Biomarkers of Alcoholic Hepatitis

酒精性肝炎的生物标志物

基本信息

批准号：
10631081
负责人：
Gavin E Arteel
金额：
$ 60.14万
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：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10631081
关键词：
Acute Address Alcoholic Hepatitis Alcoholic Liver Diseases Alcohols Algorithms Area Biological Biological Markers Biology Cells Clinical Data Collagen Data Development Disease Extracellular Matrix Extracellular Matrix Proteins Generations Goals Graph Health Hepatic Homeostasis Human Individual Informatics Injury Lead Link Liquid substance Liver Fibrosis Liver diseases Matrix Metalloproteinases Mediating Medical Methods Modeling Molecular Molecular Weight Multiple Organ Failure Multivariate Analysis Neoplasm Metastasis Outcome Patient-Focused Outcomes Patients Peptide Hydrolases Peptide Synthesis Peptides Plasma Population Predictive Value Process Production Prognosis Prognostic Marker Protease Inhibitor Protein Fragment Proteins Research Risk Role Severities Signal Transduction Supportive care Testing Tissues Translating Work acute liver injury algorithm development biomarker discovery biomarker identification chronic alcohol ingestion cohort design diagnostic tool graph learning improved individual patient insight interest learning algorithm machine learning method minimally invasive mortality mouse model new therapeutic target novel novel diagnostics organ injury outcome prediction predictive marker predictive tools profiles in patients prospective risk prediction standard of care targeted treatment tool

项目摘要

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.

抽象的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酒精性肝炎的机制（AH）。 AH的特征是急性肝脱位和多个器官衰竭。尽管对AH的支持护理有所改善，但短期死亡率仍在很大程度上仍然存在数十年来不变（30-40％）。有效预测风险的有效方法会阻碍AH的治疗。肝细胞外基质（ECM）对器官损伤的动态反应，ECM周转率增加。我们建议利用这一点为AH开发新的生物标志物。胡椒，低分子量在生物液不仅包括合成的胡椒体，还包括降解蛋白质的碎片（即“降解组”）。我们假设等离子体中的ECM降解组将产生新的生物标志物，以预测结果和 AH的机制。我们将通过以下特定目的检验该假设：1）。确定关键更改在Pepperidome中，AH作为预测的预测生物标志物。公正的肽学和多元分析将确定与预后独立联系的降解特征。可能产生的蛋白酶活动使用蛋白酶预测肽会发生显着变化。我们还将确定 ECM的离职率并行建立的酒精诱导的肝损伤模型。 2）发展概率图形模型可以预测AH中的结果。尽管我们期望目标1的结果确定患者的肽组特征与整体结果相关，仅生物标志物通常不足以准确地准确预测个体患者的结果。因此，我们将员工机器学习方法等概率混合数据类型上的图形模型（PGM），将petotomic和个体患者临床数据整合到一个概率图形框架。然后，所得图将用于推断因果相互作用在变量之间，选择信息丰富的生物标志物，以更具体地预测结果并获得新的结果对AH生物学的机械洞察力（假设产生）。 3）验证使用辣妹的使用用于确定AH结果的预测工具。使用具有前瞻性设计的大型患者队列既定结果，我们将测试本研究中产生的算法和生物标志物的能力结果。拟议工作的成功完成将在不同层面上产生重大结果：（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用于AH预后。（2）机械对AH的理解：我们的模型将产生有关不同变量之间相互作用的假设鳞片（分子，个体）将提供有关AH涉及的蛋白质的见解。（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公民变量（即生存数据）。由于上述结果，该项目可能会为AH产生新颖的诊断工具这也可能转化为其他肝病。