Novel Metabolomic Biomarkers for Latent Tuberculosis Infection: A Plasma-based Untargeted Metabolomics Approach Using Mass Spectrometry

Xinnan Wang , Yuchen Pan , Hao Zhang , Zheng Sun , Deye Liu , Cheng Chen , Leonardo Martinez , Wenliang Ji , Qiao Liu

BIO Integration ›› 2026, Vol. 7 ›› Issue (1) : 26

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BIO Integration ›› 2026, Vol. 7 ›› Issue (1) :26 DOI: 10.15212/bioi-2026-0003
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Novel Metabolomic Biomarkers for Latent Tuberculosis Infection: A Plasma-based Untargeted Metabolomics Approach Using Mass Spectrometry
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Abstract

Objective: Latent tuberculosis infection (LTBI) is difficult to diagnose due to the lack of a definitive gold standard. This study aimed to explore plasma metabolic alterations associated with LTBIs using an untargeted metabolomics approach.

Design: In this discovery-phase study, LTBI individuals (QuantiFERON-TB Gold-positive) were recruited from close contacts of tuberculosis patients, while non-LTBI individuals (QuantiFERON-TB Gold-negative) were recruited from prison detainees. Plasma samples were analyzed using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Multivariate statistical analysis combined with univariate screening was used to identify differential metabolites, followed by receiver operating characteristic analysis.

Results: A total of 43 metabolites showed significant differences between the LTBI (n = 100) and non-LTBI groups (n = 99). Among the 43 metabolites, leucylleucine, tryptophyl-phenylalanine, lysoPE(18:1(11Z)/0:0), and biliverdin displayed relatively high discriminatory ability in this discovery cohort with area under the curve values ranging from 0.975–0.981. Models combining selected metabolites achieved higher apparent classification performance under internal validation, with some area under the curve values approaching 1.00. However, because feature selection and model evaluation were performed within the same cohort and no external validation was performed, these results may overestimate true diagnostic performance.

Conclusions: This study provides exploratory evidence of plasma metabolic differences between LTBI and non-LTBI individuals and identifies four metabolites of potential interest. However, the two groups were drawn from different source populations, which may introduce selection bias and unmeasured confounding. In addition, all metabolites were identified at Metabolomics Standards Initiative level 2 without confirmation using authentic standards and no targeted validation was performed. Therefore, these findings should be interpreted as preliminary and hypothesis-generating. Independent validation in well-matched cohorts with targeted metabolomic approaches is required before any clinical interpretation.

Keywords

Biomarkers / latent tuberculosis infection / metabolomics / plasma

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Xinnan Wang, Yuchen Pan, Hao Zhang, Zheng Sun, Deye Liu, Cheng Chen, Leonardo Martinez, Wenliang Ji, Qiao Liu. Novel Metabolomic Biomarkers for Latent Tuberculosis Infection: A Plasma-based Untargeted Metabolomics Approach Using Mass Spectrometry. BIO Integration, 2026, 7 (1) : 26 DOI:10.15212/bioi-2026-0003

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References

[1]

WHO. Latent tuberculosis infection: updated and consolidated guidelines for programmatic management. Geneva: World Health Organization; 2018.

[2]

Cui X, Gao L, Cao B. Management of latent tuberculosis infection in China: Exploring solutions suitable for high-burden countries. Int J Infect Dis 2020; 92S: S37-40. [PMID: 32114201 DOI: 10.1016/j.ijid.2020.02.034]

[3]

WHO. Global tuberculosis report 2023. Geneva: World Health Organization; 2023.

[4]

Zellweger JP, Sotgiu G, Corradi M, Durando P . The diagnosis of latent tuberculosis infection (LTBI): currently available tests, future developments, and perspectives to eliminate tuberculosis (TB). Med Lav 2020; 111(3): 170-83. [PMID: 32624559 DOI: 10.23749/mdl.v111i3.9983]

[5]

Cho Y, Park Y, Sim B, Kim J, Lee H, et al. Identification of serum biomarkers for active pulmonary tuberculosis using a targeted metabolomics approach. Sci Rep 2020; 10(1): 3825. [DOI: 10.1038/s41598-020-60669-0]

[6]

Luo D, Yang BY, Qin K, Shi CY, Wei NS, et al. Untargeted metabolomics of feces reveals diagnostic and prognostic biomarkers for active tuberculosis and latent tuberculosis infection: potential application for precise and non-invasive identification. Infect Drug Resist 2023; 16: 6121-38. [PMID: 37719654 DOI: 10.2147/IDR.S422363]

[7]

Deng J, Liu L, Yang Q, Wei C, Zhang H, et al. Urinary metabolomic analysis to identify potential markers for the diagnosis of tuberculosis and latent tuberculosis. Arch Biochem Biophys 2021; 704: 108876. [PMID: 33864753 DOI: 10.1016/j.abb.2021.108876]

[8]

Zheng F, Zhao X, Zeng Z, Wang L, Lv W, et al. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography-mass spectrometry. Nat Protoc 2020; 15(8): 2519-37. [PMID: 32581297 DOI: 10.1038/s41596-020-0341-5]

[9]

Yu Y, Jiang XX, Li JC. Biomarker discovery for tuberculosis using metabolomics. Front Mol Biosci 2023; 10: 1099654. [PMID: 36891238 DOI: 10.3389/fmolb.2023.1099654]

[10]

Pai M, Zwerling A, Menzies D. Systematic review: T-cell-based assays for the diagnosis of latent tuberculosis infection: an update. Ann Intern Med 2008; 149(3): 177-84. [PMID: 18593687 DOI: 10.7326/0003-4819-149-3-200808050-00241]

[11]

Wishart DS, Guo A, Oler E, Wang F, Anjum A, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res 2022; 50(D1): D622-31. [PMID: 34986597 DOI: 10.1093/nar/gkab1062]

[12]

Brown M, Dunn WB, Dobson P, Patel Y, Winder CL, et al. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst 2009; 134(7): 1322-32. [PMID: 19562197 DOI: 10.1039/b901179j]

[13]

Xia J, Psychogios N, Young N, Wishart DS. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 2009; 37(Web Server issue): W652-60. [PMID: 19429898 DOI: 10.1093/nar/gkp356]

[14]

Wang LJ, Chou WJ, Tsai CS, Lee MJ, Lee SY, et al. Novel plasma metabolite markers of attention-deficit/hyperactivity disorder identified using high-performance chemical isotope labelling-based liquid chromatography-mass spectrometry. World J Biol Psychiatry 2021; 22(2): 139-48. [PMID: 32351159 DOI: 10.1080/15622975.2020.1762930]

[15]

Collins JM, Siddiqa A, Jones DP, Liu K, Kempker RR, et al. Tryptophan catabolism reflects disease activity in human tuberculosis. JCI Insight 2020; 5(10): e137131. [PMID: 32369456 DOI: 10.1172/jci.insight.137131]

[16]

Weiner J 3rd, Parida SK, Maertzdorf J, Black GF, Repsilber D, et al. Biomarkers of inflammation, immunosuppression and stress with active disease are revealed by metabolomic profiling of tuberculosis patients. PLoS One 2012; 7(7): e40221. [PMID: 22844400 DOI: 10.1371/journal.pone.0040221]

[17]

Vahidi S, Ripstein ZA, Juravsky JB, Rennella E, Goldberg AL, et al. An allosteric switch regulates Mycobacterium tuberculosis ClpP1P2 protease function as established by cryo-EM and methyl-TROSY NMR. Proc Natl Acad Sci USA 2020; 117(11): 5895-906. [PMID: 32123115 DOI: 10.1073/pnas.1921630117]

[18]

Famulla K, Sass P, Malik I, Akopian T, Kandror O, et al. Acyldepsipeptide antibiotics kill mycobacteria by preventing the physiological functions of the ClpP1P2 protease. Mol Microbiol 2016; 101(2): 194-209. [PMID: 26919556 DOI: 10.1111/mmi.13362]

[19]

Grzelczyk A, Gendaszewska-Darmach E. Novel bioactive glycerol-based lysophospholipids: new data - new insight into their function. Biochimie 2013; 95(4): 667-79. [PMID: 23089136 DOI: 10.1016/j.biochi.2012.10.009]

[20]

Cao X, van Putten JPM, Wösten MMSM. Chapter Two - Biological functions of bacterial lysophospholipids. In: Poole RK, Kelly DJ, editors. Advances in microbial physiology. Volume 82. Academic Press; 2023. pp. 129-54.

[21]

Tan ST, Ramesh T, Toh XR, Nguyen LN. Emerging roles of lysophospholipids in health and disease. Prog Lipid Res 2020; 80: 101068. [PMID: 33068601 DOI: 10.1016/j.plipres.2020.101068]

[22]

Zou D, Pei J, Lan J, Sang H, Chen H, et al. A SNP of bacterial blc disturbs gut lysophospholipid homeostasis and induces inflammation through epithelial barrier disruption. Ebiomedicine 2020; 52: 102652. [PMID: 32058942 DOI: 10.1016/j.ebiom.2020.102652]

[23]

Chen JX, Han YS, Zhang SQ, Li ZB, Chen J, et al. Novel therapeutic evaluation biomarkers of lipid metabolism targets in uncomplicated pulmonary tuberculosis patients. Signal Transduct Target Ther 2021; 6(1): 22. [PMID: 33462176 DOI: 10.1038/s41392-020-00427-w]

[24]

Wang C, Lou C, Yang Z, Shi J, Niu N. Plasma metabolomic analysis reveals the metabolic characteristics and potential diagnostic biomarkers of spinal tuberculosis. Heliyon 2024; 10(7): e27940. [PMID: 38571585 DOI: 10.1016/j.heliyon.2024.e27940]

[25]

Scharn CR, Collins AC, Nair VR, Stamm CE, Marciano DK, et al. Heme oxygenase-1 regulates inflammation and mycobacterial survival in human macrophages during Mycobacterium tuberculosis infection. J Immunol 2016; 196(11): 4641-9. [PMID: 27183573 DOI: 10.4049/jimmunol.1500434]

[26]

Ahmed FH, Mohamed AE, Carr PD, Lee BM, Condic-Jurkic K, et al. Rv2074 is a novel F420H2-dependent biliverdin reductase in Mycobacterium tuberculosis. Protein Sci 2016; 25(9): 1692-709. [PMID: 27364382 DOI: 10.1002/pro.2975]

[27]

Yi WJ, Han YS, Wei LL, Shi LY, Huang H, et al. L-Histidine, arachidonic acid, biliverdin, and L-cysteine-glutathione disulfide as potential biomarkers for cured pulmonary tuberculosis. Biomed Pharmacother 2019; 116: 108980. [PMID: 31125821 DOI: 10.1016/j.biopha.2019.108980]

[28]

Sun Y, Cui A, Dong H, Nie L, Yue Z, et al. Intermittent hyperglycaemia induces macrophage dysfunction by extracellular regulated protein kinase-dependent PKM2 translocation in periodontitis. Cell Prolif 2024; 57(10): e13651. [PMID: 38790140 DOI: 10.1111/cpr.13651]

[29]

Yang G, Dong C, Wu Z, Wu P, Yang C, et al. Single-cell RNA sequencing-guided engineering of mitochondrial therapies for intervertebral disc degeneration by regulating mtDNA/SPARC-STING signaling. Bioact Mater 2025; 48: 564-82. [PMID: 40104024 DOI: 10.1016/j.bioactmat.2025.02.036]

[30]

Li M, Sun X, Zeng L, Sun A, Ge J. Metabolic homeostasis of immune cells modulates cardiovascular diseases. Research (Wash D C) 2025; 8: 0679. [PMID: 40270694 DOI: 10.34133/research.0679]

[31]

Gong W, Fu H, Yang K, Zheng T, Guo K, et al. 4-Octyl itaconate blocks GSDMB-mediated pyroptosis and restricts inflammation by inactivating granzyme A. Cell Prolif 2024; 57(12): e13711. [PMID: 38982510 DOI: 10.1111/cpr.13711]

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