Genetic Analysis of Psoriasis and Psoriatic Arthritis
Ontology highlight
ABSTRACT: This study is a genome-wide study of genetic associations with psoriatic arthritis (PsA), an inflammatory musculoskeletal condition that develops in up to 30% of people who have chronic psoriasis skin lesions. 1,526 cases affected with psoriatic arthritis and 1,508 unaffected controls were recruited and typed on the Illumina HumanOmni1-Quad BeadChip array. After application of quality control measures to both samples and SNPs, genotypes for 791,217 autosomal SNPs were available for 1,430 PsA cases and 1,417 controls. Imputation using phased haplotypes for the EUR subpopulation of release 3, phase 1 of the 1000 Genomes Project as a reference, resulted in genotypes for 11,532,644 SNPs and 918,976 indels passing our imputation quality threshold (Mach r2 ≥ 0.3) that were available for downstream analysis. Association at a genome-wide threshold of significance was found for five regions (near genes TNIP1, IL12B, HLA-C, TRAF3IP2, and TYK2).
SECONDARY ACCESSION(S): PRJNA294479PRJNA294480
REPOSITORIES: dbGaP
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| README.txt | Txt | |||
| phs000982.Psoriasis_Arthritis.analysis-PI.MULTI.tar.gz | Other | |||
| phs000982.pha003894.txt | Txt | |||
| GapExchange_phs000982.v1.p1.xml | Xml | |||
| dbGaPEx2.1.5.xsd | Other |
Items per page: 5 1 - 5 of 18 |
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Project description:Characterize active synovial fluid (SF) serine proteinases in psoriatic arthritis (PsA) in comparison to osteoarthritis (OA) and rheumatoid arthritis (RA)
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2020-12-15 | GSE161500 | GEO
Project description:Psoriatic arthritis (PsA) is a chronic inflammatory joint disease, and the diagnosis is quite difficult due to the unavailability of reliable clinical markers. This study aimed to investigate the fecal metabolites in PsA by comparison with rheumatoid arthritis (RA), and to identify potential diagnostic biomarkers for PsA. The metabolic profiles of the fecal samples from 27 PsA and 29 RA patients as well as 36 healthy controls (HCs) were performed on ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). And differentially altered metabolites were screened and assessed using multivariate analysis for exploring the potential biomarkers of PsA. The results showed that 154 fecal metabolites were significantly altered in PsA patients when compared with HCs, and 45 metabolites were different when compared with RA patients. A total of 14 common differential metabolites could be defined as candidate biomarkers. Furthermore, a support vector machines (SVM) model was performed to distinguish PsA from RA patients and HCs, and 5 fecal metabolites, including α/β-turmerone, glycerol 1-hexadecanoate, dihydrosphingosine, pantothenic acid and glutamine, were determined as biomarkers for PsA. Through the metabolic pathways analysis, we found that the abnormality of amino acid metabolism, bile acid metabolism and lipid metabolism might contribute to the occurrence and development of PsA. In summary, our research provided ideas for the early diagnosis and treatment of PsA by identifying fecal biomarkers and analyzing metabolic pathways.
2022-02-23 | MTBLS4206 | MetaboLights
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2022-03-16 | MSV000089073 | MassIVE
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Methods
High-throughput 16S ribosomal RNA pyrosequencing was utilized to compare the community composition of gut microbiota in patients with PsA (n = 16), patients with psoriasis of the skin (n = 15), and healthy, matched control subjects (n = 17). Samples were further assessed for the presence and levels of fecal and serum secretory IgA (sIgA), proinflammatory proteins, and fatty acids.
Results
The gut microbiota observed in patients with PsA and patients with skin psoriasis was less diverse when compared to that in healthy controls. This could be attributed to the reduced presence of several taxa. Samples from both patient groups showed a relative decrease in abundance of Coprococcus species, while samples from PsA patients were also characterized by a significant reduction in Akkermansia, Ruminococcus, and Pseudobutyrivibrio. Supernatants of fecal samples from PsA patients revealed an increase in sIgA levels and decrease in RANKL levels. Analysis of fatty acids revealed low fecal quantities of hexanoate and heptanoate in both patients with PsA and patients with psoriasis.
Conclusion
Patients with PsA and patients with skin psoriasis had a lower relative abundance of multiple intestinal bacteria. Although some genera were concomitantly decreased in both conditions, PsA samples had a lower abundance of reportedly beneficial taxa. This gut microbiota profile in PsA was similar to that previously described in patients with inflammatory bowel disease and was associated with changes in specific inflammatory proteins unique to this group, and distinct from that in patients with skin psoriasis and healthy controls. Thus, the role of the gut microbiome in the continuum of psoriasis–PsA pathogenesis and the associated immune response merits further study.
Research is published:
http://onlinelibrary.wiley.com/doi/10.1002/art.38892/full
2015-06-08 | ST000218 | MetabolomicsWorkbench
Project description:Background: Psoriasis is a systemic inflammatory disease primarily affecting the skin. Approximately one-third of psoriasis patients develop joint involvement and are diagnosed with psoriatic arthritis (PsA). While, inIn adult-onset disease, adults, the development of arthritis usually follows skin psoriasis, but approximately 15% experience arthritis first, which can delay diagnosis. While the pathophysiology of psoriasis and PsA is incompletely understood, epigenetic dysregulation affecting CD4+ and CD8+ T-cells has been suggested. Objectives: This project aimed to identify disease-associated DNA methylation signatures in CD4+ T-cells from psoriasis and PsA patients that may be used as diagnostic and/or prognostic biomarkers. Methods: PBMCs were collected from 12 patients with chronic plaque skin psoriasis and 8 PsA patients, and 8 healthy controls. CD4+ T-cells were separated through FACS sorting, and DNA methylation profiling was performed (Illumina EPIC850K arrays). Bioinformatic analyses, including gene ontology (GO) and KEGG pathway analysis, were performed using R software. To identify genes under the control of interferon (IFN), the Interferome database was consulted, and DNA Methylation Scores were calculated. Results: Numbers and proportions of CD4+ T-cell subsets (naïve, central memory, effector memory, CD45RA re-expressing effector memory cells) did not vary between controls, skin psoriasis and PsA patients. 883 differentially methylated positions (DMPs) affecting 548 genes were identified between healthy controls and “all” psoriasis patients. Principal component and partial least-squares discriminant analysis separated controls from skin psoriasis and PsA patients. GO analysis considering promoter DMPs delivered hypermethylation of genes involved in “regulation of wound healing, spreading of epidermal cells”, “negative regulation of cell-substrate junction organization” and “negative regulation of focal adhesion assembly”. Comparing controls and “all” psoriasis, a majority of DMPs mapped to IFN-related genes (69.2%). Notably, DNA methylation profiles also distinguished skin psoriasis from PsA patients (2,949 DMPs/1,084 genes) through genes affecting “cAMP-dependent protein kinase inhibitor activity” and “cAMP-dependent protein kinase regulator activity” (GO analysis). Treatment with cytokine inhibitors (IL-17/TNF) corrected DNA methylation patterns of IL-17/TNF-associated genes, and methylation scores correlated with skin disease activity scores (PASI). Conclusion: DNA methylation profiles in CD4+ T-cells discriminate between skin psoriasis and PsA. DNA methylation signatures may be applied for quantification of disease activity and patient stratification towards individualized treatment. The aim of this study was to identify disease-associated DNA methylation signatures in CD4+ T-cells from patients with psoriasis and PsA that may be used as diagnostic and/or prognostic biomarkers to inform treatment and care.
2023-09-14 | GSE236694 | GEO
Project description:Background: Psoriasis is a systemic inflammatory disease primarily affecting the skin. Approximately one-third of psoriasis patients develop joint involvement and are diagnosed with psoriatic arthritis (PsA). While, inIn adult-onset disease, adults, the development of arthritis usually follows skin psoriasis, but approximately 15% experience arthritis first, which can delay diagnosis. While the pathophysiology of psoriasis and PsA is incompletely understood, epigenetic dysregulation affecting CD4+ and CD8+ T-cells has been suggested. Objectives: This project aimed to identify disease-associated DNA methylation signatures in CD4+ T-cells from psoriasis and PsA patients that may be used as diagnostic and/or prognostic biomarkers. Methods: PBMCs were collected from 12 patients with chronic plaque skin psoriasis and 8 PsA patients, and 8 healthy controls. CD4+ T-cells were separated through FACS sorting, and DNA methylation profiling was performed (Illumina EPIC850K arrays). Bioinformatic analyses, including gene ontology (GO) and KEGG pathway analysis, were performed using R software. To identify genes under the control of interferon (IFN), the Interferome database was consulted, and DNA Methylation Scores were calculated. Results: Numbers and proportions of CD4+ T-cell subsets (naïve, central memory, effector memory, CD45RA re-expressing effector memory cells) did not vary between controls, skin psoriasis and PsA patients. 883 differentially methylated positions (DMPs) affecting 548 genes were identified between healthy controls and “all” psoriasis patients. Principal component and partial least-squares discriminant analysis separated controls from skin psoriasis and PsA patients. GO analysis considering promoter DMPs delivered hypermethylation of genes involved in “regulation of wound healing, spreading of epidermal cells”, “negative regulation of cell-substrate junction organization” and “negative regulation of focal adhesion assembly”. Comparing controls and “all” psoriasis, a majority of DMPs mapped to IFN-related genes (69.2%). Notably, DNA methylation profiles also distinguished skin psoriasis from PsA patients (2,949 DMPs/1,084 genes) through genes affecting “cAMP-dependent protein kinase inhibitor activity” and “cAMP-dependent protein kinase regulator activity” (GO analysis). Treatment with cytokine inhibitors (IL-17/TNF) corrected DNA methylation patterns of IL-17/TNF-associated genes, and methylation scores correlated with skin disease activity scores (PASI). Conclusion: DNA methylation profiles in CD4+ T-cells discriminate between skin psoriasis and PsA. DNA methylation signatures may be applied for quantification of disease activity and patient stratification towards individualized treatment. The aim of this study was to identify disease-associated DNA methylation signatures in CD4+ T-cells from patients with psoriasis and PsA that may be used as diagnostic and/or prognostic biomarkers to inform treatment and care.
2023-09-14 | GSE236695 | GEO
Project description:Genotyping was performed using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina) to determine possible associations of genes encoding PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR,SERPNA1, PCSK6, DNAH9 to rheumatoid arthrities (RA) and ankylosing spondylitis (AS). We previously found that encoding genes of PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR, SERPNA1, PCSK6 and DNAH9 could be associated with rheumatoid arthritis (RA) and ankylosing spondylitis (AS). We used illumina customer designed microarray to verify the above finding with an increased numbers of samples. Genotyping was performed using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina) to determine possible associations of genes encoding PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR, SERPNA1, PCSK6, DNAH9 to RA and AS. A case-control study was performed with 51 AS patients, 266 rheumatoid arthritis (RA) and 163 health controls. Tag single nucleotide polymorphisms (tag SNPs) across the above genes were determined by searching the HapMap database. The tag SNPs were selected on the basis of linkage disequilibrium patterns observed in the Han Chinese in Beijing (CHB) samples that were genotyped as a part of the International HapMap Project. Only SNPs with a minor allele frequency of greater than 5% with a pair-wise r2 ≥ 0.8 in the HapMap were considered. SNPs that were in exons and untranslated regions (UTR), as well as promoters and introns within 500 bp of exons were also selected. Candidate SNPs were submitted to Illumina for a design score. The Illumina Assay Design Tool filtered out SNPs that were not suitable for the Illumina platform. Finally, 382 SNPs with a design score of “1” were selected. 382 SNPs within PADI1, PADI2, PADI3, PADI4, PADI6, PRKD3, Gc, GLRX,CDSN, PSORS1C1, TXNDC5, CA1, bub3, SORBS1, VDR,SERPNA1, PCSK6, DNAH9 encoding genes were selected. Genotyping was performed using Illumina VeraCode microarray in a case-control study including 51 AS patients, 266 rheumatoid arthritis (RA) and 163 health controls We performed genotyping using a custom-designed Illumina 384-SNP VeraCode microarray (Illumina). Peripheral blood samples were collected from patients with RA (n=266, 183 female) and AS (n=51, 10 female). RA patients had a mean age of 51.7 years, while AS patients had a mean age of 35.9 years. A total of 163 (60 female) healthy individuals with a mean age of 48.0 years were blood donors. Blood samples were put into Monovette tubes containing 3.8% sodium citrate. The genotyping was conducted with the BeadXpress Reader using the Illumina VeraCode GoldenGate Assay kit. 500 ng of sample DNA were used per assay. Genotype clustering and calling were performed using BeadStudio software (Genomestudio V2010.2 Genotyping module V1.6) (Illumina). This work was completed at the Beijing Institute of Genomics.
2012-07-22 | E-GEOD-39428 | biostudies-arrayexpress