Excellence in Research’: Mechanistic Modelling and Validation Approaches to Decontaminate (+) ssRNA Viruses using Ultra-Violet Technologies

正在进行的新冠肺炎大流行突显了需要高效和具有成本效益的技术和解决方案，以缓解和防止具有大流行潜力的病原体在包括居民楼、学校和医疗设施在内的建成环境中的传播和传播。许多正链RNA(+ssRNA)病毒，包括冠状病毒和诺如病毒，对人和动物都是致病的。这些病毒可以在空气、水分散体和表面存活。发射UV-C辐射(波长为200-280 nm)的紫外线杀菌系统已被证明能有效地灭活附着在各种介质和基质上的病毒病原体，包括呼吸气雾剂、水分散体和非多孔表面。然而，对单链RNA病毒的UV-C敏感性及其杀菌UV-C辐射灭活的程度、速率和机制的基本了解仍然难以捉摸。该项目的总体目标是开发和验证一个基于基因组的模型，该模型可用于1)估计致病单链RNA病毒的UV-C敏感性，以及2)预测在水分散体和表面灭活此类病毒所需的UV-C剂量和暴露时间。该项目的成功完成将使社会受益，方法是产生基本知识和经过验证的建模工具，以指导设计更有效的UV-C消毒系统和方案，以减轻和防止致病单链RNA病毒在建筑环境和未来疾病暴发和流行期间在社区中的传播和传播。还将通过推广和教育活动为社会带来其他好处，包括指导田纳西州立大学的一名博士后研究助理、两名研究生和四名本科生以及诺克斯维尔田纳西大学的一名研究生。UV-C照射在特定介质中灭活病毒病原体的有效性取决于几个因素，包括病毒浓度、辐射剂量和暴露时间，以及病毒UV-C敏感性(D90)，即灭活90%病毒病原体所需的辐射剂量(焦耳/平方米)。然而，由于与收集病毒病原体实验数据相关的独特挑战，包括需要生物安全3级(BSL-3)设施和专门和训练有素的工作人员，许多对人和动物致病的正链RNA(+ssRNA)病毒的测量UV-C敏感性数据无法获得。该项目的目标是开发和验证一个数学模型(基于病毒基因组参数，如基因组大小和嘧啶二核苷酸频率)，该模型可用于预测致病+单链RNA病毒在标准环境条件下在空气、水分散体和表面的UV-C敏感性。这项研究的具体目标包括：1)收集和/或生成一组模型单链RNA病毒的UV-C敏感性数据，用于模型校准和验证；2)开发和验证定量结构活性关系(QSAR)，通过分析基因组参数(基因组大小、基于序列的嘧啶二核苷酸频率值、Trp和Tyr含量)与目标1中收集的UV-C敏感性数据之间的相关性，预测+ssRNA病毒的波长特定UV-C敏感性；以及3)开发和实施Java计算机界面，该界面利用目标2中开发的QSAR来估计空气、水分散体和表面上的非活性+单链RNA病毒所需的UV-C辐射剂量和暴露时间。该项目的成功完成有可能通过产生基本知识、数据和建模工具来指导设计更有效、更具成本效益的UV-C病毒灭活和消毒系统和方案，从而产生变革性的影响。为了实施该项目的教育和外展活动，首席调查人员(PI)计划将这项研究的结果整合到田纳西州立大学、田纳西大学诺克斯维尔分校和梅哈里医学院现有的本科和研究生课程中，为学生提供新的学习模块，重点是能够指导建筑环境和食品行业中病毒病原体传播和传播的缓解和预防的技术、解决方案和建模工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The ongoing COVID-19 pandemic has highlighted the need for efficient and cost effective technologies and solutions to mitigate and prevent the spread and transmission of pathogens with pandemic potential in the built environment including residential buildings, schools, and healthcare facilities. Many positive strand RNA (+ssRNA) viruses, which include coronaviruses and noroviruses, are pathogenic to humans and animals. These viruses can survive in air, aqueous dispersions, and on surfaces. Ultraviolet germicidal systems emitting UV-C radiation (200-280 nm in wavelength) have been proven effective at inactivating viral pathogens attached to various media and substrates including respiratory aerosols, aqueous dispersions, and non-porous surfaces. However, a fundamental understanding of the UV-C sensitivity of ssRNA viruses and their extents, rates, and mechanisms of inactivation by germicidal UV-C radiation has remained elusive. The overarching goal of this project is to develop and validate a genomic-based model that could be used to 1) estimate the UV-C sensitivity of pathogenic ssRNA viruses and 2) predict the UV-C doses and exposure times required to inactivate such viruses in aqueous dispersions and surfaces. The successful completion of this project will benefit society through the generation of fundamental knowledge and validated modeling tools to guide the design of more efficient UV-C disinfection systems and protocols to mitigate and prevent the spread and transmission of pathogenic ssRNA viruses in the built environment and in communities during future disease outbreaks and epidemics. Additional benefits to society will be achieved through outreach and educational activities including the mentoring of a post-doctoral research associate, two graduate students, and four undergraduate students at Tennessee State University and one graduate student at the University of Tennessee, Knoxville.The effectiveness of UV-C irradiation at inactivating viral pathogens in a given medium depends on several factors including viral concentration, radiation dose and exposure time, and the virus UV-C susceptibility (D90) defined as the required radiation dose (Joule/m2) to inactivate 90% of the viral pathogens. However, measured UV-C susceptibility data for many positive strand RNA (+ssRNA) viruses that are pathogenic to humans and animals are not available due to the unique challenges associated with the collection of experimental data on viral pathogens including the need for biosafety level-3 (BSL-3) facilities and a specialized and highly trained workforce. The goal of this project is to develop and validate a mathematical model (based on viral genomic parameters such as genome size and pyrimidine dinucleotide frequency) that could be used to predict the UV-C susceptibility of pathogenic +ssRNA viruses in air, aqueous dispersions, and onto surfaces at standard ambient conditions. The specific objectives of the research include: 1) Collection and/or generation of UV-C susceptibility data for a set of model ssRNA viruses for model calibration and validation; 2) Development and validation of quantitative structure activity relationships (QSARs) to predict wavelength specific UV-C susceptibility of +ssRNA viruses by analyzing the correlations between genome parameters (genome size, sequence-based pyrimidine dinucleotide frequency value, Trp and Tyr content) and the UV-C susceptibility data that were collected in Objective 1 ; and 3) Development and implementation of a Java computer interface that utilizes the QSARs developed in Objective 2 to estimate the UV-C radiation dose and exposure time required to inactive +ssRNA viruses in air, aqueous dispersions, and onto surfaces. The successful completion of this project has the potential for transformative impact through the generation of fundamental knowledge, data and modeling tools to guide the design of more efficient, and cost-effective UV-C viral inactivation and disinfection systems and protocols. To implement the education and outreach activities of the project, the Principal Investigators (PIs) plan to integrate the findings from this research into existing undergraduate and graduate courses at Tennessee State University, the University of Tennessee, Knoxville, and Meharry Medical College to provide students with new learning modules with a focus on technologies, solutions, and modeling tools that could guide the mitigation and prevention of the spread and transmission of viral pathogens in the built environment and the food industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.