# P1-Genomic determinants of epidemic success in respiratory viruses

> **NIH NIH U19** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $1,728,873

## Abstract

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, ...

## Key facts

- **NIH application ID:** 10852057
- **Project number:** 1U19AI181767-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Adam Lauring
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,728,873
- **Award type:** 1
- **Project period:** 2024-06-14 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10852057

## Citation

> US National Institutes of Health, RePORTER application 10852057, P1-Genomic determinants of epidemic success in respiratory viruses (1U19AI181767-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10852057. Licensed CC0.

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