title: Functional network analysis of obese and lean Göttingen minipigs elucidates changes in oxidative and inflammatory networks in obese pigs.
abstract: The Göttingen minipig model of obesity is used in pre-clinical research to predict clinical outcome of new treatments for metabolic diseases. However, treatment effects often remain unnoticed when using single parameter statistical comparisons due to the small numbers of animals giving rise to large variation and insufficient statistical power. The purpose of this study was to perform a correlation matrix analysis of multiple multi-scale parameters describing co-segregation of traits in order to identify differences between lean and obese minipigs. More than 40 parameters, ranging from physical, cardiovascular, inflammatory and metabolic markers were measured in lean and obese animals. Correlation matrix analysis was performed using permutation test and bootstrapping at different levels of significance. Single parameter comparisons yielded significant differences between lean and obese animals mainly for known physical traits. On the other hand, functional network analysis revealed new co-segregations, particularly in the domain of inflammatory and oxidative stress markers in the obese animals that were not present in the lean. Functional networks of lean or obese minipigs could be utilised to assess drug effects and predict changes in parameters with a certain degree of precision, on the basis of the networks confidence intervals. Comparison of functional networks in minipigs with those of human clinical data may be used to identify common parameters or co-segregations related to obesity between animal models and man.
authors.affiliation: Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, 2100, Copenhagen, Denmark, hcmb@sund.ku.dk.
authors.forename: Harrie C M
authors.initials: HC
authors.lastname: Boonen
authors.forename: Sophia G
authors.initials: SG
authors.lastname: Moesgaard
authors.forename: Malene M
authors.initials: MM
authors.lastname: Birck
authors.forename: Berit O
authors.initials: BO
authors.lastname: Christoffersen
authors.forename: Susanna
authors.initials: S
authors.lastname: Cirera
authors.forename: Peter M H
authors.initials: PM
authors.lastname: Heegaard
authors.forename: Tina Rødgaard
authors.initials: TR
authors.lastname: Højbøge
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Jensen
authors.forename: Alan
authors.initials: A
authors.lastname: Mortensen
authors.forename: Lisbeth Høier
authors.initials: LH
authors.lastname: Olsen
authors.forename: Majid
authors.initials: M
authors.lastname: Sheykhzade
authors.forename: Jiaowei
authors.initials: J
authors.lastname: Tang
authors.forename: Jens
authors.initials: J
authors.lastname: Lykkesfeldt
mesh_terms: D000818:Animals; D001786:Blood Glucose / Q000378:metabolism; D001798:Blood Proteins / Q000378:metabolism; D001835:Body Weight; D002319:Cardiovascular System / Q000503:physiopathology*; D003331:Coronary Vessels / Q000473:pathology*; D005260:Female; D007249:Inflammation / Q000097:blood / Q000378:metabolism; D053858:Metabolic Networks and Pathways; D015233:Models, Statistical; D009765:Obesity / Q000097:blood / Q000378:metabolism* / Q000503:physiopathology; D018384:Oxidative Stress; D010641:Phenotype; D013552:Swine; D013556:Swine, Miniature; D014664:Vasodilation
chemical_list: D001786:Blood Glucose; D001798:Blood Proteins