Dataset Information

Negative Feedback Regulation of HIV-1 by Gene Editing Strategy.

ABSTRACT: The CRISPR/Cas9 gene editing method is comprised of the guide RNA (gRNA) to target a specific DNA sequence for cleavage and the Cas9 endonuclease for introducing breaks in the double-stranded DNA identified by the gRNA. Co-expression of both a multiplex of HIV-1-specific gRNAs and Cas9 in cells results in the modification and/or excision of the segment of viral DNA, leading to replication-defective virus. In this study, we have personalized the activity of CRISPR/Cas9 by placing the gene encoding Cas9 under the control of a minimal promoter of HIV-1 that is activated by the HIV-1 Tat protein. We demonstrate that functional activation of CRISPR/Cas9 by Tat during the course of viral infection excises the designated segment of the integrated viral DNA and consequently suppresses viral expression. This strategy was also used in a latently infected CD4+ T-cell model after treatment with a variety of HIV-1 stimulating agents including PMA and TSA. Controlled expression of Cas9 by Tat offers a new strategy for safe implementation of the Cas9 technology for ablation of HIV-1 at a very early stage of HIV-1 replication during the course of the acute phase of infection and the reactivation of silent proviral DNA in latently infected cells.

SUBMITTER: Kaminski R

PROVIDER: S-EPMC4985742 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Negative Feedback Regulation of HIV-1 by Gene Editing Strategy.

Kaminski Rafal R Chen Yilan Y Salkind Julian J Bella Ramona R Young Won-Bin WB Ferrante Pasquale P Karn Jonathan J Malcolm Thomas T Hu Wenhui W Khalili Kamel K

Scientific reports 20160816

The CRISPR/Cas9 gene editing method is comprised of the guide RNA (gRNA) to target a specific DNA sequence for cleavage and the Cas9 endonuclease for introducing breaks in the double-stranded DNA identified by the gRNA. Co-expression of both a multiplex of HIV-1-specific gRNAs and Cas9 in cells results in the modification and/or excision of the segment of viral DNA, leading to replication-defective virus. In this study, we have personalized the activity of CRISPR/Cas9 by placing the gene encodin ...[more]

PMID: 27528385

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Project description:Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference needs to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects-the model fidelity, the available data, and the numerical choices for inference-interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a detailed spatial stochastic model, a coarse-grained compartment-based multiscale model, and the standard well-mixed model, across several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance function values, we are able to substantially reduce the computational cost of running the pipeline.

Dataset Information

Negative Feedback Regulation of HIV-1 by Gene Editing Strategy.

Publications

Negative Feedback Regulation of HIV-1 by Gene Editing Strategy.

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