Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.25.221309v1?rss=1

Authors: Garzorz-Stark, N., Batra, R., Lauffer, F., Jargosch, M., Pilz, C., Roenneberg, S., Schaebitz, A., Boehner, A., Seiringer, P., Thomas, J., Fereydouni, B., Tsoi, L. C., Gudjonsson, J. E., Theis, F. F., Biedermann, T., Schmidt-Weber, C. B., Mueller, N., Eyerich, S., Eyerich, K.

Abstract:

Chronic inflammatory diseases of the cardiovascular system, brain, gut, joints, skin and lung are characterized by complex interactions between genetic predisposition and tissue-specific immune responses. This heterogeneity complicates diagnoses and the ability to exploit omics approaches to improve disease management, develop more effective therapeutics, and apply precision medicine. Using skin inflammation as a model, we developed a bio-computational approach that assigns deep clinical phenotyping information to transcriptome data of lesional and non-lesional skin (564 samples) to identify biologically-relevant gene signatures. This identified previously unknown key factors, including CCAAT Enhancer-Binding Protein Beta (CEBPB) in neutrophil invasion, and Pituitary Tumor-Transforming 2 (PTTG2) in the pathogenic epithelial response to inflammation. These were validated using genetically-modified human skin equivalents, migration assays, and in situ imaging. Thus, by combining deep clinical phenotyping and omics data with sophisticated bio-computational algorithms we present a methodological advance to identify hidden drivers of clinically-relevant biological processes within omics datasets.

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