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| − | <section id="home" class="s-home target-section" data-parallax="scroll" data-image-src="https://static.igem.org/mediawiki/2018/3/36/T--Newcastle--InterlabCover.JPEG" data-natural-width=3000 data-natural-height=2000 data-position-y=center> | + | <section id="home" class="s-home target-section" data-parallax="scroll" data-image-src="https://static.igem.org/mediawiki/2018/a/af/T--Newcastle--Interlabcover.png" data-natural-width=3000 data-natural-height=2000 data-position-y=center> |
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| − | Standardisation
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| − | Interlab | + | Protocol |
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| − | <h1 class="display-2">Standardisation and Reproducibility</h1> | + | <h1 class="display-2">Calibration</h1> |
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| − | <h3 class="subhead">The Importance</h3>
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| − | <p>The success in the commercialisation and democratisation of engineering, has often been touted to be attributed to the use of standardization (Endy 2005; Brazma 2001). Modular parts that were clearly defined can be implemented into complementary systems to produce perfectly reproducible results. This allowed for reliable, predictable and complex engineered systems to be developed with mathematical precision. Then, the amalgamation of standardization and automation brought forth a whole new era of engineering success, with the automation of both production and assembly lines revolutionising industry. </p> | + | <p><font size="3">Alternative Roots participated in the 2018 InterLab study. Three calibration steps were carried out prior to any experimental measurements being taken. Absorbance and fluorescence values were measured in 96-well plates using a Thermofisher Varioskan Lux plate reader (Thermofisher Scientific). Absorbance was measured at 600 nm and converted to a comparable OD<sub>600</sub>. Fluorescence was measured at 525 nm with excitation at 485 nm with a 12 nm bandpass width. All readings took place at 25 °C and pathlength correction was disabled. The calibrations were:</p> |
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| − | <p> There have already been some efforts to implement standardisation in biology that have been successful. Annotated DNA sequence data has been categorised by the International Nucleotide Sequence Database Collaboration (INSDC), combining the databases of DDBJ, EMBL-EBI and NCBI. Brazma and colleagues ( 2001) proposed a minimum information standard for microarray data that would allow for easy interpretation and analysable results. The Protein Data Bank ( PDB) stores protein crystallographic data from published articles and independent researchers, while the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (NC- IUBMB) specifies enzyme nomenclature. Each of these examples have advanced their given fields by allowing increased reproducibility and understanding of data, allowing collaborations to easily build from previous results. Yet, even though reproducibility is a defining feature of science, reproducibility of experimental research is often taken as an assumption instead of fact (Collins 1992; Errington et al. 2014; Schmidt 2009). </p> | + | <p><font size="3">1. A LUDOX CL-X 45 % colloidal silica suspension was used to calculate a conversion factor for the Abs<sub>600</sub> value measured by the plate reader to a comparable OD<sub>600</sub> value, considering path length and well volume (Table 1). Abs<sub>600</sub> of 1:2 dilutions of LUDOX silica suspension were taken in triplicate and a reference OD<sub>600</sub> of 0.063 (the reference value for 100 µL of LUDOX CL-X in a well of a standard 96-well flat-bottom black with clear bottom plate) divided by the mean measured value to give a conversion factor. </font></p> |
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| | + | <p><font size="2"><center>Table 1. Optical density readings for LUDOX CL-X 45% colloidal silica suspension and water used to calculate the conversion factor for absorbance readings to OD<sub>600</sub> readings for plate reader measurements. |
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| − | <p> This is shown not just in synthetic biology, but in other life sciences. Currently there is an investigation into experimental cancer biology research called the ‘Reproducibility Project’, which aims to assess studies and evaluate the reproducibility of their results (Morrison 2014). As of 2018, the project has assessed 10 high-profile studies with only four of these studies being able to be fully reproduced. However, there are conflicting opinions on the need for experimental reproducibility. Some believe that irreproducible studies will eventually be removed naturally from the scientific community through failed verification and evolution of newer, more efficient protocols that evolve naturally from exploratory research (Bissell 2013). There is also some concern among researchers that early standardisation may limit future scientific research and development (Gaisser et al. 2009). To combat this, Gaisser and colleagues (2009) suggested a stepwise standard introduction that spans 10 years, starting with reporting, before moving onto methods and components. Gaisser et al. (2009) proceeds to suggest that the standardisation process should be developed by the researchers themselves. </p>
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| | + | <p><font size="3">2. A standard curve was prepared by measuring the OD<sub>600</sub> of serial dilutions of monodisperse silica microspheres, with similar light scattering properties to <i>E. coli</i> cells. This was used to standardise OD readings across labs (Figure 1A). </p> |
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| + | <p><font size="3">3. A fluorescence standard curve was created by measuring the fluorescence of serial dilutions of the small molecule fluorescein. This has similar excitation and emission characteristics to GFP allowing conversion of fluorescence readings to an equivalent fluorescein concentration. Calibrations allowed expression measurement in units of fluorescence per OD and molecules of equivalent fluorescein (MEFL) per cell (Figure 1B). </p> |
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| + | <p><font size="2"><center>Figure 1. Curves used to calibrate A; Fluorescein per OD using dilutions of fluorescein and B; molecules of equivalent fluorescein per particle using dilutions of solutions of monodisperse silica microspheres.</font></center></p> |
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| − | <h3 class="subhead">Biodesign Automation</h3>
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| − | <p>Bio-design automation (BDA) is a new discipline that aims to automate the synthetic biology pipeline using workflows adapted from electronic design automation (EDA) (Rodrigo and Jaramillo 2013; Chen 2009). Through the adoption of BDA, the ‘Design-Build-Test’ cycle can be fully automated, with only the initial ‘Specification’ and final ‘Learn’ stages requiring human input. In the future, this could even be automated with the use of AI and machine learning. BDA can be split into two different categories; the automated hardware such as the integration of robotics, and the software that allows for in silico optimisation through computational modelling (Densmore and Bhatia 2014).</p>
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| − | <p> Automated hardware such as liquid handling robots and microfluidic systems allow for significantly reproducible, standardised workflows, while removing human error as a potential source of variation (Beal et al. 2016). Integrated software for synthetic biology allows for in silico modelling enabling the use of the available databanks. From this, complex genetic circuits can be built, designed and tested through computer simulation, without ever having to enter the laboratory for wet-lab experimentation. For an exhaustive review on the range of software available see Appleton and colleagues (2017) work. These synthetic biology workflows require data standards for BDA to be successfully implemented. Part standards must encode more than just the sequence, requiring the inclusion of additional data such as environmental and experimental information whilst also providing computational models of construct behaviour and measurements of performance (Galdzicki et al. 2014). Synthetic Biology Open Language (SBOL) is a data standard aiming to tackle this challenge. Developed by the synthetic biology community, it allows the exchange of biological circuit design in a format that can be understood by software and repositories (Galdzicki et al. 2014). Successful integration of SBOL into the wider scientific community would allow for a more directly integrated design workflow and increase the reproducibility of experimental results (Beal et al. 2012; Peccoud et al. 2011). </p>
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| − | <p> Laboratory robotics and smart lab equipment provides the platform for high reproducibility in experiments, however due to their current price bracket are unavailable to the majority of labs. This has driven recent developments in making affordable technologies for lab equipment, notably in the release of the Opentrons OT-2 liquid handling robot and the smart incubator by Incuvers Incorporated. With these developments and the ever-growing library of software, the likelihood that BDA will be integrated into most modern labs is high. </p>
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| − | <p>Following calibrations, two transformed colonies for each test device and both controls were used to inoculate LB medium containing chloramphenicol (CAM) and incubated overnight at 37 °C with shaking at 220 rpm. Overnight cultures were diluted 1:10 and the OD600 adjusted to 0.02 with LB with CAM to a final volume of 12 ml. Fluorescence and Abs600 were taken at 0h and 6 hours of incubation at 37 °C with 220 rpm shaking. Test devices, plasmid backbone and protocol workflow are shown in figure 2.</p>
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| − | <img src="https://static.igem.org/mediawiki/2018/4/4e/T--Newcastle--IntProt.PNG"> | + | <p><font size="3">Following these calibration steps, two transformed colonies for each test device and both control plasmids were used to inoculate lysogeny broth (LB) medium containing 25 µg/L of chloramphenicol (CAM) and incubated overnight at 37 °C with shaking at 220 rpm. Overnight cultures were diluted 1:10 and the OD<sub>600</sub> adjusted to 0.02 with LB with CAM to a final volume of 12 mL. Fluorescence and Abs<sub>600</sub> were taken at 0h and 6 hours of incubation at 37 °C with 220 rpm shaking. Test devices and plasmid backbone are shown in Figure 2.</font></p> |
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| − | <p>All test devices produced growth to OD600 reading in excess of 0.3, except test device (TD) 4. Despite lower growth than other transformants, TD4 produced the highest mean fluorescence reading of 79.1 a.u., as was expected as the strongest promoter of the Anderson collection (parts.igem.org/Promoters/Catalog/Anderson). Figure 3A and 3B show the colony 1 and colony 2 Abs600 values for the controls and each test device at 0 hours and 6 hours respectively. </p>
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| − | <img src="https://static.igem.org/mediawiki/2018/8/85/T--Newcastle--ABS600.PNG"> | + | <p><font size="2"><center>Figure 2. A) test devices transform into DH5α for the iGEM InterLab study. A negative control device consisting of a tetracycline resistance gene (BBa_R0040) with no associated promoter, RBS or terminator sequences was also transformed into DH5α. B) All test devices were transformed into DH5α using the pSB1C3 plasmid backbone with chloramphenicol for selection (adapted from LabGenius plasmid viewer).</font></center></p> |
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| − | <p>The relatively poor growth and high fluorescence levels effectively cancelled each other out when readings were converted to Fluorescence per OD600 and MEFL per OD600 measurements, resulting in TD4 transformants producing the highest expression levels (figure 3.1). The high fluorescence and MEFL per OD600 reading for TD4 despite lowest growth suggests expression of TD4 is not fully representative of the relative promoter strength; expression levels are interdependent with growth rate, with higher growth rates expected to produce higher expression levels (Scott et al. 2010). Expected fluorescence levels based on relative promoter strength reported for the Anderson collection of promoters did not match entirely the results produced here. In particular, TD5 utilising the promoter J23104 was expected to be the second strongest but yielded only the fourth highest fluorescence reading of 22.14 and 21.42 for colonies 1 and 2 respectively. Similarly, the highest fluorescence reading was recorded by TD1 (expected strength: third), though this test device produced the widest range in fluorescence reading between the two colonies (r = 36.2), despite both colonies having the closest OD600 reading of any of the test devices (r = 0.007). In addition to the iGEM repository documentation for relative strengths of the Anderson promoter collection, previous literature has also demonstrated that the J23101 and J23104 promoters should have almost equal strength (He et al. 2017).</p>
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| − | <p>While TD5 appeared to underperform compared to promoter activity previously reported in the literature, subsequent sequencing of test devices revealed that colonies labelled as TD5 had in fact been transformed with the positive control device. This may have simply been the result of human error when pipetting or labelling over the process of the study. The consistence of TD5 underperformance across multiple replications of the study suggests that this occurred early on. The variation in expression visible is particularly alarming for the J23101 promoter, which has been proposed and utilised in the literature as a reference promoter to characterise relative strengths of other promoters as relative promoter units (Kelly et al. 2009).</p> | + | <p> <font size="3">Results for OD<sub>600</sub> and fluorescence measurements after 6 hours are shown in Figure 3. </font></p> |
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| + | <p><font size="2"><center>Figure 3. OD<sub>600</sub> (A) and fluorescence (B) InterLab study results for positive and negative controls and all test devices after 6 hours. Tests were performed in duplicate using two colonies for each test device, results for which are shown separately to account for colony-to-colony variation. </center></font></p> |
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| + | <p><font size="3">Results for both 0 and 6 hours were converted into fluorescence per OD<sub>600</sub> and fluorescence per cell using the previously described calibrations (Figure 4). While the InterLab study is an opportunity for crowdsourcing a large amount of data on the inherent variability of a given biological system, it does not account for variations within data sets. Our team attempted to seek out and address such sources of variation, investigating biodesign automation of protocols, use of internal standards and effects of media composition. Further detail and results are available on the <a href="https://2018.igem.org/Team:Newcastle/Measurement"class="black">measurements page</a>.</font></p> |
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| − | <p>While the InterLab study is an effective way of gathering large sets of data surrounding the parts and protocols, it does not consider all sources of variation within datasets or the variability between data sets. As there are many factors which cause variability in microbial protein expression and productivity, some of these factors may be more favoured than others, leading to an overrepresentation of these in the metadata. </p>
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| | + | <p><font size="2"><center>Figure 4. iGEM InterLab study results for positive and negative controls and all test devices. Fluorescence was measured at 525 nm and readings were taken at 0 hours and 6 hours in a 96 well plate. A: Fluorescence per OD<sub>600</sub>, B: Fluorescence per <i>Escherichia coli</i> cell, calibrated to a standard curve of LUDOX silica beads. </center></font></p> |
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| − | <p>One factor that has been overlooked in the literature is the recovery period. Anecdotal evidence during all transformation protocols has indicated that the antibiotic resistance gene has a significant impact on how long the recovery time needs to be. For chloramphenicol, the widespread suggestion of a 1-hour recovery incubation for optimal TrE is quite simply incorrect, with a recovery incubation time of upwards of 2 hours being required for optimal TrE in our study. This is irrespective of volume, be it in 2 mL microcentrifuge tubes or 96 well plates. However, if using a 96 well plate format, this recovery period requires full optimisation due to its suboptimal OTR and KLa characteristics. With the additional growth inhibition of the majority of TB compositions, this recovery step needs to be fully optimised for optimal TrE. Super optimal broth with catabolite repression (SOC) is regularly used instead of SOB to enhance cells recovery, however as it includes glucose, this was not considered due to the potential inhibitory effects of increasing pH due to glucose metabolism (Islam et al. 2007; Losen et al. 2004; Marini et al. 2014). </p>
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| − | <p>Experimental observation found ampicillin (AMP) did not require longer incubation. Chloramphenicol’s requirement for increased recovery time can be explained through its mechanism of action, inhibiting protein synthesis via the inhibition of peptidyl transferase (Schifano et al. 2013; Wolfe and Hahn 1965). AMP however inhibits cell wall synthesis via inhibition of transpeptidase. Unlike AMP resistance, which involves the synthesis and excretion of either β-lactamase or penicillinase (Drawz and Bonomo 2010), chloramphenicol resistance is acquired by the synthesis of chloramphenicol acetyltransferase which is not readily excreted (Shaw 1983). This is beneficial for generating libraries as it decreases risk of satellite colonies, but the resistance mechanism may take longer to form and confer sufficient antibiotic resistance. As a result, for a further optimised protocol, using a plasmid that confers AMP resistance would be beneficial to minimise the protocol time requirement. This would also decrease the safety risk as while CAM is a known carcinogen, AMP is not. </p>
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| − | <p>Even prior to the main cell measurement protocol, irreproducibility had a significant effect in the preparation of competent cells and successful transformation. While the recommended CCMB80 and transformation protocols were followed exactly, successful expression of transformants were not guaranteed. It took 3 weeks of constant run-throughs within our lab to gain a transformation efficiency (TrE) of 5.05 x 106 before we could even start the Interlab. As such, the competent cell and transformation process was further investigated through a Bio-design Automation platform that used a design of experiments methodology to optimise transformation buffer (TB) composition. Utilising our recently acquired OT-2 liquid handling robot (Opentrons, USA), a robust automated competent cell preparation protocol was developed.</p>
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| | + | <p class="about-para"><strong>Attributions: Matthew Burridge, Kyle Stanforth, Sam Went |
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| − | <p class="about-para"><font size="2">1. Jousset, A., et al. (2009). "Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters." The Isme Journal 3: 666<font></p>
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| − | <p class="about-para"><font size="2">2. Jousset, A., et al. (2009). "Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters." The Isme Journal 3: 666<font></p>
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| − | <p class="about-para"><font size="2">3. Vanitha SC & Umesha S (2011) Pseudomonas fluorescens mediated systemic resistance in tomato is driven through an elevated synthesis of defense enzymes. Biologia Plantarum 55(2):317-322.<font></p>
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| − | <p class="about-para"><font size="2">4. United Nations, Department of Economic and Social Affairs, Population Division (2017) World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. https://population.un.org/wpp/Publications/Files/WPP2017_KeyFindings.pdf<font></p>
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| − | <p class="about-para"><font size="2">5. Food and Agriculture Organization of the United Nations (2015) World Fertilizer Trends and Outlook to 2018. http://www.fao.org/3/a-i4324e.pdf<font></p>
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| − | <p class="about-para"><font size="2">6. Usman MN, MG; Musa, I (2015) Effect of Three Levels of NPK Fertilizer on Growth Parameters and Yield of Maize-Soybean Intercrop. International Journal of Scientific and Research Publications 5(9).<font></p>
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| − | <p class="about-para"><font size="2">7. Pfromm PH (2017) Towards sustainable agriculture: Fossil-free ammonia. Journal of Renewable and Sustainable Energy 9(3):034702.<font></p>
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| − | <p class="about-para"><font size="2">8. Bitew YA, M (2017) Impact of Crop Production Inputs on Soil Health: A Review. Asian Journal of Plant Sciences 16(3):109-131.<font></p>
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| − | <p class="about-para"><font size="2">9. Yang X-e, Wu X, Hao H-l, & He Z-l (2008) Mechanisms and assessment of water eutrophication. Journal of Zhejiang University. Science. B 9(3):197-209.<font></p>
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| − | <p class="about-para"><font size="2">10. Carmichael WW (2001) Health Effects of Toxin-Producing Cyanobacteria: “The CyanoHABs”. Human and Ecological Risk Assessment: An International Journal 7(5):1393-1407.<font></p>
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| − | <p class="about-para"><font size="2">14. Gómez-Lama Cabanás C, Schilirò E, Valverde-Corredor A, & Mercado-Blanco J (2014) The biocontrol endophytic bacterium Pseudomonas fluorescens PICF7 induces systemic defense responses in aerial tissues upon colonization of olive roots. Frontiers in Microbiology 5:427.<font></p>
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| − | <p class="about-para"><font size="2">16. Sharma SB, Sayyed RZ, Trivedi MH, & Gobi TA (2013) Phosphate solubilizing microbes: sustainable approach for managing phosphorus deficiency in agricultural soils. SpringerPlus 2:587.<font></p>
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