Team:Chalmers-Gothenburg/Engineering

Engineering success

The design, build, test, learn (DBTL) cycle is an important part of the everyday life for an engineer. There are constant challenges impacting an engineer’s projects and work, and our project is no different. We have iterated through the engineering design cycle several times, where we designed an idea, which we kept building and testing only to have to redesign it. Our team went through these cycles both on a theoretical basis, and other times because of results gathered from our Wet- or Dry lab experiments.

In the planning phase the engineering cycle was used multiple times to finetune or modify an already developed project due to unforeseen factors (such as laboratory time limits or newly found gene parts). For example, the early designs of our project included dCas-proteins as a way to facilitate ON/OFF states in the expression of the different thioesterases. But due to a pre-existing Cas9 gene in the high fatty acids producing strain we originally planned to be using (Yu et al., 2018) this concept was abandoned. Later we realised that the slow growth of this strain would not make it possible to complete the project within the given timeframes of iGEM, so it was abandoned for the strain we kept using throughout the rest of the project (S. cerevisiae CEN.PK 102-5B). As such we completed engineering cycles both on a theoretical basis (pre any laboratory experiments), and as a result of such experiments. One of the initial considerations of our project was creating a functional system that would give us a chance of producing high enough yields usable for an actual product (Yu et al., 2018). Our idea was to compare the high fatty acid producing strain with a common laboratory strain. However, as the first transformation was executed, we noticed that the growth rate for the high fatty acid producing strain was much slower than anticipated. The strain had not grown enough after an overnight inoculation, hence, it was resuspended in new media and incubated for an additional 24 hours, and then 4 days. At this point the number of cells was still not enough to perform a transformation. With our limited amount of time for the project we realised that this was not a promising outcome.

Overview of our engineering cycles, image created in biorender.

During this time we also realised that the high fatty acid producing strain would be problematic to test with our design, as the fatty acid synthase had already been modified with parts of the heterologous genes from the bacterial type II fatty acid synthase system we aimed at integrating (Fernandez‐Moya et al., 2015). After some research we identified CEN.PK 102-5B was a suitable alternative. We realised that using this strain would significantly improve our chances at testing our complete design within the time frames of the iGEM project. Hence, through these rounds of experimentation we completed our first iteration cycle by selecting an appropriate working strain. At this time, we had already begun some modelling efforts aiming to compare the high fatty acid producing strain with one of its precursors - YJZ045.

Using the RAVEN toolbox (Wang et al., 2018) for genome scale metabolic model construction and testing (GEM), a tool developed by the Systems and Synthetic biology division at Chalmers University of Technology (SysBio), we constructed a GEM of YJZ045. We did this by modifying a state-of-the-art GEM for wild-type yeast (Yeast8)(Lu et al., 2019) and implementing gene and reaction changes to model the strains GEM.

Based on the results generated by the standard Yeast8 model we could observed no aerobic ethanol production (e.g., the Crabtree effect) which is a hallmark of S. cerevisiae metabolism. We therefore reconsidered the model and initiated our third iteration of the engineering cycle. After some discussion with the developers of the Yeast8 model it was decided to introduce some enzymatic constraints using the GECKO toolbox (Domenzain et al., 2021), another tool develop at SysBio. By using these constrains, ethanol production could be observed together with a higher growth rate for the precursor strain in comparison with the high producing strain.

Since the high fatty acid producing strains were not going to be used in our laboratory efforts, we had to rethink our initial plan of achieving a high fatty acid production. This initiated our fourth iteration of the engineering cycle as the heterologous bacterial type II fatty acid synthesis system we planned on introducing to CEN.PK 102-5B would have to compete with the native fatty acid synthase system in terms of precursors, this could hamper our efforts at regulating the fatty acid profiles and our overall yields. However, we realised that the idea of completely removing the native fatty acid synthase by gene knock-out could potentially hamper the growth of, or even kill our strain. After some discussion with our supervisors and research we decided that rather than a knock-out we would downregulate the native fatty acids synthase system using an antisense RNA (Saberi et al., 2016). By analysing the genome of CEN.PK 102-5B we designed a gene coding antisense RNA (BBa_K3944046) that could be integrated and allow the native fatty acid synthase to be downregulated, while still retaining some functionality. The gene fragment was ordered and integrated into an integration plasmid. Unfortunately, as all the genes of the bacterial type II fatty acid synthase system were not fully integrated, we did not get a chance to test the effect of downregulation.

References

Domenzain, I., Sánchez, B., Anton, M., Kerkhoven, E. J., Millán-Oropeza, A., Henry, C., Siewers, V., Morrisey, J. P., Sonnenschein, N., & Nielsen, J. (2021). Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. BioRxiv.

Fernandez‐Moya, R., Leber, C., Cardenas, J., & Da Silva, N. A. (2015). Functional replacement of the Saccharomyces cerevisiae fatty acid synthase with a bacterial type II system allows flexible product profiles. Biotechnology and Bioengineering, 112(12), 2618–2623.

Lu, H., Li, F., Sánchez, B. J., Zhu, Z., Li, G., Domenzain, I., Marcišauskas, S., Anton, P. M., Lappa, D., & Lieven, C. (2019). A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications, 10(1), 1–13.

Saberi, F., Kamali, M., Najafi, A., Yazdanparast, A., & Moghaddam, M. M. (2016). Natural antisense RNAs as mRNA regulatory elements in bacteria: a review on function and applications. Cellular & Molecular Biology Letters, 21(1), 1–17.

Wang, H., Marcišauskas, S., Sánchez, B. J., Domenzain, I., Hermansson, D., Agren, R., Nielsen, J., & Kerkhoven, E. J. (2018). RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Computational Biology, 14(10), e1006541.

Yu, T., Zhou, Y. J., Huang, M., Liu, Q., Pereira, R., David, F., & Nielsen, J. (2018). Reprogramming yeast metabolism from alcoholic fermentation to lipogenesis. Cell, 174(6), 1549–1558.