Severe influenza is often associated with disease manifestations outside the respiratory tract. While proinflammatory cytokines can be detected in the lungs and blood of infected patients, the role of extra-respiratory organs in the production of proinflammatory cytokines is unknown. Here, we show that both 2009 pandemic H1N1 influenza A (H1N1) virus and highly pathogenic avian influenza A (H5N1) virus induce expression of tumor necrosis factor α, interleukin-6, and interleukin-8 in the respiratory tract and central nervous system. In addition, H5N1 virus induced cytokines in the heart, pancreas, spleen, liver, and jejunum. Together, these data suggest that extra-respiratory tissues contribute to systemic cytokine responses, which may increase the severity of influenza.

With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level.