Team:Amsterdam/Project/emulsions
Evolving Romance
Moving in Together
Overview
Background
Emulsion based Culturing methods can be used to select for organisms with high yield strategies. Thus this technique has the potential use for the discovery and optimization of consortia. It can be used to fish out consortia with high yield strategies. As well as take advantage of the genetic variability in a population to optimize for individuals who interact more efficiently between each other.
Aim
Our aim is to develop a emulsion culturing method that would allow us to select for "naturally" occurring consortia. Added bonus we can use the protocol to optimize natural or genetically engineered consortia.
Approach
We adapted the method of Bachmann et al. to create emulsion. Also methods were developed to count cell of Co-cultures using the Coulter Counter and Florescence-Activated Cell Sorting(FACS).
Results
- We have demonstrated that Synechocystis a photoautotroph is able grow inside the emulsions.
- We demostrated that both Synechcystis and E.coli can be recovered from the emulsion both seperately and in co-culture.
- Develope methods for the detection and counting of Synechocystis and E.col using a Coulter Counter and FACS.
Connections
- Stable Romance: Measure stability.
- Engineering Romance: Estimate parameters of new strains.
- Simulating Romance: Provide parameters.
Fishing out a Consortia
Stable Romances tend to evolve when there is a perfect understanding between the individuals. Whether a match is a perfect fit (or not!) can be tested when partners are forced to interact for instance, when people move in together. So imagine applying the same principle to consortia were we deduced that an underlying property of all consortia is that ultimately they will display a so-called “high-yield strategy”. By High-yield strategy, it is meant that a more efficient use of natural resources leading to a greater number of individuals will be observed. This is even more so the case when instability is a serious threat. ,
What if there was a way to select these high-yield strategy consortia and actually make them interact better? How would one do this? Well… just like with people, this is possible to test by making microbes move in together.
We decided to tackle this problem by looking at studies done using emulsion based techniques selecting for high-yield strategies (Bachmann et al.). In this paper, the authors discuss the tradeoff that occurs between growth rate and growth yield in microbes. They demonstrate that compartmentalizing the availability of public goods while serial propagating leads to the selection of organisms with increased yield strategy, i.e. able to generate ever higher cell numbers (biomass) from the same portion of substrate. We apply the same logic to consortia by developing a dedicated emulsion-based protocol. The basic principle is that by randomly combining different individuals of each side of the partnership in isolated compartments (micro-droplets), perfect matches will lead to increased numbers of offspring of both photo- and chemotroph. If propagated in time, ultimately this will enrich the mixture in both photo- and chemotrophs that do well when placed in a consortium while purging the population of the ones that do not.
This method can be used to select “naturally” occurring consortia, or to evolve the synthetic consortia that have been rationally engineered. For example, chemotrophs that use more efficiently the carbon source provided by the phototroph can be co-selected at the same time that the population is enriched in phototrophs that, not only grow efficiently on the required nutrient produced by the chemotroph, but also are more capable of fixing CO2 and “willing” to release the C-source. After this process, biobricks of choice can be added to the chemotroph in order to produce a compound with the knowledge that the chemotroph consumes the produced carbon more efficiently – and hence (if all things alike) is potentially able to make the product of interest also more efficiently.
Methods summary
Emulsion method
Emulsions were created using 700ul HFE 7500 mixed with 0.2% picosurf as a surfactant. This was mixed with 300μl of medium (BG11 enriched with TES buffer, Bicarbonate and 5mM ammonium chloride) with the expectation to create 4.6x106 emulsion with an average size of 50μm (Figure 1). Incubation was performed under high light conditions at 30oC. The breaking of the emulsion was performed using breaking solution (1H,1H,2H,2H-perfluorooctanol), It is important to note that the oil in this solution was heavier than the water thus the medium phase remains on top and is easily extractable after adding 700ul of medium. Emulsion integrity is determined using a microscope and viewing average size of the emulsion.
We first determine the number of cells needed to inoculate as many droplets as possible with two cells. This is performed by calculating poisson distributions where the lambda is determined by the number of cells in 300 μl divided by the number of emulsion created by 300 μl of medium in 700 μl of oil. Once the emulsions are created and broke open, the number of cells are counted and diluted to the concentration that allows for two cell to be inside each droplet. Serial propagation is needed to test whether the method works. The duration of such seral cultivation depends greatly on the organisms being used. With Synechocystis, this technique would take a few months due to its generation time, consequently, it is not possible to do within an iGEM project. Nonetheless, since (i) all simulations suggest this is possible (link showing that final ratios are independent than starting ODs); (ii) evidence is provided below showing that we can successful recover cells of both partners; and (iii) we also show that the synthetic consortium works (link to plates with consortia); it is quite reasonable to expect that this will work in the near future when implemented.
Processing Emulsion Cell Count
Cell counts were determined using a Coulter Counter. Cells suspended in electrolyte are separated via a microchannel compartment. The cell is drawn in causing a brief change in electrical impedance, which is recorded by the machine. In our case, one could clearly distinguish between Synechocystis and E.coli cells due to the fact that E.coli is on average 1μm and Synechocystis about 2-3μm
Another method used for counting was Fluorescence-activated cell sorting (FACS). This method allowed for cell identification based upon fluorescence and specific light scattering techniques. We could clearly differentiate between Synechocystis, which presents fluorescence at the far red spectrum in contrast to E.coli that shows no autofluorescence.
Estimating parameters
Growth rate
Growth rates were estimated by fitting a regression model to the logarithm of the OD over the time during the exponential phase of growth. The slope of this model is considered as the growth rate since it shows how much the OD changes per unit of time. Its unit is 1 / h.
Production rate
Qp are obtained by dividing the the concentration of measured product by the biomass of the culture estimated from OD (1 OD unit = 0.2 gDW). This value is in fact the production yield, that is, how much product the cells excrete per amount of biomass. To get the production rate, Qp, we multiply this value by the estimated growth rate. Its units are amount of product (in mmol) / gDW * h.
Results
Stability
In Our turbidostat experiments we observed the results we expected. In the lactate strain (figure 1), a non growth coupled carbon producer, we started to observe an increase in growth rate about 300 hours after the beginning of the experiment. At the same time point we recorded a drop in the production of lactate. This result confirms our hypothesis about how mutations leading to the loss of production can be quickly fixated inasmuch as it releases the burden imposed by the carbon production increasing the fitness of the mutated cells.
On the other hand the growth coupled acetate producer, Δacs, shown a constant production and growth rate over the length of the experiment (figure 2) demonstrating that this strategy is more stable than the classical engineering approach. The Qp estimated around 400 hours shows a decrease in the production but it is probably due to experimental failure. In the turbidostat cells were grown at very low OD (threshold was set to 0.35) therefore the concentration of acetate was below the detection limit of the enzymatic assay method. To overcome this problem we dehydrate the samples by lyophilization, also called freeze-drying, then dilute them in an smaller volume therefore increasing the concentration above the detection limit. Although this method worked (we obtained similar Qp values using different culture systems), the sample processing could have introduce noise in this measurement.
Parameters
In table 2 the physiological parameters obtained for six strains producing glycerol, lactate and acetate are presented. The lactate strain shown the highest Qp followed by Δacs and Syn1413. An intriguing results is the differences in Qp and growth rate of Δacs between cultivation methods. In the turbidostat the length of the exponential phase from where the growth rate is calculated is smaller than in photostat which could be influencing the estimation. For the Qp the previously discussed argument could also be an explanation for this difference. In addition it is possible that the photostat conditions are more favourable for the growth of Synechocystis.