Team:Newcastle/Results
Our Experimental ResultsClick elements of the diagram below to see results for each section of our project.
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! |
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Biochemical Adaptor Modules: The ResultsSarcosine Oxidase (Glyphosate to Formaldehyde)BioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)
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Figure 1 Confirmation Digest of Initial Sarcosine Oxidase HiFi Assembly: Lanes 2, 3 and 5 show assembly was successful. The plasmid DNA preps with the correctly assembled SOX gBlock present were then transformed into E.coli BL21-DE3 cells. This was because BL21-DE3 cells are optimised for protein expression and because SOX was designed with a T7 promoter; DH5α cells do not produce the T7 polymerase required to express SOX whereas BL21-DE3 cells do in the presence of IPTG. To prepare SOX for testing, cell cultures were grown following this protocolto step 4. Bradley’s CFPS protocol was then followed (link it). SDS-PAGE gel electrophoresis of the samples was done to check for SOX expression. At this point, an error was spotted with the size of SOX on the SDS-PAGE gel (picture). It was approximately 7 kDa too small. It was then discovered that the sequence synthesised as a gBlock was different to the original sequence found online; parts of the sequence were missing. A new gBlock with the correct sequence was synthesised and the above methods for assembly and preparation for testing were repeated (Figure 2). 
Figure 2 Confirmation Digest of second correct Sarcosine Oxidase HiFi Assembly:All lanes show assembly was successful with the now correct Sarcosine Oxidase sequence. To test for the presence of formaldehyde, and to demonstrate this part works, larger cultures were grown following the aforementioned protocols, and the cells harvested, washed and lysed by sonication. 0 µl, 20 µl, 200 µl and 2 ml of Glyphosate at 10 mg/L concentration was added to the cell lysate and incubated at 37°C. Every 2.5 hours the lysate was tested for the presence of formaldehyde with commercial formaldehyde testing strips. After 8 hours of testing and left overnight, none of the samples had produced formaldehyde according to the testing strips. The testing strips detect a minimum formaldehyde concentration of 10 mg/L, so it was possible that formaldehyde had been produced but that there was too little of it to detect with the strips. We decided to add Sarcosine instead of Glyphosate to determine whether the part was working. Everything was repeated the same but instead we added 0 µl, 50 µl and 200 µl of Sarcosine at 0.9 g/50 ml (Figure 3). 
Figure 3 Testing of Sarcosine Oxidase with Sarcosine: Lysed cell cultures tested with formaldehyde strips. From left to right: SOX+0 µl sarcosine, SOX+IPTG+0 µl sarcosine, WT+0 µl sarcosine, WT+IPTG+0 µl sarcosine, SOX+50 µl sarcosine, SOX+IPTG+50 µl sarcosine, WT+50 µl sarcosine, WT+IPTG+50 µl sarcosine, SOX+200 µl sarcosine, SOX+IPTG+200 µl, WT+200 µl sarcosine, WT+IPTG+200 µl.
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Detector Modules: The ResultsSynthetic Promoter LibraryBioBricks used: BBa_J61002 (Arkin Lab 2006)
Diagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. 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 sensing unit present in the Sensynova platform with various synthetic promoters that are regulated by small molecules.
Due to time constraints, we lacked the time to characterise these parts into the Sensynova platform within the lab. Arsenic BiosensorBioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)
Diagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. 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. The part BBa_J33201 was made by the Edinburgh team in 2006. 
This part consists of the promoter of the E. coli JM109 chromosomal arsenic detoxification operon (ars operon), including the ArsR repressor binding site and the arsR gene encoding the arsR repressor protein, together with its ribosome binding site. Addition of any other genes to the 3' end of this part will result in their expression being dependent on the presence of sodium arsenate or sodium arsenite. Arsenite or arsenite anion binds to the repressor protein ArsR, resulting in inability to repress the promoter. Based on our experiments, a concentration of 1 micromolar sodium arsenate in LB is sufficient for essentially full expression, though this will vary according to conditions.
In order to introduce the Arsenic sensing part in the Sensinova framework, the part BBa_K2205008 containing the RBS B0034, the lasI coding sequence and the double terminator B0015 has been included in the design. The new part BBa_K2205022 presents biobrickable suffix and prefix and has been designed to have specific overhangs to be assembled in the plasmid pSB1C3 by Gibson assembly method. The part has been obtained by gBlock synthesis from IDT and subsequently assembled into the plasmid using NEB HI-Fi kit. The assembly mix was heat-shock transformed in competent DH5α and plated on Chloramphenicol LB plates. The colonies were tested through colony PCR and confirmed by sequencing. 
In the presence of arsenic, the repression will be avoided by binding the repressor ArsR This bound allows the transcription of the downstream gene, lasI. This gene encodes for the quorum sensing molecule C12, which acts as a connector to the processing cell. Brenner, K., Karing, D., Weiss, R. & Arnold, F. (2007) Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium Proc Natl Acad Sci U S A 104(44): 17300 - 17304 de Mora K, Joshi N, Balint BL, Ward FB, Elfick A, French CE. A pH-based biosensor for detection of arsenic in drinking water. Anal Bioanal Chem. 2011 May; 400(4):1031-9. Epub 2011 Mar 27. Psicose Biosensor (Evry Paris-Saclay Collaboration)BioBricks used: BBa_K2205023 (New), BBa_??? (Evry Paris-Saclay 2017)
Diagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. 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. 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. 
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. 
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.
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. |
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Processor Modules: The ResultsFim Standby SwitchBioBricks made and used: BBa_K2205005 (New), BBa_K1632013 (2015 Tokyo Tech part), BBa_K1632007(2015 Tokyo Tech part)
Figure X: The Fim Switch in the native [OFF] state where the eforRED reporter is expressed allowing direct visualisation of the cells.
Signal TunersBioBricks used: BBa_K2205024 (New),BBa_K2205025 (New), BBa_K274371 (Cambridge 2009), BBa_K274381 (Cambridge 2009)
Diagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. 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. 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. 
FIGURE LEGEND 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. 
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. 
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. 
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.  
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. 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 ResultsdeGFPBioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)
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deGFP is a variant of Green Fluorescent Protein (GFP). It was initially designed by Shin and Noireaux (2010) for expression in cell-free protein synthesis (CFPS) systems and is more efficiently translated than other variants (e.g. eGFP). Through talks with other biosensor developers (for example, Chris French), and after reviewing legislation regarding the use of synthetic biology outside of the lab environment, the importance of CFPS systems as a chassis was highlighted. Despite its importance, CFPS systems can still suffer from some issues such as lower total protein synthesis than whole cells. By standardising and characterising a GFP variant which has been modified to have enhanced expression in these systems, it is hoped that CFPS will become a more attractive option for researchers.
deGFP is a modified variant of eGFP developed by Shin and Noireaux which is more efficiently translated in CFPS systems. It was designed by truncating the N-terminal sequence and introducing silent mutations which removed internal ribosome binding like sequences. The C-terminal sequence is also truncated as this has been shown to not be necessary for maximal fluorescence (Li et al. 1997). By removing ribosome binding like sequences, Shin and Noireaux have reduced the amount of incorrect ribosome binding events and hence increased translation efficiency. The length of the protein also contributes to enhanced translation efficiency by reducing the time and resources required for this process to reach completion.
The deGFP sequence was taken from the Addgene database (Plasmid #40019). The sequence was found to have no illegal restriction sites (i.e. no EcoRI, XbaI, SpeI, or PstI sites). A strong, standard Anderson promoter (J23100) and RBS (B0034) was added before the deGFP sequence with biobrick scar sites between each part. A double terminator (B0015) was added after the deGFP sequence. The entire construct was flanked by 30 bp overhangs with the pSB1C3 plasmid, such that the construct could be Gibson assembled into a plasmid digested with XbaI and SpeI. Extra bases were added between the overhangs and the construct so that once the part was assembled into the plasmid, the XbaI and SpeI sites could be regenerated and the biobrick prefix and suffix restored. This construct (J23100-deGFP) with the overhangs was submitted to IDT for synthesis as a gBlock (https://benchling.com/brad0440/f/idABoFuR-sensynova-output-devices/seq-BANWe6pR-degfp-output/edit).
The J23100-deGFP construct described above was Gibson assembled into a pSB1C3 plasmid using the NEB Hi-Fi assembly kit. To do this, pSB1C3 was digested with XbaI and SpeI to create a linearised plasmid backbone [LINK TO DIGEST PROTOCOL]. The deGFP gBlock DNA was prepared according to the IDT protocol [LINK HERE TO PROTOCOL] and assembled into the linear plasmid backbone according to the NEB Hi-Fi Protocol [LINK]. The assembly mixture was then transformed into commercial DH5α cells and incubated on chloramphenicol plates overnight [PROTOCOL LINK]. Colonies which were green under UV light were picked and grown in 5 mL LB broth overnight [PROTOCOL] before undergoing plasmid extraction [PROTOCOL].
The expression of deGFP was firstly tested in E. coli cells using an experimental procedure similar to that used in the Interlab study. Cells transformed with pSB1C3-J23100-deGFP were grown in 10 mL LB broth overnight and OD600 nm was measured. Culture was added to 3 separate falcon tubes and made up to 12 mL with LB with chloramphenicol such that the starting OD600 of the culture was approximately 0.02. This set-up was repeated with cells containing an identical plasmid and construct, except sfGFP was in place of deGFP. As a control, untransformed cells were also prepared identically except the LB did not contain chloramphenicol. Tubes with only LB and LB+chloramphenicol were also prepared as blanks. ChromoproteinsBioBricks used: BBa_K2205016 (New),BBa_K2205017 (New),BBa_K2205018 (New), BBa_K1033915 (Uppsala 2013), BBa_K1033925 (Uppsala 2013), BBa_K1033929 (Uppsala 2013)
Diagrammatic Overview: This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. This is a caption. 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). 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. 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. 
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. 
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. 
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. 
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. 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. |
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Sensynova Framework Testing (IPTG Sensor): The ResultsBioBricks used: BBa_0123456 (New), BBa_7890123 (Team_Name 20XX)
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Therefore, we propose a modular, multicellular system for biosensor development, using a cell-to-cell communication system to eradicate the requirement for further genetic engineering of reusable biosensor devices. Cell-to-Cell communication Bacteria have native quorum sensing.systems which enable cell-to-cell communication through the production and detection of hormone-like auto-inducers. These molecules allow the synchronisation of behaviour in large populations of bacterial cells (Waters & Bassler, 2005). One such system involves the autoinducer AHL (Acylated Homoserine Lactone). AHLs compose of a lactone ring with an acyl side chain containing between 4 and 18 carbons (Churchill & Chen, 2011). Various AHL synthases exists, which produce AHL with different modifications and side change lengths. AHL receptors are sensitive to AHLs of specific length. For example, it has been found that the Rhl system, producing and detecting AHL of acyl carbon length 4 and the Las system, producing and detecting AHL of acyl carbon length 12, exhibit little crosstalk – the receptor component of the system is sensitive only to carbon chains of the correct length (Brenner et al., 2007). The orthogonal nature of the AHL family of autoinducers has enabled their use in a variety of synthetic systems. They are often used as biological “wires”, linking either inter- or intracellular processes. These “wires” have been previously used in a number of synthetic biology systems. For Example, Gupta et al. (2013) and Tasmir et al. (2011). In this project, it is proposed that modularity, and therefore the ability to use parts “off-the-shelf” without further genetic engineering, could be improved by splitting components of biosensors into different cells which communicate to coordinate responses. The orthogonal quorum sensing systems Rhl and Las will be used as biological “wires”, linking different biosensor components together. This separation of components will enable the decoupling of non-specific components from specific detection systems. Using this approach, production of biosensor variants will not require subsequent engineering steps: cells containing desired components will simply be mixed together. The splitting of biosensor components into separate cells may have additional advantages besides ease of variant production. Goni-Moreno et al. (2011) have previously suggested that the use of synthetic quorum sensing circuits enables each cell to be considered an independent logic gate, which may rectify the “fuzzy logic” seen in some biosensors, where stochastic cellular processes may produce false positive results. Quorum sensing has also been previously used to synchronise gene expressions, leading to reduced variability within a population (Danino et al., 2010).
Ten projects were unable to be reviewed because their wiki was broken. Of the remaining 111 projects, 18 projects were deemed not deemed eligible for further analysis. This was either due to a lack of information regarding biosensor mechanism provided by the team or their project was irrelevant. 3 projects were excluded as the sensing component of their project was unchanged from a previous project, to prevent the overrepresentation of biosensors in our database. Therefore, a total of 93 biosensors were used for analysis in our systematic review. (MIGHT PUT ALL THIS INTO A FIGURE)
The systematic review revealed that all biosensors could be split into four components:
Detector – The part responsible for detection of the target molecules. For example, riboswitches and transcription factors.
Processing – Adds downstream processing to a signal, which enables response turning. For example, logic gates, signal amplification and sensitivity tuning.
Output – Produces a response to the target. For example, fluorescent proteins and beta-galactosidase. Additionally, some biosensors may produce outputs which interact with the target molecule once it has been sensed, such as the production of degradation enzymes in bioremediation projects. We have termed these outputs as “effectors”
Adaptors – If the molecule is hard to detect, adaptor components can be placed before the detector unit, to convert the target molecules to something able to be sensed by the detector component. For example, for target that degrades into an easily detectable molecule, a biochemical conversion adaptor could be placed before the detector component which enzymatically degrades the target molecule into the molecule detected by the detector module.
Parts were synthesised by IDT and integration into the pSB1C3 plasmid confirmed by colony PCR and subsequent sequencing. Red boxes show part later used for biobrick production.
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Modularisation of biosensor components is ensured by the reusability of parts due to these compatible connectors. The production and detection of signalling molecules has been standardised across cells: Detector cells will produce C12 AHL, processing cells will detect C12 AHL and produce C4 AHL, and output cells will detect C4 AHL. Therefore, as long as constructs include the correct connectors, they are compatible will all other devices, without any further engineering of the system. This creates a “plug-and-play” approach to developing biosensors and allows the rapid construction of many biosensor circuit, which can be fine-tuned using only cell-mixing.
The splitting of biosensor components between different cells enables the top-down design of biosensing systems. In the top-down approach, systems are designed at a whole system level without consideration of the smaller subsystems required to generate a behaviour, as opposed to a bottom-up approach, where design begins with the smallest parts required to make a system and behaviour is built-up using the knowledge of these smaller parts. Using our platform with sub-systems of a known function already pieced together within cell, it is possible to simply add a cell to generate desired behaviour instead of having to consider the underlying biological parts. Top-down design will enable a more interdisciplinary approach to biosensor development, as knowledge of underlying biological behaviour is no longer required, and will generate biosensors better suited to their intended functions, as the design process will begin with consideration of end-user specifications, as opposed to discrete biological parts.
Brenner, K., Karing, D., Weiss, R. & Arnold, F. (2007) Engineered bidirectional communication mediates a consensus in a microbial biofilm consortium Proc Natl Acad Sci U S A 104(44): 17300 - 17304 Cambridge iGEM team (2009) Sensitivity Tuners [online] Available at: https://2009.igem.org/Team:Cambridge/Project/Amplification [Accessed 28/08/2017] Churchill, M. & Chen, L. (2011) Structural Basis of Acyl-homoserine Lactone-Dependent Signaling Chemical Reviews 111 (1): 68 - 85 Danino, T., Mondragon-Palomino, O., Tsimring, L. & Hasty, J. (2010) A synchronized quorum of genetic clocks Nature 463: 326 - 330 Ellis, T., Adie, T. & Baldwin, G. (2011) DNA assembly for synthetic biology: from parts to pathways and beyond Integrative Biology 3: 109 – 118 Goni-Moreno, A., Redondo, M., Arroyo, F. & Castellanos, J. (2011) Biocircuit design through engineering bacterial logic gates Natural Computing 10: 119 – 127 Gupta, S., Bram, E. & Weiss, R. (2013) Genetically programmable pathogen sense and destroy ACS Synthetic Biology 2 (12): 715 - 723 Tasmir, A., Tabor, J. & Voigt, C. (2011) Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’ Nature 469 (7329): 212 - 215
Waters, C. & Bassler, B. (2005) Quorum Sensing: Cell-to-cell communication in Bacteria Annual Review of Cell and Development Biology 21: 319 - 346
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Cell Free Protein Synthesis System Optimisation: The ResultsBioBricks used: BBa_K515105 (Imperial College London 2011)
Cell Free Protein Synthesis Premix Supplements: Diagrammatic overview of CFPS supplement roles in transcription and translation.
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.
Cells extracts being used in CFPS systems tend to be supplemented with a cocktail of compounds and molecules to aid the process of transcription and translation. Although exact supplement solutions can vary from protocol to protocol, most have the same basic composition; salts, nucleotides, tRNAs, co-factors, energy sources, and amino acids (Yang, et al., 2012). The supplement solution used in this study is based on the Cytomin system (figure 1.2.1) (Jewett, et al., 2008). For the cytomin supplement solution, the major energy source is sodium pyruvate, which is converted to acetate through a series of reactions catalysed by enzymes in the crude cell extract (Figure 1.2.2). The first reaction, pyruvate to acetyl-CoA, requires nicotinamide diphosphate (NAD) and Co-enzyme A (CoA) as co-factors. Both of these are components of the premix and hence added to the system to enhance flux through the reaction. The acetyl CoA is phosphorylated by inorganic phosphate, and then de-phosphorylated to produce ATP from ADP. The ATP is used as energy to drive translation of mRNA. Energy can also be derived from glutamate in the supplement solution (Jewett, et al., 2008), which is added in the form of magnesium glutamate and potassium glutamate. Glutamate is a metabolite in the tricarboxylic acid cycle, which generates NADH. In whole cells, NADH is used in oxidative phosphorylation to produce ATP. Oxidative phosphorylation relies on membrane bound proteins and proton gradients across a membrane. It has been shown previously that extracts prepared using French Press or sonication contain membrane vesicles which have ATPase activity (Futai, 1974), and that oxidative phosphorylation can be activated in CFPS systems (Jewett, et al., 2008). Sodium oxalate, another component of the supplement solution, is also used to help increase energy generation by the system. PEP synthetase, an enzyme present in E. coli, converts pyruvate into phosphoenol pyruvate (PEP) in a reaction which consumes ATP, thereby wasting ATP and directing it away from protein synthesis. Oxalate inhibits PEP synthetase by acting as a pyruvate mimic, and hence limit the energy wasted by this reaction. The ribonucleotides ATP, GTP, UTP, and CTP are also components of the supplement solution. They are used in the synthesis of mRNA for transcription of desired genes encoding on exogenous DNA added to the system, and ATP can also be used directly as energy for translation. The polyamines spermidine and putrescine are two other supplements which are added to aid with transcription. It is thought that they can bind proteins and DNA to help recruit polymerase for transcription. Polyamines may also increase translation fidelity, aid ribosome assembly, and activate tRNAs (Jelenc & Kurland, 1979; Jewett & Swartz, 2004b; Algranati & Goldemberg, 1977). To enable translation to occur, amino acids (added separately from the supplement solution) and an E. coli tRNA mixture are added to the CFPS system. Folinic acid is also added as it can be used as a source of folinate for the synthesis of f-Met; the amino acid required for initiation of translation in E. coli. Magnesium and potassium ions are also added as supplements. Both ions are ubiquitous in cells with many functions in protein synthesis, namely aiding translation by associating with ribosome subunits and stabilising RNA (Nierhaus, 2014; Pyle, 2002). While magnesium ions are essential for protein synthesis, at high concentrations they can cause inhibition of ribosome translocation and hence inhibit protein synthesis (Borg & Ehrenberg, 2015).
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.
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).
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.
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.
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).
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.
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. | |||||||||||||||||||||||||||||||||||||||||||||
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.
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).
Figure X:
Patches of the Fim Switch transformants. Patch number 6 shows the correct red colour which indicates expression of the eforRed chromoprotein.
