Modulating Gut–System Axis Metabolic Disorders: Multi-Omics Reveals the Mechanism of Mung Bean Polyphenols in Alleviating Heat Stress-Induced Damage

Abstract

Heat stress-induced systemic metabolic disorder serves as the core pathological basis of organismal damage. Although mung bean polyphenols (MBPs) had been preliminarily validated in cellular heat-stress models for their intestinal tissue-protective potential, whether they can alleviate heat-stress injury in vivo by remodeling the metabolic crosstalk network between the gut and systemic circulation remains mechanistically unclear. In this study, we innovatively employed an integrated multi-omics approach combining physiological phenotype, gut metabolome, and serum metabolome analyses based on a Balb/c heat stress (41 °C) mouse model, systematically constructing the metabolic phenotype regulatory network of MBPs. The results demonstrated that MBPs not only significantly improved oxidative stress (elevating GSH-Px and T-AOC, reducing MDA), immune-inflammation (down-regulating IL-1β and TNF-α), and stress hormone (lowering cortisol) phenotypes, but also specifically reversed the disturbances in intestinal and serum metabolic profiles induced by heat stress, particularly restoring key pro-inflammatory mediators such as Leukotriene E4 and 5-HETE. Arachidonic acid metabolism, tryptophan metabolism, histidine metabolism, and Fc epsilon RI signaling pathway constituted the core network of heat-stress metabolic disorder and MBP regulation. Furthermore, the study revealed that alterations in hub metabolites—Indolelactic Acid, Trans-Cinnamic Acid, Leukotriene E4, 5-HETE, and N(omega)-Hydroxyarginine—were significantly correlated with phenotypic improvements. This confirms that mung bean polyphenols dynamically dismantle the “pro-inflammatory-oxidative stress” pathological coupling by constructing a novel protective axis centered on the indole metabolism–melatonin–endogenous antioxidant system and successfully established a novel protective axis driven by gut-derived beneficial metabolites that promotes systemic antioxidant function, thereby elucidating the systemic mechanism underlying the alleviation of heat-stress injury at the metabolic network level.

1. Introduction

Global warming has intensified the frequency and severity of extreme heat events [1], making heat stress a major environmental threat to organismal health. The impact of heat stress extends far beyond the disruption of thermoregulation, with its core pathological basis lying in widespread metabolic homeostasis imbalance. High temperatures directly trigger a burst of intracellular reactive oxygen species, leading to oxidative damage and disturbance of protein homeostasis [2], which in turn induces dysfunction in neuroendocrine and immune regulatory networks [3]. At the metabolic level, heat stress forces the organism to undergo metabolic reprogramming, characterized mainly by a rapid shift in energy metabolic pathways (e.g., from aerobic oxidation to anaerobic glycolysis), depletion of core coenzyme systems, and direct induction of intestinal and hepatic dysfunction, acid-base imbalance, as well as systemic inflammation and immune suppression [4]. These interconnected metabolic disturbances constitute the central link in the tissue damage and performance decline caused by heat stress. Therefore, deciphering the dynamically evolving metabolic network under heat stress and identifying effective intervention strategies to restore metabolic homeostasis have emerged as frontier topics in the interdisciplinary field of stress biology and nutrition.

To address the systemic damage induced by heat stress, nutritional intervention—such as increasing dietary intake of protein, vitamins, and trace elements—is considered a feasible strategy. Its core mechanisms revolve around enhancing endogenous antioxidant defenses, maintaining energy and substrate metabolism balance, and modulating key stress signaling pathways [5]. In the realm of bioactive substance regulation, plant polyphenols have attracted significant attention as heat stress attenuators [6,7], particularly in livestock and poultry farming. Polyphenols such as lycopene, resveratrol, and curcumin have shown promising regulatory effects, primarily through mitigating oxidative damage and inflammation induced by heat stress, regulating heat shock protein expression, and alleviating intestinal inflammation and microbiota dysbiosis [8,9,10,11,12].

However, research on heat stress modulation in human diets remains limited. Our research group previously targeted polyphenols from mung bean—a traditional Chinese heat-relieving food—and conducted a series of studies using murine intestinal epithelial Mode-K cells and Caco-2 cell models under heat stress. These studies identified the FoxO signaling pathway, Rap1 signaling pathway, and PI3K-Akt signaling pathway as key environmental regulation pathways. Mung bean polyphenols were found to regulate 39 °C heat stress by inhibiting apoptosis and promoting the accumulation of lipids and amino acid-related substances. They mitigated 41 °C heat stress by modulating heat shock proteins, suppressing mitochondrial function, and affecting certain neuro-disease-related genes. Furthermore, they alleviated damage to intestinal cells under 43 °C heat stress through regulation of neuron-related genes [13,14]. Together, these findings demonstrate that nutritional interventions represented by polyphenols can deeply engage with and modulate the core metabolic networks disrupted by heat stress.

Although existing studies have preliminarily outlined the metabolic profile of heat stress and the potential benefits of nutritional intervention, significant limitations remain in the mechanistic depth and systemic understanding of metabolic regulation, which hinders the rational design of efficient intervention strategies. Firstly, there is an insufficient understanding of the dynamic and adaptive evolution of metabolic networks during heat stress and the intervention process. Secondly, metabolic responses in the organism are highly tissue-specific. How different tissues and organs “communicate” via metabolites, and how this inter-organ metabolic crosstalk determines systemic phenotypes, have not yet been elucidated. Building on these gaps, this study aims to employ a spatiotemporally integrated multi-omics strategy to systematically investigate the protective effects of mung bean polyphenol (MBP) intervention in a heat-stressed mouse model, from the novel perspective of metabolic network interactions and inter-tissue coordination. The goal is to provide a more systematic and dynamic chain of evidence elucidating the deeper metabolic mechanisms through which MBPs alleviate heat stress, thereby laying a theoretical foundation for developing precise nutritional interventions targeting metabolic homeostasis.

2. Materials and Methods

2.1. Materials and Equipment

Mung bean polyphenols (MBPs) were prepared in our laboratory. Methanol, acetonitrile, formic acid, water, and other solvents (all chromatographic grade) were obtained from Fisher Scientific. ELISA kits for Glutathione Peroxidase (GSH-Px), Malondialdehyde (MDA), Tumor Necrosis Factor-alpha (TNF-α), Mouse Interleukin-1 Beta (IL-1β), and Mouse Cortisol were purchased from Shanghai Jianglai Biotechnology Co., Ltd. The Total Antioxidant Capacity (T-AOC) assay kit was sourced from Beyotime Biotechnology (Shanghai, China).

The instruments used included: an ultra-high-performance liquid chromatography coupled with Fourier transform mass spectrometry system (UHPLC-Q Exactive HF-X, Thermo Fisher Scientific, Waltham, MA, USA); an HSS T3 column (100 mm × 2.1 mm i.d., 1.8 µm, Waters, Milford, MA, USA); a JXDC-20 nitrogen blowdown concentrator (Shanghai Jingxin Industrial Development Co., Ltd., Shanghai, China); an LNG-T88 benchtop rapid centrifugal concentrator (Taicang Huamei Biochemical Instrument Factory, Suzhou, China); a Wonbio-96c high-throughput tissue homogenizer (Shanghai Wanbai Biotechnology Co., Ltd., Shanghai, China); an SBL-10DT ultrasonic cleaner (300 W, 10 L, Ningbo Scientz Biotechnology Co., Ltd., Ningbo, China); a Centrifuge 5430 R high-speed refrigerated centrifuge (Eppendorf, Hamburg, Germany); a NewClassic MF MS105DU electronic balance (Mettler Toledo, Greifensee, Switzerland); and a BioTek ELx800 microplate reader (BioTek Instruments, Winooski, VT, USA).

2.2. Experimental Methods

2.2.1. Preparation of Mung Bean Polyphenols

Mung bean powder was sieved through an 80-mesh sieve and defatted with petroleum ether. A quantity of 3 g of the defatted powder was extracted with 80% ethanol at a solid-to-liquid ratio of 1:10 (w/v) for 60 min under ultrasonication (400 W, 40 °C). The mixture was then centrifuged at 10,000 r/min for 5 min, and the supernatant was collected. The residue underwent two additional extractions following the same procedure, resulting in a total of three extraction cycles. The residue obtained after the free polyphenol extraction was collected for bound polyphenol extraction. It was hydrolyzed with 2 mol/L NaOH solution for 1 h. Subsequently, the pH was adjusted to 2–3 using HCl, followed by triple extraction with ethyl acetate. After centrifugation, the combined supernatants were concentrated to dryness via rotary evaporation. The dried extract was dissolved in a defined volume of methanol to obtain the bound polyphenol fraction. The free and bound polyphenol extracts were combined and made up to a final volume of 10 mL. The combined extract was then freeze-dried for subsequent use. Further purification of the mung bean polyphenols was performed using D101 macroporous resin adsorption(Xi‘an Lanxiao Technology New Materials Co., Ltd., Xi’an, China ), yielding a final product with a purity of 78.93%.

2.2.2. Animal Experiment

The experimental animals were specific pathogen-free (SPF) grade Balb/c mice, with an equal number of males and females (6–7 weeks old, n = 30 in total). Mice were housed in a standard clean-grade animal room maintained at a temperature of 24 ± 1 °C and a relative humidity of 60 ± 5%, under a 12-h light/dark cycle. They were provided with a standard laboratory diet and water ad libitum. The basal diet was formulated in accordance with the GB 14924.3-2010 standard [15], with the following composition (mass fraction): corn flour 48%, soybean meal 23%, fish meal 5%, wheat middlings 6%, vegetable oil 5%, cellulose 4%, calcium hydrogen phosphate 1.5%, limestone powder 1.0%, salt 0.3%, brewer’s yeast powder 3.0%, lysine 0.2%, methionine 0.2%, vitamin premix 0.5%, and mineral premix 0.3%. The feed was pelleted after sterilization by Co60 irradiation. All mice underwent a 7-day acclimatization period prior to the experiment. The mice were randomly divided into three groups (n = 10 per group, 5 males and 5 females): (1) Control group (CON): Gavage-administered 300 µL of physiological saline daily and maintained under normal housing conditions. (2) Heat Stress group (HS): Gavage-administered 300 µL of physiological saline daily and subjected to heat stress at 41 °C for 2 h per day. (3) Mung Bean Polyphenol group (MBP): Gavage-administered 300 µL of mung bean polyphenol solution (600 mg/kg body weight) daily and subjected to heat stress at 41 °C for 2 h per day.

Experimental Procedure: The total experimental period was 7 days. On day 1, the 30 mice were randomly assigned to the three groups as described above. Daily gavage was performed at 8:00. Body weight and rectal temperature (measured before the heat stress period) were recorded for all mice on days 1, 4, and 7. Except for the CON group, mice in the HS and MBP groups were exposed to heat stress from 11:00 to 13:00 daily in an environment maintained at 41 °C and 60 ± 5% relative humidity. Food and water were available ad libitum during this period. After heat exposure, mice were returned to their original housing area under normal conditions. The behavioral and physiological status of the mice before and after heat stress was observed and recorded daily. Food and water intake were measured on days 1, 4, and 7. On day 7, the same procedures as the previous 6 days were repeated. Following the final heat stress session, mice were returned to their housing area and fasted (with water available) for 24 h for stabilization. Subsequently, all mice were euthanized via cervical dislocation following anesthesia induced by intraperitoneal injection. Blood samples were collected immediately. Intestinal contents were then collected and snap-frozen in liquid nitrogen for subsequent analysis. This experiment was conducted in strict accordance with the Author Guidelines Consensus on Animal Ethics and Welfare. The conduct of this experiment was approved by the Science and Technology Ethics Committee of Heilongjiang Bayi Agricultural University (Approval No.: spxy2024007).

2.2.3. ELISA Kits for Serum Antioxidant and Immune Indices

According to the manufacturer’s instructions of the ELISA kits for GSH-Px, T-AOC, MDA, TNF-α, IL-1β, and cortisol, serum samples were prepared and subsequently analyzed using a microplate reader (BioTek ELx800, TurnerBioSystems, Sunnyvale, CA, USA). Data processing and significance analysis were performed using SPSS 21 software, employing one-way analysis of variance (ANOVA) followed by Duncan’s test, with a significance level set at p < 0.05.

2.2.4. Untargeted Metabolomics Analysis

Sample Preparation of Intestinal Contents: Approximately 100 mg of solid sample was weighed into a 2 mL centrifuge tube containing a 6 mm diameter grinding bead. Then, 800 µL of extraction solvent (methanol–water = 4:1, v/v) spiked with four internal standards (e.g., L-2-chlorophenylalanine at 0.02 mg/mL) was added for metabolite extraction. The sample mixture was homogenized using a cryogenic tissue grinder for 6 min (−10 °C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). The sample was subsequently kept at −20 °C for 30 min and then centrifuged for 15 min (4 °C, 13,000× g). The supernatant was carefully transferred into an injection vial equipped with an insert for instrumental analysis.

Sample Preparation of Serum: A volume of 100 µL of serum sample was pipetted into a 1.5 mL centrifuge tube. Subsequently, 400 µL of extraction solvent (acetonitrile–methanol = 1:1, v/v) containing the same four internal standards was added. The mixture was vortexed for 30 s and subjected to low-temperature ultrasonic extraction for 30 min (5 °C, 40 kHz). The sample was then placed at −20 °C for 30 min, followed by centrifugation for 15 min (4 °C, 13,000× g). The supernatant was collected, dried under a gentle stream of nitrogen, and reconstituted in 100 µL of reconstitution solution (acetonitrile–water = 1:1, v/v). After vortexing, the reconstituted sample underwent low-temperature ultrasonic extraction for 5 min (5 °C, 40 kHz) and was centrifuged for 10 min (4 °C, 13,000× g). Finally, the supernatant was transferred into an injection vial with an insert for LC-MS analysis.

Analytical Method: Raw data were imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, CT, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. This process generated a data matrix containing retention time, mass-to-charge ratio (m/z), and peak intensity. The MS and MS/MS spectra were matched against public metabolite databases (HMDB and Metlin) and a self-built database (Majorbio Bio-Pharm Technology Co., Ltd., Shanghai, China) to annotate metabolites. The matrix data uploaded to the Majorbio Cloud Platform were preprocessed by applying the 80% rule to remove missing values, imputing the remaining vacancy with the minimum value of the original matrix, and normalizing using the sum normalization method to correct for systematic errors. Variables with a relative standard deviation (RSD) >30% in quality control (QC) samples were excluded. The final data matrix was obtained after log10 transformation.

The preprocessed data matrix was subjected to Principal Component Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) using the R package ropls (Version 1.6.2). The stability of the OPLS-DA model was evaluated by 7-fold cross-validation. First, the Variable Importance in Projection (VIP) values extracted from the OPLS-DA model (VIP > 1) were used as the preliminary screening indicator. Then, the p-values from Student’s t-test were subjected to FDR correction using the Benjamini-Hochberg method, with an adjusted q-value < 0.05 set as the significance threshold. Metabolites that simultaneously met both criteria (VIP > 1 and q < 0.05) were ultimately defined as differential metabolites. The identified differential metabolites were then annotated against the KEGG database to determine their associated metabolic pathways. Furthermore, pathway enrichment analysis was performed using the scipy. stats package in Python (Version1.0.0), and Fisher’s exact test was employed to identify the most significantly enriched biological pathways related to the experimental treatments.

3. Results and Discussion

3.1. Mung Bean Polyphenols Ameliorate Antioxidant Capacity and Immune Status in Heat-Stressed Mice

Following heat stress, serum activities of the antioxidant enzyme GSH-Px and total antioxidant capacity (T-AOC) in mice were significantly down-regulated, while levels of malondialdehyde (MDA) and cortisol were markedly elevated. Compared to the HS group, MBP intervention resulted in a significant and substantial up-regulation of GSH-Px activity. T-AOC was restored to a level showing no significant difference from the CON group, and MDA content was reduced to a level comparable to the CON group. Cortisol levels also showed no significant difference from the CON group after MBP treatment. These results indicate that heat stress induces oxidative stress by generating excessive free radicals, thereby disrupting the systemic oxidative balance in mice. The increase in cortisol further signifies the activation of the hypothalamic-pituitary-adrenal (HPA) axis under high-temperature conditions (Figure 1). Excessive cortisol can lead to a negative nitrogen balance and induce immunosuppression [16].

Figure 1. Measurement of serum antioxidant parameters, immune factors, and stress hormones in mice. Note: Letters (a, b, c) indicate statistical significance: groups sharing the same letter are not significantly different, while different letters denote statistically significant differences between groups.

An overabundance of free radicals can trigger inflammatory responses by structurally modifying proteins and genes, thereby activating signaling cascades [17]. Furthermore, the cell membranes of immune cells, rich in highly sensitive unsaturated fatty acids, are susceptible to damage by excessive free radicals, leading to impaired cellular function and compromised overall immune competence [18]. Serum levels of the pro-inflammatory cytokines IL-1β and TNF-α were significantly up-regulated in heat-stressed mice (p < 0.05). These cytokines are also recognized as endogenous pyrogens involved in the febrile response during inflammation, promoting heat production over dissipation and potentially contributing to a further rise in body temperature [19]. Following MBP intervention, the levels of both cytokines were significantly down-regulated (p < 0.05), with TNF-α levels restored to a state showing no significant difference from the CON group. This demonstrates that mung bean polyphenols effectively attenuate the heat stress-induced up-regulation of serum pro-inflammatory factors, thereby mitigating the inflammatory response inflicted by heat stress on the organism [20].

3.2. Metabolomic Analysis of Intestinal Contents

3.2.1. Differential Metabolite Analysis

The identified microbial metabolites in intestinal contents encompassed nine major categories: carbohydrates, hormones and transmitters, lipids, nucleic acids, organic acids, peptides, steroids, vitamins, and cofactors. Among these, lipids (primarily phospholipids and fatty acids), peptides (primarily amino acids and derivatives), nucleic acids (primarily nucleosides and nucleotides), and carbohydrates (primarily monosaccharides and oligosaccharides) were the most abundant. As revealed by the PCA and PLS-DA score scatter plots (Figure 2a,b), both heat stress and MBP intervention exerted significant influences on the metabolic profile of gut microbiota in mice.

Figure 2. Metabolomic analysis of mouse intestinal contents: PCA and PLS-DA score scatter plots, hierarchical clustering heatmap, KEGG pathway enrichment bubble chart, and network diagram. Note: (a) PCA and PLS-DA score plot: Samples are represented as coordinate points after dimensionality reduction based on principal components PC1 and PC2. (b) Each column represents a sample, and each row represents a metabolite. Colors indicate the relative abundance of the metabolite in each sample group, with the specific abundance scale shown in the color bar and its numerical labels at the bottom right. The dendrogram on the left represents metabolite clustering, and metabolite names are listed on the right. (c) The horizontal axis represents the enrichment ratio, and the vertical axis lists KEGG pathways. Bubble size corresponds to the number of compounds enriched from the metabolite set in each pathway, while bubble color indicates the enrichment significance p-value. (d) Green square nodes represent metabolites; orange circular nodes represent KEGG pathways.

By comparing the CON vs. HS groups and the LD vs. HS groups, 460 and 123 differential metabolites were screened, respectively (see Supplementary Materials Tables S1 and S2). Among these, 16 differential metabolites were common to both comparisons. Further analysis identified 15 metabolites whose levels were upregulated under heat stress but significantly reversed following MBP intervention, designating them as “core reversed metabolites” Additionally, 16 characteristic metabolites associated with heat stress injury were identified in the CON vs. HS comparison, primarily comprising key pathological molecules such as pro-inflammatory mediators, oxidative stress markers, and neurotransmitter precursors. Hierarchical clustering analysis was performed on these 32 key differential metabolites (Table 1), and the result is presented in Figure 2b.

Table 1. Differential serum metabolites in mice.

Num	Metabolite	p_Value	VIP	FC (HS/CON)	FC (LD/HS)	M/Z	Retention Time	HS/CON	LD/HS
1	DG(22:6(4Z,8Z,10Z,13Z,16Z,19Z)-OH(7)/0:0/8:0)	0	5.13	2.81	0.49	525.36	6.9217	up	down
2	PC(20:5(7Z,9Z,11E,13E,17Z)-3OH(5,6,15)/2:0)	0.0076	3.33	1.85	0.69	630.31	3.8562	up	down
3	Dihydrotestosterone diglucuronide	0.0133	2.87	1.61	0.63	677.25	3.2971	up	down
4	Oleoyl glycine	0.0054	2.76	1.44	0.71	384.27	6.5128	up	down
5	Cucurbitacin E	0.0366	2.61	1.40	1.12	537.29	7.5357	up	up
6	Ala Leu Val	0.0097	2.58	1.28	0.63	302.21	3.6213	up	down
7	Araliasaponin VIII	0	2.50	0.82	1.10	648.30	5.8743	down	up
8	Euscaphic acid	0.0106	2.39	0.75	1.28	469.33	6.2588	down	up
9	Val Glu Ser	0.0029	2.34	1.20	0.78	334.16	1.1645	up	down
10	Leukotriene E4	0.0061	1.91	1.18	0.88	498.26	3.7713	up	down
11	Allantoin	0.0134	1.73	0.89	0.92	157.04	0.643	down	down
12	N(omega)-Hydroxyarginine	0.0276	1.56	1.10	0.81	232.14	0.5942	up	down
13	Valylvaline	0.0367	1.50	1.08	0.90	217.15	2.094	up	down
14	Ser Val	0.0018	1.44	1.06	0.88	205.12	1.2976	up	down
15	Ile Val	0.0302	1.37	1.08	0.91	231.17	2.8677	up	down
16	Leucyl-Threonine	0.0262	1.16	1.04	0.91	233.15	1.4915	up	down
17	N-hydroxyl-tryptamine	0.0375	1.22	1.08	-	221.09	2.5602	up	-
18	Formiminoglutamic acid	0.0307	1.17	1.06	-	233.08	0.643	up	-
19	Trans-Cinnamic Acid	0.0044	3.21	0.63	-	131.05	5.2693	down	-
20	Ophthalmic acid	0.0016	2.99	1.43	-	328.09	3.4791	up	-
21	5-HETE	0.0002	2.98	1.32	-	319.23	6.4969	up	-
22	Gamma-Glutamyl-beta-cyanoalanine	0.0025	2.84	1.33	-	285.12	2.3522	up	-
23	Deoxycholylglutamine	0.0207	2.59	1.39	-	543.34	5.8267	up	-
24	6-Keto-prostaglandin F1a	0.0093	2.46	0.72	-	391.21	3.5161	down	-
25	Indoleacetaldehyde	0.0048	2.42	0.80		204.07	5.7008	down
26	Prostaglandin F1a	0.0330	2.30	1.30	-	379.25	5.9125	up	-
27	Resolvin E1	0.0445	2.21	0.76	-	395.18	3.5894	down	-
28	4-hydroxy-2-nonenal-glutathione conjugate	0.0120	2.16	1.23	-	461.18	5.6227	up	-
29	Imidazoleacetic acid	0.0289	1.98	1.16	-	127.05	0.656	up	-
30	3-Indoleacetonitrile	0.0432	1.98	1.25	-	174.10	3.0882	up	-
31	Urea glutamate	0.0360	1.64	1.17	-	248.05	0.8966	up	-
32	Leukotriene C5	0.0406	1.47	1.10	-	641.32	6.0375	up	-

Note: (1) p-value: Result of significance testing for differences between the two sample groups (2) VIP: Variable Importance in Projection (VIP) value from the OPLS-DA model for the metabolite between the two groups; (3) FC (HS/CON): fold change of metabolites in HS group compared to CON group; FC (LD/HS): fold change of metabolites in LD group compared to HS group; (4) M/Z: Mass-to-charge ratio, defined as the ratio of the ion mass to its charge; (5) Retention time: Chromatographic retention time of the ion; (6) up indicates increased abundance, down indicates decreased abundance.

The results indicate that heat stress induced extensive disruption in the serum metabolic profile of mice. Significant elevations were observed in pro-inflammatory lipid mediators such as Leukotriene E4, Leukotriene C5, 5-HETE, and Prostaglandin F1α. This suggests the activation of the arachidonic acid metabolism pathway via lipoxygenase and cyclooxygenase pathways, leading to a systemic inflammatory response [21,22]. Increased levels of oxidative damage-related molecules, including the 4-Hydroxynonenal-Glutathione conjugate and allantoin, along with the accumulation of ophthalmic acid, indicate exacerbated lipid peroxidation, enhanced oxidative stress status, and depletion of endogenous glutathione [23,24,25]. Concurrently, abnormalities in ammonia and one-carbon metabolism molecules, such as glutamine and formiminoglutamic acid, imply a potential burden on hepatic detoxification and antioxidant systems [26]. Alterations in neurotransmitter-related metabolites like indoleacetaldehyde, N-hydroxytyrosine, and imidazoleacetic acid may disrupt the balance of neurotransmitter synthesis precursors. The increase in deoxycholylglutamine hints at abnormalities in bile acid metabolism and the enterohepatic circulation, while the down-regulation of Resolvin E1 indicates impaired inflammatory resolution capacity. Collectively, these findings demonstrate that heat stress induces metabolic dysregulation along the neuro–endocrine–gut axis.

The findings demonstrate that MBP intervention can specifically reverse key dysregulated nodes within the aforementioned metabolic network. It significantly restored the levels of core pro-inflammatory mediators like Leukotriene E4, which aligns with the observed phenotype of reduced serum pro-inflammatory cytokines. MBP down-regulated allantoin and dihydrotestosterone diglucuronide, mitigating oxidative damage to purine and steroid hormones. It modulated the level of N(ω)-Hydroxyarginine, potentially affecting nitric oxide synthesis. The restoration of normal levels of various dipeptides/tripeptides (e.g., Ser-Val, Val-Glu-Ser) may improve protein digestion, absorption, or catabolic processes, possibly associated with restored intestinal function [27]. Furthermore, the reversal effects on plant-derived molecules such as Cucurbitacin E, Araloside VIII, and rosmarinic acid suggest that MBPs may modulate the metabolism of related exogenous compounds.

3.2.2. KEGG Pathway Enrichment Analysis

KEGG pathway enrichment analysis was performed on the 32 identified differential metabolites, revealing five significantly enriched key metabolic pathways (Figure 2c): Arachidonic acid metabolism, Tryptophan metabolism, Histidine metabolism, Biosynthesis of various alkaloids, and the Fc epsilon RI signaling pathway. A network diagram illustrating these key pathways and their associated differential metabolites is presented in Figure 2d. The enrichment of these pathways indicates that heat stress systematically perturbs lipid-mediated inflammatory responses, neuro-immune crosstalk, and the metabolism of nitrogen-containing bioactive molecules. Among these, the arachidonic acid metabolism pathway served as a core effector pathway. Its key metabolites, including Leukotriene E4, 5-HETE, and Prostaglandin F1α, were significantly upregulated in the HS group compared to the CON group. This demonstrates that the surge of potent pro-inflammatory mediators like leukotrienes and prostaglandins constitutes the direct metabolic basis for heat stress-induced systemic inflammation [28]. Perturbations in the tryptophan and histidine metabolism pathways, reflected by changes in metabolites such as indoleacetaldehyde, imidazoleacetic acid, and formiminoglutamic acid, revealed an imbalance in neurotransmitter precursor pools and activation of the histamine system. These alterations may contribute to the disruption of neuro-immune homeostasis [29]. The enrichment of the Fc epsilon RI signaling pathway provides a critical immunoregulatory link connecting upstream histamine release to the downstream synthesis of arachidonic acid metabolites [30]. Furthermore, the enrichment of the “Biosynthesis of various alkaloids” pathway reflects extensive reprogramming of the body’s nitrogenous small molecule metabolic network, providing a broader metabolic context for the aforementioned specific pathway alterations.

3.3. Serum Metabolomic Analysis in Mice

3.3.1. Analysis of Differential Metabolites in Serum

The categories of serum metabolites were similar to those of gut microbiota metabolites, with lipids and peptides being predominant. Lipids primarily consisted of phospholipids, eicosanoids, and fatty acids, while peptides were mainly amino acids and their derivatives. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) score plots (Figure 3a) revealed that both heat stress and MBP intervention significantly altered the serum metabolic profile of mice.

Figure 3. Serum metabolomic analysis in mice PCA and PLS-DA score scatter plots, hierar chicalclustering heatmap, KEGG pathway enrichment bubble chart, and network diagram. Note: (a) PCAand PLS-DA score plot: Samples are represented as coordinate points after dimensionality reduction based on principal components PC1 and PC2. (b) Each column represents a sample, and each row represents a metabolite. Colors indicate the relative abundance of the metabolite in each sample group, with the specific abundance scale shown in the color bar and its numerical labels at the bottom right. The dendrogram on the left represents metabolite clustering, and metabolite names are listed on the right. (c) The horizontal axis represents the enrichment ratio, and the vertical axis lists KEGG pathways. Bubble size corresponds to the number of compounds enriched from the metabolite set in each pathway, while bubble color indicates the enrichment significance p-value. (d) Green square nodes represent metabolites; orange circular nodes represent KEGG pathways.

Comparative analysis between the CON vs. HS groups and the MBP vs. HS groups identified 98 and 82 differential metabolites, respectively (see Supplementary Materials Tables S3 and S4), with 18 metabolites common to both comparisons. Further analysis identified 15 metabolites whose levels were downregulated after heat stress and were not restored or were further decreased following MBP intervention, classifying them as “Core non-reversed downregulated metabolites” (Table 2). The levels of these 18 shared differential metabolites were generally downregulated by heat stress and not reversed by MBP. This suggests that heat stress may not only activate certain pathways but also lead to the suppression or resource depletion of specific metabolic routes. For instance, the concurrent decrease in pro-inflammatory lipid mediators such as Thromboxane B2 (TXB2), 12-HETE, 10-HDoHE, and the prostaglandin derivative 13,14-dihydro-15-keto-PGE2 may reflect the depletion of membrane phospholipid precursors (e.g., arachidonic acid) under sustained stress or a compensatory attempt by the body to suppress excessive inflammation. The lack of reversal of these mediators by MBPs might indicate their action in assisting the body’s shift from persistent inflammation to repair, potentially by inhibiting pro-inflammatory enzyme activity at the source or promoting degradation. Meanwhile, the downregulation of the tryptophan metabolite Indolelactic Acid suggests impaired anti-inflammatory metabolic functions related to gut microbiota. The upregulation of Melatonin after heat stress and its subsequent downregulation after MBP intervention may indicate that the direct antioxidant protection provided by MBPs reduced the need for excessive endogenous melatonin secretion. The downregulation of Niacinamide hints at potential impairment in coenzyme NAD+ metabolism [31]. Furthermore, the decrease in lysosphingomyelin LysoSM(d18:1) and its subsequent recovery with MBP intervention may contribute to the restoration of cell membrane stability (Figure 3b). These alterations indicate a disturbance in metabolites associated with the neuro–immune–gut axis.

Table 2. List of differential metabolites in mouse intestinal contents.

Num	Metabolite	p_Value	VIP	FC (HS/CON)	FC (LD/HS)	M/Z	Retention Time	HS/CON	LD/HS
1	Melatonin	0.0008	3.61	1.21	0.66	233.13	3.9762	up	down
2	LysoSM(d18:1)	0.0243	2.51	0.88	1.12	504.31	6.2935	down	up
3	L-Tyrosine	0.0163	1.40	1.04	1.03	223.11	1.7885	up	up
4	9,10-Epoxy-18-hydroxy-octadecanoic acid	0.0181	2.23	1.08	0.91	295.23	5.9100	up	down
5	2-Hydroxyoctadecanoic acid	0.0039	1.72	0.96	0.97	321.24	5.8785	down	down
6	(+/−)10-HDoHE	0.0068	1.59	0.97	0.98	343.23	6.0093	down	down
7	Ureidoisobutyric acid	0.0126	1.64	1.05	1.04	188.10	0.6078	up	up
8	13,14-Dihydro-15-keto-PGE2	0.0089	1.48	0.96	0.95	335.22	5.5911	down	down
9	Hydroxyprolyl-Leucine	0.0455	1.64	0.96	0.92	267.13	1.7337	down	down
10	13-Hydroxy-9-methoxy-10-oxo-11-octadecenoic acid	0.0192	1.35	0.97	0.97	387.24	5.6321	down	down
11	Indolelactic Acid	0.0088	1.27	0.97	0.97	206.08	4.2649	down	down
12	Niacinamide	0.0176	1.27	0.98	0.97	123.06	0.6698	down	down
13	Histidylcysteine	0.0164	1.20	1.02	1.02	239.06	3.8496	up	up
14	12(R)-HETE	0.0294	1.19	0.98	0.99	319.23	6.0255	down	down
15	Thromboxane B2	0.0159	1.16	0.98	0.98	369.23	5.7118	down	down
16	N-lactoyl-phenylalanine	0.0458	1.16	0.97	0.95	238.11	4.3899	down	down
17	Aspartic Acid	0.0340	0.99	0.98	0.97	132.03	0.5639	down	down
18	12-HETE	0.0131	0.91	0.99	0.99	319.23	5.8547	down	down
19	PGB2	0.0166	1.20	0.98	-	335.22	5.7234	down	-
20	N-Acetylleucine	0.0171	1.23	0.97	-	172.10	4.3675	down	-
21	L-Pipecolic acid	0.0043	1.26	1.02	-	130.09	0.6231	up	-
22	Serotonin	0.0348	1.54	1.04	-	218.13	1.9680	up	-
23	Uric Acid	0.0340	1.42	1.03	-	169.04	1.1786	up	-
24	3-Cresotinic acid	0.0133	1.31	1.03	-	151.04	3.3802	up	-
25	(+/−)-Tryptophan	0.0431	1.38	1.04	-	246.12	2.7415	up	-
26	5,6-Dihydroxyprostaglandin F1a	0.0088	1.50	0.97	-	411.23	5.5832	down	-
27	Trans-Aconitic Acid	0.0492	1.41	0.96	-	173.01	0.6515	down	-
28	Histamine	0.0179	2.33	1.12	-	112.09	0.5301	up	-
29	Neopterin	0.0000	4.66	1.42	-	276.07	2.2967	up	-
30	Indoleacetic acid	0.0011	2.75	1.13	-	217.10	3.9840	up	-

Note: (1) p-value: Result of significance testing for differences between the two sample groups; (2) VIP: Variable Importance in Projection (VIP) value from the OPLS-DA model for the metabolite between the two groups; (3) FC (HS/CON): fold change of metabolites in HS group compared to CON group; FC (LD/HS): fold change of metabolites in LD group compared to HS group; (4) M/Z: Mass-to-charge ratio, defined as the ratio of the ion mass to its charge; (5) Retention time: Chromatographic retention time of the ion; (6) up indicates increased abundance, down indicates decreased abundance.

Twelve metabolites significantly upregulated in the CON vs. HS comparison (Table 2) delineate core pathological markers of heat stress injury. Regarding oxidative stress and immune activation, the sharp increase in Uric Acid and Neopterin confirms severe systemic redox imbalance and cellular immune (Th1-type) activation. The accumulation of Trans-Aconitic Acid suggests dysfunction in the mitochondrial tricarboxylic acid (TCA) cycle. In terms of neuroendocrine stress, the comprehensive upregulation of Tryptophan, Serotonin, and its downstream metabolite Indoleacetic acid indicates a strong shift in tryptophan metabolism towards the synthesis of neuroactive substances, driving neural stress and neurogenic inflammation. The elevation of Histamine and L-Pipecolic acid further exacerbates inflammatory responses and brain stress signaling. The downregulation of some prostaglandin metabolites, such as 5,6-dihydroxyprostaglandin F1α and PGB2, aligns with the general suppression trend of lipid mediators mentioned earlier. Together, these findings support the hypothesis that under severe heat stress, the synthesis of specific lipid inflammatory mediators may be constrained or excessively consumed.

At the serum metabolic level, heat stress induced complex metabolic reprogramming, characterized by the coexistence of significant accumulation of specific pathology-related molecules and inhibition of certain metabolic pathways. MBP intervention exhibited precise regulatory characteristics, not simply reversing all changes, but likely functioning through mechanisms such as mitigating oxidative stress, modulating the source of inflammation, and aiding in the restoration of metabolic homeostasis.

3.3.2. KEGG Pathway Enrichment Analysis of Serum Differential Metabolites

KEGG pathway enrichment analysis of the serum differential metabolites identified 20 significantly enriched metabolic pathways (Figure 3c), with key metabolites within these pathways illustrated in Figure 3d. These pathways can be categorized into three functional tiers, systematically revealing the pathophysiological network induced by heat stress and clarifying the precise regulatory role of mung bean polyphenols at each level.

The first tier is characterized by dysregulation in cellular sensing and signal decoding, manifested as significant enrichment of the neuroactive ligand-receptor interaction pathway, suggesting that heat stress leads to widespread dysregulation of signaling molecules such as neurotransmitters (serotonin, histamine) and hormones [32]. Mung bean polyphenols partially restored normal ligand-receptor signal transduction by reversing the levels of key neuroactive substances, including serotonin, melatonin, and histamine, thereby mitigating systemic signal disturbances at their source. The second tier involves the activation of multi-system specific effector networks, driven by the signal dysregulation from the first tier. This specifically includes neuroregulatory imbalance (enrichment of serotonergic synapse and circadian rhythm pathways), inflammatory and immune dysregulation (enrichment of the arachidonic acid metabolism pathway involving pro-inflammatory mediators such as Thromboxane B2 and 12-HETE; the “regulation of TRP channels by inflammatory mediators” pathway connecting peripheral inflammation to neural sensations [33]), and metabolic and endocrine stress (enrichment of pathways such as aldosterone synthesis and bile secretion). Mung bean polyphenols exhibit precise regulation at this tier, as evidenced by reversing abnormalities in serotonin and melatonin at the neuro level, significantly downregulating pro-inflammatory mediators such as 12-HETE and Thromboxane B₂ at the inflammatory level, and disrupting the “pro-inflammatory-oxidative stress” pathological metabolic coupling (e.g., the abnormal positive correlation between 5-HETE and trans-aconitic acid). At the metabolic level, they partially restore aspartic acid levels and upregulate lysosphingomyelin LysoSM(d18:1) to promote cell membrane stability. The third tier involves deep adaptive and metabolic reprogramming, reflected by the enrichment of pathways such as cocaine addiction and alcoholism (suggesting neural remodeling of the reward-stress system), as well as pathways including central carbon metabolism in cancer and nicotinate metabolism, indicating a shift in energy metabolism toward glycolysis and remodeling of NAD+ metabolism [34]. Mung bean polyphenols exert their regulatory effects through indirect mechanisms: on one hand, they reduce upstream oxidative stress and neuroinflammation (e.g., by downregulating indoleacetic acid and upregulating indolelactic acid), thereby alleviating persistent stimulation of the reward-stress circuit; on the other hand, they partially restore nicotinamide levels and alleviate mitochondrial dysfunction (indicated by downregulation of trans-aconitic acid), thus reducing excessive reliance on glycolytic compensation.

In summary, mung bean polyphenols systematically reconstitute host metabolic homeostasis through a hierarchical action model of “signal stabilization at the source—precise regulation of effector networks—reduction of deep adaptive demands.”

3.4. Integrated Cross-Omics Analysis Reveals Systemic Disruption of the Gut–System Metabolic Axis Under Heat Stress and the Network-Remodeling Effect of Mung Bean Polyphenols

To systematically elucidate the disruption of the “gut-systemic” metabolic crosstalk by heat stress and the intervention mechanism of MBPs, we conducted a multi-level integrated correlation analysis on the differential metabolites identified from intestinal contents and serum.

3.4.1. Shared Pathway Analysis Identifies the Core Dysregulated Network

Integrated KEGG pathway enrichment analysis revealed that the Arachidonic acid metabolism, Tryptophan metabolism, Histidine metabolism, and Fc epsilon RI signaling pathway were simultaneously and significantly perturbed in both intestinal contents and serum (Figure 2c and Figure 3c), constituting the core network of heat stress-induced metabolic disorder (Table 3). On one hand, this manifested as inflammation and an allergy-like response. The key effector molecules downstream of the Fc epsilon RI pathway, Histamine (in serum) and Leukotriene E4 (in intestine), were synchronously elevated. This aligned with the increase in the intestinal pro-inflammatory lipid 5-HETE, collectively pointing to a heat stress-induced “allergy-like” immune activation state [35]. On another front, it demonstrated neuroendocrine activation. For instance, Tryptophan metabolism was robustly activated both in the intestine (e.g., upregulation of 3-Indoleacetonitrile) and in serum (e.g., upregulation of Serotonin and Melatonin) [36]. Furthermore, a dissociation between local and systemic inflammatory responses was observed. While the Arachidonic acid metabolism pathway showed upregulation of pro-inflammatory mediators in the intestine (e.g., 5-HETE), it exhibited downregulation of various similar mediators in serum (e.g., 12-HETE, TXB2). This indicates that an imbalance between local inflammation and the systemic inflammatory response had already occurred [37].

Table 3. Table of Core Metabolic Pathways.

	Pathway_ID	Pathway Description	p_Value	Differentially Expressed Metabolites
intestinal flora	map00590	Arachidonic acid metabolism	0.0003	6-Keto-prostaglandin F1a (down), Leukotriene E4 (up), 5-HETE (up)
	map00380	Tryptophan metabolism	0.0004	3-Indoleacetonitrile (up), N-hydroxyl-tryptamine (up), Indoleacetaldehyde (down)
	map00340	Histidine metabolism	0.0033	Imidazoleacetic acid (up), Formiminoglutamic acid (up)
	map04664	Fc epsilon RI signaling pathway	0.0205	Leukotriene E4 (up)
serum	map00590	Arachidonic acid metabolism	0.0001	PGB2 (down), 12(R)-HETE (down), Thromboxane B2 (down), 12-HETE (down)
	map00380	Tryptophan metabolism	0.0030	Indoleacetic acid (metab_1242) (up), Melatonin (up), Serotonin (up)
	map00340	Histidine metabolism	0.0121	Histamine (up), Aspartic Acid (down)
	map04664	Fc epsilon RI signaling pathway	0.0393	Histamine (up)

Note: (1) Pathway ID: KEGG pathway identifier; (2) p_value: The p-value indicates whether the enrichment result is statistically significant. A smaller p-value represents greater statistical significance; generally, a p-value < 0.05 is considered to indicate a significantly enriched pathway.

3.4.2. Heat Stress-Induced Reprogramming of the Gut-Blood Metabolite Co-Expression Network

Hierarchical clustering heatmaps depicting the correlation analysis between the 32 differential intestinal metabolites and the 30 differential serum metabolites for the CON, HS, and LD groups are presented in Figure 4a and Figure 4b, and Figure 4c, respectively. The CON group represents the inherent “gut-systemic” metabolic crosstalk under healthy conditions. The HS group illustrates the pathological “metabolic axis” driven by abnormal metabolic couplings induced by heat stress, which underlies the observed phenotypic damage. The LD group reveals the re-establishment of beneficial associations through MBP-mediated repair of these aberrant couplings. An integrated view of the differential metabolites from all three groups is presented in Figure 4d.

Figure 4. Correlation heatmap of differential metabolites in gut and serum metabolomics. Note: In panels (a–c), metabolite names are displayed on the right, and the names of the associated metabolites are shown at the bottom. Each cell in the heatmap represents the correlation between two metabolite groups, with different colors indicating the magnitude of the correlation coefficient. Asterisks denote the significance level: * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001. In panel (d), yellow blocks represent significant correlations from panel (a), orange blocks represent those from panel (b), and green blocks represent those from panel (c).

Analysis of intra-group correlations further elucidated the specific manifestations of pathway dysregulation at the level of metabolite collaboration relationships. Firstly, physiological metabolic crosstalk was disrupted. Heat stress reversed several important gut-blood metabolite associations present under physiological conditions. For instance, the pair L-Tyrosine (serum)—Ala Leu Val (intestine), involved in maintaining nitrogen metabolism balance, shifted from a positive to a negative correlation. Similarly, the association between Ureidoisobutyric acid (serum) and 5-HETE (intestine), reflecting redox homeostasis, was inverted from positive to negative correlation. Secondly, pathological metabolic couplings were established. Heat stress fostered new networks of strong correlations, characterized chiefly by abnormal connections between pro-inflammatory lipids and oxidative/neural stress metabolites. For example, the correlation between the pro-inflammatory mediator 5-HETE (intestine) and the oxidative stress marker Trans-Aconitic Acid (serum) changed from negative to positive. The neuroactive substance L-Pipecolic acid (serum) maintained a significant positive correlation with 5-HETE (intestine) under heat stress, while Histamine (serum) maintained a significant negative correlation with the neurotransmitter precursor Indoleacetaldehyde (intestine). These newly formed couplings constitute a potential metabolic foundation driving “neuroinflammation.”

3.4.3. MBP-Mediated Metabolic Network Repair and Functional Remodeling

Following MBP intervention, the metabolic correlation network underwent directional remodeling. Its core characteristics involved attenuating pathological couplings, re-establishing homeostatic connections, and shifting the network hubs toward antioxidant and neuroprotective functions. Firstly, MBPs specifically reversed several abnormal correlations that were established or strengthened by heat stress. For example, the correlation between the pro-inflammatory metabolite pair 13-Hydroxy-9-methoxy-10-oxo-11-octadecenoic acid (serum) and 5-HETE (intestine) shifted from a significantly negative to a positive correlation. Concurrently, the association between Niacinamide (serum) and N(ω)-Hydroxyarginine (intestine) was reversed from negative to positive correlation, potentially promoting the restoration of NAD+ synthesis and NO metabolism [38]. Secondly, the MBP intervention established protective metabolic axes. Post-intervention, the correlations between serum antioxidant and neuroprotective metabolites (e.g., Melatonin, Niacinamide) and intestinal metabolites were significantly enhanced. Notably, a significant positive correlation emerged between Melatonin and the antioxidant enzyme GSH-Px. Furthermore, the gut microbiota-derived metabolite Indolelactic Acid showed a significant positive correlation with the serum total antioxidant capacity (T-AOC) (Figure 5a–c). These findings suggest that mung bean polyphenols constructed a novel protective axis centered on indole metabolism–melatonin–endogenous antioxidant systems [39].

Figure 5. Correlation heatmap analysis between serum and gut differential metabolites versus serum antioxidant, immune parameters, and stress hormones. Note: Metabolite names are displayed on the right, and the names of the associated data features are shown at the bottom. Each cell in the heatmap represents the correlation between two attributes (a metabolite and an associated feature), with color gradients indicating the magnitude of the correlation coefficient. Asterisks denote significance levels: * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01.

Finally, metabolite-phenotype correlation analysis provided a functional readout for the aforementioned network remodeling [40]. After MBP intervention, a highly significant negative correlation was observed between the pro-inflammatory mediator PGB2 and the oxidative damage marker MDA. Concurrently, newly emerged strong correlations post-intervention, such as the shift of the Ureidoisobutyric acid–Leukotriene E4 pair to a negative correlation, aligned with the downregulation trend of inflammatory cytokines. These associations confirm that the metabolic network changes induced by MBPs are functionally linked to the observed phenotypic improvements, namely enhanced antioxidant capacity (upregulated T-AOC and GSH-Px) and mitigated inflammatory damage (downregulated MDA and TNF-α) (Figure 5d–f).

In summary, Indolelactic Acid, Trans-Cinnamic Acid, Leukotriene E4, 5-HETE, and N(ω)-Hydroxyarginine were identified as five key hub metabolites, primarily originating from the two core shared pathways: tryptophan metabolism and arachidonic acid metabolism. MBP intervention, by upregulating beneficial gut microbiota metabolites (e.g., Indolelactic Acid) while downregulating intestinal pro-inflammatory mediators (e.g., Leukotriene E4, 5-HETE), not only directly corrected the levels of these metabolites but, more importantly, remodeled their correlation networks with systemic circulating metabolites and physiological phenotypes [41,42]. The hallmark of this network remodeling is the attenuation of pathological “pro-inflammatory–oxidative stress” couplings and the establishment or strengthening of beneficial connections between gut-derived protective metabolites and systemic antioxidant functions. This ultimately drives the alleviation of heat stress injury at the metabolic network level.

4. Conclusions

Through integrated analysis of physiological phenotypes and multi-omics data, this study systematically reveals that the underlying mechanism by which mung bean polyphenols (MBPs) alleviate heat stress injury lies in their ability to remodel the host metabolic network. It was found that MBPs not only directly mitigate oxidative stress and inflammation by enhancing GSH-Px activity and reducing levels of MDA and pro-inflammatory cytokines, but more importantly, they can precisely reverse the extensive disturbances in core pathways such as arachidonic acid metabolism and tryptophan metabolism induced by heat stress, specifically restoring key pathological metabolites like leukotriene E4 and 5-HETE. Based on dynamic analysis of the metabolite co-expression network, the protective effect of mung bean polyphenols is closely associated with the disruption of the “pro-inflammatory-oxidative stress” pathological metabolic coupling under heat stress, accompanied by the formation of a gut–systemic metabolic axis centered on the intestinal metabolite indolelactic acid, which correlates with improved host systemic antioxidant indicators. This axis is driven by the gut-derived beneficial metabolite indolelactic acid as a hub, promoting the restoration of systemic antioxidant function. For the first time from the perspective of dynamic metabolic network remodeling, this research elucidates a new paradigm whereby dietary polyphenols achieve multi-target protection through the systemic regulation of “gut-host” metabolic crosstalk. The findings provide a theoretical basis for the traditional use of mung beans for heat relief and establish a novel theoretical framework with candidate targets for developing functional foods or dietary supplements that target the gut–systemic metabolic axis.

Nevertheless, this study has some limitations. First, the animal experiments were conducted using a mouse model. Despite the high conservation of physiological and metabolic pathways between mice and humans, species differences may lead to heterogeneity in response to mung bean polyphenols; therefore, extrapolation of these findings to humans requires caution. Second, this study primarily relied on metabolomics-based correlation analysis to identify co-variation networks between metabolites and pathways. While this approach effectively proposes scientific hypotheses such as the “gut-systemic protective axis,” it lacks direct molecular biology or functional validation experiments to establish causal relationships. For instance, the specific mechanism by which indolelactic acid drives systemic antioxidant function, as well as the direct inhibitory effects of mung bean polyphenols on key enzyme activities, require further validation through methods such as gene knockout, enzyme activity assays, or germ-free animal models. Future research should integrate functional validation experiments and clinical trials to more comprehensively evaluate the application potential of mung bean polyphenols.