Identification of Gene Transcription Start Sites and Enhancers Responding to Pulmonary Carbon Nanotube Exposure <i>in Vivo</i>
Bibliographic record
Abstract
Increased use of nanomaterials in industry, medicine, and consumer products has raised concerns over their toxicity. To ensure safe use of nanomaterials, understanding their biological effects at the molecular level is crucial. In particular, the regulatory mechanisms responsible for the cascade of genes activated by nanomaterial exposure are not well-characterized. To this end, we profiled the genome-wide usage of gene transcription start sites and linked active enhancer regions in lungs of C57BL/6 mice 24 h after intratracheal instillation of a single dose of the multiwalled carbon nanotube (MWCNT) Mitsui-7. Our results revealed a massive gene regulatory response, where expression of key inflammatory genes (e.g., Csf3, Il24, and Fgf23) was increased >100-fold 24 h after Mitsui-7 exposure. Many of the Mitsui-7-responsive transcription start sites were alternative transcription start sites for known genes, and the number of alternative transcription start sites used in a given gene was correlated with overall Mitsui-7 response. Strikingly, genes that were up-regulated after Mitsui-7 exposure only through their main annotated transcription start site were linked to inflammatory and defense responses, while genes up-regulated only through alternative transcription start sites were functionally heterogeneous and not inflammation-associated. Furthermore, we identified almost 12 000 active enhancers, many of which were Mitsui-7-responsive, and we identified similarly responding putative target genes. Overall, our study provides the location and activity of Mitsui-7-induced enhancers and transcription start sites, providing a useful resource for targeted experiments elucidating the biological effects of nanomaterials and the identification of biomarkers for early detection of MWCNT-induced inflammation.
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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".