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Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
The Arp2/3 complex nucleates and cross-links actin filaments at the leading edge of motile cells, and its activity is stimulated by C-terminal regions of WASP/Scar proteins, called VCA domains. VCA domains contain a verprolin homology sequence (V) that binds monomeric actin and central (C) and acidic sequences (A) that bind the Arp2/3 complex. Here we show that the C domain binds to monomeric actin with higher affinity (K(d) = 10 microm) than to the Arp2/3 complex (K(d) > 200 microm). Nuclear magnetic resonance spectroscopy reveals that actin binds to the N-terminal half of the C domain and that both the V and C domains can bind actin independently and simultaneously, indicating that they interact with different sites. Mutation of conserved hydrophobic residues in the actin-binding interface of the C domain disrupts activation of the Arp2/3 complex but does not alter affinity for the complex. By chemical cross-linking the C domain interacts with the p40 subunit of the Arp2/3 complex and, by fluorescence polarization anisotropy, the binding of actin and the Arp2/3 complex are mutually exclusive. Our results indicate that both actin and Arp2/3 binding are important for C domain function but that the C domain does not form a static bridge between the two. We propose a model for activation of the Arp2/3 complex in which the C domain first primes the complex by inducing a necessary conformational change and then initiates nucleus assembly by bringing an actin monomer into proximity of the primed complex.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.