AI-powered cross-level cross-species omics data integration to elucidate mechanisms of EL

摘要 识别特殊长寿(EL)等复杂性状的分子原因是一项艰巨的任务。 大多数机器学习算法都会生成基因类型和 表型，但可能无法推断出生理上有意义的原因。机械地理解如何 单个分子组件在一个系统中一起工作，以及系统如何受到影响并适应 分子变化需要所有生物水平的分子相互作用的知识，从DNA到 从RNA到蛋白质，再到代谢物，再到生物表型。通过整合多组学数据，最新的方法 在多模式机器学习和多层网络模型中有望解决这一不足。然而， 现有的机器学习方法受到高维、非一致性、海量 跨数据资源、数据领域和物种的多组学数据的混杂因素和生物学差异 以及由于机器学习模型的黑箱性质而缺乏可解释性。我们将开发一种 变革性深度学习框架，以应对多组学数据集成和预测的挑战 因果基因型-EL关联的建模。这个项目是建立在我们实质性的初步成果上的， 阿尔茨海默病药物发现和线虫作为疾病的系统药理学的成功 以及老化模型，以及实验和计算实验室之间的密切合作。我们会 克服几个障碍，以发现EL的分子机制。我们将发展和 验证新算法以1)通过移除环境和生物来协调非均匀数据集 混杂因素(例如，年龄、物种等)和技术偏差(例如，批处理效应)，2)明确地对 生物信息流从DNA到RNA，再到蛋白质，再到代谢物，再到生物表型，3) 确定致病的遗传因素和潜在的分子相互作用。具体来说，我们将：(1)发展 MuLGIT，一个因果深度学习驱动的跨层多组学协调和集成框架 这遵循了生物学的中心教条，用于破译EL背后的分子相互作用；(2)开发一种 跨物种组学数据集成与建模的迁移学习方法PATH-AE EL的进化保守性和物种特异性的分子决定因素；(3)确定分子靶标和 通过将多组学数据集成的新方法与最新状态相结合来开发EL的药物代理 用于化学基因组学和微扰基因组学的ART方法；以及(4)实验验证计算 使用线虫模型进行预测。该项目的完成将使我们能够识别新的生物标记物， 可用药靶标，以及与非凡寿命(EL)相关的药理药剂。

Abstract It is a formidable task to identify the molecular causes of complicated traits such as exceptional longevity (EL). The majority of machine learning algorithms generate mathematical correlations between genotypes and phenotypes, but may fail to infer physiologically significant causes. A mechanistic understanding of how individual molecular components work together in a system and how the system is affected and adapted to the molecular change requires knowledge of molecular interactions across all biological levels, from DNAs to RNAs to proteins to metabolites to organismal phenotypes. By integrating multi-omics data, recent approaches in multi-modal machine learning and multi-layer network model promise to address this deficiency. However, existing machine learning approaches are hampered by high-dimensionality, non-uniformity, numerous confounders, and biological differences in multi-omics data across data resources, data domains, and species as well as lack of interpretability due to the black-box nature of machine learning models. We will develop a transformative deep learning framework to address challenges for multi-omics data integration and predictive modeling of causal genotype-EL associations. This project is established on our substantial preliminary results, successes in systems pharmacology for Alzheimer's disease drug discovery and using C. elegans as disease and aging models, and close collaborations between experimental and computational laboratories. We shall overcome several obstacles in order to discover the molecular mechanisms of EL. We will develop and validate novel algorithms to 1) harmonize non-uniform data sets by removing environmental and biological confounding factors (e.g., age, species, etc.) and technical biases (e.g., batch effect), 2) explicitly model the biological information flow from DNAs to RNAs to proteins to metabolites to organismal phenotypes, and 3) determine causal genetic factors and molecular interactions underlying EL. Specifically, we will: (1) develop MuLGIT, a causal deep learning-powered cross-layer multi-omics harmonization and integration framework that follows the central dogma of biology for deciphering the molecular interplays underlying EL; (2) develop a transfer learning method PATH-AE for cross-species omics data integration and modeling for elucidating evolutionarily conserved and species-specific molecular determinants of EL; (3) identify molecular targets and pharmaceutical agents of EL by merging new methodologies for multi-omics data integration with state-of-the- art methods for chemical genomics and perturbation genomics; and (4) experimentally validate computational predictions using C. elegans models. Completion of this project will allow us to identify novel biomarkers, druggable targets, and pharmacological agents associated with remarkable lifespan (EL).