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ranscriptomic signatures in Chlamydomonas reinhardtii as Cd biomarkers inetal mixtures
.M. Hutchinsa,∗, D.F. Simona, W. Zergesb, K.J. Wilkinsona
Department of Chemistry, University of Montreal, P.O. Box 6128, Succ. Centre-Ville, H3C 3J7 Montreal, CanadaBiology Department, Concordia University, 7141 Sherbrooke W., H4B 1R6 Montreal, Canada
r t i c l e i n f o
rticle history:eceived 7 April 2010eceived in revised form 12 July 2010ccepted 15 July 2010
In the natural environment, toxicant effects can be monitored by the signature mRNA expression patternsof genes that they generate in test organisms. The specificity and sensitivity of these transcriptome-basedbioassays to a given toxicant can be confounded by temporal changes in biomarker mRNA expression,effects of other toxicants and hardness ions, and non-linear mRNA expression responses of genes. Thisstudy provides the foundation for the development of a transcriptomic-based bioassay for bioavailableCd in the freshwater alga, Chlamydomonas reinhardtii. It characterizes: (1) the Cd regulation of nine geneswith respect to their mRNA induction kinetics; (2) the effects of two additional metals common to fresh-waters, Cu2+ and Pb2+, and (3) the relationships between metal bioaccumulation and the transcriptomicresponses. Quantitative real time PCR was used to monitor mRNA levels of nine Cd-induced genes follow-
2+
oppereadinary metal mixtures
ing an exposure to 0.01, 0.11 and 1.16 �M Cd . Several distinct mRNA expression patterns were observedwith time. While the presence of Cu2+ and Pb2+ decreased Cd biouptake, mRNA levels increased for sixgenes, showing lack of Cd2+ specificity. Nonetheless, the transcriptomic effects of binary metal exposuresrarely adhered to a simple additive model based on single metal exposures; rather most exhibited syn-ergistic or antagonistic interactions. While none of these genes could be used as a specific Cd biomarker,the signature mRNA expression profile obtained from a select subset of Cd sensitive genes was a useful
fects.
biomarker of sublethal ef
. Introduction
Ecotoxicogenomics uses variations in the transcriptome of auitable test organism to monitor sublethal responses to a givenoxicant. A transcriptomic profile is a snapshot of the relative mRNAxpression levels of a gene set, which can range from a few rele-ant genes to the entire genome (Neumann and Galvez, 2002). Intranscriptomic bioassay, a test organism is exposed to a contam-
nant solution and a select set of biomarker genes are monitoredn order to quantify the effects of a toxicant of interest. The princi-al advantage of transciptomic bioassays is that they can provideome insight into the test organism’s physiological response(s) totress. While gene expression depends upon both transcription andranslation, mRNA levels of biomarker genes of known functionre commonly used to reveal defensive responses or acclimatiza-
ion to a given toxicant. In any case, measurements of transcriptionn relationship to variable environmental conditions can serve as
useful biomarker of environmental health. While global pro-ling of an organism’s entire transcriptome provides the most
comprehensive information, this requires expensive and laboriousmicroarray analyses or massive-scale “deep” mRNA sequencing. Incontrast, the development of a transcriptome-based bioassay forhigh-throughput analyses in research and water quality testingrequires the identification of a minimum number of genes whoserelative mRNA levels can provide both sensitive and specific detec-tion of the relevant toxicant(s). It is also essential to characterizethe regulation of the biomarker genes by toxicant exposure in orderto mitigate the following three problems inherent to transciptomicbioassays.
First, toxicant exposure can induce complex temporal patternsof biomarker expression, meaning that very different transcrip-tomic profiles can be observed throughout a transcriptomicresponse. For example, phosphate deprivation induces temporalchanges in mRNA levels of distinct sets of early and late genes(Moseley et al., 2006). The few studies that have assessed thekinetics of gene regulation by metal exposure suggest a similartime-dependence (e.g. Lemaire et al., 1999). Therefore, it is essential
to know the kinetics and temporal complexity of a transcriptomicresponse on which a bioassay is based.
Second, the accuracy of transciptomic bioassays can beadversely affected by non-linearity of biomarker expression as afunction of toxicant concentration. To address this issue, rather
han using the absolute mRNA levels of biomarker genes in a tran-criptomic profile, the bioassay can also simply consider if theiomarker gene is induced and, thereby, determine whether toxi-ant concentration is above or below its “no transcriptional effectevel” (NOTEL). The NOTEL is the maximal toxicant concentra-ion that has no detectable effect on the mRNA expression of aiomarker gene (Ankley et al., 2006; Lobenhofer et al., 2004). Toate, NOTELs have not been determined for metals and many otheroxicants.
Third, external factors in the environment can alter a transcrip-omic response and, thereby, decrease the accuracy of a bioassayased upon it. For example, natural waters contain multiple metals
ncluding hardness ions (Ca2+, Mg2+) in addition to numerous lig-nds. These can alter the biouptake of the toxicant and, hence, theranscriptomic response of the organism. Therefore, it is necessaryo characterize the effects of extraneous physicochemical factors onhe transcriptomic response. Unfortunately, most toxicogenomictudies on metals have measured single metal responses withoutddressing the role of multiple metal contaminants (Dardenne etl., 2008). Compounding these issues is the rarity of biomarkerenes that are specifically induced by a given toxicant. For thiseason, the field is starting to turn to toxicant-specific signatureatterns of differential mRNA expression of genes within a tran-ciptomic profile.
The present study contributes to the development of a tran-criptomic bioassay for bioavailable Cd by characterizing theranscriptional regulation of nine genes that were previously iden-ified as potential Cd biomarkers using global gene expressionrofiling in the freshwater eukaryotic green alga, Chlamydomonaseinhardtii (Simon et al., 2008). Transcriptomic response to Cd expo-ure was assessed for kinetic variability and in the presence ofdditional metals Cu and Pb commonly found in situ in contam-nated waters. C. reinhardtii was selected because it is endogenouso both soils and freshwaters and, therefore, it is expected to berobust test organism for in situ measurements. Furthermore, it
s also an established model organism for research into metal-nduced stress and metal homeostasis. Researchers benefit from
battery of experimental tools, extensive literature on its physi-logy, cell biology and genetics, and the sequenced and annotatedenome. Our results reveal that the responses of the Cd biomarkersere not well predicted by either free Cd in solution or bioaccu-ulated Cd. Rather, the use of a small subset of genes may be more
seful for a specific and sensitive bioassay of bioavailable Cd.
. Theory
Mixed toxicant models can provide an initial understanding ofow toxicants interact (additively, synergistically or antagonisti-ally) relative to a single compound. Two models are predominantlysed to predict mixture toxicities: concentration addition (CA) and
ndependent action (IA). The CA model sums the concentrations ofhe components after adjusting for the differences in potencies andssumes the same toxic mode of action:∑
ci
ECxi(1)
here ci represents the concentration of the compound i and ECxiepresents the x% effect concentration for compound i. Unfortu-ately, when employing mRNA expression as an end point, theetermination of comparable ECx levels can prove problematicince the maximum response level is often unknown, making a clas-
ic ECx approach impossible (Dardenne et al., 2008). Alternatively,he IA model assumes differing modes of action:
(cmix) = 1 −∏
sa (2)
ology 100 (2010) 120–127 121
where E(cmix) refers to the total effect of the mixture and E(ci) isthe effect of compound i. The IA model appears better suited tomRNA expression (transcriptional) analysis. However, both modelsare necessarily oversimplifications of the complex interactions inmetal mixtures and thus results may not fit either model (Chu andChow, 2002; Preston et al., 2000).
3. Methods
3.1. Culture and exposure media
C. reinhardtii (C137) was cultured to mid-log phase in four-fold diluted Tris-acetate-phosphate (dTAP) medium (Gorman andLevine, 1965), under a 12:12 h light:dark cycle of fluorescentwhite light and orbital shaking. Cells were pelleted by centrifu-gation (3300 × g for 7 min), rinsed by resuspension in 200 mLof dTAP or HEPES (4-(2-hydroxyethyl)piperazine-1-ethanesulfonicacid, 0.01 M, pH 7.0) for 2 min and pelleted again. The cell pellet wasresuspended in 50 mL (to ca. 6.4 × 105 cells mL−1) and aliquots werediluted to a cell density of ca. 3–4 × 105 cells mL−1, corresponding to1 cm2 mL−1 in the exposure solutions (450 mL). As indicated below,the dTAP medium was employed to investigate the kinetics of thetranscriptomic response (1–8 h), while the metal mixture experi-ments (2 h) were performed in 0.01 M HEPES in order to minimizecomplexation of the metals by the buffer components.
3.2. Time series exposures
Total Cd concentrations of 0.089, 0.90 and 8.8 �M were bufferedwith 500 �M citrate in dTAP medium in order to give 0.01, 0.11 and1.16 �M of Cd2+, as determined from thermodynamic calculations(MINTEQA2, version 1.50). Experiments were performed in tripli-cate on three different days with independent cultures. Cells weresampled at 1, 2, 4, 6 and 8 h for the determination of internalisedmetal and the analysis of mRNA expression. Replicate 25 mL sam-ples were first centrifuged at 3300 × g for 10 min. The pellet wassubsequently washed in dTAP medium containing 10−3 M EDTA for1 min in order to remove metal bound to the algal cell wall (Hassleret al., 2004). For the genomic analysis, the cell suspension was thencentrifuged at 12,280 × g for 5 min. Cell pellets were frozen on dryice and then stored at −80 ◦C until RNA extraction. For the analysisof internalised metal, cells were filtered on a 5 �m nitrocellulosemembrane (Durapore, Millipore) then washed twice with dTAP.
3.3. Mixed metal exposures
Based upon the results of the time series experiments, all furthermetal exposures occurred over 2 h. Mixed metal exposure experi-ments were based upon a design described by Stratton (1988). Inorder to assess expression levels for the single metals, cells werefirst exposed to 0.05, 0.1, 0.5, 1.0 and 5.0 �M of Pb or Cu. In a sec-ond set of experiments designed to evaluate expression followingexposure to two metals, a fixed concentration of Cd2+ (0.5 �M) wasadded to all conditions. Finally, cells were exposed to 0.5 �M Cd2+
alone. All experiments were performed in 0.01 M HEPES at pH 7using three independent biological replicates. Free metal concen-trations corresponded to 99.9%, 83% and 78–80% of the total metal
concentrations for Cd, Pb and Cu, respectively (Table S1). Follow-ing metal exposure, 5 mL of 1 mM EDTA was added to 45 mL ofthe exposure solution (corresponding to ca. 1.5–1.6 × 107 cells).Cells were prepared for bioaccumulation and genomic analysis asdescribed above.
Fig. 1. Gene transcription normalised to control (no metal) exposures, representedas fold change induction (FC). Cells were exposed to 1.0 �M Cd2+ over 1–8 h andmRNA expression was measured for: (a) AOT4 (�), PBD1 (♦) and CHLH1 ( ); (b)
22 C.M. Hutchins et al. / Aquati
.4. Bioaccumulation
Following their exposure to the metal(s), cells were filtered asbove (5 �m nitrocellulose). Cells and filters were digested by theddition of 300 �L ultrapure HNO3 (JT Baker), incubated overnightt 80 ◦C then diluted to 5 mL with deionised water (R > 18 M� cm,rganic carbon <2 �g L−1). Metal concentrations were determinedy graphite furnace atomic absorption spectrometry (GFAAS) or
nductively coupled plasma mass spectrometry (ICP-MS) and nor-alized to cell surface areas that were determined using a Beckman
oulter Multisizer III.
.5. RNA extraction
�-Mercaptoethanol (1%) and glass beads (0.5 mm diameter,homas Scientific) were added to each cell pellet, which was thenortexed (4×, 15 s each). Between each manipulation, samplesere kept frozen on dry ice. Total RNA was isolated by ethanolrecipitation and purification using a Qiagen RNeasy kit (Qiagen
nc., Valencia, CA). RNA quality and quantity were estimated fromeasured absorbance at 280 nm and 260 nm and by the analysis of
RNA intactness using an Agilent 2100 BioAnalyzer (Agilent Tech-ologies). RNA quality was considered acceptable when the 28SRNA/18S rRNA ratio was ≥2.
.6. Quantitative real time PCR (qPCR)
qPCR was performed using the Applied Biosystems 7900 HTequence detection system. Primer pairs for qPCR reactions wereescribed previously (Simon et al., 2008). For each primer pair,mplification efficiency was assessed using a standard curve andalidated when >85%. To generate the standard curve, qPCR reac-ions were performed using cDNA serial dilutions of 1/5, 1/25, 1/125nd 1/625. qPCR reactions were performed using the SYBR GreenASTA ABI Universal PCR Master Mix (AmpliTaq Gold DNA Poly-erase) with a 1/5 dilution of the reverse transcription products
nd a primer concentration of 200 nM in a final volume of 10 �L. PCRonditions were: 2 min at 50 ◦C, 3 min at 95 ◦C followed by 50 cyclesf dual temperature (5 s at 95 ◦C and 30 s at 60 ◦C). The data werenalysed using the SDS 2.2 sequence detection software (Appliediosystems). Relative mRNA levels were analysed using the 2−��CT
ethod (Livak and Schmittgen, 2001) with the threshold cycle (CT),.e. the cycle at which an increase in the fluorescence is statisticallyignificant from the background, in the exponential phase of ampli-cation. Non-induced control genes 18S rRNA and N1 201 (a geneith no observable changes in intensity over the range of metals
ested, Simon et al., 2008) were used for normalisation of the qPCR.
.7. Data analysis
For each gene and each condition, mRNA expression levels wereivided by those obtained for an identical experiment containingo metals in order to obtain the fold change (FC). The following IAodel was used to predict effects in the presence of the two metals:
CM = 1 − ((1 − FCA) + (1 − FCB)) (3)
here FCM is the fold change following exposure to the metal mix-ure (M) or to the individual metals (A or B). When measured levels
f induction for the mixture fit this model, the response is consid-red as additive relative to the responses for single metals. Whenbserved levels of induction are greater than predicted, metal inter-ctions are assumed to be synergistic while less induction thanredicted suggests that interactions are antagonistic.
4.1. Biomarker genes exhibit temporal patterns of Cd regulation
The dynamics of the regulation of the nine biomarker geneswere characterized by quantifying their mRNA levels during expo-sure to 1.16 �M Cd2+ over 8 h (Fig. 1). Diverse temporal patternsof mRNA expression were observed. The earliest induction wasobserved for AOT4, DRP1, SDC1; these genes were transcription-ally maximally induced by 2 h (FC of 8.0, 3.1 and 4.7, respectively)with their mRNA levels declining thereafter. LCI11 mRNA expres-sion increased over the first 4 h (8.2 FC) and then gradually declinedto the basal level by 8 h. PBD1 showed a broad temporal patternof induction, reaching maximal mRNA levels between 4 and 6 hof exposure. SIR1 showed a bimodal temporal pattern of weaktranscriptional induction, with maxima at 1–2 and 6–8 h. Finally,CHLH1, METE and NUO11 were not transcriptionally induced by
Cd2+ in this study, but rather their mRNA expression was eitherunchanged (METE) or slightly repressed (65% and 50% repressionfor CHLH1 and NUO11, respectively). Since the highest levels ofinduction provide the best analytical signal for biomarkers, an
ig. 2. Intracellular metal concentrations (mol/cm2) for: (a) Cu or (b) Pb in singlentracellular Cd concentrations (�) are presented for the mixtures containing (c)dditional metal) are represented by the arrow. Error bars represent the standard e
xposure time of 2 h was deemed appropriate for the subsequentetal mixture experiments.In order to estimate NOTEL values for Cd2+ in C. reinhardtii, the
ame time course experiment was carried out at lower Cd2+ concen-rations (0.01 or 0.11 �M, Fig. S1). At 0.11 �M Cd2+, transcriptionalnduction was observed only for AOT4 (max. 2.3 FC) and LCI11 (max..4 FC). At 0.01 �M Cd2+, no significant effects were observed forny of the genes, suggesting that NOTEL values using the genesnvestigated were between 0.01 and 0.11 �M Cd2+.
.2. Intracellular metal concentrations
Since toxicity is presumed to be a product of bioaccumulatedetal, metal biouptake was monitored in order to assess its rela-
ionship to the elicited transcriptomic response. The addition of aecond metal to the experimental medium is predicted either toave no effect (i.e. no interaction or binding to an independentptake site) or to decrease the biouptake of the first metal (i.e.ompetition for a similar uptake site) (Slaveykova and Wilkinson,005). In contrast, toxicity assessments of metal mixtures can showdditive, synergistic or antagonistic effects when compared to thexposure of a single metal (Norwood et al., 2003).
In the absence of Cd, Cu and Pb biouptake increased as a func-ion of the external metal concentration (Cu: 0.046–0.43 nmol/cm2;b: 0.01–0.33 nmol/cm2) (open symbols in Fig. 2a and b). When.5 �M Cd2+ was added to the experimental media, Cu and Pbptake were not significantly altered except at the highest Pb2+
oncentration (5.0 �M) (closed symbols in Fig. 2a and b). Under thisondition, the addition of 0.5 �M Cd2+ doubled Pb biouptake (from.33 ± 0.02 to 0.72 ± 0.06 nmol/cm2) (Fig. 2b). While an enhance-ent of biouptake was unexpected, similar increases have been
bserved previously for Cu uptake in the presence of Zn, Pb and Nin Chlorella kesslerii (Hassler et al., 2004), for Ni uptake in C. rein-ardtii in the presence of Pb (Worms and Wilkinson, 2007) and forb uptake in the presence of Cu for C. reinhardtii (Chen et al., 2010).n contrast, in the presence of the highest Cu and Pb concentrations,d biouptake decreased, consistent with competitive interactionst the uptake sites (Fig. 2c, d). Most notably, Cd biouptake decreasedy 53% and 77% (0.026 and 0.012 ± 0.005 nmol/cm2) when 1.0 or.0 �M Pb2+ was added to the exposure media (Fig. 2d). Similarly,
n the presence of 5.0 �M Cu2+, Cd biouptake was also inhibited by5% (Fig. 2c). If the Cd-regulated genes selected for this study are
ndeed Cd specific, the observed decrease in Cd biouptake implieshat a similar decrease in the transcriptomic response would bexpected.
l exposures (open symbols) or in the presence of 0.5 �M Cd2+ (closed symbols).d (d) Pb. Intracellular Cd concentrations for the control exposure (0.5 �M Cd, nof three biological replicates.
4.3. Transcriptomic effects of Cu and Pb on the Cd-regulatedgenes (single metal exposures)
In order to assess the specificity of the biomarker genes to Cd andto determine the extent to which transcriptional regulation wasreflected by metal biouptake, biomarker gene mRNA levels werequantified in the same experimental systems as above. If the mRNAexpression of the biomarker genes is specific for Cd, then both in theabsence and presence of Cd, mRNA levels should be constant as Cu2+
or Pb2+ concentrations are increased from 0.05 to 5.0 �M. In fact,for the single metal exposures, mRNA expression changed signifi-cantly with an increasing concentration of Cu or Pb (open symbolsin Fig. 3: Cu and Fig. 4: Pb). The qPCR results suggested that thetranscriptomic responses were non-metal specific, with the excep-tion of DRP1, NUO11 and LCI11, which showed no change in mRNAexpression levels. In general, biomarker mRNA levels were directlycorrelated with Cu2+ or Pb2+ concentrations, although maximummRNA expression was often observed at 1.0 �M Cu2+ or Pb2+ (Cu:SDC1, SIR1; Pb: AOT4, PBD1, SIR1) rather than at 5 �M (Cu: AOT4,METE, CHLH1; Pb: METE, SDC1). There were a couple of exceptions:PBD1 expression inversely correlated with [Cu2+] while expressionlevels of CHLH1 inversely correlated with [Pb2+].
Finally, Pb and Cu had different effects on the biomarker mRNAlevels. For example, PBD1 mRNA levels increased from 2.6 to 4.3FC as Pb2+ concentrations increased from 0.05 to 1.0 �M whereasthe same variation in Cu2+ caused a decrease from 2.7 to 0.7 FC(Figs. 3b and 4b). Similarly, marked differences in AOT4 mRNAlevels were observed for Cu and Pb. Concentration-dependent reg-ulation of mRNA levels of these genes in cells exposed to Cu or Pbalone suggests that the interpretation of gene response in multiplemetal solutions will be even more complex.
4.4. Transcriptomic effects of binary metal mixtures
In order to assess transcriptomic responses in mixed metal solu-tions and to determine if mRNA levels were predictably based onsingle metal results, cells were exposed to similar levels of Cu2+
or Pb2+ (0.05–5.0 �M) but in the presence of 0.5 �M Cd2+. For themajority of the tested genes, the addition of 0.5 �M Cd2+ (solidpoints in Figs. 3 and 4) increased mRNA levels relative to cellsexposed only to the Cu2+ or Pb2+ (open points in Figs. 3 and 4).
For example, the addition of 0.5 �M Cd2+ generally enhanced AOT4induction with respect to exposures Cu or Pb alone (Fig. 3a and b).Furthermore, the highest concentrations of Pb appeared to have agreater negating effect on mRNA expression than did Cu, consis-tent with the greater observed effect of Pb on Cd biouptake. On the
ig. 3. mRNA levels relative to no metal controls (FC) are shown for AOT4 (a), PBD1d, Cu mixtures (�, 0.05–5.0 �M Cu; 0.5 �M Cd). The arrow indicates the mRNA leor identical conditions in the absence of metal. Error bars represent the standard e
ther hand, PBD1 mRNA levels were not significantly different inhe presence or absence of Cd (Fig. 4b) and the induction of CHLH1as similar in all cases, i.e. when alga was exposed to binary (Cd/Cu;d/Pb) or single (Cu, Pb) metal solutions (Figs. 3e and 4e).
The extent of interactive effects in the metal mixtures was notniform, differing for each gene. For example, mRNA expression
evels for CHLH1 and METE were well predicted by the simpledditive IA model (Fig. 5) as was to be expected due to minimalnduction in 0.5 �M Cd2+ only exposures (arrow Figs. 3 and 4). Onhe other hand, despite no differential mRNA expression in 0.5 �Md2+ only exposures (arrow Figs. 3 and 4), SDC1 mRNA expression
evels in the mixtures (Fig. 6) were higher than predicted from sin-le metal exposures, suggesting a synergistic interaction betweenhe metals. In contrast, for PBD1, metal interactions were antago-istic for both binary mixtures, at all concentrations. No systematicrend in the data was observed for AOT4 or SIR1, with varying syn-
rgistic or antagonistic interactions that depended on the preciseetal mixture and metal concentrations. Improved relationshipsere not obtained when biomarker mRNA levels were plotted asfunction of internalised metal concentrations (Figs. S1 and S2).
or single metal exposures, expression levels generally increased
ig. 4. mRNA levels relative to no metal controls (FC) are shown for AOT4 (a), PBD1 (b),b mixtures (�, 0.05–5.0 �M Pb; 0.5 �M Cd). The arrow indicates the mRNA levels in 0.5dentical conditions in the absence of metal. Error bars represent the standard error of th
as a function of internalised metal, until a maxima (open symbolsFigs. S1 and S2). On the other hand, when single and binary metalexposures were combined, it became clear that no single concentra-tion of intracellular metal (Cu, Pb or Cd) was a good predictor of theobserved transcriptomic effects (S1 (Cu), S2 (Pb) and S3 (Cd)). Suchan observation clearly complicates the ability to use single genesas biomarkers for metals in solutions containing multiple metals.
5. Discussion
5.1. Kinetic response following Cd exposure
Differential mRNA levels, as a biomarker for the presence ofa toxicant, only provides a snapshot of the temporally complextranscriptional response. For the majority of the examined Cdbiomarkers, short exposures of 2–4 h were sufficient to produce
maximal or near-maximal changes in the mRNA levels of thebiomarker genes. Dynamic patterns of mRNA expression after Cdtreatment have been reported for a range of genes and species (Renet al., 2003; Soetaert et al., 2007). In C. reinhardtii, transient tran-scriptional induction of genes encoding two thioredoxin isoforms
METE (c), SDC1 (d), CHLH1 (e) and SIR1 (f) exposed to Pb (�, 0.05–5.0 �M) or Cd,�M Cd exposed cells. The horizontal line at FC = 1 indicates the level obtained for
ig. 5. Fits of observed gene transcription in Cd,Cu (�) and Cd,Pb (�) mixtures withb) PBD1, (c) METE, (d) SDC1, (e) CHLH1 and (f) SIR1. The diagonal line bisecting thr inhibition.
as been reported following exposure to 100 �M Cd, with mRNAxpression maxima (4 FC) at 2 and 3 h, respectively (Lemaire etl., 1999). These data have clear implications for the appropriatexposure of C. reinhardtii as in situ biomarkers. Obviously furthernvestigation is required to confirm corresponding changes to theene product and to validate this observation for a greater range ofetals and metal-induced genes.
.2. Single metal exposures
All genes examined in this study were initially screened for theirpecificity for Cd using microarray technology (Simon et al., 2008).egulation of the six Cd2+ responsive genes: AOT4, DRP1, SDC1,CI11, PBD1, SIR1, was generally consistent with reported previousesults (Simon et al., 2008), with a few key differences. After a 2 hxposure to 1.2 �M Cd2+, transcriptional AOT4 induction was lowern this study (8 FC as opposed to 43 FC) while that of LCI11 andIR1 was greater (6.6 vs. 2.5 FC and 1.7 vs. 0.4 FC). Furthermore,n contrast to the previous results, many of the genes were not Cdpecific in their regulation, although admittedly, they had not been
reviously rigorously tested for the effects of Cu and Pb.
Even in the single metal exposures (Cd2+, Cu2+ or Pb2+),iomarker levels were not linearly related to toxicant concentra-ion. Such concentration-dependent response curves, in which theenes are transcriptionally up-regulated at low concentrations,
ig. 6. Gene transcription signatures of exposure to 1.0 �M Cd, Pb, Cu, or two metalixtures CdPb and CdCu. Error bars represent the standard error of three biological
eplicates.
ted values for genes under the assumption of a simple additive IA model: (a) AOT4,h represents the values predicted for additive effects, i.e. in the absence of synergy
with less induction at higher concentrations, are not uncommonin genotoxicity experiments. Recent studies investigating generesponses following toxicant exposure highlight the importanceof considering a dose-dependence when interpreting expressiondata (Jamers et al., 2009). Observations for which differential mRNAexpression increased with concentration to a threshold prior to sta-bilizing or decreasing clearly shows that two vastly different metalconcentrations can induce the same level of mRNA expression ofa gene. Unfortunately, this limits the practical concentration rangefor these genes as biomarkers of metal toxicity and complicates theidentification of ECx values for individual toxicants (Dardenne et al.,2008).
In addition, as the metal concentrations increase, the number ofdifferentially expressed genes related to general stress responsesalso increase, overshadowing the metal specific modes of toxicity(Poynton et al., 2008a). Indeed, of the biomarker genes encodingproteins of known function, none have previously been shown to bedirectly involved in Cd tolerance. For example, SDC1 encodes ser-ine decarboxylase, a catalyst in the production of glycinebetaine,known to be part of a general abiotic stress response (Sakamotoand Murata, 2000). Genes encoding for serine decarboxylase havebeen shown to be induced by Cu in the aquatic plant Lemna gibba(Akhtar et al., 2005) and by Ni and Mn in Arabidopsis (Fujimoriand Ohta, 2003). Similarly, CHLH1 encodes the H subunit of Mgchelatase, an enzyme in chlorophyll biosynthesis, which is also thereceptor component of a binding protein (ABAR) for abscisic acid(ABAR) (Shen et al., 2006), the biosynthesis of which is regulated bygeneral abiotic stressors (Gong et al., 2008). The products of bothSIR1 and METE function in the biosynthesis of methionine, whichis subsequently required for the synthesis of cysteine, glutathioneand phytochelatines. Both glutathione and phytochelatines arenon-metal specific and can be up-regulated following exposureto Cu (Wu et al., 2007; Guo et al., 2008; Helbig et al., 2008), Pb(Figueroa et al., 2008) or Cd (Maier et al., 2003). AOT4 encodes apredicted amino acid transporter (Simon et al., 2008). Its inductionin the presence of increasing concentrations of Cu and Pb suggeststhat its role is not limited to Cd (Fig. 2).
In this study, the absence of significant differential mRNA
expression below 0.01 �M Cd2+ suggested that the NOTEL is inthis concentration range for C. reinhardtii. This value is in sim-ilar range (0.5 nM–0.16 �M Cd) to that identified by Poynton etal. (2008a) for Daphnia magna. With regard to Cu and Pb, theobserved significant mRNA expression of a number of genes (PBD1,
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26 C.M. Hutchins et al. / Aquati
IR1, SDC1, LCI11) at the lowest exposure concentration (0.05 �M)uggested that for C. reinhardtii, NOTEL values would be at con-entrations below 0.05 �M. This observation is again quite similaro the results for Cu of Poynton et al. (2008a,b), who observed 25ifferentially transcribed genes in D. magna exposed to 0.03 �Mu while only 1 differentially transcribed gene was observed at anxposure concentration of 0.015 �M Cu. In order to ensure the accu-ate determination of NOTEL concentrations for metal exposure in. reinhardtii, it is essential that substantially larger numbers ofenes be investigated and that future studies assessing genomicetal biomarkers incorporate appropriately low (environmentally
elevant) exposure concentrations to best represent in situ contam-nation.
.3. Binary metal exposures
Biological effects of exposure to a trace metal are most oftenelated to the external concentrations of free metal (free ion activ-ty model (FIAM); Campbell, 1995; Kola and Wilkinson, 2005) oro the concentration of metal bound to sensitive biological sitesbiotic ligand model (BLM); Slaveykova and Wilkinson, 2005). TheIAM and BLM models will always predict lower (or unchanged)ioaccumulation and toxicity in the presence of a second compet-
ng ion. While decreased bioaccumulation is indeed often observedue to competition effects (Kola and Wilkinson, 2005; Worms etl., 2007; Borgmann et al., 2008), additive, synergistic or antago-istic effects are observed when applying toxicological endpointso metal mixtures (Borgmann et al., 2008; Vandenbrouck et al.,009).
For binary metal mixtures of Cd in the presence of increasing Cur Pb, the potential toxicant interaction was evaluated by a compar-son of mRNA levels with the levels predicted by a simple additive IA
odel based on single metal exposures. Deviations from the predic-ive model for PBD1, AOT4, SDC1 and SIR1 suggest that binary metalxposures can cause interactive effects which differ at the individ-al gene level. The variability of interactive effects across the genesuggests they are the product of intracellular feedback or regulatoryrocesses (i.e. translational efficiency, mRNA stability, protein sta-ility, etc.) that may be specific to each gene and each binary metalombination. Given that mRNA expression experiments performedn a given sample showed additive, synergistic and antagonisticnteractions, depending on the gene examined, competition effectsn biouptake cannot be invoked to explain the results, i.e. internalather than external processes are responsible for the differences inRNA expression. In equitoxic binary metal mixtures of Ni with Cu
nd Pb, Vandenbrouck et al. (2009) also showed that mRNA expres-ion patterns were not merely the simple sum of their individualompounds. Instead, solutions containing several metals triggeredultiple additional response pathways and interactive molecular
esponses (Vandenbrouck et al., 2009).The interactive effects in multiple metal solutions also depended
n the particular metals in binary exposures and the concentration.omplex concentration ratios and concentration-dependent syn-rgistic/antagonistic interactions between toxicants, as observedor AOT4 and SIR1, are common in assessments of mixture tox-city (Jonker et al., 2009) and have been reported for multiple
etal solutions including cadmium and lead (Bae et al., 2001) andinary solutions of Cu and diazinon (Van der Geest et al., 2000;anks et al., 2003). Clearly, the modelling of toxicant effects, basedn single contaminant exposures, will provide only a preliminarynderstanding of how contaminants interact to affect gene expres-
ion. Metal interactions and concentration-dependent effects, inddition to the gene dependent nature of the metal interactionsi.e. additive, synergistic or antagonistic), complicate the practicalpplication of using mRNA expression of a gene as a biomarker ofetal contamination.
cology 100 (2010) 120–127
The lack of metal specificity of the nine Cd biomarkers, thenon-linearity of concentration-response curves and the observedinteractive effects in mixed metal solutions limit the practicalemployment of these genes as unique biomarkers of contaminatedsystems. An alternative to the use of single gene responses as abiomarker in contaminated systems is to cluster the transcriptomicresponses for a number of genes in order to provide a distinct signa-ture for single and multiple metal solutions. For example, distinctsignature patterns from the nine biomarker genes examined herecould feasibly be employed to identify metal contamination in situ(Fig. 5). While the analysis of mRNA expression clusters is consider-ably more complex than the single gene responses, it may providethe most useful means of assessing the toxicological responses ofcomplex multiple metal solutions.
6. Conclusion
The identification of biomarkers for use in metal ecotoxicoge-nomics requires a thorough understanding of both the kineticsand specificity of the targeted genes. Extending our knowledgeof nine Cd-regulated genes, the present study identified distincttemporal dependence of their mRNA expression in C. reinhardtiifollowing Cd exposure. For the investigated genes, exposures of2–4 h maximised the level of transcriptional induction or repres-sion. Nonetheless, the observed lack of Cd specificity for six of thegenes, with varying levels of transcriptional induction/repressionobserved in the presence of Cu2+ or Pb2+, greatly reduced the prac-ticality of using these genes as individual Cd specific biomarkers.In multiple metal solutions, the presence of more than one con-taminant complicates the transcriptomic response via processesin the external exposure solution (i.e. competitive reduction ofCu uptake) and gene specific interactive (synergistic/antagonistic)metal effects. The observed lack of specificity, the absence of astrictly concentration-dependent induction and the observationof synergistic, antagonistic and additive metal interactions limitsthe potential of a using a single gene as a Cd specific biomarker.In the future, the development of a signature transcription pro-file using a cluster of genes will likely provide the best potentialfor a biomarker which can integrate both temporal and multiplecontaminant effects.
Acknowledgments
The authors gratefully acknowledge the support of the NSERCMITHE Research Network and the Fonds Quebecois de la recherchesur la nature et les technologies (FQRNT team grant KJW, WZ). Acomplete list of MITHE sponsors is available at www.mithe-morg.Technical assistance from P. Chagnon is greatly appreciated.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.aquatox.2010.07.017.
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