May 2, 2013 - Growth autoinhibition in the cyanobacterium, Microcystis aeruginosa (PCC 7806) as a research target for novel control strategies. A dissertation ...
School of Applied Sciences
Growth autoinhibition in the cyanobacterium, Microcystis aeruginosa (PCC 7806) as a research target for novel control strategies.
A dissertation submitted as part of the requirement for the BSc Environmental Science
David Hartnell
2nd May 2013
Abstract An increasing incidence of harmful cyanobacterial or blue-green algal blooms has led to concerns over detrimental effects on ecosystems, wildlife and public health. Management strategies presently consist of ‘bottom-up’, nutrient limitation or ‘topdown’, grazing by zooplankton or fish as methods of cyanobacteria control. An area of growing interest is the potential exploitation of chemical intercellular signalling interactions between cells, which can affect population growth, survival and death, as control measures. Microcystis aeruginosa is a common freshwater toxin producing cyanobacteria, two types of PCC 7806 strain were cultured, a toxin producing ‘wild type’ and a non-toxin producing ‘mutant’. Growth phases of cultures were monitored using flow cytometry, an automated high resolution cell counting method. Experiments were conducted into the growth suppression effect of chemical signals from older nutrient depleted cultures on new cultures with sufficient nutrients. The results indicated an observable effect on the growth of the cells in the treated cultures dependent upon the timing of treatment. These effects were reproducible and statistically significant at the 0.05 level. These data suggest that potential for utilising extracellular products from chlorotic cultures exists. Development of a compound to effectively control or prevent M. aeruginosa would a valuable addition to a multifaceted approach to freshwater resource management.
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Acknowledgements Thanks to Delphine Latour from the National Oceanic Centre, Southampton for the donation of the Microcystis aeruginosa (PCC 7806) cultures used in this research project. To Daniel Franklin for his supervision and enthusiasm for the project, also to his PhD student, Ian Chapman, who has always been willing to offer both practical and academic support. The technical team at Bournemouth University for answering my many and varied requests. The flow laboratory team Alf, Asha and Eddie, who’s mutual support has benefited all our research projects. But most of all I thank Rachel.
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Contents Title page
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Abstract
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Acknowledgements
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Contents
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List of Figures and Tables
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1. Introduction
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1.1. The global significance of cyanobacteria
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1.2. Cyanobacteria toxins
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1.3. Control of cyanobacterial blooms
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1.4. Microcystis spp. in laboratory culture
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1.5. Abiotic factors
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1.6. Interactions between cells
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1.7. Cell growth monitoring
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1.8. Aims and hypothesis
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2. Materials and methods
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2.1. Culture techniques
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2.2. Flow cytometry
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2.3. Accuracy and precision of the counting method
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2.4. Photosynthetic pigment extraction
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2.5. Growth suppression
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2.6. Photography
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3. Results
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3.1. Microcystis aeruginosa PCC 7806, culturing and cell monitoring
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3.1.1 Visual inspection and flow cytometry
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3.1.2 Culture medium volumes
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3.1.3 Growth of mutant and wild type
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3.2. Accuracy and precision of the counting method
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3.3. Photosynthetic pigment extraction
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3.4. Growth suppression experiments
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3.4.1. Growth curves
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3.4.2. Photography
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3.4.3. Statistical analysis of growth suppression data
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4. Discussion
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4.1. Laboratory culturing of Microcystis
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4.2. Culture medium volume
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4.3 Mutant and wild type cell densities
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4.4. Flow cytometry
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4.5. Photosynthetic pigment extraction
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4.6. Growth suppression experiments
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5. Summary and conclusion
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5.1. Summary
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5.2. Conclusion
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6. References
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Appendix I. Evaluative supplement
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Appendix II. Interim interview comments
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Appendix III. Data
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Appendix IV. SPSS Statistical outputs
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List of Figures and Tables Figures Figure 1. Molecular structure of the toxic cyclic peptide compound Microcystin 11 Figure 2. M. aeruginosa (PCC 7806) at 60x magnification
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Figure 3. An example of a flow cytometer ‘dot plot’ data output
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Figure 4. Diagram of the experimental design for growth suppression investigations
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Figure 5. Wild type and mutant cultures after inoculation
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Figure 6. Cytogram and Histogram for a 125ml WT culture day 0
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Figure 7. Cytogram and Histogram for 125ml WT culture day 13
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Figure 8. Cytogram and Histogram for 125ml WT culture day 22
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Figure 9. Cytogram and Histogram for 125ml WT culture day 35
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Figure 10. Growth curve of 125ml WT culture
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Figure 11. Comparative growth curves of 250ml, 125ml and 60ml cultures
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Figure 12. Boxplot of relative growth rates between culture media volumes
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Figure 13. Boxplot of recorded cell densities between culture media volumes
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Figure 14. Bar graphs of mean cell densities
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Figure 15. Scatter plot of counts obtained from procedures A, B and C
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Figure 16. Growth curve with chlorophyll a and total carotenoid
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Figure 17. Growth curves of wild type culture treated on days 0, 3, 6 and 10
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Figure 18. Growth curves of wild type culture treated on days 3, 6, and 10
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Figure 19. Growth curves of mutant type culture treated on days 3, 6 and 10
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Figure 20. Photographic sequence of mutant culture treated on day 6
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Figure 21. Bar graph of mean recorded cell densities between the treatments
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Figure 22. Bar graph of mean relative cell growth rates between the treatments 41 Figure 23. Bar graph of mean doubling times between the treatments
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Figure 24. Bar graphs of mean cell densities of the three treatments on the four treatment days (0, 3, 6 and 10)
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Tables Table 1. Growth of PCC7806 mutant and wild type of different media volumes. Cells were grown in BG-11 at 25°C on 12/12 L/D.
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Table 2. The results of Pearson product-moment correlation between time, cell densities, chlorophyll a and total carotenoid concentrations
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1. Introduction Cyanobacteria or blue-green algae are ubiquitous autotrophic prokaryotes, contributing significantly to primary production in freshwater & marine ecosystems and are present from the tropics to the poles (Whitton and Potts 2002). Microcystis aeruginosa is a common freshwater toxin producing colonial cyanobacteria, posing health risks to humans and ecosystems by superabundance events or blooms (Bartram et al. 1999). The occurrence of which has been linked to increasing global temperatures, hydrological modification and eutrophication of water bodies (Paerl and Huisman 2009). An understanding of factors affecting cell growth, survival and death will benefit future freshwater management strategies.
1.1. The global significance of cyanobacteria Cyanobacteria evolved early in the history of life on Earth, splitting water molecules by oxygenic photosynthesis over 3,000 million years ago (Lane 2010). Stromatolites, formations of rock created by cyanobacteria, dominate the fossil record from the Precambrian to the Mesozoic era (Schopt 2002). The oxygenation of the Earth’s atmosphere by cyanobacteria is cited as a possible driving force for the evolution of complex multicellular life, a process which defines the Phanerozoic era (Lane 2010). Cyanobacteria or a progenitor are widely acknowledged to be the ancestors of chloroplasts which were engulfed and assimilated by eukaryotic cells, symbiogenesis facilitated the evolution of green plants, red algae, and glaucophyte unicellular algae (Cavalier-Smith 2000). Symbiotic relationships between cyanobacteria are common in freshwater, marine and terrestrial ecosystems, including; diatoms, coral, sponges, ascidians (sea squirt), fungi (lichens), bryophytes, cycads (tree ferns) and ferns (aquatic and terrestrial) (Rai et al. 2002).
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1.2. Cyanobacteria toxins Many species of cyanobacteria can produce biotoxins and in recent years concerns relating to the detrimental public health and ecological impacts in freshwater bodies have increased (Bartram et al. 1999). Toxic algal blooms have been responsible for mortality and morbidity in wildlife, livestock and pets, notably dogs, in Europe and Australia (Carmichael et al. 1992, Herath 1997). Acute individual human health effects of recreational exposure to cyanobacterial toxins and community poisoning via contaminated drinking water have also been reported (Falconer 1996). Anthropogenic activities have exacerbated the risk through hydrological modifications; water basin damming for reservoirs and hydropower, river course alterations to alleviate flooding and the eutrophication of water bodies (Paerl and Huisman 2009). Cultural eutrophication is the anthropogenic loading of inorganic plant nutrients in lakes and estuaries; which causes a change in the microorganism ecology (Sigee 2005), generally due to excess Nitrogen (N) and Phosphorous (P). Cyanobacteria growth is dependent on the ratio of N to P, primarily this is due to the ability of some genera to fix atmospheric N; therefore P availability is considered the significant regulatory factor in the occurrence of harmful algal blooms (Chorus and Mur 1999). Some workers have predicted that the effects of anthropogenic driven climate change will favour increased toxic cyanobacteria blooms (Paerl and Huisman 2009, O’Neil et al. 2011). Water temperatures in excess of 20°C and increased salinity due to hydrological modification have been demonstrated to increase cyanobacteria growth (O’Neil et al. 2011). However, conversely it has been suggested that toxin producing freshwater species are poor competitors with other phytoplankton at low pH’s (Moore et al. 2008). The effects of atmospheric CO2 and associated increase in acidity of water bodies on cyanobacteria populations is not fully understood (O’Neil et al. 2011), therefore the impacts of climate change remain to be fully elucidated. The range of bio-chemicals produced by cyanobacteria is both toxicologically and chemically diverse (Sivonen and Jones 1999). Anabaena, Planktothrix and Microcystis are genera that produce microcystins, toxic cyclic peptide compounds (Figure 1.) with potent hepatotoxicity and carcinogenicity (Ding et al. 1998). The 10
secondary metabolites produced by cyanobacteria extra to cell biochemical maintenance (e.g. microcystins), are large complex molecules and their exact ecological role remains obscure (Carmichael 1992). The search for explanations for the production of physiologically expensive microcystins is an area of extensive research. It has been proposed that the toxicity is fortuitous and non-toxic strains will produce structurally similar, but non-toxic compounds (Orr and Jones 1998). However, microcystin production affords cyanobacteria protection against zooplankton grazing (Jang et al. 2007) and provides a growth advantage by protecting from photo-oxidation in high light conditions (Phelan and Downing 2011). It therefore has been considered to confer advantages to the microorganisms Dittman et al. (2001) questioned whether microcystin is a secondary metabolite, suggesting that it served as a scavenger molecule for trace metals whereas other workers have proposed it as a communication molecule for gene regulation (Pearson et al. 2004).
Figure 1. Molecular structure of the toxic cyclic peptide compound Microcystin (www.kenyon.edu)
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1.3. Control of cyanobacterial blooms Concerns over the hazard to human and animal health, and economic impacts of loss of recreational water body use coincident with blooms have prompted the development of management strategies to prevent or control cyanobacterial superabundance events (Bloon et al. 1994). These strategies have generally been either ‘bottom-up’; characterised by the restriction in essential nutrients (Bloon et al. 1994) or ‘top-down’ where biomanipulation techniques have been utilised to change the structure of the food-web or reduce phytoplankton numbers through biological control (Gragnani et al. 1999). A number of biological control options have been examined; Xie and Liu (2001) reviewed the use of herbivorous fishes to reduce blooms from Lake Donghu, China. The authors proposed that increased stocking densities of filter-feeding cyprinids could eliminate Microcystis blooms. Zooplankton grazing by e.g. Daphnia has also been demonstrated to impact upon cyanobacterial populations although a complex set of interactions between selectivity of grazers, fish predation and size of cyanobacteria has been noted (Gragnani et al. 1999, Chan et al. 2004). The use of lytic or predatory bacteria have also been proposed as potential control options for hypertrophic environments (Gumbo et al. 2010) although relatively few studies have been carried out to demonstrate the practical applicability of this approach in situ. Barley straw decomposition derived inhibitors have been shown to reduce the numbers of some cyanobacteria, specifically a toxin producing M. aeruginosa (Martin and Ridge 1999). However, the authors noted the differential susceptibility of different species and recommended that further studies were required to assess the applicability of this approach to field conditions. To date no efficacious. generically applicable strategy has been identified.
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1.4. Microcystis spp. in laboratory culture Cyanobacteria of the genus Microcystis have over 50 years of history in laboratory culture (Orr and Jones 1998) and investigations into physiology and life-history has afforded an insight into their natural function within an ecosystem. There are two main culture techniques; continuous, when medium is added to the culture vessel as nutrients are depleted and batch, when the medium is of a fixed volume and cultures complete a full growth cycle (Fog and Thake 1987). The growth patterns of batch cultures are characterised by five phases;
Lag, a period after inoculation with no measurable cell growth.
Exponential, a rapid increase in cell numbers.
Declining, a phase of declining relative growth.
Stationary, no measurable cell growth as media nutrients are depleted.
Death, media nutrients are depleted and cell numbers decrease (Fog and Thake 1987).
An advantage of continuous culturing is the cultures can be maintained in the phase most suitable for the researcher’s purpose.
Figure 2. M. aeruginosa (PCC 7806) at 60x magnification
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In the natural environment Microcystis aeruginosa are spherical cells of 4 – 6.5 µm diameters (Figure 2.) are formed into irregular colonies (Sejnohova and Marsalek 2012). The cells of M. aeruginosa are adapted to survive in the changing environment of the water column by regulating their buoyancy by the production of gas vesicles (Sejnohova and Marsalek 2012). The M. aeruginosa strain PCC 7806, used in this research was originally isolated from the Braakman reservoir, the Netherlands in 1972 (Rohrlack et al. 1999). To gain a better understanding of how and why M. aeruginosa produce toxins, a mutant strain has been manipulated through the deletion of peptide synthetase genes (mcyA-) resulting in a non-toxin producing type (Dittmann et al. 1997).
1.5. Abiotic factors Manipulation experiments of abiotic factors; light, nutrients and temperature have demonstrated effects on growth and toxin production. Higher growth rates have been demonstrated at 32°C than 25°C, although toxin production was higher at the lower temperature (Watanabe and Oishi 1985). Similarly, a higher cell growth rate was measured at higher light levels, but toxicity was unaffected (Watanabe and Oishi 1985). M. aeruginosa is a non-diazotrophic (nitrogen fixing) cyanobacterium and investigations have demonstrated nitrogen to be a critical regulating factor in cell growth rates and toxin production levels (Orr and Jones 1998). The combined effect of elevated temperature and diurnal light cycle has been investigated with respect to cells ability to cope with oxidative stress (Bouchard and Purdie 2011). Other research has been conducted into how abiotic factors influence the life stages of M. aeruginosa, i.e. benthic life phase and recruitment (Mission and Latour 2012). It is, however, unsurprising that cyanobacteria do not live in isolation in the environment and so recent investigations have focused on cell interaction both inter and intraspecific.
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1.6. Interactions between cells The latter years of the 20th century marked a paradigm shift in microbiology has been reported as bacteria went from being considered as single cells to the recognition of communication between cells and bacterial ‘multicellularity’ (Shapiro 1998). Bacteria, including cyanobacteria, monitor and respond to the presence of other bacteria in their environment by producing chemical signals in a process termed ‘quorum sensing’ (Taga and Bassler 2003). Several authors have proposed intraspecific chemical signals to be an influential factor in observed patterns of phytoplankton succession i.e. interspecific cyanobacterial dominance (Mello et al. 2012, Sigee et al. 2007) and interactions between prokaryotic and eukaryotic algae (Kearns and Hunter 2000). An interesting area of study with particular relevance to the work presented in this research project is the role of interspecies intercellular chemical signalling in Microcystis physiology and lifecycles. This has been studied with regard to longterm survival.
Dagnino and co-workers (2006) reported growth inhibition in
laboratory cultures of M. aeruginosa (including PCC 7806) at all growth phases induced by the extracellular chemical signals derived from older chlorotic cultures. Chlorotic cells lack photosynthetic pigments (chlorophyll and carotenoids) due to nutrient limitation; they are viable and will photosynthesise, grow and reproduce by binary fission when nutrients become available (Dagnino et al. 2006). The physiology of these dormant cells is important in the long-term survival of cyanobacteria, influencing cell recruitment and impacting bloom formation (Sauer et al. 2001).
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1.7. Cell growth monitoring Culture sample counting allows an accurate estimate of cell numbers per unit volume and periodic counts facilitate the calculation of population increase (Fogg and Thake 1987). There are several techniques for counting cells using light microscopy, although all are reliant on counting a small subsample in a known volume. Accuracy of counting can be improved by the use of fluorescence dyes (Guillard and Sieracki 2005). Automated cell counting using flow cytometry offers significant improvements compared to manual microscopy allowing more cells to be counted, increasing the accuracy of the analysis of cell density (Marie et al. 2005). In flow cytometry cells are hydrodynamically aligned into a narrow stream (5 to 40µm) and pass through one or two laser beams. Light emissions from the excitation of the cells are detected and recorded; these are the light scatter, forward & sideway, and a number of florescence wave lengths in the visible light spectrum. Flow cytometry as applied to the analysis of freshwater cyanobacteria phytoplankton is novel and has the potential to elucidate several research questions.
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1.8. Aims and hypothesis The aims of this research project are to successfully culture the cyanobacteria M. aeruginosa PCC 7806 under laboratory conditions, to monitor and characterise growth of cultures using flow cytometry. The flow cytometry data will be supplemented with proxy cell density data from photosynthetic pigment extraction. Moreover, the effects of extracellular chemical signals will be investigated on cell growth and physiology. All investigations have been made on toxin and non-toxin producing M. aeruginosa (PCC 7806) types, any differences between the characteristics between the two types will be recorded. H0 There will be no effect from different culture media volumes in identical culture vessels on cell densities, growth rates and doubling times. H0 There will be no observable differences in the effect of the above hypothesis between toxin producing (wild type) and non-toxin producing (mutant) cultures. H0 Extra cellular chemical signals isolated from older chlorotic cultures will have no effect on the cell densities, growth rates and doubling times of younger nutrient replete cultures.
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2. Materials and methods All experimental work was carried out at the flow cytometry laboratory (DG-47), Department of Applied Sciences, University of Bournemouth between April 2012 and March 2013. The cyanobacteria cultures used in this research originated from the Pasteur Culture Collection of Cyanobacteria (PCC) (www.pasteur.fr).
2.1. Culture techniques All procedures were carried out using two strains of Microcystis aeruginosa cyanobacteria; strain PCC7806. One was a toxin producing wild type (WT) and an artificially attenuated non-toxin producing mutant (MUT), lacking peptide synthetase genes (mcyA-) (Dittmann et al. 1997). Cultures were grown in an incubator (Conviron, CMP6010) at 25° ±1°C, on a 12 hour light/dark regime. Light was provided by a single 58 watt florescent tube (Luminex, Cool white) and cultures were shaded with a neutral density attenuation filter (Lee filters) to give a light range of 4 - 8 µmol quanta m-2 s-1. Freshwater cyanobacteria growth media BG-11 (Sigma, C3061) was prepared according to the manufacturers’ instructions to a concentration (20ml/1000ml) using 0.1µm filtered distilled water. Prior to use all glassware was acid washed (10% HCl), rinsed in distilled water. Media volumes of 60ml, 125ml and 250ml in 500ml were aliquoted in glass conical flasks with stoppers constructed of cotton wool covered in muslin capped with aluminium foil. All, glassware, stoppers, caps and media were then autoclaved at 121°C for 21 minutes (Prestige Medical, 2100 classic). For growth of MUT cultures 20 µl per 100ml of chloramphenicol (media concentration 5µg/ml) was added to the medium prior to inoculation. Inoculation of fresh cultures and all sampling was carried out in a laminar flow cabinet (Microflow, HLFWS) using sterile disposable 5ml pipettes or sterilised auto-pipette tips as appropriate. Initial inocula for all experimental work were standardised at a ratio of cell volumes to medium of 1:5 using early stationary phase cells.
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2.2. Flow cytometry An Accuri c6 flow cytometer was used to count cell numbers. Counts were made using the medium fluidics setting (Flow=35µl/min; Core=16µm) for 3 minutes. Depending on the phase of cell growth and sample dilution factor, counts ranged from 2,000 to 200,000 events per sample. A primary threshold was set at 1,000 events from florescence detector FL-3 (670nm LP) (Height) and a secondary threshold set at 10,000 events on Forward Angle Light Scatter (FSC) (Height) signal detector. Figure 3 is generated from the fluorescence detectors far-red (675 ± 12.5nm (FL-4 H)) on the y axis and red (670nm LP (FL-3 H)) on the x axis. The Accuri c6 flow cytometer uses a dual laser (blue and red) excitation system; farred florescence is excited by the blue laser. The rectangle (R1) encompasses events discriminated by red and far-red florescence and were considered ‘healthy’
Far- red 675 ± 12.5nm
cells.
Red 670nm LP
Figure 3. An example of a flow cytometer ‘dot plot’ data output (cytogram). The red rectangle (R1) encompasses events counted as PCC 7806 cells in the culture sample
Samples were diluted with 0.1µm filtered distilled water to provide events per second in the range of 500 to 2,000. Between counts the flow cytometer was run on fast fluidics with 0.1µm filtered distilled water for 1 minute. Cell density was calculated using the formula: cells per ml ( )
count x dilution factor x 1000 volume ( l)
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Relative growth rate (d-1) was calculated using the formulae (Fogg and Thake 1987): d1
ln
1 ln 2 t (d)
Doubling times (G) was calculated using the formula (Fogg and Thake 1987): G
ln 2
2.3. Accuracy and precision of the counting method The repeatability of cell counting and potential sampling error was tested at the beginning of the work. Five samples (0.2 ± 0.01ml) were taken from a 125ml culture using a disposable 5ml pipette; the culture was gently agitated between each sampling occasion. Each sample was diluted 1 in 10 (20µl to 180µl) and counted using the flow cytometry method (2.3). From one of the initial culture samples five subsamples were taken diluted 1 in 10 and counted (2.3). The first diluted sample was counted five separate times. Counts were of 3min duration on medium fluidics with 1 minute of 0.1µm filtered distilled water between counts.
2.4. Photosynthetic pigment extraction A 125ml WT culture was prepared as above (2.1) and cell growth monitored at 3-4 day intervals for up to 5 weeks. On each sampling occasion Microcystis cell counts were made using flow cytometry (2.2) and a 1ml aliquot was taken, and filtered using an ø25mm 0.7µm glass microfibre filter (Whatman GF/F). Filter papers were placed in a plastic sample vial and frozen (40 days old) (C). Test tubes were stoppered and capped, placed in a test tube rack and returned to the incubator at 25±1°C for 10 days. The 8ml subsample was retained as a positive control, incubated and sampled under identical conditions. Cell counts were made according to the methods described in section 2.2 at 3-4 day intervals. The experimental design and process is illustrated in Figure 4 below.
2.6. Photography At each cell count of the treated cultures (Section 2.5) the test tubes were photographed (Sony, Cyber-shot 12.1) to record variation in culture colour. Additionally, light microscopy images of Microcystis cells were captured using an Olympus DP70 camera microscope. An example of M. aeruginosa cells captured at 60x magnification is presented in section 1.3. (Figure 2.).
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Day 0 3 x 4ml subsamples aliquoted into sterile test tubes
Test culture
Repetition of treatments on days 3, 6 and 10
Treated subcultures were monitored for up to 10 days
125ml Microcystis culture (PCC 7806, WT or MUT) A 4ml H2O
B 4ml BG-11 (growth media)
20µl pipetted from each test tube and diluted
C 4ml Chlorotic culture (centrifuged and 0.1µm filtered)
Flow-cytometry to monitor cell growth
Figure 4. Diagram of the experimental design for growth suppression investigations, 125ml Microcystis culture and treated subsamples incubated at 25°C on 12/12 Light/Dark regime.
3. Results 3.1. Microcystis aeruginosa PCC 7806, culturing and cell monitoring Effective culturing and monitoring techniques were established before the commencement of autoinhibition experiments
3.1.1 Visual inspection and flow cytometry Visual inspections showed that cultures initially appeared pale green, the green deepened as the cells densities increased up to 25 days. Between 25 and 40 days in the stationary and death phases the green colouration paled to a yellow. Cultures older than 40 days showed an even paler milky colour. Cultures that failed to establish paled quickly and were clear in 2-3 days. Figure 5 shows at example of wild type (WT) and mutant (MUT) cultures approximately one hour after inoculation.
Figure 5. Wild type (left) and mutant (right) PCC7806 cultures after inoculation.
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Figure 6 to 9 inclusive show typical outputs from cell counts using flow cytometry, the cytogram (left) shows cells on a biparametric plot with axes of red (670nm long pass) and far-red (675 ± 12.5nm band pass) florescence. The percentage of events contained within the R1 rectangle increases from Figure 6 to 7 indicative of culture in exponential growth phase. This is supported by the histogram (right)
Far-red fluorescence
which shows a higher proportion of events towards the right of the graph.
Red fluorescence
Red fluorescence Red fluorescence
Far-red fluorescence
Figure 6. Cytogram and Histogram for a 125ml WT culture day 0, the number of cells separated by R1 was low reflecting the inocula from a culture in early stationary phase. The count corresponds to marker a on Figure 10.
Red fluorescence
Red fluorescence
Red fluorescence
Red fluorescence
Figure 7. Cytogram and Histogram for 125ml WT culture day 13, the number of cells separated by R1 was higher than previous counts. The count was taken at the end of the exponential growth phase and corresponds to marker b on Figure 10.
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Figures 7 to 8 demonstrate a decrease in the proportion of recorded events contained in R1 and towards the right of the histogram. Cells counts obtained from
Far-red fluorescence
Figure 7 and 8 indicate the culture was in a stationary growth phase.
Red fluorescence
Red fluorescence
Red fluorescence
Red fluorescence
Figure 8. Cytogram and Histogram for 125ml WT culture day 22, a thrid population of events was devloping in the red fluorescence. The count corresponds to marker c Figure 10.
Figures 8 to 9 show a significant decrease in events recorded in R1, cell counts suggest the culture reached the death phase between these sampling occasions. The histograms show that a third population of events develops as cell growth
Far-red fluorescence
decreases.
Red fluorescence
Red fluorescence
Red fluorescence
Red fluorescence
Figure 9. Cytogram and Histogram for 125ml WT culture day 35, the number of cells seperated by R1 has dimished, the culture colour was pale yellow. The count corresponds to marker d Figure 10.
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Figure 10 shows the growth curve of cell densities calculated from the above flow cytometry data. The markers a to b indicate exponential growth phase, b to c the stationary phase and c to d the growth death phase.
4.5E+7 b
4.0E+7
c
3.5E+7
Cells/ml
3.0E+7 2.5E+7 2.0E+7 1.5E+7 1.0E+7
a
5.0E+6 d 0.0E+0 0
5
10
15
20
25
30
35
40
Time (days)
Figure 10. Growth curve of 125ml WT culture as counted in Figures 6 to 9. Markers a to b indicate exponential growth phase, b to c stationary growth phase and c to d death phase.
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3.1.2 Culture medium volumes The daily growth rates and cell densities of initial cultures of PPC7806 were shown to be lower when compared to growth rates and cells densities of latter experimental work. Figure 11 shows an example growth curve of an early attempt to grow PCC7806 WT in 250ml media volume. Total cell growth as measured by flow cytometry of WT and MUT cultures show substantially lower total cell numbers in 250ml compared to 60ml volumes (Figure 11.).
Experiential work to optimise of culture volumes for both PCC7806 WT and MUT indicated that cell densities and growth cycle times varied between 250ml, 125 ml and 60 ml media volumes medium volumes
1.6E+8
1.4E+8
1.2E+8
Cells/ml
1.0E+8 250ml
8.0E+7
125ml 60ml
6.0E+7
250ml*
4.0E+7
2.0E+7
0.0E+0 0
5
10
15
20
25
30
35
40
45
Time (days)
Figure 11. Comparative growth curves of 250ml, 125ml and 60ml PCC 7806 WT cultures (Sept-Nov 2012). 250ml* indicates growth curve of initial WT culture (April-May 2012) in which cell densities across the growth cycle only varied by approximately half a log 10scale.
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Initial difficulties in repeating experimental growth in cultures of PCC 7806 were attributed in part to refinement of culturing techniques and/or the acclimation to different light and temperature regimes. Once resolved the potential effects of culture volumes and differences between WT and MUT were tested statistically. Table 1 summarises the numbers, types and volumes of cultures examined from culturing from September to December 2012.
Table 1. Growth of PCC7806 mutant and wild type of different media volumes. Cells were grown in BG-11 at 25°C on 12/12 L/D. Culture Type Volume (ml) Number of cell counts 1 WT 250 14 2 MUT 250 14 3 MUT 250 18 4 MUT 60 18 5 WT 125 17 6 WT 125 17 7 WT 60 6 8 MUT 60 6
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Figures 12 and 13 describe boxplots of relative growth rates and cell densities generated using the three media volumes (250ml, 125ml and 60ml). The distribution of measured growth rates between the three volumes appeared relatively similar, although the median value for 250ml indicated a slight increase in cells incubated in larger culture volumes (Figure 12.).
Figure 12. Boxplot of relative growth rates between the three culture media volumes. The middle line represents the mean, the top and bottom of the box represent the 25th and 75th percentile and the ‘whiskers’ represent the data range.
The distribution of cell densities in the different medium volumes showed a different pattern with higher cell densities recorded in 60ml volumes compared to 125ml and 250ml. Low outlying results (marked on Figure 13) within the 60ml volume tests were also observed, these were not excluded from further statistical analysis.
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Figure 13. Boxplot of recorded cell densities between the three culture media volumes. The middle line represents the mean, the top and bottom of the box represent the 25th and 75th percentiles, the ‘whiskers’ represent data range and other markers representing anomalous outlying results. Relative growth and cell densities of the three culture volumes were tested with a one way analysis of variance (ANOVA) with a Least Significant Difference post hoc test. No statistical differences were observed between growth rates or doubling times of cultures (graphs not presented) in 250, 125 or 60ml medium volumes (P>0.05). Statistically significant effects of culture volumes and total cell densities were observed. Larger volumes resulted in reduced cell densities; between 250ml and 125ml (P=0.018) and a larger highly significant effect measured between 250ml and 60ml (P=0.00). The differences in cell densities between volumes of 125ml and 60ml not statistically significant at the P=0.05 level.
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3.1.3 Growth of mutant and wild type The mean cell densities and relative growth rates between mutant (MUT) and wild type (WT) cultures were compared between 250ml and 60ml culture media volumes (Figure 14.). The mean growth rates obtained from the 250ml volume cultures are similar between WT and MUT with observable higher cell densities for WT cultures. Culture medium volumes of 60ml are favourable to MUT cultures in both mean cells densities and relative growth rates.
Figure 14. Bar graphs of mean cell densities (left) per ml and relative growth rates (right) between wild type (WT) and mutant (MT) in 250ml (top) and 60ml (bottom) medium volumes, error bars +/- 2 standard error.
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The distribution of cell densities and relative growth rates of WT and MUT cultures across the 60ml and 250ml culture volumes were tested by a one-sample Kolmogorov-Smirnov test. Normal distribution were observed in WT culture cell densities and relative growth rates and MUT culture relative growth rates (P>0.05). A statistical difference was observed in the distribution of MUT culture cell density (P=0.07).
3.2. Accuracy and precision of the counting method The mean count from the first five samples was 14,996,593 cells/ml and the standard deviation was 468,328 cells/ml. The second series of dilutions had a mean count of 14,440,063 cells/ml and the standard deviation was 574,927 cell/ml. The final five counts of the same sample had a mean count of 16,046,315 cells/ml and a standard deviation of 447,186 cells/ml. 1.8E+7
Cells/ml
1.6E+7 A&B B&C 1.4E+7
1.2E+7 1.2E+7
C&A
1.4E+7
1.6E+7
1.8E+7
Cells/ml
Figure 15. Scatter plot of comparing counts obtained from procedures A (Culture sampling), B (Culture dilution) and C (Flow cytometry). The mean of all 15 counts was 15,160,990 cells/ml with a standard deviation of 830,327 cells/ml. Multiplying the standard deviation by 2 provides a measure of method uncertainty (Horwitz 2003), therefore the method uncertainty for flow cytometric counting of WT and MUT was estimated at 1,660,654 cells/ml per sampling.
32
3.3. Photosynthetic pigment extraction The concentrations of chlorophyll a and total carotenoids were calculated and compared to the cell counts obtained from flow cytometry. Figure 16 depicts the curves of cell densities, chlorophyll a and total carotenoids.
4.5E+7
12
4.0E+7 10 3.5E+7 8
2.5E+7 6 2.0E+7 1.5E+7
4
µg/ml
Cells/ml
3.0E+7
Cells CHL a Carot
1.0E+7 2 5.0E+6 0.0E+0
0 0
5
10
15
20
25
30
35
40
Time (days)
Figure 16. Growth curve of PCC7806 125ml wild type culture, with chlorophyll a and total carotenoid (Carot) on the right hand vertical axis.
The results from Pearson product-moment correlation (PC) (Table 3.) analysis demonstrated a highly significant association between chlorophyll a and total carotenoids (PC - 0.945, P=0.000). Significant correlation was observed between cell count and both chlorophyll a (PC - 0.684, P=0.014) and total carotenoids (PC 0.666, P=0.018).
33
Table 2. The results of Pearson product-moment correlation between time, cell densities, chlorophyll a and total carotenoid concentrations CHL a Measurement Time (d) Total Carotenoids CHL a Total Carotenoids Cells
0.185 P=0.564 0.176 P=0.584 -0.534 P=0.074
0.945** P=0.000 0.684* P=0.014
0.666* P=0.018
Significance denoted as *significant- 0.05). A highly statistical difference was observed between the condition chlorotic culture and the other two treatments (P=0.00).
42
The results were further analysed to ascertain whether the day of treatment had any effect on the outcome of the autoinhibition growth experiment.
Day 0
Day 3
Day 6
Day 10
Figure 24. Bar graphs of mean cell densities of the three treatments (CHL = conditioned chlorotic culture) on the four treatment days (0, 3, 6 and 10), error bars +/- 2 standard error.
The recorded cells densities of the conditioned chlorotic culture treatment were tested with a one way analysis of variance (ANOVA) with a Least Significant Difference post hoc test across the four treatment days. The conditioned chlorotic culture treatment on day 6 was significantly different from days treatment on days 3 and 10 to the P=0.05 level, but day 6 was not significantly different from day 0 (P>0.05).
43
4. Discussion This research project has provided high-resolution data from flow cytometry into the growth phases of the cyanobacteria M. aeruginosa (strain PCC 7806). An investigation was made into the accuracy and precision of the sampling method and recorded data were compared to a proxy method of quantifying cultured cell densities. Acquired culturing and monitoring techniques were applied to explore potential growth autoinhibition effects of extracellular chemical signals from chlorotic cultures.
4.1. Laboratory culturing of Microcystis Initially M. aeruginosa PCC 7806 mutant (MUT) and wild type (WT) strain was acclimatised to an adjusted artificial laboratory culturing regime. The strain had been previously cultured at 30°C on a 24 hour light regime (Franklin personal communication), and in this study it was subject to 25±1°C on a 12/12 light/dark regime. Additionally, the range of light intensities available in the incubator (4 – 8 µmol quanta m-2 s-1) was lower than the previous levels (>8 µmol quanta m-2 s-1). Investigations into interactions between cells were reliant on batches of cultures that were predictably and repeatedly in an exponential growth phase. Initial culturing was not always successful with a significant number of inoculations failing to initiate a growth cycle. Furthermore, when cell growth data of successful cultures by flow cytometry monitoring were analysed, cell multiplication rates were considerably low at approximately 5 times (Figure 11.). The cell growth rates obtained from cultures from September 2012 were significantly higher, indicating cultures had acclimatised and were in sufficient growth cycles for future experimental work. M. aeruginosa PCC 7806 has been reported to have a highly plastic genome (Frangeul et al. 2008) and the genus Microcystis displays equally varied morphologies (Sejnohova and Marsalek 2012). It can be postulated the lag between acquiring the cultures and attaining high cell densities was due to a change in cultured cell morphology. Cell morphologies which were better adapted to novel regime had to increase in significant numbers to become the dominant cell morphology within the cultured cell population. 44
4.2. Culture medium volume In initial trials of differing culture medium volumes using identical 500ml conical flasks, volumes of 60ml displayed higher cell densities and shorter growth cycles than 250ml volume cultures. In latter work the apparent differences between culture medium volumes was tested systematically using 250ml, 125ml and 60ml medium volumes having identical nutrient content and light intensities. Cultures of 60ml volume were demonstrated to attain higher cell densities, although no difference was demonstrated in relative growth rates or doubling times. The differences in cell densities can be attributed to the shading effects of the Microcystis cells on each other. To clarify, lower culture volumes had a higher surface area pro rata and therefore received additional light energy for photosynthesis, attaining higher cell densities and so depleting available nutrients in a shorter growth cycle. For the purpose of subsequent scientific investigations into cell interactions culture medium volumes of 125ml were considered the most suitable; delivering 35-40 day growth and repeatable culture inoculation in the exponential growth phase.
4.3 Mutant and wild type cell densities The effects of shading by the variations in culture volume appear to be influential in comparisons between the obtained cell densities and relative growth rate of wild type (WT) and mutant (MUT) cultures. The small 60ml volume medium had a discrete advantage for MUT cultures, as measured by cell densities per ml delivering higher cell yields (Figure 14.). Other investigations have reported a growth advantage for microcystin producing toxic PCC7806 (WT) under high light energy conditions (Phelan and Downing 2011). Their research used three light energy levels, 8, 17 and 37 µmol quanta m-2 s-1, the lower being equivalent to light energy range used in this investigation. No difference in growth rates between MUT and WT cultures was detected by Phelan and Downing (2011) at the two lower light energy levels under five different media treatments, one of which was stock BG-11. The batch cultures they used were of 150ml in a 250ml conical flask providing a surface to volume ratio comparable to the 250ml media culture volumes used in this investigation. Therefore, it is probable the lighting conditions provided by the 60ml culture media volumes conveyed a cell growth advantage to 45
the MUT PCC 7806 cultures, as cells did not have the requirement expend energy on the production of complex microcystin molecules.
4.4. Flow cytometry The scientific work undertaken in this research project was enhanced substantially by the high resolution data provided by flow cytometry. Not only was the counting of cells in culture quicker with enhanced accuracy, but, differentiation of cell physiology was also possible. Understanding of growth cycles of cyanobacteria, such as M. aeruginosa, are dependent on the cells ability to photosynthesise and consequently data from red and far-red fluorescence signals were invaluable to discriminate between photosynthesising, chlorotic and dead cells. Cultures in exponential growth phase displayed two populations from the red fluorescence signal (Figures 6 & 7), the right hand population increased in proportion to the left hand population as cell density increased. It was postulated that these increasing cells were healthy photosynthesising cells and this was supported by chlorophyll a concentrations correlating with increasing cell density (see below). The left hand population was not easily categorised, possibly a mixture of dead cells and detritus. The third population of cells developing during the death phase was of great interest (Figures 8 & 9.) and could be interpreted as chlorotic cells, dormant cells which have suspended the production of photosynthetic pigments (Gorl et al. 1998). Flow cytometry as applied to cyanobacteria culturing is novel and a proportion of this research project was directed at verifying and refining monitoring techniques. The results of the precision and accuracy of the counting method indicate the automated counting by the Accuri c6 flow cytometer delivered consisted measurements. More variation was apparent from the counts subject to subsampling and pipetting for the dilutions of samples, but, all measurements were closely grouped (Figure 15.) and calculated ‘method uncertainty’ was of an acceptable tolerance.
46
4.5. Photosynthetic pigment extraction The primary rationale behind chlorophyll a and total carotenoid extraction was to provide a proxy measurement of cell culture densities and therefore further test the cell counts by flow cytometry. As a limited portion of the whole research project it was highly successful, displaying a strong correlation between chlorophyll a and total carotenoids when compared to recorded cell counts (Figure 16.). Moreover, the data collected delivered information on cell physiology throughout a batch culture growth cycle. Chlorophyll a displayed an apparent lower concentration compared with cell density during the exponential phase of the growth cycle; the ratio of chlorophyll a then appeared to increase relative to cell density during the stationary growth phase. Before the batch culture entered the death phase there was a marked increase in the ratio of chlorophyll a to cell density, which was maintained throughout this final phase. Concentrations of total carotenoids corroborated this trend, apart from lower ratios of total carotenoids were recorded in the stationary phase and higher ratios towards the end of the death phase. Owing to insufficient time for the repetition of this experiment and an absence of data for pigment extraction compared with high resolution cell counting in the literature, an explanation for the observation can only be speculated. In the exponential growth phase chlorophyll a and total carotenoids may be at a premium in rapidly dividing cells and/or those cells may be of a smaller size overall. The higher ratio in the death phase may be a result of elevated extra cellular concentrations due to cells bursting through necrosis. Total carotenoids take longer to breakdown than chlorophylls; this has been observed in the senescence of macrophytes and in eukaryotic algal cultures (Fogg and Thake 1987).
47
4.6. Growth suppression experiments Measuring any autoinhibition effect from conditioned chlorotic culture, centrifuged and 0.1µm filtered, on cell growth of nutrient replete cultures was the primary aim of this research project. Some of the results were as expected, with treated cultures exhibiting a significant drop in cell densities as measured by flow cytometry and an accompanying culture colour change from dark green to pale yellow. But other results from experimental repetitions at all-time points exhibited no visible or measurable responses to the conditioned chlorotic culture treatment. However, statistical analysis of the cell counts obtained from all 10 repetitions demonstrated the conditioned chlorotic culture treatment did have an effect decreasing the cell counts of the treated cultures. There may be several explanations for the discrepancies of the results of this investigation and those of Dagnino and co-workers (2006). The results presented in this report would suggest the time of treatment i.e. phase of growth of the treated culture, is a factor in the growth autoinhibition effects from the conditioned chlorotic culture. The one treatment concomitant with culture inoculation (Day 0) delivered the least effect from the conditioned chlorotic culture, there was some effect between day 3 and 6, but overall there is a strong correlation between the three treatments (Graph A, Figure 17.). The inoculum of cultures was designed to minimalize lag growth phase, but, all displayed a short period of unrecorded growth, generally 1 to 2 days. Therefore, it was supposed cell density was the factor in minimal recorded effect and autoinhibition of growth from the chemical signals of chlorotic cells was dependent on a critical mass of cells to respond. Treatments made on day 6 of culturing were demonstrated to have the greatest effect on cell growth, although a pronounced effect was observed on one treatment on day 3 and potentially on day 10 (monitoring ceased due to laboratory closure). The discrepancies may be attributed to shifts in the actual phases between the three cultures and to the differing culture characteristics of the mutant and wild type. Therefore, an optimum time window of maximum effect from the chlorotic conditioned culture was observed between 3 and 10 days after inoculation in this investigation.
48
However, there was no autoinhibition growth suppression effect observed from the conditioned chlorotic culture at every treatment point. The light parameters for this investigation were selected to reflect temperate environmental conditions and temperature was selected for efficient laboratory cultivation. The growth cycles of cultures are comparable with those reported by Dagnino and co-workers (2006), both were chlorotic after 25 – 30 days, there are some differences in the experimental procedures. Light intensity is higher at 30 – 45 µmol quanta m-2 s-1, light/dark cycle was 18/6 hours, temperature range was 23 - 29°C, ASM-1 growth medium was used at double concentration and cultures were constantly agitated on a rotary shaker (Dagnino et al. 2006). After completion of the experimental procedures, it was considered that the most important factor to account for discrepancies was the use of double strength media and to lesser extents the selection of ASM-1 media and the use of rotary shakers.
49
5. Summary and conclusion 5.1. Summary The prevalence of harmful cyanobacterial blooms, associated with anthropogenic actives has resulted in substantial interest and research into causal prokaryotic autotrophs. In vitro experimentation facilitates the manipulation of both abiotic and biotic factors, providing scientific data to assist understanding of physiology, morphology and life histories. An area of growing interest is the chemical intercellular signalling interactions between cells, which can affect population growth, survival and death. Recently the application of intercellular signalling to control and prevent blooms in freshwater bodies has been a subject of interest. Consequently, in vitro manipulation and techniques for the measurement of cyanbacteria have been examined by a number of workers (Kearns and Hunter 2000, Phelan and Downing 2011, Mello et al. 2012). M. aeruginosa is common toxic freshwater cyanobacteria and has been the subject of multiple investigations (Dittmann et al. 1997, Phelan and Downing 2011). The aims of this research project were the successful culturing of M. aeruginosa (PCC 7806) and monitor cell growth phases using flow cytometry. Flow cytometry is a new technique with respect to its application to phytoplankton science, both in laboratory and field investigations (Marie et al. 2005). Investigations were made into the autoinhibition effects of nutrient deplete chlorotic cultures on the cell growth of nutrient replete cultures. The results of the culturing and monitoring experiments indicated that flow cytometry is an effective and robust technique for the monitoring of cyanobacteria cultures. The high resolution data delivered has provided a valuable and reproducible technique in the context of this study. Previous studies concluded that nutrient deplete chlorotic cultures induced autoinhibition of growth in all treated cultures at all growth phases (Dagnino et al. 2006). The results of this study indicated by the time of treatment to be a factor and the conditioned chlorotic culture medium in had generally weaker growth suppression affect. Although, these data do indicate the potential utility of an intercellular signalling approach as an effective technique in the management of M. aeruginosa blooms. 50
5.2. Conclusion A cost effective, easily synthesised, inert, target specific compound which could be readily deployed to inhibit cell growth of Microcystis (and other harmful cyanobacteria) before blooms pose a threat to human and ecosystem health would be a valuable technology in freshwater management. Such a compound would offer advantages over and supplement current methods of either ‘bottom-up’ or ‘top-down’ control. ontrol of harmful cyanobacteria blooms by ‘bottom-up’ controls is achieved by limiting the nutrient input into a water body, often involving expensive infrastructure. Likewise, ‘top-down’ control by biomanipulation requires specific planning for each case, requiring changing the ecology of an ecosystem over a substantial period of time. Moreover, both methods of control are often reactionary to a cyanobacteria event, making an easily deployed rapid solution all the more desirable. Clearly, a compound of such significance is a long way from development, if it is indeed realisable. There is the requirement for the culturing of M. aeruginosa to reliably have a strong autoinhibition effect on all repetitions of the experiment. Then the compound would have to be isolated, achievable with the application of High-performance liquid chromatography (HPLC). When the compound is isolated, it would then have to undergo in vitro testing to assess its toxicity and optimum treatment in cell growth cycle. Laboratory testing would be enhanced by the application of live/dead staining techniques used in combination with flow cytometry. Lastly, an effective compound would require field trials, this could be accomplished by establishing microcosms in which biotic factors; other cyanobacteria, eukaryotic algae and zooplankton could be controlled.
51
References Bartram, J., Carmichael, W. W., Chorus, I., Jones, G., and Skulberg, O. M, 1999. Introduction. In: Chorus, I., and Bartram, J., eds. Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. London: E & F N Spon, 1 – 14. Boon, P. I., Bunn, S. E., and Shiel, R. J., 1994. Consumption of cyanobacteria by freshwater zooplankton: Implications for the success of ‘top-down’ control of cyanobacterial blooms in Australia. Australian Journal of Marine and Freshwater Research, 45(5), 875 – 887. Bouchard, J. N., and Purdie, D. A., 2011. Effect of elevated temperature, darkness and hydrogen peroxide treatment on oxidative stress and cell death in the bloomforming toxic cyanobacterium Microcystis aeruginosa. Phycological Soceity of America, 47, 1316 – 1325. Carmichael, W. W., 1992. Cyanobacteria secondary metabolites – the cyanotoxins. The Journal of Applied Bacteriology, 72, 445 – 459. Cavalier-Smith, T., 2000. Membrane heredity and early chloroplast evolution. Trends in Plant Science, 5 (4), 174 – 182. Chan, F., Pace, M. L., Howarth, R. W., and Marino, R. M., 2004. Bloom formation in heterocystic nitrogen-fixing cyanobacteria: The dependence on colony size and zooplankton grazing. The American Society of Limnology and Oceanography, 49(6), 2171 – 2178. Chorus, I., and Mur, L., 1999. Preventative measures. In: Chorus, I., and Bartram, J., eds. Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. London: E & F N Spon, 222 – 256. Dagnino. D., de Abreu Meireles, D., and de Aquino Almeida, J. C., 2006. Growth of nutrient-replete Microcystis PCC 7806 cultures is inhibited by an extracellular signal produced by chlorotic cultures. Environmental Microbiology. 8 (1), 30 – 36. Ding, W. X., Shen, H. M., Shen, Y., Zhu, H. G., and Ong, C. N., 1998. Microcystic cyanobacteria causes mitrochondrial membrane potential alteration and reactive oxygen species formation in primary cultured rat hepatocytes. Environmental Health Perspectives. 106 (7), 409 – 413. Dittmann, E., Neilman, B. A., Erhard, M., von Dohren, H., and Borner, T., 1997. Insertional mutagenesis of a peptide synthetase gene that is responsible for hepatotoxin production in the cyranobacterium Microcystis aeruginosa PCC 7806. Molecular Microbiology, 26(4), 779 – 787.
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Dittmann, E., Erhard, M., Kaebernick, M., Scheler, C., Neilman, B. A., von Dohren, H., and Borner, T., 2001. Altered expression of two light-dependent genes in a microcystin-lacking mutant of Microcystis aeruginosa PCC7806. Microbiology, 147, 3113 – 3119. Falconer, I. R., 1996. Potential impact on human health of toxic cyanobacteria. Phycologia, 35(6), 6 – 11. Fogg, G. E., and Thake, B., 1987. Algal Cultures and Phytoplankton Ecology. 3rd Edition. Wisconsin: The University of Wisconsin Press. Frangeul, L., Quillardet, P., Castets, A. M., Humbert, F., Matthijs, H. C. P., Cortez, D., Tolonen, A., Zhang, C. C., Gribaldo, S., Kehr, J. C., Zilliges, Y., Ziemert, N., Becker, S., Talla, E., Latifi, A., Billault, A., Lepelletier, A., Dittmann, E., Bouchier, C., and Tandeau de Marsac, N., 2008. Highly plastic genome of Microcystis aeruginosa PCC 7806, a ubiquitous toxic freshwater cyanobacterium. BioMed Central Genomics, 9, 274. Gorl, M., Sauer, J., and Forchhammer, K., 1998. Nitrogen-starvation-induced chlorosis in Synechococcus PCC 7942: adaption to long-term survival. Microbiology, 144, 2449 – 2458. Gragnani, A., Scheffer, M., and Rinaldi, S., 1999. Top-down control of cyanobacteria: A theoretical analysis. The American Naturalist, 153(1), 59 – 72 Guillard. R. R. L., and Sieracki, M. S., 2005. Counting cells in cultures with the light microscope. In: Andersen, R. A., ed. Algal Culturing Techniques. London: Elsevier, 239 – 252. Gumbo, J. R., Ross, G., and Cloete, T. E., 2010. The isolation of predatory bacteria from a Microcystis algal bloom. African Journal of Biotechnology, 8(5), 663 – 671. Herath, G., 1997. Freshwater algal blooms and their control: Comparison of the European and Australian experience. Journal of Environmental management, 51, 217 – 227. Horwitz, W., 2003. The certainty of uncertainty. Journal of AOAC International, 86(1), 109 – 111. Jang, M. H., Jung, J. M., and Takamura, N., 2007. Changes in microcystin production in cyanobacteria exposed to zooplankton at different population densities and infochemical concentrations. Limnology Oceanography, 52(4), 1454 – 1466. Kearns, K. D., and Hunter, M. D., 2000. Green algal extracellular products regulate antialgal toxin production in a cyanobacterium. Environmental Microbiology, 2(3), 291 – 297. 53
Lane, N., 2010. Life Ascending: The ten great inventions of evolution. London: Profile Books. Marie, D., Simon, N., and Vaulot, D., 2005. Phytoplankton Cell Counting by Flow Cytometry. In: Andersen, R. A., ed. Algal Culturing Techniques. London: Elsevier, 253 – 268. Martin, D., and Ridge, I., 1999. The relative sensitivity of algae to decomposing barley straw. Journal of Applied Phycology, 11, 285 – 291. Mello, M. M. E., Soares, M. C. S., Roland, F., and Luring, M., 2012. Growth inhibition and colony formation in the cyanobacterium Microcystis aeruginosa induced by the cyanobacterium Cylindrospermopsis raciborskii. Journal of Plankton Research, 34 (11), 987 – 994. Moore, S. K., Trainer, V. L., Mantua, N. J., Parker, M. S., Laws, E. A., Backer, L. C., and Fleming, L. E., 2008. Impacts of climate variability and future climate change on harmful algal blooms and human health. BioMed Environmental Health, 7(2), S4 Mission, B., and Latour, D., 2012. Influence of light, sediment mixing, temperature and duration of the benthic life phase on the benthic recruitment of Microcystis. Water Research 46, 1438 – 1446. Paerl, H. W., and Huisman, J., 2009. Climate change: a catalyst for global expansion of harmful cyanobacterial blooms. Environmential Microbiological Reports. 1 (1), 27 – 37. Orr, P. T., and Jones, G. J., 1998. Relationship between microcystin production and cell division rates in nitrogen-limited Microcystis aeruginosa cultures. Limnology Oceanography, 43(7), 1604 – 1614. Pearson, L. A., Hisbergues, M., Borner, T., Dittmann, E., and Neilan, B. A., 2004. Inactivation of an ABC Transporter Gene, mcyH, Results in Loss of Microcystin Production in the Cyanobacterium Microcystis aeruginosa PCC 7806. Applied and Environmental Microbiology, 70(11), 6370 – 6378. Phelan, R. R., and Downing, T. G., 2011. A growth advantage for Microcystin production by Microcystis PCC7806 under high light. Phycological Society of America, 47, 1241 – 1246. Rohrlack, T., Dittmann, E., Henning, M., Borner, T., and Kohl, J. G., 1999. Role of microcystins in poisoning and food ingestion inhibition of Daphnia galeata caused by the cyanobacterium Microcystis aeruginosa. Applied and Environmental Microbiology, 65(2), 737 – 739. Rai, A. N., Bergman, B., and Rasmussen, U., 2002. Cyanobacteria in Symbiosis. New York: Kluwer Academic Publishers. 54
Sauer, J., Schreiber, U., Schmid, R., Volker, U., and Forchhammer, K., 2001. Nitrogen starvation-induced chlorosis in Synechococcos PCC 7942. Low-level photosynthesis as a mechanism of long-term survival. American Society of Plant Physiologists, 126, 233 – 243. Schopf, J. W., 2002. The fossil record: Tracing the roots of the cyanobacterial lineage. In: Whitton, B. A., and Potts, M., eds. The Ecology of Cyanobacteria: Their Diversity in Time and Space. London: Kluwer Academic Publishers, 13 – 35. Sejnohova, L., and Marsalek, B., 2012. Microcystis. In. Whitton, B. A., ed. The Ecology of Cyanobacteria II: Their Diversity in Time and Space. New York: Springer Science, 195 – 225. Shapio, J. A., 1998. Thinking about bacterial populations as multicellular organisms. Annual Review Microbiology, 52, 81 – 104. Sigee, D. C., 2005. Freshwater Microbiology: biodiversity and dynamic interactions of microorganisms in the freshwater environment. Chichester: John Wiley and Sons Ltd. Sigee, D. C., Selwyn, A., Gallois, P., and Dean, A. P., 2007. Patterns of cell death in freshwater colonial cyanobacteria during the late summer bloom. Phycologia, 46 (3), 284 – 292. Sivonen, K., and Jones, G., 1999. Cyanobacterial Toxins. In: Chorus, I., and Bartram, J., eds. Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and management. London: E & F N Spon, 41 – 111. Taga, M. E., and Bassler, B. L. 2003. Chemical communication among bacteria. Proceedings of the National Academy of Sciences of the United States of America, 100 (2), 14549 – 14554. Watanabe, M. F., and Oishi, S., 1985. Effects of Environmental Factors on Toxicity of a Cyanobacterium (Microcystis aeruginosa) under culture conditions. Applied and Environmental Microbiology, 1342 – 1344. Wellburn, A. R., 1994. The Spectral Determination of Chlorophylls a and b, as well as Total Carotenoids, using variour solvents with Spectrophotometers of different resolution. Journal of plant Physiology, 144, 307 – 313. Whitton, B. A., and Potts, M., 2002. Introduction to the cyanobacteria. In: Whitton, B. A., and Potts, M., eds. The Ecology of Cyanobacteria: Their Diversity in Time and Space. London: Kluwer Academic Publishers, 1 – 11. Xie, P., and Liu, J., 2001. Practical success of biomanipulation using filter-feeding fish to control cyanobacteria blooms. The Scientific World, 1, 337 – 356.
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Appendix I. Evaluative supplement The
independent
research
project
titled,
‘Growth
autoinhibition
in
the
cyanobacterium. Microcystis aeruginosa (PCC 7806) as a research target for novel control strategies’ has afforded the author an invaluable insight into the cutting edge of technologies available in the field of applied environmental microbiology. Furthermore, the obtained practical laboratory experience and skills are fully transferable to the wider fields of biology, toxicology and chemistry. Although the results of the autoinhibition growth suppression were not a decisive as envisioned at the commencement of the project, all data collected were of importance to future investigations into freshwater cyanobacteria conducted at Bournemouth University. Flow cytometry is a powerful tool and its potential is only just starting to be realised in the discipline of phytoplankton science, both in the field and laboratory. What was of particular value was the ability to revisit and reanalyse the high resolution data collected from a sample. The Accuri c6 used in this investigation was on factory settings and recorded events from 6 parameters; Forward scatter (size), side scatter (granularity) and four fluorescence signals. So for instance, if you wished to look at the forward scatter of a signal in the red fluorescence, then the software could construct a ‘dot plot’ for analysis and this could be accomplished at any point after the sample was taken. In earlier cultures the events that were considered to be the cells under scrutiny would ‘move out’ of the rectangle drawn to segregate them, this was not conducive with measuring cell numbers to calculate relative growth rates. This initial ‘trial and error’ to refine the counting process was aided by the reanalyse feature allowing data not to be lost to the project and the accuracy of the cell counting improved. The accuracy and speed of counting of flow cytometry machines has improved and this performance is available to researchers at lower cost, therefore, flow cytometry is going to be a feature of phytoplankton research for some time to come.
56
The culturing of the cyanobacterium Microcystis aeruginosa (PCC 7806) was central to the success of this independent research project. In respect of this, starting culturing in April and May 2012 was a very productive use of time and allowed the honing of culturing techniques. A process which is dependent on sterility of equipment and media, prone to occasional failure but, highly rewarding when completed successfully. All the laboratory techniques used are relevant and transferable to an analytical laboratory career. The results of the autoinhibition growth suppression experiments were of a mixed success, there was an effect from the conditioned chlorotic culture, but a strong effect was not observed on all experimental repetitions. In retrospect, it was easy to blame bad practice or poor experimental design, but, it may just be the case that the conditions where such a prevalent effect was difficult to create within the time frame of the project. After all, this is microbiology and biological experiments are notorious for doing their own things. There are several aspects of the project that would be changed with the advantage of hindsight. It would have been advantageous to make more use of the variation in abiotic factors available, for example; there was a greater range of light energy available (on lower shelves in the incubator) and higher light energy would have affected growth rates. Additionally, manipulation of growth media concentrations, doubling and halving for instance, may have provided an insight into cell physiology. The temperature choice of 25°C was satisfactory, although an increase to 30°C is widely acknowledged to be optimum for Microcystis growth in the laboratory. Exploration of these variables may have produced conditioned chlorotic culture medium that was more potent for the growth suppression experiments and this would have been possible to test scientifically. There are also improvements to the biotic factors, fully potential of data from flow cytometry was not applied to culture inoculation as well as it could of and this may have a bearing on the final results. The growth suppression experiments may have benefited from the incorporation of a positive control, for example; an algaecide or barley straw.
57
The post laboratory analysis of the results was insightful, conclusions that had been reached during the collection of data were rapidly overturned, for instance; there was not as marked difference in the growth patterns between mutant and wild type cultures. There was a measurable effect on cell growth from cultures treated with the condition chlorotic culture media. The results of the photosynthetic pigment extractions were surprising and insightful; the technique would have benefited from repetition and would be valuable incorporated into the experimental design improvements listed above. Overall, however, the body of scientific work is believed to contribute significantly to the research question it addresses. Future work would benefit from integrating live/dead staining techniques, an approach that would elucidate questions about cell physiology during culture growth cycles. There are many strains of Microcystis and other cyanobacteria that have been isolated and an associated wealth of experimental findings in the literature. An investigation of the autoinhibition effects from intercellular chemical signals, both interspecific and intraspecific would be beneficial to furthering knowledge if freshwater cyanobacteria. It is hoped that this independent research project will assist future research in microbial ecology conducted at Bournemouth University.
58
Appendix II. Interim interview comments In a meeting between David Hartnell (student) and Daniel Franklin (supervisor) in December 2012, David presented:
Growth curves of mutant and wild type (PCC 7806) to date
Preliminary data of a growth suppression experiment
Gant chart of proposed future work
Daniel advised David:
To submit a draft of introduction and material & methods
Looking at various ways to present the recorded data
Consider incorporating live/dead staining into the experimental design
Outcomes:
Drafts submitted
Live/dead staining workshop attended
59
Appendix III. Experimental Data Recorded cell counts for Figure 11. Term 1 250ml wild type (Figure 11.) Day
Cell count
Volume
125ml wild type (Figure 11.)
Dilution
Day
Cell count
Volume
Dilution
0
131449
196.1
5
0
47287
103.1
10
8
131820
196.3
5
3
93961
103
10
13
163621
196.5
5
4
125738
103.2
10
16
202765
195.9
5
5
162020
108.8
10
20
301294
196.7
5
6
177398
103.2
10
23
399295
217.6
5
7
195761
103.2
10
27
280800
196.4
5
10
201357
103
10
30
235196
196.3
5
13
139873
105.7
20
38
166451
201.2
5
17
210805
124.2
20
20
251537
103
20
24
162864
103.2
30
60ml mutant (Figure 11.) Day
27
187250
103.3
30
0
Cell count 38241
Volume 196.9
Dilution 5
29
209303
103
30
1
57811
196.7
5
32
144959
103.3
50
5
271259
196.7
5
35
74740
103.1
50
7
703801
204.7
5
38
100359
103.1
50
8
460630
199.5
10
41
72057
103.1
50
11
895410
196.5
10
12
525116
196.7
20
13
573268
196.7
20
Day
14
549803
196.6
20
4
300261
131.6
1
15
566193
196.7
20
14
198148
196.6
5
18
930379
196.8
20
21
231451
196.7
5
21
667407
196.2
30
22
241147
196.8
5
25
615668
215.4
50
26
521105
196.8
5
28 32
531661 226085
207.3 197.4
50 50
28 29
348006 223674
196.7 205.1
10 20
35
165251
196.6
50
32
317241
196.1
20
40
54323
103.2
50
33
372789
196.4
20
43
30158
103.1
50
34
435025
196.8
20
35
457585
196.5
20
36
398875
196.7
20
39
336732
196.7
20
42
282785
198.7
20
250ml wild type (Figure 11.)
60
Cell count
Volume
Dilution
Additional culture data used in statistical analysis 250ml mutant (Table 1.) Day
Cell count
Volume
250ml mutant (Table 1.)
Dilution
Day
Cell count
Volume
Dilution
4
329455
131.1
1
0
50657
196.7
5
14
284495
210.4
5
1
88831
196.8
5
21
264499
196.9
5
5
205627
199.8
5
22
315393
196.7
5
7
301397
196.8
5
26
629201
168.9
5
8
186314
202.5
10
28
481069
197.5
10
11
256408
200.4
10
29
294674
205.5
20
12
160889
196.6
20
32
264643
196.7
20
13
180152
196.7
20
33
294611
196.7
20
14
178935
196.7
20
34
315164
196.8
20
15
192145
196.8
20
35
294409
196.7
20
18
215813
204
20
36
312044
196.8
20
21
174474
207.2
30
39
344062
196.7
20
25
175193
204.6
30
42
380211
201.6
20
28
177238
213.1
30
32
155731
196.6
30
35
125516
196.6
30
40
60667
103.1
30
43
32670
103.3
50
125ml wild type (Table 1.) Day
Cell count
Volume
Dilution
0
42871
111
10
3
58592
103
10
4
71455
103.2
10
5
96746
103.1
10
6
123150
103.2
10
7
140052
103.1
10
10
169876
103.1
10
13
103306
103
20
17
152096
103.2
20
20
224010
102.8
20
24
178398
110.9
30
27
218445
103.3
30
29
237968
103.1
30
32
182513
103.2
50
35
117976
108.9
50
38
113854
103.1
50
41
78732
103.2
50
61
Accuracy and precision of the counting method
Culture Sampling (A)
Culture Dilution (B)
Flow Cytometry (C)
Cell count
Volume
Dilution
Cell count
Volume
Dilution
Cell count
Volume
Dilution
158629
103.3
10
148918
103.5
10
168089
103.1
10
152457
103.2
10
151760
103.3
10
170924
103.1
10
154757
103.2
10
153757
103.3
10
166744
103.1
10
159969
103.1
10
152582
103.3
10
162666
103.2
10
148154
103.1
10
139100
103.3
10
159245
103
10
Chlorophyll extraction, cell growth data is also presented in Figure 10.
CHL a (µg/ml)
Time (d) 0 3 7 10 13 16 22 25 29 32 35 38
1.636 1.616 5.107 8.430 10.723 11.200 10.470 11.270 9.457 8.257 3.701 1.634
62
Total Carotenoids (µg/ml) 0.360 0.270 1.124 2.020 2.480 2.564 2.695 3.067 2.651 1.734 0.363 0.551
Cells/ml 6745543 13938991 30511650 34858915 40149029 40779825 36978198 26385488 22106887 13973256 741860 227686
Growth suppression data presented in Figure 17. Parent Culture 125ml wild type Day
Cell count
Volume
BG-11 Treatment Day 3
Dilution
Day
Cell count
Volume
Dilution
0
207692
103.3
5
10
50698
103
30
3
54064
103.2
30
13
65533
103.1
30
6
68215
103.2
30
16
86271
109.6
30
10
131177
103.3
30
CHL Treatment Day 3
H20 Treatment Day 0
3
47344
109.1
20
0
53786
103.4
10
6
38394
103.1
20
3
59146
103.2
20
7
33417
103.4
20
6
79294
110.3
20
10
6203
103.1
30
7
88598
103.2
20
13
2981
103.1
30
10
72892
103.3
30
16
1217
109.4
30
13
90205
103.3
30
16
94124
103.2
30
H2O Treatment Day 6
BG-11 Treatment Day 0
6
43826
103.4
20
7
65925
103.1
20
0
51740
103.2
10
10
53845
109.6
30
3
58322
103.4
20
13
55548
103.1
30
6
74011
111.1
20
16
64784
103.1
30
7
76216
103.1
20
10
66244
108.9
30
6
38516
103.4
20
13
85092
103.1
30
7
70796
103
20
16
88644
103.1
30
10
42372
103.2
30
13
46751
103
30
16
60816
103.3
30
BG-11 Treatment Day 6
CHL Treatment Day 0 0
58893
103.2
10
1
71489
103
10
2
41662
103.1
20
6
51400
109.5
20
3
59241
103.3
20
7
77304
103.2
20
6
59120
103.2
20
10
52786
102.8
30
7
63255
103.2
20
13
54298
110.5
30
10
51082
103.1
30
16
44925
110.5
30
13
64674
103.4
30
16
89801
117.8
30
CHL Treatment Day 6
H2O Treatment Day 10
H20 Treatment Day 3
10
71572
109.2
30
13
105828
110.2
30
109743
103.2
30
3
45091
103.4
20
6
59016
103.1
20
16
7
65032
103.2
20
10
63020
103.9
30
10
59884
103.2
30
13
85841
102.9
30
13
81870
108.5
30
16
89854
109.5
30
16
91877
112.3
30
BG-11 Treatment Day 10
CHL Treatment Day 10
BG-11 Treatment Day 3
10
74949
103.3
30
3
44055
103.2
20
13
86842
103
30
6
51735
110.6
20
16
72522
112.2
30
7
59132
107.9
20
63
Growth suppression data presented in Figure 18. Parent Culture 125ml wild type Day
Cell count
Volume
H20 Treatment Day 6
Dilution
Day
Cell count
Volume
Dilution
0
17,142
103.2
20
6
19,774
68.8
30
3
68,193
103.3
20
9
41,918
69
30
6
59,664
103.2
30
10
47,389
68.7
30
10
67,393
103.1
30
13
83,168
68.7
30
15
75,048
68.7
30
H20 Treatment Day 3 3
35,755
109.4
20
BG-11 Treatment Day 6
4
43,454
111
20
6
20,565
68.9
30
5
57,088
103.3
20
9
38,162
68.8
30
6
53,038
103.2
30
10
41,515
69
30
9
51,155
68.6
30
13
61,611
68.7
30
10
55,355
68.6
30
15
57,845
68.8
30
13
76,068
68.8
30
15
73,198
68.8
30
CHL Treatment Day 6
BG-11 Treatment Day 3
6
21,693
68.8
30
9
44,740
68.7
30
3
24,289
105.6
20
10
54,116
68.7
30
4
36,206
103.2
20
13
70,833
68.8
30
5
54,418
103.2
20
15
60,803
68.7
30
6
43,850
103.3
30
9
38,052
68.8
30
10
29,317
68.7
30
10
40,942
68.8
30
13
56,669
68.6
30
13
56,488
68.8
30
15
51,544
68.8
30
15
59,829
68.8
30
17
63,526
68.7
30
H2O Treatment Day 10
CHL Treatment Day 3
BG-11 Treatment Day 10
3
23,781
103.2
20
10
26,632
69
30
4
39,985
103.1
20
13
58,745
68.8
30
5
54,640
103
20
15
55,005
68.8
30
6
35,917
103.5
30
17
69,442
68.8
30
9
24,138
68.8
30
10
26,398
68.8
30
10
29,225
68.8
30
13
39,854
68.6
30
13
41,528
68.7
30
15
38,030
68.8
30
15
47,582
68.8
30
17
61,152
68.8
30
H2O Treatment Day 10
64
Growth suppression data presented in Figure 19. Parent Culture 125ml mutant Day
Cell count
Volume
H20 Treatment Day 6
Dilution
Day
Cell count
Volume
Dilution
0
20
103.1
20
6
25,302
68.8
30
3
20
103
20
9
36,273
68.9
30
6
30
103.2
30
10
51,315
68.9
30
30
103.2
30
13
83,519
68.8
30
15
79,044
68.7
30
10
H20 Treatment Day 3 3
38,561
113.9
20
BG-11 Treatment Day 6
4
35,194
103.1
20
6
24,254
68.7
30
5
46,126
103.1
20
9
45,711
68.6
30
6
36,275
103.4
30
10
44,110
68.9
30
9
45,455
70.2
30
13
68,410
68.7
30
10
90,178
131.3
30
15
71,026
68.7
30
13
84,362
68.8
30
15
83,374
68.8
30
CHL Treatment Day 6
BG-11 Treatment Day 3
6
27,769
69.2
30
9
30,297
68.7
30
3
32,967
103.5
20
10
28,961
68.8
30
4
33,129
103.1
20
13
14,803
68.9
30
5
39,885
103.1
20
15
6,049
68.7
30
6
35,166
103.4
30
9
37,949
68.8
30
10
51,865
68.8
30
10
66,562
131.3
30
13
71,458
68.7
30
13
69,817
68.6
30
15
87,402
68.6
30
15
66,016
68.6
30
17
94,278
68.9
30
H2O Treatment Day 10
CHL Treatment Day 3
BG-11 Treatment Day 10
3
30,134
103
20
10
48,180
69.1
30
4
33,706
103.2
20
13
89,174
68.7
30
5
41,322
103
20
15
84,418
68.7
30
6
37,049
103.2
30
17
83,035
68.9
30
9
28,296
68.6
30
10
68,479
131.1
30
10
66,163
68.7
30
13
92,918
68.8
30
13
81,230
68.9
30
15
87,915
68.9
30
15
61,736
68.7
30
17
76,576
68.6
30
H2O Treatment Day 10
65
Appendix IV. SPSS Statistical outputs Oneway ANOVA with LSD post hoc of growth rates between culture volumes ANOVA (K) d-1 Sum of Squares Between Groups
df
Mean Square
13.852
2
6.926
Within Groups
1606.018
69
23.276
Total
1619.870
71
F
Sig. .298
.744
Multiple Comparisons Dependent Variable: (K) d-1 (I) VolCat
(J) VolCat
Mean Difference
Std. Error
Sig.
95% Confidence Interval
(I-J)
Lower Bound
Upper Bound
125
.97953
1.26999
.443
-1.5540
3.5131
60
.47847
1.55350
.759
-2.6207
3.5776
250
-.97953
1.26999
.443
-3.5131
1.5540
60
-.50106
1.58890
.753
-3.6708
2.6687
250
-.47847
1.55350
.759
-3.5776
2.6207
125
.50106
1.58890
.753
-2.6687
3.6708
250
LSD
125
60
Oneway ANOVA with LSD post hoc of doubling times between culture volumes ANOVA G Sum of Squares
df
Mean Square
Between Groups
.004
2
.002
Within Groups
.481
69
.007
Total
.485
71
66
F
Sig. .275
.761
Multiple Comparisons Dependent Variable: G (I) VolCat
(J) VolCat
Mean Difference
Std. Error
Sig.
95% Confidence Interval
(I-J)
Lower Bound
Upper Bound
125
.01536
.02197
.487
-.0285
.0592
60
.01330
.02688
.622
-.0403
.0669
250
-.01536
.02197
.487
-.0592
.0285
60
-.00206
.02749
.941
-.0569
.0528
250
-.01330
.02688
.622
-.0669
.0403
125
.00206
.02749
.941
-.0528
.0569
250
LSD
125
60
Oneway ANOVA with LSD post hoc of cell densities between culture volumes ANOVA Cells/ml Sum of Squares
df
Mean Square
1016306319926
Sig.
5081531599632
Between Groups
2
8.849
4404.000
.000
202.000
6144767762613 Within Groups
F
5742773609918 107
0696.000
75.600
7161074082539 Total
109 5104.000 Multiple Comparisons
Dependent Variable: Cells/ml (I) VolCat
(J) VolCat
Mean Difference
Std. Error
Sig.
(I-J)
Lower Bound
Upper Bound
-12996685.622*
5419852.496
.018
-23740911.08
-2252460.17
60
-23256420.468
*
5623777.125
.000
-34404902.72
-12107938.22
250
12996685.622*
5419852.496
.018
2252460.17
23740911.08
60
-10259734.847
6002756.810
.090
-22159500.12
1640030.43
250
23256420.468*
5623777.125
.000
12107938.22
34404902.72
125
10259734.847
6002756.810
.090
-1640030.43
22159500.12
125 250
LSD
95% Confidence Interval
125
60 *. The mean difference is significant at the 0.05 level.
67
Kolmogorov-Smirnov analysis of wild type cell densities and relative growth rates across culture media volumes
Kolmogorov-Smirnov analysis of mutant cell densities and relative growth rates across culture media volumes
68
Oneway ANOVA with LSD post hoc of difference between treatments as measures by cell densities, relative growth rates and cell doubling times
Between Groups Cells/ml
Within Groups Total
Growth Rate (K) d-1
Between Groups Within Groups
Sig.
14.375
.000
308 .427
7095.933 101494.986
307 2
50747.493
4.928
.008
Within Groups
3140799.294
305
10297.703
Total
3242294.281
307
(I) Treatment
LSD
BG-11 CHL H2O
LSD
BG-11 CHL H2O
Doubling time (min)
F
.854
H2O
Growth Rate (K) d-1
Mean Square 2118234921255 2 946.500 1473546852363 306 54.200
19.750 23.136
Dependent Variable
Cells/ml
df
2 305
Total Between Groups Doubling time (min)
ANOVA Sum of Squares 4236469842511 893.000 4509053368232 4384.000 4932700352483 6280.000 39.499 7056.434
LSD
BG-11 CHL
Multiple Comparisons (J) Treatment Mean Difference (I-J)
Std. Error
BG-11 CHL H2O CHL H2O BG-11 BG-11 CHL H2O CHL H2O BG-11 BG-11 CHL H2O CHL H2O
1.692E+006 1.692E+006 1.692E+006 1.692E+006 1.692E+006 1.692E+006 .672 .672 .672 .670 .672 .670 14.175 14.175 14.175 14.141 14.175
8.109E+005 * 8.229E+006 -8.109E+005 * 7.418E+006 * -8.229E+006 * -7.418E+006 -.166 .662 .166 .828 -.662 -.828 4.384 * 40.493 -4.384 * 36.109 * -40.493
Sig. .632 .000 .632 .000 .000 .000 .805 .325 .805 .218 .325 .218 .757 .005 .757 .011 .005
95% Confidence Interval Lower Bound Upper Bound -2.52E+006 4.14E+006 4.90E+006 1.16E+007 -4.14E+006 2.52E+006 4.09E+006 1.07E+007 -1.16E+007 -4.90E+006 -1.07E+007 -4.09E+006 -1.49 1.16 -.66 1.98 -1.16 1.49 -.49 2.15 -1.98 .66 -2.15 .49 -23.51 32.28 12.60 68.39 -32.28 23.51 8.28 63.93 -68.39 -12.60
BG-11
-36.109
*
14.141
.011
-63.93
-8.28
*. The mean difference is significant at the 0.05 level.
Oneway ANOVA with LSD post hoc of difference between treatments on day of treatment as measures by cell densities, relative growth rates and cell doubling times Treatment Between Groups Cells/ml
Within Groups Total Between Groups
H2O Growth Rate (K) d-1
Within Groups Total
df 2 91
Mean Square 3486005448939 78.250 1222586728968 82.420
F
Sig.
2.851
.063
.150
.861
1.655
.197
4.564
.013
93 2
2.798
1531.228
82
18.674
1536.824
84
36037.061
2
18018.530
892722.516
82
10886.860
84
2 80
78.301 20.772
3.770
.027
Growth Rate (K) d-1
Between Groups Within Groups
928759.576 9858005799082 47.100 9828618530730 050.000 1081441911063 8298.000 156.603 1661.783 1818.386 93570.809 1109363.893
82 2 81
46785.405 13695.851
3.416
.038
Doubling time (min)
Total Between Groups Within Groups
1202934.702 2133701498221 330.500 1647722525966 6014.000 1861092675788 7344.000
83 5.892
.004
Between Groups Doubling time (min)
Within Groups Total Between Groups
Cells/ml
Within Groups Total
BG-11
Total Between Groups CHL
ANOVA Sum of Squares 6972010897879 56.500 1112553923361 6300.000 1182274032340 4256.000 5.596
Cells/ml
Within Groups Total
2 91
4929002899541 23.560 1080067970409 89.560
93
2 91 93
70
1066850749110 665.200 1810684094468 79.280
Between Groups Growth Rate (K) d-1
Within Groups Total
2
92.537
1788.173
82
21.807
1973.247
84
10961.872
2
5480.936
Within Groups
604096.834
82
7367.035
Total
615058.706
84
Between Groups Doubling time (min)
185.074
4.243
.018
.744
.478
Multiple Comparisons LSD Treatment
Dependent Variable
(I) Day
3 Cells/ml
6 10 3
H2O
Growth Rate (K) d-1
6 10 3
Doubling time (min)
6 10 3
BG-11
Cells/ml
6 10
(J) Day 6 10 3 10 3 6 6 10 3 10 3 6 6 10 3 10 3 6 6 10 3 10 3 6
Mean Difference (I-J) -4067691.002 * -6581368.421 4067691.002 -2513677.419 * 6581368.421 2513677.419 .3643 .6273 -.3643 .2630 -.6273 -.2630 -45.871 -34.625 45.871 11.247 34.625 -11.247 -4660390.492 * -7882842.105 4660390.492 -3222451.613 * 7882842.105 3222451.613
Std. Error 2676034.689 2847398.886 2676034.689 2972234.021 2847398.886 2972234.021 1.0956 1.1757 1.0956 1.2311 1.1757 1.2311 26.455 28.389 26.455 29.727 28.389 29.727 2515228.491 2676295.203 2515228.491 2793628.842 2676295.203 2793628.842
71
Sig. .132 .023 .132 .400 .023 .400 .740 .595 .740 .831 .595 .831 .087 .226 .087 .706 .226 .706 .067 .004 .067 .252 .004 .252
95% Confidence Interval Lower Bound Upper Bound -9383305.17 1247923.17 -12237376.52 -925360.33 -1247923.17 9383305.17 -8417655.18 3390300.34 925360.33 12237376.52 -3390300.34 8417655.18 -1.815 2.544 -1.712 2.966 -2.544 1.815 -2.186 2.712 -2.966 1.712 -2.712 2.186 -98.50 6.76 -91.10 21.85 -6.76 98.50 -47.89 70.38 -21.85 91.10 -70.38 47.89 -9656582.90 335801.92 -13198973.76 -2566710.46 -335801.92 9656582.90 -8771652.12 2326748.90 2566710.46 13198973.76 -2326748.90 8771652.12
6 10 3 Growth Rate (K) d-1 6 10 3 10 6 6 3 10 3 Doubling time (min) 6 10 3 10 6 6 3 10 3 Cells/ml 6 10 3 10 6 6 3 10 3 CHL Growth Rate (K) d-1 6 10 3 10 6 6 3 10 3 Doubling time (min) 6 10 3 10 6 *. The mean difference is significant at the 0.05 level. 3
1.8363 * 3.3468 -1.8363 1.5105 * -3.3468 -1.5105 -53.896 31.627 53.896 * 85.524 -31.627 * -85.524 * 8223342.275 -3610215.789 * -8223342.275 * -11833558.065 3610215.789 * 11833558.065 * 3.0429 * 2.9390 * -3.0429 -.1039 * -2.9390 .1039 25.871 17.306 -25.871 -8.565 -17.306 8.565
1.1800 1.2400 1.1800 1.3203 1.2400 1.3203 29.976 31.841 29.976 33.612 31.841 33.612 3256666.175 3465212.046 3256666.175 3617133.231 3465212.046 3617133.231 1.1840 1.2705 1.1840 1.3304 1.2705 1.3304 21.762 23.353 21.762 24.453 23.353 24.453
72
.124 .008 .124 .256 .008 .256 .076 .324 .076 .013 .324 .013 .013 .300 .013 .002 .300 .002 .012 .023 .012 .938 .023 .938 .238 .461 .238 .727 .461 .727
-.512 .879 -4.185 -1.117 -5.815 -4.138 -113.54 -31.73 -5.75 18.65 -94.98 -152.40 1754374.98 -10493433.83 -14692309.57 -19018548.88 -3273002.25 4648567.25 .687 .411 -5.398 -2.751 -5.466 -2.543 -17.42 -29.15 -69.16 -57.21 -63.76 -40.08
4.185 5.815 .512 4.138 -.879 1.117 5.75 94.98 113.54 152.40 31.73 -18.65 14692309.57 3273002.25 -1754374.98 -4648567.25 10493433.83 19018548.88 5.398 5.466 -.687 2.543 -.411 2.751 69.16 63.76 17.42 40.08 29.15 57.21
