Population-level variation and mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized. We defined prototypical insulin secretion responses to three macronutrients in islets from 140 cadaveric donors, including those with type 2 diabetes. The majority of donors\' islets exhibited the highest insulin response to glucose, moderate response to amino acid, and minimal response to fatty acid. However, 9% of donors\' islets had amino acid responses, and 8% had fatty acid responses that were larger than their glucose-stimulated insulin responses. We leveraged this heterogeneity and used multi-omics to identify molecular correlates of nutrient responsiveness, as well as proteins and mRNAs altered in type 2 diabetes. We also examined nutrient-stimulated insulin release from stem cell-derived islets and observed responsiveness to fat but not carbohydrate or protein-potentially a hallmark of immaturity. Understanding the diversity of insulin responses to carbohydrate, protein, and fat lays the groundwork for personalized nutrition.

Diabetes involves the death or dysfunction of pancreatic β-cells. Analysis of bulk sequencing from human samples and studies using in vitro and in vivo models suggest that endoplasmic reticulum and inflammatory signaling play an important role in diabetes progression. To better characterize cell type-specific stress response, we perform multiplexed single-cell RNA sequencing to define the transcriptional signature of primary human islet cells exposed to endoplasmic reticulum and inflammatory stress. Through comprehensive pair-wise analysis of stress responses across pancreatic endocrine and exocrine cell types, we define changes in gene expression for each cell type under different diabetes-associated stressors. We find that β-, α-, and ductal cells have the greatest transcriptional response. We utilize stem cell-derived islets to study islet health through the candidate gene CIB1, which was upregulated under stress in primary human islets. Our findings provide insights into cell type-specific responses to diabetes-associated stress and establish a resource to identify targets for diabetes therapeutics.

Diabetes mellitus, a significant global public health challenge, severely impacts human health worldwide. The organoid, an innovative in vitro three-dimensional (3D) culture model, closely mimics tissues or organs in vivo. Insulin-secreting islet organoid, derived from stem cells induced in vitro with 3D structures, has emerged as a potential alternative for islet transplantation and as a possible disease model that mirrors the human body\'s in vivo environment, eliminating species difference. This technology has gained considerable attention for its potential in diabetes treatment. Despite advances, the process of stem cell differentiation into islet organoid and its cultivation demonstrates deficiencies, prompting ongoing efforts to develop more efficient differentiation protocols and 3D biomimetic materials. At present, the constructed islet organoid exhibit limitations in their composition, structure, and functionality when compared to natural islets. Consequently, further research is imperative to achieve a multi-tissue system composition and improved insulin secretion functionality in islet organoid, while addressing transplantation-related safety concerns, such as tumorigenicity, immune rejection, infection, and thrombosis. This review delves into the methodologies and strategies for constructing the islet organoid, its application in diabetes treatment, and the pivotal scientific challenges within organoid research, offering fresh perspectives for a deeper understanding of diabetes pathogenesis and the development of therapeutic interventions.

The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.

Zinc deficiency has been associated with the worsening of diabetes while zinc supplementation has been proposed to ameliorate diabetes. This study examined the effects of marginal zinc deficiency (MZD) and zinc supplementation (ZS) on obesity, glycemic control, pancreatic islets, hepatic steatosis and renal function of Zucker diabetic fatty (ZDF) rats. Male ZDF rats were fed an MZD, zinc control (ZC) or ZS diet (4, 30 and 300 mg Zn/kg diet, respectively), and lean Zucker rats were fed a ZC diet for 8 weeks. MZD and ZS did not alter body weight or whole-body composition in ZDF rats. MZD ZDF rats had reduced zinc concentrations in the femur and pancreas, a greater number of enlarged pancreatic islets and a diminished response to an oral glucose load based on a 1.8-fold greater incremental area-under-the-curve (AUC) for glucose compared to ZC ZDF. ZS ZDF rats had elevated serum, femur and pancreatic zinc concentrations, unchanged pancreatic parameters and a 50% reduction in the AUC for insulin compared to ZC ZDF rats, suggesting greater insulin sensitivity. Dietary zinc intake did not alter hepatic steatosis, creatinine clearance, or levels of proteins that contribute to insulin signaling, inflammation or zinc transport in epididymal fat. Potential adverse effects of ZS were suggested by reduced hepatic copper concentrations and elevated serum urea compared to ZC ZDF rats. In summary, ZS improved the pancreatic insulin response but not the glucose handling. In contrast, reduced zinc status in ZDF rats led to impaired glucose tolerance and a compensatory increase in the number and size of pancreatic islets which could lead to β-cell exhaustion.

Diabetes imposes a huge burden worldwide. Islet transplantation is an alternative therapy for diabetes. However, tacrolimus, a kind of immunosuppressant after organ transplantation, is closely related to post-transplant diabetes mellitus. Mesenchymal stem cells (MSCs) have attracted interest for their potential to alleviate diabetes. In vivo experiments revealed that human menstrual blood-derived stem cells (MenSCs) treatment improved tacrolimus-induced blood glucose, body weight, and glucose tolerance disorders in mice. RNA sequencing was used to analyze the potential therapeutic targets of MenSCs. In this study, we illustrated that cystathionine β-synthase (CBS) contributed to tacrolimus -induced islet dysfunction. Using β-cell lines (MIN6, β-TC-6), we demonstrated that MenSCs ameliorated tacrolimus-induced islet dysfunction in vitro. Moreover, MenSC reduced the tacrolimus-induced elevation of CBS levels and significantly enhanced the viability, anti-apoptotic ability, glucose-stimulated insulin secretion (GSIS), and glycolytic flux of β-cells. We further revealed that MenSCs exerted their therapeutic effects by inhibiting CBS expression to activate the IL6/JAK2/STAT3 pathway. In conclusion, we showed that MenSCs may be a potential strategy to improve tacrolimus-induced islet dysfunction.

Non-obese diabetes (NOD) mice are an established, spontaneous model of type 1 diabetes in which diabetes develops through insulitis. Using next-generation sequencing, coupled with pathway analysis, the molecular fingerprint of early insulitis was mapped in a cohort of mice ranging from 4 to 12 weeks of age. The resulting dynamic timeline revealed an initial decrease in proliferative capacity followed by the emergence of an inflammatory signature between 6 and 8 weeks that increased to a regulatory plateau between 10 and 12 weeks. The inflammatory signature is identified by the activation of central immunogenic factors such as Infg, Il1b, and Tnfa, and activation of canonical inflammatory signaling. Analysis of the regulatory landscape revealed the transcription factor Atf3 as a potential novel modulator of inflammatory signaling in the NOD islets. Furthermore, the Hedgehog signaling pathway correlated with Atf3 regulation, suggesting that the two play a role in regulating islet inflammation; however, further studies are needed to establish the nature of this connection.

Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.

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UNASSIGNED: Type 1 diabetes (T1D) is preceded by a heterogenous pre-clinical phase, islet autoimmunity (IA). We aimed to identify pre vs. post-IA seroconversion (SV) changes in DNAm that differed across three IA progression phenotypes, those who lose autoantibodies (reverters), progress to clinical T1D (progressors), or maintain autoantibody levels (maintainers).
UNASSIGNED: This epigenome-wide association study (EWAS) included longitudinal DNAm measurements in blood (Illumina 450K and EPIC) from participants in Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, one or more islet autoantibodies on at least two consecutive visits. We compared reverters - individuals who sero-reverted, negative for all autoantibodies on at least two consecutive visits and did not develop T1D (n=41); maintainers - continued to test positive for autoantibodies but did not develop T1D (n=60); progressors - developed clinical T1D (n=42). DNAm data were measured before (pre-SV visit) and after IA (post-SV visit). Linear mixed models were used to test for differences in pre- vs post-SV changes in DNAm across the three groups. Linear mixed models were also used to test for group differences in average DNAm. Cell proportions, age, and sex were adjusted for in all models. Median follow-up across all participants was 15.5 yrs. (interquartile range (IQR): 10.8-18.7).
UNASSIGNED: The median age at the pre-SV visit was 2.2 yrs. (IQR: 0.8-5.3) in progressors, compared to 6.0 yrs. (IQR: 1.3-8.4) in reverters, and 5.7 yrs. (IQR: 1.4-9.7) in maintainers. Median time between the visits was similar in reverters 1.4 yrs. (IQR: 1-1.9), maintainers 1.3 yrs. (IQR: 1.0-2.0), and progressors 1.8 yrs. (IQR: 1.0-2.0). Changes in DNAm, pre- vs post-SV, differed across the groups at one site (cg16066195) and 11 regions. Average DNAm (mean of pre- and post-SV) differed across 22 regions.
UNASSIGNED: Differentially changing DNAm regions were located in genomic areas related to beta cell function, immune cell differentiation, and immune cell function.