Mathematical modelling of oral microbiome resilience

口腔微生物组恢复力的数学模型

• 批准号: 2748357
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2748357
• 关键词: Mathematical; modelling; oral; microbiome; resilience

Maintenance of oral health is essential. It is known that the microbiome, host response, and human behaviours (e.g. tooth brushing) play an essential role in maintaining oral health. It is also essential that consumer products that contact the oral microbiome do not adversely impact it. However, the oral microbiome is a highly complex system, with considerable diversity between individual people. The aim of this project is to develop a mathematical model to characterise oral microbiome resilience, and to use the model to identify factors predictive of dysbiosis/stability of the oral microbiome1,2. Such a description would be key to determine functions of the microbiome which need to be protected to ensure consumer health as part of microbiological risk assessments. The model will be calibrated with metagenomics and metatranscriptomics data from a longitudinal clinical of experimental gingivitis currently being carried out in NJ's laboratory at the University of Newcastle in partnership with Unilever (data available Spring 2023). This is an ideal experimental system to study resilience as it is at the reversible stage of disease that may lead to irreversible periodontitis. The model will be further supported with existing data from the literature3. Specifically, the project will: Undertake systematic reviews of existing literature and modelling approaches to oral microbiome and measures of resilience/stability of microbiomes in general. [Year 1] Develop dynamical mathematical models for competing microbial populations in the oral microbiome, based upon existing models developed in DS's laboratory as well as literature models. The models will include dynamics of growth (logistic or Monod terms as appropriate), death, physical removal, and competition of microbial populations. [Year 1] Develop suitable measures of resilience/stability of the oral microbiome, which are key to the notion of dysbiosis. Preliminary work at Unilever has shown that diversity or taxa are not in of themselves good indicator(s) of health & disease. We will define metabolic functions by processing metagenome and metatranscriptome data generated at Newcastle using advanced bioinformatics analysis being developed in the Unilever internal pipeline U-discover. This analysis will identify altered metabolic pathways and functions predictive of health/dysbiosis. [Year 2] Refine the model in the light of microbiome data, to include relevant functional populations or substrate dynamics from Objective 3. Fit the mathematical model to microbiome data from Objective 3, and appropriate literature data. These will be undertaken using Bayesian MCMC approaches, in order to infer unknown model parameters4. Priors will be taken from literature data where available. [Year 2-3] Use local and global sensitivity analyses5 and counterfactual simulations to identify factors in the model predictive of health/dysbiosis. These will be used to inform risk analysis pipelines for Unilever [Year 3-4] Disseminate research results, including KT within Unilever, and publication of results in peer-reviewed journals. [Years 2-4] References (as DOIs): (1) 10.1177/0022034517742139 (2) 10.1186/s12903-019-0889-z (3) 10.1128/mBio.03281-20 (4) 10.1098/rsif.2015.0069 (5) 10.1093/femsec/fiw040

保持口腔健康至关重要。众所周知，微生物组、宿主反应和人类行为（例如刷牙）在维持口腔健康方面起着至关重要的作用。同样重要的是，接触口腔微生物组的消费品不会对其产生不利影响。然而，口腔微生物组是一个高度复杂的系统，个体之间存在相当大的差异。该项目的目的是开发一个数学模型来预测口腔微生物组的弹性，并使用该模型来确定预测口腔微生物组生态失调/稳定性的因素1，2。这种描述对于确定微生物组的功能至关重要，这些功能需要得到保护，以确保消费者健康，作为微生物风险评估的一部分。该模型将使用宏基因组学和元转录组学数据进行校准，这些数据来自目前正在与联合利华合作在纽卡斯尔大学NJ实验室进行的实验性牙龈炎的纵向临床（数据可在2023年春季获得）。这是一个理想的实验系统，以研究弹性，因为它是在可逆的阶段的疾病，可能会导致不可逆的牙周炎。该模型将得到文献中现有数据的进一步支持3。具体而言，该项目将：对口腔微生物组的现有文献和建模方法以及微生物组的弹性/稳定性措施进行系统审查。[Year 1]根据DS实验室开发的现有模型以及文献模型，开发口腔微生物组中竞争性微生物种群的动态数学模型。模型将包括生长动力学（适当时为逻辑或Monod项）、死亡、物理去除和微生物种群竞争。[Year 1]制定口腔微生物组的弹性/稳定性的适当措施，这是生态失调概念的关键。联合利华的初步工作表明，多样性或分类群本身并不是健康和疾病的良好指标。我们将通过处理纽卡斯尔产生的宏基因组和元转录组数据，使用联合利华内部管道U-discover开发的先进生物信息学分析来定义代谢功能。该分析将确定预测健康/生态失调的改变的代谢途径和功能。[Year 2]根据微生物组数据完善模型，以包括目标3中的相关功能群体或底物动力学。将数学模型拟合到目标3的微生物组数据和适当的文献数据。这些将使用贝叶斯MCMC方法进行，以推断未知的模型参数4。将从可用的文献数据中获取先验信息。[Year 2-3]使用局部和全局敏感性分析5和反事实模拟来识别健康/生态失调预测模型中的因素。这些将用于通知联合利华的风险分析管道[第3-4年]传播研究结果，包括联合利华内部的KT，并在同行评审期刊上发表结果。[2-4年级]参考文献（作为DOI）：（1）10.1177/0022034517742139（2）10.1186/s12903-019-0889-z（3）10.1128/mBio.03281-20（4）10.1098/rsif.2015.0069（5）10.1093/femsec/fiw 040