1
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Scarinci G, Ariens JL, Angelidou G, Schmidt S, Glatter T, Paczia N, Sourjik V. Enhanced metabolic entanglement emerges during the evolution of an interkingdom microbial community. Nat Commun 2024; 15:7238. [PMID: 39174531 PMCID: PMC11341674 DOI: 10.1038/s41467-024-51702-1] [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: 03/01/2024] [Accepted: 08/15/2024] [Indexed: 08/24/2024] Open
Abstract
While different stages of mutualism can be observed in natural communities, the dynamics and mechanisms underlying the gradual erosion of independence of the initially autonomous organisms are not yet fully understood. In this study, by conducting the laboratory evolution on an engineered microbial community, we reproduce and molecularly track the stepwise progression towards enhanced partner entanglement. We observe that the evolution of the community both strengthens the existing metabolic interactions and leads to the emergence of de novo interdependence between partners for nitrogen metabolism, which is a common feature of natural symbiotic interactions. Selection for enhanced metabolic entanglement during the community evolution repeatedly occurred indirectly, via pleiotropies and trade-offs within cellular regulatory networks, and with no evidence of group selection. The indirect positive selection of metabolic dependencies between microbial community members, which results from the direct selection of other coupled traits in the same regulatory network, may therefore be a common but underappreciated driving force guiding the evolution of natural mutualistic communities.
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Affiliation(s)
- Giovanni Scarinci
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
| | - Jan-Luca Ariens
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
| | | | - Sebastian Schmidt
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
| | - Timo Glatter
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nicole Paczia
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Victor Sourjik
- Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
- Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany.
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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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Feng H, Li F, Wang T, Xing XH, Zeng AP, Zhang C. Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision. SCIENCE ADVANCES 2023; 9:eadg5296. [PMID: 37939173 PMCID: PMC10631719 DOI: 10.1126/sciadv.adg5296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.
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Affiliation(s)
- Huibao Feng
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Fan Li
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Tianmin Wang
- Tsinghua-Peking Center for Life Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xin-hui Xing
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - An-ping Zeng
- Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology, Hamburg 21073, Germany
- Center of Synthetic Biology and Integrated Bioengineering, School of Engineering, Westlake University, Hangzhou 310024, China
| | - Chong Zhang
- MOE Key Laboratory for Industrial Biocatalysis, Institute of Biochemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
- Center for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China
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4
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Sarkar S, Rammohan J. Nearly maximal information gain due to time integration in central dogma reactions. iScience 2023; 26:106767. [PMID: 37235057 PMCID: PMC10206154 DOI: 10.1016/j.isci.2023.106767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/21/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we demonstrate that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain because of time integration while also keeping the loss because of stochasticity in translation relatively low (<0.5 bits).
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Affiliation(s)
- Swarnavo Sarkar
- National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Jayan Rammohan
- National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
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5
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Rammohan J, Sarkar S, Ross D. Single-cell measurement quality in bits. PLoS One 2022; 17:e0269272. [PMID: 35951522 PMCID: PMC9371318 DOI: 10.1371/journal.pone.0269272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 05/17/2022] [Indexed: 11/18/2022] Open
Abstract
Single-cell measurements have revolutionized our understanding of heterogeneity in cellular response. However, there is no universally comparable way to assess single-cell measurement quality. Here, we show how information theory can be used to assess and compare single-cell measurement quality in bits, which provides a universally comparable metric for information content. We anticipate that the experimental and theoretical approaches we show here will generally enable comparisons of quality between any single-cell measurement methods.
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Affiliation(s)
- Jayan Rammohan
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Swarnavo Sarkar
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- * E-mail:
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6
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Okubo K, Kaneko K. Heterosis of fitness and phenotypic variance in the evolution of a diploid gene regulatory network. PNAS NEXUS 2022; 1:pgac097. [PMID: 36741431 PMCID: PMC9896930 DOI: 10.1093/pnasnexus/pgac097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 06/24/2022] [Indexed: 02/07/2023]
Abstract
Heterosis describes the phenomenon, whereby a hybrid population has higher fitness than an inbred population, which has previously been explained by either Mendelian dominance or overdominance under the general assumption of a simple genotype-phenotype relationship. However, recent studies have demonstrated that genes interact through a complex gene regulatory network (GRN). Furthermore, phenotypic variance is reportedly lower for heterozygotes, and the origin of such variance-related heterosis remains elusive. Therefore, a theoretical analysis linking heterosis to GRN evolution and stochastic gene expression dynamics is required. Here, we investigated heterosis related to fitness and phenotypic variance in a system with interacting genes by numerically evolving diploid GRNs. According to the results, the heterozygote population exhibited higher fitness than the homozygote population, indicating fitness-related heterosis resulting from evolution. In addition, the heterozygote population exhibited lower noise-related phenotypic variance in expression levels than the homozygous population, implying that the heterozygote population is more robust to noise. Furthermore, the distribution of the ratio of heterozygote phenotypic variance to homozygote phenotypic variance exhibited quantitative similarity with previous experimental results. By applying dominance and differential gene expression rather than only a single gene expression model, we confirmed the correlation between heterosis and differential gene expression. We explain our results by proposing that the convex high-fitness region is evolutionarily shaped in the genetic space to gain noise robustness under genetic mixing through sexual reproduction. These results provide new insights into the effects of GRNs on variance-related heterosis and differential gene expression.
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Affiliation(s)
- Kenji Okubo
- Research Center for Integrative Evolutionary Science, the Graduate University for Advanced Studies, SOKENDAI, Hayama, Kanagawa, 240-0193, Japan
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7
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Gerhardt KP, Rao SD, Olson EJ, Igoshin OA, Tabor JJ. Independent control of mean and noise by convolution of gene expression distributions. Nat Commun 2021; 12:6957. [PMID: 34845228 PMCID: PMC8630168 DOI: 10.1038/s41467-021-27070-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 11/03/2021] [Indexed: 11/28/2022] Open
Abstract
Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.
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Affiliation(s)
- Karl P Gerhardt
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Satyajit D Rao
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Evan J Olson
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Oleg A Igoshin
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Biosciences, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Center for Theoretical Biophysics, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Jeffrey J Tabor
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
- Department of Biosciences, Rice University, 6100 Main Street, Houston, TX, 77005, USA.
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8
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Liu Y, Huang Y, Lu R, Xin F, Liu G. Synthetic biology applications of the yeast mating signal pathway. Trends Biotechnol 2021; 40:620-631. [PMID: 34666896 DOI: 10.1016/j.tibtech.2021.09.007] [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: 08/09/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022]
Abstract
Cell fusion is a fundamental biological process that is involved in the development of most eukaryotic organisms. During the fusion process in Saccharomyces cerevisiae, cells respond to pheromones to trigger the MAPK (mitogen-activated protein kinase) cascade to initiate mating, followed by polarization, cell-wall remodeling, membrane fusion, and karyogamy. We highlight the applications of the yeast mating signal pathway in promoter engineering for tuning the expression of output genes, as well as in metabolic engineering for decoupling growth and metabolism, biosensors for sensitive detection and signal amplification, genetic circuits for programmable biological functionalities, and artificial consortia for cell-cell communication. Strategies such as exploiting rational engineering of modular circuits and optimizing the reproductive pathway to precisely maneuver physiological events have implications for scientific research and industrial development.
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Affiliation(s)
- Ying Liu
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Yuxin Huang
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Ran Lu
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Fengxue Xin
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China
| | - Guannan Liu
- College of Biological and Pharmaceutical Engineering, Nanjing Tech University, Jiangsu Province, China; Jiangsu Synergetic Innovation Center for Advanced Bio-Manufacture, Nanjing Tech University, Jiangsu Province, China.
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9
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Rammohan J, Lund SP, Alperovich N, Paralanov V, Strychalski EA, Ross D. Comparison of bias and resolvability in single-cell and single-transcript methods. Commun Biol 2021; 4:659. [PMID: 34079048 PMCID: PMC8172639 DOI: 10.1038/s42003-021-02138-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 04/16/2021] [Indexed: 11/17/2022] Open
Abstract
Single-cell and single-transcript measurement methods have elevated our ability to understand and engineer biological systems. However, defining and comparing performance between methods remains a challenge, in part due to the confounding effects of experimental variability. Here, we propose a generalizable framework for performing multiple methods in parallel using split samples, so that experimental variability is shared between methods. We demonstrate the utility of this framework by performing 12 different methods in parallel to measure the same underlying reference system for cellular response. We compare method performance using quantitative evaluations of bias and resolvability. We attribute differences in method performance to steps along the measurement process such as sample preparation, signal detection, and choice of measurand. Finally, we demonstrate how this framework can be used to benchmark different methods for single-transcript detection. The framework we present here provides a practical way to compare performance of any methods.
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Affiliation(s)
- Jayan Rammohan
- National Institute of Standards and Technology, Gaithersburg, MD, USA.
| | - Steven P Lund
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | - Vanya Paralanov
- National Institute of Standards and Technology, Gaithersburg, MD, USA
| | | | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, USA.
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10
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Durmusoglu D, Al’Abri IS, Collins SP, Cheng J, Eroglu A, Beisel CL, Crook N. In Situ Biomanufacturing of Small Molecules in the Mammalian Gut by Probiotic Saccharomyces boulardii. ACS Synth Biol 2021; 10:1039-1052. [PMID: 33843197 DOI: 10.1021/acssynbio.0c00562] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Saccharomyces boulardii is a probiotic yeast that exhibits rapid growth at 37 °C, is easy to transform, and can produce therapeutic proteins in the gut. To establish its ability to produce small molecules encoded by multigene pathways, we measured the amount and variance in protein expression enabled by promoters, terminators, selective markers, and copy number control elements. We next demonstrated efficient (>95%) CRISPR-mediated genome editing in this strain, allowing us to probe engineered gene expression across different genomic sites. We leveraged these strategies to assemble pathways enabling a wide range of vitamin precursor (β-carotene) and drug (violacein) titers. We found that S. boulardii colonizes germ-free mice stably for over 30 days and competes for niche space with commensal microbes, exhibiting short (1-2 day) gut residence times in conventional and antibiotic-treated mice. Using these tools, we enabled β-carotene synthesis (194 μg total) in the germ-free mouse gut over 14 days, estimating that the total mass of additional β-carotene recovered in feces was 56-fold higher than the β-carotene present in the initial probiotic dose. This work quantifies heterologous small molecule production titers by S. boulardii living in the mammalian gut and provides a set of tools for modulating these titers.
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Affiliation(s)
- Deniz Durmusoglu
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Ibrahim S. Al’Abri
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Scott P. Collins
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Junrui Cheng
- Plants for Human Health Institute, North Carolina State University, 600 Laureate Way, Room 3204, Kannapolis, North Carolina 28081, United States
| | - Abdulkerim Eroglu
- Plants for Human Health Institute, North Carolina State University, 600 Laureate Way, Room 3204, Kannapolis, North Carolina 28081, United States
- Department of Molecular and Structural Biochemistry, College of Agriculture and Life Sciences, North Carolina State University, 120 Broughton Drive, Room 351, Raleigh, North Carolina 27695-7622, United States
| | - Chase L. Beisel
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Centre for Infection Research (HZI), Würzburg 97080, Germany
| | - Nathan Crook
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
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11
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Bonny AR, Fonseca JP, Park JE, El-Samad H. Orthogonal control of mean and variability of endogenous genes in a human cell line. Nat Commun 2021; 12:292. [PMID: 33436569 PMCID: PMC7804932 DOI: 10.1038/s41467-020-20467-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022] Open
Abstract
Stochastic fluctuations at the transcriptional level contribute to isogenic cell-to-cell heterogeneity in mammalian cell populations. However, we still have no clear understanding of the repercussions of this heterogeneity, given the lack of tools to independently control mean expression and variability of a gene. Here, we engineer a synthetic circuit to modulate mean expression and heterogeneity of transgenes and endogenous human genes. The circuit, a Tunable Noise Rheostat (TuNR), consists of a transcriptional cascade of two inducible transcriptional activators, where the output mean and variance can be modulated by two orthogonal small molecule inputs. In this fashion, different combinations of the inputs can achieve the same mean but with different population variability. With TuNR, we achieve low basal expression, over 1000-fold expression of a transgene product, and up to 7-fold induction of the endogenous gene NGFR. Importantly, for the same mean expression level, we are able to establish varying degrees of heterogeneity in expression within an isogenic population, thereby decoupling gene expression noise from its mean. TuNR is therefore a modular tool that can be used in mammalian cells to enable direct interrogation of the implications of cell-to-cell variability.
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Affiliation(s)
- Alain R Bonny
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - João Pedro Fonseca
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Amyris Bio Products Portugal, Porto, Portugal
| | - Jesslyn E Park
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Hana El-Samad
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, 94158, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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12
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Guillemin A, Stumpf MPH. Noise and the molecular processes underlying cell fate decision-making. Phys Biol 2021; 18:011002. [PMID: 33181489 DOI: 10.1088/1478-3975/abc9d1] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Cell fate decision-making events involve the interplay of many molecular processes, ranging from signal transduction to genetic regulation, as well as a set of molecular and physiological feedback loops. Each aspect offers a rich field of investigation in its own right, but to understand the whole process, even in simple terms, we need to consider them together. Here we attempt to characterise this process by focussing on the roles of noise during cell fate decisions. We use a range of recent results to develop a view of the sequence of events by which a cell progresses from a pluripotent or multipotent to a differentiated state: chromatin organisation, transcription factor stoichiometry, and cellular signalling all change during this progression, and all shape cellular variability, which becomes maximal at the transition state.
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Affiliation(s)
- Anissa Guillemin
- School of BioSciences, University of Melbourne, Parkville, Australia
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13
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Sampaio NMV, Dunlop MJ. Functional roles of microbial cell-to-cell heterogeneity and emerging technologies for analysis and control. Curr Opin Microbiol 2020; 57:87-94. [PMID: 32919307 PMCID: PMC7722170 DOI: 10.1016/j.mib.2020.08.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 07/18/2020] [Accepted: 08/07/2020] [Indexed: 12/14/2022]
Abstract
Clonal cell populations often display significant cell-to-cell phenotypic heterogeneity, even when maintained under constant external conditions. This variability can result from the inherently stochastic nature of transcription and translation processes, which leads to varying numbers of transcripts and proteins per cell. Here, we showcase studies that reveal links between stochastic cellular events and biological functions in isogenic microbial populations. Then, we highlight emerging tools from engineering, computation, and synthetic and molecular biology that enable precise measurement, control, and analysis of gene expression noise in microorganisms. The capabilities offered by this sophisticated toolbox will shape future directions in the field and generate insight into the behavior of living systems at the single-cell level.
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Affiliation(s)
- Nadia Maria Vieira Sampaio
- Department of Biomedical Engineering, Boston University, Boston, MA, USA; Biological Design Center, Boston University, Boston, MA, USA
| | - Mary J Dunlop
- Department of Biomedical Engineering, Boston University, Boston, MA, USA; Biological Design Center, Boston University, Boston, MA, USA.
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14
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Wu F, Shim J, Gong T, Tan C. Orthogonal tuning of gene expression noise using CRISPR-Cas. Nucleic Acids Res 2020; 48:e76. [PMID: 32479612 PMCID: PMC7367181 DOI: 10.1093/nar/gkaa451] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 04/23/2020] [Accepted: 05/14/2020] [Indexed: 01/07/2023] Open
Abstract
The control of gene expression noise is important for improving drug treatment and the performance of synthetic biological systems. Previous work has tuned gene expression noise by changing the rate of transcription initiation, mRNA degradation, and mRNA translation. However, these methods are invasive: they require changes to the target genetic components. Here, we create an orthogonal system based on CRISPR-dCas9 to tune gene expression noise. Specifically, we modulate the gene expression noise of a reporter gene in Escherichia coli by incorporating CRISPR activation and repression (CRISPRar) simultaneously in a single cell. The CRISPRar uses a single dCas9 that recognizes two different single guide RNAs (sgRNA). We build a library of sgRNA variants with different expression activation and repression strengths. We find that expression noise and mean of a reporter gene can be tuned independently by CRISPRar. Our results suggest that the expression noise is tuned by the competition between two sgRNAs that modulate the binding of RNA polymerase to promoters. The CRISPRar may change how we tune expression noise at the genomic level. Our work has broad impacts on the study of gene functions, phenotypical heterogeneity, and genetic circuit control.
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Affiliation(s)
- Fan Wu
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
| | - Jiyoung Shim
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
| | - Ting Gong
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
| | - Cheemeng Tan
- Department of Biomedical Engineering, University of California Davis, Davis, CA 95616, USA
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15
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Karkaria BD, Treloar NJ, Barnes CP, Fedorec AJH. From Microbial Communities to Distributed Computing Systems. Front Bioeng Biotechnol 2020; 8:834. [PMID: 32793576 PMCID: PMC7387671 DOI: 10.3389/fbioe.2020.00834] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/29/2020] [Indexed: 12/15/2022] Open
Abstract
A distributed biological system can be defined as a system whose components are located in different subpopulations, which communicate and coordinate their actions through interpopulation messages and interactions. We see that distributed systems are pervasive in nature, performing computation across all scales, from microbial communities to a flock of birds. We often observe that information processing within communities exhibits a complexity far greater than any single organism. Synthetic biology is an area of research which aims to design and build synthetic biological machines from biological parts to perform a defined function, in a manner similar to the engineering disciplines. However, the field has reached a bottleneck in the complexity of the genetic networks that we can implement using monocultures, facing constraints from metabolic burden and genetic interference. This makes building distributed biological systems an attractive prospect for synthetic biology that would alleviate these constraints and allow us to expand the applications of our systems into areas including complex biosensing and diagnostic tools, bioprocess control and the monitoring of industrial processes. In this review we will discuss the fundamental limitations we face when engineering functionality with a monoculture, and the key areas where distributed systems can provide an advantage. We cite evidence from natural systems that support arguments in favor of distributed systems to overcome the limitations of monocultures. Following this we conduct a comprehensive overview of the synthetic communities that have been built to date, and the components that have been used. The potential computational capabilities of communities are discussed, along with some of the applications that these will be useful for. We discuss some of the challenges with building co-cultures, including the problem of competitive exclusion and maintenance of desired community composition. Finally, we assess computational frameworks currently available to aide in the design of microbial communities and identify areas where we lack the necessary tools.
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Affiliation(s)
- Behzad D. Karkaria
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Neythen J. Treloar
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
| | - Chris P. Barnes
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
- UCL Genetics Institute, University College London, London, United Kingdom
| | - Alex J. H. Fedorec
- Department of Cell and Developmental Biology, University College London, London, United Kingdom
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16
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Design of a MAPK signalling cascade balances energetic cost versus accuracy of information transmission. Nat Commun 2020; 11:3494. [PMID: 32661402 PMCID: PMC7359329 DOI: 10.1038/s41467-020-17276-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 06/22/2020] [Indexed: 01/30/2023] Open
Abstract
Cellular processes are inherently noisy, and the selection for accurate responses in presence of noise has likely shaped signalling networks. Here, we investigate the trade-off between accuracy of information transmission and its energetic cost for a mitogen-activated protein kinase (MAPK) signalling cascade. Our analysis of the pheromone response pathway of budding yeast suggests that dose-dependent induction of the negative transcriptional feedbacks in this network maximizes the information per unit energetic cost, rather than the information transmission capacity itself. We further demonstrate that futile cycling of MAPK phosphorylation and dephosphorylation has a measurable effect on growth fitness, with energy dissipation within the signalling cascade thus likely being subject to evolutionary selection. Considering optimization of accuracy versus the energetic cost of information processing, a concept well established in physics and engineering, may thus offer a general framework to understand the regulatory design of cellular signalling systems. Cellular signalling networks provide information to the cell, but the trade-off between accuracy of information transfer and energetic cost of doing so has not been assessed. Here, the authors investigate a MAPK signalling cascade in budding yeast and find that information is maximised per unit energetic cost.
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17
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Naseri G, Koffas MAG. Application of combinatorial optimization strategies in synthetic biology. Nat Commun 2020; 11:2446. [PMID: 32415065 PMCID: PMC7229011 DOI: 10.1038/s41467-020-16175-y] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 04/15/2020] [Indexed: 12/26/2022] Open
Abstract
In the first wave of synthetic biology, genetic elements, combined into simple circuits, are used to control individual cellular functions. In the second wave of synthetic biology, the simple circuits, combined into complex circuits, form systems-level functions. However, efforts to construct complex circuits are often impeded by our limited knowledge of the optimal combination of individual circuits. For example, a fundamental question in most metabolic engineering projects is the optimal level of enzymes for maximizing the output. To address this point, combinatorial optimization approaches have been established, allowing automatic optimization without prior knowledge of the best combination of expression levels of individual genes. This review focuses on current combinatorial optimization methods and emerging technologies facilitating their applications.
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Affiliation(s)
- Gita Naseri
- Institut für Chemie, Humboldt Universität zu Berlin, 12489, Berlin, Germany.
| | - Mattheos A G Koffas
- Center for Biotechnology, Rensselaer Polytechnic Institute, Troy, NY, USA.
- Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA.
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18
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Joshi YJ, Jawale YK, Athale CA. Modeling the tunability of the dual-feedback genetic oscillator. Phys Rev E 2020; 101:012417. [PMID: 32069648 DOI: 10.1103/physreve.101.012417] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Indexed: 11/07/2022]
Abstract
Oscillatory gene circuits are ubiquitous to biology and are involved in fundamental processes of cell cycle, circadian rhythms, and developmental systems. The synthesis of small, non-natural oscillatory genetic circuits has been increasingly used to test the fundamental principles of genetic network dynamics. While the "repressilator" was used to first demonstrate the proof of principle, a more recently developed dual-feedback, fast, tunable genetic oscillator has demonstrated a greater degree of robustness and control over oscillatory behavior by combining positive- and negative-feedback loops. This oscillator, combining lacI (negative-) and araC (positive-) feedback loops, was, however, modeled using multiple layers of differential equations to capture the molecular complexity of regulation, in order to explain the experimentally measured oscillations. In the search for design principles of such minimal oscillatory circuits, we have developed a reduced model of this dual-feedback loop oscillator consisting of just six differential equations, two of which are delay differential equations. The delay term is optimized, as the only free parameter, to fit the experimental dynamics of the oscillator period and amplitude tunability by the two inducers isopropyl β-D-1-thiogalactopyranoside (IPTG) and arabinose. We proceed to use our reduced and experimentally validated model to redesign the network by comparing the effect of asymmetry in gene expression at the level of (a) DNA copy numbers and the rates of (b) mRNA translation and (c) degradation, since experimental and theoretical work had predicted a need for an asymmetry in the copy numbers of activator (araC) and repressor (lacI) genes encoded on plasmids. We confirm that the minimal period of the oscillator is sensitive to DNA copy number asymmetry, and can demonstrate that while the asymmetry in the translation rate has an identical effect as the plasmid copy numbers, modulating the asymmetry in mRNA degradation can improve the tunability of the period and amplitude of the oscillator. Thus, our model predicts control at the level of translation can be used to redesign such networks, for improved tunability, while at the same time making the network robust to replication "noise" and the effects of the host cell cycle. Thus, our model predicts experimentally testable principles to redesign a potentially more robust oscillatory genetic network.
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Affiliation(s)
- Yash J Joshi
- Division of Biology, IISER Pune, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
| | - Yash K Jawale
- Division of Biology, IISER Pune, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
| | - Chaitanya A Athale
- Division of Biology, IISER Pune, Dr. Homi Bhabha Road, Pashan, Pune 411008, India
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19
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Goetz A, Mader A, von Bronk B, Weiss AS, Opitz M. Gene expression noise in a complex artificial toxin expression system. PLoS One 2020; 15:e0227249. [PMID: 31961890 PMCID: PMC6974158 DOI: 10.1371/journal.pone.0227249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 12/16/2019] [Indexed: 01/29/2023] Open
Abstract
Gene expression is an intrinsically stochastic process. Fluctuations in transcription and translation lead to cell-to-cell variations in mRNA and protein levels affecting cellular function and cell fate. Here, using fluorescence time-lapse microscopy, we quantify noise dynamics in an artificial operon in Escherichia coli, which is based on the native operon of ColicinE2, a toxin. In the natural system, toxin expression is controlled by a complex regulatory network; upon induction of the bacterial SOS response, ColicinE2 is produced (cea gene) and released (cel gene) by cell lysis. Using this ColicinE2-based operon, we demonstrate that upon induction of the SOS response noise of cells expressing the operon is significantly lower for the (mainly) transcriptionally regulated gene cea compared to the additionally post-transcriptionally regulated gene cel. Likewise, we find that mutations affecting the transcriptional regulation by the repressor LexA do not significantly alter the population noise, whereas specific mutations to post-transcriptionally regulating units, strongly influence noise levels of both genes. Furthermore, our data indicate that global factors, such as the plasmid copy number of the operon encoding plasmid, affect gene expression noise of the entire operon. Taken together, our results provide insights on how noise in a native toxin-producing operon is controlled and underline the importance of post-transcriptional regulation for noise control in this system.
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Affiliation(s)
- Alexandra Goetz
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich, Germany
| | - Andreas Mader
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich, Germany
| | - Benedikt von Bronk
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich, Germany
| | - Anna S. Weiss
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich, Germany
| | - Madeleine Opitz
- Faculty of Physics and Center for NanoScience, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, Munich, Germany
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20
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Controlling cell-to-cell variability with synthetic gene circuits. Biochem Soc Trans 2019; 47:1795-1804. [DOI: 10.1042/bst20190295] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/05/2019] [Accepted: 11/06/2019] [Indexed: 02/05/2023]
Abstract
Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.
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