1
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Glass DS, Bren A, Vaisbourd E, Mayo A, Alon U. A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover. Cell 2024; 187:931-944.e12. [PMID: 38320549 PMCID: PMC10882425 DOI: 10.1016/j.cell.2024.01.024] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/27/2023] [Accepted: 01/16/2024] [Indexed: 02/08/2024]
Abstract
Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.
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Affiliation(s)
- David S Glass
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Anat Bren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Elizabeth Vaisbourd
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
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2
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Wan Y, Mu Q, Krzysztoń R, Cohen J, Coraci D, Helenek C, Tompkins C, Lin A, Farquhar K, Cross E, Wang J, Balázsi G. Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance. Proc Natl Acad Sci U S A 2023; 120:e2303114120. [PMID: 38019857 DOI: 10.1073/pnas.2303114120] [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: 02/22/2023] [Accepted: 09/27/2023] [Indexed: 12/01/2023] Open
Abstract
Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.
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Affiliation(s)
- Yiming Wan
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Quanhua Mu
- Department of Chemical and Biological Engineering, Division of Life Science, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region 999077, China
| | - Rafał Krzysztoń
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Joseph Cohen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Damiano Coraci
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Christopher Helenek
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | | | - Annie Lin
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | - Kevin Farquhar
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
| | | | - Jiguang Wang
- Department of Chemical and Biological Engineering, Division of Life Science, State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Hong Kong Special Administrative Region 999077, China
| | - Gábor Balázsi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794
- Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794
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3
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Kong LW, Shi W, Tian XJ, Lai YC. Effects of growth feedback on gene circuits: A dynamical understanding. bioRxiv 2023:2023.06.06.543915. [PMID: 37333159 PMCID: PMC10274713 DOI: 10.1101/2023.06.06.543915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The successful integration of engineered gene circuits into host cells remains a significant challenge in synthetic biology due to circuit-host interactions, such as growth feedback, where the circuit influences cell growth and vice versa. Understanding the dynamics of circuit failures and identifying topologies resilient to growth feedback are crucial for both fundamental and applied research. Utilizing transcriptional regulation circuits with adaptation as a paradigm, we systematically study 435 distinct topological structures and uncover six categories of failures. Three dynamical mechanisms of circuit failures are identified: continuous deformation of the response curve, strengthened or induced oscillations, and sudden switching to coexisting attractors. Our extensive computations also uncover a scaling law between a circuit robustness measure and the strength of growth feedback. Despite the negative effects of growth feedback on the majority of circuit topologies, we identify a few circuits that maintain optimal performance as designed, a feature important for applications.
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4
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Halder S, Chatterjee S. Bistability regulates TNFR2-mediated survival and death of T-regulatory cells. J Biol Phys 2023; 49:95-119. [PMID: 36780123 PMCID: PMC9958227 DOI: 10.1007/s10867-023-09625-3] [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: 10/15/2022] [Accepted: 01/13/2023] [Indexed: 02/14/2023] Open
Abstract
A subgroup of T cells called T-regulatory cells (Tregs) regulates the body's immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system's dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system's bistable region, which can affect the system's normal functioning.
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Affiliation(s)
- Suvankar Halder
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001 Haryana India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd Milestone, Faridabad-Gurgaon Expressway, Faridabad, 121001 Haryana India
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5
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Wang T, Weiss A, Aqeel A, Wu F, Lopatkin AJ, David LA, You L. Horizontal gene transfer enables programmable gene stability in synthetic microbiota. Nat Chem Biol 2022; 18:1245-1252. [PMID: 36050493 PMCID: PMC10018779 DOI: 10.1038/s41589-022-01114-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/15/2022] [Indexed: 11/09/2022]
Abstract
The functions of many microbial communities exhibit remarkable stability despite fluctuations in the compositions of these communities. To date, a mechanistic understanding of this function-composition decoupling is lacking. Statistical mechanisms have been commonly hypothesized to explain such decoupling. Here, we proposed that dynamic mechanisms, mediated by horizontal gene transfer (HGT), also enable the independence of functions from the compositions of microbial communities. We combined theoretical analysis with numerical simulations to illustrate that HGT rates can determine the stability of gene abundance in microbial communities. We further validated these predictions using engineered microbial consortia of different complexities transferring one or more than a dozen clinically isolated plasmids, as well as through the reanalysis of data from the literature. Our results demonstrate a generalizable strategy to program the gene stability of microbial communities.
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Affiliation(s)
- Teng Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Andrea Weiss
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ammara Aqeel
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Feilun Wu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Allison J Lopatkin
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
| | - Lawrence A David
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
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6
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Proverbio D, Montanari AN, Skupin A, Gonçalves J. Buffering variability in cell regulation motifs close to criticality. Phys Rev E 2022; 106:L032402. [PMID: 36266798 DOI: 10.1103/physreve.106.l032402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality.
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Affiliation(s)
- Daniele Proverbio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, EX4 4QL, Exeter, United Kingdom
| | - Arthur N Montanari
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- Department of Physics and Material Science, University of Luxembourg, 162a Avenue de la Faiencerie, 1511 Luxembourg, Luxembourg
- Department of Neuroscience, University of California San Diego, 9500 Gilman Drive, La Jolla, California, United States
| | - Jorge Gonçalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- Department of Plant Sciences, University of Cambridge, CB2 3EA, Cambridge, United Kingdom
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7
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Aditya C, Bertaux F, Batt G, Ruess J. Using single-cell models to predict the functionality of synthetic circuits at the population scale. Proc Natl Acad Sci U S A 2022; 119:e2114438119. [PMID: 35271387 DOI: 10.1073/pnas.2114438119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
SignificanceAt the single-cell level, biochemical processes are inherently stochastic. For many natural systems, the resulting cell-to-cell variability is exploited by microbial populations. In synthetic biology, however, the interplay of cell-to-cell variability and population processes such as selection or growth often leads to circuits not functioning as predicted by simple models. Here we show how multiscale stochastic kinetic models that simultaneously track single-cell and population processes can be obtained based on an augmentation of the chemical master equation. These models enable us to quantitatively predict complex population dynamics of a yeast optogenetic differentiation system from a specification of the circuit's components and to demonstrate how cell-to-cell variability can be exploited to purposefully create unintuitive circuit functionality.
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8
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Wang W, Poe D, Yang Y, Hyatt T, Xing J. Epithelial-to-mesenchymal transition proceeds through directional destabilization of multidimensional attractor. eLife 2022; 11:74866. [PMID: 35188459 PMCID: PMC8920502 DOI: 10.7554/elife.74866] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [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: 10/20/2021] [Accepted: 02/06/2022] [Indexed: 11/13/2022] Open
Abstract
How a cell changes from one stable phenotype to another one is a fundamental problem in developmental and cell biology. Mathematically, a stable phenotype corresponds to a stable attractor in a generally multi-dimensional state space, which needs to be destabilized so the cell relaxes to a new attractor. Two basic mechanisms for destabilizing a stable fixed point, pitchfork and saddle-node bifurcations, have been extensively studied theoretically; however, direct experimental investigation at the single-cell level remains scarce. Here, we performed live cell imaging studies and analyses in the framework of dynamical systems theories on epithelial-to-mesenchymal transition (EMT). While some mechanistic details remain controversial, EMT is a cell phenotypic transition (CPT) process central to development and pathology. Through time-lapse imaging we recorded single cell trajectories of human A549/Vim-RFP cells undergoing EMT induced by different concentrations of exogenous TGF-β in a multi-dimensional cell feature space. The trajectories clustered into two distinct groups, indicating that the transition dynamics proceeds through parallel paths. We then reconstructed the reaction coordinates and the corresponding quasi-potentials from the trajectories. The potentials revealed a plausible mechanism for the emergence of the two paths where the original stable epithelial attractor collides with two saddle points sequentially with increased TGF-β concentration, and relaxes to a new one. Functionally, the directional saddle-node bifurcation ensures a CPT proceeds towards a specific cell type, as a mechanistic realization of the canalization idea proposed by Waddington. Cells with the same genetic code can take on many different formss, or phenotypes, which have distinct roles and appearances. Sometimes cells switch from one phenotype to another as part of healthy growth or during disease. One such change is the epithelial-to-mesenchymal transition (EMT), which is involved in fetal development, wound healing and the spread of cancer cells. During EMT, closely connected epithelial cells detach from one another and change into mesenchymal cells that are able to migrate. Cells undergo a number of changes during this transition; however, the path they take to reach their new form is not entirely clear. For instance, do all cells follow the same route, or are there multiple ways that cells can shift from one state to the next? To address this question, Wang et al. studied individual lung cancer cells that had been treated with a protein that drives EMT. The cells were then imaged at regular intervals over the course of two to three days to see how they changed in response to different concentrations of protein. Using a mathematical analysis designed to study chemical reactions, Wang et al. showed that the cells transform into the mesenchymal phenotype through two main routes. This result suggests that attempts to prevent EMT, in cancer treatment for instance, would require blocking both paths taken by the cells. This information could be useful for biomedical researchers trying to regulate the EMT process. The quantitative approach of this study could also help physicists and mathematicians study other types of transition that occur in biology.
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Affiliation(s)
- Weikang Wang
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States
| | - Dante Poe
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States
| | - Yaxuan Yang
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States
| | - Thomas Hyatt
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, United States
| | - Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States
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9
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Cui S, Lv X, Xu X, Chen T, Zhang H, Liu Y, Li J, Du G, Ledesma-Amaro R, Liu L. Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways. ACS Synth Biol 2021; 10:1587-1597. [PMID: 34213900 DOI: 10.1021/acssynbio.1c00073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.
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Affiliation(s)
- Shixiu Cui
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xueqin Lv
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Xianhao Xu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Taichi Chen
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Hongzhi Zhang
- Shandong Runde Biotechnology Co., Ltd., Tai’an 271000, China
| | - Yanfeng Liu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Jianghua Li
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Guocheng Du
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
| | - Rodrigo Ledesma-Amaro
- Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, U.K
| | - Long Liu
- Science Center for Future Foods, Jiangnan University, Wuxi 214122, China
- Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
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10
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Shakiba N, Jones RD, Weiss R, Del Vecchio D. Context-aware synthetic biology by controller design: Engineering the mammalian cell. Cell Syst 2021; 12:561-592. [PMID: 34139166 PMCID: PMC8261833 DOI: 10.1016/j.cels.2021.05.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/19/2021] [Accepted: 05/14/2021] [Indexed: 12/25/2022]
Abstract
The rise of systems biology has ushered a new paradigm: the view of the cell as a system that processes environmental inputs to drive phenotypic outputs. Synthetic biology provides a complementary approach, allowing us to program cell behavior through the addition of synthetic genetic devices into the cellular processor. These devices, and the complex genetic circuits they compose, are engineered using a design-prototype-test cycle, allowing for predictable device performance to be achieved in a context-dependent manner. Within mammalian cells, context effects impact device performance at multiple scales, including the genetic, cellular, and extracellular levels. In order for synthetic genetic devices to achieve predictable behaviors, approaches to overcome context dependence are necessary. Here, we describe control systems approaches for achieving context-aware devices that are robust to context effects. We then consider cell fate programing as a case study to explore the potential impact of context-aware devices for regenerative medicine applications.
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Affiliation(s)
- Nika Shakiba
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Ross D Jones
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Domitilla Del Vecchio
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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11
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Abstract
Growth feedback, the inherent coupling between the synthetic gene circuit and the host cell growth, could significantly change the circuit behaviors. Previously, a diverse array of emergent behaviors, such as growth bistability, enhanced ultrasensitivity, and topology-dependent memory loss, were reported to be induced by growth feedback. However, the influence of the growth feedback on the circuit functions remains underexplored. Here, we reported an unexpected damped oscillatory behavior of a self-activation gene circuit induced by nutrient-modulating growth feedback. Specifically, after dilution of the activated self-activation switch into the fresh medium with moderate nutrients, its gene expression first decreases as the cell grows and then shows a significant overshoot before it reaches the steady state, leading to damped oscillation dynamics. Fitting the data with a coarse-grained model suggests a nonmonotonic growth-rate regulation on gene production rate. The underlying mechanism of the oscillation was demonstrated by a molecular mathematical model, which includes the ribosome allocation toward gene production, cell growth, and cell maintenance. Interestingly, the model predicted a counterintuitive dependence of oscillation amplitude on the nutrition level, where the highest peak was found in the medium with moderate nutrients, but was not observed in rich nutrients. We experimentally verified this prediction by tuning the nutrient level in the culture medium. We did not observe significant oscillatory behavior for the toggle switch, suggesting that the emergence of damped oscillatory behavior depends on circuit network topology. Our results demonstrated a new nonlinear emergent behavior mediated by growth feedback, which depends on the ribosome allocation between gene circuit and cell growth.
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Affiliation(s)
- Juan Melendez-Alvarez
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
| | - Changhan He
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85281, United States
| | - Rong Zhang
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
| | - Yang Kuang
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85281, United States
| | - Xiao-Jun Tian
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona 85281, United States
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12
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Yin H, Liu S, Wen X. Optimal transition paths of phenotypic switching in a non-Markovian self-regulation gene expression. Phys Rev E 2021; 103:022409. [PMID: 33736096 DOI: 10.1103/physreve.103.022409] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 01/06/2021] [Indexed: 11/07/2022]
Abstract
Gene expression is a complex biochemical process involving multiple reaction steps, creating molecular memory because the probability of waiting time between consecutive reaction steps no longer follows exponential distributions. What effect the molecular memory has on metastable states in gene expression remains not fully understood. Here, we study transition paths of switching between bistable states for a non-Markovian model of gene expression equipped with a self-regulation. Employing the large deviation theory for this model, we analyze the optimal transition paths of switching between bistable states in gene expression, interestingly finding that dynamic behaviors in gene expression along the optimal transition paths significantly depend on the molecular memory. Moreover, we discover that the molecular memory can prolong the time of switching between bistable states in gene expression along the optimal transition paths. Our results imply that the molecular memory may be an unneglectable factor to affect switching between metastable states in gene expression.
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Affiliation(s)
- Hongwei Yin
- School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou 221111, People's Republic of China.,School of Science, Nanchang University, Nanchang 330031, People's Republic of China
| | - Shuqin Liu
- School of Science, Nanchang University, Nanchang 330031, People's Republic of China
| | - Xiaoqing Wen
- School of Mathematics and Statistics, Xuzhou University of Technology, Xuzhou 221111, People's Republic of China.,School of Science, Nanchang University, Nanchang 330031, People's Republic of China
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13
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Zhang Y, Ding W, Wang Z, Zhao H, Shi S. Development of Host-Orthogonal Genetic Systems for Synthetic Biology. Adv Biol (Weinh) 2021; 5:e2000252. [PMID: 33729696 DOI: 10.1002/adbi.202000252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/18/2020] [Indexed: 12/17/2022]
Abstract
The construction of a host-orthogonal genetic system can not only minimize the impact of host-specific nuances on fine-tuning of gene expression, but also expand cellular functions such as in vivo continuous evolution of genes based on an error-prone DNA polymerase. It represents an emerging powerful approach for making biology easier to engineer. In this review, the recent advances are described on the design of genetic systems that can be stably inherited in the host cells and are responsible for important biological processes including DNA replication, RNA transcription, protein translation, and gene regulation. Their applications in synthetic biology are summarized and the future challenges and opportunities are discussed in developing such systems.
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Affiliation(s)
- Yang Zhang
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
| | - Wentao Ding
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.,Key Laboratory of Food Nutrition and Safety (Tianjin University of Science and Technology) Ministry of Education, College of Food Engineering and Biotechnology, Tianjin University of Science and Technology, No. 29, 13th Avenue, TEDA, Tianjin, 300457, P. R. China
| | - Zhihui Wang
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Shuobo Shi
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
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14
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Kuwahara H, Gao X. Stable maintenance of hidden switches as a strategy to increase the gene expression stability. Nat Comput Sci 2021; 1:62-70. [PMID: 38217152 DOI: 10.1038/s43588-020-00001-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/02/2020] [Indexed: 01/15/2024]
Abstract
In response to severe genetic and environmental perturbations, wild-type organisms can express hidden alternative phenotypes adaptive to such adverse conditions. While our theoretical understanding of the population-level fitness advantage and evolution of phenotypic switching under variable environments has grown, the mechanism by which these organisms maintain phenotypic switching capabilities under static environments remains to be elucidated. Here, using computational simulations, we analyzed the evolution of gene circuits under natural selection and found that different strategies evolved to increase the gene expression stability near the optimum level. In a population comprising bistable individuals, a strategy of maintaining bistability and raising the potential barrier separating the bistable regimes was consistently taken. Our results serve as evidence that hidden bistable switches can be stably maintained during environmental stasis-an essential property enabling the timely release of adaptive alternatives with small genetic changes in the event of substantial perturbations.
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Affiliation(s)
- Hiroyuki Kuwahara
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
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15
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Balázsi G. Discovering evolutionary hidden treasures. Nat Comput Sci 2021; 1:18-19. [PMID: 38217151 DOI: 10.1038/s43588-020-00004-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, USA.
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16
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Jones RD, Qian Y, Siciliano V, DiAndreth B, Huh J, Weiss R, Del Vecchio D. An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells. Nat Commun 2020; 11:5690. [PMID: 33173034 PMCID: PMC7656454 DOI: 10.1038/s41467-020-19126-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/18/2020] [Indexed: 12/17/2022] Open
Abstract
Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device's output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression.
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Affiliation(s)
- Ross D Jones
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yili Qian
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Velia Siciliano
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Instituto Italiano di Tecnologia, Napoli, 80125, Italy
| | - Breanna DiAndreth
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jin Huh
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Domitilla Del Vecchio
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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17
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Xu YC, Guo YL. Less Is More, Natural Loss-of-Function Mutation Is a Strategy for Adaptation. Plant Commun 2020; 1:100103. [PMID: 33367264 PMCID: PMC7743898 DOI: 10.1016/j.xplc.2020.100103] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/08/2020] [Accepted: 08/12/2020] [Indexed: 05/12/2023]
Abstract
Gene gain and loss are crucial factors that shape the evolutionary success of diverse organisms. In the past two decades, more attention has been paid to the significance of gene gain through gene duplication or de novo genes. However, gene loss through natural loss-of-function (LoF) mutations, which is prevalent in the genomes of diverse organisms, has been largely ignored. With the development of sequencing techniques, many genomes have been sequenced across diverse species and can be used to study the evolutionary patterns of gene loss. In this review, we summarize recent advances in research on various aspects of LoF mutations, including their identification, evolutionary dynamics in natural populations, and functional effects. In particular, we discuss how LoF mutations can provide insights into the minimum gene set (or the essential gene set) of an organism. Furthermore, we emphasize their potential impact on adaptation. At the genome level, although most LoF mutations are neutral or deleterious, at least some of them are under positive selection and may contribute to biodiversity and adaptation. Overall, we highlight the importance of natural LoF mutations as a robust framework for understanding biological questions in general.
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Affiliation(s)
- Yong-Chao Xu
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ya-Long Guo
- State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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18
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Guinn MT, Wan Y, Levovitz S, Yang D, Rosner MR, Balázsi G. Observation and Control of Gene Expression Noise: Barrier Crossing Analogies Between Drug Resistance and Metastasis. Front Genet 2020; 11:586726. [PMID: 33193723 PMCID: PMC7662081 DOI: 10.3389/fgene.2020.586726] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Michael Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Stony Brook Medical Scientist Training Program, Stony Brook, NY, United States
| | - Yiming Wan
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Sarah Levovitz
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Dongbo Yang
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Marsha R Rosner
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
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19
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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.
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Affiliation(s)
| | - Harold Flohr
- Department of Physics, University of Alberta, Edmonton, AB, Canada
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20
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Wytock TP, Zhang M, Jinich A, Fiebig A, Crosson S, Motter AE. Extreme Antagonism Arising from Gene-Environment Interactions. Biophys J 2020; 119:2074-2086. [PMID: 33068537 DOI: 10.1016/j.bpj.2020.09.038] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/27/2020] [Accepted: 09/21/2020] [Indexed: 01/06/2023] Open
Abstract
Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.
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Affiliation(s)
- Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois
| | - Manjing Zhang
- The Committee on Microbiology, University of Chicago, Chicago, Illinois
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill-Cornell Medical College, New York, New York
| | - Aretha Fiebig
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan
| | - Sean Crosson
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan
| | - Adilson E Motter
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois; Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois; Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois.
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21
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Frei T, Cella F, Tedeschi F, Gutiérrez J, Stan GB, Khammash M, Siciliano V. Characterization and mitigation of gene expression burden in mammalian cells. Nat Commun 2020; 11:4641. [PMID: 32934213 PMCID: PMC7492461 DOI: 10.1038/s41467-020-18392-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/18/2020] [Indexed: 12/19/2022] Open
Abstract
Despite recent advances in circuit engineering, the design of genetic networks in mammalian cells is still painstakingly slow and fraught with inexplicable failures. Here, we demonstrate that transiently expressed genes in mammalian cells compete for limited transcriptional and translational resources. This competition results in the coupling of otherwise independent exogenous and endogenous genes, creating a divergence between intended and actual function. Guided by a resource-aware mathematical model, we identify and engineer natural and synthetic miRNA-based incoherent feedforward loop (iFFL) circuits that mitigate gene expression burden. The implementation of these circuits features the use of endogenous miRNAs as elementary components of the engineered iFFL device, a versatile hybrid design that allows burden mitigation to be achieved across different cell-lines with minimal resource requirements. This study establishes the foundations for context-aware prediction and improvement of in vivo synthetic circuit performance, paving the way towards more rational synthetic construct design in mammalian cells.
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Affiliation(s)
- Timothy Frei
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, Basel, 4058, Switzerland
| | - Federica Cella
- Istituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, Naples, 80125, Italy
- University of Genoa, Genoa, 16132, Italy
| | - Fabiana Tedeschi
- Istituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, Naples, 80125, Italy
| | - Joaquín Gutiérrez
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, Basel, 4058, Switzerland
| | - Guy-Bart Stan
- Department of Bioengineering and Centre for Synthetic Biology, Imperial College London, London, SW7 2AZ, UK
| | - Mustafa Khammash
- Department of Biosystems Science and Engineering (D-BSSE), ETH Zürich, Mattenstrasse 26, Basel, 4058, Switzerland.
| | - Velia Siciliano
- Istituto Italiano di Tecnologia-IIT, Largo Barsanti e Matteucci, Naples, 80125, Italy.
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22
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Wu J, Han X, Zhai H, Yang T, Lin Y. Evidence for rate-dependent filtering of global extrinsic noise by biochemical reactions in mammalian cells. Mol Syst Biol 2020; 16:e9335. [PMID: 32407587 PMCID: PMC7224485 DOI: 10.15252/msb.20199335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/31/2020] [Accepted: 04/03/2020] [Indexed: 12/18/2022] Open
Abstract
Recent studies have revealed that global extrinsic noise arising from stochasticity in the intracellular biochemical environment plays a critical role in heterogeneous cell physiologies. However, it remains largely unclear how such extrinsic noise dynamically influences downstream reactions and whether it could be neutralized by cellular reactions. Here, using fluorescent protein (FP) maturation as a model biochemical reaction, we explored how cellular reactions might combat global extrinsic noise in mammalian cells. We developed a novel single-cell assay to systematically quantify the maturation rate and the associated noise for over a dozen FPs. By exploiting the variation in the maturation rate for different FPs, we inferred that global extrinsic noise could be temporally filtered by maturation reactions, and as a result, the noise levels for slow-maturing FPs are lower compared to fast-maturing FPs. This mechanism is validated by directly perturbing the maturation rates of specific FPs and measuring the resulting noise levels. Together, our results revealed a potentially general principle governing extrinsic noise propagation, where timescale separation allows cellular reactions to cope with dynamic global extrinsic noise.
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Affiliation(s)
- Jiegen Wu
- Center for Quantitative Biology and Peking‐Tsinghua Joint Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- The MOE Key Laboratory of Cell Proliferation and DifferentiationSchool of Life SciencesPeking UniversityBeijingChina
- Tsinghua‐Peking Joint Center for Life SciencesTsinghua UniversityBeijingChina
| | - Xu Han
- Center for Quantitative Biology and Peking‐Tsinghua Joint Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- The MOE Key Laboratory of Cell Proliferation and DifferentiationSchool of Life SciencesPeking UniversityBeijingChina
| | - Haotian Zhai
- Center for Quantitative Biology and Peking‐Tsinghua Joint Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
| | - Tingyu Yang
- Center for Quantitative Biology and Peking‐Tsinghua Joint Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
| | - Yihan Lin
- Center for Quantitative Biology and Peking‐Tsinghua Joint Center for Life SciencesAcademy for Advanced Interdisciplinary StudiesPeking UniversityBeijingChina
- The MOE Key Laboratory of Cell Proliferation and DifferentiationSchool of Life SciencesPeking UniversityBeijingChina
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