Systems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers

Molecular Systems Biology

I-Ming Wang⁴  Bin Zhang²  Xia Yang¹³  Jun Zhu²  Serguei Stepaniants⁸  Chunsheng Zhang⁴  Qingying Meng⁵  Mette Peters¹³  Yudong He⁷  Chester Ni¹²  Deborah Slipetz¹⁶  Michael A Crackower¹⁶  Hani Houshyar¹⁷  Christopher M Tan^4,18  Ernest Asante-Appiah¹⁶  Gary O'Neill¹⁶  Mingjuan Jane Luo^4,14  Rolf Thieringer^4,15  Jeffrey Yuan^4,10  Chi-Sung Chiu¹⁷  Pek Yee Lum^4,9  John Lamb^4,6  Yves Boie^3,4  Hilary A Wilkinson^1,4  Eric E Schadt²  Hongyue Dai^4,11 

[1] Department of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA;Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA;Genomics Platform, Broad Institute, Cambridge, MA, USA;Informatics and Analysis, Merck Research Laboratories, Merck & Co., Inc., West Point, PA, USA;Department of Integrative Biology and Physiology, University of California, Los Angeles, CA, USA;Oncology Research Unit, Pfizer, San Diego, CA, USA;Investigative Toxicology Department, Amgen, Seattle, WA, USA;Covance Genomics Laboratory, Seattle, WA, USA;Product, Ayasdi Inc., Palo Alto, CA, USA;World Wide Regulatory Affairs, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA;Informatics and Analysis, Merck Research Laboratories, Merck & Co., Inc., Boston, MA, USA;Systems Immunology, Benaroya Research Institute, Seattle, WA, USA;Sage Bionetworks, Seattle, WA, USA;Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN, USA;External Scientific Affairs, Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ, USA;Department of Respiratory and Inflammation, Merck Research Laboratories, Merck & Co., Inc., Boston, MA, USA;In Vivo Pharmacology, Merck Research Laboratories, Merck & Co., Inc., Boston, MA, USA;In Vivo Pharmacology, Merck Research Laboratories, Merck & Co., Inc., Kenilworth, NJ, USA

关键词: Bayesian network;    co‐expression network;    inflammatome;    inflammatory diseases;    key regulators;   

DOI  :  10.1038/msb.2012.24

来源: Wiley

【 摘 要 】

Abstract

Common inflammatome gene signatures as well as disease-specific signatures were identified by analyzing 12 expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models. The inflammatome signature significantly overlaps with known drug targets and co-expressed gene modules linked to metabolic disorders and cancer. A large proportion of genes in this signature are tightly connected in tissue-specific Bayesian networks (BNs) built from multiple independent mouse and human cohorts. Both the inflammatome signature and the corresponding consensus BNs are highly enriched for immune response-related genes supported as causal for adiposity, adipokine, diabetes, aortic lesion, bone, muscle, and cholesterol traits, suggesting the causal nature of the inflammatome for a variety of diseases. Integration of this inflammatome signature with the BNs uncovered 151 key drivers that appeared to be more biologically important than the non-drivers in terms of their impact on disease phenotypes. The identification of this inflammatome signature, its network architecture, and key drivers not only highlights the shared etiology but also pinpoints potential targets for intervention of various common diseases.

Synopsis

A common inflammatome signature, as well as disease-specific expression patterns, was identified from 11 different rodent inflammatory disease models. Causal regulatory networks and the drivers of the inflammatome signature were uncovered and validated.

• Representative inflammatome gene signatures, as well as disease model-specific gene signatures, were identified from 12 gene expression profiling data sets derived from 9 different tissues isolated from 11 rodent inflammatory disease models.
• The inflammatome signature is highly enriched for immune response-related genes, disease causal genes, and drug targets.
• Regulatory relationships among the inflammatome signature genes were examined in over 70 causal networks derived from a number of large-scale genetic studies of multiple diseases, and the potential key drivers were uncovered and validated prospectively.
• Over 70% of the inflammatome signature genes and over 50% of the key driver genes have not been reported in previous studies of common signatures in inflammatory conditions.

【 授权许可】

CC BY-NC-SA   
Copyright © 2012 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.