Whole genome sequencing data of 1110 Mycobacterium tuberculosis isolates identifies insertions and deletions associated with drug resistance

Understanding the epidemiology and pathogenesis of Mycobacterium tuberculosis with non-redundant pangenome of epidemic strains in China

Tuberculosis is a major public health threat resulting in more than one million lives lost every year. Many challenges exist to defeat this deadly infectious disease which address the importance of a thorough understanding of the biology of the causative agent Mycobacterium tuberculosis (MTB). We generated a non-redundant pangenome of 420 epidemic MTB strains from China including 344 Lineage 2 strains, 69 Lineage 4 strains, six Lineage 3 strains, and one Lineage 1 strain. We estimate that MTB strains have a pangenome of 4,278 genes encoding 4,183 proteins, of which 3,438 are core genes. However, due to 99,694 interruptions in 2,447 coding genes, we can only confidently confirm 1,651 of these genes are translated in all samples. Of these interruptions, 67,315 (67.52%) could be classified by various genetic variations detected by currently available tools, and more than half of them are due to structural variations, mostly small indels. Assuming a proportion of these interruptions are artifacts, the number of active core genes would still be much lower than 3,438. We further described differential evolutionary patterns of genes under the influences of selective pressure, population structure and purifying selection. While selective pressure is ubiquitous among these coding genes, evolutionary adaptations are concentrated in 1,310 genes. Genes involved in cell wall biogenesis are under the strongest selective pressure, while the biological process of disruption of host organelles indicates the direction of the most intensive positive selection. This study provides a comprehensive view on the genetic diversity and evolutionary patterns of coding genes in MTB which may deepen our understanding of its epidemiology and pathogenicity.

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

BACKGROUND

Tuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium.

METHODS

In this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool.

RESULTS

We observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins.

CONCLUSION

This comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

The evolving biology of Mycobacterium tuberculosis drug resistance

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb) is an ancient disease that has remained a leading cause of infectious death. Mtb has evolved drug resistance to every antibiotic regimen ever introduced, greatly complicating treatment, lowering rates of cure and menacing TB control in parts of the world. As technology has advanced, our understanding of antimicrobial resistance has improved, and our models of the phenomenon have evolved. In this review, we focus on recent research progress that supports an updated model for the evolution of drug resistance in Mtb. We highlight the contribution of drug tolerance on the path to resistance, and the influence of heterogeneity on tolerance. Resistance is likely to remain an issue for as long as drugs are needed to treat TB. However, with technology driving new insights and careful management of newly developed resources, antimicrobial resistance need not continue to threaten global progress against TB, as it has done for decades.

Hit Compounds and Associated Targets in Intracellular Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb), the etiological agent of tuberculosis, is one of the most devastating infectious agents in the world. Chemical-genetic characterization through in vitro evolution combined with whole genome sequencing analysis was used identify novel drug targets and drug resistance genes in Mtb associated with its intracellular growth in human macrophages. We performed a genome analysis of 53 Mtb mutants resistant to 15 different hit compounds. We found nonsynonymous mutations/indels in 30 genes that may be associated with drug resistance acquisitions. Beyond confirming previously identified drug resistance mechanisms such as rpoB and lead targets reported in novel anti-tuberculosis drug screenings such as mmpL3, ethA, and mbtA, we have discovered several unrecognized candidate drug targets including prrB. The exploration of the Mtb chemical mutant genomes could help novel drug discovery and the structural biology of compounds and associated mechanisms of action relevant to tuberculosis treatment.

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype–phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of Mycobacterium tuberculosis (Mtb) complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance. It is understood that drug resistance and lineage-specific issues require an elaborate prioritization of identified variants to choose the best target for subsequent therapeutic intervention. Mycobacterium variant pipeline (MycoVarP) addresses these specific issues with a flexible battery of user-defined and default filters. It provides an end-to-end solution for WGS analysis of Mtb variants from the raw reads and performs two quality checks, viz, before trimming and after alignments of reads to the reference genome. MycoVarP maps the annotated variants to the drug-susceptible (DS) database and removes the false-positive variants, provides lineage identification, and predicts potential drug resistance. We have re-analyzed the WGS data reported by Advani et al. (2019) using MycoVarP and identified some additional variants not reported so far. We conclude that MycoVarP will help in identifying nonsynonymous, true-positive, drug resistance–associated variants more effectively and comprehensively, including those within the IDP of the PE-PPE/PGRS family, than possible from the currently available pipelines.

First-line drug resistance profiling of Mycobacterium tuberculosis: a machine learning approach

The persistence and emergence of new multi-drug resistant Mycobacterium tuberculosis (M. tb) strains continues to advance the devastating tuberculosis (TB) epidemic. Robust systems are needed to accurately and rapidly perform drug-resistance profiling, and machine learning (ML) methods combined with genomic sequence data may provide novel insights into drug-resistance mechanisms. Using 372 M. tb isolates, the combined utility of ML and bioinformatics to perform drug-resistance profiling is demonstrated. SNPs, InDels, and dinucleotide frequencies are explored as input features for three ML models, namely Decision Trees, Random Forest, and the eXtreme Gradient Boosted model. Using SNPs and InDels, all three models performed equally well yielding a 99% accuracy, 97% recall, and 99% F1-score. Using dinucleotide frequencies, the XGBoost algorithm was superior with a 97% accuracy, 94% recall and 97% F1-score. This study validates the use of variants and presents dinucleotide features as another effective feature encoding method for ML-based phenotype classification.

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

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

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

Domain-wise differentiation of Mycobacterium tuberculosis H37 Rv hypothetical proteins: A roadmap to discover bacterial survival potentials

Proteomic information revealed approximately 3,923 proteins in Mycobacterium tuberculosis H₃₇ Rv genome of which around ∼25% of proteins are hypothetical proteins (HPs). The present work comprises computational approaches to identify and characterize the HPs of M. tuberculosis that symbolize the putative target for rationale development of a drug or antituberculosis strategy. Proteins were primarily classified based on motif and domain information, which were further analyzed for the presence of virulence factors (VFs), determination of localization, and signal peptide/enzymatic cleavage sites. 863 HPs were found, and 599 HPs were finalized based on motifs, that is, GTP (525), Trx (47), SAM (14), PE-PGRS (5), and CBD (8). 80 HPs contain virulence factor (VF), 24 HPs localized in membrane region, and 4 HPs contain signal peptide/enzymatic cleavage sites. The overall parametric study finalizes four HPs Rv0679c, Rv0906, Rv3627c, and Rv3811 that also comprise GTPase domain. Structure prediction, structure-based function prediction, molecular docking and mutation analysis of selected proteins were done. Docking studies revealed that GTP and GTPase inhibitor (mac0182344) were docked with all four proteins with high affinities. In silico point mutation studies showed that substitution of aspartate with glycine within a GTPase motif showed the largest decrease in stability and pH differentiation also affects protein's stability. This analysis thus fixes a roadmap in the direction of finding potential target of this bacterium for drug development and enlightens the efficacy of GTP as a major regulator of Mycobacterial cellular pathways.

Insertion and deletion evolution reflects antibiotics selection pressure in a Mycobacterium tuberculosis outbreak

In genome evolution, genetic variants are the source of diversity, which natural selection acts upon. Treatment of human tuberculosis (TB) induces a strong selection pressure for the emergence of antibiotic resistance-conferring variants in the infecting Mycobacterium tuberculosis (MTB) strains. MTB evolution in response to treatment has been intensively studied and mainly attributed to point substitutions. However, the frequency and contribution of insertions and deletions (indels) to MTB genome evolution remains poorly understood. Here, we analyzed a multi-drug resistant MTB outbreak for the presence of high-quality indels and substitutions. We find that indels are significantly enriched in genes conferring antibiotic resistance. Furthermore, we show that indels are inherited during the outbreak and follow a molecular clock with an evolutionary rate of 5.37e-9 indels/site/year, which is 23 times lower than the substitution rate. Inherited indels may co-occur with substitutions in genes along related biological pathways; examples are iron storage and resistance to second-line antibiotics. This suggests that epistatic interactions between indels and substitutions affect antibiotic resistance and compensatory evolution in MTB.

Virtual screening of a MDR-TB WhiB6 target identified by gene expression profiling

Multidrug resistance in M. tb has become a huge global problem due to drug resistance. Hence, the treatment remains a challenge, even though short term chemotherapy is available. Therefore, it is of interest to identify novel drug targets in M.tb through gene expression profiling complimented by a subtractive proteome model. WhiB6 is a transcriptional regulator protein and a known drug resistant marker that is critical in the secretion dependent regulation of ESX-1, which is specialized for the deployment of host membrane-targeting proteins. The WhiB6 protein structure was modelled ab initio and was docked with a library of 173 phytochemicals with potential antituberculosis activity to the identified drug marker to find novel lead molecules. UDP-galactopyranose and GDP-L-galactose were identified to be potential lead molecules to inhibit the target WhiB6. The results were compared with the first line drugs for MDR-TB by docking with WhiB6. Data showed that Ethambutol showed better binding ability to WhiB6 but the afore mentioned top ranked phytochemicals were found to be better candidate molecules. The chosen candidate lead molecules should be further validated by suitable in vitro or in vivo investigation.

Investigating function roles of hypothetical proteins encoded by the Mycobacterium tuberculosis H37Rv genome

Background

Mycobacterium tuberculosis (MTB) is a common bacterium causing tuberculosis and remains a major pathogen for mortality. Although the MTB genome has been extensively explored for two decades, the functions of 27% (1051/3906) of encoded proteins have yet to be determined and these proteins are annotated as hypothetical proteins.

Methods

We assigned functions to these hypothetical proteins using SSEalign, a newly designed algorithm utilizing structural information. A set of rigorous criteria was applied to these annotations in order to examine whether they were supported by each parameter. Virulence factors and potential drug targets were also screened among the annotated proteins.

Results

For 78% (823/1051) of the hypothetical proteins, we could identify homologs in Escherichia coli and Salmonella typhimurium by using SSEalign. Functional classification analysis indicated that 62.2% (512/823) of these annotated proteins were enzymes with catalytic activities and most of these annotations were supported by at least two other independent parameters. A relatively high proportion of transporter was identified in MTB genome, indicating the potential frequent transportation of frequent absorbing essential metabolites and excreting toxic materials in MTB. Twelve virulence factors and ten vaccine candidates were identified within these MTB hypothetical proteins, including two genes (rpoS and pspA) related to stress response to the host immune system. Furthermore, we have identified six novel drug target candidates among our annotated proteins, including Rv0817 and Rv2927c, which could be used for treating MTB infection.

Conclusions

Our annotation of the MTB hypothetical proteins will probably serve as a useful dataset for future MTB studies.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5746-6) contains supplementary material, which is available to authorized users.

Genome Sequencing of Polydrug-, Multidrug-, and Extensively Drug-Resistant Mycobacterium tuberculosis Strains from South India

The genomes of 16 clinical Mycobacterium tuberculosis isolates were subjected to whole-genome sequencing to identify mutations related to resistance to one or more anti-Mycobacterium drugs. The sequence data will help in understanding the genomic characteristics of M. tuberculosis isolates and their resistance mutations prevalent in South India.