Cross Tissue Trait-Pathway Network Reveals the Importance of Oxidative Stress and Inflammation Pathways in Obesity-Induced Diabetes in Mouse

Wang X (2012) Cross Tissue Trait-Pathway Network Reveals the Importance of Oxidative Stress and Inflammation Pathways in Obesity-Induced Diabetes in Mouse. PLoS ONE 7(9): e44544. doi:10.1371/journal.pone.0044544 Cross Tissue Trait-Pathway Network Reveals the Importance of Oxidative Stress and Inflammation Pathways in Obesity-Induced Diabetes in Mouse Shouguo Gao 0 Herbert Keith Roberts 0 Xujing Wang 0 Gualtiero Colombo, Centro Cardiologico Monzino IRCCS, Italy 0 1 Department of Physics, University of Alabama at Birmingham , Birmingham , Alabama, United States of America, 2 The Comprehensive Diabetes Center, University of Alabama at Birmingham , Birmingham, Alabama , United States of America Complex disorders often involve dysfunctions in multiple tissue organs. Elucidating the communication among them is important to understanding disease pathophysiology. In this study we integrate multiple tissue gene expression and quantitative trait measurements of an obesity-induced diabetes mouse model, with databases of molecular interaction networks, to construct a cross tissue trait-pathway network. The animals belong to two strains of mice (BTBR or B6), of two obesity status (obese or lean), and at two different ages (4 weeks and 10 weeks). Only 10 week obese BTBR animals are diabetic. The expression data was first utilized to determine the state of every pathway in each tissue, which is subsequently utilized to construct a pathway co-expression network and to define trait-relevant and trait-linking pathways. Among the six tissues profiled, the adipose contains the largest number of trait-linking pathways. Among the eight traits measured, the body weight and plasma insulin level possess the most number of relevant and linking pathways. Topological analysis of the trait-pathway network revealed that the glycolysis/gluconeogenesis pathway in liver and the insulin signaling pathway in muscle are of top importance to the information flow in the network, with the highest degrees and betweenness centralities. Interestingly, pathways related to metabolism and oxidative stress actively interact with many other pathways in all animals, whereas, among the 10 week animals, the inflammation pathways were preferentially interactive in the diabetic ones only. In summary, our method offers a systems approach to delineate disease trait relevant intra- and cross tissue pathway interactions, and provides insights to the molecular basis of the obesity-induced diabetes. - Funding: Funding was provided by United States National Institutes of Health (NIH) NIH-DK080100. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Phenotypic traits are properties that emerge from the interactions of genes within a dynamic environmental framework. A major goal of systems biology is to understand how the interactions lead to the observed traits [1,2]. This is especially critical in the study of complex diseases, where it is evident that they cannot be deciphered through considering individual genes only. A disease trait normally correlates with the inability of a particular functional network module to carry out its basic function, and the pathogenesis of a complex disease can involve the perturbations of more than one module. In a complex disease like Type 2 Diabetes (T2D), a spectrum of traits, such as obesity, hyperglycemia, insulin resistance, etc, are associated to the risk of developing T2D. These suggest that multiple functional pathways are involved. Further complexities arise from the fact that multiple tissues are involved and the crosstalk among them is important to disease development. For instance, problems in both the insulin secreting pancreatic islets and target tissues of insulin action are observed in diabetics, and are believed to contribute to disease pathogenesis [38]. Therefore it will be highly valuable to map out the signaling pathways, and their interactions, both intra and cross tissue, underlying the disease traits. A basic bioinformatics question that arises is, in mapping the genetic architecture of a disease, is it more efficient to develop gene level metrics and make assessment of gene networks through their members relevance to disease; or to develop network level metrics that directly assess a whole network? In the gene expression data analysis, both approaches were developed, either starting with differentially expressed genes followed by identifying pathways with enhanced presentation among them; or starting with pathways (or predefined gene set) directly through evaluating the expression distribution shift of the whole gene set [9]. Networking individual genes is known to suffer from high noise and high false positive rate. On the other hand, networking pathways has demonstrated its advantage in providing more relevant biological insights in understanding disease pathogenesis and in establishing the inter- disease relationships [3,10]. For instance, Hu and Li proposed a framework to construct a network of pathways according to co-expression between genes in different pathways [3]. Pathways relevant to each disease are ascertained from the disease induced differential expression of their members. When applied to T2D and obesity, they demonstrated that the method can identify signature pathways for each disease and establish valid association between them. Li et al proposed an approach that first defines disease associated genes through literature mining, followed by associating pathways to diseases based on enriched disease gene presence among pathway members, and linking different diseases based on pathway sharing [11]. To our knowledge, there is no study till now that focuses on delineating the association of clinical traits related to the same disease at the molecular pathway level. Recently, Keller et al profiled gene expression in six tissues (pancreatic islet, liver, adipose, hypothalamus, gastrocnemius, and soleus), and measured eight quantitative traits (including plasma, glucose, and insulin) in a mouse model of obesity-induced diabetes [7]. They constructed co-expression networks in each tissue and linked network modules (densely connected subregions of the whole network) to traits if the average expression of members correlates to trait variations. A number of modules in islet, adipose, and soleus were found to strongly correlate with plasma glucose; several modules in islets, liver, and adipose exhibited a high correlation with insulin but not with glucose; a module in adipose correlated with inflammation. They further constructed intra- and inter-tissue networks of the pathways by linking those whose first principal components correlated. It was found that there was substantial tissue difference in the degree of intra-tissue connectivity. In both BTBR and B6 mouse strains, the two muscle tissues had (...truncated)