Chrysin ameliorates pain through regulation of the serum metabolomics in the rats

Abstract

Background

Chrysin is a natural flavonoid that exhibits various pharmacological activities including pain relief. However, the effects of chrysin on changes of metabolic profiles during pain remain unclear. The aim of this study was to analyze the biomarkers related to pain in serum and to evaluate the analgesic properties of chrysin in a rat model of pain.

Methods

Male Wister rats were divided into four groups (n = 5). Pain was induced by injecting 50 μL of formalin into the hind paw. Chrysin and diclofenac (10 mg/kg, intraperitoneal injection) was administered to the intact and pain groups. All injections were given 30 minutes before pain induction. Immediately, the behavioral test was performed. Then the serum sample was separated for ¹HNMR-based metabolite analysis.

Results

Chrysin treatment alleviated the paw licking events, flinching response, and pain score. The integrated analyses further revealed three major metabolic changes including glycine-serine-threonine, taurine-hypotaurine, and arginine by comparing the serums from intact operated rats, pain rats, and pain rats treated with chrysin, and suggested that chrysin may improve pain by regulating the biosynthesis of these metabolic pathways.

Conclusions

These findings provide insights into metabolic pathways involved in pain and the analgesic effects of chrysin and may help to identify potential targets for the anti-pain properties of chrysin.

INTRODUCTION

Pain is a complex phenomenon that can be caused by the stimulation of pain receptors in the skin, joints and many internal organs of the body or an unpleasant emotional feeling (without tissue damage) [1]. In order to understand the causes of pain, it is important to investigate microscopic metabolite changes in pathological pain conditions. Metabolomics is a new technique that has the potential to discover biomarkers from biological fluids (blood, serum, urine) and tissue. In addition, it provides insights into the diagnostic information and mechanisms of drugs for their biochemical effects. Therefore, a good understanding of the molecular changes associated with the disease is necessary to identify new pathways for treatment and diagnosis [2,3].

Management and relief pain are challenges in medical care. The most important drugs used as pain relievers include opioids and non-steroidal anti-inflammatory drugs (NSAIDs) which both have serious side effects. NSAIDs often cause side effects, especially in the upper gastrointestinal tract and cardiovascular system, and opioids can cause drug abuse and dependence. Morphine, as an analgesic, causes reproductive tract disorders [4–6]. Therefore, the use of prescription herbal drugs with low side effects, which are available and affordable, can play a significant role in disease control.

In recent years, the use of plants and their derivatives has increased due to their health benefits. This study focuses on chrysin, which is structurally based in the class of flavones. Chrysin (5,7-dihydroxyflavone) is found abundantly in plants such as Passiflora, honey, and propolis [7]. It has been reported that chrysin has various physiological and pharmacological effects, including neuroprotective, anti-cancer, anti-inflammatory, anti-diabetic, and antioxidant properties [8]. In addition, studies show that chrysin has analgesic properties, and if its effects on pain relief is confirmed, it can be used as a new pain reliever in patients [9]. Therefore, in the present study, the authors investigated the effects of chrysin on changes of serum metabolic profiles and in a rat formalin pain model.

MATERIALS AND METHODS

1. Drugs and chemicals

For the present study, chrysin (Cas No, 480-40-0, Co, USA), sodium borohydride (NaBH4) (98%), mercaptosuccinic acid (97%), cadmium chloride (CdCl2) (99.99%), sodium telluride (Na2O3Te), sodium tetraborate decahydrate (Na2B4O7) (99.5%), sodium sulfite (Na2SO3) and sodium citrate (C6H5Na3O7.2H2O) were provided from Sigma-Aldrich, and ethanol was purchased from Merck.

2. Experiment animals and drug administration

Male Wistar rats weighing 190 ± 10 g were used. Animals were kept in a laboratory for two weeks to adapt to the environment. Rats were allowed free access to food and water. The ambient temperature was set at 22°C–25°C with a light cycle of 12 hr dark/12 hr light. All experimental processes were controlled by the Ethics Committee of Mohaghegh Ardabili University (code: IR.UMA.REC.1400.029).

Male Wister rats were divided into four groups (n = 5). Normal saline was administered to the intact and pain groups. Also, intact and pain groups received chrysin (10 mg/kg, intraperitoneal injection [IP]). One group also received diclofenac (10 mg/kg, IP). All injections were performed with a single dose 30 minutes before the formalin test at 8–9 A.M.

3. Formalin-induced pain and behavior test

For pain induction, 50 μL of formalin 5% was injected subcutaneously into the hind paw plantar surface using a 30-gauge syringe. Immediately, the animal was placed in a transparent box 30 × 30 × 30 cm. Pain behaviors were examined during phase 1 (0–5 minutes) and phase 2 (15–60 minutes). The time from 5–15 minutes was considered the interphase stage, when the animal shows no behavior or little behavior (behavior response was ignored). Video recording of pain behaviors began immediately after the formalin injection and was continued for 60 minutes. Then, the behaviors measured in responses to the injection of formalin are given as follows: T0: the animal puts equal weight on both feet on the floor; T1: the foot is placed a short distance from the floor and the paw is not spread; T2: the foot is elevated completely; T3: the foot is licked. Then the duration of each behavior was noted. For the convenience of evaluating the pain score, 12 blocks of 5 minutes over 60 minutes were considered. The pain score was calculated for 5 minutes with the equation 0 × T0 + 1 × T1 + 2 × T2 + 3 × T3 / 300 sec. The final pain score is equal to the sum of the scores calculated every 5 minutes. T = Duration of each behavior [10,11].

4. Serum preparation for 1H-NMR

The rats were euthanized by Co₂ asphyxiation and decapitated using a guillotine device. Blood samples were collected in tubes. Then the samples were centrifuged at 3,000 rpm for 15 minutes. The serum sample was extracted and immediately stored at –80°C for use in 1H-NMR spectroscopy. Then, the sample was diluted by adding 100 mL D2O into the micro tube containing 300 μL of serum. HNMR spectra of serum samples were recorded using a 500 MHz ANOVA at 298 ^◦K using a 5 mM probe. The pulse program for recording spectra was a standard Carr-Purcell-Meiboom-Gill (CPMG) protocol with water suppression using weak radiation. The standard CPMG pulse was applied by means of the water suppression and a weak irradiating pulse on the water peak during the saturation delay. In order to obtain the spectrum of the sample, 100 scans were performed at 32 k data points. A relaxation delay of 2.0 seconds and an acquisition time of 3.27 seconds were considered for the ¹HNMR spectrum. Tetramethylsilane was used as an internal reference.

5. ¹HNMR spectroscopic analysis and statistical analysis

After recording the FID ¹HNMR spectra of serum samples, data were converted to frequency domain spectra using MestRenova (Mestrelab Mnova VBuild 18998) software. Then the converted spectra were subjected to phase and baseline correction and water suppression was performed in region 4.7 to 5.52 ppm. In the next step each spectrum was exported as a text file for further analysis. The text data were imported to MATLAB 2022a (Mathworks). The size of the data was 15 × 32768. In this matrix, each row shows the ¹HNMR spectra of one serum sample. In the next step, the unwanted regions, including only the noise in the spectra, were removed from the beginning and end of the spectra and then the data were aligned using a correlation optimized warping algorithm. In order to reduce the size of the data, the data was binned. After data binning, the size of the data was 15 × 1320. Then the data were scaled with range scaling [12–14]. After scaling, different multivariate methods, including principal component analysis (PCA) as an unsupervised method and partial least squares discriminant analysis as a supervised method, were applied to the data.

Data were entered into SPSS software (version 16) for analysis. One-way ANOVA was run. Then, Tukey's post-hoc test was performed to determine the significant between the groups. The level of statistical significance was considered to be P ≤ 0.05. The results were expressed as a mean ± SEM.

6. Metabolite identification

In order to find the altered metabolites in the considered system, the variable important predictor (VIP) score from the partial least squares-discriminant analysis (PLS-DA) was calculated. After finding important chemical shifts (VIP > 1), the selected chemical shifts were imported into the HMDB (www.hmdb.ca database) [15]. In the next step, the important metabolites were entered into MetaboAnalyst 5.0 [16].

RESULTS

1. Effects of chrysin on the modulation of pain responses

The effect of chrysin (10 mg/kg) administration on pain score, licking and biting events, flinching response in phase 1 (0–5 minutes) and phase 2 (15–60 minutes) was investigated. In the groups receiving chrysin (10 mg/kg) significantly reduced the licking and biting events (P = 0.036, P = 0.031), as well as the flinching response (P = 0.050, P = 0.016) compared to the control group (Figs. 1, 2). Also, the pain scores in phase1 and phase 2 in the group treated with chrysin (10 mg/kg) compared to the control group showed a significant decrease (P = 0.050, P = 0.011, Fig. 3). Moreover, administration of diclofenac 10 mg/kg significantly led to a reduction of the pain score (P = 0.021, P = 0.004), licking and biting events (P = 0.023, P = 0.027), as well as flinching response (P = 001, P = 0.014) in phase 1 and phase 2 compared to the control.

Fig. 1.

The effect of chrysin (10 mg/kg) on flinching response in rats. The results are expressed as mean ± SEM. *Compared with control (phase 1), &Compared with control (phase 2).

Fig. 2.

The effect of chrysin (10 mg/kg) on licking and biting events in rats. The results are expressed as mean ± SEM. *Compared with control (phase 1), &Compared with control (phase 2).

Fig. 3.

The effect of chrysin (10 mg/kg) on pain score in rats. Phase 1 and phase 2. The results are expressed as mean ± SEM. *Compared with control (phase 1), &Compared with control (phase 2).

2. Effects of chrysin on change metabolic profiles

The PCA, as an unsupervised analysis method (a method in which the goal is to identify patterns or groupings in data without any prior labelling or categorization), was used to compare serum metabolites between the control, chrysin treatment (pain and intact), and pain groups. As shown in Fig. 4A, group II (received formalin as a pain model group) is well separated from groups I (received saline group as a control group), group III (pain groups received chrysin 10 mg/kg) and group IV (intact groups receiving chrysin 10 mg/kg). However, groups I, II, and IV are somewhat separated from each other. Additionally, the pain group receiving chrysin (group III) has high overlap with the received saline group as a control group (group I) and intact group receiving chrysin 10 mg/kg (group IV). This shows that the composition and concentration of metabolites in the chrysin group are similar to group I. Therefore, chrysin treatment significantly prevented pathological changes caused by pain. Fig. 4B shows the loading plot from PCA which is not considered any more since the separation of the groups is not good. To detect any potential differences between groups, which may be difficult to find using PCA, PLS-DA was also performed after PCA. Fig. 4C shows the score plot obtained from the serum data analysis and shows that the groups are well separated from each other. Fig. 4D shows the loading plot resulting from PLS-DA. In order to find the important metabolites which are responsible for separation of groups in score plot, Important chemical shifts with VIP > 1 from Fig. 4D were selected to estimate the changed metabolites. To examine if chrysin treatment changed metabolites in pain conditions, the authors found 20 potential biomarkers, which were significantly different between Group 3 (pain receiving chrysin), Group 4 (intact receiving chrysin) and Group 2 (pain group) in the serum samples, and are summarized in Table 1.

Fig. 4.

Estimated (A) score with 95% confidence ellipse and (B) loading plot from applying PCA on data and (C) score plot and (D) loading plot from PLS-DA on data. Group I: received saline as a control group, Group II: received formalin as a pain model group, Group III: received chrysin 10 mg/kg as a pain mode group, Group IV: received chrysin 10 mg/kg as an intact group, PCA: principal component analysis, LV: latent variable, PLS-DA: partial least squares-discriminant analysis.

Table 1.

Important metabolites and their table of assignment in comparison to control group

Metabolite’s name	G21	G31	G41
O-Phospho-L-serine	↓	Equal	↑
L-Serine	↓	Equal	↑
L-Cystathionine	↓	Equal	↑
D-Serine	↓	Equal	↑
Guanidinoacetate	↓	Equal	↑
L-Threonine	↓	Equal	↑
Betaine	↓	Equal	↑
N,Dimethylglycine	↓	Equal	↑
Sarcosine	↓	Equal	↑
L-Cysteine	↓	Equal	↑
3-Sulfino-L-alanine	↓	Equal	↑
Hypotaurine	↓	Equal	↑
Cysteamine	↓	Equal	↑
L-Cysteate	↓	Equal	↑
Glutamate	↓	Equal	↑
L-Glutamine	↓	Equal	↑
L-Citraline	↓	Equal	↑
L-Arginno succunate	↓	Equal	↑
L-Arginine	↓	Equal	↑
L-Ornithine	↓	Equal	↑

G21: comparison of group 2 (pain group) with group 1 (control), G31: comparison of group 3 (pain receiving chrysin) with group 1 (control), G41: comparison of group 4 (intact receiving chrysin) with group 1 (control).

Table 2 shows that the most important biochemical pathways changed between Group 3 (pain receiving chrysin), Group 4 (intact receiving chrysin) and Group 2 (pain group) based on the information obtained from the MetaboAnalyst database. The authors analyzed the metabolic pathways and found that among the 24 identified pathways, glycine-serine-threonine, taurine-hypoturine and L-arginine pathways had the most changes between groups (pathways with influence values greater than P ≤ 0.05 were selected). The changes are shown in Fig. 5.

Table 2.

Results of metabolic pathway analysis of serum samples

Pathway name	Total	Hits	Raw P	False discovery rate	Impact
Aminoacyl-tRNA biosynthesis	48	17	1.26E-07	1.06E-05	0.16667
Valine, leucine and isoleucine biosynthesis	8	6	8.99E-06	0.000377	0
Cysteine and methionine metabolism	33	11	4.86E-05	0.001362	0.64008
Amino sugar and nucleotide sugar metabolism	37	11	0.000159	0.0029	0.32158
Taurine and hypotaurine metabolism	8	5	0.000202	0.0029	0.57142
Arginine and proline metabolism	38	11	0.000207	0.0029	0.43707
Alanine, aspartate and glutamate metabolism	28	9	0.000339	0.003723	0.50561
Glycine, serine and threonine metabolism	34	10	0.000355	0.003723	0.49796
Arginine biosynthesis	14	6	0.000621	0.0058	0.59898
Starch and sucrose metabolism	15	6	0.000963	0.008089	0.47689
Pyrimidine metabolism	39	10	0.001179	0.008597	0.18801
Beta-Alanine metabolism	21	7	0.001266	0.008597	0.5597
Galactose metabolism	27	8	0.001351	0.008597	0.16739
Histidine metabolism	16	6	0.001433	0.008597	0.54917
Phenylalanine metabolism	12	5	0.002142	0.011996	0.59524
Fructose and mannose metabolism	18	6	0.002873	0.015081	0.11802
Pentose and glucuronate interconversions	18	5	0.015164	0.074926	0.5
Glyoxylate and dicarboxylate metabolism	32	7	0.016361	0.075111	0.09789
Thiamine metabolism	7	3	0.016989	0.075111	0
Pantothenate and CoA biosynthesis	19	5	0.019185	0.080575	0.05714
Butanoate metabolism	15	4	0.034177	0.13671	0.03175
Phenylalanine, tyrosine and tryptophan biosynthesis	4	2	0.039516	0.15088	1
Ascorbate and aldarate metabolism	10	3	0.048064	0.17554	0.5
Glycolysis / Gluconeogenesis	26	5	0.066706	0.23347	0.1453

Fig. 5.

Pathway enrichment analysis of metabolites changed by chrysin. (A) Pathway analysis of the effects of chrysin treatment on ether glycine-serine-threonine, taurine-hypotaurine and L-arginine in rats. (B) Taurine-hypotaurine metabolism. (C) Glycine-serine-threonine. (D) L-arginine metabolism. The red color in B, C, D shows the changed metabolites.

DISCUSSION

In this study, serum samples were analyzed to observe changes in amino acid abundance between pain model and control rats. Furthermore, chrysin treatment may alleviate pain behaviors in rats through regulating metabolites and metabolic pathways. Also, in this study, three metabolic pathways involved in pain signaling were changed, including Glycine-serine-threonine, arginine, and taurine-hypoturine. Metabolites reflect a person's health status, so that identification of changes in amino acid metabolism pathways can play an important role in the pathogenesis of pain.

The present study indicates that chrysin has analgesic effects in chemical pain tests. The formalin test is a widely accepted method for assessing nociceptive responses in rodents. This test elicits a biphasic pain response. The immediate chemical activation of nociceptive primary afferent neurons causes the first phase, known as neurogenic pain, which happens seconds after a formalin injection. Increased sensitivity of dorsal horn neurons and continuous activity in primary afferents cause the phase, which is known as inflammatory pain. Thus, the test can help elucidate the potential mechanism of the analgesic effect [17]. While many NSAIDs and corticosteroids selectively inhibit the late phase, centrally acting medications, such opioids, equally inhibit both phases. The results of the present study showed that chrysin inhibited both phases of the formalin test (phase 1 and 2). The results are consistent with previous studies indicating that chrysin relieves pain in rats [18]. Chrysin possesses a variety of pharmacological characteristics, such as neuroprotective and anti-inflammatory effects. In a study on a rat model of inflammatory pain, it was shown that chrysin leads to pain relief by reducing inflammatory factors [19]. This suggests that chrysin works well for both inflammatory and neurogenic pain. Also, part of the analgesic effects of chrysin can be related to the activity of the opioid system. It has been reported that opioid receptors located in many regions of the nervous system, including the spinal cord, midbrain, thalamus, and hypothalamus, are involved in pain transmission and control [20]. The glutamatergic and opioid systems can influence each other reciprocally. For instance, elevated glutamate levels can reduce the analgesic effects of opioids, while opioids may decrease glutamate release, leading to enhanced analgesia [21,22]. Glutamate is the excitatory neurotransmitter in the brain, playing a critical role in synaptic plasticity and pain induction. Elevated levels of glutamate have been observed with pain conditions [23]. On the other hand, a study shows that chrysin has an inhibitory effect on glutamate levels in the hippocampus [24]. Therefore, one of the possible mechanisms of chrysin to reduce pain-related behaviors is to inhibit glutamatergic systems activity, which leads to an increase in the activity of the opioid system. Also, studies have shown that gamma-aminobutyric acid (GABA) neurotransmitter is involved in pain control. GABA receptors are distributed in the central and peripheral nervous system and exert analgesic effects via activating benzodiazepine-sensitive sites [25–27]. Evidence shows that baclofen, a GABA receptor (GABABR) agonist, reduces the expression of calcitonin gene-related peptide (CGRP) in pain model rats. CGRP plays an important role in pain promotion [28]. Chrysin is able to bind to the benzodiazepine sites of the GABA_A receptor [29]. Therefore, it is possible that the reduction in pain-related behaviors is exerted through the inhibition of CGRP levels due to the GABArgic effects of chrysin.

Also, the present study showed that chrysin affects the metabolism of several amino acids. Arginine is a semi-essential amino acid that plays a crucial in DNA and protein synthesis, cell division, signal transduction, and neuromodulation. Metabolism of arginine leads to the production of many important substances for the organism, including proteins, nitric oxide (NO), proline, creatine, agmatine and polyamines [30]. The results of the previous study indicate that L-arginine and its metabolic products are crucial for the modulation of pain [31]. NO and agmatine are products of arginine. Plasma concentrations of NO are increased in pain while agmatine levels are decreased. A previous study shows that, L-arginine supplementation reduces neuropathic pain by reversing the concentration of NO and agmatine [32]. In the present study, the metabolism of L-arginine was reduced in pain model rats. Probably, in rats suffering from pain, abnormal increase of NO and decrease of agmatine leads to disorders of L-arginine metabolism. L-arginine levels in rats with pain returned to normal levels after intervention with chrysin, indicating that chrysin can reduce pain by regulating L-arginine metabolism.

Taurine is a non-essential amino acid and the second most abundant amino acid after glutamate. Taurine is synthesized from cysteine through a cysteine sulfinic acid decarboxylase step-dependent pathway, and during its synthesis, hypotaurine, on intermediate product, is successively produce through cysteamine and cysteamine sulfinic acid as the intermediate product [33]. Hypothorine has a tendency to bind to the glycine receptor. Studies have shown that hypothorine suppresses acute inflammatory and neuropathic pain by increasing the activity of glycinergic neurons [34]. In the present study, the levels of hypotaurine, cysteine, and cysteamine decreased in the serum samples of formalin rats, indicating that taurine and hypotaurine metabolism was disordered in formalin rats. After administration of chrysin, the content of this metabolite was close to that of the control group. This suggests that chrysin can alleviate pain by regulating taurine-hypoturine pathway biosynthesis.

The results of this study showed that glycine-serine-threonine pathway metabolism was changed in pain model rats receiving chrysin. Glycine is the main non-essential amino acid in mammals. This compound is an essential precursor for protein, nucleic acid, lipid and glutathione synthesis. Glycine is synthesized from serine, threonine, choline, and hydroxyproline. In addition, glycine has antioxidant potential, promoting wound healing, and inflammation [34]. Glycine is also known as an inhibitory neurotransmitter. Studies show that increasing the activity of glycinergic neurotransmitters reduces the symptoms of chronic pain [35]. Glycine is one of the main compounds in the structure of glutathione. Glutathione is an antioxidant that plays a role in many body processes. Glutathione leads to pain relief by reducing free radicals [36]. In this study, the level of glycine-serine-threonine metabolism decreased in the pain group, and the decrease in glycine level can cause disruption in glutathione synthesis in the pain group. However, after chrysin injection, the level of these metabolites in the pain group was within the range of the control group. Also, glycine as a classical inhibitory neurotransmitter is released from presynaptic terminals and modulates the activity of NMDA (N-Methyl-D-aspartate) glutamate receptors. The glutamatergic system has a stimulating effect on pain transmission [37]. Therefore, in the groups treated with chrysin, increasing glycine levels in the serum can have an inhibitory effect on the activity of the glutamatergic system, which leads to pain relief.

In summary, according to this study, there were significant changes in the metabolisms of ether glycine-serine-threonine, taurine-hypotaurine, and L-arginine in rats with pain, which may be a factor in the development of pain. Chrysin could alleviate pain by modulating small molecule metabolite changes. Only recently has the significance of small molecule metabolite alterations in pain situations been investigated, and it has proven to be a very interesting area of study.

Funding Statement

FUNDING University of Mohaghegh Ardabili supported this study.