Differential Gene Expression across Breed and Sex in Commercial Pigs Administered Fenbendazole and Flunixin Meglumine

Jeremy T. Howard; Audrey T. O’Nan; Christian Maltecca; Ronald E. Baynes; Melissa S. Ashwell

doi:10.1371/journal.pone.0137830

Abstract

Characterizing the variability in transcript levels across breeds and sex in swine for genes that play a role in drug metabolism may shed light on breed and sex differences in drug metabolism. The objective of the study is to determine if there is heterogeneity between swine breeds and sex in transcript levels for genes previously shown to play a role in drug metabolism for animals administered flunixin meglumine or fenbendazole. Crossbred nursery female and castrated male pigs (n = 169) spread across 5 groups were utilized. Sires (n = 15) of the pigs were purebred Duroc, Landrace, Yorkshire or Hampshire boars mated to a common sow population. Animals were randomly placed into the following treatments: no drug (control), flunixin meglumine, or fenbendazole. One hour after the second dosing, animals were sacrificed and liver samples collected. Quantitative Real-Time PCR was used to measure liver gene expression of the following genes: SULT1A1, ABCB1, CYP1A2, CYP2E1, CYP3A22 and CYP3A29. The control animals were used to investigate baseline transcript level differences across breed and sex. Post drug administration transcript differences across breed and sex were investigated by comparing animals administered the drug to the controls. Contrasts to determine fold change were constructed from a model that included fixed and random effects within each drug. Significant (P-value <0.007) basal transcript differences were found across breeds for SULT1A1, CYP3A29 and CYP3A22. Across drugs, significant (P-value <0.0038) transcript differences existed between animals given a drug and controls across breeds and sex for ABCB1, PS and CYP1A2. Significant (P <0.0038) transcript differences across breeds were found for CYP2E1 and SULT1A1 for flunixin meglumine and fenbendazole, respectively. The current analysis found transcript level differences across swine breeds and sex for multiple genes, which provides greater insight into the relationship between flunixin meglumine and fenbendazole and known drug metabolizing genes.

Citation: Howard JT, O’Nan AT, Maltecca C, Baynes RE, Ashwell MS (2015) Differential Gene Expression across Breed and Sex in Commercial Pigs Administered Fenbendazole and Flunixin Meglumine. PLoS ONE 10(9): e0137830. https://doi.org/10.1371/journal.pone.0137830

Editor: Firas H. Kobeissy, University of Florida, UNITED STATES

Received: July 6, 2015; Accepted: August 24, 2015; Published: September 14, 2015

Copyright: © 2015 Howard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Funding provided by North Carolina Biotechnology Center (grant #2012-MRG-1108) and Food Animal Residue Avoidance Databank (USDA #2013-41480-21002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Drug use in livestock has received increased attention due to welfare concerns and food safety but a limited amount of work has been done on any potential genetic variation in drug metabolism that exists within livestock species and its impact on withdrawal times in commercial swine production. Recent work by Howard et al. [1] has shown that differences in pharmacokinetic parameters exist across four major swine breeds (i.e. Duroc, Hamphsire, Yorkshire and Landrace) and sex. This is unsurprising due to the vast body of literature in humans that has identified individual and group genetic variation in drug metabolism and response [2–6]. The pig has recently become increasing relevant as an animal model for biomedical research, mainly due to the fact that the anatomy, genetics and physiology reflect human biology more closely than classic animal models (i.e. fruit fly, zebrafish and rodents) [7]. Furthermore, differences in transcript levels have been shown to exist in commercial pigs and special breeds such as the Göttinger minipig and the Yucatan micropig [8–9]. Fink-Gremmels [10] examined liver cytochrome P450 enzymatic activity levels and found that conventional pigs have activity levels similar to those found in human livers while miniature pigs have much higher activity levels. Many drugs used to treat livestock are similar to those used in humans, which allows for knowledge to be garnered that will impact both the swine industry and human drug development [11]. Additionally, within the past decade the identification of a large number of single nucleotide polymorphisms (SNP), the sequencing of the swine genome, and a large list of quantitative trait loci across multiple economically important traits have been identified [12]. Therefore, further insight into drug-metabolizing enzyme biology using the commercial pig as a model provides great opportunity for the livestock industry and as a research tool in human medicine to move closer to the ultimate goal of giving the right drug at the right dose to the right patient at the right time.

An extremely limited amount of work in swine has been conducted on relating differences based on pharmacokinetic parameters to differences at the gene expression level, although this has been well documented in humans [13–14]. The cytochrome P450 family is a large group of monooxygenase enzymes expressed in liver that are major players in drug metabolism, accounting for ~75% of all drug metabolizing enzymes [15]. Despite the highly conserved function of the CYP450 family in most mammals, significant species differences exist at the level of expression and substrate specificity [10, 16]. It has been shown in minipigs and micropigs that females have higher enzymatic activity for Cytochrome P450 1A2 (CYP1A2) and Cytochrome P450 2E1 (CYP2E1) in comparison to males [10]. The Cytochrome P450 3A4 (CYP3A4) gene in adult humans is the most important drug-metabolizing enzyme due to its prominent expression in liver and the gut and has been shown to account for 66% to 88% of the inter-individual variation in drug response [17]. The swine homologue to CYP3A4 gene is Cytochrome P450 3A22 (CYP3A22) and Cytochrome P450 3A29 (CYP3A29) and both genes have been shown to be highly expressed in the liver of Bama miniature pigs [18].

Genes outside of the cytochrome P450 family, including The ATP-Binding Cassette Sub-Family B (ABCB1) and Sulfotransferase Family, Cytosolic, 1A, Phenol-Preferring, Member 1 (SULT1A1) have also been shown to play a role in drug metabolism. The ABCB1 gene is thought to play an important role in removing toxic substances or metabolites from cells and multiple SNP in this gene have been shown to impact drug clearance in humans [19]. Furthermore, genetic variability within the ABCB1 gene has been characterized in domestic animal species [20] and has been shown to be segregating in multiple dog breeds [21]. Lastly, genetic polymorphisms have been reported in the Sulfotransferase Family, Cytosolic, 1A, Phenol-Preferring, Member 1 (SULT1A1) gene in swine, which resulted in a significant decrease in sulfation activity [22]. Sulfation is one of the major conjugation reactions involved in the metabolism of drugs and xenobiotic compounds and a great deal of research on this particular conjugation reaction has been conducted in humans [23].

Fenbendazole and flunixin meglumine are drugs used across a variety of livestock species and pharmacokinetic differences across breed and sex have been shown to exist when these drugs are administered [1], although they are not cleared for use in humans. Fenbendazole is a widely used antihelmintic drug due to its broad-spectrum activity and is often administered as a feed additive. It undergoes CYP450-mediated oxidation and conjugation with glucoronide and sulfate [24]. Flunixin meglumine is used for the control of pyrexia associated with swine respiratory disease. Flunixin meglumine inhibits the cyclo-oxygenase enzyme, thereby decreasing prostaglandin synthesis and is hydroxylated in the liver to 5-hydroxy flunixin [25]. The objective of the study was to determine if gene expression differences exist across breed and sex for animals given flunixin meglumine or fenbendazole for genes previously shown to play a role in drug metabolism, including CYP1A2, CYP2E1, CYP3A22, CYP3A29, ABCB1 and SULT1A1.

Materials and Methods

Animals and experimental design

This study was approved by the NCSU Institutional Animal Care and Use Committee (IACUC). The experimental design was similar to the one outlined by Howard et al. [1]. Briefly, crossbred nursery female and castrated male pigs (n = 181) with an average (± SD) weight (kg) of 30.59 (±5.82) were utilized. The animals were housed in individual pens. Because only 32 pens were available, the animals were spread across five groups. The number of animals within each group was 31, 29, 25, 26, and 30 for groups from 1 to 5, respectively. The first group occurred on October 19, 2013 and the last group occurred on January 12, 2015. All animals were fed an ad libitum standard commercial corn and soybean meal based diet throughout the study.

The sires (n = 15) of the pigs were registered National Swine Registry boars mated to a common sow (Yorkshire X Landrace; n = 34) population at the North Carolina State University Swine Education Unit. Individual sows were not used for more than one mating and the average (max-min) number of pigs for a sire and dam used in the study was 12.7 (31–6) and 5.6 (14–2), respectively. The boars utilized were purebred Duroc (n = 4), Hampshire (n = 3), Landrace (n = 5) and Yorkshire (n = 3). Within each group animals were randomly placed into the following treatment groups: no drug (control), flunixin meglumine administered, or fenbendazole administered. The number of individuals within a particular breed and sex varied across groups due to conception issues, therefore some degree of unbalance did exist, but it was minimized as much as possible.

Drug administration and tissue collection

Prior to the start of the drug administration and tissue collection, pigs were moved to individual pens and jugular catheters were inserted. After a recovery period of 3 days, drugs were administered by intravenous (IV) administration. Intravenous administration could potentially fail to detect differences in drug uptake from extravascular administration across breed and potential differences may not extrapolate to extravascular administration, however the IV route was selected to remove inter- and intra-individual variability often observed with extravascular routes of administration [24, 26]; thereby focusing the variability on blood clearance mechanisms within the pig. Single IV injections of all drugs were at fractions of the estimated bio-available dose to reduce the chance of saturating metabolic enzymes.

Preparation of fenbendazole (0.4 grams) (Sigma-Aldrich, Co., St. Louis, MO, USA) was done by dissolving the drug in 20 mL DMSO (dimethyl sulfoxide) and 80 mL PG (propylene glycol or 1,2-propanediol) and used to administer an average single intravenous dose of 1 mg/kg fenbendazole to each animal. Banamine® Injectable Solution (50 mg/mL; each mL contains 50 mg flunixin as the meglumine salt) (Merck Animal Health, Summit, NJ) was used to administer an average single intravenous dose of 3 mg/kg flunixin meglumine to each animal. An initial dose was given in order to collect blood samples across a 48-hr period, although this data was not utilized in the study. Previous work by Howard et al. [1] has shown that negligible amounts of the drug and metabolite remained in the blood plasma after 48 hours. Then a second dose was given and, one hour after drug administration, animals were sacrificed via captive bolt and a liver sample from the same lobe in all animals was collected in RNAlater (Qiagen, Valencia, CA) following the manufacturers’ protocol. A liver sample collection of one hour after drug administration was chosen based on a previous study by Howard et al. [1] that found high levels of the metabolite and moderate levels of the drug in the blood plasma across both drugs. Control animals were sacrificed on the same day as the dosed pigs and a similar liver sample was collected as described above.

Quantitative real-time PCR (qPCR)

Total RNA from approximately 30mg of liver was isolated using the RNeasy Plus Mini kit (Qiagen, Valencia, CA) following the manufacturers’ protocol. Integrity and purity of the RNA were measured and quantitated on a Nanodrop-1000 spectrophotometer (Thermo Scientific, Wilmington, DE) and visualized on a 1.2% denatured agarose gel. cDNA was synthesized from 500ng of total RNA using the iScript Reverse Transcription Supermix (Bio-Rad, Hercules, CA). Primer pairs for quantitative real-time PCR (qPCR) were selected for the following target genes: CYP1A2, CYP2E1, CYP3A22, CYP3A29, ABCB1 and SULT1A1. Primer pairs for four housekeeping genes were also selected. These were Beta-actin (ACTB), hypoxanthine phosphoribosyltransferase 1 (HPRT1), ribosomal protein L4 (RPL4) and TATA box binding protein (TBP). These housekeeping genes were selected based on previous work by Fry et al. [27], where it was found that these genes were the most stable in the pig liver. For genes without previously published primer sequences, primers were designed with NCBI Primer-BLAST software (http://www.ncbi.nlm.nih.gov/tools/primer-blast) for compatibility with SYBR Green I Master Mix. The primers were designed (S1 Table) to cross an intron-exon boundary when possible. qPCR was performed in a 20μL reaction, consisting of 1μL of 1:20 diluted cDNA, 400nM of forward and reverse primers, and iTaq™ Universal SYBR® Green Supermix (Bio-Rad, Hercules, CA). A two-step amplification protocol was performed in an iCycler IQ Real-Time PCR Detection System (Bio-Rad, Hercules, CA) with the following steps: denaturation with one cycle at 95°C for 3 minutes followed by 35 cycles of 15 sec at 95°C for denaturation, 45 sec at 60°C for annealing, extension, and data collection. The gene target specificity of the reactions was evaluated with a melt curve generated at the end of the PCR amplification cycle. Melt curve analysis was 80 cycles starting at 55°C and increasing 0.5°C every cycle, with a dwell time of 15sec. Samples were amplified in duplicate, and reaction efficiency for each primer set was assessed using standard curves via a dilution series using iCycler iQ Software. Duplicate samples that differed by greater than one were removed and not included in the analysis for a given animal. No template controls were run on every plate. One cDNA product from each primer pair was sequenced to verify that the PCR product corresponded to the intended gene.

Statistical analysis

Three linear mixed models that have a similar framework as Steibel et al. [28] were used to analyze the data in order to determine the basal expression differences across breed and sex and to determine the effect of drug administration on changes in gene expression and whether this varied across breed and sex. A target gene along with the four housekeeping genes was analyzed within each drug, which resulted in 6 separate analyses within each drug across all three models. Prior to the final analysis across all models, conditional studentized residuals were checked for normality and potential outliers. A heterogeneous residual variance by gene was investigated for all models and the Akaike information criterion (AIC) value did not improve therefore a homogenous residual variance across genes was utilized. The number of individuals used in the final analysis by breed and sex for animals given flunixen meglumine or fenbendazole and the controls are outlined in Table 1 and a more thorough list by group is given in S2 Table. Due to the removal of duplicate samples that differed by greater than one cycle threshold during amplification, the total number of data points by gene may differ. Furthermore, the complete dataset used in the analysis is included as a S1 File.

Download:

Table 1. The number of animals by breed and sex for flunixin meglumine, fenbendazole and the control used in the final analysis across target and housekeeping genes^¹.

https://doi.org/10.1371/journal.pone.0137830.t001

Differences in gene expression across breed and sex for the controls.

In order to determine the basal level gene expression differences across breed and sex only control animals (those not given a drug) were utilized in the analysis. The three-way interaction between gene, group and breed was not estimable due to some breeds not being represented across all groups and therefore could not be investigated. Significance was adjusted using the Bonferroni correction due to multiple breed/sex comparisons (n = 7). The final model utilized across target genes was: (1) where Y was the CT value, μ was intercept, group was the fixed effect of group_j, group by gene was the fixed interaction of group_j and gene_k, gene by breed was the fixed interaction of gene_k and breed_l, gene by sex was the fixed interaction of gene_k and sex_m, animal was the random effect of animal_n, animal nested within gene was the random effect of animal_n within gene_k and e_jklmno was the residual.

In order to determine breed and sex expression differences for the controls the following contrasts were estimated:

Breed Contrast (TG: Target Gene; HK: Housekeeping Gene; B: Breed):

Sex Contrast (M: Male; F: Female):

Differences in gene expression across breed and sex for animals administered a drug.

The number of control animals was not very large and therefore the power to detect breed and sex differences was low. Therefore, animals that were administered a drug were utilized in the analysis to determine overall gene expression differences across breed and sex and to validate the results from the previous model that only utilized the control animals. The three-way interaction between drug, group and sex was the highest order interaction investigated and it was not significant so it was removed from the model. Furthermore, the three-way interaction between gene, group and breed was not estimable due to some breeds not being represented across all groups and therefore could not be investigated. Significance was adjusted using the Bonferroni correction due to multiple breed/sex comparisons (n = 7). The final model utilized across target genes was: (2) where Y was the CT value, μ was intercept, group was the fixed effect of group_j, group by gene was the fixed interaction of group_j and gene_k, gene by breed was the fixed interaction of gene_k and breed_l, gene by sex was the fixed interaction of gene_k and sex_m, animal was the random effect of animal_n, animal nested within gene was the random effect of animal_n within gene_k and e_jklmno was the residual.

In order to determine breed and sex expression differences for animals administered a drug the following contrasts were estimated:

Breed Contrast:

Sex Contrast:

Change in gene expression due to drug administration.

Controls and dosed animals were utilized in the analysis in order to characterize the change in gene expression after drug administration. The effect of treatment refers to animals that were or were not given a drug. The four-way interaction of group, treatment, sex and gene was investigated initially, was not significant, and was removed from the model. The four-way interaction between group, treatment, breed and gene was not estimable due to some breeds not being represented across all groups and therefore could not be investigated. Significance was adjusted using the Bonferroni correction due to multiple breed/sex comparisons (n = 13). The final model utilized across target genes was: (3) where Y was the CT value, μ was intercept, Treatment_i was the fixed effect of an animal receiving the drug or not, group was the fixed effect of group_j, group by gene was the fixed interaction of group_j and gene_k, treatment by group by gene was the fixed interaction of treatment_i, group_j and gene_k, treatment by gene by breed was the fixed interaction of treatment_i, gene_k and breed_l, treatment by gene by sex was the fixed interaction of treatment_i, gene_k and sex_m, animal was the random effect of animal_n, animal nested within gene was the random effect of animal_n within gene_k and e_jklmno was the residual

Contrast for difference between treatment (Trt) and control (Con) to estimate change in gene expression after drug administration:

Significant differences in gene expression change after drug administration was determined from a contrast between the respective mean difference between treatment and control for each breed or sex comparison.

Across all models contrast differences were converted to Fold Change (FC) using the following formula:

Results

Differences in basal gene expression across breed and sex

In order to determine whether breeds or sex differed in transcript levels prior to any drug administration, the control animals were examined. The FC differences across breeds and sex based on Model 1 are outlined in Table 2. Significant (P-value < 0.007) or trending towards significant (P-value < 0.014) basal gene expression differences after Bonferonni correction were found across breeds for SULT1A1 (Hampshire—Landrace; Hampshire—Duroc), CYP2E1 (Yorkshire—Duroc), CYP3A29 (Hampshire—Yorkshire) and CYP3A22 (Hampshire-Landrace), although no sex differences were found. An alternative analysis was also constructed due to a potential lack of power to detect differences across breeds in the controls due to a small number of animals within each breed. The fold-change differences derived from Model 2 across breed and sex for animal administered flunixin meglumine and fenbendazole are outlined in S3 and S4 Tables, respectively. Three of the five across breed fold change differences for the analysis that only used the control animals were also significant (P-value < 0.007) or trending towards significant (P-value < 0.014) based on Model 2 that included only the dosed animals.

Download:

Table 2. Breed and sex fold change (Significance^¹) comparisons derived from the control animals across target genes.

https://doi.org/10.1371/journal.pone.0137830.t002

Change in gene expression due to drug administration

In order to determine whether breeds or sex differed in gene expression post drug administration, dosed animals were compared to the controls. The FC between dosed animals and the control within each breed and sex for flunixin meglumine and fenbendazole are outlined in Tables 3 and 4, respectively. Flunixin meglumine significantly (P-value < 0.0038) suppressed transcript levels across all four breeds and sex for SULT1A1. Additionally, flunixin meglumine suppressed (P-value < 0.0038) transcript levels in Landrace and Yorkshire for ABCB1 and increased (P-value < 0.0038) transcript levels in Duroc and Yorkshire for CYP1A2. Lastly, as outlined in Fig 1, the fold change tended towards being significantly (P-value < 0.0076) different across breeds for SULT1A1 (Landrace—Yorkshire) and CYP2E1 (Yorkshire—Hampshire). Fenbendazole significantly (P-value < 0.0038) suppressed and increased transcript levels across all four breeds and sex for SULT1A1 and CYP1A2, respectively. Additionally, fenbendazole significantly (P-value < 0.0038) suppressed transcript levels in Landrace for ABCB1 and females had higher (P-value < 0.0038) transcript level after fenbendazole administration for CYP3A29. Lastly, the fold change, as outlined in Fig 1, was significantly (P-value < 0.0038) different across breeds for SULT1A1 (Duroc—Hampshire) and tended towards significance (P-value < 0.0076) across sex for CYP3A29.

Download:

Table 3. The fold-change (Significance)^¹^,^² between animals given a drug and the control within each breed and sex for flunixin meglumine across target genes.

https://doi.org/10.1371/journal.pone.0137830.t003

Download:

Table 4. The fold-change (Significance)^¹^,^² between animals given a drug and the control within each breed and sex for fenbendazole across target genes.

https://doi.org/10.1371/journal.pone.0137830.t004

Download:

Fig 1. The fold-change^1,2 between animals given a drug and the control within each breed and sex for flunixin meglumine and fenbendazole across target genes³.

¹ The significance threshold was set at 0.0038 (i.e. **) and a tendency was set at 0.0076 (i.e. *) after the Bonferonni Correction. ² Superscript a and b represent significant differences in fold change and c and d represent a tendency for differences in fold change. ³ Abbreviations: Cytochrome P450 2E1 (CYP2E1); Sulfotransferase Family, Cytosolic, 1A, Phenol-Preferring, Member 1 (SULT1A1).

https://doi.org/10.1371/journal.pone.0137830.g001

Discussion and Conclusions

The current analysis investigated whether there were differences between swine breeds and sex in transcript levels for genes previously shown to be involved in drug metabolism. Gene expression differences can be manifested due to baseline differences in gene expression regardless of whether a drug was given or differences in the change in transcript levels after drug administration. In regards to differences in baseline gene expression, multiple genes were found to display significant differences across breeds. In general, the Hampshire breed displayed the greatest number of pairwise breed differences across genes, including Landrace and Duroc for SULT1A1, Yorkshire for CYP3A29, and Landrace for CYP3A22. This is in line with previous pharmacokinetic results by Howard et al. [1], which found significant differences between Hampshire and the Landrace breeds for flunixin meglumine (clearance and volume of the distribution at steady state) and Landrace and Yorkshire for the metabolite of fenbendazole (area under the plasma concentration-time curve). No baseline gene expression differences across sex were found in the current study.

The impact of the first dose on transcript levels was assumed to be minimal due to a very small amount of drug and metabolite that remained in the blood plasma after 48 hours. To the authors’ knowledge no research has been conducted on how long gene expression is altered on the given genes when animals are administered either flunixin meglumine or fenbendazole. Furthermore, the time liver samples are collected after dosing may give rise to different gene expression results. Due to the lack of knowledge on the optimum time to collect liver samples, the results should be interpreted based on changes in transcript levels based on a liver extraction time of one hour. Future research should investigate if there is heterogeneity in transcript levels across multiple time points in order to provide a better understanding of the optimum time to collect liver samples in order to truly determine breed and sex differences. Lastly, the administration route utilized in the current study is not the approved route of drug administration for either flunixin meglumine or fenbendazole. The use of the IV route was utilized to ensure that the bioavailability of the drug was 100% and to remove inter- and intra-individual variability in drug uptake based on the approved method of oral and intramuscular administration for fenbendazole and flunixin meglumine, respectively. Peterson and Friis [24] investigated the oral bioavailability of fenbendazole in pigs and estimated it to be 27.1% and if this drug was given as a feed additive, the bioavailability may be even lower. Although the comparison between IV and oral administration on gene expression is beyond the scope of this study, one could speculate that hepatic artery delivery to the liver from IV administration may be greater than by oral administration. However, it should be noted that the feed additive is approved at 9 mg/kg BW and if bioavailability was 27%, then a pig’s liver on an oral feed additive may be exposed to more drug than the 1 mg/kg dose used in this study. The approved intramuscular method of administration for flunixin meglumine has been shown to have a bioavailability of 54–92% [26] which could be less than the IV route used in the present study and the above rational about liver exposure can also be applied.

Pharmacokinetic parameters for flunixin meglumine in swine have been well studied within a population utilizing a limited number of animals to determine the pharmacokinetic parameters [26, 29]. There is a very limited amount of research on variation across populations and no research to the author’s knowledge has been done on how flunixin meglumine impacts gene expression in swine for the genes studied. Multiple genes were found to have altered gene expression after flunixin meglumine was administered including ABCB1, SULT1A1, CYP1A2 and CYP2E1. More notably, there were differences in the change in gene expression after flunixin meglumine administration across breeds for SULT1A1 and CYP2E1. Therefore, the differences in pharmacokinetic parameters reported by Howard et al. [1] may be due to genetic variation that impacts drug metabolism at multiple levels including baseline levels and how it responds after drug administration.

In swine very little of fenbendazole is absorbed, but it is rapidly eliminated with a plasma half-life of approximately 2.6 hours [24]. Similar to flunixin meglumine, there has been no research to the authors’ knowledge on how fenbendazole impacts gene expression in swine for the genes studied. Multiple genes were found to have altered expression after fenbendazole was administered including ABCB1, SULT1A1 and CYP1A2. It has been shown in rats [29] and mice [30] that fenbendazole suppresses the activity of CYP1A1, which is in the same family of enzymes as CYP1A2. The current study found an increase in CYP1A2 expression upon fenbendazole administration. It was shown by Howard et al. [1], that the maximum metabolite concentration is reached at hour 12 for fenbendazole. Because liver samples were collected 1 hour after drug administration, the metabolite, which was shown by Murray et al. [29] to suppress CYP1A1 activity, may not have been high enough to illicit the effect of suppressing the activity of CYP1A2. Moreover, there were differences in the change in gene expression after fenbendazole administration across breeds for SULT1A1 and sex for CYP3A29.

ABCB1 has been found to play a role in multi-drug resistance in humans and other species and multiple polymorphisms have been characterized but the literature is inconclusive in regards to their phenotypic effect on disease progression [31]. The CYP1A2 has been found to be involved in the metabolism of several widely used drugs and endogenous compounds in humans and variation across ethnic groups and sex has been confirmed (see Gunes & Dahl [32], for a review). Ethnic allele frequency differences across human populations within the CYP2E1 have also been reported [33]. Characterization of regions that are associated with skatole levels in swine has resulted in the characterization of genetic polymorphisms in the SULT1A1 that impact sulfation activity, which is the primary method that skatole is eliminated in the liver [21]. The ability to use the SULT1A1 polymorphisms to reduce skatole levels in order to reduce the frequency of boar taint has been suggested, although the current analysis may provide evidence that selection for reduced sulfation activity may impact the pharmacokinetic parameters of a particular drug.

In recent years, escalating costs, risks, and uncertainty associated with human drug development have posed daunting challenges to the pharmaceutical industry. This has led to initiatives to curb costs, reduce cycle times and enhance the probability of success at each stage of drug discovery and development. Furthermore, drug efficacy is generalized on a population level therefore the safety or efficacy of a molecule at a given dose in a specific population for a pre-specified disease indication is not fully known during drug discovery and development [34]. Due to these reasons, identification of suitable animal models that can be used in drug discovery will be of paramount importance in the future. With the advent of large genotyping arrays and their reduced cost, the effectiveness of the commercial pig as an animal model is greatly increased in order to better understand drug mechanisms at the molecular level. Furthermore, the swine genome has a greater degree of linkage dis-equilibrium than humans, which allows for fewer animals and markers to genotype [35]. The current study has shown that previously identified differences in pharmacokinetic parameters across breeds translated to differences at the gene expression level for flunixin meglumine and fenbendazole in the population studied. A better understanding of the molecular basis of drug action and the genetic determinants of drug response will allow for a mechanism-based approach to the discovery and development of new medications [5].

Furthermore, the within line mating structure in swine populations allows for lines/breeds to potentially have very diverse drug response phenotypes. Additional divergence among individuals can be attained in only a few selection cycles [36–37], which could potentially identify diverse drug metabolism modes of action. The efficacy of selecting individuals based on pharmacokinetic parameters in order to create divergent lines across multiple drugs will be investigated in the future by determining the heritability of the drug concentration curve and of specific pharmacokinetic parameters. Diverse drug response phenotypes can also be used to further understand drug by genotype and drug by nutrition regimen interactions, which would shed light on drug-by-drug interaction at the molecular level. Additionally, routine phenotype collection and the dense pedigree structure allows for the ability to dissect the phenotype into its genetic components and its correlation with other traits such as growth or reproductive performance traits.

The current analysis found differences across breed and sex in expression for genes previously shown to be involved in drug metabolism, including ABCB1, SULT1A1, CYP1A2, CYP2E1 and CYP3A29. More importantly, it was found that these differences were due to inherent gene expression differences at the baseline level and differences in gene expression response after drug administration. The results provide insight into which genes are differentially expressed upon drug administration, although future research should determine the degree to which gene expression changes manifest into differences at the pharmacokinetic level. Furthermore, the differences across breeds and sex in transcript levels prior and post-drug administration confirms that breed and sex do have an effect on transcript levels for known drug metabolizing genes. Future work will determine whether genes other than the ones currently investigated are differentially expressed using RNA sequencing information and the effectiveness of clustering animals into drug efficacy groups based on pharmacokinetic parameters and gene expression data.

Supporting Information

S1 Table. Primer Information for target and housekeeping genes¹.

https://doi.org/10.1371/journal.pone.0137830.s001

(DOCX)

S2 Table. The number of animals by breed and sex within each group for flunixin meglumine, fenbendazole and the control used in the final analysis.

https://doi.org/10.1371/journal.pone.0137830.s002

(DOCX)

S3 Table. Breed and sex fold change (Significance¹) comparisons derived from animals administered Flunixin megulumine across target genes.

https://doi.org/10.1371/journal.pone.0137830.s003

(DOCX)

S4 Table. Breed and sex fold change (Significance¹) comparisons derived from animals administered Fenbendazole across target genes.

https://doi.org/10.1371/journal.pone.0137830.s004

(DOCX)

S1 File. A comma separated file that includes the complete data set used in the analysis.

https://doi.org/10.1371/journal.pone.0137830.s005

(CSV)

Acknowledgments

The authors wish to thank the many undergraduate students that helped with animal care and sample collection. Furthermore, the authors would like to thank Patty Roth, Jim Brooks, Jim Yeatts, Maria Stone and Krista Browning. The research was supported by the North Carolina Biotechnology Center (grant #2012-MRG-1108) and Food Animal Residue Avoidance Databank (USDA #2013-41480-21002).

Author Contributions

Conceived and designed the experiments: CM REB MSA. Performed the experiments: ATO MSA. Analyzed the data: JTH CM. Contributed reagents/materials/analysis tools: JTH ATO CM MSA. Wrote the paper: JTH ATO CM MSA.

References

1. Howard JT, Baynes RE, Brooks JD, Yeatts JL, Bellis B, Ashwell MS, et al. The effect of breed and sex on Sulfamethazine, Enrofloxacin, Fenbendazole and Flunixin pharmacokinetic parameters in swine. J. Vet. Pharmacol. Ther. 2014; 37: 531–541. pmid:24731191
- View Article
- PubMed/NCBI
- Google Scholar
2. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999; 286: 487–491. pmid:10521338
- View Article
- PubMed/NCBI
- Google Scholar
3. Hon YY, Fessing MY, Pui CH, Relling MV, Krynetski EY, Evans WE. Polymorphism of the thiopurine S-methyltransferase gene in African-Americans. Hum. Mol. Genet. 1999; 8: 371–376. pmid:9931346
- View Article
- PubMed/NCBI
- Google Scholar
4. Evans DA, McLeod HL, Pritchard S, Tariq M, Mobarek A. Interethnic variability in human drug responses. Drug Metab Dispos. 2001; 29: 606–610. pmid:11259361
- View Article
- PubMed/NCBI
- Google Scholar
5. McLeod HL, Evans WE. Pharmacogenomics: unlocking the human genome for better drug therapy. Annu. Rev. Pharmacool. Toxicol. 2001; 41: 101–121.
- View Article
- Google Scholar
6. Evans WE. Pharmacogenomics: marshalling the human genome to individualise drug therapy. Gut 2003; 52(Suppl 2): ii10–ii18. pmid:12651877
- View Article
- PubMed/NCBI
- Google Scholar
7. Bendixen E, Danielsen M, Larsen K, Bendixen C. Advances in porcine genomics and proteomics—a toolbox for developing the pig as a model organism for molecular biomedical research. Brief Funct Genomics. 2010; 9: 208–219. pmid:20495211
- View Article
- PubMed/NCBI
- Google Scholar
8. Sakuma T, Shimojima T, Miwa K, Kamataki T. Cloning CYP2D21 and CYP3A22 cDNAs from liver of miniature pigs. Drug Metab. Dispos. 2004; 32: 376–378. pmid:15039288
- View Article
- PubMed/NCBI
- Google Scholar
9. Vaclavikova R, Soucek P, Svobodova L, Anzenbacher P, Simek P, Guengerich FP, et al. Different in vitro metabolism of paclitaxel and docetaxel in humans, rats, pigs, and minipigs. Drug Metab. Dispos. 2004; 32: 666–674. pmid:15155559
- View Article
- PubMed/NCBI
- Google Scholar
10. Fink-Gremmels J. Implications of hepatic cytochrome P450-related biotransformation processes in veterinary sciences. Eur. J. Pharmacol. 2008; 585: 502–509. pmid:18417118
- View Article
- PubMed/NCBI
- Google Scholar
11. Landers TF, Cohen B, Wittum TE, Larson EL. A Review of Antibiotic Use in Food Animals: Perspective, Policy, and Potential. Public Health Rep. 2012; 127: 4–22. pmid:22298919
- View Article
- PubMed/NCBI
- Google Scholar
12. Ernst CW, Steibel JP. Molecular advances in QTL discovery and application in pig breeding. Trends Genet. 2013; 29:215–24. pmid:23498076
- View Article
- PubMed/NCBI
- Google Scholar
13. Ho RH, Choi L, Lee W, Mayo G, Schwarz UI, Tirona RG, et al. Effect of drug transporter genotypes on pravastatin disposition in European- and African-American participants. Pharmacogenet. Genomics. 2008; 17: 647–656.
- View Article
- Google Scholar
14. Kirchheiner J, Brockmöller J. Clinical consequences of cytochrome P450 2C9 polymorphisms. Clin. Pharmacol. Ther. 2005; 77: 1–16. pmid:15637526
- View Article
- PubMed/NCBI
- Google Scholar
15. Guengerich FP. Cytochrome p450 and chemical toxicology. Chem. Res. Toxicol. 2008; 21: 70–83. pmid:18052394
- View Article
- PubMed/NCBI
- Google Scholar
16. Mealey KL, Jabbes M, Spencer E, Akey JM. Differential expression of CYP3A12 and CYP3A26 mRNAs in canine liver and intestine. Xenobiotica. 2008; 38: 1305–1312. pmid:18841518
- View Article
- PubMed/NCBI
- Google Scholar
17. Klein K, Zanger UM. Pharmacogenomics of Cytochrome P450 3A4: Recent Progress Toward the “Missing Heritability” Problem. Front Genet. 2013; 4: 12. pmid:23444277
- View Article
- PubMed/NCBI
- Google Scholar
18. Shang H, Guo K, Liu Y, Yang J, Wei H. Constitutive expression of CYP3A mRNA in Bama miniature pig tissues. Gene 2013; 524: 261–267. pmid:23618816
- View Article
- PubMed/NCBI
- Google Scholar
19. Franke RM, Gardner ER, Sparreboom A. Pharmacogenetics of drug transporters. Curr. Pharm. Des. 2010; 16: 220–230. pmid:19835554
- View Article
- PubMed/NCBI
- Google Scholar
20. Martinez M, Modric S, Sharkey M, Troutman L, Walker L, Mealey K. The pharmacogenomics of P-glycoprotein and its role in veterinary medicine. J Vet Pharmacol Ther. 2008; 31: 285–300. pmid:18638289
- View Article
- PubMed/NCBI
- Google Scholar
21. Mealey KL, Meurs KM. Breed distribution of the ABCB1-1Delta (multidrug sensitivity) polymorphism among dogs undergoing ABCB1 genotyping. J Am Vet Med Assoc 2008; 233: 921–924. pmid:18795852
- View Article
- PubMed/NCBI
- Google Scholar
22. Lin Z, Lou Y, Squires JE. Molecular cloning and functional analysis of porcine SULT1A1 gene and its variant: a single mutation SULT1A1 causes a significant decrease in sulfation activity. Mamm Genome. 2004; 15: 218–226. pmid:15014971
- View Article
- PubMed/NCBI
- Google Scholar
23. Pacifici GM. Inhibition of human liver and duodenum sulfotransferases by drugs and dietary chemicals: a review of the literature. Int. J. Clin. Pharmacol. Ther. 2004; 42: 488–495. pmid:15487807
- View Article
- PubMed/NCBI
- Google Scholar
24. Petersen MB, Friis C. Pharmacokinetics of fenbendazole following intravenous and oral administration to pigs. Am J Vet Res. 2000; 61: 573–576. pmid:10803655
- View Article
- PubMed/NCBI
- Google Scholar
25. Königsson K, Törneke K, Engeland IV, Odensvik K, Kindahl H. Pharmacokinetics and pharmacodynamic effects of flunixin after intravenous, intramuscular and oral administration to dairy goats. Acta Vet Scand. 2003; 44: 153–159. pmid:15074628
- View Article
- PubMed/NCBI
- Google Scholar
26. Pairis-Garcia MD, Karriker LA, Johnson AK, Kukanich B, Wulf L, Sander S, et al. Pharmacokinetics of flunixin meglumine in mature swine after intravenous, intramuscular and oral administration. BMC Vet. Res. 2013; 9: 165. pmid:23941181
- View Article
- PubMed/NCBI
- Google Scholar
27. Fry RS, Ashwell MS, Lloyd KE, O’Nan AT, Flowers WL, Stewart KR, et al. Amount and source of dietary copper affects small intestine morphology, duodenal lipid peroxidation, hepatic oxidative stress,and mRNA expression of hepatic copper regulatory proteins in weanling pigs. J. Anim. Sci. 2011; 90: 3112–3119.
- View Article
- Google Scholar
28. Steibel JP, Poletoo R, Coussens PM, Rosa GJM. A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data. Genomics 2009; 94: 146–152. pmid:19422910
- View Article
- PubMed/NCBI
- Google Scholar
29. Murray M, Hudson AM, Yassa V. Hepatic Microsomal Metabolism of the Anthelmintic Benzimidazole Fenbendazole: Enhanced Inhibition of Cytochrome P450 Reactions by Oxidized Metabolites of the Drug. Chem. Res. Toxicol. 1992; 5: 60–66. pmid:1581538
- View Article
- PubMed/NCBI
- Google Scholar
30. Gardner CR, Mishin V, Laskin JD, Laskin DL. Exacerbation of Acetaminophen Hepatotoxicity by the Anthelmentic Drug Fenbendazole. Toxicol. Sci. 2012; 125: 607–612. pmid:22048645
- View Article
- PubMed/NCBI
- Google Scholar
31. Lespine A, Ménez C, Bourguinat C, Prichard RK. P-glycoproteins and other multidrug resistance transporters in the pharmacology of anthelmintics: Prospects for reversing transport-dependent anthelmintic resistance. Int J Parasitol Drugs Drug Resist 2012; 2: 58–75. pmid:24533264
- View Article
- PubMed/NCBI
- Google Scholar
32. Gunes A, Dahl ML. Variation in CYP1A2 activity and its clinical implications: influence of environmental factors and genetic polymorphisms. Pharmacogenomics J. 2008; 9: 625–637.
- View Article
- Google Scholar
33. Stephens EA, Taylor JA, Kaplan N, Yang CH, Hsieh LL, Lucier GW, et al. Ethnic variation in the CYP2E1 gene: polymorphism analysis of 695 African-Americans, European-Americans and Taiwanese. Pharmacogenet. Genomics 1994; 4: 185–192.
- View Article
- Google Scholar
34. Allerheiligen SR. Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin. Pharmacol. Ther. 2010; 88: 135–137. pmid:20531467
- View Article
- PubMed/NCBI
- Google Scholar
35. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet. 2009; 10: 381–391. pmid:19448663
- View Article
- PubMed/NCBI
- Google Scholar
36. Bender JM, See MT, Hanson DJ, Lawrence TE, Cassady JP. Correlated responses in growth, carcass, and meat quality traits to divergent selection for testosterone production in pigs. J Anim Sci. 2006; 84: 1331–1337. pmid:16699090
- View Article
- PubMed/NCBI
- Google Scholar
37. Blowe CD, Boyette KE, Ashwell MS, Eisen EJ, Robison OW, Cassady JP. Characterization of a line of pigs previously selected for increased litter size for RBP4 and follistatin. J Anim Breed Genet. 2006; 123: 389–395. pmid:17177694
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Howard JT, Baynes RE, Brooks JD, Yeatts JL, Bellis B, Ashwell MS, et al. The effect of breed and sex on Sulfamethazine, Enrofloxacin, Fenbendazole and Flunixin pharmacokinetic parameters in swine. J. Vet. Pharmacol. Ther. 2014; 37: 531–541. pmid:24731191
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Evans WE, Relling MV. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 1999; 286: 487–491. pmid:10521338
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Hon YY, Fessing MY, Pui CH, Relling MV, Krynetski EY, Evans WE. Polymorphism of the thiopurine S-methyltransferase gene in African-Americans. Hum. Mol. Genet. 1999; 8: 371–376. pmid:9931346
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Evans DA, McLeod HL, Pritchard S, Tariq M, Mobarek A. Interethnic variability in human drug responses. Drug Metab Dispos. 2001; 29: 606–610. pmid:11259361
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. McLeod HL, Evans WE. Pharmacogenomics: unlocking the human genome for better drug therapy. Annu. Rev. Pharmacool. Toxicol. 2001; 41: 101–121.
View Article
Google Scholar

[18] View Article

[19] Google Scholar

[ref6] 6. Evans WE. Pharmacogenomics: marshalling the human genome to individualise drug therapy. Gut 2003; 52(Suppl 2): ii10–ii18. pmid:12651877
View Article
PubMed/NCBI
Google Scholar

[21] View Article

[22] PubMed/NCBI

[23] Google Scholar

[ref7] 7. Bendixen E, Danielsen M, Larsen K, Bendixen C. Advances in porcine genomics and proteomics—a toolbox for developing the pig as a model organism for molecular biomedical research. Brief Funct Genomics. 2010; 9: 208–219. pmid:20495211
View Article
PubMed/NCBI
Google Scholar

[25] View Article

[26] PubMed/NCBI

[27] Google Scholar

[ref8] 8. Sakuma T, Shimojima T, Miwa K, Kamataki T. Cloning CYP2D21 and CYP3A22 cDNAs from liver of miniature pigs. Drug Metab. Dispos. 2004; 32: 376–378. pmid:15039288
View Article
PubMed/NCBI
Google Scholar

[29] View Article

[30] PubMed/NCBI

[31] Google Scholar

[ref9] 9. Vaclavikova R, Soucek P, Svobodova L, Anzenbacher P, Simek P, Guengerich FP, et al. Different in vitro metabolism of paclitaxel and docetaxel in humans, rats, pigs, and minipigs. Drug Metab. Dispos. 2004; 32: 666–674. pmid:15155559
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Fink-Gremmels J. Implications of hepatic cytochrome P450-related biotransformation processes in veterinary sciences. Eur. J. Pharmacol. 2008; 585: 502–509. pmid:18417118
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Landers TF, Cohen B, Wittum TE, Larson EL. A Review of Antibiotic Use in Food Animals: Perspective, Policy, and Potential. Public Health Rep. 2012; 127: 4–22. pmid:22298919
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Ernst CW, Steibel JP. Molecular advances in QTL discovery and application in pig breeding. Trends Genet. 2013; 29:215–24. pmid:23498076
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Ho RH, Choi L, Lee W, Mayo G, Schwarz UI, Tirona RG, et al. Effect of drug transporter genotypes on pravastatin disposition in European- and African-American participants. Pharmacogenet. Genomics. 2008; 17: 647–656.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref14] 14. Kirchheiner J, Brockmöller J. Clinical consequences of cytochrome P450 2C9 polymorphisms. Clin. Pharmacol. Ther. 2005; 77: 1–16. pmid:15637526
View Article
PubMed/NCBI
Google Scholar

[52] View Article

[53] PubMed/NCBI

[54] Google Scholar

[ref15] 15. Guengerich FP. Cytochrome p450 and chemical toxicology. Chem. Res. Toxicol. 2008; 21: 70–83. pmid:18052394
View Article
PubMed/NCBI
Google Scholar

[56] View Article

[57] PubMed/NCBI

[58] Google Scholar

[ref16] 16. Mealey KL, Jabbes M, Spencer E, Akey JM. Differential expression of CYP3A12 and CYP3A26 mRNAs in canine liver and intestine. Xenobiotica. 2008; 38: 1305–1312. pmid:18841518
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref17] 17. Klein K, Zanger UM. Pharmacogenomics of Cytochrome P450 3A4: Recent Progress Toward the “Missing Heritability” Problem. Front Genet. 2013; 4: 12. pmid:23444277
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref18] 18. Shang H, Guo K, Liu Y, Yang J, Wei H. Constitutive expression of CYP3A mRNA in Bama miniature pig tissues. Gene 2013; 524: 261–267. pmid:23618816
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref19] 19. Franke RM, Gardner ER, Sparreboom A. Pharmacogenetics of drug transporters. Curr. Pharm. Des. 2010; 16: 220–230. pmid:19835554
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref20] 20. Martinez M, Modric S, Sharkey M, Troutman L, Walker L, Mealey K. The pharmacogenomics of P-glycoprotein and its role in veterinary medicine. J Vet Pharmacol Ther. 2008; 31: 285–300. pmid:18638289
View Article
PubMed/NCBI
Google Scholar

[76] View Article

[77] PubMed/NCBI

[78] Google Scholar

[ref21] 21. Mealey KL, Meurs KM. Breed distribution of the ABCB1-1Delta (multidrug sensitivity) polymorphism among dogs undergoing ABCB1 genotyping. J Am Vet Med Assoc 2008; 233: 921–924. pmid:18795852
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref22] 22. Lin Z, Lou Y, Squires JE. Molecular cloning and functional analysis of porcine SULT1A1 gene and its variant: a single mutation SULT1A1 causes a significant decrease in sulfation activity. Mamm Genome. 2004; 15: 218–226. pmid:15014971
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref23] 23. Pacifici GM. Inhibition of human liver and duodenum sulfotransferases by drugs and dietary chemicals: a review of the literature. Int. J. Clin. Pharmacol. Ther. 2004; 42: 488–495. pmid:15487807
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref24] 24. Petersen MB, Friis C. Pharmacokinetics of fenbendazole following intravenous and oral administration to pigs. Am J Vet Res. 2000; 61: 573–576. pmid:10803655
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref25] 25. Königsson K, Törneke K, Engeland IV, Odensvik K, Kindahl H. Pharmacokinetics and pharmacodynamic effects of flunixin after intravenous, intramuscular and oral administration to dairy goats. Acta Vet Scand. 2003; 44: 153–159. pmid:15074628
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref26] 26. Pairis-Garcia MD, Karriker LA, Johnson AK, Kukanich B, Wulf L, Sander S, et al. Pharmacokinetics of flunixin meglumine in mature swine after intravenous, intramuscular and oral administration. BMC Vet. Res. 2013; 9: 165. pmid:23941181
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref27] 27. Fry RS, Ashwell MS, Lloyd KE, O’Nan AT, Flowers WL, Stewart KR, et al. Amount and source of dietary copper affects small intestine morphology, duodenal lipid peroxidation, hepatic oxidative stress,and mRNA expression of hepatic copper regulatory proteins in weanling pigs. J. Anim. Sci. 2011; 90: 3112–3119.
View Article
Google Scholar

[104] View Article

[105] Google Scholar

[ref28] 28. Steibel JP, Poletoo R, Coussens PM, Rosa GJM. A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data. Genomics 2009; 94: 146–152. pmid:19422910
View Article
PubMed/NCBI
Google Scholar

[107] View Article

[108] PubMed/NCBI

[109] Google Scholar

[ref29] 29. Murray M, Hudson AM, Yassa V. Hepatic Microsomal Metabolism of the Anthelmintic Benzimidazole Fenbendazole: Enhanced Inhibition of Cytochrome P450 Reactions by Oxidized Metabolites of the Drug. Chem. Res. Toxicol. 1992; 5: 60–66. pmid:1581538
View Article
PubMed/NCBI
Google Scholar

[111] View Article

[112] PubMed/NCBI

[113] Google Scholar

[ref30] 30. Gardner CR, Mishin V, Laskin JD, Laskin DL. Exacerbation of Acetaminophen Hepatotoxicity by the Anthelmentic Drug Fenbendazole. Toxicol. Sci. 2012; 125: 607–612. pmid:22048645
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref31] 31. Lespine A, Ménez C, Bourguinat C, Prichard RK. P-glycoproteins and other multidrug resistance transporters in the pharmacology of anthelmintics: Prospects for reversing transport-dependent anthelmintic resistance. Int J Parasitol Drugs Drug Resist 2012; 2: 58–75. pmid:24533264
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref32] 32. Gunes A, Dahl ML. Variation in CYP1A2 activity and its clinical implications: influence of environmental factors and genetic polymorphisms. Pharmacogenomics J. 2008; 9: 625–637.
View Article
Google Scholar

[123] View Article

[124] Google Scholar

[ref33] 33. Stephens EA, Taylor JA, Kaplan N, Yang CH, Hsieh LL, Lucier GW, et al. Ethnic variation in the CYP2E1 gene: polymorphism analysis of 695 African-Americans, European-Americans and Taiwanese. Pharmacogenet. Genomics 1994; 4: 185–192.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref34] 34. Allerheiligen SR. Next-generation model-based drug discovery and development: quantitative and systems pharmacology. Clin. Pharmacol. Ther. 2010; 88: 135–137. pmid:20531467
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref35] 35. Goddard ME, Hayes BJ. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet. 2009; 10: 381–391. pmid:19448663
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref36] 36. Bender JM, See MT, Hanson DJ, Lawrence TE, Cassady JP. Correlated responses in growth, carcass, and meat quality traits to divergent selection for testosterone production in pigs. J Anim Sci. 2006; 84: 1331–1337. pmid:16699090
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref37] 37. Blowe CD, Boyette KE, Ashwell MS, Eisen EJ, Robison OW, Cassady JP. Characterization of a line of pigs previously selected for increased litter size for RBP4 and follistatin. J Anim Breed Genet. 2006; 123: 389–395. pmid:17177694
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

Figures

Abstract

Introduction

Materials and Methods

Animals and experimental design

Drug administration and tissue collection

Quantitative real-time PCR (qPCR)

Statistical analysis

Differences in gene expression across breed and sex for the controls.

Differences in gene expression across breed and sex for animals administered a drug.

Change in gene expression due to drug administration.

Results

Differences in basal gene expression across breed and sex

Change in gene expression due to drug administration

Discussion and Conclusions

Supporting Information

S1 Table. Primer Information for target and housekeeping genes1.

S2 Table. The number of animals by breed and sex within each group for flunixin meglumine, fenbendazole and the control used in the final analysis.

S3 Table. Breed and sex fold change (Significance1) comparisons derived from animals administered Flunixin megulumine across target genes.

S4 Table. Breed and sex fold change (Significance1) comparisons derived from animals administered Fenbendazole across target genes.

S1 File. A comma separated file that includes the complete data set used in the analysis.

Acknowledgments

Author Contributions

References

S1 Table. Primer Information for target and housekeeping genes¹.

S3 Table. Breed and sex fold change (Significance¹) comparisons derived from animals administered Flunixin megulumine across target genes.

S4 Table. Breed and sex fold change (Significance¹) comparisons derived from animals administered Fenbendazole across target genes.