Distinct autophagy-inducing abilities of similar-sized nanoparticles in cell culture and live <i>C. elegans</i>
Bibliographic record
Abstract
Nanomaterial-induced autophagy has raised increasing concerns. A variety of nanomaterials, conventional or recently emerged, have the capability of inducing autophagy. As a consequence, it is becoming a popular belief that induction of autophagy is a common response of cells upon exposure to nanoscale materials. In order to clarify whether the "nanoscale" size is the determining factor for the nanomaterials to induce autophagy, we utilized in vitro cultured cells and an in vivo Caenorhabditis elegans (C. elegans) model to systemically investigate the autophagy-inducing ability of nanomaterials. We selected four types of representative nanomaterials with similar sizes, namely silicon nanoparticles (SiNPs), CdTe quantum dots (QDs), carbon dots (CDs) and gold nanoparticles (AuNPs). We demonstrated that, unlike most other nanomaterials tested, no autophagosome formation was detected in cultured cells or in live C. elegans with SiNP treatment. The expression of autophagy-related genes and the lipidation of LGG-1/LC3 in cells and C. elegans also remained unchanged after the treatment of SiNPs. In addition, the ability of the nanomaterials to induce autophagy appeared to correlate with those to incur subcellular organelle damage. Together, our studies demonstrate that SiNPs do not induce autophagy in vitro or in vivo in the selected model organisms and cell lines, thus clarifying that the "nanoscale" size is not the determining factor for the nanomaterials to induce autophagy. The results also suggest that the autophagy-inducing ability of most nanomaterials could be merely a reflection of their detrimental effect on cellular structures.
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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) | 0.000 | 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 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".