37 The dynamic interplay between microbial consortia and arabinoxylan reveals the remodeling of metabolism by swine gut microbiome variation.

Abstract (Objective) Dietary fiber is pivotal in enhancing intestinal development, modulating microbial diversity, and maintaining gut homeostasis in pigs. However, the dynamic processes by which dietary fiber reshapes gut microbiota remain poorly characterized. This study employed an in vitro gut microbial fermentation model to investigate the succession patterns of microbial communities and functional dynamics during arabinoxylan utilization in Jinhua (JH, Chinese native pigs) and Duroc × Landrace × Yorkshire (DLY) pigs, aiming to elucidate the microbial mechanisms underlying divergent arabinoxylan metabolic capacities and provide theoretical insights for optimizing dietary fiber utilization. (Methods) Fecal microbiota from six healthy post-weaning JH and DLY pigs was subjected to in vitro arabinoxylan fermentation. Samples were collected at 1, 3, 6, 9, 12, 15, 18, 21, 24, 48, and 72 h to analyze pH, β-xylanase and α-L-arabinofuranosidase activity, and short-chain fatty acid (SCFA) profiles via gas chromatography. 16S rRNA sequencing tracked microbial community dynamics, while time-series data integration and linear regression modeling established interactions between co-abundance response groups (CARGs) and SCFA metabolism. (Results) Key findings: 1) Gut microbiome variation exhibited similar short-term microbial responses to arabinoxylan, with microbial diversity and enzyme activity involved in arabinoxylan degradation stabilizing after rapid initial shifts. Arabinoxylan fermentation progressed through three time-dependent phases, corroborated by microbial distance and KEGG functional shifts. The microbial network closeness centrality decreased from the first fermentation stage to the second stage and then increased in the final stage, indicating that the metabolic changes primarily occurred during the middle phase. Enterobacter and Roseobacter were the primary degrading bacteria, with the enrichment of fiber-degrading taxa in JH. Clostridium and Lactobacillus were secondary degraders. 2) Niche differentiation and metabolic outputs depended on baseline microbiota. JH maintained higher α-diversity than DLY (P< 0.05) at most time points except 3, 9, 18, and 72 h, and higher total SCFAs at 1, 9, 48, and 72 h (P< 0.05). Notably, pentose and glucuronate interconversions were representative of the predicted metabolic functions that distinguish JH and DLY. 3) Time-resolved microbial networks revealed that arabinoxylan-induced metabolic divergence relied on complex ecological interactions. SCFA levels were associated with CARG abundance and microbial interactions during fermentation. Regression models identified Limosilactobacillus mucosae, Lactobacillus delbrueckii subsp. jakobsenii, Limosilactobacillus balticus, and Limosilactobacillus agrestimuris as acetate-associated taxa. (Conclusion) While both breeds shared core arabinoxylan utilization mechanisms, the microbiome in JH demonstrated superior SCFA metabolism. Higher baseline diversity facilitated enhanced fiber degradation, with niche dynamics dictating metabolic outcomes. CARGs served as robust predictors of SCFA production. This study underscores the pivotal role of intricate microecological networks in dietary fiber metabolism, advancing mechanistic understanding of fermentation kinetics for precision nutritional strategies.