<i>In Vitro</i> Exposure of A549 and J774A.1 Cells to SiO<sub>2</sub> and TiO<sub>2</sub> Nanoforms and Related Cellular- and Molecular-Level Effects: Application of Proteomics
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide There is an emerging interest in incorporating proteomic data for environmental health risk assessments. Meanwhile, the production and use of engineered nanomaterials (ENMs) with attractive physicochemical properties are expanding with the potential for exposure, thus necessitating toxicity information on these materials for health risk analysis, where proteomic data can be informative. Here, cells (A549 human lung epithelial and J774A.1 mouse monocyte/macrophage cells) were exposed to ENMs (nanoforms of SiO 2 and TiO 2 ) of different sizes and surface chemistries (dose: 0–100 μg/cm 2, 24 h) for in vitro toxicity data. Cytotoxicity (CTB, ATP, and LDH), oxidative stress (GSH oxidation), and proteomic analysis (MS- and antibody-based) were conducted post-nanoparticle (NP) exposure to determine the relative potency and identify perturbed cellular pathways. Dose-, nanoform-, and cell type-specific cytotoxicity changes were observed upon exposure to both nanoSiO 2 and nanoTiO 2 . Size, agglomeration, surface modification, and metal impurities appeared to be the determinants of cytotoxicity. Proteomic analysis identified some enriched mechanistic pathways and biological processes relevant to cell defense/phagocytosis, stress, metabolism, apoptosis, and inflammatory processes in J774A.1 cells exposed to these NPs. A549 cells exhibited enriched pathway/biological processes relevant to transport/endocytosis, stress, metabolism, and inflammatory processes post-NP exposures. Concordance was observed between the nanoform exposure- and cell type-related cytotoxicity responses, notably cellular ATP, which is critical for cell viability, oxidative stress, and cellular pathways/biological processes. These findings demonstrate the application of proteomics in regulatory toxicology and warrant further research in this direction.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".