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How to specify a promoter in de novo gene synthesis service?

How to specify a promoter in de novo gene synthesis service?



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I am trying to use a de novo gene synthesis service (Genscript) and one part confuses me:

Where do I pick the choice of promoter? Or is the promoter choice automatic based on my plasmid choice?

e.g. Say I want a regulable promoter (say lac induced by IPTG) how do I specify it? https://www.genscript.com/account/gene_services_gene_synthesis.html

I was given to understand in a related question that with a commercial gene you essentially just "pick the promoter you want"? Or must I somehow manually edit the gene sequence I have to incorporate the promoter I want?

How to put a gene under the control of a regulable promoter?

I can definitely email Genscript in case it is not a part of their default interface but I just wanted to verify my understanding of the matter before I do so.

PS. I even checked out Fischer's GeneArt service & even that interface has nowhere for me to select a specific promoter. Makes me wonder if I am getting the workflow wrong?


Synthetic biology: advancing biological frontiers by building synthetic systems

Advances in synthetic biology are contributing to diverse research areas, from basic biology to biomanufacturing and disease therapy. We discuss the theoretical foundation, applications, and potential of this emerging field.

Synthetic biology is an emerging field of interdisciplinary research that seeks to transform our ability to probe, manipulate, and interface with living systems by combining the knowledge and techniques of biology, chemistry, computer science, and engineering. Its main aim is to increase the ease and efficiency with which biological systems can be designed, constructed, and characterized. Core efforts in the field have focused on the development of tools to support this goal, including new approaches to biological design and fabrication. Although the first generation of synthetic systems demonstrated genetic circuits that encode dynamic behavior, cellular computational operations, and biological communication channels, more recent research has focused on implementing synthetic biological devices and systems in diverse applications, including disease therapy, environmental remediation, and biosynthesis of commodity chemicals. As the field matures, synthetic biology is advancing biological frontiers by expanding biomanufacturing capabilities, developing next-generation therapeutic approaches, and providing new insights into natural biological systems. Here, we review the theoretical foundations, diverse tool kits, and engineered systems that have emerged from synthetic biology and discuss current as well as potential future applications, which include in-depth studies of basic biology (such as understanding endogenous signaling pathways and feedback circuits) and new frontiers in health and medicine (such as identification of diseased cells and targeted therapeutics).


Features & Benefits

Synthetic Gene Length Turnaround Time (TAT)
<500 bp
6-8 Business Days
501-750 bp
751-1500 bp 8-10 Business Days
Custom cloning No Additional TAT

Material and methods

Cell culture

Human hepatocellular carcinoma (HepG2) (ATCC, HB-8065) were maintained in DMEM (4.5 g/l glucose, pyruvate) (Gibco), supplemented with 10% fetal bovine serum (PAA Laboratories) and penicillin/streptomycin (25 μg/ml each, Gibco) at 37 °C, 5% CO2. Cells were seeded at a density of 60,000 cells/cm 2 and allowed to proliferate for 3 days. Following medium change, cells were treated as indicated. Cells were routinely (6 times per year) tested for mycoplasma contamination.

Primary human hepatocytes (PHH) were obtained from BioIVT (donors IAN, IPH, GID) and from Lonza (donors HUM4108, HUM4055B, HUM4229, HUM181501B). Cells were seeded on collagen-coated plates (250 μg/ml rat collagen, Roche) at a density of 150,000 cells/cm 2 and cultivated for 3 h in William’s E medium (PAN Biotech) containing penicillin (100 U/ml, Gibco), streptomycin (0.1 mg/ml, Gibco), gentamycin (10 μM, PAN Biotech), dexamethasone (100 nM, Sigma), stable l -glutamine (2 mM, Sigma), insulin supplement (2 ng/ml, Sigma), and 10% Sera Plus (PAN Biotech). After 3 h for cell attachment, medium was exchanged to Sera Plus-free medium and cells were allowed to adjust for 16 h before treatment.

Generation of GFP-tagged cell lines

HepG2-GFP reporter cell lines expressing tagged Nrf2, HMOX1, or SRXN1 were constructed with bacterial artificial chromosomes that encode C-terminal GFP-tagged fusion proteins as described previously (Wink et al. 2014, 2017, 2018).

Live cell image processing

Translocation of Nrf2-GFP to the nucleus and accumulation of HMOX1-GFP and SRXN1-GFP expression was monitored by a Nikon TiE2000 confocal laser microscope equipped with an automated focus system. After Hoechst H-33342 nuclei staining, NFT or diethyl maleate (DEM) were added for up to 60 h, followed by automated live cell confocal imaging. High content image analysis pipelines were applied to quantify cellular responses as described previously in detail (Wink et al. 2014, 2017, 2018).

Cell viability

Resazurin reduction assay

Resazurin (Sigma) was added to the cell culture medium (5 μg/ml) for a period of at least 30 min. Resorufin fluorescence was determined at 530 nmex/590 nmem with a Tecan Infinite M200 reader. Cell viability was expressed as percentage of fluorescence intensity relative to untreated controls. Background fluorescence was determined in each individual experiment with cells exposed to Triton (1%) and subtracted from all other values.

Lactate dehydrogenase release assay

Lactate dehydrogenase (LDH) activity was determined in the cell homogenate and in the corresponding supernatant, respectively. Cells were lysed in PBS/Triton X-100 (0.5%) for 60 min. Cell homogenate and supernatant (10 μl) were transferred into a 96-well plate. The reaction was initiated by the addition of 190 μl reaction buffer, adjusted to pH 7.4 by titration with K2HPO4 (40 mM) and KH2PO4 (10 mM) stock solutions, supplemented with NADH (100 μM) and sodium pyruvate (600 μM). NADH consumption was detected at 340 nm, recorded in 1 min intervals over a period of 20 min. LDH release was expressed as ΔOD340 (supernatant) / ΔOD340 (supernatant + cell homogenate).

Glutathione detection

Cells in 96-well plates were lysed by the addition of 100 μl of 1% sulfosalicylic acid. Lysed cells (20 μl) were transferred to a new 96-well plate and supplemented with 80 μl water. A standard curve of GSH was prepared in 1% sulfosalicylic acid in the range of 0.1 to 10 μM. The recycling reaction was initiated by the addition of reaction buffer (100 mM sodium phosphate, pH 7.4), supplemented with 5,5′-dithiobis (2-nitrobenzoic acid) (DTNB) (100 μM) (Sigma), NADPH (100 μM) (Roth), glutathione reductase (1 U/ml) (Sigma), and EDTA (1 mM) (Sigma). Total protein amounts were detected in parallel by the BCA assay (Pierce™ BCA Protein assay Kit) for normalization of glutathione levels. If not otherwise indicated, detection of glutathione includes reduced (GSH) and oxidized (GSSG) glutathione. For separate detection of the two forms, a portion of the sample was treated with 5% 2-vinylpyridine (Sigma) for 60 min to trap GSH. Both untreated samples (containing GSH and GSSG) and 2-VP treated samples (containing GSSG) were assessed by the DTNB assay in parallel.

Nrf2 staining and translocation

For visualization and quantification of endogenous Nrf2 translocation into the nucleus, glass 96-well plates were coated with fibronectin (1 μg/ml) (Sigma) and poly- l -ornithine (40 μg/ml) over night. Plates were washed two times with water before use. HepG2 were seeded at a density of 10.000 cells/cm 2 . Following treatment, the medium was removed, cells were washed 2 times with warm PBS, and fixed with 4% formaldehyde for 20 min. Following three washing steps with PBS, cells were permeabilized with 0.1% Triton for 20 min and blocked with 2% BSA in PBS-Tween (0.5%) at room temperature for at least 1 h. The cells were then incubated with monoclonal anti-Nrf2 antibody (1:100, Santa Cruz sc-518033) at 4 °C, over-night. After five washing steps with PBS-Tween, the secondary antibody (Alexa Fluor 555 anti-mouse, Life Technologies, 1:200) was added in 2% BSA/PBS-Tween together with Hoechst 33342 (1 μg/ml) for 2 h. For visualization, an Olympus IX81 microscope, equipped with a F-view CCD camera was used. For quantitative assessment of Nrf2 translocation from the cytosol to the nucleus, an automated Cellomics ArrayScan (Thermo Fisher) microscope system was employed. Nuclei, stained with H-33342, were imaged first for automated focusing and identification of valid objects. Nrf2 was subsequently imaged in the corresponding areas. For assessment of the nuclear cytoplasmic ratio of Nrf2 signal intensity, the nucleus was defined by Hoechst H-33342 staining. The cytoplasmic area was defined as a ring region with a width of 1.9 μm and a distance of 3.3 μm from the nuclear outline.

SiRNA-mediated knockdown of Nrf2 and CYPOR

For transfection of HepG2 in (an individual well) of a 6-well plate (5 × 10 5 cells/well), solution A, consisting of 4 μl Lipofectamine™ (Life Technologies) and 150 μl Opti-MEM® (Life Technologies) were mixed. Solution B consisted of 40 μl (Nrf2) respectively 80 μl (CYPOR) of a 10 μM siRNA stock solution, mixed with 150 μl Opti-MEM®. For an individual well of a 96-well plate, solution A consisted of 0.2 μl Lipofectamine™ and 5 μl Opti-MEM® solution B consisted of 5 μl Opti-MEM® and 1.5 μl (Nrf2), respectively 3 μl (CYPOR) of a 10 μM siRNA stock solution. After an incubation period of 5 min at RT, both solutions were combined, incubated for 30 min. HepG2 (5 × 10 5 cells/cm 2 ) were washed two times with Opti-MEM® before the siRNA solution was added. After 24 h, an equal amount of DMEM containing 20% serum was added. Twenty-four hours (Nrf2), respectively 48 h (CYPOR) after seeding, treatment of the cells with NFT was initiated.

Western blot

Cells were lysed in 1× Laemmli buffer and centrifuged at 10,000 g for 2 min through a Nucleo Spin filter (Machery Nagel). Lysates were boiled at 95 °C for 5 min, 20 μg of total protein were subjected to separation by a 10% SDS gel, transferred onto nitrocellulose membranes (Amersham Biosciences) and blocked with 5% milk powder in PBS Tween (0.1%) for 1 h. The following antibodies were used: anti-Nrf2 (monoclonal, 1:1000, Santa Cruz) anti-GCLC (rabbit, 1:1000, Bioworld Technologies) anti-GCLM (rabbit, 1:2000, Proteintech) anti-CYPOR (monoclonal, 1:500, Santa Cruz) HRP-conjugated anti mouse IgG (1:5000, Jackson Immuno Research) and HRP-conjugated anti-rabbit IgG (1:5000, Amersham). Protein bands were detected by a FUSION SL™ system (Peqlab, Erlangen, Germany) and quantified by ImaEva or ImageJ.

EPR spectroscopy

Continuous wave EPR spectroscopy was performed at 20 °C with a X-band (9.6346 GHz) spectrometer (EMX-Nano, Bruker Biospin, with a cylindric cavity mode TM1110). Microsomes were prepared by sonication of 10 8 cells, removal of debris by centrifugation at 10,000 g for 15 min, and subsequent centrifugation of the resulting supernatant at 100,000 g for 60 min. Microsomes (1 mg/ml) in sodium phosphate buffer, pH 7.4 were supplemented with NADPH (10 mM) and NFT (5 mM). For analysis of intact cells, HepG2 (10 7 /ml in DMEM medium w/o serum and antibiotics) were treated with NFT (5 mM). Samples were loaded into glass capillaries (Hirschmann® ringcaps®, 1 mm inner diameter) and sealed with Hemato-Seal™ capillary tube sealant (Fisherbrand™). A microwave power of 6.3 mW and a modulation amplitude of 5 G at a modulation frequency of 100 kHz were used to acquire spectra in the range of 3367 G to 3497 G at a sweep time of 156.22 s and a conversion time of 78.11 ms. Further, 117 or 12 scans were accumulated for the samples with microsomes or whole cells, respectively. All spectra were baseline-corrected using MatLab2019b and EasySpin 5.2.25 (Stoll and Schweiger 2006). Spectra were background-corrected by subtraction of the spectrum obtained in the presence of microsomes and NADPH, respectively intact HepG, but in absence of NFT.

TempO-Seq transcriptome analysis

HepG2 cells or PHH were seeded in 96-well plates (20,000 cells/well) and exposed to different concentrations of NFT ranging from 5 to 125 μM. The plates were then washed with 1× PBS, and cells were lysed with 50 μl BioSpyder 1× lysis buffer for 15 min at RT. The plates were kept at – 80 °C until shipment. The frozen plates were shipped to BioSpyder Technologies. The Deseq2 package in R was employed for the calculation of log2 fold changes in transcript expression.

Statistics

Values are expressed as mean ± SD. Experiments were performed at least three times with at least three technical replicates in each experiment. Differences were tested for significance by one-way or two-way ANOVA, followed by Bonferroni’s post hoc test, p < 0.05. If not otherwise indicated, differences between means were considered statistically significant at p < 0.05. Statistical differences were tested using GraphPad Prism 5.0 (GraphPad Software, La Jolla, USA).


What is the CAGE method?

Cap Analysis of Gene Expression (CAGE) is a method for promoter identification and transcription profiling developed by RIKEN (Patent Number: US 6174669, US 6221599, US8809518, etc.). CAGE utilizes a “cap-trapping” technology based on the biotinylation of the 7-methylguanosine cap of Pol II transcripts, to pull down the 5’-complete cDNAs reversely transcribed from the captured transcripts. Through a massive parallel sequencing of the 5’ end of cDNA and analysis of the sequenced tags, transcription start sites and transcripts amount are inferred on a genome-wide scale. Thus, CAGE provides an effective genome-wide transcriptional profiling as an alternative to microarray and RNA-seq.

Item Specification Comment
Total RNA required 5 μg / sample Total RNA preferable
RNA entry QC Bioanalyzer We perform entry QC on all samples
DNA amount of CAGE library Several ng DNA fragments ready for illumine NGS sequencer
Sequencing platform Illumina HiSeq 2000/2500
Number of reads per lane guaranteed 75 M reads/lane
Approx. 100 - 150 M reads / lane in average
Standard conditions: 8 - 12 samples per lane.
Optional extra sequencing Number of lanes per analysis Additional lanes are available with additional charge.
Mapping rate About 75% of tags map to unique mapping position 4 M mappable CAGE tags / sample is guaranteed.
Sequence data Provided with Illumina file format Delimited text files holding sequence information and quality scores.
Data Analysis Mapping positions Tables/flat files: number of raw reads, number of extracted tags, number of mapped tags, etc.


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Synthetic Biology: Tools to Design, Build, and Optimize Cellular Processes

The general central dogma frames the emergent properties of life, which make biology both necessary and difficult to engineer. In a process engineering paradigm, each biological process stream and process unit is heavily influenced by regulatory interactions and interactions with the surrounding environment. Synthetic biology is developing the tools and methods that will increase control over these interactions, eventually resulting in an integrative synthetic biology that will allow ground-up cellular optimization. In this review, we attempt to contextualize the areas of synthetic biology into three tiers: (1) the process units and associated streams of the central dogma, (2) the intrinsic regulatory mechanisms, and (3) the extrinsic physical and chemical environment. Efforts at each of these three tiers attempt to control cellular systems and take advantage of emerging tools and approaches. Ultimately, it will be possible to integrate these approaches and realize the vision of integrative synthetic biology when cells are completely rewired for biotechnological goals. This review will highlight progress towards this goal as well as areas requiring further research.

1. Introduction

The central dogma of biology is simply and elegantly stated however it is less straightforward to engineer, control, and rewire for biotechnological purposes. This difficulty stems from our limited understanding of the multiscale, and often stochastic, operation, and regulation of biological systems [1–3]. Nevertheless, rapid progress in uncovering the basic framework and information flow within the central dogma has helped fuel the current biotechnological revolution. Yet, elucidating the specific components and control mechanisms inherent in this process has lagged significantly [4–6]. This limitation prevents the creation of custom-built cellular factories using modeling and de novo design. However, this limitation is only temporary. Recent advances in high-throughput biology are quickly uncovering the identity and details of these components and control schemes [7–10]. While not yet complete, this global, systems biology approach repeatedly depicts the central dogma as a multistep process subject to exquisite regulatory mechanisms established to maintain cellular homeostasis and to respond to environmental stimuli. Once our understanding is advanced, it will be possible to synthetically create desired functions at all levels of the central dogma.

The integrative complexity of the central dogma (and biological systems in general) has analogies and parallels to chemical or electrical systems. The rationale for drawing these analogies is twofold: (1) it helps to contextualize the various parts of a cellular process and (2) it facilitates the possible transfer of knowledge between the analogous systems. In this regard, understanding the central dogma processes, the process controls, and the environmental influences within a cell is as vital as understanding analogous components within a traditional chemical factory. Uncovering and studying these components will ultimately lead to a factory-like cellular blueprint—a detailed catalogue of parts, interactions, and functions. Moreover, compiling such a blueprint for all species will expand the number of parts we are able to access, characterize, and employ when trying to design cells and circuits from scratch. Thus, this understanding will enhance our ability to predict, control, and design cellular systems—major tenets in the emerging field of synthetic biology.

Due to its youth, the field of synthetic biology has yet to have a concrete, comprehensive definition. Yet, in its broadest sense, synthetic biology aims to harness the emergent properties of the central dogma for biotechnological and human use. This description of the field is comprehensive since even synthetically designed biological circuits actually interface with existing central dogma machinery in the cell. In this regard, tools for synthetic biology harness the complexity of the central dogma process in a predictable, designed fashion.

Within the context of engineering the central dogma, the seemingly wide variety of themes and aims in the synthetic biology research field become more unified. Considering the central dogma as a simple process diagram (Figure 1), it can be seen that the varied areas of synthetic biology research all influence the central dogma albeit at different access points in the process. As Figure 1 illustrates, this system has three tiers, specifically: (1) the central dogma process units (transcription and translation) and associated streams (DNA, RNA, and protein), (2) the intrinsic regulatory mechanisms in the cells, and (3) the extrinsic physical and chemical environment of the cells. These three tiers are depicted separately, but in reality are thoroughly enmeshed with one another as a result of evolved biological complexity. Yet, this very complexity provides a multitude of access points, or nodes, for synthetic biologists to engineer.


The central dogma with regulatory and environmental influences acting upon the process. The two central dogma process units (transcription and translation) and the three process streams (DNA, RNA, and protein) are depicted. These units and streams are all subject to control by both internal, regulatory and external, environmental conditions. These influences alter the central dogma process and regulatory mechanisms. Large bold arrows are used to indicate that proteins are the major workhorses of the cell, participating both in regulatory mechanisms and responding to the environment. Whether the system of interest is a signaling cascade or metabolic pathway, proteins are essential components and must become well understood and modifiable to bring about ground-up cellular optimization. Synthetic biology is developing tools to modify and control each unit and stream in this process.

Synthetic biology research is at the forefront of engineering the three tiers of biological systems. For example, the newly developed ability to design and chemically synthesize genetic sequences [11–13] provides a greater ability to manipulate DNA, the “feed stream’’ molecule for the first tier. Contributions from systems biology have broadened our ability to understand and engineer biological networks [14–18], providing impetus for modifying tier two intrinsic control systems and tier three extrinsic signaling interactions. Other frontiers in synthetic biology have greatly expanded our capacity to construct and improve pathways and global cellular phenotype [19–24], which engineers the third tier interaction between proteins and the chemical environment. In the same vein, protein engineering provides the synthetic biologist a great deal of flexibility for introducing and optimizing new function at any node [25–31], since proteins are such universal components throughout the central dogma process. All of these areas of synthetic biology are building toward a single goal: integrative control of the central dogma for biotechnological and human use.

From this viewpoint, developing powerful new tools that manipulate biology at each of the three tiers will empower scientists and engineers with the ability to rewire and program cellular systems for both medical and biotechnological applications. Combining these tools to work in concert would define the field of integrative synthetic biology. This culminating point of synthetic biology development will usher in the age of ground-up cellular design and optimization. However, much of current synthetic biology research is focused on tool development, a required foundation for integrative synthetic biology. As a result, it is not yet clear how to best integrate these approaches. Therefore, the purpose of this review is to provide an overview of synthetic biology research, focusing on microbial hosts, and to highlight areas where more work must be done before realizing the potential of ground-up synthetic cellular engineering.

2. The First Tier—Process Optimization of the Central Dogma

The first tier of synthetic biology focuses on altering the general process flowspecifically modifications to the function and behavior of the process units (transcription [32] and translation) and the associated process streams (DNA, RNA [33], and protein). These manipulations are made possible through detailed knowledge of the central dogma process. While this capacity has existed for several decades [34], novel capabilities and genetic tools afforded by synthetic biology may help overcome some of the limitations and time-consuming bottlenecks inherent in established techniques. In this regard, synthetic biology aims to develop foundational technologies such as large-scale, economical de novo DNA synthesis [35] that would increase the efficiency of traditional recombinant DNA technology and genetic engineering. Collectively, synthetic engineering of the central dogma aims to optimize and expand the capabilities of native cellular machinery. The methods and technologies developed from this research will contribute to a more powerful and efficient toolbox for the microbial engineer. In this section, we will review synthetic biology technologies and applications for influencing components within the first tier.

2.1. Engineering DNA

DNA manipulation began very early in the biotechnological revolution with recombinant DNA methods [36–38] and DNA sequencing technology [39–41]. Mutagenesis techniques and the establishment of standardized molecular biology methods [34] expanded these tools and empowered metabolic engineers with more powerful approaches to improve metabolic phenotypes [42–46]. Despite being straightforward and robust, these approaches are inherently limited by template-based DNA synthesis and restriction enzyme cloning. However, inexpensive, large scale synthetic (de novo) DNA manufacturing technology has the potential to revolutionize this process once again. Unlike traditional methods, de novo synthesis removes the need to engineer cellular systems using preexisting DNA as a template. In this regard, this technology brings about a new power to synthetically design genes, control elements, and circuits that do not exist in nature—thus creating novel function from the basic building blocks of nucleic acids and amino acids.

Already, there are multiple companies with expertise in synthesizing DNA (Blue Heron, DNA2.0, GENEART, IDT, etc.), from small fragments to whole genes and genetic elements. Moreover, improvements and new technologies are continually being published [12, 35, 47–50] which expand the potential applications and drive down prices. As a result, synthesis capabilities have moved beyond the scale of single genes and into the scale of chemically synthesized genomes [11, 13]. Moreover, efforts are being made to introduce this synthetic DNA into a generic host [51] in an effort to completely reprogram a cell. The combination of these powerful new DNA synthesis techniques coupled with low-cost DNA sequencing has the potential to confer a great deal of freedom to researchers. With these advances, DNA design and cloning is no longer limited by existing fragments of template DNA and available restriction sites in plasmids. In essence, this technology serves as the basis for other synthetic biology tools, since DNA is the vehicle of almost every biological perturbation, regardless of the tier of interest.

However, our ability to create DNA de novo is not equally matched by a capacity to predict the ideal DNA sequence a priori for a given application. Attempts have been made to catalogue DNA elements [52, 53] and predict the function of synthetic networks using models [9, 15, 17, 18, 54]. Nevertheless, our knowledge base for constructing predictive models of global cellular behavior is limited as is our ability to design large operons and circuits de novo. Future work on characterizing these elements as well as their dynamics and interaction will allow for synthetically created custom-designed genetic circuits.

Simply synthesizing and importing designed DNA is not enough to ensure desired function. Specifically, for these elements to operate efficiently, synthetic DNA operons must act independently and not be negatively influenced by other cellular processes. One solution to mitigate this problem embodies another area of synthetic DNA engineering research: the quest for a minimal cell [55–57]. A minimal cell only contains the essential genetic information required to maintain viability under controlled conditions. In following with the industrial process analogy, this would correspond to a factory containing only the equipment necessary for a given process application. It is clear that this minimization makes sense in a process plant as superfluous equipment would be a waste of precious resources such as money and space. However, cells contain many more parts than are necessary for a given biotechnological application. Thus, taking a cell “off the shelf’’ can result in limited efficiency. The search for a minimal cell provides a noninterfering “chassis’’ suitable for manipulation by the biological engineer. Recent advances in cataloging essential genes continue to move the minimal cell closer to reality [58, 59]. However, it is currently unclear whether the genetic definition of a minimal cell will be generic or process specific. Thus, there may be a suite of minimal cells required each one suited for different classes of bioproducts.

Another area of synthetic DNA engineering aims to expand the basic genetic code by adding synthetic base pairs [60–63]. Incorporating synthetic codons provides a means of utilizing nonnatural amino acids (see Section 2.3) and introducing nonnative DNA-protein binding pairs. Already, alternative genetic codes have led to new applications for engineered biology [61]. One of the potential difficulties of incorporating synthetic base pairs into DNA is that the three-dimensional structure of the molecule may change and key binding proteins and polymerases may not be able to recognize the new genetic language. However, initial results are promising [63, 64] and suggest that drastic changes to innate cellular architecture are not required. Thus, alternative base pairs provide a newfound flexibility in genetic code and DNA manipulation technology. Furthermore, this approach is an excellent application for de novo DNA synthesis: the coupling of synthetic base pairs with DNA synthesis technology could create a powerful tool for designing synthetic circuits. Regardless of the application, the capacity to engineer DNA using synthetic biology tools provides new access points to the cell unachievable by previous technology.

2.2. Engineering Transcription

Since the central dogma is so highly integrated, DNA-level perturbations can cause significant alterations in downstream process units (Figure 1). As a result, microbial engineers must be able to synthetically optimize each of the process units. The first process unit in the central dogma is transcription. A large number of proteins, small molecules, and even small RNAs can participate in this process step [5]. Nevertheless, the ultimate goal of this process is RNA transcription—converting DNA into an mRNA message. As a result, synthetic control of this process step influences the rate and capacity of mRNA synthesis.

Not surprisingly, the key step of RNA polymerase II binding to a promoter sequence has been targeted by synthetic tools, such as promoter engineering, for the purposes of controlling gene expression levels [65–68]. By creating a library of promoter sequence mutants, a graduated expression profile can be developed. This resulting range of expression affords a more detailed investigation of expression levels beyond traditional wild type—knockout—strong overexpression studies. Furthermore, well-characterized promoters enable more precise gene delivery [52]. A similar requirement for controlled expression is critical for genetic circuits where protein expression must be balanced to maintain a desired steady state. Often these circuits use inducible promoters, and a similar approach can be used to augment the expression capacity of inducible promoters. Thus, well-documented genetic elements will be extremely useful in creating synthetic cells and circuits. However, transcription is a two-body problem requiring both proteins and DNA. Most previous work focused on the DNA aspect of the problem however, proteins involved in transcription can also be engineered to synthetically control a cell [32]. Moreover, altering the DNA sequence focuses the change to one particular genetic locus, whereas changing the involved proteins has a profound, global impact.

It is often necessary to alter the transcriptional profile of many genes simultaneously to obtain a desired complex phenotype. This level of synthetic control in the cell is essential for rewiring cells into biofactories. In this regard, another synthetic biology tool termed global transcription machinery engineering (gTME) [30, 69, 70] aims to alter the proteins responsible for the process step of transcription in an effort to exert a pleiotropic downstream effect. The gTME approach operates by creating a mutant library of proteins responsible for transcription (such as sigma factors and TATA binding proteins) and subjecting the library to a high-throughput phenotype screen. This technique is useful for a phenotype that is typically under the control of a multitude of genes. This approach of synthetically rewiring cells at the transcriptional level provides a means of creating large changes within the transcriptome and provides a novel approach to modulating the process step of transcription.

The rationale behind gTME was recently applied to the RNA polymerase II protein itself [32]. By creating a mutant library of the polymerase α subunit and applying selective pressures, the authors demonstrated increases in the tolerance of E. coli to 1-butanol. More studies such as these are required not only to optimize the transcription process unit for synthetic biology applications, but also to gain more fundamental knowledge of the process unit. With enough information, rational design of synthetic transcriptional machinery may be possible. However, large-library based selection techniques are currently required to identify promising mutants, limiting the capacity to design transcription machinery de novo.

Also of note for future synthetic biology tool development is reverse transcription, illustrated by the double-pointed arrows in Figure 1. Reverse transcription as a method of gene delivery for disease treatment [71, 72] merits further exploration by synthetic biologists, although this type of work is generally outside the focus of microbial synthetic biologists. Yet, a great deal of work still remains prior to gaining full, synthetic control of the transcription process. Specifically, more studies focusing on the complex interactions between participatory molecules must be performed. These studies will also implicate future molecular targets for strategies similar to promoter engineering and gTME.

2.3. Engineering Translation

The second major process unit in the central dogma, translation, has also been the subject of recent synthetic biology research. Similar to transcription, translation encompasses many different classes of molecules that can serve as good targets for optimization and rewiring cells. However, less is known about the most essential molecules in this process.

One of the most successful examples of synthetically engineering translation machinery involves the incorporation of unnatural amino acids into proteins [73]. In this work, mutant aminoacyl tRNA synthetases incorporate amino acids with diverse R-group chemistries into proteins. This approach holds a great deal of promise for the synthetic biologist as a means of creating wholly new biological functions and chemistries [29, 74–76]. This direction towards designer proteins is akin to nontemplate based DNA synthesis. However, as with de novo DNA synthesis, it is not always clear which amino acid(s) should be changed to an unnatural analog to confer a desired protein function of interest. In this regard, more work is required to develop a computational linkage between sequence and function. Nevertheless, this approach gives significant leverage to synthetic biologists to create custom proteins with desired functions.

Another example of engineering translation is gene codon optimization [33]. Codon optimization is a method to bias the redundant codons for each amino acid toward the codons most commonly found in the host organism. This approach is greatly expedited by sequencing and synthesis technologies that can produce the required alternately coded genes. Codon optimization has been shown to be successful in many cases [77–82] and has led to improved translation rates, protein yields, and enzymatic activities. When combined with pathway engineering, codon-optimized pathways are typically more efficient than their unengineered counterpart. This approach is especially important when attempting to produce natural products found in systems distantly related to the host organisms (such as importing plant genes into E. coli [83]). Finally, computational techniques are continually being developed to perform the task of codon optimization and assembly design which will improve our ability to control this process step [84]. However, recent evidence has shown that codon optimization may be more effective due to changes in mRNA secondary structure, as opposed to making more abundant tRNA available for translation [85]. Engineering mRNA secondary structure is a second-tier, regulatory method of controlling the central dogma, and is discussed in that section. With these emerging questions, it remains to be seen if the combination of de novo DNA synthesis with codon optimization algorithms will greatly expedite this process and remove some translation-level limitations in cellular systems.

2.4. Engineering the First Tier—Summary

The techniques and approaches described above focus on a synthetic approach aimed to redesign the information and process flow in the central dogma. As Figure 1 suggests, manipulation of the central dogma process at any one of the nodes often results in changes at the protein level. As the major catalytic, structural, and signaling components of cells, synthetically modifying proteins is one of the primary goals of engineering biology. Some of these manipulations, such as promoter engineering and codon optimization, are designed to alter protein level, while others, such as directed evolution [25, 86, 87] and unnatural amino acids, are intended to synthetically alter protein function directly. In either case, the change must be made at the first tier in order to create the downstream effect.

The tools of synthetic biology in this arena have improved the rate and precision of changes that can be made to this first tier. Moreover, they open the capacity to design novel elements and process units that serve a higher biotechnological goal. Yet, more work is required to enable full de novo design of these custom-made elements. In addition, the complete rewiring of cells will likely require multiple modifications in the first tier, thus these approaches must be used in combination to obtain the best results for bioprocessing applications.

3. The Second Tier—Engineering Process Controls

A myriad of control systems have evolved to regulate the highly complex process steps of the central dogma. As a result of tight integration between process and control, it is sometimes difficult to clearly delineate between the biological components of control mechanisms and the central dogma process flow. For this review, we propose that microbial control mechanisms are largely those components that interface with the central dogma process steps, but do not function in a catalytic manner with respect to the process step. Using the process control analogy, these components establish, alter, and regulate the biological “set points.’’ Figure 2 depicts hypothetical control mechanisms within the cell to more clearly delineate between the various tiers of cellular processes. Generally, these control elements form a functional link between the central dogma process (the first tier) and the extrinsic environment (the third tier) and relay control messages into changes in the process units and streams. Actual control mechanisms within the cell are much more complex than this simple depiction, as studies on the RpoS subunit of E. coli RNA polymerase II have shown [88]. To this end, the systems biology approach of surveying global protein-protein interactions is pivotal to understanding these complex mechanisms.


Interaction of components illustrating synthetic biology at the three tiers. (a) Canonical signalling pathway depicting a tier three ligand-receptor binding event that induces a tier two phosphorylation cascade which subsequently alters a promoter at the first tier. (b) Canonical allosteric protein inhibition by a molecule downstream in the pathway. The inhibition binding event is a tier three interaction which results in a tier two alteration in the pathway “set point.’’ (c) Hypothetical signalling pathway in which a signal molecule alters an initiation factor at tier three which then changes the tier two protein translation set point.

Even without full knowledge of cellular regulatory machinery, synthetic biologists have developed tools that establish desirable synthetic set points for the central dogma process units. These tools are required for the function of genetic circuits and control of metabolic pathways. In this section, we will review enabling technologies and applications for synthetic engineering of process control systems within the second tier.

3.1. Transcriptional Control

As stated previously, the control elements involved in many signaling and regulatory networks are being uncovered using a systems biology approach. In particular, these studies are uncovering the important roles that transcription factors play in the process unit of transcription [7, 9, 59, 89]. Typically, these interaction networks are reconstructed using high throughput data obtained from two-hybrid, coimmunoprecipitation, or bioinformatic mining protocols. These studies have produced a wealth of data for analysis, although the data is often collected outside of biologically relevant conditions [90]. Recent work is attempting to improve upon these techniques by collecting protein interaction data in native systems with natural protein expression levels [90]. Even with preliminary data and targets, attempts to engineer transcription factor networks have shown promise [31]. If this knowledge gap is closed, imported synthetic circuits and genes could act independently and not be negatively influenced by other cellular processes. Moreover, components of transcription represent a target and provide an access point for synthetic biologists to effect change in biological systems. Therefore, transcriptional control networks should remain an active area of research for synthetic biologists in order to close this knowledge gap and open the possibility for ground-up cellular optimization.

3.2. Translational Control

In the past few years, a great interest has arisen in the synthetic engineering of RNA [91, 92]. From a process engineering perspective, RNA serves both as the “feed stream’’ to the translation process unit and as a central component of the translational machinery itself. Therefore, depending on the application and desired output, engineering of RNA to alter translation can be classified as a first-tier or a second-tier approach to synthetic biology. In terms of translational control, it has been demonstrated that modifying mRNA structure can modulate protein levels [93, 94]. As an example, by optimizing intergenic regions in an operon [95], hairpins that sequestered ribosomal start codons were introduced and afforded synthetic control of a bicistronic message. Also, similar work has been performed to identify and engineer synthetic ribosome entry (IRES) sites for polycistronic transcripts [96]. Finally, Breaker and colleagues have focused a great deal of effort to the understanding of native RNA response to small molecules [97, 98]. These findings along with research on riboswitches could begin to link translational control with signaling networks or environmental signals (for more on this topic, see Tier 3).

Despite these successes, the capacity to engineer translational control mechanisms is quite limited due to the fact that many initiation factors (IF) are yet to be fully characterized and explored. Yet, initial work is beginning to unveil the complex molecular interactions that occur at this level [99–101]. However, studies have shown that mRNA levels do not always correlate with protein levels [10, 102]. This disjoint does not reconcile with the simple, reasonable hypothesis embodied by the central dogma that increasing transcript levels should increase protein levels. However, it does provide circumstantial evidence for unknown control mechanisms influencing translation. More studies, perhaps borrowing from the systems biology approach to studying transcription factors [103, 104], are required to uncover these translation-level control mechanisms. If obtained, the full detail of translational control systems will provide a novel means to synthetically control the translation process unit. Therefore, the study of translational control mechanisms should be more fully explored by synthetic biologists.

3.3. Protein Regulation

Regulation occurs at all levels within the cell and is not limited to simply the process units of transcription and translation. In this regard, a great deal of regulation takes place at the protein level, especially upon activity and degradation rate. Also, modifying events such as glycosylation, phosphorylation, and acetylation allow protein function to be modulated and localized within the cell [105–107]. These events regulate cellular activity through activation, inhibition, and signaling. Systems biology is delving into these complex protein-protein interaction networks [108–115]. Supplied with this increasing amount of data, synthetic biologists are constructing synthetic regulatory networks [116, 117] that take advantage of these control processes.

As stated previously, protein level within a cell is partially regulated by degradation rates. Synthetic control of these processes is important to (1) aid our understanding of how to extend the half-lives of desirable proteins and (2) further our control of regulatory and signaling systems. To this end, a synthetic protein degradation network has been constructed in Saccharomyces cerevisiae [118]. Eukaryotic systems have elegant processes such as ubiquitination to control degradation, while prokaryotic systems were thought not to possess such capability. However, this has been found to not be the case. Many degradation mechanisms such as the AAA+ protease family in E. coli [119], prokaryotic ubiquitin-like protein (Pup) [120], and others have reversed this thinking. As a result, understanding and engineering protein degradation in prokaryotes can prove to be a fertile area for synthetic biology research in the future.

Protein regulation has been studied since the very beginning of the biotechnological revolution, and is presently becoming influenced by the synthetic biology paradigm. The work previously discussed exemplifies this and opens the door to ground-up cellular optimization.

3.4. Engineering the Second Tier—Summary

The regulatory mechanisms that act upon the central dogma process are vital to optimizing cellular function, from improving product titer to switching gene expression profiles. Synthetic biology has demonstrated that these mechanisms can be effectively engineered at all levels of the central dogma process. By altering regulatory and control systems, it will be possible to ensure that a cell will respond in a desired manner. This is an important trait when considering the behavior of synthetic circuits as well as the function of metabolically engineered organisms. However, more work needs to be done to establish the foundation necessary for ground-up cellular optimization and designer control elements that can leverage these critical regulatory networks.

4. The Third Tier—Engineering Environmental Signal Reception

The central dogma and the internal control mechanisms present in the cell have evolved to integrate and respond to a wide array of environmental signals. A short list of stimuli includes temperature, metabolite concentration, light, toxins, ions, and molecular signals from other cells. As we begin to probe cellular systems further, a greater appreciation is being given to cell communication and signaling pathways. New advances in small molecule detection are uncovering key small molecule elicitors. Figure 2, in addition to showing interactions at the second tier, also depicts cellular response to a hypothetical environmental signal at the third tier. This third tier is focused on extracellular molecules and sensing proteins. Once an extracellular molecule interacts with a component of either the internal control mechanisms or of the central dogma, the information has been translated into either the first or second tier. Synthetic engineering of environmental response and communication pathways provides a unique opportunity to exert control in a cellular system. In this section, we will review novel synthetic approaches for signal integration and communication.

Engineering at this third tier is producing some of the newest work at the frontier of synthetic biology. This area encompasses the work of genetic circuit construction as well as engineering bacterial quorum sensing and microbial consortia [121]. Advancing the toolbox for the microbial engineer at this level is critical for external control of microbial populations. Furthermore, this work provides a newfound method for process control of fermentations. Constructing synthetic sensors that can detect a particular signaling molecule can serve as a responsive switch in a simple genetic circuit as well as a biosensor used to detect toxins in the environment. Furthermore, these systems can be combined to achieve higher logic functions such as AND gates [122]. Engineering at this final tier is the last link in the whole-cell chain of events: environmental signal affects control mechanism, which then elicits change in central dogma, which affects protein levels, and then produces the desired result. As a result, synthetic control of molecular input capability is vital for the complex functions that are currently being designed into microbes.

4.1. Signal Receptive Genetic Circuits

Modeling biological networks has borrowed from electrical circuits theory [123], which has given rise to a great deal of analogies between the two fields. The concept of the genetic circuit has become one of the key contributions of synthetic biology [1, 124, 125]. By definition, genetic circuit design requires an input signal, which represents engineering at the third tier. Due to the many environmental properties a microbe is able to sense, there are a plethora of inputs available to engineer. These inputs can include metabolites [126], proteins [127], temperature [128], and light [129]. Using these inputs, circuits such as switches [127], oscillators [126], bandpass filters [130], and feedback loops [131] may be constructed. Also, it has been shown that genetic circuits can be improved using the directed evolution algorithm typically applied to single proteins [26].

One of the most common positive feedback loops used in genetic circuit engineering is bacterial quorum sensing [132–136]. This natural system of cell-cell communication has been used to initiate cell death for population control, cancer cell invasion [137], artificial predator-prey relationships [138], and cell motility [139]. In these studies, a synthetic quorum sensing response has been introduced into nonnative cells to drive a desired phenotype.

Beyond feedback loops, synthetic biologists could incorporate environmental signals through receptor-ligand interactions [140]. Not surprisingly, protein engineering techniques are important to manipulate these receptor-mediated interactions. Methods such as directed evolution [26, 87, 141] are proven, effective methods to accomplishing this end. However, there is also an ongoing push towards rational design of proteins [142, 143]. As this develops, synthetic biologists will have better avenues towards a priori protein design. Already, there is a great deal of published work on engineering binding pockets of proteins [140] and modeling protein folding changes based on binding events [144]. While these studies had a direct metabolic and medical [145] application, this approach could contribute to the construction of artificial regulatory networks for proteins that lack native regulation.

In addition, RNA engineering has shown that riboswitches represent a powerful and promising way to incorporate an environmental signal. These small fragments of RNA are a noncoding portion of an mRNA transcript that binds to small molecules, allosterically affecting protein activation levels in the cell [97, 146]. Moreover, the ability of these fragments to work in an independent fashion allows for a portable, modular assembly of responsive elements [98]. Riboswitches present an exciting new approach to environmental signal recognition because the sequence-structure-function relationship is more predictable than with that of a normal length protein [130]. Success has also been demonstrated designing a riboswitch as a biosensor [147], and constructing artificial switches with natural aptamer domains [148]. There is a great deal more to uncover about the function and applications of riboswitches, and ongoing work in this field will continue in that effort.

4.2. Engineering the Third Tier—Summary

Environmental signal input is essential for programming cellular function. Using the paradigm of genetic circuits, much work has been accomplished to this end. In this regard, the powerful capability of bacterial quorum sensing appears to be a very effective means of synthetically controlling cells. Also, engineering activation and inhibition sites into proteins would allow greater control over biological processes, but work in this area lags due to the unsolved protein sequence-structure-function problem. A third way to engineer environmental inputs is through riboswitches, an approach that utilizes RNA as the receptor molecule. All of these areas continue to increase the degree to which the central dogma is rationally controlled for specific uses. This capability, coupled with the powerful techniques at the other two tiers, will lead to ground-up engineering of biology.

5. Integrative Synthetic Biology

As the development of synthetic biology tools continues to mature, one can envision studies moving towards ground-up cellular optimization on a level never before seen. Armed with powerful DNA writing technology, along with the knowledge and ability to manipulate cellular processes, the possibilities for the microbial engineer may become almost limitless. This will be the stage where all of the disparate synthetic biology tools and approaches will be able to be fused with one another, creating the field of integrative synthetic biology.

To date, very few examples of this kind exist since some of the tools and understanding of the parts are lacking. However, recent work from the Keasling laboratory and colleagues provides an initial picture of the type of studies that will be done in this new field. In order to produce the medically valuable product artemisinin, many different approaches have been used, including synthetic pathway construction [83], codon optimization [83], environmental signal detection [149], and mRNA secondary structure engineering [95]. Each of these strategies and tools was used in tandem to improve the biotechnological goal, and represent the earliest examples of the power of integrative synthetic biology.

Another example illustrating the reality of integrative synthetic biology is work on orthogonal central dogma machinery. It has already been discussed that the process units of transcription and translation are under a great deal of control, and the mechanisms concerning this control are not yet fully understood. This may overly complicate genetic manipulations, since heterologous gene expression in a host organism is subject to this native central dogma machinery, and the regulatory mechanisms may be unknown or inhibitory. One approach synthetic biologists could use to overcome this difficulty is by designing orthogonal, or noninteracting, transcription and translation machinery [150]. The concept of an orthogonal biological system is an excellent way to avoid undesired host interference, and this has recently been accomplished by combining the specific T7 bacterial promoter system with a previously developed orthogonal ribosome-rRNA pair [151]. This, in effect, creates two AND gates that must be satisfied for heterologous gene expression. Using these types of constructs for engineering control systems and integration of environmental signals may provide the fundamental knowledge needed to understand the more complex and interrelated systems of natural organisms, leading us one step closer to ground-up cellular optimization.

The integrative synthetic biology approach should bring the ability to perform wholesale cellular remodeling into the scope of a single research project. However, as has been iterated at many points in this review, much more basic knowledge and foundational research must be made in order to realize this scenario. In particular, our ability to predict and design components lags behind our ability to engineer them. However, the transformative work that is continually being done in synthetic biology inspires confidence that these techniques may soon be at the fingertips of the microbial engineer.

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Copyright

Copyright © 2010 Eric Young and Hal Alper. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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Results and discussion

Combining conditional activation of TF with 4tU-labeling to capture de novo transcribed targets

We modified the cell-based TF perturbation assay called TARGET (Transient Assay Reporting Genome-wide Effects of Transcription factors), which can identify primary TF targets from either TF-regulation (by transcriptomics) or TF-binding (by ChIP-Seq) events assayed in the same cell samples [6, 15]. Herein, we extended the TARGET system to include 4tU-labeling (TARGET-tU pronounced TARGETtwo”), which enabled us to capture de novo RNA synthesis induced by the conditional nuclear import of a TF-of-interest (Fig. 1a). TARGET and TARGET-tU are comparable with the main modifications applied in TARGET-tU being the introduction of 4-thiouracil (Additional file 1: Table S1). In the TARGET-tU assay, the TF-of-interest is expressed in isolated root cells, but is retained in the cytoplasm due to the interaction between the fused glucocorticoid receptor (GR) tag and the cytoplasmic heat shock protein (HSP90). Treatment with dexamethasone (DEX) disrupts the GR-HSP90 complex, allowing nuclear import of the TF. This conditional nuclear localization of the TF in the presence of 4tU enables the incorporation of labeled UTP into actively transcribed TF-targets (Fig. 1a). By performing DEX-induction of nuclear import following a pretreatment with cycloheximide (CHX, Fig. 1c), we can identify direct targets of a TF in the TARGET system [6, 15, 16], as has also been shown previously in whole plants [17].

One major advantage of 4tU-tagging of mRNA is that it covalently labels nascent transcripts only, and therefore it is ideally suited for detecting dynamic changes in transcription of transient TF-target interactions. Using affinity capture, nascent 4tU-labeled RNA can be distinguished from pre-existing unlabeled RNA (Fig. 1b). Conditional induction of TF nuclear import combined with metabolic 4tU-labeling of nascent transcripts, to our knowledge, has not previously been used in any other organisms. Importantly, the TARGET-tU approach can be adapted to study any candidate TF, providing a robust means of identifying actively transcribed TF-targets in the context of dynamic gene regulatory networks.

Capturing actively transcribed bZIP1 targets

We applied the TARGET-tU approach to study the mode of action of a master TF Basic Leucine Zipper 1 (bZIP1), a central integrator of metabolic signaling by carbon and nitrogen in plants [6, 18–22]. Specifically, to identify actively transcribed direct bZIP1 targets, following conditional TF nuclear import, we compared 4tU-labeled fractions between bZIP1 expressing cells (4tU-bZIP1) and an empty vector control (4tU-EV, Additional file 2: Figure S1) using microarrays. This enabled us to identify 283 newly synthesized mRNAs in the bZIP1-transfected cells, compared to the empty vector control (Fig. 2a). These direct bZIP1 targets shown in the heatmap in Fig. 2a, correspond to 115 genes whose transcription is actively induced by bZIP1, and 168 genes whose transcription is repressed by bZIP1 at the time of assaying (Additional file 3: Dataset S1).

Actively transcribed targets of the master TF regulator bZIP1. a Transcriptomics profile of 4tU-labeled mRNA fractions from cells overexpressing bZIP1 (4tU-bZIP1) and empty vector control (4tU-EV), 5 h after bZIP1 activation. A heat map shows the expression profiles of 115 genes induced and 168 genes repressed by bZIP1 using 4tU-labeling and selection of actively transcribed genes. Heat map shows z-score normalized expression for genes/rows (from light yellow to dark blue gradient) using Mev [29]. Actively induced and repressed bZIP1 targets are ranked according to the number of ACGT hybrid box (0–6 boxes: from blue to red gradient) present in the 1 kb promoter regions upstream of the transcription start site (TSS). Gene ontology (GO) terms over-representation of actively induced and repressed gene sets selected from a singular enrichment analysis using Arabidopsis genome (TAIR10) as a reference with an FDR cutoff of 5 % (also see Additional file 4: Figure S2 for complete GO terms enrichment). b Identification of the known bZIP1 ACGT binding motif based on cis-elements discovery using MEME [32] (top panel, 45 sites, e-value = 6.2e −11 ) and known cis-element enrichment using Elefinder [33] (bottom panel, 299 sites, e-value = 8.2e −5 ) using 1 kb promoter regions. The frequency of ACGT hybrid box identified by Elefinder in the 1 kb promoter regions of bZIP1 targets were plotted to show the proximity of binding sites relative to the canonical transcription start site

These 283 genes whose transcription is initiated or repressed by bZIP1 nuclear import are significantly enriched for gene ontology (GO) terms associated with the known functions of bZIP1 including, regulation of transcription as well as primary and secondary metabolic processes (Additional file 4: Figure S2 [21]). In addition, cis-element analysis of the promoter regions of these target genes identified the known bZIP1-binding site (Fig. 2b [23]). The bZIP-binding site (AGCT) was found in 170 bZIP1 targets (77 induced and 93 repressed direct targets), possessing up to 6 ACGT binding boxes in their promoters (Fig. 2a, Additional file 3: Dataset S1). This represents a total of 299 ACGT binding boxes identified in target genes specifically enriched in the proximal regions of the promoters (Fig. 2b), confirming that bZIP1 binds most proximal to the transcription start site [2]. Altogether, these results are consistent with actively transcribed genes captured by 4tU-labeling being direct targets of bZIP1.

Comparison of de novo transcribed targets from 4tU-labeled RNA to targets from total RNA

The differentially transcribed targets identified from 4tU-labeled RNA fractions were then compared to previously reported bZIP1 targets identified from total mRNA (Fig. 3 [6]). In the previous study, bZIP1 was proposed to mediate metabolic signals through a “hit-and-run” model of transcription. Based on analysis of direct targets identified by TF-regulation (microarray) or TF-binding (ChIP-Seq), Para et al., [6] uncovered three different classes of direct bZIP1 targets: Class I “poised” targets (TF bound but target not regulated), Class II “stable” targets (TF bound and gene regulation), and the largest Class III “transient” targets (gene regulation without observable TF-binding). In our prior TARGET study, regulation of target genes was determined as changes in steady-state mRNA at 5 h after bZIP1 nuclear import [6]. In this current study, we started the incorporation of the 4tU-labeled nucleobase 5 h after bZIP1 nuclear activation, for an additional 20 min (Fig. 1c, Additional file 2: Figure S1). Time-course ChIP-Seq studies showed that at this late time-point, bZIP1 has “run”, and the TF is no longer bound to the promoter region of its Class III transient targets (Fig. 3, Fig. 4a [6]). In our present study, the functional read-out captured by 4tU-labeling enables us to determine if such transient bZIP1 targets are indeed de novo transcribed at times when the binding analysis shows that bZIP1 is no longer bound to the promoters.

Active transcription persists 5 h after TF-induced nuclear entry, in both stably- and transiently-bound bZIP1 targets. A comparison of the overlap between actively transcribed bZIP1 targets captured from 4tU-labeled fractions (this study), and the previously reported classes of bZIP1 targets identified from total RNA [6]. Significant overlaps of induced genes (Class IIA & IIIA) and bZIP1-repressed genes (Class IIB & IIIB) are highlighted in yellow and blue, respectively. Intersects were performed using the “Genesect” function in VirtualPlant [30]. The significance of the overlaps from bZIP1 targets captured from 4tU-labeled fractions were verified using a hypergeometric test with Class IIA (stably-bound bZIP1 induced, p = 2.2e −22 ), Class IIIA (transiently-bound bZIP1 induced, p = 6.8e −50 ) and Class IIIB (transiently-bound bZIP1 repressed, p = 2.2e −20 ) targets

Transient targets initiated by a bZIP1 “hit” are actively transcribed after the TF has “run”. a-b bZIP1 mediates rapid and catalytic transcription in response to nitrogen signal. a Time-series ChIP-Seq binding 1 kb upstream and downstream of the transcription start site (TSS) for bZIP1 transient targets, NLP3, THA1 and MCCA at 1 and 5 min (early time-points), 30 min, 1 and 5 h (late time-points) after induced nuclear localization of bZIP1. The bZIP-binding motifs identified by known motif enrichment (also see Fig. 2) were placed on the x-axis of early time-points (2 ACGT boxes in NLP3 promoter at −826 and -584 bp, and 1 box in MCCA promoter at -126 bp). The bZIP1 ChIP-Seq time-course analyzed in this study was initially performed in [6]. b Gene expression levels of transient bZIP1 targets NLP3, THA1 and MCCA in 4tU-labeled fractions of bZIP1 expressing cells (4tU-bZIP1) compared to 4tU-labeled fractions of empty vector (4tU-EV *FDR < 0.1). Please note that the ChIP-Seq time course (a) and de novo transcriptomics (b) data were performed in two independent experiments. c “Hit and Run” mode of transcription for transient TF-targets

Overall, we find significant overlaps between de novo transcribed targets captured from 4tU-labeled RNA, and known bZIP1 targets identified from total mRNA studies [6] both performed at 5 h after bZIP1 nuclear transport (Fig. 3). Further, we observed large and highly significant overlaps between actively transcribed targets identified using TARGET-tU, and previously reported bZIP1 targets including stably-bound bZIP1 targets (Class II) and transiently-bound bZIP1 targets (Class III) (Fig. 3, Additional file 3: Dataset S1). Over 50 % of the bZIP1 de novo induced targets (60 out of 115 genes), were previously identified as bZIP1 induced primary targets based on steady state mRNA [6] (Fig. 3). However, the overlap of the current 4tU studies with genes repressed by bZIP1 based on steady state mRNA [6], captured a lower number of repressed bZIP1 targets, specifically within the Class II “stable” targets. These results indicate that the new TARGET-tU and the original TARGET methods [6, 15] are more robust to capture induced targets, than repressed targets. By contrast, for bZIP1 repressed targets, down-regulation in mRNA level can be caused by reduced transcription rate, but also by active mRNA degradation (e.g. through inherent instability or through miRNA directed cleavage). Nevertheless, TARGET-tU captured a large number of repressed bZIP1 targets (Fig. 2a) that have been overlooked by our prior bZIP1 TARGET studies based on steady state mRNA [6] (Fig. 3). Thus, the TARGET-tU approach enabled us to successfully recover a highly significant number of both up- and down-regulated targets (Fig. 3 Class IIA (stable, induced) = 14 genes (p = 2.2e −22 ) Class IIIA (transient, induced) = 46 genes (p = 6.8e −50 ) Class IIIB (transient, repressed) = 27 genes (p = 2.2e −20 ), confirming that bZIP1 is a dual-mode regulator (activator and repressor). Also consistent with previous findings, Class I “poised” bZIP1 targets, to which bZIP1 is bound but are not regulated [6], were not identified in the 4tU fractions of actively transcribed bZIP1 targets. Altogether, these results confirm that we successfully captured actively transcribed TF-targets, which are a significant subset of the bZIP1 target genes identified by steady-state mRNA studies.

Here, we captured a subset of 73 transient targets identified from the original TARGET approach based on steady-state mRNA [6] and from our new TARGET-tU study which captures only actively transcribed targets, 5 h after bZIP1 nuclear import (Fig. 3). While this overlap between the TARGET and TARGET-tU experiments is significant, the numbers are low. Although both methods TARGET [6, 15] and TARGET-tU (this study) are performed in comparable experimental setups (Additional file 1: Table S1), they likely assay different profiles of mRNA targets (Additional file 5: Figure S3). Indeed, the original TARGET [6, 15] measures the steady state pools of mRNA targets accumulated during the 5 h of TF activation. By contrast, TARGET-tU only captures actively transcribed targets after introduction of labeled nucleobases (Additional file 5: Figure S3). Thus, TARGET-tU is able to distinguish whether a gene is actively transcribed without hinderance of detection above background levels of pre-existing mRNA. This may explain the large number of novel bZIP1 targets detected using TARGET-tU, especially for the targets that are actively repressed by bZIP1 (Fig. 2a), and the significant - yet partial - overlaps between TARGET and TARGET-tU (Fig. 3). Nevertheless, combining both approaches, we captured a subset of 73 dynamic bZIP1 targets with very high significance and therefore validated the “hit-and-run” mode of action for this master TF.

Capturing de novo transcription proves that “hit-and-run” transcription persists beyond TF dissociation

The TARGET-tU approach, which identifies transient targets whose transcription is bZIP1 dependent, shows that such targets are actively transcribed when bZIP1 is no longer bound. Importantly, these transient targets include bZIP1 targets previously associated with nitrogen signaling (Fig. 4a and b). Indeed, the transiently-bound bZIP1 targets include NLP3 (NIN-LIKE PROTEIN 3), which belongs to an important TF family for early response to nitrate signaling in Arabidopsis [24]. Other transient bZIP1 targets validated by our 4tU study include THA1 (THREONINE ALDOLASE 1) and MCCA (3-METHYLCROTONYL-COA CARBOXYLASE), which encode enzymes involved in the catabolism of amino acids [25, 26]. Time-series ChIP-Seq assays show that bZIP1 binds to the promoter of these transient bZIP1 targets (NLP3, THA1 and MCCA) within 1 and 5 min of the TF nuclear entry (Fig. 4a). We also confirm that ChIP-Seq signals identified in NLP3 and MCCA promoters at early time-points, coincide with the presence of bZIP-binding motifs (Fig. 4a). However, at late time-points (30 min, 1 h and 5 h), the bZIP1 ChIP-seq signal peaks (Fig. 4a, blue peaks) are not significantly higher to the DNA input control (Fig. 4a, grey peaks). Therefore, while bZIP1 is bound to the transient targets at 1–5 min, it is no longer bound to the promoters of NLP3, THA1 and MCCA at 30, 60 min or 5 h after bZIP1 nuclear import (Fig. 4a [6]). Interestingly, active transcription of these transient targets continues after bZIP1 has “run”, as shown by transcriptomics of 4tU-labeled fractions (Fig. 4b).

Since the results from the ChIP-Seq time course (Fig. 4a [6]) and de novo transcriptomics (Fig. 4b) were analyzed from two different studies, we performed a second replication of the ChIP-Seq experiments to confirm the “hit-and-run” model of transcription. This replication of the bZIP1 ChIP-Seq experiment was performed at the 5 h time-point (the same time-point as the TARGET-tU). First, we compared this new replicate to the previous binding data from Para et al. [6] and confirmed that we obtain the same bZIP1 targets (70 % overlap, pval < 1e −10 ) from the two independent ChIP-Seq experiments (Additional file 6: Figure S4A). We also confirm that bZIP1 is no longer bound to the promoter of the transient targets 5 h after its nuclear entry, using two independent experiments (Additional file 6: Figure S4B, D). To further support the “hit-and-run” model, we show 10 additional examples of transient targets captured in de novo transcribed fractions using the TARGET-tU approach and we juxtaposed their binding and TARGET-tU expression profiles (Additional file 6: Figure S4B-E), confirming the active transcription of these dynamic bZIP1 targets.

Thus, our findings provide experimental support for a “hit-and-run” transcription model, which posits that transcription is initiated when the TF “hits” the gene promoter to organize a transcriptional complex, after which transcription by RNA polymerase continues after the TF “runs”. In this model, we postulate that master signal transducers like bZIP1 may act as “catalyst TFs”, possibly by physical recruitment of other TF partners (Fig. 4c). We also propose that such dynamic mode of action rapidly activates large sets of genes in response to environmental changes.


Introduction

Metabolic engineering seeks to increase the synthesis of desired products de novo and by modification of existing metabolic pathways or by optimization of appropriate genetic elements [1–4]. The use of different biological elements may help to tune expression to achieve the desired production level, provided that cell metabolism remains co-ordinated [5–8]. Some organizations, e.g. the Genetically Engineered Machine (iGEM) Foundation, have tested the efficiency of sequence elements to function as interchangeable components or biobricks that can be used to build biological systems. The goal is to assemble libraries of these elements that can be applied for the engineering of living cells.

Current efforts in synthetic biology have focused on the evaluation of cis-sequences including promoters, ribosome binding sites (RBS), and terminators in both Escherichia coli and Saccharomyces cerevisiae [9–12]. There are several successful examples of changing promoters to alter expression in prokaryotic and eukaryotic cells. Hal Alper et al constructed a promoter library based on the bacteriophage PL-λ promoter that was generated using error-prone PCR. The library was tested for promoter strength by measuring the downstream expression of gfp and the chloramphenicol acetyltransferase gene, and those that exhibited a linear relationship between promoter strength and reporter were selected. They then expressed a series of promoter-dxs (deoxy-xylulose-P synthase) constructs in a recombinant E. coli strain overexpressing genes (ispFD and idi) of the isoprenoid synthesis pathway, and observed the linear response of lycopene yield to promoter strength. [13]. In a second example, a strong and tunable promoter library was obtained that showed a range of 400-fold at the mRNA level [14]. This library was created by combining various copies of upstream activation sequences with the native promoter AOX1 from Yarrowia lipolytica. The final expression of humanized Renilla GFP (hrGFP) in Y. lipolytica was increased eightfold over the activity of the original endogenous promoter [14]. In this system, both a high output of heterologous protein and a strictly controlled metabolic pathway were engineered [14]. In another report, a set of insulated promoters, differing in strength and context-independent behavior, was designed and applied for controlled protein production. The properties of those promoter devices in one test context were predictive of those properties in a new context allowing steady-state protein production regulated by transcriptional regulation [15]. 195 native or synthetic promoters and 192 RBSs were characterized for their ability to drive the expression of superfold GFP (sfGFP) in Streptomyces venezuelae [16]. Next, an insulator sequence, RiboJ, was introduced to reduce interference between promoters and RBSs and these combinations were again tested by examination of sfGFP levels. The insulator element RiboJ is a DNA sequence that contains both a ribozyme and hairpin acts to help expose the RBS [16]. These synthetic modular regulatory elements were then inserted upstream of the lycopene biosynthetic cluster in S. avermitilis. The correlation between lycopene production and promoter strength, elevated lycopene titer as well, confirmed the utilization and feasibility of these expression cassettes and paved the way for further application of these promoter elements in system biotechnology [17].

B. subtilis shows significant advantages as a host for protein expression due to its efficient secretion capacity and because it is a generally recognized safe organism. It is used for applications in the detergent, textile, and pharmaceutical industries [18–20]. However, due to unsuitable vectors containing weak promoters, RBS sequences, terminators, incompatible antibiotic genes, or insufficient plasmid copy numbers [21], many heterologous proteins are produced with only low yields using these vectors. In addition, overexpressing genes of secretory components is time-consuming and inefficient, particularly due to the complexity of the secretion mechanisms [22,23]. There are recent reports from large-scale omic-studies of this Gram-positive bacterium [24–26] that discussed the variation of proteins or transcripts after different conditions of cultivation. However, they compared only a few examples for specific promoter strength and did not perform a comprehensive analysis. Some promoters were discovered or modified by optimization of their key elements [27] for application in target expression systems, but no systematic promoter candidate strength evaluation has been performed using experimental assessment of reporter gene transcription. Some accessible omic-data about promoter candidates was from B. thuringiensis rather from B. subtilis. In that study, Wang et al systematically identified 1203 active promoter candidates through analysis of genome-wide transcription start sites based on RNA-seq data. Additionally, they further evaluated the characteristics of 20 highly active promoters combined with the corresponding 5’-UTR to screen the highly active promoter-5’-UTR DNA region complex by directing the expression of reporter gene lacZ [28]. Therefore, an efficient and facile approach for achieving desired production goals is selection of a suitable promoter element to strictly control related protein expression levels and to allow precise and functional modularity [29].

Here, we constructed a promoter-probe vector with GFP as a detection target and measured expression levels. We wanted to identify stress-activated promoters that could direct high-level expression under specific stress conditions, like heat shock, high salt, or ethanol to stimulate membrane stress. More than 80 promoter candidates that showed high transcription levels under multiple conditions were selected and classified according to the encoded proteins located downstream from the promoters [30]. Promoter candidate activities, relative to that of the constitutive strong promoter of P43, [31], ranged from 0.0023 to 4.53, which spanned a

1960-fold range. After heat shock, salt, or ethanol treatment, they showed changed RFU levels when compared with normal cultivation, however, the changes were less than expected. The strong promoter PtrnQ exhibited higher activity than P43 in driving the transcription of both cytoplasmic and secretory proteins (about four- and two-fold, respectively), enabling normalized measurement of the promoter candidates’ strengths.


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