Current Funded Projects




GENOME BASED INFLUENZA VACCINE STRAIN SELECTION USING MACHINE LEARNING.

Funding Source:

National Institute of Allergy and Infectious Diseases.

Summary:

Influenza A virus causes both pandemic and seasonal outbreaks, leading to loss of from thousands to millions of human lives within a short time period. Vaccination is the best option to prevent and minimize the effects of influenza outbreaks. Rapid selection of a well-matched influenza vaccine strain is the key to developing an effective vaccination program. However, this is a non-trivial task due to three major challenges in influenza vaccine strain selection: labor an time intensive virus isolation and serology-based antigenic characterization, poor growth of selected strains in chicken embryonic eggs during production, and biased sampling in influenza surveillance. Each year, many scientists worldwide, including thousands from the United States, are working altogether to select an optimal vaccine strain. However, incorrect vaccine strains have still been frequently chosen in the past decades. Recent advances in genomic sequencing allow us to rapidly and economically sequence influenza genomes from the isolates and from the clinical samples. Sequencing influenza genomes has become a routine and important component in influenza surveillance. The objectives of this project are to develop a sequence-based strategy for influenza antigenic variant identification and to optimize vaccine strain selection using genomic data. To achieve these aims, we will develop machine learning based computational methods to estimate antigenic distances among influenza viruses by directly using their genome sequences. We will then identify the key residues and mutations in influenza genomes affecting influenza antigenic drift events. Such information will allow us to select most promising virus strains as candidates for vaccine production. Since economical virus production requires the selected virus strains to grow easily in chicken embryonic eggs, we also propose the development of a machine learning based method that can predict the growth ability of a virus strain based on its sequence information. This integrated genome based influenza vaccine strain selection system will be developed for detecting antigenic variants for influenza A viruses. This project will help us provide fundamental technology that employs genomic signatures determining influenza antigenicity and growth ability in chicken embryonic eggs, which are the two key issues for efficient and effective influenza vaccine strain development. The resulting genome based vaccine strain selection strategy will significantly reduce the human labor needed for serological characterization, decrease the time required to select an effective strain that will grow well in eggs, and increase the likelihood of correct influenza vaccine candidate selection. Thus, this project will lead to significant technological advances in influenza prevention and control.

Project Link:

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USE OF CLINICAL SAMPLES TO IDENTIFY INFLUENZA VIRUS ANTIGENIC VARIANTS

Funding Source:

National Institute of Allergy and Infectious Diseases

Summary:

Use of Clinical Samples to Identify Influenza Virus Antigenic Variants Summary Influenza A viruses (IAVs) cause pandemic and seasonal outbreaks that lead to the loss of thousands to millions of human lives. Vaccination is the best option for preventing influenza outbreaks and minimizing their effects. An understanding of the antigenic evolution of influenza viruses and the rapid selection of a well- matched influenza vaccine strain is the key to developing an effective vaccination program. However, antigenic characterization for influenza viruses presents two great challenges: 1) virus propagation, which is required in conventional serologic assays, can cause culture-adapted mutations and skew antigenic properties of viruses in clinical samples, and 2) reference sera used in conventional serologic assays are produced in influenza virus–seronegative ferrets and do not represent the immunologic profiles of human serum, which often has had prior exposures to influenza viruses through vaccination, natural infection, or both. An ideal platform for determining antigenic properties of influenza viruses and for selecting influenza vaccine strain should directly use clinical samples. The objectives of this project are 1) to develop and apply a novel high-throughput technology to directly characterize antigenic properties of influenza viruses by using human clinical samples without virus isolation and propagation and 2) to understand antigenic evolution of IAVs by using clinical samples directly. The antigenic characterization will include influenza virus–positive clinical samples from which virus can or cannot be cultivated. To understand influenza virus quasispecies in clinical samples and the effect of culture-adapted mutations on antigenic characterization, we will perform next-generation genomic sequencing on the clinical samples and corresponding isolates. We will then study the effects of the sequence diversity on antigenic variations of influenza viruses. We will also determine the effect that prior exposure to influenza virus(es) has on antigenic characterization during influenza vaccine strain selection. This project will help us provide fundamental technology for characterizing the antigenicity of influenza viruses in clinical samples without propagating virus. The resulting platform for antigenic characterization will overcome biases arising from virus propagation in conventional serologic assays. In addition, this is a high- throughput method and will significantly reduce the human labor needed for serologic characterization, decrease the time required for antigenic characterization, and increase the number of samples in antigenic characterization. Thus, this project will lead to significant technologic advances in influenza vaccine strain selection and facilitate influenza prevention and control. In addition, this project will provide knowledge about molecular mechanisms in antigenic variations associated with influenza virus quasispecies and genomic diversity and knowledge about prior human exposure to influenza viruses, which will help us optimize antigenic characterization in vaccine strain selection and understand antigenic evolution of influenza viruses.

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IMPACT OF REPEATED VACCINATION ON THE EFFECTIVENESS OF SEASONAL INFLUENZA VACCINES

Funding Source:

National Institute of Allergy and Infectious Diseases

Summary:

Impact of repeated vaccination on the effectiveness of seasonal influenza vaccines Summary Influenza viruses cause pandemic and seasonal outbreaks that lead to the loss of thousands to millions of human lives. Vaccination is the best option for preventing influenza outbreaks and minimizing their effects on health. In the United States, annual influenza vaccination has been recommended since 2010 for persons 6 months of age and older. However, vaccine performance varies significantly between influenza seasons, and reduced vaccine effectiveness has been observed. Studies have reported that persons vaccinated during two consecutive influenza seasons had lower vaccine effectiveness during the second season than persons who had not been vaccinated during the prior season. These findings have caused profound confusion among the public regarding the potential benefit of annual influenza vaccination. Thus, there is a critical need to address the effect of repeated vaccination–associated pre-existing immunity on influenza vaccine performance. The objective of this project is to characterize the effects of repeated influenza vaccination on the specificity and magnitude of cross-reactive antibodies and on the effectiveness of seasonal influenza vaccines. Two specific aims are proposed: 1) determine the specificity, magnitude, and longitudinal patterns of humoral responses in humans with repeated seasonal vaccination, and 2) test the effect of repeated vaccination– associated pre-existing immunity on influenza vaccine performance in ferrets. By comparing antibodies in persons with and without repeated influenza vaccination, we expect to show whether and how pre-existing immunity achieved through repeated influenza vaccination affects the specificity and magnitude of the cross-reactivity for the resulting antibodies and, thus, vaccine effectiveness. From our studies in ferrets, we expect to show whether and how variations in repeated vaccination–associated pre- existing immunity affect influenza vaccine performance. This study will expand our understanding of molecular mechanisms that may influence how repeated vaccination affects influenza vaccine performance. Thus, this study will provide basic knowledge for evaluating the need for annual influenza vaccination and for optimizing influenza vaccine performance.

Project Link:

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RISK ASSESSMENT OF INFLUENZA A VIRUSES

Funding Source:

National Institute of Allergy and Infectious Diseases

Summary:

Risk Assessment of Influenza A Viruses Project Summary Influenza A viruses (IAVs) have caused large losses of life around the world and continue to present a great public health challenge. Risk assessment of influenza viruses is a key component in pandemic influenza preparedness (PIP), and it can help optimize resources for influenza surveillance, vaccine development and other control measurements to help minimize losses due to an emerging influenza virus. Risk assessment of influenza viruses includes emergence risk and public health impact of novel influenza viruses. Emergence risk assesses the probability for a novel influenza virus to infect and easily spread among humans; it is the first risk to be evaluated in risk assessment. Although a number of individual mutations or structural/functional motifs have been reported to be associated with influenza infection and transmission, their effects on emergence risk are difficult to predict a priori. Thus, conventional methods for assessment of the emergence risk of a novel IAV often require laboratory generation of reassortants and subsequent measurement of their infectivity, pathogenesis, and transmission, which is often done in a mammalian system. However, this strategy can lead to laboratory mutants with gain of function (i.e., mutants with new or enhanced activity on pathogenesis and/or transmissibility in mammals). Thus, an ideal system for influenza risk assessment should be able to quantify emergence risk for a novel IAV solely by using genomic sequences. Avian influenza A viruses facilitated the emergence of all four known pandemic human influenza A viruses: the hemagglutinin genes of 1918, 1957, and 1968 pandemic viruses are all of avian origin, and the polymerase PB2 and PA genes of the 2009 pandemic virus are of avian origin. Risk assessment of potential reassortants from contemporary endemic human influenza viruses and enzootic avian influenza viruses has been a key component of the pandemic influenza preparedness process. The objectives of this study are to develop and validate a machine learning method to assess the emergence risk for a novel IAV using genomic sequences. The study will focus on emergence risk from contemporary endemic human influenza viruses and enzootic avian influenza viruses. We expect to identify genetic features within and across gene segments and ascertain their synergistic effects as emergence risk determinants. We also expect to develop a computational model for estimating the probability of a possible reassortant to emerge, given the genomic sequences of one human influenza virus and one avian influenza virus. The study results should help with understanding the fundamental mechanisms for genetic reassortment and with assessing emergence risk of influenza virus; thus, the results should facilitate pandemic influenza preparedness.

Project Link:

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INFLUENZA HOST SPECIFIC GLYCAN MOTIF IDENTIFICATION THROUGH SYSTEMS BIOLOGY

Funding Source:

National Institute of Allergy and Infectious Diseases

Summary:

Influenza A viruses (IAVs) have caused large losses of life around the world and continue to present a great public health challenge. IAVs can cause infections in birds, sea mammals, lower mammals (e.g., pigs, dogs, and horses), and humans. Previous studies have demonstrated that the structures of the carbohydrate receptors determine influenza host and tissue tropisms. Thus, it is necessary to understand the receptor- binding properties for IAVs and monitor changes to them, especially for IAVs at the animal–human interface. However, this understanding is hampered by our lack of detailed knowledge of IAV glycan substructures; most of our knowledge is limited to SA2,3Gal-like and SA2,6Gal-like structures. The goals of this project are to develop and validate a machine learning method to identify host-specific glycan substructures for IAVs by using glycan array data and to identify and validate the glycan motifs associated with the host tropisms of IAVs, including those for zoonotic IAVs. The study will focus on natural hosts of IAVs: humans, swine, canines, equines, and various avian species, including common domestic poultry species and wild bird species. We expect to identify structural determinants for receptor binding with human-, swine-, canine-, and avian-origin IAVs. Such knowledge will help us understand the factors that contribute to influenza infection and transmission and thereby facilitate development of an effective influenza vaccine to prevent virus infection and block virus transmission. This knowledge will also help us develop rapid assays for monitoring emerging influenza threats at the animal–human interface. We also expect to develop a computational method for identifying glycan motifs associated with influenza host tropisms; this method will be able to be adapted to determine functional glycan motifs for other proteins, lectins, antibodies, antisera, and microorganisms, including those of other infectious pathogens, by using glycan arrays.

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LAB HAS BEEN SUPPORTED BY:


NIH     NSF     USDA