Effects of DISC1 on Alzheimer’s disease cell models assessed by iTRAQ proteomics analysis

Alzheimer’s disease (AD) is a form of neurodegenerative disease in the elderly with no cure at present. In a previous study, we found that the scaffold protein, disrupted in Schizophrenia 1 (DISC1) is down-regulated in the AD brains, and ectopic expression of DISC1 can delay the progression of AD by protecting synaptic plasticity and down-regulating BACE1. However, the underlying mechanisms remain not to be elucidated. In the present study, we compared the proteomes of normal and DISC1^high AD cells expressing the amyloid precursor protein (APP) using isobaric tag for relative and absolute quantitation (iTRAQ) and mass spectrometry (MS). The differentially expressed proteins (DEPs) were identified, and the protein–protein interaction (PPI) network was constructed to identify the interacting partners of DISC1. Based on the interaction scores, NDE1, GRM3, PTGER3 and KATNA1 were identified as functionally or physically related to DISC1, and may therefore regulate AD development. The DEPs were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases with the DAVID software, and the Non-supervised Orthologous Groups (eggNOG) database was used to determine their evolutionary relationships. The DEPs were significantly enriched in microtubules and mitochondria-related pathways. Gene set enrichment analysis (GSEA) was performed to identify genes and pathways that are activated when DISC1 is overexpressed. Our findings provide novel insights into the regulatory mechanisms underlying DISC1 function in AD.     

Alterations in immunological and neurological gene expression patterns in Alzheimer's disease tissues

Microarray technology was utilized to isolate disease-specific changes in gene expression by sampling across inferior parietal lobes of patients suffering from late onset AD or non-AD-associated dementia and non-demented controls. Primary focus was placed on understanding how inflammation plays a role in AD pathogenesis. Gene ontology analysis revealed that the most differentially expressed genes related to nervous system development and function and neurological disease followed by genes involved in inflammation and immunological signaling. Pathway analysis also implicated a role for chemokines and their receptors, specifically CXCR4 and CCR3, in AD. Immunohistological analysis revealed that these chemokine receptors are upregulated in AD patients. Western analysis demonstrated an increased activation of PKC, a downstream mediator of chemokine receptor signaling, in the majority of AD patients. A very specific cohort of genes related to amyloid beta accumulation and clearance were found to be significantly altered in AD. The most significantly downregulated gene in this data set was the endothelin converting enzyme 2 (ECE2), implicated in amyloid beta clearance. These data were subsequently confirmed by real-time PCR and Western blot analysis. Together, these findings open up new avenues of investigation and possible therapeutic strategies targeting inflammation and amyloid clearance in AD patients.     

基于单细胞转录组的基因富集方法分析星形胶质细胞与阿尔茨海默病的关系*

目的:通过生物信息学方法分析阿尔茨海默病(Alzheimer disease, AD)中与星形胶质细胞相关的糖代谢通路,为揭示AD患者的星形胶质细胞在大脑中的糖代谢过程提供理论基础。方法:首先根据细胞特异性表达基因将AD患者和健康人脑组织单细胞转录组学测序结果进行降维分析,再根据星形胶质细胞不同亚型的基因表达特征进行细胞分群,对星形胶质细胞差异表达基因进行基因注释(Gene Ontology. GO)、信号通路分析(Kyoto Encyclopedia of Genes and Genomes, KEGG)以及基因集富集分析(Gene Set Enrichment Analysis, GSEA),采用转录调控网络分析与AD的星形胶质细胞相关的转录辅助因子。结果:所有细胞降维分析结果显示AD患者脑内星形胶质细胞和兴奋性神经元数量显著减少;星形胶质细胞降维分析结果显示其可以被进一步分为6个亚群,其中在AD患者中减少的星形胶质细胞主要为RASGEF1B⁺SLC26A3⁺亚群和NRGN⁺CALM1⁺亚群;GO分析结果显示AD患者与健康对照星形胶质细胞差异表达基因主要与轴突发生、神经元的迁移、胶质细胞分化、体内锌离子稳态、突触传递的正调控、血管运输有关。KEGG结果显示,上述差异基因主要与PI3K-Akt信号通路、AMPK信号通路、钙信号通路有关。GSEA分析结果显示,AD患者差异基因在糖酵解/糖异生通路中得到富集,其中丙酮酸激酶PKM、PFKL、ACSS1、乳酸脱氢酶LDHB在AD患者星形胶质细胞中下调。转录调控网络分析结果显示,星形胶质细胞中差异表达转录辅助因子有5个,其中PKM、SOX2、SOX9在AD患者星形胶质细胞中下调。SREBF1和BCL6在AD患者星形胶质细胞中上调。结论:AD患者脑内兴奋性神经元和星形胶质细胞数量降低,以及星形胶质细胞糖酵解相关基因下调。结合星形胶质细胞作为神经元的主要乳酸供应细胞,其数量减少和糖酵解能力减低提示星形胶质细胞供能不足可能是AD发生的机制之一。     

An Effective Method to Identify Shared Pathways and Common Factors among Neurodegenerative Diseases

Groups of distinct but related diseases often share common symptoms, which suggest likely overlaps in underlying pathogenic mechanisms. Identifying the shared pathways and common factors among those disorders can be expected to deepen our understanding for them and help designing new treatment strategies effected on those diseases. Neurodegeneration diseases, including Alzheimer''s disease (AD), Parkinson''s disease (PD) and Huntington''s disease (HD), were taken as a case study in this research. Reported susceptibility genes for AD, PD and HD were collected and human protein-protein interaction network (hPPIN) was used to identify biological pathways related to neurodegeneration. 81 KEGG pathways were found to be correlated with neurodegenerative disorders. 36 out of the 81 are human disease pathways, and the remaining ones are involved in miscellaneous human functional pathways. Cancers and infectious diseases are two major subclasses within the disease group. Apoptosis is one of the most significant functional pathways. Most of those pathways found here are actually consistent with prior knowledge of neurodegenerative diseases except two cell communication pathways: adherens and tight junctions. Gene expression analysis showed a high probability that the two pathways were related to neurodegenerative diseases. A combination of common susceptibility genes and hPPIN is an effective method to study shared pathways involved in a group of closely related disorders. Common modules, which might play a bridging role in linking neurodegenerative disorders and the enriched pathways, were identified by clustering analysis. The identified shared pathways and common modules can be expected to yield clues for effective target discovery efforts on neurodegeneration.     

A genome-wide association study of Alzheimer’s disease using random forests and enrichment analysis

Alzheimer??s disease (AD) is a serious neurodegenerative disorder and its cause remains largely elusive. In past years, genome-wide association (GWA) studies have provided an effective means for AD research. However, the univariate method that is commonly used in GWA studies cannot effectively detect the biological mechanisms associated with this disease. In this study, we propose a new strategy for the GWA analysis of AD that combines random forests with enrichment analysis. First, backward feature selection using random forests was performed on a GWA dataset of AD patients carrying the apolipoprotein gene (APOE?4) and 1058 susceptible single nucleotide polymorphisms (SNPs) were detected, including several known AD-associated SNPs. Next, the susceptible SNPs were investigated by enrichment analysis and significantly-associated gene functional annotations, such as ??alternative splicing??, ??glycoprotein??, and ??neuron development??, were successfully discovered, indicating that these biological mechanisms play important roles in the development of AD in APOE?4 carriers. These findings may provide insights into the pathogenesis of AD and helpful guidance for further studies. Furthermore, this strategy can easily be modified and applied to GWA studies of other complex diseases.     

Molecular differences in Alzheimer's disease between male and female patients determined by integrative network analysis

Alzheimer's disease (AD) is a complex neurodegenerative disease and the most common cause of dementia among the elderly. There has been increasing recognition of sex differences in AD prevalence, clinical manifestation, disease course and prognosis. However, there have been few studies on the molecular mechanism underlying these differences. To address this issue, we carried out global gene expression and integrative network analyses based on expression profiles (GSE84422) across 17 cortical regions of 125 individuals with AD. There were few genes that were differentially expressed across the 17 regions between the two sexes, with only four (encoding glutamate metabotropic receptor 2, oestrogen‐related receptor beta, kinesin family member 26B, and aspartoacylase) that were differentially expressed in three regions. A pan‐cortical brain region co‐expression network analysis identified pathways and genes (eg, glycogen synthase kinase 3β) that were significantly associated with clinical characteristics of AD (such as neurofibrillary score) in males only. Similarity analyses between region‐specific networks indicated that male patients exhibited greater variability, especially in the superior parietal lobule, dorsolateral prefrontal cortex and occipital visual cortex. A network module analysis revealed an association between clinical traits and crosstalk of sex‐specific modules. An examination of temporal and spatial patterns of sex differences in AD showed that molecular networks were more conserved in females than in males in different cortical regions and at different AD stages. These findings provide insight into critical molecular pathways governing sex differences in AD pathology.     

Family‐based genome scan for age at onset of late‐onset Alzheimer's disease in whole exome sequencing data

Alzheimer's disease (AD) is a common and complex neurodegenerative disease. Age at onset (AAO) of AD is an important component phenotype with a genetic basis, and identification of genes in which variation affects AAO would contribute to identification of factors that affect timing of onset. Increase in AAO through prevention or therapeutic measures would have enormous benefits by delaying AD and its associated morbidities. In this paper, we performed a family‐based genome‐wide association study for AAO of late‐onset AD in whole exome sequence data generated in multigenerational families with multiple AD cases. We conducted single marker and gene‐based burden tests for common and rare variants, respectively. We combined association analyses with variance component linkage analysis, and with reference to prior studies, in order to enhance evidence of the identified genes. For variants and genes implicated by the association study, we performed a gene‐set enrichment analysis to identify potential novel pathways associated with AAO of AD. We found statistically significant association with AAO for three genes (WRN, NTN4 and LAMC3) with common associated variants, and for four genes (SLC8A3, SLC19A3, MADD and LRRK2) with multiple rare‐associated variants that have a plausible biological function related to AD. The genes we have identified are in pathways that are strong candidates for involvement in the development of AD pathology and may lead to a better understanding of AD pathogenesis.     

Independent Component Analysis-Based Identification of Covariance Patterns of Microstructural White Matter Damage in Alzheimer’s Disease

The existing DTI studies have suggested that white matter damage constitutes an important part of the neurodegenerative changes in Alzheimer’s disease (AD). The present study aimed to identify the regional covariance patterns of microstructural white matter changes associated with AD. In this study, we applied a multivariate analysis approach, independent component analysis (ICA), to identify covariance patterns of microstructural white matter damage based on fractional anisotropy (FA) skeletonised images from DTI data in 39 AD patients and 41 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative database. The multivariate ICA decomposed the subject-dimension concatenated FA data into a mixing coefficient matrix and a source matrix. Twenty-eight independent components (ICs) were extracted, and a two sample t-test on each column of the corresponding mixing coefficient matrix revealed significant AD/HC differences in ICA weights for 7 ICs. The covariant FA changes primarily involved the bilateral corona radiata, the superior longitudinal fasciculus, the cingulum, the hippocampal commissure, and the corpus callosum in AD patients compared to HCs. Our findings identified covariant white matter damage associated with AD based on DTI in combination with multivariate ICA, potentially expanding our understanding of the neuropathological mechanisms of AD.     

Microarray analysis in Alzheimer's disease and normal aging

The purpose of this study was to investigate gene expression in Alzheimer's disease (AD), the most common form of senile dementia. We utilized the microarray technology to simultaneously compare the expression profile of 12,000 human genes in cerebral cortex of AD and normal aging. To identify gene expression related to neurodegeneration, beside the presence of amyloid deposition, we used control brains with abundant amyloid plaques, derived from cognitively normal elderly subjects. The microarray analysis indicated that 314 genes were differentially expressed in AD cerebral cortex, with differences greater than 5 folds in 25 genes. RT-PCR performed on a selected group of genes confirmed the increased expression of the interferon-induced protein 3 in AD brain. This protein, which is highly inducible by both type I and type II interferons, was not previously associated with the neurodegenerative disease.     

Extensions to gene set enrichment

MOTIVATION: Gene Set Enrichment Analysis (GSEA) has been developed recently to capture changes in the expression of pre-defined sets of genes. We propose number of extensions to GSEA, including the use of different statistics to describe the association between genes and phenotypes of interest. We make use of dimension reduction procedures, such as principle component analysis, to identify gene sets with correlated expression. We also address issues that arise when gene sets overlap. RESULTS: Our proposals extend the range of applicability of GSEA and allow for adjustments based on other covariates. We have provided a well-defined procedure to address interpretation issues that can raise when gene sets have substantial overlap. We have shown how standard dimension reduction methods, such as PCA, can be used to help further interpret GSEA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease

Background

Alzheimer’s disease (AD) is one of the leading genetically complex and heterogeneous disorder that is influenced by both genetic and environmental factors. The underlying risk factors remain largely unclear for this heterogeneous disorder. In recent years, high throughput methodologies, such as genome-wide linkage analysis (GWL), genome-wide association (GWA) studies, and genome-wide expression profiling (GWE), have led to the identification of several candidate genes associated with AD. However, due to lack of consistency within their findings, an integrative approach is warranted. Here, we have designed a rank based gene prioritization approach involving convergent analysis of multi-dimensional data and protein-protein interaction (PPI) network modelling.

Results

Our approach employs integration of three different AD datasets- GWL,GWA and GWE to identify overlapping candidate genes ranked using a novel cumulative rank score (S_R) based method followed by prioritization using clusters derived from PPI network. S_R for each gene is calculated by addition of rank assigned to individual gene based on either p value or score in three datasets. This analysis yielded 108 plausible AD genes. Network modelling by creating PPI using proteins encoded by these genes and their direct interactors resulted in a layered network of 640 proteins. Clustering of these proteins further helped us in identifying 6 significant clusters with 7 proteins (EGFR, ACTB, CDC2, IRAK1, APOE, ABCA1 and AMPH) forming the central hub nodes. Functional annotation of 108 genes revealed their role in several biological activities such as neurogenesis, regulation of MAP kinase activity, response to calcium ion, endocytosis paralleling the AD specific attributes. Finally, 3 potential biochemical biomarkers were found from the overlap of 108 AD proteins with proteins from CSF and plasma proteome. EGFR and ACTB were found to be the two most significant AD risk genes.

Conclusions

With the assumption that common genetic signals obtained from different methodological platforms might serve as robust AD risk markers than candidates identified using single dimension approach, here we demonstrated an integrated genomic convergence approach for disease candidate gene prioritization from heterogeneous data sources linked to AD.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-199) contains supplementary material, which is available to authorized users.     

The relationship between the minor allele content and Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease. The genetic risk factors of AD remain better understood. Using previously published dataset of common single nucleotide polymorphisms (SNPs), we studied the association between the minor allele content (MAC) in an individual and AD. We found that AD patients have higher average MAC values than matched controls. We identified a risk prediction model that could predict 2.19% of AD cases. We also identified 49 genes whose expression levels correlated with both MAC and AD. By pathway and process enrichment analyses, these genes were found in pathways or processes closely related to AD. Our study suggests that AD may be linked with too many genetic variations over a threshold. The method of correlations with both MAC and traits appears to be effective in high efficiency identification of target genes for complex traits.     

Transgenic models of Alzheimer's disease: Better utilization of existing models through viral transgenesis

Animal models have been used for decades in the Alzheimer's disease (AD) research field and have been crucial for the advancement of our understanding of the disease. Most models are based on familial AD mutations of genes involved in the amyloidogenic process, such as the amyloid precursor protein (APP) and presenilin 1 (PS1). Some models also incorporate mutations in tau (MAPT) known to cause frontotemporal dementia, a neurodegenerative disease that shares some elements of neuropathology with AD. While these models are complex, they fail to display pathology that perfectly recapitulates that of the human disease. Unfortunately, this level of pre-existing complexity creates a barrier to the further modification and improvement of these models. However, as the efficacy and safety of viral vectors improves, their use as an alternative to germline genetic modification is becoming a widely used research tool. In this review we discuss how this approach can be used to better utilize common mouse models in AD research. This article is part of a Special Issue entitled: Animal Models of Disease.