Biochemical reactions are subject to stochastic fluctuations that can give rise to cell-to-cell variability. Yet, how this variability affects viral infections, which themselves involve noisy reactions, remains largely elusive. Here we present single-cell experiments and stochastic simulations that reveal a large heterogeneity between influenza A virus (IAV)-infected cells. In particular, experimental data show that progeny virus titres range from 1 to 970 plaque-forming units and intracellular viral RNA (vRNA) levels span three orders of magnitude. Moreover, the segmentation of IAV genomes seems to increase the susceptibility of their replication to noise, since the level of different genome segments can vary substantially within a cell. In addition, simulations suggest that the abortion of virus entry and random degradation of vRNAs can result in a large fraction of non-productive cells after single-hit infection. These results challenge current beliefs that cell population measurements and deterministic simulations are an accurate representation of viral infections.

BackgroundMany details in cell culture-derived influenza vaccine production are still poorly understood and approaches for process optimization mainly remain empirical. More insights on mammalian cell metabolism after a viral infection could give hints on limitations and cell-specific virus production capacities. A detailed metabolic characterization of an influenza infected adherent cell line (MDCK) was carried out based on extracellular and intracellular measurements of metabolite concentrations.ResultsFor most metabolites the comparison of infected (human influenza A/PR/8/34) and mock-infected cells showed a very similar behavior during the first 10-12 h post infection (pi). Significant changes were observed after about 12 h pi: (1) uptake of extracellular glucose and lactate release into the cell culture supernatant were clearly increased in infected cells compared to mock-infected cells. At the same time (12 h pi) intracellular metabolite concentrations of the upper part of glycolysis were significantly increased. On the contrary, nucleoside triphosphate concentrations of infected cells dropped clearly after 12 h pi. This behaviour was observed for two different human influenza A/PR/8/34 strains at slightly different time points.ConclusionsComparing these results with literature values for the time course of infection with same influenza strains, underline the hypothesis that influenza infection only represents a minor additional burden for host cell metabolism. The metabolic changes observed after12 h pi are most probably caused by the onset of apoptosis in infected cells. The comparison of experimental data from two variants of the A/PR/8/34 virus strain (RKI versus NIBSC) with different productivities and infection dynamics showed comparable metabolic patterns but a clearly different timely behavior. Thus, infection dynamics are obviously reflected in host cell metabolism.

The biological and clinical relevance of glycosylation is becoming increasingly recognized, leading to a growing interest in large-scale clinical and population-based studies. In the past few years, several methods for high-throughput analysis of glycans have been developed, but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. In this study, we compared liquid chromatography, capillary gel electrophoresis, and two MS methods for quantitative profiling of N-glycosylation of IgG in the same data set of 1201 individuals. To evaluate the accuracy of the four methods we then performed analysis of association with genetic polymorphisms and age. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and multiplexed capillary gel electrophoresis, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should inform the selection of the most appropriate method in future studies.