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

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.

Figure 1

A heat map of the inflammatome signature comprising 1499 upregulated and 984 downregulated genes. The rows represent the disease samples from the 12 data sets and the columns represent the 2483 signature genes that were grouped into two k-means clusters of upregulated and downregulated genes.

Figure 2

Topological overlap matrix (TOM) plots of weighted, gene coexpression networks constructed from one mouse studies (A–F) and four human studies including IFA (G–H), NKI (I), HLC (J) and HCC (K). Each symmetric heat map with rows and columns as genes represents the network connection strength (as indicated by the different shades of color—from white signifying not significantly correlated to red signifying highly significantly correlated) between any pair of nodes (genes) in the corresponding network. The network connection strength is measured as the topological overlap between genes. The network modules highlighted as color block along the rows and columns (each color block represents a module) were identified via an average linkage hierarchical clustering algorithm using topological overlap as the dissimilarity metric. In each network, the module highlighted with a black box is most enriched with the inflammatome signature. (A) Mouse male adipose, (B) mouse male liver, (C) mouse male muscle, (D) mouse female adipose, (E) mouse female liver, (F) mouse female muscle, (G) mouse male adipose, (H) human female adipose, (I) human breast cancer, (J) human normal liver, (K) human cancer liver.

Figure 3

A Venn diagram showing overlaps among the inflammatome, human macrophage-enriched metabolic network (MEMN), and mouse MEMN signatures. One-third of the inflammatome signature genes are in the human MEMN and the three signatures share 420 genes.

Figure 4

Inflammatome gene regulatory (Bayesian) networks and their predicted key drivers that are highlighted as large red nodes. The nodes in each network are the inflammatome signature genes and the directed links between them are derived from the causal networks reconstructed by integrating genetic and gene expression data in the corresponding cohort: (A) the human adipose IFA study; (B) the human liver HLC study. HCK, CD53, and TYROBP are the top drivers of both inflammatome subnetworks.