CLIPreg: Constructing translational regulatory networks from CLIP-, Ribo- and RNA-seq

Motivation: The creation and analysis of gene regulatory networks have been the focus of bioinformatic research and underpins much of what is known about gene regulation. However, as a result of a bias in the availability of data-types that are collected, the vast majority of gene regulatory network resources and tools have focused on either transcriptional regulation or protein-protein interactions. This has left other areas of regulation, for instance translational regulation, vastly underrepresented despite them having been shown to play a critical role in both health and disease. Results: In order to address this we have developed CLIPreg, a package that integrates RNA, Ribo and CLIP- sequencing data in order to construct translational regulatory networks coordinated by RNA-binding proteins. This is the first tool of its type to be created, allowing for detailed investigation into a previously unseen layer of regulation.

Download Full-text

RNA-Centric Approaches to Profile the RNA–Protein Interaction Landscape on Selected RNAs

Non-Coding RNA ◽

10.3390/ncrna7010011 ◽

2021 ◽

Vol 7 (1) ◽

pp. 11 ◽

Cited By ~ 1

Author(s):

André P. Gerber

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Regulatory Networks ◽

Rna Binding ◽

Rna Binding Proteins ◽

Protein Complexes ◽

Cell Protein ◽

Transcriptional Regulatory Networks ◽

Technological Advances

RNA–protein interactions frame post-transcriptional regulatory networks and modulate transcription and epigenetics. While the technological advances in RNA sequencing have significantly expanded the repertoire of RNAs, recently developed biochemical approaches combined with sensitive mass-spectrometry have revealed hundreds of previously unrecognized and potentially novel RNA-binding proteins. Nevertheless, a major challenge remains to understand how the thousands of RNA molecules and their interacting proteins assemble and control the fate of each individual RNA in a cell. Here, I review recent methodological advances to approach this problem through systematic identification of proteins that interact with particular RNAs in living cells. Thereby, a specific focus is given to in vivo approaches that involve crosslinking of RNA–protein interactions through ultraviolet irradiation or treatment of cells with chemicals, followed by capture of the RNA under study with antisense-oligonucleotides and identification of bound proteins with mass-spectrometry. Several recent studies defining interactomes of long non-coding RNAs, viral RNAs, as well as mRNAs are highlighted, and short reference is given to recent in-cell protein labeling techniques. These recent experimental improvements could open the door for broader applications and to study the remodeling of RNA–protein complexes upon different environmental cues and in disease.

Download Full-text

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

Open Biology ◽

10.1098/rsob.190096 ◽

2019 ◽

Vol 9 (6) ◽

pp. 190096 ◽

Cited By ~ 14

Author(s):

Anna Balcerak ◽

Alicja Trebinska-Stryjewska ◽

Ryszard Konopinski ◽

Maciej Wakula ◽

Ewa Anna Grzybowska

Keyword(s):

Protein Interactions ◽

Regulatory Networks ◽

Rna Binding ◽

Rna Binding Proteins ◽

Protein Complexes ◽

Binding Motifs ◽

Coding Regions ◽

Regulatory Circuits ◽

Proteins Interactions

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.

Download Full-text

Identification and Characterization of Circular Intronic RNAs Derived from Insulin Gene

International Journal of Molecular Sciences ◽

10.3390/ijms21124302 ◽

2020 ◽

Vol 21 (12) ◽

pp. 4302 ◽

Cited By ~ 1

Author(s):

Debojyoti Das ◽

Aniruddha Das ◽

Mousumi Sahu ◽

Smruti Sambhav Mishra ◽

Shaheerah Khan ◽

...

Keyword(s):

Rna Binding ◽

Cell Function ◽

Rna Binding Proteins ◽

Critical Role ◽

Large Family ◽

Normal Diet ◽

Circular Rnas ◽

Sequencing Data ◽

Reverse Transcription Pcr ◽

Β Cell Function

Circular RNAs (circRNAs) are a large family of noncoding RNAs that have emerged as novel regulators of gene expression. However, little is known about the function of circRNAs in pancreatic β-cells. Here, transcriptomic analysis of mice pancreatic islet RNA-sequencing data identified 77 differentially expressed circRNAs between mice fed with a normal diet and a high-fat diet. Surprisingly, multiple circRNAs were derived from the intron 2 of the preproinsulin 2 (Ins2) gene and are termed as circular intronic (ci)-Ins2. The expression of ci-Ins2 transcripts in mouse pancreatic islets, and βTC6 cells were confirmed by reverse transcription PCR, DNA sequencing, and RNase R treatment experiments. The level of ci-Ins2 was altered in βTC6 cells upon exposure to elevated levels of palmitate and glucose. Computational analysis predicted the interaction of several RNA-binding proteins with ci-Ins2 and their flanking region, suggesting their role in the ci-Ins2 function or biogenesis. Additionally, bioinformatics analysis predicted the association of several microRNAs with ci-Ins2. Gene ontology and pathway analysis of genes targeted by miRNAs associated with ci-Ins2 suggested the regulation of several key biological processes. Together, our findings indicate that differential expression of circRNAs, especially ci-Ins2 transcripts, may regulate β-cell function and may play a critical role in the development of diabetes.

Download Full-text

AU-rich RNA binding proteins in hematopoiesis and leukemogenesis

Blood ◽

10.1182/blood-2011-07-347237 ◽

2011 ◽

Vol 118 (22) ◽

pp. 5732-5740 ◽

Cited By ~ 35

Author(s):

Maria Baou ◽

John D. Norton ◽

John J. Murphy

Keyword(s):

Cell Growth ◽

Regulatory Networks ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Binding Protein ◽

Rna Binding Protein ◽

Growth And Survival ◽

Hematopoietic Malignancies ◽

Gene Regulatory

Abstract Posttranscriptional mechanisms are now widely acknowledged to play a central role in orchestrating gene-regulatory networks in hematopoietic cell growth, differentiation, and tumorigenesis. Although much attention has focused on microRNAs as regulators of mRNA stability/translation, recent data have highlighted the role of several diverse classes of AU-rich RNA-binding protein in the regulation of mRNA decay/stabilization. AU-rich elements are found in the 3′-untranslated region of many mRNAs that encode regulators of cell growth and survival, such as cytokines and onco/tumor-suppressor proteins. These are targeted by a burgeoning number of different RNA-binding proteins. Three distinct types of AU-rich RNA binding protein (ARE poly-U–binding degradation factor-1/AUF1, Hu antigen/HuR/HuA/ELAVL1, and the tristetraprolin/ZFP36 family of proteins) are essential for normal hematopoiesis. Together with 2 further AU-rich RNA-binding proteins, nucleolin and KHSRP/KSRP, the functions of these proteins are intimately associated with pathways that are dysregulated in various hematopoietic malignancies. Significantly, all of these AU-rich RNA-binding proteins function via an interconnected network that is integrated with microRNA functions. Studies of these diverse types of RNA binding protein are providing novel insight into gene-regulatory mechanisms in hematopoiesis in addition to offering new opportunities for developing mechanism-based targeted therapeutics in leukemia and lymphoma.

Download Full-text

Emerging Role of Circular RNA–Protein Interactions

Non-Coding RNA ◽

10.3390/ncrna7030048 ◽

2021 ◽

Vol 7 (3) ◽

pp. 48

Author(s):

Arundhati Das ◽

Tanvi Sinha ◽

Sharmishtha Shyamal ◽

Amaresh Chandra Panda

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Nuclear Export ◽

Rna Binding ◽

Rna Binding Proteins ◽

Critical Role ◽

Large Family ◽

Circular Rnas ◽

Protein Interaction Data ◽

Cell Physiology

Circular RNAs (circRNAs) are emerging as novel regulators of gene expression in various biological processes. CircRNAs regulate gene expression by interacting with cellular regulators such as microRNAs and RNA binding proteins (RBPs) to regulate downstream gene expression. The accumulation of high-throughput RNA–protein interaction data revealed the interaction of RBPs with the coding and noncoding RNAs, including recently discovered circRNAs. RBPs are a large family of proteins known to play a critical role in gene expression by modulating RNA splicing, nuclear export, mRNA stability, localization, and translation. However, the interaction of RBPs with circRNAs and their implications on circRNA biogenesis and function has been emerging in the last few years. Recent studies suggest that circRNA interaction with target proteins modulates the interaction of the protein with downstream target mRNAs or proteins. This review outlines the emerging mechanisms of circRNA–protein interactions and their functional role in cell physiology.

Download Full-text

TF–RBP–AS Triplet Analysis Reveals the Mechanisms of Aberrant Alternative Splicing Events in Kidney Cancer: Implications for Their Possible Clinical Use as Prognostic and Therapeutic Biomarkers

International Journal of Molecular Sciences ◽

10.3390/ijms22168789 ◽

2021 ◽

Vol 22 (16) ◽

pp. 8789

Author(s):

Meng He ◽

Fuyan Hu

Keyword(s):

Alternative Splicing ◽

Regulatory Networks ◽

Rna Binding ◽

Rna Binding Proteins ◽

Survival Rates ◽

Enrichment Analysis ◽

Cancer Development ◽

Pathway Enrichment Analysis ◽

Sequencing Data ◽

Computational Framework

Aberrant alternative splicing (AS) is increasingly linked to cancer; however, how AS contributes to cancer development still remains largely unknown. AS events (ASEs) are largely regulated by RNA-binding proteins (RBPs) whose ability can be modulated by a variety of genetic and epigenetic mechanisms. In this study, we used a computational framework to investigate the roles of transcription factors (TFs) on regulating RBP-AS interactions. A total of 6519 TF–RBP–AS triplets were identified, including 290 TFs, 175 RBPs, and 16 ASEs from TCGA–KIRC RNA sequencing data. TF function categories were defined according to correlation changes between RBP expression and their targeted ASEs. The results suggested that most TFs affected multiple targets, and six different classes of TF-mediated transcriptional dysregulations were identified. Then, regulatory networks were constructed for TF–RBP–AS triplets. Further pathway-enrichment analysis showed that these TFs and RBPs involved in triplets were enriched in a variety of pathways that were associated with cancer development and progression. Survival analysis showed that some triplets were highly associated with survival rates. These findings demonstrated that the integration of TFs into alternative splicing regulatory networks can help us in understanding the roles of alternative splicing in cancer.

Download Full-text

Prediction of RNA-protein interactions using a nucleotide language model

10.1101/2021.04.27.441365 ◽

2021 ◽

Author(s):

Keisuke Yamada ◽

Michiaki Hamada

Keyword(s):

Protein Interactions ◽

Rna Binding ◽

Prediction Models ◽

Rna Binding Proteins ◽

Language Model ◽

Fine Tuning ◽

Machine Learning Techniques ◽

Sequencing Data ◽

Rna Sequences ◽

Additional Information

AbstractMotivationThe accumulation of sequencing data has enabled researchers to predict the interactions between RNA sequences and RNA-binding proteins (RBPs) using novel machine learning techniques. However, existing models are often difficult to interpret and require additional information to sequences. Bidirectional encoder representations from Transformer (BERT) is a language-based deep learning model that is highly interpretable. Therefore, a model based on BERT architecture can potentially overcome such limitations.ResultsHere, we propose BERT-RBP as a model to predict RNA-RBP interactions by adapting the BERT architecture pre-trained on a human reference genome. Our model outperformed state-of-the-art prediction models using the eCLIP-seq data of 154 RBPs. The detailed analysis further revealed that BERT-RBP could recognize both the transcript region type and RNA secondary structure only from sequential information. Overall, the results provide insights into the fine-tuning mechanism of BERT in biological contexts and provide evidence of the applicability of the model to other RNA-related problems.AvailabilityPython source codes are freely available athttps://github.com/kkyamada/[email protected]

Download Full-text

The Landscape of RNA-Protein Interactions in Plants: Approaches and Current Status

International Journal of Molecular Sciences ◽

10.3390/ijms22062845 ◽

2021 ◽

Vol 22 (6) ◽

pp. 2845

Author(s):

Vesper Burjoski ◽

Anireddy S. N. Reddy

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Translational Regulation ◽

Rna Binding ◽

Rna Binding Proteins ◽

Current Status ◽

Protein Coding ◽

Rna Molecules ◽

Cellular Processes ◽

Binding Domains

RNAs transmit information from DNA to encode proteins that perform all cellular processes and regulate gene expression in multiple ways. From the time of synthesis to degradation, RNA molecules are associated with proteins called RNA-binding proteins (RBPs). The RBPs play diverse roles in many aspects of gene expression including pre-mRNA processing and post-transcriptional and translational regulation. In the last decade, the application of modern techniques to identify RNA–protein interactions with individual proteins, RNAs, and the whole transcriptome has led to the discovery of a hidden landscape of these interactions in plants. Global approaches such as RNA interactome capture (RIC) to identify proteins that bind protein-coding transcripts have led to the identification of close to 2000 putative RBPs in plants. Interestingly, many of these were found to be metabolic enzymes with no known canonical RNA-binding domains. Here, we review the methods used to analyze RNA–protein interactions in plants thus far and highlight the understanding of plant RNA–protein interactions these techniques have provided us. We also review some recent protein-centric, RNA-centric, and global approaches developed with non-plant systems and discuss their potential application to plants. We also provide an overview of results from classical studies of RNA–protein interaction in plants and discuss the significance of the increasingly evident ubiquity of RNA–protein interactions for the study of gene regulation and RNA biology in plants.

Download Full-text

PlncRNADB: A Repository of Plant lncRNAs and lncRNA-RBP Protein Interactions

Current Bioinformatics ◽

10.2174/1574893614666190131161002 ◽

2019 ◽

Vol 14 (7) ◽

pp. 621-627 ◽

Cited By ~ 3

Author(s):

Youhuang Bai ◽

Xiaozhuan Dai ◽

Tiantian Ye ◽

Peijing Zhang ◽

Xu Yan ◽

...

Keyword(s):

Protein Interactions ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Populus Trichocarpa ◽

Noncoding Rnas ◽

Reference Database ◽

Protein Coding ◽

Arabidopsis Lyrata ◽

User Friendly

Background: Long noncoding RNAs (lncRNAs) are endogenous noncoding RNAs, arbitrarily longer than 200 nucleotides, that play critical roles in diverse biological processes. LncRNAs exist in different genomes ranging from animals to plants. Objective: PlncRNADB is a searchable database of lncRNA sequences and annotation in plants. Methods: We built a pipeline for lncRNA prediction in plants, providing a convenient utility for users to quickly distinguish potential noncoding RNAs from protein-coding transcripts. Results: More than five thousand lncRNAs are collected from four plant species (Arabidopsis thaliana, Arabidopsis lyrata, Populus trichocarpa and Zea mays) in PlncRNADB. Moreover, our database provides the relationship between lncRNAs and various RNA-binding proteins (RBPs), which can be displayed through a user-friendly web interface. Conclusion: PlncRNADB can serve as a reference database to investigate the lncRNAs and their interaction with RNA-binding proteins in plants. The PlncRNADB is freely available at http://bis.zju.edu.cn/PlncRNADB/.

Download Full-text

RNA-binding proteins La and HuR cooperatively modulate translation repression of PDCD4 mRNA

Journal of Biological Chemistry ◽

10.1074/jbc.ra120.014894 ◽

2020 ◽

pp. jbc.RA120.014894

Author(s):

Ravi Kumar ◽

Dipak Kumar Poria ◽

Partho Sarothi Ray

Keyword(s):

Inflammatory Response ◽

Chronic Inflammation ◽

Rna Binding ◽

Rna Binding Proteins ◽

Critical Role ◽

Regulation Of Gene Expression ◽

Mrna Translation ◽

Suppressor Gene ◽

Cooperative Action ◽

Translation Repression

Post-transcriptional regulation of gene expression plays a critical role in controlling the inflammatory response. An uncontrolled inflammatory response results in chronic inflammation, often leading to tumorigenesis. Programmed cell death 4 (PDCD4) is a pro-inflammatory tumor-suppressor gene which helps to prevent the transition from chronic inflammation to cancer. PDCD4 mRNA translation is regulated by an interplay between the oncogenic microRNA miR-21 and the RNA-binding protein (RBP) HuR in response to LPS stimulation, but the role of other regulatory factors remain unknown. Here we report that the RBP Lupus antigen (La) interacts with the 3’UTR of PDCD4 mRNA and prevents miR-21-mediated translation repression. While LPS causes nuclear-cytoplasmic translocation of HuR, it enhances cellular La expression. Remarkably, La and HuR were found to bind cooperatively to the PDCD4 mRNA and mitigate miR-21-mediated translation repression. The cooperative action of La and HuR reduced cell proliferation and enhanced apoptosis, reversing the pro-oncogenic function of miR-21. Together, these observations demonstrate a cooperative interplay between two RBPs, triggered differentially by the same stimulus, which exerts a synergistic effect on PDCD4 expression and thereby helps maintain a balance between inflammation and tumorigenesis.

Download Full-text