Identification of HO-1 as a novel biomarker for graft acute cellular rejection and prognosis prediction after liver transplantation

Key genes and immune pathways in T-cell mediated rejection post-liver transplantation identified via integrated RNA-seq and machine learning

Liver transplantation is the definitive treatment for end-stage liver disease, yet T-cell mediated rejection (TCMR) remains a major challenge. This study aims to identify key genes associated with TCMR and their potential biological processes and mechanisms. The GSE145780 dataset was subjected to differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms to pinpoint key genes associated with TCMR. Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and regulatory networks were constructed to ascertain the biological relevance of these genes. Expression validation was performed using single-cell RNA-seq (scRNA-seq) data and liver biopsy tissues from patients. We identified 5 key genes (ITGB2, FCER1G, IL-18, GBP1, and CD53) that are associated with immunological functions, such as chemotactic activity, antigen processing, and T cell differentiation. GSEA highlighted enrichment in chemokine signaling and antigen presentation pathways. A lncRNA-miRNA-mRNA network was delineated, and drug target prediction yielded 26 potential drugs. Evaluation of expression levels in non-rejection (NR) and TCMR groups exhibited significant disparities in T cells and myeloid cells. Tissue analyses from patients corroborated the upregulation of GBP1, IL-18, CD53, and FCER1G in TCMR cases. Through comprehensive analysis, this research has identified 4 genes intimately connected with TCMR following liver transplantation, shedding light on the underlying immune activation pathways and suggesting putative targets for therapeutic intervention.

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.

Mass spectrometry-based proteomics for advancing solid organ transplantation research

Scarcity of high-quality organs, suboptimal organ quality assessment, unsatisfactory pre-implantation procedures, and poor long-term organ and patient survival are the main challenges currently faced by the solid organ transplant (SOT) field. New biomarkers for assessing graft quality pre-implantation, detecting, and predicting graft injury, rejection, dysfunction, and survival are critical to provide clinicians with invaluable prediction tools and guidance for personalized patients' treatment. Additionally, new therapeutic targets are also needed to reduce injury and rejection and improve transplant outcomes. Proteins, which underlie phenotypes, are ideal candidate biomarkers of health and disease statuses and therapeutic targets. A protein can exist in different molecular forms, called proteoforms. As the function of a protein depends on its exact composition, proteoforms can offer a more accurate basis for connection to complex phenotypes than protein from which they derive. Mass spectrometry-based proteomics has been largely used in SOT research for identification of candidate biomarkers and therapeutic intervention targets by so-called "bottom-up" proteomics (BUP). However, such BUP approaches analyze small peptides in lieu of intact proteins and provide incomplete information on the exact molecular composition of the proteins of interest. In contrast, "Top-down" proteomics (TDP), which analyze intact proteins retaining proteoform-level information, have been only recently adopted in transplantation studies and already led to the identification of promising proteoforms as biomarkers for organ rejection and dysfunction. We anticipate that the use of top-down strategies in combination with new technological advancements in single-cell and spatial proteomics could drive future breakthroughs in biomarker and therapeutic target discovery in SOT.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

Glutamate dehydrogenase combined with ferrochelatase as a biomarker of liver injury induced by antituberculosis drugs

AIMS

To explore the potential value of serum glutamate dehydrogenase (GLDH), ferrochelatase (FECH), heme oxygenase-1 (HO-1) and glutathione-S-transferase-α (GST-α) as diagnostic biomarkers for liver injury caused by antituberculosis drugs.

METHODS

We established a rat model of isoniazide-induced liver injury and recruited 122 hospitalized tuberculosis patients taking antituberculosis drugs. We detected the concentration of GLDH, FECH, HO-1 and GST-α by enzyme-linked immunosorbent assay. GraphPad Prism8 and SPSS 26.0 were used for statistical analysis.

RESULTS

In the rat model, serum GLDH concentration gradually increased during isoniazid (INH) administration, while serum FECH, HO-1 and GST-α concentrations significantly increased after INH administration was stopped. The receiver operating characteristic curve showed that the areas under the curve (AUCs) of serum GLDH and FECH for the diagnosis of anti-tuberculosis (TB) drug-induced liver injury (anti-TB-DILI) were 0.7692 (95% confidence interval [CI] 0.5442-0.9943) and 0.7284 (95% CI 0.4863-0.9705) and the diagnostic accuracies were 81.25% and 78.79%, respectively. In clinical research, the AUCs of GLDH and FECH were 0.9124 (95% CI 0.8380-0.9867) and 0.6634 (95% CI 0.5391-0.7877), and the optimal thresholds were 10.40 mIU/mL and 1.304 ng/mL, respectively. The diagnostic accuracy, specificity and positive predictive value (PPV) of GLDH were 82.61%, 79.38% and 47.22%. We performed a joint diagnostic test for GLDH and FECH. The diagnostic accuracy (90.43%), specificity (91.75%) and PPV (65.21%) of serial tests were better than for GLDH and FECH alone.

CONCLUSIONS

GLDH in the diagnosis of liver injury induced by anti-TB drugs has high sensitivity, but low specificity and low PPV. The combination of GLDH and FECH could significantly improve the specificity, PPV and diagnostic accuracy, and reduce the false-positive rate of anti-TB-DILI.

Prognostic value of protein biomarkers in liver transplantation: A systematic review

Liver transplantation is currently the preferred method for the treatment of advanced liver disease and early-stage hepatocellular carcinoma (HCC). Although advances in surgical techniques, immunosuppressive drugs and postoperative management have reduced the incidence of postoperative complications, how to effectively predict or diagnose postoperative complications earlier and reduce their incidence is still a clinical concern. We performed a comprehensive proteomics literature research to identified protein biomarkers in complications after liver transplantation. Seventeen studies met the inclusion criteria including ischemia reperfusion injury (IRI) (n = 4), acute rejection (AR) (n = 4), renal dysfunction (n = 4), HCC recurrence (n = 2), primary graft dysfunction (PGD) (n = 1), infection (n = 1), and liver fibrosis (n = 1). A total of 625 differentially expressed proteins (DEPs) have been reported between postoperative complications and controls, of which 63 have been validated by quantitative protein expression and 26 have been reported by at least two studies and showed consistently changes. The results of the bioinformation analysis show that the immune system, especially the innate immune system and cytokine signaling in immune system, is an important protein-mediated pathway that affects the prognosis of liver transplantation.

The Model for End-Stage Liver Disease Score and the Follow-Up Period Can Cause the Shift of Circulating Lymphocyte Subsets in Liver Transplant Recipients

Little is known about the shift of lymphocytes under the condition of the model for end-stage liver disease score and the follow-up period. Then, we detected the peripheral blood from liver transplant recipients by flow cytometry and compared the results. The model for end-stage liver disease score affected the percentages of T-cell subsets and B cells during the short-term follow-up period, but failed to influence the lymphocyte subsets during the long-term follow-up period. In contrast, the follow-up period not only affected the absolute counts of T-cell subsets and natural killer (NK) cells in patients with the low model for end-stage liver disease scores, but also influenced the percentages and absolute counts of T-cell subsets in patients with the high model for end-stage liver disease scores. In the two-way ANOVA, we further revealed that the model for end-stage liver disease score was associated with the percentages of T cells and CD4⁺ T cells and the absolute numbers of T-cell subsets and B cells, while the follow-up period was associated with the percentages of T-cell subsets and the absolute numbers of lymphocyte subsets. Therefore, patients with either the low model for end-stage liver disease scores or the long-term follow-up period are in a relatively activated immune condition.

Screening and identification of potential protein biomarkers for the early diagnosis of acute myocardial infarction

Background

Acute myocardial infarction (AMI) is the most serious type of heart disease. Clinically, there is an urgent need to discover diagnostic biomarkers for the early diagnosis of AMI.

Methods

Serum proteomic profiles in AMI patients, healthy controls, and stable angina pectoris (SAP) patients were explored and compared by iTRAQ-2DLC-MS/MS. The clinical data of AMI patients were also analyzed. Differentially expressed proteins were validated by enzyme linked immunosorbent assay (ELISA), and diagnostic models were constructed.

Results

A total of 39 differentially expressed proteins were identified in AMI patients. The results showed that the serum levels of apolipoprotein E (APOE) in AMI patients were notably higher than those in the healthy controls (P=0.0172). The serum levels of aspartate aminotransferase (AATC) in AMI patients were markedly higher than those in the healthy controls and SAP patients (P<0.0001 and P<0.0001, respectively). The serum levels of fibronectin (FINC) in SAP patients were significantly higher than those in the healthy controls and AMI patients (P=0.0043 and P=0.0044, respectively). Clinical data analysis showed a considerable difference in blood glucose levels, troponin I (TNI), and creatine kinase (CK) in AMI patients compared with SAP patients and healthy controls. A diagnostic model consisting of AATC and clinical indicators [lactate dehydrogenase (LDH) and CK] was established to distinguish between AMI patients and healthy controls, with an area under the curve (AUC) value of 0.993 sensitivity and specificity of 96.2% and 96.3%, respectively. A diagnostic model consisting of AATC and CK was established to distinguish between AMI patients and SAP patients, with an AUC value of 0.975 and a sensitivity and specificity of 85.2% and 79.30%, respectively.

Conclusions

In this study, differentially expressed proteins in AMI patients were combined with clinical indexes, LDH and CK, and two diagnostic models were constructed. This study may provide meaningful data for the early diagnosis of AMI.