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Noise and tunability of a programmable CRISPR platform for gene network regulation

ORAL

Abstract

Viral infections and cancer often manifest by transcriptome dysregulation in human cells. CRISPR-Cas systems could provide ways to correct transcriptome dysregulation, thereby supplementing our native immunity against viral infection, tumorigenesis and metastasis. However, the benefits and deleterious effects of such CRISPR systems could depend on the level and noise of Cas expression, which are currently unknown. Here, we adapted a negative-feedback synthetic gene circuit to precisely regulate the Cas13d effector expression. Using host cell genome engineering approaches, we integrated site-specifically Cas13d-tuning constructs into a safe harbor site in the human genome via recombinase-mediated cassette exchange. We compare gene expression data with computational models to understand the transcript-tuning capability of Cas13d-containing synthetic gene circuits. Additionally, we develop orthogonally integrated on/off switches of guide RNA expression, making the full system programmable for regulation of arbitrary human or viral genes. By targeting SARS-CoV-2 gene fragments we found dose-responsive reduction of expression, suggesting a potential for regulated viral defense systems with minimal side effects.

Presenters

  • Yiming Wan

    Stony Brook University (SUNY), Stony Brook University (SUNY), Biomedical Engineering Department and Laufer Center for Physical and Quantitative Biology

Authors

  • Yiming Wan

    Stony Brook University (SUNY), Stony Brook University (SUNY), Biomedical Engineering Department and Laufer Center for Physical and Quantitative Biology

  • Rafał Krzysztoń

    Stony Brook University, Stony Brook University (SUNY), Biomedical Engineering Department and Laufer Center for Physical and Quantitative Biology

  • Joseph Cohen

    Stony Brook University (SUNY)

  • Damiano Coraci

    Stony Brook University

  • Gábor Balázsi

    State Univ of NY - Stony Brook, Stony Brook University (SUNY), Stony Brook University (SUNY), Biomedical Engineering Department and Laufer Center for Physical and Quantitative Biology, Stony Brook University