Coleman, B. | bioRxiv Science

bioRxiv2017

The ERK MAPK pathway modulates Gq-dependent locomotion in Caenorhabditis elegans

The heterotrimeric G protein Gq regulates neuronal activity through distinct downstream effector pathways. In addition to the canonical Gq effector phospholipase C{beta}, the small GTPase Rho was recently identified as a conserved effector of Gq. To identify additional molecules important for Gq signaling in neurons, we performed a forward genetic screen in the nematode Caenorhabditis elegans for suppressors of the hyperactivity and exaggerated waveform of an activated Gq mutant. We isolated two mutations affecting the MAP kinase scaffold protein KSR-1 and found that KSR-1 modulates locomotion downstream of or in parallel to the Gq-Rho pathway. Through epistasis experiments, we found that the core ERK MAPK cascade is required for Gq-Rho regulation of locomotion, but that the canonical ERK activator LET-60/Ras may not be required. Through neuron-specific rescue experiments, we found that the ERK pathway functions in head acetylcholine neurons to control Gq-dependent locomotion. Additionally, expression of activated LIN-45/Raf in head acetylcholine neurons is sufficient to cause an exaggerated waveform phenotype and hypersensitivity to the acetylcholinesterase inhibitor aldicarb, similar to an activated Gq mutant. Taken together, our results suggest that the ERK MAPK pathway modulates the output of Gq-Rho signaling to control locomotion behavior in C. elegans.

genetics↗

bioRxiv2017

SARNAclust: Semi-Automatic Detection Of RNA Protein Binding Motifs From Immunoprecipitation Data

RNA-protein binding is critical to gene regulation, controlling fundamental processes including splicing, translation, localization and stability, and aberrant RNA-protein interactions are known to play a role in a wide variety of diseases. However, molecular understanding of RNA-protein interactions remains limited, and in particular identification of the RNA motifs that bind proteins has long been a difficult problem. To address this challenge, we have developed a novel semi-automatic algorithm, SARNAclust, to computationally identify combined structure/sequence motifs from immunoprecipitation data. SARNAclust is, to our knowledge, the first unsupervised method that can identify RNA motifs at full structural resolution while also being able to simultaneously deconvolve multiple motifs. SARNAclust makes use of a graph kernel to evaluate similarity between sequence/structure objects, and provides the ability to isolate the impact of specific features through the bulge graph formalism. SARNAclust includes a key method for predicting RNA secondary structure at CLIP peaks, RNApeakFold, which we have verified to be effective on synthetic motif data. We applied SARNAclust to 30 ENCODE eCLIP datasets, identifying known motifs and novel predictions. Notably, we predicted a new motif for the protein ILF3 similar to that for the splicing factor hnRNPC, providing evidence for interaction between these two proteins. To validate our predictions, we performed a directed RNA bind-n-seq assay for two proteins: ILF3 and SLBP, in each case revealing the effectiveness of SARNAclust in predicting RNA sequence and structure elements important to protein binding. Availability: https://github.com/idotu/SARNAclust

bioinformatics↗