An Anaerobic Biosensor Assay for the Detection of Mercury and Cadmium
Bibliographic record
Abstract
Mercury (Hg) bioavailability to microbes is a key step to toxic Hg biomagnification in food webs. Cadmium (Cd) transformations and bioavailability to bacteria control the amount that will accumulate in staple food crops. The bioavailability of these metals is dependent on their speciation in solution, but more particularly under anoxic conditions where Hg is methylated to toxic monomethylmercury (MeHg) and Cd persists in the rhizosphere. Whole-cell microbial biosensors give a quantifiable signal when a metal enters the cytosol and therefore are useful tools to assess metal bioavailability. Unfortunately, most biosensing efforts have so far been constrained to oxic environments due to the limited ability of existing reporter proteins to function in the absence of oxygen. In this study, we present our effort to develop and optimize a whole-cell biosensor assay capable of functioning anaerobically that can detect metals under anoxic condition in quasi-real time and within hours. We describe how the biosensor can help assess how chemical variables relevant to the environmental cycling of metals affect their bioavailability. The following protocol includes methods to (1) prepare Hg and Cd standards under anoxic conditions, (2) prepare the biosensor in the absence of oxygen, (3) design and execute an experiment to determine how a series of variable affects Hg or Cd bioavailability, and (4) to quantify and interpret biosensor data. We show the linear ranges of the biosensors and provide examples showing the method's ability to distinguish between metal bioavailability and toxicity by utilizing both metal-inducible and constitutive strains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".