The SAGE genetic toolkit enables highly efficient, iterative site-specific genome engineering in bacteria

Elmore, Joshua R.; Gn, Dexter; Francis, Robert C.; Riley, Lauren A.; Huenemann, Jay D.; Baldino, Henri; Am, Guss; Egbert, Robert G.

doi:10.1101/2020.06.28.176339

Cited by 10 publications

(13 citation statements)

References 64 publications

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“…Recently, this has been developed for various Gamma-and Alpha-proteobacteria in a toolset called Serine recombinase-Assisted Genome Engineering (SAGE) [33]. Together, these toolsets will help accelerate strain engineering across diverse organisms.…”

Section: Discussionmentioning

confidence: 99%

“…Because these recombinases do not require any host factors to function, this system should be adaptable to any strain in which the poly- attB “landing pad” can be inserted into the chromosome. Recently, this has been developed for various Gamma- and Alpha-proteobacteria in a toolset called Serine recombinase-Assisted Genome Engineering (SAGE) [33]. Together, these toolsets will help accelerate strain engineering across diverse organisms.…”

Section: Discussionmentioning

confidence: 99%

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Simple and rapid site-specific integration of multiple heterologous DNAs into the Escherichia coli chromosome

Riley

Payne²,

Tumen-Velasquez³

et al. 2022

Preprint

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Escherichia coli is the most studied and well understood microorganism, but research in this system can still be limited by available genetic tools, including the ability to rapidly integrate multiple DNA constructs efficiently into the chromosome. Site-specific, large serine recombinases can be useful tools, catalyzing a single, unidirectional recombination event between two specific DNA sequences, attB and attP, without requiring host proteins for functionality. Using these recombinases, we have developed a system to integrate up to twelve genetic constructs sequentially and stably into in the E. coli chromosome. A cassette of attB sites was inserted into the chromosome and the corresponding recombinases were cloned onto temperature sensitive plasmids to mediate recombination between a non-replicating, attPcontaining "cargo" plasmid and the corresponding attB site on the chromosome. The efficiency of DNA insertion into the E. coli chromosome was approximately 10 7 cfu/µg DNA for six of the recombinases when the competent cells already contained the recombinase-expressing plasmid and approximately 10 5 cfu/µg DNA or higher when the recombinase-expressing plasmid and "cargo" plasmid were co-transformed. The "cargo" plasmid contains ΦC31 recombination sites flanking the antibiotic gene, allowing for resistance markers to be removed and reused following transient expression of the ΦC31 recombinase. As an example of the utility of this system, eight DNA methyltransferases from Clostridium clariflavum 4-2a were inserted into the E. coli chromosome to methylate plasmid DNA for evasion of the C. clariflavum restriction systems, enabling the first demonstration of transformation of this cellulose-degrading species. Importance:More rapid genetic tools can help accelerate strain engineering, even in advanced hosts like Escherichia coli. Here, we adapt a suite of site-specific recombinases to enable simple, rapid, and highly efficient sitespecific integration of heterologous DNA into the chromosome. This utility of this system was demonstrated by sequential insertion of eight DNA methyltransferases into the E. coli chromosome, .

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Simple and rapid site-specific integration of multiple heterologous DNAs into the Escherichia coli chromosome

Riley

Payne²,

Tumen-Velasquez³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…On the other hand, there are many inputs that can be used to perturb the system of interest, e.g. silencing RNA [57], genetic knock-outs [58] and small chemical inducers [59]. Using these inputs, it is straightforward to reconstruct the system transfer function G ( s ) of the system [60,61], whereYfalse(sfalse)=Gfalse(sfalse)Ufalse(sfalse). Y ( s ) is a system’s output, U ( s ) a system’s input and s is the Laplace variable.…”

Section: Representing Network Interactions In Partially Measured Biological Network With Dynamical Structure Functionsmentioning

confidence: 99%

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Yeung

Kim

Yuan³

et al. 2021

J. R. Soc. Interface.

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Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.

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“…On the other hand, there are many inputs that can be used to perturb the system of interest, e.g. silencing RNA [59], genetic knock-outs [60], and small chemical inducers [61]. Using these inputs, it is straightforward to reconstruct the transfer function of the system [73], [51].…”

Section: Representing Network Interactions In Partially Measured Biological Network With Dynamical Structure Functionsmentioning

confidence: 99%

Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements

Yeung

Yuan

et al. 2021

Preprint

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Synthetic biological gene networks are typically conceptualized and visualized as static graphs with nodal and edge dynamics that are time invariant. This conceptualization of biological programming stands in stark contrast to the transient nature of biological dynamics, which are driven by labile biomolecules. Here we demonstrate the use of dynamical structure function theory to evaluate and visualize network dynamics within synthetic biological circuits. We introduce the theory of dynamical structure functions as a tool for understanding network dynamics in synthetic gene networks. We show in particular, that canonical biological crosstalk and resource loading effects in synthetic biology can be quantified directly using dynamical structure functions from simulation and experimental data. We illustrate the importance of knowing these loading effects through several example systems, showing that crosstalk imbalance in feed-forward loops can explain circuit failure or performance limitations. Finally, we show how dynamical structure functions can be used to diagnose crosstalk and network imbalance to explain failure modes in two types of synthetic biocircuits: an in vitro genelet repressilator and an E. coli based transcriptional event detector. We show that dynamical structure functions can be used as a form of inverse modeling, to pinpoint biological parts within a complex biological circuit that need revision or improvement.

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The SAGE genetic toolkit enables highly efficient, iterative site-specific genome engineering in bacteria

Cited by 10 publications

References 64 publications

Simple and rapid site-specific integration of multiple heterologous DNAs into the Escherichia coli chromosome

Simple and rapid site-specific integration of multiple heterologous DNAs into the Escherichia coli chromosome

Data-driven network models for genetic circuits from time-series data with incomplete measurements

Data-Driven Network Models for Genetic Circuits From Time-Series Data with Incomplete Measurements

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