Discovery of Mental Health and Inflammation (MHAIN) Interactome

心理健康与炎症 (MHAIN) 相互作用组的发现

• 批准号: 8505571
• 负责人: Madhavi K Ganapathiraju
• 金额: $ 14.45万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8505571
• 关键词: Address; Adverse effects; Algorithms; Alzheimer' s Disease; Anxiety; Anxiety Disorders; Area; Autophagocytosis; Behavior; Biological; Biology; Biomedical Research; Brain; Chronic; Data; Development; Disease; Drug Delivery Systems; Genes; Genetic; Goals; Happiness; Human; Immunity; Infection; Inflammation; Inflammatory; Inflammatory Response; Information Networks; Knowledge; Label; Lead; Learning; Literature; Machine Learning; Major Depressive Disorder; Malignant Neoplasms; Mental Depression; Mental Health; Mental disorders; Methods; Mind; Mining; Molecular; Multiple Sclerosis; Neurologic; Organism; Outcome; Output; Panic; Pathway interactions; Patients; Physiological; Physiological Processes; Principal Investigator; Process; Proteins; Psychiatry; Psychological Side Effects; Psychological Stress; Psychological Transfer; Psychotic Disorders; Research; Research Personnel; Resources; Role; Schizophrenia; System; Systemic disease; Systems Biology; Thinking; Viral Cancer; Virus Diseases; Work; base; body-mind; brain behavior; computerized data processing; design; environmental stressor; insight; interest; neuropsychological; protein protein interaction; psychologic; relating to nervous system; research study; response; sensor

DESCRIPTION (provided by applicant): Knowledge of protein-protein interactions (PPIs) is necessary to understand system-level aspects of organisms including that of psychiatric processes. The PPI network (interactome) acts as a vehicle for several types of biomedical research. It can be used to understand the disease mechanisms, drug targets and side effects, and genetic causes for disease. There are 1,014 human genes associated with 'brain'; for 448 of these genes not even a single PPI is known today. While it is useful to discover which pairs of proteins interact, it is also exceptionally challenging as more than 99.9% of protein pairs do not interact. The objective of this work is to carry out systematically designed computational work to discover the human mental health and inflammation (MHAIN) interactome. The MHAIN interactome refers to the network of PPIs where at least one of the two proteins is involved in either brain or inflammation. Many challenges will be addressed in discovering new protein-protein interactions towards building the interactome. Well-established algorithms from diverse computational fields, such as machine learning and signal processing will be applied to achieve the proposed goals. Further, pathways of influence of neuropsychological processes and inflammatory processes on each other will be mined from the interactome. Selected interactions will be validated by wet-lab experiments. The approaches that will be employed for predicting the MHAIN interactome are proactive-learning (to obtain the labels and features of proteins that would provide the maximum impact), transfer-learning (to transfer the knowledge of protein features and interactions from one species to another), multi-sensor fusion (to intelligently integrate output from different predictors). The predicted interactome would accelerate discovery of the biology and treatment of mental health related diseases, by serving as a central resource for biomedical research on mental health. For every protein of interest, verifiable hypothesis of its interactions will be generated that reduce the search space of interactions of that protein from about 25,000 to a few possibilities. New PPIs of proteins involved in major depressive disorder, psychosis in patients with Alzherimer's disease, and systemic inflammation (which is intricately connected to psychiatric diseases), will be put in biomedical context by co- investigators who specialize in these areas. The interactome will identify "hub-proteins" that are central to many pathways. It will provide hypothesis of "functional connectivity" of new proteins (namely, those of which nothing is currently known). Further, many diseases are found to be untreatable by suppressing a single pathway, and often require manipulating multiple pathways, and the MHAIN interactome can provide the overlap points of multiple pathways that may be used for treatment of diseases. The MHAIN interactome, which would be the outcome of the proposed research, can thus have a broad impact on our understanding of molecular basis of mental health. 1

描述（由申请人提供）：蛋白质-蛋白质相互作用（PPI）的知识对于理解生物体的系统级方面（包括精神过程）是必要的。 PPI 网络（交互组）充当多种生物医学研究的载体。它可用于了解疾病机制、药物靶点和副作用以及疾病的遗传原因。人类有 1,014 个基因与“大脑”相关；对于其中 448 个基因，目前甚至连一个 PPI 都不为人所知。虽然发现哪些蛋白质对相互作用很有用，但它也非常具有挑战性，因为超过 99.9% 的蛋白质对不相互作用。这项工作的目标是进行系统设计的计算工作，以发现人类心理健康与炎症 (MHAIN) 相互作用组。 MHAIN 相互作用组是指 PPI 网络，其中两种蛋白质中至少有一种与大脑或炎症有关。在发现新的蛋白质-蛋白质相互作用以构建相互作用组时，将解决许多挑战。来自不同计算领域（例如机器学习和信号处理）的成熟算法将用于实现所提出的目标。此外，将从相互作用组中挖掘神经心理过程和炎症过程相互影响的途径。选定的相互作用将通过湿实验室实验进行验证。用于预测 MHAIN 相互作用组的方法包括主动学习（获取可提供最大影响的蛋白质标签和特征）、迁移学习（将蛋白质特征和相互作用的知识从一个物种转移到另一个物种）、多传感器融合（智能整合不同预测因子的输出）。预测的相互作用组将作为心理健康生物医学研究的核心资源，加速心理健康相关疾病的生物学和治疗的发现。对于每种感兴趣的蛋白质，都会生成其相互作用的可验证假设，从而将该蛋白质相互作用的搜索空间从大约 25,000 种减少到几种可能性。与重度抑郁症、阿尔茨海默病患者的精神病和全身炎症（与精神疾病密切相关）相关的蛋白质的新 PPI 将由专门研究这些领域的共同研究人员置于生物医学背景下。相互作用组将识别对许多途径至关重要的“枢纽蛋白”。它将提供新蛋白质（即目前未知的蛋白质）“功能连接”的假设。此外，许多疾病被发现无法通过抑制单一途径来治疗，并且往往需要操纵多个途径，而MHAIN相互作用组可以提供可用于治疗疾病的多个途径的重叠点。 MHAIN 相互作用组是拟议研究的结果，因此可以对我们对心理健康分子基础的理解产生广泛的影响。 1