A composite immune signature parallels disease progression across T1D subjects

Time to reframe the disease staging system for type 1 diabetes

In 2015, introduction of a disease staging system offered a framework for benchmarking progression to clinical type 1 diabetes. This model, based on islet autoantibodies (stage 1) and dysglycaemia (stage 2) before type 1 diabetes diagnosis (stage 3), has facilitated screening and identification of people at risk. Yet, there are many limitations to this model as the stages combine a very heterogeneous group of individuals; do not have high specificity for type 1 diabetes; can occur without persistence (ie, reversion to an earlier risk stage); and exclude age and other influential risk factors. The current staging system also infers that individuals at risk of type 1 diabetes progress linearly from stage 1 to stage 2 and subsequently stage 3, whereas such movements are often more complex. With the approval of teplizumab by the US Food and Drug Administration in 2022 to delay type 1 diabetes in people at stage 2, there is a need to refine the definition and accuracy of type 1 diabetes staging. Theoretically, we propose that a type 1 diabetes risk calculator should incorporate any available demographic, genetic, autoantibody, metabolic, and immune data that could be continuously updated. Additionally, we call to action for the field to increase the breadth of knowledge regarding type 1 diabetes risk in non-relatives, adults, and individuals from minority populations.

Multi-omics analysis reveals drivers of loss of β-cell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study

AIMS

Heterogeneity in the rate of β-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis.

METHODS

We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in β-cell mass measured as fasting C-peptide.

RESULTS

Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in β-cell function. The second signature was related to translation and viral infection was inversely associated with change in β-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid β-cell decline.

CONCLUSIONS

Features that differ between individuals with slow and rapid decline in β-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.

Human immune phenotyping reveals accelerated aging in type 1 diabetes

The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.

Identification of an anergic BND cell-derived activated B cell population (BND2) in young-onset type 1 diabetes patients

Recent evidence suggests a role for B cells in the pathogenesis of young-onset type 1 diabetes (T1D), wherein rapid progression occurs. However, little is known regarding the specificity, phenotype, and function of B cells in young-onset T1D. We performed a cross-sectional analysis comparing insulin-reactive to tetanus-reactive B cells in the blood of T1D and controls using mass cytometry. Unsupervised clustering revealed the existence of a highly activated B cell subset we term BND2 that falls within the previously defined anergic BND subset. We found a specific increase in the frequency of insulin-reactive BND2 cells in the blood of young-onset T1D donors, which was further enriched in the pancreatic lymph nodes of T1D donors. The frequency of insulin-binding BND2 cells correlated with anti-insulin autoantibody levels. We demonstrate BND2 cells are pre-plasma cells and can likely act as APCs to T cells. These findings identify an antigen-specific B cell subset that may play a role in the rapid progression of young-onset T1D.

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

An integrated multi-omics analysis of topoisomerase family in pan-cancer: Friend or foe?

BACKGROUND

Topoisomerases are nuclear enzymes that get to the bottom of topological troubles related with DNA all through a range of genetic procedures. More and more studies have shown that topoisomerase-mediated DNA cleavage plays crucial roles in tumor cell death and carcinogenesis. There is however still a lack of comprehensive multi-omics studies related to topoisomerase family genes from a pan-cancer perspective.

METHODS

In this study, a multiomics pan-cancer analysis of topoisomerase family genes was conducted by integrating over 10,000 multi-dimensional cancer genomic data across 33 cancer types from The Cancer Genome Atlas (TCGA), 481 small molecule drug response data from cancer therapeutics response portal (CTRP) as well as normal tissue data from Genotype-Tissue Expression (GTEx). Finally, overall activity-level analyses of topoisomerase in pan-cancers were performed by gene set variation analysis (GSVA), together with differential expression, clinical relevancy, immune cell infiltration and regulation of cancer-related pathways.

RESULTS

Dysregulated gene expression of topoisomerase family were related to genomic changes and abnormal epigenetic modifications. The expression levels of topoisomerase family genes could significantly impact cancer progression, intratumoral heterogeneity, alterations in the immunological condition and regulation of the cancer marker-related pathways, which in turn caused the differences in potential drugs sensitivity and the distinct prognosis of patients.

CONCLUSION

It was anticipated that topoisomerase family genes would become novel prognostic biomarkers for cancer patients and provide new insights for the diagnosis and treatment of tumors.

Guidelines for standardizing T-cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes

Cytometric immunophenotyping is a powerful tool to discover and implement T-cell biomarkers of type 1 diabetes (T1D) progression and response to clinical therapy. Although many discovery-based T-cell biomarkers have been described, to date, no such markers have been widely adopted in standard practice. The heterogeneous nature of T1D and lack of standardized assays and experimental design across studies is a major barrier to the broader adoption of T-cell immunophenotyping assays. There is an unmet need to harmonize the design of immunophenotyping assays, including those that measure antigen-agnostic cell populations, such that data collected from different clinical trial sites and T1D cohorts are comparable, yet account for cohort-specific features and different drug mechanisms of action. In these Guidelines, we aim to provide expert advice on how to unify aspects of study design and practice. We provide recommendations for defining cohorts, method implementation, as well as tools for data analysis and reporting by highlighting and building on selected successes. Harmonization of cytometry-based T-cell assays will allow researchers to better integrate findings across trials, ultimately enabling the identification and validation of biomarkers of disease progression and treatment response in T1D.

LncRNA functional annotation with improved false discovery rate achieved by disease associations

The long non‐coding RNAs (lncRNAs) play critical roles in various biological processes and are associated with many diseases. Functional annotation of lncRNAs in diseases attracts great attention in understanding their etiology. However, the traditional co-expression-based analysis usually produces a significant number of false positive function assignments. It is thus crucial to develop a new approach to obtain lower false discovery rate for functional annotation of lncRNAs. Here, a novel strategy termed DAnet which combining disease associations with cis-regulatory network between lncRNAs and neighboring protein-coding genes was developed, and the performance of DAnet was systematically compared with that of the traditional differential expression-based approach. Based on a gold standard analysis of the experimentally validated lncRNAs, the proposed strategy was found to perform better in identifying the experimentally validated lncRNAs compared with the other method. Moreover, the majority of biological pathways (40%∼100%) identified by DAnet were reported to be associated with the studied diseases. In sum, the DAnet is expected to be used to identify the function of specific lncRNAs in a particular disease or multiple diseases.

IL-6 receptor blockade does not slow β cell loss in new-onset type 1 diabetes

BackgroundIL-6 receptor (IL-6R) signaling drives development of T cell populations important to type 1 diabetes pathogenesis. We evaluated whether blockade of IL-6R with monoclonal antibody tocilizumab would slow loss of residual β cell function in newly diagnosed type 1 diabetes patients.MethodsWe conducted a multicenter, randomized, placebo-controlled, double-blind trial with tocilizumab in new-onset type 1 diabetes. Participants were screened within 100 days of diagnosis. Eligible participants were randomized 2:1 to receive 7 monthly doses of tocilizumab or placebo. The primary outcome was the change from screening in the mean AUC of C-peptide collected during the first 2 hours of a mixed meal tolerance test at week 52 in pediatric participants (ages 6-17 years).ResultsThere was no statistical difference in the primary outcome between tocilizumab and placebo. Immunophenotyping showed reductions in downstream signaling of the IL-6R in T cells but no changes in CD4 memory subsets, Th17 cells, Tregs, or CD4+ T effector cell resistance to Treg suppression. A DC subset decreased during therapy but regressed to baseline once therapy stopped. Tocilizumab was well tolerated.ConclusionTocilizumab reduced T cell IL-6R signaling but did not modulate CD4+ T cell phenotypes or slow loss of residual β cell function in newly diagnosed individuals with type 1 diabetes.Trial RegistrationClinicalTrials.gov NCT02293837.FundingNIH National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and National Institute of Allergy and Infectious Diseases (NIAID) UM1AI109565, UL1TR000004 from NIH/National Center for Research Resources (NCRR) Clinical and Translational Science Award (CTSA), NIH/NIDDK P30DK036836, NIH/NIDDK U01DK103266, NIH/NIDDK U01DK103266, 1UL1TR000064 from NIH/NCRR CTSA, NIH/National Center for Advancing Translational Sciences (NCATS) UL1TR001878, UL1TR002537 from NIH/CTSA; National Health and Medical Research Council Practitioner Fellowship (APP1136735), NIH/NIDDK U01-DK085476, NIH/CTSA UL1-TR002494, Indiana Clinical and Translational Science Institute Award UL1TR002529, Vanderbilt Institute for Clinical and Translational Research UL1TR000445. NIH/NCATS UL1TR003142, NIH/CTSA program UL1-TR002494, Veteran Affairs Administration, and 1R01AI132774.

Parallel Multi-Omics in High-Risk Subjects for the Identification of Integrated Biomarker Signatures of Type 1 Diabetes

BACKGROUND

Biomarkers are crucial for detecting early type-1 diabetes (T1D) and preventing significant β-cell loss before the onset of clinical symptoms. Here, we present proof-of-concept studies to demonstrate the potential for identifying integrated biomarker signature(s) of T1D using parallel multi-omics.

METHODS

Blood from human subjects at high risk for T1D (and healthy controls; n = 4 + 4) was subjected to parallel unlabeled proteomics, metabolomics, lipidomics, and transcriptomics. The integrated dataset was analyzed using Ingenuity Pathway Analysis (IPA) software for disturbances in the at-risk subjects compared to controls.

RESULTS

The final quadra-omics dataset contained 2292 proteins, 328 miRNAs, 75 metabolites, and 41 lipids that were detected in all samples without exception. Disease/function enrichment analyses consistently indicated increased activation, proliferation, and migration of CD4 T-lymphocytes and macrophages. Integrated molecular network predictions highlighted central involvement and activation of NF-κB, TGF-β, VEGF, arachidonic acid, and arginase, and inhibition of miRNA Let-7a-5p. IPA-predicted candidate biomarkers were used to construct a putative integrated signature containing several miRNAs and metabolite/lipid features in the at-risk subjects.

CONCLUSIONS

Preliminary parallel quadra-omics provided a comprehensive picture of disturbances in high-risk T1D subjects and highlighted the potential for identifying associated integrated biomarker signatures. With further development and validation in larger cohorts, parallel multi-omics could ultimately facilitate the classification of T1D progressors from non-progressors.

The many faces of islet antigen-specific CD8 T cells: clues to clinical outcome in type 1 diabetes

Immune monitoring enables a better understanding of disease processes and response to therapy, but has been challenging in the setting of chronic autoimmunity because of unknown etiology, variable and protracted kinetics of the disease process, heterogeneity across patients and the complexity of immune interactions. To begin to parse this complexity, we focus here on type 1 diabetes (T1D) and CD8 T cells as a cell type that has features that are associated with different stages of disease, rates of progression and response to therapy. Specifically, we discuss the current understanding of the role of autoreactive CD8 T cells in disease outcome, which implicates particular CD8 functional subsets, rather than unique antigens or total number of autoreactive T cells. Next, we discuss how autoreactive CD8 T‐cell features can be reflected in measures of global CD8 T cells, and then pull these concepts together by highlighting immune therapies recently shown to modulate both CD8 T cells and disease progression. We end by discussing outstanding questions about the role of specific subsets of autoreactive CD8 T cells in disease progression and how they may be optimally modulated to treat and prevent T1D.

Sensitive detection of multiple islet autoantibodies in type 1 diabetes using small sample volumes by agglutination-PCR

Islet autoantibodies are predominantly measured by radioassay to facilitate risk assessment and diagnosis of type 1 diabetes. However, the reliance on radioactive components, large sample volumes and limited throughput renders radioassay testing costly and challenging. We developed a multiplex analysis platform based on antibody detection by agglutination-PCR (ADAP) for the sample-sparing measurement of GAD, IA-2 and insulin autoantibodies/antibodies in 1 μL serum. The assay was developed and validated in 7 distinct cohorts (n = 858) with the majority of the cohorts blinded prior to analysis. Measurements from the ADAP assay were compared to radioassay to determine correlation, concordance, agreement, clinical sensitivity and specificity. The average overall agreement between ADAP and radioassay was above 91%. The average clinical sensitivity and specificity were 96% and 97%. In the IASP 2018 workshop, ADAP achieved the highest sensitivity of all assays tested at 95% specificity (AS95) rating for GAD and IA-2 autoantibodies and top-tier performance for insulin autoantibodies. Furthermore, ADAP correctly identified 95% high-risk individuals with two or more autoantibodies by radioassay amongst 39 relatives of T1D patients tested. In conclusion, the new ADAP assay can reliably detect the three cardinal islet autoantibodies/antibodies in 1μL serum with high sensitivity. This novel assay may improve pediatric testing compliance and facilitate easier community-wide screening for islet autoantibodies.

Endotypes in T1D: B lymphocytes and early onset

PURPOSE OF REVIEW

Although type 1 diabetes (T1D) is characterized by destruction of the pancreatic beta cells by self-reactive T cells, it has become increasingly evident that B cells also play a major role in disease development, likely functioning as antigen-presenting cells. Here we review the biology of islet antigen-reactive B cells and their participation in autoimmune diabetes.

RECENT FINDINGS

Relative to late onset, individuals who develop T1D at an early age display increased accumulation of insulin-reactive B cells in islets. This B-cell signature is also associated with rapid progression of disease and responsiveness to B-cell depletion therapy. Also suggestive of B-cell participation in disease is loss of anergy in high-affinity insulin-reactive B cells. Importantly, loss of anergy is seen in patient's healthy first-degree relatives carrying certain T1D risk alleles, suggesting a role early in disease development.

SUMMARY

Recent studies indicate that islet-reactive B cells may play a pathogenic role very early in T1D development in young patients, and suggest utility of therapies that target these cells.

Assessing effector T cells in type 1 diabetes

PURPOSE OF REVIEW

The role of T cells specific for islet autoantigens is proven in pathogenesis of type 1 diabetes. Recently, there has been rapid expansion in the number of T-cell subsets identified, this has coincided with an increase in the repertoire of reported islet antigens mainly through the discovery of novel epitopes. A discussion of how these marry together is now warranted and timely.

RECENT FINDINGS

In this review, we will discuss the autoreactivity against neo-epitopes. We then explore the growing array of T-cell subsets for both CD4 T cells, including follicular and peripheral T helper cells, and CD8 T cells, discussing evolution from naïve to exhausted phenotypes. Finally, we detail how subsets correlate with disease stage and loss of β-cell function and are impacted by immunotherapy.

SUMMARY

The expanding list of T-cell subsets may be potentially encouraging in terms of elucidating disease mechanisms and have a role as biomarkers for disease progression. Furthermore, T-cell subsets can be used in stratifying patients for clinical trials and for monitoring immunotherapy outcomes. However, the definition of subsets needs to be refined in order to ensure that there is a uniform approach in designating T-cell subset attributes that is globally applied.