BackgroundIdentifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.ResultsRF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.ConclusionsWhile RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

BackgroundYeast Saccharomyces cerevisiae is a widely-used system for protein expression. We previously showed that heat-killed whole recombinant yeast vaccine expressing mammalian myostatin can modulate myostatin function in mice, resulting in increase of body weight and muscle composition in these animals. Foreign DNA introduced into yeast cells can be lost soon unless cells are continuously cultured in selection media, which usually contain antibiotics. For cost and safety concerns, it is essential to optimize conditions to produce quality food and pharmaceutical products.ResultsWe developed a simple but effective method to engineer a yeast strain stably expressing mammalian myostatin. This method utilized high-copy-number integration of myostatin gene into the ribosomal DNA of Saccharomyces cerevisiae. In the final step, antibiotic selection marker was removed using the Cre-LoxP system to minimize any possible side-effects for animals. The resulting yeast strain can be maintained in rich culture media and stably express mammalian myostatin for two years. Oral administration of the recombinant yeast was able to induce immune response to myostatin and modulated the body weight of mice.ConclusionsEstablishment of such yeast strain is a step further toward transformation of yeast cells into edible vaccine to improve meat production in farm animals and treat human muscle-wasting diseases in the future.

BackgroundIdentifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.ResultsRF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.ConclusionsWhile RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

BackgroundLactobacillus plantarum is a plant-associated bacterial species but it has also been found in human, mouse and porcine gastrointestinal tracts. It can ferment a broad spectrum of plant carbohydrates; it is tolerant of bile salts and low pH, and it has antagonistic potential against intestinal pathogens. However, experiments reporting the use of L. plantarum as a probiotic are limited. In this study, the effects of L. plantarum ZJ316 isolated from infant fecal samples on pig growth and pork quality were investigated.ResultsOne hundred and fifty newly weaned pigs were selected randomly and divided into five groups. Group 1 was fed a diet supplemented with the antibiotic mequindox; Groups 2, 3 and 4 were fed a diet supplemented with L. plantarum and no antibiotic; and Group 5 was fed a mixture of mequindox and L. plantarum. After a 60 days initial treatment, samples were collected for evaluation. The results showed that, the L. plantarum ZJ316 has probiotic effects on pig growth and that these effects are dose dependent. The effects of a dose of 1 × 109 CFU/d were more pronounced than those of a dose of 5 × 109 CFU/d or 1 × 1010 CFU/d. In Group 2 (1 × 109 CFU/d), the diarrhea (p = 0.000) and mortality rates (p = 0.448) were lower than in antibiotic-treated pigs (Group 1), and the daily weight gain (p = 0.001) and food conversion ratios were better (p = 0.005). Improved pork quality was associated with Lactobacillus treatment. pH (45 min, p = 0.020), hardness (p = 0.000), stickiness (p = 0.044), chewiness (p = 0.000), gumminess (p = 0.000) and restoring force (p = 0.004) were all significantly improved in Lactobacillus-treated pigs (Group 2). Although we found that L. plantarum exerted probiotic effects on pig growth and pork quality, the mechanisms underlying its action require further study. Polymerase chain reaction-denaturing gradient gel electrophoresis results showed that the gut bacterial communities in Lactobacillus- and antibiotic-treated pigs were very similar and the quantity of L. plantarum ZJ316 was below the detection limits of DGGE-band sequencing. The concentration of short-chain fatty acids in Lactobacillus- and antibiotic-treated fecal samples were not significantly different (p = 0.086). However, the villus height of ilea (p = 0.003), jejuna (p = 0.000) and duodena (p = 0.036) were found to be significantly improved by Lactobacillus treatment.ConclusionL. plantarum ZJ316 was found to have probiotic effects, improving pig growth and pork quality. The probiotic mechanism might not involve L. plantarum colonization and alteration of the gut bacterial community. Rather, it might be related to the inhibition of the growth of opportunistic pathogens and promotion of increased villus height.