Novel Biomarkers for Post-Liver Transplant NASH Fibrosis

肝移植后 NASH 纤维化的新型生物标志物

基本信息

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
项目类别：
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

项目摘要

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颖的方法肝移植后 (LT) NASH 纤维化的机制尽管 LT 是治疗 NAFLD 肝硬化的有效方法，移植后 NAFLD 的风险非常高，尤其是复发性非酒精性脂肪性肝炎 (NASH)，5 年发病率高达 70%。预测阻碍治疗的风险的有效方法。以及预防 LT 后 NASH 纤维化肝细胞外基质 (ECM) 对器官做出动态反应。损伤和 ECM 营业额增加；我们建议利用这一点来开发新的生物标记物 LT NASH 纤维化肽组（生物体液中的低分子量肽）不仅包括合成的肽。我们发现 ECM 降解组是肽，而是降解蛋白质的片段（即“降解组”）。血浆中的蛋白质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。将通过以下具体目标来检验这一假设： 1). 确定后肽组的关键变化。 LT NASH 伴纤维化。无偏肽组学和多变量分析将鉴定降解组学特征可能产生显着变化的肽的蛋白酶活性将与预后独立相关。我们还将同时确定 ECM 更新的机制作用。建立了 NAFLD/NASH 2) 开发 NASH 和 LT 后纤维化的临床可行的预测模型。特别是，我们期望目标 1 的结果能够确定患者的肽组谱与整体相关。的结果，仅生物标志物通常不足以准确预测个体患者的结果。因此，在混合数据类型上采用概率图形模型 (PGM) 等机器学习方法将肽组学和个体患者临床数据整合到单个概率图形框架中。然后生成的图表将用于推断变量之间的因果相互作用，选择信息丰富的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) 验证肽组作为确定 LT 后的预测工具的用途。 NASH 纤维化。我们将使用一个大型前瞻性设计的具有确定结果的患者队列来测试本研究中生成的算法和生物标志物预测结果的能力。拟议工作的一部分将在各个层面产生重大成果：（1）生物标志物发现：我们将确定 (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，这些假设将 (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以包括时间进程使用大型前瞻性 LT 队列来验证变量。