Team:TAU Israel/Description
Inspiration and Description
How did our project Evolve?
Our teammates were recruited from different academic backgrounds, creating a multidisciplinary team. Thus, our familiarity with synthetic biology in general and specifically with iGEM varied significantly between members. We decided that the best way to get to know iGEM (and each other) is to survey previous projects together. Our instructor asked us to present inspiring iGEM projects, that demonstrated excellence and impressed us. This assignment helped us dive into the iGEM spirit, mentality and expectations. We realized that a successful project is not measured just by its biological validity, but by a variety of factors – it is thought-invoking, it offers other teams new techniques and methods, it takes into account global impact, and it is revolutionary.
Doron (one of our team members) found the Lethbridge 2013 project and introduced us to their idea of using pseudoknots as regulatory parts. One of their goals was to create a software that would enable dual coding of proteins. Their software converts two amino-acid sequences to DNA sequences, and then tries to overlap the sequences by using different reading frames. Doron came up with many possible improvements to the software: instead of naive solutions, we can preserve original constraints such as codon bias, GC content and more, while looking for this overlap.
Our next assignment was to use the knowledge we gathered and our own unique skills to come up with an idea for a new project. Doron's presentation inspired Matan and Noa, and they introduced us to the problem of genetic stability – the fight against evolution to preserve the functionality of genetic constructs when introduced to a foreign host. Their idea was utilizing pseudoknots to create an overlap between a foreign gene of interest (hence referred to as "target gene") and an essential gene, thus increasing the target gene's stability. We found a proof-of-concept for this idea in literature (see next section) and thought that it was worth the effort.
Following this task, we brainstormed together, discussed the advantages and the weaknesses of each project, and voted for the one that seemed to best match our capabilities as a team and our vision. The idea of addressing genomic stability, as suggested by Matan and Noa, was selected.
During the brainstorming session, we asked ourselves – are we able to to generalize the solution to any given target gene? For example, what is the chance of having an overlap between a target gene and one of the essential genes in the yeast genome?
The answer to that question required some modeling, which you can find in our Engineering success page. Based on this analysis, we concluded that our initial idea had some potential but could not be generalized as we wanted. This led us to consider different approaches for achieving our end goal – a generic method for increasing the stability of a gene of interest. After further brainstorming, we came up with a new scheme, described below.
Problem Description - Evolutionary Instability of Genetically Engineered Microorganisms
Our goal this year is to address the challenge of evolutionary instability of genetically engineered microorganisms (GEM) and create a generic end-to-end solution for any given gene of interest.
A main objective of synthetic biology is to make the process of engineering genetically encoded biological systems more predictable, evolutionary robust, and efficient. However, designing devices for deployment in living organisms presents many new challenges when compared with engineering inanimate materials [2]. A key difference is that biological systems are able to actively evolve toward optimal fitness behaviors for the set of conditions they are grown under. Maintenance of deployment constructs requires energy from the cell, and increased energy consumption results reduced fitness. Thus, when a new construct is introduced to a cell, it causes an additional metabolic load and inherently decreases the cell's fitness, since it is strong arming the GEM into manufacturing large amounts of protein with all the associated metabolic load and without gaining any benefit from it at. As a result, loss-of-function becomes an evolutionarily favourable genotype. GEMs that acquire mutations that inactivate the construct (and remove the associated load) can quickly take over the population, since the evolution process involves selection for the most-fit variants in a population of organisms.
Synthetic biologists need to know that they can rely on their construct to keep its functionality for a long period of time, as it takes time and money to restart the whole genetic engineering process each time the construct is lost. More importantly, a genetic construct's stability needs to be considered while assessing its safety of use, because an unstable, mutationally prone construct that is more prone to mutations can become harmful to the environment and unusable out of the lab.
This predicament is hindering many major advances in biosynthetic designs and implementation. For example, the genetic stability of engineered bacteria serving as live therapeutic agents is a major concern for organisms intended to replicate within and/or colonize the patient’s microbiota [1]. When mutated, the strain can transfer engineered genetic material to other members of the endogenous microbiota. While this genetic transfer is not always harmful to a patient, it will eliminate the competitive advantage of the engineered strain and undermine the efficacy of treatment.
So, what should synthetic biologists do when faced with the inevitable evolution of their carefully designed construct?
Traditional approaches include [2,3]:
- Coupling the target construct to a separate selection pressure for its maintenance.
- Reducing the evolutionary instability by removing unstable genetic elements from the host genome.
- Work from a frozen stock.
Regarding the first approach, it is worth mentioning recent advances in the field, in the form of two papers in Science last year. While addressing this challenge with innovative methods, these studies still exhibit many limitations: either the solution is highly specific and requires a great deal of preliminary work, time and investment, making it economically unfeasible [4], or the solution is limited to very controlled and specific environments, greatly limiting the possibilities of biosynthetic engineering [5].
Previous iGEM projects focused on identifying and better characterizing sequences that contribute to the relative instability of genetic devices (Austin UTexas 2015) without trying to avoid these patterns, or quantifying and measuring the metabolic load caused by an introduced construct (Austin UTexas 2019). Both projects broadened the understanding of the evolutionary stability associated with genetic parts.
However, to date there is no generic end-to-end solution that combines the first two approaches – coupling of target gene with selection pressure and avoiding unstable sequences - while predicting the per-gene stability based on gathered data and bioinformatic models.
What do we suggest?
We propose interlocking a given target gene to the N-terminus of an essential gene in the host’s genome, under the same promoter. An essential gene is a gene whose activity is vital for the cell. Thus, mutations on the target gene are likely to affect the transcription of the essential gene, leading to the mortality of the mutated host.
We hypothesize that each conjugated essential gene will provide different stability levels when attached to a specific target gene. Therefore, each target gene has a specific set of essential genes that will best promote its stability. In order to find it, there is a need to predict the stability levels of the combined construct.
We base this hypothesis on literature findings [2], [6], stating that the more sequence information that the two genes share, the smaller the target size for possible mutations that inactivate the synthetic construct and maintain organismal viability. This shared information leads to a more stabilized construct, and it can be expressed in terms of codon usage, or sharing a promoter, but it could involve other features that we hope to find out utilizing machine-learning algorithms and experimental data.
The stop codon of the target gene needs to be eliminated in order to transcriptionally interlock it to an essential gene. The way we link the genes together depends on the intended usage of the inserted gene. It can vary from a simple fusion linker to a sophisticated linker with a pseudoknot and signal peptide where the target gene is clean and secreted from the cell. The various linkage options are described in our Design page.
Objective
Our end goal is to create a user-friendly software for the biotechnology industries and synthetic biology labs. This program will greatly ease the struggle of expressing, for a long duration, a high level of a target genes, whether for therapeutics or bioproduction needs.
For the creation of this robust and reliable method, we will execute empirical experiments and create algorithmic models that will simultaneously achieve two goals:
- Demonstrate (both theoretically and empirically) that N' terminally attaching a target gene to an essential gene of the host organism will greatly increase the evolutionary stability of that target gene, while still maintaining high expression levels at all time.
- Customize a construct for each target gene, composed of the best matching essential gene and linker for improved stability and higher expression levels. This will be achieved by characterizing which features of the essential gene are vital for creating a durable genetic construct, with respect to each individual target gene.
The way we plan to accomplish these goals is described in the project design page.
references
[1] Charbonneau, M. R., Isabella, V. M., Li, N., & Kurtz, C. B. (2020). Developing a new class of engineered live bacterial therapeutics to treat human diseases. Nature Communications, 11(1), 1-11.
[2] Renda, B. A., Hammerling, M. J., & Barrick, J. E. (2014). Engineering reduced evolutionary potential for synthetic biology. Molecular BioSystems, 10(7), 1668-1678.
[3] Wang, Y. H., Wei, K. Y., & Smolke, C. D. (2013). Synthetic biology: advancing the design of diverse genetic systems. Annual review of chemical and biomolecular engineering, 4, 69-102.
[4] Blazejewski, T., Ho, H. I., & Wang, H. H. (2019). Synthetic sequence entanglement augments stability and containment of genetic information in cells. Science, 365(6453), 595-598.
[5] Liao, M. J., Din, M. O., Tsimring, L., & Hasty, J. (2019). Rock-paper-scissors: Engineered population dynamics increase genetic stability. Science, 365(6457), 1045-1049.
[6] Sleight, S. C., Bartley, B. A., Lieviant, J. A., & Sauro, H. M. (2010). Designing and engineering evolutionary robust genetic circuits. Journal of biological engineering, 4(1), 12.
