BMC GenomicsBioinformaticsPeer-reviewed journal article
Identification of Novel DNA Sequence Motifs that Modulate Transcription in T cells
Nicole KnoetzeiD, Eric YungiD, Anthony BayegaiD, Scott D. BrowniD, Robert A. HoltiD
Abstract
Considerable progress has been made towards associating transcription factor binding sites (TFBS) with cell-type-specific gene expression, however, the full repertoire of DNA sequence motifs that regulate transcription remains unknown. Improving our understanding of transcriptional regulation is especially important in T cells, given the enormous potential of genetically engineered T cells as an emerging class of therapeutics. Here, we report results from a comprehensive and unbiased survey investigating whether there are novel motifs enriched in regulatory regions of genes with the highest constitutive and selective expression across diverse T- cell subsets. Using computational and experimental methods, we identified 2,036 novel motifs and 629 previously curated TFBS that are enriched, both individually and in specific combinations, in the regulatory regions of genes exhibiting T-cell-specific gene expression. We then used the self-transcribing active regulatory region sequencing (STARR-seq) assay to evaluate all possible three- way combinations of a subset of 18 candidate motifs to test their ability to modulate transcription in immortalized lymphoblastic cell lines of T-cell origin (Jurkat E6) versus myeloid origin (K562). Our results revealed novel motifs that modulate gene transcription in T cells, with some exhibiting stronger regulatory effects than TFBS for TFs with established roles in T cells. The regulatory activity of these novel motifs was influenced by the motifs orientation, position, and copy number. Overall, these results highlight our incomplete understanding of the relationship between sequence composition and T-cell gene regulation and indicate that previously annotated TFBS represent only a subset of motifs capable of modulating gene transcription in T cells.
- organism: human
- assay: DNA methylation
- assay: ChIP-seq
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 2th percentile
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