Effects of dietary Phellinus linteus polysaccharides supplementation on productive performance, egg quality, antioxidant status, immune function, cecal microbiota, jejunal morphology, and metabolism in laying hens under lipopolysaccharide challenge

Abstract

The effects of Phellinus linteus polysaccharides (PLP) on the immune responses of laying hens remain unclear. This study systematically investigated the impacts of PLP on productive performance, antioxidant status, immune response, cecal microbiota, short-chain fatty acids (SCFAs), jejunal morphology, and metabolism in laying hens challenged with lipopolysaccharide (LPS). A total of 240 Changshun green-shell laying hens were randomly assigned to 4 treatments (6 replicates × 10 hens): CON group (basal diet), PLP group (basal diet + 4.2 g/kg PLP), LPS group (basal diet + 1 mg/kg LPS), and LPS+PLP group (basal diet + 4.2 g/kg PLP + 1 mg/kg LPS). The results demonstrated that LPS challenge significantly reduced laying rate, albumen height, Haugh unit, and yolk total amino acid contents (P < 0.05). Conversely, PLP supplementation increased laying rate, yolk weight, and total essential amino acid content in yolk (P < 0.05). Regardless of LPS challenge, PLP significantly elevated serum total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD), and IgA (P < 0.05). LPS decreased hepatic T-AOC and glutathione peroxidase (GSH-Px) activities (P < 0.05), whereas PLP increased hepatic T-AOC and GSH-Px (P < 0.05). A significant PLP × LPS interaction was observed for serum and hepatic malondialdehyde (MDA), with PLP reversing LPS-induced MDA accumulation (P < 0.05). We found that PLP markedly reduced hepatic pro-inflammatory cytokines (IL-6, IL-1β, and TNF-α) and increased anti-inflammatory cytokines IL-10, irrespective of LPS (P < 0.05). Moreover, PLP increased cecal acetate and butyrate concentrations and improved jejunal morphology by increasing villus height and villus height/crypt depth ratio (P < 0.05), independent of LPS. Microbiota profiling showed that PLP enriched beneficial taxa, including Lactobacillus, Alloprevotella, and Faecalibacterium under LPS challenge and increased Faecalibacterium prausnitzii abundance regardless of LPS (P < 0.05). Metabolomics suggested that PLP modulated arginine and proline metabolism irrespective of LPS, and regulated α-linolenic acid and arachidonic acid metabolism, as well as MAPK and calcium signaling pathways under LPS stimulation. Collectively, PLP may alleviate LPS-induced impairment and support gut-liver health, potentially by enriching SCFA-producing bacteria and enhancing SCFA production, indicating its promise as a functional feed additive to improve overall performance in laying hens.

Introduction

The intestinal microbiota plays an indispensable role in modulating the host's digestive and absorptive functions, defending against pathogen invasion, and maintaining immune homeostasis (Pandit et al., 2018; Feng et al., 2023). However, the laying hens reared in intensive farming environments, where gram-negative bacteria are ubiquitous, inevitably inhale large amounts of endotoxins. They are continually exposed to various challenges, including immunological stress, pathogen infections, and oxidative damage (Zhou et al., 2023; Chen, Wang, et al., 2024; Zhang et al., 2024). These adverse factors may trigger excessive immune activation and oxidative stress responses (Nie et al., 2018; Ding et al., 2022).

Lipopolysaccharide (LPS) has been demonstrated to induce histological alterations, stimulate excessive free radical production, cause oxidative stress and apoptosis in intestinal epithelial cells, and impair intestinal structure and barrier function, thereby disrupting metabolic processes and organ functions (Zheng et al., 2022; Liu et al., 2022). Notably, impaired gut barrier integrity or microbial dysbiosis may lead to the translocation of endotoxins (e.g., LPS) into the systemic circulation, triggering hepatic inflammation and tissue damage (Albillos et al., 2020; Han et al., 2023). In recent decades, antibiotics have been frequently added to feed to mitigate stress and inflammatory responses, thereby enhancing productivity (Castanon, 2007). Unfortunately, the misuse of antibiotics has resulted in several problems, including antibiotic resistance and the emergence of foodborne illness outbreaks (Larsson et al., 2022). Therefore, there is an urgent need for nutrition and health-based strategies, such as incorporating natural medicinal fungal extracts as feed additives, to address these challenges in laying hen production.

Phellinus linteus, a natural mushroom predominantly distributed in China, South Korea, Japan, and other Asian countries, comprises primary bioactive constituents, including triterpenoids, polyphenols, and polysaccharides (Suabjakyong et al., 2015; Chen et al., 2019). The polysaccharides extracted from Phellinus linteus have garnered extensive attention due to their remarkable antioxidant, anti-inflammatory, antibacterial, and immunomodulatory activities (Hu et al., 2018; Liu et al., 2024), which are attributed to their diverse monosaccharide constituents and chemical structures (Pei et al., 2015). Several investigations have revealed that polysaccharides are generally not directly degraded by host gastrointestinal digestive enzymes. Instead, they were further fermented by beneficial intestinal microbiota, leading to the production of SCFAs (Koh et al., 2016). These metabolites regulate intestinal luminal pH, serve as a primary energy source for intestinal epithelial cells, and inhibit the growth and colonization of pathogenic bacteria, thereby contributing to the maintenance of intestinal homeostasis and overall host health (Makki et al., 2018; Hu et al., 2024). Recent studies have confirmed that supplementation with Phellinus linteus polysaccharides (PLP) may improve performance by increasing the antioxidant capacity of chickens (Liu, Wu, et al., 2023; Li et al., 2025). It has also been reported that these effects primarily occur through regulation of the gut microbiota and SCFA production. Meanwhile, PLP reduced the release of inflammatory factors and oxidative stress by inhibiting the activation of the TLR4/NF-κB/MAPK signaling pathways (Hu et al., 2024). To date, the regulation of the LPS-induced inflammatory responses and intestinal metabolism of laying hens by PLP remains unclear. Given that PLP has a favorable safety profile, high tolerability, substantial modifiability, and ready availability, it may be a promising candidate for development as a functional feed additive (Wang et al., 2014; Pei et al., 2015; Hu et al., 2024).

Hence, this study investigated the effects of PLP supplementation on productive performance, intestinal morphology, microbiota composition, SCFAs, immune function, and jejunal metabolism in laying hens under LPS challenge. We aimed to assess the potential of PLP as a novel functional feed additive in regulating inflammatory responses and productive performance in laying hens, thereby providing new evidence and insights to support its incorporation into poultry nutritional strategies.

Materials and methods

Experimental design

This study was approved by the Ethics Committee of the College of Animal Science, Guizhou University (Approval No. EAE-GZU-2025-E026). A total of 240 healthy 26-week-old Changshun green-shell laying hens with similar body weights were randomly assigned to 4 treatments in a completely randomized design (CRD) with a 2 × 2 factorial arrangement (6 replicates per treatment × 10 birds per replicate). CON group (fed basal diet; on average body weight: 1.822 ± 0.043 kg), PLP group (basal diet + 4.2g /kg PLP; on average body weight: 1.789 ± 0.042 kg), LPS group (basal diet + 1mg/kg LPS; on average body weight: 1.846 ± 0.048 kg), LPS+PLP group (basal diet + 4.2g /kg PLP + 1 mg/kg LPS; on average body weight: 1.795 ± 0.041 kg), and the level of PLP supplementation was based on the previous report (Liu, Wu, et al., 2023). To induce an inflammatory response, laying hens in the LPS and LPS+PLP groups were injected intraperitoneally with LPS on days 18, 21, and 24 of the trial period with an amount of 1 mg/kg body weight (Liu et al., 2022), while an equal volume of 0.9 % sodium chloride (NaCl) solution was administered to the CON and PLP groups. The trial period lasted 6 weeks, during which the experimental laying hens were housed in a controlled environment with free access to food and water. The temperature was maintained at 24 ± 2°C, and the relative humidity was 60 ± 10 %. The basal diet was formulated in accordance with the China National Feeding Standard for Chickens (NY/T 33-2004) (Table 1). PLP was acquired from Shaanxi Undersun Biomedtech Co., Ltd. (Shaanxi, China) as a brownish-yellow powder. LPS (Escherichia coli O55:B5) was purchased from Beijing Solarbio Science and Technology Co., Ltd (Beijing, China).

Table 1.

Ingredients and chemical composition of the experimental diets.

Items	Content
Ingredients (%)
Corn Corn germ	58.00 10.50
Soybean meal	16.00
Wheat bran	10.00
Soybean oil	1.50
Calcium hydrogen phosphate	1.00
Limestone NaCl Sodium bicarbonate Montmorillonite Lysine Methionine Premixa Total	1.19 0.20 0.10 0.20 0.21 0.10 1.00 100
Nutrients levelsb
Metabolizable energy(MJ/kg)	11.51
Crud protein	15.79
Nonphytate phosphorus	0.32
Calcium	0.97
Lysine	0.86

^aThepremix provided per kilogram of compound in the diet: Vitamin A, 11,000 IU; vitamin D₃, 16,500 IU; vitamin E, 5.5 IU; vitamin K, 4.409 mg; vitamin B₁, 0.551 mg; vitamin B₂, 1.102 mg; vitamin B₁₂, 0.07 mg; folic acid, 1.0 mg; vitamin C, 75 mg; pantothenic acid, 8.81mg; folic acid, 1.05 mg; choline chloride, 0.1 g; calcium formate, 0.2 g; complex enzyme (SFQ-081), 0.2 g.

^bCrude protein was measured values, while the others were calculated values.

Determination of total sugar content in PLP

The total sugar content was determined using the phenol-sulfuric acid method with glucose as the standard, following the modified procedure by Wang et al. (2014). Briefly, the glucose standard solutions (0.1, 0.2, 0.3, 0.4, and 0.8 mL) were mixed with ultrapure water to a final volume of 1.0 mL in test tubes. Subsequently, 0.5 mL of 5 % phenol solution was added, followed by 2.5 mL of concentrated sulfuric acid with immediate shaking. The mixtures were allowed to stand at room temperature for 30 min. A control was prepared by combining ultrapure water, phenol solution, and sulfuric acid. Absorbance at 490 nm was measured to construct the standard curve, and the polysaccharide content in the sample was calculated from its absorbance.

Measurement of the molecular weight of PLP

The molecular weights of PLP were determined by gel permeation chromatography (GPC) following the method of Pei et al. (2015), with minor modifications. The chromatographic analysis was performed using an Agilent 1100 series liquid chromatography system (Agilent Technologies, USA), equipped with a TSK-gel G3000 PWXL column (7 μm, 7.8 × 300 mm). The flow rate was set to 0.5 mL/min, with the column temperature maintained at 40°C. A differential refractive index detector was employed, and the mobile phase was a 0.05 M sodium sulfate solution (Biotyscience Co., Ltd., Beijing, China). Dextran standards with varying molecular weights, provided by the National Institute for Food and Drug Control (NIFDC, Beijing, China), were used to generate standard curves. The linear regression equation for the molecular weight standard curve was: y = –2.4034x + 23.635, R² = 0.9906.

Analysis of monosaccharide composition

The monosaccharide composition of PLP was analyzed by high-performance liquid chromatography (HPLC) using the method of Zhou et al. (2023), with slight modifications. A 10 mg sample of PLP was placed in an ampule with 1 mL of ultrapure water for dissolution, followed by the addition of 1 mL of 4 M trifluoroacetic acid (TFA). The ampoule was sealed and heated at 110°C for 4 h for hydrolysis. Afterward, the solution was extracted and neutralized with sodium hydroxide (NaOH). A 200 μL aliquot of the solution was mixed with 200 μL of an internal standard, then 100 μL was transferred to a centrifuge tube. To this, 100 μL of 0.3 M NaOH and 100 μL of 0.5 M PMP were added. The mixture was incubated in a 70°C water bath for 1 h under light protection. After cooling, 100 μL of 0.3 M HCl was added for neutralization. The sample was extracted with 500 μL of chloroform, vortexed, and then centrifuged at 7000 rpm for 5 min. The upper aqueous layer was collected, and the extraction was repeated four times. The final aqueous phase was filtered through a 0.22 μm filter and transferred to a vial for HPLC analysis. HPLC was performed using an Agilent 1200 Infinity system with an Eclipse XDB-C18 column (5 μm, 4.6 × 250 mm), at a flow rate of 1 mL/min, column temperature of 30°C, and detection at 254 nm. Mobile phases A and B consisted of 18 % and 60 % acetonitrile, respectively, with the addition of triethylamine, and gradient elution was employed.

Fourier transform infrared (FTIR) spectroscopy

FTIR spectra of the polysaccharides were obtained using a Nexus 670 FTIR spectrometer (Thermo Nicolet IS5, USA), equipped with the KBr pellet technique (Zhou et al., 2023), scanning across the wavenumber range of 500–4000 cm⁻¹.

Sample collection

On day 42 of the trial, one laying hen from each replicate was randomly chosen, and blood was collected from the wing vein. The samples were centrifuged at 3,000 rpm for 10 min at 4°C to separate the serum. Subsequently, laying hens were humanely euthanized by cervical dislocation. Under sterile conditions, cecal contents, liver, and jejunal contents were collected, rapidly frozen in liquid nitrogen, and stored at –80°C for subsequent analyses.

Laying performance and egg quality

The study evaluated several key indicators using the method described by Wang et al. (2024), including average daily feed intake (ADFI), average egg production, average egg weight, and feed conversion ratio (FCR). ADFI (g/d) = total feed consumed/number of birds × number of feeding days; FCR (g/g) = feed intake/egg mass. On days 28 and 42, thirty fresh eggs were randomly collected from each group. Eggshell strength, shell thickness, yolk color, albumen height, and Haugh Unit were measured using an automated egg quality analyzer (ETP-01; ORKA Food Technology Ltd., USA) within 5 h of egg collection.

Amino acid composition analysis of egg yolk

The amino acid composition of egg yolk was analyzed according to a previously described method with slight modifications (Tian et al., 2024). One egg was randomly selected from each experimental replicate, and its albumen and yolk were carefully separated for subsequent analysis. The yolk was stored at –80°C, lyophilized, and pulverized into a fine powder using a tissue grinder. An accurately weighed 50 mg sample was introduced into a hydrolysis tube containing 10 mL of 6 mol/L HCl. The tube was submerged in a cryogen, subjected to three cycles of vacuum and nitrogen flushing, and then sealed under a nitrogen atmosphere. Hydrolysis was performed in the oven at 110°C for 24 h (HB43-S, Sartorius AG Ltd., Germany). After cooling, the hydrolysate was filtered, transferred to a 50 mL volumetric flask, washed with deionized water, and adjusted to the final volume. A 1.0 mL aliquot was transferred to a test tube, evaporated to dryness under reduced pressure at 50°C, and reconstituted in deionized water. The sample was dried again to remove residual moisture. The resulting residue was dissolved in 5 mL of sodium citrate buffer (pH 2.2), passed through a 0.22 μm filter, and transferred to a vial. Amino acid composition was measured using the automated amino acid analyzer with a sulfonic acid-type cation exchange resin column, with detection wavelengths set at 570 nm and 440 nm (S433Dsp, Sykam GmbH Ltd., Germany). Finally, the amino acid concentrations were quantified using the external standard method based on peak area measurements.

Serum and hepatic biochemistry indicators

The activities of hepatic antioxidant enzymes were assessed using previously reported methods (Hu et al., 2025). Specifically, malondialdehyde (MDA), glutathione peroxidase (GSH-Px), total superoxide dismutase (T-SOD), and total antioxidant capacity (T-AOC) were quantified using commercially available assay kits. Serum immunoglobulins (IgA, IgG, and IgM) and hepatic inflammatory cytokines (IL-6, IL-1β, TNF-α, and IL-10) were measured with enzyme-linked immunosorbent assay (ELISA) kits. All assay kits were obtained from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). An automated biochemical analyzer was used to quantify serum albumin (ALB), triglycerides (TG), total protein (TP), and total cholesterol (TC).

Hepatic histological and jejunal morphology

Hepatic and jejunal tissues were fixed in 4 % paraformaldehyde, dehydrated through a graded ethanol series, cleared in xylene, and embedded in paraffin wax. The embedded tissues were sectioned into 3 μm slices using a Leica RM2016 microtome. The sections were mounted on glass slides and stained with hematoxylin and eosin. Morphological analysis was performed according to the method described by Zhao et al. (2024). Six intact, vertically oriented villi were selected from each sample and evaluated using Image-Pro Plus 6.0 software (Media Cybernetics, Inc., Bethesda, MD, USA).

Measurement of the cecal SCFAs

The concentrations of SCFAs in cecal digesta were determined by gas chromatography (TRACE 1300, Thermo Fisher Scientific Inc., USA) as previously described (Zhao et al., 2006), with modifications. Approximately 0.5 g of precisely weighed digesta was placed in a 1.5 mL centrifuge tube and mixed with 0.5 mL of ultrapure water. After adding 100 mg of glass beads, the sample was homogenized at 4°C for 1 min and then centrifuged at 12,000 rpm for 10 min. Centrifuged supernatant (200 μL) was carefully transferred to a new tube. To acidify the sample, add 100 μL of 15 % phosphoric acid, 20 μL of an internal standard (4-methylvaleric acid, 375 μg/mL), and 280 μL of diethyl ether. The liquid was homogenized for 1 min, then centrifuged again under the same conditions. Chromatographic separation was performed on an Agilent HP-INNOWAX capillary column using split injection mode (1 μL, 10:1 split ratio). The injection port temperature was set to 250°C, while the ion source and transfer line were maintained at 300°C. Finally, the SCFA concentrations were calculated and expressed as μmol/g, including acetic, propionic, butyric, isobutyric, valeric, and isovaleric acids.

Untargeted metabolomics profiling

Metabolite extraction was performed as described in a previous report (He et al., 2022). Briefly, 100 mg of frozen, jejunal content was transferred to an Eppendorf tube. Then, 500 μL of 80 % (v/v) methanol in water was added, followed by vortex mixing to homogenize thoroughly. Samples were kept on ice for 5 min before centrifugation at 15,000 rpm for 20 min at 4°C. After centrifugation, the supernatant was carefully collected and adjusted with MS-grade water to a final methanol content of 53 % (v/v). The supernatant was then centrifuged at 15,000 rpm for 20 min at 4°C before being utilized for LC-MS analysis (Novogene Biotechnology Co., Ltd, Beijing, China). Data obtained from mass spectrometry were processed using Compound Discoverer software (v3.3; Thermo Fisher Scientific, USA), which performed spectrum analysis and database matching to provide both qualitative and quantitative information on metabolites. Lastly, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to study metabolic processes and pathways.

Microbiota sequencing

The cecal microbiota was sequenced following the method by Yue et al. (2025). Briefly, DNA was extracted from 24 cecal contents using the QIAamp DNA Kit (QIAGEN, Hilden, Germany). The V3-V4 region of the bacterial 16S rRNA gene was amplified using primers 357F (5′-ACTCCTACGGRAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR procedure used a 50 µL mixture of 1–2 µL of DNA, 200 µM dNTPs, 0.2 µM of each primer, 10 µL of 5X buffer, and 1 U of Phusion DNA Polymerase. Following purification with the Axygen DNA Gel Extraction Kit, barcoded PCR products were quantified employing the FTC-3000 Real-Time PCR System. A second PCR amplification with dual barcodes was performed for eight cycles under identical conditions to the first, followed by sequencing on the Illumina NovaSeq platform.

Statistical analysis

All data were analysed using SPSS software (version 22.0; SPSS Inc., Armonk, NY, USA). A two-way analysis of variance (ANOVA) was conducted using the general linear model (GLM) to assess the main effects of PLP and LPS, as well as their interaction. Results are presented as mean ± standard error (SE), with P < 0.05 suggesting statistical significance. Bar charts were generated using OriginPro 2025.

Results

Microstructure and morphology of PLP

As shown in Fig. 1, scanning electron microscope images of PLP revealed that the particles are generally irregularly spherical with pronounced surface wrinkles, and their sizes are mainly distributed in the range of 5–60 μm.

Fig. 1.

Morphology of PLP observed by scanning electron microscopy at different magnifications.

Structural characterization, molecular weight, total sugar content, and monosaccharide composition of PLP

In the current investigation, PLP exhibited a total sugar content of 83.49 ± 1.08 %, as determined by the phenol-sulfuric acid method. GPC analysis indicated that the molecular weight of PLP was approximately 167.5 kDa (Fig. 2. B and Table 2). In addition, HPLC analysis revealed that PLP was primarily composed of Glc, Man, GlcN, Rha, GlcA, and Xyl, with molar percentages of 99.12 %, 0.35 %, 0.06 %, 0.02 %, 0.19 %, and 0.27 %, respectively (Fig. 2. A and Table 3). Apparently, the predominant monosaccharide in PLP was Glc. To determine the configuration of PLP initially, we conducted FTIR characterization. As shown in Fig. 2. C, the broad and strong peak at approximately 3406 cm⁻¹ is caused by the O-H stretching vibrations. The absorption peak at 2924 cm⁻¹ is associated with C-H single peak stretching vibrations. The prominent absorption peaks in the 1000–1200 cm⁻¹ range are primarily due to C-O and C-O-C stretching vibrations of the pyranose ring and glycosidic bonds. Moreover, the peak at about 846 cm⁻¹, typically linked to α-glycosidic bonds, suggests that PLP may predominantly possess an α-configuration.

Fig. 2.

Structural characterization, molecular weight, and monosaccharide composition of PLP. (A) Monosaccharide composition analysis; (B) Determination of the molecular weight of PLP; (C) Fourier transform infrared (FTIR) spectroscopy of PLP.

Table 2.

Determination of the molecular weight of PLP.

Items	Peaks	Average retention time (min)	Mw (kDa)
PLP	Peak 1	11.119	161.6
	Peak 2	15.095	3.6
	Peak 3	16.038	1.4
	Peak 4	16.935	0.6
	Peak 5	17.745	0.3
Total Mw	-	-	167.5

Abbreviation: Mw indicates molecular weight.

Table 3.

Analysis of monosaccharide composition of PLP.

Item	Monosaccharide composition (mol%)
Man	0.35
GlcN	0.06
Rha	0.02
GlcNAc	0
GlcA	0.19
GalA	0
Glc	99.12
Gal	0
Xyl	0.27
Fuc	0

Abbreviations: PLP, Phellinus linteus polysaccharides; Man, mannose; GlcN, glucosamine; Rha, rhamnose; GlcNAc, N-acetylglucosamine; GlcA, glucuronic acid; GalA, galacturonic acid; Glc, glucose; Gal, galactose; Xyl, xylose; Fuc, fucose.

Effects of PLP on the productive performance and egg quality in laying hens

Table 4 presents the effects of PLP supplementation on laying performance and egg quality in laying hens under LPS challenge. As a result, PLP supplementation significantly increased the laying rate and yolk weight (P  <  0.05), while LPS challenge decreased the laying rate (P  <  0.05). Additionally, the effects of PLP on albumen height and Haugh Unit were more pronounced in laying hens under the LPS challenge (P  <  0.05). There were no significant interactions between PLP and LPS regarding productive performance and egg quality.

Table 4.

Effects of PLP supplementation on laying performance and egg quality.

Items	Treatment						Main effect P-value
	CON	PLP	LPS	LPS+PLP	SEM	P-value	PLP	LPS	PLP × LPS
Laying performance
Laying rate (%)	83.57b	87.06a	78.91b	84.59a	0.80	<0.001	<0.001	<0.001	0.178
Average egg weight (g)	49.96	51.78	47.95	49.87	0.61	0.001	0.004	0.002	0.931
ADFI (g/d)	112.94	113.83	111.62	114.19	0.90	0.197	0.060	0.593	0.355
FCR (g/g)	2.26	2.20	2.33	2.29	0.04	0.089	0.162	0.033	0.753
Egg quality
Egg-shaped index	1.32	1.30	1.27	1.31	0.02	0.245	0.585	0.332	0.088
Eggshell strength (kgf/cm2)	45.90	48.91	42.29	44.87	1.48	0.031	0.072	0.016	0.889
Eggshell thickness (mm)	0.315	0.324	0.305	0.320	0.01	0.166	0.051	0.305	0.631
Albumen height (mm)	4.55	5.27	3.68b	4.64a	0.21	<0.001	<0.001	<0.001	0.581
Yolk color	13.08	13.17	12.42	12.66	0.33	0.375	0.630	0.096	0.809
Yolk weight (g)	15.41b	17.58a	14.84	15.99	0.43	<0.001	<0.001	0.018	0.235
Haugh Unit	76.60	79.91	71.99b	77.47a	1.51	0.007	0.006	0.027	0.484

CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides; LPS, basal diet with 1mg/kg lipopolysaccharide; LPS+PLP, basal diet with 4.2g /kg Phellinus linteus polysaccharides and an injection of 1 mg/kg lipopolysaccharide; ADFI, average daily feed intake; FCR, feed conversion ratio. Data were presented as mean ± SEM. SEM represents the standard error of the mean. Each mean represents 30 replicates per treatment in the egg quality assessment (n = 30). Different lowercase letters within a row represent significant differences (P < 0.05).

Effects of PLP on the amino acid composition of egg yolk in laying hens

As shown in Table 5, supplementation with PLP markedly increased the contents of total essential amino acids (P  <  0.05), while decreasing the contents of total non-essential amino acids (P  <  0.05). The contents of total essential amino acids and total amino acids were lower in laying hens under LPS challenge (P  <  0.05). Furthermore, PLP and LPS exhibited a significant interaction effect on total non-essential amino acids (P  <  0.05).

Table 5.

Effects of PLP supplementation on the amino acid composition of egg yolk (g/100g yolk).

Amino acids	Treatment					P-value	Main effect P-value
	CON	PLP	LPS	LPS+PLP	SEM		PLP	LPS	PLP × LPS
Essential amino acids
Lys	2.72	2.65	2.51	2.66	0.11	0.563	0.701	0.377	0.300
Ile	1.92	2.28	1.69	2.06	0.11	0.011	0.004	0.062	0.965
Leu	2.91	3.34	2.66	3.08	0.18	0.015	0.005	0.076	0.985
Met	0.72	0.78	0.71	0.70	0.06	0.807	0.738	0.442	0.625
Phe	1.39	1.53	1.29	1.36	0.07	0.171	0.180	0.080	0.648
Trp	0.92	1.18	0.82	0.94	0.08	0.041	0.035	0.054	0.428
Val	1.99	2.43	1.79	2.08	0.14	0.031	0.016	0.069	0.613
Thr	1.75	1.82	1.66	1.70	0.09	0.254	0.237	0.021	0.717
His	0.92	0.93	0.84	0.86	0.06	0.700	0.816	0.254	0.992
Σ EAAs	15.25b	16.94a	13.98b	15.46a	0.28	<0.001	<0.001	<0.001	0.715
Non-essential amino acids
Asp	3.14	3.28	3.01	3.21	0.09	0.203	0.068	0.281	0.749
Ser	2.70	2.43	2.45	2.51	0.07	0.617	0.151	0.249	0.036
Glu	4.15	3.91	4.31	4.05	0.10	0.099	0.032	0.186	0.945
Pro	1.35	1.45	1.30	1.41	0.05	0.227	0.574	0.308	0.385
Gly	1.01	0.85	0.90	0.92	0.05	0.140	0.144	0749	0.168
Ala	1.72	1.48	1.62	1.57	0.06	0.087	0.034	0.957	0.138
Tyr	1.51	1.67	1.42	1.55	0.08	0.254	0.237	0.021	0.717
Arg	2.35	2.08	2.05	2.07	0.12	0.283	0.325	0.223	0.237
Cys	0.66	0.61	0.59	0.60	0.05	0.878	0.636	0.683	0.760
Σ NEAAs	18.60a	17.77b	17.69b	17.92ab	0.20	0.023	0.168	0.081	0.020
Σ AAs	33.85	34.72	31.67b	33.38a	0.36	<0.001	0.002	<0.001	0.258

CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides; LPS, basal diet with 1mg/kg lipopolysaccharide; LPS+PLP, basal diet with 4.2g /kg Phellinus linteus polysaccharides and an injection of 1 mg/kg lipopolysaccharide. Data were presented as mean ± SEM (n = 6). SEM represents the standard error of the mean. Total essential amino acids (Σ EAAs) = Lys + Ile + Leu + Met + Phe + Trp + Val + Thr + His; Total non-essential amino acids (Σ NEAAs) = Asp + Ser + Glu + Pro + Gly + Ala + Tyr + Arg + Cys; Total amino acids (Σ AAs) = Σ EAAs + Σ NEAAs. Different lowercase letters within a row represent significant differences (P < 0.05).

Effects of PLP on the antioxidant activity and immune indicators of serum in laying hens

Serum parameters, immunoglobulins, and inflammatory cytokines of laying hens are presented in Fig. 3. LPS challenge exhibited significantly lower levels of serum T-AOC, T-SOD, GSH-Px, IgA, and IgG (P < 0.05), while PLP supplementation significantly increased the levels of T-AOC, T-SOD, and IgA regardless of LPS challenge (P < 0.05). Moreover, the effects of PLP supplementation on GSH-Px and IgG were more pronounced in laying hens under the LPS challenge (P < 0.05). Supplementation with PLP significantly reduced MDA levels and effectively inhibited the LPS-induced increase in MDA (Interaction, P < 0.05). No significant interactions between PLP and LPS were observed for serum total protein (TP), albumin (ALB), total cholesterol (TC), and triglyceride (TG).

Fig. 3.

Effects of PLP on the antioxidant activity and immune indicators of serum in laying hens. (A) Total protein, albumin, total cholesterol, and triglyceride; (B) T-AOC, T-SOD, GSH-Px, and MDA; (C) IgA, IgG, and IgM. NaCl, laying hens injected with NaCl; LPS, laying hens injected with lipopolysaccharide; CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides. Different letters above the bars indicate statistically significant differences (P < 0.05), and # represents a significant interaction between PLP and LPS (P < 0.05).

Effects of PLP on cecal SCFA levels and jejunal morphology in laying hens

As illustrated in Fig. 4, PLP supplementation increased villus height (VH) and the villus height to crypt depth (VH/CD) ratio (P < 0.05), while LPS markedly decreased villus height and the VH/CD ratio under LPS challenge (P < 0.05). There was no significant interaction between PLP and LPS in the jejunal morphology of laying hens (Fig. 4. C). Furthermore, PLP markedly increased the levels of acetic acid, butyric acid, valeric acid, and isovaleric acid (P < 0.05). Conversely, LPS significantly reduced the levels of acetic acid, propionic acid, butyric acid, and isobutyric acid (P < 0.05) (Fig. 4. B). PLP enhanced the level of propionic acid in the cecum of laying hens under LPS challenge (Interaction, P  <  0.05).

Fig. 4.

Effects of PLP on cecal SCFA levels and jejunal morphology in laying hens. (A) Jejunal morphology; (B) Acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid; (C) Villus height, crypt depth, and the ratio of villus height to crypt depth (VH/CD). NaCl, laying hens injected with NaCl; LPS, laying hens injected with lipopolysaccharide; CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides. Different letters above the bars represent statistically significant differences (P < 0.05). and # indicates a significant interaction between PLP and LPS (P < 0.05).

Effects of PLP on hepatic morphology, antioxidant status, and inflammatory factors in laying hens

The results of hematoxylin and eosin staining in the liver are presented in Fig. 5. Supplementation with PLP exhibited reduced lipid vacuole accumulation and decreased inflammatory cell infiltration. In contrast, LPS challenge increased inflammatory infiltration, an effect that PLP supplementation reversed, thereby mitigating LPS-induced effects on inflammatory cell infiltration in the liver. Additionally, we observed that PLP significantly increased the concentrations of T-AOC, GSH-Px, and IL-10 (P < 0.05), while decreasing the concentrations of MDA, IL-6, IL-1β, and TNF-α (P < 0.05) (Fig. 5. B). LPS decreased the levels of T-AOC, T-SOD, and GSH-Px (P < 0.05), while increasing the contents of MDA, IL-6, IL-1β, and TNF-α (P < 0.05). The increase of T-SOD was more pronounced in laying hens fed a PLP-supplemented diet under LPS challenge (P < 0.05). There was a significant interaction effect between LPS and PLP on the hepatic MDA level of laying hens (P < 0.05).

Fig. 5.

Effects of PLP on hepatic morphology, antioxidant status, and inflammatory factors in laying hens. (A) Hepatic tissue and morphology; (B) The concentrations of T-AOC, T-SOD, GSH-Px, MDA, IL-6, IL-1β, TNF-α, and IL-10. NaCl, laying hens injected with NaCl; LPS, laying hens injected with lipopolysaccharide; CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides. Different letters above the bars indicate statistically significant differences (P < 0.05), and # represents a significant interaction between PLP and LPS (P < 0.05).

Effects of PLP on cecal microbiota composition and diversity in laying hens

To explore the effects of PLP supplementation on LPS-induced alterations in cecal microbiota composition and community structure, bacterial 16S ribosomal RNA gene sequencing was performed for microbiome analysis. We observed that five phyla had relative abundances greater than 1 % (Fig. 6. A). The predominant bacterial phyla were Bacteroidetes, Firmicutes, Proteobacteria, Spirochaetae, and Fusobacteria among the four groups, and the top 5 genera identified through classification were Bacteroides, Parabacteroides, Phascolarctobacterium, Sutterella, and Desulfovibrio (Fig. 6. B). The Venn diagram revealed that both groups shared 842 operational taxonomic units (OTUs), with 121 specific to the CON group and 176 unique to the PLP group. The LPS and LPS+PLP groups shared 836 operational taxonomic units (OTUs), with 149 OTUs exclusive to the LPS group and 99 OTUs exclusive to the LPS+PLP group (Fig. 6. C). LPS challenge decreased the microbial alpha diversity, as evidenced by decreases in the ACE and Chao indices. In contrast, PLP supplementation increased these indices (Fig. 6. D, E). Principal component analysis (PCA) of beta diversity showed no significant differences among the four groups (R²= 0.128, P = 0.237) (Fig. 6. F). LEfSe analysis identified 47 bacterial taxa exhibiting significant differential abundance among the four groups (Fig. 6. G). Among these, g_Lactobacillus and g_Alloprevotella were significantly enriched in the LPS group. PLP supplementation significantly decreased the abundance of p_Spirochaetae, g_Campylobacter (P < 0.05), while increasing the abundance of g_Parabacteroides and s_Faecalibacterium prausnitzii (P < 0.05) (Fig. 6. H). Additionally, the abundance of g_Alistipes was lower (P < 0.05), whereas the abundance of g_Faecalibacterium was higher under LPS challenge (P < 0.05), and PLP exhibited a significant interaction effect on p_Spirochaetae and g_Parabacteroides under LPS challenge (P < 0.05).

Fig. 6.

Effects of PLP on cecal microbiota composition and diversity in laying hens. (A) Relative abundance of bacterial phyla; (B) Relative abundance of bacterial genera; (C) Venn diagram for the OTUs of the cecal microbiota; (D) ACE index of alpha diversity; (E) Chao index of alpha diversity; (F) Principal component analysis (PCA) plot was generated using operational taxonomic unit (OTU) metrics based on unweighted UniFrac distance; (G) Linear discriminant analysis (LDA) scores illustrating microbial differences, as determined by the linear discriminant analysis effect size (LEfSe) method; (H) Column chart of the microbial relative abundance. NaCl, laying hens injected with NaCl; LPS, laying hens injected with lipopolysaccharide; CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides. Different letters above the bars indicate statistically significant differences (P < 0.05), and # represents a significant interaction between PLP and LPS (P < 0.05).

Effects of PLP on jejunal metabolism in laying hens

To further understand the effects of PLP supplementation on LPS-induced metabolic changes, untargeted metabolomics of the jejunal contents was performed across four groups in laying hens (Fig. 5 and Tables S1 and S2). The volcano plot of differential metabolites showed 434 upregulated and 99 downregulated metabolites between the PLP and CON groups (Fig. 7. A). Similarly, we observed that 111 metabolites were upregulated and 566 were downregulated in the LPS and LPS+PLP groups. Additionally, the PCA analysis and cluster heatmap demonstrated apparent differences in clustering among the four groups based on metabolic compounds (Fig. 7. B). The KEGG classification diagram showed that 9 metabolites were involved in nucleotide metabolism, 6 in lipid metabolism, 5 in carbohydrate metabolism, and 12 in amino acid metabolism (Fig. 7. D). KEGG signaling pathway revealed that the PLP vs CON group showed enrichment of histidine, glutathione, arginine, proline, glycerolipid, galactose, fructose, and mannose metabolism (Fig. 7. E). In the LPS vs. LPS+PLP comparison group, linoleic acid, glycine, serine, threonine, arginine, proline, α-linolenic acid, arachidonic acid metabolism, MAPK signaling pathway, and the calcium signaling pathway were enriched. Notably, arginine and proline metabolism were simultaneously enriched in both established comparison groups. These findings revealed that PLP may alleviate LPS-induced inflammatory responses through the synergistic action of multiple pathways.

Fig. 7.

Effects of PLP on jejunal metabolism in laying hens. (A) Volcano plot of differential metabolites; (B) Principal component analysis (PCA); (C) Cluster heatmap of metabolites in the jejunal contents; (D) Histogram of KEGG classification of differential metabolites; (E) KEGG enrichment analysis of differential metabolites; (F) Correlation analyses of metabolites, and the red represents positive correlations, while the blue represents negative correlations. CON, basal diet; PLP, basal diet with 4.2g/kg Phellinus linteus polysaccharides; LPS, basal diet with 1mg/kg lipopolysaccharide; LPS+PLP, basal diet with 4.2g /kg Phellinus linteus polysaccharides and an injection of 1 mg/kg lipopolysaccharide. Differential metabolites were identified based on the thresholds of an adjusted |log₂(FC)| ≥ 1 combined with P < 0.05.

Correlations analysis of cecal microbiota with biochemistry parameters, SCFAs, and productive performance in laying hens

To elucidate the relationships between PLP supplementation and productive performance of laying hens under LPS challenge, Mantel correlation analyses were performed to evaluate the associations among immunological indicators (IgA, IgG, and IgM), inflammatory cytokines (IL-6, IL-10, IL-1β, and TNF-α), cecal microbiota composition, SCFAs, and egg performance. Mantel correlation analyses were conducted on the top 35 bacterial genera identified within the phyla Bacteroidetes, Firmicutes, Spirochaetae, and Proteobacteria (Fig. 8. A). Moreover, a Spearman correlation analysis was performed, with the results visualized as a correlation clustering heatmap (Fig. 8. B). We found that members of the cecal microbiota belonging to the phyla p_Bacteroidetes and p_Spirochaetae showed significant associations with the majority of parameters under LPS challenge. Importantly, we observed that g_Alloprevotella, g_Parabacteroides, g_Butyricimonas, and s_Faecalibacterium prausnitzii showed significant positive correlations with most antioxidant indicators, immune parameters, SCFA levels, laying ratio, and egg quality parameters, while showing negative correlations with MDA, IL-6, IL-1β, and TNF-α. These findings suggest that PLP may enhance antioxidant defense and immune responses via modulating cecal microbiota and their metabolic functions, thereby improving productive performance of laying hens.

Fig. 8.

Correlations analysis of cecal microbiota with biochemistry parameters, SCFAs, and productive performance in laying hens. (A) Mantel correlation analysis was performed between the top 35 bacterial genera identified within the phyla Bacteroidetes, Firmicutes, Spirochaetae, and Proteobacteria and multiple physiological, biochemical, as well as immune-related parameters of laying hens; (B) Spearman correlations between core bacterial genera and physiological, biochemical, immune-related parameters, and laying performance in laying hens (*P < 0.05, ^⁎⁎P < 0.01, ^⁎⁎⁎P < 0.001).

Discussion

Increasing evidence indicates that lipopolysaccharide (LPS) challenge adversely affects productive performance and egg quality in laying hens (Geng et al., 2018; Wang et al., 2023), as primarly manifested by reduced laying performance, impaired intestinal barrier, immune function, and triggered systemic inflammatory response (Nie et al., 2018; Zhou et al., 2023; Zhang et al., 2025), which may decrease the intake and absorption of essential nutrients, such as vitamins and minerals, thereby disrupting calcium and phosphorus metabolism and contributing to a decline in egg quality (Nie et al., 2018). The Changshun green-shell laying hens are a notable indigenous breed from Guizhou province, China, and are known for their strong broodiness. It is widely recognized for producing nutrient-dense green-shelled eggs with a more favorable amino acid profile, higher protein content, and lower levels of fat and cholesterol, and during the peak laying period, its egg-laying rate reaches approximately 93 % (Mu et al., 2021; Chen, Wen, et al., 2024). In the present study, we employed an LPS challenge to establish a classical model of induced inflammation in Changshun green-shell laying hens and evaluated whether PLP could mitigate LPS-induced impairments in productive performance and egg quality.

The biological functions of polysaccharides are closely associated with their molecular structures (Li et al., 2019; Duan et al., 2023). It has been reported that polysaccharides with immunomodulatory, anti-inflammatory, and antioxidant activities are mainly due to the presence of α- (1,4)-D-glucoside residues, with molecular weights ranging from 3.2 to 1900 kDa. These polysaccharides are primarily composed of glucose, rhamnose, arabinose, and xylose, and are characterized by α- (1→4)-linked glucosyl residues as the predominant glycosidic bonds (Li et al., 2019; Duan et al., 2023). This structural profile is similar to the findings reported in a previous study on PLP (Hu et al., 2024). In this study, PLP exhibited a total sugar content of approximately 83.49 %, as determined by the phenol-sulfuric acid method, with a molecular weight of 167.5 kDa. HPLC analysis revealed that PLP was primarily composed of Glc, Man, GlcN, Rha, Glc, and Xyl. FT-IR spectral analysis suggested that PLP may present an α-glycosidic configuration. As expected, our study found that the LPS challenge reduced productive performance and egg quality, as evidenced by reductions in laying rate, eggshell strength, albumen height, Haugh Unit, and amino acid content. Conversely, PLP supplementation enhanced laying performance and related egg quality parameters, antioxidant defense, and immune responses, and PLP effectively reversed the LPS-induced adverse effects on the antioxidant status and immune function of laying hens. Additionally, KEGG analysis revealed that PLP may be associated with the regulation of calcium signaling and with multiple pathways involved in amino acid metabolism, including arginine and proline metabolism, glycine, serine, and threonine metabolism, vitamin B₆ metabolism, and linoleic acid and arachidonic acid metabolism. These pathways are relevant to egg formation, nutrient utilization, inflammatory mediator synthesis, and stress responses, which could partially explain the improvements observed in inflammatory status, egg quality parameters, and yolk amino acid contents under LPS challenge.

Excessive oxidative stress is known to cause damage to cellular biomolecules, including lipids, proteins, and nucleic acids, thereby impairing normal physiological functions and performance (Ding et al., 2022; Gu et al., 2022; Zhou et al., 2023). The liver serves as a critical metabolic organ and a critical mediator of immunological functions, with essential roles in nutrient metabolism, endotoxin clearance, and immune defense against gut-derived antigens. The onset of liver injury is closely linked to its inflammatory responses and oxidative stress (Tong et al., 2022; Zhang et al., 2025). As an endotoxin, LPS induces oxidative stress by generating reactive oxygen species (ROS) in cells. Notably, hydroxyl radicals, the most reactive and harmful free radicals among ROS, can interact with nearly all biomacromolecules within living cells, causing extensive damage to neighboring biomolecules. The antioxidant defense status of an organism is commonly evaluated by determining the activities or concentrations of key biomarkers, including T-AOC, T-SOD, GSH-Px, and MDA in both serum and hepatic tissues (Ghasemi et al., 2022). Key enzymes in this system, including SOD and GSH-Px, are regulated by nuclear factor erythroid 2-related factor 2 (Nrf2). These enzymes play a crucial role in safeguarding DNA and mitochondria against oxidative harm by neutralizing free radicals, thus preserving intracellular redox balance and mitigating oxidative stress (Tripathi et al., 2024). MDA, a product of lipid peroxidation, is considered an ideal biomarker for oxidative stress due to its relatively high stability compared to other unstable oxidative stress biomarkers such as hydrogen peroxide and superoxide anions (Binder et al., 2016; Ma et al., 2023). An increase in the MDA level may induce cell damage and promote the release of inflammatory mediators, thereby further exacerbating oxidative stress and aggravating liver dysfunction (Zhang et al., 2025). Antioxidant enzymes, such as GSH-Px and T-SOD, alleviate this damage by eliminating reactive oxygen species (ROS) and stabilizing free radicals, thus protecting tissues against oxidative damage (Shao et al., 2023; Yang et al., 2025). Recent studies revealed that supplementation with PLP improved the performance and antioxidant capacity of chickens (Liu, Wu, et al., 2023; Li et al., 2025). Consistent with these findings, our results indicated that PLP supplementation significantly increased the antioxidant enzyme activity of hepatic T-AOC and GSH-Px regardless of LPS. Interestingly, PLP and LPS exhibited a significant interactive effect on the content of MDA in the liver. Immunomodulatory activity is one of the most common biological activities of polysaccharides. Therefore, we further evaluated the alteration in the immune status and inflammatory cytokines of laying hens.

Immunoglobulins (IgA, IgG, IgM, IgD, and IgE) are glycoproteins synthesized by B lymphocytes, functioning either as membrane-bound receptors on B cells or as secreted antibodies that mediate humoral immune responses (Schroeder and Cavacini, 2010). As the main effector molecules of humoral immunity, they play a central role in immune defense. Among these, IgA, IgG, and IgM are the most abundant immunoglobulin isotypes in serum, and their concentrations are widely used as essential biomarkers for evaluating the host's humoral immune function (Chen et al., 2020). These immunoglobulins not only participate in the adaptive immune response but also play a synergistic role in the innate immune response, helping mediate secondary immune defense against pathogens and exhibiting significant antiviral and antibacterial activities (Pushparaj et al., 2023; Kianpoor et al., 2025). The results of this study revealed that supplementation with PLP markedly enhanced the concentrations of serum IgA regardless of LPS, while the effect of PLP on IgG was more pronounced in laying hens under the LPS challenge, indicating that PLP may promote humoral immune responses and improve immune competence, particularly during an inflammatory challenge condition. This effect may be attributed to the reason that PLP inhibits the activation of the NF-κB signaling pathway and its interaction with the scavenger receptor class B type 1 (SCARB1) on CD19⁺ B lymphocytes, further promoting the secretion of immunoglobulins and enhancing its immunostimulatory effects (Bruno et al., 2011; Mikami et al., 2015).

Inflammation is widely considered a key factor that impairs the productive performance of laying hens (Gu et al., 2022). During inflammatory progression, aberrant activation of pathways such as NF-κB, MAPK, JAK/STAT, and PI3K/TLR4 can disrupt intestinal barrier function and promote excessive release of pro-inflammatory cytokines, thereby driving dysregulated inflammatory responses (Hu et al., 2018; Hu et al., 2024; Long et al., 2024). Polysaccharides can exert immunoregulatory effects by interacting with receptors, including Toll-like receptors (TLRs), type III complement receptor (CR3), mineralocorticoid receptor (MR), and scavenger receptor (SR), thereby activating downstream signaling networks (Leung et al., 2006; Yu et al., 2015). The MAPK family, including JNK, p38, and ERKs, is particularly important in regulating the expression of inflammatory cytokines (Paunovic and Harnett, 2013). Previous studies reported that polysaccharides from Chrysanthemum morifolium and Phellinus linteus suppress pro-inflammatory cytokines such as TNF-α, IFN-γ, IL-6, and IL-1β while enhancing IL-10 via TLR4/NF-κB/MAPK-related mechanisms (Tao et al., 2018; Hu et al., 2024). In agreement with these reports, we observed that PLP significantly reduced hepatic IL-6, IL-1β, and TNF-α levels, while increasing IL-10 levels, regardless of LPS challenge. These cytokine shifts support an anti-inflammatory role of PLP and may contribute to the observed improvements in immunologic function and antioxidant defense under LPS challenge.

Remarkably, intestinal morphology is a critical determinant of nutrient absorption efficiency and overall gut health (Sopian et al., 2024). In general, increased villus height (VH) expands the absorptive surface area and is often associated with enhanced nutrient utilization (Sopian et al., 2024; Wang et al., 2025). Crypt depth (CD) reflects epithelial cell renewal and regenerative activity, and the VH/CD ratio is widely used as an integrated indicator of intestinal functional status (Wang et al., 2025). Goblet cells contribute to mucosal protection by secreting mucus, of which MUC-2 is a major structural component (Shan et al., 2013). Consistent with previous evidence that polysaccharides can improve intestinal morphology and strengthen barrier function (Duan et al., 2023; Hu et al., 2024), our results showed that PLP significantly increased VH and the VH/CD ratio irrespective of LPS challenge, indicating that supplementation with PLP may improve intestinal structure and nutrient absorption of laying hens.

Natural polysaccharides have attracted widespread attention due to their excellent biocompatibility and potent ability to regulate gut microbiota (Yu et al., 2018; Hu et al., 2024). Several studies have reported that natural polysaccharides can serve as fermentable substrates for beneficial bacteria to produce SCFAs, thereby reducing intestinal pH and inhibiting the growth of pathogens (Zhou et al., 2022; Duan et al., 2024; Hu et al., 2024; Wei et al., 2025). The gut microbiota plays a pivotal role in host nutrient metabolism and immune modulation (Nicholson et al., 2012; Kamada et al., 2013; Saint-Martin et al., 2024). They not only form a microbial barrier alongside the intestinal mucosa to prevent pathogenic bacterial invasion but also establish the gut-liver axis pathway through interactions with the liver (Hamoud et al., 2018). SCFAs are the primary metabolites produced by bacterial fermentation of dietary fiber in the intestine, and predominantly include acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid (Koh et al., 2016), apart from serving as the dominant energy supply for the intestinal mucosa, SCFAs participate in the maintenance of metabolic homeostasis by acting as intracellular signaling molecules that bind to and modulate SCFA receptors, thereby activating host immunometabolic functions through SCFA-mediated G protein-coupled receptors (GPR41/43) and histone deacetylase (HDAC) inhibition (Den Besten et al., 2013; Koh et al., 2016). Crucially, the homeostasis of the gut-liver axis depends on the dynamic balance among the composition of the gut microbiota, the function of the intestinal barrier, and SCFA levels (Wang et al., 2023). Previous studies have shown that PLP may increase the abundance of beneficial bacterial genera, specifically g_Parabacteroides, g_Butyricimonas, and g_Faecalibacterium (Liu, Zhao, et al., 2023; Hu et al., 2024). In partial agreement with these reports, our results showed that PLP significantly increased the abundance of g_Lactobacillus, g_Alloprevotella, and g_Faecalibacterium in the cecum under LPS challenge, and PLP exhibited a significant interaction effect on g_Parabacteroides under LPS challenge. PLP significantly reduced the abundance of g_Campylobacter in the absence of LPS challenge. Regardless of LPS presence, PLP markedly increased the abundance of s_Faecalibacterium prausnitzii. More importantly, PLP significantly elevated the concentrations of acetic acid and butyric acid in the cecum of laying hens, while mitigating the detrimental effects induced by LPS. It has been reported that s_Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium of intestinal microbiota (Sokol et al., 2008), whereas g_Campylobacter are generally acknowledged as intestinal pathogens that are positively associated with inflammation and disease (Young et al., 2007). In addition, g_Alloprevotella, g_Faecalibacterium, and s_Faecalibacterium prausnitzii are widely reported as prominent producers of SCFAs and are thus regarded as key microbial contributors to SCFA production (Li et al., 2017; Moon et al., 2023; Han et al., 2024). On the other hand, g_Parabacteroides could help alleviate metabolic dysfunction by producing succinate and secondary bile acids (Wang et al., 2019). Intriguingly, Lactobacillus is widely recognized as a genus of microbiota known for its probiotic properties. Lactobacillus could inhibit the colonization of pathogenic bacteria on the host's intestinal epithelium through competitive exclusion and antagonism. It produces various bioactive compounds (e.g., SCFAs, bacteriocins, soluble peptides, and antimicrobial agents). These molecules suppress pathogen growth by disrupting cell integrity and inhibiting cellular division (Huang et al., 2022; Liu et al., 2025). Correlation analyses further demonstrated that these beneficial microbial changes were positively associated with multiple physiological, biochemical, and immune-related parameters of laying hens.

Overall, PLP supplementation alleviated LPS-induced adverse effects in Changshun green-shell laying hens, as evidenced by improved productive performance and egg quality, enhanced hepatic antioxidant capacity, suppressed pro-inflammatory cytokine production with concurrent increases in anti-inflammatory IL-10, and improved humoral immunity. These improvements might be attributed to PLP-mediated beneficial regulation of the cecal microbiota, particularly through enrichment of microbial diversity and commensal SCFA-producing bacteria, thereby promoting intestinal barrier function and gut-liver axis homeostasis.

Conclusions

Taken together, as presented in Fig. 9, this study demonstrated that PLP supplementation may improve the productive performance of laying hens via modulating the microbiota-gut-liver (MGB) axis. We speculated that the potential mechanism underlying these effects is that PLP serves as a fermentation substrate for beneficial cecal bacteria, thereby promoting SCFA production. Consequently, the increased SCFA levels may lower intestinal pH and inhibit pathogen growth, thereby enhancing intestinal barrier function and helping maintain gut-liver axis homeostasis. Mechanistically, PLP may reduce pro-inflammatory cytokine levels in laying hens by inhibiting MAPK signaling pathway activation under LPS challenge. These findings reveal that PLP holds promise as a novel functional feed additive for improving the overall performance of laying hens. Future studies are needed to investigate the precise molecular mechanisms via multi-omics integration analysis and assess the long-term effects of different supplementation levels.

Fig. 9.

Based on the current findings, PLP may mitigate the systemic inflammatory response in laying hens, enhance overall performance, and improve egg quality through these potential pathways.

CRediT authorship contribution statement

Yong Yue: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Shenglin Yang: Writing – review & editing, Methodology, Investigation. Papungkorn Sangsawad: Writing – review & editing, Methodology, Investigation. Phanthipha Laosam: Methodology, Investigation. Yingping Tian: Methodology, Investigation, Formal analysis, Conceptualization. Xu Wang: Methodology, Investigation. Muhammad Arif: Methodology, Investigation. Fuping Zhang: Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Formal analysis.

Disclosures

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data availability

The sequencing data generated in this study have been submitted to the NCBI SRA database under BioProject accession number PRJNA1335392.