Transcriptional Signatures in Tuberculosis Diagnosis

Transcriptional signatures harness patterns of gene expression in blood to distinguish active tuberculosis from latent infection and other conditions. These signatures capture the host immune response, including interferon pathways, myeloid activation and inflammatory cascades, reflecting the dynamic biology of Mycobacterium tuberculosis infection. Advances in high‐throughput sequencing and multiplex platforms have enabled reduction of complex transcriptomes to concise gene panels, improving feasibility for point‐of‐care testing. Non‐sputum‐based assays using fingerstick blood or plasma cell‐free RNA offer rapid, minimally invasive diagnostics that meet diverse programme needs, particularly in settings where sputum sample collection is challenging. Machine‐learning approaches refine signature selection and enhance robustness across populations, while ongoing validation in paediatric, HIV‐infected and geographically varied cohorts underscores global applicability and guides pathways towards clinical implementation.

Research from Nature Portfolio

Recent studies have demonstrated the promise of plasma cell‐free RNA as a sensitive diagnostic biomarker. Using RNA sequencing and machine‐learning, a six‐gene cfRNA signature achieved area‐under‐curve values above 0.90 in training, test and validation cohorts across Uganda, Vietnam and the Philippines, meeting World Health Organization targets for sensitivity and specificity irrespective of HIV status or collection method. This work highlights the stability and diagnostic power of plasma cfRNA compared with whole‐blood approaches. Earlier foundational research applied a modular transcriptomic framework to identify heterogeneity in host responses, delineating a dominant interferon‐inducible programme alongside under‐represented interferon‐γ and T‐cell modules. This modular signature not only discriminates active disease from latent infection but also distinguishes viral and bacterial co‐infections, offering a blueprint for tailoring reduced gene sets to diverse clinical contexts.

Transcriptional Signatures in Tuberculosis Diagnosis publication trend

The graph below shows the total number of articles in transcriptional signatures in tuberculosis diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Transcriptional signature: A selected set of genes whose expression levels collectively indicate a specific disease state.

Cell‐free RNA (cfRNA): Fragments of RNA released into the plasma, reflecting systemic host responses.

Area under the receiver operating characteristic curve (AUC): A measure of diagnostic accuracy, with values closer to 1.0 indicating better discrimination.

Sensitivity and specificity: Metrics of test performance defining the proportions of true positives and true negatives detected.

References

1. Diagnostic accuracy of a three-gene Mycobacterium tuberculosis host response cartridge using fingerstick blood for childhood tuberculosis: a multicentre prospective study in low-income and middle-income countries. The Lancet Infectious Diseases (2023).
2. Evaluation of the Xpert MTB Host Response assay for the triage of patients with presumed pulmonary tuberculosis: a prospective diagnostic accuracy study in Viet Nam, India, the Philippines, Uganda, and South Africa. The Lancet Global Health (2024).
3. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nature Communications (2024).
4. Concise gene signature for point‐of‐care classification of tuberculosis. EMBO Molecular Medicine (2015).
5. A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nature Communications (2018).