Gene network analysis of small molecules with autoimmune disease associated genes predicts a novel strategy for drug efficacy

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Combined protein- and nucleic acid-level effects of rs1143679 (R77H), a lupus-predisposing variant within ITGAM

Integrin alpha M (ITGAM; CD11b) is a component of the macrophage-1 antigen complex, which mediates leukocyte adhesion, migration and phagocytosis as part of the immune system. We previously identified a missense polymorphism, rs1143679 (R77H), strongly associated with systemic lupus erythematosus (SLE). However, the molecular mechanisms of this variant are incompletely understood. A meta-analysis of published and novel data on 28 439 individuals with European, African, Hispanic and Asian ancestries reinforces genetic association between rs1143679 and SLE [Pmeta = 3.60 × 10(-90), odds ratio (OR) = 1.76]. Since rs1143679 is in the most active region of chromatin regulation and transcription factor binding in ITGAM, we quantitated ITGAM RNA and surface protein levels in monocytes from patients with each rs1143679 genotype. We observed that transcript levels significantly decreased for the risk allele ('A') relative to the non-risk allele ('G'), in a dose-dependent fashion: ('AA' < 'AG' < 'GG'). CD11b protein levels in patients' monocytes were directly correlated with RNA levels. Strikingly, heterozygous individuals express much lower (average 10- to 15-fold reduction) amounts of the 'A' transcript than 'G' transcript. We found that the non-risk sequence surrounding rs1143679 exhibits transcriptional enhancer activity in vivo and binds to Ku70/80, NFKB1 and EBF1 in vitro, functions that are significantly reduced with the risk allele. Mutant CD11b protein shows significantly reduced binding to fibrinogen and vitronectin, relative to non-risk, both in purified protein and in cellular models. This two-pronged contribution (nucleic acid- and protein-level) of the rs1143679 risk allele to decreasing ITGAM activity provides insight into the molecular mechanisms of its potent association with SLE.

Protein-protein interaction networks (PPI) and complex diseases

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network.