EMERGING ANALYTICAL APPROACHES

Recent advances in multi-omics and computational biology provide new opportunities to move beyond descriptive mechanisms of probiotic action toward predictive and quantitative understanding. Metagenomics can profile gut microbiota composition and diversity, metabolomics can quantify bioactive metabolites like SCFAs, and transcriptomics enables monitoring of immune gene expression in response to probiotics [299]. By integrating these datasets, researchers can identify strain-specific effects, host–microbe interactions, and mechanistic pathways underlying immune modulation and pathogen resistance [300]. Table 5 provides a concise overview of major multi-omics tools used in poultry probiotic research, detailing their specific applications and illustrating how machine learning can leverage these datasets to generate predictive insights, such as forecasting immune enhancement or strain-specific colonization patterns [299–302].

Machine learning and artificial intelligence approaches have emerged as powerful tools for analyzing complex multi-omics datasets and predicting probiotic responses. For example, ML algorithms trained on 2025 poultry bioinformatics datasets can forecast optimal probiotic strain combinations to reduce Salmonella or enhance IgA/IgY responses [301]. Such models allow strain optimization under specific environmental or stress conditions, potentially improving feed efficiency and disease resilience. A conceptual example includes ML models predicting a 15% improvement in immunity through synergistic multi-strain formulations based on microbiota-metabolite associations [301].

Analytical tool | Main application in probiotic research | Example insight/predictive output | Relevance to precision poultry nutrition | References
Metagenomics | Characterization of gut microbiota composition, taxonomic diversity, microbial community structure, and strain-specific colonization | Identify probiotic colonization dynamics, microbial community shifts, and pathogen suppression signatures (e.g., Salmonella exclusion) | Supports selection of probiotic strains based on microbiota compatibility, colonization potential, and microbial ecosystem stability | [299, 300]
Metabolomics | Quantification of short-chain fatty acids and other bioactive microbial metabolites, including acetate, propionate, butyrate, and antimicrobial peptides | Associate microbial metabolites with epithelial integrity, regulatory T cell differentiation, and anti-inflammatory immune responses | Facilitates metabolite-guided optimization of immune resilience, gut health, and feed efficiency | [299, 300]
Transcriptomics | Analysis of host immune gene expression, epithelial barrier function, and stress-responsive signaling pathways | Predict cytokine expression profiles (e.g., IL-10, IFN-γ, and IL-1β), tight junction gene regulation, and activation of immune pathways following probiotic supplementation | Enables host response-guided selection and optimization of probiotic formulations | [299, 300]
Machine learning/artificial intelligence | Integration of multi-omics datasets with phenotypic, immunological, microbiological, and production data | Predict immune responses, identify optimal probiotic combinations, model pathogen reduction, and forecast production performance | Supports precision probiotic design, individualized nutritional strategies, and environment-specific decision-making for poultry production | [301, 302]

By combining multi-omics profiling with AI/ML-driven prediction, this section highlights a novel, data-driven framework for precision probiotic development in poultry. This integrative approach advances the field beyond descriptive summaries and aligns with emerging 2025–2026 trends in computational biology, feed optimization, and pathogen-targeted probiotic design [302].