A Rapid Active–Latent–Relapse Murine Model of Tuberculosis Based Blood Transcriptional Signature That Distinguishes Disease Stages

TL;DR

Researchers developed a fast mouse model of tuberculosis that can track disease stages and identified a blood-based gene signature to distinguish them.

Contribution

A rapid multi-stage TB murine model and a 16-gene blood transcriptional signature for tracking TB progression.

Findings

A ten-week murine model successfully recapitulates active, latent, and relapse phases of TB.

A sixteen-gene blood transcriptional signature was identified to dynamically track TB disease progression.

The model enables efficient preclinical testing and offers candidate biomarkers for LTBI surveillance.

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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