From host response to genomic targets: electrochemical biosensing of tuberculosis biomarkers.

Authors

Kaylin Cleo Januarie, Onyinyechi Vivian Uhuo, Nelia Abraham Sanga, Marlon Oranzie, Kefilwe Vanessa Mokwebo, Jaymi Leigh January, Emmanuel Iheanyichukwu Iwuoha

Published in

Bioelectrochemistry (Amsterdam, Netherlands). Volume 173. Pages 109438. Aug 28, 2026. Epub Aug 28, 2026.

Abstract

Tuberculosis (TB) remains one of the leading causes of death from a single infectious agent worldwide, with timely diagnosis continuing to be a major challenge, particularly in resource-limited settings. Conventional TB diagnostic methods are limited by low sensitivity, long turnaround times, and an inability to reliably differentiate latent from active disease. Biomarker-based diagnostic strategies have therefore gained increasing attention as they offer the potential to improve early detection, disease differentiation, and treatment monitoring. Herein, we examine electrochemical biosensing strategies for TB diagnostics using a biomarker-class-driven framework, covering host-response biomarkers (IFN-γ and TNF-α), pathogen-derived antigens (ESAT6, CFP10, CFP10-ESAT6, MPT64, Ag85, HspX and LpqH), cell-wall signatures and whole-cell markers (LAM and whole cell Mtb), and genomic markers (Mtb DNA and IS6110). Through structured comparison of recognition elements, biointerface designs, signal amplification strategies, electrochemical techniques, matrices, and validation levels, this review identifies the most promising technical approaches for different TB biomarker classes. It further highlights key translational bottlenecks, including limited clinical validation, buffer-based testing, complex multistep amplification, redox-probe dependence, matrix fouling, and insufficient evidence of manufacturability. This review therefore provides practical guidance for developing electrochemical TB biosensors that are analytically sensitive, clinically relevant, and suitable for decentralized diagnostic applications.

PMID:
42667709
Bibliographic data and abstract were imported from PubMed on 30 Aug 2026.

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