Biomarkers of Alcoholic Hepatitis

摘要 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 酒精性肝炎（AH）的发病机制。AH的特征是急性肝失代偿和多发性硬化。 器官衰竭虽然AH的支持性治疗有所改善，但短期死亡率基本保持不变 十年不变（30-40%）。有效的风险预测方法阻碍了AH的治疗。肝 细胞外基质（ECM）动态响应器官损伤和ECM周转增加，我们建议， 利用这一点开发新的AH生物标志物。肽组，低分子量肽， 生物流体不仅包括合成的肽，而且包括降解蛋白质的片段（即，'degradome'）。 我们假设血浆中的ECM降解蛋白将产生新的生物标志物来预测结果， 机制在AH。我们将通过以下具体目标来检验这一假设：1）。确定关键变更 作为AH预后的预测性生物标志物。无偏肽组学和多变量分析 将识别与预后独立相关的退化特征。蛋白酶活性可以产生 将使用Proteasix预测显著改变的肽。我们还将确定机械的作用， 在平行建立的酒精诱导的肝损伤模型中的ECM周转。2)发展概率论 图形模型来预测AH的结果。尽管我们期望目标1的结果能够确定， 患者中的肽组谱与总体结果相关，单独的生物标志物通常不足以准确 预测个体患者的结局。因此，我们将采用机器学习方法，如概率 混合数据类型的图形模型（PGM），以将肽组和个体患者临床数据整合到 单个概率图形框架。由此产生的图形将被用来推断因果关系的相互作用 在变量之间，选择将更具体地预测结果的信息生物标志物，并获得新的 对AH生物学的机械洞察（假设生成）。3)为了验证肽组的用途， 一种用于确定AH结局的预测工具。使用大型前瞻性设计的患者队列， 确定的结果，我们将测试本研究中生成的算法和生物标志物预测 结果。成功完成拟议的工作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AH预后的生物标志物和条件生物标志物。(2)机械论 理解AH：我们的模型将产生关于不同时间变量之间相互作用的假设， 规模（分子，个人），这将提供对蛋白质的见解，参与AH。(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，包括审查 变量（即，存活数据）。由于上述原因，该项目可能会产生新的AH诊断工具 这也可能转化为其他肝脏疾病。

Abstract Our overarching goal is to develop minimally invasive approaches to better predict outcome and novel mechanisms in alcoholic hepatitis (AH). AH is characterized by acute hepatic decompensation and multiple organ failure. Although supportive care for AH has improved, short-term mortality has largely remained unchanged (30-40%) for decades. Effective approaches to predict risk hamper the treatment of AH. 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 AH. 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 AH. We will test this hypothesis via the following Specific Aims: 1). To identify key changes in the peptidome as predictive biomarkers of outcome in AH. 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 models of alcohol-induced liver injury. 2) To develop probabilistic graphical models to predict outcome in AH. 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 AH (hypothesis generation). 3) To validate the use of the peptidome as a predictive tool for determining outcome in AH. 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 for AH prognosis. (2) Mechanistic understanding of AH: 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 in AH. (3) Algorithm development: through this project we will extend our mixed data graph learning algorithms to include censored variables (i.e., survival data). As a result of the above, this project is likely to yield novel diagnostic tools for AH that may also translate to other liver diseases.