icSHAPE-pipe: A comprehensive toolkit for icSHAPE data analysis and evaluation

Mapping of the influenza A virus genome RNA structure and interactions reveals essential elements of viral replication

Influenza A virus (IAV) represents a constant public health threat. The single-stranded, segmented RNA genome of IAV is replicated in host cell nuclei as a series of 8 ribonucleoprotein complexes (vRNPs) with RNA structures known to exert essential function to support viral replication. Here, we investigate RNA secondary structures and RNA interactions networks of the IAV genome and construct an in vivo structure model for each of the 8 IAV genome segments. Our analyses reveal an overall in vivo and in virio resemblance of the IAV genome conformation but also wide disparities among long-range and intersegment interactions. Moreover, we identify a long-range RNA interaction that exerts an essential role in genome packaging. Disrupting this structure displays reduced infectivity, attenuating virus pathogenicity in mice. Our findings characterize the in vivo RNA structural landscape of the IAV genome and reveal viral RNA structures that can be targeted to develop antiviral interventions.

AStruct: detection of allele-specific RNA secondary structure in structuromic probing data

BACKGROUND

Uncovering functional genetic variants from an allele-specific perspective is of paramount importance in advancing our understanding of gene regulation and genetic diseases. Recently, various allele-specific events, such as allele-specific gene expression, allele-specific methylation, and allele-specific binding, have been explored on a genome-wide scale due to the development of high-throughput sequencing methods. RNA secondary structure, which plays a crucial role in multiple RNA-associated processes like RNA modification, translation and splicing, has emerged as an essential focus of relevant research. However, tools to identify genetic variants associated with allele-specific RNA secondary structures are still lacking.

RESULTS

Here, we develop a computational tool called 'AStruct' that enables us to detect allele-specific RNA secondary structure (ASRS) from RT-stop based structuromic probing data. AStruct shows robust performance in both simulated datasets and public icSHAPE datasets. We reveal that single nucleotide polymorphisms (SNPs) with higher AStruct scores are enriched in coding regions and tend to be functional. These SNPs are highly conservative, have the potential to disrupt sites involved in m6A modification or protein binding, and are frequently associated with disease.

CONCLUSIONS

AStruct is a tool dedicated to invoke allele-specific RNA secondary structure events at heterozygous SNPs in RT-stop based structuromic probing data. It utilizes allelic variants, base pairing and RT-stop information under different cell conditions to detect dynamic and functional ASRS. Compared to sequence-based tools, AStruct considers dynamic cell conditions and outperforms in detecting functional variants. AStruct is implemented in JAVA and is freely accessible at: https://github.com/canceromics/AStruct .

Dynamic characterization and interpretation for protein-RNA interactions across diverse cellular conditions using HDRNet

RNA-binding proteins play crucial roles in the regulation of gene expression, and understanding the interactions between RNAs and RBPs in distinct cellular conditions forms the basis for comprehending the underlying RNA function. However, current computational methods pose challenges to the cross-prediction of RNA-protein binding events across diverse cell lines and tissue contexts. Here, we develop HDRNet, an end-to-end deep learning-based framework to precisely predict dynamic RBP binding events under diverse cellular conditions. Our results demonstrate that HDRNet can accurately and efficiently identify binding sites, particularly for dynamic prediction, outperforming other state-of-the-art models on 261 linear RNA datasets from both eCLIP and CLIP-seq, supplemented with additional tissue data. Moreover, we conduct motif and interpretation analyses to provide fresh insights into the pathological mechanisms underlying RNA-RBP interactions from various perspectives. Our functional genomic analysis further explores the gene-human disease associations, uncovering previously uncharacterized observations for a broad range of genetic disorders.

Structural RNA components supervise the sequential DNA cleavage in R2 retrotransposon

Retroelements are the widespread jumping elements considered as major drivers for genome evolution, which can also be repurposed as gene-editing tools. Here, we determine the cryo-EM structures of eukaryotic R2 retrotransposon with ribosomal DNA target and regulatory RNAs. Combined with biochemical and sequencing analysis, we reveal two essential DNA regions, Drr and Dcr, required for recognition and cleavage. The association of 3' regulatory RNA with R2 protein accelerates the first-strand cleavage, blocks the second-strand cleavage, and initiates the reverse transcription starting from the 3'-tail. Removing 3' regulatory RNA by reverse transcription allows the association of 5' regulatory RNA and initiates the second-strand cleavage. Taken together, our work explains the DNA recognition and RNA supervised sequential retrotransposition mechanisms by R2 machinery, providing insights into the retrotransposon and application reprogramming.

Protein-RNA interaction prediction with deep learning: structure matters

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

An ultra low-input method for global RNA structure probing uncovers Regnase-1-mediated regulation in macrophages

To enable diverse functions and precise regulation, an RNA sequence often folds into complex yet distinct structures in different cellular states. Probing RNA in its native environment is essential to uncovering RNA structures of biological contexts. However, current methods generally require large amounts of input RNA and are challenging for physiologically relevant use. Here, we report smartSHAPE, a new RNA structure probing method that requires very low amounts of RNA input due to the largely reduced artefact of probing signals and increased efficiency of library construction. Using smartSHAPE, we showcased the profiling of the RNA structure landscape of mouse intestinal macrophages upon inflammation, and provided evidence that RNA conformational changes regulate immune responses. These results demonstrate that smartSHAPE can greatly expand the scope of RNA structure-based investigations in practical biological systems, and also provide a research paradigm for the study of post-transcriptional regulation.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

SRSF5 regulates alternative splicing of DMTF1 pre-mRNA through modulating SF1 binding

ABBREVIATIONS

ARF: alternative reading frame, that is, p14ARF, or CDKN2A (cyclin-dependent kinase inhibitor 2A); β-gal: β-galactosidase; CLIP-seq: crosslinking and immunoprecipitation-sequencing; DMTF1: the cyclin D binding myb-like transcription factor 1; ESS/ESE: exonic splicing silencer/enhancer; Ex: exon; FBS: fetal bovine serum; Gluc: Gaussia luciferase; hnRNPs: heterogeneous nuclear ribonucleoproteins; In: intron; ISS/ISE: intronic splicing silencer/enhancer; PBS: phosphate-buffered saline; PCR: polymerase chain reaction; PSI: percent-splice-in; qPCR: quantitative real-time PCR; RIP: RNA immunoprecipitation; RNAseq: RNA sequencing; RT: reverse transcription; SF1: splicing factor 1; SR: serine/arginine-rich proteins; SRSF5: serine and arginine-rich splicing factor 5; TCGA: the cancer genome atlas; UCSC: University of California, Santa Cruz. WT: Wild type.

Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an "attention" strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions, with clear utility for understanding and treating human diseases.

Conserved long-range base pairings are associated with pre-mRNA processing of human genes

The ability of nucleic acids to form double-stranded structures is essential for all living systems on Earth. Current knowledge on functional RNA structures is focused on locally-occurring base pairs. However, crosslinking and proximity ligation experiments demonstrated that long-range RNA structures are highly abundant. Here, we present the most complete to-date catalog of conserved complementary regions (PCCRs) in human protein-coding genes. PCCRs tend to occur within introns, suppress intervening exons, and obstruct cryptic and inactive splice sites. Double-stranded structure of PCCRs is supported by decreased icSHAPE nucleotide accessibility, high abundance of RNA editing sites, and frequent occurrence of forked eCLIP peaks. Introns with PCCRs show a distinct splicing pattern in response to RNAPII slowdown suggesting that splicing is widely affected by co-transcriptional RNA folding. The enrichment of 3'-ends within PCCRs raises the intriguing hypothesis that coupling between RNA folding and splicing could mediate co-transcriptional suppression of premature pre-mRNA cleavage and polyadenylation.

In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.