Failed Genetic Protection: Type 1 Diabetes in the Presence of HLA-DQB1*06:02

Increased Frequency of the HLA-DRB1*04:04-DQA1*03-DQB1*03:02 Haplotype Among HLA-DQB1*06:02-Positive Children With Type 1 Diabetes

HLA-DR/DQ haplotypes largely define genetic susceptibility to type 1 diabetes (T1D). The DQB1*06:02-positive haplotype (DR15-DQ602) common in individuals of European ancestry is very rare among children with T1D. Among 4,490 children with T1D in the Finnish Pediatric Diabetes Register, 57 (1.3%) case patients with DQB1*06:02 were identified, in comparison with 26.1% of affected family-based association control participants. There were no differences between DQB1*06:02-positive and -negative children with T1D regarding sex, age, islet autoantibody distribution, or autoantibody levels, but significant differences were seen in the frequency of second class II HLA haplotypes. The most prevalent haplotype present with DQB1*06:02 was DRB1*04:04-DQA1*03-DQB1*03:02, which was found in 27 (47.4%) of 57 children, compared with only 797 (18.0%) of 4,433 among DQB1*06:02-negative case patients (P < 0.001 by χ2 test). The other common risk-associated haplotypes, DRB1*04:01-DQA1*03-DQB1*03:02 and (DR3)-DQA1*05-DQB1*02, were less prevalent in DQB1*06:02-positive versus DQB1*06:02-negative children (P < 0.001). HLA-B allele frequencies did not differ by DQB1*06:02 haplotype between children with T1D and control participants or by DRB1*04:04-DQA1*03-DQB1*03:02 haplotype between DQB1*06:02-positive and -negative children with T1D. The increased frequency of the DRB1*04:04 allele among DQB1*06:02-positive case patients may indicate a preferential ability of the DR404 molecule to present islet antigen epitopes despite competition by DQ602.

ARTICLE HIGHLIGHTS

The immunopathogenesis of narcolepsy type 1

Narcolepsy type 1 (NT1) is a chronic sleep disorder resulting from the loss of a small population of hypothalamic neurons that produce wake-promoting hypocretin (HCRT; also known as orexin) peptides. An immune-mediated pathology for NT1 has long been suspected given its exceptionally tight association with the MHC class II allele HLA-DQB1*06:02, as well as recent genetic evidence showing associations with polymorphisms of T cell receptor genes and other immune-relevant loci and the increased incidence of NT1 that has been observed after vaccination with the influenza vaccine Pandemrix. The search for both self-antigens and foreign antigens recognized by the pathogenic T cell response in NT1 is ongoing. Increased T cell reactivity against HCRT has been consistently reported in patients with NT1, but data demonstrating a primary role for T cells in neuronal destruction are currently lacking. Animal models are providing clues regarding the roles of autoreactive CD4+ and CD8+ T cells in the disease. Elucidation of the pathogenesis of NT1 will allow for the development of targeted immunotherapies at disease onset and could serve as a model for other immune-mediated neurological diseases.

Narcolepsy risk loci outline role of T cell autoimmunity and infectious triggers in narcolepsy

Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

Type 1 diabetes and inborn errors of immunity: Complete strangers or 2 sides of the same coin?

Type 1 diabetes (T1D) is a polygenic disease and does not follow a mendelian pattern. Inborn errors of immunity (IEIs), on the other hand, are caused by damaging germline variants, suggesting that T1D and IEIs have nothing in common. Some IEIs, resulting from mutations in genes regulating regulatory T-cell homeostasis, are associated with elevated incidence of T1D. The genetic spectrum of IEIs is gradually being unraveled; consequently, molecular pathways underlying human monogenic autoimmunity are being identified. There is an appreciable overlap between some of these pathways and the genetic variants that determine T1D susceptibility, suggesting that after all, IEI and T1D are 2 sides of the same coin. The study of monogenic IEIs with a variable incidence of T1D has the potential to provide crucial insights into the mechanisms leading to T1D. These insights contribute to the definition of T1D endotypes and explain disease heterogeneity. In this review, we discuss the interconnected pathogenic pathways of autoimmunity, β-cell function, and primary immunodeficiency. We also examine the role of environmental factors in disease penetrance as well as the circumstantial evidence of IEI drugs in preventing and curing T1D in individuals with IEIs, suggesting the repositioning of these drugs also for T1D therapy.

Decoding Diabetes Biomarkers and Related Molecular Mechanisms by Using Machine Learning, Text Mining, and Gene Expression Analysis

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.

Alkaloidal Phytoconstituents for Diabetes Management: Exploring the Unrevealed Potential

The main characteristic feature of diabetes mellitus is the disturbance of carbohydrate, lipid, and protein metabolism, which results in insulin insufficiency and can also lead to insulin resistance. Both the acute and chronic diabetic cases are increasing at an exponential rate, which is also flagged by the World Health Organization (WHO) and the International Diabetes Federation (IDF). Treatment of diabetes mellitus with synthetic drugs often fails to provide desired results and limits its use to symptomatic treatment only. This has resulted in the exploration of alternative medicine, of which herbal treatment is gaining popularity these days. Owing to their safety benefits, treatment compliance, and ability to exhibit effects without disturbing internal homeostasis, research in the field of herbal and ayurvedic treatments has gained importance. Medicinal phytoconstituents include micronutrients, amino acids, proteins, mucilage, critical oils, triterpenoids, saponins, carotenoids, alkaloids, flavonoids, phenolic acids, tannins, and coumarins, which play a dynamic function in the prevention and treatment of diabetes mellitus. Alkaloids found in medicinal plants represent an intriguing potential for the inception of novel approaches to diabetes mellitus therapies. Thus, this review article highlights detailed information on alkaloidal phytoconstituents, which includes sources and structures of alkaloids along with the associated mechanism involved in the management of diabetes mellitus. From the available literature and data presented, it can be concluded that these compounds hold tremendous potential for use as monotherapies or in combination with current treatments, which can result in the development of better efficacy and safety profiles.

Self-Antigens Targeted by Regulatory T Cells in Type 1 Diabetes

While progress has been made toward understanding mechanisms that lead to the development of autoimmunity, there is less knowledge regarding protective mechanisms from developing such diseases. For example, in type 1 diabetes (T1D), the immune-mediated form of diabetes, the role of pathogenic T cells in the destruction of pancreatic islets is well characterized, but immune-mediated mechanisms that contribute to T1D protection have not been fully elucidated. One potential protective mechanism includes the suppression of immune responses by regulatory CD4 T cells (Tregs) that recognize self-peptides from islets presented by human leukocyte antigen (HLA) class II molecules. In this review, we summarize what is known about the antigenic self-peptides recognized by Tregs in the context of T1D.

Association between HLA alleles and sub-phenotype of childhood steroid-sensitive nephrotic syndrome

BACKGROUND

Few studies have addressed the effects of human leukocyte antigen (HLA) alleles on different clinical sub-phenotypes in childhood steroid-sensitive nephrotic syndrome (SSNS), including SSNS without recurrence (SSNSWR) and steroid-dependent nephrotic syndrome/frequently relapse nephrotic syndrome (SDNS/FRNS). In this study, we investigated the relationship between HLA system and children with SSNSWR and SDNS/FRNS and clarified the value of HLA allele detection for precise typing of childhood SSNS.

METHODS

A total of 241 Chinese Han individuals with SSNS were genotyped using GenCap-WES Capture Kit, and four-digit resolution HLA alleles were imputed from available Genome Wide Association data. The distribution and carrying frequency of HLA alleles in SSNSWR and SDNS/FRNS were investigated. Additionally, logistic regression and mediating effects were used to examine the relationship between risk factors for disease process and HLA system.

RESULTS

Compared with SSNSWR, significantly decreased serum levels of complement 3 (C3) and complement 4 (C4) at onset were detected in SDNS/FRNS (C3, P < 0.001; C4, P = 0.018). The average time to remission after sufficient initial steroid treatment in SDNS/FRNS was significantly longer than that in SSNSWR (P = 0.0001). Low level of C4 was further identified as an independent risk factor for SDNS/FRNS (P = 0.008, odds ratio = 0.174, 95% confidence interval 0.048-0.630). The HLA-A*11:01 allele was independently associated with SSNSWR and SDNS/FRNS (P = 0.0012 and P = 0.0006, respectively). No significant HLA alleles were detected between SSNSWR and SDNS/FRNS. In addition, a mediating effect among HLA-I alleles (HLA-B*15:11, HLA-B*44:03 and HLA-C*07:06), C4 level and SDNS/FRNS was identified.

CONCLUSIONS

HLA-I alleles provide novel genetic markers for SSNSWR and SDNS/FRNS. HLA-I antigens may be involved in steroid dependent or frequent relapse in children with SSNS as mediators of immunoregulation.

Environmental Triggering of Type 1 Diabetes Autoimmunity

Type 1 diabetes (T1D) is a chronic autoimmune disease in which pancreatic islet β cells are destroyed by immune cells, ultimately leading to overt diabetes. The progressive increase in T1D incidence over the years points to the role of environmental factors in triggering or accelerating the disease process which develops on a highly multigenic susceptibility background. Evidence that environmental factors induce T1D has mostly been obtained in animal models. In the human, associations between viruses, dietary habits or changes in the microbiota and the development of islet cell autoantibodies or overt diabetes have been reported. So far, prediction of T1D development is mostly based on autoantibody detection. Future work should focus on identifying a causality between the different environmental risk factors and T1D development to improve prediction scores. This should allow developing preventive strategies to limit the T1D burden in the future.

Etiology of Autoimmune Islet Disease: Timing Is Everything

Life is about timing. -Carl LewisThe understanding of autoimmune type 1 diabetes is increasing, and examining etiology separate from pathogenesis has become crucial. The components to explain type 1 diabetes development have been known for some time. The strong association with HLA has been researched for nearly 50 years. Genome-wide association studies added another 60+ non-HLA genetic factors with minor contribution to risk. Insulitis has long been known to be present close to clinical diagnosis. T and B cells recognizing β-cell autoantigens are detectable prior to diagnosis and in newly diagnosed patients. Islet autoantibody tests against four major autoantigens have been standardized and used as biomarkers of islet autoimmunity. However, to clarify the etiology would require attention to time. Etiology may be defined as the cause of a disease (i.e., type 1 diabetes) or abnormal condition (i.e., islet autoimmunity). Timing is everything, as neither the prodrome of islet autoimmunity nor the clinical onset of type 1 diabetes tells us much about the etiology. Rather, the islet autoantibody that appears first and persists would mark the diagnosis of an autoimmune islet disease (AID). Events after the diagnosis of AID would represent the pathogenesis. Several islet autoantibodies without (stage 1) or with impaired glucose tolerance (stage 2) or with symptoms (stage 3) would define the pathogenesis culminating in clinical type 1 diabetes. Etiology would be about the timing of events that take place before the first-appearing islet autoantibody.

Proinsulin-Reactive CD4 T Cells in the Islets of Type 1 Diabetes Organ Donors

Proinsulin is an abundant protein that is selectively expressed by pancreatic beta cells and has been a focus for development of antigen-specific immunotherapies for type 1 diabetes (T1D). In this study, we sought to comprehensively evaluate reactivity to preproinsulin by CD4 T cells originally isolated from pancreatic islets of organ donors having T1D. We analyzed 187 T cell receptor (TCR) clonotypes expressed by CD4 T cells obtained from six T1D donors and determined their response to 99 truncated preproinsulin peptide pools, in the presence of autologous B cells. We identified 14 TCR clonotypes from four out of the six donors that responded to preproinsulin peptides. Epitopes were found across all of proinsulin (insulin B-chain, C-peptide, and A-chain) including four hot spot regions containing peptides commonly targeted by TCR clonotypes derived from multiple T1D donors. Of importance, these hot spots overlap with peptide regions to which CD4 T cell responses have previously been detected in the peripheral blood of T1D patients. The 14 TCR clonotypes recognized proinsulin peptides presented by various HLA class II molecules, but there was a trend for dominant restriction with HLA-DQ, especially T1D risk alleles DQ8, DQ2, and DQ8-trans. The characteristics of the tri-molecular complex including proinsulin peptide, HLA-DQ molecule, and TCR derived from CD4 T cells in islets, provides an essential basis for developing antigen-specific biomarkers as well as immunotherapies.

Next-Generation HLA Sequence Analysis Uncovers Seven HLA-DQ Amino Acid Residues and Six Motifs Resistant to Childhood Type 1 Diabetes

HLA-DQA1 and -DQB1 genes have significant and potentially causal associations with autoimmune type 1 diabetes (T1D). To follow up on the earlier analysis on high-risk HLA-DQ2.5 and DQ8.1, the current analysis uncovers seven residues (αa1, α157, α196, β9, β30, β57, and β70) that are resistant to T1D among subjects with DQ4-, 5-, 6-, and 7-resistant DQ haplotypes. These 7 residues form 13 common motifs: 6 motifs are significantly resistant, 6 motifs have modest or no associations (P values >0.05), and 1 motif has 7 copies observed among control subjects only. The motifs "DAAFYDG," "DAAYHDG," and "DAAYYDR" have significant resistance to T1D (odds ratios [ORs] 0.03, 0.25, and 0.18; P = 6.11 × 10^-24, 3.54 × 10^-15, and 1.03 × 10^-21, respectively). Remarkably, a change of a single residue from the motif "DAAYHDG" to "DAAYHSG" (D to S at β57) alters the resistance potential, from resistant motif (OR 0.15; P = 3.54 × 10^-15) to a neutral motif (P = 0.183), the change of which was significant (Fisher P value = 0.0065). The extended set of linked residues associated with T1D resistance and unique to each cluster of HLA-DQ haplotypes represents facets of all known features and functions of these molecules: antigenic peptide binding, peptide-MHC class II complex stability, β167-169 RGD loop, T-cell receptor binding, formation of homodimer of α-β heterodimers, and cholesterol binding in the cell membrane rafts. Identification of these residues is a novel understanding of resistant DQ associations with T1D. Our analyses endow potential molecular approaches to identify immunological mechanisms that control disease susceptibility or resistance to provide novel targets for immunotherapeutic strategies.