Rasmussen, A. M. | Search bioRxiv

bioRxiv2024

bayesReact: Expression-coupled regulatory motif analysis detects microRNA activity in cancer and at the single cell level

MotivationRegulatory constraints are crucial in maintaining tissue and cell integrity, and play important roles during developmental processes and environmental responses. Yet many regulatory mechanisms remain unobserved at the single-cell level and statistical inference may, in some cases, help elucidate their condition-specific activity and perturbation during disease progression. ResultsWe introduce bayesReact (BAYESian modeling of Regular Expression ACTivity), a generative model of motif occurrence across experimentally ranked sequences to infer motif-based regulatory activities. The method is evaluated for microRNAs (miRNAs), which perform post-transcriptional regulation through target mRNA destabilization and translational repression. Inferred miRNA activities positively correlate with the observed miRNA expressions in primary tumors from The Cancer Genome Atlas (TCGA) and mouse stem cells. The top miRNA activity profiles are as informative for TCGA cancer-type cluster identification as the top miRNA or mRNA expression profiles. The activity captures tissue-specific mi RNA patterns observed in the matched expression, e.g., the expression of miR-122-5p in the liver and miR-124-3p in low-grade gliomas (LGG). We observe a negative association between the activity of the two miRNAs and their target gene expressions, including between the miR-124-3p activity and the anti-neuronal REST expression in LGG. bayesReact outperforms the existing method, miReact, on sparse count data, and shows a higher correlation with the miRNA expression in single-cell data. The method recovers temporal activities of prominent miRNAs during murine stem cell differentiation, including miR-298-5p, miR-92-2-5p, and the large Sfmbt2 cluster (miR-297-669). The bayesReact model is probabilistic and quantifies the uncertainty of all provided estimates. It is unsupervised and permits screens of bulk or single-cell data to identify condition-specific regulatory motif candidates. It further improves miRNA activity inference in single-cell data. Availability and implementationbayesReact is implemented as an R-package, uses a Hamiltonian Monte-Carlo sampler for posterior approximation, and is available at https://github.com/astamr/bayesReact.

bioinformatics↗

bioRxiv2020

Transcriptome-wide profiles of circular RNA and RNA binding protein interactions reveal effects on circular RNA biogenesis and cancer pathway expression

Circular RNAs (circRNAs) are stable, often highly expressed RNA transcripts with potential to modulate other regulatory RNAs. A few circRNAs have been shown to bind RNA binding proteins (RBPs), however, little is known about the prevalence and strength of these interactions in different biological contexts. Here, we comprehensively evaluate the interplay between circRNAs and RBPs in the ENCODE cell lines, HepG2 and K562, by profiling the expression of circRNAs in fractionated total RNA-sequencing samples and analyzing binding sites of 150 RBPs in large eCLIP data sets. We show that KHSRP binding sites are enriched in flanking introns of circRNAs in both HepG2 and K562 cells, and that KHSRP depletion affects circRNA biogenesis. Additionally, we show that exons forming circRNAs are generally enriched with RBP binding sites compared to non-circularizing exons. To detect individual circRNAs with regulatory potency, we computationally identify circRNAs that are highly covered by RBP binding sites and experimentally validate circRNA-RBP interactions by RNA immunoprecipitations. We characterize circCDYL, a highly expressed circRNA with clinical and functional implications in bladder cancer, which is covered with GRWD1 binding sites. We confirm that circCDYL binds GRWD1 in vivo and functionally characterizes the effect of circCDYL-GRWD1 interactions on target genes in HepG2. Furthermore, we confirm interactions between circCDYL and RBPs in bladder cancer cells and demonstrate that circCDYL depletion affects hallmarks of cancer and perturbs the expression of key cancer genes, e.g. TP53 and MYC. Finally, we show that elevated levels of highly RBP-covered circRNAs, including circCDYL, are associated with overall survival of bladder cancer patients. Our study demonstrates transcriptome-wide and cell-type-specific circRNA-RBP interactions that could play important regulatory roles in tumorigenesis.

genomics↗