Effect of 1-Deoxynojirimycin Isolated from Mulberry Leaves on

Aug 8, 2019 - In this study, the mixed fermentation by Lactobacillus fermentum and Saccharomyces cerevisiae was used to enhance DNJ extraction efficie...
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Effect of 1‑Deoxynojirimycin Isolated from Mulberry Leaves on Glucose Metabolism and Gut Microbiota in a StreptozotocinInduced Diabetic Mouse Model Teng-Gen Hu,†,‡ Peng Wen,† Wei-Zhi Shen,‡ Fan Liu,‡ Qian Li,‡ Er-Na Li,‡ Sen-Tai Liao,‡ Hong Wu,*,† and Yu-Xiao Zou*,‡

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School of Food Science and Engineering, South China University of Technology/Guangdong Province Key Laboratory for Green Processing of Natural Products and Product Safety, Guangzhou 510640, China ‡ Sericultural and Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences/Key Laboratory of Functional Foods, Ministry of Agriculture and Rural Affairs/Guangdong Key Laboratory of Agricultural Products Processing, Guangzhou 510610, China S Supporting Information *

ABSTRACT: 1-Deoxynojirimycin (DNJ) exerts hypoglycemic effects. However, the traditional method for DNJ extraction is inefficient, and the hypoglycemic mechanism of DNJ remains unclear. In this study, the mixed fermentation by Lactobacillus fermentum and Saccharomyces cerevisiae was used to enhance DNJ extraction efficiency. It was found that this strategy was more efficient than the traditional method as the yield improved from the original 3.24 mg/g to 5.97 mg/g. The purified DNJ significantly decreased serum glucose (P < 0.01) and insulin levels (P < 0.05), improved serum lipid levels (P < 0.05), and reversed insulin resistance (P < 0.05) in diabetic mice. These changes were caused by up-regulating the protein expression of insulin receptor and glycolysis enzymes (GK, PK, and PFK) (P < 0.05) and downregulating the protein expression of insulin receptor substrate-1 and gluconeogenesis enzymes (PCB, PEPCK, FBPase, and G-6-Pase) (P < 0.05), thus alleviating glucose tolerance. Additionally, DNJ treatment relieved gut dysbiosis in diabetic mice by promoting the growth of Lactobacillus, Lachnospiraceae NK4A136 group, Oscillibacter, norank Lachnospiraceae, Alistipes, and Bif idobacterium (P < 0.05) and suppressing the growth of Ruminococcaceae UCG-014, Weissella, Ruminococcus, Prevotellaceae Ga6A1 group, Anaerostipes, Klebsiella, Prevotellaceae UCG-001, and Bacteroidales S24-7 group (P < 0.05).

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production.3 Evidence has shown that diabetes is also associated with gut microbes.4 Larsen et al. reported that the composition of the microbiome differed between diabetic and nondiabetic individuals, and the ratio of Firmicutes to Bacteroidetes was significantly negative correlated with plasma glucose concentrations.5 In addition, a review summarized that gut microbiota influenced the development of diabetes through adjusting the energy metabolism, metabolic endotoxemia production, gut permeability, and the immune system.6 Hence, regulators of glucose metabolism and the gut microbiota are potentially excellent targets for the treatment of diabetes. Currently, diabetes is treated using insulin and various oral drugs, such as α-glucosidase inhibitors, metformin, and sulfonylureas. However, many of these agents produce adverse side effects after long-term use.7,8 Therefore, safer and more effective treatment options, especially from natural sources

INTRODUCTION Diabetes mellitus is a metabolic and endocrine disorder disease which is characterized by chronic hyperglycemia due to deficient insulin secretion and/or insulin resistance.1 The incidence of diabetes has drastically increased to make it common worldwide in recent decades. According to the statistics presented by the International Diabetes Federation in 2017, 415 million individuals suffer from diabetes worldwide and the number of people with diabetes will increase to approximately 629 million by 2045.2 The high prevalence of diabetes imposes a great socio-economic burden on public health. The main causes for the diabetes epidemic are lifestyle changes with respect to the decrease in physical activity and the growing availability of high-calorie food. These changes alter the activities of key glucose metabolism enzymes, including glucokinase (GK), phosphofructokinase (PFK), pyruvate kinase (PK), pyruvate carboxylase (PCB), fructose1, 6-bisphosphatase (FBPase), phosphoenolpyruvate carboxykinase (PEPCK), and glucose-6-phosphatase (G-6-Pase), impair peripheral glucose utilization and augment glucose © XXXX American Chemical Society and American Society of Pharmacognosy

Received: March 4, 2019

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DOI: 10.1021/acs.jnatprod.9b00205 J. Nat. Prod. XXXX, XXX, XXX−XXX

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containing bioactives such as polyphenols, flavonoids, and alkaloids, should be developed. Natural products, such as quercetin, berberine, diterpenoids, and polysaccharides, that are derived from Coccinia indica,9 Phellodendron coptis,10 Salvia chamaedryoides,11 and Mori multicaulis,12 respectively, exhibit a prevention effect against the development of diabetes. The mulberry leaf has been used as traditional medicine for the treatment of diabetes, with records in the ancient materia medica in Asia dating back to 1953 BC. In China and other Asian countries, the mulberry leaf and its extracts have been used as an alternative treatment for diabetes for many years. The mulberry leaf is rich in flavonoid, alkaloid, and polysaccharide components that have antihyperglycemic, antioxidant, antitumor, antimicrobial activities, and protective effects against irradiation damage.13−17 Among these bioactive components, 1-deoxynojirimycin (DNJ, its chemical structural formula shown in Scheme 1), is a potent

antidiabetic agent with effective and specific repair of glucose metabolism by influencing a wide range of important biological processes, such as glycolysis and gluconeogensis.18 However, traditional methods of DNJ extractions are inefficient, and evidence has indicated that fermentation can improve the yield of bioactives isolated from natural resources.19,20 For example, Ryu et al. demonstrated that mulberry leaf powder extract fermented by Lactobacillus plantarum TO-2100 contains a higher level of DNJ than does the unfermented control extract because the destruction of cell walls by microorganisms facilitates the dissolution of bioactive components.21 On the other hand, the hypoglycemic mechanism of DNJ remains unclear. Herein, in order to improve the efficiency of DNJ extraction, we performed mulberry leaf fermentation using lactic acid bacteria, Bacillus subtilis, yeast, and molds. Meanwhile, the effects of DNJ on serum glucose level, serum lipid levels, and liver protection were assessed. Finally, the expression of key enzymes of glucose metabolism and the compositions of gut microbiota were measured to elucidate the hypoglycemic mechanism of DNJ.

Scheme 1. Structure of DNJ

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RESULTS AND DISCUSSION Mulberry Leaf Fermentation. It has been demonstrated that the DNJ extracts from the mulberry leaf could modulate glucose metabolism and insulin sensitivity in diabetic rodents.22 However, the yield of DNJ isolated from the

Figure 1. Optimization of mulberry leaf fermentation for DNJ extraction. (a) Strain screening. Fermentation conditions in the mulberry medium: 10 mg/mL mulberry leaf, 5 × 104 CFU/mL inoculation quantity, 35 °C for lactic acid bacteria, molds, and yeast/37 °C for bacillus, 96 h, pH 5.5; control: 10 mg/mL mulberry leaf, 50 mM HCl-30% ethanol, 80 °C, 2 h. Fermentation conditions in the basic medium (MRS broth for lactic acid bacteria; PDA broth for molds; YPD broth for yeast; LB broth for bacillus): 5 × 104 CFU/mL inoculation quantity, 35 °C for lactic acid bacteria, molds, and yeast/37 °C for bacillus, 96 h, pH 5.5. (b) Effect of mulberry leaf concentration on DNJ extraction of L. fermentum + S. cerevisiae fermentation. Conditions: 5 × 104 CFU/mL inoculation quantity, 96 h, pH 5.5, 35 °C. (c) Effect of inoculation quantity on DNJ extraction of L. fermentum + S. cerevisiae fermentation. Conditions: 15 mg/mL mulberry leaf, 96 h, pH 5.5, 35 °C. (d) Effect of fermentation temperature on DNJ extraction of L. fermentum + S. cerevisiae fermentation. Conditions: 15 mg/mL mulberry leaf, 105 CFU/mL inoculation quantity, 96 h, pH 5.5. (e) Effect of pH on DNJ extraction of L. fermentum + S. cerevisiae fermentation. Conditions: 15 mg/mL mulberry leaf, 105 CFU/mL inoculation quantity, 96 h, 30 °C. (f) Effect of fermentation time on DNJ extraction of L. fermentum + S. cerevisiae fermentation. Conditions: 15 mg/mL mulberry leaf, 105 CFU/mL inoculation quantity, pH 5.5, 30 °C. Lp: Lactobacillus plantarum, Lf: Lactobacillus fermentum, Lm: Leuconostoc mesenteroides, An: Aspergillus niger, Tr: Trichoderma reesei, Rj: Rhizopus japonicas, Tv: Trichoderma viride, Sc: Saccharomyces cerevisiae, Bs: Bacillus subtilis. B

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Figure 2. Scanning electron micrographs of mulberry leaves before (a) and after treatment with 50 mM HCl-30% ethanol solution (b) or microorganisms (c).

Figure 3. Effect of DNJ extract on FSG, OGTT, insulin and HOMA-IR and IR and IRS-1 protein expression in diabetic mice. * represents the significant difference between DNJ-treated groups and the DM group (P < 0.05); ** represents very significant difference between DNJ-treated groups and the DM group (P < 0.01); (a−d) Mean values ± SD with different letters are significantly different (P < 0.05) according to Duncan’s multiple range test.

efficiencies of DNJ extraction by fermentation with Lactobacillus plantarum, Lactobacillus fermentum, Leuconostoc mesenteroides, and Saccharomyces cerevisiae were significantly increased compared with that of the unfermented group treated with 50 mM HCl-30% ethanol solution. Meanwhile, the DNJ content in the supernatant that was fermented by microorganisms in basic media without adding mulberry leaves was not detected, which indicated the increase of DNJ was caused by the efficient fermentation of mulberry leaf tissue by microorganisms rather than the metabolites from microorganisms. Next, we selected the four strains to explore whether any two strains acted

mulberry leaf varies greatly because of the use of different extraction methods. A previous study has reported that microbial fermentation could improve the extraction efficiency of bioactive molecules from plants.19 During fermentation, microorganisms can secrete various types of extracellular hydrolases, resulting in the destruction of cell walls and facilitating the diffusion of bioactives into the solvents. In order to extract DNJ effectively, it was important to optimize several key fermentation parameters that include the strains, mulberry leaf concentration, inoculation quantity, fermentation temperature, pH, and fermentation time. As shown in Figure 1a, the C

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Figure 4. Effect of DNJ extract on LPL mRNA expression, TG, TC, HDL-C and LDL-C levels in diabetic mice. LDL-C, low density lipoproteincholesterol calculated using the Friedewald equation: LDL-C = TC − (HDL-C + estimated VLDL-C). VLDL-C (very low density lipoproteincholesterol) was estimated from the measured total TGs (TG/2.2 mmol/L, when TG was lower than 4.5 mmol/L). (a−c) Mean values ± SD with different letters are significantly different (P < 0.05) using Duncan’s multiple range test.

change afterward (Figure 1f). Hence, the optimum fermentation time was determined to be 96 h. Scanning Electron Microscope (SEM). To understand why microbial fermentation was more efficient for DNJ extraction, the microstructures of mulberry leaves before and after 50 mM HCl-30% ethanol solution or microorganism treatment were observed using SEM (Figure 2). The cell wall structure of mulberry leaf before treatment remained intact (Figure 2a) and was slightly damaged after treatment by 50 mM HCl-30% ethanol solution (Figure 2b). Mixed fermentation with L. fermentum and S. cerevisiae resulted in the complete damage of the cell wall of the mulberry leaf (Figure 2c). It is thought that these microorganisms secrete cellulolytic enzymes leading to more effective DNJ extraction, as reported previously,23 but further studies are needed. These results support the increased efficiency of DNJ release to the culture medium during mixed fermentation. Hypoglycemic Effect of DNJ on STZ-Induced Diabetic Mice. There were significant differences in food intake and body weight between control and diabetic mice (P < 0.05), as shown in Figure S1. The diabetic model (DM) group mice consumed the most food and had the greatest body weight of all groups throughout the experiment. Treatment of diabetic mice with DNJ slightly increased body weight. Treatments with medium and high doses of DNJ produced significant differences compared with the DM group (P < 0.05). Initial blood glucose levels did not significantly differ between the diabetic mice groups (Figure 3a). After 2 weeks of treatment with DNJ, the fasting serum glucose (FSG) levels were significantly lower in the treated groups than that in the DM group (P < 0.05); and FSG levels were reduced by DNJ in a dose-dependent manner. Notably, the FSG levels in the high dose (HD) group decreased very significantly at the fourth week (P < 0.01). Oral supplementation of glucose (2 g/kg body weight) increased serum glucose level within 30 min. DM group mice developed glucose intolerance, and their serum glucose levels remained high for the following 90 min. After

synergistically to improve DNJ extraction efficiency. The results showed that mixed fermentation with L. fermentum and S. cerevisiae produced the highest DNJ extraction efficiency (4.91 mg/g), which was a 0.52-fold increase compared with the control treatment. Subsequently, the L. fermentum and S. cerevisiae mixed fermentation conditions for DNJ extraction were optimized. The amount of DNJ markedly rose when the concentration of mulberry leaf increased from 5 to 15 mg/mL (Figure 1b). Further increasing the substrate concentration above 15 mg/ mL resulted in a significant drop in the DNJ content (Figure 1b). This could be due to the increase of mass transfer resistance of DNJ diffusion into the solution at a relative high substrate concentration. Thus, the suitable substrate concentration was considered as 15 mg/mL. The DNJ content was enhanced dramatically with an increase of inoculation quantity up to 105 CFU/mL (Figure 1c). A further rise in the inoculation quantity (from 105−2.5 × 105 CFU/mL) led to an appreciable fall in the DNJ content (from 5.61 to 5.14 mg/mL). The optimum inoculation quantity was determined to be 105 CFU/mL. Temperature plays an important role in fermentation. The DNJ content increased from 4.66 to 5.89 mg/mL with fermentation temperature increasing from 25 to 30 °C (Figure 1d). However, when the fermentation temperature was increased from 30 to 45 °C, the DNJ content decreased to 4.46 mg/mL. Thus, 30 °C was considered as the optimum fermentation reaction temperature. Buffer pH (5.0−7.0) had a significant impact on DNJ extraction from mulberry leaf by fermentation (Figure 1e). Changing buffer pH from 5.0 to 5.5 increased the DNJ content (from 5.52 to 5.93 mg/mL). Inversely, when buffer pH was raised from 5.5 to 7.0, the DNJ content decreased significantly. The optimal buffer pH was determined to be 5.5. The DNJ content increased significantly with increasing fermentation time up to 96 h and showed no remarkable D

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Figure 5. Effect of DNJ extract on ALT activity (a) and hepatic glycogen and pyruvic acid levels (b). (a−d) Mean values ± SD with different letters over the bars are significantly different (P < 0.05) according to Duncan’s multiple range test.

and LDL-C ratios (142.86, 47.95, and 92.52% respectively) and a 37.25% decrease of HDL-C in DM mice, compared with those in the NC group (P < 0.05). After DNJ supplementation, TG levels were markedly decreased, and HDL-C levels were significantly increased in the treated mice (P < 0.05). Only the high DNJ dose remarkably reduced TC levels (P < 0.05), while middle and high DNJ dose decreased LDL-C levels (P < 0.05). These results observed in the HD group did not significantly differ from those observed in the NC group (P > 0.05). Additionally, LPL mRNA expression was significantly decreased in the DM group relative to the NC group (P < 0.05) and remarkably increased followed by DNJ treatment (Figure 4a, P < 0.05). LPL is a key enzyme in lipid metabolism and hydrolyzes lipoprotein triglycerides to produce fatty acids for utilization or storage.28 Hence, increased LPL expression may account for the decreased TG levels in the DNJ-treated diabetic mice. Effect of DNJ on Alanine Transaminase (ALT) Activity and Hepatic Glycogen and Pyruvic Acid Content. Liver injury can impair the accumulation of hepatic glycogen and disrupt glucose homeostasis, thereby contributing to the development of hyperglycaemia.29 Higher ALT activity that was used to assess the degree of liver damage indicates more severe damge.30 Our results showed increased ALT activity in diabetic mice. This implies increased permeability, damage, and necrosis of hepatocytes, which has resulted in a marked decrease in the hepatic glycogen level and a significant increase in the pyruvic acid level (Figure 5b, P < 0.05). However, after treatment with DNJ, ALT activity significantly decreased in a dose-dependent manner (Figure 5a, P < 0.05). Furthermore, a trend toward increasing levels of hepatic glycogen and decreasing levels of pyruvic acid was observed in all treated groups, but no obvious dose-dependent relationship was evident. DNJ Regulates the Expression of Key Glucose Metabolism Enzymes. Glucose metabolism dysfunction occurs in diabetes.31 Herein, we hypothesized the changes observed in serum glucose (Figure 3a), glycogen, and pyruvic acid (Figure 5b) were related to improved hepatic glucose metabolism. To test this hypothesis, we examined the expression of key proteins controlling hepatic glucose metabolism pathways of glycolysis and gluconeogenesis. In glycolysis, GK, PFK, and PK are key rate-limiting enzymes that mediate glucose oxidation. GK is a key player in blood glucose homeostasis. GK catalyzes the phosphorylation of glucose and provides the first substrate for glycolysis, glycogenesis, and the pentose phosphate pathway.32 PFK catalyzes the phosphorylation of fructose-6-phosphate to fructose-1,6-bisphosphate, which is a key regulatory step in the glycolytic pathway.33 PK catalyzes another limited step of glycolysis to transfer a

treatment with DNJ for 4 weeks, diabetic mice exhibited improved glucose tolerance, including the rapid removal of blood glucose after 30 min (Figure 3b). Consistent with the characteristics of type 2 diabetes, the mice in the DM group had significantly higher fasting serum insulin (FSI, Figure 3c) and homeostasis model assessmentinsulin resistance (HOMA-IR, Figure 3d) index levels than that of the normal control (NC) group (P < 0.05). After administration of DNJ to diabetic mice, the FSI and HOMAIR index levels significantly decreased in a dose-dependent manner (P < 0.05). Phospho-insulin receptor (Tyr 999) and insulin receptor substrate-1 (IRS-1, Ser 307) play important roles in the insulin-signaling pathway. Insulin receptor (IR), is transmembrane tyrosine kinase receptor that is activated by insulin.24 Insulin binds the agonistic IR ligand and triggers the autophosphorylation of IR’s tyrosine residues to facilitate the insulin signaling pathway. IR phosphorylation generates an IRS-1 binding site, and IRS-1 binding subsequently activates or inhibits the downstream signaling pathway via phosphorylation. IRS-1 phosphorylation, on a tyrosine residue, is required for insulin-stimulated responses, while the phosphorylation of IRS-1 at Ser 307 inhibits this process. Phosphorylation of IR or IRS-1 alleviates or results in insulin resistance, respectively.25 The expression of these proteins was investigated as shown in Figure 3e,f. The phospho-IR (Tyr 999) and -IRS-1 (Ser 307) levels in the liver of the DM group were significantly decreased by 50.39% and increased by 63.89%, respectively (P < 0.05), compared with those in the NC group. Treatment of diabetic mice with DNJ led to a dose-dependent increase in phosphoIR (Tyr 999) expression (P < 0.05) and a dose-dependent decrease in phospho-IRS-1 (Ser 307) expression (P < 0.05). These changes could alleviate insulin resistance and were consistent with the HOMA-IR results, which suggested that the high-blood-glucose-induced signaling pathway impairment could be reversed or reactivated by DNJ. Effect of DNJ on Serum Lipid Levels. Diabetic mice also showed lipid metabolism abnormalities including increased triglyceride (TG), serum total cholesterol (TC), and low density lipoprotein-cholesterol (LDL-C) levels and decreased high density lipoprotein-cholesterol (HDL-C) and lipoprotein lipase (LPL) levels.26 The hypertriglyceridemia observed in the diabetic mice may be due to increased absorption and formation of triglycerides in the form of chylomicrons following exogenous consumption of a fat-rich diet. Hypertriglyceridemia could also result from increased endogenous TG-enriched very low density lipoprotein production and decreased TG uptake in peripheral tissues and increased dietary cholesterol absorption by the small intestine.27 As shown in Figure 4, there was a significant increase of TG, TC, E

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Figure 6. Effect of DNJ extract on the expression of key glycolysis enzymes. (a) GK, (b) PK, (c) PFK. (a−d) Mean values ± SD with different letters over the bars are significantly different (P < 0.05) based on Duncan’s multiple range test.

Figure 7. Effect of DNJ extract on the expression of key gluconeogenesis enzymes. (a) PCB, (b) PEPCK, (c) FBPase, (d) G-6-Pase. (a−e) Mean values ± SD with different letters over the bars are significantly different (P < 0.05) based on Duncan’s multiple range test.

(Figure 6, P < 0.05), causing decreased glucose utilization for energy production and triggering hyperglycemia, as was previously reported.35 After treatment with DNJ, the protein

phosphate group from phosphoenolpyruvate to adenosine diphosphate.34 Here, we found that GK, PK, and PFK expressions were significantly decreased in diabetic mice F

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Figure 8. Effect of DNJ extract on gut microbiota composition at the phylum (a) and genus (b) levels. (a−e) Mean values ± SD with different letters over the bars significantly differ (P < 0.05) based on Duncan’s multiple range test.

as FBPase in the low dose (LD) group (Figure 7c). This attenuated gluconeogenesis and inhibited glucose production. Moreover, in the diabetic mice treated with DNJ, we observed decreased pyruvic acid and increased glycogen levels (Figure 5b). These results indicated that DNJ promotes the transformation of glucose-6-phosphate to glycogen, not glucose, which improves the glucose tolerance of treated animals. Effect of DNJ on Gut Microbiota Composition. Growing evidence has shown that gut microbiota play a critical role in the regulation of diabetes development.38 Characterization of the gut microbiota in hyperglycemic patients and healthy control subjects would aid our understanding of the pathogenic mechanisms and provide insights into approaches to modulate the microbial community for preventative purposes. In this study, we demonstrated that

expressions of GK and PK in the middle dose (MD) and HD groups and PFK expression in the HD group were significantly improved (P < 0.05). Taken together, these results indicate that DNJ accelerates hepatic glucose metabolism to synthesize glycogen and decrease serum glucose levels. Furthermore, increasing evidence shows that excessive hepatic glucose production, via the gluconeogenesis pathway, elevates glucose levels in diabetic patients.36 PCB, PEPCK, FBPase, and G-6-Pase are crucial rate-limiting gluconeogenic enzymes that catalyze the synthesis of glucose from noncarbohydrate precursors.37 The increased expression of these enzymes in the liver could be responsible for the serum glucose increase in diabetic mice (Figure 7). Treatment with DNJ significantly down-regulated PCB, PEPCK, FBPase, and G-6Pase expression in the MD and HD groups (Figure 7), as well G

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Figure 9. (a) Spearman’s correlation analysis of the fecal bacterial communities and glycometabolism indices. *P < 0.05, **P < 0.01. (b) Scatter plot for the correlation between fecal bacterial communities and FSG.

ously.42−56 Lactobacillus and Bif idobacterium are important probiotic bacterial genera that are able to produce substances that inhibit pathogens or act against metabolic diseases, thus affecting the gastrointestinal tract microbial balance and improving human health.42,43 Gut bacterial populations such as Lachnospiraceae NK4A136 and Alistipes are associated with the release of intestinal hormones for regulation of insulin release and reversing insulin resistance.44 Alistipes was one of the most abundant genera of bacteria in mouse intestine and could ferment glucose and lactate to propionate, acetate, and succinate.45 Oscillibacter was negatively correlated with body mass index or postprandial glucose area under the curve.46 Metabolite examination has shown that norank Lachnospiraceae is a n-butyrate producing microorganism in the gut that influences host energy regulation and mucosal integrity.47 In addition, Ruminococcaceae UCG-014 and Prevotellaceae UCG001 are the main mucosa-associated microbial populations and are present in reduced levels in patients with immune system diseases and metabolic disorders.48,49 The Prevotellaceae family (including Prevotellaceae UCG-001 and Ga6A1 group) is thought to be associated with impaired glucose tolerance.50 Moreover, the lipoteichoic acids produced by Weissella have been shown to increase the secretion of pro-inflammatory cytokines and contribute to host inflammatory responses.51 Compared with the NC group, the changes observed in the amount of Ruminococcus and Anaerostipes in the DM group of our study were similar to those reported by Krych et al.52 and Xie et al.,53 respectively. Meanwhile, Ruminococcus may decrease the number of FoxP3+ Treg cells, thus protecting against diabetes to alleviate hyperglycemia.52 Klebsiella, the most common Gram-negative bacteria that cause severe intestinal inflammation in humans, is reported to be an important biomarker of diabetes.54 Multiple studies have

diabetes was associated with compositional changes in the intestinal microbiota primarily at the phylum and genus levels. Bacteroidetes and Firmicutes are two main communities that affect energy metabolism homeostasis, and their abundance is related to glucose tolerance.5 Our data demonstrated that the relative abundance of Firmicutes was significantly lower in diabetic mice than that in normal mice, while the proportion of Bacteroidetes was somewhat higher (Figure 8a).These results are in agreement with those of a previous report,5 but contradict those of other studies.39−41 Furthermore, the ratio of Firmicutes to Bacteroidetes was significantly reduced in the DM group compared with the NC group. However, the reverse tendency was observed in diabetic mice treated with DNJ in a dose-dependent manner (Figure 8a). At the genus level, compared with the NC group, Lactobacillus, Lachnospiraceae NK4A136 group, norank Lachnospiraceae, Alistipes, Oscillibacter, Lactococcus, Alloprevotella, Helicobacter, Roseburia, Bacteroidales, and Bif idobacterium were significantly decreased in the DM group (Figure 8b, P < 0.05); while Klebsiella, Ruminococcaceae UCG-014, Weissella, Ruminococcus, Prevotellaceae Ga6A1 group, unclassif ied Lachnospiraceae, Escherichia-Shigella, Anaeroplasma, and Anaerostipes were significantly increased in diabetic mice (P < 0.05). Treatment with DNJ significantly increased Lactobacillus, Lacknospiraceae NK4A136 group, Oscillibacter, EscherichiaShigella, and Bif idobacterium (P < 0.05) levels and decreased Ruminococcaceae UCG-014, Weissella, Ruminococcus, Prevotellaceae Ga6A1 group, Anaerostipes, and Lactococcus levels (P < 0.05). Besides, norank Lachnospiraceae and Alistipes were significantly increased (P < 0.05), and Klebsiella, Prevotellaceae UCG-001, and Bacteroidales S24-7 group were significantly decreased (P < 0.05) in MD and HD group. These gut microbiota changes were similar to those reported previH

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reported the altered abundance of Bacteroidales S24−7 group is common in diabetes-sensitive mice fed a high-fat diet,55,56 as displayed in the present study. Last but not least, compared with the DM group, DNJ treatments further increased the abundance of Escherichia-Shigella and additionally decreased Lactococcus, rather than returning them to normal levels, which was a perplexing phenomenon. We speculate that EscherichiaShigella and Lactococcus may be opportunistic pathogens in mice and that they would not cause physical damage in diabetic mice after DNJ treatment. The mechanism of this phenomenon needs to be investigated in future studies. Correlation between Bacterial Abundance and Glycometabolism Indexes. To elucidate the relationship between gut microbiota abundance and physiological indexes, Spearman’s correlation analysis was performed (Figure 9a). Prevotellaceae UCG-001, Prevotellaceae Ga6A1 group, Weissella, Klebsiella, Ruminococcus, Bacteroidales S24−7 group, and Ruminococcaceae UCG-014 were positively correlated with FSG, FSI, HOMA-IR, pyruvic acid, and protein expression of IRS-1 and the key glycoheterogenous enzymes including PCB, PEPCK, FBPase, and G-6-Pase. These same genuses were negatively correlated with the protein expression of IR and key glycolysis enzymes including GK, PK, and PFK. The opposite correlations were observed for Lacknospiraceae NK4A136 group, Alistipes, norank Lachnospiraceae, Lactobacillus, Oscillibacter, and Alloprevotella. Specifically, Prevotellaceae UCG-001 (P < 0.01), Prevotellaceae Ga6A1 group (P < 0.01), Weissella (P < 0.01), Klebsiella (P < 0.01), Ruminococcus (P < 0.01), Bacteroidales S24-7 group (P < 0.05), and Ruminococcaceae UCG-014 (P < 0.01) were significantly positively correlated with FSG (Figure 9b). Moreover, negative correlations were found between FSG and Alistipes (P < 0.01), norank Lachnospiraceae (P < 0.05), Lactobacillus (P < 0.01), and Alloprevotella (P < 0.05) (Figure 9b). These results demonstrated that specific bacterial genera were significantly correlated with diabetes indices and the expressions of key glycometabolism enzymes to relieve or aggravate disorders of glucose metabolism. On the basis of these findings, it was possible that DNJ may improve the glucose metabolism disturbance by restoring the unbalanced intestinal bacteria and the gut microbiota structure. In conclusion, L. fermentum and S. cerevisiae mixed fermentation with a mulberry leaf is an effective method that significantly (P < 0.05) enhances the DNJ yield, which lends useful consideration in the future developing practice of highly effective extraction bioactives from natural resources. After fermentation, the hypoglycemic activity of DNJ was evaluated and the mechanisms underlying the effect were examined. DNJ decreased the FSG (P < 0.01) and FSI (P < 0.05), improved serum lipid levels (P < 0.05), and reversed the insulin resistance (P < 0.05) of diabetic mice. The hypoglycemic mechanism of DNJ was clarified as follows: (i) the insulin signaling pathway was activated by up-regulating of IR (P < 0.05) and down-regulating of IRS-1 protein expression (P < 0.05), thus resulting in promotion of insulin sensitivity; (ii) the damaged glucose metabolic pathways were restored by increasing the protein expression of key glycolysis enzymes (GK, PK, and PFK) (P < 0.05) and decreasing the protein expression of gluconeogenesis key enzymes (PCB, PEPCK, FBPase, and G-6-Pase) (P < 0.05); (iii) the destructed gut microbiota community was reshaped. The results supports that DNJ is a good candidate for the treatment of diabetes mellitus. On the other hand, the method used for investigating the

hypoglycemic mechanism in this study by combination of analyzing the protein expression of key enzymes in the glucose metabolic pathways and the composition of gut microbiota is helpful in studying the hypoglycemic mechanism of other natural products.

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EXPERIMENTAL SECTION

Materials and Chemicals. Fresh mulberry (Morus atropurpurea) leaves, harvested from the Huadu Bosun field production base of Guangdong Academy of Agricultural Science, were dried by heat pump and mechanically pulverized. Nine different strains of fermentation microorganisms were examined: three lactobacillus strains (L. plantarum, L. fermentum, and L. mesenteroides), four molds (A. niger, T. reesei, R. japonicas, and T. viride), S. cerevisiae, and B. subtilis. All were purchased from the Guangdong culture collection center. The DNJ standard and streptozotocin (STZ) were purchased from Sigma (St. Louis, MO, U.S.A.). Glucobay (50 mg per tablet, 30 tablets) was purchased from Bayer (Germany). 9-Fluorenylmethyl chloroformate (FMOC-Cl, > 99%) (Guangzhou Qiyun Biotechnology Co. Ltd., China) and other chemicals and reagents used in this study were analytical grade. Inoculum of Strains. The lactic acid bacteria, molds, yeast, and bacillus were inoculated into 100 mL of MRS broth (Huankai Microbial Co. Ltd., Guangdong, China), PDA broth (Huankai Microbial Co. Ltd., Guangdong, China), YPD broth (Huankai Microbial Co. Ltd., Guangdong, China), and LB broth (Huankai Microbial Co. Ltd., Guangdong, China), respectively. The lactic acid bacteria, molds, and yeast were cultivated at 30 °C at 3000g, and the bacillus was cultivated at 37 °C and 200 rpm in a shaking incubator (THZ-D, Huamei Biochemical Instrument Co., Ltd., Taicang, China) for 24 h. Screening of Strains. Mulberry leaf powder (1 g) was suspended in 100 mL of distilled water to make a 1% solution and then autoclaved. Next, 5 ×104 CFU/mL of activated fermentation strain was added to the mulberry leaf solution or basic mediums (MRS broth for lactic acid bacteria; PDA broth for molds; YPD broth for yeast; LB broth for bacillus) for fermentation. The mulberry leaf solutions or basic mediums with activated lactic acid bacteria, molds, and yeast were incubated at 35 °C and 200 rpm for 96 h and those with activated bacillus were incubated at 37 °C and 200 rpm for 96 h. After fermentation, each sample was centrifuged at 14 000g and 4 °C for 10 min, and the supernatants were stored at −20 °C before analysis. After single-strain screening, we studied mixed fermentation using two strains (L. plantarum + L. fermentum, L. plantarum + L. mesenteroides, L. plantarum + S. cerevisiae, L. fermentum + L. mesenteroides, L. fermentum + S. cerevisiae, and L. mesenteroides + S. cerevisiae) to optimize DNJ extraction efficiency. We used 5 × 104 CFU/mL of each activated strain (1:1 ratio) and incubated them at 35 °C and 3000g for 96 h. Then, the fermentation liquors were centrifuged at 14 000g for 10 min, and the supernatants were used for further DNJ analysis. Optimizing the Mulberry Leaf Fermentation Conditions. Mulberry fermentation conditions using mixed strains were optimized. In a typical experiment, 100 mL of phosphate buffer (pH 5.0−7.0) containing 5−25 mg/mL of mulberry leaves was autoclaved at 121 °C for 30 min. Subsequently, 5 × 104−2.5 × 105 CFU/mL of activated strains were added to induce fermentation followed by incubation for 24−120 h at 25 °C−40 °C and 200 rpm. Inoculation quantity, mulberry leaf concentration, temperature, pH, and reaction time were varied for optimization. After fermentation, the broths were inactivated at 100 °C for 10 min and then centrifuged at 14 000g for 10 min. The DNJ content of the supernatant was analyzed. The precipitate was dried at 60 °C for 12 h and then characterized by SEM. Purification of DNJ. DNJ was refined with cation (001 × 7) and anion (201 × 4) exchange resin from the DNJ fermentation extract, I

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Table 1. Primer Sequences for Quantitative PCR gene

forward

reverse

β-actin LPL

5′-CGGACACGGACAGGATTGACA 5′-GAGTTTGACCGCCTTCCG

5′-CCAGACAAATCGCTCCACCAACT 5′-TCCCGTTACCGTCCATCC

the following formula: HOMA − IR = FSI (mIU/L) × FSG (mmol/ L) ÷ 22.5.59 Quantitative PCR Analysis. Total RNA was extracted using Trizol reagent according to the manufacturer’s instructions (Invitrogen, Carlsbad, U.S.A.). Reverse-transcription was performed using a PrimeScript RT reagent Kit (Takara Bio Inc., Kyoto, Japan). A Roche LightCycler 480 (Roche, Basel, Switzerland) was used for RTPCR, which was performed as follows: 1 cycle of 95 °C for 10 min, 45 cycles of 95 °C for 15 s, 55 °C for 20 s, and 72 °C for 30 s. The LPL PCR primers were synthesized by Guangzhou Aiji Biotechnology Co. Ltd., China) (Table 1). Gene expression data were normalized to βactin, and the relative expression level of each gene was calculated using the 2−ΔΔCt method. Western Blot Analysis. Liver samples collected from each group were homogenized and centrifuged at 14 000g at 4 °C for 15 min. The proteins were separated by 10% SDS-PAGE and transferred to a 0.45 μm PVDF membrane (Millipore Corp., U.S.A.). The membranes were blocked with 5% defatted milk powder for 1 h and incubated with anti-GK, anti-insulin receptor (Tyr 999), anti-insulin receptor substrate 1 (Ser 307), anti-PK, anti-PECK (Proteintech, Chicago, U.S.A.), anti-PFK, anti-PCB, anti-FBPase (Abcam Inc., U.S.A.), and anti-G-6-Pase (Shanghai Yu Bo Biological Technology Co., Ltd., China) antibodies for 1 h at 25 °C. The membranes were then washed with TBST (Tris-HCl buffer with 1% Tween 20) and incubated with antirabbit IgG horseradish peroxidase conjugated secondary antibodies (Quanshijing Biotechnology Company, Beijing, China) at 25 °C for 1 h. After washing with TBST, the proteins on the membrane were visualized by ECL (Thermo Scientific, Rockford, U.S.A.). Mouse anti-β-actin was used as an internal control. Miseq Genome Sequencing Analysis of Community Structures. The universal primer (338F_806R) was used to amplify the V3−V4 region of 16S rDNA from metagenomic DNA in mouse feces. The amplicons were normalized, pooled, and sequenced on the Illumina Miseq desktop sequencer (Illumina, America). The resultant sequences were screened using Usearch (version 7.1) including dereplication, cluster, and chimera detection analyses. Sequences with similarity ≥97% were defined as an operational taxonomic unit (OTU), and representative OTUs sequences were used to identify a species. Quantitative Insight into Microbial Ecology software (version 1.8.0) was used to analyze microbial communities and diversity, including demultiplexing and quality filtering, OTU picking, taxonomic assignment, phylogenetic reconstruction, diversity analyses, and visualizations. Statistical Analysis. The results were expressed as means ± standard deviations (SD). The differences between the mean values for the groups were analyzed by one-way analysis of variance followed by Duncan’s multiple range test. P < 0.05 was deemed significant. All statistical analyses were performed using SPSS (SPSS-17, Chicago, IL, U.S.A.).

further purified by silica gel H column according to the previous study, the purity of which was over 95%.22 Quantitative Determination of DNJ. The DNJ content was analyzed using the modified method of Kim et al.57 A 10 μL sample aliquot, 10 μL of 0.4 M potassium borate buffer (pH 8.5), and 20 μL of 5 mM FMOC-Cl were added to a tube and incubated at 20 °C for 20 min. Glycine (10 μL of 0.1 M) was added to quench the remaining FMOC-Cl and terminate the reaction, and 950 μL of 0.1% acetic acid was added to stabilize the DNJ-derived compound (DNJ-FMOC). The mixture was filtered through a 0.22 μm syringe filter and the filtrate was analyzed by HPLC (1200, Agilent, Palo Alto, CA, U.S.A.) using a C18 column (4.6 × 250 mm, 5 μm, ZORBAX Eclipse XDB C18, Agilent, Palo Alto, CA, U.S.A.) with a diode array detector (DAD; 1200, Agilent, Palo Alto, CA, U.S.A.; excitation 254 nm, emission 322 nm). The analyte was eluted at 25 °C with an acetonitrile and 0.1% acetic acid (55:45, v/v) mobile phase with a flow rate of 1.0 mL/min. All samples were independently analyzed in triplicate, and results were expressed as mg/g dried weight mulberry leaf. Characterization of Mulberry Leaf Powder by SEM. After extraction, the mulberry leaf powder was coated with Pt for SEM imaging using a sputter coater (K550, Emitech, UK) under vacuum. The morphology of the samples was then observed by SEM (Hitachi, Japan) at 100 kV. Animal Experiments. Four-week-old male Kunming mice (18 ± 2 g) and chow and high-fat diets were purchased from Guangzhou University of Chinese Medicine. All animal experiments were performed with the approval of the Sericultural and Agri-Food Research Institute, Guangdong Academy of Agricultural Sciences Institutional Animal Care and Use Committee (SYXK [Yue] 20150149). After environmental adaptation for 7 days, Kunming mice were randomly divided into NC and experimental groups. The NC and experimental groups were fed chow and high-fat diets, respectively.58 After 4 weeks, the mice were fasted overnight. The experimental group was intraperitoneally injected with 35 mg/kg body weight STZ, and the NC group was treated with an equivalent volume of saline solution. Seven days after STZ was injected, the FSG of the mice was measured from the tail tip. Mice with FSG levels above 11.1 mmol/L were used as diabetic mice and were randomly divided into five groups with 10 in each group. The five groups were the following: the DM group (saline solution, 0.5 mL); the positive drug control group (PC, glucobay 75 mg/kg body weight, 0.5 mL); HD group (DNJ 125 mg/kg body weight, 0.5 mL); MD group (DNJ 62.5 mg/kg body weight, 0.5 mL); and LD group (DNJ 31.25 mg/kg body weight, 0.5 mL). The mice were orally treated once daily via gavage and were given free access to drinking water and their respective diet for 28 days. Body weight was measured every 3 days. FSG was examined on days 0, 7, 14, 21, and 28 of the experiment. Additionally, an oral glucose tolerance test (OGTT) was performed by orally administering glucose (2.0 g/kg body weight) after 12 h fasting on treatment day 26 and measuring and recording blood glucose from the tail tip at 0, 30, 60, 90, and 120 min. On the last day of the experimental period, mice were sacrificed by decapitation after overnight fasting. Blood was collected from the orbital sinus. The plasma was separated by centrifugation at 1000g at 4 °C for 15 min and stored at −80 °C until required for analysis. The liver and feces of each mouse were immediately collected, transferred to liquid nitrogen, and stored at −80 °C for further analysis. Biochemical Estimations. FSI, ALT, glycogen, pyruvic acid, TC, TG, and HDL-C levels were measured using commercial kits (Nanjing Jiancheng Bioengineering Company, China). A HOMA-IR index was applied to evaluate the insulin sensitivity of the mice using

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jnatprod.9b00205. Effect of DNJ on food intake and body weight gain in diabetic mice (PDF) J

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AUTHOR INFORMATION

Corresponding Authors

*E-mail for H.W.: [email protected]. Tel.: +86-2022236669. *E-mail for Y.X.Z.: [email protected]. Tel.: +86-2037227141. ORCID

Hong Wu: 0000-0002-4711-9979 Yu-Xiao Zou: 0000-0002-8245-3286 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge the Science and Technology Plan Project of Guangdong Province (No. 2017A050501022), the Open Project Program of Provincial Key Laboratory of Green Processing Technology and Product Safety of Natural Products (KL-2018-02), the Innovation Teams of Modern Agricultural Industry Technology System in Guangdong Province (Nos. 2016LM1087, 2016LM2151), and the Key Research and Development Program of Guangdong Province (No. 2019B020213001) for their financial support.

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