Published online 4 January 2008
Published in J Environ Qual 37:16-21 (2008)
DOI: 10.2134/jeq2006.0556
© 2008 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
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TECHNICAL REPORTS
Ecological Risk Assessment
Bioassays with Unicellular Algae: Deviations from Exponential Growth and Its Implications for Toxicity Test Results
Rolf Altenburger*,
Mechthild Schmitt-Jansen and
Janet Riedl
UFZ- Helmholtz Centre for Environmental Research, Dep. Bioanalytical Ecotoxicology, Permoserstr.15, 04318 Leipzig, Germany
* Corresponding author (rolf.altenburger{at}ufz.de).
Received for publication December 22, 2006.
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ABSTRACT
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Growth assays with unicellular green algae are an established tool in ecotoxicological effect assessment for chemicals and environmental samples. From an ecological perspective it seems appropriate to use the growth rate as a process variable rather than a measure of biomass gain for calculating inhibitory effects of contaminants. The notion of simple exponential growth for the description of the population increase in undisturbed suspension cultures of unicellular green algae, however, seems to be an oversimplification. Experimental findings describe the increase in biomass, cell number, the development of cell volume distributions of populations, and the relationship between cell size and chlorophyll content for individual cells over one generation at a time resolution of 2-h intervals. It was observed that algal populations of Desmodesmus subspicatus show a time pattern of cell size growth; the average cell volume increases about sixfold, without corresponding increase in population size. This is followed by a distinct cell division phase with little gain in biomass. This synchronous growth behavior despite continuous illumination may be explained by the multiple fission characteristic of unicellular green algae which is an adaptation to cyclic light–dark changes in the environment. It might be controlled by an independent cell cycle clock. For routine regulatory testing fluorescence-based measurements rather than cell counting minimizes the confounding effect on toxicity determination. For investigations of time-dependent effects, e.g., by pulsed exposure, an alternative mechanistically based growth function for unicellular algae is proposed that accommodates for the observed growth pattern.
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INTRODUCTION
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ALGAL growth assays using unicellular green algae such as Desmodesmus subspicatus were among the first bioassays established for the determination of phytotoxic effects of chemicals, waste waters, and other environmental samples (Lewis, 1990). Due to this history, concepts including ecological realism in the experimental design for the effect determination can be identified in standard protocols such as OECD 201 (2006a) or ISO 8692 (2002). Growth conditions, e.g., as specified for nutrient composition and light regime, are below physiological optimum (Nyholm and Källquist, 1989; Mayer et al., 1998) and therefore do not allow utilization of the growth potential, in response to ideas of providing conditions that resemble natural growth-limiting environmental conditions. The various specificities of the standardized algal assay, however, may be the reason for the problems encountered when using algal bioassays in ecological hazard and risk assessment, like the often-reported growth stimulation (Bilnova, 2004) in waste water testing, the lack of reproducibility in chemical testing, or the relatively high coefficients of variability for the growth of controls.
The recent revision of ISO protocol 8692 and the discussion on establishing multiwell plate assays provide the opportunity to solve some of the traditional shortcomings. One commonly recommended suggestion is to replace the observation parameter inhibition of biomass yield by the specific growth rate (ISO, 2002). This proposal is based on the notion that assessment of effects on algae may be based on an ecological understanding (Nyholm, 1990; Dorgerloh, 1997) and render results suitable for process characterization like primary production. It would help to overcome the time dependency of effect estimates inherent in the currently used biomass determination, lessen the influence of suboptimal growth conditions on the results, and would also be in line with the second aquatic plant test protocol for the duckweed growth assay using Lemna minor (ISO, 2006; OECD, 2006b) where growth rate is the main endpoint for toxicity determination. Unicellular green algae, however, show the unique property of multiple fission within their cell cycle of about 24 h, which can be interpreted as an excellent adaptation to periodic illumination. During illumination phases they allocate all resources toward biomass accumulation via photosynthesis; while being in the dark, they divide their biomass gain in multiple cell division rounds (Vitova and Zachleder, 2005). This pattern led us to hypothesize that unicellular cell growth over extended time periods may in fact not follow a simple exponential growth function. In turn, this might result in problems meeting the validity criteria of standardized test protocols regarding the variation of growth in control cultures.
Moreover, there are current debates on predicting effects on aquatic organisms for fluctuating or pulsed exposures (Ashauer et al., 2006), as well as for mixtures exposure against chemicals (Altenburger et al., 2003). In both cases good understanding and description of growth behavior in model biosystems is a prerequisite to adequately apply models for effect prediction.
Our objective therefore was to study the growth of Desmodesmus subspicatus cultured under batch conditions using various observation techniques at a time resolution that allows the differentiation of individual and population growth during one generation time. For the obtained data we were interested in a growth model that captured the observations.
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Materials and Methods
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Algal Culture
Liquid cultures of the unicellular green alga Desmodesmus subspicatus strain 86.81, culture collection Pringsheim (Sammlung für Algenkulturen Göttingen, Germany), were grown photoautotrophically at 23 ± 2°C in an inorganic, sterilized medium with an additive of 1.5 mmol L–1 NaHCO3 providing a final pH of the medium of 6.9 ± 0.2 according to Riedl and Altenburger (2007). Algae were incubated in a climatic chamber (VB1514, Vötsch, Germany) under continuous light conditions using one type of fluorescent light tube (L30W/830 Warmwhite, Osram, Munich, Germany) providing a photosynthetic active radiation expressed in photon flux of 300 ± 20 µmol photon s–1 m–2. Stock culture and pre-culture were prepared as described in OECD guideline 201 (2006a).
Growth Regime
Growth assays were determined according to the standardized algal growth inhibition test protocols based on ISO 8692 (2002) and OECD 201 (2006a). The experiments were conducted in transparent microplates with 24 wells (Greiner GmbH, Frickenhausen, Germany) with an initial cell density of 5 x 106 cells L–1, and shaken at approx. 0.9 x g (i.e., 400 rpm on a TiMix, Edmund Bühler, Hechingen, Germany). Other incubation conditions were the same as used for cultivation. Algal growth was generally determined after 0, 24, 48, and 72 h and, for cell cycle analysis, measurements were performed at 2-h intervals between 24 and 50 h.
Fluorometric Measurement
Population growth was determined fluorometrically in vivo by measuring the chlorophyll a content of the cell population using a microplate reader (SpectraMax Gemini EM, Molecular Devices, Sunnyvale, USA). Fluorescence was determined six times per well with an excitation wavelength of 430 nm, emission of 690 nm, and a cut-off filter <665 nm. Sensitivity of the photomultiplier tube was set automatically. Data aquisition and storage was performed using SoftMax Pro 3.1.1. (Molecular Devices, Sunnyvale, USA).
Determination of Cell Size and Cell Number
Cell volume growth was determined, analyzing the cell volume distribution of aliquot samples for at least 2000 particles/distribution. Mean volume of the cells was determined by peak area analysis from the conductance signal in passing a defined aperture (70 µm) in an electrolyte solution using an electronic cell analyzer (CASYII, Schärfe System, Reutlingen, Germany). The cell density was determined in analogy from the particle counts of the same aliquot samples. For details see Altenburger et al. (2004).
Flow Cytometry
For cytometric analysis at each time point, aliquot samples of the algal culture were analyzed using a FACSCalibur (BD, Heidelberg, Germany) equipped with two lasers, an argon ion laser emitting at 488 nm, and a red diode laser (633 nm). Flow cytometry measurement and analysis provide different information at the level of individual cells: next to counting of the particles, the so called forward scatter (FSC) and sideward scatter (SSC) provide information about morphological characteristics of the cells, the volume, and thus growth of the living population. Fluorescence detectors are able to attain information about extinction energy emitted by autofluorescence from chlorophyll simultaneously for each individual cell of the population. A threshold was set to 100 for noise reduction in the measurement of algal cells. Ten thousand cells were measured in each run. A computer equipped with CellQuest Pro (BD, San Jose, USA) was used for data acquisition. Plot analysis was performed using WinList5.0 (Verify Software House Inc, Topsham, ME, USA). Further details are provided in Adler et al. (2007).
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Results
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First of all, growth patterns were established for the standard protocols (OECD, 2006a; ISO, 2002) for cultures of Desmodesms subspicatus grown under continuous illumination using indirect fluorometric biomass determination in 24-h time intervals. The results are depicted in Fig. 1
for a determination in eight replicates performed on 24 well microplates. When plotting the fluorescence yield as a measure of achieved biomass over time, with the yield on a log scale, the emerging pattern at first sight is consistent with an exponential population growth model as can be deduced from the straight line in the plot. However, despite the averaging of 8 x 24 subsamples, a slight irregularity as higher than expected biomass might be seen for the measurement at 48 h.
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Fig. 1. Growth of liquid cultures of Desmodesmus subspicatus over 72 h, as detected using the in vivo red chlorophyll autofluorecence signal of suspensions after blue light excitation. The line represents an exponential regression fit for the 8 independent values each derived as a mean value from a 24 well plate.
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Thereafter, the individual and population growth pattern of the suspension culture was studied at a higher time resolution of 2 h for the time interval between 24 and 50 h of the 72-h assay. Figure 2a
shows the emerging pattern for the fluorescence signals in this experiment. The resulting time course clearly consists of an exponential phase (6–8 h long) followed by a linear phase of another 8 h and finally a phase of limited growth. The measurements at times 24, 48, and 72 h in the experiment, however, were again in line with the assumption of an exponential growth of the population biomass as before (see inset).
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Fig. 2. Cell population growth of Desmodesmus subspicatus between 24 and 50 h of the 72 h growth experiment at a time resolution of 2 h. Figure 2a displays the values for in vivo chlorophyll autofluorescence and the fit of the growth model provided in Eq. 1. The inset shows the values within the 72 h full experiment. Figure 2b depicts the values for cell counts for the same experiment.
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Figure 2b shows the same experiment displaying the cell density at different time points as determined by electric particle counting. Basically, the cell number does not change between times 24 and 40 h, after which it sharply increases from about 7 x 107 cells L–1 to 45 x 107 cells L–1 within 8 h. We also analyzed the cell size distributions by particle measurements in aliquot samples, the results of which are displayed in Fig. 3
. Clearly, the cells of the population uniformly increase their cell volume, until after time 38 h when small cells, i.e., autospores are liberated from dividing mother cells and a subpopulation of small cells of about 60 fl reappear. The inset figure shows the data for the experiment aggregated for the mean cell volume of the analyzed population. Initially at time 24 h the mean cell size is about 60 fl, and increases thereafter to a mean value of 280 fl at time 38 h. From that time point to time 48 h, two subpopulations with clearly distinct size distribution exist. Furthermore, it again appears that a simple exponential growth model would not capture the behavior of the temporal changes in cell volume.
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Fig. 3. Cell size distributions of Desmodesmus subspicatus suspension samples between 24 and 50 h of the 72 h growth experiment at a time resolution of 2 h. The modal value was used to uniformly scale the distributions. The inset shows the derived mean cell volumes for each distribution at a given time point plus the fit of the growth model provided in Eq. 1.
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Simeoni et al. (2004) have recently proposed an elegant model describing the growth of human cancer cells accounting for exponential and linear growth components. For unicellular green algae, where cell division occurs via a noncanonical mechanism of multiple fission (Bisova et al., 2005), a further control mechanism often called ‘point of commitment’ (Vitova and Zachleder, 2005) that seems strictly linked to cell size has to be additionally accounted for. The point of commitment controls the passage from cell cycle G1 phase to later multiple S/M phases, though G1 can continue (Bisova et al., 2005). Linking this to the aforementioned growth model and using an additional cell clock function allows the formulation of the following growth model (Eq. [1]):
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where V represents the growth variable (cell volume or chlorophyll fluorescence), Vo is the value at the beginning of the observation and Vt at any given observation time point, µ is the exponential growth rate, while µo stands for the linear growth rate, psi is a parameter intended to capture the transition from exponential to linear growth, µ1 represents the cell clock, and K stands for the critical size at which the point of commitment is reached. Psi has been fixed to a value of 20 in line with the suggestion of Simeoni et al. (2004) which allows a sufficiently sharp transit from exponential to linear growth. The parameters µ, µ1, and K have been fitted using an Excel sheet to numerically solve the two ordinary differential functions using the Euler approach. The step size for integration was set at 0.009 h. To simplify, the linear growth rate µ0 was gained from a threshold volume, fixed as equal to two times the volume at commitment (critical size, K).
Using this approach, the exponential growth rate µ was simulated as 0.1 h for cell volume growth, the critical volume (K) amounted to 80 fl, and µ1 fitted with a value of 0.011. The model simulation is depicted in the Fig. 3 inset as line. Similarly, the increase in chlorophyll fluorescence can be described using the same model, with the same parameter values for the growth rate µ = 0.1, and µ1 = 0.011, while K is simulated as K = 20.5 and represents a fluorescence value in this case. This model is displayed in Fig. 2a.
Finally, we analyzed cytometric properties of individual cells detecting autofluorescence red emission signals after blue light excitation and simultaneously sideward scatter of the laser light using a flow cytometer. The allocation of simultaneous signals to individual cells is the unique advantage of flow cytometry and Fig. 4
shows a cytogram exemplarily for the time point 42 h with contour lines of the particles autofluorescence and sideward light scatter properties. The signal differences are about two orders of magnitude for the extremes on both scales, while the centers of the two subpopulations are about one order of magnitude apart. The sideward scatter is proportional to cell volume, while the autofluorescence signal can be interpreted as a quantification of the amount of chlorophyll a (Adler et al., 2007). These results therefore provide unanimous evidence that the cell population consisted of two subpopulations. The two subpopulations could be detected from 38 to 46 h of the growth experiment. They are composed of small cells which display little chlorophyll autofluorescence and a second population of larger cells with higher chlorophyll autofluorescence.
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Fig. 4. Contour plot of an algal sample at 42 h of a 72 h growth experiment with Desmodesmus subspicatus. Plotted are the 2000 particle specific signals for sideward light scatter against the red autofluorescence signal emitted after blue light excitation. The scale is provided in relative units.
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Discussion
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While suspensions of the unicellular green algae Desmodesmus subspicatus cultivated under continuous light and conditions in accordance with standard toxicity test protocols (OECD, 2006a; ISO, 2002) are expected to show a simple exponential growth pattern, a higher time resolution investigated in this study reveal that, in fact, the underlying growth behavior is more complex. The patterns detected for the individual cell volume growth resemble those described for synchronously growing cultures (Faust et al., 1992). The mean cell volume of a cell population at first increases during a growth phase in an exponential manner before it slows down, and eventually before multiple fission, does not change at all until the smaller sized autospores are liberated. The mean cell volume changes, observed here from about 60 fl to about 280 fl, compare well to those reported for Scenedesmus vacuolatus synchronous cultures which grow between average volumes of 20 and 240 fl (Faust et al., 1992).
Synchronization of algal suspension cultures is a standard physiological technique which is commonly achieved by applying cyclic light–dark changes and periodic dilutions (Tamiya, 1966). The degree of synchrony achieved by this technique can be characterized by the ratio of the duration for cell liberation to the duration of the complete cell cycle, which amounts to 2 h vs. 24 h for deliberately synchronized cells (Faust et al., 1992). For comparison here, the increase in cell number was observed during 8 h of the 24 h cell cycle or one third of the generation time. Heterotrophic cell cultures or cyanobacteria are considered to be synchronized when the division is restricted to half the cell cycle time. It thus appears that the algal cultures can be considered as growing at least partially synchronous rather than homocontinuous as expected, though no obvious trigger for this pattern is provided. Unintended triggers for the partial synchronous growth behavior may be sought in various environmental factors such as light, or nutrients that change on onset of the batch culture type bioassay procedure, but also in changes that occur during the duration of the standard test duration of 72 h. Also, biological factors may contribute significantly in that the time needed to perform subsequent multiple divisions pronounces the growth delays in this period. Furthermore, autospores formed, and start with reorganizing their photosynthetic apparatus before showing maximum capacity of photosynthetic yield (Altenburger et al., 2006), thus giving reason to suspect that unicellular green algae not only grow independent of their cell cycle stage but show distinct development. Alternatively, one could argue that circadian rhythms may be independent from an external trigger, as there is evidence for Chlamydomonas reinhardtii that synchrony was even kept in outer space, despite the absence of gravity, magnetism, and cyclic light–dark changes (Mittag et al., 2005). Interestingly, the observation of patterns typical for synchronous growth that can be observed for the growth in cell size and the increase in cell number also feeds through to the population level. This has been demonstrated here for the chlorophyll fluorescence and the total biovolume, i.e., cell number multiplied by the cell volume of the algal suspension, though at the level of biomass the averaging effect for autospores and mother cells dampens the visibility of the pattern.
The understanding of the specificities of the cell cycle of unicellular green algae used in ecotoxicity testing may be helpful in explaining some of the encountered problems when using standard test protocols. A growth pattern that is anticipated to undulate around the theoretical exponential growth curve easily may help to explain irregularities of intermediate observations that often are blamed as time lags in growth onset or limited growth at later stages of the test. In light of the observations reported here, some of these effects might rather be explained as observation directly before, during, or after cell divisions leading to expectable deviations from the exponential behavior. Most often with only three observations during the 72-h protocol and an estimated starting biomass, these effects may go unnoticed. However, many report that quality criteria for control cultures cannot be met, and might indicate a variation due to cell cycle phenomena rather than to varying test conditions.
The descriptions of individual cell volume growth by using two differential equations which account for exponential and linear growth processes as well as for a cell size control triggering cell division are in line with current thinking in cell growth theory. In cancer cell growth studies, growth changes from exponential to linear over time which has been attributed to a shift in the limiting steps from enzymatic to DNA-controlled processes (Simeoni et al., 2004). The role of cell size control in multiple dividing unicellular algae is well described (Vitova and Zachleder, 2005; Bisova et al., 2005). Interestingly, the value estimated for the critical volume at the point of commitment (80 fl) is well in accordance with the general observation that populations that have reached this volume will divide irrespective of any stress or exposure events.
With respect to using algal growth for hazard assessment, there is definitely room for improving the standard protocols (OECD, 2006a; ISO, 2002). The scope is not restricted to strive for better reproducibility but also allows for more understanding of observable responses. For regulatory testing of chemicals and wastewaters according to the standard protocols the strategy could be to account for the described phenomena by well defining and standardizing the precultures and by utilizing fluorescence-based measures (e.g., Mayer et al., 1997) rather than cell count-based measures for effect quantifications, as this could be shown to dampen the confounding influence of the multiple fission strategy of the algae.
So, while the growth model introduced here may be seen as primarily of a descriptive value due to its complexity when compared to the standard exponential growth model, its mechanistic basis offers certain scope. E.g., when algal assays are to be used as model systems to describe effects of pulsed exposure regimes (Ashauer et al., 2006) or for rigid quantification of combined effects from mixture exposure (Altenburger et al., 2005), good description and high measurement precision are needed.
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Conclusions
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The observed algae growth behavior described here is helpful and essential to understand phenomena occurring during standardized algae growth tests. If it seems useful to overcome the described sources of variation for regulatory testing two approaches seem possible. First of all, stricter definition of the homocontinuous behavior of the culture at the start of the experiment could be attempted by using a non-batch fermenter culture and establishing criteria for the cell size distribution of the start population. Also, the use of fluorescence-based measurements rather than cell counts should dampen the described confounding effects. Second, one may opt for shortening the whole bioassay protocol to a one generation bioassay of 24 h, thus utilizing the cell cycle as described in this paper and thereby opening opportunities to differentiate between effects on cell volume growth and cell reproduction which would be helpful to diagnose different modes of action of chemicals.
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ACKNOWLEDGMENTS
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R.A. acknowledges the receipt of an OECD fellowship grant, providing time for stimulating discussions with Donald Mager from the School of Pharmacy and Pharmaceutical Sciences, Univ. at Buffalo regarding growth models for cancer cells.
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NOTES
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All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.
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ABSTRACT
INTRODUCTION
