Depending on the stress, histone deacetylase inhibitors act as heat shock protein co-inducers in motor neurons and potentiate arimoclomol, exerting neuroprotection through multiple mechanisms in ALS models
Bibliographic record
Abstract
Upregulation of heat shock proteins (HSPs) is an approach to treatment of neurodegenerative disorders with impaired proteostasis . Many neurons, including motor neurons affected in amyotrophic lateral sclerosis (ALS), are relatively resistant to stress-induced upregulation of HSPs. This study demonstrated that histone deacetylase (HDAC) inhibitors enable the heat shock response in cultured spinal motor neurons , in a stress-dependent manner, and can improve the efficacy of HSP-inducing drugs in murine spinal cord cultures subjected to thermal or proteotoxic stress. The effect of particular HDAC inhibitors differed with the stress paradigm. The HDAC6 (class IIb) inhibitor, tubastatin A, acted as a co-inducer of Hsp70 (HSPA1A) expression with heat shock, but not with proteotoxic stress induced by expression of mutant SOD1 linked to familial ALS. Certain HDAC class I inhibitors (the pan inhibitor, SAHA , or the HDAC1/3 inhibitor, RGFP109) were HSP co-inducers comparable to the hydroxyamine arimoclomol in response to proteotoxic stress, but not thermal stress. Regardless, stress-induced Hsp70 expression could be enhanced by combining an HDAC inhibitor with either arimoclomol or with an HSP90 inhibitor that constitutively induced HSPs. HDAC inhibition failed to induce Hsp70 in motor neurons expressing ALS-linked mutant FUS, in which the heat shock response was suppressed; yet SAHA , RGFP109, and arimoclomol did reduce loss of nuclear FUS, a disease hallmark, and HDAC inhibition rescued the DNA repair response in iPSC-derived motor neurons carrying the FUS P525L mutation, pointing to multiple mechanisms of neuroprotection by both HDAC inhibiting drugs and arimoclomol.
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) | 0.001 | 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".