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Blaser, M.

Publications and source records attributed to Blaser, M..

2 recordsLinked to original sources

Dietary Lipid Oxidization Products Alter Growth, Adiposity and Gut Microbial Ecology in Prepubertal Porcine Model

Elevated levels of dietary fats in westernized diets, associated with increased risk of obesity and other chronic diseases, are increasingly consumed by children in the United States. Cooking practices such as high heat frying and increased use of oxidizable sources of fats have introduced high levels of lipid oxidation products (LOPs) into these diets. The effects of these highly reactive dietary compounds on human biology are largely unstudied, especially in the gut where these compounds are likely present at higher concentrations. Given that the gut microbiome can be influenced by dietary components and then in turn have a systemic impact, we investigated the effects of consuming LOPs on gut bacterial and fungal communities and on growth and body composition during the prepubertal period in a porcine model. The presence of LOPs in the high fat diet reduced growth and body fat gain in the model. The gut microbiome was uniquely altered by both high fat and the presence of LOPs, with notable changes in the abundances of Turicibacterales, Spriochaetales, RF39, Lactobacillales and Erysipelotrichales. The mycobiome was dominated by Kazachstania, a porcine specific yeast, which was only minimally influenced by the dietary regimen. Application of machine learning identified dietary fat and LOPs as strong predictors of body fat. The genus Methanobrevibacter was the key microbial predictor of body fat. This study highlights the need for further studies on the biological effects of LOPs which have become ubiquitous in human, livestock and pet diets in developed countries.

physiology↗

ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study

Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluated ARZIMM in comparison with the other methods.

bioinformatics↗