The Impact of Population Variation in the Analysis of microRNA Target Sites

The impact of population variation in the analysis of regulatory interactions is an underdeveloped area. MicroRNA target recognition occurs via pairwise complementarity. Consequently, a number of computational prediction tools have been developed to identify potential target sites that can be further validated experimentally. However, as microRNA target predictions are done mostly considering a reference genome sequence, target sites showing variation among populations are neglected. Here, we studied the variation at microRNA target sites in human populations and quantified their impact in microRNA target prediction. We found that African populations carry a significant number of potential microRNA target sites that are not detectable in the current human reference genome sequence. Some of these targets are conserved in primates and only lost in Out-of-Africa populations. Indeed, we identified experimentally validated microRNA/transcript interactions that are not detected in standard microRNA target prediction programs, yet they have segregating target alleles abundant in non-European populations. In conclusion, we show that ignoring population diversity may leave out regulatory elements essential to understand disease and gene expression, particularly neglecting populations of African origin

PopTargs: a database for studying population evolutionary genetics of human microRNA target sites

There is an increasing interest in the study of polymorphic variants at gene regulatory motifs, including microRNA target sites. Understanding the effects of selective forces at specific microRNA target sites, together with other factors like expression levels or evolutionary conservation, requires the joint study of multiple datasets. We have compiled information from multiple sources and compared it with predicted microRNA target sites to build a comprehensive database for the study of microRNA targets in human populations. PopTargs is a web-based tool that allows the easy extraction of multiple datasets and the joint analyses of them, including allele frequencies, ancestral status, population differentiation statistics and site conservation. The user can also compare the allele frequency spectrum between two groups of target sites and conveniently produce plots. The database can be easily expanded as new data becomes available and the raw database as well as code for creating new custom-made databases is available for downloading. We also describe a few illustrative examples

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