Dataset Information

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

ABSTRACT: Alzheimer's disease is a neurodegenerative disease characterized by the impairment of cognitive function and loss of memory, affecting millions of individuals worldwide. With the dramatic increase in the prevalence of Alzheimer's disease, it is expected to impose extensive public health and economic burden. However, this burden is particularly heavy on the caregivers of Alzheimer's disease patients eliciting neuropsychiatric symptoms that include mood swings, hallucinations, and depression. Interestingly, these neuropsychiatric symptoms are shared across symptoms of bipolar disorder, schizophrenia, and major depression disorder. Despite the similarities in symptomatology, comorbidities of Alzheimer's disease and these neuropsychiatric disorders have not been studied in the Alzheimer's disease model. Here, we explore the comprehensive changes in gene expression of genes that are associated with bipolar disorder, schizophrenia, and major depression disorder through the microarray of an Alzheimer's disease animal model, the forebrain specific PSEN double knockout mouse. To analyze the genes related with these three neuropsychiatric disorders within the scope of our microarray data, we used selected 1207 of a total of 45,037 genes that satisfied our selection criteria. These genes were selected on the basis of 14 Gene Ontology terms significantly relevant with the three disorders which were identified by previous research conducted by the Psychiatric Genomics Consortium. Our study revealed that the forebrain specific deletion of Alzheimer's disease genes can significantly alter neuropsychiatric disorder associated genes. Most importantly, most of these significantly altered genes were found to be involved with schizophrenia. Taken together, we suggest that the synaptic dysfunction by mutation of Alzheimer's disease genes can lead to the manifestation of not only memory loss and impairments in cognition, but also neuropsychiatric symptoms.

SUBMITTER: Ham S

PROVIDER: S-EPMC6013555 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Ham Suji S Kim Tae K TK Hong Heeok H Kim Yong S YS Tang Ya-Ping YP Im Heh-In HI

Frontiers in neuroscience 20180615

Alzheimer's disease is a neurodegenerative disease characterized by the impairment of cognitive function and loss of memory, affecting millions of individuals worldwide. With the dramatic increase in the prevalence of Alzheimer's disease, it is expected to impose extensive public health and economic burden. However, this burden is particularly heavy on the caregivers of Alzheimer's disease patients eliciting neuropsychiatric symptoms that include mood swings, hallucinations, and depression. Inte ...[more]

PMID: 29962931

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Project description:OBJECTIVE:Neurodevelopmental disorders presumably involve events that occur during brain development. The authors hypothesized that neuropsychiatric disorders considered to be developmental in etiology are associated with susceptibility genes that are relatively upregulated during fetal life (i.e., differentially expressed). METHOD:The authors investigated the presence of prenatal expression enrichment of susceptibility genes systematically, as composite gene sets associated with six neuropsychiatric disorders in the microarray-based "BrainCloud" dorsolateral prefrontal cortex transcriptome. RESULTS:Using a fetal/postnatal log2-fold change threshold of 0.5, genes associated with syndromic neurodevelopmental disorders (N=31 genes, p=3.37×10-3), intellectual disability (N=88 genes, p=5.53×10-3), and autism spectrum disorder (N=242 genes, p=3.45×10-4) were relatively enriched in prenatal transcript abundance, compared with the overall transcriptome. Genes associated with schizophrenia by genome-wide association studies were not preferentially fetally expressed (N=106 genes, p=0.46), nor were genes associated with schizophrenia by exome sequencing (N=212 genes, p=0.21), but specific genes within copy-number variant regions associated with schizophrenia were relatively enriched in prenatal transcript abundance, and genes associated with schizophrenia by meta-analysis were functionally enriched for some neurodevelopmental processes. In contrast, genes associated with neurodegenerative disorders were significantly underexpressed during fetal life (N=46 genes, p=1.67×10-3). CONCLUSIONS:The authors found evidence for relative prenatal enrichment of putative susceptibility genes for syndromic neurodevelopmental disorders, intellectual disability, and autism spectrum disorder. Future transcriptome-level association studies should evaluate regions other than the dorsolateral prefrontal cortex, at other time points, and incorporate further RNA sequencing analyses.

Project description:New research on assessing neuropsychiatric manifestations of Alzheimer´s Disease (AD) involves grouping neuropsychiatric symptoms into syndromes. Yet this approach is limited by high inter-subject variability in neuropsychiatric symptoms and a relatively low degree of concordance across studies attempting to cluster neuropsychiatric symptoms into syndromes. An alternative strategy that involves dichotomizing AD subjects into those with few versus multiple neuropsychiatric symptoms is both consonant with real-world clinical practice and can contribute to understanding neurobiological underpinnings of neuropsychiatric symptoms in AD patients. The aim of this study was to address whether the number of neuropsychiatric symptoms (i.e., presence of few [≤2] versus multiple [≥3] symptoms) in AD would be associated with degree of significant gray matter (GM) volume loss. Of particular interest was volume loss in brain regions involved in memory, emotional processing and salience brain networks, including the prefrontal, lateral temporal and parietal cortices, anterior cingulate gyrus, temporo-limbic structures and insula. We recruited 19 AD patients and 13 healthy controls, which underwent an MRI and neuropsychiatric assessment. Regional brain volumes were determined using voxel-based morphometry and other advanced imaging processing methods. Our results indicated the presence of different patterns of GM atrophy in the two AD subgroups relative to healthy controls. AD patients with multiple neuropsychiatric manifestations showed more evident GM atrophy in the left superior temporal gyrus and insula as compared with healthy controls. In contrast, AD subjects with few neuropsychiatric symptoms displayed more GM atrophy in prefrontal regions, as well as in the dorsal anterior cingulate ad post-central gyri, as compared with healthy controls. Our findings suggest that the presence of multiple neuropsychiatric symptoms is more related to the degree of atrophy in specific brain networks rather than dependent on the global severity of widespread neurodegenerative brain changes.

Dataset Information

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

Publications

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

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