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Designing a robust whole-cell biosensor platform for detection of toxic metals using intein splicing inhibition of Mycobacterium tuberculosis SufB protein

Mehra, A.; Nanda, A.; Nayak, S.

2025-12-25 microbiology
10.1101/2025.09.27.678972 bioRxiv
Show abstract

Disruption of the natural geochemical cycle by human activities has led to bioaccumulation of metals, posing a global health threat. Hence, there is a pressing need for simple, sensitive, yet eco-friendly biosensor setups to monitor metal contamination in the environment. Existing biosensors are limited by poor efficiency, stability issues, and complex instrumentation requiring skilled operators. To address these caveats, current study explores how intein-mediated protein splicing, a spontaneous post-translational process, can be adapted for metal-biosensing by coupling metal-dependent splicing inhibition to viability loss of native microbial cells. Toxic metal ions like Cd2 and Hg2 attenuated the splicing activity of Mtb SufB precursor protein over a concentration range of 25 {micro}M to 2 mM, while Pb2 and Cr3 failed to do so. An innovative biosensor platform was designed for colorimetric detection of metal ions via simple Alamar Blue assay, where attenuated Mtb strain (H37Ra) served as the indicator cells. Metal-induced SufB splicing inhibition led to loss of viability of H37Ra cells, while addition of metal-specific chelators reversed the effect. Multiplexing ability was evaluated by including known splicing inhibitors like Cu2, Zn2, and Pt4 over various concentration range alongside Cd2 and Hg2. The simple 96-well plate format enables multiplexed qualitative metal detection, while colorimetric absorbance measurement ensures metal quantification. The designed biosensor offers low-cost, user-friendly, and sensitive assay for high-throughput metal detection, utilizing whole-cell native organisms carrying metal-sensing precursor protein. Thus, this approach can be implemented in standard biological laboratories for robust metal screening process in environmental and industrial effluents.

Published in ACS Omega (predicted rank #3) · training set

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