Data_Sheet_1_Strategies targeting hemagglutinin cocktail as a potential universal influenza vaccine.ZIP

Vaccination is the most effective means of protecting people from influenza virus infection. The effectiveness of existing vaccines is very limited due to antigenic drift of the influenza virus. Therefore, there is a requirement to develop a universal vaccine that provides broad and long-lasting protection against influenza. CD8+ T-cell response played a vital role in controlling influenza virus infection, reducing viral load, and less clinical syndrome. In this study, we optimized the HA sequences of human seasonal influenza viruses (H1N1, H3N2, Victoria, and Yamagata) by designing multivalent vaccine antigen sets using a mosaic vaccine design strategy and genetic algorithms, and designed an HA mosaic cocktail containing the most potential CTL epitopes of seasonal influenza viruses. We then tested the recombinant mosaic antigen, which has a significant number of potential T-cell epitopes. Results from genetic evolutionary analyses and 3D structural simulations demonstrated its potential to be an effective immunogen. In addition, we have modified an existing neutralizing antibody-based seasonal influenza virus vaccine to include a component that activates cross-protective T cells, which would provide an attractive strategy for improving human protection against seasonal influenza virus drift and mutation and provide an idea for the development of a rationally designed influenza vaccine targeting T lymphocyte immunity.</p

Geographic Divisions and Modeling of Virological Data on Seasonal Influenza in the Chinese Mainland during the 2006–2009 Monitoring Years

<div><p>Background</p><p>Seasonal influenza epidemics occur annually with bimodality in southern China and unimodality in northern China. Regional differences exist in surveillance data collected by the National Influenza Surveillance Network of the Chinese mainland. Qualitative and quantitative analyses on the spatiotemporal rules of the influenza virus's activities are needed to lay the foundation for the surveillance, prevention and control of seasonal influenza.</p> <p>Methods</p><p>The peak performance analysis and Fourier harmonic extraction methods were used to explore the spatiotemporal characteristics of the seasonal influenza virus activity and to obtain geographic divisions. In the first method, the concept of quality control was introduced and robust estimators were chosen to make the results more convincing. The dominant Fourier harmonics of the provincial time series were extracted in the second method, and the VARiable CLUSter (VARCLUS) procedure was used to variably cluster the extracted results. On the basis of the above geographic division results, three typical districts were selected and corresponding sinusoidal models were applied to fit the time series of the virological data.</p> <p>Results</p><p>The predominant virus during every peak is visible from the bar charts of the virological data. The results of the two methods that were used to obtain the geographic divisions have some consistencies with each other and with the virus activity mechanism. Quantitative models were established for three typical districts: the south1 district, including Guangdong, Guangxi, Jiangxi and Fujian; the south2 district, including Hunan, Hubei, Shanghai, Jiangsu and Zhejiang; and the north district, including the 14 northern provinces except Qinghai. The sinusoidal fitting models showed that the south1 district had strong annual periodicity with strong winter peaks and weak summer peaks. The south2 district had strong semi-annual periodicity with similarly strong summer and winter peaks, and the north district had strong annual periodicity with only winter peaks.</p> </div

The comparison between raw data and sinusoidal model fit results for north district.

<p>The black line is the time series of raw data, while the red line is the sinusoidal fitting curves.</p

Geographic division results of Mainland China based on the peak performance analysis method.

<p>The provinces with the same color belong to the same district.</p

The estimated coefficients of sinusoidal fitting models for the southern districts.

<p>The estimated coefficients of sinusoidal fitting models for the southern districts.</p

Geographic division results of Mainland China based on the Fourier harmonic extraction method.

<p>The provinces with the same color belong to the same district.</p

The correlation coefficients of the nine southern provinces before and after the Fourier harmonic extraction procedure.

<p> <i>Note: The values in the parentheses represent the correlation coefficients after the Fourier harmonic extraction procedure.</i></p

The comparison between raw data and sinusoidal model fit results for the south2 district.

<p>The black line is the time series of raw data, while the red line is the sinusoidal fitting curves.</p

The Fourier harmonic extraction results of Hunan province.

<p>It's an example to illustrate the Fourier harmonic extraction method. The horizontal ordinate denotes the corresponding number of the 159 monitoring weeks in chronological order. The black line is the time series of raw data. The red line is the data after the replacement procedure and the green line is the superposition results of the extracted Fourier harmonics. More information about the results of this method is given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058434#pone.0058434.s003" target="_blank">Table S3</a> in detail.</p

Bar chart of the influenza virus subtypes in the southern area.

<p>Different colors represents different influenza subtypes as is listed in the head.</p