In vivo ligands of MDA5 and RIG-I in measles virus-infected cells.

RIG-I-like receptors (RLRs: RIG-I, MDA5 and LGP2) play a major role in the innate immune response against viral infections and detect patterns on viral RNA molecules that are typically absent from host RNA. Upon RNA binding, RLRs trigger a complex downstream signaling cascade resulting in the expression of type I interferons and proinflammatory cytokines. In the past decade extensive efforts were made to elucidate the nature of putative RLR ligands. In vitro and transfection studies identified 5'-triphosphate containing blunt-ended double-strand RNAs as potent RIG-I inducers and these findings were confirmed by next-generation sequencing of RIG-I associated RNAs from virus-infected cells. The nature of RNA ligands of MDA5 is less clear. Several studies suggest that double-stranded RNAs are the preferred agonists for the protein. However, the exact nature of physiological MDA5 ligands from virus-infected cells needs to be elucidated. In this work, we combine a crosslinking technique with next-generation sequencing in order to shed light on MDA5-associated RNAs from human cells infected with measles virus. Our findings suggest that RIG-I and MDA5 associate with AU-rich RNA species originating from the mRNA of the measles virus L gene. Corresponding sequences are poorer activators of ATP-hydrolysis by MDA5 in vitro, suggesting that they result in more stable MDA5 filaments. These data provide a possible model of how AU-rich sequences could activate type I interferon signaling.

LocTree3 prediction of localization.

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18=80±3% for eukaryotes and a six-state accuracy Q6=89±4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.

Synaptic FUS accumulation triggers early misregulation of synaptic RNAs in a mouse model of ALS.

Mutations disrupting the nuclear localization of the RNA-binding protein FUS characterize a subset of amyotrophic lateral sclerosis patients (ALS-FUS). FUS regulates nuclear RNAs, but its role at the synapse is poorly understood. Using super-resolution imaging we determined that the localization of FUS within synapses occurs predominantly near the vesicle reserve pool of presynaptic sites. Using CLIP-seq on synaptoneurosomes, we identified synaptic FUS RNA targets, encoding proteins associated with synapse organization and plasticity. Significant increase of synaptic FUS during early disease in a mouse model of ALS was accompanied by alterations in density and size of GABAergic synapses. mRNAs abnormally accumulated at the synapses of 6-month-old ALS-FUS mice were enriched for FUS targets and correlated with those depicting increased short-term mRNA stability via binding primarily on multiple exonic sites. Our study indicates that synaptic FUS accumulation in early disease leads to synaptic impairment, potentially representing an initial trigger of neurodegeneration.

Cytoplasmic FUS triggers early behavioral alterations linked to cortical neuronal hyperactivity and inhibitory synaptic defects.

Gene mutations causing cytoplasmic mislocalization of the RNA-binding protein FUS lead to severe forms of amyotrophic lateral sclerosis (ALS). Cytoplasmic accumulation of FUS is also observed in other diseases, with unknown consequences. Here, we show that cytoplasmic mislocalization of FUS drives behavioral abnormalities in knock-in mice, including locomotor hyperactivity and alterations in social interactions, in the absence of widespread neuronal loss. Mechanistically, we identified a progressive increase in neuronal activity in the frontal cortex of Fus knock-in mice in vivo, associated with altered synaptic gene expression. Synaptic ultrastructural and morphological defects were more pronounced in inhibitory than excitatory synapses and associated with increased synaptosomal levels of FUS and its RNA targets. Thus, cytoplasmic FUS triggers synaptic deficits, which is leading to increased neuronal activity in frontal cortex and causing related behavioral phenotypes. These results indicate that FUS mislocalization may trigger deleterious phenotypes beyond motor neuron impairment in ALS, likely relevant also for other neurodegenerative diseases characterized by FUS mislocalization.

ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data.

The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated.