Integrated high-throughput small RNA and transcriptome sequencing unveil the shape-dependent toxicity of nano-alumina in rat astrocytes
Bibliographic record
Abstract
Abstract Background The large-scale applications of alumina nanoparticles (Al2O3-NPs), one of the most important NPs in the global market, is causing severe damages to the environment and human health. Our previous research has revealed a critical role of nanoparticle morphology (e.g., flake and rod) in determining the toxic potencies of Al2O3-NPs, where nanorods demonstrated a significantly stronger toxic response than that of nanoflakes. However, their underlying mechanisms have not been completely elucidated yet. In the present study, we evaluated and compared the potential toxicological mechanisms of two shapes of γ-Al2O3-NPs (flake versus rod) by measuring miRNAs and mRNAs profiles of astrocytes in rat cerebral cortex, ex vivo. Results Totals of 269 mRNAs and 122 miRNAs, 180 mRNAs and 116 miRNAs were differentially expressed after nanoflakes or nanorods exposure, respectively. Among them, 55 miRNAs (e.g., miR-760-5p, miR-326-3p, and miR-35) and 105 mRNAs (e.g., Kdm4d, Wdr62, and Rps6) showed the same trend between the two shapes. These miRNAs and mRNAs were mainly involved in apoptosis, inflammatory pathways (e.g., NF-kappa B), carcinogenic pathways (e.g., MAPK, p53, Notch, Rap1, and Ras), and cellular lipid metabolisms (e.g., glycerolipid metabolism, sphingolipid, and ether lipid metabolism). However, the remaining miRNAs and mRNAs either showed an opposite trend or only changed by a particular shape. Nanorods could specifically alter the changes of PI3K/Akt, AMPK and TNF pathways, cell cycle, and cellular senescence, while nanoflakes caused the changes of Toll and lmd signaling pathways. Conclusions In this study, we demonstrate that the toxicity of nanorods might be stronger than that of nanoflakes. And this study also demonstrates the critical role of morphology in nanotoxicity of nano-alumina and reveals its potential biomolecular mechanisms.
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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 | 0.000 | 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) | 0.001 | 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".