2011 Annual Report
1a.Objectives (from AD-416)
ARS is interested in determining the impact of dietary induced changes in intestinal microflora that affect inflammatory aspects of obesity. In-house research has demonstrated that the pig can be used to model changes in immunological and anti-inflammatory responses to diets that correspond to changes in bacteria in the gut. Feeding a conventional diet containing added levels of fructose, fat, and cholesterol (HF/HFr/HC) compared to a conventional diet increased aspects of the metabolic syndrome in juvenile Ossabaw pigs.
1b.Approach (from AD-416)
Fecal material from Ossabaw sows prior to weaning and their piglets pre- and post-weaning will be evaluated for changes in microbial populations. The analysis will provide a measure of robust changes in bacterial populations that result from dramatic changes in diet pre- and post weaning. Additional changes in bacterial populations will be evaluated following a comparative regimen of feeding a conventional diet compared to one with added HF/HFr/HC and detection of single copy genes for Bifidobacterium and Lactobacillus species measured by real time PCR along with other genera and species including Eubacteria, pan Bifidobacterium, pan Lactobacillus, Escherichia coli, Clostridium difficile, and Bacteriodes fragilis. Changes in innate and acquired immune response patterns in the local tissues and selected circulating markers in whole blood will be evaluated by gene expression, histology, and physiology. This information will be used by both ARS and the cooperator to jointly develop studies that focus on how major modifications of diet affect intestinal microbial populations associated with host change that affect intestinal health.
With the advent of high-throughput sequencing methods, there is an increased interest in the fields of metagenomics and microbiomics. Often a researcher is interested in knowing which features, such as operational taxonomic units and clusters of orthologous genes, in a microbiome or metagenome data set show significant differential abundance. It can be difficult, however, for a researcher to know which statistical method is most appropriate for a particular dataset. This study compared the suitability of several statistical methods for detecting differentially abundant features in high-throughput microbiome and metagenome datasets. Among the statistical methods tested, an arcsine square root data transformation followed by the t Test (one-factor case), Gaussian generalized linear model (one- and two-factor cases), or ANOVA Type II sum of squares (2-factor case) was recommended for high-throughput microbiome and metagenome data quantified as relative abundances.
This agreement was monitored through email, visits, and telephone communication.