eLifeGenomicsPeer-reviewed journal article
Systematic analysis of naturally occurring insertions and deletions that alter transcription factor spacing identifies tolerant and sensitive transcription factor pairs
Zeyang SheniD, Rick Z. LiiD, Thomas A ProhaskaiD, Marten A. HoeksemaiD, Nathan J. Spann, Jenhan TaoiD, Gregory J. FonsecaiD, Thomas Le, Lindsey K. StolzeiD, Mashito SakaiiD, Casey E. RomanoskiiD, Christopher K. GlassiD
Abstract
Regulation of gene expression requires the combinatorial binding of sequence-specific transcription factors (TFs) at promoters and enhancers. Prior studies showed that alterations in the spacing between TF binding sites can influence promoter and enhancer activity. However, the relative importance of TF spacing alterations resulting from naturally occurring insertions and deletions (InDels) has not been systematically analyzed. To address this question, we first characterized the genome-wide spacing relationships of 75 TFs in K562 cells as determined by ChIP-sequencing. We found a dominant pattern of a relaxed range of spacing between collaborative factors, including 46 TFs exclusively exhibiting relaxed spacing with their binding partners. Next, we exploited millions of InDels provided by genetically diverse mouse strains and human individuals to investigate the effects of altered spacing on TF binding and local histone acetylation. Spacing alterations resulting from naturally occurring InDels are generally tolerated in comparison to genetic variants directly affecting TF binding sites. A remarkable range of tolerance was further established for PU.1 and C/EBP{beta}, which exhibit relaxed spacing, by introducing synthetic spacing alterations ranging from 5-bp increase to >30-bp decrease using CRISPR/Cas9 mutagenesis. These findings provide implications for understanding mechanisms underlying enhancer selection and for the interpretation of non-coding genetic variation.
- organism: human
- organism: mouse
- assay: ChIP-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 26
- FWCI
- 1.63
- Field percentile
- top 16%
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