Difference between revisions of "Team:Marburg/Results"

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One novel key feature of our toolbox are the connectors. They were designed in order to function as insulators to prevent crosstalk between neighboring transcription units <a href="https://2018.igem.org/Team:Marburg/Design">(Link to Design)</a>. Therefore a perfectly insulating connector would prevent the readthrough from backbone sequences that most probably caused the notably high expression that was measured in the promoter experiment for the dummy promoter (Verweis zum Promoter Experiment). In addition to blocking transcriptional readthrough, a good connector must not possess any cryptic promoter activity. <br>
+
One novel key feature of our toolbox are the connectors. They were designed in order to function as insulators to prevent crosstalk between neighboring transcription units <a href="https://2018.igem.org/Team:Marburg/Design">(Link to Design)</a>. Therefore a perfectly insulating connector would prevent the readthrough from backbone sequences that most probably caused the notably high expression that was measured in the promoter experiment for the dummy promoter <a href="https://2018.igem.org/Team:Marburg/Result#Promoter_Characterization">(Jump to promoter characterization)</a>. In addition to blocking transcriptional readthrough, a good connector must not possess any cryptic promoter activity. <br>
 
We focused on characterizing the 5’ Connector because we expect the stronger influence on signal strengths. For characterizing our connector parts, we created 20 test plasmids with the <i>lux</i> operon as the reporter. <br>
 
We focused on characterizing the 5’ Connector because we expect the stronger influence on signal strengths. For characterizing our connector parts, we created 20 test plasmids with the <i>lux</i> operon as the reporter. <br>
 
In our toolbox we provide five short connectors, which solely possess the fusion sites for LVL2 cloning, and five long connectors which additionally harbor self-designed insulators. Each of these ten connectors were cloned with the constitutive promoter J23100, to check for effects on an active promoter, and with the Promoter Dummy to quantify the extent of transcriptional activity that reaches the Promoter Dummy. <br>
 
In our toolbox we provide five short connectors, which solely possess the fusion sites for LVL2 cloning, and five long connectors which additionally harbor self-designed insulators. Each of these ten connectors were cloned with the constitutive promoter J23100, to check for effects on an active promoter, and with the Promoter Dummy to quantify the extent of transcriptional activity that reaches the Promoter Dummy. <br>

Revision as of 09:45, 17 October 2018

Results

However beautiful the strategy, you should occasionally look at the results
-- Winston Churchill

After having conceived our projects, we went to the lab and tried to fulfil our expectations. Successes and failure were used to improve our projects strategy by iterating theoretical and practical working steps.
On this page, we want to convince you that we managed to realize some of our visions and are able to show achievements in all of our subprojects.
Foundational experiments with V. natriegens

Flow cytometry

At many different occasions, at meetups or conferences, we showed the growth curve of V. natriegens compared to E. coli (Link). Other scientists were impressed by the extremely fast growth rate but even more by the high OD600 that we could show. We were asked many times if the high OD600 is really due to a high cell density or if it is rather caused by other components like cell debris or substances secreted to the medium which contribute to the measured absorbance.
To find out more about the growth dynamics, we decided to acquire a growth curve of V. natriegens in the most direct way, by counting cells in a flow cytometer. We inoculated three baffled flasks from stationary cultures and took samples in 15 minute intervals while the bacteria were incubated at 37 °C with shaking at 220 rpm. The OD600 of these samples was measured in a normal photometer and the cultures were then immediately analyzed by flow cytometry. The flow cytometer directs the samples through a thin capillary so that the cells pass a laser bean one by one and thus, can be counted and analyzed independently. A constant flow rate and time for data acquisition was set, which results in measuring a defined sample volume. Together with the counted events, the cells per volume can be calculated.

Figure 1: Comparison of OD600 and events/µL
OD600 is shown in red and events/µL measured with flow cytometry is shown in blue
A comparison of the OD600 to events/µL values are shown in figure xxxx.
When we planned this experiment, we were most curious about the composition of the culture in the stationary phase to answer the question if the high OD that V. natriegens can reach is the result of a high cell density or if it can be traced back to other substances. Interestingly, both values, OD600 and events/µL start to stagnate at a similar time point (165 min). We interpret this result as a confirmation that the high OD is indeed caused by bacterial cells.

By carefully comparing the shape of both growth curves, we realized that, in fact, the most striking data in this plot can be found at the beginning of the experiment. While exponentially increasing values can be seen right from the start for the curve created from the OD600 data, a short lag phase is apparent when events/µL are plotted (figure xxxx). We tried to find an explanation for this observation and realized that the absorbance of a culture does not necessarily correlate with the concentration of cells but rather with the biomass inside the flask.
Figure 1: Scatterpolt acquired by flow cytometry
A single sample after 45 minutes is shown as an example. Each dot represents one event
Fortunately, additional data can be obtained from the forward and side scatter of the laser beam in a flow cytometer which provide information about the size and inner complexity of the analyzed cells, respectively. Figure xxx exemplary shows one sample at t = 45 min. The side scatter (SSC-A) is plotted on the Y-Axis versus the side scatter (FSC-A) on the X-Axis. Each dot in this scatter plot represents one detected event and a heatmap can be used to visualize many events with the same properties (red = high event cont; blue = low event count). The population in the top right corner comprises roughly 98 % of all events and represents fully viable cells, since this is the population that increases in number throughout the growth experiment (peaking at >99% of all measured events at mid-log phase). The population in the bottom left corner most likely consists of sick or dormant cells and cell debris, since the number of events in this population remains low throughout the experiment.

Figure 1: Comparison of forward scatter versus events/µL
The measured events/µL are shown in blue and on the left Y-axis. The forward scatter is displayed in green and on the right Y-axis
We plotted the mean forward scatter values of all cells together with the events/µL. The mean side forward value dramatically increases during the first data points with a peak after 45 minutes. This is also the same time point which we identified as the beginning of the exponential growth phase. During the subsequent course of the experiment the forward scatter values decrease again, reaching a minimum when the culture goes into stationary phase.
Taking all three datasets into account, we suggest that the cells start to grow in size upon provision of fresh medium but initially without undergoing cell division. This results in an increase volume of individual cells and thus, an increase of the measured OD600 but without significant changes to the cell concentration in the culture. After 45 minutes, when the forward scatter peaks, we assume that a majority of cells reach maximum cell volume initiate rapid division, quickly entering the exponential phase. During the ensuing time points, exponential growth can be observed and the decrease of the forward scatter is a hint for a reduction in mean cell size.
Figure 1: Histograms of forward scatter during the course of the experiments
The histograms are plotted from top to bottom during the time course of the experiment
To additionally visualize the composition of the measured cells in regard to the forward scatter, we created figure xxx showing histograms of the forward scatter at different timepoints. It is apparent that the population is heterogeneous at the beginning of the experiment and at the end when the cultures again reaches stationary phase. During the period of exponential growth, the sample is more homogenous. The already discussed trend in the forward scatter curve can also be observed with these histograms which show a shift to the right when the forward scatter peaks and a shift to the left for the following time points.

We want to thank Dr. Max Mundt who carried out the experiments with us and who helped with analyzing the data.


Mutation rate in Vibrio natrigens

Many people have asked about the mutation rate of Vibrio natrigens worrying its high growth speed would be accompanied by a higher mutation rate. Some were concerned, that a mutation may ruin many days of labor, or lead to a pathogenic Vibrio natrigens mutants, and others were hoping they could speed up their mutation experiments.

The mutation rate is the frequency in with new mutations appear in an organism. A mutation in a gene may be a silent mutation meaning it has no effect, but it is also possible for the gene to loss its function, or to alter its function. These mutations occur on the one hand through DNA-damage, caused for example by radiation. And on the other spontaneously through mistakes the DNA-polymerase makes during the DNA-replication. For most organismens the spontaneous mutation rate is known, as for example for Escherichia coli and Vibrio cholerae , it was determand by selekting on mutations, and observing phenotyps, (S. E. Luria and M. Delbrück 1943) and in the most recent years by genome sequencing as this method start to be get cheaper, faster and more precise then to estimate the mutation rate based on a phenotypical change. (Patricia L. Foster 2015) (Marcus M Dillon 2017). The mutation rate plays a major role in multiple arears of biology influencing for example the rate in with pathogenic microorganisms, gain of antibiotic resistances. Or how fast microorganisms adapt to a new environment (Christopher B Ford 2013).

In order to determent the mutation rate ourselves, we conducted a mutation experiments to estimate the mutation rate (probability of mutation per cell per division or generation) of Vibrio natrigens. The mutation rate of Vibrio natrigens can be estimated with the number of mutation events in a culture and the final number of cells in the culture (Patricia L. Foster 2006). To determan the number of muation events mutants have to be identified. This can be done with rifampicin. Rifampicin is an antibiotic which inhibits mRNA transcription by obstructing its elongation path through binding to the β-chain. We used it to determine the number of mutation events, because through some specific point mutation or a specific deletion in the β-chain of the RNA-Polymerase Vibrio natrigens can gain a rifampicin resistance (Wu and Hilliker, 2017). These mutants can be selected, when plated out on rifampicin.

By testing sister colonies of Vibrio natrigens in this manner and counting the resistant colonies we estimated the mutation rate with the Lea-Coulson median estimator (Lea and Coulson, 1949). Herefor we inoculated 14 sister cultures of Vibrio natrigens with the same OD600 (About 0.0001) out of an exponential pre-culture. When the sister cultures almost reached the stationary phase, we platted them on rifampicin- and on plats without an antibiotic. After evaluating these plates, we could estimate the Median number of mutants in a culture, by counting all rifampicin resistant cultures and apliing the Lea-Coulson median estimator. The final number of cells in the culture was estimated by counting the colony forming units one the plates without antibiotics.

The calculated number of mutations per culture with the Lea-Coulson median estimator was 5,7925 and the estimated final number of cells in the culture were 3165000000 cells. By setting the number of mutations per culture in relation with the final number of cells the mutation rate can be estimated. Therefor the number of mutations per culture is divided by the final number of cell (Patricia L. Foster 2006). The mutation rate we estimated was 1,83017E-09, and there for a bit lower than the mutation rate of Escherichia coli with was estimated by Luria and Delbrück (Luria and Delbrück, 1943) in a comparable experiment for of about with was calculated to be 3,2E-09 (S. E. Luria and M. Delbrück 1943). It is noticeable that we determent the number of mutations per culture in our experiment for Vibrio natrigens with the Lea-Coulson median estimator since our number of mutations per culture was estimated to be between 4 and 15 unlike how the mutation rate of Escherichia coli, was determined. For Escherichia coli the p0 method was used, since the mutations per culture were between 0,3 and 2,3 (Patricia L. Foster 2006). Less mutations per culture occur in an Escherichia coli, growing to a lesser OD600 Escherichia coli, cultures do not undergo as many cell divisions, thus fewer cells have the opportunity to mutate and less mutation events take place. However, our experiment suggests, that the number of mutants per cells are higher in Escherichia coli, meaning for example, the possibility to choose a mutated colony is higher for Escherichia coli.

With our results, we are now able to answer the daring questions of our fellow researchers and the public audience regarding Vibrio natrigens mutation rate, sowing, that if switching form Escherichia coli to Vibrio natrigens it is not necessary to fear an increase in unwanted spontaneous mutations. We still assume Vibrio natrigens can speed up mutation experiments since there are more mutations occurring in a culture, increasing the genetic variability of the culture. Our data is not conclusive but enables an estimation of the mutation rate of Vibrio natrigens for a more precisely calculated mutation rate can be obtained by howl genome sequencing of multiple Vibrio natrigens as it has been done for Escherichia coli (Heewook Lee et al. 2012).

Results Part Collection
We created a simple and reliable workflow for the characterization of parts from the Marburg Collection in V. natriegens
(Link to Measurement). Experimental data for constitutive and inducible promoters, RBS strength, terminator readthrough, ori dependent plasmid copy number and the behavior of our newly designed connectors were obtained.

After creation of the Marburg Collection, we wanted to characterize the parts in V. natriegens. When we started with our project, we had no clue about the behavior of the genetic parts that were integrated into our toolbox. Previous research mainly focused on microbiological description rather than characterization of synthetic constructs as we already discussed in our V. natriegens review (Link to Description).
We decided to characterize the parts in our Marburg Collection and hence we did pioneering work to provide the scientific community the data that enable rational utilization of V. natriegens for various applications in synthetic biology.

Before acquiring the final data, we established a fast and convenient plate reader workflow that is tailored to the fast growth rate of V. natriegens. We demonstrate its superior performance and discuss considerations in terms of plasmidal context and data analysis on our measurement page (Link to Measurement).

Promoter Characterization

After having established an experimental and data analysis workflow and after determining the optimal plasmidal context for reporter experiments, we started to apply our knowledge to characterize the parts in our Marburg Collection.

Figure 1: Relative promoter strength of Anderson promoters
Data were normalized over the strongest construct J23100. Error bars represent the standard deviation of the measurements of three independent experiments
We started by measuring the promoter strength of the Anderson Promoter library in V. natriegens. Firstly, we assembled 19 test plasmids with golden-gate-assembly and measured their expression strength, following our selfmade workflows. The results are shown in figure 1. We observed an even distribution of the tested promoters throughout the dynamic range. The strongest promoter (J23100) yielded 40 fold stronger signal than the promoter dummy and was used as a reference to calculate relative promoter strengths. The test constructs were built with dummy connectors which did not possess insulator elements. We assume that this resulted in additional expression caused by transcription throughout the rest of the plasmid, e.g. ori and antibiotic resistance. This is thought to add the same extent of signal to all measured promoters thus reducing the overall dynamic range. To further evaluate this assumption, we could repeat this experiment with one of our insulators instead of the dummy connector.

Characterization of pTet

In addition to constitutive promoters, the Marburg Collection contains two inducible promoters, pTet and pTrc. For all experiments with inducible promoters, we added the respective inducer concentration to the preculture as well as to the main culture to ensure constant expression.The first experiments were performed with the pTet promoter that can be induced by the tetracycline derivative anhydrotetracycline (ATc). ATc is much less cytotoxic but still capable of binding and altering the structure of the repressor TetR, leading to release of the promoter and enabling transcription. To measure the dose response behavior of the pTet, we made a dilution series of ATc. Following the recommendation of our advisors (Stefano Vecchione), we started with the concentration commonly used in E. coli, started with the concentration (100 ng/mL). The starting concentration was diluted twofold in 20 subsequent steps. Our results are shown in figure 2. The absence of bars for the four highest concentrations is due to the fact that the cultures did not reach an OD600 of 0.2 in the six hours of the measurement. Remarkably, we observed reasonable growth of those same cultures in the preculture already induced with the identical amount of ATc. Knowing that luminescence is produced at the end of an enzymatic cascade, starting with intermediates of the phospholipid metabolism ( Meighen 1991), we reckon that very strong induction could decrease the fitness of cells and that after dilution in room temperature medium, strained cells are not able to recover from the stationary phase. However, we only observed this phenomenon in experiments with pTet, although we obtained higher signals for the strongest constitutive promoters as well as for the highly induced pTrc. We checked for toxicity of ATc but could not see a measurable effect. Another possibility is that TetR interacts with components inside the cell and that high ATc increases these interactions. Blast searches of TetR against the genome of V. natriegens identified one protein that shares some homology with the N-terminal part of TetR which could result in cross talk between the host and the inducible promoter.
Figure 2: Dose response of pTet with ATc.
J23100 was used as positive control and for normalization. Error bars represent the standard deviation of the measurements of three independent experiments
All measured data were normalized to the strongest constitutive promoter J23100. Saturation occurred at a dilution of 2^6 (~ 1.6 ng/mL) and an exponential reduction of luminescence signal can be observed for higher dilutions. In the absence of ATc, the signal is twelve fold lower compared to saturation.
pTet allows relatively tight control of gene expression and is therefore well suited for driving the expression of potentially toxic proteins. On the other hand, we were not able to induce strong expression that can compete with strong constitutive promoters or the fully induced pTrc.

Characterization of pTrc

pTrc is the second tested inducible promoter. It contains lac operator sites and is therefore regulated by the repressor LacI which is constitutively expressed from a downstream gene. pTrc can be induced Isoopropyl-β-D-thiogalactopyranosid (IPTG), a chemical derivative of lactose ( Camsund et al.2014). Similar to our experiments with pTet, we made a dilution series starting with the commonly used IPTG concentration for E. coli 0.5 mM. We observed a five fold induction and a saturation that occurred at a dilution of 2^5 (~15 µM). The strongest expression is similar to the expression gained from the strongest constitutive promoter J23100 while the expression in the absence of inducer equals medium strong promoters. As a consequence, we do not recommend using pTrc in constructs where a tight control of gene expression is desired. Instead, pTrc is well suited when strong expression is required.
Figure 3: Dose response of pTrc with IPTG.
J23100 was used as positive control and for normalization. Error bars represent the standard deviation of the measurements of three independent experiments
Taking the results of both inducible promoters into account, we made two observation. In both cases, the dynamic range is smaller compared to E. coli and the inducer concentration that facilitates saturation is 32 and 64 fold lower for pTrc and pTet, respectively, than the concentration that is typically used for E. coli. A possible explanation could be found in the fast growth of V. natriegens which might result in a lower concentration of the repressor proteins in the cells, finally leading to a less restricted control of the negatively regulated promoters. However, we do not have experimental support for our idea.

Characterization of Connectors

One novel key feature of our toolbox are the connectors. They were designed in order to function as insulators to prevent crosstalk between neighboring transcription units (Link to Design). Therefore a perfectly insulating connector would prevent the readthrough from backbone sequences that most probably caused the notably high expression that was measured in the promoter experiment for the dummy promoter (Jump to promoter characterization). In addition to blocking transcriptional readthrough, a good connector must not possess any cryptic promoter activity.
We focused on characterizing the 5’ Connector because we expect the stronger influence on signal strengths. For characterizing our connector parts, we created 20 test plasmids with the lux operon as the reporter.
In our toolbox we provide five short connectors, which solely possess the fusion sites for LVL2 cloning, and five long connectors which additionally harbor self-designed insulators. Each of these ten connectors were cloned with the constitutive promoter J23100, to check for effects on an active promoter, and with the Promoter Dummy to quantify the extent of transcriptional activity that reaches the Promoter Dummy.
A
B
Figure 4:
Results of Connector measurments

A) Connector constructs built with J23100 as promoter part
B) Connector constructs built with the Dummy Promoter as promoter part
The acquired data are shown in figure 4. The data were normalized over the test construct J23100, that was used in the promoter experiment and constructed with the connector dummies. For the five constructs with the active promoter and the long connectors we observed extremely varying signals (figure 4, A). We measured a range from 0.2 to 2 fold change compared to the reference construct. It has been shown that the sequence directly upstream of small synthetic promoters can greatly impact the transcription efficiency ( Carr et al.2017). In case of the long connectors, the sequence upstream of the promoter forms the terminator and could affect the efficiency of RNA-polymerase binding to the -35 and -10 regions. For the constructs built with small connectors, we also observed varying signals but to a lesser extent compared to the long connectors (figure 4, B).
For all ten connectors that are provided in our toolbox, we show a tenfold range in the measured luminescence/OD600 signal. As a conclusion, we recommend to carefully consider the combination of promoter and 5’ Connector for rationally designing constructs.

Taking a look at the constructs that were built with the Promoter Dummy, we also see a huge difference in the expression signals. For the long connectors we expected a negligibly low reporter expression which we observed for two out of five long 5’ Connectors resulting in a 14 fold signal reduction compared to the “Promoter Dummy” reference. The remarkably strong signal observed for the remaining three connectors could be due to inefficient terminators or cryptic promoters in the pretended “neutral sequence”.

For the remaining five constructs possessing the five short 5’ connectors we observed a range from 0.3 to 5.5 fold compared to the “Promoter Dummy” reference. We are not able to give an experimental explanation for this observation but we could imagine that the LVL2 fusion sites, the only four bases that differ in these constructs, could constitute a weak promoter together with surrounding sequences.
Summarizing the connector characterization, we found that sequences upstream of short synthetic promoters greatly affect reporter expression, which is in accordance with literature ( Carr et al.2017). Moreover, we demonstrated that two of our five self-designed connectors efficiently reduce the signal resulting from other sources than the actual promoter. We additionally conclude that algorithms that predict the “neutrality” of sequences alone are not sufficient to create well functioning insulators.

Characterization of Terminators

Figure 5: Terminator test construct
LVL2 plasmids were created for these experiments consisting of a RFP transcription unit with the strong constitutive promoter J23100, followed by the lux operon with the promoter dummy. The terminator located at the 3' end of the RFP transcription unit is the part which is characterized in this experiment.
The Marburg Collection contains five terminators plus one terminator dummy that can be used as a placeholder. To obtain experimental data for this category of parts, we built a set of terminator test constructs to measure the extent of transcriptional readthrough and therefore the strength of a terminator. The terminator test constructs are built as LVL2 plasmid with our toolbox. The strongest constitutive promoter J23100 drives the expression of RFP which is the first transcription unit. The Lux operon is placed downstream with the promoter dummy instead of an active promoter. Both transcription units are separated by the terminator, which is the focus of this characterization, downstream of the RFP CDS

With this setup, transcriptional activity of the RFP reporter is blocked by the terminator. Therefore the measured luminescence signal can be seen as an indicator for the efficiency of the terminator.
As discussed previously, RFP is not suitable for precise quantitative characterizations (Link to Measurement). Therefore we did not calculate ratios of the reporter upstream and downstream of the tested reporter as was described in previous experiments ( Chen et al.2013). However, we used the existence of an RFP signal as control for the correctly assembled test constructs and for the activity of the promoter driving RFP.

Figure 6: Characterization of terminator read through.
Displayed is the relative luminescence signal to the control construct J23100 obtained for constructs with the respective terminator. Error bars represent the standard deviation of the measurements of three independent experiments.
The data shown in figure 6 were acquired and analyzed following our novel workflow described on the measurement page (Link to Measurement). Like in all previous experiments, the obtained raw data for each sample were normalized over the construct J23100 from the promoter characterization.The strongest signal was observed for B1002 and B0010 with a relative signal 0.65 and 0.50 respectively, suggesting these two terminators as rather inefficient. In contrast, we could show a signal reduction four and eight fold for B0015 and B1006, respectively.

By comparing our data to the characterization provided in the iGEM registry for E. coli, we can show that the general trend is similar for both organisms. Exemplary, the terminator with the highest described efficiency in E. coli (B1006) also was found to reduce the luminescence signal most in our experiments.
In general, we found stronger signals for the reporter downstream of the terminator than what was described for E. coli. However, it has to be noted, that we used the highly sensitive reporter Lux instead of a fluorescent protein. Therefore we assume that we were able to detect a higher degree of transcriptional readthrough, which would not be distinguishable from the background when using a fluorescence reporter.
However, we cannot exclude species specific differences that cause a generally higher degree of transcriptional readthrough over terminators for V. natriegens compared to E. coli In addition to the terminators shown in figure 6 we also measured a test construct possessing B1003. The, by far, lowest signal was found for B1003 with a 15000 fold reduction of the signal. To ensure that this extremely weak signal is authentic and not caused by cross talk from neighbouring wells, we tested this sample in an otherwise empty 96 well plate and confirmed the general existence of a signal. However, we can not exclude the presence of single mutations within the lux operon dramatically diminishing the overall generation of luminescence. This result was not displayed in figure 6 to omit extreme stretching of the Y-Axis thus loosing visual information for the other tested terminators.

Measuring RBS strength

For quantifying the RBS strengths, unfortunately, we could not use our favorite reporter the lux operon because in case of this operon, each CDS possesses its own RBS. Therefore, replacing the RBS upstream of the first CDS (LuxA) alone does not suffice to achieve a difference in reporter expression dependent on the RBS strength. Consequently we used sfGFP, the reporter that showed the second best performance in our initial reporter experiment (Link to Measurement).

Figure 7: RBS strength measured with sfGFP
Test constructs were built with the same parts except for the RBS part. The sample "Empty" represents V. natriegens with a plasmid without a sfGFP reporter. The error bars indicate the standard deviation of four technical replicates.
We built test constructs that are identical in all used parts except for the used RBS. The resulting data are shown in figure 7. The sample labeled with “empty” represents V. natriegens with a plasmid without a reporter. Apparently, the difference between the tested constructs expressing sfGFP in different amounts does not differ much from the non expressing control. However, it is possible to obtain some information about the order of the tested RBS. The strongest signal was observed for B0034 followed by B0030. These RBS are also described as strong in the iGEM registry. In case of B0031 and B0032, the measured signal is lower than for the non-sfGFP expressing strain. Therefore no conclusion can be provided for these two parts.

We expected that the order of the strength of the tested RBS should be similar to E. coli. Prior to the experiment, we created an alignment of the 16S rRNA of both organisms and found that the Anti-Shine-Dalgarno sequence, the bases that are responsible for binding the Shine-Dalgarno sequence on the mRNA, do not differ between both organisms (figure 8).
Figure 8: Alignment of 16S rRNA of E.coli MG1655 and V. natriegens ATCC14048
The Anti-Shine-Dalgarno sequences is indicated with an additional annotation. The 3' sequence of the 16S rRNA does not differ between both organism

On the measurement page (Link to Measurement). we suggested the lux operon as the reporter of choice for all experiments that focus on measuring transcriptional activity . We see the RBS characterization as the confirmation that using sfGFP as reporter does not yield reliable data for very weak expression.
In future experiments, enzymatic reporters such as LacZ or β-glucuronidase (GUS) could be tested for their suitability in experiments for the quantification of translational efficiency or post translational effects (e.g. degradation tags)

Analyzing degradation tags

Our toolbox contains the three degradation tags M0050, M0051 and I11012. Similar to our RBS experiments, we could not use the lux operon as a reporter because the degradation tag is only added to the C-terminal end of the last enzyme encoded in the operon. Therefore, we again used sfGFP as reporter and test constructs were designed with one of the tags fused to the CDS of sfGFP. A non-tagged sfGFP construct serves as the reference.

Figure 9: Fluorescence signal for degradation tag test constructs
The respective degradation tags were appended to the C-terminus of sfGFP. The error bars indicate the standard deviation of four technical replicates.
As expected, appending a degradation tag to a protein decreases its concentration. The strongest decrease and therefore the highest degradation was shown for M0050 with a signal that is not distinguishable from the non-sfGFP expressing control. The second strongest signal reduction was shown for I11012, followed by M0051, which showed the least efficient degradation (figure 9). Our results are in qualitative accordance with the description of these tags for E. coli, which are provided in the registry. The tested degradation tags belong to the family of SsrA degradation tags. Naturally, they help to degrade incompletely translated proteins by labeling them for Clp proteases (Farrell et al. 2015). We checked for the presence of ClpP, one of the proteases involved in degrading SsrA tagged proteins in E. coli and found a highly homologous protein in V. natriegens. Therefore we assume that the mechanism of Clp mediated degradation is conserved between both organisms, which is in accordance with our results.

Influence of Codon optimization

Each organism has a preferred codon usage that affects the efficiency of translation through the abundance of tRNAs (Rocha, 2004). The tRNA composition differs between organism which can result in a loss of protein expression. The goal of our project is to replace E. coli in as many applications as possible. We know that scientists all over the world have been extensively working with E. coli for decades and have collected huge collections of plasmids for this organism. We already showed in many experiments that parts taken from E. coli are in general functional in V. natriegens.

Figure 10: Comparing codon optimized sfGFP with non-optimized sfGFP.
The error bars indicate the standard deviation of four technical replicates.
However, we wanted to test if optimizing the codons of a CDS can enhance the expression levels. Therefore, we ordered the DNA sequence of a sfGFP CDS with optimized codons for V. natriegens. In figure 10 we compare the signal of two constructs with and without an optimized sequence. Our experimental data suggest that codon optimization results in a considerable increase of the fluorescence signal. In conclusion, while an acceptable level of expression can be observed for parts without optimizing the codon usage, newly synthesized sequences optimized for V. natriegens can increase the expression levels. This information could prove important for industrial applications where even small changes in product yield can decide if an application is economically advantageous. We have to note that this conclusion is based on a single experiment with a single tested CDS. To further confirm the impact of codon optimization on expression levels, this experiment should be repeated with various sequences.

Characterization of origins of replication

Origins of replication (Oris) are genetic elements where DNA replication is initiated. In plasmids the Ori sequence is responsible for it’s maintenance and for the copy number inside the cell (Selzer et al., 1983; Brantl, 2014).

The origins of replication colE1, pMB1 and p15A belong to the same family. They do not code for any enzyme but are replicated by the hosts RNA polymerase (Cesareni et al., 1991; Brantl, 2014). The polymerase transcribes a region 508 bp upstream the Ori sequence (Tomizawa & Itoh, 1981; Selzer et al., 1983) synthesizing a pre-primer RNA called RNA II. During transcription the RNA II underlies conformation changes building secondary structures(Brantl, 2014). This structures contain typical loops (Cesareni et al., 1991) that binds to the plasmids’ Ori sequence building an RNA-DNA hybrid (Cesareni et al., 1991; Brantl, 2014). The RNA II is than cleaved by the hosts RNase H to become a mature primer (Cesareni et al., 1991; Brantl, 2014).

For our collection we characterized three Oris commonly used in molecular biology: colE1, pMB1 and p15A. We measured two different plasmids, one with and another without a LUX cassette. Both plasmids consist of a kanamycin resistance cassette and one of the three Oris described. The LUX expression plasmid contained a constitutively expressed LUX cassette of ~6kb. The other one contained a connector sequence to build an ‘empty’ plasmid. By comparing this constructs you may consider that the copy number is not only influenced by the LUX expression but also by the plasmids sizes. This Oris belong to the same family differing in mutations in the RNA I region (Tomizawa & Itoh, 1981; Selzer et al., 1983).

We measured the plasmids’ copy number by qPCR using the absolute quantification method.
A qPCR is set up the same way like a normal PCR but with addition of a DNA binding fluorophore in this case SYBR Green. SYBR Green binds double stranded DNA emitting a high signal while unbound SYBR Green shows only low fluorescence (Zipper et al., 2004). In every PCR cycle the number of double stranded DNA is duplicated emitting an increasing fluorescence signal. This signal is detected after every cycle by the qPCR machine and the value is saved. After the run finished, normally after ~40 cycles, a signal threshold is determined and the corresponding cycle when the threshold was reached is saved for further analysis.
For the qPCR run first total DNA from our host containing the plasmids of interest was isolated in the exponential phase (OD600 ~ 0.5), purified using the innuPREP Bacteria DNA Kit from Analytik Jena and all samples normalized to ~5ng/ul with the Qubit fluorometer from ThermoFisher scientific. Subsequently a dilution series was made in 1.5ml tubes diluting the DNA 7 times 1:2. This way the dilution series contained 8 steps reaching from 20 to 2-7. Two different primer pairs were used for the analysis: one matching the housekeeping gene dxs present once on the genome and the other matching the kanamycin resistance cassette on the plasmid. The DNA samples used for the amplification of the kanamycin cassette were the same used for the dilutions 2-4 and 2-5. The threshold cycles (Ct) acquired in triplicates from the dxs sequence were used for a standard curve. By comparing the Ct values from the resistance cassette with the corresponding standard curve the number of copies could be determined as multiples from the dxs sequence. It should be considered that the dxs sequence is coded on the first chromosome of V. natriegens at ~ one o’clock. Due to that probably the sequence is present more than once because of multifork replication of the genome.

To build the standard curve the Ct values were plotted on the y-axis and the corresponding dilution steps on the x-axis. The x-axis was set logarithmic and the standard curve was calculated with Excel. The curve’s formula was than used to calculate the corresponding x-value from the resistance cassette’s Ct values. Because the x-values describe a theoretical dilution the Ct values were multiplied with this value and with their corresponding dilution to obtain the final amount of multiplies compared to the genome. For every Ori an own standard curve was calculated.

In our experiments we showed that the plasmids’ copy number controlled by three different Oris differ a lot when comparing V. natriegens with E. coli.
One possible explanation might be different expression levels of RNA I and RNA II respecting the rate of RNA I – RNA II bounds (Cesareni et al., 1991) due to the divergent metabolism in V. natriegens and E. coli. Another plausible explanation might be the different methylation patterns in both organisms probable affecting the formation of the RNA II secondary structures and subsequently its binding affinity to the DNA (Russell & Zinder, 1987; Cesareni et al., 1991).

It was shown that mutations especially in the loop I structure might be responsible for Ori compatibility and copy number control (Selzer et al., 1983; Cesareni et al., 1991). The copy number is mainly determined by two factors: the binding efficiency of the RNA II to the DNA – specially controlled by the stabilization of stem-loop IV – (Cesareni et al., 1991) and the interference of the complementary RNA I to the RNA II pre-primer (Brantl, 2014). The RNA I is transcribed constitutively from the complementary strand from RNA II pre-primer (Brantl, 2014). Binding of RNA I to RNA II prevents the correct folding of the pre-primer (Brantl, 2014). This way the RNA-DNA hybrid can not be formed and subsequently the primer maturation can not take place (Brantl, 2014).
Figure 1: Quantification of plasmid copy number in dependency of different Oris.
The columns show the average of the calculated multiplies for the different plasmids. The blue columns show the numbers for the plasmids containing a ~6kb LUX cassette. The orange columns show the numbers for the ‘empty’ plasmids without reporter. For every column six measurements have been calculated. Looking at the ‘empty’ plasmids it is clearly shown that colE1 and p15A remain high copy plasmids like in E. coli with a copy number of ~200 copies per cell. For pMB1 the copy number is scaled down becoming a low copy number Ori in V. natriegens. Looking at the LUX plasmids it is clearly shown that the colE1 Ori remains at a high copy number while pMB1 and p15A drop down to a significantly lower level.

B. Marchal