RT Journal
A1 Barros de Andrade e Sousa, Lisa
A1 Jonkers, Iris
A1 Syx, Laurène
A1 Dunkel, Ilona
A1 Chaumeil, Julie
A1 Picard, Christel
A1 Foret, Benjamin
A1 Chen, Chong-Jian
A1 Lis, John T.
A1 Heard, Edith
A1 Schulz, Edda G.
A1 Marsico, Annalisa
T1 Kinetics of Xist-induced gene silencing can be predicted from combinations of epigenetic and genomic features
JF Genome Research 
JO Genome Research 
YR 2019 
FD July 01 
VO 29 
IS 7 
SP 1087 
OP 1099 
DO 10.1101/gr.245027.118 
UL http://genome.cshlp.org/content/29/7/1087.abstract 
AB To initiate X-Chromosome inactivation (XCI), the long noncoding 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 is highly variable across genes, with some genes even escaping XCI in somatic cells. A gene's 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. The genomic distance to the Xist locus, followed by gene density and distance to LINE elements, are the prime determinants of the speed of gene silencing. Moreover, we find two distinct gene classes associated with different silencing pathways: a class that requires Xist-repeat A for silencing, which is known to activate the SPEN pathway, and a second class in which genes are premarked by Polycomb complexes and tend to rely on the B repeat in Xist for silencing, known to recruit Polycomb complexes during XCI. Moreover, a series of features associated with active transcriptional elongation and chromatin 3D structure are enriched at rapidly silenced genes. Our machine-learning approach can thus uncover the complex combinatorial rules underlying gene silencing during X inactivation.