MicroRNAs in obesity-associated disorders

Archives of Biochemistry and Biophysics xxx (2015) 1e12

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Review article

MicroRNAs in obesity-associated disorders Eugenio J. Abente a, b, 1, Murugan Subramanian a, b, Vimal Ramachandran a, b, S.Hani Najafi-Shoushtari a, b, * a b

Department of Cell and Developmental Biology, Weill Cornell Medical College, Cornell University, New York 10021, NY, USA Weill Cornell Medical College-Qatar, Qatar Foundation, Education City, P.O. Box 24144, Doha, Qatar

a r t i c l e i n f o

a b s t r a c t

Article history: Received 30 July 2015 Received in revised form 17 September 2015 Accepted 18 September 2015 Available online xxx

The emergence of a worldwide obesity epidemic has dramatically increased the prevalence of insulin resistance and metabolic syndrome, predisposing individuals to a greater risk for the development of non-alcoholic fatty liver disease, type II diabetes and atherosclerotic cardiovascular diseases. Current available pharmacological interventions combined with diet and exercise-based managements are still poorly effective for weight management, likely in part due to an incomplete understanding of regulatory mechanisms and pathways contributing to the systemic metabolic abnormalities under disturbed energy homeostasis. MicroRNAs, small non-coding RNAs that regulate posttranscriptional gene expression, have been increasingly described to influence shifts in metabolic pathways under various obesity-related disease settings. Here we review recent discoveries of the mechanistic role that microRNAs play in regulating metabolic functions in liver and adipose tissues involved in obesity associated disorders, and briefly discusses the potential candidates that are being pursued as viable therapeutic targets. © 2015 Elsevier Inc. All rights reserved.

Keywords: Obesity MicroRNAs Lipid/fat metabolism Metabolic syndrome Insulin resistance RNA therapeutics

Contents 1. 2. 3.

4.

5. 6.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs in non-alcoholic fatty liver disease (NAFLD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs in dyslipidemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Hypertriglyceridemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Hypercholesterolemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. MicroRNA-33a/b as therapeutic targets to treat dyslipidemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs and adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. miRs that promote adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. miRs that inhibit adipogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MicroRNAs and insulin resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

00 00 00 00 00 00 00 00 00 00 00 00

1. Introduction

* Corresponding author. Weill Cornell Medical College-Qatar, Cornell University, P.O. Box 24144, Room C007, Doha, Qatar. E-mail address: [email protected] (S.Hani Najafi-Shoushtari). 1 Present address: Virus and Prion Research Unit, National Animal Disease Center, Agricultural Research Service, US Department of Agriculture, 1920 Dayton Ave, 50010 Ames IA, USA.

Abdominal obesity, genetic or of acquired origin, is a serious health issue that has been classified as a global epidemic [1e4]. In contrast to pathogenic pandemics such as AIDS or the influenza virus, where there is an etiological agent, obesity has been mostly linked to behavior, specifically the quantity and quality of food intake in addition to a sedentary lifestyle [1]. The worldwide

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prevalence of obesity nearly doubled between 1980 and 2008, with an estimated ~500 million cases reported in 2008 [5]. The WHO reports that approximately 347 million people have diabetes worldwide, and that by 2030, diabetes will be the 7th leading cause of death. Obesity is a major disorder of adipose tissue that together with the development of primary insulin resistance leads to the metabolic syndrome, a condition largely reflected in atherogenic dyslipideamia [6,7], elevated blood pressure (hypertension), increased glucose output, abnormal regulation of adipose tissue [8,9], thrombogenesis and increased inflammation. This cluster of metabolic abnormalities serve as a prelude to the development of disorders of increasing severity such as Type 2 diabetes (T2DM), atherosclerotic micro-and macro-vascular diseases (MVDs) and hepatic steatosis [10]. Eukaryotic cells of higher organisms possess a plethora of mechanisms for regulating the balance between obtaining cholesterol and fatty acids through dietary consumption or cellular biosynthesis. At the molecular level, lipid associated metabolic pathways are tightly regulated and extremely sensitive to metabolic ligands in order to facilitate rapid fine-tuning for homeostatic maintenance [11]. Within the hepatocytes, lipid/cholesterol levels are primarily regulated by two main transcription factors: sterolregulatory element binding protein (SREBP), governed by insulin and dietary fatty acids, and carbohydrate response element binding protein (CREBP), governed by ambient glucose levels. The SREBPs (SREBP-1a/SREBP-1c and SREBP-2), were reported and characterized in a series of seminal studies [12e18] and have been identified as master regulators of two arms of lipid metabolism: SREBP-1 stimulates nuclear transcription of the enzymes responsible for fatty acid synthesis and uptake, and SREBP-2 preferentially acts on cholesterol biosynthesis [19e21]. Similarly, adipose tissues play an integral role in lipid and glucose metabolism and serves two important roles: they store and release free fatty acids (FFAs) from consumption and during fasting, respectively, and serve as an immune and endocrine organ, responsible for the synthesis of adipokines such as leptin and adiponectin. Adipokines exert auto-, para, as well as endocrine functions on local and peripheral tissues such as the liver. Thereby, plasma levels of FFAs are detrimental and positively correlate with fat mass and insulin resistance, whereas leptin and adiponectin have beneficial cardiometabolic effects [22,23]. The differentiation and proliferation of pre-adipocytes is highly regulated. Adipose tissues are categorized into two different types: white adipose tissue (WAT; further divided into subcutaneous and visceral adipose tissue, SAT and VAT, respectively), and brown adipose tissue (BAT; which includes “beige” and “brite” adipose tissue that arise from de novo differentiation of WAT precursors or pre-existing white adipocytes). WAT mainly functions to secrete adipokines and store fatty acids/triglycerides, while BAT primarily functions in energy expenditure and non-shivering thermogenesis [24]. At the molecular level, C/EBPa and C/EBPb were identified as master regulators of WAT and BAT differentiation, respectively [25,26]. The discovery of functional non-coding RNAs as specific gene expression regulators heralded the advent of a new field of research and also provided novel targets for drug development. Since microRNAs (miRs) were first described [14], other non-coding RNAs that function in gene regulation have been reported such as piwi RNAs and long non-coding RNAs, highlighting the complexity of gene regulation. MiRs regulate gene translation by specifically binding to the 30 -UTR of mRNAs and blocking translation or targeting the transcript for degradation [27]. Therapeutic drugs altering miR levels are being explored as novel strategies for clinical use in various diseases, where the level of a regulatory miR is either suppressed or elevated using oligonucleotide antisense strategies or mimics, respectively [28]. In this review we will highlight recent

discoveries of how miRs play a crucial role in regulating metabolic pathways in the liver and adipose tissue, and discuss the therapeutic potential of modulating miR levels to treat obesityassociated diseases. 2. MicroRNAs in non-alcoholic fatty liver disease (NAFLD) The liver is the major metabolic organ that serves many functions, including drug and lipid metabolism, and bile acid production. Lipid accumulation in the liver results from a homeostatic defect in overall calorie intake and systemic calorie utilization. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat, mostly as triglycerides, cholesterol and phospholipids, in the absence of significant ethanol exposure [29]. The disease manifestation includes a spectrum of symptoms that includes nonalcoholic steatohepatitis (NASH) and liver injury characterized by hepatocyte ballooning, focal necrosis, inflammation and fibrosis [30]. The observed hepatic steatosis is a result of lipid accumulation and is an indicator of disrupted homeostasis of lipid metabolism, which can be both a cause and consequence of obesity and insulin resistance [31]. Dysregulated activities of several transcription factors have been shown to be involved in developing NAFLD [32]. Some of the key transcription factors that play a role in the development of NAFLD include the Pregnane X Receptor (PXR), Farnesoid X Receptor (FXR), the Liver X Receptor (LXR), Retinoid X Receptor (RXR), and Peroxisome Proliferator Activated Receptor alpha (PPARa). Early studies found that PXR forms a dimer with RXR and induces steroidinducible genes [33]. It was also reported that the activation of PXR in transgenic mice led to the development of hepatic steatosis [34], indicating a direct role in lipogenesis. The mechanistic activity of PXR is complex, however PXR can act as a co-repressor of transcription factors involved in fatty acid oxidation and gluconeogenesis such as FOXA2, FOXO1 [35,36], and also up-regulate genes involved in fatty acid uptake [34]. Previously it was reported that levels of miR-148a inversely correlated with PXR in 25 human liver samples, and that miR-148a could specifically target the PXR 30 -UTR (Table 1) [37]. In contrast to most studies that use the full length 30 -UTR, a luciferase reporter assay was employed that only contained the predicted target site of the PXR 30 -UTR. Nevertheless, they showed that miR-148a was able to regulate PXR mRNA and protein levels in a hepatic cell line (Fig. 1). A recent study found no correlation between miR-148a and PXR levels in liver samples from a Chinese Han population [38]. One possibility is that inherent SNPs within populations can affect the predicted targeting of miRs, as has been shown in other cases [39,40]. It is also possible that the detectable levels of miR-148a could differ due to the presence of isomiRs, which most likely can affect the efficiency of cDNA synthesis depending on the enzymatic assay used to generate miR cDNA, and therefore underestimate the actual miR levels [41,42]. FXR, like PXR, also forms a dimer with RXR [43]. FXR functions in triglyceride clearance, fatty acid oxidation, and down-regulation of lipogenic genes [44]. Transgenic mice lacking the FXR gene suffer from hyperlipidemia and hepatic steatosis [45,46]. FXR agonists are being explored for clinical use [47,48], although reduced nonhepatic FXR activity may be beneficial for obesity [49]. Further elucidation of the FXR pathway may provide additional therapeutic targets that can avoid potentially negative side effects observed with FXR agonists. Using a potent FXR agonist, Q. de Aguiar Vallim et al. found that miR-144 was induced by FXR activity [50]. Through in vitro and in vivo mouse studies they identified ABCA1, a highdensity lipoprotein (HDL) cholesterol transporter involved in reverse-cholesterol transport (RCT) that drives cholesterol efflux in order to protect against cholesterol overload, as a functional target

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Table 1 MicroRNAs that regulate lipid metabolism in the liver. miR

Gene target

Molecular pathway

Target validateda?

Biological systemb

References

miR-148a

PXR

Lipogenesis

miR-144

ABCA1

RCT

e e þ

miR-613 miR-1/miR-206 miR-26 miR-128-2 miR-27a miR-27b miR-27b

LXRa LXRa ABCA1, ARL7 ABCA1, ABCG1,RXRa ABCA1 PPARa, ANGPTL3, RXRa PPARg, ANGPTL3, NDST1, GPAM CRAT, MED13 FASN ACSL1 APOA5 variant Insig1

Lipogenesis Lipogenesis RCT RCT Lipogenesis Lipogenesis Lipogenesis

þ þ e e þ e e

HepG2 Clinical samples Mouse hepatocyte cell line, Hep3b, mouse model HepG2 HepG2 HepG2 HepG2 Huh7 Huh7 Huh7

[37] [38] [50] [51] [58] [59] [60] [61] [66] [65] [69]

þ þ þ þ e

Knockout mouse HepG2, primary hepatocytes HepG2, HBx transgenic mouse e Human hepatocytes, mouse model,HepG2

[71] [72] [73] [74] [77]

SREBP2 SR-B1

Lipogenesis Lipogenesis Lipogenesis Lipogenesis Lipogenesis/Cholesterol synthesis Cholesterol synthesis RCT

þ e

HepG2 HepG2

[79] [78]

HMGCS1, SC4MOL

Cholesterol synthesis

þ

Huh7

[80]

miR-378/miR-378* miR-107 miR-205 miR-485-5p miR-24 miR-185 miR-185/miR-96/miR223 miR-223 a b

þ: indicates targets that were experimentally assayed with a reporter assay and when a target seed was confirmed by site-specific mutagenesis. In cases where additional biological systems were studied (macrophages, endothelial cells, etc.), only liver specific biological systems are noted.

Fig. 1. MicroRNAs involved in lipid metabolism. This figure illustrates the complex regulation of lipid metabolism by different microRNAs. Individual microRNAs are shown together with their targets that function in cholesterol synthesis, uptake, cholesterol efflux and fatty acid metabolism. For references, please see Tables 1, 4e6. (red blunted arrows represent inhibition; green arrows represent activation).

of miR-144 (Table 1; Fig. 1). Interestingly, Ramirez et al. independently found that miR-144 regulates HDL efflux in macrophages in addition to the liver, strengthening the case for miR-144 as a potentially effective target to increase circulating HDL levels [51].

Following a successful Phase II clinical study [52], an FXR agonist is currently in a Phase III clinical trial [53]. Transgenic mice studies identified various forms of oxysterols as natural LXR ligands that promote hepatic cholesterol efflux, while

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blocking cholesterol uptake and endogenous cholesterol synthesis [54]. One mechanism involves the induction of the LXR target gene referred to as the Inducible Degrader of the low-density lipoprotein (LDL) receptor (IDOL), a protein that targets the LDL receptor (LDLR) for degradation and hence lowers levels of LDL-cholesterol uptake in the liver [55]. Another effect of increased LXR activity is the upregulation of genes in the fatty acid metabolic pathway, including SREBP-1c [56,57]. In a proposed negative feedback mechanism, Ou Z et al. found that the SREBP-1c dependent expression of miR-613, induced by LXRa activity, leads to post-transcriptional inhibition of LXRa (Table 1) [58]. miR-1/miR-206 were also shown to downregulate lipogenic genes by specifically targeting the LXRa gene in hepatocytes (Table 1) [59]. In a study that predominately focused on macrophages, but that also included a hepatic cell line, miR-26 a miR that is down-regulated by LXR activity, was found to inhibit the expression of LXR target genes ABCA1 and ARL7, both of which are involved in the RCT pathway (Table 1; Fig. 1) [60]. PPARa was first discovered as a receptor in peroxisomes that could increase fatty acid oxidation [62]. PPARa agonists, also known as fibrates, have been used to lower triglyceride levels and are currently used with statins in combination therapy [63,64]. Studies focusing on hepatitis C virus (HCV), a hepatic virus that alters the metabolic state of the liver during its replicative lifecycle, discovered that miR-27a and miR-27b both down-regulate PPARa, among other lipid related genes [65,66]. Shirasaki et al. observed that exogenously delivered miR-27a reduced PPARa and ANGPTL3 at the mRNA and protein level, and mechanistically showed that miR-27a specifically targets the ABCA1 30 -UTR and reduces ABCA1 protein levels in hepatic cells. Further more, Singaravelu et al., observed down-regulation of PPARa, ANGPTL3 and RXRa by miR-27b without showing specific targeting with a reporter assay (Table 1; Fig. 1). This study also showed that up-regulation of miR-27 in multiple cell lines and an animal model led to increased lipid accumulation in liver cells, suggesting that miR-27 inhibition may be an attractive strategy to combat hepatic lipid accumulation.

metabolism. The specific targets identified in this study are involved in fatty acid oxidation and include carnitine O-acetyltransferase (CRAT) and MED13 (Table 1; Fig. 1) [71]. Additional miRs have also been reported to target other genes in the lipogenesis pathway. Ectopic expression of miR-107 was shown to target the fatty acid synthase gene (FASN), and over-expression of miR-107 led to increased lipid accumulation and higher triglyceride levels in a hepatic cell line (Table 1; Fig. 1) [72]. miR-205 targets acyl-CoA synthetase long chain 1 (ACSL1), an enzyme that catalyzes the first step in triacylglycerol synthesis by acylating fatty acids (Table 1; Fig. 1) [73]. In an interesting population study, Caussy et al. demonstrated that a rare APOA5 haplotype generates a miR-485-5p target site in its 30 -UTR and may explain its association with hypertriglyceridemia [74]. When a 30 -UTR reporter assay containing the APOA5 c.*158C > T SNP was analyzed, the reporter activity was significantly regulated by exogenous miR-485-5p, in contrast to the c.*158C allele. This and other studies point to the importance of including non-coding regions in genome-wide association studies [75]. The ER membrane-bound transcription factors SREBP1 and SREBP2 are tightly regulated in part by Insig1. In the presence of cholesterol, Insig1 interacts with another protein, SCAP, and retains SREBPs in the ER [76]. When cholesterol levels are low, and the cells require cholesterol synthesis, Insig1 disassociates from SCAP and SREBP is transported to the Golgi for processing and then released into the cytoplasm where it will eventually translocate to the nucleus and activate cholesterogenic and lipogenic genes. Ng et al. found that miR-24 was up-regulated in mice maintained on a highfat diet, and subsequently demonstrated that miR-24 targets Insig1 for inhibition (Table 1; Fig. 1) [77]. Furthermore, they showed that antagonism of miR-24 in an obesity mouse model alleviated hyperlipidemia and fatty liver, providing support for miR-24 as a therapeutic target.

3. MicroRNAs in dyslipidemia

Several miRs that are involved in reverse-cholesterol transport and cholesterol synthesis have already been discussed above and will not be repeated here (see Table 1). MiR-185 has been reported to play a role in regulating cholesterol related pathways. Wang et al. identified that miR-185, as well as miR-96 and miR-223, regulate scavenger receptor class B type I (SR-BI), a plasma membrane protein that selectively uptakes HDL into the liver [78]. Although therapeutic approaches in an animal model were not explored, it was found that miR-96 and miR-185 were modulated in a mouse model of diet-induced obesity (DIO). Furthermore, Yang et al. observed elevated levels of miR-185 in high fat diet-fed mice, which could be correlated to a similar increase observed in humans with high cholesterol [79]. They further characterized the functional significance of miR-185 by showing that it targets SREBP2, and that miR-185 expression is regulated at the transcriptional level by SREBP-1c. Moreover, a recent study by Vickers et al. identified that miR-223 inhibits cholesterol biosynthesis and uptake, and increases cholesterol efflux through the direct repression of sterol enzymes 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) and methylsterol monooxygenase 1 (SC4MOL) (Table 1; Fig. 1) [80].

Dyslipidemia, a complex heterogeneous condition, is considered as one of the major causes of metabolic syndrome. Dyslipidemia occurs when homeostasis of fatty acid and cholesterol uptake and synthesis is disrupted leading to high levels of circulating triglycerides and cholesterol [67]. With cholesterol in particular, dysregulation leads to higher levels of circulating LDL and lower levels of HDL. The transcription factors SREBP1 and SREBP2 are important regulators of fatty acid synthesis and cholesterol biosynthesis, and many of their target genes that are involved in this process could be therapeutically targeted to reverse hypertriglyceridemia and hypercholesterolemia [21,68]. 3.1. Hypertriglyceridemia Vickers et al. identified lipid-responsive hepatic miRs from livers of C57BL/6J mice fed on a normal chow diet and on a high-fat “Western” diet. They found that miR-27b is strongly correlated with hyperlipidemia in both mouse and human plasma. They further showed that miR-27b directly regulates the expression of genes involved in lipid metabolism such as PPARg, ANGPT3, NDST1, and GPAM (Table 1; Fig. 1) [69]. The intronic miR-378/378* embedded within the PPARGC1b gene which encodes proliferator-activated receptor g coactivator 1b (PGC-1b), is coordinately expressed during adipogenesis and in the same tissues as PGC-1b [70]. Using a mouse knockout of miR-378/378*, they determined that the transgenic mice were resistant to obesity when fed a high-fat diet, and exhibited increased levels of fatty acid

3.2. Hypercholesterolemia

3.3. MicroRNA-33a/b as therapeutic targets to treat dyslipidemia It appears that around 50% of the mammalian miRs are located within introns or exons of protein-coding genes [81,82]. miR-33a and miR-33b are intronic microRNAs encoded within the SREBF2 and SREBF1 genes, respectively [83e87]. Both microRNAs play important roles in maintaining systemic cholesterol and lipid homeostasis by working in concert with their host genes. MiR-33

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increases intracellular cholesterol content by targeting multiple proteins involved in cholesterol synthesis, transport and secretion. The down-regulation of cholesterol efflux pumps, ABCA1 and ABCG1, by miR-33 reduces the export of cholesterol as HDL particles (HDL-C). The repression of CYP7A1, a rate-limiting enzyme in bile acid synthesis, and the biliary transporters ABCB11 and ATP8B1 by miR-33 also reduces conversion of cholesterol to bile and its secretion. In addition to cholesterol, miR-33 also raises intracellular lipid levels by targeting several factors involved in fatty acid oxidation and insulin signaling. Numerous studies characterizing the targets of miR-33a/b are summarized in Tables 4e6. Thus, the silencing of miR-33 is an attractive therapeutic strategy against pathologic conditions involving hypercholesterolemia and dyslipidemia, and pre-clinical studies involving antagonism of miR-33 have delivered promising results [88,89]. Amelioration of miR-33 in genetic mouse models of hyperlipidemia and hypercholesterolemia helps reduce the severity of the condition. miR33/ Apoe/ double knockout mice show enhanced reverse cholesterol transport and reduced atherosclerotic plaque sizes compared to miR33þ/þ Apoe/ mice [90]. Inhibition of miR-33 in Ldlr/ mice also produces similar atheroprotective effects with both short term and long term treatments [91]; although one report claims that miR-33 inhibition only works in the short term to raise circulating HDL-C [92]. Subcutaneous injection of antisense oligos against miR-33 up-regulate HDL-C significantly in both chow-fed mice and obese mice fed western-type diets, while infusion of adenovirus expressing miR33 leads to a 29% drop in serum HDL-C [85]. However, questions have been raised over chronic in vivo silencing of miR-33 in rodents. Prolonged miR-33 inhibition for 20 weeks in mice maintained on a high-fat diet resulted in adverse effects such as elevated triglycerides, lipid biosynthesis and hepatic fat content [93]. In this context, anti-miR-33a studies in non-human primates is critical because they are more physiologically and clinically relevant. Male African green monkeys treated with antisense oligos against both miR-33a and miR-33b for 12 weeks show notable increase in HDL-C

5

and reduction in plasma triglycerides [89]. Short 8-mer LNA antimiRs that target both miR-33a/b have also been found to effectively increase HDL-C by up to 40% in obese and insulin-resistant primates, while being well tolerated for over 100 days, demonstrating not only the efficacy of the therapy but also its safety [88]. Apart from its anti-atherogenic effects, miR-33 silencing has also been found to be effective against cholestasis and hepatotoxicity resulting from administration of statins to treat hypercholesterolemia. Statins block cholesterol synthesis that transcriptionally up-regulate miR-33 along with its host gene, SREBF2. MiR-33 antagomiRs rescue statin- and lithogenic diet-induced cholestasis in mice, lowers hepatosteatosis and restores normal plasma lipid profiles [94]. 4. MicroRNAs and adipogenesis One of the hallmarks of obesity is a higher level of fat mass and energy storage in adipose tissue. Higher levels of fat mass can be due to differentiated adipocytes that undergo hypertrophy (increased size) or hyperplasia (increased numbers due to proliferation) [95]. From a therapeutic standpoint, it would be beneficial to regulate white adipogenesis to reduce the excess fat mass observed in obesity, and to increase the quantity of brown adipogenesis that helps burn excess fat. The regulatory network controlling adipogenesis and the different types of adipose tissue is now better understood with miRs emerging as important players in the process [96]. Among the challenges of functional miR analysis is that miRs can have tissue-specific and time dependent functions, some of which will be discussed below. Here we highlight miRs that are involved in regulating adipogenesis and describe their roles in obesity-associated disorders (Table 2). 4.1. miRs that promote adipogenesis Ahn et al. studied miRs that were dysregulated during differentiation of a commonly used mouse adipocyte cell line, 3T3-L1,

Table 2 MicroRNAs that regulate adipogenesis. miRa

Gene target

Molecular pathway

Target validatedb?

Biological systemc

References

miR-146b miR-143 miR-26a/b miR-196a miR-21 miR-486-5p miR-137 miR-27a

Sirt1 MAP2K5 ADAM17 HoxC8 STAT3 Sirt1 CDC42 Prdm16, PPARa, CREB1, Pgc1b Prohibitin E2F3 MAP2K5 EGR2, ACSL2

þ þ þ e e e þ þ

3T3-L1, mouse model Adipose tissue-derived stromal cells Human multipotent adipose-derived stem Progenitor cells from human white adipose tissue, transgenic mouse Human adipose tissue-derived mesenchymal stem cells Human adipose tissue-derived mesenchymal stem cells Adipose tissue-derived stromal cells Primary immortalized brown adipocytes, SAT SVF cells

[97] [99] [101] [103] [104] [112] [113] [114]

þ þ þ þ

Human adipose stem cells Adipose tissue-derived stromal cells Adipose tissue-derived stromal cells 3T3-L1

[115] [99] [99] [118]

e

Primary mouse stromal vascular fraction (SVF) cells from BAT

[119]

Primary mouse stromal vascular fraction (SVF) cells from BAT, knockout mouse Porcine dedifferentiated fat Primary mouse stromal vascular fraction (SVF) cells from BAT and SAT Induced brown adipocytes in vivo, mouse model Primary mouse stromal vascular fraction (SVF) cells from BAT and WAT, knockout mouse ADSC- Somatic stem cells

[120]

miR-106b/ miR-93 miR-155

Ucp1

Adipogenesis Adipogenesis Adipogenesis Adipogenesis Adipogenesis Adipogensis Adipogenesis SAT/BAT adipogenesis Adipogenesis Adipogenesis Adipogenesis Adipogenesis/ lipogenesis Adipogenesis

C/EBPb

Adipogenesis

þ

miR-145 miR-133 miR-133 miR-133a

IRS1 PRDM16 PRDM16 PRDM16

Adipogenesis Adipogenesis Adipogenesis Adipogenesis

þ þ þ þ

miR-540

PPARd

Adipogenesis

þ

miR-27a/b miR-363 miR-143 miR-224-5p

a b c

[127] [124] [125] [126] [129]

miRs in bold promote adipogenesis, and miRs italicized inhibit adipogenesis. þ: indicates targets that were experimentally assayed with a reporter assay and when a target seed was confirmed by site-specific mutagenesis. In cases where additional biological systems were studied (macrophages, endothelial cells, etc.), only adipose specific biological systems are noted.

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Table 3 MicroRNAs involved in insulin signaling. miR

Gene target

Molecular pathway

Target validateda?

Biological systemb

References

miR-126 miR-221 miR-221 miR-93 miR-130a-3p miR-190b miR-802 miR-122 miR-181a miR-99a miR-26a miR-26a miR-26a

CCL2 ADIPOR1 ADIPOR1, ETS1 GLUT4 GRB10 IGF1 Hnf1b PTP1B Sirt1 mTOR TCF7L2, PCK1, ACSL3, ACSL4 GSk3b, PKCd, PKCq

Adipose inflammation Insulin resistance Insulin resistance Insulin resistance Insulin resistance/Liver steatosis Insulin resistance Insulin resistance Insulin resistance Insulin resistance Insulin resistance Gluconeogenesis Fatty acid synthesis Insulin signaling

e þ e e þ þ e e þ þ þ þ þ

Subcutaneous white adipose tissue HepG2 Primary human adipocytes Human subcutaneaous adipose tissue, 3T3-L1 HepG2, primary hepatocytes, mouse model Huh7 Hepa1-6, mouse model HepG2 HepG2, primary hepatocytes HepG2, HL77002 Mouse model Mouse model Mouse model

[134] [137] [152] [138] [143] [139] [144] [146] [148] [151] [102] [102] [102]

a b

þ: indicates targets that were experimentally assayed with a reporter assay and when a target seed was confirmed by site-specific mutagenesis. In cases where additional biological systems were studied (macrophages, endothelial cells, etc.), only adipose and liver specific biological systems are noted.

Table 4 Genes involved in lipid and fatty acid metabolism that are regulated by miR-33a in hepatic and adipose tissue. Gene target

Molecular pathway

Target validateda?

Biological systemb

References

ABCA1

RCT

ABCG1

RCT

NPC1

Intracellular trafficking of cholesterol

HADHB

Beta-oxidation

CPT1A

Beta-oxidation

CROT

Beta-oxidation

ATP8B1 ABCB11

Hepatic bile metabolism Hepatic bile metabolism

CYP7A1 SRC1 SRC3 NFYC SREBP-1 NSF

Hepatic bile metabolism Lipid metabolism Lipid metabolism Lipid metabolism Fatty acid synthesis VLDL trafficking

þ þ þ þ þ NA NA NA þ þ þ þ e þ NA þ NA NA e þ NA NA e e þ þ NA þ e e þ þ þ

HepG2, mouse model HepG2, HEPA, mouse model Hep3B, mouse model Human primary hepatocyte, knockout mouse HepG2 African green monkey (4/12 wk post-treatment) Mouse model African green monkey HepG2, HEPA, mouse model Hep3B, mouse model HEK293 (luciferase reporter assay) HepG2, HEPA, mouse model Huh7 HepG2 African green monkey (4/12 wk post-treatment) HepG2 African green monkey (12 wk post-treatment) Mouse model Huh7 HepG2 African green monkey (4/12 wk post-treatment) African green monkey Huh7 Huh7 Primary murine hepatocytes, Huh7, mouse model Primary murine hepatocytes, Huh7, mouse model Mouse model Mouse model Huh7 Huh7 Huh7 HepG2, mouse model Huh7, mouse model

[86] [87] [85] [84] [83] [89] [153] [88] [87] [85] [84] [87] [21] [83] [89] [83] [89] [153] [154] [83] [89] [88] [78] [154] [94] [94] [153] [155] [154] [154] [154] [156] [157]

a þ: Targets that were experimentally validated with a reporter assay and when a target seed was confirmed by site-specific mutagenesis. In the case of animal models testing therapeutic efficacy, predicted target genes that were confirmed to be affected by miR modulation are shown and target validation is listed as non-applicable (NA). b In cases where additional biological systems were studied (macrophages, endothelial cells, etc.), only liver/adipose specific biological systems are noted.

and observed that miR-146b was up-regulated during adipogenesis [97]. They identified sirtuin 1 (SIRT1) as a potential target and confirmed the interaction using a reporter assay. They propose a mechanism in which miR-146b down-regulates SIRT1, and therefore represses SIRT1-mediated deacetylation of the transcription factor FOXO1. The acetylated form of FOXO1 has reduced transcriptional activity, and miR-146b appears to be pro-adipogenic by targeting this pathway. They further demonstrated the therapeutic potential of a miR-146b antagomiR treatment in a DIO mouse model and observed alleviation of obesity-associated disorders (Table 2; Fig. 2). Adipose tissue-derived stromal cells (ADSCs) can differentiate

into adipocytes through a series of regulated stages, including commitment and differentiation into preadipocytes, growth arrest and clonal expansion, and terminal differentiation into adipocytes [98]. MiR-143 was identified as a modulator of adipose differentiation via targeting a member of the MAPK signaling pathway, MAP2K5 (Table 2; Fig. 2) [99]. They determined that overexpression of miR-143 could inhibit adipogenesis in the earlier stages of differentiation (clonal stage), and promote adipogenesis in later stages (growth arrest and terminal differentiation stage). The proposed mechanism is that miR-143 regulates the transition from clonal expansion to terminal differentiation, and provides a target for controlling dysregulated adipogenesis.

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Table 5 Genes involved in lipid and fatty acid metabolism that are regulated by miR-33a* in hepatic and adipose tissue. Gene target

Molecular pathway

Target validateda?

Biological systemb

Reference

ABCA1 NPC1 CPT1A CROT IRS2 SRC1 SRC3 NFYC

RCT Intracellular trafficking of cholesterol Beta-oxidation Beta-oxidation Insulin signaling Lipid metabolism Lipid metabolism Lipid metabolism

e þ e þ þ þ þ þ

Huh7 Huh7 Huh7 Huh7 Huh7 Huh7 Huh7 Huh7

[154] [154] [154] [154] [154] [154] [154] [154]

a b

þ: Targets that were experimentally validated with a reporter assay and target seed was confirmed by site-specific mutagenesis. In cases where additional biological systems were studied (macrophages, endothelial cells, etc.), only liver/adipose specific biological systems are noted.

Table 6 Genes involved in lipid and fatty acid metabolism that are regulated by miR-33b/miR-33b* in hepatic and adipose tissue. miR

Gene target

Molecular Pathway

Target Validateda?

Biological Systemb

Reference

miR-33b

CROT CPT1a HADHB AMPKa IRS2 PCK1 G6PC SRC1 RORa CREB1 NFYC

Beta-oxidation Beta-oxidation Beta-oxidation Beta-oxidation Insulin signaling Gluconeogenesis Gluconeogenesis Gluconeogenesis Gluconeogenesis Gluconeogenesis Lipid metabolism

þ þ þ þ þ þ þ þ þ þ þ

HepG2 HepG2 HepG2 HepG2 HepG2, Huh7 HepG2 HepG2 HepG2 HepG2 HepG2 Huh7

[158] [158] [158] [158] [158] [159] [159] [159] [159] [159] [154]

miR-33b* a

þ: Targets that were experimentally assayed with a reporter assay and when a target seed was confirmed by site-specific mutagenesis. In the case of animal models testing therapeutic efficacy, predicted target genes that were confirmed to be affected by miR modulation are shown and target validation is listed as non-applicable (NA). b In cases where additional biological systems were studied (macrophages, endothelial cells, etc.), only liver/adipose specific biological systems are noted.

Fig. 2. MicroRNAs implicated in adipogenesis. This figure summarizes the involvement of microRNAs in brown and white adipogenesis further described in greater detail in the text. The abbreviations are explained in the text. For references, please see Table 2. (red blunted arrows represent inhibition; green arrows represent activation).

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Higher content and activity of BAT can help counter the adverse effects of obesity [100], and understanding the molecular mechanisms of brown adipogenesis is of clinical interest. MiR-26a/b was identified as a determinant for brown adipogenesis and energy dissipation activity by targeting the peptidase gene, ADAM17 (Table 2; Fig. 2) [101]. MiR-26a appears to be biologically relevant because in mice subjected to prolonged cold exposure, miR-26a increased significantly, and modulation of miR-26a levels in vivo could potentially be used to shift adipose tissue to promote energy expenditure. Similarly, miR-196a also promotes brown adipogenesis and provides another path to enhance energy expenditure [103]. MiR-196a induces brown adipocyte differentiation in WATprogenitor cells by down-regulating a WAT associated gene, Hoxc8 (Table 2; Fig. 2). Hoxc8 is a repressor of C/EBPb, a master regulator that activates brown adipogenesis. Importantly, increased BAT content and enhanced energy expenditure was observed in transgenic mice over-expressing miR-196a, and these transgenic mice were resistant to obesity when fed a high-fat diet. MiR-21 is differentially expressed in WAT during the development of obesity in a mouse model and may have two functional roles: low levels of miR-21 in the early stages of obesity correlates with proliferation of adipocyte precursors, and high levels of miR21 in the latter stages of obesity contributes to enhanced adipogenic differentiation [104]. MiR-21 specifically targets STAT3 and may mediate proliferation of adipocyte precursors through this interaction because STAT3 inhibition reduces adipocyte precursor proliferation. Further elucidation of the functional roles of miR-21 in this complex process will help determine its therapeutic value. 4.2. miRs that inhibit adipogenesis SIRT1 is associated with both aging and metabolic processes, and in addition the reduced activity and expression of SIRT1 are known to exacerbate metabolic disorders [105e111]. miR-486-5p is expressed at higher levels in adipose tissue-derived mesenchymal stem cells (hADSCs) isolated from old human individuals and was shown to target SIRT1 [112]. To simulate a biological context of metabolic disorder, hADSCs were incubated with excess glucose and this triggered up-regulation of miR-486-5p, suggesting that it might be a viable therapeutic target to enhance SIRT1 activity and expression, although they did not validate the predicted SIRT1 30 UTR target site with site-specific mutagenesis (Table 2; Fig. 2). The hADSC cell line has been used to identify other functional roles in adipogenesis that involves miR-137 and miR-363. miR-137 inhibits proliferation and adipogenesis in hADSCs by targeting the gene that codes for cell division control protein 42 homolog (CDC42) [113]. miR-363 inhibits growth and proliferation of hADSCs by down-regulating E2F3, blocking the transition from clonal expansion to terminal differentiation (Table 2; Fig. 2) [99]. Previous studies have shown that miR-27 plays an important role in regulating adipogenesis. Mir-27 was identified as an important regulator of brown adipogenesis by targeting several transcription factors characteristic of BAT including PRDM16, PPARa, and CREB [114]. Additionally, further studies demonstrated that miR-27 inhibits adipogenesis in hADSCs by targeting Prohibitin (PHB) [115]. Thus, miR-27 appears to have a clear anti-adipogenic property in adipose tissue. Since miR-27 has important roles in other biological disorders such as cancer [116,117], targeting miR27 for therapy may have to be tissue specific and will have to be carefully monitored for undesired side-effects (Table 2; Fig. 2). Mir-224-5p has a stage-dependent functional role in adipogenesis and metabolic function of adipocytes [118]. During differentiation of 3T3-L1 cells, miR-224-5p is at low levels in the early stages of adipogenesis, and increases to higher levels during the latter stages. In the early stages of adipogenesis, higher miR-224-5p

levels can inhibit adipogenesis by targeting EGR2. Subsequently, during terminal differentiation miR-224-5p can regulate fatty acid metabolism by targeting Acyl-CoA synthetase long-chain family member 4 (ACSL4), which results in increased free fatty acids (Table 2; Fig. 2). Mir-106b and miR-93 were identified as inhibitors of brown adipogenesis, and their levels inversely correlated with Ucp1. However, 30 -UTR target validation has not been reported (Table 2; Fig. 2) [119]. Both miRs were higher in BAT from mice on a high-fat diet, suggesting a physiological relevance, although their therapeutic potentials were not explored. A recent report identified miR-155 to be enriched in BAT using deep sequencing analysis, and found that it inhibits BAT adipogenesis by targeting C/EBPb [120]. MiR-155 is highly expressed during preadipocyte proliferation, but then decreases during maturation and is predicted to form a bistable loop with C/EBPb to regulate adipogenesis. The role of miR-155 in brown adipogenesis was confirmed with transgenic mice that either overexpressed miR-155 or had the gene knocked out (Table 2; Fig. 2). The Prdm16 gene drives brown adipogenesis from myoblasts, fibroblasts and SAT [121e123], and understanding how it is regulated is of interest in order to promote BAT adipogenesis for clinical therapy. Trajkovski et al. determined that miR-133, and its transcriptional regulator Mef2, play a key role in controlling Prdm16 expression and brown adipogenesis (Table 2; Fig. 2) [124]. It was shown subsequently that miR-133 regulates Prdm16 in satellite cells, and likewise could determine brown adipogenic lineages [125]. Lastly, the biological relevance of miR-133a was observed in transgenic mice where knocking out miR-133 resulted in higher levels in brown adipocytes [126]. Mir-145 expression varies during differentiation of porcine dedifferentiated fat (DFAT) cells, increasing significantly during the latter stages of differentiation [127]. Over-expression of miR-145 in DFAT cells inhibited differentiation and reduced triglyceride accumulation. Mir-145 was shown to specifically regulate IRS1, although other adipogenic genes were also down-regulated, most likely as a consequence of IRS1 modulation given IRS1-dependent regulation of adipogenic gene expression (Table 2; Fig. 2) [128]. A recent study showed that the expression of miR-540 was reduced during differentiation of rat ADSC somatic stem cells. They found that miR-540 impairs adipogenesis by regulating the expression of PPARg by binding to its 30 UTR (Table 2) [129]. 5. MicroRNAs and insulin resistance Insulin, a glucose homeostatic regulator, is released by the pancreas triggering a signaling cascade by binding to the insulin receptor that results in increased levels of glucose uptake [130]. Insulin resistance is characterized by an impaired cellular response to insulin reflected mostly in the inability of normal amounts of insulin to achieve normal glucose homeostasis. In such a scenario, to achieve normoglycemia an increase in insulin biosynthesis and release occurs, that with the passage of time, causes functional defects in pancreatic insulin secretion. Consequently, hyperglycemia causes compensatory hyperinsulinemia to fail and allows prediabetes and T2DM to supervene [131]. The major sites of insulin resistance are the liver, skeletal muscle and adipose tissues [132]. Cytokines released during obesity-associated inflammation (ADIPOR1, TNF-a, CCL2) is one of the leading causes of reduced insulin sensitivity [133]. Understanding the underlying molecular cues that regulate insulin sensitivity and signaling is of great clinical importance. CCL2 is an important cytokine released by adipocytes that is a consequence of the inflammatory response caused by obesity. Analysis of SAT samples determined that miR-126 and miR-193b,

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among others, are down-regulated in obese patients [134]. miR-126 was shown to directly target CCL2 while miR-193b indirectly affects CCL2 levels (Table 3; Fig. 3). Blocking the inflammatory response observed in obesity is an important target to hinder the development of insulin resistance. In a population study that characterized differential miR expression in SAT from human patients, miR-221 was identified as being up-regulated in obese patients. They observed that in primary human adipocytes miR-221 directly targets ADIPOR1, a receptor for adiponectin that is down-regulated in insulin resistance [135], and ETS1, a transcription factor involved in metabolic homeostasis and associated with insulin resistance [136]. Another study also found that ADIPOR1 was targeted by miR-221 in both muscle and liver cells (Table 3; Fig. 3) [137]. Insulin resistance has been shown to be associated with other diseases as well. For example, insulin resistance is associated with women with polycystic ovary syndrome. MiR-93 was observed to be up-regulated in patients with polycystic ovary syndrome or insulin resistance [138]. Further analysis determined that miR-93 could target GLUT4, a glucose transporter found in adipocytes, explaining in part how elevated levels of miR-93 can contribute to insulin resistance (Table 3; Fig. 3). MiR-190b was shown to be upregulated in hepatocellular carcinoma samples compared to nontumor samples [139]. MiR-190b targets insulin-like growth factor (IGF-1), which participates in glucose homeostasis and contributes to enhanced insulin sensitivity [140,141]. It also regulates cellular proliferation, differentiation and the suppression of apoptosis. Thereby, altered miR-190b expression may be useful as an early predictor of insulin resistance caused by hepatocellular dysfunction (Table 3; Fig. 3). Several miRs including miR-130a-3p have been identified as a direct effector on the various subcellular events involved in glucose-stimulated insulin secretion and was shown to be involved

9

in insulin resistance [142]. Further, Xiao et al. reported that miR130a-3p affects insulin resistance in hepatic cells by targeting the growth factor receptor-bound protein 10 (GRB10), an adapter protein that regulates receptor tyrosine kinase signaling cascades, and confirmed their finding in a mouse model [143]. Mice overexpressing miR-130a-3p had improved glucose clearance, and concomitant over-expression of GRB10 could overcome this beneficial outcome. Additionally, they found that over-expression of miR-130a-3p resulted in lower levels of hepatic triglyceride accumulation, suggesting an important role in liver steatosis (Table 3; Fig. 3). MiR-802 was identified as being highly expressed in the liver of obese patients and mouse models. Kornfeld et al. modulated miR802 in several mouse models under different conditions and observed that high levels of miR-802 was associated with glucose intolerance and insulin resistance, and reduction of miR-802 improved metabolic parameters [144]. Mechanistically, they discovered that miR-802 represses the hepatocyte nuclear factor 1 beta (HNF1B) gene, validated it as a target both in vitro and in vivo, and provided strong support for targeting miR-802 as a novel therapeutic to reverse insulin resistance (Table 3; Fig. 3). In a similar approach, Fu et al. detected down-regulated levels of miR-26a in overweight patients and in 2 obese mouse models (Table 3) [102]. Further investigation by Fu et al. determined that over-expression of miR-26 improved insulin sensitivity, decreased hepatic glucose production, and decreased fatty acid synthesis in obese mouse models. Repression of miR-26a in conventional dietfed mice led to insulin resistance complications such as impaired insulin sensitivity, enhanced glucose production, and increased fatty acid synthesis. Confirmed targets, tested both in vitro and in vivo, are involved in gluconeogenesis (TCF7L2, PCK1), fatty acid synthesis (ACSL3, ACSL4) and insulin signaling (GSk3b, PKCd, PKCq). Along with previously described results above [60,101], miR-26a

Fig. 3. MicroRNAs involved in insulin signaling and glucose metabolism. This figure shows the function of various miRs in inhibiting the different key players in insulin signaling and glucose metabolism. The mechanistic actions of the miRs are described in detail in the text. For references, please see Table 3. (red blunted arrows represent inhibition; green arrows represent activation).

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appears to play an important role in several pathways that lead to metabolic disorders and should be clinically explored. MiR-122, the most abundant miR in the liver [145], has been shown to be down-regulated in mice fed with a high-fat diet and inversely correlated with protein tyrosine phosphatase 1B (PTP1B) levels [146]. MiR-122 regulates the PTP1B expression by binding to its 30 -UTR, however the target site was not validated by mutagenesis analysis (Table 3; Fig. 3). They found that the hepatocyte nuclear factor 4a (HNF4a) drives the expression of miR-122, and HNF4a in turn is regulated by its repressor, c-Jun N-terminal kinase 1 (JNK1). The elucidation of the signaling cascade that regulates miR-122, and the availability of chemical inhibitors of JNK, provides novel targets for insulin resistance treatment. SIRT1, an NAD-dependent protein deacetylase, has been shown as a potential therapeutic target to combat insulin resistance and Type 2 diabetes [147]. Zhou et al. found that miR-181a specifically regulates SIRT1 in hepatic cells, and led to insulin resistance. Ectopic expression of miR-181a reduced levels of SIRT1, and inversely, down-regulation of miR-181a led to increased SIRT1 levels. Furthermore, modulation of miR-181a levels in a dietinduced mouse model was sufficient to impair insulin signaling or improve glucose homeostasis. (Table 3; Fig. 3) [148]. Mammalian target of rapamycin (mTOR) is a serine/threonine kinase that regulates many cellular functions and is implicated in many diseases such as cancer, cardiovascular diseases and other metabolic disorders [149]. Pyruvate kinase M2 (PKM2), a ratelimiting glycolytic enzyme, is regulated by the mTOR pathway and is a key regulator of glucose metabolism [150]. Hepatic cell lines treated with insulin induced miR-99a expression and subsequent experiments showed that by down-regulating mTOR, PKM2 and glucose metabolism were regulated (Table 3; Fig. 3) [151]. Further experiments are needed to determine the role of miR-99a in a biological context of insulin resistance both in cell culture and with an animal model. 6. Future directions Metabolic syndrome and Type 2 diabetes are on the rise, and new drugs are necessary to combat the obesity epidemic. A better understanding of the molecular mechanisms underlying obesityassociated disorders and disease progression is a primary step towards developing more efficacious drugs and discovering novel therapeutics. The widespread function of miRs in major pathways of metabolic processes involved in these disorders and disease settings have revealed the potential of miRs as novel therapeutic targets and diagnostic markers. Current clinical advances in targeted microRNA antisense-based strategies using locked nucleic acid modified-anti-miRs (LNAs) or antagomiRs, have proven effective at inhibiting miRs in vivo with low amount of off-target effects. Several other methods have been shown to block miR activity in cells such as miR sponges, miR erasers and miR decoys, however, the design and efficiency of these methods, as well as their mode of delivery by plasmid or viral vectors, needs to be thoroughly tested to validate clinical use. Furthermore, the combination of these technological advances with tissue-specific delivery of RNA-based therapeutics will further enhance the possibility of their use as novel therapeutic agents. To date, few miR-targeted therapeutics are in advanced development for clinical use although Miravirsen (anti-miR-122), an LNA-modified oligonucleotide capable of suppressing hepatitis C virus, is currently undergoing clinical trials. A miR-34 mimic (MRX34 - a liposomeencapsulated miR-34 mimic), encapsulated using an innovative liposomal formulation called SMARTICLES, is currently being studied in a multicenter, Phase 1 clinical trial in patients with primary liver cancer or solid cancers with liver involvement. With

regards to metabolic syndrome and obesity, anti-miR-33a/b oligonucleotides is progressing towards being one of the first potential miR therapeutics for treating cardio-metabolic disorders. miRs are proving to be valuable targets for the development of novel therapeutics, however continued assessment of additional miR candidates in vivo is necessary to evaluate their clinical potential. References [1] B.A. Swinburn, G. Sacks, K.D. Hall, K. McPherson, D.T. Finegood, M.L. Moodie, S.L. Gortmaker, Lancet 378 (2011) 804e814. [2] C.C. Imes, L.E. Burke, Curr. Epidemiol. Rep. 1 (2014) 82e88. [3] Y. Wang, M.A. Beydoun, Epidemiol. Rev. 29 (2007) 6e28. [4] C.A. Roberto, B. Swinburn, C. Hawkes, T.T. Huang, S.A. Costa, M. Ashe, L. Zwicker, J.H. Cawley, K.D. Brownell, Lancet 385 (2015) 2400e2409. [5] W.H. Organization, World Health Statistics Report 2012, World Health Organization, 2012. [6] E. Jahangir, A. De Schutter, C.J. Lavie, Transl. Res. J. Lab. Clin. Med. 164 (2014) 336e344. [7] B. Klop, J.W. Elte, M.C. Cabezas, Nutrients 5 (2013) 1218e1240. [8] M. Bluher, Best Pract. Res. Clin. Endocrinol. Metab. 27 (2013) 163e177. [9] M. Desai, M. Beall, M.G. Ross, Curr. Diabetes Rep. 13 (2013) 27e33. [10] G.M. Reaven, Diabetes 37 (1988) 1595e1607. [11] A.A. van der Klaauw, I.S. Farooqi, Cell 161 (2015) 119e132. [12] M.R. Briggs, C. Yokoyama, X. Wang, M.S. Brown, J.L. Goldstein, J. Biol. Chem. 268 (1993) 14490e14496. [13] X. Hua, C. Yokoyama, J. Wu, M.R. Briggs, M.S. Brown, J.L. Goldstein, X. Wang, Proc. Natl. Acad. Sci. U. S. A. 90 (1993) 11603e11607. [14] R.C. Lee, R.L. Feinbaum, V. Ambros, Cell 75 (1993) 843e854. [15] P. Tontonoz, J.B. Kim, R.A. Graves, B.M. Spiegelman, Mol. Cell. Biol. 13 (1993) 4753e4759. [16] X. Wang, M.R. Briggs, X. Hua, C. Yokoyama, J.L. Goldstein, M.S. Brown, J. Biol. Chem. 268 (1993) 14497e14504. [17] C. Yokoyama, X. Wang, M.R. Briggs, A. Admon, J. Wu, X. Hua, J.L. Goldstein, M.S. Brown, Cell 75 (1993) 187e197. [18] X. Wang, R. Sato, M.S. Brown, X. Hua, J.L. Goldstein, Cell 77 (1994) 53e62. [19] M.S. Brown, J.L. Goldstein, Cell 89 (1997) 331e340. [20] J.D. Horton, J.L. Goldstein, M.S. Brown, J. Clin. Investig. 109 (2002) 1125e1131. [21] W. Shao, P.J. Espenshade, Cell Metab. 16 (2012) 414e419. [22] E.E. Kershaw, J.S. Flier, J. Clin. Endocrinol. Metab. 89 (2004) 2548e2556. [23] G.R. Hajer, T.W. van Haeften, F.L. Visseren, Eur. Heart J. 29 (2008) 2959e2971. [24] E.D. Rosen, B.M. Spiegelman, Cell 156 (2014) 20e44. [25] H.G. Linhart, K. Ishimura-Oka, F. DeMayo, T. Kibe, D. Repka, B. Poindexter, R.J. Bick, G.J. Darlington, Proc. Natl. Acad. Sci. U. S. A. 98 (2001) 12532e12537. [26] T. Tanaka, N. Yoshida, T. Kishimoto, S. Akira, EMBO J. 16 (1997) 7432e7443. [27] D.P. Bartel, Cell 136 (2009) 215e233. [28] P. Hydbring, G. Badalian-Very, F1000Research 2 (2013) 136. [29] N.N. Than, P.N. Newsome, Atherosclerosis 239 (2015) 192e202. [30] D.E. Kleiner, E.M. Brunt, M. Van Natta, C. Behling, M.J. Contos, O.W. Cummings, L.D. Ferrell, Y.C. Liu, M.S. Torbenson, A. Unalp-Arida, M. Yeh, A.J. McCullough, A.J. Sanyal, Nonalcoholic Steatohepatitis Clinical Research Network, Hepatol. 41 (2005) 1313e1321. [31] T. Karlas, J. Wiegand, T. Berg, Best Pract. Res. Clin. Endocrinol. Metab. 27 (2013) 195e208. [32] D.B. Jump, S. Tripathy, C.M. Depner, Annu. Rev. Nutr. 33 (2013) 249e269. [33] B. Blumberg, W. Sabbagh Jr., H. Juguilon, J. Bolado Jr., C.M. van Meter, E.S. Ong, R.M. Evans, Genes Dev. 12 (1998) 3195e3205. [34] J. Zhou, Y. Zhai, Y. Mu, H. Gong, H. Uppal, D. Toma, S. Ren, R.M. Evans, W. Xie, J. Biol. Chem. 281 (2006) 15013e15020. [35] K. Nakamura, R. Moore, M. Negishi, T. Sueyoshi, J. Biol. Chem. 282 (2007) 9768e9776. [36] L. Valenti, R. Rametta, P. Dongiovanni, M. Maggioni, A.L. Fracanzani, M. Zappa, E. Lattuada, G. Roviaro, S. Fargion, Diabetes 57 (2008) 1355e1362. [37] S. Takagi, M. Nakajima, T. Mohri, T. Yokoi, J. Biol. Chem. 283 (2008) 9674e9680. [38] Z. Wei, M. Chen, Y. Zhang, X. Wang, S. Jiang, Y. Wang, X. Wu, S. Qin, L. He, L. Zhang, Q. Xing, PloS One 8 (2013) e59141. [39] L. Qi, Y. Hu, Y. Zhan, J. Wang, B.B. Wang, H.F. Xia, X. Ma, Neurosci. Lett. 515 (2012) 1e6. [40] X. Zhang, Y. Li, X. Li, W. Zhang, Z. Pan, F. Wu, C. Wang, Z. Chen, T. Jiang, D. Xu, Z. Ping, J. Liu, C. Liu, Z. Li, J.C. Li, BMC Infect. Dis. 15 (2015) 41. [41] L.W. Lee, S. Zhang, A. Etheridge, L. Ma, D. Martin, D. Galas, K. Wang, RNA 16 (2010) 2170e2180. [42] L. Guo, F. Chen, Gene 544 (2014) 1e7. [43] N.Y. Kalaany, D.J. Mangelsdorf, Annu. Rev. Physiol. 68 (2006) 159e191. [44] Y. Zhang, L.W. Castellani, C.J. Sinal, F.J. Gonzalez, P.A. Edwards, Genes Dev. 18 (2004) 157e169. [45] B. Kong, J.P. Luyendyk, O. Tawfik, G.L. Guo, J. Pharmacol. Exp. Ther. 328 (2009) 116e122. [46] C.J. Sinal, M. Tohkin, M. Miyata, J.M. Ward, G. Lambert, F.J. Gonzalez, Cell 102

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E.J. Abente et al. / Archives of Biochemistry and Biophysics xxx (2015) 1e12 (2000) 731e744. [47] C. Thomas, R. Pellicciari, M. Pruzanski, J. Auwerx, K. Schoonjans, Nat. Rev. Drug Discov. 7 (2008) 678e693. [48] T.Q. de Aguiar Vallim, E.J. Tarling, P.A. Edwards, Cell Metab. 17 (2013) 657e669. [49] J. Prawitt, M. Abdelkarim, J.H. Stroeve, I. Popescu, H. Duez, V.R. Velagapudi, J. Dumont, E. Bouchaert, T.H. van Dijk, A. Lucas, E. Dorchies, M. Daoudi, S. Lestavel, F.J. Gonzalez, M. Oresic, B. Cariou, F. Kuipers, S. Caron, B. Staels, Diabetes 60 (2011) 1861e1871. [50] T.Q. de Aguiar Vallim, E.J. Tarling, T. Kim, M. Civelek, A. Baldan, C. Esau, P.A. Edwards, Circ. Res. 112 (2013) 1602e1612. [51] C.M. Ramirez, N. Rotllan, A.V. Vlassov, A. Davalos, M. Li, L. Goedeke, J.F. Aranda, D. Cirera-Salinas, E. Araldi, A. Salerno, A. Wanschel, J. Zavadil, A. Castrillo, J. Kim, Y. Suarez, C. Fernandez-Hernando, Circ. Res. 112 (2013) 1592e1601. [52] G.M. Hirschfield, A. Mason, V. Luketic, K. Lindor, S.C. Gordon, M. Mayo, K.V. Kowdley, C. Vincent, H.C. Bodhenheimer Jr., A. Pares, M. Trauner, H.U. Marschall, L. Adorini, C. Sciacca, T. Beecher-Jones, E. Castelloe, O. Bohm, D. Shapiro, Gastroenterology 148 (2015), 751-761 e758. [53] I. Pharmaceuticals, Intercept Pharmaceuticals Announces Pivotal Phase 3 Clinical Trial of Obeticholic Acid in NASH, 2015. [54] A.J. Pommier, J. Dufour, G. Alves, E. Viennois, H. De Boussac, A. Trousson, D.H. Volle, F. Caira, P. Val, P. Arnaud, J.M. Lobaccaro, S. Baron, PLoS Genet. 9 (2013) e1003483. [55] N. Zelcer, C. Hong, R. Boyadjian, P. Tontonoz, Science 325 (2009) 100e104. [56] J. Zhou, M. Febbraio, T. Wada, Y. Zhai, R. Kuruba, J. He, J.H. Lee, S. Khadem, S. Ren, S. Li, R.L. Silverstein, W. Xie, Gastroenterology 134 (2008) 556e567. [57] J.J. Repa, G. Liang, J. Ou, Y. Bashmakov, J.M. Lobaccaro, I. Shimomura, B. Shan, M.S. Brown, J.L. Goldstein, D.J. Mangelsdorf, Genes Dev. 14 (2000) 2819e2830. [58] Z. Ou, T. Wada, R. Gramignoli, S. Li, S.C. Strom, M. Huang, W. Xie, Mol. Endocrinol. 25 (2011) 584e596. [59] D. Zhong, G. Huang, Y. Zhang, Y. Zeng, Z. Xu, Y. Zhao, X. He, F. He, Cell. Signal. 25 (2013) 1429e1437. [60] D. Sun, J. Zhang, J. Xie, W. Wei, M. Chen, X. Zhao, FEBS Lett. 586 (2012) 1472e1479. [61] Y.K. Adlakha, S. Khanna, R. Singh, V.P. Singh, A. Agrawal, N. Saini, Cell Death Dis. 4 (2013) e780. [62] I. Issemann, S. Green, Nature 347 (1990) 645e650. [63] P.D. Rosenblit, Curr. Cardiol. Rep. 14 (2012) 112e124. [64] S.A. Saha, R.R. Arora, Curr. Opin. Lipidol. 22 (2011) 270e276. [65] R. Singaravelu, R. Chen, R.K. Lyn, D.M. Jones, S. O'Hara, Y. Rouleau, J. Cheng, P. Srinivasan, N. Nasheri, R.S. Russell, D.L. Tyrrell, J.P. Pezacki, Hepatology 59 (2014) 98e108. [66] T. Shirasaki, M. Honda, T. Shimakami, R. Horii, T. Yamashita, Y. Sakai, A. Sakai, H. Okada, R. Watanabe, S. Murakami, M. Yi, S.M. Lemon, S. Kaneko, J. Virol. 87 (2013) 5270e5286. [67] L. Wu, K.G. Parhofer, Metab. Clin. Exp. 63 (2014) 1469e1479. [68] X. Xiao, B.L. Song, Acta Biochim. Biophys. Sin. 45 (2013) 2e10. [69] K.C. Vickers, B.M. Shoucri, M.G. Levin, H. Wu, D.S. Pearson, D. Osei-Hwedieh, F.S. Collins, A.T. Remaley, P. Sethupathy, Hepatology 57 (2013) 533e542. [70] I. Gerin, G.T. Bommer, C.S. McCoin, K.M. Sousa, V. Krishnan, O.A. MacDougald, Am. J. Physiol. Endocrinol. Metab. 299 (2010) E198eE206. [71] M. Carrer, N. Liu, C.E. Grueter, A.H. Williams, M.I. Frisard, M.W. Hulver, R. Bassel-Duby, E.N. Olson, Proc. Natl. Acad. Sci. U. S. A. 109 (2012) 15330e15335. [72] H. Bhatia, G. Verma, M. Datta, Biochim. Biophys. Acta 1839 (2014) 334e343. [73] M. Cui, Y. Wang, B. Sun, Z. Xiao, L. Ye, X. Zhang, Biochem. Biophys. Res. Commun. 444 (2014) 270e275. [74] C. Caussy, S. Charriere, C. Marcais, M. Di Filippo, A. Sassolas, M. Delay, V. Euthine, A. Jalabert, E. Lefai, S. Rome, P. Moulin, Am. J. Hum. Genet. 94 (2014) 129e134. [75] S.L. Edwards, J. Beesley, J.D. French, A.M. Dunning, Am. J. Hum. Genet. 93 (2013) 779e797. [76] M.T. Bengoechea-Alonso, J. Ericsson, Curr. Opin. Cell Biol. 19 (2007) 215e222. [77] R. Ng, H. Wu, H. Xiao, X. Chen, H. Willenbring, C.J. Steer, G. Song, Hepatology 60 (2014) 554e564. [78] L. Wang, X.J. Jia, H.J. Jiang, Y. Du, F. Yang, S.Y. Si, B. Hong, Mol. Cell. Biol. 33 (2013) 1956e1964. [79] M. Yang, W. Liu, C. Pellicane, C. Sahyoun, B.K. Joseph, C. Gallo-Ebert, M. Donigan, D. Pandya, C. Giordano, A. Bata, J.T. Nickels Jr., J. Lipid Res. 55 (2014) 226e238. [80] K.C. Vickers, S.R. Landstreet, M.G. Levin, B.M. Shoucri, C.L. Toth, R.C. Taylor, B.T. Palmisano, F. Tabet, H.L. Cui, K.A. Rye, P. Sethupathy, A.T. Remaley, Proc. Natl. Acad. Sci. U. S. A. 111 (2014) 14518e14523. [81] S. Griffiths-Jones, H.K. Saini, S. van Dongen, A.J. Enright, Nucleic Acids Res. 36 (2008) D154eD158. [82] H.K. Saini, A.J. Enright, S. Griffiths-Jones, BMC Genom. 9 (2008) 564. [83] I. Gerin, L.A. Clerbaux, O. Haumont, N. Lanthier, A.K. Das, C.F. Burant, I.A. Leclercq, O.A. MacDougald, G.T. Bommer, J. Biol. Chem. 285 (2010) 33652e33661. [84] T. Horie, K. Ono, M. Horiguchi, H. Nishi, T. Nakamura, K. Nagao, M. Kinoshita, Y. Kuwabara, H. Marusawa, Y. Iwanaga, K. Hasegawa, M. Yokode, T. Kimura, T. Kita, Proc. Natl. Acad. Sci. U. S. A. 107 (2010) 17321e17326.

11

[85] T.J. Marquart, R.M. Allen, D.S. Ory, A. Baldan, Proc. Natl. Acad. Sci. U. S. A. 107 (2010) 12228e12232. [86] S.H. Najafi-Shoushtari, F. Kristo, Y. Li, T. Shioda, D.E. Cohen, R.E. Gerszten, A.M. Naar, Science 328 (2010) 1566e1569. [87] K.J. Rayner, Y. Suarez, A. Davalos, S. Parathath, M.L. Fitzgerald, N. Tamehiro, E.A. Fisher, K.J. Moore, C. Fernandez-Hernando, Science 328 (2010) 1570e1573. [88] V. Rottiers, S. Obad, A. Petri, R. McGarrah, M.W. Lindholm, J.C. Black, S. Sinha, R.J. Goody, M.S. Lawrence, A.S. deLemos, H.F. Hansen, S. Whittaker, S. Henry, R. Brookes, S.H. Najafi-Shoushtari, R.T. Chung, J.R. Whetstine, R.E. Gerszten, S. Kauppinen, A.M. Naar, Sci. Transl. Med. 5 (2013), 212ra162. [89] K.J. Rayner, C.C. Esau, F.N. Hussain, A.L. McDaniel, S.M. Marshall, J.M. van Gils, T.D. Ray, F.J. Sheedy, L. Goedeke, X. Liu, O.G. Khatsenko, V. Kaimal, C.J. Lees, C. Fernandez-Hernando, E.A. Fisher, R.E. Temel, K.J. Moore, Nature 478 (2011) 404e407. [90] T. Horie, O. Baba, Y. Kuwabara, Y. Chujo, S. Watanabe, M. Kinoshita, M. Horiguchi, T. Nakamura, K. Chonabayashi, M. Hishizawa, K. Hasegawa, N. Kume, M. Yokode, T. Kita, T. Kimura, K. Ono, J. Am. Heart Assoc. 1 (2012) e003376. [91] N. Rotllan, C.M. Ramirez, B. Aryal, C.C. Esau, C. Fernandez-Hernando, Arterioscler. Thromb. Vasc. Biol. 33 (2013) 1973e1977. [92] T.J. Marquart, J. Wu, A.J. Lusis, A. Baldan, Arterioscler. Thromb. Vasc. Biol. 3 (2013) 455e458. [93] L. Goedeke, A. Salerno, C.M. Ramirez, L. Guo, R.M. Allen, X. Yin, S.R. Langley, C. Esau, A. Wanschel, E.A. Fisher, Y. Suarez, A. Baldan, M. Mayr, C. FernandezHernando, EMBO Mol. Med. 6 (2014) 1133e1141. [94] R.M. Allen, T.J. Marquart, C.J. Albert, F.J. Suchy, D.Q. Wang, M. Ananthanarayanan, D.A. Ford, A. Baldan, EMBO Mol. Med. 4 (2012) 882e895. [95] E.D. Rosen, O.A. MacDougald, Nat. Rev. Mol. Cell Biol. 7 (2006) 885e896. [96] M. Trajkovski, H. Lodish, Trends Endocrinol. Metab. TEM 24 (2013) 442e450. [97] J. Ahn, H. Lee, C.H. Jung, T.I. Jeon, T.Y. Ha, EMBO Mol. Med. 5 (2013) 1602e1612. [98] X. Ma, P. Lee, D.J. Chisholm, D.E. James, Front. Endocrinol. 6 (2015) 1. [99] L. Chen, J. Hou, L. Ye, Y. Chen, J. Cui, W. Tian, C. Li, L. Liu, Sci. Rep. 4 (2014) 3819. [100] M.L. Bonet, P. Oliver, A. Palou, Biochim Biophys. Acta 1831 (2013) 969e985. [101] M. Karbiener, D.F. Pisani, A. Frontini, L.M. Oberreiter, E. Lang, A. Vegiopoulos, K. Mossenbock, G.A. Bernhardt, T. Mayr, F. Hildner, J. Grillari, G. Ailhaud, S. Herzig, S. Cinti, E.Z. Amri, M. Scheideler, Stem Cells 32 (2014) 1578e1590. [102] X. Fu, B. Dong, Y. Tian, P. Lefebvre, Z. Meng, X. Wang, F. Pattou, W. Han, X. Wang, F. Lou, R. Jove, B. Staels, D.D. Moore, W. Huang, J. Clin. Investig. 125 (2015) 2497e2509. [103] M. Mori, H. Nakagami, G. Rodriguez-Araujo, K. Nimura, Y. Kaneda, PLoS Biol. 10 (2012) e1001314. [104] Y.J. Kim, S.H. Hwang, H.H. Cho, K.K. Shin, Y.C. Bae, J.S. Jung, J. Cell. Physiol. 227 (2012) 183e193. [105] M.L. Balestrieri, M. Rienzo, F. Felice, R. Rossiello, V. Grimaldi, L. Milone, A. Casamassimi, L. Servillo, B. Farzati, A. Giovane, C. Napoli, Biochim Biophys. Acta 1784 (2008) 936e945. [106] J.A. Baur, K.J. Pearson, N.L. Price, H.A. Jamieson, C. Lerin, A. Kalra, V.V. Prabhu, J.S. Allard, G. Lopez-Lluch, K. Lewis, P.J. Pistell, S. Poosala, K.G. Becker, O. Boss, D. Gwinn, M. Wang, S. Ramaswamy, K.W. Fishbein, R.G. Spencer, E.G. Lakatta, D. Le Couteur, R.J. Shaw, P. Navas, P. Puigserver, D.K. Ingram, R. de Cabo, D.A. Sinclair, Nature 444 (2006) 337e342. [107] J.N. Feige, M. Lagouge, C. Canto, A. Strehle, S.M. Houten, J.C. Milne, P.D. Lambert, C. Mataki, P.J. Elliott, J. Auwerx, Cell Metab. 8 (2008) 347e358. [108] M.C. Haigis, L.P. Guarente, Genes Dev. 20 (2006) 2913e2921. [109] K.T. Howitz, K.J. Bitterman, H.Y. Cohen, D.W. Lamming, S. Lavu, J.G. Wood, R.E. Zipkin, P. Chung, A. Kisielewski, L.L. Zhang, B. Scherer, D.A. Sinclair, Nature 425 (2003) 191e196. [110] M. Lagouge, C. Argmann, Z. Gerhart-Hines, H. Meziane, C. Lerin, F. Daussin, N. Messadeq, J. Milne, P. Lambert, P. Elliott, B. Geny, M. Laakso, P. Puigserver, J. Auwerx, Cell 127 (2006) 1109e1122. [111] J. Rutanen, N. Yaluri, S. Modi, J. Pihlajamaki, M. Vanttinen, P. Itkonen, S. Kainulainen, H. Yamamoto, M. Lagouge, D.A. Sinclair, P. Elliott, C. Westphal, J. Auwerx, M. Laakso, Diabetes 59 (2010) 829e835. [112] Y.J. Kim, S.H. Hwang, S.Y. Lee, K.K. Shin, H.H. Cho, Y.C. Bae, J.S. Jung, Stem Cells Dev. 21 (2012) 1749e1760. [113] K.K. Shin, Y.S. Kim, J.Y. Kim, Y.C. Bae, J.S. Jung, Cell. Physiol. Biochem. Int. J. Exp. Cell. Physiol Biochem. Pharmacol. 33 (2014) 758e768. [114] L. Sun, M. Trajkovski, Metabolism Clin. Exp. 63 (2014) 272e282. [115] T. Kang, W. Lu, W. Xu, L. Anderson, M. Bacanamwo, W. Thompson, Y.E. Chen, D. Liu, J. Biol. Chem. 288 (2013) 34394e34402. [116] C.E. Fletcher, D.A. Dart, A. Sita-Lumsden, H. Cheng, P.S. Rennie, C.L. Bevan, Hum. Mol. Genet. 21 (2012) 3112e3127. [117] H. Zhu, H. Wu, X. Liu, B.R. Evans, D.J. Medina, C.G. Liu, J.M. Yang, Biochem. Pharmacol. 76 (2008) 582e588. [118] Y. Peng, H. Xiang, C. Chen, R. Zheng, J. Chai, J. Peng, S. Jiang, Int. J. Biochem. Cell Biol. 45 (2013) 1585e1593. [119] Y. Wu, J. Zuo, Y. Zhang, Y. Xie, F. Hu, L. Chen, B. Liu, F. Liu, Biochem. Biophys. Res. Commun. 438 (2013) 575e580. [120] Y. Chen, F. Siegel, S. Kipschull, B. Haas, H. Frohlich, G. Meister, A. Pfeifer, Nat. Commun. 4 (2013) 1769. [121] S. Kajimura, P. Seale, K. Kubota, E. Lunsford, J.V. Frangioni, S.P. Gygi,

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B.M. Spiegelman, Nature 460 (2009) 1154e1158. [122] P. Seale, B. Bjork, W. Yang, S. Kajimura, S. Chin, S. Kuang, A. Scime, S. Devarakonda, H.M. Conroe, H. Erdjument-Bromage, P. Tempst, M.A. Rudnicki, D.R. Beier, B.M. Spiegelman, Nature 454 (2008) 961e967. [123] P. Seale, S. Kajimura, W. Yang, S. Chin, L.M. Rohas, M. Uldry, G. Tavernier, D. Langin, B.M. Spiegelman, Cell Metab. 6 (2007) 38e54. [124] M. Trajkovski, K. Ahmed, C.C. Esau, M. Stoffel, Nat. Cell Biol. 14 (2012) 1330e1335. [125] H. Yin, A. Pasut, V.D. Soleimani, C.F. Bentzinger, G. Antoun, S. Thorn, P. Seale, P. Fernando, W. van Ijcken, F. Grosveld, R.A. Dekemp, R. Boushel, M.E. Harper, M.A. Rudnicki, Cell Metab. 17 (2013) 210e224. [126] W. Liu, S. Kuang, J. Mol. Cell Biol. 5 (2013) 432e434. [127] Y. Guo, Y. Chen, Y. Zhang, Y. Zhang, L. Chen, D. Mo, Int. J. Biol. Sci. 8 (2012) 1408e1417. [128] H. Miki, T. Yamauchi, R. Suzuki, K. Komeda, A. Tsuchida, N. Kubota, Y. Terauchi, J. Kamon, Y. Kaburagi, J. Matsui, Y. Akanuma, R. Nagai, S. Kimura, K. Tobe, T. Kadowaki, Mol. Cell. Biol. 21 (2001) 2521e2532. [129] L. Chen, Y. Chen, S. Zhang, L. Ye, J. Cui, Q. Sun, K. Li, H. Wu, L. Liu, J. Cell. Biochem. 116 (2015) 969e976. [130] J.P. Mayer, F. Zhang, R.D. DiMarchi, Biopolymers 88 (2007) 687e713. [131] J. Ye, Front. Med. 7 (2013) 14e24. [132] J.I. Odegaard, A. Chawla, Cold Spring Harb. Perspect. Med. 2 (2012) a007724. [133] O. Osborn, J.M. Olefsky, Nat. Med. 18 (2012) 363e374. [134] E. Arner, N. Mejhert, A. Kulyte, P.J. Balwierz, M. Pachkov, M. Cormont, S. Lorente-Cebrian, A. Ehrlund, J. Laurencikiene, P. Heden, K. DahlmanWright, J.F. Tanti, Y. Hayashizaki, M. Ryden, I. Dahlman, E. van Nimwegen, C.O. Daub, P. Arner, Diabetes 61 (2012) 1986e1993. [135] T. Yamauchi, T. Kadowaki, Cell Metab. 17 (2013) 185e196. [136] Y. Yang, Y. Wang, K. Zhou, A. Hong, Gene 539 (2014) 68e74. [137] Y. Lustig, E. Barhod, R. Ashwal-Fluss, R. Gordin, N. Shomron, K. BaruchUmansky, R. Hemi, A. Karasik, H. Kanety, Diabetes 63 (2014) 433e445. [138] Y.H. Chen, S. Heneidi, J.M. Lee, L.C. Layman, D.W. Stepp, G.M. Gamboa, B.S. Chen, G. Chazenbalk, R. Azziz, Diabetes 62 (2013) 2278e2286. [139] T.M. Hung, C.M. Ho, Y.C. Liu, J.L. Lee, Y.R. Liao, Y.M. Wu, M.C. Ho, C.H. Chen, H.S. Lai, P.H. Lee, PloS One 9 (2014) e89446. [140] S. Yakar, J.L. Liu, A.M. Fernandez, Y. Wu, A.V. Schally, J. Frystyk, S.D. Chernausek, W. Mejia, D. Le Roith, Diabetes 50 (2001) 1110e1118. [141] R. Yu, S. Yakar, Y.L. Liu, Y. Lu, D. LeRoith, D. Miao, J.L. Liu, Mol. Cell. Endocrinol. 204 (2003) 31e42.

[142] M.D. Williams, G.M. Mitchell, Exp. Diabetes Res. 2012 (2012) 484696. [143] F. Xiao, J. Yu, B. Liu, Y. Guo, K. Li, J. Deng, J. Zhang, C. Wang, S. Chen, Y. Du, Y. Lu, Y. Xiao, Z. Zhang, F. Guo, Diabetes 63 (2014) 2631e2642. [144] J.W. Kornfeld, C. Baitzel, A.C. Konner, H.T. Nicholls, M.C. Vogt, K. Herrmanns, L. Scheja, C. Haumaitre, A.M. Wolf, U. Knippschild, J. Seibler, S. Cereghini, J. Heeren, M. Stoffel, J.C. Bruning, Nature 494 (2013) 111e115. [145] S. Bandiera, S. Pfeffer, T.F. Baumert, M.B. Zeisel, J. Hepatol. 62 (2015) 448e457. [146] Y.M. Yang, S.Y. Seo, T.H. Kim, S.G. Kim, Hepatology 56 (2012) 2209e2220. [147] F. Liang, S. Kume, D. Koya, Nat. Rev. Endocrinol. 5 (2009) 367e373. [148] B. Zhou, C. Li, W. Qi, Y. Zhang, F. Zhang, J.X. Wu, Y.N. Hu, D.M. Wu, Y. Liu, T.T. Yan, Q. Jing, M.F. Liu, Q.W. Zhai, Diabetologia 55 (2012) 2032e2043. [149] Z. Yang, X.F. Ming, Obes. Rev. Off. J. Int. Assoc. Study Obes. 13 (Suppl. 2) (2012) 58e68. [150] S. Mazurek, Int. J. Biochem. Cell Biol. 43 (2011) 969e980. [151] W. Li, J. Wang, Q.D. Chen, X. Qian, Q. Li, Y. Yin, Z.M. Shi, L. Wang, J. Lin, L.Z. Liu, B.H. Jiang, PloS One 8 (2013) e64924. [152] A. Meerson, M. Traurig, V. Ossowski, J.M. Fleming, M. Mullins, L.J. Baier, Diabetologia 56 (2013) 1971e1979. [153] T.J. Marquart, J. Wu, A.J. Lusis, A. Baldan, Arterioscler. Thromb. Vasc. Biol. 33 (2013) 455e458. [154] L. Goedeke, F.M. Vales-Lara, M. Fenstermaker, D. Cirera-Salinas, A. ChamorroJorganes, C.M. Ramirez, J.A. Mattison, R. de Cabo, Y. Suarez, C. FernandezHernando, Mol. Cell. Biol. 33 (2013) 2339e2352. [155] T. Li, J.M. Francl, S. Boehme, J.Y. Chiang, Hepatology 58 (2013) 1111e1121. [156] T. Horie, T. Nishino, O. Baba, Y. Kuwabara, T. Nakao, M. Nishiga, S. Usami, M. Izuhara, N. Sowa, N. Yahagi, H. Shimano, S. Matsumura, K. Inoue, H. Marusawa, T. Nakamura, K. Hasegawa, N. Kume, M. Yokode, T. Kita, T. Kimura, K. Ono, Nat. Commun. 4 (2013) 2883. [157] R.M. Allen, T.J. Marquart, J.J. Jesse, A. Baldan, Circ. Res. 108 (2011) 9232e9237. [158] A. Davalos, L. Goedeke, P. Smibert, C.M. Ramirez, N.P. Warrier, U. Andreo, D. Cirera-Salinas, K. Rayner, U. Suresh, J.C. Pastor-Pareja, E. Esplugues, E.A. Fisher, L.O. Penalva, K.J. Moore, Y. Suarez, E.C. Lai, C. Fernandez-Hernando, Proc. Natl. Acad. Sci. U. S. A. 108 (2011) 9232e9237. [159] C.M. Ramirez, L. Goedeke, N. Rotllan, J.H. Yoon, D. Cirera-Salinas, J.A. Mattison, Y. Suarez, R. de Cabo, M. Gorospe, C. Fernandez-Hernando, Mol. Cell. Biol. 33 (2013) 2891e2902.

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