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

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

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.Funding for this study was provided by Cobb-Vantress, Inc. Authors RH and RO are commercially affiliated with the funder. The funder provided support in the form of salaries for SDS, and RH and RO, employed by the funder had active roles in the study as stated in the authors’ contributions.Peer reviewe

On-farm Food Safety: Aquaponics

Good agricultural practices and produce handling advice to prevent zoonoses in fish-and-produce production systems are detailed

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 fa

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

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

Abstract 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