Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

ABSTRACT: A major health care problems today is that drug treatment is ineffective for many patients. This may depend on the involvement of thousands of genes, which vary between patients with the same diagnosis at different time points. Identification and prioritization of upstream regulatory (UR) genes for precision medicine are therefore great challenges. In this study, we present a framework to model dynamic cellulome- and genome-wide changes in digital twins, to prioritize UR genes. We used seasonal allergic rhinitis (SAR) as a disease model because the external trigger (pollen) is known, so that the disease process can be mimicked by time series analyses of in vitro allergen-stimulated PBMC from SAR patients. Based on single cell profiling data from different stages of the disease, we constructed network models, hypothesizing that the directed molecular interactions between cell types could be traced to find an UR cell type and gene. This hypothesis was rejected because the cellular interactions formed multi-directional networks, rather than linear hierarchies. Instead, we were successfully able to rank and prioritize the URs based on their relative effects on the different cell types. We propose that this ranking strategy is a scalable approach for organizing and analyzing cellulome- and genome-wide data in digital twins.

ORGANISM(S): Homo sapiens

PROVIDER: GSE180697 | GEO | 2022/04/12

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

Project description:A dynamic single cell-based framework for digital twins to prioritize disease genes and drug targets

| PRJNA749249 | ENA

Network analyses of single cell-based digital twins for individualized treatment of antigen induced arthritis in mice.

Project description:Computational treatment of digital twins of individual patients has been proposed as a strategy to personalize treatment. Systematic solutions are though currently lacking for inflammatory diseases. Here, we propose a strategy (scDrugPrio) that creates scRNA-seq based multicellular disease models that incorporate the key biological and pharmacological properties such as gene interactions within and between cell types, varying gene expression levels between cells and individuals, and inhibitory or enhancing drug effects. scDrugPrio was constructed based on joint scRNA-seq data from a mouse model of antigen induced arthritis. It was and validated by improved precision/recall for known drug targets and in vitro studies of predicted but yet unknown drug targets. When applied to human multiple sclerosis and Crohn’s disease, scDrugPrio was able to stratify predicted treatments based on interindividual variations. We hence propose scDrugPrio as a solution to personalize treatment by projecting network-based drug screening to digital twins of individual patients on cellulome-, genome-and drugome-wide scales.

2023-07-31 | GSE193536 | GEO

Allergen-challenged CD4+ cells from patients with seasonal allergic rhinitis

Project description:Seasonal allergic rhinitis (SAR) is a complex disease that is caused by many interacting genes and environmental factors. It is also an excellent model disease for clinical studies; it is common, it is seasonal, and since it takes place in the nasal cavity it can be studied in vivo non-invasively. Furthermore, the key disease cell, the Th2 cell is known. We study SAR using allergen-challenged CD4+ cells from allergic patients.

2009-10-16 | GSE18574 | GEO

Quantitative Versus Qualitative Fecal Immunochemical Tests (FIT) to Prioritize Urgency of Colonoscopy Referral

Project description:The main aim of this study is to determine whether there is a difference in time to diagnosis of advanced colorectal neoplasms using quantitative Fecal Immunochemical Tests (FIT) to prioritize referral for colonoscopy (intervention) compared to usual care (qualitative FIT and appointment-based referral).

| 2158887 | ecrin-mdr-crc

Affymetrix 6.0 study of sporadic motor neuron disease patients and controls.

Project description:This data was divided into three experiment sets: 1. A somatic study of sporadic motor neuron disease (SMND) brain samples that were compared to the blood from the same individual, normal control brains and disease control brans (Parkinson Disease patients); 2. A twin study comparing blood and other tissue samples from twins that were discordant for MND, concordant for MND and control twins and 3. A trio study of blood samples MND patients compared to their unaffected parents.

2010-07-31 | GSE15826 | GEO

DNA Methylation Changes in Allergic Patients Correlate with Symptom Severity [methylation]

Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21; GSE50223) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.

2013-12-01 | GSE50222 | GEO

DNA Methylation Changes in Allergic Patients Correlate with Symptom Severity [gene expression]

Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.

2013-12-01 | GSE50101 | GEO

DNA Methylation Changes in Allergic Patients Correlate with Symptom Severity [methylation]

2013-12-01 | E-GEOD-50222 | biostudies-arrayexpress

DNA Methylation Changes in Allergic Patients Correlate with Symptom Severity [gene expression]

Project description:Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients= 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina’s HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. Moreover, we found that this methylation signature correlated with symptom severity. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols= 9), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells. Total RNA was isolated from CD4+ T-cells of patients with seasonal allergic rhinitis and healthy controls both during and outside the pollen season. Total RNA was amplified and hybridized to Illumina HT12 version 4 human whole-genome arrays (Illumina, San Diego, CA).

2013-12-01 | E-GEOD-50101 | biostudies-arrayexpress

Phenotypically concordant and discordant monozygotic twins display different DNA copy number variation profiles (BAC)

Project description:The exploration of copy number variation (CNV), notably of somatic cells, is an understudied aspect of genome biology. Any differences in the genetic make-up between twins derived from the same zygote represent an extreme example of somatic variation. We studied 19 pairs of monozygotic twins with either concordant or discordant phenotype using two platforms for genome-wide CNV analyses and show that CNVs exist within pairs in both groups. These findings impact our views of genotypic and phenotypic diversity in monozygotic twins, and suggest that CNV analysis in phenotypically discordant monozygotic twins may provide a powerful tool in identifying disease predisposition loci. Our results also imply that caution should be exercised with the interpretation of disease causality of de novo CNVs found in patients based on analysis of a single tissue in routine disease-related DNA diagnostics Keywords: copy number variation, concordant and discordant monozygotic twins

2007-12-11 | GSE9559 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data