Mannan-oligosaccharides promote gut microecological recovery after antibiotic disturbance.

Antibiotic treatment often causes collateral damage to the gut microbiota, including changes in its diversity and composition. Dietary fiber helps maintain intestinal health, regulate short-chain fatty acids, and promote the recovery of the intestinal microbiome. However, it is currently unknown which specific plant-based dietary fiber is optimal as a dietary supplement for restoring the intestinal microbiota after antibiotic disturbance. Previously, we proposed predictive recovery-associated bacterial species (p-RABs) and identified the most important interventions. This study aimed to identify an optimal form of dietary fiber to recover the gut microbiome after antibiotic treatment. Therefore, we examined the types of dietary fibers associated with p-RABs through a p-RAB-metabolite bilayer network constructed from prior knowledge; we searched for dietary fiber that could provide nutritional support for Akkermansia muciniphila and Bacteroides uniformis. C57BL/6J mice were fed with 500 mg kg-1 of different types of dietary fibers daily for one week after being treated with ampicillin. The results showed that mannan-oligosaccharides could better promote the diversity of intestinal microbial growth, enhance the recovery of most genera, including Akkermansia and Bacteroides, and inhibit certain pathogenic bacteria, such as Proteus, compared to the other fiber types. Furthermore, mannan-oligosaccharides could regulate the levels of short-chain fatty acids, especially butyric acid. Functional predictions showed that starch metabolism, galactose metabolism, and the metabolism of other carbohydrates played key roles in the early recovery process. In conclusion, mannan-oligosaccharides could enhance the recovery of the intestinal microbiome after antibiotic treatment, offering valuable insights for targeted dietary strategies.

Statistical modeling of gut microbiota for personalized health status monitoring.

BACKGROUND: The gut microbiome is closely associated with health status, and any microbiota dysbiosis could considerably impact the host's health. In addition, many active consortium projects have generated many reference datasets available for large-scale retrospective research. However, a comprehensive monitoring framework that analyzes health status and quantitatively present bacteria-to-health contribution has not been thoroughly investigated. METHODS: We systematically developed a statistical monitoring diagram for personalized health status prediction and analysis. Our framework comprises three elements: (1) a statistical monitoring model was established, the health index was constructed, and the health boundary was defined; (2) healthy patterns were identified among healthy people and analyzed using contrast learning; (3) the contribution of each bacterium to the health index of the diseased population was analyzed. Furthermore, we investigated disease proximity using the contribution spectrum and discovered multiple multi-disease-related targets. RESULTS: We demonstrated and evaluated the effectiveness of the proposed monitoring framework for tracking personalized health status through comprehensive real-data analysis using the multi-study cohort and another validation cohort. A statistical monitoring model was developed based on 92 microbial taxa. In both the discovery and validation sets, our approach achieved balanced accuracies of 0.7132 and 0.7026, and AUC of 0.80 and 0.76, respectively. Four health patterns were identified in healthy populations, highlighting variations in species composition and metabolic function across these patterns. Furthermore, a reasonable correlation was found between the proposed health index and host physiological indicators, diversity, and functional redundancy. The health index significantly correlated with Shannon diversity ([Formula: see text]) and species richness ([Formula: see text]) in the healthy samples. However, in samples from individuals with diseases, the health index significantly correlated with age ([Formula: see text]), species richness ([Formula: see text]), and functional redundancy ([Formula: see text]). Personalized diagnosis is achieved by analyzing the contribution of each bacterium to the health index. We identified high-contribution species shared across multiple diseases by analyzing the contribution spectrum of these diseases. CONCLUSIONS: Our research revealed that the proposed monitoring framework could promote a deep understanding of healthy microbiomes and unhealthy variations and served as a bridge toward individualized therapy target discovery and precise modulation. Video Abstract.

Preparation, characterization, and encapsulation capability of the hydrogel cross-linked by esterified tapioca starch.

The modified starch-based hydrogels were prepared by crosslinking modified starch with sodium trimetaphosphate. Modified starch was obtained by esterification of tapioca starch with maleic anhydride. The degree of substitution (DS) increased significantly from 0.078 to 0.258 as the content of maleic anhydride increased from 6.67% to 33.33%. Fourier transform infrared spectroscopy demonstrated that starch was successfully esterified. In addition, the thermal properties of modified starch-based hydrogels were investigated by differential scanning calorimeter and thermogravimetry analysis, which proved that hydrogels had better thermal stability. Esterified starch-based hydrogels showed excellent pH sensitivity by measuring of swelling degree. When DS was 0.250, the adsorption capacity and encapsulation efficiency of starch-based hydrogels were 399.23 µg/g and 80%, respectively, which exhibited satisfactory embedding properties for curcumin. Therefore, esterified tapioca starch-based hydrogels could be as the encapsulating materials to protect bioactive substances, which provided a theoretical basis for their application in food field and pharmaceuticals industry.