Teplizumab, a humanized anti-CD3 mAb, represents a breakthrough in autoimmune type 1 diabetes (T1D) treatment, by delaying clinical onset in stage 2 and slowing progression in early stage 3 of the disease. However, therapeutic responses are heterogeneous. To better understand this variability, we applied single-cell transcriptomics to paired peripheral blood and pancreas samples from anti-mouse CD3-treated nonobese diabetic (NOD) mice and identified distinct gene signatures associated with the therapy outcome, with consistent patterns across compartments. Success-associated signatures were enriched in NK or CD8+ T cells and other immune cell types, whereas resistance signatures were predominantly expressed by neutrophils. The immune cell communities underlying these response signatures were confirmed in human whole blood sequencing data from the Autoimmunity-blocking Antibody for Tolerance (AbATE) study at 6 months, which assessed teplizumab therapy in individuals with stage 3 T1D. Furthermore, baseline expression profiling in the human TrialNet Anti-CD3 Prevention (TN10) (stage 2) and AbATE (stage 3) cohorts identified immune signatures predictive of therapy response, T cell-enriched signatures in responders, and neutrophil-enriched signatures in nonresponders, highlighting the roles of both adaptive and innate immunity in determining teplizumab treatment outcomes. Using an elastic net logistic regression model, we developed a 26-gene blood-based signature predicting the response to teplizumab (AUC = 0.97). These findings demonstrate the predictive potential of immune gene signatures and the value of transcriptomics profiling in guiding individualized treatment strategies with teplizumab in individuals with T1D.

Introduction & Objective: Islet antigen reactive (IAR) T cells play an important role in pancreatic β-cell destruction in type 1 diabetes (T1D) and are clonally expanded after T1D diagnosis, indicative of in vivo antigen exposure. The objective of this study was to determine if clonal expansion of IAR CD4 T cells occurs prior to T1D onset and is associated with risk for disease progression. Methods: The study included prospective blood samples collected from islet autoantibody (Aab) positive individuals enrolled in the TrialNet Pathways to Prevention and Long Term Investigative Follow-up in TrialNet studies. IAR CD4 T cells were isolated using a CD154 activation assay from PBMC samples of a cross-sectional cohort of Aab negative (n=6), 1 Aab+ >18y (n=6), 1 Aab+ <14y (n=3), and >1 Aab+ (n=6) donors with increasing risk for progression to T1D. Cells were analyzed by single-cell RNA-sequencing to identify T cell receptor α/β pairs and full transcript profiles. Clonal expansion was visualized using circos plots and analyzed using Simpson’s diversity. Transcript profiles were analyzed by unsupervised clustering and differential gene expression using Seurat v4. Results: Clonal expansion of IAR CD4 T cells was observed primarily in the >1 Aab+ at-risk group compared to the single Aab+ or Aab negative groups. The transcript profiles of IAR CD4 T cells formed clusters based on differentiation status from naïve to T effector memory phenotypes and revealed an enrichment of T effector memory cells in >1 Aab+ at-risk subjects. Conclusion: These results suggest that IAR CD4 T cell clonal expansion and enrichment of an effector memory transcript profile may accompany the progression to multiple Aab and increased risk for development of T1D. Further studies with longitudinal samples from subjects progressing from single to multiple Aab and T1D onset will validate and extend these findings to determine if IAR CD4 T cells are a biomarker of disease progression. T.H. Edwards: Employee; Wavely Diagnostics, Inc. J. Chen: None. M. Dufort: None. R. Hartley: None. C. Speake: None. C. Greenbaum: Other Relationship; Sanofi, Takeda Pharmaceutical Company Limited, Bristol-Myers Squibb Company, Imcyse. P. Linsley: None. K. Cerosaletti: Research Support; GentiBio, Cour Pharmaceuticals. American Diabetes Association (1-19-ICTS-006)

Recent breakthroughs by deep neural networks (DNNs) in natural language processing (NLP) and computer vision have been driven by a scale-up of models and data rather than the discovery of novel computing paradigms. Here, we investigate if scale can have a similar impact for models designed to aid small molecule drug discovery. We address this question through a large-scale and systematic analysis of how DNN size, data diet, and learning routines interact to impact accuracy on our Phenotypic Chemistry Arena (Pheno-CA) benchmark: a diverse set of drug development tasks posed on image-based high content screening data. Surprisingly, we find that DNNs explicitly supervised to solve tasks in the Pheno-CA do not continuously improve as their data and model size is scaled-up. To address this issue, we introduce a novel precursor task, the Inverse Biological Process (IBP), which is designed to resemble the causal objective functions that have proven successful for NLP. We indeed find that DNNs first trained with IBP then probed for performance on the Pheno-CA significantly outperform task-supervised DNNs. More importantly, the performance of these IBP-trained DNNs monotonically improves with data and model scale. Our findings reveal that the DNN ingredients needed to accurately solve small molecule drug development tasks are already in our hands, and project how much more experimental data is needed to achieve any desired level of improvement. We release our Pheno-CA benchmark and code to encourage further study of neural scaling laws for small molecule drug discovery.

Exhausted CD8 T cells (TEX) are associated with worse outcome in cancer yet better outcome in autoimmunity. Building on our past findings of increased TIGIT+KLRG1+ TEX with teplizumab therapy in type 1 diabetes (T1D), in the absence of treatment we found that the frequency of TIGIT+KLRG1+ TEX is stable within an individual but differs across individuals in both T1D and healthy control (HC) cohorts. This TIGIT+KLRG1+ CD8 TEX population shares an exhaustion-associated EOMES gene signature in HC, T1D, rheumatoid arthritis (RA), and cancer subjects, expresses multiple inhibitory receptors, and is hyporesponsive in vitro, together suggesting co-expression of TIGIT and KLRG1 may broadly define human peripheral exhausted cells. In HC and RA subjects, lower levels of EOMES transcriptional modules and frequency of TIGIT+KLRG1+ TEX were associated with RA HLA risk alleles (DR0401, 0404, 0405, 0408, 1001) even when considering disease status and cytomegalovirus (CMV) seropositivity. Moreover, the frequency of TIGIT+KLRG1+ TEX was significantly increased in RA HLA risk but not non-risk subjects treated with abatacept (CTLA4Ig). The DR4 association and selective modulation with abatacept suggests that therapeutic modulation of TEX may be more effective in DR4 subjects and TEX may be indirectly influenced by cellular interactions that are blocked by abatacept.

Human type 1 diabetes (T1D) is caused by autoimmune attack on the insulin-producing pancreatic beta cells by islet antigen-reactive T cells. How human islet antigen-reactive (IAR) CD4+ memory T cells from peripheral blood affect T1D progression in the pancreas is poorly understood. Here, we aim to determine if IAR T cells in blood could be detected in pancreas. We identify paired αβ (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new-onset, and established T1D donors, and measured sequence overlap with TCRs in pancreata from healthy, at risk and T1D organ donors. We report extensive TRA junction sharing between IAR T cells and pancreas-infiltrating T cells (PIT), with perfect-match or single-mismatch TRA junction amino acid sequences comprising ~29% total unique IAR TRA junctions (942/3,264). PIT-matched TRA junctions were largely public and enriched for TRAV41 usage, showing significant nucleotide sequence convergence, increased use of germline-encoded versus non-templated residues in epitope engagement, and a potential for cross-reactivity. Our findings thus link T cells with distinctive germline-like TRA chains in the peripheral blood with T cells in the pancreas.

Biomarkers are critical to the staging and diagnosis of type 1 diabetes (T1D). Functional biomarkers offer insights into T1D immunopathogenesis and are often revealed using "omics" approaches that integrate multiple measures to identify involved pathways and functions. Application of the omics biomarker discovery may enable personalized medicine approaches to circumvent the more recently appreciated heterogeneity of T1D progression and treatment. Use of omics to define functional biomarkers is still in its early years, yet findings to date emphasize the role of cytokine signaling and adaptive immunity in biomarkers of progression and response to therapy. Here, we share examples of the use of omics to define functional biomarkers focusing on two signatures, T-cell exhaustion and T-cell help, which have been associated with outcomes in both the natural history and treatment contexts.

Supplementary Table E from Coordinated Regulation of Cell Cycle Transcripts by p53-Inducible microRNAs, miR-192 and miR-215

Supplementary Figure and Table Legend from Identification of SULF2 as a Novel Transcriptional Target of p53 by Use of Integrated Genomic Analyses

A genetic variant in the gene PTPN22 (R620W, rs2476601) is strongly associated with increased risk for multiple autoimmune diseases and linked to altered TCR regulation and T cell activation. Here, we utilize Crispr/Cas9 gene editing with donor DNA repair templates in human cord blood-derived, naive T cells to generate PTPN22 risk edited (620W), non-risk edited (620R), or knockout T cells from the same donor. PTPN22 risk edited cells exhibited increased activation marker expression following non-specific TCR engagement, findings that mimicked PTPN22 KO cells. Next, using lentiviral delivery of T1D patient-derived TCRs against the pancreatic autoantigen, islet-specific glucose-6 phosphatase catalytic subunit-related protein (IGRP), we demonstrate that loss of PTPN22 function led to enhanced signaling in T cells expressing a lower avidity self-reactive TCR, but not a high-avidity TCR. In this setting, loss of PTPN22 mediated enhanced proliferation and Th1 skewing. Importantly, expression of the risk variant in association with a lower avidity TCR also increased proliferation relative to PTPN22 non-risk T cells. Together, these findings suggest that, in primary human T cells, PTPN22 rs2476601 contributes to autoimmunity risk by permitting increased TCR signaling and activation in mildly self-reactive T cells, thereby potentially expanding the self-reactive T cell pool and skewing this population toward an inflammatory phenotype.

The thymus is an intricate organ consisting of a diverse population of thymic epithelial cells (TECs). Cortical and medullary TECs and their subpopulations have distinct roles in coordinating the development and selection of functionally competent and self-tolerant T cells. Recent advances made in technologies such as single-cell RNA sequencing have made it possible to investigate and resolve the heterogeneity in TECs. These findings have provided further understanding of the molecular mechanisms regulating TEC function and expression of tissue-restricted Ags. In this brief review, we focus on the newly characterized subsets of TECs and their diversity in relation to their functions in supporting T cell development. We also discuss recent discoveries in expression of self-antigens in the context of TEC development as well as the cellular and molecular changes occurring during embryonic development to thymic involution.

Supplementary Table 1 from Identification of SULF2 as a Novel Transcriptional Target of p53 by Use of Integrated Genomic Analyses

Abstract Human islet antigen reactive CD4 + memory T cells (IAR T cells) from peripheral blood have been studied extensively for their role in the pathogenesis of autoimmune type 1 diabetes (T1D). However, IAR T cells are rare, and it remains poorly understood how they affect T1D progression in the pancreas. Using single cell RNA-sequencing coupled with a multiplexed activation induced marker (AIM) enrichment assay, we identified paired TCR alpha/beta ( TRA/TRB ) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new onset, and established T1D donors. Using TCR sequences as barcodes, we measured infiltration of IAR T cells from blood into pancreas of organ donors with and without T1D. We detected extensive TCR sharing between IAR T cells from peripheral blood and pancreatic infiltrating T cells (PIT), with perfectly matched or single mismatched TRA junctions and J gene regions, comprising ~ 34% of unique IAR TCRs. PIT-matching IAR T cells had public TRA chains that showed increased use of germline-encoded residues in epitope engagement and a propensity for cross-reactivity. The link with T cells in the pancreas implicates autoreactive IAR T cells with shared TRA junctions and increased levels in blood with the prediabetic and new onset phases of T1D progression.

BACKGROUND Low-dose anti–thymocyte globulin (ATG) transiently preserves C-peptide and lowers HbA1c in individuals with recent-onset type 1 diabetes (T1D); however, the mechanisms of action and features of the response remain unclear. Here, we characterized the post hoc immunological outcomes of ATG administration and their potential use as biomarkers of metabolic response to therapy (i.e., improved preservation of endogenous insulin production).METHODS We assessed gene and protein expression, targeted gene methylation, and cytokine concentrations in peripheral blood following treatment with ATG (n = 29), ATG plus granulocyte colony–stimulating factor (ATG/G-CSF, n = 28), or placebo (n = 31).RESULTS Treatment with low-dose ATG preserved regulatory T cells (Tregs), as measured by stable methylation of FOXP3 Treg-specific demethylation region (TSDR) and increased proportions of CD4+FOXP3+ Tregs (P < 0.001) identified by flow cytometry. While treatment effects were consistent across participants, not all maintained C-peptide. Responders exhibited a transient rise in IL-6, IP-10, and TNF-α (P < 0.05 for all) 2 weeks after treatment and a durable CD4+ exhaustion phenotype (increased PD-1+KLRG1+CD57– on CD4+ T cells [P = 0.011] and PD1+CD4+ Temra MFI [P < 0.001] at 12 weeks, following ATG and ATG/G-CSF, respectively). ATG nonresponders displayed higher proportions of senescent T cells (at baseline and after treatment) and increased methylation of EOMES (i.e., less expression of this exhaustion marker).CONCLUSION Altogether in these exploratory analyses, Th1 inflammation-associated serum and CD4+ exhaustion transcript and cellular phenotyping profiles may be useful for identifying signatures of clinical response to ATG in T1D.TRIAL REGISTRATION ClinicalTrials.gov NCT02215200.FUNDING The Leona M. and Harry B. Helmsley Charitable Trust (2019PG-T1D011), the NIH (R01 DK106191 Supplement, K08 DK128628), NIH TrialNet (U01 DK085461), and the NIH NIAID (P01 AI042288).

Supplementary Figure 1 from Identification of SULF2 as a Novel Transcriptional Target of p53 by Use of Integrated Genomic Analyses

Exhausted CD8 T cells (TEX) are associated with worse outcome in cancer and better outcome in autoimmunity. However, factors contributing to reduced TEX in the context of autoimmunity are not well understood. Here, we identify co-expression of TIGIT and KLRG1 as a broad identifier of human TEX: TIGIT+KLRG1+ CD8 memory T cells share EOMES signature genes in healthy controls (HC), T1D and Rheumatoid Arthritis (RA) subjects, are increased with age and in chronic viral-specific cells, and are hyporesponsive (reduced proliferation and cytokine production) in vitro. We applied this inclusive, sample sparing and high throughput measure of TEX to larger longitudinal and autoimmune cohorts. Consistent with a genetic determinant, TEX are stable within HC and T1D individuals (Interclass correlation coefficient=92.0 and 83.7%, respectively), but display high interindividual variation (range 0.37–28.47 of CD8). In a large cohort of HC and RA subjects (n>100/cohort), lower levels of EOMES modules and TEX were associated with the HLA Class II DR4 autoimmune risk allele, regardless of disease status. While the DR4 locus is complex, these findings suggest that TEX may be indirectly influenced by APC or CD4 T cell help. Moreover, therapeutic modulation of TEX may differ in DR4 risk and non risk subjects, with implications for precision medicine. Supported by grants from the NIH (R01 AI141952, R21 5R21AR073508-02) and JDRF.

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 life span, 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-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 that may have implications in other autoimmune diseases.

ABSTRACT The association between acute pancreatitis (AP) and diabetes mellitus (DM) has long been established, with the initial descriptions of AP patients presenting with DM after a bout of AP published in the 1940s and 50s. However, the potential mechanisms involved, particularly those components related to the immune system, have not been well defined. The Diabetes RElated to Acute pancreatitis and its Mechanisms (DREAM) study is a multicenter clinical study designed to understand the frequency and phenotype of DM developing after AP. This article describes one objective of the DREAM study: to determine the immunologic mechanisms of DM after AP, including the contribution of β-cell autoimmunity. This component of the study will assess the presence of islet autoimmunity, as well as the magnitude and kinetics of the innate and adaptive immune response at enrollment and during longitudinal follow-up after 1 or more episodes of AP. Finally, DREAM will evaluate the relationship between immune features, DM development, and pancreatitis etiology and severity.

In the article cited above, the affiliation for author Marjana Marinac appeared incorrectly due to a journal composition error; Dr. Marinac's correct affiliation is JDRF International, New York, NY. Additional typographical errors introduced during production have also been corrected. In Table 1, the term "B cell" was used instead of "β-cell" three times. The correct table text is "β-cell-directed therapies," "β-cell survival," and "β-cell secretory capacity." In Fig. 1, upper right quadrant, the heading appeared as "Trial Risk." The correct heading is "Trial Risks." In Table 1, minor formatting and language edits have been made for clarity and consistency within columns. In Duality of Interest, the following statement was given for author Randy L. Anderson: "R.L.A. was a JDRF board director at the time of ideation of the manuscript and has since become an employee of Provention Bio, Inc." It would be more accurate to state, "R.L.A. was a JDRF board director at the time of ideation of the manuscript and is a consultant to Provention Bio, Inc." The online version of the article (https://doi.org/10.2337/dc22-0308) has been updated to reflect these changes.

Elevated levels and enhanced sensing of the pro-inflammatory cytokine interleukin-6 (IL-6) are key features of many autoimmune and inflammatory diseases. To better understand how IL-6 signaling may influence human T cell fate, we investigated the relationships between levels of components of the IL-6R complex, pSTAT responses, and transcriptomic and translational changes in CD4 + and CD8 + T cell subsets from healthy individuals after exposure to IL-6. Our findings highlight the striking heterogeneity in mbIL-6R and gp130 expression and IL-6-driven pSTAT1/3 responses across T cell subsets. Increased mbIL-6R expression correlated with enhanced signaling via pSTAT1 with less impact on pSTAT3, most strikingly in CD4 + naïve T cells. Additionally, IL-6 rapidly induced expression of transcription factors and surface receptors expressed by T follicular helper cells and altered expression of markers of apoptosis. Importantly, many of the features associated with the level of mbIL-6R expression on T cells were recapitulated both in the setting of tocilizumab therapy and when comparing donor CD4 + T cells harboring the genetic variant, IL6R Asp358Ala (rs2228145), known to alter mbIL-6R expression on T cells. Collectively, these findings should be taken into account as we consider the role of IL-6 in disease pathogenesis and translating IL-6 biology into effective therapies for T cell-mediated autoimmune disease.