RESUMEN
The gut microbiome and lipid metabolism are both recognized as essential components in the maintenance of metabolic health. The mechanisms involved are multifactorial and (especially for microbiome) poorly defined. A strategic approach to investigate the complexity of the microbial influence on lipid metabolism would facilitate determination of relevant molecular mechanisms for microbiome-targeted therapeutics. E. coli is associated with obesity and metabolic syndrome and we used this association in conjunction with gnotobiotic models to investigate the impact of E. coli on lipid metabolism. To address the complexities of the integration of the microbiome and lipid metabolism, we developed transcriptomics-driven lipidomics (TDL) to predict the impact of E. coli colonization on lipid metabolism and established mediators of inflammation and insulin resistance including arachidonic acid metabolism, alterations in bile acids and dietary lipid absorption. A microbiome-related therapeutic approach targeting these mechanisms may therefore provide a therapeutic avenue supporting maintenance of metabolic health.
RESUMEN
Development of NGS has revolutionized the analysis in microbial ecology contributing to our deeper understanding of microbiota in health and disease. However, the quality, quantity and confidence of summarized taxonomic abundances are in need of further scrutiny due to sample dependent and independent effects. In this article we introduce 'AVIT (Abundance and Variability In Taxonomy), an unbiased method to enrich for assigned members of microbial communities. As opposed to using a priori thresholds, 'AVIT uses inherent abundance and variability of taxa in a dataset to determine the inclusion or rejection of each taxa for further downstream analysis. Using in-vitro and in-vivo studies, we benchmarked performance and parameterized 'AVIT to establish a framework for investigating the dynamic range of microbial community membership in clinically relevant scenarios.
