Abstract
Prediction of protein–RNA interactions is important to understand post-transcriptional events taking place in the cell. Here we introduce catRAPID omics v2.0, an update of our web server ...dedicated to the computation of protein–RNA interaction propensities at the transcriptome- and RNA-binding proteome-level in 8 model organisms. The server accepts multiple input protein or RNA sequences and computes their catRAPID interaction scores on updated precompiled libraries. Additionally, it is now possible to predict the interactions between a custom protein set and a custom RNA set. Considerable effort has been put into the generation of a new database of RNA-binding motifs that are searched within the predicted RNA targets of proteins. In this update, the sequence fragmentation scheme of the catRAPID fragment module has been included, which allows the server to handle long linear RNAs and to analyse circular RNAs. For the top-scoring protein–RNA pairs, the web server shows the predicted binding sites in both protein and RNA sequences and reports whether the predicted interactions are conserved in orthologous protein–RNA pairs. The catRAPID omics v2.0 web server is a powerful tool for the characterization and classification of RNA-protein interactions and is freely available at http://service.tartaglialab.com/page/catrapid_omics2_group along with documentation and tutorial.
Graphical Abstract
Graphical Abstract
catRAPID omicsv2.0 calculates protein–RNA interaction propensities between user submitted sequences and proteomes/transcriptomes. Transcripts are also scanned for known RNA-binding motifs. For top-scoring pairs, interaction maps and evolutionary conservation analyses are provided.
Multiple human diseases are associated with a liquid-to-solid phase transition resulting in the formation of amyloid fibers or protein aggregates. Here, we present an alternative mechanism for ...cellular toxicity based on a concentration-dependent liquid-liquid demixing. Analyzing proteins that are toxic when their concentration is increased in yeast reveals that they share physicochemical properties with proteins that participate in physiological liquid-liquid demixing in the cell. Increasing the concentration of one of these proteins indeed results in the formation of cytoplasmic foci with liquid properties. Demixing occurs at the onset of toxicity and titrates proteins and mRNAs from the cytoplasm. Focus formation is reversible, and resumption of growth occurs as the foci dissolve as protein concentration falls. Preventing demixing abolishes the dosage sensitivity of the protein. We propose that triggering inappropriate liquid phase separation may be an important cause of dosage sensitivity and a determinant of human disease.
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•Dosage-sensitive proteins in yeast have a high propensity for liquid-liquid demixing•Increased protein concentration can force a liquid phase separation, titrating proteins and RNAs from the cytoplasm•Preventing liquid-liquid demixing averts dosage sensitivity•Inappropriate liquid phase separation may be a determinant of human genetic disease
Bolognesi et al. report that proteins that are harmful when overexpressed have properties associated with liquid-liquid demixing and that increased protein concentration can force a liquid phase transition causing cellular toxicity.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The maintenance of protein solubility is a fundamental aspect of cellular homeostasis because protein aggregation is associated with a wide variety of human diseases. Numerous proteins unrelated in ...sequence and structure, however, can misfold and aggregate, and widespread aggregation can occur in living systems under stress or aging. A crucial question in this context is why only certain proteins appear to aggregate readily in vivo, whereas others do not. We identify here the proteins most vulnerable to aggregation as those whose cellular concentrations are high relative to their solubilities. We find that these supersaturated proteins represent a metastable subproteome involved in pathological aggregation during stress and aging and are overrepresented in biochemical processes associated with neurodegenerative disorders. Consequently, such cellular processes become dysfunctional when the ability to keep intrinsically supersaturated proteins soluble is compromised. Thus, the simultaneous analysis of abundance and solubility can rationalize the diverse cellular pathologies linked to neurodegenerative diseases and aging.
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Protein supersaturation is an intrinsic aspect of protein homeostasisSupersaturated proteins form a metastable subproteomeSupersaturated proteins are overrepresented in neurodegenerative pathwaysSupersaturated proteins undergo aggregation upon cellular stress or aging
Widespread protein aggregation can occur in disease, stress, and aging, but it is unclear why only certain proteins aggregate, whereas others do not. Morimoto, Dobson, Vendruscolo, and colleagues identify the proteins most vulnerable to aggregation as those whose cellular concentrations are high relative to their solubilities. These supersaturated proteins form a metastable subproteome involved in pathological aggregation and are overrepresented in biochemical processes associated with neurodegeneration. Consequently, such processes become dysfunctional when the ability to keep supersaturated proteins soluble is compromised.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Here we introduce catRAPID omics, a server for large-scale calculations of protein-RNA interactions. Our web server allows (i) predictions at proteomic and transcriptomic level; (ii) use of protein ...and RNA sequences without size restriction; (iii) analysis of nucleic acid binding regions in proteins; and (iv) detection of RNA motifs involved in protein recognition.
We developed a web server to allow fast calculation of ribonucleoprotein associations in Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae and Xenopus tropicalis (custom libraries can be also generated). The catRAPID omics was benchmarked on the recently published RNA interactomes of Serine/arginine-rich splicing factor 1 (SRSF1), Histone-lysine N-methyltransferase EZH2 (EZH2), TAR DNA-binding protein 43 (TDP43) and RNA-binding protein FUS (FUS) as well as on the protein interactomes of U1/U2 small nucleolar RNAs, X inactive specific transcript (Xist) repeat A region (RepA) and Crumbs homolog 3 (CRB3) 3'-untranslated region RNAs. Our predictions are highly significant (P < 0.05) and will help the experimentalist to identify candidates for further validation.
catRAPID omics can be freely accessed on the Web at http://s.tartaglialab.com/catrapid/omics. Documentation, tutorial and FAQs are available at http://s.tartaglialab.com/page/catrapid_group.
Insoluble protein aggregates are the hallmarks of many neurodegenerative diseases. For example, aggregates of TDP-43 occur in nearly all cases of amyotrophic lateral sclerosis (ALS). However, whether ...aggregates cause cellular toxicity is still not clear, even in simpler cellular systems. We reasoned that deep mutagenesis might be a powerful approach to disentangle the relationship between aggregation and toxicity. We generated >50,000 mutations in the prion-like domain (PRD) of TDP-43 and quantified their toxicity in yeast cells. Surprisingly, mutations that increase hydrophobicity and aggregation strongly decrease toxicity. In contrast, toxic variants promote the formation of dynamic liquid-like condensates. Mutations have their strongest effects in a hotspot that genetic interactions reveal to be structured in vivo, illustrating how mutagenesis can probe the in vivo structures of unstructured proteins. Our results show that aggregation of TDP-43 is not harmful but protects cells, most likely by titrating the protein away from a toxic liquid-like phase.
Protein aggregation causes many devastating neurological and systemic diseases and represents a major problem in the preparation of recombinant proteins in biotechnology. Major advances in ...understanding the causes of this phenomenon have been made through the realisation that the analysis of the physico-chemical characteristics of the amino acids can provide accurate predictions about the rates of growth of the misfolded assemblies and the specific regions of the sequences that promote aggregation. More recently it has also been shown that the toxicity in vivo of protein aggregates can be predicted by estimating the propensity of polypeptide chains to form protofibrillar assemblies. In this tutorial review we describe the development of these predictions made through the Zyggregator method and the applications that have been explored so far.
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.
Cellular chaperone networks prevent potentially toxic protein aggregation and ensure proteome integrity. Here, we used Escherichia coli as a model to understand the organization of these networks, ...focusing on the cooperation of the DnaK system with the upstream chaperone Trigger factor (TF) and the downstream GroEL. Quantitative proteomics revealed that DnaK interacts with at least ∼700 mostly cytosolic proteins, including ∼180 relatively aggregation-prone proteins that utilize DnaK extensively during and after initial folding. Upon deletion of TF, DnaK interacts increasingly with ribosomal and other small, basic proteins, while its association with large multidomain proteins is reduced. DnaK also functions prominently in stabilizing proteins for subsequent folding by GroEL. These proteins accumulate on DnaK upon GroEL depletion and are then degraded, thus defining DnaK as a central organizer of the chaperone network. Combined loss of DnaK and TF causes proteostasis collapse with disruption of GroEL function, defective ribosomal biogenesis, and extensive aggregation of large proteins.
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► The DnaK (Hsp70) chaperone interactome comprises ∼700–1000 different proteins ► About 180 proteins are highly enriched on DnaK and are relatively aggregation-prone ► Loss of Trigger factor or GroEL markedly alters the DnaK interactome ► Loss of DnaK and Trigger factor disrupts proteostasis and protein flux to GroEL
All cells possess chaperone networks that prevent protein aggregation and ensure proteome integrity. E. coli serves as a model for Hartl and colleagues in understanding the organization of these networks, focusing on the central role of the DnaK (Hsp70) chaperone system. Quantitative proteomics reveal that DnaK interacts with at least ∼700 mostly cytosolic proteins. Deletion of the upstream chaperone Trigger factor or depletion of the downstream GroEL markedly alters the DnaK interactome. These effects are highly informative as to the cooperativity of chaperone modules.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The mechanisms by which mutations in FUS and other RNA binding proteins cause ALS and FTD remain controversial. We propose a model in which low-complexity (LC) domains of FUS drive its ...physiologically reversible assembly into membrane-free, liquid droplet and hydrogel-like structures. ALS/FTD mutations in LC or non-LC domains induce further phase transition into poorly soluble fibrillar hydrogels distinct from conventional amyloids. These assemblies are necessary and sufficient for neurotoxicity in a C. elegans model of FUS-dependent neurodegeneration. They trap other ribonucleoprotein (RNP) granule components and disrupt RNP granule function. One consequence is impairment of new protein synthesis by cytoplasmic RNP granules in axon terminals, where RNP granules regulate local RNA metabolism and translation. Nuclear FUS granules may be similarly affected. Inhibiting formation of these fibrillar hydrogel assemblies mitigates neurotoxicity and suggests a potential therapeutic strategy that may also be applicable to ALS/FTD associated with mutations in other RNA binding proteins.
•FUS phase transitions between monomer, liquid droplet, and hydrogel states•FUS mutants induce further phase transition into irreversible fibrillar hydrogels•Irreversible hydrogels sequester RNP cargo and impair RNP granule function•Formation of non-amyloid fibrillar hydrogels provides a compelling causative mechanism for neurodegeneration
Murakami et al. show that FUS transitions between monomer, liquid droplet, and hydrogel states during uptake and release of RNP granule cargo. FUS mutations accelerate transition into fibrillar hydrogels that trap RNP cargo, impair RNP granule function, and cause neurodegeneration.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically ...restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP–RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor–target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.
Graphical Abstract
Graphical Abstract
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