iGEM 2020 — Team Linkoping

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# Team:Linkoping/Notebook

* Project

  [Description](https://2020.igem.org/Team%3ALinkoping/Description)
  [Design](https://2020.igem.org/Team%3ALinkoping/Design)
  [Contribution](https://2020.igem.org/Team%3ALinkoping/Contribution)
  [Implementation](https://2020.igem.org/Team%3ALinkoping/Implementation)
  [Engineering Success](https://2020.igem.org/Team%3ALinkoping/Engineering)
  [Results](https://2020.igem.org/Team%3ALinkoping/Results)
  [Notebook](https://2020.igem.org/Team%3ALinkoping/Notebook)
* Software

  [ClusteRsy](https://2020.igem.org/Team%3ALinkoping/Software)
  [Proof of Concept](https://2020.igem.org/Team%3ALinkoping/Proof_Of_Concept)
* Lab

  [Experiments](https://2020.igem.org/Team%3ALinkoping/Experiments)
  [Safety](https://2020.igem.org/Team%3ALinkoping/Safety)
  [Parts](https://2020.igem.org/Team%3ALinkoping/Parts)
* Model

  [Model](https://2020.igem.org/Team%3ALinkoping/Model)
* People

  [Team Members](https://2020.igem.org/Team%3ALinkoping/Team)
  [Attributions](https://2020.igem.org/Team%3ALinkoping/Attributions)
  [Collaborations](https://2020.igem.org/Team%3ALinkoping/Collaborations)
  [Partnership](https://2020.igem.org/Team%3ALinkoping/Partnership)
  [Sponsors](https://2020.igem.org/Team%3ALinkoping/Sponsorship)
* Human Practices

  [Human Practices](https://2020.igem.org/Team%3ALinkoping/Human_Practices)
  [Science Communication](https://2020.igem.org/Team%3ALinkoping/Education)
* Poster

  [Poster](https://2020.igem.org/Team%3ALinkoping/Poster)

**Left column: Webtool Notebook**

**Right column: Lab notebook**

# Week 18:

We divided the team into an experimental subgroup and a programming subgroup. At this stage there was a third subgroup that was a hybrid group that later was dissolved.

We divided the team into an experimental subgroup and a programming subgroup. At this stage there was a third subgroup that was a hybrid group that later was dissolved.

# Week 19:

We in the programming group had our first meeting where we set up a GANTT chart and a WBS for our project. In addition to that, we discussed modeling criterias, which was yet to be known, and read up on disease models.

Decided focusing on asthma

# Week 20:

We had a meeting with our supervisor to discuss the project and to get us started on using GitHub.

Planned for the work that will be performed during summer. Some research on the components of inflammation and touched up on basic knowledge about immunology was also done.

# Week 21:

During this week, we all installed the MODifieR package to our computers since it is necessary for our project. We also started working in groups with smaller projects for the webtool in preparation for the real project.

Literature study on inflammation and immunology.

# Week 22:

We had a workshop with our supervisor in preparation of the project and continued with the smaller projects. A new design from the design team was also applied to the webtool.

Started researching asthma, different types and what proteins are involved

# Week 23:

We all had this week off since it was exam period.

We all had this week off since it was exam period.

# Week 24:

We read articles about MODifieR this week and finished the smaller projects.

Literature study of asthma.

# Week 25:

This week was the official start for the project and we were all very excited! We started to implement inference modules and PPI networks to the webtool.

Literature study, aiming to find 3 good asthma biomarkers each.

# Week 26:

We started implementing ClusterProfiler and maintaining the main page and continued implementing inference methods and PPI networks. We had also got some feedback from potential users which we discussed how we could implement the webtool and a new potential named for it.

Deciding on creating a biosensor, which will help in asthma diagnostic

# Week 27:

We continued to implement ClusterProfiler and maintain the main page during this week.

Literature search regarding biosensors.

# Week 28:

We had a half time evaluation about the project so far and continued with the work from the former week.

Compiling a list of asthma biomarkers and choosing 3 ones that is the best fit for our project.

# Week 29:

Started preparing for beta testing and it was the last week working on ClusterProfiler and maintaining main page.

Educational week, project advisors were showing how to work in Benchling.

# Week 30:

This week some of us started working on GWAS along with some from the experimental group while the others continued with maintaining the webtool. We also held a beta testing for potential users.

Designing chosen biomarkers in Benchling

# Week 31:

We continued working on GWAS and updating the webtool from feedback we received from the beta testing

Literature search to find protocols regarding protein expression, purification and labeling for lab work.

# Week 32:

This week we started filming for the promotional video which was a lot of fun, especially since we got to meet each other for the first time in all summer. We also worked on improving the mainpage and making the webtool more user friendly. We even got some data to run with MODifieR.

This week we worked on how implement our biosensor theoretically together with biomarkers.

# Week 33.

We are still implementing improvements from the beta testing feedback and preparing data to run on the cluster.

Ordering earlier constructed plasmid in Benchling from IDT and continue looking for protocols.

# Week 34.

We continued to update the webtool from the beta testing and started preparing us to finish up the summer part of the project.

Educational week, during which the lab advisor and PIs teached experimental team members some basis of lab work with the bacteria. We also had safety laboratory instruction.

# Week 35.

There are only three issues left on GitHub meaning the webtool is almost done! We had meetings about how we will proceed with the project during the fall and fixed some bugs in the webtool.

Successfully expressed EDN and ECP in E.coli BL21 strain and purified it.

# Week 36.

The webtool is finished! Just in time before school starts again. We worked on joining the experimental and programming parts of the project. We decided a new date for another beta testing.

Dialysis and nanoDFS was performed on ECP in order to ensure protein fold.

# Week 37.

We decided on a name for the webtool - Clustersy, and added some new functions to the webtool and planned for some enrichment analysis.

This week dialysis and nanoDFS was still performed on ECP in order to ensure protein fold.

# Week 38.

The enrichment analysis is done and we started to study the results. We also found a way to validate the biomarkers. The v-NIC also happened this week which was great.

Data was analyzed from the dialysis and nanoDFS and samples were run on MALDI - TOF.

# Week 39.

We started to prepare for the next beta testing.

The data from MALDI - TOF was analyzed.

---

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* [![GitHub](https://static.igem.org/mediawiki/2020/4/44/T--Linkoping--GitHub_mini.svg)](https://github.com/igemsoftware2020/ClusteRsy-Linkoping)

---

##### Special thanks to our sponsors

[![”Normal](https://static.igem.org/mediawiki/2020/3/31/T--Linkoping--liu.png)](https://liu.se)
[![”Normal](https://static.igem.org/mediawiki/2020/7/73/T--Linkoping--qq_labs_ab.png)](https://qandqlabs.se)

---

###### Code release under MIT license.

###### Based on [Bootstrap](https://getbootstrap.com/) and theme [Flatly](https://bootswatch.com/flatly/) from [Bootswatch](https://bootswatch.com/).

###### Web fonts from [Google](https://fonts.google.com/). Logos from [Bootstrap Icons](https://icons.getbootstrap.com/). Scroll animation by [AOS](https://michalsnik.github.io/aos/). Lightbox by [Bootstrap Lightbox](https://ashleydw.github.io/lightbox/).

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# Team:Linkoping/Engineering

* Project

  [Description](https://2020.igem.org/Team%3ALinkoping/Description)
  [Design](https://2020.igem.org/Team%3ALinkoping/Design)
  [Contribution](https://2020.igem.org/Team%3ALinkoping/Contribution)
  [Implementation](https://2020.igem.org/Team%3ALinkoping/Implementation)
  [Engineering Success](https://2020.igem.org/Team%3ALinkoping/Engineering)
  [Results](https://2020.igem.org/Team%3ALinkoping/Results)
  [Notebook](https://2020.igem.org/Team%3ALinkoping/Notebook)
* Software

  [ClusteRsy](https://2020.igem.org/Team%3ALinkoping/Software)
  [Proof of Concept](https://2020.igem.org/Team%3ALinkoping/Proof_Of_Concept)
* Lab

  [Experiments](https://2020.igem.org/Team%3ALinkoping/Experiments)
  [Safety](https://2020.igem.org/Team%3ALinkoping/Safety)
  [Parts](https://2020.igem.org/Team%3ALinkoping/Parts)
* Model

  [Model](https://2020.igem.org/Team%3ALinkoping/Model)
* People

  [Team Members](https://2020.igem.org/Team%3ALinkoping/Team)
  [Attributions](https://2020.igem.org/Team%3ALinkoping/Attributions)
  [Collaborations](https://2020.igem.org/Team%3ALinkoping/Collaborations)
  [Partnership](https://2020.igem.org/Team%3ALinkoping/Partnership)
  [Sponsors](https://2020.igem.org/Team%3ALinkoping/Sponsorship)
* Human Practices

  [Human Practices](https://2020.igem.org/Team%3ALinkoping/Human_Practices)
  [Science Communication](https://2020.igem.org/Team%3ALinkoping/Education)
* Poster

  [Poster](https://2020.igem.org/Team%3ALinkoping/Poster)

[Programming](#Programming)
 [Experimental](#Experimental)

# Engineering principles

## Engineering principles programming

[![”Alttext”](https://static.igem.org/mediawiki/2020/4/40/T--Linkoping--engineering_cycle.jpg)](https://static.igem.org/mediawiki/2020/4/40/T--Linkoping--engineering_cycle.jpg)

**Figure 1.** Brief overview of the engineering cycle.

We sought out to develop a tool that would be comprehensive for first-time users. Doing this within the realm of bioinformatics was expected by us to be an uphill battle. The field itself is more than just writing R scripts and consulting Google when encountering malfunction. Hence, we reasoned that a good tool would be able to inform the user of the processes within the pipeline enough for the user to be willing to try new things out. That would hopefully encourage new users to try different options and learn along the way, and give experienced users familiar and customizable tools.

The logic behind it builds upon the idea of simplicity and user-friendliness. The only thing required from the user is curiosity. An experienced user would know what some of the packages within MODifieR offers, recognize an enrichment method or two and have probably written a script by hand before. Encountering new, already integrated options to experiment with and simultaneously having a consulting user guide within the tool itself will hopefully make ClusteRsy the go-to option for even experienced bioinformaticians. Or at least that is our vision.

Still, anyone can use ClusteRsy to generate figures from data. You can randomly select an inference method and a type of enrichment and voila. Being able to play around within our ClusteRsy was important to us, especially since curiosity is said to be the driving force behind science in general. By offering users the option to change if not all default parameters the tool becomes highly customizable. This leads to another engineering principle, an abundance of options.

Clusterly features eight different disease module generators, gene, and disease ontology analysis, pathway analysis, the ability to upload your own PPI network, save and delete results, and much much more. This will hopefully make it the go-to tool for finding overrepresented genes within disease modules, thus helping understand complex disease by pointing out new potential biomarkers.

To summarize, our most important engineering principles whilst building the tool have been user-friendliness alongside complexity, offering users new and old clear insight into the various methods that they try out whilst simultaneously being simple to navigate and understand.

## Engineering principles experimental

The experimental group spent several weeks researching known asthma biomarkers, especially in T2 asthma, and came up with many potential candidates. The biomarkers of highest interest were then chosen, with the criteria that they should be proteins and preferably enzymes since that could aid in the production of a biosensor. Three proteins were chosen, eosinophil derived neurotoxin (EDN), eosinophil cationic protein (ECP), and histamine n-methyltransferase (HNMT). These proteins were then designed in Benching where T7-RNA-polymerase promoter, as well as a 5'-UTR region, was added to the constructs and the constructs were ordered from IDT (Figure 2). The T7 promoter was used to enable a high expression of the chosen proteins.

[![”Alttext”](https://static.igem.org/mediawiki/2020/2/24/T--Linkoping--engineering_success1.png)](https://static.igem.org/mediawiki/2020/2/24/T--Linkoping--engineering_success1.png)

**Figure 2.** Schematic representation of the method Liu iGEM 2020 used for expression of biomarkers. A strong expression system was used to produced biomarkers in the cytosol.

In the wet lab, these constructs were transformed into Escherichia coli (E. coli) and expression levels were measured. In the laboratory, it was noted that the bacteria that were to express HNMT did not grow as well as the other bacteria, and it was determined that E. coli could not express this protein sufficiently due to it probably being toxic to the bacteria. EDN and ECP were successfully transformed into bacteria, but the levels of EDN that were expressed were very low. ECP was expressed successfully, and after the NanoDFS that was performed to control the folding of the proteins, one could observe that the ECP protein had at least partially correctly folded.

From this experiment we did more research about these constructs and learned that ECP and EDN contain a lot of disulfide bonds, each one has four of them, and this makes it hard to express them in the construct we choose for this wet-lab. From this, we learned that we need to use another method to express these proteins (Figure 3). Using a signal peptide for the transfer of proteins to the periplasm of the bacteria could ensure better folding because the environment in the periplasm is better suited for disulfide formation. We also learned that there are enzymes (DsbA) that help the formation of disulfide bonds in the periplasm [[1]](#[1]). To enable transportation, we understood that it is not only the signal peptide that is needed but also a certain fold of the protein [[2]](#[2]). The safest way to be sure that the transport happens, one of the native transported proteins can be used as a fusion. For example, Ecotin, a highly expressed protease inhibitor in some strains of E. coli, can be used as a fusion to transport another protein to the periplasm [[3](#[3]),[4]](#[4]). With the addition of the His-tag and TEV site between Ecotin-ECP/EDN, one could separate the proteins after purification (building upon Sydney Australia iGEM 2017 construct: BBa\_K2417002). We believe that this could be a good way to express proteins with many disulfide bonds. Since some of these proteins may be toxic to the host, we also learned that the use of T7 lysozyme can promote the survival of the host. This is usually put in a second plasmid, which will inhibit the low leaking expression of T7-RNA-polymerase [[5]](#[5]). Without the leaking expression of toxic products, the cells will hopefully grow normally until the point of induction.

[![Alttext](https://static.igem.org/mediawiki/2020/2/23/T--Linkoping--engineering_success2.png)](https://static.igem.org/mediawiki/2020/2/23/T--Linkoping--engineering_success2.png)

**Figure 3.** A schematic representation of an improved method for disulfide rich-proteins. Where ECP/EDN (four disulfide bonds each) would be expressed from plasmid 1. Fused to ECP/EDN is also Ecotin with its native signal sequence for transport to the periplasmic space. While in the periplasmic space, isomerases such as DsbA will help form the disulfide bonds. The Ecotin-ECP/EDN fusion protein can then be purified using its His-tag, in the linker region, a TEV-site is also placed to cleave off both the His-tag and Ecotin. Since both ECP/EDN are toxic proteins, and slow growth was noted, plasmid two can carry T7 lysozyme, which will inhibit leaking expression of ECP/EDN. This will ensure better growth up until the point of induction.

# References

[1] Denoncin K, Collet JF. Disulfide bond formation in the bacterial periplasm: major achievements and challenges ahead. Antioxid Redox Signal. 2013;19(1):63-71.

[2] Holland IB. Translocation of bacterial proteins—an overview, Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 2004; 694(1-3), 5-16.

[3] McGrath ME, Erpel T, Browner MF, Fletterick RJ. Expression of the protease inhibitor ecotin and its co-crystallization with trypsin. J Mol Biol. 1991;222(2):139-42.

[4] Paal M, Heel T, Schneider R, et al. A novel Ecotin-Ubiquitin-Tag (ECUT) for efficient, soluble peptide production in the periplasm of Escherichia coli. Microb Cell Fact. 2009; 8(7).

[5] Moffatt BA and Studier FW. T7 Lysozyme Inhibits Transcription by T7 RNA Polymerase. Cell. 1987; 49: 221-227

---

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* [![GitHub](https://static.igem.org/mediawiki/2020/4/44/T--Linkoping--GitHub_mini.svg)](https://github.com/igemsoftware2020/ClusteRsy-Linkoping)

---

##### Special thanks to our sponsors

[![”Normal](https://static.igem.org/mediawiki/2020/3/31/T--Linkoping--liu.png)](https://liu.se)
[![”Normal](https://static.igem.org/mediawiki/2020/7/73/T--Linkoping--qq_labs_ab.png)](https://qandqlabs.se)

---

###### Code release under MIT license.

###### Based on [Bootstrap](https://getbootstrap.com/) and theme [Flatly](https://bootswatch.com/flatly/) from [Bootswatch](https://bootswatch.com/).

###### Web fonts from [Google](https://fonts.google.com/). Logos from [Bootstrap Icons](https://icons.getbootstrap.com/). Scroll animation by [AOS](https://michalsnik.github.io/aos/). Lightbox by [Bootstrap Lightbox](https://ashleydw.github.io/lightbox/).

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

# Team:Linkoping/Experiments

* Project

  [Description](https://2020.igem.org/Team%3ALinkoping/Description)
  [Design](https://2020.igem.org/Team%3ALinkoping/Design)
  [Contribution](https://2020.igem.org/Team%3ALinkoping/Contribution)
  [Implementation](https://2020.igem.org/Team%3ALinkoping/Implementation)
  [Engineering Success](https://2020.igem.org/Team%3ALinkoping/Engineering)
  [Results](https://2020.igem.org/Team%3ALinkoping/Results)
  [Notebook](https://2020.igem.org/Team%3ALinkoping/Notebook)
* Software

  [ClusteRsy](https://2020.igem.org/Team%3ALinkoping/Software)
  [Proof of Concept](https://2020.igem.org/Team%3ALinkoping/Proof_Of_Concept)
* Lab

  [Experiments](https://2020.igem.org/Team%3ALinkoping/Experiments)
  [Safety](https://2020.igem.org/Team%3ALinkoping/Safety)
  [Parts](https://2020.igem.org/Team%3ALinkoping/Parts)
* Model

  [Model](https://2020.igem.org/Team%3ALinkoping/Model)
* People

  [Team Members](https://2020.igem.org/Team%3ALinkoping/Team)
  [Attributions](https://2020.igem.org/Team%3ALinkoping/Attributions)
  [Collaborations](https://2020.igem.org/Team%3ALinkoping/Collaborations)
  [Partnership](https://2020.igem.org/Team%3ALinkoping/Partnership)
  [Sponsors](https://2020.igem.org/Team%3ALinkoping/Sponsorship)
* Human Practices

  [Human Practices](https://2020.igem.org/Team%3ALinkoping/Human_Practices)
  [Science Communication](https://2020.igem.org/Team%3ALinkoping/Education)
* Poster

  [Poster](https://2020.igem.org/Team%3ALinkoping/Poster)

[Bacterial growth, transformation, plasmid preparation](#Growth)
 [Protein expression, harvest, purification](#Expression)
 [Protein characterization](#Characterization)
 [Phase II](#PhaseII)

# Bacterial growth, transformation, plasmid preparation

## Bacterial Growth

All experiments using Escherichia coli (E. coli) BL21 cells were grown in LB-Miller (Table 1,  [USBiological](http://www.usbio.net/protocols/lb-broth-miller)) for liquid cultures and LB-Miller agar (Table 1,  [chsProtocols](http://cshprotocols.cshlp.org/content/2009/3/pdb.rec11683.full)) for solid plated bacteria. When carrying kanamycin resistance, 50 µg/mL kanamycin was added to both the liquid and solid cultures. In smaller cultures (1-25 mL), the shaking was set to 200 rpm. In large cultures (1 L), the shaking was set to 120 rpm. Small cultures were inoculated in 15- or 50 mL tubes, while larger ones in 3 L baffled flasks.

**Table 1.** Recipe for 1X and 2X LB-Miller media.

[![Alttext](https://static.igem.org/mediawiki/2020/d/da/T--Linkoping--exp_data_table1.png)](https://static.igem.org/mediawiki/2020/d/da/T--Linkoping--exp_data_table1.png)

**Table 2.** Recipe for LB media agar.

[![”Alttext”](https://static.igem.org/mediawiki/2020/5/53/T--Linkoping--exp_data_table2.png)](https://static.igem.org/mediawiki/2020/5/53/T--Linkoping--exp_data_table2.png)

## Chemically competent cell preparation

To enable transformation, 25 mL E. coli (BL21) cells were cultured for 6 hours at 37 °C with appropriate antibiotics (if already carrying plasmids) in a shaking incubator. Harvest of the cells was done by centrifugation for 5 minutes at 3000 g. The supernatant was discarded and the pellet was resuspended in 30 mL 0.1 M CaCl2 (Table 3). The centrifugation and resuspension were repeated once and afterward the cells were resuspended in 0.1 M CaCl2 with 15 % glycerol. Aliquots of the cells were made in a volume of 50 µL in 1.5 mL tubes. The now competent cells were placed directly into -80 °C, without prior use of liquid nitrogen.

**Table 3.** Recipe for competent cell buffer.

[![”Alttext”](https://static.igem.org/mediawiki/2020/c/c5/T--Linkoping--exp_data_table3.png)](https://static.igem.org/mediawiki/2020/c/c5/T--Linkoping--exp_data_table3.png)

## Heatshock

The dry DNA from Integrated DNA Technologies was centrifuged briefly and resuspended in 50 µL autoclaved dH2O. A volume of 1 µL of the DNA solution was transferred into a tube containing 50 µL BL21 competent cells. This solution with bacterial cells and plasmid DNA was mixed by flicking the tube lightly. The tubes were incubated on ice for 30 min. Heatshock was carried out at 42 °C for 45 seconds and directly afterward, the tube was placed back on ice. The tubes were left on ice until they could be transferred into 1 mL LB-Miller media and incubated at 37°C for 1 hour. To plate these transformed bacteria, the culture was moved into a suitable tube and centrifuged at 500 g briefly. To concentrate the cells, 900 µL of the media was discarded, and the pelleted cells were resuspended in the remaining 100 µL. Plating was done on LB-Miller agar with 50 µg/mL kanamycin, all of the 100 µL was plated. The plates were then incubated at 37 °C until colonies had formed.

## Plasmid preparation

Plasmid preparation was done using [Monarch®](http://international.neb.com/protocols/2015/11/20/monarch-plasmid-dna-miniprep-kit-protocol-t1010) Plasmid DNA Miniprep Kit (New England Biolabs) protocol. A volume of 5 mL of LB media was added to a 50 mL tube together with suitable antibiotics. An inoculated colony was then added to the mixture and it was incubated overnight at 37 °C. The tubes were then centrifuged for 5 minutes at 3500 g. The supernatant was discarded and replaced with 200 µL Plasmid Resuspension Buffer (B1). The tubes were vortexed to ensure no clumps formed. The cells were then lysed by adding 200 µL Plasmid Lysis Buffer (B2). The tubes were inverted gently 5-6 times and then incubated for 1 minute. After this incubation, 400 µL of Plasmid Neutralization Buffer (B3) was added to the tubes. The tubes were inverted gently until the color changed to a uniform yellow. Tubes were then incubated for 2 minutes. The mixture was then centrifuged at 16000 g for 5 minutes. The supernatant was removed and placed in the spin column and centrifuged for 1 minute. Flowthrough was discarded. The spin-column was inserted in the collection tube and 200 µL of Plasmid Wash Buffer 1 was added and centrifuged for 1 minute. A volume of 400 µL of Plasmid Wash Buffer 2 was then added and centrifuged for 1 minute. The column was then transferred to a 1.5 mL microfuge tube and 50 µL autoclaved dH2O was added to the center of the matrix. The microfuge tubes were incubated for 1 minute, then spun for 1 minute to elute DNA. DNA concentration was measured using a Nanodrop. Samples were afterward loaded onto an agarose gel for gel electrophoresis to confirm the size of the plasmids.

## Agarose Gel

Preparation of an agarose gel was done by mixing 1.3 g of agarose in 100 mL of TAE 1X buffer (Table 4). The mixture was microwaved until it boiled and then poured into a mold. Before the gel had hardened, a comb was added to form the wells. To prepare the samples to run on the gel, 7 µL of loading dye was mixed with 7 µL of each DNA sample. EDN, ECP, and HNMT were loaded, as well as 7 µL of a ladder for reference (1 kb plus DNA ladder, New England Biolabs). The gel was run at 100 V for 60 min. After finishing, it was stained with ethidium bromide for 5 min (with light shaking). To remove excess EtBr, destaining with TAE buffer was done for 1 minute (with light shaking). To analyze the results, the gel was placed in a gel camera (C150, Azure Biosystems).

**Table 4.** Recipe for 50X TAE-buffer (pH 8.5).

[![”Alttext”](https://static.igem.org/mediawiki/2020/9/99/T--Linkoping--exp_data_table4.png)](https://static.igem.org/mediawiki/2020/9/99/T--Linkoping--exp_data_table4.png)

# Protein expression, harvest, purification

## Protein expression

Seeds were prepared by spreading 100 µL from a liquid culture of pIDTSmart-ECP/EDN E. coli (BL21) onto LB-Miller agar plates with 50 µg/mL kanamycin. A total of 3 plates were made for each construct. These plates were incubated at 37 °C for 16 hours. To inoculate the three plates, 400 µL of LB-Miller was poured onto them and released from the plate using a spreader. Cells from all the plates were then moved into a culture flask with 1 L 2X LB-Miller and 50 µg/mL kanamycin. This inoculation was then left to grow until an OD600 at 0.6 for both of the constructs. The induction of the T7 system was done with 1 mM IPTG and the temperature of the incubator was lowered to 20 °C. The cells were incubated for 16 hours. The optical density (600 nm) at harvest was measured to 5.7 for both of the constructs. Harvest was done by centrifugation at 3500 g 4 °C for 20 minutes using a swing-out rotor. The bacterial pellets were then resuspended using Buffer G (Table 5) and placed at -80 °C [[1, prion protein section]](#[1]).

## Bacterial lysis

Bacteria lysis was done using a sonication method. Pellets of harvested cells were resuspended in the appropriate buffer and frozen at -80 °C. The cells were then thawed at 37 °C. A sonication probe was placed in the bacterial slurry and sonication was done at 30 % amplitude with 30 seconds ON 30 seconds OFF for a total ON time of 4 minutes. Samples were kept on ice during sonication to prevent excessive heating of the sample. After sonication samples were centrifuged at 10000 g for 40 minutes. The supernatant was saved for further purification.

## IMAC purification

IMAC purification can be used for His-tagged proteins. The protocol was adapted from [[1, prion protein section]](#[1]). A volume of 1 mL of Nickel-nitrilotriacetic acid agarose was added to a PD10 column and washed with 10 mL of wash buffer (Buffer B). The lysate was poured onto the IMAC column and flowthrough was saved until protein has been detected in the purified sample. A volume of 10 mL buffer G was added, followed by adding 7 mL of 50 % buffer G and 50 % buffer B (Table 5), and 7 mL buffer B afterward. Protein was eluted using 10 mL of buffer E (Table 5). All fractions were then run on the SDS-PAGE (Table 6) to verify the purity of the protein.

**Table 5.** Recipes for purification buffers.

[![”Alttext”](https://static.igem.org/mediawiki/2020/8/8d/T--Linkoping--exp_data_table5.png)](https://static.igem.org/mediawiki/2020/8/8d/T--Linkoping--exp_data_table5.png)

**Table 6.** Recipe for polyacrylamide gel.

[![”Alttext”](https://static.igem.org/mediawiki/2020/6/60/T--Linkoping--exp_data_table6.png)](https://static.igem.org/mediawiki/2020/6/60/T--Linkoping--exp_data_table6.png)

The separation gel was cast first, with the stacking gel directly after the separation gel had hardened (gel mold, Mini-Protean Tetra Casting Module, Bio-Rad). A comb (10 well 1 mm, 40 µL) was added when the stacking gel was cast.

# Protein characterization

## Sample preparation

To prepare samples to run on the SDS-PAGE gel, 20 µL sample was mixed with 18 µL sample buffer (Table 7) and 2 µL β-mercaptoethanol. All of this solution could then be loaded onto the gel.

**Table 7.** Recipe for 2X SDS-PAGE sample buffer (pH 6.8).

[![”Alttext”](https://static.igem.org/mediawiki/2020/6/60/T--Linkoping--exp_data_table7.png)](https://static.igem.org/mediawiki/2020/6/60/T--Linkoping--exp_data_table7.png)

## Running the gel

The gel was placed in the gel electrophoresis tray and 1X running buffer (Table 8) was added (~0.5 L). Voltage was set to 100 V and the gel was run until all samples wandered into the separation gel. Then the voltage was then increased to 200 V and the gel was run until the sample buffer color front reached the bottom.

**Table 8.** Recipe for 10X running buffer (pH 8.3).

[![”Alttext”](https://static.igem.org/mediawiki/2020/d/db/T--Linkoping--exp_data_table8.png)](https://static.igem.org/mediawiki/2020/d/db/T--Linkoping--exp_data_table8.png)

## Staining

The gel was removed from the gel electrophoresis tray and rinsed with water twice, a final rinse was done with water and heated to near-boiling temperature. Following, the water was removed and the staining solution (Table 9) was added, this solution was heated to a near-boiling temperature as well. The gel was incubated with the staining solution for 30 minutes with light shaking. Afterward, the staining solution was removed and the destaining solution (Table 9) was added and left to destain for 30 minutes with a light shake. The destaining solution was changed once.

**Table 9.** Recipes for staining and destaining solutions.

[![”Alttext”](https://static.igem.org/mediawiki/2020/7/78/T--Linkoping--exp_data_table9.png)](https://static.igem.org/mediawiki/2020/7/78/T--Linkoping--exp_data_table9.png)

## Protein dialysis

Protein dialysis was done in order to change the buffer conditions. A membrane (Standard RC tubing, MWCO 3.5 kDa, Spectrum Laboratories) was incubated in dH2O for 5 minutes and samples were loaded into the dialysis tubing. Dialysis was then done in 5 mM EDTA buffer (1 L) for 16 hours at 4 °C. The dialysis was repeated once but using a 5 mM borate buffer (1 L) instead.

## Nano-format of Differential Scanning Fluorimetry (nanoDSF)

To investigate the protein fold and stability, nanoDSF (Prometheus, NanoTemper) was performed. A volume of 10 µL of protein sample was loaded into capillaries (using capillary force) and simultaneously scanned at 330/350 nm wavelengths. The temperature was set to gradient and started at 20 °C, rising 0.5 °C/min until it reached 110 °C. For eosinophil cationic protein, 10 mM dithiothreitol was also added to investigate the state of the disulfide bonds.

# Phase II

## Validation of the chosen biomarkers

ClusteRsy has revealed interesting biomarkers from the input data retrieved in the study GSE75011. These biomarkers and the biomarkers found from the literature study will need to be further validated to see that they indeed significantly over- or underexpressed in asthma patients. If found significantly differentially expressed they will move on to the next step where they will be expressed in bulk for the biosensor optimization. One idea we have for this step of phase II is also to create the antibodies used for the biosensor ourselves. This will be done by using a protocol for antibody production created by the Rochester iGEM team this year and then optimizing this protocol for our purpose ([link](https://static.igem.org/mediawiki/2020/7/74/T--Rochester--BioBrick_Instructions.pdf)). The biomarkers that are shown to be significant and have functionally expressed antibodies will finally make it to the biosensor assay.

[![Overview of phase II](https://static.igem.org/mediawiki/2020/3/39/T--Linkoping--overview_phase2.png)](https://static.igem.org/mediawiki/2020/3/39/T--Linkoping--overview_phase2.png)

**Figure 1.** Overview of phase II.

Direct sandwich ELISA has been chosen as the method of validation due to its robustness and since all the biomarkers chosen have a matching pair of antibodies. The procedure of the validation will be as follows:

**1.** The capture antibodies are immobilized on the bottom of the wells.
**2.** The microplate is washed.
**3.** Our sample is added into the wells.
**4.** The microplate is washed.
**5.** The detection antibody with linked enzymes is added to their respective well.
**6.** The microplate is washed.
**7.** Enzyme substrate added to each well.
**8.** The reaction is noted. The amount of binding can now be determined.

## Design of the biosensor

Lateral flow assays (LFA) are simple and economic devices used as biosensors. During phase II of this project, an LFA will be designed that can detect the presence of overexpressed genes related to asthma, for which biomarkers were found using the software ClusteRsy. The development of ClusteRsy has been the focus of phase I of this project.

A LFA biosensor consists of several parts, but most important is the porous material designed to transport fluid and the test- and control lines where the detection of biomarkers occurs. At the test line, there are immobilized antibodies for the biomarkers. When they bind to the antibodies there is a colorimetric change, which is easy to detect for the person conducting the test. How this color change occurs depends on the type of LFA, it can either be a competitive assay or a sandwich assay. At the control line, there is a different antibody and if there is a colorimetric change there the test can be considered invalid.

For this project the idea is to design an LFA biosensor where there are several test lines for the different biomarkers. This can then be used to find which genes are overexpressed by the patient. This way the type of asthma the patient has can be detected which can then be used to determine the correct treatments. In order to accurately detect significantly overexpressed genes the sensitivity of the LFA will be adjusted to [[2]](#[2]).

[![Design of the theoretical biosensor](https://static.igem.org/mediawiki/2020/a/a3/T--Linkoping--lfa_phase2.png)](https://static.igem.org/mediawiki/2020/a/a3/T--Linkoping--lfa_phase2.png)

**Figure 2.** Design of the theoretical biosensor.

# References

[1]. Sandberg A, Nyström S. Purification and fibrillation of recombinant human amyloid-β, prion protein, and tau under native conditions. In: Sigurdsson EM, Calero M, Gasset M, editors. Methods in Molecular Biology. New York, NY: Springer New York; 2018. p. 147–66.

[2]. Shuai Zhao et al. State of the art: Lateral flow assay (LFA) biosensor for on-site rapid detection. Chinese Chemical Letters. 2018;29(11):1567-1577. https://doi.org/10.1016/j.cclet.2017.12.008.

---

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# Team:Linkoping/Results

* Project

  [Description](https://2020.igem.org/Team%3ALinkoping/Description)
  [Design](https://2020.igem.org/Team%3ALinkoping/Design)
  [Contribution](https://2020.igem.org/Team%3ALinkoping/Contribution)
  [Implementation](https://2020.igem.org/Team%3ALinkoping/Implementation)
  [Engineering Success](https://2020.igem.org/Team%3ALinkoping/Engineering)
  [Results](https://2020.igem.org/Team%3ALinkoping/Results)
  [Notebook](https://2020.igem.org/Team%3ALinkoping/Notebook)
* Software

  [ClusteRsy](https://2020.igem.org/Team%3ALinkoping/Software)
  [Proof of Concept](https://2020.igem.org/Team%3ALinkoping/Proof_Of_Concept)
* Lab

  [Experiments](https://2020.igem.org/Team%3ALinkoping/Experiments)
  [Safety](https://2020.igem.org/Team%3ALinkoping/Safety)
  [Parts](https://2020.igem.org/Team%3ALinkoping/Parts)
* Model

  [Model](https://2020.igem.org/Team%3ALinkoping/Model)
* People

  [Team Members](https://2020.igem.org/Team%3ALinkoping/Team)
  [Attributions](https://2020.igem.org/Team%3ALinkoping/Attributions)
  [Collaborations](https://2020.igem.org/Team%3ALinkoping/Collaborations)
  [Partnership](https://2020.igem.org/Team%3ALinkoping/Partnership)
  [Sponsors](https://2020.igem.org/Team%3ALinkoping/Sponsorship)
* Human Practices

  [Human Practices](https://2020.igem.org/Team%3ALinkoping/Human_Practices)
  [Science Communication](https://2020.igem.org/Team%3ALinkoping/Education)
* Poster

  [Poster](https://2020.igem.org/Team%3ALinkoping/Poster)

[Programming](#Programming)
 [Model](#Model)
[Experimental](#Experimental)

# Programming

The final version of the ClusteRsy software is a sophisticated, intuitive, and easy to use software for analysis of big-RNA-data. One of the ambitions of the software was to make it easy to use for people who are not familiar with bioinformatics, so through the clickable explanations, user guide, and Youtube tutorials the user is guided through the use of ClusteRsy. During the first meeting with the clinicians, this ambition was set. Throughout the two rounds of beta testing, we witnessed a great increase in the experienced user-friendliness. Another of the wishes opinionated at the first beta testing that we took into account was the hiding of all settings that were not vital for the user to change for each analysis and setting those to an appropriate default value. Another of the aspirations we set ourselves was to make ClusteRsy look like a modern tool that is pleasing for the eye. This was accomplished by using contrasting, yet matching colors combined with rounded shapes whenever it was possible. We also divided the information to separate tabs where it would be easy to find all relevant information without the tool being too cluttered.

To conclude, ClusteRsy is a software developed for transcriptome analysis and it will enable other teams as well as clinicians to easily access biomarker discovery and give new insight into complex diseases.

[

](https://static.igem.org/mediawiki/2020/2/28/T--Linkoping--clustersy_movie.mp4)

**Video:** An introduction of the ClusteRsy interface.

# Model

Here we will showcase the power of ClusteRsy and our modeled computational pipeline that anyone can use. We have used RNA-seq data from the NCBI GEO database from the asthma study [GSE75011](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75011) on Th2 cells. Without prior knowledge, we were able to find a connection between asthma and RSV bronchiolitis using the results from ClusteRsy and our pipeline.

## Input data

From the [GSE75011](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75011) count matrix an edgeR table was calculated using a built-in function in MODifieR which in turn uses generalized linear models to calculate which genes were differentially expressed. The resulting input data contained 12923 genes and 2306 of them were considered to be differentially expressed (P-value < 0.05).

## MODifieR

Using the prepared [GSE75011](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75011) dataset we ran all 8 methods from MODifieR to produce in total 8 different disease modules. These modules contain genes that are highly linked together and therefore form a cluster in the network. The next step was to validate how well these modules performed. This was done using PASCAL to compare the SNP P-values from the genome-wide association study. Before that could be done the modules DiffCoEx and WGCNA were too big which means they contained more than 10.000 genes and therefore had to be split into submodules. This resulted in 30 different submodules for DiffCoEX and 9 different submodules for WGCNA.

## Validation of modules

In order to validate the modules we ran PASCAL on all the MODifieR modules. The threshold was set to a P-value < 0.05. As seen in Figure 1, the negative logarithm of the P-values are displayed, hence significant modules are above this threshold.

From validation with PASCAL we concluded that the DIAMOnD, DiffCoEX\_skyblue1 (splitted module), WGCNA, WGCNA\_splitted\_module\_by\_color\_3, Correlation clique, Clique sum permutation (CSP) and module discoverer modules were significant.

The final modules that were chosen to continue with were DIAMOnD, Correlation clique, CSP, and Module discoverer. DiffCoEx\_skyblue1, WGCNA, and WGCNA\_splitted\_module\_by\_color\_3 were discarded, this was due to the small size of some modules and none of them gave any significant results further on. As seen in Table 1, the final modules that were chosen to continue with had varying sizes. The original input data contained 12923 genes.

[![”Alttext”](https://static.igem.org/mediawiki/2020/a/a8/T--Linkoping--graph_best_modules.png)](https://static.igem.org/mediawiki/2020/a/a8/T--Linkoping--graph_best_modules.png)

**Figure 1.** Results from PASCAL. The negative logarithm of the P-values are being displayed on the y-axis and all the MODifieR modules on the x-axis. Red is seed based methods, blue is co-expression based and yellow is Clique based methods. The dashed line is the threshold, alpha, set to a P-value = 0.05. All the significant modules are above this dashed line.
Investigating the resulting modules further DiffCoEX\_skyblue1 (splitted module), WGCNA and WGCNA\_splitted\_module\_by\_color\_3 was discarded.

**Table 1.** The final modules chosen to continue with. The first column is the MODifieR method used and the second column is the total number of genes present in the MODifieR module. \*The total number of genes also includes genes that were picked up from the PPI network. These genes doesn't contain any experimental data.

[![”Alttext”](https://static.igem.org/mediawiki/2020/f/ff/T--Linkoping--data_table_nr_genes.png)](https://static.igem.org/mediawiki/2020/f/ff/T--Linkoping--data_table_nr_genes.png)

## Enrichment analysis

The final modules, DIAMOnD, Correlation clique, CSP, and Module discoverer contain information about the disease module, i.e. the resulting modules are networks of genes that are considered significant for the modules and are highly linked together. To find out any information about the disease genes from these networks enrichment analysis is needed. We performed disease analysis using the DGN database on DIAMOnD, Correlation clique, CSP, and Modules discoverer.

As seen in Table 2, the disease analysis is significant with a p-value < 0.05 for all diseases of interest as well as all the modules. The gene ratio is the number of genes involved in the disease divided by the total number of genes in that module after the disease analysis had been performed.

**Table 2.** Enrichment analysis results using the disease analysis algorithm and DGN database. The column module shows all the significant modules that were retrieved after PASCAL. In the disease column Allergic asthma, RSV and viral Bronchiolitis are listed for each module. The gene ratio is the number of genes involved with the disease divided by the total number of genes in the module. The P-value and P-adjusted value shows the significance for the genes being involved with the listed disease.

[![”Alttext”](https://static.igem.org/mediawiki/2020/4/4e/T--Linkoping--data_table_module_disease.png)](https://static.igem.org/mediawiki/2020/4/4e/T--Linkoping--data_table_module_disease.png)

## How well our model preformed

In order to find out how well our model using MODifieR actually performed we wanted to test it against the case of not using the model. We, therefore, filtered the input data and did not use MODifieR. The input data was filtered to only contain differentially expressed genes that were significant, P-value < 0.05, and performed disease analysis at this filtered input data as well.

We can show that our model using MODifieR outperforms input data only when performing enrichment analysis.

As seen in Table 3 using our model with MODifieR produces significant results with P-values < 0.05 for all diseases investigated, compared to using input data only, which returns non-significant results with P-value > 0.05 and P-adjusted = 1 for all diseases. This supports our findings and that the connections discovered are indeed correlated to the disease as well as showing the power of this computational approach.

With this we can show the importance of analyzing transcriptome data using a modular approach. Our pipeline will therefore increase the chance to get significant results when doing enrichment analysis.

**Table 3.** Enrichment analysis using the disease analysis algorithm and DGN database. In yellow the filtered input data is shown together with the P-value and P-adjusted value for the investigated diseases and is compared to DIAMOnD in green, CSP in light red, Module discoverer in blue and Correlation clique in pink.

[![”Alttext”](https://static.igem.org/mediawiki/2020/b/b0/T--Linkoping--data_table_input_value.png)](https://static.igem.org/mediawiki/2020/b/b0/T--Linkoping--data_table_input_value.png)

## Predicted biomarkers

[![”Alttext”](https://static.igem.org/mediawiki/2020/2/20/T--Linkoping--module_discoverer.png)](https://static.igem.org/mediawiki/2020/2/20/T--Linkoping--module_discoverer.png)

[![”Alttext”](https://static.igem.org/mediawiki/2020/2/2d/T--Linkoping--diamond.png)](https://static.igem.org/mediawiki/2020/2/2d/T--Linkoping--diamond.png)

[![”Alttext”](https://static.igem.org/mediawiki/2020/2/24/T--Linkoping--correlation_clique.png)](https://static.igem.org/mediawiki/2020/2/24/T--Linkoping--correlation_clique.png)

[![”Alttext”](https://static.igem.org/mediawiki/2020/9/9b/T--Linkoping--csp.png)](https://static.igem.org/mediawiki/2020/9/9b/T--Linkoping--csp.png)

**Figure 2.** Network representation of the different modules, Module Discoverer upper left, DIAMOnD upper right, Correlation Clique bottom left and CSP bottom right. Red is overexpressed genes with a logFC > 0.5, yellow is genes with -0.5 < logFC < 0.5 and are not considered significant for our purpose. Blue is underexpressed genes with a logFC < -0.5. The green area is the genes involved in viral Bronchiolitis, orange area is genes involved in Asthma and the purple area is genes involved in RSV. The intersection between the disease are genes present in both or all diseases.

When the modules had been validated and proven to be better than using only input data, we wanted to find any biological relevance out of these modules. The disease analysis provided us with very interesting results, as seen in Figure 2 there is a big overlap between Asthma, RSV, and viral bronchiolitis. This shows the power of this computational pipeline, the amount of insight into asthma we got was more than we had expected. The connection between Asthma and severe RSV Bronchiolitis was first established at Linköping University and has since then been reported in several studies, although the causality has not been proven yet, the connection is well established [[1-3]](#[1]). From Figure 2 the asthma genes are shown in the orange area, viral bronchiolitis in green and RSV in purple. Red is overexpressed genes with a logFC > 0.5, yellow is genes with -0.5 < logFC < 0.5 and are not considered significant for our purpose. In blue underexpressed genes are shown.

These results made us think about the idea of being able to differentiate between patients with asthma and patients who had severe childhood RSV Bronchiolitis and asthma. But first, we needed to find out if there were any clinical value in being able to differentiate between the groups. We contacted Maria Jenmalm, Professor of Experimental Allergology for a consultation about this idea. After a very informative meeting, we came to the conclusion that the intersection between asthma, RSV, and Bronchiolitis, shown in Figure 2, is a perfect indicator for infants at high risk for severe RSV Bronchiolitis. These results could therefore be used for screening of infants with a higher risk for severe RSV Bronchiolitis and vaccination of this high-risk group should therefore be considered.

### First selection of biomarkers

When selecting the predicted biomarkers of interest we started with collecting all the genes that were considered to be overexpressed (logFC > 0.5) or underexpressed (logFC < -0.5) with a P-value < 0.05 and that were involved in asthma, the intersection of asthma and RSV as well as the intersection of asthma, RSV and Bronchiolitis.

The results can be seen in table 4. The logFC value was given a gradient ranging from light red (low positive logFC) to red (large positive logFC) and light blue (low negative value) to blue (large negative logFC) this was done to easily spot the most differentially expressed genes.

**Table 4.** In the left column the different MODifieR modules (methods) are listed. The disease column shows if the gene is present in Asthma alone or Asthma and RSV as well as Asthma, RSV and Bronchiolitis. The logFC value contains a gradient where 0.5 is light red, 1.2 and above is red. For negative values -0.5 is light blue and the minimum logFC is blue. The P-values is the P-value for the differential expression between control group and patient group for that gene.

[![”Alttext”](https://static.igem.org/mediawiki/2020/9/92/T--Linkoping--data_table_chosen_biomarkers.png)](https://static.igem.org/mediawiki/2020/9/92/T--Linkoping--data_table_chosen_biomarkers.png)

Once this first selection was done we wanted to refine our finding even more. In order to make it to the final biomarker assay, we needed to know whether it was practically doable given these biomarkers. We, therefore, set up a few criteria to narrow down these biomarkers to a final list.

### Criteria for selecting the final biomarkers

The following criteria for the final selection were based on the following:
There should be purchasable antibodies in order for us to validate our results using ELISA.
The ones with the highest or lowest logFC should be picked and they should be easy to moderately easy to express in order to ease the work during phase II of the project.
We also looked into whether the predicted biomarkers had been reported in asthma before by doing a literature search.

### The final biomarkers

In Table 5, the final 13 biomarkers are shown. These are biomarkers that have been reported in asthma, have polyclonal antibodies to purchase, and are easy or moderately easy to express. The biomarkers have been divided into asthma, the intersection of asthma and RSV as well as the intersection of asthma, RSV, and viral Bronchiolitis. All these 13 biomarkers will be validated using a sandwich ELSA during phase 2 of the project before finally making it to the biosensor assay. From these 13 biomarkers, all of them could potentially be used for asthma diagnosis and we also purpose that from the intersection between Asthma, RSV, and Bronchiolitis we can use these biomarkers to screen for infants who are at high risk to develop severe RSV Bronchiolitis.

**Table 5.** The final biomarkers that were selected from the criteria listed above. The biomarkers are divided into asthma, the intersection of asthma and RSV as well as the intersection asthma, RSV and viral Bronchiolitis. The logFC value contains a gradient where 0.5 is light red, 1.2 and above is red. For negative values -0.5 is light blue and the minimum logFC is blue. The P-values is the P-value for the differential expression between control group and patient group for that gene.

[![””](https://static.igem.org/mediawiki/2020/9/97/T--Linkoping--data_table_for_asthma.png)](https://static.igem.org/mediawiki/2020/9/97/T--Linkoping--data_table_for_asthma.png)

[![”Alttext”](https://static.igem.org/mediawiki/2020/d/df/T--Linkoping--data_table_biomarker2.png)](https://static.igem.org/mediawiki/2020/d/df/T--Linkoping--data_table_biomarker2.png)

[![”Alttext”](https://static.igem.org/mediawiki/2020/5/50/T--Linkoping--data_table_biomarkers3.png)](https://static.igem.org/mediawiki/2020/5/50/T--Linkoping--data_table_biomarkers3.png)

# Conclusion

Not only can we show that a modular approach using our computational pipeline improves the results significantly, but the amount of detail in the results is sufficient enough to be able to distinguish between heterogeneity within a disease.

We could use this computational pipeline to successfully predict 13 biomarkers for asthma diagnosis. We also purpose that with our findings we could with a screening program identify infants with high risk for severe RSV bronchiolitis.

# Experimental

Due to the ongoing pandemic the time that our team could spend in the lab was limited. Because of this time limitation, our main goals were to express and purify chosen proteins to simplify laboratory work for phase 2 of the project that could be performed next year. To minimize the time spent in the laboratory, constructs were ordered from IDT precloned in vectors (pIDTSmart-Kan) ready to be transformed and expressed directly, removing the time spent on manual cloning.

[![”Alttext”](https://static.igem.org/mediawiki/2020/1/16/T--Linkoping--agarose_gel_analysis.png)](https://static.igem.org/mediawiki/2020/1/16/T--Linkoping--agarose_gel_analysis.png)

**Figure 3.** Agarose gel analysis of plasmids expression. A 1.3 % agarose Tris- acetic acid- EDTA gel was run for 60 minutes at 100 V in TAE-buffer. Lane 1 contains a 1kb DNA ladder (New England Biolabs), lane 2 pIDTSmart-Kan-EDN plasmid, lane 3 pIDTSmart-Kan-ECP plasmid, lane 4 pIDTSmart-Kan-HNMT plasmid.

# Plasmid purification

To investigate the presence of our plasmids, and agarose gel analysis of plasmid DNA was done. Positive bands were seen for pIDTSmart-EDN/ECP from purified plasmid DNA which was transformed into Escherichia coli (BL21). These bands indicate a functioning plasmid and a non-lethal construct. However, due to the nature of EDN/ECP, the BL21 cells which carried these constructs grew slow. The most intense bands in Figure 3. indicate the presence of a near-correct-sized plasmid, bands above it indicate different conformations of that same plasmid. Both constructs are ~2700 bp, however, showing up at a slightly lighter size can be explained by supercoiling.

[![”Alttext”](https://static.igem.org/mediawiki/2020/c/c6/T--Linkoping--SDS-PAGE_gel_analysis.png)](https://static.igem.org/mediawiki/2020/c/c6/T--Linkoping--SDS-PAGE_gel_analysis.png)

**Figure 4.** SDS-PAGE gel after protein purification. Lane 4 is a precision plus protein ladder (Bio-rad), lanes 1-3 are ECP, lanes 5-7 are EDN.

# Protein purification

After purification, the fractions were loaded onto an SDS-PAGE gel to investigate the content (for protocol, see Experiments). As seen in Figure 4, purified ECP could be seen at 26-27 kDa. The ECP protein has a molecular weight of 17.4 kDa (with His-tag and TEV-site), the slightly larger size on the gel can be explained by its high positive charge. While EDN (Lane 5-7) showed a similar size as ECP, the amount was less, and the sample was more unpure than ECP.

# Protein folding verification

Based on previous results from the SDS-PAGE the folding of ECP was decided to be investigated. That was done by using nano Differential Scanning Fluorimetry (nanoDSF). Using this method a thermal unfolding curve was generated. ECP has tyrosine and tryptophan residues, which has intrinsic fluorescence. The environments of those residues will alter as they become more exposed to the solvent, which will change the fluorescence intensity. The relation between the change in fluorescence intensity and temperature change can be measured using a melting temperature (Tm). Tm indicates that half of the proteins in the sample are unfolded, which corresponds to the midpoint transition between folded and unfolded states.

Due to the 4 disulfide bonds in ECP, dithiothreitol (DTT) was added (10 mM) as a control to find out the state of these bonds and a nanoDSF experiment was conducted (between 20-100 °C). As seen in Figure 5 the first conformational change (tighter conformation as seen in Figure 5, negative derivate) occurred at 49 °C (10 mM DTT, 5 mM borate, pH 7.5) and 51 °C (5 mM borate, pH 7.5), which is quite far from the literature value at 39.4 °C (50 mM HEPES, pH 7.5), possibly due to the different buffers used. The thermal unfolding of the protein can be seen at 65 °C in the control sample with DTT and 69.5 °C in the sample without any DTT. This is in accordance with the literature, which showed 68.6 °C (50 mM HEPES, pH 7.5) [[4]](#[4]). These results indicate that the disulfide bonds and the fold of the protein were at least partially correct. These results also show that the construct design, expression, and purification of potential biomarkers have a good basis for further experiments during phase II. With slight improvements, which are discussed in the Engineering part of the wiki, we believe that human disulfide bridge-rich proteins can be produced at good yields.

[![”Alttext”](https://static.igem.org/mediawiki/2020/8/8e/T--Linkoping--lab_results_ecp_ecpdtt.png)](https://static.igem.org/mediawiki/2020/8/8e/T--Linkoping--lab_results_ecp_ecpdtt.png)

**Figure 5.** Thermal unfolding curve of ECP with and without DTT (10 mM). Left y-axis depicts fluorescence intensity at 350 nm (exposed Trp) divided by fluorescence intensity at 330 nm (buried Trp). Right y-axis depicts the change in the left y-axis ratio. The experiment was done in triplicate, where the average of the replicates are shown.

## References

[1]. Sigurs N, Bjarnason R, Sigurbergsson F, Kjellman B. Respiratory syncytial virus bronchiolitis in infancy is an important risk factor for asthma and allergy at age 7. Am J Respir Crit Care Med. 2000;161(5):1501-7. doi: 10.1164/ajrccm.161.5.9906076.

[2]. Wu P, Hartert TV. Evidence for a causal relationship between respiratory syncytial virus infection and asthma. Expert Rev Anti Infect Ther. 2011;9(9):731-745. doi:10.1586/eri.11.92

[3]. Altman MC, Beigelman A, Ciaccio C, Gern JE, Heymann PW, Jackson DJ, Kennedy JL, Kloepfer K, Lemanske RF Jr, McWilliams LM, Muehling L, Nance C, Peebles RS Jr. Evolving concepts in how viruses impact asthma: A Work Group Report of the Microbes in Allergy Committee of the American Academy of Allergy, Asthma & Immunology. J Allergy Clin Immunol. 2020 May;145(5):1332-1344. doi: 10.1016/j.jaci.2019.12.904.

[4]. Nikolovski Z, Buzón V, Ribó M, Moussaoui M, Vilanova M, Cuchillo, CM, Nogués MV. Thermal unfolding of eosinophil cationic protein/ribonuclease 3: a nonreversible process. Protein science, 2006;15(12):2816-2827

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