Towards unraveling the influenza A (H1N1) immunome, this work aims at constructing the murine host response pathway interactome. To accomplish that, an ensemble of dynamic and time-varying Gene Regulatory Network Inference methodologies was recruited to set a confident interactome based on mouse time series transcriptome data (day 1-day 60). The proposed H1N1 interactome demonstrated significant transformations among activated and suppressed pathways in time. Enhanced interplay was observed at day 1, while the maximal network complexity was reached at day 8 (correlated with viral clearance and iBALT tissue formation) and one interaction was present at day 40. Next, we searched for common interactivity features between the murine-adapted PR8 strain and other influenza A subtypes/strains. For this, two other interactomes, describing the murine host response against H5N1 and H1N1pdm, were constructed, which in turn validated many of the observed interactions (in the period day 1-day 7). The H1N1 interactome revealed the role of cell cycle both in innate and adaptive immunity (day 1-day 14). Also, pathogen sensory pathways (e.g., RIG-I) displayed long-lasting association with cytokine/chemokine signaling (until day 8). Interestingly, the above observations were also supported by the H5N1 and H1N1pdm models. It also elucidated the enhanced coupling of the activated innate pathways with the suppressed PPAR signaling to keep low inflammation until viral clearance (until day 14). Further, it showed that interactions reflecting phagocytosis processes continued long after the viral clearance and the establishment of adaptive immunity (day 8-day 40). Additionally, interactions involving B cell receptor pathway were evident since day 1. These results collectively inform the emerging field of public health omics and future clinical studies aimed at deciphering dynamic host responses to infectious agents.

BackgroundThe immune response to viral infection is a temporal process, represented by a dynamic and complex network of gene and protein interactions. Here, we present a reverse engineering strategy aimed at capturing the temporal evolution of the underlying Gene Regulatory Networks (GRN). The proposed approach will be an enabling step towards comprehending the dynamic behavior of gene regulation circuitry and mapping the network structure transitions in response to pathogen stimuli.ResultsWe applied the Time Varying Dynamic Bayesian Network (TV-DBN) method for reconstructing the gene regulatory interactions based on time series gene expression data for the mouse C57BL/6J inbred strain after infection with influenza A H1N1 (PR8) virus. Initially, 3500 differentially expressed genes were clustered with the use of k-means algorithm. Next, the successive in time GRNs were built over the expression profiles of cluster centroids. Finally, the identified GRNs were examined with several topological metrics and available protein-protein and protein-DNA interaction data, transcription factor and KEGG pathway data.ConclusionsOur results elucidate the potential of TV-DBN approach in providing valuable insights into the temporal rewiring of the lung transcriptome in response to H1N1 virus.

While significant strides have been made in designing brain-machine interfaces for use in humans, efforts to decode truly dexterous movements in real time have been hindered by difficulty extracting detailed movement-related information from the most practical human neural interface, the electrocorticogram (ECoG). We explore a potentially rich, largely untapped source of movement-related information in the form of cortical connectivity computed with time-varying dynamic Bayesian networks (TV-DBN). We discover that measures of connectivity between ECoG electrodes derived from the local motor potential vary with dexterous movement in 65% of movement-related electrode pairs tested, and measures of connectivity derived from spectral features vary with dexterous movement in 76%. Due to the large number of features generated with connectivity methods, the TV-DBN a promising tool for dexterous decoding.

No abstract