Journal of Animal Science and Technology最新文献

Influenza A virus (FLUAV) causes serious diseases in both poultry and humans. Various host proteins, including Mx1, are considered candidates for avian influenza (AI) resistance. After infecting Jeju Native chicken embryo fibroblasts (CEFs) with three types of AI viruses, we performed gene expression profiling, identified single nucleotide polymorphisms (SNPs) through RNA-sequencing, and confirmed phenotypes showing antiviral activity in vitro. Highly pathogenic AI viruses upregulated FGF2, LYN, and FLT4 and downregulated HGF, ANGPT1, and ROR2, while a low pathogenicity AI upregulated PARK7, RACK1, and DTX3L and downregulated SIRT1, LRRK2, and WAC. However, no virus affected Mx1 expression. Although SNPs in Mx1 could not discriminate antiviral activity alone, the only CEF resistant to H5N6, strain AN4, contained the Mx1 631 R/R genotype and strongly expressed an oligoadenylate synthetase-like (OASL) variant with a unique SNP: c.G880A (p.E294K). Using transfected cell lines, H5N6-infected cells expressing OASL with the c.G880A SNP showed minimal cytopathic effects and the lowest M gene expression. This study confirms that Jeju Native chickens with specific SNP combinations in both Mx1 and OASL showed H5N6 resistance and demonstrates the interplay of genetic factors in host-pathogen dynamics, suggesting a need for integrated analyses of multiple resistance genes to inform AI prevention strategies.

Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9 (CRISPR/Cas9) technology has significantly facilitated the generation of gene-edited (GE) pigs. Although GE pigs are promising for agricultural and biomedical applications, the entire process of generating useful GE pigs is time- and labor-intensive. To overcome this, in vivo gene-editing techniques have been developed, where Cas9 nuclease and single guide RNA (sgRNA) are directly injected into animals; however, their efficiency remains low owing to the large size of the nuclease. In this study, we generated a Cas9-expressing pig by inserting the Cas9 gene into the ROSA26 locus, resulting in its constitutive expression in various tissues. We also confirmed the pig's fertility. In vitro experiments with primary cells from the pig confirmed effective gene deletion by adding only sgRNAs. These results suggest that the Cas9-expressing pig generated in this study could serve as an effective platform for in vivo and in vitro gene editing in agricultural and biomedical research.

MicroRNAs (miRNAs) are small noncoding RNAs that play a pivotal role in the regulation of gene expression. Analysis of miRNAs is important for understanding a variety of biological processes. Sequencing of miRNAs within milk-derived extracellular vesicles (EVs) provides valuable insights into the molecular mechanisms through which these EVs influence recipient cells. Comparative miRNA sequencing of colostrum and mature milk from different cow breeds can demonstrate breed-specific differences and improve the understanding of potential therapeutic applications in immune regulation and gut health. Therefore, this study was conducted to compare the miRNA profiles and characteristics of colostrum- and mature milk-derived EVs from Holstein and Jersey breeds and determine their effects on intestinal epithelial cells. The miRNA profiles of EVs isolated from the colostrum and mature milk of Holstein and Jersey cows were analyzed via small RNA sequencing. Holstein colostrum-derived EVs exhibited the most diverse miRNA profile with 421 identified miRNAs compared with 259 in mature milk-derived EVs. Jersey colostrum EVs had 198 miRNAs, whereas mature milk EVs had 282. Differential expression analysis revealed considerable miRNA differences between colostrum and mature milk, particularly in Holstein cows. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that miRNAs from colostrum EVs predominantly regulated immune-related pathways. Transcriptomic analysis of human colon cell line HT-29 treated with Holstein colostrum EVs confirmed the modulation of genes associated with immune responses. These findings indicate that colostrum-derived EVs, particularly from Holstein cows, play a pivotal role in immune regulation and could be potential candidates for therapeutic applications.

Melatonin plays a crucial role in various behavioral and physiological aspects of animals, including regulating their circadian rhythms. This review provides a comprehensive evaluation of the multifaceted effects of melatonin on animal behavior, such as temperament, stress, and aggression regulation. The focus is on the complex interactions between melatonin and the hormonal and neurotransmitter systems, highlighting how melatonin interacts with cortisol, serotonin, and dopamine to influence behavior. Additionally, it investigates the effects of melatonin on the hypothalamic-pituitary-gonada (HPG) axis and stress responses, emphasizing its potential to improve stress management and social interactions, thereby enhancing animal welfare. The review also examines the seasonal variations of melatonin and its impact on aggression and reproductive activities related to photoperiods, as well as its effects on learning and memory to suggest improvements in animal training methods and practices. Furthermore, it discusses the influence of melatonin on appetite and physical activity regulation, implying its involvement in metabolic processes. In conclusion, further research is needed to elucidate the complex mechanisms underlying the extensive influence of melatonin on animal behavior. Through this review, the aim is to integrate the overall knowledge about melatonin and animal behavioral temperament and to propose new research areas for animal management based on behavioral and hormonal regulation.

The growing demands of sustainable, efficient, and welfare-conscious pig husbandry have necessitated the adoption of advanced technologies. Among these, RGB imaging and machine vision technology may offer a promising solution for early disease detection and proactive disease management in advanced pig husbandry practices. This review explores innovative applications for monitoring disease symptoms by assessing features that directly or indirectly indicate disease risk, as well as for tracking body weight and overall health. Machine vision and image processing algorithms enable for the real-time detection of subtle changes in pig appearance and behavior that may signify potential health issues. Key indicators include skin lesions, inflammation, ocular and nasal discharge, and deviations in posture and gait, each of which can be detected non-invasively using RGB cameras. Moreover, when integrated with thermal imaging, RGB systems can detect fever, a reliable indicator of infection, while behavioral monitoring systems can track abnormal posture, reduced activity, and altered feeding and drinking habits, which are often precursors to illness. The technology also facilitates the analysis of respiratory symptoms, such as coughing or sneezing (enabling early identification of respiratory diseases, one of the most significant challenges in pig farming), and the assessment of fecal consistency and color (providing valuable insights into digestive health). Early detection of disease or poor health supports proactive interventions, reducing mortality and improving treatment outcomes. Beyond direct symptom monitoring, RGB imaging and machine vision can indirectly assess disease risk by monitoring body weight, feeding behavior, and environmental factors such as overcrowding and temperature. However, further research is needed to refine the accuracy and robustness of algorithms in diverse farming environments. Ultimately, integrating RGB-based machine vision into existing farm management systems could provide continuous, automated surveillance, generating real-time alerts and actionable insights; these can support data-driven disease prevention strategies, reducing the need for mass medication and the development of antimicrobial resistance.

In contrast to conventional genomic prediction, which typically targets a single breed and circumvents the necessity for population structure adjustments, multi-breed genomic prediction necessitates accounting for population structure to mitigate potential bias. The presence of this structure in multi-breed datasets can influence prediction accuracy, rendering proper modeling crucial for achieving unbiased results. This study aimed to address the effect of population structure on multi-breed genomic prediction, particularly focusing on crossbred reference populations. The prediction accuracy of genomic models was assessed by incorporating genomic breed composition (GBC) or principal component analysis (PCA) into the genomic best linear unbiased prediction (GBLUP) model. The accuracy of five different genomic prediction models was evaluated using data from 354 Duroc × Korean native pig crossbreds, 1,105 Landrace × Korean native pig crossbreds, and 1,107 Landrace × Yorkshire × Duroc crossbreds. The models tested were GBLUP without population structure adjustment, GBLUP with PCA as a fixed effect, GBLUP with GBC as a fixed effect, GBLUP with PCA as a random effect, and GBLUP with GBC as a random effect. The highest prediction accuracies for backfat thickness (0.59) and carcass weight (0.50) were observed in Models 1, 4, and 5. In contrast, Models 2 and 3, which included population structure as a fixed effect, exhibited lower accuracies, with backfat thickness accuracies of 0.40 and 0.53 and carcass weight accuracies of 0.34 and 0.38, respectively. These findings suggest that in multi-breed genomic prediction, the most efficient and accurate approach is either to forgo adjusting for population structure or, if adjustments are necessary, to model it as a random effect. This study provides a robust framework for multi-breed genomic prediction, highlighting the critical role of appropriately accounting for population structure. Moreover, our findings have important implications for improving genomic selection efficiency, ultimately enhancing commercial production by optimizing prediction accuracy in crossbred populations.

In this study, we analyzed effects of drone pupae aqueous extract powder (DEP) on proliferation and differentiation of Hanwoo myosatellite cells (HSC). Results of amino acid, vitamin, and mineral analysis of drone pupae revealed the presence of branched-chain amino acids, Glu, essential amino acids, vitamins B6, C and Mg, K, and so on. Additionally, drone pupae were shown to have an antioxidant ability. HSC were cultured for proliferation by adding 0, 10, 100, 200, and 400 μg/mL DEP to the medium. As a result of MTS analysis, DEP increased the proliferation capacity of HSC, with cell viability being significantly higher after treatment with DEP, especially when DEP was used at 100 μg/mL (p < 0.05). To measure the differentiation ability of HSC, 0 and 100 μg/mL DEP (CON, D100) were added to the medium, and cells were cultured. Myotube formation was confirmed through images using immunofluorescence staining. Fusion index and myotube area in the D100 were higher than those in the CON (p < 0.01). DEP promoted differentiation ability and myotube formation by increasing the expression of MYH2, MYOG, and DES genes and MYH2 and DES proteins in HSC. Additionally, in HSC differentiation culture, proteome expression intensity was higher in D100 than in CON. Proteins upregulated in the D100 group included Myosin, IL18, MYO1D, and so on. In conclusion, characteristics of various components present in DEP could improve the proliferation and differentiation ability of HSC. This suggests that drone pupae can be used as a functional substance to enhance muscle growth.

This review examines the application of video processing and convolutional neural network (CNN)-based deep learning for animal face recognition, identification, and re-identification. These technologies are essential for precision livestock farming, addressing challenges in production efficiency, animal welfare, and environmental impact. With advancements in computer technology, livestock monitoring systems have evolved into sensor-based contact methods and video-based non-contact methods. Recent developments in deep learning enable the continuous analysis of accumulated data, automating the monitoring of animal conditions. By integrating video processing with CNN-based deep learning, it is possible to estimate growth, identify individuals, and monitor behavior more effectively. These advancements enhance livestock management systems, leading to improved animal welfare, production outcomes, and sustainability in farming practices.

Corynebacterium sp. SCR221107 was isolated from the rumen fluid of healthy male Holstein dairy cows from a research farm at Suncheon, Jeollanam-do, Korea. Corynebacterium sp. SCR221107 is a functional probiotic candidate that produces vitamin B₁₂. All Corynebacterium sp. SCR221107 was sequenced using the PacBio RS II and Illumina HiSeq platforms and assembled de novo. The complete genome sequence of Corynebacterium sp. SCR221107 contained one circular chromosome (3,043,024 bp) with a guanine + cytosine (GC) content of 60.1%. Annotation analysis showed the presence of 2,639 protein-coding sequences, 15 rRNA genes, and 57 tRNA genes. Genome analysis found that Corynebacterium sp. SCR221107 encodes various genes associated with vitamin B12 synthesis and transport. The genomic information provided a detailed understanding of Corynebacterium sp. SCR221107, suggesting that this isolate may have potential probiotic applications.

The purpose of the current study was to investigate the effects of gum Arabic supplementation on short-chain fatty acids, cecal microbiota, immune-related gene expression, and small intestinal morphology in post-hatched broiler chicks. On the day of hatching, four hundred thirty-two commercial male broiler chicks were randomly allocated into six treatments with twelve cages as replicates of six chicks each for 24 days. Dietary treatments (T1 to T6) were supplemented with 0.0, 0.12, 0.25, 0.50, 0.75, and 1.0% gum Arabic to the basal diet, respectively. Performance parameters, short-chain fatty acid concentration, quantification of microbiota and immune response gene expression (pre-inflammatory cytokines, mucin-2, and secretory immunoglobulin A), and histomorphometry of the small intestine were measured. According to our results, daily weight gains in T2 and the production efficiency index increased in T2 to T4, whereas daily feed intake decreased in T2, T3, T5, and T6, but feed conversion ratio improved. Concentration of lactate, acetate, butyrate, and total short-chain fatty acid increased in T2, T3, T5, and T6. Propionate ‎in T2 T3, T4, and T6 and format in T2, T5, and T6 also increased. Lactobacillus spp. quantitatively increased from T3 to T6, whereas Bacteroides spp. decreased in T3 and T5. Other microbiota quantitatively showed no effect of dietary supplements. IL-1β, TNF-α, and MUC-2 decreased in T2 to T6 and IL-12 in T3, whereas INF-Y increased in T4 to T6 and SIgA in T4. All histometeric parameters of the duodenum, jejunum, and ileum improved with dietary supplementation. We conclude that the administration of gum Arabic resulted in an improvement in overall performance, fermentation metabolites, and modification of microbiota and immune response with improved histomorphometry in the intestines of young chicks.