Transcriptional profiling identifies physicochemical properties of nanomaterials that are determinants of the in vivo pulmonary response
Bibliographic record
Abstract
We applied transcriptional profiling to elucidate the mechanisms associated with pulmonary responses to titanium dioxide (TiO2 ) nanoparticles (NPs) of different sizes and surface coatings, and to determine if these responses are modified by NP size, surface area, surface modification, and embedding in paint matrices. Adult C57BL/6 mice were exposed via single intratracheal instillations to free forms of TiO2 NPs (10, 20.6, or 38 nm in diameter) with different surface coatings, or TiO2 NPs embedded in paint matrices. Controls were exposed to dispersion medium devoid of NPs. TiO2 NPs were characterized for size, surface area, chemical impurities, and agglomeration state in the exposure medium. Pulmonary transcriptional profiles were generated using microarrays from tissues collected one and 28 d postexposure. Property-specific pathway effects were identified. Pulmonary protein levels of specific inflammatory cytokines and chemokines were confirmed by ELISA. The data were collapsed to 659 differentially expressed genes (P ≤ 0.05; fold change ≥ 1.5). Unsupervised hierarchical clustering of these genes revealed that TiO2 NPs clustered mainly by postexposure timepoint followed by particle type. A pathway-based meta-analysis showed that the combination of smaller size, large deposited surface area, and surface amidation contributes to TiO2 NP gene expression response. Embedding of TiO2 NP in paint dampens the overall transcriptional effects. The magnitude of the expression changes associated with pulmonary inflammation differed across all particles; however, the underlying pathway perturbations leading to inflammation were similar, suggesting a generalized mechanism-of-action for all TiO2 NPs. Thus, transcriptional profiling is an effective tool to determine the property-specific biological/toxicity responses induced by nanomaterials.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".