Biology subjects

Pratt, H.

Publications and source records attributed to Pratt, H..

4 recordsLinked to original sources

bioRxiv2022

MafB, WDR77, and ss-catenin interact with each other and have similar genome association profiles

MafB (a bZIP transcription factor), {beta}-catenin (the ultimate target of the Wnt signal transduction pathway that acts as a transcriptional co-activator of LEF/TCF proteins), and WDR77 (a transcriptional co-activator of multiple hormone receptors) are important for breast cellular transformation. Unexpectedly, these proteins interact directly with each other, and they have similar genomic binding profiles. Furthermore, while some of these common target sites coincide with those bound by LEF/TCF, the majority are located just downstream of transcription initiation sites at a position near paused RNA polymerase (Pol II) and the +1 nucleosome. Occupancy levels of these factors at these promoter-proximal sites are strongly correlated with the level of paused Pol II and transcriptional activity.

molecular biology↗

bioRxiv2021

YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation

The YAP and TAZ paralogues are transcriptional co-activators recruited to target sites, primarily by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB and STAT3, key factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, co-occupy many target sites in vivo via AP-1 and (to a lesser extent) STAT3 sequence motifs, and stimulate transcriptional activation by AP-1 proteins. A few target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes. YAP/TAZ, JUNB, and STAT3 directly regulate a common set of target genes that overlap, but are distinct from, those regulated by YAP/TAZ and TEADs. The set of genes regulated by YAP/TAZ, STAT3, and JUNB is associated with poor survival in breast cancer patients with the triple-negative form of the disease.

molecular biology↗

bioRxiv2020

Annotation of Chromatin States in 66 Complete Mouse Epigenomes During Development

The morphologically and functionally distinct cell types of a multicellular organism are maintained by epigenomes and gene expression programs. Phase III of the ENCODE Project profiled 66 mouse epigenomes across twelve tissues at daily intervals from embryonic day 10.5 to birth. Applying the ChromHMM algorithm to these epigenomes, we annotated eighteen chromatin states with characteristics of promoters, enhancers, transcribed regions, repressed regions, and quiescent regions throughout the developmental time course. Our integrative analyses delineate the tissue specificity and developmental trajectory of the loci in these chromatin states. Approximately 0.3% of each epigenome is assigned to a bivalent chromatin state, which harbors both active marks and the repressive mark H3K27me3. Highly evolutionarily conserved, these loci are enriched in silencers bound by Polycomb Repressive Complex proteins and the transcription start sites of their silenced target genes. This collection of chromatin state assignments provides a useful resource for studying mammalian development.

bioinformatics↗

bioRxiv2019

A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods

Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes. To facilitate the development of computational methods for predicting target genes, we developed a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the Registry of cCREs we developed recently with experimentally-derived genomic interactions. We used BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the supervised learning methods TargetFinder and PEP. We found that while TargetFinder was the best performing method, it was modestly better than a baseline distance method for most benchmark datasets while trained and tested within the same cell type and that TargetFinder often did not outperform the distance method when applied across cell types. Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

bioinformatics↗