To date, most existing cellular or immunological evaluations of psoriasis inflammation have been limited by the low-resolution data used for analysis and often rely on gross clinical endpoints. The overall goal of the CORT is to combine new bioinformatic methodologies with advanced murine and human experimental approaches to translate scientific findings into clinical applications that more nimbly advance therapy for psoriasis and related inflammatory comorbidities. Systems biology provides an unbiased approach to generating a comprehensive assessment of the host responses involved in psoriasis-mediated inflammation that may lead to new therapy targets for psoriasis and ideally, those tailored for the specific patient. This evolving investigative paradigm driven by modern high-throughput technologies that generate enormous datasets (?Big Data?) has engendered the need for a centralized ?omics core service and analysis platforms that the CORT Applied Meta?Omics Core (AMC) will provide. The AMC is designed to serve as an innovative collaborative resource with bidirectional experimental ties to the Collaborative Research Project (CRP) and Preclinical Modeling Core (PMC). The AMC will receive samples from the CRP and PMC, generate transcriptomic, metabolomic, microbiome and mycobiome datasets, and provide the bioinformatic pipelines to analyze those datasets in conjunction with clinical outcomes and/or other data types, including incorporation of artificial intelligence, data mining, network techniques and machine learning to enable identification of novel pathways and differentially-expressed gene targets that will allow for identification of new and/or re-purposed drugs. The AMC supports the CORT CRP?s overall objectives via the following aims: (1) perform SOP-driven sample acquisition, sample preparation, and ?omic assays for the PMC and CRP; (2) identify differential metabolomic, gene and pathway expression signatures, as well as changes in the micro/mycobiome, between psoriatic involved, uninvolved and control skin from human and psoriasiform murine models for the CRP; (3) (i) identify systems biology donor profiles (endotypes) and candidate biomarkers that may define the pathogenesis of psoriasis in humans and mouse models and (ii) identify FDA-approved drugs that are predicted to target the biomarkers and/or pathways associated with psoriasis. Highly interactive Cores synergistically interacting with the central CRP, the AMC team's expertise, innovation and extensive resources will drive the CORT's transforming and sustainable impact on psoriasis understanding and clinical care. The AMC will shed deeper insight into the pathological events in psoriasis, which can be translated into targeted diagnostic and therapeutic strategies to manage psoriasis at the individual level. By combining myco/microbiome, metabolomic, and transcriptomic analyses to identify pathological or protective correlates that are perturbed in disease, as well as their predicted drug targets, the AMC is envisioned to be critical in supporting the PMC and CRP in their goals of targeted therapeutics and precision, individualized treatments.

National Institute of Health (NIH)
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Specialized Center (P50)
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Special Emphasis Panel (ZAR1)
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Case Western Reserve University
United States
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Santus, Pierachille; Rizzi, Maurizio; Radovanovic, Dejan et al. (2018) Psoriasis and Respiratory Comorbidities: The Added Value of Fraction of Exhaled Nitric Oxide as a New Method to Detect, Evaluate, and Monitor Psoriatic Systemic Involvement and Therapeutic Efficacy. Biomed Res Int 2018:3140682
Damiani, Giovanni; Conic, Rosalynn R Z; de Vita, Valerio et al. (2018) When IL-17 inhibitors fail: Real-life evidence to switch from secukinumab to adalimumab or ustekinumab. Dermatol Ther :e12793
Wang, QuanQiu; McCormick, Thomas S; Ward, Nicole L et al. (2017) Combining mechanism-based prediction with patient-based profiling for psoriasis metabolomics biomarker discovery. AMIA Annu Symp Proc 2017:1734-1743