Cell Signalling and MicroRNAs: Regulation and Evolution
Cell signalling is among the most studied topics in modern biology, it allows cells to communicate with each other and their environment and thus, orchestrates the entire functioning of organisms. Despite being studied for more than a century, the field of cell signalling has recently evolved as cell signalling started to be looked at in the context of sophisticated networks that incorporates several loops and regulatory mechanisms. One of the major regulators of cell signalling are microRNAs. MicroRNAs are small non-coding sequences that regulate gene expression post-transcriptionally. As all other non-coding sequences, the significance of microRNAs has only been established recently. Today, microRNAs are known as one of the major regulators of gene expression that are able to target more than 60% of all human protein-coding genes as well as being involved in many diseases. ​While the role of microRNAs in regulating several components of signalling networks is known, our current knowledge lacks a systematic overview of the patterns of microRNA-mediated regulation of signalling networks. In this work, I provide a comprehensive analysis of the evolution of microRNA-mediated regulation through the incorporation of several bioinformatic tools. The results of this work show that microRNA-mediated regulation in signalling networks is particularly important onreceptors. In addition, the evolutionary analysis shows that r​odents and humans microRNA-mediated regulation of receptors have diverged significantly, limiting the validity of these animals models to study human disease related to cell signalling. Finally, the analysis of the precision of microRNA target prediction shows that multiple target sites close to each other significantly increase the chances of microRNA regulation. ​In summary, the main addition to knowledge provided by this work is a novel representation of a comprehensive evolutionary overview of microRNA regulation among different cell signalling networks in addition to tackling some of the issues currently present in microRNA target prediction.
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