https://orcid.org/0000-0003-0876-2205
Abstract
To explore the in vivo anti-obesity effect of chikusetsusaponin V and explore the underlying mechanism by transcriptomic and metabonomic methods.
The physiological parameters of high-fat-diet induced obese mice administered with or without 25 mg/kg and 100 mg/kg of chikusetsusaponin V by gavage for 16 weeks were recorded. In addition, the RNA-sequencing and UHPLC-Q-TOF techniques were applied to obtain the transcriptomic and metabolomic profiling, respectively.
Chikusetsusaponin V could significantly alleviate the high-fat-diet induced increase in the weight of the whole body and obesity-related organs or tissues, and ameliorate the lipid content in the blood, the lipid accumulation in the livers, as well as the hypertrophy of the fat tissues. Importantly, transcriptomic results revealed that more than 30 genes involved in the pathway which closely associates with obesity, were significantly altered. Moreover, metabolomic data indicated the key differential metabolites enriched in the pathways such as the activated protein kinase signaling pathway which is a vital mediator of obesity and other processes.
The integrative analysis highlighted that chikusetsusaponin V significantly influenced the activated protein kinase signaling pathway at both transcriptomic and metabolomic levels, thereby exerting anti-obesity effects.
Introduction
Obesity is one of the major risk factors for many chronic diseases, leading to additional healthcare costs and reduction in life expectancy.[1] In 2015–2016, the prevalence of obesity in US children was 18.5%,[2] while the prevalence of obesity reached 39.8 and 18.5% among US adults and youth, respectively.[3] Likewise, some developing countries have reported similar rises in the obesity rate. As the largest developing country in the world, China has already become a major country with a rising obesity rate.[4] According to the latest epidemiological study, the prevalence of overweight and obesity among children and adolescents (aged 7–18 years) was 15.5 and 8.8%, respectively.[5] More importantly, several epidemiological studies collectively pointed out that obesity can induce a variety of serious diseases, for example, diabetes, hypertension, hyperlipidemia and nonalcoholic fatty liver disease, thereby increasing morbidity and mortality.[6]
Many factors, including genetic predisposition, metabolism, physiology, endocrine and behavior, contribute to the development of obesity.[7] Thus the therapeutic strategies of obesity are correspondingly based on these factors. Notably, two compounds, orlistat and sibutramine had been licensed by the FDA for pharmacological weight management in 2010. However, soon afterward, sibutramine was withdrawn from the market due to its side effects. Similarly, the application of many other anti-obesity agents was also limited by its typical side effects, such as nausea, dizziness, increased thirst and even severe cardiovascular damages.[8] To meet the challenge, many researchers are pursuing an effective and safe weight-loss drug. Growing small molecules and natural products with anti-obesity effects have been identified and reported.[6] In particular, some compounds from Traditional Chinese Medicine have been reported to possess anti-obesity effects, for example, astragaloside IV,[9] glycyrrhizin[10] and berberine[11] in the past decades. Tujia ethnomedicine is an important part of the national medical system in China. The Tujia ethnomedicinal system possesses its unique theories and methods for treating and preventing disease, which heavily relies on the accumulating knowledge and experience of the ethnic Tujia people.[12] Chikusetsusaponin V (CV) is a triterpenoid saponin extracted from the classic Tujia ethnomedicine Panax japonicus Meyer.[13] The potential anti-obesity effects of chikusetsusaponins, together with its some analogs including chikusetsusaponin IVa, ginsenoside Rg1 and ginsenoside Rb1, have been reported.[14–18] However, it remains undetermined whether chikusetsusaponin V has in vivo anti-obesity effects and how chikusetsusaponin V exerts the anti-obesity effects. To address these questions on chikusetsusaponin V, HFD-induced obese mice were treated with or without different doses of chikusetsusaponin V for a reasonable period, followed by a series of analyses including physiological assay, transcriptomics and metabolomics in this study.
Materials and Methods
Materials
Chikusetsusaponin V was isolated from the root of P. japonicus Meyer according to a previously published method.[13] The structure, 1H NMR and 13C NMR spectrum of chikusetsusaponin V were shown in Figures S1–S3, respectively. High-fat diet (D12492) was purchased from Research Diets, Inc. (New Brunswick, NJ, USA). Normal chow was purchased from Silaikejingda Experimental Animal Co. (Changsha, China, Permit Number: SCXK 2014-0002). Glucometer was purchased from Johnson & Johnson Medicinal Ltd (Suzhou, China). Lipid assay kits (TC, TG, HDL-C and LDL-C) were purchased from Mindary Bio-Medical Electronics Co., Ltd (Shenzhen, China). The tissue OCT-Freeze Medium was purchased from Sakura Finetek USA Inc (Torrance, CA, USA). Ethanol, xylene, hydrochloric acid, ammonium hydroxide and neutral balsam were purchased from Sinopharm Chemical Reagent Co., Ltd (Beijing, China). Hematoxylin–eosin stain, paraformaldehyde, oil red O stain, glycerin jelly were purchased from Wuhan servicebio technology Co., Ltd (Wuhan, China).
Animal experiment
Six-week-old C57BL/6 male mice were purchased from Silaikejingda Experimental Animal Co. (Changsha, China, Permit Number: SCXK 2016-0002) and housed under the controlled conditions of 16°C and 12 h light-dark circle. The investigation was approved by the Ethics Committee for Animals of the Hunan University of Chinese Medicine and followed the guidelines for the use of animals in experimental studies. Mice were randomly assigned into four groups, with eight mice per treatment. Group A (Chow) was fed with normal diet plus ddH2O (0.1 mL/10 g bodyweight) daily by intragastric gavage; Group B (HFD) was fed with high-fat diet plus ddH2O (0.1 mL/10 g bodyweight) daily by intragastric gavage; Group C (HFD + LCV) was fed with high-fat diet plus chikusetsusaponin V (25 mg/kg body weight) daily by intragastric gavage; Group D (HFD + HCV) was fed with high-fat diet plus chikusetsusaponin V (100 mg/kg body weight) daily by intragastric gavage. Food consumption was measured daily, and mice's bodyweight was measured every week. After 16 weeks of treatment, the mice were euthanized, the tissues (heart, liver, subcutaneous fat and epididymal fat) were dissected out and weighed.
Histological examination
For histological examination, liver, subcutaneous fat and epididymal fat were fixed in 4% paraformaldehyde solution. The subcutaneous fat and epididymal fat were dehydrated and embedded in paraffin, sectioned into 4 µm thickness, and subsequently stained with hematoxylin–eosin. The liver was dehydrated, embedded and frozen in OCT-Freeze Medium, sectioned into 8 µm thickness, and subsequently stained with oil red O and hematoxylin. The sections were photographed at a magnification of 100× and 400×. For statistic calculation, at least three fields per section, and three sections per tissue were randomly chosen. The Image J software was used to analyse the size and number of adipocytes, and the area of lipid droplet.
Analysis of lipid parameters in serum
Blood samples were collected orbit before sacrifice. The serum was obtained by centrifugation at 1200 g for 5 min, then stored at −80°C. The concentration of triglyceride, total cholesterol, high-density lipoprotein-cholesterol and low-density lipoprotein-cholesterol were analysed by an automatic biochemical analyzer (BS 190, Mindary, China).
RNA sequencing and data acquisition
The livers of mice were chosen for RNA sequencing (five samples per group). Total RNA was extracted using Trizol reagent (Invitrogen, Carlsbad, CA, USA), followed by quality evaluation using Bioanalyzer 2100 and RNA 6000 Nano LabChip Kit (Agilent, Santa Clara, CA, USA). The cDNA libraries were obtained by reverse transcription of the cleaved RNA Fragments using mRNA Seq sample preparation kit (Illumina, San Diego, USA). Subsequently, the samples were qualified and sequenced on Illumina Hiseq4000 ((LC Sciences, USA) with a read length of 300 bp (±50 bp) paired-end reads. Low-quality reads were filtered out based on the following rules: (1) removing reads containing sequencing adaptors; (2) removing reads containing sequencing primer; and (3) removing nucleotide with q quality score lower than 10. For RNA sequencing data analysis, the reads of samples were aligned to the UCSC (http://genome.ucsc.edu/) Mus musculus reference genome using HISAT package. The mapped reads of each sample were assembled using StringTie. Then, all transcriptomes from samples were merged to reconstruct a comprehensive transcriptome using Perl scripts. After the final transcriptome was generated, StringTie and Ballgown were used to estimate the expression levels of all transcripts. StringTie was used to determine expression level for mRNAs by calculating fragments per kilobase million (FPKM). The differentially expressed mRNAs and genes were selected with log2 (fold change) >1 or log2 (fold change) <−1 and with statistical significance (P-value < 0.05) by R package – Ballgown.
Metabolites extraction and metabonomic analysis of liver tissues
The livers of mice were selected for metabonomic analysis (eight samples per group). For this purpose, 200 mg of liver samples were extracted with 1 mL of precooled 80% aqueous methanol by a homogenizer. After centrifugation at 15 000 r/min for 10 min, the supernatant was completely dried, and then the dried sample was dissolved in 80% aqueous methanol for analysis.
The UPLC-Q-TOF system (Waters, Milford, MA) was used to analyse the liver samples. UPLC parameters were as follows: an ACQUITY UPLC BEH Amide column (100 × 2.1 mm, 1.7 µm, Waters, UK) was used for the reversed phase separation. The column oven was maintained at 35°C. The flow rate was 0.4 mL/min and the mobile phase consisted of solvent A (25 mM ammonium acetate ± 25 mM NH4H2O) and solvent B (IPA : ACN = 9 : 1 ± 0.1% formic acid). Gradient elution conditions were set as follows: 0–0.5 min, 95% B; 0.5–9.5 min, 95–65% B; 9.5–10.5 min, 65–40% B; 10.5–12 min, 40% B; 12–12.2 min, 40–95% B; 12.2–15 min, 95% B. The injection volume for each sample was 4 µL. For the MS parameter, a high-resolution mass spectrometer AB SCIEX TripleTOF5600plus (Framingham, MA) was used to detect metabolites. The Q-TOF was operated in both positive and negative ion modes. The curtain gas, Ion source gas1, Ion source gas2 were set at 30, 60 and 60 PSI, respectively, and an interface heater temperature was 650℃. For positive ion mode and negative ion mode, the ion spray voltage floating was set at 5 and −4.5 kV, respectively. The MS data were collected in the range of m/z 60–1200. During the acquisition, the mass accuracy was calibrated every 20 samples. Additionally, a quality control sample (A mixture of all samples) was acquired after every 10 samples to evaluate the stability of the LC–MS during the whole acquisition.
For LC–MS data analysis, the acquired MS data pretreatments, including peak picking, peak grouping, retention time correction, second peak grouping and annotation of isotopes and adducts were analysed using XCMS software. The online KEGG and HMDB databases were used to annotate the metabolites by matching the exact molecular weight (m/z) of samples with those from databases. If a mass difference between the observed and the database value was less than 10 ppm, the metabolite would be annotated, and the molecular formula of metabolites would further be identified and validated by the isotopic distribution measurements. The in-house second-fragment spectrum library of metabolites was also applied to validate the metabolite identification. An in-house software metaX further preprocessed the intensity of peak data. Wilcoxon tests were conducted to detect differences in metabolite concentrations between two phenotypes. The P-value was adjusted for multiple tests using an FDR (Benjamini–Hochberg). Supervised learning method partial least squares discrimination analysis (PLS-DA) was conducted to discriminate the different variables between groups. The variable important for the projection (VIP) value was calculated, and a VIP cut-off value of 1.0 was used to select important features.
Statistical analysis
Statistical analysis was performed using SPSS software (version 22.0). The differences of physiological parameters among four study groups were determined by one-way ANOVA and followed by the least significant difference (LSD) test (equal variances) or Dunnett T3 posthoc test (unequal variances) for multiple comparisons. P ≤ 0.05 was set for statistical significance.
Results
Chikusetsusaponin V effectively prevents HFD-induced obesity in mice
After 16 weeks of HFD feeding, a noticeable difference in body shape among these groups was observed. To confirm the anti-obesity effects of chikusetsusaponin V, the basic physiological parameters such as body weight, the weight of organs and tissues and blood lipid profile were measured. The results indicated that HFD feeding for three weeks led to significant increases in mice bodyweight. The intragastrical administration of chikusetsusaponin V could markedly alleviate HFD-induced increase in the weight of the body, heart and liver, epididymal fat and subcutaneous fat (Figure 1A–E). Treatment with a low dose of chikusetsusaponin V (LCV) displayed better effects on reducing the weight of heart and liver, epididymal fat, and subcutaneous fat than a high dose of chikusetsusaponin V (HCV). Notably, the inhibitory effects of chikusetsusaponin V on body weight and other parameters were independent of the alteration in food consumption, as indicated in Figure 1F.
Effects of chikusetsusaponin V on body/tissue weight and food consumption. Shown are the effects of CV treatment on heart weight (A), liver weight (B), subcutaneous fat (C), epididymal fat (D), data are expressed as mean ± s.d. (n = 8). *P < 0.05, **P < 0.01, ***P < 0.001, NS = not significant. The body weight (E) and food consumption (F) during 15-week feeding for each group was also recorded and compared (#P < 0.05 compared with chow group, *P < 0.05 compared with HFD group, **P < 0.01 compared with HFD group and ***P < 0.001 compared with HFD group).
Likewise, the fasting serum triglyceride (TG) level in mice fed with HFD plus LCV and HCV was significantly reduced when compared with that in HFD-fed mice (Figure 2A). In spite of less effect on the total cholesterol (TC) levels (Figure 2B), the promoting effect of chikusetsusaponin V on the high density lipoprotein-cholesterol (HDL-C) levels (Figure 2C) and inhibitory effect of it on the low density lipoprotein-cholesterol (LDL-C) levels (Figure 2D) were easily identified. For instance, the LDL-C levels were reduced by 52.65 (LCV) and 64.60% (HCV) compared with those of HFD-fed mice.
Effects of CV on blood lipid profile. Shown are the effects of CV on triglyceride (A), total cholesterol (B), high density lipoprotein cholesterol (C) and low density lipoprotein cholesterol (D) in the blood. Data are expressed as mean ± s.d. (n = 8). *P < 0.05, **P < 0.01, ***P < 0.001, NS = not significant.
To further characterise the alterations in lipid accumulation and adipocyte hypertrophy after chikusetsusaponin V treatment, the histological sections of mice liver, subcutaneous fat pad, and epididymal fat pad were carefully examined. In contrast to normal chow-fed mice, HFD-fed mice had apparent fat droplet accumulation in the Oil red O stained liver sections (Figure 3A). Likewise, these mice also displayed markedly hypertrophic adipocytes in the H&E stained subcutaneous fat (Figure 3B) and epididymal fat (Figure 3C). However, these adverse phenotypes were significantly attenuated in HFD-fed mice in the presence of chikusetsusaponin V, as indicated by reduced lipid accumulation in the livers (Figure 3A), and decreased hypertrophic adipocytes in the subcutaneous fat pad (Figure 3B) and epididymal fat pad (Figure 3C) of these mice. Between two doses of CV groups, the LCV group performed better in reducing liver fat accumulation and inhibiting adipocyte hypertrophy. These results were consistent with the statistical data of the area of lipid droplet, and the size and number of adipocytes (Figure S4).
Effects of CV on lipid accumulation and hypertrophy of adipocytes. The effects of it on lipid accumulation in liver (A), subcutaneous adipocyte size (B) and epididymal adipocyte size (C) are shown, respectively. The liver sections were stained with oil red O, while the subcutaneous fat and epididymal fat sections were stained with H&E. For each group, at least three samples were chosen for histological analysis. The above pictures were photographed at a magnification of 400× to show the obvious difference.
Transcriptomic profiles of mouse livers
To explore the potential molecular mechanisms underlying the regulation of chikusetsusaponin V on the development of obesity, the transcriptomic analysis was performed in the livers of mice treated with or without chikusetsusaponin V. These groups were included: Chow-fed, HFD-fed and HFD-fed plus low dose of chikusetsusaponin V (HFD-CV) treated groups, since low dose of chikusetsusaponin V displayed better inhibitory effects according to the results aforementioned. The analysis of transcriptomic profiling indicated that compared to the Chow group, 229 differentially expressed genes (DEGs) were identified in the HFD group (132 up-regulated and 97 down-regulated genes, shown in Figure S5A and C), while 455 DEGs were identified between HFD-CV group and HFD group (172 up-regulated and 283 down-regulated genes, shown in Figure S5B and C). Moreover, Venn diagrams revealed the overlap in DEGs between HFD group and HFD-CV group. Briefly, 21 genes were significantly altered in the livers of mice under the condition of HFD and/or CV treatment.
To further identify the major molecular pathways and gene functions, the DEGs were mapped to terms in the KEGG database for gene annotation. The analysis disclosed that many pathways involved in a wide range of physiological and pathophysiological processes were affected by chikusetsusaponin V treatment. The top 20 enriched pathways included fatty acid metabolism, peroxisome proliferator-activated receptor (PPAR) signaling, (AMP)-activated protein kinase (AMPK) signaling, insulin signaling, and fatty acid degradation, which are the tight ones related to obesity (Figure 4). In more detail, a number of genes, which include ELOVL family member 6 (Elovl6), fatty acid desaturase 1 (Fads1), fatty acid synthase (Fasn), stearoyl-Coenzyme A desaturase 1(Scd1) involved in fatty acid metabolism were markedly down-regulated (Figure 5A). As to those in PPAR signaling pathway, genes belong to family 4 of cytochrome P450 were up-regulated, such as Cyp4a10, Cyp4a14 and Cyp4a31. Likewise, the angiopoietin-like 4 (Angptl4) and carnitine palmitoyltransferase 1a (Cpt1a) were also up-regulated (Figure 5B). In addition, chikusetsusaponin V supplementation resulted in the up-regulation of AMPK signaling pathway-related genes such as insulin receptor substrate 2 (Irs2), leptin receptor (Lepr), and malonyl-CoA decarboxylase (Mlycd) (Figure 5C).
KEGG pathway analysis of differentially expressed the genes between HFD-CV group and HFD group.
A heat map of the genes involved in fatty acid metabolism (A), PPAR signaling pathway (B), AMPK signaling pathway (C), insulin signaling pathway (D) and fatty acid degradation (E).
Metabolomic profiling of mouse livers
Considering that the liver is a critical organ for systemic metabolism, the liver metabolites from Chow-fed mice, HFD-fed mice, and HFD-fed plus low dose of chikusetsusaponin V treated mice were analysed to obtain metabolomic profiling. The analysis of metabolomic profiling indicated that comparing to the Chow group, 1499 differential metabolites were annotated in the HFD group (750 up-regulated and 749 down-regulated metabolites, shown in Figure S6A) under the positive mode, as well as 1144 differential metabolites (567 up-regulated and 577 down-regulated metabolites, shown in Figure S6B) under negative mode. Besides, 2923 differential metabolites (1156 up-regulated and 1767 down-regulated metabolites, shown in Figure S6C) and 2623 differential metabolites (1013 up-regulated and 1613 down-regulated metabolites, shown in Figure S6D) were observed in HFD-CV group under positive mode and negative mode respectively when compared with HFD group. Multivariate statistical analyses were used to distinguish the metabolomic differences among those three groups. PLS-DA score plot was firstly performed to visualize the distinct separation among those three groups (Figure S7), suggesting that obvious biochemical alternations occurred in the presence of chikusetsusaponin V.
To further find the reliable key biomarkers, the above differential metabolites were subsequently screened and confirmed based on the comparison of the spectrum of secondary fragment ions via MS/MS mode with those of in-house database resources. Thus, a total of 54 reliable key biomarkers were found via the above method. Among them, the contents of 15 metabolites were significantly increased, whereas 39 metabolites were markedly decreased in the presence of chikusetsusaponin V. The retention time, HMDB classification and change tendency of key biomarkers were presented in Table S1. For further characterisation, the pathway enrichment analysis was carried out based on KEGG annotation. As indicated in Figure 6, the significantly altered biomarkers were distributed into various metabolic pathways, including purine metabolism, taste transduction, biosynthesis of amino acids, ABC transporters, and AMPK signaling pathway.
Pathway enrichment analysis of potential biomarkers between HFD-CV and HFD groups.
Integrative analysis of transcriptomic and metabonomic results
As described above, many differentially altered transcripts and metabolites enriched in several pathways were determined. For further holistic analysis, we integrated the transcriptomic and metabolomic profiles to obtain insights about the potential pathways which may be responsible for the anti-obesity action of chikusetsusaponin V or required for in-depth functional investigation. The Integrated Molecular Pathway Level Analysis (IMPaLA) was applied for integrated pathway enrichment. As a result, 12 differential genes and four differential metabolites implicated in AMPK signaling pathway were identified, suggesting that AMPK signaling pathway may be the most relevant one. Specifically, downregulated genes included acetyl-CoA carboxylase 1 (Acaca), acetyl-CoA carboxylase 2 (Acacb), cyclin D1 (Ccnd1), fatty acid synthase (Fasn), glucose-6-phosphatase (G6pc), HMG-CoA reductase (Hmgcr), stearoyl-CoA desaturase (Scd1) and sterol regulatory element-binding transcription factor 1 (Srebf1) (Figure 7). Whereas, the expression of carnitine palmitoyltransferase I (Cpt1a), insulin receptor substrate 2 (Irs2) and leptin receptor (Lepr) were pronouncedly upregulated (Figure 7). Meanwhile, four metabolites related to the AMPK signaling pathway were significantly decreased. These contained AMP, ADP, D-Fructose 6-phosphate and D-Fructose 1,6-bisphosphate (Figure 7). Based on these results and analyses, anti-obesity effects of chikusetsusaponin V should be at least partly mediated by the AMPK signaling pathway, thereby influencing relevant targets at both transcriptomic and metabolomic levels.
AMPK signaling pathway featuring transcriptomic and metabolomic regulation of HFD-CV versus HFD. Differential transcripts are presented as green rectangles, while differential metabolites are presented as green ovals.
Discussion
Obesity is a common disease characterised by the excessive accumulation and storage of fat in the body. Given the complexity of the pathogenesis of obesity, the therapeutic strategies and therapeutic targets for obesity are also diverse. However, the primary inducer of obesity is uncontrolled food intake, especially food containing high amounts of fat and carbohydrates.[19] Diet affects whole body metabolism and regulation through effects on hormones, glucose metabolism, and lipid metabolism.[20] Over the past few decades, numerous dietary approaches were developed to induce obesity model because the diet-induced obesity model is similar to humans with obesity.[21] Compared with other models, diet-induced obesity model has the advantages of simplicity and affordability. In this study, we confirmed the in vivo anti-obesity effects of chikusetsusaponin V in the HFD-induced obese mice, as demonstrated by the improvement of weight gain, the lipid accumulation in the livers, as well as the hypertrophy of the epididymal and subcutaneous fat tissues. Importantly, we also applied a combined analysis of transcriptomic and metabolomic profiling to explore the potential mechanism underlying the anti-obesity action of it. It was clear and undoubted that the observed comprehensive alterations in the HFD-fed mice were derived from the long-term chikusetsusaponin V treatment, these mainly included genes involved in fatty acid metabolism, PPAR signaling pathway, AMPK signaling pathway, insulin signaling pathway, and fatty acid degradation. Among them, the AMPK signaling pathway was more prominent in the holistic analysis, suggesting that it may largely or at least partly mediate the action of chikusetsusaponin V.
According to the integrative analysis, a series of transcripts and metabolites associated with the AMPK signaling pathway were significantly altered by the presence of chikusetsusaponin V. For instance, the gene encoding important metabolic enzymes such as Acaca, Acacb and, Srebf1 which function in the rate-limiting steps for fatty acid synthesis, were down-regulated in this study. Accordingly, the downstream target genes of Srebf1, the Fasn, Acaca, and Scd1, which stimulate fatty acid synthesis,[22] were also repressed. The liver plays a significant role in the control of glucose homeostasis by regulating various pathways of insulin-mediated glucose metabolism, including glycogenesis, glycogenolysis, glycolysis and gluconeogenesis.[23] AMPK is a known suppressor of hepatic gluconeogenesis. The activated AMPK can decrease the expression of key gluconeogenic enzymes and transcription factors, thereby inhibiting the hepatic gluconeogenesis and reducing the blood glucose level.[24] In this study, the down-regulation happened in gluconeogenesis, the gene encoding glucose 6-phosphatase (G6pc) was down-expressed, and two metabolites, D-Fructose 6-phosphate and D-Fructose 1,6-bisphosphate involved in glycogenolysis/glycolysis were also decreased. In addition, the expression of HMG-CoA reductase (Hmgcr), which is the rate-controlling enzyme in the process of cholesterol synthesis was decreased.[25] Inhibition of HMG-CoA reductase can reduce cholesterol synthesis. These current observations, together with previous findings, collectively imply that chikusetsusaponin V may exert its anti-obesity action via activating AMPK signaling pathway. AMPK, a critical energy sensor, holds considerable potential as the target to ameliorate metabolism-related diseases such as obesity and type 2 diabetes.[26] In this regard, many anti-diabetic and anti-obesity agents exert their effects by activating AMPK, such as metformin[27] and berberine.[23] Ginseng extracts and ginsenosides effectively treat and/or prevent metabolic diseases and cancer through AMPK activation.[24] Additionally, it has been reported that the anti-obesity effects of some analogues of chikusetsusaponin V, such as ginsenoside Rh2[28] and ginsenoside Rg3[29] are also associated with the activation of AMPK signaling pathway. It is believable that chikusetsusaponin V has a similar action on this critical signaling, thereby displaying its anti-obesity function.
In this study, the transcriptomic and metabolomic profiles definitely provide broadened sights for classifying and understanding the anti-obesity effects of chikusetsusaponin V. However, here are some limitations of this study. First, only two doses of CV were tested because of the limited quantity of the compound. In this study, low dose of CV exerted a better preventive effect on mice. This suggests that 25 mg/kg dose might be the saturating dose. The dose–response relationship of CV needs further study. Second, the changes in transcripts need further validation by some analytical techniques of molecular biology, such as real-time polymerase chain reaction and western blot.
Conclusion
The intragastrical administration of chikusetsusaponin V could significantly alleviate HFD-induced increase in the weight of the whole body and obesity-related organs or tissues, and ameliorate the lipid content in the blood, the lipid accumulation in the livers, as well as the hypertrophy of the epididymal and subcutaneous fat tissues. The transcriptomic results revealed the expression more than 30 genes involved in fatty acid metabolism pathway, PPAR signaling pathway, AMPK signaling pathway and insulin signaling pathway which closely associate with the obesity, were significantly altered, suggesting that the anti-obesity effects of chikusetsusaponin V might at least partially be mediated by these genes and pathways. From the metabolomics study, a total of 54 key biomarkers enriched in the pathways of purine metabolism, taste transduction, biosynthesis of amino acids, and AMPK signaling pathway, were markedly changed. Importantly, integrative analysis of transcriptomic and metabolomic results collectively revealed that AMPK signaling pathway was the dominant potential pathway under the anti-obesity activities of chikusetsusaponin V. To our best knowledge, this is the first study investigating the anti-obesity effects of chikusetsusaponin V on mice and integrating transcriptomic and metabolomic profiles to investigate the potential mechanisms underlying chikusetsusaponin V action.
Funding
This work was funded by National Natural Science Foundation of China, grant numbers 81673579, 81703819 and 81803708, Ministry of Science and Technology of PR China, grant number 2018FY100703, Hunan Science and Technology Department, grant numbers 2019JJ50315, 2018SK 2110 and 2081 and ‘Shennong’ Scholar Funding.
Author contributions
W.W., A.W. and Y.Q. designed the experiment. Y.Q. performed the experiments, analyzed the data, and wrote the paper. X.L., Y.H. and S.G. helped performed the experiments and evaluated the data. Y.J. and B.L. isolated the active compounds from herbs. C.P. and L.L. made recommendations on experimental data analysis. Y.Q. modified the language of the manuscript. W.W. and A.W. supervised the experiment, supported all the experiments, and modified the manuscript.
Conflict of Interest
The authors declare no conflicts of interest.
Acknowledgements
We thank LC Bio Technologies for the RNA-sequencing and LC–MS supports.
