Metabolomic analysis of insulin resistance across different mouse strains and diets

Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity

Skeletal muscle NOX4 is required for adaptive responses that prevent insulin resistance

Reactive oxygen species (ROS) generated during exercise are considered integral for the health-promoting effects of exercise. However, the precise mechanisms by which exercise and ROS promote metabolic health remain unclear. Here, we demonstrate that skeletal muscle NADPH oxidase 4 (NOX4), which is induced after exercise, facilitates ROS-mediated adaptive responses that promote muscle function, maintain redox balance, and prevent the development of insulin resistance. Conversely, reductions in skeletal muscle NOX4 in aging and obesity contribute to the development of insulin resistance. NOX4 deletion in skeletal muscle compromised exercise capacity and antioxidant defense and promoted oxidative stress and insulin resistance in aging and obesity. The abrogated adaptive mechanisms, oxidative stress, and insulin resistance could be corrected by deleting the H2O2-detoxifying enzyme GPX-1 or by treating mice with an agonist of NFE2L2, the master regulator of antioxidant defense. These findings causally link NOX4-derived ROS in skeletal muscle with adaptive responses that promote muscle function and insulin sensitivity

Insulin signaling in AgRP neurons regulates meal size to limit glucose excursions and insulin resistance

The importance of hypothalamic insulin signaling on feeding and glucose metabolism remains unclear. We report that insulin acts on AgRP neurons to acutely decrease meal size and thereby limit postprandial glucose and insulin excursions. The promotion of insulin signaling in AgRP neurons decreased meal size without altering total caloric intake, whereas the genetic ablation of the insulin receptor had the opposite effect. The promotion of insulin signaling also decreased the intake of sucrose-sweetened water or high-fat food over standard chow, without influencing food-seeking and hedonic behaviors. The ability of heightened insulin signaling to override the hedonistic consumption of highly palatable high-fat food attenuated the development of systemic insulin resistance, without affecting body weight. Our findings define an unprecedented mechanism by which insulin acutely influences glucose metabolism. Approaches that enhance insulin signaling in AgRP neurons may provide a means for altering feeding behavior in a nutrient-dense environment to combat the metabolic syndrome

MKP1 promotes nonalcoholic steatohepatitis by suppressing AMPK activity through LKB1 nuclear retention

Abstract Nonalcoholic steatohepatitis (NASH) is triggered by hepatocyte death through activation of caspase 6, as a result of decreased adenosine monophosphate (AMP)-activated protein kinase-alpha (AMPKα) activity. Increased hepatocellular death promotes inflammation which drives hepatic fibrosis. We show that the nuclear-localized mitogen-activated protein kinase (MAPK) phosphatase-1 (MKP1) is upregulated in NASH patients and in NASH diet fed male mice. The focus of this work is to investigate whether and how MKP1 is involved in the development of NASH. Under NASH conditions increased oxidative stress, induces MKP1 expression leading to nuclear p38 MAPK dephosphorylation and decreases liver kinase B1 (LKB1) phosphorylation at a site required to promote LKB1 nuclear exit. Hepatic deletion of MKP1 in NASH diet fed male mice releases nuclear LKB1 into the cytoplasm to activate AMPKα and prevents hepatocellular death, inflammation and NASH. Hence, nuclear-localized MKP1-p38 MAPK-LKB1 signaling is required to suppress AMPKα which triggers hepatocyte death and the development of NASH

PTP1B is an intracellular checkpoint that limits T cell and CAR T cell anti-tumor immunity.

Immunotherapies aimed at alleviating the inhibitory constraints on Tcells have revolutionised cancer management. To date, these have focused on the blockade of cell surface checkpoints such as PD-1. Herein we identify protein-tyrosine-phosphatase-1B (PTP1B) as an intracellular checkpoint that is upregulated in T cells in tumors. We show that the increased PTP1B limits T cell expansion and cytotoxicity to contribute to tumor growth. T cell-specific PTP1B deletion increased STAT-5 signaling and this enhanced the antigen-induced expansion and cytotoxicity of CD8+ T cells to suppress tumor growth. The pharmacological inhibition of PTP1B recapitulated the T cell-mediated repression of tumor growth and enhanced the response to PD-1 blockade. Furthermore, the deletion or inhibition of PTP1B enhanced the efficacy of adoptively-transferred chimeric-antigen-receptor (CAR) T cells against solid tumors. Our findings identify PTP1B as an intracellular checkpoint whose inhibition can alleviate the inhibitory constraints on T cells and CAR T cells to combat cancer

Metabolomic analysis of insulin resistance across different mouse strains and diets

Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity