Dissecting the molecular mechanisms of lncRNA function in X chromosome inactivation across mammalian gestation evolution

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Background:In eutherian mammals, X-chromosome inactivation (XCI) is required in females to ensure equal transcription levels of most X-linked genes for both males and females. In female (XX) preimplantation embryos, both X-chromosomes are transcriptionally active from embryonic genome activation until a long non-coding RNA (X-inactive specific transcript: XIST) mediates the inactivation of one. Weknow that species with different types of placental development have substantial diversity in the timing and regulation of XCI initiation but we do not know how this process works. Objectives:1-To determine the RNA, DNA and protein components that XIST interacts with in embryos from different placental phenotypes.2-To integrate interaction data and extract the mechanism/s of function of XIST as it relates to placental evolution.3-To identify evolutionary conserved and divergent molecular mechanisms in the XIST functional complex across different placental and embryo phenotypes. Novelty:This cutting edge project combines reproductive biology, RNA biology and computational evolutionary biology to address the fundamental questions of how the lncRNA XIST regulates XCI and the evolution of the mechanism across different placental types. Timeliness:This will be the first time the functional evolution of lncRNAs will be addressed in vivo. The in vivo pull-down methods are at the cutting edge of transcriptomic and proteomic technologies. Our recentlyfounded LeedsOmics will provide the bioinformatics training required. Experimental Approaches:Embryos from mouse, bovine, sheep and pig (i.e. different placenta types) will be cultured with sex-sorted semen where possible. Lysates will be generated from the female embryos and then XIST RNA will be pulled-down with antisense biotinylated oligos (in vivo). The components of these XIST complexes will be identified by nano-LC MS/MS (proteins), RNA-seq (RNA) and ChIP-Seq(DNA). Resulting NGS data will be analysed computationally and will be compared to high quality mammal genomes to identify regions of conservation and variation.