Novel Biomarkers for Post-Liver Transplant NASH Fibrosis

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 肝移植后NASH纤维化的机制。虽然肝移植是治疗NAFLD肝硬变的有效方法， 移植后非酒精性脂肪肝的风险高得令人担忧，尤其是对于复发的非酒精性脂肪性肝炎。 (NASH)，5年发病率高达70%。预测风险的有效方法阻碍了治疗 和预防肝移植后NASH纤维化。肝脏细胞外基质(ECM)对器官的动态反应 损伤和ECM周转率增加；我们建议利用这一点开发新的生物标志物，用于后 纳什纤维症。生物体液中的小分子多肽，不仅包括合成的 多肽，但降解的蛋白质片段(即‘降解穹顶’)。我们假设ECM降解的穹顶 将产生新的生物标志物来预测LT NASH术后纤维化的结果和机制。我们 我将通过以下具体目标来检验这一假说：1)。以确定后更年期多肽组的主要变化 纳什少校患有纤维化..。无偏见的多肽分析和多变量分析将识别降解性特征 与预后独立相关。可能产生显著变化的多肽的蛋白酶活性将是 用ProteaSix预测的。我们还将确定ECM周转在并行的 建立了NAFLD/NASH。2)建立临床可操作的肝移植后NASH和纤维化预测模型。 鉴于我们预计AIM 1的结果将确定患者的多肽图谱与总体 结果，仅有生物标记物往往不足以准确预测个体患者的结果。我们会 因此，在混合数据类型上使用类似概率图形模型(PGMS)的机器学习方法 将多肽症和个别患者的临床数据集成到一个单一的概率图形框架中。这个 生成的曲线图将被用来推断变量之间的因果作用，选择信息丰富的生物标志物 这将更具体地预测结果，并获得新的机制洞察后LT纳什的生物学 (假设生成)。3)验证多肽作为确定肝移植后的预测工具的使用 纳什纤维症。使用一个大型前瞻性设计的具有既定结果的患者队列，我们将测试 这项研究中产生的算法和生物标记物预测结果的能力。圆满完成 将在不同层面产生重大成果：(1)生物标记物的发现：我们将确定 生物标志物和条件性生物标志物。(2)对肝移植后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，这将 提供对所涉及的蛋白质和潜在的新的可用药靶点的见解。(3)算法 发展：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-过程 变量将使用较大的前瞻性LT队列进行验证。

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.