bioRxivCancer Biology
Lineage specific transcription factor waves reprogram neuroblastoma from self-renewal to differentiation
D. Banerjee, B. Gryder, S. Bagchi, Z. Liu, H.-C. Chen, M. Xu, M. Sun, Z. Vaksman, S. J. Diskin, J. Khan, C. Thiele
Abstract
Temporal regulation of super-enhancer (SE) driven lineage specific transcription factors (TFs) underlies normal developmental programs. Neuroblastoma (NB) arises from an inability of sympathoadrenal progenitors to exit a self-renewal program and terminally differentiate. To identify critical SEs driving TF regulators of NB, we utilized NB cells in which all-trans retinoic acid (ATRA) induces growth arrest and differentiation. H3K27ac ChIP-seq paired with RNA-seq over a time course of ATRA treatment revealed SEs moving in a coordinated manner with four distinct temporal patterns (clusters). SEs that decreased with ATRA linked to 24 TFs involved in stem cell development/specialization (MYCN, GATA3, SOX11) along with LMO1, a transcriptional coregulator and oncogene identified via a genome-wide association study (GWAS) of NB. H3K27ac levels and GATA3 binding at the NB-associated rs2168101 site of the LMO1 SE were reduced with ATRA treatment, resulting in 1.46 fold decreased LMO1 expression. The SOX11 SE was lost coincident with a 50% decrease in mRNA after 8 days of ATRA treatment. CRISPR-Cas9 screening and siRNA inhibition showed a dependency on SOX11 for cell growth in NB cell lines. Silencing of the SOX11 SE using dCAS9-KRAB targeted guides caused a 40% decrease in SOX11 mRNA and inhibited cell growth. Three other TF SE clusters had sequential waves of activation at 2, 4 and 8 days of ATRA treatment and involved TFs regulating neural development (GATA2 and SOX4). Silencing of the gained SOX4 SE using dCAS9-KRAB targeting, caused a 50% decrease in SOX4 expression and attenuated expression of ATRA-induced differentiation genes. Our study has identified candidate oncogenic lineage drivers of NB self-renewal and TFs critical for implementing a differentiation program.
- organism: human
- assay: ChIP-seq
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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