iGEM 2022 — Team afcm-egypt

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* [Protocol](#Protocol)

Protocols

We created this notebook to record the work we did in the wet lab in iGEM 2022. This notebook contains all relevant lab information, including the people who participated in the experiment, the tools we used, and the procedures we followed to obtain the results.

![](https://static.igem.wiki/teams/4140/wiki/lab-timeline-1.png)

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[Parts](https://2022.igem.wiki/afcm-egypt/Parts)

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Iterations:* [(1)](#Iteration-1)
* [(2)](#Iteration-2)
* [(3)](#Iteration-3)
* [(4)](#Iteration-4)
* [(5)](#Iteration-5)
* [(6)](#Iteration-6)
* [(7)](#Iteration-7)
* [(8)](#Iteration-8)
* [(9)](#Iteration-9)
* [(10)](#Iteration-10)
* [(11)](#Iteration-11)
* [(12)](#Iteration-12)

Engineering Success
Our Project’s Little story

For our team
to design and produce a working Diagnostic platform, our engineering journey
had to pass through many design-build-test-learn & Improve cycles to get
the platform to be able to:-

1)
Have
Optimal Function & Diagnostic accuracy

2)
Be
as cost efficient as possible

3)
Serve
as a modular diagnostic Platform that can be applied for multiple diseases

4)
Add
a therapeutic element to our diagnostic kit

This Journey
started early with the research & brainstorming stage. We tried to tackle
the national problem of inborn errors of metabolism as we have found out that
our country faces an absolute catastrophe with one of the highest numbers of
children affected with Inborn error of metabolisms (IEMs).

**Brainstorming
Phase**

**Iteration 1**

**Design
& Evaluate**

Our
First approach to tackle this national problem was to try to use synthetic
biology to design a cost-efficient and reliable screening kit for the public
and that’s where our early research led us to try and work on **Microfluidic chips**
as our first iteration to design our diagnostic kit. We found through literature
that the Microfluidics-based immunoassay system is a promising example for the
point-of care diagnostics being affordable, sensitive, rapid, and robust. Thus
providing an easy way to rapidly detect diseases at extremely low cost and
short time.

**Build
& Solve**

Our
First iteration to build such a kit made us contact experts in the field of
Microfluidics & Engineers which helped us thoroughly in designing our **preliminary plan**
and model for the chip. We designed that model to solve the problems that we
might encounter in the lab while using this system. Then began our search to
contact labs all over the country that might be able to sustain our needs by
providing us the required equipment and lab setting for designing that kit.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/0.png)

**Test
& Research**

After
Contacting several Experts and laboratories we faced many problems such as:

1)
Lack
of needed tools and equipment needed for the production and designing of the
chip

2)
Absence
of clear rooms in the country that are required for the fabrication and
scalable production of microfluidic chips. Because microfluidic chips have very
narrow pores and could easily be clogged by tiny dust particles making the
whole system fail.

3)
Unsustainability
of long term production of the chip even if it was to be ordered from across
the country

4)
Hard
Use of the microfluidic chip that contradicted our need of the chip to become a
screening tool with wide use in the country, with only proper experts being
able to work on it.

5)
Presence
of many faults that can happen to the chip & the need for the test to be
conducted exclusively under certain conditions.

**Learn
& Improve**

Our search
provided us with the knowledge to go through and re-research a more Viable
solution that would go hand in hand with what we need to provide a sustainable
and cost efficient solution and led us to disregard the use of microfluidic chips
as a whole.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/1.png)

**Iteration 2**

**Design**

After
Finding out that the use of microfluidic chips is something to be considered
problematic we needed to start searching for a new solution to be used for
designing the kit. We went on a visit to the nanocentre in AUC (American
University in Cairo) to search for other lab facilities that could help us find
another application for our diagnostic system. We met there Dr. Reda Abdelbaset
who was of great help to us and told us about another system similar to the
Microfluidic chips, it is called Printed Circuit Boards (PCB). But we faced the
same problems because these kinds of chips are not available in Egypt and had
to be purchased and imported to be able to use it for our system.

**Build**

After
contacting several doctors such as Dr. Eran Andrechek, associate professor in
physiology. and telling him about our project, we had many discussions over
some aspects regarding our project and what we might need to be able to
practically apply our idea in a simple way. He recommended the use of Lateral
flow assays as it would be much cheaper,  more affordable and if executed
properly would enable us to implement our design in a very easy and effective
way.

In Addition
to that Dr. Farid AboElelaa Associate Professor of Biophysics in Zewail City of
science and technology.  suggested the use of aptamers, which are
oligonucleotide molecules that can selectively bind to a specific target at a
very small scale. Aptamers can be more specific antibodies as a biological
recognition element that can easily detect small quantities of analyte in a
given sample.

**Test**

We started
conducting our own research on lateral flow assays. We even estimated the
prices to conduct similar tests & their effectiveness with what we had in
mind as well as started researching aptamers in comparison to the use of
antibodies.

**Learn
& Improve**

We learned
that lateral flow assays best possible candidate and would be both cost
effective and cheap to produce, as well as be easier for people to use at home as point of care diagnostic, and we were able find the lab material
for it in our country also we found that aptamers would be a great benefit to us
and our diagnostic kit as it would add much more to us regarding specificity
and sensitivity to the intended targets

![](https://static.igem.wiki/teams/4140/wiki/enginnering/2.png)

**Iteration 3**

**Design
& Evaluate**

In the early phases of designing our project
we needed to know how to make our kit as user friendly as possible so we
thought first about using fluorescence as our mainstay for the detection method
so it would be as simple as possible for the people to understand and work with
the test results as we were considering our people while working. We face
another problem: how the cell detects substances outside it.

.

**Build
& Solve**

In
working then to build a solution we started studying about fluorescence and
different types of it and how we could build a circuit
that can incorporate fluorescence, but we were faced with a brick wall as it
was found out that fluoroscopy would be very expensive and hard to reach
modality in our country so we started asking
professionals about alternatives and we had meetings
with Dr. Eran Andrechek who suggested that the use of colorimetry would
be a feasible alternative as a detection system in general and would serve our
goal for easy test readability & understanding as well as be much more less
in cost if designed and built properly

**Test & Research**

We
started testing his hypothesis and researching literature to find a method that
in addition to the easy readability of colorimetry would maybe help our kit to
be not only be a qualitative method of detection (Positive & Negative) but
also have semi quantitative factor to maybe actually give the user a hint of
the severity of his current disease and we found that gold nanoparticles (AuNP)
would be the key to as we found out induced aggregation of the particles can
cause different color changes that can actually by interpreted according to the
amount of present detected biomarker.

**Learn
& Improve**

So we learned that colorimetry induced using
AuNP aggregation would be a the best fit for our project and long-term goals of
designing a point of care diagnostic kit

**![](https://static.igem.wiki/teams/4140/wiki/enginnering/3.png)**

**Iteration 4**

**Design & Evaluate**

In
the process of determining how we are going to make a diagnostic kit for PKU we
needed to start first researching what are the most relevant biomarkers that
would actually indicate the presence of a disease and avoid using insensitive
biomarkers for our diagnostic device. Accordingly we had to filter from a panel
of possible target molecules.

**Build
& Solve**

So
in order to do that we had meetings with pediatricians and collected more
information about this disease as well as conducting meetings with multiple
experts in the field such as Dr. Nashwa ElKhazrage.

**Test
& Research**

After doing our own research & contact
with her and multiple meetings which helped us determine that the best marker
to be detected would be phenylalanine

**Learn
& Improve**

We decided that the biomarker to be targeted
would finally be phenylalanine because it would be the most sensitive to PKU as
well as easy to detect; however this posed another problem to us.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/4.png)

**Iteration 5**
**Design & Evaluate**

Although using phenylalanine as a biomarker
would be a good fit in our project and a sensitive indicator of the disease in
excess and represent enzymatic deficiency, It had normal blood levels so our
kit needed to not only be able to detect the biomarker but to distinguish
between its mere presence and excess of it.

**Build**

As Always we started our
work by regrouping, researching and contact with experts to try and determine
how we could design a kit that is able to detect only the excess of the
biomarker that’s when we came with a novel idea of making our Lateral flow
assay containing a line that would consume all the phenylalanine that can be
present in normal humans and only for excess of phenylalanine to be able to
interact with the second line of our LFT indicating presence of disease the
lines of which we call the The Jury & Fugitive lines.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/5.png)

**Test**

In order to test and validate our consumption
line we started by preparing 5 tubes of different concentrations of aptamers
after that we started introducing a constant amount of phenylalanine which is
considered the upper limit of the normal range in humans which is 20 mg/dl
after that we witnessed an increase of the color change in all tubes except for
tube no. 5 which matched the color of tube no. 4 indicating that the
concentration of aptamers no. 4 was enough to consume all the normal levels of
phenylalanine.

**Learn
& Improve**

By doing our experimental test we were able to
recognize the concentration needed in order to prepare our consumption line to
be able to accurately reduce false positive results and enhance the design of
our Lateral flow assay.

**Lab
Phase**

**Iteration 6**

**Design**

Our protocol first was to start by preparing 2 DNA fragments
that are functionalized with Gold Nanoparticles that form purple aggregate as
long as they are annealed to each other. Nanoparticles 1 and 2 are
functionalized with two different DNA molecules through thiol-gold chemistry.

The
bonds with the initial part of the aptamer are stable at room temperature.
However, they are more likely to disrupt once the aptamer changes to its
secondary structure upon recognizing the biomarker of interest.

Once
the aptamer binds to the analyte, it detaches from the second DNA fragment and
surrounds the biomarker, leaving the second DNA fragment paired only with the
Penta-nucleotide sequence. which are very weak, unstable and prone to disintegration
in that case. As a result, the two nanoparticles functionalized with dna
molecules are disassembled and separated from each other, changing the color of
the gold nanoparticles from purple to red.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/6.gif)

**Build**:

After
starting to prepare our protocol we found a problem with it as we intended for
it to be used as a consumption line on lateral flow assay however we found
that  It was too hard to actually stabilize the aptamers on the lateral flow
assay as we have found out that the protocol would leave the aptamers unstable
at room temperature.

**ReDesign:**

So as always we started researching and
redesigning our protocol to find a protocol that would give us accurate results
and sensitive binding as well as be able to stay stable under room temperature
that’s when we came across the duplex dissociation mechanism which is a
protocol that would allow our aptamers to maintain a fast paced detection and
high sensitivity for small molecule, this method works by the ability of
nucleic acids to form Watson-Crick-Franklin base pairing, making the end result
being a three dimensional configuration that is formed of the desired target
analyte with high sensitivity and specificity which uses a complementary strand
attached to the aptamer which upon introduction with a ligand would displaced
from its place due to conformational changes. This induces signal transduction
by the complementary strand’s separation.

This protocol has many advantages over the
previous one, because it was more applicable on lateral flow assay. As we can
easily immobilize the control probe DNA using biotin labeling technique as well
as being much easier to integrate with the consumption line using
streptavidin-biotin DNA conjugates technique.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/7.png)

**Test**

We prepared for the validation of our protocol
by preparing test tubes with the aptamers using the new protocol by preparation
of different concentrations of aptamers in test tubes followed by introduction
of phenylalanine samples with the same concentration  which was predetermined
using spectrophotometry.

(Edit:Sowar Test Tubes & Revision of
testing methodology)

**Learn
& Improve**

Our results indicated colorimetric changes in
the test tubes upon introduction of phenylalanine with different color grades
indicating appropriate and successful binding of phenylalanine with the
aptamers designed under the new protocol which would be stable under room
temperature.

**Iteration 7**

**Design**:

After receiving our order from IDT and
finishing our drylab work and theoretical preparation we made designs using
Synthetic Biology Open Language (SBOL) of our circuit and started preparing our
protocols needed to be able to work in the lab.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/8.png)

**Build**:

In order to start making our circuit into an
actual device to be tested and used the first step to do was first doing
resuspension of the parts followed by digestion and ligation of it and
preparation of it to be transformed

**Test**:

However to go through the next step we needed
to characterize our parts and to first use western plotting and the use of our
gel electrophoresis to characterize and validate our parts and the process of
digestion and proper ligation of these parts using our knowledge of their
molecular weight and the DNA ladder used as a reference and control. So we can
go through the steps of transformation.

However after doing that we have found that
the gel showed weak bands in both the digested and ligated parts indicating
that there is weak concentration of the parts

**Learn
& Improve:**

We learned that we had weaker concentrations
than what would be needed and that we need to use PCR to amplify our parts in
order to get stronger bands and be able to correctly characterize and verify
our parts.

**Iteration 8**

**Design**

In our
First attempts and designs to use and introduce colorimetry and to design our
whole cell biosensor that would be able to detect the excess of phenylalanine
from the consumption line we intended to use CRPG outside the cell to react
with beta-galactosidase enzyme as it converts the CPRG substrate which is
yellow-orange into a red chromophore called chlorophenol red turning the final
solution into dark red color.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/11.png)

**Build**

However
after the construction of the idea and design we were faced by the lack of CPRG
in our country as it there were no sellers for it in here so we needed to start
researching and redesigning

After
searching for an alternative to CRPG we found an organic compound called X-gal
which is made from galactose bound to a substituted indole

**Test**

To test
X-Gal in the lab we introduced it to E.Coli that was pre-made using transformation techniques and
prepared an agar to try and see for ourselves
how it would look like in the lab as well as be able to use the result as a
control for future lab work as this would be considered as the control and
normal expected outcome of phenylalanine detection and its forthcoming
expression of b-galactosidase.

**Learn
& Improve**

After
doing our experiment & studying the compound we learned that X-gal would
even be a better solution to our problem and even better than our early
suggestion of cprg because it gives blue color which is more appropriate for
color blinded people making our future device more friendly for those with
disabilities.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/12.png)

**Iteration 9**

**Design
& Evaluate**

We
designed our project to sense phenylalanine using a whole cell-based biosensor
that works by our genetic circuit, which involves a phenylalanine-sensitive
regulator called TyrR to express b-galactosidase in the presence of
phenylalanine for it to react with X-gal and produce blue color .

**Build
& Solve**

We
started building this circuit by transforming it into E.coli using
transformation techniques and started our process of assessment of color
production in the case of phenylalanine introduction.

**Test
& Research**

After
introduction of phenylalanine to the circuit we found that there were almost no
color change that was presented or happened so we started search in for the
cause of this and we found that there were a flaw in design because
phenylalanine is extracellular and TyrR is intracellular so we were unable to
express Phe to TyrR

**Learn
& Improve**

To solve
this problem we added another circuit to produce the permease enzyme which
makes the cell membrane permeable to phenylalanine

.![](https://static.igem.wiki/teams/4140/wiki/enginnering/13.png)

**Iteration 10**

**ReTest**

After
retesting again with the addition of permease expression we found that there
were still weak colors unlike what was seen  in the control with the X-gal and
b-galactosidase was due to the low concentration of beta-galactosidase outside
the cell

**ReDesign**

So we
started searching for a signal amplifier to add to our circuit that would be
able to increase the color in the case of detection of phenylalanine and after
research and meeting with doctors we decided to ligate KP-SP to the Lacz
promoter to be able to secrete beta-galactosidase extracellularly.

**Build**

We started
Transforming our new design again into E.coli in order to determine whether the
addition of Kp-Sp which is a secreted signal peptide and whether it would be
able to carry b-galactosidase extracellularly by its tagging to lacz alpha

**Test
& Research**

And to test our
new circuit we used comparative methods to compare between the previously made
agar with another with the same concentration of phenylalanine with the only
change about it being the addition of Kp-Sp tagging to the second plasmid.

**Learn
& Improve**

We
learned that the addition of Kp-Sp made a remarkable change in the color
concentration and the amplification of the color signal and so this
modification in our circuit was finalized

![](https://static.igem.wiki/teams/4140/wiki/enginnering/14.png)

**Iteration 11**

**Design
& Evaluate**

When we primarily
designed our first design for the therapeutic system for PKU the design would
work by the detection of phenylalanine and tyrosine levels and then further
expression and regulation of phenyl hydroxylase enzymes in accordance to the
levels of the selected biomarkers above.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/15.png)

However  we knew
In our design of the therapeutic circuit that the primary biomarker that would
indicate the success of our therapeutic circuit is the expression of
phenylalanine hydroxylase as  we needed to to monitor degrees of PAH synthesis
within the cells so we planned to use PAH elisa kit. .

**Build
& Solve**

Prior to the preparation of our therapeutic circuit and
beginning to work on the protocol for transformation we have found out that the
process to extract the media and seperate it from the cells to repeatedly be
used on elisa kit for verification would be time and material consuming as well
as it can be something that would increase future costs of manufacture of the
product

**ReDesign**

So we started
searching for an alternative to the usage of an external kit for validation and
we started thinking of methods that are inherently built in the circuit and to
find a solution that would be able for our circuit to be tested under any
condition so we thought of incorporating one of our previously used parts which
is the Lacz alpha that would produce b-galactosidase and by using P2A as a
linker between it and PAH, so production of color in the media in presence of
X-Gal would represent PAH expression.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/16.png)

**Test
& Research**

In order to test
whether our system can effectively produce PAH and LacZ alpha as a reporter
gene simultaneously, as well as whether attaching LacZ alpha to PAH will
decrease the amounts of PAH expression, we transformed the newly designed
circuit into E.coli and used phenylalanine samples with concentrations
predetermined using spectrophotometery to induce the expression of
phenylalanine hydroxylase.

**Learn
& Improve**

After conducting
our experiment we have found out that there have been color expression
indicating the release of phenylalanine hydroxylase from the circuit therefore
indicating success of the circuit’s design and ability to express its intended
target outcome.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/17.png)

**Iteration 12**

**Design
& Evaluate**

In order
to tune our circuit’s activity and prevent over-expression of PAH, we had to
integrate a regulatory system with our circuit, so after research we decided to
utilize CRISPR system which originally was found in the genome of bacteria as
an immune system against bacteriophages, and to incorporate this system into
our design we added an additional circuit which would consist of two parts.

The
first part would be a guide RNA which would have complementary sequence to the
target sequence with high affinity for it and this complementary sequence would
be placed upstream of the PAH codon sequence that needs editing, and second
part would be, Cas9, an RNA-guided protein that edits genes by cleaving the two
strands of the target gene simultaneously through its nuclease activity after
recognizing PAM sequence tied to the PAH gene. This will terminate the
expression of PAH to sustain its concentration within the normal level.
![](https://static.igem.wiki/teams/4140/wiki/enginnering/18.png)

**Build
& Solve**

So we started designing our new circuit that
incorporated the crispr system and we started transforming this circuit with
the previously made circuit to E.coli to test it.

**Test
& Research**

To
assess how well our circuit could regulate and tune the level of PAH expression
under various conditions, after we transferred it to E. coli. We discovered
that PAH expression is reduced when phenylalanine levels are low, but even when
we increased the concentration of phenylalanine, the levels of PAH were still
relatively low to phenylalanine concentration. As a result, we performed DNA
sequencing to identify the cause of PAH non-functionality.

**Learn
& Improve**

DNA analysis
revealed frameshift mutations in the PAH coding sequence and an off-targeting
effect due to interactions with other coding sequences, so we had to evolve our
regulatory system to operate at the mRNA level rather than genomic level so
that that the crispr system and safety mechanism would only be modulatory and
repressive of the expression of PAH rather than complete termination of the
function of the circuit as well as prevent mutations and accidentally knock
down other genes containing the PAM sequence. That’s why we replaced Cas9 with
dCas12g, which acts as a post-transcriptional modifier by targeting the mRNA of
PAH and restricting its translation.

![](https://static.igem.wiki/teams/4140/wiki/enginnering/19.png)

![](https://static.igem.wiki/teams/4140/wiki/enginnering/20.png)

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[engineering](https://2022.igem.wiki/afcm-egypt/engineering-parts)

References

1. https://www.biorender.com/

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* [inspiration](#inspiration)
* [Problem](#Problem)
* [Background](#Background)
* [diagnostic approach](#diagnostic-approach)
* [therapeutic approach](#therapeutic-approach)
* [References](#References)

Description...

![](./page1.png)

Our inspiration

This year we have been inspired by one of the PKU survivor called Kevin Alexander who said “Had I not always been on diet and had I not always been on my formula, it is undeniable that I would have been in an institution, that I would have become mentally retarded”. The words of a PKU survivor who has abided to his diet no matter how hard it was because he had the knowledge since an early age about what it would have been like if he didn’t follow the strict diet prescribed by his doctor. But not everyone is too lucky to be diagnosed early, some people in the developing countries might not have the privilege of availability of such a diagnostic tool due to either inadequate knowledge or the high cost.

![](pku.png)

Problem and rationale

Inborn errors of metabolism (IEM) have been a major issue that threatens the lives of our newborns if not detected. The earlier we discover them the more chances we have to prevent their detrimental consequences to our children. According to statistics, phenylketonuria affects 1 child from every 10,000 worldwide. While in Egypt, this rate increases to reach about 5 from every 5,000 children in 2020 figure (1). Egypt has the third highest prevelance rate of phenylketonuria (PKU) all over the world after Italy and Ireland. That’s why we have chosen our project this year to be about Phenylketonuria (PKU).

In Egypt, the cost of the available diagnostic tool for PKU is 27$ per test, which puts a great burden on both the government and families. Moreover, this diagnostic tool is invasive, expensive and requires about 1 to 2 weeks to give results. Moreover, we have recently paid much money to detect IEM using Next Generation Sequencing (NGS) technique which imposes an extra burden on our country. We aim to reduce morbidity that results from late diagnosis and management of IEM. So, in our project, we have created a novel diagnostic tool for PKU as a prototype which is less invasive, requiring less time, user friendly and cost-effective to diagnose PKU patients. Our project hasn’t stopped to that point but it has extended to develop a novel approach to treat PKU using synthetic biology as our cornerstone. We would like to establish a deep learning algorithm (SIDEA) for directed evolution of aptamers to be the fittest in terms of affinity, stability, and physicochemical parameters, local and global evolutionary contexts.
.

![](1.png)

Background

Phenylketonuria is an inherited autosomal recessive (AR) disorder caused by different mutations in the phenylalanine hydroxylase (PAH) gene. These mutations result in a decrease in metabolism of phenylalanine (Phe) in the hepatocytes, causing accumulation of excess amount of phenylalanine. PAH hydroxylates Phe to tyrosine in the presence of tetrahydrobiopterin (BH4) as a co-factor figure (2). Defects in either PAH or the production or recycling of BH4 may result in hyperphenilalaninemia which can cause intellectual disability if untreated (1).

![](2.png)

Our diagnostic approach

There have been several method for neonatal screening of PKU such as the Guthrie test, others based on high performance liquid chromatography (HPLC) and tandem mass spectrometry (MS/MS). The latter method has been the most worldwide used in the previous 20 years using dry blood spot (2).

This year, we have adopted a novel method for detection of excess Phe. Frist, we will get a blood drop from any newborn heel. This drop will pass through a separation pad that will get rid of blood cells leaving cell free analyte. The analytic will then flow through the conjugation pad that will consume the normal amount of Phe leaving excess Phe to pass to the whole cell biosensor platform. On that pad, we have immobilized gold nano-particle labeled ssDNA (Au-NP labeled ssDNA) bonded to an aptamer that fit to Phe. In the presence of Phe, those aptamers will undergo conformational change separating Au-NP labeled ssDNA and only Excess Phe will pass to the control line. After that, ssDNA will anneal with its complementary strands emitting a color ensuring the flow in the kit. Excess Phe will then pass to the whole cell biosensor platform. Upon the degree of change of the color, the amount of Phe can be measured.

Our therapeutic approach

But what will happen when we diagnose a PKU patient??
There are many therapeutic approach for treatment of PKU figure (3). Phenylalanine free diet remains the cornerstone in management of PKU patients. However, non-compliance to diet regimen is the main obstacle that faces every patient. There has been an enzyme replacement therapy either using Phenylalanine hydroxylase enzyme (PAH) or Phenylalanine amino-lyase to decrease the level of phenylalanine (Phe) in the blood (3). Moreover, Tetrahydropterin (sapropterin) has shown to be effective in management of some patients of PKU reducing its level about 30% from baseline level (4). In addition, Gene therapy for the management of PKU has been introduced in various research groups during the last period. For example, in a mouse model of PKU, there has been a great advance made by the use of an adenovirus as a vector to deliver PAH gene into hepatocytes as they are hepatotropic viruses (5). At last, cell-directed therapy has been introduced recently but still under research. In this approach, liver repopulation by PAH encoding cells is achieved by hepatocytes transplantation but results till now are not conclusive on humans (6).

![](33.png)

In our project we are hitting a new approach based on synthetic biology. We have adopted a novel protocol called SEND platform (Selective Endogenous eNcapsidation for cellular Delivery) figure (3). SEND is superior to other gene delivery systems for at least two reasons. First, it uses proteins that do not stimulate immune cells so reducing the risk of adverse immune reactions. Second, it could be modulated with engineered fusogens that are capable of targeting specific cell types with high cellular tropism. Consequently, such fusogens could deliver therapeutic cargo to the target cells reducing the probability and severity of adverse effects. Following this approach we will be able to deliver mRNA encoding PAH into hepatocytes to be translated. This process will be regulated by Cas12g that will control PAH activity according to the level of Phe and tyrosine in the blood.

![](3.png)

Implementation >

References

1. Blau N., van Spronsen F.J., Levy H.L. Phenylketonuria. Lancet. 2010;376:1417–1427.
2. Blau, N., Shen, N., and Carducci, C. (2014). Molecular genetics and diagnosis of phenylketonuria: state of the art. Expert Rev. Mol. Diagn. 14, 655–671. doi: 10.1586/14737159.2014.923760
3. Sarkissian C.N., Shao Z., Blain F., Peevers R., Su H., Heft R., Chang T.M., Scriver C.R. A different approach to treatment of phenylketonuria: Phenylalanine degradation with recombinant phenylalanine ammonia lyase. Proc. Natl. Acad. Sci. USA. 1999;96:2339–2344. doi: 10.1073/pnas.96.5.2339.
4. Fiege B., Blau N. Assessment of tetrahydrobiopterin (BH4) responsiveness in phenylketonuria. J. Pediatrics. 2007;150:627–630. doi: 10.1016/j.jpeds.2007.02.017.
5. Ding Z., Georgiev P., Thöny B. Administration-route and gender-independent long-term therapeutic correction of phenylketonuria (PKU) in a mouse model by recombinant adeno-associated virus 8 pseudo typed vector-mediated gene transfer. Gene Ther. 2006;13:587–593. doi: 10.1038/sj.gt.3302684.
6. Harding C.O. Progress toward cell-directed therapy for phenylketonuria. Clin. Genet. 2008;74:97–104. doi: 10.1111/j.1399-0004.2008.01027.x.

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* [Overview](#Overview)
* [Bioinformatics](#Bioinformatics)
* [Wetlab](#Wetlab)

Our Overview

In order to develop a screening tool with high specificity, low false positives, and false negatives, we set out to develop an easy and straightforward test to detect phenylalanine. Ultimately, the test performed as planned and was able to detect semi-quantitative results of phenylalanine. More experiments will be required in the future to improve the color of the test strips, improve the limit of detection, and reduce false positive and negative results.

![](https://static.igem.wiki/teams/4140/wiki/results/1.png)

Bioinformatics

We started thinking about developing a detection method for Phe by using aptamers and whole cell biosensors.And to find a unique aptamer sequence that is specific for Phe, we used our very own aptamer prediction, affinity tools to Phe and compare between it and others from literature.

Prediction of binding site:

![](https://static.igem.wiki/teams/4140/wiki/results/6.png)

This figure Shows the architecture of binding site prediction model of the aptamer in addition to confusion matrices and ROC curves showing the performance of our classification model. The training of the model shows an AUC of 0.96 and the testing to another dataset has shown an AUC of 95%

Calculating the affinity and physicochemical parameters of the resulting aptamers:

![](https://static.igem.wiki/teams/4140/wiki/results/7.png)

This figure shows the results of our affinity regression model when applied to a dataset of aptamer-protein complexes, including both DNA and RNA aptamers in the selected data. (A) Shows a decrease in the Mean Absolute Squared Error, which could be used as a reference to predict the accuracy of the training results. (B) Shows decrease in Root-mean-square deviation (RMSE) indicating that the predicted values are nearer to the regression line. (C) Shows an increase in the accuracy of the training and validation scores over epochs. (D) Shows an increase in the r-squared scores of training and validation over epochs indicating that our model strongly explains the observed data.

![](https://static.igem.wiki/teams/4140/wiki/results/8.png)

This figure shows our models ability to predict the ΔG values of aptamer-protein complexes. (A) Shows the prediction results of the training process of the model. (B) Shows our models performance when applied to another testing set and its ability to accurately predict the ΔG values of the formed complexes.

Docking simulations of aptamer and Phenylalanine:

![](https://static.igem.wiki/teams/4140/wiki/results/9.png)

This figure Shows the structure of the Phenylalanine amino acid, generated using PyMOL.

![](https://static.igem.wiki/teams/4140/wiki/results/10.png)

This figure Shows the structure of the Phenylalanine Aptamer molecule, generated using PyMOL.

![](https://static.igem.wiki/teams/4140/wiki/results/11.png)

This figure Shows the docking model of the Phenylalanine-Aptamer, which was generated using Poseview.

![](https://static.igem.wiki/teams/4140/wiki/results/12.png)

This figure Shows the docking model of the Phenylalanine-Aptamer, which was generated using HDOCK.

![](https://static.igem.wiki/teams/4140/wiki/results/13.png)

This figure Shows an analysis of the non-covalent interaction between the phenylalanine amino acid and its respective aptamer. It shows that the amino acid forms Hydrogen bonds with its aptamer, in addition to Pi-Cation interactions between the amino acid and residues of the aptamer.

Directed evolution

Then we applied the concept of directed evolution to the diagnostic parts to choose the mutant variant with the highest ranking stability, physicochemical parameters and evolutionary fitness to increase the specificity and sensitivity of the test.

![](https://static.igem.wiki/teams/4140/wiki/results/2.png)

The (C374R) mutation, as depicted in the chart, had the greatest score when compared to other mutations.

![](https://static.igem.wiki/teams/4140/wiki/results/3.png)

The (N99D) mutation, as depicted in the chart, had the greatest score when compared to other mutations.

![](https://static.igem.wiki/teams/4140/wiki/results/4.png)

The (W77S) mutation, as depicted in the chart, had the greatest score when compared to other mutations.

![](https://static.igem.wiki/teams/4140/wiki/results/5.png)

The (H37K) mutation, as depicted in the chart, had the greatest score when compared to other mutations.

Wetlab

1)Preparation of gold nanoparticles:

\*We used the help of Nanogate company in Egypt to synthesis and characterize the gold nanoparticle
1) Prepare aqua regia by mixing 3:1 concentrated HCl:HNO3 in a large beaker in a fume hood.
The capped aqua regia bottle may explode. Render it safe by dilution and neutralization.
2) Soak the 200 ml two-neck flask, magnetic stir bar, stopper and condenser in aqua regia for at least 15 min. Rinse the glassware with copious amounts of deionized water and then Millipore-filtered water21.
3) Load 98 ml of Millipore water into the two-neck flask. Add 2 ml of 50 mM HAuCl4 solution so that the final HAuCl4 concentration is 1 mM.
4) Connect the condenser to one neck of the flask, and place the stopper in the other neck. Put the flask on the hot plate to reflux while stirring.
5) When the solution begins to reflux, remove the stopper. Quickly add 10 ml of 38.8 mM sodium citrate, and replace the stopper. The color should change from pale yellow to deep red in 1 min. Allow the system to reflux for another 20 min.
6) Turn off heating and allow the system to cool to room temperature (23–25 °C) under stirring. The diameter of such prepared nanoparticles is ∼13 nm. The extinction value of the 520-nm plasmon peak is ∼2.4, and the nanoparticle concentration is ∼13 nM. The color should be burgundy red, and the nanoparticle shape should be spherical under transmission electron microscopy (TEM).

Absorption spectrum of nanoparticles:

![](https://static.igem.wiki/teams/4140/wiki/results/spectrum.png)

This figure shows the surface plasmon resonance of the prepared AuNPs. UV-Vis absorption spectra were obtained on Cary series UV-Vis- NIR, Australia.

Transmission electron microscopy

![](https://static.igem.wiki/teams/4140/wiki/results/electron.jpg)

This figure shows the TEM images of the prepared AuNPs. TEM were performed on JEOL JEM-2100 high resolution transmission electron microscope at an accelerating voltage of 200 kV.

Optimization the ratio of aptamer-AuNPs able to consume the normal level of phe:

Colorimetric detection with aptamer-gold nanoparticle conjugates coupled to a paper strip, showing gradual change of color based on different concentrations of phenylalanine added at each drop, We started by titrating a different concentration of aptamer in 20 mg/dL phe and cDNA3.
till no color changes, and the results show that at concentration 3ng of aptamer all the 20 ml/dL of phe are consumed and the excess of phe will be passed to the next line (WCB)

Three methods were conducted
1. GNP +aptamer then phenylalanine then NaCl
2. Aptamer + phenylalanine then GNP then NaCl
3. Aptamer + phenylalanine then NaCl then gold
In the method 3, Very weak aggregation was observed.
![](https://static.igem.wiki/teams/4140/wiki/results/opt2.jpg)

This figure shows colourimetric change based on the concentration of phenylalanine added at each trial, showing Red color reflecting maximum saturation of the 3ng of aptamers when phenylalanine is added at the given concentration of 20 mg/dL.

![](https://static.igem.wiki/teams/4140/wiki/modeling/image33.png)

-This graph represents plotting both analyte in free state (A) and aptamer analyte complex (RA) after the binding occurs between them. It’s shown that the analyte (phenylalanine) is loaded to the test strip through the sample at the beginning. (A) shows gradual decrease of analyte over the time units. On the other hand, There’s a gradual increase in the aptamer-analyte complex over the time units via binding between the aptamer with its target.
This model was beneficial in the optimization of aptamer to analyte concentration that is essential in constructing the consumption line in our lateral flow assay (LFA).

Validation of the diagnostic:

According to the single tube transformation protocol, which is provided on the Notebook page, we transformed the circuit to validate its function. In addition to providing X-gal media, which is the substrate of the beta-galactosidase enzyme, which is released as a reporter after sensing excess levels of phenylalanine. The transformation of the bacteria by plating them on LB-agar plates included negative and positive controls to validate the transferred circuits and the cell viability.
Testing the plasmids and performing transformation protocols with them allowed us to determine whether these transformations were successful or not.

![](https://static.igem.wiki/teams/4140/wiki/results/15.jpg)

The figures display the chosen outcome of the transformation, providing the proof of concept and showing the different iterations as follows:

Verifying the function of the regulatory circuit (kink turn -cas12g):

![](https://static.igem.wiki/teams/4140/wiki/results/27.png)

This figure on the left (Phe-ve/X-gal +ve) shows that the WCB emits a low signal of the blue color at 0 mg/dL of phenylalanine. The results are shown to the right (Phe-ve/X-gal +ve), which shows that the WCB emits a negligible signal of the blue color at 0 mg/dL of phenylalanine. which is also a background noise from the circuit but with a very low signal. This iteration was essential as it proved the concept of background noise without using the regulatory circuit unless the result after transforming it.

This figure on the left (Phe-ve/X-gal +ve) shows that the WCB emits a low signal of the blue color at 0 mg/dL of phenylalanine. The results are shown to the right (Phe-ve/X-gal +ve), which shows that the WCB emits a negligible signal of the blue color at 0 mg/dL of phenylalanine. which is also a background noise from the circuit but with a very low signal. This iteration was essential as it proved the concept of background noise without using the regulatory circuit unless the result after transforming it.

![](https://static.igem.wiki/teams/4140/wiki/results/23.png)

This graph illustrates the modeled results of riboswitch kinetics where L7Ae is expressed and bound to its kink-turns ,therefore inhibiting the expression of cas12g. However, M represents no expression of L7Ae in which cas12g would be expressed to control the circuit if the phenylalanine is absent.

![](https://static.igem.wiki/teams/4140/wiki/results/mnb.png)

For this plate, it showed an incomplete negative percentage of dominant control color in the test sample. To confirm the results, we plotted the absorbance (nM) of both the tested sample and the control sample as they showed slight overlapping ranges of wavelength (nM).

![](https://static.igem.wiki/teams/4140/wiki/results/2211.png)

The graph on the left shows the absorbance of the test sample (nM) which shows a slight overlap between the absorbance range of the control sample (nM) which confirms the incomplete negativity.

This figure shows a very negligible signal of the blue color at the periphery of the plate at 0 mg/dL of phenylalanine. which is also a background noise from the circuit but with a very low signal after the usage of the regulatory circuit (kin-turn and cas12g).
For this plate, it showed a negative percentage of dominant control color in the test sample. To confirm the results, we plotted the absorbance (nM) of both the tested sample and the control sample as they showed different ranges of wavelength (nM).

![](https://static.igem.wiki/teams/4140/wiki/results/21.jpg)
![](https://static.igem.wiki/teams/4140/wiki/results/3311.png)

The graph on the left shows the absorbance of the test sample (nM) which shows different absorbance ranges of the control sample (nM) which confirms the negativity.

Verifying the construction of the improved part:

We enhanced the LacZ alpha gene by incorporating a peptide signal that controls the secretion of B-galactosidase extracellularly (KP-SP tagging), which acts as a reporter protein in our diagnostic circuit, in an effort to improve the cleavage capacity of the X-gal, heighten the intensity of the dark blue color emitted by the X-gal product, and reduce the amount of time required to receive the results after performing the test on the chip.

![](https://static.igem.wiki/teams/4140/wiki/results/25.png)

This figure shows a characterization of KP-SP and lacZ alpha part as it's validated through gel electrophoresis as it is in lane 3.

![](https://static.igem.wiki/teams/4140/wiki/results/26.png)

This figure shows (Phe/X-gal with KP-SP) +ve, shows that the WCB emitted blue color with good intensity at a standard 20 mg/dL concentration of phenylalanine. The results shown to the right (Phe/X-gal) +ve, without the tag KP-SP, show that the WCB emitted a low level of blue color at the same concentration of phenylalanine.

Construction and verification of the function of the diagnostic circuits:

Our genetic circuit activity was dependent on Phe concentration.
The increase in Phe concentration leads to an increase in the color intensity due to increased TyrR and TyrP activities and LacZ expression.

![](https://static.igem.wiki/teams/4140/wiki/results/16.jpg)

This plate (Phe/X-gal) +ve shows that the WCB emitted a bright blue color at a phenylalanine concentration of 20mg/dL. This is the best outcome after the improvements and making good use of our iterations.

We interpreted the results using our own built software tool in which the user uses a reference image as a control to analyze the color of the other image according to it. This control image should show the optimum results.
For this plate, it showed a positive percentage of dominant control color in the test sample. To confirm the results, we plotted the absorbance (nM) of both the tested sample and the control sample as they showed overlapping within the same range of wavelength (nM)

![](https://static.igem.wiki/teams/4140/wiki/results/1122.png)

The graph on the left shows the absorbance of the test sample (nM) which shows an overlap between the absorbance range of the control sample (nM) which confirms the positivity.

![](https://static.igem.wiki/teams/4140/wiki/results/17.png)

This graph illustrates the modeled results from the modeling of the direct relationship between the biomarker and beta-galactosidase. As the biomarker increases, the released amount of beta-galactosidase increases till it reaches a constant value after about 30 time units. Therefore, the maximum amount of the biomarker releases the maximum amount of beta-galactosidase, which is achieved from the test after iterations of troubleshooting.

Validation of the parts via Gel electrophoresis:

![](https://static.igem.wiki/teams/4140/wiki/results/18.png)

This figure shows characterization of (T7P-TyrR RBS-TyrR-TyrPromoter), which is ordered from IDT as it's validated through gel electrophoresis as it is in lane 6 (the last one).

![](https://static.igem.wiki/teams/4140/wiki/results/19.png)

This figure shows characterization of the ParoF promoter-P2A-L7Ae, which is ordered from IDT as it's validated through gel electrophoresis as it is in lane 5.

![](https://static.igem.wiki/teams/4140/wiki/results/20.png)

This figure shows the lacZ alpha part as it's validated through gel electrophoresis as it is in lane 3, which is ordered from IDT.

Validation of the consumption and WCB lines:

To make sure that our approach goes well, we tested the WCB if it senses any aptamer-phenylalanine complex or not by using a different concentration of the aptamer binded with Phe, so by the advantage of the permease and TyrR parts that help the entry and sensing of Phe, there is no Apt-Phe complex that will pass to the cell to sense it.

![](https://static.igem.wiki/teams/4140/wiki/results/24.jpg)

This plate (Apt-Phe complex +ve/X-gal +ve) shows that the WCB emits a low signal of the blue color at 20 mg/dL of phenylalanine. which validates the specificity of the TyrR for Phe only, not the Apt-Phe complex.

In vitro validation of the Therapeutic circuit:

1. Cell preparation from hippocampus tissue:

The dissected hippocampi were transferred into 10 ml of preparation medium [Hank’s balanced salt solution (HBBS),1 mM sodium pyruvate, and 10 mM HEPES on a 35-mm Petri dish on ice.
The hippocampi were transferred using a fire-polished glass Pasteur pipette into a 15-ml polypropylene Falcon tube with 5 ml of papain (0.5 mg papain,10 μg DNase I in 5 ml Papain Buffer) buffer. The tissue was incubated for 10 min at 37°C. After the tissue had sunk to the bottom, excess papain solution was discarded using a glass pipette.

Tissue is suspended in 10 mL of PBS pH 7.4, centrifugated at 900 rpm for 10 minutes, The clear supernatant was discarded. The cell pellet was resuspended in 1 ml DMEM/F12 with 10% FBS after the supernatant was discarded. Minced and digested hippocampal tissues were seeded into T25 flasks in DMEM/F12 medium (Gibco, Thermosientific, Germany) containing 10% foetal bovine serum (FBS) (Gibco, Thermosientific, Germany) and 1% penicillin G sodium (10.000 UI), streptomycin (10 mg), and amphotericin B (25 g) (PSA) (Invitrogen Life Technologies). Flasks were incubated at 37o C in a 5% CO2 environment. Non-adherent cells were eliminated by changing the media every two days after single cells had adhered to the plastic surface. Plastic adherent cells were multiplied until they reached a confluence of about 80%. During this time, phase-contrast microscopy was employed to examine cells, and cells from passages 1 to 5 were used in all experiments. Because it appears that asymmetrically dividing progenitor cells ratify after passing, early passages were employed.

![](https://static.igem.wiki/teams/4140/wiki/results/h1.jpg)

this figure shows a microscopic image showed the cultured Hippocampi cells. The magnification power is 40X and the scale bar is 100µm. Images are captured with LABOMED inverted microscope, USA Labomed-USA

2. Transfection of hippocampi cells with pcDNA3.1+/therapeutic circuit:

The hippocampi cells were equally distributed onto 6-well culture plate that are intended for the three different experimental groups including: (1) naïve cells (negative control), (2) cells transfected with empty vector (pcDNA3.1+), and (3): cells transfected with pcDNA3.1+ therapeutic circuit) (pcDNA3.1+). Transfected cells are incubated at 37o C in a 5% CO2 for 72 hours. One day before conducting the experiment, the Hippocampi cells were seeded in 6-well culture plate. An average 1x106 cells were seeded in 200 µL of Dulbecco's Modified Eagle Medium (DMEM) (Gibco, Thermosientific, Germany) containing 10% fetal bovine serum (FBS) (Gibco, Thermosientific, Germany) and 1% of penicillin G sodium (10.000 UI), streptomycin (10 mg) and amphotericin B (25 μg) (PSA) (Gibco, Thermosientific, Germany). Culture plates were incubated at 37 °C in an atmosphere of 5% CO2 for 24 hours to reach the 70% confluence. On the next day, cells are transfected with pcDNA3.1 vector + therapeutic circuit using Lipofectamine 3000 (Thermosientific, Germany). In addition, the carrier solvent (0.1% DMSO) was used for control cells. The treated Hippocampi cells were incubated at 37 °C in an atmosphere of 5% CO2 for 72 hours.

At the end of the incubation period, cells were harvested using 0.25% Trypsin EDTA Gibco, Thermosientific, Germany). The harvested cells washed three times with phosphate buffer saline (PBS) pH 7.4, then the cell pellet is suspended in 2.0 mL DMEM media and stored at -800 C until used for PCR analysis.

3. Validation of therapeutic circuit by measuring the PKH gene by Syber-green Real Time PCR:

To validate the efficacy of the therapeutic circuit, the Phenyl alanine hydroxylase gene was measured in harvested cells using Syber-green-based Real time PCR. The following steps are performed:

3.1. Tissue homogenization:

Disruption and homogenization for harvested cells was performed using the  **Tissue Ruptor II (Qiagen, Hilden, Germany)**, a rotor–stator homogenizer that thoroughly disrupts and simultaneously homogenizes single tissue samples in the presence of lysis buffer in 15–90 seconds, depending on the toughness and size of the sample. Then the mixture is centrifugated for 20 minutes at 4000rpm. Finally, the cell supernatant is collected for RNA extraction.

3.2. Total RNA extraction:

Following disruption and homogenization of liver tissue by bead-milling in a guanidine-thiocyanate– containing lysis buffer. the sample is loaded onto RNeasy Mini spin column. Total RNA binds to the RNeasy silica-membrane, contaminants are efficiently washed away, and high-quality RNA is eluted in RNase-free water. The RNA extraction & purification was performed using RNeasy blood/ tissue Mini kit, Qiagen, Hilden, Germany. The process was conducted according to the manufacturer protocol.

3.3. cDNA synthesis (Reverse transcription):

The reverse transcription step was performed by the  **QuantiTect Reverse Transcription Kit** , cat. No: 205310, **(Qiagen, Hilden, Germany)**. The reverse-transcription master mix was prepared on ice in total volume of 20µL, which was composed of 1µL of Quantitect Reverse transcriptase enzyme, 4 µL of RT buffer, 1 µL of RT primer mix, and 14 µL of genomic DNA, the reaction mix was mixed and then kept on ice. The reverse-transcription master mix contains all components required for first-strand cDNA synthesis except template RNA. The reaction mix was incubated for 15 min at 42°C, then it was incubated for 3 min at 95°C to inactivate Quantiscript Reverse Transcriptase. The reverse-transcription reactions were placed on ice and then real-time PCR was proceeded directly.

3.4. Gene amplification by Syber-green based qPCR:

The gene expression level was amplified from mRNA using specific primer sequences for PAH gene “the sequence is listed” and **ACTB\_1\_SG QuantiTect Primer Assay** (β-actin) cat no: 249900, as housekeeper gene. All samples were analyzed using the 5 plex Rotor Gene PCR Analyzer (Qiagen, Germany). The PCR reaction mix was prepared by adding as follow, 2x QuantiTect SYBR Green PCR Master Mix, 10x QuantiTect Primer Assay, template cDNA, and RNase-free water were thawed at room temperature (15–25ºC). Then, the reaction mix was prepared for a final volume 18 μl per well reaction volume as following: 10 μl of 2x QuantiTect SYBR Green PCR Master Mix, 2 μl 10x t Universal Primer, 2 μl 10x Quantitect Primer Assay and 4 μl RNase-free water. The reaction mix was mixed thoroughly but gently, and dispensed appropriate volumes into the Rotor-Disc wells then 2 μl template cDNA was added, to reach 20 μl as final volume. Carefully, tightly the disc was sealed with Rotor-Disc Heat-Sealing Film.
Consequently, the real-time cycler Initial was programmed as: activation step for 15 minutes at 95ºC for Hot-Star Taq DNA Polymerase activation. Three-step cycling: denaturation for 15 seconds at 94ºC, annealing for 30 seconds at 55ºC, extension for 30 seconds at 70ºC, for 40 cycles. Moreover, the expression levels were normalized to β-actin levels as a reference gene. The relative gene expression level (fold change) for APP was normalized to an internal control (β-actin) and relative to the calibrator (negative control sample) were calculated using the equation 2-∆∆Ct test/ control.

![](https://static.igem.wiki/teams/4140/wiki/results/h2.jpg)

TCt: cycle threshold, Δ: delta, FC: fold change of PAH gene after normalization to untreated Hippocampi cells, ACTB: β-actin housekeeper gene used for normalization

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