Design, optimization and validation of genes commonly used in expression studies on DMH/AOM rat colon carcinogenesis model

Submitted 23 November 2018 Accepted 30 December 2018 Published 29 January 2019 Corresponding author Carme Piñol-Felis, [email protected] Academic editor Emiliano Maiani Additional Information and Declarations can be found on page 13 DOI 10.7717/peerj.6372 Copyright 2019 Bars-Cortina et al. Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS Design, optimization and validation of genes commonly used in expression studies on DMH/AOM rat colon carcinogenesis model David Bars-Cortina1,2, Antoni Riera-Escamilla3, Gemma Gou4,5, Carme Piñol-Felis2,6and María-José Motilva7 1Food Technology Department, XaRTA-TPV, Agrotecnio Center, Escola Tècnica Superior d’Enginyeria Agrària, Universitat de Lleida, Lleida, Catalonia 2Department of Medicine, Universitat de Lleida, Lleida, Catalonia 3Andrology Department, Fundació Puigvert, Universitat Autònoma de Barcelona, Instituto de Investigaciones Biomédicas Sant Pau (IIB-Sant Pau), Barcelona, Catalonia, Spain 4Molecular Physiology of the Synapse Laboratory, Biomedical Research Institute Sant Pau (IIB Sant Pau), Barcelona, Spain 5Universitat Autónoma de Barcelona, Bellaterra, Spain 6Institut de Recerca Biomèdica de Lleida Fundació Dr. Pifarré-IRBLLeida, Lleida, Spain 7Instituto de Ciencias de la Vid y del Vino (ICVV) (CSIC Universidad de la Rioja-Gobierno de La Rioja), Logroño, Spain ABSTRACT Colorectal cancer (CRC), also known as colon cancer, is the third most common form of cancer worldwide in men and the second in women and is characterized by several genetic alterations, among them the expression of several genes. 1,2-dimethylhydrazine (DMH) and its metabolite azoxymethane (AOM) are procarcinogens commonly used to induce colon cancer in rats (DMH/AOM rat model). This rat model has been used to study changes in mRNA expression in genes involved in this pathological condition. However, a lack of proper detailed PCR primer design in the literature limits the reproducibility of the published data. The present study aims to design, optimize and validate the qPCR, in accordance with the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines, for seventeen genes commonly used in the DMH/AOM rat model of CRC (Apc, Aurka, Bax, Bcl2, β-catenin, Ccnd1, Cdkn1a, Cox2, Gsk3beta, IL-33, iNOs, Nrf2, p53, RelA, Smad4, Tnf α and Vegfa) and two reference genes (Actb or β-actin and B2m). The specificity of all primer pairs was empirically validated on agarose gel, and furthermore, the melting curve inspection was checked as was their efficiency (%) ranging from 90 to 110 with a correlation coefficient of r2>0.980. Finally, a pilot study was performed to compare the robustness of two candidate reference genes. Subjects Food Science and Technology, Molecular Biology, Nutrition, Oncology Keywords qPCR, Dimethylhydrazine (DMH), Azoxymethane (AOM), Colon, SYBR, Gene validation How to cite this article Bars-Cortina D, Riera-Escamilla A, Gou G, Piñol-Felis C, Motilva M-J. 2019. Design, optimization and validation of genes commonly used in expression studies on DMH/AOM rat colon carcinogenesis model. PeerJ 7:e6372 http://doi.org/10.7717/peerj.6372 INTRODUCTION Colorectal cancer (CRC) is the third most common form of cancer worldwide in men (surpassed by lung and prostate cancer) and the second in women (overtaken by breast cancer). The incidence of CRC varies significantly between populations, Australia and New Zealand being the countries with the highest rate of new diagnoses, while the countries of western Africa have the lowest incidence (Fact Sheets by Cancer, 2018). In the United States, CRC represents the fourth most prevalent cancer with 135430 new cases diagnosed in 2017 and representing 8.0% of all new cancer cases (Colorectal Cancer-Cancer Stat Facts, 2018). In Europe, CRC is the second most common cancer in both sexes (Ferlay et al., 2013). In Asia, especially in the industrialized regions, the incidence of CRC has increased over the last decade due to the adoption of the western lifestyle (Koo et al., 2012). Interestingly, a similar situation is taking place in Eastern Europe, Latin America and the Caribbean countries (Arnold et al., 2017). Unfortunately, the global incidence of CRC is expected to increase by about 60% and it is predicted that, in 2030, more than 2.2 million new cases will be diagnosed and 1.1 million people will die from this disease (Arnold et al., 2017). It is well established that lifestyle and especially eating patterns play an important role in the risk of developing cancer in the digestive tract (Slattery et al., 1998;Pan, Yu & Wang, 2018;Kurotani et al., 2010;Willett, 1994). Hence, several studies have focused on diet as the major strategy to counteract and prevent colon cancer (Grosso et al., 2017). Different animal models have been used to evaluate the effect of food components on colon cancer prevention. Such models of colon carcinogenesis can be divided into two broad categories: transgenic and chemically-induced (Perše & Cerar, 2011;Femia & Caderni, 2008). A classic example of a transgenic animal CRC model concerns adenomatous polyposis coli (Apc) gene mutations. Nevertheless, these animal models have been generated to study familial adenomatous polyposis (FAP) and hereditary nonpolyposis colorectal cancer (HNPCC) syndromes which only account for approximately 5% of all cases (Perše & Cerar, 2011; Femia & Caderni, 2008). Dimethylhydrazine (DMH) and its metabolite azoxymethane (AOM) are frequently used to generate chemically-induced models for CRC (Perše & Cerar, 2011;Megaraj et al., 2014). These models share many similarities with human sporadic colon cancer since DMH/AOM colon carcinogenesis occurs as a multistep process. The stepwise development of CRC from dysplastic crypts, adenomas to carcinomas provides the opportunity to investigate and identify molecular alterations in each stage of tumour development (Perše & Cerar, 2011). Interestingly, genes that have been found mutated in human sporadic colon cancer have also been found to mutate in DMH/AOM-induced colon carcinogenesis (Perše & Cerar, 2011). Nutrigenomics, the study of the effects of food components on gene expression, is a broad approach used in CRC animal models. In this regard, a wealth of data addresses this issue through different dietary regimes (more than 100 results appear when searching for ‘‘rat colon cancer diet gene expression’’ in Pubmed). However, the data generated is often confusing when it is carefully evaluated. For instance, in some published articles, the accession number of the reference sequence used is not indicated or the qPCR is not well designed or described (i.e., the primer sequence is missing or contains mistakes, primers Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 2/18 are not specific). On the basis of the aforementioned data, in this study we provide a welldesigned and described qPCR protocol according to the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines (Bustin et al., 2009) for 17 genes routinely studied in DMH/AOM CRC rat model (El-Shemi et al., 2016; Kensara et al., 2016;Islam et al., 2016;Qie & Diehl, 2016;Rivera-Rivera & Saavedra, 2016; Rubio, 2017;Al-Henhena et al., 2015;Tan et al., 2015;Gamallat et al., 2016;Walter et al., 2010). Moreover, we also analyse two reference genes commonly used in this carcinogenesis model. MATERIALS & METHODS Animals All animal care and experimental procedures were in accordance with the EU Directive 2010/63/EU guidelines for animal experiments and approved by the Animal Ethics Committee at the University of Lleida (CEEA 02/06-16). The project approved (CEEA 02/06-16) allowed the performance of a parallel study, described briefly on Fig. S1. However, from the same project, a group of remnants healthy adult male Wistar rats weighing between 200 to 250 g and maintained in the animal facilities at the University of Lleida were used for primer validation as a necessary previous step to perform a gene expression study. The animals were housed in polyvinyl cages at a controlled temperature (21 ◦C±1◦C) and humidity (55% ±10% RH), maintained under a constant 12 h light-dark cycle. All the animals were fed with water and a standard diet for rodents (Envigo Teklad Global Diet 2014, batch 3201, Settimo Milanese, Italy) ad libitum. Three randomly-selected animals were sacrificed by intracardiac puncture after isoflurane anaesthesia (ISOFlo, Veterinaria Esteve, Bologna, Italy). Distal colon tissue (the most relevant region in CRC studies with DMH/AOM induced models) (Megaraj et al., 2014) was extracted and immediately frozen in liquid nitrogen and then stored at −80 ◦C until it was analysed. RNA isolation & cDNA synthesis Tissue Lyser LT (Quigen, Hilden, Germany) was used as a tissue homogenizer (four cycles of 50 Hz for 30 s. with a 1 min. pause within each cycle). Total RNA was extracted using the TrizolTM Plus PureLinkTM Kit RNA Mini Kit (Invitrogen, USA) following the kit instructions. RNA quantity and purity (260/280 and 260/230 ratios) were assessed with a ND-1000 Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Furthermore, the integrity of the total RNA obtained was evaluated through 1% agarose gel (Derveaux, Vandesompele & Hellemans, 2010). Reverse transcription was performed with the Maxima H Minus First Strand cDNA Synthesis kit with dsDNase (Ref. K1682; Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions (≤5µg of total RNA as template and using 100 pmol random hexamer primer). The resulting material was diluted with nuclease free water (BP561-1; Fisher Scientific, Waltham, MA, USA) for the qPCR reaction. Primer pairs design Primer pairs for seventeen different CRC related genes (Apc, Aurka, Bax, Bcl2, β-catenin, Ccnd1, Cdkn1a, Cox2, Gsk3beta, IL-33, iNOs, Nrf2, p53, RelA, Smad4, Tnf αand Vegfa) Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 3/18 and two candidate reference genes (Actb and B2m) were designed and evaluated for their suitability through a number of bioinformatics tools summarized in Fig. 1A. Briefly, we selected the genes to be studied (from a literature search) and obtained their accession number. Then, the nucleotide sequence was retrieved from the NCBI Nucleotide database which is feed from genome assembly of Rnor_6.0 (https: //www.ncbi.nlm.nih.gov/nucleotide/). Afterwards, if no previously published primers were used, we checked for their splice variants (through Ensembl Release 87) (Zerbino et al., 2018) (https://www.ensembl.org/index.html). In the case of spliced genes and also in order to avoid the presence of SNPs (single-nucleotide polymorphisms), multiple sequence alignment was performed to find common regions within the splice variants (https://www.ebi.ac.uk/Tools/msa/clustalo/). Zerbino et al. (2018) The selected sequence was transferred to Primer3Plus software version 2.4.2 (Untergasser et al., 2012) to pick up primers (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi). The technical parameters used in the design of the primers were based on Thornton & Basu (2011). Finally, the primer pairs obtained were submitted to further in silico analysis in order to avoid strong primer secondary structures (through OligoAnalyzer 3.1, Integrated DNA Technologies; https://eu.idtdna.com/calc/analyzer) (Owczarzy et al., 2008), robust amplicon secondary structures (with the UNAFold tool, Integrated DNA Technologies; https://eu.idtdna.com/UNAFold?) (Owczarzy et al., 2008) and unspecificity (with the In-Silico PCR tool of the UCSC Genome Browser Database (https://genome.ucsc.edu/cgi-bin/hgPcr) (Rhead et al., 2010) and the Nucleotide BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE_TYPE=BlastSearch) (Johnson et al., 2008)). The primer pairs selected after these bioinformatics tool tests were acquired from the Sigma-Aldrich custom oligo facilities (Haverhill, UK). PCR reaction & empirical validation PCR reactions were performed in a total reaction volume of 25 µl comprising 2.5 µl of 10X Dream Taq Buffer, 0.5 µl of dNTP mix (R0191; Thermo Fisher Scientific, Waltham, MA, USA), 0.5 µl of gene-specific primer pair at 10 µM, 2 µl of cDNA template, 0.625 U Dream Taq DNA Polymerase (EP0701; Thermo Fisher Scientific, Waltham, MA, USA) and filled up to 25 µl with nuclease free water (BP561-1; Fisher Scientific, Waltham, MA, USA). The PCR conditions used were 3 min of polymerase activation at 95 ◦C followed by 35 cycles of denaturation at 95 ◦C for 30 s, an annealing step at 57 ◦C (or between 51 ◦C and 61 ◦C in the case of a gradient) for 30 s and extension at 72 ◦C for 30 s. Final extension (72 ◦C) was performed for 5 min followed by an infinite 4 ◦C step. After the previous in silico steps described above, all the primer pairs were submitted to further analysis (Fig. 1B). Although the specificity of a pair of primers and absence of primer dimers is assessed in a more sensitive way using the melting curve in the qPCR reaction, it has been also considered opportune to check it through PCR. Primer specificity was assessed through conventional PCR followed by agarose gel electrophoresis in order to check that unique band with the expected molecular weight according to the amplicon size was obtained. The annealing temperature was set at 57 ◦C by default but, in some cases, an annealing temperature gradient was needed (see above). Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 4/18 Figure 1 Flowchart indicating the strategy followed to design and validate the candidate primers. (A) In silico validation flowchart. (B) Empirical validation flowchart. Full-size DOI: 10.7717/peerj.6372/fig-1 Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 5/18 qPCR reaction, empirical validation and analysis Real-time PCR reactions were performed in a total reaction volume of 20 µl comprising 10 µl of SYBRTM Select Master Mix (2X) (Thermo Fisher Scientific, Waltham, MA, USA), µl needed of each gene-specific primer (for every primer the concentration has been optimized from 100 nM to 400 nM), 2 µl of cDNA, and filled up to 20 µl with nuclease free water (BP561-1; Fisher Scientific, Waltham, MA, USA). The qPCR reactions were carried out on a Bio-Rad CFX96 real-time PCR system (Bio-Rad Laboratories, Hercules, CA, USA) under the following conditions: 2 min of uracil-DNA glycosylase (UDG) activation at 50 ◦C, 2 min of polymerase activation at 95 ◦C, followed by 40 cycles of denaturation at 95 ◦C for 15 s and annealing/extension at the corresponding annealing temperature for 1 min. A melting curve analysis was done immediately after the qPCR analysis. Once the unique band had been obtained in the previous PCR step, qPCR efficiency, linearity and specificity (unique and clear melt curve) were assessed taking into account (Taylor et al., 2010), and therefore the MIQE guidelines (Bustin et al., 2009). qPCR efficiency must be within a range of 90 to 110% and with a standard curve correlation coefficient (R2)≥0.98 (Taylor et al., 2010;Kennedy & Oswald, 2011). Each point on the standard curve was performed in triplicate. Whenever possible, the standard curve comprised three orders of magnitude. Cqvalues >38 were not considered for data analysis due to their low efficiency (Bustin et al., 2009). Furthermore, in triplicate, no template control (NTC) was included for each primer pair in every run. The data resulting from the qPCR were analysed using the Bio-Rad CFX Maestro 1.1 software. Baseline correction and threshold setting were performed using the automatic calculation offered by the same software. Reference gene selection The primer validation described in this paper is the necessary first step before to perform future relative gene expression studies using these primer pairs. In addition, in order to normalize the data, a reference gene choice is mandatory. The selection of an adequate reference gene is crucial because the expression levels of the reference genes may change between tissues and species and might be also influenced by experimental conditions of an experiment. Hence, for each experiment it is highly recommended to empirically choose the best reference gene for our study apart from a bibliographic search. As an example of this issue, and in parallel to the primers validation, we have conducted an experiment addressing the possible effect of dietary supplementation with a particular fruit (whiteand red-fleshed apples) and cyanidin galactoside (the main anthocyanin in red-fleshed apples) on these genes in the early phases of rat colon cancer induced by AOM (Fig. S1). For this reason, two reference genes commonly used in DMH/AOM rat model experiments were selected and submitted to check their expression stability in the different experimental groups (Fig. S1). In detail, two distal colon from two rats per treatment group were analysed with three technical replicates each one. The amount of cDNA used in each reaction was 100 ng. Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 6/18 The stability (aptitude) of the candidate reference genes was evaluated with two software tools (web-based RefFinder platform: http://leonxie.esy.es/RefFinder/ accessed 08/05/2018) and Bio-Rad CFX Maestro 1.1. software, based on the geNorm algorithm). RESULTS Genetic material used As stated in the previous section, three healthy adult male Wistar rats were selected randomly and sacrificed. The distal region of the colon was obtained and immediately frozen. The distal colon samples were pooled prior to total RNA extraction. The quality and quantity of the RNA was good (ratio 260/280 =1.89, ratio 260/230 =2.05, 186.6 ng/µl). Furthermore, the integrity of the total RNA obtained was evaluated through 1% agarose gel (Derveaux, Vandesompele & Hellemans, 2010). In all cases, 18S and 28S ribosomal RNA bands were clearly detected and no degraded RNA (illustrated as smear in the gel lane) was identified (pdf S1). Primer design and validation through agarose gel The primer pairs detailed in Table 1 passed all the bioinformatics tests described in Fig. 1A. In particular, Table 1 specifies the nucleotide sequence of all primers from each gene studied (with their gene accession number); their map on mRNA rat genome (Rnor_6.0); their amplicon size; their annealing temperature used; and, if the primers were in-house designed or not. Furthermore, as stated on Fig. 1B, a PCR +agarose gel has been performed in order to check that a single band with the expected molecular weight was obtained. Some examples of figures showing the agarose gel results are attached in pdf S2. These primers were selected for further analysis through qPCR. On the contrary, the primers which do not pass some step in the validation process are shown in Table S1. The majority of the primer pairs (12, if we also consider the reference genes) were in-house designed. The remainder were from published intact sequences or, in some cases, were obtained after some in silico mismatch corrections (indicated as ‘‘Based on’’ in Table 1 and Table S1). Primer validation through qPCR Through qPCR technique the primer pairs were submitted to an extra-control of their specificity because is a more sensitive analysis: melting curve. As can be verified in Fig. S2, a unique peak was detected for each primer pair, demonstrating their specificity and demonstrating the absence of primer dimers. In addition, we also need to validate the qPCR assay, mainly qPCR efficiency. Table 2 summarizes the qPCR validation results obtained. In detail for each gene, the linearity range, lowest and highest Cq value used, qPCR efficiency (in %), coefficient of determination (R2) and primer concentration used were detailed. Some examples of efficiency qPCR assays output are attached in pdf S2. Furthermore, repeatability and reproducibility has been assessed (Fig. 2) from the expression analysis study mentioned previously (Fig. S1). In detail, the approach followed Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 7/18 Table 1 List of the primer pairs validated. Gene (Accession no.) Primer sequences (50–30) Gene region Amplicon size Ta Reference F: TCTGTGTGGATTGGTGGCT Exon 6, CDS Actb* (NM_031144.3)R: TCATCGTACTCCTGCTTGCT Exon 6, CDS 80 bp 57 ◦C / 59.3 ◦CIn-house F: ACTCCTTACTGCTTCTCACG Exon 15, CDS Apc (NM_012499.1)R: GTCCTTACTTTCTTTGCCCTTT Exon 15, CDS 114 bp 57 ◦C In-house F: AGTGCTATCTGTCCATCAACC Exon 8, 30UTR Aurka (NM_153296.2)R: ACCCGCATTTCCAGTCATCT Exon 8, 30UTR 98 bp 59.3 ◦C In-house F: AGAGGATGATTGCTGATGTGG Exon 3, CDS Bax (NM_017059.2)R: CCCAGTTGAAGTTGCCGT Exon 4, CDS 93 bp 57 ◦C In-house F: GATTGTGGCCTTCTTTGAG Exon 1, CDS Bcl2 (NM_016993.1)R: CAGGCTGAGCAGCGTCTTC Exon 2, CDS 232 bp 59.3 ◦CBased on Zucchini et al. (2005) F: CCCACCCTCATGGCTACTTC Exon 4, 30UTR B2m* (NM_012512.2)R: GATGAAAACCGCACACAGGC Exon 4, 30UTR 157 bp 57 ◦C / 59.3 ◦CTan et al. (2015) F:CAAGTGGGTGGCATAGAGG Exon 8, CDS β-catenin (AF121265.1)R: ATGACGAAGAGCACAGATGG Exon 8, CDS 93 bp 57 ◦C In-house F: AGTTGCTGCAAATGGAACTG Exon 2, CDS Ccnd1 (NM_171992.4)R: TGGAGAGGAAGTGTTCGATG Exon 3, CDS 93 bp 57 ◦CBased on Wu et al. (2012) F: ATGTCCGATCCTGGTGATGT Exon 1, CDS Cdkn1a (NM_080782.3)R: GCTCAACTGCTCACTGTCCA Exon 1, CDS 90 bp 57 ◦C In-house F: TGTATGCTACCATCTGGCTTCGG Exon 7, CDS Cox2 (AF233596.1)R: GTTTGGAACAGTCGCTCGTCATC Exon 7, CDS 94 bp 57 ◦CPeinnequin et al. (2004) F: TGGGTCATTTGGTGTGGT Exon 2, CDS Gsk3beta (NM_032080.1)R: GGTTCTTAAATCGCTTGTCCT Exon 2-3, CDS 95 bp 57 ◦C In-house F: TTCAGTCCTGCCCTTTCCTT Exon 9, 30UTR IL-33 (NM_001014166.1)R: TGTGGTGCGTGCTCTTCT Exon 9, 30UTR 84 bp 57 ◦C In-house F: CACCACCCTCCTTGTTCAAC Exon 19, CDS iNOs (NM_012611.3)R: CAATCCACAACTCGCTCCAA Exon 19, CDS 132 bp 57 ◦CNergiz et al. (2012) F: GTGACTCGGAAATGGAAGAG Exon 5, CDS Nrf2 (NM_031789.2)R: AGAAGAATGTGTTGGCTGTG Exon 5, CDS 83 bp 57 ◦C In-house F: GCAGAGTTGTTAGAAGGC Exon 4, CDS p53 (NM_030989.3)R: TTGAGAAGGGACGGAAGA Exon 4, CDS 138 bp 57 ◦C In-house F: TCACCAAAGACCCACCTCA Exon 4, CDS RelA (NM_199267.2)R: GTTCAGCCTCATAGAAGCCA Exon 4, CDS 81 bp 57 ◦C In-house F: CCACCAACTTCCCCAACATT Exon 5, CDS Smad4 (AB010954.1)R: TGCAGTCCTACTTCCAGTCCAG Exon 7, CDS 191 bp 57 ◦CKensara et al. (2016) F: ACCACGCTCTTCTGTCTACTG Exon 1, CDS Tnf α (NM_012675.3)R: CTTGGTGGTTTGCTACGAC Exon 3-4, CDS 169 bp 59.3 ◦CLi et al. (2015) F: GACACACCCACCCACATAC Exon 7, 30UTR Vegfa (ENSRNOG00000019598)R: TCCAGTGAAGACACCAATAACA Exon 7, 30UTR 141 bp 57 ◦C In-house Notes. *denotes reference gene. Ta, annealing temperature. Bars-Cortina et al. (2019), PeerJ, DOI 10.7717/peerj.6372 8/18 Table 2 qPCR efficiency and correlation coefficient (R2) obtained for each selected gene. Gene (Accession no.) Linearity range (ng cDNA) Lowest Cq value Highest Cq value qPCR efficiency R2[primer], nM 2 to 128 (Ta: 57 ◦C) 17.4 23.1 Actb* (NM_031144.3)22.5 to 114 (Ta: 59.3 ◦C) 16.4 19.2 108.9% 90.5% 0.998 0.999 100 Apc (NM_012499.1) 2 to 128 23.7 29.4 108.5% 0.998 100 Aurka (NM_153296.2) 9 to 243 28.6 33.2 108.8% 0.998 200 Bax (NM_017059.2) 0.16 to 100 25.4 34.6 106.3% 0.994 200 Bcl2 (NM_016993.1) 0.5 to 128 29 37.1 102.9% 0.990 200 1.6 to 148.8 (Ta:57 ◦C) 22.8 29.5 B2m* (NM_012512.2)2 to 128 (Ta:59.3 ◦C) 23.1 26.8 100.4% 108.7% 0.996 0.997 200 β-catenin (AF121265) 2 to 128 21.6 27.5 109.8% 0.980 150 Ccnd1 (NM_171992.4) 22.5 to 114 22.1 24.3 108.5% 0.997 100 Cdkn1a (NM_080782.3) 0.16 to 100 27 36.4 101.2% 0.994 200 Cox2 (AF233596.1) 0.5 to 100 30.4 37.1 106.6% 0.998 200 Gsk3beta (NM_032080) 1.56 to 100 23.8 29.7 106.8% 0.997 200 IL-33 (NM_001014166.1) 2 to 128 25.5 31.1 110.0% 0.996 100 iNOs (NM_012611.3) 20 to 338.8 30.8 35 100.0% 0.990 200 Nrf2 (NM_031789.2) 0.16 to 100 23.1 32.3 106.6% 0.996 200 p53 (NM_030989.3) 8 to 128 32.5 35.9 102.6% 0.997 100 RelA (NM_199267.2) 0.16 to 100 24.9 34.1 103.0% 0.997 200 Smad4 (AB010954.1) 6.4 to 100 23.7 27.6 104.5% 0.993 400 Tnf α (NM_012675.3) 10 to 114 30 34.4 90.9% 0.987 100 Vegfa (ENSRNOG00000019598) 0.16 to 100 22.5 30.1 106.9% 0.992 200 Notes. *denotes reference gene. 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