A Predictive Modeling Framework to Dissect the Dynamic Immunometabolic Responses to Pathogenic infection and the Kinetic Reprogramming of Metabolism in Cancer Cell System

剖析对病原体感染的动态免疫代谢反应和癌细胞系统代谢的动力学重编程的预测模型框架

• 批准号: 10667580
• 负责人: Rajib Saha
• 金额: $ 36.76万
• 依托单位: UNIVERSITY OF NEBRASKA LINCOLN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10667580
• 关键词: 2019-nCoV; Biological Models; Cells; Cellular Metabolic Process; Communities; Computer Models; Data Set; Databases; Disease; Drug Targeting; Epigenetic Process; Genes; Genetic; Immune; Immune response; Immune system; Immunological Models; Infection; Kinetics; Laboratories; Lead; Macrophage; Malignant Neoplasms; Metabolic; Metabolism; Methods; Modeling; Molecular; Organ; Outcome; Pathogenicity; Play; Prognostic Marker; Proteins; Reaction; Regulation; Research; Role; Severities; Severity of illness; Staphylococcus aureus infection; Symptoms; System; Testing; Work; cancer cell; data integration; experience; improved; kinetic model; metabolic phenotype; multi-scale modeling; neoplastic cell; novel therapeutic intervention; pancreatic ductal adenocarcinoma cell; pathogen; predictive modeling; programs; response; skills; therapeutic target; therapeutically effective; translational scientist

Cellular metabolism is emerging as a critical factor to control the immune responses and their impact on the pathogens. In addition, recent studies pinpoint a more prominent role of the aberrant metabolism in controlling both genetic and epigenetic cellular phenomena of any form of cancer. Thus, investigating the dynamic metabolic shift in immune cells upon pathogenic infection and temporal ‘reactomics’ (defined as a combination of reaction mechanisms, regulations, and kinetic parameters) and associated vulnerabilities of tumor cells holds immense potential to develop novel therapeutic approaches. While the existing multi-scale modeling of immune cells tries to bridge the gap between multiple scales (i.e., molecular to organ-level), none of the existing approaches can simultaneously do that by building a proper, predictive ‘full-scale’ model. Furthermore, whether or to what extent metabolic shifts occur in the host’s immune system is still not known. In case of cancer cell, some of the critical challenges include defining the systems-level cellular metabolic phenotype and tracking the temporal changes in reactomics which are critical for reverting the cell metabolism to more healthy state. Herein, PI Saha proposes to develop and iteratively improve a systems-level, comprehensive, and integrative metabolic modeling framework: i) to dissect the dynamic shifts in the immunometabolic responses associated with pathogenic Infection, and ii) investigate the changes in temporal reactomics associated with the metabolic reprogramming in a specific cancer cell. The proposed research program will leverage the unique combination of computational modeling skills and rich research experience in Saha’s laboratory that are crucial for characterizing the metabolic phenomena associated with any disease. His research team recently developed the first computationally tractable and accurate modeling framework to track the temporal dynamics of cellular metabolism and also established a new method to estimate the reactomics of each of the metabolic reactions involved in a cellular system when ‘omics’ datasets are incomplete or missing and, thereby, develop a predictive kinetic modeling framework. Thus, the proposed modeling framework can potentially investigate the metabolic dynamics associated with a cluster of cells (e.g., immune cells) interacting with a pathogen or the temporal reactomics of a specific cell (e.g., cancer cell). As a first step, Saha will investigate the dynamic metabolic shifts in a specific type of immune cell (i.e., macrophage) upon SARS-Cov-2 and Staphylococcus aureus infection and the temporal reprogramming and reactomics of pancreatic ductal adenocarcinoma (PDAC) cell metabolism and test the hypothesis that if the degree to these changes gives rise to the severity of the disease symptoms. Overall, the proposed framework as well as the associated ‘predictome’ database (containing the predictions of key genes/proteins/reactions playing critical roles) will provide the broader scientific community including molecular biologists, computational biologists, clinicians, and translational scientists with a basic understanding of the role of metabolism in dictating disease severity and also a useful template to investigate other diseases.

细胞代谢正在成为控制免疫应答及其对免疫应答的影响的关键因素。 病原体此外，最近的研究指出，异常代谢在控制糖尿病中的作用更为突出。 任何形式癌症的遗传和表观遗传细胞现象。因此，研究动态代谢 病原性感染时免疫细胞的转移和暂时的“反应性”（定义为反应性 机制、调节和动力学参数）和相关的肿瘤细胞脆弱性具有巨大的 开发新的治疗方法的潜力。虽然现有的免疫细胞多尺度建模试图 为了桥接多个刻度之间的差距（即，分子到器官水平），现有的方法都不能 同时通过建立一个适当的、预测性的“全面”模型来做到这一点。此外，是否或在多大程度上 代谢变化发生在宿主的免疫系统仍然是未知的。在癌细胞的情况下，一些关键的 挑战包括定义系统水平的细胞代谢表型和跟踪时间变化 这对于将细胞代谢恢复到更健康的状态至关重要。在此，PI Saha建议 开发和迭代改进系统级、全面和综合的代谢建模 框架：i）剖析与致病性疾病相关的免疫代谢反应的动态变化， 感染，以及ii）调查与代谢重编程相关的时间反应的变化 在特定的癌细胞中。拟议的研究计划将利用计算的独特组合， Saha实验室的建模技能和丰富的研究经验对于表征代谢至关重要， 与任何疾病有关的现象。他的研究小组最近开发了第一个计算 跟踪细胞代谢的时间动态的易处理和准确的建模框架， 建立了一种新的方法来估计每一个参与细胞代谢反应的反应组学， 当“组学”数据集不完整或缺失时，系统，从而开发预测动力学建模 框架.因此，所提出的建模框架可以潜在地研究代谢动力学 与小区簇相关联（例如，免疫细胞）与病原体相互作用或 特定小区（例如，癌细胞）。作为第一步，Saha将研究一个特定的细胞中的动态代谢变化， 免疫细胞的类型（即，SARS-Cov-2和金黄色葡萄球菌感染时， 胰腺导管腺癌（PDAC）细胞代谢的重编程和反应性切除术，并测试 如果这些变化的程度引起疾病症状的严重性的假设。总体看 建议的框架以及相关的“预测”数据库（包含关键的预测 基因/蛋白质/反应发挥关键作用）将提供更广泛的科学界，包括分子 生物学家，计算生物学家，临床医生和翻译科学家，对角色有基本的了解， 代谢在决定疾病严重程度方面的作用，也是研究其他疾病的有用模板。

项目成果

Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence.

机器学习和代谢模型引导的 CRISPRi 揭示了磷酸甘油酸变位酶在沙眼衣原体持久性中的核心作用。

• DOI: 10.1101/2023.12.18.572198
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Chowdhury,NiazBahar;Pokorzynski,Nick;Rucks,ElizabethA;Ouellette,ScotP;Carabeo,ReyA;Saha,Rajib
• 通讯作者: Saha,Rajib