Person:

Kahn, C.

Insulin and insulin-like growth factor 1 (IGF-1) play important roles in adipocyte differentiation, glucose tolerance and insulin sensitivity. Here, to assess how these pathways can compensate for each other, we created mice with a double tissue-specific knockout of insulin and IGF-1 receptors to eliminate all insulin/IGF-1 signaling in fat. These FIGIRKO mice had markedly decreased white and brown fat mass and were completely resistant to high fat diet (HFD) induced obesity and age- and HFD-induced glucose intolerance. Energy expenditure was increased in FIGIRKO mice despite a >85% reduction in brown fat mass. However, FIGIRKO mice were unable to maintain body temperature when placed at (4^{\circ}C). Brown fat activity was markedly decreased in FIGIRKO mice but was responsive to (\beta3)-receptor stimulation. Thus, insulin/IGF-1 signaling has a crucial role in the control of brown and white fat development, and, when disrupted, leads to defective thermogenesis and a paradoxical increase in basal metabolic rate.

Background: Diabetes, obesity, and the metabolic syndrome are multifactorial diseases dependent on a complex interaction of host genetics, diet, and other environmental factors. Increasing evidence places gut microbiota as important modulators of the crosstalk between diet and development of obesity and metabolic dysfunction. In addition, host genetics can have important impact on the composition and function of gut microbiota. Indeed, depending on the genetic background of the host, diet and other environmental factors may produce different changes in gut microbiota, have different impacts on host metabolism, and create different interactions between the microbiome and the host. Scope of review In this review, we highlight how appropriate animal models can help dissect the complex interaction of host genetics with the gut microbiome and how diet can lead to different degrees of weight gain, levels of insulin resistance, and metabolic outcomes, such as diabetes, in different individuals. We also discuss the challenges of identifying specific disease-associated microbiota and the limitations of simple metrics, such as phylogenetic diversity or the ratio of Firmicutes to Bacteroidetes. Major conclusions Understanding these complex interactions will help in the development of novel treatments for microbiome-related metabolic diseases. This article is part of a special issue on microbiota.