Cross-species gene expression analysis identifies a novel set of genes implicated in human insulin sensitivity

• Published in: NPJ systems biology and applications Vol. 1; no. 1; p. 15010
• Main Authors: Chaudhuri, Rima, Khoo, Poh Sim, Tonks, Katherine, Junutula, Jagath R, Kolumam, Ganesh, Modrusan, Zora, Samocha-Bonet, Dorit, Meoli, Christopher C, Hocking, Samantha, Fazakerley, Daniel J, Stöckli, Jacqueline, Hoehn, Kyle L, Greenfield, Jerry R, Yang, Jean Yee Hwa, James, David E
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.11.2015; Nature Publishing Group
• Subjects: 631/114; 631/553; Bioinformatics; Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Life Sciences; Systems Biology
• ISSN: 2056-7189; 2056-7189
• DOI: 10.1038/npjsba.2015.10

Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavored to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single gene, gene set, and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in three independent human data sets ( n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared with routine clinical measures across multiple independent data sets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Metabolic disease: Systems approach identifies genetic signature of insulin resistance A new molecular profile for insulin resistance (IR) has been identified by integrating gene expression (GE) profiles from two different species. Prof James from the University of Sydney and colleagues from Australia and the US sought an easier way to diagnose IR–one of the earliest predictors of type 2 diabetes. Generating a generic GE-based signature of the condition is difficult because, though gene activity changes, they vary among individuals. To solve this problem, James’s team combined GE patterns from mice fed high-fat diets and humans to generate a signature of IR including 93 different genes. When examined in 115 individuals from three different studies, this gene collection was able to better discriminate between insulin resistant and insulin sensitive patients compared to standard measures. Further investigation of this geneset could prove useful in the clinic.