The regulatory map of hormone metabolism
Following synthesis in eukaryotic cells and before their translation, processes such as alternative splicing and polyadenylation regulate RNA molecules. The fate and distribution of RNA molecules largely depends on a number of regulatory factors such as RNA-binding proteins (RBPs), microRNAs, and long non-coding RNAs (lncRNA). The primary objective of the EU-funded RNAREGMAP (Condition specific RNA regulatory maps) project was to identify regulatory RBPs that are implicated in human steroid hormone metabolism. Given that inappropriate production of hormone in the adrenal cortex causes major physiological disorders including Cushing’s and Conn’s syndromes, the outcome of the study is of great clinical significance. Scientists generated a computational approach that examined approximately 80 global RBP-RNA datasets involved in diverse functions such as microRNA-mediated regulation, RNA stability and translation. In addition, they optimised genomic methods for calculating RNA production, processing and decay rates in human cell lines. This approach indicated that lncRNAs were substantially less stable than protein-coding RNAs. In another part of the project, researchers used a human adrenocortical cell culture model to identify gene expression responses to angiotensin and forskolin. These two compounds are known for their capacity to stimulate hormone production. They discovered open reading frames in protein-coding genes and non-coding RNA genomic regions involved in the molecular response to hormone production. In addition, they identified a number of putative regulatory transcription factors — RBPs, microRNAs, and lncRNAs. Particular emphasis was given to RBP ZFP36L2, an important post-transcriptional regulator of steroid hormone metabolism. Taken together, the metabolic labelling of RNA in this project provided important technical and biological insights into gene expression. Importantly, the findings of the study improve our understanding of the regulation of steroid hormone metabolism and could be further exploited to correct hormonal imbalance.
Keywords
Hormone metabolism, modelling, RNA, RNA-binding proteins, microRNA, steroid, angiotensin, forskolin, RBP ZFP36L2
