Biochemical and bioinformatic methods for elucidating the role of RNA-protein interactions in posttranscriptional regulation

Enhanced immunoprecipitation techniques for the identification of RNA-binding protein partners: IGF2BP1 interactions in mammary epithelial cells

RNA-binding proteins (RBPs) regulate the expression of large cohorts of RNA species to produce programmatic changes in cellular phenotypes. To describe the function of RBPs within a cell, it is key to identify their mRNA-binding partners. This is often done by crosslinking nucleic acids to RBPs, followed by chemical release of the nucleic acid fragments for analysis. However, this methodology is lengthy, which involves complex processing with attendant sample losses, thus large amounts of starting materials and prone to artifacts. To evaluate potential alternative technologies, we tested “exclusion-based” purification of immunoprecipitates (IFAST or SLIDE) and report here that these methods can efficiently, rapidly, and specifically isolate RBP–RNA complexes. The analysis requires less than 1% of the starting material required for techniques that include crosslinking. Depending on the antibody used, 50% to 100% starting protein can be retrieved, facilitating the assay of endogenous levels of RBPs; the isolated ribonucleoproteins are subsequently analyzed using standard techniques, to provide a comprehensive portrait of RBP complexes. Using exclusion-based techniques, we show that the mRNA-binding partners for RBP IGF2BP1 in cultured mammary epithelial cells are enriched in mRNAs important for detoxifying superoxides (specifically glutathione peroxidase [GPX]-1 and GPX-2) and mRNAs encoding mitochondrial proteins. We show that these interactions are functionally significant, as loss of function of IGF2BP1 leads to destabilization of GPX mRNAs and reduces mitochondrial membrane potential and oxygen consumption. We speculate that this underlies a consistent requirement for IGF2BP1 for the expression of clonogenic activity in vitro.

A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences

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RNA are master players in various cellular and biological processes and RNA-protein interactions are vital for proper functioning of cellular machineries.

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Knowledge of binding sites is crucial to decipher their functional implications.

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RNA NC-triplet and NC-quartet features could give reasonably high performance.

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RF model outperformed other machine learning classifiers with 85% accuracy and 0.93 AUC and performed better than few existing methods.

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An online webserver “Nucpred” is developed with trained model and freely accessible for scientific community.

RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. In such a scenario, efficient computational algorithms are required for identification of protein-binding interfaces of RNA in the absence of known structures. We have investigated several machine learning classifiers and various features derived from nucleotide sequences to identify protein-binding nucleotides in RNA. We achieve best performance with nucleotide-triplet and nucleotide-quartet feature-based random forest models. An overall accuracy of 84.8%, sensitivity of 83.2%, specificity of 86.1%, MCC of 0.70 and AUC of 0.93 is achieved. We have further implemented the developed models in a user-friendly webserver “Nucpred”, which is freely accessible at “http://www.csb.iitkgp.ac.in/applications/Nucpred/index”.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers

BACKGROUND

Nucleic acid-binding proteins play major roles in different biological processes, such as transcription, splicing and translation. Therefore, the nucleic acid-binding function prediction of proteins is a step toward full functional annotation of proteins. The aim of our research was the improvement of nucleic-acid binding function prediction.

METHODS

In the current study, nine machine-learning algorithms were used to predict RNA- and DNA-binding proteins and also to discriminate between RNA-binding proteins and DNA-binding proteins. The electrostatic features were utilized for prediction of each function in corresponding adapted protein datasets. The leave-one-out cross-validation process was used to measure the performance of employed classifiers.

RESULTS

Radial basis function classifier gave the best results in predicting RNA- and DNA-binding proteins in comparison with other classifiers applied. In discriminating between RNA- and DNA-binding proteins, multilayer perceptron classifier was the best one.

CONCLUSION

Our findings show that the prediction of nucleic acid-binding function based on these simple electrostatic features can be improved by applied classifiers. Moreover, a reasonable progress to distinguish between RNA- and DNA-binding proteins has been achieved.

Transcriptome-wide analysis uncovers the targets of the RNA-binding protein MSI2 and effects of MSI2's RNA-binding activity on IL-6 signaling

The RNA-binding protein Musashi 2 (MSI2) has emerged as an important regulator in cancer initiation, progression, and drug resistance. Translocations and deregulation of the MSI2 gene are diagnostic of certain cancers, including chronic myeloid leukemia (CML) with translocation t(7;17), acute myeloid leukemia (AML) with translocation t(10;17), and some cases of B-precursor acute lymphoblastic leukemia (pB-ALL). To better understand the function of MSI2 in leukemia, the mRNA targets that are bound and regulated by MSI2 and their MSI2-binding motifs need to be identified. To this end, using photoactivatable ribonucleoside cross-linking and immunoprecipitation (PAR-CLIP) and the multiple EM for motif elicitation (MEME) analysis tool, here we identified MSI2's mRNA targets and the consensus RNA-recognition element (RRE) motif recognized by MSI2 (UUAG). Of note, MSI2 knockdown altered the expression of several genes with roles in eukaryotic initiation factor 2 (eIF2), hepatocyte growth factor (HGF), and epidermal growth factor (EGF) signaling pathways. We also show that MSI2 regulates classic interleukin-6 (IL-6) signaling by promoting the degradation of the mRNA of IL-6 signal transducer (IL6ST or GP130), which, in turn, affected the phosphorylation statuses of signal transducer and activator of transcription 3 (STAT3) and the mitogen-activated protein kinase ERK. In summary, we have identified multiple MSI2-regulated mRNAs and provided evidence that MSI2 controls IL6ST activity that control oncogenic signaling networks. Our findings may help inform strategies for unraveling the role of MSI2 in leukemia to pave the way for the development of targeted therapies.

The PARA-suite: PAR-CLIP specific sequence read simulation and processing

Background

Next-generation sequencing technologies have profoundly impacted biology over recent years. Experimental protocols, such as photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), which identifies protein–RNA interactions on a genome-wide scale, commonly employ deep sequencing. With PAR-CLIP, the incorporation of photoactivatable nucleosides into nascent transcripts leads to high rates of specific nucleotide conversions during reverse transcription. So far, the specific properties of PAR-CLIP-derived sequencing reads have not been assessed in depth.

Methods

We here compared PAR-CLIP sequencing reads to regular transcriptome sequencing reads (RNA-Seq) to identify distinctive properties that are relevant for reference-based read alignment of PAR-CLIP datasets. We developed a set of freely available tools for PAR-CLIP data analysis, called the PAR-CLIP analyzer suite (PARA-suite). The PARA-suite includes error model inference, PAR-CLIP read simulation based on PAR-CLIP specific properties, a full read alignment pipeline with a modified Burrows–Wheeler Aligner algorithm and CLIP read clustering for binding site detection.

Results

We show that differences in the error profiles of PAR-CLIP reads relative to regular transcriptome sequencing reads (RNA-Seq) make a distinct processing advantageous. We examine the alignment accuracy of commonly applied read aligners on 10 simulated PAR-CLIP datasets using different parameter settings and identified the most accurate setup among those read aligners. We demonstrate the performance of the PARA-suite in conjunction with different binding site detection algorithms on several real PAR-CLIP and HITS-CLIP datasets. Our processing pipeline allowed the improvement of both alignment and binding site detection accuracy.

Availability

The PARA-suite toolkit and the PARA-suite aligner are available at https://github.com/akloetgen/PARA-suite and https://github.com/akloetgen/PARA-suite_aligner, respectively, under the GNU GPLv3 license.

A network-based analysis of colon cancer splicing changes reveals a tumorigenesis-favoring regulatory pathway emanating from ELK1

Splicing aberrations are prominent drivers of cancer, yet the regulatory pathways controlling them are mostly unknown. Here we develop a method that integrates physical interaction, gene expression, and alternative splicing data to construct the largest map of transcriptomic and proteomic interactions leading to cancerous splicing aberrations defined to date, and identify driver pathways therein. We apply our method to colon adenocarcinoma and non-small-cell lung carcinoma. By focusing on colon cancer, we reveal a novel tumor-favoring regulatory pathway involving the induction of the transcription factor MYC by the transcription factor ELK1, as well as the subsequent induction of the alternative splicing factor PTBP1 by both. We show that PTBP1 promotes specific RAC1,NUMB, and PKM splicing isoforms that are major triggers of colon tumorigenesis. By testing the pathway's activity in patient tumor samples, we find ELK1,MYC, and PTBP1 to be overexpressed in conjunction with oncogenic KRAS mutations, and show that these mutations increase ELK1 levels via the RAS-MAPK pathway. We thus illuminate, for the first time, a full regulatory pathway connecting prevalent cancerous mutations to functional tumor-inducing splicing aberrations. Our results demonstrate our method is applicable to different cancers to reveal regulatory pathways promoting splicing aberrations.

Revealing protein-lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein–RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP–lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein–lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.