Phase separation drives X-chromosome inactivation: a hypothesis

The long non-coding RNA Xist induces heterochromatinization of the X chromosome by recruiting repressive protein complexes to chromatin. Here we gather evidence, from the literature and from computational analyses, showing that Xist assemblies are similar in size, shape and composition to phase-separated condensates, such as paraspeckles and stress granules. Given the progressive sequestration of Xist’s binding partners during X-chromosome inactivation, we formulate the hypothesis that Xist uses phase separation to perform its function

MAPK/MAK/MRK overlapping kinase (MOK) controls microglial inflammatory/type-I IFN responses via Brd4 and is involved in ALS

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease affecting motor neurons and characterized by microglia-mediated neurotoxic inflammation whose underlying mechanisms remain incompletely understood. In this work, we reveal that MAPK/MAK/MRK overlapping kinase (MOK), with an unknown physiological substrate, displays an immune function by controlling inflammatory and type-I interferon (IFN) responses in microglia which are detrimental to primary motor neurons. Moreover, we uncover the epigenetic reader bromodomain-containing protein 4 (Brd4) as an effector protein regulated by MOK, by promoting Ser-phospho-Brd4 levels. We further demonstrate that MOK regulates Brd4 functions by supporting its binding to cytokine gene promoters, therefore enabling innate immune responses. Remarkably, we show that MOK levels are increased in the ALS spinal cord, particularly in microglial cells, and that administration of a chemical MOK inhibitor to ALS model mice can modulate Ser-phospho-Brd4 levels, suppress microglial activation, and modify the disease course, indicating a pathophysiological role of MOK kinase in ALS and neuroinflammation.We are extremely grateful to Prof. Kevan Shokat and Dr. Flora Rutaganira, University of California, San Francisco (San Francisco, USA) for kindly supplying us with C13 compound. We thank patients’ associations (Saca la lengua a la ELA, Juntos contra la ELA, and Reto Todos Unidos/Miquel Valls Fundació Catalana D’ELA) for supporting the PAIDI Research Group (CTS-0160) global mission. Financial support to C.R. was provided by the Spanish Ministry of Economy (RTI2018-098432-B-I00), Fundación Ramón Areces (CIVP19A5938), the Andalusian Regional Government-FEDER (US-1265227), and I+D PAIDI (PY20-01097). Funding to D.P. was given by the PAIDI research group (CTS-0160) and Regional Ministry of Health (PI-0232-2022). V.C-G. is supported by the Institute of Health Carlos III, Spain, cofunded by the European Social Fund (CP19/00046). S.M. was supported by Generalitat Valenciana (GVA: Prometeo/2018/041 and CIPROM/2021/018); the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the Spanish State Research Agency (AEI) cofunded by European Regional Development Fund (ERDF), European Union (PID2020-118171RB-100); and Instituto de Salud Carlos III (RD16/001/0010, cofunded by ERDF)

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

Computational characterization of protein-RNA interactions and implications for phase separation

Despite what was previously considered, the role of RNA is not only to carry the genetic information from DNA to proteins. Indeed, RNA has proven to be implicated in more complex cellular processes. Recent evidence suggests that transcripts have a regulatory role on gene expression and contribute to the spatial and temporal organization of the intracellular environment. They do so by interacting with RNA-binding proteins (RBPs) to form complex ribonucleoprotein (RNP) networks, however the key determinants that govern the formation of these complexes are still not well understood. In this work, I will describe algorithms that I developed to estimate the ability of RNAs to interact with proteins. Additionally, I will illustrate applications of computational methods to propose an alternative model for the function of Xist lncRNA and its protein network. Finally, I will show how computational predictions can be integrated with high throughput approaches to elucidate the relationship between the structure of the RNA and its ability to interact with proteins. I conclude by discussing open questions and future opportunities for computational analysis of cell’s regulatory network. Overall, the underlying goal of my work is to provide biologists with new insights into the functional association between RNAs and proteins as well as with sophisticated tools that will facilitate their investigation on the formation of RNP complexesA pesar de lo que se consideraba anteriormente, el papel del ARN no es solo transportar la información genética del ADN a las proteínas. De hecho, el ARN ha demostrado estar implicado en muchos procesos celulares más complejos. La evidencia reciente sugiere que los transcriptos tienen un papel regulador en la expresión génica y contribuyen a la organización espacial y temporal del entorno intracelular. Lo hacen interactuando con proteínas de unión a ARN (RBP) para formar redes complejas de ribonucleoproteína (RNP), sin embargo, los determinantes clave que rigen la formación de estos complejos aún no se conocen bien. En este trabajo, describiré algoritmos que he desarrollado para estimar la capacidad de los ARN de interactuar con las proteínas. Además, ilustraré aplicaciones de métodos computacionales para proponer una maquinaria alternativa para el Xist lncRNA y su red de interacciones. Finalmente, mostraré cómo las predicciones computacionales pueden integrarse con enfoques de alto rendimiento para dilucidar la relación entre la estructura del ARN y su capacidad para interactuar con las proteínas. Concluyo discutiendo preguntas abiertas y oportunidades futuras para el análisis computacional de la red reguladora de la célula. En general, el objetivo subyacente de mi trabajo es proporcionar a los biólogos nuevas ideas sobre la asociación funcional entre ARN y proteínas, así como herramientas sofisticadas que facilitarán su investigación sobre la formación de complejos RNP

Computational characterization of protein-RNA interactions and implications for phase separation

Despite what was previously considered, the role of RNA is not only to carry the genetic information from DNA to proteins. Indeed, RNA has proven to be implicated in more complex cellular processes. Recent evidence suggests that transcripts have a regulatory role on gene expression and contribute to the spatial and temporal organization of the intracellular environment. They do so by interacting with RNA-binding proteins (RBPs) to form complex ribonucleoprotein (RNP) networks, however the key determinants that govern the formation of these complexes are still not well understood. In this work, I will describe algorithms that I developed to estimate the ability of RNAs to interact with proteins. Additionally, I will illustrate applications of computational methods to propose an alternative model for the function of Xist lncRNA and its protein network. Finally, I will show how computational predictions can be integrated with high throughput approaches to elucidate the relationship between the structure of the RNA and its ability to interact with proteins. I conclude by discussing open questions and future opportunities for computational analysis of cell’s regulatory network. Overall, the underlying goal of my work is to provide biologists with new insights into the functional association between RNAs and proteins as well as with sophisticated tools that will facilitate their investigation on the formation of RNP complexesA pesar de lo que se consideraba anteriormente, el papel del ARN no es solo transportar la información genética del ADN a las proteínas. De hecho, el ARN ha demostrado estar implicado en muchos procesos celulares más complejos. La evidencia reciente sugiere que los transcriptos tienen un papel regulador en la expresión génica y contribuyen a la organización espacial y temporal del entorno intracelular. Lo hacen interactuando con proteínas de unión a ARN (RBP) para formar redes complejas de ribonucleoproteína (RNP), sin embargo, los determinantes clave que rigen la formación de estos complejos aún no se conocen bien. En este trabajo, describiré algoritmos que he desarrollado para estimar la capacidad de los ARN de interactuar con las proteínas. Además, ilustraré aplicaciones de métodos computacionales para proponer una maquinaria alternativa para el Xist lncRNA y su red de interacciones. Finalmente, mostraré cómo las predicciones computacionales pueden integrarse con enfoques de alto rendimiento para dilucidar la relación entre la estructura del ARN y su capacidad para interactuar con las proteínas. Concluyo discutiendo preguntas abiertas y oportunidades futuras para el análisis computacional de la red reguladora de la célula. En general, el objetivo subyacente de mi trabajo es proporcionar a los biólogos nuevas ideas sobre la asociación funcional entre ARN y proteínas, así como herramientas sofisticadas que facilitarán su investigación sobre la formación de complejos RNP

RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data

Protein-RNA interactions are implicated in a number of physiological roles as well as diseases, with molecular mechanisms ranging from defects in RNA splicing, localization and translation to the formation of aggregates. Currently, ∼1400 human proteins have experimental evidence of RNA-binding activity. However, only ∼250 of these proteins currently have experimental data on their target RNAs from various sequencing-based methods such as eCLIP. To bridge this gap, we used an established, computationally expensive protein-RNA interaction prediction method, catRAPID, to populate a large database, RNAct. RNAct allows easy lookup of known and predicted interactions and enables global views of the human, mouse and yeast protein-RNA interactomes, expanding them in a genome-wide manner far beyond experimental data (http://rnact.crg.eu).European Research Council [RIBOMYLOME_309545]; European Union's Horizon 2020 research and innovation programme [727658, IASIS]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P, BFU2017-86970-P]; Spanish Ministry of Economy and Competitiveness; ‘Centro de Excelencia Severo Ochoa 2013-2017’; CERCA Programme of the Generalitat de Catalunya; Marie Sklodowska-Curie Individual Fellowship from the European Union's Horizon 2020 research and innovation programme (793135, ‘DeepRNA’ to B.L.). Funding for open access charge: European Research Council [RIBOMYLOME_309545], European Union [793135]

Aggregation is a context-dependent constraint on protein evolution

Solubility is a requirement for many cellular processes. Loss of solubility and aggregation can lead to the partial or complete abrogation of protein function. Thus, understanding the relationship between protein evolution and aggregation is an important goal. Here, we analysed two deep mutational scanning experiments to investigate the role of protein aggregation in molecular evolution. In one data set, mutants of a protein involved in RNA biogenesis and processing, human TAR DNA binding protein 43 (TDP-43), were expressed in S. cerevisiae. In the other data set, mutants of a bacterial enzyme that controls resistance to penicillins and cephalosporins, TEM-1 beta-lactamase, were expressed in E. coli under the selective pressure of an antibiotic treatment. We found that aggregation differentiates the effects of mutations in the two different cellular contexts. Specifically, aggregation was found to be associated with increased cell fitness in the case of TDP-43 mutations, as it protects the host from aberrant interactions. By contrast, in the case of TEM-1 beta-lactamase mutations, aggregation is linked to a decreased cell fitness due to inactivation of protein function. Our study shows that aggregation is an important context-dependent constraint of molecular evolution and opens up new avenues to investigate the role of aggregation in the cell.The research leading to these results was supported by the Dementia Research Institute (REI 3556 and AlzUK) (ARUK-PG2019B-020), European Research Council (RIBOMYLOME 309545 and ASTRA 855923), the H2020 projects (IASIS 727658 and INFORE 25080), the Spanish Ministry of Economy and Competitiveness BFU 2017-86970-P as well as the collaboration with Peter St. George-Hyslop financed by the Wellcome Trust

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

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