Robust Prediction of Expression Differences among Human Individuals Using Only Genotype Information

Many genetic variants that are significantly correlated to gene expression changes across human individuals have been identified, but the ability of these variants to predict expression of unseen individuals has rarely been evaluated. Here, we devise an algorithm that, given training expression and genotype data for a set of individuals, predicts the expression of genes of unseen test individuals given only their genotype in the local genomic vicinity of the predicted gene. Notably, the resulting predictions are remarkably robust in that they agree well between the training and test sets, even when the training and test sets consist of individuals from distinct populations. Thus, although the overall number of genes that can be predicted is relatively small, as expected from our choice to ignore effects such as environmental factors and trans sequence variation, the robust nature of the predictions means that the identity and quantitative degree to which genes can be predicted is known in advance. We also present an extension that incorporates heterogeneous types of genomic annotations to differentially weigh the importance of the various genetic variants, and we show that assigning higher weights to variants with particular annotations such as proximity to genes and high regional G/C content can further improve the predictions. Finally, genes that are successfully predicted have, on average, higher expression and more variability across individuals, providing insight into the characteristics of the types of genes that can be predicted from their cis genetic variation.     

Matrix Metalloproteinase 9 Exerts Antiviral Activity against Respiratory Syncytial Virus

Increased lung levels of matrix metalloproteinase 9 (MMP9) are frequently observed during respiratory syncytial virus (RSV) infection and elevated MMP9 concentrations are associated with severe disease. However little is known of the functional role of MMP9 during lung infection with RSV. To determine whether MMP9 exerted direct antiviral potential, active MMP9 was incubated with RSV, which showed that MMP9 directly prevented RSV infectivity to airway epithelial cells. Using knockout mice the effect of the loss of Mmp9 expression was examined during RSV infection to demonstrate MMP9’s role in viral clearance and disease progression. Seven days following RSV infection, Mmp9 ^-/- mice displayed substantial weight loss, increased RSV-induced airway hyperresponsiveness (AHR) and reduced clearance of RSV from the lungs compared to wild type mice. Although total bronchoalveolar lavage fluid (BALF) cell counts were similar in both groups, neutrophil recruitment to the lungs during RSV infection was significantly reduced in Mmp9 ^-/- mice. Reduced neutrophil recruitment coincided with diminished RANTES, IL-1β, SCF, G-CSF expression and p38 phosphorylation. Induction of p38 signaling was required for RANTES and G-CSF expression during RSV infection in airway epithelial cells. Therefore, MMP9 in RSV lung infection significantly enhances neutrophil recruitment, cytokine production and viral clearance while reducing AHR.     

A Unified Approach to Linking Experimental,Statistical and Computational Analysis of Spike Train Data

A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.     

On the evacuation of sperm from the spermatophore of the tick, Ornithodoros savignyi

The paper deals with the different mechanisms involved in the process of spermatophore evagination, its triggering, its intermediate steps, and the cause of the flow of spermiophores from the ectospermatophore to the endospermatophore.The methods employed involved exposure of the spermatophores to the influence of various media and different environmental conditions, as well as mass spectrometric measurements in respect of the gas formed in the spermatophore during evagination.Results showed the possibility of retardation, interruption, and/or total inhibition of the process and produced conclusive proof that the gas generated in the spermatophore and responsible for the flow of spermiophores was CO₂.