# Core 3: Translational Genomics and Data Integration Core

> **NIH NIH P50** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $161,508

## Abstract

Abstract / Project Summary 
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.

## Key facts

- **NIH application ID:** 10022106
- **Project number:** 5P50AR060780-09
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** MICHAEL L WHITFIELD
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $161,508
- **Award type:** 5
- **Project period:** 2011-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10022106

## Citation

> US National Institutes of Health, RePORTER application 10022106, Core 3: Translational Genomics and Data Integration Core (5P50AR060780-09). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10022106. Licensed CC0.

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