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Identifying hidden drivers of heterogeneous inflammatory diseases

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.

2020-07-26 immunology
10.1101/2020.07.25.221309 bioRxiv
Show abstract

Chronic inflammatory diseases 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 method that integrates deep clinical phenotyping information (phenomics) with transcriptome data of lesional and non-lesional skin (564 samples) to identify clinically-relevant gene signatures. It led us to discover so-far unexplored 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 factors were validated using genetically-modified human skin equivalents, migration assays, and in situ imaging. Thus, by meaningful integration of deep clinical phenotyping and omics data we reveal hidden drivers of clinically-relevant biological processes.

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