Influenza A viruses are important pathogens that cause acute respiratory diseases and annual epidemics in humans. Macrophages recognize influenza A virus infection with their pattern recognition receptors, and are involved in the activation of proper innate immune response. Here, we have used high-throughput subcellular proteomics combined with bioinformatics to provide a global view of host cellular events that are activated in response to influenza A virus infection in human primary macrophages. We show that viral infection regulates the expression and/or subcellular localization of more than one thousand host proteins at early phases of infection. Our data reveals that there are dramatic changes in mitochondrial and nuclear proteomes in response to infection. We show that a rapid cytoplasmic leakage of lysosomal proteins, including cathepsins, followed by their secretion, contributes to inflammasome activation and apoptosis seen in the infected macrophages. Also, our results demonstrate that P2X7 receptor and src tyrosine kinase activity are essential for inflammasome activation during influenza A virus infection. Finally, we show that influenza A virus infection is associated with robust secretion of different danger-associated molecular patterns (DAMPs) suggesting an important role for DAMPs in host response to influenza A virus infection. In conclusion, our high-throughput quantitative proteomics study provides important new insight into host-response against influenza A virus infection in human primary macrophages.

Proliferating cells require coordinated gene expression between the nucleus and mitochondria in order to divide, ensuring sufficient organelle number in daughter cells [1]. However, the machinery and mechanisms whereby proliferating cells monitor mitochondria and coordinate organelle biosynthesis remain poorly understood. Antibiotics inhibiting mitochondrial translation have emerged as therapeutics for human cancers because they block cell proliferation [2, 3]. These proliferative defects were attributable to modest decreases in mitochondrial respiration [3, 4], even though tumors are mainly glycolytic [5] and mitochondrial respiratory chain function appears to play a minor role in cell proliferation in vivo [6]. Here we challenge this interpretation by demonstrating that one class of antiproliferative antibiotic induces stalled mitochondrial ribosomes, which triggers a mitochondrial ribosome and RNA decay pathway. Rescue of the stalled mitochondrial ribosomes initiates a retrograde signaling response to block cell proliferation and occurs prior to any loss of mitochondrial respiration. The loss of respiratory chain function is simply a downstream effect of impaired mitochondrial translation and not the antiproliferative signal. This mitochondrial ribosome quality-control pathway is actively monitored in cells and constitutes an important organelle checkpoint for cell division.

The present study reports an in-depth proteome analysis of two Lactobacillus rhamnosus strains, the well-known probiotic strain GG and the dairy strain Lc705. We used GeLC-MS/MS, in which proteins are separated using 1-DE and identified using nanoLC-MS/MS, to generate high-quality protein catalogs. To maximize the number of identifications, all data sets were searched against the target databases using two search engines, Mascot and Paragon. As a result, over 1600 high-confidence protein identifications, covering nearly 60% of the predicted proteomes, were obtained from each strain. This approach enabled identification of more than 40% of all predicted surfome proteins, including a high number of lipoproteins, integral membrane proteins, peptidoglycan associated proteins, and proteins predicted to be released into the extracellular environment. A comparison of both data sets revealed the expression of more than 90 proteins in GG and 150 in Lc705, which lack evolutionary counterparts in the other strain. Differences were noted in proteins with a likely role in biofilm formation, phage-related functions, reshaping the bacterial cell wall, and immunomodulation. The present study provides the most comprehensive catalog of the Lactobacillus proteins to date and holds great promise for the discovery of novel probiotic effector molecules.

Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP’s performance and accuracy by analysing various simulated and real longitudinal -omics datasets.