RNA-protein Binding Sites Research Articles

Abstract MP219: Hnrnpa2b1-dependent Selective Sorting Of Mir-486a-5p Into Msc-derived Exosomes Contributes To Cardioprotection

Exosomes (Exo) are a class of extracellular vesicles and involvement of stem cell-derived Exo in cardiac repair and cardioprotection is thought to be an important in the heart. Our HYPOTHESIS Is that specific microRNAs (miRs) from mesenchymal stem cell (MSC)-derived exosomes are actively and selectively sorted into Exo by RNA binding proteins and motifs on the miR, to serve specific functions of the Exo, including Cardioprotection. Methods: We characterized the miR populations of parental MSCs and their Exo via RNA Seq and confirmed by QRT-PCR the subpopulation of miRs that is increased in Exo vs . MSC cells. We then used Multiple Em for Motif Elicitation (MEME) Version 5.3.3 and determined the predicted conserved motifs. From these, we predicted RNA binding protein sites from the literature. In parallel, we performed mass spectrometry and western blot analyses to determine RNA binding proteins in MSC and Exo. Predicting that hnRNPA2B1 was a likely RNA binding protein for the new motif, we knocked out the cognate gene (CRISPR) in MSC and evaluated the KO Exo vs. the WT Exo by RNA Seq and QRT-PCR. We performed protein and RNA pulldowns, and EMSA to validate binding of hnRNPA2B1 to several of the miRs, and investigated the effects of these miRs on cell survival after simIR and in an in vivo mouse model of MI. Results: We found a set of eight miRs that are selectively concentrated in the MSC Exo. MEME software predicted a conserved binding motif of gAGu, which is close to canonical sites for binding of hnRNPA2B1 and hnRNPA1. We determined hnRNPA2B1 was in MSC and Exo and showed KO hnRNPA2B1 cells and Exo had no compensatory perturbation of other RNA binding proteins. The KO MSC Exo show reduction of the selective sorting of the miRs of interest. Pulldowns and binding assay results verify binding of hnRNPA2B1 to both miR-486a-5p and miR-122a. Finally, we showed that miR-486a-5p is protective in H9C2 cells submitted to simIR and results in significant 68% reduction of infarct size (n=7, P=0.0175) in vivo in association with repression of PDCD4 expression and apoptosis. Conclusions: We determined that a set of miRs is selectively concentrated in MSC Exo and demonstrated the necessity of hnRNPA2B1 in that process. This appears to involve a conserved RNA sequence motif (mutational analysis underway). A major miR affected is miR-486a-5p, which is strongly cardioprotective. Our results support that miR-486a-5p is selectively concentrated in MSC Exo and contributes to cardioprotection by reducing PDCD4 activity in apoptosis.

RNAProt: an efficient and feature-rich RNA binding protein binding site predictor.

BackgroundCross-linking and immunoprecipitation followed by next-generation sequencing (CLIP-seq) is the state-of-the-art technique used to experimentally determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). However, it relies on gene expression, which can be highly variable between conditions and thus cannot provide a complete picture of the RBP binding landscape. This creates a demand for computational methods to predict missing binding sites. Although there exist various methods using traditional machine learning and lately also deep learning, we encountered several problems: many of these are not well documented or maintained, making them difficult to install and use, or are not even available. In addition, there can be efficiency issues, as well as little flexibility regarding options or supported features.ResultsHere, we present RNAProt, an efficient and feature-rich computational RBP binding site prediction framework based on recurrent neural networks. We compare RNAProt with 1 traditional machine learning approach and 2 deep-learning methods, demonstrating its state-of-the-art predictive performance and better run time efficiency. We further show that its implemented visualizations capture known binding preferences and thus can help to understand what is learned. Since RNAProt supports various additional features (including user-defined features, which no other tool offers), we also present their influence on benchmark set performance. Finally, we show the benefits of incorporating additional features, specifically structure information, when learning the binding sites of an hairpin loop binding RBP.ConclusionsRNAProt provides a complete framework for RBP binding site predictions, from data set generation over model training to the evaluation of binding preferences and prediction. It offers state-of-the-art predictive performance, as well as superior run time efficiency, while at the same time supporting more features and input types than any other tool available so far. RNAProt is easy to install and use, comes with comprehensive documentation, and is accompanied by informative statistics and visualizations. All this makes RNAProt a valuable tool to apply in future RBP binding site research.

Aging-associated lncRNAs are evolutionarily conserved and participate in NFκB signaling.

The transcriptome undergoes global changes during aging, including both protein-coding and noncoding RNAs. Using comparative genomics, we identify aging-associated long noncoding RNAs (lncRNAs) that are under evolutionary constraint and are more conserved than lncRNAs that do not change with age. Aging-associated lncRNAs are enriched for functional elements, including binding sites for RNA-binding proteins and transcription factors, in particular nuclear factor kappa B (NFκB). Using CRISPR screening, we discovered that 13 of the aging-associated lncRNAs were regulators of the NFκB pathway, and we named this family 'NFκB modulating aging-related lncRNAs (NFKBMARLs)'. Further characterization of NFκBMARL-1 reveals it can be traced to 29 Ma before humans and is induced by NFκB during aging, inflammation and senescence. Reciprocally, NFκBMARL-1 directly regulates transcription of the NFκB inhibitor NFKBIZ in cis within the same topologically associated domain by binding to the NFKBIZ enhancer and recruiting RELA to the NFKBIZ promoter. These findings reveal many aging-associated lncRNAs are evolutionarily conserved components of the NFκB pathway.

Identification and analysis of splicing quantitative trait loci across multiple tissues in the human genome

Alternative splicing (AS) is a fundamental step in eukaryotic mRNA biogenesis. Here, we develop an efficient and reproducible pipeline for the discovery of genetic variants that affect AS (splicing QTLs, sQTLs). We use it to analyze the GTEx dataset, generating a comprehensive catalog of sQTLs in the human genome. Downstream analysis of this catalog provides insight into the mechanisms underlying splicing regulation. We report that a core set of sQTLs is shared across multiple tissues. sQTLs often target the global splicing pattern of genes, rather than individual splicing events. Many also affect the expression of the same or other genes, uncovering regulatory loci that act through different mechanisms. sQTLs tend to be located in post-transcriptionally spliced introns, which would function as hotspots for splicing regulation. While many variants affect splicing patterns by altering the sequence of splice sites, many more modify the binding sites of RNA-binding proteins. Genetic variants affecting splicing can have a stronger phenotypic impact than those affecting gene expression.

Transcriptome-wide analysis reveals spatial correlation between N6-methyladenosine and binding sites of microRNAs and RNA-binding proteins

N6-methyladenosine (m6A), the most prevalent epitranscriptomic modification in eukaryotes, is enriched in 3′-untranslated regions (3′UTRs) of mRNAs. As 3′UTRs are major binding sites of RNA-binding proteins (RBPs) and microRNAs (miRNAs), m6A-dependent local RNA structure change may alter the accessibility of RBPs and miRNAs to their target sites and regulate mRNA function. Using a human transcriptome-wide computational analysis to investigate the relation between m6A, RBPs and miRNAs, we find a strong positive correlation between number of m6A sites, miRNAs and RBPs binding to mRNAs, suggesting m6A-modified mRNAs are more targeted by miRNAs and RBPs. Moreover, m6A sites are located proximally to miRNA target sites and binding sites of multiple RBPs. Further, miRNA target sites and RBP-binding sites located close to each other are also located proximally to m6A. This study indicates three-way interplay between m6A, microRNA and RBP binding, suggesting the influence of mRNA modifications on the miRNA and RBP interactomes.

Deep neural networks for inferring binding sites of RNA-binding proteins by using distributed representations of RNA primary sequence and secondary structure

BackgroundRNA binding proteins (RBPs) play a vital role in post-transcriptional processes in all eukaryotes, such as splicing regulation, mRNA transport, and modulation of mRNA translation and decay. The identification of RBP binding sites is a crucial step in understanding the biological mechanism of post-transcriptional gene regulation. However, the determination of RBP binding sites on a large scale is a challenging task due to high cost of biochemical assays. Quite a number of studies have exploited machine learning methods to predict binding sites. Especially, deep learning is increasingly used in the bioinformatics field by virtue of its ability to learn generalized representations from DNA and protein sequences.ResultsIn this paper, we implemented a novel deep neural network model, DeepRKE, which combines primary RNA sequence and secondary structure information to effectively predict RBP binding sites. Specifically, we used word embedding algorithm to extract features of RNA sequences and secondary structures, i.e., distributed representation of k-mers sequence rather than traditional one-hot encoding. The distributed representations are taken as input of convolutional neural networks (CNN) and bidirectional long-term short-term memory networks (BiLSTM) to identify RBP binding sites. Our results show that deepRKE outperforms existing counterpart methods on two large-scale benchmark datasets.ConclusionsOur extensive experimental results show that DeepRKE is an efficacious tool for predicting RBP binding sites. The distributed representations of RNA sequences and secondary structures can effectively detect the latent relationship and similarity between k-mers, and thus improve the predictive performance. The source code of DeepRKE is available at https://github.com/youzhiliu/DeepRKE/.

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

BackgroundRNA-binding proteins (RBPs) play crucial roles in various biological processes. Deep learning-based methods have been demonstrated powerful on predicting RBP sites on RNAs. However, the training of deep learning models is very time-intensive and computationally intensive.ResultsHere we present a deep learning-based RBPsuite, an easy-to-use webserver for predicting RBP binding sites on linear and circular RNAs. For linear RNAs, RBPsuite predicts the RBP binding scores with them using our updated iDeepS. For circular RNAs (circRNAs), RBPsuite predicts the RBP binding scores with them using our developed CRIP. RBPsuite first breaks the input RNA sequence into segments of 101 nucleotides and scores the interaction between the segments and the RBPs. RBPsuite further detects the verified motifs on the binding segments gives the binding scores distribution along the full-length sequence.ConclusionsRBPsuite is an easy-to-use online webserver for predicting RBP binding sites and freely available at http://www.csbio.sjtu.edu.cn/bioinf/RBPsuite/.

Genetic analyses support the contribution of mRNA N6-methyladenosine (m6A) modification to human disease heritability.

N6-methyladenosine (m6A) plays important roles in regulating mRNA processing. Despite rapid progress in this field, little is known about genetic determinants of m6A modification and their role in common diseases. In this work, we mapped quantitative trait loci (QTLs) of m6A peaks in 60 Yoruba lymphoblast cell lines (LCLs). We find that m6A-QTLs are largely independent of expression and splicing QTLs, and are enriched with binding sites of RNA-binding proteins (RBPs), RNA structure-changing variants and transcriptional features. Joint analysis of QTLs of m6A and related molecular traits suggests that downstream effects of m6A are heterogeneous and context-dependent. We identified proteins that mediate m6A effects on translation. Integrating with data from genome-wide association studies (GWAS), we show that m6A-QTLs contribute to heritability of various immune and blood-related traits at levels comparable to splicing-QTLs and roughly half of eQTLs. Leveraging m6A-QTLs in a transcriptome-wide association study (TWAS) framework, we identified putative risk genes of these traits.

RMAPS2: an update of the RNA map analysis and plotting server for alternative splicing regulation.

The rMAPS2 (RNA Map Analysis and Plotting Server 2) web server, freely available at http://rmaps.cecsresearch.org/, has provided the high-throughput sequencing data research community with curated tools for the identification of RNA binding protein sites. rMAPS2 analyzes differential alternative splicing or CLIP peak data obtained from high-throughput sequencing data analysis tools like MISO, rMATS, Piranha, PIPE-CLIP and PARalyzer, and then, graphically displays enriched RNA-binding protein target sites. The initial release of rMAPS focused only on the most common alternative splicing event, skipped exon or exon skipping. However, there was a high demand for the analysis of other major types of alternative splicing events, especially for retained intron events since this is the most common type of alternative splicing in plants, such as Arabidopsis thaliana. Here, we expanded the implementation of rMAPS2 to facilitate analyses for all five major types of alternative splicing events: skipped exon, mutually exclusive exons, alternative 5′ splice site, alternative 3′ splice site and retained intron. In addition, by employing multi-threading, rMAPS2 has vastly improved the user experience with significant reductions in running time, ∼3.5 min for the analysis of all five major alternative splicing types at once.

The Circular RNA circSKA3 Binds Integrin β1 to Induce Invadopodium Formation Enhancing Breast Cancer Invasion.

Metastatic cancer cells invade surrounding tissues by forming dynamic actin-based invadopodia, which degrade the surrounding extracellular matrix and allow cancer cell invasion. Regulatory RNAs, including circular RNA, have been implicated in this process. By microarray, we found that the circular RNA circSKA3 was highly expressed in breast cancer cells and human breast cancer tissues. We further found that the invasive capacity of breast cancer cells was positively correlated with circSKA3 expression, through the formation of invadopodia. Mechanistically, we identified Tks5 and integrin β1 as circSKA3 binding partners in these tumor-derived invadopodia. Ectopic circSKA3 expression conferred increased tumor invasiveness invitro and invivo. We further identified the RNA-protein binding sites between circSKA3, Tks5 and integrin β1. In tumor formation assays, we found that circSKA3 expression promoted tumor progression and invadopodium formation. Mutation of the circSKA3 binding sites or transfection with blocking oligos abrogated the observed effects. Thus, we provide evidence that the circular RNA circSKA3 promotes tumor progression by complexing with Tks5 and integrin β1, inducing invadopodium formation.

Use of Fluorescent Nucleotides to Map RNA-Binding Sites on Protein Surface.

Currently, studies of RNA/protein interactions occupy a prominent place in molecular biology and medicine. The structures of RNA-protein complexes may be determined by X-ray crystallography or NMR for further analyses. These methods are time-consuming and difficult due to the versatility and dynamics of the RNA structure. Furthermore, due to the need to solve the "phase problem" for each dataset in crystallography, crystallographic structures of RNA are still underrepresented. Structure determination of single ribonucleotide-protein complexes is a useful tool to identify the position of single-stranded RNA-binding sites in proteins. We describe here a structural approach that incorporates affinity measurement of a protein for various single ribonucleotides, ranking the RNA/protein complexes according to their stability. This chapter describes how to perform these measurements, including a perspective for the analysis of RNA-binding sites in protein and single-nucleotide crystal soaking.

A Review About RNA–Protein-Binding Sites Prediction Based on Deep Learning

RNA-binding proteins (RBPs) play crucial roles in gene regulation. The advent of high-throughput experimental methods, has generated a huge volume of experimentally verified binding sites of RNA-binding proteins and greatly advanced the genome-wide studies of RNA-protein interactions. Many computational approaches have been proposed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we discuss machine learning and deep learning approaches, mainly focusing on the prediction of RNA and proteins binding sites on RNAs by deep learning. Furthermore, we discuss the advantages and disadvantages of these approaches. The workflow of deep learning is also revealed. We recommend some promising future directions of deep learning models in the study of RBP-binding sites on RNAs, especially the embedding, generative adversarial net, and attention model. Extraction and visualization methods involving motif are illustrated. Finally, we summarize the previous studies, and then compare the performance on different dataset.

DSreg: a Bayesian model to integrate changes in splicing and RNA-binding protein activity.

MotivationAlternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two-step framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis.ResultsTo overcome these limitations, we propose dSreg, a Bayesian model that integrates RNA-seq with data from regulatory features, e.g. binding sites of RNA-binding proteins. dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified AS changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RNA-binding proteins’ experiments from ENCODE, as opposed to traditional enrichment methods, such as over-representation analysis and gene set enrichment analysis. dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments.Availability and implementationdSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg.Supplementary information Supplementary data are available at Bioinformatics online.

FLASH: ultra-fast protocol to identify RNA-protein interactions in cells.

Determination of the in vivo binding sites of RNA-binding proteins (RBPs) is paramount to understanding their function and how they affect different aspects of gene regulation. With hundreds of RNA-binding proteins identified in human cells, a flexible, high-resolution, high-throughput, highly multiplexible and radioactivity-free method to determine their binding sites has not been described to date. Here we report FLASH (Fast Ligation of RNA after some sort of Affinity Purification for High-throughput Sequencing), which uses a special adapter design and an optimized protocol to determine protein–RNA interactions in living cells. The entire FLASH protocol, starting from cells on plates to a sequencing library, takes 1.5 days. We demonstrate the flexibility, speed and versatility of FLASH by using it to determine RNA targets of both tagged and endogenously expressed proteins under diverse conditions in vivo.

Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins.

Gene regulation plays an important role in all cells. Transcriptional, post-transcriptional (or RNA processing), translational, and post-translational steps are used to regulate specific genes. Sequence-specific nucleic acid-binding proteins target specific sequences to control spatial or temporal gene expression. The binding sites in nucleic acids are typically characterized by mutational analysis. However, numerous proteins of interest have no known binding site for such characterization. Here we describe an approach to identify previously unknown binding sites for RNA-binding proteins. It involves iterative selection and amplification of sequences starting with a randomized sequence pool. Following several rounds of these steps-transcription, binding, and amplification-the enriched sequences are sequenced to identify a preferred binding site(s). Success of this approach is monitored using in vitro binding assays. Subsequently, in vitro and in vivo functional assays can be used to assess the biological relevance of the selected sequences. This approach allows identification and characterization of a previously unknown binding site(s) for any RNA-binding protein for which an assay to separate protein-bound and unbound RNAs exists.

Prediction of the RBP binding sites on lncRNAs using the high-order nucleotide encoding convolutional neural network

Long non-coding RNA (lncRNA) plays an important role in cells through the interaction with RNA-binding proteins (RBPs). Finding the RBPs binding sites on the lncRNA chains can help to understand the post-transcriptional regulatory mechanism, exploring the pathogenesis of cancers and possible roles in other diseases. Although many genome-wide RBP experimental techniques can identify the RNA-protein interactions and detect the binding sites on RNA chains, they are still time-consuming, labor-intensive and cost-heavy. Thus, many computational methods have been developed to predict the RBPs sites by integrating the RNA sequence, structure and domain specific features, etc. However, current approaches that focus on predicting the RBPs binding sites on RNA chains lack a consideration of the dependencies among nucleotides. In this work, we propose a higher-order nucleotide encoding convolutional neural network-based method (namely HOCNNLB) to predict the RBPs binding sites on lncRNA chains. HOCNNLB first employs a high-order one-hot encoding strategy to encode the lncRNA sequences by considering the dependence among nucleotides, then the encoded lncRNA sequences are fed into the convolutional neural network (CNN) to predict the RBP binding sites. We evaluate HOCNNLB on 31 experimental datasets of 12 lncRNA binding proteins. The average AUC of HOCNNLB achieves 0.953, which is 0.247, 0.175 higher than that of iDeepS and DeepBind, respectively. The average accuracy is 90.2%, which is 26.8%, 19.5% higher than that of iDeepS and DeepBind, respectively. These results demonstrate that HOCNNLB can reliably predict the RBP binding sites on lncRNA chains and outperforms the state-of-the-art methods. The source code of HOCNNLB and the datasets used in this work are available at https://github.com/NWPU-903PR/HOCNNLB for academic users.

Integration of CLIP experiments of RNA-binding proteins: a novel approach to predict context-dependent splicing factors from transcriptomic data

BackgroundSplicing is a genetic process that has important implications in several diseases including cancer. Deciphering the complex rules of splicing regulation is crucial to understand and treat splicing-related diseases. Splicing factors and other RNA-binding proteins (RBPs) play a key role in the regulation of splicing. The specific binding sites of an RBP can be measured using CLIP experiments. However, to unveil which RBPs regulate a condition, it is necessary to have a priori hypotheses, as a single CLIP experiment targets a single protein.ResultsIn this work, we present a novel methodology to predict context-specific splicing factors from transcriptomic data. For this, we systematically collect, integrate and analyze more than 900 CLIP experiments stored in four CLIP databases: POSTAR2, CLIPdb, DoRiNA and StarBase. The analysis of these experiments shows the strong coherence between the binding sites of RBPs of similar families. Augmenting this information with expression changes, we are able to correctly predict the splicing factors that regulate splicing in two gold-standard experiments in which specific splicing factors are knocked-down.ConclusionsThe methodology presented in this study allows the prediction of active splicing factors in either cancer or any other condition by only using the information of transcript expression. This approach opens a wide range of possible studies to understand the splicing regulation of different conditions. A tutorial with the source code and databases is available at https://gitlab.com/fcarazo.m/sfprediction.

Recent methodology progress of deep learning for RNA-protein interaction prediction.

Interactions between RNAs and proteins play essential roles in many important biological processes. Benefitting from the advances of next generation sequencing technologies, hundreds of RNA-binding proteins (RBP) and their associated RNAs have been revealed, which enables the large-scale prediction of RNA-protein interactions using machine learning methods. Till now, a wide range of computational tools and pipelines have been developed, including deep learning models, which have achieved remarkable performance on the identification of RNA-protein binding affinities and sites. In this review, we provide an overview of the successful implementation of various deep learning approaches for predicting RNA-protein interactions, mainly focusing on the prediction of RNA-protein interaction pairs and RBP-binding sites on RNAs. Furthermore, we discuss the advantages and disadvantages of these approaches, and highlight future perspectives on how to design better deep learning models. Finally, we suggest some promising future directions of computational tasks in the study of RNA-protein interactions, especially the interactions between noncoding RNAs and proteins. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Evolution and Genomics > Computational Analyses of RNA RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition.

SpliceV: analysis and publication quality printing of linear and circular RNA splicing, expression and regulation

BackgroundIn eukaryotes, most genes code for multiple transcript isoforms that are generated through the complex and tightly regulated process of RNA splicing. Despite arising from identical precursor transcripts, alternatively spliced RNAs can have dramatically different functions. Transcriptome complexity is elevated further by the production of circular RNAs (circRNAs), another class of mature RNA that results from the splicing of a downstream splice donor to an upstream splice acceptor. While there has been a rapid expansion of circRNA catalogs in the last few years through the utilization of next generation sequencing approaches, our understanding of the mechanisms and regulation of circular RNA biogenesis, the impact that circRNA generation has on parental transcript processing, and the functions carried out by circular RNAs remains limited.ResultsHere, we present a visualization and analysis tool, SpliceV, that rapidly plots all relevant forward- and back-splice data, with exon and single nucleotide level coverage information from RNA-seq experiments in a publication quality format. SpliceV also integrates analysis features that assist investigations into splicing regulation and transcript functions through the display of predicted RNA binding protein sites and the configuration of repetitive elements along the primary transcript.ConclusionsSpliceV is an easy-to-use splicing visualization tool, compatible with both Python 2.7 and 3+, and distributed under the GNU Public License. The source code is freely available for download at https://github.com/flemingtonlab/SpliceV and can be installed from PyPI using pip.

HPV shapes tumor transcriptome by globally modifying the pool of RNA binding protein-binding motif.

Human papillomavirus (HPV) positive head and neck cancer displayed specific transcription landscape but the underlying molecular mechanisms are not fully determined. Here, we interestingly found that HPV infection could globally elongate the 3’-untranslated regions (3’UTRs) in the majority of alternative polyadenylation (APA)-containing genes. Counterintuitively, the 3’UTR elongation does not affect their resident gene expression. Rather, they significantly increase the number of binding sites for RNA-binding proteins (RBPs) and subsequently upregulate a group of oncogenic genes by absorbing RBPs. A significant fraction of HPV affected genes are regulated through such mechanism that is 3’UTR-mediated recruitment of RBPs. As an example, we observed that HPV infection increases the length of 3’UTR of RBM25 transcript and hence recruits much more RNA binding protein including FUS and DGCR8. Consequently, in the absence of FUS and DGCR8 regulation, PD-1 was rescued and up-regulated after HPV infection. Taken together, our findings not only suggest a novel paradigm of how oncogenic viruses shape tumor transcriptome by modifying the 3’UTR, but also present a previously unrecognized layer of APA—RBP interplay in this molecular hierarchy. Modification of the pool of RBP-binding motif might expand our understandings into virus-associated carcinogenesis.