PDF(Pubmed)
Abstract：
UNASSIGNED: Pulmonary hypertension (PH) is a pathological condition that affects approximately 1% of the population. The prognosis for many patients is poor, even after treatment. Our knowledge about the pathophysiological mechanisms that cause or are involved in the progression of PH is incomplete. Additionally, the mechanism of action of many drugs used to treat pulmonary hypertension, including sotatercept, requires elucidation.
UNASSIGNED: Using our graph-powered knowledge mining software Lifelike in combination with a very small patient metabolite data set, we demonstrate how we derive detailed mechanistic hypotheses on the mechanisms of PH pathophysiology and clinical drugs.
UNASSIGNED: In PH patients, the concentration of hypoxanthine, 12(S)-HETE, glutamic acid, and sphingosine 1 phosphate is significantly higher, while the concentration of L-arginine and L-histidine is lower than in healthy controls. Using the graph-based data analysis, gene ontology, and semantic association capabilities of Lifelike, led us to connect the differentially expressed metabolites with G-protein signaling and SRC. Then, we associated SRC with IL6 signaling. Subsequently, we found associations that connect SRC, and IL6 to activin and BMP signaling. Lastly, we analyzed the mechanisms of action of several existing and novel pharmacological treatments for PH. Lifelike elucidated the interplay between G-protein, IL6, activin, and BMP signaling. Those pathways regulate hallmark pathophysiological processes of PH, including vasoconstriction, endothelial barrier function, cell proliferation, and apoptosis.
UNASSIGNED: The results highlight the importance of SRC, ERK1, AKT, and MLC activity in PH. The molecular pathways affected by existing and novel treatments for PH also converge on these molecules. Importantly, sotatercept affects SRC, ERK1, AKT, and MLC simultaneously. The present study shows the power of mining knowledge graphs using Lifelike\'s diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions. We believe that Lifelike and our presented approach will be valuable for future mechanistic studies of PH, other diseases, and drugs.
UNASSIGNED: Using our graph-powered knowledge mining software Lifelike in combination with a very small patient metabolite data set, we demonstrate how we derive detailed mechanistic hypotheses on the mechanisms of PH pathophysiology and clinical drugs.
UNASSIGNED: In PH patients, the concentration of hypoxanthine, 12(S)-HETE, glutamic acid, and sphingosine 1 phosphate is significantly higher, while the concentration of L-arginine and L-histidine is lower than in healthy controls. Using the graph-based data analysis, gene ontology, and semantic association capabilities of Lifelike, led us to connect the differentially expressed metabolites with G-protein signaling and SRC. Then, we associated SRC with IL6 signaling. Subsequently, we found associations that connect SRC, and IL6 to activin and BMP signaling. Lastly, we analyzed the mechanisms of action of several existing and novel pharmacological treatments for PH. Lifelike elucidated the interplay between G-protein, IL6, activin, and BMP signaling. Those pathways regulate hallmark pathophysiological processes of PH, including vasoconstriction, endothelial barrier function, cell proliferation, and apoptosis.
UNASSIGNED: The results highlight the importance of SRC, ERK1, AKT, and MLC activity in PH. The molecular pathways affected by existing and novel treatments for PH also converge on these molecules. Importantly, sotatercept affects SRC, ERK1, AKT, and MLC simultaneously. The present study shows the power of mining knowledge graphs using Lifelike\'s diverse set of data analytics functionalities for developing knowledge-driven hypotheses on PH pathophysiological and drug mechanisms and their interactions. We believe that Lifelike and our presented approach will be valuable for future mechanistic studies of PH, other diseases, and drugs.
摘要：
■肺动脉高压（PH）是一种影响约1％人群的病理状况。许多患者的预后很差，即使在治疗后。我们对引起或参与PH进展的病理生理机制的了解是不完整的。此外,许多用于治疗肺动脉高压的药物的作用机制，包括sotatercept,需要说明。
■使用我们的图形驱动的知识挖掘软件Lifelike结合非常小的患者代谢物数据集，我们展示了我们如何得出关于PH病理生理学和临床药物机制的详细机制假设。
■在PH患者中，次黄嘌呤的浓度，12(S)-HETE,谷氨酸，和鞘氨醇1磷酸盐明显更高，而L-精氨酸和L-组氨酸的浓度低于健康对照组。使用基于图形的数据分析，基因本体论，和Lifelike的语义关联能力，引导我们将差异表达的代谢物与G蛋白信号和SRC联系起来。然后，我们将SRC与IL6信号联系起来。随后，我们发现了连接SRC的关联，和IL6到激活素和BMP信号。最后,我们分析了几种现有的和新的药物治疗PH的作用机制。逼真地阐明了G蛋白之间的相互作用，IL6,activin,和BMP信号。这些途径调节PH的标志性病理生理过程，包括血管收缩,内皮屏障功能,细胞增殖，和凋亡。
■结果突出了SRC的重要性，ERK1,AKT,和PH中的MLC活性。受现有和新的PH治疗影响的分子途径也集中在这些分子上。重要的是,sotatercept影响SRC,ERK1,AKT,同时，MLC。本研究显示了使用Lifelike的各种数据分析功能来开发知识驱动的PH病理生理学和药物机制及其相互作用的假设的能力。我们相信Lifelike和我们提出的方法将对未来的PH机理研究有价值，其他疾病,和毒品。
■使用我们的图形驱动的知识挖掘软件Lifelike结合非常小的患者代谢物数据集，我们展示了我们如何得出关于PH病理生理学和临床药物机制的详细机制假设。
■在PH患者中，次黄嘌呤的浓度，12(S)-HETE,谷氨酸，和鞘氨醇1磷酸盐明显更高，而L-精氨酸和L-组氨酸的浓度低于健康对照组。使用基于图形的数据分析，基因本体论，和Lifelike的语义关联能力，引导我们将差异表达的代谢物与G蛋白信号和SRC联系起来。然后，我们将SRC与IL6信号联系起来。随后，我们发现了连接SRC的关联，和IL6到激活素和BMP信号。最后,我们分析了几种现有的和新的药物治疗PH的作用机制。逼真地阐明了G蛋白之间的相互作用，IL6,activin,和BMP信号。这些途径调节PH的标志性病理生理过程，包括血管收缩,内皮屏障功能,细胞增殖，和凋亡。
■结果突出了SRC的重要性，ERK1,AKT,和PH中的MLC活性。受现有和新的PH治疗影响的分子途径也集中在这些分子上。重要的是,sotatercept影响SRC,ERK1,AKT,同时，MLC。本研究显示了使用Lifelike的各种数据分析功能来开发知识驱动的PH病理生理学和药物机制及其相互作用的假设的能力。我们相信Lifelike和我们提出的方法将对未来的PH机理研究有价值，其他疾病,和毒品。
