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Principal Investigator: Adam Lauring
Organization: UNIVERSITY OF MICHIGAN AT ANN ARBOR
Fiscal Year: 2024
Award: $1,728,873
Funding agency: National Institute of Allergy and Infectious Diseases
Project Summary – Project 1 (Virus)
Respiratory viruses impose a significant public health burden, in large part due to their rapid evolution and ability
to evade host immunity. Influenza, COVID-19, and now RSV infection, are all vaccine-preventable diseases, yet
our current system of genomic surveillance is limited by significant knowledge gaps in forecasting new antigenic
drift mutations and remarkably little attention to determinants of within-host and population-level viral fitness
outside of antigenic sites. The long-term goal of this research is to advance the field of virus genomics by
improving inference of natural selection in genomic surveillance data. The core objective of this project is to
combine large-scale genomic surveillance and functional genomics to define determinants of epidemic success
in three respiratory viruses. Preliminary data demonstrate: (i) a system for genomic surveillance of SARS-CoV-
2, influenza, and RSV across four major health systems in Michigan that serve a racially, geographically, and
socioeconomically diverse population; and (ii) Novel laboratory approaches to measuring an array of viral
phenotypes. This project will take an integrated bidirectional approach, in which genomic surveillance is used to
identify strains and mutations for experimental analysis, and in which functional genomics data are used to
improve population-level inference of adaptive viral evolution. Key knowledge gaps in forecasting antigenic drift
and selection elsewhere in the genome will be addressed in three specific aims: (Aim 1) Develop a Bayesian
model to identify SARS-CoV-2 mutations positively selected within hosts. A comprehensive dataset of fitness
values for amino acid substitutions in the Omicron spike protein will be used to parameterize a Bayesian model
for identifying positively selected mutations within hosts and applied to deep sequence data of serially sampled
individuals. (Aim 2) Use mutational antigenic profiling (MAP) of the RSV fusion (F) protein to define and anticipate
antigenic drift. High throughput MAP will precisely identify immune selection on all mutations in F, making it
possible to generate complete maps of antibody selection. Targeted epitopes and antibody escape mutations
will be compared using sera from unvaccinated adults with prior infection, vaccinated adults with prior infection,
and vaccinated pregnant women. (Aim 3) Leverage influenza virus genomic surveillance to identify determinants
of epidemic success. Phylodynamic models will be applied to regional whole genome surveillance data to identify
strains and mutations conferring a population-level fitness advantage. These will be further evaluated in
laboratory and animal models. This project is innovative because it will combine large-scale genomic surveillance
and functional genomics to define antigenic drift and other determinants of epidemic success in influenza virus,
SARS-CoV-2, and RSV. While different approaches are taken for each virus, they are universally applicable and
will advance the field. By addressing knowledge gaps for these three respiratory viruses, this project will provide
a foundation for more accurate prediction of emerging strains and the development of more protective vaccines.
Terms: <2019 novel corona virus><2019 novel coronavirus><2019-nCoV><21+ years old><Ab-mediated immunity><Ab-mediated protection><Acylneuraminyl hydrolase><Address><Adult><Adult Human><Amino Acid Substitution><Animal Model><Animal Models and Related Studies><Antibodies><Antibody Response><Antibody immunity><Antibody protection><Antibody-mediated protection><Antigenic Determinants><Assay><Attention><Bayesian Modeling><Bayesian adaptive designs><Bayesian adaptive models><Bayesian belief network><Bayesian belief updating model><Bayesian framework><Bayesian hierarchical model><Bayesian network model><Bayesian nonparametric models><Bayesian spatial data model><Bayesian spatial image models><Bayesian spatial models><Bayesian statistical models><Bayesian tracking algorithms><Binding Determinants><Bioassay><Biological Assay><Blood Serum><COVID surveillance><COVID-19><COVID-19 surveillance><COVID-19 virus><COVID19 surveillance><COVID19 virus><CV-19><Cell Surface Glycoproteins><Chimera Protein><Chimeric Proteins><CoV-2><CoV2><Coronavirus Infectious Disease 2019><Data><Data Set><Development><Disease><Disorder><Epidemic><Epitopes><Event><Evolution><Foundations><Frequencies><Fusion Protein><Genetic Alteration><Genetic Change><Genetic defect><Genome><Genomic Centers for Infectious Diseases><Genomics><Geography><Glycoproteins><Goals><Grippe><Health system><Hemagglutinin><History><Household><Immune><Immunes><Immunity><Individual><Infection><Influenza><Influenza Virus><Knowledge><Laboratories><Libraries><Location><Maps><Measures><Membrane Glycoproteins><Michigan><Modeling><Mutation><N-Acylneuraminate Glycohydrolases><Natural Selections><Neuraminidase><Oligosaccharide Sialidase><Phenotype><Population><Population Heterogeneity><Pregnant Women><Proteins><Public Health><RSV infection><Race><Races><Recording of previous events><Research><Respiratory Syncytial Virus Infections><SARS corona virus 2><SARS-CO-V2><SARS-COVID-2><SARS-CoV-2><SARS-CoV-2 surveillance><SARS-CoV2><SARS-associated corona virus 2><SARS-associated coronavirus 2><SARS-coronavirus-2><SARS-related corona virus 2><SARS-related coronavirus 2><SARSCoV2><Sampling><Serum><Severe Acute Respiratory Coronavirus 2><Severe Acute Respiratory Distress Syndrome CoV 2><Severe Acute Respiratory Distress Syndrome Corona Virus 2><Severe Acute Respiratory Distress Syndrome Coronavirus 2><Severe Acute Respiratory Syndrome CoV 2><Severe Acute Respiratory Syndrome-associated coronavirus 2><Severe Acute Respiratory Syndrome-related coronavirus 2><Severe acute respiratory syndrome associated corona virus 2><Severe acute respiratory syndrome coronavirus 2><Severe acute respiratory syndrome related corona virus 2><Sialidase><Site><Surface Glycoproteins><System><Vaccinated><Vaccination><Vaccines><Variant><Variation><Viral><Virus><Wuhan coronavirus><adulthood><antibody-mediated immunity><coronavirus disease 2019><coronavirus disease 2019 surveillance><coronavirus disease 2019 virus><coronavirus disease surveillance><coronavirus disease-19><coronavirus disease-19 virus><coronavirus infectious disease-19><developmental><diverse populations><entire genome><exo alpha sialidase><expectant mother><expecting mother><experimental analysis><fitness><flu surveillance><full genome><functional genomics><genome mutation><genomic data><genomic data-set><genomic dataset><hCoV19><heterogeneous population><heuristics><histories><improved><influenza surveillance><influenzavirus><innovate><innovation><innovative><model of animal><mutation scanning><mutation screening><nCoV2><non vaccinated><not vaccinated><novel><population diversity><pregnant mothers><racial><racial background><racial origin><respiratory virus><seasonal flu><seasonal influenza><severe acute respiratory syndrome coronavirus 2 surveillance><socio-economic><socio-economically><socioeconomically><socioeconomics><success><surveillance data><unvaccinated><viral fitness><viral genomics><virus genomics><whole genome>