However beautiful the strategy, you should occasionally look at the results
-- Winston Churchill
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.
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 about the extremely fast growth but even more by the high OD that we could show. We were asked many times if the high OD is really due to a high cell density or if it is rather caused by other components like secreted substances to the medium which contribute to the absorbance.
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 flask from stationary pre cultures and took sample in 15 minute intervals while the bacteria were incubated at 37 °C and 220 rpm. The OD600 of these samples was measured in a normal photometer and the cultures are thereafter immediately analyzed by flow cytometry. The flow cytometer directs the samples through a thin capillary so that single cells 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 culture can be calculated.
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
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.
A single sample after 45 minutes is shown as an example. Each dot represents one event
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
By considering the course of all three sets of data, we suggest that the cells start to expand upon provision of fresh medium but without undergoing cell division. This results in an increase of the cell volumes and thus the measured OD600 but without an increase in cell concentration in the culture. After 45 minutes, when the forward scatter peaks, we assume that a majority of cells reach maximum cell volume and enter the exponential phase thereafter. During the following time points, exponential growth can be observed and the decrease of the forward scatter could be seen as a hint for a reduction in mean cell size.
The histograms are plotted from top to bottom during the time course of the experiment
It shows histograms of the forward scatter. 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.
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).
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!!!).
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.
After having established an experimental and data analysis workflow and after determining the optimal plasmid context for reporter experiments, we started to apply our knowledge to characterize the parts in our Marburg Collection.
Data were normalized over the strongest construct J23100. Error bars represent the standard deviation of the measurements of three independent experiments
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 xxx. The absence of bars for the four highest concentrations means that the cultures did not reach an OD of 0.2 in the seven 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 (figurexxxxx). 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.
J23100 was used as positive control and for normalization. Error bars represent the standard deviation of the measurements of three independent experiments
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.
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. However, pTrc is well suited when strong expression is required.
J23100 was used as positive control and for normalization. Error bars represent the standard deviation of the measurements of three independent experiments
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 zu 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 (Verweis zum Promoter Experiment). 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
BResults of Connector measurmenet
A) Connector constructs built with J23100 as promoter part
B) Connector constructs built with the Dummy Promoter as promoter part
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.
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 Dummy Promoter instead of an active promoter. Both transcription units are separated by the terminator downstream of the RFP CDS which is the focus of this characterization. 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. 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 an control for the correctly assembled test constructs and for the activity of the promoter driving RFP.
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.
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 xxx 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 is 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 xxx to omit extreme stretching of the Y-Axis thus loosing visual information for the other tested terminators.
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).
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.