Expression of target and reference genes in Daphnia magna exposed to ibuprofen

Toxicogenomics is an evolving discipline investigating stressor impact at the genome level [1]. Microarrays are state-of-the-art tools for global gene expression profiling of the response of an organism in a particular biological context. However, expression levels of key genes responding on the microarray need to be validated [2]. The preferred technique of validation is by real-time quantitative polymerase chain reaction (QPCR) [3, 4]. Although QPCR is a fairly reliable technique, amplification can vary depending on factors such as RNA integrity, reverse transcriptase (RT) efficiencies, sample-to-sample variations in amplification efficiency, and variation in cDNA sample loading. Using the same sample sizes, assessing RNA integrity and equalising RNA concentrations prior to RT are some of the basic normalisation steps in QPCR [5]. However, normalisation to some internal control is essential for accurate QPCR to balance sample-to-sample variations within the RT and PCR reactions. Internal control is generally achieved using reference genes, also known as housekeeping genes.

Some of the most commonly used reference genes, such as β-actin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), are historical carryovers from northern blotting, a "predecessor" of QPCR [5]. It is good practice to rigorously validate the suitability of these reference genes under the specific experimental condition to which they are applied [5–7]. Total RNA and rRNA have also been suggested as internal controls, although again differences between tissue, individuals and experimental conditions apply [8]. Moreover, using RNA as an internal control has the disadvantage of not controlling for variation inherent in the RT and PCR reactions [5]. None of these genes or RNA products are completely invariable, and thus each needs validation before use [5–7]. A further problem of using only one reference gene for normalisation, is that it theoretically should have been normalised itself prior to target gene normalisation [9]. Various methods have been proposed to overcome these problems. One involves the use of a normalisation factor (NF) based on the geometric mean of multiple carefully selected reference genes. This NF can be calculated by geNorm (see Vandesompele et al., 2002 [10] and the geNorm website [11]) or other freely available Excel (Microsoft) based software applications, e.g. BestKeeper [12] (see Pfaffl et al., 2004 [9]). Vandesompele et al., 2002 [10] recommend that NF s should be estimated from a minimum of three reference genes. The advantage of this approach is that it allows for the cumulative error of the entire process from RNA extraction to the QPCR.

We use the geNorm algorithm [10] to estimate the variability of the reference genes, and to discover an optimal normalisation factor (NF), based on the geometric mean of three of them. geNorm estimates reference gene variability by calculating an expression level ratio for any two candidate reference genes. The variation between the expression ratios is then used as an inverse measure to estimate the variability of the analysed reference genes (please see Methods or Vandesompele et al., (2002) [10] for further information). The approach is illustrated using as target gene the D. magna ortholog of leukotriene B4 12-hydroxydehydrogenase (Ltb4dh), which was identified as being up-regulated in a suppressive subtractive hybridisation following 24 h exposure to 63–81 mg IB l^-1 [13]. Ltb4dh is of special interest because NSAIDs are known to inhibit the mammalian biosynthesis of various eicosanoids (e.g. prostaglandins, epoxyeicosatrienoic acids and leukotriene) that play important regulatory and signalling functions, for instance regulation of ion flux [14, 15]. As anticipated, use of the NF clarified the response of Ltb4dh to ibuprofen.

We investigated the variability of 10 candidate reference genes in D. magna following a 24 h exposure to IB to discover the least variable internal control(s) for QPCR normalisation. A comparison of the reference genes, using the geNorm software, ranked the least variable genes as; UBC = GAPDH <Act <WARS <SDH <TBP <18S <28S. Atb and Cyp were not included in the geNorm analysis as they were down-regulated by IB. Furthermore, geNorm identified the optimal normalisation factor as NF₃ based on the geometric mean expression of UBC, GAPDH and Act. This NF was based on the lowest recommended number of reference genes with the lowest level of variation [10]. The response to IB of an example target gene, Ltb4dh, was little changed by application of NF₃ (Fig. 2), although at the highest concentration of IB, there was a slight increase in the response, and a diminution of the variation between replicates. Such reduction in the variation between replicates is exactly what one hopes to achieve through normalisation.

QPCR confirmed that Ltb4dh was up-regulated following exposure to IB [13]. NSAIDs are known to inhibit the biosynthesis of various eicosanoids that play important regulatory and signalling functions, e.g. regulation of ion flux. In mammals, Ltb4dh is involved in the metabolism of leukotriene B₄ an eicosanoid that is formed in the lipoxygenase pathway [15]. There is evidence of eicosanoid pathways in invertebrates being similar to the mammalian pathways [16]. Further experimentation, involving a global expression profile based on cDNA microarrays, is underway to support this hypothesis and reveal the overall molecular stress response of D. magna to IB. To our knowledge, this is one of the first genomic studies to look at the impact of a NSAID on an aquatic invertebrate. The only non-mammalian studies are on yeast Saccharomyces cerevisiae [17], a Drosophila cell line [18] and Bacillus megaterium [19].

Following normalisation to NF₃ it transpired that Atb, assayed as a candidate reference gene, was affected by IB, and so should be regarded as an IB target gene. In a review on the molecular mechanism of NSAIDs in mammals, Tarnawski and Jones, 2003 [20] reveal that it is likely that NSAIDs exert many inhibitory effects, one of which is the inhibition of cell division. The observed suppression of Atb following exposure to IB could be linked to this, since alpha-tubulin helps form microtubules that are involved in cell division, cell structure, and transportation of vesicles and organelles.

Of the remaining candidate reference genes, Act, GAPDH, UBC, WARS, SDH and TBP had a constant expression following exposure to IB. But although, GAPDH and UBC were the most stable reference genes they both had a weakly significant (p < 0.10) linear trend of down-regulation. Thus, relying solely on either of these two reference genes for normalisation could obscure the output.

Using reference genes as an internal control in QPCR is currently the recommended approach [5]. However, Bustin and Nolan, 2004 [21] advocate the introduction of more standard analysis and reporting procedures in QPCR similar to the MIAME guidelines [22] established for microarray technology. In union with other authors [5, 10, 21, 23] we would like to recommend some basic applications for QPCR: (i) apply a dissociation curve in every QPCR, and omit any samples diverging from the curve; (ii) use a minimum of two non-template controls (NTCs) in every QPCR, and omit any samples having a cycle threshold (C_t) value close to or higher than the mean NTC C_t-value; (iii) avoid inter-assay comparison if possible, as it is more appropriate to duplicate the samples in one QPCR than using triplicates of each sample, but having to run two QPCRs (S.N. Peirson, personal communication); (iv) use a NF based on a minimum of three validated reference genes for target gene normalisation [10]; (v) integrating the DART-PCR approach [23], which verifies sample-to-sample variations in amplification efficiency with a normalisation factor software package, e.g. geNorm [10], creates a powerful tool for analysing relative gene expression of target genes.

Investigators engaging in toxicogenomic research using invertebrates, or vertebrate cell lines, exposed to NSAIDs may be able to apply our findings directly under similar experimental conditions, e.g. acute exposure. However, we strongly recommend validating reference genes prior to commencing target gene expression profiling under experimental conditions different than those described herein. Our findings stress the importance of performing fundamental research, by validating the most invariant internal QPCR control(s) of a particular experimental condition, prior to investigating the expression of target genes examined by QPCR only, or as QPCR validation of microarray data.