Synaptic Vesicle Protein 2B Negatively Regulates the Amyloidogenic Processing of AβPP as a Novel Interaction Partner of BACE1

Genetic architecture of hippocampus subfields volumes in Alzheimer's disease

BACKGROUND

The hippocampus is a heterogeneous structure, comprising histologically and functionally distinguishable hippocampal subfields. The volume reductions in hippocampal subfields have been demonstrated to be linked with Alzheimer's disease (AD). The aim of our study is to investigate the hippocampal subfields' genetic architecture based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set.

METHODS

After preprocessing the downloaded genetic variants and imaging data from the ADNI database, a co-sparse reduced rank regression model was applied to analyze the genetic architecture of hippocampal subfields volumes. Homology modeling, docking, molecular dynamics simulations, and Co-IP experiments for protein-protein interactions were used to verify the function of target protein on hippocampal subfields successively. After that, the association analysis between the candidated genes on the hippocampal subfields volume and clinical scales were performed.

RESULTS

The results of the association analysis revealed five unique genetic variants (e.g., ubiquitin-specific protease 10 [USP10]) changed in nine hippocampal subfields (e.g., the granule cell and molecular layer of the dentate gyrus [GC-ML-DG]). Among five genetic variants, USP10 had the strongest interaction effect with BACE1, which affected hippocampal subfields verified by MD and Co-IP experiments. The results of association analysis between the candidated genes on the hippocampal subfields volume and clinical scales showed that candidated genes influenced the volume and function of hippocampal subfields.

CONCLUSIONS

Current evidence suggests that hippocampal subfields have partly distinct genetic architecture and may improve the sensitivity of the detection of AD.

Acute inhibition of AMPA receptors by perampanel reduces amyloid β-protein levels by suppressing β-cleavage of APP in Alzheimer's disease models

Hippocampal hyperexcitability is a promising therapeutic target to prevent Aβ deposition in AD since enhanced neuronal activity promotes presynaptic Aβ production and release. This article highlights the potential application of perampanel (PER), an AMPA receptor (AMPAR) antagonist approved for partial seizures, as a therapeutic agent for AD. Using transgenic AD mice combined with in vivo brain microdialysis and primary neurons under oligomeric Aβ-evoked neuronal hyperexcitability, the acute effects of PER on Aβ metabolism were investigated. A single oral administration of PER rapidly decreased ISF Aβ40 and Aβ42 levels in the hippocampus of J20, APP transgenic mice, without affecting the Aβ40 /Aβ42 ratio; 5 mg/kg PER resulted in declines of 20% and 31%, respectively. Moreover, PER-treated J20 manifested a marked decrease in hippocampal APP βCTF levels with increased FL-APP levels. Consistently, acute treatment of PER reduced sAPPβ levels, a direct byproduct of β-cleavage of APP, released to the medium in primary neuronal cultures under oligomeric Aβ-induced neuronal hyperexcitability. To further evaluate the effect of PER on ISF Aβ clearance, a γ-secretase inhibitor was administered to J20 1 h after PER treatment. PER did not influence the elimination of ISF Aβ, indicating that the acute effect of PER is predominantly on Aβ production. In conclusion, acute treatment of PER reduces Aβ production by suppressing β-cleavage of amyloid-β precursor protein effectively, indicating a potential effect of PER against Aβ pathology in AD.

Changes of tRNA-Derived Fragments by Alzheimer's Disease in Cerebrospinal Fluid and Blood Serum

BACKGROUND

Alzheimer's disease (AD) is the most common type of dementia, affecting individuals over 65. AD is also a multifactorial disease, with disease mechanisms incompletely characterized, and disease-modifying therapies are marginally effective. Biomarker signatures may shed light on the diagnosis, disease mechanisms, and the development of therapeutic targets. tRNA-derived RNA fragments (tRFs), a family of recently discovered small non-coding RNAs, have been found to be significantly enhanced in human AD hippocampus tissues. However, whether tRFs change in body fluids is unknown.

OBJECTIVE

To investigate whether tRFs in body fluids are impacted by AD.

METHODS

We first used T4 polynucleotide kinase-RNA-seq, a modified next-generation sequencing technique, to identify detectable tRFs in human cerebrospinal fluid and serum samples. The detectable tRFs were then compared in these fluids from control, AD, and mild cognitive impairment patients using tRF qRT-PCR. The stability of tRFs in serum was also investigated by checking the change in tRFs in response to protein digestion or exosome lysis.

RESULTS

Among various tRFs, tRF5-ProAGG seemed to be impacted by AD in both cerebrospinal fluid and serum. AD-impacted serum tRF5-ProAGG showed a correlation with the AD stage. Putative targets of tRF5-ProAGG in the hippocampus were also predicted by a computational algorithm, with some targets being validated experimentally and one of them being in a negative correlation with tRF5-ProAGG even using a small size of samples.

CONCLUSIONS

tRF5-ProAGG showed the potential as an AD biomarker and may play a role in disease progression.

ADeditome provides the genomic landscape of A-to-I RNA editing in Alzheimer's disease

A-to-I RNA editing, contributing to nearly 90% of all editing events in human, has been reported to involve in the pathogenesis of Alzheimer's disease (AD) due to its roles in brain development and immune regulation, such as the deficient editing of GluA2 Q/R related to cell death and memory loss. Currently, there are urgent needs for the systematic annotations of A-to-I RNA editing events in AD. Here, we built ADeditome, the annotation database of A-to-I RNA editing in AD available at https://ccsm.uth.edu/ADeditome, aiming to provide a resource and reference for functional annotation of A-to-I RNA editing in AD to identify therapeutically targetable genes in an individual. We detected 1676 363 editing sites in 1524 samples across nine brain regions from ROSMAP, MayoRNAseq and MSBB. For these editing events, we performed multiple functional annotations including identification of specific and disease stage associated editing events and the influence of editing events on gene expression, protein recoding, alternative splicing and miRNA regulation for all the genes, especially for AD-related genes in order to explore the pathology of AD. Combing all the analysis results, we found 108 010 and 26 168 editing events which may promote or inhibit AD progression, respectively. We also found 5582 brain region-specific editing events with potentially dual roles in AD across different brain regions. ADeditome will be a unique resource for AD and drug research communities to identify therapeutically targetable editing events. Significance: ADeditome is the first comprehensive resource of the functional genomics of individual A-to-I RNA editing events in AD, which will be useful for many researchers in the fields of AD pathology, precision medicine, and therapeutic researches.

Bioinformatics analysis of potential core genes for glioblastoma

Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes.

Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool.

Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis.

Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.