Abstract
Acupuncture is a traditional Chinese medicine therapy that has been
found useful for treating various diseases. The treatments involve the
insertion of fine needles at acupoints along specific meridians
(meridian specificity). This study aims to investigate the metabolic
basis of meridian specificity using proton nuclear magnetic resonance
(^1H NMR)-based metabolomics. Electro-acupuncture (EA) stimulations
were performed at acupoints of either Stomach Meridian of Foot-Yangming
(SMFY) or Gallbladder Meridian of Foot-Shaoyang (GMFS) in healthy male
Sprague Dawley (SD) rats. ^1H-NMR spectra datasets of serum, urine,
cortex, and stomach tissue extracts from the rats were analysed by
multivariate statistical analysis to investigate metabolic
perturbations due to EA treatments at different meridians. EA treatment
on either the SMFY or GMFS acupoints induced significant variations in
31 metabolites, e.g., amino acids, organic acids, choline esters and
glucose. Moreover, a few meridian-specific metabolic changes were found
for EA stimulations on the SMFY or GMFS acupoints. Our study
demonstrated significant metabolic differences in response to EA
stimulations on acupoints of SMFY and GMFS meridians. These results
validate the hypothesis that meridian specificity in acupuncture is
detectable in the metabolome and demonstrate the feasibility and
effectiveness of a metabolomics approach in understanding the mechanism
of acupuncture.
Introduction
Acupuncture is a traditional Chinese medicine therapy that has been
practised in China for 4,000 years^[40]1. More recently, it has emerged
as a complementary therapeutic method for health restoration,
promotion, and maintenance^[41]1, [42]2. The practice of acupuncture
involves the insertion of fine needles into acupuncture points
(acupoints) in the body. To date, more than 400 acupoints affiliated to
12 meridians across the human body have been systemically described.
Application of acupuncture on a specific median exerts a therapeutic
effect on a specific part of the human body. This specific effect,
known as meridian specificity, forms the theoretical basis of
acupuncture therapy^[43]3. Researchers have reported the existence of
meridian specificity with functional neuroimaging
techniques^[44]4–[45]7.
With the aid of modern clinical and experimental techniques, there is
increasing evidence regarding the efficacy of acupuncture in
ameliorating symptoms of diseases, such as gastroesophageal reflux
disease^[46]8, functional dyspepsia^[47]9, insomnia^[48]10,
migraines^[49]11, and rheumatoid arthritis^[50]12. However, the
biological mechanism underlying how acupuncture actually works remains
elusive.
Metabolomics serves as an emerging tool with rapid expansion for
high-throughput biomedical analyses^[51]13 and provides a “snapshot” of
the metabolic status of a biological sample^[52]14, [53]15. Interest in
applying NMR-based metabolomics to study the metabolic changes
following acupuncture treatment is increasing^[54]16–[55]20. In this
study, we used a proton nuclear magnetic resonance- (^1H NMR-) based
metabolomics approach to investigate the metabolic perturbations due to
acupuncture at two different specific meridians, namely, the Stomach
Meridian of Foot-Yangming (SMFY) and Gallbladder Meridian of
Foot-Shaoyang (GMFS) meridians. According to previous reports,
electro-acupuncture (EA) treatment on acupoints along the SMFY meridian
proved effective in enhancing gastrointestinal motility, improving
gastric mucosal blood flow, and protecting gastric mucosa from
injury^[56]21–[57]23. On the other hand, EA treatment on acupoints in
the GMFS meridian was found to promote bile production, secretion, and
release to facilitate digestion, improve insomnia and to alleviate
migraines^[58]24, [59]25.
The flow chart of experiments in this study is shown as Fig. [60]1. Our
primary purpose is to validate the hypothesis that meridian specificity
in acupuncture is detectable in the metabolome and to provide specific
metabolic patterns induced by acupuncture stimuli on different
meridians.
Figure 1.
Figure 1
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Flowchart of experimental design and analyses. Twenty-four healthy SD
rats were divided into three groups: control, SMFY and GMFS groups.
Samples of serum, urine, stomach and cortex were measured with ^1H NMR
spectroscopy to examine altered metabolism due to acupuncture
treatments.
Assignments: 2-HB, 2-hydroxybutyrate; 3-HB, 3-hydroxybutyrate; Ace,
acetate; Ade, adenosine; AH, aminohippurate; Ala, alanine; All,
allantion; Asp, asparate; Ben, benzoate; Bet, betaine; Ci, citrate; Cn,
creatinine; Cr, creatine; DMA, dimethylamine; DMG, N,N-dimethylglycine;
EthA, ethanolamine; For, formate; GABA, γ-aminobutyrate; Gln,
glutamine; Glu, glutamate; Gly, glycine; GPC, glycerophosphocholine;
Hip, hippurate; HX, hypoxanthine; Ino, inosine; Lac, lactate; LDL, low
density lipoprotein; Lys, lysine; Met, methionine; m-I, myo-inositol;
MM, methylmalonate; MN, N-methylnicotinamide; m-HPA,
meta-hydroxyphenylacetate; NAA, N-acetylaspartate; OAS,
O-acetylglycoprotein; o-HPA, ortho-hydroxyphenylacetate; PAG,
phenylacetylglycine; PC, phosphocholine; Phe, phenylalanine; Tau,
taurine; Thr, threonine; Tyr, tyrosine; Uc, urocanate; Ura, uracil;
α-Glc, α-glucose; α-KG, α-ketoglutarate; β-Glc, β-glucose.
Results
^1H NMR spectra of multiple biological samples
Typical ^1H NMR spectra for the control group are shown as Fig. [62]2.
In the figure, high-intensity peaks are assigned based on published
literature^[63]26 and the HMDB database ([64]http://www.hmdb.ca/).
Generally, the spectra show common peaks (which are present in
different types of samples) including choline, purine, amino acids,
carboxylic acids, and glycolysis and TCA cycle intermediates. Some of
the metabolites are tissue-specific; for example, cortex tissue extract
contains neurotransmitter (GABA), serum contains lipoprotein and
glycoprotein, and urinary samples present gut microbiota-related
metabolites, such as hippurate, benzoate, and urocanate.
Figure 2.
Figure 2
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Typical ^1H NMR spectra (at 600 MHz) of multiple matrices in the
control group. (a) serum, (b) urine, (c) stomach extract, (d) cortex
extract.
Pattern recognition analyses
Next, we used multivariate statistical analyses to examine the
metabolic differences between EA stimulations on the SMFY and GMFS
meridians. Analysis of the NMR data using PCA shows good separation
between the control group and EA-treated groups (i.e., SMFY and GMFS)
for all types of biological samples (Figs [66]3 and [67]S1). The result
showed that EA treatment contributed most to the metabolic variation
among the groups.
Figure 3.
Figure 3
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PCA scores plots of the control ( Inline graphic ), SMFY ( Inline
graphic ) and GMFS ( Inline graphic ) groups for (a) serum, (b) urine,
(c) stomach extract, and (d) cortex extract.
PLS-DA modelling was further used to examine class discrimination. In
addition to group separation between the controls and EA-stimulated
rats, the PLS-DA scores plots further showed good separation between
the SMFY and the GMFS groups (Fig. [69]4). The result suggested that
the meridian-specific metabolic changes due to acupuncture are
detectable in the metabolome of biological samples. In particular, both
EA-treated groups (i.e., SMFY and GMFS) were well separated from the
control group along the first latent component (t[1]), indicating that
metabolic profiles of rat bio-samples were distinctly altered by the EA
stimulation. Apart from the separation by t[1], the second latent
component (t[2]) further showed separation between the SMFY and GMFS
groups. Furthermore, comparisons between the control and the two
treatment groups (i.e., control and SMFY, control and GMFS) were
carried out using the PLS-DA and OPLS-DA analyses. The models were
found robust following a 7-fold cross-validation and permutation test
(200 permutations) (Figs [70]S2 and [71]S3).
Figure 4.
Figure 4
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PLS-DA scores plots of the control ( Inline graphic ), SMFY ( Inline
graphic ) and GMFS ( Inline graphic ) groups for (a) serum, (b) urine,
(c) stomach extract, and (d) cortex extract.
We will next discuss two aspects of the metabolic analyses: first, a
comparison between the EA-stimulated and the control groups to
investigate the non-specific metabolic responses due to EA stimulation
(meridian independent), and second, a comparison between the SMFY and
GMFS groups to investigate the specific metabolic perturbation due to
EA stimulations on different meridians.
EA-perturbed metabolic changes
The corresponding coefficient loading plots and enhanced volcano plots
were then used to identify candidate metabolites that contributed to
the inter-group separation. In the enhanced volcano plots, the fold
change was defined as the ratio of average concentration of a given
metabolite between the EA-treated groups (SMFY or GMFS) and the control
group. Therefore, the concentrations for those metabolites located at
positive side of horizontal axis in Fig. [73]5 are higher in the
EA-treated group, compared to the controls. Detailed cut-off values for
parameters to select significantly changed metabolites in enhanced
volcano plots are listed in Table [74]S1. In the enhanced volcano
plots, |r| ≥ 0.6 (absolute correlation coefficient) and VIP ≥ top 20%
(variable importance in projection) were set as the criteria to select
metabolites with statistically significant changes between different
groups. The cut-off values for p-value and fold change are indicated by
straight lines in each plot. Generally, candidate metabolites
identified by multivariate statistical analyses tend to locate at the
upper left or upper right zones of the enhanced volcano plot (segmented
by horizontal and vertical threshold lines into six zones) in larger
circle shapes and warmer colours.
Figure 5.
Figure 5
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Enhanced volcano plots of multiple matrices for screening if metabolite
markers. Figures (a,c,e) show comparison between SMFY and control
groups, and (b,d,f) between GMFS and control groups; (a and b), (c and
d), and (e and f) correspond to cortex extract, serum, and urine
samples, respectively. VIP together with |r| is introduced with being
represented by circles size and colour, respectively. For each
comparison, VIP values are categorized into four segments: top 5%, top
10%, top 20% and rest 80%, with each represented by a circle of
decreasing size.
In comparison with the cortex metabolome of the control group,
metabolic alterations in the SMFY group include decreased
concentrations of aspartate, phenylalanine, acetate, fumarate,
γ-aminobutyrate (GABA), ethanolamine (EthA), and phosphocholine (PC),
together with increased concentrations of threonine, lysine, and
myo-inositol (Figs [76]5a and [77]S4a). By contrast, the GMFS group
shows significant decreased levels of aspartate, phenylalanine,
fumarate, GABA, and PC, together with increased levels of threonine,
lysine, myo-inositol, and glutamate in cortex (Figs [78]5b and
[79]S4b). Among the changes, eight metabolites were found to have
consistent changes following acupuncture and were considered as
non-specific metabolic responses to the EA stimulation regardless of
the meridian, and several others (e.g., EthA, acetate, and glutamate)
were considered as exclusive responses due to stimulations on different
meridians.
A combination of the enhanced volcano plot with correlation coefficient
and variable importance projection (VIP) from OPLS-DA model offers a
comprehensive and straightforward method to study differential
metabolites. The results from enhanced volcano plots are consistent
with the corresponding coefficient loading plots where metabolites with
major difference between groups appear in hot colours.
Following EA stimulations, the serum samples of EA-treated groups
(i.e., SMFY and GMFS) are highlighted with significant increases of
lactate, along with reductions of glutamine, citrate, 3-hydroxybutyrate
(3-HB), β-glucose, N , N-dimethylglycine (DMG), and low density
lipoprotein (LDL) in comparison with the control group (Fig. [80]5c and
d). In addition, urinary metabolome analyses indicated significant
lower levels of hippurate, urocanate, benzoate, allantoin,
α-ketoglutarate (α-KG), phenylacetylglycine (PAG), and
ortho-hydroxyphenylacetate (o-HPA) in both the SMFY and GMFS groups
compared to the control group (Fig. [81]5e and f). Notably, the altered
metabolites due to EA stimulations in blood and urinary samples are
mainly scattered in the upper left region of the volcano plot, showing
that EA stimulations primarily led to decreased metabolite
concentration in serum and urine samples. In conclusion, these
metabolites are potential non-meridian-specific markers due to EA
stimulations.
The results also showed a number of meridian-specific markers due to EA
treatment on the SMFY meridian, e.g., 2-hydroxybutyrate (2-HB,
increase), creatine (Cr, increase), methylmalonate (MM, increase),
aminohippurate (AH, decrease), acetate (Ace, decrease), and
ethanolamine (EthA, decrease). On the other hand, treatment on the GMFS
meridian is highlighted with meridian-specific changes, including
increased concentrations of N-methylnicotinamide (MN) and glutamate,
and accompanied by a reduction in O-acetylglycoprotein (OAS). Notably,
methionine in blood serum was found to be decreased in the SMFY group
but increased in the GMFS group (Fig. [82]5).
The stomach metabolome of the SMFY group does not show similarity with
the GMFS group (shown in Fig. [83]S5). These results support the
distinctive effect of acupuncture at SMFY (the stomach meridian)
acupoints on the stomach metabolome, different from acupuncture on the
gallbladder meridian, GMFS. Since urine and serum reveal global
metabolic variations, and cerebral-cortex-related neuro-regulation is
of interest in this study, we primarily focused on the influences on
urine, serum, and cortex tissue induced by acupuncture in following
analyses.
Pathway enrichment analysis
A metabolic pathway analysis was conducted for serum, urine, and cortex
extract samples using the MetPA (Metabolomics Pathway Analysis)^[84]27.
The identified metabolic markers were analysed using the MetPA to
facilitate further biological interpretation and thereby reveal the
most relevant pathways involved in EA stimulations at acupoints on the
SMFY or GMFS meridian. The most affected pathways in the experimental
groups were found to be alanine, aspartate, and glutamate metabolism;
phenylalanine metabolism; and phenylalanine, tyrosine and tryptophan
biosynthesis (Impact value ≥ 0.4 and -log(p) ≥ 2) (Fig. [85]6 and
Table [86]S2). The EA stimulations on acupoints of SMFY and GMFS
meridians share similar effects on a number of metabolic pathways; for
example, metabolites in the TCA cycle were found changed in the same
direction (but to a different degree) with EA treatment on either SMFY
or GMFS acupoints. In contrast, marked increased metabolic changes in
glycolysis can only be observed in the SMFY group, while glutamine and
glutamate changed significantly only for the GMFY group.
Figure 6.
Figure 6
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Bubble plots of altered metabolic pathways in multiple bio-samples
(serum, urine, and cortex extract). (a) shows comparison between SMFY
and control, and (b) between GMFS and control. Bubble area is
proportional to the impact of each pathway with colour denoting the
significance from highest (in red) to lowest (in white). 1, alanine,
aspartate and glutamate metabolism; 2, arginine and proline metabolism;
3, butanoate metabolism; 4, D-glutamine and D-glutamate metabolism; 5,
glycolysis or gluconeogenesis; 6, glyoxylate and dicarboxylate
metabolism; 7, histidine metabolism; 8, phenylalanine metabolism; 9,
phenylalanine, tyrosine and tryptophan biosynthesis; 10, TCA cycle.
Statistical power analysis
To determine the achieved statistical power of our study, a post hoc
power analysis was carried out on the current results using the online
statistics software G*power 3.1^[88]28, [89]29
([90]http://www.gpower.hhu.de/). First, the effect size was computed as
the absolute difference between the experimental mean and the control
mean divided by a standard deviation for the NMR bio-sample data. The
resultant large effect sizes indicated the sufficient difference
between the experimental groups and the control group (Fig. [91]7).
These variable effect sizes were then used to calculate the achieved
statistical power of the selected metabolites as a function of
specified values for critical significance level (α = 0.01) and given
sample size (n = 8) (refer to Fig. [92]S6 for test specification). The
statistical power for all significant metabolites were found to be
greater than 0.8 (Fig. [93]7). The means statistical power of 0.95
(SD = 0.13) and 0.97 (SD = 0.09) were obtained for the SMFY group and
the GMFS group, respectively. The great effect size and achieved
statistical power establish the reliability of our results.
Figure 7.
Figure 7
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The post hoc power analysis of the selected metabolites calculated in
G*power. (a) colour coded according to the different bio-samples,
Inline graphic (light green), Inline graphic (soft red), and Inline
graphic (light yellow) present cortex extract, serum, and urine
respectively. (b) The relative concentration (percentage of the
normalized integrals, mean ± SD). (c) Red coloured numbers denote
significant increase whereas the blue coloured ones indicate
significant decrease and the black coloured ones denote no significant
changes in the EA-treated group (i.e., SMFY and GMFS) compared to the
control group. The criteria of selection are as shown in Table [95]S1.
(d) The effect size computed based on the standardized mean difference
between the EA-treated group (i.e., SMFY and GMFS) and the control
group in G*power. (e) The statistical power calculated as a function of
critical significance level (α = 0.01), given sample size (n = 8), and
obtained variable effect size in G*power.
Discussion
The experimental results showed a number of common metabolic changes
caused by EA stimulation as well as specific metabolic perturbations
due to EA treatment on either the SMFY or GMFS meridians. These
EA-induced metabolic changes primarily involved metabolites in the
amino acid metabolism, energy metabolism, fatty acid β-oxidation,
choline metabolism and gut microbiota-related metabolism (Fig. [96]8).
These pathways are affected to different extents based on results from
pathway analysis (Fig. [97]6). The current results suggested that
meridian-specific metabolic changes can be detected using a NMR-based
metabolomics approach.
Figure 8.
Figure 8
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Summaries of metabolic pathways altered after EA stimulations. (a)
delineates SMFY group, and (b) GMFS group. Metabolites displayed with
red (or blue) present concentration significantly increases (or
decreases) in comparison with the control group; those in black
indicate no significant changes; the metabolites with ★ indicate
exclusive metabolites changes for SMFY or GMFS groups. PEP,
Phosphoenolpyruvate.
In this study, we found that GABA concentration was lowered by EA
stimulations relative to the control group. GABA is a key mediator of
inhibitory neurotransmission in mammalian central nervous system
(CNS)^[99]30. It can couple and activate GABA[A] and GABA[B] receptors
to reduce brain anxiety. Known to exert an antagonistic effect upon
excitatory neurotransmitters, such as dopamine and glutamate, GABA can
be synthesized from glutamate via glutamate decarboxylase. Mutual
transformations among glutamine, glutamate (excitatory), and GABA
(inhibitory) maintain the internal balance between excitation and
inhibition in the CNS^[100]31. The decreased levels of GABA and
glutamine in both SMFY and GMFS groups indicated that EA may partially
reduce the inhibition of excitement in the rat brain. With the
development of neuroimaging techniques, the use of positron emission
tomography (PET) and functional magnetic resonance imaging (MRI) to
explore the central mechanism of acupuncture has been an active area of
research^[101]32. Notably, functional MRI studies report that EA
stimulation on SMFY and GMFS acupoints contribute to activation of
brain regions^[102]33–[103]36. Recent papers have described the central
regulations over the benign and comprehensive regulatory effects of
acupuncture by brain, and certain findings are similar to the results
of the current study. Zeng have suggested that acupuncture stimulation
not only affect the activity of the common pathway of somatic and
visceral sensation but also modulate the activity of certain brain
functional regions^[104]37. Accordingly, our study further found
neurotransmitter (such as GABA, glutamate) changes by EA stimulation on
the SMFY or GMFS acupoints. Moreover, EA at SMFY acupoints or GMFS
acupoints can enhance CNS excitement significantly, with the GMFS group
exhibiting more prominent changes. The brain seems to play an active
role to convey the effect of acupuncture treatment.
Altered levels of glucose, lactate, fumarate, citrate, α-KG, and ATP in
the present study suggested that the EA stimulations induced the change
of energy metabolism. As intermediates of the TCA cycle, fumarate,
citrate, and α-KG decreased in SMFY and GMFS groups in comparison with
the control group, unveiling the enhanced aerobic TCA cycle by EA
stimulations (Fig. [105]6).
From the above analyses, significant changes of neurotransmitter (GABA,
glutamate) levels and increased energy metabolism may affect the
catabolism of amino acids. Phenylalanine acts as a precursor for the
biosynthesis of catecholamine (i.e., dopamine, epinephrine, and
norepinephrine)^[106]19. In comparison with the control group, the
observed decreased levels of phenylalanine in the SMFY and GMFS groups
were probably related to demands for catecholamine biosynthesis, which
will lead to excitation of central nerves. Lysine is a structural
component of carnitine, which is capable of increasing fatty acid
β-oxidation^[107]38. High levels of lysine in the SMFY and GMFS groups
may be associated with the slow oxidization of fatty acids into
acetyl-CoA after EA stimulations. This can be reflected by the
decreased level of 3-hydroxybutyrate (3-HB), which is a final product
and marker of fatty acid β-oxidation in mitochondria (Fig. [108]8). The
phosphatide methylation of the liver cell membrane will enhance the
membrane mobility to prevent bile deposition in liver cells and then
promote detoxification, thus providing liver protection with
methionine. The GMFS meridian is tightly linked to liver function, and
EA at the GMFY acupoints will effectively accelerate bile release to
facilitate the normal physiological function of liver cells. Because EA
stimulations directly accelerate bile release, the dependence on
methionine fades, eliciting the accumulation of methionine in the GMFS
group (higher level in comparison with the control group).
Abnormalities in lipid metabolism, including significantly elevated
LDL/VLDL and cholesterol, have been observed in the serum of normal
weight migraine patients^[109]39 and pregnant migraineurs^[110]40. In
our study, serum LDL in both the SMFY and GMFS group were dramatically
decreased, with an approximately 0.9-fold change in comparison with the
control group, revealing that EA at SMFY acupoints and GMFS acupoints
may lower cholesterol and slow the conversion from cholesterol to LDL
and therefore help to provide potential prevention and treatment for
cardiovascular diseases such as hypercholesterolemia and
atherosclerosis. Furthermore, an increased severity of liver fibrosis
has been reported to be associated with higher tyrosine, phenylalanine,
methionine, citrate, and LDL^[111]41. EA stimulations at the SMFY and
GMFS acupoints can lower phenylalanine, LDL, and citrate levels,
resulting in an increased capacity for citrate clearance in cirrhotic
patients and a lower rate of lipogenesis.
Hippurate, benzoate, and urocanate are common metabolites produced by
gut microbiota. Significant decreased levels of those urinary
metabolites were observed in the SMFY and GMFS groups, indicating that
EA stimulations at SMFY and GMFS acupoints alter the metabolism of gut
microbiota in the gastrointestinal tract. This is consistent with the
practical EA applications that SMFY is generally selected as the
primary meridian with GMFY as the complementary approach for treatment
of gastrointestinal diseases^[112]42, [113]43.
Accumulating research shows an interconnection between the SMFY
meridian and the stomach. In an acupuncture study on zusanli (ST36)
acupoint in the SMFY meridian, Zhang utilized bioinformatics analysis
to determine the molecular function of differentially expressed
proteins^[114]23; the most interesting functional categories were
“gastric acid secretion” and “pancreatic secretion”, representing 2.53%
and 6.33% of all the proteins identified, respectively. The result
would help to explain why the ST36 point has been traditionally used in
human acupuncture to treat gastrointestinal disorders. After
comprehensive analyses of the metabolic markers, we found that EA
treatment on SMFY meridian seems to promote glycolysis. Therefore, EA
treatments can be adopted to enhance the energy supplement under
pathologies and thus facilitate the physical recovery. Previous
studies^[115]44, [116]45 have shown that the effects of EA at acupoints
of SMFY on gastric motility were related to brain-gut peptides.
Therefore, our study provides supporting evidence that brain metabolism
plays an important role in acupuncture treatment.
In this study, we chose healthy rats with no EA treatment as controls
instead of using controls with EA on non-acupoints near to the studied
acupoints as the baseline to determine metabolic variations. This
experimental design aimed to observe the maximum possible EA-induced
metabolic variations, since it has been reported that EA on
near-acupoint regions will also introduce certain metabolic variations
similar to EA on acupoints. In other words, acupoint-specific metabolic
variation will decrease with the distance between EA point and actual
acupoint, thus reducing the contrast between the EA group and control
group. Moreover, acupoints for some meridians are close to each other,
and EA on near-acupoint region may introduce metabolic variation
originating from a different acupoint.
The results and conclusions of the current study should be cautiously
interpreted due to the following limitations. First, meridian
specificity consists of different aspects, e.g., biophysics
specificity, dynamic temporal specificity, spatial specificity, and
viscera-related specificity^[117]17, [118]46, [119]47. Therefore,
future studies may be focused on these aspects to provide an in-depth
systematic understanding of meridian specificity. Second, although the
sufficient statistical power on the sample size (n = 8) was tested
using software G*power ([120]http://www.gpower.hhu.de/), further study
with a larger sample size is recommended to validate the current
conclusions. Third, future study could include female rats to examine
the interaction between gender effect and acupuncture treatment.
Finally, the current study applied a single metabolomic platform (NMR)
and therefore does not cover a wide range of metabolomes due to the
diversity of physicochemical properties and the broad range of
metabolite concentrations. Thus, future study with multiple
metabolomics platforms (e.g., GC-MS and LC-MS) or even multiple omics
technologies (e.g., proteomics and transciptomics) can be integrated to
provide more comprehensive and definitive meridian-specific
markers^[121]48–[122]50.
In conclusion, we performed EA stimulations on the acupoints of SMFY
and GMFS meridians to investigate meridian specificity. The ^1H
NMR-based metabolomics approach was adopted to identify differential
rat metabolic profiles of multiple biological matrices (i.e., serum,
urine, stomach and cortex extracts) induced by EA stimulations. Our
current study demonstrated significant metabolic pattern differences in
response to EA stimulations on the acupoints of the SMFY and GMFS
meridians. These results demonstrate the feasibility and effectiveness
of the metabolomics approach in understanding the effects of
acupuncture, provide a metabolic basis for meridian specificity in
acupuncture treatments, and constitute a reference for the clinical
practice of acupuncture.
Methods
Ethical Statement
All animal procedures were approved and conducted in strict accordance
with the guidelines of the Animal Care and Use Committee of Hunan
University of Chinese Medicine (Permit Number: SCXK2016–0015). The
study was carried out adhering to guidelines provided by the National
Institutes of Health for the Care and Use of Laboratory Animals and all
efforts were made to minimize the suffering of animals.
Animals and housing
Sprague Dawley (SD) rats in a single gender (male) were used in our
experiment. It should be noted that female rats generally show higher
metabolic variation due to differences in hormonal profiles, food
intake and energy metabolism at different stages of the oestrous cycle.
To prevent metabolic interference due to gender factors, we only used
male rats in the current study. Briefly, 24 healthy male SD rats
(150 ± 20 g, 8-weeks-old) were individually housed in metabolic cages
under controlled conditions (temperature, humidity, and 12-h light/dark
cycle). Food and water were available ad libitum. After a one-week
acclimation, all 24 rats were randomly divided into three groups
(n = 8): control group (without EA treatments), SMFY group (EA at SMFY
acupoints), and GMFS group (EA at GMFS acupoints).
The sample size used in the current study was calculated based on our
preliminary study before the current experiment. Briefly, we conducted
a Power Analysis using MetaboAnalyst 3.0 software
([123]http://www.metaboanalyst.ca) to analyse rat blood serum data
(with FDR = 0.13, power = 0.8). It was found that n = 8 is adequate to
meet the requirements (Fig. [124]S7).
EA stimulations
For the SMFY group, three classical acupoints along SMFY meridian
(including Sibai (ST 2), Liangmen (ST 21), and Zusanli (ST 36),
represent acupoints of head, trunk, and limb, respectively) were
selected for EA stimulations. Yangbai (GB 14), Riyue (GB 24), and
Yanglingquan (GB 34) in the same segments along the GMFS meridian were
selected for EA treatment for the GMFS group (shown in Fig. [125]S8).
Locations for both SMFY and GMFS acupoints were determined according to
Government Channel and Points Standard GB12346–90 of China and “The
Veterinary Acupuncture of China”. State-licensed acupuncturists
performing all treatment procedures had at least 2 years of experience
with acupuncture treatment. For acupuncture treatment, two-channel
electrical stimulations were performed with a pulse generator (Model
G6805-II; Qingdao Xinsheng Medical Instrument Factory, Shandong, China)
with four sterile stainless-steel acupuncture needles (diameter:
0.25 mm) being inserted into the acupoints. The electrical stimuli
consisted of both intermittent and irregular waves (intermittent wave:
4 Hz, irregular wave: 50 Hz) with voltages ranging from 2 to 4 V.
Electrical intensities were increased from 0.1 mA to 1.0 mA until the
rats’ hind limbs began to twitch slightly. Rats of the SMFY and GMFS
groups were given EA stimulations for 30 minutes per day for a
consecutive seven days.
Sample collections
After the seven-day treatment course, 24-hour urine samples for each of
the rats were collected into 5 mL Eppendorf (EP) tubes on ice. Each of
the tubes contained a drop of NaN[3] solution (0.1 g/mL NaN[3]). In
addition, blood samples (~1 mL) drawn from rats’ carotid arteries were
collected into 5 mL EP tubes with no anticoagulation and left to clot
at room temperature for 20 min. The supernatants (serum) were obtained
by centrifugation at 11,000 ×
[MATH: g :MATH]
, at 4 °C for 10 min. Finally, the rats were sacrificed by rapid
decapitation without anaesthesia in 30 min or less; the specimens of
cortex and stomach tissues were then dissected immediately (typically
within 30 s), and snap-frozen in liquid nitrogen. All biological
samples were stored at −80 °C until further treatments.
Sample preparations for NMR analysis
For urinary samples, 300 μL urine was added with 300 μL phosphate
buffer solution (1.5 M K[2]HPO[4]/NaH[2]PO[4], pH 7.4, 99.9% D[2]O with
0.3 mM TSP (3-trimethylsilyl- propionic-2,2,3,3-d4 acid sodium salt).
The D[2]O provided the NMR spectrometer with a field frequency for
locking, and TSP was used as a reference for chemical shift (0 ppm).
After centrifugation (11,000 ×
[MATH: g :MATH]
, 4 °C, 10 min), 500 μL supernatants were transferred into 5-mm NMR
tubes. For blood serum samples, an aliquot of 400 μL was added with
200 μL phosphate buffer solution (90 mM K[2]HPO[4]/NaH[2]PO[4], pH 7.4,
99.9% D[2]O). After centrifugation (11,000 ×
[MATH: g :MATH]
, 4 °C, 10 min) to remove precipitates, 500 μL supernatants were
transferred into 5 mm NMR tubes.
The frozen excisions of cortex and stomach tissues (~300 mg) were
ground on dry ice using a mortar and pestle, then transferred into
micro-centrifuge tubes with adding an icy cold solvent mixture of
methanol and water (2:1, v/v). After sonication for 15 min in a water
bath, the resulting homogenates were added with chloroform and water
(1:1, v/v), placed on ice for 15 min, and then centrifuged (11,000 ×
[MATH: g :MATH]
, 4 °C, 10 min). Supernatants (upper aqueous phases) were collected
into 5 mL EP tubes and lyophilized under liquid nitrogen for
approximately 36 h to remove methanol. The dried residues were
re-dissolved with a 550 μL phosphate buffer solution (90 mM
K[2]HPO[4]/NaH[2]PO[4], pH 7.4, 99.9% D[2]O with 0.3 mM TSP). Following
vortex and centrifugation (11,000 ×
[MATH: g :MATH]
, 4 °C, 10 min), 500 μL supernatants (tissue extracts) were pipetted
into 5 mm NMR tubes. All prepared biological samples (i.e., serum,
urine, cortex, and stomach) in NMR tubes were stored at 4 °C until NMR
analyses.
NMR experiments
^1H NMR spectra of serum, urine, cortex, and stomach were acquired
using a 600 MHz Bruker NMR system at 298 K. Serum samples were analysed
by the Carr-Purcell-Meiboom-Gill (CPMG) sequence (awaiting time ~ π/2 ~
[τ ~ π ~ τ][n] ~ acquisition) with a free relaxation duration (2nτ) of
100 ms and an echo time (τ) of 250 μs. For urine, cortex and stomach
samples, Nuclear Overhauser Effect Spectroscopy (NOESY, awaiting time ~
π/2 ~ t[1] ~ π/2 ~ t[m] ~ π/2 ~ acquisition) was used with a water
suppression lasting 2 s and a mixing time (t[m]) of 120 ms. For data
acquisition, 64 scans were performed with 32 k data points under a
spectral width of 12,000 Hz for each free induction decay (FID).
All ^1H NMR spectra were manually phased and baseline corrected using
MestReNova v.8.1.2 software (Mestrelab Research S.L.). TSP (at δ 0.00)
was used as a spectral reference for urine, stomach and cortex, and the
left split (-CH[3]) from the doublet of lactate centring at δ 1.336
(methyl group) was used as chemical shift reference for serum samples.
Residual water signals (serum: δ 4.7–5.0, urine: δ 4.76–5.06, cortex: δ
4.97–5.02 and stomach: δ 4.95–5.04), urea resonances (δ 5.60–6.35), and
peak-free regions were excluded from further analysis. The remaining
spectra over ranges of δ 0.5–9.0 for serum, δ 0.5–10.0 for urine, δ
0.5–8.5 for cortex and δ 0.5–9.2 for stomach were binned into bucketed
data with a fixed width of 0.004 ppm (2.4 Hz). Prior to multivariate
data analysis, data normalization was carried out using the method of
probabilistic quotient normalization (PQN)^[126]51 to take the
sample-concentration variations into considerations.
Multivariate statistical data analyses
The pre-processed bucketed data were imported into the SIMCA-P software
(version 12.0.1, Umetrics AB, Ume°a, Sweden) for multivariate analyses.
The normalized bucket data were scaled by mean centre (Ctr) and
subjected to principal components analysis (PCA) to overview the data
distribution and potential outliers. Supervised partial least
squares-discriminate analysis (PLS-DA) and orthogonal partial least
squares discrimination analysis (OPLS-DA) were then implemented on
Pareto scaled NMR data to examine metabolite differences between
groups. The validation of the model was performed with a 7-fold
cross-validation and permutation test (200 permutations)^[127]52. The
loading plots from OPLS-DA models were generated with an in-house
MATLAB program, and signals were colour-encoded with correlation
coefficients to exhibit metabolites with significant changes.
Statistical analyses were also performed using methods of fold-change
and the Student’s t-test with a Bonferroni correction. The resulting t
statistic, such as transformed p-value, can be used to determine
metabolites with statistically significant changes in metabolomics. The
fold-change is a method to evaluate the log ratio of concentrations
between two conditions to identify significant metabolite variations
above an arbitrary cut-off value. The metabolites were quantified by
integrals over corresponding spectral range in reference to the
internal standards. To avoid the influences induced by spectral
congestion, we chose peaks with least overlapping for quantifications
for corresponding metabolites of interest.
In this study, we used the volcano plot to summarize both t-test and
fold-change criteria in a single plot. Typically, it is a scatter plot
of -log[10](p-value) against log[2](fold-change)^[128]53. The variable
importance projection (VIP) and absolute correlation coefficient values
(|r|) constructed from the OPLS-DA analysis were introduced as two new
variables in the original volcano plot and were represented by circles
size and colour, respectively (i.e., larger circle size corresponds to
larger VIP value, warmer colour to higher |r|). This enhanced
four-dimensional volcano plot may provide an integrated and effective
method to identify potential biomarker candidates from a global view.
Electronic supplementary material
[129]Supplementary Information^ (1.8MB, pdf)
Acknowledgements