Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

Webster, Jennifer; Gibbs, J. Raphael; Clarke, Jennifer; Ray, Monika; Zhang, Weixiong; Holmans, Peter Alan; Rohrer, Kristen; Zhao, Alice; Marlowe, Lauren; Kaleem, Mona; McCorquodale, Donald; Cuello, Cindy; Leung, Doris G.; Bryden, Leslie; Nath, Priti; Zismann, Victoria; Joshipura, Keta; Huentelman, Matthew J.; Hu-Lince, Diane; Coon, Keith D.; Craig, David W.; Pearson, John V.; Heward, Christopher B.; Reiman, Eric M.; Stephan, Dietrich A.; Hardy, John; Myers, Amanda

doi:10.1016/j.ajhg.2009.03.011

Cited by 279 publications

(320 citation statements)

References 46 publications

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“…the transcriptional response associated with its onset and progression (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43). These studies have revealed that this transcriptional response involves genes corresponding to proteins that can cause the disease and those associated with the cellular processes engaged in combating it (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A transcriptional signature of Alzheimer’s disease is associated with a metastable subproteome at risk for aggregation

Ciryam

Kundra

Freer

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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It is well-established that widespread transcriptional changes accompany the onset and progression of Alzheimer’s disease. Because of the multifactorial nature of this neurodegenerative disorder and its complex relationship with aging, however, it remains unclear whether such changes are the result of nonspecific dysregulation and multisystem failure or instead are part of a coordinated response to cellular dysfunction. To address this problem in a systematic manner, we performed a meta-analysis of about 1,600 microarrays from human central nervous system tissues to identify transcriptional changes upon aging and as a result of Alzheimer’s disease. Our strategy to discover a transcriptional signature of Alzheimer’s disease revealed a set of down-regulated genes that encode proteins metastable to aggregation. Using this approach, we identified a small number of biochemical pathways, notably oxidative phosphorylation, enriched in proteins vulnerable to aggregation in control brains and encoded by genes down-regulated in Alzheimer’s disease. These results suggest that the down-regulation of a metastable subproteome may help mitigate aberrant protein aggregation when protein homeostasis becomes compromised in Alzheimer’s disease.

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Section: Discussionmentioning

confidence: 99%

“…These studies have revealed that this transcriptional response involves genes corresponding to proteins that can cause the disease and those associated with the cellular processes engaged in combating it (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43).…”

Section: Discussionmentioning

confidence: 99%

A transcriptional signature of Alzheimer’s disease is associated with a metastable subproteome at risk for aggregation

Ciryam

Kundra

Freer

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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show abstract

“…Complex diseases, including AD, show a continuum of clinical phenotypes, such as the levels of Aβ accumulation. Transcriptome analysis, therefore, is preferred, because it is highly concordant with the disease state and expected to provide an accurate molecular view of a complex disease (25). Additionally, although quantitative trait loci analysis and a genome-wide association study identify genetic markers, they do not point to specific genes, whereas transcriptomics does (17).…”

Section: Discussionmentioning

confidence: 99%

Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-β accumulation modifier

Morita

Hayashi

Yokokoji

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Alzheimer's disease (AD) is characterized by the accumulation of amyloid-β (Aβ). The genes that govern this process, however, have remained elusive. To this end, we combined distinct mouse strains with transcriptomics to directly identify disease-relevant genes. We show that AD model mice (APP-Tg) with DBA/2 genetic backgrounds have significantly lower levels of Aβ accumulation compared with SJL and C57BL/6 mice. We then applied brain transcriptomics to reveal the genes in DBA/2 that suppress Aβ accumulation. To avoid detecting secondarily affected genes by Aβ, we used non-Tg mice in the absence of Aβ pathology and selected candidate genes differently expressed in DBA/2 mice. Additional transcriptome analysis of APP-Tg mice with mixed genetic backgrounds revealed kinesin light chain-1 (Klc1) as an Aβ modifier, indicating a role for intracellular trafficking in Aβ accumulation. Aβ levels correlated with the expression levels of Klc1 splice variant E and the genotype of Klc1 in these APP-Tg mice. In humans, the expression levels of KLC1 variant E in brain and lymphocyte were significantly higher in AD patients compared with unaffected individuals. Finally, functional analysis using neuroblastoma cells showed that overexpression or knockdown of KLC1 variant E increases or decreases the production of Aβ, respectively. The identification of KLC1 variant E suggests that the dysfunction of intracellular trafficking is a causative factor of Aβ pathology. This unique combination of distinct mouse strains and model mice with transcriptomics is expected to be useful for the study of genetic mechanisms of other complex diseases.mouse-to-human translation | alternative splicing

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“…We selected an AD gene expression data set from Gene Expression Omnibus (GEO, GSE15222). 24 This data set included 24 350 probes, 363 samples, 187 controls and 176 cases. First, we performed pretreatments, including using the mean value of gene expression when multiple probes corresponded to one gene and removing missing data lines and outliers.…”

Section: Materials and Methods Datamentioning

confidence: 99%

DBGSA: a novel method of distance-based gene set analysis

Wang

et al. 2012

J Hum Genet

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When compared with single gene functional analysis, gene set analysis (GSA) can extract more information from gene expression profiles. Currently, several gene set methods have been proposed, but most of the methods cannot detect gene sets with a large number of minor-effect genes. Here, we propose a novel distance-based gene set analysis method. The distance between two groups of genes with different phenotypes based on gene expression should be larger if a certain gene set is significantly associated with the given phenotype. We calculated the distance between two groups with different phenotypes, estimated the significant P-values using two permutation methods and performed multiple hypothesis testing adjustments. This method was performed on one simulated data set and three real data sets. After a comparison and literature verification, we determined that the gene resampling-based permutation method is more suitable for GSA, and the centroid statistical and average linkage statistical distance methods are efficient, especially in detecting gene sets containing more minor-effect genes. We believe that this distance-based method will assist us in finding functional gene sets that are significantly related to a complex trait. Additionally, we have prepared a simple and publically available Perl and R package

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Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

Cited by 279 publications

References 46 publications

A transcriptional signature of Alzheimer’s disease is associated with a metastable subproteome at risk for aggregation

A transcriptional signature of Alzheimer’s disease is associated with a metastable subproteome at risk for aggregation

Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-β accumulation modifier

DBGSA: a novel method of distance-based gene set analysis

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