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, XL1-Blue, 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. 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.

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. 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. 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.

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

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. 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€:

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. 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. 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.