iGEM 2024 — Team athens

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{% block title %}Notebook{% endblock %}
{% block lead %}Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.{% endblock %}
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![Contribution Icon](https://static.igem.wiki/teams/5406/images/icons/sprite-07.png)

# Notebook

Wet Lab Notebook
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

This page is dedicated to the daily record keeping of our laboratory experiments. More specifically, we provide all the necessary information taken through real-time documentation of our wet lab activities in chronological order, including the progress of each week, as well as challenges we faced along the different experimental procedures. A summary of the experimental sequences is found at the beginning of each week, followed by a detailed analysis of each day spent at the laboratory, concluded by our highlighted observations and results.

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![Engineering Icon](https://static.igem.wiki/teams/5406/images/icons/sprite-12.png)

# Engineering

Engineering Overview
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

Welcome to the Engineering page of iGEM Athens 2024: **e-PHAESTUS**. Our journey to
engineering success followed the foundational Design-Build-Test-Learn cycle. This
approach helped us to improve our ideas and successfully get over the challenges we
encountered.

Throughout the development and experimentation of our project , we applied these cycles to
four key aspects of our project: **Conceptualization, Gene Cloning Experiments, Protein
Expression, and Chemical Engineering**. We welcomed both success and failure as
chances for development at every turn. This mentality was crucial to making breakthroughs
and accomplishing our goals.

![Engineering Image 1](https://static.igem.wiki/teams/5406/images/engineering/engineering0.webp)

### Cycle 1: Conceptualization

#### Design

The three months of constant research in e-waste management and bioleaching resulted in
our final gene selection : HCN Synthase. More specifically, we are intrigued by the HCNabc
operon that was found in pseudomonas putida and pseudomonas fluorescens. This operon
enables the production of the valuable HCN from cheap amino acids like glycine and
threonine. HCN is crucial because CN- ions are the most traditional leachate in gold
dissolution, yielding very high recovery rates. [1],[2],[3]

Further literature review indicated that glycine is the optimal amino acid and that a pH level
of 9 would minimize the emission of the highly toxic HCN, while enhancing CN- activity. 10
T o recover the metals, we consulted professors from the Chemistry Department specializing
in Inorganic and Polymer Chemistry. Dr. Efthimiadou, Methenitis, and Pitsikalis guided us in
synthesizing an amphiphilic copolymer. The first part is made from polystyrene (PS) ensures
that the polymer is insoluble, while the second hydrophilic part from polyacrylic acid (PAA) is
responsible for binding metallic ions. This membrane can interact with and bind metals
depending on the solution's pH. Specifically, at pH < 3, the hydrophilic group exists in a
molecular form (OH), whereas at pH > 9, the ionized group interacts as shown below. [1],[2],[3]

![Engineering Image 2](https://static.igem.wiki/teams/5406/images/engineering/engineering2.webp)

Overall, the PCBs (printed circuit boards) would be placed in a culture of transformed
bacteria during their exponential phase. The pH of the growth medium would be regulated at
9, and the metals would be left for one week. Afterward, the solution would be filtered,
retaining the metal residues and bacteria in the original container. The filtrate would contain
soluble Au-CN complexes. T o recover the metals, the filtrate would pass into a vessel
containing the aforementioned copolymer. Subsequently, the copolymer would be acidified in
a new vessel, releasing the desired metallic ions.

#### Build

As the overall design of the project was over, our team began the “building” process. We started experimenting with the genes and we are one step before the purchase of the DNA from IDT.

Given the complexity of the overall gene design and the fact that this set of experiments did not actually proceed, we will only showcase the basic principles. The hcnABC operon contains three genes, hcnA which codes for a formate dehydrogenase and hcnB and hcnC which code for amino acid oxidases. As said before, these genes are part of the same regulatory unit, something we hoped to employ. In the paper, “Characterization of the hcnABC Gene Cluster Encoding Hydrogen Cyanide Synthase and Anaerobic Regulation by ANR in the Strictly Aerobic Biocontrol Agent Pseudomonas fluorescens CHA0” , the authors present the sequence of the hcnABC operon as can be seen in the picture bellow: [4]

![Engineering Image 3](https://static.igem.wiki/teams/5406/images/engineering/engineering3.webp)

In addition, they showcase several systems through which we could achieve expression of the above proteins, into a complete HCN-producing system, with the following one showing important potential: [4]

![Engineering Image 4](https://static.igem.wiki/teams/5406/images/engineering/engineering4.webp)

This particular system showcased good expression of all three proteins and was what we sought to replicate, based on the DNA sequence above. While several steps were undertaken to prepare the DNA sequence for a potential order from IDT , this plan did not materialize given the considerations showcased in the test part of this cycle.

#### Test

Rather than conducting immediate experiments, we rigorously evaluated our HCN Synthase prospect's theoretical and safety potential. Potential investors analyzed it first, followed by scientific committees from the Chemical and Biological Department of the National and Kapodistrian University of Athens. However, the most important test was the iGEM competition's safety form, which revealed serious dangers related to our proposed experiments.

When integrating the theoretical design into a lab-ready project, it was apparent that multiple safety and technical difficulties would arise:

* **Risk Level:** Our lab lacked the necessary equipment, such as an appropriate modern fume hood, to safely conduct HCN synthesis.
* **Economic Viability:** Competing with established HCN production technologies created substantial cost obstacles. Traditional pyrometallurgy and hydrometallurgy techniques were more cost-effective.
* **Limited Synthetic Biology Component:** The project relied heavily on chemical engineering. Therefore, the synthetic biology aspect was relatively underdeveloped.
* **Toxic By-products:** The by-products from synthetic HCN synthesis were as hazardous as those from traditional NaCN leaching methods.
* **Complex Stability:** The stability of the Au(CN)2- complexes was much higher than anticipated, making dissolution difficult and expensive.
* **Membrane Protein Challenges:** The purification process was complicated since the project included membranous proteins.

As Dr. Labrou thoroughly reviewed the whole project and it was ultimately discontinued Upon thorough review by Dr. Labrou, the project was ultimately discontinued due to these constraints.

#### Learn

Although we didn't proceed with actual experimentation, this stage gave us invaluable
knowledge and guided the future of our project. Here's what we learned:

* **Upcycling Over Recycling:**
  Significant financial barriers were incurred when competing with well-established
  leaching technologies. Conventional methods of hydrometallurgy and pyrometallurgy
  were more economical.
* **Prioritizing Green Chemistry:**
  Safety and sustainability became the main point of our approach. A project in order
  to be characterized as successful should use safe, green leachates that avoid toxic
  by-products. This is the way to ensure not only compliance with safety regulations
  but also strengthen the environmental aspect of the project.
* **Chemical Viability and Balance:**
  We learned that the chemical processes behind metal complex formation must be
  both kinetically and thermodynamically viable. However, overly stable complexes can
  impede the dissolution of metals and burden the project financially.
* **Scalability and Enzyme Purification:**
  To ensure practical industry application, we recognized the importance of enzyme
  immobilization. This method simplifies purification, improves scalability and aligns with
  the legislation of the EU making it easier to integrate our solution into industrial
  settings.

These crucial realizations enabled us to improve our idea and switch to **glutathione**, a more bio-friendly and viable solution in the long run.

### Cycle 2: Gene Cloning Experiments

After the selection of the appropriate genes, namely GshFss and PPKT e, alongside the
plasmid vector pET28a(+), the system needed to be designed to be fit for purpose. The
vector pET28a was selected as it featured: 1) a 6xHis-tag which could be used for the
isolation of the genes through affinity chromatography and 2) a T7-promoter which ensured
a controlled but also high expression of the protein. In the picture below these two regions
are highlighted on the pET28a(+) plasmid:

![Engineering Image 5](https://static.igem.wiki/teams/5406/images/engineering/engineering5.webp)

We sought to replicate successful experiments done by Zhang, X. et al (1) and so we chose
to use the restriction enzymes BamHI and HindIII for the cloning of GshFss and EcoRI and
SalI for the cloning of PPKT e. As can be seen in the picture above, the restriction sites for
these enzymes are between the T7 promoter and the 6xHis-tag.

Designing the genes also posed some challenges:

The GshFss gene was sourced from the paper of Zhang, X. et al (1) (supplementary
material) and was already optimized for *E.coli* expression. Therefore, after adding the
restriction sites at the end of the gene, it was noted that the gene that resulted violated both
the Biobrick standard and the Type IIS standard as it contained “illegal” restriction sites. In
order to cause the least silent mutations on the gene, it was selected to make the gene
compatible with the Type IIS standard, which required several silent mutations (please refer
to part [BBa\_K5406000](https://parts.igem.org/Part%3ABBa_K5406000)):

i) A SapI site was removed by changing A at position 1065 to a G

ii) A BsaI site was removed by changing G at position 891 to an A

The PPKT e gene was sourced from the NCBI database. It was not optimized for *E.coli*
expression and therefore had to be optimized, which was done using Snapgene(R) software.
After adding the restriction sites at the end of the gene, it was noted that the gene that
resulted violated the Biobrick standard as it contained “illegal” restriction sites. As EcoRI was
to be used for cloning, the gene had to be compatible with the Type IIS standard. When it
was time to order the gene from Integrated DNA T echnologies, our sequence was too
problematic to be synthesized by IDT . Therefore, using IDT software, the gene was once
again optimized to be compatible with IDT synthesis procedures. This optimization produced
an internal EcoRI site which was promptly removed (change of T at position 911 to an A,
please refer to part [BBa\_K5406001](https://parts.igem.org/Part%3ABBa_K5406001)).

The following pictures showcase the final position of the GshFss and PPKT e genes respectively in the final plasmid:

![Engineering Image 6](https://static.igem.wiki/teams/5406/images/engineering/engineering6.webp)
![Engineering Image 7](https://static.igem.wiki/teams/5406/images/engineering/engineering7.webp)

#### Build

To acquire the recombinant plasmids, we performed double digestion on both of the genes. The desired digested products were visualized through loading on an agarose gel (outlined on Figure 4), and then extracted using a “Gel Extraction kit” . Through electrophoresis of the extracted product, clear bands were seen at ~2250 bps, as expected for the genes and at ~5300 bps for the plasmid.

![Engineering Image 8](https://static.igem.wiki/teams/5406/images/engineering/engineering8.webp)

#### Test

In order to test if the cloning was successful, we proceeded to chemical transformation of
DH5a cells with the ligation product. A positive (pET28a plasmid) and negative (only cells)
transformation control were added. Some results of our attempts were the following:

![Engineering Image 9](https://static.igem.wiki/teams/5406/images/engineering/engineering9.webp)
![Engineering Image 10](https://static.igem.wiki/teams/5406/images/engineering/engineering10.webp)

Given the above results, we were hopeful that we had successfully cloned our genes into
pET28a. But for that to be ensured, we needed to isolate the recombinant plasmids and
visualize them through agarose gel electrophoresis. This was done with a plasmid isolation
kit and results were the following:

![Engineering Image 11](https://static.igem.wiki/teams/5406/images/engineering/engineering11.webp)

Therefore, the bands serve as a clear indication of having successfully created the
recombinant plasmid.

#### Learn

Given the above, we showcase that we have managed to create the recombinant plasmids, something that is further verified through the expression experiments (see further engineering cycles). These experiments have showcased the following: 1) That a 1:1 molar vector:insert ratio was enough to result in successful recombination for our system, and not a 2:1 or 3:1 as it has been suggested, 2) The pET28a(+) plasmid is an appropriate expression vector for genes of about 2200 bp. For a substantial confirmation of our molecular cloning experiments success, an examination of the plasmid DNA and gene constructs is yet to be completed, something we aim for in our future plans.

### Cycle 3: Protein Expression and Isolation

*This section is dedicated to the cycle of experimental procedures aiming at the heterologous
overexpression of the enzymatic system of GshF/PPK through bacterial systems.*

#### Design

As it was mentioned before, we opted for the enzymatic biosynthesis in vitro for the
production of GSH, since it offers a multitude of advantages, adapting well to our project’s
effort. Therefore, it was required to produce the recombinant proteins whose gene constructs
were previously designed, and then isolate both enzymes through protein purification.

Having concluded on the enzymes to be studied, namely GshFSS (Streptococcus sanguinis
SK36) and PPKTe (*Thermosynechococcus vestitus (nee. elongatus)* BP-1), sequences
encoding the respective enzymes were inserted in suggested plasmid DNA vectors,
pET28a(+). Besides the expression cells transformed with pET28a-GshF and pET28a-PPK
recombinant vectors, we also made use of Control samples, transformed with an empty
plasmid vector (pET-28b), in order to investigate the native expression of the proteins GshF
and PPK in E. coli strains. It is also important to highlight that so far there is no indication of
a systemic impact of the heterologous expression of GshF in *E. coli* bacterial cells.

#### Build

Our goal was to transform each of the two target - genes, gshf and ppk, in different
competent expression cell strains of Escherichia coli, mainly characterized as DE3 according
to research papers of Zhang et. al (2017)[4] among others. Based on the availability of
biological materials in our laboratory, we opted for the use of SHuffle T7, Rosetta2/PlysS
(DE3) and later with BL21/PlysS (DE3), in order to achieve and further optimize protein
overexpression.

#### Test

During the first experimental trial of protein overexpression (Weeks 05/08 - 09/08), the
transformation of *E. coli* Rosetta2/PlysS with either the pET28a-GshF or pET28a-PPK
plasmids resulted in no observable cell growth. This lack of growth was attributed to potential
toxicity effects caused by the expression of these recombinant proteins, as control cultures
transformed with the empty pET28b vector demonstrated healthy growth during the same
incubation period. In contrast, *E. coli* SHuffle T7 cells that were transformed with the same
plasmids exhibited significant growth. Consequently, these SHuffle T7 cultures were selected
for further development into liquid bacterial cell cultures aimed at induced protein
overexpression. A portion of these successful cultures was preserved at -80°C in sterilized
YT liquid culture medium supplemented with glycerol to ensure viability for future
experiments.

In order to evaluate the results at the end of our first protein expression trial, samples from
both the supernatant and cell pellet collected were subjected to Bradford protein
quantification as well as SDS-PAGE Gel Electrophoresis.[9]

In the second trial (Weeks 09/09 - 13/09), both gshF and ppk genes were again transformed
into the SHuffle T7 strain, as well as into BL21/PlysS(DE3) cells. However, no growth was
observed in the BL21/PlysS(DE3) cultures transformed with the recombinant plasmids,
which echoed the previous findings from the Rosetta2/PlysS strain. This lack of growth was
similarly attributed to toxicity effects on the culture. In contrast, the SHuffle T7 strain again
demonstrated significant growth with both recombinant plasmids.

We then decided to conduct a liquid bacterial cell culture in a larger volume (Weeks 09/09 -
13/09) to obtain a substantial quantity of protein samples heterologously expressed. The
culture was grown in YT medium using IPTG as well, a common practice for the induction of
protein expression in in vitro bacterial systems. Given the aforementioned trials, competent
*E. coli* SHuffle T7 cells previously transformed with pET28a-GshFSs and pET28a-PPKT e
(Weeks 05/08 - 09/08), in order to proceed with isolating the enzymes through Ni-NTA
affinity chromatography. In order to evaluate the conclusive results, samples from the
supernatant of SHuffle T7 competent expression cells collected were subjected to Bradford
protein quantification, using both crude enzyme samples as well as the samples after the
purification.

Protein electrophoresis results were assessed, by detecting the corresponding zones of
each sample before and after the purification. More specifically, significant overexpression
was noted especially for GshFSs, as a zone of high intensity at 85 kDa, compared to the
crude sample of the expression cells protein content. In the case of PPKT e, overexpression
was achieved and purification was successful, detecting a distinct zone at approximately 85
kDa based on the protein marker, however it could have been improved compared to the
GshFSs samples success.

![Engineering Image 13](https://static.igem.wiki/teams/5406/images/engineering/engineering13.webp)
![Engineering Image 12](https://static.igem.wiki/teams/5406/images/engineering/engineering12.webp)

#### Learn

The results of both studies highlighted the importance of selecting the appropriate competent *E. coli* strain for protein expression. Cell growth was not observed in either Rosetta2/PlysS or BL21/PlysS, suggesting that these strains may not be capable of supporting the overexpression and protein folding modifications of GshF and PPK. In contrast, the SHuffle T7 strain yielded excellent results, with considerable growth observed in both experiments. This implies that SHuffle T7 competent cells were more compatible with the expression of the GshF and PPK respective genes, presumably because of its capacity of facilitating the appropriate folding as well as post-translational modifications of proteins that contain disulfide bonds, which is a frequently occurring issue in *E. coli* systems.

Furthermore, the results of SDS-PAGE electrophoresis of enzyme elution samples before
and after purification, compared to the crude samples, demonstrated that both enzymes
were satisfyingly isolated. We received this as an indication of a successful first effort in the
protein purification through affinity chromatography to be successful.

The characteristics of PPK and more importantly, the association of this enzyme with cell
membranes is an area we find worth exploring further. While reviewing research papers, we
found that members of the polyphosphate kinase family are partially insoluble and
associated with the outer cell membrane[4] [10]. More specifically, the PPK enzyme exhibits a
soluble behavior, despite 90% or more of the enzyme, even when overproduced, being firmly
bound in the membrane fraction. After purification, the PPK protein is further associated with
the outer cell membrane. Nevertheless, according to researchers, the in vitro activity of the
enzyme seems to remain unaffected by its contact with the membrane.[10]

We speculated whether a cell-free expression system would have been a more suited
approach towards the efficient production and isolation of PPK, besides heterologous
overexpression through bacterial systems. Thus, our immediate future plans involve the use
of a cell - free expression system, in order to compare, evaluate and amplify protein
expression in vitro, specifically in the case of PPKT e, needed for the ATP regeneration in our
multi-enzyme system.

### Cycle 4: Chemical Engineering

In the chemical engineering aspect of the project our team wanted to test the results of the
leaching between glutathione and metals since there is not any literature available.

#### Design

In designing the leaching experiments, modeling and computational chemistry were
essential tools. For optimal results, Computational chemistry was utilized to establish the
initial conditions, particularly stabilizing the pH at 11 for optimal results. This valuable step
ensured that the reaction conditions were suited for effective metal solubilization.

To maximize the yield and performance of the glutathione leaching process, **sodium
thiosulfate** was used. It plays a critical role in enhancing the solubilization of metals like
gold (Au) during the initial phase of the leaching process. Later, sodium thiosulfate is
replaced by glutathione, which takes over to further increase efficiency and support the
formation of glutathione-coated gold nanoparticles—an essential component for the success
of the project. Modeling studies demonstrated that glutathione-metal complexes are
significantly more favorable than thiosulfate-metal complexes. This preference indicates that
glutathione not only enhances metal recovery but also improves the stability of the resulting
complexes, making it a superior choice for our leaching strategy.

![Engineering Image 15](https://static.igem.wiki/teams/5406/images/engineering/engineering15.webp)

Interaction between glutathione and gold, picture taken from Gaussian

Based on the research done by Konado et al. we decided to introduce common and
cost-effective reducing agent, sodium sulfite (Na2SO3).This is crucial for preventing the
oxidation of glutathione and the formation of disulfide bonds between glutathione molecules,
ensuring the stability and continuity of the leaching process. [11]

![Engineering Image 14](https://static.igem.wiki/teams/5406/images/engineering/engineering14.webp)

Sodium Sulfite anion, picture taken from Gaussian

#### Build

For the leaching experiments aimed at extracting copper and silver, the following conditions and starting quantities are outlined:

**Experimental Conditions:**

* **Temperature:** 37 °C
* **Pressure:** 1 atm
* **Shaking Speed:** 150 rpm
* **pH:**Regulated at 11 with an appropriate solution.

**Copper**

| Solution | Total Volume (mL) | Concentration of Cu (mg/mL) | Concentration of GSH (mg/mL) | Concentration of Na2SO3 (mg/mL) | Concentration of Na2S2O3 (mg/mL) |
| --- | --- | --- | --- | --- | --- |
| A | 5 | 4 | 1 | 0 | 0 |
| B | 5 | 4 | 1 | 2 | 0 |
| C | 5 | 4 | 1 | 2 | 1 |

**Silver**

| Solution | Total Volume (mL) | Concentration of Ag (mg/mL) | Concentration of GSH (mg/mL) | Concentration of Na2SO3 (mg/mL) | Concentration of Na2S2O3 (mg/mL) |
| --- | --- | --- | --- | --- | --- |
| D | 5 | 4 | 1 | 0 | 0 |
| E | 5 | 4 | 1 | 2 | 0 |
| F | 5 | 4 | 1 | 2 | 1 |

#### Test

**Investigation of Solutions:** After the first 48 hours, the solubility and condition of the
solutions should be visually assessed.

**End of Leaching Period:** The leaching should be stopped after 168 hours to quantify the
solubility yield.

**Quantification of Solubility Yield:** The quantification process will be conducted under the
guidance of Dr. Karavoltsos, a professor of Environmental Chemistry at the National and
Kapodistrian University of Athens with the use of ICP-MS T echnique (Inductively Coupled
Plasma Mass Spectrometry)

Due to the limited time and the delay of shipment of the metals, we did not have the time to
effectively test our system. The metals arrived 5 days before the wiki freeze, and despite
planning all the experiments, we were not able to gain results. However, supposing we had
enough time, we would have constantly changed the concentrations of all above in order to
find the optimized system. Modeling helped us to find the best conditions, but pH=7 should
be tested as well.

#### Learn

In this segment we would include the finalized and optimized concentrations and conditions
for the leaching experiments. The knowledge after these tests would be valuable since the
optimized system would be transferred in the final system that will contain stabilized
enzymes.

### Engineering Cycle Conclusion

In conclusion, our project successfully investigated the enzymatic synthesis of glutathione
(GSH) and its application in the bioleaching of metals from electronic waste. We developed a
potentially effective GSH biosynthesis system employing the GshF/PPK enzyme cascade
after conducting a series of rigorous testing. The E. coli SHuffle T7 competent expression
cell strain was the best option among others for the heterologous overproduction of the
targeted enzymes, highlighting the importance of selecting optimal E. coli strains for protein
expression. Overall, our study highlighted the possibility of employing GSH as a chelating
agent in the environmental cleanup and stressed the significance of optimizing conditions for
better metal recovery. These conditions will be clarified after the chemical engineering
experiments.

References
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

1. Lee, J., & Pandey, B. D. (2012). Bio-processing of solid wastes and secondary resources for metal extraction – A review. Waste Management, 32(1), 3-18.
2. Laville, J., Blumer, C., Von Schroetter, C., Gaia, V., Défago, G., Keel, C., & Haas, D. (1998). Characterization of the hcnABC gene cluster encoding hydrogen cyanide synthase and anaerobic regulation by ANR in the strictly aerobic biocontrol agent Pseudomonas fluorescens CHA0. Journal of Bacteriology, 180(12), 3187-3196.
3. Işıldar, A., van de Vossenberg, J., Rene, E. R., van Hullebusch, E. D., & Lens, P . N. L. (2016). Two-step bioleaching of copper and gold from discarded printed circuit boards (PCB). Waste Management, 57, 149-157.
4. Zhang, X., Wu, H., Huang, B., Li, Z., & Ye, Q. (2017). One-pot synthesis of glutathione by a two-enzyme cascade using a thermophilic ATP regeneration system. Journal of Biotechnology, 241, 163–169.
5. Xiong, Z., Kong, L., Wang, G., Xia, Y ., Zhang, H., Yin, B., & Ai, L. (2018). Functional analysis and heterologous expression of bifunctional glutathione synthetase from Lactobacillus. Journal of Dairy Science, 101(8), 6937–6945.
6. Zhang, X., Cui, X., & Li, Z. (2020). Characterization of Two Polyphosphate Kinase 2 Enzymes Used for ATP Synthesis. Applied Biochemistry and Biotechnology, 191(2), 881–892.
7. Cui, Xiangwei & Li, Zhimin (2021). High production of glutathione by in vitro enzymatic cascade after thermostability enhancement. AIChE Journal, Volume 67 – Issue 1. doi:10.1002/aic.17055
8. Bonjoch, N.P. and Tamayo, P.R. (2001) ‘Protein Content Quantification By Bradford Method’, in Handbook of Plant Ecophysiology Techniques. doi:10.1007/0-306-48057-3
9. Nowakowski, A. B., Wobig, W. J., & Petering, D. H. (2014). Native SDS-PAGE: high resolution electrophoretic separation of proteins with retention of native properties including bound metal ions. Metallomics : integrated biometal science, 6(5), 1068–1078. doi:10.1039/c4mt00033a
10. Akiyama, M., Crooke, E., & Kornberg, A. (1992). The polyphosphate kinase gene of Escherichia coli. Isolation and sequence of the ppk gene and membrane location of the protein. The Journal of biological chemistry, 267(31), 22556–22561.
11. Konadu, K. T., & Sasaki, K. (2023). Sulfidic gold ore leaching by cysteine in the presence of Na₂SO₃. Hydrometallurgy, 216, 106018.

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{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your iGEM project.{% endblock %}
{% block page\_content %}

![Contribution Icon](https://static.igem.wiki/teams/5406/images/icons/sprite-06.png)

# Experiments

Wet Lab Experiments
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

In this page, we offer a collective description of the experimental protocols we followed during the wet lab activities of our project. The protocols include details about the laboratory procedures as a comprehensive guide, representative of our teams’ work for the past months, that could potentially serve as a point of reference or even inspiration for the laboratory work of future teams. The procedures followed according to the protocols were further optimized with notes from team members. We gladly offer this collection of laboratory procedures as a supportive resource for future projects and experiments!

{% endblock %}

{% extends "layout\_no\_header.html" %}
{% block title %}Results{% endblock %}
{% block lead %}You can describe the results of your project and your future plans here.{% endblock %}
{% block page\_content %}

![Results Icon](https://static.igem.wiki/teams/5406/images/icons/sprite-08.png)

# Results

Wet Lab Notebook
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

Dry Lab - Computational Chemistry for Bioleaching
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

The computational chemistry study with the software GaussView and Gaussian provided us with these interesting results. The photos and difference between its complex is available at the [Model page](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D).

## Table 1: Complexes between Cu, Glutathione, and Thiosulfate

| Complex | ΔG (kcal/mol) | K constant |
| --- | --- | --- |
| Cu-1 | -81.76 | 4 \* 1057 |
| Cu-2 | -69.07 | 5 \* 1048 |
| Cu-3 | -48.62 | 2 \* 1034 |
| Cu-4 | -29.90 | 1 \* 1021 |

For the reaction: Thiosulfate-Cu + GS- 🡪 GS-Cu + Thiosulfate

ΔG = -51.78 kcal/mol, K = 4 \* 1036

## Table 2: Complexes between Ag, Glutathione, and Thiosulfate

| Complex | ΔG (kcal/mol) | K constant |
| --- | --- | --- |
| Ag-1 | -26.74 | 7 \* 1018 |
| Ag-2 | -29.53 | 7 \* 1020 |
| Ag-3 | -41.44 | 2 \* 1029 |
| Ag-4 | -5.83 | 1 \* 104 |

For the reaction: Thiosulfate-Ag + GS- 🡪 GS-Ag + Thiosulfate

ΔG = -35.61 kcal/mol, K = 1 \* 1025

## Table 3: Complexes between Au, Glutathione, and Thiosulfate

| Complex | ΔG (kcal/mol) | K constant |
| --- | --- | --- |
| Au-1 | -35.88 | 2 \* 1025 |
| Au-2 | -63.49 | 7 \* 1020 |
| Au-3 | -83.63 | 9 \* 1058 |
| Au-4 | -61.48 | 2 \* 1043 |
| Au-5 | -34.82 | 4 \* 1024 |
| Au-6 | -19.74 | 8 \* 1013 |

For the reaction: Thiosulfate-Au + GS- 🡪 GS-Au + Thiosulfate

ΔG = -48.81 kcal/mol, K = 3 \* 1034

## In addition to the previous research, we explored the effect of the oxidation of gold.

## Table 4: Complex of Au(III) and Glutathione

| Complex | ΔG (kcal/mol) | K constant |
| --- | --- | --- |
| Au-7 | -407.64 | 3 \* 10287 |

For the reaction: Thiosulfate-Au + GS- 🡪 GS-Au + Thiosulfate

ΔG = -48.81 kcal/mol, K = 3 \* 1034

In addition to the previous research, we explored the effect of the oxidation of gold.

The results and the comparisons are clear in the graph below.

![Results Dry Lab Gaussian](https://static.igem.wiki/teams/5406/images/results/dry-gaussian/gaussian1.webp)

Graph 1: Comparison of ΔG for each metal in pH 7 and 11

We also performed the same calculations for Gold when its oxidation state was 0, since it is an inert metal that cannot be easily oxidized. We got the following results.

## Table 5: Complexes between Au(0), Glutathione, and Thiosulfate

| Leaching Agent | ΔG (kcal/mol) | K constant |
| --- | --- | --- |
| Glutathione | +20.63 | 3 \* 10-15 |
| Thiosulfate | -52.78 | 2 \* 1037 |

From all this study, many valuable conclusions may be drawn.

* It is evident that Glutathione, indeed, has the capacity to interact and form stabilized metal complexes. This fact truly supports the leaching activity of the tripeptide, and we expect it to perform as we expected.
* The interactions between Gold and Copper are superior to the one of Silver. Surprisingly, even though gold is an inert metal, it interacts when oxidized better than the silver. Gold is a soft acid, and it binds to soft metals like S-. We are truly encouraged about Au and Cu because these two metals seem to really be a big part of electronic devices and therefore our project.
* The ΔG was lower during the calculations of a basic solution than a neutral one. As a result, the optimized pH for the solution of the leaching reaction is indeed 11. It encourages the belief that the interaction of the S- is more important than the one with the O- groups.
* The ΔG of Au(III) was extremely low. This cation is not stable, and it interacts strongly. So, the increase of the oxidation state of the Au will opt to better results. However, this oxidation is extremely difficult and expensive.
* The interaction between these soft metals and sulfur is stronger than the one with oxygen. We are encouraged to inquire about a new molecule with more S- groups.
* The ΔG of the thiosulfate complexes was significantly lower. The K between the reactions M-Thiosulfate + Glutathione 🡪 M-Glutathione + Thiosulfate is extremely high. This data was really important for our project optimization.
* The ΔG of Au(0) and Glutathione was positive. Consequently, it is necessary to increase the quantity of Au(I) in the solution. Thiosulfate, however, had a negative one. Combined with the previous fact, we can now validate that the combination of Glutathione and Thiosulfate is viable and compulsory. These two chemical compounds need to be reduced to act as a leachate. For that reason, we will use sodium sulfite.

## Future Experiments

Since the time was limited, we were not able to conclude the whole of our research.

We should repeat all the experiments for a larger number of metals like Pt (Platinum), Ni (Nickel), and Pd (Palladium). These metals are a big part of e-waste and have similar chemical properties to the metals we examined.

Furthermore, the use of a more suited basis set would increase the accuracy of our calculations. A new version of the Gaussian Software would include a larger number of basis sets, and the calculations should be repeated.

Last but not least, as it was natural, there was a divergence between the theoretical number of ΔG and the one found experimentally. Unfortunately, there is not any available data regarding the interaction of Glutathione and Metals but only for the well-established Thiosulfate. For this reason, we should revise the calculations and sync them with the experimental data.

References
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

1. Gudkov, A.S., Zhuchkov, I.A. & Mineev, G.G. Thermodynamics of interaction between sulfite-thiosulfate solutions and noble metals. Russ. J. Non-ferrous Metals 51, 233–236 (2010).

Dry Lab - Glutathione production membrane system
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

# Results and Discussion

This study aimed to model and simulate the production and transport of Glutathione (GSH) between two containers connected by selective membranes with differing pore sizes. The system was designed to investigate the concentration dynamics of GSH under various experimental conditions, including production rates, membrane pore sizes, and fouling probabilities. By doing so, we hoped to gain insight into the behavior of small biomolecules in a controlled environment, which could provide valuable information for optimizing biochemical production processes.

## Experimental Setup

The system consists of two 1-liter containers, A and B, each connected by membranes with specific pore sizes. Container A is linked to a membrane with a pore size of 0.65 nanometers, which allows GSH and smaller amino acids to pass through. Container B, on the other hand, is connected to a membrane with a smaller pore size of 0.45 nanometers, effectively blocking the passage of GSH while allowing amino acids to move freely between containers.

In this experiment, GSH is continuously produced in Container A at a rate of 0.00025 mol/L per minute. The system was designed to simulate the transport of GSH from Container A to Container B, based on the selective permeability of the membranes. The membranes’ fouling probabilities were also incorporated into the model, with fouling rates of 0.001/min for Membrane A and 0.0005/min for Membrane B. This setup allowed us to explore the dynamics of molecular transport and fouling under varying conditions.

## Parameter Variation and Exploration

To explore the impact of different system parameters, we conducted experiments by varying the pore sizes of the membranes, fouling rates, and production rates of GSH. Initially, the base case involved a pore size of 0.65 nm for Membrane A and 0.45 nm for Membrane B, with a production rate of 0.00025 mol/L/min. We then experimented with different pore sizes and production rates to understand how these factors influenced GSH transport and membrane fouling.

For example, when we increased the pore size of Membrane A to 0.75 nm, we observed a faster accumulation of GSH in Container B due to the increased flux rate, while reducing the pore size to 0.55 nm slowed down the transport process significantly. Similarly, altering the fouling rates for both membranes helped us understand the long-term effects of membrane degradation on the system's efficiency. Higher fouling rates resulted in a slower accumulation of GSH in Container B over time, as the membranes became increasingly clogged and less effective in transporting molecules.

## Membrane Fouling Probability Results

The first graph below illustrates the fouling probabilities of both Membrane A and Membrane B over time. As expected, Membrane A showed a higher fouling probability compared to Membrane B, owing to its higher base fouling rate of 0.001/min. Fouling probability for both membranes increased linearly over time, but at different rates. After 100 minutes, the fouling probability of Membrane A reached approximately 0.09, while Membrane B exhibited a fouling probability of around 0.06.

![Results Dry Lab](https://static.igem.wiki/teams/5406/images/results/dry-gpmss/gsh.webp)

The linear growth of fouling probability is consistent with our model, which accounts for membrane degradation over time due to continuous usage. As fouling increases, the effectiveness of the membranes in transporting GSH and other molecules diminishes. This result suggests that the system will need maintenance or membrane replacement after extended periods of operation, especially in cases where high production rates are maintained.

## GSH Concentration Dynamics

The second graph demonstrates the change in GSH concentration in both Container A and Container B over time. As shown, Container A experiences a rapid increase in GSH concentration due to the constant production of the molecule at a rate of 0.00025 mol/L/min. Initially, GSH concentrations in both containers start at zero, but as time progresses, Container A accumulates GSH at a much faster rate compared to Container B.

![Results Dry Lab](https://static.igem.wiki/teams/5406/images/results/dry-gpmss/prob.webp)

This discrepancy can be attributed to the selective permeability of the membranes, which favors the retention of GSH in Container A. After 120 minutes, the concentration of GSH in Container A reaches approximately 0.021 mol/L, while the concentration in Container B lags behind at 0.007 mol/L. These results align with our hypothesis that the membrane pore sizes and fouling effects significantly impact the overall efficiency of GSH transport between the containers.

## Achievements and Insights

The key achievements of this study include the successful development of a dynamic model that accurately describes the transport and production of GSH in a membrane-based system. By experimenting with different parameters such as membrane pore size, fouling rates, and production rates, we were able to better understand the factors that influence the efficiency of GSH transport.

One of the main insights gained from this work is the critical role that membrane pore size plays in controlling molecular flux rates. Larger pore sizes lead to faster transport but may also increase the risk of membrane fouling, which can compromise the long-term functionality of the system. Moreover, the fouling rates for each membrane provide valuable information for predicting system maintenance needs, especially in large-scale industrial applications.

Overall, the results of this study have important implications for the design of biochemical systems involving selective molecular transport. By optimizing membrane characteristics and monitoring fouling over time, it is possible to enhance the efficiency and sustainability of systems used in industrial applications, such as bioremediation, biochemical production, and molecular separation processes.

References
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

1. Shelly C. Lu. “Glutathione Synthesis”. In: Biochimica et Biophysica Acta 1830.5 (2013), pp. 3143–3153. doi: 10.1016/j.bbagen.2012.09.008.
2. National Institute for Physiological Sciences. Discovery of the Mechanism of Cell Death Involving Glutathione in Neurons 2013
3. J.G. Kunkel. Amino Acid and Peptide Buffers.

Dry Lab - GSH Biosynthesis Simulation
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

# Results and Discussion

This analysis aims to elucidate the dynamic behavior of the glutathione biosynthesis pathway by comparing the outcomes generated by two methodologies RRE and SSA.
The python script implements the simulation of the chemical system we study, the glutathione biosynthesis, with reaction rate equation and stochastic simulation algorithm.
the python script below simulates the evolution of the system over time assuming random numbers of molecules of the chemical species at the initial state.
The number of molecules at the initial state at t=0s is a parameter that can be changed.

The goal here is to show the simulation process.
This code can be very useful for wet lab members to simulate the system and calculate useful data (i.e. concentrations of the chemical species involved in GSH biosynthesis) saving resources and time. Also, this code can easily be transformed to simulate any other chemical system.

![GSH Biosynthesis Simulation](https://static.igem.wiki/teams/5406/images/results/ssa/simulation-of-gsh-biosynthesis.webp)

The plot above shows the evolution of the GSH biosynthesis chemical system over time.
The left-hand side plot depicts the implementation of SSA algorithm for the chemical system and the right-hand side plot depicts the deterministic approach solving the RRE.
The number of molecules of chemical species are initialized randomly with the values L-Glu=100, Cys=80, γ-GC=0, Gly=50.

At the right-hand plot that implements the deterministic approach, the lines are smoother than those from the left-hand plot.
This is because of the RRE models the system using continuous, averaged concentrations of species, which are governed by ordinary differential equations (ODEs).
The SSA models the system as a series of discrete reaction events that happen at random times.
Instead of tracking continuous concentrations, the SSA tracks the exact number of molecules of each species, and reactions occur with certain probabilities.

## Insights

The developed Python script successfully implements both the Stochastic Simulation Algorithm (SSA) and the Reaction Rate Equation (RRE) to simulate the biosynthesis of glutathione, capturing the dynamic behavior of the system with high fidelity. The script provides a comparative analysis between the deterministic and stochastic approaches, demonstrating the distinct advantages of each method. In particular, the RRE yields smooth, continuous curves, reflecting the average behavior of the system in scenarios where molecular populations are large, while the SSA captures the inherent randomness and fluctuations typical in smaller-scale systems. The simulation offers valuable insights into the kinetics of glutathione biosynthesis, highlighting the exponential-like behavior in product formation during the early stages due to enzyme-substrate interactions. Additionally, the script enhances our understanding of reaction dynamics, particularly how substrate availability and enzyme saturation influence the overall reaction rates.
By combining both stochastic and deterministic frameworks, this tool provides a robust platform for exploring the complex biochemical pathways involved in glutathione production.

References
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

1. Higham, D. J. (2008). Modeling and simulating chemical reactions. SIAM Review, 50(2), 347–368.
2. Howard, P. (2023). 2009-Peter-Howard-Modeling With ODE. SIMIODE, QUBES Educational Resources.
3. Vergauwen, B., De Vos, D., & Van Beeumen, J. J. (2006). Characterization of the bifunctional γ-glutamate-cysteine ligase/glutathione synthetase (GshF) of Pasteurella multocida. Journal of Biological Chemistry, 281(7), 4380–4394.
4. Gillespie, D. T. (1976). A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. Journal of Computational Physics, 22(4), 403–434.
5. Gillespie, D., & Petzold, L. (2006). Numerical simulation for biochemical kinetics. In Complex systems and biomolecular processes (pp. 331–354). MIT Press.
6. Crampin EJ, Schnell S. New approaches to modelling and analysis of biochemical reactions, pathways and networks. Prog Biophys Mol Biol. 2004 Sep.
7. Adalsteinsson D, McMillen D, Elston TC. Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks. BMC Bioinformatics. 2004 Mar 8

Investigating thermostability of gshF enzyme using computational methods
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

# Results and Discussion

In this study, the aim is to investigate the thermostability of the glutathione synthetase enzyme (GshF) using computational approaches.
By employing Python scripts, we calculate key structural properties such as B-factors and loop depths, which are critical indicators of protein flexibility and stability. These computational analyses are designed to provide insights into the relationship between the dynamic behavior of the enzyme's loops and its overall thermostability.
The ultimate goal of this research is to identify structural features that could enhance the thermal resilience of GshF, which has potential implications for improving its functionality in industrial applications.

![gshF depth of loops](https://static.igem.wiki/teams/5406/images/results/depth-loops.webp)
![B factors of residues in gshF enzyme](https://static.igem.wiki/teams/5406/images/results/thermostability-plot/bfactor-gshf.webp)

The depth of the loops,shown at the bar chart, has been calculated by comparing the spatial positions of residues in the loops to the overall center of mass of the protein. Firstly, we calculated the center of mass of the entire protein structure based on its atomic coordinates and then we calculated as the Euclidean distance between the center of the loop and the center of mass of the protein.
The more buried the loops are, the more stable tend to be and play important role at the structure of the gshF.

The line chart shows the b-factors of residues in gshF.
The b-factor for individual atoms within a residue can vary. By examining the b-factors of all atoms in a residue, one can assess the overall stability of that residue.
The b-factor as a measurement for displacement of atoms within th crystal structure provide insights into the dynamic behaviour of the position of atoms in the residue.
Low b-factor values indicate that the residue is relatively well-ordered and stable, while high b-factors indicate that the residue is more flexible and possibly unstable.
Here we observe from the line chart that the b-factors show slight deviation and the values mainly are in the range from 0.7 Å² to 0.95 Å²

Below are shown arithmetic data, calculated from the python scripts, for the b-factor and the depth of the loops.

## B-factors of loops of gshF enzyme

| Loop Start Residue | Loop End Residue | Average B-Factor(Å²) |
| --- | --- | --- |
| 1 | 5 | 0.14 |
| 11 | 11 | 0.81 |
| 36 | 42 | 0.86 |
| 47 | 47 | 0.90 |
| 72 | 73 | 0.79 |
| 92 | 92 | 0.87 |
| 99 | 103 | 0.91 |
| 114 | 119 | 0.81 |
| 134 | 135 | 0.83 |
| 139 | 142 | 0.91 |
| 150 | 150 | 0.86 |
| 162 | 165 | 0.84 |
| 193 | 197 | 0.91 |
| 209 | 210 | 0.79 |
| 223 | 223 | 0.73 |
| 228 | 230 | 0.83 |
| 257 | 257 | 0.80 |
| 264 | 264 | 0.89 |
| 275 | 277 | 0.87 |
| 282 | 286 | 0.91 |
| 294 | 295 | 0.92 |
| 312 | 317 | 0.70 |
| 335 | 335 | 0.87 |
| 340 | 341 | 0.86 |
| 362 | 364 | 0.81 |
| 380 | 380 | 0.86 |
| 384 | 384 | 0.87 |
| 427 | 427 | 0.87 |
| 441 | 442 | 0.91 |
| 475 | 475 | 0.80 |
| 496 | 502 | 0.90 |
| 522 | 522 | 0.80 |
| 543 | 544 | 0.86 |
| 559 | 560 | 0.86 |
| 567 | 570 | 0.87 |
| 590 | 591 | 0.92 |
| 600 | 600 | 0.81 |
| 624 | 628 | 0.85 |
| 639 | 640 | 0.85 |
| 645 | 646 | 0.82 |
| 654 | 659 | 0.85 |
| 663 | 664 | 0.93 |
| 674 | 674 | 0.85 |
| 689 | 689 | 0.91 |
| 705 | 706 | 0.83 |
| 712 | 713 | 0.87 |
| 723 | 723 | 0.94 |
| 729 | 729 | 0.89 |
| 734 | 737 | 0.90 |

## Depth of loops

| Loop Start Residue | Loop End Residue | Estimated Depth (Å) |
| --- | --- | --- |
| 1 | 5 | 0.76 |
| 11 | 11 | 8.96 |
| 36 | 42 | 14.2 |
| 47 | 47 | 16 |
| 72 | 73 | 3.29 |
| 92 | 92 | 8.03 |
| 99 | 103 | 2.52 |
| 114 | 119 | 24.43 |
| 134 | 135 | 19.59 |
| 139 | 142 | 8.14 |
| 150 | 150 | 0.35 |
| 162 | 165 | 6.26 |
| 193 | 197 | 0.61 |
| 209 | 210 | 4.19 |
| 223 | 223 | 15.3 |
| 228 | 230 | 14.1 |
| 257 | 257 | 14.37 |
| 264 | 264 | 13.84 |
| 275 | 277 | 10.33 |
| 282 | 286 | 2.36 |
| 294 | 295 | 5.21 |
| 312 | 317 | 1.93 |
| 335 | 335 | 5.49 |
| 340 | 341 | 11.38 |
| 362 | 364 | 16.67 |
| 380 | 380 | 18.48 |
| 384 | 384 | 11.21 |
| 427 | 427 | 4.12 |
| 441 | 442 | 12.85 |
| 475 | 475 | 11.88 |
| 496 | 502 | 3.53 |
| 522 | 522 | 9.53 |
| 543 | 544 | 19.66 |
| 559 | 560 | 17.36 |
| 567 | 570 | 6.77 |
| 590 | 591 | 14.42 |
| 600 | 600 | 25.62 |
| 624 | 628 | 21.02 |
| 639 | 640 | 26.12 |
| 645 | 646 | 24.42 |
| 654 | 659 | 16.2 |
| 663 | 664 | 12.94 |
| 674 | 674 | 9.17 |
| 689 | 689 | 11.96 |
| 705 | 706 | 9.6 |
| 712 | 713 | 3.85 |
| 723 | 723 | 1.42 |
| 729 | 729 | 0.99 |
| 734 | 737 | 5.63 |

---

It is easily understood from the charts and the tables above, that wild-type gshF is a very stable enzyme.

## Insights and Future Goals

The insights gained from this study will pave the way for future investigations into the impact of single-point mutations on the thermostability and melting temperatures of the GshF enzyme.
By systematically introducing mutations, we can explore how specific alterations in the protein structure affect its thermal resilience and stability.
This research not only enhances our understanding of the relationship between amino acid composition and enzyme performance under thermal stress but also provides a framework for designing more robust enzymes for various applications. Ultimately, the findings could inform directed evolution strategies aimed at optimizing GshF for industrial processes, thereby contributing to advancements in enzyme engineering and biotechnological applications.
The computational method we are gonna use to make single point mutations is to use Rosetta program to calculate the ΔΔG between the mutated enzyme and the wild-type. Negative ΔΔG leads to a more stable enzyme while positive ΔΔG mean that the mutation destabilized the enzyme’s structure and thus the thermostability did not possibly enhanced.
Unfortunately, in the current stage we could not access to Rosetta to make single point mutations.

References
![Close](https://static.igem.wiki/teams/5406/images/icons/x-scaled.png)

1. Yu H, Yan Y, Zhang C, Dalby PA. Two strategies to engineer flexible loops for improved enzyme thermostability. Sci Rep. 2017 Feb 1.
2. Kim H. S., Le Q. A. T. & Kim Y. H. Development of thermostable lipase B from Candida antarctica (CalB) through in silico design employing B-factor and RosettaDesign. Enzyme and Microbial Technology 47, 1–5 (2010).
3. Thiltgen G. & Goldstein R. A. Assessing predictors of changes in protein stability upon mutation using self-consistency. PloS one 7, e46084 (2012).
4. Paul M., Hazra M., Barman A. & Hazra S. Comparative molecular dynamics simulation studies for determining factors contributing to the thermostability of chemotaxis protein “CheY”. Journal of biomolecular structure & dynamics 32, 928–949 (2014).
5. Yu H. & Huang H. Engineering proteins for thermostability through rigidifying flexible sites. Biotechnology advances 32, 308–315 (2014).

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