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Transcriptomics profiling of Alzheimer’s disease reveal novel molecular targets

ABSTRACT: Our data provide a comprehensive list of transcriptomics alterations and warrant holistic approach including both coding and non-coding RNAs in functional studies aimed to understand the pathophysiology of LOAD

ORGANISM(S): Homo sapiens

PROVIDER: GSE67333 | GEO | 2015/04/01

SECONDARY ACCESSION(S): PRJNA279526

REPOSITORIES: GEO

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Transcriptomics profiling of Alzheimer's disease reveal novel molecular targets

Project description:Our data provide a comprehensive list of transcriptomics alterations and warrant holistic approach including both coding and non-coding RNAs in functional studies aimed to understand the pathophysiology of LOAD We performed directional RNA sequencing on high quality RNA samples extracted from hippocampi of 4 late onset Alzheimer's diseas (LOAD) and 4 age-matched controls.

2015-04-01 | E-GEOD-67333 | biostudies-arrayexpress

Late-onset sporadic Alzheimer’s disease modeling using patient-derived cells: A review

Project description:Late-onset sporadic Alzheimer’s disease (LOAD) is the most common form of dementia. The disease is characterized by progressive loss of memory and behavioral changes followed by neurodegeneration of all cortical areas. While the contribution of genetic and environmental factors is important, advanced aging remains the most important disease risk factor. Because LOAD does not naturally occur in most know animal species, except humans, studies have traditionally relied on the use of transgenic mice modeling rare, early-onset genetic forms of Alzheimer’s disease. Hence, the development of more representative LOAD models through reprograming of patient-derived cells into neuronal, glial, and immune cells became a necessity to better understand the disease origin and pathophysiology. Herein, and focusing on neurons, we review current work in the field and compare results obtained with two different reprograming methods to generate patient-derived neuronal cells: the induced pluripotent stem cell and induced neuron technologies. We also evaluate if these models can faithfully mimic cellular and molecular pathologies observed in LOAD patient’s brains. Here, we show new ATAC-seq data that support that iPSC can faithfully model LOAD, with an increased number of peaks showing chromatin relaxation in LOAD neurons

2026-01-14 | GSE314553 | GEO

Genetic control of human brain transcript expression in Alzheimer’s disease.

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.

2009-04-10 | GSE15222 | GEO

Integrated proteomics and single-cell transcriptomics reveal potential therapeutic targets in Wilson's disease patients.

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Integrative network analyses of transcriptomics data reveal potential drug targets for acute radiation syndrome.

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Single-cell and spatial transcriptomics reveal pre-metastatic subsets and therapeutic targets in penile carcinoma.

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Integrated multiplexed assays of variant effect reveal determinants of catechol-O-methyltransferase gene expression

Project description:Multiplexed assays of variant effect are powerful methods to profile the consequences of rare variants on gene expression and organismal fitness. Yet, few studies have integrated several multiplexed assays to map variant effects on gene expression in coding sequences. Here, we pioneered a multiplexed assay based on polysome profiling to measure variant effects on translation at scale, uncovering single-nucleotide variants that increase and decrease ribosome load. By combining high-throughput ribosome load data with multiplexed mRNA and protein abundance readouts, we mapped the cis-regulatory landscape of thousands of catechol-O-methyltransferase (COMT) variants from RNA to protein and found numerous coding variants that alter COMT expression. Finally, we trained machine learning models to map signatures of variant effects on COMT gene expression and uncovered both directional and divergent impacts across expression layers. Our analyses reveal expression phenotypes for thousands of variants in COMT and highlight variant effects on both single and multiple layers of expression. Our findings prompt future studies that integrate several multiplexed assays for the readout of gene expression

2023-11-06 | GSE246139 | GEO

RNA Sequencing (RNA-Seq) analysis of sex-matched neural stem cells derived from early-onset Alzheimer’s disease (EOAD), late-onset Alzheimer’s disease (LOAD), and wild-type controls

Project description:Neural stem cells derived from early-onset Alzheimer’s disease (EOAD) patients with PSEN1 mutations, late-onset Alzheimer’s disease (LOAD) patients with APOE4 genotypes, and sex-matched wild-type (WT) controls were cultured and differentiated to neurons. Undifferentiated cells (UD, cultured for 1 day) and differentiated cells (DIF, cultured for 35 days) were compared to study gene expression changes. Oligomeric amyloid beta (Aβ, 0.5 µM) was applied to differentiated WT neurons for 24 hours prior to RNA sequencing. Differential gene expression analysis revealed that genetic background differences between healthy controls, as well as between EOAD and LOAD, contribute as many expression changes as AD-specific mutations. Comparing differentiation-induced gene expression changes between AD neurons and corresponding healthy controls circumvents background variability and highlights disease-specific alterations.Analysis of over 100 AD-associated genes showed altered expression in differentiated EOAD and LOAD neurons, consistent with roles in neuronal functions and AD etiopathogenesis. Notably, LOAD neurons, but not EOAD neurons, exhibited significant deficiencies in heme and redox homeostasis, with reduced expression of genes associated with heme metabolism, oxidative phosphorylation, and NAD/NADH homeostasis. These deficiencies may underlie neuronal dysfunction and contribute to LOAD pathogenesis. Gene Ontology enrichment analysis identified pathways uniquely dysregulated in EOAD and LOAD, offering insights into the molecular mechanisms distinguishing these forms of Alzheimer’s disease. This study emphasizes the critical role of redox and heme homeostasis in LOAD and identifies gene expression patterns altered specifically by AD mutations.

2025-09-21 | GSE288917 | GEO

Transcriptomic profiling of sporadic Alzheimer’s disease patients

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.

2022-10-12 | GSE203206 | GEO

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