What Is Hot and New in Basic and Translational Science in Liver Transplantation in 2023? Report of the Basic and Translational Research Committee of the International Liver Transplantation Society.

The 2023 Joint Annual Congress of the International Liver Transplantation Society, European Liver and Intestine Transplant Association, and Liver Intensive Care Group of Europe were held in Rotterdam, the Netherlands, from May 3 to 6, 2023. This year, all speakers were invited to attend the Congress in person for the first time since the COVID-19 pandemic. The congress was attended by 1159 registered delegates from 54 countries representing 5 continents, with the 10 countries comprising the bulk of the delegates. Of the 647 abstracts initially submitted, 542 were eventually presented at the meeting, coming from 38 countries (mainly North America, Europe, and Asia) and 85% of them (462 abstracts) came from only 10 countries. Fifty-three (9.8%) abstracts, originated from 17 countries, were submitted under the Basic/Translational Scientific Research category, a similar percentage as in 2022. Abstracts presented at the meeting were classified as (1) ischemia and reperfusion injury, (2) machine perfusion, (3) bioengineering and liver regeneration, (4) transplant oncology, (5) novel biomarkers in liver transplantation, (6) liver immunology (rejection and tolerance), and (7) artificial intelligence and machine learning. Finally, we evaluated the number of abstracts commented in the Basic and Translational Research Committee-International Liver Transplantation Society annual reports over the past 5 y that resulted in publications in peer-reviewed journals to measure their scientific impact in the field of liver transplantation.

Rapid intestinal and systemic metabolic reprogramming in an immunosuppressed environment.

Intrinsic metabolism shapes the immune environment associated with immune suppression and tolerance in settings such as organ transplantation and cancer. However, little is known about the metabolic activities in an immunosuppressive environment. In this study, we employed metagenomic, metabolomic, and immunological approaches to profile the early effects of the immunosuppressant drug tacrolimus, antibiotics, or both in gut lumen and circulation using a murine model. Tacrolimus induced rapid and profound alterations in metabolic activities within two days of treatment, prior to alterations in gut microbiota composition and structure. The metabolic profile and gut microbiome after seven days of treatment was distinct from that after two days of treatment, indicating continuous drug effects on both gut microbial ecosystem and host metabolism. The most affected taxonomic groups are Clostriales and Verrucomicrobiae (i.e., Akkermansia muciniphila), and the most affected metabolic pathways included a group of interconnected amino acids, bile acid conjugation, glucose homeostasis, and energy production. Highly correlated metabolic changes were observed between lumen and serum metabolism, supporting their significant interactions. Despite a small sample size, this study explored the largely uncharacterized microbial and metabolic events in an immunosuppressed environment and demonstrated that early changes in metabolic activities can have significant implications that may serve as antecedent biomarkers of immune activation or quiescence. To understand the intricate relationships among gut microbiome, metabolic activities, and immune cells in an immune suppressed environment is a prerequisite for developing strategies to monitor and optimize alloimmune responses that determine transplant outcomes.

Immune landscape of the kidney allograft in response to rejection.

Preventing kidney graft dysfunction and rejection is a critical step in addressing the nationwide organ shortage and improving patient outcomes. While kidney transplants (KT) are performed more frequently, the overall number of patients on the waitlist consistently exceeds organ availability. Despite improved short-term outcomes in KT, comparable progress in long-term allograft survival has not been achieved. Major cause of graft loss at 5 years post-KT is chronic allograft dysfunction (CAD) characterized by interstitial fibrosis and tubular atrophy (IFTA). Accordingly, proactive prevention of CAD requires a comprehensive understanding of the immune mechanisms associated with either further dysfunction or impaired repair. Allograft rejection is primed by innate immune cells and carried out by adaptive immune cells. The rejection process is primarily facilitated by antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR). It is essential to better elucidate the actions of individual immune cell subclasses (e.g. B memory, Tregs, Macrophage type 1 and 2) throughout the rejection process, rather than limiting our understanding to broad classes of immune cells. Embracing multi-omic approaches may be the solution in acknowledging these intricacies and decoding these enigmatic pathways. A transition alongside advancing technology will better allow organ biology to find its place in this era of precision and personalized medicine.

Impact of Deceased-donor Acute Kidney Injury on Kidney Transplantation.

Even as record numbers of deceased donors are undergoing organ recovery, the global transplant community continues to struggle with a shortage of donor organs and a high organ discard rate. Acute kidney injury (AKI) occurs in many hospitalized patients, including up to 25% of patients in critical condition. Registry studies have shown a significant increase in nonrecovery or organ discard rates in AKI donors, despite most studies reporting similar clinical outcomes compared with non-AKI donors. This review aims to capture the salient information learned from these studies and to summarize the efforts that have been made to gain a more granular understanding of how kidneys from donors with AKI behave posttransplant. In particular, we reviewed the studies that analyzed the clinical outcomes in different stages of AKI and AKI in marginal donors, such as kidney donor profile index of >85%, older donors, and donation after circulatory death donors. We summarized studies investigating molecular biomarkers, transcriptomics, and possible future therapeutic targets for postdonation AKI.

Rapid intestinal and systemic metabolic reprogramming in an immunosuppressed environment.

Intrinsic metabolism shapes the immune environment associated with immune suppression and tolerance in settings such as organ transplantation and cancer. However, little is known about the metabolic activities in an immunosuppressive environment. In this study, we employed metagenomic, metabolomic, and immunological approaches to profile the early effects of the immunosuppressant drug tacrolimus, antibiotics, or both in gut lumen and circulation using a murine model. Tacrolimus induced rapid and profound alterations in metabolic activities within two days of treatment, prior to alterations in gut microbiota composition and structure. The metabolic profile and gut microbiome after seven days of treatment was distinct from that after two days of treatment, indicating continuous drug effects on both gut microbial ecosystem and host metabolism. The most affected taxonomic groups are Clostriales and Verrucomicrobiae (i.e., Akkermansia muciniphila), and the most affected metabolic pathways included a group of interconnected amino acids, bile acid conjugation, glucose homeostasis, and energy production. Highly correlated metabolic changes were observed between lumen and serum metabolism, supporting their significant interactions. Despite a small sample size, this study explored the largely uncharacterized microbial and metabolic events in an immunosuppressed environment and demonstrated that early changes in metabolic activities can have significant implications that may serve as antecedent biomarkers of immune activation or quiescence. To understand the intricate relationships among gut microbiome, metabolic activities, and immune cells in an immune suppressed environment is a prerequisite for developing strategies to monitor and optimize alloimmune responses that determine transplant outcomes.

European Society for Organ Transplantation Consensus Statement on Biomarkers in Liver Transplantation.

Currently, one-year survival following liver transplantation (LT) exceeds 90% in large international registries, and LT is considered definitive treatment for patients with end-stage liver disease and liver cancer. Recurrence of disease, including hepatocellular carcinoma (HCC), significantly hampers post-LT outcomes. An optimal approach to immunosuppression (IS), including safe weaning, may benefit patients by mitigating the effect on recurrent diseases, as well as reducing adverse events associated with over-/under-IS, including chronic kidney disease (CKD). Prediction of these outcome measures-disease recurrence, CKD, and immune status-has long been based on relatively inaccurate clinical models. To address the utility of new biomarkers in predicting these outcomes in the post-LT setting, the European Society of Organ Transplantation (ESOT) and International Liver Transplant Society (ILTS) convened a working group of experts to review literature pertaining to primary disease recurrence, development of CKD, and safe weaning of IS. Summaries of evidence were presented to the group of panelists and juries to develop guidelines, which were discussed and voted in-person at the Consensus Conference in Prague November 2022. The consensus findings and recommendations of the Liver Working Group on new biomarkers in LT, clinical applicability, and future needs are presented in this article.

Single-cell RNA sequencing reveals peripheral blood mononuclear immune cell landscape associated with operational tolerance in a kidney transplant recipient.

Operational tolerance (OT) after kidney transplantation is defined as stable graft acceptance without the need for immunosuppression therapy. However, it is not clear which cellular and molecular pathways are driving tolerance in these patients. In this first-of-its-kind pilot study, we assessed the immune landscape associated with OT using single-cell analyses. Peripheral mononuclear cells from a kidney transplant recipient with OT (Tol), 2 healthy individuals (HC), and a kidney transplant recipient with normal kidney function on standard-of-care immunosuppression (SOC) were evaluated. The immune landscape of the Tol was drastically different from that of SOC and emerged closer to the profile of HC. TCL1A+ naive B cells and LSGAL1+ regulatory T cells (Tregs) were in higher proportions in Tol. We were unable to identify the Treg subcluster in SOC. The ligand-receptor analysis in HC and Tol identified interactions between B cells, and Tregs that enhance the proliferation and suppressive function of Tregs. SOC reported the highest proportion of activated B cells with more cells in the G2M phase. Our single-cell RNA sequencing study identified the mediators of tolerance; however, it emphasizes the requirement of similar investigations on a larger cohort to reaffirm the role of immune cells in tolerance.

Single nuclei transcriptomics delineates complex immune and kidney cell interactions contributing to kidney allograft fibrosis.

Chronic allograft dysfunction (CAD), characterized histologically by interstitial fibrosis and tubular atrophy, is the major cause of kidney allograft loss. Here, using single nuclei RNA sequencing and transcriptome analysis, we identified the origin, functional heterogeneity, and regulation of fibrosis-forming cells in kidney allografts with CAD. A robust technique was used to isolate individual nuclei from kidney allograft biopsies and successfully profiled 23,980 nuclei from five kidney transplant recipients with CAD and 17,913 nuclei from three patients with normal allograft function. Our analysis revealed two distinct states of fibrosis in CAD; low and high extracellular matrix (ECM) with distinct kidney cell subclusters, immune cell types, and transcriptional profiles. Imaging mass cytometry analysis confirmed increased ECM deposition at the protein level. Proximal tubular cells transitioned to an injured mixed tubular (MT1) phenotype comprised of activated fibroblasts and myofibroblast markers, generated provisional ECM which recruited inflammatory cells, and served as the main driver of fibrosis. MT1 cells in the high ECM state achieved replicative repair evidenced by dedifferentiation and nephrogenic transcriptional signatures. MT1 in the low ECM state showed decreased apoptosis, decreased cycling tubular cells, and severe metabolic dysfunction, limiting the potential for repair. Activated B, T and plasma cells were increased in the high ECM state, while macrophage subtypes were increased in the low ECM state. Intercellular communication between kidney parenchymal cells and donor-derived macrophages, detected several years post-transplantation, played a key role in injury propagation. Thus, our study identified novel molecular targets for interventions aimed to ameliorate or prevent allograft fibrogenesis in kidney transplant recipients.

Contribution of Proteomics in Transplantation: Identification of Injury and Rejection Markers.

Solid organ transplantation saves thousands of lives suffering from end-stage diseases. Although early transplants experienced acute organ injury, medical breakthroughs, such as tissue typing, and use of immunosuppressive agents have considerably improved graft survival. However, the overall incidence of allograft injury and chronic rejection remains high. Often the clinical manifestations of organ injury or rejection are nonspecific and late. Current requirement for successful organ transplantation is the identification of reliable, accurate, disease-specific, noninvasive methods for the early diagnosis of graft injury or rejection. Development of noninvasive techniques is important to allow routine follow-ups without the discomfort and risks associated with a graft biopsy. Multiple biofluids have been successfully tested for the presence of potential proteomic biomarkers; these include serum, plasma, urine, and whole blood. Kidney transplant research has provided significant evidence to the potential of proteomics-based biomarkers for acute and chronic kidney rejection, delayed graft function, early detection of declining allograft health. Multiple proteins have been implicated as biomarkers; however, recent observations implicate the use of similar canonical pathways and biofunctions associated with graft injury/rejection with altered proteins as potential biomarkers. Unfortunately, the current biomarker studies lack high sensitivity and specificity, adding to the complexity of their utility in the clinical space. In this review, we first describe the high-throughput proteomics technologies and then discuss the outcomes of proteomics profiling studies in the transplantation of several organs. Existing literature provides hope that novel biomarkers will emerge from ongoing efforts and guide physicians in delivering specific therapies to prolong graft survival.

An optimized protocol for single nuclei isolation from clinical biopsies for RNA-seq.

Single nuclei RNA sequencing (snRNA-seq) has evolved as a powerful tool to study complex human diseases. Single cell resolution enables the study of novel cell types, biological processes, cell trajectories, and cell-cell signaling pathways. snRNA-seq largely relies on the dissociation of intact nuclei from human tissues. However, the study of complex tissues using small core biopsies presents many technical challenges. Here, an optimized protocol for single nuclei isolation is presented for frozen and RNAlater preserved human kidney biopsies. The described protocol is fast, low cost, and time effective due to the elimination of cell sorting and ultra-centrifugation. Samples can be processed in 90 min or less. This method is effective for obtaining normal nuclei morphology without signs of structural damage. Using snRNA-seq, 16 distinct kidney cell clusters were recovered from normal and peri-transplant acute kidney injury allograft samples, including immune cell clusters. Quality control measurements demonstrated that these optimizations eliminated cellular debris and allowed for a high yield of high-quality nuclei and RNA for library preparation and sequencing. Cellular disassociation did not induce cellular stress responses, which recapitulated transcriptional patterns associated with standardized methods of nuclei isolation. Future applications of this protocol will allow for thorough investigations of small biobank biopsies, identifying cell-specific injury pathways and driving the discovery of novel diagnostics and therapeutic targets.

Pretransplant kidney transcriptome captures intrinsic donor organ quality and predicts 24-month outcomes.

With the development of novel prognostic tools derived from omics technologies, transplant medicine is entering the era of precision medicine. Currently, there are no established predictive biomarkers for posttransplant kidney function. A total of 270 deceased donor pretransplant kidney biopsies were collected and posttransplant function was prospectively monitored. This study first assessed the utility of pretransplant gene expression profiles in predicting 24-month outcomes in a training set (n = 174). Nearly 600 differentially expressed genes were associated with 24-month graft function. Grafts that progressed to low function at 24 months exhibited upregulated immune responses and downregulated metabolic processes at pretransplantation. Using penalized logistic regression modeling, a 55 gene model area under the receiver operating curve (AUROC) for 24-month graft function was 0.994. Gene expression for a subset of candidate genes was then measured in an independent set of pretransplant biopsies (n = 96) using quantitative polymerase chain reaction. The AUROC when using 13 genes with three donor characteristics (age, race, body mass index) was 0.821. Subsequently, a risk score was calculated using this combination for each patient in the validation cohort, demonstrating the translational feasibility of using gene markers as prognostic tools. These findings support the potential of pretransplant transcriptomic biomarkers as novel instruments for improving posttransplant outcome predictions and associated management.

Nucleic acid biomarkers to assess graft injury after liver transplantation.

Many risk factors and complications impact the success of liver transplantation, such as ischaemia-reperfusion injury, acute rejection, and primary graft dysfunction. Molecular biomarkers have the potential to accurately diagnose, predict, and monitor injury progression or organ failure. There is a critical opportunity for reliable and non-invasive biomarkers to reduce the organ shortage by enabling i) the assessment of donor organ quality, ii) the monitoring of short- and long-term graft function, and iii) the prediction of acute and chronic disease development. To date, no established molecular biomarkers have been used to guide clinical decision-making in transplantation. In this review, we outline the recent advances in cell-free nucleic acid biomarkers for monitoring graft injury in liver transplant recipients. Prior work in this area can be divided into two categories: biomarker discovery and validation studies. Circulating nucleic acids (CNAs) can be found in the extracellular environment pertaining to different biological fluids such as bile, blood, urine, and perfusate. CNAs that are packaged into extracellular vesicles may facilitate intercellular and interorgan communication. Thus, decoding their biological function, cellular origins and molecular composition is imperative for diagnosing causes of graft injury, guiding immunosuppression and improving overall patient survival. Herein, we discuss the most promising molecular biomarkers, their state of development, and the critical aspects of study design in biomarker research for early detection of post-transplant liver injury. Future advances in biomarker studies are expected to personalise post-transplant therapy, leading to improved patient care and outcomes.

Biomarkers and predictive models of early allograft dysfunction in liver transplantation - A systematic review of the literature, meta-analysis, and expert panel recommendations.

BACKGROUND: Prompt identification of early allograft dysfunction (EAD) is critical to reduce morbidity and mortality in liver transplant (LT) recipients. OBJECTIVES: Evaluate the evidence supporting biomarkers that can provide diagnostic and predictive value for EAD. DATA SOURCES: Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central. METHODS: Systematic review following PRISMA guidelines and recommendations using the GRADE approach was derived from an international expert panel. Studies that investigated biomarkers or models for predicting EAD in adult LT recipients were included for in-depth evaluation and meta-analysis. Olthoff's criteria were used as the standard reference for the diagnostic accuracy evaluation. PROSPERO ID: CRD42021293838 RESULTS: Ten studies were included for the systematic review. Lactate, lactate clearance, uric acid, Factor V, HMGB-1, CRP to ALB ratio, phosphocholine, total cholesterol, and metabolomic predictive model were identified as potential early EAD predictive biomarkers. The sensitivity ranged between .39 and .92, while the specificity ranged from .63 to .90. Elevated lactate level was most indicative of EAD after adult LT (pooled diagnostic odds ratio of 7.15 (95%CI: 2.38-21.46)). The quality of evidence (QOE) for lactate as indicator was moderate according to the GRADE approach, whereas the QOE for other biomarkers was very low to low likely as consequence of study design characteristics such as single study, small sample size, and large ranges of sensitivity or specificity. CONCLUSIONS: Lactate is an early indicator to predict EAD after LT (Quality of Evidence: Moderate | Grade of Recommendation: Strong). Further multicenter studies and the use of machine perfusion setting should be implemented for validation.

The cellular landscape of the normal kidney allograft: Main players balancing the alloimmune response.

Despite recent advances made in short-term outcomes; minimal improvements have been observed in long-term kidney transplantation outcomes. Due to an imbalance between organ transplant availability and patient waiting list, expanding kidney allograft longevity is a critical need in the field. Prior studies have either focused on early ischemic and immunological conditions affecting kidney allografts (e.g., delayed graft function, acute rejection) or late stage chronic injury when interventions are no longer feasible. However, studies characterizing kidney allografts with normal function by its cellular distribution, cell-cell interactions, and associated molecular pathways are lacking. Herein, we used single nuclei RNA-sequencing to uncover the cellular landscape and transcriptome of the normal kidney allograft. We profiled 40,950 nuclei from seven human kidney biopsies (normal native, N = 3; normal allograft, N = 4); normal allograft protocol biopsies were collected ≥15-months post-transplant. A total of 17 distinct cell clusters were identified with proximal tubules (25.70 and 21.01%), distal tubules (15.22 and 18.20%), and endothelial cells (EC) (4.26 and 9.94%) constituting the major cell populations of normal native and normal allograft kidneys, respectively. A large proportion of cycling cells from normal native kidneys were in G1-phase (43.96%) whereas cells from normal allograft were predominantly in S-phase (32.69%). This result suggests that transcriptional differences between normal native and normal allograft biopsies are dependent on the new host environment, immunosuppression, and injury-affliction. In the normal allograft, EC-specific genes upregulated metabolism, the immune response, and cellular growth, emphasizing their role in maintaining homeostasis during the ongoing alloreactive stress response. Immune cells, including B (2.81%), macrophages (24.96%), monocytes (15.29%), natural killer (NK) (12.83%), neutrophils (8.44%), and T cells (14.41%, were increased in normal allografts despite lack of histological or clinical evidence of acute rejection. Phenotypic characterization of immune cell markers supported lymphocyte activation and proinflammatory cytokines signaling pathways (i.e., IL-15, IL-32). The activation of B, NK, and T cells reveals potential immune cells underlying subclinical inflammation and repair. These single nuclei analyses provide novel insights into kidney and immune cell associated signaling pathways that portray kidney grafts with normal allograft function beyond 2-years post-transplant, revealing a novel perspective in understanding long-term allograft graft survival.

Artificial Intelligence May Predict Early Sepsis After Liver Transplantation.

Background: Sepsis, post-liver transplantation, is a frequent challenge that impacts patient outcomes. We aimed to develop an artificial intelligence method to predict the onset of post-operative sepsis earlier. Methods: This pilot study aimed to identify "physiomarkers" in continuous minute-by-minute physiologic data streams, such as heart rate, respiratory rate, oxygen saturation (SpO2), and blood pressure, to predict the onset of sepsis. The model was derived from a cohort of 5,748 transplant and non-transplant patients across intensive care units (ICUs) over 36 months, with 92 post-liver transplant patients who developed sepsis. Results: Using an alert timestamp generated with the Third International Consensus Definition of Sepsis (Sepsis-3) definition as a reference point, we studied up to 24 h of continuous physiologic data prior to the event, totaling to 8.35 million data points. One hundred fifty-five features were generated using signal processing and statistical methods. Feature selection identified 52 highly ranked features, many of which included blood pressures. An eXtreme Gradient Boost (XGB) classifier was then trained on the ranked features by 5-fold cross validation on all patients (n = 5,748). We identified that the average sensitivity, specificity, positive predictive value (PPV), and area under the receiver-operator curve (AUC) of the model after 100 iterations was 0.94 ± 0.02, 0.9 ± 0.02, 0.89 ± 0.01, respectively, and 0.97 ± 0.01 for predicting sepsis 12 h before meeting criteria. Conclusion: The data suggest that machine learning/deep learning can be applied to continuous streaming data in the transplant ICU to monitor patients and possibly predict sepsis.