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.

我们的总体目标是开发最低侵入性的方法，以更好地预测结果和新颖 肝后移植（LT）NASH纤维化中的机制。尽管LT是NAFLD肝硬化的有效疗法，但 移植后NAFLD的风险令人震惊，尤其是反复出现的非酒精性脂肪性肝炎 （NASH）在5年时的事件高达70％。有效预测风险妨碍治疗的方法 并预防LT纳什纤维化后。肝细胞外基质（ECM）动态响应以组织 受伤和ECM周转率增加；我们建议利用这一优势来开发新的生物标志物来供邮政 LT NASH纤维化。辣妹低分子量在生物学流体中的宠物，不仅包括合成的 肽，但降解蛋白质的碎片（即“降解组”）。我们假设ECM降解组 在血浆中，将产生新的生物标志物，以预测LT纳什纤维化后的结果和机制。我们 将通过以下特定目的检验该假设：1）。确定后辣椒的关键变化 - LT NASH带有纤维化。无偏的肽组学和多元分析将识别降解特征 与预后独立联系。可能会产生明显变化宠物的蛋白酶活动将是 使用蛋白酶预测。我们还将确定ECM离职率在同行中的机理作用 已建立的NAFLD/NASH。 2）在LT后开发NASH和纤维化的临床可行预测模型。 而我们期望AIM 1的结果确定患者的辣妹概况与整体相关 结果，仅生物标志物通常不足以准确预测个体的患者结果。我们将 因此，员工机器学习方法等混合数据类型等概率图形模型（PGM） 将肽组和个体患者临床数据整合到一个概率图形框架中。这 然后，结果图将用于推断变量之间的因果相互作用，选择信息性的生物标志物 这将更具体地预测结果，并获得对LT NASH后生物学的新机械洞察力 （假设产生）。 3）验证使用辣妹作为确定LT后的预测工具 纳什纤维化。使用具有既定结果的大型前瞻性设计的患者队列，我们​​将测试 本研究中产生的算法和生物标志物的能力预测结果。成功完成 拟议的工作将在不同层面产生重大结果：（1）生物标志物发现：我们将确定 生物标志物和有条件的生物标志物。 （2）对LT后NASH纤维化的机械理解：我们的模型将 产生关于不同尺度（分子，个体）变量之间相互作用的假设 提供有关所涉及的蛋白质和潜在可吸毒靶标的蛋白质的见解。 （3）算法 开发：通过这个项目，我们将扩展混合数据图学习算法以包括时间表 使用大型前瞻性LT队列验证的变量。