iGEM 2021 — Team Rio_UFRJ_Brazil

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# Team:Rio UFRJ Brazil/Notebook

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Notebook

* [A long journey](#first)
* [2020](#second)
* [2021](#third)

# A long journey

---

Osiris Rio UFRJ team was founded in 2017 with the goal of being the first Synthetic Biology team from the State of Rio de Janeiro, Brazil to participate in the iGEM competition. Since then we had many challenges to make this dream come true: assembling a team, finding a Principal Investigator to support us and finally accomplishing financial aid to get us to the competition, which for a long time was the main bottleneck that prevented us from participating in iGEM before.

Considering we were trying to participate in the competition for such a long time, we have accomplished a few things even before the year of the competition. From the achievement of the funding to participate in the competition until the final weeks before the wiki freeze, we have endured a long journey, with highs and lows, but overall very knowledgeable and fun! Check out below our main milestones!

![](https://static.igem.org/mediawiki/2021/8/87/T--Rio_UFRJ_Brazil--notebook-foto.png)

# 2020

---

### Finally received the investment from the government!

* In 2019 we received governmental aid through a Parliamentary Amendment granted by the Brazilian political party Novo to fund our diagnostic project aimed at arboviral infections detection. However we only received it in 2020, but not in time to participate in this year’s edition.

### Idea brainstorm

* We had many ideas since the creation of the team, all with the goal to mitigate the problems associated with arboviral infections misdiagnosis. But only in 2020 we established the present concept of how we would address this problem: using multi-epitope proteins.

### Other achievements

* Initial conversation with professors about protein and its expression system, immunology and biosensors

* Multi-epitope protein initial design steps and modeling

* Multi-epitope synthesis by GenScript Biotech

* DME-BR purified by our partner

# 2021

---

### January

* Expression and solubility test of DME-C (DME-C was fortunately on soluble fraction!)

### February

* Due to an increase in the pandemic cases, access to the laboratory was reduced and we had to interrupt the experiments.

### March

* DME-C expression in higher volume.

### April

* iGEM Registration Fee payment!

### May

* DME-C Histrap chromatography

* We finally purified the protein! However, we still needed to refine the purification.

* First meeting with our iGEM mentor!

* We are participating in the iGEM mentorship program and finally, in May we had our first meeting with Jorge Luis Hernandez Ayal, our mentor!

* Starting to write the Safety Forms

* Initial interviews with physicians

### June

* Working on the Promo Video!

* Issues with the protein concentration determination method

* We had tried using different methods (Bradford, Lowry) but it took us some time to realize that the imidazole used in the purification method was interfering with our measurements.

* Talking with the Calgary team!

* First talk with the Queens team!

* Building the forms to evaluate the arboviral infection diagnostics problem among other physicians.

* Talk with a specialist from a Diagnostic Clinic

### July

* Final Promo Video Editing!

* Started to build our business model

* First talk with the Rochester team!

### August

* Participation in the Brazilian Symposium on Synthetic Biology!
* Start working on the wiki!
* The Queens team helped us to understand our docking results.
* Diafiltration of DME-C to remove Imidazole.
* Our team was selected for the iGEM Impact Grant!
* With the funding we were able to pay the Giant Jamboree fee

### September

* Western blots and ELISA assays anti-Dengue to check our protein’s ability to recognize anti-dengue antibodies

* Participation in iGEM LATAM fest!

### October

* Western blots and ELISA assays anti-Zika to evaluate cross reaction.

  + Wiki final edition

+ Final discussions about the partnership with the Rochester team

* ###### Sponsers & Partnerships

* [![](https://static.igem.org/mediawiki/2021/2/24/T--Rio_UFRJ_Brazil--iibqm.png)](http://ibqm.bioqmed.ufrj.br/)
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###### Contact

osirisrioufrj@gmail.com
Avenida Carlos Chagas Filho, 373, Bloco D, Subsolo, Sala SS-11
Cidade Universitária, Rio de Janeiro -
RJ, 21941-902

© Osiris

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:Rio UFRJ Brazil/Engineering

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  + [Proof of concept](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Proof_Of_Concept)
* Labwork
  + [Safety](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Safety)
  + [Experiments](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Experiments)
  + [Notebook](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Notebook)
  + [Results](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Results)
* Human Practices
  + [Human Practices](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Human_Practices)
  + [Entrepreneurship](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Entrepreneurship)
* Team
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Engineering

Success

* [Overview](#first)
* [DBTL Cycle 1 - Epitope selection](#second)
* [DBTL Cycle 2 - Protein Design](#third)
* [Wet lab Implementation](#fourth)
* [References](#fifth)

# Overview

In order to build Ammit, we used the Design, Build, Test, and Learn (DBTL) cycle for both the selection of epitopes and their combination. The idea is to use a rational and systematic approach to develop more specific antigens for serodiagnostics. Although the DBTL cycle is commonly used for biological circuits design and metabolic engineering, here we show its potential application for protein engineering. As a proof of concept, we decided to test our methodology for designing a multi-epitope protein for the detection of antibodies present during Dengue 2 (DENV2) infections, since this is the main circulating dengue serotype in our country Brazil. So we can separate our strategy in two different DBTL cycles:

Epitope Selection
Protein Design

When choosing epitopes for serodiagnostics, some considerations are needed:

Unlike multi-epitope proteins used for vaccine development, which uses both B-cell epitopes and T-cell epitopes in its constitution, serodiagnostic multi-epitope protein uses only B-cell epitopes.
Those epitopes must be immunodominant;

Usually, linear epitopes are preferred over conformational ones.

When a chimeric protein is designed, mostly we cannot guarantee that each domain structure will remain the same as it is in the native protein. Besides, linear epitopes are easier to predict and screen than conformational ones. Therefore, the first step of our project was to select which linear B-cell epitopes to use for recognizing anti-dengue antibodies in a specific manner. After epitope selection, the next challenge was to assemble them and check the final protein physico-chemical properties and surface exposure for interacting with antibodies. This way, each iteration throughout the Epitope selection and Protein design cycles should theoretically provide more specific antigens for serodiagnostics.

# DBTL Cycle 1 - Epitope selection

![]( https://static.igem.org/mediawiki/2021/2/20/T--Rio_UFRJ_Brazil--Engineering_imagem1.png)

## First iteration: Design & Build - Literature review The studies regarding the use of Multi-Epitope proteins for serologic diagnostics usually screen experimentally synthetic peptide libraries using infected patient samples in order to evaluate the most promising epitopes in terms of sensibility and specificity [1]. Another option, especially when the pathogen is an emerging one such as the SARS-CoV-2 is to predict B-cell epitopes using bioinformatic tools such as Discotope. Since there are already characterized Dengue epitopes, we have decided to start with known epitopes described in previous studies. In an initial literature research, we found a study that had already designed a multi-epitope protein for capturing anti-Dengue virus (DENV) IgG by joining 15 linear B-cell epitopes from different DENV proteins and serotypes, as shown in the table below [2,3]. They have chosen epitopes from Envelope protein, since it is the most immunogenic of all DENV proteins and has well characterized immunodominant epitopes. Also, epitopes from the Non structural proteins 1 and 3 (NS1 and NS3 respectively) were used since they can elicit strong antibody responses, especially during secondary infections [2,3]. It is important to note that secondary infections (that is infection of individuals seropositive for DENV due to prior exposure) are really important to be aware of since they are usually associated with complications such as dengue hemorrhagic fever and dengue shock syndrome. However, the authors have used sequences from Asian strains and did not evaluate the possibility of cross-reaction against other flaviviruses such as the Zika virus (ZIKV). Still, since they have used this protein for the development of a lateral flow assay that presented good results, we decided to start our initial epitope pools with the ones they used previously. Table 1 - Epitopes used by Anandarao [2,3] | Epitope | Protein | Sequence | | --- | --- | --- | | Ep1 | Envelope | ETLVTFKNPHAKKQDVVVLGS | | Ep2 | Envelope | NLLFTG | | Ep3 | Envelope | PFGDSYIIIGVE | | Ep4 | Envelope | QLKLNWFKKGSS | | Ep5 | Envelope | TAWDFGSLGGVFTSIG | | Ep6 | Envelope | VIITWIG | | Ep7 | Envelope | STSLSV | | Ep8 | Envelope | VTLYLGA | | Ep9 | NS1 | EHKYSWKS | | Ep10 | NS1 | DSGCVVSWKNKELKC | | Ep11 | NS1 | KFQPESPARLASAILNA | | Ep12 | NS1 | LKYSWKTWGKAK | | Ep13 | NS1 | FLIDGPDTSECPNERRA | | Ep14 | NS1 | WYGMEIRPLSEKEENMV | | Ep15 | NS3 | ILEENMEVEIWTREGEKKKL | Test - Evaluating criteria for cross-reaction minimizationLinear alignments The first criteria we used to evaluate the epitopes was conservation among the DENV Brazilian strains. We compared the epitope sequences to 13 Brazilian DENV 2 strains that circulated in the country between 2008 and 2019 (GenBank: AGK36289.1; AGK36290.1; AGK36293.1; AGK36296.1; AGK36295.1; AGK36299.1; QGW05368.1; QGW05372.1; AGK36292.1; AGN94882.1; QGJ02362.1; ACY70783.1; QGW05381.1). Using the multiple alignment software tool from Clustal server (https://www.ebi.ac.uk/Tools/msa/clustalo/), we identified some differential residues in the epitope sequences that were conserved within Brazilian strain sequences. These residues are highlighted in blue in the sequence below. Figure 1 - Differential residues between AnanadaRao sequence and Brazilian Strains. ![](https://static.igem.org/mediawiki/2021/1/1c/T--Rio_UFRJ_Brazil--Engineering_imgem2_c%C3%B3digo.png) Also, you can download the alignment file in the button below. [![](https://static.igem.org/mediawiki/2021/9/92/T--Rio_UFRJ_Brazil--Engineering-alignment.png)](https://static.igem.org/mediawiki/2021/f/f7/T--Rio_UFRJ_Brazil--alignment_denvbr-ep.txt) As the aim of the present work was to express a multi-epitope protein with greater specificity for recognizing antibodies during infection by Brazilian DENV2 strains, epitopes 2, 4, 5, 6, 7, 8 and 12 from table 1 were not used ​​for the construction of Ammit because they do not present differential residues. Epitope 1, despite not having differential residues either, was kept as it was pointed out as a potential immunodominant diagnostic peptide specific for Dengue in a previous bioinformatics study [5]. We decided to evaluate if changing those differential residues would cause any impact on antibody recognition. Therefore, we decided to build two multi-epitope Ammit proteins: DME-C, which will use the epitopes we select during the first DBTL cycle; and DME-BR, which will be constituted by the same epitopes, but with some few mutations in order to make the sequence closer to Brazilian strains. After analysing the differential residues, we decided to change only those in which the mutation would be for another residue with similar polarity. Changes between a neutral polar residue and a charged one were not made. Check below the changes: ![](https://static.igem.org/mediawiki/2021/4/4b/T--Rio_UFRJ_Brazil--design-alinha.png) Conservancy analysis To verify epitopes that could cross-react with other arboviruses, we analysed the epitopes selected in the previous step in the Epitope Conservancy Analysis tool, available in the Immune Epitope Database (http://tools.iedb.org/conservancy /) (BUI et al., 2007). For this analysis, the epitopes were compared with sequences from 41 yellow fever (YF) and Zika virus strains circulating in Brazil between 2008 and 2019. Check in the buttons below to check the Zika and YF strains used. [![](https://static.igem.org/mediawiki/2021/2/2e/T--Rio_UFRJ_Brazil--Engineering_zikayf.png)](https://static.igem.org/mediawiki/2021/3/37/T--Rio_UFRJ_Brazil--Zika_YFV_strains_Brazil_2008-2019.txt) The software generates a table that provides information about the maximum and minimum identity found among the alignments and also the percentage of sequences that had identity higher than a preset identity threshold [6]. According to some studies, 60% [7] to 80% [8] of identity can be found in highly conserved flavivirus regions, which might correlate with cross-reactivity among this virus family. In order to be more restrictive, we decided to use the 60% as a threshold. Table 2 - Conservancy analysis with Zika and Yellow fever sequences | Epitope | Percent of protein sequence matches at identity <= 60% | Minimum identity | Maximum identity | | --- | --- | --- | --- | | Ep1 | 78.05 | 42.86 | 71.43 | | Ep3 | 100 | 75 | 83.33 | | Ep9 | 0.00 | 37.5 | 50 | | Ep10 | 0.00 | 46.67 | 46.67 | | Ep11 | 0.00 | 41.18 | 47.06 | | Ep13 | 0.00 | 47.06 | 52.94 | | Ep14 | 78.05 | 52.94 | 76.47 | | Ep15 | 0.00 | 35.00 | 45.00 | Structural alignments Another analysis we performed were structural alignments in the software Pymol between the epitope structure in the Dengue native protein (PDB code: Envelope, 1OKE; NS1, 4O6B; NS3,2VBC) e and their corresponding structure in Zika virus (PDB code: Envelope, 5JHM; NS1, 5GS6, NS3, 6ADW). Unfortunately, epitope 9 did not have its structure elucidated, so we could not run this analysis for this sequence. We measured the Root-mean-square deviation of atomic positions (RMSD) to evaluate the structural similarities between those regions. The closer the RMSD is to zero, the more similar are the structures (Figure 3). ![](https://static.igem.org/mediawiki/2021/0/01/T--Rio_UFRJ_Brazil--Eng-struc-alig.png) Researchers feedback Besides the analysis we have talked to researchers in order to improve our design choices: We confirmed the importance of using B-cell epitopes for serodiagnostics after talking to the researcher Adriana Vallochi, from Fiocruz, who has expertise with immunology. As mentioned in the project description, the B-cells are the ones associated with the humoral immune response, producing antibodies when stimulated by an antigenic region: a b-cell epitope. Those epitopes are also recognized by antibodies, and that is their importance for serodiagnostic [4]. Also, we talked to Professor Marcius Almeida, who recommended us to also evaluate if the epitopes we analysed had cysteine residues forming disulfide bonds. According to him, those bonds might help stabilize the protein structures, which might impact their proper folding in chimeric proteins, so we should be careful. After analyzing the epitope structures in Pymol, we have identified that epitopes 10 and 13 had disulfide bonds in their structure. ![](https://static.igem.org/mediawiki/2021/3/3c/T--Rio_UFRJ_Brazil--Eng-dissulfide.png) Learn - Discarding problematic epitopes Based on those analyses, we decided to eliminate the epitopes that both presented bad conservancy results and a low RMSD: epitopes 3 and 14. According to literature, RMSD values below 2.0 Angstrom already indicate that the proteins have very similar structure, so we thought combining both sequence and structural identity instead of only using RMDS values or identity values would be a more robust approach to filter epitopes. Regarding the epitopes containing disulfide bonds, we decided to keep them only because we are using linear epitopes which can be recognized even when denatured. However, knowing which epitopes have this bond might be important in case problems are identified during the final protein expression and folding. Second iteration: Design & Build - Finding a new epitope After another round of literature research, we found a study that identified potential epitopes to use for differentiating dengue from zika. After using a cohort of human sera, the authors highlighted the potential of the envelope protein epitope YENLKYTVIITVHTGDQH, mentioned as peptide 17 in the study, for detecting Dengue specific antibodies [1]. After a multiple alignment with the 13 DENV2 strains used in previous alignments, some mutations were made in this peptide so that its sequence would be close to the sequence present and conserved in Brazilian strains, obtaining the peptide PENLEYTIVITPHSGEEH. Test - Linear and structural alignments The conservancy analysis comparing with the previous Zika and Yellow Fever of this epitope showed that it had a maximum identity of 44%, highlighting its potential. Table 3 - Conservancy analysis for peptide 17 | Epitope | Percent of protein sequence matches at identity <= 60% | Minimum identity | Maximum identity | | --- | --- | --- | --- | | Ep1 | 0.00 | 38.89 | 44.44 | Regarding structural alignments, it showed an RMSD value of 1.744, higher than all the other epitopes tested. ![]( https://static.igem.org/mediawiki/2021/5/59/T--Rio_UFRJ_Brazil--Eng-pept17-alig.png) Learn - Incorporating a new epitope Considering the promising linear and structural alignments, we decided to incorporate this epitope in our design.

# DBTL Cycle 2 - Protein Design

![]( https://static.igem.org/mediawiki/2021/b/be/T--Rio_UFRJ_Brazil--dbtl2.png)

## First iteration: Design - Epitope and linker assembly Once we chose the epitopes, we had to decide the order of epitopes and the linkers we would use to join them. Initially, we considered the linkers below, commonly used for vaccine multi-epitope design. AAY GPGPG GGG Linker selection is important, due to its role in ensuring protein stability and facilitating epitope presentation [9]. Although glycine linkers are commonly used flexible linkers, less flexible linkers such as AAY and GPGPG have their relevance, since they can help preventing the formation of junctional epitopes, created by the juxtaposition of epitopes, which might cause the redirection of the immune response (and also antibody recognition) to undesired regions in the sequence [9]. For deciding which linker to choose, we again consulted Professor Marcius Almdeia, who advised us to use the proline linker, since they can also help stabilize the protein structure. Regarding the epitopes order in the assembly, we decided to keep the original order used by Anandarao and collaborators, keeping only the selected epitopes and replacing the third epitope by peptide 17. At the end, we designed two Ammit multi-epitope proteins: DME-C, with the chosen epitopes with their unchanged sequences, and DME-BR, which contains some mutations highlighted in order to make the sequence more identical to the conserved Dengue Brazilian strain proteome. ![]( https://static.igem.org/mediawiki/2021/d/d1/T--Rio_UFRJ_Brazil--seqdmes.png) Build - Structure model In order to evaluate the protein design, we used the PSIPRED server DMPfold tool to predict the 3D structure of the proteins (Check the modeling page for more details). [![]( https://static.igem.org/mediawiki/2021/f/f9/T--Rio_UFRJ_Brazil--check-model.png)](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Model) Test - Structure validation and antibody simulations Using the protein primary structure, we build hydrophobicity plots in order to evaluate the presence of exposed regions in the protein, which would thus be able to interact with the antibodies. Fortunately both DME-C and DME-BR presented exposed regions. Regarding the 3D structure validation, we have generated Ramachandran plots that indicated the presence of a high percentage of residues in preferred regions, which shows the quality of our prediction. Finally, we used the 3D structures we have generated in order to make antigen-docking simulations. We were not considering performing docking since we only used linear B-cell epitopes. However, after the Queens team suggestions, we thought it would be a good idea to check if our protein could bind to anti-Zika antibodies. So we used the software ClusPro and identified epitopes that might be able to cross react with anti-Zika antibodies (Check modeling for more details). [![]( https://static.igem.org/mediawiki/2021/f/f9/T--Rio_UFRJ_Brazil--check-model.png)](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Model) Design - Epitope and linker assembly Although both DME-C and DME-BR presented exposed surface regions, there is a chance of cross-reaction as the docking simulations presented. However, when we talked to the Queens team who suggested us to use ClusPro, we had already synthesized the plasmid containing the coding sequence for DME-C and DME-BR and unfortunately we could not implement modifications to resolve this matter. Nevertheless, since our final experiments showed the occurrence of cross reaction, now we can have an idea of the potential epitopes that might be associated with this event and new iterations of both DBTL cycles might improve the specificity of the antigens.

# Wet lab implementation

Once we optimized Ammit design for minimization of cross-reaction using the DBTL cycle, we defined cloning strategy (Check Design for more information), and proceeded to the experiments needed its expression in Escherichia coli and finally the immunoassays to validate Ammit’s capacity of recognizing antibodies in a more specific way.
Check out Experiments and Results for more detail.

[![](https://static.igem.org/mediawiki/2021/0/05/T--Rio_UFRJ_Brazil--check-results.png)](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Results)

# References

1- Amrun SN, Yee WX, Abu Bakar F, Lee B, Kam YW, Lum FM, et al. Novel differential linear b‐cell epitopes to identify Zika and dengue virus infections in patients. Clinical & Translational Immunology. 2019;8(7).
2- AnandaRao R, Swaminathan S, Fernando S, Jana AM, Khanna N. A custom-designed recombinant multiepitope protein as a dengue diagnostic reagent. Protein Expression and Purification. 2005;41(1):136–47.
3- AnandaRao R, Swaminathan S, Fernando S, Jana AM, Khanna N. Recombinant multiepitope protein for early detection of dengue infections. Clinical and Vaccine Immunology. 2006;13(1):59–67.
4- Sela-Culang I, Kunik V, Ofran Y. The structural basis of antibody-antigen recognition. Frontiers in Immunology. 2013;4.
5- Lee AJ, Bhattacharya R, Scheuermann RH, Pickett BE. Identification of diagnostic peptide regions that distinguish zika virus from related mosquito-borne flaviviruses. PLOS ONE. 2017;12(5).
6- Bui H-H, Sidney J, Li W, Fusseder N, Sette A. Development of an epitope conservancy analysis tool to facilitate the design of epitope-based diagnostics and vaccines. BMC Bioinformatics. 2007;8(1).
7- Salvador EA, Pires de Souza GA, Cotta Malaquias LC, Wang T, Leomil Coelho LF. Identification of relevant regions on structural and nonstructural proteins of zika virus for vaccine and diagnostic test development: An in silico approach. New Microbes and New Infections. 2019;29:100506.
8- Xu X, Vaughan K, Weiskopf D, Grifoni A, Diamond MS, Sette A, et al. Identifying candidate targets of immune responses in zika virus based on homology to epitopes in other flavivirus species. PLoS Currents. 2016.
9- X. Chen, J.L. Zaro, W-C, Shen, Fusion protein linkers: Property, design and functionality. Adv Drug Deliv Ver, 65 (10) (2013) 1357-1369.

* ###### Sponsers & Partnerships

* [![](https://static.igem.org/mediawiki/2021/2/24/T--Rio_UFRJ_Brazil--iibqm.png)](http://ibqm.bioqmed.ufrj.br/)
  [![](https://static.igem.org/mediawiki/2021/7/7c/T--Rio_UFRJ_Brazil--iiq.png)](https://www.iq.ufrj.br/)

* [![](https://static.igem.org/mediawiki/2021/3/3c/T--Rio_UFRJ_Brazil--logo-novo-paulo-ganime.png)](https://pauloganime.com.br/)
  [![](https://static.igem.org/mediawiki/2021/4/47/T--Rio_UFRJ_Brazil--test1.png)](https://cenabio.ccs.ufrj.br/index.php/plataforma/historico-pab)
  [![](https://static.igem.org/mediawiki/2021/6/6d/T--Rio_UFRJ_Brazil--logo_faperj.png)](http://www.faperj.br/)

###### Contact

osirisrioufrj@gmail.com
Avenida Carlos Chagas Filho, 373, Bloco D, Subsolo, Sala SS-11
Cidade Universitária, Rio de Janeiro -
RJ, 21941-902

© Osiris

[![](https://static.igem.org/mediawiki/2021/8/8b/HQ_page_logo.jpg)](https://2021.igem.org)

# Team:Rio UFRJ Brazil/Experiments

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###### Contact

osirisrioufrj@gmail.com
Avenida Carlos Chagas Filho, 373, Bloco D, Subsolo, Sala SS-11
Cidade Universitária, Rio de Janeiro -
RJ, 21941-902

© Osiris

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# Team:Rio UFRJ Brazil/Results

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Results

* [Overview](#first)
* [Production of multi-epitope proteins](#second)
* [Expression in larger volume](#third)
* [Immunoassays](#fourth)
* [Final conclusions](#fifth)
* [References](#sixth)

# Overview

With the coding sequences designed (for more details see the [Design](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Design) and [Engineering success](https://2021.igem.org/Team%3ARio_UFRJ_Brazil/Engineering) pages), we requested the synthesis of genes cloned in the pET-28(a) vector by Genscript. With the plasmids in hand, we need to define a workflow for the production, purification and testing of this protein.

---

### Production

For production, it was necessary to transform the inserts in our chassis, the E. coli Bl21(DE3), to carry out the cultivation, induction and expression of the proteins. Unfortunately, due to the pandemic and restrictions on access to the laboratory during late 2020 and early 2021, we were unable to perform some of these experiments and chose to hire the service of Protein Advanced Biochemistry (PAB), a laboratory from the UFRJ specialized in recombinant protein expression, execution the transformation of coding inserts for DME-C and DME-BR in E. coli.
PAB was also responsible for the expression and purification of DME-BR, so we will not include these experiments here and we will focus on the steps of cultivation, induction, expression and purification of DME-C.

#### Purification

As described on the design page, we have chosen to include a 6xHis-tag at the carboxy-terminal end of both DME-C and DME-BR to facilitate their purification process via immobilized metal affinity chromatography-IMAC. As best described in the sessions, it took additional purification steps to solve some issues we had.

#### Immunoassays

The main objective of our project is to identify anti-Dengue antibodies in a specific way, preventing or minimizing the recognition of anti-Zika antibodies. For this, we first need to verify through western blot assays whether our protein was in fact capable of recognizing anti-Dengue antibodies. Subsequently carry out anti-Zika antibody recognition assays to check for cross-reactions. And finally, mimic ELISA assays with commercial antibodies to partially assess the sensitivity of the protein we designed.

# Production of multi-epitope proteins

---

### Plasmid confirmation

Once we received the plasmids, with the help of the PAB, we cloned them in E.coli BL21(DE3) and performed a miniprep for plasmid extraction.

#### Expression and purification of DME-BR

As previously mentioned, PAB was responsible for the expression and purification of the DME-BR protein. A titer of 31.3 mg/L of DME-BR was obtained, with an estimated purity of 97.5% ± 1.4. During expression, they reported that under non-denaturing lysis conditions, the protein aggregates and therefore to solubilize it it was necessary to add dithiothreitol (DTT) during the lysis and purification steps. At the end, the protein was stored in a solution of 50 mM sodium phosphate, 250 nM NaCl, 5 mM DTT, pH 8.0.

#### Expression test and DME-C solubility test

For the expression of DME-C, E. coli BL21 (DE3) was transformed with the constructs by heat shock method, with the aid of PAB. The next steps of culture growth and protein purification were carried out by our team. Transformed positive clones were grown in liquid LB medium with kanamycin (30 μg/mL) as a selection marker, and protein expression was induced with 1mM IPTG during 4 hours. The culture conditions were adapted from the work of Anandarao and collaborators (2005 and 2006), who also expressed multi-epitope recombinant proteins in E. coli [1,2]. Aliquots of culture (1mL) were collected before and after induction, after, 2h and 4h. Cells were centrifuged, resuspended in native (50 mM sodium phosphate and 300 mM NaCl) or denaturant (50 mM sodium phosphate, 300 mM NaCl and 8M Urea) lysis buffer, and lysed by sonication. We tested these two lysis conditions to assess the need for a denaturing agent to keep the proteins soluble.
Analysis of the protein profile by 15% SDS-PAGE showed a band below 26 kDa, possibly corresponding to DME-C (16.83 kDa) in samples collected after 2 and 4 h of induction, both in the soluble fraction and in the insoluble fraction, when using buffer without urea. When the denaturing agent urea was added, the band was observed only in the soluble fraction (Figure 2). Since part of the protein was found in the soluble fraction without urea, we chose to follow the next lysis experiments in native lysis buffer without urea.

![](https://static.igem.org/mediawiki/2021/d/d8/T--Rio_UFRJ_Brazil--fig1labwork.png)

#### Small-Scale DME-C Purification

To confirm the presence of DME-C, we first performed a small scale purification using the His Microspin Purification kit (Amershan Biosciences) according to the manufacturer's instructions and analyzed the different fractions by both 15% SDS-PAGE and Western Blot anti-his. Analysis by 15% SDS-PAGE showed the presence of some contaminating proteins of higher molecular weight in the eluate, which
highlighted the need for further additional purification steps (Figure 2).

![](https://static.igem.org/mediawiki/2021/0/06/T--Rio_UFRJ_Brazil--fig2labwork.png)

#### Western blot anti-his confirmation from DME-C and DME-BR.

Regarding the western blot, different dilutions of uninduced samples, induced by 0h and 4h (lysis without urea) and of the eluates obtained in the purification were used in order to avoid saturation of the detection signal. Anti-mouse monoclonal antibody 6x-His-Tag (Invitrogen) at 1:2000 dilution was used, and as a secondary anti-mouse antibody conjugated to HRP, at 1:10,000 dilution. The analysis revealed the presence of DME-C, since the anti-His-Tag antibody was able to recognize the over-expressed band with a molecular weight close to that expected. Furthermore, it was observed that one of the bands with the highest contaminating molecular weight present in the purification eluates was also recognized in the assay. As this band was not present either in the uninduced sample or in the sample with 0h of induction, it possibly corresponds to a protein aggregate of DME-C (Figure 3).

![](https://static.igem.org/mediawiki/2021/d/d9/T--Rio_UFRJ_Brazil--result-fig3.png)

Regarding DME-BR, a western blot analysis was performed using anti-6 HIS-TAG antibody (1:2000 dilution) and developed with anti-mouse antibody conjugated to HRP as the secondary antibody (1:10,000 dilution) using ECL (Amersham)

![](https://static.igem.org/mediawiki/2021/1/12/T--Rio_UFRJ_Brazil--result-fig4.png)

# Expression in larger volume

---

### Cultivation and induction

Once the expression of DME-C was confirmed by the western blot, we expressed the protein in a larger scale (500 mL of LB medium and 30 ug/mL kanamycin), following the same conditions used for small-scale expression. To improve the lysis yield we tested the addition of Cellytic (Sigma Co), to the previously used lysis buffer (without the addition of urea) to resuspend the cell pellet equivalent to 40 ml of culture. By analyzing the 15% SDS-PAGE gel of these lysates, we verified that the protein yield was higher in the soluble fraction using Cellytic, so we chose to continue the use of this reagent.

![](https://static.igem.org/mediawiki/2021/4/4d/T--Rio_UFRJ_Brazil--result-fig5.png)

#### Purification

After lysis, the supernatant was filtered using a 45 μm filter and we proceed to perform the affinity chromatography using a His-Trap FF. The whole protocol and method used can be checked on the Experiments page. We tested 3 different elution methods, in order to evaluate changes in the chromatographic profile.

Table 1 - Parameters of Chromatography analysis

| Parameters | First run | Second run | Third run |
| Equilibration volume | 2 CV | 3 CV | 5CV |
| Sample | 30.0 mL | 35.0 mL | 35.0 mL |
 Buffer B gradient | 0 - 100 % | 0 - 100 % | 0 - 60 % || Elution volume | 6 CV | 8 CV | 6 CV |

CV - Colum Volume

At the last run, we decreased the final elution concentration because we had imidazole interference at the used absorbance wavelength. In the end, not many differences were observed among the three performed analyses and fractions enriched with DME-C proteins were obtained (Figure 6).

![](https://static.igem.org/mediawiki/2021/8/82/T--Rio_UFRJ_Brazil--fig6labwork.png)

#### Diafiltration

Diafiltration is a technique that can remove or lower the concentration of salts like imidazole from the medium by the use of ultrafiltration membranes. We used a 5Kda membrane to retain the DMEC protein and washout imidazole. We pooled the following fractions from the three different elutions from His-trap columns where we obtained the greatest visibility of the bands, as previously shown in Figures 6:
1st (F4-F7), 2nd (F3-F6), and 3rd run (F3-F6),.
After diafiltration we measured the protein concentration by absorbance at 280nm using the Diodrop instrument. The parameters used were: molecular weight (16.83 kDa) and extinction coefficient (1948 M-1 cm-1), both calculated at https://web.expasy.org/.

The absorbance of concentrated protein in diafiltration at 280nm was 0.596 which is equivalent to 0.202μg/μl.

# Immunoassays

---

### Anti-dengue assays

In order to evaluate if our proteins could recognize anti-Dengue antibodies, we performed western blot analysis with two different commercial antibodies: anti-Dengue NS1, and anti-Dengue envelope protein. Fortunately, both DME-C and DME-BR were capable of recognizing anti-Dengue E antibodies, showing that Ammit fullfiled at least its main function. However, we still needed to evaluate its cross reactivity.

![](https://static.igem.org/mediawiki/2021/a/a2/T--Rio_UFRJ_Brazil--result-fig7.png)

#### Anti-Zika assays

Considering that our project is aimed at cross-reaction mitigation, we performed a western blot with Zika virus NS1 antibodies, so we could measure if our multi-epitope proteins designed for Dengue infection diagnostics were recognizing anti-Zika antibodies. When analyzing the membrane with ZiKa antibodies, it is possible to observe a band, which should not occur, since the protein was developed in order to avoid cross-reaction.

#### Western blot anti-Zika NS1 confirmation from DME-C and DME-BR

Regarding this Western Blot, different dilutions of DME-C and DME-BR protein were used. For this purpose, the Anti Mouse monoclonal antibody was used at a 1:10,000 dilution with the Anti-Zika NS1 at a 1:1000 dilution. The analysis revealed the presence of DME-C and DME-BR, since the anti-Zika NS1 antibody was able to recognize the very apparent band with the approximate molecular weight of the anti-histag. As the experiment was performed on the same membrane that was used to perform the anti-histag Western Blot, a wash-related error may have occurred, at the time of stripping the antibodies were not completely removed and therefore when the Zika membrane was revealed the result was not what was expected.

![](https://static.igem.org/mediawiki/2021/e/ea/T--Rio_UFRJ_Brazil--fig8labwork.png)

# Final conclusions

Observing the analyses, we concluded that to minimize the detected cross-reaction, as shown in the anti-Zika NS1 Western blot, we should iterate more times over the build test and learn design cycle. However, a very valid option would also be to test with antibodies against other proteins.

# References

1. AnandaRao R, Swaminathan S, Fernando S, Jana AM, Khanna N. A custom-designed recombinant multiepitope protein as a dengue diagnostic reagent. Protein Expression and Purification. 2005;41(1):136–47.

2. AnandaRao R, Swaminathan S, Fernando S, Jana AM, Khanna N. Recombinant multiepitope protein for early detection of dengue infections. Clinical and Vaccine Immunology. 2006;13(1):59–67.

3. Molina F, Rueda A, Bosque-Sendra JM, Megias L. Determination of proteins in the presence of imidazole buffers. Journal of Pharmaceutical and Biomedical Analysis. 1996;14(3):273–80.

4. Qiagen. FAQ [Internet]. QIAGEN. [cited 2021Oct17]. Available from: https://www.qiagen.com/no/resources/faq?id=6a331f4d-8716-4adf-992c-298fdddbb0be&lang=en

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###### Contact

osirisrioufrj@gmail.com
Avenida Carlos Chagas Filho, 373, Bloco D, Subsolo, Sala SS-11
Cidade Universitária, Rio de Janeiro -
RJ, 21941-902

© Osiris