Copper toxicity in the microalga Chlamydomonasreinhardtii: an integrated approach
An Jamers • Ronny Blust • Wim De Coen •
Julian L. Griffin • Oliver A. H. Jones
Received: 11 October 2012 / Accepted: 12 June 2013 / Published online: 18 June 2013
� Springer Science+Business Media New York 2013
Abstract The effects of copper exposure at five
different concentrations on the freshwater alga Chla-
mydomonas reinhardtii were studied at the biochem-
ical (metabolite), physiological (uptake kinetics and
flow cytometry) and growth level. Changes at the
physiological level were evident at the lowest expo-
sure concentration while effects on the metabolome
and on growth only occurred at the highest copper
concentration tested. Flow cytometry revealed the
presence of higher reactive oxygen species concen-
trations in algae exposed to higher copper concentra-
tions and this was confirmed by a significant reduction
in glutathione levels as part of the metabolomics
assessment. Cu2? uptake kinetic data contributed
information on possible mechanisms of copper toxic-
ity, revealing that, a decrease in efflux pumping might
be at the basis of an increased metal accumulation at
higher exposure levels. This study demonstrates the
value of using a comparative approach to investigating
the mechanisms of toxicity rather than focusing on a
single level of organization or effect.
Keywords Algae � Copper � Metabolomics �Transcriptomics � Systems toxicology
Introduction
In recent years, a noticeable shift from a reductionist to
a more integrative approach has been evident in life
science research. Although the reductionist approach
has proven to be very useful in elucidating key
individual components in complex systems, informa-
tion from just one level cannot fully explain the
behavior of the whole system. The availability of
complete genome sequences and the advent of high
throughput techniques for transcripts, protein and
metabolite analysis has lead to the development of
systems biology. This field aims to develop a
comprehensive and consistent knowledge of a biolog-
ical system by investigating the behavior of, and
interactions between, its individual parts; with the aim
of gaining a new level of understanding of cells and
organisms (Kitano 2002). Similarly, the field of
A. Jamers � R. Blust � W. De Coen
Laboratory for Ecophysiology, Biochemistry
and Toxicology, Department of Biology, University
of Antwerp, Groenenborgerlaan 171, 2020 Antwerp,
Belgium
Present Address:
A. Jamers
Apeiron-Team Consulting, Pluyseghemstraat 69,
2550 Antwerp, Belgium
J. L. Griffin
Department of Biochemistry, University of Cambridge,
The Sanger Building, Tennis Court Road,
Cambridge CB2 1QW, UK
O. A. H. Jones (&)
School of Applied Sciences, RMIT University,
GPO Box 2476, Melbourne, VIC 3001, Australia
e-mail: [email protected]
123
Biometals (2013) 26:731–740
DOI 10.1007/s10534-013-9648-9
toxicology is moving away from the measurement of
single endpoints (such as growth or mortality) and
towards the integration of data obtained at different
levels of biological organization, with the intention of
generating a greater understanding of the potential
effect mechanisms of the contaminant under study; as
well as how organisms deal with toxicant exposure at
the cellular level (Spurgeon et al. 2010).
Microalgae play an important role in ecotoxicolog-
ical research and are also of commercial importance.
Knowledge of separate algal subsystems has increased
exponentially in recent years but the challenge of
integrating these data to produce new knowledge has not
yet been overcome. There is therefore great interest in
gaining more in depth knowledge of their basic
functions in order to improve our understanding of
metabolic networks and biological systems (Grossman
2005). The freshwater alga Chlamydomonas rein-
hardtii, commonly found in fresh water and soils and
extensively used in environmental research, is particu-
larly suited to such complex studies: it is a robust
organism, easily grown in the lab. It has the advantage of
a sequenced genome and it has also been metabolically
profiled in detail (Bolling and Fiehn 2005; Matthew
et al. 2009; Merchant et al. 2007). As knowledge of the
components of this species has increased so has the
number of studies comparing observations on different
levels of biological organization. For example Mus et al.
(2007) used metabolite, genomic and transcriptomic
data to assess the regulation of the metabolic networks
utilized by C. reinhardtii under anaerobic conditions
associated with H2 production. The levels of over 500
transcripts increased significantly during acclimation of
the cells to anoxic conditions and elevated levels of
transcripts encoding proteins associated with the pro-
duction of H2, organic acids, and ethanol were observed
in congruence with the accumulation of fermentation
products. Similarly, Castruita et al. (2011) used tran-
scriptomic, proteomic, metabolomic and immunoblot
analyses to show that study copper nutrition is linked to
multiple steps on the metabolic pathway of this species.
Copper itself is an interesting environmental con-
taminant. It is released into the environment from both
natural sources (e.g. wind-blown dust, decaying vege-
tation, forest fires and sea spray) as well as anthropo-
genic activities such as mining, and fertilizer production
(Jamers et al. 2006). Although widespread, the majority
of copper (and copper compounds) occur bound to either
sediments or soil particles and are not bioavailable.
Soluble copper compounds are a threat to environmental
health and these substances often occur in the environ-
ment through their application as pesticides (Arnal et al.
2011). However, copper is also an essential trace metal,
due to its key roles in oxygen-requiring chemical
reactions. Copper ions aid electron transfer to molecular
oxygen during aerobic respiration, act as co-factors in
superoxide dismutases, and are an essential component
of plastocyanin, an electron carrier in oxygenic photo-
synthesis (Bertoni 2011). At the same time, however,
large concentrations of copper can cause toxic effects
especially in invertebrate species (El-Gendy et al. 2009;
Spurgeon et al. 2003). Organisms are therefore pre-
sented with the challenging problem of maintaining
copper concentrations within a narrow range.
In this study we compare and contrast the effects of
various levels of copper exposure on the unicellular
green alga C. reinhardtii. Effects were assessed at the
biochemical (metabolite), physiological (viability,
presence of reactive oxygen species—ROS and uptake
kinetics) and organism level (growth rate). The
potential of this comparative approach to provide
insights into the mechanism(s) of toxicity was also
explored.
Materials and methods
Chlamydomonas culture conditions
Chlamydomonas reinhardtii (11-32a, Culture Collec-
tion of Algae (SAG), University of Gottingen, Ger-
many) were maintained in 1 l bottles (filled to the 1 l
mark) Tris–Acetate–Phosphate (TAP) liquid medium
at 25 ± 1 �C under light:dark cycles of 14:10 h with
light provided by a photosynthetic light bank (six Gro-
lux F18 W/Gro lamps (Sylvania, Antwerp, Belgium)
arranged parallel to each other). To prevent cultures
from being contaminated with metals, all glassware
was soaked in 2.5 M HCl overnight and then washed
five times with ultra pure (18.2 MX) MilliQ water
before use. Based on a pilot dose–response experi-
ment, copper was added to the growth media to create
a range of different exposure concentrations of 0, 8,
25, 55 and 125 lM (total CuSO4). Since it is generally
accepted that toxic effects of copper are related to free
ion rather than to total concentrations, the free ion
exposure levels will be used in the presentation of the
results in this study. These were 0 (L1), 2.31 9 10-3
732 Biometals (2013) 26:731–740
123
(L2), 0.92 (L3), 4.31 (L4), and 17 (L5) nM Cu2? ions
respectively. The experimental media were inoculated
from the stock solution at a density of 10,000 cells per
ml (determined via a Multisizer 3 Coulter Counter–
Beckman Coulter, California, USA) as per the OECD
guidelines as modified by Jamers et al. (2006). In order
to ensure enough biological material for the compar-
ative part of the study each experiment was run three
times; one to provide for the metabolomics measure-
ments, one for flow cytometry assessment and a third
to allow the calculation of Cu uptake kinetics. In each
experiment all exposure media were set up in triplicate
and media were spiked with 65Cu for the uptake
kinetics measurements.
Metal content and speciation
Initial metal exposure concentrations were measured
according to the procedure described by Jamers et al.
(2006). Briefly, algae were collected by centrifugation
and acidified with HNO3, after which their copper
content was determined with atomic absorption spec-
troscopy (Agilent, Melbourne, Australia). At the same
time, the free copper concentration in each of the
exposure media was determined using the Visual
Minteq chemical speciation model (Gustafsson 2005).
Stability constants used to calculate the metal speci-
ation were these included in the standard database
except for Tris(hydroxymethyl) methylamine for
which stability constants were taken from the Critical
Stability Constants database (Smith et al. 2005).
Growth rates
Growth was determined by counting cell numbers at 0,
24, 48 and 72 h after the start of each experiment
[when a volume of algal culture was diluted in
balanced electrolyte solution (Coulter Isoton II Dilu-
ent, Beckman Coulter)]. Cells were counted using a
Multisizer 3 Coulter counter (Beckman Coulter). The
growth rate l (day-1) was calculated via the formula
l = (Nt - N0)/tn where Nt was the final density (cells
ml-1), N0 the initial cell density (cells ml-1) and tn the
time (days) after the initiation of the test.
Metabolomics
The metabolomic measurements and analyses were as
described previously in Jones et al. (2008). In brief, 48
and 72 h after initiation of the exposure algal cells were
harvested by centrifugation at 2009g for 2 min and the
pellet was flash frozen in liquid nitrogen to quench
metabolic activity. It proved necessary to wait 48 h after
the initiation of the experiment in order to allow enough
algae to grow to provide reliable NMR data. Metabolites
were then extracted using the standard methanol:chlo-
roform:water method (Le Belle et al. 2002).
The aqueous fraction was analysed via nuclear
magnetic resonance (NMR) spectroscopy. Samples
were dried down and then rehydrated in D2O. 3-(Tri-
methylsilyl)-2,2,3,3-tetradeuteriopropionate (TSP)
(Cambridge Isotope Laboratories, Inc., Hook, UK)
was added as an internal standard. Analysis was
carried out using an AVANCE II NMR spectrometer
operating at 500.13 MHz for the 1H frequency (Bru-
ker, Germany) using a 5 mm Broadband TXI Inverse
ATMA (Automatic Tuning and MAtching) probe.
Individual metabolites were identified in conjunction
with reference to the chemical shifts detailed in the
literature (Fan 1996) as well as NMR Suite Profes-
sional, version 5.1 (Chenomx, Alberta, Canada), the
online Madison Metabolomics Consortium NMR
Database (http://mmcd.nmrfam.wisc.edu/index.html)
and the Madison Biological Magnetic Resonance Data
Bank (http://www.bmrb.wisc.edu/metabolomics/).
Cu uptake kinetics
The uptake kinetics of copper were monitored using the
stable isotope 65Cu. Growth media were spiked with 65Cu
at the desired range of concentrations and left to
equilibrate. At t = 0.5, 1, 2, 3, 6, 9, 12, 24, 48, 72 and
96 h after inoculation the algal cell number was deter-
mined using a Coulter counter (see ‘‘Growth rates’’) as
well as metal uptake (internalized and strongly bound
fractions of 65Cu). For the isotopic measurements cells
were harvested by centrifugation and washed with a
3 mM Na2EDTA solution. Following this, the algae were
filtered using a 0.45 lm cellulose nitrate filter and
washed with modified TAP medium. The filter was dried
and acidified with HNO3. The 63Cu and 65Cu content of
the samples were measured using a quadrupole induc-
tively coupled plasma mass spectrometer (ICP-MS)
(Agilent, Melbourne, Australia). Based on the measured
concentrations of the non-spiked isotope (63Cu) and its
known natural abundance, background concentrations of
the spiked isotope (65Cu) were calculated as well as the
amount of metals already present in the algae before the
Biometals (2013) 26:731–740 733
123
beginning of the experiment. The 65Cu accumulation was
modeled using GraphPad Prism 4 (GraphPad Prism
2009).
Flow cytometry
The staining protocols for C. reinhardtii were as
previously described (Jamers et al. 2009; Jamers and
De Coen 2010). Briefly, after 48 and 72 h of exposure
algae were stained with either fluorescein diacetate
(FDA) or dihydrorhodamine methyl ester 123 (DHR123)
at final concentrations of 25 and 10 lM, respectively,
and incubated in the dark for 20 min. Chlorophyll
autofluorescence (Chl a) and forward and side scattering
signals (FSC and SSC respectively) were also assessed.
A BD LSRII System (Becton–Dickinson, California,
USA) equipped with a solid state 488 nm laser was used
for this part of the study. Green fluorescence was
collected through a 530/30 nm band pass filter for
DHR123 and FDA stained samples, while red autofluo-
rescence (RAF) was collected through a 695/40 nm band
pass filter. Data acquisition and analysis were performed
using the BD FACSDiva Software (Becton–Dickinson).
Statistical analysis
Results are reported as mean and ±standard deviation
(SD). Where relevant, data were tested for normality and
homogeneity of variance using the Kolmogorov–Smir-
nov and Levene test, respectively. One-way analysis of
variance (ANOVA) was performed to test for differ-
ences in growth and growth rate or in flow cytometric
results (mean fluorescence intensity). A p value \0.05
was considered statistically significant. The Tukey
honestly significant difference (Tukey HSD) was per-
formed as a post hoc test. The metabolomics data were
imported into SIMCA-P version 11.0 (Umetrics, Umea,
Sweden) and analysed using principal components
analysis (PCA) and partial least squares-discriminate
analysis (PLS-DA) using mean centering and pareto
scaling (Eriksson et al. 1999; Jones et al. 2008).
Results
Metal content and speciation
The initial metal content of the media, speciation
modeling results (T = 298 K; I = 0.022; c = 0.56)
and calculated number of copper ions per cell results
show that variation between the total metal concen-
trations in the L3–L5 dosed media were low (coeffi-
cients of variation of 9.01, 16.48 and 10.33 %,
respectively). Conversely the background concentra-
tions in the L2 (the lowest exposure) media showed
quite considerable variation (Table 1).
Although they did not define a homeostatic range or
a threshold value above which toxic effects arise, Hill
et al. (1996) reported that approximately 8 9 106 to
9 9 106 copper Cu ions per cell are sufficient to satisfy
the demands of the plastocyanin (the main copper
containing protein) biosynthetic pathway in C. rein-
hardtii). Cultures with copper concentrations below
this value may therefore considered copper deficient.
According to this criterion, in our study, the L1 and L2
cultures would be severely deficient in copper while
the three remaining exposure media were not Cu
deficient. The L3 exposure medium (with 0.92 nM
Cu2?) approximates the proposed ion demand best,
whereas the L5 media exposed algae to a very high ion
quantity of 1.24 9 109 nM Cu2?per cell.
Growth rates
After 72 h algae grown in the L2 media had the highest
cell density while the average number of cells/ml in the
L5 media was significantly lower than all other cultures.
‘‘Statistical analysis’’ section revealed that algae exposed
to L5 Cu2? were the only ones to exhibit a significantly
lower growth rate than the other cultures (Fig. 1).
Metabolomics
The NMR analysis was found to give good coverage of
the metabolism of C. reinhardtii. We detected over 30
Table 1 Total concentration (mean ± 1 SD), free ion con-
centration and activity (lM) for Cu in each of the exposure
media (T = 298 K; I = 0.022; c = 0.56) (Mean taken over all
three experiments)
Total [Cu]
(lM)
Free ion
conc. (nM)
Free ion
act. (nM)
# Cu2? ions/
cell (t = 0 h)
L1 2.20 9 10-6 1.23 9 10-6 110
L2 2.31 9 10-3 1.30 9 10-3 0.12 9 106
L3 0.92 0.51 50.37 9 106
L4 4.31 2.42 257 9 106
L5 17 9.52 1.24 9 106
L1 control culture (no Cu added)
734 Biometals (2013) 26:731–740
123
metabolites with the most prominent peaks being
lysine, dimethylglycine, N-acetylglutamate, succinate,
lactate, formate, histidine and AMP, ADP and ATP.
Our results match previously published data on the C.
reinhardtii metabolome (Bolling and Fiehn 2005;
Matthew et al. 2009). PCA was used to obtain a
measure of the variability of the metabolite profiles
between the different treatments (Fig. 2). Overall
however, NMR metabolomics proved less sensitive to
assessing the effects of copper than originally
expected. Whereas an effect gradient could be
discerned related to increases in copper concentration
for other endpoints, very few changes were detected at
the metabolome level. After 72 h of exposure, PCA
analysis indicated that only algae exposed to the
highest copper concentration were significantly dif-
ferent from the control algae. While small reductions
were observed in concentrations of lactate and the
amino acids isoleucine, leucine and valine, the only
significant change was in the reduced form of gluta-
thione, which was significantly decreased in these
algae (p \ 0.05). This leads us to speculate that
metabolomics might not be a suitable technique for the
detection of effects of exposure to an essential element
for which complex homeostatic mechanisms likely
exist.
Uptake kinetics
As opposed to the metabolomics analysis, the Cu
uptake kinetics results contributed data on possible
mechanisms of toxicity, revealing that at the highest
exposure a decrease in efflux pumping might be
responsible for increased accumulation of Cu. The
total copper concentration in the algae at the beginning
of the experiment was 97.26 ± 0.82 lg/g dry weight.
Our results show that concentrations of spiked 65Cu
increase with increasing exposure concentrations, with
the exception of the 65Cu content of algae exposed to
2.31 9 10-3 and 0.92 nM Cu2?, where the content
was similar in both cultures (Fig. 3). Comparison of the
rate of Cu uptake between the different cultures
indicated that all cultures were significantly different
from each other in this regard (t test, p \ 0.005).
A decrease in internal 63Cu concentration, con-
comitant with 65Cu uptake, was found over time.
Interestingly this decrease did not occur to the same
extent in all cultures. Whereas the final 63Cu content
(t = 96 h) for the L1, L2, L3 and L4 media was only
1.4–7.2 % of the starting concentration, for algae
grown in the L5 media the level was 36.5 %.
Num
ber
of c
ells
per
ml
1750000
1500000
1250000b
a1000000
c750000
500000
250000
0*abc
0 20 40 60 80
Time (h)
L1L2L3L4L5
Fig. 1 Growth of C. reinhardtii upon exposure to Cu2? (mean
± 1 SD). Significant differences between cultures are indicated
with identical characters
-1.0
-0.5
0.0
0.5
1.0
-1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
Principle Component 1
Prin
cipl
e C
ompo
nent
2
R2X = 0.949Q2= 0.691
Fig. 2 PCA plot of L1 (filled circle) and the L5 Cu2? exposure (filled diamond) after 72 h. NB not all algae survived at the highest Cu
dose at this time
Biometals (2013) 26:731–740 735
123
Flow cytometry
After 48 h the L5 exposure exhibited a significantly
lower FSC signal than all other cultures (Fig. 4). At the
same time, the FSC signal of algae grown at L4
exposure was significantly lower than that of algae
grown at the L3 and 2.31 10-3 nM (ANOVA,
p \ 0.05). After 72 h algae exposed to the L5 concen-
tration also exhibited a significantly higher FSC than
that of the other cultures (p \ 0.05).
L2 =
L3 =
L4 =
L5 =
(a) (b)
(c) (d)
Fig. 3 Flow cytometric results for both stained and unstained
parameters. Results represent the mean fluorescence intensities
of the exposed cultures expressed as a percentage of the control.
Data are given as mean ± SD. Significant differences with the
control at a significance level of 0.05 (p \ 0.05) are indicated
with asterisk. Differences between exposed cultures are indicated
with identical characters. Left panel exposure duration of 48
hours, right panel exposure duration of 72 hours. FSC forward
scattering signal, SSC side scattering signal, Chl a chlorophyll a,
FDA fluorescein diacetate, DHR123 dihydrorhodamine
Time (hours) Time (hours)
Time (hours)
(a) (b)
(c) L1L2L3L4
L5
Fig. 4 Uptake kinetics of copper. Internal concentrations (lg/g) of a spiked 65Cu; b total Cu, c 63Cu
736 Biometals (2013) 26:731–740
123
Discussion
In this study, we attempted to used a comparative
approach to assess the effects of copper exposure on
the unicellular green alga C. reinhardtii. Effects were
assessed at biochemical (metabolite), physiological
(viability, presence of ROS and uptake kinetics) and
organism (growth and growth rate) levels (Fig. 5).
At the organism level, growth results revealed that
algae exposed to L3 media exhibited the highest growth
rate, corroborating the view that this culture probably
approaches the ideal Cu availability for this species.
However, under conditions of copper deficiency, many
green algae can adapt by inducing the synthesis of heme-
containing cyt c6, which functions as an alternative
electron transfer catalyst (Merchant and Bogorad 1986a,
b; Quinn et al. 1999). The existence of this alternative
pathway likely explains why algae in the L1 and L2
cultures were still viable. Conversely, the significantly
inhibited growth rate of algae grown at the highest
copper concentration clearly indicates the toxic nature
of L5 exposure.
The Cu uptake kinetics hint at how this toxicity
arises. A clear increase in algal 65Cu content can be
observed with increasing Cu2? exposure concentra-
tion with a concomitant decrease in 63Cu. This can be
rationalized on the basis of the fact that the algae used
to inoculate the exposure media originated from a
stock culture prepared with 6.3 lM total CuSO4 and
were then transferred to media which had been spiked
with 65Cu only. Thus, the spiked media were 63Cu
depleted and over time a decrease in algal 63Cu is
logical. Interestingly, this decrease did not occur to the
same extent in all cultures with the final 63Cu content
Fig. 5 Hierarchical
representation of effects of
copper on C. reinhardtii at
different levels of biological
organization, expressed as
changes in comparison to
the control culture. On the
left hand side specific effects
are indicated. The upwards
arrow and downwards
arrow represent an increase
or decrease, respectively.
The more arrows are
present, the greater the
effect. The biological levels
at which effects occurred are
listed on the right of the
figure
Biometals (2013) 26:731–740 737
123
of algae in the L5 media being 36.5 % of the starting
concentration, whereas that of all other cultures was
only 1.4–7.2 %. This observation could point to a
disturbance of the copper efflux mechanisms in the
algae grown at the highest Cu2? concentration leading
to accumulation and eventually toxicity (Monteiro
et al. 2012; Nishikawa and Tominaga 2001; Franklin
et al. 2002). Another interesting observation is the
similar 65Cu content at the end of the exposure period
of algae grown at L2 (originally classified as copper
deficient) and those grown in L3 media (originally
classified as copper replete). One would expect the65Cu content to be higher in the replete than in the
deficient culture (which in the first 24 h of exposure
was indeed the case). A possible explanation for the
observations post 24 h is the fact that algae exposed to
L2 were likely have a sufficient Cu supply at the
beginning of the exposure, but may suffer from copper
deficiency once cell density had increased.
The toxic effects of copper are generally are
thought to be due to the generation of oxidative stress,
caused by the production of ROS (Pinto et al. 2003).
This was recently confirmed by the finding of a strong
upregulation of the expression of a glutathione S
transferase gene and the differential expression of
several genes related to the thioredoxin system upon
copper exposure (Jamers et al. 2006). In the current
study, similar results were found in both the meta-
bolomic and flow cytometry measurements. At the
highest exposure metabolomics analysis revealed that
concentration levels of the reduced form of glutathi-
one (GSH) decreased significantly. GSH plays an
important role in oxidative stress defense. It conju-
gates a variety of cytotoxic ROS eventually leading to
their detoxification. The presence of ROS was also
monitored flow cytometrically using DHR123. Results
showed an increase in DHR123 mean fluorescence
intensity with increasing copper concentration, again
pointing to oxidative stress being a likely cause of
toxicity.
Fluorescein fluorescence originating from the
hydrolysis of FDA was monitored because it reflects
esterase activity and membrane integrity, both of
which are a measure of cell viability A potential
mechanism for the clear increase in mean FDA
fluorescence intensity with increasing copper concen-
tration is the possible activation and upregulation of
detoxification processes, in which esterases may take
part. Our results confirm certain previous findings
concerning fluorescein fluorescence in microalgae
exposed to heavy metals. Franklin et al. (2001)
investigated the suitability of flow cytometry as a tool
for toxicity testing with marine and freshwater algae
exposed to copper and used FDA hydrolysis as a
measure of cell viability, reflected in esterase activity
and membrane permeability. They found a stimulation
of fluorescein fluorescence at low copper concentra-
tions for exposures of 24 h or less; however, they
attributed this increase to a probable increase in FDA
uptake resulting from cell membrane hyperpolariza-
tion rather than to an increased esterase activity.
Franklin et al. (2001) also developed a rapid
toxicity test based on inhibition of esterase activity
in marine and freshwater algae and found that esterase
activity was a sensitive indicator of copper toxicity in
Selenastrum capricornutum and Entomoneis cf punc-
tulata. As copper concentrations increased, esterase
activity decreased in a concentration-dependent man-
ner. It should however, be noted that only short-term
effects (1–24 h were assessed. The findings of Yang
et al. (2007) are also in line with our observations after
72 h. That study used flow cytometry to determine
short-term copper toxicity in a multispecies micro
algal population and found that after 2 h of copper
exposure, a stimulation in esterase activity appeared at
the lower exposure concentrations. Above concentra-
tions of 21 lg/l, esterase activity decreased as copper
concentrations increased.
Similar results were also previously found for
copper toxicity C. reinhardtii (Jamers et al. 2006). In
that study a total of 2407 expression sequence tags
(ESTs) were differentially expressed in response to
copper exposure. Of those, 362 sequences could be
annotated based on EST description and searches
through public genome databases. Almost 50 % of all
differentially expressed genes belonged to the energy
metabolism (28 %) or protein metabolism (21 %).
Stress related genes made up 7 %. Affected genes
related to copper deficiency were coproporphyrinogen
III oxidase and Crd1. At high Cu levels a probable
glutathione S transferase, glutathione peroxidase,
thioredoxin system, protein damage related genes
and heat shock proteins were all differentially
expressed.
These results demonstrate the value of approaching
a scientific question from different angles. At the same
time, however, it was concluded that adding an extra
level of organization does not always significantly
738 Biometals (2013) 26:731–740
123
contribute to the acquired knowledge. Notably, NMR
based metabolomics proved to be relatively insensi-
tive to the effects of copper. Whereas for other
endpoints often an effect gradient could be discerned
related to increases in copper concentration, only very
few changes were detected at the metabolome level.
This leads us to speculate that NMR based metabolo-
mics might not be a suitable technique for the
detection of effects of exposure to an essential element
where complex homostatic mechanisms likely exist. A
more sensitive method such as mass spectrometry may
be more appropriate for the assessment of early effects
of essential metals/elements at environmentally rele-
vant concentrations.
Acknowledgments This work was funded by the Institute for
the Promotion of Innovation by Science and Technology in
Flanders, Belgium and the European Union (European
Commission, FP6 Contract No. 003956). OAHJ thanks Robin
Jones for helpfull advice.
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