Dataset Information

Synthetic design of strong promoters.

ABSTRACT: We have taken a synthetic biology approach to the generation and screening of transcription factor binding sites for activity in human cells. All possible 10-mer DNA sequences were printed on microarrays as 100-mers containing 10 repeats of the same sequence in tandem, yielding an oligonucleotide library of 52,429 unique sequences. This library of potential enhancers was introduced into a retroviral vector and screened in multiple cell lines for the ability to activate GFP transcription from a minimal CMV promoter. With this method, we isolated 100 bp synthetic enhancer elements that were as potent at activating transcription as the WT CMV immediate early enhancer. The activity of the recovered elements was strongly dependent on the cell line in which they were recovered. None of the elements were capable of achieving the same levels of transcriptional enhancement across all tested cell lines as the CMV enhancer. A second screen, for enhancers capable of synergizing with the elements from the original screen, yielded compound enhancers that were capable of twofold greater enhancement activity than the CMV enhancer, with higher levels of activity than the original synthetic enhancer across multiple cell lines. These findings suggest that the 10-mer synthetic enhancer space is sufficiently rich to allow the creation of synthetic promoters of all strengths in most, if not all, cell types.

SUBMITTER: Schlabach MR

PROVIDER: S-EPMC2823900 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Synthetic design of strong promoters.

Schlabach Michael R MR Hu Jimmy K JK Li Mamie M Elledge Stephen J SJ

Proceedings of the National Academy of Sciences of the United States of America 20100121 6

We have taken a synthetic biology approach to the generation and screening of transcription factor binding sites for activity in human cells. All possible 10-mer DNA sequences were printed on microarrays as 100-mers containing 10 repeats of the same sequence in tandem, yielding an oligonucleotide library of 52,429 unique sequences. This library of potential enhancers was introduced into a retroviral vector and screened in multiple cell lines for the ability to activate GFP transcription from a m ...[more]

PMID: 20133776

Similar Datasets

Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

Dataset Information

Synthetic design of strong promoters.

Publications

Synthetic design of strong promoters.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets