Nature GeneticsGenomicsPeer-reviewed journal article
Sequence determinants of human gene regulatory elements
Biswajyoti SahuiD, Tuomo HartoneniD, Päivi PihlajamaaiD, Bei WeiiD, Kashyap DaveiD, Fangjie ZhuiD, Eevi KaasineniD, Katja LidschreiberiD, Michael LidschreiberiD, Carsten O. DaubiD, Patrick CrameriD, Teemu KiviojaiD, Jussi TaipaleiD
Abstract
DNA determines where and when genes are expressed, but the full set of sequence determinants that control gene expression is not known. To obtain a global and unbiased view of the relative importance of different sequence determinants in gene expression, we measured transcriptional activity of DNA sequences that are in aggregate [~]100 times longer than the human genome in three different cell types. We show that enhancers can be classified to three main types: classical enhancers1, closed chromatin enhancers and chromatin-dependent enhancers, which act via different mechanisms and differ in motif content. Transcription factors (TFs) act generally in an additive manner with weak grammar, with classical enhancers increasing expression from promoters by a mechanism that does not involve specific TF-TF interactions. Few TFs are strongly active in a cell, with most activities similar between cell types. Chromatin-dependent enhancers are enriched in forkhead motifs, whereas classical enhancers contain motifs for TFs with strong transactivator domains such as ETS and bZIP; these motifs are also found at transcription start site (TSS)-proximal positions. However, some TFs, such as NRF1 only activate transcription when placed close to the TSS, and others such as YY1 display positional preference with respect to the TSS. TFs can thus be classified into four non-exclusive subtypes based on their transcriptional activity: chromatin opening, enhancing, promoting and TSS determining factors - consistent with the view that the binding motif is the only atomic unit of gene expression.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 197
- FWCI
- 13.05
- Field percentile
- top 1%
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