Computer model uses virus 'appearance' to better predict winter flu
strains

Date:
October 13, 2020
Source:
eLife
Summary:
Combining genetic and experimental data into models about the
influenza virus can help predict more accurately which strains
will be most common during the next winter, says a study published
recently in eLife.



FULL STORY ========================================================================== Combining genetic and experimental data into models about the influenza
virus can help predict more accurately which strains will be most common
during the next winter, says a study published recently in eLife.


==========================================================================
The models could make the design of flu vaccines more accurate, providing fuller protection against a virus that causes around half a million
deaths each year globally.

Vaccines are the best protection we have against the flu. But the
virus changes its appearance to our immune system every year, requiring researchers to update the vaccine to match. Since a new vaccine takes
almost a year to make, flu researchers must predict which flu viruses
look the most like the viruses of the future.

The gold-standard ways of studying influenza involve laboratory
experiments looking at a key molecule that coats the virus called haemagglutinin. But these methods are labour-intensive and take a long
time. Researchers have focused instead on using computers to predict how
the flu virus will evolve from the genetic sequence of haemagglutinin
alone, but these data only give part of the picture.

"The influenza research community has long recognised the importance of
taking into account physical characteristics of the flu virus, such as
how haemagglutinin changes over time, as well as genetic information,"
explains lead author John Huddleston, a PhD student in the Bedford Lab
at Fred Hutchinson Cancer Research Center and Molecular and Cell Biology Program at the University of Washington, Seattle, US. "We wanted to see
whether combining genetic sequence-only models of influenza evolution
with other high-quality experimental measurements could improve the
forecasting of the new strains of flu that will emerge one year down the
line." Huddleston and the team looked at different components of virus 'fitness' - - that is, how likely the virus is to thrive and continue
to evolve. These included how similar the antigens of the virus are to previously circulating strains (antigens being the components of the virus
that trigger an immune response). They also measured how many mutations
the virus has accumulated, and whether they are beneficial or harmful.

Using 25 years of historical flu data, the team made forecasts one year
into the future from all available flu seasons. Each forecast predicted
what the future virus population would look like using the virus' genetic
code, the experimental data, or both. They compared the predicted and
real future populations of flu to find out which data types were more
helpful for predicting the virus' evolution.

They found that the forecasts that combined experimental measures of the
virus' appearance with changes in its genetic code were more accurate than forecasts that used the genetic code alone. Models were most informative
if they included experimental data on how flu antigens changed over
time, the presence of likely harmful mutations, and how rapidly the flu population had grown in the past six months. "Genetic sequence alone
could not accurately predict future flu strains -- and therefore should
not take the place of traditional experiments that measure the virus' appearance," Huddleston says.

"Our results highlight the importance of experimental measurements to
quantify the effects of changes to virus' genetic code and provide a
foundation for attempts to forecast evolutionary systems," concludes
senior author Trevor Bedford, Principal Investigator at the Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center,
Seattle, Washington. "We hope the open- source forecasting tools we have developed can immediately provide better forecasts of flu populations,
leading to improved vaccines and ultimately fewer illnesses and deaths
from flu."

========================================================================== Story Source: Materials provided by eLife. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. John Huddleston, John R Barnes, Thomas Rowe, Xiyan Xu, Rebecca
Kondor,
David E Wentworth, Lynne Whittaker, Burcu Ermetal, Rodney Stuart
Daniels, John W McCauley, Seiichiro Fujisaki, Kazuya Nakamura,
Noriko Kishida, Shinji Watanabe, Hideki Hasegawa, Ian Barr,
Kanta Subbarao, Pierre Barrat-Charlaix, Richard A Neher, Trevor
Bedford. Integrating genotypes and phenotypes improves long-term
forecasts of seasonal influenza A/H3N2 evolution. eLife, 2020;
9 DOI: 10.7554/eLife.60067 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201013124050.htm

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