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Heard, E.

Publications and source records attributed to Heard, E..

20 records · Page 2Linked to original sources

Differential epigenetic landscapes and transcription factors explain X-linked gene behaviours during X-chromosome reactivation in the mouse inner cell mass

X-chromosome inactivation (XCI) is established in two waves during mouse development. First, silencing of the paternal X chromosome (Xp) is triggered, with transcriptional repression of most genes and enrichment of epigenetic marks such as H3K27me3 being achieved in all cells by the early blastocyst stage. XCI is then reversed in the inner cell mass (ICM), followed by a second wave of maternal or paternal XCI, in the embryo-proper. Although the role of Xist RNA in triggering XCI is now clear, the mechanisms underlying Xp reactivation in the inner cell mass have remained enigmatic. Here we use in vivo single cell approaches (allele-specific RNAseq, nascent RNA FISH and immunofluorescence) and find that different genes show very different timing of reactivation. We observe that the genes reactivate at different stages and that initial enrichment in H3K27me3 anti-correlates with the speed of reactivation. To define whether this repressive histone mark is lost actively or passively, we investigate embryos mutant for the X-encoded H3K27me3 demethylase, UTX. Xp genes that normally reactivate slowly are retarded in their reactivation in Utx mutants, while those that reactive rapidly are unaffected. Therefore, efficient reprogramming of some X-linked genes in the inner cell mass is very rapid, indicating minimal epigenetic memory and potentially driven by transcription factors, whereas others may require active erasure of chromatin marks such as H3K27me3.

developmental biology↗

Effective normalization for copy number variation in Hi-C data

Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data. Chromosome conformation data, such as Hi-C, is not different. The most widely used type of normalization of Hi-C data casts estimations of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact as much as any other. Here, we show that these approaches, while very effective on fully haploid or diploid genome, fail to correct for unwanted effects in the presence of copy number variations. We propose a simple extension to matrix balancing methods that properly models the copy-number variation effects. Our approach can either retain the copy-number variation effects or remove it. We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genome.

bioinformatics↗