Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic Pathways

项目摘要/摘要 在我们对许多疾病的新陈代谢如何变化的理解上有一个根本的差距，因为我们 缺乏高通量、不偏不倚地发现间接代谢物-蛋白质联系的方法。继续前- 认识到这一知识差距是公共卫生和NIH使命的一个重大问题，因为， 在它被填满之前，许多疾病的治疗方法的开发将在很大程度上仍然是棘手的。多元经济学分析 来自同一系统的蛋白质组和代谢组的组合为发现隐藏的新陈代谢提供了一条有希望的途径 途径，但对人类专家解释的要求是阻碍完整价值的关键障碍 从多组实验中提取。迈耶医学院迈耶研究小组的长期目标 威斯康星州将揭示之前隐藏的代谢途径。这里的总体目标，这是第一步 在实现这一愿景时，就是要使多组学数据收集和数据解释民主化，从而增加 代谢途径发现的速度。中心假设是人工智能模型可以学习 在代谢物和蛋白质之间建立新的代谢联系。这一假设是基于初步的 申请者生成的数据和出版的文献，显示了该策略如何揭示已知和新的 代谢物和蛋白质之间的联系。这项拟议研究的理由是，公正、数据- 利用人工智能算法(如深度神经网络)推动发现新的新陈代谢连接将导致 新的和创新的治疗靶点，可以积极或消极地操纵，以预防或治疗疾病。 放松点。在初步数据和文献的指导下，这一假说将通过追求两个互补来检验 重点领域：(1)多组学数据集成，(2)多组学数据收集。多体数据集成 Focus使用在申请人的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 行为。人工智能模型将使用现有的公共数据进行优化，模型将使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。这个 第二个重点领域为多组数据收集建立了新的、快速的方法，以将数据输入人工智能模型，从 来自申请人最近发表的进展(Meyer等人，ChemRxiv 2020，自然-Meth-接受- 消耗臭氧层物质)。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，并扩大 一种在单一平台上实现多组分析的方法。这种方法是创新的，因为它脱离了 缓慢的多组数据解释的现状需要专家通过构建和验证新的、 代谢物途径发现的自动人工智能方法。多组学数据收集的重点是创新的BE-BE 因为它不同于需要多个平台和每小时进行缓慢的多组数据收集的现状 通过在几分钟内实现统一的多组分分析来实现样本。这一贡献将是重大的，因为多- 最终，该项目产生的知识、有效的方法和资源数据集将开辟新的天地-- 针对新陈代谢改变的疾病，如癌症和糖尿病，药物开发中的区域。

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.