The unfolded protein response transcription factor XBP1s ameliorates Alzheimer’s disease by improving synaptic function and proteostasis
Bibliographic record
Abstract
Alteration in the buffering capacity of the proteostasis network is an emerging feature of Alzheimer's disease (AD), highlighting the occurrence of endoplasmic reticulum (ER) stress. The unfolded protein response (UPR) is the main adaptive pathway to cope with protein folding stress at the ER. Inositol-requiring enzyme-1 (IRE1) operates as a central ER stress sensor, enabling the establishment of adaptive and repair programs through the control of the expression of the transcription factor X-box binding protein 1 (XBP1). To artificially enforce the adaptive capacity of the UPR in the AD brain, we developed strategies to express the active form of XBP1 in the brain. Overexpression of XBP1 in the nervous system using transgenic mice reduced the load of amyloid deposits and preserved synaptic and cognitive function. Moreover, local delivery of XBP1 into the hippocampus of an 5xFAD mice using adeno-associated vectors improved different AD features. XBP1 expression corrected a large proportion of the proteomic alterations observed in the AD model, restoring the levels of several synaptic proteins and factors involved in actin cytoskeleton regulation and axonal growth. Our results illustrate the therapeutic potential of targeting UPR-dependent gene expression programs as a strategy to ameliorate AD features and sustain synaptic function.
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".