Implementing a coupled system of integrative ML modeling and data validation for elucidating microglial therapeutic targets in neurodegenerative disease

实施集成机器学习建模和数据验证的耦合系统，以阐明神经退行性疾病中的小胶质细胞治疗靶点

• 批准号: 10699794
• 负责人: Karen SACHS
• 金额: $ 145.73万
• 依托单位: MODULO BIO, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10699794
• 关键词: Acceleration; Active Learning; Address; Affect; Antisense Oligonucleotides; Area; Biological; Biological Assay; Biological Models; Biology; Cells; Classification; Clinical; Coculture Techniques; Computer Models; Coupled; Data; Data Set; Databases; Disease; Disease Pathway; Disease Progression; Disease model; Disparate; Drug Targeting; Elements; Etiology; Genes; Goals; Health; Image; In Vitro; Individual; Knowledge; Learning; Link; Methods; Microglia; Modeling; Molecular; Mus; Nerve Degeneration; Neurodegenerative Disorders; Neuroimmune; Neurons; Organism; Pathogenesis; Pathway Analysis; Pathway interactions; Patients; Persons; Physiological; Research; Science; System; Testing; Therapeutic; Therapeutic Intervention; Tissues; Validation; Work; causal model; causal variant; cell type; comparative; data framework; data integration; design; disorder subtype; drug development; effective therapy; flexibility; frontotemporal lobar dementia amyotrophic lateral sclerosis; heterogenous data; in silico; in vitro Model; in vivo; individual patient; induced pluripotent stem cell; insight; large scale data; learning network; machine learning framework; machine learning model; mouse model; multidimensional data; multiple datasets; network models; neuroimmunologic disease; neuroprotection; new therapeutic target; personalized medicine; predictive modeling; risk variant; screening; small molecule; stem cell model; therapeutic target; tool

Project Summary/Abstract: ALS and FTD are fatal neurodegenerative diseases that presently have no cure. To date, one focus area in ALS research has been developing model systems to characterize the condition, with over 20 different ALS mouse models, and more recently, numerous iPSC based models, each gradually contributing to our overall knowledge of the mechanisms behind neurodegeneration, and the contribution of the neuro-immune interface. Despite the multitude of disease models, there is no overarching, computational modeling framework for integrating disparate datasets, towards the goal of characterizing disease networks, and identifying therapeutic targets. Moreover, while standard ML models for target prediction have become ubiquitous in the biomedical sciences, they fail to learn causality, shedding little insight into underlying disease etiology and failing to make effective target predictions. Our proposal’s long-term goal is to create a flexible pipeline, applicable to ND diseases, to characterize the neuro-immune interface and its contribution to ND etiology, to enable therapeutic intervention by creating an integrated workflow to identify ND microglial disease networks in health, disease, and disease subsets. We will capitalize on existing experimental data as well as internal iPSC based in vitro models, paired with a causal ML model. Each component of this workflow can work independently, or can be linked to the other in a powerful ‘active learning’ framework, in which the ML model makes predictions, the co-culture system validates or disproves the prediction, and in each such round the in silico model is refined by integrating the new experimental data. Our causal machine learning model characterizes ND neuro-immune networks from analysis of combined molecular, clinical, and functional data in a multi-layered format with individual layers for ND disease state, data platform, and cell state analyzed simultaneously to bolster confidence for inferences shared among numerous layers and identify unique, and therapeutically relevant, network elements. We will focus initially on therapeutic interventions for ALS, followed by related ND diseases also characterized in the network model. The objectives of this proposal are: (1) to refine an in silico framework for data integration across NDs, microglial subsets, and heterogeneous datasets/data platforms enabling a robust model for therapeutic target prediction and (2) to validate predicted targets in our iPSC microglia and neuron co-culture system using in vitro perturbations (including antisense-oligonucleotides and small molecules) and high-content imaging analysis. The central hypothesis is that comprehensively integrating available data across public datasets and databases, ND diseases, model species, data platforms, and tissue types, with data from our co-culture screening platform, in a powerful mechanistic model, will enable elucidation of causal disease pathways, comparative analysis across conditions, and the identification of therapeutic targets. Ultimately, characterization of individuals can even enable personalized therapy approaches as well as identification of disease subtypes. 1

项目摘要/摘要：ALS 和 FTD 是致命的神经退行性疾病，目前无法治愈。 迄今为止，ALS 研究的一个重点领域是开发模型系统来表征这种状况， 超过 20 种不同的 ALS 小鼠模型，以及最近的许多基于 iPSC 的模型，每种模型都逐渐 有助于我们对神经退行性变背后机制的整体了解，以及 尽管有多种疾病模型，但没有总体的计算模型。 用于整合不同数据集的建模框架，以实现描述疾病网络的目标， 此外，用于目标预测的标准机器学习模型已经成为。 它们在生物医学中无处不在，但它们无法了解因果关系，对潜在疾病几乎没有了解 我们建议的长期目标是创建一个灵活的目标预测。 适用于 ND 疾病的管道，用于表征神经免疫界面及其对 ND 的贡献 病因学，通过创建集成工作流程来识别 ND 小胶质细胞疾病，从而实现治疗干预 我们将利用现有的实验数据以及健康、疾病和疾病子集的网络。 基于内部 iPSC 的体外模型，与因果 ML 模型配对，该工作流程的每个组件都可以发挥作用。 独立地，或者可以在强大的“主动学习”框架中与另一个框架联系起来，其中机器学习模型 做出预测，共培养系统验证或反驳预测，并且在每一轮中 通过整合新的实验数据来完善硅模型。 通过分析组合的分子、临床和功能数据来描述 ND 神经免疫网络的特征 多层格式，其中包含 ND 疾病状态、数据平台和细胞状态分析的各个层 同时增强对多个层之间共享的推论的信心，并识别独特的和 我们将重点关注 ALS 的初始治疗干预措施。 该提案的目标是：（1） 完善跨 ND、小胶质细胞子集和异构数据集成的计算机框架 数据集/数据平台为治疗目标预测提供强大的模型，并且（2）验证预测 我们的 iPSC 小胶质细胞和神经元共培养系统中使用体外扰动（包括 反义寡核苷酸和小分子）和高内涵成像分析。 假设是全面整合公共数据集和数据库中的可用数据，ND 疾病、模型物种、数据平台和组织类型，以及来自我们共培养筛选平台的数据， 一个强大的机制模型将能够阐明致病途径，并进行比较分析 最终，甚至可以对个体进行表征。 实现个性化治疗方法以及疾病亚型的识别。 1