CatRAPID signature: identification of ribonucleoproteins and RNA-binding regions

Motivation: Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis.RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity.AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html.CONTACT: [email protected] INFORMATION: Supplementary data are available at Bioinformatics online

Long non-coding RNA uc.291 controls epithelial differentiation by interfering with the ACTL6A/BAF complex.

The mechanisms that regulate the switch between epidermal progenitor state and differentiation are not fully understood. Recent findings indicate that the chromatin remodelling BAF complex (Brg1-associated factor complex or SWI/SNF complex) and the transcription factor p63 mutually recruit one another to open chromatin during epidermal differentiation. Here, we identify a long non-coding transcript that includes an ultraconserved element, uc.291, which physically interacts with ACTL6A and modulates chromatin remodelling to allow differentiation. Loss of uc.291 expression, both in primary keratinocytes and in three-dimensional skin equivalents, inhibits differentiation as indicated by epidermal differentiation complex genes down-regulation. ChIP experiments reveal that upon uc.291 depletion, ACTL6A is bound to the differentiation gene promoters and inhibits BAF complex targeting to induce terminal differentiation genes. In the presence of uc.291, the ACTL6A inhibitory effect is released, allowing chromatin changes to promote the expression of differentiation genes. Thus, uc.291 interacts with ACTL6A to modulate chromatin remodelling activity, allowing the transcription of late differentiation genes

SAMMSON fosters cancer cell fitness by concertedly enhancing mitochondrial and cytosolic translation

Synchronization of mitochondrial and cytoplasmic translation rates is critical for the maintenance of cellular fitness, with cancer cells being especially vulnerable to translational uncoupling. Although alterations of cytosolic protein synthesis are common in human cancer, compensating mechanisms in mitochondrial translation remain elusive. Here we show that the malignant long non-coding RNA (lncRNA) SAMMSON promotes a balanced increase in ribosomal RNA (rRNA) maturation and protein synthesis in the cytosol and mitochondria by modulating the localization of CARF, an RNA-binding protein that sequesters the exo-ribonuclease XRN2 in the nucleoplasm, which under normal circumstances limits nucleolar rRNA maturation. SAMMSON interferes with XRN2 binding to CARF in the nucleus by favoring the formation of an aberrant cytoplasmic RNA-protein complex containing CARF and p32, a mitochondrial protein required for the processing of the mitochondrial rRNAs. These data highlight how a single oncogenic lncRNA can simultaneously modulate RNA-protein complex formation in two distinct cellular compartments to promote cell growth

Approaches to characterize structural properties of RNA

The secondary structure of an RNA molecule is fundamental for its function. However, structural conservation and the structure of RNA in vivo are still poorly understood. Data from recent high-throughput experiments can provide new insights, but they have not yet been systematically exploited. The aim of my doctoral studies was to exploit these experimental data to develop computational approaches for discovering and analyzing structural properties of RNA. I developed two algorithms: CROSS predicts the secondary structure propensity profile of an RNA, and CROSSalign discovers structural similarities among different RNAs. In addition, I studied the effect of the presence of protein binding motifs on the prediction of the RNA structure, in vivo and investigated how the propensity of RNAs to bind to proteins could be exploited to create a predictive tool. The suite of tools that I developed opens new possibilities for studying the structural properties of long RNA molecules and for investigating structural conservation in large-scale analyses.La estructura secundaria del ARN es fundamental para su función. Sin embargo, la conservación estructural y la estructura del ARN in vivo son poco conocidas. Los datos provenientes de experimentos de alto rendimiento pueden proporcionar nuevos conocimientos, pero aun no han sido usados sistemáticamente. El objetivo de mis estudios de doctorado fue emplear estos datos experimentales con el fin de desarrollar metodos computacionales para el descubrimiento y el analisis de las propiedades estructurales del ARN. Como resultado de mi tesis desarrollé dos algoritmos: CROSS, que predice el perfil de propensión de estructura secundaria de un ARN; y CROSSalign, que busca similitudes estructurales entre diferentes ARNs. Además, estudié el efecto de la presencia de dominios de unión protéinica en la predicción de la estructura del ARN in vivo; e investigué como la propensión de los ARNs a unirse a las proteínas podría usarse para crear un modelo predictivo. El conjunto de herramientas que desarrollé abre nuevas posibilidades para estudiar las propiedades estructurales de moléculas de ARN largas y para investigar la conservación estructural en análisis a gran escala

Using gene expression to study specialized metabolism - a practical guide

Plants produce a vast array of chemical compounds that we use as medicines and flavors, but these compounds' biosynthetic pathways are still poorly understood. This paucity precludes us from modifying, improving, and mass-producing these specialized metabolites in suitable bioreactors. Many of the specialized metabolites are expressed in a narrow range of organs, tissues, and cell types, suggesting a tight regulation of the responsible biosynthetic pathways. Fortunately, with unprecedented ease of generating gene expression data and with >200,000 publicly available RNA sequencing samples, we are now able to study the expression of genes from hundreds of plant species. This review demonstrates how gene expression can elucidate the biosynthetic pathways by mining organ-specific genes, gene expression clusters, and applying various types of co-expression analyses. To empower biologists to perform these analyses, we showcase these analyses using recently published, user-friendly tools. Finally, we analyze the performance of co-expression networks and show that they are a valuable addition to elucidating multiple the biosynthetic pathways of specialized metabolism.Ministry of Education (MOE)Published versionDS was supported by Singaporean Ministry of Education grant MOE2018-T2-2-053

A method for RNA structure prediction shows evidence for structure in lncRNAs

To compare the secondary structure profiles of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure profile at single-nucleotide resolution and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. CROSSalign performs pair-wise comparisons and is able to find homologs between thousands of matches identifying the exact regions of similarity between profiles of different lengths. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modeling. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/crossalign.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P and BFU2017-86970-P). We also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013–2017 (SEV-2012-0208). We also thank the CRG fellowship to SM

A high-throughput approach to profile RNA structure

Here we introduce the Computational Recognition of Secondary Structure (CROSS) method to calculate the structural profile of an RNA sequence (single- or double-stranded state) at single-nucleotide resolution and without sequence length restrictions. We trained CROSS using data from high-throughput experiments such as Selective 2΄-Hydroxyl Acylation analyzed by Primer Extension (SHAPE; Mouse and HIV transcriptomes) and Parallel Analysis of RNA Structure (PARS; Human and Yeast transcriptomes) as well as high-quality NMR/X-ray structures (PDB database). The algorithm uses primary structure information alone to predict experimental structural profiles with >80% accuracy, showing high performances on large RNAs such as Xist (17 900 nucleotides; Area Under the ROC Curve AUC of 0.75 on dimethyl sulfate (DMS) experiments). We integrated CROSS in thermodynamics-based methods to predict secondary structure and observed an increase in their predictive power by up to 30%.The research leading to these results has received funding from European Union Seventh Framework Programme [FP7/2007-2013]; European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]; AGAUR [2014 SGR 00685 to GGT]; Spanish Ministry of Economy and Competitiveness, European Research Development Fund ERDF, 'Centro de Excelencia Severo Ochoa 2013-2017' [SEV-2012-0208]. Funding for open access charge: European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]. The authors also thank the CRG fellowship to SM

Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses

Most pandemics of recent decades can be traced to RNA viruses, including HIV, SARS, influenza, dengue, Zika, and SARS-CoV-2. These RNA viruses impose considerable social and economic burdens on our society, resulting in a high number of deaths and high treatment costs. As these RNA viruses utilize an RNA genome, which is important for different stages of the viral life cycle, including replication, translation, and packaging, studying how the genome folds is important to understand virus function. In this review, we summarize recent advances in computational and high-throughput RNA structure-mapping approaches and their use in understanding structures within RNA virus genomes. In particular, we focus on the genome structures of the dengue, Zika, and SARS-CoV-2 viruses due to recent significant outbreaks of these viruses around the world