Low C-peptide levels and decreased expression of TNF and CD45 in children with high risk of type 1 diabetes

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort

Background

Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk.

Methods

Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA.

Results

All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated.

Conclusions

Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Investigating immune profile by CyTOF in individuals with long-standing type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease caused by T-cell mediated destruction of pancreatic beta cells. Eosinophils are found in pancreatic tissue from individuals with T1D. Eosinophilic suppression of T cells is dependent of the protein galectin-10. Little is known when it comes to the role of eosinophil granulocytes in type 1 diabetes. Here we show that individuals with long-standing T1D had lower levels of galectin-10^hi eosinophils and a subgroup of galectin-10^hi eosinophils were entirely absent in all T1D patients. In addition, 7% immature eosinophils were present in the circulation of T1D patients whereas 0.8% in healthy individuals. Furthermore, higher levels of CD4+CD8+ T cells and Th17 cells were observed in patients with T1D. Blood samples from 12 adult individuals with long-standing T1D and 12 healthy individuals were compared using cytometry by time-of-flight. Lower levels of galectin-10^hi eosinophils, which are potent T cell suppressors, in individuals with T1D could indicate that activated T cells are enabled to unrestrictedly kill the insulin producing beta cells. This is the first study showing absence of galectin-10^hi eosinophilic subgroup in individuals with T1D compared with healthy controls. This study is a first important step toward unraveling the role of the eosinophils in patients with T1D.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets

Background: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. Methods: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. Results: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the “golden ratio” of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. Conclusions: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Identification of Shared Genes and Pathways in Periodontitis and Type 2 Diabetes by Bioinformatics Analysis

INTRODUCTION

It is well known that the presence of diabetes significantly affects the progression of periodontitis and that periodontitis has negative effects on diabetes and diabetes-related complications. Although this two-way relationship between type 2 diabetes and periodontitis could be understood through experimental and clinical studies, information on common genetic factors would be more useful for the understanding of both diseases and the development of treatment strategies.

MATERIALS AND METHODS

Gene expression data for periodontitis and type 2 diabetes were obtained from the Gene Expression Omnibus database. After preprocessing of data to reduce heterogeneity, differentially expressed genes (DEGs) between disease and normal tissue were identified using a linear regression model package. Gene ontology and Kyoto encyclopedia of genes and genome pathway enrichment analyses were conducted using R package 'vsn'. A protein-protein interaction network was constructed using the search tool for the retrieval of the interacting genes database. We used molecular complex detection for optimal module selection. CytoHubba was used to identify the highest linkage hub gene in the network.

RESULTS

We identified 152 commonly DEGs, including 125 upregulated and 27 downregulated genes. Through common DEGs, we constructed a protein-protein interaction and identified highly connected hub genes. The hub genes were up-regulated in both diseases and were most significantly enriched in the Fc gamma R-mediated phagocytosis pathway.

DISCUSSION

We have identified three up-regulated genes involved in Fc gamma receptor-mediated phagocytosis, and these genes could be potential therapeutic targets in patients with periodontitis and type 2 diabetes.

Analysis of key genes and their functions in placental tissue of patients with gestational diabetes mellitus

BACKGROUND

This study was aimed at screening out the potential key genes and pathways associated with gestational diabetes mellitus (GDM).

METHODS

The GSE70493 dataset used for this study was obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in the placental tissue of women with GDM in relation to the control tissue samples were identified and submitted to protein-protein interaction (PPI) network analysis and subnetwork module mining. Functional enrichment analyses of the PPI network and subnetworks were subsequently carried out. Finally, the integrated miRNA-transcription factor (TF)-DEG regulatory network was analyzed.

RESULTS

In total, 238 DEGs were identified, of which 162 were upregulated and 76 were downregulated. Through PPI network construction, 108 nodes and 278 gene pairs were obtained, from which chemokine (C-X-C motif) ligand 9 (CXCL9), CXCL10, protein tyrosine phosphatase, receptor type C (PTPRC), and human leukocyte antigen (HLA) were screened out as hub genes. Moreover, genes associated with the immune-related pathway and immune responses were found to be significantly enriched in the process of GDM. Finally, miRNAs and TFs that target the DEGs were predicted.

CONCLUSIONS

Four candidate genes (viz., CXCL9, CXCL10, PTPRC, and HLA) are closely related to GDM. miR-223-3p, miR-520, and thioredoxin-binding protein may play important roles in the pathogenesis of this disease.

Treg activation defect in type 1 diabetes: correction with TNFR2 agonism

Activated T-regulatory cells (aTregs) prevent or halt various forms of autoimmunity. We show that type 1 diabetics (T1D) have a Treg activation defect through an increase in resting Tregs (rTregs, CD4⁺CD25⁺Foxp3⁺CD45RA) and decrease in aTregs (CD4⁺CD25⁺Foxp3⁺CD45RO) (n= 55 T1D, n=45 controls, P=0.01). The activation defect persists life long in T1D subjects (T1D=45, controls=45, P=0.01, P=0.04). Lower numbers of aTregs had clinical significance because they were associated with a trend for less residual C-peptide secretion from the pancreas (P=0.08), and poorer HbA1C control (P=0.03). In humans, the tumor necrosis factor receptor 2 (TNFR2) is obligatory for Treg induction, maintenance and expansion of aTregs. TNFR2 agonism is a method for stimulating Treg conversion from resting to activated. Using two separate in vitro expansion protocols, TNFR2 agonism corrected the T1D activation defect by triggering conversion of rTregs into aTregs (n=54 T1D, P<0.001). TNFR2 agonism was superior to standard protocols and TNF in proliferating Tregs. In T1D, TNFR2 agonist-expanded Tregs were homogeneous and functionally potent by virtue of suppressing autologous cytotoxic T cells in a dose-dependent manner comparable to controls. Targeting the TNFR2 receptor for Treg expansion in vitro demonstrates a means to correct the activation defect in T1D.

Exploring Oxidovanadium(IV) Complexes as YopH Inhibitors: Mechanism of Action and Modeling Studies

YopH tyrosine phosphatase, a virulence factor produced by pathogenic species of Yersinia, is an attractive drug target. In this work, three oxidovanadium(IV) complexes were assayed against recombinant YopH and showed strong inhibition of the enzyme in the nanomolar range. Molecular modeling indicated that their binding is reinforced by H-bond, cation−π, and π–π interactions conferring specificity toward YopH. These complexes are thus interesting lead molecules for phosphatase inhibitor drug discovery.