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Article

Using Integrated Microbiome–Metabolome–Genome Axis Data to Elucidate the Mechanism by Which Polyphenol Content in the Extract from C. osmantha Leaves (PECOL) Regulates Broiler Flavor

by
Manting Ma
2,
Wanxi He
3,
Xiajin Lin
2,
Yibing Wang
2,
Shouqun Jiang
2,
Li Yang
1,
Guizhen Li
1,* and
Yao Gu
1,*
1
Guangxi Laboratory of Forestry, Guangxi Forestry Research Institute, Nanning 530002, China
2
Institute of Animal Science, Guangdong Academy of Agricultural Sciences, State Key Laboratory of Swine and Poultry Breeding Industry, Key Laboratory of Animal Nutrition and Feed Science in South China, Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangzhou 510640, China
3
State Key Laboratory of Swine and Poultry Breeding Industry, Lingnan Guangdong Laboratory of Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
*
Authors to whom correspondence should be addressed.
Foods 2026, 15(5), 862; https://doi.org/10.3390/foods15050862
Submission received: 19 January 2026 / Revised: 10 February 2026 / Accepted: 11 February 2026 / Published: 4 March 2026
(This article belongs to the Section Foodomics)
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Abstract

The quality and flavor of chicken meat are the key factors that influence consumers’ purchase decisions. Recent studies have demonstrated that polyphenol can modulate meat quality. In this study, an integrated multi-omics approach was utilized to systematically identify the regulatory effect of dietary supplementation with polyphenols extracts of C. osmantha leaves (PECOL) on chicken flavor. It was found that dietary PECOL supplementation enhanced breast meat flavor and increased fatty acid ethyl ester compounds in the breast muscle. Moreover, PECOL supplementation reshaped the composition and proportions of gut microbiota across multiple taxonomic levels, with a notable enrichment of taxa within the phylum Firmicutes (e.g., g_Massilistercora). Furthermore, the addition of PECOL altered the contents of cecal metabolites related to lipid and glucose metabolism, such as PC (14:1(9Z)/21:0), PC (P-16:0/15:1(9Z)), LysoPE (20:4(8Z, 11Z, 14Z, 17Z)/0:0), and glycerol 3-phosphate. Notably, we found that g_Massilistercora was significantly correlated with the content of these metabolites related to lipid and glucose metabolism. Further analysis revealed that these metabolites might interact with GPAT4 to jointly regulate chicken flavor. These findings further clarify the regulatory role played by PECOL in shaping the flavor of broiler meat.

Graphical Abstract

1. Introduction

Chicken is one of the most widely consumed meats globally, and its consumption is continuing to increase every year [1]. As consumers’ living standards continue to rise, their dietary preferences have gradually shifted from focusing on quantity to quality. On the market, high-quality meat products have become the mainstream [2], where distinct flavor characteristics have become a critical factor in consumer satisfaction [3]. Meat flavor is a complex characteristic, mainly composed of both volatile and non-volatile compounds [4], and it is influenced by multiple factors, such as breed, feed, and additives, among others. The nutrient composition of feed is one of the key factors influencing the meat quality [5]. Plant-based additives are natural compounds that are rich in various nutrients and biological activities; they do not cause tissue residues or pose the risk of drug resistance [6]. Therefore, they are a popular alternative to antibiotics in the livestock and poultry industries. Moreover, studies have shown that the addition of plant-based additives to feed can improve the pH and L* value of meat, reduce its shear force and nutrient loss when cooking, and alter the crude protein and inosine content [7].
Camellia osmantha (C. osmantha), a newly discovered species within the genus Camellia, was first identified in Guangxi, China, in 2012 [8]. Currently, research on C. osmantha primarily focuses on the processing and utilization of its seed oil [9]. However, its leaves are among the main by-products generated during the pruning and shaping of C. osmantha trees, containing various bioactive compounds such as phenolic compounds [8]. Polyphenols have been shown to possess antioxidant, anti-tumor, and lipid-lowering properties [10]. Previous studies have established that phenolic compound supplementation elevates meat antioxidant levels and quality, and improves the fatty acid composition of broiler chicken [11,12]. However, little is known regarding how dietary supplementation of phenolic compounds affects broiler meat flavor and the underlying regulatory mechanisms.
Guangxi Ma chickens are one of China’s indigenous yellow broilers, highly favored by the market for their delicious meat flavor and high resistance to coarse feed [13]. Studies have shown that polyphenolic additives can enhance poultry growth performance by regulating intestinal microorganisms [11]. However, limited research has explored the effects of polyphenol extraction of C. osmantha leaves (PECOL) on the meat flavor compounds of Guangxi Ma chickens. This study systematically evaluated the effects of dietary PECOL supplementation on meat flavor in the breast muscle of Guangxi Ma chickens, with a focus on clarifying how PECOL supplementation mediate intestinal microbiota, intestinal metabolites, and key regulatory genes to regulate effects on muscle flavor and quality.

2. Materials and Methods

2.1. Preparation of PECOL

The C. osmantha leaves were collected from the Camellia oleifera germplasm resource collection garden at Tiger Ridge, located in the northern suburbs of Nanning City, Guangxi Zhuang Autonomous Region. Polyphenols were extracted following the method described in the reference literature [14] with slight modifications. The specific extraction and purification steps are as follows: the leaves were naturally air-dried, ground and sieved for subsequent use. A total of 100 g of the above powder was subjected to ultrasound-microwave-assisted extraction with 40% ethanol at a microwave power of 350 W and ultrasonic power of 50 W for 100 s (CW-2000, Shanghai Xintwo Analytical Instrument Technology Co., Ltd., Shanghai, China). After UMAE, the extraction and residual materials were performed with 60% ethanol in a water bath at 70 °C, followed by centrifugation at 4000 rpm for 15 min to remove residues; this process was repeated three times. A total of 1 L of the leachate was measured, mixed with 30 g of activated clay and stirred at 300 rpm for 2 h, and the filtrate was collected by suction filtration. Then 40 g of activated carbon was added to the filtrate and stirred at 300 rpm for 30 min, and the filtrate was collected again by suction filtration. Ethanol in the filtrate was removed by vacuum distillation at 55 °C and 0.09 MPa. The concentrate was subjected to alcohol precipitation with anhydrous ethanol (4:1, v/v), and the filtrate was collected after filtration. The filtrate was concentrated again by vacuum distillation and then extracted thrice with equal volumes of ethyl acetate. The ethyl acetate phase was evaporated at 45 °C and 0.09 MPa. The resulting semi-solid residue was lyophilized for 24 h using a vacuum freeze dryer (DGJ-25S, Shanghai Borden Biotechnology Co., Ltd., Shanghai, China) to obtain purified polyphenol extraction of C. osmantha leaves. The purified extract was ground into fine powder (designated as PECOL), sealed, and stored at −20 °C until the determination of total polyphenol content, which was measured to be 60.0%.

2.2. Animals, Experimental Design, and Diets

Guangxi Ma chickens were purchased from Guangxi Nanning Baodu Biotechnology Co., Ltd (Nanning, Guangxi, China). A total of 360 60-day-old female Guangxi Ma chickens were randomly divided into 2 groups with 6 replicates each, and each replicate included 30 birds. Chickens in the control group were fed a basal diet, while those in the experimental groups received the same basal diet supplemented with PECOL. The PECOL supplementation dosages were calibrated based on its 60% polyphenol content, corresponding to polyphenol levels of 400, 800, and 1200 mg/kg in the respective groups. The basal diet had been formulated according to the nutritional requirements of yellow chickens (NY/T 3645-2020) [15]; its specific formulation is presented in Table 1. All chickens were raised in three-story cages under the recommended environmental conditions and were allowed access to feed and water ad libitum. The breeding period lasted for 60 days. Preliminary experiments showed that 800 mg/kg PECOL significantly improved growth performance and breast amino acid content in Guangxi Ma chicken, so this dosage was selected for subsequent omics analyses. At the end of the trial, all animal slaughter procedures were conducted in strict accordance with the national standard of China (GB/T 19478–2018) [16]. All chickens were transported to the abattoir and fasted for 12 h with free access to water. Following electrical stunning to induce unconsciousness, the animals were exsanguinated via transection of the cervical artery and jugular vein, with complete bleeding achieved within 3 min. The breast and cecum samples were immediately frozen in liquid nitrogen and stored at −80 °C for subsequent analysis.

2.3. Electronic Nose Analysis

Fresh breast muscle tissue (10 g per sample) was placed in a 100 mL beaker and allowed to stand at room temperature for 30 min before undergoing analysis. Using an electronic nose (PEN3, AIRSENSE, Schwerin, Germany), a syringe needle was inserted directly into the sealed sample-containing beaker for measurement, which comprised a sensor array consisting of 10 distinct metal oxide sensors. The samples were analyzed and detected with an electronic nose for 80 s [17].

2.4. Two-Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry (GC×GC-TOF MS)

Analyses were performed on a LECO Pegasus® 4D instrument (LECO, St. Joseph, MI, USA) comprising an Agilent 8890A GC system (Agilent Technologies, Palo Alto, CA, USA) with a split/splitless injector, a dual-stage cryogenic modulator (LECO), and a TOFMS detector (LECO). A DB-Heavy Wax column (30 m × 250 μm I.D., 0.5 μm; Agilent, USA) and an Rxi-5Sil MS column (2.0 m × 150 μm I.D., 0.15 μm; Restek, Bellefonte, PA, USA) were assigned as the 1D and 2D columns, respectively. The carrier gas used was high-purity helium (>99.999%) at 1.0 mL/min constant flow. The oven temperature program included the following conditions: 50 °C hold for 2 min, 5 °C/min ramp to 230 °C, and 230 °C hold for 5 min. The secondary oven and modulator temperatures were 5 °C above the primary oven and 15 °C above the 2D column, respectively, with a 6.0 s modulator period and 250 °C injector temperature. Flavor substances were detected on the LECO Pegasus BT 4D instrument; the transfer line and ion source temperatures were set at 250 °C, with an acquisition frequency of 200 spectra/s. The mass spectrometer was operated in EI mode (70 eV, m/z 35–550, 1960 V detector voltage). Metabolites from GC×GC-TOF MS (LECO, Michigan, USA) were linked to sensory characteristics in terms of species and content.
The Relative Odor Activity Values (ROAVs) were calculated according to the formula described in reference [18]: ROAV = Ci/OTi, where Ci represents the concentration of volatile organic compounds (VOCs) in μg/g, and OTi denotes the odor threshold of the corresponding VOCs in water, as obtained from the standard reference literature [19].

2.5. Gut Microbiota Analysis

DNA was extracted from cecal digesta samples of the control and PECOL groups using the OMEGA Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA), per the manufacturer’s instructions. PCR amplification of the bacterial 16S rRNA gene V3–V4 region was conducted with primers 338F/806R (sequences listed above) and 7 bp barcodes for multiplex sequencing, using a reaction system containing 5 μL of 5× buffer, 0.25 μL of Fast Pfu DNA Polymerase, 2 μL of 2.5 mM dNTPs, 1 μL of each primer, 1 μL of DNA template, and 14.75 μL of ddH2O. Thermal cycling included initial denaturation (98 °C, 5 min), 25 cycles of denaturation (98 °C, 30 s), annealing (53 °C, 30 s), and extension (72 °C, 45 s), plus a final extension (72 °C, 5 min). Purified, quantified amplicons were pooled equally for paired-end 2 × 250 bp sequencing on an Illumina NovaSeq platform (Shanghai Personal Biotechnology Co., Ltd, Shanghai, China).
Raw reads were trimmed and filtered with fastp (v0.20.0), and the reads aligned to the chicken genome via BWA (v0.7.9a) were discarded with their mate pairs. MEGAHIT (v1.1.2) was used for contig assembly based on succinct de Bruijn graphs. Prodigal predicted ORFs in contigs, with ORFs > 100 bp translated to amino acid sequences. A non-redundant gene catalog was constructed with CD-HIT (v4.6.1), retaining sequences with ≥90% identity and coverage [20].

2.6. Metabolomic Analysis

Cecal metabolites were extracted using liquid nitrogen grinding and resuspension in prechilled 80% methanol. Equal-volume extracts from individual samples served as the QC samples, and blank samples were used to exclude background ions. LC-MS/MS-based relative quantification was carried out in positive and negative ion modes, with metabolites identified and quantified via retention time and m/z matching against databases. Following background subtraction and data normalization, metabolomics data were generated. Metabolite annotation relied on the PerSonalbio Next-Generation Metabolomics Database (PSNGM), which integrates an in-house standard library, mzCloud, LIPID MAPS, HMDB, MoNA, NIST 2020 MS/MS Library, and the AI-predicted MS/MS spectral library.

2.7. Transcriptome Analysis

Total RNA was extracted using Trizol (Takara Bio, Otsu, Japan), and the concentration of RNA was measured using NanoDrop 2000 (Thermo Fisher, Waltham, MA, USA) according to the manufacturer’s instructions. The transcriptome libraries were generated for sequencing using the KAPA-Stranded RNA-Seq Library Prep Kit (Illumina). Differentially expressed genes (DEGs) between the two groups were identified by p < 0.05 and fold change >1.5.

2.8. Enrichment Analysis

The Gene Ontology (GO) analysis and Genes and Genomes (KEGG) enrichment analysis of DEGs and differential metabolites were performed to explain the functional enrichment of differential genes and clarify the differences between samples at the gene function level. ClusterProfiler R software (version 4.10.1) package was used for GO function enrichment and KEGG pathway enrichment analysis.

2.9. Statistical Analysis

Data were log-transformed (log2 and/or log10, as appropriate) and Z-score standardized during preprocessing. An unsupervised principal component analysis (PCA) was subsequently applied to the Z-score standardized dataset. OPLS-DA was used to assess group separation, and model overfitting was evaluated by permutation testing (200 permutations). For univariate comparisons between the two groups of metabolomics and transcriptomics data, differences were assessed using two-sided independent sample t-tests. The Kruskal-Wallis (KW) test was applied for 16S rRNA gene sequencing data by default. Spearman’s correlation coefficients were calculated to evaluate associations between variables, with statistical significance defined as p < 0.05. Values are presented as mean ± SEM; p < 0.05 was regarded as significant and p < 0.01 as highly significant.

3. Results

3.1. Electronic Nose (E-Nose) Detection of Breast Muscle from Chickens Supplemented with PECOL

Based on the e-nose data of the PECOL and control groups, principal component analysis (PCA) was performed to generate Figure 1A. The results outline that the two sample groups showed clear separation along the PC1 and PC2 axes. Furthermore, a cluster heatmap analysis was conducted using the electronic nose sensor data of the PECOL and control group (Figure 1B).
Additionally, the e-nose radar plots of breast meat from the PECOL and control groups showed clear discrimination, demonstrating that dietary PECOL supplementation affected the flavor formation of chicken breast (Figure 1C). The results further revealed that samples from the PECOL group exhibited higher sensitivity to hydrogen sulfide (W1W), aromatic compounds and organic sulfides (W2W) (Figure 1C).

3.2. Detection of Volatile Organic Compounds (VOCs) in the Breast Muscles of Chickens Supplemented with PECOL Was Conducted

After identifying differences in meat flavor between the PECOL and control groups, we determined the VOCs of the two groups using GC×GC-TOF MS. A total of 3867 flavor compounds were identified, and they were entered into the Chemical Abstract Service database (CAS, https://www.cas.org/cas-data/cas-registry, accessed on 8 August 2025). Among them, the PECOL group and the control group detected 2696 and 2655 flavor compounds, respectively. OPLS-DA was employed to identify differential VOCs between the PECOL group and the control group. The results indicate that there is no overfitting in the verification model (Figure 2A,B), meaning that all initial OPLS-DA models were valid, and the corresponding results were reliable. The concentrations of lipids and lipid-like molecules, alcohol and heterocyclic compounds in the PECOL group were higher than those in the control group (Figure 2C), suggesting that dietary PECOL supplementation might affect the flavor profile of chicken meat. A differential analysis was conducted on the detected VOCs (p < 0.05 and VIP > 1). A total of 18 compounds exhibited higher concentrations, and 11 exhibited lower concentrations in the PECOL group compared with those in the control group (Figure 2D; Table S1). The heatmap shows the contents of differential VOCs in the PECOL group and control group; the VOC profiles differed significantly between the two groups (Figure 2E). Notably, we found that dietary PECOL effectively promoted fatty acid ethyl ester compounds in the breast muscle, such as decanoic acid ethyl ester, nonanoic acid ethyl ester, octanoic acid ethyl ester, and pentanoic acid ethyl ester (Figure 2F). This observation indicates that supplementation with PECOL will effectively alter the VOCs of chicken meat. To further define how VOCs contribute to chicken breast aroma, we evaluated the ROAVs. Compared with the control group, dietary PECOL supplementation significantly enhanced the sweet, waxy, alkane, and ethereal flavor profiles in chicken breast (Figure 2G). Overall, the PECOL-supplemented group exhibited enhanced flavor profiles in most aroma notes relative to the control group. Interestingly, we found that the strong and unique sweet, fruity, and waxy-like flavors in the PECOL-supplemented group were mainly attributed to the four ethyl ester compounds of fatty acids (Figure 2H).

3.3. Supplementation with PECOL Altered the Microbiome Composition

We speculated that PECOL supplementation would affect the poultry’s intestinal microbial richness. Comparative analysis of cecal microbiota between groups revealed higher microbial diversity in the PECOL group at the operational taxonomic unit (OTU) level (Figure 3A). The similarity of gut microbial communities between the two groups was evaluated using principal coordinate analysis (PCoA), and the results showed significant segregation of gut microbial communities between the PECOL and control groups (Figure 3B). Moreover, further analysis of α-diversity between the PECOL group and the control group revealed a significant increase in microbial community diversity in the PECOL group (Figure 3C). The overall microbial composition was analyzed at the phylum level. The abundances of Firmicutes_A and Synergistota increased in the PECOL group, while those of Bacteroidota and Firmicutes_C decreased (Figure 3D). At the genus level, in the PECOL group, the proportions of Mediterraneibacter_A and g_Massilistercora increased, while those of Phascolarctobacterium_A and Coprenecus decreased (Figure 3E). Using the linear discriminant analysis (LDA) effect size (LEfSe) analysis, it was found that in the cecal microbiota of chickens supplemented with PECOL, microbial communities such as f_Anaerovoracaceae, f_Oscillospiraceae_o_Oscillospirales, o_Oscillospirales, and g_Massilistercora significantly increased (all LDA scores (log10) > 3) (Figure 3F). Interestingly, these microorganisms belong to the phylum Firmicutes, suggesting that PECOL supplementation might alter the composition of Firmicutes. The metabolic functions of the bacterial community were further predicted using the KEGG pathway database via PICRUSt2 (Figure 3G). The results revealed that the microbial communities were mainly involved in amino acid metabolism, carbohydrate metabolism, and the metabolism of cofactors and vitamins.

3.4. Alteration in Metabolic Signature Between the PECOL and Control Group

Given that PECOL supplementation was found to affect the cecal microbiota characteristics of Guangxi Ma chicken, we speculated that these microbial changes might contribute to alterations in metabolic pathways. Therefore, we conducted a further analysis of the changes in metabolites in the cecal contents of chickens in the PECOL and control groups. The results of the OPLS-DA model indicate that the detection data are reliable (Figure 4A,B), meaning that the original model is also reliable. Furthermore, the differential metabolites with VIP > 1 and p < 0.05 were identified. Compared with the control group, 128 metabolites in the PECOL group showed increased expression levels, while 177 metabolites showed decreased expression levels (Figure 4C,D; Table S2). These differential metabolites consisted of various biochemical classes, including 74 organic acids, 69 lipids, 50 organic heterosis compounds, 25 benzenoids, 11 organic nitrogen compounds, 11 phenylpropanoids and polyketides, 10 organic oxygen compounds, 8 alkaloids, 1 acyclic compound, and 6 nucleotides and analogs (Figure 4E). We conducted a KEGG pathway analysis on these differential metabolites and found that they were mainly enriched in the biosynthesis of unsaturated fatty acids in glycerophospholipid metabolism (Figure 4F).

3.5. Differences in Gene Expression Patterns Between the PECOL and Control Groups

We analyzed differences in gene expression profiles between the PECOL and control groups using mRNA-seq. A total of 1146 DEGs were identified, with 185 upregulated and 961 downregulated in the PECOL group relative to the control (Figure 5A; Table S3). The results of the cluster analysis illustrated the expression differences between the two groups (Figure 5B). We further analyzed the expression trends of these common genes and found that the expression levels of 65 genes in subcluster_1 (such as AVD, SLC43A2, and HYI) were lower in the PECOL group than in the control group (Figure 5C), and these genes were mainly enriched in the ribosome pathway and the AMPK signaling pathway (Figure 5D). Furthermore, subcluster_9 contains 185 similar genes, and these genes have higher expression levels in the PECOL group (Figure 5E). Enrichment analysis indicated that these genes were mainly concentrated in pathways such as glycosaminoglycan biosynthesis and starch and sucrose metabolism (Figure 5F). Subsequently, we performed GO analysis on the functions of 1146 DEGs. In terms of biological processes, the top three terms were anatomical structure development, developmental process, and system development (Figure 5G). The KEGG enrichment analysis revealed that these differentially expressed genes were mainly enriched in pathways related to muscle growth, such as the MAPK and PPAR signaling pathways, and extracellular matrix-receptor interaction pathways (Figure 5H).

3.6. Correlations Between the PECOL Supplementation-Induced Gut Microbiome and Metabolome

Spearman’s correlation analysis was performed to assess associations between differential metabolites and differential microbiota. Interestingly, we found that g_Massilistercora, enriched in the PECOL group, was highly correlated with most flavor-related differentially expressed metabolites (Figure 6A). Moreover, we further investigated the correlation between g_Massilistercora and lipid metabolism and glucose metabolites using Spearman’s correlation coefficient analysis. We found that g_Massilistercora was significantly positively correlated with PC(14:1(9Z)/21:0), PC(P-16:0/15:1(9Z)), LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0), and glycerol 3-phosphate contents (Figure 6B–E).

3.7. Multi-Omics Integrated Analysis of Metabolomics and Transcriptomics

The changes in the differential metabolites and DEGs (with |r| > 0.80 and p < 0.05) across each comparison were visualized using a nine-quadrant diagram (Figure 6F). A total of 4889 positively correlated and 173 negatively correlated metabolite-gene pairs were identified (Figure 6F). Furthermore, the KEGG pathway analysis revealed that the differentially expressed metabolites and genes were co-enriched in pathways such as glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids, and linoleic acid metabolism (Figure 6G). To extract the interactions among all of these metabolites and gene expressions, we screened genes related to metabolites significantly associated with g_Massilistercora, and a network diagram was constructed (|r| > 0.80, p < 0.05). We found that genes including GPAT4, LYPLA1, ECI2, and PSMD9 were significantly associated with lipid and glucose metabolism (Figure 6H). Among them, GPAT4 significantly increased in the PECOL group (Table S3).

4. Discussion

The meat quality and flavor of poultry are influenced by various factors such as nutrition and slaughter conditions [21]. Polyphenols are a key category of these bioactive phytochemicals, as they exhibit multiple physiological properties such as antioxidation, anti-inflammation, antibacterial activity, immunomodulation, and intestinal health promotion [22,23,24]. In recent years, polyphenols have been extensively applied as feed additives in animal husbandry [25,26]. Various studies have demonstrated that dietary polyphenols significantly improve production performance, meat quality, immune response, antioxidant capacity, and metabolic function [11]. Previously, our team extracted substances rich in phenolic compounds from C. osmantha leaves, and the content of phenolic compounds reached 60%. However, whether the changes in the gut microbial community, metabolites, and transcriptomic of chicken breast are correlated in chickens supplemented with PECOL remains unknown. In this study, we observed that PECOL supplementation improved meat quality and flavor in broiler chickens for the microbiome–metabolome–genome axis.
Sensory evaluation has long been the most effective method for obtaining comprehensive, high-quality information about poultry muscle. Electronic nose technology can be used for an overall assessment of the flavor characteristics of food samples [19,27]. In this study, we conducted a PCA on the data from the electronic nose. The results show that PECOL supplementation can enhance the flavor intensity and richness of chicken breast meat and improve the overall flavor characteristics of Guangxi Ma chicken. However, the metabolites responsible for these differences in flavor formation still require further investigation.
Flavor and quality are key indicators for consumers in meat assessment and serve as critical determinants of their purchase decisions [28]. We further employed GC×GC-TOF MS to detect breast muscle metabolites in Guangxi chickens. The results suggested that the flavor improvement induced by PECOL supplementation is possibly closely correlated with elevated fatty acid ethyl ester levels. Esters are usually formed by the reaction of free fatty acids and alcohols, which are produced by lipid oxidation, and exhibit fruity or flowery aromas [29]. Studies have shown that ester compounds are one of the main sources of aroma in chicken drumsticks, and they exhibit fruity or flowery aromas [30]. Hence, esters may play a supportive role in chicken meat flavor. We suspect that PECOL is a natural flavor enhancer that enhances the quality of poultry meat and drives advancements in the industry.
Various studies have shown that a polyphenol- rich diet can regulate the composition of the gut microbiota [31,32]. Compared with the controls, there were more Firmicutes and fewer Bacteroidota in the PECOL group. Firmicutes are involved in the synthesis of short-chain fatty acids including butyrate and propionic acid, which exert multiple beneficial effects on the intestine [33]. We found that PECOL supplementation increased Mediterraneibacter_A and g_Massilistercora, which belong to the phylum Firmicutes. Massilistercora has been confirmed to be positively correlated with certain phospholipids and plant-based metabolites, and it can serve as a microbial biomarker to predict host intramuscular fat (IMF) [34,35].
We found that after PECOL supplementation, the main types of differentially upregulated metabolites in the muscles were organic acids and lipids. Phospholipids, as the main component of the IMF, are related to the juiciness, flavor, and tenderness of meat [36,37]. Interestingly, we found that the upregulated differential metabolites mainly participated in unsaturated fatty acid biosynthesis and glycerophospholipid metabolism. Furthermore, PECOL supplementation promoted the accumulation of phospholipids (such as PC(P-16:0/15:1(9Z)), PC(14:1(9Z)/21:0), and LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0). Glycerol 3-phosphate is an important precursor for the synthesis of glycerol phospholipids and participates in the lipid synthesis process such as in phospholipids and fats. In the PECOL treatment group, glycerol 3-phosphate content also increased significantly. These results hinted that PECOL supplementation is involved in the regulation of glycerophospholipid composition in the muscles. The chicken cecum is a key site for microbial fermentation of dietary components. Cecal microbiota contributes to fat deposition and correlates with breast muscle metabolic profiles [38,39]. Dietary prebiotics can also alter the cecal metabolome through microbial metabolites such as SCFAs, thereby significantly affecting chicken meat flavor [40]. Similarly, adding bifidobacterium to pig diets increases the proportions of cecal Firmicutes and boosts lipid metabolism. In this study, we found that g_Massilistercora was significantly positively correlated with PC(14:1(9Z)/21:0), PC(P-16:0/15:1(9Z)), LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0), and glycerol 3-phosphate contents. These results suggest that gut microbiota may modulate lipid metabolism, thereby further affecting the formation of VOCs in the breast muscle.
The analysis integrated metabolomics and transcriptomics data, and it was found that these differential metabolites and DEGs were mainly enriched in glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids, and linoleic acid metabolism. We further analyzed the correlations between these differential phospholipids and the differential genes and screened out the genes that were significantly related to all four phospholipids as candidate genes. A total of 39 genes were screened out and significantly correlated with PC(14:1(9Z)/21:0). Among these, GPAT4 has been found to be a key gene regulating glycerophospholipid metabolism and lipid metabolism [41,42]. Therefore, we hypothesize that these metabolites may exert their effects by interacting with GPAT4. However, the regulatory relationships among key microorganisms, metabolites and genes in this study remain merely at the level of correlation analysis, and their causal associations and underlying mechanisms have not yet been elucidated. In the future, targeted functional validation experiments will systematically uncover the regulatory interaction pathways among the three, providing a basis for dissecting the multi-factor interaction network underlying the formation of meat flavor in broilers.

5. Conclusions

In conclusion, PECOL dietary supplementation potentially improved the flavor of Guangxi Ma chickens. This change may be due to the beneficial effects it has on the gut microbiota, particularly by altering the relative abundances of microbiota with the phylum Firmicutes. PECOL dietary supplementation also modified the contents of cecal metabolites associated with flavor, including multiple phospholipids, and induced differential gene expression linked to the MAPK and PPAR signaling pathways. Collectively, PECOL supplementation mediated complex interactions among the gut microbiome, metabolites, and gene expression in chickens, thereby contributing to the regulation of meat quality and flavor.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods15050862/s1; Table S1: Differential volatile organic compounds in the PECOL and control group; Table S2: Differential metabolites in the PECOL and control group; Table S3: DEGs in the PECOL and control group.

Author Contributions

M.M.: Writing—review and editing, writing—original draft, supervision, methodology, investigation, formal analysis, project administration, and data curation. W.H.: Writing—review and editing, supervision, and investigation. X.L.: Methodology and formal analysis. Y.W.: Investigation. S.J.: Supervision and resources. L.Y.: Methodology and formal analysis. G.L.: Writing—review and editing, resources, and project administration. Y.G.: Writing—review and editing, validation, supervision, resources, project administration, methodology, and conceptualization. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Guangxi Forestry Science and Technology Extension Project (Guilin Scientific Research [2025KX] No.03), the Natural Scientific Foundation of China (U25A20701), the China Agriculture Research System of MOF and MARA (CARS-41-G06), the Science and Technology Program of Guangdong Academy of Agricultural Sciences (R2024YJ-YB3004 and R2023PY-QY012), the Science and Technology Plan Project of Guangzhou (2024E04J0562), and Guangdong Basic and Applied Basic Research Foundation (2023A1515111031).

Institutional Review Board Statement

All animal experimental protocols were conformed to “The Instructive Notions with Respect to Caring for Laboratory Animals” issued by the Ministry of Science and Technology of the People’s Republic of China, and approved by the Institutional Animal Care and Use Committee eat the Guangdong Academy of Agricultural Sciences (approval ID: W2025001 and approval date: 5 January 2025).

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. E-nose analysis of the PECOL and control groups. (A–C). The PCA (principal component analysis) (A) (n = 6), heatmap (B) (n = 6), and radar plot (C) (n = 6) based on the e-nose dataset from the PECOL and control groups. In panel (A), blue represents the PECOL group, and orange represents the control group. In panel (C), the concentric circles in the diagram represent the relative intensity of sensor responses, with 0, 1, and 2 indicating the minimum, moderate, and maximum response levels, respectively.
Figure 1. E-nose analysis of the PECOL and control groups. (A–C). The PCA (principal component analysis) (A) (n = 6), heatmap (B) (n = 6), and radar plot (C) (n = 6) based on the e-nose dataset from the PECOL and control groups. In panel (A), blue represents the PECOL group, and orange represents the control group. In panel (C), the concentric circles in the diagram represent the relative intensity of sensor responses, with 0, 1, and 2 indicating the minimum, moderate, and maximum response levels, respectively.
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Figure 2. Mass spectrum from GC × GC-TOF MS analysis in the PECOL and control groups. (A,B). The OPLS-DA score scatter plots (A) (n = 6) and permutation test (B) (n = 6) results of the PECOL and control groups. (C–E). Composition (C) (n = 6), statistics (D) (n = 6), and heatmap (E) (n = 6) of volatile flavor compounds in the PECOL and control groups. (F). Relative fatty acid ethyl ester compound content in the PECOL and control groups (n = 6). (G). The flavor intensity radar map of the number of volatile compounds (n = 6). (H). The interaction map of VOCs and each flavor, indicating the degree of contribution of VOCs to flavor (n = 6). In panel (F), the results are shown as mean ± SEM. Statistical significance of differences between means was assessed using an independent sample t-test. (* p < 0.05; ** p < 0.01). In panel (B), the diagonal line represents the linear regression trend line of Q², and the gray horizontal line serves as the reference line, which is used to determine the positive or negative value of Q². In panel (H), blue represents flavor, while red represents metabolites.
Figure 2. Mass spectrum from GC × GC-TOF MS analysis in the PECOL and control groups. (A,B). The OPLS-DA score scatter plots (A) (n = 6) and permutation test (B) (n = 6) results of the PECOL and control groups. (C–E). Composition (C) (n = 6), statistics (D) (n = 6), and heatmap (E) (n = 6) of volatile flavor compounds in the PECOL and control groups. (F). Relative fatty acid ethyl ester compound content in the PECOL and control groups (n = 6). (G). The flavor intensity radar map of the number of volatile compounds (n = 6). (H). The interaction map of VOCs and each flavor, indicating the degree of contribution of VOCs to flavor (n = 6). In panel (F), the results are shown as mean ± SEM. Statistical significance of differences between means was assessed using an independent sample t-test. (* p < 0.05; ** p < 0.01). In panel (B), the diagonal line represents the linear regression trend line of Q², and the gray horizontal line serves as the reference line, which is used to determine the positive or negative value of Q². In panel (H), blue represents flavor, while red represents metabolites.
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Figure 3. Relative gut microbiota abundance in the PECOL and control groups. (A). Rank abundance curves of gut microbiota at the OUT level of the PECOL and control groups (n = 6). (B). PCA based on the unweighted UniFrac analysis of the OUT level (n = 6). (C). The observed species, Shannon, and chao1 indices of the PECOL and control groups (n = 6). (D,E). Relative gut microbiota abundance at the phylum (D) (n = 6) and genus (E) (n = 6) level in cecum samples of the PECOL and control groups. (F). LEfSe analysis of the PECOL and control groups (n = 6). (G). The metabolic functions of the 16S rRNA gene sequencing data with the PECOL group and the control group were predicted using PICRUSt2 (n = 6). In panels (C), the results are shown as mean ± SEM. The statistical significance of the differences between means was assessed using an independent sample t-test.
Figure 3. Relative gut microbiota abundance in the PECOL and control groups. (A). Rank abundance curves of gut microbiota at the OUT level of the PECOL and control groups (n = 6). (B). PCA based on the unweighted UniFrac analysis of the OUT level (n = 6). (C). The observed species, Shannon, and chao1 indices of the PECOL and control groups (n = 6). (D,E). Relative gut microbiota abundance at the phylum (D) (n = 6) and genus (E) (n = 6) level in cecum samples of the PECOL and control groups. (F). LEfSe analysis of the PECOL and control groups (n = 6). (G). The metabolic functions of the 16S rRNA gene sequencing data with the PECOL group and the control group were predicted using PICRUSt2 (n = 6). In panels (C), the results are shown as mean ± SEM. The statistical significance of the differences between means was assessed using an independent sample t-test.
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Figure 4. Analysis of the metabolome in the PECOL and control groups. (A,B). The OPLS-DA score scatter plots (A) (n = 6) and permutation test (B) (n = 6) results of the PECOL and control groups. (C–E). Statistics (C) (n = 6), heatmap (D) (n = 6), and chemical taxonomy (E) (n = 6) of differential metabolites of the PECOL and control groups. (F). KEGG pathway analysis of differential metabolites of the PECOL and control groups (n = 6). In panel (B), the diagonal line represents the linear regression trend line of Q², and the gray horizontal line serves as the reference line, which is used to determine the positive or negative value of Q².
Figure 4. Analysis of the metabolome in the PECOL and control groups. (A,B). The OPLS-DA score scatter plots (A) (n = 6) and permutation test (B) (n = 6) results of the PECOL and control groups. (C–E). Statistics (C) (n = 6), heatmap (D) (n = 6), and chemical taxonomy (E) (n = 6) of differential metabolites of the PECOL and control groups. (F). KEGG pathway analysis of differential metabolites of the PECOL and control groups (n = 6). In panel (B), the diagonal line represents the linear regression trend line of Q², and the gray horizontal line serves as the reference line, which is used to determine the positive or negative value of Q².
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Figure 5. Analysis of transcriptome data in the PECOL and control groups. (A,B). DEGs (A) (n = 6) and hierarchical clustering analysis (B) (n = 6) of the PECOL and control groups. (C,E). Subcluster analysis of DEGs in the PECOL and control groups (n = 6). (D,F). KEGG pathway analysis of DEGs with similar expression trends between the PECOL and control groups (n = 6). (G,H). GO terms analysis (G) (n = 6) and KEGG pathways analysis (H) (n = 6) of DEGs in the PECOL and control groups. In panel (B), The green on the right represents the control group, while the red represents the PECOL group. In panel (C and E), the green lines represent the average expression of genes in the cluster, while the dotted lines represent the individual expression levels of all the genes.
Figure 5. Analysis of transcriptome data in the PECOL and control groups. (A,B). DEGs (A) (n = 6) and hierarchical clustering analysis (B) (n = 6) of the PECOL and control groups. (C,E). Subcluster analysis of DEGs in the PECOL and control groups (n = 6). (D,F). KEGG pathway analysis of DEGs with similar expression trends between the PECOL and control groups (n = 6). (G,H). GO terms analysis (G) (n = 6) and KEGG pathways analysis (H) (n = 6) of DEGs in the PECOL and control groups. In panel (B), The green on the right represents the control group, while the red represents the PECOL group. In panel (C and E), the green lines represent the average expression of genes in the cluster, while the dotted lines represent the individual expression levels of all the genes.
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Figure 6. Correlation analysis of the gut microbiota, metabolites, and genes. (A). Correlation analysis of differential metabolites and differentially abundant microbiota (n = 6) (* p < 0.05; ** p < 0.01; *** p < 0.001). (B–E). Correlation analysis of LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0) (B) (n = 6), PC(14:1(9Z)/21:0) (C) (n = 6), PC(P-16:0/15:1(9Z)) (D) (n = 6), and glycerol 3-phosphate (E) (n = 6) with g_Massilistercora. (F). Nine-quadrant diagrams analysis of differential metabolites and DEGs in the PECOL and control groups (n = 6). (G). KEGG pathway analysis of differential metabolites and DEGs in the PECOL and control groups (n = 6). (H). Regulatory network between PC(14:1(9Z)/21:0) (|R| > 0.80 and p < 0.05) and DEGs in the PECOL and control groups (n = 6). In panel (F), The diagonal on the left represents pairs of metabolites and genes with consistent differential expression patterns, while the diagonal on the right represents pairs of metabolites and genes with opposite differential expression patterns. The red and green dots indicate that the metabolites remain unchanged, while the genes are upregulated or downregulated or the metabolites are upregulated or downregulated despite the gene’s unchanged state. The black dots represent pairs of metabolites and genes with no significant association. Among them, the significantly different metabolites were screened with VIP > 1 and p < 0.05, while the DEGs were identified at a threshold of p < 0.05. In panel (G), The red dotted line indicates the position of p = 0.05. Above the dotted line are the results where the p< 0.05. In panel (H), blue represents metabolites, and red represents genes.
Figure 6. Correlation analysis of the gut microbiota, metabolites, and genes. (A). Correlation analysis of differential metabolites and differentially abundant microbiota (n = 6) (* p < 0.05; ** p < 0.01; *** p < 0.001). (B–E). Correlation analysis of LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0) (B) (n = 6), PC(14:1(9Z)/21:0) (C) (n = 6), PC(P-16:0/15:1(9Z)) (D) (n = 6), and glycerol 3-phosphate (E) (n = 6) with g_Massilistercora. (F). Nine-quadrant diagrams analysis of differential metabolites and DEGs in the PECOL and control groups (n = 6). (G). KEGG pathway analysis of differential metabolites and DEGs in the PECOL and control groups (n = 6). (H). Regulatory network between PC(14:1(9Z)/21:0) (|R| > 0.80 and p < 0.05) and DEGs in the PECOL and control groups (n = 6). In panel (F), The diagonal on the left represents pairs of metabolites and genes with consistent differential expression patterns, while the diagonal on the right represents pairs of metabolites and genes with opposite differential expression patterns. The red and green dots indicate that the metabolites remain unchanged, while the genes are upregulated or downregulated or the metabolites are upregulated or downregulated despite the gene’s unchanged state. The black dots represent pairs of metabolites and genes with no significant association. Among them, the significantly different metabolites were screened with VIP > 1 and p < 0.05, while the DEGs were identified at a threshold of p < 0.05. In panel (G), The red dotted line indicates the position of p = 0.05. Above the dotted line are the results where the p< 0.05. In panel (H), blue represents metabolites, and red represents genes.
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Table 1. Composition and nutrient levels of basal diets (air-dry basis).
Table 1. Composition and nutrient levels of basal diets (air-dry basis).
IngredientsContent/%Nutrient Levels (2)Content
Corn67.30Metabolizable Energy/(MJ/kg)12.13
Soybean meal21.15Crude Protein/%14.82
Rice bran3.85Crude Fiber/%5.30
Premix (1)3.85Calcium/%0.20
Limestone3.85Total Phosphorus/%0.06
Total100Ether Extract/(g/kg)77.00
(1) The premix provided the following per kg of diets: 500 mg of Cu (as copper sulfate), 5000 mg of Fe (as ferrous sulfate), 3000 mg of Zn (as zinc sulfate), 3000 mg of Mn (as manganese sulfate), VA 250,000 IU, VD3 100,000 IU, 200 mg of VE, 10 mg of VK3, 15 mg of VB1, 50 mg of VB2, 10 mg of VB6, 0.1 mg of VB12, 250 mg of nicotinic acid, and 80 mg of D-pantothenic acid. (2) ME was a calculated value, while the others were measured values.
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Ma, M.; He, W.; Lin, X.; Wang, Y.; Jiang, S.; Yang, L.; Li, G.; Gu, Y. Using Integrated Microbiome–Metabolome–Genome Axis Data to Elucidate the Mechanism by Which Polyphenol Content in the Extract from C. osmantha Leaves (PECOL) Regulates Broiler Flavor. Foods 2026, 15, 862. https://doi.org/10.3390/foods15050862

AMA Style

Ma M, He W, Lin X, Wang Y, Jiang S, Yang L, Li G, Gu Y. Using Integrated Microbiome–Metabolome–Genome Axis Data to Elucidate the Mechanism by Which Polyphenol Content in the Extract from C. osmantha Leaves (PECOL) Regulates Broiler Flavor. Foods. 2026; 15(5):862. https://doi.org/10.3390/foods15050862

Chicago/Turabian Style

Ma, Manting, Wanxi He, Xiajin Lin, Yibing Wang, Shouqun Jiang, Li Yang, Guizhen Li, and Yao Gu. 2026. "Using Integrated Microbiome–Metabolome–Genome Axis Data to Elucidate the Mechanism by Which Polyphenol Content in the Extract from C. osmantha Leaves (PECOL) Regulates Broiler Flavor" Foods 15, no. 5: 862. https://doi.org/10.3390/foods15050862

APA Style

Ma, M., He, W., Lin, X., Wang, Y., Jiang, S., Yang, L., Li, G., & Gu, Y. (2026). Using Integrated Microbiome–Metabolome–Genome Axis Data to Elucidate the Mechanism by Which Polyphenol Content in the Extract from C. osmantha Leaves (PECOL) Regulates Broiler Flavor. Foods, 15(5), 862. https://doi.org/10.3390/foods15050862

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