Burden-driven feedback control of gene expression

Ceroni, Francesca; Furini, Simone; Gorochowski, Thomas E.; Boo, Alice; Borkowski, Olivier; Ladak, YN; Awan, Ali R.; Gilbert, Charlie; Stan, Guy-Bart; Ellis, Tom

doi:10.1101/177030

Cited by 67 publications

(97 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our findings highlight the importance of considering the burden a synthetic construct places on a cell (e.g. the sequestering shared resources like RNAPs or ribosomes) and their indirect effects on integrated stress responses 13,14,40,64 .…”

Section: Discussionmentioning

confidence: 86%

“…This is supported by the fact that dnaJ, groL/S, and grpE were transcriptionally upregulated during PK induction as well as ftsh, which encodes the protease that degrades σ 32 . The precise mechanism of σ 32 upregulation by expressing synthetic constructs is not known, although it has been reported to be a general response 64 .…”

Section: Cellular Response To a Strong Synthetic Pseudoknotmentioning

confidence: 99%

See 1 more Smart Citation

Absolute quantification of translational regulation and burden using combined sequencing approaches

Gorochowski

Chelysheva

Eriksen

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

1Translation of mRNAs into proteins is a key cellular process. Ribosome binding sites and stop 2 codons provide signals to initiate and terminate translation, while stable secondary mRNA structures 3 can induce translational recoding events. Fluorescent proteins are commonly used to characterize 4 such elements but require the modification of a part's natural context and allow only a few 5 parameters to be monitored concurrently. Here, we develop an approach that combines ribosome 6 profiling (Ribo-seq) with quantitative RNA sequencing (RNA-seq) to enable the high-throughput 7 characterization of genetic parts controlling translation in absolute units. We simultaneously 8 measure 743 translation initiation rates and 746 termination efficiencies across the Escherichia coli 9 transcriptome, in addition to translational frameshifting induced at a stable RNA pseudoknot 10 structure. By analyzing the transcriptional and translational response, we discover that sequestered 11 ribosomes at the pseudoknot contribute to a σ 32 -mediated stress response, codon-specific pausing, 12 and a drop in translation initiation rates across the cell. Our work demonstrates the power of 13 integrating global approaches towards a comprehensive and quantitative understanding of gene 14 regulation and burden in living cells. 15 42 2016). When used for characterization, the part of interest is usually placed into a new genetic 43 backbone (often a plasmid) and its behavior is directly linked to the expression of one or more 44 fluorescent proteins (Cambray et al, 2013). When debugging a circuit failure, it is not possible to 45 extract the part of interest as the context of the circuit is important. For circuits that use transcription 46 rate (i.e. RNAP flux) as a common signal between components (Canton et al, 2008), debugging 47 plasmids containing a promoter responsive to the signal of interest have been used to track the 48 propagation of signals and reveal the root cause of failures (Nielsen et al, 2016). Alternatively, any 49 4genes whose expression is controlled by the part of interest can be tagged by a fluorescent protein 50 (Snapp, 2005). Such modifications allow for a readout of protein level but come at the cost of 51 alterations to the circuit. This is problematic as there is no guarantee the fluorescent tag itself will not 52 affect a part's function (Baens et al, 2006; Margolin, 2012). 53 The past decade has seen tremendous advances in sequencing technologies. This has 54 resulted in continuously falling costs and a growing range of information that can be captured 55 (Goodwin et al, 2016). Sequencing methods exist to measure chromosomal architecture 56 (Lieberman-aiden et al, 2009), RNA secondary structure (Lucks et al, 2011), DNA and RNA 57 abundance (Conesa et al, 2016), and translation efficiency (Ingolia, 2014). New developments have 58 expanded the capabilities even further towards more quantitve measurements of transcription and 59 protein synthesis rates with native elongating transcript sequencing (NET-seq) (Maye...

show abstract

Section: Discussionmentioning

confidence: 86%

Section: Cellular Response To a Strong Synthetic Pseudoknotmentioning

confidence: 99%

Absolute quantification of translational regulation and burden using combined sequencing approaches

Gorochowski

Chelysheva

Eriksen

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…(B) In vitro cell-free protein expression can be applied for prototyping and optimisation of genetic regulatory networks [46,48,138], enzymes [49], and biosensors [50,51]. (C) By engineering and constructing novel genetic architectures, native sensory pathways and regulatory networks can be used for an array of dynamic sensing, computation, and regulatory applications [20,23,89].…”

Section: Regulation Of Transcription Initiationmentioning

confidence: 99%

“…Harnessing native starvation systems allows us to design gene regulation networks in which production is activated under resource-limiting conditions [23,90,127]. (B) Constructing autoresponsive, smart genetic circuits would allow us to ultimately construct genetic regulatory networks that are able to sense, respond, and remediate causes of excessive burden or loss of viability [20,121]. Devices such as these may enable harmonious production while reducing inhibition on biomass accumulation.…”

Section: Autonomous Regulation Of Gene Expression To Alleviate Metabomentioning

confidence: 99%

“…robust and predictable genetic circuits it is essential that a balance be struck between circuit function and cell viability, particularly if we hope to apply the given genetic circuitry for large-scale fermentations over extended timescales. Modern tools for regulating gene expression allow construction of better production platforms through fine-tuning of gene expression, dynamic regulation, and autonomous, burden-responsive, feedback [10,[17][18][19][20][21] to help overcome this barrier.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Contemporary Tools for Regulating Gene Expression in Bacteria

Kent

Dixon

2020

Trends in Biotechnology

View full text Add to dashboard Cite

Insights from novel mechanistic paradigms in gene expression control have led to the development of new gene expression systems for bioproduction, control, and sensing applications. Coupled with a greater understanding of synthetic burden and modern creative biodesign approaches, contemporary bacterial gene expression tools and systems are emerging that permit fine-tuning of expression, enabling greater predictability and maximisation of specific productivity, while minimising deleterious effects upon cell viability. These advances have been achieved by using a plethora of regulatory tools, operating at all levels of the so-called 'central dogma' of molecular biology. In this review, we discuss these gene regulation tools in the context of their design, prototyping, integration into expression systems, and biotechnological application. From Overexpression to Precise Regulation: Engineering Gene Expression Control Historically, researchers have relied on a relatively small number of mechanisms to regulate heterologous gene expression [1]. These traditional systems have focussed on massive overexpression of genes in an effort to maximise product yield (see Glossary). Often systems developed for overexpression are adapted ad hoc for integration into genetic circuits. While suited for the rapid accumulation of high levels of protein, these tools are often insufficient for developing the more precise systems required for many modern applications in sensing, computation and production. Recent years have seen improvements in the functionality of gene regulation tools, enhancing utility for dynamic, tuneable regulation. With any bioproduction effort, there is often an important tradeoff between yield, biomass accumulation, and titre that must be considered (Figure 1A). This balance is important for an array of biotechnological applications to ensure process viability and stability. As our ability to engineer more complex systems increases, the importance of harmonising gene expression with the metabolic capacity of the host organism, or chassis, by reducing the burden associated with the engineered function only increases. In the case of recombinant protein production, aberrant protein synthesis can lead to an overload in cellular capacity, such as synthesis or secretion. Both resource limitations, such as limited amino acid and ribosome availability [2], and symptoms of cellular capacity overload, such as impaired protein folding and secretion capacity [3-5], can impact cellular viability. Resource demand and overload also present a challenge to metabolic engineering efforts where substrate, intermediate, and product concentration can all have deleterious effects on cell viability [6]. This issue is exacerbated when central metabolites and/or cofactors are consumed, creating further competition for cellular resources.

show abstract

Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture

2019

Biotech & Bioengineering

View full text Add to dashboard Cite

Monod and Logistic growth models have been widely used as basic equations to describe cell growth in bioprocess engineering. In the case of the Monod equation, the specific growth rate is governed by a limiting nutrient, with the mathematical form similar to the Michaelis-Menten equation. In the case of the Logistic equation, the specific growth rate is determined by the carrying capacity of the system, which could be growth-inhibiting factors (i.e., toxic chemical accumulation) other than the nutrient level. Both equations have been found valuable to guide us build unstructured kinetic models to analyze the fermentation process and understand cell physiology. In this work, we present a hybrid Logistic-Monod growth model, which accounts for multiple growth-dependent factors including both the limiting nutrient and the carrying capacity of the system. Coupled with substrate consumption and yield coefficient, we present the analytical solutions for this hybrid Logistic-Monod model in both batch and continuous stirred tank reactor (CSTR) culture. Under high biomass yield (Y x/s ) conditions, the analytical solution for this hybrid model is approaching to the Logistic equation; under low biomass yield condition, the analytical solution for this hybrid model converges to the Monod equation. This hybrid Logistic-Monod equation represents the cell growth transition from substrate-limiting condition to growth-inhibiting condition, which could be adopted to accurately describe the multi-phases of cell growth and may facilitate kinetic model construction, bioprocess optimization, and scale-up in industrial biotechnology. K E Y W O R D S analytical solution, batch and CSTR culture, cell growth, logistic growth, monod equation, hybrid model

show abstract

Burden-driven feedback control of gene expression

Cited by 67 publications

References 36 publications

Absolute quantification of translational regulation and burden using combined sequencing approaches

Absolute quantification of translational regulation and burden using combined sequencing approaches

Contemporary Tools for Regulating Gene Expression in Bacteria

Analytical solution for a hybrid Logistic‐Monod cell growth model in batch and continuous stirred tank reactor culture

Contact Info

Product

Resources

About