Pharmacokinetic interaction between regorafenib and atorvastatin in rats

BACKGROUND

Regorafenib is used in the treatment of colorectal cancer and hepatocellular carcinoma. Due to the co-morbidity of hyperlipidemia in these conditions, statins, including atorvastatin, are used as potential adjuvant therapy agents. Both regorafenib and atorvastatin are metabolized by CYP3A4. In addition, atorvastatin is a P-gp and BCRP substrate, whereas regorafenib and its active metabolites M-2 and M-5 are inhibitors of these transporters. Hence, the concomitant use of both drugs may increase the risk of a clinically significant drug-drug interaction. Therefore, the present study aimed to assess the pharmacokinetic interactions of atorvastatin and regorafenib and their active metabolites.

METHODS

Male Wistar rats were assigned to three groups (eight animals in each) and were orally administered: regorafenib and atorvastatin (IREG+ATO), a carrier with regorafenib (IIREG), and atorvastatin with a carrier (IIIATO). Blood samples were collected for 72 h. UPLC-MS/MS was the method of measurement of regorafenib and atorvastatin concentrations. The pharmacokinetic parameters were calculated with a non-compartmental model.

RESULTS

A single administration of atorvastatin increased the exposure to regorafenib and its active metabolites. In the IREG+ATO group, the Cmax, AUC0-t, and AUC0-∞ of regorafenib increased 2.7, 3.2, and 3.2-fold, respectively. Atorvastatin also significantly increased the Cmax, AUC0-t, and AUC0-∞ of both regorafenib metabolites. Regorafenib, in turn, decreased the AUC0-t and AUC0-∞ of 2-OH atorvastatin by 86.9% and 67.3%, and the same parameters of 4-OH atorvastatin by 45.0% and 46.8%, respectively.

CONCLUSIONS

This animal model study showed a significant pharmacokinetic interaction between regorafenib and atorvastatin. While this interaction may be clinically significant, this needs to be confirmed in clinical trials involving cancer patients.

Assessment of omeprazole and famotidine effects on the pharmacokinetics of tacrolimus in patients following kidney transplant-randomized controlled trial

Tacrolimus is metabolized in the liver with the participation of the CYP3A4 and CYP3A5 enzymes. Proton pump inhibitors are used in kidney transplant patients to prevent duodenal and gastric ulcer disease due to glucocorticoids. Omeprazole, unlike famotidine, is a substrate and inhibitor of the enzymes CYP2C19, CYP3A4, CYP3A5. The aim of this study was to compare the impact of omeprazole and famotidine on the pharmacokinetics of tacrolimus. A randomized, non-blinded study involving 22 stabilized adult kidney transplant patients was conducted. Patients received the standard triple immunosuppression regimen and omeprazole 20 mg (n = 10) or famotidine 20 mg (n = 12). The study material consisted of blood samples in which tacrolimus concentrations were determined using the Chemiluminescent Microparticle Immuno Assay method. A single administration of omeprazole increased tacrolimus concentrations at 2 h (day 2) = 11.90 ± 1.59 ng/mL vs. 2 h (day 1 - no omeprazole administration) = 9.40 ± 0.79 ng/mL (p = 0.0443). AUC0-6 amounted to 63.07 ± 19.46 ng × h/mL (day 2) vs. 54.23 ± 10.48 ng × h/mL (day 1), (p = 0.0295). AUC2-6 amounted to 44.32 ± 11.51 ng × h/mL (day 2) vs. 38.68 ± 7.70 ng × h/mL (day 1), (p = 0.0130). Conversely, no significant changes in values of pharmacokinetic parameters were observed for famotidine. Omeprazole significantly increases blood exposure of tacrolimus. The administration of famotidine instead of omeprazole seems safer for patients following kidney transplantation. Clinical Trial Registration: clinicaltrials.gov, identifier NCT05061303.

Genomic Regions and Candidate Genes Affecting Response to Heat Stress with Newcastle Virus Infection in Commercial Layer Chicks Using Chicken 600K Single Nucleotide Polymorphism Array

Heat stress results in significant economic losses to the poultry industry. Genetics plays an important role in chickens adapting to the warm environment. Physiological parameters such as hematochemical parameters change in response to heat stress in chickens. To explore the genetics of heat stress resilience in chickens, a genome-wide association study (GWAS) was conducted using Hy-Line Brown layer chicks subjected to either high ambient temperature or combined high temperature and Newcastle disease virus infection. Hematochemical parameters were measured during three treatment phases: acute heat stress, chronic heat stress, and chronic heat stress combined with NDV infection. Significant changes in blood parameters were recorded for 11 parameters (sodium (Na+, potassium (K+), ionized calcium (iCa2+), glucose (Glu), pH, carbon dioxide partial pressure (PCO2), oxygen partial pressure (PO2), total carbon dioxide (TCO2), bicarbonate (HCO3), base excess (BE), and oxygen saturation (sO2)) across the three treatments. The GWAS revealed 39 significant SNPs (p < 0.05) for seven parameters, located on Gallus gallus chromosomes (GGA) 1, 3, 4, 6, 11, and 12. The significant genomic regions were further investigated to examine if the genes within the regions were associated with the corresponding traits under heat stress. A candidate gene list including genes in the identified genomic regions that were also differentially expressed in chicken tissues under heat stress was generated. Understanding the correlation between genetic variants and resilience to heat stress is an important step towards improving heat tolerance in poultry.

Bidirectional pharmacokinetic drug interactions between olaparib and metformin

OBJECTIVE

Olaparib is a PARP (poly-ADP-ribose polymerase) inhibitor used for maintenance therapy in BRCA-mutated cancers. Metformin is a first-choice drug used in the treatment of type 2 diabetes. Both drugs are commonly co-administered to oncologic patients with add-on type 2 diabetes mellitus. Olaparib is metabolized by the CYP3A4 enzyme, which may be inhibited by metformin through the Pregnane X Receptor. In vitro studies have shown that olaparib inhibits the following metformin transporters: OCT1, MATE1, and MATE2K. The aim of the study was to assess the influence of 'the perpetrator drug' on the pharmacokinetic (PK) parameters of 'the victim drug' after a single dose. To evaluate the effect, the AUC0→∞ (area under the curve) ratio was determined (the ratio between AUC0→∞ in the presence of the perpetrator and AUC0→∞ without the presence of the perpetrator).

METHODS

Male Wistar rats were assigned to three groups (eight animals in each group), which were orally administered: metformin and olaparib (IMET+OLA), vehiculum with metformin (IIMET), and vehiculum with olaparib (IIIOLA). Blood samples were collected after 24 h. HPLC was applied to measure the concentrations of olaparib and metformin. The PK parameters were calculated in a non-compartmental model.

RESULTS

Metformin did not affect the olaparib PK parameters. The AUC0→∞ IMET+OLA/IIIOLA ratio was 0.99. Olaparib significantly increased the metformin Cmax (by 177.8%), AUC0→t (by 159.8%), and AUC0→∞ (by 74.1%). The AUC0→∞ IMET+OLA/IIMET ratio was 1.74.

CONCLUSIONS

A single dose of metformin did not affect the PK parameters of olaparib, nor did it inhibit the olaparib metabolism, but olaparib significantly changed the metformin pharmacokinetics, which may be of clinical importance.

Utility of Feathers for Avian Influenza Virus Detection in Commercial Poultry

The present study evaluated the potential utility of feather samples for the convenient and accurate detection of avian influenza virus (AIV) in commercial poultry. Feather samples were obtained from AIV-negative commercial layer facilities in Iowa, USA. The feathers were spiked with various concentrations (106 to 100) of a low pathogenic strain of H5N2 AIV using a nebulizing device and were evaluated for the detection of viral RNA using a real-time RT-PCR assay immediately or after incubation at -20, 4, 22, or 37 °C for 24, 48, or 72 h. Likewise, cell culture medium samples with and without the virus were prepared and used for comparison. In the spiked feathers, the PCR reliably (i.e., 100% probability of detection) detected AIV RNA in eluates from samples sprayed with 103 EID50/mL or more of the virus. Based on half-life estimates, the feathers performed better than the corresponding media samples (p < 0.05), particularly when the samples were stored at 22 or 37 °C. In conclusion, feather samples can be routinely collected from a poultry barn as a non-invasive alternative to blood or oropharyngeal-cloacal swab samples for monitoring AIV.

Assessment of serum concentration and urinary excretion of tumor necrosis factor receptor 1 and 2 and their potential as markers of immunoglobulin A nephropathy activity

BACKGROUND

Tumor necrosis factor receptor 1 (TNFR1) and 2 (TNFR2) can be cleaved from the cell surface and circulate alone or in combination with tumor necrosis factor alpha (TNF-α). These soluble receptors may play a key role in regulating the inflammatory response.

OBJECTIVES

The study aimed to evaluate the role of TNFRs in regulating the inflammatory response in immunoglobulin A nephropathy (IgAN).

MATERIAL AND METHODS

The study included 26 patients with newly diagnosed and biopsy-confirmed IgAN and 20 healthy controls. Study material included blood and fresh urine collected the morning before kidney biopsy and therapy. The serum concentrations of TNFR1 (STNFR1) and TNFR2 (STNFR2) and urinary excretion of TNFR1 (UTNFR1) and TNFR2 (UTNFR2) were determined with immunoassay. Subsequently, the data were evaluated statistically.

RESULTS

The STNFR1 and STNFR2 levels were higher in IgAN patients than in healthy subjects (4747.87 pg/mL and 2817.62 pg/mL compared to 2755.68 pg/mL (95% CI: from -2948.41 to -1035.97; p = 0.001) and 1437.83 pg/mL (95% CI: from -1958.50 to -419.60; p = 0.001). The power of the test was 98.5% for STNFR1 and 96% for STNFR2. Urinary concentrations only increased for TNFR1 (3551.29 compared to 2338.95 pg/mg of creatinine (Cr) (95% CI: from -2247.03 to -177.66; p = 0.023). The STNFR1 marker was characterized by a sensitivity of 73.08% and a specificity of 90.00% (p < 0.001).

CONCLUSIONS

Our results suggest that TNFR1 and TNFR2 are good markers of TNF-α pathway activation in IgAN patients.

Genome wide association analysis of cuticle deposition in laying hens

The cuticle is an invisible barrier that protects the internal egg contents from microorganisms entering through gas exchange pores. Eggs which have a good cuticle are least likely to be penetrated by microorganisms and improved cuticle cover should reduce vertical transmission of microorganisms and improve biosecurity. The aim was to carry out a genome wide association study for cuticle deposition in 3 independent populations of laying hens using tartrazine and lissamine green staining. Eggs from ∼8,000 hens represented 2 White Leghorn and 1 Rhode Island Red breed. Estimates of heritability using pedigree or genomic relationship matrices were in the 0.2 to 0.3 range. The results were breed specific. Across the populations, genomic regions on chromosomes 1, 2, 4, 5, and 8 were identified as significantly associated with cuticle deposition. No single loci had a large effect. A comparison was made with genes differentially expressed in the shell gland when cuticle deposition was manipulated, however none were obvious candidates for cuticle deposition. The results support the polygenic nature of the trait and the information will help in the future to understand the genetic variance and what might control cuticle deposition and the microbiological safety of the egg.

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

The effect of amylase supplementation on individual variation, growth performance, and starch digestibility in broiler chickens

The objective of this study was to evaluate the variance of starch digestibility in broilers individually fed diets without or with supplemental exogenous amylase. A total of 120 d-of-hatch male chicks were individually reared from 5 to 42 d in metallic cages and fed maize-based basal diets or diets containing 80 kilo-novo-α-amylase units/kg (60 birds or replicates per treatment). Beginning on d 7, feed intake, body weight gain, and feed conversion ratio were recorded; partial excreta collection was conducted every Monday, Wednesday, and Friday until 42 d, when all birds were sacrificed for individual collection of duodenal and ileal digesta. Lower feed intake (4,675 vs. 4,815 g) and feed conversion ratio (1.470 vs. 1.508) were observed in amylase-fed broilers during the overall period (7-43 d; P < 0.01), whereas body weight gain was not affected. Amylase supplementation improved total tract starch (TTS) digestibility (P < 0.05) on each day of excreta collection (except for d 28, where no difference was found), averaging 0.982 vs. 0.973 compared to basal-fed broilers from d 7 to 42. Both apparent ileal starch (AIS) digestibility and apparent metabolizable energy (AME_N) were increased (P <0.05) from 0.968 to 0.976 and from 3,119 to 3,198 kcal/kg, respectively, with enzyme supplementation. Activity of amylase in the duodenum was higher (18.6 vs. 50.1 IU/g of digesta) in supplemented birds. Amylase supplementation led to a reduced coefficient of variation for both TTS (averaged 2.41 vs. 0.92% from 7 to 42 d) and AIS digestibilities (1.96 vs. 1.03%), as well as AME_N (0.49 vs. 0.35%), when compared to the nonsupplemented group, indicating lower individual heterogenity. An age effect was detected for TTS digestibility, as both groups saw an increase during the first weeks (slightly more pronounced in the supplemented group); older birds (d 30 onwards) presented a lower TTS digestibility compared to ages between 7 and 25 d. In conclusion, amylase supplementation in maize diets for broilers can attenuate individual bird variation for starch and energy utilization by increasing amylase activity and enhancing starch digestibility.

The Chicken A and E Blood Systems Arise from Genetic Variation in and around the Regulators of Complement Activation Region

The tightly linked A and E blood alloantigen systems are 2 of 13 blood systems identified in chickens. Reported herein are studies showing that the genes encoding A and E alloantigens map within or near to the chicken regulator of complement activation (RCA) gene cluster, a region syntenic with the human RCA. Genome-wide association studies, sequence analysis, and sequence-derived single-nucleotide polymorphism information for known A and/or E system alleles show that the most likely candidate gene for the A blood system is C4BPM gene (complement component 4 binding protein, membrane). Cosegregation of single-nucleotide polymorphism–defined C4BPM haplotypes and blood system A alleles defined by alloantisera provide a link between chicken blood system A and C4BPM. The best match for the E blood system is the avian equivalent of FCAMR (Fc fragment of IgA and IgM receptor). C4BPM is located within the chicken RCA on chicken microchromosome 26 and is separated from FCAMR by 89 kbp. The genetic variation observed at C4BPM and FCAMR could affect the chicken complement system and differentially guide immune responses to infectious diseases.

Application of Bayesian genomic prediction methods to genome-wide association analyses

Background

Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses.

Results

By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance.

Conclusions

Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

Genome-wide association studies for egg quality traits in White Leghorn layers using low-pass sequencing and SNP chip data

Low-pass sequencing data have been proposed as an alternative to single nucleotide polymorphism (SNP) chips in genome-wide association studies (GWAS) of several species. However, it has not been used in layer chickens yet. This study aims at comparing the GWAS results of White Leghorn chickens using low-pass sequencing data (1×) and 54 k SNP chip data. Ten commercially relevant egg quality traits including albumen height, shell strength, shell colour, egg weight and yolk weight collected from up to 1,420 White Leghorn chickens were analysed. The results showed that the genomic heritability estimates based on low-pass sequencing data were higher than those based on SNP chip data. Although two GWAS analyses showed similar overall landscape for most traits, low-pass sequencing captured some significant SNPs that were not on the SNP chip. In GWAS analysis using 54 k SNP chip data, after including more individuals (up to 5,700), additional significant SNPs not detected by low-pass sequencing data were found. In conclusion, GWAS using low-pass sequencing data showed similar results to those with SNP chip data and may require much larger sample sizes to show measurable advantages.

Genetic analysis of disease resilience of wean-to-finish pigs under a natural disease challenge model using reaction norms

Background

Disease resilience is the ability to maintain performance across environments with different disease challenge loads (CL). A reaction norm describes the phenotypes that a genotype can produce across a range of environments and can be implemented using random regression models. The objectives of this study were to: (1) develop measures of CL using growth rate and clinical disease data recorded under a natural polymicrobial disease challenge model; and (2) quantify genetic variation in disease resilience using reaction norm models.

Methods

Different CL were derived from contemporary group effect estimates for average daily gain (ADG) and clinical disease phenotypes, including medical treatment rate (TRT), mortality rate, and subjective health scores. Resulting CL were then used as environmental covariates in reaction norm analyses of ADG and TRT in the challenge nursery and finisher, and compared using model loglikelihoods and estimates of genetic variance associated with CL. Linear and cubic spline reaction norm models were compared based on goodness-of-fit and with multi-variate analyses, for which phenotypes were separated into three traits based on low, medium, or high CL.

Results

Based on model likelihoods and estimates of genetic variance explained by the reaction norm, the best CL for ADG in the nursery was based on early ADG in the finisher, while the CL derived from clinical disease traits across the nursery and finisher was best for ADG in the finisher and for TRT in the nursery and across the nursery and finisher. With increasing CL, estimates of heritability for nursery and finisher ADG initially decreased, then increased, while estimates for TRT generally increased with CL. Genetic correlations for ADG and TRT were low between high versus low CL, but high for close CL. Linear reaction norm models fitted the data significantly better than the standard genetic model without genetic slopes, while the cubic spline model fitted the data significantly better than the linear reaction norm model for most traits. Reaction norm models also fitted the data better than multi-variate models.

Conclusions

Reaction norm models identified genotype-by-environment interactions related to disease CL. Results can be used to select more resilient animals across different levels of CL, high-performance animals at a given CL, or a combination of these.

Pharmacokinetic Drug Interaction Study of Sorafenib and Morphine in Rats

A combination of the tyrosine kinase inhibitor—sorafenib—and the opioid analgesic—morphine—can be found in the treatment of cancer patients. Since both are substrates of P-glycoprotein (P-gp), and sorafenib is also an inhibitor of P-gp, their co-administration may affect their pharmacokinetics, and thus the safety and efficacy of cancer therapy. Therefore, the aim of this study was to evaluate the potential pharmacokinetic drug–drug interactions between sorafenib and morphine using an animal model. The rats were divided into three groups that Received: sorafenib and morphine (I_SOR+MF), sorafenib (II_SOR), and morphine (III_MF). Morphine caused a significant increase in maximum plasma concentrations (C_max) and the area under the plasma concentration–time curves (AUC_0–t, and AUC_0–∞) of sorafenib by 108.3 (p = 0.003), 55.9 (p = 0.0115), and 62.7% (p = 0.0115), respectively. Also, the C_max and AUC_0–t of its active metabolite—sorafenib N-oxide—was significantly increased in the presence of morphine (p = 0.0022 and p = 0.0268, respectively). Sorafenib, in turn, caused a significant increase in the C_max of morphine (by 0.5-fold, p = 0.0018). Moreover, in the presence of sorafenib the C_max, AUC_0–t, and AUC_0–∞ of the morphine metabolite M3G increased by 112.62 (p < 0.0001), 46.82 (p = 0.0124), and 46.78% (p = 0.0121), respectively. Observed changes in sorafenib and morphine may be of clinical significance. The increased exposure to both drugs may improve the response to therapy in cancer patients, but on the other hand, increase the risk of adverse effects.

PSVII-27 Genetic analysis of disease resilience of nursery-to-finish pigs under a natural disease challenge model using reaction norms

Disease resilience is the ability of an animal to maintain performance across environments with different disease challenge loads (CL) and can be quantified using random regression reaction norm models that describe phenotype as a function of CL. Objectives of this study were to: 1) develop measures of CL using growth rate and clinical disease phenotypes under a natural disease challenge; 2) evaluate genetic variation in disease resilience. Data used were late nursery and finisher growth rates and clinical disease phenotypes, including medical treatment and mortality rates, and subjective health scores, collected on 50 batches of 60/75 crossbred (LRxY) barrows under a polymicrobial natural disease challenge. All pigs were genotyped using a 650K SNP panel. Different CL were derived from estimates of contemporary group effects and used as environmental covariates in reaction norm analyses of average daily gain (ADG) and treatment rate (TRT). The CL were compared based on model loglikelihoods and estimates of genetic variance, using both linear and cubic spline reaction norm models. Linear reaction norm models fitted the data significantly better than the standard genetic model and the cubic spline models fitted the data significantly better than the linear reaction norm model for most traits. CL based on early finisher ADG provided the best fit for nursery ADG, while CL based on clinical disease phenotypes was best for finisher ADG and TRT. With increasing CL, estimates of heritability for ADG initially decreased and then increased, while estimates of heritability for TRT generally increased with CL. Genetic correlations were low between ADG or TRT at high versus low CL but high for close CLs. Results can be used to select more resilient pigs across different CL levels, or high-performance animals at a given CL level, or a combination of these. Funded by Genome Canada, Genome Alberta, USDA-NIFA, and PigGenCanada.

Purebred-crossbred genetic parameters for reproductive traits in swine

For swine breeding programs, testing and selection programs are usually within purebred (PB) populations located in nucleus units that are generally managed differently and tend to have a higher health level than the commercial herds in which the crossbred (CB) descendants of these nucleus animals are expected to perform. This approach assumes that PB animals selected in the nucleus herd will have CB progeny that have superior performance at the commercial level. There is clear evidence that this may not be the case for all traits of economic importance and, thus, including data collected at the commercial herd level may increase the accuracy of selection for commercial CB performance at the nucleus level. The goal for this study was to estimate genetic parameters for five maternal reproductive traits between two PB maternal nucleus populations (Landrace and Yorkshire) and their CB offspring: Total Number Born (TNB), Number Born Alive (NBA), Number Born Alive > 1 kg (NBA > 1 kg), Total Number Weaned (TNW), and Litter Weight at Weaning (LWW). Estimates were based on single-step GBLUP by analyzing any two combinations of a PB and the CB population, and by analyzing all three populations jointly. The genomic relationship matrix between the three populations was generated by using within-population allele frequencies for relationships within a population, and across-population allele frequencies for relationships of the CB with the PB animals. Utilization of metafounders for the two PB populations had no effect on parameter estimates, so the two PB populations were assumed to be genetically unrelated. Joint analysis of two (one PB plus CB) vs. three (both PB and CB) populations did not impact estimates of heritability, additive genetic variance, and genetic correlations. Heritabilities were generally similar between the PB and CB populations, except for LWW and TNW, for which PB populations had about four times larger estimates than CB. Purebred-crossbred genetic correlations (rpc) were larger for Landrace than for Yorkshire, except for NBA > 1 kg. These estimates of rpc indicate that there is potential to improve selection of PB animals for CB performance by including CB information for all traits in the Yorkshire population, but that noticeable additional gains may only occur for NBA > 1 kg and TNW in the Landrace population.

Environmental Sampling for Avian Influenza Virus Detection in Commercial Layer Facilities

The present study was designed to evaluate the utility of environmental samples for convenient but accurate detection of avian influenza virus (AIV) in commercial poultry houses. First, environmental samples from AIV-negative commercial layer facilities were spiked with an H5N2 low pathogenic AIV and were evaluated for their effect on the detection of viral RNA immediately or after incubation at -20 C, 4 C, 22 C, or 37 C for 24, 48, or 72 hr. Second, Swiffer pads, drag swabs, and boot cover swabs were evaluated for their efficiency in collecting feces and water spiked with the H5N2 LPAIV under a condition simulated for a poultry facility floor. Third, environmental samples collected from commercial layer facilities that experienced an H5N2 highly pathogenic AIV outbreak in 2014-15 were evaluated for the effect of sampling locations on AIV detection. The half-life of AIV was comparable across all environmental samples but decreased with increasing temperatures. Additionally, sampling devices did not differ significantly in their ability to collect AIV-spiked environmental samples from a concrete floor for viral RNA detection. Some locations within a poultry house, such as cages, egg belts, house floor, manure belts, and manure pits, were better choices for sampling than other locations (feed trough, ventilation fan, and water trays) to detect AIV RNA after cleaning and disinfection. Samples representing cages, floor, and manure belts yielded significantly more PCR positives than the other environmental samples. In conclusion, environmental samples can be routinely collected from a poultry barn as noninvasive samples for monitoring AIV.

Heritability of perching behavior and its genetic relationship with incidence of floor eggs in Rhode Island Red chickens

Background

As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines.

Results

The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were − 0.26 ± 0.14 and − 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability.

Conclusions

In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.

The impact of endogenous Avian Leukosis Viruses (ALVE) on production traits in elite layer lines

Avian Leukosis Virus subgroup E (ALVE) integrations are endogenous retroviral elements found in the chicken genome. The presence of ALVE has been reported to have negative impacts on multiple traits, including egg production and body weight. The recent development of rapid, inexpensive and specific ALVE detection methods has facilitated their characterization in elite commercial egg production lines across multiple generations. The presence of 20 ALVE was examined in 8 elite lines, from 3 different breeds. Seventeen of these ALVE (85%) were informative and found to be segregating in at least one of the lines. To test for an association between specific ALVE inserts and traits, a large genotype by phenotype study was undertaken. Genotypes were obtained for 500 to 1500 males per line, and the phenotypes used were sire-daughter averages. Phenotype data were analyzed by line with a linear model that included the effects of generation, ALVE genotype and their interaction. If genotype effect was significant, the number of ALVE copies was fitted as a regression to estimate additive ALVE gene substitution effect. Significant associations between the presence of specific ALVE inserts and 18 commercially relevant performance and egg quality traits, including egg production, egg weight and albumen height, were observed. When an ALVE was segregating in more than one line, these associations did not always have the same impact (negative, positive or none) in each line. It is hypothesized that the presence of ALVE in the chicken genome may influence production traits by 3 mechanisms: viral protein production may modulate the immune system and impact overall production performance (virus effect); insertional mutagenesis caused by viral integration may cause direct gene alterations or affect gene regulation (gene effect); or the integration site may be within or adjacent to a quantitative trait region which impacts a performance trait (linkage disequilibrium, marker effect).

In vivo assessment of potential for UGT-inhibition-based drug-drug interaction between sorafenib and tapentadol

Sorafenib (SR) is one of the most potent UGT (1A1, 1A9) inhibitors (in in vitro tests). The inhibition of UGT1A1 may cause hyperbilirubinaemia, whereas the inhibition of UGT1A9 and 1A1 may result in drug-drug interactions (DDIs). Tapentadol (TAP) is a synthetic μ-opioid agonist and is used to treat moderate to severe acute pain. Tapentadol is highly glucuronidated by the UGT1A9 and UGT2B7 isoenzymes. The aim of the study was to assess the DDI between SR and TAP. Wistar rats were divided into three groups, with eight animals in each. The rats were orally treated with SR (100 mg/kg) or TAP (4.64 mg/kg) or in combination with 100 mg/kg SOR and 4.64 TAP mg/kg. The concentrations of SR and sorafenib N-oxide, TAP and tapentadol glucuronide were respectively measured by means of high-performance liquid chromatography (HPLC) with ultraviolet detection and by means of ultra-performance liquid chromatography-tandem mass spectrometry. The co-administration of TAP with SR caused TAP maximum plasma concentration (C_max) to increase 5.3-fold whereas its area under the plasma concentration-time curve (AUC_0-∞) increased 1.5-fold. The tapentadol glucuronide C_max increased 5.3-fold and whereas its AUC_0-∞ increased 2.0-fold. The tapentadol glucuronide/TAP AUC_0-∞ ratio increased 1.4-fold (p = 0.0118). TAP also increased SR C_max 1.9-fold, whereas its AUC_0-∞ increased 1.3-fold. The sorafenib N-oxide C_max increased 1.9-fold whereas its AUC_0-∞ increased 1.3-fold. The sorafenib N-oxide/SR AUC_0-t ratio increased 1.4-fold (p = 0.0127). The results show that the co-administration of sorafenib and tapentadol increases the exposure to both drugs and changes their metabolism. In consequence, the pharmacological effect may be intensified, but the toxicity may increases, too.

Pharmacokinetic Interaction between Sorafenib and Atorvastatin, and Sorafenib and Metformin in Rats

The tyrosine kinase inhibitor sorafenib is the first-line treatment for patients with hepatocellular carcinoma (HCC), in which hyperlipidemia and type 2 diabetes mellitus (T2DM) may often coexist. Protein transporters like organic cation (OCT) and multidrug and toxin extrusion (MATE) are involved in the response to sorafenib, as well as in that to the anti-diabetic drug metformin or atorvastatin, used in hyperlipidemia. Changes in the activity of these transporters may lead to pharmacokinetic interactions, which are of clinical significance. The study aimed to assess the sorafenib−metformin and sorafenib−atorvastatin interactions in rats. The rats were divided into five groups (eight animals in each) that received sorafenib and atorvastatin (I_SOR+AT), sorafenib and metformin (II_SOR+MET), sorafenib (III_SOR), atorvastatin (IV_AT), and metformin (V_MET). Atorvastatin significantly increased the maximum plasma concentration (C_max) and the area under the plasma concentration–time curve (AUC) of sorafenib by 134.4% (p < 0.0001) and 66.6% (p < 0.0001), respectively. Sorafenib, in turn, caused a significant increase in the AUC of atorvastatin by 94.0% (p = 0.0038) and its metabolites 2−hydroxy atorvastatin (p = 0.0239) and 4−hydroxy atorvastatin (p = 0.0002) by 55.3% and 209.4%, respectively. Metformin significantly decreased the AUC of sorafenib (p = 0.0065). The AUC ratio (II_SOR+MET group/III_SOR group) for sorafenib was equal to 0.6. Sorafenib did not statistically significantly influence the exposure to metformin. The pharmacokinetic interactions observed in this study may be of clinical relevance in HCC patients with coexistent hyperlipidemia or T2DM.

Research Note: Comparison of chicken blood chemistry and electrolyte parameters between the portable i-STAT1 clinical analyzer and VetScan VS2 serum biochemistry panel using Hy-Line commercial white-egg laying hens

The i-STAT1 clinical analyzer has become an increasingly popular tool in clinical production animal medicine as it can provide pen-side results in a cost effective and timely manner when compared to standard benchtop serum biochemistry blood gas and chemistry analyses. This study compares the results of the portable Abbott i-STAT1 analyzer and the Abaxis VetScan VS2 for glucose (Glu, mg/dL), ionized Ca (mmol/L), Na (mmol/L), and K (mmol/L) values. Three genetically distinct commercial varieties (CV) of Hy-Line white-egg laying hens are used in this study (Hy-Line W-36, Hy-Line W-80, and Hy-Line W-80+). Thirty blood samples (n = 10 per CV) were obtained in the production house from the brachial vein and concurrently analyzed by the i-STAT1 portable device. Serum from 22 of these same samples was analyzed via VetScan VS2, a benchtop serum clinical biochemistry analyzer, using VetScan Avian/Reptilian Profile Plus reagent rotors. A paired T-test was used to test for statistical differences in means between the 2 instruments for each of the parameters. Parameters with significant mean differences were then subject to correlation and regression analysis to further evaluate relationships between the results from the 2 methods. Significant differences between means were found for Glu, Na, and K levels. Ca levels were found to be not directly comparable by the 2 analysis instruments. This comparison elucidates the importance of clinical analyzer validations when applying different strategies of diagnostic medicine in poultry.

In vivo assessment of the drug interaction between sorafenib and paracetamol in rats

Purpose

Sorafenib is a multi-targeted tyrosine kinase inhibitor (TKI) used for the treatment of advanced renal cell carcinoma, hepatocellular carcinoma and radioactive iodine resistant thyroid carcinoma. Neoplastic diseases are the cause of pain, which may occur regardless of the stage of the disease. Paracetamol is a non-opioid analgesic used alone or in combination with opioids for the treatment of cancer pain. Numerous studies have pointed out changes in the pharmacokinetic parameters of TKIs when co-administered with paracetamol. The aim of the study was to assess drug–drug interactions (DDIs) between sorafenib and paracetamol.

Methods

Rats were divided into three groups, each consisting of eight animals. The first group received sorafenib (II_S), the second group received sorafenib + paracetamol (I_S+PA), whereas the third group received only paracetamol (III_PA). A single dose of sorafenib (100 mg/kg b.w.) and paracetamol (100 mg/kg b.w.) was administered orally. The plasma concentrations of sorafenib and its metabolite–N-oxide as well as paracetamol and its glucuronide and sulphate metabolites were measured using validated high-performance liquid chromatography (HPLC) method with ultraviolet detection.

Results

The co-administration of sorafenib and paracetamol increased the maximum concentration (C_max) of paracetamol by 33% (p = 0.0372). In the I_S+ PA group the C_max of paracetamol glucuronide was reduced by 48% (p =  < 0.0001), whereas the C_max of paracetamol sulphate was higher by 153% (p = 0.0012) than in the III_PA group. Paracetamol increased sorafenib and sorafenib N-oxide C_max by 60% (p = 0.0068) and 83% (p = 0.0023), respectively.

Conclusions

A greater knowledge of DDI between sorafenib and paracetamol may help adjust dose properly and avoid toxicity effects in individual patients.

A biphasic curve for modeling, classifying, and predicting egg production in single cycle and molted flocks

Egg production on a flock level can be summarized into several phases determined by biology of individual birds: rapid increase in production reflecting achieving sexual maturity, peak production related to maximum laying potential, followed by gradual decrease in the rate of lay as the birds age. In 1989 Yang et al. proposed a mathematical model (modified compartmental model) to describe this process. In this study a biphasic modified compartmental model was proposed for modeling, classifying, and predicting egg production in single cycle and molted flocks. Goodness-of-fit was high for both single cycle (average R² = 0.99) and molted flocks (average R² = 0.97), suggesting that the model could be used for benchmarking molted flocks. The difference in R² between the biphasic model and the model used by Yang et al in 1989 can be used to differentiate between single cycle and molted flocks. The biphasic model was shown to predict future records well up to 8 wk in advance, but as with any regression model, caution is recommended when predicting records outside of the observed age range.

Accuracy of genomic prediction of shell quality in a White Leghorn line

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens.

Influence of obesity on pharmacokinetics and analgesic effect of ketoprofen administered intravenously to patients after laparoscopic cholecystectomy

BACKGROUND

Ketoprofen is an analgesic drug commonly applied in the postoperative period, e.g., to patients after laparoscopic cholecystectomy. Many patients who undergo this procedure are obese. As pathophysiological changes are observed in obesity, the efficacy of ketoprofen may be altered in this group of patients. The aim of the study was to compare the pharmacokinetic parameters and analgesic effect of ketoprofen administered to obese and non-obese patients after laparoscopic cholecystectomy.

METHODS

The study was conducted on 41 patients after laparoscopic cholecystectomy, who were divided into two groups: obese (n = 21) and non-obese (n = 20). Ketoprofen was administered intravenously at a dose of 100 mg. Plasma ketoprofen concentrations were measured by means of validated high-performance liquid chromatography with ultraviolet detection. The pharmacokinetic parameters of the drug were calculated using the non-compartmental method. Additionally, pain intensity was assessed during the study using NRS scale.

RESULTS

The obese patients had significantly lower AUC_0-∞ (1.4-fold), AUMC_0-t (1.8-fold), AUMC_0-∞ (3.2-fold), MRT_0-t (1.4-fold), MRT_0-∞ (2.3-fold), t_0.5 (2.3-fold) and V_z/kg (2.3-fold) and higher k_el (2.2-fold) than the non-obese group. Moreover, 4 h and 6 h after the administration of the drug, pain intensity was significantly higher in the obese patients.

CONCLUSIONS

The drug was eliminated faster and the analgesic effect of ketoprofen in the obese patients was decreased as compared with the non-obese subjects. However, pain intensity did not increase to the level, which required additional analgesic treatment. Therefore, it seems that dosage adjustment of intravenous ketoprofen is not necessary in obese patients.

Genetic determinants of resistance to highly pathogenic avian influenza in chickens

Highly pathogenic avian influenza (HPAI) poses a huge threat to poultry production and also introduces an epidemiological risk in the human population. Thus far, HPAI has been controlled mainly through widespread implementation of biosecurity, and in the case of an outbreak, liquidation of flocks and establishment of protection zones. Alternative strategies for combating HPAI include the use of vaccines, genetic modification, and genetic selection for increased general and specific immunity in birds. These kinds of strategies often require identification of the genes involved in the immune response to the pathogen. Many genes have been identified as potentially associated with differences in the response to HPAI between poultry species and between individuals. Thus far, the most attention has been focused on genes taking part in regulating the innate immune response, which is responsible for preventing infection and limiting the replication and spread of the virus. The most commonly mentioned candidates for layer chickens include interferon-stimulated genes (ISGs) and RIG-I-like receptors. Proteins encoded by genes of the BTLN family, defensins, and proteins involved in apoptosis have also been associated with differences in the response to HPAI. Recent years have seen an increasing number of studies on the genetic determinants of individual differences in the response to HPAI in chickens. Data from HPAI outbreaks in the US in the spring of 2015 and Mexico in the years 2012-2016 have enabled a more precise analysis of this problem. A number of genes have been identified as associated with the immune response, but their specific role in determining the survival of birds requires further study. Preliminary results indicate that genetic determinants of resistance to HPAI are highly complex and can vary depending on the virus strain and the genetic line of birds.

Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens

Background

The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL (quantitative trait loci) for recombination rate in two purebred layer chicken lines.

Methods

A total of 1200 white-egg layers (WL) were genotyped with 580 K SNPs and 5108 brown-egg layers (BL) were genotyped with 42 K SNPs (single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each 0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL.

Results

Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL, respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines.

Conclusions

Differences in the recombination rates, hotspot locations, and QTL regions associated with genome-wide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait.

Electronic supplementary material

The online version of this article (10.1186/s40104-019-0332-y) contains supplementary material, which is available to authorized users.

Investigating the genetic determination of clutch traits in laying hens

Clutch traits were proposed as a more detailed description of egg-laying patterns than simple total egg production. In this study, egg production of 23,809 Rhode Island Red (RIR) and 22,210 White Leghorn (WL) hens was described in terms of number of clutches, average and maximum clutch size, age at first egg, total saleable egg production, and percentage of egg defects. Genetic parameters were estimated using a six-trait animal model. Of the phenotyped birds, 1433 RIR hens and 1515 WL hens were genotyped with line specific 50K Affymetrix Axiom single nucleotide polymorphism chips to perform genome-wide association analyses. Moderate heritabilities were estimated for clutch traits of 0.20 to 0.42 in the RIR line and 0.29 to 0.41 in the WL line. Average and maximum clutch size was positively genetically correlated with total saleable egg number in both lines. Genome-wide association analysis identified seven regions that were associated with egg production in the RIR line and 12 regions in the WL line. The regions identified were line and trait specific, except for one region on chromosome 6 from 28 to 29 Mb that influenced number of clutches and maximum and average clutch size in WL hens. Regions associated with egg production identified here overlapped with 260 genes, with some strong positional candidates based on gene ontology including WASH1, which is involved in oocyte maturation, NPVF, involved in regulation of follicle-stimulating hormone secretion, and FOXO3, involved in oocyte maturation and ovulation from the ovarian follicle. Confirmation of the role of these genes in regulation of egg production pattern will require further studies.

Genetics and Genomic Regions Affecting Response to Newcastle Disease Virus Infection under Heat Stress in Layer Chickens

Newcastle disease virus (NDV) is a highly contagious avian pathogen that poses a tremendous threat to poultry producers in endemic zones due to its epidemic potential. To investigate host genetic resistance to NDV while under the effects of heat stress, a genome-wide association study (GWAS) was performed on Hy-Line Brown layer chickens that were challenged with NDV while under high ambient temperature to identify regions associated with host viral titer, circulating anti-NDV antibody titer, and body weight change. A single nucleotide polymorphism (SNP) on chromosome 1 was associated with viral titer at two days post-infection (dpi), while 30 SNPs spanning a quantitative trait loci (QTL) on chromosome 24 were associated with viral titer at 6 dpi. Immune related genes, such as CAMK1d and CCDC3 on chromosome 1, associated with viral titer at 2 dpi, and TIRAP, ETS1, and KIRREL3, associated with viral titer at 6 dpi, were located in two QTL regions for viral titer that were identified in this study. This study identified genomic regions and candidate genes that are associated with response to NDV during heat stress in Hy-Line Brown layer chickens. Regions identified for viral titer on chromosome 1 and 24, at 2 and 6 dpi, respectively, included several genes that have key roles in regulating the immune response.

Detection of copy number variations in brown and white layers based on genotyping panels with different densities

Background

Copy number variations (CNV) are an important source of genetic variation that has gained increasing attention over the last couple of years. In this study, we performed CNV detection and functional analysis for 18,719 individuals from four pure lines and one commercial cross of layer chickens. Samples were genotyped on four single nucleotide polymorphism (SNP) genotyping platforms, i.e. the Illumina 42K, Affymetrix 600K, and two different customized Affymetrix 50K chips. CNV recovered from the Affymetrix chips were identified by using the Axiom^® CNV Summary Tools and PennCNV software and those from the Illumina chip were identified by using the cnvPartition in the Genome Studio software.

Results

The mean number of CNV per individual varied from 0.50 to 4.87 according to line or cross and size of the SNP genotyping set. The length of the detected CNV across all datasets ranged from 1.2 kb to 3.2 Mb. The number of duplications exceeded the number of deletions for most lines. Between the lines, there were considerable differences in the number of detected CNV and their distribution. Most of the detected CNV had a low frequency, but 19 CNV were identified with a frequency higher than 5% in birds that were genotyped on the 600K panel, with the most common CNV being detected in 734 birds from three lines.

Conclusions

Commonly used SNP genotyping platforms can be used to detect segregating CNV in chicken layer lines. The sample sizes for this study enabled a detailed characterization of the CNV landscape within commercially relevant lines. The size of the SNP panel used affected detection efficiency, with more CNV detected per individual on the higher density 600K panel. In spite of the high level of inter-individual diversity and a large number of CNV observed within individuals, we were able to detect 19 frequent CNV, of which, 57.9% overlapped with annotated genes and 89% overlapped with known quantitative trait loci.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0428-4) contains supplementary material, which is available to authorized users.

Genetic Analysis of a Commercial Egg Laying Line Challenged With Newcastle Disease Virus

In low income countries, chickens play a vital role in daily life. They provide a critical source of protein through egg production and meat. Newcastle disease, caused by avian paramyxovirus type 1, has been ranked as the most devastating disease for scavenging chickens in Africa and Asia. High mortality among flocks infected with velogenic strains leads to a devastating loss of dietary protein and buying power for rural households. Improving the genetic resistance of chickens to Newcastle Disease virus (NDV), in addition to vaccination, is a practical target for improvement of poultry production in low income countries. Because response to NDV has a component of genetic control, it can be influenced through selective breeding. Adding genomic information to a breeding program can increase the amount of genetic progress per generation. In this study, we challenged a commercial egg-laying line with a lentogenic strain of NDV, measured phenotypic responses, collected genotypes, and associated genotypes with phenotypes. Collected phenotypes included viral load at 2 and 6 days post-infection (dpi), antibody levels pre-challenge and 10 dpi, and growth rates pre- and post-challenge. Six suggestive QTL associated with response to NDV and/or growth were identified, including novel and known QTL confirming previously reported associations with related traits. Additionally, previous RNA-seq analysis provided support for several of the genes located in or near the identified QTL. Considering the trend of negative genetic correlation between antibody and Newcastle Disease tolerance (growth under disease) and estimates of moderate to high heritability, we provide evidence that these NDV response traits can be influenced through selective breeding. Producing chickens that perform favorably in challenging environments will ultimately increase the supply of quality protein for human consumption.

Genomic prediction of avian influenza infection outcome in layer chickens

Avian influenza (AI) is a devastating poultry disease that currently can be controlled only by liquidation of affected flocks. In spite of typically very high mortality rates, a group of survivors was identified and genotyped on a 600K single nucleotide polymorphism (SNP) chip to identify genetic differences between survivors, and age- and genetics-matched controls from unaffected flocks. In a previous analysis of this dataset, a heritable component was identified and several regions that are associated with outcome of the infection were localized but none with a large effect. For complex traits that are determined by many genes, genomic prediction models using all SNPs across the genome simultaneously are expected to optimally exploit genomic information. In this study, we evaluated the diagnostic value of genomic estimated breeding values for predicting AI infection outcome within and across two highly pathogenic avian influenza viral strains and two genetic lines of layer chickens using receiver operating curves. We show that genomic prediction based on the 600K SNP chip has the potential to predict disease outcome especially within the same strain of virus (area under receiver operating curve above 0.7), but did not predict well across genetic varieties (area under receiver operating curve of 0.43).

A pharmacokinetic study on lapatinib in type 2 diabetic rats

BACKGROUND

Diabetes mellitus (DM) is a complex metabolic disorder which affects the function of numerous tissues and alters the pharmacokinetic parameters of many drugs. As many oncological patients are diabetics, it is important to determine the influence of this chronic disease on the pharmacokinetics (PK) of anticancer drugs. Lapatinib is a tyrosine kinase inhibitor (TKI), approved for the treatment of human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer. The aim of the study was to compare the PK of lapatinib in normal and type 2 diabetes mellitus (T2DM) model rats. Additionally, the effect of lapatinib on blood glucose concentrations was examined.

METHODS

The PK of lapatinib was studied in healthy rats (n=6, the healthy group) and T2DM model rats (n=6, the diabetic group). The rats received lapatinib orally as a single dose of 50mg. Plasma concentrations of lapatinib were measured with high-performance liquid chromatography method coupled with a tandem mass spectrometry.

RESULTS

The plasma concentrations of lapatinib were increased in the T2DM model rats. There were statistically significant differences between the groups in C_max (p=0.0104) and AUC_0-t (p=0.0265). The reduction of glycaemia in the range of 1.2-41.5% and in the range of 4.1-36.8% was observed in the diabetic and healthy animals, respectively.

CONCLUSIONS

Higher concentrations of lapatinib in the diabetic rats may suggest the need for application of lower doses of this TKI in patients with DM.

Repeatability vs. multiple-trait models to evaluate shell dynamic stiffness for layer chickens

Shell quality is one of the most important traits for improvement in layer chickens. Proper consideration of repeated records can increase the accuracy of estimated breeding values and thus genetic improvement of shell quality. The objective of this study was to compare different models for genetic evaluation of the collected data. For this study, 81,646 dynamic stiffness records on 21,321 brown egg layers and 93,748 records on 24,678 white egg layers from 4 generations were analyzed. Across generations, data were collected at 2 to 4 ages (at approximately 26, 42, 65, and 86 wk), with repeated records at each age. Seven models were compared, including 5 repeatability models with increasing complexity, a random regression model, and a multitrait model. The models were compared using Akaike Information Criteria with significance testing of nested models with a Log Likelihood Ratio test. Estimates of heritability were 0.31-0.36 for the brown line and 0.23-0.26 for the white line, but repeatability was higher for the model with age-specific permanent environment effects (0.59 for both lines) than for the model with an overall permanent environmental effects (0.47 for the brown and 0.41 for the white line). The model that allowed for permanent environmental effect within age and heterogeneous residual variance between ages resulted in improved fit compared to the traditional model that fits single permanent environment and residual effects, but was inferior in fit and predictive ability to the full multiple-trait model. The random regression model had better fit to the data than repeatability models but slightly worse than the multiple-trait model. For traits with repeated records at different ages, repeatability within and across ages as well as genetic correlations should be considered while choosing the number of records collected per individual as well as the model for genetic evaluation.

Pharmacokinetic drug-drug interaction between erlotinib and paracetamol: A potential risk for clinical practice

BACKGROUND

Erlotinib is a tyrosine kinase inhibitor available for the treatment of non-small cell lung cancer. Paracetamol is an analgesic agent, commonly used in cancer patients. Because these drugs are often co-administered, there is an increasing issue of interaction between them.

OBJECTIVE

The aim of the study was to investigate the effect of paracetamol on the pharmacokinetic parameters of erlotinib, as well as the influence of erlotinib on the pharmacokinetics of paracetamol.

METHODS

The rabbits were divided into three groups: the rabbits receiving erlotinib (I_ER), the group receiving paracetamol (II_PR), and the rabbits receiving erlotinib+paracetamol (III_ER+PR). A single dose of erlotinib was administered orally (25mg) and was administered intravenously (35mg/kg). Plasma concentrations of erlotinib, its metabolite (OSI420), paracetamol and its metabolites - glucuronide and sulphate were measured with the validated method.

RESULTS

During paracetamol co-administration we observed increased erlotinib maximum concentration (C_max) and area under the plasma concentration-time curve from time zero to infinity (AUC_0-∞) by 87.7% and 31.1%, respectively. In turn, erlotinib lead to decreased paracetamol AUC_0-∞ by 35.5% and C_max by 18.9%. The mean values of paracetamol glucuronide/paracetamol ratios for C_max were 32.2% higher, whereas paracetamol sulphate/paracetamol ratios for C_max and AUC_0-∞ were 37.1% and 57.1% lower in the II_PR group, when compared to the III_ER+PR group.

CONCLUSIONS

Paracetamol had significant effect on the enhanced plasma exposure of erlotinib. Additionally, erlotinib contributed to the lower concentrations of paracetamol. Decreased glucuronidation and increased sulphation of paracetamol after co-administration of erlotinib were also observed.

The alteration of pharmacokinetics of erlotinib and OSI420 in type 1 diabetic rabbits

BACKGROUND

Alterations in blood glucose levels observed in diabetes, may change the pharmacokinetics of co-administered drugs and in consequence, the efficacy and safety of therapy. Many oncological patients are diabetics and it is important to determine the interaction of anticancer drugs with this chronic disease. Erlotinib is a tyrosine kinase inhibitor (TKI), approved for the treatment of patients with non-small-cell lung cancer and pancreatic cancer in combination with gemcitabine. The aim of the study was to investigate the influence of the diabetes on the pharmacokinetics of erlotinib in rabbits. Additionally, the effect of erlotinib on glucose levels was examined.

METHODS

The pharmacokinetics of erlotinib was studied in healthy rabbits (n=6, control group) and type 1 diabetic rabbits (n=6, diabetic group). Erlotinib was administered in a single oral dose of 25mg. Plasma concentrations of erlotinib and its metabolite (OSI420) were measured with the validated method.

RESULTS

The plasma concentrations of erlotinib and OSI420 were markedly increased in diabetic rabbits. Statistically significant differences between the groups were revealed for almost all analysed pharmacokinetic parameters for erlotinib and OSI420. The maximum glycaemia drop of 7.7-33.5% was observed in the diabetic animals, but no significant changes in glucose concentration were observed in the control group.

CONCLUSIONS

The research proved the significant influence of diabetes on the pharmacokinetics of erlotinib and OSI420. Due to higher exposure to erlotinib, there may be an increased risk of adverse drug reactions in diabetic patients. Therefore, in some cases lower doses of the drug should be considered.

Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

BACKGROUND

Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.

METHODS

Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel.

RESULTS

Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP.

CONCLUSIONS

In conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.

A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1

BACKGROUND

The major histocompatibility complex (MHC) is present within the genomes of all jawed vertebrates. MHC genes are especially important in regulating immune responses, but even after over 80 years of research on the MHC, much remains to be learned about how it influences adaptive and innate immune responses. In most species, the MHC is highly polymorphic and polygenic. Strong and highly reproducible associations are established for chicken MHC-B haplotypes in a number of infectious diseases. Here, we report (1) the development of a high-density SNP (single nucleotide polymorphism) panel for MHC-B typing that encompasses a 209,296 bp region in which 45 MHC-B genes are located, (2) how this panel was used to define chicken MHC-B haplotypes within a large number of lines/breeds and (3) the detection of recombinants which contributes to the observed diversity.

METHODS

A SNP panel was developed for the MHC-B region between the BG2 and CD1A1 genes. To construct this panel, each SNP was tested in end-point read assays on more than 7500 DNA samples obtained from inbred and commercially used egg-layer lines that carry known and novel MHC-B haplotypes. One hundred and one SNPs were selected for the panel. Additional breeds and experimentally-derived lines, including lines that carry MHC-B recombinant haplotypes, were then genotyped.

RESULTS

MHC-B haplotypes based on SNP genotyping were consistent with the MHC-B haplotypes that were assigned previously in experimental lines that carry B2, B5, B12, B13, B15, B19, B21, and B24 haplotypes. SNP genotyping resulted in the identification of 122 MHC-B haplotypes including a number of recombinant haplotypes, which indicate that crossing-over events at multiple locations within the region lead to the production of new MHC-B haplotypes. Furthermore, evidence of gene duplication and deletion was found.

CONCLUSIONS

The chicken MHC-B region is highly polymorphic across the surveyed 209-kb region that contains 45 genes. Our results expand the number of identified haplotypes and provide insights into the contribution of recombination events to MHC-B diversity including the identification of recombination hotspots and an estimation of recombination frequency.