Genetic and Environmental Factors in Complex Neurodevelopmental Disorders

Complex neurodevelopmental disorders, such as schizophrenia, autism, attention deficit (hyperactivity) disorder, (manic) depressive illness and addiction, are thought to result from an interaction between genetic and environmental factors. Association studies on candidate genes and genome-wide linkage analyses have identified many susceptibility chromosomal regions and genes, but considerable efforts to replicate association have been surprisingly often disappointing. Here, we summarize the current knowledge of the genetic contribution to complex neurodevelopmental disorders, focusing on the findings from association and linkage studies. Furthermore, the contribution of the interaction of the genetic with environmental and epigenetic factors to the aetiology of complex neurodevelopmental disorders as well as suggestions for future research are discussed.Key Words: Neurodevelopmental disorders, susceptibility genes, environmental factors, gene-environment interactions, association studies, linkage analysis.     

ReSynPlex: Respiratory Syndrome Linked Pathogens Multiplex Detection and Characterization

Background

Pneumonia is a common disease that is a leading cause of mortality worldwide and frequently requires hospital admission. A large number of infectious agents may be involved and as a result of the potential severity of the infection, a clear and quick diagnostic is mandatory in order to take the appropriate decision for the health of the patient. However, a real gap exists between the multiplicity of information required by the practitioner and the diagnostic tools available.

Quantitative Characterization of Metastability and Heterogeneity of Amyloid Aggregates

Amyloids are heterogeneous assemblies of extremely stable fibrillar aggregates of proteins. Although biological activities of the amyloids are dependent on its conformation, quantitative evaluation of heterogeneity of amyloids has been difficult. Here we use disaggregation of the amyloids of tetramethylrhodamine-labeled Aβ (TMR-Aβ) to characterize its stability and heterogeneity. Disaggregation of TMR-Aβ amyloids, monitored by fluorescence recovery of TMR, was negligible in native buffer even at low nanomolar concentrations but the kinetics increased exponentially with addition of denaturants such as urea or GdnCl. However, dissolution of TMR-Aβ amyloids is different from what is expected in the case of thermodynamic solubility. For example, the fraction of soluble amyloids is found to be independent of total concentration of the peptide at all concentrations of the denaturants. Additionally, soluble fraction is dependent on growth conditions such as temperature, pH, and aging of the amyloids. Furthermore, amyloids undissolved in a certain concentration of the denaturant do not show any further dissolution after dilution in the same solvent; instead, these require higher concentrations of the denaturant. Taken together, our results indicate that amyloids are a heterogeneous ensemble of metastable states. Furthermore, dissolution of each structurally homogeneous member requires a unique threshold concentration of denaturant. Fraction of soluble amyloids as a function of concentration of denaturants is found to be sigmoidal. The sigmoidal curve becomes progressively steeper with progressive seeding of the amyloids, although the midpoint remains unchanged. Therefore, heterogeneity of the amyloids is a major determinant of the steepness of the sigmoidal curve. The sigmoidal curve can be fit assuming a normal distribution for the population of the amyloids of various kinetic stabilities. We propose that the mean and the standard deviation of the normal distribution provide quantitative estimates of mean kinetic stability and heterogeneity, respectively, of the amyloids in a certain preparation.     

Simulating Genetic Circuits in Bacterial Populations with Growth Heterogeneity

We computationally study genetic circuits in bacterial populations with heterogeneities in the growth rate. To that end, we present a stochastic simulation method for gene circuits in populations of cells and propose an efficient implementation that we call the “Next Family Method”. Within this approach, we implement different population setups, specifically Chemostat-type growth and growth in an ideal Mother Machine and show that the population structure and its statistics are different for the different setups whenever there is growth heterogeneity. Such dependence on the population setup is demonstrated, in the case of bistable systems with different growth rates in the stable states, to have distinctive signatures on quantities including the distributions of protein concentration and growth rates, and hysteresis curves. Applying this method to a bistable antibiotic resistance circuit, we find that as a result of the different statistics in different population setups, the estimated minimal inhibitory concentration of the antibiotic becomes dependent on the population setup in which it is measured.     

Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

Fluorescence recovery after photobleaching (FRAP) is an important tool used by cell biologists to study the diffusion and binding kinetics of vesicles, proteins, and other molecules in the cytoplasm, nucleus, or cell membrane. Although many FRAP models have been developed over the past decades, the influence of the complex boundaries of 3D cellular geometries on the recovery curves, in conjunction with regions of interest and optical effects (imaging, photobleaching, photoswitching, and scanning), has not been well studied. Here, we developed a 3D computational model of the FRAP process that incorporates particle diffusion, cell boundary effects, and the optical properties of the scanning confocal microscope, and validated this model using the tip-growing cells of Physcomitrella patens. We then show how these cell boundary and optical effects confound the interpretation of FRAP recovery curves, including the number of dynamic states of a given fluorophore, in a wide range of cellular geometries—both in two and three dimensions—namely nuclei, filopodia, and lamellipodia of mammalian cells, and in cell types such as the budding yeast, Saccharomyces pombe, and tip-growing plant cells. We explored the performance of existing analytical and algorithmic FRAP models in these various cellular geometries, and determined that the VCell VirtualFRAP tool provides the best accuracy to measure diffusion coefficients. Our computational model is not limited only to these cells types, but can easily be extended to other cellular geometries via the graphical Java-based application we also provide. This particle-based simulation—called the Digital Confocal Microscopy Suite or DCMS—can also perform fluorescence dynamics assays, such as number and brightness, fluorescence correlation spectroscopy, and raster image correlation spectroscopy, and could help shape the way these techniques are interpreted.     

Structural Characterization of N-WASP Domain V Using MD Simulations with NMR and SAXS Data

Because of their large conformational heterogeneity, structural characterization of intrinsically disordered proteins (IDPs) is very challenging using classical experimental methods alone. In this study, we use NMR and small-angle x-ray scattering (SAXS) data with multiple molecular dynamics (MD) simulations to describe the conformational ensemble of the fully disordered verprolin homology domain of the neural Aldrich syndrome protein involved in the regulation of actin polymerization. First, we studied several back-calculation software of SAXS scattering intensity and optimized the adjustable parameters to accurately calculate the SAXS intensity from an atomic structure. We also identified the most appropriate force fields for MD simulations of this IDP. Then, we analyzed four conformational ensembles of neural Aldrich syndrome protein verprolin homology domain, two generated with the program flexible-meccano with or without NMR-derived information as input and two others generated by MD simulations with two different force fields. These four conformational ensembles were compared to available NMR and SAXS data for validation. We found that MD simulations with the AMBER-03w force field and the TIP4P/2005s water model are able to correctly describe the conformational ensemble of this 67-residue IDP at both local and global level.