Evidence map›Paper›PMID 42643736›Full record

ReviewFrontiers in molecular biosciences2026

Adaptive-to-maladaptive IRE1α signaling as a driver of amyloidogenic APP processing in Alzheimer's disease.

Daniel Wang, Qinan Yin

Abstract readReview
In one paragraph

Review in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Daniel WangBullis School, Potomac, MD, United States.
Qinan YinDepartment of Radiation Oncology, Cancer Institute, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is increasingly recognized as a disorder of proteostatic failure characterized by progressive disruption of neuronal protein quality control, culminating in amyloid-β (Aβ) accumulation and synaptic dysfunction. Chronic activation of the endoplasmic reticulum (ER) stress response represents one of the earliest molecular alterations detected in vulnerable brain regions and correlates with Braak progression before overt plaque deposition. Inositol-requiring enzyme 1 alpha (IRE1α), the most evolutionarily conserved sensor of the unfolded protein response (UPR), functions as a signaling rheostat within this network through its divergent downstream outputs. Under moderate proteotoxic stress, adaptive IRE1α- X-box binding protein 1 (XBP1) signaling supports ER proteostasis, preserves amyloid precursor protein (APP) quality control, and favors non-amyloidogenic α-secretase processing. Persistent ER stress, however, drives sustained IRE1α hyperactivation and engages regulated IRE1α-dependent decay (RIDD), which destabilizes microRNA (miRNA) networks that normally constrain beta-site APP-cleaving enzyme 1 (BACE1) expression, thereby favoring amyloidogenic APP processing. Accumulating evidence suggests that aging progressively compromises ER proteostatic capacity, thereby redirecting IRE1α signaling away from adaptive XBP1s-mediated responses toward a predominantly RIDD-driven state. This shift may reinforce a self-sustaining cycle in which accumulating Aβ further amplifies ER stress signaling. Here, we examine how dynamic changes in IRE1α signaling bias contribute to amyloidogenic progression in AD and consider whether selective modulation of adaptive versus maladaptive IRE1α outputs may offer stage-dependent therapeutic benefit.

Indexed as

Alzheimer’s diseaseamyloid-βAPP processingBACE1ER stressIRE1αRIDDXBP1s

Identifiers

PMID42643736
PMCPMC13503739

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.