Alcohol-related hepatitis (AH) is plagued with high mortality and difficulty in identifying at-risk patients. The extracellular matrix undergoes significant remodeling during inflammatory liver injury that can be detected in biological fluids and potentially used for mortality prediction. EDTA plasma samples were collected from AH patients (n= 62); Model for End-Stage Liver Disease (MELD) score defined AH severity as moderate (12-20; n=28) and severe (>20; n=34). The peptidome data was collected by high resolution, high mass accuracy UPLC-MS. Univariate and multivariate analyses identified differentially abundant peptides, which were used for Gene Ontology, parent protein matrisomal composition and protease involvement. Machine learning methods were used on patient-specific peptidome and clinical data to develop mortality predictors. Analysis of plasma peptides from AH patients and healthy controls identified over 1,600 significant peptide features corresponding to 130 proteins. These were enriched for ECM fragments in AH samples, likely related to turnover of hepatic-derived proteins. Analysis of moderate versus severe AH peptidomes showed a shift in abundance of peptides from collagen 1A1 and fibrinogen A proteins. The dominant proteases for the AH peptidome spectrum appear to be CAPN1 and MMP12. Increase in hepatic expression of these proteases was orthogonally-validated in RNA-seq data of livers from AH patients. Causal graphical modeling identified four peptides directly linked to 90-day mortality in >90% of the learned graphs. These peptides improved the accuracy of mortality prediction over MELD score and were used to create a clinically applicable mortality prediction assay. A signature based on plasma peptidome is a novel, non-invasive method for prognosis stratification in AH patients. Our results could also lead to new mechanistic and/or surrogate biomarkers to identify new AH mechanisms.

中文翻译：

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血浆肽组学特征揭示细胞外基质重塑并预测酒精相关性肝炎的预后

酒精相关性肝炎（AH）死亡率高且难以识别高危患者。细胞外基质在炎症性肝损伤期间经历显着的重塑，可以在生物体液中检测到并可能用于死亡率预测。 EDTA 血浆样本采集自 AH 患者 (n= 62)；终末期肝病模型(MELD)评分将AH严重程度定义为中度(12-20；n＝28)和重度(＞20；n＝34)。通过高分辨率、高质量准确度 UPLC-MS 收集肽组数据。单变量和多变量分析确定了差异丰度的肽，这些肽用于基因本体论、亲本蛋白基质体组成和蛋白酶参与。使用机器学习方法分析患者特异性肽组和临床数据来开发死亡率预测因子。对 AH 患者和健康对照者血浆肽的分析确定了与 130 种蛋白质相对应的 1,600 多个重要肽特征。这些在 AH 样本中富集了 ECM 片段，可能与肝源性蛋白质的周转有关。对中度和重度 AH 肽组的分析显示，胶原蛋白 1A1 和纤维蛋白原 A 蛋白的肽丰度发生了变化。 AH 肽组谱的主要蛋白酶似乎是 CAPN1 和 MMP12。这些蛋白酶的肝脏表达增加在 AH 患者肝脏的 RNA-seq 数据中得到了正交验证。因果图模型在超过 90% 的学习图中确定了四种与 90 天死亡率直接相关的肽。这些肽提高了 MELD 评分的死亡率预测准确性，并用于创建临床适用的死亡率预测测定。基于血浆肽组的特征是一种用于 AH 患者预后分层的新型非侵入性方法。我们的结果还可能导致新的机制和/或替代生物标志物来识别新的 AH 机制。