Team:William and Mary/Description

Page Title



Overview of Project

Orthogonality, defined as the lack of unintended interactions among parts of a circuit or between a circuit and host physiology, is a fundamental tenet of synthetic biology. While methodology for identifying undesirable interactions among circuit parts themselves is well studied, assessment of orthogonality between the circuit and the host physiology that goes beyond metabolic burden, "an unnatural load consuming cellular resources," (Ceroni et al., 2015) has received surprisingly little attention, with the few existing methods both costly and technically challenging. To address this universal need, W&M iGEM is developing an accessible toolkit for circuit-host orthogonality assessment in E. coli. This toolkit employs circuits that provide measurements not only of classic burden, but also more encompassing orthogonality markers identified by extensive analysis of RNA-seq data. These measurements serve as inputs for our integrative, comprehensive mathematical model that provides the end user with overall orthogonality metrics, with the goal of enhancing the efficiency, functionality, and safety of all synthetic biology circuits.


Inspiration: Why Orthogonality?

As with many other teams, our W&M 2021 iGEM team continues to be impacted by COVID-19. In particular, this year, nearly all of our team was new to the concept of synthetic biology and had no experience in the lab due to 18 months of COVID-19 lab restrictions. However, we were determined to conduct a wet lab project in an important area of synthetic biology that addresses a global problem, even though we were still operating under restricted conditions. Given the inexperience of our team, we decided it would be essential to start our iGEM season with an intensive review of the fundamentals of synthetic biology. Beginning with the basics, we systematically started with the key tenets of synthetic biology: modularity, standardization, measurement, and orthogonality. While modularity, standardization, and measurement are intensively discussed in the current literature and widely applied in the field of synthetic biology, we were surprised that this was not the case for orthogonality.

The lack of widespread, rigorous assessment of orthogonality could be due in part to the lack of standardization of a definition of orthogonality. Definitions differ from those stating that orthogonality involves no interactions between the circuit and the environment as a whole, to those that define a lack of interactions between parts as orthogonal. Overall, orthogonality has many different levels: specifically a part-to-part level and a circuit-host level. Our team is defining orthogonality as a lack of interactions between a circuit and a host. This definition of orthogonality is often conflated with burden; however, orthogonality expands beyond burden as it encompasses all cellular processes, not just protein production, which may be impacted by the addition of a circuit. For example, our team has found through an extensive literature review that many heat shock proteins are upregulated in response to circuit addition.

Our team has found a lack of orthogonality assessment in the field. In a review of all ACS Synthetic Biology journal publications in 2020, we found approximately 20% mentioned orthogonality. Related terms burden and toxicity were mentioned in approximately 20% and 81% of papers, respectively. Outside of burden and toxicity, orthogonality was measured in approximately 9.55% of papers, and modeled in approximately 1.82%. In the general literature, we noticed parts were often assumed to be orthogonal without assessment. Out of 190 iGEM projects conducted in 2019, approximately 33% mentioned orthogonality or a synonym, and approximately 11% assessed orthogonality. 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 whether they had measured orthogonality.

Our team wanted to find a way to start filling this gap in orthogonality assessment with an easily accessible method for iGEM teams to assess orthogonality in E. coli. To fill this need, we created our 2021 iGEM project: Orthogonality.


Importance of Orthogonality

These unintended interactions are extremely important as they can cause circuits to lose efficiency, harm their host, release toxins into their environment, break completely, and more, which prevents the reliability necessary for field implementation. In Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage, Christopher A. Voigt and his team discovered the magnitude of these interactions, writing “We were shocked at the number of internal failures. Many parts do not function as designed, bring unintended regulatory sequences in their DNA (e.g., cryptic promoters) or introduce new functions due to the new sequences formed when two parts are connected. ” (Borujeni et al., 2020). The circuits they tested and confirmed to be functional operated in completely different ways than they expected. Evidently, even theoretically sound or functionally confirmed circuits can be unpredictable. In the diverse and crowded environment of a cell, synthetic biologists do not know the full range of their circuits’ interactions, which can lead to unwanted effects or complete breakage.

Overall, orthogonality is directly related to:

  • Predictability
  • Host fitness
  • Circuit efficiency
  • Modularity
  • Safety
  • Fieldability

Therefore, the entire synthetic biology community would greatly benefit from:

  • A clear definition of orthogonality
  • A technique for measuring the orthogonality of their system
  • A standardized method of quantifying orthogonality

As orthogonality is so important to circuit function, we wanted to start the discussion surrounding a lack of orthogonality assessment. To do this, our team developed our project to provide a method with which circuit-host orthogonality can be assessed in E. coli.


What is our Orthogonality Toolkit?

Our team has developed a framework for evaluating and assessing circuit-host orthogonality. We noticed that despite the necessity of orthogonality assessment, most of the “gold standard” orthogonality assessment methods are highly expensive and not accessible to most teams. As a result, our team’s project strives to make orthogonality assessment widely available. We accomplish our goal through a mathematical model with various input levels and a system of sensor circuits designed to produce inputs to our model.

To extend beyond a burden model, our model incorporates RNA-seq data. 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, from any level, 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 three 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 the associated model level. Using their data, our model will output an assessment of their circuit’s orthogonality to the host.


Input Levels for Our Model

The bottom level of our model receives inputs from a collection of genetic circuits that our team designed. These circuits each provide fluorescence output. To use our system, teams will transform an additional plasmid that they want to test into the host with our circuit. The addition of this plasmid will increase circuit-host interactions and will lead to a change in our sensor circuit’s fluorescence output. This change in fluorescence will then be used as an input into our model to derive a specific circuit-host orthogonality assessment.

The creation of sensors to specific host functions will provide teams with insight into which host functions the circuit is directly interfering with, offering a clearer picture of how to fix these issues. Three of our sensor circuits measure the transcriptional, translation, and post-translational burden placed on the host cell by a circuit, providing specific information on how the circuit affects the functioning of its host cell processes. With the goal of expanding our range in the assessment of circuit-host orthogonality, our team conducted a literature search for RNA-seq data comparing gene expression of host cells with and without an introduced plasmid containing a circuit. We found that there were a few genes that were commonly differentially expressed, providing a “marker” for circuit-host interactions. Hence, our additional circuits, the “beyond burden” sensors, were designed using our analysis of the RNA-seq data. The overall sensor design consists of circuits that produce fluorescence in response to changes in the levels of these marker genes, allowing our team to assess differences in the expression of these genes, and therefore, changes in orthogonality levels within the cell over time.

In addition to our sensor circuit input, our model is able to accept other forms of input data, such as RNA-seq and qRT-PCR. RNA-seq is the “gold standard” of orthogonality assessment in the field; however, it is also the most expensive method. As a result, we wanted to provide teams with the option of using RNA-seq data, but also wanted to ensure that this method was not the only input for our circuit, prompting us to create our sensor circuits. Through an input of RNA-seq data comparing gene expression with and without a host, our model will be able to provide teams with an orthogonality assessment. While not as detailed as RNA-seq, qRT-PCR can also provide useful measurements of the changes in cellular functions before and after the introduction of a circuit into a host cell. Measurements from qRT-PCR can be used as inputs for the model. However, this method is still expensive and not accessible to most teams.


Our Team’s Designed Circuits

This collection of circuits, detailed in our Design page, measures the effect of a circuit on a host cell’s:

  • Transcriptional Functions - measures the transcription of a green-fluorescent Broccoli aptamer
  • Translational Functions - measures the translation of fluorescent sfGFP
  • Post-Translational Functions
    • Phosphorylation - senses changes in phosphorylation levels
      • The phosphorylation circuit is based on the EnzV/OmpR E. coli osmolarity regulation system. As OmpR regulates the expression of ompC by phosphorylation, this circuit uses the ompC promoter to express sfGFP, indicating host phosphorylation levels with fluorescence levels.
    • Proteolysis - senses lon protease expression
      • The lon circuit places sfGFP under the control of the lon promoter, indicating expression levels of lon, a protease important for degradation of misfolded proteins as well as some properly-folded proteins. Lon has been observed to degrade about half of misfolded proteins in E. coli (He et al., 2018).
    • Glycosylation
      • The peptidoglycan glycosylation sensor circuit senses the expression levels of the mur operon, responsible for glycoprotein synthesis in E. coli peptidoglycan (Barreteau et al., 2008). It uses the LexA-repressed ftsLp2 promoter, which initiates transcription of the endogenous mur operon, to express sfGFP at levels proportional to mur operon expression in response to circuit-host interactions (Vicente et al., 1998).
    • Heat-Shock Response
      • dnaK and dnaJ expression
        • In addition to the already-available heat-shock response sensors by Ceroni et al. (2018) for htpG, ibpAB, and groSL, which we used in integration with our toolkit, we designed a circuit for sensing the changes in the expression of the dnaK and dnaJ genes which are regulated under the same dnaKJ operon. Both DnaK and DnaJ are chaperone proteins, regulated by the σ-32 factor, and are upregulated when the cell is under stress conditions such as burden by heterologous gene expression (Ceroni et al., 2018). The dnaKJ sensor circuit uses the operon promoter dnaK and produces sfGFP, which is a reporter for the expression of these heat-shock response genes.
      • clpB expression
        • The clpB circuits place sfGFP under the control of the clpB promoter, thereby using fluorescence to indicate expression levels of clpB, a heat shock gene. ClpB is the ATPase subunit of Clp protease, but it does not interact with ClpP, the proteolytic component. Rather, ClpB binds to protein aggregates and rearranges them upon hydrolyzing ATP, allowing for their disaggregation by the chaperone system consisting of DnaK, DnaJ, and GrpE (Goloubinoff et al., 1999; Li et al., 2015).
    • Secretion Function
      • flu expression
        • The AG43/secretion sensor circuit senses the downregulation of secreted Ag-43 biofilm caused by the insertion of a circuit. This sensor circuit uses the flup2 promoter that endogenously regulates the flu gene, which is responsible for Ag-43 production in E. coli (Santos-Zavaleta et al., 2018).



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