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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.

Project description:Almost all regulation of gene expression in eukaryotic genomes is mediated by the action of distant non-coding transcriptional enhancers upon proximal gene promoters. Enhancer locations cannot be accurately predicted bioinformatically because of the absence of a defined sequence code, and thus functional assays are required for their direct detection. Here we used a massively parallel reporter assay, Self-Transcribing Active Regulatory Region sequencing (STARR-seq), to generate the first comprehensive genome-wide map of enhancers in Anopheles coluzzii, a major African malaria vector in the Gambiae species complex. The screen was carried out by transfecting reporter libraries created from the genomic DNA of 60 wild A. coluzzii from Burkina Faso into A. coluzzii 4a3A cells, in order to functionally query enhancer activity of the natural population within the homologous cellular context. We report a catalog of 3,288 active genomic enhancers that were significant across three biological replicates, 74% of them located in intergenic and intronic regions. The STARR-seq enhancer screen is chromatin-free and thus detects inherent activity of a comprehensive catalog of enhancers that may be restricted in vivo to specific cell types or developmental stages. Testing of a validation panel of enhancer candidates using manual luciferase assays confirmed enhancer function in 26 of 28 (93%) of the candidates over a wide dynamic range of activity from two to at least 16-fold activity above baseline. The enhancers occupy only 0.7% of the genome, and display distinct composition features. The enhancer compartment is significantly enriched for 15 transcription factor binding site signatures, and displays divergence for specific dinucleotide repeats, as compared to matched non-enhancer genomic controls. The genome-wide catalog of A. coluzzii enhancers is publicly available in a simple searchable graphic format. This enhancer catalogue will be valuable in linking genetic and phenotypic variation, in identifying regulatory elements that could be employed in vector manipulation, and in better targeting of chromosome editing to minimize extraneous regulation influences on the introduced sequences. Importance: Understanding the role of the non-coding regulatory genome in complex disease phenotypes is essential, but even in well-characterized model organisms, identification of regulatory regions within the vast non-coding genome remains a challenge. We used a large-scale assay to generate a genome wide map of transcriptional enhancers. Such a catalogue for the important malaria vector, Anopheles coluzzii, will be an important research tool as the role of non-coding regulatory variation in differential susceptibility to malaria infection is explored and as a public resource for research on this important insect vector of disease.

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.

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