Kinetics of Xist-induced gene silencing can be predicted from combinations of epigenetic and genomic features

To initiate X-chromosome inactivation (XCI), the long non-coding RNA Xist mediates chromosome-wide gene silencing of one X chromosome in female mammals to equalize gene dosage between the sexes. The efficiency of gene silencing, however is highly variable across genes, with some genes even escaping...

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Published inbioRxiv
Main Authors Lisa Barros De Andrade E Sousa, Jonkers, Iris, Syx, Laurene, Chaumell, Julie, Picard, Christel, et, Benjamin, Chong-Jian, Chen, Lis, John T, Heard, Edith, Schulz, Edda G, Marsico, Annalisa
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 03.01.2019
Subjects
Artificial intelligence
Chromosomes
Epigenetics
Gene dosage
Gene silencing
Genes
Learning algorithms
Non-coding RNA
Polycomb group proteins
Somatic cells
Transcription
X-chromosome inactivation
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Summary:To initiate X-chromosome inactivation (XCI), the long non-coding RNA Xist mediates chromosome-wide gene silencing of one X chromosome in female mammals to equalize gene dosage between the sexes. The efficiency of gene silencing, however is highly variable across genes, with some genes even escaping XCI in somatic cells. A genes susceptibility to Xist-mediated silencing appears to be determined by a complex interplay of epigenetic and genomic features; however, the underlying rules remain poorly understood. We have quantified chromosome-wide gene silencing kinetics at the level of the nascent transcriptome using allele-specific Precision nuclear Run-On sequencing (PRO-seq). We have developed a Random Forest machine learning model that can predict the measured silencing dynamics based on a large set of epigenetic and genomic features and tested its predictive power experimentally. While the genomic distance to the Xist locus is the prime determinant of the speed of gene silencing, we find that also pre-marking of gene promoters with polycomb complexes is associated with fast silencing. Moreover, a series of features associated with active transcription and the O-GlcNAc transferase Ogt are enriched at rapidly silenced genes. Our machine learning approach can thus uncover the complex combinatorial rules underlying gene silencing during X inactivation.
DOI:10.1101/510198