Differential effects of pesticides on dioxin receptor signaling and p53 activation
Bibliographic record
Abstract
As modern agricultural practices increase their use of chemical pesticides, it is inevitable that we will find a number of these xenobiotics within drinking water supplies and disseminated throughout the food chain. A major problem that arises from this pollution is that the effects of most of these pesticides on cellular mechanisms in general, and how they interact with each other and affect human cells are still poorly understood. In this study we make use of cultured human cancer cells to measure by qRT-PCR how pesticides affect gene expression of stress pathways. Immunoblotting studies were performed to monitor protein expression levels and activation of signaling pathways. We make use of immunofluorescence and microscopy to visualize and quantify DNA damage events in those cells. In the current study, we evaluate the potential of a subset of widely used pesticides to activate the dioxin receptor pathway and affect its crosstalk with estrogen receptor signaling. We quantify the impact of these chemicals on the p53-dependent cellular stress response. We find that, not only can the different pesticides activate the dioxin receptor pathway, most of them have better than additive effects on this pathway when combined at low doses. We also show that different pesticides have the ability to trigger crosstalk events that may generate genotoxic estrogen metabolites. Finally, we show that some, but not all of the tested pesticides can induce a p53-dependent stress response. Taken together our results provide evidence that several xenobiotics found within the environment have the potential to interact together to elicit significant effects on cell systems. Our data warrants caution when the toxicity of substances that are assessed simply for individual chemicals, since important biological effects could be observed only in the presence of other compounds, and that even at very low concentrations.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".