Project Description

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Why did we pick this project? What have we achieved? Discover it on this page!

Introduction

Over two billion tons of waste cooking oil (WCO) were produced in the world in 2016 alone1. Belgium is renowned for its fried foods, and according to data by the company Valorfrit, 88 thousand metric tons of WCO were produced in the country in 20212. These are large volumes, moreover the amount of waste cooking oil that is produced annually is expected to further increase by 70% by 20501.

As WCO is a dangerous product, this increase is a problem. One liter of waste cooking oil can pollute over a million liters of water3. It is a threat to the environment and to wildlife. Additionally, if people simply wash it down the sink, it can lead to fatbergs. Fatbergs are large piles of debris that clog up sewers and that cost hundreds of thousands of dollars each to remove4.

Over the past decade, the EU has focused its efforts into recycling WCO by transforming it into biodiesel. WCO-derived biodiesel is referred to as UCOME (Used Cooking Oil Methyl Ester). Nearly one fifth of the total biodiesel consumption in the EU and UK is derived from WCO. By 2030, it is expected that the UCOME consumption will substantially increase, reaching 6.1 to 6.4 million tons per year5.

This being said, making biodiesel certainly isn’t the most planet-friendly alternative available. Burning it still emits CO2, which contributes to climate change. Hence, it’s not a bad idea to look for alternatives for our project.

AI-generated image of oil on beach Figure 1. We all know what oil spills look like and can do. The way WCO can poison and harm ecosystems is very similar.

The steroid drug market is valued around 10 billion USD per year, second to antibiotics. Because of its diverse applications, annual global production of this class of molecule exceeds one million tons per year. This can be explained by the fact that they have many diverse applications. Probably the most prescribed class are corticosteroids, used for treatment of inflammatory diseases (rheumatoid arthritis, inflammatory bowel disease, multiple sclerosis, and others). Another well-known class of steroid drugs are anabolic steroids such as testosterone or sex hormones6.

This being said, steroid drugs are highly complex molecules that are difficult to synthesize chemically. This is not only due to their basic structure, but also to the presence of several chiral centers. While current methods exist to produce steroids from intermediates like campesterol, these intermediates still rely on extraction from plants or animals (Figure 2). Intermediates are then generally converted into steroid drugs by chemical methods or by engineered organisms6.

Figure 2. Current synthesis methods of steroidal active pharmaceutical ingredient synthesis.

While, at first sight, steroid drug synthesis and WCO may seem unrelated, organisms such as Yarrowia lipolytica (an oleaginous yeast) can use WCO as a carbon source to produce useful molecules, such as terpenoids (Figure 3). There are several examples of this approach, including making perfume fragrances and biofuels, but another possibility is making campesterol – a key intermediate for the industrial production of steroid medicines. This creates a direct link between WCO and medicine, and this is what our project is all about7-16.

AI-generated image of oil on beach Figure 3. Metabolic pathways in Yarrowia. Reproduced under permission9.

Metabolic engineering of Yarrowia lipolytica to guide it towards making useful molecules, including campesterol, has been done in the past and despite synthetic biology tools, it remains a slow process, not compatible with the iGEM timetable. Thus, we focused on maximizing the bioavailability of WCO as a carbon source instead, by engineering a chassis for biosurfactant (mainly hydrophobin) production17-20.

It has been shown that adding surfactants to microbial cultures producing terpenoids, like campesterol, increases yield. Moreover, studies involving WCO have also used surfactants to emulsify WCO and increase its cellular uptake. However, synthetic molecules such as polysorbates (e.g., tween 80, as shown below), rather than large biomolecules such as proteins (hydrophobins, for example) are used. It would be interesting to get an organism that is capable of transforming WCO into drug precursors to also express their own biosurfactants, which is exactly what our team focused on. After all, it stands to reason that diverting organism resources to make its own surfactant will make the process more sustainable. Besides, this isn’t just useful for the production of medicine precursors. Surfactants are also used in many other industrial applications in the food and the pharmaceutical industries to stabilize emulsions11,12.

We begun with a proof-of-principle demonstration of the biosurfactants in E. coli as a stepping stone towards establishing more relevant hosts, such as S. cerevisiae and Y. lipolytica itself.

Figure 4. Different types of surfactants. Left side: tween 80, a synthetic surfactant used in many applications, including in the pharmaceutical sector. Right: HFBI, one of the hydrophobins expressed in E. coli by our team.

Methods

Wet Lab

The wet lab component of our project focused on generating expression vectors for biosurfactant proteins, such as natural or engineered fungal hydrophobins, as well as one other sequence with demonstrated emulsion formation properties (MBSP1)22. To do so, we assembled, via Gibson Assembly, his-tagged GFP-hydrophobin fusions on a pET29 backbone. After successful induction of recombinant protein expression in E. coli BL21 cells, we also monitored our producer strain using live-cell time-lapse imaging. The full (hypothetical) pipeline of our wet lab work is shown in Figure 5. As should be clear from the explanation above, we are halfway through this pipeline.

We went beyond working in E. coli and monitored the growth of a Po1d-derived Yarrowia lipolytica strain using glucose and oil at various concentrations. This was done in preparation for a future experiment in which we use WCO as a substrate for campesterol production. The growth of Y. lipolytica was monitored across multiple days by the optical density of cultures across the lag, exponential, and stationary phases. You can read more about our how we designed and implemented our project here!

Figure 5. Summary of wet lab activities performed in E. coli. Genes encoding putative bioemulsifiers were identified from literature, commercially synthesised and cloned. Sequence-verified transformants were induced to validate biosurfactant expression and lack of toxicity to the producer. The next steps in the project would include testing the purified biosurfactants with Yarrowia as well as assembling production modules in yeasts.

Dry Lab

We worked on four distinctive projects: 7-Dehydrocholesterol reductase (DHCR7) enzyme screening, making a growth model for Yarrowia lipolytica, hydrophobin design, and a genetic switch. Starting from AlphaFold23 predictions, our objective was to screen DHCR7 variants in search of the most effective enzyme for the final stage of campesterol synthesis. To achieve this, we employed various techniques, including molecular docking (utilizing the AutoDock Vina 1.1.224 plugin in UCSF Chimera25) and molecular dynamics simulations, employing the Amber2226 package. With the assistance of our wet lab team, who provided us with crucial growth data, we engaged in the modeling of Yarrowia lipolytica's growth on two distinct media. These models were meticulously developed using the biogrowth27 and growthrates28 packages from R, along with a few Python libraries. Furthermore, we explored the potential to enhance hydrophobin efficiency by reducing its isoelectric point (pI). Our approach involved a combination of a custom Python script, AlphaFold23 and PredictSNP29, to identify sets of mutations that were anticipated to lower the protein's pI while preserving its structural integrity. In addition, in order to design a genetic switch that would allow Yarrowia to swap from biosurfactant expression to campesterol production, we looked into multiple switch types and explored various triggers.

Below, you can find a visual summary of all our dry lab work. Even though all subprojects touch upon different aspects, they all contribute to our project. Thanks to enzyme screening, we can better understand campesterol biosynthesis. Designing a hydrophobin with improved properties will enhance wet lab performance. By modeling the growth of Y. lipolytica, we gain insights into the organism's characteristics, and designing a switch will enable us to engineer the cells to reduce their workload.

Results

Wet Lab

We successfully created vectors for expression of the MBSP1 protein, as well as the natural and mutated sequence of HFBI from Trichoderma reesei. We purified proteins extracted from transformed BL21 cells and showed GFP fluorescence coupled with aggregate formation in a live-cell time-lapse imaging experiment. The aggregation of biosurfactant proteins agrees with their natural properties and indicates ability to assemble at the oil-water interface. However, this could also represent a challenge in large-scale production of these molecules, which we addressed by designing mutations aimed at making the proteins easier to secrete. To read more in-depth look at our results you can head over to our Results page!

Dry Lab

After conducting DHCR7 enzyme screening, we investigated the ergosta-5.7-reduction reaction leading to campesterol production. Furthermore, we identified potential DHCR7 enzymes as candidates for our pathway, which required further testing in the wet lab. With the assistance of our wet lab team, we delved into the growth of Y. lipolytica, developing a basic growth model. Our focus was on exploring the impact of glucose versus an oil-based medium on Yarrowia's growth. In addition, we conducted sequence mutations on HFBI to lower its isoelectric point, and our preliminary analysis suggests that these mutations do not disrupt protein stability. Regarding our genetic bioswitch, we proposed a straightforward toggle switch design. We also introduced several triggering mechanisms to facilitate transitions between different states. These mechanisms encompassed ideas such as a thermal switch and morphological transitions from yeast-to-hyphal growth, among others.

Why YarroWCO?

Picture of a frituur or Belgian fries shop

“YarroW” refers to our organism of choice, Yarrowia lipolytica. WCO is an acronym that describes one half of the problems we want to address. A general consensus during one of our preliminary brainstorms was the desire to pursue a sustainability-centered theme. We’re all young people that care for the future of our planet and are worried about the direction our world is headed towards.

The explanation above still describes many possible projects. The next thing we considered is whether we could solve a local problem. It was tempting to try and tackle some huge issue like plastic pollution or climate change, but we were more interested in interacting with local stakeholders. With additional research, we ran across the problem of waste cooking oil.

Fried foods are heavily engrained in Belgian culture, and thus about 88 thousand tons of waste cooking oil are generated in Belgium every year. Based on the rationale above, we believe that our community also has the responsibility for dealing with this waste. This gave us an opportunity to be a part of the change that needs to happen. As anticipated, picking a local problem really helped us with going beyond the lab, and interacting with people from the community. We even gave back to this community by raising awareness and setting up an oil collection point in Leuven, as can be read on our Integrated Human Practices page!

The reasons above were our key drivers but our thoughts on the project went much further: We considered many technical and practical aspects, all of which are detailed in the decision trees on our integrated human practices page. These practical aspects also led us to work on expressing biosurfactants mainly. Doing metabolic engineering to actually produce campesterol wasn’t novel enough, was too ambitious to do over the summer, and would have left us completely empty handed if any step were to require optimization (from ordering strains to analysis). Coming up with a good iGEM project turned out to be very challenging but also very fun, hence why we wanted to document it on our wiki.

Harnessing an organism for the synthesis of a commodity chemical is well established, but we opted for campesterol because of the possibility to link with the large Belgian pharmaceutical sector and because of its potential impact.

Second, and more importantly, the idea of upcycling waste into something that can benefit hundreds of thousands of patients worldwide is incredibly motivating. It is the true embodiment of how we envision changing the world. Our world cannot cope with how we live; we need to change and we need to do it now. This is us taking the first step. The other products would have certainly been very interesting, but not nearly as symbolic as what we chose to do. To us, it is with this mindset that we will turn the tide and save our planet. This is what YarroWCO truly stands for, and this is why we tackled our project with such passion and enthusiasm!

  1. Foo, W. H. et al. The conundrum of waste cooking oil: Transforming hazard into energy. Journal of Hazardous Materials 417, 126129 (2021).
  2. Valorfrit yearly report from 2021 obtained by our team upon request.
  3. Waste oil. Energy, Climate Change, Environment. https://tinyurl.com/mrxuhfzw. (2023), consulted 11/10/2023.
  4. Campbell, L. Workers clear 'huge, disgusting' fatberg from London sewer. Th Guardian (2021).
  5. Van Grinsven, A. Used Cooking Oil (UCO) as biofuel feedstock in the EU. CE Delft (2021).
  6. Fernández-Cabezón, L., Galán, B. & García, J. L. New Insights on Steroid Biotechnology. Frontiers in Microbiology 9, (2018).
  7. Du, H.-X. et al. Engineering Yarrowia lipolytica for Campesterol overproduction. PLOS ONE 11, (2016).
  8. Zhang, Y. et al. Improved campesterol production in engineered Yarrowia lipolytica strains. Biotechnology Letters 39, 1033–1039 (2017).
  9. Arnesen, J. A. & Borodina, I. Engineering of Yarrowia lipolytica for terpenoid production. Metabolic Engineering Communications 15, (2022).
  10. Xu, S. & Li, Y. Yeast as a promising heterologous host for steroid bioproduction. Journal of Industrial Microbiology and Biotechnology 47, 829–843 (2020).
  11. Pang, Y. et al. Engineering the oleaginous yeast Yarrowia lipolytica to produce limonene from waste cooking oil. Biotechnology for Biofuels 12, (2019).
  12. Gao, Z., Ma, Y., Liu, Y. & Wang, Q. Waste cooking oil used as carbon source for microbial lipid production: Promoter or inhibitor. Environmental Research 203, 111881 (2022).
  13. Lopes, M., Miranda, S. M. & Belo, I. Microbial valorization of waste cooking oils for valuable compounds production – a review. Critical Reviews in Environmental Science and Technology 50, 2583–2616 (2019).
  14. Rong, L. et al. Engineering yarrowia lipolytica to produce itaconic acid from waste cooking oil. Frontiers in Bioengineering and Biotechnology 10, (2022).
  15. Liu, Y. et al. Engineering yarrowia lipolytica for the sustainable production of β-farnesene from waste oil feedstock. Biotechnology for Biofuels and Bioproducts 15, (2022).
  16. Arnesen, J. A. et al. Yarrowia lipolytica strains engineered for the production of Terpenoids. Frontiers in Bioengineering and Biotechnology 8, (2020).
  17. Liepins, J. et al. Glycolipid biosurfactant production from waste cooking oils by yeast: Review of substrates, producers and products. Fermentation 7, 136 (2021).
  18. Linder, M. B., Szilvay, G. R., Nakari-Setälä, T. & Penttilä, M. E. Hydrophobins: The protein-amphiphiles of filamentous fungi. FEMS Microbiology Reviews 29, 877–896 (2005).
  19. Khalesi, M., Gebruers, K. & Derdelinckx, G. Recent advances in fungal hydrophobin towards using in industry. The Protein Journal 34, 243–255 (2015).
  20. Berger, B. W. & Sallada, N. D. Hydrophobins: Multifunctional biosurfactants for Interface Engineering. Journal of Biological Engineering 13, (2019).
  21. Kirby, J. et al. Use of nonionic surfactants for improvement of terpene production in saccharomyces cerevisiae. Applied and Environmental Microbiology 80, 6685–6693 (2014).
  22. Araújo, S. C. et al. MBSP1: A biosurfactant protein derived from a metagenomic library with activity in oil degradation. Scientific Reports 10, (2020).
  23. Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
  24. Trott, O. & Olson, A. J. Autodock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry (2009).
  25. Pettersen, E. F. et al. UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry 25, 1605–1612 (2004).
  26. D.A. Case, H.M. Aktulga, K. Belfon, I.Y. Ben-Shalom, J.T. Berryman, S.R. Brozell, D.S. Cerutti, T.E. Cheatham, III, G.A. Cisneros, V.W.D. Cruzeiro, T.A. Darden, N. Forouzesh, G. Giambaşu, T. Giese, M.K. Gilson, H. Gohlke, A.W. Goetz, J. Harris, S. Izadi, S.A. Izmailov, K. Kasavajhala, M.C. Kaymak, E. King, A. Kovalenko, T. Kurtzman, T.S. Lee, P. Li, C. Lin, J. Liu, T. Luchko, R. Luo, M. Machado, V. Man, M. Manathunga, K.M. Merz, Y. Miao, O. Mikhailovskii, G. Monard, H. Nguyen, K.A. O’Hearn, A. Onufriev, F. Pan, S. Pantano, R. Qi, A. Rahnamoun, D.R. Roe, A. Roitberg, C. Sagui, S. Schott-Verdugo, A. Shajan, J. Shen, C.L. Simmerling, N.R. Skrynnikov, J. Smith, J. Swails, R.C. Walker, J. Wang, J. Wang, H. Wei, X. Wu, Y. Wu, Y. Xiong, Y. Xue, D.M. York, S. Zhao, Q. Zhu, and P.A. Kollman (2023), Amber 2023, University of California, San Francisco.
  27. Garre, A., Koomen, J., den Besten, H. M. & Zwietering, M. H. Modeling population growth in r with the biogrowth package. Journal of Statistical Software 107, (2023).
  28. Hall, B. G., Acar, H., Nandipati, A. & Barlow, M. Growth rates made easy. Molecular Biology and Evolution 31, 232–238 (2013).
  29. Bendl, J. et al. PredictSNP: Robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Computational Biology 10, (2014).