Team:William and Mary/Design
Design
Overview
The goal of the William and Mary 2021 iGEM project is to provide an easily accessible measure of the orthogonality of E. coli circuits, specifically orthogonality with regard to circuit-host interactions. While methodology for identifying undesirable interactions among circuit parts themselves is well studied, assessment of orthogonality between the circuit and the host physiology that goes beyond metabolic burden has received surprisingly little attention, with the few existing methods both costly and technically challenging. Our toolkit attempts to address this need. This toolkit employs circuits that provide measurements not only of classic burden at the transcriptional, translational, and post-translational level, but also includes more encompassing orthogonality markers for protease and heat shock, and identified by extensive data-driven analysis of RNA-seq data. These measurements serve as inputs for our integrative, comprehensive mathematical model that provides the end user with overall orthogonality metrics, with the goal of enhancing the efficiency, functionality, and safety of all synthetic biology circuits. Our design strategy is depicted in the flowchart below with more detail provided in the accompanying narrative.
1. Search for Orthogonality Assessment Methods
Our team completed a literature review of orthogonality assessment methods to understand how other researchers are assessing orthogonality. Our team found that many of these methods are expensive and not readily accessible to all iGEM teams. As a result, our team wanted to create a method which other teams could use to assess their circuit's orthogonality.
2. Determining Genes for Sensor Systems
2a. Select Burden Markers for all levels of the Central Dogma “Burden” Sensor System
After conducting our literature review, our team searched for “markers” of burden for our central dogma circuit, which is designed to sense burden at the transcriptional, translational, and post-translational levels. We choose these markers based on what we thought would best fit our needs and our model’s needs. However, after receiving input from individuals in the field through IHP, it was clear that we needed to tweak our design. Our Central Dogma sensor system consists of a set of circuits that are able to measure burden at every step of the central dogma: transcription, translation, and post-translation. Please see below to learn more about the marker genes we chose as our Central Dogma sensors.
Transcriptional Sensor:
After a thorough literature search, it became clear that the best method with which to sense transcriptional changes was through aptamers. When deciding which aptamer to use for our sensor, we focused on three aptamers specifically: Mango, Spinach, and Broccoli. These aptamers are widely regarded as “the most promising fluorescent RNA aptamers” (Shanaa et al., 2021). As Broccoli binds to a non-toxic, membrane-permeable dye (DFHBI-1T) with minimal effects on host activities, is widely used in vivo, and has a relatively simple use procedure, our team decided to use Broccoli as our aptamer (Okuda et al., 2017). However, we still needed to choose which form of Broccoli to use. We eventually decided on F30-Broccoli (Filonov et al.) as it has been shown that its scaffold is not recognized by nucleases for degradation like other aptamers, that it is regarded as the “best-performing aptamer”, and that it is widely used (Filonov et al., 2015; Thorn et al., 2017). Due to all these factors, our team decided to use F30-Broccoli (Filonov et al.).
Using this aptamer, we designed two transcriptional burden sensors. Our first design, WM21_011 (BBa_K3773511), places control of F30-Broccoli expression under the strong, constitutive Anderson promoter BBa_J23119 (Yan and Fong), without incorporation of any RBS or spacers. Our second design, WM21_012 (BBa_K3773512), places control of F30-Broccoli expression under the strong, constitutive P70a promoter without incorporation of any RBS or spacers. In both circuits, F30-Broccoli (Filonov et al.) is directly followed by the strong, synthetic, bidirectional terminator BBa_B1006.
In addition, we used the 3WJ dimeric Broccoli (3WJdb) circuit (pUC19-P70a-3WJdB-T), which was designed and constructed entirely by researchers Alam et al. (2017). This circuit contains the dimeric Broccoli aptamer placed within the three-way junction (3WJ) RNA motif, and this aptamer forms a secondary structure that is able to bind to a membrane-permeable, fluorescent dye known as DFHBI-1T. DFHBI-1T only fluoresces after binding to the Broccoli aptamer. This circuit places 3WJdb under the control of the constitutive P70a promoter. This circuit was acquired from Addgene (#87311).
Once the correct aptamer dye has been applied to cells transformed with this circuit, a fluorescence output will be produced from the aptamer. As the circuit does not include an RBS, this circuit is able to detect decreases in burden on the transcriptional level; therefore, based on the amount of aptamer transcribed, the fluorescence output will fluctuate providing a measure of the additional circuit’s impact on the host.
Translational Sensor:
Our team decided to use sfGFP as our marker gene in this circuit. As the amount of sfGFP translated is directly proportional to the amount of fluorescence produced, sfGFP can be used as an indicator of changes in translation levels. The translational sensor aims to detect changes in host translation with an additional circuit added. Decreases in sfGFP fluorescence with and without the desired tested circuit will reflect the impact of the additional circuit on hosts functions, reflecting circuit-host burden levels.
For our translational burden sensor circuits, we tested two designs. The first version of this circuit, WM21_016, was designed by researchers Ceroni et al. (2015) to test translational burden of genetic constructs within a cell. However, while researchers Ceroni et al. incorporated their circuit into the host genome, we utilized their circuit in plasmid form. Researchers Ceroni et al. used the strong constitutive promoter BBa_J23100 and an RBS designed (using an RBS calculator) specifically for codon-optimized sfGFP. The sequence for codon-optimized sfGFP was created for high expression levels using DNA2.0, and sfGFP was followed by a spacer and the synthetic terminator BBa_B1002. Based on Ceroni et al.’s circuit concept and design, our team designed a translational burden sensor (WM21_013) consisting of the strong, constitutive Anderson promoter BBa_J23119, the RBS B0034 with a spacer included, the codon optimized sfGFP sequence used by researchers Ceroni et al. (2015), and the strong synthetic, bidirectional terminator BBa_B1006.
Post-translational Sensor:
Our team developed our post-translational sensor circuits based on three different post-translational modifications: phosphorylation, glycosylation, and protease cleavage. Given that phosphorylation is so common in the cell, our team wanted to develop a sensor of this post-translational modification. In order to sense phosphorylation, our team is utilizing the EnvZ/OmpR system found natively in E. coli. EnvZ is an outer membrane protein that responds to changes in the osmolarity of the surrounding environment. When this protein detects osmolarity changes, it auto-phosphorylates then passes the phosphate to the OmpR protein. Depending on the osmolarity of the surrounding medium, OmpR will either bind to the OmpC promoter in high osmolarity states or to the OmpF promoter in low osmolarity states, producing these genes coding for additional outer membrane proteins (Cai et al., 2002). Our post-translational modification sensor uses this EnvZ/OmpC system to look at phosphorylation rates in the cell, as the phosphorylation of EnvZ causes OmpC production.
When we discussed our project with Dr. Barrick from UT Austin, he suggested that phosphorylation may not be an accurate measure of circuit-host burden due to the high levels of phosphate in the cell. To address this concern, our team also designed two sensors to indicate changes in levels of proteolysis. Each of these sensors places sfGFP after a promoter which normally controls the expression of a proteolysis-related protein. First, the lon promoter was chosen due to its importance as a protease and frequent upregulation upon introduction of a heterologous circuit. In E. coli, this promoter controls expression of the ATP-dependent Lon protease, which selectively degrades certain proteins. This protease is also upregulated in response to other forms of stress (https://www.uniprot.org/uniprot/P0A9M0, Oct 10. 2021). Lon has been observed to degrade about 50% of misfolded proteins in E. coli (He et al., 2018). Second, the hslVU (a.k.a. clpQY) promoter was chosen, as the hslU gene it controls was also found to be even more frequently upregulated in response to a heterologous circuit. hslU, also known as clpY, is the ATPase subunit of the HslVU complex. The HslVU complex forms a protease which is also part of the heat shock response (Lien et al., 2009).
In addition to phosphorylation and proteases as post-translational modifications, our team also looked at glycosylation to quantify the effect of circuit-imposed burden on the post-translational modification stage of the central dogma. The mur operon was the best candidate for protein glycosylation in non-pathogenic strains of E. coli because of its role in peptidoglycan synthesis. The ftsLp2 promoter was chosen because it initiates the transcription of genes responsible for a N-acetylglucosaminyltransferase (murG) and a UDP-N-acetylmuramate-L-alanine ligase (murC) in peptidoglycan synthesis (Barreteau et al., 2008; Vicente et al., 1998). This promoter is regulated by the DNA-binding transcriptional repressor LexA.
2b. Select orthogonality markers for “Beyond Burden” Sensors
2b1. RNA-Seq Analysis & Venn Diagrams:
In order to determine the effect a circuit has on all host processes, our team decided to use RNA-sequencing technology. Before designing our circuits, we conducted a literature review for entire genome RNA sequencing on E. coli strains before and after circuit induction or implementation. We collected the differentially expressed (DE) genes, along with their adjusted p-value, fold change, up or down regulation, plasmid copy number, E. coli strain, and circuit details. See the spreadsheet we created from published data in the literature here.
Next, our team used this data to find the most commonly reported DE genes after circuit implementation or induction. A hierarchy of DE genes was developed, ranking the genes based on the number of circuits in which they were DE, and their adjusted p-values. Lower tier priorities included how many sources reported them to be DE, fold changes, how many experiments they appeared DE in, and the consistency of their up or down regulation. After the hierarchy was established, protein products, GO molecular functions, and GO biological processes were collected for the genes that were most common and strongly expressed in E. coli with induced circuits. See the hierarchy spreadsheet here.
The names of the shared and unshared DE genes in the venn diagram above can be found here.
The names of the shared and unshared DE genes in the venn diagram above can be found here.
The names of the shared and unshared DE genes in the venn diagram above can be found here.
2b2. Choice of “Beyond Burden” sensors:
Our team used the data we collected from our DE analysis to build sensors of well-known differentially expressed genes. As these genes are DE, they are clear markers of a lack of orthogonality between a circuit and host. Looking at changes in the expression of these genes can therefore provide additional insight into the orthogonality of a circuit.
Heat Shock Proteins
Our venn diagram analysis revealed that genes coding for heat-shock proteins were the most consistently differentially expressed genes among a variety of experiments using different circuits and conditions. These common DE genes were ibpA, ibpB, htpG, dnaK, dnaJ, groS, and clpB. In general, these genes code for small heat shock proteins, chaperone proteins, and chaperonins that play a role in bacterial response to stressors such as fluctuating temperatures, limited availability of nutrients and exposure to harmful chemicals (Cha et al., 2020; Ceroni et al., 2018). With the reliance on chassis machinery to express products of synthetic circuits, it is expected that the host resources would be unnaturally consumed in this process and induce the heat-shock response (Ceroni et al., 2018). Thus, we aimed to use the changes in the expression of these genes as a marker for measuring how much synthetic constructs burdened their host cells and contributed to unwanted interactions between the host and the construct.
We first looked into the literature to see if sensors for these genes were already available. We found that in their 2018 paper “Burden-driven feedback control of gene expression”, Ceroni et al. had successfully created and tested GFP-expressing sensor plasmids that used promoters for ibpA and ibpB (both in the ibpAB operon) and htpG (regulated by two overlapping promoters htpG1 and htpG2). In addition to these genes which Ceroni et al. found to be commonly DE from their own RNA sequencing analysis, they had created another sensor for the gene groS which was also consistently highly upregulated in our venn diagram analysis. Although groS was not commonly DE for Ceroni et al. (2018), they utilized its expression as a burden marker because all the heat-shock genes identified as commonly DE were under the control of σ32. The groSL operon encodes heat-shock genes that control the amount and activity of σ32 in cells (Ceroni et al., 2018). Overall, we were able to order their sensor constructs ibpAB-GFP, htpG1-GFP, htpG2-GFP, and groSL-GFP from Addgene.org. Our team designed the remaining heat shock sensor circuits for dnaK, dnaJ, and clpB.
Secretion (AG43)
This circuit senses the downregulation of AG43 biofilm production in response to the burden imposed on a cell by the addition of a circuit.
Why AG43?Our venn diagram analysis revealed that the flu gene is usually significantly downregulated when a circuit is introduced to an E. coli cell. This consistent downregulation means that we can use flu gene transcription levels as yet another means to assess the effect of circuit-host interactions on the host cell. The flu gene controls biofilm production, specifically that of AG43, and we chose to use this gene as an indicator of cellular secretion being affected by burden.
As justification for our choice, we looked through the literature to see if anyone has had success with similar circuits and found some helpful prior studies. We found a study in which the flu promoter has already been successfully used with a reporter (Beloin et al., 2006). We found evidence of the flu promoter being operational when in combination with the coding region of another gene (Waldron et al., 2002). Lastly, we looked at a construct that enables amplification of the signal from the flu promoter by replacing its coding sequence with the T7 RNA polymerase gene. The resulting strain, LeoL194, was then transformed with the pHL32 plasmid carrying the gfpmut3.1 gene controlled by the T7 polymerase promoter (Lim et al., 2007). This experiment showed us that the flu promoter and reporter combination works even when integrated into the genome, something we anticipate doing with our circuits. We inferred from these prior studies that the flup1 and flup2 promoters should work well with our chosen sfGFP reporter.
3. Select Delivery Platform
After our team decided what markers to use for our circuits, it was necessary to decide which circuit delivery method to use. Originally, our team was deciding between three different delivery methods: in vivo, in vitro, and genome integration. After meeting with Dr. Barrick, Dr. Smith, Mr. Ortiz, and Mr. Marken, our team was able to receive input on the potential methods.
There was wide agreement that the in vitro method is the most widely accessible method cost-wise. However, Dr. Smith and Dr. Barrick pointed out that an in vitro system may not be as accurate. Dr. Smith discussed potential issues with a lack of compartmentalization in the in vitro system. He worried that since these compartments are so crucial to cell dynamics, without them the system may not be as accurate. Dr. Barrick stated that these systems may not be able to effectively reproduce the fitness costs associated with an engineered cell. As the accessibility of our model is a key part of our project, we wanted to find a way to utilize the in vitro method without compromising accuracy. To address these issues, our team decided to use whole cell lysate as part of our in vitro protocol without any additional materials added to the system. Hopefully, this change will help to increase the capabilities of the system to reproduce fitness costs. However, Mr. Marken and Mr. Ortiz pointed out that it is difficult to get in vitro systems to work correctly. Mr. Ortiz pointed out that in vitro systems are cheap when a team is able to make their own mixes. However, the process is very difficult and can become expensive if teams are not able to make the mixes. Mr. Marken suggested that we develop all of these methods in parallel and Mr. Ortiz suggested that we use an in vitro system.
In response to Mr. Marken’s and Mr. Ortiz’s suggestions, our team also developed an in vitro method. Our in vitro method consists of plasmids which will be transformed into the cell along with the plasmid to be tested. This method seemed to provide some accessibility with the advantage of being an easier system to operate.
Finally, our team created a method to integrate our system into the genome. Integrating our sensor circuits into the host cell genome would decrease the burden caused by plasmid DNA and unintended interactions between the foreign plasmid and the host genome. This integration would also result in less steps required for teams to assess orthogonality, as only the circuit being tested would need to be transformed into our sensor strain. We decided on a method of integration based on the lambda RED and CRISPR Cas9 systems, avoiding the long process of expressing burdensome antibiotic resistance selection genes and then potentially having to attempt to remove them after selection. Additionally, this method is self-curing, so all plasmids transformed to facilitate integration will cease propagation, preventing further burden to the host. By using pCas and pTargetF from Jiang et al. and modifying pTargetF to target yjcS and carry our sensor circuits (WM21_013 and WM21_021) with yjcS homology, we would be able to integrate our translational and stress sensor circuits into the E. coli genome for teams to assess the orthogonality of their circuit with a simple transformation.
4. Design Circuits
These circuits were designed to sense changes in orthogonality and burden levels after the introduction of an additional circuit. When transformed with the plasmid to be tested, there will be a change in fluorescence output. This change can be compared to the fluorescence output without the additional circuit. The accompanying change in fluorescence can then be input into our model to receive an orthogonality assessment.
*We understand that some of these circuits fit into both the Post-Translational and “Beyond Burden” categories and were, therefore, grouped for clarity.
4a. In vivo System:
Transcriptional:
The transcriptional sensor consists of a strong Anderson promoter (BBa_J23102) driving transcription of the F30-Broccoli aptamer with a strong terminator (BBa_B1006) (Fong et al., 2017; Filonov et al., 2015; Cambray et al., 2013).
Translational:
This sensor encodes sfGFP under the control of a strong Anderson promoter (BBa_J23119) and B0034 with BBa_B1006 as the terminator (Fong et al., 2017;Clifton et al. 2018; Cambray et al., 2013).
Post-Translational:
Phosphorylation
Our circuit consists of the OmpC promoter and BBa_B0034 as the RBS, driving production of sfGFP with Bba_B0015 as the terminator (Clifton et al., 2018; Ceroni, et al., 2015; Hartvig et al., 1996; Genbank V01146). Our construct builds off of the 2012 Tokyo-NoKoGen construct (BBa_K769001), using their same promoter sequence; however, our team is using sfGFP instead of the original GFP (Tokyo-NoKoGen 2012 iGEM Team, http://parts.igem.org/Part:BBa_R0082 ).
Proteases
These two circuits consist of the lon and hslVU promoters driving production of sfGFP (Ceroni et al., 2015). The strong RBS BBa_B0034 was used to promote transcription, and transcription is halted by the BBa_B1002 terminator (Prakash, 2018).
Glycosylation
This circuit consists of the ftsLp2 promoter, which is responsible for the N-acetylglucosaminyltransferase (murG) and a UDP-N-acetylmuramate-L-alanine ligase (murC) in peptidoglycan synthesis (Barreteau et al., 2008; Vicente et al., 1998). Downstream of ftsLp2 is a codon-optimized sfGFP gene that allows fluorescence to be expressed at levels proportional to the mur operon for easy quantification of post-translational modification activity in response to circuit-imposed burden. This circuit also utilizes BBa_B0034 as an RBS driving production of sfGFP with BBa_B0015 as the terminator (Clifton et al., 2018, Ceroni, et al., 2015, Hartvig et al., 1996, Genbank V01146).
“Beyond Burden” Heat shock circuit:
Our overall design for the remaining heat-shock sensor circuits for dnaK, dnaJ and clpB consisted of a promoter that was upstream of the individual heat-shock gene coding region or its operon in the native E. coli genome followed by sfGFP. Since the dnaK and dnaJ genes are under the control of the same dnaK operon promoter (Cowing et al., 1985), we designed a single sensor for measuring the expression levels of both genes. The promoter sequence obtained from Cowing et al. (1985) included the spacer and RBS regions, thus we only added the sfGFP and terminator (BBa_B1006) sequences (Ceroni et al., 2015; Cambray et al., 2013). For clpB, we created two versions of our sensor circuit. Each version uses the promoter for the native gene in E. coli followed by one of two RBS regions: either BBa_B0034 or the native RBS preceding the gene in E. coli. The promoter and RBS control the expression of sfGFP, which is halted by BBa_B1002 in both circuits (Ceroni et al., 2015; Prakash, 2018).
In vivo “Beyond Burden”AG43:
The flu gene is part of an operon including the isrC and flu genes. The operon contains three promoters: flup, flup1, and flup2. Flup activates both isrC and flu, whereas flup1 and flup2 each only activate the flu gene, therefore we eliminated the possibility of using flup as our promoter due to its lack of specificity (RegulonDB, 2019). We designed two versions of the Secretion Circuit, one using flup1 as the promoter, and the other using flup2. Since flup2 is downstream and thus closer to the start of the gene, we expected this circuit to perform better, however we tested both versions in case this hypothesis was incorrect.
This circuit also utilizes BBa_B0034 as an RBS driving production of sfGFP with BBa_B0015 as the terminator (Clifton et al., 2018, Ceroni, et al., 2015, Hartvig et al., 1996, Genbank V01146).
4b. In vitro:
Transcriptional:
The transcriptional portion of our in vitro system utilizes the Spinach2 aptamer to sense transcription levels in the host cell (Strack et al., 2013). Our team chose to use two different aptamers for our in vitro and in vitro circuits as Spinach has been shown to be more effective in in vitro systems than Broccoli (Autour et al., 2016). In this circuit, the Spinach2 aptamer is under the control of the constitutive P70a promoter (Marshall et al., 2019). Through the utilization of a constitutive promoter, the user receives a real time comprehensive analysis of the transcription rates of the cell over time. Like all of the other circuits in this system, transcription is ended by the T500 terminator, which is widely used in many in vitro systems (Marshall et al., 2019). With a cotransformation of this circuit and the circuit to be tested, teams will be able to compare changes in fluorescence outputs, providing an input for our mathematical model. This value can then be plugged into our team’s model to output an orthogonality assessment, which will enable teams to determine if their circuit is affecting host transcription rates.
Translational:
To drive transcription of sfGFP, this circuit contains the constitutive P70a promoter and associated RBS, with the T500 terminator halting transcription (Ceroni et al., 2015; Marshall et al., 2019).
Post-Translational:
As the EnvZ/OmpR system requires an intact plasma membrane to function, our team was not able to use our OmpC promoter construct for the in vitro system. As a result, we decided to use our proteases sensor. Our post-translational in vitro sensing circuit measures the impact of the circuit on protease activity. Using this measurement, teams will be able to look at a change in fluorescence output as a representation of a decrease in protease activity due to unwanted host interactions. This circuit contains lon promoter driving transcription of sfGFP halted by the T500 terminator (Cha et. al, 1999; Ceroni et al., 2015; Marshall et al., 2019).
Test Circuits
In order to test the efficacy of our circuits, our team designed a protocol to determine if our circuits are able to accurately sense orthogonality changes.
To build our circuits, we ordered this sequence as a gene block from IDT DNA with UNS1 and UNS10 sequences flanking the gene block in order to insert it into the 1C3 plasmid backbone. Once our gene block arrived, we gibsoned it into the appropriate backbone and transformed it into NEB 5-alpha cells. To confirm the efficacy of our transformation, we performed a colony PCR using a forward primer of UNS1 and a reverse primer of UNS10. Once the PCR was run, we performed a gel electrophoresis on the product to confirm that the insert was the appropriate length. Our gel electrophoresis was able to confirm that colonies contained the appropriate plasmid lengths. These confirmed colonies were grown overnight and converted into glycerol stocks, which were then used for our official circuit testing protocol.
Within our official testing protocol, colonies are grown overnight and diluted in the morning in a 1:50 dilution. These diluted colonies were then left to grow and measurements of fluorescence were taken at the following time intervals: 0, 1, 6, 12, 24, and 48 hours. These measurements were taken using a plate reader with excitation and emission values set to 485 and 528, respectively. Each experimental replicate included a positive control, WM21_013 which we confirmed to be fluorescent, and a negative control, the untransformed bacterial strain.
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