Gold nanoclusters elicit homeostatic perturbations in glioblastoma cells and adaptive changes of lysosomes
Bibliographic record
Abstract
Unique physicochemical features place gold nanoclusters at the forefront of nanotechnology for biological and biomedical applications.To date, information on the interactions of gold nanoclusters with biological macromolecules is limited and restricts their use in living cells.Methods: Our multidisciplinary study begins to fill the current knowledge gap by focusing on lysosomes and associated biological pathways in U251N human glioblastoma cells.We concentrated on lysosomes, because they are the intracellular destination for many nanoparticles, regulate cellular homeostasis and control cell survival.Results: Quantitative data presented here show that gold nanoclusters (with 15 and 25 gold atoms), surface-modified with glutathione or PEG, did not diminish cell viability at concentrations ≤1 µM.However, even at sublethal concentrations, gold nanoclusters modulated the abundance, positioning, pH and enzymatic activities of lysosomes.Gold nanoclusters also affected other aspects of cellular homeostasis.Specifically, they stimulated the transient nuclear accumulation of TFEB and Nrf2, transcription factors that promote lysosome biogenesis and stress responses.Moreover, gold nanoclusters also altered the formation of protein aggregates in the cytoplasm.The cellular responses elicited by gold nanoclusters were largely reversible within a 24-hour period.Conclusions: Taken together, this study explores the subcellular and molecular effects induced by gold nanoclusters and shows their effectiveness to regulate lysosome biology.Our results indicate that gold nanoclusters cause homeostatic perturbations without marked cell loss.Notably, cells adapt to the challenge inflicted by gold nanoclusters.These new insights provide a framework for the further development of gold nanocluster-based applications in biological sciences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".