iGEM 2020 — Team Patras

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# Team:Patras/Notebook

# Notebook

Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.

### What should this page have?

* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

### Inspiration

You can see what others teams have done to organize their notes:

* [2018 Munich](https://2018.igem.org/Team%3AMunich/Notebook)
* [2014 ATOMS-Turkiye](https://2014.igem.org/Team%3AATOMS-Turkiye/Notebook)
* [2014 Tec Monterrey](https://2014.igem.org/Team%3ATec-Monterrey/ITESM14_project.html#tab_notebook)
* [2014 Kyoto](https://2014.igem.org/Team%3AKyoto/Notebook/Magnetosome_Formation#title)
* [2014 Cornell](https://2014.igem.org/Team%3ACornell/notebook)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:Patras/Engineering

Home

[![Mobirise](https://static.igem.org/mediawiki/2020/e/e1/T--Patras--hippocrates.png)](https://2020.igem.org/Team%3APatras)

* [Home](https://2020.igem.org/Team%3APatras)
* Team

  [Meet our Team](https://2020.igem.org/Team%3APatras/Team)[Our road to Giant Jamboree](https://2020.igem.org/Team%3APatras/Road)[Attributions](https://2020.igem.org/Team%3APatras/Attributions)
* * Project

    [Overview](https://2020.igem.org/Team%3APatras/Project_Overview)[Description](https://2020.igem.org/Team%3APatras/Description)[Proposed Solution](https://2020.igem.org/Team%3APatras/Proposed_Solution)[Contribution](https://2020.igem.org/Team%3APatras/Contribution)[Parts](https://2020.igem.org/Team%3APatras/Parts)[Poster](https://2020.igem.org/Team%3APatras/Poster)
  * Wet Lab

    [Overview](https://2020.igem.org/Team%3APatras/Wet_Lab_Overview)[Engineering](https://2020.igem.org/Team%3APatras/Engineering)[Protocols](https://2020.igem.org/Team%3APatras/Protocols)[Results](https://2020.igem.org/Team%3APatras/Results)[Proof Of Concept](https://2020.igem.org/Team%3APatras/Proof_Of_Concept)[Notebook](https://2020.igem.org/Team%3APatras/Wet_Lab_Notebook)
  * Dry Lab

    [Overview](https://2020.igem.org/Team%3APatras/Dry_Lab_Overview)[Artificial Intelligence](https://2020.igem.org/Team%3APatras/Artificial_Intelligence)[System](https://2020.igem.org/Team%3APatras/System)[Notebook](https://2020.igem.org/Team%3APatras/Dry_Lab_Notebook)
  * Human Centric Design

    [Overview](https://2020.igem.org/Team%3APatras/Human_Practices_Overview)[(Integrated) Human Practices](https://2020.igem.org/Team%3APatras/Human_Practices)[Science Communication](https://2020.igem.org/Team%3APatras/Education)[Collaborations](https://2020.igem.org/Team%3APatras/Collaborations)[Proposed Implementation](https://2020.igem.org/Team%3APatras/Implementation)[Entrepreneurship](https://2020.igem.org/Team%3APatras/Entrepreneurship)
  * [Safety](https://2020.igem.org/Team%3APatras/Safety)
  * [Judging](https://igem.org/2020_Judging_Form?id=3464)

# **Engineering**

Engineering Success **- Click here to relive our wet-lab troubleshooting story!**

After the team's recruitment, we brainstormed to decide the Project to participate in the iGEM competition. One of our first thoughts was to work on a certain drug category, considering that the team's scientific background is mainly in pharmaceutical science.

We began with bibliographic research focusing on finding widely administered drugs with high clinical impact involving in gene-drug interactions. Our findings led us to anti-cancer medications, anti-malarian drugs, and drugs for cardiovascular diseases. We realized that working on oncology was quite difficult due to the complexity of the disease's genomic profile. We also rejected the option of anti-malarian drugs because it is not widely administered, especially in western countries.

So, we ended up with cardiovascular diseases, and the most common and most widely administrated drug in this category, statins. According to the latest update of the World Health Organization, cardiovascular diseases is the first cause of death in developed countries. The statins are the most frequently prescribed drug to regulate the high LDL cholesterol levels and the first that broke the barrier of 10 billion dollars within a year.

Despite the high clinical impact of the drug, it appears that one out of three patients suffers from Adverse Drug Reactions due to the lack of insufficient regulation of the dosage. According to the PharmGKB, the drug has a directly associated genomic profile, especially the gene [*SLCO1B1*](#words) and the polymorphism rs4149056, so we decided to build a diagnostic test adjust the dosage and to prevent the ADRs.

We had to make the diagnostic test accessible to everyone, even those located far from cities, where medical centers can perform a similar genomic analysis. We came up with the idea of conducting the analysis in a portable laboratory that can be transported in remote areas. After research, we ended up using the [BentoLab](#bento), a mini-lab in the size of a hand-luggage.

Considering its portability, we designed an [experimental protocol](#blood), fast and easy to perform without any other benchtop laboratory equipment. We could not use restriction enzymes to detect the targeted variant because of the lack of time and the equipment to perform such a procedure. The [Allele-Specific PCR](#pcr) was the best option because there is no need to use restriction enzymes and is as accurate as many other SNP detection methods, though cheaper and quicker.

Thinking that the physicians are not adequately qualified to perform such an analysis, we provide a[step-by-step](https://2020.igem.org/Team%3APatras/Implementation#testimonials3-68) guide with the details, advice, photos, and videos through a web application. Also, we implemented an [Artificial Intelligence](https://2020.igem.org/Team%3APatras/Artificial_Intelligence) system to analyze the results from the electrophoresis gel and directly propose the adjustment of the simvastatin dose.

The main challenge of developing a portable diagnostic test is to reach the sensitivity and the accuracy of conventional diagnostic analysis. To test if the BentoLab-AI system can provide reliable genotype results, we examined several DNA samples and [compared the findings](https://2020.igem.org/Team%3APatras/Proof_Of_Concept) to those provided from a gold standard and conventional laboratory equipment. We find out that it can perform a genomic analysis with an accuracy of 94%.

Through our experiments, many improvements have been made. Our future plan is to test many more DNA samples to achieve greater accuracy and establish the BentoLab-AI system as a diagnostic tool in clinical practice. All the following experiments helped us solve all the above-mentioned problems and presented us the steps we need to follow to succeed in our future plans.

#### **- A few words about our gene and the target polymorphism**

Our target gene is the *SLCO1B1* located on chromosome 12. In humans, encodes a protein transporter OATP (organic anion transporter protein) that regulates the transport of statins into tissues. If increased levels of simvastatin are concentrated in the blood circulation, there is a high risk of severe adverse effects such as myopathy. People with polymorphism rs4149056 suffer from muscle pain and damage, including myopathy (rhabdomyolysis). The rs4149056 is a functional polymorphism (c.T521C, p.V174A) that leads to higher statin circulating concentration. The rs4149056 is a missense variant (T/C, with the ancestral allele T) located in exon 5 on chromosome 12. Regarding rs4149056 polymorphism, the C allele frequency found in Caucasian descent is 0.146, and in the European population 0.161, therefore rs4149056 is a variant that occurs with great frequency among the population worldwide. Moreover, the frequencies of T/T, T/C, and C/C in the Europeans are 0.698, 0.282, and 0.020, respectively (1000 Genomes Project).

![Snow](https://static.igem.org/mediawiki/2020/8/83/T--Patras--ceu.jpg)

![Forest](https://static.igem.org/mediawiki/2020/3/33/T--Patras--Eur.jpg)

![Mountains](https://static.igem.org/mediawiki/2020/1/1e/T--Patras--freq.jpg)

**Click on numbers or titles below to find out more about our experiments!**

1

#### **BentoLab Specifications**

2

#### **Selecting the blood samples.**

3

#### **DNA extraction from the whole blood samples using DNA extraction Kit.**

4

#### **Nanodrop**

5

#### **PCR Allele - specific**

6

#### **Primer designing for Allele- Specific PCR**

7

#### **Standardization of the PCR allele-specific based on samples with known genotype**

8

#### **PCR-Allele specific for the analysed samples (samples with unknown genotype)**

9

#### **Amplification**

10

#### **Electrophoresis**

11

#### **Results – Blue LED light**

#### **- BentoLab Specifications**

To conduct our experiments BentoLab, a fully equipped portable molecular biology laboratory was used as a quick and straight-forward method for rs4149056 pharmacogenomics testing. This portable laboratory has been already successfully used for educational purposes in high school and college students all over Greece and abroad yielding comparable genotyping results compared with conventional laboratory methods and instrumentation.

Combining all the necessary tools for molecular biology, Bentolab includes a portable PCR Thermocycler, a microcentrifuge, Gel electrophoresis and blue LED transilluminator. Specifically, the thermocycler includes 32 wells suitable for PCR tubes with a Ramp rate up to 2.5 ℃ and temperature ranges between 12 ℃ and 105 ℃. The centrifuge has capacity of 6 tubes of 1,5 mL and max RCF of 8,000g. Regarding the gel electrophoresis, the Voltage ranges between 50V-120V, and gives the option of 9 or 12 wells on the gel. Last but not least, it is equipped with a blue LED array for the visualisation of the results. All of these devices is provided in the size of a hand baggage.

![Mobirise](https://static.igem.org/mediawiki/2020/4/4b/T--Patras--Bento22.jpg)

#### **- Selecting the blood samples.**

A total of eighty-one volunteers originated from Greece were enrolled in this study after their written informed consent. The whole blood samples were already collected and stored from the Laboratory of Pharmacogenomics and Individualized Therapy of the University of Patras for the purposes of a previous clinical study. The whole blood samples were obtained in blood tubes treated with anticoagulants and labelled according to our code system connected with each volunteer's relative details to avoid confusion. The blood samples were stored in the freezer (-20 ℃) and prepared for DNA extraction and an Allele-Specific PCR. The Institutional Ethics Committee approved the study of the University of Patras.

All the volunteers DNA were sequenced using a Sanger-based genotyping process (KASP assays), for the determination of the rs4149056 single-nucleotide polymorphism (SNP) of *SLCO1B1* gene, using the Hydrocycler-4 (LGC Genomics) and FLUOstar Omega SNP (BMG LABTECH), as a gold standard genotyping method.

#### **- DNA extraction from the whole blood samples using DNA extraction Kit**

DNA extraction is essential, and the critical step for the genotyping process of the studied variant rs4149056. In this particular study, all genomic DNA were extracted from peripheral blood using a DNA extraction Kit (NucleoSpin ® Blood, Macherey-Nagel), which contains all the required reagents and laboratory supplies. From a wide range of available DNA extraction protocols, a DNA extraction commercial Kit was used, mainly because of its time-efficiency, but also for the high purity and high yield (A260 / A280 ratio between 1.60 and 1.90 and a typical concentration of 50–100 ng/ul) while providing minimum contamination of DNA samples. Another advantage is that the extracted genomic DNA is ready to be used for the PCR.

**Before starting any NucleoSpin® Blood protocol, prepare the following:**

* Wash Buffer B5 (NucleoSpin® Blood): Add the indicated volume of ethanol (96–100 %) to Wash Buffer B5 Concentrate. Mark the label of the bottle to indicate that ethanol was added. Store Wash Buffer B5 at room temperature (18–25 °C) for up to one year.
* Proteinase K: Add the indicated volume of Proteinase Buffer PB to dissolve lyophilized Proteinase K. Proteinase K solution is stable at -20 °C for up to 6 months.

The protocol has been adjusted to BentoLab device specifications.

![Mobirise](https://static.igem.org/mediawiki/2020/d/d1/T--Patras--extra.png)

#### **- Nanodrop**

After DNA extraction, all the samples were assessed for high purity and their concentration using the NanoDrop Spectrophotometer Q6000 (200-750nm) (Quawell).
The amount of sample required is just 1μl. The system uses fiber optic technology and natural surface tension properties without cell. Thanks to the surface tension, the sample is held between two optical surfaces, which have optical fibers bridging their edges (Figure 2.1). This enables highly concentrated samples to be measured.

![Mobirise](https://static.igem.org/mediawiki/2020/1/15/T--Patras--nano.jpg)

Clean the upper and lower optical surface with which the sample will be on contact. Reset with the BE buffer provided in the extraction kit. Place 1μl of the samples on the lower optical surface with a pipette and lower the arm. Select the "Measure" command using the software on the PC. The DNA concentration and purity ratios are automatically calculated ("NanoDrop 2000 / 2000c Spectrophotometer HandBook" 2000). Each molecule absorbs electromagnetic radiation at a specific wavelength. Nucleic acids absorb at 260nm, proteins at 280nm, while salts and phenol at 230nm (in the spectrum of ultraviolet (UV) radiation, with a range of 60nm-380nm.) Therefore, the optical density (OD) is measured or absorption (A) emitted by the sample at 230nm, 260nm, and 280nm.
In addition, the software on the PC also calculates the following ratios:

* A260nm / A280nm: the ratio between 1.8-2.0 means that the sample has pure DNA. If less than 1.8, the presence of proteins, phenol, or other substances used in DNA isolation is possible. If it takes values greater than 2.0, this indicates the presence of RNA in the sample.
* A260nm / A230nm: if the ratio is greater than 2.0, it is accepted as "pure" for DNA. Lower values are attributed to the presence of salts, phenol, proteins.

#### **- PCR Allele - specific**

The allele-specific polymerase chain reaction (AS-PCR) is an application of the polymerase chain reaction (PCR) that permits the detection of any known SNPs (single nucleotide polymorphism). AS-PCR is based on the primers' modification for different alleles: the 3' end of the primers is modified so as one set of the primers is designed to amplify the ancestral allele (T) and the other one the alternative (C ). The mismatch single base is introduced at the 3' end of the primer and allows each primer to amplify one allele. The mismatch is the crucial factor in achieving a single mismatching. If the mismatch near the 3' end is weak the chance of amplification is higher, whereas a strong mismatch near the 3' end in the non-complementary allele terminates the amplification since it is not complementary to the target DNA sequence.

It should be highlighted that in PCR protocol, it is preferred to use high fidelity Taq DNA Polymerase because of its 3' to 5' exonuclease activity. On the contrary, in AS-PCR, this kind of Taq Polymerase could potentially remove the mismatch bases essential for AS-PCR, so it is suggested to use only Taq polymerase, which lacks 3' to 5' exonuclease activity.

#### **- Primer designing for Allele- Specific PCR**

Three different oligonucleotide primers were designed to perform AS PCR, two forward and one reverse. This process is very critical and essential, and that is because, without functional primers, there will not be produced any of the desired PCR products. Our goal was to design such primers that would be both specific and efficient for the PCR amplification of gene fragments that contain the two different alleles (T, C) of the studied variant. The allele specific forward primers were design to have the same sequence, except for the last nucleotide at the 3' end, where the SNP site is creating an artificial mismatch. This modification of the primers is due to the different alleles because of the presence of the SNP. One set of the primers amplify to the normal allele and the other one to the mutant. So, two separate PCR reactions of three primers are required to detect each allele. The design of the primers was conducted with computer analysis. More specifically, we used primer design tools and options provided by the Primer3 website. Moreover, for the assessment of the primers and the PCR products, regarding the folding and hybridization parameters, we relied on the DINAMelt Web Server.

More details about the primer designing procedure can be found in the ["WetLab-Parts"](https://2020.igem.org/Team%3APatras/Parts)section.

#### **- Standardization of the PCR allele-specific based on samples with known genotype**

To standardize the PCR protocol, we used control samples with known genotype providing sequencing analysis to be aware of the expected genotype to make the proper changes. After the primer design, we had to standardize the temperatures and the adding volumes of the reagents in the PCR protocol. Using a gradient PCR at a range of 55 ℃ – 67 ℃, we resulted that the suitable annealing temperature for our primers was the 65 ℃. After that, we tested different Taq Polymerase enzymes, and the one was chosen regarding the better intensity of the DNA bands on the electrophoresis gel. Accordingly, we adjust the volumes of the adding reagents so as not to appear on the gel by-products: extra bands of DNA bands on a different length of the expected 535 bp, a band of DNA in the well for the alternative allele in a sample with genotype homozygous for the ancestral allele (T/T) or band of DNA in the well for the ancestral allele in a sample with genotype homozygous for the alternative allele (C/C). For that, we also had to adjust the time for each step of the PCR conditions.

#### **- PCR-Allele specific for the analysed samples (samples with unknown genotype)**

At the end of the standardization experiments with known genotype samples, we tested 81 samples with unknown genotype applying the protocol describing in the table below:

| PCR reagents | Initial concentrations | Adding volume for one (1) sample |
| --- | --- | --- |
| Distilled H2O (ddH2O) |  | 17 μl |
| Buffer Solution | 10x | 2.5 μl |
| MgCl2 | 25 mM | 2.5 μl |
| Forward primer for the ancestral allele (Fw) /  Forward primer for the alternative allele (Fm) | 100 u/μl | 0.75 μl |
| Reverse primer | 100 u/μl | 0.75 μl |
| dNTPs | 100 u/μl | 0.25 μl |
| Taq Polymerase | 5 u/μl | 0.25 μl |
| DNA template | 50 - 100 ng/μl | The volume of DNA template is depended on the initial concentration of the sample |

#### **- Amplification:**

We calculated the appropriate concentration of PCR reagents by multiplying the given concentrations for one sample, on the following table with A (A= the number of samples + 1 blank sample + 1 extra).

| PCR reagents | Initial concentrations | Adding volume for one (1) sample | Adding volume for N samples |
| --- | --- | --- | --- |
| Distilled H2O (ddH2O) |  | 17 μl | 17 \* A μl |
| Buffer Solution | 10x | 2.5 μl | 2.5 \* A μl |
| MgCl2 | 25 mM | 2.5 μl | 2.5 \* A μl |
| Forward primer for the ancestral allele (Fw) /  Forward primer for the alternative allele (Fm) | 100 u/μl | 0.75 μl | 0.75 \* A μl |
| Reverse primer | 100 u/μl | 0.75 μl | 0.75 \* A μl |
| dNTPs | 100 u/μl | 0.25 μl | 0.25 \* A μl |
| Taq Polymerase | 5 u/μl | 0.25 μl | 0.25 \* A μl |
| DNA template | 50 - 100 ng/μl | The volume of DNA template is depended on the initial concentration of the sample |  |

To standardize the PCR protocol, we used control samples with known genotype providing sequencing analysis to be aware of the expected genotype to make the proper changes. After the primer design, we had to standardize the temperatures and the adding volumes of the reagents in the PCR protocol. Using a gradient PCR at a range of 55 ℃ – 67 ℃, we resulted that the suitable annealing temperature for our primers was the 65 ℃. After that, we tested different Taq Polymerase enzymes, and the one was chosen regarding the better intensity of the DNA bands on the electrophoresis gel. Accordingly, we adjust the volumes of the adding reagents so as not to appear on the gel by-products: extra bands of DNA bands on a different length of the expected 535 bp, a band of DNA in the well for the alternative allele in a sample with genotype homozygous for the ancestral allele (T/T) or band of DNA in the well for the ancestral allele in a sample with genotype homozygous for the alternative allele (C/C). For that, we also had to adjust the time for each step of the PCR conditions.

![Mobirise](https://static.igem.org/mediawiki/2020/d/d7/T--Patras--pcrcon.png)

#### **- Electrophoresis**

We prepared an 1% agarose gel dissolved in Buffer TBE 1x at high temperature in a microwave oven. When agarose had been dissolved, we added 7 μl of gel stain. The gel was placed on the electrophoresis device of the BentoLab, created wells, and was left for at least 30 min.

#### **- Results – Blue LED light**

Once the electrophoresis ended, we placed the agarose gel on the Blue light on the BentoLab to see the results.
We expected to observe the following results:

* Only one band in the well of the ancestral allele is Homozygous for the ancestral.
* Two bands (one in the well of the ancestral and one of the alternative) is the Heterozygous.
* Only one band in the well of the alternative allele is Homozygous for the alternative.
* No band in the Blank samples (absence of contamination).

![Mobirise](https://static.igem.org/mediawiki/2020/0/0e/T--Patras--meme.jpg)

References **- Click here to reveal all references!**

1. Ahrberg CD, Ilic BR, Manz A, and Neuzˇil P. (2016). Handheld
real-time PCR device. Lab Chip 16, 586–592.

2. Brunham LR, Baker S, Mammen A, et al. (2018). Role of genetics in the prediction of statin-associated muscle symptoms
and optimization of statin use and adherence. Cardiovasc Res
114, 1073–1081.

3. Jangam SR, Agarwal AK, Sur K, and Kelso DM. (2013).
A point-of-care PCR test for HIV-1 detection in resourcelimitedsettings. Biosens Bioelectron 42, 69–75.

4. Khine H, Yuet WC, Adams-Huet B, and Ahmad Z. (2016).
Statin-associated muscle symptoms and *SLCO1B1* rs4149056
genotype in patients with familial hypercholesterolemia. Am
Heart J 179, 1–9.

5. Koutsilieri S, Tzioufa F, Sismanoglou DC, et al. (2020). Unveiling the guidance heterogeneity for genome-informed drug
treatment interventions among regulatory bodies and research
consortia. Pharmacol Res 153, 104590.

6. Psarias G. Iliopoulou E. et al. (2020) Development of Rapid Pharmacogenomic Testing Assay in a Mobile Molecular Biology Laboratory (2MoBiL). OMICS: A Journal of Integrative Biology. doi:10.1089/omi.2020.0168

7. Lim S, Nan H, Lee M, and Kang SH. (2014). Fast on-site diagnosis of influenza A virus by Palm PCR and portable
capillary electrophoresis. J Chromatogr B Analyt Technol
Biomed Life Sci 963, 134–139.

8. Macleod JA, Nemeth AC, Dicke WC, et al. (2016). Fast, sensitive point of care electrochemical molecular system for
point mutation and select agent detection. Lab Chip 16,
2513–2520.

9. Marx V. (2015). PCR heads into the field. Nat Methods 12,
393–397.

10. McDonagh EM, Thorn CF, Bautista JM, et al. (2012).
PharmGKB summary: Very important pharmacogene information for G6PD. Pharmacogenet Genomics 22, 219.

11. Meade T, Sleight P, Collins R, et al. (2008). *SLCO1B1* variants
and statin-induced myopathy—A genomewide study. N Engl
J Med 359, 789–799.

12. Menounos P, Zervas C, Garinis G, et al. (2000). Molecular
heterogeneity of the glucose-6-phosphate dehydrogenase deficiency in the Hellenic population. Hum Hered 50, 237–241.

13. Nair CB, Manjula J, Subramani PA, et al. (2016). Differential
diagnosis of malaria on truelab Uno, a portable, real-time,
MicroPCR device for point-of-care applications. PLoS One
11, e0146961.

14. Pasanen MK, Fredrikson H, Neuvonen PJ, and Niemi M.
(2007). Different effects of *SLCO1B1* polymorphism on the
pharmacokinetics of atorvastatin and rosuvastatin. Clin
Pharmacol Ther 82, 726–733.

15. Pasanen MK, Neuvonen M, Neuvonen PJ, and Niemi M. (2006).
*SLCO1B1* polymorphism markedly affects the pharmacokinetics of simvastatin acid. Pharmacogenet Genomics 16, 873–879.

16. Pirmohamed M. (2001). Pharmacogenetics and pharmacogenomics. Br J Clin Pharmacol 52, 345–347.

17. Relling MV, Mcdonagh EM, Chang T, et al. (2014). Clinical
Pharmacogenetics Implementation Consortium (CPIC)
guidelines for rasburicase therapy in the context of G6PD
deficiency genotype. Clin Pharmacol Ther 96, 169–174.

18. Siamoglou S, Karamperis K, Mitropoulou C, and Patrinos GP.
(2020). Costing methods as a means to measure the costs of
pharmacogenomics testing. J Appl Lab Med 5, 1005–1016.

19. Siamoglou S, and Patrinos GP. (2019). 2MoBiL laboratory in
educational activities in the field of molecular genetics and
Personalised Medicine [in Greek]. EnasolijetlAmg Iasqija
1, 17–25.

20. Stroes ES, Thompson PD, Corsini A, et al. (2015). Statin-associated
muscle symptoms: Impact on statin therapy—European Atherosclerosis Society consensus panel statement on assessment,
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### **Sponsors**

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[![Mobirise](https://static.igem.org/mediawiki/2020/2/2d/T--Patras--patras-logo.png)](https://2020.igem.org/Team%3APatras)

##### **About**

16 students from Patras blending Pharmacogenomics with Artificial Intelligence to redefine medicine

##### **Follow Us**

[![Mobirise](https://static.igem.org/mediawiki/2020/e/e1/T--Patras--hippocrates.png)](https://2020.igem.org/Team%3APatras)

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# Team:Patras/Experiments

# Experiments

Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

### What should this page contain?

* Protocols
* Experiments

### Inspiration

* [2014 Colombia](https://2014.igem.org/Team%3AColombia/Protocols)
* [2014 Imperial](https://2014.igem.org/Team%3AImperial/Protocols)
* [2014 Caltech](https://2014.igem.org/Team%3ACaltech/Project/Experiments)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:Patras/Results

Home

[![Mobirise](https://static.igem.org/mediawiki/2020/e/e1/T--Patras--hippocrates.png)](https://2020.igem.org/Team%3APatras)

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  * [Judging](https://igem.org/2020_Judging_Form?id=3464)

# **Results**

According to our methodology, after the DNA extraction, the concentration of the extracted DNA samples and their ratio of purity, regarding the presence of proteins and ethanol is being determined by using Nanodrop. Using the Nucleospin Blood extraction kit results in concentrations ranging from 50 – 100 ng/uL. Herein we represent the concentrations of the 81 examined DNA samples, with the majority being between this range.

As described previously, 81 DNA samples were tested using two methods, on our portable laboratory BentoLab, and on conventional laboratory equipment to double-check the reliability of BentoLab. The genotypes obtained from each method were compared against the KASP assay, referred to as the gold standard method.

Our experiments started by testing the designed primers for the Allele-Specific PCR (AS-PCR) to find the annealing temperature of their function. The check was performed by implementing a Gradient AS-PCR on conventional laboratory equipment for a control sample with a known genotype. According to the image below, the proper annealing temperature is 65℃, where the sample appeared with the correct genotype, homozygous for the ancestral allele.

![Mobirise](https://static.igem.org/mediawiki/2020/1/19/T--Patras--Eikona1.png)

**Figure 1**: Representation of the Gradient AS-PCR (62-67℃) of the rs4149056 genotype for a control sample with genotype, homozygous for the ancestral allele T, by using the conventional PCR device. For each of the DNA samples, two wells match: for the ancestral allele (‘‘w,’’ referring to T allele) and for the alternative allele (‘‘m,’’ referring to C allele). In the first well, a ladder 100bp was loaded, and the expected molecular weight per PCR product is 535 bp.

Continuing to standardize the AS-PCR protocol, we tested three DNA samples in a conventional benchtop device: IGEM20-020, IGEM20-023, IGEM20-024, applying the annealing temperature 65℃. The obtained results are provided in the photo below. The presented genotypes are correct when compared with the genotypes provided from the gold standard method. Thus, we continue testing the remaining samples implementing this protocol on the conventional laboratory equipment.

![Mobirise](https://static.igem.org/mediawiki/2020/d/d1/T--Patras--eikona2.jpg)

**Figure 2**: Representation of the rs4149056 genotype for three different DNA samples (IGEM20-020, IGEM20-023, IGEM20-024) using the conventional PCR device and applying the standardized protocol for AS-PCR. For each of the DNA samples, two wells match: for the ancestral allele (‘‘w,’’ referring to T allele) and for the alternative allele (‘‘m,’’ referring to C allele). In the first well, a ladder 100bp was loaded, and the expected molecular weight per PCR product is 535 bp.

The next step was to standardize the AS-PCR protocol again on BentoLab. We implemented the same protocol with the conventional PCR device, but the results were not satisfying, and we had to modify it.

![Mobirise](https://static.igem.org/mediawiki/2020/2/21/T--Patras--eikona3.jpg)

**Figure 3**: Representation of the rs4149056 genotype for three different DNA samples (IGEM20-024, IGEM20-023, IGEM20-020) using the BentoLab method with the standardized protocol from the conventional PCR equipment. For the visualization of the results, we used a) the blue LED light of BentoLab (left) and b) the UV device (right). For each of the DNA samples, two wells match: for the ancestral allele (‘‘w,’’ referring to T allele) and for the alternative allele (‘‘m,’’ referring to C allele). In the first well, a ladder 100bp was loaded, and the expected molecular weight per PCR product is 535 bp. The results are not satisfying, and the protocols have to be modified.Figure 3: Representation of the rs4149056 genotype for three different DNA samples (IGEM20-024, IGEM20-023, IGEM20-020) using the BentoLab method with the standardized protocol from the conventional PCR equipment. For the visualization of the results, we used a) the blue LED light of BentoLab (left) and b) the UV device (right). For each of the DNA samples, two wells match: for the ancestral allele (‘‘w,’’ referring to T allele) and for the alternative allele (‘‘m,’’ referring to C allele). In the first well, a ladder 100bp was loaded, and the expected molecular weight per PCR product is 535 bp. The results are not satisfying, and the protocols have to be modified.

We modify the PCR protocol, the conditions, the electrophoresis protocol respecting the BentoLab’s specifications. More importantly, in the preparation step of the agarose gel, we added the gel stain to visualize the results on the blue LED light of BentoLab because, as we can see in Figure 3, using the Midori gel stain is visible only in UV device. The obtained results respecting three DNA samples (IGEM20-020, IGEM20-023, IGEM20-024 ) are presented in the image below.

![Mobirise](https://static.igem.org/mediawiki/2020/5/50/T--Patras--eikona4.jpg)

**Figure 4**: Representation of the rs4149056 genotype for three different DNA samples (IGEM20-024, IGEM20-023, IGEM20-020) using the BentoLab method. For each of the DNA samples, two wells match: for the ancestral allele (‘‘w,’’ referring to T allele) and for the alternative allele (‘‘m,’’ referring to C allele). In the first well, a ladder 100bp was loaded, and the expected molecular weight per PCR product is 535 bp.

After standardizing the protocols, all the 81 DNA samples were tested on BentoLab. The following table presents the genotypes of the 81 samples as derived from the BentoLab against the gold standard. The ancestral allele is T, and the alternative one is C. N/A indicates that the method results in a different genotype from the gold standard method.

According to the above table, a comparison between the findings obtained from the BentoLab against the given genotypes provided from the gold standard method was conducted. According to that comparison, the BentoLab resulted in 77 out of 81 samples with correctly identified genotypes.

![Mobirise](https://static.igem.org/mediawiki/2020/5/5b/T--Patras--rabdo.jpg)

**Figure 5**: Representation of the number of samples using the BentoLab. True stands for the samples with correct genotype regarding the gold standard method and false for the samples that Bentolab results in a different genotype from the gold standard method.

Below, we attach some indicative photos of the agarose gels after the visualization on the blue LED on BentoLab. For each of the DNA samples, two wells match: for the ancestral allele (‘‘w,’’ referring to T allele) and for the alternative allele (‘‘m,’’ referring to C allele). In the first well, a ladder 100bp was loaded, and the expected molecular weight per PCR product is 535 bp.

![](https://static.igem.org/mediawiki/2020/4/44/T--Patras--results1.jpg)

![](https://static.igem.org/mediawiki/2020/7/79/T--Patras--results2.jpg)

![](https://static.igem.org/mediawiki/2020/b/bc/T--Patras--results3.jpg)

![](https://static.igem.org/mediawiki/2020/d/d8/T--Patras--results4.jpg)

![](https://static.igem.org/mediawiki/2020/0/0a/T--Patras--results5.jpg)

![](https://static.igem.org/mediawiki/2020/8/88/T--Patras--results6.jpg)

![](https://static.igem.org/mediawiki/2020/0/0f/T--Patras--results7.jpg)

![](https://static.igem.org/mediawiki/2020/a/ab/T--Patras--results8.jpg)

![](https://static.igem.org/mediawiki/2020/0/02/T--Patras--results9.jpg)

![](https://static.igem.org/mediawiki/2020/9/93/T--Patras--results10.jpg)

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The agarose gel electrophoresis' images were used by the dry lab team to train and test the [Artificial Intelligence](https://2020.igem.org/Team%3APatras/Artificial_Intelligence)model.

### **Sponsors**

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##### **About**

16 students from Patras blending Pharmacogenomics with Artificial Intelligence to redefine medicine

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