Department of Epidemiology, University of Florida, Gainesville, Florida, USA.
Department of Electrical & Computer Engineering and Computer Science, University of New Haven, West Haven, Connecticut, USA.
Hepatol Commun. 2024 Jul 31;8(8). doi: 10.1097/HC9.0000000000000510. eCollection 2024 Aug 1.
Alcohol-associated hepatitis (AH) is plagued with high mortality and difficulty in identifying at-risk patients. The extracellular matrix undergoes significant remodeling during inflammatory liver injury and could potentially be used for mortality prediction.
EDTA plasma samples were collected from patients with AH (n = 62); Model for End-Stage Liver Disease score defined AH severity as moderate (12-20; n = 28) and severe (>20; n = 34). The peptidome data were 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 to develop mortality predictors.
Analysis of plasma peptides from patients with AH and healthy controls identified over 1600 significant peptide features corresponding to 130 proteins. These were enriched for extracellular matrix fragments in AH samples, likely related to the turnover of hepatic-derived proteins. Analysis of moderate versus severe AH peptidomes was dominated by changes in peptides from collagen 1A1 and fibrinogen A proteins. The dominant proteases for the AH peptidome spectrum appear to be CAPN1 and MMP12. Causal graphical modeling identified 3 peptides directly linked to 90-day mortality in >90% of the learned graphs. These peptides improved the accuracy of mortality prediction over the Model for End-Stage Liver Disease score and were used to create a clinically applicable mortality prediction assay.
A signature based on plasma peptidome is a novel, noninvasive method for prognosis stratification in patients with AH. Our results could also lead to new mechanistic and/or surrogate biomarkers to identify new AH mechanisms.
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过程中发生显著重塑,可能可用于预测死亡率。
收集 AH 患者(n=62)的 EDTA 血浆样本;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、高质量精度 UPLC-MS 采集肽组数据。单变量和多变量分析鉴定差异丰度肽,用于基因本体、母蛋白基质组成和蛋白酶参与分析。采用机器学习方法开发死亡率预测模型。
分析 AH 患者和健康对照者的血浆肽,鉴定出 1600 多个与 130 种蛋白对应的显著肽特征。这些特征在 AH 样本中富含细胞外基质片段,可能与肝源性蛋白的周转有关。分析中度与重度 AH 肽组,胶原 1A1 和纤维蛋白原 A 蛋白的肽变化占主导地位。AH 肽组谱的主要蛋白酶似乎是 CAPN1 和 MMP12。因果图形建模确定了 3 个直接与 90 天死亡率相关的肽,在 90%以上的学习图中都存在。与 MELD 评分相比,这些肽提高了死亡率预测的准确性,并用于创建一种临床适用的死亡率预测检测方法。
基于血浆肽组的特征是一种新型的、非侵入性的 AH 患者预后分层方法。我们的结果还可能导致新的机制和/或替代生物标志物,以识别新的 AH 机制。