ArticleScientific reports2023
Metabolomic analysis of Mycobacterium tuberculosis reveals metabolic profiles for identification of drug-resistant tuberculosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- Metabolomics and Artificial Intelligence (AI) assisted metabolomics in the diagnosis of non-tuberculous mycobacterial infections: progress and challenges.New microbes and new infections · 2026Review
- Applications of Metabolomics in the Development of Biomedicine and Biotechnology.Advances in experimental medicine and biology · 2026Review
- Rapid discrimination ofBMJ health & care informatics · 2025Article
- Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.Cell systems · 2025Article
- The Utility of Metabolomics in Spinal Cord Injury: Opportunities for Biomarker Discovery and Neuroprotection.International journal of molecular sciences · 2025Review
- Recent progress in tuberculosis diagnosis: insights into blood-based biomarkers and emerging technologies.Frontiers in cellular and infection microbiology · 2025Review
- Identification ofInfection and drug resistance · 2024Review
- Clinical metabolomics by NMR revealed serum metabolic signatures for differentiating sarcoidosis from tuberculosis.Metabolomics : Official journal of the Metabolomic Society · 2023Article
- Advances in Diagnostics and Drug Discovery against Resistant and Latent Tuberculosis Infection.Pharmaceutics · 2023Review
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Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The detection of pre-extensively (pre-XDR) and extensively drug-resistant tuberculosis (XDR-TB) is challenging. Drug-susceptibility tests for some anti-TB drugs, especially ethambutol (ETH) and ethionamide (ETO), are problematic due to overlapping thresholds to differentiate between susceptible and resistant phenotypes. We aimed to identify possible metabolomic markers to detect Mycobacterium tuberculosis (Mtb) strains causing pre-XDR and XDR-TB. The metabolic patterns of ETH- and ETO-resistant Mtb isolates were also investigated. Metabolomics of 150 Mtb isolates (54 pre-XDR, 63 XDR-TB and 33 pan-susceptible; pan-S) were investigated. Metabolomics of ETH and ETO phenotypically resistant subgroups were analyzed using UHPLC-ESI-QTOF-MS/MS. Orthogonal partial least-squares discriminant analysis revealed distinct separation in all pairwise comparisons among groups. Two metabolites (meso-hydroxyheme and itaconic anhydride) were able to differentiate the pre-XDR and XDR-TB groups from the pan-S group with 100% sensitivity and 100% specificity. In comparisons of the ETH and ETO phenotypically resistant subsets, sets of increased (ETH = 15, ETO = 7) and decreased (ETH = 1, ETO = 6) metabolites specific for the resistance phenotype of each drug were found. We demonstrated the potential for metabolomics of Mtb to differentiate among types of DR-TB as well as between isolates that were phenotypically resistant to ETO and ETH. Thus, metabolomics might be further applied for DR-TB diagnosis and patient management.
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