Team:GO Paris-Saclay/Poster

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HuGenesS: a project on gene entanglement

Abstract

HuGenesS is based on the concept of overlapping genes, where a DNA stretch encodes two genes, depending on the reading frame. The genes are interlaced, as if in a loving hug! While such genetic arrangements exist in nature, this phenomenon could not be easily harnessed for synthetic biology… until the CAMEOS software was developed by Harris Wang’s Team. We used it to generate several entangled genes encoding reporter and antibiotic resistance proteins. We cloned the sequence encoding GFP and KNT. Since the cloned entangled genes lost functionality, we improved CAMEOS to generate sequences preserving conserved amino acids and to choose the best overlap. We have written comprehensive tutorials on utilizing CAMEOS. The improved software generated sequences that are currently tested by a research team optimizing lipid synthesis in yeast. Genetic entanglement has many potential applications from designing minimal genomes to developing improved, safer and more stable genetic constructs. Confined hugs!

Team Members

Students

- Alice Caron - Inès Yousfi - Delphine Polvé - Maxime Mahout - Guillaume Garnier - Godefroy Glaude - Florent Poubanne

- Clémentine Sejotte - Baptiste Ortheau - Mathieu Nairabeze - Nathan Chatelet - Ekaterina Aleksenko - Ibrahim Soumana Adamou

Principal Instructors

- Dr. Ombeline Rossier
- Dr Philippe Bouloc
- Pr. Stéphanie Bury-Moné

Instructors

- Laetitia Maroc
- Téo Hebra
- Luis Ramirez

Introduction - Project goals

Introduction

In this changing world, the promise of synthetic biology is to help to feed the world, to care for people and to use renewable energies, in a more sustainable way.

One solution is the use of biological systems, to produce in a more sustainable way. By 2025, the biobased chemical sales are expected to grow about 15% (Paul et al. 2019), and therefore will take more and more relevance in our future. Therefore, we need biological machines that are safe and adapted to our needs, so that their use is economically viable, practical and sustainable.

Project Goals

We believe HuGenesS could be implemented for three main goals:

• to stabilize genetic information;
• to secure bioconfinement of GEMs and prevent horizontal gene transfer;
• to increase genetic information quantity in small genomes.

Stabilization of genetic information

Genetically engineered organisms often experience a reduction in fitness that leads to genetic instability of the engineered functions. In most cases, this organism will naturally tend to lose this gene.

Our project aims to produce biobricks that contain two overlapping genes, generating so-called “entangled genes”. Entangling a gene of interest with an essential gene is likely to constrain its evolution! So that the introduced gene is now stable over time!

Securing biocontainment of GEMs and prevent horizontal transfert

The 2020 sanitary context reminded us that containment remains a crucial issue for GEMs (Torres et al, 2016). Many methods have been developed with the aim of confining these organisms, such as addiction strategies or even kill-switches to self-destroy, without finding a miracle solution (Simon et al, 2016:).

With HuGenesS, we aim to intertwine our gene of interest with a toxin gene in such a way that they become dependent on each other. For your designed GEM to survive, it will need to produce an antitoxin that would only be present/produced if the GEM is in its proper bio confined environment (a fermenter for example).

Compaction of genetic information in small genomes

Finally, whether you want to secure or stabilise your construction, in all cases the added genetic information will be compacted. The genetic burden will be less and we can then imagine adding huge genes into very small genomes.

The future in GEMs might rely on organisms with very small genomes, and bottom up approaches to synthetic microorganisms (Liu et al. 2020). HuGenesS is also intending to develop this line of research !

Problems

Synthetic biology is bringing a multitude of new possibilities. However, the execution of genetic constructs and their use still meets issues and limits.

Genetic Instability

One of them being to stabilize genetic constructs. For instance, to produce second-generation biofuels, modified bacteria and fungi are used because they are the only organisms capable of performing the degradation of lignocellulosic waste. Morevoer, the production of third-generation biofuels requires the use of modified microalgae. However, the machinery of these microalgae has been observed to have a strong tendency to inactivate artificially introduced genes via mutations (Rosenberg et al. 2008). In both cases, wether or not the mutations are frequent, they can lead to inactivate the gene of interest which makes the GMO genetic construct useless.

Spread of Genetically Engineered Machines

Furthermore, the biocontainment of microalgae in bioreactors are also an issue.
This brings us to our next point : bioconfinement. It is known that GMOs are not well received by the general public and one of the main arguments for that is the possibility of leaks. To prevent this, kill switches are implemented. One example is the Passcode Switch, it necessitates the combination of 3 different signals to prevent the production of a toxin. That way, if the GMO escapes from its environment, the toxin will be produced and kill the GMO. Yet, this system is not fail-proof. Once again, mutations can occur, leading to the evolution of the toxin gene which would result in the loss of the toxin production.

Principle

Entanglement of genes

Overlapping genes can exist due to the way proteins are encoded in DNA. Since the genetic information is encoded by DNA codons, triplets of nucleotides, one or two nucleotide shifts of the reading frame of the nucleotide sequence will completely change the protein that will be produced. For example in the diagram, the purple gene is read from the first nucleotide, while the yellow gene is read from the third nucleotide. We therefore have two different proteins, one violet and one yellow, produced from the same DNA sequence read in different frames (frame 1 for the purple gene, frame 3 for the yellow gene).

All of our entanglements engineered with CAMEOS follow the format: aaa...aaBBB...BBBaa...aaa where sequence a is on frame 1 and sequence b is on frame 2 or 3.

Inspirations

What are our inspirations ?

During the iGEM 2020 competition, our project took a long time to emerge from our minds. Indeed, initially, we wanted to work on phagotherapy as it could be used to limit the use of antibiotics. We initially wanted to add new genes in a phage to help it fight against resistant bacteria. We encountered a problem: the maximum size of a stable viral particle was too small to accommodate the genes we wanted to add. Moreover, we thought that the mutation rate of the introduced genes would also be high.

Then we read an article about the phage φX174. It was about the discovery of 2 unidirectional entangled genes. We were intrigued by the genetic entanglement: we definitely had the genesis of our project for iGEM 2020. We discovered the extent of the possibilities concerning entangled genes and all its advantages (increased storage capacity of genetic information, ...).

We finally discovered the article by Blazejewski et al, 2019. These researchers generated sequences of entangled genes using a new CAMEOS software. We learned how to use this software. We also interviewed 2 of the 3 co-authors of this article, Thomasz Blazejewski and Dr Harris H. Wang. For example, we discussed the difficulties of the CAMEOS software and its potential applications.

Methodology

Our Methodology

The first step of the project is to choose the two genes to entangle. For the sake of the lab experiments, we made the decision to pick genes encoding for reporter proteins and antibiotic resistance protein.

Both gene sequences as well as consensus sequences are then provided as inputs to the software CAMEOS. The best in silico entanglement is then selected (based on its BLASTp identity scores and Phyre² results) and optimized using the Pareto optimization. Restriction sites and RBS are added as well as other elements if needed.

The entangled DNA sequence is then ordered and ligated in a plasmid via the Golden Gate cloning method. The plasmids are then transformed into E.coli cells. Transformants are selected and the DNA is amplified. Finally, the proteins are characterized.

Cameos

Mathematical Model

Our mathematical model describes the entire procedure to entangle gene sequences in the software CAMEOS and suggests ways to improve it. To entangle genes with CAMEOS, a mathematical structure (Hidden Markov Model, HMM) (Krogh et al, 1994) is explicitly constructed that captures the digital fingerprint of a family of proteins. This structure is used to generate and study new entangled sequences. To take into account the 3D structure of proteins, a classical mathematical tool (Markov Random Fields, MRF) (Seemayer et al, 2014) is used to perform a second enhancement of these sequences.

Improving CAMEOS

In addition, we developed improvements to the sequence entanglement software CAMEOS in the form of a Bash script (bash.sh) that speeds up the generation of CAMEOS inputs, and two scripts (pareto.py and extract_data.jl) that facilitate the analysis of CAMEOS outputs using the Pareto optimization technique. The Python script also generates plots of the Pareto optimal sequences which were very helpful in our engineering process.

Results

CAMEOS

12 entangled genes were generated by Cameos. This 12 HuGenesS were also modified to be optimized in the lab (see Methodology design).

The Team proposes an improvement of the software Cameos to take more biological aspects in the program

The IOANNINA 2021 team accepted our offer to test our CAMEOS tutorial course and gave us a feedback about their experience. Two members tried to follow the steps however one of them could not go through with it since she was working with windows and not Linux. The other member was pleased with the tutorial overall and especially with the explanations to entangle their own sequences. He advised us :

• To give more explanation of the commands,
• To provide further information on the function of CAMEOS for those interested,
• To produce a video tutorial as it is usually easier to follow than text.

Thanks to this collaboration, we will update our work to make our tutorial easier and more understandable.

YFP_LOV

Our search for potential reporter proteins to entangle led us to successfully characterize a novel thermostable flavin-based fluorescent protein stable_YFP_LOV.

Fluorescence-detection :

Bacteria were grown to exponential phase and analyzed by flow cytometry on two separate occasions, using different flow cytometers to widen the options of excitation wavelengths and emission filters.

The fluorescence of cells producing the stable_YFP_LOV clearly above background (see panel A and B). Therefore, our cloned biobrick stable_YFP_LOV confers fluorescence to our chassis.

Conclusion :

Sequencing confirmed that we were able to clone a new biobrick, encoding our small (113 aa residues) thermostable protein with a Cterminal histidine tag. Flow cytometry experiments show that this gene confers fluorescence (excitation 488 nm, emission 536 nm). Thus, this biobrick is correctly produced and functional.

knt-GFP

The second biobrick we succeeded in cloning was one of the HuGenesS that combined Knt, a kanamycin nucleotidyltransferase (resistance to aminoglycoside family antibiotics) and green fluorescent protein (GFP).

Antibiotic-resistance :

We performed antibiograms to evaluate whether Knt provided resistance to kanamycin but also tested spectinomycin and streptomycin (aminoglycoside family) in case it had changed specificity. E. coli strains carrying pBAD24 derivatives were struck on antibiotic gradient plates. Arabinose is used because Knt-GFP are under an inducible promoter to arabinose.

1. positive control for spectinomycin resistance
2. Knt-GFP transformants
3. negative control

The strain carrying knt-gfp was not able to grow well beyond the area of lowest antibiotic concentration, even when arabinose was included in the medium to induce gene expression. It is possible that the knt gene is expressed too lowly and/or that its gene product is not functional.

Fluorescence-detection :

Bacteria were grown and analyzed as YFP_LOV transformants were. We could not observe a significant difference in cell fluorescence for cells carrying the entangled genes knt-gfp and the negative control. Therefore, the GFP encoded by the entangled genes is either not functional or not produced.

Conclusion :

We cloned a DNA fragment with entangled knt and gfp genes, but unfortunately the genes are either not expressed or their products are not functional. The choice of two genes of almost the same size might explain it. Future work should test additional entangled genes found by Cameos.

Human Aspects

Human Practices and Implementations

- Linking industrial problems of stability of genetically engineered machines and design of a HuGenesS bioblock for the research project of Dr. Tristan Rossignol.

- Exploration of the social, legal and ethical complexities surrounding genetically modified organisms (GMOs), in a context of health crisis and lockdown:

• European law regulating GMOs,
• virtual workshops with iGEMers,
• entanglement with the CcdB toxin gene to improve existing killswitch.

- Communication and science education with a broad audience : videos and workshops that make DNA tangible, and open the discussion about DNA modifications in GEMs.

- Communication with scientists, and creation of a tutorial on CAMEOS to enhance the comprehension and the adoption of gene entanglement.

- Market analysis to understand the existing methods, the disadvantages and advantages of HuGenesS.

Human Practices Pictures from left to right :

1. Dr. Tristan Rossignol
2. Sciences Education Communication-DNA workshops
3. Sciences Education Communication-DNA workshops
4. Illustration of the Passcode killswitch

Collaborations

• Parisian Teams (Ionis, Bettencourt, Sorbonne_U and Evry Paris-Saclay) : Sciences Education at Cité des Sciences et de l'Industrie, the most important french museum about sciences
• Parisian Teams : Organization of the Parisian Meet-up
• All the French Teams + Lausanne team : all the projects of each team in one video
• Marburg Team : meeting, and organisation of a workshop at their German Virtual Meetup
• IONNINA Team : test of our CAMEOS tutorial course and gave us a feedback about their experience
• Patras Team : organisation of a workshop at their Mare Nostrum Meetup
• Tel Aviv University Team : exchanges about our projects, which both aim to improve the stability of genetic constructs
• UCSC Team : virtual meeting about our projects
• TU Delft Team : participation to their video “The world of Synthetic Biology”
• Moscou Team : participation to their #iGEMap_Flashmob, for the unity of iGEM Teams
• Uppsala Team :participation to their Corona Outreach Program Initiative

Pictures of some collaborations that our team made. From up to bottom, from left to right:

1. Meeting with Marburg team
2. French Teams + Lausanne Team Projects in one video
3. Parisian Team collaborated to do Science Education at Cité des Sciences et de l'Industrie
4. The Parisian meet-up

Conclusions - Perspectives

Finally

To conclude, we were able to carry out this project at different levels:
The Cameos software was used to entangled genes. These HuGenesS genes give several advantages:
-increase the genetic stability of the overlapped genes
-improve biocontainment
-optimization of the storage space for genetic information.

The Cameos software has been tested and improved to incorporate a biological reality. A tutorial has also been created to facilitate its use.

One biobrick was produced and characterized : a yellow fluorescence protein (LOV YFP)

We have also been able to raise public awareness about biology and particularly synthetic biology. We have developed popularization workshops on DNA that have worked very well with the public.

The Safety was our priority. So we used E.coli, a safe chassis, lab members followed an online course on different risks in laboratory (fire, biological, chemicals) given by the CNRS (Centre National de la Recherche Scientifique), the safety department of our University gave advices...

Following these results, several avenues seem interesting to explore:
- testing HuGenesS genes in different species
- focus on minimal genome or GMO securisation
- look at the interlacing efficiency of Cameos by comparing with interlaced genes already existing in nature

Finally, our team enjoyed participating in this competition, which was very rewarding despite the presence of an uninvited member: Covid-19. Indeed, we were able to have a different professional experience which opened our horizons other than scientific (science education, impact of our scientific project on society, wiki coding, graphic design...).

Reference & Acknowledgements

References

Paul et al. 2019: Paul, Sangeetha, & Deepika (2019) Emerging Trends in the Industrial Production of Chemical Products by Microorganisms. Recent Developments in Applied Microbiology and Biochemistry, doi:10.1016/b978-0-12-816328-3.00009-x

 

Torres et al, 2016: Leticia Torres, Antje Krüger, Eszter Csibra, Edoardo Gianni, Vitor B Pinheiro, Synthetic biology approaches to biological containment, pre-emptively tackling potential risks, Essays in Biochemistry (2016) 60 393–410, DOI: 10.1042/EBC20160013

 

Simon et al, 2016: Anna J. Simon, Andrew D. Ellington, Recent advances in synthetic biosafety, 31 Aug 2016, 5(F1000 Faculty Rev):2118 , https://doi.org/10.12688/f1000research.8365.1

 

Blazejewski et al. (2019). Synthetic sequence entanglement augments stability and containment of genetic information in cells. Science, 365(6453), 595-598.

 

Krogh et al. (1994). Hidden Markov models in computational biology. Applications to protein modeling. Journal of molecular biology, 235(5), 1501-1531

 

Seemayer et al. (2014) CCMpred--fast and precise prediction of protein residue-residue contacts from correlated mutations. Bioinformatics. Nov 1;30(21):3128-30. doi: 10.1093/bioinformatics/btu500. Epub 2014 Jul 26. PMID: 25064567; PMCID: PMC4201158.*

 

Rosenberg et al, 2008: Rosenberg, Oyler, Wilkinson, Betenbaugh (2008) A green light for engineered algae: redirecting metabolism to fuel a biotechnology revolution. Current Opinion in Biotechnology , doi.org/10.1016/j.copbio.2008.07.008

Acknowledgements

Coachs

We would particularly like to thank our Instructors for having helped us to build this amazing project. Thank you very much for having supported us in all the steps, from the reading of the article treating of Cameos to the lab design and the wiki edit. You gave us a lot of precious advice and you have taught us much more in this competition than in any course. Only the experience can bring that.

We also want to thank our University and the I2BC for the possibility to participate at this competition and to lend facilities (lab, meeting room, coffee room and this verdant campus)

Sponsors

Thank you very much to our generous sponsors :

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