Diet shapes the gut microbiome of pigs during nursing and weaning.

BACKGROUND: The newborn mammal is rapidly colonized by a complex microbial community, whose importance for host health is becoming increasingly clear. Understanding the forces that shape the early community, especially during the nursing period, is critical to gain insight into how this consortium of microbes is assembled. Pigs present an attractive model for nursing humans, given physiological and compositional similarity of pig and human milk and the utility of pigs in experimental studies. However, there is a paucity of data examining the gut microbiome in nursing pigs from birth through weaning using modern molecular methods and fewer experimental studies that examine the impact of diet on these microbial communities. RESULTS: We characterized the fecal microbiome of pigs from birth through 7 weeks of age, during which the animals were transitioned from an exclusive diet of sow milk to a starter diet composed of plant and animal-based components. Microbial communities were clearly distinguishable based on diet, being relatively stable absent dietary changes. Metagenomic sequencing was used to characterize a subset of animals before and after weaning, which identified glycan degradation pathways differing significantly between diets. Predicted enzymes active on milk-derived glycans that are otherwise indigestible to the host animal were enriched in the microbial metagenome of milk-fed animals. In contrast, the bacterial metagenome of weaned animals was enriched in functional pathways involved in plant glycan deconstruction and consumption. CONCLUSIONS: The gut microbiome in young pigs is dramatically shaped by the composition of dietary glycans, reflected by the different functional capacities of the microbiome before and after weaning.

Computer modeling of obesity links theoretical energetic measures with experimental measures of fuel use for lean and obese men.

The goal of this research was to use a computational model of human metabolism to predict energy metabolism for lean and obese men. The model is composed of 6 state variables representing amino acids, muscle protein, visceral protein, glucose, triglycerides, and fatty acids (FAs). Differential equations represent carbohydrate, amino acid, and FA uptake and output by tissues based on ATP creation and use for both lean and obese men. Model parameterization is based on data from previous studies. Results from sensitivity analyses indicate that model predictions of resting energy expenditure (REE) and respiratory quotient (RQ) are dependent on FA and glucose oxidation rates with the highest sensitivity coefficients (0.6, 0.8 and 0.43, 0.15, respectively, for lean and obese models). Metabolizable energy (ME) is influenced by ingested energy intake with a sensitivity coefficient of 0.98, and a phosphate-to-oxygen ratio by FA oxidation rate and amino acid oxidation rate (0.32, 0.24 and 0.55, 0.65 for lean and obese models, respectively). Simulations of previously published studies showed that the model is able to predict ME ranging from 6.6 to 9.3 with 0% differences between published and model values, and RQ ranging from 0.79 to 0.86 with 1% differences between published and model values. REEs >7 MJ/d are predicted with 6% differences between published and model values. Glucose oxidation increases by ∼0.59 mol/d, RQ increases by 0.03, REE increases by 2 MJ/d, and heat production increases by 1.8 MJ/d in the obese model compared with lean model simulations. Increased FA oxidation results in higher changes in RQ and lower relative changes in REE. These results suggest that because fat mass is directly related to REE and rate of FA oxidation, body fat content could be used as a predictor of RQ.