Type 2 diabetes candidate genes, including PAX5, cause impaired insulin secretion in human pancreatic islets

Pan-cancer analysis of SLC2A family genes as prognostic biomarkers and therapeutic targets

Background

The major facilitator superfamily glucose transporters (GLUTs), encoded by solute carrier 2A (SLC2A) genes, mediate the transmembrane movement and uptake of glucose. To satisfy the improved energy demands, glycolysis flux is increased in cancers compared with healthy tissues. Multiple diseases, including cancer, have been associated with GLUTs. Nevertheless, not much research has been done on the functions of SLC2As in pan-cancer prognosis or their clinical treatment potential.

Methods

The SLC2A family genes' level of expression and prognostic values were analyzed in relation to pan-cancer. We then examined the association among SLC2As expression and TME, Stemness score, clinical characteristics, immune subtypes, and drug sensitivity. We merged bioinformatics analysis techniques with up-to-date public databases. Additionally, SLC2As from the KOBAS database were subjected to enrichment analysis.

Results

We discovered that SLC2As' gene expression differed significantly between normal tissues and many malignancies. A number of tumors from various databases demonstrate a relationship between prognosis and SLC2A family gene expression. For instance, SLC2A2 and SLC2A5 were associated with the overall survival (OS) of hepatocellular carcinoma. SLC2A1 was associated with the OS of lung adenocarcinoma and pancreatic adenocarcinoma. Moreover, the SLC2A family gene expression is significantly correlated with the pan-cancer stromal and immune scores, and the RNA and DNA stemness scores. Furthermore, we found that the majority of SLC2As had a strong correlation with the tumor stages in KIRC. The immunological subtypes and all members of the SLC2A gene family exhibited a substantial correlation. Moreover, pathways containing insulin resistance and adipocytokine signaling pathway may influence the progression of some cancers. Finally, there is a significant positive or negative connection between drug sensitivity and SLC2A1 expression.

Conclusion

Our research highlights the significant promise of SLC2As as prognostic indicators and offers insightful approaches for upcoming exploration of SLC2As as putative therapeutic targets in malignancies.

Unique features of β-cell metabolism are lost in type 2 diabetes

Pancreatic β cells play an essential role in the control of systemic glucose homeostasis as they sense blood glucose levels and respond by secreting insulin. Upon stimulating glucose uptake in insulin-sensitive tissues post-prandially, this anabolic hormone restores blood glucose levels to pre-prandial levels. Maintaining physiological glucose levels thus relies on proper β-cell function. To fulfill this highly specialized nutrient sensor role, β cells have evolved a unique genetic program that shapes its distinct cellular metabolism. In this review, the unique genetic and metabolic features of β cells will be outlined, including their alterations in type 2 diabetes (T2D). β cells selectively express a set of genes in a cell type-specific manner; for instance, the glucose activating hexokinase IV enzyme or Glucokinase (GCK), whereas other genes are selectively "disallowed", including lactate dehydrogenase A (LDHA) and monocarboxylate transporter 1 (MCT1). This selective gene program equips β cells with a unique metabolic apparatus to ensure that nutrient metabolism is coupled to appropriate insulin secretion, thereby avoiding hyperglycemia, as well as life-threatening hypoglycemia. Unlike most cell types, β cells exhibit specialized bioenergetic features, including supply-driven rather than demand-driven metabolism and a high basal mitochondrial proton leak respiration. The understanding of these unique genetically programmed metabolic features and their alterations that lead to β-cell dysfunction is crucial for a comprehensive understanding of T2D pathophysiology and the development of innovative therapeutic approaches for T2D patients.

Gene expression analysis reveals diabetes-related gene signatures

BACKGROUND

Diabetes is a spectrum of metabolic diseases affecting millions of people worldwide. The loss of pancreatic β-cell mass by either autoimmune destruction or apoptosis, in type 1-diabetes (T1D) and type 2-diabetes (T2D), respectively, represents a pathophysiological process leading to insulin deficiency. Therefore, therapeutic strategies focusing on restoring β-cell mass and β-cell insulin secretory capacity may impact disease management. This study took advantage of powerful integrative bioinformatic tools to scrutinize publicly available diabetes-associated gene expression data to unveil novel potential molecular targets associated with β-cell dysfunction.

METHODS

A comprehensive literature search for human studies on gene expression alterations in the pancreas associated with T1D and T2D was performed. A total of 6 studies were selected for data extraction and for bioinformatic analysis. Pathway enrichment analyses of differentially expressed genes (DEGs) were conducted, together with protein-protein interaction networks and the identification of potential transcription factors (TFs). For noncoding differentially expressed RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which exert regulatory activities associated with diabetes, identifying target genes and pathways regulated by these RNAs is fundamental for establishing a robust regulatory network.

RESULTS

Comparisons of DEGs among the 6 studies showed 59 genes in common among 4 or more studies. Besides alterations in mRNA, it was possible to identify differentially expressed miRNA and lncRNA. Among the top transcription factors (TFs), HIPK2, KLF5, STAT1 and STAT3 emerged as potential regulators of the altered gene expression. Integrated analysis of protein-coding genes, miRNAs, and lncRNAs pointed out several pathways involved in metabolism, cell signaling, the immune system, cell adhesion, and interactions. Interestingly, the GABAergic synapse pathway emerged as the only common pathway to all datasets.

CONCLUSIONS

This study demonstrated the power of bioinformatics tools in scrutinizing publicly available gene expression data, thereby revealing potential therapeutic targets like the GABAergic synapse pathway, which holds promise in modulating α-cells transdifferentiation into β-cells.

Predicting cell-type specific disease genes of diabetes with the biological network

Type 2 diabetes (T2D) is a chronic condition that can lead to significant harm, such as heart disease, kidney disease, nerve damage, and blindness. Although T2D-related genes have been identified through Genome-wide association studies (GWAS) and various computational methods, the biological mechanism of T2D at the cell type level remains unclear. Exploring cell type-specific genes related to T2D is essential to understand the cellular mechanisms underlying the disease. To address this issue, we introduce DiGCellNet (predicting Disease Genes with Cell type specificity based on biological Networks), a model that integrates graph convolutional network (GCN) and multi-task learning (MTL) to predict T2D-associated cell type-specific genes based on the biological network. Our work represents the first attempt to predict cell type-specific disease genes using GCN and MTL. We evaluate our approach by predicting genes specific to four cell types and demonstrate that the proposed DiGCellNet outperforms other models that combine node embeddings with traditional machine learning algorithms. Moreover, DiGCellNet successfully identifies CALM1 as a gene specific to beta cell type in T2D cases, and this association is confirmed using an independent dataset. The code is available at https://github.com/23AIBox/23AIBox-DiGCellNet.

Genes with epigenetic alterations in human pancreatic islets impact mitochondrial function, insulin secretion, and type 2 diabetes

Epigenetic dysregulation may influence disease progression. Here we explore whether epigenetic alterations in human pancreatic islets impact insulin secretion and type 2 diabetes (T2D). In islets, 5,584 DNA methylation sites exhibit alterations in T2D cases versus controls and are associated with HbA1c in individuals not diagnosed with T2D. T2D-associated methylation changes are found in enhancers and regions bound by β-cell-specific transcription factors and associated with reduced expression of e.g. CABLES1, FOXP1, GABRA2, GLR1A, RHOT1, and TBC1D4. We find RHOT1 (MIRO1) to be a key regulator of insulin secretion in human islets. Rhot1-deficiency in β-cells leads to reduced insulin secretion, ATP/ADP ratio, mitochondrial mass, Ca2+, and respiration. Regulators of mitochondrial dynamics and metabolites, including L-proline, glycine, GABA, and carnitines, are altered in Rhot1-deficient β-cells. Islets from diabetic GK rats present Rhot1-deficiency. Finally, RHOT1methylation in blood is associated with future T2D. Together, individuals with T2D exhibit epigenetic alterations linked to mitochondrial dysfunction in pancreatic islets.

An Integrated Map of Cell Type-Specific Gene Expression in Pancreatic Islets

Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in β-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology.

ARTICLE HIGHLIGHTS

Bioinformatics analysis to reveal the potential comorbidity mechanism in psoriasis and nonalcoholic steatohepatitis

PURPOSE

An increasing amount of evidence suggests that psoriasis and nonalcoholic steatohepatitis (NASH) may occur simultaneously, whereas the underlying mechanisms remain unclear. Our research aims to explore the potential comorbidity mechanism in psoriasis and nonalcoholic steatohepatitis.

MATERIALS AND METHODS

The expression profiles of psoriasis (GSE30999, GSE13355) and NASH (GSE24807, GSE17470) were downloaded from GEO datasets. Next, common differently expressed genes (DEGs) of psoriasis and NASH were investigated. Then, GO and KEGG enrichment, protein interaction network (PPI) construction, and hub gene identification for DEGs were performed. Finally, immune cells expression, target genes predicted by common miRNAs, and transcription factors interaction analysis for hub genes were carried out.

RESULTS

Twenty DEGs were identified in totally. GO analysis revealed response to the virus was the most enriched term, and hepatitis C and coronavirus disease-COVID-19 infection-associated pathways were mainly enriched in KEGG. A total of eight hub genes were collected, including IFIT1, IFIT3, OAS1, HPGDS, IFI27, IFI44, CXCL10, IRF9, and 11 TFs were predicted. Then, neutrophils and monocytes were identified as immune cells that express the most hub genes. Moreover, five common miRNAs for psoriasis and NASH and one common miRNAs (hsa-miR-1305)-mRNAs (CHL1, MBNL2) network were presented.

CONCLUSION

CHL1 and MBNL2 may participate in the process of psoriasis and NASH via regulating hsa-miR-1305, and together with eight hub genes may be potential therapeutic targets for future treatment for the co-occurrence of these two diseases. This comprehensive bioinformatic analysis provides new insights on molecular pathogenesis and identification of potential therapeutic targets for the co-occurrence of them.

TET (Ten-eleven translocation) family proteins: structure, biological functions and applications

Ten-eleven translocation (TET) family proteins (TETs), specifically, TET1, TET2 and TET3, can modify DNA by oxidizing 5-methylcytosine (5mC) iteratively to yield 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxycytosine (5caC), and then two of these intermediates (5fC and 5caC) can be excised and return to unmethylated cytosines by thymine-DNA glycosylase (TDG)-mediated base excision repair. Because DNA methylation and demethylation play an important role in numerous biological processes, including zygote formation, embryogenesis, spatial learning and immune homeostasis, the regulation of TETs functions is complicated, and dysregulation of their functions is implicated in many diseases such as myeloid malignancies. In addition, recent studies have demonstrated that TET2 is able to catalyze the hydroxymethylation of RNA to perform post-transcriptional regulation. Notably, catalytic-independent functions of TETs in certain biological contexts have been identified, further highlighting their multifunctional roles. Interestingly, by reactivating the expression of selected target genes, accumulated evidences support the potential therapeutic use of TETs-based DNA methylation editing tools in disorders associated with epigenetic silencing. In this review, we summarize recent key findings in TETs functions, activity regulators at various levels, technological advances in the detection of 5hmC, the main TETs oxidative product, and TETs emerging applications in epigenetic editing. Furthermore, we discuss existing challenges and future directions in this field.

DNA methylation partially mediates antidiabetic effects of metformin on HbA1c levels in individuals with type 2 diabetes

AIMS

Despite metformin being used as first-line pharmacological therapy for type 2 diabetes, its underlying mechanisms remain unclear. We aimed to determine whether metformin altered DNA methylation in newly-diagnosed individuals with type 2 diabetes.

METHODS AND RESULTS

We found that metformin therapy is associated with altered methylation of 26 sites in blood from Scandinavian discovery and replication cohorts (FDR<0.05), using MethylationEPIC arrays. The majority (88%) of these 26 sites were hypermethylated in patients taking metformin for ∼3 months compared to controls, who had diabetes but had not taken any diabetes medication. Two of these blood-based methylation markers mirrored the epigenetic pattern in muscle and adipose tissue (FDR<0.05). Four type 2 diabetes-associated SNPs were annotated to genes with differential methylation between metformin cases and controls, e.g., GRB10, RPTOR, SLC22A18AS and TH2LCRR. Methylation correlated with expression in human islets for two of these genes. Three metformin-associated methylation sites (PKNOX2, WDTC1 and MICB) partially mediate effects of metformin on follow-up HbA1c levels. When combining methylation of these three sites into a score, which was used in a causal mediation analysis, methylation was suggested to mediate up to 32% of metformin's effects on HbA1c.

CONCLUSION

Metformin-associated alterations in DNA methylation partially mediates metformin's antidiabetic effects on HbA1c in newly-diagnosed individuals with type 2 diabetes.