Dissecting the expression dynamics of RNA-binding proteins in posttranscriptional regulatory networks. Proc Natl Acad Sci U S A. 2009;106:20300-20305. [PMID: 19918083 DOI: 10.1073/pnas.0906940106]

Unveiling the veil of RNA binding protein phase separation in cancer biology and therapy

RNA-binding protein (RBP) phase separation in oncology reveals a complex interplay crucial for understanding tumor biology and developing novel therapeutic strategies. Aberrant phase separation of RBPs significantly influences gene regulation, signal transduction, and metabolic reprogramming, contributing to tumorigenesis and drug resistance. Our review highlights the integral roles of RBP phase separation in stress granule dynamics, mRNA stabilization, and the modulation of transcriptional and translational processes. Furthermore, interactions between RBPs and non-coding RNAs add a layer of complexity, providing new insights into their collaborative roles in cancer progression. The intricate relationship between RBPs and phase separation poses significant challenges but also opens up novel opportunities for targeted therapeutic interventions. Advancing our understanding of the molecular mechanisms and regulatory networks governing RBP phase separation could lead to breakthroughs in cancer treatment strategies.

Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models

Ground-breaking progress has been made in structure prediction of biomolecular assemblies, including the recent breakthrough of AlphaFold 3. However, it remains challenging for AlphaFold 3 and other state-of-the-art deep learning-based methods to accurately predict protein-RNA complex structures, in part due to the limited availability of evolutionary and structural information related to protein-RNA interactions that are used as inputs to the existing approaches. Here, we introduce ProRNA3D-single, a new deep-learning framework for protein-RNA complex structure prediction with only single-sequence input. Using a novel geometric attention-enabled pairing of biological language models of protein and RNA, a previously unexplored avenue, ProRNA3D-single enables the prediction of interatomic protein-RNA interaction maps, which are then transformed into multi-scale geometric restraints for modeling 3D structures of protein-RNA complexes via geometry optimization. Benchmark tests show that ProRNA3D-single convincingly outperforms current state-of-the-art methods including AlphaFold 3, particularly when evolutionary information is limited; and exhibits remarkable robustness and performance resilience by attaining better accuracy with only single-sequence input than what most methods can achieve even with explicit evolutionary information. Freely available at https://github.com/Bhattacharya-Lab/ProRNA3D-single, ProRNA3D-single should be broadly useful for modeling 3D structures of protein-RNA complexes at scale, regardless of the availability of evolutionary information.

PUF-8, a C. elegans ortholog of the RNA-binding proteins PUM1 and PUM2, is required for robustness of the cell death fate

During C. elegans development, 1090 somatic cells are generated, of which 959 survive and 131 die, many through apoptosis. We present evidence that PUF-8, a C. elegans ortholog of the mammalian RNA-binding proteins PUM1 and PUM2, is required for the robustness of this 'survival and death' pattern. We found that PUF-8 prevents the inappropriate death of cells that normally survive, and we present evidence that this anti-apoptotic activity of PUF-8 is dependent on the ability of PUF-8 to interact with ced-3 (a C. elegans ortholog of caspase) mRNA, thereby repressing the activity of the pro-apoptotic ced-3 gene. PUF-8 also promotes the death of cells that are programmed to die, and we propose that this pro-apoptotic activity of PUF-8 may depend on the ability of PUF-8 to repress the expression of the anti-apoptotic ced-9 gene (a C. elegans ortholog of Bcl2). Our results suggest that stochastic differences in the expression of genes within the apoptosis pathway can disrupt the highly reproducible and robust survival and death pattern during C. elegans development, and that PUF-8 acts at the post-transcriptional level to level out these differences, thereby ensuring proper cell number homeostasis.

Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores

Metabolic stability of proteins plays a vital role in various dedicated cellular processes. Traditional methods of measuring the metabolic stability are time-consuming and expensive. Therefore, we developed a more efficient computational approach to understand the protein dynamic action mechanisms in biological process networks. In this study, we collected 341 short-lived proteins and 824 non-short-lived proteins from U2OS; 342 short-lived proteins and 821 non-short-lived proteins from HEK293T; 424 short-lived proteins and 1153 non-short-lived proteins from HCT116; and 384 short-lived proteins and 992 non-short-lived proteins from RPE1. The proteins were encoded by GO and KEGG enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. We also incorporated the protein interaction information from STRING into the features and obtained 19,247 node features. Boruta and mRMR methods were used for feature filtering, and IFS method was used to obtain the best feature subsets and create the models with the highest performance. The present study identified 42 features that did not appear in previous studies and classified them into eight groups according to their functional annotation. By reviewing the literature, we found that the following three functional groups were critical in determining the stability of proteins: synaptic transmission, post-translational modifications, and cell fate determination. These findings may serve as a valuable reference for developing drugs that target protein stability.

Evaluating native-like structures of RNA-protein complexes through the deep learning method

RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.

Modular architecture and functional annotation of human RNA-binding proteins containing RNA recognition motif

RNA-binding proteins (RBPs) are structurally and functionally diverse macromolecules with significant involvement in several post-transcriptional gene regulatory processes and human diseases. RNA recognition motif (RRM) is one of the most abundant RNA-binding domains in human RBPs. The unique modular architecture of each RBP containing RRM is crucial for its diverse target recognition and function. Genome-wide study of these structurally conserved and functionally diverse domains can enhance our understanding of their functional implications. In this study, modular architecture of RRM containing RBPs in human proteome is identified and systematically analysed. We observe that 30% of human RBPs with RNA-binding function contain RRM in single or multiple repeats or with other domains with maximum of six repeats. Zinc-fingers are the most frequently co-occurring domain partner of RRMs. Human RRM containing RBPs mostly belong to RNA metabolism class of proteins and are significantly enriched in two functional pathways including spliceosome and mRNA surveillance. Various human diseases are associated with 18% of the RRM containing RBPs. Single RRM containing RBPs are highly enriched in disorder regions. Gene ontology (GO) molecular functions including poly(A), poly(U) and miRNA binding are highly depleted in RBPs with single RRM, indicating the significance of modular nature of RRMs in specific function. The current study reports all the possible domain architectures of RRM containing human RBPs and their functional enrichment. The idea of domain architecture, and how they confer specificity and new functionalities to RBPs, can help in re-designing of modular RRM containing RBPs with re-engineered function.

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

Rationally designed inhibitors of the Musashi protein-RNA interaction by hotspot mimicry

RNA-binding proteins (RBPs) are key post-transcriptional regulators of gene expression, and thus underlie many important biological processes. Here, we developed a strategy that entails extracting a "hotspot pharmacophore" from the structure of a protein-RNA complex, to create a template for designing small-molecule inhibitors and for exploring the selectivity of the resulting inhibitors. We demonstrate this approach by designing inhibitors of Musashi proteins MSI1 and MSI2, key regulators of mRNA stability and translation that are upregulated in many cancers. We report this novel series of MSI1/MSI2 inhibitors is specific and active in biochemical, biophysical, and cellular assays. This study extends the paradigm of "hotspots" from protein-protein complexes to protein-RNA complexes, supports the "druggability" of RNA-binding protein surfaces, and represents one of the first rationally-designed inhibitors of non-enzymatic RNA-binding proteins. Owing to its simplicity and generality, we anticipate that this approach may also be used to develop inhibitors of many other RNA-binding proteins; we also consider the prospects of identifying potential off-target interactions by searching for other RBPs that recognize their cognate RNAs using similar interaction geometries. Beyond inhibitors, we also expect that compounds designed using this approach can serve as warheads for new PROTACs that selectively degrade RNA-binding proteins.

RBPmap: A Tool for Mapping and Predicting the Binding Sites of RNA-Binding Proteins Considering the Motif Environment

RNA-binding proteins (RBPs) play a key role in post-transcriptional regulation via binding to coding and non-coding RNAs. Recent development in experimental technologies, aimed to identify the targets of RBPs, has significantly broadened our knowledge on protein-RNA interactions. However, for many RBPs in many organisms and cell types, experimental RNA-binding data is not available. In this chapter we describe a computational approach, named RBPmap, available as a web service via http://rbpmap.technion.ac.il/ and as a stand-alone version for download. RBPmap was designed for mapping and predicting the binding sites of any RBP within a nucleic acid sequence, given the availability of an experimentally defined binding motif of the RBP. The algorithm searches for a sub-sequence that significantly matches the RBP motif, considering the clustering propensity of other weak matches within the motif environment. Here, we present different applications of RBPmap for discovering the involvement of RBPs and their targets in a variety of cellular processes, in health and disease states. Finally, we demonstrate the performance of RBPmap in predicting the binding targets of RBPs in large-scale RNA-binding data, reinforcing the strength of the tool in distinguishing cognate binding sites from weak motifs.

hnRNP A1B, a Splice Variant of HNRNPA1, Is Spatially and Temporally Regulated

RNA binding proteins (RBPs) play a key role in cellular growth, homoeostasis and survival and are tightly regulated. A deep understanding of their spatiotemporal regulation is needed to understand their contribution to physiology and pathology. Here, we have characterized the spatiotemporal expression pattern of hnRNP A1 and its splice variant hnRNP A1B in mice. We have found that hnRNP A1B expression is more restricted to the CNS compared to hnRNP A1, and that it can form an SDS-resistant dimer in the CNS. Also, hnRNP A1B expression becomes progressively restricted to motor neurons in the ventral horn of the spinal cord, compared to hnRNP A1 which is more broadly expressed. We also demonstrate that hnRNP A1B is present in neuronal processes, while hnRNP A1 is absent. This finding supports a hypothesis that hnRNP A1B may have a cytosolic function in neurons that is not shared with hnRNP A1. Our results demonstrate that both isoforms are differentially expressed across tissues and have distinct localization profiles, suggesting that the two isoforms may have specific subcellular functions that can uniquely contribute to disease progression.

PARP1-RNA interaction analysis: PARP1 regulates the expression of extracellular matrix-related genes in HK-2 renal proximal tubular epithelial cells

Recent studies suggest that Poly(ADP-ribose) polymerase 1 (PARP1) acts as an RNA-binding protein in a majority of renal diseases with tubular cell injury. However, detailed knowledge of RNA targets and the RNA-binding regions for PARP1 is unknown. Herein, mapping of iRIP-seq reads in HK-2 renal tubular epithelial cells showed a biased distribution at coding sequence (CDS) and intron regions that is specific to these cells. A total of 1708 differentially expressed genes were identified after PARP1 knockdown using RNA-seq. Furthermore, transcriptome analysis also showed that selective variable splicing was globally regulated by PARP1 in HK-2 cells. By comparison of PARP1 RNA-seq and iRIP-seq data, we found 68 overlapping genes that are enriched in 'extracellular matrix' pathway. Follow-up identification of their interactions may contribute vital insights into the regulatory role of PARP1 as an RNA-binding protein in HK-2 cells.

A New RBPs-Related Signature Predicts the Prognosis of Colon Adenocarcinoma Patients

The dysregulation of RNA binding proteins (RBPs) is closely related to tumorigenesis and development. However, the role of RBPs in Colon adenocarcinoma (COAD) is still poorly understood. We downloaded COAD’s RNASeq data from the Cancer Genome Atlas (TCGA) database, screened the differently expressed RBPs in normal tissues and tumor, and constructed a protein interaction network. COAD patients were randomly divided into a training set (N = 315) and a testing set (N = 132). In the training set, univariate Cox analysis identified 12 RBPs significantly related to the prognosis of COAD. By multivariate COX analysis, we constructed a prognostic model composed of five RBPs (CELF4, LRRFIP2, NOP14, PPARGC1A, ZNF385A) based on the lowest Akaike information criterion. Each COAD patient was scored according to the model formula. Further analysis showed that compared with the low-risk group, the overall survival rate (OS) of patients in the high-risk group was significantly lower. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve was 0.722 in the training group and 0.738 in the test group, which confirmed a good prediction feature. In addition, a nomogram was constructed based on clinicopathological characteristics and risk scores. C-index and calibration curve proved the accuracy in predicting the 1-, 3-, and 5-year survival rates of COAD patients. In short, we constructed a superior prognostic and diagnostic signature composed of five RBPs, which indicates new possibilities for individualized treatment of COAD patients.

Prediction of lncRNA-Protein Interactions via the Multiple Information Integration

The long non-coding RNA (lncRNA)-protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the prediction of lncRNA-protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence, which are important. Traditional experimental methods for detecting lncRNA-protein interactions are expensive and time-consuming. Therefore, computational methods provide many effective strategies to deal with this problem. In recent years, most computational methods only use the information of the lncRNA-lncRNA or the protein-protein similarity and cannot fully capture all features to identify their interactions. In this paper, we propose a novel computational model for the lncRNA-protein prediction on the basis of machine learning methods. First, a feature method is proposed for representing the information of the network topological properties of lncRNA and protein interactions. The basic composition feature information and evolutionary information based on protein, the lncRNA sequence feature information, and the lncRNA expression profile information are extracted. Finally, the above feature information is fused, and the optimized feature vector is used with the recursive feature elimination algorithm. The optimized feature vectors are input to the support vector machine (SVM) model. Experimental results show that the proposed method has good effectiveness and accuracy in the lncRNA-protein interaction prediction.

Upregulation of μ-Opioid Receptor in the Rat Spinal Cord Contributes to the α2-Adrenoceptor Agonist Dexmedetomidine-Induced Attenuation of Chronic Morphine Tolerance in Cancer Pain

Background

Sustained morphine treatment for cancer pain has been limited due to analgesic tolerance. Opioid receptor internalization and desensitization mediated by downregulation of mu-opioid receptor (MOR) expression have been confirmed as one of the mechanisms of chronic morphine tolerance. In addition to the opiate system, the α2-adrenergic system is involved in the development of morphine tolerance. Several studies reported that co-administration of α2-adrenoceptor agonist dexmedetomidine inhibits morphine tolerance in normal or neuropathic pain animals. However, the effect of dexmedetomidine on morphine tolerance has not been studied in cancer pain. Therefore, we investigated the effect of intrathecal injection of dexmedetomidine on the development of morphine tolerance in cancer pain and on the expression of MOR in the spinal cord of morphine-tolerant cancer pain rats.

Methods

The model was established using a rat’s right hind paw injection of Walker 256 cancer cells. Subcutaneous morphine (10mg/kg) was administrated twice daily for 7 days; meanwhile, the rats received intrathecal α2-adrenoceptor agonist dexmedetomidine (10μ/kg) or antagonist MK-467 (0.25mg/kg) in test groups. Rats receiving drug vehicle served as the control group. Antinociception was detected by von Frey filaments and hot-plate tests. The expression of MOR in the spinal cord was examined through real-time reverse transcription polymerase chain reaction and Western blotting. The data were analyzed via analysis of variance followed by Student t-test with Bonferroni correction.

Results

Seven-day chronic morphine administration elicited notable analgesic tolerance in the rats with cancer pain. Co-administration of α2-adrenoceptor agonist dexmedetomidine enhanced morphine analgesia and attenuated morphine tolerance, which could be blocked by α2-adrenoceptor antagonist MK-467. Furthermore, pre-treatment of dexmedetomidine significantly upregulated MOR protein expression without a notable change in MOR mRNA expression in the spinal cord.

Conclusion

Our findings suggest that intrathecal injection of dexmedetomidine enhanced morphine analgesia and attenuated morphine tolerance in cancer pain, potentially by upregulating MOR expression in the spinal cord. The α2-adrenoceptor agonist may provide a more versatile analgesia option for morphine treatment for cancer pain.

PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions

Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol⁻¹, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.

SliceIt: A genome-wide resource and visualization tool to design CRISPR/Cas9 screens for editing protein-RNA interaction sites in the human genome

Several protein-RNA cross linking protocols have been established in recent years to delineate the molecular interaction of an RNA Binding Protein (RBP) and its target RNAs. However, functional dissection of the role of the RBP binding sites in modulating the post-transcriptional fate of the target RNA remains challenging. CRISPR/Cas9 genome editing system is being commonly employed to perturb both coding and noncoding regions in the genome. With the advancements in genome-scale CRISPR/Cas9 screens, it is now possible to not only perturb specific binding sites but also probe the global impact of protein-RNA interaction sites across cell types. Here, we present SliceIt (http://sliceit.soic.iupui.edu/), a database of in silico sgRNA (single guide RNA) library to facilitate conducting such high throughput screens. SliceIt comprises of ~4.8 million unique sgRNAs with an estimated range of 2-8 sgRNAs designed per RBP binding site, for eCLIP experiments of >100 RBPs in HepG2 and K562 cell lines from the ENCODE project. SliceIt provides a user friendly environment, developed using advanced search engine framework, Elasticsearch. It is available in both table and genome browser views facilitating the easy navigation of RBP binding sites, designed sgRNAs, exon expression levels across 53 human tissues along with prevalence of SNPs and GWAS hits on binding sites. Exon expression profiles enable examination of locus specific changes proximal to the binding sites. Users can also upload custom tracks of various file formats directly onto genome browser, to navigate additional genomic features in the genome and compare with other types of omics profiles. All the binding site-centric information is dynamically accessible via "search by gene", "search by coordinates" and "search by RBP" options and readily available to download. Validation of the sgRNA library in SliceIt was performed by selecting RBP binding sites in Lipt1 gene and designing sgRNAs. Effect of CRISPR/Cas9 perturbations on the selected binding sites in HepG2 cell line, was confirmed based on altered proximal exon expression levels using qPCR, further supporting the utility of the resource to design experiments for perturbing protein-RNA interaction networks. Thus, SliceIt provides a one-stop repertoire of guide RNA library to perturb RBP binding sites, along with several layers of functional information to design both low and high throughput CRISPR/Cas9 screens, for studying the phenotypes and diseases associated with RBP binding sites.

Human protein-RNA interaction network is highly stable across mammals

BACKGROUND

RNA-binding proteins (RBPs) are crucial in modulating RNA metabolism in eukaryotes thereby controlling an extensive network of RBP-RNA interactions. Although previous studies on the conservation of RBP targets have been carried out in lower eukaryotes such as yeast, relatively little is known about the extent of conservation of the binding sites of RBPs across mammalian species.

RESULTS

In this study, we employ CLIP-seq datasets for 60 human RBPs and demonstrate that most binding sites for a third of these RBPs are conserved in at least 50% of the studied vertebrate species. Across the studied RBPs, binding sites were found to exhibit a median conservation of 58%, ~ 20% higher than random genomic locations, suggesting a significantly higher preservation of RBP-RNA interaction networks across vertebrates. RBP binding sites were highly conserved across primates with weak conservation profiles in birds and fishes. We also note that phylogenetic relationship between members of an RBP family does not explain the extent of conservation of their binding sites across species. Multivariate analysis to uncover features contributing to differences in the extents of conservation of binding sites across RBPs revealed RBP expression level and number of post-transcriptional targets to be the most prominent factors. Examination of the location of binding sites at the gene level confirmed that binding sites occurring on the 3' region of a gene are highly conserved across species with 90% of the RBPs exhibiting a significantly higher conservation of binding sites in 3' regions of a gene than those occurring in the 5'. Gene set enrichment analysis on the extent of conservation of binding sites to identify significantly associated human phenotypes revealed an enrichment for multiple developmental abnormalities.

CONCLUSIONS

Our results suggest that binding sites of human RBPs are highly conserved across primates with weak conservation profiles in lower vertebrates and evolutionary relationship between members of an RBP family does not explain the extent of conservation of their binding sites. Expression level and number of targets of an RBP are important factors contributing to the differences in the extent of conservation of binding sites. RBP binding sites on 3' ends of a gene are the most conserved across species. Phenotypic analysis on the extent of conservation of binding sites revealed the importance of lineage-specific developmental events in post-transcriptional regulatory network evolution.

Condition-Specific Modeling of Biophysical Parameters Advances Inference of Regulatory Networks

Large-scale inference of eukaryotic transcription-regulatory networks remains challenging. One underlying reason is that existing algorithms typically ignore crucial regulatory mechanisms, such as RNA degradation and post-transcriptional processing. Here, we describe InfereCLaDR, which incorporates such elements and advances prediction in Saccharomyces cerevisiae. First, InfereCLaDR employs a high-quality Gold Standard dataset that we use separately as prior information and for model validation. Second, InfereCLaDR explicitly models transcription factor activity and RNA half-lives. Third, it introduces expression subspaces to derive condition-responsive regulatory networks for every gene. InfereCLaDR’s final network is validated by known data and trends and results in multiple insights. For example, it predicts long half-lives for transcripts of the nucleic acid metabolism genes and members of the cytosolic chaperonin complex as targets of the proteasome regulator Rpn4p. InfereCLaDR demonstrates that more biophysically realistic modeling of regulatory networks advances prediction accuracy both in eukaryotes and prokaryotes.

The RNA-binding protein TcUBP1 up-regulates an RNA regulon for a cell surface-associated Trypanosoma cruzi glycoprotein and promotes parasite infectivity

The regulation of transcription in trypanosomes is unusual. To modulate protein synthesis during their complex developmental stages, these unicellular microorganisms rely largely on post-transcriptional gene expression pathways. These pathways include a plethora of RNA-binding proteins (RBPs) that modulate all steps of the mRNA life cycle in trypanosomes and help organize transcriptomes into clusters of post-transcriptional regulons. The aim of this work was to characterize an RNA regulon comprising numerous transcripts of trypomastigote-associated cell-surface glycoproteins that are preferentially expressed in the infective stages of the human parasite Trypanosoma cruzi. In vitro and in vivo RNA-binding assays disclosed that these glycoprotein mRNAs are targeted by the small trypanosomatid-exclusive RBP in T. cruzi, U-rich RBP 1 (TcUBP1). Overexpression of a GFP-tagged TcUBP1 in replicative parasites resulted in >10 times up-regulated expression of transcripts encoding surface proteins and in changes in their subcellular localization from the posterior region to the perinuclear region of the cytoplasm, as is typically observed in the infective parasite stages. Moreover, RT-quantitative PCR analysis of actively translated mRNAs by sucrose cushion fractionation revealed an increased abundance of these target transcripts in the polysome fraction of TcUBP1-induced samples. Because these surface proteins are involved in cell adherence or invasion during host infection, we also carried out in vitro infections with TcUBP1-transgenic trypomastigotes and observed that TcUBP1 overexpression significantly increases parasite infectivity. Our findings provide evidence for a role of TcUBP1 in trypomastigote stage-specific gene regulation important for T. cruzi virulence.

IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction

Long non-coding RNA (lncRNA) plays an important role in many important biological processes and has attracted widespread attention. Although the precise functions and mechanisms for most lncRNAs are still unknown, we are certain that lncRNAs usually perform their functions by interacting with the corresponding RNA- binding proteins. For example, lncRNA-protein interactions play an important role in post transcriptional gene regulation, such as splicing, translation, signaling, and advances in complex diseases. However, experimental verification of lncRNA-protein interactions prediction is time-consuming and laborious. In this work, we propose a computational method, named IRWNRLPI, to find the potential associations between lncRNAs and proteins. IRWNRLPI integrates two algorithms, random walk and neighborhood regularized logistic matrix factorization, which can optimize a lot more than using an algorithm alone. Moreover, the method is semi-supervised and does not require negative samples. Based on the leave-one-out cross validation, we obtain the AUC of 0.9150 and the AUPR of 0.7138, demonstrating its reliable performance. In addition, by means of case study in the “Mus musculus,” many lncRNA-protein interactions which are predicted by our method can be successfully confirmed by experiments. This suggests that IRWNRLPI will be a useful bioinformatics resource in biomedical research.

LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction

RNA-protein interactions are essential for understanding many important cellular processes. In particular, lncRNA-protein interactions play important roles in post-transcriptional gene regulation, such as splicing, translation, signaling and even the progression of complex diseases. However, the experimental validation of lncRNA-protein interactions remains time-consuming and expensive, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. Here, we presented eigenvalue transformation-based semi-supervised link prediction (LPI-ETSLP) to uncover the relationship between lncRNAs and proteins. Moreover, it is semi-supervised and does not need negative samples. Based on 5-fold cross validation, an AUC of 0.8876 and an AUPR of 0.6438 have demonstrated its reliable performance compared with three other computational models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is indicated that LPI-ETSLP would be a useful bioinformatics resource for biomedical research studies.

RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.

Brain tumor initiating cells: with great technology will come greater understanding

The discovery of the brain tumor initiating cells resulted in a paradigm shift within the cancer research community to consider brain tumors as an outcome of developmental mechanisms gone awry. This review will guide the reader through the technological advances that hold the powerful potential to allow brain cancer researchers to develop an intimate understanding of the dynamic and complex mechanism governing brain tumor behavior.

Mutational landscape of RNA-binding proteins in human cancers

RNA Binding Proteins (RBPs) are a class of post-transcriptional regulatory molecules which are increasingly documented to be dysfunctional in cancer genomes. However, our current understanding of these alterations is limited. Here, we delineate the mutational landscape of ∼1300 RBPs in ∼6000 cancer genomes. Our analysis revealed that RBPs have an average of ∼3 mutations per Mb across 26 cancer types. We identified 281 RBPs to be enriched for mutations (GEMs) in at least one cancer type. GEM RBPs were found to undergo frequent frameshift and inframe deletions as well as missense, nonsense and silent mutations when compared to those that are not enriched for mutations. Functional analysis of these RBPs revealed the enrichment of pathways associated with apoptosis, splicing and translation. Using the OncodriveFM framework, we also identified more than 200 candidate driver RBPs that were found to accumulate functionally impactful mutations in at least one cancer. Expression levels of 15% of these driver RBPs exhibited significant difference, when transcriptome groups with and without deleterious mutations were compared. Functional interaction network of the driver RBPs revealed the enrichment of spliceosomal machinery, suggesting a plausible mechanism for tumorogenesis while network analysis of the protein interactions between RBPs unambiguously revealed the higher degree, betweenness and closeness centrality for driver RBPs compared to non-drivers. Analysis to reveal cancer-specific Ribonucleoprotein (RNP) mutational hotspots showed extensive rewiring even among common drivers between cancer types. Knockdown experiments on pan-cancer drivers such as SF3B1 and PRPF8 in breast cancer cell lines, revealed cancer subtype specific functions like selective stem cell features, indicating a plausible means for RBPs to mediate cancer-specific phenotypes. Hence, this study would form a foundation to uncover the contribution of the mutational spectrum of RBPs in dysregulating the post-transcriptional regulatory networks in different cancer types.

Oncogenic Activation of the RNA Binding Protein NELFE and MYC Signaling in Hepatocellular Carcinoma

Global transcriptomic imbalance is a ubiquitous feature associated with cancer, including hepatocellular carcinoma (HCC). Analyses of 1,225 clinical HCC samples revealed that a large numbers of RNA binding proteins (RBPs) are dysregulated and that RBP dysregulation is associated with poor prognosis. We further identified that oncogenic activation of a top candidate RBP, negative elongation factor E (NELFE), via somatic copy-number alterations enhanced MYC signaling and promoted HCC progression. Interestingly, NELFE induces a unique tumor transcriptome by selectively regulating MYC-associated genes. Thus, our results revealed NELFE as an oncogenic protein that may contribute to transcriptome imbalance in HCC through the regulation of MYC signaling.

Overexpression of the human antigen R suppresses the immediate paradoxical proliferation of melanoma cell subpopulations in response to suboptimal BRAF inhibition

Tumor plasticity and the heterogeneous response of melanoma cells to targeted therapies are major limits for the long‐term efficacy of this line of therapy. Targeting tumor plasticity is theoretically possible through the modulation of the expression of RNA‐binding proteins which can affect many different compensatory mechanisms of the adaptive response of malignant cells to targeted therapies. Human antigen R (HuR) is a modulator of gene expression and a transacting factor in the mRNA‐processing machinery used in the cell stress response, and is a potential target for reducing tumor plasticity. In this experiment, we exploit the inherent heterogeneous response of the A375 melanoma line to suboptimal BRAF inhibition as a model of immediate adaptive response. We first observe that HuR overexpression can prevent the heterogeneous response and thus the immediate paradoxical proliferation induced by low‐doses vemurafenib treatment. We then use single‐cell mass cytometry to characterize subpopulations, including those that paradoxically proliferate, based on their proliferation rate and the expression patterns of markers involved in the reversible adaptive resistance to BRAF inhibition and/or recognized as HuR targets involved in cell cycle regulation. Under suboptimal BRAF inhibition, HuR overexpression affects these subpopulations and their expression pattern with contrasting responses depending on their proliferation rate: faster‐proliferating vemurafenib‐sensitive or ‐resistant subpopulations showed higher death tendency and reduced size, and slower‐proliferating subpopulations showed an attenuated resistant expression response and their paradoxical proliferation was inhibited. These observations pave the way to new therapeutic strategies for preventing the heterogeneous response of tumors to targeted therapies.

Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles

RNA-binding proteins (RBPs) control the regulation of gene expression in eukaryotic genomes at post-transcriptional level by binding to their cognate RNAs. Although several variants of CLIP (crosslinking and immunoprecipitation) protocols are currently available to study the global protein-RNA interaction landscape at single-nucleotide resolution in a cell, currently there are very few tools that can facilitate understanding and dissecting the functional associations of RBPs from the resulting binding maps. Here, we present Seten, a web-based and command line tool, which can identify and compare processes, phenotypes, and diseases associated with RBPs from condition-specific CLIP-seq profiles. Seten uses BED files resulting from most peak calling algorithms, which include scores reflecting the extent of binding of an RBP on the target transcript, to provide both traditional functional enrichment as well as gene set enrichment results for a number of gene set collections including BioCarta, KEGG, Reactome, Gene Ontology (GO), Human Phenotype Ontology (HPO), and MalaCards Disease Ontology for several organisms including fruit fly, human, mouse, rat, worm, and yeast. It also provides an option to dynamically compare the associated gene sets across data sets as bubble charts, to facilitate comparative analysis. Benchmarking of Seten using eCLIP data for IGF2BP1, SRSF7, and PTBP1 against their corresponding CRISPR RNA-seq in K562 cells as well as randomized negative controls, demonstrated that its gene set enrichment method outperforms functional enrichment, with scores significantly contributing to the discovery of true annotations. Comparative performance analysis using these CRISPR control data sets revealed significantly higher precision and comparable recall to that observed using ChIP-Enrich. Seten's web interface currently provides precomputed results for about 200 CLIP-seq data sets and both command line as well as web interfaces can be used to analyze CLIP-seq data sets. We highlight several examples to show the utility of Seten for rapid profiling of various CLIP-seq data sets. Seten is available on http://www.iupui.edu/∼sysbio/seten/.

A pair-conformation-dependent scoring function for evaluating 3D RNA-protein complex structures

Computational prediction of RNA-protein complex 3D structures includes two basic steps: one is sampling possible structures and another is scoring the sampled structures to pick out the correct one. At present, constructing accurate scoring functions is still not well solved and the performances of the scoring functions usually depend on used benchmarks. Here we propose a pair-conformation-dependent scoring function, 3dRPC-Score, for 3D RNA-protein complex structure prediction by considering the nucleotide-residue pairs having the same energy if their conformations are similar, instead of the distance-only dependence of the most existing scoring functions. Benchmarking shows that 3dRPC-Score has a consistent performance in three test sets.

ceRNA crosstalk stabilizes protein expression and affects the correlation pattern of interacting proteins

Gene expression is a noisy process and several mechanisms, both transcriptional and post-transcriptional, can stabilize protein levels in cells. Much work has focused on the role of miRNAs, showing in particular that miRNA-mediated regulation can buffer expression noise for lowly expressed genes. Here, using in silico simulations and mathematical modeling, we demonstrate that miRNAs can exert a much broader influence on protein levels by orchestrating competition-induced crosstalk between mRNAs. Most notably, we find that miRNA-mediated cross-talk (i) can stabilize protein levels across the full range of gene expression rates, and (ii) modifies the correlation pattern of co-regulated interacting proteins, changing the sign of correlations from negative to positive. The latter feature may constitute a potentially robust signature of the existence of RNA crosstalk induced by endogenous competition for miRNAs in standard cellular conditions.

A Brief Review of RNA-Protein Interaction Database Resources

RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions.

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.

Regulation of RNA binding proteins in trypanosomatid protozoan parasites

Posttranscriptional mechanisms have a critical role in the overall outcome of gene expression. These mechanisms are especially relevant in protozoa from the genus Trypanosoma, which is composed by death threatening parasites affecting people in Sub-saharan Africa or in the Americas. In these parasites the classic view of regulation of transcription initiation to modulate the products of a given gene cannot be applied. This is due to the presence of transcription start sites that give rise to long polycistronic units that need to be processed costranscriptionally by trans-splicing and polyadenylation to give mature monocistronic mRNAs. Posttranscriptional mechanisms such as mRNA degradation and translational repression are responsible for the final synthesis of the required protein products. In this context, RNA-binding proteins (RBPs) in trypanosomes have a relevant role as modulators of mRNA abundance and translational repression by associating to the 3’ untranslated regions in mRNA. Many different RBPs have been proposed to modulate cohorts of mRNAs in trypanosomes. However, the current understanding of their functions lacks a dynamic view on the different steps at which these RBPs are regulated. Here, we discuss different evidences to propose regulatory events for different RBPs in these parasites. These events vary from regulated developmental expression, to biogenesis of cytoplasmic ribonucleoprotein complexes in the nucleus, and condensation of RBPs and mRNA into large cytoplasmic granules. Finally, we discuss how newly identified posttranslational modifications of RBPs and mRNA metabolism-related proteins could have an enormous impact on the modulation of mRNA abundance. To understand these modifications is especially relevant in these parasites due to the fact that the enzymes involved could be interesting targets for drug therapy.

Conserved mRNA-binding proteomes in eukaryotic organisms

RNA-binding proteins (RBPs) are essential for the post-transcriptional regulation of gene expression. Recent high-throughput screens have dramatically increased the number of experimentally identified RBPs; however, comprehensive identification of RBPs within living organisms is elusive. Here we describe the repertoire of 765 and 594 proteins that reproducibly interact with polyadenylated mRNAs in Saccharomyces cerevisiae and Caenorhabditis elegans, respectively. Furthermore, we report the differential association of mRBPs upon apoptosis induction in C. elegans L4 stage larvae. Strikingly, most proteins comprising mRNA-binding proteomes (mRBPomes) are evolutionarily conserved between yeast and C. elegans, including components of early metabolic pathways and the proteasome. Based on our evidence that glycolytic enzymes bind to distinct glycolytic mRNAs, we speculate that enzyme-mRNA interactions relate to an ancient mechanism for post-transcriptional coordination of metabolic pathways, perhaps established during the transition from the early RNA to the protein ‘world’.

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.

The PfAlba1 RNA-binding protein is an important regulator of translational timing in Plasmodium falciparum blood stages

Background

Transcriptome-wide ribosome occupancy studies have suggested that during the intra-erythrocytic lifecycle of Plasmodium falciparum, select mRNAs are post-transcriptionally regulated. A subset of these encodes parasite virulence factors required for invading host erythrocytes, and are currently being developed as vaccine candidates. However, the molecular mechanisms that govern post-transcriptional regulation are currently unknown.

Results

We explore the previously identified DNA/RNA-binding protein PfAlba1, which localizes to multiple foci in the cytoplasm of P. falciparum trophozoites. We establish that PfAlba1 is essential for asexual proliferation, and subsequently investigate parasites overexpressing epitope-tagged PfAlba1 to identify its RNA targets and effects on mRNA homeostasis and translational regulation. Using deep sequencing of affinity-purified PfAlba1-associated RNAs, we identify 1193 transcripts that directly bind to PfAlba1 in trophozoites. For 105 such transcripts, 43 % of which are uncharacterized and 13 % of which encode erythrocyte invasion components, the steady state levels significantly change at this stage, evidencing a role for PfAlba1 in maintaining mRNA homeostasis. Additionally, we discover that binding of PfAlba1 to four erythrocyte invasion mRNAs, Rap1, RhopH3, CDPK1, and AMA1, is linked to translation repression in trophozoites whereas release of these mRNAs from a PfAlba1 complex in mature stages correlates with protein synthesis.

Conclusions

We show that PfAlba1 binds to a sub-population of asexual stage mRNAs and fine-tunes the timing of translation. This mode of post-transcriptional regulation may be especially important for P. falciparum erythrocyte invasion components that have to be assembled into apical secretory organelles in a highly time-dependent manner towards the end of the parasite’s asexual lifecycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0771-5) contains supplementary material, which is available to authorized users.

The human RBPome: from genes and proteins to human disease

RNA binding proteins (RBPs) play a central role in mediating post transcriptional regulation of genes. However less is understood about them and their regulatory mechanisms. In this study, we construct a catalogue of 1344 experimentally confirmed RBPs. The domain architecture of RBPs enabled us to classify them into three groups - Classical (29%), Non-classical (19%) and unclassified (52%). A higher percentage of proteins with unclassified domains reveals the presence of various uncharacterised motifs that can potentially bind RNA. RBPs were found to be highly disordered compared to Non-RBPs (p<2.2e-16, Fisher's exact test), suggestive of a dynamic regulatory role of RBPs in cellular signalling and homeostasis. Evolutionary analysis in 62 different species showed that RBPs are highly conserved compared to Non-RBPs (p<2.2e-16, Wilcox-test), reflecting the conservation of various biological processes like mRNA splicing and ribosome biogenesis. The expression patterns of RBPs from human proteome map revealed that ~40% of them are ubiquitously expressed and ~60% are tissue-specific. RBPs were also seen to be highly associated with several neurological disorders, cancer and inflammatory diseases. Anatomical contexts like B cells, T-cells, foetal liver and foetal brain were found to be strongly enriched for RBPs, implying a prominent role of RBPs in immune responses and different developmental stages. The catalogue and meta-analysis presented here should form a foundation for furthering our understanding of RBPs and the cellular networks they control, in years to come. This article is part of a Special Issue entitled: Proteomics in India.

Uncovering RNA binding proteins associated with age and gender during liver maturation

In the present study, we perform an association analysis focusing on the expression changes of 1344 RNA Binding proteins (RBPs) as a function of age and gender in human liver. We identify 88 and 45 RBPs to be significantly associated with age and gender respectively. Experimental verification of several of the predicted associations in mice confirmed our findings. Our results suggest that a small fraction of the gender-associated RBPs (~40%) are expressed higher in males than females. Altogether, these observations show that several of these RBPs are important and conserved regulators in maintaining liver function. Further analysis of the protein interaction network of RBPs associated with age and gender based on the centrality measures like degree, betweenness and closeness revealed that several of these RBPs might be prominent players in aging liver and impart gender specific alterations in gene expression via the formation of protein complexes. Indeed, both age and gender-associated RBPs in liver were found to show significantly higher clustering coefficients and network centrality measures compared to non-associated RBPs. The compendium of RBPs and this study will help us gain insight into the role of post-transcriptional regulatory molecules in aging and gender specific expression of genes.

RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ∼94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ∼83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred.

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.

Rapid proteasomal degradation of posttranscriptional regulators of the TIS11/tristetraprolin family is induced by an intrinsically unstructured region independently of ubiquitination

The TIS11/tristetraprolin (TTP) CCCH tandem zinc finger proteins are major effectors in the destabilization of mRNAs bearing AU-rich elements (ARE) in their 3' untranslated regions. In this report, we demonstrate that the Drosophila melanogaster dTIS11 protein is short-lived due to its rapid ubiquitin-independent degradation by the proteasome. Our data indicate that this mechanism is tightly associated with the intrinsically unstructured, disordered N- and C-terminal domains of the protein. Furthermore, we show that TTP, the mammalian TIS11/TTP protein prototype, shares the same three-dimensional characteristics and is degraded by the same proteolytic pathway as dTIS11, thereby indicating that this mechanism has been conserved across evolution. Finally, we observed a phosphorylation-dependent inhibition of dTIS11 and TTP degradation by the proteasome in vitro, raising the possibility that such modifications directly affect proteasomal recognition for these proteins. As a group, RNA-binding proteins (RNA-BPs) have been described as enriched in intrinsically disordered regions, thus raising the possibility that the mechanism that we uncovered for TIS11/TTP turnover is widespread among other RNA-BPs.

Trans-regulation of RNA-binding protein motifs by microRNA

The wide array of vital functions that RNA performs is dependent on its ability to dynamically fold into different structures in response to intracellular and extracellular changes. RNA-binding proteins regulate much of this activity by targeting specific RNA structures or motifs. One of these structures, the 3-way RNA junction, is characteristically found in ribosomal RNA and results from the RNA folding in cis, to produce three separate helices that meet around a central unpaired region. Here we demonstrate that 3-way junctions can also form in trans as a result of the binding of microRNAs in an unconventional manner with mRNA by splinting two non-contiguous regions together. This may be used to reinforce the base of a stem-loop motif being targeted by an RNA-binding protein. Trans interactions between non-coding RNA and mRNA may be used to control the post-transcriptional regulatory code and suggests a possible role for some of the recently described transcripts of unknown function expressed from the human genome.

MicroRNA buffering and altered variance of gene expression in response to Salmonella infection

One potential role of miRNAs is to buffer variation in gene expression, although conflicting results have been reported. To investigate the buffering role of miRNAs in response to Salmonella infection in pigs, we sequenced miRNA and mRNA in whole blood from 15 pig samples before and after Salmonella challenge. By analyzing inter-individual variation in gene expression patterns, we found that for moderately and lowly expressed genes, putative miRNA targets showed significantly lower expression variance compared with non-miRNA-targets. Expression variance between highly expressed miRNA targets and non-miRNA-targets was not significantly different. Further, miRNA targets demonstrated significantly reduced variance after challenge whereas non-miRNA-targets did not. RNA binding proteins (RBPs) are significantly enriched among the miRNA targets with dramatically reduced variance of expression after Salmonella challenge. Moreover, we found evidence that targets of young (less-conserved) miRNAs showed lower expression variance compared with targets of old (evolutionarily conserved) miRNAs. These findings point to the importance of a buffering effect of miRNAs for relatively lowly expressed genes, and suggest that the reduced expression variation of RBPs may play an important role in response to Salmonella infection.

Selective nuclear export of specific classes of mRNA from mammalian nuclei is promoted by GANP

The nuclear phase of the gene expression pathway culminates in the export of mature messenger RNAs (mRNAs) to the cytoplasm through nuclear pore complexes. GANP (germinal- centre associated nuclear protein) promotes the transfer of mRNAs bound to the transport factor NXF1 to nuclear pore complexes. Here, we demonstrate that GANP, subunit of the TRanscription-EXport-2 (TREX-2) mRNA export complex, promotes selective nuclear export of a specific subset of mRNAs whose transport depends on NXF1. Genome-wide gene expression profiling showed that half of the transcripts whose nuclear export was impaired following NXF1 depletion also showed reduced export when GANP was depleted. GANP-dependent transcripts were highly expressed, yet short-lived, and were highly enriched in those encoding central components of the gene expression machinery such as RNA synthesis and processing factors. After injection into Xenopus oocyte nuclei, representative GANP-dependent transcripts showed faster nuclear export kinetics than representative transcripts that were not influenced by GANP depletion. We propose that GANP promotes the nuclear export of specific classes of mRNAs that may facilitate rapid changes in gene expression.

A knowledge-based scoring function for protein-RNA interactions derived from a statistical mechanics-based iterative method

Protein-RNA interactions play important roles in many biological processes. Given the high cost and technique difficulties in experimental methods, computationally predicting the binding complexes from individual protein and RNA structures is pressingly needed, in which a reliable scoring function is one of the critical components. Here, we have developed a knowledge-based scoring function, referred to as ITScore-PR, for protein-RNA binding mode prediction by using a statistical mechanics-based iterative method. The pairwise distance-dependent atomic interaction potentials of ITScore-PR were derived from experimentally determined protein–RNA complex structures. For validation, we have compared ITScore-PR with 10 other scoring methods on four diverse test sets. For bound docking, ITScore-PR achieved a success rate of up to 86% if the top prediction was considered and up to 94% if the top 10 predictions were considered, respectively. For truly unbound docking, the respective success rates of ITScore-PR were up to 24 and 46%. ITScore-PR can be used stand-alone or easily implemented in other docking programs for protein–RNA recognition.

Dissecting the expression landscape of RNA-binding proteins in human cancers

BACKGROUND

RNA-binding proteins (RBPs) play important roles in cellular homeostasis by controlling gene expression at the post-transcriptional level.

RESULTS

We explore the expression of more than 800 RBPs in sixteen healthy human tissues and their patterns of dysregulation in cancer genomes from The Cancer Genome Atlas project. We show that genes encoding RBPs are consistently and significantly highly expressed compared with other classes of genes, including those encoding regulatory components such as transcription factors, miRNAs and long non-coding RNAs. We also demonstrate that a set of RBPs, numbering approximately 30, are strongly upregulated (SUR) across at least two-thirds of the nine cancers profiled in this study. Analysis of the protein-protein interaction network properties for the SUR and non-SUR groups of RBPs suggests that path length distributions between SUR RBPs is significantly lower than those observed for non-SUR RBPs. We further find that the mean path lengths between SUR RBPs increases in proportion to their contribution to prognostic impact. We also note that RBPs exhibiting higher variability in the extent of dysregulation across breast cancer patients have a higher number of protein-protein interactions. We propose that fluctuating RBP levels might result in an increase in non-specific protein interactions, potentially leading to changes in the functional consequences of RBP binding. Finally, we show that the expression variation of a gene within a patient group is inversely correlated with prognostic impact.

CONCLUSIONS

Overall, our results provide a roadmap for understanding the impact of RBPs on cancer pathogenesis.

Leveraging transcription factors to speed cellobiose fermentation by Saccharomyces cerevisiae

BACKGROUND

Saccharomyces cerevisiae, a key organism used for the manufacture of renewable fuels and chemicals, has been engineered to utilize non-native sugars derived from plant cell walls, such as cellobiose and xylose. However, the rates and efficiencies of these non-native sugar fermentations pale in comparison with those of glucose. Systems biology methods, used to understand biological networks, hold promise for rational microbial strain development in metabolic engineering. Here, we present a systematic strategy for optimizing non-native sugar fermentation by recombinant S. cerevisiae, using cellobiose as a model.

RESULTS

Differences in gene expression between cellobiose and glucose metabolism revealed by RNA deep sequencing indicated that cellobiose metabolism induces mitochondrial activation and reduces amino acid biosynthesis under fermentation conditions. Furthermore, glucose-sensing and signaling pathways and their target genes, including the cAMP-dependent protein kinase A pathway controlling the majority of glucose-induced changes, the Snf3-Rgt2-Rgt1 pathway regulating hexose transport, and the Snf1-Mig1 glucose repression pathway, were at most only partially activated under cellobiose conditions. To separate correlations from causative effects, the expression levels of 19 transcription factors perturbed under cellobiose conditions were modulated, and the three strongest promoters under cellobiose conditions were applied to fine-tune expression of the heterologous cellobiose-utilizing pathway. Of the changes in these 19 transcription factors, only overexpression of SUT1 or deletion of HAP4 consistently improved cellobiose fermentation. SUT1 overexpression and HAP4 deletion were not synergistic, suggesting that SUT1 and HAP4 may regulate overlapping genes important for improved cellobiose fermentation. Transcription factor modulation coupled with rational tuning of the cellobiose consumption pathway significantly improved cellobiose fermentation.

CONCLUSIONS

We used systems-level input to reveal the regulatory mechanisms underlying suboptimal metabolism of the non-glucose sugar cellobiose. By identifying key transcription factors that cause suboptimal cellobiose fermentation in engineered S. cerevisiae, and by fine-tuning the expression of a heterologous cellobiose consumption pathway, we were able to greatly improve cellobiose fermentation by engineered S. cerevisiae. Our results demonstrate a powerful strategy for applying systems biology methods to rapidly identify metabolic engineering targets and overcome bottlenecks in performance of engineered strains.

BORIS/CTCFL is an RNA-binding protein that associates with polysomes

BACKGROUND

BORIS (CTCFL), a paralogue of the multifunctional and ubiquitously expressed transcription factor CTCF, is best known for its role in transcriptional regulation. In the nucleus, BORIS is particularly enriched in the nucleolus, a crucial compartment for ribosomal RNA and RNA metabolism. However, little is known about cytoplasmic BORIS, which represents the major pool of BORIS protein.

RESULTS

We show, firstly, that BORIS has a putative nuclear export signal in the C-terminal domain. Furthermore, BORIS associates with mRNA in both neural stem cells and young neurons. The majority of the BORIS-associated transcripts are different in the two cell types. Finally, by using polysome profiling we show that BORIS is associated with actively translating ribosomes.

CONCLUSION

We have demonstrated the RNA binding properties of cellular BORIS and its association with actively translating ribosomes. We suggest that BORIS is involved in gene expression at both the transcriptional and post-transcriptional levels.

Mass spectrometry-based identification of proteins interacting with nucleic acids

The identification of the regulatory proteins that control DNA transcription as well as RNA stability and translation represents a key step in the comprehension of gene expression regulation. Those proteins can be purified by DNA- or RNA-affinity chromatography, followed by identification by mass spectrometry. Although very simple in the concept, this represents a real technological challenge due to the low abundance of regulatory proteins compared to the highly abundant proteins binding to nucleic acids in a nonsequence-specific manner. Here we review the different strategies that have been set up to reach this purpose, discussing the key parameters that should be considered to increase the chances of success. Typically, two categories of biological questions can be distinguished: the identification of proteins that specifically interact with a precisely defined binding site, mostly addressed by quantitative mass spectrometry, and the identification in a non-comparative manner of the protein complexes recruited by a poorly characterized long regulatory region of nucleic acids. Finally, beside the numerous studies devoted to in vitro-assembled nucleic acid-protein complexes, the scarce data reported on proteomic analyses of in vivo-assembled complexes are described, with a special emphasis on the associated challenges.