Difference between revisions of "Team:Amsterdam/Project/Eng rom/Photosyn car"

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 the initial 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 in the most stable way possible such that its relationships with other consortia-species last more than one or a few nights. Based on the results of our software tools, which parsed the genome-scale metabolic map to search for compounds that could be produced in a growth-coupled way, we decided to engineer acetate production.

As the genetic engineering of carbon production in Synechocystis involved relatively simple constructs, standard restriction-digestion cloning protocols were used for most engineering efforts. Synechocystis knock-outs were created using the specific markerless knock-out method described by [ref]. This two-round knock-out approach allowed for the cultivation of knock-out strains without the need of antibiotics and thus increased flexibility of implementation in a potential consortium.

Acetate production involved the knock-out of the native acs gene and heterologous over-expression of the ackA1 and pta genes.

The following constructs were used for the knocking out of acs and insertion of ackA1, pta, or fused AckA1/pta:

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.

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 Q_p 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 Q_p values using different culture systems), the sample processing could have introduce noise in this measurement.

In table 2 the physiological parameters obtained for six strains producing glycerol, lactate and acetate are presented. The lactate strain shown the highest Q_p followed by Δacs and Syn1413. An intriguing results is the differences in Q_p 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 Q_p 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.