Showing papers on "Nucleic acid secondary structure published in 2019"

RNA structure maps across mammalian cellular compartments

Abstract: RNA structure is intimately connected to each step of gene expression. Recent advances have enabled transcriptome-wide maps of RNA secondary structure, called 'RNA structuromes'. However, previous whole-cell analyses lacked the resolution to unravel the landscape and also the regulatory mechanisms of RNA structural changes across subcellular compartments. Here we reveal the RNA structuromes in three compartments, chromatin, nucleoplasm and cytoplasm, in human and mouse cells. The cytotopic structuromes substantially expand RNA structural information and enable detailed investigation of the central role of RNA structure in linking transcription, translation and RNA decay. We develop a resource with which to visualize the interplay of RNA-protein interactions, RNA modifications and RNA structure and predict both direct and indirect reader proteins of RNA modifications. We also validate a novel role for the RNA-binding protein LIN28A as an N6-methyladenosine modification 'anti-reader'. Our results highlight the dynamic nature of RNA structures and its functional importance in gene regulation.

Replication of G Quadruplex DNA.

Abstract: A cursory look at any textbook image of DNA replication might suggest that the complex machine that is the replisome runs smoothly along the chromosomal DNA. However, many DNA sequences can adopt non-B form secondary structures and these have the potential to impede progression of the replisome. A picture is emerging in which the maintenance of processive DNA replication requires the action of a significant number of additional proteins beyond the core replisome to resolve secondary structures in the DNA template. By ensuring that DNA synthesis remains closely coupled to DNA unwinding by the replicative helicase, these factors prevent impediments to the replisome from causing genetic and epigenetic instability. This review considers the circumstances in which DNA forms secondary structures, the potential responses of the eukaryotic replisome to these impediments in the light of recent advances in our understanding of its structure and operation and the mechanisms cells deploy to remove secondary structure from the DNA. To illustrate the principles involved, we focus on one of the best understood DNA secondary structures, G quadruplexes (G4s), and on the helicases that promote their resolution.

A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming.

Abstract: In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA in vivo using an iterative method to meet the minimum energy or other constraints. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future.

DMfold: A Novel Method to Predict RNA Secondary Structure With Pseudoknots Based on Deep Learning and Improved Base Pair Maximization Principle.

Abstract: While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, ‘DMfold’, a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database.

The majority of A-to-I RNA editing is not required for mammalian homeostasis

Abstract: Adenosine-to-inosine (A-to-I) RNA editing, mediated by ADAR1 and ADAR2, occurs at tens of thousands to millions of sites across mammalian transcriptomes. A-to-I editing can change the protein coding potential of a transcript and alter RNA splicing, miRNA biology, RNA secondary structure and formation of other RNA species. In vivo, the editing-dependent protein recoding of GRIA2 is the essential function of ADAR2, while ADAR1 editing prevents innate immune sensing of endogenous RNAs by MDA5 in both human and mouse. However, a significant proportion of A-to-I editing sites can be edited by both ADAR1 and ADAR2, particularly within the brain where both are highly expressed. The physiological function(s) of these shared sites, including those evolutionarily conserved, is largely unknown. To generate completely A-to-I editing-deficient mammals, we crossed the viable rescued ADAR1-editing-deficient animals (Adar1E861A/E861AIfih1−/−) with rescued ADAR2-deficient (Adarb1−/−Gria2R/R) animals. Unexpectedly, the global absence of editing was well tolerated. Adar1E861A/E861AIfih1−/−Adarb1−/−Gria2R/R were recovered at Mendelian ratios and age normally. Detailed transcriptome analysis demonstrated that editing was absent in the brains of the compound mutants and that ADAR1 and ADAR2 have similar editing site preferences and patterns. We conclude that ADAR1 and ADAR2 are non-redundant and do not compensate for each other’s essential functions in vivo. Physiologically essential A-to-I editing comprises a small subset of the editome, and the majority of editing is dispensable for mammalian homeostasis. Moreover, in vivo biologically essential protein recoding mediated by A-to-I editing is an exception in mammals.

Comprehensive catalog of dendritically localized mRNA isoforms from sub-cellular sequencing of single mouse neurons

Abstract: RNA localization involves cis-motifs that are recognized by RNA-binding proteins (RBP), which then mediate localization to specific sub-cellular compartments. RNA localization is critical for many different cell functions, e.g., in neuronal dendrites, localization is a critical step for long-lasting synaptic potentiation. However, there is little consensus regarding which RNAs are localized and the role of alternative isoforms in localization. A comprehensive catalog of localized RNA can help dissect RBP/RNA interactions and localization motifs. Here, we utilize a single cell sub-cellular RNA sequencing approach to profile differentially localized RNAs from individual cells across multiple single cells to help identify a consistent set of localized RNA in mouse neurons. Using independent RNA sequencing from soma and dendrites of the same neuron, we deeply profiled the sub-cellular transcriptomes to assess the extent and variability of dendritic RNA localization in individual hippocampal neurons, including an assessment of differential localization of alternative 3′UTR isoforms. We identified 2225 dendritic RNAs, including 298 cases of 3′UTR isoform-specific localization. We extensively analyzed the localized RNAs for potential localization motifs, finding that B1 and B2 SINE elements are up to 5.7 times more abundant in localized RNA 3′UTRs than non-localized, and also functionally characterized the localized RNAs using protein structure analysis. We integrate our list of localized RNAs with the literature to provide a comprehensive list of known dendritically localized RNAs as a resource. This catalog of transcripts, including differentially localized isoforms and computationally hypothesized localization motifs, will help investigators further dissect the genome-scale mechanism of RNA localization.

In vivo RNA structural probing of uracil and guanine base-pairing by 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC).

Abstract: Many biological functions performed by RNAs arise from their in vivo structures. The structure of the same RNA can differ in vitro and in vivo owing in part to the influence of molecules ranging from protons to secondary metabolites to proteins. Chemical reagents that modify the Watson-Crick (WC) face of unprotected RNA bases report on the absence of base-pairing and so are of value to determining structures adopted by RNAs. Reagents have thus been sought that can report on the native RNA structures that prevail in living cells. Dimethyl sulfate (DMS) and glyoxal penetrate cell membranes and inform on RNA secondary structure in vivo through modification of adenine (A), cytosine (C), and guanine (G) bases. Uracil (U) bases, however, have thus far eluded characterization in vivo. Herein, we show that the water-soluble carbodiimide 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) is capable of modifying the WC face of U and G in vivo, favoring the former nucleobase by a factor of ∼1.5, and doing so in the eukaryote rice, as well as in the Gram-negative bacterium Escherichia coli While both EDC and glyoxal target Gs, EDC reacts with Gs in their typical neutral state, while glyoxal requires Gs to populate the rare anionic state. EDC may thus be more generally useful; however, comparison of the reactivity of EDC and glyoxal may allow the identification of Gs with perturbed pKas in vivo and genome-wide. Overall, use of EDC with DMS allows in vivo probing of the base-pairing status of all four RNA bases.

G-Quadruplex DNA and RNA.

Abstract: G-quadruplexes (G4s) have become one of the most exciting nucleic acid secondary structures. A noncanonical, four-stranded structure formed in guanine-rich DNA and RNA sequences, G-quadruplexes can readily form under physiologically relevant conditions and are globularly folded structures. DNA is widely recognized as a double-helical structure essential in genetic information storage. However, only ~3% of the human genome is expressed in protein; RNA and DNA may form noncanonical secondary structures that are functionally important. G-quadruplexes are one such example which have gained considerable attention for their formation and regulatory roles in biologically significant regions, such as human telomeres, oncogene-promoter regions, replication initiation sites, and 5'- and 3'-untranslated region (UTR) of mRNA. They are shown to be a regulatory motif in a number of critical cellular processes including gene transcription, translation, replication, and genomic stability. G-quadruplexes are also found in nonhuman genomes, particularly those of human pathogens. Therefore, G-quadruplexes have emerged as a new class of molecular targets for drug development. In addition, there is considerable interest in the use of G-quadruplexes for biomaterials, biosensors, and biocatalysts. The First International Meeting on Quadruplex DNA was held in 2007, and the G-quadruplex field has been growing dramatically over the last decade. The methods used to study G-quadruplexes have been essential to the rapid progress in our understanding of this exciting nucleic acid secondary structure.

Quantitative principles of cis -translational control by general mRNA sequence features in eukaryotes

Abstract: General translational cis-elements are present in the mRNAs of all genes and affect the recruitment, assembly, and progress of preinitiation complexes and the ribosome under many physiological states. These elements include mRNA folding, upstream open reading frames, specific nucleotides flanking the initiating AUG codon, protein coding sequence length, and codon usage. The quantitative contributions of these sequence features and how and why they coordinate to control translation rates are not well understood. Here, we show that these sequence features specify 42–81% of the variance in translation rates in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Arabidopsis thaliana, Mus musculus, and Homo sapiens. We establish that control by RNA secondary structure is chiefly mediated by highly folded 25–60 nucleotide segments within mRNA 5′ regions, that changes in tri-nucleotide frequencies between highly and poorly translated 5′ regions are correlated between all species, and that control by distinct biochemical processes is extensively correlated as is regulation by a single process acting in different parts of the same mRNA. Our work shows that general features control a much larger fraction of the variance in translation rates than previously realized. We provide a more detailed and accurate understanding of the aspects of RNA structure that directs translation in diverse eukaryotes. In addition, we note that the strongly correlated regulation between and within cis-control features will cause more even densities of translational complexes along each mRNA and therefore more efficient use of the translation machinery by the cell.

The ribosomal protein S1-dependent standby site in tisB mRNA consists of a single-stranded region and a 5 ' structure element

Abstract: In bacteria, stable RNA structures that sequester ribosome-binding sites (RBS) impair translation initiation, and thus protein output In some cases, ribosome standby can overcome inhibition by str

Expanding the CRISPR Toolbox with ErCas12a in Zebrafish and Human Cells

Abstract: CRISPR and CRISPR-Cas effector proteins enable the targeting of DNA double-strand breaks to defined loci based on a variable length RNA guide specific to each effector. The guide RNAs are generally similar in size and form, consisting of a ∼20 nucleotide sequence complementary to the DNA target and an RNA secondary structure recognized by the effector. However, the effector proteins vary in protospacer adjacent motif requirements, nuclease activities, and DNA binding kinetics. Recently, ErCas12a, a new member of the Cas12a family, was identified in Eubacterium rectale. Here, we report the first characterization of ErCas12a activity in zebrafish and expand on previously reported activity in human cells. Using a fluorescent reporter system, we show that CRISPR-ErCas12a elicits strand annealing mediated DNA repair more efficiently than CRISPR-Cas9. Further, using our previously reported gene targeting method that utilizes short homology, GeneWeld, we demonstrate the use of CRISPR-ErCas12a to integrate reporter alleles into the genomes of both zebrafish and human cells. Together, this work provides methods for deploying an additional CRISPR-Cas system, thus increasing the flexibility researchers have in applying genome engineering technologies.

Conserved Secondary Structures in Viral mRNAs.

Abstract: RNA secondary structure in untranslated and protein coding regions has been shown to play an important role in regulatory processes and the viral replication cycle. While structures in non-coding regions have been investigated extensively, a thorough overview of the structural repertoire of protein coding mRNAs, especially for viruses, is lacking. Secondary structure prediction of large molecules, such as long mRNAs remains a challenging task, as the contingent of structures a sequence can theoretically fold into grows exponentially with sequence length. We applied a structure prediction pipeline to Viral Orthologous Groups that first identifies the local boundaries of potentially structured regions and subsequently predicts their functional importance. Using this procedure, the orthologous groups were split into structurally homogenous subgroups, which we call subVOGs. This is the first compilation of potentially functional conserved RNA structures in viral coding regions, covering the complete RefSeq viral database. We were able to recover structural elements from previous studies and discovered a variety of novel structured regions. The subVOGs are available through our web resource RNASIV (RNA structure in viruses).

Single-molecule correlated chemical probing reveals large-scale structural communication in the ribosome and the mechanism of the antibiotic spectinomycin in living cells

Abstract: The ribosome moves between distinct structural states and is organized into multiple functional domains. Here, we examined hundreds of occurrences of pairwise through-space communication between nucleotides in the ribosome small subunit RNA using RNA interaction groups analyzed by mutational profiling (RING-MaP) single-molecule correlated chemical probing in bacterial cells. RING-MaP revealed four structural communities in the small subunit RNA, each distinct from the organization defined by the RNA secondary structure. The head domain contains 2 structural communities: the outer-head contains the pivot for head swiveling, and an inner-head community is structurally integrated with helix 44 and spans the entire ribosome intersubunit interface. In-cell binding by the antibiotic spectinomycin (Spc) barely perturbs its local binding pocket as revealed by the per-nucleotide chemical probing signal. In contrast, Spc binding overstabilizes long-range RNA–RNA contacts that extend 95 A across the ribosome that connect the pivot for head swiveling with the axis of intersubunit rotation. The two major motions of the small subunit—head swiveling and intersubunit rotation—are thus coordinated via long-range RNA structural communication, which is specifically modulated by Spc. Single-molecule correlated chemical probing reveals trans-domain structural communication and rationalizes the profound functional effects of binding by a low–molecular-mass antibiotic to the megadalton ribosome.

An algebraic language for RNA pseudoknots comparison

Abstract: RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation. In this work, we introduce an algebraic language to represent RNA secondary structures with arbitrary pseudoknots. Each structure is associated with a unique algebraic RNA tree that is derived from a tree grammar having concatenation, nesting and crossing as operators. From an algebraic RNA tree, an abstraction is defined in which the primary structure is neglected. The resulting structural RNA tree allows us to define a new measure of similarity calculated exploiting classical tree alignment. The tree grammar with its operators permit to uniquely represent any RNA secondary structure as a tree. Structural RNA trees allow us to perform comparison of RNA secondary structures with arbitrary pseudoknots without taking into account the primary structure.

Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter

Abstract: RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity. To address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs. Comparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods.

Integrative Analysis of Somatic Mutations in Non-coding Regions Altering RNA Secondary Structures in Cancer Genomes.

Abstract: RNA secondary structure may influence many cellular processes, including RNA processing, stability, localization, and translation. Single-nucleotide variations (SNVs) that alter RNA secondary structure, referred to as riboSNitches, are potentially causative of human diseases, especially in untranslated regions (UTRs) and noncoding RNAs (ncRNAs). The functions of somatic mutations that act as riboSNitches in cancer development remain poorly understood. In this study, we developed a computational pipeline called SNIPER (riboSNitch-enriched or depleted elements in cancer genomes), which employs MeanDiff and EucDiff to detect riboSNitches and then identifies riboSNitch-enriched or riboSNitch-depleted non-coding elements across tumors. SNIPER is available at github: https://github.com/suzhixi/SNIPER/ . We found that riboSNitches were more likely to be pathogenic. Moreover, we predicted several UTRs and lncRNAs (long non-coding RNA) that significantly enriched or depleted riboSNitches in cancer genomes, indicative of potential cancer driver or essential noncoding elements. Our study highlights the possibly neglected importance of RNA secondary structure in cancer genomes and provides a new strategy to identify new cancer-associated genes.

3D based on 2D: Calculating helix angles and stacking patterns using forgi 2.0, an RNA Python library centered on secondary structure elements.

Abstract: We present forgi , a Python library to analyze the tertiary structure of RNA secondary structure elements. Our representation of an RNA molecule is centered on secondary structure elements (stems, bulges and loops). By fitting a cylinder to the helix axis, these elements are carried over into a coarse-grained 3D structure representation. Integration with Biopython allows for handling of all-atom 3D information. forgi can deal with a variety of file formats including dotbracket strings, PDB and MMCIF files. We can handle modified residues, missing residues, cofold and multifold structures as well as nucleotide numbers starting at arbitrary positions. We apply this library to the study of stacking helices in junctions and pseudoknots and investigate how far stacking helices in solved experimental structures can divert from coaxial geometries.

Stacking of nucleic acid bases: optimization of the computational approach—the case of adenine dimers

Abstract: Stacking interactions play an important role in stabilizing DNA and RNA secondary structure. To select a computational level to study the stacking interactions, both energy and geometric criteria, as well as the time necessary to optimize the system, should be taken into account. In this work, an attempt was made to find the most optimal level of theory describing the stacking interactions in adenine dimers. The obtained results have shown that for this purpose, wB97XD/6-311G(p,d), wB97XD/aug-cc-pvdz, or B97D3/aug-cc-pvdz should be used. What is more, geometry of the most preferable arrangements of molecules was also pointed out, ensuring an optimal starting system for further analyses.

Visualizing RNA Conformational Changes via Pattern Recognition of RNA by Small Molecules

Abstract: Conformational changes in RNA play vital roles in the regulation of many biological systems, yet these changes can be challenging to visualize. Previously, we demonstrated that Pattern Recognition of RNA by Small Molecules (PRRSM) can unbiasedly cluster defined RNA secondary structure motifs utilizing an aminoglycoside receptor library. In this work, we demonstrate the power of this method to visualize changes in folding at the secondary structure level within two distinct riboswitch structures. After labeling at three independent positions on each riboswitch, PRRSM accurately classified all apo and ligand-bound riboswitch structures, including changes in the size of a structural motif, and revealed modification sites that prevented folding and/or led to a mixture of states. These data underscore the utility and robustness of the PRRSM assay for rapid assessment of RNA structural changes and for gaining ready insight into nucleotide positions critical to RNA folding.

Global importance of RNA secondary structures in protein-coding sequences.

Abstract: MOTIVATION The protein-coding sequences of messenger RNAs are the linear template for translation of the gene sequence into protein. Nevertheless, the RNA can also form secondary structures by intramolecular base-pairing. RESULTS We show that the nucleotide distribution within codons is biased in all taxa of life on a global scale. Thereby, RNA secondary structures that require base-pairing between the position 1 of a codon with the position 1 of an opposing codon (here named RNA secondary structure class c1) are under-represented. We conclude that this bias may result from the co-evolution of codon sequence and mRNA secondary structure, suggesting that RNA secondary structures are generally important in protein-coding regions of mRNAs. The above result also implies that codon position 2 has a smaller influence on the amino acid choice than codon position 1. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Demonstration of protein cooperativity mediated by RNA structure using the human protein PUM2.

Abstract: Posttranslational gene regulation requires a complex network of RNA-protein interactions. Cooperativity, which tunes response sensitivities, originates from protein-protein interactions in many systems. For RNA-binding proteins, cooperativity can also be mediated through RNA structure. RNA structural cooperativity (RSC) arises when binding of one protein induces a redistribution of RNA conformational states that enhance access (positive cooperativity) or block access (negative cooperativity) to additional binding sites. As RSC does not require direct protein-protein interactions, it allows cooperativity to be tuned for individual RNAs, via alterations in sequence that alter structural stability. Given the potential importance of this mechanism of control and our desire to quantitatively dissect features that underlie physiological regulation, we developed a statistical mechanical framework for RSC and tested this model by performing equilibrium binding measurements of the human PUF family protein PUM2. Using 68 RNAs that contain two to five PUM2-binding sites and RNA structures of varying stabilities, we observed a range of structure-dependent cooperative behaviors. To test our ability to account for this cooperativity with known physical constants, we used PUM2 affinity and nearest-neighbor RNA secondary structure predictions. Our model gave qualitative agreement for our disparate set of 68 RNAs across two temperatures, but quantitative deviations arise from overestimation of RNA structural stability. Our results demonstrate cooperativity mediated by RNA structure and underscore the power of quantitative stepwise experimental evaluation of mechanisms and computational tools.

Modulation of PDCD1 exon 3 splicing

Abstract: The PDCD1 gene encodes PD-1, an important immune checkpoint protein and key immunotherapy target to treat cancer. PDCD1 is alternatively spliced to generate an exon 3-skipped isoform PD-1Δ3 that has been suggested to play an antagonistic role to PD-1, but the mechanism underlying alternative splicing of PDCD1 has never been explored. Here using a minigene system, we analysed the splicing pattern of PDCD1 in multiple cell lines and confirmed exon 3 skipping as the main alternative splicing event. Using deletion analysis of exon 3, we mapped two splicing enhancers in the exon: ESE3a and ESE3b. Using mutagenesis, RNA-affinity chromatography, mass spectrometry as well as depletion and overexpression of MATR3, we defined MATR3 as a splicing activator during PDCD1 exon 3 splicing that operates through binding to ESE3b. MATR3's splicing-stimulatory activity is counteracted by an RNA secondary structure around ESE3b and an RNA helicase DDX5. Furthermore, we identified ASOs that efficiently promotes PDCD1 exon 3 skipping in both minigene and endogenous-gene contexts. Our data support further study of the ASOs as potential drug candidates to treat cancer.