MAP4K4 impairs energy metabolism in endothelial cells and promotes insulin resistance in obesity

The blood vasculature responds to insulin, influencing hemodynamic changes in the periphery, which promotes tissue nutrient and oxygen delivery and thus metabolic function. The lymphatic vasculature regulates fluid and lipid homeostasis, and impaired lymphatic function can contribute to atherosclerosis and obesity. Recent studies have suggested a role for endothelial cell (EC) Mitogen activated protein kinase kinase kinase kinase 4 (Map4k4) in developmental angiogenesis and lymphangiogenesis as well as atherosclerosis. Here, we show that inducible EC Map4k4 deletion in adult mice ameliorates metabolic dysfunction in obesity despite the development of chylous ascites and a concomitant striking increase in adipose tissue lymphocyte content. Despite these defects, animals lacking endothelial Map4k4 were protected from skeletal muscle microvascular rarefaction in obesity, and primary ECs lacking Map4k4 displayed reduced senescence and increased metabolic capacity. Thus, endothelial Map4k4 has complex and opposing functions in the blood and lymphatic endothelium post-development. Whereas blood endothelial Map4k4 promotes vascular dysfunction and impairs glucose homeostasis in adult animals, lymphatic endothelial Map4k4 is required to maintain lymphatic vascular integrity and regulate immune cell trafficking in obesity

Inducible Deletion of Protein Kinase Map4k4 in Obese Mice Improves Insulin Sensitivity in Liver and Adipose Tissues

Studies in vitro suggest that mitogen-activated protein kinase kinase kinase kinase 4 (Map4k4) attenuates insulin signaling, but confirmation in vivo is lacking since Map4k4 knockout is lethal during embryogenesis. We thus generated mice with floxed Map4k4 alleles and a tamoxifen-inducible Cre/ERT2 recombinase under the control of the ubiquitin C promoter to induce whole-body Map4k4 deletion after these animals reached maturity. Tamoxifen administration to these mice induced Map4k4 deletion in all tissues examined, causing decreased fasting blood glucose concentrations and enhanced insulin signaling to AKT in adipose tissue and liver but not in skeletal muscle. Surprisingly, however, mice generated with a conditional Map4k4 deletion in adiponectin-positive adipocytes or in albumin-positive hepatocytes displayed no detectable metabolic phenotypes. Instead, mice with Map4k4 deleted in Myf5-positive tissues, including all skeletal muscles tested, were protected from obesity-induced glucose intolerance and insulin resistance. Remarkably, these mice also showed increased insulin sensitivity in adipose tissue but not skeletal muscle, similar to the metabolic phenotypes observed in inducible whole-body knockout mice. Taken together, these results indicate that (i) Map4k4 controls a pathway in Myf5-positive cells that suppresses whole-body insulin sensitivity and (ii) Map4k4 is a potential therapeutic target for improving glucose tolerance and insulin sensitivity in type 2 diabetes

Protein Kinase Mitogen-activated Protein Kinase Kinase Kinase Kinase 4 (MAP4K4) Promotes Obesity-induced Hyperinsulinemia

Previous studies revealed a paradox whereby mitogen-activated protein kinase kinase kinase kinase 4 (Map4k4) acted as a negative regulator of insulin sensitivity in chronically obese mice, yet systemic deletion of Map4k4 did not improve glucose tolerance. Here, we report markedly reduced glucose-responsive plasma insulin and C-peptide levels in whole body Map4k4-depleted mice (M4K4 iKO) as well as an impaired first phase of insulin secretion from islets derived from M4K4 iKO mice ex vivo After long-term high fat diet (HFD), M4K4 iKO mice pancreata also displayed reduced beta cell mass, fewer proliferating beta cells and reduced islet-specific gene mRNA expression compared with controls, although insulin content was normal. Interestingly, the reduced plasma insulin in M4K4 iKO mice exposed to chronic (16 weeks) HFD was not observed in response to acute HFD challenge or short term treatment with the insulin receptor antagonist S961. Furthermore, the improved insulin sensitivity in obese M4K4 iKO mice was abrogated by high exogenous insulin over the course of a euglycemic clamp study, indicating that hypoinsulinemia promotes insulin sensitivity in chronically obese M4K4 iKO mice. These results demonstrate that protein kinase Map4k4 drives obesity-induced hyperinsulinemia and insulin resistance in part by promoting insulin secretion from beta cells in mice

Endothelial protein kinase MAP4K4 promotes vascular inflammation and atherosclerosis

Signalling pathways that control endothelial cell (EC) permeability, leukocyte adhesion and inflammation are pivotal for atherosclerosis initiation and progression. Here we demonstrate that the Sterile-20-like mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), which has been implicated in inflammation, is abundantly expressed in ECs and in atherosclerotic plaques from mice and humans. On the basis of endothelial-specific MAP4K4 gene silencing and gene ablation experiments in Apoe(-/-) mice, we show that MAP4K4 in ECs markedly promotes Western diet-induced aortic macrophage accumulation and atherosclerotic plaque development. Treatment of Apoe(-/-) and Ldlr(-/-) mice with a selective small-molecule MAP4K4 inhibitor also markedly reduces atherosclerotic lesion area. MAP4K4 silencing in cultured ECs attenuates cell surface adhesion molecule expression while reducing nuclear localization and activity of NFkappaB, which is critical for promoting EC activation and atherosclerosis. Taken together, these results reveal that MAP4K4 is a key signalling node that promotes immune cell recruitment in atherosclerosis

Mutation in the <em>γ</em>2-Subunit of AMP-activated protein kinase stimulates cardiomyocyte proliferation and hypertrophy independent of glycogen storage

RATIONALE: AMP-activated protein kinase (AMPK) is a master regulator of cell metabolism and an attractive drug target for cancer, metabolic and cardiovascular diseases. Point mutations in the regulatory γ2-subunit of AMPK (encoded by Prkag2 gene) caused a unique form of human cardiomyopathy characterized by cardiac hypertrophy, ventricular pre-excitation and glycogen storage. Understanding the disease mechanisms of Prkag2 cardiomyopathy is not only beneficial for the patients but also critical to the utility of AMPK as a drug target. OBJECTIVE: We sought to identify the pro-growth signaling pathway(s) triggered by Prkag2 mutation and to distinguish it from the secondary response to glycogen storage. METHODS AND RESULTS: In a mouse model of N488I mutation of the Prkag2 (R2M), we rescued the glycogen storage phenotype by genetic inhibition of glucose-6-phosphate stimulated glycogen synthase activity. Ablation of glycogen storage eliminated the ventricular pre-excitation but did not affect the excessive cardiac growth in R2M mice. The pro-growth effect in R2M hearts was mediated via increased insulin sensitivity and hyperactivity of Akt, resulting in activation of mTOR and inactivation of FoxO signaling pathways. Consequently, cardiac myocyte proliferation during the postnatal period was enhanced in R2M hearts followed by hypertrophic growth in adult hearts. Inhibition of mTOR activity by rapamycin or restoration of FoxO activity by overexpressing FoxO1 rescued the abnormal cardiac growth. CONCLUSIONS: Our study reveals a novel mechanism for Prkag2 cardiomyopathy independent of glycogen storage. The role of γ2-AMPK in cell growth also has broad implications in cardiac development, growth and regeneration

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

<div><p>Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the <i>i</i>MDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (<u>m</u>ultiple <u>d</u>ifferential <u>m</u>odules). We applied <i>i</i>MDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that <i>i</i>MDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. <i>i</i>MDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.</p></div

Performance comparison of the <i>i</i>MDM algorithm.

<p><i>i</i>MDM DCN, method using multiple differential co-expression networks; <i>i</i>MDM Co-expression, method using multiple co-expression networks but no differential gene expression information. A, Specificity of the algorithms. Gene modules found by each method were evaluated using a set of gold-standard pathway annotations. Specificity was defined as the fraction of predicted modules that significantly overlaps with reference pathways. B, Sensitivity of the algorithms. Sensitivity was defined as the fraction of reference pathways that significantly overlaps with predicted modules. Pathway overlap P-values were computed using the hypergeometric distribution. P-values for the difference in specificity and sensitivity were computed using Fisher’s exact test. C, Percentage of predicted modules that significantly overlapped with genes whose deletions lead to cardiovascular phenotypes. P-values for the difference in the percentage of overlapped modules was computed using Fisher’s exact test. All p-values were corrected for multiple testing using the method of Benjamin-Hochberg. *, <i>p-value</i> < 0.05.</p

Overview of the iMDM algorithm.

<p>The algorithm has two major steps. First, gene expression profiles across multiple conditions are used to build differential gene co-expression networks (DCNs). To build DCNs, a binary co-expression network is constructed first in which edges are chosen based on the absolute value of Pearson correlation of the expression profiles of two genes. Only edges whose correlation exceeds a pre-defined threshold <b><i>δ</i></b> are included in the binary network. Edges in the binary network are then weighted (<b><i>wi,j</i></b>) based on the p-values (<b><i>pi</i></b> and <b><i>pj</i></b>) of differential gene expression between the baseline and disease conditions. Second, multiple differential co-expression networks are analyzed to identify shared and unique multiple differential modules (M-DMs) under different conditions. 1-DM are modules that are only found in one condition whereas M-DMs with M ≥ 2 are modules that are found in multiple conditions.</p