Proteomic Profiling of Acute Cardiac Allograft Rejection

Artificial intelligence, big data and heart transplantation: Actualities

BACKGROUND

As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx.

METHOD

A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).

RESULTS

Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center.

CONCLUSIONS

While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.

The Proteomic Signature of Intestinal Acute Rejection in the Mouse

Intestinal acute rejection (AR) lacks a reliable non-invasive biomarker and AR surveillance is conducted through frequent endoscopic biopsies. Although citrulline and calprotectin have been suggested as AR biomarkers, these have limited clinical value. Using a mouse model of intestinal transplantation (ITx), we performed a proteome-wide analysis and investigated rejection-related proteome changes that may eventually be used as biomarkers. ITx was performed in allogenic (Balb/C to C57Bl) and syngeneic (C57Bl) combinations. Graft samples were obtained three and six days after transplantation (n = 4/time point) and quantitative proteomic analysis with iTRAQ-labeling and mass spectrometry of whole tissue homogenates was performed. Histology showed moderate AR in all allografts post-transplantation at day six. Nine hundred and thirty-eight proteins with at least three unique peptides were identified in the intestinal grafts. Eighty-six proteins varying by >20% between time points and/or groups had an alteration pattern unique to the rejecting allografts: thirty-seven proteins and enzymes (including S100-A8 and IDO-1) were significantly upregulated whereas forty-nine (among other chromogranin, ornithine aminotransferase, and arginase) were downregulated. Numerous proteins showed altered expression during intestinal AR, several of which were previously identified to be involved in acute rejection, although our results also identified previously unreported proteome changes. The metabolites and downstream metabolic pathways of some of these proteins and enzymes may become potential biomarkers for intestinal AR.

The current state of artificial intelligence in cardiac transplantation

PURPOSE OF REVIEW

The field of heart transplantation is a complex practice that combines both science and art to optimize the quality and quantity of an organ transplant recipient's life span. In the current age of Transplant Medicine there are many limiting factors that prevent potentially usable organs to be transplanted in addition to the many unknown factors in assessing the risk of posttransplant complications in a proactive manner. This review focuses on the current state, and potential use, and implementation of artificial intelligence technologies in the field of heart transplantation. Furthermore, the utilization of predictive algorithms to assess donor quality, graft function, posttransplant complications and prediction of high-risk complications will be discussed. Artificial intelligence technologies in the pretransplant population is also explored.

RECENT FINDINGS

Artificial intelligence process use has been increasing over the past decade. Early adoption in radiology and laboratory medicine have shown promise for future applications. Implementation of nascent technologies within the field of transplant medicine remains in its infancy. Cardiac and renal medicine have been recent focuses of large-scale artificial intelligence projects because of the wealth of data, the main limiting factor for producing accurate models. Understanding the true role of artificial intelligence in medicine is key - and has been divided into three areas of focus: data quality, interpretation, and clinical application. These areas allow the clinician to translate problems facing patients into algorithms utilized by data scientists to create solutions, which may provide in-depth analysis and detection of relationships not immediately clear. Although some published data has led to commercial products for cardiac, diabetic, and dermatologic applications -- widespread adoption remains limited to specialized centers.

SUMMARY

Artificial intelligence applications with clinically relevant models in transplant medicine have the potential to optimize organ utilization, prediction of complications, and potential pretransplant management, which may mitigate the need for transplant. Further translational projects are under development at major centers, with proof of concepts demonstrating validity and safety in the clinical setting. Limiting factors of infrastructure, expertise, and data availability continue to be addressed. Ongoing efforts for commercialization and large-scale trials will provide a foundation for the development of artificial intelligence applications in transplant medicine.

Discovery of potential plasma protein biomarkers for acute myocardial infarction via proteomics

Background

Acute myocardial infarction (AMI) is an acute disease with high mortality and seriously threatens human health. The identification of new effective biological markers for AMI is a prerequisite for treatment. Most proteomic studies have focused on atherosclerotic plaques, vascular cells, monocytes and platelets in the blood; however, the concentration of these factors in plasma is low, making it difficult to measure the complexity of plasma components. Moreover, some studies have examined the plasma protein of patients with acute coronary syndrome with histochemistry; however, the results are not consistent. Therefore, it is necessary to further investigate the differential proteins in the plasma of patients with AMI via proteomics to identify new biomarkers of AMI.

Methods

In this study, immunodepletion of high-abundance plasma proteins followed by an isobaric tagging for relative and absolute quantitation (iTRAQ)-based quantitative proteomic approach was used to analyze plasma samples from 5 control individuals and 10 AMI patients.

Results

Four hundred sixty-eight proteins were identified from two samples, and 33 proteins were differentially expressed in AMI patients compared to the controls. Among the 33 proteins, 12 proteins showed a ≥1.5-fold change between AMI and control samples. These proteins included fatty acid binding protein 3 (FABP3, ratio =6.36), creatine kinase-MB (CK-MB ratio =4.89), adenylate kinase1 (AK1 ratio =4.16), pro-platelet basic protein (PPBP ratio =3.29), creatine kinase (CK ratio =2.88), platelet factor 4 (PF4 ratio =2.62), peptidyl prolyl isomerase Cyclophilin A (PPIA ratio =2.05), Cofilin-1 (CFL1 ratio =1.81), coronin1A (CORO1A ratio =1.71), protein kinase M (PKM ratio =1.63), ribonuclease inhibitor (RNH1, ratio =1.67), and triose phosphate isomerase (TPI1 ratio =1.56). By contrast, there was a decrease of 19 proteins, such as adiponectin (ADIPOQ ratio =0.70), insulin-like growth factor binding protein6 (IGFBP6 ratio =0.70), Dickkopf-related protein 3 (DKK3 ratio =0.70) and complement 4B (C4B ratio =0.68). The most over-represented term was regulation of cell proliferation in the cellular component category of Gene Ontology (GO). The top 3 biological process terms were regulation of cell proliferation, response to wounding and wound healing. These proteins included immune proteins, blood coagulation proteins, lipid metabolism proteins, cytoskeleton proteins, energy metabolism proteins, gene regulation proteins, myocutaneous proteins, and myocardial remodeling proteins and were highly connected with each other, which indicates that the functional network of these processes contribute to the pathophysiology of AMI.

Conclusions

In conclusion, the present quantitative proteomic study identified novel AMI biomarker candidates and might provide fundamental information for the development of an AMI biomarker.

Novel Biomarker Approaches for Managing Patients With Cardiac Transplantation

Despite major advances in the medical care of patients following heart transplantation (HTx) and a steady increase in long-term survival, allograft surveillance is still based on endomyocardial biopsy, the gold standard since the 1970s. This invasive procedure calls for less burdening and more cost-effective approaches. In recent years, impressive progress has been made in utilizing blood-based biomarkers for the diagnosis and management of diseases in a variety of fields. Hence, a number of trials have been performed testing the usefulness of circulating molecules or other technical methods to overcome the need for surveillance myocardial biopsy in HTx patients. Here, we review current approaches and the state of research on novel biomarkers for the management of patients following heart transplantation.

Expression of hemopexin in acute rejection of rat liver allograft identified by serum proteomic analysis

Acute rejection (AR) and acceptance of allograft after liver transplantation (LTx) remain critical issues that need addressing to improve prognosis. We therefore performed rat orthotopic LTx and proteomic analyses to screen for immune response-related biomarkers in sera. Markers identified were validated at the mRNA and/or protein levels, and the molecules of interest were functionally explored. Compared with syngeneic controls, signs of AR as well as spontaneous acceptance were observed in hematoxylin and eosin-stained sections of liver allografts. In accordance with the severity of AR, 30 protein spots displaying significant changes in abundance were identified using two-dimensional differential gel electrophoresis. Ultimately, 14 serum proteins were sequenced and five spots of interest were identified as hemopexin (HPX). Expression of HPX was significantly and inversely associated with the severity of AR at both the mRNA and protein levels. In vitro, Mt-1, Ho-1, Fth, Ifn-γ, and Il-17 transcripts were significantly upregulated in lysates of lymphocytes stimulated with HPX, whereas Il-10 markedly was remarkably downregulated. Interferon-γ, IL-10, and IL-17 proteins in the supernatant of HPX-stimulated lymphocytes were significantly altered in keeping with the mRNA level. Our data facilitated the generation of a proteomic profile to enhance the understanding of rat liver AR. In view of finding that the HPX serum level is negatively associated with the severity of AR of rat liver allograft, we propose that in vitro treatment with HPX regulates cytokine expression in rat lymphocytes.

Proteomics in transplantation

Proteomics and biochemical profiling have emerged as exciting and powerful tools in clinical biomarker research. In the field of transplantation, proteomics aims not only at developing noninvasive means for immune monitoring but also to gain mechanistic insights into the pathophysiology of the alloimmune response and hence defining new therapeutic targets. This chapter provides an overview of proteomic biomarker-driven approaches and its underlying concepts and discusses the advantages, clinical implications, challenges, and limitations of this novel modality as it relates to solid organ transplantation.

Murine cervical heart transplantation model using a modified cuff technique

Mouse models are of special interest in research since a wide variety of monoclonal antibodies and commercially defined inbred and knockout strains are available to perform mechanistic in vivo studies. While heart transplantation models using a suture technique were first successfully developed in rats, the translation into an equally widespread used murine equivalent was never achieved due the technical complexity of the microsurgical procedure. In contrast, non-suture cuff techniques, also developed initially in rats, were successfully adapted for use in mice(1-3). This technique for revascularization involves two major steps I) everting the recipient vessel over a polyethylene cuff; II) pulling the donor vessel over the formerly everted recipient vessel and holding it in place with a circumferential tie. This ensures a continuity of the endothelial layer, short operating time and very high patency rates(4). Using this technique for vascular anastomosis we performed more than 1,000 cervical heart transplants with an overall success rate of 95%. For arterial inflow the common carotid artery and the proximal aortic arch were anastomosed resulting in a retrograde perfusion of the transplanted heart. For venous drainage the pulmonary artery of the graft was anastomosed with the external jugular vein of the recipient(5). Herein, we provide additional details of this technique to supplement the video.

Identification of common blood gene signatures for the diagnosis of renal and cardiac acute allograft rejection

To test, whether 10 genes, diagnostic of renal allograft rejection in blood, are able to diagnose and predict cardiac allograft rejection, we analyzed 250 blood samples from heart transplant recipients with and without acute rejection (AR) and with cytomegalovirus (CMV) infection by QPCR. A QPCR-based logistic regression model was built on 5 of these 10 genes (AR threshold composite score >37%  = AR) and tested for AR prediction in an independent set of 109 samples, where it correctly diagnosed AR with 89% accuracy, with no misclassifications for AR ISHLT grade 1b. CMV infection did not confound the AR score. The genes correctly diagnosed AR in a blood sample within 6 months prior to biopsy diagnosis with 80% sensitivity and untreated grade 1b AR episodes had persistently elevated scores until 6 months after biopsy diagnosis. The gene score was also correlated with presence or absence of cardiac allograft vasculopathy (CAV) irrespective of rejection grade. In conclusion, there is a common transcriptional axis of immunological trafficking in peripheral blood in both renal and cardiac organ transplant rejection, across a diverse recipient age range. A common gene signature, initially identified in the setting of renal transplant rejection, can be utilized serially after cardiac transplantation, to diagnose and predict biopsy confirmed acute heart transplant rejection.

Protein and peptide biomarkers in organ transplantation

Organ transplantation is the optimal treatment choice for end-stage organ failure in pediatric patients. The ideal maintenance of a transplanted organ requires efficient monitoring tools and an effective individualized post-transplant treatment plan. Currently available post-transplant monitoring options are not ideal because of their invasiveness or their lack of sensitivity and specificity when providing an accurate assessment of transplant injury. Current research on proteins and peptides, including mass spectrometry-based proteomics, can identify novel surrogate protein and peptide biomarkers that can assist in monitoring the graft in order to correctly assess the status of the transplanted organ. In this article, we have critically reviewed current relevant literature to highlight the importance of protein and peptide biomarkers in the field of pediatric organ transplantation, the status of research findings in the field of protein and peptide biomarkers in different organ transplantation and factors that impact and inhibit the progression of protein biomarker discovery in the field of solid-organ transplantation in pediatrics.

Recent advances in biomarker discovery in solid organ transplant by proteomics

The identification and clinical use of more sensitive and specific biomarkers in the field of solid organ transplantation is an urgent need in medicine. Solid organ transplantation has seen improvements in the short-term survival of transplanted organs due to recent advancements in immunosuppressive therapy. However, the currently available methods of allograft monitoring are not optimal. Recent advancements in assaying methods for biomolecules such as genes, mRNA and proteins have helped to identify surrogate biomarkers that can be used to monitor the transplanted organ. These high-throughput 'omic' methods can help researchers to significantly speed up the identification and the validation steps, which are crucial factors for biomarker discovery efforts. Still, the progress towards identifying more sensitive and specific biomarkers remains a great deal slower than expected. In this article, we have evaluated the current status of biomarker discovery using proteomics tools in different solid organ transplants in recent years. This article summarizes recent reports and current status, along with the hurdles in efficient biomarker discovery of protein biomarkers using proteomics approaches. Finally, we will touch upon personalized medicine as a future direction for better management of transplanted organs, and provide what we think could be a recipe for success in this field.

Distinct host cell proteins incorporated by SIV replicating in CD4+ T cells from natural disease resistant versus non-natural disease susceptible hosts

BACKGROUND

Enveloped viruses including the simian immunodeficiency virus (SIV) replicating within host cells acquire host proteins upon egress from the host cells. A number of studies have catalogued such host proteins, and a few have documented the potential positive and negative biological functions of such host proteins. The studies conducted herein utilized proteomic analysis to identify differences in the spectrum of host proteins acquired by a single source of SIV replicating within CD4+ T cells from disease resistant sooty mangabeys and disease susceptible rhesus macaques.

RESULTS

While a total of 202 host derived proteins were present in viral preparations from CD4+ T cells from both species, there were 4 host-derived proteins that consistently and uniquely associated with SIV replicating within CD4+ T cells from rhesus macaques but not sooty mangabeys; and, similarly, 28 host-derived proteins that uniquely associated with SIV replicating within CD4+ T cells from sooty mangabeys, but not rhesus macaques. Of interest was the finding that of the 4 proteins uniquely present in SIV preparations from rhesus macaques was a 26 S protease subunit 7 (MSS1) that was shown to enhance HIV-1 'tat' mediated transactivation. Among the 28 proteins found in SIV preparations from sooty mangabeys included several molecules associated with immune function such as CD2, CD3ε, TLR4, TLR9 and TNFR and a bioactive form of IL-13.

CONCLUSIONS

The finding of 4 host proteins that are uniquely associated with SIV replicating within CD4+ T cells from disease susceptible rhesus macaques and 28 host proteins that are uniquely associated with SIV replicating within CD4+ T cells from disease resistant sooty mangabeys provide the foundation for determining the potential role of each of these unique host-derived proteins in contributing to the polarized clinical outcome in these 2 species of nonhuman primates.

Biomarkers of immunoregulatory status in stable liver transplant recipients undergoing weaning of immunosuppressive therapy

Biomarkers that reflect immune response recovery and predict clinical events during withdrawal or minimization of immunosuppressive therapy have not been evaluated. This study aimed to evaluate whether immune response recovers after withdrawal of long-term immunosuppressive treatment in stable liver transplant patients and to determine whether specific biomarkers reflect immune response reactivity and predict rejection. Pharmacokinetic-pharmacodynamic profiles were determined in 24 patients and 80 healthy donors before immunosuppressive treatment reduction began, at 50%, and at complete withdrawal. In patients who rejected, effector-T-cell response mediated by soluble IFN-γ, %CD4(+)IFN-γ and %CD8(+)IL-2/IFN-γ were significantly increased, while TGF-β1 production and the TGF-β1/IFN-γ ratio were significantly decreased. In patients with rejection, soluble IFN-γ and %CD8(+)IL-2 were significantly higher before immunosuppressive treatment was reduced. Further studies are required, but this battery of biomarkers performed in whole blood could be a useful tool to monitor immunosuppressive treatment minimization or withdrawal protocols and identify patients at increased risk of rejection.