Big Data Analysis of Genes Associated With Neuropsychiatric Disorders in an Alzheimer's Disease Animal Model

Two-sample Mendelian randomization analysis of 91 circulating inflammatory protein levels and amyotrophic lateral sclerosis

Introduction

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood pathophysiology. Recent studies have highlighted systemic inflammation, especially the role of circulating inflammatory proteins, in ALS.

Methods

This study investigates the potential causal link between these proteins and ALS. We employed a two-sample Mendelian Randomization(MR) approach, analyzing data from large-scale genome-wide association studies to explore the relationship between 91 circulating inflammatory proteins and ALS. This included various MR methods like MR Egger, weighted median, and inverse-variance weighted, complemented by sensitivity analyses for robust results.

Results

Significant associations were observed between levels of inflammatory proteins, including Adenosine Deaminase, Interleukin-17C, Oncostatin-M, Leukemia Inhibitory Factor Receptor, and Osteoprotegerin, and ALS risk. Consistencies were noted across different P-value thresholds. Bidirectional MR suggested that ALS risk might influence levels of certain inflammatory proteins.

Discussion

Our findings, via MR analysis, indicate a potential causal relationship between circulating inflammatory proteins and ALS. This sheds new light on ALS pathophysiology and suggests possible therapeutic targets. Further research is required to confirm these results and understand the specific roles of these proteins in ALS.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Anti-Aβ Antibody Aducanumab Regulates the Proteome of Senile Plaques and Closely Surrounding Tissue in a Transgenic Mouse Model of Alzheimer’s Disease

Background: Alzheimer’s disease (AD) is characterized by accumulation of amyloid-β (Aβ) species and deposition of senile plaques (SPs). Clinical trials with the anti-Aβ antibody aducanumab have been completed recently. Objective: To characterize the proteomic profile of SPs and surrounding tissue in a mouse model of AD in 10-month-old tgAPPPS1-21 mice after chronic treatment with aducanumab for four months with weekly dosing (10 mg/kg). Methods: After observing significant reduction of SP numbers in hippocampi of aducanumab-treated mice, we applied a localized proteomic analysis by combining laser microdissection and liquid chromatography-tandem mass spectrometry (LC-MS/MS) of the remaining SPs in hippocampi. We microdissected three subregions, containing SPs, SP penumbra level 1, and an additional penumbra level 2 to follow the proteomic profile as gradient. Results: In the aducanumab-treated mice, we identified 17 significantly regulated proteins that were associated with 1) mitochondria and metabolism (ACAT2, ATP5J, ETFA, EXOG, HK1, NDUFA4, NDUFS7, PLCB1, PPP2R4), 2) cytoskeleton and axons (ADD1, CAPZB, DPYSL3, MAG), 3) stress response (HIST1H1C/HIST1H1D, HSPA12A), and 4) AβPP trafficking/processing (CD81, GDI2). These pathways and some of the identified proteins are implicated in AD pathogenesis. Proteins associated with mitochondria and metabolism were mainly upregulated while proteins associated with AβPP trafficking/processing and stress response pathways were mainly downregulated, suggesting that aducanumab could lead to a beneficial proteomic profile around SPs in tgAPPPS1-21 mice. Conclusion: We identified novel proteomic patterns of SPs and surrounding tissue indicating that chronic treatment with aducanumab could inhibit Aβ toxicity and increase phagocytosis and cell viability.

Target Genes of Autism Risk Loci in Brain Frontal Cortex

Autism spectrum disorder (ASD) is a complex neuropsychiatric disorder. A number of genetic risk loci have been identified for ASD from genome-wide association studies (GWAS); however, their target genes in relevant tissues and cell types remain to be investigated. The frontal cortex is a key region in the human brain for communication and cognitive function. To identify risk genes contributing to potential dysfunction in the frontal cortex of ASD patients, we took an in silico approach integrating multi-omics data. We first found genes with expression in frontal cortex tissue that correlates with ASD risk loci by leveraging expression quantitative trait loci (eQTLs) information. Among these genes, we then identified 76 genes showing significant differential expression in the frontal cortex between ASD cases and controls in microarray datasets and further replicated four genes with RNA-seq data. Among the ASD GWAS single nucleotide polymorphisms (SNPs) correlating with the 76 genes, 20 overlap with histone marks and 40 are associated with gene methylation level. Thus, through multi-omics data analyses, we identified genes that may work as target genes of ASD risk loci in the brain frontal cortex.

Comparative study of microarray and experimental data on Schwann cells in peripheral nerve degeneration and regeneration: big data analysis

A Schwann cell has regenerative capabilities and is an important cell in the peripheral nervous system. This microarray study is part of a bioinformatics study that focuses mainly on Schwann cells. Microarray data provide information on differences between microarray-based and experiment-based gene expression analyses. According to microarray data, several genes exhibit increased expression (fold change) but they are weakly expressed in experimental studies (based on morphology, protein and mRNA levels). In contrast, some genes are weakly expressed in microarray data and highly expressed in experimental studies; such genes may represent future target genes in Schwann cell studies. These studies allow us to learn about additional genes that could be used to achieve targeted results from experimental studies. In the current big data study by retrieving more than 5000 scientific articles from PubMed or NCBI, Google Scholar, and Google, 1016 (up- and downregulated) genes were determined to be related to Schwann cells. However, no experiment was performed in the laboratory; rather, the present study is part of a big data analysis. Our study will contribute to our understanding of Schwann cell biology by aiding in the identification of genes. Based on a comparative analysis of all microarray data, we conclude that the microarray could be a good tool for predicting the expression and intensity of different genes of interest in actual experiments.

Comprehensive MicroRNAome Analysis of the Relationship Between Alzheimer Disease and Cancer in PSEN Double-Knockout Mice

Purpose

Presenilins are functionally important components of γ-secretase, which cleaves a number of transmembrane proteins. Manipulations of PSEN1 and PSEN2 have been separately studied in Alzheimer disease (AD) and cancer because both involve substrates of γ-secretase. However, numerous clinical studies have reported an inverse correlation between AD and cancer. Interestingly, AD is a neurodegenerative disorder, whereas cancer is characterized by the proliferation of malignant cells. However, this inverse correlation in the PSEN double-knockout (PSEN dKO) mouse model of AD has been not elucidated, although doing so would shed light onto the relationship between AD and cancer.

Methods

To investigate the inverse relationship of AD and cancer under conditions of PSEN loss, we used the hippocampus of 7-month-old and 18-month-old PSEN dKO mice for a microRNA (miRNA) microarray analysis, and explored the tumorsuppressive or oncogenic role of differentially-expressed miRNAs.

Results

The total number of miRNAs that showed changes in expression level was greater at 18 months of age than at 7 months. Most of the putative target genes of the differentially-expressed miRNAs involved Cancer pathways.

Conclusions

Based on literature reviews, many of the miRNAs involved in Cancer pathways were found to be known tumorsuppressive miRNAs, and their target genes were known or putative oncogenes. In conclusion, the expression levels of known tumor-suppressive miRNAs increased at 7 and 18 months, in the PSEN dKO mouse model of AD, supporting the negative correlation between AD and cancer.