Thrombopoietin signaling to chromatin elicits rapid and pervasive epigenome remodeling within poised chromatin architectures

Abstract

Thrombopoietin (TPO) is a critical cytokine regulating hematopoietic stem cell maintenance and differentiation into the megakaryocytic lineage. However, the transcriptional and chromatin dynamics elicited by TPO signaling are poorly understood. Here, we study the immediate early transcriptional and cis-regulatory responses to TPO in hematopoietic stem/progenitor cells (HSPCs) and use this paradigm of cytokine signaling to chromatin to dissect the relation between cisregulatory activity and chromatin architecture. We show that TPO profoundly alters the transcriptome of HSPCs, with key hematopoietic regulators being transcriptionally repressed within 30 minutes of TPO. By examining cis-regulatory dynamics and chromatin architectures, we demonstrate that these changes are accompanied by rapid and extensive epigenome remodeling of cis-regulatory landscapes that is spatially coordinated within topologically associating domains (TADs). Moreover, TPO-responsive enhancers are spatially clustered and engage in preferential homotypic intra-and inter-TAD interactions that are largely refractory to TPO signaling. By further examining the link between cis-regulatory dynamics and chromatin looping, we show that rapid modulation of cis-regulatory activity is largely independent of chromatin looping dynamics. Finally, we show that, although activated and repressed cis-regulatory elements share remarkably similar DNA sequence compositions, transcription factor binding patterns accurately predict rapid cis-regulatory responses to TPO.

Dissection of acute stimulus-inducible nucleosome remodeling in mammalian cells

“…Least absolute shrinkage and selection operator (lasso) logistic regression models were used to discriminate symmetric from asymmetric nucleosomal patterns at intergenic enhancers based on DNA sequence features, and to predict nucleosome remodeling events associated with TF dynamics during LPS stimulation. Models were trained and evaluated as described (Comoglio et al 2015;Comoglio et al 2018). For modeling of nucleosomal symmetry patterns at enhancers, DNA sequences were extracted from the reference genome (mm9) are scored as follow.…”

“…To this end, we considered three sets of features: i) k-mers frequencies (with 2 ≤ k ≤ 4); ii) DNA shape features (Chiu et al 2016) and iii) motif scores from a curated collection of >1700 TF motifs (Diaferia et al 2016). These features were used alone or in combination to train lasso logistic regression classifiers (Comoglio et al 2015;Comoglio et al 2018) and prediction accuracies were evaluated on an independent test set. This analysis revealed that TF motifs were only marginally predictive for nucleosomal symmetry at enhancers (mean area under the receiver operating characteristic curve (AUC) = 0.59) and that DNA shape features (AUC = 0.65) and particularly 4-mer sequences (AUC = 0.72) were able to achieve more accurate predictions ( Figure 2F).…”

Divergent wiring of repressive and active chromatin interactions between mouse embryonic and trophoblast lineages

“…PCHi-C experiments were performed in duplicate for each cell line. Thirty to 40 million ESCs or TSCs were fixed in 2% formaldehyde (Agar Scientific) for 10 min at room temperature, and Hi-C libraries were prepared as described 49 , with minor modifications. Briefly, crosslinked chromatin was digested with Hin dIII (NEB), and restriction fragment ends were labelled using biotin-14-dATP (Life Technologies) and DNA Polymerase I (Large Klenow Fragment; NEB).…”

Dissection of acute stimulus-inducible nucleosome remodeling in mammalian cells

“…To this end, we considered three sets of features: (1) k-mer frequencies (with 2 ≤ k ≤ 4), (2) DNA shape features (Chiu et al 2016), and (3) motif scores from a curated collection of >1700 TF motifs (Diaferia et al 2016). These features were used alone or in combination to train classifiers (Comoglio et al 2015(Comoglio et al , 2018 using cross-validation and to evaluate the prediction accuracies on a test set. This analysis revealed that models combining all of the considered feature sets could achieve a fair classification accuracy (mean area under the receiver operating characteristic curve [AUC] = 0.72) but did not outperform models based solely on 4-mers ( Fig.…”

“…Lasso logistic regression models were used to discriminate symmetric from asymmetric nucleosomal patterns at intergenic enhancers based on DNA sequence features and predict nucleosome remodeling events associated with TF dynamics during LPS stimulation. Models were trained and evaluated as described (Comoglio et al 2015(Comoglio et al , 2018.…”