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Kabaria SR, Bae Y, Ehmann ME, Beitz AM, Dorn BA, Peterman EL, Love KS, Ploessl DS, Galloway KE. Programmable promoter editing for precise control of transgene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599813. [PMID: 38948694 PMCID: PMC11212971 DOI: 10.1101/2024.06.19.599813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Subtle changes in gene expression direct cells to distinct cellular states. Identifying and controlling dose-dependent transgenes requires tools for precisely titrating expression. To this end, we developed a framework called DIAL for building editable promoters that allows for fine-scale, heritable changes in transgene expression. Using DIAL, we increase expression by recombinase-mediated excision of spacers between the binding sites of a synthetic zinc-finger transcription factor and the core promoter. By nesting varying numbers and lengths of spacers, DIAL generates a tunable range of unimodal setpoints from a single promoter construct. Through small-molecule control of transcription factors and recombinases, DIAL supports temporally defined, user-guided control of transgene expression. Integration of DIAL promoters into lentivirus allows for efficient delivery to primary cells. As promoter editing generates stable states, DIAL setpoints are heritable, facilitating mapping of transgene levels to phenotypes. The highly modular and extensible DIAL framework opens up new opportunities for screening and tailoring transgene expression to regulate gene and cell-based therapies. Highlights Promoter editing generates a range of unimodal setpoints from DIAL, a synthetic promoter systemLength of the excisable spacer and identity of the zinc-finger activator tune setpointsNested, excisable spacers expand the number of unimodal setpointsDIAL generates stable setpoints that are robust to varying transactivator levelsDIAL transmits transient inputs into heritable statesThe TET-DIAL system enables small molecule activation of defined setpointsDIAL controls expression in primary cells and iPSCs; regulates physiologically-relevant transgenes. One Sentence Summary DIAL offers an extensible framework for designing synthetic promoters that generate heritable setpoints of gene expression and performs across a range of cell types and delivery systems.
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Du R, Flynn MJ, Honsa M, Jungmann R, Elowitz MB. miRNA circuit modules for precise, tunable control of gene expression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.12.583048. [PMID: 38559239 PMCID: PMC10979901 DOI: 10.1101/2024.03.12.583048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The ability to express transgenes at specified levels is critical for understanding cellular behaviors, and for applications in gene and cell therapy. Transfection, viral vectors, and other gene delivery methods produce varying protein expression levels, with limited quantitative control, while targeted knock-in and stable selection are inefficient and slow. Active compensation mechanisms can improve precision, but the need for additional proteins or lack of tunability have prevented their widespread use. Here, we introduce a toolkit of compact, synthetic miRNA-based circuit modules that provide precise, tunable control of transgenes across diverse cell types. These circuits, termed DIMMERs (Dosage-Invariant miRNA-Mediated Expression Regulators) use multivalent miRNA regulatory interactions within an incoherent feed-forward loop architecture to achieve nearly uniform protein expression over more than two orders of magnitude variation in underlying gene dosages or transcription rates. They also allow coarse and fine control of expression, and are portable, functioning across diverse cell types. In addition, a heuristic miRNA design algorithm enables the creation of orthogonal circuit variants that independently control multiple genes in the same cell. These circuits allowed dramatically improved CRISPR imaging, and super-resolution imaging of EGFR receptors with transient transfections. The toolbox provided here should allow precise, tunable, dosage-invariant expression for research, gene therapy, and other biotechnology applications.
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Affiliation(s)
- Rongrong Du
- Howard Hughes Medical Institute and Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Michael J. Flynn
- Howard Hughes Medical Institute and Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Monique Honsa
- Max Planck Institute of Biochemistry, Martinsried, Germany; Faculty of Physics, Ludwig Maximilian University, Munich, Germany
| | - Ralf Jungmann
- Max Planck Institute of Biochemistry, Martinsried, Germany; Faculty of Physics, Ludwig Maximilian University, Munich, Germany
| | - Michael B. Elowitz
- Howard Hughes Medical Institute and Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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Chan DTC, Baldwin GS, Bernstein HC. Revealing the Host-Dependent Nature of an Engineered Genetic Inverter in Concordance with Physiology. BIODESIGN RESEARCH 2023; 5:0016. [PMID: 37849456 PMCID: PMC10432152 DOI: 10.34133/bdr.0016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 07/17/2023] [Indexed: 10/19/2023] Open
Abstract
Broad-host-range synthetic biology is an emerging frontier that aims to expand our current engineerable domain of microbial hosts for biodesign applications. As more novel species are brought to "model status," synthetic biologists are discovering that identically engineered genetic circuits can exhibit different performances depending on the organism it operates within, an observation referred to as the "chassis effect." It remains a major challenge to uncover which genome-encoded and biological determinants will underpin chassis effects that govern the performance of engineered genetic devices. In this study, we compared model and novel bacterial hosts to ask whether phylogenomic relatedness or similarity in host physiology is a better predictor of genetic circuit performance. This was accomplished using a comparative framework based on multivariate statistical approaches to systematically demonstrate the chassis effect and characterize the performance dynamics of a genetic inverter circuit operating within 6 Gammaproteobacteria. Our results solidify the notion that genetic devices are strongly impacted by the host context. Furthermore, we formally determined that hosts exhibiting more similar metrics of growth and molecular physiology also exhibit more similar performance of the genetic inverter, indicating that specific bacterial physiology underpins measurable chassis effects. The result of this study contributes to the field of broad-host-range synthetic biology by lending increased predictive power to the implementation of genetic devices in less-established microbial hosts.
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Affiliation(s)
- Dennis Tin Chat Chan
- Faculty of Biosciences, Fisheries and Economics, UiT, The Arctic University of Norway, 9019 Tromsø, Norway
| | - Geoff S. Baldwin
- Department of Life Sciences, Imperial College London, South Kensington, London SW7 2AZ, UK
- Imperial College Centre for Synthetic Biology, Imperial College London, South Kensington, London SW7 2AZ, UK
| | - Hans C. Bernstein
- Faculty of Biosciences, Fisheries and Economics, UiT, The Arctic University of Norway, 9019 Tromsø, Norway
- The Arctic Centre for Sustainable Energy, UiT, The Arctic University of Norway, 9019 Tromsø, Norway
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Li Z, Fattah A, Timashev P, Zaikin A. An Account of Models of Molecular Circuits for Associative Learning with Reinforcement Effect and Forced Dissociation. SENSORS (BASEL, SWITZERLAND) 2022; 22:5907. [PMID: 35957464 PMCID: PMC9371404 DOI: 10.3390/s22155907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/23/2022] [Accepted: 07/28/2022] [Indexed: 06/15/2023]
Abstract
The development of synthetic biology has enabled massive progress in biotechnology and in approaching research questions from a brand-new perspective. In particular, the design and study of gene regulatory networks in vitro, in vivo, and in silico have played an increasingly indispensable role in understanding and controlling biological phenomena. Among them, it is of great interest to understand how associative learning is formed at the molecular circuit level. Mathematical models are increasingly used to predict the behaviours of molecular circuits. Fernando's model, which is one of the first works in this line of research using the Hill equation, attempted to design a synthetic circuit that mimics Hebbian learning in a neural network architecture. In this article, we carry out indepth computational analysis of the model and demonstrate that the reinforcement effect can be achieved by choosing the proper parameter values. We also construct a novel circuit that can demonstrate forced dissociation, which was not observed in Fernando's model. Our work can be readily used as reference for synthetic biologists who consider implementing circuits of this kind in biological systems.
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Affiliation(s)
- Zonglun Li
- Department of Mathematics, University College London, London WC1E 6BT, UK
- Institute for Women’s Health, University College London, London WC1E 6BT, UK
| | - Alya Fattah
- Department of Mathematics, University College London, London WC1E 6BT, UK
| | - Peter Timashev
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov University, Moscow 119991, Russia
| | - Alexey Zaikin
- Department of Mathematics, University College London, London WC1E 6BT, UK
- Institute for Women’s Health, University College London, London WC1E 6BT, UK
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov University, Moscow 119991, Russia
- Department of Applied Mathematics and Laboratory of Systems Biology of Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603022, Russia
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Zhu D, Johnson HJ, Chen J, Schaffer DV. Optogenetic Application to Investigating Cell Behavior and Neurological Disease. Front Cell Neurosci 2022; 16:811493. [PMID: 35273478 PMCID: PMC8902366 DOI: 10.3389/fncel.2022.811493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/04/2022] [Indexed: 11/13/2022] Open
Abstract
Cells reside in a dynamic microenvironment that presents them with regulatory signals that vary in time, space, and amplitude. The cell, in turn, interprets these signals and accordingly initiates downstream processes including cell proliferation, differentiation, migration, and self-organization. Conventional approaches to perturb and investigate signaling pathways (e.g., agonist/antagonist addition, overexpression, silencing, knockouts) are often binary perturbations that do not offer precise control over signaling levels, and/or provide limited spatial or temporal control. In contrast, optogenetics leverages light-sensitive proteins to control cellular signaling dynamics and target gene expression and, by virtue of precise hardware control over illumination, offers the capacity to interrogate how spatiotemporally varying signals modulate gene regulatory networks and cellular behaviors. Recent studies have employed various optogenetic systems in stem cell, embryonic, and somatic cell patterning studies, which have addressed fundamental questions of how cell-cell communication, subcellular protein localization, and signal integration affect cell fate. Other efforts have explored how alteration of signaling dynamics may contribute to neurological diseases and have in the process created physiologically relevant models that could inform new therapeutic strategies. In this review, we focus on emerging applications within the expanding field of optogenetics to study gene regulation, cell signaling, neurodevelopment, and neurological disorders, and we comment on current limitations and future directions for the growth of the field.
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Affiliation(s)
- Danqing Zhu
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, United States
| | - Hunter J. Johnson
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States
- Graduate Program in Bioengineering, University of California, San Francisco, San Francisco, CA, United States
- Graduate Program in Bioengineering, University of California, Berkeley, Berkeley, CA, United States
| | - Jun Chen
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, United States
| | - David V. Schaffer
- California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA, United States
- Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, United States
- Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, United States
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- *Correspondence: David V. Schaffer
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Bacteriophage self-counting in the presence of viral replication. Proc Natl Acad Sci U S A 2021; 118:2104163118. [PMID: 34916284 DOI: 10.1073/pnas.2104163118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2021] [Indexed: 11/18/2022] Open
Abstract
When host cells are in low abundance, temperate bacteriophages opt for dormant (lysogenic) infection. Phage lambda implements this strategy by increasing the frequency of lysogeny at higher multiplicity of infection (MOI). However, it remains unclear how the phage reliably counts infecting viral genomes even as their intracellular number increases because of replication. By combining theoretical modeling with single-cell measurements of viral copy number and gene expression, we find that instead of hindering lambda's decision, replication facilitates it. In a nonreplicating mutant, viral gene expression simply scales with MOI rather than diverging into lytic (virulent) and lysogenic trajectories. A similar pattern is followed during early infection by wild-type phage. However, later in the infection, the modulation of viral replication by the decision genes amplifies the initially modest gene expression differences into divergent trajectories. Replication thus ensures the optimal decision-lysis upon single-phage infection and lysogeny at higher MOI.
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Chu HY, Wong ASL. Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays. ADVANCED GENETICS (HOBOKEN, N.J.) 2021; 2:2100038. [PMID: 36619853 PMCID: PMC9744531 DOI: 10.1002/ggn2.202100038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/08/2021] [Indexed: 01/11/2023]
Abstract
Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild-type variant. Even with a high-throughput screening on pooled libraries and Next-Generation Sequencing to boost the scale of read-outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in-silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino-acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio-physical rules for protein folding. Using machine learning-guided approaches, researchers can build more focused libraries, thus relieving themselves from labor-intensive screens and fast-tracking the optimization process. Here, we describe the current advances in massive-scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants.
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Affiliation(s)
- Hoi Yee Chu
- Laboratory of Combinatorial Genetics and Synthetic BiologySchool of Biomedical SciencesThe University of Hong KongHong Kong852China
| | - Alan S. L. Wong
- Laboratory of Combinatorial Genetics and Synthetic BiologySchool of Biomedical SciencesThe University of Hong KongHong Kong852China,Electrical and Electronic EngineeringThe University of Hong KongPokfulamHong Kong852China
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Abstract
Synthetic biology increasingly enables the construction of sophisticated functions in mammalian cells. A particularly promising frontier combines concepts drawn from industrial process control engineering-which is used to confer and balance properties such as stability and efficiency-with understanding as to how living systems have evolved to perform similar tasks with biological components. In this review, we first survey the state-of-the-art for both technologies and strategies available for genetic programming in mammalian cells. We then discuss recent progress in implementing programming objectives inspired by engineered and natural control mechanisms. Finally, we consider the transformative role of model-guided design in the present and future construction of customized mammalian cell functions for applications in biotechnology, medicine, and fundamental research.
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