Data Sheet 5_A plasma proteomic signature of the actin-coagulation axis accurately predicts progression to active tuberculosis.pdf
收藏NIAID Data Ecosystem2026-05-10 收录
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https://figshare.com/articles/dataset/Data_Sheet_5_A_plasma_proteomic_signature_of_the_actin-coagulation_axis_accurately_predicts_progression_to_active_tuberculosis_pdf/31147624
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BackgroundPredicting progression to active tuberculosis (TB) is a critical unmet need, as current immunological tests only detect infection but cannot discriminate those who will develop active disease. A reliable prognostic biomarker could enable targeted preventive therapy and transform TB control strategies.
MethodsWe performed deep plasma proteomics using data-independent acquisition mass spectrometry on a prospective cohort of 60 TB-exposed students in Jiangsu Province, China, comprising 40 individuals with Mycobacterium tuberculosis infection and 20 uninfected controls.
ResultsOver 2 years of follow-up, 21 of the 40 infected participants progressed to active disease, and a LASSO-Cox model with internal cross-validation for tuning the regularization parameter (λ) was used to develop a prognostic signature from baseline plasma samples. A 33-protein signature predicted progression to active TB with high discriminatory performance in this cohort (AUC = 0.992, 95% CI 0.977–1.0). This signature revealed a distinct pre-symptomatic state in progressors, defined by a pro-thrombotic shift in the coagulation cascade and profound disruption of actin cytoskeleton dynamics. The proteomic alterations were detectable up to 2 years before clinical diagnosis, providing a potential window for intervention. Functional network analysis identified key hub proteins including ACTR3, ACTN1, and MYH9 (actin remodeling) and F2 (coagulation).
ConclusionWe identified a plasma protein signature that accurately predicts progression from latent to active tuberculosis, linking disease onset to dysregulation of the actin cytoskeleton and coagulation. This biomarker provides a basis for precision preventive therapy and identifies novel host-directed therapeutic targets.
创建时间:
2026-01-26



