Poultry meat and egg quality result from complex interactions among host genetics, metabolism, nutrition, microbiome ecology, physiology, management practices, and environmental conditions. These multidimensional interactions limit the predictive capacity of conventional phenotype-based approaches and increasingly necessitate systems-level frameworks capable of capturing biological complexity. Recent advances in multi-omics technologies have transformed poultry quality research by enabling integrated analyses of genomic, transcriptomic, proteomic, metabolomic, lipidomic, epigenomic, and microbiome datasets. These approaches have substantially enhanced understanding of the molecular, cellular, physiological, and ecological networks associated with product quality, production efficiency, physiological resilience, and environmental adaptation. Integrated multi-omics analyses, particularly when combined with artificial intelligence and machine-learning approaches, have the potential to identify biologically interpretable biomarkers, candidate mechanistic pathways, and predictive signatures associated with meat and egg quality traits; however, most proposed signatures remain at early stages of validation and require rigorous external testing before commercial deployment. This review synthesizes current advances in omics-driven poultry research and critically evaluates emerging applications in precision nutrition, breeding, health monitoring, environmental adaptation, and sustainable production systems. To provide a unifying biological framework, we propose the Adaptive Systems Theory of Poultry Quality (ASTPQ), which conceptualizes poultry quality as an emergent adaptive phenotype arising from coordinated interactions among mitochondrial function, redox homeostasis, immune competence, metabolic flexibility, physiological resilience, endocrine–immune regulation, and host–microbiome dynamics. Within this conceptual framework, adaptive-system capacity is proposed as the principal integrative mechanism linking molecular regulation with phenotypic quality outcomes across diverse production environments. Despite substantial advances, commercial implementation remains constrained by biological heterogeneity, methodological variability, limited external validation, computational complexity, challenges in data integration, infrastructure requirements, and economic barriers. Current evidence suggests that predictive performance depends less on increasing molecular dimensionality than on developing biologically interpretable, externally validated, economically feasible, and operationally scalable systems. Future progress will likely require integrated precision-production frameworks that combine molecular biomarkers, physiological monitoring, environmental sensing, microbiome-informed interventions, explainable artificial intelligence, and rigorous large-scale field validation to support sustainable, resilient, and commercially applicable poultry production systems.
This review evaluates multi-omics applications in dairy research across nutrition, safety, and health. Through multi-omics integration, we reveal nutrient differences driven by species, rearing practices, and processing techniques, identify protein patterns and allergen profiles, and construct adulteration detection fingerprints and species-specific peptide markers, thereby improving the timeliness and accuracy of safety assessment. The coupling of metagenomics and metabolomics effectively predicts spoilage-related microbial risks, enabling better risk control. Furthermore, multi-omics approaches systematically elucidate the functional mechanisms of bioactive peptides (e.g., ACE-inhibitory peptides), clarify the prebiotic effects of functional oligosaccharides, and build interaction networks between dairy components and gut microbiota. The introduction of machine learning enables origin and shelf-life prediction, as well as the discovery of novel biomarkers, promoting personalized nutrition and precision fermentation strategies. However, the field is currently constrained by severe reproducibility issues arising from the absence of standardized operating procedures, excessive optimism regarding machine learning models that rarely generalize across laboratories or product matrices, and a persistent disconnect between laboratory-scale biomarker discovery and industrial implementation. Without rigorous cross-platform validation and openly shared multi-omics reference datasets, most published markers remain unfit for regulatory or industrial application. Future efforts should establish standardized workflows and expand the evidence base to drive the dairy industry toward safer, healthier, and more traceable directions.
Despite the challenges in standardization, cost, and large-scale validation, advances in artificial intelligence, portable analytics, and open access omics data bases are accelerating the translation of these technologies into practical feed innovation.
Mir Ishfaq Nazir, Pankaj Gargotra, O. Asimi et al.· Frontiers in Fish Science· 0 citations
This review outlines foundational applications of omics in aquaculture and highlights the characteristics and current applications of distinct omics approaches, and summarizes three core application domains of omics in aquaculture.
Chao Guo, De-Qi Sun, Ben Yang et al.· Fishes· 0 citations
Organic dairy farming has expanded rapidly over the past three decades, driven by regulatory reforms, consumer demand, and growing recognition of its environmental, animal welfare, and potential human health benefits. Despite this growth, evidence comparing organic and conventional dairy systems remains fragmented across genetics, phenomics, animal health, and human health outcomes. This review synthesizes current knowledge through the lens of genotype × environment interactions, integrating evidence from four complementary domains: (1) genomic architecture and breeding strategies; (2) phenotypic performance, including milk production and composition, meat quality, nutrition, and reproductive traits; (3) animal health, disease resistance, antimicrobial use, and welfare; and (4) implications for human health. Holstein–Friesian cattle remain the predominant breed in both systems; however, organic production favors animals with greater robustness, longevity, grazing efficiency, and disease resilience. Genetic studies further demonstrate that highly heritable production traits share similar genetic architecture across production systems, whereas health, fertility, longevity, and other low-heritability functional traits exhibit stronger genotype × environment interactions and more system-specific genomic signatures. These findings suggest that breeding strategies developed for high-input conventional systems are unlikely to maximize performance under organic management. Collectively, the evidence supports a shift from selection focused primarily on milk yield toward genomic improvement of robustness, disease resistance, reproductive resilience, grazing adaptation, and lifetime productivity. We propose Genomically Optimized Organic Dairy (GOOD) as an emerging framework that integrates genomic selection, precision phenotyping, health monitoring, and environmental adaptation to develop dairy cattle better suited to organic production.
Priunka Bhowmik, Amy L. Zinski, Qing-Qing Wu et al.· Genes· 0 citations
BACKGROUND
The intensification of food production systems highlights the need for poultry gut health strategies aligned with One Health goals. Central to this is a balanced gut microbiota, which supports nutrient absorption, immunity, and disease resilience.
RESULTS
We applied integrative multi-omics, combining untargeted LC-MS metabolomics and shotgun metagenomics, to explore the caecal responses of commercial Ross-308 broilers to two widely used gut health interventions: ionophore supplementation (T1) and anticoccidial vaccination (T2). Across 7,554 detected metabolites, we identified candidate metabolic signatures: T1 was marked by trends in prenol lipids, including multiple soyasaponins, and enrichment of cellular stress-related pathways (e.g. glutathione pathway). T2 instead was associated with shifts in aromatic amino acid metabolism, elevating tryptophan-derived indoles such as 5-methoxyindole. While global metabolic profiles did not differ significantly (PERMANOVA p > 0.05), supervised integration (DIABLO algorithm) identified 405 potential metabolite-MAG correlations. Bacteroides fragilis emerged as a dominant associate, correlating positively with a diverse range of metabolites (n = 271). Functional gene analysis suggested a link between Mediterraneibacter spp. and soyasaponin deglycosylation, while Ruminococcaceae UBA3818 showed genomic potential for tryptophan utilisation and indole-linked metabolic steps.
CONCLUSION
Our exploratory findings suggest that prophylactic interventions impact the gut microbiome, resulting in divergent subsets of metabolic features. This highlights the potential of microbiome-informed strategies to improve enteric disease management and advance gut health centred approaches in both veterinary and human contexts.
G. Pangga, A. Richmond, C. Hughes et al.· Animal Microbiome· 0 citations
The integration of multiple omics strategies represents a transformative paradigm in farm animal genetics and breeding. By capturing molecular complexity across biological layers, integrative omics offers new opportunities to reveal the regulatory mechanisms underlying economically important traits. Here, we characterize the hepatic transcriptomic landscape of Nelore cattle and its relationship with meat and carcass quality phenotypes. For that, we integrated gene expression data, co-expression networks, expression quantitative trait locus (eQTL) mapping, single-nucleotide polymorphism (SNP)–phenotype associations, chromatin accessibility, and transcription factor motif analyses using hepatic RNA-seq data from 90 animals and assay for transposase‑accessible chromatin using sequencing (ATAC-seq) data from two animals. Weighted gene co-expression network analysis (WGCNA) identified 10 gene modules associated with our phenotypes of interest, particularly the blue module (r = −0.4), which is linked to meat color and enriched in insulin and mTOR signaling pathways. eQTL mapping revealed 1,198 cis- and 39,227 trans-eQTLs (false discovery rate [FDR] < 0.05), including hotspots on chromosome 25. Notably, rs449155362 was found to regulate 848 genes, within them MLXIPL, a transcription factor involved in glucose and lipid metabolism. Phenotype–eQTL associations revealed 54 SNPs (FDR < 0.05) related to meat and carcass traits, among which rs110069409, within an open chromatin region, modulates PLA2G2D1 expression and was associated with meat color (yellowness 24 h after the slaughter—b*₀), representing a convergence point across regulatory layers. These findings provide novel insights into the multilayered genetic architecture of the liver that controls meat quality traits in beef cattle, supporting the use of integrative omics to guide functional genomic selection.
Thaís Ribeiro da Silva, Juliana Afonso, B. Silva-Vignato et al.· Journal of Animal Science an...· 0 citations