Abstract
The mouse is the main model organism used to study the functions of human genes because most biological processes in the mouse are highly conserved in humans. Recent reports that compared identical transcriptomic datasets of human inflammatory diseases with datasets from mouse models using traditional gene‐to‐gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. To reduce susceptibility to biased interpretation, all genes of interest for the biological question under investigation should be considered. Thus, standardized approaches for systematic data analysis are needed. We analyzed the same datasets using gene set enrichment analysis focusing on pathways assigned to inflammatory processes in either humans or mice. The analyses revealed a moderate overlap between all human and mouse datasets, with average positive and negative predictive values of 48 and 57% significant correlations. Subgroups of the septic mouse models (i.e., Staphylococcus aureus injection) correlated very well with most human studies. These findings support the applicability of targeted strategies to identify the optimal animal model and protocol to improve the success of translational research.
Highlights
The mouse has been used as model for heredity analysis since the 19th century
Gene set enrichment analysis (GSEA), which was established for use in metabolic studies in the 2000s (Subramanian et al, 2005), first maps all detected unfiltered transcripts to the intended pathways to allow the analysis of thousands of transcripts potentially involved in the concerted regulation of specific pathways
Because mouse models aim to mimic a specific disorder using different experimental manipulations, we focused on pathways involved in immunological processes annotated by the BioCarta, Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases
Summary
The relevance of mice as a valuable model organism for translational research has become increasingly controversial This discussion has been renewed by the study of Seok et al (2013) that compared transcriptomic data from human inflammatory diseases with data from mice challenged with inflammatory stimuli and the work of Takao & Miyakawa (2015) that reanalyzed the same datasets using different strategies and algorithms. To identify regulated signaling pathways, a statistical evaluation is subsequently performed using running sum statistics This approach is fundamentally different from assigning Gene Ontology (GO) terms or pathways to genes after filtering for strongly regulated genes and avoids the problems associated with cutoff values.
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