Inserm, U1167, Univ. Lille, Institut Pasteur de Lille, FHU-REMOD-VHF, F-59000, Lille, France.
Inserm, U1167, Univ. Lille, Institut Pasteur de Lille, FHU-REMOD-VHF, F-59000, Lille, France.
Biochim Biophys Acta Mol Basis Dis. 2017 Jun;1863(6):1445-1453. doi: 10.1016/j.bbadis.2017.02.001. Epub 2017 Feb 3.
To elucidate the time-resolved molecular events underlying the LV remodeling (LVR) process, we developed a large-scale network model that integrates the 24 molecular variables (plasma proteins and non-coding RNAs) collected in the REVE-2 study at four time points (baseline, 1month, 3months and 1year) after MI. The REVE-2 network model was built by extending the set of REVE-2 variables with their mechanistic context based on known molecular interactions (1310 nodes and 8639 edges). Changes in the molecular variables between the group of patients with high LVR (>20%) and low LVR (<20%) were used to identify active network modules within the clusters associated with progression of LVR, enabling assessment of time-resolved molecular changes. Although the majority of molecular changes occur at the baseline, two network modules specifically show an increasing number of active molecules throughout the post-MI follow up: one involved in muscle filament sliding, containing the major troponin forms and tropomyosin proteins, and the other associated with extracellular matrix disassembly, including matrix metalloproteinases, tissue inhibitors of metalloproteinases and laminin proteins. For the first time, integrative network analysis of molecular variables collected in REVE-2 patients with known molecular interactions allows insight into time-dependent mechanisms associated with LVR following MI, linking specific processes with LV structure alteration. In addition, the REVE-2 network model provides a shortlist of prioritized putative novel biomarker candidates for detection of LVR after MI event associated with a high risk of heart failure and is a valuable resource for further hypothesis generation.
为了阐明导致左心室重构(LVR)过程的时间分辨分子事件,我们开发了一个大规模的网络模型,该模型整合了在 MI 后四个时间点(基线、1 个月、3 个月和 1 年)的 REVE-2 研究中收集的 24 个分子变量(血浆蛋白和非编码 RNA)。REVE-2 网络模型是通过基于已知分子相互作用扩展 REVE-2 变量集及其机制背景(1310 个节点和 8639 个边)构建的。将高 LVR(>20%)和低 LVR(<20%)患者组之间的分子变量变化用于识别与 LVR 进展相关的聚类中的活跃网络模块,从而能够评估时间分辨的分子变化。尽管大多数分子变化发生在基线,但两个网络模块特别显示出在 MI 后随访期间活跃分子数量不断增加:一个涉及肌丝滑动,包含主要肌钙蛋白形式和原肌球蛋白蛋白,另一个与细胞外基质解体相关,包括基质金属蛋白酶、金属蛋白酶组织抑制剂和层粘连蛋白蛋白。这是首次对具有已知分子相互作用的 REVE-2 患者中收集的分子变量进行综合网络分析,深入了解与 MI 后 LVR 相关的时间依赖性机制,将特定过程与 LV 结构改变联系起来。此外,REVE-2 网络模型为 MI 后检测与心力衰竭高风险相关的 LVR 提供了一份优先级较高的潜在新型生物标志物候选名单,是进一步生成假设的宝贵资源。