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Updated: Mar 29, 2026

A 3D Human Lung Tissue Model for Functional Studies on Mycobacterium tuberculosis Infection
Published on: October 5, 2015
A Rapid Active-Latent-Relapse Murine Model of Tuberculosis Based Blood Transcriptional Signature That Distinguishes
Haifeng Li1,2,3,4,5,6, Junfei Wang1, Yu Wang1
1Institute of Laboratory Animal Science, CAMS & PUMC, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing 100021, China.
Abstract:
The lack of reliable diagnostic tools and relapse monitoring for latent tuberculosis infection (LTBI) constitutes a major obstacle to global tuberculosis (TB) control. This highlights an urgent need for robust animal models and predictive biomarkers. To address this, we report the successful establishment of a rapid murine model of recapitulating the active, latent, and relapse phases of TB within a compressed ten-week timeframe-hence termed the rapid multi-stage TB murine model. In this model, mice were first intravenously infected with Mycobacterium tuberculosis, followed by a four-week isoniazid (INH) regimen starting at two weeks post-infection. By week six, pulmonary bacterial loads in most mice dropped below the detection limit, signifying the establishment of latency. Reactivation was subsequently triggered by a four-week administration of anti-TNF-α (Tumor Necrosis Factor-α) monoclonal antibody. Leveraging this reproducible and time-efficient model, we performed transcriptomic profiling of peripheral blood and identified a distinct sixteen-gene signature (including Ets2, Fam111a, Fosl2, Gadd45b, Nfkbid, Rgs1, Bhlhe40, Il1r2, Clec2d, Kmo, Lynx1, Papd4, Trim34a, Wrb, Nlrp12, Spns1) that dynamically tracks disease progression. Collectively, these findings not only provide a valuable and efficient preclinical tool but also deliver transformable candidate biomarkers with immediate potential to guide the development of novel diagnostic strategies for LTBI surveillance and management.
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