Developing computational models to predict the immune response to B. pertussis booster vaccination

开发计算模型来预测百日咳博德特氏菌加强疫苗接种的免疫反应

基本信息

批准号：
10246590
负责人：
Bjoern Peters
金额：
$ 27.45万
依托单位：
LA JOLLA INSTITUTE FOR IMMUNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-10 至 2025-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10246590
关键词：
Address Adult Age Antibodies Antigen-Antibody Complex Antigens B-Lymphocytes Bacteria Biological Assay Bordetella pertussis CCL3 gene Case Study Cell Communication Cell surface Cells Child Code Communities Computer Models Country Coupled Data Demographic Impact Development Educational workshop Ensure Epidemiology Evaluation Event Feedback Fine needle aspiration biopsy Frequencies Gene Expression Gene Expression Profile Generations Grant IgG4 Immune Immune response Immune system Immunity Individual Infant Infection Measurement Measures Modeling Morbidity - disease rate Outcome Peripheral Blood Mononuclear Cell Pertussis Pertussis Vaccine Phenotype Plasma Process Protocols documentation Proxy Publications Reporting Research Personnel Sampling Secondary Immunization Seeds Specificity Stains Structure of germinal center of lymph node T-Lymphocyte Teaching Materials Teenagers Testing Time United States National Institutes of Health Vaccination Vaccine Research Vaccines age group antigen-specific T cells base booster vaccine cell type cohort improved individual response infancy lymph nodes model building novel vaccines outreach peripheral blood predictive modeling recruit response sex side effect single-cell RNA sequencing success transcriptome sequencing vaccine response vaccine-induced immunity web platform web site

项目摘要

Project Summary/Abstract: Bordetella pertussis (PT) is the causative agent of whooping cough. Highly effective vaccines to protect from PT infection have been in use since the early 1940s. However, the last fifteen years have witnessed a consistent increase in the number of whooping cough cases, and the reasons for this are not completely understood. This highlights the need to better understand how immune responses against PT vary between individuals, which we can determine by measuring immune responses to PT booster vaccination as a proxy for immune responses to infectious challenge. We will use these data to develop computational models that will help establish a quantitative and predictive understanding of the factors that impact immune responses resulting from PT booster vaccination. Specifically, we will examine the impact of demographic variables such as age and sex, the type of vaccine received in infancy, and the immune state prior to vaccination. Understanding how these variables can impact vaccine responses is of importance not only for PT, but for vaccine research in general. To achieve this, we are proposing an iterative process of model building, model evaluation, experimental data generation, and model refinement. We will engage the wider scientific community in this process by hosting an annual prediction contest. We have placed particular emphasis on making this process transparent and open, to ensure that the value of the models and the results from the evaluation are accepted by the community at large. Specifically, we will: 1) Establish and seed an open platform to build and evaluate computational models of PT booster vaccination. 2) Generate new experimental data with staggered release dates to test and iteratively improve computational models. 3) Engage the broader scientific community in this modeling effort.

项目摘要/摘要：百日咳博德特氏菌 (PT) 是百日咳的病原体。高效疫苗可预防 PT 感染自 20 世纪 40 年代初以来一直在使用。然而，过去十五年见证了始终如一的百日咳病例数量增加，其原因尚不完全清楚。这强调需要更好地了解针对 PT 的免疫反应在个体之间有何差异，我们对此进行了研究。可以通过测量对 PT 加强疫苗接种的免疫反应来确定，作为对 PT 加强疫苗接种的免疫反应的代理传染性的挑战。我们将使用这些数据来开发计算模型，这将有助于建立定量和预测的模型。了解影响 PT 加强疫苗接种免疫反应的因素。具体来说，我们将研究人口变量的影响，例如年龄和性别、接种疫苗的类型婴儿期和接种疫苗前的免疫状态。了解这些变量如何影响疫苗反应不仅对 PT 很重要，而且对一般疫苗研究也很重要。为了实现这一目标，我们提出了模型构建、模型评估、实验数据的迭代过程生成和模型细化。我们将通过举办一次活动让更广泛的科学界参与这一进程年度预测大赛。我们特别强调使这一过程透明和开放，以便确保模型的价值和评估结果被整个社区接受。具体来说，我们将： 1) 建立并培育一个开放平台，以构建和评估 PT 加强疫苗接种的计算模型。 2）生成具有交错发布日期的新实验数据来测试和迭代改进计算模型。 3) 让更广泛的科学界参与到这一建模工作中。