This year, our team has contributed to the field of synthetic biology in several important ways:  Raising awareness of the importance of assessing orthogonality for circuit efficacy and safety;  Providing synthetic biologists with 13 sensor circuits to assess orthogonality of their circuits in an accessible and straightforward manner;  Developing a mathematical model for physiological burden (“an unnatural load consuming cellular resources” - Ceroni et. al) that incorporates burden at the transcriptional, translation and post-translational levels; and a model that assesses orthogonality beyond just burden;  Providing an analysis of existing RNA-Seq data with respect to orthogonality;  Developing a pamphlet and powerpoint presentation for talking to retirement homes about synthetic biology;  Creating a Guide for Producing Graphics;  Writing a review of orthogonality assessment methods.
1. Raising Awareness to the Importance of Orthogonality Testing
When our team started our initial literature review of orthogonality, we were shocked by the lack of assessment in this area. In order to quickly communicate this issue to other teams, our team conducted a review of all ACS Synthetic Biology journal publications in 2020, and we found that approximately 20% mentioned orthogonality. Related terms, burden and toxicity, were mentioned in approximately 20% and 81% of papers, respectively. In the general literature, we noticed a trend of parts being assumed to be orthogonal without proper assessment. In the ACS Synthetic Biology Review, orthogonality, extending beyond burden and toxicity, was measured in approximately 9.55% of papers, and modeled in approximately 1.82%.
Our team wanted to determine if this trend of a lack of orthogonality assessment also extended to iGEM. Out of 190 iGEM projects conducted in 2019, approximately 33% mentioned orthogonality or a synonym, and approximately 11% assessed orthogonality. Due to the nature of the iGEM competition, it was unclear to our team whether teams were not assessing orthogonality because they did not see it as relevant to their project or due to a lack of time. Our team released a survey to help us answer this question. In a survey released to 2021 iGEM teams, approximately 21.1% reported having no concern at all for orthogonality, crosstalk, or burden, and approximately 100% of teams responded “no” to if they had measured their circuit’s orthogonality. Therefore, it was clear to our team that the importance of orthogonality has been lost in the chaos of the iGEM competition.
As a result, our team designed our 2021 project with the goal of helping to raise awareness of this issue. Our sensor circuits and model, see below for more detail, provide teams with the ability to assess their circuit’s orthogonality in an easily accessible way. However, in no way is our one iGEM project going to completely solve this issue. Instead, we hope that this project will bring orthogonality assessment to the forefront of people’s minds and will encourage others to consider assessing the orthogonality of their circuits.
2. Development of Sensor Circuits
In order to provide team’s with easy access to orthogonality assessment equipment, our team developed sensor circuits to provide fluorescence outputs which can then be incorporated into our model for an orthogonality assessment. We developed two sets of circuits for this purpose: the Central Dogma system and the “Beyond Burden” system*. The Central Dogma system consists of three plasmids each of which senses burden at either the transcriptional, translational, or post-translational modification level. These sensors produce a fluorescence output. By a comparison of host fluorescence values with and without an additional circuit added, teams will produce a value which can be input into our model to provide an orthogonality assessment. Our second circuit set, the “Beyond Burden” system, utilizes a series of “marker” genes found in our RNA-seq literature analysis. Our analysis found these “marker” genes to be commonly differentially expressed between hosts with a circuit and hosts without a circuit, leading their differential expression to be sensors of circuit-host interactions. Our sensor circuits are able to measure changes in expression of these genes, through changes in fluorescence, which will then indicate differences in host expression due to introduction of a circuit. Therefore, these circuits are able to sense changes in circuit-host orthogonality.
3. Mathematical Model
Our team was able to contribute to the field of synthetic biology by designing a widely accessible orthogonality assessment method, our model. Our model incorporates both a mechanistic model, representing the metabolic functions of the cell, and a model of differential gene expression. The metabolic portion of our model allows teams to assess potential burden caused by circuit-host interactions. To extend beyond a burden model, our model also includes an analysis of host differential gene expression. After completing a literature review of RNA-seq data sets comparing host genome expression with and without an additional circuit, we found that specific genes were commonly differentially expressed in the presence of a circuit. Our team incorporated these genes into our model as “marker” genes with changes in expression illustrating changes in circuit-host orthogonality. Teams will then be able to input their own data into the model. This data will allow the model to quantify changes in circuit-host marker gene expression by comparing the baseline host expression to host expression with a circuit, enabling our model to provide an orthogonality assessment.
Our mathematical model has four data input levels: RNA-seq, qrt-PCR, and our designed sensor circuits. Each level has differing associated costs and accuracy levels. Based on each team’s resources, they can conduct experiments and input their data into one or many of the model’s levels. Using their data, our model will output an assessment of their circuit’s orthogonality to the host.
4. RNA-seq Data Orthogonality Analysis
Our team utilized RNA-sequencing data to understand the impact of biological circuitry on all cell processes. We gathered every published paper that conducted RNA-sequencing before and after circuit implementation or induction in E. coli. Using this data, we identified the reported differentially expressed (DE) genes, along with their adjusted p-values, fold changes, whether they were up or down regulated, plasmid copy number, E. coli strain, and any important circuit details. See our findings here:
Using this data, our team was able to identify which genes were most commonly differentially expressed after circuit implementation or induction. We constructed a hierarchy of genes that were found to be DE in at least two circuits. The genes were ranked on how many circuits reported them DE, and their adjusted p-values. Additional but lower weighted features included how many papers reported them DE, fold changes, how many experiments found them DE, and the consistency of their up or down regulation. Protein products, GO molecular functions, and GO biological processes were also gathered for these DE genes. These attributes allowed us to assess which cellular mechanisms in E. coli were most impacted by insertion of genetic circuitry. Find our hierarchy here:
Overall, thanks to RNA-sequencing technology, our team was able to identify which genes were most commonly differentially expressed and which cellular processes were most affected when circuits are implemented in E. coli. These genes were incorporated into our sensor circuits and models. We have included all of our analysis spreadsheets on our wiki to provide others with the opportunity to use this data set, and hopefully expand on it.
5. Developed a Pamphlet and Powerpoint Presentation for Talking to Retirement Homes about Synthetic Biology
Our team greatly enjoyed discussing synthetic biology with retirement communities. As a result, we wanted to establish a way for other teams to have positive experiences interacting with these communities. We developed an educational pamphlet with tips and tricks about presenting to retirement communities, including advice for creating the presentation, giving the presentation, and what content to cover. Additionally, we attached our presentation for other teams to use. This presentation was reviewed by two individuals at a retirement home for feedback to make the science of the presentation as clear as possible.
6. Created a Guide for Producing Graphics
Our team created a guide to using Google Drawings to produce graphics. Our team found Google Drawings to be a helpful software for our project due to its ability to produce images in many different file formats and we wanted to help other teams have access to this resource. This guide covers basic principles of design, such as contrast, negative space, and balance. In addition, it offers an overall user guide for using Google Drawings including features such as how to develop a color palette and how to export files, including a description of the differences between different file forms.
7. Writing a Review of Orthogonality Assessment Methods
In addition to providing teams with our own circuit-host orthogonality assessment system, we wrote a literature review systematically summarizing the methods that teams can use for measuring the mutual orthogonality of their own synthetic parts. We believe that this goal complements both our “beyond burden” sensors and the Burdenometer project by the 2019 UT Austin team where using a genome-integrated fluorescence cassette, designed by Ceroni et al. (2015), they measured the cellular burden of synthetic parts from the BioBricks from the iGEM Registry of Standard Biological Parts. This review specifically focuses on the phenotypic assessment methodology of part-to-part orthogonality at the transcriptional and translational levels of the central dogma. By providing this overview of phenotypic and non-genomic expression analysis based orthogonality assessment methods, our goal is that teams without access to any advanced genomic analysis tools will ideally be able to gain inspiration for how to measure the mutual orthogonality of their own synthetic parts, and hopefully, integrate it into our circuit-host orthogonality measurement system. In the near future, we will be sharing in our Wiki page a thorough review of other methodology such as global and single gene analysis, to address the state of the field in assessing orthogonality at all levels.
*Our team understands that many of these circuits can fit into both the Central Dogma sensor circuit category and "Beyond Burden" sensor circuit category; therefore, these circuits were grouped together for clarity.
Ceroni, F., Algar, R., Stan, G., & Ellis, T. (2015). Quantifying cellular capacity identifies gene expression designs with reduced burden. Nature Methods, 12(5):415-418. Doi: 10.1038/nmeth.3339