Abstract
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.
Highlights
Phenotypic traits are properties that emerge from the interactions of genes within a dynamic environmental framework
We identified a number of trait-relevant tissue-specific KEGG pathways: 186 for insulin, 32 for glucose, 19 for islet number, 34 for PAI1, 100 for resistin, 107 for TG, 209 for weight, and 9 for adiponectin
It utilizes the Pathway Connectivity Index (PCI) to capture the overall activity of each pathway under given experimental conditions, which incorporates contributions from both the individual genes and how they are geometrically situated in the network
Summary
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. 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. It will be highly valuable to map out the signaling pathways, and their interactions, both intra and cross tissue, underlying the disease traits
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