ArticleCurrent Alzheimer research2025
An
Article in Current Alzheimer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlzheimer's disease (AD) is a neurodegenerative disorder characterized by synaptic dysfunction and the accumulation of amyloid plaques. The molecular mechanisms linking gene dysregulation, pathogenic variants, and protein interaction networks to these core pathologies remain incompletely understood. This study aimed to integrate transcriptomic data with mutation and structural modeling to uncover disease mechanisms and identify therapeutic targets.
methodsWe performed differential gene expression analysis on the GSE138260 microarray dataset using GEO2R to identify DEGs in AD brain tissue. Missense mutations in DEGs were retrieved from the Alzheimer's Disease Variant Portal (ADVP). Protein-protein interaction networks were constructed using the STRING database to identify connections with the amyloid precursor protein (APP). Molecular dynamics simulations were conducted to evaluate the structural consequences of the BDNF V66M mutation.
resultsA total of 1,588 DEGs were identified, including upregulation of immune-related genes and downregulation of neuroplasticity-associated genes (e.g., BDNF, GRIN2B, GRM8). PPI analysis revealed a core network centered on APP, including BDNF as a direct interactor. The V66M variant in BDNF, confirmed to be downregulated in AD brains, showed increased rigidity and localized flexibility in structural models. DISCUSSION: The integration of transcriptomics and protein modeling revealed a critical link between BDNF dysfunction and APP interaction in AD. The V66M mutation was found to structurally alter BDNF, potentially disrupting its neuroprotective roles. The findings suggested that impaired BDNF signaling, driven by transcriptional repression and structural mutation, contributes to amyloid pathology and synaptic failure.
conclusionThis multi-omics investigation has identified BDNF as a converging point of gene dysregulation and pathogenic mutation within an APP-centric network. Structural alterations induced by the V66M mutation may exacerbate amyloid accumulation and neuronal dysfunction, supporting therapeutic strategies aimed at enhancing BDNF signaling in AD.
Indexed as
Identifiers
41116277What OpenQuestion holds
Registered trials
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.