Identification of MYC intron 2 regions that modulate expression

MYC pre-mRNA is spliced with high fidelity to produce the transcription factor known to regulate cellular differentiation, proliferation, apoptosis, and alternative splicing. The mechanisms underpinning the pre-mRNA splicing of MYC, however, remain mostly unexplored. In this study, we examined the interaction of heterogeneous nuclear ribonucleoprotein C (HNRNPC) with MYC intron 2. Building off published eCLIP studies, we confirmed this interaction with poly(U) regions in intron 2 of MYC and found that full binding is correlated with optimal protein production. The interaction appears to be compensatory, as mutational disruption of all three poly(U) regions was required to reduce both HNRNPC binding capacity and fidelity of either splicing or translation. Poly(U) sequences in MYC intron 2 were relatively conserved across sequences from several different species. Lastly, we identified a short sequence just upstream of an HNRNPC binding region that when removed enhances MYC translation.

Thermodynamic and structural characterization of an EBV infected B-cell lymphoma transcriptome

Epstein-Barr virus (EBV) is a widely prevalent human herpes virus infecting over 95% of all adults and is associated with a variety of B-cell cancers and induction of multiple sclerosis. EBV accomplishes this in part by expression of coding and noncoding RNAs and alteration of the host cell transcriptome. To better understand the structures which are forming in the viral and host transcriptomes of infected cells, the RNA structure probing technique Structure-seq2 was applied to the BJAB-B1 cell line (an EBV infected B-cell lymphoma). This resulted in reactivity profiles and secondary structural analyses for over 10000 human mRNAs and lncRNAs, along with 19 lytic and latent EBV transcripts. We report in-depth structural analyses for the human MYC mRNA and the human lncRNA CYTOR. Additionally, we provide a new model for the EBV noncoding RNA EBER2 and provide the first reported model for the EBV tandem terminal repeat RNA. In-depth thermodynamic and structural analyses were carried out with the motif discovery tool ScanFold and RNAfold prediction tool; subsequent covariation analyses were performed on resulting models finding various levels of support. ScanFold results for all analyzed transcripts are made available for viewing and download on the user-friendly RNAStructuromeDB.

ScanFold 2.0: a rapid approach for identifying potential structured RNA targets in genomes and transcriptomes

A major limiting factor in target discovery for both basic research and therapeutic intervention is the identification of structural and/or functional RNA elements in genomes and transcriptomes. This was the impetus for the original ScanFold algorithm, which provides maps of local RNA structural stability, evidence of sequence-ordered (potentially evolved) structure, and unique model structures comprised of recurring base pairs with the greatest structural bias. A key step in quantifying this propensity for ordered structure is the prediction of secondary structural stability for randomized sequences which, in the original implementation of ScanFold, is explicitly evaluated. This slow process has limited the rapid identification of ordered structures in large genomes/transcriptomes, which we seek to overcome in this current work introducing ScanFold 2.0. In this revised version of ScanFold, we no longer explicitly evaluate randomized sequence folding energy, but rather estimate it using a machine learning approach. For high randomization numbers, this can increase prediction speeds over 100-fold compared to ScanFold 1.0, allowing for the analysis of large sequences, as well as the use of additional folding algorithms that may be computationally expensive. In the testing of ScanFold 2.0, we re-evaluate the Zika, HIV, and SARS-CoV-2 genomes and compare both the consistency of results and the time of each run to ScanFold 1.0. We also re-evaluate the SARS-CoV-2 genome to assess the quality of ScanFold 2.0 predictions vs several biochemical structure probing datasets and compare the results to those of the original ScanFold program.

Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control

Experimental breakthroughs have provided unprecedented insights into the genes involved in cancer. The identification of such cancer driver genes is a major step in gaining a fuller understanding of oncogenesis and provides novel lists of potential therapeutic targets. A key area that requires additional study is the posttranscriptional control mechanisms at work in cancer driver genes. This is important not only for basic insights into the biology of cancer, but also to advance new therapeutic modalities that target RNA—an emerging field with great promise toward the treatment of various cancers. In the current study we performed an in silico analysis on the transcripts associated with 800 cancer driver genes (10,390 unique transcripts) that identified 179,190 secondary structural motifs with evidence of evolutionarily ordered structures with unusual thermodynamic stability. Narrowing to one transcript per gene, 35,426 predicted structures were subjected to phylogenetic comparisons of sequence and structural conservation. This identified 7,001 RNA secondary structures embedded in transcripts with evidence of covariation between paired sites, supporting structure models and suggesting functional significance. A select set of seven structures were tested in vitro for their ability to regulate gene expression; all were found to have significant effects. These results indicate potentially widespread roles for RNA structure in posttranscriptional control of human cancer driver genes.

Secondary Structure of Subgenomic RNA M of SARS-CoV-2

SARS-CoV-2 belongs to the Coronavirinae family. Like other coronaviruses, SARS-CoV-2 is enveloped and possesses a positive-sense, single-stranded RNA genome of ~30 kb. Genomic RNA is used as the template for replication and transcription. During these processes, positive-sense genomic RNA (gRNA) and subgenomic RNAs (sgRNAs) are created. Several studies presented the importance of the genomic RNA secondary structure in SARS-CoV-2 replication. However, the structure of sgRNAs has remained largely unsolved so far. In this study, we probed the sgRNA M model of SARS-CoV-2 in vitro. The presented model molecule includes 5'UTR and a coding sequence of gene M. This is the first experimentally informed secondary structure model of sgRNA M, which presents features likely to be important in sgRNA M function. The knowledge of sgRNA M structure provides insights to better understand virus biology and could be used for designing new therapeutics.

A map of the SARS-CoV-2 RNA structurome

SARS-CoV-2 has exploded throughout the human population. To facilitate efforts to gain insights into SARS-CoV-2 biology and to target the virus therapeutically, it is essential to have a roadmap of likely functional regions embedded in its RNA genome. In this report, we used a bioinformatics approach, ScanFold, to deduce the local RNA structural landscape of the SARS-CoV-2 genome with the highest likelihood of being functional. We recapitulate previously-known elements of RNA structure and provide a model for the folding of an essential frameshift signal. Our results find that SARS-CoV-2 is greatly enriched in unusually stable and likely evolutionarily ordered RNA structure, which provides a large reservoir of potential drug targets for RNA-binding small molecules. Results are enhanced via the re-analyses of publicly-available genome-wide biochemical structure probing datasets that are broadly in agreement with our models. Additionally, ScanFold was updated to incorporate experimental data as constraints in the analysis to facilitate comparisons between ScanFold and other RNA modelling approaches. Ultimately, ScanFold was able to identify eight highly structured/conserved motifs in SARS-CoV-2 that agree with experimental data, without explicitly using these data. All results are made available via a public database (the RNAStructuromeDB: https://structurome.bb.iastate.edu/sars-cov-2) and model comparisons are readily viewable at https://structurome.bb.iastate.edu/sars-cov-2-global-model-comparisons.

Targeting the SARS-CoV-2 RNA Genome with Small Molecule Binders and Ribonuclease Targeting Chimera (RIBOTAC) Degraders

COVID-19 is a global pandemic, thus requiring multiple strategies to develop modalities against it. Herein, we designed multiple bioactive small molecules that target a functional structure within the SARS-CoV-2's RNA genome, the causative agent of COVID-19. An analysis to characterize the structure of the RNA genome provided a revised model of the SARS-CoV-2 frameshifting element, in particular its attenuator hairpin. By studying an RNA-focused small molecule collection, we identified a drug-like small molecule (C5) that avidly binds to the revised attenuator hairpin structure with a K d of 11 nM. The compound stabilizes the hairpin's folded state and impairs frameshifting in cells. The ligand was further elaborated into a ribonuclease targeting chimera (RIBOTAC) to recruit a cellular ribonuclease to destroy the viral genome (C5-RIBOTAC) and into a covalent molecule (C5-Chem-CLIP) that validated direct target engagement and demonstrated its specificity for the viral RNA, as compared to highly expressed host mRNAs. The RIBOTAC lead optimization strategy improved the bioactivity of the compound at least 10-fold. Collectively, these studies demonstrate that the SARS-CoV-2 RNA genome should be considered druggable.

A survey of RNA secondary structural propensity encoded within human herpesvirus genomes: global comparisons and local motifs

There are nine herpesviruses known to infect humans, of which Epstein-Barr virus (EBV) is the most widely distributed (>90% of adults infected). This ubiquitous virus is implicated in a variety of cancers and autoimmune diseases. Previous analyses of the EBV genome revealed numerous regions with evidence of generating unusually stable and conserved RNA secondary structures and led to the discovery of a novel class of EBV non-coding (nc)RNAs: the stable intronic sequence (sis)RNAs. To gain a better understanding of the roles of RNA structure in EBV biology and pathogenicity, we revisit EBV using recently developed tools for genome-wide motif discovery and RNA structural characterization. This corroborated previous results and revealed novel motifs with potential functionality; one of which has been experimentally validated. Additionally, since many herpesviruses increasingly rival the seroprevalence of EBV (VZV, HHV-6 and HHV-7 being the most notable), analyses were expanded to include all sequenced human Herpesvirus RefSeq genomes, allowing for genomic comparisons. In total 10 genomes were analyzed, for EBV (types 1 and 2), HCMV, HHV-6A, HHV-6B, HHV-7, HSV-1, HSV-2, KSHV, and VZV. All resulting data were archived in the RNAStructuromeDB (https://structurome.bb.iastate.edu/herpesvirus) to make them available to a wide array of researchers.

RNA structural analysis of the MYC mRNA reveals conserved motifs that affect gene expression

The MYC gene encodes a human transcription factor and proto-oncogene that is dysregulated in over half of all known cancers. To better understand potential post-transcriptional regulatory features affecting MYC expression, we analyzed secondary structures in the MYC mRNA using a program that is optimized for finding small locally-folded motifs with a high propensity for function. This was accomplished by calculating folding metrics across the MYC sequence using a sliding analysis window and generating unique consensus base pairing models weighted by their lower-than-random predicted folding energy. A series of 30 motifs were identified, primarily in the 5' and 3' untranslated regions, which show evidence of structural conservation and compensating mutations across vertebrate MYC homologs. This analysis was able to recapitulate known elements found within an internal ribosomal entry site, as well as discover a novel element in the 3' UTR that is unusually stable and conserved. This novel motif was shown to affect MYC expression, potentially via the modulation of miRNA target accessibility or other trans-regulatory factors. In addition to providing basic insights into mechanisms that regulate MYC expression, this study provides numerous, potentially druggable RNA targets for the MYC gene, which is considered “undruggable” at the protein level.