MiRNA-mediated control of HLA-G expression and function

HLA-G is a non-classical HLA class-Ib molecule expressed mainly by the extravillous cytotrophoblasts (EVT) of the placenta. The expression of HLA-G on these fetal cells protects the EVT cells from immune rejection and is therefore important for a healthy pregnancy. The mechanisms controlling HLA-G expression are largely unknown. Here we demonstrate that miR-148a and miR-152 down-regulate HLA-G expression by binding its 3'UTR and that this down-regulation of HLA-G affects LILRB1 recognition and consequently, abolishes the LILRB1-mediated inhibition of NK cell killing. We further demonstrate that the C/G polymorphism at position +3142 of HLA-G 3'UTR has no effect on the miRNA targeting of HLA-G. We show that in the placenta both miR-148a and miR-152 miRNAs are expressed at relatively low levels, compared to other healthy tissues, and that the mRNA levels of HLA-G are particularly high and we therefore suggest that this might enable the tissue specific expression of HLA-G.     

H5-type influenza virus hemagglutinin is functionally recognized by the natural killer-activating receptor NKp44

Antiviral immune defenses involve natural killer (NK) cells. We previously showed that the NK-activating receptor NKp44 is involved in the functional recognition of H1-type influenza virus strains by NK cells. In the present study, we investigated the interaction of NKp44 and the hemagglutinin of a primary influenza virus H5N1 isolate. Here we show that recombinant NKp44 recognizes H5-expressing cells and specifically interacts with soluble H5 hemagglutinin. H5-pseudotyped lentiviral particles bind to NK cells expressing NKp44. Following interaction with target cells expressing H5, pseudotyped lentiviral particles, or membrane-associated H5, NK cells show NKp44-mediated induced activity. These findings indicate that NKp44-H5 interactions induce functional NK activation.     

Influenza virus infection augments NK cell inhibition through reorganization of major histocompatibility complex class I proteins

The killing by natural killer (NK) cells is regulated by inhibitory, costimulatory, and activating receptors. The inhibitory receptors recognize mainly major histocompatibility complex (MHC) class I molecules, while the activating NK receptors recognize stress-induced ligands and viral products. Thus, changes in the expression of the various inhibitory and activating ligands will determine whether target cells will be killed or protected. Here, we demonstrate that after influenza virus infection the binding of the two NK inhibitory receptors, KIR2DL1 and the LIR1, to the infected cells is specifically increased. The increased binding occurs shortly after the influenza virus infection, prior to the increased recognition of the infected cells by the NK activating receptor, NKp46. We also elucidate the mechanism responsible for this effect and demonstrate that, after influenza virus infection, MHC class I proteins redistribute on the cell surface and accumulate in the lipid raft microdomains. Such redistribution allows better recognition by the NK inhibitory receptors and consequently increases resistance to NK cell attack. In contrast, T-cell activity was not influenced by the redistribution of MHC class I proteins. Thus, we present here a novel mechanism, developed by the influenza virus, of inhibition of NK cell cytotoxicity, through the reorganization of MHC class I proteins on the cell surface.     

Slow oscillations in neural networks with facilitating synapses

The synchronous oscillatory activity characterizing many neurons in a network is often considered to be a mechanism for representing, binding, conveying, and organizing information. A number of models have been proposed to explain high-frequency oscillations, but the mechanisms that underlie slow oscillations are still unclear. Here, we show by means of analytical solutions and simulations that facilitating excitatory (E _f) synapses onto interneurons in a neural network play a fundamental role, not only in shaping the frequency of slow oscillations, but also in determining the form of the up and down states observed in electrophysiological measurements. Short time constants and strong E _f synapse-connectivity were found to induce rapid alternations between up and down states, whereas long time constants and weak E _f synapse connectivity prolonged the time between up states and increased the up state duration. These results suggest a novel role for facilitating excitatory synapses onto interneurons in controlling the form and frequency of slow oscillations in neuronal circuits.     

An experimental design and a statistical analysis separating interference from exploitation competition

Previous experimental studies of competition among foragers rarely distinguished between exploitation and interference competition. In many systems this separation is experimentally impossible without interfering with the natural behavior of the animals. Consequently, these studies can only demonstrate the combined effect of interference and exploitation on the forager’s feeding rate, namely, it usually decreases in a decelerating rate as a function of density. We suggest here a simple experimental and statistical procedure that facilitates the separation of the effects of interference from those of exploitation. This procedure includes manipulation of both predator density and the foraging experiment duration. The statistical analysis is based on multiple linear regression. The working assumption is that exploitation can be neglected at the beginning of the foraging experiment because, initially, predators do not experience diminishing returns in prey capture rates. Using both the results of an individual-based simulation and a field experiment dataset of gerbils foraging for seeds in an artificial food patch located in the field, we demonstrate that our procedure can successfully detect and separate the effect of interference from the combined overall effect of competition (i.e., interference plus exploitation). Inon Scharf and Ido Filin contributed equally to this paper.     

Phylogenetic and Genome-Wide Deep-Sequencing Analyses of Canine Parvovirus Reveal Co-Infection with Field Variants and Emergence of a Recent Recombinant Strain

Canine parvovirus (CPV), a fast-evolving single-stranded DNA virus, comprises three antigenic variants (2a, 2b, and 2c) with different frequencies and genetic variability among countries. The contribution of co-infection and recombination to the genetic variability of CPV is far from being fully elucidated. Here we took advantage of a natural CPV population, recently formed by the convergence of divergent CPV-2c and CPV-2a strains, to study co-infection and recombination. Complete sequences of the viral coding region of CPV-2a and CPV-2c strains from 40 samples were generated and analyzed using phylogenetic tools. Two samples showed co-infection and were further analyzed by deep sequencing. The sequence profile of one of the samples revealed the presence of CPV-2c and CPV-2a strains that differed at 29 nucleotides. The other sample included a minor CPV-2a strain (13.3% of the viral population) and a major recombinant strain (86.7%). The recombinant strain arose from inter-genotypic recombination between CPV-2c and CPV-2a strains within the VP1/VP2 gene boundary. Our findings highlight the importance of deep-sequencing analysis to provide a better understanding of CPV molecular diversity.