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.

The RHCE gene encodes the chicken blood system I.

BACKGROUND: There are 13 known chicken blood systems, which were originally detected by agglutination of red blood cells by specific alloantisera. The genomic region or specific gene responsible has been identified for four of these systems (A, B, D and E). We determined the identity of the gene responsible for the chicken blood system I, using DNA from multiple birds with known chicken I blood system serology, 600K and 54K single nucleotide polymorphism (SNP) data, and lowpass sequence information. RESULTS: The gene responsible for the chicken I blood system was identified as RHCE, which is also one of the genes responsible for the highly polymorphic human Rh blood group locus, for which maternal/fetal antigenic differences can result in fetal hemolytic anemia with fetal mortality. We identified 17 unique RHCE haplotypes in the chicken, with six haplotypes corresponding to known I system serological alleles. We also detected deletions in the RHCE gene that encompass more than 6000 bp and that are predicted to remove its last seven exons. CONCLUSIONS: RHCE is the gene responsible for the chicken I blood system. This is the fifth chicken blood system for which the responsible gene and gene variants are known. With rapid DNA-based testing now available, the impact of I blood system variation on response against disease, general immune function, and animal production can be investigated in greater detail.

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.

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.

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.

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.

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 concomitant use of lapatinib and paracetamol - the risk of interaction.

Lapatinib is a tyrosine kinase inhibitor used for the treatment of breast cancer. Paracetamol is an analgesic commonly applied to patients with mild or moderate pain and fever. Cancer patients are polymedicated, which involves high risk of drug interactions during therapy. The aim of the study was to assess the interaction between lapatinib and paracetamol in rats. The rats were divided into three groups of eight animals in each. One group received lapatinib + paracetamol (IL + PA), another group received lapatinib (IIL), whereas the last group received paracetamol (IIIPA). A single dose of lapatinib (100 mg/kg b.w.) and paracetamol (100 mg/kg b.w.) was administered orally. Plasma concentrations of lapatinib, paracetamol and its metabolites - glucuronide and sulphate, were measured with the validated HPLC-MS/MS method and HPLC-UV method, respectively. The pharmacokinetic parameters of both drugs were calculated using non-compartmental methods. The co-administration of lapatinib and paracetamol increased the area under the plasma concentration-time curve (AUC) and the maximum concentration (Cmax) of lapatinib by 239.6% (p = 0.0030) and 184% (p = 0.0011), respectively. Lapatinib decreased the paracetamol AUC0-∞ by 48.8% and Cmax by 55.7%. In the IL + PA group the Cmax of paracetamol glucuronide was reduced, whereas the Cmax of paracetamol sulphate was higher than in the IIIPA group. Paracetamol significantly affected the enhanced plasma exposure of lapatinib. Additionally, lapatinib reduced the concentrations of paracetamol. The co-administration of lapatinib decreased the paracetamol glucuronidation but increased the sulphation. The findings of this study may be of clinical relevance to patients requiring analgesic therapy.

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.

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).

THE EFFECT OF TOTAL AND PARTIAL NEPHRECTOMY ON THE PHARMACOKINETICS OF INTRAVENOUS PARACETAMOL IN HUMANS.

Paracetamol is one of the most common analgesic and antipyretic drugs. Recently intravenous paracetamol has been widely used to treat moderate postoperative pain. Surgery is the main method of treatment of renal cancer. Total or partial nephrectomy can be performed, depending on the size and location of the tumor. Pharmacokinetics of drugs may depend on the type of surgery. The aim of the study was to compare the postinfusion pharmacokinetics of paracetamol in patients after total nephrectomy (TN) and nephron sparing surgery (NSS).The research was carried out on two groups of patients after nephrectomy: total (TN n = 37; mean [SD], age, 60.4 [10.9] years; BMI, 26.5 [3.8] kg/m2; creatinine clearance, Cl, 80.9 [37.1] mL/min) and nephron sparing surgery (NSS n = 17; 57.9 [16.5] years; BMI, 29.5 [5.3] kg/m2; Cl, 97.6 [27.8] mL/min). The patients were treated with paracetamol (PerfalganO Bristol-Myers Squibb) at an intravenous dose of 1.000 mg, which was infused for 15 minutes after surgery. The concentrations of paracetamol in the patients' plasma were determined by the HPLC method with UV detection (X = 261 run). The main pharmacokinetic parameters of paracetamol in the TN vs. NSS group were as follows: C.. 29.08 [17.39] vs. 27.54 [15.70] pg/mL (p = 0.6692); AUC5, 29.24 [13.86] vs. 34.85 [14.28] pg.h/mL (p = 0.2896); AUMC5,,,, 47.58 [26.08] vs. 62.02 [27.64] pg-h/mL (p = 0.1345); to. 2.34 [0.96] vs. 1.93 [0.50] h (p = 0.1415), respectively. In both groups the exposure to paracetamol was comparable. The t1/2 after nephron sparing surgery was shorter than after total nephrectomy. Therefore, these patients may demand more frequent drug administration. In the NSS group the C. of the analgesic was considerably reduced in men.

Effects of number of training generations on genomic prediction for various traits in a layer chicken population.

BACKGROUND: Genomic estimated breeding values (GEBV) based on single nucleotide polymorphism (SNP) genotypes are widely used in animal improvement programs. It is typically assumed that the larger the number of animals is in the training set, the higher is the prediction accuracy of GEBV. The aim of this study was to quantify genomic prediction accuracy depending on the number of ancestral generations included in the training set, and to determine the optimal number of training generations for different traits in an elite layer breeding line. METHODS: Phenotypic records for 16 traits on 17,793 birds were used. All parents and some selection candidates from nine non-overlapping generations were genotyped for 23,098 segregating SNPs. An animal model with pedigree relationships (PBLUP) and the BayesB genomic prediction model were applied to predict EBV or GEBV at each validation generation (progeny of the most recent training generation) based on varying numbers of immediately preceding ancestral generations. Prediction accuracy of EBV or GEBV was assessed as the correlation between EBV and phenotypes adjusted for fixed effects, divided by the square root of trait heritability. The optimal number of training generations that resulted in the greatest prediction accuracy of GEBV was determined for each trait. The relationship between optimal number of training generations and heritability was investigated. RESULTS: On average, accuracies were higher with the BayesB model than with PBLUP. Prediction accuracies of GEBV increased as the number of closely-related ancestral generations included in the training set increased, but reached an asymptote or slightly decreased when distant ancestral generations were used in the training set. The optimal number of training generations was 4 or more for high heritability traits but less than that for low heritability traits. For less heritable traits, limiting the training datasets to individuals closely related to the validation population resulted in the best predictions. CONCLUSIONS: The effect of adding distant ancestral generations in the training set on prediction accuracy differed between traits and the optimal number of necessary training generations is associated with the heritability of traits.

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.

Response and inbreeding from a genomic selection experiment in layer chickens.

BACKGROUND: Genomic selection (GS) using estimated breeding values (GS-EBV) based on dense marker data is a promising approach for genetic improvement. A simulation study was undertaken to illustrate the opportunities offered by GS for designing breeding programs. It consisted of a selection program for a sex-limited trait in layer chickens, which was developed by deterministic predictions under different scenarios. Later, one of the possible schemes was implemented in a real population of layer chicken. METHODS: In the simulation, the aim was to double the response to selection per year by reducing the generation interval by 50 %, while maintaining the same rate of inbreeding per year. We found that GS with retraining could achieve the set objectives while requiring 75 % fewer reared birds and 82 % fewer phenotyped birds per year. A multi-trait GS scenario was subsequently implemented in a real population of brown egg laying hens. The population was split into two sub-lines, one was submitted to conventional phenotypic selection, and one was selected based on genomic prediction. At the end of the 3-year experiment, the two sub-lines were compared for multiple performance traits that are relevant for commercial egg production. RESULTS: Birds that were selected based on genomic prediction outperformed those that were submitted to conventional selection for most of the 16 traits that were included in the index used for selection. However, although the two programs were designed to achieve the same rate of inbreeding per year, the realized inbreeding per year assessed from pedigree was higher in the genomic selected line than in the conventionally selected line. CONCLUSIONS: The results demonstrate that GS is a promising alternative to conventional breeding for genetic improvement of layer chickens.

The effect of sunitinib on the plasma exposure of intravenous paracetamol and its major metabolite: paracetamol glucuronide.

The study aimed to examine the effect of sunitinib on the plasma exposure of intravenous paracetamol and its major metabolite, paracetamol glucuronide. Both drugs share metabolic pathways in the liver, and the drug interactions between sunitinib and paracetamol administered in higher doses were reported. These interactions resulted in hepatotoxicity. The adult New Zealand male rabbits were divided into three groups (6 animals each): rabbits receiving sunitinib and paracetamol (SUN + PC), rabbits receiving sunitinib (SUN), and a control group receiving paracetamol (PC). Sunitinib was administered orally (25 mg) and paracetamol was administrated intravenously (35 mg/kg). Blood samples for sunitinib and SU12662 assays were collected up to 96 h after drug administration and for paracetamol and paracetamol glucuronide up to 300 min after drug administration. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), and bilirubin were analysed before and after drug administration. A number of pharmacokinetic parameters were analysed. There were no differences in the levels of AST, ALT, and bilirubin among the groups at either time point. Significantly higher values of AUC0-t , AUC0-∞ , and C max and lower clearance and volume of distribution of paracetamol were observed in group PC vs. group SUN + PC (p < 0.01). The maximum plasma concentration of paracetamol glucuronide tended to be higher in group PC 213.27 µg/mL (90 % CI 1.06, 1.25; p = 0.0267). Statistically significant differences were revealed for paracetamol glucuronide mean residence time (MRT); MRT was higher in group SUN + PC than in group PC (p = 0.0375). The mean t max of paracetamol glucuronide was similar in both groups: SUN + PC and group PC (15 and 20 min, respectively). The mean t max of sunitinib was different in groups SUN + PC and SUN (10.0 and 7.0, respectively; p = 0.0134). At the studied doses, neither of the drugs, whether administered alone or together, had hepatotoxic effects. The present study was not able to confirm that sunitinib, administered at low doses in conjunction with paracetamol, displays a hepatoprotective effect. Significant differences were observed in some pharmacokinetic parameters of paracetamol.

Multifactorial analysis of the follicular environment is predictive of oocyte morphology in cattle.

Numerous attempts have been recently made in the search for a reliable, fast and noninvasive assay for selection of oocytes suitable for in vitro embryo production. Potential markers have been described in the follicle such as follicular fluid (FF) or cumulus cells (CCs). However, the reported findings are contradictory, which may reflect the complexity of metabolism of the ovarian follicle. In the present experiment, a data set from individual follicles of known diameter was obtained: cumulus-oocyte complex (COC) morphology, fatty acid composition and glucose concentration in FF as well as apoptotic index in CCs. The obtained data was statistically analyzed either separately (univariate analysis) or simultaneously (multivariate analysis) to examine its predictive value in morphology assessment of bovine COCs. Although the univariate analysis yielded a complex relation system of the selected parameters, no clear outcome could be established. In multivariate analysis, the concentration of the four fatty acids (C16:0, C16:1, C18:1cis9, C22:5n3) and Δ(9)-desaturase (16) as well as elongase activities were selected as covariates. This allowed prediction of the morphology of a COC with an accuracy of 72%, which is the most interesting finding of the experiment. The present study indicates that the multifactorial model comprising of selected parameters related to the follicle appeared more effective in predicting the morphology of a bovine COC, which may improve the effectiveness of in vitro production systems.

Genetic Variation in Natural and Induced Antibody Responses in Layer Chickens.

Selection of livestock for disease resistance is challenging due to the difficulty in obtaining reliable phenotypes. Antibodies are immunological molecules that provide direct and indirect defenses against infection and link the activities of both the innate and adaptive compartments of the immune system. As a result, antibodies have been used as a trait in selection for immune defense. The goal of this study was to identify genomic regions associated with natural and induced antibodies in chickens using low-pass sequencing. Enzyme-linked immunosorbent assays were used to quantify innate (natural) antibodies binding KLH, OVA, and PHA and induced (adaptive) antibodies binding IBD, IBV, NDV, and REO. We collected plasma from four White Leghorn (WL), two White Plymouth Rock (WPR), and two Rhode Island Red (RIR) lines. Samples numbers ranged between 198 and 785 per breed. GWAS was performed within breed on data pre-adjusted for Line-Hatch-Sex effects using GCTA. A threshold of p = 10-6 was used to select genes for downstream annotation and enrichment analysis with SNPEff and Panther. Significant enrichment was found for the defense/immunity protein, immunoglobulin receptor superfamily, and the antimicrobial response protein in RIR; and the immunoglobulin receptor superfamily, defense/immunity protein, and protein modifying enzyme in WL. However, none were present in WPR, but some of the selected SNP were annotated in immune pathways. This study provides new insights regarding the genetics of the antibody response in layer chickens.

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.

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.

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.