Animal Molecular Breeding, 2024, Vol.14, No.6, 380-387 http://animalscipublisher.com/index.php/amb 383 genotype-dependent manner, influencing fat deposition and energy utilization (Liu et al., 2015). L-arginine supplementation has been shown to regulate genes involved in lipid metabolism, promoting lipolysis in adipose tissue and improving the metabolic profile. Additionally, dietary interventions during gestation, such as L-arginine supplementation, can enhance placental growth and fetal survival by modulating genes related to nutrient metabolism and energy efficiency (Li et al., 2022). 5 Advances in Nutritional Genomics for Swine 5.1 Identification of gene-nutrient interactions through transcriptomic studies The application of RNA sequencing (RNA-Seq) technology has significantly advanced the understanding of gene-nutrient interactions in swine. RNA-Seq allows for comprehensive profiling of gene expression in response to dietary interventions, providing insights into how nutrients affect cellular processes and phenotypic outcomes (Hasan et al., 2019). This technology has been used to study the impact of various dietary components, such as fatty acids, proteins, and bioactive compounds, on gene expression in key metabolic tissues like the liver and muscle (Liao and Hasan, 2020). These studies highlight the potential of transcriptomics to uncover complex nutrient-gene interactions, which are crucial for optimizing swine nutrition and improving production efficiency (Liao et al., 2019). 5.2 Application of nutrigenomics to precision swine nutrition Nutrigenomics, which examines the effects of nutrients on gene expression, is paving the way for precision nutrition in swine. By understanding the genome-wide influences of nutrition, researchers can tailor diets to enhance growth, health, and production performance (Osorio and Moisá, 2019; Hassan et al., 2022). This approach involves using genomic data to predict how individual animals will respond to specific nutrients, allowing for more targeted and effective feeding strategies. The integration of nutrigenomics into swine nutrition research is expected to lead to more efficient and sustainable production systems by optimizing nutrient utilization and minimizing waste (Bionaz et al., 2015; Abdelrahman et al., 2022). 5.3 Integration of omics technologies for personalized feeding strategies The integration of various omics technologies, including genomics, transcriptomics, and metabolomics, is essential for developing personalized feeding strategies in swine. These technologies provide a comprehensive view of how nutrients interact with the genome and influence metabolic pathways (Di Renzo et al., 2019; Hashemi et al., 2020). By combining data from different omics approaches, researchers can develop more precise nutritional interventions that consider the genetic and phenotypic variability among swine populations. This holistic approach aims to enhance animal performance and health by aligning dietary formulations with the specific genetic makeup and metabolic needs of individual animals (Ramos-López et al., 2021). 6 Case Study 6.1 Background and context of the selected case study The selected case study focuses on the impact of dietary prebiotics and arachidonic acid (ARA) on gene expression in piglets experiencing gastrointestinal disturbances. Gastrointestinal (GI) issues are a significant concern in the swine industry due to their economic impact. Nutritional interventions, such as the use of prebiotics and ARA, have been explored to manage inflammation and optimize microbial colonization in the GI tract (Figure 2). The study of He et al. (2019) specifically investigates the differential gene expression in piglets subjected to an acute colitis model induced by dextran sodium sulfate (DSS). 6.2 Nutritional intervention details and observed effects on gene expression In this study, piglets were divided into four dietary groups: 0.5% ARA, 0.5% ARA with prebiotics (galactooligosaccharide and polydextrose), 2.5% ARA, and 2.5% ARA with prebiotics. The intervention aimed to assess the effects of these diets on gene expression in the context of DSS-induced colitis. The results showed that prebiotic supplementation significantly reduced the number of differentially expressed (DE) genes in piglets with colitis, from 83 to 33, indicating a potential protective effect against inflammation. A total of 133 DEgenes were identified, with prebiotics and ARA affecting gene expression differently but without interaction effects. These DE
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