Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes

Abstract

The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding therapeutic decisions, and monitoring interventions. We previously demonstrated that plasma samples from recent-onset Type 1 diabetes (RO T1D) patients induce a proinflammatory transcriptional signature in freshly drawn peripheral blood mononuclear cells (PBMCs) relative to that of unrelated healthy controls (HC). Here, using cryopreserved PBMC, we analyzed larger RO T1D and HC cohorts, examined T1D progression in pre-onset samples, and compared the RO T1D signature to those associated with three disorders characterized by airway infection and inflammation. The RO T1D signature, consisting of interleukin-1 cytokine family members, chemokines involved in immunocyte chemotaxis, immune receptors, and signaling molecules, was detected during early pre-diabetes and found to resolve post-onset. The signatures associated with cystic fibrosis patients chronically infected with Pseudomonas aeruginosa, patients with confirmed bacterial pneumonia, and subjects with H1N1 influenza all reflected immunological activation, yet each were distinct from one another and negatively correlated with that of T1D. This study highlights the remarkable capacity of cells to serve as biosensors capable of sensitively and comprehensively differentiating immunological states.

Functional relevance for type 1 diabetes mellitus-associated genetic variants by using integrative analyses

“…These studies were performed with original purposes of identifying transcriptional signatures as a disease-specific and predictive inflammatory biomarker underlying type 1 DM in peripheral blood mononuclear cells (PBMCs) or monocytes [11,12]. Details on sample quality control and experimental procedures were previously described in the original publication [11][12][13][14]. Then, we performed T-test to identify differential expression of the eQTL genes by comparing mean gene expression signals in peripheral blood mononuclear cells (PBMCs) and monocytes between type 1 DM cases and controls.…”

Gene expression analysis for pneumonia caused by Gram-positive bacterial infection

“…Expression profiles of gene arrays were downloaded from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/). Two independent datasets, GSE6269 (14) and GSE35716 (15), were selected to analyze the DEGs. The dataset GSE6269 consisted of 44 samples from the blood leukocytes of pediatric patients with Strepto coccus pneumonia infection and 7 unrelated healthy controls based on the platform Affymetrix Human Genome U133 Array (Affymetrix; Thermo Fisher Scientific, Inc, Waltham, MA, USA).…”

Revealing pathway cross-talk related to diabetes mellitus by Monte Carlo Cross-Validation analysis

“…In the present study, the gene expression dataset (accession number: E-GEOD-35725) [13] was downloaded from the ArrayExpress Archive of Functional Genomics Data (http://www.ebi.ac.uk/arrayexpress/) which is an international functional genomics database at the European Bioinformatics Institute (EMBL-EBI). This data was derived from samples that were analyzed by the platform of A-AFFY-44 -Affymetrix GeneChip Human Genome U133 Plus 2.0 [HG-U133_Plus_2].…”