Modulation of Amyloid-β Protein Precursor Expression by HspB1
Bibliographic record
Abstract
Upregulation of heat shock proteins, such as Hsp70 and HspB1/Hsp27, have been associated with an amelioration of the deficits in animal models of Alzheimer's disease (AD). HspB1 is reported to be increased in AD brains and to accumulate in plaques, but whether this localization is an attempt by HspB1 to ameliorate the detrimental effects of amyloid-β (Aβ) on cells or part of the disease process is unknown. Here we explore the potential effects of the HspB1 on amyloid-β protein precursor (AβPP) processing and distribution within HEK293 stable cell lines expressing either AβPPwt or AβPPsw. We compare AβPP production, distribution, and release of proteolytic products (including Aβ40 and Aβ42) to determine possible modifications in the presence of HspB1. We also investigate whether HspB1 interacts with Aβ or its precursor, AβPP, and whether, through this interaction, it is able to alter AβPP processing or release of Aβ peptide. Coexpression of HspB1 resulted in increased cellular holoAβPP as well as C-terminal fragments. Further, expression of HspB1 attenuated the release of Aβ42 from the AβPPsw cells. In summary, we have shown that expression of HspB1 alters AβPP expression and processing in cell lines expressing AβPPwt and AβPPsw. Furthermore, the presence of HspB1 decreased the amount of Aβ42 released by the cell lines. Thus in addition to its effects on protecting cells from the potentially toxic effects of Aβ, HspB1 also appears to be involved in modulating cellular levels of AβPP, although an understanding of the underlying mechanisms requires further investigation.
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 | 0.001 |
| 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".