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Short Communication
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Evaluation of reference genes for real-time PCR quantification of gene
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expression in the Australian sheep blowfly, Lucilia cuprina.
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N.H. Bagnall and A.C. Kotze
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CSIRO Livestock Industries, 306 Carmody Rd., St. Lucia, Brisbane, QLD 4067,
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Australia
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Corresponding author:
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Andrew Kotze, email: [email protected]; ph: 07-32142355
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No. of words in text: 3533
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Abstract:
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The Australian sheep blowfly, Lucilia cuprina, causes significant animal distress and
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financial loss to the sheep industry in Australia, and other parts of the world.
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However, the paucity of information on many fundamental molecular aspects of this
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species limits our ability to exploit functional genomics techniques for the discovery
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of new drug targets for control of this parasite. Our study aimed to facilitate gene
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expression studies in this species by identifying the most suitable reference genes for
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normalisation of mRNA expression data. We performed quantitative real-time PCR
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with 11 genes across a total 40 RNA samples (eggs, L1, L3, pupae and adult life-
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stages), and applied two normalisation programs, Normfinder and geNorm to the data.
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The results showed an ideal set of genes (18S rRNA, 28S rRNA, GST1, β-tubulin and
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RPLPO) for data normalisation across all life-stages. We also identified the most
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suitable reference genes for studies within specific life-stages. Both Normfinder and
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geNorm identified GAPDH as a poor choice of reference gene. The reference gene
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recommendations in this study will be of use to laboratories investigating gene
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expression in L. cuprina and related blowfly species.
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Keywords: Lucilia cuprina, reference gene, housekeeping gene, real-time PCR,
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geNorm, Normfinder.
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1. Introduction
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Quantitative reverse transcription real-time PCR (qRT-PCR) is a sensitive and
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powerful technique for quantifying messenger RNA (mRNA) expression levels.
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Confident results with this technique are dependent on using a reliable normalisation
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method when analysing data. The most common method for normalising gene
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expression levels is to relate the gene of interest mRNA levels to that of endogenous
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control genes, often referred to as housekeeping or reference genes. Other methods,
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including the use of total RNA and ribosomal RNA (rRNA), have been used to
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normalise gene expression (Lea et al., 2004), however these techniques may be
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adversely affected by differences in RNA quality between samples, as well as errors
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that arise during reverse transcription. Furthermore, normalisation to total RNA is
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reliant on a constant rRNA:mRNA ratio, which can change between samples (Solanas
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et al., 2001). In addition, RNA measurements primarily measure rRNA, not mRNA,
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which accounts for less than 1% of total RNA (Steinau et al., 2006) and which is the
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target of qRT-PCR. It’s primarily for these reasons that reference genes are most
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often used for data normalisation when assessing mRNA expression levels between
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samples.
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Ideally reference genes should be expressed at constant levels across all samples
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containing an equivalent amount of RNA or cDNA regardless of treatment, and
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exhibit similar levels of expression as the target gene under investigation. No study
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has yet identified a single reference gene that is suitable for use across all
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developmental stages and tissue types within an organism. For these reasons the use
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of multiple reference genes to normalise data (Vandesompele et al., 2002) as well as
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establishing the most suitable reference genes for each tissue and life-stage for the
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species under investigation is recommended.
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The Australian sheep blowfly (Lucilia cuprina) is of significant concern for the
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welfare issues and production losses that arise when gravid female blowflies deposit
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eggs on sheep, giving rise to flystrike. Flystrike costs the Australian sheep industry
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AUD$280 million annually (Smith 2007), both in production losses and control
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measures. Control of L. cuprina lies in effective animal husbandry and appropriate
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chemical use. However, the blowfly has shown an ability to develop resistance to
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chemical groups used to control it over the years (Levot 1995; Smith 2007). The
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continued use of chemicals to control the blowfly is dependent on the development of
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new drugs. The discovery of these drugs through target based screening will most
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likely rely on functional genomics to characterise specific blowfly genes that are
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essential for larval development and survival, and hence represent ideal drug targets.
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This application of functional genomics to the blowfly requires a robust method to
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monitor gene expression changes either during normal larval development, or in
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response to genetic manipulations such as RNAinterference (RNAi).
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In considering the need for a reliable means to monitor gene expression in L. cuprina
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we aimed to evaluate the most suitable reference genes across each life-stage of this
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species. To achieve this we performed qRT-PCR on 11 genes, including 7 classically
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used reference genes, across the egg, L1, L3, pupae and adult life-stages. We used
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Normfinder (Andersen et al., 2004) and geNorm (Vandesompele et al., 2002) software
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to normalise qRT-PCR data and identify the most suitable reference genes to assist in
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defining mRNA expression levels.
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2. Materials and Methods
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2.1. Parasites
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Lucilia cuprina (LS strain) were kept at 28°C and 80% humidity with a daily
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photoperiod of 16h. Adults were maintained on a diet of sugar and water, while eggs,
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L1 and L3 L. cuprina were raised on a wheat germ culture medium as outlined by
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Tachibana and Numata (2001). Gravid females were allowed to oviposit onto bovine
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liver, before eggs were transferred to the wheat germ culture medium shortly
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thereafter.
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2.2. RNA isolation and cDNA synthesis
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Extractions were performed separately with the following life-stages:
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a) 0.01g eggs, approximately 2h after oviposition,
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b) 0.025g first instar larvae (L1), collected from wheat germ culture medium
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approximately 24h after egg hatch,
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c) 3 third instar larvae (L3), collected from wheat germ culture medium
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approximately 4 days after oviposition,
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d) 3 pupae, approximately 3 days following the onset of pupation,
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e) 3 adult flies (sex undetermined), approximately 4 days after eclosion.
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Samples for extraction were placed in a tube containing 1ml of Trizol® (Invitrogen)
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reagent with four 4mm glass beads, and vigorously agitated using a Savant 101
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homogeniser for 30s. Total RNA was then extracted as per manufacturer’s
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instructions. For each life-stage, eight separate RNA extractions were performed.
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Following extraction, RNA samples were treated with Turbo DNase (Ambion),
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following the manufacturer’s instructions to remove remaining genomic DNA. RNA
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pellets were eluted in 30µl of RNase-free water. RNA integrity was assessed using a
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non-denaturing agarose gel where samples were run alongside RNA from Bos taurus
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(prepared as described by Bagnall et al., 2009) and the nematode Haemonchus
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contortus (prepared as described in Bagnall and Kotze, 2004). RNA samples were
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stored at –80°C.
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Complimentary DNA (cDNA) was generated using 2µg of total RNA with oligo–d(T)
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priming and Superscript™ III (Invitrogen), according to the manufacturer’s
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instructions.
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2.3. Primer design, cloning and sequencing
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The genes examined in this study are shown in Table 1. Primers for 18S rRNA, 28S
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rRNA, GST1, AChl, Per55 and aE7 were designed using Primer3 software (Rosen and
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Skaletsky, 2000) from publicly available sequence information. For the remaining
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candidate genes, ClustalW alignments of up to 10 sequences from species including
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Drosophila melanogaster, Anguilla anguilla, Rhipicephalus (Boophilus) microplus,
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Culex pipiens, Rattus rattus, and Homo sapiens were used to look for conserved
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regions across which to design degenerate primers. These primers were used in PCRs
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containing L. cuprina L3 life-stage cDNA. PCR products were generated using 10ng
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of cDNA, 250nm of forward and reverse primers and Platinum® taq DNA polymerase
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(Invitrogen) following the manufacturers’ instructions. Amplicons were run on
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agarose gels to verify the presence of single bands of predicted size, and cloned into
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pCR®2.1-Topo® (Invitrogen) according to the manufacturer’s instructions. Clones
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were sequenced using Big Dye® terminator v3.1 (Applied Biosystems) with 3.2pmol
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M13F or M13R primers and 20ng plasmid DNA on the ABI Prism Genetic Analyser.
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Sequences were aligned with other species to verify their identity, and submitted to
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GenBank. Specific L. cuprina primers using the new sequence information were then
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designed using Primer 3 (Rosen and Skaletsky, 2000; see Table 1).
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2.4. qRT-PCR
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Each qRT-PCR contained 10ng of cDNA, 250nm each of forward and reverse primer,
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and 5µl of SYBR® Green PCR Master Mix (ABI), in a total volume of 10µl. Three
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technical replicates were performed from each of the eight cDNAs generated for each
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life-stage. Standard cycling conditions were used on the ABI Prism 7900HT
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Sequence Detection System. RNA that had not been used in reverse transcription
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reactions (no-RT control) were used to verify the absence of genomic DNA. The
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inclusion of a product dissociation stage at the end of each qRT-PCR run allowed
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each PCR assay to be checked for the presence of a single product, and the absence of
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primer–dimers. Amplification efficiencies for each qRT-PCR assay were calculated
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from the slope of standard curves using 10-fold serially-diluted cloned plasmid DNA.
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Efficiency was calculated using the formula, E = 10 (-1/slope).
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To identify gene(s) for optimal data normalisation we used two software programs
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geNorm (Vandesompele et al., 2002) and Normfinder (Andersen et al., 2004). As
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both programs rely on the input of relative values, we converted the mean of the three
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technical replicate Ct values from the qRT-PCR to relative quantities using the delta-
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Ct method, where the lowest Ct value for each gene was set to 1 (see Vandesompele
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et al., 2002).
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3. Results
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In this study we chose seven common reference genes as potential candidates for gene
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expression with L. cuprina (18S rRNA, 28S rRNA, actin, cAMP-dependent protein
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kinase A (PKA), β-tubulin, acidic ribosomal phosphoprotein PO (RPLPO), and
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glyceraldehyde 3-phosphate dehydrogenase (GAPDH)) as well as four genes for
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which L. cuprina sequence information was publicly available (glutathione S-
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transferase (GST1), acetylcholinesterase (AChl), peritrophin-55 (Per55) and alpha
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esterase (aE7)). While 18S rRNA does not have a polyA tail, we included it in this
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analysis as previous work has noted that internal priming with oligo-d(T) does occur
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(Perrin et al., 2004). In our own trial with L. cuprina RNA comparing oligo-d(T) with
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random hexamers as the rtPCR priming method we found random hexamers did yield
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lower Ct values for 18S rRNA in qRT-PCR indicating they were more efficient at
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generating cDNA for this gene (data not shown). However, for actin, PKA, β-tubulin,
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RPLPO and GAPDH, the Ct values were significantly higher when using random
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hexamers, indicating that rtPCR priming with oligo-d(T) is preferable for the primer
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set developed for these genes (may reflect amplicon proximity to 3’), and for this
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reason we chose to use oligo-d(T) in this study.
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All RNA samples used in this study showed rRNA banding patterns equivalent to 18S
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and 5 / 5.8S of B. taurus and H. contortus rRNA. No band was evident in L. cuprina
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RNA samples at the equivalent 28S rRNA position in B. taurus or H. contortus. The
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concentration of total RNA isolated was between 1.7 to 3.8µg/µl. Aliquots of RNA
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collected following DNAse treatment (no-RT control) did not generate an amplicon
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with RPLPO primers within a 40 cycle qRT-PCR, demonstrating the absence of
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genomic DNA.
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Figure 1 shows the range, median and standard deviations of Ct values for each gene
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across all life stages. The lowest Ct values were for 18S rRNA and 28S rRNA. The
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range and standard deviations shown in Figure 1 reflect differences between life-stage
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expression rather than variation within individual life-stages.
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When considering the whole data set across all life-stages, Normfinder identified
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GST1 as the best gene for data normalisation followed by 28S rRNA, 18S rRNA and
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RPLPO with stability values of 0.101, 0.163, 0.168 and 0.174 respectively (Table 2).
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Normfinder relies on a model based approach to assign stability values that is based
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on expression variation within and between groups, rather than expression the entire
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data set (Anderson et al., 2004). When data for each life-stage was analysed
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separately, there was substantial variation in the rankings of genes. However,
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throughout all life-stages 28S rRNA, 18S rRNA, and RPLPO were always ranked at 6
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or higher for the 11 genes investigated. While GST1 ranked best for the whole data
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set, in the egg and pupal life-stages it ranked poorly (9 and11) when compared to
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most other genes (Table 2).
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The geNorm software assigns the gene-stability measure (M) assuming that two
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optimal reference genes exist within all experimental samples, regardless of
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differences that may exist within an experiment (e.g. cell type). Within our study M
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showed that 18S rRNA and 28S rRNA as the most stable genes when data from all life-
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stages was collated (Table 3). Using the geNorm stepwise inclusion strategy, which
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is a pair-wise variation (V) measure of the minimum number of genes required when
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normalising a data set, the combination of 18S rRNA, 28S rRNA with β-tubulin
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resulted in the lowest pair-wise variation measure (V = 0.037) and provided the
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optimum number of genes for normalisation.
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When data was analysed separately for each life stage, geNorm showed that only two
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genes were required for optimal data normalisation for the egg, L1, L3 and pupal life-
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stages, however the actual pair of genes generally differed for each life-stage.
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The pair wise variation measures for these optimal pairs for each life-stage were: eggs
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V= 0.012, L1 V= 0.006, L3 V= 0.006, pupae V = 0.015. For adults, geNorm indicated
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that three genes (18S rRNA, RPLPO and 28S rRNA) were the appropriate choice when
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normalising mRNA expression data (V =0.009).
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Both the geNorm and Normfinder programs identified the same 5 genes as being the
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most suitable housekeepers when all life-stage data were collated: 18S rRNA, 28S
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rRNA, GST1, β-tubulin and RPLPO. Both programs also identified PKA, Per 55, aE7
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and GAPDH as the poorest choices for data normalisation when all life-stage data was
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considered together.
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4. Discussion
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This study is the first to examine the most suitable reference genes for data
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normalisation in gene expression studies with the important livestock ectoparasite, L.
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cuprina. Overlap in the results between the Normfinder and geNorm programs,
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although not complete, provides a degree of confidence in the use of these genes for
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data normalisation. There were some differences in the genes identified as the most
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appropriate housekeepers when assessed using pooled data from all life-stages
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compared to those genes highlighted as being most suitable within a specific life-
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stage. Our data can therefore be used to identify the most appropriate reference genes
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in studies where expression over a number of life-stages is compared as well those
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studies examining expression within a single life-stage.
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A notable feature of our analysis was the poor performance of GAPDH. While
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GAPDH has been used extensively as a housekeeping gene for expression studies it is
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not always ideal. The variation in GAPDH expression probably reflects its role as a
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catalytic enzyme in the glycolytic pathway and the immediate energy requirements of
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cells, tissues, or organisms at the time of sampling. Our data consistently showed
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GAPDH was the most variable and therefore poorest choice as a reference gene within
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L. cuprina. This poor result for GAPDH may reflect the sampling method used in this
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study, where different groups of individuals were at approximately the same
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developmental stage, they may have differed in their immediate energy status at the
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actual time of sampling.
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An important consideration when choosing suitable reference genes is that they
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should ideally be expressed at similar levels as the target gene of interest (Bustin
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2000). This consideration is not a component of the geNorm and Normfinder
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programs. Hence, choosing the most suitable housekeepers for studies with L. cuprina
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should consider both the relative expression levels of genes under investigation
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(Figure 1) as well as the results from the Normfinder and geNorm analysis (Tables 2
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and 3). For example, the 18S rRNA and 28S rRNA genes may not be ideal for some
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studies due to their significantly higher expression levels than other genes examined
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here.
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An interesting property of the L. cuprina RNA samples was the absence of the 28S
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rRNA band usually present in eukaryotic cells when viewed on non-denaturing
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agarose gels. This suggests that the rRNA of this species possesses the ‘hidden break’
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described by Ishikawa (1977) which results in the dissociation of the 28S rRNA
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molecule into two 18S rRNA components upon exposure to a brief heat-treatment.
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This property has been reported previously for some Dipteran species (e.g. Balazs and
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Agosin, 1968: Lava-Sanchez and Puppo, 1975).
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In conclusion, this study has examined a number of L. cuprina genes and identified
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those most appropriate for use as reference genes in qRT-PCR studies with this
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species, either across all life-stages, or within individual life-stages. This information
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will be important to laboratories examining gene expression in this species and in
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other closely related pests (e.g. Lucilia sericata) which impact significantly on sheep
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grazing enterprises.
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Acknowledgements
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Funding for this work was provided by the L.W Bett Trust. We would like to thank
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Geoff Brown and Peter Green (Queensland Department of Primary Industries and
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Fisheries, Brisbane, Australia) for kindly supplying the L. cuprina to establish the
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colony used in this study.
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References
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Bagnall, NH., Kotze, AC., 2004. cDNA cloning and expression patterns of a
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specific sites of 26S ribosomal RNA of Musca carnaria. J Mol. Biol. 95, 9-20.
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Altarejos, M., Matsuo, K., Hintze, TH., Stanley, WC., Recchia, FA., 2004.
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Paradoxical down regulation of the glucose oxidation pathway despite enhanced flux
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in severe heart failure. J Mol. Cell. Cardiol. 36, 567-576.
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Levot, GW., 1995. Resistance and the control of sheep ectoparasties. Int. J Para, 25,
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1355-1362.
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Perrin, R., Lange, H., Grienenberger, JM., Gagliardi, D., 2004. AtmtPNPase is
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required for multiple aspects of the 18S rRNA metabolism in Arabidopsis thaliana
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mitochondria. Nucl. Acids Res. 32, 5174–5182.
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Rosen, S., and Skaletsky, HJ., 2000. Primer3 on the WWW for general users and for
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biologist programmers. In: Krawetz S, Misener S, (Eds.). Bioinformatics methods and
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protocols: methods in molecular biology. Humana Press, New Jersey, pp. 365–386.
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Smith, R., 2007. Beyond the Bale suppl. Battling the Blowfly. 2, 2.
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Solanas, M., Moral, R., Escrich, E., 2001. Unsuitability of using ribosomal RNA as
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loading control for Northern blot analyses related to the imbalance between
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messenger and ribosomal RNA content in rat mammary tumours. Anal. Biochem.
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288, 99-102.
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Steinau, M., Rajeevan, MS., Unger, ER., 2006. DNA and RNA references for qRT-
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PCR assays in exfoliated cervical cells. J Mol. Diag. 8, 113-118.
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Tachibana, SI., and Numata, H., 2001. An artificial diet for blow fly larvae, Lucilia
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sericata (Meigen). Appl. Entomol. Zool. 36, 521-523.
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Vandesompele, J., De Preter, K., Pattyn, F., Poppe, B., Van Roy, N., DePaepe, A.,
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2002. Accurate normalization of real-time quantitative RT-PCR data by geometric
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averaging of multiple internal control genes. Gen. Biol. 3, 1-12.
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Fig. 1. Distribution of Critical threshold (Ct) values for candidate reference genes
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obtained using qRT-PCR in L. cuprina. Data for all life-stages was pooled for each
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gene. Boxes show range of Ct values within each gene, black centre line indicates
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the median, extended vertical bars show standard deviation of the mean (n= 120 Ct
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values, derived from 8 RNA samples for each life-stage, each with three technical
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replicates).
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25
20
15
10
5
Gene
16
7
aE
Pe
r5
5
hl
AC
PK
A
βtu
bu
lin
R
PL
PO
G
AP
D
H
G
ST
1
28
s
18
s
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Ac
tin
0
rR
N
A
rR
N
A
Critical threshold (Ct)
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Table 1. Genes evaluated in the present study, and primers used for qRT-PCR.
Gene Name
Accession No.
Forward Primer (5'-3')
Reverse Primer (5'-3')
18S rRNA
AF322425
AGCAGTTTGGGGGCATTAG
GCTGGCATCGTTTATGGTTAG
28S rRNA
EU626548
CCAAAGAGTCGTGTTGCTTG
ATTCAGGTTCATCGGGCTTA
Actin
GQ284559
GTACGACCGGAGGCATACAA
GCCAACCGTGAGAAGATGAC
cAMP-dependent protein kinase A (PKA)
GQ284557
GATAGCGTAGGGAAACCAAGAA CAACACAAGCCGACAAAAGC
β-tubulin
GQ284558
AAGCTGACGACACCCACATAC
CGGGCATGAAGAAGTGAAGA
Acidic ribosomal phosphoprotein PO
GQ284556
ACCCATAAGGACGACACC
GGTGCTGACAATGTTGGTTC
GQ284555
GGCGGTGCCAAGAAGGTC
GCATGCACAGTGGTCATGA
Glutathione S-transferase (GST1)
L23126
GCCAGTGTCAGCACCTTTG
GCAACCTTCCCAGTTTTCATC
Acetylcholinesterase (AChl)
U88631
CTTCTTTACCTGCCCCACAA
CATCCATTCACCCCACAAG
Peritrophin-55 (Per55)
AF515826
CAAACCACCGATGTTGAATG
AGTGGAAGGAGGGGCAGTAG
Alpha esterase (aE7).
U56636
CCGGCTATAAGGGTGAGGA
CCGAGGATGTTTGGGTAAGA
(RPLPO)
Glyceraldehyde 3-phosphate
dehydrogenase (GAPDH)
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Table 2. Normfinder ranking of candidate genes for data normalisation across egg,
L1, L3, pupae, adult (n = 8 separate RNA preparations) and overall (includes all lifestage data, n = 40) for L. cuprina. The stability value is shown in brackets under each
gene.
Rank
Life-stage
Overall
Egg
L1
L3
Pupae
Adult
1
GST1
(0.101)
RPLPO
(0.033)
RPLPO
(0.005)
18S rRNA
(0.007)
β-tubulin
(0.015)
RPLPO
(0.010)
2
28S rRNA
(0.163)
28S rRNA
(0.034)
Per55
(0.005)
PKA
(0.007)
28S rRNA
(0.015)
aE7
(0.020)
3
18S rRNA
(0.168)
aE7
(0.053)
Actin
(0.007)
28S rRNA
(0.016)
RPLPO
(0.027)
β-tubulin
(0.022)
4
RPLPO
(0.174)
18S rRNA
(0.057)
GST1
(0.010)
RPLPO
(0.058)
Actin
(0.031)
28S rRNA
(0.032)
5
β-tubulin
(0.175)
Actin
(0.058)
18S rRNA
(0.011)
Actin
(0.086)
PKA
(0.042)
GST1
(0.041)
6
Actin
(0.224)
β-tubulin
(0.070)
28S rRNA
(0.026)
aE7
(0.093)
18S rRNA
(0.077)
18S rRNA
(0.042)
7
AChl
(0.311)
PKA
(0.811)
PKA
(0.032)
GST1
(0.100)
AChl
(0.113)
Actin
(0.043)
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PKA
(0.353)
GAPDH
(0.094)
β-tubulin
(0.053)
β-tubulin
(0.126)
Per55
(0.147)
AChl
(0.073)
9
Per55
(0.415)
GST1
(0.095)
aE7
(0.072)
AChl
(0.147)
GST1
(0.150)
PKA
(0.083)
10
aE7
(0.534)
Per55
(0.108)
AChl
(0.118)
GAPDH
(0.195)
aE7
(0.155)
Per55
(0.141)
11
GAPDH
(0.724)
AChl
(0.136)
GAPDH
(0.195)
Per55
(0.422)
GAPDH
(0.201)
GAPDH
(0.265)
18
Table 3. geNorm ranking of candidate genes for data normalisation across egg, L1,
L3, pupae, adult (n = 8 separate RNA preparations) and overall (includes all life-stage
data, n = 40) for L. cuprina. The gene-stability measure (M) is shown in brackets
under each gene.
Rank
Life-stage
Overall
Egg
L1
L3
Pupae
Adult
1
18S rRNA
(0.059)
β-tubulin
(0.037)
RPLPO
(0.013)
18S rRNA
(0.021)
RPLPO
(0.024)
RPLPO
(0.033)
1
28S rRNA
(0.059)
28S rRNA
(0.037)
Per55
(0.013)
PKA
(0.021)
28S rRNA
(0.024)
18S rRNA
(0.033)
3
β-tubulin
(0.118)
18S rRNA
(0.040)
18S rRNA
(0.018)
28S rRNA
(0.039)
β-tubulin
(0.039)
28S rRNA
(0.034)
4
RPLPO
(0.141)
RPLPO
(0.055)
Actin
(0.022)
RPLPO
(0.065)
PKA
(0.050)
β-tubulin
(0.037)
5
GST1
(0.187)
Per55
(0.064)
GST1
(0.028)
aE7
(0.082)
18S rRNA
(0.062)
Actin
(0.045)
6
Actin
(0.232)
Actin
(0.090)
28S rRNA
(0.034)
Actin
(0.094)
Actin
(0.072)
aE7
(0.055)
7
AChl
(0.307)
GST1
(0.108)
PKA
(0.040)
β-tubulin
(0.105)
Per55
(0.104)
AChl
(0.069)
8
PKA
(0.373)
AChl
(0.122)
β-tubulin
(0.051)
AChl
(0.120)
AChl
(0.130)
GST1
(0.079)
9
Per55
(0.445)
aE7
(0.134)
aE7
(0.064)
GST1
(0.135)
aE7
(0.154)
PKA
(0.097)
10
aE7
(0.536)
PKA
(0.338)
AChl
(0.084)
GAPDH
(0.177)
GST1
(0.175)
Per55
(0.113)
11
GAPDH
(0.643)
GAPDH
(0.152)
GAPDH
(0.121)
Per55
(0.257)
GAPDH
(0.200)
GAPDH
(0.163)
19