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We investigated the role of 3D genome architecture in instructing functional properties of glioblastoma stem cells (GSCs) by generating sub-5-kb resolution 3D genome maps by in situ Hi-C. Contact maps at sub-5-kb resolution allow identification of individual DNA loops, domain organization, and large-scale genome compartmentalization. We observed differences in looping architectures among GSCs from different patients, suggesting that 3D genome architecture is a further layer of inter-patient heterogeneity for glioblastoma. Integration of DNA contact maps with chromatin and transcriptional profiles identified specific mechanisms of gene regulation, including the convergence of multiple super enhancers to individual stemness genes within individual cells. We show that the number of loops contacting a gene correlates with elevated transcription. These results indicate that stemness genes are hubs of interaction between multiple regulatory regions, likely to ensure their sustained expression. Regions of open chromatin common among the GSCs tested were poised for expression of immune-related genes, including CD276 We demonstrate that this gene is co-expressed with stemness genes in GSCs and that CD276 can be targeted with an antibody-drug conjugate to eliminate self-renewing cells. Our results demonstrate that integrated structural genomics data sets can be employed to rationally identify therapeutic vulnerabilities in self-renewing cells.
Pubmed ID: 31249064
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Set of software modules for performing common ChIP-seq data analysis tasks across the whole genome, including positional correlation analysis, peak detection, and genome partitioning into signal-rich and signal-poor regions. The tools are designed to be simple, fast and highly modular. Each program carries out a well defined data processing procedure that can potentially fit into a pipeline framework. ChIP-Seq is also freely available on a Web interface.
View all literature mentionsFunctional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.
View all literature mentionsA tool for mapping and performing quality control on Hi-C data.
View all literature mentionsA powerful toolset for genome arithmetic allowing one to address common genomics tasks such as finding feature overlaps and computing coverage. Bedtools allows one to intersect, merge, count, complement, and shuffle genomic intervals from multiple files in widely-used genomic file formats such as BAM, BED, GFF/GTF, VCF. While each individual tool is designed to do a relatively simple task (e.g., intersect two interval files), quite sophisticated analyses can be conducted by combining multiple bedtools operations on the UNIX command line.
View all literature mentionsSoftware tool for sequence mapping.The next version of BWA-MEM. Used for aligning sequencing reads against large reference genome.
View all literature mentionsSoftware tool for rapid detection of structural variants and indels for germline and cancer sequencing applications. Used to call structural variants and indels from mapped paired end sequencing reads.
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