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

Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk

INTRODUCTION

Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection.

METHODS

We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation.

RESULTS

Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model.

CONCLUSION

Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.

Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting

The impact of pre-existing immunity on the efficacy of artemisinin combination therapy is largely unknown. We performed in-depth profiling of serological responses in a therapeutic efficacy study [comparing artesunate-mefloquine (ASMQ) and artemether-lumefantrine (AL)] using a proteomic microarray. Responses to over 200 Plasmodium antigens were significantly associated with ASMQ treatment outcome but not AL. We used machine learning to develop predictive models of treatment outcome based on the immunoprofile data. The models predict treatment outcome for ASMQ with high (72–85%) accuracy, but could not predict treatment outcome for AL. This divergent treatment outcome suggests that humoral immunity may synergize with the longer mefloquine half-life to provide a prophylactic effect at 28–42 days post-treatment, which was further supported by simulated pharmacokinetic profiling. Our computational approach and modeling revealed the synergistic effect of pre-existing immunity in patients with drug combination that has an extended efficacy on providing long term treatment efficacy of ASMQ.

Natural killer cells in liver transplantation: Can we harness the power of the immune checkpoint to promote tolerance?

The roles that natural killer (NK) cells play in liver disease and transplantation remain ill‐defined. Reports on the matter are often contradictory, and the mechanisms elucidated are complex and dependent on the context of the model tested. Moreover, NK cell attributes, such as receptor protein expression and function differ among species, make study of primate or rodent transplant models challenging. Recent insights into NK function and NK‐mediated therapy in the context of cancer therapy may prove applicable to transplantation. Of specific interest are immune checkpoint molecules and the mechanisms by which they modulate NK cells in the tumor micro‐environment. In this review, we summarize NK cell populations in the peripheral blood and liver, and we explore the data regarding the expression and function of immune checkpoint molecules on NK cells. We also hypothesize about the roles they could play in liver transplantation and discuss how they might be harnessed therapeutically in transplant sciences.

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.