Mots-Clés
X-chromosome Inactivation
Primates
Multi-omics Analysis
Allele-Specific Genomics
Cross-species comparisons
Description
Title: Evolution of the regulation of X-chromosome inactivation in primates
Project Overview
X-chromosome inactivation (XCI) is a fundamental epigenetic mechanism that induces the transcriptional silencing one of the two X chromosomes in female cells. It is required for the proper development of human female embryos. XCI results in a differential treatment of the two X chromosomes within the cell nucleus: one being active and one being silent. This project interrogates whether the modalities of X-linked gene regulation through XCI are conserved over a short evolutionary timescale. To do this, we compare human and rhesus macaque embryonic stem (ES) cells in which XCI has been experimentally perturbed.
Research Objectives & Computational Analyses
The primary objective is to integrate multi-omics datasets generated from both species, including:
- High-depth RNA sequencing (RNA-seq) to characterize transcriptional changes;
- Cut&Run profiling of multiple histone modifications to investigate chromatin dynamics;
- ChIRP-seq to map the genomic localization of XIST, the XCI-associated regulatory RNA.
In addition, the genomes of all cell lines used in this study have been fully sequenced, enabling robust allele-specific analyses.
Several complementary approaches will be used:
- Differential Multi-omics Analysis: Comparative analyses will be performed between wild-type and knockout conditions across multiple time points to identify changes in gene expression and chromatin states associated with XCI perturbation.
- Allele-Specific Genomics: Using available WGS data and established analysis pipelines, sequencing reads will be assigned to their parental alleles. These analyses will enable the characterization of allele-specific X-linked gene expression, chromatin modifications, and XCI dynamics at high resolution.
- Comparative Genomics: Cross-species comparisons between human and rhesus macaque datasets will be used to identify differences in XCI regulation.
- Predictive Modeling of Gene Activity: development of computational models capable of predicting gene expression states from chromatin features.
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Training Environment
The project will be co-supervised by members of the Rougeulle laboratory and by Laurène Syx and Nicolas Servant from the bioinformatics platform at Institut Curie. This collaborative framework will provide extensive training in the analysis of large-scale sequencing datasets, allele-specific genomics, comparative epigenomics, and modelling of gene activity.
Candidate profile
We are looking for a highly motivated student to help us finalize this project.
- Scientific skills: basic knowledge in epigenetics and gene regulation
- Language skills: English
- Abilities: work in a team and communicate