Proteomic changes in human lung epithelial cells (A549) in response to carbon black and titanium dioxide exposures
Bibliographic record
Abstract
This study combined cytotoxicity assays with proteomic analysis to characterize the unique biological responses of the A549 human lung epithelial cell line to two physicochemically distinct respirable particles titanium dioxide (TiO2) and carbon black (CB). Cellular LDH, ATP, BrdU incorporation and resazurin reduction indicated that CB was more potent than TiO2. Proteomic analysis was done using 2D-GE and MALDI-TOF-TOF-MS. Proteomic changes reflected common and particle-specific responses. Particle-specific proteomic responses were associated with cell death (necrosis and apoptosis), viability and proliferation pathways. Our results suggested that these pathways were consistent with the cytotoxicity data. For instance, increased expressions of anti-proliferative proteins LMNA and PA2G4 were in agreement with the decreased BrdU incorporation in A549 cells after exposure to CB. Similarly, increased expression of HSPA5 that is associated with ATPase activity was consistent with decreased cellular ATP levels in these cells. These findings reveal that proteomic changes can explain the cellular cytotoxicity characteristics of the particles. In essence, our results demonstrate that the in vitro toxicoproteomic approach is a promising tool to gain insight into molecular mechanisms underlying particle exposure-specific cytotoxicity. In this study we have shown that toxicoproteomics is a sensitive and informative method to resolve the toxicity characteristics of particles with different physicochemical properties. This approach can be useful in the investigation of molecular mechanisms underpinning cellular cytotoxic responses elicited by particle exposures. Thus, the toxicoproteomic approach can be valuable in assessing the risk associated with particle exposures in vitro.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".