Patidar Krutika, Deng Jennifer H, Mitchell Cassie S, Ford Versypt Ashlee N
Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY 14260, USA.
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Int J Mol Sci. 2024 Apr 19;25(8):4503. doi: 10.3390/ijms25084503.
Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study's goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains-DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/β-catenin pathway, the renin-angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein-protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments.
糖尿病肾病（DKD）是全球终末期肾病的主要原因。本研究的目标是通过基于人工智能的文献发现来识别调节DKD中肾小球内皮功能障碍的信号驱动因素和信号通路。使用SemNet 2.0对3300多万篇PubMed文章进行跨领域文本挖掘，以识别与DKD相关的多尺度和多因素病理生理概念并进行排名。使用归一化平均HeteSim分数对一组已识别的调节与DKD相关的不同病理事件的相关基因和蛋白质进行分析和排名。排名靠前的基因和蛋白质涉及三个领域——DKD、免疫反应和肾小球内皮细胞。排名前10%的概念被映射到以下生物学功能：血管生成、凋亡过程、细胞粘附、趋化性、生长因子信号传导、血管通透性、一氧化氮反应、氧化应激、细胞因子反应、巨噬细胞信号传导、NFκB因子活性、TLR途径、葡萄糖代谢、炎症反应、ERK/MAPK信号反应、JAK/STAT途径、T细胞介导的反应、WNT/β-连环蛋白途径、肾素-血管紧张素系统和NADPH氧化酶活性。排名靠前的基因和蛋白质被用于生成蛋白质-蛋白质相互作用网络。研究结果对DKD中信号失调所涉及的相互作用或分子进行了优先排序，可通过生化网络模型或实验进一步评估。
