The Cistrome Response to Hypoxia in Human Umbilical Vein Endothelial Cells
Singh, A.; Pastukh, V. M.; Roberts, J. T.; Turpin, Z. M.; Glover, Z. S.; Daly, G. T.; Benoit, J. M.; Gillespie, M. N.; Bass, H. W.
Show abstract
Hypoxic stress triggers transcriptional signaling mainly through hypoxia-inducible transcription factors (HIFs), which bind hypoxia response elements (HREs) in gene regulatory regions. However, only a small proportion ([~]1%) of known HREs are occupied by HIFs during hypoxia, suggesting the involvement of additional hypoxia-responsive factors. To address this gap, we utilized MOA-seq. This MNase-based assay enables genome-wide, high-resolution (<30 bp) identification of transcription factor (TF) occupancy footprints embedded within larger regions, most of which were previously annotated as open or accessible chromatin. Applying this native cistrome mapping to endothelial cells under normoxia or hypoxia (1, 3, or 24 hours) revealed thousands of hypoxia-responsive genomic sites with dynamic TF footprints. The affected genes were enriched in canonical hypoxia-induced pathways, such as angiogenesis. Motif analysis identified over 100 candidate TFs potentially mediating these multifaceted genomic responses. By grouping the gain/loss footprint patterns over time, we defined 10 distinct TF kinetic clusters, half of which were associated with HIF1A. HIF1A-proximal binding sites suggested co-activators, while non-HIF1A clusters pointed to additional TFs with HIF1A-independent roles. This analysis provides insight into how multiple TF networks coordinate hypoxia responses and highlights the power of cistrome profiling to deepen understanding of genomic regulation under low oxygen conditions. KEY POINTSO_LIMOA-seq mapped TF occupancy at 21,765 sites in normoxia, including 7,444 beyond the known ENCODE cCREs. C_LIO_LIHypoxia for 1, 3, and 24h changes the cistrome occupancy at thousands of genes. C_LIO_LIClustering analysis of hypoxia-responsive footprints consolidated cistrome kinetics into HIF1A-associated and HIF1A-independent TFs. C_LI
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs 96%
- Prediction of G4 formation in live cells with epigenetic data: a deep learning approach 94%
- Underlying causes for prevalent false positives and false negatives in STARR-seq data 94%
Similar papers in this journal
- DNA methylation entropy is associated with DNA sequence featuresand developmental epigenetic divergence 94%
- Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities 94%
- Genome-wide mapping of G-quadruplex structures with CUT&Tag 94%
Similar papers in this journal
- Functional non-coding SNPs in human endothelial cells fine-map vascular trait associations 95%
- Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model 94%
- Aberrant homeodomain-DNA cooperative dimerization underlies distinct developmental defects in two dominant CRX retinopathy models 94%
Similar papers in this journal
- β-actin dependent chromatin remodeling mediates compartment level changes in 3D genome architecture 95%
- Transcriptional Responses of Cancer Cells to Heat Shock-Inducing Stimuli Involve Amplification of Robust HSF1 Binding 94%
- Chondrogenic Enhancer Landscape of Limb and Axial Skeleton Development 94%
Similar papers in this journal
- Allele-specific DNA methylation is increased in cancers and its dense mapping in normal plus neoplastic cells increases the yield of disease-associated regulatory SNPs 93%
- Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq 93%
- CpG island turnover events predict evolutionary changes in enhancer activity 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.