1
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El Meouche I, Jain P, Jolly MK, Capp JP. Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity. Transl Oncol 2024; 49:102069. [PMID: 39121829 PMCID: PMC11364053 DOI: 10.1016/j.tranon.2024.102069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 07/17/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.
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Affiliation(s)
- Imane El Meouche
- Université Paris Cité, Université Sorbonne Paris Nord, INSERM, IAME, F-75018 Paris, France.
| | - Paras Jain
- Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, India
| | - Jean-Pascal Capp
- Toulouse Biotechnology Institute, INSA/University of Toulouse, CNRS, INRAE, Toulouse, France.
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2
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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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3
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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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4
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Glass DS, Bren A, Vaisbourd E, Mayo A, Alon U. A synthetic differentiation circuit in Escherichia coli for suppressing mutant takeover. Cell 2024; 187:931-944.e12. [PMID: 38320549 PMCID: PMC10882425 DOI: 10.1016/j.cell.2024.01.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/27/2023] [Accepted: 01/16/2024] [Indexed: 02/08/2024]
Abstract
Differentiation is crucial for multicellularity. However, it is inherently susceptible to mutant cells that fail to differentiate. These mutants outcompete normal cells by excessive self-renewal. It remains unclear what mechanisms can resist such mutant expansion. Here, we demonstrate a solution by engineering a synthetic differentiation circuit in Escherichia coli that selects against these mutants via a biphasic fitness strategy. The circuit provides tunable production of synthetic analogs of stem, progenitor, and differentiated cells. It resists mutations by coupling differentiation to the production of an essential enzyme, thereby disadvantaging non-differentiating mutants. The circuit selected for and maintained a positive differentiation rate in long-term evolution. Surprisingly, this rate remained constant across vast changes in growth conditions. We found that transit-amplifying cells (fast-growing progenitors) underlie this environmental robustness. Our results provide insight into the stability of differentiation and demonstrate a powerful method for engineering evolutionarily stable multicellular consortia.
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Affiliation(s)
- David S Glass
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
| | - Anat Bren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Elizabeth Vaisbourd
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Avi Mayo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Uri Alon
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel.
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5
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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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6
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Ruess J, Ballif G, Aditya C. Stochastic chemical kinetics of cell fate decision systems: From single cells to populations and back. J Chem Phys 2023; 159:184103. [PMID: 37937934 DOI: 10.1063/5.0160529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/14/2023] [Indexed: 11/09/2023] Open
Abstract
Stochastic chemical kinetics is a widely used formalism for studying stochasticity of chemical reactions inside single cells. Experimental studies of reaction networks are generally performed with cells that are part of a growing population, yet the population context is rarely taken into account when models are developed. Models that neglect the population context lose their validity whenever the studied system influences traits of cells that can be selected in the population, a property that naturally arises in the complex interplay between single-cell and population dynamics of cell fate decision systems. Here, we represent such systems as absorbing continuous-time Markov chains. We show that conditioning on non-absorption allows one to derive a modified master equation that tracks the time evolution of the expected population composition within a growing population. This allows us to derive consistent population dynamics models from a specification of the single-cell process. We use this approach to classify cell fate decision systems into two types that lead to different characteristic phases in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events inside single cells to growth of cell populations in dynamically changing environments.
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Affiliation(s)
- Jakob Ruess
- Inria Saclay, 91120 Palaiseau, France
- Institut Pasteur, Université Paris Cité, 75015 Paris, France
| | | | - Chetan Aditya
- Institut Pasteur, Université Paris Cité, 75015 Paris, France
- Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA
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7
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Shepherd MJ, Pierce AP, Taylor TB. Evolutionary innovation through transcription factor rewiring in microbes is shaped by levels of transcription factor activity, expression, and existing connectivity. PLoS Biol 2023; 21:e3002348. [PMID: 37871011 PMCID: PMC10621929 DOI: 10.1371/journal.pbio.3002348] [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: 05/22/2023] [Revised: 11/02/2023] [Accepted: 09/25/2023] [Indexed: 10/25/2023] Open
Abstract
The survival of a population during environmental shifts depends on whether the rate of phenotypic adaptation keeps up with the rate of changing conditions. A common way to achieve this is via change to gene regulatory network (GRN) connections-known as rewiring-that facilitate novel interactions and innovation of transcription factors. To understand the success of rapidly adapting organisms, we therefore need to determine the rules that create and constrain opportunities for GRN rewiring. Here, using an experimental microbial model system with the soil bacterium Pseudomonas fluorescens, we reveal a hierarchy among transcription factors that are rewired to rescue lost function, with alternative rewiring pathways only unmasked after the preferred pathway is eliminated. We identify 3 key properties-high activation, high expression, and preexisting low-level affinity for novel target genes-that facilitate transcription factor innovation. Ease of acquiring these properties is constrained by preexisting GRN architecture, which was overcome in our experimental system by both targeted and global network alterations. This work reveals the key properties that determine transcription factor evolvability, and as such, the evolution of GRNs.
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Affiliation(s)
- Matthew J. Shepherd
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, United Kingdom
- Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, United Kingdom
| | - Aidan P. Pierce
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, United Kingdom
| | - Tiffany B. Taylor
- Milner Centre for Evolution, Department of Life Sciences, University of Bath, Bath, United Kingdom
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8
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Charlebois DA. Quantitative systems-based prediction of antimicrobial resistance evolution. NPJ Syst Biol Appl 2023; 9:40. [PMID: 37679446 PMCID: PMC10485028 DOI: 10.1038/s41540-023-00304-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.
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Affiliation(s)
- Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, AB, T6G-2E1, Canada.
- Department of Biological Sciences, University of Alberta, Edmonton, AB, T6G-2E9, Canada.
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9
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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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10
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Wang T, Weiss A, Aqeel A, Wu F, Lopatkin AJ, David LA, You L. Horizontal gene transfer enables programmable gene stability in synthetic microbiota. Nat Chem Biol 2022; 18:1245-1252. [PMID: 36050493 PMCID: PMC10018779 DOI: 10.1038/s41589-022-01114-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/15/2022] [Indexed: 11/09/2022]
Abstract
The functions of many microbial communities exhibit remarkable stability despite fluctuations in the compositions of these communities. To date, a mechanistic understanding of this function-composition decoupling is lacking. Statistical mechanisms have been commonly hypothesized to explain such decoupling. Here, we proposed that dynamic mechanisms, mediated by horizontal gene transfer (HGT), also enable the independence of functions from the compositions of microbial communities. We combined theoretical analysis with numerical simulations to illustrate that HGT rates can determine the stability of gene abundance in microbial communities. We further validated these predictions using engineered microbial consortia of different complexities transferring one or more than a dozen clinically isolated plasmids, as well as through the reanalysis of data from the literature. Our results demonstrate a generalizable strategy to program the gene stability of microbial communities.
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Affiliation(s)
- Teng Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Andrea Weiss
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ammara Aqeel
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Feilun Wu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Allison J Lopatkin
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
| | - Lawrence A David
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA.
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA.
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11
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Guthrie J, Charlebois D. Non-genetic resistance facilitates survival while hindering the evolution of drug resistance due to intraspecific competition. Phys Biol 2022; 19. [PMID: 35998624 DOI: 10.1088/1478-3975/ac8c17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 08/23/2022] [Indexed: 11/11/2022]
Abstract
Rising rates of resistance to antimicrobial drugs threaten the effective treatment of infections across the globe. Drug resistance has been established to emerge from non-genetic mechanisms as well as from genetic mechanisms. However, it is still unclear how non-genetic resistance affects the evolution of genetic drug resistance. We develop deterministic and stochastic population models that incorporate resource competition to quantitatively investigate the transition from non-genetic to genetic resistance during the exposure to static and cidal drugs. We find that non-genetic resistance facilitates the survival of cell populations during drug treatment while hindering the development of genetic resistance due to competition between the non-genetically and genetically resistant subpopulations. Non-genetic resistance in the presence of subpopulation competition increases the fixation times of drug resistance mutations, while increasing the probability of mutation before population extinction during cidal drug treatment. Intense intraspecific competition during drug treatment leads to extinction of susceptible and non-genetically resistant subpopulations. Alternating between drug and no drug conditions results in oscillatory population dynamics, increased resistance mutation fixation timescales, and reduced population survival. These findings advance our fundamental understanding of the evolution of resistance and may guide novel treatment strategies for patients with drug-resistant infections.
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Affiliation(s)
- Joshua Guthrie
- Department of Physics, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, T6G 2E1, CANADA
| | - Daniel Charlebois
- Departments of Physics and Biological Sciences, University of Alberta, 11455 Saskatchewan Drive NW, Edmonton, Alberta, T6G 2E1, CANADA
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12
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Engineering of Synthetic Transcriptional Switches in Yeast. Life (Basel) 2022; 12:life12040557. [PMID: 35455048 PMCID: PMC9030632 DOI: 10.3390/life12040557] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 03/31/2022] [Accepted: 04/03/2022] [Indexed: 02/04/2023] Open
Abstract
Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.
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13
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Drug-dependent growth curve reshaping reveals mechanisms of antifungal resistance in Saccharomyces cerevisiae. Commun Biol 2022; 5:292. [PMID: 35361876 PMCID: PMC8971432 DOI: 10.1038/s42003-022-03228-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Accepted: 03/07/2022] [Indexed: 11/15/2022] Open
Abstract
Microbial drug resistance is an emerging global challenge. Current drug resistance assays tend to be simplistic, ignoring complexities of resistance manifestations and mechanisms, such as multicellularity. Here, we characterize multicellular and molecular sources of drug resistance upon deleting the AMN1 gene responsible for clumping multicellularity in a budding yeast strain, causing it to become unicellular. Computational analysis of growth curve changes upon drug treatment indicates that the unicellular strain is more sensitive to four common antifungals. Quantitative models uncover entwined multicellular and molecular processes underlying these differences in sensitivity and suggest AMN1 as an antifungal target in clumping pathogenic yeasts. Similar experimental and mathematical modeling pipelines could reveal multicellular and molecular drug resistance mechanisms, leading to more effective treatments against various microbial infections and possibly even cancers. Combined growth curve experiments and quantitative modeling reveal antifungal responses of planktonic yeast, providing a future framework to examine antimicrobial drug resistance.
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14
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Aditya C, Bertaux F, Batt G, Ruess J. Using single-cell models to predict the functionality of synthetic circuits at the population scale. Proc Natl Acad Sci U S A 2022; 119:e2114438119. [PMID: 35271387 PMCID: PMC8931247 DOI: 10.1073/pnas.2114438119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
SignificanceAt the single-cell level, biochemical processes are inherently stochastic. For many natural systems, the resulting cell-to-cell variability is exploited by microbial populations. In synthetic biology, however, the interplay of cell-to-cell variability and population processes such as selection or growth often leads to circuits not functioning as predicted by simple models. Here we show how multiscale stochastic kinetic models that simultaneously track single-cell and population processes can be obtained based on an augmentation of the chemical master equation. These models enable us to quantitatively predict complex population dynamics of a yeast optogenetic differentiation system from a specification of the circuit's components and to demonstrate how cell-to-cell variability can be exploited to purposefully create unintuitive circuit functionality.
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Affiliation(s)
- Chetan Aditya
- Inria Paris, Inria, 75012 Paris, France
- Department of Computational Biology, Institut Pasteur, 75015 Paris, France
- Université de Paris, 75006 Paris, France
| | - François Bertaux
- Inria Paris, Inria, 75012 Paris, France
- Department of Computational Biology, Institut Pasteur, 75015 Paris, France
| | - Gregory Batt
- Inria Paris, Inria, 75012 Paris, France
- Department of Computational Biology, Institut Pasteur, 75015 Paris, France
| | - Jakob Ruess
- Inria Paris, Inria, 75012 Paris, France
- Department of Computational Biology, Institut Pasteur, 75015 Paris, France
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15
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Hall R, Charlebois DA. Lattice-based Monte Carlo simulation of the effects of nutrient concentration and magnetic field exposure on yeast colony growth and morphology. In Silico Biol 2021; 14:53-69. [PMID: 34924371 PMCID: PMC8842992 DOI: 10.3233/isb-210233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Yeasts exist in communities that expand over space and time to form complex structures and patterns. We developed a lattice-based framework to perform spatial-temporal Monte Carlo simulations of budding yeast colonies exposed to different nutrient and magnetic field conditions. The budding patterns of haploid and diploid yeast cells were incorporated into the framework, as well as the filamentous growth that occurs in yeast colonies under nutrient limiting conditions. Simulation of the framework predicted that magnetic fields decrease colony growth rate, solidity, and roundness. Magnetic field simulations further predicted that colony elongation and boundary fluctuations increase in a nutrient- and ploidy-dependent manner. These in-silico predictions are an important step towards understanding the effects of the physico-chemical environment on microbial colonies and for informing bioelectromagnetic experiments on yeast colony biofilms and fungal pathogens.
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Affiliation(s)
- Rebekah Hall
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | - Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, AB, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
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16
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Farquhar KS, Rasouli Koohi S, Charlebois DA. Does transcriptional heterogeneity facilitate the development of genetic drug resistance? Bioessays 2021; 43:e2100043. [PMID: 34160842 DOI: 10.1002/bies.202100043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 05/30/2021] [Accepted: 06/07/2021] [Indexed: 12/24/2022]
Abstract
Non-genetic forms of antimicrobial (drug) resistance can result from cell-to-cell variability that is not encoded in the genetic material. Data from recent studies also suggest that non-genetic mechanisms can facilitate the development of genetic drug resistance. We speculate on how the interplay between non-genetic and genetic mechanisms may affect microbial adaptation and evolution during drug treatment. We argue that cellular heterogeneity arising from fluctuations in gene expression, epigenetic modifications, as well as genetic changes contribute to drug resistance at different timescales, and that the interplay between these mechanisms enhance pathogen resistance. Accordingly, developing a better understanding of the role of non-genetic mechanisms in drug resistance and how they interact with genetic mechanisms will enhance our ability to combat antimicrobial resistance. Also see the video abstract here: https://youtu.be/aefGpdh-bgU.
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Affiliation(s)
| | - Samira Rasouli Koohi
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada
| | - Daniel A Charlebois
- Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada.,Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
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17
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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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18
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Zhang Y, Ding W, Wang Z, Zhao H, Shi S. Development of Host-Orthogonal Genetic Systems for Synthetic Biology. Adv Biol (Weinh) 2021; 5:e2000252. [PMID: 33729696 DOI: 10.1002/adbi.202000252] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/18/2020] [Indexed: 12/17/2022]
Abstract
The construction of a host-orthogonal genetic system can not only minimize the impact of host-specific nuances on fine-tuning of gene expression, but also expand cellular functions such as in vivo continuous evolution of genes based on an error-prone DNA polymerase. It represents an emerging powerful approach for making biology easier to engineer. In this review, the recent advances are described on the design of genetic systems that can be stably inherited in the host cells and are responsible for important biological processes including DNA replication, RNA transcription, protein translation, and gene regulation. Their applications in synthetic biology are summarized and the future challenges and opportunities are discussed in developing such systems.
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Affiliation(s)
- Yang Zhang
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
| | - Wentao Ding
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China.,Key Laboratory of Food Nutrition and Safety (Tianjin University of Science and Technology) Ministry of Education, College of Food Engineering and Biotechnology, Tianjin University of Science and Technology, No. 29, 13th Avenue, TEDA, Tianjin, 300457, P. R. China
| | - Zhihui Wang
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
| | - Huimin Zhao
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Shuobo Shi
- Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing, 100029, P. R. China
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19
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Kuwahara H, Gao X. Stable maintenance of hidden switches as a strategy to increase the gene expression stability. NATURE COMPUTATIONAL SCIENCE 2021; 1:62-70. [PMID: 38217152 DOI: 10.1038/s43588-020-00001-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/02/2020] [Indexed: 01/15/2024]
Abstract
In response to severe genetic and environmental perturbations, wild-type organisms can express hidden alternative phenotypes adaptive to such adverse conditions. While our theoretical understanding of the population-level fitness advantage and evolution of phenotypic switching under variable environments has grown, the mechanism by which these organisms maintain phenotypic switching capabilities under static environments remains to be elucidated. Here, using computational simulations, we analyzed the evolution of gene circuits under natural selection and found that different strategies evolved to increase the gene expression stability near the optimum level. In a population comprising bistable individuals, a strategy of maintaining bistability and raising the potential barrier separating the bistable regimes was consistently taken. Our results serve as evidence that hidden bistable switches can be stably maintained during environmental stasis-an essential property enabling the timely release of adaptive alternatives with small genetic changes in the event of substantial perturbations.
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Affiliation(s)
- Hiroyuki Kuwahara
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xin Gao
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
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20
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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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21
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Jones RD, Qian Y, Siciliano V, DiAndreth B, Huh J, Weiss R, Del Vecchio D. An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells. Nat Commun 2020; 11:5690. [PMID: 33173034 PMCID: PMC7656454 DOI: 10.1038/s41467-020-19126-9] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 09/18/2020] [Indexed: 12/17/2022] Open
Abstract
Synthetic biology has the potential to bring forth advanced genetic devices for applications in healthcare and biotechnology. However, accurately predicting the behavior of engineered genetic devices remains difficult due to lack of modularity, wherein a device's output does not depend only on its intended inputs but also on its context. One contributor to lack of modularity is loading of transcriptional and translational resources, which can induce coupling among otherwise independently-regulated genes. Here, we quantify the effects of resource loading in engineered mammalian genetic systems and develop an endoribonuclease-based feedforward controller that can adapt the expression level of a gene of interest to significant resource loading in mammalian cells. Near-perfect adaptation to resource loads is facilitated by high production and catalytic rates of the endoribonuclease. Our design is portable across cell lines and enables predictable tuning of controller function. Ultimately, our controller is a general-purpose device for predictable, robust, and context-independent control of gene expression.
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Affiliation(s)
- Ross D Jones
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Yili Qian
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Velia Siciliano
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Instituto Italiano di Tecnologia, Napoli, 80125, Italy
| | - Breanna DiAndreth
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jin Huh
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ron Weiss
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Domitilla Del Vecchio
- Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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22
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Blackstone NW, Gutterman JU. Can natural selection and druggable targets synergize? Of nutrient scarcity, cancer, and the evolution of cooperation. Bioessays 2020; 43:e2000160. [PMID: 33165962 DOI: 10.1002/bies.202000160] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 10/03/2020] [Accepted: 10/06/2020] [Indexed: 01/21/2023]
Abstract
Since the dawn of molecular biology, cancer therapy has focused on druggable targets. Despite some remarkable successes, cell-level evolution remains a potent antagonist to this approach. We suggest that a deeper understanding of the breakdown of cooperation can synergize the evolutionary and druggable-targets approaches. Complexity requires cooperation, whether between cells of different species (symbiosis) or between cells of the same organism (multicellularity). Both forms of cooperation may be associated with nutrient scarcity, which in turn may be associated with a chemiosmotic metabolism. A variety of examples from modern organisms supports these generalities. Indeed, mammalian cancers-unicellular, glycolytic, and fast-replicating-parallel these examples. Nutrient scarcity, chemiosmosis, and associated signaling may favor cooperation, while under conditions of nutrient abundance a fermentative metabolism may signal the breakdown of cooperation. Manipulating this metabolic milieu may potentiate the effects of targeted therapeutics. Specific opportunities are discussed in this regard, including avicins, a novel plant product.
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Affiliation(s)
- Neil W Blackstone
- Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois, USA
| | - Jordan U Gutterman
- Department of Leukemia, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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23
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Guinn MT, Wan Y, Levovitz S, Yang D, Rosner MR, Balázsi G. Observation and Control of Gene Expression Noise: Barrier Crossing Analogies Between Drug Resistance and Metastasis. Front Genet 2020; 11:586726. [PMID: 33193723 PMCID: PMC7662081 DOI: 10.3389/fgene.2020.586726] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Michael Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States.,Stony Brook Medical Scientist Training Program, Stony Brook, NY, United States
| | - Yiming Wan
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Sarah Levovitz
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
| | - Dongbo Yang
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Marsha R Rosner
- Ben May Department for Cancer Research, The University of Chicago, Chicago, IL, United States
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, United States
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24
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Wytock TP, Zhang M, Jinich A, Fiebig A, Crosson S, Motter AE. Extreme Antagonism Arising from Gene-Environment Interactions. Biophys J 2020; 119:2074-2086. [PMID: 33068537 DOI: 10.1016/j.bpj.2020.09.038] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/27/2020] [Accepted: 09/21/2020] [Indexed: 01/06/2023] Open
Abstract
Antagonistic interactions in biological systems, which occur when one perturbation blunts the effect of another, are typically interpreted as evidence that the two perturbations impact the same cellular pathway or function. Yet, this interpretation ignores extreme antagonistic interactions wherein an otherwise deleterious perturbation compensates for the function lost because of a prior perturbation. Here, we report on gene-environment interactions involving genetic mutations that are deleterious in a permissive environment but beneficial in a specific environment that restricts growth. These extreme antagonistic interactions constitute gene-environment analogs of synthetic rescues previously observed for gene-gene interactions. Our approach uses two independent adaptive evolution steps to address the lack of experimental methods to systematically identify such extreme interactions. We apply the approach to Escherichia coli by successively adapting it to defined glucose media without and with the antibiotic rifampicin. The approach identified multiple mutations that are beneficial in the presence of rifampicin and deleterious in its absence. The analysis of transcription shows that the antagonistic adaptive mutations repress a stringent response-like transcriptional program, whereas nonantagonistic mutations have an opposite transcriptional profile. Our approach represents a step toward the systematic characterization of extreme antagonistic gene-drug interactions, which can be used to identify targets to select against antibiotic resistance.
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Affiliation(s)
- Thomas P Wytock
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois
| | - Manjing Zhang
- The Committee on Microbiology, University of Chicago, Chicago, Illinois
| | - Adrian Jinich
- Division of Infectious Diseases, Weill Department of Medicine, Weill-Cornell Medical College, New York, New York
| | - Aretha Fiebig
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan
| | - Sean Crosson
- Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan
| | - Adilson E Motter
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois; Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois; Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois.
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25
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Farquhar KS, Flohr H, Charlebois DA. Advancing Antimicrobial Resistance Research Through Quantitative Modeling and Synthetic Biology. Front Bioeng Biotechnol 2020; 8:583415. [PMID: 33072732 PMCID: PMC7530828 DOI: 10.3389/fbioe.2020.583415] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 09/02/2020] [Indexed: 11/13/2022] Open
Abstract
Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.
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Affiliation(s)
| | - Harold Flohr
- Department of Physics, University of Alberta, Edmonton, AB, Canada
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26
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Abstract
Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses.
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Affiliation(s)
- Luca Agozzino
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, 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
| | - Jin Wang
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, USA
| | - Ken A Dill
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA; .,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, USA
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27
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Calles B, Goñi‐Moreno Á, de Lorenzo V. Digitalizing heterologous gene expression in Gram-negative bacteria with a portable ON/OFF module. Mol Syst Biol 2019; 15:e8777. [PMID: 31885200 PMCID: PMC6920698 DOI: 10.15252/msb.20188777] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Revised: 10/16/2019] [Accepted: 10/24/2019] [Indexed: 01/24/2023] Open
Abstract
While prokaryotic promoters controlled by signal-responding regulators typically display a range of input/output ratios when exposed to cognate inducers, virtually no naturally occurring cases are known to have an OFF state of zero transcription-as ideally needed for synthetic circuits. To overcome this problem, we have modelled and implemented a simple digitalizer module that completely suppresses the basal level of otherwise strong promoters in such a way that expression in the absence of induction is entirely impeded. The circuit involves the interplay of a translation-inhibitory sRNA with the translational coupling of the gene of interest to a repressor such as LacI. The digitalizer module was validated with the strong inducible promoters Pm (induced by XylS in the presence of benzoate) and PalkB (induced by AlkS/dicyclopropyl ketone) and shown to perform effectively in both Escherichia coli and the soil bacterium Pseudomonas putida. The distinct expression architecture allowed cloning and conditional expression of, e.g. colicin E3, one molecule of which per cell suffices to kill the host bacterium. Revertants that escaped ColE3 killing were not found in hosts devoid of insertion sequences, suggesting that mobile elements are a major source of circuit inactivation in vivo.
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Affiliation(s)
- Belén Calles
- Systems Biology ProgramCentro Nacional de Biotecnología‐CSICMadridSpain
| | - Ángel Goñi‐Moreno
- Systems Biology ProgramCentro Nacional de Biotecnología‐CSICMadridSpain
- Present address:
School of ComputingNewcastle UniversityNewcastle upon TyneUK
| | - Víctor de Lorenzo
- Systems Biology ProgramCentro Nacional de Biotecnología‐CSICMadridSpain
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28
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Abstract
Natural or synthetic genetic modules can lose their function over long-term evolution if the function is costly. How populations can evolve to restore such broken function is poorly understood. To test the reversibility of evolutionary breakdown, we use yeast cell populations with a chromosomally integrated synthetic gene circuit. In previous evolution experiments the gene circuit lost its costly function through various mutations. By exposing such mutant populations to conditions where regaining gene circuit function would be beneficial, we find adaptation scenarios with or without repairing lost gene circuit function. These results are important for drug resistance or future synthetic biology applications where evolutionary loss and regain of function play a significant role. Evolutionary reversibility—the ability to regain a lost function—is an important problem both in evolutionary and synthetic biology, where repairing natural or synthetic systems broken by evolutionary processes may be valuable. Here, we use a synthetic positive-feedback (PF) gene circuit integrated into haploid Saccharomyces cerevisiae cells to test if the population can restore lost PF function. In previous evolution experiments, mutations in a gene eliminated the fitness costs of PF activation. Since PF activation also provides drug resistance, exposing such compromised or broken mutants to both drug and inducer should create selection pressure to regain drug resistance and possibly PF function. Indeed, evolving 7 PF mutant strains in the presence of drug revealed 3 adaptation scenarios through genomic, PF-external mutations that elevate PF basal expression, possibly by affecting transcription, translation, degradation, and other fundamental cellular processes. Nonfunctional mutants gained drug resistance without ever developing high expression, while quasifunctional and dysfunctional PF mutants developed high expression nongenetically, which then diminished, although more slowly for dysfunctional mutants where revertant clones arose. These results highlight how intracellular context, such as the growth rate, can affect regulatory network dynamics and evolutionary dynamics, which has important consequences for understanding the evolution of drug resistance and developing future synthetic biology applications.
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29
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Diversity in lac Operon Regulation among Diverse Escherichia coli Isolates Depends on the Broader Genetic Background but Is Not Explained by Genetic Relatedness. mBio 2019; 10:mBio.02232-19. [PMID: 31719176 PMCID: PMC6851279 DOI: 10.1128/mbio.02232-19] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The lac operon of Escherichia coli is a classic model for studying gene regulation. This study has uncovered features such as the environmental input logic controlling gene expression, as well as gene expression bistability and hysteresis. Most lac operon studies have focused on a few lab strains, and it is not known how generally those findings apply to the diversity of E. coli strains. We examined the environmental dependence of lac gene regulation in 20 natural isolates of E. coli and found a wide range of regulatory responses. By transferring lac genes from natural isolate strains into a common reference strain, we found that regulation depends on both the lac genes themselves and on the broader genetic background, indicating potential for still-greater regulatory diversity following horizontal gene transfer. Our results reveal that there is substantial natural variation in the regulation of the lac operon and indicate that this variation can be ecologically meaningful. Transcription of bacterial genes is controlled by the coordinated action of cis- and trans-acting regulators. The activity and mode of action of these regulators can reflect different requirements for gene products in different environments. A well-studied example is the regulatory function that integrates the environmental availability of glucose and lactose to control the Escherichia colilac operon. Most studies of lac operon regulation have focused on a few closely related strains. To determine the range of natural variation in lac regulatory function, we introduced a reporter construct into 23 diverse E. coli strains and measured expression with combinations of inducer concentrations. We found a wide range of regulatory functions. Several functions were similar to the one observed in a reference lab strain, whereas others depended weakly on the presence of cAMP. Some characteristics of the regulatory function were explained by the genetic relatedness of strains, indicating that differences varied on relatively short time scales. The regulatory characteristics explained by genetic relatedness were among those that best predicted the initial growth of strains following transition to a lactose environment, suggesting a role for selection. Finally, we transferred the lac operon, with the lacI regulatory gene, from five natural isolate strains into a reference lab strain. The regulatory function of these hybrid strains revealed the effect of local and global regulatory elements in controlling expression. Together, this work demonstrates that regulatory functions can be varied within a species and that there is variation within a species to best match a function to particular environments.
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Camellato B, Roney IJ, Azizi A, Charlebois D, Kaern M. Engineered gene networks enable non‐genetic drug resistance and enhanced cellular robustness. ENGINEERING BIOLOGY 2019. [DOI: 10.1049/enb.2019.0009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Brendan Camellato
- Ottawa Institute of Systems Biology University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Department of Cellular and Molecular Medicine University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Institute of Systems Genetics New York University Langone Health 550 1st Avenue New York NY 10016 USA
| | - Ian J. Roney
- Ottawa Institute of Systems Biology University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Department of Cellular and Molecular Medicine University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
| | - Afnan Azizi
- Ottawa Institute of Systems Biology University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Department of Cellular and Molecular Medicine University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
| | - Daniel Charlebois
- Ottawa Institute of Systems Biology University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Department of Physics University of Ottawa 150 Louis‐Pasteur Pvt Ottawa Ontario K1N 6N5 Canada
| | - Mads Kaern
- Ottawa Institute of Systems Biology University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Department of Cellular and Molecular Medicine University of Ottawa 451 Smyth Road Ottawa Ontario K1H 8M5 Canada
- Department of Physics University of Ottawa 150 Louis‐Pasteur Pvt Ottawa Ontario K1N 6N5 Canada
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Cortes MG, Krog J, Balázsi G. Optimality of the spontaneous prophage induction rate. J Theor Biol 2019; 483:110005. [PMID: 31525321 DOI: 10.1016/j.jtbi.2019.110005] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 08/30/2019] [Accepted: 09/10/2019] [Indexed: 10/26/2022]
Abstract
Lysogens are bacterial cells that have survived after genomically incorporating the DNA of temperate bacteriophages infecting them. If an infection results in lysogeny, the lysogen continues to grow and divide normally, seemingly unaffected by the integrated viral genome known as a prophage. However, the prophage can still have an impact on the host's phenotype and overall fitness in certain environments. Additionally, the prophage within the lysogen can activate the lytic pathway via spontaneous prophage induction (SPI), killing the lysogen and releasing new progeny phages. These new phages can then lyse or lysogenize other susceptible nonlysogens, thereby impacting the competition between lysogens and nonlysogens. In a scenario with differing growth rates, it is not clear whether SPI would be beneficial or detrimental to the lysogens since it kills the host cell but also attacks nonlysogenic competitors, either lysing or lysogenizing them. Here we study the evolutionary dynamics of a mixture of lysogens and nonlysogens and derive general conditions on SPI rates for lysogens to displace nonlysogens. We show that there exists an optimal SPI rate for bacteriophage λ and explain why it is so low. We also investigate the impact of stochasticity and conclude that even at low cell numbers SPI can still provide an advantage to the lysogens. These results corroborate recent experimental studies showing that lower SPI rates are advantageous for phage-phage competition, and establish theoretical bounds on the SPI rate in terms of ecological and environmental variables associated with lysogens having a competitive advantage over their nonlysogenic counterparts.
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Affiliation(s)
- Michael G Cortes
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Jonathan Krog
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794, USA.
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Guinn MT, Balázsi G. Noise-reducing optogenetic negative-feedback gene circuits in human cells. Nucleic Acids Res 2019; 47:7703-7714. [PMID: 31269201 PMCID: PMC6698750 DOI: 10.1093/nar/gkz556] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2019] [Revised: 06/10/2019] [Accepted: 06/12/2019] [Indexed: 12/16/2022] Open
Abstract
Gene autorepression is widely present in nature and is also employed in synthetic biology, partly to reduce gene expression noise in cells. Optogenetic systems have recently been developed for controlling gene expression levels in mammalian cells, but most have utilized activator-based proteins, neglecting negative feedback except for in silico control. Here, we engineer optogenetic gene circuits into mammalian cells to achieve noise-reduction for precise gene expression control by genetic, in vitro negative feedback. We build a toolset of these noise-reducing Light-Inducible Tuner (LITer) gene circuits using the TetR repressor fused with a Tet-inhibiting peptide (TIP) or a degradation tag through the light-sensitive LOV2 protein domain. These LITers provide a range of nearly 4-fold gene expression control and up to 5-fold noise reduction from existing optogenetic systems. Moreover, we use the LITer gene circuit architecture to control gene expression of the cancer oncogene KRAS(G12V) and study its downstream effects through phospho-ERK levels and cellular proliferation. Overall, these novel LITer optogenetic platforms should enable precise spatiotemporal perturbations for studying multicellular phenotypes in developmental biology, oncology and other biomedical fields of research.
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Affiliation(s)
- Michael Tyler Guinn
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794, USA
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
- Stony Brook Medical Scientist Training Program, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY 11794, USA
| | - Gábor Balázsi
- Biomedical Engineering Department, Stony Brook University, Stony Brook, NY 11794, USA
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA
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Kuzdzal‐Fick JJ, Chen L, Balázsi G. Disadvantages and benefits of evolved unicellularity versus multicellularity in budding yeast. Ecol Evol 2019; 9:8509-8523. [PMID: 31410258 PMCID: PMC6686284 DOI: 10.1002/ece3.5322] [Citation(s) in RCA: 20] [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: 04/12/2019] [Accepted: 04/15/2019] [Indexed: 12/18/2022] Open
Abstract
Multicellular organisms appeared on Earth through several independent major evolutionary transitions. Are such transitions reversible? Addressing this fundamental question entails understanding the benefits and costs of multicellularity versus unicellularity. For example, some wild yeast strains form multicellular clumps, which might be beneficial in stressful conditions, but this has been untested. Here, we show that unicellular yeast evolve from clump-forming ancestors by propagating samples from suspension after larger clumps have settled. Unicellular yeast strains differed from their clumping ancestors mainly by mutations in the AMN1 (Antagonist of Mitotic exit Network) gene. Ancestral yeast clumps were more resistant to freeze/thaw, hydrogen peroxide, and ethanol stressors than their unicellular counterparts, but they grew slower without stress. These findings suggest disadvantages and benefits to multicellularity and unicellularity that may have impacted the emergence of multicellular life forms.
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Affiliation(s)
- Jennie J. Kuzdzal‐Fick
- Department of Systems BiologyThe University of Texas MD Anderson Cancer CenterHoustonTexas
- Department of Biology and BiochemistryUniversity of HoustonHoustonTexas
| | - Lin Chen
- Department of Systems BiologyThe University of Texas MD Anderson Cancer CenterHoustonTexas
| | - Gábor Balázsi
- Department of Systems BiologyThe University of Texas MD Anderson Cancer CenterHoustonTexas
- Louis and Beatrice Laufer Center for Physical & Quantitative BiologyStony Brook UniversityStony BrookNew York
- Department of Biomedical EngineeringStony Brook UniversityStony BrookNew York
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Farquhar KS, Charlebois DA, Szenk M, Cohen J, Nevozhay D, Balázsi G. Role of network-mediated stochasticity in mammalian drug resistance. Nat Commun 2019; 10:2766. [PMID: 31235692 PMCID: PMC6591227 DOI: 10.1038/s41467-019-10330-w] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 05/03/2019] [Indexed: 11/11/2022] Open
Abstract
A major challenge in biology is that genetically identical cells in the same environment can display gene expression stochasticity (noise), which contributes to bet-hedging, drug tolerance, and cell-fate switching. The magnitude and timescales of stochastic fluctuations can depend on the gene regulatory network. Currently, it is unclear how gene expression noise of specific networks impacts the evolution of drug resistance in mammalian cells. Answering this question requires adjusting network noise independently from mean expression. Here, we develop positive and negative feedback-based synthetic gene circuits to decouple noise from the mean for Puromycin resistance gene expression in Chinese Hamster Ovary cells. In low Puromycin concentrations, the high-noise, positive-feedback network delays long-term adaptation, whereas it facilitates adaptation under high Puromycin concentration. Accordingly, the low-noise, negative-feedback circuit can maintain resistance by acquiring mutations while the positive-feedback circuit remains mutation-free and regains drug sensitivity. These findings may have profound implications for chemotherapeutic inefficiency and cancer relapse. The role of gene expression noise in the evolution of drug resistance in mammalian cells is unclear. Here, by uncoupling noise from mean expression of a drug resistance gene in CHO cells the authors show that noisy expression aids adaptation to high drug levels, but delays it at low drug levels.
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Affiliation(s)
- Kevin S Farquhar
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.,Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center, UT Health Graduate School of Biomedical Sciences, Houston, TX, 77030, USA
| | - Daniel A Charlebois
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.,Department of Physics, University of Alberta, Edmonton, AB, 4-181 CCIS, T6G-2E1, Canada
| | - Mariola Szenk
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA.,Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Joseph Cohen
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Dmitry Nevozhay
- School of Biomedicine, Far Eastern Federal University, 8 Sukhanova Street, Vladivostok, 690950, Russia.,Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA. .,Genetics and Epigenetics Graduate Program, The University of Texas MD Anderson Cancer Center, UT Health Graduate School of Biomedical Sciences, Houston, TX, 77030, USA. .,Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, 11794, USA. .,Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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35
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Firman T, Amgalan A, Ghosh K. Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network. J Phys Chem B 2019; 123:343-355. [PMID: 30507199 DOI: 10.1021/acs.jpcb.8b07465] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Single-cell protein expression time trajectories provide rich temporal data quantifying cellular variability and its role in dictating fitness. However, theoretical models to analyze and fully extract information from these measurements remain limited for three reasons: (i) gene expression profiles are noisy, rendering models of averages inapplicable, (ii) experiments typically measure only a few protein species while leaving other molecular actors-necessary to build traditional bottom-up models-unnoticed, and (iii) measured data are in fluorescence, not particle number. We recently addressed these challenges in an alternate top-down approach using the principle of Maximum Caliber (MaxCal) to model genetic switches with one and two protein species. In the present work we address scalability and broader applicability of MaxCal by extending to a three-gene (A, B, C) feedback network that exhibits oscillation, commonly known as the repressilator. We test MaxCal's inferential power by using synthetic data of noisy protein number time traces-serving as a proxy for experimental data-generated from a known underlying model. We notice that the minimal MaxCal model-accounting for production, degradation, and only one type of symmetric coupling between all three species-reasonably infers several underlying features of the circuit such as the effective production rate, degradation rate, frequency of oscillation, and protein number distribution. Next, we build models of higher complexity including different levels of coupling between A, B, and C and rigorously assess their relative performance. While the minimal model (with four parameters) performs remarkably well, we note that the most complex model (with six parameters) allowing all possible forms of crosstalk between A, B, and C slightly improves prediction of rates, but avoids ad hoc assumption of all the other models. It is also the model of choice based on Bayesian information criteria. We further analyzed time trajectories in arbitrary fluorescence (using synthetic trajectories) to mimic realistic data. We conclude that even with a three-protein system including both fluorescence noise and intrinsic gene expression fluctuations, MaxCal can faithfully infer underlying details of the network, opening future directions to model other network motifs with many species.
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36
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Charlebois DA, Balázsi G. Modeling cell population dynamics. In Silico Biol 2019; 13:21-39. [PMID: 30562900 PMCID: PMC6598210 DOI: 10.3233/isb-180470] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 09/13/2018] [Accepted: 10/16/2018] [Indexed: 12/27/2022]
Abstract
Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.
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Affiliation(s)
- Daniel A. Charlebois
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA
- Department of Physics, University of Alberta, Edmonton, AB, Canada
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA
- Department of Biomedical Engineering, Stony Brook University, NY, USA
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37
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Abstract
Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.
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Affiliation(s)
- Daniel A Charlebois
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA.,Department of Physics, University of Alberta, Edmonton, AB, Canada
| | - Gábor Balázsi
- The Louis and Beatrice Laufer Center for Physical and Quantitative Biology, Stony Brook University, NY, USA.,Department of Biomedical Engineering, Stony Brook University, NY, USA
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38
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Firman T, Amgalan A, Ghosh K. Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network. J Phys Chem A 2018. [DOI: 10.1021/acs.jpca.8b07465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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39
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Meredith HR, Andreani V, Ma HR, Lopatkin AJ, Lee AJ, Anderson DJ, Batt G, You L. Applying ecological resistance and resilience to dissect bacterial antibiotic responses. SCIENCE ADVANCES 2018; 4:eaau1873. [PMID: 30525104 PMCID: PMC6281428 DOI: 10.1126/sciadv.aau1873] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 11/07/2018] [Indexed: 05/14/2023]
Abstract
An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a notable change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, although their interpretations are often subject to debate. Here, we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended-spectrum β-lactamases (ESBLs) when treated by a β-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a β-lactam and a β-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.
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Affiliation(s)
| | - Virgile Andreani
- Inria Saclay–Île-de-France, Palaiseau, France
- Institut Pasteur, Paris, France
| | - Helena R. Ma
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Anna J. Lee
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Deverick J. Anderson
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Duke Center for Antimicrobial Stewardship and Infection Prevention, Duke University School of Medicine, Durham, NC, USA
| | - Gregory Batt
- Inria Saclay–Île-de-France, Palaiseau, France
- Institut Pasteur, Paris, France
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
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Abstract
Fluctuating environments such as changes in ambient temperature represent a fundamental challenge to life. Cells must protect gene networks that protect them from such stresses, making it difficult to understand how temperature affects gene network function in general. Here, we focus on single genes and small synthetic network modules to reveal four key effects of nonoptimal temperatures at different biological scales: (i) a cell fate choice between arrest and resistance, (ii) slower growth rates, (iii) Arrhenius reaction rates, and (iv) protein structure changes. We develop a multiscale computational modeling framework that captures and predicts all of these effects. These findings promote our understanding of how temperature affects living systems and enables more robust cellular engineering for real-world applications. Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae. We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.
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Székely T, Balázsi G. Beyond Promoters: How Genes Tweak Their Own Expression. Trends Genet 2018; 34:733-735. [PMID: 30119990 DOI: 10.1016/j.tig.2018.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 07/17/2018] [Indexed: 10/28/2022]
Abstract
The correct expression of genes is vital for cells to function. Schikora-Tamarit et al. show that, in addition to obeying their promoters, most genes can modulate their own expression by either buffering or amplification. This could help to avoid costly overexpression of proteins.
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Affiliation(s)
- Tamás Székely
- 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.
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Scott LH, Mathews JC, Flematti GR, Filipovska A, Rackham O. An Artificial Yeast Genetic Circuit Enables Deep Mutational Scanning of an Antimicrobial Resistance Protein. ACS Synth Biol 2018; 7:1907-1917. [PMID: 29979580 DOI: 10.1021/acssynbio.8b00121] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Understanding the molecular mechanisms underlying antibiotic resistance requires concerted efforts in enzymology and medicinal chemistry. Here we describe a new synthetic biology approach to antibiotic development, where the presence of tetracycline antibiotics is linked to a life-death selection in Saccharomyces cerevisiae. This artificial genetic circuit allowed the deep mutational scanning of the tetracycline inactivating enzyme TetX, revealing key functional residues. We used both positive and negative selections to confirm the importance of different residues for TetX activity, and profiled activity hotspots for different tetracyclines to reveal substrate-specific activity determinants. We found that precise positioning of FAD and hydrophobic shielding of the tetracycline are critical for enzymatic inactivation of doxycycline. However, positioning of FAD is suboptimal in the case of anhydrotetracycline, potentially explaining its comparatively poor degradation and potential as an inhibitor for this family of enzymes. By combining artificial genetic circuits whose function can be modulated by antimicrobial resistance determinants, we establish a framework to select for the next generation of antibiotics.
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Affiliation(s)
- Louis H. Scott
- Harry Perkins Institute of Medical Research and Centre for Medical Research, The University of Western Australia, Nedlands 6009, Australia
| | - James C. Mathews
- Harry Perkins Institute of Medical Research and Centre for Medical Research, The University of Western Australia, Nedlands 6009, Australia
| | - Gavin R. Flematti
- School of Molecular Sciences, The University of Western Australia, Crawley 6009, Australia
| | - Aleksandra Filipovska
- Harry Perkins Institute of Medical Research and Centre for Medical Research, The University of Western Australia, Nedlands 6009, Australia
- School of Molecular Sciences, The University of Western Australia, Crawley 6009, Australia
| | - Oliver Rackham
- Harry Perkins Institute of Medical Research and Centre for Medical Research, The University of Western Australia, Nedlands 6009, Australia
- School of Molecular Sciences, The University of Western Australia, Crawley 6009, Australia
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Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat Biotechnol 2018; 36:614-623. [PMID: 29889214 PMCID: PMC6035058 DOI: 10.1038/nbt.4151] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 04/13/2018] [Indexed: 12/30/2022]
Abstract
Precise control over microbial cell growth conditions could enable detection of minute phenotypic changes, which would improve our understanding of how genotypes are shaped by adaptive selection. Although automated cell-culture systems such as bioreactors offer strict control over liquid culture conditions, they often do not scale to high-throughput or require cumbersome redesign to alter growth conditions. We report the design and validation of eVOLVER, a scalable DIY framework that can be configured to carry out high-throughput growth experiments in molecular evolution, systems biology, and microbiology. We perform high-throughput evolution of yeast across systematically varied population density niches to show how eVOLVER can precisely characterize adaptive niches. We describe growth selection using time-varying temperature programs on a genome-wide yeast knockout library to identify strains with altered sensitivity to changes in temperature magnitude or frequency. Inspired by large-scale integration of electronics and microfluidics, we also demonstrate millifluidic multiplexing modules that enable multiplexed media routing, cleaning, vial-to-vial transfers and automated yeast mating.
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44
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von Bronk B, Götz A, Opitz M. Complex microbial systems across different levels of description. Phys Biol 2018; 15:051002. [PMID: 29757151 DOI: 10.1088/1478-3975/aac473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Complex biological systems offer a variety of interesting phenomena at the different physical scales. With increasing abstraction, details of the microscopic scales can often be extrapolated to average or typical macroscopic properties. However, emergent properties and cross-scale interactions can impede naïve abstractions and necessitate comprehensive investigations of these complex systems. In this review paper, we focus on microbial communities, and first, summarize a general hierarchy of relevant scales and description levels to understand these complex systems: (1) genetic networks, (2) single cells, (3) populations, and (4) emergent multi-cellular properties. Second, we employ two illustrating examples, microbial competition and biofilm formation, to elucidate how cross-scale interactions and emergent properties enrich the observed multi-cellular behavior in these systems. Finally, we conclude with pointing out the necessity of multi-scale investigations to understand complex biological systems and discuss recent investigations.
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Affiliation(s)
- Benedikt von Bronk
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, D-80539 Munich, Germany
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45
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Dar RD, Weiss R. Perspective: Engineering noise in biological systems towards predictive stochastic design. APL Bioeng 2018; 2:020901. [PMID: 31069294 PMCID: PMC6481707 DOI: 10.1063/1.5025033] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Accepted: 04/16/2018] [Indexed: 01/24/2023] Open
Abstract
Significant progress has been made towards engineering both single-cell and multi-cellular systems through a combination of synthetic and systems biology, nanobiotechnology, pharmaceutical science, and computational approaches. However, our ability to engineer systems that begin to approach the complexity of natural pathways is severely limited by important challenges, e.g. due to noise, or the fluctuations in gene expression and molecular species at multiple scales (e.g. both intra- and inter-cellular fluctuations). This barrier to engineering requires that biological noise be recognized as a design element with fundamentals that can be actively controlled. Here we highlight studies of an emerging discipline that collectively strives to engineer noise towards predictive stochastic design using interdisciplinary approaches at multiple-scales in diverse living systems.
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Affiliation(s)
- Roy D. Dar
- Author to whom correspondence should be addressed:
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Rodrigo G. Post-transcriptional bursting in genes regulated by small RNA molecules. Phys Rev E 2018; 97:032401. [PMID: 29776125 DOI: 10.1103/physreve.97.032401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Indexed: 11/07/2022]
Abstract
Gene expression programs in living cells are highly dynamic due to spatiotemporal molecular signaling and inherent biochemical stochasticity. Here we study a mechanism based on molecule-to-molecule variability at the RNA level for the generation of bursts of protein production, which can lead to heterogeneity in a cell population. We develop a mathematical framework to show numerically and analytically that genes regulated post transcriptionally by small RNA molecules can exhibit such bursts due to different states of translation activity (on or off), mostly revealed in a regime of few molecules. We exploit this framework to compare transcriptional and post-transcriptional bursting and also to illustrate how to tune the resulting protein distribution with additional post-transcriptional regulations. Moreover, because RNA-RNA interactions are predictable with an energy model, we define the kinetic constants of on-off switching as functions of the two characteristic free-energy differences of the system, activation and formation, with a nonequilibrium scheme. Overall, post-transcriptional bursting represents a distinctive principle linking gene regulation to gene expression noise, which highlights the importance of the RNA layer beyond the simple information transfer paradigm and significantly contributes to the understanding of the intracellular processes from a first-principles perspective.
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Affiliation(s)
- Guillermo Rodrigo
- Institute for Integrative Systems Biology, CSIC, Universidad de Valencia, 46980 Paterna, Spain
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Norred SE, Caveney PM, Chauhan G, Collier LK, Collier CP, Abel SM, Simpson ML. Macromolecular Crowding Induces Spatial Correlations That Control Gene Expression Bursting Patterns. ACS Synth Biol 2018; 7:1251-1258. [PMID: 29687993 DOI: 10.1021/acssynbio.8b00139] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Recent superresolution microscopy studies in E. coli demonstrate that the cytoplasm has highly variable local concentrations where macromolecular crowding plays a central role in establishing membrane-less compartmentalization. This spatial inhomogeneity significantly influences molecular transport and association processes central to gene expression. Yet, little is known about how macromolecular crowding influences gene expression bursting-the episodic process where mRNA and proteins are produced in bursts. Here, we simultaneously measured mRNA and protein reporters in cell-free systems, showing that macromolecular crowding decoupled the well-known relationship between fluctuations in the protein population (noise) and mRNA population statistics. Crowded environments led to a 10-fold increase in protein noise even though there were only modest changes in the mRNA population and fluctuations. Instead, cell-like macromolecular crowding created an inhomogeneous spatial distribution of mRNA ("spatial noise") that led to large variability in the protein production burst size. As a result, the mRNA spatial noise created large temporal fluctuations in the protein population. These results highlight the interplay between macromolecular crowding, spatial inhomogeneities, and the resulting dynamics of gene expression, and provide insights into using these organizational principles in both cell-based and cell-free synthetic biology.
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Affiliation(s)
- S Elizabeth Norred
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
- Bredesen Center for Interdisciplinary Research and Graduate Education , University of Tennessee Knoxville and Oak Ridge National Laboratory , Knoxville , Tennessee 37996 , United States
| | - Patrick M Caveney
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
- Bredesen Center for Interdisciplinary Research and Graduate Education , University of Tennessee Knoxville and Oak Ridge National Laboratory , Knoxville , Tennessee 37996 , United States
| | - Gaurav Chauhan
- Chemical and Biomolecular Engineering Department , University of Tennessee Knoxville , Knoxville , Tennessee 37996 , United States
| | - Lauren K Collier
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - C Patrick Collier
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
| | - Steven M Abel
- Chemical and Biomolecular Engineering Department , University of Tennessee Knoxville , Knoxville , Tennessee 37996 , United States
| | - Michael L Simpson
- Center for Nanophase Materials Sciences , Oak Ridge National Laboratory , Oak Ridge , Tennessee 37831 , United States
- Bredesen Center for Interdisciplinary Research and Graduate Education , University of Tennessee Knoxville and Oak Ridge National Laboratory , Knoxville , Tennessee 37996 , United States
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Lee AJ, Wang S, Meredith HR, Zhuang B, Dai Z, You L. Robust, linear correlations between growth rates and β-lactam-mediated lysis rates. Proc Natl Acad Sci U S A 2018; 115:4069-4074. [PMID: 29610312 PMCID: PMC5910845 DOI: 10.1073/pnas.1719504115] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
It is widely acknowledged that faster-growing bacteria are killed faster by β-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against β-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics. Due to the lack of thorough quantification, however, it is unclear whether and to what extent the bacterial growth rate can predict the lysis rate upon β-lactam treatment under diverse conditions. Enabled by experimental automation, here we measured >1,000 growth/killing curves for eight combinations of antibiotics and bacterial species and strains, including clinical isolates of bacterial pathogens. We found that the lysis rate of a bacterial population linearly depends on the instantaneous growth rate of the population, regardless of how the latter is modulated. We further demonstrate that this predictive power at the population level can be explained by accounting for bacterial responses to the antibiotic treatment by single cells. This linear dependence of the lysis rate on the growth rate represents a dynamic signature associated with each bacterium-antibiotic pair and serves as the quantitative foundation for designing combination antibiotic therapy and predicting the population-structure change in a population with mixed phenotypes.
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Affiliation(s)
- Anna J Lee
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Shangying Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Hannah R Meredith
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Bihan Zhuang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Zhuojun Dai
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Lingchong You
- Department of Biomedical Engineering, Duke University, Durham, NC 27708;
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC 27710
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de la Cruz R, Perez-Carrasco R, Guerrero P, Alarcon T, Page KM. Minimum Action Path Theory Reveals the Details of Stochastic Transitions Out of Oscillatory States. PHYSICAL REVIEW LETTERS 2018; 120:128102. [PMID: 29694079 DOI: 10.1103/physrevlett.120.128102] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Revised: 12/13/2017] [Indexed: 06/08/2023]
Abstract
Cell state determination is the outcome of intrinsically stochastic biochemical reactions. Transitions between such states are studied as noise-driven escape problems in the chemical species space. Escape can occur via multiple possible multidimensional paths, with probabilities depending nonlocally on the noise. Here we characterize the escape from an oscillatory biochemical state by minimizing the Freidlin-Wentzell action, deriving from it the stochastic spiral exit path from the limit cycle. We also use the minimized action to infer the escape time probability density function.
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Affiliation(s)
- Roberto de la Cruz
- Centre de Recerca Matemàtica, Edifici C, Campus de Bellaterra, 08193 Bellaterra (Barcelona), Spain and Departament de Matemàtiques, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain
| | - Ruben Perez-Carrasco
- Department of Mathematics, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Pilar Guerrero
- Department of Mathematics, University College London, Gower Street, London WC1E 6BT, United Kingdom
| | - Tomas Alarcon
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain
- Centre de Recerca Matemàtica, Edifici C, Campus de Bellaterra, 08193 Bellaterra (Barcelona), Spain
- Departament de Matemàtiques, Universitat Autònoma de Barcelona, 08193 Bellaterra (Barcelona), Spain and Barcelona Graduate School of Mathematics (BGSMath), Barcelona, Spain
| | - Karen M Page
- Department of Mathematics, University College London, Gower Street, London WC1E 6BT, United Kingdom
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Firman T, Wedekind S, McMorrow TJ, Ghosh K. Maximum Caliber Can Characterize Genetic Switches with Multiple Hidden Species. J Phys Chem B 2018; 122:5666-5677. [PMID: 29406749 DOI: 10.1021/acs.jpcb.7b12251] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Gene networks with feedback often involve interactions between multiple species of biomolecules, much more than experiments can actually monitor. Coupled with this is the challenge that experiments often measure gene expression in noisy fluorescence instead of protein numbers. How do we infer biophysical information and characterize the underlying circuits from this limited and convoluted data? We address this by building stochastic models using the principle of Maximum Caliber (MaxCal). MaxCal uses the basic information on synthesis, degradation, and feedback-without invoking any other auxiliary species and ad hoc reactions-to generate stochastic trajectories similar to those typically measured in experiments. MaxCal in conjunction with Maximum Likelihood (ML) can infer parameters of the model using fluctuating trajectories of protein expression over time. We demonstrate the success of the MaxCal + ML methodology using synthetic data generated from known circuits of different genetic switches: (i) a single-gene autoactivating circuit involving five species (including mRNA), (ii) a mutually repressing two-gene circuit (toggle switch) with seven species (including mRNA) considering stochastic time traces of two proteins, and (iii) the same toggle switch circuit considering stochastic time traces of only one of the two proteins. To further challenge the MaxCal + ML inference scheme, we repeat our analysis for the second and third scenario with traces expressed in noisy fluorescence instead of protein number to closely mimic typical experiments. We show that, for all of these models with increasing complexity and obfuscation, the minimal model of MaxCal is still able to capture the fluctuations of the trajectory and infer basic underlying rate parameters when benchmarked against the known values used to generate the synthetic data. Importantly, the model also yields an effective feedback parameter that can be used to quantify interactions within these circuits. These applications show the promise of MaxCal's ability to characterize circuits with limited data, and its utility to better understand evolution and advance design strategies for specific functions.
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Affiliation(s)
- Taylor Firman
- Molecular and Cellular Biophysics , University of Denver , Denver , Colorado 80209 , United States
| | - Stephen Wedekind
- Department of Physics and Astronomy , University of Denver , Denver , Colorado 80209 , United States
| | - T J McMorrow
- Department of Physics and Astronomy , University of Denver , Denver , Colorado 80209 , United States
| | - Kingshuk Ghosh
- Department of Physics and Astronomy , University of Denver , Denver , Colorado 80209 , United States
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