Notable Recent Publications

These are some recent publications which give a flavour of the research from the Barclay lab. For a complete list of publications, please see below.

Species difference in ANP32A underlies influenza A virus polymerase host restriction. Nature (2016).
Jason S. Long, Efstathios S. Giotis, Olivier Moncorgé, Rebecca Frise, Bhakti Mistry, Joe James, Mireille Morisson, Munir Iqbal, Alain Vignal, Michael A. Skinner & Wendy S. Barclay

This paper identified a key factor that explained why the polymerases from avian influenza viruses are restricted in humans.  For more, please see the associated New and Views.

See our latest ANP32 papers here: eLIFE, Journal of Virology, Journal of Virology.

The mechanism of resistance to favipiravir in influenza. PNAS (2018).
Daniel H. Goldhill, Aartjan J. W. te Velthuis, Robert A. Fletcher, Pinky Langat, Maria Zambon, Angie Lackenby & Wendy S. Barclay

This paper showed how influenza could evolve resistance to favipiravir, an antiviral that may be used to treat influenza. The residue that mutated to give resistance was highly conserved suggesting that the mechanism of resistance may be applicable to other RNA viruses.

Internal genes of a highly pathogenic H5N1 influenza virus determine high viral replication in myeloid cells and severe outcome of infection in mice. Plos Path. (2018).
Hui Li*, Konrad C. Bradley*, Jason S. Long, Rebecca Frise, Jonathan W. Ashcroft, Lorian C. Hartgroves, Holly Shelton, Spyridon Makris, Cecilia Johansson, Bin Cao & Wendy S. Barclay

Why do avian influenza viruses like H5N1 cause such severe disease in humans? This paper demonstrated that H5N1 viruses replicate better than human viruses in myeloid cells from mice leading to a cytokine storm and more severe disease.

Citation

BibTex format

@article{Yan:2020:10.1016/j.epidem.2020.100406,
author = {Yan, AWC and Zhou, J and Beauchemin, CAA and Russell, CA and Barclay, WS and Riley, S},
doi = {10.1016/j.epidem.2020.100406},
journal = {Epidemics: the journal of infectious disease dynamics},
pages = {1--10},
title = {Quantifying mechanistic traits of influenza viral dynamics using in vitro data.},
url = {http://dx.doi.org/10.1016/j.epidem.2020.100406},
volume = {33},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - When analysing in vitro data, growth kinetics of influenza virus strains are often compared by computing their growth rates, which are sometimes used as proxies for fitness. However, analogous to mathematical models for epidemics, the growth rate can be defined as a function of mechanistic traits: the basic reproduction number (the average number of cells each infected cell infects) and the mean generation time (the average length of a replication cycle). Fitting a model to previously published and newly generated data from experiments in human lung cells, we compared estimates of growth rate, reproduction number and generation time for six influenza A strains. Of four strains in previously published data, A/Canada/RV733/2003 (seasonal H1N1) had the lowest basic reproduction number, followed by A/Mexico/INDRE4487/2009 (pandemic H1N1), then A/Indonesia/05/2005 (spill-over H5N1) and A/Anhui/1/2013 (spill-over H7N9). This ordering of strains was preserved for both generation time and growth rate, suggesting a positive biological correlation between these quantities which have not been previously observed. We further investigated these potential correlations using data from reassortant viruses with different internal proteins (from A/England/195/2009 (pandemic H1N1) and A/Turkey/05/2005 (H5N1)), and the same surface proteins (from A/Puerto Rico/8/34 (lab-adapted H1N1)). Similar correlations between traits were observed for these viruses, confirming our initial findings and suggesting that these patterns were related to the degree of human adaptation of internal genes. Also, the model predicted that strains with a smaller basic reproduction number, shorter generation time and slower growth rate underwent more replication cycles by the time of peak viral load, potentially accumulating mutations more quickly. These results illustrate the utility of mathematical models in inferring traits driving observed differences in in vitro growth of influenza strains.
AU  - Yan,AWC
AU  - Zhou,J
AU  - Beauchemin,CAA
AU  - Russell,CA
AU  - Barclay,WS
AU  - Riley,S
DO  - 10.1016/j.epidem.2020.100406
EP  - 10
PY  - 2020///
SN  - 1755-4365
SP  - 1
TI  - Quantifying mechanistic traits of influenza viral dynamics using in vitro data.
T2  - Epidemics: the journal of infectious disease dynamics
UR  - http://dx.doi.org/10.1016/j.epidem.2020.100406
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33096342
UR  - https://www.sciencedirect.com/science/article/pii/S1755436520300311?via%3Dihub
UR  - http://hdl.handle.net/10044/1/83950
VL  - 33
ER  -
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Contact us

For any enquiries related to this group, please contact:

Professor Wendy Barclay
Chair in Influenza Virology 
+44 (020) 7594 5035
w.barclay@imperial.ac.uk