Democratizing Multi-Omics to Expedite Discovery of Hidden Metabolic Pathways

项目总结/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性的差距，因为我们 缺乏用于高通量、无偏地发现间接代谢物-蛋白质连接的方法。续前- 这种知识差距的存在代表了公共卫生和NIH使命的一个主要问题，因为， 在它被填满之前，许多疾病的治疗方法的发展将在很大程度上仍然是棘手的。多组学分析 蛋白质组和代谢组来自同一系统提供了一个有前途的途径，发现隐藏的代谢 途径，但对人类专家解释的要求是一个关键的障碍， 从多组学实验中提取。迈耶研究小组的长期目标是， 威斯康星州将揭示以前隐藏的代谢途径。这里的总体目标，也就是第一步 在实现这一愿景，是民主化的多组学数据收集和数据解释，从而增加 代谢途径发现的速度。核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。这项研究的基本原理是，无偏见的数据- 用人工智能算法（如深度神经网络）驱动发现新的代谢联系将导致 新的和创新的治疗目标，可以积极或消极地操纵，以预防或治疗疾病， 放松。在初步数据和文献的指导下，这一假设将通过追求两个互补的 重点领域：（1）多组学数据整合，以及（2）多组学数据收集。多组学数据集成 Focus使用已经在申请人的实验室中建立的可行的AI模型来预测代谢物-蛋白质间 行动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，新的代谢联系将使用经典的遗传和生物化学技术进行验证。的 第二个重点领域是建立新的、快速的多组学数据收集方法，将数据输入人工智能模型， 根据申请人最近公布的进展（Meyer等人，ChemRxiv 2020，在Nature Meth- ods）。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，并且还扩展了 在单一平台上实现多组学分析的方法。这种方法是创新的，因为它脱离了 缓慢的多组学数据解释的现状需要专业人员通过构建和验证新的， 自动AI方法用于代谢途径发现。多组学数据收集重点是创新的， 因为它从缓慢的多组学数据收集的现状出发，需要多个平台和小时， 通过在几分钟内实现统一的多组学分析，这一贡献将是巨大的，因为多- 因此，该项目产生的知识、经验证的方法和资源数据集将打开新的视野， 在药物开发方面，如癌症和糖尿病等代谢改变的疾病。

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.