Genetics and quantum chemistry as tools for unknown metabolite identification

遗传学和量子化学作为未知代谢物鉴定的工具

基本信息

批准号：
10173229
负责人：
ARTHUR S EDISON
金额：
$ 35.14万
依托单位：
UNIVERSITY OF GEORGIA
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10173229
关键词：
2019-nCoV Affect Animal Housing Animal Model Animals Area Attenuated Biology COVID-19 COVID-19 pandemic Characteristics Chemicals Cohort Studies Collaborations Communicable Diseases Communication Containment Data Data Analytics Development Disease Disease Outbreaks Environment Environmental Exposure Equipment Etiology Exposure to Female Ferrets Funding Genetic Healthcare Human Human Resources Immune response Immune system Immunologics Immunology Infection Infrastructure Laboratories Lipids Measurement Measures Metabolic Modeling Molecular Monitor Mus Outcome Pharmaceutical Preparations Phenotype Protocols documentation Public Health Research Research Personnel Respiratory Tract Infections Role Sampling Science Serum Severities Signal Transduction Social Distance Study of serum Symptoms System Systems Biology Testing Time Tissues Training United States National Institutes of Health Vaccines Viral Virus Virus Diseases Work acute infection co-infection cohort cooking cost environmental chemical exposure experience global health immunotoxicity male metabolome metabolomics novel vaccines pandemic disease perfluorooctanoic acid predictive marker quantum chemistry respiratory respiratory virus screening tool transmission process trend vaccine development vaccine discovery virus host interaction

项目摘要

Project Summary/Abstract The SARS-CoV-2 virus and resulting COVID-19 pandemic has created the biggest global health crisis in our lifetime. We have assembled a team of investigators with expertise in vaccine development, environmental exposures, immunology, metabolomics, lipidomics, and modeling to discover metabolic predictive biomarkers (MPBs) of infection in ferrets. We will use ferrets, because they have already been shown to be an effective animal model for human COVID-19 disease, and they are currently being used for vaccine development. Our study builds upon an NIH funded co-infection study in which ferrets will be infected with 4 different common respiratory viruses before infection by SARS-CoV-2. That study will determine the severity of infections and immune responses, but it did not include metabolomics measurements. The hypothesis of the co-infections is that the severity of SARS-CoV-2 infection will be attenuated with co-infection by another virus. We will be adding a group of ferrets that will be exposed to per- and polyfluoroalkyl substances (PFAS) prior to infection by SARS- CoV-2. PFAS have been shown to suppress the immune system in mice, and a limited number of studies have demonstrated associations between severity of virus infection and levels of PFAS. PFAS bioaccumulate in tissues and are common chemicals used in many everyday items such as plastic bottles and non-stick cooking pans, so this common environmental exposure could be an important variable in COVID-19 symptoms. The ferret model provides an ideal way to study the effect of PFAS on SARS-CoV-2 infection progression and outcomes. For each group in the study (co-infection, PFAS, or control), 15 serum samples will be collected from each animal (n=6 for each group) over about 1 month, with SARS-CoV-2 infection occurring at the midpoint of the sampling. Thus, we will be able to derive detailed time-course measurements of metabolites and lipids and associate these signals with phenotypic outcomes. We have 3 specific aims: 1) Conduct the co-infection and PFAS exposure studies in BSL-3 containment and collect immunological and infectivity data. Serum samples will be collected and inactivated by a biosafety- approved protocol. 2) Measure metabolites and lipids using non-targeted LC-MS and NMR. NMR is faster and less expensive and will be used to prioritize samples for LC-MS. Background PFAS signals from animal housing equipment will be determined. 3) Model the metabolites and lipids with phenotypic outcomes. We will also model the influence of PFAS exposure on the lipidome to better understand the molecular mechanisms of PFAS immunotoxicity. We have also started a Slack workspace for communication between different groups around the world working on COVID-19 metabolomics. This workspace provides for sharing of protocols and data, posting the latest research in this area, as well as a forum for questions and answers. All data generated from our study will be shared publicly as soon as it passes our system suitability tests.

项目概要/摘要 SARS-CoV-2 病毒和由此引发的 COVID-19 大流行在我们国家造成了最大的全球健康危机寿命。我们组建了一支在疫苗开发、环境方面拥有专业知识的研究人员团队暴露、免疫学、代谢组学、脂质组学和建模以发现代谢预测生物标志物 (MPB) 雪貂感染。我们将使用雪貂，因为它们已被证明是一种有效的人类 COVID-19 疾病的动物模型，目前正用于疫苗开发。我们的研究建立在 NIH 资助的一项共同感染研究的基础上，其中雪貂将感染 4 种不同的常见病毒感染 SARS-CoV-2 之前的呼吸道病毒。该研究将确定感染的严重程度和免疫反应，但不包括代谢组学测量。合并感染的假设是 SARS-CoV-2 感染的严重程度会因另一种病毒的共同感染而减弱。我们将添加一群雪貂在感染 SARS 之前会接触全氟烷基物质和多氟烷基物质 (PFAS) 冠状病毒-2。 PFAS 已被证明会抑制小鼠的免疫系统，并且有限的研究表明证明病毒感染的严重程度与 PFAS 水平之间存在关联。 PFAS 在体内生物富集纸巾，是许多日常用品（例如塑料瓶和不粘锅烹饪）中使用的常见化学品因此，这种常见的环境暴露可能是 COVID-19 症状的一个重要变量。这雪貂模型为研究 PFAS 对 SARS-CoV-2 感染进展的影响提供了一种理想的方法结果。对于研究中的每个组（合并感染、PFAS 或对照），将从每只动物（每组 n = 6 只）持续约 1 个月，SARS-CoV-2 感染发生在中点抽样。因此，我们将能够获得代谢物和脂质的详细时间过程测量，将这些信号与表型结果相关联。我们有 3 个具体目标：1) 在 BSL-3 遏制和收集免疫学和感染性数据。血清样本将通过生物安全设备收集并灭活。批准的协议。 2) 使用非靶向 LC-MS 和 NMR 测量代谢物和脂质。核磁共振速度更快成本较低，并将用于优先考虑 LC-MS 样品。来自动物圈舍的背景 PFAS 信号装备将被确定。 3) 用表型结果对代谢物和脂质进行建模。我们也会建模 PFAS 暴露对脂质组的影响，以更好地了解 PFAS 的分子机制免疫毒性。我们还启动了 Slack 工作空间，用于世界各地不同群体之间的交流致力于 COVID-19 代谢组学。该工作区提供协议和数据的共享，发布该领域的最新研究，以及问答论坛。我们研究产生的所有数据将在通过我们的系统适用性测试后立即公开共享。