Abstract
As essential players in the field of diabetes treatment, resveratrol (RSV) has received much attention in recent years. However, it is unclear whether it can improve insulin resistance by regulating the long-chain non-coding RNA (lncRNA). The objective of this study was to investigate whether RSV improves high-fat diet-induced insulin resistance in mice by regulating thelncRNANONMMUT008655.2 in vivo and in vitro. To this end, animal and cell insulin resistance models were developed. Specifically, C57BL/6J mice were fed a high-fat diet (HFD) and administered RSV for eight weeks. Additionally, mouse Hepa cells were treated with palmitic acid, transfected with siRNA NONMMUT008655.2, and treated with RSV. Treated mice and cells were then compared to normal controls that were not exposed to RSV. In the animal model, RSV was found to decrease the levels of fasting blood glucose, triglycerides, and low-density lipoprotein cholesterol, as well as the insulin index and area under the curve; while increasing the insulin sensitivity index. Besides, RSV decreased the expression levels of SOCS3, G6PC, and FOXO1 yet increased that of p-Akt and p-FOXO1 in mice. The same results were observed following knockdown of NONMMUT008655.2 in cells. Overall, our results suggest that RSV may improve hepatic insulin resistance and control blood glucose levels by downregulating lncRNA NONMMUT008655.2.
Keywords: Blood glucose, insulin resistance, resveratrol, long-chain non-coding RNA
Introduction
Type-2 diabetes mellitus (T2DM) is one of the leading non-infectious diseases worldwide [1]. Its occurrence is increasing rapidly, with the number of T2DM adults expected to reach 590 million by 2035 [2]. Currently, no specific biomarkers have been identified for T2DM-concurring diseases, and effective treatment options that prevent disease progression are also unavailable. Although a variety of factors lead to T2DM development, insulin resistance (IR) is one of the most significant. IR is a state in which the muscles, fat cells, and liver reduce the uptake and utilisation of glucose [3]. Consequently, the beta cells of the pancreatic islets secrete more insulin to stabilise blood sugar, leading to hyperinsulinemia [4].
Resveratrol (3,4’,5-trihydroxy-1,2-stilbene; RSV) is a non-flavonoid polyphenol first isolated in 1940 from the roots of white squash, and subsequently detected in more than 70 plant species, including red grapes, blueberries, mulberries, peanuts, and raspberries [5]. Numerous biological benefits have been reported for RSV, including anti-tumour, antioxidant, anti-inflammatory, cardioprotective, and neuroprotective properties [6]. Several studies have also reported on its anti-IR effects via blood sugar regulation and protection of the islet beta cells [7,8].
Long non-coding RNA (lncRNA) is a transcription product consisting of more than 200 nucleotides and lacking protein-coding ability. Most lncRNAs are conserved and exhibit lower expression levels than mRNA [9]. Recent studies have shown that lncRNAs are important regulators of numerous biological processes, including proliferation, differentiation, invasion, and apoptosis, while also being associated with insulin resistance-related signalling pathways and metabolic diseases [10,11]. However, the role of the lncRNA NONMMUT008655.2 in RSV-mediated improvement of hepatic IR remains unclear. Therefore, the objectives of this study were to investigate the effects of RSV on IR in the mouse liver in vivo and in vitro, as well as to explore the interrelationship of RSV and NONMMUT008655.2 in improving IR. Cumulatively, our data may provide novel insights into the prevention and treatment of T2DM.
Materials and methods
Animals
Thirty six-week-old, clean grade (body weight, 21.0-23.0 g), male C57BL/6J mice obtained from the Beijing Vital River Laboratory Animal Centre (Licence number: SCXK [Beijing] 2016-0006) were housed in the animal laboratory at the Clinical Research Centre, Hebei Provincial People’s Hospital (temperature, 20-25°C; relative humidity, 40-60%; photoperiod, 12 h light/12 h dark) with ad libitum access to food and water. The experiment was approved by the Ethics Committee of Hebei General Hospital (approval number: 201920, 19-09-2018) and complies with the International Regulations for the Administration of Laboratory Animals.
Establishment of the animal model
Thirty-six C57BL/6J mice were adaptively fed for one week and then, randomly divided into three groups: 12 in the control (CON) group were fed a regular diet (D12450J; 20% protein, 70% carbohydrates, 10% fat; 3.85 kcal g-1), 12 in the high-fat diet (HFD) group were fed a D1249 diet (20% protein, 20% carbohydrates, 60% fat; 5.24 kcal g-1), and 12 mice in the HFD+RSV group were fed a high-fat diet and administered RSV solution. RSV (Sigma-Aldrich, St. Louis, MO, USA) was dissolved in dimethyl sulfoxide (DMSO; Sigma-Aldrich; 30 mg ml-1) and diluted 1:1 in NaCl 0.9%. The HFD+RSV group was intragastrically administered RSV (100 mg kg-1) daily for six weeks [12], whereas CON and HFD mice were administered NaCl 0.9%, containing 0.1% DMSO.
Weekly body weight and food intake were recorded during mouse breeding. IPGTT was performed after eight weeks of feeding and 12 h of dry fasting. Blood glucose was measured with a blood glucose metre (Johnson & Johnson, New Brunswick, NJ, USA) using blood samples collected from the tail vein at 0 min, 15 min, 30 min, 60 min, and 120 min after a 1.5 g kg-1 intraperitoneal injection of 50% glucose diluted 1:1 in NaCl 0.9%. Area under the curve (AUC) was used to validate the establishment of the animal IR model.
Serum and tissue specimens
After six weeks of feeding and 12 h of dry fasting, three randomly selected mice from each group were intraperitoneally administered 37.5 IU kg-1 of insulin (Sigma-Aldrich) and euthanised 20 min later by cervical dislocation. Blood was collected by cardiac puncture and centrifuged at 5,000 rpm min-1 for 15 min. The upper layer containing serum was transferred into a microcentrifuge tube and stored at -80°C. The liver was quickly dissected and washed with saline solution. A small piece was fixed in 4% paraformaldehyde, whereas the rest of the tissue was placed in a cryotube, frozen in liquid nitrogen, and stored at -80°C.
Determination of blood indicators
Total cholesterol (TC), TG, HDL-C, and LDL-C were determined using kits purchased from the Nanjing Jiancheng Bioengineering Institute (Jiangsu Sheng, China). Serum insulin was determined using an ELISA kit (ALPCO Diagnostics, Salem, NH, USA). All protocols were performed in accordance with manufacturer’s instructions.
Haematoxylin-eosin staining
The liver tissue fixed in 4% paraformaldehyde was dehydrated within 24 h of collection with a conventional alcohol gradient (100%, 95%, 80%, 75%). Tissues were then made transparent with xylene, embedded in paraffin, and serially sliced at a thickness of 5 μm. The sections were deparaffinised, treated with haematoxylin for 5 min, differentiated with 70% ethanol for 10 s, and washed with distilled water. After staining with eosin, the sections were dehydrated and sealed with resin. The morphological features of liver sections were observed under a light microscope.
Oil Red O staining
Liver tissue sections were placed in Oil Red O solution for 8-10 min with light protection, rinsed with distilled water, differentiated with 75% alcohol, and washed with distilled water. After staining with haematoxylin, the tablets were sealed with glycerine gelatine. The morphological features of liver sections were observed under a light microscope.
Western blot analysis
Proteins were separated by sodium dodecyl sulphate-polyacrylamide gel electrophoresis, transferred to a polyvinylidene difluoride membrane, and then, blocked with 5% skim milk for 2 h. The primary antibodies were diluted in a blocking solution as follows: β-actin: mouse antibody, 1:1000; t-Akt: rabbit antibody, 1:2000; p-Akt (Ser 473): rabbit antibody, 1:1000; FOXO1: rabbit antibody, 1:1000; G6PC: rabbit antibody, 1:2000; SOCS3: rabbit antibody, 1:1000; and Anti-p-FOXO1 (p-Ser256): rabbit antibody, 1:1000. Antibodies were purchased from Cell Signalling Technology (Danvers, MA, USA), Proteintech Group (Rosemont, IL, USA), and Abcam (Cambridge, UK). The membrane was incubated with the primary antibodies at 4°C overnight and washed thrice for 10 min each. Next, the membrane was incubated with the secondary antibody at 18-30°C for approximately 50 min and washed thrice for 10 min each. Bands were displayed with a gel imager and quantified using Image-J. Standardisation was performed against β-actin.
RT-qPCR
Total RNA was extracted using the RNAsimple Total RNA Kit (Tiangen Biotech, Beijing, China), and the concentrations were determined using the NanoDrop 2000 (Fisher Scientific, Hampton, NH, USA). Reverse transcription was performed using PrimeScript RT with gDNA Eraser (Takara, Kusatsu, Japan). PCR was carried out using the Applied Biosystems 7300 Real-Time PCR System (Fisher Scientific) with SYBR Premix Ex Taq II (Takara) as follows: 3 min at 95°C and 41 cycles of 30 sec at 95°C, 5 sec at 95°C, and 31 sec at 60°C. The melting curve was constructed over a temperature of 60-95°C. Gene expression levels were normalised to β-actin using the 2-ΔΔCt method [13]. All primers used in this study are listed in Table 1.
Table 1.
Real-time quantitative polymerase chain reaction primers used in this study
| Gene | Forward primer (5’-3’) | Reverse primer (5’-3’) |
|---|---|---|
| Actin | GGCGCTTTTGACTCAGGATT | GGGATGTTTGCTCCAACCAA |
| NONMMUT001470.2 | GTGTGGCAGTAATTGAGGCATA | TCCGTCCAGAAGAAAAGACAATA |
| NONMMUT017329.2 | CGGGTTTTTCTCCGTATTCTAA | CACACACACACACAAATGGAAG |
| NONMMUT047231.2 | TTGCAGAGTCGTTTTTCTTATCC | CTCTCAGGAGGAAGAAGCTGAA |
| NONMMUT031874.2 | GCGTGGGACTTATCTTCAGC | ACACACTGCTTCACCACAGG |
| NONMMUT034345.2 | TGGTGAGGAGCAGAAGTGG | TGTGGATGCTATGCTGGAAA |
| NONMMUT008655.2 | TGAGCAAGTTCCACCTGTACC | GTCCCTTCTCTCCTCATTTGC |
| NONMMUT003114.2 | CCTTCACTAGCCCTCCATCA | GTTTCAGGTTCCAGCGACTT |
| Akt | AAGGAGGTCATCGTCGCCAA | ACAGCCCGAAGTCCGTTATC |
| FOXO1 | AAGGCCATCGAGAGCTCAGC | GATTTTCCGCTCTTGCCTCC |
| G6PC | TTGCATTCCTGTATGGTAGTGG | TAGGCTGAGGAGGAGAAAACTG |
| SOCS3 | CTGCTTTGTCTCTCCTATGTGG | GAATCCCTCAACTCTCTGCCTA |
High-throughput sequencing
Total RNA was extracted using the RNeasy mini kit (Qiagen, Hilden, Germany). The sequencing library was constructed using the TruSeq RNA Sample Preparation Kit (Illumina, San Diego, USA), according to the manufacturer’s instructions. Ribo-Zero rRNA was used to remove ribosomal RNA from the total RNA. After purification, the mRNA was fragmented using divalent cations at 94°C for 8 min. Using the RNA fragment as a template, the first-strand cDNA was replicated using reverse transcriptase and random primers. Second-strand cDNA synthesis was then performed using DNA polymerase I and RNase H. cDNA fragments underwent a terminal repair process, adding a single “A” base and then, joining the linker sequences. The purified product was amplified by PCR to generate the final cDNA library. Purified libraries were verified for insert size, and molar concentrations were calculated by Qubit 2.0 (Life Technologies, Carlsbad, CA, USA) and by Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Clusters were generated by cBot, diluted to a 10-pM library, and then, sequenced by the Illumina NovaSeq 6000. Library construction and sequencing were performed at Shanghai Sinomics (Shangai, China). High-throughput sequencing results have been uploaded to the GEO database. GEO accession number is GSE137840.
Identification and expression analysis of lncRNAs and mRNAs
lncRNA and mRNA were obtained from authoritative databases, lncRNA included RefSeq, Ensembls and Genebank, and mRNA included Noncode and Ensembls. Fragments in each gene segments were compared using StringTie [14] (Johns Hopkins University, Baltimore, MD, USA) and normalised with the trimmed mean of M values algorithm to calculate the Fragments Per Kilobase Million (FPKM) value of each gene. Differences in gene expressions were identified using the “edge” package in R based on the FPKM value. F-values were corrected by controlling the False Discovery Rate.
Establishment of cell model and analysis
Mouse liver cancer cells (Hepa) were purchased from the cell bank of the Chinese Academy of Sciences and stored at the Clinical Research Centre of Hebei General Hospital. Hepa cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Gibco) supplemented with 10% foetal bovine serum (Hyclone Laboratories, UT, USA), 1% non-essential amino acids (Gibco, Waltham, MA, USA), and 1% streptomycin (Hyclone Laboratories) and incubated in a 5% CO2 atmosphere at 37°C. To establish the IR model, the cells were transferred to DMEM with 0.25 mmol L-1 palmitic acid (PA) upon reaching approximately 80% confluency [15]. Glucose concentration was determined using the Glucose Oxidase Assay kit (Applygen, Beijing, China) at 0 h, 8 h, 16 h, and 24 h after the transfer was used to validate the establishment of the cell IR model.
Hepa cells were cultured in 96-well plates and treated with 10 μM, 20 μM, 30 μM, 40 μM, or 50 μM RSV dissolved in DMSO (50 mmol L-1) upon reaching approximately 80% confluency. Cell viability was calculated using CCK-8 (Dojindo, Kumamoto, Japan).
The cells were transfected in an incubator at 37°C upon reaching approximately 60% confluency. The transfection complex (200 μL OPTI-MEM, 5 μL RNA oligo stock solution, and 10 μL siRNA-Mate transfection reagent) was added to the medium at a final concentration of 50 nM siRNA. Hepa cells were divided into a control group and a knockdown group. RNA was extracted 24 h after transfection, and the knockdown efficiency of NONMMUT008655.2 was detected by RT-PCR. Hepa cells were seeded in 6-well plates, 24 h after transfection. PA and RSV were added to the corresponding groups, and cells were stimulated with insulin 24 h after treatment. Proteins were extracted for western blot 40 min after insulin stimulation.
Statistical analysis
All data were processed using SPSS 22.0 (IBM, Armonk, NY, USA), and the results were expressed as means ± standard deviation. One-way analysis of variance (ANOVA) was used in conjunction with the Student’s t-test to identify significant differences at P < 0.05. The differential expression of lncRNAs and mRNAs was expressed as q-value < 0.05, and the absolute value of fold-change was greater than or equal to a two-fold-change.
Results
Establishment of a high-fat diet-induced IR animal model
The baseline body weight of the control group (CON), and the high-fat diet group (HFD) did not differ significantly. However, after two weeks of diet intervention, the body weight of the HFD mice was significantly higher than that of CON (Figure 1A), although the mean daily caloric intake was similar between the two groups (Figure 1B). Further, the intraperitoneal glucose tolerance test (IPGTT) after eight weeks of diet intervention showed that the blood glucose levels of HFD were significantly higher than those of the CON at 0 min, 30 min, 60 min, and 120 min after the glucose saline injection (Figure 1C). Additionally, the AUC for the HFD group was significantly higher than that of CON (Figure 1D), indicating that the IR model was established successfully.
Figure 1.
Body weights and food intake of C57BL/6J mice before and IPGTT experiment result after high-fat diet intervention for 8 weeks. A. Body weight in control and high-fat diet groups; B. Food intake in CON group and HFD group; C. Blood glucose levels at 0, 15, 30, 60, and 120 min following glucose intraperitoneal injection; D. The area under the curve for glucose levels. Data are presented as the mean ± SD (n=12 in CON and n=24 in HFD). Student’s t-test was used for statistical analysis. *P < 0.05 vs CON group.
General indices and blood lipid levels
The body weight of HFD mice was significantly higher than that of CON throughout the eight weeks of diet intervention, and compared to the HFD mice treated with RSV (HFD+RSV) after four weeks of diet intervention (Figure 2A), although the daily caloric intake did not differ significantly among the groups (Figure 2B). Further, the fasting blood glucose and insulin levels were significantly higher in HFD mice compared with those in CON and HFD+RSV (Figure 2C, 2D); while the quantitative insulin sensitivity check index (QUICKI) of HFD mice was significantly lower than that of CON and HFD+RSV (Figure 2E).
Figure 2.
Body weight and insulin sensitivity indicators after resveratrol treatment. A. Body weights in control, high-fat diet and resveratrol-treated groups; B. Daily caloric intake in the three groups; C. Fasting blood glucose levels in the three groups; D. Fasting plasma insulin levels; E. Quantitative insulin sensitivity check index. Data are presented as the mean ± SD (n=12). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test. *P < 0.05 vs CON group. #P < 0.05 vs HFD group.
The triglycerides (TG), TC, and low-density lipoprotein cholesterol (LDL-C) levels were significantly higher in HFD mice compared to those in the CON group. Moreover, RSV treatment significantly reduced TG and LDL-C, and increased HDL-C, however, was not observed to have an effect on TC (Figure 3A-D).
Figure 3.
Lipid profiles after resveratrol treatment. A. Triglyceride; B. Total cholesterol; C. High-density lipoprotein cholesterol; D. Low-density lipoprotein cholesterol. Data are presented as the mean ± SD (n=12). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test. *P < 0.05 vs CON group. #P < 0.05 vs HFD group.
Liver lipid deposition
In the CON mice, the cellular structure of liver tissue was clear and intact, the cytoplasm was uniformly red-stained, and the lipid droplets were less vacuolated. Alternatively, in HFD mice, the cell structure of the liver tissue was disordered, and lipid droplets were irregularly sized in the cytoplasm. In the HFD+RSV group, the lipid droplets displaced the nucleus to the cell edge, the morphology of the liver tissue and the number of lipid droplets was intermediate to that of the CON and HFD groups (Figure 4A-C). Tissue from the CON group contained blue nuclei with a small number of orange-red lipid droplets, whereas numerous orange-red lipid droplets was observed in HFD (Figure 4D-F).
Figure 4.
Hepatic lipid deposition after resveratrol treatment. A-C. H&E staining of liver tissue after resveratrol treatment; D-F. Oil Red O staining of liver tissue after resveratrol treatment; A, D. CON group had normal liver morphology; B, E. The HFD group had a large number of lipid droplet vacuoles; C, F. HFD+RSV group had fewer vacuoles compared with HFD alone. One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test. *P < 0.05 vs CON group. #P < 0.05 vs HFD group.
Expression profiles of lncRNAs and mRNAs
The expression of lncRNAs and mRNAs in CON, HFD, and HFD+RSV was determined using high-throughput sequencing. After standardisation, a total of 51,024 lncRNAs and 31,055 mRNAs were detected in mouse liver tissues. Compared with CON, 503 differentially expressed lncRNAs (344 upregulated and 159 downregulated) and 655 differentially expressed mRNAs (418 upregulated and 237 downregulated) were detected in HFD. Compared with HFD, 95 differentially expressed lncRNAs (46 upregulated and 49 downregulated) and 102 differentially expressed mRNAs (34 upregulated and 68 downregulated) were detected in HFD+RSV (Figure 5A-D).
Figure 5.
Volcano plot of lncRNA and mRNA variation between groups. A and B. lncRNAs; C and D. mRNAs; A and C. HFD vs CON; B and D. HFD+RSV vs HFD. Red area contains lncRNAs (or mRNAs) with P < 0.05 and fold-change ≥ 2. Blue area contains lncRNAs (or mRNAs) with P < 0.05 and fold-change ≤ -2.
Of the lncRNAs and mRNAs upregulated in HFD, 25 lncRNAs and 50 mRNAs were downregulated in HFD+RSV. Of the lncRNAs and mRNAs downregulated in HFD, 25 lncRNAs and 23 mRNAs were upregulated in HFD+RSV. The expression patterns of lncRNAs and mRNAs were consistent within each group, however, differed significantly between the CON, HFD, and HFD+RSV groups (Figure 6A and 6B; Tables 2 and 3).
Figure 6.
Clustering heat map of differentially expressed lncRNAs and mRNAs. A. lncRNAs; B. mRNAs. Red area contains lncRNAs (or mRNAs) with P < 0.05 and fold-change ≥ 2. Blue area contains lncRNAs (or mRNAs) with P < 0.05 and fold-change ≤ -2.
Table 2.
Expression patterns of lncRNAs in mice fed a regular diet (CON), a high-fat diet (HFD), or an HFD and treated with resveratrol (HFD+RSV)
| Seq name | Log2 (Fold-change) | Regulation (HFD vs CON) | Log2 (Fold-change) | Regulation (HFD+RSV vs HFD) |
|---|---|---|---|---|
| NONMMUT056862.2 | 6.53438 | Up | -6.63876 | Down |
| NONMMUT060510.2 | 5.23091 | Up | -5.35853 | Down |
| NONMMUT031873.2 | 4.87425 | Up | -3.78681 | Down |
| NONMMUT051901.2 | 4.85021 | Up | -4.94198 | Down |
| NONMMUT149177.1 | 4.81429 | Up | -3.35448 | Down |
| NONMMUT031874.2 | 4.38032 | Up | -2.83355 | Down |
| ENSMUST00000181265 | 3.66591 | Up | -4.15283 | Down |
| NONMMUT153837.1 | 3.49033 | Up | -1.90271 | Down |
| NONMMUT058999.2 | 3.38641 | Up | -3.27548 | Down |
| NONMMUT003114.2 | 3.13425 | Up | -1.20431 | Down |
| NONMMUT119418.1 | 3.15295 | Up | -2.57853 | Down |
| NONMMUT068763.2 | 3.10618 | Up | -3.62327 | Down |
| MSTRG.16066.11 | 2.79283 | Up | -2.73949 | Down |
| NONMMUT008655.2 | 2.73349 | Up | -1.49769 | Down |
| NONMMUT047505.2 | 2.55273 | Up | -2.64522 | Down |
| NONMMUT059480.2 | 2.34636 | Up | -2.63681 | Down |
| NONMMUT044184.2 | 1.93205 | Up | -2.01973 | Down |
| NONMMUT017329.2 | 1.60991 | Up | -1.52653 | Down |
| ENSMUST00000180982 | -7.99741 | Down | 8.66425 | Up |
| NONMMUT034345.2 | -6.54169 | Down | 6.58842 | Up |
| NONMMUT069202.2 | -6.24163 | Down | 6.00385 | Up |
| NONMMUT053361.2 | -5.44591 | Down | 6.47468 | Up |
| NONMMUT147866.1 | -5.44114 | Down | 5.91095 | Up |
| NONMMUT010559.2 | -5.19587 | Down | 5.39012 | Up |
| NONMMUT039378.2 | -4.57962 | Down | 4.67744 | Up |
| NONMMUT059852.2 | -4.46208 | Down | 5.13582 | Up |
| NONMMUT077969.1 | -4.33904 | Down | 3.31442 | Up |
| NONMMUT062675.2 | -4.23117 | Down | 3.67421 | Up |
| NONMMUT027048.2 | -3.95038 | Down | 3.85579 | Up |
| NONMMUT001352.2 | -3.86435 | Down | 3.81448 | Up |
| NONMMUT057244.2 | -3.80355 | Down | 3.90188 | Up |
| NONMMUT000701.2 | -3.56772 | Down | 2.65998 | Up |
| NONMMUT001470.2 | -2.88807 | Down | 3.22941 | Up |
| NONMMUT025510.2 | -2.72927 | Down | 2.76835 | Up |
| NONMMUT110312.1 | -2.64181 | Down | 2.51996 | Up |
| NONMMUT152649.1 | -2.50569 | Down | 2.82238 | Up |
| NONMMUT047231.2 | -2.35594 | Down | 1.80211 | Up |
| ENSMUST00000196744 | -2.07075 | Down | 1.94977 | Up |
The table lists only some of the results for lncRNAs with an up- or downregulation in expression in the HFD group compared with the CON group and in the HFD+RSV group compared with the HFD group. Seq name, lncRNA name; fold-change, absolute fold-change between the compared groups.
Table 3.
Top upregulated and downregulated mRNAs in mice fed a regular diet (CON), a high-fat diet (HFD), or an HFD and treated with resveratrol (HFD+RSV)
| Gene name | Log2 (Fold-change) | Regulation (HFD vs CON) | Log2 (Fold-change) | Regulation (HFD+RSV vs HFD) |
|---|---|---|---|---|
| Saa2 | 7.86092 | Up | -6.81613 | Down |
| Cidea | 7.29545 | Up | -3.84703 | Down |
| Saa1 | 7.21934 | Up | -5.66038 | Down |
| Gm43756 | 7.06443 | Up | -2.16895 | Down |
| Lcn2 | 7.02521 | Up | -5.33297 | Down |
| Cfd | 6.96657 | Up | -3.67247 | Down |
| Orm2 | 5.70148 | Up | -4.75652 | Down |
| Chil1 | 5.25805 | Up | -4.40776 | Down |
| Gm27551 | 5.11722 | Up | -2.28461 | Down |
| Mt2 | 4.24141 | Up | -4.66979 | Down |
| Chac1 | 3.58132 | Up | -1.73976 | Down |
| Aacs | 3.57367 | Up | -1.81265 | Down |
| Foxq1 | 3.56353 | Up | -1.60571 | Down |
| Iqgap3 | 3.53768 | Up | -1.71281 | Down |
| Dlgap5 | 3.49875 | Up | -2.18024 | Down |
| Socs3 | 2.42581 | Up | -1.77296 | Down |
| Gm43738 | 2.41071 | Up | -1.73832 | Down |
| Bhlhe41 | 2.29883 | Up | -1.54587 | Down |
| Gm3571 | 2.23722 | Up | -1.32981 | Down |
| Irx3 | 2.23361 | Up | -2.10145 | Down |
| Zbed6 | -6.12237 | Down | 6.35215 | Up |
| Gm28373 | -5.34677 | Down | 4.68876 | Up |
| Gm27640 | -4.52201 | Down | 5.12685 | Up |
| Capn11 | -4.48551 | Down | 4.46449 | Up |
| Gm20427 | -3.22439 | Down | 2.66816 | Up |
| Rnu3b4 | -2.9713 | Down | 6.33207 | Up |
| Eif4ebp3 | -2.72848 | Down | 1.18786 | Up |
| Gm43314 | -2.07988 | Down | 1.96045 | Up |
| Gm28323 | -2.06383 | Down | 1.79928 | Up |
| Gm38283 | -1.96239 | Down | 2.37695 | Up |
| Gm38357 | -1.92091 | Down | 1.90174 | Up |
| Gm29453 | -1.82934 | Down | 1.51896 | Up |
| Igfbp1 | -1.78184 | Down | 2.20251 | Up |
| Gm15344 | -1.73828 | Down | 1.67884 | Up |
| 4933431G14Rik | -1.72561 | Down | 2.15402 | Up |
| 4930453N24Rik | -1.68061 | Down | 1.51409 | Up |
| Noct | -1.40321 | Down | 1.62225 | Up |
| Tat | -1.31374 | Down | 1.14191 | Up |
| Gm43359 | -1.29315 | Down | 1.02345 | Up |
| Sik1 | -1.14932 | Down | 1.23115 | Up |
The table lists only the top 20 of the results for mRNAs with an up- or downregulation in expression in the HFD group compared with the CON group and in the HFD+RSV group compared with the HFD group. Gene name, mRNA name; fold-change, absolute fold-change between the compared groups.
Gene ontology (GO) and kyoto encyclopaedia of genes and genomes (KEGG) analysis
GO analysis classified differentially expressed mRNAs into three categories: biological process (BP), molecular function (MF), and cellular component (CC). The differential expression of mRNAs primarily included negative signal regulation, negative molecular function regulation, enzyme-linked receptor protein signalling pathway, enzyme inhibitor activity, and endopeptidase activity (Figure 7A). KEGG analysis further classified differentially expressed mRNAs into metabolic pathways, the mitogen-activated protein kinase signalling pathway, Janus kinase/signal transducers and activators of transcription (JAK/STAT) signalling pathway, insulin signalling pathway, as well as the cell cycle and adipocytokine signalling pathway (Figure 7B). The insulin signalling pathway was selected, and the closely related target gene mRNA SOCS3 was detected.
Figure 7.
GO and pathway analysis of differentially-expressed mRNAs. A. Gene ontology analysis of differentially-expressed mRNAs; B. KEGG pathway analysis of differentially-expressed mRNAs.
Verification of lncRNAs
Four lncRNAs upregulated in HFD and downregulated in HFD+RSV (NONMMUT031874.2, NONMMUT003114.2, NONMMUT008655.2, and NONMMUT017329.2) along with three lncRNAs downregulated in HFD and upregulated in HFD+RSV (NONMMUT034345.2, NONMMUT001470.2, and NONMMUT047231.2) were selected for real-time quantitative polymerase chain reaction (RT-qPCR) to verify sequencing results. The expression levels of the selected lncRNAs were consistent with those of the sequencing analysis (Figure 8A-G).
Figure 8.
Validation of four HFD-upregulated and RSV-downregulated lncRNAs by RT-qPCR. A. NONMMUT031874.2; B. NONMMUT003114.2; C. NONMMUT017329.2; G. NONMMUT008655.2. Validation results of three HFD-downregulated and RSV-upregulated lncRNAs by RT-qPCR. D. NONMMUT001470.2; E. NONMMUT034345.2; F. NONMMUT047231.2. All results were obtained from three independent experiments. Data are presented as the mean ± SD (n=4). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test. *P < 0.05 vs CON, #P < 0.05 vs HFD.
mRNA levels of insulin signalling pathway-related genes
No differences were identified in the mRNA level of Akt among CON, HFD, and HFD+RSV (Figure 9A). However, the mRNA levels of Forkhead Box O (FOXO1), Suppressor of Cytokine Signalling 3 (SOCS3), and Glucose-6-Phosphatase Catalytic Subunit (G6PC) were significantly higher in HFD compared to the CON group, and significantly lower in HFD+RSV compared to HFD (Figure 9B-D).
Figure 9.
Relative mRNA expression of insulin signal pathway indicators in liver. A. Akt; B. FOXO1; C. G6PC; D. SOCS3. Data are presented as the mean ± SD (n=4). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test. *P < 0.05 vs CON, #P < 0.05 vs HFD.
Protein expression related to insulin signalling pathway
Compared with CON, the expression levels of FOXO1, SOCS3, and G6PC were significantly increased in HFD, however, significantly decreased in HFD+RSV. Alternatively, no significant differences were identified in the expression levels of Akt among the three groups. The phosphorylation levels of FOXO1 and Akt were decreased in HFD compared with CON, and increased in HFD+RSV compared with HFD (Figure 10A-G).
Figure 10.
Relative protein expression of insulin signalling pathway indicators in liver. (A) Protein bands of insulin signal pathway molecules; (B) Akt; (C) p-Akt/Akt; (D) G6PC; (E) FOXO1; (F) p-FOXO1 and (G) SOCS3. Densitometric analysis of protein expression. Data are presented as the mean ± SD (n=3). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test. *P < 0.05 vs CON, #P < 0.05 vs HFD.
NONMMUT008655.2 lncRNA-miRNA-mRNA co-expression network
From the seven selected lncRNAs, NONMMUT008655.2 exhibited the highest FPKM value (120.2). Path analysis and prediction revealed that the mRNA SOCS3 was related to NONMMUT008655.2, presenting a consistent trend. To clarify the interaction between SOCS3 and NONMMUT008655.2, we constructed a map of the lncRNA-miRNA-mRNA network and found that NONMMUT008655.2 regulates SOCS3 via miRNA mmu-miR-133c, mmu-miR-3569-5p, mmu-miR-504-3p, and mmu-miR-7076-5p. It was further revealed that both SOCS3 and NONMMUT008655.2 may play a key role in improving IR (Figure 11).
Figure 11.
The NONMMUT008655.2 lncRNA-miRNA-mRNA network. This co-expression network suggests an inter-regulation of lncRNAs, miRNA and mRNAs.
Establishment of cell model
Glucose concentration was measured at 0 h, 8 h, 16 h, and 24 h after the transfer of cells to medium with or without PA and CON, respectively). At 0 h, 8 h, and 16 h, no significant differences were identified in glucose concentration between CON and PA. However, the glucose concentration in PA was significantly higher than that in CON at 24 h after transfer, indicating that the IR model was established successfully (Figure 12A) [16]. The glucose concentrations in the 0 h, 8 h, 16 h, and 24 h media of Hepa cells were impacted by both PA and RSV. Although, no significant difference in glucose concentration was observed in the medium between the three groups at 0 and 8 hours, by 16 and 24 hours, the glucose concentration in the PA group was significantly higher than that in the CON group. Moreover, at 24 hours, the glucose concentration in the RSV group was significantly lower than that in the HFD group, indicating that RSV effectively reduces the glucose concentration and improves IR (Figure 12B).
Figure 12.
Establishment of an insulin-resistant cell model and transfection efficiency after NONMMUT008655.2 knockdown. A. Glucose concentration in the culture medium after PA treatment for 0, 8, 16, and 24 hours; B. Glucose concentrations in Hepa cells after PA and RSV treatments; C. Cell survival rate after 24 hours of treatment with different concentrations of resveratrol; D. Cell survival rates of the different groups after PA and resveratrol treatments; E. Relative mRNA level of NONMMUT008655.2. Data are presented as the mean ± SD (n=8). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test.*P < 0.05 vs CON, #P < 0.05 vs HFD.
Cell viability
Cell viability was monitored after the treatment of Hepa cells with 0-50 μM RSV for 24 h. The results showed that 10 μM, 20 μM, and 30 μM RSV did not significantly affect the viability of Hepa cells (Figure 12C). Besides, the cell survival rate in PA (80.6%) was lower than that in the CON (87.1%), or PA+30 μM RSV (84.6%), however, the differences were not statistically significant (Figure 12D).
Effect of RSV on insulin signalling pathway after knockdown of NONMMUT008655.2
After the successful knockdown of NONMMUT008655.2 (Figure 12E), compared with CON, the expression of FOXO1, G6PC, and SOCS3 was significantly higher in PA, however the expression of p-Akt and p-FOXO1 was significantly lower. Moreover, the knockdown of NONMMUT008655.2 significantly increased the expression of p-Akt and p-FOXO1 compared with that in PA, yet significantly decreased the expression of FOXO1, G6PC, and SOCS3. The expression of FOXO1, G6PC, and SOCS3 was significantly lower in PA+RSV than in PA, whereas the expression of p-Akt and p-FOXO1 was significantly higher. Compared with the knockdown of NONMMUT008655.2, PA+RSV significantly increased expression of p-Akt and p-FOXO1, however, significantly decreased the expression of FOXO1 (Figure 13A-G).
Figure 13.
Effect of resveratrol on the insulin signalling pathway after knockdown of NONMMUT008655.2. A. Protein bands of insulin signalling pathway molecules; B. Akt; C. p-Akt/Akt; D. G6PC; E. FOXO1; F. p-FOXO1; G. SOCS3. Data are presented as the mean ± SD (n=3). One-way ANOVA was used for statistical analysis followed by a post hoc least significant difference test or Tamhane’s multiple comparison test. *P < 0.05 vs CON, #P < 0.05 vs PA, &P < 0.05 vs PA + siRNA-NONMMUT008655.2.
Discussion
In addition to its anti-tumour, anti-inflammatory, antioxidant, anti-ageing, cardioprotective, and neuroprotective properties, RSV has also been reported to improve IR [17,18]. The pathogenesis of T2DM is complex as the target organs of insulin include the skeletal muscle, liver, and fat cells [19]. It has been reported that RSV reduces blood sugar, improves IR in T2DM, and protects the function of islet beta cells by activating the insulin signalling pathway thereby allowing insulin to bind insulin receptors on the cell membrane and activate the insulin receptor substrate protein [20]. The liver is an insulin-sensitive organ, and an important site for glycolipid metabolism. Previous studies on the HFD-induced T2DM animal model has shown that RSV effectively reduces hepatocyte IR and hepatic steatosis, and improves abnormalities in glycolipid metabolism by activating the SIRT1 and AMPK signalling pathways in hepatocytes, while inhibiting inflammatory signalling pathways [7,21].
In the present study, RSV not only reduced the blood glucose, insulin index, and AUC in HFD mice, but also improved QUICKI, blood lipid levels, and the histomorphology of hepatocytes. These results indicate that mice fed a HFD experience inhibition of the insulin signalling pathway, and RSV improves IR while reducing hepatic lipid deposition, TG, and LDL-C, however, does not significantly impact TC or HDL-C. The effect of RSV on different blood lipid indicators is related to the subject type, drug concentration, administration route, experimental duration, and test tissue [22].
Although lncRNAs were originally considered to be transcriptional gene “noise”, numerous studies have since indicated that they regulate gene expression and participate in a variety of important biological processes, including chromatin modification, transcription and post-transcriptional regulation, cell proliferation, differentiation, and apoptosis. Besides, lncRNAs are closely associated with the development of human diseases, including T2DM, cardiovascular, blood, neurodegenerative, and lung diseases, as well as various cancers [23-27]. To our knowledge, this is the first study to report on the modulation of lncRNAs in the insulin signalling pathway of an HFD-induced insulin-resistant mouse model administrated RSV.
High-throughput sequencing revealed 503 differentially expressed lncRNAs in HFD mice compared with the CON group and a further 95 differentially expressed lncRNAs compared with the HFD+RSV group. Moreover, the expression of 50 lncRNAs in the HFD+RSV group was opposite to that in HFD; of these, 25 upregulated lncRNAs in HFD were downregulated in HFD+RSV. Many of these lncRNAs are intergenic regions that may regulate the expression of genes encoding adjacent proteins [28]. RT-qPCR verified the sequencing results, indicating that RSV improves HFD-induced IR by regulating the expression of lncRNAs in the mouse liver. However, the role of these lncRNAs in increasing insulin sensitivity remains unclear. Additionally, the expression of 50 lncRNAs was reversed by RSV within the HFD group. Moreover, GO and KEGG analysis classified these lncRNAs as part of the insulin signalling pathway and indicated that NONMMUT008655.2 exhibited the highest expression. The lncRNA-miRNA-mRNA network map revealed that NONMMUT008655.2 regulates SOCS3 through mmu-miR-133c, mmu-miR-3569-5p, mmu-miR-504-3p, and mmu-miR-7076-5p [29]. SOCS3 is an important suppressor of cytokine signalling that regulates JAK/STAT, and is likely related to IR [30]. Akt regulates a variety of signalling pathways, including those associated with cell survival, metabolism, differentiation, and proliferation. Akt is also a key molecule in the insulin signalling pathway as it regulates glycogen synthesis and glucose transport in the liver. Phosphorylated Akt inhibits FOXO1 expression and activates that of phosphorylated FOXO1, which transfers FOXO1 from the nucleus to the cytoplasm, inhibiting its transcriptional activity [31]. In the liver, FOXO1 promotes G6PC expression, which is the rate-limiting enzyme of the gluconeogenesis pathway, thereby increasing hepatic glucose production and, consequently, blood sugar. Inhibition of FOXO1 downregulates G6PC, decreases blood glucose, and improves IR [32]. Previous studies have reported that overexpression of SOCS3 inhibits the phosphorylation and activation of Akt, as well as the phosphorylation of JAK2 and STAT3, which in turn inhibits Akt activation, indicating an important link between Akt and SOCS3 [33].
NONMMUT008655.2 is expressed in mouse hepatocytes and is considered to be associated with RSV to improve IR. In the present study, Hepa cells were used to establish an in vitro IR model to explore the relationship between RSV and NONMMUT008655.2 [16]. Our results indicate that administration of 30 μΜ RSV for 24 h reduced the glucose concentration of PA-treated Hepa cells and improved IR [34].
Previous studies showed that lncRNAs play an important role in improving IR as a key regulator of gene expression in the insulin signalling pathway [35]. It has been suggested that lncRNAs affect the development of T2DM by regulating the development of islet beta cells and insulin secretion [36]; however, the role of NONMMUT008655.2 remained unclear. In the present study, we verified the associated molecules in the Akt-FOXO1 pathway by RT-qPCR and western blot and identified the potential regulatory role of RSV and NONMMUT008655.2. After knocking down NONMMUT008655.2, we analysed changes in the insulin signalling pathways and observed an improvement in IR in vitro. Compared with knockdown NONMMUT008655.2, RSV improved IR more effectively via regulating the expression of p-Akt, p-FOXO1, and FOXO1; however, no significant changes in the expression were observed for G6PC and SOCS3. These results indicate that the pharmacological effects of RSV are similar to the downregulation of NONMMUT008655. Similarly, RSV improves HFD-induced IR by downregulating NONMMUT008655.2 in vivo.
In summary, high-throughput sequencing revealed that RSV may improve hepatic IR by downregulating NONMMUT008655.2. Additionally, the lncRNA and the encoded protein showed similar expression patterns, suggesting that the lncRNA may regulate the interaction of the encoded transcript and protein in the insulin signalling pathway. Finally, our data suggests that NONMMUT008655.2 miRNA mmu-miR-133c, mmu-miR-3569-5p, mmu-miR-504-3p, mmu-miR-7076-5p and mRNA SOCS3 could represent novel pathways for T2DM treatment using RSV.
Conclusions
Our data suggests that RSV improves hepatic IR and controls blood sugar levels by downregulating NONMMUT008655.2. Hence, RSV and NONMMUT008655.2 may serve as potential therapeutic targets for IR and T2DM.
Acknowledgements
We sincerely thank the teachers at the Clinical Medical Research Centre of Hebei General Hospital who helped us during the experiment. We thank Ning Li for responding to reviewers’ and editor’s comments. This study was supported by the grant from the Natural Science Foundation of Hebei Province (No. H2018307071).
Disclosure of conflict of interest
None.
Abbreviations
- RSV
Resveratrol
- LncRNA
Long-chain non-coding RNA
- HFD
High-fat diet
- SOCS3
Suppressor of cytokine signalling 3
- SOCS3
Suppressor of cytokine signalling 3
- G6PC
Glucose-6-phosphatase catalytic subunit
- FOXO1
Forkhead box O1
- Akt
Protein kinase B
- T2DM
Type-2 diabetes mellitus
- IR
Insulin resistance
- IPGTT
Intraperitoneal glucose tolerance test
- AUC
Area under the curve
- QUICKI
Quantitative insulin sensitivity check index
- TG
Triglycerides
- TC
Total cholesterol
- HDL-L
High-density lipoprotein cholesterol
- LDL-L
Low-density lipoprotein cholesterol
- mRNA
Messenger RNA
- BP
Biological process
- BP
Biological process
- MF
Molecular function
- CC
Cellular component
- JAK
Janus kinase
- STAT
Signal transducers and activators of transcription
- miRNA
microRNA
- PA
Palmitic acid
- SIRT1
Silent mating type information regulation 2 homolog-1
- AMPK
AMP-activated protein kinase
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