Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution
- University of Washington, United States
- Centers for Disease Control and Prevention (CDC), United States
- The Francis Crick Institute, United Kingdom
- The Francis Crick Insitute, United Kingdom
- National Institute of Infectious Diseases, Japan
- National Instituite of Infectious Diseases, Japan
- Peter Doherty Institute for Infection and Immunity, United States
- Peter Doherty Institute for Infection and Immunity, Australia
- University of Basel, Switzerland
- Fred Hutchinson Cancer Research Center, United States
Abstract
Seasonal influenza virus A/H3N2 is a major cause of death globally. Vaccination remains the most effective preventative. Rapid mutation of hemagglutinin allows viruses to escape adaptive immunity. This antigenic drift necessitates regular vaccine updates. Effective vaccine strains need to represent H3N2 populations circulating one year after strain selection. Experts select strains based on experimental measurements of antigenic drift and predictions made by models from hemagglutinin sequences. We developed a novel influenza forecasting framework that integrates phenotypic measures of antigenic drift and functional constraint with previously published sequence-only fitness estimates. Forecasts informed by phenotypic measures of antigenic drift consistently outperformed previous sequence- only estimates, while sequence-only estimates of functional constraint surpassed more comprehensive experimentally-informed estimates. Importantly, the best models integrated estimates of both functional constraint and either antigenic drift phenotypes or recent population growth.
Data availability
Sequence data are available from GISAID using accession ids provided in Supplemental File S1.Source code, derived data from serological measurements, fitness metric annotations, and resulting fitness model performance data are available in the project's GitHub repository (https://github.com/blab/flu-forecasting).Raw serological measurements are restricted from public distribution by previous data sharing agreements.
Article and author information
Author details
Pierre Barrat-Charlaix
Funding
Cancer Research UK (FC001030)
- Lynne Whittaker
- Burcu Ermetal
- Rodney Stuart Daniels
- John W McCauley
National Institute of Allergy and Infectious Diseases (U19AI117891-01)
- Trevor Bedford
National Institute of Allergy and Infectious Diseases (R01AI127893-01)
- Pierre Barrat-Charlaix
- Richard A Neher
- Trevor Bedford
Medical Research Council (FC001030)
- Lynne Whittaker
- Burcu Ermetal
- Rodney Stuart Daniels
- John W McCauley
Wellcome (FC001030)
- Lynne Whittaker
- Burcu Ermetal
- Rodney Stuart Daniels
- John W McCauley
Ministry of Health, Labour and Welfare (10110400)
- Seiichiro Fujisaki
- Kazuya Nakamura
- Noriko Kishida
- Shinji Watanabe
- Hideki Hasegawa
Japan Agency for Medical Research and Development (JPfk0108118)
- Shinji Watanabe
Australian Government Department of Health
- Ian Barr
- Kanta Subbarao
National Institute of Allergy and Infectious Diseases (F31AI140714)
- John Huddleston
National Institute of General Medical Sciences (R35GM119774-01)
- Trevor Bedford
Pew Charitable Trusts
- Trevor Bedford
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution
eLife 9:e60067.
https://doi.org/10.7554/eLife.60067
Further reading
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- Evolutionary Biology
- Epidemiology and Global Health
- Microbiology and Infectious Disease
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eLife is pleased to present a Special Issue to highlight recent advances in the growing and increasingly interdisciplinary field of evolutionary medicine.
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- Evolutionary Biology
Gene duplication drives evolution by providing raw material for proteins with novel functions. An influential hypothesis by Ohno (1970) posits that gene duplication helps genes tolerate new mutations and thus facilitates the evolution of new phenotypes. Competing hypotheses argue that deleterious mutations will usually inactivate gene duplicates too rapidly for Ohno’s hypothesis to work. We experimentally tested Ohno’s hypothesis by evolving one or exactly two copies of a gene encoding a fluorescent protein in Escherichia coli through several rounds of mutation and selection. We analyzed the genotypic and phenotypic evolutionary dynamics of the evolving populations through high-throughput DNA sequencing, biochemical assays, and engineering of selected variants. In support of Ohno’s hypothesis, populations carrying two gene copies displayed higher mutational robustness than those carrying a single gene copy. Consequently, the double-copy populations experienced relaxed purifying selection, evolved higher phenotypic and genetic diversity, carried more mutations and accumulated combinations of key beneficial mutations earlier. However, their phenotypic evolution was not accelerated, possibly because one gene copy rapidly became inactivated by deleterious mutations. Our work provides an experimental platform to test models of evolution by gene duplication, and it supports alternatives to Ohno’s hypothesis that point to the importance of gene dosage.
