Molecular and genetic inflammation networks in major human diseases

Abstract

It has been well-recognized that inflammation alongside tissue repair and damage maintaining tissue homeostasis determines the initiation and progression of complex diseases. Albeit with the accomplishment of having captured the most critical inflammation-involved molecules, genetic susceptibilities, epigenetic factors, and environmental factors, our schemata on the role of inflammation in complex diseases remain largely patchy, in part due to the success of reductionism in terms of research methodology per se. Omics data alongside the advances in data integration technologies have enabled reconstruction of molecular and genetic inflammation networks which shed light on the underlying pathophysiology of complex diseases or clinical conditions. Given the proven beneficial role of anti-inflammation in coronary heart disease as well as other complex diseases and immunotherapy as a revolutionary transition in oncology, it becomes timely to review our current understanding of the molecular and genetic inflammation networks underlying major human diseases. In this review, we first briefly discuss the complexity of infectious diseases and then highlight recently uncovered molecular and genetic inflammation networks in other major human diseases including obesity, type II diabetes, coronary heart disease, late onset Alzheimer's disease, Parkinson's disease, and sporadic cancer. The commonality and specificity of these molecular networks are addressed in the context of genetics based on genome-wide association study (GWAS). The double-sword role of inflammation, such as how the aberrant type 1 and/or type 2 immunity leads to chronic and severe clinical conditions, remains open in terms of the inflammasome and the core inflammatome network features. Increasingly available large Omics and clinical data in tandem with systems biology approaches have offered an exciting yet challenging opportunity toward reconstruction of more comprehensive and dynamic molecular and genetic inflammation networks, which hold great promise in transiting network snapshots to video-style multi-scale interplays of disease mechanisms, in turn leading to effective clinical intervention.

Figure 1. Example immune response networks

A) A pharmacologically addressable response network is shown. By inhibiting CHEK1, viral protein production is severely limited. Node colors (cell viability) and node border colors (viral replication) refer to negative (blue) and positive (red) Z-scores after RNAi analysis indicating low and high viral replication/cell viability, respectively. B) Regulation of the mTOR pathway by a naphthalimide compound. REDD1 (DDIT4) is activated by the compound. Node colors indicate up- (red) and down- (blue) regulated genes between compound and DMSO (control) treated A549 cells. Edge colors denote edge scores (yellow edges have high score).

Figure 2

The relationship between immune, inflammasome, and inflammatome gene signatures.

Figure 3

A bar chart illustrating all possible intersections among the GWAS gene sets of the six common complex diseases and the inflammatory gene signatures. Complex diseases include obesity (OB), type II diabetes (T2DM), coronary heart disease (CHD), late onset Alzheimer Disease (AD), Parkinson disease (PD), and sporadic cancer (CA). Immune gene sets inflammatome (MSB) and innateDB curated immune genes (IMDB). Meanwhile, host factors (I1) and immune response signature (I2) are included. The matrix of solid and empty circles at the bottom illustrates the “presence” (solid green) or “absence” (empty) of the gene sets in each intersection. The numbers to the right of the matrix are set sizes. The colored bars on the top of the matrix represent the intersection sizes with the color intensity showing the P value significance.

Figure 4

A core causal network in inflammation. The predicted key drivers are highlighted in larger size and red color.