Application of high-throughput gene expression technology to systemic sclerosis (SSc) skin biopsies, isolated SSc cell lines and peripheral blood cell (PBC) samples has shown that it will be an important tool for understanding the diversity in rheumatic diseases, as well as changes to the underlying gene expression pathways. The Translational Genomics and Data Integration (TGDI) core will use novel bioinformatic and genomic methods that have been developed and already successfully implemented in the core PI's laboratory to analyze SSc samples and healthy controls. High quality RNA will be prepared and analyzed by RNA-sequencing using protocols established in the PIs laboratory. All data are processed using standard and novel methods that use a combination of algorithms that test for differential gene expression, enriched pathways analysis and put the changes into the context of the all publicly available SSc high- throughput data. The TGDI core provides network analyses using a Scleroderma Specific Network (SSN) to analyze data from cells lines, mouse models, clinical trials, and single cell RNA-seq (scRNA-seq), thus providing a measure of how well a therapy eliminates the aberrant gene expression we observe in SSc. The goals of this core are to 1) Provide high quality RNA-seq analyses for individual projects and process the resulting data in a rigorously controlled analysis pipeline to provide differential gene expression and patient subset assignments, 2) provide a systems biology and network analysis of gene expression data in SSc using our novel SSN, and 3) perform meta- analyses of SSc clinical trials using both existing data as well as new data generated as part of the CORT research projects. Relevance: High-throughput gene expression analysis has allowed the definition of subsets of SSc and identified deregulated pathways that can be targeted therapeutically. Recent studies have shown that a patient's subset or activated pathways at baseline can be predictive of clinical response. This core, provides high-throughput studies, data analyses for CORT investigators, bioinformatics and systems biology analyses of SSc samples.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Specialized Center (P50)
Project #
2P50AR060780-06A1
Application #
9370325
Study Section
Special Emphasis Panel (ZAR1)
Project Start
Project End
Budget Start
2017-09-15
Budget End
2018-06-30
Support Year
6
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Type
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
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Franks, Jennifer M; Cai, Guoshuai; Whitfield, Michael L (2018) Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data. Bioinformatics 34:1868-1874
Apostolidis, Sokratis A; Stifano, Giuseppina; Tabib, Tracy et al. (2018) Single Cell RNA Sequencing Identifies HSPG2 and APLNR as Markers of Endothelial Cell Injury in Systemic Sclerosis Skin. Front Immunol 9:2191
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Meiners, Silke; Evankovich, John; Mallampalli, Rama K (2018) The ubiquitin proteasome system as a potential therapeutic target for systemic sclerosis. Transl Res 198:17-28
Rice, Lisa M; Mantero, Julio C; Stratton, Eric A et al. (2018) Serum biomarker for diagnostic evaluation of pulmonary arterial hypertension in systemic sclerosis. Arthritis Res Ther 20:185
Taroni, Jaclyn N; Greene, Casey S; Martyanov, Viktor et al. (2017) A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis. Genome Med 9:27
Makino, Katsunari; Makino, Tomoko; Stawski, Lukasz et al. (2017) Blockade of PDGF Receptors by Crenolanib Has Therapeutic Effect in Patient Fibroblasts and in Preclinical Models of Systemic Sclerosis. J Invest Dermatol 137:1671-1681
Goswami, Rishov; Cohen, Jonathan; Sharma, Shweta et al. (2017) TRPV4 ION Channel Is Associated with Scleroderma. J Invest Dermatol 137:962-965

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