The Timing of IFNβ Production Affects Early Innate Responses to Listeria monocytogenes and Determines the Overall Outcome of Lethal Infection

Dendritic cells (DCs) and natural killer (NK) cells are essential components of the innate immunity and play a crucial role in the first phase of host defense against infections and tumors. Listeria monocytogenes (Lm) is an intracellular pathogen that colonizes the cytosol of eukaryotic cells. Recent findings have shown Lm specifically in splenic CD8a(+) DCs shortly after intravenous infection. We examined gene expression profiles of mouse DCs exposed to Lm to elucidate the molecular mechanisms underlying DCs interaction with Lm. Using a functional genomics approach, we found that Lm infection induced a cluster of late response genes including type I IFNs and interferon responsive genes (IRGs) in DCs. Type I INFs were produced at the maximal level only at 24 h post infection indicating that the regulation of IFNs in the context of Lm infection is delayed compared to the rapid response observed with viral pathogens. We showed that during Lm infection, IFNγ production and cytotoxic activity were severely impaired in NK cells compared to E. coli infection. These defects were restored by providing an exogenous source of IFNβ during the initial phase of bacterial challenge. Moreover, when treated with IFNβ during early infection, NK cells were able to reduce bacterial titer in the spleen and significantly improve survival of infected mice. These findings show that the timing of IFNβ production is fundamental to the efficient control of the bacterium during the early innate phase of Lm infection.     

Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits

We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5?% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.     

A bistable model of cell polarity

Ultrasensitivity, as described by Goldbeter and Koshland, has been considered for a long time as a way to realize bistable switches in biological systems. It is not as well recognized that when ultrasensitivity and reinforcing feedback loops are present in a spatially distributed system such as the cell plasmamembrane, they may induce bistability and spatial separation of the system into distinct signaling phases. Here we suggest that bistability of ultrasensitive signaling pathways in a diffusive environment provides a basic mechanism to realize cell membrane polarity. Cell membrane polarization is a fundamental process implicated in several basic biological phenomena, such as differentiation, proliferation, migration and morphogenesis of unicellular and multicellular organisms. We describe a simple, solvable model of cell membrane polarization based on the coupling of membrane diffusion with bistable enzymatic dynamics. The model can reproduce a broad range of symmetry-breaking events, such as those observed in eukaryotic directional sensing, the apico-basal polarization of epithelium cells, the polarization of budding and mating yeast, and the formation of Ras nanoclusters in several cell types.     

Bearded reedlings adjust their pair-bond behaviour in relation to the sex and attractiveness of unpaired conspecifics

An individual's investment in mating or keeping a pair bond intact may be influenced not only by the attractiveness of its current mate, but also by that of other potential mates. In this study, we investigated the effect of relative attractiveness on pair-bond behaviour in bearded reedlings, Panurus biarmicus. We showed that mate attractiveness, in terms of beard length in males and tail length in females, influenced courtship behaviour when the pair was kept isolated. In the presence of a conspecific, contact initiations within a pair increased. This increment was mainly related to the sex of the unpaired conspecific, however, and less to differences in attractiveness between the current partner and the unpaired conspecific. Female contact initiations towards potential extra mates were independent of male attractiveness, whereas male contact behaviour was significantly influenced by female attractiveness. However, females displayed more contact initiations to their current mate when they were less attractive than the unpaired females. Males decreased their overtures towards other females with increasing attractiveness of their current mates. Overall, our results suggested that, when there was a risk of losing their mate, bearded reedlings adjust their pair-bond investment mainly in response to the presence or absence of a competitor, and fine-tune investment to a lesser extent in response to the attractiveness of that potential competitor.     

WNP: a novel algorithm for gene products annotation from weighted functional networks

Predicting the biological function of all the genes of an organism is one of the fundamental goals of computational system biology. In the last decade, high-throughput experimental methods for studying the functional interactions between gene products (GPs) have been combined with computational approaches based on Bayesian networks for data integration. The result of these computational approaches is an interaction network with weighted links representing connectivity likelihood between two functionally related GPs. The weighted network generated by these computational approaches can be used to predict annotations for functionally uncharacterized GPs. Here we introduce Weighted Network Predictor (WNP), a novel algorithm for function prediction of biologically uncharacterized GPs. Tests conducted on simulated data show that WNP outperforms other 5 state-of-the-art methods in terms of both specificity and sensitivity and that it is able to better exploit and propagate the functional and topological information of the network. We apply our method to Saccharomyces cerevisiae yeast and Arabidopsis thaliana networks and we predict Gene Ontology function for about 500 and 10000 uncharacterized GPs respectively.     

Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

Background

The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.

Methodology/Principal Findings

We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures'' representative pdf''s via the Kullback-Leibler divergence (KL). Species turnover, or diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.

Conclusions/Significance

Texture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.     

The Impact of Winter and Spring Temperatures on Temperate Tree Budburst Dates: Results from an Experimental Climate Manipulation

Budburst phenology is a key driver of ecosystem structure and functioning, and it is sensitive to global change. Both cold winter temperatures (chilling) and spring warming (forcing) are important for budburst. Future climate warming is expected to have a contrasting effect on chilling and forcing, and subsequently to have a non-linear effect on budburst timing. To clarify the different effects of warming during chilling and forcing phases of budburst phenology in deciduous trees, (i) we conducted a temperature manipulation experiment, with separate winter and spring warming treatments on well irrigated and fertilized saplings of beech, birch and oak, and (ii) we analyzed the observations with five temperature-based budburst models (Thermal Time model, Parallel model, Sequential model, Alternating model, and Unified model). The results show that both winter warming and spring warming significantly advanced budburst date, with the combination of winter plus spring warming accelerating budburst most. As expected, all three species were more sensitive to spring warming than to winter warming. Although the different chilling requirement, the warming sensitivity was not significantly different among the studied species. Model evaluation showed that both one- and two- phase models (without and with chilling, respectively) are able to accurately predict budburst. For beech, the Sequential model reproduced budburst dates best. For oak and birch, both Sequential model and the Thermal Time model yielded good fit with the data but the latter was slightly better in case of high parameter uncertainty. However, for late-flushing species, the Sequential model is likely be the most appropriate to predict budburst data in a future warmer climate.