The Q-soluble N-Ethylmaleimide-sensitive Factor Attachment Protein Receptor (Q-SNARE) SNAP-47 Regulates Trafficking of Selected Vesicle-associated Membrane Proteins (VAMPs).

SNAREs constitute the core machinery of intracellular membrane fusion, but vesicular SNAREs localize to specific compartments via largely unknown mechanisms. Here, we identified an interaction between VAMP7 and SNAP-47 using a proteomics approach. We found that SNAP-47 mainly localized to cytoplasm, the endoplasmic reticulum (ER), and ERGIC and could also shuttle between the cytoplasm and the nucleus. SNAP-47 preferentially interacted with the trans-Golgi network VAMP4 and post-Golgi VAMP7 and -8. SNAP-47 also interacted with ER and Golgi syntaxin 5 and with syntaxin 1 in the absence of Munc18a, when syntaxin 1 is retained in the ER. A C-terminally truncated SNAP-47 was impaired in interaction with VAMPs and affected their subcellular distribution. SNAP-47 silencing further shifted the subcellular localization of VAMP4 from the Golgi apparatus to the ER. WT and mutant SNAP-47 overexpression impaired VAMP7 exocytic activity. We conclude that SNAP-47 plays a role in the proper localization and function of a subset of VAMPs likely via regulation of their transport through the early secretory pathway.

Chr21 protein-protein interactions: enrichment in proteins involved in intellectual disability, autism, and late-onset Alzheimer's disease.

Down syndrome (DS) is caused by human chromosome 21 (HSA21) trisomy. It is characterized by a poorly understood intellectual disability (ID). We studied two mouse models of DS, one with an extra copy of the <i>Dyrk1A</i> gene (189N3) and the other with an extra copy of the mouse Chr16 syntenic region (Dp(16)1Yey). RNA-seq analysis of the transcripts deregulated in the embryonic hippocampus revealed an enrichment in genes associated with chromatin for the 189N3 model, and synapses for the Dp(16)1Yey model. A large-scale yeast two-hybrid screen (82 different screens, including 72 HSA21 baits and 10 rebounds) of a human brain library containing at least 10⁷ independent fragments identified 1,949 novel protein-protein interactions. The direct interactors of HSA21 baits and rebounds were significantly enriched in ID-related genes (<i>P</i>-value &lt; 2.29 × 10^-8). Proximity ligation assays showed that some of the proteins encoded by HSA21 were located at the dendritic spine postsynaptic density, in a protein network at the dendritic spine postsynapse. We located HSA21 DYRK1A and DSCAM, mutations of which increase the risk of autism spectrum disorder (ASD) 20-fold, in this postsynaptic network. We found that an intracellular domain of DSCAM bound either DLGs, which are multimeric scaffolds comprising receptors, ion channels and associated signaling proteins, or DYRK1A. The DYRK1A-DSCAM interaction domain is conserved in <i>Drosophila</i> and humans. The postsynaptic network was found to be enriched in proteins associated with ARC-related synaptic plasticity, ASD, and late-onset Alzheimer's disease. These results highlight links between DS and brain diseases with a complex genetic basis.

HENA, heterogeneous network-based data set for Alzheimer's disease.

Alzheimer's disease and other types of dementia are the top cause for disabilities in later life and various types of experiments have been performed to understand the underlying mechanisms of the disease with the aim of coming up with potential drug targets. These experiments have been carried out by scientists working in different domains such as proteomics, molecular biology, clinical diagnostics and genomics. The results of such experiments are stored in the databases designed for collecting data of similar types. However, in order to get a systematic view of the disease from these independent but complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous network-based data set for Alzheimer's disease (HENA). Additionally, we demonstrate the application of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze their own results in the broader context of Alzheimer's disease research.

A pairwise alignment algorithm which favors clusters of blocks.

Pairwise sequence alignments aim to decide whether two sequences are related and, if so, to exhibit their related domains. Recent works have pointed out that a significant number of true homologous sequences are missed when using classical comparison algorithms. This is the case when two homologous sequences share several little blocks of homology, too small to lead to a significant score. On the other hand, classical alignment algorithms, when detecting homologies, may fail to recognize all the significant biological signals. The aim of the paper is to give a solution to these two problems. We propose a new scoring method which tends to increase the score of an alignment when "blocks" are detected. This so-called Block-Scoring algorithm, which makes use of dynamic programming, is worth being used as a complementary tool to classical exact alignments methods. We validate our approach by applying it on a large set of biological data. Finally, we give a limit theorem for the score statistics of the algorithm.