MOIR - Machine Learning and Modeling Core

MOIR - 机器学习和建模核心

• 批准号: 10731663
• 负责人: Ashish Arunkumar Sharma
• 金额: $ 11.37万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-03 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731663
• 关键词: Acceleration; Adaptive Immune System; Binding; Bioinformatics; Biological Assay; Biological Models; Biological Process; Biometry; CCR5 gene; CD4 Positive T Lymphocytes; Cells; Clinical Data; DNA; Data; Data Set; Diet; Environment; Environmental Risk Factor; Epigenetic Process; FAIR principles; Future; Generations; Genes; HIV; HIV-1; Heterogeneity; Human; Individual; Infection; Infusion procedures; Innate Immune System; Machine Learning; Maintenance; Maps; Microbe; Mission; Modeling; National Institute of Allergy and Infectious Disease; Outcome; Process; Proteins; Publications; Recurrence; Regulation; Reproducibility; Research; Resources; Sampling; Services; Therapeutic; Therapeutic Intervention; Validation; Viral Load result; Virus Replication; Visualization; anti-PD1 antibodies; antibody immunotherapy; data management; epigenome; experimental study; immune function; immunological intervention; immunoregulation; insight; large scale data; metabolome; microbiome; multiple omics; neutralizing antibody; novel; power analysis; programs; public repository; transcriptome; viral rebound

PROJECT SUMMARY Despite the durable suppression of viral replication by ART, HIV persists indefinitely in infected individuals. Several promising avenues to cure HIV-1 infection have come to light, including gene editing of the CCR5 receptor in CD4 T cells, anti-PD1 monoclonal antibody immunotherapy and the infusion of broadly neutralizing antibodies. These therapeutic approaches constitute a prime opportunity to extensively understand the underlying mechanisms associated with the establishment and maintenance of the HIV reservoir, which will ultimately serve to identify key novel targets for future more refined therapies. Furthermore, the heterogeneity in human immune function has been mapped to multiple environmental factors such as microbiome, metabolome and diet, some of which have been associated with the maintenance of the HIV-1 reservoir. In this P01, we hypothesize that specific key metabolites, microbes and other environmental factors influence the responsiveness to different therapeutic approaches targeting the HIV reservoir. The main objective of the Machine Learning and Modeling Core (MLMC) will be to bring together all datasets generated by Projects 1-3 into a cohesive whole to generate mechanistic models of HIV reservoir maintenance. We shall look into how metabolites modulate the immune transcriptome and epigenome of many subsets. In Aim 1, the MLMC will provide statistical and bioinformatics support for all projects and identify key correlates of HIV viral rebound and HIV DNA decay from all large-scale datasets (OMICs). In Aim 2, the MLMC will perform integrative analysis using novel datasets generated in Aim 1. In Aim 3, the Core will integrate parallel models of regulation of the HIV reservoir into a global unified model where key recurrent features will be identified as prime targets for future therapeutic avenues. The MLMC will thus serve as the central resource for this U19 for the integration of all datasets and generation of mechanistic insights.

项目摘要 尽管抗逆转录病毒疗法能持久抑制病毒复制，但艾滋病毒在感染者体内会无限期存在。 几种有希望治愈HIV-1感染的方法已经出现，包括对CCR 5进行基因编辑。 CD 4 T细胞受体，抗PD 1单克隆抗体免疫治疗和广泛中和的输注 抗体的这些治疗方法构成了广泛了解 与建立和维持艾滋病毒库有关的基本机制， 最终有助于为未来更精细的疗法确定关键的新靶点。此外， 人类免疫功能已被映射到多种环境因素，例如微生物组、代谢组 和饮食，其中一些与HIV-1储存库的维持有关。在P01中，我们 假设特定的关键代谢物，微生物和其他环境因素影响 不同的治疗方法针对艾滋病毒库的反应。的主要目标 机器学习和建模核心（MLMC）将汇集项目生成的所有数据集 1-3整合成一个有凝聚力的整体，以生成HIV储存库维护的机械模型。我们要看看 代谢物如何调节免疫转录组和表观基因组的许多子集。在目标1中， 将为所有项目提供统计和生物信息学支持，并确定艾滋病毒反弹的关键相关因素 和HIV DNA衰变从所有大规模数据集（OMIC）。在目标2中，MLMC将进行综合分析 使用Aim 1中生成的新数据集。在目标3中，核心方案将整合监管的并行模式， 将艾滋病毒储存库纳入全球统一模型，其中将确定关键复发特征为未来的主要目标 治疗途径因此，MLMC将成为U19的核心资源，以整合所有人 数据集和机械见解的生成。