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Acute respiratory distress syndrome (ARDS) has a fibroproliferative phase that may be followed by pulmonary fibrosis. This has been described in patients with COVID-19 pneumonia, but the underlying mechanisms have not been completely defined. We hypothesized that protein mediators of tissue remodeling and monocyte chemotaxis are elevated in the plasma and endotracheal aspirates of critically ill patients with COVID-19 who subsequently develop radiographic fibrosis.We enrolled COVID-19 patients admitted to the ICU who had hypoxemic respiratory failure, were hospitalized and alive for at least 10 days, and had chest imaging done during hospitalization (n= 119). Plasma was collected within 24h of ICU admission and at 7d. In mechanically ventilated patients, endotracheal aspirates (ETA) were collected at 24h and 48-96h. Protein concentrations were measured by immunoassay. We tested for associations between protein concentrations and radiographic evidence of fibrosis using logistic regression adjusting for age, sex, and APACHE score.We identified 39 patients (33%) with features of fibrosis. Within 24h of ICU admission, plasma proteins related to tissue remodeling (MMP-9, Amphiregulin) and monocyte chemotaxis (CCL-2/MCP-1, CCL-13/MCP-4) were associated with the subsequent development of fibrosis whereas markers of inflammation (IL-6, TNF-α) were not. After 1 week, plasma MMP-9 increased in patients without fibrosis. In ETAs, only CCL-2/MCP-1 was associated with fibrosis at the later timepoint.This cohort study identifies proteins of tissue remodeling and monocyte recruitment that may identify early fibrotic remodeling following COVID-19. Measuring changes in these proteins over time may allow for early detection of fibrosis in patients with COVID-19.

OBJECTIVES: The metabolic abnormalities that lead to diabetes mellitus (DM) following an episode of acute pancreatitis (AP) have not been extensively studied. This manuscript describes the objectives, hypotheses, and methods of mechanistic studies of glucose metabolism that comprise secondary outcomes of the Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) Study. METHODS: Three months after an index episode of AP, participants without pre-existing DM will undergo baseline testing with an oral glucose tolerance test. Participants will be followed longitudinally in three sub-cohorts with distinct metabolic tests. In the first and largest subcohort, oral glucose tolerance tests will be repeated 12 months after AP and annually to assess changes in β-cell function, insulin secretion, and insulin sensitivity. In the second, mixed meal tolerance tests will be performed at 3 and 12 months, then annually, and following incident DM to assess incretin and pancreatic polypeptide responses. In the third, frequently-sampled intravenous glucose tolerance tests will be performed at 3 months and 12 months to assess the first-phase insulin response and more precisely measure β-cell function and insulin sensitivity. CONCLUSIONS: The DREAM study will comprehensively assess the metabolic and endocrine changes that precede and lead to the development of DM after AP.

Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM following AP and understanding the underlying mechanisms. The Diabetes RElated to Acute Pancreatitis and its Mechanisms (DREAM) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM following AP. In the following article, we summarize literature regarding the epidemiology of DM following AP, and provide the rationale and an overview of the DREAM study.

Inflammation and oxidative stress in pancreatic islets amplify the appearance of various posttranslational modifications to self-proteins. In this study, we identified a select group of carbonylated islet proteins arising before the onset of hyperglycemia in NOD mice. Of interest, we identified carbonyl modification of the prolyl-4-hydroxylase β subunit (P4Hb) that is responsible for proinsulin folding and trafficking as an autoantigen in both human and murine type 1 diabetes. We found that carbonylated P4Hb is amplified in stressed islets coincident with decreased glucose-stimulated insulin secretion and altered proinsulin-to-insulin ratios. Autoantibodies against P4Hb were detected in prediabetic NOD mice and in early human type 1 diabetes prior to the onset of anti-insulin autoimmunity. Moreover, we identify autoreactive CD4+ T-cell responses toward carbonyl-P4Hb epitopes in the circulation of patients with type 1 diabetes. Our studies provide mechanistic insight into the pathways of proinsulin metabolism and in creating autoantigenic forms of insulin in type 1 diabetes.

BackgroundHeterogeneity exists in type 1 diabetes (T1D) development and presentation. Islet autoantibodies form the foundation for T1D diagnostic and staging efforts. We hypothesized that autoantibodies can be used to identify heterogeneity in T1D before, at, and after diagnosis, and in response to disease modifying therapies. at clinically relevant timepoints throughout T1D progression.MethodsWe performed a systematic review assessing 10 years of original research studies examining relationships between autoantibodies and heterogeneity during disease progression, at the time of diagnosis, after diagnosis, and in response to disease modifying therapies in individuals at risk for T1D or within 1 year of T1D diagnosis.Results10,067 papers were screened. Out of 151 that met data extraction criteria, 90 studies characterized heterogeneity before clinical diagnosis. Autoantibody type/target was most commonly examined, followed by autoantibody number, titer, order of seroconversion, affinity, and novel islet autoantibodies/epitopes. Recurring themes included positive relationships of autoantibody number and specific types and titers with disease progression, differing clinical phenotypes based on the order of autoantibody seroconversion, and interactions with age and genetics. Overall, reporting of autoantibody assay performance was commonly included; however, only 43% (65/151) included information about autoantibody assay standardization efforts. Populations studied were almost exclusively of European ancestry.ConclusionsCurrent evidence most strongly supports the application of autoantibody features to more precisely define T1D before clinical diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly when considered in relation to age and genetic risk, could offer more precise stratification. Increased participation in autoantibody standardization efforts is a critical step to improving future applicability of autoantibody-based precision medicine in T1D.Plain Language SummaryWe performed a systematic review to ascertain whether islet autoantibodies, biomarkers of autoimmunity against insulin-producing cells, could aid in stratifying individuals with different clinical presentations of type 1 diabetes. We found existing evidence most strongly supporting the application of these biomarkers to the period before clinical diagnosis, when certain autoantibody features (number, type) and the age when they develop, can provide important information for patients and care providers on what to expect for future type 1 diabetes progression.

BackgroundType 1 diabetes (T1D) results from immune-mediated destruction of insulin-producing beta cells. Efforts to prevent T1D have focused on modulating immune responses and supporting beta cell health; however, heterogeneity in disease progression and responses to therapies have made these efforts difficult to translate to clinical practice, highlighting the need for precision medicine approaches to T1D prevention.MethodsTo understand the current state of knowledge regarding precision approaches to T1D prevention, we performed a systematic review of randomized-controlled trials from the past 25 years testing disease-modifying therapies in T1D and/or identifying features linked to treatment response, analyzing bias using a Cochrane-risk-of-bias instrument.ResultsWe identified 75 manuscripts, 15 describing 11 prevention trials for individuals with increased risk for T1D, and 60 describing treatments aimed at preventing beta cell loss in individuals at disease onset. Seventeen agents tested, mostly immunotherapies, showed benefit compared to placebo (only two prior to T1D onset). Fifty-seven studies employed precision analyses to assess features linked to treatment response. Age, measures of beta cell function and immune phenotypes were most frequently tested. However, analyses were typically not prespecified, with inconsistent methods reporting, and tended to report positive findings.ConclusionsWhile the quality of prevention and intervention trials was overall high, low quality of precision analyses made it difficult to draw meaningful conclusions that inform clinical practice. Thus, prespecified precision analyses should be incorporated into the design of future studies and reported in full to facilitate precision medicine approaches to T1D prevention.Plain Language SummaryType 1 diabetes (T1D) results from the destruction of insulin-producing cells in the pancreas, necessitating lifelong insulin dependence. T1D prevention remains an elusive goal, largely due to immense variability in disease progression. Agents tested to date in clinical trials work in a subset of individuals, highlighting the need for precision medicine approaches to prevention. We systematically reviewed clinical trials of disease-modifying therapy in T1D. While age, measures of beta cell function, and immune phenotypes were most commonly identified as factors that influenced treatment response, the overall quality of these studies was low. This review reveals an important need to proactively design clinical trials with well-defined analyses to ensure that results can be interpreted and applied to clinical practice.

Mislabeling samples or data with the wrong participant information can affect study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of human leukocyte antigen (HLA) class I alleles. We measured HLA-A*02 and HLA-B*07 expression in three longitudinal samples from 41 participants using a 33-marker CyTOF panel designed to identify major immune cell types. 3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples' cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting sample-labeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross-sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry.

The use of a standardized outcome metric enhances clinical trial conduct, interpretation, and cross-trial comparison. If a disease course is predictable, comparing modeled predictions with outcome data affords the precision and confidence needed to accelerate precision medicine. We demonstrate the power of this approach in type 1 diabetes (T1D) trials aiming to preserve endogenous insulin secretion measured by C-peptide. C-peptide is a predictable outcome given an individual’s age and baseline value; quantitative response (QR) adjusts for these variables and represents the difference between the observed and predicted outcome. Validated across 13 trials, the QR metric reduces each trial’s variance and markedly increases statistical power. As smaller studies are especially subject to random sampling variability, using QR as the outcome metric introduces alternative interpretations of previous clinical trial results analyzed by traditional statistical methods. QR can provide model-based estimates that quantify whether individuals or groups did better or worse than expected. QR also provides a purer metric to associate with biomarker data, enabling improved mechanistic insights. Using data from more than 1,300 participants, we demonstrate the value of QR in advancement of disease-modifying therapy (DMT) in T1D. QR applies to any disease where outcome is predictable by baseline covariates, rendering it useful for defining responders to therapy, comparing the effectiveness of different therapies, and understanding causal pathways in disease.

The naive T cell compartment undergoes multiple changes across age that associate with altered susceptibility to infection and autoimmunity. In addition to the acquisition of naive-like memory T cell subsets, mouse studies describe substantial molecular reprogramming of the naive compartment in adults compared with adolescents. However, these alterations are not well delineated in human aging. Using a new trimodal single cell technology (TEA-seq), we discovered that the composition and transcriptional and epigenetic programming of the naive T cell compartment in children (11-13 yrs) is distinct from that of older adults (55-65 yrs). Naive CD4 T cells, previously considered relatively resistant to aging, exhibited far more pronounced molecular reprogramming than naive CD8 T cells, in which alterations are preferentially driven by shifts in naive-like memory subsets. These data reveal the complex nature of the naive T cell compartment that may contribute to differential immune responses across the spectrum of human age.One Sentence Summary:The naive CD8 and CD4 T cell compartments in humans are heterogeneous and impacted differently with age, in which naive CD8 T cell subsets dramatically shift in composition and true naive CD4 T cells display significant molecular re-programming.

Genome-wide association studies have identified numerous loci with allelic associations to Type 1 Diabetes (T1D) risk. Most disease-associated variants are enriched in regulatory sequences active in lymphoid cell types, suggesting that lymphocyte gene expression is altered in T1D. We assayed gene expression between T1D cases and healthy controls in two autoimmunity-relevant lymphocyte cell types, memory CD4+/CD25+ T-regulatory cells (Treg) and memory CD4+/CD25- T-cells, using a splicing event-based approach to characterize tissue-specific transcriptomes. Limited differences in isoform usage between T1D cases and controls were observed in memory CD4+/CD25- T-cells. In Tregs, 553 genes demonstrated differences in isoform usage between cases and controls, particularly RNA recognition and splicing factor genes. Many of these genes are regulated by the variable inclusion of exons that can trigger nonsense mediated decay. Our results suggest that dysregulation of gene expression, through shifts in alternative splicing in Tregs, contributes to T1D etiology.

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., Studies implicate particular autoreactive CD8 T‐cell functional subsets, rather than certain self‐antigen specificities or the total number of cells, in type 1 diabetes (T1D) outcome. Specifically, more hyporesponsive and terminal subsets (i.e. exhausted cells) are associated with preservation of insulin‐producing beta islet cells while more progenitor, polyfunctional subsets are associated with poor outcome. A better understanding of the development of such CD8 T‐cell subsets in T1D will inform how they may be specifically and optimally modulated to treat and prevent T1D.

Context The presence of islet autoimmunity identifies individuals likely to progress to clinical type 1 diabetes (T1D). In clinical research studies, autoantibody screening followed by regular metabolic monitoring every 6 months reduces incidence of diabetic ketoacidosis (DKA) at diagnosis. Objective We hypothesized that DKA reduction can be achieved on a population basis with a reduced frequency of metabolic monitoring visits. We reasoned that prolonged time between the development of T1D and the time of clinical diagnosis (“undiagnosed time”) would more commonly result in DKA and thus that limiting undiagnosed time would decrease DKA. Methods An analysis was conducted of data from TrialNet's Pathway to Prevention (PTP), a cross-sectional longitudinal study that identifies and follows at-risk relatives of people with T1D. PTP is a population-based study enrolling across multiple countries. A total of 6193 autoantibody (AAB)-positive individuals participated in PTP from March 2004 to April 2019. We developed models of progression to clinical diagnosis for pediatric and adult populations with single or multiple AAB, and summarized results using estimated hazard rate. An optimal monitoring visit schedule was determined for each model to achieve a minimum average level of undiagnosed time for each population. Results Halving the number of monitoring visits usually conducted in research studies is likely to substantially lower the population incidence of DKA at diagnosis of T1D. Conclusion Our study has clinical implications for the metabolic monitoring of at-risk individuals. Fewer monitoring visits would reduce the clinical burden, suggesting a path toward transitioning monitoring beyond the research setting.

Inflammation and oxidative stress in pancreatic islets amplify the appearance of various post-translational modifications (PTMs) to self-proteins. Herein, we identified a select group of carbonylated islet proteins arising before the onset of hyperglycemia in non-obese diabetic mice. Of interest, we identified carbonyl modification of the prolyl-4-hydroxylase beta subunit (P4Hb) that is responsible for proinsulin folding and trafficking as an autoantigen in both human and murine type 1 diabetes. We found the carbonylated P4Hb is amplified in stressed islets coincident with decreased glucose-stimulated insulin secretion and altered proinsulin to insulin ratios. Autoantibodies against P4Hb were detected in prediabetic NOD mice and in early human type 1 diabetes prior to the onset of anti-insulin autoimmunity. Moreover, we identify autoreactive CD4+ T cell responses toward carbonyl-P4Hb epitopes in the circulation of patients with type 1 diabetes. Our studies provide mechanistic insight into the pathways of proinsulin metabolism and in creating autoantigenic forms of insulin in type 1 diabetes.

Diabetic ketoacidosis (DKA) is often the sentinel event to a diagnosis of clinical type 1 diabetes. Research studies involving antibody positive (AAB+) individuals at risk for developing type 1 diabetes have demonstrated that metabolic monitoring every 6 months with oral glucose tolerance testing can dramatically reduce the incidence of DKA at diagnosis. It is unknown whether other schedules can reduce the population level burden of metabolic monitoring in AAB+ individuals while retaining the potential benefit of reducing DKA. We reasoned that the shorter the time time-period during which clinical type 1 diabetes (T1D) remains undetected, the lower the likelihood of DKA. We aimed to develop models of optimized monitoring visit schedules to limit undiagnosed (UnDx) time for the population. Using OGTT data from 6193 islet AAB+ relatives identified in TrialNet’s Pathway to Prevention study, we developed models and an algorithm to optimize visit schedules to limit population-based UnDx time. The results of modeling were presented using estimated hazard rate as a function of age. An "optimal" visit schedule was determined for each model to achieve a minimum average level of UnDx time for the population. This was applied for risk patterns identified by each model thus producing optimal schedules for pediatric and adult populations with single or multiple antibodies. Modeling an optimized visit schedule to keep UnDx time ≤ 6 months at a population level, we find that conducting half the number of metabolic monitoring visits usually done in research studies is likely to be effective in significantly impacting the population incidence of DKA at diagnosis of T1D. Our approach has implications for public health screening and monitoring for T1D risk. Disclosure C. Orourke: None. C. Speake: Advisory Panel; Vertex Pharmaceuticals Incorporated. A. Ylescupidez: None. C. Bender: None. S. Lord: Other Relationship; Pfizer Inc. Funding Helmsley Charitable Trust (2103-05008) ; Helmsley Charitable Trust (2210-05590)

T cells isolated from the pancreatic infiltrates of nonobese diabetic mice have been shown to recognize epitopes formed by the covalent cross-linking of proinsulin and secretory granule peptides. Formation of such hybrid insulin peptides (HIPs) was confirmed through mass spectrometry, and responses to HIPs were observed among the islet-infiltrating T cells of pancreatic organ donors and in the peripheral blood of individuals with type 1 diabetes (T1D). However, questions remain about the prevalence of HIP-specific T cells in humans, the sequences they recognize, and their role in disease. We identified six novel HIPs that are recognized in the context of DRB1*04:01, discovered by using a library of theoretical HIP sequences derived from insulin fragments covalently linked to one another or to fragments of secretory granule proteins or other islet-derived proteins. We demonstrate that T cells that recognize these HIPs are detectable in the peripheral blood of subjects with T1D and exhibit an effector memory phenotype. HIP-reactive T-cell clones produced Th1-associated cytokines and proliferated in response to human islet preparations. These results support the relevance of HIPs in human disease, further establishing a novel posttranslational modification that may contribute to the loss of peripheral tolerance in T1D.

The pool of beta cell-specific CD8+ T cells in type 1 diabetes (T1D) sustains an autoreactive potential despite having access to a constant source of antigen. To investigate the long-lived nature of these cells, we established a DNA methylation-based T cell 'multipotency index' and found that beta cell-specific CD8+ T cells retained a stem-like epigenetic multipotency score. Single-cell assay for transposase-accessible chromatin using sequencing confirmed the coexistence of naive and effector-associated epigenetic programs in individual beta cell-specific CD8+ T cells. Assessment of beta cell-specific CD8+ T cell anatomical distribution and the establishment of stem-associated epigenetic programs revealed that self-reactive CD8+ T cells isolated from murine lymphoid tissue retained developmentally plastic phenotypic and epigenetic profiles relative to the same cells isolated from the pancreas. Collectively, these data provide new insight into the longevity of beta cell-specific CD8+ T cell responses and document the use of this methylation-based multipotency index for investigating human and mouse CD8+ T cell differentiation.

Exposure histories to SARS-CoV-2 variants and vaccinations will shape the specificity of antibody responses. To understand the specificity of Delta-elicited antibody immunity, we characterize the polyclonal antibody response elicited by primary or mRNA vaccine-breakthrough Delta infections. Both types of infection elicit a neutralizing antibody response focused heavily on the receptor-binding domain (RBD). We use deep mutational scanning to show that mutations to the RBD’s class 1 and class 2 epitopes, including sites 417, 478, and 484–486 often reduce binding of these Delta-elicited antibodies. The anti-Delta antibody response is more similar to that elicited by early 2020 viruses than the Beta variant, with mutations to the class 1 and 2, but not class 3 epitopes, having the largest effects on polyclonal antibody binding. In addition, mutations to the class 1 epitope (e.g., K417N) tend to have larger effects on antibody binding and neutralization in the Delta spike than in the D614G spike, both for vaccine- and Delta-infection-elicited antibodies. These results help elucidate how the antigenic impacts of SARS-CoV-2 mutations depend on exposure history.

A recent discovery effort resulted in identification of novel splice variant and secretory granule antigens within the HLA class I peptidome of human islets and documentation of their recognition by CD8+ T cells from peripheral blood and human islets. In the current study, we applied a systematic discovery process to identify novel CD4+ T cell epitopes derived from these candidate antigens. We predicted 145 potential epitopes spanning unique splice junctions and within conventional secretory granule antigens and measured their in vitro binding to DRB1*04:01. We generated HLA class II tetramers for the 35 peptides with detectable binding and used these to assess immunogenicity and isolate T cell clones. Tetramers corresponding to peptides with verified immunogenicity were then used to label T cells specific for these putative epitopes in peripheral blood. T cells that recognize distinct epitopes derived from a cyclin I splice variant, neuroendocrine convertase 2, and urocortin-3 were detected at frequencies that were similar to those of an immunodominant proinsulin epitope. Cells specific for these novel epitopes predominantly exhibited a Th1-like surface phenotype. Among the three epitopes, responses to the cyclin I peptide exhibited a distinct memory profile. Responses to neuroendocrine convertase 2 were detected among pancreatic infiltrating T cells. These results further establish the contribution of unconventional antigens to the loss of tolerance in autoimmune diabetes.

The coinhibitory receptor lymphocyte activation gene 3 (LAG-3) is an immune checkpoint molecule that negatively regulates T cell activation, proliferation, and homeostasis. Blockade or deletion of LAG-3 in autoimmune-prone backgrounds or induced-disease models has been shown to exacerbate disease. We observed significantly fewer LAG-3+ CD4 and CD8 T cells from subjects with relapsing-remitting multiple sclerosis (RRMS) and type 1 diabetes. Low LAG-3 protein expression was linked to alterations in mRNA expression and not cell surface cleavage. Functional studies inhibiting LAG-3 suggest that in subjects with RRMS, LAG-3 retains its ability to suppress T cell proliferation. However, LAG-3 expression was associated with the expression of markers of apoptosis, indicating a role for low LAG-3 in T cell resistance to cell death. In T cells from subjects with RRMS, we observed a global dysregulation of LAG-3 expression stemming from decreased transcription and persisting after T cell stimulation. These findings further support the potential clinical benefits of a LAG-3 agonist in the treatment of human autoimmunity.

Individuals with Down syndrome show cellular and clinical features of dysregulated aging of the immune system, including a shift from naïve to memory T cells and increased incidence of autoimmunity. However, a quantitative understanding of how various immune compartments change with age in Down syndrome remains lacking. Here we performed deep immunophenotyping of a cohort of individuals with Down syndrome across the lifespan, selecting for autoimmunity-free individuals. We simultaneously interrogated age- and sex-matched healthy controls and people with type 1 diabetes as a representative autoimmune disease. We built an analytical software, IMPACD (Iterative Machine-assisted Permutational Analysis of Cytometry Data), that enabled us to rapidly identify many features of immune dysregulation in Down syndrome shared with other autoimmune diseases. We found quantitative and qualitative dysregulation of naïve CD4(+) and CD8(+) T cells in individuals with Down syndrome and identified interleukin (IL)-6 as a candidate driver of some of these changes, thus extending the consideration of immunopathologic cytokines in Down syndrome beyond interferons. We used immune cellular composition to generate three linear models of aging (immune clocks) trained on control participants. All three immune clocks demonstrated advanced immune aging in individuals with Down syndrome. One of these clocks, informed by Down syndrome-relevant biology, also showed advanced immune aging in individuals with type 1 diabetes. Orthologous RNA sequencing-derived immune clocks also demonstrated advanced immune aging in individuals with Down syndrome. Together, our findings demonstrate an approach to studying immune aging in Down syndrome which may have implications in the context of other autoimmune diseases.

Multiple sclerosis (MS) is a demyelinating inflammatory disease of the CNS treated by diverse disease-modifying therapies that suppress the immune system. Severe acute respiratory syndrome coronavirus 2 mRNA vaccines have been very effective in immunocompetent individuals, but whether MS patients treated with modifying therapies are afforded the same protection is not known. This study determined that dimethyl fumarate caused a momentary reduction in anti-Spike (S)-specific Abs and CD8 T cell response. MS patients treated with B cell–depleting (anti-CD20) or sphingosine 1-phosphate receptor agonist (fingolimod) therapies lack significant S-specific Ab response. Whereas S-specific CD4 and CD8 T cell responses were largely compromised by fingolimod treatment, T cell responses were robustly generated in anti-CD20–treated MS patients, but with a reduced proportion of CD4+CXCR5+ circulating follicular Th cells. These data provide novel information regarding vaccine immune response in patients with autoimmunity useful to help improve vaccine effectiveness in these populations.

Human islet antigen reactive CD4+ memory T cells (IAR T cells) play a key role in the pathogenesis of autoimmune type 1 diabetes (T1D). Using single-cell RNA sequencing (scRNA-Seq) to identify T cell receptors (TCRs) in IAR T cells, we have identified a class of TCRs that share TCRα chains between individuals ("public" chains). We isolated IAR T cells from blood of healthy, new-onset T1D and established T1D donors using multiplexed CD154 enrichment and identified paired TCRαβ sequences from 2767 individual cells. More than a quarter of cells shared TCR junctions between 2 or more cells ("expanded"), and 29/47 (~62%) of expanded TCRs tested showed specificity for islet antigen epitopes. Public TCRs sharing TCRα junctions were most prominent in new-onset T1D. Public TCR sequences were more germline like than expanded unique, or "private," TCRs, and had shorter junction sequences, suggestive of fewer random nucleotide insertions. Public TCRα junctions were often paired with mismatched TCRβ junctions in TCRs; remarkably, a subset of these TCRs exhibited cross-reactivity toward distinct islet antigen peptides. Our findings demonstrate a prevalent population of IAR T cells with diverse specificities determined by TCRs with restricted TCRα junctions and germline-constrained antigen recognition properties. Since these "innate-like" TCRs differ from previously described immunodominant TCRβ chains in autoimmunity, they have implications for fundamental studies of disease mechanisms. Self-reactive restricted TCRα chains and their associated epitopes should be considered in fundamental and translational investigations of TCRs in T1D.

Mislabeling samples or data with the wrong participant information can impact study integrity and lead investigators to draw inaccurate conclusions. Quality control to prevent these types of errors is commonly embedded into the analysis of genomic datasets, but a similar identification strategy is not standard for cytometric data. Here, we present a method for detecting sample identification errors in cytometric data using expression of HLA class I alleles. We measured HLA-A*02 and HLA-B*07 expression in 3 longitudinal samples from 41 participants using a 33-marker CyTOF panel designed to identify major immune cell types. 3/123 samples (2.4%) showed HLA allele expression that did not match their longitudinal pairs. Furthermore, these same three samples’ cytometric signature did not match qPCR HLA class I allele data, suggesting that they were accurately identified as mismatches. We conclude that this technique is useful for detecting sample labeling errors in cytometric analyses of longitudinal data. This technique could also be used in conjunction with another method, like GWAS or PCR, to detect errors in cross-sectional data. We suggest widespread adoption of this or similar techniques will improve the quality of clinical studies that utilize cytometry.

AIMS/HYPOTHESIS: Age is known to be one of the most important stratifiers of disease progression in type 1 diabetes. However, what drives the difference in rate of progression between adults and children is poorly understood. Evidence suggests that many type 1 diabetes disease predictors do not have the same effect across the age spectrum. Without a comprehensive analysis describing the varying risk profiles of predictors over the age continuum, researchers and clinicians are susceptible to inappropriate assessment of risk when examining populations of differing ages. We aimed to systematically assess and characterise how the effect of key type 1 diabetes risk predictors changes with age. METHODS: Using longitudinal data from single- and multiple-autoantibody-positive at-risk individuals recruited between the ages of 1 and 45 years in TrialNet’s Pathway to Prevention Study, we assessed and visually characterised the age-varying effect of key demographic, immune and metabolic predictors of type 1 diabetes by employing a flexible spline model. Two progression outcomes were defined: participants with single autoantibodies (n=4893) were analysed for progression to multiple autoantibodies or type 1 diabetes, and participants with multiple autoantibodies were analysed (n=3856) for progression to type 1 diabetes. RESULTS: Several predictors exhibited significant age-varying effects on disease progression. Amongst single-autoantibody participants, HLA-DR3 (p=0.007), GAD65 autoantibody positivity (p=0.008), elevated BMI (p=0.007) and HOMA-IR (p=0.002) showed a significant increase in effect on disease progression with increasing age. Insulin autoantibody positivity had a diminishing effect with older age in single-autoantibody-positive participants (p

People with Down syndrome show cellular and clinical features of dysregulated aging of the immune system, including naïve-memory shift in the T cell compartment and increased incidence of autoimmunity. However, a quantitative understanding of how various immune compartments change with age in Down syndrome remains lacking. Here we performed deep immunophenotyping of a cohort of individuals with Down syndrome across the lifespan, selecting for individuals not affected by autoimmunity. We simultaneously interrogated age- and sex-matched healthy neurotypical controls and people with type 1 diabetes, as a representative autoimmune disease. We built a new analytical software, IMPACD, that enabled us to rapidly identify many features of immune dysregulation in Down syndrome that are recapitulated in other autoimmune diseases. We found significant quantitative and qualitative dysregulation of naïve CD4+and CD8+T cells in Down syndrome and identified IL-6 as a candidate driver of some of these changes, thus extending the consideration of immunopathologic cytokines in Down syndrome beyond interferons. Notably, we successfully used immune cellular composition to generate three quantitative models of aging (i.e. immune clocks) trained on control subjects. All three immune clocks demonstrated significantly advanced immune aging in people with Down syndrome. Notably, one of these clocks, informed by Down syndrome-relevant biology, also showed advanced immune aging in people with type 1 diabetes. Together, our findings demonstrate a novel approach to studying immune aging in Down syndrome which may have implications in the context of other autoimmune diseases.One Sentence SummaryPermutational analysis of immune landscape reveals advanced immune aging in people with Down syndrome and in people with type 1 diabetes.