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de Freitas Magalhães B, Fan G, Sontag E, Josić K, Bennett MR. Pattern Formation and Bistability in a Synthetic Intercellular Genetic Toggle. ACS Synth Biol 2024; 13:2844-2860. [PMID: 39214591 DOI: 10.1021/acssynbio.4c00272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Differentiation within multicellular organisms is a complex process that helps to establish spatial patterning and tissue formation within the body. Often, the differentiation of cells is governed by morphogens and intercellular signaling molecules that guide the fate of each cell, frequently using toggle-like regulatory components. Synthetic biologists have long sought to recapitulate patterned differentiation with engineered cellular communities, and various methods for differentiating bacteria have been invented. Here, we couple a synthetic corepressive toggle switch with intercellular signaling pathways to create a "quorum-sensing toggle". We show that this circuit not only exhibits population-wide bistability in a well-mixed liquid environment but also generates patterns of differentiation in colonies grown on agar containing an externally supplied morphogen. If coupled to other metabolic processes, circuits such as the one described here would allow for the engineering of spatially patterned, differentiated bacteria for use in biomaterials and bioelectronics.
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Affiliation(s)
| | - Gaoyang Fan
- Department of Mathematics, University of Houston, Houston, Texas 77204, United States
| | - Eduardo Sontag
- Department of Bioengineering and Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, Texas 77204, United States
| | - Matthew R Bennett
- Department of Biosciences, Rice University, Houston, Texas 77005, United States
- Department of Bioengineering, Rice University, Houston, Texas 77005, United States
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2
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Yang X, Yang J, Huang H, Yan X, Li X, Lin Z. Achieving robust synthetic tolerance in industrial E. coli through negative auto-regulation of a DsrA-Hfq module. Synth Syst Biotechnol 2024; 9:462-469. [PMID: 38634002 PMCID: PMC11021974 DOI: 10.1016/j.synbio.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/29/2024] [Accepted: 04/06/2024] [Indexed: 04/19/2024] Open
Abstract
In industrial fermentation processes, microorganisms often encounter acid stress, which significantly impact their productivity. This study focused on the acid-resistant module composed of small RNA (sRNA) DsrA and the sRNA chaperone Hfq. Our previous study had shown that this module improved the cell growth of Escherichia coli MG1655 at low pH, but failed to obtain this desired phenotype in industrial strains. Here, we performed a quantitative analysis of DsrA-Hfq module to determine the optimal expression mode. We then assessed the potential of the CymR-based negative auto-regulation (NAR) circuit for industrial application, under different media, strains and pH levels. Growth assay at pH 4.5 revealed that NAR-05D04H circuit was the best acid-resistant circuit to improve the cell growth of E. coli MG1655. This circuit was robust and worked well in the industrial lysine-producing strain E. coli SCEcL3 at a starting pH of 6.8 and without pH control, resulting in a 250 % increase in lysine titer and comparable biomass in shaking flask fermentation compared to the parent strain. This study showed the practical application of NAR circuit in regulating DsrA-Hfq module, effectively and robustly improving the acid tolerance of industrial strains, which provides a new approach for breeding industrial strains with tolerance phenotype.
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Affiliation(s)
- Xiaofeng Yang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Jingduan Yang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Haozheng Huang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Xiaofang Yan
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Xiaofan Li
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
| | - Zhanglin Lin
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006, China
- School of Biomedicine, Guangdong University of Technology, Guangzhou 510006, China
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3
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Choi HJ, Lo TW, Cutler KJ, Huang D, Will WR, Wiggins PA. Protein overabundance is driven by growth robustness. ARXIV 2024:arXiv:2408.11952v1. [PMID: 39253634 PMCID: PMC11383435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.
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Affiliation(s)
- H James Choi
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - Teresa W Lo
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - Kevin J Cutler
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - Dean Huang
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - W Ryan Will
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA
| | - Paul A Wiggins
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
- Department of Microbiology, University of Washington, Seattle, Washington 98195, USA
- Department of Bioengineering, University of Washington, Seattle, Washington 98195, USA
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4
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Choi HJ, Lo TW, Cutler KJ, Huang D, Will WR, Wiggins PA. Protein overabundance is driven by growth robustness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.14.607847. [PMID: 39185236 PMCID: PMC11343162 DOI: 10.1101/2024.08.14.607847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.
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Affiliation(s)
- H. James Choi
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - Teresa W. Lo
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - Kevin J. Cutler
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - Dean Huang
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
| | - W. Ryan Will
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, USA
| | - Paul A. Wiggins
- Department of Physics, University of Washington, Seattle, Washington 98195, USA
- Department of Microbiology, University of Washington, Seattle, Washington 98195, USA
- Department of Bioengineering, University of Washington, Seattle, Washington 98195, USA
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5
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Helenek C, Krzysztoń R, Petreczky J, Wan Y, Cabral M, Coraci D, Balázsi G. Synthetic gene circuit evolution: Insights and opportunities at the mid-scale. Cell Chem Biol 2024; 31:1447-1459. [PMID: 38925113 PMCID: PMC11330362 DOI: 10.1016/j.chembiol.2024.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 05/07/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024]
Abstract
Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.
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Affiliation(s)
- Christopher Helenek
- 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
| | - Rafał Krzysztoń
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Julia Petreczky
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yiming Wan
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Mariana Cabral
- 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
| | - Damiano Coraci
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- 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; Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY 11794, USA.
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6
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Pal S, Dhar R. Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making. FEBS Lett 2024; 598:1673-1691. [PMID: 38724715 DOI: 10.1002/1873-3468.14898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 07/23/2024]
Abstract
The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.
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Affiliation(s)
- Sampriti Pal
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
| | - Riddhiman Dhar
- Department of Bioscience and Biotechnology, IIT Kharagpur, India
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Lewis DD, Pablo M, Chen X, Simpson ML, Weinberger L. Evidence for Behavioral Autorepression in Covid-19 Epidemiological Dynamics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.07.24308626. [PMID: 38883757 PMCID: PMC11178008 DOI: 10.1101/2024.06.07.24308626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
It has long been hypothesized that behavioral reactions to epidemic severity autoregulate infection dynamics, for example when susceptible individuals self-sequester based on perceived levels of circulating disease. However, evidence for such 'behavioral autorepression' has remained elusive, and its presence could significantly affect epidemic forecasting and interventions. Here, we analyzed early COVID-19 dynamics at 708 locations over three epidemiological scales (96 countries, 50 US states, and 562 US counties). Signatures of behavioral autorepression were identified through: (i) a counterintuitive mobility-death correlation, (ii) fluctuation-magnitude analysis, and (iii) dynamics of SARS-CoV-2 infection waves. These data enabled calculation of the average behavioral-autorepression strength (i.e., negative feedback 'gain') across different populations. Surprisingly, incorporating behavioral autorepression into conventional models was required to accurately forecast COVID-19 mortality. Models also predicted that the strength of behavioral autorepression has the potential to alter the efficacy of non-pharmaceutical interventions. Overall, these results provide evidence for the long-hypothesized existence of behavioral autorepression, which could improve epidemic forecasting and enable more effective application of non-pharmaceutical interventions during future epidemics. Significance Challenges with epidemiological forecasting during the COVID-19 pandemic suggested gaps in underlying model architecture. One long-held hypothesis, typically omitted from conventional models due to lack of empirical evidence, is that human behaviors lead to intrinsic negative autoregulation of epidemics (termed 'behavioral autorepression'). This omission substantially alters model forecasts. Here, we provide independent lines of evidence for behavioral autorepression during the COVID-19 pandemic, demonstrate that it is sufficient to explain counterintuitive data on 'shutdowns', and provides a mechanistic explanation of why early shutdowns were more effective than delayed, high-intensity shutdowns. We empirically measure autorepression strength, and show that incorporating autorepression dramatically improves epidemiological forecasting. The autorepression phenomenon suggests that tailoring interventions to specific populations may be warranted.
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Sun J, Yang R, Li Q, Zhu R, Jiang Y, Zang L, Zhang Z, Tong W, Zhao H, Li T, Li H, Qi D, Li G, Chen X, Dai Z, Liu Z. Living Synthelectronics: A New Era for Bioelectronics Powered by Synthetic Biology. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2400110. [PMID: 38494761 DOI: 10.1002/adma.202400110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/23/2024] [Indexed: 03/19/2024]
Abstract
Bioelectronics, which converges biology and electronics, has attracted great attention due to their vital applications in human-machine interfaces. While traditional bioelectronic devices utilize nonliving organic and/or inorganic materials to achieve flexibility and stretchability, a biological mismatch is often encountered because human tissues are characterized not only by softness and stretchability but also by biodynamic and adaptive properties. Recently, a notable paradigm shift has emerged in bioelectronics, where living cells, and even viruses, modified via gene editing within synthetic biology, are used as core components in a new hybrid electronics paradigm. These devices are defined as "living synthelectronics," and they offer enhanced potential for interfacing with human tissues at informational and substance exchange levels. In this Perspective, the recent advances in living synthelectronics are summarized. First, opportunities brought to electronics by synthetic biology are briefly introduced. Then, strategic approaches to designing and making electronic devices using living cells/viruses as the building blocks, sensing components, or power sources are reviewed. Finally, the challenges faced by living synthelectronics are raised. It is believed that this paradigm shift will significantly contribute to the real integration of bioelectronics with human tissues.
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Affiliation(s)
- Jing Sun
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, P. R. China
| | - Ruofan Yang
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qingsong Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Runtao Zhu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Ying Jiang
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Lei Zang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhibo Zhang
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Wei Tong
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Hang Zhao
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Tengfei Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Hanfei Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Dianpeng Qi
- MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage, National and Local Joint Engineering Laboratory for Synthesis, Transformation and Separation of Extreme Environmental Nutrients, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, 150001, P. R. China
| | - Guanglin Li
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Xiaodong Chen
- School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore
| | - Zhuojun Dai
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhiyuan Liu
- Soft Bio-interface Electronics Lab, Center of Neural Engineering, CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- Standard Robots Co.,Ltd,Room 405, Building D, Huafeng International Robot Fusen Industrial Park, Hangcheng Avenue, Guxing Community, Xixiang Street, Baoan District, Shenzhen, 518055, China
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O’Brien NLV, Holland B, Engelstädter J, Ortiz-Barrientos D. The distribution of fitness effects during adaptive walks using a simple genetic network. PLoS Genet 2024; 20:e1011289. [PMID: 38787919 PMCID: PMC11156440 DOI: 10.1371/journal.pgen.1011289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/06/2024] [Accepted: 05/04/2024] [Indexed: 05/26/2024] Open
Abstract
The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.
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Affiliation(s)
- Nicholas L. V. O’Brien
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Barbara Holland
- School of Natural Sciences, University of Tasmania, Hobart, Tasmania, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, University of Tasmania, Hobart, Tasmania, Australia
| | - Jan Engelstädter
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
| | - Daniel Ortiz-Barrientos
- School of the Environment, The University of Queensland, Brisbane, Queensland, Australia
- ARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, QLD, Australia
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10
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Andrews SS, Kochen M, Smith L, Feng S, Wiley HS, Sauro HM. Signal integration and integral feedback control with biochemical reaction networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591337. [PMID: 38746178 PMCID: PMC11092504 DOI: 10.1101/2024.04.26.591337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Biochemical reaction networks perform a variety of signal processing functions, one of which is computing the integrals of signal values. This is often used in integral feedback control, where it enables a system's output to respond to changing inputs, but to then return exactly back to some pre-determined setpoint value afterward. To gain a deeper understanding of how biochemical networks are able to both integrate signals and perform integral feedback control, we investigated these abilities for several simple reaction networks. We found imperfect overlap between these categories, with some networks able to perform both tasks, some able to perform integration but not integral feedback control, and some the other way around. Nevertheless, networks that could either integrate or perform integral feedback control shared key elements. In particular, they included a chemical species that was neutrally stable in the open loop system (no feedback), meaning that this species does not have a unique stable steady-state concentration. Neutral stability could arise from zeroth order decay reactions, binding to a partner that was produced at a constant rate (which occurs in antithetic control), or through a long chain of covalent cycles. Mathematically, it arose from rate equations for the reaction network that were underdetermined when evaluated at steady-state.
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11
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Stevanovic M, Teuber Carvalho JP, Bittihn P, Schultz D. Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures. Phys Biol 2024; 21:036002. [PMID: 38412523 PMCID: PMC10988634 DOI: 10.1088/1478-3975/ad2d64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/25/2024] [Accepted: 02/27/2024] [Indexed: 02/29/2024]
Abstract
Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistancetetoperon inE. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.
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Affiliation(s)
- Mirjana Stevanovic
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States of America
| | - João Pedro Teuber Carvalho
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States of America
| | - Philip Bittihn
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Daniel Schultz
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States of America
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12
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Andrews SS, Wiley HS, Sauro HM. Design patterns of biological cells. Bioessays 2024; 46:e2300188. [PMID: 38247191 PMCID: PMC10922931 DOI: 10.1002/bies.202300188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 12/03/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.
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Affiliation(s)
- Steven S. Andrews
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - H. Steven Wiley
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Herbert M. Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
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13
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Llanos CD, Xie T, Lim HE, Segatori L. A Computational Modeling Approach for the Design of Genetic Control Systems that Respond to Transcriptional Activity. Methods Mol Biol 2024; 2774:99-117. [PMID: 38441761 DOI: 10.1007/978-1-0716-3718-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
Recent progress in synthetic biology has enabled the design of complex genetic circuits that interface with innate cellular functions, such as gene transcription, and control user-defined outputs. Implementing these genetic networks in mammalian cells, however, is a cumbersome process that requires several steps of optimization and benefits from the use of predictive modeling. Combining deterministic mathematical models with software-based numerical computing platforms allows researchers to quickly design, evaluate, and optimize multiple circuit topologies to establish experimental constraints that generate the desired control systems. In this chapter, we present a systematic approach based on predictive mathematical modeling to guide the design and construction of gene activity-based sensors. This approach enables user-driven circuit optimization through iterations of sensitivity analyses and parameter scans, providing a universal method to engineer sense and respond cells for diverse applications.
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Affiliation(s)
- Carlos D Llanos
- Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, USA
| | - Tianyi Xie
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Ha Eun Lim
- Department of Bioengineering, Rice University, Houston, TX, USA
| | - Laura Segatori
- Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, USA.
- Department of Bioengineering, Rice University, Houston, TX, USA.
- Department of Chemical and Biochemical Engineering, Rice University, Houston, TX, USA.
- Department of Biosciences, Rice University, Houston, TX, USA.
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14
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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] [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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15
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Adhikary R, Roy A, Jolly MK, Das D. Effects of microRNA-mediated negative feedback on gene expression noise. Biophys J 2023; 122:4220-4240. [PMID: 37803829 PMCID: PMC10645566 DOI: 10.1016/j.bpj.2023.09.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 07/19/2023] [Accepted: 09/28/2023] [Indexed: 10/08/2023] Open
Abstract
MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression post-transcriptionally in eukaryotes by binding with target mRNAs and preventing translation. miRNA-mediated feedback motifs are ubiquitous in various genetic networks that control cellular decision making. A key question is how such a feedback mechanism may affect gene expression noise. To answer this, we have developed a mathematical model to study the effects of a miRNA-dependent negative-feedback loop on mean expression and noise in target mRNAs. Combining analytics and simulations, we show the existence of an expression threshold demarcating repressed and expressed regimes in agreement with earlier studies. The steady-state mRNA distributions are bimodal near the threshold, where copy numbers of mRNAs and miRNAs exhibit enhanced anticorrelated fluctuations. Moreover, variation of negative-feedback strength shifts the threshold locations and modulates the noise profiles. Notably, the miRNA-mRNA binding affinity and feedback strength collectively shape the bimodality. We also compare our model with a direct auto-repression motif, where a gene produces its own repressor. Auto-repression fails to produce bimodal mRNA distributions as found in miRNA-based indirect repression, suggesting the crucial role of miRNAs in creating phenotypic diversity. Together, we demonstrate how miRNA-dependent negative feedback modifies the expression threshold and leads to a broader parameter regime of bimodality compared to the no-feedback case.
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Affiliation(s)
- Raunak Adhikary
- Department of Biological Sciences, Indian Institute of Science Education And Research Kolkata Mohanpur, Nadia, West Bengal, India
| | - Arnab Roy
- Department of Biological Sciences, Indian Institute of Science Education And Research Kolkata Mohanpur, Nadia, West Bengal, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, India
| | - Dipjyoti Das
- Department of Biological Sciences, Indian Institute of Science Education And Research Kolkata Mohanpur, Nadia, West Bengal, India.
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16
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Stevanovic M, Carvalho JPT, Bittihn P, Schultz D. Dynamical model of antibiotic responses linking expression of resistance to metabolism explains emergence of heterogeneity during drug exposures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.558994. [PMID: 37790326 PMCID: PMC10542528 DOI: 10.1101/2023.09.22.558994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug's action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistance tet operon in E. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.
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Affiliation(s)
- Mirjana Stevanovic
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - João Pedro Teuber Carvalho
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Philip Bittihn
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
- Institute for the Dynamics of Complex Systems, University of Göttingen, Göttingen, Germany
| | - Daniel Schultz
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH, United States
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17
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Torres A, Cockerell S, Phillips M, Balázsi G, Ghosh K. MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division. Biophys J 2023; 122:2623-2635. [PMID: 37218129 PMCID: PMC10397576 DOI: 10.1016/j.bpj.2023.05.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/03/2023] [Accepted: 05/18/2023] [Indexed: 05/24/2023] Open
Abstract
Gene expression is inherently noisy due to small numbers of proteins and nucleic acids inside a cell. Likewise, cell division is stochastic, particularly when tracking at the level of a single cell. The two can be coupled when gene expression affects the rate of cell division. Single-cell time-lapse experiments can measure both fluctuations by simultaneously recording protein levels inside a cell and its stochastic division. These information-rich noisy trajectory data sets can be harnessed to learn about the underlying molecular and cellular details that are often not known a priori. A critical question is: How can we infer a model given data where fluctuations at two levels-gene expression and cell division-are intricately convoluted? We show the principle of maximum caliber (MaxCal)-integrated within a Bayesian framework-can be used to infer several cellular and molecular details (division rates, protein production, and degradation rates) from these coupled stochastic trajectories (CSTs). We demonstrate this proof of concept using synthetic data generated from a known model. An additional challenge in data analysis is that trajectories are often not in protein numbers, but in noisy fluorescence that depends on protein number in a probabilistic manner. We again show that MaxCal can infer important molecular and cellular rates even when data are in fluorescence, another example of CST with three confounding factors-gene expression noise, cell division noise, and fluorescence distortion-all coupled. Our approach will provide guidance to build models in synthetic biology experiments as well as general biological systems where examples of CSTs are abundant.
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Affiliation(s)
- Andrew Torres
- Department of Physics and Astronomy, University of Denver, Denver, Colorado
| | - Spencer Cockerell
- Department of Physics and Astronomy, University of Denver, Denver, Colorado
| | - Michael Phillips
- Department of Physics and Astronomy, University of Denver, Denver, Colorado
| | - Gábor Balázsi
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York
| | - Kingshuk Ghosh
- Molecular and Cellular Biophysics, University of Denver, Denver, Colorado; Department of Physics and Astronomy, University of Denver, Denver, Colorado.
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18
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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: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [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.
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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.
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19
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Gautam P, Sinha SK. Theoretical investigation of functional responses of bio-molecular assembly networks. SOFT MATTER 2023; 19:3803-3817. [PMID: 37191191 DOI: 10.1039/d2sm01530g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Cooperative protein-protein and protein-DNA interactions form programmable complex assemblies, often performing non-linear gene regulatory operations involved in signal transductions and cell fate determination. The apparent structure of those complex assemblies is very similar, but their functional response strongly depends on the topology of the protein-DNA interaction networks. Here, we demonstrate how the coordinated self-assembly creates gene regulatory network motifs that corroborate the existence of a precise functional response at the molecular level using thermodynamic and dynamic analyses. Our theoretical and Monte Carlo simulations show that a complex network of interactions can form a decision-making loop, such as feedback and feed-forward circuits, only by a few molecular mechanisms. We characterize each possible network of interactions by systematic variations of free energy parameters associated with the binding among biomolecules and DNA looping. We also find that the higher-order networks exhibit alternative steady states from the stochastic dynamics of each network. We capture this signature by calculating stochastic potentials and attributing their multi-stability features. We validate our findings against the Gal promoter system in yeast cells. Overall, we show that the network topology is vital in phenotype diversity in regulatory circuits.
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Affiliation(s)
- Pankaj Gautam
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
| | - Sudipta Kumar Sinha
- Theoretical and Computational Biophysical Chemistry Group, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, Punjab 140001, India.
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20
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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21
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Qiao L, Ghosh P, Rangamani P. Design principles of improving the dose-response alignment in coupled GTPase switches. NPJ Syst Biol Appl 2023; 9:3. [PMID: 36720885 PMCID: PMC9889403 DOI: 10.1038/s41540-023-00266-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/17/2023] [Indexed: 02/02/2023] Open
Abstract
"Dose-response alignment" (DoRA), where the downstream response of cellular signaling pathways closely matches the fraction of activated receptor, can improve the fidelity of dose information transmission. The negative feedback has been experimentally identified as a key component for DoRA, but numerical simulations indicate that negative feedback is not sufficient to achieve perfect DoRA, i.e., perfect match of downstream response and receptor activation level. Thus a natural question is whether there exist design principles for signaling motifs within only negative feedback loops to improve DoRA to near-perfect DoRA. Here, we investigated several model formulations of an experimentally validated circuit that couples two molecular switches-mGTPase (monomeric GTPase) and tGTPase (heterotrimeric GTPases) - with negative feedback loops. In the absence of feedback, the low and intermediate mGTPase activation levels benefit DoRA in mass action and Hill-function models, respectively. Adding negative feedback has versatile roles on DoRA: it may impair DoRA in the mass action model with low mGTPase activation level and Hill-function model with intermediate mGTPase activation level; in other cases, i.e., the mass action model with a high mGTPase activation level or the Hill-function model with a non-intermediate mGTPase activation level, it improves DoRA. Furthermore, we found that DoRA in a longer cascade (i.e., tGTPase) can be obtained using Hill-function kinetics under certain conditions. In summary, we show how ranges of activity of mGTPase, reaction kinetics, the negative feedback, and the cascade length affect DoRA. This work provides a framework for improving the DoRA performance in signaling motifs with negative feedback.
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Affiliation(s)
- Lingxia Qiao
- Department of Mechanical and Aerospace Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Pradipta Ghosh
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA.
- Moores Comprehensive Cancer Center, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, Jacob's School of Engineering, University of California San Diego, La Jolla, CA, USA.
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22
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Genome-Wide Analysis of Gene Expression Noise Brought About by Transcriptional Regulation in Pseudomonas aeruginosa. mSystems 2022; 7:e0096322. [PMID: 36377899 PMCID: PMC9765613 DOI: 10.1128/msystems.00963-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The part of expression noise that is brought about by transcriptional regulation (represented here as NTR) is an important criterion for estimating the regulatory mode of a gene. However, characterization of NTR is an under-explored area, and there is little knowledge regarding the genome-wide NTR in the model pathogen Pseudomonas aeruginosa. Here, with a library of dual-color transcriptional reporters, we estimated the NTR for over 90% of the promoters in P. aeruginosa. Most promoters exhibit low NTR, while 42 and 115 promoters with high NTR were screened out in the exponential and the stationary growth phases, respectively. Specifically, a rearrangement of NTR was found in promoters involved in amino acid metabolism when bacteria enter the exponential phase. In addition, during the stationary phase, high NTR was found in a wide range of iron-related promoters involving siderophore synthesis and heme uptake, ExsA-regulated promoters involving bacterial virulence, and FleQ-regulated promoters involving biofilm development. We also found a large-scale negative dependence of transcriptional regulation between high-NTR promoters belonging to different functional categories. Our findings offer a global view of transcriptional heterogeneity in P. aeruginosa. IMPORTANCE The phenotypic diversity of Pseudomonas aeruginosa is frequently observed in research, suggesting that bacteria adopt strategies such as bet-hedging to survive ever-changing environments. Gene expression noise (GEN) is the major source of phenotypic diversity. Large GEN from transcriptional regulation (represented as NTR) represent an evolutionary necessity to maintain the copy number diversity of certain proteins in the population. Here, we provide a system-wide view of NTR in P. aeruginosa under nutrient-rich and stressed conditions. High NTR was found in genes involved in flagella biosynthesis and amino acid metabolism under both conditions. Specially, iron acquisition genes exhibited high NTR in the stressed condition, suggesting a great diversity of iron physiology in P. aeruginosa. We further revealed a global negative dependence of transcriptional regulation between those high-NTR genes under the stressed condition, suggesting a mutually exclusive relationship between different bacterial survival strategies.
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23
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Barajas C, Del Vecchio D. Synthetic biology by controller design. Curr Opin Biotechnol 2022; 78:102837. [PMID: 36343564 DOI: 10.1016/j.copbio.2022.102837] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/26/2022] [Accepted: 10/07/2022] [Indexed: 11/06/2022]
Abstract
Natural biological systems display complex regulation and synthetic biomolecular systems have been used to understand their natural counterparts and to parse sophisticated regulations into core design principles. At the same time, the engineering of biomolecular systems has unarguable potential to transform current and to enable new, yet-to-be-imagined, biotechnology applications. In this review, we discuss the progression of control systems design in synthetic biology, from the purpose of understanding the function of naturally occurring regulatory motifs to that of creating genetic circuits whose function is sufficiently robust for biotechnology applications.
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Affiliation(s)
- Carlos Barajas
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Domitilla Del Vecchio
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
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24
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Schultz D, Stevanovic M, Tsimring LS. Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures. Biophys J 2022; 121:4137-4152. [PMID: 36168291 PMCID: PMC9675034 DOI: 10.1016/j.bpj.2022.09.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 08/22/2022] [Accepted: 09/23/2022] [Indexed: 11/17/2022] Open
Abstract
Cellular responses to the presence of toxic compounds in their environment require prompt expression of the correct levels of the appropriate enzymes, which are typically regulated by transcription factors that control gene expression for the duration of the response. The characteristics of each response dictate the choice of regulatory parameters such as the affinity of the transcription factor to its binding sites and the strength of the promoters it regulates. Although much is known about the dynamics of cellular responses, we still lack a framework to understand how different regulatory strategies evolved in natural systems relate to the selective pressures acting in each particular case. Here, we analyze a dynamical model of a typical antibiotic response in bacteria, where a transcriptionally repressed enzyme is induced by a sudden exposure to the drug that it processes. We identify strategies of gene regulation that optimize this response for different types of selective pressures, which we define as a set of costs associated with the drug, enzyme, and repressor concentrations during the response. We find that regulation happens in a limited region of the regulatory parameter space. While responses to more costly (toxic) drugs favor the usage of strongly self-regulated repressors, responses where expression of enzyme is more costly favor the usage of constitutively expressed repressors. Only a very narrow range of selective pressures favor weakly self-regulated repressors. We use this framework to determine which costs and benefits are most critical for the evolution of a variety of natural cellular responses that satisfy the approximations in our model and to analyze how regulation is optimized in new environments with different demands.
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Affiliation(s)
- Daniel Schultz
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
| | - Mirjana Stevanovic
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire
| | - Lev S Tsimring
- Synthetic Biology Institute, University of California, San Diego, La Jolla, California
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25
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Chagas MDS, Medeiros F, dos Santos MT, de Menezes MA, Carvalho-Assef APD, da Silva FAB. An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851. Mem Inst Oswaldo Cruz 2022; 117:e220111. [PMID: 36259790 PMCID: PMC9565603 DOI: 10.1590/0074-02760220111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/09/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.
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Affiliation(s)
- Márcia da Silva Chagas
- Fundação Oswaldo Cruz-Fiocruz, Programa de Computação Científica, Rio de Janeiro, RJ, Brasil,+ Corresponding authors: /
| | - Fernando Medeiros
- Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Infectologia, Laboratório de Pesquisa Clínica em Doenças Febris Agudas, Rio de Janeiro, RJ, Brasil
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26
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Angermayr SA, Pang TY, Chevereau G, Mitosch K, Lercher MJ, Bollenbach T. Growth-mediated negative feedback shapes quantitative antibiotic response. Mol Syst Biol 2022; 18:e10490. [PMID: 36124745 PMCID: PMC9486506 DOI: 10.15252/msb.202110490] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/19/2022] [Accepted: 08/26/2022] [Indexed: 11/15/2022] Open
Abstract
Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.
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Affiliation(s)
- S Andreas Angermayr
- Institute for Biological PhysicsUniversity of CologneCologneGermany
- Institute of Science and Technology AustriaKlosterneuburgAustria
- Present address:
CeMM Research Center for Molecular Medicine of the Austrian Academy of SciencesViennaAustria
| | - Tin Yau Pang
- Institute for Computer ScienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Department of BiologyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | | | - Karin Mitosch
- Institute of Science and Technology AustriaKlosterneuburgAustria
- Genome Biology UnitEuropean Molecular Biology Laboratory (EMBL)HeidelbergGermany
| | - Martin J Lercher
- Institute for Computer ScienceHeinrich Heine University DüsseldorfDüsseldorfGermany
- Department of BiologyHeinrich Heine University DüsseldorfDüsseldorfGermany
| | - Tobias Bollenbach
- Institute for Biological PhysicsUniversity of CologneCologneGermany
- Center for Data and Simulation ScienceUniversity of CologneCologneGermany
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Dynamics and Sensitivity of Signaling Pathways. CURRENT PATHOBIOLOGY REPORTS 2022; 10:11-22. [PMID: 36969954 PMCID: PMC10035447 DOI: 10.1007/s40139-022-00230-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Purpose of Review Signaling pathways serve to communicate information about extracellular conditions into the cell, to both the nucleus and cytoplasmic processes to control cell responses. Genetic mutations in signaling network components are frequently associated with cancer and can result in cells acquiring an ability to divide and grow uncontrollably. Because signaling pathways play such a significant role in cancer initiation and advancement, their constituent proteins are attractive therapeutic targets. In this review, we discuss how signaling pathway modeling can assist with identifying effective drugs for treating diseases, such as cancer. An achievement that would facilitate the use of such models is their ability to identify controlling biochemical parameters in signaling pathways, such as molecular abundances and chemical reaction rates, because this would help determine effective points of attack by therapeutics. Recent Findings We summarize the current state of understanding the sensitivity of phosphorylation cycles with and without sequestration. We also describe some basic properties of regulatory motifs including feedback and feedforward regulation. Summary Although much recent work has focused on understanding the dynamics and particularly the sensitivity of signaling networks in eukaryotic systems, there is still an urgent need to build more scalable models of signaling networks that can appropriately represent their complexity across different cell types and tumors.
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Chaturvedi S, Pablo M, Wolf M, Rosas-Rivera D, Calia G, Kumar AJ, Vardi N, Du K, Glazier J, Ke R, Chan MF, Perelson AS, Weinberger LS. Disrupting autorepression circuitry generates "open-loop lethality" to yield escape-resistant antiviral agents. Cell 2022; 185:2086-2102.e22. [PMID: 35561685 PMCID: PMC9097017 DOI: 10.1016/j.cell.2022.04.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 03/01/2022] [Accepted: 04/14/2022] [Indexed: 12/27/2022]
Abstract
Across biological scales, gene-regulatory networks employ autorepression (negative feedback) to maintain homeostasis and minimize failure from aberrant expression. Here, we present a proof of concept that disrupting transcriptional negative feedback dysregulates viral gene expression to therapeutically inhibit replication and confers a high evolutionary barrier to resistance. We find that nucleic-acid decoys mimicking cis-regulatory sites act as "feedback disruptors," break homeostasis, and increase viral transcription factors to cytotoxic levels (termed "open-loop lethality"). Feedback disruptors against herpesviruses reduced viral replication >2-logs without activating innate immunity, showed sub-nM IC50, synergized with standard-of-care antivirals, and inhibited virus replication in mice. In contrast to approved antivirals where resistance rapidly emerged, no feedback-disruptor escape mutants evolved in long-term cultures. For SARS-CoV-2, disruption of a putative feedback circuit also generated open-loop lethality, reducing viral titers by >1-log. These results demonstrate that generating open-loop lethality, via negative-feedback disruption, may yield a class of antimicrobials with a high genetic barrier to resistance.
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Affiliation(s)
- Sonali Chaturvedi
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA.
| | - Michael Pablo
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Marie Wolf
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Daniel Rosas-Rivera
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Giuliana Calia
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Arjun J Kumar
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Noam Vardi
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Kelvin Du
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Joshua Glazier
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Ruian Ke
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Matilda F Chan
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Ophthalmology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Alan S Perelson
- Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Leor S Weinberger
- Gladstone/UCSF Center for Cell Circuitry, Gladstone Institutes, San Francisco, CA 94158, USA; Gladstone Institute of Virology, Gladstone Institutes, San Francisco, CA 94158, USA; Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
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29
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Balázsi G. New antivirals exploit viral feedback tricks for a cure without resistance. Cell 2022; 185:2210-2212. [DOI: 10.1016/j.cell.2022.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 05/24/2022] [Accepted: 05/24/2022] [Indexed: 10/17/2022]
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30
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Wang Y, Carter BZ, Li Z, Huang X. Application of machine learning methods in clinical trials for precision medicine. JAMIA Open 2022; 5:ooab107. [PMID: 35178503 PMCID: PMC8846336 DOI: 10.1093/jamiaopen/ooab107] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/15/2021] [Accepted: 12/01/2021] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE A key component for precision medicine is a good prediction algorithm for patients' response to treatments. We aim to implement machine learning (ML) algorithms into the response-adaptive randomization (RAR) design and improve the treatment outcomes. MATERIALS AND METHODS We incorporated 9 ML algorithms to model the relationship of patient responses and biomarkers in clinical trial design. Such a model predicted the response rate of each treatment for each new patient and provide guidance for treatment assignment. Realizing that no single method may fit all trials well, we also built an ensemble of these 9 methods. We evaluated their performance through quantifying the benefits for trial participants, such as the overall response rate and the percentage of patients who receive their optimal treatments. RESULTS Simulation studies showed that the adoption of ML methods resulted in more personalized optimal treatment assignments and higher overall response rates among trial participants. Compared with each individual ML method, the ensemble approach achieved the highest response rate and assigned the largest percentage of patients to their optimal treatments. For the real-world study, we successfully showed the potential improvements if the proposed design had been implemented in the study. CONCLUSION In summary, the ML-based RAR design is a promising approach for assigning more patients to their personalized effective treatments, which makes the clinical trial more ethical and appealing. These features are especially desirable for late-stage cancer patients who have failed all the Food and Drug Administration (FDA)-approved treatment options and only can get new treatments through clinical trials.
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Affiliation(s)
- Yizhuo Wang
- Department of Biostatistics, The University of Texas
MD Anderson Cancer Center, Houston, Texas, USA
| | - Bing Z Carter
- Section of Molecular Hematology and Therapy,
Department of Leukemia, The University of Texas MD Anderson Cancer
Center, Houston, Texas, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas
MD Anderson Cancer Center, Houston, Texas, USA
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas
MD Anderson Cancer Center, Houston, Texas, USA
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31
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Jones RD, Qian Y, Ilia K, Wang B, Laub MT, Del Vecchio D, Weiss R. Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles. Nat Commun 2022; 13:1720. [PMID: 35361767 PMCID: PMC8971529 DOI: 10.1038/s41467-022-29338-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 02/16/2022] [Indexed: 02/06/2023] Open
Abstract
Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.
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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
| | - Katherine Ilia
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Benjamin Wang
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Michael T Laub
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Howard Hughes Medical Institute, 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.
| | - 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.
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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32
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Goetz H, Stone A, Zhang R, Lai Y, Tian X. Double-edged role of resource competition in gene expression noise and control. ADVANCED GENETICS (HOBOKEN, N.J.) 2022; 3:2100050. [PMID: 35989723 PMCID: PMC9390979 DOI: 10.1002/ggn2.202100050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/08/2022] [Indexed: 04/30/2023]
Abstract
Despite extensive investigation demonstrating that resource competition can significantly alter the deterministic behaviors of synthetic gene circuits, it remains unclear how resource competition contributes to the gene expression noise and how this noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: competition decreases noise through introducing a resource constraint but generates its own type of noise which we name as "resource competitive noise." Utilization of orthogonal resources enables retainment of the noise reduction conferred by resource constraint while removing the added resource competitive noise. The noise reduction effects are studied using three negative feedback types: negatively competitive regulation (NCR), local, and global controllers, each having four placement architectures in the protein biosynthesis pathway (mRNA or protein inhibition on transcription or translation). Our results show that both local and NCR controllers with mRNA-mediated inhibition are efficacious at reducing noise, with NCR controllers demonstrating a superior noise-reduction capability. We also find that combining feedback controllers with orthogonal resources can improve the local controllers. This work provides deep insights into the origin of stochasticity in gene circuits with resource competition and guidance for developing effective noise control strategies.
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Affiliation(s)
- Hanah Goetz
- School for Engineering of Matter, Transport and EnergyArizona State UniversityTempeAZ85287USA
| | - Austin Stone
- School of Biological and Health Systems EngineeringArizona State UniversityTempeAZ85287USA
| | - Rong Zhang
- School of Biological and Health Systems EngineeringArizona State UniversityTempeAZ85287USA
| | - Ying‐Cheng Lai
- School of Electrical, Computer and Energy EngineeringArizona State UniversityTempeAZ85287USA
- Department of PhysicsArizona State UniversityTempeAZ85287USA
| | - Xiao‐Jun Tian
- School of Biological and Health Systems EngineeringArizona State UniversityTempeAZ85287USA
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33
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Doshi A, Bandey I, Nevozhay D, Varadarajan N, Cirino PC. Design and characterization of a salicylic acid-inducible gene expression system for Jurkat cells. J Biotechnol 2022; 346:11-14. [PMID: 35051448 PMCID: PMC9618363 DOI: 10.1016/j.jbiotec.2022.01.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 12/11/2021] [Accepted: 01/13/2022] [Indexed: 11/29/2022]
Abstract
With continued progress in cell and gene therapies, there is an immediate need for exogenously tunable gene expression systems with safe and predictable behavior in specific human cell types. Here, we demonstrate the ability of the salicylic acid (SA)-inducible MarR repressor protein from Escherichia coli to regulate target gene expression in a human T lymphocyte cell line. Two lentiviral vectors, one encoding an enhanced green fluorescent protein (EGFP) reporter cassette and the other a repressor cassette, were sequentially transduced into Jurkat cells, using fluorescence-activated cell sorting (FACS) to isolate stable Jurkat progeny. As a result, EGFP expression was repressed by MarR and was inducible upon the addition of SA (~1.3 fold). This represents the first example of functional expression of bacterial MarR in mammalian cells, and opens the possibility for further development of regulated, SA-tunable gene expression system for T-cells.
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Affiliation(s)
- Aarti Doshi
- Dept. of Biology and Biochemistry, University of Houston, Houston, TX, USA.
| | - Irfan Bandey
- Dept. of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA.
| | - Dmitry Nevozhay
- Dept. of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; School of Biomedicine, Far Eastern Federal University, Vladivostok, Russia.
| | - Navin Varadarajan
- Dept. of Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA.
| | - Patrick C. Cirino
- Dept. of Biology and Biochemistry, University of Houston, Houston, TX,Dept. of Chemical and Biomolecular Engineering, University of Houston, Houston, TX
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A Platform Technology for Monitoring the Unfolded Protein Response. Methods Mol Biol 2022; 2378:45-67. [PMID: 34985693 PMCID: PMC10053305 DOI: 10.1007/978-1-0716-1732-8_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
The unfolded protein response (UPR) is a complex signal transduction pathway that remodels gene expression in response to proteotoxic stress in the endoplasmic reticulum (ER) and is linked to the development of a range of diseases, including Alzheimer's disease, diabetes, and several types of cancer. UPR induction is typically monitored by measuring the expression level of UPR marker genes. Most tools for quantifying gene expression, including DNA microarrays and quantitative PCR with reverse transcription (RT-PCR), produce snapshots of the cell transcriptome, but are not ideal for measurements requiring temporal resolution of gene expression dynamics. Reporter assays for indirect detection of the UPR typically rely on extrachromosomal expression of reporters under the control of minimal or synthetic regulatory sequences that do not recapitulate the native chromosomal context of the UPR target genes. To address the need for tools to monitor chromosomal gene expression that recapitulate gene expression dynamics from the native chromosomal context and generate a readily detectable signal output, we developed a gene signal amplifier platform that links transcriptional and post-translational regulation of a fluorescent output to the expression of a chromosomal gene marker of the UPR. The platform is based on a genetic circuit that amplifies the output signal with high sensitivity and dynamic resolution and is implemented through chromosomal integration of the gene encoding the main control element of the genetic circuit to link its expression to that of the target gene, thereby generating a platform that can be easily adapted to monitor any UPR target through integration of the main control element at the appropriate chromosomal locus. By recapitulating the transcriptional and translational control mechanisms underlying the expression of UPR targets with high sensitivity, this platform provides a novel technology for monitoring the UPR with superior sensitivity and dynamic resolution.
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35
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Chakravarty S, Csikász-Nagy A. Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops. PLoS Comput Biol 2021; 17:e1009622. [PMID: 34860832 PMCID: PMC8641863 DOI: 10.1371/journal.pcbi.1009622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 11/08/2021] [Indexed: 11/19/2022] Open
Abstract
Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures. Cellular behavior can be affected by noise in molecular interactions. Signaling pathways should process noisy input signals and support cellular decision making by properly transducing the signals, while removing noise from them. Three component networks of feed-forward loops (FFLs) have been proposed to serve as ideal noise reducers, while linear pathways were shown to be good signal transducers. These signaling units do not work in isolation, so there is a possibility that a combination of various feed-forward loops can provide good noise reduction, while maintaining good signal transduction. To test this hypothesis, we have systematically tested the noise reducing and signal transducing capabilities of all possible combinations of feed-forward loops and compared them with the performance of individual FFLs. We built mathematical models of all these systems and compared their capabilities at reducing noise in the input signal while maintaining responses to meaningful changes in the incoming signal. We found that a combination of two copies of a special type of fully positive signaling FFLs is the best noise reducer, while a combination of two incoherent (one positive, one negative signal) FFLs can provide the best signal transduction. The combination of these two FFLs could provide good signal processing where both noise reduction and signal transduction are achieved.
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Affiliation(s)
- Suchana Chakravarty
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- * E-mail: (SC); (AC-N)
| | - Attila Csikász-Nagy
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
- Randall Center for Cell and Molecular Biophysics, King’s College London, London, United Kingdom
- * E-mail: (SC); (AC-N)
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36
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Gerhardt KP, Rao SD, Olson EJ, Igoshin OA, Tabor JJ. Independent control of mean and noise by convolution of gene expression distributions. Nat Commun 2021; 12:6957. [PMID: 34845228 PMCID: PMC8630168 DOI: 10.1038/s41467-021-27070-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 11/03/2021] [Indexed: 11/28/2022] Open
Abstract
Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.
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Affiliation(s)
- Karl P Gerhardt
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Satyajit D Rao
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Evan J Olson
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Oleg A Igoshin
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Biosciences, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Center for Theoretical Biophysics, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Jeffrey J Tabor
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
- Department of Biosciences, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
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37
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Weisenberger C, Hathcock D, Hinczewski M. Cellular Signaling beyond the Wiener-Kolmogorov Limit. J Phys Chem B 2021; 125:12698-12711. [PMID: 34756045 DOI: 10.1021/acs.jpcb.1c07894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Accurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory, originally developed for engineering problems, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However, WK theory has one assumption that potentially limits its applicability: it relies on a linear, continuum description of the reaction dynamics. Despite this, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence of the bound being broken. To accomplish this, we develop a theoretical framework for multilevel signaling cascades, including the possibility of feedback interactions between input and output. In the absence of feedback, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback, we rely on numerical solutions of the system's master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However, the magnitude of the violation is biologically negligible, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds, its predictions for performance limits are excellent approximations, even for nonlinear systems.
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Affiliation(s)
- Casey Weisenberger
- Department of Physics, Case Western Reserve University, Cleveland, Ohio 44106, United States
| | - David Hathcock
- Department of Physics, Cornell University, Ithaca, New York 14853, United States
| | - Michael Hinczewski
- Department of Physics, Case Western Reserve University, Cleveland, Ohio 44106, United States
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Kashiwagi FM, Wendler Miranda B, de Oliveira Pedrosa F, de Souza EM, Müller-Santos M. Control of Gene Expression With Quercetin-Responsive Modular Circuits. Front Bioeng Biotechnol 2021; 9:730967. [PMID: 34604189 PMCID: PMC8481877 DOI: 10.3389/fbioe.2021.730967] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/23/2021] [Indexed: 12/02/2022] Open
Abstract
Control of gene expression is crucial for several biotechnological applications, especially for implementing predictable and controllable genetic circuits. Such circuits are often implemented with a transcriptional regulator activated by a specific signal. These regulators should work independently of the host machinery, with low gratuitous induction or crosstalk with host components. Moreover, the signal should also be orthogonal, recognized only by the regulator with minimal interference with the host operation. In this context, transcriptional regulators activated by plant metabolites as flavonoids emerge as candidates to control gene expression in bacteria. However, engineering novel circuits requires the characterization of the genetic parts (e.g., genes, promoters, ribosome binding sites, and terminators) in the host of interest. Therefore, we decomposed the QdoR regulatory system of B. subtilis, responsive to the flavonoid quercetin, and reassembled its parts into genetic circuits programmed to have different levels of gene expression and noise dependent on the concentration of quercetin. We showed that only one of the promoters regulated by QdoR worked well in E. coli, enabling the construction of other circuits induced by quercetin. The QdoR expression was modulated with constitutive promoters of different transcriptional strengths, leading to low expression levels when QdoR was highly expressed and vice versa. E. coli strains expressing high and low levels of QdoR were mixed and induced with the same quercetin concentration, resulting in two stable populations expressing different levels of their gene reporters. Besides, we demonstrated that the level of QdoR repression generated different noise levels in gene expression dependent on the concentration of quercetin. The circuits presented here can be exploited in applications requiring adjustment of gene expression and noise using a highly available and natural inducer as quercetin.
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Affiliation(s)
- Fernanda Miyuki Kashiwagi
- Postgraduate Program in Science (Biochemistry), Department of Biochemistry and Molecular Biology, Nitrogen Fixation Laboratory, Federal University of Paraná (UFPR), Curitiba, Brazil
| | - Brenno Wendler Miranda
- Biological Sciences Undergraduate Course, Department of Biochemistry and Molecular Biology, Nitrogen Fixation Laboratory, Federal University of Paraná (UFPR), Curitiba, Brazil
| | - Fabio de Oliveira Pedrosa
- Nitrogen Fixation Laboratory, Department of Biochemistry and Molecular Biology, Federal University of Paraná (UFPR), Curitiba, Brazil
| | - Emanuel Maltempi de Souza
- Nitrogen Fixation Laboratory, Department of Biochemistry and Molecular Biology, Federal University of Paraná (UFPR), Curitiba, Brazil
| | - Marcelo Müller-Santos
- Nitrogen Fixation Laboratory, Department of Biochemistry and Molecular Biology, Federal University of Paraná (UFPR), Curitiba, Brazil
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39
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Krzysztoń R, Wan Y, Petreczky J, Balázsi G. Gene-circuit therapy on the horizon: synthetic biology tools for engineered therapeutics. Acta Biochim Pol 2021; 68:377-383. [PMID: 34460209 PMCID: PMC8590856 DOI: 10.18388/abp.2020_5744] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 07/19/2021] [Indexed: 01/17/2023]
Abstract
Therapeutic genome modification requires precise control over the introduced therapeutic functions. Current approaches of gene and cell therapy fail to deliver such command and rely on semi-quantitative methods with limited influence on timing, contextuality and levels of transgene expression, and hence on therapeutic function. Synthetic biology offers new opportunities for quantitative functionality in designing therapeutic systems and their components. Here, we discuss synthetic biology tools in their therapeutic context, with examples of proof-of-principle and clinical applications of engineered synthetic biomolecules and higher-order functional systems, i.e. gene circuits. We also present the prospects of future development towards advanced gene-circuit therapy.
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Affiliation(s)
- Rafał Krzysztoń
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11974, USA
- The Louis & Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yiming Wan
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11974, USA
- The Louis & Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Julia Petreczky
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11974, USA
- The Louis & Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11974, USA
- The Louis & Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
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40
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Azizoglu A, Brent R, Rudolf F. A precisely adjustable, variation-suppressed eukaryotic transcriptional controller to enable genetic discovery. eLife 2021; 10:69549. [PMID: 34342575 PMCID: PMC8421071 DOI: 10.7554/elife.69549] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/02/2021] [Indexed: 11/13/2022] Open
Abstract
Conditional expression of genes and observation of phenotype remain central to biological discovery. Current methods enable either on/off or imprecisely controlled graded gene expression. We developed a 'well-tempered' controller, WTC846, for precisely adjustable, graded, growth condition independent expression of genes in Saccharomyces cerevisiae. Controlled genes are expressed from a strong semisynthetic promoter repressed by the prokaryotic TetR, which also represses its own synthesis; with basal expression abolished by a second, 'zeroing' repressor. The autorepression loop lowers cell-to-cell variation while enabling precise adjustment of protein expression by a chemical inducer. WTC846 allelic strains in which the controller replaced the native promoters recapitulated known null phenotypes (CDC42, TPI1), exhibited novel overexpression phenotypes (IPL1), showed protein dosage-dependent growth rates and morphological phenotypes (CDC28, TOR2, PMA1 and the hitherto uncharacterized PBR1), and enabled cell cycle synchronization (CDC20). WTC846 defines an 'expression clamp' allowing protein dosage to be adjusted by the experimenter across the range of cellular protein abundances, with limited variation around the setpoint.
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Affiliation(s)
| | - Roger Brent
- Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, United States
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41
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Yang J, Lee J, Land MA, Lai S, Igoshin OA, St-Pierre F. A synthetic circuit for buffering gene dosage variation between individual mammalian cells. Nat Commun 2021; 12:4132. [PMID: 34226556 PMCID: PMC8257781 DOI: 10.1038/s41467-021-23889-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 05/19/2021] [Indexed: 02/06/2023] Open
Abstract
Precise control of gene expression is critical for biological research and biotechnology. However, transient plasmid transfections in mammalian cells produce a wide distribution of copy numbers per cell, and consequently, high expression heterogeneity. Here, we report plasmid-based synthetic circuits - Equalizers - that buffer copy-number variation at the single-cell level. Equalizers couple a transcriptional negative feedback loop with post-transcriptional incoherent feedforward control. Computational modeling suggests that the combination of these two topologies enables Equalizers to operate over a wide range of plasmid copy numbers. We demonstrate experimentally that Equalizers outperform other gene dosage compensation topologies and produce as low cell-to-cell variation as chromosomally integrated genes. We also show that episome-encoded Equalizers enable the rapid generation of extrachromosomal cell lines with stable and uniform expression. Overall, Equalizers are simple and versatile devices for homogeneous gene expression and can facilitate the engineering of synthetic circuits that function reliably in every cell.
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Affiliation(s)
- Jin Yang
- Department of Bioengineering, Rice University, Houston, TX, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jihwan Lee
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA
| | - Michelle A Land
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Shujuan Lai
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Oleg A Igoshin
- Department of Bioengineering, Rice University, Houston, TX, USA
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA
- Department of Biosciences, Rice University, Houston, TX, USA
- Department of Chemistry, Rice University, Houston, TX, USA
| | - François St-Pierre
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA.
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA.
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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: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [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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43
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Cortes MG, Lin Y, Zeng L, Balázsi G. From Bench to Keyboard and Back Again: A Brief History of Lambda Phage Modeling. Annu Rev Biophys 2021; 50:117-134. [PMID: 33957052 DOI: 10.1146/annurev-biophys-082020-063558] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Cellular decision making is the process whereby cells choose one developmental pathway from multiple possible ones, either spontaneously or due to environmental stimuli. Examples in various cell types suggest an almost inexhaustible plethora of underlying molecular mechanisms. In general, cellular decisions rely on the gene regulatory network, which integrates external signals to drive cell fate choice. The search for general principles of such a process benefits from appropriate biological model systems that reveal how and why certain gene regulatory mechanisms drive specific cellular decisions according to ecological context and evolutionary outcomes. In this article, we review the historical and ongoing development of the phage lambda lysis-lysogeny decision as a model system to investigate all aspects of cellular decision making. The unique generality, simplicity, and richness of phage lambda decision making render it a constant source ofmathematical modeling-aided inspiration across all of biology. We discuss the origins and progress of quantitative phage lambda modeling from the 1950s until today, as well as its possible future directions. We provide examples of how modeling enabled methods and theory development, leading to new biological insights by revealing gaps in the theory and pinpointing areas requiring further experimental investigation. Overall, we highlight the utility of theoretical approaches both as predictive tools, to forecast the outcome of novel experiments, and as explanatory tools, to elucidate the natural processes underlying experimental data.
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Affiliation(s)
- Michael G Cortes
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, USA
| | - Yiruo Lin
- Department of Computer Science and Engineering, Texas A&M University, College Station, Texas 77843, USA
| | - Lanying Zeng
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, Texas 77843, USA; .,Center for Phage Technology, Texas A&M University, College Station, Texas 77843, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York 11794, USA
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44
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Capp J. Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability. Evol Appl 2021; 14:893-901. [PMID: 33897810 PMCID: PMC8061278 DOI: 10.1111/eva.13204] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 12/11/2022] Open
Abstract
Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.
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Affiliation(s)
- Jean‐Pascal Capp
- Toulouse Biotechnology InstituteINSACNRSINRAEUniversity of ToulouseToulouseFrance
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45
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Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks. ENTROPY 2021; 23:e23030357. [PMID: 33802879 PMCID: PMC8002683 DOI: 10.3390/e23030357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 11/24/2022]
Abstract
Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time—recorded at frequent intervals—over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data.
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46
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Pomeroy AE, Peña MI, Houser JR, Dixit G, Dohlman HG, Elston TC, Errede B. A predictive model of gene expression reveals the role of network motifs in the mating response of yeast. Sci Signal 2021; 14:14/670/eabb5235. [PMID: 33593998 DOI: 10.1126/scisignal.abb5235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.
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Affiliation(s)
- Amy E Pomeroy
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - Matthew I Peña
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - John R Houser
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gauri Dixit
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Henrik G Dohlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Timothy C Elston
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. .,Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Beverly Errede
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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47
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Cuba Samaniego C, Franco E. Ultrasensitive molecular controllers for quasi-integral feedback. Cell Syst 2021; 12:272-288.e3. [PMID: 33539724 DOI: 10.1016/j.cels.2021.01.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 09/22/2020] [Accepted: 01/11/2021] [Indexed: 12/24/2022]
Abstract
Feedback control has enabled the success of automated technologies by mitigating the effects of variability, unknown disturbances, and noise. While it is known that biological feedback loops reduce the impact of noise and help shape kinetic responses, many questions remain about how to design molecular integral controllers. Here, we propose a modular strategy to build molecular quasi-integral feedback controllers, which involves following two design principles. The first principle is to utilize an ultrasensitive response, which determines the gain of the controller and influences the steady-state error. The second is to use a tunable threshold of the ultrasensitive response, which determines the equilibrium point of the system. We describe a reaction network, named brink controller, that satisfies these conditions by combining molecular sequestration and an activation/deactivation cycle. With computational models, we examine potential biological implementations of brink controllers, and we illustrate different example applications.
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Affiliation(s)
- Christian Cuba Samaniego
- Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Elisa Franco
- Mechanical and Aerospace Engineering, University of California at Los Angeles, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California at Los Angeles, Los Angeles, CA 90095, USA; Bioengineering, University of California at Los Angeles, Los Angeles, CA 90095, USA; Mechanical Engineering, University of California at Riverside, Riverside, CA 92521, USA.
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48
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Bonny AR, Fonseca JP, Park JE, El-Samad H. Orthogonal control of mean and variability of endogenous genes in a human cell line. Nat Commun 2021; 12:292. [PMID: 33436569 PMCID: PMC7804932 DOI: 10.1038/s41467-020-20467-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022] Open
Abstract
Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.
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Affiliation(s)
- Alain R Bonny
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - João Pedro Fonseca
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Amyris Bio Products Portugal, Porto, Portugal
| | - Jesslyn E Park
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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49
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Bokes P, Hojcka M, Singh A. MicroRNA Based Feedforward Control of Intrinsic Gene Expression Noise. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:272-282. [PMID: 31484129 DOI: 10.1109/tcbb.2019.2938502] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intrinsic noise, which arises in gene expression at low copy numbers, can be controlled by diverse regulatory motifs, including feedforward loops. Here, we study an example of a feedforward control system based on the interaction between an mRNA molecule and an antagonistic microRNA molecule encoded by the same gene, aiming to quantify the variability (or noise) in molecular copy numbers. Using linear noise approximation, we show that the mRNA noise is sub-Poissonian in case of non-bursty transcription, and exhibits a nonmonotonic response both to the species natural lifetime ratio and to the strength of the antagonistic interaction. Additionally, we use the Chemical Reaction Network Theory to prove that the mRNA copy number distribution is Poissonian in the absence of spontaneous mRNA decay channel. In case of transcriptional bursts, we show that feedforward control can attenuate the super-Poissonian gene-expression noise that is due to bursting, and that the effect is more considerable at the protein than at the mRNA level. Our results indicate that the strong coupling between mRNA and microRNA in the sense of burst stoichiometry and also of timing of production events renders the microRNA based feedforward motif an effective mechanism for the control of gene expression noise.
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50
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Zhao EM, Lalwani MA, Lovelett RJ, García-Echauri SA, Hoffman SM, Gonzalez CL, Toettcher JE, Kevrekidis IG, Avalos JL. Design and Characterization of Rapid Optogenetic Circuits for Dynamic Control in Yeast Metabolic Engineering. ACS Synth Biol 2020; 9:3254-3266. [PMID: 33232598 PMCID: PMC10399620 DOI: 10.1021/acssynbio.0c00305] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The use of optogenetics in metabolic engineering for light-controlled microbial chemical production raises the prospect of utilizing control and optimization techniques routinely deployed in traditional chemical manufacturing. However, such mechanisms require well-characterized, customizable tools that respond fast enough to be used as real-time inputs during fermentations. Here, we present OptoINVRT7, a new rapid optogenetic inverter circuit to control gene expression in Saccharomyces cerevisiae. The circuit induces gene expression in only 0.6 h after switching cells from light to darkness, which is at least 6 times faster than previous OptoINVRT optogenetic circuits used for chemical production. In addition, we introduce an engineered inducible GAL1 promoter (PGAL1-S), which is stronger than any constitutive or inducible promoter commonly used in yeast. Combining OptoINVRT7 with PGAL1-S achieves strong and light-tunable levels of gene expression with as much as 132.9 ± 22.6-fold induction in darkness. The high performance of this new optogenetic circuit in controlling metabolic enzymes boosts production of lactic acid and isobutanol by more than 50% and 15%, respectively. The strength and controllability of OptoINVRT7 and PGAL1-S open the door to applying process control tools to engineered metabolisms to improve robustness and yields in microbial fermentations for chemical production.
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Affiliation(s)
- Evan M. Zhao
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
| | - Makoto A. Lalwani
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
| | - Robert J. Lovelett
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
- Department of Chemical and Biomolecular Engineering, 221 Maryland
Hall, Johns Hopkins University, 2400 N. Charles Street, Baltimore, Maryland 21218, United States
| | - Sergio A. García-Echauri
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
| | - Shannon M. Hoffman
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
| | - Christopher L. Gonzalez
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
| | - Jared E. Toettcher
- Department of Molecular Biology, 140 Lewis Thomas Laboratory, Princeton University, Washington Road, Princeton, New Jersey 08544, United States
| | - Ioannis G. Kevrekidis
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
- Department of Chemical and Biomolecular Engineering, 221 Maryland
Hall, Johns Hopkins University, 2400 N. Charles Street, Baltimore, Maryland 21218, United States
| | - José L. Avalos
- Department of Chemical and Biological Engineering, Hoyt Laboratory
101, Princeton University, William Street, Princeton, New Jersey 08544, United States
- Department of Molecular Biology, 140 Lewis Thomas Laboratory, Princeton University, Washington Road, Princeton, New Jersey 08544, United States
- The Andlinger Center for Energy and the Environment, Princeton University, 86 Olden Street, Princeton, New Jersey 08544, United States
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