Lycium barbarum L. leaves ameliorate type 2 diabetes in rats by modulating metabolic profiles and gut microbiota composition

Biomedicine & Pharmacotherapy 121 (2020) 109559

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Lycium barbarum L. leaves ameliorate type 2 diabetes in rats by modulating metabolic profiles and gut microbiota composition

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Xue-qin Zhaoa,1, Sheng Guoa,*,1, You-yuan Lua, Yue Huaa, Fang Zhanga, Hui Yana, Er-xin Shanga, Han-qing Wangb, Wen-hua Zhangc, Jin-ao Duana,* a Jiangsu Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, State Administration of Traditional Chinese Medicine Key Laboratory of Chinese Medicinal Resources Recycling Utilization, Nanjing University of Chinese Medicine, Nanjing 210023, China b School of Pharmacy, Ningxia Medical University, Yinchuan 750021, China c Bairuiyuan Goqi.Corp., Yinchuan 750002, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Type 2 diabetes mellitus Leaves of Lyciumbarbarum L. Metabolomics Gut microbiota

The leaf of Lycium barbarum L. (LLB) has been widely used as a tea, vegetable, and herb in China and Southeast Asia for centuries; this is because of the hypoglycemic effect it has, but the mechanism behind this effect is still unclear. In this study, a type 2 diabetic mellitus (T2DM) rat model, induced by a high-fat diet combined with low-dose streptozotocin (STZ) injections, was adopted. The biochemical index was determined and the histopathological and metabolomics analyses of serum and urine and 16S rDNA sequencing of the gut microbiota were performed. We evaluated the hypoglycemic effects and the mechanism of action of the water extract from LLB, which contained neochlorogenic acid, chlorogenic acid, caffeic acid, and rutin (up to 6.06%). The relationships between biochemical indexes, serum and urine metabolites, and gut microbiota were analyzed. The results showed that the LLB extract could noticeably modulate the levels of blood glucose and lipids in diabetic rats as well as repair injuries in livers, kidneys and pancreas. The changes in serum and urine metabolites caused by T2DM were reversed after the administration of LLB; these changes were found to mainly be correlated with the following pathways: nicotinate and nicotinamide metabolism, arachidonic acid metabolism, and purine metabolism. Sequencing of the 16S rDNA from fecal samples showed that the LLB extract could reverse the gut microbiota dysbiosis that T2DM had induced. Therefore, we conclude that T2DM, which altered the metabolic profiles and gut microbiota, could be alleviated effectively using the LLB extract.

1. Introduction Type 2 diabetes mellitus (T2DM) is a complex disease; many factors, such as lifestyle and genetic factors, can affect its emergence and development. It is characterized by metabolic disorders, which are caused by a deficiency in insulin secretion or insulin action or both. Chronic hyperglycemia often causes various complications, such as nerve damage [1], diseases in the kidney [2], and cardiovascular diseases [3,4]. The pathogenesis of diabetes is complex and can be related to family

history, aging, obesity, hypertension, energy intake, and other risk factors [5,6]. As a typical metabolic disease, its development process not only includes abnormal metabolism of dietary substances, such as sugar, fat, and protein, but also the existence of histopathological and intestinal microflora changes [7]. In the past few decades, the prevalence of T2DM has increased in many countries. It is estimated that the global number of adult patients will rise to 592 million in 2035 [8,9]; moreover, the number of global diabetes-related deaths have increased from 1.5 million in 2012 to 1.6 million in 2015 [10].

Abbreviations: LLB, leaves of Lycium barbarum L; T2DM, type 2 diabetes mellitus; TCM, traditional chinese medicine; STZ, streptozotocin; UPLC-Q-TOF/MS, ultrahigh performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry; SPF, specific pathogen free; FBG, fasting blood glucose; T-CHO, total cholesterol; TG, triglyceride; LDL-C, low density lipoprotein cholesterin; FFA, free fatty acid; OX-LDL, oxidized low density lipoprotein; INS, insulin; ALT/GPT, alanine transaminase; AST, Aspartate aminotransferase; PCA, principal component analysis; OPLS-DA, orthogonal partial least squares discrimination analysis; KEGG, genes and genomes; HMDB, human metabolome database; ANOVA, one-way analysis of variance; ESI, Electrospray ionization; OTU, operational taxonomic unit; LEfSe, linear discriminate analysis effect size; H&E, hematoxylin and eosin; QC, quality control; LDA, linear discriminant analysis; COG, cluster of orthologous groups; PLS-DA, partial least squares discrimination analysis; lysoPC, lysophosphatidylcholine ⁎ Corresponding authors. E-mail addresses: [email protected] (S. Guo), [email protected] (J.-a. Duan). 1 These authors have contributed equally to this work. https://doi.org/10.1016/j.biopha.2019.109559 Received 6 September 2019; Received in revised form 8 October 2019; Accepted 16 October 2019 0753-3322/ © 2019 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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were obtained from the Experimental Animal Center of Zhejiang Province, Zhejiang, China (SCXK(Zhe)2014-0001). The animal experiment ethics committee of Nanjing University of Traditional Chinese Medicine confirmed that the experiments conformed to the Regulations on the Administration of Laboratory Animals issued by the State Science and Technology Commission and the Detailed Rules for the Implementation of the Administration of Medical Laboratory Animals issued by the Ministry of Health. All animals were kept under SPF conditions with a 12-h occulting cycle and had free access to water and feed. The temperature and relative humidity of the environment were 23 ± 2 °C and 60 ± 2%, respectively. After one week of adaptive feeding, tail vein blood samples of all rats were collected to determine the fasting blood glucose (FBG) level using a glucose meter and glucose test papers (Sannuo Biosensor Co., Ltd., Shenzhen, China). The rats with FBG (4.0 ± 1.0 mmol/L) were randomly divided into a healthy control group (N, n = 8) and a diabetic group. The schematic diagram for the time design of the experiment is shown in Figure S1. The rats in the healthy control group were fed with a conventional diet, and the rats in the diabetic group were fed with a high-fat diet (formula for 67.5% basic feed, 2.5% egg yolk, 20% sugar, and 10% lard, Experimental Animal Center of Zhejiang Province, China) for 4 weeks. Subsequently, a single intraperitoneal injection of 1% STZ (Lot. WXBC7268 V, Sigma-Aldrich, USA) dissolved in aseptic citric acid buffer (pH = 4.2) was administered to the diabetic group with a dosage of 30 mg/kg for 3 days [20]. The rats with FBG ≥ 11.1 mmol/L were considered as diabetic rats and then randomly divided into four groups (8 rats per group): model group (M), diabetic rats treated with high-fat diet; positive group (P), diabetic rats treated with high-fat diet and metformin hydrochloride (175 mg/kg, Lot. AA03572, Shi Guibao Pharmaceutical Co., Ltd., Shanghai, China); high dose group (HL), diabetic rats treated with high-fat diet and high dose of LLB extract (2.08 g/kg); low dose group (LL), diabetic rats treated with highfat diet and low dose of LLB extract (1.04 g/kg). The rats in each treatment group were intragastrically administrated with LLB or metformin hydrochloride for 4 weeks, and the rats in the healthy control group and the model group were administrated the same volume of physiological saline. During the experiment, the rat mental activity, hair changes, body weight, and water and food intake were monitored, and FBG was measured once a week.

Therefore, the epidemic of the disease has become a major public health problem globally. At present, insulin sensitizers, insulin secretory drugs, and insulin are used in the clinical treatment of T2DM, and their main mechanism is to promote the uptake and utilization of glucose in tissues and cells in the body and to inhibit glycogen decomposition and gluconeogenesis. However, their effects are not always satisfactory, and their use can cause side effects such as hypoglycemia [11] and gastrointestinal discomfort [12]. Therefore, the development of cheaper, more effective, and safer antidiabetic drugs is needed. Traditional Chinese medicine (TCM) has unique advantages in the treatment of chronic diseases because of its multi-active components and few side effects [13]. As a result, many studies have focused on developing effective therapies and drugs with few side effects using TCM. Lycium barbarum L. is a traditional Chinese medicinal material in China, and its mature fruits have been used as traditional medicine and functional food for more than 2000 years in China. In addition, its leaves (also known as Tianjingcao in China) were noted with the activities of reinforcing deficiency and benefiting essence, antithermic and eye-clearing effects, which are also widely used as tea, vegetables and herbs in China and Southeast Asia [14,15]. Previous studies showed that the leaves of L. barbarum (LLB) are mainly composed of polysaccharide, phenolic acids, flavonoids, coumarins, carotenoids, and alkaloids [16]. Among them, polysaccharides, phenolic acids, and flavonoids are the main active components, with anti-oxidative, anti-aging, and hypolipidemic activities [17]. In addition, LLB is also used as a functional food with hypoglycemic effect in China [18,19]. However, its mechanism of action is still unclear, limiting its clinical application. In the present study, we examined the effects of LLB extract on rats with T2DM induced by high-fat diet and low-dose streptozotocin (STZ) administration. Ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to investigate the serum and urine metabolic profiles, and 16S rDNA sequencing was used to investigate the composition of gut microbiota. Moreover, integrative analysis of the relationships between the biochemical indexes, serum and urine metabolites, and specific gut microbiota profiles was performed to understand the potential antidiabetes mechanisms of oral LLB extract. 2. Materials and methods

2.4. Sample collection and determination of biochemical indicators 2.1. Materials

LLB (907.2 g) was extracted by boiling in water (10 L × 2) for 1.5 h and 1 h. The filtrate was merged, concentrated, and freeze-dried [20]. Obtained LLB extracts (218.3 g) were stored at 4 °C for further use. When administered to animals, the extracts, 0.208 g and 0.104 g, were dissolved in 1 mL distilled water to obtain solutions of 0.208 g/mL and 0.104 g/mL, respectively. The qualitative and quantitative analysis of the composition of LLB extract was performed on UPLC-Q-TOF/MS (Waters, USA) and UPLC-PDA (Waters, USA), respectively. The apparatus characteristics and chromatographic conditions are provided in Supplementary Methods.

After the last administration, fresh feces were collected and preserved at −80 °C for 16S rDNA sequencing. The rats were fasted in the metabolic cage, and 12-h urine samples were collected. After the experiment, the animals were euthanized using chloral hydrate (Lot. Z13D8Y50658, Yuanye Biotechnology Co., Ltd., Shanghai, China). The blood samples, livers, kidneys, and pancreas were collected after dissecting rats. Among them, the livers, kidneys, and pancreas were stored in 4% paraformaldehyde (Lot. 1808266, Biosharp, Hefei, China). The serum was obtained by centrifuging blood samples at 860 × g for 15 min at 4 °C. Urine, serum, and various organs were stored at −80 °C for further study. Total cholesterol (T-CHO), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), free fatty acid (FFA), and alanine aminotransferase (ALT) activities in the serum, as well as, proteinuria in urine were estimated by using commercially available assay kits (Nanjing Jiancheng Bioengineering Institute, China). Insulin (INS) and oxidized low-density lipoprotein (OX-LDL) levels were estimated using ELISA kits (Nanjing Jiancheng Bioengineering Institute). Aspartate aminotransferase (AST) kits were purchased from Solarbio Science &Technology Co., Ltd. (Beijing, China).

2.3. Animal study

2.5. Histopathological analysis

LLB was collected from Yongning county of Ningxia province, China, in August 2017. The herb was identified as Lycium barbarum L. by professor Jin-ao Duan, Nanjing University of Chinese Medicine. After collection, the leaves were dried, by airing them at an average temperature of 25 °C, to a moisture content of 10%, and kept under closed and dry conditions at 25 ± 5 °C. 2.2. Preparation and composition determination of LLB extract

After dissection, the tissues fixed with 4% paraformaldehyde

Healthy specific pathogen free (SPF)-grade rats (male, 200–240 g) 2

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on samples of different groups in order to find the species that had significant differences in sample classification using linear discriminate analysis effect size (LEfSe) software. ANOVA was used to test the significance of the differences between groups. The copy number of the 16S marker gene in the species genome was removed using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States software. The family information and Kyoto Encyclopedia of Genes and Genomes (KEGG) information of OTU Cluster of Orthologous Groups (COG) were obtained using Greengene ID corresponding to each OTU. Based on the information of the COG database, the description information and function information of each COG were analyzed from the eggNOG database, and the function abundance spectrum was obtained.

solution (Lot. 1810898, Biosharp, Hefei, China) were dehydrated, embedded in paraffin, and cut into 4-μm-thick slices. The slides were observed under light microscopy at 200× using an optical microscope after the sections were stained with hematoxylin and eosin (H&E). 2.6. Metabolomics study on serum and urine After being thawed at room temperature, the serum and urine samples were extracted with three times of acetonitrile to precipitate protein. The mixture was vortexed for 30 s and centrifuged at 13,000 rpm for 15 min at 4 °C. The supernatant was transferred into a 200 μL liquid vial, and an aliquot of 2 μL was injected into UPLC-MS for metabolism analysis. To monitor the stability of the UPLC-QTOF/MS, two samples were selected randomly from each group and mixed at the same amount to obtain the quality control (QC) sample. Prior to the sample injection, QC sample was continuously injected for 6 times to adjust and balance the system and was injected every eight samples to monitor the stability of the analysis [21,22]. The separation was performed on an ACQUITY UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 μm), which was maintained at 35 °C, and the mobile phase was composed of 0.1% formic acid solution (A) and acetonitrile (B) at a flow rate of 0.4 mL/min. For serum analysis, the gradient elution conditions were as follows: 0.0–3.0 min, 5–45% B; 3.0–13.0 min, 45–95% B; 13.0–14.0 min, 95% B; 14.0–15.0 min, 95–5% B. For urine analysis, the gradient elution conditions were as follows: 0.0–8.0 min, 5–30% B; 8.0–11.0 min, 30–70% B; 11.0–13.0 min, 70–95% B; 13.0–14.0 min, 95% B; 14.0–15.0 min, 95–5% B. Electrospray ionization (ESI) mass spectra were acquired in both positive and negative ionization modes by scanning over the m/z range of 100–1000 Da. The conditions were as follows: extraction voltage, 2.0 V; cone voltage, 30 V; capillary voltage, 3.0 kV; collision energy, 20–50 eV; ion source temperature, 120 °C; desolvation temperature, 350 °C; cone gas flow rate, 50 L/h; and desolvation gas flow rate, 600 L/ h. High purity nitrogen and leucine-enkephalin (ESI +: 556.2771 m/z, ESI −: 555.2615 m/z) were the gas collision and locked mass solution, respectively.

2.8. Statistical analysis SPSS 24.0 software and GraphPad Prism version 7.0 were used to analyze the data. The data were expressed in terms of mean ± standard deviation, and significance levels were indicated as 0.01 < *p < 0.05, 0.001 < **p < 0.01, and ***p < 0.001. The original data obtained from mass spectrometry were processed using Masslynx v4.1 software. The intensity of each ion was normalized to the total ion count, and a list of data consisting of retention time, m/z value, and normalized peak area was generated. The data were imported into EZinfo 2.0 for principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA). The variables with VIP > 1 and P < 0.05 were selected as potential biomarkers for further statistical analysis. For the identification of potential markers and metabolic pathways, the following databases were used: Human Metabolome Database (HMDB) (http://www.hmdb.ca/), Metabo Analyst (http:// www.metaboanalyst.ca/) and KEGG database (http://www.genome.jp/ kegg/). Pearson correlation coefficient was used to show the relationships between parameters, and a heat map was constructed using Pyplot. The correlation coefficient was always between −1 and +1. If the absolute value of the correlation coefficient was closer to 1, the linear relationship was better.

2.7. Gut microbiota analysis 3. Results The total DNA was extracted from fecal samples (200 mg) using the E.Z.N.A. soil kit (American BioTek instruments Co., Ltd., USA) as per manufacturer instructions. The concentration and purity of DNA were detected using NanoDrop 2000, and the quality of DNA was detected using 1% agarose (Yingjie Life Technology Co., Ltd., USA) gel electrophoresis. The variable region of V3–V4 was amplified using < the 338 F (5′-ACTCCTACGGGAGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTA-3′) primers. The amplification procedure was as follows: pre-denaturation at 95 °C for 3 min, followed by 27 cycles of denaturation at 95 °C for 30 s, annealing for 30 s, and extension at 72 °C for 30 s, and last extension at 72 °C for 10 min. PCR products were recovered using 2% agarose gel electrophoresis, purified using AxyPrep DNA Gel Extraction kit (Axygen, USA), eluted using Tris-HCl, and detected using 2% agarose gel electrophoresis. QuantiFluor™-ST (Promega, USA) was used for quantitative detection. According to the standard operating procedure of Illumina MiSeq platform (Illumina, USA), the PE 2 × 300 library was constructed [22]. Trimmomatic software was used to control the original sequence, and FLASH software was used to assemble the sequence. Using UPARSE software (version 7.0, http://drive5.com/uparse/) based on 97% similarity, the non-repeated sequences (excluding single sequences) were clustered into operational taxonomic units (OTUs), and the representative sequences of OTUs were obtained. The abundance and diversity of single sample microbial community were reflected by single sample diversity (Alpha diversity) analysis. Additionally, at the OTU level, Sobs index was calculated to evaluate the species diversity of samples at different sequencing quantities. Linear discriminant analysis (LDA) was performed

3.1. Qualitative and quantitative analysis of LLB extract composition Based on the total ion chromatogram in negative ion mode (Figure S2A) and mass spectrometry information, we identified the chemical composition of LLB extract compared to the references [15,23] (Table 1). Neochlorogenic acid, chlorogenic acid, caffeic acid, and rutin contents were quantified. The contents of neochlorogenic acid, chlorogenic acid, caffeic acid, and rutin in LLB extract were 13.47 mg/ g, 32.41 mg/g, 3.67 mg/g, and 11.02 mg/g, respectively (Figure S2B). 3.2. Biochemical indexes The results of biochemical analysis are shown in Fig. 1. Model group showed significant increase in FBG, T-CHO, TG, LDL-C, FFA, and OXLDL levels, as well as a significant decrease in INS level compared to those of the normal group (P < 0.05). All the above results suggested that the T2DM rat model was successful and could be used for further experiments. In addition, elevated levels of ALT, AST, and proteinuria suggested that diabetes could cause liver and kidney diseases in the rats of the model group. Compared to the model group, the levels of FBG were reduced by 30.5%, 19.5%, and 19.9%, respectively, after the administration of metformin hydrochloride and high and low dose of LLB extract. As shown in Fig. 1, most of the biochemical indexes such as TCHO, TG, LDL-C, FFA, and OX-LDL tended to be normal after treatment, significantly different from the model group (P < 0.05). The levels of ALT, AST, and proteinuria were reduced to varying degrees after 3

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Table 1 Identification of chemical constituents of LLB extract. Peak no.

Retention/ min

λ

max

(nm)

[M-H]− (m/z)

1 2 3 4 5

3.10 5.57 6.26 6.52 6.85

324; 325; 326; 353; 353;

239 241 242 254 255

353.0862 353.0857 179.0352 771.1991 771.1964

6 7 8 9 10 11 12 13

7.30 8.35 9.01 11.96 12.81 14.43 16.08 17.18

340; 265 318; 266 327; 246 279; 224 353; 255 340; 296; 265 248; 268; 252

755.2025 755.2047 367.1039 245.0916 609.1447 593.1500 285.0396 374.2429

MS fragments (m/z)

Identified components −

191.0564 [M-H-caffeoyl] 191.0564 [M-H-caffeoyl]−; 179.0359 [M-H-Quinic]− 135.0451 [M-H-CO2]− 609.1479 [M-H-glucosyl]− 625.0609 [M-H-rhamnosyl] −; 301.0350 [M-H- rhamnosyl -sophorosyl]593.1510 [M-H-glucosyl]− 593.1484 [M-H-glucosyl]− 191.0543 [M-H-ferruloyl]− 203.0822 300.0272 [M-H-rutinosyl]− 285.0391 [M-H-rutinosyl]− 175.0402; 133.0305 158.9279; 176.9375; 194.9463

Neochlorogenic acid Chlorogenic acid Caffeic acid Quercetin-3-O-rutinoside-7-O-glucoside Quercetin-3-O-sophoroside-7-Orhamnoside Kaempferol-3-O-rutinoside-7-O-glucoside Not identified 5-O-Ferruloylquinic acid Not identified Rutin Kaempferol-3-O-rutinoside Luteolin Not identified

Fig. 1. Determination of FBG, TG, T-CHO, LDL-C, OX-LDL, FFA, INS, Proteinuria, ALT, AST and ALT/AST among all groups. (N) normal group; (M) model group; (P) positive group; (HL) high dose group of LLB extract; (LL) low dose group of LLB extract. Values are presented as mean ± SD, n = 8. 0.01 < #p < 0.05, 0.001 < ##p < 0.01, ###p < 0.001: model vs control; 0.01 < *p < 0.05, 0.001 < **p < 0.01, ***p < 0.001: treatment vs model. 4

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Fig. 2. Effects of LLB components on histopathological changes of Livers, kidneys and pancreas in the normal group (N), model group (M), positive group (P), high dose group of LLB extract (HL); low dose group of LLB extract (LL).(200, HE staining).

model group. Besides, the tubular epithelial cells were swollen, and nucleus was pyknotic and hyperstained. All treatment groups showed reduced pathological changes at different degrees, and only a small amount of eosinophilic substance and glomerular capillary blood stasis were detected in the treatment groups. In the pancreatic tissue, the islet was regularly round-shaped, the acinar boundaries were clear, and there was no obvious abnormality. Compared to the normal group, the shape of the pancreatic islet in the model group was irregular, and the distribution was uneven. A small amount of vacuoles occurred in cytoplasm, and part of the cells became swollen and loosened. However, islet cells in the treatment groups appeared less damaged, indicating

administration of LLB, indicating that LLB could improve liver and kidney injury from T2DM. In the model group, the ratio of ALT to AST showed an upward trend, while there was no significant difference among all groups. 3.3. Histological examination Based on the images of histological examination of the kidney (Fig. 2), the renal tubule in the normal group was closely arranged, and its epithelial cells were regular and normal in shape. Renal capillary congestion and a small amount of neutrophils could be observed in the 5

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Fig. 3. PCA model results between normal (N) and T2DM (M) rats in positive mode (A1: plot of serum; A3: plot of urine) and negative mode (A2: plot of serum; A4: plot of urine). OPLS-DA model between N and M rats in positive mode (B1: plot of serum; B3: plot of urine), and negative mode (B2: plot of serum; B4: plot of urine). S-plot of OPLS-DA model for M vs. N group in positive mode (C1: serum; C3: urine) and negative mode (C2: serum; C4: urine). Metabolic pathways involved in all markers in serum and urine for T2DM (D): (a) Pantothenate and CoA biosynthesis, (b) Pentose and glucuronate interconversions, (c) Nicotinate and nicotinamide metabolism, (d) Arachidonic acid metabolism. Metabolic pathways involved in potential markers in serum and urine regulated by LLB (E): (e) Nicotinate and nicotinamide metabolism, (f) Arachidonic acid metabolism, (g) Purine metabolism.

6

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indicated that the microflora of each group had a high richness, and there was no significant difference between the different groups. The histograms of the community (Fig. 5A, B) showed that Firmicutes and norank_f_Bacteroidales_S24-7_group were dominant after the merger of species, with the abundance less than 0.1% at the phylum and genus level, respectively. At the genus level, the genera with significant differences between the model group and normal group were identified using LEfSe analysis (Fig. 5C). Overall, 45 genera were found to have significant differences, most of which belonged to Firmicutes and Bacteroidetes. Besides, Escherichia_Shigella, Romboutsia, and Ruminoccaceae_NK4A214_group had the strongest association with the normal group, while Bifidobacterium, Prevotella_9, and Blautia were the most representative bacteria in the model group. Among them, the levels of six genera, including Marvinbryantia, Parasutterella, Prevotellaceae_NK3B31_group, Blautia, Ruminococcus_1, and Coprococcus_2, were reversed to the normal level after administration of LLB extract (Fig. 6). Additionally, a total of 25 metabolic functions were found to be related to the intestinal flora of diabetes rats (Figure S3B). Among them, carbohydrate transport and metabolism, amino acid transport and metabolism, as well as DNA replication, recombination, and repair were the most important functions. Based on the information of the KEGG database, the metabolic pathways in Table S2 were screened out, and carbohydrate, amino acid, energy, and abnormal lipid metabolisms, glucose biosynthesis, and metabolic pathways contributed directly or indirectly to the development of diabetes.

that metformin hydrochloride and LLB extract could ameliorate the pathology. The liver tissues of normal rats exhibited normal cellular structure with clear liver cord, abundant cytoplasm, clean lumen, and complete and normal portal vein endothelia. In contrast, there were red blood cells in the lumen and a small amount of inflammatory cells infiltrated around the vein of T2DM rats. Compared to the model group, the liver tissues of the treatment groups tended to be normal. 3.4. Metabolism analysis UPLC-QTOF/MS was used to detect and collect the metabolic information of serum and urine in positive and negative ion modes. Firstly, the data of serum and urine samples from the N and M groups was analyzed using PCA. The PCA score plots (Fig. 3A1–A4) showed significant clustering in serum and urine samples of the N and M groups in both positive and negative ion modes, which indicated that the samples from the M group deviated from normal levels and showed metabolic disorders. Subsequently, potential markers of interest (marked in red boxes) were extracted from the S-plots (Fig. 3C1–C4) constructed after OPLS-DA (Fig. 3B1–B4). The metabolites in serum and urine samples were identified based on MS/MS data, KEGG, and HMDB 3.6, and a total of 22 metabolites were annotated (7 from serum and 15 from urine). The mass spectrometry data and their changing trends in the model group compared to the normal group are shown in Table S1. Among the total 22 metabolites, the levels of nine metabolites, including neriantogenin, prostaglandin H2, lysophosphatidylcholine (lysoPC) (18:0), xanthosine, histidinal, pantetheine 4′-phosphate, L-allothreonine, homovanillin, and niacinamide, significantly increased in the model group compared to the normal group, whereas other 13 metabolite levels dramatically decreased. The relative contents of 22 potential biomarkers in each group were analyzed using One-way analysis of variance (ANOVA), and the differences between the model group and other groups were compared. The results (Fig. 4) showed that the contents of 12 biomarkers were reversed (P < 0.05) after administration of LLB extract. Neriantogenin (M2) and prostaglandin H2 (M3) were detected in serum, while xanthosine (M8), malonic acid (M9), histidinal (M10), homovanillin (M14), niacinamide (M15), 5amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxylate (M16), hippuric acid (M17), 2-phenylacetamide (M18), L-allothreonine (M19), and trans-S-(1-propenyl)-L-cysteine (M20) were detected in urine. These results indicated that the administration of LLB extract could effectively regulate the abnormal changes in these potential biomarkers. To explore the possible pathway influenced by the LLB extract, the different metabolites identified in serum and urine in the model and administration groups were analyzed using the MetPA Analyst database to construct analytical metabolic pathway. As shown in Fig. 3D, the larger the -log (P) and the redder the color, the higher the significance of the metabolites, and the larger the impact, the more nodes were hit by the metabolites. In this study, the pathway with influence value greater than 0.1 was screened as the potential target pathway. The results showed that the occurrence of T2DM might be associated with pantothenate and CoA biosynthesis, pentose and glucuronate interconversions, nicotinate and nicotinamide metabolism, and arachidonic acid metabolism. Nicotinate and nicotinamide metabolism was considered as the most important metabolic pathway modified by LLB extract, with an impact-value of 0.2381 (Fig. 3E), suggesting that the administration of LLB could modify this metabolic pathway and related biomarkers to exert a curative effect for T2DM.

3.6. Potential relationships between biochemical indexes, serum and urine metabolites, and gut microbiota In order to comprehensively analyze the relationships between nine biochemical indexes, 22 metabolites, and 47 genera of gut microbiota, a correlation matrix was established calculating Pearson correlation coefficient. As shown in Fig. 7, eight biochemical indexes, including FBG, T-CHO, TG, LDL-C, FFA, ALT/GPT, OX-LDL, and proteinuria were positively correlated with neriantogenin, prostaglandin H2, xanthosine, histidinal, homovanillin, and niacinamide, as well as with Prevotellaceae_NK3B31_group, Marvinbryantia, and Blautia, while INS was negatively correlated. Besides, TG was negatively correlated with malonic acid, 5-amino-1-(5-phospho-D-ribosyl) imidazole-4-carboxylate, hippuric acid, 2-phenylacetamide, and trans-S-(1-propenyl)-Lcysteine. Regarding the relationships between metabolites and gut microbiota, Marvinbryantia and Prevotellaceae_NK3B31_group were positively correlated with neriantogenin, histidinal, homovanillin, and niacinamide, while they were negatively correlated with hippuric acid, 2-phenylacetamide, and 5-amino-1-(5-phospho-D-ribosyl)imidazole-4carboxylate. Moreover, Coprococcus_2 was positively correlated with neriantogenin and niacinamide, and Blautia was positively correlated with histidinal, homovanillin, and L-allothreonine. These relationships suggested that biochemical indexes, metabolites, and gut microbiota could affect each other. 4. Discussion At present, it is generally believed that the diabetic rat model induced by high-fat diet combined with low-dose STZ injection is very similar to type 2 diabetes in humans, which is mainly characterized by increased blood glucose level, impaired islet cell function, and decreased immune function [24]. Moreover, some studies showed that multiple administration of low-dose STZ could ensure the stability of the model [25,26]. Therefore, the pathogenesis and characteristics of the animal model established in this study are relatively similar to the clinical pathogenesis and metabolic characteristics of type 2 diabetes patients, ensuring the reliability of the diabetes model and providing a good animal model for the pharmacological study of LLB. The purpose of this study was to evaluate the therapeutic effect of LLB extract and to elucidate its anti-diabetes mechanism by metabolomics and intestinal

3.5. Gut microbiota analysis In order to explore whether the antidiabetic effect of LLB extract was associated with gut microbiota, we analyzed the fecal flora of rats after 4 weeks of treatment. The species diversity of the samples was evaluated at the OTU level. The curves of Sobs exponent (Figure S3A) 7

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Fig. 4. Relative peak area of potential biomarkers identified in serum in positive and negative ion mode. (N) normal group; (M) model group; (P) positive group; (HL) high dose group of LLB extract; (LL) low dose group of LLB extract. Values are presented as mean ± SD, n = 6. 0.01 < #p < 0.05, 0.001 < ##p < 0.01, ###p < 0.001: model vs control; 0.01 < *p < 0.05, 0.001 < **p < 0.01, ***p < 0.001: treatment vs model. M1, LysoPC (22:6(4Z,7Z,10Z,13Z,16Z,19Z)); M2, Neriantogenin; M3, Prostaglandin H2; M4, LysoPC (20:5(5Z,8Z,11Z,14Z,17Z)); M5, LysoPC (16:1(9Z)/0:0); M6, LysoPC (20:3(8Z,11Z,14Z)); M7, LysoPC (18:0); M8, Xanthosine; M9, Malonic acid; M10, Histidinal; M11, Pantetheine 4′-phosphate; M12, Dodecanoic acid; M13, Octanoylglucuronide; M14, Homovanillin; M15, Niacinamide; M16, 5-amino-1-(5-phospho-D-ribosyl)imidazole-4-carboxylate; M17, Hippuric acid; M18, 2-Phenylacetamide; M19, L-Allothreonine; M20, trans-S-(1Propenyl)-L-cysteine; M21, Pyrroline hydroxycarboxylic acid; M22, Indole-3-carboxylic acid.

flora profiling. After 4 weeks of treatment, LLB extract was proved to regulate blood glucose and lipid levels, reduce proteinuria, transaminase levels, and insulin resistance, and protect glomeruli, renal tubules, and islet cells in diabetic rats, consistent with the reports on the antidiabetic effect of leaf of Lycium chinense, a plant from the same genus [27]. LLB is rich in polysaccharides, phenolic acids, flavonoids, and other active ingredients. Among them, caffeic acid ameliorates diabetic nephropathy via regulation of AMPK signaling and PI3 K pathway [28], chlorogenic acid can modulate lipid and glucose metabolism regulation [29], and rutin shows anti-hyperglycemia and protective effects against the development of diabetic complications [30]. Finally, polysaccharides in L. barbarum also exhibit hypoglycemic effects and

improve insulin resistance activities [31]. Thus, the anti-diabetes effect of LLB may be an integrated effect of the above components. In our study, the contents of neochlorogenic acid, chlorogenic acid, caffeic acid, and quercetin-3-O-sophoroside-7-O-rhamnoside in LLB extract were relatively high. They may play an important role in ameliorating T2DM, which should be studied in the future. Metabolomics explains the physiological and pathological conditions of subjects and reflects the rule that subjects are affected by internal and external factors in their overall metabolism by analyzing the compositions and changes of endogenous metabolites in biological samples [32–35]. Analysis results of metabolomics demonstrated that LLB extract could modify the level of neriantogenin, prostaglandin H2, 8

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Fig. 5. Effects of LLB extract on the structure and abundance of gut microbiota in diabetic rats. The percent of community abundance on phylum (A) and genus (B) level. LDA scores (C) was generated from LEfSe analysis, showing the differences of bacterial abundance on genus level. (N) normal group; (M) model group; (P) positive group; (HL) high dose group of LLB extract; (LL) low dose group of LLB extract.

increase lipid peroxides, stimulate arachidonic acid metabolism, and promote inflammation [38,39]. Interestingly, after the administration of the LLB extract, the level of prostaglandin H2 was significantly reduced, indicating that LLB might contribute to protect islet β-cells and modify insulin release. The catabolism of xanthosine eventually leads to the production of high uric acid levels, which are associated with diabetic complications [40,41]. Additionally, hippuric acid is a metabolite of phenylalanine, which can be converted from dietary aromatic compounds by intestinal microbes [42], and evidences indicate that blood levels of amino acids are related to the development of T2DM. Moreover, the increase in tyrosine and phenylalanine leads to an increased relative risk of T2DM [43]. The variation tendencies of xanthosine, hippuric acid, and L-allothreonine in diabetes rats in our study were the same as previously described [44]. The level of nicotinamide was reversed after the administration of LLB extract. Niacinamide is a

xanthosine, malonic acid, histidinal, homovanillin, niacinamide, hippuric acid, 2-phenylacetamide, L-allothreonine, 5-amino-1-(5-phosphoD-ribosyl)imidazole-4-carboxylate, and trans-S-(1-propenyl)-L-cysteine. Arachidonic acid is an important polyunsaturated fatty acid that plays an important role in inflammatory response as the precursor of many compounds with strong pro-inflammatory and anti-inflammatory effects [36]. Normal levels of arachidonic acid can promote the synthesis and secretion of insulin, while excessive levels of arachidonic acid can lead to the apoptosis of islet β-cells and reduce the synthesis and secretion of insulin [37]. In this study, the levels of prostaglandin H2 in the model group were obviously higher than that in the normal group. Prostaglandins are related to pathological processes, such as inflammation, allergic reactions, and cardiovascular diseases. They can regulate the conversion of arachidonic acid to secondary metabolites and the disorder of lipid metabolism. In addition, oxidative stress can 9

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Fig. 6. Average relative abundance of different genera of gut microbiota in each group. (N) normal group; (M) model group; (P) positive group; (HL) high dose group of LLB extract; (LL) low dose group of LLB extract. Values are presented as mean ± SD, n = 6. 0.01 < #p < 0.05, 0.001 < ##p < 0.01, ###p < 0.001: model vs control; 0.01 < *p < 0.05, 0.001 < **p < 0.01, ***p < 0.001: treatment vs model.

related to INS. Prevotella_1 has the ability to degrade fiber sources [51], while there are no reports about the relationships between Ruminococcus_torques_group and T2DM. Therefore, Prevotella_1 and Ruminococcus_torques_group may be the candidate genera for predicting and treating T2DM. However, further studies should be performed to confirm the hypothesis.

metabolite produced by the amidation of niacin, which is closely related to many metabolic processes, including glucose glycolysis, fat metabolism, and pyruvate metabolism [45]. Thus, from the above metabolomics results, we could speculate that the components of LLB could regulate nicotinate and nicotinamide, arachidonic acid, purine, and amino acids metabolisms to interfere in the development of T2DM. Studying the interactions between TCM and microbiota composition and metabolism and transformation of TCM compounds, we can understand and use TCM more effectively [46]. Analysis results of the fecal microbiota showed that the abundances of Parasutterella, Ruminococcus_1, and Prevotellaceae_NK3B31_group in the gut microbiota of diabetic rats significantly increased compared to those in normal rats, consistent previous observations [47]. Prevotellaceae is associated with impaired glucose tolerance [48], and the increased abundance of Prevotellaceae in this study further confirms that the changes in intestinal flora are closely related to the occurrence of diabetes. The genus Blautia was found to be increased in diabetic rats, in contrast to the report that Blautia was associated with lower blood glucose levels [47]. These contradictory results may be related to different environments, including air quality and food intake [49]. Moreover, the abundance of Collinsella was negatively related to INS found in this study, confirming that the abundance of Collinsella increased in T2DM patients [50]. Prevotella_1 and Ruminococcus_torques_group were also negatively

5. Conclusions In general, LLB extract can improve liver, kidney, and pancreas injury and regulate metabolic profiles in T2DM model rats. This may be associated with the regulation of metabolic disorders through arachidonic acid, purine, and amino acid metabolisms, as well as, the reversion of intestinal flora disorders caused by T2DM. To confirm the regulation of lipid metabolism, the key genes should be analyzed further. The results may provide useful hints for the treatment of T2DM and be helpful for the utilization of LLB resources and the development of new therapeutic drugs. Author contributions X.-q. Z., S. G. and J.-a. D. designed the study. X.-q. Z., Y.-y. L. and Y. H. performed the experiment. X.-q. Z., S. G. and E.-x. S. participated in 10

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Fig. 7. Correlation analysis between biochemical indexes, metabolites and gut microbiota.

data analysis. F. Z., H. Y., H.-q. W. and W.-h. Z. contributed to the preparation of the samples. X.-q. Z. and S. G. wrote the manuscript. S. G. and J.-a. D. supervised the study and revised the manuscript.

Talent Peaks Project in Jiangsu Province (No.YY-026), the Key R & D Program of Ningxia Hui Autonomous Region (East-West Science and Technology Cooperation, No. 2017BY079) and China Agriculture Research System (CARS-21)Special subsidy for public health services of traditional Chinese medicine (CS [2018] No. 43)

Funding This work was supported by the National Natural Science Foundation of China (No. 81873189, 81773837 and 81473538), Six 11

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Declaration of Competing Interest The authors declare that there are no conflicts of interest.

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Appendix A. Supplementary data

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Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.biopha.2019.109559.

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