Background: Bile acids mediate gut-liver cross-talk through bile acid receptors. Serum, hepatic, and microbial bile acid metabolism was evaluated in HCV-compensated chronic liver disease. Methods: Patients underwent liver biopsy; portal and peripheral blood were obtained before (HCVi), and 6 months after sustained virologic response (SVR), splenic blood was obtained only after SVR. The fecal microbiome and liver transcriptome were evaluated using RNA-Seq. Twenty-four bile acids were measured in serum, summed as free, taurine-conjugated bile acids (Tau-BAs), and glycine-conjugated bile acids. Results: Compared to SVR, HCVi showed elevated conjugated bile acids, predominantly Tau-BA, compounded in HCVi cirrhosis. In the liver, transcription of bile acids uptake, synthesis, and conjugation was decreased with increased hepatic spillover into systemic circulation in HCVi. There was no difference in the transcription of microbial bile acid metabolizing genes in HCVi. Despite an overall decrease, Tau-BA remained elevated in SVR cirrhosis, mainly in splenic circulation. Only conjugated bile acids, predominantly Tau-BA, correlated with serum proinflammatory markers and hepatic proinflammatory pathways, including NLRP3 and NFKB. Among hepatic bile acid receptors, disease-associated conjugated bile acids showed the strongest association with hepatic spingosine-1-phosphate receptor 2 (S1PR2). Conclusions: Enhanced expression of hepatic S1PR2 in HCVi and HCVi-cirrhosis and strong associations of S1PR2 with Tau-BAs suggest pathological relevance of Tau-BA-hepatic S1PR2 signaling in chronic liver disease. These findings have therapeutic implications in chronic liver diseases. Export

INTRODUCTION: Perturbations in aromatic (AAAs) and branched-chain amino acids (BCAAs) are seen in decompensated liver disease. The aim of this study was to evaluate the dynamic, postprandial relationship between hepatitis C virus-induced liver disease and amino acid concentrations in patients with compensated liver disease. METHODS: Patients infected with hepatitis C virus underwent a baseline liver biopsy to determine Ishak Fibrosis Score and evaluate the liver transcriptome. Patients ate a standard meal and underwent peripheral vein sampling at defined intervals. Quantitative analysis of amino acids was performed using liquid chromatography-tandem mass spectrometry. RESULTS: At baseline, there was no difference in AAA and BCAA concentrations between patients with cirrhosis and non-cirrhotic patients. After a standard meal, AAAs, but not BCAAs, were elevated in patients with cirrhosis compared with non-cirrhotic patients at every time point. The HepQuant SHUNT fraction was significantly higher in patients with cirrhosis and positively correlated with AAA concentration at all time points, but not BCAA. Analysis of the hepatic transcriptome demonstrated greater downregulation of the AAA degradation pathways than the BCAA degradation pathways. DISCUSSION: At baseline, cirrhotic patients with compensated liver disease have adequate reserve liver function to metabolize AAAs and BCAAs. When faced with a metabolic stressor, such as a standard meal, patients with cirrhosis are less able to metabolize the increased load of AAAs. This impairment correlates with portosystemic shunting. Further evaluation of AAA levels in compensated liver disease might further the understanding of the liver-muscle axis and the role it may play in the development of sarcopenia in liver disease.

Cell-free RNAs (cfRNAs) are promising analytes as non-invasive biomarkers and have even greater potential if tied in with metabolomics. Plasma is an optimal source for cfRNAs but is often derived from a variety of anticoagulants. Plasma obtained in heparin is suitable for metabolomics but is difficult to utilize for qPCR-based downstream analysis. In the present study, we aimed to develop a simple, time-efficient, and cost-effective heparinase protocol, followed by library preparation and sequencing of human plasma cfRNAs drawn and stored in heparin at −80 °C for several years. Blood was collected in CPT™ sodium heparin tubes from patients with chronic HCV infection (NCT02400216) at the National Institutes of Health (NIH) Clinical Center. Plasma cfRNAs were treated with heparinase I and used for library preparation and next-generation sequencing (NGS). Heparinase treatment maintained RNA integrity and allowed for successful library preparation for all the study subjects even with 7 ng of cfRNAs as starting material. The classification report derived from Pavian R package v1.2.0 showed no artificial reads. The abundance of chordate over microbial reads suggests no addition of experimental error through heparinase I treatment. We report a novel and practical approach to heparinase treatment for human plasma collected and frozen in sodium heparin for several years. This is an effective demonstration of utilizing heparin plasma for NGS and downstream transcriptomic research, which could then be integrated with metabolomics from the same samples, maximizing efficiency and minimizing blood draws.

Cells coordinate diverse biological processes through interactions among thousands of molecules, but mapping these interactions comprehensively and systematically remains an open problem. Pathway reconstruction algorithms address this problem by linking molecules of interest, identified from high-throughput omics experiments, using prior knowledge encoded as background interaction networks. This process recovers intermediate molecules and interactions that were not directly measured in the experimental data but plausibly connect the observed molecules. It generates testable hypotheses about interactions that drive cell behavior and informs the choice of follow-up experiments. Many algorithms have been created over decades, each optimizing different computational objectives and relying on different assumptions. The resulting heterogeneity has made benchmarking challenging, limiting systematic comparisons. Therefore, selecting an algorithm for a given biological context remains a non-trivial and poorly informed task. This registered report presents a large-scale benchmark of pathway reconstruction algorithms, evaluating 14 algorithms across 822 datasets from four biological settings. To enable this benchmark, we introduce Signaling Pathway Reconstruction Analysis Streamliner (SPRAS), which standardizes algorithm inputs, outputs, and execution into a formal framework, enabling systematic comparison that was previously infeasible. We will assess each algorithm on reconstruction performance against gold standard pathways, algorithm similarity, and computational performance across different biological contexts. Together, these evaluations will provide quantitative evidence for understanding pathway reconstruction algorithm behavior and guiding algorithm selection.