Yousong Peng

Sequential Reassortments Underlie Diverse Influenza H7N9 Genotypes in China

Initial genetic characterizations have suggested that the influenza A (H7N9) viruses responsible for the current outbreak in China are novel reassortants. However, little is known about the pathways of their evolution and, in particular, the generation of diverse viral genotypes. Here we report an in-depth evolutionary analysis of whole-genome sequence data of 45 H7N9 and 42 H9N2 viruses isolated from humans, poultry, and wild birds during recent influenza surveillance efforts in China. Our analysis shows that the H7N9 viruses were generated by at least two steps of sequential reassortments involving distinct H9N2 donor viruses in different hosts. The first reassortment likely occurred in wild birds and the second in domestic birds in east China in early 2012. Our study identifies the pathways for the generation of diverse H7N9 genotypes in China and highlights the importance of monitoring multiple sources for effective surveillance of potential influenza outbreaks.

mapping of H3n2 influenza antigenic evolution in China reveals a strategy for vaccine strain recommendation

One of the primary efforts in influenza vaccine strain recommendation is to monitor through gene sequencing the viral surface protein haemagglutinin (HA) variants that lead to viral antigenic changes. Here we have developed a computational method, denoted as PREDAC, to predict antigenic clusters of influenza A (H3N2) viruses with high accuracy from viral HA sequences. Application of PREDAC to large-scale HA sequence data of H3N2 viruses isolated from diverse regions of Mainland China identified 17 antigenic clusters that have dominated for at least one season between 1968 and 2010. By tracking the dynamics of the dominant antigenic clusters, we not only find that dominant antigenic clusters change more frequently in China than in the United States/Europe, but also characterize the antigenic patterns of seasonal H3N2 viruses within China. Furthermore, we demonstrate that the coupling of large-scale HA sequencing with PREDAC can significantly improve vaccine strain recommendation for China.

A comprehensive surveillance of adamantane resistance among human influenza A virus isolated from mainland China between 1956 and 2009

BACKGROUND Adamantane-derived drugs have been used for treatment and prophylaxis of influenza A virus infection for many years worldwide. Rapid surveillance of antiviral drug resistance is important for appropriate clinical guideline development. Here, we retrospectively assessed adamantane resistance among different influenza A subtypes (H1N1, H3N2 and H5N1) over 53 years (1956-2009) in mainland China. METHODS A total of 1,451 viruses, including 773 H3N2 viruses, 647 H1N1 viruses and 31 human H5N1 viruses, were analysed by matrix gene sequencing and assayed for drug resistance. RESULTS Our results show that the prevalence of adamantane-resistant H3N2 viruses was low between 1956 and 2002, but substantially increased in 2003 to the extent that since 2006 all H3N2 viruses have been drug resistant. The percentage of adamantane-resistant H1N1 viruses also increased from 50.0% in 2004 to 98.7% in 2007; however, this decreased to 46.7% in 2009. Only three adamantane-resistant H5N1 viruses have been detected since 2003, when the first case of human H5N1 virus infection was detected in mainland China. Phylogenetic analysis demonstrated that the increase of adamantane-resistant isolates was caused by point mutations or intrasubtype reassortment instead of intersubtype reassortment. CONCLUSIONS Because of the high percentage of adamantane-resistant H3N2 and H1N1 viruses in mainland China, the use of amantadine and rimantadine drugs for prophylaxis and treatment of current seasonal influenza A infection is not recommended.

Correlation of influenza virus excess mortality with antigenic variation: application to rapid estimation of influenza mortality burden.

The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention.

Inferring the antigenic epitopes for highly pathogenic avian influenza H5N1 viruses.

The evasion of influenza virus from host immune surveillance is mainly mediated through its surface protein hemagglutinin (HA), the main component of influenza vaccine. Thus, identification of influenza virus antigenic epitopes on HA can not only help us understand the molecular mechanisms of viral immune escape but also facilitate vaccine strain selection. Despite previous efforts, there is a lack of systematic definition of the antigenic epitopes for the highly pathogenic avian influenza (HPAI) H5N1 viruses. In this study, we infer the HA antigenic epitopes for H5N1 viruses by integrating the antigenic sites mapped from the HA of human influenza H3N2 viruses, the sites which were reported to be associated with immune escape in H5 viruses and the mutation hotspot sites identified in the evolutionary history of HPAI H5N1 viruses. We show that these inferred antigenic epitopes play significant roles in antigenic variation of HPAI H5N1 viruses. Based on inferred antigenic epitopes, we further develop a computational method to effectively predict antigenic variants for HPAI H5N1 viruses (available at http://biocloud.hnu.edu.cn/predict/html/index.html). Therefore, our work has not only inferred the antigenic epitopes for HPAI H5N1 viruses but also provided an effective computational method to assist vaccine recommendations for protection against the deadly bird flu.

Antigenic variation of the human influenza A (H3N2) virus during the 2014–2015 winter season

The human influenza A (H3N2) virus dominated the 2014–2015 winter season in many countries and caused massive morbidity and mortality because of its antigenic variation. So far, very little is known about the antigenic patterns of the recent H3N2 virus. By systematically mapping the antigenic relationships of H3N2 strains isolated since 2010, we discovered that two groups with obvious antigenic divergence, named SW13 (A/Switzerland/9715293/2013-like strains) and HK14 (A/Hong Kong/5738/2014-like strains), co-circulated during the 2014–2015 winter season. HK14 group co-circulated with SW13 in Europe and the United States during this season, while there were few strains of HK14 in mainland China, where SW13 has dominated since 2012. Furthermore, we found that substitutions near the receptor-binding site on hemagglutinin played an important role in the antigenic variation of both the groups. These findings provide a comprehensive understanding of the recent antigenic evolution of H3N2 virus and will aid in the selection of vaccine strains.

PREDAC-H3: a user-friendly platform for antigenic surveillance of human influenza a(H3N2) virus based on hemagglutinin sequences

MOTIVATION Timely surveillance of the antigenic dynamics of the influenza virus is critical for accurate selection of vaccine strains, which is important for effective prevention of viral spread and infection. RESULTS Here, we provide a computational platform, called PREDAC-H3, for antigenic surveillance of human influenza A(H3N2) virus based on the sequence of surface protein hemagglutinin (HA). PREDAC-H3 not only determines the antigenic variants and antigenic cluster (grouped for similar antigenicity) to which the virus belongs, based on HA sequences, but also allows visualization of the spatial distribution and temporal dynamics of antigenic clusters of viruses isolated from around the world, thus assisting in antigenic surveillance of human influenza A(H3N2) virus. AVAILABILITY AND IMPLEMENTATION It is publicly available from: http://biocloud.hnu.edu.cn/influ411/html/index.php CONTACTS : yshu@cnic.org.cn or taijiao@moon.ibp.ac.cn.

Identification of genome-wide nucleotide sites associated with mammalian virulence in influenza A viruses

Motivation The virulence of influenza viruses is a complex multigenic trait. Previous studies about the virulence determinants of influenza viruses mainly focused on amino acid sites, ignoring the influence of nucleotide mutations. Results We collected more than 200 viral strains from 21 subtypes of influenza A viruses with virulence in mammals and obtained over 100 mammalian virulence-related nucleotide sites across the genome by computational analysis. Interestingly, 50 of these nucleotide sites only experienced synonymous mutations. Further experiments showed that synonymous mutations in the top two of these nucleotide sites, i.e., PB1-2031 and PB1-633, enhanced the pathogenicity of the viruses in mice. Finally, machine-learning models with accepted accuracy for predicting mammalian virulence of influenza A viruses were built. Overall, this study highlighted the importance of nucleotide mutations, especially synonymous mutations in viral virulence, and provided rapid methods for evaluating the virulence of influenza A viruses. It could be helpful for early warning of newly emerging influenza A viruses.

Influenza incidence prediction for the United States: an update for the 2018-2019 season

Introduction Seasonal influenza causes a high disease burden every year in the United States and worldwide. Anticipating epidemic size ahead of season can contribute to preparedness and more targetted control and prevention of seasonal influenza. Methods A recently developed process-based epidemiological model that incorporates evolutionary change of the virus and generates incidence forecasts for the H3N2 subtype ahead of the season, was previously validated by several statistical criteria, including an accurate real-time prediction for the 2016-2017 influenza season. With this model, a new forecast is generated here for the upcoming 2018-2019 season. The accuracy of predictions published for the 2017-2018 season is also retrospectively evaluated. Results For 2017-2018, the model correctly predicted the dominance of the H3N2 subtype and its higher than average incidence. Based on surveillance and sequence data up to June 2018, the new forecast for the upcoming 2018-2019 season indicates low levels for H3N2, and suggests an H1N1 dominant season with low incidence of influenza B. Discussion Real-time forecasts, those generated with a model that was parameterized based on data preceding the predicted season, allows valuable evaluation of the approach. Anticipating the dominant subtype and the size of the upcoming epidemic ahead of season informs disease control. Further studies are needed to promote more accurate ahead-of-season forecasts and extend the approach to multiple subtypes. Funding statement This work was funded by the Sun Yat-sen University. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Predicting the receptor-binding domain usage of the coronavirus based on kmer frequency on spike protein

Please cite this article as: Zhaozhong Zhu, Zheng Zhang, Wenjun Chen, Zena Cai, Xingyi Ge, Haizhen Zhu, Taijiao Jiang, Wenjie Tan, Yousong Peng , Predicting the receptorbinding domain usage of the coronavirus based on kmer frequency on spike protein. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Meegid(2018), doi:10.1016/j.meegid.2018.03.028