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.

Structural insights into branch site proofreading by human spliceosome.

Selection of the pre-mRNA branch site (BS) by the U2 small nuclear ribonucleoprotein (snRNP) is crucial to prespliceosome (A complex) assembly. The RNA helicase PRP5 proofreads BS selection but the underlying mechanism remains unclear. Here we report the atomic structures of two sequential complexes leading to prespliceosome assembly: human 17S U2 snRNP and a cross-exon pre-A complex. PRP5 is anchored on 17S U2 snRNP mainly through occupation of the RNA path of SF3B1 by an acidic loop of PRP5; the helicase domain of PRP5 associates with U2 snRNA; the BS-interacting stem-loop (BSL) of U2 snRNA is shielded by TAT-SF1, unable to engage the BS. In the pre-A complex, an initial U2-BS duplex is formed; the translocated helicase domain of PRP5 stays with U2 snRNA and the acidic loop still occupies the RNA path. The pre-A conformation is specifically stabilized by the splicing factors SF1, DNAJC8 and SF3A2. Cancer-derived mutations in SF3B1 damage its association with PRP5, compromising BS proofreading. Together, these findings reveal key insights into prespliceosome assembly and BS selection or proofreading by PRP5.

Computational prediction and experimental validation identify functionally conserved lncRNAs from zebrafish to human.

Functional studies of long noncoding RNAs (lncRNAs) have been hindered by the lack of methods to assess their evolution. Here we present lncRNA Homology Explorer (lncHOME), a computational pipeline that identifies a unique class of long noncoding RNAs (lncRNAs) with conserved genomic locations and patterns of RNA-binding protein (RBP) binding sites (coPARSE-lncRNAs). Remarkably, several hundred human coPARSE-lncRNAs can be evolutionarily traced to zebrafish. Using CRISPR-Cas12a knockout and rescue assays, we found that knocking out many human coPARSE-lncRNAs led to cell proliferation defects, which were subsequently rescued by predicted zebrafish homologs. Knocking down coPARSE-lncRNAs in zebrafish embryos caused severe developmental delays that were rescued by human homologs. Furthermore, we verified that human, mouse and zebrafish coPARSE-lncRNA homologs tend to bind similar RBPs with their conserved functions relying on specific RBP-binding sites. Overall, our study demonstrates a comprehensive approach for studying the functional conservation of lncRNAs and implicates numerous lncRNAs in regulating vertebrate physiology.

Pervasive downstream RNA hairpins dynamically dictate start-codon selection.

Translational reprogramming allows organisms to adapt to changing conditions. Upstream start codons (uAUGs), which are prevalently present in mRNAs, have crucial roles in regulating translation by providing alternative translation start sites1-4. However, what determines this selective initiation of translation between conditions remains unclear. Here, by integrating transcriptome-wide translational and structural analyses during pattern-triggered immunity in Arabidopsis, we found that transcripts with immune-induced translation are enriched with upstream open reading frames (uORFs). Without infection, these uORFs are selectively translated owing to hairpins immediately downstream of uAUGs, presumably by slowing and engaging the scanning preinitiation complex. Modelling using deep learning provides unbiased support for these recognizable double-stranded RNA structures downstream of uAUGs (which we term uAUG-ds) being responsible for the selective translation of uAUGs, and allows the prediction and rational design of translating uAUG-ds. We found that uAUG-ds-mediated regulation can be generalized to human cells. Moreover, uAUG-ds-mediated start-codon selection is dynamically regulated. After immune challenge in plants, induced RNA helicases that are homologous to Ded1p in yeast and DDX3X in humans resolve these structures, allowing ribosomes to bypass uAUGs to translate downstream defence proteins. This study shows that mRNA structures dynamically regulate start-codon selection. The prevalence of this RNA structural feature and the conservation of RNA helicases across kingdoms suggest that mRNA structural remodelling is a general feature of translational reprogramming.

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.

A paternal protein facilitates sperm RNA delivery to regulate zygotic development.

Sperm contributes essential paternal factors, including the paternal genome, centrosome, and oocyte-activation signals, to sexual reproduction. However, it remains unresolved how sperm contributes its RNA molecules to regulate early embryonic development. Here, we show that the Caenorhabditis elegans paternal protein SPE-11 assembles into granules during meiotic divisions of spermatogenesis and later matures into a perinuclear structure where sperm RNAs localize. We reconstitute an SPE-11 liquid-phase scaffold in vitro and find that SPE-11 condensates incorporate the nematode RNA, which, in turn, promotes SPE-11 phase separation. Loss of SPE-11 does not affect sperm motility or fertilization but causes pleiotropic development defects in early embryos, and spe-11 mutant males reduce mRNA levels of genes crucial for an oocyte-to-embryo transition or embryonic development. These results reveal that SPE-11 undergoes phase separation and associates with sperm RNAs that are delivered to oocytes during fertilization, providing insights into how a paternal protein regulates early embryonic development.

PrismNet: predicting protein-RNA interaction using in vivo RNA structural information.

Fundamental to post-transcriptional regulation, the in vivo binding of RNA binding proteins (RBPs) on their RNA targets heavily depends on RNA structures. To date, most methods for RBP-RNA interaction prediction are based on RNA structures predicted from sequences, which do not consider the various intracellular environments and thus cannot predict cell type-specific RBP-RNA interactions. Here, we present a web server PrismNet that uses a deep learning tool to integrate in vivo RNA secondary structures measured by icSHAPE experiments with RBP binding site information from UV cross-linking and immunoprecipitation in the same cell lines to predict cell type-specific RBP-RNA interactions. Taking an RBP and an RNA region with sequential and structural information as input ('Sequence & Structure' mode), PrismNet outputs the binding probability of the RBP and this RNA region, together with a saliency map and a sequence-structure integrative motif. The web server is freely available at http://prismnetweb.zhanglab.net.

Structural basis of membrane skeleton organization in red blood cells.

The spectrin-based membrane skeleton is a ubiquitous membrane-associated two-dimensional cytoskeleton underneath the lipid membrane of metazoan cells. Mutations of skeleton proteins impair the mechanical strength and functions of the membrane, leading to several different types of human diseases. Here, we report the cryo-EM structures of the native spectrin-actin junctional complex (from porcine erythrocytes), which is a specialized short F-actin acting as the central organizational unit of the membrane skeleton. While an α-/ß-adducin hetero-tetramer binds to the barbed end of F-actin as a flexible cap, tropomodulin and SH3BGRL2 together create an absolute cap at the pointed end. The junctional complex is strengthened by ring-like structures of dematin in the middle actin layers and by patterned periodic interactions with tropomyosin over its entire length. This work serves as a structural framework for understanding the assembly and dynamics of membrane skeleton and offers insights into mechanisms of various ubiquitous F-actin-binding factors in other F-actin systems.

CryoRes: Local Resolution Estimation of Cryo-EM Density Maps by Deep Learning.

Recent progress in cryo-EM research has ignited a revolution in biological macromolecule structure determination. Resolution is an essential parameter for quality assessment of a cryo-EM density map, and it is known that resolution varies in different regions of a map. Currently available methods for local resolution estimation require manual adjustment of parameters and in some cases necessitate acquisition or de novo generation of so-called "half maps". Here, we developed CryoRes, a deep-learning algorithm to estimate local resolution directly from a single final cryo-EM density map, specifically by learning resolution-aware patterns of density map voxels through supervised training on a large dataset comprising 1,174 experimental cryo-EM density maps. CryoRes significantly outperforms all of the state-of-the-art competing resolution estimation methods, achieving an average RMSE of 2.26 Å for local resolution estimation relative to the currently most reliable FSC-based method blocres, yet requiring only the single final map as input. Further, CryoRes is able to generate a molecular mask for each map, with accuracy 12.12% higher than the masks generated by ResMap. CryoRes is ultra-fast, fully automatic, parameter-free, applicable to cryo-EM subtomogram data, and freely available at https://cryores.zhanglab.net.

Prediction of Dynamic RBP-RNA Interactions Using PrismNet.

A capacity to detect the binding profiles of RNA targets for an RNA-binding protein (RBP) under different cellular conditions is essential to understand the functions of the RBP in posttranscriptional regulation. However, the prediction of RBP binding sites in vivo remains challenging. Tools that predict RBP-RNA interactions using sequence and/or predicted structures cannot reflect the exact state of RNA in vivo. PrismNet, which uses both sequences and in vivo RNA structure information from probing experiments, can accurately predict RBP binding under different cellular conditions by deep learning, and can be applied for functional studies of RBPs. Here, we provide a detailed protocol showing how to train a PrismNet model of RBP-RNA interactions for an RBP, and how to apply the model for predictions of the RBP binding under different conditions.

Enhanced transcriptome-wide RNA G-quadruplex sequencing for low RNA input samples with rG4-seq 2.0.

BACKGROUND: RNA G-quadruplexes (rG4s) are non-canonical structural motifs that have diverse functional and regulatory roles, for instance in transcription termination, alternative splicing, mRNA localization and stabilization, and translational process. We recently developed the RNA G-quadruplex structure sequencing (rG4-seq) technique and described rG4s in both eukaryotic and prokaryotic transcriptomes. However, rG4-seq suffers from a complicated gel purification step and limited PCR product yield, thus requiring a high amount of RNA input, which limits its applicability in more physiologically or clinically relevant studies often characterized by the limited availability of biological material and low RNA abundance. Here, we redesign and enhance the workflow of rG4-seq to address this issue. RESULTS: We developed rG4-seq 2.0 by introducing a new ssDNA adapter containing deoxyuridine during library preparation to enhance library quality with no gel purification step, less PCR amplification cycles and higher yield of PCR products. We demonstrate that rG4-seq 2.0 produces high-quality cDNA libraries that support reliable and reproducible rG4 identification at varying RNA inputs, including RNA mounts as low as 10 ng. rG4-seq 2.0 also improved the rG4-seq calling outcome and nucleotide bias in rG4 detection persistent in rG4-seq 1.0. We further provide in vitro mapping of rG4 in the HEK293T cell line, and recommendations for assessing RNA input and sequencing depth for individual rG4 studies based on transcript abundance. CONCLUSIONS: rG4-seq 2.0 can improve the identification and study of rG4s in low abundance transcripts, and our findings can provide insights to optimize cDNA library preparation in other related methods.

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space.

Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

Advances and opportunities in RNA structure experimental determination and computational modeling.

Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.

LncRNA-Smad7 mediates cross-talk between Nodal/TGF-ß and BMP signaling to regulate cell fate determination of pluripotent and multipotent cells.

Transforming growth factor ß (TGF-ß) superfamily proteins are potent regulators of cellular development and differentiation. Nodal/Activin/TGF-ß and BMP ligands are both present in the intra- and extracellular milieu during early development, and cross-talk between these two branches of developmental signaling is currently the subject of intense research focus. Here, we show that the Nodal induced lncRNA-Smad7 regulates cell fate determination via repression of BMP signaling in mouse embryonic stem cells (mESCs). Depletion of lncRNA-Smad7 dramatically impairs cardiomyocyte differentiation in mESCs. Moreover, lncRNA-Smad7 represses Bmp2 expression through binding with the Bmp2 promoter region via (CA)12-repeats that forms an R-loop. Importantly, Bmp2 knockdown rescues defects in cardiomyocyte differentiation induced by lncRNA-Smad7 knockdown. Hence, lncRNA-Smad7 antagonizes BMP signaling in mESCs, and similarly regulates cell fate determination between osteocyte and myocyte formation in C2C12 mouse myoblasts. Moreover, lncRNA-Smad7 associates with hnRNPK in mESCs and hnRNPK binds at the Bmp2 promoter, potentially contributing to Bmp2 expression repression. The antagonistic effects between Nodal/TGF-ß and BMP signaling via lncRNA-Smad7 described in this work provides a framework for understanding cell fate determination in early development.

Single-cell transcriptome and translatome dual-omics reveals potential mechanisms of human oocyte maturation.

The combined use of transcriptome and translatome as indicators of gene expression profiles is usually more accurate than the use of transcriptomes alone, especially in cell types governed by translational regulation, such as mammalian oocytes. Here, we developed a dual-omics methodology that includes both transcriptome and translatome sequencing (T&T-seq) of single-cell oocyte samples, and we used it to characterize the transcriptomes and translatomes during mouse and human oocyte maturation. T&T-seq analysis revealed distinct translational expression patterns between mouse and human oocytes and delineated a sequential gene expression regulation from the cytoplasm to the nucleus during human oocyte maturation. By these means, we also identified a functional role of OOSP2 inducing factor in human oocyte maturation, as human recombinant OOSP2 induced in vitro maturation of human oocytes, which was blocked by anti-OOSP2. Single-oocyte T&T-seq analyses further elucidated that OOSP2 induces specific signaling pathways, including small GTPases, through translational regulation.

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure.

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

Recent advances in RNA structurome.

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

ILF3 represses repeat-derived microRNAs targeting RIG-I mediated type I interferon response.

MicroRNAs (miRNAs) play important roles in regulated gene expression and miRNA biogenesis is also subject to regulation, together constituting critical regulatory circuitries in numerous physiological and pathological processes. As a dsRNA binding protein, interleukin enhancer binding factor 3 (ILF3) has been implicated as a negative regulator in miRNA biogenesis, but the mechanism and specificity have remained undefined. Here, combining small-RNA-seq and CLIP-seq, we showed that ILF3 directly represses many miRNAs or perhaps other types of small RNAs annotated in both miRBase and MirGeneDB. We demonstrated that ILF3 preferentially binds to A/U-enriched motifs, which tend to lengthen and/or stabilize the stem-loop in pri-miRNAs, thereby effectively competing with the Microprocessor to block miRNA biogenesis. Focusing on the biological function of ILF3-suppressed miR-582-3p, we discovered that this LINE-derived miRNA targets a critical interferon-inducible gene RIG-I for repression, thus establishing a novel ILF3/miR-582/RIG-I axis in the antiviral response.

CD127 imprints functional heterogeneity to diversify monocyte responses in inflammatory diseases.

Inflammatory monocytes are key mediators of acute and chronic inflammation; yet, their functional diversity remains obscure. Single-cell transcriptome analyses of human inflammatory monocytes from COVID-19 and rheumatoid arthritis patients revealed a subset of cells positive for CD127, an IL-7 receptor subunit, and such positivity rendered otherwise inert monocytes responsive to IL-7. Active IL-7 signaling engaged epigenetically coupled, STAT5-coordinated transcriptional programs to restrain inflammatory gene expression, resulting in inverse correlation between CD127 expression and inflammatory phenotypes in a seemingly homogeneous monocyte population. In COVID-19 and rheumatoid arthritis, CD127 marked a subset of monocytes/macrophages that retained hypoinflammatory phenotypes within the highly inflammatory tissue environments. Furthermore, generation of an integrated expression atlas revealed unified features of human inflammatory monocytes across different diseases and different tissues, exemplified by those of the CD127high subset. Overall, we phenotypically and molecularly characterized CD127-imprinted functional heterogeneity of human inflammatory monocytes with direct relevance for inflammatory diseases.

Comparison of viral RNA-host protein interactomes across pathogenic RNA viruses informs rapid antiviral drug discovery for SARS-CoV-2.

In contrast to the extensive research about viral protein-host protein interactions that has revealed major insights about how RNA viruses engage with host cells during infection, few studies have examined interactions between host factors and viral RNAs (vRNAs). Here, we profiled vRNA-host protein interactomes for three RNA virus pathogens (SARS-CoV-2, Zika, and Ebola viruses) using ChIRP-MS. Comparative interactome analyses discovered both common and virus-specific host responses and vRNA-associated proteins that variously promote or restrict viral infection. In particular, SARS-CoV-2 binds and hijacks the host factor IGF2BP1 to stabilize vRNA and augment viral translation. Our interactome-informed drug repurposing efforts identified several FDA-approved drugs (e.g., Cepharanthine) as broad-spectrum antivirals in cells and hACE2 transgenic mice. A co-treatment comprising Cepharanthine and Trifluoperazine was highly potent against the newly emerged SARS-CoV-2 B.1.351 variant. Thus, our study illustrates the scientific and medical discovery utility of adopting a comparative vRNA-host protein interactome perspective.