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In mammals, changes in the metabolic state, including obesity, fasting, cold challenge, and high-fat diets (HFDs), activate complex immune responses. In many strains of rodents, HFDs induce a rapid systemic inflammatory response and lead to obesity. Little is known about the molecular signals required for HFD-induced phenotypes. We studied the function of the receptor for advanced glycation end products (RAGE) in the development of phenotypes associated with high-fat feeding in mice. RAGE is highly expressed on immune cells, including macrophages. We found that high-fat feeding induced expression of RAGE ligand HMGB1 and carboxymethyllysine-advanced glycation end product epitopes in liver and adipose tissue. Genetic deficiency of RAGE prevented the effects of HFD on energy expenditure, weight gain, adipose tissue inflammation, and insulin resistance. RAGE deficiency had no effect on genetic forms of obesity caused by impaired melanocortin signaling. Hematopoietic deficiency of RAGE or treatment with soluble RAGE partially protected against peripheral HFD-induced inflammation and weight gain. These findings demonstrate that high-fat feeding induces peripheral inflammation and weight gain in a RAGE-dependent manner, providing a foothold in the pathways that regulate diet-induced obesity and offering the potential for therapeutic intervention.

BackgroundThe role of the microbiota in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of the roles of specific microbes is necessary to better understand the mechanisms involved in diseases related to microbiome perturbations. MethodsHere, we introduce a new microbiome-based group association testing method, optimal microbiome-based association test (OMiAT). OMiAT is a data-driven testing method which takes an optimal test throughout different tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). We illustrate that OMiAT efficiently discovers significant association signals arising from varying microbial abundances and different relative contributions from microbial abundance and phylogenetic information. We also propose a way to apply it to fine-mapping of diverse upper-level taxa at different taxonomic ranks (e.g., phylum, class, order, family, and genus), as well as the entire microbial community, within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM).ResultsOur extensive simulations demonstrate that OMiAT is highly robust and powerful compared with other existing methods, while correctly controlling type I error rates. Our real data analyses also confirm that MiCAM is especially efficient for the assessment of upper-level taxa by integrating OMiAT as a group analytic method.ConclusionsOMiAT is attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical association map for numerous microbial taxa and can also be used as a guideline for further investigation on the roles of discovered taxa in human health and disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0262-x) contains supplementary material, which is available to authorized users.

Women with HL may survive a subsequent diagnosis of BC, only to experience significant excesses of death from other primary cancers and cardiac disease. Greater awareness of screening for cardiac disease and subsequent primary cancers in patients with HL-BC is warranted.