Basal and Zinc-Induced Metallothionein in Resistance to Cadmium, Cisplatin, Zinc, and tertButyl Hydroperoxide: Studies Using MT Knockout and Antisense-Downregulated MT in Mammalian Cells
Bibliographic record
Abstract
Metallothioneins (MTs) mediate resistance to metal and non-metal toxicants. To differentiate the role of MTs from other protective factors, resistance to zinc (Zn), cadmium (Cd), tertbutyl hydroperoxide (tBH), and cisplatin (CDDP) was compared in renal cell lines from wild type (MT-WT) and MT-1/MT-2 knockout (MT-KO) mice. MT-WT cells were more resistant to tBH than MT-KO cells but, unexpectedly, were more sensitive to Zn, Cd, and CDDP. Thus, basal expression of MT conferred resistance to tBH, but not to Cd or CDDP. Pretreatment with Zn increased MT expression and enhanced resistance to Cd and CDDP only in MT-WT cells, indicating a critical role for MT in this form of resistance. By contrast, Zn-pretreatment increased resistance to subsequent Zn exposure, but did not alter resistance to tBH, regardless of MT-status. Therefore, Zn-induced resistance to subsequent exposure to Zn (but not to Cd or CDDP) was mediated by non-MT factors, and neither Zn-induced MT nor other factors affected tBH sensitivity. Furthermore, antisense down-regulation of MT in human HeLa cells reduced basal MT levels and resistance to TBH, but not to Cd or CDDP. Therefore, basal MT alone can mediate resistance to TBH (but not to Cd or CDDP) in mouse and human cells. These data suggest that MT can mediate resistance to toxicants by different mechanisms, some of which correlate with the cellular content of MT protein. Moreover, resistance to some agents (Cd and CDDP) can be enhanced by inducing MT. Resistance to other agents (tBH) requires only basal (non-induced) MT levels.
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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 | 0.000 | 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".