Tumenbayar Bat-Ider, Pham Khanh, Biber John C, Drewes Rhonda, Bae Yongho
Department of Pharmacology and Toxicology, University at Buffalo, Buffalo, NY 14203 USA; Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA.
Department of Pathology and Anatomical Sciences, University at Buffalo, Buffalo, NY 14203 USA.
IEEE Trans Mol Biol Multiscale Commun. 2025 Mar;11(1):78-90. doi: 10.1109/tmbmc.2024.3501576. Epub 2024 Nov 18.
Cardiovascular diseases (CVDs) and pathologies are often driven by changes in molecular signaling and communication, as well as in cellular and tissue components, particularly those involving the extracellular matrix (ECM), cytoskeleton, and immune response. The fine-wire vascular injury model is commonly used to study neointimal hyperplasia and vessel stiffening, but it is not typically considered a model for CVDs. However, applying this model to study CVDs in conjunction with established processes could offer valuable insights. In this paper, we hypothesize that vascular injury induces changes in gene expression, molecular communication, and biological processes similar to those observed in CVDs at both the transcriptome and protein levels. To investigate this, we analyzed gene expression in microarray datasets from injured and uninjured femoral arteries in mice two weeks post-injury, identifying 1,467 significantly and differentially expressed genes involved in several CVDs such as including vaso-occlusion, arrhythmia, and atherosclerosis. We further constructed a protein-protein interaction network with seven functionally distinct clusters, with notable enrichment in ECM, metabolic processes, actin-based process, and immune response. Significant molecular communications were observed between the clusters, most prominently among those involved in ECM and cytoskeleton organizations, inflammation, and cell cycle. Machine Learning Disease pathway analysis revealed that vascular injury-induced crosstalk between ECM remodeling and immune response clusters contributed to aortic aneurysm, neovascularization of choroid, and kidney failure. Additionally, we found that interactions between ECM and actin cytoskeletal reorganization clusters were linked to cardiac damage, carotid artery occlusion, and cardiac lesions. Overall, through multi-scale bioinformatic analyses, we demonstrated the robustness of the vascular injury model in eliciting transcriptomic and molecular network changes associated with CVDs, highlighting its potential for use in cardiovascular research.
心血管疾病（CVDs）及其病变通常由分子信号传导与通讯的变化以及细胞和组织成分的改变所驱动，特别是那些涉及细胞外基质（ECM）、细胞骨架和免疫反应的成分。细线血管损伤模型常用于研究内膜增生和血管硬化，但通常不被视为心血管疾病的模型。然而，将该模型与既定过程相结合用于研究心血管疾病可能会提供有价值的见解。在本文中，我们假设血管损伤会在转录组和蛋白质水平上诱导与心血管疾病中观察到的类似的基因表达、分子通讯和生物学过程的变化。为了对此进行研究，我们分析了损伤后两周小鼠受伤和未受伤股动脉的微阵列数据集中的基因表达，鉴定出1467个与包括血管阻塞、心律失常和动脉粥样硬化等几种心血管疾病相关的显著差异表达基因。我们进一步构建了一个具有七个功能不同簇的蛋白质-蛋白质相互作用网络，在细胞外基质、代谢过程、基于肌动蛋白的过程和免疫反应方面有显著富集。在这些簇之间观察到了显著的分子通讯，最显著的是在参与细胞外基质和细胞骨架组织、炎症和细胞周期的那些簇之间。机器学习疾病通路分析表明，血管损伤诱导的细胞外基质重塑和免疫反应簇之间的串扰促成了主动脉瘤、脉络膜新生血管形成和肾衰竭。此外，我们发现细胞外基质和肌动蛋白细胞骨架重组簇之间的相互作用与心脏损伤、颈动脉阻塞和心脏病变有关。总体而言，通过多尺度生物信息学分析，我们证明了血管损伤模型在引发与心血管疾病相关的转录组和分子网络变化方面的稳健性，突出了其在心血管研究中的应用潜力。
