Joy Shengnan Xiang

The global impact of the COVID-19 pandemic highlights the need for a deeper understanding of SARS-CoV-2 replication. Although the essential roles of the RNA-dependent RNA polymerase (NSP12), primase (NSP8), and nucleocapsid (N) proteins are established, their contact points with the full-length viral RNA genome during active infection require further characterization. Here, we used enhanced crosslinking and immunoprecipitation (eCLIP) to map direct interactions of NSP8, NSP12, and N across the SARS-CoV-2 genome during live virus infection at early and late stages of replication. We mapped NSP8 and NSP12 binding to the 5′ and 3′ ends of positive- and negative-sense RNA, consistent with their roles in viral replication, transcription, and translation. We also observed an NSP12 binding site within the conserved Y1 domain of NSP3 that modulates RNA and protein abundance. In addition, N binds the 5′ untranslated region (UTR), supporting a role in translational regulation early in infection, and associates with the frameshift element, where it modulates ribosomal frameshifting. Together, these findings map SARS-CoV-2 RNA–protein interactions across the viral genome and delineate specific regulatory regions for potential antiviral intervention.

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron–exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update RNASeek’s generation policy toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3′ UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3′ UTR sequences exceed the performance of the training data and benchmarked AI-generated 3′ UTRs. Together, RNASeek establishes a unified pretrain–predict–optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

Long-range RNA-RNA pairing impacts the genome structure and function of SARS-CoV-2 variants. To understand the structure and function relationships of different SARS-CoV-2 variants that have emerged during the COVID-19 pandemic, we perform high-throughput structure probing and modelling of the genomic structures of the wildtype (WT), Alpha, Beta, Delta and Omicron variants of SARS-CoV-2. We observe that genomes of SARS-CoV-2 variants are generally structurally conserved, and that single-nucleotide variations and interactions with RNA binding proteins can impact RNA structures across the viruses. Importantly, using proximity ligation sequencing, we identify many conserved ultra-long-range RNA-RNA interactions, including one that spans more than 17 kb in both the WT virus and the Omicron variant. We show that mutations that disrupt this 17 kb long-range interaction reduce viral fitness at later stages of its infection cycle, while compensatory mutations partially restore virus fitness. Additionally, we show that this ultra-long-range RNA-RNA interaction structure binds directly to ADAR1 to alter the RNA editing levels on the viral genome. These studies deepen our understanding of RNA structures in the SARS-CoV-2 genome and their ability to interact with host factors to facilitate virus infectivity.

The global impact of the COVID-19 pandemic underscores the critical need for a comprehensive understanding of SARS-CoV-2 replication mechanisms. While the central roles of the RNA dependent RNA polymerase (NSP12), primase protein (NSP8), and nucleocapsid protein (N) in the virus life cycle are extensively studied, the precise nature of their interactions with the full-length viral RNA genome remain incompletely characterized. In this study, we sought to address this knowledge gap by employing enhanced crosslinking and immunoprecipitation (eCLIP) to map the binding sites of NSP8, NSP12, and N proteins across the SARS-CoV-2 genome at early stages of viral RNA and protein synthesis and late stages of virion assembly. Our findings revealed interactions of NSP8 and NSP12 to the 5’ and 3’ untranslated regions (UTRs) of both positive and negative sense RNA, regions known to regulate viral replication, transcription, and translation. We identified a surprising and essential NSP12 binding site within the RNA sequence encoding the conserved Y1 domain of NSP3, which regulates RNA abundance upstream of the site. Additionally, we found that N protein interacts with the 5’ UTR and influences translation efficiency. Finally, we report a novel regulatory function of N protein in modulating ribosomal frameshifting proximal to the frameshift element, a crucial process for maintaining viral protein stoichiometry. Our results provide a detailed molecular map of SARS-CoV-2 protein-RNA interactions, revealing potential therapeutic targets for attenuating viral fitness and informing the development of next-generation antiviral strategies.

Protein–RNA interactions are central to all RNA processing events, with pivotal roles in the regulation of gene expression and cellular functions. Dysregulation of these interactions has been increasingly linked to the pathogenesis of human diseases. High-throughput approaches to identify RNA-binding proteins and their binding sites on RNA — in particular, ultraviolet crosslinking followed by immunoprecipitation (CLIP) — have helped to map the RNA interactome, yielding transcriptome-wide protein–RNA atlases that have contributed to key mechanistic insights into gene expression and gene-regulatory networks. Here, we review these recent advances, explore the effects of cellular context on RNA binding, and discuss how these insights are shaping our understanding of cellular biology. We also review the potential therapeutic applications arising from new knowledge of 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/β. 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.

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Here, using aptamer-coupled ribozyme libraries and a ribozyme regeneration method, de novo rapid in vitro evolution of RNA biosensors (DRIVER) enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identify and validate biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors are applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors are also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

Biosensors are key components in engineered biological systems, providing a means of measuring and acting upon the large biochemical space in living cells. However, generating small molecule sensing elements and integrating them into in vivo biosensors have been challenging. Using aptamer-coupled ribozyme libraries and a novel ribozyme regeneration method, we developed de novo rapid in vitro evolution of RNA biosensors (DRIVER) that enables multiplexed discovery of biosensors. With DRIVER and high-throughput characterization (CleaveSeq) fully automated on liquid-handling systems, we identified and validated biosensors against six small molecules, including five for which no aptamers were previously found. DRIVER-evolved biosensors were applied directly to regulate gene expression in yeast, displaying activation ratios up to 33-fold. DRIVER biosensors were also applied in detecting metabolite production from a multi-enzyme biosynthetic pathway. This work demonstrates DRIVER as a scalable pipeline for engineering de novo biosensors with wide-ranging applications in biomanufacturing, diagnostics, therapeutics, and synthetic biology.

Synthetic RNA-based genetic devices dynamically control a wide range of gene-regulatory processes across diverse cell types. However, the limited throughput of quantitative assays in mammalian cells has hindered fast iteration and interrogation of sequence space needed to identify new RNA devices. Here we report developing a quantitative, rapid and high-throughput mammalian cell-based RNA-Seq assay to efficiently engineer RNA devices. We identify new ribozyme-based RNA devices that respond to theophylline, hypoxanthine, cyclic-di-GMP, and folinic acid from libraries of ~22,700 sequences in total. The small molecule responsive devices exhibit low basal expression and high activation ratios, significantly expanding our toolset of highly functional ribozyme switches. The large datasets obtained further provide conserved sequence and structure motifs that may be used for rationally guided design. The RNA-Seq approach offers a generally applicable strategy for developing broad classes of RNA devices, thereby advancing the engineering of genetic devices for mammalian systems.

Single Nucleotide Polymorphisms (SNPs) can influence patient outcome such as drug response and toxicity after drug intervention. The purpose of this study is to develop a systematic pathway approach to accurately and efficiently predict novel non-synonymous SNPs (nsSNPs) that could be causative to gemcitabine-based chemotherapy treatment outcome in Singaporean non-small cell lung cancer (NSCLC) patients. Using a pathway approach that incorporates comprehensive protein-protein interaction data to systematically extend the gemcitabine pharmacologic pathway, we identified 77 related nsSNPs, common in the Singaporean population. After that, we used five computational criteria to prioritize the SNPs based on their importance for protein function. We specifically selected and screened six candidate SNPs in a patient cohort with NSCLC treated with gemcitabine-based chemotherapy. We performed survival analysis followed by hematologic toxicity analyses and found that three of six candidate SNPs are significantly correlated with the patient outcome (P < 0.05) i.e. ABCG2 Q141K (rs2231142), SLC29A3 S158F (rs780668) and POLR2A N764K (rs2228130). Our computational SNP candidate enrichment workflow approach was able to identify several high confidence biomarkers predictive for personalized drug treatment outcome while providing a rationale for a molecular mechanism of the SNP effect. NCT00695994. Registered 10 June, 2008 ‘retrospectively registered’.