A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Summary

We provide a standardized protocol for the use of gene set enrichment analysis of transcriptomic data to identify an ideal mouse model for translational research.
This protocol can be used with DNA microarray and RNA sequencing data and can further be extended to other omics data if data are available.

Abstract

Recent studies that compared transcriptomic datasets of human 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. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models. Thus, standardized approaches for systematic data analysis are needed. To overcome subjective gene filtering and ineffective gene-to-gene comparisons, we recently demonstrated that gene set enrichment analysis (GSEA) has the potential to avoid these problems. Therefore, we developed a standardized protocol for the use of GSEA to distinguish between appropriate and inappropriate animal models for translational research. This protocol is not suitable to predict how to design new model systems a-priori, as it requires existing experimental omics data. However, the protocol describes how to interpret existing data in a standardized manner in order to select the most suitable animal model, thus avoiding unnecessary animal experiments and misleading translational studies.

Introduction

Animal models are widely used to study human diseases, because of their assumed similarity to humans in terms of genetics, anatomy, and physiology. Moreover, animal models often serve as gatekeepers to clinical therapies and can have a huge impact on the success of translational research. Careful selection of the optimal animal model can reduce the number of misleading animal studies. Recently, the relevance of animal models for translational research has been controversially discussed, particularly because analyzing the same datasets obtained from human inflammatory diseases and related mouse models led to contradictory conclusions ^1,

Protocol

1. Download of the GSEA Software and the Molecular Signatures Database

1. Go to the official GSEA Broad Institute website (http://software.broadinstitute.org/gsea/index.jsp) and register to get access to the GSEA software tool and the Molecular Signatures Database (MSigDB).
2. Download the javaGSEA desktop application or an alternative software option (e.g., R script).
   NOTE: All options implement exactly the same algorithm. The GSEA software is freely available to individuals in academia and industry for internal research purposes.
3. For further details on the GSEA software go to the documentation website (http://software.broadins....

Results

The GSEA workflow and screenshots of exemplary data are demonstrated. Figure 1 shows the gene expression data file that contains the transcriptomic data of interest. For every study a descriptive phenotype file is required that is shown in Figure 2. Annotated gene sets (e.g., pathways) are defined in the gene set database file (Figure 3). Figure 4 shows a step-b.......

Discussion

Animal models have long been applied for the investigation of disease mechanisms and the development of novel therapeutic strategies. However, skepticism regarding the predictivity of animal models started to spread following failures of clinical trials¹². Furthermore, controversial discussions about appropriate strategies for analyzing and interpreting big omics data from preclinical trials were raised by opposite conclusions drawn from the same data after applying differing data analysis strateg.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Excel	Microsoft Corporation