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: 8176786
• 负责人: Madhavi K Ganapathiraju
• 金额: $ 35.91万
• 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/8176786
• 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 PUBLIC HEALTH RELEVANCE: The proposed research discovers interactions among proteins that are involved in mental health and inflammation. The knowledge of such interactions would generate hypotheses about the role of these proteins in biological functional pathways and would contribute towards the understanding of the biology of diseases and towards development of mechanisms of treatment.

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水平方面（包括精神过程）是必要的。PPI网络（interactome）是几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”有关，其中448个基因至今还没有一个PPI。虽然发现哪些蛋白质对相互作用是有用的，但由于超过99.9%的蛋白质对不相互作用，因此也极具挑战性。这项工作的目的是进行系统设计的计算工作，以发现人类心理健康和炎症（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参与大脑或炎症。在发现新的蛋白质-蛋白质相互作用以构建相互作用组方面将面临许多挑战。来自不同计算领域（如机器学习和信号处理）的成熟算法将被应用于实现所提出的目标。此外，神经心理过程和炎症过程相互影响的途径将从相互作用组中挖掘出来。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相互作用组的方法是主动学习（以获得将提供最大影响的蛋白质的标签和特征），转移学习（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（智能地整合来自不同预测器的输出）。预测的相互作用组将作为心理健康生物医学研究的中心资源，加速发现与心理健康相关疾病的生物学和治疗。对于每个感兴趣的蛋白质，将生成其相互作用的可验证假设，将该蛋白质相互作用的搜索空间从大约25，000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性炎症（与精神疾病密切相关）有关的蛋白质的新PPI将由专门研究这些领域的合作研究者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”。它将提供新蛋白质的“功能连接”的假设（即，目前还不知道的那些）。此外，发现许多疾病不能通过抑制单一途径来治疗，并且通常需要操纵多个途径，并且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，这将是拟议研究的结果，因此可以对我们理解心理健康的分子基础产生广泛的影响。1 公共卫生相关性：拟议的研究发现了与心理健康和炎症有关的蛋白质之间的相互作用。这种相互作用的知识将产生关于这些蛋白质在生物功能途径中的作用的假设，并将有助于理解疾病的生物学和治疗机制的发展。