Mathematical ecology models of host-microbiota interaction in auto microbiota transplants (auto-FMT)

自体微生物移植（auto-FMT）中宿主与微生物相互作用的数学生态学模型

• 批准号: 10089385
• 负责人: Ying Taur
• 金额: $ 80.47万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10089385
• 关键词: Address; Allogenic; Anaerobic Bacteria; Antibiotic Prophylaxis; Antibiotic Therapy; Antibiotics; Autologous; Bioreactors; Blood Cells; Cancer Patient; Clinical; Clinical Data; Clinical Research; Clostridium difficile; Communities; Complement; Complete Blood Count; Complication; Computational Technique; Data; Data Science; Data Set; Databases; Development; Dose; Ecology; Ecosystem; Enrollment; Experimental Models; Frequencies; Future; Genetic; Goals; Health; Hematopoietic; Hematopoietic Stem Cell Transplantation; Human; Immune system; In Vitro; Infection; Intervention; Laboratories; Learning; Life; Machine Learning; Mathematics; Medical; Memorial Sloan-Kettering Cancer Center; Metadata; Modeling; Monitor; Mus; Neutropenia; Patients; Pharmaceutical Preparations; Play; Process; Randomized; Randomized Controlled Trials; Recovery; Research; Resources; Ribosomal RNA; Role; Sampling; Series; Shotgun Sequencing; Time; Transplantation; Validation; base; cancer therapy; clinical database; commensal bacteria; design; experimental study; fecal transplantation; gut microbiota; host microbiome; host microbiota; human microbiota; improved; improved outcome; in vivo; in vivo Model; indexing; large datasets; mathematical model; microbial; microbial community; microbial composition; microbiome; microbiota; microbiota transplantation; microorganism interaction; mortality; mouse model; next generation; patient population; patient subsets; predictive modeling; prevent; prophylactic; reconstitution; response; simulation; stem cell engraftment; success

Project Summary Mathematical ecology models of host-microbiota interaction in auto microbiota transplants (auto-FMT) We aim to develop mathematical models for the rational design of microbiota transplants that can restore compositional diversity and function to the damaged microbiota of antibiotic-treated patients. We will focus on hospitalized cancer patients receiving allogeneic hematopoietic stem cell transplants (allo-HSCT). Allo-HSCT is a potentially curative cancer treatment that compromises the immune system, and requires that patients receive massive antibiotic treatments to prevent and treat life-threatening infections. We will build on a vast clinical database, in vitro experiments in bioreactors and in vivo experiments with mice to develop dynamic mathematical models that describe how antibiotics cause changes in the microbial composition, and how that can impact the recovery of the host's immune system after allo-HSCT. The model expands approaches pioneered by our team—the Generalized Lotka Volterra Ecological Regression (GLOVER) and agent-based models—towards a model that can assist in the development of microbiota therapies for patients undergoing allo-HSCT. In aim 1 we will use data from a unique clinical resource available at the Memorial Sloan Kettering Cancer center—a sample bank obtained from >1,500 allo-HSCT patients (including microbiome 16S rRNA and shotgun sequencing) and extensive clinical metadata (including time series of complete blood counts and time and doses of all drugs given while the patients are hospitalized); we will also use data from a first-of-its-kind controlled randomized trial of autologous fecal microbiota transplant (auto-FMT) undergoing in allo-HSCT patients. We will use these unique resources to parameterize our models and investigate how the microbiota composition influences the recovery of the host immune system. In aim 2 we will validate the microbial component of our mathematical model using experimental data from anaerobic laboratory reactors that recreate—in vitro—the human microbiota dynamics during antibiotic treatment and auto-FMT in the absence of a living host. In aim 3 we will develop mouse models to investigate those same microbiota dynamics experimentally but now in the context of a living host. The data obtained from these clinical studies, in vitro experiments and in vivo models will refine our mathematical models in close cycles of simulation and quantitative experimentation. Our ultimate goal is to develop models that can define optimal microbial cocktails and reconstitute the perturbed microbiota of allo- HSCT patients. In the process we hope to uncover general principles of microbiota ecology for future therapies in other patient populations whose microbiota is damaged by antibiotic treatments.

项目摘要 宿主-微生物群相互作用的数学生态学模型 自体微生物群移植（auto-FMT） 我们的目标是开发数学模型，用于合理设计微生物群移植， 组成的多样性和功能，以受损的微生物群的抗肿瘤治疗的患者。我们将专注于 接受异基因造血干细胞移植（allo-HSCT）的住院癌症患者。allo-HSCT 是一种潜在的治愈癌症的治疗方法，损害免疫系统，并要求患者 接受大量抗生素治疗，以预防和治疗危及生命的感染。我们将建立在一个巨大的 临床数据库，生物反应器中的体外实验和小鼠体内实验，以开发动态 数学模型描述了抗生素如何引起微生物组成的变化，以及 会影响异基因造血干细胞移植后宿主免疫系统的恢复。该模型扩展了 由我们的团队开创-广义洛特卡沃尔泰拉生态回归（格洛弗）和基于代理的 模型-建立一个模型，可以帮助开发微生物群疗法， allo-HSCT。 在目标1中，我们将使用来自Memorial Sloan Kettering的独特临床资源的数据。 癌症中心-从> 1，500名allo-HSCT患者获得的样本库（包括微生物组16 S rRNA和 鸟枪测序）和广泛的临床元数据（包括全血细胞计数和时间的时间序列 以及患者住院期间给予的所有药物的剂量）;我们还将使用来自首次 在allo-HSCT中进行自体粪便微生物群移植（auto-FMT）的对照随机试验 患者我们将使用这些独特的资源来参数化我们的模型，并研究微生物群如何 组合物影响宿主免疫系统的恢复。在目标2中，我们将验证微生物 我们的数学模型的组成部分，使用来自厌氧实验室反应器的实验数据， 在体外重建-在抗生素治疗期间的人类微生物群动力学和在缺乏 一个活生生的宿主在aim 3中，我们将开发小鼠模型来研究这些相同的微生物群动态 实验性的，但现在在一个活生生的宿主的背景下。 从这些临床研究、体外实验和体内模型中获得的数据将完善我们的 数学模型在模拟和定量实验的封闭循环。我们的最终目标是 开发模型，可以定义最佳的微生物鸡尾酒，并重建异源微生物的扰动微生物群， HSCT患者在这个过程中，我们希望为未来的治疗方法揭示微生物群生态学的一般原则。 在其他微生物群被抗生素治疗破坏的患者群体中。