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

Multi-Omic blood analysis reveals differences in innate inflammatory sensitivity between species

Vertebrates differ greatly in responses to pro-inflammatory agonists such as bacterial lipopolysaccharide (LPS), complicating use of animal models to study human sepsis or inflammatory disorders. We compared transcriptomes of resting and LPS-exposed blood from six LPS-sensitive species (rabbit, pig, sheep, cow, chimpanzee, human) and four LPS-resilient species (mice, rats, baboon, rhesus), as well as plasma proteomes and lipidomes. Unexpectedly, at baseline, sensitive species already had enhanced expression of LPS-responsive genes relative to resilient species. After LPS stimulation, maximally different genes in resilient species included genes that detoxify LPS, diminish bacterial growth, discriminate sepsis from SIRS, and play roles in autophagy and apoptosis. The findings reveal the molecular landscape of species differences in inflammation, and may inform better selection of species for pre-clinical models.

Global proteomics reveals insulin abundance as a marker of human islet homeostasis alterations

AIM

The variation in quality between the human islet samples represents a major problem for research, especially when used as control material. The assays assessing the quality of human islets used in research are non-standardized and limited, with many important parameters not being consistently assessed. High-throughput studies aimed at characterizing the diversity and segregation markers among apparently functionally healthy islet preps are thus a requirement. Here, we designed a pilot study to comprehensively identify the diversity of global proteome signatures and the deviation from normal homeostasis in randomly selected human-isolated islet samples.

METHODS

By using Tandem Mass Tag 16-plex proteomics, we focused on the recurrently observed disparity in the detected insulin abundance between the samples, used it as a segregating parameter, and analyzed the correlated changes in the proteome signature and homeostasis by pathway analysis.

RESULTS

In this pilot study, we showed that insulin protein abundance is a predictor of human islet homeostasis and quality. This parameter is independent of other quality predictors within their acceptable range, thus being able to further stratify islets samples of apparent good quality. Human islets with low amounts of insulin displayed changes in their metabolic and signaling profile, especially in regard to energy homeostasis and cell identity maintenance. We further showed that xenotransplantation into diabetic hosts is not expected to improve the pre-transplantation signature, as it has a negative effect on energy balance, antioxidant activity, and islet cell identity.

CONCLUSIONS

Insulin protein abundance predicts significant changes in human islet homeostasis among random samples of apparently good quality.

Local Dexamethasone Administration Delays Allogeneic Islet Graft Rejection in the Anterior Chamber of the Eye of Non-Human Primates

Pancreatic islet transplantation into the anterior chamber of the eye (ACE) has been shown to improve glycemic control and metabolic parameters of diabetes in both murine and primate models. This novel transplantation site also allows the delivery of therapeutic agents, such as immunosuppressive drugs, locally to prevent islet graft rejection and circumvent unwanted systemic side effects. Local intravitreal administration of micronized dexamethasone implant was performed prior to allogeneic islet transplantation into the ACEs of non-human primates. Two study groups were observed namely allogeneic graft without immunosuppression (n = 4 eyes) and allogeneic graft with local immunosuppression (n = 8 eyes). Survival of islet grafts and dexamethasone concentration in the ACE were assessed in parallel for 24 weeks. Allogeneic islet grafts with local dexamethasone treatment showed significantly better survival than those with no immunosuppression (median survival time- 15 weeks vs 3 weeks, log-rank test p<0.0001). Around 73% of the grafts still survived at week 10 with a single local dexamethasone implant, where the control group showed no graft survival. Dexamethasone treated islet grafts revealed a good functional response to high glucose stimulation despite there was a transient suppression of insulin secretion from week 8 to 12. Our findings show a significant improvement of allografts survival in the ACE with local dexamethasone treatment. These results highlight the feasibility of local administration of pharmacological compounds in the ACE to improve islet graft survival and function. By eliminating the need for systemic immunosuppression, these findings may impact clinical islet transplantation in the treatment of diabetes, and the ACE may serve as a novel therapeutic islet transplantation site with high potential for local pharmacological intervention.

Proteomics Analysis of Aqueous Humor and Rejected Graft in Pig-to-Non-Human Primate Corneal Xenotransplantation

Although pig-to-non-human primate (NHP) corneal xenotransplantation has shown long-term graft survival, xenogeneic antigen-related immune responses are still stronger than allogeneic antigen-associated responses. Therefore, there is an unmet need to investigate major rejection pathways in corneal xenotransplantation, even with immunosuppression. This study aimed to identify biomarkers in aqueous humor for predicting rejection and to investigate rejection-related pathways in grafts from NHPs transplanted with porcine corneas following the administration of steroids combined with tacrolimus/rituximab. NHPs who had received corneas from wild-type (WT) or α-1,3-galactosyltransferase gene-knockout (GTKO) pigs were divided into groups with or without rejection according to clinical examinations. Liquid chromatography-mass spectrometry (LC-MS) was used to analyze the proteomes of corneal tissues or aqueous humor. The biological functions of differentially expressed proteins (DEPs) were assessed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for pathways and protein–protein interaction network analysis. Among the 66 DEPs in aqueous humor, complement proteins (C3, C5, and C9) and cholesterol metabolic proteins (APOA1 and APOA2) were related to xenogeneic rejection as biomarkers, and alternative pathways of the complement system seemed to be important in xenogeneic graft rejection. Among the 416 DEPs of the cornea, NF-κB1 and proteosomes (PSMD7, PSMA5, and PSMD3) seemed to be related to xenogeneic graft rejection. Additionally, oxidative phosphorylation and leukocyte activation-related pathways are involved in rejection. Overall, our proteomic approach highlights the important role of NF-κB1, proteosomes, oxidative phosphorylation, and leukocyte activation-related inflammation in the cornea and the relevance of complement pathways of the aqueous humor as a predictive biomarker of xenogeneic rejection.

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.

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.

The Eye as a Transplantation Site to Monitor Pancreatic Islet Cell Plasticity

The endocrine cells confined in the islets of Langerhans are responsible for the maintenance of blood glucose homeostasis. In particular, beta cells produce and secrete insulin, an essential hormone regulating glucose uptake and metabolism. An insufficient amount of beta cells or defects in the molecular mechanisms leading to glucose-induced insulin secretion trigger the development of diabetes, a severe disease with epidemic spreading throughout the world. A comprehensive appreciation of the diverse adaptive procedures regulating beta cell mass and function is thus of paramount importance for the understanding of diabetes pathogenesis and for the development of effective therapeutic strategies. While significant findings were obtained by the use of islets isolated from the pancreas, in vitro studies are inherently limited since they lack the many factors influencing pancreatic islet cell function in vivo and do not allow for longitudinal monitoring of islet cell plasticity in the living organism. In this respect a number of imaging methodologies have been developed over the years for the study of islets in situ in the pancreas, a challenging task due to the relatively small size of the islets and their location, scattered throughout the organ. To increase imaging resolution and allow for longitudinal studies in individual islets, another strategy is based on the transplantation of islets into other sites that are more accessible for imaging. In this review we present the anterior chamber of the eye as a transplantation and imaging site for the study of pancreatic islet cell plasticity, and summarize the major research outcomes facilitated by this technological platform.

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 β-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-β, 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.