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This study examined the effects of road transportation on metabolic and immunological responses in dairy heifers. Twenty Holstein heifers in early pregnancy were divided into non-transported (NT; n = 7) and transported (T; n = 13) groups. Blood was collected before transportation (BT), immediately after transportation for 100 km (T1) and 200 km (T2), and 24 h after transportation (AT). The T heifers had higher (P < 0.05) blood cortisol and non-esterified fatty acid concentrations after T1 and T2 than did NT heifers. By contrast, the T heifers had lower (P < 0.05) serum triglyceride concentrations after T1 and T2 than had the NT heifers. The serum cortisol and triglyceride concentrations returned (P > 0.05) to the BT concentrations at 24 h AT in the T heifers. The granulocyte-to-lymphocyte ratio and the percentage of monocytes were higher (P < 0.05) after T2 in the T heifers than in the NT heifers, suggesting that transportation stress increased the numbers of innate immune cells. T heifers had higher (P < 0.01) plasma haptoglobin concentrations than NT heifers 24 h AT. In conclusion, transportation increased cortisol secretion and was correlated with increased metabolic responses and up-regulation of peripheral innate immune cells in dairy heifers.

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A genome wide association study was conducted using estimated breeding value (EBV) for milk production traits from 1st to 4th lactation. Significant single nucleotide polymorphism (SNP) markers were selected for each trait and the differences were compared by lactation. DNA samples were taken from 456 animals with EBV which are Holstein proven bulls whose semen is being sold or the daughters of old proven bulls whose semen is no longer being sold in Korea. High density genome wide SNP genotype was investigated and the significance of markers associated with traits was tested using the breeding value estimated by a multiple lactation model as a dependent variant. As the result of significance comparisons by lactations, several differences were found between the first lactation and subsequent lactations (from second to 4th lactation). A similar trend was noted in mean deviation and correlation of the estimated effects by lactation. Since there was a difference in the genes associated with EBV for each trait between first and subsequent lactations, a multi-lactation model in which lactation is considered as a different trait is genetically useful. Also, significant markers in all lactations and common markers for different traits were detected, which can be used as markers for quantitative trait loci exploration and marker assisted selection in milk production traits.

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Pork is a major source of animal protein for humans. The subcutaneous, intermuscular and the intramuscular fat are the factors responsible for meat quality. RNA-seq is rapidly adopted for the profiling of the transcriptomes in the studies related to gene regulation. The discovery of differentially expressed genes (DEGs) between adult animals of Jeju Native Pig (JNP) and Berkshire breeds are of particular interest for the current study. RNA-seq was used to investigate the transcriptome profiling in the fat tissue. Sequence reads were obtained from Ilumina HiSeq2000 and mapped to the pig genome using Tophat2. Total 153 DEGs were identified and 71 among the annotated genes, have BLAST matches in the non- redundant database. Metabolic, immune response and protein binding are enriched pathways in the fat tissue. In our study, biological adhesion, cellular, developmental and multicellular organismal processes in fat were up-regulated in JNP as compare to Berkshire. Multicellular organismal process, developmental process, embryonic morphogenesis and skeletal system development were the most significantly enriched terms in fat of JNP and Berkshire breeds (p = 1.17E-04, 0.044, 3.47E-04 and 4.48E-04 respectively). COL10A1, COL11A2, PDK4 and PNPLA3 genes responsible for skeletal system morphogenesis and body growth were down regulated in JNP. This study is the first statistical analysis for the detection of DEGs from RNA-seq data generated from fat tissue sample. This analysis can be used as stepping stone to understand the difference in the genetic mechanisms that might influence the identification of novel transcripts, sequence polymorphisms, isoforms and noncoding RNAs.

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As more whole-genome sequences become available, there is an increasing demand for high-throughput methods that link genes to phenotypes, facilitating discovery of new gene functions. In this study, we describe a new version of the Tn-seq method involving a modified EZ:Tn5 transposon for genome-wide and quantitative mapping of all insertions in a complex mutant library utilizing massively parallel Illumina sequencing. This Tn-seq method was applied to a genome-saturating Salmonella enterica serotype Typhimurium mutant library recovered from selection under 3 different in vitro growth conditions (diluted Luria-Bertani [LB] medium, LB medium plus bile acid, and LB medium at 42°C), mimicking some aspects of host stressors. We identified an overlapping set of 105 protein-coding genes in S. Typhimurium that are conditionally essential under at least one of the above selective conditions. Competition assays using 4 deletion mutants (pyrD, glnL, recD, and STM14_5307) confirmed the phenotypes predicted by Tn-seq data, validating the utility of this approach in discovering new gene functions. With continuously increasing sequencing capacity of next generation sequencing technologies, this robust Tn-seq method will aid in revealing unexplored genetic determinants and the underlying mechanisms of various biological processes in Salmonella and the other approximately 70 bacterial species for which EZ:Tn5 mutagenesis has been established.

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The Porcine SNP database has a huge number of SNPs, but these SNPs are mostly found by computer data-mining procedures and have not been well characterized. We re-sequenced 1,439 porcine public SNPs from four commercial pig breeds and one Korean domestic breed (Korean Native pig, KNP) by using two DNA pools from eight unrelated animals in each breed. These SNPs were from 419 protein-coding genes covering the 18 autosomes, and the re-sequencing in breeds confirmed 690 public SNPs (47.9%) and 226 novel mutations (173 SNPs and 53 insertions/deletions). Thus, totally, 916 variations were found from our study. Of the 916 variations, 148 SNPs (16.2%) were found across all the five breeds, and 199 SNPs (21.7%) were breed specific polymorphisms. According to the SNP locations in the gene sequences, these 916 variations were categorized into 802 non-coding SNPs (785 in intron, 17 in 3'-UTR) and 114 coding SNPs (86 synonymous SNPs, 28 non-synonymous SNPs). The nucleotide substitution analyses for these SNPs revealed that 70.2% were from transitions, 20.0% from transversions, and the remaining 5.79% were deletions or insertions. Subsequently, we genotyped 261 SNPs from 180 genes in an experimental KNP × Landrace F2 cross by the Sequenom MassARRAY system. A total of 33 traits including growth, carcass composition and meat quality were analyzed for the phenotypic association tests using the 132 SNPs in 108 genes with minor allele frequency (MAF)>0.2. The association results showed that five marker-trait combinations were significant at the 5% experiment-wise level (ADCK4 for rear leg, MYH3 for rear leg, Hunter B, Loin weight and Shearforce) and four at the 10% experiment-wise level (DHX38 for average daily gain at live weight, LGALS9 for crude lipid, NGEF for front leg and LIFR for pH at 24 h). In addition, 49 SNPs in 44 genes showing significant association with the traits were detected at the 1% comparison-wise level. A large number of genes that function as enzymes, transcription factors or signalling molecules were considered as genetic markers for pig growth (RNF103, TSPAN31, DHX38, ABCF1, ABCC10, SCD5, KIAA0999 and FKBP10), muscling (HSPA5, PTPRM, NUP88, ADCK4, PLOD1, DLX1 and GRM8), fatness (PTGIS, IDH3B, RYR2 and NOL4) and meat quality traits (DUSP4, LIFR, NGEF, EWSR1, ACTN2, PLXND1, DLX3, LGALS9, ENO3, EPRS, TRIM29, EHMT2, RBM42, SESN2 and RAB4B). The SNPs or genes reported here may be beneficial to future marker assisted selection breeding in pigs.

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