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

Abstract

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.

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

“…As presented in Tables 1 and 2, a total of eight studies have reported the connections between omics data and phenotypes in clinical LT. 31,[37][38][39][42][43][44][45] Metabolomics is the most-used assay involved in all omics studies. Most researchers prefer to collect samples from graft tissues and omics data from recipient sera, but such were only assayed in three studies.…”

“…It is noteworthy that dysfunctional glycerophospholipid metabolism was also shown to be a major cause of macrosteatosis (MaS) and graft failure (GF) in a prior study from our center. 39 We speculated that a derangement in glycerophospholipid might be a link to inferior organ quality, such as MaS, to early graft dysfunction and final organ failure in LT.…”

“…6,[27][28][29][30][31][32][33][34] Otherwise, the molecular mechanisms underlying key issues, such as ischemic reperfusion injury (IRI) and operational tolerance in LT were also discussed in a previous multiomics study. [35][36][37][38][39] A variety of omics studies involving solid organ transplantation have been collectively named transplantomics as a potential approach for the individualized treatment of transplant patients (Fig. 1).…”

“…40,41 Despite the number of studies that have been published, the application of omics data for clinical LT is rarely summarized. 31,[37][38][39][42][43][44][45] Based on our previous study, the major peri-operative risk factors for LT were divided into four categories (including recipients, donors, grafts, and surgery). 39,46 We summarized the prior achievements of clinical omics studies that were categorized according to the above-…”

Identification of Urine Metabolic Biomarkers for Vogt-Koyanagi-Harada Disease

“…The metabolome represents the downstream products of the genome and proteome, finally resulting in a specific omics composition ( Yan et al, 2018 ; Rinschen et al, 2019 ). It has commonly been used as an approach to identify biomarkers in the diagnosis and prediction of disease ( Li et al, 2019 ; Rinschen et al, 2019 ; Zhou et al, 2019 ; Abooshahab et al, 2020 ; Jiang et al, 2020 ; Liu Z. et al, 2020 ; Souto-Carneiro et al, 2020 ). In view of the unique characteristics of urine, an increasing number of studies have used urinary metabolomics for biomarker discovery in recent years ( Kwon et al, 2020 ; Liu W. et al, 2020 ; Olesova et al, 2020 ).…”