UC Irvine

Developmental processes are extremely complex and precisely coordinated sets of orchestrated changes in the transcriptomic landscape within the cells or tissues involved. These changes are the result of concerted efforts across multiple layers of transcriptional and post-transcriptional regulation. MicroRNAs (miRNAs) are a key class of short, non-coding post-transcriptional regulators with a prominent role in early development and differentiation. The main aim of my research has been to study the role of miRNAs in dynamic processes such as embryonic development in conjunction with the transcriptional changes during those processes. To achieve this goal, we integrated the analysis of miRNA and mRNA data from a set of multiple tissues across different stages of embryonic mouse development. In our study, we first cluster miRNAs and mRNAs separately using a regression-based tool. Then, we used analysis of negative partial correlation of these clusters with each other in parallel with enrichment analysis of the predicted targets for each miRNA cluster across mRNA clusters. Using this approach, we are able to identify clusters of miRNAs that repress, in a tissue specific manner, the undesired developmental processes pertaining to other tissues.

MicroRNAs affect the steady-state expression of their target mRNAs by destabilizing and degrading them. However, mRNA steady-state expression levels are affected by both transcription and degradation rates, and the changes in steady-state expression measured by RNA-seq can be attributed to either process. A higher resolution of miRNA-mRNA analysis requires studying the dynamics of transcription at the level of individual mRNA molecules that are being made or degraded. Furthermore, many miRNA binding sites fall in UTR regions or exonic/intronic regions of the gene that can vary between isoforms. Identifying exactly which isoforms are expressed can be extremely helpful in distinguishing the degradation rates between different isoform species. Hence, we developed long-TUC-seq, a long-read sequencing protocol that utilizes TUC-seq chemistry (4-thiouridine labeling and its conversion to cytidine using osmium tetroxide) in order to identify RNA molecules that are recently made (or degraded in the case of chase experiments) at transcript isoform resolution.

Finally, in order to consider the dynamics of miRNA biogenesis and degradation itself, we developed micro-TUC-seq, which is a novel method relying on TUC-seq chemistry to identify mature miRNAs generated in a given labeling time window. We apply this method together with regular TUC-seq to decipher the role of miRNAs during HL-60 macrophage differentiation.