F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.14140.1 Research Note Articles Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019

[version 1; peer review: 2 approved, 1 not approved]

 Paessler Slobodan Conceptualization Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 2 Veljkovic Veljko Conceptualization Formal Analysis Investigation Methodology Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0002-1980-0927 a 3 Department of Pathology, Galveston National Laboratory, University of Texas Medical Branch at Galveston, Galveston, TX, USA Galveston National Laboratory, Institute for Human Infections and Immunity, University of Texas Medical Branch at Galveston, Galveston, TX, USA Biomed Protection, Galveston, TX, USA veljko@biomedprotection.com

No competing interests were disclosed.

 8 3 2018 2018 7 298 28 2 2018 Copyright: © 2018 Paessler S and Veljkovic V 2018 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

 influenza vaccine efficacy H3N2 electronic biology The author(s) declared that no grants were involved in supporting this work. Introduction

Vaccine effectiveness (VE) against H3N2 viruses is typically lower than VE against influenza H1N1 and/or influenza B viruses. It's not uncommon to see VE of about 30 percent against H3N2 viruses. Furthermore, during the flu season 2017 in Australia, VE of the seasonal flu vaccine was around 10% resulting in record-high numbers of laboratory-confirmed influenza A infections, hospitalizations and deaths 1 . This situation raised concerns that similar could happen in the United States during the flu season 2017/2018, in which H3N2 viruses were predominant. The concerns were based on assumptions that H3N2 viruses in Australia and US were similar, as the classical phylogeny indicated, and because the vaccine composition was identical one could expect comparable levels of VE. Therefore, predicted VE of the flu vaccine in the USA at the beginning of the flu season was around 10% 2 . This prediction was justified and rationalized using the assumption that H3N2 viruses circulating in Australia in the flu season 2016–17 are similar to viruses in the Northern Hemisphere.

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 . Our prediction was recently confirmed in the interim CDC estimation of 2017–18 seasonal influenza VE, published and released in February 2018 1 . Moreover, the risk for a (H3N2) associated medically-attended influenza illness was reduced through vaccination by 59% among children aged 6 months through 8 years 1 .

 Methods

To improve VE for the flu season 2018, WHO selected in September 2017 the new vaccine virus A/Singapore/INFIMH-16-0019/2016, which is better adapted to H3N2 viruses circulating in the South Hemisphere ( See WHO recommendation of vaccine compositions for the Southern Hemisphere). The WHO in February 2018 selected the same virus for the vaccine for the season 2018–19 in the North Hemisphere ( See WHO recommendation of vaccine compositions for the Northern Hemisphere).

In order to assess VE against H3N2 viruses for the next flu season 2018–19 in the United States, we analyzed compatibility between new vaccine virus A/Singapore/INFIMH-16-0019/2016 and H3N2 viruses isolated in 2018 in US. 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 .

 Results and discussion

In Figure 1 the ISM-based phylogenetic tree is presented for hemagglutinin HA1 from 68 H3N2 viruses collected in the United States from January to February 2018 and stored in the publicly open database GISAID. As can be seen in this figure, the H3N2 viruses are grouped into two separate clusters and the novel vaccine virus A/Singapore/INFIMH-16-0019/2016 belongs to the small cluster encompassing only 8.8% of analyzed viruses. Previously we showed that 71% of H3N2 viruses isolated in the beginning of the US season 2017–18 were informationally compatible with vaccine virus 3 . This compatibility resulted in good protection against H3N2 viruses in this season 1 . The low informational compatibility between new vaccine virus and H3N2 viruses circulating in US suggests that VE for the next flu season in US could be very low. Of note is that H3N2 virus A/Hong Kong/4801/2014 in vaccine for the season 2017–2018 better fits US viruses than new vaccine virus A/Singapore/INFIMH-16-0019/2016. This suggests possibility that VE of the current vaccine could be even higher than that for the new vaccine.

The ISM-based phylogenetic tree of HA1 from human H3N2 influenza viruses collected in the United States from January to February 2018.

The vaccine viruses are marked with asterisk (green).

We propose the “ISM-based phylogenetic algorithm ISTREE analysis” for rapid and accurate analysis of different influenza A viruses that can be used for VE prediction. This is a first report VE prediction prior to flu season using computational analysis. Our prediction has been recently confirmed through laboratory reports released by CDC. Based on current data, we predict low VE for the season 2018/2019 for US due to vaccine virus selection.

Data availability

The data referenced by this article are under copyright with the following copyright statement: Copyright: � 2018 Paessler S and Veljkovic V

Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Sequence data of the viruses were obtained from the GISAID EpiFlu TM Database. To access the database each individual user should complete the “Registration Form For Individual Users”. This form, together with detailed instructions, are available on the website. After submission of the Registration form, the user will receive a password. There are no any other restrictions for the access to GISAID. Conditions of access to, and use of, the GISAID EpiFlu TM Database and Data are defined by the Terms of Use.

Dataset 1: Human H3N2 influenza viruses collected in the United States from January to February 2018 (GISAID EpiFlu TM database, accessed February 20, 2018). 10.5256/f1000research.14140.d196223 5

Flannery B Chung JR Belongia EA : Interim estimates of 2017-18 seasonal influenza vaccine effectiveness - United States, February 2018. MMWR Morb Mortal Wkly Rep. 2018;67(6):180–185. 29447141 10.15585/mmwr.mm6706a2 5815489 Paules CI Sullivan SG Subbarao K : Chasing Seasonal Influenza - The Need for a Universal Influenza Vaccine. N Engl J Med. 2018;378(1):7–9. 29185857 10.1056/NEJMp1714916 Paessler S Veljkovic V : Prediction of influenza vaccine effectiveness for the influenza season 2017/18 in the US [version 1; referees: 2 approved]. F1000Res. 2017;6:2067–2075. 10.12688/f1000research.13198.1 Perovic V : Novel algorithm for phylogenetic analysis of proteins: application to analysis of the evolution of H5N1 influenza viruses. J Mathem Chem. 2013;51(8):2238–2255. 10.1007/s10910-013-0212-1 Paessler S Veljkovic V : Dataset 1 in: Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019. F1000Research. 2018. Data Source 10.5256/f1000research.15380.r31708 Reviewer response for version 1 Harder Timm C. 1 Referee Federal Research Institute for Animal Health, Friedrich Loeffler Institute, Greifswald, Germany

Competing interests: No competing interests were disclosed.

 27 3 2018 Copyright: © 2018 Harder TC 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

The authors provide an analysis of HA sequences of recent H3N2 influenza viruses from the ongoing season using the protein sequence-based clustering algorithm ISTREE. This method has been developed and published by one of the authors several years ago. ISTREE seeks to combine molecular data and provide a clustering with respect to antigenic relatedness of the influenza virus HA. Previous publications provided a retrospective analysis of genetic and antigenic data. Basically, ISTREE is advertised as an alternative to antigenic cartography of influenza viruses using serologic data based on hemagglutination inhibition (HI).

In fact, such algorithms would be of great interest and value in terms of vaccine selection. The prospective statements re appropriate vaccine strains for a future influenza season made by the authors here are quite clear but they are solely based on ISTREE assessments. No flanking/supporting data based on standard HI assays is provided.  Assessing the validity of the ISTREE data therefore is not possible. The data may well turn out to be useful but, as it currently stands, they may be totally misleading as well.

Solid antigenic data will be required to validate the potentially useful ISTREE algorithm.

Is the work clearly and accurately presented and does it cite the current literature?

Partly

If applicable, is the statistical analysis and its interpretation appropriate?

I cannot comment. A qualified statistician is required.

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

No

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Virology, diagnostics, and epidemiology of influenza A

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Veljkovic Veljko Biomed Protection, Galveston, USA, USA

Competing interests: No competing interest

 4 4 2018

The major Referee’s criticism concerning our study is the lack of serology/virology data to support our approach for making valid predictions. This criticism misses the major point of the study, which is to rapidly release predictions based on in silico screening to allow other scientists to look at it as early as possible and to test it through the flu season. However, as we have mentioned in our paper we used this method previously and if the reviewer would have read the paper carefully, he would have realized that our prediction for the flu season 2017/18 was confirmed several months later by CDC using various serological assays. These data can be found in CDC laboratory reports (see the latest CDC report: https://www.cdc.gov/flu/weekly/index.htm) and present a totally independent validation of our approach.

 10.5256/f1000research.15380.r31709 Reviewer response for version 1 Arafa Abdel-Satar 1 Referee National Laboratory for Veterinary Quality Control on Poultry Production, Animal Health Research Institute, Giza, Egypt

Competing interests: No competing interests were disclosed.

 22 3 2018 Copyright: © 2018 Arafa AS 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

The article “Using electronic biology based platform to predict flu vaccine efficacy for 2018/2019” is clearly presented and technically sound enough for publication in F1000Research online. It is well-written and supported by computational well-developed bioinformatics analysis.

I just suggest one observations that should be clarified to enhance the work:

-          Fig 1 The ISM tree based analysis is not supported by a tool to predict grouping like bootstrapping method to confirm accurate grouping

Finally,  the article is recommended for publication in its present format.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Virology, avian influenza, genetic analysis

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.15380.r31711 Reviewer response for version 1 Perez Daniel R. 1 Referee https://orcid.org/0000-0002-6569-5689 Department of Population Health, Poultry Diagnostic and Research Center (PDRC), University of Georgia, Athens, GA, USA

Competing interests: No competing interests were disclosed.

 22 3 2018 Copyright: © 2018 Perez DR 2018 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

This is a provocative research article based on the authors' previous development of the informational spectrum method that takes into account how virus/receptor interactions modulate the antigenic cross-reactive phenotype of the HA protein of influenza A viruses. Using this method, the authors were able to shed light on the lack of efficacy of the vaccine against the H3 subtype during the 2014/15. They were also able to predict that in the US, the vaccine against the H3 subtype was going to be more efficacious than previously anticipated by the CDC. Later in the 2017/18 season, the CDC confirmed the authors' prediction. In this latest article, the authors predict that the selection of the vaccine candidate for the 2018/19 is less than ideal and suggest that vaccine efficacy against the H3 subtype will be lower than in this past season. This type of work is fascinating and necessary and hopefully it becomes part of the toolkit for the selection of vaccine candidates against influenza.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

NA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.