Background: Whole genome sequencing (WGS) can elucidates Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based transmissibility study of 4,000 TB patients in Lima, Peru, we identified a large MIRU-VNTR Mtb cluster with a range of resistance phenotypes and studied host and bacterial factors contributing to its spread.
Methods: WGS was performed on 61 of 148 isolates in the cluster. We compared transmission link inference using epidemiological or genomic data with and without the inclusion of controversial variants, and estimated the dates of emergence of the cluster and antimicrobial drug resistance acquisition events by generating a time-calibrated phylogeny. We validated our findings in genomic data from an outbreak of 325 TB cases in London. Using a larger set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages.
Findings: Four isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95% HPD: 1945-1985). Isoniazid resistance arose once, whereas rifampicin resistance emerged subsequently at least three times. Amplification of other drug resistance occurred as recently as within the last year of sampling. High quality PE/PPE variants and indels added information for transmission inference. We identified five cluster-defining SNPs, including esxV S23L to be potentially contributing to transmissibility.
Interpretation: Clusters defined by MIRU-VNTR typing, could be circulating for decades in a high-burden setting. WGS allows for an improved understanding of transmission, as well as bacterial resistance and fitness factors.
The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic drug susceptibility but gaps remain for predicting phenotype accurately from genotypic data. Using targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601 Mycobacterium tuberculosis strains, 1,228 of which were multidrug resistant, we implemented the first multitask deep learning framework to predict phenotypic drug resistance to 10 anti-tubercular drugs. The proposed wide and deep neural network (WDNN) acheived improved predicted performance compared to regularized logistic regression and random forest: the average sensitivities and specificities, respectively, were 92.7% and 92.7% for first-line drugs and 82.0% and 92.8% for second-line drugs during cross-validation. On an independent validation set, the multitask WDNN showed significant performance gains over baseline models, with average sensitivities and specificities, respectively, of 84.5% and 93.6% for first-line drugs and 64.0% and 95.7% for second-line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multitask architecture shares information across different anti-tubercular drugs and genes to provide a more accurate phenotypic prediction. We use t-distributed Stochastic Neighbor Embedding (t-SNE) visualization and feature importance analyses to examine inter-drug similarities. Deep learning has a clear role in improving drug resistance predictive performance over traditional methods and holds promise in bringing sequencing technologies closer to the bedside.
Bacteria and other microbes play a crucial role in human health and disease. Medicine and clinical microbiology have traditionally attempted to identify the etiological agents that causes disease, and how to eliminate them. Yet this traditional paradigm is becoming inadequate for dealing with a changing disease landscape. Major challenges to human health are noncommunicable chronic diseases, often driven by altered immunity and inflammation, and persistent communicable infections whose agents harbor antibiotic resistance. It is increasingly recognized that microbe-microbe interactions, as well as human-microbe interactions are important. Here, we review the "Evolutionary Medicine" framework to study how microbial communities influence human health. This approach aims to predict and manipulate microbial influences on human health by integrating ecology, evolutionary biology, microbiology, bioinformatics and clinical expertise. We focus on the potential promise of evolutionary medicine to address three key challenges: 1) detecting microbial transmission; 2) predicting antimicrobial resistance; 3) understanding microbe-microbe and human-microbe interactions in health and disease, in the context of the microbiome.
Drug resistance is threatening attempts at tuberculosis epidemic control. Molecular diagnostics for drug resistance that rely on the detection of resistance-related mutations could expedite patient care and accelerate progress in TB eradication. We performed minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We then used a linear mixed model to evaluate genome wide associations between mutations in MTB genes or noncoding regions and drug resistance, followed by validation of our findings in an independent dataset of 792 patient isolates. Novel associations at 13 genomic loci were confirmed in the validation set, with 2 involving noncoding regions. We found promoter mutations to have smaller average effects on resistance levels than gene body mutations in genes where both can contribute to resistance. Enabled by a quantitative measure of resistance, we estimated the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. We also report the proportion of variation in resistance levels explained by the novel loci identified here. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causative or compensatory loci, and emphasizes the contribution of the noncoding portion of the genome.
Whole genome sequencing (WGS) can elucidate Mycobacterium tuberculosis (Mtb) transmission patterns but more data is needed to guide its use in high-burden settings. In a household-based TB transmissibility study in Peru, we identified a large MIRU-VNTR Mtb cluster (148 isolates) with a range of resistance phenotypes, and studied host and bacterial factors contributing to its spread. WGS was performed on 61 of the 148 isolates. We compared transmission link inference using epidemiological or genomic data and estimated the dates of emergence of the cluster and antimicrobial drug resistance (DR) acquisition events by generating a time-calibrated phylogeny. Using a set of 12,032 public Mtb genomes, we determined bacterial factors characterizing this cluster and under positive selection in other Mtb lineages. Four of the 61 isolates were distantly related and the remaining 57 isolates diverged ca. 1968 (95%HPD: 1945-1985). Isoniazid resistance arose once and rifampin resistance emerged subsequently at least three times. Emergence of other DR types occurred as recently as within the last year of sampling. We identified five cluster-defining SNPs potentially contributing to tranmissibility. In conclusion, clusters (as defined by MIRU-VNTR typing) may be circulating for decades in a high-burden setting. WGS allows for an enhanced understanding of transmission, drug resistance, and bacterial fitness factors.
Drug-resistant TB remains a public health challenge. Rifamycins are among the most potent anti-TB drugs. They are known to target the RpoB subunit of RNA polymerase; however, our understanding of how rifamycin resistance is genetically coded remains incomplete. Here we investigated rpoB genetic diversity and cross-resistance between the two rifamycin drugs rifampicin and rifabutin.
We performed WGS of 1003 Mycobacterium tuberculosis clinical isoltes and determined MICs of both rifamycin agents on 7H10 agar using the indirect proportion method. We generated rpoB mutants in a laboratory strain and measured their antibiotic susceptibility using the alamarBlue reduction assay.
Of the 1003 isolates, 766 were rifampicin resistant and 210 (27%) of these were ribabutin susceptible; j102/210 isolates had the rpoB mutation D435V (Escherichia coli D516V). Isolates with discordant resistance were 17.2 times more likely to harbour a D435V mutation than those resistant to both agents (OR 17.2, 95% CI 10.5-27.9, P value <10−40). Compared with WT, the D435V in vitro mutant had an increased IC50 of both rifamycins; however, in both cases to a lesser degree than the S450L (E. coli S531L) mutation.
The observation that the rpoB D435V mutation produces an increase in the IC50 of both drugs contrasts with findings from previous smaller studies that suggested that isolates with the D435V mutation remain rifabutin susceptible despite being rifampicin resistant. Our finding thus suggests that the recommended critical testing concentration for rifabutin should be revised.
Conor J Meehan, G.G. Serrano, Thomas Kohl, Lennert Verboven, Anzaan Dippenaar, Matthew Ezewudo, Maha R Farhat, Jennifer Guthrie, Kris Laukens, Paulo Miotto, Boatema Ofori-Anyinam, Viola Dreyer, Philip Supply, Anita Suresh, Christian Utpatel, Dick van Soolingen, Yang Zhou, Philip Ashton, Daniela Brites, Andrea Cabibbe, Bouke de Jong, Margareta De Vos, Menardo Fabrizio, Sebastien Gagneux, Qian Gao, Tim Heupink, Qingyun Liu, Chloe Marie Louiseau, Leen Rigouts, Tim Rodwell, Elisa Tagliani, Timothy Walker, Robin Warren, Yanlin Zhao, Matteo Zignol, Marco Schito, Jennifer L Gardy, Daniella Cirillo, Stefan Niemann, I Comas, and Annelies Van Rie. 2019. “Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues.” Nature Reviews Microbiology.
Genome analysis should allow the discovery of interdependent loci that together cause antibiotic resistance. In practive, however, the vast number of possible epistatic interactions erodes statistical power. Here, we extend an approach that has been successfully used to identify epistatic residues in proteins to infer genomic loci that are strongly coupled. This approach reduces the number of tests required for an epistatic genome-wide association study of antibiotic resistance and increases the likelihood of identifying causal epistasis. We discovered 38 loci and 240 epistatic pairs that influence the minimum inhibitory concentrations of 5 different antibiotics in 1,102 isolates of Neisseria gonorrhoeae that were confirmed in a second dataset of 495 isolates. Many known resistance-affecting loci were recovered; however, the majority of associations occurred in unreported genes, such as murE. About half of the discovered epistasis involved at least one locus previously associated with antibiotic resistance, including interactions between gyrA and parC. Still, many combinations involved unreported loci and genes. While most variation in minimum inhibitory concentrations could be explained by identified loci, epistasis substantially increased explained phenotypic variance. Our work provides a systematic identification of epistasis affecting antibiotic resistance in N. gonorrhoeae and a generalizable approach for epistatic genome-wide association studies.
BACKGROUND The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear.
METHODS We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance.
RESULTS A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted.
CONCLUSIONS Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.)
Identification of pathogen-specific biomarkers present in patients' serum or urine samples can be a useful diagnostic approach. In efforts to discover Mycobacterium tuberculosis (Mtb) biomarkers we identified by mass spectroscopy a unique 21-mer Mtb peptide sequence (VVLGLTVPGGVELLPGVALPR) present in the urines of TB patients from Zimbabwe. This peptite has 100% sequence homology with the protein TBCG_03312 from the C strain of Mtb (a clinical isolate identified in New York, NY, USA) and 95% sequence homology with Mtb oxidoreductase (MRGA423_21210) from the clinical isolate MTB423 (identified in Kerala, India). Alignment of the genes coding for these proteins show an insertion point mutation relative to Rv3368c of the reference H37Rv strain, which generated a unique C-terminus with no sequence homology with any other described protein. Phylogenetic analysis utilizing public sequence data shows that the insertion mutation is apparently a rare event. However, sera from TB patients from distinct geographical areas of the world (Peru, Vietnam, and South Africa) contain antibodies that recognize a purified recombinant C-terminus of the protein, thus suggesting a wider distribution of isolates that produce this protein.
RATIONALE: Successful transmission of tuberculosis depends on the interplay of human behavior, host immune responses and Mycobacterium tuberculosis virulence factors. Previous studies have focused on identifying host risk factors associated with increased transmission, while the contribution of specific genetic variations in mycobacterial strains themselves are still unknown. OBJECTIVES: To identify mycobacterial genetic markers associated with increased transmissibility, and examine whether these markers lead to altered in vitro immune responses. METHODS: Using a comprehensive (n = 10,389) tuberculosis registry and strain collection in the Netherlands, we identified a set of 100 M. tuberculosis strains either least or most likely to be transmitted after controlling for host factors. We subjected these strains to whole genome sequencing and evolutionary convergence analysis. We repeated this analysis in an independent validation cohort. A subset of the original strains was used to perform functional immunological experiments to measure in vitro cytokine production and neutrophil responses to strains with or without the identified mutations associated to increased transmissibility. MEASUREMENTS AND MAIN RESULTS: We identified the loci espE, PE-PGRS56, Rv0197, Rv2813-2814c and Rv2815-2816c as targets of convergent evolution among transmissible strains. We validated four of these regions in an independent set of strains, and demonstrated that mutations in these targets affected in vitro monocyte and T-cell cytokine production, neutrophil reactive oxygen species release and apoptosis. CONCLUSIONS: This study identifies genetic markers in convergent evolution of M. tuberculosis towards enhanced transmissibility in vivo that are associated with altered immune responses in vitro.
RATIONALE: The development of molecular diagnostics that detect both the presence of Mycobacterium tuberculosis in clinical samples and drug resistance-conferring mutations promises to revolutionize patient care and interrupt transmission by ensuring early diagnosis. However, these tools require the identification of genetic determinants of resistance to the full range of antituberculosis drugs. OBJECTIVES: To determine the optimal molecular approach needed, we sought to create a comprehensive catalog of resistance mutations and assess their sensitivity and specificity in diagnosing drug resistance. METHODS: We developed and validated molecular inversion probes for DNA capture and deep sequencing of 28 drug-resistance loci in M. tuberculosis. We used the probes for targeted sequencing of a geographically diverse set of 1,397 clinical M. tuberculosis isolates with known drug resistance phenotypes. We identified a minimal set of mutations to predict resistance to first- and second-line antituberculosis drugs and validated our predictions in an independent dataset. We constructed and piloted a web-based database that provides public access to the sequence data and prediction tool. MEASUREMENTS AND MAIN RESULTS: The predicted resistance to rifampicin and isoniazid exceeded 90% sensitivity and specificity but was lower for other drugs. The number of mutations needed to diagnose resistance is large, and for the 13 drugs studied it was 238 across 18 genetic loci. CONCLUSIONS: These data suggest that a comprehensive M. tuberculosis drug resistance diagnostic will need to allow for a high dimension of mutation detection. They also support the hypothesis that currently unknown genetic determinants, potentially discoverable by whole-genome sequencing, encode resistance to second-line tuberculosis drugs.
In regard to tuberculosis (TB) and other major global epidemics, the use of new diagnostic tests is increasing dramatically, including in resource-limited countries. Although there has never been as much digital information generated, this data source has not been exploited to its full potential. In this opinion paper, we discuss lessons learned from the global scale-up of these laboratory devices and the pathway to tapping the potential of laboratory-generated information in the field of TB by using connectivity. Responding to the demand for connectivity, innovative third-party players have proposed solutions that have been widely adopted by field users of the Xpert(®) MTB/RIF assay. The experience associated with the utilisation of these systems, which facilitate the monitoring of wide laboratory networks, stressed the need for a more global and comprehensive approach to diagnostic connectivity. In addition to facilitating the reporting of test results, the mobility of digital information allows the sharing of information generated in programme settings. When they become easily accessible, these data can be used to improve patient care, disease surveillance and drug discovery. They should therefore be considered as a public health good. We list several examples of concrete initiatives that should allow data sources to be combined to improve the understanding of the epidemic, support the operational response and, finally, accelerate TB elimination. With the many opportunities that the pooling of data associated with the TB epidemic can provide, pooling of this information at an international level has become an absolute priority.
Molecular diagnostics that rapidly and accurately predict resistance to fluoroquinolone drugs and especially later-generation agents promise to improve treatment outcomes for patients with multidrug-resistant tuberculosis and prevent the spread of disease. Mutations in the gyr genes are known to confer most fluoroquinolone resistance, but knowledge about the effects of gyr mutations on susceptibility to early- versus later-generation fluoroquinolones and about the role of mutation-mutation interactions is limited. Here, we sequenced the full gyrA and gyrB open reading frames in 240 multidrug-resistant and extensively drug-resistant tuberculosis strains and quantified their ofloxacin and moxifloxacin MIC by testing growth at six concentrations for each drug. We constructed a multivariate regression model to assess both the individual mutation effects and interactions on the drug MICs. We found that gyrB mutations contribute to fluoroquinolone resistance both individually and through interactions with gyrA mutations. These effects were statistically significant. In these clinical isolates, several gyrA and gyrB mutations conferred different levels of resistance to ofloxacin and moxifloxacin. Consideration of gyr mutation combinations during the interpretation of molecular test results may improve the accuracy of predicting the fluoroquinolone resistance phenotype. Further, the differential effects of gyr mutations on the activity of early- and later-generation fluoroquinolones requires further investigation and could inform the selection of a fluoroquinolone for treatment.
BACKGROUND: Little is known about intraoperative adverse events (iAEs) in emergency surgery (ES). We sought to describe iAEs in ES and to investigate their clinical and financial impact. METHODS: The 2007 to 2012 administrative and American College of Surgeons-National Surgical Quality Improvement Program databases at our tertiary academic center were: (1) linked, (2) queried for all ES procedures, and then (3) screened for iAEs using the ICD-9-CM-based Patient Safety Indicator "accidental puncture/laceration". Flagged cases were systematically reviewed to: (1) confirm or exclude the occurrence of iAEs (defined as inadvertent injuries during the operation) and (2) extract additional variables such as procedure type, approach, complexity (measured by relative value units), need for adhesiolysis, and extent of repair. Univariate and multivariate analyses were performed to assess the independent impact of iAEs on 30-day morbidity, mortality, and hospital charges. RESULTS: Of a total of 9,288 patients, 1,284 (13.8%) patients underwent ES, of which 23 had iAEs (1.8%); 18 of 23 (78.3%) of the iAEs involved the small bowel or spleen, 10 of 23 (43.5%) required suture repair, and 8 of 23 (34.8%) required tissue or organ resection. Compared with those without iAEs, patients with iAEs were older (median age 62 vs 50; P = .04); their procedures were more complex (total relative value unit 46.7, interquartile range [27.5 to 52.6] vs 14.5 [.5 to 30.2]; P < .001), longer in duration (>3 hours: 52% vs 8%; P < .001), and more often required adhesiolysis (39.1% vs 13.5% P = .001). Patients with iAEs had increased total charges ($31,080 vs $11,330, P < .001), direct charges ($20,030 vs $7,387, P < .001), and indirect charges ($11,460 vs $4,088, P < .001). On multivariable analyses, iAEs were independently associated with increased 30-day morbidity (odds ratio, 3.56 [CI, 1.10 to 11.54]; P = .03) and prolonged postoperative length of stay (LOS; LOS >7 days; odds ratio, 5.60 [1.54 to 20.35]; P = .01]. A trend toward increased mortality did not reach statistical significance. CONCLUSIONS: In ES, iAEs are independently associated with significantly higher postoperative morbidity and prolonged LOS.
Fluoroquinolone (FQ) drug susceptibility testing (DST) is an important step in the design of effective treatment regimens for multidrug-resistant tuberculosis. Here we compare ciprofloxacin, ofloxacin and moxifloxacin (MFX) resistance results from 226 multidrug-resistant samples. The low level of concordance observed suggests that DST should be performed for the specific FQ planned for clinical use. The results also support the new World Health Organization recommendation for testing MFX at a critical concentration of 2.0 μg/ml.
Improving our understanding of the relationship between the genotype and the drug resistance phenotype of Mycobacterium tuberculosis will aid the development of more accurate molecular diagnostics for drug-resistant tuberculosis. Studies that use direct genetic manipulation to identify the mutations that cause M. tuberculosis drug resistance are superior to associational studies in elucidating an individual mutation's contribution to the drug resistance phenotype.
We systematically reviewed the literature for publications reporting allelic exchange experiements in any of the resistance-associated M. tuburculosis genes. We included studies that introduced single point mutations using specialized linkage transduction or site-directed/in vitro mutagenesis and documented a change in the resistance phenotype.
We summarize evidence supporting the causal relationship of 54 different mutations in eight genes (katG, inhA, kasA, embB, rpoB, gyrA and gyrB) and one intergenic region (furA-katG) with resistance to isoniazid, the rifamycins, ethambutol and fluoroquinolones. We observed a significant role for the strain of genomic background in modulating the resistance phenotype of 21 of these mutations and found examples of where the same drug resistance mutations caused varying levels of resistance to different members of the same drug class.
This systematic review highlights those mutations that have been shown to causally change phenotypic resistance in M. tuberculosis and brings attention to a notable lack of allelic exchange data for several of the genes known to be associated with drug resistance.
Whole genome sequencing is increasingly used to study phenotypic variation among infectious pathogens and to evaluate their relative transmissibility, virulence, and immunogenicity. To date, relatively little has been published on how and how many pathogen strains should be selected for studies associating phenotype and genotype. There are specific challenges when identifying genetic associations in bacteria which often comprise highly structured populations. Here we consider general methodological questions related to sampling and analysis focusing on clonal to moderately recombining pathogens. We propose that a matched sampling scheme constitutes an efficient study design, and provide a power calculator based on phylogenetic convergence. We demonstrate this approach by applying it to genomic datasets for two microbial pathogens: Mycobacterium tuberculosis and Campylobacter species.