HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence.

RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier (HydRA), which leverages information from both intermolecular protein interactions and internal protein sequence patterns to predict RNA-binding capacity with unparalleled specificity and sensitivity using support vector machines (SVMs), convolutional neural networks (CNNs), and Transformer-based protein language models. Occlusion mapping by HydRA robustly detects known RBDs and predicts hundreds of uncharacterized RNA-binding associated domains. Enhanced CLIP (eCLIP) for HydRA-predicted RBP candidates reveals transcriptome-wide RNA targets and confirms RNA-binding activity for HydRA-predicted RNA-binding associated domains. HydRA accelerates construction of a comprehensive RBP catalog and expands the diversity of RNA-binding associated domains.

Long-read Ribo-STAMP simultaneously measures transcription and translation with isoform resolution.

Transcription and translation are intertwined processes where mRNA isoforms are crucial intermediaries. However, methodological limitations in analyzing translation at the mRNA isoform level have left gaps in our understanding of critical biological processes. To address these gaps, we developed an integrated computational and experimental framework called long-read Ribo-STAMP (LR-Ribo-STAMP) that capitalizes on advancements in long-read sequencing and RNA-base editing-mediated technologies to simultaneously profile translation and transcription at both gene and mRNA isoform levels. We also developed the EditsC metric to quantify editing and leverage the single-molecule, full-length transcript information provided by long-read sequencing. Here, we report concordance between gene-level translation profiles obtained with long-read and short-read Ribo-STAMP. We show that LR-Ribo-STAMP successfully profiles translation of mRNA isoforms and links regulatory features, such as upstream open reading frames (uORFs), to translation measurements. We apply LR-Ribo-STAMP to discovering translational differences at both gene and isoform levels in a triple-negative breast cancer cell line under normoxia and hypoxia and find that LR-Ribo-STAMP effectively delineates orthogonal transcriptional and translation shifts between conditions. We also discover regulatory elements that distinguish translational differences at the isoform level. We highlight GRK6, where hypoxia is observed to increase expression and translation of a shorter mRNA isoform, giving rise to a truncated protein without the AGC Kinase domain. Overall, LR-Ribo-STAMP is an important advance in our repertoire of methods that measure mRNA translation with isoform sensitivity.

Robust single-cell discovery of RNA targets of RNA-binding proteins and ribosomes.

RNA-binding proteins (RBPs) are critical regulators of gene expression and RNA processing that are required for gene function. Yet the dynamics of RBP regulation in single cells is unknown. To address this gap in understanding, we developed STAMP (Surveying Targets by APOBEC-Mediated Profiling), which efficiently detects RBP-RNA interactions. STAMP does not rely on ultraviolet cross-linking or immunoprecipitation and, when coupled with single-cell capture, can identify RBP-specific and cell-type-specific RNA-protein interactions for multiple RBPs and cell types in single, pooled experiments. Pairing STAMP with long-read sequencing yields RBP target sites in an isoform-specific manner. Finally, Ribo-STAMP leverages small ribosomal subunits to measure transcriptome-wide ribosome association in single cells. STAMP enables the study of RBP-RNA interactomes and translational landscapes with unprecedented cellular resolution.

From Protein-RNA Predictions toward a Peptide-RNA Code.

The RNA field is undergoing a renaissance, with a deluge of proteins being identified to bind RNA. Two reports now introduce proteome-wide approaches that identify the peptides that are crosslinked to RNA (Castello et al., 2016; He et al., 2016).

SONAR Discovers RNA-Binding Proteins from Analysis of Large-Scale Protein-Protein Interactomes.

RNA metabolism is controlled by an expanding, yet incomplete, catalog of RNA-binding proteins (RBPs), many of which lack characterized RNA binding domains. Approaches to expand the RBP repertoire to discover non-canonical RBPs are currently needed. Here, HaloTag fusion pull down of 12 nuclear and cytoplasmic RBPs followed by quantitative mass spectrometry (MS) demonstrates that proteins interacting with multiple RBPs in an RNA-dependent manner are enriched for RBPs. This motivated SONAR, a computational approach that predicts RNA binding activity by analyzing large-scale affinity precipitation-MS protein-protein interactomes. Without relying on sequence or structure information, SONAR identifies 1,923 human, 489 fly, and 745 yeast RBPs, including over 100 human candidate RBPs that contain zinc finger domains. Enhanced CLIP confirms RNA binding activity and identifies transcriptome-wide RNA binding sites for SONAR-predicted RBPs, revealing unexpected RNA binding activity for disease-relevant proteins and DNA binding proteins.

A relay velocity model infers cell-dependent RNA velocity.

RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data. Conventional RNA velocity models infer universal kinetics from all cells in an scRNA-seq experiment, resulting in unpredictable performance in experiments with multi-stage and/or multi-lineage transition of cell states where the assumption of the same kinetic rates for all cells no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers velocity for each cell from its neighbors and then relays a series of local velocities to provide single-cell resolution inference of velocity kinetics. In the simulation benchmark, cellDancer shows robust performance in multiple kinetic regimes, high dropout ratio datasets and sparse datasets. We show that cellDancer overcomes the limitations of existing RNA velocity models in modeling erythroid maturation and hippocampus development. Moreover, cellDancer provides cell-specific predictions of transcription, splicing and degradation rates, which we identify as potential indicators of cell fate in the mouse pancreas.

Discovery and functional interrogation of SARS-CoV-2 protein-RNA interactions.

The COVID-19 pandemic is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The betacoronvirus has a positive sense RNA genome which encodes for several RNA binding proteins. Here, we use enhanced crosslinking and immunoprecipitation to investigate SARS-CoV-2 protein interactions with viral and host RNAs in authentic virus-infected cells. SARS-CoV-2 proteins, NSP8, NSP12, and nucleocapsid display distinct preferences to specific regions in the RNA viral genome, providing evidence for their shared and separate roles in replication, transcription, and viral packaging. SARS-CoV-2 proteins expressed in human lung epithelial cells bind to 4773 unique host coding RNAs. Nine SARS-CoV-2 proteins upregulate target gene expression, including NSP12 and ORF9c, whose RNA substrates are associated with pathways in protein N-linked glycosylation ER processing and mitochondrial processes. Furthermore, siRNA knockdown of host genes targeted by viral proteins in human lung organoid cells identify potential antiviral host targets across different SARS-CoV-2 variants. Conversely, NSP9 inhibits host gene expression by blocking mRNA export and dampens cytokine productions, including interleukin-1α/ß. Our viral protein-RNA interactome provides a catalog of potential therapeutic targets and offers insight into the etiology of COVID-19 as a safeguard against future pandemics.

Discovery and functional interrogation of SARS-CoV-2 protein-RNA interactions.

The COVID-19 pandemic is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The betacoronvirus has a positive sense RNA genome which encodes for several RNA binding proteins. Here, we use enhanced crosslinking and immunoprecipitation to investigate SARS-CoV-2 protein interactions with viral and host RNAs in authentic virus-infected cells. SARS-CoV-2 proteins, NSP8, NSP12, and nucleocapsid display distinct preferences to specific regions in the RNA viral genome, providing evidence for their shared and separate roles in replication, transcription, and viral packaging. SARS-CoV-2 proteins expressed in human lung epithelial cells bind to 4773 unique host coding RNAs. Nine SARS-CoV-2 proteins upregulate target gene expression, including NSP12 and ORF9c, whose RNA substrates are associated with pathways in protein N-linked glycosylation ER processing and mitochondrial processes. Furthermore, siRNA knockdown of host genes targeted by viral proteins in human lung organoid cells identify potential antiviral host targets across different SARS-CoV-2 variants. Conversely, NSP9 inhibits host gene expression by blocking mRNA export and dampens cytokine productions, including interleukin-1α/ß. Our viral protein-RNA interactome provides a catalog of potential therapeutic targets and offers insight into the etiology of COVID-19 as a safeguard against future pandemics.

Evaluation of Engineered CRISPR-Cas-Mediated Systems for Site-Specific RNA Editing.

Site-directed RNA editing approaches offer great potential to correct genetic mutations in somatic cells while avoiding permanent off-target genomic edits. Nuclease-dead RNA-targeting CRISPR-Cas systems recruit functional effectors to RNA molecules in a programmable fashion. Here, we demonstrate a Streptococcus pyogenes Cas9-ADAR2 fusion system that uses a 3' modified guide RNA (gRNA) to enable adenosine-to-inosine (A-to-I) editing of specific bases on reporter and endogenously expressed mRNAs. Due to the sufficient nature of the 3' gRNA extension sequence, we observe that Cas9 gRNA spacer sequences are dispensable for directed RNA editing, revealing that Cas9 can act as an RNA-aptamer-binding protein. We demonstrate that Cas9-based A-to-I editing is comparable in on-target efficiency and off-target specificity with Cas13 RNA editing versions. This study provides a systematic benchmarking of RNA-targeting CRISPR-Cas designs for reversible nucleotide-level conversion at the transcriptome level.