Multi-compartment spatiotemporal metabolic modeling of the chicken gut guides the design of dietary interventions

Background Understanding the interactions between diet and the gut microbiome is critical for identifying dietary interventions that support gut health. This is of particular importance for poultry where the elimination of antibiotic growth promoters has resulted in an alarming rise in enteric infections with significant economic consequences. While research has identified promising interventions including prebiotics, probiotics, and organic acids, these produce inconsistent outcomes across farms and production systems. This variability reflects a fundamental challenge: intervention efficacy depends on baseline conditions including diet composition and microbiota structure. Computational metabolic models offer powerful tools for dissecting diet-microbiome interactions, yet current approaches remain limited, largely ignoring the physiological parameters and spatial organization of the gastrointestinal tract that critically shape microbial metabolism and community dynamics. Results We developed the first multi-compartment, spatiotemporally resolved metabolic model of the chicken gastrointestinal tract. Our six-compartment framework integrates avian-specific physiological features including bidirectional flow through peristalsis and reverse peristalsis, feeding-fasting cycles with diurnal shifts in gut motility, and compartment-specific environmental parameters including pH gradients, oxygen levels, and transit times. The model captured distinct metabolic specialization along the gut, with upper compartments enriched for bile salt hydrolases, membrane lipid synthesis, and fatty acid biosynthesis, while cecal and colonic communities specialized in short-chain fatty acid synthesis pathways and polysaccharide degradation. In silico screening of 34 dietary supplements revealed context-dependent metabolic responses and predicted cellulose, starch, and L-threonine as robust enhancers of short-chain fatty acid production. A controlled feeding trial confirmed the model’s directional predictions for butyrate production, with cellulose and starch supplementation producing significant increases consistent with predictions. Quantitative rank-order agreement across multiple metabolites improved substantially in a trial-informed two-compartment model, demonstrating that predictive accuracy is primarily determined by the match between modeled and in vivo microbial community composition. Conclusions Our findings demonstrate that microbial community composition is a primary determinant of metabolic outcomes and underscore the critical importance of context-specific modeling for precision nutrition strategies. This framework provides a mechanistic platform for rational design of dietary interventions that modulate gut microbial metabolism in poultry and is broadly adaptable to other livestock and human gastrointestinal systems.