Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US

Paessler, Slobodan; Veljković, Veljko

doi:10.12688/f1000research.13198.1

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…This analysis was performed using the informational spectrum method (ISM) based phylogenetic algorithm, the Informational Spectrum-based Phylogenetic Analysis (ISTREE), which we previously used to assess VE for the flu season 2017–18 3 . This algorithm, which is based on the informational hallmark of proteins that determines their biological function, was previously described in more detail 4 .…”

Section: Methodsmentioning

confidence: 99%

“…Comparison of Australian H3N2 viruses and viruses isolated in the USA at the beginning of the flu season 2017–18, performed using a novel functional phylogenetic tool, demonstrated significant difference between these two groups of viruses 3 . This new information led us to predict that the flu vaccine in US should work in the season 2017–18 just as well as in 2016–17 3 .…”

Section: Introductionmentioning

confidence: 99%

“…This new information led us to predict that the flu vaccine in US should work in the season 2017–18 just as well as in 2016–17 3 . Our prediction was recently confirmed in the interim CDC estimation of 2017–18 seasonal influenza VE, published and released in February 2018 1 .…”

Section: Introductionmentioning

confidence: 99%

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Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019

Paessler

Veljković

2018

F1000Res

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Flu epidemics and potential pandemics pose great challenges to public health institutions, scientists and vaccine producers. Creating right vaccine composition for different parts of the world is not trivial and has been historically very problematic. This often resulted in decrease in vaccinations and reduced trust in public health officials. To improve future protection of population against flu we urgently need new methods for vaccine efficacy prediction and vaccine virus selection.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019

Paessler

Veljković

2018

F1000Res

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“…Inactivated influenza vaccines significantly reduce mortality rates in children and severe disease in adults . However, vaccine protection is notoriously strain‐specific, and mismatches between vaccine and circulating strains through antigenic drift lead to low vaccine efficacy …”

Section: Ferrets For Influenza Surveillance and Vaccine Developmentmentioning

confidence: 99%

Improving immunological insights into the ferret model of human viral infectious disease

Wong

Layton

Wheatley

et al. 2019

Influenza Resp Viruses

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Influenza Other Respi Viruses. 2019;13:535-546. | 535 wileyonlinelibrary.com/journal/irv 1 | INTRODUC TI ON Animal models have proved critical in developing concepts of immunity to infection. Non-human primates (NHP) are a highly humanrelevant disease model for infectious agents due to high genetic conservation between humans and NHP. 1-3 However, significant cost and ethical issues often necessitate the use of smaller animal models for both basic and translational research. Mice (Mus musculus) are a favoured small animal model due to widespread availability, incisive transgenic models, comprehensive genomic information 4 and readily available reagents. However, for many viruses, alternative animal models better recapitulate human physiology and disease. Ferrets (Mustela putorius furo) have been employed to study the pathogenesis of a variety of human pathogens, including human and avian influenzas, coronaviruses including severe acute respiratory syndrome (SARS-CoV), 5-14 human respiratory syncytial virus (HRSV), 15-20 human metapneumovirus (HMPV), 21 Ebola virus 22-28 and henipavirus (Nipah virus and Hendra virus). 29-34 While ferretscan be productively infected with many of these viruses, a lack of some tools to interrogate ferret immunological responses to infection limits insights that might impact the development of vaccines and/or therapeutics. Here, we review recent insights gained from ferret models of human respiratory diseases, with a major focus on influenza, and highlight several knowledge gaps whose closure will greatly enhance the informational gain from ferret models to advance development of human vaccines and therapeutics. AbstractFerrets are a well-established model for studying both the pathogenesis and transmission of human respiratory viruses and evaluation of antiviral vaccines. Advanced immunological studies would add substantial value to the ferret models of disease but are hindered by the low number of ferret-reactive reagents available for flow cytometry and immunohistochemistry. Nevertheless, progress has been made to understand immune responses in the ferret model with a limited set of ferret-specific reagents and assays. This review examines current immunological insights gained from the ferret model across relevant human respiratory diseases, with a focus on influenza viruses. We highlight key knowledge gaps that need to be bridged to advance the utility of ferrets for immunological studies. K E Y W O R D S ferret, immunology, influenza How to cite this article: Wong J, Layton D, Wheatley AK, Kent SJ. Improving immunological insights into the ferret model of human viral infectious disease. Influenza Other Respi Viruses. 2019;13:535-546. https ://doi.

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“…The informational spectrum method (ISM), a virtual spectroscopy method for analysis of proteins, is based on the fundamental electronic properties of amino acids and requires only nucleotide sequence availability to investigate proteins 1 . For this reason, ISM was previously used for analysis of novel viruses for which little or no information were available [2][3][4][5] . Here, the 2019-nCoV was analyzed with ISM to identify its possible origin and natural host, as well as putative therapeutic and vaccine targets.…”

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Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target

et al. 2020

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A novel coronavirus recently identified in Wuhan, China (2019-nCoV) has expanded the number of highly pathogenic coronaviruses affecting humans. The 2019-nCoV represents a potential epidemic or pandemic threat, which requires a quick response for preparedness against this infection. The present report uses the informational spectrum methodology to identify the possible origin and natural host of the new virus, as well as putative therapeutic and vaccine targets. The performed in silico analysis indicates that the newly emerging 2019-nCoV is closely related to severe acute respiratory syndrome (SARS)-CoV and, to a lesser degree, Middle East respiratory syndrome (MERS)-CoV. Moreover, the well-known SARS-CoV receptor (ACE2) might be a putative receptor for the novel virus as well. Additional results indicated that civets and poultry are potential candidates for the natural reservoir of the 2019-nCoV, and that domain 288-330 of S1 protein from the 2019-nCoV represents promising therapeutic and/or vaccine target. PubMed Abstract | Publisher Full Text | Free Full Text 9. Li W, Moore MJ, Vasilieva N, et al.: Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature. 2003; 426(6965): 450-4. PubMed Abstract | Publisher Full Text 10. Raj VS, Mou H, Smits SL, et al.: Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC. Nature. 2013; 495(7440): 251-4. PubMed Abstract | Publisher Full Text 11. Müller MA, Raj VS, Muth D, et al.: Human coronavirus EMC does not require the SARS-coronavirus receptor and maintains broad replicative capability in mammalian cell lines. mBio. 2012; 3(6): pii: e00515-12.PubMed Abstract | Publisher Full Text | Free Full Text

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Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US

Cited by 12 publications

References 8 publications

Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019

Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019

Improving immunological insights into the ferret model of human viral infectious disease

Use of the informational spectrum methodology for rapid biological analysis of the novel coronavirus 2019-nCoV: prediction of potential receptor, natural reservoir, tropism and therapeutic/vaccine target

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