Ganoderma lucidum polysaccharides improve insulin sensitivity by regulating inflammatory cytokines and gut microbiota composition in mice

Journal of Functional Foods 38 (2017) 545–552

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Ganoderma lucidum polysaccharides improve insulin sensitivity by regulating inflammatory cytokines and gut microbiota composition in mice S. Xu a,1, Y. Dou b,1, B. Ye c, Q. Wu a, Y. Wang d, M. Hu d, F. Ma d, X. Rong a,⇑, J. Guo a,⇑ a Guangdong Metabolic Diseases Research Centre of Integrated Chinese and Western Medicine, Institute of Chinese Medicine Sciences, Guangdong Pharmaceutical University, Guangzhou, China b School of Chinese Meteria Medica, Guangzhou University of Chinese Medicine, Guangzhou, China c Department of Nursing, Medical College of Jiaying University, Meizhou 514031, China d Infinitus Chinese Herbal Immunity Research Centre, Guangzhou, China

a r t i c l e

i n f o

Article history: Received 20 April 2017 Received in revised form 16 August 2017 Accepted 14 September 2017

Keywords: Ganoderma lucidum polysaccharides Insulin resistance Gut microbiota Type 2 diabetes Inflammatory cytokines

a b s t r a c t Ectopic lipid accumulation and low-grade chronic inflammation are critical pathogenesis of insulin resistance development. Ganoderma lucidum is a traditional Chinese herb for balancing energy homeostasis. In this research, we determined the effects of G. lucidum polysaccharides (GLP) on high-fat diet (HFD)induced insulin resistant mice. We observed that GLP treatment decreased plasma insulin concentration and reversed HFD-induced systemic insulin resistance. Meanwhile, GLP ameliorated low-grade chronic inflammation, inducing lipolysis in adipose tissues. GLP decreased plasma triglyceride and nonesterified fatty acid outflux by suppressing mRNA expressions of hormone-sensitive lipase, fatty acid binding protein 4, tumor necrosis factor-a, and interleukin-6 in epididymal fat. Finally, GLP treatment suppressed ectopic lipid accumulation in peripheral tissues and hepatic insulin-regulated lipogenesis. GLP also regulated composition of gut microbiota implicated in type 2 diabetes mellitus development. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Western diet, which contains high levels of saturated fats and fructose, plays a significant role in obesity development (Tiniakos, Vos, & Brunt, 2010). Obesity induces insulin resistance in the liver, skeletal muscles, and adipose tissues, strongly develop-

Abbreviations: ACC, acetyl-CoA carboxylase; ACOX1, peroxisomal acylcoenzyme A oxidase 1; AUC, area under the curve; CAD, cardiovascular disease; CPT1a, carnitine palmitoyltransferase 1; FABP4, fatty acid binding protein 4; FAS, fatty acid synthase; GLP, ganoderma lucidum polysaccharides; HFD, high fat diet; HOMA-IR, homeostasis model of assessment for insulin resistance; HSL, hormone sensitive lipase; IFN-c, interferon c; IL-1a/b, interleukin 1a/b; LDL-C, low-density lipoprotein cholesterol; MCP1, monocyte chemotactic protein 1; NAFLD, nonalcoholic fatty liver disease; NEFA, non-esterified fatty acid; PCoA, UniFrzc-based principal coordinates analysis; PPARc, peroxisome proliferator activated receptor c; SREBP1, sterol regulatory element binding transcription factor 1; sTNFR1/2, soluble tumor necrosis factor receptors 1/2; TC, total cholesterol; T2DM, type 2 diabetes mellitus; TG, triglyceride; TNFa, tumor necrosis factor a. ⇑ Corresponding authors at: Guangdong Metabolic Diseases Research Centre of Integrated Chinese, and Western Medicine, Institute of Chinese Medicine Sciences, Guangdong Pharmaceutical University, Guangzhou 510006, China. E-mail addresses: [email protected] (X. Rong), [email protected] (J. Guo). 1 These authors contributed equally. https://doi.org/10.1016/j.jff.2017.09.032 1756-4646/Ó 2017 Elsevier Ltd. All rights reserved.

ing into type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and cardiovascular disease (CAD) (Olefsky & Glass, 2010). The World Health Organization estimated that over 300 million people will suffer from metabolic syndrome in 2025 (Symonds, Sebert, Hyatt, & Budge, 2009). In current studies, ectopic lipid accumulation and systemic low-grade chronic inflammation are several pathogenesis of insulin resistance (Samuel & Shulman, 2012). Obesity causes excess lipid accumulation in the liver, skeletal muscles, and adipose tissue and generates high amounts of lipid intermediates. These intermediates, such as diacylglycerol, nonesterified fatty acid (NEFA), and ceramide, impair insulin signaling pathways through different mechanisms and reduce insulinstimulated glycometabolism (Samuel & Shulman, 2012). Finally, these mechanisms exacerbate insulin resistance and result in hyperglycemia, hyperinsulinemia, and T2DM. Concomitantly, numerous studies demonstrated systemic low-grade chronic inflammation implicated with insulin resistance development (Glass & Olefsky, 2012; Odegaard & Chawla, 2013; Sell, Habich, & Eckel, 2012). Specifically, studies have observed high amounts of macrophage infiltration and production of monocyte chemoattractant protein-1 (MCP1) in adipose tissues (Sartipy & Loskutoff,

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2003; Weisberg et al., 2003). These polarized macrophages activate classic Jun N-terminal kinase and inhibitor of kB kinase b pathway to inhibit insulin signaling by phosphorylation of serine residues in insulin receptor substrate 1 (Odegaard & Chawla, 2013; Olefsky & Glass, 2010). Meanwhile, tumor necrosis factor (TNF)-a, interleukin (IL)-6, IL-1b, and other macrophage-secreted inflammatory cytokines stimulate lipolysis in adipocytes and impair insulin sensitivity in distal tissues through endocrine effects (Cai et al., 2005; Guilherme, Virbasius, Puri, & Czech, 2008; Hotamisligil, Shargill, & Spiegelman, 1993; Shoelson, Lee, & Yuan, 2004). Consequently, inflammation-induced lipid outflux enhances lipid concentrations in circulation and peripheral tissues, exacerbating lipotoxicity and increasing risks for CAD (Guilherme et al., 2008; Samuel & Shulman, 2012). In the last few decades, growing evidence demonstrated that gut microbiota composition plays an important role in the development of obesity, insulin resistance, and T2DM (Patterson et al., 2016; Saad, Santos, & Prada, 2016; Shen, Obin, & Zhao, 2012). The human intestine is densely populated by trillions of bacteria. Gut microbiota composition plays a significant role in gut barrier and normal intestinal functions (Brun et al., 2007). However, disturbance in composition can disrupt gut barrier function, causing metabolic endotoxemia that initiates low-grade chronic inflammation and suppresses insulin signaling by several pathways (Cani et al., 2008; Saad et al., 2016). Ganoderma lucidum is a classic traditional Chinese herb for modulating immune responses (Lin, Liang, Lee, & Chiang, 2005; Liu et al., 2016). Interestingly, some studies illustrated that G. lucidum exert significant effects on inhibiting diabetes and obesity development (Chang et al., 2015; Pan et al., 2013). In this research, we aim to investigate effects and mechanism of water extract of G. lucidum polysaccharides (GLP) in treating insulin resistance development. 2. Methods 2.1. Preparation of GLP G. lucidum mycelia were mixed with water at a ratio of 1:10 ml after pulverization. The mixture was agitated for 4 h under 70– 80 °C, and filter residue was harvested. Extraction of filtered residue was repeated thrice. Extracting solution was mixed, and vacuum concentration was performed. Supernatant was harvested by centrifugation. We mixed the supernatant with 95% ethanol at a ratio of 1:9 and left the resulting mixture to stand at 4 °C overnight. Sediments were harvested as GLP by centrifugation and vacuum freeze-drying. The specific composition of GLP were showed in Table 3. 2.2. Mice and nutrients Normal chow and purified high-fat diet (HFD) (45% fat) were purchased from MediScience Ltd. (Yangzhou, China) (Table 1) Eight-week-old male C57BL/6J mice were purchased from Guangdong Medical Laboratory Animal Center. Mice were housed under pathogen-free conditions and in a temperature-controlled room illuminated for 12 h daily. Animals received humane care in accordance with study guidelines established by the Guangzhou University of Chinese Medicine Laboratory Animal Holding Care. Following acclimation for 1 week, mice were designated into three groups, namely, the normal chow group (n = 6), the HFD group (n = 8), and the GLP group (n = 8). The normal chow group mice were fed with normal chow and treated with 5% acacia gum solution. The HFD group and GLP group were fed with HFD and treated with 5% acacia gum solution or 200 mg/kg GLP by intragastric

Table 1 Nutritional data of diet. Nutrients

kcal/g

Normal chow MD12032 high fat diet

3.5 4.73

administration, respectively. After 13 weeks, mice were sacrificed by cervical dislocation after anesthesia. Tissues were snap-frozen or fixed in formalin.

2.3. Biochemical assays and metabolomics Blood sample was collected from retinal vein plexus after fasting overnight or with feeding at 9:00 a.m. Mice were anesthetized by ether. Plasma was harvested after being centrifuged. Plasma glucose (Glu), triglyceride (TG), total cholesterol (TC), and lowdensity lipoprotein cholesterol (LDL-C) were determined using commercial kits from Rsbio (Shanghai, China). NEFA was determined using NEFA assay kit from Wako (Osaka, Japan). Plasma insulin was determined using enzyme-linked immunosorbent assay commercial kits from Cusabio (Wuhan, China). Plasma inflammatory cytokines were determined using mouse inflammation antibody array C1 from RayBiotech (Norcross, USA). Hepatic TG and TC were extracted by isopropanol (1 mg tissue/20 mL isopropanol) standing at 4 °C overnight after homogenizing and harvesting supernatants and were determined using commercial kits after centrifuging at 3500 rpm for 15 min at 4 °C. For fatty acids assay, 50 mg tissue was pulverized in 600ul CHCL3-MeOH (2:1 v/ v). Pulverized tissue was mixed by vortexing with 200ul ddH2O. Samples were centrifuged at 10000 rpm for 5 min. 200ul lower extraction fluid was harvested and dried under nitrogen flow. Each dry sample was re-dissolved in 150ul CHCL3-MeOH (2:1 v/v). 40ul Trimethyl-3-trifluoromethylphenylammonium Hydroxide (TCI, Japan) was mixed with each sample and standing for 1 h. Supernatant was harvested for Gas Chromatography-Mass Spectrometer analysis. The GC separation was achieved on Agilent DB-23 capillary column at a flow rate of 1 ml/min. The acquisition of results and data analyzed were performed on a 7890B-5977B Agilent mass spectrometer (California, USA). The abbreviations of NEFA were listed in Table 4.

2.4. Oral glucose tolerance tests Glucose (2 g/kg body weight) was intragastrically administered to mice fasted overnight. Insulin sensitivity was evaluated using homeostatic model assessment of insulin resistance [HOMA-IR, fasting blood glucose (mmol/L)
fasting serum insulin ([mIU/ L)/22.5].

2.5. Histology Livers were fixed in formalin and paraffin-embedded, sectioned, and stained with hematoxylin and eosin.

2.6. Real-time polymerase chain reaction (PCR) Total RNA was isolated by homogenizing epididymal fat in Tiangen TRIzol reagent (Beijing, China), and single standard cDNA was synthesized by using Tiangen cDNA kit (Beijing, China). Quantitative real-time PCR was performed with Thermo Scientific PikoReal 96 Real-Time PCR System (Waltham, USA). Table 2 illustrates primer sequences obtained from Takara (Dalian, China).

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S. Xu et al. / Journal of Functional Foods 38 (2017) 545–552 Table 2 Primer sequences for real-time PCR assays.

Table 4 Abbreviation of fatty acids.

Gene

Primer

Abbreviation

Name

18s

F CGGCTACCACATCCAAGGA R CCAATTACAGGGCCTCGAAA

Actin

F R F R

C4:0 C6:0 C8:0 C10:0 C11:0 C12:0 C13:0 C14:0 C14:1 C15:0 C15:1 C16:0 C16:1 C17:0 C17:1 C18:0 C18:1n9c C18:1n9t C18:2n6c C18:2n6t C18:3n6 C18:3n3 C20:0 C20:1n9 C20:2 C20:3n6 C20:3n3 C20:4n6 C20:5n3 C21:0 C22:0 C22:1n9 C22:2 C22:6n3 C23:0 C24:0 C24:1n9

Butryic acid Caproic acid Caprylic acid Capric acid Undecanoic acid Lauric acid Tridecanoic acid Myristic acid Myristoleic acid Pentadecanoic acid cis-10-Pentadecenoic acid Palmitic acid (Hexadacanoic acid) Palmitoleic acid Heptadecanoic acid cis-10-Heptadecenoic acid Stearic acid (Octadecanoic acid) Oleic acid (Octadecenoic acid) Elaidic acid Linoleic acid Linolelaidic acid c-Linolenic acid a-Linolenic acid Arachidic acid cis-11-Eicosenoic acid cis-11,14-Eicosadienoic acid cis-8,11,14-Eicosatrienoic acid cis-11,14,17-Eicosatrienoic acid Arachidonic acid cis-5,8,11,14,17-Eicosapentaenoic acid Henicosanoic acid Behenic acid Erucic acid cis-13,16-Docosadienoic acid cis-4,7,10,13,16,19-Docosahexaenoic acid Tricosanoic acid Lignoceric acid Nervonic acid

PPARc

ATGGAGGGGAATACAGCCC TTCTTTGCAGCTCCTTCGTT GGAGCCTAAGTTTGAGTTTGCTGTG TGCAGCAGGTTGTCTTGGATG

HSL

F TCCTGGAACTAAGTGGACGCAAG R CAGACACACTCCTGCGCATAGAC

FABP4

F TGGGAACCTGGAAGCTTGTCTC R GAATTCCACGCCCAGTTTGA

FAS

F TGCTGTTGGAAGTCAGCTATGAA R GATGCCTCTGAACCACTCACAC

ACC

F AGCGACATGAACACCGTACTGAA R TAGGGTCCCGGCCACATAAC

ACOX1

F GCTTGGAAACCACTGCCACA R CTGAGCCAGGACTATCGCATGA

CPT1a

F GGGTCGAAAGCCCATGTTGTA R CAGTGCTGTCATGCGTTGGA

TNFa

F TATGGCCCAGACCCTCACA R GGAGTAGACAAGGTACAACCCATC

IL-6

F CCACTTCACAAGTCGGAGGCTTA R CCAGTTTGGTAGCATCCATCATTTC

Sequences: 50 to 30 . Forward primers are designated by f and reverse primers by r.

Table 3 Composition of GLP. Component

Percentage

Total polysaccharide Alduronic acid Protein

71.99% 19.27% 5.39%

2.7. Western blot Total protein extracts were fractionated by sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to polyvinylidene difluoride membranes. Then, the membranes were blocked with 5% nonfat milk in Tris-buffered saline with Tween-20 (TBST) for 1 h at room temperature. Membranes were incubated with anti-SREBP1 (Cell Signaling Technology, USA) and antitubulin (Sigma, USA) at 4 °C overnight. The membranes were rinsed thrice with TBST and incubated with respective secondary antibodies for 1 h at room temperature. Protein bands were visualized with Thermo Fisher Scientific SuperSignal West Femto Maximum Sensitivity Substrate (Rockford, USA) and captured using an Image Quant LAS4000 imaging system (Shanghai, China).

each OTU. Beta diversity was analyzed based on UniFrac-based principal coordinate analysis (PCoA). Significant differences were analyzed by Metastats (v2.15.3) and revised by Benjamini–Hochberg method. 2.9. Data analysis All results are expressed as means ± standard error of the mean. Data from more than two groups were analyzed by one-way analysis of variance. Student’s t test was performed to identify differences between two groups. P < 0.05 was considered significant. 3. Results 3.1. Effects of GLP on insulin resistance

2.8. Determination of fecal microbial composition Every cage of feces samples was merged and snap-frozen in liquid nitrogen before storage at 80 °C (Supplemental Fig. 1). Microbiota 16S rDNA amplicon sequencing and operational taxonomic unit (OTU) analysis were relegated to The Beijing Genomics Institute (Shenzhen, China). After removing unqualified reads, tags were obtained based on connecting overlapped pair-ends (V1.2.3.3) (Liu et al., 2012). After selecting effective clean tags by mothur (v1.31.2) and UCHIME (v4.2) (Mccafferty et al., 2013; Schloss et al., 2009; Zhao et al., 2013), total clean tags were clustered into OTUs based on 97% sequence similarity according to average neighbor clustering algorithm (Schloss et al., 2009). Species profiles were classified based on over 51% same clean tags in

After mice feeding with HFD for 11 weeks, mice exhibited impaired Glu tolerance and elevated HOMA-IR index (Fig. 1A–D). Mouse plasma Glu also increased with HFD feeding (Fig. 1B). However, mouse-impaired Glu tolerance was suppressed with GLP treatment (Fig. 1A). GLP treatment also decreased HOMA-IR index, fasting plasma insulin, and feeding plasma Glu (Fig. 1B–D). 3.2. Effects of GLP on inflammatory cytokine expression We determined plasma inflammatory cytokine expression in plasma to investigate GLP mechanism in treating insulin resistance. We observed that mouse plasma IL-1b increased significantly, and interferon-gamma (IFN-c) content also increased with

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Fig. 1. Effects of GLP on insulin resistance. Plasma glucose kinetics and area under the curve of kinetics profiles after 2 g/kg oral glucose administration (A). Plasma glucose (B), Fasting insulin (C) and Homeostatic model assessment of insulin resistance of normal chow group (black circles), HFD group (black squares) and GLP group (black trigons) (D) after treatment with GLP 10 weeks. Values are means ± SEMs, n = 6–8 per group. *P < 0.05, **P < 0.01 versus normal chow group. #P < 0.05, ##P < 0.01 versus HFD group.

HFD feeding (Fig. 2C and D). These two cytokines, which induce M1 macrophage polarizing and secreting IL-6 and TNF-a (Gutierrez, Puglisi, & Hasty, 2009). Consistent with these results, plasma IL6, TNF-a, and soluble TNF receptor 2 increased significantly in HFD group mice (Fig. 2E–H). However, GLP treatment can decrease IL-1b, IL-6, and TNF-a concentration in plasma (Fig. 2C, E, and F). MCP1 and IFN-c contents were also suppressed by GLP treatment (Fig. 2A and D). 3.3. Effects of GLP on lipid outflux from adipose tissues Based on low-grade chronic inflammation-induced lipolysis and ectopic steatosis, we determined lipid metabolic gene expression in epididymal fat and plasma lipid content. As expected, plasma NEFA and TG levels enhanced significantly with HFD feeding (Fig. 3A and B). Meanwhile, the NEFA content in adipose tissue was decreased significantly (Supplemental Fig. 2A). On the contrary, hepatic NEFA content were significant enhanced (Supplemental Fig. 2B). These results were consistent with aforementioned concept. HFD feeding also suppressed mRNA expression of peroxisome proliferator-activated receptor c (PPARc) in epididymal fat (Fig. 3C). These results were supported by rapid degradation of PPARc mRNA by TNF-a treatment (Guilherme et al., 2008). By contrast, mRNA expression of hormone-sensitive lipase (HSL) was enhanced with HFD feeding (Fig. 3D). HSL and fatty acid binding protein 4 (FABP4) mRNA expression were suppressed after treatment with GLP (Fig. 3D and E). Concomitantly, TNF-a and IL-6 mRNA expression were also reduced by GLP treatment (Fig. 3F and G). Fasting plasma NEFA and TG also decreased (Fig. 3A and B). Interestingly, the NEFA content of adipose tissue was enhanced accompanied by the

decreased plasma lipid (Supplemental Fig. 2A). These observations suggest that GLP inhibits inflammation-induced lipolysis and segregate the lipid in adipose tissue, which can prevent lipid outflux and hyperlipidemia. This mechanism may be beneficial in treating peripheral lipotoxicity and insulin resistance. In the species of NEFA, palmitoleic acid (C16:1) in adipose tissue was significant reduced by HFD feeding and restored with GLP treatment (Supplemental Fig. 2C). In previous research, palmitoleic acid was demonstrated as a anti-inflammation metabolite (Li et al., 2013). However, GLP exhibited no significant effects on elevated plasma TC and LDL-C (Supplemental Fig. 4A and B). 3.4. Effects of GLP on obesity and ectopic steatosis Consistent with hyperlipidemia induced by HFD feeding, mice became obese and showed ectopic lipid accumulation in peripheral tissues (liver and skeletal muscle) after feeding with HFD (Fig. 4A– C, Supplemental Figs. 2 and 3). Hepatic peroxisomal acyl-coenzyme A oxidase 1 and carnitine palmitoyltransferase 1 mRNA expression were suppressed by HFD (Fig. 4F and G), exacerbating NAFLD development. However, mouse hepatic TG, TC, and skeletal muscle TG content decreased significantly with GLP treatment (Fig. 4A–C). Thus, GLP attenuated hepatic macrovesicular steatosis (Supplemental Fig. 4D). Based on results of GLP treatment reversing hyperinsulinemia, we also noted that GLP treatment downregulated hepatic SREBP1 protein and fatty acid synthase (FAS) mRNA expression (Fig. 4D and H). The hepatic NEFA and oleic acid (C18:1n9c) which had a cytotoxicity in hepatocytes (Pang, Xi, Jin, Han, & Zhang, 2013) were reduced with GLP treatment as a consequence (Supplemental Fig. 2B and D). These observations strongly support aforementioned amelioration of inflammation-induced

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Fig. 2. Effects of GLP on inflammatory cytokines expression. Plasma MCP1 (A), IL-1a (B), IL-1b (C), IFN-c (D), IL-6 (E), TNFa (F), sTNFR1 (G) and sTNFR2 (H) levels in normal chow group (white bars), HFD group (black bars) and GLP group (gray bars) after treatment with GLP 13 weeks. Values are means ± SEMs, n = 4 per group. *P < 0.05, **P < 0.01 versus normal chow group. #P < 0.05, ##P < 0.01 versus HFD group.

Fig. 3. Effects of GLP on lipid outflux from adipose tissue. Plasma NEFA (A) and TG (B) levels of normal chow group, HFD group and GLP group. PPARc (C), HSL (D), FABP4 (E), TNFa (F) and IL-6 (G) mRNA relative expression in epididymal fat after GLP treatment 13 weeks. Values are means ± SEMs, n = 4–7 per group. *P < 0.05, **P < 0.01 versus normal chow group. #P < 0.05, ##P < 0.01 versus HFD group.

lipolysis and ectopic lipid accumulation by GLP. Then, GLP normalized systemic insulin resistance and decreased plasma insulin, which inhibited insulin–SREBP1-regulated lipogenesis in the liver. Surprisingly, GLP treatment attenuated NAFLD, but GLP treatment exerted no remarkable effects on obesity development and liver weight (Supplemental Figs. 3B–D and 4C).

3.5. Effects of GLP on gut microbiota composition We identified specific bacterial phylotypes based on redundancy analysis. After removing unqualified sequences, an average of 1118 ± 328 OTUs per sample were obtained. Average OTUs for each group and overlap were performed using Veen diagram

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Fig. 4. Effects of GLP on obesity and ectopic steatosis. Hepatic TG (A), hepatic TC (B) and skeletal muscle TG (C) contents of normal chow group (white bars), HFD group (black bars) and GLP group (gray bars). Hepatic FAS (D), ACC (E), ACOX1 (F), CPT1a mRNA (G) and SREBP1 protein relative expression (H) after GLP treatment 13 weeks. Values are means ± SEMs, n = 6–8 per group. *P < 0.05, **P < 0.01 versus normal chow group. #P < 0.05, ##P < 0.01 versus HFD group.

B

A Nornal chow

HFD

HFD

GLP

Normal Normal HFD1 HFD2 HFD3 chow1 chow2

C

D

GLP1 GLP2 GLP3 HFD1 HFD2 HFD3 Fig. 5. Effects of GLP on gut microbiota composition. Veen Diagram of OTUs of each group (A). Bacterial taxonomic profiling in the genus level (B, C) of intestinal bacteria from different groups. Relative abundance of Enterococcus from each group after GLP treatment 12 weeks. Values are means ± SEMs, n = 2–3 per group. *P < 0.05, **P < 0.01 versus normal chow group. #P < 0.05, ##P < 0.01 versus HFD group.

S. Xu et al. / Journal of Functional Foods 38 (2017) 545–552

(Fig. 5A). PCoA revealed visual clustering of gut microbiota composition for each group (Supplemental Fig. 5). After feeding with HFD, gut microbiota composition has been recombined (Supplemental Fig. 5). Detailed analysis showed that Actinobacteria abundance was increased by HFD at the family level (Supplemental Data 1). In previous research, increased Actinobacteria have been associated with increased serum lipopolysaccharide (LPS) level, which is a significant factor for systemic chronic low-grade inflammation and insulin resistance (Caricilli & Saad, 2013). Leuconostoc also increased at the genus level in the HFD group (Supplemental Data 1). This genus of microbiota contributed to inflammation and insulin resistance in published research (Kong et al., 2013). Prevotella, Parasutterella, and Akkermansia muciniphila (Akkermansia) were exhausted by HFD (Fig. 5B, Supplemental Data 1). In current studies, both these strains benefit anti-inflammation (De et al., 2017; Kreutzer et al., 2017; Liu et al., 2017). Interestingly, Lactobacillus spp. (Lactobacillus frumenti, Lactobacillus helveticus, and Lactobacillus intestinalis) were enriched with HFD feeding (Supplemental Data 1). Enterococcus (De et al., 2017; Mcgavigan et al., 2017; Yuan, Wang, Chen, Zhu, & Meng, 2016), which poses beneficial effects in insulin sensitivity and anti-inflammation and inhibits endoplasmic reticulum stress, was enriched by GLP treatment (Fig. 5D).

4. Discussion In conventional opinions, excess calorie uptake leads to obesity and ectopic lipid accumulation. However, these phenotypes cause hypertrophy of adipose tissues (Guilherme et al., 2008). During adipose tissue expansion, hypoxia and apoptosis can occur and increase chronic inflammation by macrophage recruitment (Cinti et al., 2005; Hosogai et al., 2007). Development of inflammationinduced adipocytes and macrophages results in secretion of several inflammatory cytokines, including IL 1 family (IL-1a and IL-1b), IL6, and TNF-a to peripheral tissues. These proinflammatory cytokines induce lipolysis, lipid transport and exacerbate lipotoxicity, impairing insulin signaling in peripheral tissues through endocrine effects. In our research, GLP decreased IL-1b, IL-6, and TNF-a concentration in plasma. Current studies demonstrate that TNF-a enhances lipolysis by upregulating HSL and inhibiting PPARc (Guilherme et al., 2008; Langin & Arner, 2006). This mechanism is also consistent with GLP-reduced HSL, FABP4, TNFa, and IL-6 mRNA expression in epididymal fat. GLP suppressed lipid outflux and consequently ameliorated hypertriglyceridemia. Similar to IL-1b, IL-6 and TNF-a impair insulin signaling pathways by altering phosphorylation of insulin receptor substrate (Fève & Bastard, 2009; Hotamisligil et al., 1996). Circulating NEFA was also elevated in insulin resistance states (Glass & Olefsky, 2012). Numerous studies demonstrate NEFA as a proinflammatory lipid compound that enhances inflammatory signaling in insulin resistance development (Nguyen et al., 2005, 2007). Our observations suggest that GLP treatment can attenuate systemic low-grade chronic inflammation. Thus, GLP inhibit inflammatory cytokine-induced lipolysis in epididymal fat and prevent NEFA outflux. This phenomenon is a potential mechanism for improving systemic insulin resistance by GLP. As expected, GLP also improved systemic insulin sensitivity in our research. Our results indicate that hepatic TG, TC, NEFA and skeletal muscle TG content reduced with GLP treatment. These results strongly support the mechanism of GLP-ameliorated inflammation-induced lipolysis in adipose tissues. Thus, GLP prevent lipid outflux-induced dyslipidemia and ectopic lipid accumulation, which is another pathogenesis in insulin resistance development. Notably, we observed that hepatic SREBP1 protein and downstream FAS mRNA expression decreased with GLP treat-

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ment. We consider this finding as a consequence of GLP normalizing plasma insulin concentration. Our research showed that GLP also change composition of gut microbiota. Current studies indicate that microbiota composition is related to obesity and insulin resistance (Shen et al., 2012). Several reports suggest that HFD induced disorder of gut microbiota and changed gut permeability, leading to excess LPS in blood. Finally, these pathophysiological changes will result in metabolic endotoxemia and exacerbate inflammation development (Saad et al., 2016). Further studies revealed the relationship between numerous bacterial phylotypes and metabolic diseases. Increased Actinobacteria and Leuconostoc have been demonstrated as contributors to increased LPS levels, insulin resistance, obesity, and metabolic syndrome (Caricilli & Saad, 2013; Kong et al., 2013). Findings on both these phenotypes are consistent with our results. By contrast, Prevotella and Parasutterella were both exposed as probiotics to anti-inflammation in adipose tissues and hypothalamus (Kreutzer et al., 2017; Liu et al., 2017). However, in our research, these two phylotypes were exhausted by HFD. Thus, mice presented systemic inflammation, insulin resistance, and metabolic syndrome after feeding with HFD. We also observed that relative abundance of Enterococcus was enhanced with GLP treatment. This finding concurs with studies of anti-inflammation effects of Enterococcus in diabetic mice (Yuan et al., 2016; Mcgavigan et al., 2017). Another research revealed that abundance of Enterococcus casseliflavus was low in T2DM patients and reversed by metformin treatment (De et al., 2017). These studies both suggest that Enterococcus exhibits beneficial effects in preventing low-grade chronic systemic inflammation and T2DM. This finding may be a potential mechanism of GLP in reducing inflammatory cytokine secretion and insulin resistance development. Intriguingly, Lactobacillus spp. were enriched with HFD feeding, and GLP exerted no significant effects on Lactobacillus spp. Lactobacillus spp. were also considered one of probiotics in most cases. However, a clinical research showed abundance of Lactobacillus spp. in feces of diabetic patients (Sato et al., 2014). Thus, further studies are needed to investigate the role of Lactobacillus in insulin resistance. GLP also exerts no effects on ratios of Firmicutes to Bacteroidetes (Supplemental Data 1). Numerous studies also suggest Firmicutes to Bacteroidetes ratios as compositional biomarker for obesity. However, some researchers still disagree, thus requiring clarification of this conception (Murphy et al., 2010; Patterson et al., 2016; Schwiertz et al., 2010). Nonetheless, our data still indicate that GLP can change composition of gut microbiota in relation to inflammation and T2DM development. However, the causality between the specific phylotypes, GLP and inflammation are not illustrated very clearly. More researches, such as microbiota transplant may be performed to investigate which phylotypes contribute to the anti-inflammation effects. 5. Conclusion The present findings suggest that GLP can change gut microbiota and attenuate inflammation response. Then, GLP improve systemic insulin sensitivity by suppressing inflammation and inflammation-induced ectopic lipotoxicity. Finally, GLP decrease insulin concentration and insulin-regulated lipogenesis in the liver. These effects of GLP may benefit prevention of T2DM, NAFLD, and CAD development. Acknowledgment This research was supported by the Natural Science Foundation of Guangdong Province, China (Grant No. 2015A030311033). S. Xu and X. Rong designed the research protocol. S. Xu, Y. Dou, and B. Ye

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