Protein subcellular localization is fundamental to the establishment of the body axis, cell migration, synaptic plasticity, and a vast range of other biological processes. Protein localization occurs through three mechanisms: protein transport, mRNA localization, and local translation. However, the relative contribution of each process to neuronal polarity remains unknown. Using neurons differentiated from mouse embryonic stem cells, we analyze protein and RNA expression and translation rates in isolated cell bodies and neurites genome-wide. We quantify 7323 proteins and the entire transcriptome, and identify hundreds of neurite-localized proteins and locally translated mRNAs. Our results demonstrate that mRNA localization is the primary mechanism for protein localization in neurites that may account for half of the neurite-localized proteome. Moreover, we identify multiple neurite-targeted non-coding RNAs and RNA-binding proteins with potential regulatory roles. These results provide further insight into the mechanisms underlying the establishment of neuronal polarity.

This communication reports a further examination of venom gland transcripts and venom composition of the Mexican scorpion Thorellius atrox using RNA-seq and tandem mass spectrometry. The RNA-seq, which was performed with the Illumina protocol, yielded more than 20,000 assembled transcripts. Following a database search and annotation strategy, 160 transcripts were identified, potentially coding for venom components. A novel sequence was identified that potentially codes for a peptide with similarity to spider ω-agatoxins, which act on voltage-gated calcium channels, not known before to exist in scorpion venoms. Analogous transcripts were found in other scorpion species. They could represent members of a new scorpion toxin family, here named omegascorpins. The mass fingerprint by LC-MS identified 135 individual venom components, five of which matched with the theoretical masses of putative peptides translated from the transcriptome. The LC-MS/MS de novo sequencing allowed to reconstruct and identify 42 proteins encoded by assembled transcripts, thus validating the transcriptome analysis. Earlier studies conducted with this scorpion venom permitted the identification of only twenty putative venom components. The present work performed with more powerful and modern omic technologies demonstrates the capacity of accomplishing a deeper characterization of scorpion venom components and the identification of novel molecules with potential applications in biomedicine and the study of ion channel physiology.

BackgroundThe initiation of translation via cellular internal ribosome entry sites plays an important role in the stress response and certain physiological conditions in which canonical cap-dependent translation initiation is compromised. Currently, only a limited number of these regulatory elements have been experimentally identified. Notably, cellular internal ribosome entry sites lack conservation of both the primary sequence and mRNA secondary structure, rendering their identification difficult. Despite their biological importance, the currently available computational strategies to predict them have had limited success. We developed a bioinformatic method based on a support vector machine for the prediction of internal ribosome entry sites in fungi using the 5’-UTR sequences of 20 non-redundant fungal organisms. Additionally, we performed a comparative analysis and characterization of the functional relationships among the gene products predicted to be translated by this cap-independent mechanism.ResultsUsing our method, we predicted 6,532 internal ribosome entry sites in 20 non-redundant fungal organisms. Some orthologous groups were enriched with our positive predictions. This is the case of the HSP70 chaperone family, which remarkably has two verified internal ribosome entry sites, one in humans and the other in flies. A second example is the orthologous group of the eIF4G repression protein Sbp1p, which has two homologous genes known to be translated by this cap-independent mechanism, one in mice and the other in yeast. These examples emphasize the wide conservation of these regulatory elements as a result of selective pressure. In addition, we performed a protein-protein interaction network characterization of the gene products of our positive predictions using Saccharomyces cerevisiae as a model, which revealed a highly connected and modular topology, suggesting a functional association. A remarkable example of this functional association is our prediction of internal ribosome entry sites elements in three components of the RNA polymerase II mediator complex.ConclusionsWe developed a method for the prediction of cellular internal ribosome entry sites that may guide experimental and bioinformatic analyses to increase our understanding of protein translation regulation. Our analysis suggests that fungi show evolutionary conservation and functional association of proteins translated by this cap-independent mechanism.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2266-x) contains supplementary material, which is available to authorized users.

BackgroundMetabolic reactions are chemical transformations commonly catalyzed by enzymes. In recent years, the explosion of genomic data and individual experimental characterizations have contributed to the construction of databases and methodologies for the analysis of metabolic networks. Some methodologies based on graph theory organize compound networks into metabolic functional categories without preserving biochemical pathways. Other methods based on chemical group exchange and atom flow trace the conversion of substrates into products in detail, which is useful for inferring metabolic pathways.MethodsHere, we present a novel rule-based approach incorporating both methods that decomposes each reaction into architectures of compound pairs and loner compounds that can be organized into tree structures. We compared the tree structure-compound pairs to those reported in the KEGG-RPAIR dataset and obtained a match precision of 81%. The generated tree structures naturally clustered all reactions into general reaction patterns of compounds with similar chemical transformations. The match precision of each cluster was calculated and used to suggest reactant-pairs for which manual curation can be avoided because this is the main goal of the method. We evaluated catalytic processes in the clusters based on Enzyme Commission categories that revealed preferential use of enzyme classes.ConclusionsWe demonstrate that the application of simple rules can enable the identification of reaction patterns reflecting metabolic reactions that transform substrates into products and the types of catalysis involved in these transformations. Our rule-based approach can be incorporated as the input in pathfinders or as a tool for the construction of reaction classifiers, indicating its usefulness for predicting enzyme catalysis.Electronic supplementary materialThe online version of this article (10.1186/s12918-018-0583-9) contains supplementary material, which is available to authorized users.