Evolution of 2014/15 H3N2 Influenza Viruses Circulating in US: Consequences for Vaccine Effectiveness and Possible New Pandemic

Prediction of Antigenic Distance in Influenza A Using Attribute Network Embedding

Owing to the rapid changes in the antigenicity of influenza viruses, it is difficult for humans to obtain lasting immunity through antiviral therapy. Hence, tracking the dynamic changes in the antigenicity of influenza viruses can provide a basis for vaccines and drug treatments to cope with the spread of influenza viruses. In this paper, we developed a novel quantitative prediction method to predict the antigenic distance between virus strains using attribute network embedding techniques. An antigenic network is built to model and combine the genetic and antigenic characteristics of the influenza A virus H3N2, using the continuous distributed representation of the virus strain protein sequence (ProtVec) as a node attribute and the antigenic distance between virus strains as an edge weight. The results show a strong positive correlation between supplementing genetic features and antigenic distance prediction accuracy. Further analysis indicates that our prediction model can comprehensively and accurately track the differences in antigenic distances between vaccines and influenza virus strains, and it outperforms existing methods in predicting antigenic distances between strains.

Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning

The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus inoculations to confer immunity against PRRSV on farms. While understanding cross-protection among strains is crucial for the continued success of these interventions, understanding how genetic diversity translates to antigenic diversity remains elusive. We developed machine learning algorithms to estimate antigenic distance in silico, based on genetic sequence data, and identify differences in specific amino acid sites associated with antigenic differences between viruses. First, we obtained antigenic distance estimates derived from serum neutralization assays cross-reacting PRRSV monospecific antisera with virus isolates from 27 PRRSV1 viruses circulating in Europe. Antigenic distances were weakly to moderately associated with ectodomain amino acid distance for open reading frames (ORFs) 2 to 4 (ρ < 0.2) and ORF5 (ρ = 0.3), respectively. Dividing the antigenic distance values at the median, we then categorized the sera-virus pairs into two levels: low and high antigenic distance (dissimilarity). In the machine learning models, we used amino acid distances in the ectodomains of ORFs 2 to 5 and site-wise amino acid differences between the viruses as potential predictors of antigenic dissimilarity. Using mixed-effect gradient boosting models, we estimated the antigenic distance (high versus low) between serum-virus pairs with an accuracy of 81% (95% confidence interval, 76 to 85%); sensitivity and specificity were 86% and 75%, respectively. We demonstrate that using sequence data we can estimate antigenic distance and potential cross-protection between PRRSV1 strains. IMPORTANCE Understanding cross-protection between cocirculating PRRSV1 strains is crucial to reducing losses associated with PRRS outbreaks on farms. While experimental studies to determine cross-protection are instrumental, these in vivo studies are not always practical or timely for the many cocirculating and emerging PRRSV strains. In this study, we demonstrate the ability to rapidly estimate potential immunologic cross-reaction between different PRRSV1 strains in silico using sequence data routinely collected by production systems. These models can provide fast turn-around information crucial for improving PRRS management decisions such as selecting vaccines/live virus inoculation to be used on farms and assessing the risk of outbreaks by emerging strains on farms previously exposed to certain PRRSV strains and vaccine development among others.

Antigenicity prediction and vaccine recommendation of human influenza virus A (H3N2) using convolutional neural networks

The rapid evolution of influenza A viruses poses a great challenge to vaccine development. Analytical and machine learning models have been applied to facilitate the process of antigenicity determination. In this study, we designed deep convolutional neural networks (CNNs) to predict Influenza antigenicity. Our model is the first that systematically analyzed 566 amino acid properties and 141 amino acid substitution matrices for their predictability. We then optimized the structure of the CNNs using particle swarm optimization. The optimal neural networks outperform other predictive models with a blind validation accuracy of 95.8%. Further, we applied our model to vaccine recommendations in the period 1997 to 2011 and contrasted the performance of previous vaccine recommendations using traditional experimental approaches. The results show that our model outperforms the WHO recommendation and other existing models and could potentially improve the vaccine recommendation process. Our results show that WHO often selects virus strains with small variation from year to year and learns slowly and recovers once coverage dips very low. In contrast, the influenza strains selected via our CNN model can differ quite drastically from year to year and exhibit consistently good coverage. In summary, we have designed a comprehensive computational pipeline for optimizing a CNN in the modeling of Influenza A antigenicity and vaccine recommendation. It is more cost and time-effective when compared to traditional hemagglutination inhibition assay analysis. The modeling framework is flexible and can be adopted to study other type of viruses.

Prediction of the effectiveness of COVID-19 vaccine candidates

A safe and effective vaccine is urgently needed to bring the current SARS-CoV-2 pandemic under control. The spike protein (SP) of SARS-CoV-2 represents the principal target for most vaccines currently under development. This protein is highly conserved indicating that vaccine based on this antigen will be efficient against all currently circulating SARS-CoV-2 strains. The present analysis of SP suggests that mutation D614G could significantly decrease the effectiveness of the COVID-19 vaccine through modulation of the interaction between SARS-CoV-2 and its principal receptor ACE2.

Evolution of H5-Type Avian Influenza A Virus Towards Mammalian Tropism in Egypt, 2014 to 2015

Highly pathogenic avian influenza viruses (HPAIV) of the H5-subtype have circulated continuously in Egypt since 2006, resulting in numerous poultry outbreaks and considerable sporadic human infections. The extensive circulation and wide spread of these viruses in domestic poultry have resulted in various evolutionary changes with a dramatic impact on viral transmission ability to contact mammals including humans. The transmitted viruses are either (1) adapted well enough in their avian hosts to readily infect mammals, or (2) adapted in the new mammalian hosts to improve their fitness. In both cases, avian influenza viruses (AIVs) acquire various host-specific adaptations. These adaptive variations are not all well-known or thoroughly characterized. In this study, a phylogenetic algorithm based on the informational spectrum method, designated hereafter as ISM, was applied to analyze the affinity of H5-type HA proteins of Egyptian AIV isolates (2006-2015) towards human-type cell receptors. To characterize AIV H5-HA proteins displaying high ISM values reflecting an increased tendency of the HA towards human-type receptors, recombinant IV expressing monobasic, low pathogenic (LP) H5-HA versions in the background of the human influenza virus A/PR/8/1934(H1N1) (LP 7+1), were generated. These viruses were compared with a LP 7+1 expressing a monobasic H5-HA from a human origin virus isolate (human LP-7271), for their receptor binding specificity (ISM), in vitro replication efficiency and in vivo pathogenicity in mammals. Interestingly, using ISM analysis, we identified a LP 7+1 virus (LP-S10739C) expressing the monobasic H5-HA of AIV A/Chicken/Egypt/S10739C/2015(H5N1) that showed high affinity towards human-type receptors. This in silico prediction was reflected by a higher in vitro replication efficiency in mammalian cell cultures and a higher virulence in mice as compared with LP-7271. Sequence comparison between the LP-S10739C and the LP-7271 H5-HA, revealed distinct amino acid changes. Their contribution to the increased mammalian receptor propensity of LP-S10739C demands further investigation to better deduce the molecular determinant behind the reported high morbidity of 2014 to 2015 HPAI H5N1 virus in humans in Egypt. This study provides insights into the evolution of Egyptian H5 HPAIVs and highlights the need to identify the viral evolution in order to recognize emerging AIV with the potential to threaten human and animal populations.

The effectiveness of influenza vaccination in preventing hospitalizations in elderly in Beijing, 2016-18

BACKGROUND

Influenza vaccinations play an important role in preventing influenza related hospitalizations. The objective of this study was to estimate the effectiveness of vaccination in protecting Beijing residents aged ≥60 years from influenza related hospitalizations during the 2016/17 and 2017/18 influenza seasons.

METHODS

Patients who met the definition of severe acute respiratory infection (SARI) and were hospitalized in the nine sentinel hospitals in Beijing during the 2016/17 and 2017/18 influenza seasons were identified as the study population. The vaccination status of patients was obtained from a vaccination registry. Real-time reversetranscription polymerasechainreaction (RT-PCR) experiments were conducted to test pharyngeal or lower respiratory tract samples collected from SARI patients for influenza A and B viruses. Vaccine effectiveness (VE) was examined using a test-negative design that compare the odds of vaccination among influenza positives and negatives, adjusting for calendar week of illness onset, age, and underlying medical conditions.

RESULTS

We identified 50,364 patients in the study, in which there were 145 influenza cases and 528 influenza-negative controls aged ≥60 years in 2016/17 season and 149 cases and 358 controls aged ≥60 years in 2017/18 season. The most commonly identified subtype among participants was influenza A(H3N2) in 2016/17 and 2017/18 season (78.5% and 70.6%). Among the adults aged ≥60 years, the adjusted VE of vaccination against any influenza virus for serious acute respiratory infection (SARI) patients was 32.8% (95% confidence interval [CI]: -22.0 to 63.0%) in 2016/17 season. While the adjusted VE in 2017/18 season were 4.6% (95% CI: -72.4 to 47.2%) against any types of influenza, 29.2% (95% CI: -92.9 to 74%) against influenza A(H1N1)pdm09, -37.7% (95% CI: -293.8; 51.9%) against influenza A(H3N2) viruses, and 3.6% (95% CI: -113.8 to 56.5%) against influenza B.

CONCLUSION

The influenza vaccine provided moderate protection in 2016/17 season and mild protection in 2017/18 season for influenza related inpatients of adults aged ≥60 years in Beijing.

Optimizing the impact of low-efficacy influenza vaccines

The efficacy of influenza vaccines varies from one year to the next, with efficacy during the 2017-2018 season anticipated to be lower than usual. However, the impact of low-efficacy vaccines at the population level and their optimal age-specific distribution have yet to be ascertained. Applying an optimization algorithm to a mathematical model of influenza transmission and vaccination in the United States, we determined the optimal age-specific uptake of low-efficacy vaccine that would minimize incidence, hospitalization, mortality, and disability-adjusted life-years (DALYs), respectively. We found that even relatively low-efficacy influenza vaccines can be highly impactful, particularly when vaccine uptake is optimally distributed across age groups. As vaccine efficacy declines, the optimal distribution of vaccine uptake shifts toward the elderly to minimize mortality and DALYs. Health practitioner encouragement and concerted recruitment efforts are required to achieve optimal coverage among target age groups, thereby minimizing influenza morbidity and mortality for the population overall.

The Hard Way towards an Antibody-Based HIV-1 Env Vaccine: Lessons from Other Viruses

Although effective antibody-based vaccines have been developed against multiple viruses, such approaches have so far failed for the human immunodeficiency virus type 1 (HIV-1). Despite the success of anti-retroviral therapy (ART) that has turned HIV-1 infection into a chronic disease and has reduced the number of new infections worldwide, a vaccine against HIV-1 is still urgently needed. We discuss here the major reasons for the failure of “classical” vaccine approaches, which are mostly due to the biological properties of the virus itself. HIV-1 has developed multiple mechanisms of immune escape, which also account for vaccine failure. So far, no vaccine candidate has been able to induce broadly neutralizing antibodies (bnAbs) against primary patient viruses from different clades. However, such antibodies were identified in a subset of patients during chronic infection and were shown to protect from infection in animal models and to reduce viremia in first clinical trials. Their detailed characterization has guided structure-based reverse vaccinology approaches to design better HIV-1 envelope (Env) immunogens. Furthermore, conserved Env epitopes have been identified, which are promising candidates in view of clinical applications. Together with new vector-based technologies, considerable progress has been achieved in recent years towards the development of an effective antibody-based HIV-1 vaccine.

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

Vaccination against seasonal influenza viruses is the most effective way to prevent infection. A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. The high evolutionary rate, antigenic shift and antigenic drift of influenza viruses, represents the main obstacle for correct prediction of the vaccine effectiveness for an upcoming flu season. Conventional structural and phylogenetic approaches for assessment of vaccine effectiveness have had a limited success in prediction of vaccine efficacy in the past. Recently, a novel bioinformatics approach for assessment of effectiveness of seasonal influenza vaccine was proposed. Here, this approach was used for prediction of the vaccine effectiveness for the influenza season 2017/18 in US.

Seasonal influenza vaccine effectiveness against laboratory-confirmed influenza in 2015-2016: a hospital-based test-negative casecontrol study in Lithuania

OBJECTIVE

A case-control study was conducted to assess seasonal influenza vaccine effectiveness (SIVE) during the 2015-2016 influenza season.

METHODS

A study was performed in three departments in Lithuania between 1 December 2015 and 1 May 2016. Data on demographic and clinical characteristics including influenza vaccination status were collected from the patients recommended to receive the seasonal influenza vaccine. Influenza virus infection was confirmed by multiplex reverse transcription polymerase chain reaction (RT-PCR) .

RESULTS

Ninety-one (56.4%) of the 163 included subjects were ≥65 years old. Fifteen (9.2%) subjects were vaccinated against influenza at least 2 weeks before the onset of influenza symptoms, 12 of them were ≥65 years old. Of the 72 (44.2%) influenza virus positive cases, 65 (39.9%) were confirmed with influenza A (including 50 cases of influenza A(H1N1)pdm09), eight (4.9%) were confirmed with influenza B and one was a co-infection. Unadjusted SIVE against any influenza, influenza type A and influenza A(H1N1)pdm09 was 57% (95% CI -41% to 87%), 52% (95% CI -57% to 85%) and 70% (95% CI -43% to 94%) respectively.

CONCLUSION

Although SIVE estimates were not statistically significant the point estimates suggest moderate effectiveness against influenza type A.

Predicted Enhanced Human Propensity of Current Avian-Like H1N1 Swine Influenza Virus from China

Influenza A virus (IAV) subtypes against which little or no pre-existing immunity exists in humans represent a serious threat to global public health. Monitoring of IAV in animal hosts is essential for early and rapid detection of potential pandemic IAV strains to prevent their spread. Recently, the increased pandemic potential of the avian-like swine H1N1 IAV A/swine/Guangdong/104/2013 has been suggested. The virus is infectious in humans and the general population seems to lack neutralizing antibodies against this virus. Here we present an in silico analysis that shows a strong human propensity of this swine virus further confirming its pandemic potential. We suggest mutations which would further enhance its human propensity. We also propose conserved antigenic determinants which could serve as a component of a prepandemic vaccine. The bioinformatics tool, which can be used to further monitor the evolution of swine influenza viruses towards a pandemic virus, are described here and are made publically available (http://www.vin.bg.ac.rs/180/tools/iav_mon.php; http://www.biomedprotection.com/iav_mon.php).

Possible repurposing of seasonal influenza vaccine for prevention of Zika virus infection

The in silico analysis shows that the envelope glycoproteins E of Zika viruses (ZIKV) isolated in Asia, Africa and South and Central America encode highly conserved information determining their interacting profile and immunological properties. Previously it was shown that the same information is encoded in the primary structure of the hemagglutinin subunit 1 (HA1) from pdmH1N1 influenza A virus.  This similarity suggests possible repurposing of the seasonal influenza vaccine containing pdmH1N1 component for prevention of the ZIKV infection.