Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases

Abstract

Objective

To explore the metabolic regulatory mechanisms of sodium houttuyfonate (SH), a herbal-originated anti-inflammatory drug that has been used clinically in China for many years, in preventing diseases.

Methods

Rats received SH intragastrically at 24 mg/kg body weight once a day for 4 consecutive weeks, based on the equivalent dose of SH in rat vs. human. The abdominal aorta blood of the rats was collected to separate serum for metabolomic analyses by ultraperformance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS).

Results

Compared to the blank control, SH treatment induced 337 differential metabolites (DMs) with a fold change (FC) ≥ 2.0 or ≤0.50. Among them, 191 DMs can be annotated in the Human Metabolome Database (HMDB), and 98 DMs can be enriched in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, including 11 hormones and hormone-related compounds, 18 organic acids and their derivatives, 8 nucleotides and their metabolites, 6 amino acid-related metabolites, etc. These DMs were enriched in 143 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, with the tricarboxylic acid (TCA) cycle and steroid hormone biosynthesis significantly altered. Finally, progesterone (MW0062186) and monoacylglycerols (MEDL02326) were the top up-regulated (5.47E+2) and the most down-regulated (1.36E-3) metabolites in the annotated DMs, respectively.

Conclusion

Our findings suggest a metabolic reprogramming mechanism of SH in preventing diseases, and this mechanism may guide the possible use of SH in the future.

Keywords

sodium houttuyfonate metabolomics progesterone steroid hormone biosynthesis metabolic reprogramming

Introduction

Houttuynia cordata Thunb. (HCT) is a traditional Chinese medicine (TCM) with the function of reducing fever and detoxifying. In Asia, especially in China, it has a long history of treating lung-related diseases, such as pneumonia, bronchitis, and lung abscesses.¹ As a perennial herb, HCT is not only a Chinese herbal medicine but also a food, known as Yu-Xing-Cao in Chinese.² During the outbreak of severe acute respiratory syndrome (SARS) in late 2002 to mid-2003, HCT was recommended to the public in China as a preventive ingredient and showed a significant anti-complementary activity.³ Houttuynin (decanoyl acetaldehyde) is the main active compound in the volatile oil extracted from HCT, but it is difficult to directly convert into a clinical drug due to its instability and low solubility in water.⁴ To overcome these defects, sodium houttuyfonate (SH, C₁₂H₂₃O₅SNa), an addition compound of sodium bisulfite and houttuynin, was developed as an alternative drug that has been extensively applied in antimicrobial areas in China and proven to have the same pharmacological effects as houttuynin, which is more stable and soluble in water.⁵ SH exhibits a variety of pharmacological effects, such as antibacterial, antiviral, anti-inflammatory, and injury -protective activities.⁶ The underlying mechanisms include preserving the normal glutamatergic system,⁷ regulating the TNF-α/JAK-STAT signaling pathway,⁸ inhibiting the NLRP3/GSDMD pathway,⁹ interfering with the Dectin-1/NF-κB/miR-32-5p/NFKBIZ axis,¹⁰ suppressing the TLR4/NF-ĸB signaling pathway,¹¹ etc. We postulated that clarifying the metabolic regulatory mechanism of SH is crucial to expand its clinical applications. The aim of this study is to explore the metabolic regulatory mechanisms of SH in preventing diseases by rat serum metabolomic analyses. Our findings suggest a metabolic reprogramming mechanism of SH in preventing diseases, and this mechanism may guide the possible use of SH in the future.

Materials and Methods

Drugs and Chemical Reagents

SH tablets (30 mg ×100 tablets), Beijing Saier Biological Pharmaceutical Co., Ltd (Beijing, China), approval No. H11020222, production date 18/12/2023; methanol (CH₃OH),CAS # 67-56-1,LCMS grade,Merck (Darmstadt, Germany); acetonitrile (CH₃CN), CAS # 75-05-8, LCMS grade, Merck (Darmstadt, Germany); acetic acid (CH₃COOH), CAS # 64-19-7, LCMS grade, Ron Company (Brandon, USA); ammonium formate (CH₅NO₂), CAS # 540-69-2, LCMS grade, Aladdin (Shanghai, China); formic acid (HCOOH), CAS # 64-18-6, LCMS grade, Aladdin (Shanghai, China); ammonia solution (NH₄OH), CAS # 1336-21-6, LCMS grade, Aladdin(Shanghai, China); reference material‌, purity>98%, isoreag/TRC/TCI/Sigma. The abbreviations were listed in Table 1.

Table 1.

Abbreviations

Abbreviation	Official Name
SH	Sodium houttuyfonate
HCT	Houttuynia cordata Thunb
UPLC-Q-TOF-MS	Ultraperformance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry
DMs	Differential metabolites
PCA	Principal Component Analysis
HCA	Hierarchical cluster analysis
OPLS-DA	Orthogonal partial least squares discrimination analysis
SMPDB	Small molecule pathway database
TCM	Traditional Chinese medicine
MS	Mass spectrometer
UPLC	Ultra Performance Liquid Chromatography
IDA	Information-dependent acquisition
FDR	False detection rate
PCC	Pearson Correlation Coefficients
QC	Quality control
HMDB	Human metabolome database
KEGG	Kyoto Encyclopedia of Genes and Genomes
DA	Differential abundance
GP	Glycerophospholipids

Instruments

-80°C freezer, 906-ULTS, Thermo Scientific (Waltham, MA, USA); mass spectrometer (MS), TripleTOF 6600+, SCIEX (Foster City, CA, USA); ultra performance liquid chromatography (UPLC), LC-30A, Shimadzu (Kyoto, Japan); centrifuge, 5424R, Eppendorf (Hamburg, Germany);one hundred thousandth electronic balance, MS105DΜ, Mettler Toledo Instrument Co., Ltd. (Zurich, Switzerland); centrifugal concentrator, CentriVap, LABCONCO (Kansas City, MO, USA); pipette, Research plus, Eppendorf (Hamburg, Germany);automation workstation, Biomek i5, Beckman Coulter (California, USA).

Rat Treatment With SH

Twelve ten-week-old Wistar female rats (body weight:110±10g) were obtained from Henan Provincial Medical Laboratory Animal Center (license number SCXK (YU) 2019–0002) and were randomly divided into two groups: normal group and SH group, with 6 rats in each group. Animal experiments were conducted at the Henan University Laboratory Animal Center, and the time duration was 5 weeks (including a one-week adaptation period). The rats were kept in an environment with a temperature of 22 ± 2 °C, a humidity of 50 ± 10%, and a light/dark cycle of 12 h. All animal procedures were authorized by the Medical Ethics Committee of Henan University (ethical batch number HUSAM 2024-168) and carried out according to the Guide for the Care and Use of Laboratory Animals. Rats in the SH group received SH at a dose of 24 mg/kg body weight (the tablet was triturated and suspended in 0.5% of the CMC-Na solution) via gavage once a day for 4 consecutive weeks based on the equivalent dose of rat vs. human, while rats in the normal group received intragastrically an equal volume of the CMC-Na solution. At the end of the treatment, the rats were fasted for 12 h but allowed free access to drinking water, and then the abdominal aorta blood was collected under anesthesia with pentobarbital sodium (60 mg/kg body weight) to separate serum for metabolomic analyses.¹² The reporting of this study conforms to ARRIVE 2.0 guidelines.¹³Non-targeted serum metabolomics was performed by ultraperformance liquid chromatography combined with quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS) at Wuhan Metware Bio. Co., Ltd. (Wuhan, China) according to the standardized protocol,¹⁴ and the time duration was 4 weeks.

Sample Preparation and Extraction

Liquid Samples Class I

The method was optimized based on a previous report.¹⁵ Briefly, the serum samples stored in the -80 °C refrigerator were thawed on ice and vortexed for 10 s. Then, 50 μL of sample and 300 μL of extraction solution (ACN: Methanol = 1:4) containing internal standards were added into a 2 mL microcentrifuge tube. The sample was vortexed for 3 min and then centrifuged at 12,000 rpm at 4 °C for 10 min. Next, 200 μL of the supernatant was collected and cooled at -20 °C for 30 min and then centrifuged at 12,000 rpm at 4 °C for 3 min. Finally, a 180 μL aliquot of supernatant was transferred for UPLC-MS analysis.

UPLC Conditions

All samples were performed with two UPLC/MS methods.¹⁶ A 4 μL aliquot was separated using a Waters ACQUITY UPLC HSS T3 column (1.8 µm, 2.1 mm × 100 mm) on a Waters Acquity UPLC Class system. The analytical conditions were as follows: column temperature, 40 °C; flow rate, 0.4 mL/min; injection volume, 4 μL. Solvent A was 0.1 % formic acid in water, and solvent B was 0.1 % formic acid in acetonitrile. The flow gradient was shown in Table 2: 5 to 20 % in 2 min, increased to 60 % in the following 3 min, increased to 99 % in 1 min and held for 1.5 min, then converted to 5 % mobile phase B within 0.1 min, and held for 2.4 min. Another aliquot adopted the negative ion mode, and its elution gradient was the same as that of the positive ion mode.

Table 2.

T3 column flow gradient

Time (min)	Solvent A (%)	Solvent B (%)
0.0	95	5
2.0	80	20
5.0	40	60
6.0	1	99
7.5	1	99
7.6	95	5
10.0	95	5

Solvent A: 0.1 % formic acid in water;

Solvent B: 0.1 % formic acid in acetonitrile.

MS Conditions (AB)

Based on standardized and optimized protocols,¹⁴ the TripleTOF 6600+ mass spectrometer equipped with an electrospray ionization source was operated in both positive and negative ion modes, with spray voltages of 5000 V and 4000 V, respectively. The data acquisition was operated using the information-dependent acquisition (IDA) mode with the Analyst TF 1.7.1 software (Sciex, Concord, ON, Canada). The source parameters were set as shown in Table 3: ion source gas 1 (GAS1), 50 psi; ion source gas 2 (GAS2), 50 psi; curtain gas (CUR), 25 psi; temperature (TEM), 550 °C; and declustering potential (DP), 60 V or -60 V in positive or negative modes, respectively. The TOF MS scan parameters were set as follows: mass range, 50–1000 Da; accumulation time, 200 ms; and dynamic background subtract, on. The product ion scan parameters were set as follows: mass range, 25–1000 Da; accumulation time, 40 ms; collision energy, 30 or -30 V in positive or negative modes; collision energy spread, 15; resolution, UNIT; charge state, 1 to 1; intensity, 100 cps; exclude isotopes within 4 Da; mass tolerance, 10 ppm; maximum number of candidate ions to monitor per cycle, 18. System calibration and quality control (QC) were rigorously performed.

Table 3.

AB TripleTOF 6600 mass parameters

Parameters	ESI+	ESI-
Duration (min)	10	10
IonSpray Voltage (V)	5000	-4000
Temperature (°C)	550	450
Ion Source Gas1 (psi)	50	50
Ion Source Gas2 (psi)	60	60
Curtain Gas (psi)	35	35
Declustering Potential (V)	60	-60
MS1 Collision Energy (V)	10	-10
MS2 Collision Energy (V)	30	-30
Collision Energy Spread (V)	15	15
MS1 TOF Masses (Da)	50∼1000	50∼1000
MS2 TOF Masses (Da)	25∼1000	25∼1000
MS1 Accumulation time (s)	0.2s	0.2s
MS2 Accumulation time (s)	0.04s	0.04s
Candidate ions	18	18
Mass errors	10 ppm	10 ppm
Exclude former target ions	Always, for 3 seconds, after3 occurences

Mass error (ppm) = [(experimentally observed m/z - theoretical m/z)/theoretical m/z] × 10⁶.

Data Analyses

The original data file obtained by LC-MS was converted into mzXML format by the ProteoWizard software.¹⁷ Peak extraction, peak alignment, and retention time correction were performed by the XCMS program, respectively.¹⁸ The “SVR” method was used to correct the peak area.¹⁹ The peaks with a detection rate lower than 50 % in each group of samples were discarded. After that, metabolic identification information was obtained by searching the laboratory’s self-built database, integrated public database, AI database, and metDNA. The process included peak deconvolution, alignment, and integration. The software used in data analyses was shown in Table 4.

Table 4.

The software used in data analysis

Analysis	Software	Version
PCA	R (base package)	4.1.2
Pearson correlation coefficient	R (base package)	4.1.2
Sample correlation	R (corrplot)	0.92
Heatmap	R (ComplexHeatmap)	2.9.4
OPLS-DA	R (MetaboAnalystR)	1.0.1
Radar map	R (fmsb)	0.7.1
Chord diagram	R (igraph; ggraph)	1.2.11; 2.0.5
Relevance networks	R (igraph)	1.2.11
Network chart	R (FELLA)	1.2.0

The raw data were standardized by zero-centering and Z-score.

Zero-centering (x’) = x-µ; Z-score (x’) = (x-µ)/σ, x’ is the calibrated data, x is the raw data, µ is the mean and σ is the standard deviation.

The components with a score > 0.5 and coefficient of variation (CV) < 0.3 were integrated in the positive and negative ion modes.

Principal Component Analysis (PCA)

Unsupervised PCA was performed by statistics function prcomp in R (https://www.r-project.org). The data were unit variance scaled before unsupervised PCA. In order to find out the maximum differences between two groups, Partial Least Squares Discriminant Analysis (PLS-DA) was utilized.²⁰ Differential variables were chosen and validated using the false detection rate (FDR)-corrected Mann-Whitney U test.²¹

Hierarchical Cluster Analysis (HCA) and Pearson Correlation Coefficients (PCC)

The HCA results of samples and metabolites were presented as heatmaps with dendrograms, while PCC between samples was calculated by the cor function in R and presented as heatmaps. Both HCA and PCC were carried out by the R package ComplexHeatmap. For HCA, normalized signal intensities of metabolites (unit variance scaling) were visualized as a color spectrum.²²

Enrichment for the metabolic pathways was recognized as significant when their p values were < 0.05 based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Differential Metabolites (DMs) Selected

Metabolite identification relied on mass error, PubChem, score, and level. For two-group analyses, DMs were determined by fold change (FC) ≥ 2 or ≤ 0.5.²³

KEGG Annotation and Enrichment Analyses

Identified metabolites were annotated using the KEGG Compound database (https://www.kegg.jp/kegg/compound/), and annotated metabolites were then mapped to the KEGG Pathway database (https://www.kegg.jp/kegg/pathway.html). Significantly enriched pathways were identified with a hypergeometric test’s p-value for a given list of metabolites.²⁴

Results

Metabolite Identification

In this study, based on 10 ppm of the error thresholds, the orthogonal partial least squares discrimination analysis (OPLS-DA) indicated that a total of 918 metabolites were identified in PubChem, including 565 metabolites in the positive ion mode and 353 metabolites in the negative ion mode (Supplementary Table 1 and Supplementary Table 2). PCA variances, score 2D plots, and score 3D plots of the serum metabolomics in ESI+ and ESI- modes were shown in Figure 1A–1F, which revealed the gathered well QC of each group under different ion modes, confirming the repeatability and stability of the method. Among the 918 metabolites, 337 metabolites with FC ≥ 2 or ≤ 0.5 (212 metabolites in the positive ion mode and 125 metabolites in the negative ion mode) were selected as primary DMs; the detailed information on 337 primary DMs was shown in Supplementary Table 3. The heatmaps of all compounds, classes and col-row clusters were shown in Figure 2A–2C, which involved 23 class compounds.

Figure 1.

Principal component analyses (PCA) variances, score 2D plots and score 3D plots. (A) Variance explained by each principal component in the positive ion mode; (B) Variance explained by each principal component in the negative ion mode; (C) PCA score 2D plot in the positive ion mode; (D) PCA score 2D plot in the negative ion mode; (E) PCA score 3D plot in the positive ion mode; (F) PCA score 3D plot in the negative ion mode. PC1, the first principal components; PC2, the second principal components; Percentage represents the variance of the data set explained by the principal components

Figure 2.

The heatmaps of all metabolites in two groups. (A) The heatmaps of the noted compounds; (B) The heatmaps of the compound class; (C) The heatmaps of the compound col-row_cluster. Horizontal represents sample groups, vertical represents metabolites, red represents upregulation and green represents downregulation

Metabolite Enrichment and Metabolic Pathway Analyses

337 DMs were mapped to KEGG metabolic pathways, and 143 KEGG pathways were obtained (142 KEGG pathways except ko01100 were shown in supplementary Table 4). The differential abundance (DA) score and rich factor of the top 20 pathways were shown in Figure 3A and 3B. It was shown that the tricarboxylic acid (TCA) cycle (ko00020) and steroid hormone biosynthesis (ko00140) were significantly enriched metabolic pathways when comparing SH vs blank control groups (p<0.05). Furthermore, 13 DMs were enriched in the steroid hormone biosynthesis, and 6 DMs were enriched in the TCA cycle (Figure 4A and 4B). The KEGG regulation network was shown in Figure 5A. In addition, DMs were also mapped to the small molecule pathway database (SMPDB), and 608 action pathways were obtained, of which the action pathways of ibuprofen and celecoxib were two major highlights of enrichment (Figure 5B).

Figure 3.

Differential abundance (DA) score and rich factor of the top 20 KEGG pathways. (A) DA score of the top 20 KEGG pathways; (B) Rich factor of the top 20 KEGG pathways. Vertical represents pathways in ascending order of p-value; the point size represents the number of metabolites enriched in pathways. The higher the DA and rich factor the more significant the enrichment

Figure 4.

Differential metabolites (DMs) in two KEGG pathways. (A) DMs enriched in the steroid hormone biosynthesis; (B) DMs enriched in the crtrate cycle. Horizontal represents sample groups, vertical represents metabolites, red represents upregulation, and green represents downregulation

Figure 5.

KEGG regulation network and SMPDB action pathways. (A) KEGG regulation network; (B) The top 20 SMPDB action pathways.Vertical represents pathways in ascending order of p-value; the point size represents the number of metabolites enriched in pathways.The higher the rich factor the more significant the enrichment

Identification of Significant Changes in Metabolites

Among the 337 DMs, the most enriched metabolites set included hormones and hormone-related compounds, glycerophospholipids (GP), organic acids and their derivatives, nucleotides and their metabolites, heterocyclic compounds, benzene and substituted derivatives, amino acids and their metabolites, fatty acyls, carboxylic acids and their metabolites, etc. To accurately understand the changes in metabolites, the top twenty DMs and the top ten upregulated and downregulated DMs were obtained by absolute Log₂FC (Figure 6A and 6B). However, only 191 DMs can be annotated in HMDB, and 98 DMs can be enriched in KEGG pathways (78 of 98 DMs are enriched in metabolic pathways). After non-enriched metabolites were excluded, in the top ten upregulated and downregulated DMs, progesterone (MW0062186) and monoacylglycerols (MEDL02326) were the first upregulated (5.47E+2) and downregulated (1.36E-3) metabolites, respectively. The corrected top ten upregulated and downregulated DMs were shown in Table 5. Detailed information on the enriched 98 DMs was shown in supplementary Table 5.

Figure 6.

The top differential metabolites (DMs) in two groups. (A) The top twenty DMs with Log₂FC; (B) The top ten upregulated and downregulated DMs with Log₂FC

Table 5.

Detailed information on the corrected top of ten upregulated and downregulated DMs

Mode	Index	Compounds	Formula	Molecular weight (Da)	RT (min)	Adduct	Mass error	Fold_Change	Level	PubChem	kegg_map
T3_positive	MW0062186	Progesterone	C21H30O2	314.2246	5.9289	[M+H]+	2.5263	547.0465	2	5994	ko00140,ko01100,ko04080,ko04114,ko04913, ko04914,ko04917,ko04925,ko04927,ko04934, ko05200,ko05207,ko05215,ko05224
T3_positive	MEDL00830	Urobilin	C33H42N4O6	590.3104	4.2179	[M+H]+	1.7438	119.3708	2	5280819	ko00860
T3_positive	MEDN1519	Adenosine-5'-triphosphate	C10H16N5O13P3	506.9957	2.2241	[M+K+HCOOH]+	2.0609	47.4372	1b	5957	ko00190,ko00230,ko01100,ko01232,ko01240, ko04020,ko04066,ko04080,ko04142,ko04217, ko04611,ko04621,ko04714,ko04721,ko04742, ko04750,ko04911,ko04917,ko04924,ko04930, ko05012,ko05022,ko05133,ko05410,ko05415
T3_positive	MW0107822	leukotriene C4	C30H47N3O9S	625.3033	5.598	[M+H]+	1.5344	32.3357	2	5280493	ko00590,ko01100,ko04080,ko04664,ko04726, ko04976,ko05310
T3_negative	MW0015787	Androsterone glucuronide	C25H38O8	466.2567	5.4083	[M-H2O-H]-	9.5497	9.6513	3	114833	ko00140,ko01100
T3_positive	MW0103526	Deoxyuridine triphosphate	C9H15N2O14P3	467.9736	0.8688	[M+Na]+	1.8136	8.9976	3	65070	ko00240,ko01100,ko01232
T3_positive	MW0159685	Glutathione	C10H17N3O6S	307.0838	1.3615	[M+]+	0.4208	8.9398	2	124886	ko00270,ko00480,ko01100,ko01240,ko02010, ko04216,ko04918,ko04976,ko05208,ko05415
T3_positive	MW0006140	Benzaldehyde	C7H6O	106.0419	3.2822	[M+H]+	0.9979	8.7434	3	240	ko01100
T3_negative	MW0013231	20-HETE	C20H32O3	320.2351	7.061	[2M-H]-	5.5970	8.4257	2	5283157	ko00590,ko01100,ko04270
T3_negative	MEDN0202	α-Ketoglutaric Acid (α-KG)	C5H6O5	146.0215	1.3484	[M+Na-2H]-	0.0500	8.2406	1b	51	ko00020,ko00040,ko00053,ko00220,ko00250, ko00310,ko00340,ko00430,ko00470,ko00630, ko00650,ko00785,ko01100,ko01200,ko01210, ko01230,ko01240,ko04066,ko04727,ko04922, ko04964,ko04976,ko05230
T3_positive	MW0003976	3-Ethyl-1,2-benzenediol	C8H10O2	138.0681	9.5457	[M-H+2K]+	0.4500	0.2681	3	70278	ko01100
T3_negative	MW0062272	Prostaglandin h2	C20H32O5	352.225	6.7505	[M+K-2H]-	5.0750	0.2607	3	445049	ko00590,ko01100,ko04611,ko04723,ko04726, ko04921
T3_negative	MW0009432	Phenylpyruvic acid	C9H8O3	164.0473	4.0953	[M-H]-	0.8500	0.2395	2	997	ko00360,ko00400,ko00470,ko01100,ko01210, ko01230
T3_negative	MW0157906	(Z)-7-[(2R,3S,4S,6S)-4,6-dihydroxy-2-[(E,3S)-3-hydroxyoct-1-enyl]oxan-3-yl]hept-5-enoic acid	C20H34O6	370.2355	6.7712	[M-H2O-H]-	5.8500	0.1416	3	5280890	ko00590,ko01100,ko04726,ko04976
T3_positive	MW0148995	Erythronolide B	C21H38O7	402.2618	5.0396	[M+H-2H2O]+	1.3750	0.1355	3	441113	ko01100
T3_negative	MEDP1201	8,8a-deoxy-oleane	C20H36O6	372.2512	4.8569	[M+NH4-2H]-	4.7250	0.1305	3	193898	ko01100
T3_positive	MW0007328	Homovanillic acid	C9H10O4	182.0579	2.6727	[M+H-H2O]+	5.2239	0.1008	2	1738	ko00350,ko01100,ko04728
T3_negative	MW0003047	2-Fluorobenzoic acid	C7H5FO2	140.0274	4.4682	[M+Cl]-	2.3921	0.0951	3	9935	ko01100
T3_positive	MW0143816	6-deoxyerythronolide B	C21H38O6	386.2668	5.9289	[M+H-2H2O]+	1.3500	0.0020	3	121904	ko01100
T3_positive	MEDL02326	MG(18:4/0:0/0:0)	C21H34O4	350.2457	6.2926	[M+H]+	1.5315	0.0014	3	53480980	ko00561,ko01100,ko04714,ko04923,ko04975, ko04977

Level: 1b represents a medium match with standard, MS1, RT and MS2; 2 represents a close match with MS1, RT and MS2; 3 represents a medium match with MS1, RT and MS2.

It is worth noting that progesterone is enriched in 14 KEGG pathways, including steroid hormone biosynthesis (ko00140), metabolic pathways (ko01100), neuroactive ligand-receptor interaction (ko04080), ovarian steroidogenesis (ko04913), prolactin signaling pathway (ko04917), aldosterone synthesis and secretions (ko04925), pathways in cancer (ko05200), oocyte meiosis (ko04114), progesterone-mediated oocyte maturation (ko04914), cortisol synthesis and secretion (ko04927), Cushing syndrome (ko04934), chemical carcinogenesis-receptor activation (ko05207), prostate cancer (ko05215), and breast cancer (ko05224); while monoacylglycerols (MG) are enriched in 7 KEGG pathways, including glycerolipid metabolism (ko00561), metabolic pathways (ko01100), thermogenesis (ko04714), regulation of lipolysis in adipocytes (ko04923), fat digestion and absorption (ko04975),and vitamin digestion and absorption (ko04977).

Discussion

SH represents the various pharmacological properties of HCT, including but not limited to anti-inflammatory,¹¹ antibacterial,²⁵ antiviral and anticancer.²⁶ Previous studies have shown that SH demonstrates promising potential in preventing obesity,²⁷ protecting against myocardial infarction²⁸ and neuronal damage.⁷ However, the mechanisms underlying these effects remain largely unclear. LC-MS-based non-targeted metabolomics analyses have become increasingly important in exploring the action mechanisms of drugs, as it can simultaneously detect thousands of metabolites in a high-throughput manner.²⁹ Thus, in this study, we performed in-depth rat serum metabolomic analyses to identify SH-treatment-altered metabolites as compared with normal rats. A total of 191 DMs were annotated in the HMDB, and significant alterations in the metabolomic composition were observed in the SH group. These findings suggest that metabolic reprogramming occurs in the SH group, which exerts the preventive effect on some latent diseases and may be a compensatory mechanism for the action of SH.

Anti-inflammation and immune regulation are main mechanisms in SH treatment, which are related to both innate immunity and adaptive immunity.³⁰ Immune cells have the energy demands for their activation and effector functions by metabolic reprogramming, which is mainly influenced by steroid hormones that modulate immune and inflammatory responses through intracellular metabolic pathways, such as the tricarboxylic acid cycle and oxidative phosphorylation.³¹ In this study, we observed significant upregulation of certain metabolites associated with the TCA cycle and steroid hormone biosynthesis in the SH group. Notably, progesterone, a metabolite derived from cholesterol, showed a 547-fold upregulation in the SH group, and adenosine-5′-triphosphate (ATP, MEDN1519) was upregulated by 47.4-fold. It is known that progesterone is involved in regulating both innate and adaptive immune responses and plays a crucial role in suppressing inflammation for maintaining immune homeostasis.³² The effect of stimulating steroid hormone biosynthesis and inducing progesterone is a possible mechanism of SH in preventing diseases. Progesterone not only regulates the menstrual cycle and maintains pregnancy, but also prevents hyperplasia, atypical hyperplasia, and uterine endometrial cancer.³³ Multiple epidemiological studies indicate that there is a negative association between full-term pregnancies and the incidence of ovarian and endometrial cancer.³⁴ The reduced risk of ovarian or endometrial cancer is mainly due to the increase in progesterone and the decrease in oestrogen during pregnancy.³⁵ In clinic, the analogues of progesterone, such as medroxyprogesterone acetate (MPA) and megestrol acetate (MA), are used to treat endometrial and cervical cancers with promising results.³⁶ Regarding the significant upregulation of progesterone in the SH group, it is possible that SH can prevent sex hormone-related diseases, for example, obesity, endometrial hyperplasia, cancer, and polycystic ovary syndrome.

Lipid metabolism dysregulation in the body is a major risk factor for many metabolism-related diseases,³⁷ including diabetes, thyroid diseases, adrenal gland diseases, and cranial nerve diseases. In this study, we also observed significant downregulation of certain metabolites associated with glycerolipid metabolism (ko00561), glycerophospholipid metabolism (ko00564) and retrograde endocannabinoid signalling (ko04723) in the SH group. Notably, monoacylglycerols (MAGs) showed a 735-fold downregulation in the SH group with a 2-3-fold downregulation of some glycerophospholipid metabolites, such as 1,2-Diarachidoyl-sn-glycero-3-phosphocholine (MW0011863) and LPC (0:0/20:2, MEDP1330) which are involved in glycerophospholipid metabolism and retrograde endocannabinoid signalling. These pathways and their associated metabolites have been reported to cause lipid metabolism dysregulation leading to excessive production of too much ROS and inflammatory factors, which in turn damage tissue cells.^38,39 In contrast, pathway analyses of DMs in the SH group demonstrated significant enrichment of several processes, including glutathione metabolism (ko00480), biosynthesis of cofactors (ko01240), ferroptosis (ko04216), and thyroid hormone synthesis (ko04918), which are regulated by glutathione (upregulated 8.94-fold), a protective metabolite on tissue injury. These results are consistent with previous studies indicating that SH can induce the browning of inguinal white adipose tissue by inhibiting ROS and ferroptosis.²⁷ These data indicate that the altered metabolites and their enriched functional pathways in the SH group not only prevent lipid metabolism dysregulation but also promote antioxidant protection against tissue injury, suggesting their possible use in the treatment of metabolism-related diseases.

It should be acknowledged that this study has some limitations, especially the absence of disease-related and small-sample animal experiments. Further animal-related experiments and clinical trials are needed to confirm the effectiveness of this approach.

Conclusions

In summary, our analyses suggest that SH induces metabolic reprogramming by activating the steroid hormone biosynthesis pathway to prevent inflammation and maintain immunity homeostasis, while inhibiting lipid metabolism-related pathways to prevent lipid metabolism dysregulation. At the same time, SH also enhances antioxidant protection against tissue injury by activating the glutathione-regulated metabolic pathways. These characteristics of SH in regulating metabolic reprogramming may maximise clinical benefits while minimising toxicity. These results not only explain the reasons why SH exhibits various pharmacological properties but also expand its application scope in preventing diseases, providing a possible theoretical basis for the future use of SH.

Supplemental Material

Supplemental Material - Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases

Supplemental Material for Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases by Jin-cheng Du, Zhi-jian Long, Jia-huan Li in Natural Product Communications

Supplemental Material

Supplemental Material - Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases

Supplemental Material

Supplemental Material - Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases

Supplemental Material

Supplemental Material - Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases

Supplemental Material

Supplemental Material - Rat Serum Metabolomics Reveals a Metabolic Reprogramming Mechanism of Sodium Houttuyfonate in Preventing Diseases

Footnotes

Acknowledgments

We thank Henan University for providing the experimental platform for this study, the Wuhan Metware Bio. Co., Ltd for detection and analysis of non-targeted serum metabolomics, and Prof. Shao-feng Duan and Prof. Yan-ming Wang for editing this manuscript.

ORCID iD

Jia-huan Li

Ethical Considerations

The experimental protocol was approved by the Medical Ethics Committee of Henan University (authorization No. HUSAM 2024-168).

Consent to Participate

There are no human subjects in this article and informed consent is not applicable.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Declaration of conflicting interests

The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data related to this study can be requested from the corresponding author.*

Trial Registration

This article does not contain any clinical trials.

Statement of Human and Animal Rights

We confirm that guidelines on animal rights and treatment have been met and any details of approval obtained are indicated within the text of the submitted manuscript.

Supplemental Material

Supplemental material for this article is available online.

References

Wei

Luo

Hou

Zhao

Meng

. Houttuynia Cordata Thunb.: A comprehensive review of traditional applications, phytochemistry, pharmacology and safety. Phytomedicine. 2024;123:155195. doi:10.1016/j.phymed.2023.155195.

Ghosh

Parihar

, et al. Nutraceutical prospects of Houttuynia cordata against the infectious viruses. Food Biosci. 2022;50:101977. doi:10.1016/j.fbio.2022.101977.

Wang

Dempsey

Corr

Kukula-Koch

Sasse

Sheridan

. The traditional Chinese medicine Houttuynia cordata Thunb decoction alters intestinal barrier function via an EGFR dependent MAPK (ERK1/2) signaling pathway. Phytomedicine. 2022;105:154353. doi:10.1016/j.phymed.2022.154353.

Ling

Ren

, et al. The synergistic effect and mechanisms of flavonoids and polysaccharides from Houttuynia cordata on H1N1-induced pneumonia in mice. J Ethnopharmacol. 2023;302(Pt A):115761. doi:10.1016/j.jep.2022.115761.

Zhang

Wang

, et al. Chemical profiling of Houttuynia cordata Thunb. by UPLC-Q-TOF-MS and analysis of its antioxidant activity in C2C12 cells. J Pharm Biomed Anal. 2021;204:114271. doi:10.1016/j.jpba.2021.114271.

Wang

Zhong

. Sodium houttuyfonate plays a protective role in the asthmatic airway by alleviating the NLRP3-related pyroptosis and Th1/Th2 immune imbalance. Mol Immunol. 2023;160:103–111. doi:10.1016/j.molimm.2023.06.013.

Chang

Chen

Wang

. Sodium houttuyfonate prevents seizures and neuronal cell Loss by maintaining glutamatergic system stability in male rats with kainic acid-induced seizures. Biomedicines. 2024;12(6):1312. doi:10.3390/biomedicines12061312.

Yang

Sun

Zhang

, et al. Sodium houttuyfonate prevents NiO-NPs induced lung injury in rats through the TNF-alpha/JAK-STAT signaling pathway. Ecotoxicol Environ Saf. 2025;298:118306. doi:10.1016/j.ecoenv.2025.118306.

Zhao

Tian

Feng

. Sodium houttuyfonate ameliorates beta-amyloid(1-42)-induced memory impairment and neuroinflammation through inhibiting the NLRP3/GSDMD pathway in alzheimer's disease. Mediators Inflamm. 2021;2021:8817698. doi:10.1155/2021/8817698.

10.

Yang

Cheng

Wang

Liu

Shao

. Sodium houttuyfonate ameliorates DSS-induced colitis aggravated by candida albicans through dectin-1/NF-kappaB/miR-32-5p/NFKBIZ axis based on intestinal microRNA profiling. Inflammation. 2025;48(2):820–838. doi:10.1007/s10753-024-02091-6.

11.

Wang

Shen

Zhang

. Sodium houttuyfonate inhibits LPS-induced inflammatory response via suppressing TLR4/NF-qB signaling pathway in bovine mammary epithelial cells. Microb Pathog. 2017;107:12–16. doi:10.1016/j.micpath.2017.03.011.

12.

Mohammed

SAD

Liu

Baldi

Chen

Liu

. GJD modulates cardiac/vascular inflammation and decreases blood pressure in hypertensive rats. Mediators Inflamm. 2022;2022:7345116–7345119. doi:10.1155/2022/7345116.

13.

Percie du Sert

Hurst

Ahluwalia

, et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. Br J Pharmacol. 2020;177(16):3617-3624. doi:10.1111/bph.15193.

14.

Zhang

Pan

Zheng

Liu

Sun

. The metabolic analysis in human aortic tissues of aortic dissection. J Clin Lab Anal. 2022;36(9):e24623. doi:10.1002/jcla.24623.

15.

Usoltseva

Ioutsi

Panov

, et al. Serum vitamin D metabolites by HPLC-MS/MS combined with differential ion mobility spectrometry: aspects of sample preparation without derivatization. Int J Mol Sci. 2023;24(9):8111. doi:10.3390/ijms24098111.

16.

Zhu

Chen

Zhang

, et al. Serum metabolomics in pulmonary sarcoidosis: metabolic signatures across prognoses. BMC Pulm Med. 2025;25(1):373. doi:10.1186/s12890-025-03863-y.

17.

Bhamber

Jankevics

Deutsch

Jones

Dowsey

. mzMLb: a future-proof raw mass spectrometry data format based on standards-compliant mzML and optimized for speed and storage requirements. J Proteome Res. 2021;20(1):172–183.16. doi:10.1021/acs.jproteome.0c00192.

18.

Xue

Derks

RJE

Hoang

Giera

Siuzdak

. Proteomics with enhanced in-source fragmentation/annotation: applying XCMS-EISA informatics and Q-MRM high-sensitivity quantification. J Am Soc Mass Spectrom. 2021;32(11):2644–2654. doi:10.1021/jasms.1c00188.

19.

Jin

Cao

Qian

Zhang

Wang

. Study on an assembly prediction method of RV reducer based on IGWO algorithm and SVR model. Sensors (Basel). 2022;23(1):366. doi:10.3390/s23010366.

20.

Nicolotti

Hack

Herderich

Lloyd

. MStractor: R workflow package for enhancing metabolomics data pre-processing and visualization. Metabolites. 2021;11(8):492. doi:10.3390/metabo11080492.

21.

Edmands

WMB

Hayes

Rappaport

. SimExTargId: a comprehensive package for real-time LC-MS data acquisition and analysis. Bioinformatics. 2018;34(20):3589-3590. doi:10.1093/bioinformatics/bty218.

22.

Rainer

Vicini

Salzer

, et al. A modular and expandable ecosystem for metabolomics data annotation in R. Metabolites. 2022;12(2):173. doi:10.3390/metabo12020173.

23.

Chang

Liu

Zhang

, et al. A comparative metabolomic analysis reveals the metabolic variations among cartilage of Kashin-Beck disease and osteoarthritis. Bone Joint Res. 2024;13(7):362–371. doi:10.1302/2046-3758.137.BJR-2023-0403.R1.

24.

Kalmar

Garrard

Muddiman

. GlycoHunter: an open-source software for the detection and relative quantification of INLIGHT-labeled N-linked glycans. J Proteome Res. 2021;20(4):1855–1863. doi:10.1021/acs.jproteome.0c00840.

25.

Zhuang

Wang

, et al. Sodium houttuyfonate effectively treats acute pulmonary infection of Pseudomonas aeruginosa by affecting immunity and intestinal flora in mice. Front Cell Infect Microbiol. 2022;12:1022511. doi:10.3389/fcimb.2022.1022511.

26.

Chi

Wang

, et al. Sodium houttuyfonate inhibits the cariogenic virulence of Streptococcus mutans through the downregulation of VicRK two components pathway. J Oral Microbiol. 2025;17(1):2465345. doi:10.1080/20002297.2025.2465345.

27.

Liu

Zou

You

Zhang

. Sodium houttuybonate promotes the browning of white adipose tissue by inhibiting ferroptosis via the AMPK-NRF2-HO1 pathway. Antioxidants (Basel). 2024;13(9):1057. doi:10.3390/antiox13091057.

28.

Zheng

Lin

, et al. Sodium houttuyfonate alleviates post-infarct remodeling in rats via AMP-activated protein kinase pathway. Front Pharmacol. 2018;9:1092. doi:10.3389/fphar.2018.01092.

29.

Guo

Sun

Ding

Wang

Qiu

. Mechanism of simiaowan in the treatment of hyperuricemia based on serum metabolomics. Drug Des Devel Ther. 2025;19:5761-5779. doi:10.2147/DDDT.S533394.

30.

Jin

Zhang

, et al. Sodium houttuyfonate induces bacterial lipopolysaccharide shedding to promote macrophage M1 polarization against acute bacterial lung infection. Biomed Pharmacother. 2024;179:117358. doi:10.1016/j.biopha.2024.117358.

31.

Ong

Jiang

, et al. Targeting P4HA1 promotes CD8(+) T cell progenitor expansion toward immune memory and systemic anti-tumor immunity. Cancer Cell. 2025;43(2):213–231.e9. doi:10.1016/j.ccell.2024.12.001.

32.

Memi

Pavli

Papagianni

Vrachnis

Mastorakos

. Diagnostic and therapeutic use of oral micronized progesterone in endocrinology. Rev Endocr Metab Disord. 2024;25(4):751–772. doi:10.1007/s11154-024-09882-0.

33.

Nagy

Szekeres-Barthó

Kovács

, et al. Key to Life: Physiological role and clinical implications of progesterone. Int J Mol Sci. 2021;22(20):11039. doi:10.3390/ijms222011039.

34.

Main

Chen

Zhao

Chamley

Chen

. Understanding how pregnancy protects against ovarian and endometrial cancer development: fetal antigens may be involved. Endocrinology. 2022;163(11):bqac141. doi:10.1210/endocr/bqac141.

35.

Tuesley

Spilsbury

Pearson

, et al. Long-acting, progestin-based contraceptives and risk of breast, gynecological, and other cancers. J Natl Cancer Inst. 2025;117(5):1046–1055. doi:10.1093/jnci/djae282.

36.

Fedotcheva

Shimanovsky

. Progestins as anticancer drugs and chemosensitizers, new targets and applications. Pharmaceutics. 2021;13(10):1616. doi:10.3390/pharmaceutics13101616.

37.

Dalhaimer

. Lipid droplets in disease. Cells. 2019;8(9):974. doi:10.3390/cells8090974.

38.

Chen

Wang

, et al. Microbial and metabolic profiles unveil mutualistic microbe-microbe interaction in obesity-related colorectal cancer. Cell Rep Med. 2024;5(3):101429. doi:10.1016/j.xcrm.2024.101429.

39.

Zhao

Nian

. Tissue metabolomics reveals metabolic dysregulation associated with intimal hyperplasia in arteriovenous fistula stenosis. Front Physiol. 2025;16:1638179. doi:10.3389/fphys.2025.1638179.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.83 MB

1.30 MB

0.87 MB

0.16 MB

0.24 MB