Designing a robust whole-cell biosensor for detection of toxic metals using intein splicing inhibition of <i>Mycobacterium tuberculosis</i> SufB protein
Bibliographic record
Abstract
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 Cd 2₊ and Hg 2₊ attenuated the splicing activity of Mtb SufB precursor protein over a concentration range of 25 µM to 2 mM, while Pb 2₊ and Cr 3₊ 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 Cu 2₊ , Zn 2₊ , and Pt 4₊ over various concentration range alongside Cd 2₊ and Hg 2₊ . 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".