Novel Biomarkers for Post-Liver Transplant NASH Fibrosis

肝移植后 NASH 纤维化的新型生物标志物

• 批准号: 10518842
• 负责人: Gavin E Arteel
• 金额: $ 71.25万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-18 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518842
• 关键词: Address; Algorithms; Area; Biological; Biological Markers; Biology; Cardiovascular Diseases; Cells; Chronic; Cirrhosis; Clinical Data; Collagen; Data; Development; Disease; Extracellular Matrix; Extracellular Matrix Proteins; Fibrosis; Generations; Goals; Graph; Health; Hepatic; Homeostasis; Human; Incidence; Individual; Lead; Lifestyle Therapy; Link; Liquid substance; Liver Fibrosis; Matrix Metalloproteinases; Mediating; Medical; Methods; Modeling; Molecular; Molecular Weight; Multivariate Analysis; Neoplasm Metastasis; Outcome; Patient risk; Patient-Focused Outcomes; Patients; Peptide Fragments; Peptide Hydrolases; Peptides; Pharmacotherapy; Plasma; Population; Predictive Value; Prevention; Production; Prognosis; Prognostic Marker; Protease Inhibitor; Protein Fragment; Proteins; Recurrence; Risk; Role; Signal Transduction; Steatohepatitis; Surrogate Markers; Testing; Time; Tissues; Work; acute liver injury; algorithm development; base; biomarker discovery; clinically actionable; clinically significant; cohort; design; effective therapy; end stage liver disease; fatty liver disease; graph learning; individual patient; insight; interest; learning algorithm; liver development; liver injury; liver transplantation; machine learning method; minimally invasive; mouse model; multimodal data; new therapeutic target; non-alcoholic fatty liver disease; nonalcoholic steatohepatitis; novel; novel marker; organ injury; outcome prediction; post-transplant; predictive modeling; predictive tools; profiles in patients; prospective; risk stratification; simple steatosis; targeted treatment; tool

Our overarching goal is to develop minimally invasive approaches to better predict outcome and novel mechanisms in post-liver transplant (LT) NASH fibrosis. Although LT is an effective therapy for NAFLD cirrhosis, the risk of post-transplant NAFLD is alarmingly high, particularly for recurrent non-alcoholic steatohepatitis (NASH) with an incidence of up to 70% at 5 years. Effective approaches to predict risk hamper the treatment and prevention of post-LT NASH fibrosis. The hepatic extracellular matrix (ECM) responds dynamically to organ injury and ECM turnover increases; we propose to take advantage of this to develop new biomarkers for post- LT NASH fibrosis. The peptidome, low molecular weight peptides in biologic fluids, includes not only synthesized peptides, but fragments of degraded proteins (i.e., ‘degradome’). We hypothesize that the ECM degradome in plasma will yield new biomarkers to predict outcome and mechanisms in post-LT NASH fibrosis. We will test this hypothesis via the following Specific Aims: 1). To identify key changes in the peptidome of post- LT NASH with fibrosis.. Unbiased peptidomics and multivariate analyses will identify degradomic features independently linked to prognosis. Protease activity that could produce significantly changed peptides will be predicted using Proteasix. We will also determine the mechanistic role of ECM turnover in the in parallel established NAFLD/NASH. 2) To develop clinically-actionable predictive models of NASH and fibrosis post-LT. Whereas we expect the results of Aim 1 to establish that the peptidome profile in patients correlates with overall outcome, biomarkers alone are often insufficient to accurately predict individual patient outcome. We will therefore employ machine learning methods like probabilistic graphical models (PGMs) over mixed data types to integrate peptidomic and individual patient clinical data, into a single probabilistic graphical framework. The resulting graphs will then be used to infer causal interactions between variables, select informative biomarkers that will more specifically predict the outcome, and gain new mechanistic insight into the biology of post-LT NASH (hypothesis generation). 3) To validate the use of the peptidome as a predictive tool for determining post-LT NASH fibrosis. Using a large prospectively-designed patient cohort with established outcomes, we will test the ability of the algorithms and biomarkers generated in this study to predict outcome. The successful completion of the proposed work will produce significant results at various levels: (1) Biomarker discovery: we will identify biomarkers and conditional biomarkers. (2) Mechanistic understanding of post-LT NASH fibrosis: our models will generate hypotheses about the interactions between variables at different scales (molecular, individual) that will provide insights on the proteins that are involved and potentially new druggable targets. (3) Algorithm development: through this project we will extend our mixed data graph learning algorithms to include time-course variables to be validated using a large prospective LT cohort.

我们的首要目标是开发微创方法，以更好地预测结果和新的 肝移植（LT）后NASH纤维化的机制。虽然LT是NAFLD肝硬化的有效治疗方法， 移植后NAFLD风险高得惊人，特别是复发性非酒精性脂肪性肝炎 （NASH），5年时发病率高达70%。有效的风险预测方法阻碍了治疗 和预防LT后NASH纤维化。肝细胞外基质（ECM）对器官的动态反应 损伤和ECM周转增加;我们建议利用这一点来开发新的生物标志物， LT NASH纤维化。生物体液中的低分子量肽不仅包括合成的肽， 肽，但降解蛋白质的片段（即，'degradome'）。我们假设细胞外基质降解了 将产生新的生物标志物来预测LT后NASH纤维化的结果和机制。我们 我们将通过以下具体目标来检验这一假设：1）。为了确定后- LT NASH伴纤维化无偏肽组学和多变量分析将识别降解组特征 与预后独立相关。蛋白酶活性，可以产生显着改变的肽将是 使用Proteasix预测。我们还将确定ECM周转的机制作用， 建立NAFLD/NASH。2)开发可用于临床的肝移植后NASH和纤维化预测模型。 然而，我们期望目标1的结果确定患者中的肽组谱与总体相关性。 然而，单独的生物标志物通常不足以准确地预测个体患者的结果。我们将 因此，在混合数据类型上使用概率图模型（PGM）等机器学习方法 将肽组和个体患者临床数据整合到单个概率图形框架中。的 然后，将使用所得图推断变量之间的因果相互作用，选择信息性生物标志物， 这将更具体地预测结果，并获得对LT后NASH生物学的新机制见解 （假设生成）。3)验证肽组作为预测工具用于确定LT后 NASH纤维化。我们将使用一个具有既定结局的大型前瞻性设计患者队列， 本研究中生成的算法和生物标志物预测结果的能力。圆满完成 的拟议工作将产生重大成果，在各个层面：（1）生物标志物的发现：我们将确定 生物标志物和条件性生物标志物。(2)LT后NASH纤维化的机制理解：我们的模型将 生成关于不同尺度（分子，个体）变量之间相互作用的假设， 提供了有关蛋白质和潜在的新药物靶点的见解。(3)算法 开发：通过这个项目，我们将扩展我们的混合数据图学习算法，包括时间过程 使用大型前瞻性LT队列验证变量。