Design

Growth rate, cell density and lipid accumulation are key factors determining the yield of final products when considering microbial industrial production. So to better understand the strain and set the basis for further engineering, we plan to first test and optimize cultivation conditions (including supporting media) for Yarrowia lipolytica growth and lipid accumulation.

The standard and very basic media for cultivating the yeast is YPD and YNB, but there are lots more variables to be explored. Carbon and nitrogen sources are the 2 main components that determine the growth of the strain, and the C/N ratio has a significant impact on the lipid accumulation. There are also papers reporting that phosphate limitation is crucial to DHA accumulation. Therefore, we divided the experiments in cycle 0 mainly into 2 parts by its purpose, and under each part the following factors are investigated:

Part 1. Optimization for Growth

• Different carbon source (glucose, glycerol, glucose+glycerol)
• Conc. of carbon source

Absorption mechanism

Part 2. Optimization for lipid accumulation

• Effect of nitrogen source depletion
• Effect of phosphate source depletion

Regulation mechanism

Our chassis Yarrowia lipolytica is known for being able to grow on various carbon sources, among which glucose and glycerol are the most common ones. In our case carbon source also serves as the substrate for DHA synthesis. It contributes to one of the main costs in industrial fermentation. This was noticed through our interviews with experts in the current bioproduction industry, Dr. Young-Kyuong Park working with Yarrowia lipolytica and Dr. Zhu Yuanmin working with DHA-producing microalgae Schizochytrium sp.. Various researches tried to use a cheaper alternative carbon source for bioproduction including crude glycerol or food waste extract. Therefore we also designed experiments to verify the feasibility of doing so and its impact on the growth of our chassis.

Besides, the scale of culture is another key factor to be considered, and will be further discussed in our Engineering Cycle 4 together with some other key factors such as temperature, pH and oxygen supply.

Build

To further understand how different substrates affect the metabolism and growth of Yarrowia lipolytica, our dry lab established a Genome-scale Metabolic Model (See our Dry Lab page for more detail), which helps us identify the crucial factors, make predictions and design our experiments correspondingly.

Our target strain Yarrowia lipolytica Po1f is kindly provided by Dr. Du Fei from Nanjing Normal University, who’s group is working on a similar topic with Yarrowia lipolytica. For other media preparation, please refer to the Experiment page.

For testing the growth in crude glycerol, we reached out to ASB-Biodiesel, a local company specialized in biofuel production and generates crude glycerol as one of their main by-products. (more to add here)

Test

The change of OD600 over time was measured by taking the sample every 1 hour, which gives us important information about the growth rate and final biomass, as a linear coloration between cell dry mass (CDM) per unit volume and OD600 was suggested by various researches3 and past iGEM teams (2023 KUleuven - Model). As a result, the maximum growth rate during the exponential phase and the time taken to reach the stationary phase under different conditions were also obtained.

(graph)

(conclusion)

The results also assemble the prediction made by our models.

For lipid accumulation, we used the lipid staining method to visualize the lipid accumulation in the cells under different growth phases and conditions using a microscope. As we know from research that lipids tend to accumulate when cells are entering the stationary phase, by taking the sample every 1 hour near the exponential phase, we also characterized the timing when lipid accumulation starts and how it develops with time and couple with the growth curve. Microscope pictures are taken and analyzed by the QuPath software.

A GC-MS analysis was also conducted to characterize the lipid profile in the original strain, at the same time providing a quantified result for reference along with the lipid-staining method, and serving as the control for GC-MS analysis in later cycles. For more details about the experiment and results, please refer to our Experiment page.

Learn

By analyzing the data derived, we found that glycerol will lead to a higher growth rate of Yarrowia lipolytica, which is important to the efficient industrial production. Also nitrogen and phosphate limiting conditions will lead to a higher lipid accumulation by some number. However, these might not be the optimal conditions for growth, suggesting a separation of growth and production stages under different conditions, which lead to our Engineering Cycle 3.1 and will be discussed later in detail.

More importantly, in cycle 0, we found out that the lipid accumulation in the native strain, though optimized, is still far from the reported cases for industrial production. Therefore, we would like to further increase the lipid accumulation in Yarrowia lipolytica before introducing the DHA synthesis pathway, which redirects the existing fatty acid to DHA. Here comes our engineering cycle 1.

Design

In order to improve lipid accumulation in Yarrowia lipolytica, we refer to various researches and found that preventing fatty acid degradation is one of the more effective strategies. Fatty acid including DHA is degraded through the beta-oxidation pathway in Yarrowia lipolytica, which happens in an organelle called peroxisome where most enzymes for beta-oxidation are localized. Knowing that, we consulted Prof. R. A. Rachubinski, expert in peroxisome biogenesis, and further confirmed that termination of the peroxisome biogenesis lead to the total disruption of beta-oxidation, and effectively promotes fatty acid accumulation in the cell in form of triacylglycerol (TAG), which was suggested to be a more effective strategy than increasing the precursors.

Through the discussion and relevant research, we know that the Pex proteins are the key proteins in peroxisome biogenesis, which help form the peroxisomal membrane ring complex and transport other important proteins during peroxisome formation. Knockout of Pex genes will lead to disruption of peroxisome biogenesis, especially Pex10, which was proved to promote lipid accumulation to the largest extent among other existing strategies and not affecting the normal cell growth. So in our engineering cycle 1, we decided to construct a Pex10 knockout strain to improve lipid accumulation.

We designed a construct with the 5’ and 3’ homologous arms of Pex10 gene flanking a LEU2 selection marker, containing a LEU2 native promoter, LEU2 coding sequence and a terminator, which enables part of the Pex10 to be replaced by the LEU2 expression cassette through homologous recombination.

However, for selection after future transformations with the same LEU2 marker gene, we need to release the LEU2 gene from the genome. For this part we consulted Prof. Jee Loon Foo from NUS and he kindly provided us a solution using the Cre-loxP system and gave us the plasmid containing the Cre recombinase, which can recognize the region flanked by the loxP sites and enable its release. Therefore we also added the loxP sites flanking our previous LEU2 expression cassette.

Build

According to our design, we ordered the gBlocks and assembled them with Gibson assembly at once in E. coli, verified, linearized with restriction digestion, then transformed into Yarrowia lipolytica with lithium acetate method. For more details about the cloning process, please refer to the Cloning page.

Initial confirmation of the successful knockout strain is carried out through colony PCR using primers that bind to the homologous arm. Given the low chance of homologous recombination in our chassis Yarrowia lipolytica, say, 100 colonies are selected, and 1 of them shows the expected result, which was inoculated for further testing.

Test

Thanks to Prof. Rachubinski, we obtained a peroxisomal-targeting antibody, anti-SKL, to detect the presence of peroxisome before and after the knockout through observing if the immune-staining marker clusters (have peroxisomes) or spread uniformly in the cytosol (no peroxisome). At the same time we do lipid staining to observe and compare the lipid accumulation in the strains. After the knockout of Pex10, the peroxisome almost disappears, and the lipid accumulation is increased by some number, according to our image analysis, suggesting a general success in constructing the Pex10-KO strain.

To investigate the impact of Pex10 knockout on the lipid profile, we also did a GC-MS analysis of the Pex10 KO strain and compared the result with the original one, which suggests that all difference fluctuations are within the error range statistically and no significant change was detected.

Learn

In this cycle we successfully constructed a Pex10-KO strain, which shows a good ability to accumulate lipids up to some percentage under what condition. This level of accumulation can be further raised by combining with other strategies such as knocking down the MFE2 gene, another key gene catalyzing the beta-oxidation in Yarrowia lipolyticaYarrowia lipolytica. Due to the time limit, we are unable to test this through experiment, but our successful knockout of pex10 can serve as a proof-of-concept, and similar approaches can be directly applied to the knockout of other genes.

Up to this point, we finished optimizing the lipid accumulation, but since Yarrowia lipolytica normally do not produce DHA, we have to introduce a new pathway and redirect the synthesis of fatty acid to DHA to get a DHA-accumulating Yarrowia lipolytica strain. Here comes our next cycle to introduce the PUFA synthase pathway for DHA production.

Design

Thanks for feedbacks from Prof. Ye Chao of Nanking Normal University and Prof. Henry Lam in HKUST, we first established a genome-scale metabolic model (GEM) to predict the change in flux and identify potential targets for metabolic engineering after introducing the DHA synthesis pathway. We also build a DHA yield model using ODE analysis with Matlab and some AI-aided parameter prediction tools, which can predict the DHA production curve over time. Please refer to our Dry Lab page for more details. These models indicate that introducing the PUFA synthase pathway into the cell can indeed lead to predicted DHA production, and the correlation between the expression level and DHA yield, which guide us through the design of this cycle and further optimization strategies in the next cycle.

In order to express the PUFA synthase described on the Design page in Yarrowia lipolytica, we designed to first put each subunits under the same strong constitutive promoter pTEFin (BBa_K3629001) and before the common Yarrowia lipolytica terminator LIP2t (BBa_K4343115) from the registry, after consulting Prof. Yong Lai specialized in yeast synthetic biology in HKUST, and experienced researcher of Yarrowia lipolytica, Dr. Young-Kyuong Park. In order to detect the expression, we also added a His-tag after each subunit and with a linker. Though we are using the same tag, expression of different subunits can still be differentiated according to their diverse size. We also made sure that the addition of the His-tags will not change the proteins’ conformation and affect the assembly by predicting the structures with software (See Dry Lab page), after discussion with Prof. David Banfield, another expert in yeast study in HKUST. All the coding sequences are codon-optimized for Yarrowia lipolytica, and the open reading frames are divided into shorter fragments to facilitate the gBlock synthesis.

Expressing such a large enzyme with a total gene length of 20kb is not a common practice, therefore we again reached out to Dr. Du Fei who has experience in a similar project. From her feedback we decided to transform linearized fragments into Yarrowia lipolytica, and make use of the non-homologous end-joining mechanism, which has a higher chance in Yarrowia lipolytica, to randomly integrate the gene into the yeast's genome.

Build

Given the length of the construct and the time limit, also thanks to the suggestion from our PI, we finally decided not to get all subunits expressed in Yarrowia lipolytica, but rather complete the successful expression and detection of activity for subunit C as a proof-of concept that all subunits, with the same promoter and terminator, can be successfully expressed in Yarrowia lipolytica and cluster together to achieve its function of synthesizing DHA. To support this assumption, our Dry Lab also simulated the assembly of the cluster in cytosol.

To increase the cloning efficiency, we first constructed a new plasmid backbone containing the TEFin promoter and the LIP2 terminator with spacer, then inserted our ORF_C into the new backbone and finally added the LEU2 selection marker cassette to the plasmid by digestion and ligation. The same assembly and transformation approach (except the addition of LEU2) was employed as the previous cycle. Refer to the Cloning page for more details.

Test

The successful expression of each protein subunit, with the His-tag added, was detected by Western Blot, and the expression level is quantified through image analysis. Given the fact that individual subunits of PUFA synthase, if expressed correctly, still contain certain functioning domains that can catalyze the reaction, for example, dehydration, during the fatty acid synthesis on its own, a change in fatty acid profile after the expression is expected. Therefore, we further verified the activity of the subunit C with GC-MS to compare the change in fatty acid profile before and after expressing the subunit.

Result

For experimental details, please refer to our Experiment page.

Learn

After the proof-of-concept expression of a functioning subunit in Yarrowia lipolytica and measuring the expression level, we can make a relatively more accurate prediction of the DHA yield using our genome-scale metabolic model and mathematical yield model, which can be further improved according to the models. This leads to the next cycle, which is the optimizations for increasing DHA yield.

Design

During our discussion with Prof. David Banfield, we realized that constitutive expression of such a large enzyme may not be good for the yeast, and a controlled expression is preferred. While according to our engineering cycle 0, there are different optimal conditions for cell growth and lipid accumulation, so our design divided the whole process into two stages: growth stage and production stage. We first modified our yield model (see Model page) to predict if this strategy will help increase the yield by comparing the case to constitutive expression, and show a favorable result.

In order to achieve the control between stages, we need to use an inducible promoter to control the gene expression. However, we found that there are only few inducible promoters characterized in Yarrowia lipolytica, though it is already a common strain for industrial production, which was also mentioned during our interview with Dr. Young-Kyoung Park, an expert in Yarrowia lipolytica research, to be a major challenge. Given this situation, in this cycle we changed our focus to characterizing more inducible promoters in Yarrowia lipolytica.

From the result of cycle 0, we found out that nitrogen-limiting conditions are very beneficial for lipid accumulation, in our case DHA accumulation. So we choose nitrogen-limitation to be our inducing condition and identified several native promoters induced by nitrogen starvation, cleaving 1500 bp upstream of the coding gene. Among them two promoters pCIT1 and pIDP2 are chosen for testing. We use GFP under the promoters to test its activity and sensitivity to the nitrogen level by fluorescence measurement, and compare them to the standard TEF strong constitutive promoter to quantify the promoter strength.

Knowing that the native inducible promoters do not usually have a high enough expression level for production cases, we would also aim to improve the promoters by increasing its strength. We add Upstream Activating Sequence (UAS), in our case most-studied UAS1B from upstream of the native XPR promoter, to amplify the inducible promoters, which can significantly increase their strength with increasing copy numbers added.

We know the UAS functions in a distance-dependent manner, which means they need to be closer to the core promoter region in order to work well. However currently the promoters are all simply a cut of 1500 or 1000 base pairs in front of the native gene, and not well characterized where exactly the promoter region is. So after attempting to predict the conserved elements and known regulatory sequences, we decided to sequentially truncate the promoter from the 5’ end and try to identify the actual promoter region. At the same time we also try to add an intron sequence from pTEFin after the promoter, which is another reported strategy to increase its strength.

IMAGE+DESCRIPTION WILL BE ADDED SOON

Build

Using the same approaches mentioned above, we planned to construct 9 plasmid by inserting different promoter or promoter modifications into a GFP-Lip2t-pSB1C3 new backbone, and 3 of them are successfully constructed and tested:

• pTEF (BBa_)
• pCIT1 (BBa_)
• pIDP2 (BBa_)
• 1UAS1B+pCIT1 (BBa_)
• 2UAS1B+pCIT1
• 4UAS1B+pCIT1
• pCIT1+TEF intron (BBa_)
• pCIT1-810
• pCIT1-500 or pCIT-1000 depending on the testing results of pCIT1-810

Test

Strength and sensitivity of pCIT1 to [nitrogen source]

We acquired YNB without nitrogen source as the control medium and tested the promoter by GFP in different media with a serial nitrogen source concentration. Measurements are repeated to get more data points near the threshold conc. and the change of fluorescence over time was obtained. The fluorescence values are normalized with OD600 and compared to the GFP fluorescence under pTEF. For experimental details, please refer to the Experiment page. Influence of copy numbers integrated into the genome was minimized statistically by comparing the average value of multiple fluorescent colonies.

Graph 1: Initial fluorescence under difference [nitrogen source]

Result: the threshold conc. for activating the promoter / the relation between nitrogen source and the promoter strength

Graph 2: Change in fluorescence over time

We find out that the depletion of nitrogen over time does/does not affect the promoter strength. Estimate the nitrogen uptake rate.

Due to time constraints, the improvement strategies proposed are not tested in our Wet Lab. We will perform more experiments in the future to better characterize and improve the CIT1 inducible promoter.

Learn

In this cycle, we identified an inducible promoter that can be used for the stage control strategy in Yarrowia lipolytica. With the promoter characterized, people can also apply the strategy to many other biomanufacturing cases in Yarrowia lipolytica where a controlled expression and lipid accumulation are preferred.

With interchanges between 2 different conditions, the stage control strategy is highly associated with the overall design of the bioproduction process and hardwares involved, which lead to our Engineering Cycle 4, the bioreactor design.

Design

With our Dry Lab models, we perform sensitivity analysis to identify the metabolic substrates and pathways that affect DHA yield the most, and NADPH was found to be one of the key limiting factors. During fatty acid synthesis, NADPH is sorely used and oxidized to NADP+ to provide the reduction power. Extensive fatty acid synthesis leads to the depletion of NADPH without enough replenishment. Thus we identified an enzyme that can change NADP+ back to NADPH. It is achieved by enabling cell metabolism to prefer using NADP+ instead of normally NAD+ during glycolysis to provide the oxidation power.

Before introducing GapC: NAD+

After introducing GapC: NADP+

Then we introduced the new metabolic pathway with GapC enzyme into our Genome-scale Metabolic Model and predicted the engineering outcome, which shows an increased DHA flux and turns out to be the most significant improvement predicted among other potential engineering strategies. Prediction with the yield model by adding the equation representing this pathway further verified our optimization strategy. Therefore, we designed to introduce the codon-optimized GapC gene into our Yarrowia lipolytica with a constitutive TEF promoter and Lip2 terminator.

Design

As a biomanufacturing project, the final goal is to reach an industrial-scale production of DHA with our engineered strain. According to cycle 3.1, a two-stage fermentation approach was adopted, so a corresponding reactor system was proposed and modeled, which was shown on our Hardware page.

One of the biggest problems here is that lab-scale results cannot be directly used to calculate the final yield, cost and productivity, and the scale-up effect must be considered.

Our initial plan was to design and prototype the hardware for two-stage DHA production with our strain, however, after consulting somebody, founder of AlGreen and Prof. Marshal Liu in HKUST expertise in industrial biomanufacturing, we understand that such hardware is already mature and in use in existing facilities, which can be directly applied to our case, while it is unrealistic to redesign an production scale hardware without prior experience in the industry (please refer to Hardware page for more details). Interviews with Prof. Henry Lam and Dr. Zhu Yuanmin further confirmed this idea. Therefore, we decided to instead focus on modeling and testing the scale-up effect of our engineered chassis.

Build

A scale-up model was built. [need to know more about the model]

Test

Thanks to the suggestion and helps from Prof. Marshal Liu and technician Mr. Bruce Chan, we managed to conduct fermentation experiments with the 1 L bioreactor in the food lab under Chemical and Biological Engineering (CBE) department, HKUST, to test the growth and lipid accumulation of our strain under a preliminary scale-up scenario. We cultivated our engineered strain in the 1L-reactor with how many what media for some hours and obtained the growth curve with the same method described in Cycle 1. The results were compared to our model predictions:

(Show result below)

Learn

From the data derived, we modified the parameters in our scale-up model and thus predicted the conditions required during the industrial scale operation at some ton or liter scale. Based on the results, we further built a cost model to analyze the cost-effectiveness of our system. For more details, please refer to the Implementation page.

Reference:
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