Genetic deficiency of Itgb2 or ItgaL prevents autoimmune diabetes through distinctly different mechanisms in NOD/LtJ mice

Comprehensive Analysis of CircRNA Expression Profiles in Multiple Tissues of Pigs

Circular RNAs (circRNAs) are a class of non-coding RNAs with diverse functions, and previous studies have reported that circRNAs are involved in the growth and development of pigs. However, studies about porcine circRNAs over the past few years have focused on a limited number of tissues. Based on 215 publicly available RNA sequencing (RNA-seq) samples, we conducted a comprehensive analysis of circRNAs in nine pig tissues, namely, the gallbladder, heart, liver, longissimus dorsi, lung, ovary, pituitary, skeletal muscle, and spleen. Here, we identified a total of 82,528 circRNAs and discovered 3818 novel circRNAs that were not reported in the CircAtlas database. Moreover, we obtained 492 housekeeping circRNAs and 3489 tissue-specific circRNAs. The housekeeping circRNAs were enriched in signaling pathways regulating basic biological tissue activities, such as chromatin remodeling, nuclear-transcribed mRNA catabolic process, and protein methylation. The tissue-specific circRNAs were enriched in signaling pathways related to tissue-specific functions, such as muscle system process in skeletal muscle, cilium organization in pituitary, and cortical cytoskeleton in ovary. Through weighted gene co-expression network analysis, we identified 14 modules comprising 1377 hub circRNAs. Additionally, we explored circRNA-miRNA-mRNA networks to elucidate the interaction relationships between tissue-specific circRNAs and tissue-specific genes. Furthermore, our conservation analysis revealed that 19.29% of circRNAs in pigs shared homologous positions with their counterparts in humans. In summary, this extensive profiling of housekeeping, tissue-specific, and co-expressed circRNAs provides valuable insights into understanding the molecular mechanisms of pig transcriptional expression, ultimately deepening our understanding of genetic and biological processes.

Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning

Background

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.

Methods

Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.

Results

Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.

Conclusion

By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Background

Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Methods

Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes.

Results

GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset.

Conclusion

Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Objectives:

The underlying molecular mechanisms of diabetic nephropathy have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of diabetic nephropathy.

Methods:

We downloaded next-generation sequencing data set GSE142025 from Gene Expression Omnibus database having 28 diabetic nephropathy samples and nine normal control samples. The differentially expressed genes between diabetic nephropathy and normal control samples were analyzed. Biological function analysis of the differentially expressed genes was enriched by Gene Ontology and REACTOME pathways. Then, we established the protein–protein interaction network, modules, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network. Hub genes were validated by using receiver operating characteristic curve analysis.

Results:

A total of 549 differentially expressed genes were detected including 275 upregulated and 274 downregulated genes. The biological process analysis of functional enrichment showed that these differentially expressed genes were mainly enriched in cell activation, integral component of plasma membrane, lipid binding, and biological oxidations. Analyzing the protein–protein interaction network, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB, and NR4A1 by the Cytoscape software. The receiver operating characteristic curve analysis confirmed that hub genes were of diagnostic value.

Conclusions:

Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in diabetic nephropathy, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for diabetic nephropathy.

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Investigation of the underlying genes and mechanism of familial hypercholesterolemia through bioinformatics analysis

Background

Familial hypercholesterolemia (FH) is one of the commonest inherited metabolic disorders. Abnormally high level of low-density lipoprotein cholesterol (LDL-C) in blood leads to premature atherosclerosis onset and a high risk of cardiovascular disease (CVD). However, the specific mechanisms of the progression process are still unclear. Our study aimed to investigate the potential differently expressed genes (DEGs) and mechanism of FH using various bioinformatic tools.

Methods

GSE13985 and GSE6054 were downloaded from the Gene Expression Omnibus (GEO) database for bioinformatic analysis in this study. First, limma package of R was used to identify DEGs between blood samples of patients with FH and those from healthy individuals. Then, the functional annotation of DEGs was carried out by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Ontology (GO) analysis. Based on Search Tool for the Retrieval of Interacting Genes (STRING) tool, we constructed the Protein-Protein Interactions (PPIs) network among DEGs and mined the core genes as well.

Results

A total of 102 communal DEGs (49 up-regulated and 53 down-regulated) are identified in FH samples compared with control samples. The functional changes of DEGs are mainly associated with the focal adhere and glucagon signaling pathway. Ten genes (ITGAL, TLN1, POLR2A, CD69, GZMA, VASP, HNRNPUL1, SF1, SRRM2, ITGAV) were identified as core genes. Bioinformatic analysis showed that the core genes are mainly enriched in numerous processes related to cell adhesion, integrin-mediated signaling pathway and cell-matrix adhesion. In the transcription factor (TF) target regulating network, 219 nodes were detected, including 214 DEGs and 5 TFs (SP1, EGR3, CREB, SEF1, HOX13). In conclusion, the DEGs and hub genes identified in this study may help us understand the potential etiology of the occurrence and development of AS.

Conclusion

Up-regulated ITGAL, TLN1, POLR2A, VASP, HNRNPUL1, SF1, SRRM2, and down-regulated CD69, GZMA and ITGAV performed important promotional effects for the formation of atherosclerotic plaques those suffering from FH. Moreover, SP1, EGR3, CREB, SEF1 and HOX13 were the potential transcription factors for DEGs and could serve as underlying targets for AS rupture prevention. These findings provide a theoretical basis for us to understand the potential etiology of the occurrence and development of AS in FH patients and we may be able to find potential diagnostic and therapeutic targets.

Molecular Footprints of the Immune Assault on Pancreatic Beta Cells in Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic disease caused by the selective destruction of the insulin-producing pancreatic beta cells by infiltrating immune cells. We presently evaluated the transcriptomic signature observed in beta cells in early T1D and compared it with the signatures observed following in vitro exposure of human islets to inflammatory or metabolic stresses, with the aim of identifying "footprints" of the immune assault in the target beta cells. We detected similarities between the beta cell signatures induced by cytokines present at different moments of the disease, i.e., interferon-α (early disease) and interleukin-1β plus interferon-γ (later stages) and the beta cells from T1D patients, identifying biological process and signaling pathways activated during early and late stages of the disease. Among the first responses triggered on beta cells was an enrichment in antiviral responses, pattern recognition receptors activation, protein modification and MHC class I antigen presentation. During putative later stages of insulitis the processes were dominated by T-cell recruitment and activation and attempts of beta cells to defend themselves through the activation of anti-inflammatory pathways (i.e., IL10, IL4/13) and immune check-point proteins (i.e., PDL1 and HLA-E). Finally, we mined the beta cell signature in islets from T1D patients using the Connectivity Map, a large database of chemical compounds/drugs, and identified interesting candidates to potentially revert the effects of insulitis on beta cells.

Immune cell trafficking to the islets during type 1 diabetes

Inhibition of immune cell trafficking to the pancreatic islets during type 1 diabetes (T1D) has therapeutic potential, since targeting of T cell and B cell trafficking has been clinically effective in other autoimmune diseases. Trafficking to the islets is characterized by redundancy in adhesion molecule and chemokine usage, which has not enabled effective targeting to date. Additionally, cognate antigen is not consistently required for T cell entry into the islets throughout the progression of disease. However, myeloid cells are required to enable T cell and B cell entry into the islets, and may serve as a convergence point in the pathways controlling this process. In this review we describe current knowledge of the factors that mediate immune cell trafficking to pancreatic islets during T1D progression.

Identification of gene coexpression modules, hub genes, and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway "natural killer cell-mediated cytotoxicity" were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the "natural killer cell-mediated cytotoxicity" pathway.

System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes

Genome-wide association studies (GWASs) have discovered >50 risk loci for type 1 diabetes (T1D). However, those variations only have modest effects on the genetic risk of T1D. In recent years, accumulated studies have suggested that gene-gene interactions might explain part of the missing heritability. The purpose of our research was to identify potential and novel risk genes for T1D by systematically considering the gene-gene interactions through network analyses. We carried out a novel system network analysis of summary GWAS statistics jointly with transcriptomic gene expression data to identify some of the missing heritability for T1D using weighted gene co-expression network analysis (WGCNA). Using WGCNA, seven modules for 1852 nominally significant (P ≤ 0.05) GWAS genes were identified by analyzing microarray data for gene expression profile. One module (tagged as green module) showed significant association (P ≤ 0.05) between the module eigengenes and the trait. This module also displayed a high correlation (r = 0.45, P ≤ 0.05) between module membership (MM) and gene significant (GS), which indicated that the green module of co-expressed genes is of significant biological importance for T1D status. By further describing the module content and topology, the green module revealed a significant enrichment in the "regulation of immune response" (GO:0050776), which is a crucially important pathway in T1D development. Our findings demonstrated a module and several core genes that act as essential components in the etiology of T1D possibly via the regulation of immune response, which may enhance our fundamental knowledge of the underlying molecular mechanisms for T1D.

Long term outcome of eight patients with type 1 Leukocyte Adhesion Deficiency (LAD-1): Not only infections, but high risk of autoimmune complications

Leukocyte Adhesion Deficiency type 1 (LAD-1) is a rare primary immunodeficiency due to mutations in the gene encoding for the common β-chain of the β2 integrin family (CD18). Herein, we describe clinical manifestations and long-term complications of eight LAD-1 patients. Four LAD-1 patients were treated with hematopoietic stem cell transplantation (HSCT), while the remaining four, including two with moderate LAD-1 deficiency, received continuous antibiotic prophylaxis. Untreated patients presented numerous infections and autoimmune manifestations. In particular, two of them developed renal and intestinal autoimmune diseases, despite the expression of Beta-2 integrin was partially conserved. Other two LAD-1 patients developed type 1 diabetes and autoimmune cytopenia after HSCT, suggesting that HSCT is effective for preventing infections in LAD-1, but does not prevent the risk of the autoimmune complications.

SDF-1-CXCR4 differentially regulates autoimmune diabetogenic T cell adhesion through ROBO1-SLIT2 interactions in mice

AIMS/HYPOTHESIS

We had previously reported that stromal cell-derived factor 1 (SDF-1) mediates chemorepulsion of diabetogenic T cell adhesion to islet microvascular endothelium through unknown mechanisms in NOD mice. Here we report that SDF-1-mediated chemorepulsion occurs through slit homologue (SLIT)2-roundabout, axon guidance receptor, homologue 1 (Drosophila) (ROBO1) interactions.

METHODS

C-X-C receptor (CXCR)4 and ROBO1 protein expression was measured in mouse and human T cells. Parallel plate flow chamber adhesion and detachment studies were performed to examine the molecular importance of ROBO1 and SLIT2 for SDF-1-mediated T cell chemorepulsion. Diabetogenic splenocyte transfer was performed in NOD/LtSz Rag1(-/-) mice to examine the effect of the SDF-1 mimetic CTCE-0214 on adoptive transfer of diabetes.

RESULTS

CXCR4 and ROBO1 protein expression was elevated in diabetic NOD/ShiLtJ T cells over time and coincided with the onset of hyperglycaemia. CXCR4 and ROBO1 expression was also increased in human type 1 diabetic T cells, with ROBO1 expression maximal at less than 1 year post diagnosis. Cell detachment studies revealed that immunoneutralisation of ROBO1 prevented SDF-1-mediated chemorepulsion of NOD T cell firm adhesion to TNFα-stimulated islet endothelial cells. SDF-1 increased NOD T cell adhesion to recombinant adhesion molecules, a phenomenon that was reversed by recombinant SLIT2. Finally, we found that an SDF-1 peptide mimetic prevented NOD T cell adhesion in vitro and significantly delayed adoptive transfer of autoimmune diabetes in vivo.

CONCLUSIONS/INTERPRETATION

These data reveal a novel molecular pathway, which regulates diabetogenic T cell recruitment and may be useful in modulating autoimmune diabetes.

Absence of β2 integrins impairs regulatory T cells and exacerbates CD4+ T cell-dependent autoimmune carditis

The immunopathogenic mechanisms mediating inflammation in multiorgan autoimmune diseases may vary between the different target tissues. We used the K/BxN TCR transgenic mouse model to investigate the contribution of CD4(+) T cells and β(2) integrins in the pathogenesis of autoimmune arthritis and endocarditis. Depletion of CD4(+) T cells following the onset of arthritis specifically prevented the development of cardiac valve inflammation. Genetic absence of β(2) integrins had no effect on the severity of arthritis and unexpectedly increased the extent of cardiovascular pathology. The exaggerated cardiac phenotype of the β(2) integrin-deficient K/BxN mice was accompanied by immune hyperactivation and was linked to a defect in regulatory T cells. These findings are consistent with a model in which the development of arthritis in K/BxN mice relies primarily on autoantibodies, whereas endocarditis depends on an additional contribution of effector T cells. Furthermore, strategies targeting β(2) integrins for the treatment of systemic autoimmune conditions need to consider not only the role of these molecules in leukocyte recruitment to sites of inflammation, but also their impact on the regulation of immunological tolerance.

Loss of T cell CD98 H chain specifically ablates T cell clonal expansion and protects from autoimmunity

CD98 H chain (4F2 Ag, Slc3a2) was discovered as a lymphocyte-activation Ag. Deletion of CD98 H chain in B cells leads to complete failure of B cell proliferation, plasma cell formation, and Ab secretion. In this study, we examined the role of T cell CD98 in cell-mediated immunity and autoimmune disease pathogenesis by specifically deleting it in murine T cells. Deletion of T cell CD98 prevented experimental autoimmune diabetes associated with dramatically reduced T cell clonal expansion. Nevertheless, initial T cell homing to pancreatic islets was unimpaired. In sharp contrast to B cells, CD98-null T cells showed only modestly impaired Ag-driven proliferation and nearly normal homeostatic proliferation. Furthermore, these cells were activated by Ag, leading to cytokine production (CD4) and efficient cytolytic killing of targets (CD8). The integrin-binding domain of CD98 was necessary and sufficient for full clonal expansion, pointing to a role for adhesive signaling in T cell proliferation and autoimmune disease. When we expanded CD98-null T cells in vitro, they adoptively transferred diabetes, establishing that impaired clonal expansion was responsible for protection from disease. Thus, the integrin-binding domain of CD98 is required for Ag-driven T cell clonal expansion in the pathogenesis of an autoimmune disease and may represent a useful therapeutic target.

Interference with islet-specific homing of autoreactive T cells: an emerging therapeutic strategy for type 1 diabetes

Pathogenesis of type 1 diabetes involves the activation of autoimmune T cells, consequent homing of activated lymphocytes to the pancreatic islets and ensuing destruction of insulin-producing b cells. Interaction between activated lymphocytes and endothelial cells in the islets is the hallmark of the homing process. Initial adhesion, firm adhesion and diapedesis of lymphocytes are the three crucial steps involved in the homing process. Cell-surface receptors including integrins, selectins and hyaluronate receptor CD44 mediate the initial steps of homing. Diapedesis relies on a series of proteolytic events mediated by matrix metalloproteinases. Here, molecular mechanisms governing transendothelial migration of the diabetogenic effector cells are discussed and resulting pharmacological strategies are considered.

Gene network analysis of bone marrow mononuclear cells reveals activation of multiple kinase pathways in human systemic lupus erythematosus

Background

Gene profiling studies provide important information for key molecules relevant to a disease but are less informative of protein-protein interactions, post-translational modifications and regulation by targeted subcellular localization. Integration of genomic data and construction of functional gene networks may provide additional insights into complex diseases such as systemic lupus erythematosus (SLE).

Methodology/Principal Findings

We analyzed gene expression microarray data of bone marrow mononuclear cells (BMMCs) from 20 SLE patients (11 with active disease) and 10 controls. Gene networks were constructed using the bioinformatic tool Ingenuity Gene Network Analysis. In SLE patients, comparative analysis of BMMCs genes revealed a network with 19 central nodes as major gene regulators including ERK, JNK, and p38 MAP kinases, insulin, Ca²⁺ and STAT3. Comparison between active versus inactive SLE identified 30 central nodes associated with immune response, protein synthesis, and post-transcriptional modification. A high degree of identity between networks in active SLE and non-Hodgkin's lymphoma (NHL) patients was found, with overlapping central nodes including kinases (MAPK, ERK, JNK, PKC), transcription factors (NF-kappaB, STAT3), and insulin. In validation studies, western blot analysis in splenic B cells from 5-month-old NZB/NZW F1 lupus mice showed activation of STAT3, ITGB2, HSPB1, ERK, JNK, p38, and p32 kinases, and downregulation of FOXO3 and VDR compared to normal C57Bl/6 mice.

Conclusions/Significance

Gene network analysis of lupus BMMCs identified central gene regulators implicated in disease pathogenesis which could represent targets of novel therapies in human SLE. The high similarity between active SLE and NHL networks provides a molecular basis for the reported association of the former with lymphoid malignancies.