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Phylogenomic analysis and Mycobacterium tuberculosis antibiotic resistance prediction by whole-genome sequencing from clinical isolates of Caldas, Colombia.

ABSTRACT: Mycobacterium tuberculosis (M. tuberculosis) was the pathogen responsible for the highest number of deaths from infectious diseases in the world, before the arrival of the COVID-19 pandemic. Whole genome sequencing (WGS) has contributed to the understanding of genetic diversity, the mechanisms involved in drug resistance and the transmission dynamics of this pathogen. The object of this study is to use WGS for the epidemiological and molecular characterization of M. tuberculosis clinical strains from Chinchiná, Caldas, a small town in Colombia with a high incidence of TB. Sputum samples were obtained during the first semester of 2020 from six patients and cultured in solid Löwenstein-Jensen medium. DNA extraction was obtained from positive culture samples and WGS was performed with the Illumina HiSeq 2500 platform for subsequent bioinformatic analysis. M. tuberculosis isolates were typified as Euro-American lineage 4 with a predominance of the Harlem and LAM sublineages. All samples were proven sensitive to antituberculosis drugs by genomic analysis, although no phenotype antimicrobial tests were performed on the samples, unreported mutations were identified that could require further analysis. The present study provides preliminary data for the construction of a genomic database line and the follow-up of lineages in this region.

SUBMITTER: Sanchez-Corrales L

PROVIDER: S-EPMC8496870 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Phylogenomic analysis and Mycobacterium tuberculosis antibiotic resistance prediction by whole-genome sequencing from clinical isolates of Caldas, Colombia.

Sánchez-Corrales Lusayda L Tovar-Aguirre Olga Lucía OL Galeano-Vanegas Narmer Fernando NF Castaño Jiménez Paula Alejandra PA Martínez-Vega Ruth Arali RA Maldonado-Londoño Carlos Ernesto CE Hernández-Botero Johan Sebastián JS Siller-López Fernando F

PloS one 20211007 10

Mycobacterium tuberculosis (M. tuberculosis) was the pathogen responsible for the highest number of deaths from infectious diseases in the world, before the arrival of the COVID-19 pandemic. Whole genome sequencing (WGS) has contributed to the understanding of genetic diversity, the mechanisms involved in drug resistance and the transmission dynamics of this pathogen. The object of this study is to use WGS for the epidemiological and molecular characterization of M. tuberculosis clinical strains ...[more]

PMID: 34618869

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Project description:BackgroundWhole genome sequencing has revolutionised the interrogation of mycobacterial genomes. Recent studies have reported conflicting findings on the genomic stability of Mycobacterium tuberculosis during the evolution of drug resistance. In an age where whole genome sequencing is increasingly relied upon for defining the structure of bacterial genomes, it is important to investigate the reliability of next generation sequencing to identify clonal variants present in a minor percentage of the population. This study aimed to define a reliable cut-off for identification of low frequency sequence variants and to subsequently investigate genetic heterogeneity and the evolution of drug resistance in M. tuberculosis.MethodsGenomic DNA was isolated from single colonies from 14 rifampicin mono-resistant M. tuberculosis isolates, as well as the primary cultures and follow up MDR cultures from two of these patients. The whole genomes of the M. tuberculosis isolates were sequenced using either the Illumina MiSeq or Illumina HiSeq platforms. Sequences were analysed with an in-house pipeline.ResultsUsing next-generation sequencing in combination with Sanger sequencing and statistical analysis we defined a read frequency cut-off of 30% to identify low frequency M. tuberculosis variants with high confidence. Using this cut-off we demonstrated a high rate of genetic diversity between single colonies isolated from one population, showing that by using the current sequencing technology, single colonies are not a true reflection of the genetic diversity within a whole population and vice versa. We further showed that numerous heterogeneous variants emerge and then disappear during the evolution of isoniazid resistance within individual patients. Our findings allowed us to formulate a model for the selective bottleneck which occurs during the course of infection, acting as a genomic purification event.ConclusionsOur study demonstrated true levels of genetic diversity within an M. tuberculosis population and showed that genetic diversity may be re-defined when a selective pressure, such as drug exposure, is imposed on M. tuberculosis populations during the course of infection. This suggests that the genome of M. tuberculosis is more dynamic than previously thought, suggesting preparedness to respond to a changing environment.

Project description:BackgroundEfforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predicting drug resistance of four anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) in MTB using whole-genome sequence and clinical data from Uganda. We also assessed the model's generalizability on another dataset from South Africa.ResultsWe trained ten machine learning algorithms on a dataset comprising of 182 MTB isolates with clinical data variables (age, sex, HIV status) and SNP mutations across the entire genome as predictor variables and phenotypic drug-susceptibility data for the four drugs as the outcome variable. Model performance varied across the four anti-TB drugs after a five-fold cross validation. The best model was selected considering the highest Mathews Correlation Coefficient (MCC) and Area Under the Receiver Operating Characteristic Curve (AUC) score as key metrics. The Logistic regression excelled in predicting rifampicin resistance (MCC: 0.83 (95% confidence intervals (CI) 0.73-0.86) and AUC: 0.96 (95% CI 0.95-0.98) and streptomycin (MCC: 0.44 (95% CI 0.27-0.58) and AUC: 0.80 (95% CI 0.74-0.82), Extreme Gradient Boosting (XGBoost) for ethambutol (MCC: 0.65 (95% CI 0.54-0.74) and AUC: 0.90 (95% CI 0.83-0.96) and Gradient Boosting (GBC) for isoniazid (MCC: 0.69 (95% CI 0.61-0.78) and AUC: 0.91 (95% CI 0.88-0.96). The best performing model per drug was only trained on the SNP dataset after excluding the clinical data variables because intergrating them with SNP mutations showed a marginal improvement in the model's performance. Despite the high MCC (0.18 to 0.72) and AUC (0.66 to 0.95) scores for all the best models with the Uganda test dataset, LR model for rifampicin and streptomycin didn't generalize with the South Africa dataset compared to the GBC and XGBoost models. Compared to TB profiler, LR for RIF was very sensitive and the GBC for INH and XGBoost for EMB were very specific on the Uganda dataset. TB profiler outperformed all the best models on the South Africa dataset. We identified key mutations associated with drug resistance for these antibiotics. HIV status was also identified among the top significant features in predicting drug resistance.ConclusionLeveraging machine learning applications in predicting antimicrobial resistance represents a promising avenue in addressing the global health challenge posed by antimicrobial resistance. This work demonstrates that integration of diverse data types such as genomic and clinical data could improve resistance predictions while using machine learning algorithms, support robust surveillance systems and also inform targeted interventions to curb the rising threat of antimicrobial resistance.

Project description:BackgroundZoonotic human tuberculosis (TB) caused by Mycobacterium bovis (M. bovis) is as vital as Mycobacterium tuberculosis, however with scarce available information. We aimed to use whole-genome sequencing (WGS) technology to take a deep insight into the circulating genotypes of human M. bovis and the genomic characteristics underlying virulence and drug resistance.MethodsThe study included smear positive Ziehl-Neelsen samples from patients with suspected tuberculosis. Samples were cultured on Lowenstein-Jensen media and suspected colonies of M. bovis were selected to undergo DNA extraction and WGS. Data was analysed using the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), and online bioinformatics tools. A phylogenetic tree was constructed for our sequenced strains, in addition to a set of 59 previously sequenced M. bovis genomes from different hosts and countries.ResultsOut of total 112 mycobacterial positive cultures, five M. bovis were isolated and underwent WGS. All sequenced strains belonged to Mycobacterium tuberculosis var bovis, spoligotype BOV_1; BOV_11. Resistance gene mutations were determined in 100% of strains to pyrazinamide (pncA and rpsA), isoniazid (KatG and ahpC), ethambutol (embB, embC, embR and ubiA), streptomycin (rpsl) and fluoroquinolones (gyrA and gyrB). Rifampin (rpoB and rpoC) and delamanid (fbiC) resistance genes were found in 80% of strains. The major represented virulence classes were the secretion system, cell surface components and regulation system. The phylogenetic analysis revealed close genetic relatedness of three sequenced M. bovis strains to previous reported cow strains from Egypt and human strains from France, as well as relatedness of one M. bovis strain to four human Algerian strains. One sequenced strain was related to one cow strain from Egypt and a human strain from South Africa.ConclusionsAll sequenced M. bovis isolates showed the same spoligotype, but diverse phylogeny. Resistance gene mutations were detected for anti-TB drugs including pyrazinamide, isoniazid, streptomycin, ethambutol, fluoroquinolones, cycloserine, rifampin and delamanid. The virulence profile comprised genes assigned mainly to secretion system, cell surface components and regulation system. Phylogenetic analysis revealed genetic relatedness between our isolates and previously sequenced bovine strains from Egypt as well as human strains from other nearby countries in the region.

Dataset Information

Phylogenomic analysis and Mycobacterium tuberculosis antibiotic resistance prediction by whole-genome sequencing from clinical isolates of Caldas, Colombia.

Publications

Phylogenomic analysis and Mycobacterium tuberculosis antibiotic resistance prediction by whole-genome sequencing from clinical isolates of Caldas, Colombia.

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