bioRxivSynthetic BiologyPreprint
Identifying triplex binding rules in vitro leads to creation of a new synthetic regulatory tool in vivo
Beate Kaufmann, Or WillingeriD, Noa Eden, Lisa Kermas, Leon AnavyiD, Oz SolomoniD, Orna AtariD, Zohar Yakhini BiD, Sarah GoldbergiD, Roee AmitiD
- Citations
- 2
Abstract
Nature provides a rich toolbox of dynamic nucleic acid structures that are widespread in cells and affect multiple biological processes1. Recently, non-canonical structures gained renewed scientific and biotechnological interest2,3. One particularly intriguing form of such structures are triplexes4 in which a single-stranded nucleic acid molecule interacts via Hoogsteen bonds with a DNA/RNA double helix5. Despite extensive research in vitro6-9, the underlying rules for triplex formation remain debated and evidence for triplexes in vivo is circumstantial10-12. Here, we demonstrate the development of a deep-sequencing platform termed Triplex-Seq to systematically refine the DNA triplex code and identify high affinity triplex forming oligo (TFO) variants. We identified a preference for short G-rich motifs using an oligo-library with a mix of all four bases. These high-information content motifs formed specific high-affinity triplexes in a pH-independent manner and stability was increased with G-rich double-stranded molecules. We then conjugated one high-affinity and one low-affinity variant to a VP48 peptide and studied these synthetic biomolecules in mammalian cells. Using these peptide-oligo constructs (POCs), we demonstrated possible triplex-induced down-regulation activity in 544 differentially expressed genes. Our results show that deep-sequencing platforms can substantially expand our understanding of triplex binding rules, which in turn has led to the development of a functional non-genetically encoded regulatory tool for in vivo applications.
- organism: human
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: benchmarking
- research focus: database / resource
- approach: deep learning
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