Excellence in Another Area
This year, our team was able to fulfill the Excellence in Another Area criteria in several different ways:  through completing a in depth analysis of PETase, a PET degradation enzyme, implementation,  through producing a guide for other teams to use animation software,  by conducting an RNA-seq experiment and producing data that can be used by other teams,  by writing an in depth literature review of various accessible methods of orthogonality assessment.
Developing an In Depth Analysis of PETase degradation
Our team was initially interested in a plastic degradation project. Although we did not decide to pursue this project in wet lab, we wanted to make our research available to other teams interested in plastic degradation. This guide provides a brief overview of polyethylene terephthalate (PET) degradration enzyme PETase, an active site optimized PETase mutant, a method to display PETase on the cell membrane to prevent protein aggregation, full PET degradation via PETases native partner MHETase, and further mitigation of the the PETase/PET interaction via rhamnolipids.
Producing a Graphic Guide
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. As a result, we created a guide 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 information about 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.
Conducting RNA-seq Experiments
When we conducted our literature review to find differentially expressed genes with and without a circuit, we found that there was a serious lack of global gene expression data in this area. Our team therefore conducted our own RNA-seq analysis to help increase the pool of available data for host gene expression with and without a circuit. We conducted these experiments using three different synthetic systems: TS_pluxlac (Barbier et al., 2020), pCDF_LuxR (Barbier et al., 2020), pBbB8k-csg-amylase (Birnbaum et al., 2021), and pDawn-AG43 (Jin et al., 2018). We grew cells transformed with these plasmids overnight and took samples of cellular RNA before and after circuit induction. While the resulting data are certainly relevant to our project, they are even more useful as a dataset for the entire community of synthetic biologists. The DeSeq2 results are available on this wiki and the raw reads are currently being uploaded to GEO. This data can be found here.
Producing a Literature Review of Orthogonality Assessment
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.
Barbier, I., Perez-Carrasco, R., & Schaerli, Y. (2020). Controlling spatiotemporal pattern formation in a concentration gradient with a synthetic toggle switch. Molecular Systems Biology. 16. https://doi.org/10.15252/msb.20199361
Birnbaum, D., Manjula-Basavanna, A., Kan, A., Tardy, B., & Joshi, N. (2021). Hybrid Living Capsules Autonomously Produced by Engineered Bacteria. Advanced Science. 8(11), https://doi.org/10.1002/advs.202004699
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: http://dx.doi.org/10.1038/nmeth.3339
Jin, X., & Riedel-Kruse, I. (2018). Biofilm Lithography enables high-resolution cell patterning via optogenetic adhesin expression. 115(15). doi: 10.1073/pnas.1720676115