Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Smoking induced expression of the GPR15 gene indicates its potential role in chronic inflammatory pathologies

ABSTRACT: The aim of present study was to study the changes in peripheral blood gene expression profile induced by smoking. RNA was extracted from the PBMCs of 48 persons with detailed smoking history (24 were non-smokers, 16 were current smokers and 8 were ex-smokers). Gene expression profile was evaluated with the RNA sequencing and results were analyzed separately in males (24) and females (24). In male smokers, 13 genes were statistically significantly (FDR < 0.1) differentially expressed. In female smoker, 5 genes were differentially expressed with the FDR<0.1. Most of the differentially expressed genes were different between male and female smokers. However, GPR15, expression was changed in male and female smokers compared to non-smokers. Further analysis on the methylation of the genes GPR15 identified significant hypomethylation in smokers compared to non-smokers. GPR15 is the chemo-attractant receptor that regulates T-cell migration and immunity. Up-regulation of GPR15 could explain to some extend the health hazards of smoking for chronic inflammatory diseases

ORGANISM(S): Homo sapiens

PROVIDER: GSE68549 | GEO | 2018/04/30

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Specific induction of GPR15 expression in heterogeneous blood lymphocytes by smoking

Project description:Although a mucosal and cutaneous chemokine ligand for G protein –linked receptor GPR15 was recently identified, the physiological role of GPR15 remains yet elusive. In blood, in has been shown that smoking is, to date, the only specific condition leading to an increase in GPR15+ T cells. We therefore used transcriptome sequencing to describe GPR15-expressing blood T cells in more detail.

2019-01-04 | GSE114837 | GEO

GPR15+ vs GPR15- tumor-associated Tregs

Project description:GPR15+ vs GPR15- tumor-associated Tregs were sorted from colorectal cancer mice (Foxp3-RFP x GPR15-GFP)

2021-03-12 | GSE168744 | GEO

Expression data from GPR15+CD4+ and GPR15-CD4+ T cells in LILP

Project description:GPR15 is an orphan G-protein coupled receptor and its expression is abundant among T cells in the large intestine lamina propria. We used microarrays to examine charateristics of GPR15- vs GPR15+ CD4+ T cells in LILP and identified distinct classes of up-regulated genes in GPR15+ CD4+ T cells. We have generated GFP knock-in mice in GPR15 locus and used GFP as a maker for GPR15 expression. In heterozygous mice, GPR15+CD4+ T cells and GPR15-CD4-T cells were sorted and RNA was prepared from them.

2013-05-09 | E-GEOD-45773 | biostudies-arrayexpress

Expression data from GPR15+CD4+ and GPR15-CD4+ T cells in LILP

2013-05-09 | GSE45773 | GEO

GPR15+ vs GPR15- tumor-associated Tregs

Project description:GPR15+ vs GPR15- tumor-associated Tregs

| PRJNA713801 | ENA

Gene expression in male and female smokers

Project description:Smoking is the second leading cause of preventable death in the United States. Cohort epidemiological studies have demonstrated that women are more vulnerable to cigarette-smoking induced diseases than their male counterparts, however, the molecular basis of these differences has been remained unknown to date. In this study, we explored if there were differences in the gene expression patterns between male and female smokers, and how these patterns might reflect different gender-specific responses to the stress of smoking. Global gene expression was measured in the peripheral blood cells of 12 each male and female smokers and non-smokers using the Agilent one-color workflow and human whole genome microarrays.

2015-05-01 | E-GEOD-47415 | biostudies-arrayexpress

Radiation responses in peripheral white blood cells of smokers and non-smokers

Project description:Understanding the possible impact of potential confounding factors is necessary for any approach to radiation biodosimetry. Potential confounding factors have not been fully addressed for gene expression-based biodosimetry approaches, such as we are developing. To begin addressing this need, we have used an ex vivo irradiated peripheral blood cell model to investigate the potential effect of smoking on the global radiation gene expression response, and looked for genes that respond to radiation differently in smokers and non-smokers, and also in males and females. The results indicate that only a small number of genes may be significantly confounded by either factor, supporting the idea of developing peripheral blood gene expression strategies for radiation biodosimetry. Blood from each of 24 different donors was exposed to four doses of ionizing radiation (0, 0.1, 0.5, or 2 Gy) and analyzed using single-color microarray hybridization. The donors represented equal numbers of male and female smokers (1 or more packs a day) and non-smokers. There are 95 data sets in the study as the sample from one of the female smokers exposed to 2 Gy was lost.

2011-03-16 | E-GEOD-23515 | biostudies-arrayexpress

A Pilot Study Of Immunological and Microbiome Profiling Reveals Distinct Inflammatory Profiles For Smokers and Electronic Cigarette Users [re-analysis]

Project description:Background: Smoking increases pulmonary inflammation, but an effect by electronic cigarette (EC) vaping is less understood. We previously reported smokers (SM) had increased lung immune cell counts and inflammatory gene expression in bronchial epithelial cells compared to EC users and never-smokers (NS). Here we report association of smoking and vaping with immune cell subtypes and gene expression in bronchoalveolar lavage. Methods: SM, EC users, and NS underwent bronchoscopy (n=28). RNASeq and the CIBERSORT computational algorithm were used to determine immune cell subtypes, along with inflammatory gene expression and microbiome metatranscriptomics. Correlations and associations were assessed across and within the tobacco-use groups, corrected for false discovery rates. Results: Classification of macrophage subtypes revealed a 2-fold increase in M0 macrophages for smokers and EC users relative to never-smokers, with a concordant decrease in M2 macrophages. There were 68, 19, and 1 significantly differentially expressed inflammatory genes (DEG) (FDR<0.05; log2-fold change>1) between SM/NS, SM/EC users, and EC users/NS respectively. CSF-1 and GATA3 expression correlated positively and inversely with M0 and M2 macrophages respectively. Correlation profiling for DEG showed distinct lung profiles for each participant group. There were 3 bacteria genera-DEG correlations and 3 bacteria genera-macrophage subtype correlations (Diffcorr>0.5 and FDR<0.1). Conclusions: In this pilot study, smoking and EC use were associated with an increase in undifferentiated M0 macrophages, but smokers differed from EC users and NS for inflammatory gene expression. The preliminary data support the hypothesis that smoking and EC use have toxic lung effects influencing inflammatory responses, but not via changes in the microbiome.

2023-07-05 | GSE227547 | GEO

Gene expression profiles of GPR15 knock out macrophages

Project description:To confirm the changed gene expression profiles in GPR15 knock out (KO) macrophages, we applied a SurePrint G3 Mouse Gene Expression service from Takara Bio Inc (Kusatsu, Shiga, Japan) to analyze gene expression profiles in lipopolysaccharide (LPS)-stimulated GPR15KO macrophages, comparing with wild type (WT) macrophages. Most significantly up-regulated genes in GPR15KO macropahges included Il6, Il17a, Il23, Tnfsf8, Il1b, Ifna2 and Ccnd2. On the contrary, several inflammation-related genes, including Ccl17, Itgax, Nrp1 and Rasgrf2, were down-regulated in WT macrophages, compared to GPR15 KO macrophages. Abdominal macrophages from WT and GPR15 KO mice were stimulated with PBS or LPS (100 ng/ml) for 4 hrs. Total RNA were extracted using a TRIzol-chloroform based method.

2016-08-25 | E-GEOD-86020 | biostudies-arrayexpress

Watanabe2018_Simple markov model

Project description:Simple Markov model. There are 3 disease states: Healthy, Sick, and Dead, where the Dead state is terminal. The yearly transition probabilities are: Healthy to Dead: 0.01; Healthy to Sick: 0.2 for Male and 0.1 for Female; Sick to Healthy: 0.1; Sick to Dead: 0.3. The transition probability now depends on the cohort (Male or Female) and can be expressed as a function of a Boolean covariate Male. Initial conditions: Healthy = (50 Male, 50 Female), Sick = (0,0) and Dead = (0,0). Output: Number of men and women in each disease state for years 1-10.

2019-01-30 | MODEL1803120004 | BioModels

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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