Biology subjects

Evans, A.

Publications and source records attributed to Evans, A..

4 recordsLinked to original sources

bioRxiv2018

ADAR2-mediated Q/R editing of GluK2 regulates kainate receptor upscaling in response to suppression of synaptic activity

Kainate receptors (KARs) regulate neuronal excitability and network function. Most KARs contain the subunit GluK2 and the properties of these receptors are determined in part by ADAR2-mediated mRNA editing of GluK2 that changes a genomically encoded glutamine (Q) to arginine (R). Suppression of synaptic activity reduces ADAR2-dependent Q/R editing of GluK2 with a consequential increase in GluK2-containing KAR surface expression. However, the mechanism underlying this reduction in GluK2 editing has not been addressed. Here we show that induction of KAR upscaling results in proteasomal degradation of ADAR2, which reduces GluK2 Q/R editing. Because KARs incorporating unedited GluK2(Q) assemble and exit the ER more efficiently this leads to an upscaling of KAR surface expression. Consistent with this, we demonstrate that partial ADAR2 knockdown phenocopies and occludes KAR upscaling. Moreover, we show that although the AMPAR subunit GluA2 also undergoes ADAR2-dependent Q/R editing, this process does not mediate AMPAR upscaling. These data demonstrate that activity-dependent regulation of ADAR2 proteostasis and GluK2 Q/R editing are key determinants of KAR, but not AMPAR, trafficking and upscaling.\n\nSummary statementSynaptic suppression promotes proteasomal degradation of the mRNA-editing enzyme ADAR2. Decreased ADAR2 levels reduce Q/R editing of the kainate receptor subunit GluK2 leading to enhanced surface expression and homeostatic upscaling.

neuroscience↗

bioRxiv2018

The neonatal Fc receptor is a pan-echovirus receptor

Echoviruses are the main causative agents of aseptic meningitis worldwide and are particularly devastating in the neonatal population, where they are associated with severe hepatitis, neurological disease including meningitis and encephalitis, and even death. Here, we identify the neonatal Fc receptor (FcRn) as a pan-echovirus receptor. We show that loss of expression of FcRn or its binding partner beta 2 microglubulin ({beta}2M) renders human brain microvascular cells resistant to infection by a panel of echoviruses at the stage of virus attachment and that a blocking antibody to {beta}2M inhibit echovirus infection in cell lines and in primary human fetal intestinal epithelial cells. We also show that expression of human, but not mouse, FcRn renders non-permissive human and mouse cells sensitive to echovirus infection and that the extracellular domain of human FcRn directly binds echoviral particles and neutralizes infection. Lastly, we show that primary cells isolated from mice that express human FcRn are highly susceptible to echovirus infection. Our findings thus identify FcRn as a pan-echovirus receptor, which may explain the enhanced susceptibility of neonates to echovirus infections.\n\nSignificanceEchoviruses are associated with aseptic meningitis and induce severe disease, and even death, in neonates and young infants. Here, we identify the neonatal Fc receptor (FcRn) as a pan-echovirus receptor. FcRn is expressed on the surface of the human placenta, and throughout life in intestinal enterocytes, liver hepatocytes, and in the microvascular endothelial cells that line the blood-brain barrier. This pattern of expression is consistent with the organ sites targeted by echoviruses in humans, with the primary entry site of infection in the intestinal tract and subsequent infection of secondary tissues including the liver and brain. These findings provide important insights into echovirus pathogenesis and may explain the enhanced susceptibility of infants and neonates to echovirus-induced disease.

microbiology↗

bioRxiv2018

Unbiased Age-Appropriate Structural Brain Atlases for Chinese Pediatrics

In magnetic resonance imaging (MRI) studies of children brain development, structural brain atlases usually serve as important references of pediatric population in which individual images are spatially normalized into a common or standard stereotactic space. However, the existing popular children brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD atlases) are made mostly based on MR images from Western populations, and are thus insufficient to characterize the brains of Chinese children due to the neuroanatomical differences that are relevant to genetic and environmental factors. By collecting high-quality T1- and T2- weighted MR images from 328 typically developing Chinese children aged from 6 to 12 years old, we created a set of age-appropriate Chinese pediatric (CHN-PD) atlases using an unbiased template construction algorithm. The CHN-PD atlases included the head/brain templates, the symmetric brain template, the gender-specific brain templates and the corresponding tissue probability atlases. Moreover, the atlases contained multiple age-specific templates with a one-year interval. A direct comparison of the CHN-PD and the NIH-PD atlases revealed remarkable anatomical differences bilaterally in the lateral frontal and parietal regions and somatosensory cortex. While applying the CHN-PD atlases to two independent Chinese pediatric datasets (N = 114 and N = 71, respectively), machine-learning regression approaches revealed higher prediction accuracy on brain ages than the usage of NIH-PD atlases. These results suggest that the CHN-PD brain atlases are necessary and important for future typical and atypical developmental studies in Chinese pediatric population. Currently, the CHN-PD atlases have been released on the NITRC website (https://www.nitrc.org/projects/chn-pd).

neuroscience↗

bioRxiv2017

An analytic approach for interpretable predictive models in high dimensional data, in the presence of interactions with exposures

Predicting a phenotype and understanding which variables improve that prediction are two very challenging and overlapping problems in analysis of high-dimensional data such as those arising from genomic and brain imaging studies. It is often believed that the number of truly important predictors is small relative to the total number of variables, making computational approaches to variable selection and dimension reduction extremely important. To reduce dimensionality, commonly-used two-step methods first cluster the data in some way, and build models using cluster summaries to predict the phenotype.\n\nIt is known that important exposure variables can alter correlation patterns between clusters of high-dimensional variables, i.e., alter network properties of the variables. However, it is not well understood whether such altered clustering is informative in prediction. Here, assuming there is a binary exposure with such network-altering effects, we explore whether use of exposure-dependent clustering relationships in dimension reduction can improve predictive modelling in a two-step framework. Hence, we propose a modelling framework called ECLUST to test this hypothesis, and evaluate its performance through extensive simulations.\n\nWith ECLUST, we found improved prediction and variable selection performance compared to methods that do not consider the environment in the clustering step, or to methods that use the original data as features. We further illustrate this modelling framework through the analysis of three data sets from very different fields, each with high dimensional data, a binary exposure, and a phenotype of interest. Our method is available in the eclust CRAN package.

genomics↗