Prolonged or uncontrolled B-cell receptor (BCR) signaling is associated with autoimmunity. We previously demonstrated a role for actin in BCR signal attenuation. This study reveals that actin-binding protein 1 (Abp1/HIP-55/SH3P7) is a negative regulator of BCR signaling and links actin to negative regulatory pathways of the BCR. In both Abp1^−/− and bone marrow chimeric mice, in which only B cells lack Abp1 expression, the number of spontaneous germinal center and marginal zone B cells and the level of autoantibody are significantly increased. Serum levels of T-independent antibody responses and total antibody are elevated, whereas T-dependent antibody responses are markedly reduced and fail to undergo affinity maturation. Upon activation, surface BCR clustering is enhanced and B-cell contraction delayed in Abp1^−/− B cells, concurrent with slow but persistent increases in F-actin at BCR signalosomes. Furthermore, BCR signaling is enhanced in Abp1^−/− B cells compared with wild-type B cells, including Ca²⁺ flux and phosphorylation of B-cell linker protein, the mitogen-activated protein kinase kinase MEK1/2, and ERK, coinciding with reductions in recruitment of the inhibitory signaling molecules hematopoietic progenitor kinase 1 and SH2-containing inositol 5-phosphatase to BCR signalosomes. Our results indicate that Abp1 negatively regulates BCR signaling by coupling actin remodeling to B-cell contraction and activation of inhibitory signaling molecules, which contributes to the regulation of peripheral B-cell development and antibody responses. Significance B cells are the major mediators of humoral immunity, producing antibody to aid in the elimination of pathogens. B cells signal through their membrane receptors, resulting in transcriptional modification for the maintenance of B-cell survival, proliferation, and antibody production. B-cell receptor (BCR) signaling is tightly regulated, and elevated or sustained BCR signals lead to loss of B-cell tolerance and subsequent autoimmunity. This study reveals that the adaptor molecule Abp1 negatively regulates development of marginal zone and spontaneous germinal center B cells, as well as autoantibody production. As a link between the actin cytoskeleton and BCR signaling, Abp1 assists BCR signal attenuation by promoting BCR central cluster formation, as well as recruitment of inhibitory signaling molecules to BCR signalosomes.

Insulin-like growth factors (IGFs), specifically IGF1 and IGF2, promote glucose metabolism, with their availability regulated by IGF-binding proteins (IGFBPs). We hypothesized that IGF1 and IGF2 levels, or their bioavailability, are reduced during type 1 diabetes development. Total serum IGF1, IGF2, and IGFBP1–7 levels were measured in an age-matched, cross-sectional cohort at varying stages of progression to type 1 diabetes. IGF1 and IGF2 levels were significantly lower in autoantibody (AAb)⁺ compared with AAb⁻ relatives of subjects with type 1 diabetes. Most high-affinity IGFBPs were unchanged in individuals with pre–type 1 diabetes, suggesting that total IGF levels may reflect bioactivity. We also measured serum IGFs from a cohort of fasted subjects with type 1 diabetes. IGF1 levels significantly decreased with disease duration, in parallel with declining β-cell function. Additionally, plasma IGF levels were assessed in an AAb⁺ cohort monthly for a year. IGF1 and IGF2 showed longitudinal stability in single AAb⁺ subjects, but IGF1 levels decreased over time in subjects with multiple AAb and those who progressed to type 1 diabetes, particularly postdiagnosis. In sum, IGFs are dysregulated both before and after the clinical diagnosis of type 1 diabetes and may serve as novel biomarkers to improve disease prediction.

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.

Autoimmune destruction of pancreatic β cells results in type 1 diabetes (T1D), with pancreatic immune infiltrate representing a key feature in this process. However, characterization of the immunological processes occurring in human pancreatic lymphatic tissues is lacking. Here, we conduct a comprehensive study of immune cells from pancreatic, mesenteric, and splenic lymphatic tissues of non-diabetic control (ND), β cell autoantibody-positive non-diabetic (AAb+), and T1D donors using flow cytometry and CITEseq. Compared to ND pancreas-draining lymph nodes (pLN), AAb+ and T1D donor pLNs display decreased CD4+ Treg and increased stem-like CD8+ T cell signatures, while only T1D donor pLNs exhibit naive T cell and NK cell differentiation. Mesenteric LNs have modulations only in CD4+ Tregs and naive cells, while splenocytes lack these perturbations. Further, T cell expression of activation markers and IL7 receptor correlate with T1D genetic risk. These results demonstrate tissue-restricted immune changes occur before and after T1D onset.

Type 1 diabetes (T1D) is a T cell–mediated disease with a strong immunogenetic human leukocyte antigen (HLA) dependence. HLA allelic influence on the T cell receptor (TCR) repertoire shapes thymic selection and controls activation of diabetogenic clones yet remains largely unresolved in T1D. We sequenced the circulating TCRβ chain repertoire from 2250 HLA-typed participants across three cross-sectional cohorts, including individuals with T1D and healthy related and unrelated controls. We found that HLA risk alleles show higher restriction of TCR repertoires in individuals with T1D. We leveraged deep learning to identify T1D-associated TCR subsequence motifs that were also observed in independent TCR cohorts residing in pancreas-draining lymph nodes of individuals with T1D. Collectively, our data demonstrate T1D-related TCR motif enrichment based on genetic risk, offering a potential metric for autoreactivity and groundwork for TCR-based diagnostics and therapeutics.

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by limited animal models, the length and cost of clinical trials, difficulties in identifying individuals who will progress faster to a clinical diagnosis of type 1 diabetes, and heterogeneous clinical responses in intervention trials. Classic placebo-controlled intervention trials often include monotherapies, broad participant populations and extended follow-up periods focused on clinical endpoints. While this approach remains the ‘gold standard’ of clinical research, efforts are underway to implement new approaches harnessing the power of artificial intelligence and machine learning to accelerate drug discovery and efficacy testing. Here, we review emerging approaches for repurposing agents used to treat diseases that share pathogenic pathways with type 1 diabetes and selecting synergistic combinations of drugs to maximise therapeutic efficacy. We discuss how emerging multi-omics technologies, including analysis of antigen processing and presentation to adaptive immune cells, may lead to the discovery of novel biomarkers and subsequent translation into antigen-specific immunotherapies. We also discuss the potential for using artificial intelligence to create ‘digital twin’ models that enable rapid in silico testing of personalised agents as well as dose determination. To conclude, we discuss some limitations of artificial intelligence and machine learning, including issues pertaining to model interpretability and bias, as well as the continued need for validation studies via confirmatory intervention trials. Graphical Abstract