Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1327335 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Alzheimer's disease (AD) is a chronic neurodegenerative disorder characterized by progressive deterioration of cognitive function. Evidence suggests a role for epigenetic regulation, in particular the cytosine modifications 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC,) in AD. 5hmC is highly enriched in the nervous system and displays neurodevelopment and age-related changes. To determine the role of 5hmC in AD, we performed genome-wide analyses of 5hmC in DNA from prefrontal cortex of post-mortem AD as well as RNA-Seq to correlate changes in methylation status with transcriptional changes. We also utilized the existing AD fly model to further test the functional significance of these epigenetically altered loci. We identified 325 genes containing differentially hydroxymethylated loci (DhMLs) in both the discovery and replication datasets, and these are enriched for pathways involved in neuron projection development and neurogenesis. Of the 325 genes identified, 140 also showed changes in gene expression by RNA-Seq. Proteins encoded by genes identified in the current analysis form direct protein-protein interactions with AD-associated genes, expanding the network of genes implicated in AD. Furthermore, we identified AD-associated single nucleotide polymorphisms (SNPs) located within or near DhMLs, suggesting that these SNPs may identify regions of epigenetic gene regulation that play a role in AD pathogenesis. Finally using the existing AD fly model we showed that some of these genes could modulate the toxicity associated with AD. Our data implicate neuron projection development and neurogenesis pathways as potential targets in AD. These results indicate that incorporating epigenomic and transcriptomic data with GWAS data can expand the known network of genes involved in disease pathogenesis. Combination of epigenome profiling and Drosophila model enables us to identify the epigenetic modifiers of Alzheimer's disease. University of Kentucky Alzheimer's Disease Research Center (3 control, 3 Alzheimer's) and Emory University Alzheimer's Disease Research Center (2 control, 2 Alzheimer's)
2016-07-20 | E-GEOD-72782 | biostudies-arrayexpress
Project description:Natural products exhibit potential as candidates for developing multi-target agents for Alzheimer's disease treatment. The aim of this study is to utilize network-based medicine to identify novel natural products for Alzheimer's disease, and investigate their efficacy and mechanisms of action. In this study, we identified (-)-Vestitol and Salviolone as new potential natural products for treating Alzheimer's disease via an Alzheimer's disease-related pathway-gene network. Both natural products improved the cognition of APP/PS1 transgenic mice, reduced Aβ deposition, and lowered soluble toxic Aβ levels in the brain. Notably, a synergistic effect was observed when the two natural products were combined. Transcriptomic analysis and qRT-PCR experiments revealed that the synergistic mechanism of (-)-Vestitol and Salviolone combination is associated with the regulation of a broader range of AD-related pathways and genes, particularly the neuroactive ligand-receptor interaction pathway and calcium signaling pathway.
2025-02-19 | GSE270100 | GEO
Project description:Background: Alcohol-associated hepatitis (AH) is the clinical manifestation of alcohol-associated liver disease (ALD). AH is a complex disease encompassing the dysregulation of many cells and cell subpopulations. This study used a hepatic spatial transcriptomic and proteomic approach (10X Genomics Visium) to identify hepatic cell populations and their associated transcriptomic and proteomic alterations in human AH. Methods: Formalin fixed parraffin embedded liver tissue from AH patients (n=2) and non-ALD controls (donors) (n=2) were used for Visium spatial transcriptomic and proteomic analysis. Results: AH cell clusters and cell markers were drastically different in regard to tissue pattern and number of cell type as compared to non-ALD controls. Cholangiocytes, endothelial cells, macrophages, and stellate cells were more profuse in AH relative to non-ALD controls. Transcriptionally, proliferating cell nuclear antigen positive (PCNA+) hepatocytes in AH more closely resembled cholangiocytes suggesting they were non-functional hepatocytes derived from cholangiocytes. Further, mitochondria protein coding genes were reduced in AH vs non-ALD control hepatocytes, suggesting reduced functionality and loss of regenerative mechanisms. Macrophages in AH exhibited elevated gene expression involved in exosomes as compared to non-ALD controls. The most upregulated macrophage genes observed in AH were those involved in exosome trafficking and cellular migration. Gene and protein signatures of disease associated hepatocytes (ANXA2+/CXCL1+/CEACAM8+) were elevated in AH and could visually identify a pre-malignant lesion. Conclusions: This study identified global cell type alterations in AH and distinct transcriptomic changes between AH and non-ALD controls. These findings characterizes cellular plasticity and profuse transcriptomic and proteomic changes that are apparent in AH and contributes to the identification of novel therapeutics.
2024-12-02 | GSE278662 | GEO
Project description:Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder. Grasping the pathological transformations in early-phase AD is important. Integrating single-cell and spatial transcriptomics with the pathophysiology of AD from the same origin permits to delineate molecular characteristics and spatial arrangement of cellular populations without individual differences, however, such experimental designs have been ignored in current studies. Here, we use a single-source experimental framework to create a multimodal dataset of disease-susceptible region in early-onset Alzheimer's Disease (EOAD) mouse. Predicated on amyloid-beta (Aβ) immunopathology, a profile of disease-associated alterations is discerned. Mature myelinating oligodendrocytes within fiber tracts exhibit compromised fatty acid synthesis and energy-related processes. Region-specific glutamatergic neurons demonstrate impaired synaptic formation, coupled with transcriptomic modifications that encompass energy homeostasis, lipid metabolism and cellular adhesion. Disease-associated astrocytes, aggregating around Aβ deposits, exhibit neuron-related changes in oxidative phosphorylation (OxPhos) and metal ion responses. This investigation serves as a resource for exploring disease signature across multimodal, which provides a basis for finding the progression mechanisms of AD.
2025-06-20 | GSE253570 | GEO
Project description:In this study, we coupled microarray-based transcriptomics and MS-based phosphoproteomics assay to determine mRNA, protein, and phosphopeptide expression levels from 71 autopsied temporal cortical samples, with varying degree of Alzheimer's Disease (AD)-related neurofibrillary pathology. With computational analysis, we identified disease-related transcript, protein and phosphopeptide expression patterns, associated with distinct biological processes and cell types.
2018-12-16 | GSE106241 | GEO
Project description:Understanding the pathological basis of the neurological symptoms observed following SARS-CoV2 infection is essential to optimizing outcomes and developing therapeutics. We performed proteomics profiling across eight cortical and subcortical brain regions, frontal lobe, temporal lobe, occipital lobe, hippocampus, thalamus, basal ganglia, midbrain, and pons, using postmortem brain samples from severe acute COVID-19 patients and matched controls (n=16), all preserved as formalin-fixed paraffin-embedded (FFPE) tissue. Integrating proteomics and additional transcriptomic analyses, we identified board dysregulation of mitochondrial and synaptic pathways in deep-layer excitatory neurons and changes exhibited similarities with those seen in various age-related neurodegenerative diseases, such as Parkinson's disease (PD) and Alzheimer's disease (AD).
2024-03-12 | MSV000094297 | MassIVE
Project description:Endometriosis is a debilitating gynecological disorder affecting approximately 10% of the female population. Despite its prevalence, robust methods to classify and treat endometriosis remain elusive. Changes throughout the menstrual cycle in tissue size, architecture, cellular composition, and individual cell phenotypes make it extraordinarily challenging to identify markers or cell types associated with uterine pathologies since disease-state alterations in gene and protein expression are convoluted with cycle phase variations. Here, we developed an integrated workflow to generate both proteomic and single-cell RNA-sequencing (scRNA-seq) data sets using tissues and cells isolated from the uteri of control and endometriotic donors. Using a linear mixed effect model (LMM), we identified proteins associated with cycle stage and disease, revealing a set of genes that drive separation across these two biological variables. Further, we analyzed our scRNA-seq data to identify cell types expressing cycle and disease- associated genes identified in our proteomic data. A module scoring approach was used to identify cell types driving the enrichment of certain biological pathways, revealing several pathways of interest across different cell subpopulations. Finally, we identified ligand-receptor pairs including Axl/Tyro3 – Gas6, that may modulate interactions between endometrial macrophages and/or endometrial stromal/epithelial cells. Analysis of these signaling pathways in an independent cohort of endometrial biopsies revealed a significant decrease in Tyro3 expression in patients with endometriosis compared to controls, both transcriptionally and through histological staining. This measured decrease in Tryo3 in patients with disease could serve as a novel diagnostic biomarker or treatment avenue for patients with endometriosis. Taken together, this integrated approach provides a framework for integrating LMMs, proteomic and RNA-seq data to deconvolve the complexities of complex uterine diseases and identify novel genes and pathways underlying endometriosis.
2023-09-05 | PXD045115 | Pride
Project description:AML is a heterogeneous disease with current genomic and cytogenetic based classifications do not fully account for patient-to-patient variability. By decoupling lineage-related changes from transcriptomic signatures and integrating orthogonal genomic and proteomic based assays, we uncovered AML group of patients with inflammatory and metabolic signatures that inform outcomes.
2021-07-18 | GSE165656 | GEO
Project description:In the current study we examined several proteomic- and RNA-Seq-based datasets of cardiac-enriched, cell-surface and membrane-associated proteins in human fetal and mouse neonatal ventricular cardiomyocytes. By integrating available microarray and tissue expression profiles along with MGI phenotypic analysis, we identified 173 membrane-associated proteins that are cardiac-enriched, conserved amongst eukaryotic species, and have not yet been linked to a ‘cardiac’ Phenotype-Ontology. To highlight the utility of this dataset, we selected several proteins to investigate more carefully, including FAM162A, MCT1, and COX20, to show cardiac enrichment, subcellular distribution and expression patterns in disease. Three-dimensional imaging was used to validate subcellular localization and expression in adult mouse ventricular cardiomyocytes. FAM162A, MCT1, and COX20 were differentially expressed at the transcriptomic and proteomic levels in multiple models of mouse and human heart diseases and may represent potential diagnostic and therapeutic targets for human dilated and ischemic cardiomyopathies. Altogether, we believe this comprehensive cardiomyocyte membrane proteome dataset will prove instrumental to future investigations aimed at characterizing heart disease markers and/or therapeutic targets for heart failure.
2020-11-17 | PXD017732 | Pride
Project description:Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular prognostic markers. To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers. Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4x44K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n=634) by Kaplan-Meier estimates and Cox regression analyses. Combination of gene expression-based classification and established prognostic markers improves risk estimation of LR/IR neuroblastoma patients. We propose to implement our revised treatment stratification system in a prospective clinical trial.
2014-10-09 | E-MTAB-1781 | biostudies-arrayexpress