Transgenic nematodes as biosensors for metal stress in soil pore water samples
Bibliographic record
Abstract
Caenorhabditis elegans strains carrying stress-reporter green fluorescent protein transgenes were used to explore patterns of response to metals. Multiple stress pathways were induced at high doses by most metals tested, including members of the heat shock, oxidative stress, metallothionein (mtl) and xenobiotic response gene families. A mathematical model (to be published separately) of the gene regulatory circuit controlling mtl production predicted that chemically similar divalent metals (classic inducers) should show additive effects on mtl gene induction, whereas chemically dissimilar metals should show interference. These predictions were verified experimentally; thus cadmium and mercury showed additive effects, whereas ferric iron (a weak inducer) significantly reduced the effect of mercury. We applied a similar battery of tests to diluted samples of soil pore water extracted centrifugally after mixing 20% w/w ultrapure water with air-dried soil from an abandoned lead/zinc mine in the Murcia region of Spain. In addition, metal contents of both soil and soil pore water were determined by ICP-MS, and simplified mixtures of soluble metal salts were tested at equivalent final concentrations. The effects of extracted soil pore water (after tenfold dilution) were closely mimicked by mixtures of its principal component ions, and even by the single most prevalent contaminant (zinc) alone, though other metals modulated its effects both positively and negatively. In general, mixtures containing similar (divalent) metal ions exhibited mainly additive effects, whereas admixture of dissimilar (e.g. trivalent) ions often resulted in interference, reducing overall levels of stress-gene induction. These findings were also consistent with model predictions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".