Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint.

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Evaluation of rapid optimized flow cytometry crossmatch (Halifaster) in living donor kidney transplantation.

Kidney transplantation is often the preferred treatment for end-stage renal disease. However, the presence of preformed donor-specific antibodies (DSA), including those against HLA, can lead to antibody-mediated rejection and significantly impact transplant outcomes. The Flow Cytometry Crossmatch (FCXM) is a crucial tool in kidney transplantation, as it also enables the measurement of low levels of anti-HLA DSA antibodies. However, current methodologies for detecting these antibodies, however, are time-consuming and require extensive reagents. In this study, we analyzed the performance of the Halifaster FCXM protocol in 133 consecutive living kidney donor pairs, correlating these results with single antigen-based anti-HLA DSA results. Anti-HLA DSA was identified in 31 patients (23.3%). Both T and B lymphocyte FCXM assays demonstrated high sensitivity and specificity in detecting anti-HLA DSA. Furthermore, a Tree model to determine the levels of anti-HLA DSA to produce a flow crossmatch positivity, was developed offering an accuracy of 93% and 90% for T and B lymphocytes, respectively. Both approaches point to a thresh old of 1000-2000 MFI for T lymphocytes and 3000 MFI for B lymphocytes. Our findings indicate that the Halifaster protocol facilitates fast and efficient FCXM testing without compromising accuracy, marking a significant advancement in the field of kidney transplantation. The inclusion of HLA-specific antibody analysis underscores the protocol's comprehensive approach to improving transplant outcomes.

Exosomes and microvesicles in kidney transplantation: the long road from trash to gold.

Kidney transplantation significantly enhances the survival rate and quality of life of patients with end-stage kidney disease. The ability to predict post-transplantation rejection events in their early phases can reduce subsequent allograft loss. Therefore, it is critical to identify biomarkers of rejection processes that can be acquired on routine analysis of samples collected by non-invasive or minimally invasive procedures. It is also important to develop new therapeutic strategies that facilitate optimisation of the dose of immunotherapeutic drugs and the induction of allograft immunotolerance. This review explores the challenges and opportunities offered by extracellular vesicles (EVs) present in biofluids in the discovery of biomarkers of rejection processes, as drug carriers and in the induction of immunotolerance. Since EVs are highly complex structures and their composition is affected by the parent cell's metabolic status, the importance of defining standardised methods for isolating and characterising EVs is also discussed. Understanding the major bottlenecks associated with all these areas will promote the further investigation of EVs and their translation into a clinical setting.

Blood molecular profile to predict genotoxicity from exposure to antineoplastic drugs.

Genotoxicity is an important information that should be included in human biomonitoring programmes. However, the usually applied cytogenetic assays are laborious and time-consuming, reason why it is critical to develop rapid and economic new methods. The aim of this study was to evaluate if the molecular profile of frozen whole blood, acquired by Fourier Transform Infrared (FTIR) spectroscopy, allows to assess genotoxicity in occupational exposure to antineoplastic drugs, as obtained by the cytokinesis-block micronucleus assay. For that purpose, 92 samples of peripheral blood were studied: 46 samples from hospital professionals occupationally exposed to antineoplastic drugs and 46 samples from workers in academia without exposure (controls). It was first evaluated the metabolome from frozen whole blood by methanol precipitation of macromolecules as haemoglobin, followed by centrifugation. The metabolome molecular profile resulted in 3 ratios of spectral bands, significantly different between the exposed and non-exposed group (p < 0.01) and a spectral principal component-linear discriminant analysis (PCA-LDA) model enabling to predict genotoxicity from exposure with 73 % accuracy. After optimization of the dilution degree and solution used, it was possible to obtain a higher number of significant ratios of spectral bands, i.e., 10 ratios significantly different (p < 0.001), highlighting the high sensitivity and specificity of the method. Indeed, the PCA-LDA model, based on the molecular profile of whole blood, enabled to predict genotoxicity from the exposure with an accuracy, sensitivity, and specificity of 92 %, 93 % and 91 %, respectively. All these parameters were achieved based on 1 µL of frozen whole blood, in a high-throughput mode, i.e., based on the simultaneous analysis of 92 samples, in a simple and economic mode. In summary, it can be conclude that this method presents a very promising potential for high-dimension screening of exposure to genotoxic substances.

Proteomics-Driven Biomarkers in Pancreatic Cancer.

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

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.

Plasma versus Serum Analysis by FTIR Spectroscopy to Capture the Human Physiological State.

Fourier Transform InfraRed spectroscopy of serum and plasma has been highly explored for medical diagnosis, due to its general simplicity, and high sensitivity and specificity. To evaluate the plasma and serum molecular fingerprint, as obtained by FTIR spectroscopy, to acquire the system metabolic state, serum and plasma spectra were compared to characterize the metabolic state of 30 human volunteers, between 90 days consumption of green tea extract rich in Epigallocatechin-3-gallate (EGCG). Both plasma and serum spectra enabled the high impact of EGCG consumption on the biofluid spectra to be observed, as analyzed by the spectra principal component analysis, hierarchical-cluster analysis, and univariate data analysis. Plasma spectra resulted in the prediction of EGCG consumption with a slightly higher specificity, accuracy, and precision, also pointing to a higher number of significant spectral bands that were different between the 90 days period. Despite this, the lipid regions of the serum spectra were more affected by EGCG consumption than the corresponding plasma spectra. Therefore, in general, if no specific compound analysis is highlighted, plasma is in general the advised biofluid to capture by FTIR spectroscopy the general metabolic state. If the lipid content of the biofluid is relevant, serum spectra could present some advantages over plasma spectra.

Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection.

Renal transplantation is currently the treatment of choice for end-stage kidney disease, enabling a quality of life superior to dialysis. Despite this, all transplanted patients are at risk of allograft rejection processes. The gold-standard diagnosis of graft rejection, based on histological analysis of kidney biopsy, is prone to sampling errors and carries high costs and risks associated with such invasive procedures. Furthermore, the routine clinical monitoring, based on urine volume, proteinuria, and serum creatinine, usually only detects alterations after graft histologic damage and does not differentiate between the diverse etiologies. Therefore, there is an urgent need for new biomarkers enabling to predict, with high sensitivity and specificity, the rejection processes and the underlying mechanisms obtained from minimally invasive procedures to be implemented in routine clinical surveillance. These new biomarkers should also detect the rejection processes as early as possible, ideally before the 78 clinical outputs, while enabling balanced immunotherapy in order to minimize rejections and reducing the high toxicities associated with these drugs. Proteomics of biofluids, collected through non-invasive or minimally invasive analysis, e.g., blood or urine, present inherent characteristics that may provide biomarker candidates. The current manuscript reviews biofluids proteomics toward biomarkers discovery that specifically identify subclinical, acute, and chronic immune rejection processes while allowing for the discrimination between cell-mediated or antibody-mediated processes. In time, these biomarkers will lead to patient risk stratification, monitoring, and personalized and more efficient immunotherapies toward higher graft survival and patient quality of life.

A new method to predict genotoxic effects based on serum molecular profile.

It is critical to develop new methods to assess genotoxic effects in human biomonitoring since the conventional methods are usually laborious, time-consuming, and expensive. It is aimed to evaluate if the analysis of a drop of serum by Fourier Transform Infrared spectroscopy, allow to assess genotoxic effects in occupational exposure to cytostatic drugs in hospital professionals, as obtained by the lymphocyte cytokinesis-block micronucleus assay. It was considered peripheral blood from hospital professionals exposed to cytostatic drugs (n = 22) and from a non-exposed group (n = 36). It was observed that workers occupationally exposed presented a higher number of micronuclei (p < 0.05) in lymphocytes, in relation to the non-exposed group. The serum Fourier Transform Infrared spectra from exposed workers presented diverse different peaks (p < 0.01) in relation to the non-exposed group. The hierarchical cluster analysis of serum spectra separated serum samples of the exposed group from the non-exposed group with 61% sensitivity and 88% specificity. A support vector machine model of serum spectra enables to predict exposure with high accuracy (0.91), precision (0.89), sensitivity (0.86), F1 score (0.87) and AUC (0.96). Therefore, Fourier Transform Infrared spectroscopic analysis of a drop of serum enabled to predict in a rapid and simple mode the genotoxic effects of cytostatic drugs. The method presents therefore potential for high-dimension screening of exposure of genotoxic substances, due to its simplicity and rapid setup mode.

Improving HLA matching in living donor kidney transplantation using kidney paired exchange program.

BACKGROUND: The inclusion of compatible pairs within kidney paired exchange programs has been described as a way to enhance these programs. Improved immunological matching for the recipient in compatible pair has been described to be a possible benefit. METHODS: The main purpose of our study was to determine if the introduction of compatible pairs in the Portuguese kidney paired exchange program would result in a better match for these patients, but also to assess if this strategy would increase the number of incompatible pairs with a possible match. We included 17 compatible pairs in kidney paired exchange pool of 35 pairs and performed an in-silico simulation determining HLA eplet mismatch load between the co-registered and matched pairs using HLA MatchMaker, version 3.0. RESULTS: Our study showed that the inclusion of fully HLA-A, -B, -DR mismatched compatible pairs within the national Portuguese KEP increased matched rate within ICP (0.71%) and improved HLA eplet matching within compatible pairs. 16 of 17 (94.12%) of the CP obtained one or more transplants possibilities and 13 (81.25%) would have been transplanted with significantly lower HLA class I and class II total and antibody-verified eplet mismatch load (83.9 ± 16.9 vs. 59.8 ± 12.2, P = .002 and 30.1 ± 5.5 vs. 21.2 ± 3.0, P = .003, respectively). CONCLUSIONS: This strategy is a viable alternative for compatible pairs seeking a better matched kidney and Portuguese KEP program should allow them this possibility.

A Simple, Label-Free, and High-Throughput Method to Evaluate the Epigallocatechin-3-Gallate Impact in Plasma Molecular Profile.

Epigallocatechin-3-gallate (EGCG), the major catechin present in green tea, presents diverse appealing biological activities, such as antioxidative, anti-inflammatory, antimicrobial, and antiviral activities, among others. The present work evaluated the impact in the molecular profile of human plasma from daily consumption of 225 mg of EGCG for 90 days. Plasma from peripheral blood was collected from 30 healthy human volunteers and analyzed by high-throughput Fourier transform infrared spectroscopy. To capture the biochemical information while minimizing the interference of physical phenomena, several combinations of spectra pre-processing methods were evaluated by principal component analysis. The pre-processing method that led to the best class separation, that is, between the plasma spectral data collected at the beginning and after the 90 days, was a combination of atmospheric correction with a second derivative spectra. A hierarchical cluster analysis of second derivative spectra also highlighted the fact that plasma acquired before EGCG consumption presented a distinct molecular profile after the 90 days of EGCG consumption. It was also possible by partial least squares regression discriminant analysis to correctly predict all unlabeled plasma samples (not used for model construction) at both timeframes. We observed that the similarity in composition among the plasma samples was higher in samples collected after EGCG consumption when compared with the samples taken prior to EGCG consumption. Diverse negative peaks of the normalized second derivative spectra, associated with lipid and protein regions, were significantly affected (p < 0.001) by EGCG consumption, according to the impact of EGCG consumption on the patients' blood, low density and high density lipoproteins ratio. In conclusion, a single bolus dose of 225 mg of EGCG, ingested throughout a period of 90 days, drastically affected plasma molecular composition in all participants, which raises awareness regarding prolonged human exposure to EGCG. Because the analysis was conducted in a high-throughput, label-free, and economic analysis, it could be applied to high-dimension molecular epidemiological studies to further promote the understanding of the effect of bio-compound consumption mode and frequency.