Gold nanourchins and celastrol reorganize the nucleo- and cytoskeleton of glioblastoma cells
Bibliographic record
Abstract
The physicochemical properties and cytotoxicity of diverse gold nanoparticle (AuNP) morphologies with smooth surfaces have been examined extensively. Much less is known about AuNPs with irregular surfaces. This study focuses on the effects of gold nanourchins in glioblastoma cells. With limited success of monotherapies for glioblastoma, multimodal treatment has become the preferred regimen. One possible example for such future therapeutic applications is the combination of AuNPs with the natural cytotoxic agent celastrol. Here, we used complementary physical, chemical and biological methods to characterize AuNPs and investigate their impact on glioblastoma cells. Our results show that gold nanourchins altered glioblastoma cell morphology and reorganized the nucleo- and cytoskeleton. These changes were dependent on gold nanourchin surface modification. PEGylated nanourchins had no significant effect on glioblastoma cell morphology or viability, unless they were combined with celastrol. By contrast, CTAB-nanourchins adversely affected the nuclear lamina, microtubules and filamentous actin. These alterations correlated with significant glioblastoma cell death. We identified several mechanisms that contributed to the impact of AuNPs on the cytoskeleton and cell survival. Specifically, CTAB-nanourchins caused a significant increase in the abundance of Rock1. This protein kinase is a key regulator of the cytoskeleton. In addition, CTAB-nanourchins led to a marked decline in pro-survival signaling via the PI3 kinase-Akt pathway. Taken together, our study provides new insights into the molecular pathways and structural components altered by gold nanourchins and their implications for multimodal glioblastoma therapy.
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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".