Journal article
Master Regulators of Infiltrate Recruitment in Autoimmune Disease Identified through Network-Based Molecular Deconvolution
Cell systems, Vol.1(5), pp.326-337
11/25/2015
DOI: 10.1016/j.cels.2015.11.001
PMCID: PMC4670983
PMID: 26665180
Abstract
Network-based molecular modeling of physiological behaviors has proven invaluable in the study of complex diseases such as cancer, but these approaches remain largely untested in contexts involving interacting tissues such as in autoimmunity. Here, using Alopecia Areata (AA) as a model, we have adapted regulatory network analysis to specifically isolate physiological behaviors in the skin that contribute to the recruitment of immune cells in autoimmune disease. We use context-specific regulatory networks to deconvolve and identify skin-specific regulatory modules with IKZF1 and DLX4 as master regulators (MRs). These MRs are sufficient to induce AA-like molecular states in vitro in three cultured cell lines, resulting in induced NKG2D-dependent cytotoxicity. This work demonstrates the feasibility of a network-based approach for compartmentalizing and targeting molecular behaviors contributing to interactions between tissues in autoimmune disease.
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•Deconvolution of mixed tissue signatures using context-specific regulatory networks•IKZF1 and DLX4 are master regulators of Alopecia Areata pathogenesis•IKZF1 and DLX4 induce Alopecia Areata-like immune infiltration in cultured cells•This framework is applicable to other autoimmune diseases
Modeling complex diseases with pathology involving interacting tissues is difficult when using reverse-engineered regulatory networks due to the presence of different networks in a biopsy sample containing multiple cell types. Chen et al. introduce an approach for deconvolving regulatory modules that originate uniquely in one tissue and apply it to identify IKZF1 and DLX4 as key transcriptional regulators in Alopecia Areata.
Details
- Title: Subtitle
- Master Regulators of Infiltrate Recruitment in Autoimmune Disease Identified through Network-Based Molecular Deconvolution
- Creators
- James C Chen - Department of Dermatology, Columbia University, 1150 Saint Nicholas Avenue, New York, NY 10032, USAJane E Cerise - Department of Dermatology, Columbia University, 1150 Saint Nicholas Avenue, New York, NY 10032, USAAli Jabbari - Department of Dermatology, Columbia University, 1150 Saint Nicholas Avenue, New York, NY 10032, USARaphael Clynes - Department of Dermatology, Columbia University, 1150 Saint Nicholas Avenue, New York, NY 10032, USAAngela M Christiano - Department of Dermatology, Columbia University, 1150 Saint Nicholas Avenue, New York, NY 10032, USA
- Resource Type
- Journal article
- Publication Details
- Cell systems, Vol.1(5), pp.326-337
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.cels.2015.11.001
- PMID
- 26665180
- PMCID
- PMC4670983
- ISSN
- 2405-4712
- eISSN
- 2405-4720
- Language
- English
- Date published
- 11/25/2015
- Academic Unit
- Dermatology
- Record Identifier
- 9984025373402771
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