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Transcriptional enhancers induce insertional gene deregulation independently from the vector type and design.

ABSTRACT: The integration characteristics of retroviral (RV) vectors increase the probability of interfering with the regulation of cellular genes, and account for a tangible risk of insertional mutagenesis in treated patients. To assess the potential genotoxic risk of conventional or self-inactivating (SIN) gamma-RV and lentiviral (LV) vectors independently from the biological consequences of the insertion event, we developed a quantitative assay based on real-time reverse transcriptase--PCR on low-density arrays to evaluate alterations of gene expression in individual primary T-cell clones. We show that the Moloney leukemia virus long terminal repeat (LTR) enhancer has the strongest activity in both a gamma-RV and a LV vector context, while an internal cellular promoter induces deregulation of gene expression less frequently, at a shorter range and to a lower extent in both vector types. Downregulation of gene expression was observed only in the context of LV vectors. This study indicates that insertional gene activation is determined by the characteristics of the transcriptional regulatory elements carried by the vector, and is largely independent from the vector type or design.

SUBMITTER: Maruggi G

PROVIDER: S-EPMC2835143 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Transcriptional enhancers induce insertional gene deregulation independently from the vector type and design.

Maruggi Giulietta G Porcellini Simona S Facchini Giulia G Perna Serena K SK Cattoglio Claudia C Sartori Daniela D Ambrosi Alessandro A Schambach Axel A Baum Christopher C Bonini Chiara C Bovolenta Chiara C Mavilio Fulvio F Recchia Alessandra A

Molecular therapy : the journal of the American Society of Gene Therapy 20090317 5

The integration characteristics of retroviral (RV) vectors increase the probability of interfering with the regulation of cellular genes, and account for a tangible risk of insertional mutagenesis in treated patients. To assess the potential genotoxic risk of conventional or self-inactivating (SIN) gamma-RV and lentiviral (LV) vectors independently from the biological consequences of the insertion event, we developed a quantitative assay based on real-time reverse transcriptase--PCR on low-densi ...[more]

PMID: 19293778

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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

Transcriptional enhancers induce insertional gene deregulation independently from the vector type and design.

Publications

Transcriptional enhancers induce insertional gene deregulation independently from the vector type and design.

Similar Datasets

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