Bone Metabolite Profile Differs between Normal and Femur Head Necrosis (FHN/BCO)-Affected Broilers: Implications for Dysregulated Metabolic Cascades in FHN Pathophysiology.

Femur head necrosis (FHN), also known as bacterial chondronecrosis with osteomyelitis (BCO), has remained an animal welfare and production concern for modern broilers regardless of efforts to select against it in primary breeder flocks. Characterized by the bacterial infection of weak bone, FHN has been found in birds without clinical lameness and remains only detectable via necropsy. This presents an opportunity to utilize untargeted metabolomics to elucidate potential non-invasive biomarkers and key causative pathways involved in FHN pathology. The current study used ultra-performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS) and identified a total of 152 metabolites. Mean intensity differences at p < 0.05 were found in 44 metabolites, with 3 significantly down-regulated and 41 up-regulated in FHN-affected bone. Multivariate analysis and a partial least squares discriminant analysis (PLS-DA) scores plot showed the distinct clustering of metabolite profiles from FHN-affected vs. normal bone. Biologically related molecular networks were predicted using an ingenuity pathway analysis (IPA) knowledge base. Using a fold-change cut off of -1.5 and 1.5, top canonical pathways, networks, diseases, molecular functions, and upstream regulators were generated using the 44 differentially abundant metabolites. The results showed the metabolites NAD+, NADP+, and NADH to be downregulated, while 5-Aminoimidazole-4-carboxamide ribonucleotide (AICAR) and histamine were significantly increased in FHN. Ascorbate recycling and purine nucleotides degradation were the top canonical pathways, indicating the potential dysregulation of redox homeostasis and osteogenesis. Lipid metabolism and cellular growth and proliferation were some of the top molecular functions predicted based on the metabolite profile in FHN-affected bone. Network analysis showed significant overlap across metabolites and predicted upstream and downstream complexes, including AMP-activated protein kinase (AMPK), insulin, collagen type IV, mitochondrial complex, c-Jun N-terminal kinase (Jnk), extracellular signal-regulated kinase (ERK), and 3ß-hydroxysteroid dehydrogenase (3ß HSD). The qPCR analysis of relevant factors showed a significant decrease in AMPKα2 mRNA expression in FHN-affected bone, supporting the predicted downregulation found in the IPA network analysis. Taken as a whole, these results demonstrate a shift in energy production, bone homeostasis, and bone cell differentiation that is distinct in FHN-affected bone, with implications for how metabolites drive the pathology of FHN.

Using fecal microbiota as biomarkers for predictions of performance in the selective breeding process of pedigree broiler breeders.

Much work has been dedicated to identifying members of the microbial gut community that have potential to augment the growth rate of agricultural animals including chickens. Here, we assessed any correlations between the fecal microbiome, a proxy for the gut microbiome, and feed efficiency or weight gain at the pedigree chicken level, the highest tier of the production process. Because selective breeding is conducted at the pedigree level, our aim was to determine if microbiome profiles could be used to predict feed conversion or weight gain in order to improve selective breeding. Using 16s rRNA amplicon sequencing, we profiled the microbiomes of high and low weight gain (WG) birds and good and poor feed efficient (FE) birds in two pedigree lineages of broiler chickens. We also aimed to understand the dynamics of the microbiome with respect to maturation. A time series experiment was conducted, where fecal samples of chickens were collected at 6 points of the rearing process and the microbiome of these samples profiled. We identified OTUs differences at different taxonomic levels in the fecal community between high and low performing birds within each genetic line, indicating a specificity of the microbial community profiles correlated to performance factors. Using machine-learning methods, we built a classification model that could predict feed conversion performance from the fecal microbial community. With respect to maturation, we found that the fecal microbiome is dynamic in early life but stabilizes after 3 weeks of age independent of lineage. Our results indicate that the fecal microbiome profile can be used to predict feed conversion, but not weight gain in these pedigree lines. From the time series experiments, it appears that these predictions can be evaluated as early as 20 days of age. Our data also indicates that there is a genetic factor for the microbiome profile.

Chicken muscle mitochondrial content appears co-ordinately regulated and is associated with performance phenotypes.

Mitochondrial content is a fundamental cellular bioenergetic phenotype. Previous work has hypothesised possible links between variation in muscle mitochondrial content and animal performance. However, no population screens have been performed in any production species. Here, we have designed a high throughput molecular approach to estimate mitochondrial content in commercial broilers. Technical validity was established using several approaches, including its performance in monoclonal DF-1 cells, cross-tissue comparisons in tissues with differing metabolic demands (white fat

Broilers with low serum Mannose-binding Lectin show increased fecal shedding of Salmonella enterica serovar Montevideo.

Mannose-binding lectin (MBL) is a key molecule in innate immunity. MBL binds to carbohydrates on the surface of pathogens, initiating the complement system via the lectin-dependent pathway or facilitates opsonophagocytosis. In vivo studies using inbred chicken lines differing in MBL serum concentration indicate that chicken MBL affects Salmonella resistance; further studies are imperative in conventional broiler chickens. In this study 104 conventional day-old chickens (offspring from a cross between Cobb 500 male and female parent breeders) were orally infected with Salmonella enterica subsp. enterica serovar Montevideo. The chickens were divided into two groups based on polymorphisms in their MBL promoter region, designated L/L for low serum concentrations of MBL and L/H for medium serum concentrations of MBL. A semi-quantitative real-time PCR method for detection of Salmonella in cloacal swabs was used, the log10 CFU quantification was based on a standard curve from artificially spiked cloacal swab samples pre-incubated for 8 h with known concentrations of Salmonella ranging from 10(1) to 10(6) CFU/swabs, with an obtained amplification efficiency of 102% and a linear relationship between the log10 CFU and the threshold cycle Ct values of (R(2) = 0.99). The L/L chickens had significantly higher Log10 CFU/swab at week 5 post infection (pi) than the L/H chickens. A repetition of the study with 86 L/L and 18 L/H chickens, also gave significantly higher log10 CFU ± SEM in cloacal swabs, using the semi-quantitative real-time PCR method from L/L chickens than from the L/H chickens at week 5 pi. These results indicate that genetically determined basic levels of MBL may influence S. Montevideo susceptibility.

Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.

The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.

Comparison and contrast of genes and biological pathways responding to Marek's disease virus infection using allele-specific expression and differential expression in broiler and layer chickens.

BACKGROUND: Marek's disease (MD) is a commercially important neoplastic disease of chickens caused by the Marek's disease virus (MDV), a naturally occurring oncogenic alphaherpesvirus. Enhancing MD genetic resistance is desirable to augment current vaccines and other MD control measures. High throughput sequencing was used to profile splenic transcriptomes from individual F1 progeny infected with MDV at 4 days of age from both outbred broilers (meat-type) and inbred layer (egg-type) chicken lines that differed in MD genetic resistance. The resulting information was used to identify SNPs, genes, and biological pathways exhibiting allele-specific expression (ASE) in response to MDV infection in each type of chicken. In addition, we compared and contrasted the results of pathway analyses (ASE and differential expression (DE)) between chicken types to help inform on the biological response to MDV infection. RESULTS: With 7 individuals per line and treatment group providing high power, we identified 6,132 single nucleotide polymorphisms (SNPs) in 4,768 genes and 4,528 SNPs in 3,718 genes in broilers and layers, respectively, that exhibited ASE in response to MDV infection. Furthermore, 548 and 434 genes in broilers and layers, respectively, were found to show DE following MDV infection. Comparing the datasets, only 72 SNPs and 850 genes for ASE and 20 genes for DE were common between the two bird types. Although the chicken types used in this study were genetically different, at the pathway level, both TLR receptor and JAK/STAT signaling pathways were enriched as well as exhibiting a high proportion of ASE genes, especially at the beginning of both above mentioned regulatory pathways. CONCLUSIONS: RNA sequencing with adequate biological replicates is a powerful approach to identify high confidence SNPs, genes, and pathways that are associated with transcriptional response to MDV infection. In addition, the SNPs exhibiting ASE in response to MDV infection provide a strong foundation for determining the extent to which variation in expression influences MD incidence plus yield genetic markers for genomic selection. However, given the paucity of overlap among ASE SNP sets (broilers vs. layers), it is likely that separate screens need to be incorporated for each population. Finally, comparison of gene lists obtained between these two diverse chicken types indicate the TLR and JAK/STAT signaling are conserved when responding to MDV infection and may be altered by selection of genes exhibiting ASE found at the start of each pathway.

Integrated analysis of microRNA expression and mRNA transcriptome in lungs of avian influenza virus infected broilers.

BACKGROUND: Avian influenza virus (AIV) outbreaks are worldwide threats to both poultry and humans. Our previous study suggested microRNAs (miRNAs) play significant roles in the regulation of host response to AIV infection in layer chickens. The objective of this study was to test the hypothesis if genetic background play essential role in the miRNA regulation of AIV infection in chickens and if miRNAs that were differentially expressed in layer with AIV infection would be modulated the same way in broiler chickens. Furthermore, by integrating with parallel mRNA expression profiling, potential molecular mechanisms of host response to AIV infection can be further exploited. RESULTS: Total RNA isolated from the lungs of non-infected and low pathogenic H5N3 infected broilers at four days post-infection were used for both miRNA deep sequencing and mRNA microarray analyses. A total of 2.6 M and 3.3 M filtered high quality reads were obtained from infected and non-infected chickens by Solexa GA-I Sequencer, respectively. A total of 271 miRNAs in miRBase 16.0 were identified and one potential novel miRNA was discovered. There were 121 miRNAs differentially expressed at the 5% false discovery rate by Fisher's exact test. More miRNAs were highly expressed in infected lungs (108) than in non-infected lungs (13), which was opposite to the findings in layer chickens. This result suggested that a different regulatory mechanism of host response to AIV infection mediated by miRNAs might exist in broiler chickens. Analysis using the chicken 44 K Agilent microarray indicated that 508 mRNAs (347 down-regulated) were differentially expressed following AIV infection. CONCLUSIONS: A comprehensive analysis combining both miRNA and targeted mRNA gene expression suggests that gga-miR-34a, 122-1, 122-2, 146a, 155, 206, 1719, 1594, 1599 and 451, and MX1, IL-8, IRF-7, TNFRS19 are strong candidate miRNAs or genes involved in regulating the host response to AIV infection in the lungs of broiler chickens. Further miRNA or gene specific knock-down assay is warranted to elucidate underlying mechanism of AIV infection regulation in the chicken.

The development and characterization of a 60K SNP chip for chicken.

BACKGROUND: In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). To be of value in a wide variety of breeds and populations, the success rate of the SNP genotyping assay, the distribution of the SNP across the genome and the minor allele frequencies (MAF) of the SNPs used are extremely important. RESULTS: We describe the design of a moderate density (60k) Illumina SNP BeadChip in chicken consisting of SNPs known to be segregating at high to medium minor allele frequencies (MAF) in the two major types of commercial chicken (broilers and layers). This was achieved by the identification of 352,303 SNPs with moderate to high MAF in 2 broilers and 2 layer lines using Illumina sequencing on reduced representation libraries. To further increase the utility of the chip, we also identified SNPs on sequences currently not covered by the chicken genome assembly (Gallus_gallus-2.1). This was achieved by 454 sequencing of the chicken genome at a depth of 12x and the identification of SNPs on 454-derived contigs not covered by the current chicken genome assembly. In total we added 790 SNPs that mapped to 454-derived contigs as well as 421 SNPs with a position on Chr_random of the current assembly. The SNP chip contains 57,636 SNPs of which 54,293 could be genotyped and were shown to be segregating in chicken populations. Our SNP identification procedure appeared to be highly reliable and the overall validation rate of the SNPs on the chip was 94%. We were able to map 328 SNPs derived from the 454 sequence contigs on the chicken genome. The majority of these SNPs map to chromosomes that are already represented in genome build Gallus_gallus-2.1.0. Twenty-eight SNPs were used to construct two new linkage groups most likely representing two micro-chromosomes not covered by the current genome assembly. CONCLUSIONS: The high success rate of the SNPs on the Illumina chicken 60K Beadchip emphasizes the power of Next generation sequence (NGS) technology for the SNP identification and selection step. The identification of SNPs from sequence contigs derived from NGS sequencing resulted in improved coverage of the chicken genome and the construction of two new linkage groups most likely representing two chicken micro-chromosomes.

Structural variation in the chicken genome identified by paired-end next-generation DNA sequencing of reduced representation libraries.

BACKGROUND: Variation within individual genomes ranges from single nucleotide polymorphisms (SNPs) to kilobase, and even megabase, sized structural variants (SVs), such as deletions, insertions, inversions, and more complex rearrangements. Although much is known about the extent of SVs in humans and mice, species in which they exert significant effects on phenotypes, very little is known about the extent of SVs in the 2.5-times smaller and less repetitive genome of the chicken. RESULTS: We identified hundreds of shared and divergent SVs in four commercial chicken lines relative to the reference chicken genome. The majority of SVs were found in intronic and intergenic regions, and we also found SVs in the coding regions. To identify the SVs, we combined high-throughput short read paired-end sequencing of genomic reduced representation libraries (RRLs) of pooled samples from 25 individuals and computational mapping of DNA sequences from a reference genome. CONCLUSION: We provide a first glimpse of the high abundance of small structural genomic variations in the chicken. Extrapolating our results, we estimate that there are thousands of rearrangements in the chicken genome, the majority of which are located in non-coding regions. We observed that structural variation contributes to genetic differentiation among current domesticated chicken breeds and the Red Jungle Fowl. We expect that, because of their high abundance, SVs might explain phenotypic differences and play a role in the evolution of the chicken genome. Finally, our study exemplifies an efficient and cost-effective approach for identifying structural variation in sequenced genomes.

Genomic regions associated with dermal hyperpigmentation, polydactyly and other morphological traits in the Silkie chicken.

The Silkie chicken has been a model of melanoctye precursor and neural crest cell migration and proliferation in the developing embryo due to its extensive hyperpigmentation of dermal and connective tissues. Although previous studies have focused on the distribution and structure of the Silkie's pigment or the general mechanisms by which this phenotype presents itself, the causal genetic variants have not been identified. Classical breeding experiments have determined this trait to be controlled by 2 interacting genes, the sex-linked inhibitor of dermal melanin (Id) and autosomal fibromelanosis (Fm) genes. Genome-wide single nucleotide polymorphism (SNP)-trait association analysis was used to detect genomic regions showing significant association with these pigmentation genes in 2 chicken mapping populations designed to segregate independently for Id and Fm. The SNP showing the highest association with Id was located at 72.3 Mb on chromosome Z and 10.3-13.1 Mb on chromosome 20 showed the highest association with Fm. Prior to this study, the linkage group to which Fm belonged was unknown. Although the primary focus of this study was to identify loci contributing to dermal pigmentation in the Silkie chicken, loci associated with various other morphological traits segregating in these populations were also detected. A single SNP in a highly conserved cis-regulatory region of Sonic Hedgehog was significantly associated with polydactyly (Po). Genomic regions in association with silkie feathering or hookless (h), feathered legs (Pti), vulture hock (V), rose comb (R), and duplex comb (D) were also identified.

Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds.

Breed utilization, genetic improvement, and industry consolidation are predicted to have major impacts on the genetic composition of commercial chickens. Consequently, the question arises as to whether sufficient genetic diversity remains within industry stocks to address future needs. With the chicken genome sequence and more than 2.8 million single-nucleotide polymorphisms (SNPs), it is now possible to address biodiversity using a previously unattainable metric: missing alleles. To achieve this assessment, 2551 informative SNPs were genotyped on 2580 individuals, including 1440 commercial birds. The proportion of alleles lacking in commercial populations was assessed by (1) estimating the global SNP allele frequency distribution from a hypothetical ancestral population as a reference, then determining the portion of the distribution lost, and then (2) determining the relationship between allele loss and the inbreeding coefficient. The results indicate that 50% or more of the genetic diversity in ancestral breeds is absent in commercial pure lines. The missing genetic diversity resulted from the limited number of incorporated breeds. As such, hypothetically combining stocks within a company could recover only preexisting within-breed variability, but not more rare ancestral alleles. We establish that SNP weights act as sentinels of biodiversity and provide an objective assessment of the strains that are most valuable for preserving genetic diversity. This is the first experimental analysis investigating the extant genetic diversity of virtually an entire agricultural commodity. The methods presented are the first to characterize biodiversity in terms of allelic diversity and to objectively link rate of allele loss with the inbreeding coefficient.

The Dominant white, Dun and Smoky color variants in chicken are associated with insertion/deletion polymorphisms in the PMEL17 gene.

Dominant white, Dun, and Smoky are alleles at the Dominant white locus, which is one of the major loci affecting plumage color in the domestic chicken. Both Dominant white and Dun inhibit the expression of black eumelanin. Smoky arose in a White Leghorn homozygous for Dominant white and partially restores pigmentation. PMEL17 encodes a melanocyte-specific protein and was identified as a positional candidate gene due to its role in the development of eumelanosomes. Linkage analysis of PMEL17 and Dominant white using a red jungle fowl/White Leghorn intercross revealed no recombination between these loci. Sequence analysis showed that the Dominant white allele was exclusively associated with a 9-bp insertion in exon 10, leading to an insertion of three amino acids in the PMEL17 transmembrane region. Similarly, a deletion of five amino acids in the transmembrane region occurs in the protein encoded by Dun. The Smoky allele shared the 9-bp insertion in exon 10 with Dominant white, as expected from its origin, but also had a deletion of 12 nucleotides in exon 6, eliminating four amino acids from the mature protein. These mutations are, together with the recessive silver mutation in the mouse, the only PMEL17 mutations with phenotypic effects that have been described so far in any species.