Metabolomics discloses donor liver biomarkers associated with early allograft dysfunction

Cross-species metabolomic profiling reveals phosphocholine-mediated liver protection from cold and ischemia/reperfusion

Cold and ischemia/reperfusion (IR)-associated injuries are seemingly inevitable during liver transplantation and hepatectomy. Because Syrian hamsters demonstrate intrinsic tolerance to transplantation-like stimuli, cross-species comparative metabolomic analyses were conducted with hamster, rat, and donor liver samples to seek hepatic cold and IR-adaptive mechanisms. Lower hepatic phosphocholine contents were found in recipients with early graft-dysfunction and with virus-caused cirrhosis or high model for end-stage liver disease scores (≥30). Choline/phosphocholine deficiency in cultured human THLE-2 hepatocytes and animal models weakened hepatocellular cold tolerance and recovery of glutathione and ATP production, which was rescued by phosphocholine supplements. Among the biological processes impacted by choline/phosphocholine deficiency, 3 lipid-related metabolic processes were downregulated, whereas phosphocholine elevated the expression of genes in methylation processes. Consistently, in THLE-2, phosphocholine enhanced the overall RNA m6A methylation, among which the transcript stability of fatty acid desaturase 6 (FADS6) was improved. FADS6 functioned as a key phosphocholine effector in the production of polyunsaturated fatty acids, which may facilitate the hepatocellular recovery of energy and redox homeostasis. Thus, our study reveals the choline-phosphocholine metabolism and its downstream FADS6 functions in hepatic adaptation to cold and IR, which may inspire new strategies to monitor donor liver quality and improve recipient recovery from the liver transplantation process.

Serum Arginine Level for Predicting Early Allograft Dysfunction in Liver Transplantation Recipients by Targeted Metabolomics Analysis: A Prospective, Single-Center Cohort Study

Early allograft dysfunction (EAD) is a frequent phenomenon, leading to increased graft loss and higher mortality after liver transplantation (LT). Despite significant efforts for early diagnosis of EAD, there is no existing approach that can predict EAD on the first post-operative day. The aim is to define a metabolite-based biomarker on the first day after LT complicated with EAD. Ten patients diagnosed with EAD and 26 non-EAD are recruited for the study. A HPLC-MS/MS is used to determine 14 amino acids and 15 bile acids serum concentration. Comparative analyses are conducted between EAD and non-EAD groups. Arginine is identified as the most significant metabolite distinguishing the EAD and non-EAD groups, and therefore, is identified as a potential biomarker of EAD. The optimal cut-off value for arginine is 52.09 µmol L-1, with an AUROC of 0.804 (95% confidence interval: 0.638-0.917, p < 0.001), yielding a sensitivity of 100%, specificity of 53.8%, and Youden index of 0.54, NPVof 100%, and PPV of 45.45%. In summary, the study indicated that targeted metabolomics analysis would be a promising strategy for discovering novel biomarkers to predict EAD. The identified arginine may be helpful in developing an objective diagnostic method for EAD.

Advancements in Predictive Tools for Primary Graft Dysfunction in Liver Transplantation: A Comprehensive Review

Orthotopic liver transplantation stands as the sole curative solution for end-stage liver disease. Nevertheless, the discrepancy between the demand and supply of grafts in transplant medicine greatly limits the success of this treatment. The increasing global shortage of organs necessitates the utilization of extended criteria donors (ECD) for liver transplantation, thereby increasing the risk of primary graft dysfunction (PGD). Primary graft dysfunction (PGD) encompasses early allograft dysfunction (EAD) and the more severe primary nonfunction (PNF), both of which stem from ischemia-reperfusion injury (IRI) and mitochondrial damage. Currently, the only effective treatment for PNF is secondary transplantation within the initial post-transplant week, and the occurrence of EAD suggests an elevated, albeit still uncertain, likelihood of retransplantation urgency. Nonetheless, the ongoing exploration of novel IRI mitigation strategies offers hope for future improvements in PGD outcomes. Establishing an intuitive and reliable tool to predict upcoming graft dysfunction is vital for early identification of high-risk patients and for making informed retransplantation decisions. Accurate diagnostics for PNF and EAD constitute essential initial steps in implementing future mitigation strategies. Recently, novel methods for PNF prediction have been developed, and several models for EAD assessments have been introduced. Here, we provide an overview of the currently scrutinized predictive tools for PNF and EAD evaluation strategies, accompanied by recommendations for future studies.

An integrated proteomics and metabolomics approach to assess graft quality and predict early allograft dysfunction after liver transplantation: a retrospective cohort study

BACKGROUND

Early allograft dysfunction (EAD) is a common complication after liver transplantation (LT) and is associated with poor prognosis. Graft itself plays a major role in the development of EAD. We aimed to reveal the EAD-specific molecular profiles to assess graft quality and establish EAD predictive models.

METHODS

A total of 223 patients who underwent LT were enrolled and divided into training ( n =73) and validation ( n =150) sets. In the training set, proteomics was performed on graft biopsies, together with metabolomics on paired perfusates. Differential expression, enrichment analysis, and protein-protein interaction network were used to identify the key molecules and pathways involved. EAD predictive models were constructed using machine learning and verified in the validation set.

RESULTS

A total of 335 proteins were differentially expressed between the EAD and non-EAD groups. These proteins were significantly enriched in triglyceride and glycerophospholipid metabolism, neutrophil degranulation, and the MET-related signaling pathway. The top 12 graft proteins involved in the aforementioned processes were identified, including GPAT1, LPIN3, TGFB1, CD59, and SOS1. Moreover, downstream metabolic products, such as lactate dehydrogenase, interleukin-8, triglycerides, and the phosphatidylcholine/phosphorylethanolamine ratio in the paired perfusate displayed a close relationship with the graft proteins. To predict the occurrence of EAD, an integrated model using perfusate metabolic products and clinical parameters showed areas under the curve of 0.915 and 0.833 for the training and validation sets, respectively. It displayed superior predictive efficacy than that of currently existing models, including donor risk index and D-MELD scores.

CONCLUSIONS

We identified novel biomarkers in both grafts and perfusates that could be used to assess graft quality and provide new insights into the etiology of EAD. Herein, we also offer a valid tool for the early prediction of EAD.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

Low C-reactive Protein and Urea Distinguish Primary Nonfunction From Early Allograft Dysfunction Within 48 Hours of Liver Transplantation

Primary nonfunction (PNF) is a life-threatening complication of liver transplantation (LT), but in the early postoperative period, it can be difficult to differentiate from early allograft dysfunction (EAD). The aim of this study was to determine if serum biomarkers can distinguish PNF from EAD in the initial 48 h following LT.

Materials and Methods

A retrospective study of adult patients that underwent LT between January 2010 and April 2020 was performed. Clinical parameters, absolute values and trends of C-reactive protein (CRP), blood urea, creatinine, liver function tests, platelets, and international normalized ratio in the initial 48 h after LT were compared between the EAD and PNF groups.

Results

There were 1937 eligible LTs, with PNF and EAD occurring in 38 (2%) and 503 (26%) patients, respectively. A low serum CRP and urea were associated with PNF. CRP was able to differentiate between the PNF and EAD on postoperative day (POD)1 (20 versus 43 mg/L; P < 0.001) and POD2 (24 versus 77; P < 0.001). The area under the receiver operating characteristic curve (AUROC) of POD2 CRP was 0.770 (95% confidence interval [CI] 0.645-0.895). The urea value on POD2 (5.05 versus 9.0 mmol/L; P = 0.002) and trend of POD2:1 ratio (0.71 versus 1.32 mmol/L; P < 0.001) were significantly different between the groups. The AUROC of the change in urea from POD1 to 2 was 0.765 (95% CI 0.645-0.885). Aspartate transaminase was significantly different between the groups, with an AUROC of 0.884 (95% CI 0.753-1.00) on POD2.

Discussion

The biochemical profile immediately following LT can distinguish PNF from EAD; CRP, urea, and aspartate transaminase are more effective than ALT and bilirubin in distinguishing PNF from EAD in the initial postoperative 48 h. Clinicians should consider the values of these markers when making treatment decisions.

Lysophosphatidylcholines modulate immunoregulatory checkpoints in peripheral monocytes and are associated with mortality in people with acute liver failure

BACKGROUND & AIMS

Acute liver failure (ALF) is a life-threatening disease characterised by high-grade inflammation and immunoparesis, which is associated with a high incidence of death from sepsis. Herein, we aimed to describe the metabolic dysregulation in ALF and determine whether systemic immune responses are modulated via the lysophosphatidylcholine (LPC)-autotaxin (ATX)-lysophosphatidylcholinic acid (LPA) pathway.

METHODS

Ninety-six individuals with ALF, 104 with cirrhosis, 31 with sepsis and 71 healthy controls (HCs) were recruited. Pathways of interest were identified by multivariate statistical analysis of proton nuclear magnetic resonance spectroscopy and untargeted ultraperformance liquid chromatography-mass spectrometry-based lipidomics. A targeted metabolomics panel was used for validation. Peripheral blood mononuclear cells were cultured with LPA 16:0, 18:0, 18:1, and their immune checkpoint surface expression was assessed by flow cytometry. Transcript-level expression of the LPA receptor (LPAR) in monocytes was investigated and the effect of LPAR antagonism was also examined in vitro.

RESULTS

LPC 16:0 was highly discriminant between ALF and HC. There was an increase in ATX and LPA in individuals with ALF compared to HCs and those with sepsis. LPCs 16:0, 18:0 and 18:1 were reduced in individuals with ALF and were associated with a poor prognosis. Treatment of monocytes with LPA 16:0 increased their PD-L1 expression and reduced CD155, CD163, MerTK levels, without affecting immune checkpoints on T and NK/CD56+T cells. LPAR1 and 3 antagonism in culture reversed the effect of LPA on monocyte expression of MerTK and CD163. MerTK and CD163, but not LPAR genes, were differentially expressed and upregulated in monocytes from individuals with ALF compared to controls.

CONCLUSION

Reduced LPC levels are biomarkers of poor prognosis in individuals with ALF. The LPC-ATX-LPA axis appears to modulate innate immune response in ALF via LPAR1 and LPAR3. Further investigations are required to identify novel therapeutic agents targeting these receptors.

IMPACT AND IMPLICATIONS

We identified a metabolic signature of acute liver failure (ALF) and investigated the immunometabolic role of the lysophosphatidylcholine-autotaxin-lysophosphatidylcholinic acid pathway, with the aim of finding a mechanistic explanation for monocyte behaviour and identifying possible therapeutic targets (to modulate the systemic immune response in ALF). At present, no selective immune-based therapies exist. We were able to modulate the phenotype of monocytes in vitro and aim to extend these findings to murine models of ALF as a next step. Future therapies may be based on metabolic modulation; thus, the role of specific lipids in this pathway require elucidation and the relative merits of autotaxin inhibition, lysophosphatidylcholinic acid receptor blockade or lipid-based therapies need to be determined. Our findings begin to bridge this knowledge gap and the methods used herein could be useful in identifying therapeutic targets as part of an experimental medicine approach.

Metabolomics Differences of the Donor Livers Between In Situ and Ex Situ Conditions During Ischemia-free Liver Transplantation

BACKGROUND

Ischemia-free liver transplantation (IFLT) has been innovated to avoid graft ischemia during organ procurement, preservation, and implantation. However, the metabolism activity of the donor livers between in the in situ and ex situ normothermic machine perfusion (NMP) conditions, and between standard criteria donor and extend criteria donor remains unknown.

METHODS

During IFLT, plasma samples were collected both at the portal vein and hepatic vein of the donor livers in situ during procurement and ex situ during NMP. An ultra-high performance liquid chromatography-mass spectrometry was conducted to investigate the common and distinct intraliver metabolite exchange.

RESULTS

Profound cysteine and methionine metabolism, and aminoacyl-tRNA biosynthesis were found in both in situ and ex situ conditions. However, obvious D-arginine and D-ornithine metabolism, arginine and proline metabolism were only found in the in situ condition. The suppressed activities of the urea cycle pathway during ex situ condition were confirmed in an RNA expression level. In addition, compared with extend criteria donor group, standard criteria donor group had more active intraliver metabolite exchange in metabonomics level. Furthermore, we found that the relative concentration of p-cresol, allocystathionine, L-prolyl-L-proline in the ex situ group was strongly correlated with peak alanine aminotransferase and aspartate aminotransferase at postoperative days 1-7.

CONCLUSIONS

In the current study, we show the common and distinct metabolism activities during IFLT. These findings might provide insights on how to modify the design of NMP device, improve the perfusate components, and redefine the criteria of graft viability.

Assessing Donor Liver Quality and Restoring Graft Function in the Era of Extended Criteria Donors

Liver transplantation (LT) is the final treatment option for patients with end-stage liver disease. The increasing donor shortage results in the wide usage of grafts from extended criteria donors across the world. Using such grafts is associated with the elevated incidences of post-transplant complications including initial nonfunction and ischemic biliary tract diseases, which significantly reduce recipient survival. Although several clinical factors have been demonstrated to impact donor liver quality, accurate, comprehensive, and effective assessment systems to guide decision-making for organ usage, restoration or discard are lacking. In addition, the development of biochemical technologies and bioinformatic analysis in recent years helps us better understand graft injury during the perioperative period and find potential ways to restore graft function. Moreover, such advances reveal the molecular profiles of grafts or perfusate that are susceptible to poor graft function and provide insight into finding novel biomarkers for graft quality assessment. Focusing on donors and grafts, we updated potential biomarkers in donor blood, liver tissue, or perfusates that predict graft quality following LT, and summarized strategies for restoring graft function in the era of extended criteria donors. In this review, we also discuss the advantages and drawbacks of these potential biomarkers and offer suggestions for future research.

Elevation of Lipid Metabolites in Deceased Liver Donors Reflects Graft Suffering

Liver transplantation can be performed with deceased or living donor allografts. Deceased liver grafts are donated from brain- or circulation-death patients, and they have usually suffered from a certain degree of damage. Post-transplant graft function and patient survival are closely related to liver allograft recovery. How to define the damage of liver grafts is unclear. A total of 47 liver donors, 23 deceased and 24 living, were enrolled in this study. All deceased donors had suffered from severe brain damage, and six of them had experienced cardio-pulmonary-cerebral resuscitation (CPR). The exploration of liver graft metabolomics was conducted by liquid chromatography coupled with mass spectrometry. Compared with living donor grafts, the deceased liver grafts expressed higher levels of various diacylglycerol, lysophosphatidylcholine, lysophosphatidylethanolamine, oleoylcarnitine and linoleylcarnitine; and lower levels of cardiolipin and phosphatidylcholine. The liver grafts from the donors with CPR had higher levels of cardiolipin, phosphatidic acid, phosphatidylcholine, phatidylethanolamine and amiodarone than the donors without CPR. When focusing on amino acids, the deceased livers had higher levels of histidine, taurine and tryptophan than the living donor livers. In conclusion, the deceased donors had suffered from cardio-circulation instability, and their lipid metabolites were increased. The elevation of lipid metabolites can be employed as an indicator of liver graft suffering.

Metabolomics as a Critical Tool for Studying Clinical Surgery

Metabolomics enables the analysis of metabolites within an organism, which offers the closest direct measurement of the physiological activity of the organism, and has advanced efforts to characterize metabolic states, identify biomarkers, and investigate metabolic pathways. A high degree of innovation in analytical techniques has promoted the application of metabolomics, especially in the study of clinical surgery. Metabolomics can be employed as a clinical testing method to maximize therapeutic outcomes, and has been applied in rapid diagnosis of diseases, timely postoperative monitoring, prognostic assessment, and personalized medicine. This review focuses on the use of mass spectrometry and nuclear magnetic resonance-based metabolomics in clinical surgery, including identifying metabolic changes before and after surgery, finding disease-associated biomarkers, and exploring the potential of personalized therapy. Challenges and opportunities of metabolomics in organ transplantation are also discussed, with a particular emphasis on metabolomics in donor organ evaluation and protection, prognostic outcome prediction, as well as postoperative adverse reaction monitoring. In the end, current limitations of metabolomics in clinical surgery and future research directions are presented.

Current Status of Biomarkers and Molecular Diagnostic Tools for Rejection in Liver Transplantation: Light at the End of the Tunnel?

Strategies to minimize immune-suppressive medications after liver transplantation are limited by allograft rejection. Biopsy of liver is the current standard of care in diagnosing rejection. However, it adds to physical and economic burden to the patient and has diagnostic limitations. In this review, we aim to highlight the different biomarkers to predict and diagnose acute rejection. We also aim to explore recent advances in molecular diagnostics to improve the diagnostic yield of liver biopsies.

Metabolomics profiling in acute liver transplant rejection in a pediatric population

Pediatric liver transplantation rejection affects 20% of children. Currently, liver biopsy, expensive and invasive, is the best method of diagnosis. Discovery and validation of clinical biomarkers from blood or other biospecimens would improve clinical care. For this study, stored plasma samples were utilized from two cross-sectional cohorts of liver transplant patients at Children's Healthcare of Atlanta. High resolution metabolic profiling was completed using established methods. Children with (n = 18) or without (n = 25) acute cellular rejection were included in the analysis (n = 43 total). The mean age of these racially diverse cohorts ranged from 12.6 years in the rejection group and 13.6 years in the no rejection group. Linear regression provided 510 significantly differentiating metabolites between groups, and OPLS-DA showed 145 metabolites with VIP > 2. A total of 95 overlapping significant metabolites between OPLS-DA and linear regression analyses were detected. Pathway analysis (p < 0.05) showed bile acid biosynthesis and tryptophan metabolism as the top two differentiating pathways. Network analysis also identified tryptophan and clustered with liver enzymes and steroid use. We conclude metabolic profiling of plasma from children with acute liver transplant rejection demonstrates > 500 significant metabolites. This result suggests that development of a non-invasive biomarker-based test is possible for rejection screening.

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.

Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation

Omics data address key issues in liver transplantation (LT) as the most effective therapeutic means for end-stage liver disease. The purpose of this study was to review the current application and future direction for omics in LT. We reviewed the use of multiomics to elucidate the pathogenesis leading to LT and prognostication. Future directions with respect to the use of omics in LT are also described based on perspectives of surgeons with experience in omics. Significant molecules were identified and summarized based on omics, with a focus on post-transplant liver fibrosis, early allograft dysfunction, tumor recurrence, and graft failure. We emphasized the importance omics for clinicians who perform LTs and prioritized the directions that should be established. We also outlined the ideal workflow for omics in LT. In step with advances in technology, the quality of omics data can be guaranteed using an improved algorithm at a lower price. Concerns should be addressed on the translational value of omics for better therapeutic effects in patients undergoing LT.

Quantitative Metabolomics of Tissue, Perfusate, and Bile from Rat Livers Subjected to Normothermic Machine Perfusion

Machine perfusion (MP) allows the maintenance of liver cells in a metabolically active state ex vivo and can potentially revert metabolic perturbations caused by donor warm ischemia, procurement, and static cold storage (SCS). The present preclinical research investigated the metabolic outcome of the MP procedure by analyzing rat liver tissue, bile, and perfusate samples by means of high-field (600 MHz) nuclear magnetic resonance (NMR) spectroscopy. An established rat model of normothermic MP (NMP) was used. Experiments were carried out with the addition of an oxygen carrier (OxC) to the perfusion fluid (OxC-NMP, n = 5) or without (h-NMP, n = 5). Bile and perfusate samples were collected throughout the procedure, while biopsies were only taken at the end of NMP. Two additional groups were: (1) Native, in which tissue or bile specimens were collected from rats in resting conditions; and (2) SCS, in which biopsies were taken from cold-stored livers. Generally, NMP groups showed a distinctive metabolomic signature in all the analyzed biological matrices. In particular, many of the differentially expressed metabolites were involved in mitochondrial biochemical pathways. Succinate, acetate, 3-hydroxybutyrate, creatine, and O-phosphocholine were deeply modulated in ex vivo perfused livers compared to both the Native and SCS groups. These novel results demonstrate a broad modulation of mitochondrial metabolism during NMP that exceeds energy production and redox balance maintenance.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

Primary Nonfunction of the Liver Allograft

Severe allograft dysfunction, as opposed to the expected immediate function, following liver transplantation is a major complication, and the clinical manifestations of such that lead to either immediate retransplant or death are the catastrophic end of the spectrum. Primary nonfunction (PNF) has declined in incidence over the years, yet the impact on patient and healthcare teams, and the burden on the organ pool in case of the need for retransplant should not be underestimated. There is no universal test to define the diagnosis of PNF, and current criteria are based on various biochemical parameters surrogate of liver function; moreover, a disparity remains within different healthcare systems on selecting candidates eligible for urgent retransplantation. The impact on PNF from traditionally accepted risk factors has changed somewhat, mainly driven by the rising demand for organs, combined with the concerted approach by clinicians on the in-depth understanding of PNF, optimal graft recipient selection, mitigation of the clinical environment in which a marginal graft is reperfused, and postoperative management. Regardless of the mode, available data suggest machine perfusion strategies help reduce the incidence further but do not completely avert the risk of PNF. The mainstay of management relies on identifying severe allograft dysfunction at a very early stage and aggressive management, while excluding other identifiable causes that mimic severe organ dysfunction. This approach may help salvage some grafts by preventing total graft failure and also maintaining a patient in an optimal physiological state if retransplantation is considered the ultimate patient salvage strategy.

Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy

Liver has an ability to regenerate itself in mammals, whereas the mechanism has not been fully explained. Here we used a GC/MS-based metabolomic method to profile the dynamic endogenous metabolic change in the serum of C57BL/6J mice at different times after 2/3 partial hepatectomy (PHx), and nine machine learning methods including Least Absolute Shrinkage and Selection Operator Regression (LASSO), Partial Least Squares Regression (PLS), Principal Components Regression (PCR), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (xgbDART), Neural Network (NNET) and Bayesian Regularized Neural Network (BRNN) were used for regression between the liver index and metabolomic data at different stages of liver regeneration. We found a tree-based random forest method that had the minimum average Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and the maximum R square (R²) and is time-saving. Furthermore, variable of importance in the project (VIP) analysis of RF method was performed and metabolites with VIP ranked top 20 were selected as the most critical metabolites contributing to the model. Ornithine, phenylalanine, 2-hydroxybutyric acid, lysine, etc. were chosen as the most important metabolites which had strong correlations with the liver index. Further pathway analysis found Arginine biosynthesis, Pantothenate and CoA biosynthesis, Galactose metabolism, Valine, leucine and isoleucine degradation were the most influenced pathways. In summary, several amino acid metabolic pathways and glucose metabolism pathway were dynamically changed during liver regeneration. The RF method showed advantages for predicting the liver index after PHx over other machine learning methods used and a metabolic clock containing four metabolites is established to predict the liver index during liver regeneration.

HR-MS Based Untargeted Lipidomics Reveals Characteristic Lipid Signatures of Wilson's Disease

Background and Aims: The diagnosis of Wilson's disease (WD) is challenging by clinical or genetic criteria. A typical early pathological change of WD is the increased liver lipid deposition and lowered serum triglyceride (TG). Therefore, the contents of serum lipids may provide evidence for screening of biomarkers for WD. Methods: 34 WD patients, 31 WD relatives, and 65 normal controls were enrolled in this study. Serum lipidomics data was acquired by an ultra-high-performance liquid chromatography high-resolution mass spectrometry system, and the data were analyzed by multivariate statistical methods. Results: Of all 510 identified lipids, there are 297 differential lipids between the WD and controls, 378 differential lipids between the relatives and controls, and 119 differential lipids between the patients and relatives. In WD, the abundances of most saturated TG were increased, whereas other unsaturated lipids decreased, including phosphatidylcholine (PC), sphingomyelin (SM), lysophosphatidylcholine (LPC), ceramide (Cer), and phosphatidylserine (PS). We also found many serum lipid species may be used as biomarkers for WD. The areas under the receiver operating characteristic curve (AUC) of PS (35:0), PS (38:5), and PS (34:0) were 0.919, 0.843, and 0.907. The AUCs of TG (38:0) and CerG1 (d42:2) were 0.948 and 0.915 and the AUCs of LPC (17:0) and LPC (15:0) were 0.980 and 0.960, respectively. The lipid biomarker panel exhibits good diagnostic performance for WD. The correlation networks were built among the different groups and the potential mechanisms of differential lipids were discussed. Interestingly, similar lipid profile of WD is also found in their relatives, which indicated the changes may also related to the mutation of the ATP7B gene. Conclusions: Lipid deregulation is another important hallmark of WD besides the deposition of copper. Our lipidomic results provide new insights into the diagnostic and therapeutic targets of WD.

Can ultrasound elastography assess liver quality in brain-dead donors and predict early allograft dysfunction after transplantation?

PURPOSE

To evaluate the role of two-dimensional shear wave elastography (2D SWE) in assessing graft quality before liver transplantation and the relationship between donor liver stiffness (LS) and early allograft dysfunction (EAD) after transplantation.

METHODS

Eighty-three donors from January 2018 to December 2018 were involved in this prospective study. Liver stiffness measurements (LSMs) were performed using 2D SWE. The differences in LS values between discarded and transplanted grafts were analyzed. The relationship of donor LS with recipient EAD was also evaluated.

RESULTS

Our results suggest that the donor LS values were higher in discarded grafts than in transplanted grafts (24.0 ± 10.9 kPa vs 10.0 ± 2.6 kPa, p < 0.001). LSM failed in one donor. According to multivariate logistic regression analysis, the donor LS values ≥10.9 kPa (odds ratio [OR] 4.042, 95% confidence interval [CI] 1.133-14.421, p = 0.031), BMI (OR 1.287, 95% CI 1.025-1.616, p = 0.030) and INR (OR 6.703, 95% CI 1.338-33.589, p = 0.021) were independently associated with EAD.

CONCLUSION

Donor LSM conducted by 2D SWE might represent an effective quantitative method to evaluate graft quality. Donor LS might predict recipient EAD after liver transplantation.

An overview of lipidomics utilizing cadaver derived biological samples

INTRODUCTION

We present lipidomic studies that have utilized cadaveric biological samples, including tissues and bodily fluids (excluding blood or serum). Analyses of lipids from cadaveric-derived tissues play vital roles in many different fields, such as in anthropogeny to understand food habits of ancient people, in forensics for postmortem analyses, and in biomedical research to study human diseases.

AREAS COVERED

The goal of the review is to demonstrate how cadavers can be utilized for study of lipidome to get biological insight in different fields. Several important considerations need to be made when analyzing lipids from cadaver samples. For example, what important postmortem changes occur due to environmental or other intrinsic factors that introduce deviations in the observed differences versus true differences? Do these factors affect distinct classes of lipids differently? How do we arrive at a reasonable level of certainty that the observed differences are truly biological rather than artifacts of sample collection, changes during transportation, or variations in analytical procedures? These are pressing questions that need to be addressed when performing lipidomics investigations utilizing postmortem tissues, which inherently presents hurdles and unknowns beginning with harvesting methods, transportation logistics, and at analytical techniques. In our review, we have purposefully omitted blood and serum studies since they pose greater challenges in this regard. Several studies have been carried out with cadaveric tissues and fluids that support the successful use of cases of these samples; however, many control studies are still necessary to provide insight into full potential of the cadaveric tissue and fluid resources. Most importantly, additional control studies will allow us to gain important insights into the opportunities lipidomics presents for biomedical studies of complex human disease and disorders. Another goal of the review is to generate awareness about limitations and pitfalls of use of cadaver materials for study of lipidome.

EXPERT OPINION

We comment on the current state of lipidomics studies that utilize cadaveric tissues, provide a few pertinent examples, and discuss perspectives on both future technological directions and the applications they will enable.

Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial

Metabolomics has become an invaluable tool for both studying metabolism and biomarker discovery. The great technical advances in analytical chemistry and bioinformatics have considerably increased the number of measurable metabolites, yet an important part of the human metabolome remains uncovered. Among the various MS hyphenated techniques available, LC-MS stands out as the most used. Here, we aimed to show the capabilities of LC-MS to uncover part of the metabolome and how to best proceed with sample preparation and LC to maximise metabolite detection. The analyses of various open metabolite databases served us to estimate the size of the already detected human metabolome, the expected metabolite composition of most used human biospecimens and which part of the metabolome can be detected when LC-MS is used. Based on an extensive review and on our experience, we have outlined standard procedures for LC-MS analysis of urine, cells, serum/plasma, tissues and faeces, to guide in the selection of the sample preparation method that best matches with one or more LC techniques in order to get the widest metabolome coverage. These standard procedures may be a useful tool to explore, at a glance, the wide spectrum of possibilities available, which can be a good starting point for most of the LC-MS metabolomic studies.

An integrative atlas of chicken long non-coding genes and their annotations across 25 tissues

Long non-coding RNAs (LNC) regulate numerous biological processes. In contrast to human, the identification of LNC in farm species, like chicken, is still lacunar. We propose a catalogue of 52,075 chicken genes enriched in LNC (http://www.fragencode.org/), built from the Ensembl reference extended using novel LNC modelled here from 364 RNA-seq and LNC from four public databases. The Ensembl reference grew from 4,643 to 30,084 LNC, of which 59% and 41% with expression ≥ 0.5 and ≥ 1 TPM respectively. Characterization of these LNC relatively to the closest protein coding genes (PCG) revealed that 79% of LNC are in intergenic regions, as in other species. Expression analysis across 25 tissues revealed an enrichment of co-expressed LNC:PCG pairs, suggesting co-regulation and/or co-function. As expected LNC were more tissue-specific than PCG (25% vs. 10%). Similarly to human, 16% of chicken LNC hosted one or more miRNA. We highlighted a new chicken LNC, hosting miR155, conserved in human, highly expressed in immune tissues like miR155, and correlated with immunity-related PCG in both species. Among LNC:PCG pairs tissue-specific in the same tissue, we revealed an enrichment of divergent pairs with the PCG coding transcription factors, as for example LHX5, HXD3 and TBX4, in both human and chicken.

Prognostic value of endogenous and exogenous metabolites in liver transplantation

Liver transplantation has been widely accepted as an effective intervention for end-stage liver diseases and early hepatocellular carcinomas. However, a variety of postoperative complications and adverse reactions have baffled medical staff and patients. Currently, transplantation monitoring relies primarily on nonspecific biochemical tests, whereas diagnosis of multiple complications depends on invasive pathological examination. Therefore, a noninvasive monitoring method with high selectivity and specificity is desperately needed. This review summarized the potential of endogenous small-molecule metabolites as biomarkers for assessing graft function, ischemia-reperfusion injury and liver rejection. Exogenous metabolites, mainly those immunosuppressive agents with high intra- and inter-individual variability, were also discussed for transplantation monitoring.

Metabonomic Profile of Macrosteatotic Allografts for Orthotopic Liver Transplantation in Patients With Initial Poor Function: Mechanistic Investigation and Prognostic Prediction

BACKGROUND

Our previous study revealled amplified hazardous effects of macrosteatosis (MaS) on graft failure (GF) in recipients with severe liver damage in short post-operative days, with vague mechanism inside.

AIM

We aimed to uncover the molecular mechanism of donor MaS on GF, and construct the predictive model to monitor post-transplant prognosis based on "omics" perspective.

METHODS

Ultra-performance liquid chromatography coupled to mass spectrometry metabolomic analysis was performed in allograft tissues from 82 patients with initial poor function (IPF) from multi-liver transplant (LT) centers. Pathway analysis was performed by on-line toolkit Metaboanalyst (v 3.0). Predictive model was constructed based on combinative metabonomic and clinical data extracted by stepwised cox proportional analysis.

RESULTS

Principle component analysis (PCA) analysis revealled stratification on metabolic feature in organs classified by MaS status. Differential metabolits both associated with MaS and GF were significantly enriched on pathway of glycerophospholipid metabolism (P < 0.05). Phosphatidylcholine (PC) and phosphatidylethanolamine (PE) involved in glycerophospholipid metabolism was significantly decreased in cases with MaS donors and GF (P < 0.05). Better prediction was observed on graft survival by combinative model (area under the curve = 0.91) and confirmed by internal validation.

CONCLUSION

Metabonomic features of allografts can be clearly distinguished by MaS status in patients with IPF. Dysfunction on glycerophospholipid metabolism was culprit to link donor MaS and final GF. Decrement on PC and PE exerted the fatal effects of MaS on organ failure. Metabonomic data might help for monitoring long-term graft survival after LT.

Dynamic Metabolomics Study of the Bile Acid Pathway During Perioperative Primary Hepatic Carcinoma Following Liver Transplantation

Background

There are many situations of abnormal metabolism influencing liver graft function. This study aims to provide data for the development of liver function recovery after liver transplantation by dynamically analyzing metabolites of bile acids pathway in serum.

Material/Methods

A comprehensive metabolomics profiling of serum of 9 liver transplantation patients before transplantation, on the 1^st, 3^rd, and 7^th days after liver transplantation, and healthy individuals were performed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Multivariate data and dynamic analysis were used to search for biomarkers between the metabolomics profiles present in perioperative liver transplantation and normal controls.

Results

Thirty-three differential endogenous metabolites were screened by the threshold of variable importance in the projection (VIP) from an orthogonal partial least square discriminant analysis (OPLS-DA) greater than 1.0, q-value <0.05, and fold change (FC) ≤0.8 or ≥1.2 between the preoperative group and the normal controls in negative mode. The metabolite intensities of taurocholic acid, taurochenodeoxycholic acid, chenodeoxycholic acid glycine conjugate, and glycocholic acid pre-transplantation were significantly higher than those of normal controls. The average metabolite intensities of taurocholic acid and taurochenodesoxycholic acid on the first day after liver transplantation were lower than those observed pre-transplantation. The average metabolite intensities on day 3 after liver transplantation showed a sudden increase and then decreased after 7 postoperative days. The average metabolite intensities of glycocholic acid and chenodeoxycholic acid glycine conjugate showed an increasing trend on the 1^st, 3^rd, and 7^th days after liver transplantation.

Conclusions

Use of taurocholic acid and taurochenodeoxycholic acid-related bile secretion, liver regeneration, and de novo bile acid synthesis may help clinical evaluation and provide data for the development of liver function recovery after liver transplantation.

Tryptophan Metabolism via the Kynurenine Pathway: Implications for Graft Optimization during Machine Perfusion

Access to liver transplantation continues to be hindered by the severe organ shortage. Extended-criteria donor livers could be used to expand the donor pool but are prone to ischemia-reperfusion injury (IRI) and post-transplant graft dysfunction. Ex situ machine perfusion may be used as a platform to rehabilitate discarded or extended-criteria livers prior to transplantation, though there is a lack of data guiding the utilization of different perfusion modalities and therapeutics. Since amino acid derivatives involved in inflammatory and antioxidant pathways are critical in IRI, we analyzed differences in amino acid metabolism in seven discarded non-steatotic human livers during normothermic- (NMP) and subnormothermic-machine perfusion (SNMP) using data from untargeted metabolomic profiling. We found notable differences in tryptophan, histamine, and glutathione metabolism. Greater tryptophan metabolism via the kynurenine pathway during NMP was indicated by significantly higher kynurenine and kynurenate tissue concentrations compared to pre-perfusion levels. Livers undergoing SNMP demonstrated impaired glutathione synthesis indicated by depletion of reduced and oxidized glutathione tissue concentrations. Notably, ATP and energy charge ratios were greater in livers during SNMP compared to NMP. Given these findings, several targeted therapeutic interventions are proposed to mitigate IRI during liver machine perfusion and optimize marginal liver grafts during SNMP and NMP.

Assessment of Organ Quality in Kidney Transplantation by Molecular Analysis and Why It May Not Have Been Achieved, Yet

Donor organ shortage, growing waiting lists and substantial organ discard rates are key problems in transplantation. The critical importance of organ quality in determining long-term function is becoming increasingly clear. However, organ quality is difficult to predict. The lack of good measures of organ quality is a serious challenge in terms of acceptance and allocation of an organ. The underlying review summarizes currently available methods used to assess donor organ quality such as histopathology, clinical scores and machine perfusion characteristics with special focus on molecular analyses of kidney quality. The majority of studies testing molecular markers of organ quality focused on identifying organs at risk for delayed graft function, yet without prediction of long-term graft outcome. Recently, interest has emerged in looking for molecular markers associated with biological age to predict organ quality. However, molecular gene sets have not entered the clinical routine or impacted discard rates so far. The current review critically discusses the potential reasons why clinically applicable molecular quality assessment using early kidney biopsies might not have been achieved yet. Besides a critical analysis of the inherent limitations of surrogate markers used for organ quality, i.e., delayed graft function, the intrinsic methodological limitations of studies assessing organ quality will be discussed. These comprise the multitude of unpredictable hits as well as lack of markers of nephron mass, functional reserve and regenerative capacity.

Deregulation of the Purine Pathway in Pre-Transplant Liver Biopsies Is Associated with Graft Function and Survival after Transplantation

The current shortage of livers for transplantation has increased the use of marginal organs sourced from donation after circulatory death (DCD). However, these organs have a higher incidence of graft failure, and pre-transplant biomarkers which predict graft function and survival remain limited. Here, we aimed to find biomarkers of liver function before transplantation to allow better clinical evaluation. Matched pre- and post-transplant liver biopsies from DCD (n = 24) and donation after brain death (DBD, n = 70) were collected. Liver biopsies were analysed using mass spectroscopy molecular phenotyping. Discrimination analysis was used to parse metabolites differentiated between the two groups. Five metabolites in the purine pathway were investigated. Of these, the ratios of the levels of four metabolites to those of urate differed between DBD and DCD biopsies at the pre-transplantation stage (q < 0.05). The ratios of Adenosine monophosphate (AMP) and adenine levels to those of urate also differed in biopsies from recipients experiencing early graft function (EGF) (q < 0.05) compared to those of recipients experiencing early allograft dysfunction (EAD). Using random forest, a panel consisting of alanine aminotransferase (ALT) and the ratios of AMP, adenine, and hypoxanthine levels to urate levels predicted EGF with area under the curve (AUC) of 0.84 (95% CI (0.71, 0.97)). Survival analysis revealed that the metabolite classifier could stratify six-year survival outcomes (p = 0.0073). At the pre-transplantation stage, a panel composed of purine metabolites and ALT could improve the prediction of EGF and survival.

Systematic Review: Clinical Metabolomics to Forecast Outcomes in Liver Transplantation Surgery

Liver transplantation is an effective intervention for end-stage liver disease, fulminant hepatic failure, and early hepatocellular carcinoma. Yet, there is marked patient-to-patient variation in liver transplantation outcomes. This calls for novel diagnostics to enable rational deployment of donor livers. Metabolomics is a postgenomic high-throughput systems biology approach to diagnostic innovation in clinical medicine. We report here an original systematic review of the metabolomic studies that have identified putative biomarkers in the context of liver transplantation. Eighteen studies met the inclusion criteria that involved sampling of blood (n = 4), dialysate fluid (n = 4), bile (n = 5), and liver tissue (n = 5). Metabolites of amino acid and nitrogen metabolism, anaerobic glycolysis, lipid breakdown products, and bile acid metabolism were significantly different in transplanted livers with and without graft dysfunction. However, criteria for defining the graft dysfunction varied across studies. This systematic review demonstrates that metabolomics can be deployed in identification of metabolic indicators of graft dysfunction with a view to implicated molecular mechanisms. We conclude the article with a horizon scanning of metabolomics technology in liver transplantation and its future prospects and challenges in research and clinical practice.

Primary liver graft dysfunction and non-function: integrative literature review

Avoiding deaths in the waiting list for an organ is no longer the only focus of the transplant teams attention. Research and care in clinical practice has been increasingly focused on post transplant graft survival and functioning. In the present work, we performed an integrative literature review to identify the terminology used about liver graft dysfunction and non-function, as well as to investigate the incidence and risk factors of these clinical events. We chosen articles written in Portuguese, English and Spanish between 2012 and 2016, based on CINAHL, MEDLINE, Cochrane, LILACS, BDENF, IBECS, EMBASE and Web of Science. We selected 14 studies, in which we identified the incidence of hepatic graft dysfunction ranging from 7% to 27%. The terminology used to describe this clinical event was initial malfunction, graft hypofunction, marginal function or delay in function. The primary non-function of the liver graft was found in 1.4% to 8.4% of the patients, and the terminology used to describe the event was early dysfunction or graft loss. The risk factors found are related to donor, recipient, graft and transplant logistics variables. We conclude that knowledge of the different terminologies employed in the literature, related to dysfunction and primary non- function incidence, and of their risk factors are fundamental to qualify the control of the events, aiming to improve patients' survival after liver transplantation.

A Lipidomics Study Reveals Lipid Signatures Associated with Early Allograft Dysfunction in Living Donor Liver Transplantation

Liver transplantation has become the ultimate treatment for patients with end stage liver disease. However, early allograft dysfunction (EAD) has been associated with allograft loss or mortality after transplantation. We aim to utilize a metabolomic platform to identify novel biomarkers for more accurate correlation with EAD using blood samples collected from 51 recipients undergoing living donor liver transplantation (LDLT) by 1H-nuclear magnetic resonance spectroscopy (NMR) and liquid chromatography coupled with mass spectrometry (LC-MS). Principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were used to search for a relationship between the metabolomic profiles and the presence of EAD.Cholesteryl esters (CEs), triacylglycerols (TGs), phosphatidylcholines (PCs) and lysophosphatidylcholine (lysoPC) were identified in association with EAD and a combination of cholesterol oleate, PC (16:0/16:0), and lysoPC (16:0) gave an optimal area under the curve (AUC) of 0.9487 and 0.7884 in the prediction of EAD and in-hospital mortality, respectively after LDLT. Such biomarkers may add as a potential clinical panel for the prediction of graft function and mortality after LDLT.

Impact of real-time metabolomics in liver transplantation: Graft evaluation and donor-recipient matching

BACKGROUND & AIMS

There is an emerging need to assess the metabolic state of liver allografts especially in the novel setting of machine perfusion preservation and donor in cardiac death (DCD) grafts. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS-NMR) could be a useful tool in this setting as it can extemporaneously provide untargeted metabolic profiling. The purpose of this study was to evaluate the potential value of HR-MAS-NMR metabolomic analysis of back-table biopsies for the prediction of early allograft dysfunction (EAD) and donor-recipient matching.

METHOD

The metabolic profiles of back-table biopsies obtained by HR-MAS-NMR, were compared according to the presence of EAD using partial least squares discriminant analysis. Network analysis was used to identify metabolites which changed significantly. The profiles were compared to native livers to identify metabolites for donor-recipient matching.

RESULTS

The metabolic profiles were significantly different in grafts that caused EAD compared to those that did not. The constructed model can be used to predict the graft outcome with excellent accuracy. The metabolites showing the most significant differences were lactate level >8.3 mmol/g and phosphocholine content >0.646 mmol/g, which were significantly associated with graft dysfunction with an excellent accuracy (AUROC_lactates = 0.906; AUROC_{phosphocholine} = 0.816). Native livers from patients with sarcopenia had low lactate and glycerophosphocholine content. In patients with sarcopenia, the risk of EAD was significantly higher when transplanting a graft with a high-risk graft metabolic score.

CONCLUSION

This study underlines the cost of metabolic adaptation, identifying lactate and choline-derived metabolites as predictors of poor graft function in both native livers and liver grafts. HR-MAS-NMR seems a valid technique to evaluate graft quality and the consequences of cold ischemia on the graft. It could be used to assess the efficiency of graft resuscitation on machine perfusion in future studies.

LAY SUMMARY

Real-time metabolomic profiles of human grafts during back-table can accurately predict graft dysfunction. High lactate and phosphocholine content are highly predictive of graft dysfunction whereas low lactate and phosphocholine content characterize patients with sarcopenia. In these patients, the cost of metabolic adaptation may explain the poor outcomes.

Role of biobanks in transplantation

The establishment of bio-banks together with high throughput technologies, such as genomics, transcriptomics and proteomics has opened new frontiers in biomarker discovery and the development of systems biology approaches to identifying key pathways that could be exploited to improve outcomes of solid organ transplantation. One of the major challenges in organ donation has been the lack of access to large scale well characterised material to facilitate projects that aim to characterise injury to donor organs and identify biomarkers. This may have hampered research in the field of organ donation by not allowing researchers to materials of high quality and lower pre-analytical variability. We describe in this manuscript the need for bio-banks in organ donation, research opportunities and the particular challenges in establishing such an initiative.

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We address:

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The main challenges in transplantation.

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Underpinning cellular processes of injury and repair.

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The role of biobanks can be used in transplantation.

A new index predicts early allograft dysfunction following living donor liver transplantation: A propensity score analysis

OBJECTIVE/AIM

The aim of this study was to identify a new index to predict early allograft dysfunction following living donor liver transplantation.

METHODS

The study enrolled 260 adult living donor liver transplantation recipients. Postoperative laboratory variables were assessed for their association with the prevalence of early allograft dysfunction using the inverse probability of treatment weighting and propensity-score matching (n=93 pairs) analysis.

RESULTS

Forty-seven recipients (18.1%) developed early allograft dysfunction. In multivariable analysis, the alanine aminotransferase and gamma-glutamyl transpeptidase levels on postoperative day 1 were independent predictors of early allograft dysfunction. The alanine aminotransferase to gamma-glutamyl transpeptidase ratio (AGR) was developed. All cases were divided into two groups (Group 1 [AGR≥8.47, n=103] and Group 2 [AGR<8.47, n=157]). AGR≥8.47 (OR 10.345, 95%CI 4.502-23.772, p<0.001), hepatorenal syndrome (OR 3.016, 95%CI 1.119-8.125, p=0.029), and graft to recipient weight ratio <0.8% (OR 2.155, 95%CI 1.004-4.624, p=0.049) were independent risk factors for early allograft dysfunction. The prevalence of early allograft dysfunction was higher in group 1 (after adjusting for inverse probability of treatment weighting [n=39; 37.9% vs n=8; 5.1%] and propensity-score matching [n=33; 35.5% vs n=2; 2.2%]) than that in group 2 (p<0.001).

CONCLUSIONS

The postoperative AGR is a practical index for predicting early allograft dysfunction after living donor liver transplantation.

A Metabolomics Signature Linked To Liver Fibrosis In The Serum Of Transplanted Hepatitis C Patients

Liver fibrosis must be evaluated in patients with hepatitis C virus (HCV) after liver transplantation because its severity affects their prognosis and the recurrence of HCV. Since invasive biopsy is still the gold standard to identify patients at risk of graft loss from rapid fibrosis progression, it becomes crucial the development of new accurate, non-invasive methods that allow repetitive examination of the patients. Therefore, we have developed a non-invasive, accurate model to distinguish those patients with different liver fibrosis stages. Two hundred and three patients with HCV were histologically classified (METAVIR) into five categories of fibrosis one year after liver transplantation. In this cross-sectional study, patients at fibrosis stages F0-F1 (n = 134) were categorised as “slow fibrosers” and F2-F4 (n = 69) as “rapid fibrosers”. Chloroform/methanol serum extracts were analysed by reverse ultra-high performance liquid chromatography coupled to mass spectrometry. A diagnostic model was built through linear discriminant analyses. An algorithm consisting of two sphingomyelins and two phosphatidylcholines accurately classifies rapid and slow fibrosers after transplantation. The proposed model yielded an AUROC of 0.92, 71% sensitivity, 85% specificity, and 84% accuracy. Moreover, specific bile acids and sphingomyelins increased notably along with liver fibrosis severity, differentiating between rapid and slow fibrosers.

The release of microRNA-122 during liver preservation is associated with early allograft dysfunction and graft survival after transplantation

Early allograft dysfunction (EAD) after liver transplantation (LT) is associated with inferior graft survival. EAD is more prevalent in grafts from donation after circulatory death (DCD). However, accurate prediction of liver function remains difficult because of the lack of specific biomarkers. Recent experimental and clinical studies highlight the potential of hepatocyte-derived microRNAs (miRNAs) as sensitive, stable, and specific biomarkers of liver injury. The aim of this study was to determine whether miRNAs in graft preservation fluid are predictive for EAD after clinical LT and in an experimental DCD model. Graft preservation solutions of 83 liver grafts at the end of cold ischemia were analyzed for miRNAs by reverse transcription polymerase chain reaction. Of these grafts, 42% developed EAD after transplantation. Results were verified in pig livers (n = 36) exposed to different lengths of warm ischemia time (WIT). The absolute miR-122 levels and miR-122/miR-222 ratios in preservation fluids were significantly higher in DCD grafts (P = 0.001) and grafts developing EAD (P = 0.004). In concordance, the miR-122/miR-222 ratios in perfusion fluid correlate with serum transaminase levels within the first 24 hours after transplantation. Longterm graft survival was significantly diminished in grafts with high miR-122/miR-222 ratios (P = 0.02). In the porcine DCD model, increased WIT lead to higher absolute miR-122 levels and relative miR-122/miR-222 ratios in graft perfusion fluid (P = 0.01 and P = 0.02, respectively). High miR-122/miR-222 ratios in pig livers were also associated with high aspartate aminotransferase levels after warm oxygenated reperfusion. In conclusion, both absolute and relative miR-122 levels in graft preservation solution are associated with DCD, EAD, and early graft loss after LT. As shown in a porcine DCD model, miRNA release correlated with the length of WITs. Liver Transplantation 23 946-956 2017 AASLD.

Peritransplant Energy Changes and Their Correlation to Outcome After Human Liver Transplantation

BACKGROUND

The ongoing shortage of donor livers for transplantation and the increased use of marginal livers necessitate the development of accurate pretransplant tests of viability. Considering the importance energy status during transplantation, we aimed to correlate peritransplant energy cofactors to posttransplant outcome and subsequently model this in an ex vivo setting.

METHODS

Sequential biopsies were taken from 19 donor livers postpreservation, as well as 30 minutes after portal venous reperfusion and hepatic arterial reperfusion and analyzed by liquid chromatography-mass spectrometry for energetic cofactors (adenosine triphosphate [ATP]/adenosine diphosphate [ADP]/adenosine monophosphate [AMP], nicotinamide adenine dinucleotide /NAD, nicotinamide adenine dinucleotide phosphate / nicotinamide adenine dinucleotide phosphate , flavin adenine dinucleotide , glutathione disulfide/glutathione). Energy status was correlated to posttransplant outcome. In addition, 4 discarded human donation after circulatory death livers were subjected to ex vivo reperfusion, modeling reperfusion injury and were similarly analyzed for energetic cofactors.

RESULTS

A rapid shift toward higher energy adenine nucleotides was observed following clinical reperfusion, with a 2.45-, 3.17- and 2.12-fold increase in ATP:ADP, ATP:AMP and energy charge after portal venous reperfusion, respectively. Seven of the 19 grafts developed early allograft dysfunction. Correlation with peritransplant cofactors revealed a significant difference in EC between early allograft dysfunction and normal functioning grafts (0.09 vs 0.31, P < 0.05). In the simulated reperfusion model, a similar trend in adenine nucleotide changes was observed.

CONCLUSIONS

A preserved energy status appears critical in the peritransplant period. Levels of adenine nucleotides change rapidly after reperfusion and ratios of ATP/ADP/AMP after reperfusion are significantly correlated to graft function. Using these markers as a viability test in combination with ex vivo reperfusion may provide a useful predictor of outcome that incorporates donor, preservation, and reperfusion factors.

A lipidomic cell-based assay for studying drug-induced phospholipidosis and steatosis

Phospholipidosis and steatosis are two toxic effects, which course with overaccumulation of different classes of lipids in the liver. MS-based lipidomics has become a powerful tool for the comprehensive determination of lipids. LC-MS lipid profiling of HepG2 cells is proposed as an in vitro assay to study and anticipate phospholipidosis and steatosis. Cells with and without preincubation with a mixture of free fatty acids (FFA; i.e. oleic and palmitic) were exposed to a set of well-known steatogenic and phospholipidogenic compounds. The use of FFA preloading accelerated the accumulation of phospholipids, thus leading to a better discrimination of phospholipidosis, and magnified the lipidomic alterations induced by steatogenic drugs. Phospholipidosis was characterized by increased levels of phosphatidylcholines, phosphatidylethanolamines, phosphatidylserines, and phosphatidylinositols, while steatosis induced alterations in FA oxidation and triacylglyceride (TG) synthesis pathways (with changes in the levels of FFA, acylcarnitines, monoacylglycerides, diacylglycerides, and TG). Interestingly, palmitic and oleic acids incorporation into lipids differed. A characteristic pattern was observed in the fold of change of particular TG species in the case of steatosis (TG(54:3) > TG(52:2) > TG(50:1) > TG(48:0)). Based on the levels of those lipids containing only palmitic and/or oleic acid moieties a partial least squares-discriminant analysis model was built, which showed good discrimination among nontoxic, phospholipidogenic and steatogenic compounds. In conclusion, it has been shown that the use of FFA preincubation together with intracellular LC-MS based lipid profiling could be a useful approach to identify the potential of drug candidates to induce phospholipidosis and/or steatosis.

Biomarkers in Transplantation--Proteomics and Metabolomics

Modern multianalyte "omics" technologies allow for the identification of molecular signatures that confer significantly more information than measurement of a single parameter as typically used in current medical diagnostics. Proteomics and metabolomics bioanalytical assays capture a large set of proteins and metabolites in body fluids, cells, or tissues and, complementing genomics, assess the phenome. Proteomics and metabolomics contribute to the development of novel predictive clinical biomarkers in transplantation in 2 ways: they can be used to generate a diagnostic fingerprint or they can be used to discover individual proteins and metabolites of diagnostic potential. Much fewer metabolomics than proteomics biomarker studies in transplant patients have been reported, and, in contrast to proteomics discovery studies, new lead metabolite markers have yet to emerge. Most clinical proteomics studies have been discovery studies. Several of these studies have assessed diagnostic sensitivity and specificity. Nevertheless, none of these newly discovered protein biomarkers have yet been implemented in clinical decision making in transplantation. The currently most advanced markers discovered in proteomics studies in transplant patients are the chemokines CXCL-9 and CXCL-10, which have successfully been validated in larger multicenter trials in kidney transplant patients. These chemokines can be measured using standard immunoassay platforms, which should facilitate clinical implementation. Based on the published evidence, it is reasonable to expect that these chemokine markers can help guiding and individualizing immunosuppressive regimens, may be able to predict acute and chronic T-cell-mediated and antibody-mediated rejection, and may be useful tools for risk stratification of kidney transplant patients.

Analytical Aspects of the Implementation of Biomarkers in Clinical Transplantation

In response to the urgent need for new reliable biomarkers to complement the guidance of the immunosuppressive therapy, a huge number of biomarker candidates to be implemented in clinical practice have been introduced to the transplant community. This includes a diverse range of molecules with very different molecular weights, chemical and physical properties, ex vivo stabilities, in vivo kinetic behaviors, and levels of similarity to other molecules, etc. In addition, a large body of different analytical techniques and assay protocols can be used to measure biomarkers. Sometimes, a complex software-based data evaluation is a prerequisite for appropriate interpretation of the results and for their reporting. Although some analytical procedures are of great value for research purposes, they may be too complex for implementation in a clinical setting. Whereas the proof of "fitness for purpose" is appropriate for validation of biomarker assays used in exploratory drug development studies, a higher level of analytical validation must be achieved and eventually advanced analytical performance might be necessary before diagnostic application in transplantation medicine. A high level of consistency of results between laboratories and between methods (if applicable) should be obtained and maintained to make biomarkers effective instruments in support of therapeutic decisions. This overview focuses on preanalytical and analytical aspects to be considered for the implementation of new biomarkers for adjusting immunosuppression in a clinical setting and highlights critical points to be addressed on the way to make them suitable as diagnostic tools. These include but are not limited to appropriate method validation, standardization, education, automation, and commercialization.

Mass Spectrometry Based Metabolomics Comparison of Liver Grafts from Donors after Circulatory Death (DCD) and Donors after Brain Death (DBD) Used in Human Orthotopic Liver Transplantation

Use of marginal liver grafts, especially those from donors after circulatory death (DCD), has been considered as a solution to organ shortage. Inferior outcomes have been attributed to donor warm ischaemic damage in these DCD organs. Here we sought to profile the metabolic mechanisms underpinning donor warm ischaemia. Non-targeted Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry metabolomics was applied to biopsies of liver grafts from donors after brain death (DBD; n = 27) and DCD (n = 10), both during static cold storage (T₁) as well as post-reperfusion (T₂). Furthermore 6 biopsies from DBD donors prior to the organ donation (T₀) were also profiled. Considering DBD and DCD together, significant metabolic differences were discovered between T₁ and T₂ (688 peaks) that were primarily related to amino acid metabolism, meanwhile T₀ biopsies grouped together with T₂, denoting the distinctively different metabolic activity of the perfused state. Major metabolic differences were discovered between DCD and DBD during cold-phase (T₁) primarily related to glucose, tryptophan and kynurenine metabolism, and in the post-reperfusion phase (T₂) related to amino acid and glutathione metabolism. We propose tryptophan/kynurenine and S-adenosylmethionine as possible biomarkers for the previously established higher graft failure of DCD livers, and conclude that the associated pathways should be targeted in more exhaustive and quantitative investigations.

Early allograft dysfunction after liver transplantation: an intermediate outcome measure for targeted improvements

BACKGROUND

The term early allograft dysfunction (EAD) identifies liver transplant (LT) allografts with initial poor function and portends poor allograft and patient survival. Aims of this study are to use EAD as an intermediate outcome measure in a large single center cohort and identify donor, recipient and peri-operative risk factors.

MATERIAL AND METHODS

In 1950 consecutive primary LT, donor, recipient and peri-operative data were collected. EAD was defined by the presence of one or more of the following: total bilirubin ≥ 10 mg/dL (171 μmol/L) or, INR ≥ 1.6 on day 7, and ALT/AST > 2,000 IU/L within the first 7 days.

RESULTS

The incidence of EAD was 26.5%. 1-, 3-, and 5-year allograft and patient survival for patients who developed EAD were significantly inferior to those who did not (P < 0.01 at all time points). Multivariate analysis demonstrated associations in the development of EAD with recipient pre-operative ventilator status, donation after cardiac death allografts, donor age, allograft size, degree of steatosis, operative time and intra-operative transfusion requirements (all P < 0.01). Patients with EAD had a significantly longer hospitalization at 20.9 ± 38.9 days (median: 9; range: 4-446) compared with 10.7 ± 13.5 days (median: 7; range: 3-231) in patients with no EAD (P < 0.01).

CONCLUSIONS

This is the largest single center experience demonstrating incidence of EAD and identifying factors associated with development of EAD. EAD is a useful intermediate outcome measure for allograft and patient survival. Balancing recipient pretransplant conditions, donor risk factors and intra-operative conditions are necessary for avoiding EAD.

Serum metabolomic signatures discriminate early liver inflammation and fibrosis stages in patients with chronic hepatitis B

Chronic HBV (CHB) infected patients with intermediate necroinflammation and fibrosis are recommended to receive antiviral treatment. However, other than liver biopsy, there is a lack of sensitive and specific objective method to determine the necroinflammation and fibrosis stages in CHB patients. This study aims to identify unique serum metabolomic profile associated with histological progression in CHB patients and to develop novel metabolite biomarker panels for early CHB detection and stratification. A comprehensive metabolomic profiling method was established to compare serum samples collected from health donor (n = 67), patients with mild (G < 2 and S < 2, CHB1, n = 52) or intermediate (G ≥ 2 or S ≥ 2, CHB2, n = 36) necroinflammation and fibrosis. Multivariate models were developed to differentiate CHB1 and CHB2 from controls. A set of CHB-associated biomarkers was identified, including lysophosphatidylcholines, phosphatidylcholines, phosphatidylinositol, phosphatidylserine, and bile acid metabolism products. Stratification of CHB1 and CHB2 patients by a simple logistic index, the PIPSindex, based on phosphatidylinositol (PI) and phosphatidylserine (PS), was achieved with an AUC of 0.961, which outperformed all currently available markers. A panel of serum metabolites that differentiate health control, CHB1 and CHB2 patients has been identified. The proposed metabolomic biosignature has the potential to be used as indicator for antiviral treatment for CHB management.

A metabolomics cell-based approach for anticipating and investigating drug-induced liver injury

In preclinical stages of drug development, anticipating potential adverse drug effects such as toxicity is an important issue for both saving resources and preventing public health risks. Current in vitro cytotoxicity tests are restricted by their predictive potential and their ability to provide mechanistic information. This study aimed to develop a metabolomic mass spectrometry-based approach for the detection and classification of drug-induced hepatotoxicity. To this end, the metabolite profiles of human derived hepatic cells (i.e., HepG2) exposed to different well-known hepatotoxic compounds acting through different mechanisms (i.e., oxidative stress, steatosis, phospholipidosis, and controls) were compared by multivariate data analysis, thus allowing us to decipher both common and mechanism-specific altered biochemical pathways. Briefly, oxidative stress damage markers were found in the three mechanisms, mainly showing altered levels of metabolites associated with glutathione and γ-glutamyl cycle. Phospholipidosis was characterized by a decreased lysophospholipids to phospholipids ratio, suggestive of phospholipid degradation inhibition. Whereas, steatosis led to impaired fatty acids β-oxidation and a subsequent increase in triacylglycerides synthesis. The characteristic metabolomic profiles were used to develop a predictive model aimed not only to discriminate between non-toxic and hepatotoxic drugs, but also to propose potential drug toxicity mechanism(s).

Metabolic profiling during ex vivo machine perfusion of the human liver

As donor organ shortages persist, functional machine perfusion is under investigation to improve preservation of the donor liver. The transplantation of donation after circulatory death (DCD) livers is limited by poor outcomes, but its application may be expanded by ex vivo repair and assessment of the organ before transplantation. Here we employed subnormothermic (21 °C) machine perfusion of discarded human livers combined with metabolomics to gain insight into metabolic recovery during machine perfusion. Improvements in energetic cofactors and redox shifts were observed, as well as reversal of ischemia-induced alterations in selected pathways, including lactate metabolism and increased TCA cycle intermediates. We next evaluated whether DCD livers with steatotic and severe ischemic injury could be discriminated from ‘transplantable’ DCD livers. Metabolomic profiling was able to cluster livers with similar metabolic patterns based on the degree of injury. Moreover, perfusion parameters combined with differences in metabolic factors suggest variable mechanisms that result in poor energy recovery in injured livers. We conclude that machine perfusion combined with metabolomics has significant potential as a clinical instrument for the assessment of preserved livers.