Transcriptome-Wide, Unbiased Profiling of Ribonuclease Targeting Chimeras.

Various approaches have been developed to target RNA and modulate its function with modes of action including binding and cleavage. Herein, we explored how small molecule binding is correlated with cleavage induced by heterobifunctional ribonuclease targeting chimeras (RiboTACs), where RNase L is recruited to cleave the bound RNA target, in a transcriptome-wide, unbiased fashion. Only a fraction of bound targets was cleaved by RNase L, induced by RiboTAC binding. Global analysis suggested that (i) cleaved targets generally form a region of stable structure that encompasses the small molecule binding site; (ii) cleaved targets have preferred RNase L cleavage sites nearby small molecule binding sites; (iii) RiboTACs facilitate a cellular interaction between cleaved targets and RNase L; and (iv) the expression level of the target influences the extent of cleavage observed. In one example, we converted a binder of LGALS1 (galectin-1) mRNA into a RiboTAC. In MDA-MB-231 cells, the binder had no effect on galectin-1 protein levels, while the RiboTAC cleaved LGALS1 mRNA, reduced galectin-1 protein abundance, and affected galectin-1-associated oncogenic cellular phenotypes. Using LGALS1, we further assessed additional factors including the length of the linker that tethers the two components of the RiboTAC, cellular uptake, and the RNase L-recruiting module on RiboTAC potency. Collectively, these studies may facilitate triangulation of factors to enable the design of RiboTACs.

The RNA secondary structure of androgen receptor-FL and V7 transcripts reveals novel regulatory regions.

The androgen receptor (AR) is a ligand-dependent nuclear transcription factor belonging to the steroid hormone nuclear receptor family. Due to its roles in regulating cell proliferation and differentiation, AR is tightly regulated to maintain proper levels of itself and the many genes it controls. AR dysregulation is a driver of many human diseases including prostate cancer. Though this dysregulation often occurs at the RNA level, there are many unknowns surrounding post-transcriptional regulation of AR mRNA, particularly the role that RNA secondary structure plays. Thus, a comprehensive analysis of AR transcript secondary structure is needed. We address this through the computational and experimental analyses of two key isoforms, full length (AR-FL) and truncated (AR-V7). Here, a combination of in-cell RNA secondary structure probing experiments (targeted DMS-MaPseq) and computational predictions were used to characterize the static structural landscape and conformational dynamics of both isoforms. Additionally, in-cell assays were used to identify functionally relevant structures in the 5' and 3' UTRs of AR-FL. A notable example is a conserved stem loop structure in the 5'UTR of AR-FL that can bind to Poly(RC) Binding Protein 2 (PCBP2). Taken together, our results reveal novel features that regulate AR expression.

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.

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.

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.

Expansion of the RNAStructuromeDB to include secondary structural data spanning the human protein-coding transcriptome.

RNA plays vital functional roles in almost every component of biology, and these functional roles are often influenced by its folding into secondary and tertiary structures. An important role of RNA secondary structure is in maintaining proper gene regulation; therefore, making accurate predictions of the structures involved in these processes is important. In this study, we have expanded on our previous work that led to the creation of the RNAStructuromeDB. Unlike this previous study that analyzed the human genome at low resolution, we have now scanned the protein-coding human transcriptome at high (single nt) resolution. This provides more robust structure predictions for over 100,000 isoforms of known protein-coding genes. Notably, we also utilize the motif identification tool, ScanFold, to model structures with high propensity for ordered/evolved stability. All data have been uploaded to the RNAStructuromeDB, allowing for easy searching of transcripts, visualization of data tracks (via the Integrative Genomics Viewer or IGV), and download of ScanFold data-including unique highly-ordered motifs. Herein, we provide an example analysis of MAT2A to demonstrate the utility of ScanFold at finding known and novel secondary structures, highlighting regions of potential functionality, and guiding generation of functional hypotheses through use of the data.

Transcriptome-Wide Mapping of Small-Molecule RNA-Binding Sites in Cells Informs an Isoform-Specific Degrader of QSOX1 mRNA.

The interactions between cellular RNAs in MDA-MB-231 triple negative breast cancer cells and a panel of small molecules appended with a diazirine cross-linking moiety and an alkyne tag were probed transcriptome-wide in live cells. The alkyne tag allows for facile pull-down of cellular RNAs bound by each small molecule, and the enrichment of each RNA target defines the compound's molecular footprint. Among the 34 chemically diverse small molecules studied, six bound and enriched cellular RNAs. The most highly enriched interaction occurs between the novel RNA-binding compound F1 and a structured region in the 5' untranslated region of quiescin sulfhydryl oxidase 1 isoform a (QSOX1-a), not present in isoform b. Additional studies show that F1 specifically bound RNA over DNA and protein; that is, we studied the entire DNA, RNA, and protein interactome. This interaction was used to design a ribonuclease targeting chimera (RIBOTAC) to locally recruit Ribonuclease L to degrade QSOX1 mRNA in an isoform-specific manner, as QSOX1-a, but not QSOX1-b, mRNA and protein levels were reduced. The RIBOTAC alleviated QSOX1-mediated phenotypes in cancer cells. This approach can be broadly applied to discover ligands that bind RNA in cells, which could be bioactive themselves or augmented with functionality such as targeted degradation.

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.

Prediction and analysis of functional RNA structures within the integrative genomics viewer.

In recent years, interest in RNA secondary structure has exploded due to its implications in almost all biological functions and its newly appreciated capacity as a therapeutic agent/target. This surge of interest has driven the development and adaptation of many computational and biochemical methods to discover novel, functional structures across the genome/transcriptome. To further enhance efforts to study RNA secondary structure, we have integrated the functional secondary structure prediction tool ScanFold, into IGV. This allows users to directly perform structure predictions and visualize results-in conjunction with probing data and other annotations-in one program. We illustrate the utility of this new tool by mapping the secondary structural landscape of the human MYC precursor mRNA. We leverage the power of vast 'omics' resources by comparing individually predicted structures with published data including: biochemical structure probing, RNA binding proteins, microRNA binding sites, RNA modifications, single nucleotide polymorphisms, and others that allow functional inferences to be made and aid in the discovery of potential drug targets. This new tool offers the RNA community an easy to use tool to find, analyze, and characterize RNA secondary structures in the context of all available data, in order to find those worthy of further analyses.

Analysis of key genes in Mycobacterium ulcerans reveals conserved RNA structural motifs and regions with apparent pressure to remain unstructured.

Buruli Ulcer is a neglected tropical disease that results in disfiguring and dangerous lesions in affected persons across a wide geographic area, including much of West Africa. The causative agent of Buruli Ulcer is Mycobacterium ulcerans, a relative of the bacterium that causes tuberculosis and leprosy. Few therapeutic options exist for the treatment of this disease beyond antibiotics in the early stages, which are frequently ineffective, and surgical removal in the later stage. In this study we analyze six genes in Mycobacterium ulcerans that have high potential of therapeutic targeting. We focus our analysis on a combined in silico and comparative sequence study of potential RNA secondary structure across these genes. The result of this work was the comprehensive local RNA structural landscape across each of these significant genes. This revealed multiple sites of ordered and evolved RNA structure interspersed between sequences that either have no bias for structure or, indeed, appear to be ordered to be unstructured and (potentially) accessible. In addition to providing data that could be of interest to basic biology, our results provide guides for efforts aimed at targeting this pathogen at the RNA level. We explore this latter possibility through the in silico analysis of antisense oligonucleotides that could potentially be used to target pathogen RNA.