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This Week in Genome Research: Oct 11, 2017

Investigators from Albert Einstein College of Medicine and elsewhere describe hydroxymethylation shifts at sites in the pancreatic cancer genome with regulatory roles related to oncogenic processes. Using approaches such as hmC-seal 5'-hydroxymethylation enrichment, bisulfite sequencing, ATAC-seq, and RNA-seq, the team tracked patterns for 5'-hydroxymethylation and cytosine methylation in pancreatic cancer cell lines, patient-derived xenograft tumors, and cancer-free pancreatic samples. The hydroxymethylation modifications were over-represented at sites with open chromatin, the authors report, and turned near sites with enhanced expression in pancreatic cancer, including apparent oncogenes such as BRD4, MYC, and KRAS.

A University of Colorado-led team search for genes regulated by the tumor suppressor gene TP53 in three cancer cell lines. Using a combination of chromatin immunoprecipitation sequencing, GRO-seq, RNA-seq, and polysome-associated messenger RNA sequencing, the researchers tracked binding and expression patterns in the cancer cell lines after activating TP53 with Nutlin-3, a small molecule inhibitor compound that interferes with the interaction between the TP53 product and proto-oncogene MDM2. To make sense of the target gene network, they followed up with small hairpin RNA screening. "[A]lthough TP53 elicits vastly divergent signaling cascades across cell lines," the authors note, "it directly activates a core transcription program of [approximately 100] genes with diverse biological functions, regardless of cell type or cellular response to TP53 activation."

Researchers from the MRC Weatherall Institute of Molecular Medicine describe a computational strategy called Sasquatch that is designed to predict non-coding variant impacts on transcription factor binding based on DNase-seq data. By analyzing DNase enzyme footprints from a single DNase-seq dataset, the team explains, the Sasquatch method provides an estimate of SNP effects on transcription factor patterns in specific cell or tissue types. After testing this approach on known functional variants well-characterized human and mouse erythroid cells, the authors applied Sasquatch to other tissue types and to assess potential functional effects of SNPs linked to disease in prior genome-wide association studies.