Team:Amsterdam/Project/Eng rom/Photosyn car
Engineering Stable Carbon Production
Sharing is Carbon
Overview
Background
The driving force of our consortium's romance is the phototrophic carbon-sharing module: an engineered Synechocystis that fixates CO2 and produces compounds that can be used by a chemotroph to create desired end-products like biofuels.
Aim
Productive relationships need to stand the test of time. Having experienced the perils of instability firsthand, we sought to engineer carbon production in way that would last.
Approach
Using genetic engineering strategies guided by modelling results, we used a combination of gene knock-outs and over-expressions to target a pathway that would result in growth-coupled acetate production.
Results
- We engineered growth-coupled acetate production by knocking out the acs gene, showing that this indeed leads to stable acetate production
- We over-expressed the Pta and AckA1 genes to increase the flux towards acetate formation.
Connections
- Stable Romance: Measure stability.
- Engineering Romance: Estimate parameters of new strains.
- Simulating Romance: Provide parameters.
Change of plans
Different carbon compounds produced by Synechocystis could serve as fuel for E. coli in our consortium: Glucose, lactate, and glycerol for example, are all products that an engineered cyanobacteria can produce. Although glucose seems like a great compound to drive sustainable bio-production with, our initial plan was to develop Synechocystis strains that produce all of these products, and to compare their performances in a consortium with E. coli generating different products.
After after the inital results of lactase production came in, however, we decided to make genetic stability the central focus of our carbon fixation efforts: engineering a Synechocystis that produces a carbon compound - acetate, based on our modelling results - in the most stable way possible such that its relationships with other consortia-species last more than one or a few nights.
This system allow us to reduce the time of observing a mutation but now it comes the second problem. How do we know if a mutation leading to a change in the parameters has happened? The first option that comes to our synthetic biologist mind is by sequencing periodically the genome of the population and screen it for mutations. The major drawback of sequencing is that it does not inform whether the mutation affects the parameters or not. The remaining solution is then to estimate the physiological parameters with high time definition to spot when the mutation has occurred. Using the turbidostat it would be possible to store all the values recorded by the spectrophotometer and calculate the growth rate from these data. This is done by fitting a linear model to the logarithm of the OD over the time. The production rate can obtained by periodically measuring the amount of product in the medium (link to protocols for enzymatic assays and HPLC).
Luckily for us when we came to the lab there was a master student, Joeri Jongbloets{link}, who had transformed a multicultivator (MC) like this{link} in a 8 channel turbidostat. For his internship he wrote the program that connect with the MC hardware and control the pumps to dilute the culture when necessary. In addition the software stores the measurements from the MC spectrophotometers in a database and analyzes it to obtain the growth rate. Thanks to this impressive piece of software engineering we were able to run long term experiments and observe evolution.
Methods summary
Cultivation conditions
The strains producing glycerol, lactate and acetate (Δacs) were grown in turbidostats for 900 hours under constant LED lights providing 20 μE/ m2* s * OD.
The Pta1, Pta2, Δacs and Ack strains were cultured on photostats. This systems, also implemented by Joeri Jongbloets in the MC, is basically a batch culture where the light intensity per OD provided to the cells is maintained constant. In this case the light intensity per OD was set to 20 μE/ m2* s.
In both cases the culture medium was BG-11 supplemented with NO3- and TES buffer {link to recipe}.
Measuring product
Samples were taken from the cultures and centrifuged (5 min, 14.500 rpm) to obtain the supernatant. Product concentration was then estimated by enzymatic assays {links to protocols} in the case of lactate and acetate and by HPLC for glycerol {links to protocols}.
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.
