Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic Pathways

民主化多组学加速发现隐藏的代谢途径

• 批准号: 10470828
• 负责人: Jesse Meyer
• 金额: $ 41.75万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-28 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10470828
• 关键词: Area; Artificial Intelligence; Biochemical; Data; Data Analyses; Data Collection; Data Set; Diabetes Mellitus; Disease; Genetic; Goals; Hour; Human; Knowledge; Learning; Literature; Longevity; Malignant Neoplasms; Metabolic; Metabolic Pathway; Metabolism; Methods; Mission; Modeling; Multiomic Data; Nature; Organism; Partner in relationship; Pathway interactions; Proteins; Proteome; Public Health; Publishing; Research; Resources; Sampling; System; Techniques; Testing; United States National Institutes of Health; Vision; Wisconsin; Yeasts; artificial intelligence algorithm; base; data integration; deep neural network; drug development; experimental study; innovation; medical schools; metabolome; multiple omics; new technology; novel; novel therapeutics; prevent; protein metabolite; therapeutic target; therapy development

PROJECT SUMMARY/ABSTRACT There is a fundamental gap in our understanding of how metabolism changes in many diseases because we lack methods for high-throughput, unbiased discovery of indirect metabolite-protein connections. Continued ex- istence of this knowledge gap represents a major issue for public health and the mission of the NIH because, until it is filled, development of treatments for many diseases will remain largely intractable. Multi-omic analysis of proteomes and metabolomes from the same system offers a promising path to discover hidden metabolic pathways, but the requirement for human expert interpretation is a critical barrier that prevents complete value extraction from multi-omic experiments. The long-term goal of the Meyer Research Group at Medical College of Wisconsin is to reveal previously hidden metabolic pathways. The overall objective here, which is the first step in realizing this vision, is to democratize multi-omic data collection and data interpretation, thereby increasing the pace of metabolic pathway discovery. The central hypothesis is that artificial intelligence models can learn to draw new metabolic connections between metabolites and proteins. This hypothesis is based on preliminary data generated by the applicant and published literature, which shows how the strategy reveals known and new connections between metabolites and proteins. The rationale for the proposed research is that unbiased, data- driven discovery of new metabolic connections with AI algorithms (such as deep neural networks) will result in new and innovative therapeutic targets that can be manipulated positively or negatively to prevent or treat dis- ease. Guided by preliminary data and literature, this hypothesis will be tested by pursuing two complementary focus areas: (1) multi-omic data integration, and (2) multi-omic data collection. The multi-omic data integration focus uses AI models, already established as feasible in the applicant’s lab, to predict metabolite-protein inter- actions. AI models will be optimized with existing public data, models will be validated with newly collected data, and then novel metabolic connections will be validated using classic genetic and biochemical techniques. The second focus area builds new, fast methods for multi-omic data collection to feed data into AI models, starting from a recent advancement published by the applicant (Meyer et al., ChemRxiv 2020, accepted at Nature Meth- ods). The applicant’s lab will further develop this method to quantify the full yeast proteome, and also extend the method to enable multi-omic analysis on a single platform. This approach is innovative because it departs from the status quo of slow multi-omic data interpretation requiring expert humans by building and validating a new, automated AI method for metabolite pathway discovery. The multi-omic data collection focus is innovative be- cause it departs from the status quo of slow multi-omic data collection requiring multiple platforms and hours per sample by enabling unified multi-omic analysis in minutes. This contribution will be significant because ulti- mately, the knowledge, validated methods, and resource datasets generated by this project will open new hori- zons in drug development for diseases with altered metabolism, such as cancers and diabetes.

项目概要/摘要 我们对许多疾病中新陈代谢如何变化的理解存在根本差距，因为我们 缺乏高通量、公正地发现间接代谢物-蛋白质联系的方法。 这种知识差距的存在是公共卫生和 NIH 使命的一个重大问题，因为， 在它被填补之前，许多疾病的治疗方法的开发在很大程度上仍然是棘手的。 来自同一系统的蛋白质组和代谢组的研究为发现隐藏的代谢提供了一条有希望的途径 路径，但对人类专家解释的要求是阻碍完整价值的关键障碍 从多组学实验中提取 医学院迈耶研究小组的长期目标。 威斯康星州的总体目标是揭示以前隐藏的代谢途径，这是第一步。 实现这一愿景的目标是使多组学数据收集和数据解释民主化，从而提高 代谢途径发现的速度的中心假设是人工智能模型可以学习。 绘制代谢物和蛋白质之间新的代谢联系这一假设是基于初步的。 申请人生成的数据和已发表的文献，显示该策略如何揭示已知和新的 代谢物和蛋白质之间的联系所提出的研究的基本原理是公正的、数据的。 与人工智能算法（例如深度神经网络）驱动的新代谢联系的发现将导致 可以积极或消极地操纵以预防或治疗疾病的新的和创新的治疗目标 在初步数据和文献的指导下，将通过追求两个互补的方法来检验这一假设。 重点领域：(1) 多组学数据集成，(2) 多组学数据收集。 focus 使用申请人实验室中已经建立的可行的人工智能模型来预测代谢物-蛋白质间的关系 人工智能模型将利用现有的公共数据进行优化，模型将利用新收集的数据进行验证， 然后将使用经典的遗传和生化技术来验证新的代谢联系。 第二个重点领域构建新的、快速的多组学数据收集方法，将数据输入人工智能模型，开始 来自申请人最近发表的进展（Meyer 等人，ChemRxiv 2020，被 Nature Meth 接受） 申请人的实验室将进一步开发这种方法来量化完整的酵母蛋白质组，并扩展该方法。 这种方法是创新的，因为它偏离了单一平台上的多组学分析。 缓慢的多组学数据解释现状需要专家通过构建和验证新的、 用于代谢途径发现的自动化人工智能方法。多组学数据收集的重点是创新性的。 因为它偏离了缓慢的多组学数据收集的现状，需要多个平台和每个小时 通过在几分钟内实现统一的多组学分析来获得样本，这一贡献将是巨大的，因为多组学。 相应地，该项目生成的知识、经过验证的方法和资源数据集将开启新的视野。 针对代谢改变疾病（例如癌症和糖尿病）的药物开发领域。