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

CROSSalive: a web server for predicting the in vivo structure of RNA molecules

MOTIVATION: RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. RESULTS: Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. AVAILABILITY AND IMPLEMENTATION: CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.The research leading to these results has received funding from European Research Council RIBOMYLOME_309545, European Union's Horizon 2020 IASIS_727658 and INFORE_825070, as well as Spanish Ministry of Economy and Competitiveness BFU2017-86970-

Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.The research leading to these results has been supported by European Research Council grant agreements RIBOMYLOME (309545) and ASTRA (855923), and the European Union’s Horizon 2020 research and innovation programme grant agreements IASIS (727658), DeepRNA (793135), and INFORE (825080). We would like to thank for the financial support the project Grant SysBioNet, Italian Roadmap Research Infrastructures 2012

RNA-protein interactions: central players in coordination of regulatory networks

Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration-dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post-transcriptional layer of gene regulation. We describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid-like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid-to-solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0.The research leading to these results has been supported by European Research Council (RIBOMYLOME_309545 and ASTRA_855923), the H2020 projects IASIS_727658 and INFORE_825080, the Spanish Ministry of Economy and Competitiveness BFU2017‐86970‐P, the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No 754490 within the MINDED project.We also acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Cataluny

A high-throughput approach to predict A-to-I effects on RNA structure indicates a change of double-stranded content in noncoding RNAs

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also noncoding RNAs. Here, we introduce the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic noncoding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD, and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data and catRAPID predictions. We found that A-to-I editing is linked to the alteration of interaction sites with proteins involved in phase separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive.The research leading to these results has been supported by European Research Council [RIBOMYLOME_309545 and ASTRA_855923], the H2020 projects [IASIS_727658 and INFORE_825080], the Spanish Ministry of Science and Innovation (RYC2019-026752-I, PID2020-117454RA-I00/AEI/10.13039/501100011033, and PID2020-114627RB-I00/AEI/10.13039/501100011033) and L'Oréal-UNESCO For Women in Science Programme 2021

Structural analysis of SARS-CoV-2 genome and predictions of the human interactome

Specific elements of viral genomes regulate interactions within host cells. Here, we calculated the secondary structure content of >2000 coronaviruses and computed >100 000 human protein interactions with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The genomic regions display different degrees of conservation. SARS-CoV-2 domain encompassing nucleotides 22 500-23 000 is conserved both at the sequence and structural level. The regions upstream and downstream, however, vary significantly. This part of the viral sequence codes for the Spike S protein that interacts with the human receptor angiotensin-converting enzyme 2 (ACE2). Thus, variability of Spike S is connected to different levels of viral entry in human cells within the population. Our predictions indicate that the 5' end of SARS-CoV-2 is highly structured and interacts with several human proteins. The binding proteins are involved in viral RNA processing, include double-stranded RNA specific editases and ATP-dependent RNA-helicases and have strong propensity to form stress granules and phase-separated assemblies. We propose that these proteins, also implicated in viral infections such as HIV, are selectively recruited by SARS-CoV-2 genome to alter transcriptional and post-transcriptional regulation of host cells and to promote viral replication.European Research Council [RIBOMYLOME_309545, ASTRA_855923]; H2020 projects [IASIS_727658 and INFORE_825080]; Spanish Ministry of Economy and Competitiveness [BFU2017-86970-P]; collaboration with Peter St. George-Hyslop financed by the Wellcome Trust. Funding for open access charge: ERC [ASTRA 855923]

RNA-binding and prion domains: the Yin and Yang of phase separation

Proteins and RNAs assemble in membrane-less organelles that organize intracellular spaces and regulate biochemical reactions. The ability of proteins and RNAs to form condensates is encoded in their sequences, yet it is unknown which domains drive the phase separation (PS) process and what are their specific roles. Here, we systematically investigated the human and yeast proteomes to find regions promoting condensation. Using advanced computational methods to predict the PS propensity of proteins, we designed a set of experiments to investigate the contributions of Prion-Like Domains (PrLDs) and RNA-binding domains (RBDs). We found that one PrLD is sufficient to drive PS, whereas multiple RBDs are needed to modulate the dynamics of the assemblies. In the case of stress granule protein Pub1 we show that the PrLD promotes sequestration of protein partners and the RBD confers liquid-like behaviour to the condensate. Our work sheds light on the fine interplay between RBDs and PrLD to regulate formation of membrane-less organelles, opening up the avenue for their manipulation.European Research Council (ERC) [RIBOMYLOME_309545 to G.G.T., ASTRA_855923 to G.G.T., METAMETA_311522 to R.M.V.]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P, BFU2017-86970-P]; H2020 Projects [IASIS_727658, INFORE_825080]; ‘Centro de Excelencia Severo Ochoa 2013-2017’; CERCA Programme/Generalitat de Catalunya (to EMBL partnership); Spanish Ministry for Science and Competitiveness (MINECO) (to EMBL partnership). Funding for open access charge: ERC [855923]

The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates

Biological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here, we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress granules, processing bodies and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein–protein, protein–RNA and RNA–RNA interactions. For proteins, it reports the liquid–liquid phase separation and liquid–solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates’ physical properties.This work was supported by the ERC [ASTRA_855923] and H2020 projects [IASIS_727658 and INFORE_825080]