Transcriptomics profiling of Alzheimer’s disease reveal novel molecular targets
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE67333 | GEO | 2015/04/01
SECONDARY ACCESSION(S): PRJNA279526
REPOSITORIES: GEO
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Project description:We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We now have analyzed additional samples with a confirmed pathologic diagnosis of late onset Alzheimer's disease (LOAD, final n=187 controls, 176 cases). Nine percent of the cortical transcripts we analyzed had expression profiles correlated with their genotypes in the combined cohort and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power to find risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease. see DOI:10.1016/j.ajhg.2009.03.011 for further details and complete author list.
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Project description:Alzheimer’s disease (AD) manifested before age 65 is commonly referred to as early-onset AD (EOAD). While the majority (> 90%) of EOAD cases are not caused by autosomal-dominant mutations in PSEN1, PSEN2, and APP, they do have a higher heritability (92–100%) than sporadic late-onset AD (LOAD, 70%). Although the endpoint clinicopathological changes, i.e., Aβ plaques, tau tangles, and cognitive decline, are common across EOAD and LOAD, the disease progression is highly heterogeneous. This heterogeneity, leading to temporally distinct age at onset (AAO) and stages of cognitive decline, may be caused by myriad combinations of distinct disease-associated molecular mechanisms. We and others have used transcriptome profiling in AD patient-derived neuron models of autosomal-dominant EOAD and sporadic LOAD to identify disease endotypes. Further, analyses of large postmortem brain cohorts demonstrate that only one-third of AD patients show hallmark disease endotypes like increased inflammation and decreased synaptic signaling. Areas of the brain less affected by AD pathology at early disease stages—such as the primary visual cortex—exhibit similar transcriptomic dysregulation as those regions traditionally affected and, therefore, may offer a view into the molecular mechanisms of AD without the associated inflammatory changes and gliosis induced by pathology. To this end, we analyzed AD patient samples from the primary visual cortex (19 EOAD, 20 LOAD) using transcriptomic signatures to identify patient clusters and disease endotypes. Interestingly, although the clusters showed distinct combinations and severity of endotypes, each patient cluster contained both EOAD and LOAD cases, suggesting that AAO may not directly correlate with the identity and severity of AD endotypes.
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