A reciprocal activating interaction between NK cells and dendritic cells (DC) has been suggested to play a role in the functional regulation of these cells in immunity, but it has been studied only using in vitro generated bone marrow- or monocyte-derived DC. We report that human peripheral blood plasmacytoid DC (pDC) and myeloid DC are necessary to induce NK cell function depending on the type of microbial stimulus. pDC and myeloid DC are required for strongly increased NK cytolytic activity and CD69 expression, in response to inactivated influenza virus or CpG-containing oligonucleotides and poly(I:C), respectively. Secreted type I IFN is required and sufficient for the augmentation of NK cell cytolytic activity in the coculture with pDC or myeloid DC, whereas CD69 expression is dependent on both type I IFN and TNF. In addition, in response to poly(I:C), myeloid DC induce NK cells to produce IFN-γ through a mechanism dependent on both IL-12 secretion and cell contact between NK cells and myeloid DC, but independent of type I IFN. IL-2-activated NK cells have little to no cytolytic activity for immature myeloid DC and pDC, but are able to induce maturation of these cells. Moreover, IL-2-activated NK cells induce, in the presence of a suboptimal concentration of CpG-containing oligonucleotides, a strong IFN-α and TNF production. These data suggest that the reciprocal functional interaction between NK cells and either pDC or myeloid DC may play an important physiological role in the regulation of both innate resistance and adaptive immunity to infections.
Motivation: Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a ‘mutually exclusive’ manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive.Results: We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks.Availability and implementation: BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.htmlContact: iorio@ebi.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
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