Abstract

Background

   Capsaicin is the main compound found in chili pepper and has complex
   pharmacologic effects. This study aimed to elucidate the mechanism of
   the effect of capsaicin on physiological processes by analyzing changes
   in metabolites and metabolic pathways.

Methods

   Female C57BL/6 mice were divided into two groups(n = 10/group) and fed
   with capsaicin-soybean oil solution(group T) or soybean oil(group C)
   for 6 weeks. Ultra-high performance liquid chromatography/quadrupole
   time-of-flight mass spectrometry (UHPLC-qTOF-MS) based metabolomics was
   undertaken to assess plasma and skin metabolic profile changes and
   identify differential metabolites through multivariate analysis.

Results

   According to the OPLS-DA score plots, the plasma and skin metabolic
   profiles in the group T and group C were significantly separated. In
   plasma, 38 significant differential metabolites were identified. KEGG
   pathway enrichment analysis revealed that the most significant plasma
   metabolic pathways included pyruvate metabolism and ABC transporters.
   In skin, seven significant differential metabolites were found. Four
   metabolic pathways with p values < 0.05 were detected, including
   sphingolipid metabolism, sphingolipid signaling pathway, apoptosis, and
   necroptosis.

Conclusion

   These findings will provide metabolomic insights to assess the
   physiological functions of capsaicin and contribute to a better
   understanding of the potential effects of a capsaicin-rich diet on
   health.

   Keywords: capsaicin, UHPLC-QTOF-MS, metabolomics, plasma, skin

Background

   Peppers have been an essential element of culinary traditions across
   the world in recent years, and they have a long history of usage for
   flavoring, coloring, preserving food, and medicinal purposes.[32]1 The
   rising use of peppers in food is a worldwide trend.[33]2 The impact of
   chili peppers on health is a subject of growing interest. Capsaicinoids
   are a group of molecules unique to fruits of plants from the genus
   Capsicum (chili peppers). Although more than ten structures exist, the
   most prominent forms are capsaicin and dihydrocapsaicin, accounting for
   almost 90% of capsaicinoids.
   Capsaicin(8-METHYL-N-Vanillyl-6-Nonenamide) harbors many benefits
   extensively documented in many studies. Capsaicin consumption and
   supplementation positively affect glucose and insulin levels in
   people[34]3–5 and may help reduce body mass index in obese
   patients.[35]6,[36]7 There is evidence that capsaicin can activate
   transient receptor potential vanilloid subfamily member 1, which may
   benefit the cardiovascular system.[37]8,[38]9 Besides, in vivo studies
   corroborate capsaicin’s anticancer action.[39]10,[40]11

   On the other hand, it has been noticed that eating chili peppers often
   causes some pathological conditions such as sore throat, mouth ulcer,
   constipation, etc.[41]12 Besides, the increased risk of carcinogenesis
   with the usage of capsaicin has been reported.[42]13–15 Of note,
   evidence shows a link between the pepper diet and some skin conditions.
   A spicy diet rich in capsaicin may trigger and worsen rosacea
   symptoms.[43]16 In rat pups, capsaicin injection can cause recurrent
   pruritic dermatitis.[44]17

   Metabolomics, a new “omics” technique, may be used to reflect the
   comprehensive, unbiased metabolic profile of biological samples,
   providing details of the metabolic responses of the living organism to
   external stimuli. Unlike genomics, transcriptomics, and proteomics,
   metabolomics represents the physiological condition of a biological
   system most directly since metabolites are most closely related to an
   organism’s phenotype.[45]18 Analyzing all the metabolites in biological
   samples can provide information about what transpired in the
   organisms.[46]19 Recently, ultra-high-performance liquid chromatography
   and Q-TOF mass spectrometry (UHPLC/Q-TOF MS) offer a new dimension to
   metabolic investigations due to their improved reproducibility,
   detection limits, and higher chromatographic resolution.[47]20,[48]21

   To date, no study has investigated the effect of capsaicin on the
   plasma and skin metabolome in C57BL/6 mice. In our study, we compared
   the changes in plasma and skin metabolic profiles in mice after
   capsaicin supplement based on UHPLC-Q-TOF MS. Our study will help to
   elucidate the mechanism of the effect of capsaicin on physiological
   processes by analyzing changes in metabolites and metabolic pathways.

Materials and Methods

Experimental Animals and Chemicals

   Twenty healthy C57BL/6 female mice aged 6 weeks were purchased from
   Hunan SJA Laboratory Animal Co., Ltd (Hunan, China). All mice were
   allowed to acclimatize in cages for 1 week before treatment. Mice had
   free access to standard diet and water and were maintained under normal
   laboratory conditions (temperature of 22–23°C, relative humidity of
   45%-50%, and 12 h/12 h light/dark cycle). Synthesized capsaicin (97%
   capsaicin) and soybean oil were obtained from Sinopharm, China. The
   synthetic capsaicin was dissolved in soybean oil to form a 0.5 mg/mL
   capsaicin-soybean oil solution. Ammonium acetate (NH4AC) and ammonium
   hydroxide (NH4OH) were purchased from Sigma Aldrich. Acetonitrile was
   purchased from Merck.

Animal Treatment and Sample Collection

   After 1 week of adaptation, the mice were randomly assigned to either
   group C (control group, n = 10) or the group T(treatment group, n =
   10). In the group T, the mice were treated with capsaicin-soybean oil
   solution (capsaicin of 5 mg/kg mice body weight) on Monday, Wednesday,
   and Friday (16:00–17:00) of each week for a total of six weeks. The
   mice in the group C were fed with the corresponding volume of soybean
   oil simultaneously. Before each gavage, their body weights were
   determined and recorded. Regular monitoring of animal health and
   well-being was done three times weekly, including the mice’s overall
   health and their motor activity, eating, drinking, and weight records.
   At the end of 6 weeks, all mice were euthanized. The blood was
   immediately harvested from the heart and centrifuged at 1500g for 15min
   to obtain plasma samples. Skin samples were taken from the backs of
   mice and quickly frozen in liquid nitrogen. All plasma samples and skin
   samples were stored at −80°C. All animal experiments were performed
   according to the regulation of institutional guidelines for the care
   and use of experimental animals and approved by the Animal Ethical
   Committee of The Second Affiliated Hospital of Nanchang University.

Sample Preparation for UHPLC-Q-TOF MS Analysis

Plasma Sample Preparation

   The plasma samples stored at −80°C were gradually thawed at 4°C. A
   100µL aliquot of sample was added to a 400µL pre-cooled
   methanol/acetonitrile mixed solution (v/v, 1:1). The samples were
   sequentially vortexed (5min), static-placed (−20°C, 60min) and
   centrifuged (14000g, 4°C, 20min), and the supernatant was then
   collected and vacuum dried. For metabolomics analysis, the samples were
   re-dissolved in 100 μL acetonitrile/water (1:1, v/v) solvent.

Skin Sample Preparation

   The frozen skin tissue (100 mg) was thawed at 4 °C and homogenized in 1
   mL of precooled methanol/acetonitrile/ddH2O (2:2:1, v/v/v) with a
   homogenizer (FastPrep-24^TM, MP Biomedicals LLC., Santa Ana,
   California, USA) at 6.0 M/S (20 s each, three times). Then, the mixture
   was centrifuged for 15 min (14000g, 4°C), and the supernatant was
   lyophilized under a vacuum and stored at − 80 °C until redissolution in
   100 μL of an acetonitrile/water (1:1, v/v) solvent for metabolomics
   analysis.

UHPLC-Q-TOF MS Analysis

   This study detected twenty plasma samples and twenty skin samples by
   UHPLC-Q-TOF MS. The UHPLC-Q-TOF MS approaches were described in detail
   as follows. Briefly, analyses were performed using a UHPLC (1290
   Infinity LC, Agilent Technologies) coupled to a quadrupole
   time-of-flight (AB Sciex TripleTOF 6600) in Shanghai Applied Protein
   Technology Co., Ltd. The flow rate was 0.5 mL/min, and the column
   temperature was 25 °C. The quality control (QC) samples were inserted
   regularly and analyzed in every 5 samples to evaluate the system’s
   stability and the experimental data’s reliability.

Data Processing

   The raw MS data (whiff. scan files) were converted to MzXML files using
   ProteoWizard MSConvert before importing them into XCMS software. For
   peak picking, the following parameters were used: centWave m/z=25 ppm,
   peak width = c (10, 60), prefilter = c (10, 100). For peak grouping, bw
   = 5, mzwid = 0.025, minfrac = 0.5 were used. CAMERA (Collection of
   Algorithms of MEtabolite pRofile Annotation) was sued for annotation of
   isotopes and adducts. In the extracted ion features, only the variables
   having more than 50% of the nonzero measurement values in at least one
   group were kept.

   Compound identification of metabolites was performed by comparing
   accuracy m/z value (<10 ppm) and MS/MS spectra with an in-house
   database established with available authentic standards. The structural
   identification of metabolites in biological samples was performed by
   comparing with the information of retention time, molecular mass
   (molecular mass error <10ppm), secondary fragmentation spectra and
   collision energy of metabolites in the database, and the identification
   results were strictly checked and confirmed by manual secondary
   inspection. The identification level is above level 2. Level 2 is
   defined as matched to literature data or databases by diagnostic
   evidence, at least two orthogonal pieces of information, including
   evidence that excludes all other candidates.

   Metabolites were compared with free online databases KEGG
   ([49]http://www.genome.jp/kegg/) and HMDB ([50]http://www.hmdb.ca/),
   and the corresponding KEGG pathways were extracted. KEGG enrichment
   analysis was performed using MetaboAnalyst([51]www.metaboanalyst.ca).

Statistical Analysis

   After normalized to total peak intensity, the processed data were
   uploaded into before importing into SIMCA-P (version 16.1, Umetrics,
   Umea, Sweden), where it was subjected to multivariate data analysis,
   including Pareto-scaled principal component analysis (PCA) and
   orthogonal partial least-squares discriminant analysis (OPLS-DA). The
   7-fold cross-validation and response permutation testing were used to
   evaluate the robustness of the model. The variable importance in the
   projection (VIP) value of each variable in the OPLS-DA model was
   calculated to indicate its contribution to the classification.
   Metabolites with the VIP value >1 were further applied to Student’s
   t-test at the univariate level to measure the significance of each
   metabolite, the p values less than 0.05 were considered statistically
   significant. In addition, Student’s t test was used to compare the body
   weight of mice in groups T and C before and after the experiment.

Results

Effect of Capsaicin on Body Weight in C57BL/6 Mice

   As shown in [52]Figure 1, the group T significantly decreased in body
   weight compared with the group C after 6 weeks of capsaicin feeding (p
   < 0.05).

Figure 1.

   [53]Figure 1
   [54]Open in a new tab

   Comparison of body weight of mice in group C and group T. Data were
   presented as the mean±standard deviation, *p<0.05 vs group C.

Analytical Method Assessment

   QC samples were prepared to validate the metabolomic methodology. PCA
   analysis was performed on all extraction peaks of all experimental and
   QC samples after Pareto-scaling. The relatively tight clustering of the
   QC samples in the PCA score plots ([55]Figures S1 and [56]S2) suggested
   the experiment had good repeatability and stability.

Effects of Capsaicin on Plasma and Skin Metabolic Profiles of C57BL/6 Mice

   OPLS-DA is a supervised discriminant analysis statistical method.
   According to the OPLS-DA score plots ([57]Figure 2A), the plasma
   metabolic profiles in the group T and group C were obviously separated
   both in positive and negative modes. Similarly, a clear differentiation
   in skin metabolomic profiles between the two groups was observed
   ([58]Figure 2B). These results indicated capsaicin can induce
   significant changes in the plasma and skin metabolic profile.

Figure 2.

   [59]Figure 2
   [60]Open in a new tab

   The score plots from OPLS-DA mode. (A) Plasma OPLS-DA score plots in
   positive and negative modes of the group C and group T. (B) Skin
   OPLS-DA score plots in positive and negative mode of the group C and
   group T.

   Abbreviations: POS, positive mode; NEG, negative mode.

Identification of Significantly Differential Plasma Metabolites and Metabolic
Pathways

   In plasma, 272 metabolites were identified, including 138 metabolites
   in positive ion mode and 134 in negative ion mode.The chemical
   classification attribution map revealed that the highest proportion of
   differential metabolites were organic acids and derivatives(20.22%),
   lipids and lipid-like molecules(16.17%), and organoheterocyclic
   compounds(6.98%) ([61]Figure 3).Based on an OPLS-DA model with VIP > 1
   and P-value < 0.05, 38 significant differential plasma metabolites were
   identified ([62]Table S1). Their relationships were revealed by
   correlation analysis ([63]Figure 4A and [64]B). The hierarchical
   clustering heatmap indicated that the plasma metabolites of the group T
   were significantly separated from the group C ([65]Figure 4C). The ROC
   curves were used to assess the predictive value of the screened
   potential markers.AUC values between 0.7 and 0.9 have “medium”accuracy,
   and values greater than 0.9 have “high” accuracy. The ROC curves of the
   top 10 differential plasma metabolites in the VIP value were shown in
   [66]Figure 5. The results indicated that, except
   gamma-Glutamylcysteines, other differential metabolites have good
   diagnostic performance.

Figure 3.

   [67]Figure 3
   [68]Open in a new tab

   The proportion of identified plasma metabolites in each chemical
   classification.

Figure 4.

   [69]Figure 4
   [70]Open in a new tab

   Correlation heatmap and hierarchical clustering heatmap. (A)
   correlation heatmap analysis of differential metabolites in plasma in
   positive mode. (B) correlation heatmap analysis of differential
   metabolites in plasma in negative mode. (C) Hierarchical clustering
   heatmap of 38 differential plasma metabolites between group C and group
   T.

Figure 5.

   [71]Figure 5
   [72]Open in a new tab

   ROC curves for potential plasma biomarkers with VIP values in the top
   10.

   To identify significantly affected metabolic pathways, fisher’s exact
   test was used to analyze and calculate the significance level of
   metabolite enrichment for each pathway. The smaller the p-value, the
   more significant the difference in the metabolic pathway. A total of 42
   metabolic pathways were significantly changed (p < 0.05) in group C and
   group T. The most significant metabolic pathways included pyruvate
   metabolism and ABC transporters ([73]Figure 6).

Figure 6.

   [74]Figure 6
   [75]Open in a new tab

   Statistics of KEGG enrichment in plasma. The x axis indicates the rich
   factor(number of significantly differentiated metabolites/total
   metabolites in this pathway), and the y axis indicates name of the KEGG
   metabolic pathway. The size and color of bubbles represent the number
   of metabolites and the p-value, respectively.

Identification of Significantly Differential Skin Metabolites and Metabolic
Pathways

   A total of 392 metabolites were identified in the skin. Seven
   significant differential skin metabolites were found (VIP >1, and p <
   0.05) ([76]Table S2). The differentially expressed skin metabolites of
   group C and group T were visualized in a clustering heatmap ([77]Figure
   7A), indicating an altered skin metabolic profile in mice after
   capsaicin supplementation. Besides, four metabolic pathways with p
   values < 0.05 were detected, including sphingolipid metabolism,
   sphingolipid signaling pathway, apoptosis, and necroptosis ([78]Figure
   7B).

Figure 7.

   [79]Figure 7
   [80]Open in a new tab

   Hierarchical clustering heatmap and KEGG enrichment analysis chart in
   skin. (A) Hierarchical clustering heatmap of 7 differential metabolites
   in skin. (B) Statistics of KEGG enrichment in skin. The x axis
   indicates the rich factor corresponding to each pathway, and the y axis
   indicates name of the KEGG metabolic pathway. The size and color of
   bubbles represent the number of metabolites and the p-value,
   respectively.

Discussion

   In the current study, we demonstrated that the plasma and skin
   metabolic profiles based on UHPLC-QTOF could distinguish group T from
   group C. Meanwhile,38 significant differential metabolites were
   identified, and a total of 40 metabolism pathways were perturbed in
   plasma. Furthermore, seven significant differential metabolites were
   identified, and four metabolic pathways were detected in skin.

   Numerous studies have discovered that capsaicin has a beneficial effect
   on weight management.For example, capsaicin can suppress obesity by
   modulating energy metabolism, reducing adipose tissue weight, and
   increasing lipid oxidation.[81]22 The possible mechanism is that
   capsaicin can activate of TRPV1, leading to a rapid increase in Ca2+,
   which causes changes in the expression of obesity-related factors such
   as PPAR-γ, SREBP-1, and FAS in cells.[82]23 However, the underlying
   mechanisms are not fully understood. In our study, we found that the
   body weight of group T was significantly lower than group C at the end
   of the 6-week intervention(P<0.05), indicating capsaicin inhibited the
   weight increase of C57BL/6 mice. Of note, some differential metabolites
   (D-lactate, L-lactate, and L-Malate) and metabolic pathways(ABC
   transporters and Pyruvate metabolism) associated with energy metabolism
   were identified in our study. DL-lactate and L-malate are engaged in
   the pyruvate metabolism pathway, a critical pathway for energy
   metabolism.[83]24 L-Malate is a three shuttle acid cycle intermediate
   metabolite, directly involved in mitochondrial energy metabolism.[84]25
   Besides, the ABC transporters metabolic pathway is characterized by the
   use of ATP-hydrolyzed energy to transport specific compounds across
   cell membranes. It can mediate fatty acid transport that is associated
   with obesity and insulin resistant states.[85]26 Therefore, we
   speculate that capsaicin may alter the energy metabolism of mice, which
   in turn affects their body weight.

   Due to its antioxidant properties, capsaicin has been experimentally
   demonstrated to regulate cellular oxidative stress. It has beneficial
   effects in preventing or improving oxidative stress-mediated diseases
   (cardiovascular disease, diabetes, cancer, etc.).[86]27–29 In the
   current study, some plasma differential metabolites associated with
   oxidative stress were identified, such as cholic acid, deoxycholic
   acid, pipecolic acid, carnosine, taurine, pyroglutamic acid,
   gamma-Glutamylcysteines, and 3-Indolepropionic acid. Pipecolic acid can
   cause oxidative stress in the cerebral cortex of young rats in
   vitro.[87]30 Carnosine and taurine have antioxidant
   properties,[88]31–33 whose deficits may be detrimental to the disease’s
   recovery from oxidative stress damage. Gamma-glutamylcysteine,
   pyroglutamic acid and taurine also play a role in regulating oxidative
   stress due to their connection with glutathione.[89]34–37 Hence, our
   results provide evidence that capsaicin may exert physiological effects
   by modulating oxidative stress.

   According to published studies, capsaicin is effective in reducing
   anxiety and other emotional responses.[90]38 For example, dietary
   capsaicin improved depressive-like behavior of C57BL/6J mice.[91]39
   However, its exact antidepressant mechanism remain unclear. Our data
   demonstrated that the plasma level of sarcosine was increased in group
   T(VIP = 1.25, FC = 1.74). Sarcosine, an N-methyl-d-aspartate receptor
   enhancer, can improve depression-like behavior in rodent models and
   depression in humans.[92]40 Hence, our results suggest that capsaicin
   may exert an antidepressant effect by regulating sarcosine. However,
   further researches are needed to confirm this.

   There were seven metabolites were markedly altered in the skin of group
   T. Of these metabolites, sphingosine(VIP = 4.89, FC = 1.41) was
   upregulated, indicating that the sphingolipid metabolism and
   sphingolipid signaling pathways were significantly perturbed pathways
   in the skin of group T. Interestingly, our previous studies found that
   sphingolipid metabolism and sphingolipid signaling pathway were
   enriched in the rabbit ear acne model.[93]41 In addition, the
   sphingolipid signaling pathway was the most altered in the plasma of
   patients with moderate to severe acne.[94]42 It is well known that
   spicy foods can exacerbate acne.Hence, we speculate capsaicin may
   affect the progress of acne through the sphingolipid metabolism and
   sphingolipid signaling pathway.

   This study had some shortcomings: 1) The number of unclassified
   metabolites was relatively large.The reason may be limited number of
   reliable spectral reference databases for metabolite identification; 2)
   It is only a preliminary exploration of the changes in plasma and skin
   metabolites in mice after capsaicin intervention, and the related
   disease models were not established.

Conclusions

   In summary, our work has demonstrated that capsaicin can induce
   apparent plasma and skin metabolic profile changes in C57BL/6 mice. Our
   findings provide metabolomic insights to assess the physiological
   functions of capsaicin and contribute to a better understanding of the
   potential effects of a capsaicin-rich diet on health.

Preprint

   A previous version of this manuscript was published as a
   preprint.[95]43

Acknowledgments