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. 2023 Aug 11;9(8):e19108. doi: 10.1016/j.heliyon.2023.e19108

A comparative metabolomics study between grain-sized moxibustion and suspended moxibustion on rats with gastric ulcers

Guhang Wei a,1, Yufeng Xie a,∗,1, Mengran Pei a, Jinlan Yang b, Yunjin Yu b, Yanbin Cheng c, Baohua Chen d, Jingjing Guo c, Zongbao Yang c,∗∗, Jun Feng a,∗∗∗
PMCID: PMC10469062  PMID: 37664739

Abstract

Grain-sized moxibustion (GS-Moxi) and suspended moxibustion (S-Moxi) represent the two typical local heat therapies in Traditional Chinese Medicine (TCM) and have been extensively used in treating gastric ulcers (GU) in China. However, the difference in biological response between the two moxibustion therapies in treating GU remains unclear. Here we investigated the therapeutic effect and potential mechanistic difference underlying the two moxibustion methods. Ethanol-induced GU model was established and was treated with GS-Moxi or S-Moxi at ST36 and ST21 for 5 days separately. And then, gastric histopathological examination, immunohistochemical staining for repair factors (EGFR, VEGF, Ki67), and 1H NMR-based metabolomics analysis of plasma and stomach of rats were conducted. We found GS-Moxi and S-Moxi effectively alleviated gastric damage and significantly increased the expression of related repair factors. However, S-Moxi corrected aberrant energy metabolism and lipids metabolism in GU rats but had little effect on neurotransmitter-related metabolism, while GS-Moxi regulated energy metabolism and neurotransmitter-related metabolism in GU rats but had no effect on lipids metabolism. We further proposed that the main target of S-Moxi may be liver and vasculature, whereas GS-Moxi specially targeted the stomach via regulating nervous system. This study strongly verified the outstanding gastroprotective effects of moxibustion and enriched our understanding of the varied biological responses triggered by different moxibustion methods.

Keywords: 1H NMR, Metabolomics, Moxibustion, Suspended moxibustion, Grain-sized moxibustion, Local heat therapy, Gastric ulcer

1. Introduction

Gastric ulcer (GU) is one of the most common gastrointestinal diseases nowadays and seriously imperils human health. Although proton pump inhibitors combined with antibiotics or histamine 2 receptor antagonists are recommended for managing and preventing GU [1], a set of issues following the current treatment options such as high recurrence rate, increasing antimicrobial resistance, and long treatment course, have generated serious challenges in treating GU [2]. Therefore, new alternatives or adjunctive therapies to overcome these problems are highly desired.

In China, moxibustion is widely used to treat gastrointestinal disorders including GU [[3], [4], [5], [6]]. S-Moxi and GS-Moxi are two quite distinct moxibustion methods, but both of them show repairing effects on gastric lesions. S-Moxi, the application of a burning moxa stick above the acupoints from a distance of 2 cm–5 cm, commonly provides constant and stable heat flux at temperatures according to the temperature tolerance of the individual (usually below 44 °C) on the surface of the acupoint for 10∼30 min to exert its therapeutic effect. Many studies have reported that S-Moxi could effectively modulate the intestinal flora [7], improve gastrointestinal motility [8], regulate signaling factors associated with cell proliferation and apoptosis, and subsequently promote GU repair [9,10]. GS-Moxi directly places ignited wheat grain-sized moxa cones on the skin of acupoints thus generating an intense burning sensation and mild scald on acupoints in seconds. The transient temperature of the attached skin can reach over 50 °C according to Arrhenius burn integration [11]and Okazaki's study [12]. GS-Moxi can regulate the immune response [13], and exert a good anti-inflammatory and analgesic effect by stimulating the activation of the thermal sensor STM1/ORAI1 [14]. It can also stimulate the expression of related tight junction proteins occluding and ZO-1, and promote the repair of intestinal mucosal damage [15]. In general, GS-Moxi and S-Moxi methods represent different and complex heat transfer behaviors coupled with multiple variables (temperature, duration time, stimulus intensity, and contact area), which will inevitably cause different biological reactions [[16], [17], [18]]. However, the systemic contrast research focused on the two representative moxibustion methods in treating GU has not been mentioned.

Metabolomics has unique advantages in understanding the overall intervention effect of moxibustion via its holistic research model, and further elucidating the difference between different moxibustion methods via observing the relevant metabolic responses [19]. The stomach is the target organ of GU while blood circulation involves the exchange of molecules among multiple organs across the body. Therefore, gastric metabolomics analysis provides specifically localized responses to stimuli, and plasma metabolomics analysis can provide complementary information from extra-gastric tissues. In this study, 1H NMR based metabolomics of plasma and gastric tissues combined with molecular biology techniques were used to observe the therapeutic properties and underlying mechanisms of S-Moxi and GS-Moxi on GU rats. Altogether, our study advances the application of different moxibustion methods in the clinical environment and enriches our understanding of the overwhelming complexity of moxibustion.

2. Materials and methods

2.1. Experimental animals

Male SD rats weighing about 200 g were purchased from the Silaike Experimental Animal Limited Liability Company (Shanghai, China). The animal study protocol was approved by the Animal Care and Use Committee of Xiamen University (Permit Number: XMULAC20190142). The study was performed in accordance with the National Institutes of Health for the Care and Use of Laboratory Animals.

The rats were randomly divided into 4 groups (7 per group): control group (Con), Gastric ulcer model group (Mod); Grain-sized moxibustion-treated group (GS-Moxi), and Suspended moxibustion-treated group (S-Moxi). All animals were maintained in a controlled environmental condition (21 ± 1 °C, relative humidity of 65 ± 5%, and 12 h light/dark cycle) with free access to water and food. After a 1-week adaptation, all rats except for the control rats were constructed GU modeling with alcohol given by gavage with 75% ethanol solution at 4 mL/kg after being deprived of food for 24 h [20]. The control rats were administered with the same amount of saline during the period. After the establishment of the ethanol-induced GU model, one rat in each group was randomly selected for histopathological examination to verify the success of the model.

2.2. GS-Moxi and S-Moxi treatment

Moxibustion treatments were carried out on the second day of the ethanol-induced GU model after ethanol gavage (Fig. 1A). Bilateral Liangmen acupoint (ST 21, located 5 mm horizontally to the spot above 2 cm from the navel) and Zusanli acupoint (ST 36, located 5 mm below the fibular head and lateral to the anterior tubercle of the tibia) were selected as the rat body stimulation sites and shaved to expose. ST21 and ST36 are important stomach meridian acupoints and were selected frequently in acupuncture mechanism studies for healing gastrointestinal disorders [21,22].

Fig. 1.

Fig. 1

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Moxibustion improved gastric lesions in ethanol-induced GU model. (A) Experimental condition of GS-Moxi treatment (a) and S-Moxi treatment (b). (B) Body weight alteration of rats in 4 groups. The sample size in each group is 6. #P < 0.05 and NS indicate no significant difference vs. Con group by ANOVA. (C) HE-stained histological images of gastric tissues from 4 groups. Red arrow: inflammatory cell infiltration. Black arrow: exfoliation of gastric epithelial cells. Green arrow: disordered lamina propria gland. Blud arrow: submucosal edema. Scale bars represent 200 μm for the top row and 100 μm for the bottom row.).

For GS-Moxi treatment, moxa floss (Nanyang Hanyi Moxibustion Technology Development Co., Ltd., China) was made into grain-sized moxa cones (cone shape, wheat grain size with a length of 5 mm, and 1 mm center diameter). After applying Vaseline to the skin of bilateral ST21 and ST36 acupoints, grain-sized moxa cones were placed on the acupoints and ignited to stimulate the skin of acupoints directly. A new grain-sized moxa cone was applied when the prior one was burned up. A total of ignited three grain-sized moxa cones were applied at each acupoint in succession for 5 days. For the S-Moxi treatment, moxa sticks (φ8mm x 70 mm) made by the same moxa floss of grain-sized moxa cones were ignited and placed at 2 cm above the selected acupoints in the S-Moxi group. The temperature of the acupoints was maintained at 40–42 °C. S-Moxi intervention for 10min/day for 5 consecutive days. The rats in the Con group and Mod group had applied no treatment but similar restraint for 10 min/d.

2.3. Biological sample collection

By the end of the experiment, rats in all groups were anesthetized by intraperitoneal injection of 2% pentobarbital (0.3 mL/100 g body weight), and the toe-pinch reflex was used to confirm the depth of anesthesia. Blood was collected by cardiac puncture into sodium heparin tubes and then centrifuged at 1600 g for 20min at 4 °C to collect plasma. After collecting blood, rats were killed by rapid decapitation. Then the gastric tissue samples were excised and snap-frozen in liquid nitrogen immediately for tissue extraction. All of the samples were stored in a −80 °C refrigerator for subsequent analysis.

2.4. Histopathology and immunohistochemistry

The gastric tissues were harvested and fixed in 4% paraformaldehyde overnight, and then embedded in paraffin. The 3-μm thick sections were sliced.

For pathologic examination, after deparaffinization and rehydration, the sections were stained with a hematoxylin and eosin kit (Shanghai Yuanye S&T Co., Ltd., China) following the manufacturer's protocol for routine histopathological observations.

For immunohistochemical experiments, all the reagents for immunohistochemistry were obtained from Maixin Biotech Co., Ltd., China. Tissue sections were de-paraffinized and rehydrated followed by antigen retrieval in EDTA buffer at pH9.0. After blocking endogenous peroxidase activity by treating the sections with 3% hydrogen peroxide, we performed primary antibodies targeting EGFR, VEGF, and, Ki67 to cover the sections separately and incubated the sections overnight at 4 °C in a humidified chamber. After 3 washes with PBS, secondary antibodies were added and followed incubation for 50min at 37 °C. PBS washing steps were repeated and DAB color substrate was applied to sections subsequently for color development. Finally, after nuclear counterstaining with hematoxylin and quick differentiation in the differentiation solution, the sections were conventionally dehydrated, transparentized, and mounted. All sections were observed under a light microscope (Olympus, Japan) and photographed by CellSens Standard software (Olympus, Japan). Five independent biological samples in each group were chosen randomly for immunohistochemical staining and five visual fields per section were captured. Further image quantitative measurements were made by using Image-Pro Plus 6.0 software (Media Cybernetics, USA).

2.5. Sample preparation and NMR measurement

Prior to NMR analysis, 200 μL aliquots of the plasma samples were mixed with 400 μL 0.9%NaCl solution containing 40 μL D2O (Sigma-Aldrich, America) and then centrifuged at 12000g for 5min at 4 °C. Subsequently, 550 μL of the supernatant solution was transferred into a 5 mm NMR tube for further NMR analysis. Gastric tissue (200 mg) was homogenized in a buffer containing 300 μL of H2O and 600 μL CH3OH (Xilong Scientific Co., Ltd., China) for 1min and 300 μL CHCL3 (Xilong Scientific Co., Ltd., China) was then added into the mixture. After centrifugation of the mixture at 10000 g for 10min at 4 °C, the supernatant was obtained and followed by concentration and removal of methanol with a dry nitrogen-blowing instrument (Hengao Technology Development Co., Ltd., China). Freeze-dried extracts were then redissolved with 600 μL D2O solution containing 1 mM of TSP (sodium 3-(trimethylsilyl)-propionate-d4, Sigma-Aldrich, America) and mixed thoroughly. Finally, the mixture was centrifuged at 12000g for 5min at 4 °C and the supernatant solution (550 μL) of the mixture was then transferred into a 5-mm NMR tube for NMR spectroscopy.

The NMR experiments were conducted using an Avance-Ⅲ 600mz NMR spectrometer (Bruker, Germany) at a temperature of 298 K. All One-dimensional (1D) 1H NOESY spectra were acquired employing the pulse sequence Noesypr1d [RD-90°-t1-90°-tm-90°-Acq] with water suppression during the relaxation delay and mixing time. The relaxation delay time (RD) was 2s and the shortly delay time (t1) was 4 μs. 90° represents a 90° RF pulse which width was adjusted to approximately 10 μs for each sample. The mixing time (tm) was 150 ms, the spectral width was 12 ppm. And a total of 40 transients were collected into 32 K points with an acquisition time (Acq) of 1.88s. Fourier transform was applied to the free induction delay (FID) signal with a window function with a line broadening of 0.3 Hz, then the 1D 1H spectra were obtained.

2.6. Multivariate data analysis

All NMR spectra were meticulously subjected to phase and baseline correction using MestReNova v9.0.1 software (Mestrelab Research, Santiago de Compostella, Spain). Reference for the plasma 1H NMR spectra was given to a double peak of CH3 from the internal lactate at 1.33 ppm, whereas the stomach 1H NMR spectra were referenced on the TSP peak at 0.0 ppm. To eliminate the interference of residual Methanol and water signals, areas from 3.34 to 3.35 ppm and 4.7–5.20 ppm were omitted. Subsequently, the spectra region δ 0.60–9.50 was partitioned and integrated into binning data with a size of 0.01 ppm. Every bucketed region was then normalized to the totality of spectral integrals to counteract the overall concentration differences before statistical data analysis.

The acquired data was imported into SIMCA-P 14.1 software for further multivariate statistical analysis. Next, the unsupervised Principal component analysis (PCA) models were conducted to visualize the original distribution of samples. Furthermore, we applied successively Partial least square-discriminant analysis (PLS-DA) model and orthogonal partial least square-discriminant analysis (OPLS-DA) model to observe the discrimination in different groups after the Pareto-scaled procedure. PLS-DA is a potent supervised statistical method that is widely used in pattern recognition. PLS-DA models obtained were validated by 200-permutation testing. OPLS-DA is a major development of the PLS-DA technique which constructs a linear combination of all peak intensities and further maximizes the variation between the different study groups. The robustness of OPLS-DA model is determined by its R2Y cum and Q2 cum scores. R2Y values access the explanation ability of the model while Q2 values assess the predictability of the model. When R2Y > 0.5 and Q2>0.5, the OPLS-DA model was considered a valid model. And the closer the accumulated values of R2Y and Q2 are to 1, the more reliable the model is [23].

Subsequently, the important metabolites contributing to the separation between different groups were evaluated by the variable importance in the project (VIP) list and the corresponding S-plot of the OPLS-DA model. In addition, we filtered the significant characteristic metabolites according to the VIP of the established OPLS-DA model (VIP>1.0) and an independent-sample t-test (P < 0.05) using SPSS software (version 13.0, SPSS Inc., Chicago, IL, USA).

2.7. Metabolic pathway analysis

The identified characteristic metabolites were imported into the Pathway Analysis module of Metaboanalyst 5.0, and significantly altered metabolic pathways were identified according to metabolite sets enrichment analysis (P < 0.05) and pathway topological analysis (Pathway impact value ≥ 0.05).

2.8. Univariate analysis

All data are expressed as the mean ± SD and graphs were generated using GraphPad Prism 4.0 software (Hearne Scientific Software). The differences in the concentrations of metabolites between the two groups were analyzed using independent-sample t-tests with SPSS 13.0 software. Additionally, one-way ANOVA with Turkey's multiple comparisons post-test in SPSS 13.0 software was applied to verify the difference in the body weight and the positive expression of EGFR, VEGF, and Ki67 among four groups. A statistically significant difference was considered when the P value < 0.05.

3. Results

3.1. Effects of moxibustion treatment on ethanol-induced GU rats

As shown in Fig. 1B, after GU modeling (D0∼D1), the body weight of rats in any other groups significantly decreased except for the Con group. During the intervention period (D1∼D5), the body weight of each group all showed an upward trend, and the weight growth rate of rats in the Mod group was lower than that of the GS-Moxi group and S-Moxi group. On the 5th day after moxibustion treatment, there was no significant difference in body weight between the GS-Moxi group and the Con group, while rats in the S-Moxi group and Mod group remained lower body weights than the control rats.

Pathologic changes of the gastric tissues in various groups were evaluated by HE staining (Fig. 1C). Compared with the Con group, rats in the Mod group showed an abnormal structure of the gastric mucosal including thin gastric mucosa layer, disordered lamina propria gland, obvious submucosal edema, and extensive inflammatory cells infiltration, which indicated that the ethanol-induced GU model was successfully established. However, after GS-Moxi or S-Moxi intervention, the gastric epithelium and glands became intact and arranged neatly, and a significant reduction in inflammation and submucosal edema was observed in the moxibustion groups. The histological evaluation indicated the powerful gastroprotective effect of GS-Moxi and S-Moxi on GU rats.

Immunohistochemical analyses revealed the expression of Ki67 (Fig. 2, A and a), VEGF (Fig. 2, B and b) and EGFR (Fig. 2, C and c) in the gastric tissues across the four groups. The levels of the three repair factors (EGFR, VEGF, Ki67) in the gastric tissues of the Mod group were higher compared to those in the Con group, which was consistent with the previous studies because the activation of hypoxia-inducible factor target genes after gastric damage would stimulate the expression of related repair factors [24,25]. However, the GS-Moxi group had higher expression of Ki67, VEGF, and EGFR than the Mod group, in parallel, the S-Moxi group had higher expression of Ki67 and VEGF than the Mod group. In addition, the expression levels of Ki67, VEGF, and EGFR were distinct between the GS-Moxi group and the S-Moxi group. The GS-Moxi group had lower VEGF positive expression level and higher EGFR, and Ki67 positive expression level than did the S-Moxi group. Taken together, these results illustrated GS-Moxi or S-Moxi treatment promoted GU healing by stimulating the expression of related repair factors, but there is some specificity underlying their regulation mechanisms of related repair factors.

Fig. 2.

Fig. 2

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The immunohistochemical analysis of Ki67, VEGF, and EGFR expression in the gastric tissues. (A), (B), and (C) Representative pictures for Ki67, VEGF, and EGFR positive expression in gastric tissues of 4 groups. (The scale bars in (A) represent 100 μm, and the scale bars in (B) and (C) represent 50 μm). (a), (b) and (c) were the quantified results of the expression of Ki67, VEGF, and EGFR in stomach, respectively. Data represent the mean ± s.d. (error bars) from the indicated samples (n = 5 per group). **P < 0.01, ***P < 0.001 and NS indicate no significant difference vs. Mod group by ANOVA. △△P < 0.01 vs. GS-Moxi group by ANOVA.

3.2. NMR spectra and recognition analysis

The typical 1H NMR spectra of plasma and aqueous extracts of gastric tissues are shown in Fig. 3. Additionally, a total of 49 metabolites from the spectra were identified according to Human Metabolome Database (HMDB), literature data, and the metabolomics database aided by the Chenomx NMR Suite 8.1. Two lists of quantified metabolites from plasma and stomach are shown in Tables S1 and S2.

Fig. 3.

Fig. 3

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Typical 1H NMR spectra of plasma and aqueous extract from the stomach. (1, Low density lipoprotein/very low density lipoprotein (LDL/VLDL); 2, Cholesterol; 3, ketoleucine; 4, Leucine; 5, Valine; 6, Isoleucine; 7, 3-Hydroxybutyrate (3-HB); 8, Lactate; 9, Alanine; 10, Acetate; 11, N-Actyl-glycoprotein; 12, Glutamate; 13, Acetone; 14, Acetoacetate; 15, Pyruvate; 16, Saccharopine; 17, Glutamine; 18, Citrate; 19, Glutathione; 20, Homocysteine; 21, Aspartate; 22, Trimethylamine; 23, Creatine; 24, Phophocreatine; 25, Choline; 26, Glycero-3-phosphocholine; 27, Taurine; 28, Betaine; 29, Methanol; 30, Glucose; 31, myo-Inositol; 32, Glycine; 33, Glycerol; 34, Guanidoacetate; 35, Methylhistidine; 36, Phosphorylcholine; 37, Adenosine monophosphate; 38, Allantoin; 39, UDP-Glucose; 40, Nicotinamide adenine dinucleotide phosphate (NADP+); 41, Inosinic acid; 42, Fumarate; 43, Tyrosine; 44, Uridine 5′-monophosphate; 45, Hypoxanthine; 46, Niacinamide; 47, Nicotinate; 48, Adenosine; 49, Formic acid).

For an overview of the metabolic profiles of the four groups, unsupervised PCA with two principal components was applied. The separation tendency among the four groups could be observed (Fig. S1). Next, the supervised PLS-DA scoring plots were constructed to describe the separation and comparison among multiple groups in stomach and plasma samples. And the robustnesses of the PLS-DA models were confirmed by the 200-permutation test. As shown in Fig. 4 (A and a), Fig. 5 (A and a), and Fig. 6 (A and B), a good separation between the Con and Mod group was observed, indicating that either the stomach or plasma metabolic pattern was significantly changed due to GU modeling. Moreover, the aggregation pattern of the GS-Moxi (Fig. 4, B and b; Fig. 5, B and b) or S-Moxi group (Fig. 4, C and c; Fig. 5, C and c) was always located between the Con group and Mod group, whether in stomach or plasma samples. And the clear cluster separation of GS-Moxi (Fig. 6C and D) and S-Moxi (Fig. 6E and F) away from the Mod group was also observed in OPLS-DA scoring plots. These results strongly suggested that GS-Moxi and S-Moxi treatment could effectively regulate the aberrant metabolic patterns of GU rat.

Fig. 4.

Fig. 4

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PLS-DA scoring plots (upper images) and corresponding validation plots (lower images) for 1H NMR spectra recorded on plasma samples in Con(n = 6), Mod (n = 6), GS-Moxi (n = 6), and S-Moxi (n = 6) groups of SD rat. A and a, Con vs. Mod; B and b, Con vs. GS-Moxi vs. Mod; C and c, Con vs. S-Moxi. vs. Mod. The corresponding validation plots were obtained by using the 200-permutation test.

Fig. 5.

Fig. 5

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PLS-DA scoring plots (upper images) and corresponding validation (lower images) for 1H NMR spectra recorded on aqueous extracts derived from the stomach in Con(n = 6), Mod (n = 6), GS-Moxi (n = 6) and S-Moxi (n = 6) groups of SD rat. A and a, Con vs. Mod; B and b, Con vs. GS-Moxi vs. Mod; C and c, Con vs. S-Moxi vs. Mod. The corresponding validation plots were obtained by using the 200-permutation test.

Fig. 6.

Fig. 6

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OPLS-DA scoring plots and corresponding S-plots of the 1H NMR spectra from the plasma (left images) and stomach (right images) in Con(n = 6), Mod (n = 6), GS-Moxi (n = 6) and S-Moxi (n = 6) groups of SD rat. A and a, B and b: Con vs. Mod; C and c, D and d: Mod vs. GS-Moxi; E and e, F and f: Mod vs. S-Moxi. (Lac: Lactate; Glc: Glucose; Glu: Glutamate; Gln: Glutamine; Asp: Aspartate; MI: Myo-inositol; Pcr: Phophocreatine; Cr: Creatine; Tau: Taurine; Gro: Glycerol; Val: Valine; 3-HB: 3-Hydroxybutyrate; Ala: Alanine; LDL/VLDL: Low density lipoprotein/Very low density lipoprotein; Cho: Choline).

3.3. Characteristic metabolites of Con vs. Mod, GS-Moxi vs. Mod, and S-Moxi vs. Mod

Based on the metabolite lists with VIP values exceeding 1 obtained from the OPLS-DA models, we performed t-tests and constructed S-plots to identify the characteristic metabolites distinguishing Con vs. Mod (Fig. 6a and b), GS-Moxi vs. Mod (Fig. 6c and d), and S-Moxi vs. Mod groups (Fig. 6e and f). The results indicate that the characteristic metabolites’ change observed in the moxibustion-treated groups were largely congruent with those in the Con group, as opposed to the Mod group (Table 1, Table 2, and Fig. 7A).

Table 1.

Quantitative comparison of characteristic metabolite levels of plasma samples among Con, Mod, GS-Moxi, and S-Moxi groups of rats based on relative NMR integrals.

Metabolites (Plasma) Mean ± SD
 VIP score
Con (n = 6) Mod (n = 6) GS-Moxi (n = 6) S-Moxi (n = 6) VIP1 VIP2 VIP3
LDL/VLDL 0.0203 ± 0.0034* 0.0142 ± 0.0027 0.014 ± 0.0016 0.018 ± 0.0024* 1.49219 0.691 1.55583
Valine 0.0025 ± 0.0005*** 0.0061 ± 0.0009 0.0057 ± 0.0008 0.0034 ± 0.0005*** 1.11648 0.239048 1.30842
3-Hydroxybutyric acid 0.012 ± 0.0017** 0.0085 ± 0.0016 0.0095 ± 0.0023 0.0125 ± 0.0013** 1.06048 0.393012 1.58905
Lactate 0.0823 ± 0.0072** 0.1029 ± 0.0081 0.0857 ± 0.0086** 0.0914 ± 0.0052* 2.6984 3.40229 2.60426
Alanine 0.0054 ± 0.0005** 0.0091 ± 0.0022 0.0089 ± 0.0017 0.0068 ± 0.0004* 1.10346 0.7418 1.10006
Glutamate 0.0301 ± 0.0032** 0.0243 ± 0.0017 0.0282 ± 0.0034* 0.0255 ± 0.0034 1.44631 1.67679 0.856526
Glucose 0.0407 ± 0.0054*** 0.0201 ± 0.006 0.0312 ± 0.007* 0.0297 ± 0.0054* 2.82433 2.81807 2.57889
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Note: (1) Statistical significances: *P < 0.05; **P < 0.01, ***P < 0.001 vs. Mod group by t-test. (2) VIP1, VIP2 and VIP3 represent variable importance in the projection (VIP) scores of metabolites for pair-wise comparisons of Con vs. Mod, GS-Moxi vs. Mod and S-Moxi vs. Mod from the OPLS-DA models, respectively. (3) Red/blue colors denote increased/decreased characteristic metabolite levels in Con, GS-Moxi and S-Moxi groups compared with those in the Mod group.

Table 2.

Quantitative comparison of characteristic metabolite levels of stomach among Con, Mod, GS-Moxi, and S-Moxi groups of rats based on relative NMR integrals.

Metabolites (Stomach) Mean ± SD
 VIP score
Con (n = 6) Mod (n = 6) GS-Moxi (n = 6) S-Moxi (n = 6) VIP1 VIP2 VIP3
Lactate 0.0579 ± 0.0072*** 0.0883 ± 0.0029 0.0671 ± 0.007*** 0.0762 ± 0.0074** 3.18695 2.56741 2.52005
Phosphocreatine 0.0231 ± 0.0039* 0.0179 ± 0.0021 0.0284 ± 0.0067** 0.0243 ± 0.0079 1.27296 1.87605 1.97012
Taurine 0.0696 ± 0.0036*** 0.0534 ± 0.0063 0.0648 ± 0.0036** 0.0581 ± 0.0038 2.29906 1.90597 1.59093
Myo-Inositol 0.0264 ± 0.0027* 0.0216 ± 0.0023 0.0261 ± 0.0013** 0.0237 ± 0.0014 1.16113 1.18704 1.04281
Glycerol 0.0475 ± 0.0018** 0.0416 ± 0.0038 0.0503 ± 0.0023** 0.0459 ± 0.0036 1.29825 1.67148 1.46235
Aspartate 0.0188 ± 0.0008* 0.0218 ± 0.0028 0.0165 ± 0.0009** 0.0166 ± 0.003* 1.00454 1.13526 1.31886
Creatine 0.02 ± 0.0027 0.0174 ± 0.0015 0.0233 ± 0.0047* 0.0209 ± 0.0051 0.864033 1.38661 1.48766
Glutamine 0.009 ± 0.0032 0.0072 ± 0.0014 0.0109 ± 0.0026* 0.0099 ± 0.0024 0.557946 1.02777 1.17006
Glutamate 0.0286 ± 0.0037* 0.0225 ± 0.0025 0.0311 ± 0.0025*** 0.0254 ± 0.0035 1.19581 1.63307 0.767745
Alanine 0.0147 ± 0.0006* 0.0174 ± 0.0025 0.0166 ± 0.0019 0.0138 ± 0.0018* 0.823899 0.54215 1.29422
Choline 0.0264 ± 0.0047 0.0207 ± 0.0037 0.021 ± 0.0013 0.0259 ± 0.0026* 0.981492 0.62347 1.67547
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Note: (1) Statistical significances: *P < 0.05; **P < 0.01, ***P < 0.001 vs. Mod group by t-test. (2) VIP1, VIP2 and VIP3 represent variable importance in the projection (VIP) scores of metabolites for pair-wise comparisons of Con vs. Mod, GS-Moxi vs. Mod and S-Moxi vs. Mod from the OPLS-DA models, respectively. (3) Red/blue colors denote increased/decreased characteristic metabolite levels in Con, GS-Moxi, and S-Moxi groups compared with those in the Mod group.

Fig. 7.

Fig. 7

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Analyses of characteristic metabolites and corresponding metabolic pathways of GS-Moxi, Con and S-Moxi. (A) Venn diagrams of characteristic metabolites identified with two criteria of VIP>1 from the OPLS-DA models and P < 0.05 from the t-test. a, Venn diagram of characteristic metabolites for GS-Moxi vs. Mod and Con vs. Mod. b, Venn diagram of characteristic metabolites for S-Moxi vs. Mod and Con vs. Mod. Red/blue colors denote increased/decreased characteristic metabolite levels in Con, GS-Moxi and S-Moxi groups compared with those in the Mod group. Characteristic metabolites in the dotted boxes were shared by Con group. (B), (C) and (D) Metabolic pathway analyses for GS-Moxi, Con and S-Moxi, respectively. (a. Aminoacyl-tRNA biosynthesis; b. Alanine, aspartate and glutamate metabolism; c. Arginine biosynthesis; d. Butanoate metabolism; e. Histidine metabolism; f. Pantothenate and CoA biosynthesis; g. Glycolysis/Gluconeogenesis; h. Synthesis and degradation of ketone bodies; i. d-Glutamine and d-glutamate metabolism; j. Nitrogen metabolism; k. Arginine and proline metabolism).

The Con group exhibited 7 characteristic metabolites in plasma and 7 in the stomach relative to the Mod group. Among these characteristic metabolites, the GS-Moxi group shared 3 in plasma (elevated glucose and glutamate, as well as reduced lactate) and 7 in the stomach (down-regulated lactate and aspartate, and up-regulated phosphocreatine, taurine, myo-inositol, glycerol, and glutamate) with the Con group (Fig. 7a). In contrast, the S-Moxi group shared 6 characteristic metabolites in plasma (increased glucose, LDL/VLDL, and 3-Hydroxybutyrate (3-HB), as well as decreased lactate, valine, and alanine), and 2 (decreased lactate and aspartate) in the stomach with the Con group (Fig. 7b). Moreover, the GS-Moxi group exhibited 2 unique metabolites in stomach, including increased creatine and glutamine. Similarly, the S-Moxi group displayed 2 distinctive metabolites in the stomach: decreased alanine, and increased choline.

On the other hand, our research also indicates that the application of two distinct moxibustion methods elicit divergent effects on both plasma and gastric metabolism. This is evidenced by the distinct combinations of characteristic metabolites observed in response to each respective moxibustion treatment (Fig. 7A).

3.4. Significantly altered metabolic pathway analysis

Using a combination of metabolite sets enrichment analysis (P < 0.05) and pathway impact value (PIV≥0.05), we have identified a total of 10 significantly altered metabolic pathways in the Con group (Fig. 7C), 8 significantly altered metabolic pathways in the GS-Moxi group (Fig. 7B) and 5 significantly altered metabolic pathways in the S-Moxi group (Fig. 7D) compared to the Mod group. Among these pathways, GS-Moxi and Con group shared 7 significant metabolic pathways, while S-Moxi and Con group shared 5 significant metabolic pathways (Table 3). In addition, the Con group exhibited a unique significant pathway, namely Butanoate metabolism, while the GS-Moxi group exhibited another unique significant pathway: Arginine and proline metabolism. Overall, the majority of the significant metabolic pathways observed in the moxibustion treated groups were consistent with those observed in the Con group.

Table 3.

Significantly altered metabolic pathways identified in Con, GS-Moxi, and S-Moxi groups in GU rats relative to the Mod group.

Pathway Con vs. Mod GS-Moxi vs. Mod S-Moxi vs. Mod
a. Aminoacyl-tRNA biosynthesis ✓ ✓ ✓
b. Alanine, aspartate and glutamate metabolism ✓ ✓ ✓
c. Arginine biosynthesis ✓ ✓
d. Butanoate metabolism ✓
e. Histidine metabolism ✓ ✓
f. Pantothenate and CoA biosynthesis ✓ ✓
g. Glycolysis/Gluconeogenesis ✓ ✓ ✓
h. Synthesis and degradation of ketone bodies ✓ ✓
i. d-Glutamine and d-glutamate metabolism ✓ ✓
j. Nitrogen metabolism ✓ ✓
k. Arginine and proline metabolism ✓
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Furthermore, S-Moxi and GS-Moxi shared 3 significant metabolic pathways, including Glycolysis/Gluconeogenesis, Alanine, aspartate and glutamate metabolism, and Aminoacyl-tRNA biosynthesis. Moreover, S-Moxi had 2 unique metabolic pathways including Pantothenate and CoA biosynthesis and Synthesis and degradation of Ketone bodies, while GS-Moxi had 5 unique metabolic pathways including Arginine biosynthesis, Nitrogen metabolism, Arginine and proline metabolism, d-Glutamine and d-glutamate metabolism, and Histidine metabolism.

According to the KEGG database, MetaboAnalyst 5.0, and the physiological properties of each characteristic metabolite, a metabolic map (Fig. 8) was constructed to visualize the aberrant metabolic processes during GU and the effects of the two moxibustion methods on GU rats. On the one hand, we observed that the changing trends of levels of most characteristic metabolites in the Moxi-teated groups compared with the Mod group were opposite to those in the Mod group compared with the Con group, indicating that GS-Moxi and S-Moxi could effectively reverse the abnormal metabolic changes in GU rats. On the other hand, we also observed different metabolic regulation processes between GS-Moxi and S-Moxi treatment, which would be explored further in the Discussion section.

Fig. 8.

Fig. 8

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Visual metabolic map of metabolic disorders during GU and therapeutic effect of GS-Moxi and S-Moxi. The box with dashed lines represents characteristic metabolites. Dotted arrows indicate multiple biochemical reactions, whereas solid arrows denote single biochemical reactions. Blue arrows indicate changing trends of characteristic metabolites in the Mod group compared to the Con group. Red arrows indicate changing trends of characteristic metabolites in the GS-Moxi group compared to the Mod group. Green arrows indicate changing trends of characteristic metabolites in the S-Moxi group compared to the Mod group. (3-HB: 3-Hydroxybutyrate; 3-HIB: 3-hydroxyisobutyrate).

4. Discussion

This study amply demonstrates the therapeutic effects of moxibustion treatments in GU healing. On the one hand, GS-Moxi and S-Moxi both facilitated GU healing via significantly up-regulating the expression of the relevant repair factors in different degrees (Fig. 2). EGFR, VEGF, and Ki67 are indispensable in the GU-healing process. EGFR plays a vital role in re-epithelialization [26], and VEGF stimulates angiogenesis and revascularization [27]. Ki67, a proliferation-related factor, can reflect the degree of cell proliferation in GU healing [28]. It is clear that a higher expression of the three factors benefits GU repairing. On the other hand, GS-Moxi or S-Moxi treatment significantly normalized GU-associated metabolic phenotype in plasma and stomach (Fig. 4, Fig. 5, Fig. 6). The GS-Moxi group demonstrated a shared presence of 10 characteristic metabolites and 9 significantly pathways with the Con group, whereas the S-Moxi group exhibited a shared presence of 8 characteristic metabolites and 5 significantly pathways with the Con group. Evidently, the metabolic phenotype of the GS-Moxi group displayed a greater similarity to that of the Con group when compared to the S-Moxi group. Therefore, it can be inferred that the therapeutic effects of GS-Moxi on GU rats may be superior to those of S-Moxi.

Although GS-Moxi and S-Moxi are effective in improving gastric damage and metabolic phenotypes, they likely involve different mechanisms that have been demonstrated in two ways. First, GS-Moxi showed a superior effect on EGFR and Ki67 expression than did S-Moxi, whereas S-Moxi had a more obvious impact on VEGF expression than did GS-Moxi (Fig. 2). This observation points out that the responses related to GS-Moxi and S-Moxi may have a different emphasis, such as re-epithelialization or vascular reconstruction. However, it's necessary to detect the expression of related kinase signaling of re-epithelialization and revascularization at the molecular level during GU, which will be carried out in our future work. Second, different characteristic metabolites in plasma and gastric tissue in response to GS-Moxi and S-Moxi treatment revealed different metabolic regulatory processes. Taken as a whole, 6 metabolites in plasma and 4 metabolites in the stomach were significantly affected by S-Moxi, while 3 metabolites in plasma and 9 metabolites in the stomach were significantly influenced by GS-Moxi (Fig. 7A). The differing responses observed in the two tissues suggested that GS-Moxi had a much more profound effect on the stomach while the effect of S-Moxi appeared more obvious in the bloodstream. The two moxibustion treatments may act on different targets. Focusing on the specific metabolite changes allows for interpretations of the underlying mechanism. Thus, the physiological function of each characteristic metabolite was analyzed. Glucose, lactate, phosphocreatine, creatine, glycerol, alanine, and aspartate were involved in energy metabolism. LDL/VLDL, 3-HB, and valine were involved in lipid metabolism. Glutamate, glutamine, taurine, myo-inositol, and choline were involved in neurotransmitter-related metabolism. And GS-Moxi corrected aberrant energy and neurotransmitter-related metabolism in GU rats, while S-Moxi modulated metabolic perturbations in energy and lipid metabolism. These details are discussed below.

4.1. Energy metabolism

In our study, energy metabolism disturbance is the most predominant pathological feature in GU, which is in line with the previous findings [29]. Mechanically, the destruction of blood vessels in GU would necessarily be accompanied by insufficient nutrients and oxygen supply [25]. Both GS-Moxi and S-Moxi effectively corrected disrupted energy metabolism in GU rats, indicating the excellent repairment effects of both moxibustion methods.

Glucose is the main substrate of energy metabolism, the decreased circulating glucose level inevitably leads to insufficient ATP production. Lactate is the end product of glycolysis and an important biomarker evaluating the balance between oxygen supply and its consumption in the body [30]. Phosphocreatine, a rapidly mobilizable reserve of high-energy phosphates, can convert into creatine and release energy quickly through the creatine-phosphocreatine shuttle system [31]. The accumulation of lactate and the decreased contents of glucose and phosphocreatine in GU rats reflected that the reliance on anaerobic metabolism (lactate generation, phosphocreatine degradation) was increased, meanwhile, the oxygen extraction and electron flux through the electron transport chain (ETC) was reduced. Alanine and glycerol can be converted into pyruvate, providing a carbon resource for gluconeogenesis [32]. Pyruvate can further stimulate aspartate synthesis in an aspartate aminotransferase-dependent fashion for rescuing the proliferation of cells with ETC dysfunction [33]. Thus, increased aspartate concentration was partly due to the compensatory proliferation function upon ETC inhibition, in addition, which was also closely associated with enhanced proteolysis and decreased amino acid availability under inflammation conditions [34].

The significant fluctuation of the concentrations of the above-discussed metabolites after GU modeling strongly indicated energy metabolism homeostasis was disrupted, including glucose metabolism disorders, ETC inhibition, and perturbed creatine-phosphocreatine shuttle system. However, both moxibustion methods showed beneficial effects on energy metabolism. GS-Moxi effectively regulated the level of glucose, lactate, aspartate, glycerol, phosphocreatine, and creatine, whereas S-Moxi reversed the level of glucose, lactate, aspartate, and alanine. Notably, both the two moxibustion treatments facilitated the efficient utilization of aspartate. Increased Aspartate availability is potent at promoting cell proliferation and vascular repair [35], which might be the important material basis for the therapeutic effects of GS Moxi and S-Moxi.

4.2. Lipid metabolism

Lipid metabolism plays a crucial role in whole-body metabolic homeostasis. Prior studies [36,37] have observed lipid metabolism dysfunction in GU. In this study, disorders in hepatic lipid metabolism and trans-endothelial fatty acids (FA) transport of GU rats were identified. Moreover, S-Moxi showed a powerful effect in regulating lipid homeostasis, which GS-Moxi lacked.

LDL/VLDL are the most common lipoproteins synthesized in the liver and transport nonpolar lipids to peripheral tissue. Ethanol feeding causes gastric mucosal damage and decreases LDL/VLDL secretion [38]. 3-HB, an endogenous product of fatty acid oxidation in hepatic mitochondria, serves as an important energy source in extrahepatic tissues [39]. The decreased plasma level of LDL/VLDL and 3-HB indicated disrupted hepatic lipid metabolism in GU. Valine is closely associated with impaired lipid metabolism [40,41]. The catabolic intermediate of valine produced in skeletal muscle, 3-hydroxyisobutyrate (3-HIB), is an important paracrine regulator of trans-endothelial FA transport [42]. High circulating valine level reflected the inefficient valine catabolism in GU because inflammatory signals would inhibit valine transport from the blood to muscles for further degradation [43]. Notably, valine accumulation aggravates endothelial dysfunction by generating ROS [44], rather, 3-HB exhibits antioxidant effects, promoting VEGF generation and antagonizing endothelial injury [45].

S-Moxi could effectively increase the plasma LDL/VLDL and 3-HB levels, while decreasing the plasma valine level, indicating its beneficial effect on lipids metabolism and endothelial injury repair. Previous studies have demonstrated S-Moxi could ameliorate hepatic metabolism and promote blood circulation [46,47]. Taken together, we speculate the main acting sites of S-Moxi may be the liver and vasculature.

4.3. Neurotransmitter-related metabolism

Glutamate and taurine are important neurotransmitters existing in the stomach and are involved in the multiple gastric functions’ regulation [48,49]. Glutamate activates ionotropic and metabotropic receptors of afferent vagal fibers, thus activating neurons in the nucleus of the solitary tract and forming the vago-vagal reflex, and ultimately exerts autonomic, endocrine, and behavioral responses [50]. Taurine can excite the cholinergic neurons and follow by the release of Ach via binding GABAA receptors on cholinergic neurons [49]. The postganglionic cholinergic pathway has a major role in setting the level of basal gastric tone and motility [48]. Noteworthy, accumulating data have revealed the therapeutic effects of glutamate and taurine in GU rats [51,52]. Glutamine can be converted to glutamate via glutaminase. Beyond this, glutamine is involved in various biochemical pathways and is important to cell survival and proliferation [53]. Myo-inositol is fundamental for signal transduction in organs in response to various neurotransmitters [54]. Choline is primarily from the degradation of lipids. As a key precursor of the neurotransmitter acetylcholine, improving choline supply is beneficial to counter deficits in the cholinergic system [55,56].

Therefore, the depletion of glutamate, taurine, and Myo-inositol in GU rats revealed low neuron excitability during GU and was followed by the disruption of multiple gastric functions. Interestingly, GS-Moxi could effectively upregulate the concentration of glutamate, glutamine, taurine, and myo-inositol, whereas S-Moxi only increased choline's level, which indicated GS-Moxi showed a more potent effect in regulating neurotransmitters-related metabolism and recovering impaired gastric functions than S-Moxi. This observation suggested the underlying therapeutic mechanism of GS-Moxi may be a rapid and target-specific response based on neuronal activity in autonomic nervous system.

In all, our findings systematically illuminated the potential differences in metabolic regulations between GS-Moxi and S-Moxi in GU rats for the first time. Both GS-Moxi and S-Moxi significantly regulated energy balance in GU rats. However, GS-Moxi exhibited a better effect on the neurotransmitter-related metabolism than S-Moxi, meanwhile, S-Moxi has a superior effect in regulating lipid metabolism than GS-Moxi. We also proposed the effect of GS-Moxi and S-Moxi on different organs is diverse. The main acting sites of S-Moxi may be the liver and vasculature, whereas G-s Moxi specially targeted the stomach via regulating nervous system. This study offers new perspectives for understanding the mechanistic variances underlying different bio-heat transfer behavior of the different moxibustion therapies. Future studies based on integrated analysis of the metabolic changes in all organs that responded to different moxibustion treatments will contribute to revealing holistic biological information about moxibustion.

Ethics statement

The animal study protocol was approved by the Animal Care and Use Committee of Xiamen University (Permit Number: XMULAC20190142).

Author contribution statement

Guhang Wei: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Yufeng Xie, Zongbao Yang: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data.

Jinlan Yang, Yunjin Yu, Yanbin Cheng, Baohua Chen, Jingjing Guo: Performed the experiments.

Mengran Pei: Performed the experiments; Analyzed and interpreted the data.

Jun Feng: Contributed reagents, materials, analysis tools or data.

Funding statement

Mr. Yufeng Xie was supported by Public Health Research Projects of Futian, Shenzhen {FTWS2021011}, Shenzhen Fundamental Research Scheme-General Program {JCYJ20220531091404010}.

Jun Feng was supported by Public Health Research Projects of Futian, Shenzhen {FTWS2020014}.

Data availability statement

Data will be made available on request.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank Qiaoping Li and Junru Wen for their comments and revisions on the manuscript.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e19108.

Contributor Information

Yufeng Xie, Email: yufengxie1983@163.com.

Zongbao Yang, Email: yangzb@xmu.edu.cn.

Jun Feng, Email: 906959073@qq.com.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (316.1KB, docx)

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