In iGEM, the engineering mindset is in a spotlight to turn scientific research into innovations that promote bioindustry, sustainable solutions and creating something unforeseen. To create optimized and the most efficient solutions the engineering cycle – Design, Build, Test and Learn (DBTL) – is often used as a guideline. To create the most optimized strain of Synechocystis, our host organism, there were nested engineering cycles (Figure 1).
Design
MercuLess in about remediating waters from the toxic methylmercury with the means of synthetic biology. To
achieve this we had to find effective genes merA and merB
of the proteins MerA and MerB, respectively, which would work in our host organism Synechocystis sp. PCC 6803. The MerB protein is an alkylmercury lyase that separates the
mercury from the methyl group creating ionic mercury. The MerA is a mercuric reductase that reduces the ionic
mercury into its elemental form. We also had to choose the right ribosomal binding sites (RBS) for our genes
of interest but this was not simple. Although Synechocystis is a model organism, it
is much less known about than for example Escherichia coli and required more effort
and creativity from our team to find the best choice. Because there are no standard in
silico models for choosing the right RBSs in Synechocystis we had to go
with the old trial and error way of engineering. We were also not certain how well the genes would work. Here
we saw the possibility for testing two different merA and merB homologs to find the most suitable one.
While deciding which homologs to use we had to make sure the genes of interest were compatible to be
translated efficiently in Synechocystis . This issue was especially relevant since
half our genes are from Pseudomonas Aeruginosa , a Gram-negative aerobic bacteria,
which has a really high guanine-cytosine concentration unlike Synechocystis and a
whole different codon usage. To tackle this problem, we decided to try codon harmonizing software
“CodonWizard”
(Rehbein et al., 2019),
which matches the codons by their corresponding rarity.
To measure the performance of our constructs and to help us select the best candidate for the future, we had
to design a suitable measuring protocol. Because not much work is done with methylmercury and its measurement,
there are no standard protocols for measuring it, especially for Synechocystis. Due
to this we had to also engineer our measurement system to suit our purposes. We decided to take inspiration
from team Minnesota's 2014 project with merA and merB and
their experimental setup to study the activity of our genes of interest, and modified it for our needs. More
information on the modification process can be found from our
Design page.
Build
Because of the need for modularity in our construct design we were stirred into the way of Golden Gate
assembly. Fortunately for our team the Department of Molecular Plant Biology at the University of Turku had
just finished their new Golden Gate Cloning based assembly system for RBS optimization. This means they
already had a library of parts for the assembly, so we only had to design our genes of interest to fit the
assembly. We got the opportunity to use their cloning system, which was great because it allowed us to
conveniently make multiple different constructs with different RBSs and genes of interest. This assembly
system also allowed us to speed up the engineering cycle by studying multiple variables of the same construct
simultaneously.
To find out more about our part design and our final construct with optimized RBS go check our
Parts page.
Test
To test the performance of our genetically modified Synechocystis the object is to
test their efficiency in catalyzing methylmercury into elemental mercury. This is to be done by treating our
cells with methylmercury and then measuring the concentration afterwards.
(Click here for a more detailed protocol).
To get better certainty of our constructs’ toxicity to the cells, we also measure the oxygen usage
of the cells. To get the most optimal result,, the aim is to test different variations of the same construct.
To get certainty of the proper function of our measurement system the objective is to use a
negative control
A controlling method that gives us the information of the success when it does not work in given conditions
.
To save time we decided to go for a setup that was fast, affordable and did not require a collaboration with a
third party. This was an essential decision, since shipping samples requires a lot of extra time and effort
especially because our samples contain methylmercury and are extremely dangerous.
Our experimental setup needed to prove to us that both of our genes of interest, merA and merB , had activity. Because our genes are operating
under the same promoter, we had to somehow separately measure which changes are caused by merA and which by merB. To achieve this, we made single gene
constructs, where the other gene of interest was replaced with a reporter gene, either sYFP2 or EFE . This way even though we know that merA catalyzes the reduction of the ionic mercury and merB the
cleavage of the methyl group, we can with certainty know what causes what. We will also be treating the cells
with methylmercury and mercury chloride separately, to be sure.
Learn
To get the most optimal construct to transform methylmercury into elemental mercury in Synechocystis, we would iterate the process and learn from all results. In the next iterations the aim is to try other RBSs to see if more efficient dissociation of methylmercury and reduction of the ionic mercury would be achieved. The objective is also to multiply the number of merA genes to find out if that would make the reaction more efficient. Also, since there are multiple varieties of merA homologs we would test them out and see if any of them would be better than the one we are currently using. On top of these iteration cycles the aim is to test the effects of adding IPTG Isopropyl β-D-1-thiogalactopyranoside, a molecular biology reagent in our system and studying the effects of different promoters.
DBTL within building our constructs
Our laboratory work faced some trouble when trying to assemble our constructs. Although this slowed down our
process of measuring the process of turning methylmercury into elemental mercury we got valuable experience of
iterating the engineering cycle (Figure 2 a-e).
Designing the construct:
we designed as good a construct as we were able to with our current knowhow by using the
pDF backbone
The base of the plasmid, where our genes of interest are added
.
Building the construct:
we built the construct carefully with the new Golden Gate inspired assembly method.
Testing the construct:
we tested if the constructs are transformed into our host organism with antibiotic resistance using an
antibiotic, Spectinomycin. We also see that the constructs are correct by amplifying the constructs with PCR
and by running an electrophoresis.
Learning of the results:
when getting the results of the antibiotic selection and the electrophoresis we see if the resulting bands are
correct. If the bands are not what we were looking for, we get back to the design phase and make another
iteration of the engineering cycle to get a properly functioning assembly.
Figure 2. Our engineering cycles on creating the optimized construct visualized. A) The first iteration of our engineering cycle. The goal was to create a seven-part construct including our genes of interest with linear fragments with the new assembly method based on the Golden Gate. B) The second iteration of our engineering cycle. The first cycle was modified by having higher concentrations of the linear fragment to enhance the reaction. C) The third iteration of our engineering cycle. The objective was to achieve creating optimal constructs by using circular fragments subcloned from the linear fragments used in the previous cycle. D) The fourth iteration of our engineering cycle. The goal was to build the constructs by using the modified Golden Gate assembly method but using commercial fragments. E) The fifth iteration of our engineering cycle. Repeating and modifying the previous cycle with new knowledge.