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是目前无法治愈的致死性神经退行性疾病。