Importance of Correlation between Gene Expression Levels: Application to the Type I Interferon Signature in Rheumatoid Arthritis

Frédéric Reynier,Fabien Petit,Arnaud Hot,Fanny Turrel-Davin,Malick Paye,Pierre-Emmanuel Imbert,Bruno Mougin,Pierre Miossec,Pierre Bobé

doi:10.1371/journal.pone.0024828

Abstract

BackgroundThe analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals.Methodology/Principal FindingsUsing blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients.ConclusionsIn conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation.

Highlights

A wide range of methods for microarray data analysis have evolved, ranging from simple fold-change approaches to many complex and computationally demanding techniques [1]
In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Summary

Introduction

A wide range of methods for microarray data analysis have evolved, ranging from simple fold-change approaches to many complex and computationally demanding techniques [1]. Gene expression profiling by microarray technology has become a widely used strategy for investigating the molecular mechanisms underlying many complex diseases [2]. A common observation in the analysis of gene expression is that many genes show similar expression patterns [3] which may share biological functions under common regulatory control. These co-expressed genes are frequently clustered according to their expression patterns in subset of experimental conditions [4]. Bi-clustering methods seek gene similarity in subsets of available conditions, which is more appropriate for functionally heterogeneous data [5,6]. We investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals

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Discussion

Conclusion