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. 2021 Jan 28;35(4):e5020. doi: 10.1002/bmc.5020

The related mechanism of complete Freund's adjuvant‐induced chronic inflammation pain based on metabolomics analysis

Weibo Zhang 1, Jie Lyu 1, Juxiang Xu 3, Piao Zhang 1, Shuxia Zhang 1, Yeru Chen 1, Yongjie Wang 2, Gang Chen 1,✉
PMCID: PMC7988654  PMID: 33159321

Abstract

Chronic inflammation pain is a debilitating disease, and its mechanism still remains poorly understood. This study attempted to illuminate the metabolic mechanism of chronic inflammation pain induced by complete Freund’s adjuvant (CFA) injection, especially at spinal level. The chronic inflammation pain model was established by CFA administration. Behavioral testing including mechanical allodynia and thermal hyperalgesia was performed. Meanwhile, a liquid chromatography–mass spectrometry‐based metabolomics approach was applied to analyze potential metabolic biomarkers. The orthogonal partial least squares discrimination analysis mode was employed for determining metabolic changes, and a western blot was performed to detect the protein expression change. The results showed that 27 metabolites showed obviously abnormal expression and seven metabolic pathways were significantly enriched, comprising aminoacyl‐tRNA biosynthesis, arginine and proline metabolism, histidine metabolism, purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism, and phenylalanine metabolism. Meanwhile, the results showed that the expression of arginase I and nitric oxide levels were elevated in the CFA group compared with the control group, while the argininosuccinate synthetase and argininosuccinatelyase proteins were not significantly different between the groups. These findings demonstrate that metabolic changes of the spinal cord may be implicated in neurotransmitter release and pain conductivity following CFA administration.

Keywords: chronic inflammation pain, complete Freund’s adjuvant, metabolomics

1. INTRODUCTION

Chronic pain results in dramatic decline in life quality, substantial medical expenses and a massive economic burden (Henderson & Keay, 2018). Survey data demonstrate that the prevalence of chronic pain ranges from 13.5 to 47% globally, and afflicts at least 50 million American adults (Dahlhamer et al., 2018; Tsuji et al., 2019). Generally, patients with chronic pain have symptoms of anxiety and depression, poor concentration and irritability (Gureje, Von Korff, Simon, & Gater, 1998). Some research shows that patients with chronic pain have multiple inflammatory and neuropathic conditions (Finnerup, 2013). Additionally, chronic inflammation pain, as one type of chronic pain, is attracting growing interest from clinicians and scientists. A previous study documented that chronic inflammation pain was derived from chemical stimuli, tissue damage or autoimmune processes. These stimuli directly caused the release of inflammatory mediators comprising prostaglandins, histamine and neurogenic factors, and elicited a series of chain reactions, thereby contributing to pain sensation by stimulating the peripheral afferent fibers (Kidd, Photiou, & Inglis, 2004). The potential mechanism regarding the chronic inflammation pain has been extensively investigated. Yet, it is of great importance in the clinical practice, while its pathogenesis has not been clarified comprehensively.

More recently, systems biology strategies such as metabolomics have been widely applied in medical fields to investigate the pathogenic mechanism, which facilitated the development of novel biomarkers for disease diagnosis and therapy (Hocher & Adamski, 2017; Yang et al., 2018; Zhang et al., 2018). Metabolism is a complex dynamic process including generating energy and producing macromolecules for sustaining cell growth and survival (Patti et al., 2012). Metabolites are downstream molecules of gene transcription and translation processes, which are closely correlated to the disease phenotype (Lains et al., 2019). Metabolic shift is identified as a hallmark of disease, and provides a noninvasive method to monitor the disease progress (Ohman & Forsgren, 2015). Furthermore, emerging evidence has revealed the relationship between inflammatory and metabolic dysregulation (Jha et al., 2015; Jiang et al., 2016; Palomer, Salvado, Barroso, & Vazquez‐Carrera, 2013). A previous study revealed that aberrant metabolism may be involved in triggering inflammatory cascade reactions (O'Neill & Hardie, 2013). Notwithstanding, there are no adequate data to uncover the role of metabolism alteration in chronic inflammation pain. Therefore, the complete Freund’s adjuvant (CFA) model was established to investigate the potential mechanism for chronic inflammation pain in this study. Moreover, a metabolomics method was employed to analyze the changes in spinal metabolites. Interestingly, our results indicated that certain metabolic pathways were obviously enriched in the chronic inflammatory process, and these findings may provide new perspectives for comprehending the underlying mechanism of chronic inflammation pain.

2. MATERIALS AND METHODS

2.1. Animals

All experiments were performed on 8–12‐week‐old male C57BL/6 mice purchased from Shanghai SLAC Laboratory Animal Co. Ltd. For experiments, mice (20–35 g) were housed four or five per cage at constant room temperature (25 ± 1°C) and relative humidity (50 ± 5%) under a 12 h light/dark schedule (lights on 07:00–19:00); food and water were available ad libitum. For behavioral tests, the mice were allowed to adapt to laboratory conditions for about one week and to habituate to the testing situation for at least 15 min before experiments. The Animal Care and Use Committee of Zhejiang University approved all of the mouse protocols (approval no. 11978).

2.2. CFA‐induced chronic inflammation pain

Animals were randomly divided into two groups as follows: (a) a control group, injected with 10 μl saline (n = 10); and (b) a CFA group, injected with 10 μl 50% CFA in saline (n = 10). Chronic inflammatory pain was induced by administration of CFA as described previously (Pan et al., 2014). Briefly, an emulsion containing 10 μl of CFA with saline (proportion 1:1) was injected into the left posterior plantar of mice (n = 10). The control group received the same procedure with saline (Y. Liu et al., 2017b). Mice were allowed to acclimatize to the home cage and environment.

2.3. Behavioral testing

2.3.1. Mechanical allodynia

Mice were placed in individual black wood boxes without a bottom and allowed to acclimatize for at least 30 min to quantify the mechanical sensitivity of the hindpaw according to the previous literature (Chaplan, Bach, Pogrel, Chung, & Yaksh, 1994). Mechanical paw withdrawal threshold in response to the stimulation of von Frey filaments was measured using the “up–down” method (Chaplan, Bach, Pogrel, Chung, & Yaksh, 1994). Filaments were applied to the plantar surface of left hindpaw until they bent. A quick withdrawal or shaking of the stimulated paw or biting or licking of the paw was regarded as a positive withdrawal response, while other responses were regarded as a negative withdrawal response. A positive withdrawal response was followed by the application of a lower force filament and vice versa for a negative response until a change in behavior occurred (Zhao, Hiraoka, Ogawa, & Tanaka, 2018). The test started with the application of a 0.16 g filament. Every trial was repeated three times at ~2 min intervals. According to the method described by Dixon, the 50% paw withdrawal threshold was calculated based on this assessment.

2.3.2. Thermal hyperalgesia

To assess thermal hyperalgesia, mouse paw withdrawal latency (PWL) was measured using radiant heat (Bao et al., 2014; Bao et al., 2015). Mice were placed individually in plastic cages and allowed to acclimatize at least for 30 min. Each left hind paw received at least three stimuli with a 10 min interval between, and the average of the three values was defined as the PWL. The heat was maintained at a constant intensity and the cut‐off time was set to 21 s to prevent paw damage.

2.3.3. Sample preparation

Animals were anesthetized with 3% isoflurane on day 7 after CFA administration. Then these mice were sacrificed through decapitation. A laminectomy of L4–6 was carried out, and the spinal cord tissues were exposed. Complete incision of L4–6 was performed and the intervening tissue was removed. Thereafter, the spinal cord was removed and stored in a liquid nitrogen box immediately for future use.

2.3.4. Metabolite extraction

In brief, the spinal cord tissue was homogenized in 1,500 μl methanol with water (1:1) in a 2 ml glass tissue homogenizer, and centrifuged at 15,000g for 10 min (Tube 1). The supernatant was transferred to a 2 ml centrifuge tube, tube 2, then concentrated at room temperature in vacuum. A 120 μl methanol–water (1:1) solution was applied to dissolve the concentrated product. The precipitate of tube 1 was homogenized with 1,600 μl cold dichloromethane–methanol (3:1), then centrifuged at 15,000g for 10 min. The culture liquid was transferred to 2 ml centrifuge tube, tube 3, which was concentrated at room temperature in vacuum, and redissolved with 120 μl methanol–water (1:1). The solutions of centrifuge tubes 2 and 3 were mixed and centrifuged once again (15,000g for 10 min). The supernatant was determined by HPLC–MS. During the study, 10 QC samples were pooled from all spinal cord samples to equilibrate the HPLC–MS system (Zhou et al., 2018).

2.3.5. Liquid chromatography–mass spectrometry analysis

The metabolomics data were determined using a Nexera UHPLC LC‐30A system (Shimadzu, Japan), while the chromatographic separation was processed on a Waters HSS T3 (150 × 3 mm, 1.8 μm) column at 25°C, with a flow rate of 0.3 ml/min. The analysis was completed with mobile phases A (acetonitrile) and B (0.1% CH3COOH–H2O). The gradient program was 100% B at 0–10 min; 50% A and 50% B at 10–13 min; 95% A and 5% B at 13–14 min; 100% B at 14–15 min. All samples were kept at 4°C during the procedures.

The high‐resolution MS system was performed using a TripleTOF5600 + mass spectrometer (AB SCIEX™, USA). Both positive and negative modes was used to acquire the data. Source parameters are defined as follows: scanning range, m/z 100–1,500; scanning mode,data‐independent acquisition (DIA); capillary voltage, 5,000 V (positive) and 4,500 V (negative); capillary temperature, 500°C; declustering potential (DP), 60 V; collision energy (CE), 35 V; collision energy spared (CES), 15 V.

2.3.6. Data processing

The raw LC–MS data was imported into MS‐DIAL3.96 software for preprocessing, then peak extraction, de‐noise, deconvolution and peak alignment, and a 3D data matrix in CSV format was exported. The peak information was compared with metabolites from online databases including MassBank, Respect and GNPS. The three‐dimensional matrix comprising sample information, retention time, mass nuclear ratio and mass spectrometry response intensity (peak area) was analyzed. Principal components analysis, partial least squares discriminate analysis and orthogonal partial least squares discrimination analysis were carried out to make multivariate statistical analysis using SIMCA‐P (version 11.0, Umetrics, Umea, Sweden) software (Rezig et al., 2018).

2.3.7. Western blot analysis

The mouse spinal cord tissues (L4–6) were harvested and homogenized using RIPA buffer (Beyotime, P0013B) supplemented with 1× protease inhibitor cocktail (Sigma‐Aldrich; P8304), phosphatase inhibitor cocktail II and III (Sigma‐Aldrich; P5726). The supernatant was collected by centrifugation at 12,000g for 10 min, and the protein concentration was detected using a bicinchoninic acid protein assay kit (Beyotime, P0012S). An aliquot of 50 μg protein from each sample was separated using SDS‐PAGE and transferred to a PVDF membrane, then blocked with 5% nonfat milk in TBST (pH 7.4). Thereafter, the membranes were incubated with primary antibodies including arginase I (1:1000; CST; #93668), argininosuccinate synthetase (1:1000; abcam; ab17095), argininosuccinatelyase (1:1000; abcam; ab97370) and actin (1:1000; ABclonal; AC026). After incubation with the appropriate horseradish peroxidase (HRP) conjugated secondary antibodies (IgG, against rabbit, 1:1000; ABclonal; AS014), the immune complexes were visualized using the SuperSignal West Pico Substrate (34,077, Pierce). The digital images were quantified using densitometric measurements by Quantity‐One software (Bio‐Rad).

2.3.8. NO level detection

The spinal cord tissues (L4–6) were acquired and the level of nitric oxide (NO) was determined. Briefly, the NO detection kit (A012‐1‐2; Nanjing Jiancheng Biotechnology Co. Ltd; China) was purchased and the experiment protocol was performed according to the operating manual.

2.3.9. Statistical analysis

Data are presented as the mean ±standard deviation. An unpaired Student’s t‐test was conducted using GraphPad Prism 8.0 (Graphpad, CA, USA). A value of P < 0.05 was considered statistically significant.

3. RESULTS

3.1. CFA‐induced mechanical and thermal hypersensitivities

The mechanical and thermal hypersensitivities were examined on the fifth day after CFA injection. The results showed that the PWL and paw withdrawal threshold values were remarkable decreased in the CFA group compared with the control group (Figure 1a,b; P < 0.05).

FIGURE 1.

FIGURE 1

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Mechanical and thermal allodynia in mice induced by complete Freund's adjuvant (CFA) injection. Effect of CFA injection on the paw withdrawal responses to thermal (a) and mechanical (b) stimuli at 5 days. *P < 0.05, compared with control group

3.2. Metabolic profiling analysis

To confirm whether chronic inflammation pain induced dramatic shifts in the metabolites in the spinal cord, an LC–MS method was applied to analyze the differences between the control and CFA groups. Principal components analysis (Figure 2a) and partial least squares discrimination analysis methods (Figure 2b) were used to detect the differences. The results showed that the two methods did not isolate differentially expressed metabolites (Figure 2a,b). Therefore, orthogonal partial least squares discrimination analysis mode was employed, and the metabolites were separated into two categories (Figure 2c). Meanwhile, the model was subjected to a parametric test, and the results indicated that the prediction rate of metabolites was 14.4%, the prediction rate of the grouping was 75.4% and the accuracy of model prediction was 58.6% (Figure 2d). In order to prevent a false positive of the model, it was detected by response arrangement tests (100 runs), and the results showed that the prediction rate of grouping was 99.0%, and the accuracy of model prediction was 72.3% (Figure 2e). To obtain different metabolite candidates, P‐value < 0.05 and fold change > 2 were set as threshold values. The heat map and volcano plot of metabolites are separately shown in Figure 2f and g, and the details of the different metabolites are attached to Table 1.

FIGURE 2.

FIGURE 2

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Metabolic profiling analysis: (a) principal components analysis; (b) partial least squares discrimination analysis; (c) orthogonal partial least squares discrimination analysis; (d, e) parametric test. (f) Heat map analysis of metabolites between control group and CFA group (the color scale shows the relative metabolites expression in certain slide: blue indicates low relative expression levels; red indicates high relative expression levels; yellow indicates no change); (g) volcano plot of metabolites between control group and CFA group (red indicates the metabolites expression was significantly down/up‐regulated in CFA group compared with control group; P < 0.05). R 2 X represents the prediction rate of metabolites, R 2 Y represents the prediction rate of grouping, and Q 2 represents the accuracy of model prediction

TABLE 1.

The details of metabolites in different groups

Alignment ID Average retention time (min) Average Mz Metabolite name Adduct type MS/MS assigned Reference m/z Formula Ontology INCHIKEY SMILES MS1 isotopic spectrum MS/MS spectrum m‐CON‐1‐1 m‐CON‐1‐2 m‐CON‐2‐1 m‐CON‐2‐2 m‐CON‐3‐1 m‐CON‐3‐2 m‐CON‐4‐1 m‐CON‐4‐2 m‐CON‐5‐1 m‐CON‐5‐2 m‐CFA‐1‐1 m‐CFA‐1‐2 m‐CFA‐2‐1 m‐CFA‐2‐2 m‐CFA‐3‐1 m‐CFA‐3‐2 m‐CFA‐4‐1 m‐CFA‐4‐2 m‐CFA‐5‐1 m‐CFA‐5‐2 QC‐1 QC‐2 QC‐3 QC‐4 QC‐5
CON CON CON CON CON CON CON CON CON CON CFA CFA CFA CFA CFA CFA CFA CFA CFA CFA QC QC QC QC QC VIP (CFA vs. CON) FC (CFA vs. CON) TTEST (CFA vs. CON)
44 3.798 104.05289 N‐Methylalanine [M + H]+ True 104.0706 C4H9NO2 Alanine and derivatives GDFAOVXKHJXLEI‐VKHMYHEASA‐N CN[C@@H](C)C(O)=O 104.05318:10556 105.05653:4518 106.05989:904 58.07356:42104.11706:42 2,194 813 6,113 23,888 45,403 29,632 46,466 46,437 50,186 6,318 52,019 38,489 45,164 35,816 72,491 40,667 48,518 38,682 43,410 72,211 3,369 2,412 35,398 1967 37,982 0.060775 1.8934433871 0.0041166709
47 2.967 104.07158 a‐Aminoisobutyrate [M + H]+ True 104.0706 C4H9NO2 Alpha amino acids FUOOLUPWFVMBKG‐UHFFFAOYSA‐N CC(C)(N)C(O)=O 104.06766:5184 105.07101:4230 106.07437:2256 56.05714:83 58.07238:2343 58.11862:142 58.14444:98 58.21546:42 58.37488:42 59.05377:43 59.08196:319 59.1004:63 59.91335:48 60.08711:1660 60.11774:104 60.19324:42 60.48803:42 61.00925:63 69.03474:42 71.08587:42 87.05281:63104.10562:1224 104.28419:42 62,703 54,945 78,732 42,530 87,202 80,753 98,610 104,309 96,175 82,126 91,306 110,792 92,627 107,516 125,306 114,844 154,416 108,947 156,350 139,787 20,239 3,645 41,971 67,113 61,782 0.10933 1.5250778787 0.0002315302
634 3.507 132.101 Isoleucine [M + H]+ True 132.1028 C6H13NO2 Isoleucine and derivatives AGPKZVBTJJNPAG‐UHFFFAOYNA‐N O=C(O)C(N)C(C)cc 132.101:275158 133.10435:40078 134.10771:5289 53.00695:63 53.02544:42 55.02148:42 55.06335:83 56.06026:171 57.06399:150 57.07038:149 58.05942:83 58.0691:63 58.07985:63 62.94052:21 69.04636:63 69.07684:478 69.10146:102 69.21879:42 71.07623:42 72.06087:146 72.08722:42 72.94139:146 73.06555:63 74.0699:42 85.8249:42 86.09304:87 86.1022:2372 86.20303:179 86.27247:83 86.34325:83 86.53212:42 87.06313:210 87.08025:133 87.09867:104 89.60625:44 90.05688:982 90.90903:42114.07255:63115.05105:63117.0801:42119.07763:21127.86925:43132.07666:840132.11559:42 14,260 715 869 106,976 120,636 816 154,014 34,887 3,356 859 15,242 1,267 1,462,686 8,721 1,404,377 1,314,712 1,672,418 3,525 1,578 6,252 49,284 40,378 42,667 43,929 38,667 1.4409 13.4680832579 0.017904852
1,006 4.964 146.16362 Spermidine [M + H]+ True 146.16518 C7H19N3 Dialkylamines ATHGHQPFGPMSJY‐UHFFFAOYSA‐N NCCCCNCCCN 146.16512:4739 147.16847:688148.17183:0 56.96902:21 58.07324:42 72.08226:146 72.09304:104 84.08359:63112.11149:42 53 69 1,045 3,522 8,376 7,891 12,487 8,258 28,946 582 12,503 25,642 29,045 21,045 21,998 26,846 70,867 113,281 56,294 62,733 741 71 346 63 856 0.097503 6.1808252257 0.0010790023
1,093 3.779 150.05882 Methionine [M + H]+ True 150.05832 C5H11NO2S Methionine and derivatives FFEARJCKVFRZRR‐UHFFFAOYNA‐N CSCCC(N)C(O)=O 150.056:51970 151.05935:5422 152.06271:8131 53.04919:42 53.06153:42 55.87885:31 56.05933:1175 56.10476:77 56.12907:45 58.99855:42 60.82431:42 60.85072:48 61.01922:1374 61.07874:47 61.54713:21 66.04822:31 70.9967:21 73.64085:21 74.02877:191 74.05183:31 74.06519:73 75.03236:31 77.00658:31 84.04641:83 84.05804:63 85.03094:83 87.0238:53 87.02644:234 90.04098:31 93.06465:21102.05724:190104.05369:350105.00134:31120.0837:21129.12486:10133.03197:514134.11136:42135.12294:31150.05905:83150.07806:42150.12646:52 991 2,193 3,376 7,331 5,339 1925 248,496 8,429 224,333 1867 12,403 9,580 203,402 208,821 281,816 185,274 234,987 262,779 342,045 404,916 410 4,593 2,204 824 219,231 0.43378 4.2556179107 0.0022510742
1,149 4.075 156.0755 Histidine [M + H]+ True 156.07675 C6H9N3O2 Histidine and derivatives HNDVDQJCIGZPNO‐UHFFFAOYNA‐N O=C(O)C(N)CC1=CN=CN1 156.07379:27948 157.07714:3718 158.0805:3718 50.02816:21 54.04945:63 56.06555:147 66.04694:83 68.05582:83 71.95312:42 81.04317:42 81.04952:169 82.05473:167 82.07135:63 83.05714:44 83.06357:331 83.10729:42 86.06815:21 93.04813:366 95.05525:22 95.06213:63109.721:65110.07299:883110.1026:69111.05206:42115.50999:21156.07678:83 2,559 3,836 10,967 5,033 9,758 57,934 130,906 8,141 45,796 2,921 167,330 124,687 40,716 143,928 67,014 47,851 26,308 44,872 318,432 290,404 3,874 2,741 3,303 6,566 4,009 0.26255 4.5763448755 0.0061254643
1,254 3.967 161.12683 l‐β‐Homolysine [M + H]+ True 161.12845 C7H16N2O2 β Amino acids and derivatives PJDINCOFOROBQW‐LURJTMIESA‐N NCCCC[C@H](N)CC(O)=O 161.12852:5683 162.13187:1610 163.13523:1610 70.07452:21 72.08102:125 84.08356:83 84.10168:63139.03064:21144.10316:63146.07782:42161.1118:42 4,751 1,240 3,089 56,286 40,507 44,075 15,866 60,653 13,569 372 77,838 74,484 15,263 81,451 31,709 26,618 22,144 20,078 22,976 94,105 3,417 867 2,832 2,743 7,909 0.059781 1.9411417257 0.0421885982
1,285 3.391 162.11143 l‐carnitine [M + H]+ True 162.11247 C7H15NO3 Carnitines PHIQHXFUZVPYII‐ZCFIWIBFSA‐N C[N+](C)(C)C[C@H](O)CC([O‐])=O 162.11374:29728 163.11709:5364 164.12045:1490 57.03634:42 58.07023:104 59.07545:63 60.09476:104 85.03355:104102.09293:104103.03878:104103.04737:146146.09703:63162.11127:503 137,818 126,043 189,429 120,297 182,996 226,630 171,583 230,633 230,463 1,617 354,025 440,909 1,259,888 305,598 292,446 335,317 1,768,962 338,206 430,799 398,995 1,008 1,020 117,255 123,861 125,978 1.1382 3.6631295405 0.007597821
1,291 3.753 162.112 Carnitine [M + H]+ True 162.11247 C7H15NO3 Carnitines PHIQHXFUZVPYII‐ZCFIWIBFSA‐N C[N+](C)(C)C[C@H](O)CC([O‐])=O 162.11185:524328 163.1152:89133 164.11856:11269 54.93892:42 55.95581:43 57.04489:618 57.06727:96 57.09925:54 58.07563:878 59.07764:378 59.35122:63 60.01388:48 60.08713:1754 60.11119:177 60.12213:95 60.13635:111 61.0302:125 61.06437:42 84.08521:42 84.76316:63 85.03355:2327 85.0882:99 86.05917:42 97.97429:42 98.96687:42102.09148:1763 103.04305:2444 103.08173:102103.22644:83104.41814:42114.96052:42146.10208:63161.58708:83162.10399:264162.11296:4560 162.25131:66162.34656:125162.44545:104 114,299 2,126,919 2,391,938 55,919 39,264 15,737 7,897 45,777 2049 2,570,282 27,836 16,605 9,793 17,451 1,601 9,851 14,734 10,385 11,670 23,891 2,141,254 2,090,007 104,083 166,766 41,578 1.9093 0.0195136254 0.0288782904
1,572 3.702 182.08066 Tyrosine [M + H]+ True 182.08118 C9H11NO3 Tyrosine and derivatives OUYCCCASQSFEME‐UHFFFAOYNA‐N O=C(O)C(N)CC1=CC=C(O)C=C1 182.08034:80922 183.08369:11783 184.08705:2561 51.03576:63 53.04057:148 53.06729:42 55.02642:31 64.8437:32 65.04722:246 67.05714:42 74.79868:21 77.0445:333 77.07794:36 79.05739:73 79.08247:21 81.03526:21 81.06955:52 88.02511:21 90.77292:42 91.05811:2809 91.10388:61 91.16583:43 93.05737:31 94.04394:42 94.75534:52 95.05221:776 95.08797:26 95.10586:26 99.93855:21101.04308:31103.05828:94106.06608:22107.05055:501108.08651:21109.06851:52117.05843:83118.06648:167118.66663:42119.05116:1528 119.09734:34120.05537:31121.0638:83122.64606:21123.04481:1296 123.37523:21135.06503:31135.66908:32136.07527:2217 137.07275:42145.26024:21147.04427:415153.06671:32165.05525:458182.07918:52 8,060 623 884 226,568 388,549 282,080 350,601 507,047 346,685 2 433,140 282,783 263,639 318,859 309,281 265,574 326,698 310,226 493,607 582,405 230 1,033 343,879 8,427 382,659 0.38975 1.6987417454 0.0247046305
2,218 1.391 220.11549 d‐(+)‐pantothenic acid [M + H]+ True 220.11795 C9H17NO5 Secondary alcohols GHOKWGTUZJEAQD‐ZETCQYMHSA‐N CC(C)(CO)[C@@H](O)C(O)=NCCC(O)=O 220.11549:34997 221.11884:3891 222.1222:1196 56.01493:42 57.07793:63 59.06138:42 67.06334:83 69.08517:42 70.03354:63 72.04784:63 77.04002:42 79.05535:42 83.05476:42 85.06741:63 87.08044:42 90.05307:85 90.05977:337 94.07184:42 95.05688:83 98.01903:42 98.02183:83115.06196:42118.09472:63122.10828:21124.07578:146129.08815:42131.08987:42142.08527:104145.0995:42156.11259:42177.12654:104184.09784:125202.10573:104202.14784:21205.12944:63205.1638:167205.17592:104220.11571:125220.14291:42 210,166 167,154 220,827 109,474 147,563 108,886 56,756 58,130 89,778 218,550 133,007 121,659 44,553 87,255 76,023 81,978 30,429 92,765 114,592 121,395 95,250 291,906 285,777 247,110 218,772 0.12778 0.6513850084 0.0240294256
2,298 4.286 227.11293 l‐Carnosine [M + H]+ True 227.11386 C9H14N4O3 Hybrid peptides CQOVPNPJLQNMDC‐ZETCQYMHSA‐N NCCC(O)=N[C@@H](CC1=CN=CN1)C(O)=O 227.1124:74781 228.11575:11134 229.11911:1465 55.04132:42 68.05227:42 82.05465:63 83.06734:215 83.08278:64 84.96175:42 93.0494:146 93.84448:43 95.0634:235109.72236:47110.07436:1878 110.10692:69110.16467:57119.07593:63122.07659:361122.0875:147136.08853:63141.10925:21146.08275:63151.03488:21152.08218:83155.33369:43156.07483:712161.68329:21164.07979:230172.06091:21180.08009:63181.10577:125192.07181:42210.0862:188210.1046:104224.81111:21227.10995:104227.12697:83 18,611 8,659 3,375 691,174 7,229 672,318 676,762 765,788 534,700 11,967 994,556 706,764 307,627 838,688 664,714 681,011 67,650 941,312 717,540 803,866 459 802 451,869 13,045 501,460 0.88068 1.9830595505 0.0156686685
2,657 1.416 245.07281 Uridine [M + H]+ True 245.07681 C9H12N2O6 Pyrimidine nucleosides DRTQHJPVMGBUCF‐UHFFFAOYNA‐N O=C1N=C(O)C=CN1C2OC(co)C(O)C2O 245.07242:3838 246.07577:452247.07913:0 70.02979:42 71.02264:21 96.01913:63 97.0379:42113.02923:65113.03673:537113.06073:42245.22159:63 69,991 42,498 24,696 39,146 37,751 48,829 31,960 31,420 14,001 31,382 9,996 37,357 14,007 17,556 2,538 28,768 2,617 11,325 1824 1,048 30,913 36,493 47,935 42,544 40,265 0.064638 0.3417941529 0.0004340064
2,948 6.998 261.03534 d‐Mannose‐6‐phosphate [M + H]+ True 261.03699 C6H13O9P Hexose phosphates NBSCHQHZLSJFNQ‐QTVWNMPRSA‐N OC1O[C@H](COP(O)(O)=O)[C@@H](O)[C@H](O)[C@@H]1O 261.03534:50962 262.03869:4518 263.04205:1061 53.04903:63 57.04477:63 63.03564:21 67.02851:21 69.03809:106 71.04882:42 80.98725:85 81.03551:281 85.03593:230 93.06441:21 97.04065:43 98.98625:426 99.02415:71 99.04521:149100.90723:42103.3968:44109.03044:725109.09969:23118.94981:21127.04094:383127.05207:127145.04797:63160.98993:21207.00189:42225.00177:42225.01447:104243.02344:146 62,030 31,393 42,363 123,232 220,245 70,522 88,688 424,738 442,372 825,961 540,513 331,605 354,822 396,896 424,848 373,235 413,106 497,578 948,371 689,780 426 6,442 11,014 48,095 52,117 0.69733 2.1319580501 0.0090084967
3,133 2.362 269.08701 Inosine [M + H]+ True 269.08804 C10H12N4O5 Purine nucleosides UGQMRVRMYYASKQ‐KQYNXXCUSA‐N OC[C@H]1O[C@H]([C@H](O)[C@@H]1O)N1C=NC2=C1N=CN=C2O 269.08591:112501 270.08926:17870 271.09262:3310 55.02217:63 55.03473:42 57.04338:83 57.06256:63 67.03049:42 69.03893:63 71.02114:63 73.03501:83 82.05434:83 85.02518:42 85.03168:63 85.04599:63 92.02988:42 94.03973:213 95.21723:21 97.02918:42 99.83833:42103.04815:43110.03708:674115.04003:85118.6619:43119.03718:769120.0197:104133.04759:83136.60722:209137.04619:23885 137.17505:1682 137.26263:667137.3519:250137.39655:42137.48917:42137.55205:167137.64471:125137.81856:125138.30925:63138.40051:42140.32079:42215.12384:42219.2112:21 1,744,192 2,301,859 1,189,128 1,606,597 1,083,286 1,455,366 1,113,897 1,444,050 899,913 1,494,445 1,074,040 1,458,989 656,759 1,296,131 533,352 1,233,329 494,316 966,957 696,056 1,027,346 1,380,913 1,675,873 1,554,619 1,649,054 1,345,126 1.2935 0.6584421129 0.0042157925
4,947 6.973 364.06473 Guanosine 5′‐monophosphate [M + H]+ True 364.06528 C10H14N5O8P Purine ribonucleoside monophosphates RQFCJASXJCIDSX‐UUOKFMHZSA‐N O[C@@H]1[C@@H](COP(O)(O)=O)O[C@H]([C@@H]1O)N1C=NC2=C1NC(=N)N=C2O 364.05899:1155 365.06234:434366.0657:87 110.05524:10135.02759:21149.02536:21152.05627:125152.08411:63169.94139:10264.26987:10 683 2,643 263 6,031 1,126 1,516 323 5,970 5,029 4,968 1877 7,575 1,051 9,004 11,751 1790 17,554 11,665 7,104 8,026 49 129 273 28 443 #N/A 2.710738302 0.007436557
55 1.398 115.00401 Maleic acid [M − H]− True 115.00368 C4H4O4 Dicarboxylic acids and derivatives VZCYOOQTPOCHFL‐UHFFFAOYSA‐N O=C(O)C=cc(=O)O 115.00503:9050 116.00838:577117.01174:576 71.05444:21 71.05563:21 55,633 77,348 48,858 62,670 32,151 49,493 32,421 69,355 21,203 39,650 26,928 50,276 11,637 51,514 32,987 25,449 20,385 39,037 39,291 29,654 45,173 36,089 41,432 43,091 38,137 0.042704 0.6693331587 0.0155992248
217 3.937 154.06157 His [M − H]− True 154.06219 C6H9N3O2 Histidine and derivatives HNDVDQJCIGZPNO‐YFKPBYRVSA‐N N[C@@H](CC1=CN=CN1)C(O)=O 154.06317:5394 155.06652:675156.06988:630 80.04959:42 91.03069:21 93.0517:83137.04097:42154.07217:63 2,130 584 2,731 2,805 1,596 25,392 32,088 2,772 46,927 1924 37,518 31,430 44,829 28,002 50,461 38,582 49,224 31,520 50,570 2,223 1,664 3,377 1898 4,881 3,229 0.064842 3.063153116 0.0012762611
263 3.67 164.07458 l‐(−)‐Phenylalanine [M − H]− True 164.0717 C9H11NO2 Phenylalanine and derivatives COLNVLDHVKWLRT‐QMMMGPOBSA‐N N[C@@H](CC1=CC=CC=C1)C(O)=O 164.07387:12947 165.07722:2343 166.08058:1340 72.01447:42 91.05745:21103.06355:42134.04286:21147.05013:104164.08469:63 59,723 1,231 4,593 37,941 77,746 57,819 75,580 88,057 81,639 137 100,927 61,536 6,396 65,599 92,867 78,271 85,666 57,850 103,083 116,152 59,012 58,157 61,426 63,599 64,054 0.075006 1.5859668171 0.0361989234
301 6.907 171.00775 Glycerophosphate(2) [M − H]− True 171.00639 C3H9O6P Glycerophosphates AWUCVROLDVIAJX‐GSVOUGTGSA‐N OC[C@@H](O)COP(O)(O)=O 171.01015:13672 172.0135:624173.01686:286 77.99744:43 78.96368:579 81.30538:21 193,915 455,002 195,998 313,002 258,938 165,165 293,164 184,300 291,288 304,236 128,549 153,126 10,999 162,820 9,070 9,053 9,921 189,087 256,611 127,301 125,714 155,695 163,806 208,021 204,086 0.42234 0.3979411738 0.0003830492
312 1.694 173.00899 cis‐Aconitate [M − H]− True 173.00916 C6H6O6 Tricarboxylic acids and derivatives GTZCVFVGUGFEME‐IWQZZHSRSA‐N OC(=O)C\C(=C\C(O)=O)C(O)=O 173.01173:4774 174.01508:988175.01844:188 85.03636:63 26,885 37,094 29,617 1902 1,439 1,126 1,095 1,447 1725 52,483 1791 2,320 682 1,536 1,215 1,062 1,274 1,506 2057 2,623 24,108 19,080 22,670 20,288 20,534 #N/A 0.1037768146 0.0177163028
317 4.026 173.10483 l‐(+)‐Arginine [M − H]− True 173.1044 C6H14N4O2 l‐α‐Amino acids ODKSFYDXXFIFQN‐BYPYZUCNSA‐N N[C@@H](CCCNC(N)=N)C(O)=O 173.10464:16345 174.10799:2098 175.11135:74 105.03072:21131.0858:378173.11749:42 9,483 93,266 131,938 8,595 20,461 7,508 6,163 15,823 9,093 60,177 10,077 6,734 1,693 9,333 6,493 6,422 4,270 7,258 14,847 34,069 17,284 17,708 8,559 20,185 9,741 0.069043 0.2791559887 0.0419059935
803 6.769 229.0134 d‐Ribulose 5‐phosphate [M − H]− True 229.01187 C5H11O8P Pentose phosphates FNZLKVNUWIIPSJ‐UHNVWZDZSA‐N OCC(=O)[C@H](O)[C@H](O)COP(O)(O)=O 229.00995:5537 230.0133:849231.01666:282 78.95767:63 78.96269:294 91.06715:21 96.97551:125 97.03944:42 59,792 54,366 146,915 51,521 86,127 4,758 31,327 69,766 35,181 109,452 14,016 7,827 28,820 11,103 33,714 42,339 35,598 68,688 61,508 9,504 38,938 49,583 50,449 52,324 56,902 0.0888 0.4823083618 0.0170326847
1718 5.74 322.0506 Cytidine‐3′‐monophosphate [M − H]− True 322.04459 C9H14N3O8P Ribonucleoside 3′‐phosphates UOOOPKANIPLQPU‐XVFCMESISA‐N OC[C@H]1O[C@H]([C@H](O)[C@@H]1OP(O)(O)=O)N1C=CC(=N)N=C1O 322.0498:4108 323.05315:759324.05651:333 78.96099:125 96.97363:42211.01308:42322.06519:63 33,820 128,567 23,557 160,602 21,139 14,234 34,175 26,041 13,700 33,224 10,321 34,112 10,600 21,033 19,820 12,858 16,402 12,454 13,615 9,089 29,710 32,516 39,322 35,315 39,940 0.086863 0.3277804927 0.0305979778
1731 6.877 323.02869 Uridine 5′‐monophosphate [M − H]− True 323.02859 C9H13N2O9P Pyrimidine ribonucleoside monophosphates DJJCXFVJDGTHFX‐XVFCMESISA‐N O[C@@H]1[C@@H](COP(O)(O)=O)O[C@H]([C@@H]1O)N1C=CC(O)=NC1=O 323.03195:4429 324.0353:820325.03866:412 78.96327:167 80.51807:21 96.97334:83111.02354:42211.00215:63323.03467:63 29,233 149,656 17,963 202,163 18,277 21,042 29,042 20,533 14,499 28,719 13,747 23,658 1,557 17,368 1978 2031 2,303 20,274 14,242 5,319 34,845 38,533 36,869 43,415 47,390 0.11326 0.1929425542 0.0284777076
3,024 7.264 476.09399 8‐Methylthiooctyl glucosinolate [M − H]− True 476.10883 C16H31NO9S3 Alkylglucosinolates CWOJBEDMJKZKAB‐STPBKMPXSA‐N CSCCCCCCCC\C(S[C@@H]1O[C@H](CO)[C@@H](O)[C@H](O)[C@H]1O)=N/OS(O)(=O)=O 476.0961:3018 477.09945:479478.10281:695 96.98495:42357.10968:42389.07697:104458.12701:21476.10638:167476.12793:188 33,371 34,322 19,035 24,637 14,148 28,477 7,809 21,548 27,341 22,910 13,362 10,208 10,621 9,090 6,317 19,145 6,381 14,649 9,622 6,535 9,484 13,047 20,190 11,853 19,247 0.033732 0.4534713482 0.0001821421
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3.3. Protein expression and pathway analysis

The decrease in arginine levels may be involved in the alteration of key enzymes of the arginine–NO cycle including argininosuccinate synthetase and argininosuccinatelyase, and NO level and arginase I expression. To validate the hypothesis, the Western Blot (WB) assay was performed, and the results showed that the expression of arginase I was elevated in the CFA group compared with the control group, while the proteins of argininosuccinate synthetase and argininosuccinatelyase were not significantly different between the CFA group and the control group (Figure 3a). The NO level was obviously increased in the CFA group compared with the control group (Figure 3b, P < 0.05). In order to screen significantly enriched pathways, the different metabolites were analyzed based on the KEGG and HMDB databases. In Table 2, metabolic pathways with raw P and impact values are listed. In addition, the impact of metabolic pathway is delineated in Figure 3c, and the pathways marked with letters were severely affected by chronic inflammation pain, with the details as follows (A–G): (A) aminoacyl‐tRNA biosynthesis; (B) arginine and proline metabolism; (C) histidine metabolism; (D) purine metabolism; (E) phenylalanine; (F) tyrosine and tryptophan biosynthesis; and (G) glutathione metabolism and phenylalanine metabolism. Moreover, to provide insight into the pathobiological mechanism of chronic inflammation pain, the interaction networks among these seven metabolic pathways were generated and are presented in Figure 3d.

FIGURE 3.

FIGURE 3

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Pathway analysis of the different metabolites: (a) Western Blot (WB) assay for key protein expression; (b) NO level detection; and (c) bubble plot for pathway analysis of the different metabolites. The x‐axis represents the pathway impact and the y‐axis describes the impact value. The circular area is proportional to the number of metabolites assigned to the term and the color accords with the P‐value; (d) the regulatory relationship of metabolic pathways in response to CFA‐induced inflammatory pain. Potential biomarkers are marked as red (up‐regulated), green (down‐regulated) and blue (without significant changes). Other undetected metabolites in the metabolic pathway are labeled in black. The names of related metabolic pathways are marked in red in the corresponding dashed box

TABLE 2.

The potential metabolic pathways

Total Expected Hits Raw P #name? Holm adjust FDR Impact
Aminoacyl‐tRNA biosynthesis 69 3.6034 17 1.81 × 10−8 17.827 1.48E‐06 1.48E‐06 0.12903
Arginine and proline metabolism 44 2.2978 10 4.84 × 10−5 9.9365 0.0039184 0.0019834 0.36034
Histidine metabolism 15 0.78335 4 0.0060344 5.1103 0.48275 0.11698 0.24194
Alanine, aspartate and glutamate metabolism 24 1.2534 5 0.0066071 5.0196 0.52196 0.11698 0.60232
Purine metabolism 68 3.5512 9 0.0071327 4.9431 0.55635 0.11698 0.23524
Nitrogen metabolism 9 0.47001 3 0.0091513 4.6939 0.70465 0.12507 0
Phenylalanine, tyrosine and tryptophan biosynthesis 4 0.20889 2 0.015078 4.1945 1 0.15742 1
Valine, leucine and isoleucine biosynthesis 11 0.57445 3 0.016665 4.0944 1 0.15742 0.99999
Pyrimidine metabolism 41 2.1411 6 0.017278 4.0583 1 0.15742 0.1534
Glycine, serine and threonine metabolism 31 1.6189 5 0.019841 3.92 1 0.1627 0.26989
d‐Glutamine and d‐glutamate metabolism 5 0.26112 2 0.024285 3.7179 1 0.18103 1
Pantothenate and CoA biosynthesis 15 0.78335 3 0.039543 3.2304 1 0.26972 0.02041
Glutathione metabolism 26 1.3578 4 0.04276 3.1522 1 0.26972 0.44179
Cysteine and methionine metabolism 27 1.41 4 0.048266 3.031 1 0.2827 0.17491
β‐Alanine metabolism 17 0.88779 3 0.054848 2.9032 1 0.29984 0
Biosynthesis of unsaturated fatty acids 42 2.1934 5 0.063642 2.7545 1 0.32333 0
Glycerophospholipid metabolism 30 1.5667 4 0.067032 2.7026 1 0.32333 0.15371
Phenylalanine metabolism 11 0.57445 2 0.10909 2.2156 1 0.49694 0.40741
Amino sugar and nucleotide sugar metabolism 37 1.9323 4 0.12318 2.0941 1 0.53164 0.16012
Ubiquinone and other terpenoid–quinone biosynthesis 3 0.15667 1 0.14873 1.9056 1 0.60979 0
Lysine biosynthesis 4 0.20889 1 0.19328 1.6436 1 0.75471 0
Biotin metabolism 5 0.26112 1 0.23553 1.4459 1 0.86 0
Glyoxylate and dicarboxylate metabolism 18 0.94001 2 0.24122 1.4221 1 0.86 0.38709
Cyanoamino acid metabolism 6 0.31334 1 0.27559 1.2888 1 0.886 0
Linoleic acid metabolism 6 0.31334 1 0.27559 1.2888 1 0.886 1
Citrate cycle (TCA cycle) 20 1.0445 2 0.28093 1.2697 1 0.886 0.09164
Sphingolipid metabolism 21 1.0967 2 0.30076 1.2014 1 0.91341 0.01504
Valine, leucine and isoleucine degradation 38 1.9845 3 0.31867 1.1436 1 0.93324 0
Lysine degradation 23 1.2011 2 0.34013 1.0784 1 0.9556 0
Taurine and hypotaurine metabolism 8 0.41778 1 0.34961 1.0509 1 0.9556 0.42857
Ascorbate and aldarate metabolism 9 0.47001 1 0.38377 0.95772 1 0.9834 0
Methane metabolism 9 0.47001 1 0.38377 0.95772 1 0.9834 0.4
Riboflavin metabolism 11 0.57445 1 0.44686 0.80551 1 1 0
Nicotinate and nicotinamide metabolism 13 0.6789 1 0.50357 0.68603 1 1 0.2381
Pentose and glucuronate interconversions 16 0.83557 1 0.57805 0.54809 1 1 0.2
Glycerolipid metabolism 18 0.94001 1 0.62147 0.47567 1 1 0.0256
Starch and sucrose metabolism 19 0.99224 1 0.64149 0.44396 1 1 0.13815
Fatty acid biosynthesis 43 2.2456 2 0.6688 0.40227 1 1 0
Fructose and mannose metabolism 21 1.0967 1 0.67845 0.38794 1 1 0.15342
Butanoate metabolism 22 1.1489 1 0.6955 0.36313 1 1 0
Galactose metabolism 26 1.3578 1 0.7552 0.28077 1 1 0
Fatty acid elongation in mitochondria 27 1.41 1 0.76823 0.26367 1 1 0
Porphyrin and chlorophyll metabolism 27 1.41 1 0.76823 0.26367 1 1 0
Arachidonic acid metabolism 36 1.88 1 0.85855 0.15251 1 1 0.32601
Fatty acid metabolism 39 2.0367 1 0.88011 0.12771 1 1 0
Tryptophan metabolism 40 2.0889 1 0.88655 0.12042 1 1 0.17715
Tyrosine metabolism 44 2.2978 1 0.90906 0.095343 1 1 0.14045
Primary bile acid biosynthesis 46 2.4023 1 0.9186 0.084902 1 1 0.02976
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4. DISCUSSION

Chronic inflammatory pain is universally regarded as a difficult medical problem worldwide and only partial therapy options are available. Various methods have been employed to investigate the potential mechanisma. However, the complex biochemical processes of chronic inflammatory pain remain poorly understood and little relief has been achieved in spite of the enormous efforts that have been made in basic medical and clinical research. Therefore, illuminating the underlying mechanism may provide novel strategies to alleviate pain with fewer side effects. Recently, systems biology strategies including metabolomics analysis have been widely applied to explore the pathogenic mechanism. In this study, the metabolites of CFA‐induced chronic inflammation pain were analyzed based on a metabolomics method. The analysis showed that 27 metabolites were significantly altered in response to CFA injection and seven metabolic pathways were obviously enriched.

4.1. The association between chronic inflammatory pain and metabolites

Inflammatory pain is a complex symptom involving multiple modulators consisting of neurotransmitters, receptors, ion channels and signaling pathways (Jiao et al., 2020). Previous studies documented that NF‐κB, as a ubiquitously expressed transcription factor, could effectively initiate the inflammatory response to mediate cell proliferation, apoptosis and metastasis (Sethi, Sung, & Aggarwal, 2008). Insulin resistance was enhanced by the NF‐κB pathway to accelerate the progress of inflammatory reactions (Wang, Zhang, Wang, Wang, & Liu, 2019). Inflammatory and oxidative stress were closely correlated with the development of metabolic complications, and NF‐κB signaling may promote the deterioration of nonalcoholic fatty liver disease by inducing the accumulation of triacylglycerol in the liver (Kang et al., 2017; Valenzuela & Videla, 2020). Moreover, emerging evidence has shown that metabolic disturbance may participate in regulating excitable membranes, synaptic transmission and synaptic plasticity. Surveys suggested several metabolites as biological markers that are sensitive to pain pathology induced by CFA injection. Similarly, the differentially expressed metabolites were screened, and the results showed that the expression of 26 metabolites was significantly changed in response to CFA injection. Hence, the potential regulatory network was analyzed, and a hub metabolite was sought out for developing a therapeutic method of chronic inflammation pain.

4.2. The metabolic alterations elicited by chronic inflammatory pain

Several metabolites induced by chronic inflammatory pain were identified, which may be implicated in nervous impulse transmission. To clarify the metabolic process, spinal cord tissues were acquired and the regulatory process of metabolites was analyzed. Generally, arginine is susceptible to the level of guanidine compounds, and thereby results in citrullination (Wang et al., 2019). In addition, a previous study showed that arginine downregulation exacerbated the inflammatory reactions, and thereby resulted in the degradation of amino acids (Schroecksnadel et al., 2006). In this study, our results showed that the level of arginine was significantly decreased in the CFA group compared with the control group, which may directly mediate the inflammatory response and cause inflammatory pain. In addition, related documents revealed that arginine participated in the synthesis of NO neurotransmitter, which could produce anti‐nociceptive natural opioids and N‐methyl‐d‐aspartate receptor‐mediated pain‐promoting effect (Chen et al., 2016; Rondon et al., 2018), whereas neurotransmitter depletion derived from arginine decrease may contribute to inflammatory pain. Moreover, histidine is closely related to the inflammation processes by regulating the synthesis of histamine neurotransmitters (Shell et al., 2016). The metabolomics data showed that histidine expression was enhanced following CFA injection and ultimately led to chronic inflammation pain.

4.3. Phenylalanine and tyrosine metabolism

Tyrosine is an essential amino acid, which is partially synthetized from phenylalanine. The accumulation of phenylpyruvate is toxic to the central nervous system (Rausell et al., 2019). Previous research found that the levels of phenylalanine and tyrosine were remarkably increased in cerebrospinal fluid of patients with regional pain syndrome (Meissner et al., 2014). Dopamine, norepinephrine and epinephrine produced by the phenylalanine and tyrosine metabolic reactions play a critical role in the brain. Norepinephrine released from the sympathetic nerves can activate β2ARs receptors, and result in production and secretion of the proinflammatory cytokine, subsequently causing hyperalgesia of sensory neurons and increasing chronic inflammatory pain (Li et al., 2013). Interestingly, our findings indicated that the pronounced increase of phenylalanine and tyrosine may accelerate pain signal transduction by increasing the concentration of neurotransmitters in spinal cord.

Currently, data suggest that metabolic changes are relevant to many diseases, and metabolites obtained from accessible samples such as urine or plasma may serve as potential biomarkers for diagnosis of chronic inflammatory pain in the clinic (Liu et al., 2017a; Liu et al., 2017b). The spinal cord is the primary center of transmission signals. The signals of nociceptive stimuli are transmitted to the posterior horn of the spinal cord by fine fibers, and eventually pass to the cerebral cortex after processing in the spinal cord (Descalzi et al., 2015; Meacham, Shepherd, Mohapatra, & Haroutounian, 2017).

Therefore, investigating the alteration of metabolites in spinal cord may help to illuminate the neuronal communication mechanism regarding CFA injection‐induced chronic inflammation pain. Collectively, this study provides a new perspective for comprehending the pathological process of CFA‐induced chronic inflammation pain, and enhancing efforts to develop new therapeutic strategies.

COMPETING INTERESTS

The authors declare that they have no competing interests.

ACKNOWLEDGEMENTS

This study was supported by the National Natural Science Foundation of China (nos 81371214 and 81671063), the Natural Science Foundation of Zhejiang Province, China (no. LY16H090008), and the Key Program of the Natural Science Foundation of Zhejiang Province, China (no. LZ19H090003).

Zhang W, Lyu J, Xu J, et al. The related mechanism of complete Freund's adjuvant‐induced chronic inflammation pain based on metabolomics analysis. Biomedical Chromatography. 2021;35:e5020. 10.1002/bmc.5020

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