Effects of dietary fiber on the gut microbiota, intestinal barrier, and inflammatory factors in Xinjiang black pigs

Abstract

Background

Alfalfa meal is rich in insoluble dietary fiber and bioactive compounds, which undergo fermentation in the hindgut of pigs. Its metabolites are closely associated with intestinal barrier function and immune responses. In this study, we investigated the effects of varying dietary fiber levels on the gut microbiota, Gut metabolites, the intestinal barrier, and inflammatory cytokines in Xinjiang black pigs.

Methods

A total of 36 experimental pigs (21.55 ± 4.22 kg) were randomly assigned to three groups, with six replicates per group and two pigs per replicate. They were fed a basal diet (CON), a low-fiber diet (LF), or a high-fiber diet (HF), with the dietary fiber levels adjusted using alfalfa meal.

Results

The experimental results were as follows: In the LF group, the beneficial bacterium Lactobacillus was significantly upregulated (P < 0.05), whereas the pathogenic bacteria Streptococcus and Opportunistic pathogen Clostridium_sensu_stricto_1 were significantly downregulated (P < 0.05). Metabolic pathway analysis revealed significant enrichment in Choline metabolism in cancer, Biosynthesis of various antibiotics, and ‘Prodigiosin biosynthesis (P < 0.05). The levels of IL-10, MUC1, MUC2, SOD, and CAT were significantly increased (P < 0.05), while the levels of IL-1β, IL-6, and TNF-α were significantly decreased (P < 0.05), with no significant difference in D-LA levels (P > 0.05). In the HF group, the abundance of the phylum Actinobacteria increased (P < 0.05), leading to a significant decrease in the abundance of the phylum Firmicutes (P < 0.05). The abundances of Clostridium_sensu_stricto_1 and Corynebacterium were significantly elevated (P < 0.05). Metabolic pathways were enriched in histamine-related pathways such as Asthma, Fc epsilon RI signaling pathway, Synaptic vesicle cycle, and Histidine metabolism (P < 0.05). The gene expression of tight junction proteins was significantly downregulated (P < 0.05). The serum D-LA concentration significantly increased (P < 0.05), whereas the SOD and CAT activities significantly decreased (P < 0.05). Furthermore, the gene expression of IL-6 and TNF-α was significantly upregulated (P < 0.05).

Conclusions

In summary, a moderate-fiber diet enhances beneficial bacterial abundance, suppresses pathogenic bacteria, strengthens intestinal barrier function, improves antioxidant capacity, and maintains intestinal homeostasis. In contrast, an excessive-fiber diet reduces the abundance of Firmicutes, promotes the colonization of pathogenic bacteria, leads to gut microbiota dysbiosis, and triggers the release of inflammatory cytokines. This is accompanied by reduced antioxidant capacity and increased intestinal permeability, thereby exacerbating intestinal inflammation.

Introduction

The intestine is not only a central organ for digestion and absorption but also responsible for protecting the host from dietary toxins and microorganisms [1]. The intestinal mucosal barrier is composed of physical, chemical, and immune barriers. The physical barrier consists of intestinal epithelial cells and their intercellular connections. The cellular junctional complexes include tight junctions, adherens junctions, desmosomes, and gap junctions [2]. Tight junction proteins (TJPs) are intercellular protein complexes that maintain tissue homeostasis and integrity by regulating paracellular permeability and cell polarity, thereby preventing the entry of potentially harmful substances or pathogens into the body [3]. The chemical barrier is composed of mucus secreted by the intestinal mucosal epithelium, digestive fluids, and beneficial substances produced by the normal flora residing in the intestinal lumen. The immune barrier consists of gut-associated lymphoid tissue (GALT), immune cells, and diffuse immune cells. Cytokines influence intestinal barrier integrity by modulating inflammation [4]. Therefore, the intestinal mucosal barrier can fully exert its functions—such as defending against pathogenic microorganism invasion, regulating antigen presentation, and suppressing inflammation—only when the integrity and coordination of its various barriers are maintained [5].

The gut microbiota refers to the diverse microbial communities colonizing the host’s gastrointestinal tract [6]. It has been confirmed to be closely associated with the host’s healthy growth and immune regulation [7]. Pigs lack specific endogenous digestive enzymes in their intestines to degrade dietary fiber, which can instead be fermented by microbes in the hindgut to produce short-chain fatty acids (SCFAs) [8]. SCFAs are considered a primary energy source for intestinal cells and play important roles in promoting immunity, reducing inflammation, and enhancing barrier function [9]. Relevant studies have shown that moderate fiber intake can effectively enhance intestinal barrier function and prevent excessive inflammation [10, 11]. However, much research has focused on the effects of fiber-rich diets on the gut microbiota and microbial metabolism while neglecting the interdependent changes between such diets and both intestinal barrier function and inflammatory cytokines in pigs, thereby overlooking their impact on pig health. Xinjiang black pigs have developed breed characteristics of tolerance to coarse forage and strong stress resistance under long-term extensive feeding environments. Alfalfa meal is primarily composed of insoluble dietary fiber (IDF), which has a moderate protein content, a balanced amino acid profile, and abundant vitamins and minerals [12]. Feeding alfalfa meal can alter the intestinal microbial community in pigs [13]. However, the fiber tolerance of Xinjiang black pigs remains unknown, and research on the interaction between intestinal barrier function and cytokines under fiber intake is lacking. This study aimed to investigate the interactions among the intestinal microbiota, intestinal barrier, and inflammatory cytokines in Xinjiang black pigs fed a fiber-rich diet via 16 S rRNA gene sequencing, transcriptome sequencing, and quantitative real-time PCR (qRT‒PCR) techniques. These findings provide a theoretical basis for the healthy breeding of Xinjiang black pigs.

Methods

Materials and methods

This study employed a single-factor experimental design. A total of 36 experimental pigs (21.55 ± 4.22 kg) were randomly allocated into 3 groups, with 6 replicates per group and 2 pigs per replicate (The experimental pigs were all sourced from the Ruikang Breeding Specialized Cooperative in Shihezi City, Xinjiang Uygur Autonomous Region, China.). The diets were formulated according to the Chinese National Standard GBT 39,235 − 2020 Nutrient Requirements of Pigs and the NRC Nutrient Requirements of Swine (11th edition). The diets were primarily based on corn-expanded soybean. Alfalfa meal was used to replace corn-expanded soybean to adjust the neutral detergent fiber (NDF) level of the diets. For the growth phase, the basal diet contained 1.0% alfalfa meal (CON, control group). The experimental diets contained 18.4% alfalfa meal for the low-fiber diet (LF) group and 35.5% alfalfa meal for the high-fiber diet (HF) group. For the finishing phase (days 71 to 120), the basal diet contained 4.25% alfalfa meal (CON), while the experimental diets contained 21.63% alfalfa meal (LF) and 38.8% alfalfa meal (HF). The trial included a 7-day adaptation period followed by a 120-day formal feeding period. The detailed feed composition and nutritional content can be found in our previous study [14].

Sample collection and storage

Blood samples were collected from the jugular vein of all the experimental pigs on days 0, 40, 80, and 120 of the formal trial period. The samples were allowed to stand until serum separation and then centrifuged at 3000 r/min for 15 min. The supernatant was aspirated into centrifuge tubes and stored at -20 °C for subsequent analysis. At the conclusion of the feeding trial, six pigs from each group were randomly selected for slaughter (Electrical stunning for humane euthanasia). Intestinal tissues were immediately isolated upon slaughter. A segment of 2–3 cm was excised from the mid-cecum and fixed in 4% paraformaldehyde, while tissue samples and luminal contents from the same region were collected into cryotubes, flash-frozen in liquid nitrogen, and stored at − 80 °C for further analysis. To ensure representativeness and consistency of sampling, the ceco-ileal junction was first ligated, the cecal contents were thoroughly homogenized, and then samples were taken from the mid-cecum.

Determination of serum antioxidant indices and intestinal barrier permeability indices

Serum superoxide dismutase (SOD), catalase (CAT), and D-lactic acid (D-LA) levels were determined via the spectrophotometric method according to the instructions provided in the kits manufactured by Suzhou Geruisi Biotechnology Co., Ltd.

Total RNA extraction, cDNA synthesis, and quantitative real-time PCR (qRT‒PCR)

Total RNA extraction

Total RNA extraction was performed according to the instructions of the Total RNA Extraction Kit (TransZol Up Plus RNA Kit).

cDNA synthesis

cDNA was synthesized via reverse transcription following the instructions of the reverse transcription kit (EasyScript® One-Step gDNA Removal and cDNA Synthesis SuperMix). The reaction system consisted of 5 µg of total RNA, 5 µL of anchored oligo(dT)18 primer (0.5 µg/µL), 5 µL of EasyScript® RT/RI Enzyme Mix, and 5 µL of gDNA Remover. RNase-free water was then added to bring the total volume to 100 µL. The mixture was incubated at 42 °C for 15 min, and the reaction was terminated by heating at 85 °C for 5 s. The resulting cDNA was stored at -20 °C.

Quantitative real-time PCR detection

Primer design and synthesis

The gene sequences for ZO-1, Claudin-1, Occludin, MUC1, MUC2, IL-1β, IL-6, TNF-α, and IL-10 were obtained from the NCBI official website. The primers were designed via NCBI Primer-BLAST, with β-actin serving as the reference gene. All primers were synthesized by Shanghai Jierui Biological Engineering Co., Ltd. The gene primer sequences are listed in Table 1.

Table 1.

Primer sequences for quantitative real-time PCR (Swine)

Target gene	Forward primer5’-3’	Reverse primer5’-3’	Product length	Accession number
ZO-1	GAAAGCCCTAAGTTCAATC	GTGTCAACGCCACTATCAA	146	XM_013993251.1
Claudin-1	GGTCTGGCTATCTTAGTTGC	CATATCTGGCATTGACTGG	91	NM_001244539.1
Occludin	CAGGTGCACCCTCCAGATTG	GGACTTTCAAGAGGCCTGGAT	110	NM_001163647.2
MUC1	GGAAGCAGGCACCTATAACC	GTTCTTTCGTCGGCACTGACACACA	190	XM_021089729.1
MUC2	CCCAGTGTAACATCTCCTTTG	GACCCGTATCTCGTAGTCGTA	95	XM_021082584.1
IL-1β	TCTGCATGAGCTTTGTGCAAG	ACAGGGCAGACTCGAATTCAAC	225	NM_001302388.2
IL-6	CCCTGAGGCAAAAGGGAAAGA	AGGAAATCCTCAAGGCTGCG	148	NM_214399.1
TNF-α	CGCTCTTCTGCCTACTGCACTTC	CTGTCCCTCGGCTTTGACATT	164	NM_214022.1
IL-10	CCTGGAAGACGTAATGCCGA	CACGGCCTTGCTCTTGTTTT	148	NM_214041.1
β-action	TCTGGCACCACACCTTCTA	AAGGTCTCGAACATGATCTG	127	XM_021086047.1

qRT‒PCR detection

Quantitative real-time PCR (qRT‒PCR) was performed according to the instructions of the PerfectStart® Green qPCR SuperMix Kit. A 20 µL reaction system containing 10 µL of 2× PerfectStart® Green qPCR SuperMix, 0.4 µL each of the forward and reverse primers, 1 µL of cDNA, and 8.2 µL of nuclease-free water was used. The qRT‒PCR procedure was as follows: predenaturation at 94 °C for 30 s, followed by 45 cycles of denaturation at 95 °C for 5 s, annealing for 20 s, and extension at 72 °C for 10 s. After the reaction, Ct values were exported to Excel, and the relative gene expression levels were calculated via the 2−ΔΔCt method.

Bacterial DNA extraction and PCR amplification

DNA extraction was performed via an E.Z.N.A.^®soil DNA kit (Omega Biotek, Norcross, GA, U.S.) according to the manufacturer’s instructions. The quality of the extracted DNA was assessed via 1% agarose gel electrophoresis, while its concentration and purity were determined via a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA).

PCR amplification of the 16S rRNA V3-V4 region was performed via the following barcode-index primers: forward primer 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and reverse primer 806R (5’-GGACTACHVGGGTWTCTAAT-3’). The reaction mixture composition is provided in Table 2.

Table 2.

PCRreaction system

Component	Volume (µL)	Content
5× TransStart FastPfu Buffer	4	1×
dNTPs	2	2.5 mM
Forward primer	0.8	5 µM
Reverse primer	0.8	5 µM
TransStart FastPfu DNA Polymerase	0.4	-
Template DNA	-	10 ng
Nuclease-free Water	To 20	-

PCR amplification was conducted via an ABI GeneAmp^® 9700 PCR system. The amplified products were electrophoresed on a 2% agarose gel for extraction, purified via a DNA Gel Recovery and Purification Kit (PCR Clean-Up Kit, Yuhua, China), and quantified via a Qubit 4.0 fluorometer (Thermo Fisher Scientific, USA). Finally, a sequencing library was constructed from the purified PCR products via the NEXTFLEX^® Rapid DNA-Seq Kit.

High-throughput sequencing and bioinformatics analysis

Paired-end raw sequencing reads were quality controlled via fastp software and assembled via FLASH software with the following criteria: (1) trimmed bases with quality scores below 20 at the tail of reads; (2) exact primer matching with a maximum of 2-nucleotide mismatches was needed, and reads containing ambiguous bases were removed; and (3) overlapping sequences with an overlap length exceeding 10 bp were merged. Using UPARSE v7.1 software (http://drive5.com/uparse/), quality-filtered and assembled sequences were clustered into operational taxonomic units (OTUs) at 97% similarity, and chimeras were removed. Taxonomic classification of each 16 S rRNA gene sequence was performed via the RDP classifier algorithm (http://rdp.cme.msu.edu/) against the Silva (SSU128) 16 S rRNA database with a confidence threshold of 70%. Alpha diversity indices (ACE, Chao1, and Shannon) were calculated via the Wilcoxon rank-sum test to assess sample biodiversity. Beta diversity measures based on Bray–Curtis and unweighted UniFrac distances were computed via mothur. Linear discriminant analysis effect size (LEfSe) was performed to identify differentially abundant bacterial taxa (biomarkers) from the phylum to the genus level between different groups via a one-against-all (less stringent) comparison strategy.

Metabolite identification

The raw mass spectrometry files were converted to mzXML format via the Proteowizard MSConvert. Peak detection, filtering, and alignment were performed in R via XCMS, with support vector regression (SVR) correction based on the QC samples. Metabolites with a coefficient of variation (CV) < 30% in the QC samples were retained. Metabolite identification was conducted by querying against the MDB, MassBank, LipidMaps, mzCloud, KEGG, and in-house databases with a mass tolerance of < 30 ppm. Multivariate statistical and differential analyses (PCA, PLS-DA, OPLS-DA) were carried out via the Ropls package. Potential biomarker metabolites were screened on the basis of a p value < 0.05, a variable importance in projection (VIP) score > 2 from the OPLS-DA model, and fold change values. Pathway enrichment analysis was performed on the basis of the hypergeometric distribution, and significantly enriched pathways were visualized via KEGG Mapper. A bidirectional clustering heatmap of scaled metabolite quantitative values was generated via the Pheatmap package in R.

Statistical analysis

The experimental data were preliminarily organized via Excel 2003 and subsequently analyzed via one-way ANOVA with SPSS 26.0 software. Significant differences were assessed via Duncan’s multiple range test. The data are presented as the means ± standard errors (SEs). Differences were considered statistically significant at P < 0.05, not significant at P > 0.05.

Results

Effects of different dietary fiber levels on serum antioxidant indices in Xinjiang black pigs

As shown in Table 3, the superoxide dismutase (SOD) content in the low-fiber group was significantly greater than that in the CON group at 40, 80, and 120 d (P < 0.05). In contrast, in the high-fiber group, the SOD activity was significantly greater than that in the CON group at 40 d (P < 0.05) but was significantly lower at both 80 and 120 d (P < 0.05). Compared with that in the CON group, the catalase (CAT) content in the low-fiber group was significantly greater at 40, 80, and 120 d (P < 0.05). The CAT content in the high-fiber group was significantly greater than that in the CON group at 40 d but was significantly lower at both 80 and 120 d (P < 0.05).

Table 3.

Effects of different dietary fiber levels on antioxidant indices in Xinjiang black pigs

Items	Day	Control Group CON	Low-Fiber Group LF	High-Fiber Group HF	P value
Superoxide Dismutase SOD U/ml	0 d	8.87 ± 1.43	8.93 ± 1.20	8.98 ± 0.25	0.998
	40 d	9.63 ± 0.88b	12.59 ± 0.93a	11.31 ± 0.52ab	0.051
	80 d	10.80 ± 0.83ab	12.62 ± 1.25a	8.47 ± 0.67b	0.015
	120 d	13.65 ± 1.06ab	15.49 ± 1.26a	8.12 ± 1.60b	0.004
Catalase CAT µmoL/min/mL	0 d	138.70 ± 25.53	136.86 ± 13.54	136.53 ± 19.05	0.997
	40 d	136.06 ± 7.77b	165.37 ± 9.27ab	176.22 ± 2.28a	0.003
	80 d	145.64 ± 14.47a	178.14 ± 0.81a	89.59 ± 9.03b	<0.01
	120 d	135.73 ± 17.10a	154.09 ± 12.88a	66.79 ± 7.87b	0.001

Within the same row, values with no letters or the same letter superscripts indicate no significant difference (P > 0.05), whereas different letter superscripts indicate a significant difference (P < 0.05). The same notation applies to the tables below

Effects of different dietary fiber levels on intestinal mucin expression in Xinjiang black pigs

As shown in Fig. 1a, in cecal tissues, the expression levels of MUC2 in both the low-fiber and high-fiber groups were significantly greater than those in the CON group (P < 0.05). Compared with that in the CON group, the expression of MUC1 was significantly upregulated in the low-fiber group (P < 0.05), whereas it was significantly downregulated in the high-fiber group (P < 0.05).

Fig. 1.

Effects of Different Dietary Fiber Levels on Cecal Mucin, Tight Junction, and Inflammatory Cytokine Expression in Xinjiang Black Pigs. a Gene expression levels of mucins; b Gene expression levels of tight junction proteins; c Gene expression levels of inflammatory cytokines

Note: In the figure above, "*" and "**" indicate a significant difference (P < 0.05), "***" and "****" indicate a highly significant difference (P < 0.01), and the absence of asterisks indicates no significant difference (P > 0.05). All comparisons were made relative to the CON group

Effects of Different Dietary Fiber Levels on Intestinal Barrier Permeability and Tight Junction Protein Expression in Xinjiang Black Pigs

As shown in Table 4, at 40 d, 80 d, and 120 d, the D-LA content in the low-fiber group was not significantly different from that in the CON group (P > 0.05). At 80 d and 120 d, the D-LA content in the high-fiber group was significantly greater than that in the CON group (P < 0.05).

Table 4.

Effects of different dietary fiber levels on the intestinal barrier permeability of Xinjiang black pigs

项目 Items	天数 Day	对照组 CON	低纤维组 LF	高纤维组 HF	P值 P value
D-乳酸 D-LA µmoL/mL	0d	0.60 ± 0.05	0.56 ± 0.01	0.58 ± 0.06	0.814
	40d	0.66 ± 0.02	0.61 ± 0.02	0.67 ± 0.03	0.165
	80d	0.52 ± 0.05b	0.59 ± 0.06ab	0.71 ± 0.05a	0.048
	120d	0.43 ± 0.04b	0.40 ± 0.03b	0.77 ± 0.05a	<0.01

As shown in Fig. 1b, in the cecal tissues, the expression levels of tight junction proteins (claudin-1, occludin, and ZO-1) in the HF group were significantly lower than those in the CON group (P < 0.05). In contrast, the gene expression level of occludin in the LF group was significantly greater than that in the CON group (P < 0.05), whereas no significant differences were detected for the other indicators (P > 0.05).

Effects of different dietary fiber levels on the immune performance of Xinjiang black pigs

As shown in Fig. 1c, in cecal tissues, the gene expression levels of the proinflammatory cytokines IL-1β, IL-6, and TNF-α in the low-fiber group were significantly lower than those in the CON group (P < 0.05). Compared with those in the CON group, the gene expression levels of IL-6 and TNF-α in the high-fiber group were significantly greater (P < 0.05), whereas no significant difference in the expression of IL-1β was detected (P > 0.05). The anti-inflammatory cytokine IL-10 initially increased but then decreased with increasing dietary fiber levels. Specifically, the gene expression level of IL-10 in the low-fiber group was significantly greater than that in the CON group (P < 0.05), whereas no significant difference was detected in the high-fiber group (P > 0.05).

Richness and diversity of the cecal microbiota

The composition and structure of the gut microbiota were revealed via 16 S rRNA gene sequencing. After merging and filtering, a total of 2,097,232 high-quality sequences were retained for subsequent analysis, with an average of 116,513 sequences per sample. Cluster analysis based on 97% sequence similarity ultimately yielded 41,699 operational taxonomic units (OTUs). The coverage index consistently exceeded 0.99, indicating that the sequencing results sufficiently represented the breadth and depth of the samples. As shown in Fig. 2a, there were no significant differences in the species richness or diversity of the cecal microbiota (P > 0.05). According to principal coordinate analysis (PCoA), the first principal coordinate (PC1) accounted for 41.55% of the variation, whereas the second principal coordinate (PC2) accounted for 19.44% of the variation (Fig. 2b). Permutational multivariate analysis of variance (PERMANOVA) based on Bray-Curtis distances further indicated that dietary treatment explained 24.0% (R² = 0.24) of the variance in microbial community structure, with a trend toward statistical significance (P = 0.15).

Fig. 2.

Analysis of the alpha and beta diversity of the cecal microbiota: a ACE, Chao1, Shannon, and Simpson indices of the cecal content; b PCoA and NMDS analysis of the cecal content; c Microbial composition at the phylum level in the cecal content; d Microbial composition at the genus level in the cecal content; e LDA analysis of the cecal microbiota

Composition and structure of the cecal microbiota

At the phylum level (Fig. 2c), a total of 22 bacterial phyla were detected across the three groups. The dominant phylum in the CON and LF groups was Firmicutes, accounting for 98.17% and 98.91% of the population, respectively. In contrast, the dominant phyla in the HF group were Firmicutes (82.65%) and Actinobacteria (15.45%). When the NDF concentration exceeded 15%, the relative abundance of Firmicutes decreased significantly (P < 0.05), whereas the relative abundance of Actinobacteria increased significantly (P < 0.05). At the genus level (Fig. 2d), the ten predominant genera (with a relative abundance ≥ 5% in at least one group) were Lactobacillus, Clostridium_sensu_stricto_1, Terrisporobacter, Streptococcus, Turicibacter, and Corynebacterium. Compared with that in the CON group, the abundance of Lactobacillus was significantly greater in the LF group (P < 0.05) but significantly lower in the HF group (P < 0.05). The abundance of Clostridium_sensu_stricto_1 was significantly lower in the LF group (P < 0.05) but significantly greater in the HF group (P < 0.05). The abundance of Streptococcus was significantly lower in both the LF and HF groups (P < 0.05). Additionally, the relative abundance of Corynebacterium was significantly greater in the HF group than in the other groups (P < 0.05).

On the basis of linear discriminant analysis (LDA), key microbial biomarkers were identified using an LDA score threshold of > 2. As shown in Fig. 2e, a total of 17 microbial biomarkers were identified in the LF and HF groups. Most of these biomarkers belong to the phylum Firmicutes, such as s__Enterococcus_faecium_g__Enterococcus, f__Veillonellaceae, and s__Novibacillus_thermophilus, as well as g__Saccharopolyspora from the phylum Actinobacteria. These microbial communities may be associated with host immune performance [15, 16]. Additionally, genera such as Clostridium and Lactobacillus, which are involved in fiber degradation, were also identified.

Metabolomic analysis of cecal contents

The OPLS-DA score plot of the cecal content metabolites (Fig. 3a) revealed distinct separations between any two groups, indicating clear differences in metabolite distributions. For all comparison groups, the R2Y values were close to 1, and the Q2 values exceeded 0.5, demonstrating that the model had strong explanatory power and good predictive ability. In the permutation test, the R2 and Q2 points were lower than the original R2 and Q2 points in the upper right quadrant, confirming the model’s reliability and low risk of overfitting (Fig. 3b). In the analysis of cecal metabolites, we identified 5,720 and 5,120 metabolites in positive and negative ion modes, respectively. For LF vs. CON, there were 576 and 416 significantly differentially abundant metabolites in positive and negative ion modes, respectively (Fig. 3c). For HF vs. CON, there were 973 and 815 significantly differentially abundant metabolites in positive and negative ion modes, respectively (Fig. 3d). To further investigate metabolic differences between groups, differentially abundant metabolites were screened on the basis of P values, VIP scores, and fold change (FC) values (Fig. 3e). The results revealed that for LF vs. CON in positive ion mode, 107 significantly differentially abundant metabolites were identified, with 56 upregulated and 51 downregulated in the LF group. In negative ion mode, 106 significantly differentially abundant metabolites were identified, with 63 upregulated and 43 downregulated in the LF group. For HF vs. CON in positive ion mode, 126 significantly differentially abundant metabolites were identified, with 97 upregulated and 29 downregulated in the HF group. In negative ion mode, 131 significantly differentially abundant metabolites were identified, 93 of which were upregulated and 38 of which were downregulated in the HF group.

Fig. 3.

Volcano plots and abundance heatmaps of differentially abundant metabolites between different dietary fiber level groups. a OPLS‒DA analysis of cecal content in positive and negative ion modes; b Permutation test of cecal content in positive and negative ion modes; c Volcano plot of primary differentially abundant metabolites in positive and negative ion modes for the LF vs. CON groups; d Volcano plot of primary differentially abundant metabolites in positive and negative ion modes for the HF vs. CON groups; e Abundance heatmaps of primary differentially abundant metabolites in positive and negative ion modes for the LF, HF, and CON groups; f Abundance heatmaps of secondary differentially abundant metabolites for the LF vs. CON groups; g Abundance heatmaps of secondary differentially abundant metabolites for the HF vs. CON groups

As illustrated in Fig. 3f, 25 differentially abundant metabolites were identified in the LF vs. CON comparison. Among these metabolites, metabolites such as (E)-2-octenal, aurachin B epoxide, aurachin D, and glycerophosphorylcholine were significantly elevated in the LF group (P < 0.05). As shown in Fig. 3g, 31 differentially abundant metabolites were identified in the HF vs. CON comparison. Among these, AdoMet was significantly elevated in the HF group (P < 0.05), whereas metabolites such as N-acetylhistamine and histamine were markedly reduced (P < 0.05).

KEGG pathway analysis of cecal metabolites

As shown in Fig. 4a, the KEGG enrichment analysis revealed three metabolic pathways with significant differences between the LF and CON groups: choline metabolism in cancer, biosynthesis of various antibiotics, and prodigiosin biosynthesis. Among these pathways, the biosynthesis of various antibiotics pathway contained the greatest number of enriched differentially abundant metabolites, whereas choline metabolism in cancer exhibited the most statistically significant differences. According to Fig. 4b, all annotated differentially abundant metabolites in the choline metabolism in cancer, biosynthesis of various antibiotics, and prodigiosin biosynthesis pathways tended to be upregulated.

Fig. 4.

Enrichment analysis of metabolic pathways for differentially abundant metabolites in different dietary fiber level groups. a Bubble plot of pathway impact factors for the LF vs. CON groups; b differential enrichment score plot for the LF vs. CON groups; c bubble plot of pathway impact factors for the HF vs. CON groups; d differential enrichment score plot for the HF vs. CON groups

As shown in Fig. 4c, five metabolic pathways exhibited significant differences between the HF and CON groups: asthma, the sulfur relay system, the Fc epsilon RI signaling pathway, the synaptic vesicle cycle, and histidine metabolism. Among these pathways, the histidine metabolism pathway contained the greatest number of enriched differentially abundant metabolites, whereas pathways such as asthma, the sulfur relay system, and the Fc epsilon RI signaling pathway presented the most statistically significant differences. According to Fig. 4d, all annotated differentially abundant metabolites in the asthma, Fc epsilon RI signaling, synaptic vesicle cycle, and histidine metabolism pathways tended to be downregulated, whereas all annotated differentially abundant metabolites in the sulfur relay system pathway tended to be upregulated.

Correlation analysis

Correlation analysis among the gut microbiota, differential microbial metabolites, and gut barrier function-related molecules is shown in Fig. 5. Clostridium_sensu_stricto_1 was positively correlated with TNF-α and IL-6. Terrisporobacter showed positive correlations with aurachin B epoxide and Turicibacter. Turicibacter was positively correlated with aurachin B epoxide. Corynebacterium was positively correlated with IL-10. Claudin-1 was significantly positively correlated with ZO-1, whereas Occludin was negatively correlated with histamine. MUC2 was significantly positively correlated with (E)-2-octenal and Aurachin D. A significant positive correlation was also observed between (E)-2-octenal and Aurachin D. N-Acetylhistamine was positively correlated with histamine.

Fig. 5.

Correlation analysis of gut microbiota, differential metabolites of gut microbiota, and gut barrier function-related molecules

“*”indicates P < 0.05; “** ”indicates P < 0.01

Discussion

The intestine, as a core organ of the digestive system, is not only responsible for the digestion, absorption, and metabolic regulation of nutrients but also serves as an integral component of the endocrine and immune systems. Oxidative stress may contribute to the development of inflammatory diseases. Superoxide dismutase (SOD) is a key antioxidant enzyme that scavenges free radicals and reduces oxidative damage [17]. Catalase (CAT), on the other hand, is an important indicator of the body’s antioxidant capacity and the extent of oxidative damage, as it prevents the accumulation of hydrogen peroxide and subsequent cytotoxicity [18]. Studies have shown that dietary fiber intake can modulate the activity of antioxidant enzymes, thereby increasing the body’s antioxidant capacity [19]. Liu et al. [20] reported that a diet containing 6.86% fiber significantly increased the serum CAT and T-SOD levels in Taoyuan pigs. This finding is consistent with the results of the low-fiber group (7.46% crude fiber) in the present study, where the SOD and CAT levels were significantly higher than those in the CON group at 40, 80, and 120 d. These results suggest that dietary fiber supplementation can improve the antioxidant capacity of pigs. This effect may be attributed to the flavonoid compounds (such as tricin and apigenin glycosides) present in alfalfa, which exhibit greater antioxidant activity than do conventional antioxidants and can scavenge free radicals within a certain range, thereby increasing its antioxidant capacity [21]. However, Hu Ping [22] reported that a high-fiber diet significantly reduced SOD and CAT activity compared with a low-fiber diet. Another study [23] indicated that while a 6.58% fiber diet improved the antioxidant capacity of pigs, increasing the fiber level to 8.08% reduced their antioxidant capacity. This aligns with the observations in the high-fiber group of the present study, where the CAT and SOD levels were significantly lower than those in the CON group at 80 and 120 d. These findings suggest that an appropriate amount of dietary fiber can increase the antioxidant capacity [24]. However, excessive fiber content in feed may reduce feed intake and slow weight gain, leading to an increase in free radicals. These free radicals can disrupt intestinal integrity and microbial balance. When the antioxidant system fails to eliminate excess free radicals, oxidative damage occurs, resulting in decreased SOD and CAT levels [25–27].

Cytokines play crucial roles in regulating intestinal inflammation. Maintaining lower concentrations of inflammatory cytokines is an important strategy for preserving intestinal barrier integrity and reducing inflammation during stress responses [28]. In this study, the anti-inflammatory cytokine IL-10 reached its highest level in the low-fiber group but decreased under high-fiber conditions, indicating that the low-fiber diet represents the optimal level for enhancing anti-inflammatory responses and balancing pro- and anti-inflammatory cytokines. This finding is consistent with previous research [29], which demonstrated that dietary fiber, when fermented by the gut microbiota, produces short-chain fatty acids (SCFAs) that promote mucin secretion by goblet cells and upregulate IL-10 expression. Conversely, the increase in proinflammatory cytokines under high-fiber conditions aligns with studies showing that high-fiber diets induce epithelial damage in the gastrointestinal tract of goats and upregulate the gene expression of proinflammatory cytokines such as TNF-α, IL-1β, and IL-6, suggesting that excessive fiber intake may trigger gastrointestinal inflammation [30, 31]. Furthermore, measurements of serum D-LA in Xinjiang black pigs revealed that the low-fiber group presented enhanced intestinal barrier function, whereas the high-fiber group presented increased serum D-LA levels after 80 days, indicating elevated intestinal permeability. This observation is consistent with the aforementioned discussion on the upregulation of tight junction proteins and mucins in high-fiber diets. Tight junction proteins (e.g., claudin-1, occludin, and ZO-1) play critical roles in maintaining intestinal barrier integrity by effectively sealing intercellular spaces and preventing the translocation of gut bacteria and antigens. The core function of the intestinal mucosal barrier lies in the selective partitioning of luminal contents by the epithelial layer, which physically blocks the translocation of pathogenic antigens. Intestinal permeability, as a key physiological characteristic, dynamically regulates the exchange of substances between the lumen and mucosa and is essential for maintaining gut homeostasis. In a study by Luo et al. [32], dietary fiber supplementation not only promoted the secretion of the anti-inflammatory cytokine IL-10 but also increased the expression of MUC1 and MUC2 in the posterior intestinal mucosa of pigs. In the present study, the gene expression levels of MUC1 and MUC2 were significantly increased. Additionally, the expression of tight junction proteins increased across all fiber groups, reflecting the promoting effects of appropriate dietary fiber levels on the intestinal barrier function of Xinjiang black pigs.

Analysis of the effects of dietary factors on the gut microbial composition indicates that dietary fiber is likely a primary driving force shaping the structure of the gut microbiota [33]. Key fiber-degrading bacteria in the intestine, such as Lactobacillus and anaerobic Clostridium, belong to the phylum Firmicutes. These bacteria degrade fiber to produce short-chain fatty acids (SCFAs), which play critical roles in energy metabolism, immune regulation, and intestinal barrier function [34]. In this study, the microbial structure of the LF group did not change at the phylum level but significantly increased the abundance of beneficial genera such as Lactobacillus while suppressing the abundance of opportunistic pathogens such as Streptococcus and opportunistic pathogen Clostridium_sensu_stricto_1. This beneficial effect can be attributed to the proliferation of Lactobacillus, which reduces the pH and thereby inhibits the growth of pathogenic bacteria [35, 36]. As an anaerobic probiotic, Lactobacillus can enhance intestinal barrier integrity by promoting the differentiation of regulatory T cells (Tregs), reducing proinflammatory cytokine levels, inhibiting the proliferation of pathogenic bacteria, and suppressing endotoxin production, thereby restoring balance to the intestinal immune system [37–39]. These findings demonstrate that appropriate dietary fiber supplementation can promote host health. Furthermore, studies have shown that enriching the gut with Firmicutes, particularly beneficial genera such as Lactobacillus and Faecalibacterium, can increase host health and may play a key role in mitigating metabolic and inflammatory diseases [40]. High-fiber diets have been reported to reduce the relative abundance of Firmicutes and Bacteroidetes [41], which aligns with the results of this study. In the HF group, the abundance of Firmicutes decreased, whereas that of Actinobacteria increased. The increase in Actinobacteriota may be associated with microbial dysbiosis [42]. In the HF group, Lactobacillus was suppressed, whereas Clostridium_sensu_stricto_1 became the dominant genus, and the abundance of Corynebacterium significantly increased.

Analysis of the overall community structure (β-diversity) showed a notable trend, with diet explaining a considerable proportion of the variation (R² = 0.24), although this did not reach formal statistical significance (P = 0.15). This may reflect limited sample size for detecting community-wide shifts and the high natural variability between individual animals. However, clearer and more significant signals were obtained at the genus level. Consistent with the overall trend, we observed statistically significant changes: in the HF group, the opportunistic pathogens Clostridium_sensu_stricto_1 and Corynebacterium increased significantly, while the beneficial Lactobacillus decreased significantly. This suggests the effect of high dietary fiber was not a uniform restructuring of all microbes, but a specific shift in key bacterial groups.

Clostridium_sensu_stricto_1 is a opportunistic pathogen whose increased relative abundance can promote the release of IL-4,5-HT, TNF-α, and IFN-γ, thereby exacerbating intestinal inflammation and contributing to conditions such as ulcerative colitis [43, 44]. Most Corynebacterium species are pathogenic and closely linked to immune dysfunction [45]. These specific changes observed at the genus level likely constitute the core linking mechanism between the high-fiber diet and a series of host physiological outcomes. These outcomes include impaired intestinal barrier function (manifested as elevated serum D-LA levels), exacerbated systemic inflammation (upregulated expression of IL-6 and TNF-α), and diminished antioxidant capacity (reduced activity of SOD and CAT). Thus, a high-fiber diet may facilitate the colonization of pathogenic bacteria while suppressing beneficial bacteria, leading to intestinal dysbiosis [46].

Microbially derived metabolites modulate immune homeostasis. A growing body of evidence demonstrates the importance of gut microbiota-derived metabolites in host immunity [47, 48]. Aurachin B epoxide and Aurachin D are quinoline alkaloids that exhibit bactericidal effects against Gram-positive bacteria and inhibit the oxidation of NADH within the mitochondrial membrane [49]. These two metabolites were significantly enriched in the Biosynthesis of various antibiotics pathway. (E)-2-octenal is a natural flavor component isolated from plant essential oils and can also be synthesized by animals and microorganisms [50]. (E)-2-octenal possesses antifungal activity [51] and was primarily enriched in the Prodigiosin biosynthesis pathway. Studies [52] have shown that Prodigiosin biosynthesis can reduce inflammatory cytokines in rabbits under heat stress and effectively improve their growth. Glycerophosphorylcholine (GPC) was significantly enriched in the Choline metabolism in cancer pathway. On one hand, GPC can promote the rapid proliferation of tumor cells; on the other hand, it inhibits the NF-κB signaling pathway, reducing the release of pro-inflammatory cytokines. This anti-inflammatory effect may also contribute to an immunosuppressive microenvironment [53]. In the HF group, differential metabolites were mainly enriched in pathways including Asthma, Sulfur relay system, Fc epsilon RI signaling pathway, Synaptic vesicle cycle, and Histidine metabolism. Histamine is a major metabolite in these four pathways—Asthma, Fc epsilon RI signaling pathway, Synaptic vesicle cycle, and Histidine metabolism—and was significantly downregulated in all of them. Despite the downregulation of these histamine-related pro-inflammatory pathways in the HF group, a significant inflammatory state was still observed. This suggests that the inflammation induced by the HF diet may not be primarily mediated through histamine pathways but rather driven by other mechanisms, such as the direct stimulation of pro-inflammatory cytokine production by the increased abundance of the opportunistic pathogen Clostridium_sensu_stricto_1^54,55. The downregulation of histamine metabolism might represent a secondary or overwhelmed compensatory response [54, 55]. The “Sulfur relay system” was another significantly different pathway, which has also been associated with intestinal inflammation [56, 57]. It should be noted that the functional annotations of the metabolic pathways discussed here are primarily cited from general databases such as KEGG, where the knowledge base largely originates from studies on humans, model organisms, or in vitro systems. Therefore, directly extrapolating these functions to the intestinal environment of pigs, especially Xinjiang black pigs, may have limitations due to potential species and physiological differences, and caution is warranted in interpretation. In summary, dietary fiber levels influence intestinal immunity by regulating microbial metabolites. A low-fiber diet suppresses inflammation by enriching antimicrobial and anti-inflammatory metabolites, whereas a high-fiber diet, despite downregulating pro-inflammatory histamine metabolism, ultimately leads to intestinal inflammation due to factors such as the proliferation of pathogenic bacteria.

To further integrate the above findings, we performed a correlation analysis to investigate the interactions among the gut microbiota, differential microbial metabolites, and gut barrier function-related molecules. The results revealed that Clostridium_sensu_stricto_1 was positively correlated with the pro-inflammatory factors TNF-α and IL-6, further confirming the role of this pathogen in promoting intestinal inflammation [54] and aligning with the observed enhanced inflammatory response under the high-fiber diet mentioned earlier. Terrisporobacter showed positive correlations with both aurachin B epoxide and Turicibacter, while Turicibacter was also positively correlated with aurachin B epoxide, suggesting that these microbial groups may be associated with the production of antibacterial metabolites, potentially suppressing the activity of certain bacteria by generating compounds with antibacterial activity [58]. Corynebacterium was positively correlated with the anti-inflammatory factor IL-10, which may indicate its involvement in immunomodulation under certain conditions, although its overall pathogenicity requires cautious interpretation [59].Regarding gut barrier function, Claudin-1 was significantly positively correlated with ZO-1, highlighting the synergistic effect of tight junction proteins in maintaining barrier integrity [60, 61]. Furthermore, the positive correlation between N-Acetylhistamine and histamine reflects the continuity of the histamine metabolic pathway, which can influence inflammatory and immune responses [62]. Collectively, these correlation analyses reinforce the mechanism by which dietary fiber modulates gut barrier function and inflammatory status through regulating microbial composition and metabolite production, underscoring the benefits of appropriate fiber levels and the potential risks of excessive intake.

Several limitations should be considered when interpreting the findings of this study. First, in the multiple comparison analyses of microbiota and metabolites, screening was primarily based on P-values (P < 0.05) and fold change(FC). Although the key findings showed high consistency across different levels, which reduces the risk of conclusions being driven by false positives, applying more stringent correction methods in future studies would improve the robustness of the conclusions. Second, dietary fiber levels in this study were adjusted by varying the proportion of alfalfa meal. Alfalfa meal is a complex raw material containing not only fiber but also bioactive components such as saponins and flavonoids. Therefore, the observed effects should be regarded as the integrated outcome of alfalfa meal, representing a potential confounding factor in this study. Finally, conclusions regarding intestinal barrier function were primarily based on gene expression data. Nevertheless, the downregulation of tight junction protein genes in the high-fiber group was highly consistent with increased serum intestinal permeability marker (D-LA), upregulation of pro-inflammatory cytokines (IL-6, TNF-α), and decreased antioxidant enzyme (SOD, CAT) activity. This mutually supportive chain of evidence across multiple indicators strengthens the plausibility of our core inference. Future research should include direct validation at the protein and histological levels and employ more precisely defined fiber sources to clarify their independent effects.

Conclusion

In summary, an appropriate dietary fiber level helps maintain gut microbiota homeostasis, enhances the abundance of beneficial bacteria such as Lactobacillus, suppresses pathogenic bacteria such as Streptococcus and Clostridium_sensu_stricto_1, strengthens intestinal barrier function, and improves systemic antioxidant capacity. In contrast, excessive dietary fiber reduces the abundance of Firmicutes, increases the abundance of Actinobacteria, promotes the colonization of pathogenic bacteria such as Clostridium_sensu_stricto_1 and Corynebacterium, and stimulates the release of proinflammatory cytokines. Metabolomic analysis further revealed that the high-fiber group was enriched in histamine-related pathways. Additionally, excessive fiber significantly decreased antioxidant enzyme activity in later stages and increased intestinal permeability. These findings indicate that excessive dietary fiber exacerbates intestinal inflammation through microbial dysbiosis, metabolic disturbances, and oxidative damage.

Authors’ contributions

ZZ、JW wrote the main manuscript, and SZ、FW prepared Figs. 1, 2, 3, 4 and 5. TL prepared Tables 1, 2 and 3. CN and JN. jointly supervised the paper. All authors reviewed and approved the manuscript.

Funding

This research received funding from several sources, including the Key Science and Technology Program of XPCC, the Science and Technology Program of Shihezi City, and the Science and Technology Program of Ili Prefecture, Xinjiang, under Grant Nos. 2022AB012, 2025RC08, and YZD2024A08, respectively.

Data availability

DNA and RNA sequences and sequencing data have been deposited in the NCBI database under BioProject: PRJNA1367337.

Declarations

Ethics approval and consent to participate

All animal experimental procedures were reviewed and approved by the Animal Ethics Committee of Shihezi University (Ethics Approval No. A2025-1210). Informed consent for the experimental pigs was obtained from the Ruikang Breeding Cooperative in Shihezi City.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.