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Identifying Mechanisms of Gene Circuit Evolution that Elicit Mammalian Drug Resistance

ORAL

Abstract

Stochasticity in gene expression is a prime determinant of drug resistance in mammalian cell populations, but the biological mechanisms that elicit such behaviors are poorly understood. Recently we used synthetic gene circuits harboring positive feedback (PF) and negative feedback (NF) regulation to tune mammalian drug resistance transgene (DRT) expression noise. Cell populations harboring PF circuitry exhibit high DRT expression noise, which favors the evolution of drug resistance in high stress environments. Cell populations harboring NF circuitry exhibit minimal DRT expression noise, which favors the evolution of drug resistance in low stress environments. Biological mechanisms that drive these observations are unknown. Here, we investigate transcriptional profiles of experimentally evolved NF and PF mammalian cell populations that became drug resistant. Identifying how mammalian populations combine and evolve drug-specific and pleiotropic drug resistance will provide insight into drug resistance mechanisms and may guide future applications of synthetic gene circuits in cell research and medicine.

Publication: Farquhar, K. S., Charlebois, D. A., Szenk, M., Cohen, J., Nevozhay, D., & Balázsi, G. (2019). Role of network-mediated stochasticity in mammalian drug resistance. Nature communications, 10(1), 1-14.

Presenters

  • Joseph Cohen

    Stony Brook University (SUNY)

Authors

  • Joseph Cohen

    Stony Brook University (SUNY)

  • Quanhua Mu, Ph.D

    Hong Kong University of Science and Technology

  • Yiming Wan

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

  • Kevin Farquar, Ph.D

    Independent Researcher

  • 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