iGEM 2020 — Team GO_Paris-Saclay

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

# Team:GO Paris-Saclay/Notebook

Team

[Team members](https://2020.igem.org/Team%3AGO_Paris-Saclay/Team)
 [Attributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Attributions)
 [Collaborations](https://2020.igem.org/Team%3AGO_Paris-Saclay/Collaborations)
 [Partnership](https://2020.igem.org/Team%3AGO_Paris-Saclay/Partnership)
 [Award](https://2020.igem.org/Team%3AGO_Paris-Saclay/Award)

Project

[Description](https://2020.igem.org/Team%3AGO_Paris-Saclay/Description)
 [Design](https://2020.igem.org/Team%3AGO_Paris-Saclay/Design)
 [Engineering](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering)
 [Results](https://2020.igem.org/Team%3AGO_Paris-Saclay/Results)
 [Notebook](https://2020.igem.org/Team%3AGO_Paris-Saclay/Notebook)

Model

[Model](https://2020.igem.org/Team%3AGO_Paris-Saclay/Model)
 [Software](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software)

Lab

[Lab Experiments](https://2020.igem.org/Team%3AGO_Paris-Saclay/Experiments)
 [Parts Collection](https://2020.igem.org/Team%3AGO_Paris-Saclay/Part_Collection)
 [Contributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Contribution)
 [Safety](https://2020.igem.org/Team%3AGO_Paris-Saclay/Safety)

Human Practices

[Integrated Human Practices](https://2020.igem.org/Team%3AGO_Paris-Saclay/Human_Practices)
 [Implementation](https://2020.igem.org/Team%3AGO_Paris-Saclay/Implementation)
 [Science Education](https://2020.igem.org/Team%3AGO_Paris-Saclay/Education)

[Poster](https://2020.igem.org/Team%3AGO_Paris-Saclay/Poster)

[CAMEOS Tutorial](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course)

[Judging Form](https://igem.org/2020_Judging_Form?id=3427)

☰

Notebook

|  |  |
| --- | --- |
| **Human Practices** | **Lab book** |
| **February 28th :** - Participation of the team in Aperiscience **March 12th :** - Meeting with the German team of Marburg **June 5th :** - Choice of gene entanglement as project’s subject **July 1st :** - Interview Harris WANG, Thomasz Blazejewski and Hsing-I Ho (authors of Cameos, the software used to entangle genes) **July ? th :** Participation at the conception, realisation and shot of the video iGEM France + Switzerland  Done by Clémentine, Inès, Alice, Delphine and Mathieu **July or August ? th :** Participation at a Zoom with Santa Cruz Team  Done by Clémentine, Inès, Nathan and Mathieu **September 9th :** - Parisian Meetup  **September 16th :** - Interview Mathieu Falques  Done by Delphine, Baptiste and Florent  **- Video collab France** **September 17th :** - Interview Tristan Rossignol, Researcher at INRAE  Done by Delphine and Alice **September 18th :** - Interview Anne Lopes  Done by Mathieu, Godefroy and Florent **September 24th and October 15th :** - Interview Gaëlle Lelandais   Done by Delphine and Alice **October 3rd-4th :** - Stand presentation at “La Cité des Sciences et de l’Industrie” - Service Médiation Scientifique (= City of Sciences and the Industry - Department of Scientific Mediation) in Paris.  This museum has as target to initiate and promote all Sciences domains to the population and specially young public (< 15 years) by ludic approach.  Organisation and coordination of iGEM Paris Teams (Ionis, Sorbonne Université, Evry Paris-Saclay, Bettencourt) on this event with the Cité des Sciences et de l’Industrie.  It was done at the occasion of the Fête de la Sciences (= Sciences Party) organised by the French Ministers of Superior Education and the Research, Minister of National Education… , an event organised on 2 weeks.  Presentation of our subject, initiation of the public to Molecular Biology and Synthetic Biology ( DNA extraction ok kiwi Stand for example).  Done by Delphine, Alice, Mathieu, Godefroy, Clémentine, Baptiste and Florent | **July 6th to 10th :** - Evaluation of the output sequences generated by CAMEOS and comparison to the input genes through BLAST.  - Design of entangled gene sequences on benchling of our sequences ( entangled genes ) generated with CAMEOS ( add of RBS sequences ) and design of primers compatible with Golden Gate cloning (add BsaI Sites)  - Send designed sequences (primers and genes) for synthesis.  Done by Clémentine and Florent **July 13th to 17th :** -Amplification, miniprep, and evaluation of recipient and template plasmids pBAD-MoClo, pZE12, pZA21, and pZA31.  Done by Clémentine, Ines and Mathieu Stable YFP**July 22th :** - Dilution and evaluation of synthetic DNA from IDT of genes stable\_YFP and ilvA\_ccdB  - GG reaction to clone biobricks stable\_YFP or ilvA\_ccdB into pBAD-MoClo (GG#1)  - Transformation of E. coli DH5alpha cells with GG#1 reaction products: stable\_YFP+pBAD-MoClo and ilvA\_ccdB+pBAD-MoClo  Done by Clémentine, Ines and Mathieu **July 23th :** - Start pre-cultures from colonies obtained after GG#1 transformation  - Colony PCR from transformants GG#1 (colony\_PCR#1)  - Evaluation of amplicons by Agarose gel electrophoresis from colony\_PCR#1  Done by Clémentine, Ines and Mathieu **August 3rd :** - PCR amplification and GG cloning of pZA31 and pBAD-MoClo inserts (GG#6)  Done by Mathieu, Baptiste, Godefroy and Florent **August 5th :** - Comparison of protocols for GG cloning of knt\_GFP and stable\_YFP into pBAD-MoClo (GG#7)  - Colony PCR of GG#7 transformants (colony\_PCR#7)  - Agarose gel electrophoresis from colony\_PCR#7  Done by Mathieu, Baptiste, Godefroy and Florent **August 6th :** - Strike and store GG#7 transformants  Done by Mathieu, Baptiste, Godefroy and Florent August 7th : - Culture of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP on selective media  Done by Mathieu, Baptiste, Godefroy and Florent **August 11th :** - Colony PCR from cells transformed with GG#7 (pBAD-YFP), GG#8 (pBAD-ilvA\_ccdB), and GG#8 (pBAD-knt\_GFP)  - Miniprep of pBAD-YFP from GG#7 clone 5  Done by Baptiste, Godefroy and Florent and Ibrahim **August 12th :** - Colony PCR for clones 1 to 5 from GG#7 (pBAD-YFP), (colony\_PCR#11,5)  -Agarose gel electrophoresis of colony\_PCR#11,5 products  - Miniprep of pBAD-YFP from GG#7 clone 5  - OD measurements from overnight cultures of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP  - Preparation of cultures from strains E. coli pBAD-knt\_GFP and E. coli pBAD-YFP for microscopique fluorescence observation and Clariostar observation  - Assessing fluorescence of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells in liquid cultures  - Sample preparation and sending for DNA sequencing: pBAD-YFP from GG#7  - Fluorescence microscopy observation 1  Done by Baptiste, Godefroy and Florent and Ibrahim **August 13th :** - Antibiotic susceptibility testing of E. coli pBAD-knt\_GFP cells on spectinomycin gradient  Done by Baptiste, Godefroy and Florent and Ibrahim **August 14th :** - Evaluation of fluorescence from E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells by spectrometry (Clariostar)  - Preparation of samples for Fluorescence microscopy  - Fluorescent microscopy observation 2  Done by Baptiste, Godefroy and Florent and Ibrahim **September 2nd :** - Flow cytometry of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells - Test 1 **September 3rd :** - Flow cytometry of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells - Test 2  - Antibiotic susceptibility testing of E. coli pBAD-knt\_GFP cells on spectinomycin gradient  Done by Baptiste **September 30th :** - Comparison sequences YFP designed and YFP sequenced  Done by Mathieu and Florent **knt-GFP****July 29th :** - Dilution and evaluation of synthetic DNA from IDT of genes knt-GFP  - GG#3 (Golden Gate) and transformation with its products  Done by Florent, Ines and Mathieu **July 30th :** - Second transformation GG#3 products  - PCR#5 from GG#3 and GG#4 products  -Agarose gel electrophoresis of colony\_PCR#5 products  Done by Florent, Ines and Mathieu **August 5th :** - Comparison of protocols for GG cloning of knt\_GFP and stable\_YFP into pBAD-MoClo (GG#7)  - Colony PCR of GG#7 transformants (colony\_PCR#7)  Done by Mathieu, Baptiste, Godefroy and Florent **August 6th :** - Sub-cloning transformation GG#7  Done by Mathieu, Baptiste, Godefroy and Florent **August 7th :** - Culture of colonies of sub-cloning GG#7 on selective middle + stocking + preparation tubes for PCR#9  Done by Mathieu, Baptiste, Godefroy and Florent **August 10th :** - Colony PCR #10  - Electrophoresis PCR colony #10 from sub-clones knt-GFP from GG#7  Done by Baptiste, Godefroy and Florent and Ibrahim **August 11th :** - Electrophoresis PCR colony #10 from sous clones knt-GFP from GG#7 ( 2 )  - Observation of knt-GFP and YFP strains with UV table  - Miniprep pBAD knt\_GFP sub-clone GG#7  Done by Baptiste, Godefroy and Florent and Ibrahim **August 12th :** - OD measurements from overnight cultures of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP  - Culture by night of colonies knt-GFP and YFP for microscopique fluorescence observation and Clariostar observation + results observations  - Preparation of cultures from strains E. coli pBAD-knt\_GFP and E. coli pBAD-YFP for microscopique fluorescence observation and Clariostar observation  - Assessing fluorescence of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells in liquid cultures  - Sample preparation and sending for DNA sequencing: pBAD-YFP from GG#8  - Fluorescence microscopy observation 1  Done by Baptiste, Godefroy and Florent and Ibrahim **August 13th :** - Culture of colonies knt-GFP on selective middle  - Culture by night of colonies knt-GFP and YFP for fluorescence microscopy  - Culture of knt-GFP colonies one spectinomycin gradient  Done by Baptiste, Godefroy and Florent and Ibrahim **August 14th :** - Evaluation of fluorescence from E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells by spectrometry (Clariostar)  - Preparation of samples for Fluorescence microscopy  - Fluorescent microscopy observation 2  Done by Baptiste, Godefroy and Florent and Ibrahim **September 2nd :** - Flow cytometry of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells - Test 1  Done by Baptiste **September 3rd :** - Flow cytometry of E. coli pBAD-knt\_GFP and E. coli pBAD-YFP cells - Test 2  - Culture of YFP and knt-GFP transformants on middles with different antibiotics concentration gradients  Done by Baptiste **September 30th :** - Comparison sequences knt-GFP designed and sequenced  Done by Mathieu and Florent |

##### **Back to the top:**

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### **Follow us**

email us :  igem.parissaclay@gmail.com

|  |  |  |
| --- | --- | --- |
| ![Faculté des Sciences d'Orsay- Université Paris-Saclay-Logo](https://static.igem.org/mediawiki/2020/9/9e/T--GO_Paris-Saclay--Logo_Facult%C3%A9_des_Sciences_d%27Orsay.png) | Team GO Paris-Saclay Université Paris-Saclay Faculté des Sciences d'Orsay   Building n°400 91 405 Cedex, Orsay | ![GO Paris-Saclay logo - like Eiffel Tower with a DNA strand](https://static.igem.org/mediawiki/2020/6/6c/T--GO_Paris-Saclay--GOPS_logo_Eiffel_Tower.png) |

### Thank you very much to our generous Sponsors

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# Team:GO Paris-Saclay/Engineering

Team

[Team members](https://2020.igem.org/Team%3AGO_Paris-Saclay/Team)
 [Attributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Attributions)
 [Collaborations](https://2020.igem.org/Team%3AGO_Paris-Saclay/Collaborations)
 [Partnership](https://2020.igem.org/Team%3AGO_Paris-Saclay/Partnership)
 [Award](https://2020.igem.org/Team%3AGO_Paris-Saclay/Award)

Project

[Description](https://2020.igem.org/Team%3AGO_Paris-Saclay/Description)
 [Design](https://2020.igem.org/Team%3AGO_Paris-Saclay/Design)
 [Engineering](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering)
 [Results](https://2020.igem.org/Team%3AGO_Paris-Saclay/Results)
 [Notebook](https://2020.igem.org/Team%3AGO_Paris-Saclay/Notebook)

Model

[Model](https://2020.igem.org/Team%3AGO_Paris-Saclay/Model)
 [Software](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software)

Lab

[Lab Experiments](https://2020.igem.org/Team%3AGO_Paris-Saclay/Experiments)
 [Parts Collection](https://2020.igem.org/Team%3AGO_Paris-Saclay/Part_Collection)
 [Contributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Contribution)
 [Safety](https://2020.igem.org/Team%3AGO_Paris-Saclay/Safety)

Human Practices

[Integrated Human Practices](https://2020.igem.org/Team%3AGO_Paris-Saclay/Human_Practices)
 [Implementation](https://2020.igem.org/Team%3AGO_Paris-Saclay/Implementation)
 [Science Education](https://2020.igem.org/Team%3AGO_Paris-Saclay/Education)

[Poster](https://2020.igem.org/Team%3AGO_Paris-Saclay/Poster)

[CAMEOS Tutorial](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course)

[Judging Form](https://igem.org/2020_Judging_Form?id=3427)

☰

Engineering

# **ENGINEERING SUCCESS**

The following **cycle chart** describes our procedure for engineering new entangled sequences. To run CAMEOS we need two gene sequences to entangle as input, as well as protein Multiple Sequence Alignments (MSAs) for each of the two sequences. CAMEOS then randomly computes hundreds of sequences for this specific entanglement, which we also call entanglement variants. We select optimal variants in hope to make new [BioBricks](https://2020.igem.org/Team%3AGO_Paris-Saclay/Parts) that would be useful for the iGEM community. From the failures we encountered when entangling sequences, selecting variants, and doing lab experiments, we can improve our methodology for the next sequence entanglements.

![](https://static.igem.org/mediawiki/2020/f/f8/T--GO_Paris-Saclay--Engineering-cycle-cameos.png)

We successfully generated CAMEOS entanglements and selected optimal variants with [Pareto optimization](#pareto) for the following couples of sequences, among others:

|  |  |  |
| --- | --- | --- |
| *luc x gfp*  [[FASTA]](https://static.igem.org/mediawiki/2020/6/69/T--GO_Paris-Saclay--opt_entangled_genes_luc_gfp.txt) | *knt¹ x ccdB* [[FASTA]](https://static.igem.org/mediawiki/2020/b/b0/T--GO_Paris-Saclay--opt_entangled_genes_knt_1_ccdB.txt) | *knt² x gfp* [[FASTA]](https://static.igem.org/mediawiki/2020/c/c5/T--GO_Paris-Saclay--opt_entangled_genes_knt_2_ccdB.txt) |
| *luc x knt¹* [[FASTA]](https://static.igem.org/mediawiki/2020/1/1f/T--GO_Paris-Saclay--opt_entangled_genes_luc_knt_1.txt) | *knt² x ccdB* [[FASTA]](https://static.igem.org/mediawiki/2020/0/09/T--GO_Paris-Saclay--opt_entangled_genes_knt_2_gfp.txt) | *luc x ccdB* [[FASTA]](https://static.igem.org/mediawiki/2020/8/83/T--GO_Paris-Saclay--opt_entangled_genes_luc_ccdB.txt) |

*Yarrowia lipolytica* entanglements:

|  |  |  |
| --- | --- | --- |
| *hph x nat* [[FASTA]](https://static.igem.org/mediawiki/2020/9/98/T--GO_Paris-Saclay--opt_entangled_genes_hph_nat.txt) | *hph x Turquoise* [[FASTA]](https://static.igem.org/mediawiki/2020/4/41/T--GO_Paris-Saclay--opt_entangled_genes_hph_Turquoise.txt) | *RedStar x nat* [[FASTA]](https://static.igem.org/mediawiki/2020/b/bf/T--GO_Paris-Saclay--opt_entangled_genes_RedStar_nat.txt) |
| *hph x RedStar* [[FASTA]](https://static.igem.org/mediawiki/2020/6/60/T--GO_Paris-Saclay--opt_entangled_genes_hph_RedStar.txt) | *hph x yfp* [[FASTA]](https://static.igem.org/mediawiki/2020/6/65/T--GO_Paris-Saclay--opt_entangled_genes_hph_yfp.txt) | *Turquoise x nat* [[FASTA]](https://static.igem.org/mediawiki/2020/9/99/T--GO_Paris-Saclay--opt_entangled_genes_Turquoise_nat.txt) |

The links in the table above should redirect you to a FASTA file containing the optimal entangled sequences.
Additionally, here is [a ZIP file](https://static.igem.org/mediawiki/2020/e/eb/T--GO_Paris-Saclay--pareto-figures.zip) containing plots of CAMEOS' scores annotated with Pareto optimal sequences for all entanglements.
Plots and optimal sequences were computed from CAMEOS outputs using [our scripts](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software) `pareto.py` and `extract_data.jl`.

*Look for ideas*
 *(Research & Imagine)*

We were first inspired by the ideas proposed by Blazejewski et al in their article, including entangling a gene of interest with a toxin to contain it within a chassis that provides the antitoxin and entangling a biosynthetic gene with an essential gene to restrict accumulation of mutations.

We were also inclined to try reproducing the case of entangled sequences in bacteriophage [phiX174](https://en.wikipedia.org/wiki/Phi_X_174) which is one of the few examples of the kind of entangled sequences generated by CAMEOS in nature.

For the sake of having lab experiments accessible to do for iGEM, we finally opted for genes encoding mostly **reporter proteins** and **antibiotic resistance proteins**.

*Design* *(Design)*

For the first set of entanglements, the coding and protein sequences were retrieved on Uniprot. Our sequences include *luc*, encoding for the firefly luciferase, two *knt* sequences encoding for kanamycin resistance, *ccdB* encoding for bacterial toxin CcdB, and *gfp*, which really should be *sfgfp*, since it encodes for the *superfolder* Green Fluorescent Protein.

For the second set of entanglements, the sequences were provided to us by Tristan Rossignol (a researcher who works at the INRAE in Jouy-en-Josas) as part of a [Human Practices](https://2020.igem.org/Team%3AGO_Paris-Saclay/Human%20Practices) collaboration aiming to entangle a sequence in the yeast *Yarrowia lipolytica*. It includes *hph* confering hygromycin resistance, streptothricin acetyltransferase *nat*, and various colored GFP variants.

All sequences used in these entanglements are provided in the [proteins.fasta](https://static.igem.org/mediawiki/2020/9/91/T--GO_Paris-Saclay--proteins.fasta.txt) and [cds.fasta](https://static.igem.org/mediawiki/2020/d/df/T--GO_Paris-Saclay--cds.fasta.txt) files.
 From these same sequences we realized BLAST alignments to retrieve protein MSAs to give as input to CAMEOS.

*Entangle* *(Build)*

Using the protein and coding sequence from the two genes to entangle, and the protein MSAs retrieved for each gene, CAMEOS generated sets of **300 variants**.

We used our bash script to help the generation of inputs from the MSAs to speed up the process of entangling our own sequences.

All of our entanglements presented here follow this format: `aaa...aaBBB...BBBaa...aaa` where sequence *a* is on frame 1 and sequence *b* is on frame 2 or 3.

In addition, we always picked a small and a large sequence to entangle: the small sequence ends up being the one inside the large sequence.

*Select variants* *(Test)*

Optimal entangled sequences were calculated with [Pareto optimization](#pareto) using our scripts as described in the [Software](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software) page as well as in the [CAMEOS Course](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course/Part_3).

This is an important part: we want to eliminate all suboptimal variants and check with BLASTp identity scores if our optimal variants are close to the original proteins.

*Lab experiments* *(Test)*

Some of these entanglements were used for **lab experiments**, these are colored in red.

We selected the sequences among these that were found to have good enough BLASTp identity scores to be considered for the lab. Most of these lab experiments unfortunately did not turn out well.

We also used Phyre² to model some of the proteins entanglements, here are two examples of .pdb files obtained by running Phrye² in intensive mode on *gfp* and *knt²* for one of our [BioBricks](https://2020.igem.org/Team%3AGO_Paris-Saclay/Parts): [knt.pdb](https://static.igem.org/mediawiki/2020/f/f2/T--GO_Paris-Saclay---knt-gfp--knt.pdb.txt) and [gfp.pdb](https://static.igem.org/mediawiki/2020/8/82/T--GO_Paris-Saclay---knt-gfp--gfp.pdb.txt).

**Note:** the knt-gfp BioBrick did not come from the FASTA file presented above as *knt² x gfp*, but from a previous *knt² x gfp* entanglement.

*Improve chances of success*
 *(Learn & Improve)*

More often than not, we didn't perform any lab experiments, but we still managed to **find improvements** to make to our entanglement and selection process.

→ Selecting sequences with Phylogeny analysis and BLASTp

CAMEOS took a long time to be familiar with. At first we arbitrarily selected a few sequences and ran them through a phylogeny analysis in order to find the ones who would be the most resemblant to the original proteins.

However, we found their BLASTp scores to be a bit lacking, which makes sense if we didn't select the best entangled sequences in the lot.

→ Selecting sequences with Pareto optimization of CAMEOS' scores

Then we discovered CAMEOS' top scoring system, which already included scores ranking the entangled sequences by resemblance to the first and second protein sequences.
Wanting to go further with CAMEOS' scoring, we developed a script using Pareto optimization to select the optimal entangled sequences.

→ Taking tridimensional structure into consideration

Fearing that our entanglements proteins end up having dysfunctional tridimensional structures, we also decided to take structural analysis into consideration for the selection process.

In particular, this lead us to use Phyre² as explained above, but also to search if our protein sequences have mutations on important binding sites, which are for some of them described on the protein's Uniprot page.

In many cases, the entanglement variants actually had mutations on very important spots, which reduced the likelihood of it being functional in lab.

→ Checking Open Reading Frames and Ribosome Binding Sites

For our [BioBrick](https://2020.igem.org/Team%3AGO_Paris-Saclay/Results) *knt² x gfp*, there was an ATG codon **in upstream** of the second coding sequence (the one that ends up in the middle of the other sequence) with a better RBS. This is a major problem: we don't want the ribosome to translate the wrong reading frame.
This can be avoided by replacing such starting codons or generating synonymous mutations and it underlines how important it is to check all frames for ORFs with a tool such as ORF FINDER.

→ Find out which MSAs result in the best entanglements

Finally, in most of the cases the biggest problem for us to trust the entanglement sequences to make for good BioBricks was the BLASTp identity scores, which are especially low for the second coding sequence: it very rarely reaches an identity percentage of 70%.

For example, in the first optimal sequence of the *hph x nat* entanglements provided above, the *hph* sequence has an identity score of [about 92%](https://static.igem.org/mediawiki/2020/f/fd/T--GO_Paris-Saclay--gene_64_hph_nat_hph.png) with hygromycin phosphotransferase, while the *nat* sequence only has an identity score of [about 42%](https://static.igem.org/mediawiki/2020/2/26/T--GO_Paris-Saclay--gene_64_hph_nat_nat.png) with N-acetyltransferase.

To remedy to this problem, we hypothesized that the obtained BLASTp identity scores were purely dependant in the quality of the protein MSAs given to CAMEOS and decided to run the **following investigation**.

→ Case study: Comparing MSAs from several genomic databases

For the couple *knt¹* and *ccdB*, we did not only run one CAMEOS execution. Since CAMEOS depends on the MSAs given in input, we selected 5 MSAs from different databases (BLAST, Uniprot, etc.) for both *knt¹* and *ccdB* and generated a total of 25 entanglements, one for each MSA combination.

The entanglements in question are not provided here but their analysis and comparison lead to interesting results which are presented in detail in the [CAMEOS Case Study](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course/Case_Study).

In particular, we generate HMM consensus sequences for each MSA, and the results suggest that the closest the HMM consensus sequence is to the original protein sequence, the better CAMEOS scores for the outer sequences are, meaning that taking distant homologues in MSAs to have diversity in our HMMs would not always be the right choice.

To learn more about how CAMEOS uses HMMs and MRFs generated from the protein MSA given in input, check the [Model](https://2020.igem.org/Team%3AGO_Paris-Saclay/Model) description.

In **conclusion**, we engineered a remarkable number of CAMEOS entanglements and provided an engineering methodology for sequence entanglement softwares. First, we carefully choose sequences and corresponding MSAs to entangle with CAMEOS.
Secondly, we select the entanglement variants using Pareto optimization of CAMEOS' scores, BLASTp identity scores, and structural analysis tools. If the optimal sequences aren't acceptable, we search for possible improvements with the chosen sequences and MSAs.
Conversely, we select some entangled sequences for the lab, and the observations done at the lab give us insights for the future sequence entanglements. Further details can be found in the [CAMEOS tutorial course](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course/).

### Pareto optimization

Our selection technique is based on Pareto optimization or [multiobjective optimization.](https://en.wikipedia.org/wiki/Multi-objective_optimization)
In the figure below we attempt to explain Pareto optimization applied to entangled sequences in a nutshell.
We have two parameters to optimize: we want to select the sequences which minimize the divergence from Protein A and Protein B.
In this case, the sequences that appear on the graph in yellow are said to be Pareto-optimal. They constitute a Pareto front.

![](https://static.igem.org/mediawiki/2020/d/db/T--GO_Paris-Saclay--pareto.png)

Pareto-optimal sequences are sequences that are not dominated by any other sequence in both categories.
Intuitively, for each sequence that does not belong to the Pareto front, there is another sequence on the front which scores at least as good in resemblance to both Protein A and Protein B, being a better choice.

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

# Team:GO Paris-Saclay/Experiments

Team

[Team members](https://2020.igem.org/Team%3AGO_Paris-Saclay/Team)
 [Attributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Attributions)
 [Collaborations](https://2020.igem.org/Team%3AGO_Paris-Saclay/Collaborations)
 [Partnership](https://2020.igem.org/Team%3AGO_Paris-Saclay/Partnership)
 [Award](https://2020.igem.org/Team%3AGO_Paris-Saclay/Award)

Project

[Description](https://2020.igem.org/Team%3AGO_Paris-Saclay/Description)
 [Design](https://2020.igem.org/Team%3AGO_Paris-Saclay/Design)
 [Engineering](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering)
 [Results](https://2020.igem.org/Team%3AGO_Paris-Saclay/Results)
 [Notebook](https://2020.igem.org/Team%3AGO_Paris-Saclay/Notebook)

Model

[Model](https://2020.igem.org/Team%3AGO_Paris-Saclay/Model)
 [Software](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software)

Lab

[Lab Experiments](https://2020.igem.org/Team%3AGO_Paris-Saclay/Experiments)
 [Parts Collection](https://2020.igem.org/Team%3AGO_Paris-Saclay/Part_Collection)
 [Contributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Contribution)
 [Safety](https://2020.igem.org/Team%3AGO_Paris-Saclay/Safety)

Human Practices

[Integrated Human Practices](https://2020.igem.org/Team%3AGO_Paris-Saclay/Human_Practices)
 [Implementation](https://2020.igem.org/Team%3AGO_Paris-Saclay/Implementation)
 [Science Education](https://2020.igem.org/Team%3AGO_Paris-Saclay/Education)

[Poster](https://2020.igem.org/Team%3AGO_Paris-Saclay/Poster)

[CAMEOS Tutorial](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course)

[Judging Form](https://igem.org/2020_Judging_Form?id=3427)

☰

Experiments

# **EXPERIMENTAL DESIGN**

Once the Cameos team generated entangled genes, the Cameos and Lab teams collaborated to select variants of entangled genes which were deemed most likely to be functional (%identity, BLASTx, BLASTp, Phyre²...). Then the Lab team optimized the HuGenesS sequences for cloning (addition of BsaI sites for Golden Gate claning and improvement of ribosome binding sites)

Here are the different steps that were performed by the Lab team during six weeks in the summer:
1) DNA fragments with the designed HuGenesS were ordered to IDT.
2) Golden Gate cloning of the DNA fragments into the pBAD24-Moclo vector.
3) Transformation of the Golden Gate reaction into *E.coli* DH5alpha and selection on selective LB agar medium
4) Amplification + Purification of the obtained transformants
5) Verification of the selected transformants by PCR and electrophoresis
6) Plasmid DNA purification from PCR transformants -> sent to a company for DNA sequencing
7) Characterisation(s) of the genes to determine their ability -> fluorescence measurement of bacteria by flow cytometry and antibiograms

Of the seven sequences that were ordered, we succeeded in cloning two. These two cloned biobricks/ parts were characterized by our Team this summer : **stable\_YFP\_LOV** coding for a small thermostable YFP and the  **HugenesS Knt-GFP**  predicted to confer resistance to aminoglycoside antibotics and to encode GFP.

**The lab books for these two parts are below**

![Experimental design using pBAD-Moclo_24 plasmid as vector of our HuGenesS](https://static.igem.org/mediawiki/2020/3/32/T--GO_Paris-Saclay--Lab_design_illustration_GG_pBAD_Moclo_plasmid%2BHuGenesS.png)

|  |  |
| --- | --- |
| Lab book on Stable\_YFP\_LOV | Lab book on knt-gfp HuGenesS |
| Open the PDF file [Stable\_LOV-YFP Lab book](https://static.igem.org/mediawiki/2020/2/2e/T--GO_Paris-Saclay--Lab_Book_stable_LOV-YFP_complete_lab_book.pdf). | Open the PDF file [Knt-GFP Lab book](https://static.igem.org/mediawiki/2020/1/16/T--GO_Paris-Saclay--complete_Lab_Book_Knt-GFP.pdf). |

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

# Team:GO Paris-Saclay/Results

Team

[Team members](https://2020.igem.org/Team%3AGO_Paris-Saclay/Team)
 [Attributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Attributions)
 [Collaborations](https://2020.igem.org/Team%3AGO_Paris-Saclay/Collaborations)
 [Partnership](https://2020.igem.org/Team%3AGO_Paris-Saclay/Partnership)
 [Award](https://2020.igem.org/Team%3AGO_Paris-Saclay/Award)

Project

[Description](https://2020.igem.org/Team%3AGO_Paris-Saclay/Description)
 [Design](https://2020.igem.org/Team%3AGO_Paris-Saclay/Design)
 [Engineering](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering)
 [Results](https://2020.igem.org/Team%3AGO_Paris-Saclay/Results)
 [Notebook](https://2020.igem.org/Team%3AGO_Paris-Saclay/Notebook)

Model

[Model](https://2020.igem.org/Team%3AGO_Paris-Saclay/Model)
 [Software](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software)

Lab

[Lab Experiments](https://2020.igem.org/Team%3AGO_Paris-Saclay/Experiments)
 [Parts Collection](https://2020.igem.org/Team%3AGO_Paris-Saclay/Part_Collection)
 [Contributions](https://2020.igem.org/Team%3AGO_Paris-Saclay/Contribution)
 [Safety](https://2020.igem.org/Team%3AGO_Paris-Saclay/Safety)

Human Practices

[Integrated Human Practices](https://2020.igem.org/Team%3AGO_Paris-Saclay/Human_Practices)
 [Implementation](https://2020.igem.org/Team%3AGO_Paris-Saclay/Implementation)
 [Science Education](https://2020.igem.org/Team%3AGO_Paris-Saclay/Education)

[Poster](https://2020.igem.org/Team%3AGO_Paris-Saclay/Poster)

[CAMEOS Tutorial](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course)

[Judging Form](https://igem.org/2020_Judging_Form?id=3427)

☰

Results

# **RESULTS**

Below is a list of the results obtained both *in silico* and in the laboratory.

More informations on how these results were achieved can be found in the related links.

1) We installed CAMEOS and ran it successfully ([Link](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering))

2) We developed software to streamline input and output processes ([Software page](https://2020.igem.org/Team%3AGO_Paris-Saclay/Software))

3) We generated 12 gene entanglements using CAMEOS ([Link](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering))

4) We analyzed the variants and selected 6 variants to be tested by us in the lab, and six to be tested by a researcher in metabolic engineering eager on adopting gene entanglement ([Link](https://2020.igem.org/Team%3AGO_Paris-Saclay/Engineering))

5) We succeeded in cloning and characterizing one of these variants, but it was not functional ([Knt-GFP HuGenesS](http://parts.igem.org/Part%3ABBa_K3427001))

6) We explained the mathematical tools behind CAMEOS and suggested other tools that could improve it ([Model Page](https://2020.igem.org/Team%3AGO_Paris-Saclay/Model))

7) We developed a tutorial course on using CAMEOS ([Cameos Course](https://2020.igem.org/Team%3AGO_Paris-Saclay/Course))

8) We developed a workshop on DNA for the general public ([DNA workshops](https://static.igem.org/mediawiki/2020/b/b0/T--GO_Paris-Saclay--DNA_workshops_bis.pdf))

9) We cloned and characterized a new Biobrick, that encodes a small thermostable fluorescent protein ([stable\_YFP\_LOV part](http://parts.igem.org/Part%3ABBa_K3427000))