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: oliver.jones@rmit.edu.au

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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