Responses of A549 human lung epithelial cells to cristobalite and α‐quartz exposures assessed by toxicoproteomics and gene expression analysis
Bibliographic record
Abstract
In this study, we used cytotoxicity assays, proteomic and gene expression analyses to examine the difference in response of A549 cells to two silica particles that differ in physical properties, namely cristobalite (CR) and α-quartz (Min-U-Sil 5, MI). Cytotoxicity assays such as lactate dehydrogenase release, 5-bromo-2'-deoxyuridine incorporation and cellular ATP showed that both silica particles could cause cell death, decreased cell proliferation and metabolism in the A549 human lung epithelial cells. While cytotoxicity assays revealed little difference between CR and MI exposures, proteomic and gene expression analyses unveiled both similar and unique molecular changes in A549 cells. For instance, two-dimensional gel electrophoresis data indicated that the expression of proteins in the cell death (e.g., ALDH1A1, HTRA2 and PRDX6) and cell proliferation (e.g., FSCN1, HNRNPAB and PGK1) pathways were significantly different between the two silica particles. Reverse transcription-polymerase chain reaction data provided additional evidence supporting the proteomic findings. Preliminary assessment of the physical differences between CR and MI suggested that the extent of surface interaction between particles and cells could explain some of the observed biological effects. However, the differential dose-response curves for some other genes and proteins suggest that other physical attributes of particulate matter can also contribute to particulate matter-related cellular toxicity. Our results demonstrated that toxicoproteomic and gene expression analyses are sensitive in distinguishing subtle toxicity differences associated with silica particles of varying physical properties compared to traditional cytotoxicity endpoints. Copyright © 2016 Her Majesty the Queen in Right of Canada. Journal of Applied Toxicology published by John Wiley & Sons, Ltd.
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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".