Specific gene expression signature associated with development of autoimmune type-I diabetes using whole-blood microarray analysis

Importance of correlation between gene expression levels: application to the type I interferon signature in rheumatoid arthritis

Background

The 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 Findings

Using 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.

Conclusions

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.

Immune profiling by multiple gene expression analysis in patients at-risk and with type 1 diabetes

There is a need for biomarkers to monitor the development and progression of type 1 DM. We analyzed mRNA expression levels for granzyme B, perforin, fas ligand, TNF-α, IFN-γ, Foxp3, IL-10, TGF-β, IL-4, IL-6, IL-17, Activation-induced cytidine deaminase (AID) and Immunoglobulin G gamma chain (IgG<gamma>) genes in peripheral blood of at-risk, new-onset and long-term type 1 DM , and healthy controls. The majority of the genes were suppressed in long-term type 1 DM compared to controls and new-onset patients. IFN-γ, IL-4 and IL-10 mRNA levels were significantly higher in new-onset compared to at-risk and long-term groups. There was decreased mRNA expression for AID and IgG<gamma> and up-regulation of IFN-γ with age in controls. Data suggest an overall depressed immunity in long-term type 1 DM. Increased gene expression levels for IFN-γ, IL-4 and IL-10 in new-onset patients from at-risk patients might be used as potential markers for progression of the disease.

Global gene expression changes in type 1 diabetes: insights into autoimmune response in the target organ and in the periphery

Type 1 diabetes (T1D) is an autoimmune disease caused by the selective destruction of the insulin-producing β cells. Research into the pathogenesis of T1D has been hindered by the lack of detection of the autoimmune process during the asymptomatic period and by the inaccessibility to the target tissue. Therefore current understanding of the immunological phenomena that take place in the pancreas of the patients is very limited and much of the current knowledge on T1D has been obtained using animal models. Microarray technology and bioinformatics allow the comparison of the gene expression profile - transcriptome - in normal and pathological conditions, creating a global picture of altered processes. Microarray experiments have defined new transcriptional alterations associated with several autoimmune diseases, and are focused on the identification of specific biomarkers. In this review we summarize current data on gene expression profiles in T1D from an immunological point of view. Reported transcriptome studies have been performed in T1D patients and Non-Obese Diabetic mouse models analyzing peripheral blood, lymphoid organs and pancreas/islets. In the periphery, the distinctive profiles are inflammatory pathways inducible by IL-1β and IFNs that can help in the identification of new biomarkers. In the target organ, a remarkable finding is the overexpression of inflammatory and innate immune response genes and the active autoimmune response at longstanding stages, contrary to the pre-existing concept of acute autoimmune process in T1D.

Advances and challenges in biomarker development for type 1 diabetes prediction and prevention using omic technologies

IMPORTANCE OF THE FIELD: Biomarkers are essential for the identification of high risk children as well as monitoring of prevention outcomes for type 1 diabetes (T1D). AREAS COVERED IN THIS REVIEW: This review discusses progress, opportunities and challenges in biomarker discovery and validation using high throughput genomic, transcriptomic and proteomic technologies. The authors also suggest potential solutions to deal with the current challenges. WHAT THE READER WILL GAIN: Readers will gain an overview of the current status on T1D biomarkers, an integrated review of three omic technologies, their applications and limitations for biomarker discovery and validation, and a critical discussion of the major issues encountered in biomarker development. TAKE HOME MESSAGE: Better biomarkers are still urgently needed for T1D prediction and prevention. The high throughput omic technologies offer great opportunities but also face significant challenges that have to be solved before their potential for biomarker development is fully realized.