samples in India and Madagascar. A diversity of semiquantitative smear positivity-level samples were included. Sequencing was performed using Illumina and MinION (monoplex and multiplex) technologies. We measured the impact of bacterial inoculum and sequencing platforms on genomic read depth, drug susceptibility prediction performance, and typing accuracy. M. tuberculosis was identified by direct sputum sequencing in 45/51 samples using Illumina, 34/38 were identified using MinION-monoplex sequencing, and 20/24 were identified using MinION-multiplex sequencing. The fraction of M. tuberculosis reads from MinION sequencing was lower than from Illumina, but monoplexing grade 3+ samples on MinION produced higher read depth than Illumina (P < 0.05) and MinION multiplexing (P < 0.01). No significant differences in sensitivity and specificity of drug susceptibility predictions were seen across sequencing modalities or within each technology when stratified by smear grade. Illumina sequencing from sputum accurately identified 1/8 (rifampin) and 6/12 (isoniazid) resistant samples, compared to 2/3 (rifampin) and 3/6 (isoniazid) accurately identified with Nanopore monoplex. Lineage agreement levels between direct and culture-based sequencing were 85% (MinION-monoplex), 88% (Illumina), and 100% (MinION-multiplex). M. tuberculosis direct-from-sample whole-genome sequencing remains challenging. Improved and affordable sample treatment protocols are needed prior to clinical deployment.

Performances of bioinformatics tools for the analysis of sequencing data of Mycobacterium tuberculosis complex strains

Whole-genome sequencing of Mycobacterium tuberculosis complex (MTBC) strains is a rapidly growing tool to obtain results regarding the resistance and phylogeny of the strains. We evaluated the performances of two bioinformatics tools for the analysis of whole-genome sequences of MTBC strains. Two hundred and twenty-seven MTBC strains were isolated and whole-genome sequenced at the laboratory of Avicenne Hospital between 2015 and 2021. We investigated the resistance and susceptibility status of strains using two online tools, Mykrobe and PhyResSE. We compared the genotypic and phenotypic resistance results obtained by drug susceptibility testing. Unlike with the Mykrobe tool, sequencing quality data were obtained using PhyResSE: average coverage of 98% and average depth of 119X. We found a similar concordance between phenotypic and genotypic results when determining susceptibility to first-line anti-tuberculosis drugs (95%) with both tools. The sensitivity and specificity of each tool compared to the phenotypic method were respectively 72% [52-87] and 98% [96-99] for Mykrobe and 76% [57-90] and 97% [94-99] for PhyResSE. Mykrobe and PhyResSE were easy to use and efficient. These platforms are accessible to people not trained in bioinformatics and constitute a complementary approach to phenotypic methods for the study of MTBC strains.

Evaluation of Nanopore sequencing for Mycobacterium tuberculosis drug susceptibility testing and outbreak investigation: a genomic analysis

BACKGROUND

Mycobacterium tuberculosis whole-genome sequencing (WGS) has been widely used for genotypic drug susceptibility testing (DST) and outbreak investigation. For both applications, Illumina technology is used by most public health laboratories; however, Nanopore technology developed by Oxford Nanopore Technologies has not been thoroughly evaluated. The aim of this study was to determine whether Nanopore sequencing data can provide equivalent information to Illumina for transmission clustering and genotypic DST for M tuberculosis.

METHODS

In this genomic analysis, we analysed 151 M tuberculosis isolates from Madagascar, South Africa, and England, which were collected between 2011 and 2018, using phenotypic DST and matched Illumina and Nanopore data. Illumina sequencing was done with the MiSeq, HiSeq 2500, or NextSeq500 platforms and Nanopore sequencing was done on the MinION or GridION platforms. Using highly reliable PacBio sequencing assemblies and pairwise distance correlation between Nanopore and Illumina data, we optimise Nanopore variant filters for detecting single-nucleotide polymorphisms (SNPs; using BCFtools software). We then used those SNPs to compare transmission clusters identified by Nanopore with the currently used UK Health Security Agency Illumina pipeline (COMPASS). We compared Illumina and Nanopore WGS-based DST predictions using the Mykrobe software and mutation catalogue.

FINDINGS

The Nanopore BCFtools pipeline identified SNPs with a median precision of 99·3% (IQR 99·1-99·6) and recall of 90·2% (88·1-94·2) compared with a precision of 99·6% (99·4-99·7) and recall of 91·9% (87·6-98·6) using the Illumina COMPASS pipeline. Using a threshold of 12 SNPs for putative transmission clusters, Illumina identified 98 isolates as unrelated and 53 as belonging to 19 distinct clusters (size range 2-7). Nanopore reproduced 15 out of 19 clusters perfectly; two clusters were merged into one cluster, one cluster had a single sample missing, and one cluster had an additional sample adjoined. Illumina-based clusters were also closely replicated using a five SNP threshold and clustering accuracy was maintained using mixed Illumina and Nanopore datasets. Genotyping resistance variants with Nanopore was highly concordant with Illumina, having zero discordant SNPs across more than 3000 SNPs and four insertions or deletions (indels), across 60 000 indels.

INTERPRETATION

Illumina and Nanopore technologies can be used independently or together by public health laboratories performing M tuberculosis genotypic DST and outbreak investigations. As a result, clinical and public health institutions making decisions on which sequencing technology to adopt for tuberculosis can base the choice on cost (which varies by country), batching, and turnaround time.

FUNDING

Academy for Medical Sciences, Oxford Wellcome Institutional Strategic Support Fund, and the Swiss South Africa Joint Research Award (Swiss National Science Foundation and South African National Research Foundation).

Rapid emergence of extensively drug-resistant Shigella sonnei in France

Shigella sonnei, the main cause of bacillary dysentery in high-income countries, has become increasingly resistant to antibiotics. We monitored the antimicrobial susceptibility of 7121 S. sonnei isolates collected in France between 2005 and 2021. We detected a dramatic increase in the proportion of isolates simultaneously resistant to ciprofloxacin (CIP), third-generation cephalosporins (3GCs) and azithromycin (AZM) from 2015. Our genomic analysis of 164 such extensively drug-resistant (XDR) isolates identified 13 different clusters within CIP-resistant sublineage 3.6.1, which was selected in South Asia ∼15 years ago. AZM resistance was subsequently acquired, principally through IncFII (pKSR100-like) plasmids. The last step in the development of the XDR phenotype involved various extended-spectrum beta-lactamase genes (bla_CTX-M-3, bla_CTX-M-15, bla_CTX-M-27, bla_CTX-M-55, and bla_CTX-M-134) carried by different plasmids (IncFII, IncI1, IncB/O/K/Z) or even integrated into the chromosome, and encoding resistance to 3GCs. This rapid emergence of XDR S. sonnei, including an international epidemic strain, is alarming, and good laboratory-based surveillance of shigellosis will be crucial for informed decision-making and appropriate public health action.

TransFlow: a Snakemake workflow for transmission analysis of Mycobacterium tuberculosis whole-genome sequencing data

MOTIVATION

Whole-genome sequencing (WGS) is increasingly used to aid the understanding of Mycobacterium tuberculosis (MTB) transmission. The epidemiological analysis of tuberculosis based on the WGS technique requires a diverse collection of bioinformatics tools. Effectively using these analysis tools in a scalable and reproducible way can be challenging, especially for non-experts.

RESULTS

Here, we present TransFlow (Transmission Workflow), a user-friendly, fast, efficient and comprehensive WGS-based transmission analysis pipeline. TransFlow combines some state-of-the-art tools to take transmission analysis from raw sequencing data, through quality control, sequence alignment and variant calling, into downstream transmission clustering, transmission network reconstruction and transmission risk factor inference, together with summary statistics and data visualization in a summary report. TransFlow relies on Snakemake and Conda to resolve dependencies among consecutive processing steps and can be easily adapted to any computation environment.

AVAILABILITY AND IMPLEMENTATION

TransFlow is free available at https://github.com/cvn001/transflow.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High Throughput Sequencing for Clinical Tuberculosis: An Overview

High throughput sequencing (HTS) can identify the presence of Mycobacterium tuberculosis DNA in a clinical sample while also providing information on drug susceptibility. Multiple studies have provided a context for exploring the clinical application of HTS for TB diagnosis. The workflow challenges, strengths and limitations of the various sequencing platforms, and tools used for analysis are presented to provide a framework for further innovations in the field.

Phylodynamic signatures in the emergence of community-associated MRSA

Community-associated, methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) lineages have emerged in many geographically distinct regions around the world during the past 30 y. Here, we apply consistent phylodynamic methods across multiple community-associated MRSA lineages to describe and contrast their patterns of emergence and dissemination. We generated whole-genome sequencing data for the Australian sequence type (ST) ST93-MRSA-IV from remote communities in Far North Queensland and Papua New Guinea, and the Bengal Bay ST772-MRSA-V clone from metropolitan communities in Pakistan. Increases in the effective reproduction number (R_e) and sustained transmission (R_e &gt; 1) coincided with spread of progenitor methicillin-susceptible <i>S. aureus</i> (MSSA) in remote northern Australian populations, dissemination of the ST93-MRSA-IV genotype into population centers on the Australian East Coast, and subsequent importation into the highlands of Papua New Guinea and Far North Queensland. Applying the same phylodynamic methods to existing lineage datasets, we identified common signatures of epidemic growth in the emergence and epidemiological trajectory of community-associated <i>S. aureus</i> lineages from America, Asia, Australasia, and Europe. Surges in R_e were observed at the divergence of antibiotic-resistant strains, coinciding with their establishment in regional population centers. Epidemic growth was also observed among drug-resistant MSSA clades in Africa and northern Australia. Our data suggest that the emergence of community-associated MRSA in the late 20th century was driven by a combination of antibiotic-resistant genotypes and host epidemiology, leading to abrupt changes in lineage-wide transmission dynamics and sustained transmission in regional population centers.

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

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

Unraveling the mechanisms of intrinsic drug resistance in Mycobacterium tuberculosis

Tuberculosis (TB) is among the most difficult infections to treat, requiring several months of multidrug therapy to produce a durable cure. The reasons necessitating long treatment times are complex and multifactorial. However, one major difficulty of treating TB is the resistance of the infecting bacterium, Mycobacterium tuberculosis (Mtb), to many distinct classes of antimicrobials. This review will focus on the major gaps in our understanding of intrinsic drug resistance in Mtb and how functional and chemical-genetics can help close those gaps. A better understanding of intrinsic drug resistance will help lay the foundation for strategies to disarm and circumvent these mechanisms to develop more potent antitubercular therapies.

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

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

Mutations in rv0678 Confer Low-Level Resistance to Benzothiazinone DprE1 Inhibitors in Mycobacterium tuberculosis

Tuberculosis (TB) is the leading cause of death from any bacterial infection, causing 1.5 million deaths worldwide each year. Due to the emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) there have been significant efforts aimed at developing novel drugs to treat TB. One promising drug target in Mtb is the arabinogalactan biosynthetic enzyme DprE1, and there have been over a dozen unique chemical scaffolds identified which inhibit the activity of this protein. Among the most promising lead compounds are the benzothiazinones BTZ043 and PBTZ169, both of which are currently in or have completed phase IIa clinical trials. Due to the potential clinical utility of these drugs, we sought to identify potential synergistic interactions and new mechanisms of resistance using a genome-scale CRISPRi chemical-genetic screen with PBTZ169. We found that knockdown of rv0678, the negative regulator of the mmpS5/L5 drug efflux pump, confers resistance to PBTZ169. Mutations in rv0678 are the most common form of resistance to bedaquiline and there is already abundant evidence of these mutations emerging in bedaquiline-treated patients. We confirmed that rv0678 mutations from clinical isolates confer low level cross-resistance to BTZ043 and PBTZ169. While it is yet unclear whether rv0678 mutations would render benzothiazinones ineffective in treating TB, these results highlight the importance of monitoring for clinically prevalent rv0678 mutations during ongoing BTZ043 and PBTZ169 clinical trials.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Evaluation of the broth microdilution plate methodology for susceptibility testing of Mycobacterium tuberculosis in Peru

BACKGROUND

Tuberculosis (TB) is a communicable, preventable and curable disease caused by the bacterium Mycobacterium tuberculosis (MTB). Peru is amongst the 30 countries with the highest burden of multidrug-resistant tuberculosis (MDR-TB) worldwide. In the fight against drug-resistant tuberculosis, the UKMYC6 microdilution plate was developed and validated by the CRyPTIC project. The objective of the study was to evaluate the use of the broth microdilution (BMD) plate methodology for susceptibility testing of drug-resistant MTB strains in Peru.

METHODS

MTB strains isolated between 2015 and 2018 in Peru were used. 496 nationally-representative strains determined as drug-resistant by the routine 7H10 Agar Proportion Method (APM) were included in the present study. The Minimum Inhibitory Concentration (MIC) of 13 antituberculosis drugs were determined for each strain using the UKMYC6 microdilution plates. Diagnostic agreement between APM and BMD plate methodology was determined for rifampicin, isoniazid, ethambutol, ethionamide, kanamycin and levofloxacin. Phenotypes were set using binary (or ternary) classification based on Epidemiological cut-off values (ECOFF/ECV) proposed by the CRyPTIC project. Whole Genome Sequencing (WGS) was performed on strains with discrepant results between both methods.

RESULTS

MIC distributions were determined for 13 first- and second-line anti-TB drugs, including new (bedaquiline, delamanid) and repurposed (clofazimine, linezolid) agents. MIC results were available for 80% (397/496) of the strains at 14 days and the remainder at 21 days. The comparative analysis determined a good agreement (0.64 ≤ k ≤ 0.79) for the drugs rifampicin, ethambutol, ethionamide and kanamycin, and the best agreement (k > 0.8) for isoniazid and levofloxacin. Overall, 12% of MIC values were above the UKMYC6 plate dilution ranges, most notably for the drugs rifampicin and rifabutin. No strain presented MICs higher than the ECOFF/ECV values for the new or repurposed drugs. Discrepant analysis using genotypic susceptibility testing by WGS supported half of the results obtained by APM (52%, 93/179) and half of those obtained by BMD plate methodology (48%, 86/179).

CONCLUSIONS

The BMD methodology using the UKMYC6 plate allows the complete susceptibility characterization, through the determination of MICs, of drug-resistant MTB strains in Peru. This methodology shows good diagnostic performances for rifampicin, isoniazid, ethambutol, ethionamide, kanamycin and levofloxacin. It also allows for the characterization of MICs for other drugs used in previous years against tuberculosis, as well as for new and repurposed drugs recently introduced worldwide.

Phylogenetic lineages of tuberculosis isolates and their association with patient demographics in Tanzania

BACKGROUND

Mycobacterium tuberculosis presents several lineages each with distinct characteristics of evolutionary status, transmissibility, drug resistance, host interaction, latency, and vaccine efficacy. Whole genome sequencing (WGS) has emerged as a new diagnostic tool to reliably inform the occurrence of phylogenetic lineages of Mycobacterium tuberculosis and examine their relationship with patient demographic characteristics and multidrug-resistance development.

METHODS

191 Mycobacterium tuberculosis isolates obtained from a 2017/2018 Tanzanian drug resistance survey were sequenced on the Illumina Miseq platform at Supranational Tuberculosis Reference Laboratory in Uganda. Obtained fast-q files were imported into tools for resistance profiling and lineage inference (Kvarq v0.12.2, Mykrobe v0.8.1 and TBprofiler v3.0.5). Additionally for phylogenetic tree construction, RaxML-NG v1.0.3(25) was used to generate a maximum likelihood phylogeny with 800 bootstrap replicates. The resulting trees were plotted, annotated and visualized using ggtree v2.0.4 RESULTS: Most [172(90.0%)] of the isolates were from newly treated Pulmonary TB patients. Coinfection with HIV was observed in 33(17.3%) TB patients. Of the 191 isolates, 22(11.5%) were resistant to one or more commonly used first line anti-TB drugs (FLD), 9(4.7%) isolates were MDR-TB while 3(1.6%) were resistant to all the drugs. Of the 24 isolates with any resistance conferring mutations, 13(54.2%) and 10(41.6%) had mutations in genes associated with resistance to INH and RIF respectively. The findings also show four major lineages i.e. Lineage 3[81 (42.4%)], followed by Lineage 4 [74 (38.7%)], the Lineage 1 [23 (12.0%)] and Lineages 2 [13 (6.8%)] circulaing in Tanzania.

CONCLUSION

The findings in this study show that Lineage 3 is the most prevalent lineage in Tanzania whereas drug resistant mutations were more frequent among isolates that belonged to Lineage 4.