Collaborative Research: Infection Multiplicity and Virus Evolution, from Experiments to Large Scale Multi-Population Stochastic Computations

合作研究：感染多重性和病毒进化，从实验到大规模多群体随机计算

• 批准号: 1662096
• 负责人: David Levy
• 金额: $ 41.4万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1662096&HistoricalAwards=false
• 关键词: Collaborative; Research; Infection; Multiplicity; Virus

To understand how organisms evolve with time requires the development of predictive mathematical descriptions of evolutionary theories along with experimental studies to test these predictions. Because incremental and inheritable genetic changes happen over multiple generations evolutionary theory is difficult to study. Viruses obey the same basic evolutionary rules as higher organisms, but replicate and evolve much faster. Therefore they provide a near-ideal model system for the test of evolutionary and selection theories. Viruses are also capable of social interactions which can change their evolutionary behaviors. Social interactions arise by a process called co-infection, whereby multiple virus genomes infect and replicate in a single cell, and can result in a number of poorly understood interactions. The research team will perform a series of integrated experimental and mathematical analyses aimed at providing insights into these complex processes. This research also has significance to other epidemiological situations in which co-infection patterns are observed when different pathogen species simultaneously infect the same host and impact each others' ability to spread. Thus the novel numerical techniques being developed have relevance and significance in more general health areas including cancer studies. RNA viruses are characterized by a high mutation rate, allowing them to rapidly diversify and readily adapt to environmental challenges. They provide, therefore, a near-ideal model system for the testing of evolutionary and selection theories that are difficult to approach in more complex organisms. Typically, virus genomes are considered as isolated entities, however, multiple infection (coinfection) of cells is a common occurrence and results in a series of poorly understood social interactions that have the power to shape evolutionary trajectories. A highly tractable experimental system for assessing the model is replication of the human immunodeficiency virus (HIV-1) in vitro. HIV-1 multiple infection is promoted by cell-to-cell contact and the formation of virological synapses, in which multiple viruses are simultaneously transferred from one cell to another. In contrast, spread via the release of free virus particles promotes single infection. The relative occurrence of synaptic and free virus transmission, and hence infection multiplicity, can be elegantly manipulated with innovative experimental techniques. Several experimental techniques will be used to generate data on virus growth and virus evolution at different infection multiplicities. Transmission pathways (synaptic and free virus) will be manipulated to change infection multiplicity during virus spread. Evolutionary dynamics will be explored in the context of different mutant types that undergo a variety of social interactions. New techniques will be introduced in order to manipulate the relative importance of cell-free and synaptic transmission, and thus infection multiplicity. Novel computational algorithms will be developed in order to fully understand how multiple infection and social interactions impact the dynamics. Mathematical models will be tested, parameterized, and employed to explore the evolutionary dynamics at large population sizes. This research will provide greater insights on the relative significance of mechanisms that impact evolution of organisms.

要了解生物体如何随时间演化，需要发展演化理论的预测性数学描述，沿着进行实验研究，以检验这些预测。由于渐进的和可遗传的遗传变化发生在多代人身上，进化理论很难研究。病毒遵循与高等生物相同的基本进化规则，但复制和进化得更快。因此，它们为进化和选择理论的检验提供了一个近乎理想的模型系统。病毒也能够进行社会互动，这可以改变它们的进化行为。社会相互作用是由一个称为共感染的过程引起的，多个病毒基因组在单个细胞中感染和复制，并可能导致许多知之甚少的相互作用。研究团队将进行一系列综合实验和数学分析，旨在深入了解这些复杂的过程。 这项研究对其他流行病学情况也具有重要意义，在这些情况下，当不同的病原体物种同时感染同一宿主并影响彼此的传播能力时，观察到共同感染模式。因此，正在开发的新的数值技术在更广泛的健康领域，包括癌症研究的相关性和意义。RNA病毒的特点是突变率高，使它们能够迅速多样化，并容易适应环境挑战。因此，它们提供了一个近乎理想的模型系统，用于测试在更复杂的生物体中难以接近的进化和选择理论。通常情况下，病毒基因组被认为是孤立的实体，然而，细胞的多重感染（共感染）是一种常见的现象，并导致一系列知之甚少的社会互动，这些互动具有塑造进化轨迹的能力。用于评估该模型的高度易处理的实验系统是体外复制人类免疫缺陷病毒（HIV-1）。HIV-1多重感染是通过细胞与细胞的接触和病毒学突触的形成而促进的，其中多种病毒同时从一个细胞转移到另一个细胞。相反，通过释放游离病毒颗粒的传播促进了单一感染。突触和自由病毒传播的相对发生，以及因此感染的多样性，可以用创新的实验技术优雅地操纵。几种实验技术将用于生成不同感染多重性下病毒生长和病毒进化的数据。在病毒传播过程中，将操纵传播途径（突触和游离病毒）以改变感染多样性。进化动力学将在经历各种社会相互作用的不同突变类型的背景下进行探索。新技术将被引入，以操纵无细胞和突触传递的相对重要性，从而感染的多样性。将开发新的计算算法，以充分了解多重感染和社会互动如何影响动态。 数学模型将进行测试，参数化，并探讨在大人口规模的进化动力学。这项研究将为影响生物进化的机制的相对重要性提供更多的见解。

项目成果

Multiple infection of cells changes the dynamics of basic viral evolutionary processes

细胞的多重感染改变了基本病毒进化过程的动态

• DOI: 10.1002/evl3.95
• 发表时间: 2019
• 期刊: Evolution Letters
• 影响因子: 5
• 作者: Wodarz, Dominik;Levy, David N.;Komarova, Natalia L.
• 通讯作者: Komarova, Natalia L.

NNRTI-induced HIV-1 protease-mediated cytotoxicity induces rapid death of CD4 T cells during productive infection and latency reversal

• DOI: 10.1186/s12977-019-0479-9
• 发表时间: 2019-06
• 期刊: Retrovirology
• 影响因子: 3.3
• 作者: B. Trinité;Hongtao Zhang;D. N. Levy

PSGL-1 restricts HIV-1 infectivity by blocking virus particle attachment to target cells

• DOI: 10.1073/pnas.1916054117
• 发表时间: 2020-04-28
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Fu, Yajing;He, Sijia;Wu, Yuntao
• 通讯作者: Wu, Yuntao