Understanding the pathogenesis of type-I diabetes (T1D) is hindered in humans by the long autoimmune process occurring before clinical onset and by the difficulty to study the pancreas directly. Alternatively, exploring body fluids and particularly peripheral blood can provide some insights. Indeed, circulating cells can function as 'sentinels', with subtle changes in gene expression occurring in association with disease. Therefore, we investigated the gene expression profiles of circulating blood cells using Affymetrix microarrays. Whole-blood samples from 20 first-degree relatives of T1D children with autoimmune diabetes-related antibodies, 19 children immediately after the onset of clinical T1D and 20 age-and sex-matched healthy controls were collected in PAXgene tubes. A global gene expression analysis with MDS approach allowed the discrimination of pre-diabetic subjects, diabetic patients and healthy controls. Univariate statistical analysis highlighted 107 distinct genes differently expressed between these three groups. Two major gene expression profiles were characterized, including type-I IFN-regulated genes and genes associated with biosynthesis and oxidative phosphorylation. Our results showed the presence of early functional modifications associated with T1D, which could help to understand the disease and suggest possible avenues for therapeutic interventions.

Recent advances in sequencing, mass spectrometry and cytometry technologies have enabled researchers to collect large-scale omics data from the same set of biological samples. The joint analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omic layers. In this work, we present a thorough comparison of a selection of recent integrative clustering approaches, including Bayesian (BCC and MDI) and matrix factorization approaches (iCluster, moCluster, JIVE and iNMF). Based on simulations, the methods were evaluated on their sensitivity and their ability to recover both the correct number of clusters and the simulated clustering at the common and data-specific levels. Standard non-integrative approaches were also included to quantify the added value of integrative methods. For most matrix factorization methods and one Bayesian approach (BCC), the shared and specific structures were successfully recovered with high and moderate accuracy, respectively. An opposite behavior was observed on non-integrative approaches, i.e. high performances on specific structures only. Finally, we applied the methods on the Cancer Genome Atlas breast cancer data set to check whether results based on experimental data were consistent with those obtained in the simulations.

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.