iGEM 2023 — Team sheffield

| Growth Slower | Status |
| --- | --- |
| J23100 | Successful |
| gp2 | Successful |
| J23119 | Successful |
| RBS (B0015) | Successful |
| gp240 | Successful |
| gp0.7 | Successful |
| gp79 | Successful |
| AsiA | Successful |
| Alc | Successful |
| J23104 | Unsuccessful |
| gp106 | Unsuccessful |

{% extends "layout.html" %}
{% block title %}Engineering Cycle{% endblock %}
{% block lead %}Sheffield 2023 followed the design-build-test-learn cycle many times, especially through wet lab experiments. Here, we describe how the engineering cycle was used to improve our plasmid design.{% endblock %}
{% block page\_content %}

Every day, the team went through engineering design cycle iterations in most experiments and protocols used. From decreasing the amount of DNA loaded on a gel so as not to saturate the imager, to increasing the extension time in a PCR to get more discrete and clearer bands. However, a few significant changes stood out as part of the design, build, test and learn approach.

![](https://static.igem.wiki/teams/4939/wiki/assets/assets/igem-eng-cycle.png)

At the early stages of the project, the team selected one of the JUMP backbones (pJUMP28-a) as the vector to make the constructs with. The team also intended to use Golden Gate cloning to build entire transcriptional units (TUs) comprising a promoter, ribosome binding site (RBS), coding sequence (CDS) and terminator, using only parts from the distribution kit.

Upon completion of the first attempt to assemble the first set of plasmids, each with their own 4 parts and vector, the team quickly realised that the method to screen colonies for the right plasmid we utilised (colony PCR), did not yield any meaningful results. What is more, pJUMP28-a contains a sfGFP by default and the intention was to clone in efasGFP (another green fluorescent reporter), which meant there was no way to differentiate the colonies with the ligation plasmid from the colonies with empty backbone visually. In theory both, successful ligation colonies and colonies from non-digested or recircularised pJUMP28-a would have appeared green. Additionally to this, the plates also showed non-fluorescent colonies, which made results even more puzzling.

The team tried to get around this problem by digesting pJUMP28-a and pJUMP29-a and swapping their inserts. This way the high copy number Ori from pJUMP28-a would have been preserved but it would have been combined with a lacZ gene, allowing for blue-white screening, potentially solving the problem. Nevertheless, when gel extraction was attempted to recover the correct insert and the correct backbone, a very low yield was produced. This was attempted a few times prior to switching tactics one last time.

The final decision was to use a pET28a vector instead, which encodes its own RBS, terminator and inducible pLac. Harnessing the Lac mechanism allowed for the attainment of different levels of gene expression by varying the concentrations of the inducer. This way, for fluorescent constructs it was very easy to tell which colonies had the right construct, since only correct ligation colonies would express a GFP. Anyhow, the team still screened them through colony PCR and sent them to sequence for a definitive check. For non-fluorescent constructs, colony PCR also yielded significantly better results than when using JUMP backbones. An explanation for a better success rate is that doing four ligations at once is considerably more error-prone than doing a single one.

Another example of a very significant learning outcome from the engineering design cycle pertains to the incorporation of DpnI digests.
To get the version of pET28a that was needed, inverse PCR was used to linearise, amplify and give BsaI restriction sites to the backbone. After the team attempted to ligate growth slowing genes (GS) into pET28a, with DNA from colonies that went through the screening process were sequenced as a last check. Nonetheless, the results indicated the DNA contained just the empty pET28a vector, without an insert.

This most likely means that some of the original circular backbone made its way through the purification process (including a gel extraction) and ended up mixed with the linearised backbone and transformed into the *Escherichia coli* that was subsequently mini-prepped. Most of the sequencing results received showed the empty pET vector.

The team immediately brainstormed to find a solution and thought of the DpnI enzyme, which cleaves DNA when its recognition site is methylated. This means products from a PCR (which are unmethylated) wouldn't be cleaved, but the original PCR template would be, producing only linearised pET28 as a result.
After making this single addition to the methods, 7 of our 8 following ligations were successful.

Another instance in which the engineering cycle was applied and reflected upon was after completion of the first two plate reading experiments, where fluorescence of vsfGFP in pET28a was measured under different concentrations of IPTG. In the first experiment the concentrations of IPTG ranged from 0 to 1000 µM in 100 µM steps and most cultures saturated the plate reader's fluorescence sensor. As a result, in the second trial the concentration range was reduced to 0 to 100 µM with smaller steps. Even in this trial, all the cultures with concentrations above 6 µM saturated the fluorescence sensor.
Hence, for the final fluorescence reading of pLac-driven plasmids, the range of IPTG concentrations employed was 0 to 5 µM. This ensured that all the curves remained within the sensor's working range and the data could be extracted and analysed. This iterative approach allowed for the acquisition of more useful data.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}How we developed our experiments, and the protocols we used to get our results{% endblock %}
{% block page\_content %}

## Introduction

### iGEM Sheffield Biological Context

Growth modulation is a foundational-level biotechnology, with multiple applications. PARSE aims to produce a proof of concept plasmid that enables the modulation of the growth of *E. coli* in a fine-tunable manner, in this case, using an IPTG-inducible promoter (pLac) and phage-derived growth slowing (GS) genes as modulators. Additionally, it aims to lay a foundation whereby any user can calibrate their own inducible systems and utilise their own reporters to achieve a similar objective.

To achieve this, the devised strategy was to assemble the following constructs:

1. **Anderson promoter- vsfGFP-0** in pET28a: A fluorescent reporter expressed by a range of constitutive promoters of known strength relative to the wild type (J23119).
2. **pLac- vsfGFP-0** in pET28a: A newly characterised fluorescent reporter expressed by an inducible promoter.
3. **pLac- GS** in pET28a : A GS expressed by an inducible promoter.

pET28a was the vector of choice due to its inbuilt Lac inducible system, most importantly containing pLac, an IPTG-inducible promoter (further considerations are explained here ). As a result, a wide range of levels of gene expression can be obtained by varying the concentrations of IPTG.

The purpose behind the first set of plasmids is to establish a baseline for gene expression measurements in terms of a constant scale. The second plasmids allow calibration of any inducible system (Lac in this case) in relation to the Anderson scale. The third set of plasmids, allow to test the functionality of the GS as well as to characterise them thoroughly by providing a link between the level of gene expression and the bacterial growth achieved.

The four Anderson promoters used in assembly were J23100, J23106, J23110 and J23114, from strongest to weakest promoter strength respectively (**Figure 1**), and the six GS that were successfully inserted into pET28a are listed in **Figure 2**.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a1.png)![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a2.png)
![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a3.png)![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a4.png)

Figure 1: Anderson-vsfGFP constructs in pET28a backbone, created on Benchling

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a5.png)

Figure 2: Growth slower constructs in pET28a backbone, created on Benchling.

The purpose of computational modelling is to determine the strength of the inducible promoters (used in plasmid constructs 2 and 3) that express the green fluorescent protein gene, causing the transformed bacteria to fluoresce. The strength of each promoter is quantified by the amount of fluorescence per cell to describe the amount of gene expression of the reporter gene within the microbial culture. Additionally, the levels of the gene expression of the GS could be further visualised with the predicted decrease of the specific growth rate. See our [biomodelling](https://2023.igem.wiki/sheffield/biological-modelling) page for more information.

### Hypothesis

Growth rate is a crucial parameter to be considered during the expression of genetic constructs in bacteria as it gives insight into the relation between gene product generation and the metabolic state of the culture [1]. Gene expression and biomass increase remain constant as long as nutrient requirements are satisfied. Otherwise, output efficiency is reduced [2,3]. The development of a strategy to actively regulate the growth rate will ensure a constant product output and will open the possibility of including intermediate growth rates when convenient.

Growth modulator genes can be used as synthetic biology tools to achieve this objective. Their effect on bacterial growth can be positive (“growth enhancer”) or negative (“growth slower”). The latter includes several phage-derived genes which hinder host DNA and/or mRNA synthesis and thus growth rate while the expression of phage-derived genes is maintained [4]. The assembly of an artificial genetic system that takes advantage of growth-slower genes to precisely control bacterial growth requires tight control of their expression in the first place. Our proposed system is composed of a plasmid containing one growth-slower gene expressed under an IPTG-inducible Lac promoter. A calibration with plasmids containing Anderson promoters and GFP-containing plasmids relates the presence of a reporter molecule to the expression level of our genetic unit and makes it possible to link this overall expression with the observed values of optical density (OD) in the culture.

Our working hypothesis is that the expression of our growth slower-based genetic system will result in a decrease in the bacterial growth rate depending on the promoter strength. The activation of the system (with an inducer molecule such as IPTG) will be reflected in a lower growth rate of the culture. The growth inhibition level will be proportional to the concentration of the inductor molecule.

### Anderson calibration

At the first stage of the project, four plasmids that combined a promoter from the collection of Anderson promoters of different strengths and one fluorescent reporter gene (vsfGFP-0) were constructed in a pET28a vector (Figure 1) and transformed into E. coli BL21-Lemo bacteria.

Through using these plasmids, promoters of specific strengths (Table 1) (which have been extensively characterised and quantified) can be linked to the respective level of gene expression they produce, using the fluorescence of the reporter gene as a measure of it. This makes these plasmids essential as a first step towards the calibration of a fine-tunable inducible system.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a6.png)

Figure 3: Construct for GFP expression calibration under the control of different Anderson promoters.

| **Promoter** | **Measured Strength** |
| --- | --- |
| J23100 | 1.00 |
| J23106 | 0.47 |
| J23110 | 0.33 |
| J23114 | 0.10 |

Table 2: Selected Anderson promoters and their strengths. From: http://parts.igem.org/Promoters/Catalog/Anderson

Fluorescence was measured at EM: 535nm and, EX: 485nm (these were the closest wavelengths available in the plate reader used. The protein properties are actually EM: 510nm and EX: 485nm). The expected standard curve that can be plotted from these results is shown in Figure 4, where higher measured fluorescence values correspond to Anderson promoters of higher strengths. Optical Density was also measured so that a normalised value representing “fluorescence per cell” could be extracted.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a7.png)

Figure 4: GFP expression under the control of different Anderson promoters. A maximum fluorescence intensity value was obtained for each promoter. Promoter strength is shown in each case.

The second set of plasmids constructed involves utilising the Lac IPTG-inducible promoter the pET28a contains by default, and coupling it with the same reporter gene (vsfGFP-0) to measure the different levels of fluorescence generated by the constructs when induced with different concentrations of IPTG [5]. (Figure 5). This allows to relate the constant Anderson promoter strengths to the concentrations of inducer, a more arbitrary parameter, and build a standard curve in relation to inducer concentration, which will make the desired system more fine-tuneable than the discrete and limited catalogue of Anderson promoters.

The expected standard curve is shown in Figure 6, where increasing concentrations of inducer result in higher levels of fluorescence. Results of the experiments conducted can be consulted [here](https://2023.igem.wiki/sheffield/results).

It is important to stress that, anyone who intends to use PARSE may choose their own reporter gene as well as their own inducible system. The calibration of their inducible system should be done in a similar fashion, using the Anderson promoters as the link between a consistent measuring tool and an inducible mechanism to be calibrated.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a8.png)

Figure 5: Calibration construct for IPTG-inducible GFP expression.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a9.png)

Figure 6: GFP expression under the control of the Lac promoter and different concentrations of IPTG

The last combination of plasmids built contain pLac coupled to one of six growth slowing genes (see Table 2 and Figure 7- initially we planned on testing seven but did not manage to clone gp0.7). Inducing the transformed cells with IPTG should slow down their growth as the growth slowing genes begin to be expressed. Their growth repressor function mechanism has been previously described in literature (See References)

|  |  |  |
| --- | --- | --- |
| **Gene** | **Target in Host** | **Reference** |
| gp79 | RNAP (core) | [ 6 ] |
| gp104 | Dnal (helicase loader) | [ 7 ] |
| gp2 | RNAP (β′ subunit) | [ 8 ] |
| gp240 | DNA Pol III (β subunit) | [ 9 ] |
| asiA | σ70 factor | [ 10 ] |
| alc | RNAP (core) | [ 11 ] |

Table 2: Selected Growth Slower Genes

Experiments to measure optical density of E. coli transformed with these plasmids were carried out. By measuring OD at different inducer concentrations and having established a relationship between the concentration of inducer and gene expression, this allows for thorough characterisation of the growth slowing genes and the effect they produce on growth rate depending on how much they are expressed. See the expected behaviour in Figure 8.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a10.png)

Figure 7: Construct for growth slower genes (GS) expression under the control of an IPTG-inducible promoter

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a11.png)

Figure 8: Construct for growth slower (GS) genes expression under the control of an IPTG-inducible promoter

Part of our future perspective includes obtaining a set of final constructs that combine an Anderson promoter with one GS gene. The previously obtained parameters on gene expression and effect on OD allowed us to calculate the relative gene expression of our genetic system and relate this parameter with growth rate. For this purpose, the proposed general structure of the set of plasmids for growth control included a combination of an Anderson promoter, an RBS and one GS gene (Figure 9). The total number of constructs obtained was 35, including all the combinations of the 5 selected Anderson promoters and the 7 GS genes.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a12.png)

Figure 9: Constructs for growth slower genes expression under the control of an Anderson promoter

### Determination of Relative Gene Expression

Using the set of generated constructs, the expression and function of our growth control genetic system were tested as follows:

**A**. We measured the GFP fluorescence when the corresponding GFP was expressed under different relative strength Anderson promoters to determine a relationship between the relative strength of the Anderson Promoters, the amount of GFP they produce, and what concentration of IPTG this equals to (Figure 10). Promoter strength is shown in each case.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a13.png)

Figure 10: Determination of Relative GFP expression under the selected Anderson promoters

**B**. We also used the previous data gathered during the calibration stage to model what the growth rate of E. coli would be if the GSs were expressed by constitutive Anderson Promoters of different specific strengths (Figure 11). Promoter strength is shown in each case.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a14.png)

Figure 11: Determination of Growth Rate as the effect of the expression of our genetic system under the selected Anderson promoters

The overall strategy to monitor the expression and the effect on E. coli growth of our genetic system is stated within Biology and Notebook in more detail, but a 96 well plate reader was used to measure OD (600nm) and fluorescence (Ex λ 485 and Em λ 510). Anderson promoters have constitutive expression so the GS gene is also constitutively expressed, which results in a decrease in the growth rate. The final decrease in bacterial growth rate will depend on the promoter strength and the GS gene used.

## Required Input

Biological modelling requires definition of the known parameters of the considered elements in our system (such as Anderson promoters' strength) and the relationship between them to generate a computational model. In addition, experimental data gathered from our laboratory work was integrated to obtain an enhanced model that better represented the real system. The integrated data is listed as follows:

### Optical Density of *E. coli* cultures

The main input parameter constantly monitored during our experiments was the Optical Density (OD) values of the culture media using a 600nm light beam, as the standard physical property to measure bacterial growth. OD can be defined as the negative of the logarithm (base 10) of light transmission, where the transmission varies between 0 and 1 (Figure 13). OD is calibrated at the beginning of the culture with fresh culture media as blank and it increases as more cell numbers are present because of a decrease in light transmission. $$\begin{equation}\text{OD} = -\log\_{10}\left(\frac{I}{I\_0}\right)\end{equation}$$

Formula to calculate OD (\(I\): Intensity of light transmitted through a sample, \(I\_0\): Intensity of light transmitted through a reference sample (blank)).

After bacteria inoculation in a fresh culture, OD was followed through the different bacterial growth phases: Lag, Exponential (or Log) and Stationary (Figure 14). The monitoring of the OD over time in a bacterial culture follows a sigmoidal curve. The duration of the full cycle depends on the bacterial replication time, being about 20 minutes in E. coli. For data analysis growth rate was defined as the change of OD over time, during the exponential phase. Maximum OD was also measured in each culture corresponding to when the culture reached maximum cell density. An additional phase can be present at the end of the growth cycle (Death or Decline phase) but these values were not considered in our experiments.

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a15.png)

Figure 14: Bacterial growth phases

## Fluorescence Intensity

Fluorescence intensity measurement parameters depends on the nature of the fluorescent protein employed. For our experiments, the measurements were taken considering the values for the maximum excitation and emission of the vsfGFP protein, which are 485nm and 510nm, respectively (Figure 14).

![](https://static.igem.wiki/teams/4939/wiki/assets/protocols/figs/a16.png)

Figure 14: vsfGFP spectra to measure fluorescence intensity (from http://parts.igem.org/Part:BBa\_E0040)

Expression Inducer Concentration will be the main input variable in our system. IPTG (in mM) was the molecule used during the calibration stage. For other potential applications (such as drug or antibiotic effects) any other molecule can be used. The genetic system requires to be inducible by each of these molecules.

---

## Protocols

---

### Competent Cells

DH5α strain of *Escherichia coli* was used for DNA amplification *in vivo* and BL21 LEMO strain was used as the protein expression strain. Heat shock transformations were carried out using this protocol. Initially, the iGEM protocol for competent cell production was used. However, due to a low transformation efficiency, it was changed to a protocol provided by the Perak Lab team as of the 22nd of August, and all subsequent transformations were in cells that resulted from this method.

[Initial competent cell protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/chang-et-al-jemi-methods-vol-1-pg-22-25.pdf)
[Perak Competent Cell Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/competent-cells.pdf)

---

### Agar Plates and Overnight Cultures

LB agar (2.5% Millers) for 25 mL plates, or LB for 5 mL overnights (2.5% Millers) was mixed with either ampicillin (100 ng /μL), kanamycin (50 ng /μL) and chloramphenicol (25 ng /μL) dependent upon the antibiotic resistant gene present in the plasmid transformed into *E. coli*.
Agar plates were grown at 37 C overnight, and liquid overnights were grown at 37 C and 220 RPM overnight.

[Overnight Culture and LB Broth Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/liquid-culture.pdf)

---

### DNA Resuspension

DNA from the iGEM distribution kits was resuspended following iGEM's protocol.
Primers and gBlocks from Integrated DNA Technologies (IDT) were resuspended to 100 µM and 10 ng /µL respectively, following their advice.

[iGEM Distribution Kit Resuspension Protocol](https://technology.igem.org/distribution/handbook)
[IDT Primer Resuspension Protocol](https://eu.idtdna.com/pages/education/decoded/article/tips-for-resuspending-and-diluting-your-oligonucleotides)
[IDT gBlock Resuspension Protocol](https://eu.idtdna.com/pages/education/decoded/article/tips-for-working-with-gblocks-gene-fragments)

---

### Minipreps

The Nippon GeneJet Miniprep kit was used, and protocol was followed. Solutions were left to elute for five minutes instead of two before the final centrifugation step, and elutions were made with 30 μL.

[Mini-Prep Time Saver Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/mini-prep-time-saver.pdf)

---

### PCR

50 μL PCR solutions were made following New England Biolab's (NEB) Q5 High-Fidelity 2× Master Mix protocol. 1 ng from gBlocks (Integrated DNA Technologies) or 1 µL from previously miniprepped DNA was used as template, along with 2.5 μL of 10 μM primers, 25 μL of 2x Q5 High-Fidelity Master Mix, and made up to volume with autoclaved Milli-Q or DEPC-treated water from Invitrogen-ThermoFisher.

[PCR Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/pcr.pdf)

---

### Colony PCR

10 μL PCR solutions were made with 0.2 μL of 10 μM primers, 25 μL of 2× PCRBIO Taq DNA Polymerase, and made up to volume with autoclaved MQ or DEPC-treated water from Invitrogen-ThermoFisher. Samples of colonies from a ligation plate were taken and submerged onto the solution in the tube to use as template, and the same colonies were then scratched onto an agar plate. Every time protocol was carried out, at least 3 colonies were screened.

[Colony PCR Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/colony-pcr.pdf)

---

### Gel Electrophoresis

Gel electrophoresis was carried out on 0.7%, 1% and 3% m/v agarose gels depending on BP length and gel extraction. All gels were made up using a 1× TAE buffer and dyed with either SYBR Safe (Thermo Fisher) or Midori Green Advance (Nippon Genetics). Gels were run at 100 V for one hour. A 1 kb Plus Ladder (Thermo Fisher) was used, and 6× Purple Gel Loading Dye (New England Biolabs) was added. Gel extractions were carried out using a Gel Extraction Kit (Nippon Genetics) following the standard protocol.

[Gel Electrophoresis Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/gel-electrophoresis.pdf)

---

### Heat Shock Transformations

Heat shock transformations were carried out using the iGEM transformation protocol.

[iGEM Transformation Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/heat-shock-transformation-protocol-26-7-23.pdf)

---

### Restriction Digests

The NEBcloner restriction digest tool was used to find the correct protocol for each restriction enzyme. *BsaI-HFv2, BsmBI-v2, NcoI* and *DpnI* (all New England Biolabs), were used at moments to perform restriction digests.

---

### Building Plasmid Constructs

---

### 96-Well Plate Experiments

A 96 well plate reader recorded fluorescence (where relevant) and OD readings at 15 minute intervals, for a day (97 cycles). All wells contained 150 μL of culture. All cultures were diluted to a standardised OD of 0.05 and 20 μL of them were added to the relevant wells. The remaining 130 μL volume contained LB media and the relevant antibiotics and inducer in their respective required concentrations. See the linked file below.
The plate reader employed was a Hidex Sense Microplate Reader Type 425-311 (g/n 320-0250).

[Plate Map produced](https://static.igem.wiki/teams/4939/wiki/assets/protocols/96-well-plate-maps-trial-1-6-9-23.csv), with concentrations and volumes for each well marked.

---

### Glycerol Stocks

Glycerol stocks for long term culture storage were made following Addgene's protocol.

[Glycerol Stocks Protocol](https://static.igem.wiki/teams/4939/wiki/assets/protocols/creating-a-glycerol-stock.pdf)

---

### References

[1] S. Klumpp, Z. Zhang, and T. Hwa, "Growth rate-dependent global effects on gene expression in bacteria," Cell, vol. 139, no. 7, pp. 1366–1375, Jul. 2009. doi: https://doi.org/10.1016/j.cell.2009.12.001.
[2] D. J. Jin, C. Cagliero, and Y. N. Zhou, "Growth rate regulation in Escherichia coli," FEMS Microbiology Reviews, vol. 36, no. 2, pp. 269–287, Feb. 2012. doi: https://doi.org/10.1111/j.1574-6976.2011.00279.x.
[3] C. N. Peterson, M. J. Mandel, and T. J. Silhavy, "Escherichia coli starvation diets: essential nutrients weigh in distinctly," Journal of Bacteriology, vol. 187, no. 22, pp. 7549–7553, Nov. 2005. doi: https://doi.org/10.1128/JB.187.22.7549-7553.2005.
[4] B. Roucourt and R. Lavigne, "The role of interactions between phage and bacterial proteins within the infected cell: a diverse and puzzling interactome," Environmental Microbiology, vol. 11, no. 11, pp. 2789–2805, Nov. 2009. doi: https://doi.org/10.1111/j.1462-2920.2009.02029.x.
[5] "Anderson Promoter," . doi: http://parts.igem.org/Promoters/Catalog/Anderson.
[6] "BBa\_K4451009," . doi: http://parts.igem.org/Part:BBa\_K4451009.
[7] J. Liu, M. Dehbi, G. Moeck, F. Arhin, P. Bauda, D. Bergeron, M. Callejo, V. Ferretti, N. Ha, T. Kwan, J. McCarty, R. Srikumar, D. Williams, J. J. Wu, P. Gros, J. Pelletier, and M. DuBow, "Antimicrobial drug discovery through bacteriophage genomics," Nature Biotechnology, vol. 22, no. 2, pp. 185–191, Feb. 2004. doi: https://doi.org/10.1038/nbt932.
[8] B. Bae, E. Davis, D. Brown, E. A. Campbell, S. Wigneshweraraj, and S. A. Darst, "Phage T7 Gp2 inhibition of Escherichia coli RNA polymerase involves misappropriation of σ70 domain 1.1," Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 49, pp. 19772–19777, Dec. 2013. doi: https://doi.org/10.1073/pnas.1314576110.
[9] B. A. Hesselbach and D. Nakada, ""Host shutoff" function of bacteriophage T7: involvement of T7 gene 2 and gene 0.7 in the inactivation of Escherichia coli RNA polymerase," Journal of Virology, vol. 24, no. 3, pp. 736–745, Mar. 1977. doi: https://doi.org/10.1128/JVI.24.3.736-745.1977.
[10] M. Ouhammouch, K. Adelman, S. R. Harvey, G. Orsini, and E. N. Brody, "Bacteriophage T4 MotA and AsiA proteins suffice to direct Escherichia coli RNA polymerase to initiate transcription at T4 middle promoters," Proceedings of the National Academy of Sciences of the United States of America, vol. 92, no. 5, pp. 1451–1455, Mar. 1995. doi: https://doi.org/10.1073/pnas.92.5.1451.
[11] M. Kashlev, E. Nudler, A. Goldfarb, T. White, and E. Kutter, "Bacteriophage T4 Alc protein: a transcription termination factor sensing local modification of DNA," Cell, vol. 75, no. 1, pp. 147–154, Jan. 1993.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results and Analysis{% endblock %}
{% block lead %}The results of our project are found here, as well as analysis of the data.{% endblock %}
{% block page\_content %}

## Experimental Setup

Our experimental data were gathered by transforming E. coli with the constructed plasmids and measuring the optical density (OD) and fluorescence of the corresponding cultures. This series of experiments allowed us to characterise vsfGFP-0 with respect to sfGFP, the strength of the Lac promoter, as well as to characterise the Growth Slowing (GS) genes. A typical experiment was conducted in a 96 well plate and its contents were measured using a Hidex Sense Microplate Reader Type 425-311 (g/n 320-0250). There were 150µL of culture in each well in total. 130µL of these were made up of 2.5% Millers LB media with kanamycin (50ng/µL) and chloramphenicol (25ng/µL). The remaining 20µL were made of the relevant culture diluted to an OD of 0.05.

These experiments involved monitoring the optical density (OD-600nm) (and fluorescence for fluorescent cultures) of LEMO BL21 transformed *E. coli* every 15 minutes during a 24-hour period. For the experiments involving pLac, several IPTG concentrations were tested, ranging from 0 to 1 mM. For the Anderson promoter experiments, a different promoter from the catalogue was used in each construct. Our computational analysis was based on visual comparisons of the bacterial growth and fluorescence behaviour using the R software to produce the required graphs.

### vsfGFP-0 Characterisation (Construct 1): First Attempt

Our first experiment intended to measure OD and fluorescence values of the construct containing vsfGFP-0 under the control of pLac, for an IPTG range from 0µM to 1000µM. The findings shown in Figure 1 indicate that the induction of the promoter resulted in a sustained increase in fluorescence when the vsfGFP-0 was present, compared to the empty pET28a backbone. A different sfGFP-containing plasmid (pJUMP28-1a) was included as a positive fluorescence control. In later measurements, at high IPTG concentrations, all the samples with this construct overpower the sensor after 2,400,000 relative fluorescence units (RFU). In addition, it was also seen that samples with high IPTG concentrations exhibited shorter lag phase periods. Although our results confirmed the fluorescence increment after IPTG-inducible promoter activation, some data recordings were incomplete as the optical sensor became saturated before we could observe a proper plateau in them. The 96-well plate maps (found here: [supplementary data](https://2023.igem.wiki/sheffield/supplemental)) include a detailed description of all the assessed well plates.

| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0µM | 10µM | 50µM | 100µM | 200µM | 300µM | 400µM | 500µM | 600µM | 700µM | 800µM | 1000µM |

| A | B | C | D | E | F | G | H |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pET28a with pLac + vsfGFP | pET28a with pLac + vsfGFP | Negative Control- Only pET28a | Positive Control-pJUMP | pET28a with Lac +vsfGFP | pET28a with Lac + vsfGFP | Negative Control- Only pET28a | Positive Control- pJUMP plasmid |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

Table:1 Well-Plate Key used in Figure 1 below.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/constryct-1-f.png)

Figure 1: Effect of IPTG induction of vsfGFP-containing plasmids on fluorescence.
Fluorescence increased proportionally to IPTG concentration only when the vsfGFP-0 insert was present in the plasmid (rows A and B). The empty pET28a plasmid showed no fluorescence increase (rows C and D).

![](https://static.igem.wiki/teams/4939/wiki/assets/results/construct-1-od.png)

Figure 2: Effect of IPTG induction of vsfGFP-containing plasmids on OD.

OD was increased after IPTG administration to a greater extent when the vsfGFP insert was present in the pET28A plasmid (rows A, B, E, F). The optical sensor was overwhelmed in these conditions but not in the rest of the samples. These included negative controls of PET-28 without any fluorescent genes or the inducible promoter, respectively (rows C and G). Also a different plasmid pJUMP28-1A was included as reference (row D). Lastly, a pET28a plasmid with fluorescent genes but lacking the promoter was also included as negative control (row H).

### vsfGFP-0 Characterisation (Construct 1 V2): Second Attempt

In our previous experiment, the optical sensor was saturated by the fluorescence of most of the induced cultures. As a result, the working IPTG concentration range was reduced to 0 to 5 µM. vsfGFP-0 expressed by the constitutive Anderson promoter J23110 was also added as a positive fluorescence control. Additionally, this experiment intends to compare the fluorescence of vsfGFP-0 and its parent molecule, sfGFP-0. Therefore, the fluorescence of cultures containing plasmids with these genes (in a pET28a vector) was measured. In a similar way as before, fluorescence and OD were measured every 15 minutes during a 24 hour period and the results are shown in Figure 3 and Figure 4.

| A | B | C | D | E | F | G | H |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pET28a with pLac + vsfGFP | pET28a with pLac + vsfGFP | Negative Control- Only pET28a | Positive Control-pJUMP | pET28a with Lac +vsfGFP | pET28a with Lac + vsfGFP | Negative Control- Only pET28a | Positive Control- pJUMP plasmid |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

| A | B | C | D | E | F | G | H |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pET28a with pLac + vsfGFP | pET28a with pLac + vsfGFP | Negative Control- Only pET28a | Positive Control-pJUMP | pET28a with Lac +vsfGFP | pET28a with Lac + vsfGFP | Negative Control- Only pET28a | Positive Control- pJUMP plasmid |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

Table 2: Key of Well Plate Maps used for Construct 1 V2 analysis

All inducible constructs showed an increase in fluorescence as the concentration of IPTG increased, although those from rows A and B had a much steeper increase than those in rows C and D, as shown in Figure 2. This suggests an intrinsic superiority in the brightness of the protein expressed by the bacteria. The growth control, as expected, showed near-zero levels of fluorescence and the fluorescence control saturated the sensor. Well D1, which was meant to be a blank, was contaminated when loading the plate, which matches the unexpected growth shown by the measurements.

When formatting the plots depicting fluorescence with respect to time, it is very easy to see that for every sfGFP construct at a particular IPTG concentration there is a vsfGFP-0 analogous that exhibits a higher fluorescence corresponding to the same inducer concentration every time. See Figure 3 where the fluorescence values have been corrected against the fluorescence of the blank.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/copy-of-fluorescence-construct-2-3.svg)

Figure 3. Shows mean corrected fluorescence (RFU) measurements with respect to time (h) for different concentrations of IPTG in cultures transformed with vsfGFP-0 in pET28a, and a positive fluorescence control under the control of the J23110 Anderson promoter is also included.

Processing the data further gives some more interesting insight. If the mean values of fluorescence at a given time are divided by the optical density at that time, the quotient will be a magnitude representing "fluorescence per cell". This is the most important metric. Since GFPs (and in particular sfGFPs) are so stable, it is necessary to obtain a normalised value for fluorescence (as indicated in Equation 9 (from [biological modelling](https://2023.igem.wiki/sheffield/biological-modelling))), since in time the GFPs will build up and the values of fluorescence will become an indicator of how long the bacteria have been expressing the protein rather than how fluorescent the protein actually is. In Figure 4 it can, once more, be clearly seen that the F/OD increases as the inducer concentration increases and the vsfGFP-0 always leads in magnitude with respect to its sfGFP analogous when both are subjected to the same IPTG concentrations. Moreover, it is important to note the trough in F/OD at approximately four hours. This might indicate an offset between the times when the logarithmic phase begins for growth rate and when it begins for GFP production, or it could also denote the required maturation time for GFPs to begin fluorescing. It seems as though cells begin dividing rapidly roughly five hours before they begin producing protein, and are subsequently surpassed by a rapid increase in GFP production. This would explain the decrease in the F/OD quotient from when measurements started to be recorded until around five hours later.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/mean-fod-construct-2-1.png)

Figure 4. Shows fluorescence per cell (RFU/OD) measurements for different concentrations of IPTG in cultures transformed with vsfGFP-0 in pET28a, and a positive fluorescence control under the control of the J23110 Anderson promoter is also included.

Finally, utilising all the data points measuring fluorescence per IPTG concentration we can plot the trend followed by the cultures, shown in Figure 5. This indicates that the fluorescence of the cultures with each of the reporters tested increases with the concentration of inducer. However, vsfGFP-0 increases significantly faster than sfGFP establishing, indicating considerably higher brightness. At the highest IPTG concentration, overall, the measured fluorescence of the culture containing the vsfGFP-0 gene was 2.5 times more fluorescent than that of sfGFP. It should be highlighted that these trendlines do not cross the origin. Additional to potential systematic errors in measuring, this could depict leaky expression of the T7-pLac which manages the expression of the GFP gene, as has been previously described in literature[2]. The error bars were measured based on the standard error of mean of each point. It should be noted that mean was done between the corresponding plasmids (A and B or B and C). Moreover, as seen at 3.5μΜ, the error bars overlap. This happens because the two values have a range higher than 500,000 RFU OD-1. In addition, these relationships could be used further to predict relative maximum F/OD depending on the concentration of inducer. This shall be explored further.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/mean-fod-per-iptg-1.svg)

Figure 5. Shows the trends in fluorescence (RFU) per IPTG concentration (uM) for cultures transformed with vsfGFP-0 in pET28a, sfGFP in pET28a and pET28a with no insert.

In conclusion, it seems reasonably clear the improvement vsfGFP-0 presents over its parent molecule sfGFP as a reporter, as equivalent gene expression results in significantly higher fluorescence in the former.

![](https://static.igem.wiki/teams/4939/wiki/assets/assets/registry-assets/bba-k4939000-gfp-5um-comparison.png)

Figure 6. Depicts the case where the concentration of IPTG is 5uM, and draws a clear representation of the conclusion that has been arrived at.

![](https://static.igem.wiki/teams/4939/wiki/assets/assets/registry-assets/bba-k4939000-pretty-pictures.png)

Figure 7. Allows for a visual comparison between brightness of the two cultures transformed with the two different fluorescent reporters.

For additional information see our registry part page: [http://parts.igem.org/Part:BBa\_K4939000](http://parts.igem.org/Part%3ABBa_K4939000)

### Promoter Activity Calculation

As shown in Figure 1 from the Biological Modelling section, the promoter activity is a magnitude that quantifies transcription and translation of vsfGFP-0 stimulated by the pLac promoter. This measure is extremely important as it can be used as a means of calibrating the pLac inducible promoters against constitutive promoters such as the Anderson promoters. Based on the modelling Equation (11), it is expected that the promoter activity is affected by the growth rate (\(\mu\)), the fluorescence per cell at steady-state (\(f\_{ss}\)) and the maturation constant (\(m\)).

The parameters are determined from the graphs illustrated above. The growth is determined from the relationship of the log2OD over time, where the slope is calculated. The \(f\_{ss}\) is determined from the F/OD graph illustrated in Figure 4 above, where the plateau indicates the maximum fluorescence per cell, while the maturation constant is determined from the Equation (1) below after finding the maturation time. The maturation time is the period taken when the non-fluorescent vsfGFP-0 protein begins to fluoresce. Assuming that the maturation time is similar to the parent strain, sfGFP, is 13.6 min, the maturation constant is calculated as follows:$$\begin{equation}\frac{\ln(2)}{\text{GFP Maturation time (h)}} = m\end{equation}$$$$\begin{equation}\frac{\ln(2)}{13.6/60} = 3.0059\end{equation}$$

As a result, The table of the measurements and calculations can be seen below in table 2 Promoter activity Calculation:

| A | B | C | D | E | F | G | H |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pET28a with pLac + vsfGFP | pET28a with pLac + vsfGFP | Negative Control- Only pET28a | Positive Control-pJUMP | pET28a with Lac +vsfGFP | pET28a with Lac + vsfGFP | Negative Control- Only pET28a | Positive Control- pJUMP plasmid |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

![](https://static.igem.wiki/teams/4939/wiki/assets/results/promoter-activity-graph.png)

The graph illustrating the sigmoidal relationship of the Promoter Activity (RFU OD-1h-1) with the log [IPTG] of the vsfGFP construct.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/relative-promoter-activity.svg)

Figure 8b: Red represents the vsfGFP-0. This graph is obtained by dividing the promoter activity by the corresponding concentration of IPTG.

The trend shown in the relationship between the two variables is an illustration of a sigmoidal curve as modelled by Hill’s kinetics..It represents the non-linear behaviour of the promoter activity rate as it slowly decreases. Therefore, it is estimated that the maximum promoter activity is at \(1.45\times 10^6\text{RFU}\;\text{OD}^{-1}\text{h}^{-1}\) with the equilibrium constant being \(0.225\times 10^6\text{RFU}\;\text{OD}^{-1}\text{h}^{-1}\)

Using the Anderson promoter activity values,the promoter activity of pLac at varying [IPTG] can be reduced by taking the same particular F/OD value for Anderson and pLac promoters and assuming that the level of fluorescence is indicative of the same level of gene expression and thus promoter activity. This can be validated by calculating the promoter activity of two of the Anderson promoters, and using their strength relative to one another, to confirm that the change in promoter activity between the two is still correlated to this difference in strength. This methodology constitutes a way to determine absolute promoter activity metrics for virtually any inducible promoter, with the only parameter needing to be swapped out within the experimentation being the promoter and associated inducer.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/comparing-anderson-and-iptg.png)

Figure 9: Calculating promoter activity of Andersons based on previously calculated promoter activity for pLAC at various [IPTG].

However, this is only theoretical as the Andersons over-saturated the sensor and therefore, the steady state was never measured. Furthermore, more data would be needed in order to predict the promoter activity with more accuracy and for more ranges of IPTG.

### Anderson Promoters + vsfGFP-0 (Construct 2)

A relevant part of our project consists of characterising the strength of the selected Anderson promoters in our genetic system. Anderson promoter collection represents a versatile option to test transcription activity. It comprises a wide-covering catalogue of constitutive promoters with constant activities with known values (promoter strength), defined by their particular nucleotide sequence. In our case, this series of experiments would have helped to determine the fluorescence values that we should expect during a lesser or greater activity of our PARSE genetic system.

We found that fluorescence increased for all the Anderson constructs whereas it did not for the positive and negative growth controls (uninduced pLac-vsfGFP-0 and LB respectively). The fluorescence control with sfGFP-pJUMP28-a showed an increase in fluorescence as well. The rate of fluorescence increase of the Anderson constructs was different depending on the promoter used and followed the qualitative behaviour expected based on their relative strengths described in the [Anderson catalogue page](http://parts.igem.org/Promoters/Catalog/Anderson).

The fluorescence increase rate and the maximum fluorescence values were taken into account for the calculation of the overall activity of our system. These calculations were essential for the subsequent comparison and estimation of the PARSE system activity using an inducible promoter.

| A | B | C | D | E | F | G | H |
| --- | --- | --- | --- | --- | --- | --- | --- |
| pET28a with pLac + vsfGFP | pET28a with pLac + vsfGFP | Negative Control- Only pET28a | Positive Control-pJUMP | pET28a with Lac +vsfGFP | pET28a with Lac + vsfGFP | Negative Control- Only pET28a | Positive Control- pJUMP plasmid |
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |

![](https://static.igem.wiki/teams/4939/wiki/assets/results/anderson-plot.svg)

Figure 10: Effect of Anderson promoters on fluorescence increase.

Fluorescence intensity increased at a high rate for the plasmids cloned both with J23106 and J23110 and a lower rate for J23114 (see figure 10), which was expected given that their strengths are meant to be comparable and high with respect to J23114 based on the Anderson catalogue page. However, J23106 showed a slightly lower increase rate than J23110 which contradicts the relative strengths recorded in the Anderson catalogue. J23100 was also cloned and tested but showed a very slow fluorescence increase rate, comparable to the uninduced pLac, which was very unexpected since this was, theoretically, the strongest promoter. We later confirmed that the genetic sequence cloned was not right through sequencing.Fluorescence intensity increased at a high rate for the plasmids cloned both with J23106 and J23110 and a lower rate for J23114 (see figure 10), which was expected given that their strengths are meant to be comparable and high with respect to J23114 based on the Anderson catalogue page. However, J23106 showed a slightly lower increase rate than J23110 which contradicts the relative strengths recorded in the Anderson catalogue. J23100 was also cloned and tested but showed a very slow fluorescence increase rate, comparable to the uninduced pLac, which was very unexpected since this was, theoretically, the strongest promoter. We later confirmed that the genetic sequence cloned was not right through sequencing.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/andy3-plot.svg)

It is worth noting that, initially, the team intended to form the link between gene expression and fluorescence (or growth rate in the case of the GS) with respect to the less-arbitrary (i.e. constitutive) Anderson promoters, rather than the inducible pLac. Nevertheless, since the data for the Andersons could not be measured entirely as the plate reader’s sensor was overwhelmed, we resorted to characterising vsfGFP-0 and the GS employing our inducible system.

In parallel, the effect of the Anderson promoters on the bacterial growth in E. coli was studied. The OD600 values were simultaneously measured in the same samples and collected during the full duration of the experiment. We found that bacteria transformed with a plasmid containing vsfGFP-0 under the control of an Anderson Promoter divided slower when compared to vsfGFP-0 under the control of pLac, as well as the empty pET28a vector and a GFP-containing plasmid (pJUMP28-1a) as independent constitutive GFP expression control(Figure 11).

One possible explanation for the decrease in this natural cell function can be attributed to global protein synthesis restraints caused by the metabolic burden of augmented GFP production in these genetic profiles [1]. The metabolic burden can be defined as the draining of nutrients, raw materials and energy from the cell metabolism as a result of an abnormal increase in protein production. In our case, the directly affected function is the bacterial growth. Further experiments would be required to confirm this hypothesis.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/anderso-plot-od.svg)

Figure 11: Plots of the Log2OD versus time of all wells, exhibiting the effect of gene expression driven by Anderson Promoters on OD.

## Growth Slowers (Construct 3): Characterisation of the expression of the PARSE system on the bacterial growth in E.coli

The PARSE genetic system developed by iGEM Sheffield is composed of a pET28a plasmid containing one GS gene. When activated, by means of IPTG (the inducer), the GS is expressed and the expected response is a decrease in the rate of growth of the culture. This response can be modulated by varying the concentration of IPTG. To confirm that the GS genes worked in E.coli, and to calculate the growth slowing impact they have on the bacteria, a series of experiments were performed.

The first test consisted in determining if the sole presence of the pET28a vector produced any inhibitory response on bacterial growth. For this purpose, the effect of different concentrations of IPTG was compared in pET28a-transformed E. coli (see figure 14). Despite some inhibition observed in the lowest concentration tested (1µM), the higher concentration (2 -4µM) did not show any inhibitory effect on growth. With this result, we discarded the possibility of an intrinsic inhibitory effect of the vector.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/plot-pet28-gs1.svg)

Figure 12: Effect of IPTG on bacterial growth in the presence of the pET28a vector.

Considering that a PARSE plasmid contains both a GS and a fluorescent reporter gene, the next objective was to confirm that the GS was the only significant cause of the resulting growth inhibition. From the previous experiments, the highest tested concentration of IPTG was taken (4 µM) to compare the bacterial growth when the vector contained a GS gene and when it contained vsfGFP-0. This would help determine whether the growth-slowing effect observed was due to the metabolic burden imposed by sheer protein production rather than the identity of the protein produced.

A representative example of our findings is shown in Figure 15, where bacterial growth was markedly affected when the GS was present in the plasmid. If growth-slowing depended mostly on metabolic burden only it would be likely that the constructs transformed with vsfGFP-0 would grow at a lower rate than the ones transformed with the GS, since compared to most GS, vsfGFP-0 is made of at least three times as many residues.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/plot-vsfgfp-gp2.svg)

Figure 13: Comparison between the effect of gp2 and vsfGFP expressions on bacterial growth in the presence of 4 µM IPTG.

We decided to test a variety of growth slowers with different functions, some from E. coli phages and some from Staphylococcus phages. This way we tested both for feasibility proposing different applications as well as context-independence.

By comparing the increase in OD at different IPTG concentrations with a growth control we can determine whether the GS have indeed worked or not. Figures 14-19 show this. Out of the six cloned growth slowers, gp2, gp240 and AsiA yielded positive results. Gp240 is a DNA clamp inhibitor derived from a Staphylococcus phage. The two other successful growth slowers come from Escherichia phages. This could suggest homology between the clamp structures of both species, and may further implicate their broad applicability to several genera of bacteria. Additionally, gp240 has a different function to gp 2, which is an RNA polymerase inhibitor, and having interventions at different levels in metabolic pathways allows for a variety of applications.

On the other hand, gp79, gp104 and AsiA did not yield any significant change in growth rate with respect to the growth control. Later, it was confirmed through sequencing that the Alc construct had an incorrect DNA sequence.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/gp2.png)

Figure 14: Growth curves showcasing the effect of gp2 expression driven by different concentrations of IPTG.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/gp240.png)

Figure 15: Growth curves showcasing the effect of gp240 expression driven by different concentrations of IPTG.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/asia.png)

Figure 16: Growth curves showcasing the effect of AsiA expression driven by different concentrations of IPTG.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/gp79.png)

Figure 17: Growth curves showcasing the effect of gp79 expression driven by different concentrations of IPTG.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/gp104.png)

Figure 18: Growth curves showcasing the effect of gp104 expression driven by different concentrations of IPTG.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/alc.png)

Figure 19: Growth curves showcasing the effect of Alc expression driven by different concentrations of IPTG.

The next comparison was focused on the growth inhibition effect of a wider IPTG concentration range. An example of this comparison is shown in Figure 20 (for gp240 gene) and Figure 21 below (for AsiA gene). As expected, a higher expression of the GS gene (by means of a higher IPTG concentration) was reflected in reduced bacterial growth. The highest concentration included (1000 µM) exhibited the maximum growth inhibition. A similar comparison was performed for each GS gene.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/plot-gp240-gs2.svg)

Figure 20: Effect of gp240 expression on bacterial growth

Bacterial growth (in OD units) was progressively reduced when gp240 expression was induced by inductor presence (0-1000 µM). The greatest decrease occurred in the presence of 1000 µM IPTG.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/log-plot-asia-gs2.svg)

Figure 21: Effect of IPTG on bacterial growth in the presence of the AsiaA GS.

Bacterial growth (in OD units) was progressively reduced when AsiA expression was induced by inductor presence (0-1000 µM). The greatest decrease occurred in the presence of 1000 µM IPTG.

Finally, several GS were simultaneously compared to identify differences between their growth inhibition effects. Taking into account that each GS acts through a different mechanism of action, this comparison could help to select the best GS-inducer concentration - response profile according to particular experimental needs. This comparison for the three successful GS is shown in figure 22.

![](https://static.igem.wiki/teams/4939/wiki/assets/results/plot-that-gs2.svg)

Figure 22: Comparison of the IPTG effect on bacterial growth in the presence of the Alc, gp104 and gp2.

Bacterial growth (OD) was progressively reduced as GS expression was increased by increasing inducer concentration (0-1000 µM). In all cases, the greatest decrease occurred in the presence of 5µM IPTG.

Supporting Information for the R-code and supporting files can be found in this [Gitlab repository](https://gitlab.igem.org/2023/software-tools/sheffield/-/tree/main/MOD-BIO/R_official_documents)

## References

[1] Bentley, W. E., Mirjalili, N., Andersen, D. C., Davis, R. H., & Kompala, D. S. (1990). Plasmid-encoded protein: the principal factor in the "metabolic burden" associated with recombinant bacteria. Biotechnology and bioengineering, 35(7), 668–681. https://doi.org/10.1002/bit.260350704
Rahmen, N., Fulton, A., Ihling, N., Magni, M., Jaeger, K. E., & Büchs, J. (2015). Exchange of single amino acids at different positions of a recombinant protein affects metabolic burden in Escherichia coli. Microbial cell factories, 14, 10. https://doi.org/10.1186/s12934-015-0191-y

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