DRY LAB OVERVIEW

Overview

BCoated revolutionises the way seeds are coated. We produce sustainable, modular seed coatings from bacterial cellulose (BC) through our unique production plaform. One of the actions we took to improve the production platform was to co-culture Komagataeibacter sucrofermentans with Saccharomyces cerevisiae. To form a stable co-culture, we engineered K. sucrofermentans and S. cerevisiae to be dependent on each other. In the co-culture, S. cerevisiae produces ethanol for K. sucrofermentans, as low concentrations of ethanol (1% (v/v)) have been shown to increase BC production1. However, S. cerevisiae produces ethanol at a higher rate than K. sucrofermentans consumes ethanol1,2. Consequently, to regulate ethanol production in S. cerevisiae and to control external ethanol levels, we developed a genetic circuit (ethanol homeostasis circuit) in S. cerevisiae. Additionally, the co-culture can be used to express proteins in S. cerevisiae that become incorporated in the BC produced by K. sucrofermentans3. The invertase of S. cerevisiae can also help to increase available carbon sources for K. sucrofermentans3. We did not model these latter two advantages. However, we did test S. cerevisiae protein secretion in the wet lab.

Implementing this co-culture in the lab is costly in terms of time and money. To guide the experimental implementation, and to keep costs and time investment low, models are often used. Models aim to provide a mathematical-based prediction or simulation of a biological phenomenon. For our project, we developed two models. The first model is an ordinary differential equations-based model (ODE-based model) that describes the dynamics of K. sucrofermentans and S. cerevisiae within the consortium. The second model is an ODE-based model that simulates the behaviour of the ethanol homeostasis circuit in S. cerevisiae.

This page gives an overview of the two models. The models themselves can be found on GitLab for future teams to use to design engineered co-cultures.

Consortium model

To better understand the complex metabolic interactions and cross-feeding mechanisms within the K. sucrofermentans - S. cerevisiae consortium, we developed an ODE-based model. This model also helped in the optimisation of growth conditions and in the assessment of the stability of the consortium: this is important since competition for substrates and nutrients from the media ultimately compromises the yield of BC. The simulations made by the consortium model can be used to help scale-up and identify the ideal growth conditions for maximal BC yield, saving time and money. We defined the stability of the consortium based on biomass growth curves, of both K. sucrofermentans and S. cerevisiae, in the consortium over time.

The consortium model simplifies complex metabolic pathways as singular reactions, in which the concentrations of substrates and products are measured over time. We assume that the metabolic pathways of biomass growth, cross-feeding, and carbon source metabolism all interlink at acetyl-CoA, thus resulting in an acetyl-CoA junction (Figure 1). This simplification of the mathematical model allows for easier troubleshooting, while accounting for the most relevant pathways. This is used to improve the model when contradictory results between computational simulation and laboratory experiments are obtained.

Our model for the consortium takes inspiration and builds on previously established ODE models for monoculture of E. coli4. Following their workflow, we built two monoculture models for S. cerevisiae and K. sucrofermentans. We then fit the monoculture models to the individual growth data, based on wet-lab experiments, from which we combined the two models to build the consortium model.

Our model accounts for three compartments, namely environment, S. cerevisiae, and K. sucrofermentans, and 17 Ordinary Differential Equations (ODE) (Figure 1), where each ODE represents a crucial metabolic step. Each metabolic step, with the exception of ethanol and acetate diffusion, is modelled according to irreversible Michaelis-Menten equations. Growth is modelled as the result of acetyl-CoA entering the TCA cycle4, under the assumption that there is constant yield of biomass per mole of acetyl-CoA5.

Metabolic overview of the consortium as accounted for by the model. The consortium used for the production platform consists of K. sucrofermentans and S. cerevisiae, which utilise different carbon sources to avoid competition. S. cerevisiae only grows on maltose since all glucose transporters are knocked out and produces ethanol that K. sucrofermentans requires for energy generation and assimilation. Additionally, S. cerevisiae consumes acetate, to inhibit acidification of the medium. Through an acetyl-CoA (AccoA) junction, a cross-feeding mechanism is established to ensure consortium stability.

Based on laboratory data, we observed that the growth rate of S. cerevisiae was faster with a higher carrying capacity than that of K. sucrofermentans. To reduce competition for the same substrate, a fermentative strain of S. cerevisiae, growing purely on maltose was used, alongside K. sucrofermentans growing purely on glucose.

We used the optimised monoculture models to simulate a consortium, and upon running multiple iterations, we observed an overproduction of ethanol through the acetyl-CoA junction, which is detrimental to the consortium, thus establishing the need to ensure a constant ethanol concentration in the environment.

Curious? Learn more about this dynamic model and our findings

Ethanol homeostasis circuit model

In the co-culture, S. cerevisiae produces ethanol for K. sucrofermentans, as low concentrations of ethanol (1% (v/v)) have been shown to increase BC production1. However, S. cerevisiae produces ethanol at a higher rate than K. sucrofermentans consumes ethanol1,2. Consequently, to regulate ethanol production in S. cerevisiae and to control external ethanol levels, we developed a genetic circuit (ethanol homeostasis circuit) in S. cerevisiae. We designed our ethanol homeostasis circuit to reduce the ethanol production rate of S. cerevisiae in response to higher external ethanol concentrations. To achieve this purpose, our ethanol homeostasis circuit needed to be sensitive to the external ethanol concentration. For this reason, we made use of an ethanol-inducible promoter (Figure 2).

The ethanol-inducible promoter expresses an enzyme called pTEV+ , which degrades a sequence called an N-degron6,7. The N-degron is fused to either phosphoglycerate kinase 1 (PGK1) or phosphoglycerate mutase (GPM1). Once the N-degron is cleaved by pTEV+, the degron is activated and the tagged protein is degraded7. Both enzymes are involved in the fermentation of glucose. We reasoned that by degrading the enzymes involved in fermentation, we would reduce the ethanol production rate of S. cerevisiae.

Overview of ethanol homeostasis circuit. From the medium, glucose is taken up by S. cerevisiae and fermented into ethanol. PGK1 and GPM1 are two enzymes involved in fermentation. Ethanol diffuses into the environment across the membrane of S. cerevisiae. Ethanol in the external environment may be taken up by K. sucrofermentans for biomass production. The ethanol inside S. cerevisiae induces transcription of pTEV+. pTEV+ degrades either PGK1 or GPM1. We hypothesised that this will reduce the ethanol production rate in S. cerevisiae. DPG: 3-phospho-D-glyceroyl-phosphate. G3P: 3-phospho-D-glycerate. 2-PG: 2-phospho-D-glycerate.

Our ODE-based model was simulated to check the biological plausibility of the model and was later further fine-tuned to match experimental data (Figure 3). Being able to describe experimental data, we used the predictive power of the model to assess the effect of degrading either PGK1 or GPM1 on the ethanol production rate of S. cerevisiae. Moreover, we assessed which of the experimentally tuneable parameters were important for reducing the ethanol production rate of S. cerevisiae, and we further investigated the qualitative effect of some of these important parameters. These findings enabled us to give concrete directions to the successful implementation of the ethanol homeostasis circuit in the wet lab.

Comparison between the rescaled null model simulation and the S. cerevisiae experimental data collected in the lab for initial glucose concentrations of 2%, 5%, and 10% (w/v). The top panels show the model simulation. The bottom panels show the experimental data. Error bars represent standard deviation. 2% (w/v) condition is indicated in brown. 5% (w/v) condition is indicated in blue. 10% (w/v) condition is indicated in yellow.

We also used the model to investigate the effect of initial S. cerevisiae biomass and ethanol uptake by K. sucrofermentans on the external ethanol concentration. These findings highlighted the importance of carefully regulating the population dynamics of the two organisms in order for the ethanol homeostasis circuit to be successful in the consortium.

Curious? Learn more about this dynamic model and our findings

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