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Local translation allows spatial control of gene expression. Here, we performed a dual protein-mRNA localization screen, using smFISH on 523 human cell lines expressing GFP-tagged genes. 32 mRNAs displayed specific cytoplasmic localizations with local translation at unexpected locations, including cytoplasmic protrusions, cell edges, endosomes, Golgi, the nuclear envelope, and centrosomes, the latter being cell-cycle-dependent. Automated classification of mRNA localization patterns revealed a high degree of intercellular heterogeneity. Surprisingly, mRNA localization frequently required ongoing translation, indicating widespread co-translational RNA targeting. Interestingly, while P-body accumulation was frequent (15 mRNAs), four mRNAs accumulated in foci that were distinct structures. These foci lacked the mature protein, but nascent polypeptide imaging showed that they were specialized translation factories. For β-catenin, foci formation was regulated by Wnt, relied on APC-dependent polysome aggregation, and led to nascent protein degradation. Thus, translation factories uniquely regulate nascent protein metabolism and create a fine granular compartmentalization of translation., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Translational regulation plays a central role in the global gene expression of a cell, and detection of such regulation has allowed deciphering of critical biological mechanisms. Genome-wide studies of the regulation of translation (translatome) performed on microarrays represent a substantial proportion of studies, alongside with recent advances in deep-sequencing methods. However, there has been a lack of development in specific processing methodologies that deal with the distinct nature of translatome array data. In this study, we confirm that polysome profiling yields skewed data and thus violates the conventional transcriptome analysis assumptions. Using a comprehensive simulation of translatome array data varying the percentage and symmetry of deregulation, we show that conventional analysis methods (Quantile and LOESS normalizations) and statistical tests failed, respectively, to correctly normalize the data and to identify correctly deregulated genes (DEGs). We thus propose a novel analysis methodology available as a CRAN package; Internal Control Analysis of Translatome (INCATome) based on a normalization tied to a group of invariant controls. We confirm that INCATome outperforms the other normalization methods and allows a stringent identification of DEGs. More importantly, INCATome implementation on a biological translatome data set (cells silenced for splicing factor PSF) resulted in the best normalization performance and an improved validation concordance for identification of true positive DEGs. Finally, we provide evidence that INCATome is able to infer novel biological pathways with superior discovery potential, thus confirming the benefits for researchers of implementing INCATome for future translatome studies as well as for existing data sets to generate novel avenues for research., (© 2017 Sbarrato et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.)

A growing body of work demonstrates the importance of post-transcriptional control, in particular translation initiation, in the overall regulation of gene expression. Here we focus on the contribution of regulatory elements within the 5' and 3' untranslated regions of mRNA to gene expression in eukaryotic cells including terminal oligopyrimidine tracts, internal ribosome entry segments, upstream open reading frames and cytoplasmic polyadenylation elements. These mRNA regulatory elements may adopt complex secondary structures and/or contain sequence motifs that allow their interaction with a variety of regulatory proteins, RNAs and RNA binding proteins, particularly hnRNPs. The resulting interactions are context-sensitive, and provide cells with a sensitive and fast response to cellular signals such as hormone exposure or cytotoxic stress. Importantly, an increasing number of diseases have been identified, particularly cancers and those associated with neurodegeneration, which originate either from mutation of these regulatory motifs, or from deregulation of their cognate binding partners.