Molecular Composition of Staufen2-Containing Ribonucleoproteins in Embryonic Rat Brain

Messenger ribonucleoprotein particles (mRNPs) are used to transport mRNAs along neuronal dendrites to their site of translation. Numerous mRNA-binding and regulatory proteins within mRNPs finely regulate the fate of bound-mRNAs. Their specific combination defines different types of mRNPs that in turn are related to specific synaptic functions. One of these mRNA-binding proteins, Staufen2 (Stau2), was shown to transport dendritic mRNAs along microtubules. Its knockdown expression in neurons was shown to change spine morphology and synaptic functions. To further understand the molecular mechanisms by which Stau2 modulates synaptic function in neurons, it is important to identify and characterize protein co-factors that regulate the fate of Stau2-containing mRNPs. To this end, a proteomic approach was used to identify co-immunoprecipitated proteins in Staufen2-containing mRNPs isolated from embryonic rat brains. The proteomic approach identified mRNA-binding proteins (PABPC1, hnRNP H1, YB1 and hsc70), proteins of the cytoskeleton (α- and β-tubulin) and RUFY3 a poorly characterized protein. While PABPC1 and YB1 associate with Stau2-containing mRNPs through RNAs, hsc70 is directly bound to Stau2 and this interaction is regulated by ATP. PABPC1 and YB1 proteins formed puncta in dendrites of embryonic rat hippocampal neurons. However, they poorly co-localized with Stau2 in the large dendritic complexes suggesting that they are rather components of Stau2-containing mRNA particles. All together, these results represent a further step in the characterization of Stau2-containing mRNPs in neurons and provide new tools to study and understand how Stau2-containing mRNPs are transported, translationally silenced during transport and/or locally expressed according to cell needs

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets

An analysis-ready and quality controlled resource for pediatric brain white-matter research

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. BárbaraAvelar-Pereira 9 , EthanRoy2 , Valerie J.Sydnor3,4,5, JasonD.Yeatman1,2, The Fibr Community Science Consortium*, TheodoreD.Satterthwaite3,4,5,88 & Ariel Roke