Team:ASIJ Tokyo/Contribution
Protein-Coding Sequence Modification
For our synthesis of biomarkers experiment, the protein-coding sequences found from NCBI, were unfortunately not compatible with the BioBrick RFC[10] and iGEM Type IIS RFC[1000] assembly methods.
Figure 1: Mammaglobin B Protein Coding Sequence
When the raw sequence from NCBI is uploaded to iGEM Registry, it is evident that the sequence is not compatible with any of the Biobrick or iGEM standards.
Therefore, we decided to troubleshoot this by removing the illegal restriction sites by inducing silent mutations via ApE plasmid editing software. This would cause the produced protein to remain the same but allow for the sequence to be compatible with iGEM assembly standards.
Figure 2: ApE Plasmid Editor
The grey areas in the figure indicate illegal restriction sites and we resolved this by coding for the same amino acid via a silent mutation.
We removed all the illegal restriction sites for the protein-coding sequences of Mucin, Mammaglobin B and HER-2(found from NCBI) to be compatible with the BioBrick RFC[10] and iGEM Type IIS RFC[1000] assembly methods. We hope that for future teams who plan on assembling or synthesizing Mucin, Mammaglobin B, or Her-2 in a prokaryotic system, this contribution will help make assembly more convenient and universal!
Please see our parts page for more information!
2021 iGEM Database
Inspired by the After iGEM Phoenix Project, we decided to create the 2021 iGEM Database to keep track of teams and the various projects they were working on for the current competition season.
In iGEM, while most collaboration opportunities are done through social media or email, ASIJ_Tokyo noticed the lack of an organized database that listed team projects and topics, which often resulted in missed opportunities by the end of the season. For example, without the database, we would not have realized that Korea_HS, a HS team, was actually working on the same area of interest as our team.
2021 iGEM Database Overall View
2021 iGEM Database Team View
The aim of the database was to provide a space for teams to share their project ideas and also for other teams to come find collaborations if needed. The inaugural launch of the 2021 iGEM database was fairly successful, and we were able to accumulate information regarding more than 50 teams.
Our database, using the Airtable format from the After iGEM Phoenix Project, sorts teams by region and has a search bar to allow for teams to select specific keywords. We even included a section where teams could advertise their surveys and social media accounts for other teams to view.
We realize that the 2021 iGEM Database is not complete, as we were not able to get the information of all the teams participating in the jamboree. Nevertheless, we hope that this provides a framework for future teams who would like to engage in similar opportunities.
We thank all the teams who filled out our automated database entry survey and After iGEM for the inspiration behind the project.
Literature Review
When we were researching aptamer sequences for our project, we often encountered difficulties finding the aptamer sequence and relevant information regarding the aptamer.
We have included the following information about the aptamers we found and their effectiveness(from literature) for future teams. We believe that this information will be useful for teams who want to work with aptamers in the future and do not have the resources to perform the SELEX procedure(making aptamers).
MUCIN
MUC1 S1.1:
TAAGAACAGGGCGTCGTGTTACGAG
MUC1 S1.2:
GTGGCTTACTGCGAGGACGGGCCCA
MUC1 S1.3
GCAGTTGATCCTTTGGATACCCTGG
MUC1 S1.4
AACCCTATCCACTTTTCGGCTCGGG
MUC1 S1.5
CGAATGGGCCCGTCCTCGCTGTAAG
MUC1 S1.6
GCAACAGGGTATCCAAAGGATCAAA
Literature review has stated that there is quite a lot of research regarding the MUC1 S1.3 aptamer such as its secondary structure and it is used in many studies regarding aptamers (Ferreira et al., 2006).
HER-2
HeA2_1:
ATTAAGAACCATCACTCTTCCAAATGGATATACGACTGGG
HeA2_3:
TCTAAAAGGATTCTTCCCAAGGGGATCCAATTCAAACAGC
HeA2_4:
CAAACAGAAAATTGCCTATTTAGCTGTCTCTGAGATTCGA
HeA2_5:
AGTCAGTAGTTCGGCTTATGATTTTATACATCTTACCCCT
HeA2_6:
TCGCATCAGTGTTTTAATAGTCAACCGGTAAATGTTTCCC
Two aptamers, HeA2_1 and HeA2_3 have been shown to bind to HER2 with high affinities from our literature review. It is not known if the HeA2_2 aptamer exists or is effective for experiments (Gijs et al., 2016).
Mammaglobin-B
Aptamer 1:
CATGCTTACCTATAGTGAACCCAACGTCGAACTGAATCCCGTGTCCTTTGAGAACTGACTCATAC
Aptamer 2:
CATGCTTACCTATAGTGAACCCGGGACAGAACGTGCGCTTTGAGCTTTGAGAACTGACTCATAC
We report that aptamer-based studies regarding Mammaglobin B are largely nonexistent. We hypothesize that this is largely due to the long sequence of the mammaglobin B. Based on advice from Dr Kazunori Ikebukuro, aptamers with more than 40 nucleotides often fold several different structures and this causes low reproducibility of binding assay results. We recommend that teams working with Mammaglobin-B either use SELEX to screen for aptamers or attempt to find truncated mutants of the two aptamers listed above (Hassan, 2017).
Aptamer-Biomarker Binding Verification
We verified the binding between aptamer and biomarker for the following aptamer sequences and also derived their Kd values.
This verification was conducted by performing an ELISA assay and Kd values were calculated through using a non-linear regression with equation y = (x × Bmax) / (x + Kd) to model the absorbance vs concentration data, where Bmax is the maximal binding and Kd is the dissociation constant.
However, we were unable to receive our parts in time to perform the ELISA assay, but have included methods to derive the value of Kd and sample Kd values that were found in literature for teams who would like to do similar experiments in the future.
Additionally, from our modeling experiments, we also helped verify the interaction between aptamer and biomarker despite not being able to prove this experimentally.
Derivation Method
In any type of binding experiment, the binding affinity can be modeled via a nonlinear regression with equation y = (x × Bmax) / (x + Kd) where Y is the net fluorescence intensity at each concentration, X is the concentration, Bmax is the net fluorescence intensity at saturation, and Kd is the dissociation constant which helps measure the binding affinity (Cho et al., 2013).
Using this equation, we can help screen and monitor for the efficiency of aptamers and their ability in detection. Therefore, by employing an ELISA assay or fluorescence intensity assay which constructs a graph of concentration vs absorbance, we can easily measure and derive the value of Kd.
Our math modeling results via the Hill function indicate that the value of Kd is inversely proportional to the binding affinity. Therefore, aptamers with lower Kd values are likely to bind strongly with the biomarker and will serve as better detection probes.
Aptamer and Kd Values
| Aptamer Name | Sequence | Kd value(Literature) |
| MUC1 S1.1 | TAAGAACAGGGCGTCGTGTTACGAG | 33.38 nM |
| MUC1 S1.2 | GTGGCTTACTGCGAGGACGGGCCCA | 0.221 nM |
| MUC1 S1.3 | GCAGTTGATCCTTTGGATACCCTGG | 0.135 nM |
| MUC1 S1.4 | AACCCTATCCACTTTTCGGCTCGGG | 25.00 nM |
| MUC1 S1.5 | CGAATGGGCCCGTCCTCGCTGTAAG | 25.1 nM |
| MUC1 S1.6 | GCAACAGGGTATCCAAAGGATCAAA | 21.00 nM |
| HeA2_1 | ATTAAGAACCATCACTCTTCCAAATGGATATACGACTGGG | 28.9 nM |
| HeA2_3 | TCTAAAAGGATTCTTCCCAAGGGGATCCAATTCAAACAGC | 6.2 nM |
| HeA2_4 | CAAACAGAAAATTGCCTATTTAGCTGTCTCTGAGATTCGA | Not reported in literature |
| HeA2_5 | AGTCAGTAGTTCGGCTTATGATTTTATACATCTTACCCCT | Not reported in literature |
| HeA2_6 | TCGCATCAGTGTTTTAATAGTCAACCGGTAAATGTTTCCC | Not reported in literature |
| MAMB1 | CATGCTTACCTATAGTGAACCCAACGTCGAACTGAATCCCGT GTCCTTTGAGAACTGACTCATAC |
13.0 nM |
| MAMA2 | CATGCTTACCTATAGTGAACCCGGGACAGAACGTGCGCTTTG AGCTTTGAGAACTGACTCATAC |
3.0 nM |