An enormous mutation rate, coupled with reassortment (a process analogous to chromosomal segregation) of RNA segments of the viral genome, and natural zoonosis, have led to influenza epidemics and pandemics in humans. It has been estimated seasonal influenza causes approximately 5 million cases annually, resulting in 250,000 to 500,000 deaths. Deaths resulting from pandemic influenza can reach millions and each year, the World Health Organization predicts which strains of the virus are most likely to be circulating in the following year. This approach relies on yearly global surveillance data to monitor ongoing infections in humans and livestock and is prone to significant error. A major shortcoming of these data is that they represent a partial landscape of circulating viruses due to limitations in surveillance and the selective pressures are generated in an uncontrolled setting making it difficult to assess the effect of complex evolutionary processes. However, even when such predictions are accurate, a particular influenza vaccine usually confers protection for no more We propose to overcome these limitations by integrating the experimental expertise of the Shapira and Garcia-Sastre laboratories together with than a few years due to the high mutation rate of the virus. mathematical modeling and computational framework of the Rabadan laboratory, a member of the Center for the Multiscale Analysis of Genomic and Cellular Networks (MAGNet; which provides a core framework for applying systems biology approaches to studying complex biological systems such as influenza evolution). While it is well established that reassortment between influenza isolates from different host species can generate viruses with pandemic potential, the relationship between reassortment, viral mutation rates, as well as the selective pressures imposed on this virus remain key questions in influenza biology and are major issues for global public health. Not only does influenza represent an ideal laboratory model for quantitative studies of evolution, In collaboration with MAGNet, we propose to use modern genomic approaches, coupled with genetically tractable mammalian systems, to determine, in an unprecedented fashion, precisely what evolutionary changes the virus undergoes as it adapts across species, or as it interacts with the host innate and adaptive immune systems, to compute the evolutionary trajectory of individual viral sequences, and to identify selection pressures exerted on the virus. We expect that the experimental and computational platform described in this proposal will foster new insights into the evolutionary constrains that govern viral evolution. Coupled with ongoing surveillance efforts, even modest improvements on the current understanding of the influenza fitness landscape will allow for better assessment of pandemic risk potential of circulating strains. Understanding the variables that influence antigenic drift, and viral adaptation is paramount.
In this project, we seek to use modern genomic approaches, coupled with genetically tractable mammalian systems, to determine, in an unprecedented fashion, the genetic changes that influenza virus undergoes as it adapts across species, or as it interacts with the host innate and adaptive immune systems. We aim to study the population structures of actively adapting influenza viruses, compute fitness landscapes of viral sequences, examine selection pressures exerted on the virus, and provide evidence for altered physical and transcriptional interactions that arise during viral adaption. We expect that the experimental and computational platform will foster new insights into the evolutionary constrains that govern influenza evolution.
|Abe, Takayuki; Lee, Albert; Sitharam, Ramaswami et al. (2017) Germ-Cell-Specific Inflammasome Component NLRP14 Negatively Regulates Cytosolic Nucleic Acid Sensing to Promote Fertilization. Immunity 46:621-634|
|Stockman, Victoria B; Ghamsari, Lila; Lasso, Gorka et al. (2016) A High-Throughput Strategy for Dissecting Mammalian Genetic Interactions. PLoS One 11:e0167617|
|Garzón, José Ignacio; Deng, Lei; Murray, Diana et al. (2016) A computational interactome and functional annotation for the human proteome. Elife 5:|
|Tripathi, Shashank; Pohl, Marie O; Zhou, Yingyao et al. (2015) Meta- and Orthogonal Integration of Influenza ""OMICs"" Data Defines a Role for UBR4 in Virus Budding. Cell Host Microbe 18:723-35|