Type 1 Diabetes Mellitus-Associated Genetic Variants Contribute to Overlapping Immune Regulatory Networks

A Review of Stage 0 Biomarkers in Type 1 Diabetes: The Holy Grail of Early Detection and Prevention?

Type 1 diabetes mellitus (T1D) is an incurable autoimmune disease characterized by the destruction of pancreatic islet cells, resulting in lifelong dependency on insulin treatment. There is an abundance of review articles addressing the prediction of T1D; however, most focus on the presymptomatic phases, specifically stages 1 and 2. These stages occur after seroconversion, where therapeutic interventions primarily aim to delay the onset of T1D rather than prevent it. This raises a critical question: what happens before stage 1 in individuals who will eventually develop T1D? Is there a "stage 0" of the disease, and if so, how can we detect it to increase our chances of truly preventing T1D? In pursuit of answers to these questions, this narrative review aimed to highlight recent research in the field of early detection and prediction of T1D, specifically focusing on biomarkers that can predict T1D before the onset of islet autoimmunity. Here, we have compiled influential research from the fields of epigenetics, omics, and microbiota. These studies have identified candidate biomarkers capable of predicting seroconversion from very early stages to several months prior, suggesting that the prophylactic window begins at birth. As the therapeutic landscape evolves from treatment to delay, and ideally from delay to prevention, it is crucial to both identify and validate such "stage 0" biomarkers predictive of islet autoimmunity. In the era of precision medicine, this knowledge will enable early intervention with the potential for delaying, modifying, or completely preventing autoimmunity and T1D in at-risk children.

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

Pharmacological inhibition of tyrosine protein-kinase 2 reduces islet inflammation and delays type 1 diabetes onset in mice

Tyrosine protein-kinase 2 (TYK2), a member of the Janus kinase family, mediates inflammatory signaling through multiple cytokines, including interferon-α (IFNα), interleukin (IL)-12, and IL-23. Missense mutations in TYK2 are associated with protection against type 1 diabetes (T1D), and inhibition of TYK2 shows promise in the management of other autoimmune conditions. Here, we evaluated the effects of specific TYK2 inhibitors (TYK2is) in pre-clinical models of T1D. First, human β cells, cadaveric donor islets, and iPSC-derived islets were treated in vitro with IFNα in combination with a small molecule TYK2i (BMS-986165 or a related molecule BMS-986202). TYK2 inhibition prevented IFNα-induced β cell HLA class I up-regulation, endoplasmic reticulum stress, and chemokine production. In co-culture studies, pre-treatment of β cells with a TYK2i prevented IFNα-induced activation of T cells targeting an epitope of insulin. In vivo administration of BMS-986202 in two mouse models of T1D (RIP-LCMV-GP mice and NOD mice) reduced systemic and tissue-localized inflammation, prevented β cell death, and delayed T1D onset. Transcriptional phenotyping of pancreatic islets, pancreatic lymph nodes (PLN), and spleen during early disease pathogenesis highlighted a role for TYK2 inhibition in modulating signaling pathways associated with inflammation, translational control, stress signaling, secretory function, immunity, and diabetes. Additionally, TYK2i treatment changed the composition of innate and adaptive immune cell populations in the blood and disease target tissues, resulting in an immune phenotype with a diminished capacity for β cell destruction. Overall, these findings indicate that TYK2i has beneficial effects in both the immune and endocrine compartments in models of T1D, thus supporting a path forward for testing TYK2 inhibitors in human T1D.

Exploring the theranostic potentials of miRNA and epigenetic networks in autoimmune diseases: A comprehensive review

BACKGROUND

Autoimmune diseases (AD) are severe pathophysiological ailments that are stimulated by an exaggerated immunogenic response towards self-antigens, which can cause systemic or site-specific organ damage. An array of complex genetic and epigenetic facets majorly contributes to the progression of AD, thus providing significant insight into the regulatory mechanism of microRNA (miRNA). miRNAs are short, non-coding RNAs that have been identified as essential contributors to the post-transcriptional regulation of host genome expression and as crucial regulators of a myriad of biological processes such as immune homeostasis, T helper cell differentiation, central and peripheral tolerance, and immune cell development.

AIMS

This article tends to deliberate and conceptualize the brief pathogenesis and pertinent epigenetic regulatory mechanism as well as miRNA networks majorly affecting five different ADs namely rheumatoid arthritis (RA), type 1 diabetes, multiple sclerosis (MS), systemic lupus erythematosus (SLE) and inflammatory bowel disorder (IBD) thereby providing novel miRNA-based theranostic interventions.

RESULTS & DISCUSSION

Pertaining to the differential expression of miRNA attributed in target tissues and cellular bodies of innate and adaptive immunity, a paradigm of scientific expeditions suggests an optimistic correlation between immunogenic dysfunction and miRNA alterations.

CONCLUSION

Therefore, it is not astonishing that dysregulations in miRNA expression patterns are now recognized in a wide spectrum of disorders, establishing themselves as potential biomarkers and therapeutic targets. Owing to its theranostic potencies, miRNA targets have been widely utilized in the development of biosensors and other therapeutic molecules originating from the same.

Novel regulators of islet function identified from genetic variation in mouse islet Ca2+ oscillations

Insufficient insulin secretion to meet metabolic demand results in diabetes. The intracellular flux of Ca2+ into β-cells triggers insulin release. Since genetics strongly influences variation in islet secretory responses, we surveyed islet Ca2+ dynamics in eight genetically diverse mouse strains. We found high strain variation in response to four conditions: (1) 8 mM glucose; (2) 8 mM glucose plus amino acids; (3) 8 mM glucose, amino acids, plus 10 nM glucose-dependent insulinotropic polypeptide (GIP); and (4) 2 mM glucose. These stimuli interrogate β-cell function, α- to β-cell signaling, and incretin responses. We then correlated components of the Ca2+ waveforms to islet protein abundances in the same strains used for the Ca2+ measurements. To focus on proteins relevant to human islet function, we identified human orthologues of correlated mouse proteins that are proximal to glycemic-associated single-nucleotide polymorphisms in human genome-wide association studies. Several orthologues have previously been shown to regulate insulin secretion (e.g. ABCC8, PCSK1, and GCK), supporting our mouse-to-human integration as a discovery platform. By integrating these data, we nominate novel regulators of islet Ca2+ oscillations and insulin secretion with potential relevance for human islet function. We also provide a resource for identifying appropriate mouse strains in which to study these regulators.

Maternal type 1 diabetes and relative protection against offspring transmission

Type 1 diabetes is around twice as common in the offspring of men with type 1 diabetes than in the offspring of women with type 1 diabetes, but the reasons for this difference are unclear. This Review summarises the evidence on the rate of transmission of type 1 diabetes to the offspring of affected fathers compared with affected mothers. The findings of nine major studies are presented, describing the magnitude of the effect observed and the relative strengths and weaknesses of these studies. This Review also explores possible underlying mechanisms for this effect, such as genetic mechanisms (eg, the selective loss of fetuses with high-risk genes in mothers with type 1 diabetes, preferential transmission of susceptibility genes from fathers, and parent-of-origin effects influencing gene expression), environmental exposures (eg, exposure to maternal hyperglycaemia, exogenous insulin exposure, and transplacental antibody transfer), and maternal microchimerism. Understanding why type 1 diabetes is more common in the offspring of men versus women with type 1 diabetes will help in the identification of individuals at high risk of the disease and can pave the way in the development of interventions that mimic the protective elements of maternal type 1 diabetes to reduce the risk of disease in individuals at high risk.

Biobank-scale methods and projections for sparse polygenic prediction from machine learning

In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.

3D genome organization and epigenetic regulation in autoimmune diseases

Three-dimensional (3D) genomics is an emerging field of research that investigates the relationship between gene regulatory function and the spatial structure of chromatin. Chromatin folding can be studied using chromosome conformation capture (3C) technology and 3C-based derivative sequencing technologies, including chromosome conformation capture-on-chip (4C), chromosome conformation capture carbon copy (5C), and high-throughput chromosome conformation capture (Hi-C), which allow scientists to capture 3D conformations from a single site to the entire genome. A comprehensive analysis of the relationships between various regulatory components and gene function also requires the integration of multi-omics data such as genomics, transcriptomics, and epigenomics. 3D genome folding is involved in immune cell differentiation, activation, and dysfunction and participates in a wide range of diseases, including autoimmune diseases. We describe hierarchical 3D chromatin organization in this review and conclude with characteristics of C-techniques and multi-omics applications of the 3D genome. In addition, we describe the relationship between 3D genome structure and the differentiation and maturation of immune cells and address how changes in chromosome folding contribute to autoimmune diseases.

Juvenile idiopathic arthritis-associated genetic loci exhibit spatially constrained gene regulatory effects across multiple tissues and immune cell types

Juvenile idiopathic arthritis (JIA) is an autoimmune, inflammatory joint disease with complex genetic etiology. Previous GWAS have found many genetic loci associated with JIA. However, the biological mechanism behind JIA remains unknown mainly because most risk loci are located in non-coding genetic regions. Interestingly, increasing evidence has found that regulatory elements in the non-coding regions can regulate the expression of distant target genes through spatial (physical) interactions. Here, we used information on the 3D genome organization (Hi-C data) to identify target genes that physically interact with SNPs within JIA risk loci. Subsequent analysis of these SNP-gene pairs using data from tissue and immune cell type-specific expression quantitative trait loci (eQTL) databases allowed the identification of risk loci that regulate the expression of their target genes. In total, we identified 59 JIA-risk loci that regulate the expression of 210 target genes across diverse tissues and immune cell types. Functional annotation of spatial eQTLs within JIA risk loci identified significant overlap with gene regulatory elements (i.e., enhancers and transcription factor binding sites). We found target genes involved in immune-related pathways such as antigen processing and presentation (e.g., ERAP2, HLA class I and II), the release of pro-inflammatory cytokines (e.g., LTBR, TYK2), proliferation and differentiation of specific immune cell types (e.g., AURKA in Th17 cells), and genes involved in physiological mechanisms related to pathological joint inflammation (e.g., LRG1 in arteries). Notably, many of the tissues where JIA-risk loci act as spatial eQTLs are not classically considered central to JIA pathology. Overall, our findings highlight the potential tissue and immune cell type-specific regulatory changes contributing to JIA pathogenesis. Future integration of our data with clinical studies can contribute to the development of improved JIA therapy.

Pathophysiology of Type 1 Diabetes and Gut Microbiota Role

Type 1 diabetes (T1D) is a multifactorial autoimmune disease driven by T-cells against the insulin-producing islet β-cells, resulting in a marked loss of β-cell mass and function. Although a genetic predisposal increases susceptibility, the role of epigenetic and environmental factors seems to be much more significant. A dysbiotic gut microbial profile has been associated with T1D patients. Moreover, new evidence propose that perturbation in gut microbiota may influence the T1D onset and progression. One of the prominent features in clinically silent phase before the onset of T1D is the presence of a microbiota characterized by low numbers of commensals butyrate producers, thus negatively influencing the gut permeability. The loss of gut permeability leads to the translocation of microbes and microbial metabolites and could lead to the activation of immune cells. Moreover, microbiota-based therapies to slow down disease progression or reverse T1D have shown promising results. Starting from this evidence, the correction of dysbiosis in early life of genetically susceptible individuals could help in promoting immune tolerance and thus in reducing the autoantibodies production. This review summarizes the associations between gut microbiota and T1D for future therapeutic perspectives and other exciting areas of research.

Therapeutic Strategies Targeting Pancreatic Islet β-Cell Proliferation, Regeneration, and Replacement

Diabetes results from insufficient insulin production by pancreatic islet β-cells or a loss of β-cells themselves. Restoration of regulated insulin production is a predominant goal of translational diabetes research. Here, we provide a brief overview of recent advances in the fields of β-cell proliferation, regeneration, and replacement. The discovery of therapeutic targets and associated small molecules has been enabled by improved understanding of β-cell development and cell cycle regulation, as well as advanced high-throughput screening methodologies. Important findings in β-cell transdifferentiation, neogenesis, and stem cell differentiation have nucleated multiple promising therapeutic strategies. In particular, clinical trials are underway using in vitro-generated β-like cells from human pluripotent stem cells. Significant challenges remain for each of these strategies, but continued support for efforts in these research areas will be critical for the generation of distinct diabetes therapies.

LipGene: Lipschitz Continuity Guided Adaptive Learning Rates for Fast Convergence on Microarray Expression Data Sets

Hyperparameter tuning, specifically tuning of learning rate, can often be a time-consuming process, especially when dealing with large data sets. A mathematical foundation in the choice of learning rate can minimize tuning efforts. We propose the application of a novel adaptive learning rate paradigm, guided by Lipschitz continuity of the loss functions (LipGene), to the task of Gene Expression Inference using shallow neural networks. We utilize Mean Absolute Error and Quantile loss separately for training. Our adaptive learning rate, which is dynamically computed for each epoch, is based on the principle of Lipschitz constant and requires no tuning. Experimentally, we prove that our proposed approach greatly surpasses conventional choices of learning rates in terms of both speed of convergence and generalizability. Advocating the principle of Parsimonious Computing, our method can reduce compute infrastructure required for training by using smaller networks with a minimal compromise on the prediction error.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.

The rs2304256 Polymorphism in TYK2 Gene Is Associated with Protection for Type 1 Diabetes Mellitus

BACKGROUND

Tyrosine kinase 2 (TYK2) is a candidate gene for type 1 diabetes mellitus (T1DM) since it plays an important role in regulating apoptotic and pro-inflammatory pathways in pancreatic β-cells through modulation of the type I interferon signaling pathway. The rs2304256 single nucleotide polymorphism (SNP) in TYK2 gene has been associated with protection for different autoimmune diseases. However, to date, only two studies have evaluated the association between this SNP and T1DM, with discordant results. This study thus aimed to investigate the association between the TYK2 rs2304256 SNP and T1DM in a Southern Brazilian population.

METHODS

This case-control study comprised 478 patients with T1DM and 518 non-diabetic subjects. The rs2304256 (C/A) SNP was genotyped by real-time polymerase chain reaction technique using TaqMan minor groove binder (MGB) probes.

RESULTS

Genotype and allele frequencies of the rs2304256 SNP differed between T1DM patients and non-diabetic subjects (P<0.0001 and P=0.001, respectively). Furthermore, the A allele was associated with protection against T1DM under recessive (odds ratio [OR], 0.482; 95% confidence interval [CI], 0.288 to 0.806) and additive (OR, 0.470; 95% CI, 0.278 to 0.794) inheritance models, adjusting for human leukocyte antigen (HLA) DR/DQ genotypes, gender, and ethnicity.

CONCLUSION

The A/A genotype of TYK2 rs2304256 SNP is associated with protection against T1DM in a Southern Brazilian population.

A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk

Type 1 diabetes (T1D) etiology is complex. We developed a machine learning approach that ranked the tissue-specific transcription regulatory effects for T1D SNPs and estimated their relative contributions to conversion to T1D by integrating case and control genotypes (Wellcome Trust Case Control Consortium and UK Biobank) with tissue-specific expression quantitative trait loci (eQTL) data. Here we show an eQTL (rs6679677) associated with changes to AP4B1-AS1 transcript levels in lung tissue makes the largest gene regulatory contribution to the risk of T1D development. Luciferase reporter assays confirmed allele-specific enhancer activity for the rs6679677 tagged locus in lung epithelial cells (i.e. A549 cells; C > A reduces expression, p = 0.005). Our results identify tissue-specific eQTLs for SNPs associated with T1D. The strongest tissue-specific eQTL effects were in the lung and may help explain associations between respiratory infections and risk of islet autoantibody seroconversion in young children.

HERV-W and Mycobacteriumavium subspecies paratuberculosis Are at Play in Pediatric Patients at Onset of Type 1 Diabetes

The etiology of T1D remains unknown, although a variety of etiological agents have been proposed as potential candidates to trigger autoimmunity in susceptible individuals. Emerging evidence has indicated that endogenous human retrovirus (HERV) may play a role in the disease etiopathogenesis; although several epigenetic mechanisms keep most HERVs silenced, environmental stimuli such as infections may contribute to the transcriptional reactivation of HERV-Wand thus promote pathological conditions. Previous studies have indicated that also Mycobacterium avium subspecies paratuberculosis (MAP) could be a potential risk factor for T1D, particularly in the Sardinian population. In the present study, the humoral response against HERV-W envelope and MAP-derived peptides was analyzed to investigate their potential role in T1D etiopathogenesis, in a Sardinian population at T1D onset (n = 26), T1D (45) and an age-matched healthy population (n = 45). For the first time, a high serum-prevalence of anti-Map and anti-HERV-W Abs was observed in pediatric patients at onset of T1D compared to T1D patients and healthy controls. Our results support the hypothesis that external infections and internal reactivations are involved in the etiology of T1D, and that HERV-W activation may be induced by infectious agents such as MAP.

Natural Killer Cells as Key Mediators in Type I Diabetes Immunopathology

The immunopathology of type I diabetes (T1D) presents a complicated case in part because of the multifactorial origin of this disease. Typically, T1D is thought to occur as a result of autoimmunity toward islets of Langerhans, resulting in the destruction of insulin-producing cells (β cells) and thus lifelong reliance on exogenous insulin. However, that explanation obscures much of the underlying mechanism, and the actual precipitating events along with the associated actors (latent viral infection, diverse immune cell types and their roles) are not completely understood. Notably, there is a malfunctioning in the regulation of cytotoxic CD8+ T cells that target endocrine cells through antigen-mediated attack. Further examination has revealed the likelihood of an imbalance in distinct subpopulations of tolerogenic and cytotoxic natural killer (NK) cells that may be the catalyst of adaptive immune system malfunction. The contributions of components outside the immune system, including environmental factors such as chronic viral infection also need more consideration, and much of the recent literature investigating the origins of this disease have focused on these factors. In this review, the details of the immunopathology of T1D regarding NK cell disfunction is discussed, along with how those mechanisms stand within the context of general autoimmune disorders. Finally, the rarer cases of latent autoimmune, COVID-19 (viral), and immune checkpoint inhibitor (ICI) induced diabetes are discussed as their exceptional pathology offers insight into the evolution of the disease as a whole.

Untangling the genetic link between type 1 and type 2 diabetes using functional genomics

There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D. We identified 195 pleiotropic genes that are modulated by tissue-specific spatial eQTLs associated with both T1D and T2D. The pleiotropic genes are enriched in inflammatory and metabolic pathways that include mitogen-activated protein kinase activity, pertussis toxin signaling, and the Parkinson's disease pathway. We identified 8 regulatory elements within the TCF7L2 locus that modulate transcript levels of genes involved in immune regulation as well as genes important in the etiology of T2D. Despite the observed gene and pathway overlaps, there was no significant genetic correlation between variant effects on T1D and T2D risk using European ancestral summary data. Collectively, our findings support the hypothesis that T1D and T2D specific genetic variants act through genetic regulatory mechanisms to alter the regulation of common genes, and genes that co-locate in biological pathways, to mediate pleiotropic effects on disease development. Crucially, a high risk genetic profile for T1D alters biological pathways that increase the risk of developing both T1D and T2D. The same is not true for genetic profiles that increase the risk of developing T2D. The conversion of information on genetic susceptibility to the protein pathways that are altered provides an important resource for repurposing or designing novel therapies for the management of diabetes.

Emerging Roles of MHC Class I Region-Encoded E3 Ubiquitin Ligases in Innate Immunity

The major histocompatibility complex (MHC) class I (MHC-I) region contains a multitude of genes relevant to immune response. Multiple E3 ubiquitin ligase genes, including tripartite motif 10 (TRIM10), TRIM15, TRIM26, TRIM27, TRIM31, TRIM38, TRIM39, TRIM40, and RING finger protein 39 (RNF39), are organized in a tight cluster, and an additional two TRIM genes (namely TRIM38 and TRIM27) telomeric of the cluster within the MHC-I region. The E3 ubiquitin ligases encoded by these genes possess important roles in controlling the intensity of innate immune responses. In this review, we discuss the E3 ubiquitin ligases encoded within the MHC-I region, highlight their regulatory roles in innate immunity, and outline their potential functions in infection, inflammatory and autoimmune diseases.

Genome-wide search for genes affecting the age at diagnosis of type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease affecting individuals in the early years of life. Although previous studies have identified genetic loci influencing T1D diagnosis age, these studies did not investigate the genome with high resolution.

OBJECTIVE AND METHODS

We performed a genome-wide meta-analysis for age at diagnosis with cohorts from Finland (Finnish Diabetic Nephropathy Study), the United Kingdom (UK Genetic Resource Investigating Diabetes) and Sardinia. Through SNP associations, transcriptome-wide association analysis linked T1D diagnosis age and gene expression.

RESULTS

We identified two chromosomal regions associated with T1D diagnosis age: multiple independent variants in the HLA region on chromosome 6 and a locus on chromosome 17q12. We performed gene-level association tests with transcriptome prediction models from two whole blood datasets, lymphocyte cell line, spleen, pancreas and small intestine tissues. Of the non-HLA genes, lower PNMT expression in whole blood, and higher IKZF3 and ZPBP2, and lower ORMDL3 and GSDMB transcription levels in multiple tissues were associated with lower T1D diagnosis age (FDR = 0.05). These genes lie on chr17q12 which is associated with T1D, other autoimmune diseases, and childhood asthma. Additionally, higher expression of PHF20L1, a gene not previously implicated in T1D, was associated with lower diagnosis age in lymphocytes, pancreas, and spleen. Altogether, the non-HLA associations were enriched in open chromatin in various blood cells, blood vessel tissues and foetal thymus tissue.

CONCLUSION

Multiple genes on chr17q12 and PHF20L1 on chr8 were associated with T1D diagnosis age and only further studies may elucidate the role of these genes for immunity and T1D onset.

Comprehensive analysis of macrophage-related multigene signature in the tumor microenvironment of head and neck squamous cancer

Macrophages are among the most abundant cells of the tumor microenvironment in head and neck squamous cancer (HNSC). Although the marker gene sets of macrophages have been found, the mechanism by which they affect macrophages and whether they further predict the clinical outcome is unclear. In this study, a univariate COX analysis and a random forest algorithm were used to construct a prognostic model. Differential expression of the key gene, methylation status, function, and signaling pathways were further analyzed. We cross-analyzed multiple databases to detect the relationship between the most critical gene and the infiltration of multiple immune cells, as well as its impact on the prognosis of pan-cancer. FANCE is recognized as hub gene by different algorithms. It was overexpressed in HNSC, and high expression was predictive of better prognosis. It might promote apoptosis through the Wnt/β-catenin pathway. The expression of FANCE is inversely proportional to the infiltration of CD4 + T cells and their subsets, tumor-associated macrophages (TAMs), M2 macrophages, but positively co-expressed with M1 macrophages. In summary, FANCE was identified as the hub gene from the macrophage marker gene set, and it may improve the prognosis of HNSC patients by inhibiting lymphocytes and tumor-associated macrophages infiltration.

Immune checkpoint inhibitors and diabetes: Mechanisms and predictors

The emergence of immune checkpoint inhibitors in the arsenal of cancer immunotherapy was a breakthrough which provided hope to many cancer patients. However, not long has passed since their discovery that some adverse effects were associated with these promising therapeutic agents. Immune checkpoint inhibitors dysregulate host immunity and may precipitate autoimmune diseases including diabetes mellitus. In this review, we go beyond the case reports towards understanding the underlying mechanisms by which Programmed cell death 1 (PD-1) and Programmed death ligand-1 (PD-L1) inhibitors precipitate diabetes. We discuss the role of PD-1/PD-L1 in autoimmunity and the use of mice models to describe their involvement in diabetes. We also reviewed the genetic anomalies in PD-1/PD-L1genes and their link to diabetes. Finally, we present the studies conducted to identify patients at risk of developing autoimmune diseases as an adverse effect for PD-1/PD-L1 use. Understanding these issues can guide researchers to find a way to circumvent the autoimmune adverse reactions seen with PD-1/PD-L1 inhibitors without affecting their antitumor activity.

In silico analysis reveals interrelation of enriched pathways and genes in type 1 diabetes

Type 1 diabetes (T1D) is a multifactorial, polygenic complex autoimmune disease damaging pancreatic islet β cells. Numerous genes linked to T1D have been discovered through genetical studies, GWAS and polymorphisms. Most genetical studies focused on independent genes while others overemphasized on SNPs. Here, a collective analysis of documented T1D-associated genes was performed using bioinformatics tools. Enriched biological pathways, functions, enrichment clustering, networks and interactomes were analysed. Besides, meta-analyses of T1D-associated genes and T1D-related genes from SNPs were investigated to find common genes, pathways, enrichment and interrelationships. Notable enriched pathways comprised of cytokine-mediated signalling, cytokine production, interferon gamma production, myeloid leukocyte activation, activation of immune response, lymphocyte activation, adaptive immune response, Th17 cell differentiation etc. Enrichment analysis of T1D-associated genes emphasized the role of immune-linked machineries in metabolism, disease progression and aetiology of type 1 diabetes. Interactome analysis revealed overrepresentation of T1D-associated genes compared with T1D-related genes from SNPs. MCODE components highlighted the significance of pathways linked to vitamin D metabolism, signalling by interleukins, toll-like receptors, chemokines, PD-1, NOTCH, antigen processes etc. About 153 genes from MCODE complexes representing enriched pathways of T1D-associated genes and T1D-related genes from SNPs play a crucial role and may be important for further investigations. The information may be valuable for designing precision medicine-based therapeutics.

Artificial Pancreas Control Strategies Used for Type 1 Diabetes Control and Treatment: A Comprehensive Analysis

This paper presents a comprehensive survey about the fundamental components of the artificial pancreas (AP) system including insulin administration and delivery, glucose measurement (GM), and control strategies/algorithms used for type 1 diabetes mellitus (T1DM) treatment and control. Our main focus is on the T1DM that emerges due to pancreas’s failure to produce sufficient insulin due to the loss of beta cells (β-cells). We discuss various insulin administration and delivery methods including physiological methods, open-loop, and closed-loop schemes. Furthermore, we report several factors such as hyperglycemia, hypoglycemia, and many other physical factors that need to be considered while infusing insulin in human body via AP systems. We discuss three prominent control algorithms including proportional-integral- derivative (PID), fuzzy logic, and model predictive, which have been clinically evaluated and have all shown promising results. In addition, linear and non-linear insulin infusion control schemes have been formally discussed. To the best of our knowledge, this is the first work which systematically covers recent developments in the AP components with a solid foundation for future studies in the T1DM field.

Enteroviruses and T1D: Is It the Virus, the Genes or Both which Cause T1D

Type 1 diabetes (T1D) is a chronic autoimmune disorder that results from the selective destruction of insulin-producing β-cells in the pancreas. Up to now, the mechanisms triggering the initiation and progression of the disease are, in their complexity, not fully understood and imply the disruption of several tolerance networks. Viral infection is one of the environmental factors triggering diabetes, which is initially based on the observation that the disease’s incidence follows a periodic pattern within the population. Moreover, the strong correlation of genetic susceptibility is a prerequisite for enteroviral infection associated islet autoimmunity. Epidemiological data and clinical findings indicate enteroviral infections, mainly of the coxsackie B virus family, as potential pathogenic mechanisms to trigger the autoimmune reaction towards β-cells, resulting in the boost of inflammation following β-cell destruction and the onset of T1D. This review discusses previously identified virus-associated genetics and pathways of β-cell destruction. Is it the virus itself which leads to β-cell destruction and T1D progression? Or is it genetic, so that the virus may activate auto-immunity and β-cell destruction only in genetically predisposed individuals?

Genetic Association Study of IL2RA, IFIH1, and CTLA-4 Polymorphisms With Autoimmune Thyroid Diseases and Type 1 Diabetes

Autoimmune thyroid diseases (AITDs) which include Graves' disease (GD) and Hashimoto's thyroiditis (HT) as well as type 1 diabetes (T1D) are common autoimmune disorders in children. Many genes are involved in the modulation of the immune system and their polymorphisms might predispose to autoimmune diseases development. According to the literature genes encoding IL2RA (alpha subunit of Interleukin 2 receptor), IFIH1 (Interferon induced with helicase C domain 1) and CTLA-4 (cytotoxic T cell antigen 4) might be associated with autoimmune diseases pathogenesis. The aim of the study was to assess the association of chosen single nucleotide polymorphisms (SNPs) of IL2RA, IFIH1, and CTLA-4 genes in the group of Polish children with AITDs and in children with T1D. We analyzed single nucleotide polymorphisms (SNPs) in the IL2RA region (rs7093069), IFIH1 region (rs1990760) and CTLA-4 region (rs231775) in group of Polish children and adolescents with type 1 diabetes (n = 194) and autoimmune thyroid diseases (GD n = 170, HT n = 81) and healthy age and sex matched controls for comparison (n = 110). There were significant differences observed between T1D patients and control group in alleles of IL2RA (rs7093069 T > C) and CTLA-4 (rs231775 G > A). In addition, the study revealed T/T genotype at the IL2RA locus (rs7093069) and G/G genotype at the CTLA-4 locus (rs231775) to be statistically significant more frequent in children with T1D. Moreover, genotypes C/T and T/T at the IFIH1 locus (rs1990760) were significantly more frequent in patients with T1D than in controls. We observed no significant differences between AITD patients and a control group in analyzed SNPs. In conclusion, we detected that each allele T of rs7093069 SNP at the IL2RA locus and G allele of rs231775 SNP at the CTLA-4 locus as well as C/T and T/T genotypes of rs1990760 SNP at the IFIH1 locus are predisposing in terms of T1D development. Thereby, we confirmed that IL2RA, IFIH1, and CTLA-4 gene locus have a role in T1D susceptibility. The analysis of selected SNPs revealed no association with AITDs in a group of Polish children and adolescents.

BTN3A2 serves as a prognostic marker and favors immune infiltration in triple-negative breast cancer

Immune infiltration is reported to be highly associated with tumor progress. Since butyrophilin subfamily 3 member A2 (BTN3A2) serves as a crucial mediator in immune activation, we aimed to investigate the correlation of BTN3A2 in immune infiltration and tumor prognosis via extensive-cancer analysis. The levels of BTN3A2 expression in extensive cancers were analyzed with Oncomine and TIMER databases. Univariate cox and multivariate cox regression analyses were conducted to assess the associations of BTN3A2 to prognosis of various cancers. The correlations of BTN3A2 with immune infiltration were assessed by TIMER database. It suggested that BTN3A2 was a potential prognosis signature for breast cancer (BRCA) and ovarian cancer (OV). However, immune infiltrations were highly correlated with BTN3A2 in triple-negative breast cancer (TNBC), compared with OV and other subtypes of BRCA. Multivariate cox regression analysis revealed that BTN3A2 was an independently prognostic signature of TNBC, as well as weighted correlation network analysis suggested BTN3A2 was only correlated with TNBC, rather than other subtypes of BRCA. Immune cell subtypes correlation analysis showed that BTN3A2 was highly correlated with general T, CD8+ T, T helper type 1, exhausted T cells, and dendritic cells in TNBC. And the coexpression geneset of BTN3A2 was mainly involved in T-cell receptor interaction and the nuclear factor-κB (NF-κB) signaling pathway. Collectively, BTN3A2 that was positively associated with better prognosis could be served as a special diagnostic and independently prognostic marker for TNBC by regulating the T-cell receptor interaction and NF-κB signaling pathways.

The Genetic Contribution to Type 1 Diabetes

PURPOSE OF REVIEW

To provide an updated summary of discoveries made to date resulting from genome-wide association study (GWAS) and sequencing studies, and to discuss the latest loci added to the growing repertoire of genetic signals predisposing to type 1 diabetes (T1D).

RECENT FINDINGS

Genetic studies have identified over 60 loci associated with T1D susceptibility. GWAS alone does not specifically inform on underlying mechanisms, but in combination with other sequencing and omics-data, advances are being made in our understanding of T1D genetic etiology and pathogenesis. Current knowledge indicates that genetic variation operating in both pancreatic β cells and in immune cells is central in mediating T1D risk. One of the main challenges is to determine how these recently discovered GWAS-implicated variants affect the expression and function of gene products. Once we understand the mechanism of action for disease-causing variants, we will be well placed to apply targeted genomic approaches to impede the premature activation of the immune system in an effort to ultimately prevent the onset of T1D.

The Association between rs2292239 Polymorphism in ERBB3 Gene and Type 1 Diabetes: A Meta-Analysis

Objectives

The purpose of this study was to explore the association between rs2292239 polymorphism in ERBB3 gene and type 1 diabetes (T1D).

Methods

A systematic search of studies on the association of rs2292239 polymorphism in ERBB3 gene with T1D susceptibility was conducted in PubMed, Web of science, Elsevier Science Direct, and Cochrane Library. Eventually, 9 published studies were included. The strength of association between rs2292239 polymorphism and T1D susceptibility was assessed by odds ratios (ORs) with its 95% confidence intervals (CIs).

Results

A total of 9 case-control studies, consisting of 5369 T1D patients and 6920 controls, were included in the meta-analysis. This meta-analysis showed significant association between ERBB3 rs2292239 polymorphism and T1D susceptibility in overall population (A vs. C, OR: 1.292, 95% CI= 1.224-1.364, P _H=0.450, P _H is P value for the heterogeneity test). Similar results were found in subgroup analysis by ethnicity.

Conclusions

ERBB3 rs2292239 polymorphism is associated with T1D susceptibility and rs2292239-A allele is a risk factor for T1D. However, more large-scale studies are warranted to replicate our findings.

Machine Learning SNP Based Prediction for Precision Medicine

In the past decade, precision genomics based medicine has emerged to provide tailored and effective healthcare for patients depending upon their genetic features. Genome Wide Association Studies have also identified population based risk genetic variants for common and complex diseases. In order to meet the full promise of precision medicine, research is attempting to leverage our increasing genomic understanding and further develop personalized medical healthcare through ever more accurate disease risk prediction models. Polygenic risk scoring and machine learning are two primary approaches for disease risk prediction. Despite recent improvements, the results of polygenic risk scoring remain limited due to the approaches that are currently used. By contrast, machine learning algorithms have increased predictive abilities for complex disease risk. This increase in predictive abilities results from the ability of machine learning algorithms to handle multi-dimensional data. Here, we provide an overview of polygenic risk scoring and machine learning in complex disease risk prediction. We highlight recent machine learning application developments and describe how machine learning approaches can lead to improved complex disease prediction, which will help to incorporate genetic features into future personalized healthcare. Finally, we discuss how the future application of machine learning prediction models might help manage complex disease by providing tissue-specific targets for customized, preventive interventions.