Dataset Information

Discovery and Confirmation of a Protein Biomarker Panel with Potential to Predict Response to Biologic Therapy in Psoriatic Arthritis

ABSTRACT: An integrated discovery to targeted proteomics approach was used to investigate the protein profiles of good and non–responders to anti-TNF-alpha and T-cell inhibitor treatments in PsA patients. Reverse phase liquid chromatography coupled to tandem mass spectrometry was used to generate protein profiles of synovial tissue obtained at baseline from 10 PsA patients who then commenced anti-TNF-alpha therapy (adalimumab). Targeted proteomics using multiple reaction monitoring was used to confirm and pre-validate a potential protein biomarker panel in 18 and 7 PsA patient samples respectively.

INSTRUMENT(S): 6520A Quadrupole Time-of-Flight LC/MS

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Synovial Tissue

DISEASE(S): Psoriatic Arthritis

SUBMITTER: Stephen Pennington

LAB HEAD: Stephen Roy Pennington

PROVIDER: PXD000707 | Pride | 2019-12-17

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	110408_CR_Stella_Sample_A2.mzXML	Mzxml
	110408_CR_Stella_Sample_A2.tandem.pep.xml	Pepxml
	110408_CR_Stella_Sample_A3.mzXML	Mzxml
	110408_CR_Stella_Sample_A3.tandem.pep.xml	Pepxml
	110408_CR_Stella_Sample_A4.mzXML	Mzxml

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Publications

Discovery and confirmation of a protein biomarker panel with potential to predict response to biological therapy in psoriatic arthritis.

Ademowo Opeyemi S OS Hernandez Belinda B Collins Emily E Rooney Cathy C Fearon Ursula U van Kuijk Arno W AW Tak Paul-P PP Gerlag Danielle M DM FitzGerald Oliver O Pennington Stephen R SR

Annals of the rheumatic diseases 20140903 1

<h4>Objective</h4>Biological therapies, which include antitumour necrosis factor-α and T-cell inhibitors, are potentially effective treatments for psoriatic arthritis (PsA) but are costly and may induce a number of side effects. Response to treatment in PsA is variable and difficult to predict. Here, we sought to identify a panel of protein biomarkers that could be used to predict which patients diagnosed with PsA will respond to biologic treatment.<h4>Methods</h4>An integrated discovery to targ ...[more]

PMID: 25187158

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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.