Transcriptome Sequencing (RNA-seq) Analysis of the Effects of Metal Nanoparticle Exposure on the Transcriptome of Chlamydomonas reinhardtii
Bibliographic record
Abstract
The widespread use of nanoparticles (NPs) raises concern over their potential toxicological effects in humans and ecosystems. Here we used transcriptome sequencing (RNA-seq) to evaluate the effects of exposure to four different metal-based NPs, nano-Ag (nAg), nano-TiO2 (nTiO2), nano-ZnO (nZnO), and CdTe/CdS quantum dots (QDs), in the eukaryotic green alga Chlamydomonas reinhardtii. The transcriptome was characterized before and after exposure to each NP type. Specific toxicological effects were inferred from the functions of genes whose transcripts either increased or decreased. Data analysis resulted in important differences and also similarities among the NPs. Elevated levels of transcripts of several marker genes for stress were observed, suggesting that only nZnO caused nonspecific global stress to the cells under environmentally relevant conditions. Genes with photosynthesis-related functions were decreased drastically during exposure to nTiO2 and slightly during exposures to the other NP types. This pattern suggests either toxicological effects in the chloroplast or effects that mimic a transition from low to high light. nAg exposure dramatically elevated the levels of transcripts encoding known or predicted components of the cell wall and the flagella, suggesting that it damages structures exposed to the external milieu. Exposures to nTiO2, nZnO, and QDs elevated the levels of transcripts encoding subunits of the proteasome, suggesting proteasome inhibition, a phenomenon believed to underlie the development and progression of several major diseases, including Alzheimer's disease, and used in chemotherapy against multiple myeloma.
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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 | 0.001 |
| 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".