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Revision as of 23:20, 15 October 2018
Alternative Roots
InterLab Study
Introduction
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Biodesign Automation
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Design of Experiments
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Minimal Information Standards
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This Study
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Improved Interlab Measurement Plasmids
Inclusion of an RFP internal standard into the 2018 Interlab test plasmids
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Comparisons between the existing GFPmut3b test devices and a new series of mNeon green test devices
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InterLab Protocol
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.
Abs600 analysis of each test device
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.
Fluorescein/OD600 and MEFL/particle analysis
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).
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).
Sources of variation in the InterLab study design
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.
Competent cell protocols
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.
Recovery period
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).
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.
Use of mut3GFP as a reporter
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.
Competent cell protocols
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.
Competent cell protocols
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.
InterLab
REFERENCES
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
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
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.
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
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
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).
7. Pfromm PH (2017) Towards sustainable agriculture: Fossil-free ammonia. Journal of Renewable and Sustainable Energy 9(3):034702.
8. Bitew YA, M (2017) Impact of Crop Production Inputs on Soil Health: A Review. Asian Journal of Plant Sciences 16(3):109-131.
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.
10. Carmichael WW (2001) Health Effects of Toxin-Producing Cyanobacteria: “The CyanoHABs”. Human and Ecological Risk Assessment: An International Journal 7(5):1393-1407.
11. New Partnership for Africa's Development (2013) Agriculture in Africa - Transformation and Outlook. http://www.un.org/en/africa/osaa/pdf/pubs/2013africanagricultures.pdf
12. Food and Agriculture Organization of the United Nations (2017) World Fertilizer Trends and Outlook to 2020. http://www.fao.org/3/a-i6895e.pdf
13. Bergey, D. H., et al. (1984). Bergey's manual of systematic bacteriology. Baltimore, MD, Williams & Wilkins.
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.
15. Gross, H. and J. Loper (2009). Genomics of secondary metabolite production by Pseudomonas spp.
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.
17. Ruffner, B., et al. (2013). "Oral insecticidal activity of plant-associated pseudomonads." Environmental Microbiology 15(3): 751-763.
18. Jousset, A., et al. (2009). "Predators promote defence of rhizosphere bacterial populations by selective feeding on non-toxic cheaters." The Isme Journal 3: 666
19. 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.
20. Maheshwari DK (2012) Bacteria in Agrobiology: Plant Probiotics (Springer Berlin Heidelberg).
21. Despommier D (2011) The vertical farm: Controlled environment agriculture carried out in tall buildings would create greater food safety and security for large urban populations. J fur Verbraucherschutz und Leb 6(2):233–236.
22.World Health Organization. (2018). Q&A: genetically modified food. [online] Available at: http://www.who.int/foodsafety/areas_work/food-technology/faq-genetically-modified-food/en/ [Accessed 13 Sep. 2018].