1
|
Wan Y, Cohen J, Szenk M, Farquhar KS, Coraci D, Krzysztoń R, Azukas J, Van Nest N, Smashnov A, Chern YJ, De Martino D, Nguyen LC, Bien H, Bravo-Cordero JJ, Chan CH, Rosner MR, Balázsi G. Nonmonotone invasion landscape by noise-aware control of metastasis activator levels. Nat Chem Biol 2023; 19:887-899. [PMID: 37231268 PMCID: PMC10299915 DOI: 10.1038/s41589-023-01344-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 04/18/2023] [Indexed: 05/27/2023]
Abstract
A major pharmacological assumption is that lowering disease-promoting protein levels is generally beneficial. For example, inhibiting metastasis activator BACH1 is proposed to decrease cancer metastases. Testing such assumptions requires approaches to measure disease phenotypes while precisely adjusting disease-promoting protein levels. Here we developed a two-step strategy to integrate protein-level tuning, noise-aware synthetic gene circuits into a well-defined human genomic safe harbor locus. Unexpectedly, engineered MDA-MB-231 metastatic human breast cancer cells become more, then less and then more invasive as we tune BACH1 levels up, irrespective of the native BACH1. BACH1 expression shifts in invading cells, and expression of BACH1's transcriptional targets confirm BACH1's nonmonotone phenotypic and regulatory effects. Thus, chemical inhibition of BACH1 could have unwanted effects on invasion. Additionally, BACH1's expression variability aids invasion at high BACH1 expression. Overall, precisely engineered, noise-aware protein-level control is necessary and important to unravel disease effects of genes to improve clinical drug efficacy.
Collapse
Affiliation(s)
- Yiming Wan
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Joseph Cohen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mariola Szenk
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Kevin S Farquhar
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center, UT Health Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Damiano Coraci
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Rafał Krzysztoń
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Joshua Azukas
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Nicholas Van Nest
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Alex Smashnov
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Yi-Jye Chern
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
- Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Daniela De Martino
- Department of Medicine, Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Long Chi Nguyen
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Harold Bien
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Jose Javier Bravo-Cordero
- Department of Medicine, Division of Hematology and Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chia-Hsin Chan
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
- Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY, USA
| | - Marsha Rich Rosner
- Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA
| | - Gábor Balázsi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
- Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA.
| |
Collapse
|
2
|
Farquhar KS, Rasouli Koohi S, Charlebois DA. Does transcriptional heterogeneity facilitate the development of genetic drug resistance? Bioessays 2021; 43:e2100043. [PMID: 34160842 DOI: 10.1002/bies.202100043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 12/24/2022]
Abstract
Non-genetic forms of antimicrobial (drug) resistance can result from cell-to-cell variability that is not encoded in the genetic material. Data from recent studies also suggest that non-genetic mechanisms can facilitate the development of genetic drug resistance. We speculate on how the interplay between non-genetic and genetic mechanisms may affect microbial adaptation and evolution during drug treatment. We argue that cellular heterogeneity arising from fluctuations in gene expression, epigenetic modifications, as well as genetic changes contribute to drug resistance at different timescales, and that the interplay between these mechanisms enhance pathogen resistance. Accordingly, developing a better understanding of the role of non-genetic mechanisms in drug resistance and how they interact with genetic mechanisms will enhance our ability to combat antimicrobial resistance. Also see the video abstract here: https://youtu.be/aefGpdh-bgU.
Collapse
Affiliation(s)
| | - Samira Rasouli Koohi
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada
| | - Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| |
Collapse
|
3
|
Farquhar KS, Flohr H, Charlebois DA. Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology. Front Bioeng Biotechnol 2020; 8:583415. [PMID: 33072732 PMCID: PMC7530828 DOI: 10.3389/fbioe.2020.583415] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/02/2020] [Indexed: 11/13/2022] Open
Abstract
Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.
Collapse
Affiliation(s)
| | - Harold Flohr
- Department of Physics, University of Alberta, Edmonton, AB, Canada
| | | |
Collapse
|
4
|
Farquhar KS, Charlebois DA, Szenk M, Cohen J, Nevozhay D, Balázsi G. Role of network-mediated stochasticity in mammalian drug resistance. Nat Commun 2019; 10:2766. [PMID: 31235692 PMCID: PMC6591227 DOI: 10.1038/s41467-019-10330-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 05/03/2019] [Indexed: 11/11/2022] Open
Abstract
A major challenge in biology is that genetically identical cells in the same environment can display gene expression stochasticity (noise), which contributes to bet-hedging, drug tolerance, and cell-fate switching. The magnitude and timescales of stochastic fluctuations can depend on the gene regulatory network. Currently, it is unclear how gene expression noise of specific networks impacts the evolution of drug resistance in mammalian cells. Answering this question requires adjusting network noise independently from mean expression. Here, we develop positive and negative feedback-based synthetic gene circuits to decouple noise from the mean for Puromycin resistance gene expression in Chinese Hamster Ovary cells. In low Puromycin concentrations, the high-noise, positive-feedback network delays long-term adaptation, whereas it facilitates adaptation under high Puromycin concentration. Accordingly, the low-noise, negative-feedback circuit can maintain resistance by acquiring mutations while the positive-feedback circuit remains mutation-free and regains drug sensitivity. These findings may have profound implications for chemotherapeutic inefficiency and cancer relapse. The role of gene expression noise in the evolution of drug resistance in mammalian cells is unclear. Here, by uncoupling noise from mean expression of a drug resistance gene in CHO cells the authors show that noisy expression aids adaptation to high drug levels, but delays it at low drug levels.
Collapse
Affiliation(s)
- Kevin S Farquhar
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.,Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center, UT Health Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Daniel A Charlebois
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.,Department of Physics, University of Alberta, Edmonton, AB, 4-181 CCIS, T6G-2E1, Canada
| | - Mariola Szenk
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.,Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Joseph Cohen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Dmitry Nevozhay
- School of Biomedicine, Far Eastern Federal University, 8 Sukhanova Street, Vladivostok, 690950, Russia.,Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA. .,Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center, UT Health Graduate School of Biomedical Sciences, Houston, TX, 77030, USA. .,Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. .,Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
| |
Collapse
|