Viral factors in influenza pandemic risk assessment
- Harvard TH Chan School of Public Health, United States
- Imperial College London, United Kingdom
- Massachusetts Institute of Technology, United States
- St Jude Children's Research Hospital, United States
- Centers for Disease Control and Prevention, United States
- University of Chicago, United States
- University of Virginia, United States
- University of California, Los Angeles, United States
- Agency for Science, Technology and Research, Singapore
- Ryerson University, Canada
- Fred Hutchinson Cancer Research Center, United States
- University of Wisconsin School of Veterinary Medicine, United States
- University of Georgia, United States
- Erasmus Medical Center, Netherlands
- MRC-University of Glasgow Centre For Virus Research, United Kingdom
- Institut de Recherche pour le Développement, France
- The University of Texas at Austin, United States
- University of Cambridge, United Kingdom
Abstract
The threat of an influenza A virus pandemic stems from continual virus spillovers from reservoir species, a tiny fraction of which spark sustained transmission in humans. To date, no pandemic emergence of a new influenza strain has been preceded by detection of a closely related precursor in an animal or human. Nonetheless, influenza surveillance efforts are expanding, prompting a need for tools to assess the pandemic risk posed by a detected virus. The goal would be to use genetic sequence and/or biological assays of viral traits to identify those non-human influenza viruses with the greatest risk of evolving into pandemic threats, and/or to understand drivers of such evolution, to prioritize pandemic prevention or response measures. We describe such efforts, identify progress and ongoing challenges, and discuss three specific traits of influenza viruses (hemagglutinin receptor binding specificity, hemagglutinin pH of activation, and polymerase complex efficiency) that contribute to pandemic risk.
Article and author information
Author details
James O Lloyd-Smith
Catherine AA Beauchemin
Funding
National Health and Medical Research Council (12/1/06/24/5793)
- Sebastian Maurer-Stroh
Wellcome (200861/Z/16/Z)
- Steven Riley
Medical Research Council (MR/J008761/1)
- Steven Riley
National Institute of General Medical Sciences (MIDAS U01 GM110721-01)
- Steven Riley
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VIDI grant 91715372)
- Sander Herfst
Agency for Science, Technology and Research (12/1/06/24/5793)
- Sebastian Maurer-Stroh
National Institute of Allergy and Infectious Diseases (Centers of Excellence for Influenza Research and Surveillance (Contract HHSN272201400006C))
- Charles J Russell
National Institutes of Health (R01 GM088344)
- Claus O Wilke
National Institutes of Health (R01 GM098304)
- Peter M Kasson
Royal Society (University Research Fellowship)
- Colin A Russell
Medical Research Council (G0801822)
- Pablo R Murcia
Wellcome (Project 093488/Z/10/Z)
- Steven Riley
Wellcome (200187/Z/15/Z)
- Steven Riley
National Institute of General Medical Sciences (MIDAS Center of Excellence Cooperative Agreement U54GM088558)
- Marc Lipsitch
Natural Sciences and Engineering Research Council of Canada (Discovery Grant (355837-2013))
- Catherine AA Beauchemin
Ministry of Research and Innovation (Early Researcher Award Award (ER13-09-040))
- Catherine AA Beauchemin
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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Viral factors in influenza pandemic risk assessment
eLife 5:e18491.
https://doi.org/10.7554/eLife.18491
Further reading
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- Epidemiology and Global Health
Background:
The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).
Methods:
Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.
Results:
Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).
Conclusions:
Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.
Funding:
Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.
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- Epidemiology and Global Health
- Microbiology and Infectious Disease
Several areas of the world suffer a notably high incidence of Shiga toxin-producing Escherichia coli. To assess the impact of persistent cross-species transmission systems on the epidemiology of E. coli O157:H7 in Alberta, Canada, we sequenced and assembled E. coli O157:H7 isolates originating from collocated cattle and human populations, 2007–2015. We constructed a timed phylogeny using BEAST2 using a structured coalescent model. We then extended the tree with human isolates through 2019 to assess the long-term disease impact of locally persistent lineages. During 2007–2015, we estimated that 88.5% of human lineages arose from cattle lineages. We identified 11 persistent lineages local to Alberta, which were associated with 38.0% (95% CI 29.3%, 47.3%) of human isolates. During the later period, six locally persistent lineages continued to be associated with human illness, including 74.7% (95% CI 68.3%, 80.3%) of reported cases in 2018 and 2019. Our study identified multiple locally evolving lineages transmitted between cattle and humans persistently associated with E. coli O157:H7 illnesses for up to 13 y. Locally persistent lineages may be a principal cause of the high incidence of E. coli O157:H7 in locations such as Alberta and provide opportunities for focused control efforts.
