Browsing publications of the research group bioinformatics in infection research ([BRICS] BIFO) by Journal
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Adaptive Mutations That Occurred during Circulation in Humans of H1N1 Influenza Virus in the 2009 Pandemic Enhance Virulence in Mice.During the 2009 H1N1 influenza pandemic, infection attack rates were particularly high among young individuals who suffered from pneumonia with occasional death. Moreover, previously reported determinants of mammalian adaptation and pathogenicity were not present in 2009 pandemic H1N1 influenza A viruses. Thus, it was proposed that unknown viral factors might have contributed to disease severity in humans. In this study, we performed a comparative analysis of two clinical 2009 pandemic H1N1 strains that belong to the very early and later phases of the pandemic. We identified mutations in the viral hemagglutinin (HA) and the nucleoprotein (NP) that occurred during pandemic progression and mediate increased virulence in mice. Lethal disease outcome correlated with elevated viral replication in the alveolar epithelium, increased proinflammatory cytokine and chemokine responses, pneumonia, and lymphopenia in mice. These findings show that viral mutations that have occurred during pandemic circulation among humans are associated with severe disease in mice.
Computational prediction of vaccine strains for human influenza A (H3N2) viruses.Human influenza A viruses are rapidly evolving pathogens that cause substantial morbidity and mortality in seasonal epidemics around the globe. To ensure continued protection, the strains used for the production of the seasonal influenza vaccine have to be regularly updated, which involves data collection and analysis by numerous experts worldwide. Computer-guided analysis is becoming increasingly important in this problem due to the vast amounts of generated data. We here describe a computational method for selecting a suitable strain for production of the human influenza A virus vaccine. It interprets available antigenic and genomic sequence data based on measures of antigenic novelty and rate of propagation of the viral strains throughout the population. For viral isolates sampled between 2002 and 2007, we used this method to predict the antigenic evolution of the H3N2 viruses in retrospective testing scenarios. When seasons were scored as true or false predictions, our method returned six true positives, three false negatives, eight true negatives, and one false positive, or 78% accuracy overall. In comparison to the recommendations by the WHO, we identified the correct antigenic variant once at the same time and twice one season ahead. Even though it cannot be ruled out that practical reasons such as lack of a sufficiently well-growing candidate strain may in some cases have prevented recommendation of the best-matching strain by the WHO, our computational decision procedure allows quantitative interpretation of the growing amounts of data and may help to match the vaccine better to predominating strains in seasonal influenza epidemics. Importance: Human influenza A viruses continuously change antigenically to circumvent the immune protection evoked by vaccination or previously circulating viral strains. To maintain vaccine protection and thereby reduce the mortality and morbidity caused by infections, regular updates of the vaccine strains are required. We have developed a data-driven framework for vaccine strain prediction which facilitates the computational analysis of genetic and antigenic data and does not rely on explicit evolutionary models. Our computational decision procedure generated good matches of the vaccine strain to the circulating predominant strain for most seasons and could be used to support the expert-guided prediction made by the WHO; it thus may allow an increase in vaccine efficacy.