Department of Psychiatry, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98, H-4032 Debrecen, Hungary.
Department of Pulmonology, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98, H-4032 Debrecen, Hungary.
Int J Mol Sci. 2024 Sep 6;25(17):9682. doi: 10.3390/ijms25179682.
The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedentary lifestyle behaviors. These are all linked to the altered function of the mesocorticolimbic (MCL) system. As the mesocorticolimbic circuit is central to the reward pathway and is involved in risk behaviors and mental disorders, we set out to test the hypothesis that these pathologies may be approached therapeutically as a group. To address these questions, the identification of novel targets by exploiting knowledge-based, network-based and disease similarity algorithms in two major Thomson Reuters databases (MetaBase™, a database of manually annotated protein interactions and biological pathways, and Integrity, a unique knowledge solution integrating biological, chemical and pharmacological data) was performed. Each approach scored proteins from a particular approach-specific standpoint, followed by integration of the scores by machine learning techniques yielding an integrated score for final target prioritization. Machine learning identified characteristic patterns of the already known targets (control targets) with high accuracy (area under curve of the receiver operator curve was ~93%). The analysis resulted in a prioritized list of 250 targets for MCL disorders, many of which are well established targets for the mesocorticolimbic circuit e.g., dopamine receptors, monoamino oxidases and serotonin receptors, whereas emerging targets included DPP4, PPARG, NOS1, ACE, ARB1, CREB1, POMC and diverse voltage-gated Ca channels. Our findings support the hypothesis that disorders involving the mesocorticolimbic circuit may share key molecular pathology aspects and may be causally linked to NCDs, yielding novel targets for drug repurposing and personalized medicine.
导致最常见的慢性非传染性疾病（NCDs）的风险行为包括酒精滥用、不健康饮食、吸烟和久坐不动的生活方式行为。这些都与中脑边缘多巴胺系统（MCL）的功能改变有关。由于中脑边缘多巴胺系统是奖励途径的核心，与风险行为和精神障碍有关，我们着手测试以下假设：这些病理学可能作为一个整体进行治疗。为了解决这些问题，我们在两个主要的汤姆森路透数据库（MetaBaseTM，一个手动注释蛋白质相互作用和生物途径的数据库，以及Integrity，一个集成生物、化学和药理学数据的独特知识解决方案）中，利用基于知识、基于网络和疾病相似性的算法，确定了新的靶点。每种方法都从特定方法的角度对蛋白质进行评分，然后通过机器学习技术对评分进行整合，得出最终目标优先级的综合评分。机器学习以高精度（接收者操作曲线下的面积约为 93%）识别了已经知道的目标（对照目标）的特征模式。分析结果得出了一个 MCL 疾病的 250 个优先目标列表，其中许多是中脑边缘多巴胺系统的既定目标，例如多巴胺受体、单胺氧化酶和血清素受体，而新兴的目标包括 DPP4、PPARG、NOS1、ACE、ARB1、CREB1、POMC 和多种电压门控钙通道。我们的研究结果支持这样的假设，即涉及中脑边缘多巴胺系统的疾病可能具有共同的关键分子病理学特征，并且可能与 NCDs 有因果关系，为药物再利用和个性化医学提供了新的靶点。
