{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T13:07:01Z","timestamp":1791810421633,"version":"4.3.4"},"reference-count":35,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T00:00:00Z","timestamp":1766361600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82200015"],"award-info":[{"award-number":["82200015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Science and Technology Health Project of the Health Commission of Hubei Province","award":["WJ2025Q005"],"award-info":[{"award-number":["WJ2025Q005"]}]},{"name":"Basic Applied Research Project of Wuhan Science and Technology Bureau","award":["2023020201010214"],"award-info":[{"award-number":["2023020201010214"]}]},{"name":"Science and Technology Project of Shenzhen","award":["JCYJ20220530163216036"],"award-info":[{"award-number":["JCYJ20220530163216036"]}]},{"name":"Bao'an District Medical Association","award":["BAYXH2023007"],"award-info":[{"award-number":["BAYXH2023007"]}]},{"name":"Wuhan Pulmonary Hospital Research Fund","award":["YNZZ202216"],"award-info":[{"award-number":["YNZZ202216"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Background<\/jats:title>\n The differential diagnosis between Tuberculosis (TB) and Non-tuberculous Mycobacteria (NTM) has historically been constrained by the inadequate sensitivity and specificity of current diagnostic methods. Furthermore, distinguishing between Active Tuberculosis (ATB) and Latent Tuberculosis Infection (LTBI) poses significant challenges. This study aims to develop a molecular differentiation system for ATB, LTBI, and NTM by integrating plasma proteomics with multi-dimensional analytical techniques, while also exploring key biomarkers associated with disease progression and treatment response.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Using label-free quantitative technology, we conducted a plasma proteomics analysis across five groups: ATB, LTBI, NTM, Cured Patients (CPs), and Healthy Donors (HD). Differentially Expressed Proteins (DEPs) were identified through screening (FC\u2009>\u20091.5 or <0.67,\n P<\/jats:italic>\n \u2009<\u20090.05), followed by Gene Ontology\/KEGG pathway enrichment, STRING interaction network, and Mfuzz dynamic clustering analysis to systematically elucidate molecular characteristics. Experimental data were validated through a multidimensional quality control system (Pearson correlation coefficient, peptide distribution, molecular weight distribution, etc.). Enzyme-linked immunosorbent assay (ELISA) was employed to detect the plasma expression levels of target proteins across the groups and to facilitate comparisons.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n This study identified 1,338 non-redundant proteins across five cohorts. Comparative analysis revealed 142 DEPs across the three comparative groups (ATB, LTBI, and NTM), which were primarily localized in the extracellular domain. Key findings include: 27 DEPs in the ATB-LTBI group, primarily enriched in inflammatory responses (such as A2M, IL-1R2) and epithelial barrier functions (TGM3, KRT3); 69 DEPs in the ATB-NTM group, characterized by significant changes in immunoglobulin light chains (IGLV2\u201311) and innate immune effector molecules (S100A8); 46 DEPs in the NTM-LTBI group, closely related to lipid metabolism (APOC3) and extracellular matrix remodeling (FN1). KEGG pathway analysis revealed that DEPs in the ATB-LTBI group were enriched in nitrogen metabolism pathways, those in the ATB-NTM group were associated with thyroid hormone synthesis, and the NTM-LTBI group was involved in phagosome function. Dynamic clustering results showed six treatment response modules: Cluster 1\/2 (riboflavin metabolism, complement coagulation pathway) were activated post-treatment, Cluster 3\/4 (proteasome, cardiac signaling pathway) exhibited partial reversal in expression, and Cluster 5\/6 (platelet activation, cytoskeleton) showed delayed regression. Research confirmed 10 differential proteins between the ATB-CPs and ATB-HD groups, including S100A8, LTA4H, and DEFA1B, which constitute a molecular fingerprint specific to ATB. ELISA validation confirmed significantly elevated S100A8 and GPX3 in ATB group, while NTM group showed higher FGB and lower ATRN levels.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study systematically reveals the plasma proteomic characteristics under infection statuses caused by different mycobacteria. A discrimination framework for ATB\/LTBI\/NTM was constructed based on disease-specific differential proteins, overcoming the limitations of traditional diagnostic techniques in distinguishing infection states. Through dynamic analysis of six temporal therapeutic modules, the reprogramming patterns of the host protein network during tuberculosis treatment were elucidated. This research lays a multidimensional molecular foundation for the precise typing, personalized treatment, and prognostic evaluation of mycobacterial infections.<\/jats:p>\n <\/jats:sec>","DOI":"10.1371\/journal.pone.0339558","type":"journal-article","created":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T18:28:33Z","timestamp":1766428113000},"page":"e0339558","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identification of diagnostic and prognostic biomarkers for tuberculosis based on plasma 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