Gene Expression Profiling Following Maternal Deprivation: Involvement of the Brain Renin-Angiotensin System

The postnatal development of the mouse is characterized by a stress hypo-responsive period (SHRP), where basal corticosterone levels are low and responsiveness to mild stressors is reduced. Maternal separation is able to disrupt the SHRP and is widely used to model early trauma. In this study we aimed at identifying of brain systems involved in acute and possible long-term effects of maternal separation. We conducted a microarray-based gene expression analysis in the hypothalamic paraventricular nucleus after maternal separation, which revealed 52 differentially regulated genes compared to undisturbed controls, among them are 37 up-regulated and 15 down-regulated genes. One of the prominently up-regulated genes, angiotensinogen, was validated using in-situ hybridization. Angiotensinogen is the precursor of angiotensin II, the main effector of the brain renin-angiotensin system (RAS), which is known to be involved in stress system modulation in adult animals. Using the selective angiotensin type I receptor [AT(1)] antagonist candesartan we found strong effects on CRH and GR mRNA expression in the brain and ACTH release following maternal separation. AT(1) receptor blockade appears to enhance central effects of maternal separation in the neonate, suggesting a suppressing function of brain RAS during the SHRP. Taken together, our results illustrate the molecular adaptations that occur in the paraventricular nucleus following maternal separation and contribute to identifying signaling cascades that control stress system activity in the neonate

Self-similar chain conformations in polymer gels

We use molecular dynamics simulations to study the swelling of randomly end-cross-linked polymer networks in good solvent conditions. We find that the equilibrium degree of swelling saturates at Q_eq = N_e**(3/5) for mean strand lengths N_s exceeding the melt entanglement length N_e. The internal structure of the network strands in the swollen state is characterized by a new exponent nu=0.72. Our findings are in contradiction to de Gennes' c*-theorem, which predicts Q_eq proportional N_s**(4/5) and nu=0.588. We present a simple Flory argument for a self-similar structure of mutually interpenetrating network strands, which yields nu=7/10 and otherwise recovers the classical Flory-Rehner theory. In particular, Q_eq = N_e**(3/5), if N_e is used as effective strand length.Comment: 4 pages, RevTex, 3 Figure

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Dynamik von Polymerschmelzen und Quellverhalten ungeordneter Netzwerke

Deutsch:Mit Hilfe eines parallelen Molekulardynamik-Programmswurden einfach Modelle von Homopolymerschmelzen simuliert.Langkettige Schmelzen zeigten eine sehr gute Übereinstimmungmit den Vorhersagendes Reptationsmodells. Die intermediären Reptationsbereichemit den vorhergesagtenExponenten konnten wesentlich klarer als bisher verifiziertwerden. Es stellteVerschiedene, gebräuchliche Analyse-Methoden führten jedochzuunterschiedlichen Aussagen für die Verhakungslänge. Fürkurze Kettenbzw. kurze Unterketten wurden in dichten Schmelzendie Abweichungen vom Rouse-Modell aufgezeigt. DieseAbweichungenkönnen als Korrelationslocheffekt interpretiert werden undsteht in teilweiser Übereinstimmung zu den Vorhersagenrenormierter Rouse-Modelle. Aus den Schmelzen wurden Netzwerke in einem speziellenZufallsvernetzungsprozeßhergestellt, der die Bildung von Defektstrukturenunterbindet. Diesewurden bzgl. ihres Quell- und Deformationsverhaltenuntersucht. Der maximaleQuellgrad eines Netzwerkes war bereits bei verhaltnismäßigkurzen Kettenlängenverhakungslimitiert. Die Struktur der Ketten in einem biszum osmotischenGleichgewicht gequollenen Netzwerk unterhalb derMaschengröße ist die überstreckter,selbstvermeidender Ketten mit einer Fraktaldimension von D =1.4,jenseits der Maschengröße (bzw. Verhakungslänge) nehmen siedie Struktur einesIrrfluges an (D = 2). Gequollene Netzwerke zeigten, wie auch in Experimenten, einestarkeZunahme von Dichtefluktuationen, welche unter Verstreckunganisotropwurde und in der Streufunktion zu sogenanntenButterfly-Mustern führt.Diese Fluktuationen sind statischer Natur als Folge desEinfrierenseines ungeordneten Zustandes während der Vernetzung.English:Simulation results for simple models of homopolymer meltsgenerated with a newly developed parallel molecular dynamicsprogram are presented. Long chains showed a good agreementwith the established reptation model. The regime of localreptation with its associated dynamical exponents wasverified with remarkable clarity. However differentestablished methods for measuring the entanglement lengthyield different answers.Short chains or sub-chains showed deviations from thestandardRouse picture which can be explained by a correlation holeeffectand were in partial agreement with renormalized Rousemodels. The melt systems were crosslinked in a special simulatedprocesswhich eleminated defect structures completely. Thesenetworkswere swollen and uni-axially stretched. The maximum swellingdegree of such neutral networks in good solvent is limitedby entanglements. The chain structure below the entanglementlength in such network is that of self-avoiding walks butwith a fractal dimension of D = 1.4. Above the entanglementlength the chains become gaussian. In the swollen networks density fluctuations were stronglyenhanced and under additional uniaxial stretching showedbutterfly patterns in the total structure factor as was seenby many experiments. These flucatuations are static andare to be viewed as a consequence of the disorder of thecrosslinking process

Self-Consistent Integral Equation Theory for Polyolefins: Comparison to Molecular Dynamics Simulations and X-Ray Scattering

No abstract prepared

DeepOF: a Python package for supervised and unsupervised pattern recognition in mice motion tracking data

DeepOF (Deep Open Field) is a Python package that provides a suite of tools for analysing behaviour in freely-moving rodents. Specifically, it focuses on post-processing time-series data extracted from videos using DeepLabCut (Mathis et al., 2018). This paper included code and functionality peer-review, which we think is an important milestone for our package moving forward