Constitutive patterns of gene expression regulated by RNA-binding proteins

Prediction of protein-RNA interactions from single-cell transcriptomic data

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.

Exosomal circLPAR1 functions in colorectal cancer diagnosis and tumorigenesis through suppressing BRD4 via METTL3–eIF3h interaction

Background

Exosomes have emerged as vital biomarkers of multiple cancers and contain abundant circular RNAs (circRNAs). However, the potential for exosomal circRNAs to be used in diagnostics and their molecular mechanism of action in colorectal cancer (CRC) remain unclear.

Methods

CRC-specific exosomal circRNAs were identified by RNA sequencing, exoRBase database and a tissue microarray. The diagnostic performance of plasma exosomal circRNAs was evaluated among cancer-free controls, precancer individuals, CRC patients, and patients with other types of cancer. The corresponding biological functions were mainly assessed using circRNA pull-down, proteomic analysis, and RNA immunoprecipitation assay underlying cellular and mouse models.

Results

CircLPAR1 was encapsulated in exosomes with high stability and detectability, and its expression in plasma exosomes was remarkably decreased during CRC development but recovered after surgery. Exosomal circLPAR1 showed cancer specificity in CRC diagnosis and increased the diagnostic performance to an area under the receiver operating characteristic curve of 0.875, as determined by analysing its performance in combination with common clinical biomarkers CEA and CA19–9. Additionally, circLPAR1 was downregulated in CRC tissues and was associated with overall survival. Mechanistically, exosomal circLPAR1 was internalized by CRC cells, and it suppressed tumor growth, likely because exosomal circLPAR1 directly bound with eIF3h specifically suppressed the METTL3-eIF3h interaction, decreasing the translation of oncogene BRD4.

Conclusions

This comprehensive study highlights plasma exosomal circLPAR1 as a promising predictor in CRC diagnosis and describes its biological regulation of colorectal tumorigenesis. This study provides a new perspective on early diagnosis in the clinic and pathogenesis in disease development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-021-01471-y.

catRAPID omics v2.0: going deeper and wider in the prediction of protein-RNA interactions

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.

Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

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.

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.

NRF1-enhanced miR-4458 alleviates cardiac hypertrophy through releasing TTP-inhibited TFAM

Growing evidence suggests the crucial role of microRNAs (miRNAs) in regulating basic cell functions, and therefore participating in the pathologic development of diverse human diseases, including cardiac hypertrophy. Herein, we explained that miR-4458 was distinctly stimulated in Ang II-stimulated hypertrophic H9c2 cells. Intriguingly, miR-4458 inhibition led to exacerbated hypertrophic phenotypes in Ang II-treated H9c2 cells. In addition, the compensatory upregulation of miR-4458 in Ang II-treated H9c2 cells was ascribed to its transcriptional enhancement by NRF1, a transcription factor previously identified to be activated in early cardiac hypertrophy. Moreover, we discovered that miR-4458 served as a negative modulator in cardiac hypertrophy by prompting TFAM, a well-recognized myocardial protective protein. TTP, a RBP that always leads to degradation of recognized mRNAs, was predicted to interact with both miR-4458 and TFAM mRNA. Importantly, we verified that miR-4458 facilitated TFAM expression in cardiomyocytes by directly targeting TTP and releasing TTP-destabilized TFAM mRNA. On the whole, these findings demonstrated that NRF1-induced miR-4458 boosted TFAM via targeting TTP to dampen the exacerbation of cardiac hypertrophy, which indicates miR-4458 as a promising biomarker for the cardiac hypertrophy treatment.

RNA structure drives interaction with proteins

The combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet, the molecular determinants governing interactions in protein-RNA networks are not well understood. Here we investigated the relationship between the structure of an RNA and its ability to interact with proteins. Analysing in silico, in vitro and in vivo experiments, we find that the amount of double-stranded regions in an RNA correlates with the number of protein contacts. This relationship -which we call structure-driven protein interactivity- allows classification of RNA types, plays a role in gene regulation and could have implications for the formation of phase-separated ribonucleoprotein assemblies. We validate our hypothesis by showing that a highly structured RNA can rearrange the composition of a protein aggregate. We report that the tendency of proteins to phase-separate is reduced by interactions with specific RNAs.

RNA-protein interactions: disorder, moonlighting and junk contribute to eukaryotic complexity

RNA-protein interactions are crucial for most biological processes in all organisms. However, it appears that the complexity of RNA-based regulation increases with the complexity of the organism, creating additional regulatory circuits, the scope of which is only now being revealed. It is becoming apparent that previously unappreciated features, such as disordered structural regions in proteins or non-coding regions in DNA leading to higher plasticity and pliability in RNA-protein complexes, are in fact essential for complex, precise and fine-tuned regulation. This review addresses the issue of the role of RNA-protein interactions in generating eukaryotic complexity, focusing on the newly characterized disordered RNA-binding motifs, moonlighting of metabolic enzymes, RNA-binding proteins interactions with different RNA species and their participation in regulatory networks of higher order.

Post-transcriptional regulatory patterns revealed by protein-RNA interactions

The coordination of the synthesis of functionally-related proteins can be achieved at the post-transcriptional level by the action of common regulatory molecules, such as RNA–binding proteins (RBPs). Despite advances in the genome-wide identification of RBPs and their binding transcripts, the protein–RNA interaction space is still largely unexplored, thus hindering a broader understanding of the extent of the post-transcriptional regulation of related coding RNAs. Here, we propose a computational approach that combines protein–mRNA interaction networks and statistical analyses to provide an inferred regulatory landscape for more than 800 human RBPs and identify the cellular processes that can be regulated at the post-transcriptional level. We show that 10% of the tested sets of functionally-related mRNAs can be post-transcriptionally regulated. Moreover, we propose a classification of (i) the RBPs and (ii) the functionally-related mRNAs, based on their distinct behaviors in the functional landscape, hinting towards mechanistic regulatory hypotheses. In addition, we demonstrate the usefulness of the inferred functional landscape to investigate the cellular role of both well-characterized and novel RBPs in the context of human diseases.

An exact test for comparing a fixed quantitative property between gene sets

Motivation

A significant difference in the distribution of a feature between two gene sets can provide insight into function or regulation. This statistical setting differs from much of hypothesis testing theory because the genome is often considered to be effectively fixed, finite and entirely known in commonly studied organisms, such as human. The Mann-Whitney U test is commonly employed in this scenario despite the assumptions of the test not being met, leading to unreliable and generally underpowered results. Permutation tests are also commonly employed for this purpose, but are computationally burdensome and are not tractable for obtaining small P values or for multiple comparisons.

Results

We present an exact test for the null hypothesis that gene set membership is independent of the quantitative gene feature of interest. We derive an analytic expression for the randomization distribution of the median of the quantitative feature under the null hypothesis. Efficient implementation permits calculation of precise P values of arbitrary magnitude and makes thousands of simultaneous tests of transcriptome-sized gene sets computationally tractable. The flexibility of the hypothesis testing framework presented permits extension to a variety of related tests commonly found in genomics. The exact test is used to identify signatures of translation control and protein function in the human genome.

Availability and implementation

The exact test presented here is implemented in R in the package kpmt available on CRAN.

Contact

map2085@med.cornell.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genomic and regulatory characteristics of significant transcription factors in colorectal cancer metastasis

The dysregulation of transcription factors has an important impact on the oncogenesis and tumor progression. Nonetheless, its functions in colorectal cancer metastasis are still unclear. In this study, four transcription factors (HNF4A, HSF1, MECP2 and RAD21) were demonstrated to be associated with the metastasis of colorectal cancer in both RNA and protein levels. To comprehensively explore the intrinsic mechanisms, we profiled the molecular landscape of these metastasis-related transcription factors from multiple perspectives. In particular, as the crucial factors affecting genome stability, both copy number variation and DNA methylation exerted their strengths on the expression of these transcription factors (except MECP2). Additionally, based on a series of bioinformatics analyses, putative long non-coding RNAs were identified as functional regulators. Besides that, rely on the ATAC-Seq and ChIP-Seq profiles, we detected the target genes regulated by each transcription factor in the active chromatin zones. Finally, we inferred the associations between the target genes by Bayesian networks and identified LMO7 and ARL8A as potential clinical biomarkers. Taken together, our research systematically characterized the regulatory cascades of HNF4A, HSF1, MECP2 and RAD21 in colorectal cancer metastasis.

Discovering the 3' UTR-mediated regulation of alpha-synuclein

Recent evidence indicates a link between Parkinson's Disease (PD) and the expression of a-synuclein (SNCA) isoforms with different 3′ untranslated regions (3′UTRs). Yet, the post-transcriptional mechanisms regulating SNCA expression are unknown. Using a large-scale in vitro /in silico screening we identified RNA-binding proteins (RBPs) that interact with SNCA 3′ UTRs. We identified two RBPs, ELAVL1 and TIAR, that bind with high affinity to the most abundant and translationally active 3′ UTR isoform (575 nt). Knockdown and overexpression experiments indicate that both ELAVL1 and TIAR positively regulate endogenous SNCA in vivo. The mechanism of regulation implies mRNA stabilization as well as enhancement of translation in the case of TIAR. We observed significant alteration of both TIAR and ELAVL1 expression in motor cortex of post-mortem brain donors and primary cultured fibroblast from patients affected by PD and Multiple System Atrophy (MSA). Moreover, trans expression quantitative trait loci (trans-eQTLs) analysis revealed that a group of single nucleotide polymorphisms (SNPs) in TIAR genomic locus influences SNCA expression in two different brain areas, nucleus accumbens and hippocampus. Our study sheds light on the 3′ UTR-mediated regulation of SNCA and its link with PD pathogenesis, thus opening up new avenues for investigation of post-transcriptional mechanisms in neurodegeneration.

Whole-transcriptome microarray analysis reveals regulation of Rab4 by RBM5 in neurons

RNA binding motif 5 (RBM5) is a nuclear protein that modulates gene transcription and mRNA splicing in cancer cells. The brain is among the highest RBM5-expressing organ in the body but its mRNA target(s) or functions in the CNS have not been elucidated. Here we knocked down (KO) RBM5 in primary rat cortical neurons and analyzed total RNA extracts by gene microarray vs. neurons transduced with lentivirus to deliver control (non-targeting) shRNA. The mRNA levels of Sec23A (involved in ER-Golgi transport) and the small GTPase Rab4a (involved in endocytosis/protein trafficking) were increased in RBM5 KO neurons relative to controls. At the protein level, only Rab4a was significantly increased in RBM5 KO extracts. Also, elevated Rab4a levels in KO neurons were associated with decreased membrane levels of oligomeric serotonin transporters (SERT). Finally, RBM5 KO was associated with increased uptake of membrane-derived monomeric SERT.

SIGNIFICANCE

Rab4a is involved in the regulation of endocytosis and protein trafficking in cells. In the CNS it regulates diverse neurobiological functions including (but not limited to) trafficking of transmembrane proteins involved in neurotransmission (e.g. SERT), maintaining dendritic spine size, promoting axonal growth, and modulating cognition. Our findings suggest that RBM5 regulates Rab4a in rat neurons.

The hnRNP RALY regulates transcription and cell proliferation by modulating the expression of specific factors including the proliferation marker E2F1

The heterogeneous nuclear ribonucleoproteins (hnRNP) form a large family of RNA-binding proteins that exert numerous functions in RNA metabolism. RALY is a member of the hnRNP family that binds poly-U-rich elements within several RNAs and regulates the expression of specific transcripts. RALY is up-regulated in different types of cancer, and its down-regulation impairs cell cycle progression. However, the RALY's role in regulating RNA levels remains elusive. Here, we show that numerous genes coding for factors involved in transcription and cell cycle regulation exhibit an altered expression in RALY-down-regulated HeLa cells, consequently causing impairments in transcription, cell proliferation, and cell cycle progression. Interestingly, by comparing the list of RALY targets with the list of genes affected by RALY down-regulation, we found an enrichment of RALY mRNA targets in the down-regulated genes upon RALY silencing. The affected genes include the E2F transcription factor family. Given its role as proliferation-promoting transcription factor, we focused on E2F1. We demonstrate that E2F1 mRNA stability and E2F1 protein levels are reduced in cells lacking RALY expression. Finally, we also show that RALY interacts with transcriptionally active chromatin in both an RNA-dependent and -independent manner and that this association is abolished in the absence of active transcription. Taken together, our results highlight the importance of RALY as an indirect regulator of transcription and cell cycle progression through the regulation of specific mRNA targets, thus strengthening the possibility of a direct gene expression regulation exerted by RALY.

Computational Prediction of RNA-Protein Interactions

Experimental methods for identifying protein(s) bound by a specific promoter-associated RNA (paRNA) of interest can be expensive, difficult, and time-consuming. This chapter describes a general computational framework for identifying potential binding partners in RNA-protein complexes or RNA-protein interaction networks. Protocols for using three web-based tools to predict RNA-protein interaction partners are outlined. Also, tables listing additional webservers and software tools for predicting RNA-protein interactions, as well as databases that contain valuable information about known RNA-protein complexes and recognition sites for RNA-binding proteins, are provided. Although only one of the tools described, lncPro, was designed expressly to identify proteins that bind long noncoding RNAs (including paRNAs), all three approaches can be applied to predict potential binding partners for both coding and noncoding RNAs (ncRNAs).

Advances in the characterization of RNA-binding proteins

From transcription, to transport, storage, and translation, RNA depends on association with different RNA-binding proteins (RBPs). Methods based on next-generation sequencing and protein mass-spectrometry have started to unveil genome-wide interactions of RBPs but many aspects still remain out of sight. How many of the binding sites identified in high-throughput screenings are functional? A number of computational methods have been developed to analyze experimental data and to obtain insights into the specificity of protein-RNA interactions. How can theoretical models be exploited to identify RBPs? In addition to oligomeric complexes, protein and RNA molecules can associate into granular assemblies whose physical properties are still poorly understood. What protein features promote granule formation and what effects do these assemblies have on cell function? Here, we describe the newest in silico, in vitro, and in vivo advances in the field of protein-RNA interactions. We also present the challenges that experimental and computational approaches will have to face in future studies. WIREs RNA 2016, 7:793-810. doi: 10.1002/wrna.1378 For further resources related to this article, please visit the WIREs website.

Non-random distribution of homo-repeats: links with biological functions and human diseases

The biological function of multiple repetitions of single amino acids, or homo-repeats, is largely unknown, but their occurrence in proteins has been associated with more than 20 hereditary diseases. Analysing 122 bacterial and eukaryotic genomes, we observed that the number of proteins containing homo-repeats is significantly larger than expected from theoretical estimates. Analysis of statistical significance indicates that the minimal size of homo-repeats varies with amino acid type and proteome. In an attempt to characterize proteins harbouring long homo-repeats, we found that those containing polar or small amino acids S, P, H, E, D, K, Q and N are enriched in structural disorder as well as protein- and RNA-interactions. We observed that E, S, Q, G, L, P, D, A and H homo-repeats are strongly linked with occurrence in human diseases. Moreover, S, E, P, A, Q, D and T homo-repeats are significantly enriched in neuronal proteins associated with autism and other disorders. We release a webserver for further exploration of homo-repeats occurrence in human pathology at http://bioinfo.protres.ru/hradis/.

Dissecting the expression relationships between RNA-binding proteins and their cognate targets in eukaryotic post-transcriptional regulatory networks

RNA-binding proteins (RBPs) are pivotal in orchestrating several steps in the metabolism of RNA in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Here, we employed CLIP (cross-linking immunoprecipitation)-seq datasets for 60 human RBPs and RIP-ChIP (RNP immunoprecipitation-microarray) data for 69 yeast RBPs to construct a network of genome-wide RBP- target RNA interactions for each RBP. We show in humans that majority (~78%) of the RBPs are strongly associated with their target transcripts at transcript level while ~95% of the studied RBPs were also found to be strongly associated with expression levels of target transcripts when protein expression levels of RBPs were employed. At transcript level, RBP - RNA interaction data for the yeast genome, exhibited a strong association for 63% of the RBPs, confirming the association to be conserved across large phylogenetic distances. Analysis to uncover the features contributing to these associations revealed the number of target transcripts and length of the selected protein-coding transcript of an RBP at the transcript level while intensity of the CLIP signal, number of RNA-Binding domains, location of the binding site on the transcript, to be significant at the protein level. Our analysis will contribute to improved modelling and prediction of post-transcriptional networks.

A synthetic gene circuit for measuring autoregulatory feedback control

Autoregulatory feedback loops occur in the regulation of molecules ranging from ATP to MAP kinases to zinc. Negative feedback loops can increase a system's robustness, while positive feedback loops can mediate transitions between cell states. Recent genome-wide experimental and computational studies predict hundreds of novel feedback loops. However, not all physical interactions are regulatory, and many experimental methods cannot detect self-interactions. Our understanding of regulatory feedback loops is therefore hampered by the lack of high-throughput methods to experimentally quantify the presence, strength and temporal dynamics of autoregulatory feedback loops. Here we present a mathematical and experimental framework for high-throughput quantification of feedback regulation and apply it to RNA binding proteins (RBPs) in yeast. Our method is able to determine the existence of both direct and indirect positive and negative feedback loops, and to quantify the strength of these loops. We experimentally validate our model using two RBPs which lack native feedback loops and by the introduction of synthetic feedback loops. We find that RBP Puf3 does not natively participate in any direct or indirect feedback regulation, but that replacing the native 3'UTR with that of COX17 generates an auto-regulatory negative feedback loop which reduces gene expression noise. Likewise, RBP Pub1 does not natively participate in any feedback loops, but a synthetic positive feedback loop involving Pub1 results in increased expression noise. Our results demonstrate a synthetic experimental system for quantifying the existence and strength of feedback loops using a combination of high-throughput experiments and mathematical modeling. This system will be of great use in measuring auto-regulatory feedback by RNA binding proteins, a regulatory motif that is difficult to quantify using existing high-throughput methods.

A Computational Approach for the Discovery of Protein-RNA Networks

Protein-RNA interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of genes to host defense against pathogens. In this chapter we present the computational approach catRAPID to predict protein-RNA interactions and discuss how it could be used to find trends in ribonucleoprotein networks. We envisage that the combination of computational and experimental approaches will be crucial to unravel the role of coding and noncoding RNAs in protein networks.

Protein aggregation, structural disorder and RNA-binding ability: a new approach for physico-chemical and gene ontology classification of multiple datasets

Background

Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities.

Results

We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis.

Conclusions

In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal. Each of the pages contains links to the corresponding documentation and tutorial.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2280-z) contains supplementary material, which is available to authorized users.

Neurodegeneration and Cancer: Where the Disorder Prevails

It has been reported that genes up-regulated in cancer are often down-regulated in neurodegenerative disorders and vice versa. The fact that apparently unrelated diseases share functional pathways suggests a link between their etiopathogenesis and the properties of molecules involved. Are there specific features that explain the exclusive association of proteins with either cancer or neurodegeneration? We performed a large-scale analysis of physico-chemical properties to understand what characteristics differentiate classes of diseases. We found that structural disorder significantly distinguishes proteins up-regulated in neurodegenerative diseases from those linked to cancer. We also observed high correlation between structural disorder and age of onset in Frontotemporal Dementia, Parkinson's and Alzheimer's diseases, which strongly supports the role of protein unfolding in neurodegenerative processes.

By the company they keep: interaction networks define the binding ability of transcription factors

Access to genome-wide data provides the opportunity to address questions concerning the ability of transcription factors (TFs) to assemble in distinct macromolecular complexes. Here, we introduce the PAnDA (Protein And DNA Associations) approach to characterize DNA associations with human TFs using expression profiles, protein–protein interactions and recognition motifs. Our method predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89). PAnDA is freely available at http://service.tartaglialab.com/new_submission/panda.

A census of human RNA-binding proteins

Post-transcriptional gene regulation (PTGR) concerns processes involved in the maturation, transport, stability and translation of coding and non-coding RNAs. RNA-binding proteins (RBPs) and ribonucleoproteins coordinate RNA processing and PTGR. The introduction of large-scale quantitative methods, such as next-generation sequencing and modern protein mass spectrometry, has renewed interest in the investigation of PTGR and the protein factors involved at a systems-biology level. Here, we present a census of 1,542 manually curated RBPs that we have analysed for their interactions with different classes of RNA, their evolutionary conservation, their abundance and their tissue-specific expression. Our analysis is a critical step towards the comprehensive characterization of proteins involved in human RNA metabolism.

The cleverSuite approach for protein characterization: predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities

Motivation: The recent shift towards high-throughput screening is posing new challenges for the interpretation of experimental results. Here we propose the cleverSuite approach for large-scale characterization of protein groups.

Description: The central part of the cleverSuite is the cleverMachine (CM), an algorithm that performs statistics on protein sequences by comparing their physico-chemical propensities. The second element is called cleverClassifier and builds on top of the models generated by the CM to allow classification of new datasets.

Results: We applied the cleverSuite to predict secondary structure properties, solubility, chaperone requirements and RNA-binding abilities. Using cross-validation and independent datasets, the cleverSuite reproduces experimental findings with great accuracy and provides models that can be used for future investigations.

Availability: The intuitive interface for dataset exploration, analysis and prediction is available at http://s.tartaglialab.com/clever_suite.

Contact:gian.tartaglia@crg.es

Supplementary information:Supplementary data are available at Bioinformatics online.

Of RNA-binding proteins and their targets: interaction determines expression

Combining the prediction of interactions between mRNAs and RNA-binding proteins with experimental expression profiles uncovers novel regulatory paradigms concerning proliferation and differentiation processes.

See related research, http://genomebiology.com/2014/15/1/R13

Discovery of protein–RNA networks

We review the latest advances and future challenges in experimental and computational investigation of protein–RNA networks.