# High-throughput experiments to guide influenza vaccine strain selection

> **NIH NIH R01** · FRED HUTCHINSON CANCER RESEARCH CENTER · 2020 · $422,967

## Abstract

PROJECT SUMMARY
Every year, seasonal influenza infects 5-15% of the human population, resulting in over 250,000
deaths worldwide. The annual influenza vaccine is the primary public-health intervention against
these epidemics. The strains in the vaccine must be selected before the influenza season.
Unfortunately, the selected strains sometimes fail to closely match those that end up actually
circulating in the human population; such strain mismatches reduce vaccine efficacy. Methods
for better selecting vaccine strains are therefore of paramount importance to public health.
We will use innovative new experimental and computational techniques to guide better vaccine-
strain selection. Two key properties determine which influenza strains dominate a season:
successful strains have high inherent fitness (manifested by a low load of deleterious mutations)
and an abundance of antigenic mutations in the epitopes recognized by human immunity. We
will measure how each of these properties is affected by every possible amino-acid mutation to
the viral surface protein hemagglutinin. To make these high-throughput measurements, we will
generate pools of viruses carrying all possible codon mutations to hemagglutinin, and then
passage these mutant viruses in the presence and absence of human serum. We will then use
ultra-accurate deep sequencing to count the frequency of every mutation pre- and post-
selection, enabling us to quantify how each mutation affects both deleterious load and antigenic
recognition by serum from a cross-section of the human population.
To improve vaccine-strain selection, we will use a real-time web platform to overlay our
measurements of deleterious mutational load and the antigenic change onto an influenza
phylogeny. This platform will enable decision makers to intuitively visualize the “Big Data”
generated by our experiments as they weigh all sources of evidence during the strain-selection
process. In addition, we will make our data and computer code readily available, so that others
can leverage our work for their own efforts to better predict influenza strain dynamics.
This work has direct relevance to public health in that it will help guide better vaccine-strain
selection at a fraction of the cost of current approaches, and thereby improve seasonal influenza
vaccine effectiveness.

## Key facts

- **NIH application ID:** 9987261
- **Project number:** 5R01AI127893-05
- **Recipient organization:** FRED HUTCHINSON CANCER RESEARCH CENTER
- **Principal Investigator:** Jesse D Bloom
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $422,967
- **Award type:** 5
- **Project period:** 2016-09-26 → 2022-02-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987261

## Citation

> US National Institutes of Health, RePORTER application 9987261, High-throughput experiments to guide influenza vaccine strain selection (5R01AI127893-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9987261. Licensed CC0.

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