Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.

BACKGROUND: Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. METHODS: This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. RESULTS: the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. CONCLUSIONS: the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease.

A Composite Biomarker Signature of Type 1 Diabetes Risk Identified via Augmentation of Parallel Multi-Omics Data from a Small Cohort.

Background: Biomarkers of early pathogenesis of type 1 diabetes (T1D) are crucial to enable effective prevention measures in at-risk populations before significant damage occurs to their insulin producing beta-cell mass. We recently introduced the concept of integrated parallel multi-omics and employed a novel data augmentation approach which identified promising candidate biomarkers from a small cohort of high-risk T1D subjects. We now validate selected biomarkers to generate a potential composite signature of T1D risk. Methods: Twelve candidate biomarkers, which were identified in the augmented data and selected based on their fold-change relative to healthy controls and cross-reference to proteomics data previously obtained in the expansive TEDDY and DAISY cohorts, were measured in the original samples by ELISA. Results: All 12 biomarkers had established connections with lipid/lipoprotein metabolism, immune function, inflammation, and diabetes, but only 7 were found to be markedly changed in the high-risk subjects compared to the healthy controls: ApoC1 and PON1 were reduced while CETP, CD36, FGFR1, IGHM, PCSK9, SOD1, and VCAM1 were elevated. Conclusions: Results further highlight the promise of our data augmentation approach in unmasking important patterns and pathologically significant features in parallel multi-omics datasets obtained from small sample cohorts to facilitate the identification of promising candidate T1D biomarkers for downstream validation. They also support the potential utility of a composite biomarker signature of T1D risk characterized by the changes in the above markers.

Fluorescence Angiography with Dual Fluorescence for the Early Detection and Longitudinal Quantitation of Vascular Leakage in Retinopathy.

BACKGROUND: Diabetic retinopathy (DR) afflicts more than 93 million people worldwide and is a leading cause of vision loss in working adults. While DR therapies are available, early DR development may go undetected without treatment due to the lack of sufficiently sensitive tools. Therefore, early detection is critically important to enable efficient treatment before progression to vision-threatening complications. A major clinical manifestation of early DR is retinal vascular leakage that may progress from diffuse to more localized focal leakage, leading to increased retinal thickness and diabetic macular edema (DME). In preclinical research, a hallmark of DR in mouse models is diffuse retinal leakage without increased thickness or DME, which limits the utility of optical coherence tomography and fluorescein angiography (FA) for early detection. The Evans blue assay detects diffuse leakage but requires euthanasia, which precludes longitudinal studies in the same animals. METHODS: We developed a new modality of ratiometric fluorescence angiography with dual fluorescence (FA-DF) to reliably detect and longitudinally quantify diffuse retinal vascular leakage in mouse models of induced and spontaneous DR. RESULTS: These studies demonstrated the feasibility and sensitivity of FA-DF in detecting and quantifying retinal vascular leakage in the same mice over time during DR progression in association with chronic hyperglycemia and age. CONCLUSIONS: These proof-of-concept studies demonstrated the promise of FA-DF as a minimally invasive method to quantify DR leakage in preclinical mouse models longitudinally.

Exploring Computational Data Amplification and Imputation for the Discovery of Type 1 Diabetes (T1D) Biomarkers from Limited Human Datasets.

BACKGROUND: Type 1 diabetes (T1D) is a devastating disease with serious health complications. Early T1D biomarkers that could enable timely detection and prevention before the onset of clinical symptoms are paramount but currently unavailable. Despite their promise, omics approaches have so far failed to deliver such biomarkers, likely due to the fragmented nature of information obtained through the single omics approach. We recently demonstrated the utility of parallel multi-omics for the identification of T1D biomarker signatures. Our studies also identified challenges. METHODS: Here, we evaluated a novel computational approach of data imputation and amplification as one way to overcome challenges associated with the relatively small number of subjects in these studies. RESULTS: Using proprietary algorithms, we amplified our quadra-omics (proteomics, metabolomics, lipidomics, and transcriptomics) dataset from nine subjects a thousand-fold and analyzed the data using Ingenuity Pathway Analysis (IPA) software to assess the change in its analytical capabilities and biomarker prediction power in the amplified datasets compared to the original. These studies showed the ability to identify an increased number of T1D-relevant pathways and biomarkers in such computationally amplified datasets, especially, at imputation ratios close to the "golden ratio" of 38.2%:61.8%. Specifically, the Canonical Pathway and Diseases and Functions modules identified higher numbers of inflammatory pathways and functions relevant to autoimmune T1D, including novel ones not identified in the original data. The Biomarker Prediction module also predicted in the amplified data several unique biomarker candidates with direct links to T1D pathogenesis. CONCLUSIONS: These preliminary findings indicate that such large-scale data imputation and amplification approaches are useful in facilitating the discovery of candidate integrated biomarker signatures of T1D or other diseases by increasing the predictive range of existing data mining tools, especially when the size of the input data is inherently limited.

Challenges in stem cell-derived islet replacement therapy can be overcome.

In this Commentary, we echo the conclusions of a recent review titled "The promise of stem cell-derived islet replacement therapy," which highlighted recent advances in producing glucose responsive "islets" from stem cells and the benefits of their use in islet transplant therapy in type 1 diabetes (T1D). The review also outlined the status of clinical islet transplantation and the challenges that have prevented it from reaching its full therapeutic promise. We agree with the conclusions of the review and suggest that the identified challenges may be overcome by using the eye anterior chamber as an islet transplant site. We anticipate that the combination of stem cell-derived islets and intraocular transplant could help this promising T1D therapy reach full fruition.

HGAL inhibits lymphoma dissemination by interacting with multiple cytoskeletal proteins.

Human germinal center-associated lymphoma (HGAL) is an adaptor protein specifically expressed in germinal center lymphocytes. High expression of HGAL is a predictor of prolonged survival of diffuse large B-cell lymphoma (DLBCL) and classic Hodgkin lymphoma. Furthermore, HGAL expression is associated with early-stage DLBCL, thus potentially limiting lymphoma dissemination. In our previous studies, we demonstrated that HGAL regulates B-cell receptor signaling and cell motility in vitro and deciphered some molecular mechanisms underlying these effects. By using novel animal models for in vivo DLBCL dispersion, we demonstrate here that HGAL decreases lymphoma dissemination and prolongs survival. Furthermore, by using an unbiased proteomic approach, we demonstrate that HGAL may interact with multiple cytoskeletal proteins thereby implicating a multiplicity of effects in regulating lymphoma motility and spread. Specifically, we show that HGAL interacts with tubulin, and this interaction may also contribute to HGAL effects on cell motility. These findings recapitulate previous observations in humans, establish the role of HGAL in dissemination of lymphoma in vivo, and explain improved survival of patients with HGAL-expressing lymphomas.

Integrated Metabolomics and Proteomics Analyses in the Local Milieu of Islet Allografts in Rejection versus Tolerance.

An understanding of the immune mechanisms that lead to rejection versus tolerance of allogeneic pancreatic islet grafts is of paramount importance, as it facilitates the development of innovative methods to improve the transplant outcome. Here, we used our established intraocular islet transplant model to gain novel insight into changes in the local metabolome and proteome within the islet allograft's immediate microenvironment in association with immune-mediated rejection or tolerance. We performed integrated metabolomics and proteomics analyses in aqueous humor samples representative of the graft's microenvironment under each transplant outcome. The results showed that several free amino acids, small primary amines, and soluble proteins related to the Warburg effect were upregulated or downregulated in association with either outcome. In general, the observed shifts in the local metabolite and protein profiles in association with rejection were consistent with established pro-inflammatory metabolic pathways and those observed in association with tolerance were immune regulatory. Taken together, the current findings further support the potential of metabolic reprogramming of immune cells towards immune regulation through targeted pharmacological and dietary interventions against specific metabolic pathways that promote the Warburg effect to prevent the rejection of transplanted islets and promote their immune tolerance.

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 ß-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-ß, 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.

A machine learning approach to predict pancreatic islet grafts rejection versus tolerance.

The application of artificial intelligence (AI) and machine learning (ML) in biomedical research promises to unlock new information from the vast amounts of data being generated through the delivery of healthcare and the expanding high-throughput research applications. Such information can aid medical diagnoses and reveal various unique patterns of biochemical and immune features that can serve as early disease biomarkers. In this report, we demonstrate the feasibility of using an AI/ML approach in a relatively small dataset to discriminate among three categories of samples obtained from mice that either rejected or tolerated their pancreatic islet allografts following transplant in the anterior chamber of the eye, and from naïve controls. We created a locked software based on a support vector machine (SVM) technique for pattern recognition in electropherograms (EPGs) generated by micellar electrokinetic chromatography and laser induced fluorescence detection (MEKC-LIFD). Predictions were made based only on the aligned EPGs obtained in microliter-size aqueous humor samples representative of the immediate local microenvironment of the islet allografts. The analysis identified discriminative peaks in the EPGs of the three sample categories. Our classifier software was tested with targeted and untargeted peaks. Working with the patterns of untargeted peaks (i.e., based on the whole pattern of EPGs), it was able to achieve a 21 out of 22 positive classification score with a corresponding 95.45% prediction accuracy among the three sample categories, and 100% accuracy between the rejecting and tolerant recipients. These findings demonstrate the feasibility of AI/ML approaches to classify small numbers of samples and they warrant further studies to identify the analytes/biochemicals corresponding to discriminative features as potential biomarkers of islet allograft immune rejection and tolerance.

NOD Mice-Good Model for T1D but Not Without Limitations.

The nonobese diabetic (NOD) mouse model of type 1 diabetes (T1D) was discovered by coincidence in the 1980s and has since been widely used in the investigation of T1D and diabetic complications. The current in vivo study was originally designed to prospectively assess whether hyperglycemia onset is associated with physical destruction or functional impairment of beta cells under inflammatory insult during T1D progression in diabetes-prone female NOD mice. Prediabetic 16- to 20-wk-old NOD mice were transplanted with green fluorescent protein (GFP)-expressing reporter islets in the anterior chamber of the eye (ACE) that were monitored longitudinally, in addition to glycemia, with and without immune modulation using anti-CD3 monoclonal antibody therapy. However, there was an early and vigorous immune reaction against the GFP-expressing beta cells that lead to their premature destruction independent of autoimmune T1D development in progressor mice that eventually became hyperglycemic. This immune reaction also occurred in nonprogressor NOD recipients. These findings showed a previously unknown reaction of NOD mice to GFP that prevented achieving the original goals of this study but highlighted a new feature of the NOD mice that should be considered when designing experiments using this model in T1D research.

Studying the biology of cytotoxic T lymphocytes in vivo with a fluorescent granzyme B-mTFP knock-in mouse.

Understanding T cell function in vivo is of key importance for basic and translational immunology alike. To study T cells in vivo, we developed a new knock-in mouse line, which expresses a fusion protein of granzyme B, a key component of cytotoxic granules involved in T cell-mediated target cell-killing, and monomeric teal fluorescent protein from the endogenous Gzmb locus. Homozygous knock-ins, which are viable and fertile, have cytotoxic T lymphocytes with endogeneously fluorescent cytotoxic granules but wild-type-like killing capacity. Expression of the fluorescent fusion protein allows quantitative analyses of cytotoxic granule maturation, transport and fusion in vitro with super-resolution imaging techniques, and two-photon microscopy in living knock-ins enables the visualization of tissue rejection through individual target cell-killing events in vivo. Thus, the new mouse line is an ideal tool to study cytotoxic T lymphocyte biology and to optimize personalized immunotherapy in cancer treatment.

Cytotoxic, or killer, T cells are a key part of the immune system. They carry a lethal mixture of toxic chemicals, stored in packages called cytotoxic granules. Killer T cells inject the contents of these granules into infected, cancerous or otherwise foreign cells, forcing them to safely self-destruct. In test tubes, T cells are highly efficient serial killers, moving from one infected cell to the next at high speed. But, inside the body, their killing rate slows down. Researchers think that this has something to do with how killer T cells interact with other immune cells, but the details remain unclear. To get to grips with how killer T cells work in their natural environment, researchers need a way to follow them inside the body. One approach could be to use genetic engineering to attach a fluorescent tag to a protein found inside killer T cells. That tag then acts as a beacon, lighting the cells up and allowing researchers to track their movements. Tagging a protein inside the cytotoxic granules would allow close monitoring of T cells as they encounter, recognize and kill their targets. But fluorescent tags are bulky, and they can stop certain proteins from working as they should. To find out whether it is possible to track killer T cells with fluorescent tags, Chitirala, Chang et al. developed a new type of genetically modified mouse. The modification added a teal-colored tag to a protein inside the granules of the killer T cells. Chitirala, Chang et al. then used a combination of microscopy techniques inside and outside of the body to find out if the T cells still worked. This analysis showed that, not only were the tagged T cells able to kill diseased cells as normal, the tags made it possible to watch it happening in real time. Super-resolution microscopy outside of the body allowed Chitirala, Chang et al. to watch the killer T cells release their toxic granule content. It was also possible to follow individual T cells as they moved into, and destroyed, foreign tissue that had been transplanted inside the mice. These new mice provide a tool to understand how killer T cells really work. They could allow study not only of the cells themselves, but also their interactions with other immune cells inside the body. This could help to answer open questions in T cell research, such as why T cells seem to be so much more efficient at killing in test tubes than they are inside the body. Understanding this better could support the development of new treatments for viruses and cancer.

Longitudinal proteomics analysis in the immediate microenvironment of islet allografts during progression of rejection.

The applicability and benefits of pancreatic islet transplantation are limited due to various issues including the need to avoid immune-mediated rejection. Here, we used our experimental platform of allogeneic islet transplant in the anterior chamber of the eye (ACE-platform) to longitudinally monitor the progress of rejection in mice and obtain aqueous humor samples representative of the microenvironment of the graft for accurately-timed proteomic analyses. LC-MS/MS-based proteomics performed on such mass-limited samples (~5 µL) identified a total of 1296 proteins. Various analyses revealed distinct protein patterns associated with the mounting of the inflammatory and immune responses and their evolution with the progression of the rejection. Pathway analyses indicated predominant changes in cytotoxic functions, cell movement, and innate and adaptive immune responses. Network prediction analyses revealed transition from humoral to cellular immune response and exacerbation of pro-inflammatory signaling. One of the proteins identified by this localized proteomics as a candidate biomarker of islet rejection, Cystatin 3, was further validated by ELISA in the aqueous humor. This study provides (1) experimental evidence demonstrating the feasibility of longitudinal localized proteomics using small aqueous humor samples and (2) proof-of-concept for the discovery of biomarkers of impending immune attack from the immediate local microenvironment of ACE-transplanted islets. SIGNIFICANCE: The combination of the ACE-platform and longitudinal localized proteomics offers a powerful approach to biomarker discovery during the various stages of immune reactions mounted against transplanted tissues including pancreatic islets. It also supports proteomics-assisted drug discovery and development efforts aimed at preventing rejection through efficacy assessment of new agents by noninvasive and longitudinal graft monitoring.

Islet Transplantation to the Anterior Chamber of the Eye-A Future Treatment Option for Insulin-Deficient Type-2 Diabetics? A Case Report from a Nonhuman Type-2 Diabetic Primate.

Replacement of the insulin-secreting beta cells through transplantation of pancreatic islets to the liver is a promising treatment for type-1 diabetes. However, low oxygen tension, shear stress, and the induction of inflammation lead to significant islet dysfunction and loss. The anterior chamber of the eye (ACE) has gained considerable interest and represents an alternative therapeutic islet transplantation site because of its accessibility, high oxygen tension, and immune-privileged milieu. We have previously demonstrated the feasibility of intraocular islet transplant in mouse and nonhuman primate models of type-1 diabetes and are now assessing its efficacy on glucose homeostasis in a nonhuman primate model of type-2 diabetes. We transplanted allogeneic donor islets (1,500 islet equivalents/kg) into the anterior chamber of one eye in a cynomolgus monkey with high-fat-diet-induced type-2 diabetes. Repeated examinations of the anterior and posterior segments of both eyes were done to monitor the engrafted islets and assess the overall ocular health. Fasting blood glucose level, blood biochemistry, and other metabolic parameters were routinely evaluated to determine the function of the islet graft and diabetes status. The transplanted islets were rapidly engrafted onto the iris and became vascularized 1 month after transplantation. We did not detect changes in intraocular pressure, cataract formation, ophthalmitis, or retinal vessel deformation. A significant lower fasting blood glucose level was observed while the graft was in place, and the transplantation reverts the progression of diabetes. The metabolic markers, hemoglobin A1C and fructosamine, demonstrated improvement following islet transplantation. As a conclusion, intraocular islet transplantation in one eye of a cynomolgus monkey with type-2 diabetes improved its overall plasma glucose homeostasis, as evidenced by short-term measures and long-term metabolic markers. These results further support the future application of the ACE as an alternative site for clinical islet transplants in the context of type-2 diabetes.

Feasibility of Localized Metabolomics in the Study of Pancreatic Islets and Diabetes.

(1) Background: Disruption of insulin production by native or transplanted pancreatic islets caused by auto/allo-immunity leads to hyperglycemia, a serious health condition and important therapeutic challenge due to the lifelong need for exogeneous insulin administration. Early metabolic biomarkers can prompt timely interventions to preserve islet function, but reliable biomarkers are currently lacking. We explored the feasibility of "localized metabolomics" where initial biomarker discovery is made in aqueous humor samples for further validation in the circulation. (2) Methods: We conducted non-targeted metabolomic studies in parallel aqueous humor and plasma samples from diabetic and nondiabetic mice. Metabolite levels and associated pathways were compared in both compartments as well as to an earlier longitudinal dataset in hyperglycemia-progressor versus non-progressor non-obese diabetic (NOD) mice. (3) Results: We confirmed that aqueous humor samples can be used to assess metabolite levels. About half of the identified metabolites had well-correlated levels in the aqueous humor and plasma. Several plasma metabolites were significantly different between diabetic and nondiabetic animals and between males and females, and many of them were correlated with the aqueous humor. (4) Conclusions: This study provides proof-of-concept evidence that aqueous humor samples enriched with islet-related metabolites and representative of the immediate islet microenvironment following intraocular islet transplant can be used to assess metabolic changes that could otherwise be overlooked in the general circulation. The findings support localized metabolomics, with and without intraocular islet transplant, to identify biomarkers associated with diabetes and islet allograft rejection.

Interplay between HGAL and Grb2 proteins regulates B-cell receptor signaling.

Human germinal center (GC)-associated lymphoma (HGAL) is an adaptor protein expressed in GC B cells. HGAL regulates cell motility and B-cell receptor (BCR) signaling, processes that are central for the successful completion of the GC reaction. Herein, we demonstrate phosphorylation of HGAL by Syk and Lyn kinases at tyrosines Y80, Y86, Y106Y107, Y128, and Y148. The HGAL YEN motif (amino acids 107-109) is similar to the phosphopeptide motif pYXN used as a binding site to the growth factor receptor-bound protein 2 (Grb2). We demonstrate by biochemical and molecular methodologies that HGAL directly interacts with Grb2. Concordantly, microscopy studies demonstrate HGAL-Grb2 colocalization in the membrane central supramolecular activation clusters (cSMAC) following BCR activation. Mutation of the HGAL putative binding site to Grb2 abrogates the interaction between these proteins. Further, this HGAL mutant localizes exclusively in the peripheral SMAC and decreases the rate and intensity of BCR accumulation in the cSMAC. Furthermore, we demonstrate that Grb2, HGAL, and Syk interact in the same complex, but Grb2 does not modulate the effects of HGAL on Syk kinase activity. Overall, the interplay between the HGAL and Grb2 regulates the magnitude of BCR signaling and synapse formation.

Correction to: In vivo imaging of type 1 diabetes immunopathology using eye-transplanted islets in NOD mice.

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In vivo imaging of type 1 diabetes immunopathology using eye-transplanted islets in NOD mice.

AIMS/HYPOTHESIS: Autoimmune attack against the insulin-producing beta cells in the pancreatic islets results in type 1 diabetes. However, despite considerable research, details of the type 1 diabetes immunopathology in situ are not fully understood mainly because of difficult access to the pancreatic islets in vivo. METHODS: Here, we used direct non-invasive confocal imaging of islets transplanted in the anterior chamber of the eye (ACE) to investigate the anti-islet autoimmunity in NOD mice before, during and after diabetes onset. ACE-transplanted islets allowed longitudinal studies of the autoimmune attack against islets and revealed the infiltration kinetics and in situ motility dynamics of fluorescence-labelled autoreactive T cells during diabetes development. Ex vivo immunostaining was also used to compare immune cell infiltrations into islet grafts in the eye and kidney as well as in pancreatic islets of the same diabetic NOD mice. RESULTS: We found similar immune infiltration in native pancreatic and ACE-transplanted islets, which established the ACE-transplanted islets as reliable reporters of the autoimmune response. Longitudinal studies in ACE-transplanted islets identified in vivo hallmarks of islet inflammation that concurred with early immune infiltration of the islets and preceded their collapse and hyperglycaemia onset. A model incorporating data on ACE-transplanted islet degranulation and swelling allowed early prediction of the autoimmune attack in the pancreas and prompted treatments to intercept type 1 diabetes. CONCLUSIONS/INTERPRETATION: The current findings highlight the value of ACE-transplanted islets in studying early type 1 diabetes pathogenesis in vivo and underscore the need for timely intervention to halt disease progression.

Operational immune tolerance towards transplanted allogeneic pancreatic islets in mice and a non-human primate.

AIMS/HYPOTHESIS: Patients with autoimmune type 1 diabetes transplanted with pancreatic islets to their liver experience significant improvement in quality of life through better control of blood sugar and enhanced awareness of hypoglycaemia. However, long-term survival and efficacy of the intrahepatic islet transplant are limited owing to liver-specific complications, such as immediate blood-mediated immune reaction, hypoxia, a highly enzymatic and inflammatory environment and locally elevated levels of drugs including immunosuppressive agents, all of which are injurious to islets. This has spurred a search for new islet transplant sites and for innovative ways to achieve long-term graft survival and efficacy without life-long systemic immunosuppression and its complications. METHODS: We used our previously established approach of islet transplant in the anterior chamber of the eye in allogeneic recipient mouse models and a baboon model of diabetes, which were treated transiently with anti-CD154/CD40L blocking antibody in the peri-transplant period. Survival of the intraocular islet allografts was assessed by direct visualisation in the eye and metabolic variables (blood glucose and C-peptide measurements). We evaluated longitudinally the cytokine profile in the local microenvironment of the intraocular islet allografts, represented in aqueous humour, under conditions of immune rejection vs tolerance. We also evaluated the recall response in the periphery of the baboon recipient using delayed-type hypersensitivity (DTH) assay, and in mice after repeat transplant in the kidney following initial transplant with allogeneic islets in the eye or kidney. RESULTS: Results in mice showed >300 days immunosuppression-free survival of allogeneic islets transplanted in the eye or kidney. Notably, >70% of tolerant mice, initially transplanted in the eye, exhibited >400 days of graft survival after re-transplant in the kidney without immunosuppression compared with ~30% in mice that were initially transplanted in the kidney. Cytokine and DTH data provided evidence of T helper 2-driven local and peripheral immune regulatory mechanisms in support of operational immune tolerance towards the islet allografts in both models. CONCLUSIONS/INTERPRETATION: We are currently evaluating the safety and efficacy of intraocular islet transplantation in a phase 1 clinical trial. In this study, we demonstrate immunosuppression-free long-term survival of intraocular islet allografts in mice and in a baboon using transient peri-transplant immune intervention. These results highlight the potential for inducing islet transplant immune tolerance through the intraocular route. Therefore, the current findings are conceptually significant and may impact markedly on clinical islet transplantation in the treatment of diabetes.

Paracrine Interactions within the Pancreatic Islet Determine the Glycemic Set Point.

Every animal species has a signature blood glucose level or glycemic set point. These set points are different, and the normal glycemic levels (normoglycemia) of one species would be life threatening for other species. Mouse normoglycemia can be considered diabetic for humans. The biological determinants of the glycemic set point remain unclear. Here we show that the pancreatic islet imposes its glycemic set point on the organism, making it the bona fide glucostat in the body. Moreover, and in contrast to rodent islets, glucagon input from the alpha cell to the insulin-secreting beta cell is necessary to fine-tune the distinctive human set point. These findings affect transplantation and regenerative approaches to treat diabetes because restoring normoglycemia may require more than replacing only the beta cells. Furthermore, therapeutic strategies using glucagon receptor antagonists as hypoglycemic agents need to be reassessed, as they may reset the overall glucostat in the organism.

Effect of Arginase-1 Inhibition on the Incidence of Autoimmune Diabetes in NOD Mice.

Metabolism of the amino acid L-arginine is implicated in many physiological and pathophysiological processes including autoimmune conditions such as type 1 diabetes (T1D). Alternate arginine metabolism through the citrulline-nitric oxide (NO) or the ornithine pathways can lead to proinflammatory or immune regulatory effects, respectively. In this report, we blocked the arginine-ornithine metabolic pathway by inhibiting the enzyme arginase-1 with Nω-hydroxy-nor-arginine (nor-NOHA) to make arginine more available to the alternate citrulline pathway for augmented NO production and increased incidence of autoimmune T1D in female non-obese diabetic (NOD) mice. Unexpectedly, mice receiving nor-NOHA did not develop diabetes although increased NO production is proinflammatory and expected to increase diabetes incidence. These results warrant further studies of the mechanism of action of nor-NOHA, and highlight its potential as a therapeutic agent for the treatment or prevention of T1D.