Protein Phosphorylation in Depolarized Synaptosomes: Dissecting Primary Effects of Calcium from Synaptic Vesicle Cycling.

Synaptic transmission is mediated by the regulated exocytosis of synaptic vesicles. When the presynaptic membrane is depolarized by an incoming action potential, voltage-gated calcium channels open, resulting in the influx of calcium ions that triggers the fusion of synaptic vesicles (SVs) with the plasma membrane. SVs are recycled by endocytosis. Phosphorylation of synaptic proteins plays a major role in these processes, and several studies have shown that the synaptic phosphoproteome changes rapidly in response to depolarization. However, it is unclear which of these changes are directly linked to SV cycling and which might regulate other presynaptic functions that are also controlled by calcium-dependent kinases and phosphatases. To address this question, we analyzed changes in the phosphoproteome using rat synaptosomes in which exocytosis was blocked with botulinum neurotoxins (BoNTs) while depolarization-induced calcium influx remained unchanged. BoNT-treatment significantly alters the response of the synaptic phoshoproteome to depolarization and results in reduced phosphorylation levels when compared with stimulation of synaptosomes by depolarization with KCl alone. We dissect the primary Ca2+-dependent phosphorylation from SV-cycling-dependent phosphorylation and confirm an effect of such SV-cycling-dependent phosphorylation events on syntaxin-1a-T21/T23, synaptobrevin-S75, and cannabinoid receptor-1-S314/T322 on exo- and endocytosis in cultured hippocampal neurons.

SEAweb: the small RNA Expression Atlas web application.

We present the Small RNA Expression Atlas (SEAweb), a web application that allows for the interactive querying, visualization and analysis of known and novel small RNAs across 10 organisms. It contains sRNA and pathogen expression information for over 4200 published samples with standardized search terms and ontologies. In addition, SEAweb allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAweb's user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns. We provide evidence for SEAweb's fidelity by (i) generating a set of 591 tissue specific miRNAs across 29 tissues, (ii) finding known and novel bacterial and viral infections across diseases and (iii) determining a Parkinson's disease-specific blood biomarker signature using novel data. We believe that SEAweb's simple semantic search interface, the flexible interactive reports and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases and organisms.

Explainable Deep Learning for Augmentation of Small RNA Expression Profiles.

The lack of well-structured metadata annotations complicates the reusability and interpretation of the growing amount of publicly available RNA expression data. The machine learning-based prediction of metadata (data augmentation) can considerably improve the quality of expression data annotation. In this study, we systematically benchmark deep learning (DL) and random forest (RF)-based metadata augmentation of tissue, age, and sex using small RNA (sRNA) expression profiles. We use 4243 annotated sRNA-Seq samples from the sRNA expression atlas database to train and test the augmentation performance. In general, the DL machine learner outperforms the RF method in almost all tested cases. The average cross-validated prediction accuracy of the DL algorithm for tissues is 96.5%, for sex is 77%, and for age is 77.2%. The average tissue prediction accuracy for a completely new data set is 83.1% (DL) and 80.8% (RF). To understand which sRNAs influence DL predictions, we employ backpropagation-based feature importance scores using the DeepLIFT method, which enable us to obtain information on biological relevance of sRNAs.

Oasis 2: improved online analysis of small RNA-seq data.

BACKGROUND: Small RNA molecules play important roles in many biological processes and their dysregulation or dysfunction can cause disease. The current method of choice for genome-wide sRNA expression profiling is deep sequencing. RESULTS: Here we present Oasis 2, which is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. CONCLUSIONS: Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment. AVAILABILITY AND IMPLEMENTATION: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at https://oasis.dzne.de.