Difference between revisions of "Team:Newcastle/Results"

Our Experimental Results

Hover over elements of the diagram below to see what each part represents.
Click elements of the diagram below to see results for each section of our project.

	Biochemical Adaptor
Target		Detector Modules		Multicellular Framework Testing
		C12 HSL: Connector 1
		Processor Modules
				Framework in Cell Free Protein Synthesis Systems
		C4 HSL: Connector 2
		Reporter Modules
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Alternatively, click here to see a list of our experiments and results.
Want to learn more about our framework (above)? Head over to our description page!

Biochemcial Adaptor Modules: The Results

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Sarcosine Oxidase (Glyphosate to Formaldehyde)

BioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)

Rationale and Aim

Sarcosine Oxidase (SOX) is an enzyme that oxidatively demethylates sarcosine to form glycine, hydrogen peroxide and formaldehyde (Trickey et al. 1999). SOX was selected to be an example of a possible solution to one of the 5 problems in biosensor production that we identified - unconventional substrates. We defined an unconventional substrate as a substrate that we have little prior knowledge of but that can be adapted into something with an existing biosensor. SOX was specifically chosen to demonstrate that glyphosate, an unconventional substrate which there is not a lot information on, can be converted into formaldehyde which there are existing biosensors for (Ling and Heng 2010).

As part of our project, SOX was designed to be an ‘adapter’ that could link glyphosate into our framework via a formaldehyde detector module. This concept could then be applied to other molecules that have easily detectable substrates in their degradation pathways. The aim of this part of the project was to demonstrate that SOX can be expressed by E. coli cells and that when glyphosate is added SOX can convert it to formaldehyde to be detected via a biosensor.

Background Information

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Design Stage

To ensure the codon usage of our SOX protein was not differing significantly from the average codon usage of E. coli, rare codons were removed from the sequence using the IDT codon optimisation toolto produce high protein expression.

E. coli BL21-DE3 cells have higher levels of protein expression than DH5α cells and so were a more practical choice. This led to the expression of SOX being placed under the control of a T7 promoter due to BL21-DE3 cells producing T7 polymerase after the addition of IPTG.

During the initial design stage of the protein, parts of the sequence were lost between optimisation and sending it to be synthesised into a gBlock. This was not discovered until expression of SOX was induced by IPTG in BL21-DE3 cells and a sample analysed by SDS-Page gel electrophoresis (picture). It was noticed that the band we were expecting was of a lower weight than what it should have been; ~35kDa instead of ~42kDa. It was realised that the sequence in the PSB1C3 plasmid was different to the sequence origin. Therefore a new gBlock was synthesised using the proper sequence and an SDS-Page gel used to confirm that the protein expressed was of the correct weight (picture).

Implementation

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Characterisation

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Conclusions and Future Work

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References

Ling YP, Heng LY (2010). A Potentiometric Formaldehyde Biosensor Based on Immobilization of Alcohol Oxidase on Acryloxysuccinimide-modified Acrylic Microspheres. Sensors 10:9963-9981.

Trickey P, Wagner MA, Jorns MS, Mathews FS (1999). Monomeric sarcosine oxidase: structure of a covalently flavinylated amine oxidizing enzyme. Structure 7:331-345.

Detector Modules: The Results

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Synthetic Promoter Library

BioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)

Rationale and Aim

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Background Information

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Design Stage

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Implementation

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Characterisation

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Conclusions and Future Work

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References

Arsenic Biosensor

BioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)

Rationale and Aim

The Sensynova multicellular biosensor platform has been developed to overcome the limitations identified by our team [hyperlink to human practices] that hamper the success in biosensors development. One of these limits regards the lack of modularity and reusability of the various components. Our platform design, based on the expression of three main modules (Detector, Processor and Output) by three E. coli strains in co-culture, allows the switch of possible variances for each module and the production of multiple customised biosensors.

This section of the project is based on testing the modularity of the system by replacing the IPTG detector part of the Sensynova design with different detecting parts. In particular, an Arsenic sensing part will be used.

Background Information

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Design Stage Implementation Characterisation Conclusions and Future Work References

Psicose Biosensor (Evry Paris-Saclay Collaboration)

BioBricks used: BBa_K2205023 (New), BBa_??? (Evry Paris-Saclay 2017)

Rationale and Aim

The Sensynova multicellular biosensor platform has been developed to overcome the limitations identified by our team [hyperlink to human practices] that hamper the success in biosensors development. One of these limits regards the lack of modularity and reusability of the various components. Our platform design, based on the expression of three main modules (Detector, Processor and Output) by three E.coli strains in co-culture, allows the switch of possible variances for each module and the production of multiple customised biosensors.

This section of the project is based on testing the modularity of the system by implementing the biosensor created by the 2017 Evry Paris-Saclay iGEM team into the Sensynova platform as part of our collaboration requirement.

Background Information

This biosensor was designed, made and submitted to the iGEM registry by the Evry Paris-Saclay 2017 team.

We chose to use this system as a variant to the IPTG detector module present in the Sensynova platform in order to fulfil the requirement of collaborating with another iGEM team.

The image below, provided to us by the Evry Paris-Saclay 2017 team, details the psicose biosensor design. It features the pLac derivative promoter pTAC (BBa_K180000), a RBS (BBa_B0034), the PsiR coding sequence, the terminator (BBa_B0015), the synthetic promoter pPsitac, a RBS (BBa_B0034), a mCherry coding sequence and finally the terminator (BBa_B0015) flanked by the iGEM prefix and suffix.

The inducible system works as detailed in the diagram below. When pTAC is induced due to the presence of IPTG, PsiR is transcribed and binds to the pPsitac promoter repressing the transcription of the mCherry protein. When psicose is present, the sugar binds to PsiR, freeing up the promoter and subsequently the colour output.

Design Stage

In order to implement the psicose biosensor variant to the Sensynova platform, a design was created by replacing the IPTG sensing system in the original detector module with the construct detailed above, creating part K2205023.

We chose to replace the pTAC promoter with the constitutive promoter present within the platform in order to eliminate the need for induction with IPTG. In place of the colour output present in the Evry Paris-Saclay design, we have added our part K2205008, which produces our first connector in order to trigger a response from following modules of the Sensynova platform.

Part K2205023 detailed above was designed using Benchling and ordered for synthesis through IDT. Using Benchling, virtual digestions and ligations were simulated resulting in the plasmid map detailed below.

Implementation

The Psicose detector construct obtained by gBlock synthesis has been designed to include required overhangs for Gibson assembly into the linearized plasmid pSB1C3.

The plasmid backbone was acquired by digestion [Protocol link] of the part K2205015 with XbaI and SpeI, cutting out the original sfGFP construct.

The Psicose detector construct was assembled into the plasmid backbone using the NEB Hi-Fi kit [Protocol link] and transformed into DH5α E. coli cells [Protocol link].

Colonies picked from streaked plates and cultures were prepared for miniprepping [Protocol link]. DNA samples were then sent off for sequencing [Website link] to ensure that the constructs were correct.

Characterisation

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Future Work

Due to time constraints resulted from synthesis delays, we lacked the time to co-culture this part with the Sensynova platform's multiple modules in order for the creation of variants. The part K2205023, the Evry Pasir-Sclay's psicose biosensor system as the detecting unit of the platform, has been submitted to the iGEM registry for future work and characterisation by future teams.

Processor Modules: The Results

Click the headings to see experiments and results

Fim Standby Switch

BioBricks used: BBa_0123456 (New), BBa_K1632013, BBa_K1632007(2015 Tokyo Tech part)

Rationale and Aim

Sensynova multicellular biosensor platform has been developed to overcome the limitations that hamper the success in biosensors development. One of these limits regards the lack of modularity and reusability of the various components. Our platform design, based on the expression of three main modules (Detector, Processor and Output) by three E.coli strains in co-culture, allows the switch of possible variances for each module and the production of multiple customised biosensors.

This part can be used within the platform as a Processor unit. Real world applications of biosensors are limited by many factors, one of which is that with most biosensors there is not a readout signal showing if the biosensor is working when not in use, i.e that the cells are still alive and have not lost their biosensor phenotypes. This can make them difficult to use, as well as market, since their viability comes into question as well as leading to false negatives/positives. Biosensors which rely on expression of a reporter signal may also suffer from unobserved activation due to weak or inconstant induction.

For this section of the project, as an improvement on a part by the Tokyo Tech team, we aim to produce a biobrick compatible part which is able to constitutively express a reporter signal prior to activation (to show that it is functioning) and to amplify a weak or inconsistent induction signal by permanently switching from an [OFF] to [ON] state after induction.

Background Information

Expression of the E. coli type 1 fimbriae gene is tightly regulated and phase dependent, i.e expression is either completely [ON] or [OFF] (Klemm., 1986). This change in expression is controlled by the action of two proteins FimB and FimE which independently act upon a 300bp promoter region upstream of the fimbriae gene. The 300bp promoter region is inverted to either activate or suppress expression (McClain et al., 1991). Typical gene regulation mechanisms rely on up or down regulation of a promoter from a baseline expression, the fimbriae mechanism of ‘ALL’ or ‘NONE’ makes it a useful tool for synthetic biology applications. While the FimB protein inverts the promoter back and forth between [ON] and [OFF] states the FimE protein permanently inverts the promoter from [ON] to [OFF]. This inversion can be used to amplify weak or inconsistent induction signals.

Since the part we are making is designed to amplify a weak signal which can then be detected by a downstream ‘reporter’ cell the quorum sensing system from P. aeruginosa was adapted to allow for signal transfer between cells. The rhlI gene from P. aeruginosa produces the quorum sensing molecule N-butyryl-HSL (C4-HSL) (Parsek et al.,2000), this molecule is membrane permeable and able to induce expression of a promoter upstream of sfGFP in another cell.

LINNNNKKKK HHHEEEEEERRRRRREEEE!!!!!!!!!! Figure X: Representation of the switching mechanism of the Fim Switch, in the native [OFF] state the eforRED reporter is expressed (shown in red) allowing direct visualisation of the cells. Following the inversion of the promoter region (K1632004), eforRED expression is halted and the RhlI gene is expressed (J64718), this is now the [ON] state.

Design Stage

To construct the Fim reporter switch 3 separate gBlocks were designed with overlapping adapter regions homologous to the iGEM prefix and suffix to allow for Gibson assembly into the pSB1C3 backbone whilst retaining biobrick compatibility. The individual genes and other components are shown in (Table 1). The 1st gBlock sequence starts with a RBS (B0034) upstream of the fimE ORF (K137007) with no promoter region, this is to allow for other promoters to be cloned in upstream of the part. Downstream of the fimE gene is a double terminator (B0015). All RBS and terminator sequences used are B0034 and B0015 respectively. The switching mechanism consists of the Fim promoter sequence (K1632004) flanked by two RBS-ORF-Terminator sequences. While in the native [OFF] state the Fim promoter drives expression of eforRed (K592012) and when flipped to the [ON] state drives expression of rhlI (J64718). The rationale behind using the fimE gene instead of is that it permanently inverts the promoter region meaning weak induction signals can be amplified by the Fim switch.

TABLE HERE

Implementation

To assemble the Fim switch part the isothermal Gibson assembly cloning method was chosen as it would significantly shorten the time taken to assemble 3 separate sequences compared to traditional cloning methods. The 3 gBlock DNA fragments shown in (Table 1) were amplified by high fidelity Q5 PCR, the pSB1C3 backbone was digested with restriction enzymes EcoRI and PstI.

Figure X: High fidelity amplification of the 3 gBlock fragments for assembly of the Fim Switch. The gBlock-1 amplification is shown in lanes 1+2 (819 bp), gBlock-2 amplification is shown in lanes 3+4 (1148 bp) and the gBlock-3 amplification is shown in lanes 5+6 (939bp).

The Gibson assembly reaction re-forms the iGEM prefix and suffix regions at the 5’ and 3’ ends of the Fim switch part making the component biobrick compatible while leaving no scarring regions. Following assembly, the plasmid was transformed into chemically competent DH5a E. coli >>Protocol link<< and colonies patched onto LB Chloramphenicol agar plates. A single patch showed the correct red colour indicative of the eforRed chromoprotein >>Figure X<<.

Figure X: Patches of the Fim Switch transformants. Patch number 6 shows the correct red colour which indicates expression of the eforRed chromoprotein.

The red patch was cultured in LB chloramphenicol overnight and the plasmid DNA extracted by miniprep >>Protocol link<<. The plasmid was digested with restriction enzymes XbaI and PstI. The image in >>FigureX<< shows the DNA bands from the digested Fim switch plasmid.


Figure X: Restriction digestion of the Fim switch plasmid to confirm successful integration into the iGEM pSB1C3 backbone. The Fim switch plasmid (Lane 1) was digested with XbaI and PstI with expected band sizes of (2840 bp and 2044 bp). The pSB1C3 plasmid (Lane 2) containing sfGFP as a control was also digested with XbaI and PstI with expected band sizes of (811 bp and 2044 bp).

The Fim switch insert is 2882 bp in length which makes performing standard short sequencing reads challenging as multiple reactions are required to completely sequence the entire part. To overcome this we used our in-house Illumina MiSEQ to completely sequence the entire plasmid. Following quality control analysis the sequence was assembled and shown to be a match to the expected Fim switch part.

A problem we found with the Fim switch was that a subset of the colonies were prematurely switching from red to white. This is likely due to a low level of leaky expression of the fimE gene which then inverts the promoter region upstream of the eforRed gene. A single white colony was picked and cultured for use in downstream testing as a control as the switching of the promoter should express the RhlI gene and therefor produce the C4 quorum sensing molecule.

Characterisation

The fim Standby Switch has two main functions; a visual signal to show that the target compound has been detected and AHL production so that the part can be detected by a reporter cell. To characterise the part these functions are individually tested, in aim to further isolate issues if they occur.
To test the detecting function of the Fim Standby Switch it was assembled with the PBAD/AraC promotor. The PBAD/AraC promotor with Standby Switch parts were plated out onto four different LB plates containing chloramphenicol, two plates with different concentrations of glucose and chloramphenicol, and another containing arabinose. As colonies for the Fim Switch section were red and white when plated onto chloramphenicol plates due to leakiness, the plates with glucose in theory should suppress this switching and a greater percentage colonies on these plates should be red after transformation. The colonies on the arabinose plate should be white as translation of fimE leads to the flipping fimS, and expression of RHlI.

The number of colonies on the plate that are white and red confirm inversion, this will show the percentage of colonies in the [ON] and [OFF] states. DNA sequencing will show inversion of the switch. As fimE is unidirectional over time the colonies should all become white on the plate containing arabinose. The plate containing glucose should repress leakage and the medium is supplemented by some percentage glucose.

To test the functionality of the Fim switch part, to ensure that C4 (AHL) is produced, the strain was cultured with the reporter strain >>REPORTER??<< which produces GFP in response to the quorum sensing molecule C4. Due to a small sub-population of the Fim switch strain being white, a single white colony was picked and cultured separately form the main culture. This culture is then co-cultured with a successfully independently tested reporter cell.This reporter cell detects C4 production and as a result GFP produced. This strain was used as a reference as it should produce activate expression of GFP in the reporter. This also shows that the reversed sequences for RHLI and B0034 are working as expected. Since the issue of premature inversion of the Fim promoter may cause a problem with the Fim switch.




Figure X: Expression of GFP in the reporter strain in co-culture with the Fim switch strains. The assay was performed using methods described in >>Protocol<<. The data shows the expression of GFP in the reporter strain over a standard growth curve. The FimW and FimR strains represent the white and red variants of the Fim switch strain respectively, these were co-cultured with the reporter strain in a 1:14 ratio. Each data point is the mean of 3 biological repeats. RFU stands for relative fluorescence units.

Conclusions and Future Work

TEXT HERE

References

P. Klemm, Two regulatory fim genes, fimB and fimE, control the phase variation of type 1 fimbriae in Escherichia coli. EMBO J 5, 1389-1393 (1986).

M. S. McClain, I. C. Blomfield, B. I. Eisenstein, Roles of fimB and fimE in site-specific DNA inversion associated with phase variation of type 1 fimbriae in Escherichia coli. J Bacteriol 173, 5308-5314 (1991).

M. R. Parsek, E. P. Greenberg, Acyl-homoserine lactone quorum sensing in gram-negative bacteria: a signaling mechanism involved in associations with higher organisms. Proc Natl Acad Sci U S A 97, 8789-8793 (2000).

Signal Tuners

BioBricks used: BBa_K2205024 (New),BBa_K2205025 (New), BBa_K274371 (Cambridge 2009), BBa_K274381 (Cambridge 2009)

Rationale and Aim

The Sensynova multicellular biosensor platform has been developed to overcome the limitations identified by our team [hyperlink to human practices] that hamper the success in biosensors development. One of these limits regards the lack of modularity and reusability of the various components. Our platform design, based on the expression of three main modules (Detector, Processor and Output) by three E.coli strains in co-culture, allows the switch of possible variances for each module and the production of multiple customised biosensors.

This section of the project is based on testing the modularity of the system by inserting two different sensitivity tuner constructs between the processing units of the Sensynova platform; BBa_K274371 and BBa_K274381.

Background Information

Both selected sensitivity tuner constructs were made and submitted to the iGEM registry by the Cambridge 2009 team.

They were chosen as variants to the empty processing module present in the Sensynova platform due to the fact that, although they have been included in the iGEM distribution kit since their submission in 2009, they have yet to be successfully implemented into a team’s system, as far as we are aware.

The 2007 Cambridge iGEM team built 15 different constructs that amplified the PoPS output of the promoter pBad/AraC detailed by image below taken from the Cambridge 2009 team's wiki.

The 2009 Cambridge iGEM team then re-designed these constructs to be PoPS converters, as image below taken from their wiki details, and generated a set sensitivity tuners corresponding to Cambridge 2007’s amplifiers.

BBa_K274371

This part is made up of a RBS (BBa_B0034), an org activator coding sequence (BBa_I746350) from P2 phage, the double terminator BBa_B0015 (made up of BBa_B0010 and BBa_B0012) and the inducible promoter PO (BBa_I746361) from P2 phage.

BBa_ K274381

This part is made up of a RBS (BBa_B0034), a pag activator coding sequence (BBa_I746351) from PSP3 phage, the double terminator BBa_B0015 (made up of BBa_B0010 and BBa_B0012) and the inducible promoter PO (BBa_I746361) from P2 phage.

Design Stage

In order to implement these two sensitivity tuner variants into the Sensynova platform, designs were made by inserting the above parts between the two constructs forming the empty processor module of our framework.

Using Benchling, virtual digestions of the two sensitivity tuners and ligations to the part K2205010, the connector 1 receiver module, were carried out. These two new constructs were then virtual digested and ligated to the part K2205011, the connector 2 reporter module, resulting in the two plasmid maps detailed below; parts K2205024 and K2205025.

Implementation

The sensitivity tuners parts BBa_K274371 and BBa_K274381 were requested from the iGEM parts registry. Upon arrival, parts were transformed in DH5α E. coli cells [Protocol link]. Colonies were picked and cultures were prepared for miniprepping [Protocol link]. Minipreps were digested [Protocol link] with XbaI and PstI for BioBrick assembly [Protocol link].

The part K2205010 contained in pSB1C3, was digested [Protocol link] using SpeI and PstI to allow for the insertion of the processing variants directly after the Las controlled promoter (pLas) that would trigger transcription of sensitivity tuners in the presence of connector 1 of the Sensynova platform.

Ligations were set up overnight [Protocol link] using NEB’s T4 ligase and transformed in DH5α E. coli cells [Protocol link]. Colony PCR [Protocol link] was performed to check ligations. Colonies picked for this protocol were streaked onto a LB-agar plate.

Colonies picked from streaked plates and cultures were prepared for miniprepping [Protocol link]. Minipreps were digested [Protocol link] with SpeI and PstI to allow for the insertion of the part K2205011 directly after the PO promoter.

The part K2205010 contained in pSB1C3, was digested [Protocol link] using XbaI and PstI for BioBrick assembly [Protocol link]. Ligations were set up overnight [Protocol link] using NEB’s T4 ligase and transformed in DH5α E. coli cells [Protocol link]. Colony PCR [Protocol link] was performed to check ligations. Colonies picked for this protocol were streaked onto a LB-agar plate.

Colonies picked from streaked plates and cultures were prepared for miniprepping [Protocol link]. DNA samples were then sent off for sequencing [Website link] to ensure that the constructs were correct.

Future Work

Due to time constraints, we lacked the time to characterise these parts into the Sensynova platform within the lab. The parts K2205024 and K2205025, the parts BBa_K274371 and BBa_K274381 respectively as processing units of the platform, were been submitted to the iGEM registry for future work and characterisation by future teams.

Reporter Modules: The Results

Click the headings to see experiments and results

deGFP

BioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)

Rationale and Aim
Background Information
Design Stage
Implementation
Characterisation
Conclusions and Future Work
References

Chromoproteins

BioBricks used: BBa_K2205016 (New),BBa_K2205017 (New),BBa_K2205018 (New), BBa_K1033915 (Uppsala 2013), BBa_K1033925 (Uppsala 2013), BBa_K1033929 (Uppsala 2013)

Rationale and Aim

The Sensynova multicellular biosensor platform has been developed to overcome the limitations identified by our team [hyperlink to human practices] that hamper the success in biosensors development. One of these limits regards the lack of modularity and reusability of the various components. Our platform design, based on the expression of three main modules (Detector, Processor and Output) by three E.coli strains in co-culture, allows the switch of possible variances for each module and the production of multiple customised biosensors.

This section of the project is based on testing the modularity of the system by replacing the sfGFP output part of the Sensynova platform design with three different output chromoprotein variants; BBa_K1033929 (aeBlue), BBa_K1033925 (spisPink) and BBa_K1033915 (amajLime).

Background Information

All three selected chromoproteins were made and submitted to the iGEM registry by the Uppsala 2013 team.

They were chosen as variants to the sfGFP present in the Sensynova platform as they exhibit of strong colour readily observed in both LB cultures and in agar plates when expressed.

All three proteins have significant sequence homologies with proteins in the GFP family.

BBa_K1033915 – amajLime

The amajLime protein is a yellow-green chromoprotein extracted from the coral Anemonia majano. It was first extracted and characterized by Matz et al. under the name amFP486 (UniProtKB/Swiss-Prot: Q9U6Y6.1 GI: 56749103 GenBank: AF168421.1) and codon optimized for E coli by Genscript. The protein has an absorption maximum at 458 nm giving it a yellow-green colour visible to the naked eye.

BBa_K1033925 – spisPink

The spisPink protein is a pink chromoprotein extracted from the coral Stylophora pistillata. It was first extracted and characterized by Alieva et al. under the name spisCP (GenBank: ABB17971.1) and codon optimized for E. coli by Genscript. The protein has an absorption maximum at 560 nm giving it a pink colour visible to the naked eye. The strong colour is readily observed in both LB or on agar plates after less than 24 hours of incubation.

BBa_K1033929 – aeBlue

The aeBlue protein is a blue chromoprotein extracted from the basal disk of a beadlet anemone Actinia equine. It was first extracted and characterized by Shkrob et al. 2005 under the name aeCP597 and codon optimised for E. coli by Bioneer Corp. The protein has an absorption maximum at 597nm and a deep blue colour visible to the naked eye. The protein aeBlue has significant sequence homologies with proteins in the GFP family. The coding sequence for this protein was originally submitted to the registry as BBa_K1033916 by the 2012 Uppsala iGEM team.

Design Stage

In order to implement these three chromoprotein variants into the Sensynova platform, designs were made by replacing the sfGFP in the original reporter module with the parts detailed above that were ordered from the iGEM parts registry.

Using Benchling, virtual digestions of the three chromoproteins and ligations to the part K2205013, the connector 2 receiver module detailed above, were carried out resulting in the three plasmid maps detailed below; parts K2205016, K2205017 and K220518.

Implementation

The chromoproteins aeBlue (BBa_K1033929), amajLime (BBa_K1033915) and spisPink (BBa_K1033925) parts were requested from the iGEM parts registry. Upon arrival, parts were transformed in DH5α E. coli cells [Protocol link]. Colonies were picked and overnight cultures were prepared for miniprepping [Protocol link]. Minipreps were digested [Protocol link] with XbaI and PstI for BioBrick assembly [Protocol link].

The part K2205013 contained in pSB1C3, was digested [Protocol link] using SpeI and PstI to allow for the insertion of the chromoproteins directly after the RhI controlled promoter (pRhI) that would trigger transcription of colour proteins in the presence of connector 2 of the Sensynova platform.

Stared colonies picked from streaked plates and cultures were prepared for miniprepping [Protocol link]. DNA samples were then sent off for sequencing [Website link] to ensure that the constructs were correct.

Characterisation
Conclusions and Future Work
References

Alieva, N., Konzen, K., Field, S., Meleshkevitch, E., Hunt, M., Beltran-Ramirez, V., Miller, D., Wiedenmann, J., Salih, A. and Matz, M. (2008). Diversity and Evolution of Coral Fluorescent Proteins. PLoS ONE, 3(7), p.e2680.

Matz, M., Fradkov, A., Labas, Y., Savitsky, A., Zaraisky, A., Markelov, M. and Lukyanov, S. (1999). Nature Biotechnology, 17(10), pp.969-973.

Shkrob, M., Yanushevich, Y., Chudakov, D., Gurskaya, N., Labas, Y., Poponov, S., Mudrik, N., Lukyanov, S. and Lukyanov, K. (2005). Far-red fluorescent proteins evolved from a blue chromoprotein fromActinia equina. Biochemical Journal, 392(3), pp.649-654.

Sensynova Framework Testing (IPTG Sensor): The Results

BioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)

Rationale and Aim

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Background Information

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Design Stage

TEXT HERE

Implementation

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Characterisation

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Conclusions and Future Work

TEXT HERE

References

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Cell Free Protein Synthesis System Optimisation: The Results

BioBricks used: BBa_K515105 (Imperial College London 2011)

Rationale and Aim

Cell free protein synthesis (CFPS) systems have large potential as alternative chassis for applications such biosensors or diagnostic tests. This is because generally, biosensors are needed to function outside of the laboratory environment. Whole cells, which are traditionally used as chassis, can be problematic in these scenarios due to issues with containment and release of genetically modified organisms.

While CFPS systems are promising alternatives to whole cells, there are currently some drawbacks, primarily their cost and variability between batches of cell extract. To address these issues, this part of the project had the following aims: (i) develop a functional CFPS system, (ii) demonstrate the usefulness of a Design of Experiments (DoE) approach towards identifying which supplements are the most crucial for maximal CFPS activity, and (iii) demonstrate how DoE can be used to optimise each batch of cell extract for maximal CFPS activity.

Background Information

Cell Free Protein Synthesis Systems
Cell free protein synthesis (CFPS) systems are capable of performing transcription and translation of exogenous DNA in vitro. CFPS systems have been in use for many decades (Nirenberg & Matthaei, 1961), however the field of synthetic biology has resulted in a CFPS renaissance (Lu, 2017; Lee & Kim, 2013). Commonly, CFPS systems are based on cell extracts, which provide the transcription/translation machinery, as well as enzymes required to generate ATP required for protein synthesis.

While CFPS systems have a lot of potential, they also suffer from some drawbacks. Two of the major issues are the large variation in CFPS activity between cell extracts (Katsura, et al., 2017), and the high costs compared to whole cells (although cost have been reduced significantly in the past decade) (Carlson, et al., 2012). These issues can hinder the uptake of CFPS systems as an alternative chassis to whole cells, and as research tools.

Click to show/hide more information about the CFPS premix

Previous research has shown that the concentration of some components of the supplement solution are crucial for efficient protein synthesis, and that for each batch of extract produced the optimal concentration may need to be found (Yang, et al., 2012). Studies which have explored this have only focused on, at most, a few components at a time (Garamella, et al., 2016; Kelwick, et al., 2016), which means that important interactions between the components may have been missed.

Multifactorial Design of Experiments
Traditionally, biologists tend to use One Factor At a Time (OFAT) approaches to determine the effect and importance of factors on a system. This method can sometimes be a poor choice. By only determining the effect that a single factor has on a system at a time, important interactions can be missed. For example, removing only factor A may have no effect, and removing only factor B may also have no effect, but removing both may cause an adverse effect. Therefore, it is important to take a multifactorial approach when investigating the importance of conditions or components of a system, or when trying to optimise a system. An issue with this approach is that a large number of experiments may be required to fully investigate all factors. By using statistical methods, a Design of Experiments (DoE) can be determined which has the minimum number of experiments required to explore questions such as the importance of factors in a system. This approach also allows for robustness testing or determining batch-batch variation (Anderson & Whitcomb, 2010). As mentioned previously, CFPS systems can be plagued with issues rising from variation, so this approach offers a method to investigate the causes. There are several different types of DoE designs. One of these is the screening design (SD), which is used to create experimental designs to determine the factors with the highest effect on a system. Another design is the surface response design (SRD), which makes experimental designs to collect data for generating models which can predict optimal settings for many factors (SAS Institute Inc., 2016). Software tools, such as JMP (SAS Institute Inc., 2016), have been developed to create these experimental designs.

Implementation

Cell free extract preparation procedures were based on methods reported in literature previously (Kwon & Jewett, 2015). Cell free extracts were prepared from Escherichia coli BL21 and Bacillus subtilis 168. Cells were streak plated out from glycerol stocks on LB agar (15 mg/mL agar, 10 mg/mL tryptone, 5 mg/mL yeast extract, 0.17 M sodium chloride) and incubated overnight at 37^oC. A single colony was used to inoculate 10 mL LB broth (10 mg mL^-1 tryptone, 5 mg mL^-1 yeast extract, 0.17 M sodium chloride) before shake-incubation at 37^oC for approximately 16 hours overnight. 2 mL of overnight liquid culture was used to inoculate 200 mL LB broth in a 2 L flask and shake-incubated at 37^oC until late exponential phase was reached (OD_{600 nm} of approximately 2.5 for E. coli BL21 cells). The culture was split in half and cells were harvested by centrifugation at 4,500 RPM and 4^oC for 20 minutes in pre-weighed falcon tubes. The wet cell pellet weight was determined before storage at -20^oC. Cells were defrosted on ice for approximately 1.5 hours and resuspended in approximately 10 mL of ice-cold CFPS wash buffer (60 mM potassium glutamate, 14 mM magnesium glutamate, 10 mM TRIS (pH 8.2 with acetic acid); autoclave sterilised; supplemented with 2 mM DTT immediately before use) per gram of wet cell pellet. Resuspended cells were centrifuged at 4,500 RPM and 4oC for 20 mins. The supernatant was discarded and cell pellets were resuspended and centrifuged in CFPS wash buffer twice more. The washed pellets were then resuspended in 1 mL CFPS wash buffer per gram of wet cell pellet and aliquoted to 1 mL in 2 mL tubes. Cells were lysed by sonication (20% amplitude, cycles of 40 seconds on – 59.9 seconds off, 432.5 Joules) and the lysates were clarified by centrifugation at 12,000 RPM for 10 mins, flash frozen in liquid nitrogen, and stored at -80oC. A CFPS supplement solution was prepared based on previously reported protocols (Yang, et al., 2012). Briefly, amino acid stock solutions were prepared according to table 1. Briefly, amino acids were weighed in 2 mL tubes, dissolved in 5 M potassium hydroxide, and stored at -20oC. A 10x amino acid solution was prepared by mixing the stock solutions together in amounts according to Appendix.2 and the pH was adjusted to 7.9 with acetic acid. The solution was aliquoted to 1.5 mL and stored at -80oC. The following solutions were prepared in autoclave sterilised MiliQ water and stored at -80oC: 100x magnesium glutamate solution (1.2 M magnesium glutamate), 10x salt solution (1.3 M potassium glutamate, 40 mM sodium oxalate, 10 mM ammonium acetate), 25x NTPS & co-factor mix (37.5 mM spermidine, 30 mM ATP, 21.25 mM GTP, UTP, and CTP, 25 mM putrescine, 8.25 mM nicotinamide diphosphate, 4.25 mg mL-1 E. coli tRNA (Roche), 0.85 mg mL-1 folinic acid, N xX co-enzyme A), 25x sodium pyruvate solution (825 mM sodium pyruvate, pH to 7.3 with potassium hydroxide), unless stated otherwise. A 5x CFPS supplement solution premix (5% v/v nuclease free water, 5% v/v magnesium glutamate solution, 50% v/v salt solution, 20% v/v NTPS & co-factor mix, 20% v/v sodium pyruvate solution, unless stated otherwise) was prepared and stored at -80oC. CFPS activity of systems prepared as above were tested by expression of 1.7 μg pSB1C3-J23100-sfGFP (Figure CFPS-1). Firstly, enough CFPS master mix was prepared for 7 reactions by mixing 112 μL cell extract, 70 μL CFPS supplement premix, and 21 μL amino acid solution in a 1.5 mL tube and stored on ice. A further six 1.5 mL tubes were put on ice; 21 μL of nuclease free water was added to three tubes, and 1.7 μg pSB1C3-J23100-sfGFP plasmid DNA from the same stock solution was added to the remaining three. Tubes containing DNA were made up to 21 μL with nuclease-free water. CFPS master mix (29 μL) was then added to all tubes, which were vortexed and transferred to a 96-well plate. The plate was incubated in a BMG Labtech Fluostar Optima at 370C for 4.25 hours with fluorescence readings (excitation: 485 nm, emission: 510 nm) every 15 mins. Figure CFPS-2 shows that over time, fluorescence intensity increased in systems with DNA encoding for sfGFP compared to systems with no DNA. Hence, the system had CFPS activity.

Figure CFPS-1 Plasmid Map for pSB1C3-J23100-sfGFP: Plasmid map for pSB1C3-sfGFP. Construct is standard biobrick part BBa_ K515105.

Figure CFPS-2 Initial Testing of CFPS Systems: Negative corrected fluorescence for E. coli BL21 extract-based CFPS systems. Each data point is an average of 3 replicate reactions, and error bars represent +/- standard error.

Design Stage-1

Previous research has shown that the concentration of certain salts in the CFPS supplement premix are crucial for maximal protein synthesis activity [REF]. A Design of Experiments approach was used to determine which of the four salts (magnesium glutamate, potassium glutamate, sodium oxalate, and ammonium acetate) are the most important using the JMP software. A classical screening design was created with all four salts as continuous factors and CFPS activity as the response to be maximised. A concentration of ‘0’ was used as the lower limit for each factor, and the concentration used normally in CFPS supplement premixes was used as the upper limit. The main effects screening design was then used to generate the experimental design.

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Experimental Procedure-1

Cell extracts were prepared from E. coli BL21 cells using sonication. A CFPS supplement premix solution was prepared as above, except the salts were omitted. Separate solutions for each salt were prepared and added to each CFPS reaction according to the main effects screening design. Reactions were performed as above and CFPS activity was measured as fluorescence at each time point minus fluorescence at 15 mins (Figure below). Endpoint data was then used, along with the JMP software, to build a model predicting the important factors (Bar chart below).

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Design Stage-2

The three salts which the screening design determined as being the most important (magnesium glutamate, potassium glutamate, and sodium oxalate) were analysed further. A surface response design was used to help determine optimal concentrations for each salt in the CFPS supplement premix solution. The JMP software was used to create a classical surface response design for magnesium glutamate, potassium glutamate, and sodium oxalate. Each factor was given a lower limit of 0.5 times their ‘normal’ concentration, and an upper limit of 1.5 times their ‘normal’ concentration. Four types of surface response designs were constructed and compared. Ultimately the central composite design – orthogonal was chosen.

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Experimental Procedure-2

Cell extracts were prepared and CFPS reactions performed as before, except the magnesium glutamate, potassium glutamate, and sodium oxalate concentrations were according to the surface response experimental design. Ammonium acetate was kept at the default amount. CFPS activity was measured as fluorescence at each time point minus fluorescence at 15 mins (Figure below). Endpoint data was then used, along with the JMP software, to build a model predicting optimal concentrations for the three salts analysed (predictions visualised in the cube plot below). These predictions were then tested by preparing a supplement solution premix with amounts of magnesium glutamate, potassium glutamate, and sodium oxalate at concentrations of 6 mM, 195 mM, and 2 mM respectively. This supplement solution premix was used to supplement two batches of cell extract which were prepared identically. The first batch was the same extract used to collect data on which the predictions were made, whereas the second batch was newly prepared (Figure below). It was found that for the first extract, CFPS activity was enhanced when the premix containing ‘optimised’ concentrations of salts was used compared to the un-altered supplement solution premix. Additionally, CFPS activity was observed as being within the confidence intervals predicted by the DoE model.

When CFPS reactions were performed using the second extract and the new premix, CFPS activity was lower compared to reactions using the second extract and the original premix. This supports the view that each extract requires separate optimisation of the supplement solution premix to reach optimal CFPS activity, and that a DoE approach is suitable for achieving this.

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Design Stage-3

All 15 supplements in the supplement solution premix were analysed similarly to how the four salts were initially analysed (i.e. a main effects screening design). A classical screening design was created with all supplements as continuous factors (the nucleotides UTP, GTP, and CTP were combined to form a single factor), and CFPS activity as the response to be maximised. A concentration of ‘0’ was used as the lower limit for each factor, and the concentration used normally in CFPS supplement premixes was used as the upper limit. The main effects screening design was then used to generate the experimental design (below).

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Experimental Procedure-3

CFPS reactions were prepared and performed as usual, except the supplement solution had components at concentrations according to the main effects screening design. The experiment was repeated using two separate batches of cell extract; one which was initially moderately active (extract one) and one which initially had low activity (extract two). The results for each are shown below. It can be seen that for extract 1, the CFPS reaction with the highest CFPS activity was that with the original premix composition (R21), suggesting that the supplement solution was already near optimal. This is not surprising as the extract was already showing moderately high activity. Conversely, for extract 2, the reaction using the original premix was not the one with the highest CFPS activity.

Similar to the first main effects screening design analysis performed, endpoint data was used with JMP to build a model predicting the important factors for each extract (bar charts below). This analysis shows that the most important factors vary greatly between each extract.

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Conclusions and Future Work

This study has begun multifactorial analysis on the components of the supplemental solution for cell free protein synthesis systems. It has provided evidence that some supplements have a greater effect on a systems protein synthesis activity than others, and that the important factors may differ between cell extract batches. The ability to use a Design of Experiments approach towards the optimisation of CFPS systems has also been demonstrated. While this study has provided evidence towards these claims, further work should be performed to validate the findings. A DoE screening design for the supplements of CFPS systems should be used on the same cell extract batch repeatedly. This will help confirm that the screening models derived from the experimental design data are accurate. The screening design should also be performed on many different batches of at least moderately active cell extracts to confirm that important supplements do differ between batches.

Following the above, a surface response design could be used for all commonly important supplements of the CFPS system to determine its effectiveness at optimising CFPS activity. The information could also be used to determine commonly unimportant supplements so they can be eliminated from the supplement solution, hence decreasing the cost per reaction.

Implementation
Characterisation
Conclusions and Future Work
References