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.

Correlation between plasma levels of arginine and citrulline in preterm and full-term neonates: Therapeutical implications.

BACKGROUND: Preterm neonates exhibit several deficiencies that endanger their lives. Understanding those disturbances will provide tools for the management of preterm neonates. The present work focuses on arginine and citrulline which has been flagged among the biochemical landmarks of prematurity. METHODS: We examined blood samples of preterm newborns as compared with mature neonates to determine the levels of arginine and citrulline by capillary zone electrophoresis with laser induced fluorescence detection (CZE-LIFD). RESULTS: Significantly lower levels of arginine and citrulline were found in preterm neonates than in mature neonates (P<.01). Interestingly there was a highly significant correlation between the two amino acids in mature neonates (P<.0001). Such correlation was present in preterm neonates too (P<.01). Pearson coefficient showed that 60% of the citrulline concentration depends on arginine concentration in mature neonates. Only 20% of the citrulline concentration depends on arginine concentration in preterm neonates. Although the ratio arginine/citrulline was lower in preterm neonates than in mature neonates the difference was not statistically significant. CONCLUSIONS: These results suggest that less arginine is converted to citrulline to form nitric oxide in preterm than in full-term neonates. The result is discussed in terms of the immature enzymatic systems in the preterm neonate.

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.