iGEM 2023 — Team insaenslyon1

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![Notebook](https://static.igem.wiki/teams/4818/wiki/notebook/notebook.svg)

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## pEdit5 historical

**Zoom and move around the map to view all our constructions !**

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![Engineering](https://static.igem.wiki/teams/4818/wiki/engineering/engineering.svg)

**Engineering** can be described as the branch of science and technology concerned
with the design, building, and use of engines, machines, and structures. Based on this definition, the engineering
principles are **key elements** in **constructing** our tools:
whether for our software, our biological tool, our model... Our reflection is guided through time by a **series
of
steps**. There are four stages that include **Design, Build, Test** and
**Learn**. We wanted to represent it as a rond because, after the learning step, you can easily redo all
the previous stages to improve your tool or even consider new parameters or information. This is a way to
**learn from errors** or missing steps in our **creation process**, not miss a single
parameter, and **build a tool that is as effective and complete as possible**.

![Description de l'image](https://static.igem.wiki/teams/4818/wiki/engineering/cyclebg.png)

We present the five main cycles we used in our engineering approach. This is how we tackled the
various challenges that arose during our thinking and project construction. First, you'll learn about our software
design approach, a necessary step in creating our biological tool. Then, you'll discover the biological design of our
SuperBugBuster. The modeling part of our project also involved an engineering process. Finally, surprising as it may
seem, the engineering cycle also applies to constructing a social and historical work. Indeed, it's a way of building
a precise reflection on a specific subject. As human practices are at the heart of this project against antibiotic
resistance, the cycle of its reflection is represented on this page.

---

[Software](#Software)

[Crispr](#Biology)

[Model](#Model)

[IHP](#HP)

[BacProtacs](#BP)

## **Software Cycle**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

When looking for CRISPR motifs, we came up against a difficulty: we wanted to eliminate carbapenemase
resistance. As
this is a class of genes with the same function, some of the genes in this class may have homology
relationships. We,
therefore, wanted to obtain a single motif capable of guiding the CRISPR module over several same-class
genes. However,
current CRISPR tools were unable to find CRISPR motifs capable of binding to a gene database. We created
this program
using two existing software packages:
<https://cstb.ibcp.fr/> and
<http://www.rgenome.net/be-designer/>.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

Firstly, we contacted the IBCP team, who created one of the programs we based ourselves on. The
program takes
two databases as input, the first containing the list of genes for which we are looking for CRISPR
motifs. The second
database contains the bacterial genome(s) in which we don't want CRISPR motifs to be recognized. Then
our program follows these
steps:

1. We look for all 20-nucleotide motifs that end in the NGG motif on the first database. We repeat this
   process on the second
   database.
2. We compare the two lists of motifs to identify only those present in the first database and not in
   the second one. We use a
   binary search method to perform this search as it reduces complexity and speeds up the program
   execution time.
3. While all the motifs obtained through this process can be used for a classic CRISPR, they cannot be
   utilized for the cas9 CRISPR we use, which is fused to a cytidine deaminase. Therefore, for each
   gene/pattern pair, we will test by
   mutating specific nucleotides in the 13 to 17 nucleotide window, a C->T mutation on the direct strand
   and a G->A mutation on the
   complementary strand, to see if this mutation will
   produce a nonsense codon.
4. The program adds up the results to create a data table showing the probability of producing a
   nonsense codon using all
   the motifs.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

To test the program, we conducted tests between our program and <http://www.rgenome.net/be-designer/>
to ensure that the program functioned correctly. In addition, we also carried out manual tests
simulating the use of
CRISPR in specific sequences to check that the program behaved correctly.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

We realized that our initial hypothesis was that, since a homology relationship linked the genes, the
same motif could
hybridize on a large number of genes in the same class. In some cases, a motif may hybridize with
several genes, but
this number remains limited.

## **Biology Cycle**

**1rst iteration: pEDIT1**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

The first plasmid we need is the entry vector of the Gateway system. This plasmid must contain a
transfer origin, allowing
conjugation with a wide range of bacteria to make our tool applicable to different species.
Additionally, this plasmid must provide a carefully chosen replication origin.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

A Gateway entry vector must include specific recombination sites attR3 and attR4, between which the
sequences of interest are located. Outside of these recombination sites, the vector must contain
antibiotic resistance for plasmid selection, as well as a pUC replication origin.
The sequences of interest provided by this plasmid will be the replication origin, the transfer origin,
and the resistance
necessary to exert selection pressure on the final plasmid. We have chosen an RP4 plasmid transfer
origin, allowing conjugation of
the plasmid across a broader spectrum of bacteria. For the replication origin, we decided to design a
mini F plasmid containing the
ori2 replication origin and the associated replication protein, RepE. This replication origin is tightly
regulated, and the F
plasmid is present in a single copy per cell. We have removed partition genes to allow easy loss of this
plasmid without selection
pressure.
This plasmid is constructed in five fragments assembled by Golden Gate cloning using the BsaI enzyme and
T4 DNA ligase. Each
fragment was previously obtained by PCR from a template plasmid using appropriate primers. These primers
have 5' overhangs with
BsaI sites possessing suitable sticky ends to allow directional assembly during Golden Gate cloning.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

In a first step, the sizes of the PCR products are verified by agarose gel electrophoresis. The expected
sizes are as follows:

* **PCR Cm\_OriT RP4**: 1131 bp
* **PCR Ori2-repE**: 2007 bp
* **PCR attR4-ccdB**: 934 bp
* **PCR Rifr-pUC ori**: 1435 bp.

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/pedit1deltaccdb.png)

*Fig.1 : Agarose electrophoresis gel on pEDIT1*

Once ligation is completed, the size of the resulting plasmid is verified by XhoI enzymatic digestion.

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/ph2.png)

*Fig.2 : Agarose electrophoresis gel on pEDIT1 after Xhol digestion*

Finally, sequencing covering the ligated regions is performed to ensure that the correct sequence is
obtained(see [Results](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D) page).

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

The results seem to indicate that we have successfully constructed the expected plasmid.

**2nd iteration: pEDIT2**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

The second plasmid must incorporate an inducible editing system capable of inactivating a
specific
target gene.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

DNA base editors, combining a deaminase with an inactive Cas-9 protein, are genetic editing tools
adapted to make specific modifications at the genome level. There are various base editors, and in
this project, we used the Cytidine deaminase module derived from the marine lamprey Petromyzon
marinus. This module induces conversions of Cytidine to Uridine within the DNA. It is fused with
the uracil glycosylase inhibitor (UGI) from the PBS1 bacteriophage of Bacillus subtilis, which
inhibits bacterial uracil-DNA glycosylase and prevents the reversion of Uridine to Cytidine. This
protein combination with the CRISPR-dCas9 system allows guide RNAs (gRNAs) to precisely target the
genome region where the cytosine-to-thymine base mutation is intended to occur. The base editor is
guided to a specific site called the protospacer by a gRNA. A 20-nucleotide segment called a
spacer, located at the 5' end of the gRNA, forms a heteroduplex with the complementary DNA strand
of the protospacer. This complex exposes the protospacer DNA strand, making it accessible to the
deaminase. In the case of the Cytidine deaminase base editor coupled with the CRISPR-dCas9 system,
the deamination window is located 13 to 17 bases upstream of the Protospacer Adjacent Motif (PAM)
NGG recognized by the Cas9 protein.

![](https://static.igem.wiki/teams/4818/wiki/engineering/unnamed-11.png)

*Fig.3 : A:Schema of dCas9 fonction B:Cytosine becomes thymine by méthylation*

We decided to place our editing system under the control of the tetR induction system. This
repressor
regulates the expression of the tetA gene encoding the TetA efflux pump capable of exporting
tetracycline antibiotics out of the bacterial cell. In the absence of tetracycline, the TetR
repressor
blocks the expression of the tetA gene. However, in the presence of tetracycline, it interacts
with the
TetR regulator, which changes conformation and loses its affinity for the tetO operators.
Consequently,
the ptet promoter is released, and the tetA gene is transcribed.

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/tetr.jpg)

*Fig.4 : Functioning diagram of the Tetr system*

This plasmid was constructed by performing a double enzymatic digestion (SpeI + BamHI) of
two-parent
plasmids and a ligation using T4 DNA ligase.
After obtaining the plasmid resulting from the previous ligation, we need to remove the region
responsible for chloramphenicol resistance to avoid having two chloramphenicol resistance regions
on the
final pEDIT5 plasmid. To achieve this, we perform a double enzymatic digestion (ZraI + SpeI),
followed
by blunting the protruding ends, and finally, ligation using T4 ligase.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

We tested the sensitivity of the obtained strains to chloramphenicol. The strains should be
sensitive to
chloramphenicol.
The size of the resulting plasmid is verified by (Kpn1 + SpeI) enzymatic digestion.

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/pedit2-gel-figure2.png)

*Fig.5 : Electrophoresis gel on pEDIT2 by KnpI and SpeI enzymatic digestion.*

Finally, to ensure that the correct sequence is obtained, sequencing covering the ligated regions
is
performed (see [Results](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D) page).

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

The only plasmids we could obtain with this method are the initial plasmids
(chloramphenicol
resistant). We think that the source of error is the blunting kit, as it is likely
inefficient. We
have decided to perform different enzymatic digestion of the first intermediate plasmid; we are
conducting a double digestion with ZraI + MlsI in order to partially delete the region carrying
the
chloramphenicol resistance, hoping for a loss of function.

**3rd iteration: pEDIT3**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

This plasmid must contain between attL1 and attL2 sites, the guide RNAs (two or four guide RNAs)
assembled in tandem, each under the control of an Anhydrotetracycline-inducible
promoter.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

This plasmid requires the construction of three intermediate plasmids:

* The host plasmids for specific spacers, pHost-spacer
* The expression plasmids for guide RNAs, pEx-gRNA1 and pEx-gRNA2
* The recipient plasmids for tandem guide RNAs, pV2-mScarlet and pV4-mScarlet

The pHost-spacer plasmids contain: the ptet promoter, a ccdB suicide cassette, the guide RNA
backbone
necessary for interaction with the Cas9 protein.

Each of these plasmids will accept a specific spacer.

The pHost-spacer plasmids differ in the presence of distinct BbsI sites, allowing directional
assembly of guide RNAs into the recipient vectors pV2-mScarlet or pV4-mScarlet.

The pHost-spacer plasmids consist of two fragments obtained by PCR from the template plasmids
pINS-Rif and pT1, using primers with 5' overhangs carrying distinct BbsI sites.

The expression plasmids pEx-gRNA1 and pEx-gRNA2 contain the spacers S1-oxa48 and S2-oxa48,
respectively, targeting [specific target]. They were obtained through Golden Gate cloning using
BsaI
and T4 DNA ligase into the plasmids pHost-spacer1 and pHost-spacer2.

The host plasmids for tandem guide RNAs, pV2-mScarlet and pV4-mScarlet, are derived from the
pENTR4-dual plasmid, marketed by Thermofisher and containing the recombination sites attL1 and
attL2.
The objective is to replace the chloramphenicol resistance gene and the ccdB suicide gene
located
between attL1 and attL2 with the gene encoding mScarlet, flanked by two BbsI restriction sites.
These
BbsI sites will enable the oriented insertion of tandem guide RNAs through Golden Gate cloning.
The
construction of these plasmids involves a series of steps:

* The first step involves removing an unwanted BbsI site from the pENTR4-dual plasmid. This
  was
  achieved through deletion using the blunt-end enzymes ZraI and MbiI, resulting in the plasmid
  pENTR4∆BbsI.
* The second step involves amplifying the mScarlet gene from the template plasmid
  pMCF-mScarlet
  using primers that introduce XhoI and KpnI sites at the ends, as well as distinct internal
  BbsI
  sites.
* Finally, the last step involves cloning the mScarlet PCR fragment into the plasmid
  pENTR4∆BbsI
  through double digestion with XhoI and KpnI.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

We tested the loss of the BbsI site by enzymatic digestion.

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/figure-6.png)

*Fig.6 : Electrophoresis gel on enzymatic digestion BbsI and ApaI of pENTR4-dual 3 Clones of pENTR4∆BbsI.*

Finally, to ensure that the correct sequence is obtained, sequencing covering the ligated
regions is
performed (see [Results](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27results%27%29%20%7D%7D) page).

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/figure-7.png)

*Fig 7 : Electrophoresis gel on pV2-mScarlet and pV4-mScarlet by BbsI enzymatic digestion.*

![Safety guidelines](https://static.igem.wiki/teams/4818/wiki/engineering/fig-8-pedit3.png)

*Fig 8 : Electrophoresis gel on pEDIT3 by XhoI enzymatic digestion.*

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

We noticed that we had an issue with selection pressure. Indeed, since the pEDIT3 plasmid is
KanR, and
the plasmids used for its construction are also KanR, we had difficulty isolating the correct
plasmid.
Therefore, we decided to make pEDIT3 SmR to enhance our selection.

## **Model Cycle**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

The design of the differential equation model on the Crispr part of our project was based on the
needs
of
the team in charge of laboratory manipulations. This model enables us to explore the tests of our
in
silico tool, and therefore had to answer questions asked of our biological tool, in order to
support
future tests but also to test specific things that would not be feasible in the time available.
The main
questions we had to answer were:

* At what scale should our model be placed (bacterial, bacterial population, DNA...)?
* What were the main variables to be taken into account?
* What were the interactions between these variables to be explored?
* What simplification hypotheses could be taken into account in the model without causing it to
  lose
  its
  biological meaning?

After answering these questions, we came up with a population dynamics model showing plasmid
transitions
that move bacteria between the following categories: susceptible, resistant, susceptible with
CRISPR
plasmid, resistant with CRISPR plasmid.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

The creation of the model was still very much related to biological issues, but the more
mathematical
aspects came into play. Many types of equations were available, so we had to choose the most
appropriate
ones for our situation. We therefore chose to use two different models: one with an Allee effect
and the
other with a logistic effect (Verhulst). This model-building stage led to a lot of backtracking to
the
previous design stage, as we repeatedly realized that our first design had certain shortcomings,
or even
errors, in relation to biological reality. It's up to the model to adapt to the experiments, not
the
other
way round, hence the feedback. Once our final design appeared to be the right one, we still had to
find
the parameter values. We therefore undertook bibliographical research and had to use certain
arbitrary
values due to lack of knowledge, which were then adjusted in the next stage of the engineering
cycle.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

The mathematical equations were translated into Python code to run numerical simulations. Euler's
method
was used to approximate the evolution of the variables. The test phase may revert to the build
phase at
times, as certain parameters need to be adjusted. The test consists in displaying the curves and
observing
the evolution of the populations. We know what result we want to achieve, a result that
corresponds to
the
correct functioning of our tool, and we try to understand how to write the model in a way that is
biologically and mathematically correct.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

We went back and forth between the learn, test and build phases. In fact, observing the curves
allows us
to understand any inconsistencies that may have remained in our model. At a certain point, our
model
meets
our expectations and we decide that the curves obtained are our final results. These results allow
us to
learn about the effectiveness of our tool. This effectiveness remains an assumption, as it is
based on
simplifications, but these have been made sparingly and appropriately, so we can rely on our
model. Our
SuperBugBuster tool is effective, since it enables us to eliminate resistance and, in the end,
obtain
only
susceptible bacteria. This last phase also enabled us to revisit our simplifications, re-discuss
them
and
envisage new complexities to bring our model even closer to biological reality.The model can be
used to
test different initial conditions and observe the behavior of bacterial populations. It can
therefore be
used to prepare test experiments in the laboratory!

## **Human Practices Cycle**

**1rst iteration: Onion Method**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

For the first iteration of our design part of human practices, we did real research work on
bibliography.
We had a few questions in mind about our subject. So, we searched for information on the theme of
antibiotic resistance: The history of antibiotics, the economic context around it, the
consequences and
where do we go with our actual situation. This permitted us to create our real base of work to
design
our
reflection of human practices. But with information leading to new questions, we extended our work
to
also
search data and about the imaginary around science in general, how people perceive science.
Moreover,
the
notion of One Health came up.
Then we asked around how we could image all of this, how we could represent all this information,
in an
easy and understandable way. That is when the onion method came up to us, and seemed to us to be a
great
tool to use in our Human Practice approach of the antibiotic resistance subject. Indeed, this
allows us
to
structure our ideas, take a step back, and think critically about our subject.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

Then, we build up our onion method, which you can find on the Human Practice page. This tool was
created
and imagined by Jean François Trégouët and Céline Nguyen, teachers at INSA Lyon. So we have
surrounded
ourselves with teachers and humanity teachers to help us build our onions. Their expertise in the
subject
led to the design that we have now. Layers, by layers we looked into our bibliography to fill in
them.
We
also included the One Health problematic, by a color code.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

For the test of our tool, as it is a written form, we decided to test it around us, by
proofreading our
onion method by different persons. We tried to include in this trial phase people from different
environments, friends, family, teachers, to have a maximum of feedback on it. Like this, we had
many
different points of view on the subject: people close to the scientist environment, others totally
out
of
it, or even others who specialize more into social sciences. Their feedback was an important step
in our
reflection that we seriously take into consideration. This was a real help in our Human Practices
journey.
The test phase was effective because it pointed out that the onion was a great tool to have a
global
idea
of the subject, but was only based on bibliography. We needed to have real testimonies on the
subject to
have a more important knowledge of the antibiotic resistance subject.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

To finish our first iteration of the Human Practice Engineering cycle, we took into account the
different
feedback of our helpers. At this step, we needed more material on antibiotic resistance, and
needed it
in
a different way than just with bibliography. Indeed, the theme of antibiotic resistance being
first of
all
a consumption problem, we needed to have testimonies, to have real people telling their experience
of
it.
But this onion method step wasn’t useless, for sure, because it was this first approach of the
subject
that permitted us the following of our Human Practice Journey; to include and integrate people in
the
core
of this part of the Super Bug Buster subject. Indeed, the work by Human Practice at this point is
not
finished at all and we are at the beginning of our second iteration of the engineering cycle !

**2nd iteration: Studies**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

Our second iteration of the cycle began with the creation of our qualitative study; Indeed, after
our
research work and the creation of our bibliography, we needed to go further in the antibiotic
resistance
problem. To do so, we research how we could do so. It is natural that we turned to a total study
of the
subject: both qualitative and quantitative studies.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

The building of this full study consisted in two branches.
First we created our qualitative study by creating a full interview with basic questions that we
built
thanks to the firsts research that we made. We modified it according to the type of person that we
wanted
to interview. We ended up with 5 different interviews according to the interviewed profile. Of
course,
during the actual interview new questions came up, and through the interviews, a new profile
appeared,
so
new questions again. Indeed each interview ended up influencing the next one, and the type of
people to
interview, by opening new questions on the subject. It was a real work of adaptation to build our
qualitative study !
Then when we finished our qualitative study, by creating a Reflection tree, permitting to arrange
our
reflection, through the interviews. This permitted us to see all the links between interviews, and
another
time, it was important for us to include this notion of One Health, that is really important in
the
context of antibiotic resistance.
Furthermore, for our quantitative aspect, we decided to get surrounded by a Science Po student, to
help
us
to create a survey. This was for us to have global information on several big questions that
raised our
bibliography work. Indeed we asked: what people know about antibiotic resistance, their
consumptions of
antibiotics and how they think it affects the world. The creation of this survey was the final
point of
our Integrated Human Practices journey.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

The test step of this iteration of the Human Practice work mainly consisted of the deployment of
our
survey. but we realized that it was biased. Indeed, we, as students in science, are surrounded by
people
from this field. So by deploying our survey around us, by social media, mails … we will
automatically
touch younger people, and related to science, so that they necessarily have an idea of what
antibiotic
resistance is or at list that they have to have a reasonable use of. We didn’t want to have false
results,
with false interpretation.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

So to not create this, thanks to our test, we learned that we will keep this survey as a
proof-of-concept,
like a potential survey that we could deploy, if we had a larger audience, or a bigger impact. We
detail
all of this in the Integrated Human Practices page.

**3rd iteration: Studies**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

Last iteration, but not least, for the Human Practice work, after realizing the importance of
educating
people about antibiotic resistance, we decided to focus on education. We started by identifying
the
educational objectives we want to achieve such as equipping individuals with knowledge, skills,
and
values
needed for personal development and societal contribution. Through education, individuals gain the
tools
to navigate the world, make informed decisions, and actively participate in shaping a better
future. We
conducted research, gathering information about existing educational games, successful awareness
campaigns, and effective social media strategies used in education. Additionally, we defined our
target
audience, considering factors such as age group, educational level, and demographics. With all
this
information, we brainstormed creative ideas that aligned with our objectives and target audience.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

We designed and created educational games that are interactive, engaging, easily adaptable and
accessible
to all. Simultaneously, we developed an awareness campaign about antibioresistance through flyers
and
brochures. Talking about our project and its key concepts, we used our social media to do some
science
popularization. The idea was to reach as many people as possible, around the world ! We encourage
user
engagement, discussions, and sharing of educational resources.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

To test our educational tools, we wanted to deploy our games in schools or summer camps. However,
out of
the fifty establishments we contacted, only one middle school answered. We did a presentation of
our
project, focusing on the educational part, for 170 students. For the awareness campaign, we
contacted
doctors and sent them our flyers and brochure. Two of them agreed to display them in their waiting
rooms.
For the first-year’s students of Biosciences at INSA Lyon, we gathered feedback to evaluate if our
games
and social media contents were engaging and enjoyable and if they were suitable for children.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

Finally, after the tests, we reviewed the feedback received for the games, awareness campaign, and
social
media content. We identified areas of improvement and areas that have been successful. This helped
us
refine and enhance our educational tools. We implemented necessary modifications to improve their
effectiveness and impact. By sharing our experiences, we contribute to the collective knowledge
and
improvement of education.

## **BacProtacs Cycle**

**General**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

The first goal was to find a solution to degrade OXA48 protein in the cytoplasm. We came up with
an idea of a BacPROTACs, with ClpX directly involved.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

To design our PROTAC, we needed two proteins, one that could bind to the OXA48 protein and one to
ClpX. We decided to use ClpX directly because it was simpler that attempting to find a lingant
protein from scratch (something we didn’t know how to do). We also found in the literature a
nanobody specific to CMY-2, a protein from the Oxa48 family, that we thought would interact with
our protein of interest since the two molecules were pretty alike. To link those two proteins, we
decided to use a really common linker, that is normally good: Asn Phe Phe Asn Leu.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

When we presented our project to a group of scientists, from diverse specialties, even though they
found the idea really interesting, they quickly pointed out that the nanobody wouldn't bound
with the oxa48 protein because it was really specific to CMY-2. They also said that a linker is
not something we can choose out of the blue, and we need to make some modelisation to find it so
the BacPROTACs would work.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

During this meeting, we learned several things. First, it is important to present our work to
scientists so they can point out some of the mistakes and miscalculations we made during the
process. Also, building BacPROTACs is a lot more complicated than we thought and just putting
three proteins all together won’t work. So we needed to think about it further.

**2nd iteration : NANOBODY**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

We took into account what the scientists said and tried to do some modelisations of the
interaction OXA-48-nanobody. As there wasn’t any interaction, we decided to modify this nanobody,
thanks to the help of Riccardo Pelarin, a scientist from the bioinformatics field, who gave us the
idea.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

To do so, we used a new algorithm: RF Diffusion (see [Docking/](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27dry_exp%27%29%20%7D%7D) page ). It allowed us
to transform the nanobody CMY-2 specific and to
obtain potential ones interacting with OXA48.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

We modeled in silico the interaction of the best we had and we thought of doing some tests in vitro, for example an ELISA
test (see [Experiments/](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27wet_exp%27%29%20%7D%7D) page) in order to find which nanobody interacts with the protein.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

Here, we learned pretty much about how time-consuming is in silico modelization and about the
difficulty to have a good result. Indeed, the algorithms are really complicated, so difficult to
really understand, and because it’s biology, it’s even more complicated to model.
Thanks to that manipulation, we succeeded to have three nanobody candidates, which, when
implemented in modelisation platforms, produced an interaction with OXA48.

**2nd iteration bis: LINKER**

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/design.png)

The first linker we chose was a simple and basic linker: Asn Phe Phe Asn Leu. But it wasn’t
necessarily the best option. Indeed, we realized later, by consulting a researcher on the idea,
that we didn’t know if we could avoid steric clashes with this linker and if it would be long
enough to allow both the nanobody and ClpX to do their function fine. So we decided to try to
model it.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/build.png)

Based on the previous, we planned to use an algorithm called IMP-develop that allows to ​​detail structural
characterization of assemblies. So it could help us to build our linker by visualizing the interaction between the
nanobody, ClpX and the chain we wanted to put.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/test.png)

To do so, we needed to supply the IMP tool with the nanobody’s and ClpX’s sequence, and the
most basic linker possible, so it can modify it. The nanobody and ClpX models are frozen, so
that only the linker can take on all possible conformations in this rigid environment, in order to
obtain the best possible results on the possible coordinates of the linker.

![Image](https://static.igem.wiki/teams/4818/wiki/engineering/learn.png)

Although we didn't use this tool, as we would have had to start a script from scratch. We were
able to determine that we needed to link the linker to the C-terminal part of ClpX since studies
using GFP as the protein to be degraded had linked GFP to the C-terminal part of this same
protein. This allows us to switch from two binding possibilities (C-terminal or N-terminal) to a
single one, which we can implement right from the start in the software to have a reduced data
set.

---

**Click on the buttons to see the cycles appear !**

{% endblock %}


{% extends "layout.html" %}
{% block title %}RESULTS{% endblock %}
{% block IMG\_URL %}
"https://static.igem.wiki/teams/4818/wiki/results/logo-result.svg"
{% endblock %}
{% block background %}bg-orange{% endblock %}
{% block page\_content %}

![Results](https://static.igem.wiki/teams/4818/wiki/results/result.svg)

---

## RESULTS MODEL:

The first result that was useful for us was linked with our model. Indeed, our model permitted us to have a first apprehension of how our tool works. You can find all the creation fo it in the [Model](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27model%27%29%20%7D%7D) page and the python code used is available on our [gitlab](https://gitlab.igem.org/2023/software-tools/insaenslyon1/-/tree/main/Model).

We have these followed parameters:

\( R(t) \), population of resistant bacteria at time \(t\),
\(S(t)\), population of sensitive bacteria at time \(t\),
\(Rc(t)\), population of resistant bacteria containing the CRISPR tool plasmid at time \(t\),
\(Sc(t)\), population of sensitive bacteria that are recipients containing the CRISPR tool plasmid at time \(t\),
\(C(t)\), population of donor bacteria containing the CRISPR tool plasmid at time \(t\).

We ended up with the next evolution of our parameters, with at t=2 there is induction by anhydrotetracycline:

![Model](https://static.igem.wiki/teams/4818/wiki/model/results.png)

Fig.1 : Simulations of our SuperBugBuster model

The results are conclusive regarding the effectiveness of our tool. Indeed, the population of sensitive bacteria then becomes significantly dominant, leading to the extinction of resistant bacteria. Thus, we observe a pronounced growth curve for the S population.
The model that permits us to have an in silico study of our CRISPR dCas9 plasmid tool concludes that it works !
Thanks to it, by modifying the parameters, we are able to predict the different behavior possible of our tool, which is of great importance for the wet lab part and also for the safety aspect.
This is therefore a major support for predicting the behaviour of bacterial populations in the laboratory testing phase!

---

## RESULTS SOFTWARE:

Our software also created some important results for our SuperBugBuster creation ! To get an idea of what is the principle of it, you can look at the
[Software](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27software%27%29%20%7D%7D) page and at the python code available on our [gitlab](https://gitlab.igem.org/2023/software-tools/insaenslyon1/-/tree/main/Model).

Indeed, thanks to it and to the run of our program, we had as a result the motifs of our RNAg. We ended up with those next motifs:

![Model](https://static.igem.wiki/teams/4818/wiki/results/soft.png)

Fig.2 : Crispr parttern for oxa48

These results were really important for our plasmids creation and construction. Indeed, it is thanks to that, that we target the antibiotic resistance gene, to induce its modification and inactivate it.

---

## RESULTS PROTAC:

For our BacProtac part, our results lie on the finding of 3 potential nanobodies that will permit protein deterioration. These Nanobodies will permit us to create our BacPROTAC system specific for our interest protein. Thanks to docking experiments, we ended up with the 10 most compatible Nanobodies, but decided to perform our tests only on the top 3. The next list presents sequences of these ones, that seem to be good candidate for our tool:

* >1/1-390
  ATGCAGGTGCAGCTGGTGGAAAGCGGCGGCGGCATGGTGCAGCCGGGCGGCAGCCTGCGCCTGAGCTGCGCGGCGAGCGGCATTGATTTTAGCCTGTATGATATGAGCTGGGTGCGCCGCGCGCCGGGCAAAGGCCCGGAATGGGTGAGCACCATTAAAAGCGATACCGGCGAAACCAGCTATGCGGATAGCGTGAAAGGCCGCTTTACCATTAGCCGCGATAACGCGAAAAACACCCTGTATCTGCAGATGAACAGCCTGAAACCGGAAGATACCGCGCTGTATTATTGCACCATTGATCGCAACGGCCGCAAAGGCGATATTAACTATGGCGTGACCTATAACAGCGGCACCCAGGTGACCGTGAGCAGCCATCATCATCATCATCATTAA
* >5/1-373
  ATGCAGGTGCAGCTGGTGGAAAGCGGCGGCGGCATGGTGCAGCCGGGCGGCAGCCTGCGCCTGAGCTGCGCGGCGAGCGGCATTGATTTTAGCCTGTATGATATGAGCTGGGTGCGCCGCGCGCCGGGCAAAGGCCCGGAATGGGTGAGCACCATTAGCAGCGATGGCAAAGATACCAGCTATGCGGATAGCGTGAAAGGCCGCTTTACCATTAGCCGCGATAACGCGAAAAACACCCTGTATCTGCAGATGAACAGCCTGAAACCGGAAGATACCGCGCTGTATTATTGCACCATTGATCGCGGCCATGATTATGGCGGCGCGGGCCTGGATGCGAGCCGCAACCCGGGCACCCAGGTGACCGTGAGCAGCCATCATCATCATCATCATTAA
* >9/1-373
  ATGCAGGTGCAGCTGGTGGAAAGCGGCGGCGGCATGGTGCAGCCGGGCGGCAGCCTGCGCCTGAGCTGCGCGGCGAGCGGCATTGATTTTAGCCTGTATGATATGAGCTGGGTGCGCCGCGCGCCGGGCAAAGGCCCGGAATGGGTGAGCACCATTAGCCCGGATGGCAGCACCACCAGCTATGCGGATAGCGTGAAAGGCCGCTTTACCATTAGCCGCGATAACGCGAAAAACACCCTGTATCTGCAGATGAACAGCCTGAAACCGGAAGATACCGCGCTGTATTATTGCACCATTGATCGCACCCCGGTGGTGGGCAGCGCGCTGAGCGATATGCCGGTGGATAGCGGCACCCAGGTGACCGTGAGCAGCCATCATCATCATCATCATTAA

Next step is to test them, to see if they are actually working on our protein. To do so, we thought of 3 different tests such as: Fluorescent, NMR and ELISA (you can find more information on that on our [Experiments](%7B%7B%20url_for%28%27pages%27%2C%20page%3D%27wet_exp%27%29%20%7D%7D) page).

---

## RESULTS CRISPR:

During this project we achieved the build of much than 14 different plasmid, here are our success results :
To check our result, for each plasmid we realized the check of PCR on gel and sequencing. These two steps permitted us to see if our plasmids created were the good ones and the ones expected.

* The first step is check our PCR products on gel, and enzymatic digestion
* The second step to check our plasmid is the sequencing. It allowed us to confirm the identity of diverse plasmids and ensure that it matches the intended sequence. This is crucial when working with genetically modified organisms or conducting experiments that rely on specific DNA sequences. Sanger sequencing was carried out by Microsynth. We aligned the sequences with the theoretical ones on SnapGene and analyzed the results. Some plasmids have more than one aligned sequence to ensure proper sequencing or check different regions.

---

### pEDIT1 version 1 (without ccdB) :

**Gel on PCR :**

The first step of the construction of pEDIT1 was the PCR product, which was checked in this gel.
(0,7% agarose, loading dye (6X), 1kb+ ladder)

Here is our gel result :

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel1.png)

Fig.3 : PCR product on gel of pEDIT1

The lengths obtained from the PCR amplification products were as expected.

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel2.png)

Fig.4 : Enzymatic digestion of pEDIT1

Then we check out the length of the linearised plasmid from the isolated clone, and it was the good one:

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel3.png)

Fig.5 : linearized pEDIT1 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel4.png)

After sequencing, we can say that we obtained the good plasmid. That could have been pretty cool, but we realized that we needed other verification tools (RifR resistance, ccdB cassette) in order to really select our final plasmid at the end of the final transformation.

---

### pEDIT1 version 2 (with ccdB) :

**Gel on PCR :**

We are expecting these fragments after enzyme digestion of pEDIT1 version 2 (with ccdB).

* PCR Cm\_OriT RP4: **1131 bp**
* PCR Ori2-repE: **2007 bp**
* PCR attR4-ccdB: **934 bp**
* PCR Rifr-pUC ori: **1435 bp**

Here is our gel result:

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel5.png)

Fig.6 : PCR products on gel of pEDIT1 V2

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel6.png)

Fig.7 : Enzyme digestion on gel of pEDIT1 V2

We have obtained that good length for the PCR, and as we ordered this plasmid, we can say that what we have in our colonies is the plasmid we want. So we have obtained the host plasmid of our construction, with the origins of replication and transcription and the CmR resistance. The double resistance (CmR, RifR) and the ccdB gene will allow us to eliminate cells that kept this plasmid during the final assembly. Indeed, the RifR and ccdB cassette should be lost during the process, so cells having a resistance to RifR won’t be the good ones so we will eliminate them, and on top of that, the ccdb cassette should kill them. This double verification is necessary since we’re at the final stage of our construction.

---

### pEDIT2-Cm :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel7.png)

Fig.8 : Enzymatic digestion of pEDIT2-Cm on gel

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel.png)

Fig.9 : Linearized pEDIT2-Cm on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel10.png)

We obtained the right length for the pEDIT2-Cm plasmid without digestion (ND) for each of the 4 clones. After digestion by EcoRI, we had two fragments as expected. So we can conclude that we obtained the right pEDIT2-Cm plasmid.

---

### pEDIT2:

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel11.png)

Fig.10 : Enzymatic digestion of pEDIT2 on gel

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel12.png)

Fig.11 : linearized pEDIT2 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel13.png)

We obtained the right length for the pEDIT2 plasmid without digestion (ND). After digestion by KnpI and SpeI, we had two fragments as expected corresponding to the correct lengths. So we can conclude that we obtained the right pEDIT2-Cm plasmid.

---

### pHost-spacer1 :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel15.png)

Fig.12 : PCR products on gel of pHost-spacer

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel16.png)

Fig.13 : linearized pHost-spacer1 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel17.png)

We obtained the correct length for the PCR pT1 and PCR INS-rif that allow us to build the pHost-spacer1. So we can conclude that we obtained the right pHost-spacer1 plasmid after ligation. So it’s ready to host the backbone of the 1st gRNA that will target the oxa gene.

---

### pHost-spacer2 :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel18.png)

Fig.14 : linearized pHost-spacer2 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel19.png)

We obtained the correct length for the PCR pT1 and PCR INS-rif that allow us to build the pHost-spacer1. So we can conclude that we obtained the right pHost-spacer2 plasmid after ligation. So it’s ready to host the backbone of the 2nd gRNA that will target the oxa gene.

---

### pHost-spacer3 :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel20.png)

Fig.15 : linearized pHost-spacer3 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel21.png)

We obtained the correct length for the PCR pT1 and PCR INS-rif that allow us to build the pHost-spacer1. So we can conclude that we obtained the right pHost-spacer3 plasmid after ligation. So it’s ready to host the backbone of the 3rd gRNA, that will target our plasmid so it will be inactivated.

---

### pHost-spacer4 :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel22.png)

Fig.16 : linearized pHost-spacer4 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel23.png)

We obtained the correct length for the PCR pT1 and PCR INS-rif that allow us to build the pHost-spacer1. So we can conclude that we obtained the right pHost-spacer4 plasmid after ligation. So it’s ready to host the backbone of the 4th gRNA, that will target our plasmid so it will be inactivated.

---

### pHost-spacer1 :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel24.png)

Fig.17 : Enzymatic digestion of pEX-gRNA1/2-oxa48 on gel

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel25.png)

Fig.18 : linearized pEX-gRNA1-oxa48 on Snapgene

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel27.png)

Fig.19 : linearized pEX-gRNA2-oxa48 on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel26.png)

pEX-gRNA1-oxa48

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel28.png)

pEX-gRNA2-oxa48

We obtained the right length for the pEx-gRNA1-oxa48 and pEx-gRNA2-oxa48 plasmids without digestion (ND). After digestion by ZraI, we had one fragment meaning that the plasmid has been linearized as expected and the fragment obtained is corresponding to the correct lengths. So we can conclude that we obtained the right pEx-gRNA1-oxa48 and pEx-gRNA2-oxa48 plasmids, the plasmids with the gRNA that will guide our cas9 to the oxa gene.

---

### pENTR4-Delta-BbsI :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel29.png)

Fig.20 : Enzymatic digestion of pENTR4-Delta-BbsI on gel

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel30.png)

Fig.21 : linearized pENTR4-Delta-BbsI on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel31.png)

We obtained the right length for the pENTR4-Delta-BbsI plasmid without digestion (ND). After digestion by BbsI and ApaI, we had two fragments as expected and not one like in the original pENTR4 and they are corresponding to the correct lengths. So we can conclude that we obtained the pENTR4-Delta-BbsI plasmid wanted.

---

### pV2-mScarlet :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel32.png)

Fig.22 : Enzymatic digestion of pV2-mScarlet on gel

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel33.png)

Fig.23 : linearized pV2-mScarlet on Snapgene

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel34.png)

We chose 2 different regions because we cannot sequence only one (length regulation) and we see that we have the expected plasmid since mismatches and gaps / insertions located at the ends are consistent with the sequence. This shows that errors are often made at the beginning and the end during a sequencing but with two different regions, we checked that these ends were correct.
We obtained the right length for the pV2-mScarlet plasmid without digestion (ND). After digestion by BbsI, we had two fragments as expected and they are corresponding to the correct lengths. So we can conclude that we obtained the right pV2-mScarlet plasmid.

---

### pet28-ox48 :

**Gel on PCR :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel35.png)

Fig.24: linearized pet28-ox48 on Snapgene

We obtained the right length for the pet28-oxa48 plasmid without digestion (ND). After digestion by BbsI, we had two fragments as expected and they are corresponding to the correct lengths. So we can conclude that we obtained the right pet28-oxa48 plasmid.

**Sequencing :**

![Model](https://static.igem.wiki/teams/4818/wiki/results/gel40.png)

We obtained the right length for the pet28-oxa48 plasmid without digestion (ND). After digestion, we had two fragments as expected and they are corresponding to the correct lengths. So we can conclude that we obtained the right pet28-oxa48 plasmid.

---

### Sequencing conclusions :

Our results ended up positive, all our plasmids were successfully built.

* The 4 intermediate plasmids, the tests we carried out to get there
* Overprod of oxa48
* The oxa plasmid showing that it was not resistant

{% endblock %}