New insights into c-Ret signalling pathway in the enteric nervous system and its relationship with ALS.

The receptor tyrosine kinase Ret (c-Ret) transduces the glial cell line-derived neurotrophic factor (GDNF) signal, one of the neurotrophic factors related to the degeneration process or the regeneration activity of motor neurons in amyotrophic lateral sclerosis (ALS). The phosphorylation of several tyrosine residues of c-Ret seems to be altered in ALS. c-Ret is expressed in motor neurons and in the enteric nervous system (ENS) during the embryonic period. The characteristics of the ENS allow using it as model for central nervous system (CNS) study and being potentially useful for the research of human neurological diseases such as ALS. The aim of the present study was to investigate the cellular localization and quantitative evaluation of marker c-Ret in the adult human gut. To assess the nature of c-Ret positive cells, we performed colocalization with specific markers of cells that typically are located in the enteric ganglia. The colocalization of PGP9.5 and c-Ret was preferentially intense in enteric neurons with oval morphology and mostly peripherally localized in the ganglion, so we concluded that the c-Ret receptor is expressed by a specific subtype of enteric neurons in the mature human ENS of the gut. The functional significance of these c-Ret positive neurons is discussed

On the page number of RNA secondary structures with pseudoknots

Let S denote the set of (possibly noncanonical) base pairs {i, j} of an RNA tertiary structure; i. e. {i, j} ∈ S if there is a hydrogen bond between the ith and jth nucleotide. The page number of S, denoted π(S), is the minimum number k such that S can be decomposed into a disjoint union of k secondary structures. Here, we show that computing the page number is NP-complete; we describe an exact computation of page number, using constraint programming, and determine the page number of a collection of RNA tertiary structures, for which the topological genus is known. We describe an approximation algorithm from which it follows that ω(

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; in particular, identification of RNA motifs that bind proteins has long been challenging, especially when such motifs depend on both sequence and structure. Moreover, although RNA binding proteins (RBPs) often contain more than one binding domain, algorithms capable of identifying more than one binding motif simultaneously have not been developed. In this paper we present a novel pipeline to determine binding peaks in crosslinking immunoprecipitation (CLIP) data, to discover multiple possible RNA sequence/structure motifs among them, and to experimentally validate such motifs. At the core is a new semi-automatic algorithm SARNAclust, the first unsupervised method to identify and deconvolve multiple sequence/structure motifs simultaneously. SARNAclust computes similarity between sequence/structure objects using a graph kernel, providing the ability to isolate the impact of specific features through the bulge graph formalism. Application of SARNAclust to synthetic data shows its capability of clustering 5 motifs at once with a V-measure value of over 0.95, while GraphClust achieves only a V-measure of 0.083 and RNAcontext cannot detect any of the motifs. When applied to existing eCLIP sets, SARNAclust finds known motifs for SLBP and HNRNPC and novel motifs for several other RBPs such as AGGF1, AKAP8L and ILF3. We demonstrate an experimental validation protocol, a targeted Bind-n-Seq-like high-throughput sequencing approach that relies on RNA inverse folding for oligo pool design, that can validate the components within the SLBP motif. Finally, we use this protocol to experimentally interrogate the SARNAclust motif predictions for protein ILF3. Our results support a newly identified partially double-stranded UUUUUGAGA motif similar to that known for the splicing factor HNRNPC.JHC was supported by NIH grants R21 HG007554 and R01 NS094637. ERA and EE were supported by the MINECO and FEDER (BIO2014-52566-R), AGAUR (SGR2014-1121), and the Sandra Ibarra Foundation for Cancer (FSI2013)

SARNAclust motif discovered for SLBP.

<p>Consensus sequence/structure motif found for SLBP by SARNAclust with graph transformation options 7 and 10.</p

RBNS-like validation using known SLBP motif.

<p>a) Percentage shift in the sequences of each group of RNAs for SLBP RNA-bind-n-seq. GST-SBP samples are used as a non-specific binding control b) Gel shift results for select probes tested in the RBNS when incubated with purified GST-SBP-SLBP. The Consensus A (CA) probe shows more binding relative to Consensus B (CB), Consensus Loop Only A (CLA), Consensus Loop Only B (CLB), Loop In Bulge (LIB) and Loop Stem Only (LST). Sequences for each probe and their RBNS results can be found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006078#pcbi.1006078.s014" target="_blank">S4 Data</a>. * indicates p < 0.05, ** indicates p < 0.005 assessed by t-test.</p