Team:Valencia Biocampus/Results

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Revision as of 14:33, 15 October 2014

Results

Stability

E. coli is the lab rat of the bacterial world. Not only is E. coli easy to grow, with a doubling time of only 20 minutes, but, genetic modification is fairly easy in strains of these species. Just as well, more details about the molecular biology of this organism are known that any other.

We have performed the most complete characterization of the stability of E. coli in order to confirm its optimality as a chassis for Synthetic Biology. We have subjected wild type strains to a range of stressful conditions (see just below "the limits of E. coli") and measured how our Biobricks behaved in these non-optimal conditions.

1 . The Limits of E.coli

Temperature

E.coli is a fully studied and characterized bacterium. The optimal growth of this specie is 37-38ºC with continuous agitation (200 r.p.m.) but out of this range the growth slows down. First of all, we measured the growth of E.coli at temperatures ranging from 30 to 50ºC. To achieve our goal, we used a fully original technique: instead of an incubator, we used a thermal cycler programmed to display a gradient of temperatures.

The results obtained are shown below:

Figure 1
Figure 1: Growth of E. coli strains DH5-alpha (left) and XL1-blue (right) under different temperatures (30ºC-50ºC) set in a thermal cycler. The two strains display clearly different temperature profiles.

Conclusion

Is E. coli stable at a wide range of temperatures?

Yes it is! We have detected some variations between the two strains. For example, DH5-alpha resists low temperatures better than XL1-blue, but both grow at a very large range of non-optimal temperatures.

Material fatigue

Our modeling sub-team suggested an original idea. Is there something similar to material fatigue in biology? For example, how would an E.coli culture behave if subjected to a temperature fluctuation? We carried out some experiments with our thermal cycler by cycling the temperature of the medium every minute (37-41ºC) for 3h and maintaining controls at fixed temperatures (typically 37 and 41ºC). In the case of XL1-blue using ANOVA for three groups and 16 replicas, we cannot reject the null hypothesis of equal, and with DH5-alpha using ANOVA for three groups and 8 replicas, we can reject the null hypothesis of equal mean. OD600 values(F=63.95, p<0.001). mean OD600 values (F=0.870, p=0.433).

Figure 2
Figure 2: XL1-blue and DH5-alpha subjected to cycling (37-41ºC) temperature conditions. Compared to the controls, the former strain did not exhibit any significant change in the growth rate, as deduced by the OD600 after 3 hours (DH5-alpha) and 15 hours (XL1-blue). By contrast, DH5-alpha significantly increased its growth compared to both controls. Data shown are the average of 16 independent assays at each condition. For XL1-blue ANOVA test 95% shows not significant differences, see text. Nonetheless, in the case of DH5-alpha, the ANOVA test 95% equal mean values hypothesis rejected, see text.

Conclusion

Is E. coli sensitive to fatigue of materials in the form of cycling conditions?

Yes! at least DH5-alpha, but in a positive way. Cycling temperatures resulted in an emergent property: increased growth. In order to check whether this effect was due to a mechanical resuspension (convection) linked to the cycling conditions, we performed a specific experiment with dead, antibiotic-treated E. coli cells (Kanamicine and Cloranfenicol) subjected to extreme (30 to 50ºC) thermal fluctuations. The following figures shows that convection forces might not be the reason of such increased growth. A biological explanation may be hypothesized: cycling conditions allow a range of optimal enzymatic activities to be met. Using t-Student test for 2 groups and 16 replicas, we cannot reject the null hypothesis of equal mean OD600 values (t=1.371, p=0.180 )

Figure 3
Figure 3: O.D.600 of antibiotic-killed E. coli suspensions of strain XL1 incubated under cycling (30ºC-50ºC) and average (40ºC) conditions. No significant differences suggest that convection forces are not the main key players in the increased growth of E. coli incubated under thermally cycling condition. t-Student test 95% shows not significant differences, see text.

UV Radiation

UV radiation generally carries out its biological effect by damaging nucleic acids. Depending on the radiation intensity and exposure time the damage caused to the genome is reversible; cells can repair their DNA and continue to grow, although they might carry point mutations. Since UV studies are scarce, our team decided to assay the maximum resistance of E. coli strains to UV radiation of a fixed intensity (340 µW/cm2). The experiment consisted of subjecting spot plated E.coli strains to timed pulses of UV light and comparing the output to a control set of bacteria which received no radiation at all.

Figure 4
Figure 4: Characterization of XL1-blue (left) and DH5-alpha (right) subjected to UV light (340 µW/cm2). Both strains are rather sensitive, but DH5-alpha resists UV light better than XL1-blue.

We also studied how sensitive these strains are when transformed with our Biobricks. As shown in Fig 5, Biobrick-expressing strains were more sensitive to UV light compared to plasmid-only strains. However, Biobrick7 provided some resistance to radiation in DH5-alpha compared with the controls.

Figure 5
Figure 5: Resistance of DH5α (left) and XL1 (right) to UV pulses (340 µW/cm2). X axis values are given in seconds. Both strains are very sensitive to UV light with a maximum resistance of around 6 seconds.

pH & Salinity (1)

Finally, we also characterized E. coli resistance to a range of extreme pH values and salt concentrations. As shown in Table 1, none of the extreme conditions tested resulted in a total inhibition of E. coli’s growth.

Table 1.

pH

5

6

7

8

9

XL1-blue

+

++

+++

++

+

DH5-alpha

++

+++

+++

++

+

Salt

Without salt

LB

LB+1% salt

LB+2% salt

LB+4%salt

XL1-blue

+++

+++

++

+

+

DH5-alpha

+++

++

+

+

+

BL21. Chromosomal T7 polymerase gene is unstable!

BL21 is an Escherichia coli strain that bears the T7 phage polymerase gene into its genome. Therefore, it is used to express proteins that are controlled by T7 phage promoters, as is the case of three of our Biobricks (Biobricks 8, 9 & 10). However, when we transformed the strain with the Biobricks (blue and yellow chromoproteins), we could not measure any signal. After many tests, we hypothesized that the T7 phage polymerase gene was unstable and did not resist stressful situations such as competence or transformation protocols.

We performed a PCR screening to find out whether the T7 gene was present. We used colonies of BL21 transformed with Biobrick 8 (blue chromoprotein) and grew them on LBA medium. We performed a colony-PCR with a couple of T7 specific oligos designed by ourselves targeting a 1.5 kb region of the T7 polymerase coding sequence.

Figure 6
Figure 6: 0.8% agarose gel electrophoresis. We saw that the bacteria forming the colonies did not have the gene that codes for the polymerase.
We then decided to check whether our glycerol stock of bacteria contained the gene. Maybe during the process of competence the culture was contaminated. To our surprise, the gene was present in every single colony we analyzed. Therefore, we decided to do a final colony PCR using the bacteria from the glycerol stock as positive controls, and using colonies from the non-transformed competent stock.

Figure 7
Figure 7: 0,8% agarose gel electrophoresis. In the BL21-glicerol stock the gene was present. Nonetheless, almost all BL21-competent cells lack T7 phage polymerase gene.

Our results reveal that BL21 loses the T7 polymerase gene at some point during the process of making the cells competent, as only three out of the ten colonies analyzed did result in (a rather faint) amplification of the gene.

% BL21 colonies with T7 polymerase gene

Glycerol Stock

100

Competent cells (non transformed)

30

Transformed cells

0

2 . Behavior of different Biobricks in sub-optimal conditions

We show below a summary of our Biobrick characterization program, in which we have subjected selected Biobrick-expressing strains to a range of environmental stresses. Our results clearly show that whereas UV light, pH variation and salt content do not significantly affect Biobrick expression, sub-optimal temperatures can in some cases decrease the Biobrick output. Anaerobic, vacuum culture resulted in a dramatic decrease of Biobrick 2 (RFP) activity.

Temperature Stress

37ºC and 200 rpm are the standard growth conditions of E.coli. Varying these conditions and measuring the growth after three hours, (O.D.600) as well as Biobrick expression (fluorescence), was used to test the effect on Biobrick expression (all data are the average of at least three measurements; fluorescence values are normalized to OD600).

Figure 5A
Figure 5, A: The strain XL1 Blue transformed with a Biobrick which encodes GFP (Biobrick 1, Bb1) was grown at different temperatures in a thermocycler for three hours, the cells were collected and O.D600 was measured. Fluorescence was also determined and compared to the non-transformed/wildtype or control strain. As the plot shows, Biobrick expression is affected by growth at different temperatures, fluorescence (normalized to O.D600) is higher around E.coli optimal growth temperature (37-41 ºC); over or below this rather short temperature range fluorescence decreases.
Figure 5B
Figure 5, B: The strain XL1 Blue transformed with a Biobrick which encodes GFP (Biobrick 3, Bb3) placed under the control of a weak promoter. The micro-cultures were grown at different temperatures in a thermo cycler for three hours, the cells were collected and O.D600 was measured. Fluorescence was also determined and compared to the non-transformed/wildtype or control strain. Fluorescence values in the different temperatures tested are very similar to the control and only slightly higher around optimal temperatures. This indicates a low expression of Bb3 in all cases.

Temperature Fatigue

As described in the first parts of this section “The limits of E. coli”, the strain DH5α grows better under temperature cycling conditions "thermal fatigue" compared to the optimal growth temperature (37ºC), but the same effect was not observed for the strain XL1 Blue. Hence, we decided to check what is the effect of Biobrick expression in XL1 (Fig 6), which reveals a lack of effect of the cycling temperatures on the Biobrick output. Using ANOVA for three groups and 16 replicas, we cannot reject the null hypothesis of equal mean OD600 values (F=1.682, p=0.200)at 95% of significance.

Figure 6
Figure 6: Biobrick 1 expression under different temperatures, including temperature fluctuation (from 37ºC to 41ºC, every minute each) in a thermocycler. Controls were incubated at 37 and 41ºC. After an incubation period of 3 hours, O.D.600 alongside fluorescence was measured. The results show that the expression of the Biobrick is not affected by cycling temperatures. ANOVA test 95% shows not significant differences, see text.

pH & Salinity (2)

Large proteins, including enzymes, are affected by pH. Usually, the catalytic properties of the enzymes are lost and metabolism is halted, which eventually leads to death. The pH range determined for E.coli optimal growth is 6 to 7, but does Biobrick expression affect this? Displayed below are the results of the experiment in which the pH of the medium was varied. The results show that, as expected Bb1 displays a higher fluorescence and that it is stably active independently of the pH value.

DH5α pH

Using ANOVA for three groups and 16 replicas, we can reject the null hypothesis of equal mean OD600 values in the cases of Ampicilin (F=3.842, p=0.024) and BB3 (F=7.6, p=0.001) at a 95% of significance, but not in the case of BB1 (F=2.654, p=0.075).

Figure 7
Figure 7: The plot displayed above shows the variation in fluorescence of the transformed DH5α cells grown in LB with pH ranging from 5 to 8. Cells were transformed with GFP constructs, one of which is controlled by a strong constitutive promoter (BB1) and the other, BB3, controlled by a weak housekeeping promoter. The output from these is compared to a cell transformed with an empty plasmid (Ø Ampicillin). ANOVA test 95%, equal mean values hypothesis rejected, see text.

Regarding salinity, a major environmental factor, we also tested the stability of our Biobricks by measuring their output in increasingly salty media (Fig. 8). Again, there was no significant differences associated with salt concentration.

DH5α salinity

Using ANOVA for three groups and nine replicas, we can reject the null hypothesis of equal mean Fluorescence/ OD600 values in the cases of Ampicilin (F=30.65, p<0.001) and BB3 (F=3.149, p=0.045) at a 95% of significance, but not in the case of BB1 (F=0.832, p=0.491).

Figure 8
Figure 8: Using the same approach as the previous experiment, the same strains (DH5 alpha) were grown in mediums with different salt concentrations (LB without salt, regular LB, and LB supplemented with 1% and 2% NaCl). Fluorescence of the GFP Biobricks was measured and results normalized to the O.D.600. As before, Bb1 was significantly expressed compared to the control at values relatively independent of the salt concentration. ANOVA 95% test equal mean values hypothesis rejected, see text.

UV Radiation

In the section "Limits of E.coli" we tested the resistance of E.coli (Wild Type and transformed strains) to UV pulses, but what happens to the Biobrick when subjected to UV? Does it still work after the UV shock? Below we display the results of Biobrick behavior upon irradiation by measuring fluorescence of the surviving cells. We found out that the fluorescence was not affected by UV exposure (Figures 9 and 10).Using t-Student test for two groups and three replicas, we cannot reject the null hypothesis of equal mean Fluorescence/ OD600 values at a 95% of significance (BB1: t=1.011, p=0.418; BB3: t=1.240, p=0.341)

GFP

Figure 9
Figure 9: Biobrick 1 and 3 behaviors before and after a 2 second UV pulse. Left, DH5α; Right, XL1. The activity of both Biobricks is not affected by previous exposure to UV. In both cases, t-Student test 95% shows not significant differences, see text

RFP

DH5-alpha: Using t-Student test for two groups and three replicas, we cannot reject the null hypothesis of equal mean Fluorescence/ OD600 values at a 95% of significance (BB2: t=-0.852, p=0.499). XL1-blue: Using t-Student test for two groups and three replicas, we cannot reject the null hypothesis of equal mean Fluorescence/ OD600 values at a 95% of significance (BB2: t=0.428, p=0.754).

Figure 10
Figure 10: Biobrick 2 behavior before and after a 2 second UV pulse. Left, DH5α; Right, XL1. The activity this Biobrick is not affected by previous exposure to UV. Unexpectedly Bb2 does not show any significant fluorescence even in the non-UV-treated samples. In both cases, t-Student test 95% shows not significant differences, see text.

Vacuum

We designed a simple experiment which allowed testing the growth of a facultative anaerobe, like E.coli, not only without oxygen, but under Mars-like vacuum. It consisted of incubating the Petri dishes in deep vacuum during 48 hours. After this incubation period, cells were recollected and O.D.600 as well as fluorescence, determined. Surprisingly, the Biobrick 2 in both strains proved extremely sensitive to vacuum, since normalized fluorescence dramatically decreased compared to the normal pressure-incubated cultures (Figure 11).

RFP

Figure 11
Figure 11: Effect of vacuum on the expression of the Biobrick 2 (RFP) in two strains of E.coli. In both cases vacuum-cultured cells display a much lesser normalized fluorescence that the normal pressure-incubated ones (all assays were conducted at 37ºC).

Standardization

Biobrick parts are supposed to be standard, this is, they are expected to behave in a similar and predictable way in different host organisms or “chassis”. In this part of the project we used six different E. coli strains commonly used in molecular biology labs (DH5α, XL1Blue, JM109, DH10B, HB101, BL21(DE3)) to test whether the behavior of our Biobrick parts was homogeneous among them (Bb1 to Bb6, BBa_K1468000-BBa_K1468005 in the Registry of Standard Biological Parts). Surprisingly, only one of the constructions we tested displayed a relatively standard output among the different strains (BB5).

General protocol

  • Strike the desired strains on LB plates or LB plates supplemented with the appropriate antibiotic (depending on the bacteria we are working with: the wild type, sensitive to the antibiotic, or the transformed ones, resistant to the selective agent). Allow to grow by incubating at 37°C at least 24 hours.
  • Gather a large amount of bacteria with an inoculation loop and transfer to 1 mL of LB or LB supplemented with the antibiotic. Incubate during 20 minutes at 37°C and 200 rpm.
  • Inoculate 100 µL of bacterial suspension in a tube containing 3 mL of LB with antibiotic. Perform four biological replica of each culture. The expression of Bb4 was induced with a heat shock at 42 °C for 2 min. The expression of Bb5 and Bb6 was induced by adding doxycycline and IPTG, respectively, to the medium.
  • Incubate until OD595 is between 0.15-0.4 (1 hour aprox.).
  • Measure OD595.
  • In the case of Biobrick parts 4, 5 and 6, sonicate an aliquot of the culture (it allows us working with the pretein extract, avoiding the interference of cell density in the measurement of the blue, product of the XGal reaction), eliminate cell debris by centrifugation (2 min, max. rpm), add X-Gal, incubate 6 min at RT, and stop the reaction by adding Na2CO3.
  • Measure either GFP fluorescence (Bb1 and Bb3; λex= 493nm; λem=505nm), RFP fluorescence (Bb2; λex= 576 nm; λem= 592 nm), or absorbance at 630 nm (Bb4, Bb5, and Bb6). Fluorescence measurements were performed in a Hitachi F7000 plate reader, whereas a spectrophotometer was used for absorbance measurements.
  • With Excel, normalize fluorescence by cell density, and calculate the average and standard deviation taking into account the four biological replicas. Represent the data.

Results

The behavior of all the Biobrick parts we tested is shown in Figure 1. From our results, it is evident that the expression of all but one of the constructions, Bb5, clearly differed depending on the host strain of choice. Even for Bb5, using an ANOVA test for six groups and 4 replicas, one can reject the null hypothesis of equal mean values (F=4.253, p=0.010) which means that the expresion of the Biobrick is indeed different. Each strain proved to be more “efficient” at hosting a different Biobrick part. For instance, Bb1 and Bb5 are highly expressed in strain JM109, whereas the expression of the other biobricks was low or nearly undetectable. Note that Bb3, containing the weak promoter J23110, could not be detected in four out of the six strains. It is also remarkable the fact that in 1A and in 1B (Bb1 and Bb2 are under the control of the same promoter) the pattern obtained is almost equal among strains except in JM109.

Figure 1
Figure 1. Behavior of Biobrick parts in six different E. coli strains. Fluorescence intensity of Bb1, Bb2, and Bb3 (A, B, and C, respectively) was corrected by the fluorescence of cells containing an empty plasmid. Absorbance values of Bb4, Bb5, and Bb6 (D, E, and F, respectively) was corrected by the absorbance displayed by non-transformed cells (bacteria containing an empty plasmid where not used because the white-blue screening sytem present in the vector).

Discussion and conclusions

We demonstrate here a difference in the output of the Biobricks depending on the host strain. If we assume that they are in the same physiological state this results could be related to epistasis, since Epistasis describes how gene interactions can affect phenotypes (Miko, I. Epistasis: Gene interaction and phenotype effects. Nature Education (2008) 1(1):197). In other words, the behaviour of a particular gene strongly depends on the presence of others, on the genetic background of each Escherichia coli strain.

The different behavior can be a consequence of the synthesis rate, which also depends on the genetic background. The measures were made in the exponential phase of growing, where the bacteria are constantly dividing, in such a way that the protein synthesized is also being divided. Maybe, if the same experiment was carried out when the bacterial cells had reached the stationary phase, the output obtained could be more homogeneous. We are currently testing that.

In any case, with the results we obtained, we have to conclude that Biobrick parts do not behave in a standard way even whitin strains of the same bacterial species.

Orthogonality

In Synthetic Biology, two constructions can be considered orthogonal when they only interact at specific and predictable interphases, and do not disturb each other. We have studied this desirable feature of Biobrick parts by combining two constructions in the same cell and comparing this output with the one produced by simple transformations. According to our experiments, that were carried out in two different E. coli strains, Biobrick parts do not behave orthogonal when present in the same cell, being the expression of one of the parts 2 to 3 times higher than the other one. Then, we wanted to go un step further: what is the effect of a simple transformation (a plasmid with a Biobrick part) into the cell architecture? To get some insights about this, we are currently waiting for a proteomic analysis in which the whole proteome of an E. coli strain transformed with a Biobrick part will be compared to that of the non-transformed, control strain.

1. Standard fluorimetry assays

We performed experiments with two different combinations of Biobrick parts containing a fluorescent protein under the control of a promoter sequence from Anderson’s promoter collection: Bb1 (GFP with strong promoter J23104) + Bb2 (RFP with strong promoter J23104) and Bb2 + Bb3 (GFP with less strong promoter J23110). In all cases, several controls were used: wild type (non-transformed) cells, simple transformations with either the biobrick or an empty plasmid, and co-transformations of the biobricks and the empty plasmids. It has to be noted that there was a high background emission of red fluorescence by the cells in all cases. Our results are shown in Figures 1 and 2.

General protocol

  • Strike the desired strains on LB plates with the appropriate antibiotic. Allow to grow by incubating at 37°C at least 24 hours.
  • Gather a large amount of bacteria with an inoculation loop and transfer to 1 mL of LB. Incubate during 20 minutes at room temperature.
  • Inoculate 100 µL of bacterial suspension in a tube containing 3 mL of LB with antibiotic. Perform three biological replicas of each combination of Biobrick parts.
  • Incubate until OD600 is between 0.1-0.3.
  • Measure OD600.
  • Measure GFP fluorescence (exc.= 493nm; emis.=505nm) and RFP fluorescence (exc.= 576 nm; emis.= 592 nm) in a FP6200 spectrofluorimeter (Jasco, Easton, MD) fluorometer using standard plastic cuvettes.
  • With excel, normalize fluorescence by cell density, calculate the average and standard deviation taking into account the three biological replicas, and represent the data.

Results

Figure 1
Figure 1. Fluorescence emission in XL1-Blue E. coli strains co-expressing Biobrick parts 1, 2, and 3 reveals asymmetry of behaviors (lack of orthogonality). Left, cells containing both Biobrick parts 1 and 2, Biobrick part 1 and an empty kanamycin plasmid (e.k.p), Biobrick part 2 and an empty ampicillin plasmid (e.a.p.), Biobrick part 1, and Biobrick part 2. Right: Cells containing both Biobrick parts 2 and 3, Biobrick part 2 and an empty kanamycin plasmid (e.k.p), Biobrick part 3 and an empty ampicillin plasmid (e.a.p.), Biobrick part 2, and Biobrick part 3. XL1-Blue E. coli cells containing no plasmids or empty plasmids were used as controls. Both green (505 nm) and red (592 nm) fluorescence intensity (FI) were measured for all samples and normalized by the optical density at 600 nm (OD600).

Figure 2
Figure 2. Fluorescence emission by DH5-α E. coli strains co-expressing Biobrick parts 1 and 2 also reveals non orthogonal expression of Biobrick parts. Cells containing both Biobrick parts 1 and 2, Biobrick part 1 and an empty kanamycin plasmid (e.k.p), Biobrick part 2 and an empty ampicillin plasmid (e.a.p.), Biobrick part 1, and Biobrick part 2 were analyzed. XL1-Blue E. coli cells containing no plasmids or empty plasmids were used as controls. Both green (505 nm) and red (592 nm) fluorescence intensity (FI) were measured for all samples and normalized by the optical density at 600 nm (OD600).

When in co-transformation, Bb1 and Bb2 showed a clearly asymmetric expression . Bb1 was highly expressed in comparison to Bb2 (2-fold in strain XL1-Blue and 3-fold in DH5α). A similar phenomenon was observed when either Bb1 or Bb2 were cotransformed with empty plasmids. Again, less fluorescence was detected in comparison to the simple transformants. Surprisingly, this fluorescence decay was strikingly different between both E. coli strains: Bb1 strongly lowered its expression in DH5α, whereas Bb2 showed a higher decay in XL1-Blue cells.

However, the result was completely different when Bb2 and Bb3 were present in the same cell. In the XL1-Blue strain, the fluorescence intensity of Bb2 was as high as that of the single transformant, whereas the fluorescence of Bb3 –containing a less strong promoter- dropped dramatically. Fluorescence intensity of Bb3 was not detectable in strain DH5α under our experimental conditions.

With these results with co-transformants, we have to conclude that Biobrick parts, even if controlled by the same promoter and cloned into the same plasmid, are not expressed at similar rates. But, is this lack of orthogonality stable? In other words, is the asymmetric output always the same or it fluctuates depending on the culture? We designed a second experiment to answer this question. By performing several subcultures of the co-transformant strains -with five independent replica of each- and measuring the fluorescence output of each replicate after the subculturing steps, we will check whether fluorescence asymmetry is stable. On the other hand, we have only studied the average fluorescence in a population of cells in our experiments… but what are the variations between single cells? A simple experiment using flow cytometry can help us solve this question…

2. Flow cytometry

Figure 3
Figure 3. Caption.

Figure 4
Figure 4. Caption.

Figure 5
Figure 5. Caption.

3. Proteomics

Open License (and Responsible Research and Innovation)

Not unlike scientific research, RRI is about common sense, about combining self-profit with others’ profit, about taking care of our planet. We believe that being responsible is not the opposite of being selfish; responsibility is just a broad-minded way of selfishness that points at the long-term benefit. And there is no long-term benefit without a sustainable society in a sustainable environment. What is good for our neighbors and for our home must be good for us as well.

Our Proposal: Responsible Research and Innovation as a tool to choose within the IP ecosystem.

Since we, our Human Practices sub-team, are a biotechnology and a law student, we are also glad to see that another key concept in IP is a beautiful science-law metaphor: Jane Calvert’s “diverse ecology” scenario. This image fits well with our interdisciplinary spirit and we found it is very useful because it evokes very well the range of different solutions out there to choose from in order to use a scientific invention in a given way.

Having reached this point, it is obvious which is the next step: there is a whole ecosystem of IP figures (see the poetic cover illustration by Paula, another team member), and one has to be chosen. How? Our bet is simple: by using RRI as the guiding principle of the decision process.

The following figure illustrates what we propose:

Figure 1
Figure 1. RRI-based decision process on a diverse ecology scenario. From a diverse array of IP protection possibilities, we propose RRI as a framework to choose the most suitable one. In order to assist in the decision process, experts’ opinion is the major force, and we propose here two tools to contribute to this process: a common language exemplified by our dictionaries (Annex I), and a formula-based assistance procedure (Annex II).

In our view, the decision process has to fall upon the responsibility of the experts or examiners, as it is the case today. But we propose that a formation in RRI for patent examiners and other IP actors is introduced. In the same way that scientist need a basic law background for transferring their knowledge into applied solutions, we envisage a future in which everybody involved in the production of a given IP figure (a patent, for example, from scientists and engineers to ethics specialists, lawyers and other experts) should share a common interdisciplinary formation on RRI. A common language is the first step towards a common goal.

Additionally, we propose in this report two tools that might contribute to avoid misunderstandings and to make a simplified decision process available for a wide audience. In order to accomplish the former objective, we have prepared the two common-languages short dictionaries listed in Annex I. To achieve the latter, we propose, for the first time for the best of our knowledge, a math-based equation that could contribute to make IP issues available to a wide audience (Annex II). We have prepared the equation in two steps. First, we combined the patentability requirements (novelty, inventive step, industrial application) in a mathematically logical way to reach our draft equation (F0). Then, we submitted the formula to several experts to have their feedback, modify F0 accordingly to yield F1 (this reflexive process, combining design with improvement rounds, elegantly evokes a biotechnology strategy called directed evolution).

Figure 2
Figure 2. Human decisions are only acceptable if no help is used? Left, chess player. Right, graphic representation of the equation F0 we propose for patentability determination, as a function of the Industrial application (I) and the Inventive step (A). See Annex II for more details.

The very idea of using maths to decide whether something is patentable may seem odd. The opinion of the law experts was useful; but we wanted to have a broader feedback from other social actors. We thus carried out a survey to probe the opinion of iGEMers, scientists/engineers and law students, but also of general population to compare their view on our proposal. The C section of Annex II describes the results of this survey that can be summarize as follows: the majority of the respondents found that a math-based approach could be a useful tool, but should not substitute humans as the final decision makers. We could not agree more on that.

Report

Original watercolor by Paula Villaescusa

Annex 1. Brief dictionary of IP and SB Annex 2. Patentability index

The seat of the ST2OOL

One of the goals of our project was to select standard, stable, and orthogonal parts naturally occurring in nature. To do that, we carried out a functional metagenomics strategy aiming at selecting promoters from a library of metagenomic DNA. As a first step, we wanted to isolate metagenomic DNA from environmental samples, but instead of using traditional manual kits, we decided to build a robot able to automatically extract metagenomic DNA for us… It is a pleasure for our team to introduce you our TOOL robot:

“The TOOL” robot

How does it work?

The main goal of this machine was the automation of the DNA isolation process. This robot can be used for both E. coli (mini/maxipreps) and for DNA extraction from metagenomic samples.

The robotic system was developed in a continuous philosophy and all the components were assembled on a piece of wood. In short, it consisted of the following modules:

  • The control and powering system: the system was controlled by an Arduino Mega 2560 microchip, powered by a computer via USB. The other components of the machine were powered by an ATX power supply.
  • The injection system, which consisted of several screw stepper motors controlled by an A4988 driver. We chose this kind of motors because they allowed a quite accurate control of the position and the speed of the injections. A torque of 35N*m was also included. The system had three-way stopcocks in order to refill the syringes easily.
  • The temperature modules: high temperatures were achieved with a system that combined hot water and Peltier cells, whereas low temperatures were achieved by ice cooling. Temperature was measured with a dbs1820 temperature sensor.
  • The mixing: a homogeneous mixture of all the reagents was obtained thanks to the use of tubes allowing a turbulent regime.

The complete process was as follows:

  1. Manually inject a suspension of the cells into 3 mL of ice-cold solution I.
  2. From this step until step 8, the procedure is automatic (indicated in italics).
  3. 200 µL of solution II are injected and mixed during the flow into the silicone tubes.
  4. The mixture is moved through the silicone tubes to a water bath at 65 ºC and incubated for 30 minutes.
  5. 60 µL of ice-cold Potassium Acetate 3M (pH 5.0) are injected and mixed during the flow into the silicone tubes.
  6. The mixture is moved through the silicone tubes to an ice bath and incubated 20 minutes at -20ºC.
  7. The resulting suspension is then filtered through a membrane and insoluble particles are removed.
  8. An equal volume of isopropanol is injected and mixed with to the filtered solution during the flow into the silicone tubes, and the mixture is incubated at room temperature for 5 minutes.
  9. At this point, the solution is collected in Eppendorf tubes and these tubes are centrifuged at maximum speed for 10 minutes.
  10. The supernatant is discarded and the pellet washed with 500 µL of 70% (v/v) ethanol.
  11. The tubes are centrifuged again at maximum speed for 3 minutes and the supernatant is discarded.
  12. Finally, the pellet is dried and resuspended in 50 µL of water or elution buffer.

Results

As a proof of concept, we used a suspension of soil bacteria (obtained after several mild centrifugations of a suspension of compost in PBS sterile buffer) to isolate metagenomic DNA. Up to now, we have been able to perform steps 1-8 with our robot, and we are still working hard on the implementation of the final steps of the protocol. Figure 1A shows the Nanodrop analysis on the metagenomic DNA we obtained from the suspension. We isolated pure (A260/A280 and A260/A230 ratios close to 2) and quite concentrated (90.5 ng/µL) metagenomic DNA, the integrity of which was also checked on a 0.8% agarose gel (Figure 1B). Interestingly, we also observed the bands corresponding to the 16S and 23S ribosomal RNA on the gel.

Figure 1
Figure 1. A) Screenshot of the Nanodrop output of compost metagenomic DNA isolated with the robot as described in the main text. B) TBE 0.8% agarose gel showing the nucleic acids isolated with our robot. Lane 1: M.W. marker; lanes 2-5: metagenomic DNA aliquots. Arrows indicate metagenomic DNA (upper part) and 16S/23S ribosomal RNA.

Small budget, big ideas

Table 1 shows a list of all the components and materials used to build the robot. We decided to buy cheap and easy-to-find materials, so anyone can re-build or improve the robot! We only spent around 500€:

Table 1.Materials and components used during the construction of “The TOOL” robot. The price of each component is indicated on the right.
MATERIAL NUMBER Price (€)
ARDUINO MEGA 2560 1 12
WIRES 100 10
PROTOBOARD 1 3
DRIVER A4988 5 15
SCREW STEPPER MOTOR 4 130
SYRINGE 4 1
SCREW S 50 5
ALUMINUM BARS 1 3
WOOD TABLE 3 3
CLAMP 8 3
DRAWER GUIDE 4 3
SILICONE TUBES 5 1
PERISTALTIC PUMP 2 10
BEAKERS 3 7
SAW 12
FISH 1 2
DRILL 1 20
STOPCOCK 3 WAYS 6 12
SCROLL SAW 1 8
FILTER 1 per sample -
FILTER HOLDER (own design) 1 4
AID SILICONE TUBES 4 5
RESISTOR (400 h) 4 3
CAPACITOR(0.5F) 1 2
LIMIT SWITCH 2 3
WELDER 1 15
TIN COIL 1 11
RULE 1 3
LUB 1 6
SILICONE PISTOL 1 6
SILICONE 4 1
SCREW DRIVER 2 5
ATX POWER SUPPLY 1 10
dbs1820 SENSOR 2 6
PELTIER CELL 3 12
RELAY 1 6
PERISTALTIC PUMP 2 16
TRANSISTOR 2 1
Hours of work… A lot! A lot!

Selecting natural standard promoters

Once we had the metagenomic DNA extraction performed by our robot, we wanted to clone a library of fragments coming from this DNA in the pAcGFP1-1 plasmid (Clontech Laboratories, Inc.). This plasmid contains a GFP protein placed downstream the MCS, so if a promoter sequence is inserted in the polylinker, the expression of the fluorescent protein is triggered and thus fluorescent transformants can be easily detected.

We digested both the plasmid and the metagenomic DNA with the restriction enzymes EcoRI and BamHI and then purified the resulting fragments (Figure 2).

Figure 2
Figure 2. TBE 0.8% agarose gel showing the plasmid and the metagenomic DNA fragments obtained. Lane 1: MW marker; lane 2: undigested mgDNA; lane 3: digested mgDNA; lane 4: digested pAcGFP1-1 plasmid.

We set up a ligation reaction, incubated it overnight, and then transformed E. coli DH5α competent cells. We obtained around 300 colonies, but none was fluorescent… We performed a colony PCR with oligonucleotides flanking the MCS of the pAcGFP1-1 plasmid in order to check whether the transformant colonies were carrying inserts of metagenomic DNA, and confirmed their presence as you can see in Figure 3.

Figure 3
Figure 3. TBE 0.8% agarose gel showing the results of the colony-PCRs. Lanes 1 and 10: MW marker; lanes 2-9 and 11-16: independent colonies; lane 17: negative control.

We are working now on improving the efficiency of the transformation, so we can increase the probability of "catching" a good promoter sequence… more results coming soon!

Stability We have modeled the variation of fluorescence per cell of Biobricks with temperature, pH and salt concentration by considering changes in their transcription and synthesis rates. Our model captures the measured shape of the fluorescence per cell as a function of temperature. We have found that transcription rate goes up with temperature by the increase of free σ factors and synthesis rate goes down above the optimal temperature. Unfortunately, this is not all the story. Our model dramatically fails to fit the amount of variation, which is much larger experimentally than the predicted one. Let us compare theoretical predictions and experimental results. In both studies, strain XL1 Blue transformed with Biobricks Bb1 or Bb3, we find that the ratio between theoretical prediction and experimental result is the same at temperatures lower than the optimal (in fact, accidentally too close given the error bars). This fact suggests that the extra temperature dependence not included in our model should come from cell mechanisms independent on parameters of the Biobricks (promoter strength, ...). Quite on the contrary, the comparison of theoretical predictions and experimental results at larger temperatures show that our model has not yet capture the dependence of the magnitude of the decrease in activity with the Biobrick parameters.

Standardization We have built an app that computes the standardization-stability index and generates a promoter sequence for a given expression rate. Our wet-lab results show that de Biobrick 5 is the more standard one. Our model predicts that systems with less modules or factors will be the more stable and standard. Biobrick 5 has a tetracycline depending promoter, whose limitating step is the binding of tetracycline to TetR. In this scenario, the intracellular concentration of aTc and the binding to the TetR repressor do not depend on the strain, so there is only one factor in which the strain could have an important effect: the TetR intracellular concentration, which has a constitutive expression. Analyzing the other Biobricks, we found that the strains with less expression in the constitutive promoter are the ones with higher expression when we use the Ptet promoter (but JM109 and BB1), which agree with our prediction.

Orthogonality We have studied deterministic and stochastic models of the plasmid growth to learn about the unspecific interaction between two plasmids in a cell. We found that this system is unstable and the only way to keep the plasmid levels is introducing a resistance. However, it is not a good way to maintain those levels: bacteria which have non-desired levels of plasmids are killed. This leads to a lower yield. Also, there is an important variability, which can drive the system away from the expected behaviour. In conclusion, plasmids with identical origin of replication are not orthogonal per se. Although we can introduce use different resistances to tune the system, it could not behave as expected due to a great variability. As a solution, we could use plasmids with different mechanisms of replication, but they could show different dynamics, making the system even mor difficult to control. Therefore, new plasmids must be designed to make engineered cells more reliable and predictable to make progress in synthetic biology.

Open Licence We have modeled the susceptibility to patent a human creation and define the factors that influence it. Inventive is the most relevant variable but is regulated by the industrial application. The highest inventive step can be reduced by almost a factor 2 with the lowest industrial application. On the contrary, the lowest inventive step is not affected by the industrial application. After computing all the discussed factors, the patentability index can run from 0 to 10. The reader can find a detailed discussion of the questionnaires, tests, analysis of the index and references in the area Human Practices.