Daphnia magna Straus (Clone Type 5 – IRCHA), originally obtained from the Water Research Centre (WRc), Medmenham, UK, were investigated. For further information on culturing conditions of D. magna please see Hooper et al., 2006 .
Experimental design and analytical chemistry
The experimental design consisted of three replicates of a control and three treatments with ibuprofen-sodium (Sigma-Aldrich, CAS no. 31121-93-4) containing 20, 40 and 80 mg IB l-1, respectively. Each replicate consisted of 100 third brood neonates (< 24 h old) placed in a 1000 ml glass beaker containing one litre of reconstituted freshwater with or without the addition of IB. Neonates were exposed to IB for 24 h under the same conditions as stock cultures (see Hooper et al., 2006 ), but without feeding. To quantify IB, 1.5 ml was sampled from each replicate at 0 h and 24 h and stored at -20°C. Subsequently, the free base concentration of IB was measured at 217 nm by UV-spectrophotometry using an Ultrospec3000 (Biochrom) after the method of Pascoe et al., 2003 . Conductivity, pH and water temperature were checked at the beginning and at the end of the exposure to verify stable water chemistry.
Tissue preparation, RNA extraction and reverse transcription
Following exposure, the neonates were immediately placed in 0.2 ml of RNAlater® (Ambion) and stored at -80°C. Subsequently, total RNA was extracted using the RNeasy Mini kit with on-column DNase treatment (Qiagen) to remove any traces of genomic DNA following the manufacturer's instructions. RNA concentrations were determined by spectrophotometry using GeneQuant Pro (Biochrom). The integrity of the RNA was verified by 1% agarose gel electrophoresis. Four micrograms of DNase-treated total RNA was reverse transcribed with Oligo(dT)12–18 primers (Invitrogen) using the Omniscript Reverse Transcriptase kit (Qiagen) following the manufacturer's instructions. Once synthesised, cDNA was diluted 10-fold resulting in total RNA concentrations of 10 ng/μl and stored at -20°C.
Real-time quantitative PCR
Ten expressed sequence tags (ESTs) were obtained from the NCBI website  and the Daphnia Genomics Consortium website  (see Colbourne et al., 2005 ). ESTs were verified by BLASTN and BLASTX analysis . Primers were designed using Primer3 , and synthesised by MWG. The investigated ESTs, accession numbers, primer sequences and gene ontology are shown in Table 1.
QPCR was conducted on the GeneAmp 5700 Sequence Detection System (SDS) (Applied Biosystems) using ABsolute™ QPCR SYBR® Green ROX (500 nM) mix (ABgene). Each reaction was run in triplicate and contained 2.5 μl of cDNA template (equivalent to 25 ng total RNA) along with 900 nM primers in a final volume of 25 μl. Cycling parameters were 95°C for 15 min to activate the DNA polymerase, then 40 cycles of 95°C for 15 s and 60°C for 1 min. Melting curves were performed by using dissociation curve SDS software version 1.3 (Applied Biosystems) to verify that only a single product with no primer-dimers were amplified.
Data analysis and statistics
Following QPCR, the raw data was exported to an Excel workbook (Microsoft), entitled Data Analysis for Real-Time PCR (DART-PCR) . This programmed workbook enables calculation of threshold cycles and amplification efficiencies for every sample. Differences in amplification efficiency were assessed using one-way analysis of variance (ANOVA), based upon the null hypotheses: (i) that amplification efficiency is comparable within sample groups (outlier detection) and (ii) that amplification efficiency is comparable between sample groups (amplification equivalence) . Outliers identified by DART-PCR and samples diverging from the dissociation curve were omitted prior to further analysis.
Differences in relative expression were assessed on log-transformed data using ANOVA and Tukey's honestly significant difference (HSD) for post hoc comparisons. Equality of variance was tested using Levene's test. The relative expression of the reference genes calculated by DART-PCR was exported as an Excel workbook (Microsoft) to geNorm version 3.4 . The "geNorm" algorithm is based on the proposition that, in any set of assays, the expression levels of suitable reference genes should be perfectly correlated. However, because of technical difficulties in assessing correlation in circumstances when there is little variation, a modified procedure is adopted as follows . First, the ratio of expression levels is calculated for any two candidate reference genes. Assuming that the expression ratios should not vary between assays, the variation between the expression ratios is used as an inverse measure, here referred to as gene variability, of the adequacy of the reference genes to be used for normalisation. Thus, the lower the variation, the better the reference genes. Variation is here measured as standard deviation. Given a set of assays that covers the treatments of interest, this procedure can be used to identify the best two reference genes from all possible pairs of reference genes available to the experimenter. A normalisation factor (NF2) is then produced for these two genes, calculated as the geometric mean of their expression levels. The procedure is then extended to assess whether stepwise inclusion of additional reference genes – producing NF3 to NF
– will reduce the average pairwise variation between them. The optimal number of reference genes to be included in a NF is estimated by comparing the pairwise variation between sequential normalisation factors , e.g. NF3 vs. NF4. All statistical tests were performed in SPSS 12.0.1 for Windows (SPSS, Chicago, IL, USA) applying a significant level of 5%, except reference gene variability and NF analysis, which was computed in geNorm version 3.4 .