Induction of heat shock proteins in differentiated human neuronal cells following co-application of celastrol and arimoclomol
Bibliographic record
Abstract
Few effective therapies exist for the treatment of neurodegenerative diseases that have been characterized as protein misfolding disorders. Upregulation of heat shock proteins (Hsps) mitigates against the accumulation of misfolded, aggregation-prone proteins and synaptic dysfunction, which is recognized as an early event in neurodegenerative diseases. Enhanced induction of a set of Hsps in differentiated human SH-SY5Y neuronal cells was observed following co-application of celastrol and arimoclomol, compared to their individual application. The dosages employed did not affect cell viability or neuronal process morphology. The induced Hsps included the little studied HSPA6 (Hsp70B'), a potentially neuroprotective protein that is present in the human genome but not in rat and mouse and hence is missing in current animal models of neurodegenerative disease. Enhanced induction of HSPA1A (Hsp70-1), DNAJB1 (Hsp40), HO-1 (Hsp32), and HSPB1 (Hsp27) was also observed. Celastrol activates heat shock transcription factor 1 (HSF1), the master regulator of Hsp gene transcription, and also exhibits potent anti-inflammatory and anti-oxidant activities. Arimoclomol is a co-activator that prolongs the binding of activated HSF1 to heat shock elements (HSEs) in the promoter regions of inducible Hsp genes. Elevated Hsp levels peaked at 10 to 12 h for HSPA6, HSPA1A, DNAJB1, and HO-1 and at 24 h for HSPB1. Co-application of celastrol and arimoclomol induced higher Hsp levels compared to heat shock paired with arimoclomol. The co-application strategy of celastrol and arimoclomol targets multiple neurodegenerative disease-associated pathologies including protein misfolding and protein aggregation, inflammatory and oxidative stress, and synaptic dysfunction.
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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 | 0.001 |
| 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".