The management of animal experiments involved in the research shall refer to the “Regulations on the Administration of Laboratory Animals” (Ministry of Science and Technology, China, revised in June 2004). Sample collection was approved by the Institutional Animal Care and Use Committee of Sichuan Agricultural University, Sichuan, China (No. 20181105), and operated in strict accordance with ethical guidelines.

The experiment selected at 60 days of age Taoyuan pigs (Average weight: 13.87 ± 0.58 kg; Purchased from Xiangcun Hi-Tech Agriculture Co., Ltd.), Duroc pigs (Average weight 18.50 ± 1.09 kg; Purchased from Linli Tianxin Seed Industry Co., Ltd) and their cross-bred varieties Xiangcun pigs (Average weight 14.47 ± 0.15 kg; Purchased from Xiangcun Hi-Tech Agriculture Co., Ltd.), and adopted a 3 × 2 factorial design, that is, 3 varieties of pigs (20 pigs for each variety, that is, each 10 pigs in each treatment group, 60 pigs in total) were fed a high-fiber diet (crude fiber: 6–7%; digestible energy: 3.5%; crude protein: 19.16%.) and a low-fiber diet (crude fiber: 2–3%; Digestible Energy: 3.49%; Crude Protein: 19.15%). Wheat bran fiber was purchased from Chengdu Tubaite Technology Co., Ltd. (manufacturer: JRS, model: WF200, purity >95%). All pigs were housed in single pens, each pen was equipped with feeders and nipple drinkers, the room temperature was 28°C, water and food were free, and the experimental period was 28 days. Pigs were slaughtered on the last day of the experiment and colonic contents and feces were collected.

Paired-end libraries were constructed by NEXTFLEX Rapid DNA Seq (Bioo Scientific, Austin, TX, United States) using 1 μg of high-quality DNA constructs, and then sequenced using the Novaseq6000 platform from Shanghai Majorbio BioPharmaceuticals Biotechnology Co., Ltd. (Shanghai, China). Raw data were filtered using Trimmomatic v0.38 to remove reads containing more than three ambiguous nucleotides with an average quality score of <20 and to remove artificially duplicated reads. Clean reads were then assembled into contigs using MEGAHIT (14) (https://github.com/voutcn/megahit, version 1.1.2). Only overlapping clusters of ≥300 bp were retained for subsequent analysis. Open reading frames (ORFs) of all assembled overlap clusters were predicted using MetaGene and translated into amino acid sequences. A catalog of non-redundant genes was constructed using CD-HIT (15) (http://www.bioinformatics.org/cd-hit/, version 4.6.1) with 90% sequence identity and 90% coverage. High-quality reads were aligned to the non-redundant gene catalog to calculate gene abundance with 95% identity using SOAPaligner (http://soap.genomics.org.cn/, version 2.21). Virulence factor annotation was performed using Diamond (16) (http://www.diamondsearch.org/index.php, version 0.8.35 http://www.mgc.ac.cn/VFs/) e value cutoff was 1 × 10⁻⁵. Sequence data associated with this project have been deposited in the NCBI Short Read Archive database (Accession Number: PRJNA849732).

Weighed about 1 g of samples (colon contents and fecal samples) in a 2 mL centrifuge tube, added ultrapure water in the ratio of 1:2.5, mixed well and let it stand for 30 min. Took 2 mL of supernatant in a new centrifuge tube, and incubated for 30 min at 4°C and 12,000 r/min for 10 min. Took 1 mL of supernatant and added 0.2 mL of 25% (w/v) metaphosphoric acid solution, mixed well, and incubated for 30 min at 4°C for 10 min at 12,000 r/min. Added 0.2 mL of 25% (w/v) metaphosphoric acid solution to 1 mL of the supernatant, mixed well, and incubated at 4°C for 30 min. Incubated for 10 min at 12,000 r/min for 10 min. Took 0.6 mL of the supernatant and added 11.67 μL of crotonic acid solution (210 mmol/L) to the supernatant, mixed well, and then took 0.3 mL of supernatant into a new centrifugal tube, and then added 0.9 mL of methanol solution. The SCFA concentration in the samples was determined using a gas chromatography system (CP-3800 GC, Varian, Inc., Walnut Creek, CA, United States) and following the method of Franklin et al. (17).

DNA was extracted from feces and in vitro fermentation samples according to the instructions of the Fecal Genomic DNA Extraction Kit (D2700, Solarbio, China). The concentration and integrity of the DNA samples were determined by using a NanoDrop Microspectrophotometer (NanoDrop 2000, NanoDrop, United States) and Gel Imaging (GelDocXR, Bio-Rad, United States) to detect the concentration, purity and integrity of DNA samples. The quantitative analysis of VFs gene was performed by qPCR using fecal DNA as template (CFX96 real-time fluorescence quantification instrument, ABI7900, United States). Three replicates were set up for each sample, and the results were averaged. The reaction system consisted of 5 μL of SYBR Premix Ex Taq^™ II, 0.5 μL of each of the upstream and downstream primers, 2 μL of the DNA template, and 2 μL of ddH₂O. The abundance of 46 genes in the fecal samples was examined by qPCR (QS6FX, ABI, United States) (Supplementary Table S5) (11). Absolute abundance of VFs was calculated according to the method of Xie et al. (11).

In vitro fermentation experiments were conducted according to existing methods (18). Duroc pigs were in normal body condition for 1 month prior to fecal collection. The collected feces were strongly mixed under aseptic conditions with 32% (w/v) inoculum (fecal slurry) in phosphate buffer, pH adjusted to 7.0 with 0.1 M HCl, and then filtered through 4 layers of gauze. The filtrate was bubbled with N₂. Then, 8 mL of fermentation medium, 2 mL of fecal inoculum and treatment agent were added to each 50 mL fermentation tube under anaerobic (N₂) and aseptic conditions. The fermentation tubes were then incubated for 20 h at 37°C at 120 rpm. After 20 h of fermentation, the fermentation was stopped by immersion in ice water for 15 min and the fermentation broth was collected for qPCR analysis of VFs and MGEs. The fermentation medium and reducing solution were prepared according to the established method (18). Based on the content of SCFAs in the feces of the high-fiber diet group, we determined the final concentrations of AA, PA, and BA added in the in vitro fermentation, which were 70 μmol/mL, 30 μmol/mL, and 20 μmol/mL, respectively.

Data preprocessing was performed using Excel 2019 (Microsoft, United States), and data statistics were performed using SPSS 22.0 (IBM Corp., United States). Graphical display of results was performed using GraphPad Prism 10. RPKM value was used for heat map data, Z-score was used for data standardization, and average clustering was used for cluster analysis. Correlation analysis used Spearman. The “protest” function in the vegan package was used to analyze the Procrustes correlation between the bacteriome and the VFs. Principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) analyses were performed using the pure vegetation package in R software, using VFs and normalized abundance values of bacterial communities with a Bray–Curtis distance. Distribution of VFs in bacteria of different taxonomic levels was plotted as a Sankey diagram using the networkD3 package¹ in R (v3.6.2). Mantel test correlation heatmaps were plotted using the ggplot2 package (3.3.3) in R (v4.1.3) and the two matrix correlations were tested using the linkET package (0.0.7.4). The mulberry graph is drawn using the plotly package in the R software. Circos analysis was produced and analyzed using the tools of the Megi Bio cloud platform.

Reads per kilobase million (RPKM):

R i represents the abundance value of Genei in a sample, that is, the number of reads aligned to Genei in the sample; L i represents the nucleotide length of Genei; ∑ 1 n R j represents the sum of the reads corresponding to all genes in the sample.

Experimental design using a 3 × 2 design, two-way ANOVA was used to analyze two main effects and interaction effects. Results were expressed as mean ± standard error, and p < 0.05 indicated a significant difference.

To research the effects of fiber diet and genetics on the diversity, abundance and distribution characteristics of VFs in the colonic contents of different breeds of pigs, we performed metagenomic analyses of the colonic contents of Duroc, Taoyuan and Xiangcun pigs on a high and low fiber diet (Figure 1A). In the colonic contents, 1,236 VFs were identified (Supplementary Table S1). The mechanistic classification revealed that both Offensive and Defensive virulence factors predominated, constituting over 70% of VFs. There was no significant disparity in the overall VF abundance across varying fiber levels. Yet, distinct differences in VF abundance were evident between breeds, with Taoyuan and Xiangcun pigs showcasing heightened VF levels (Figure 1B). PCoA analysis disclosed distinctive clustering for Duroc and Taoyuan pigs across fiber levels, while Xiangcun pigs resembled both breeds to some extent (Figures 1C,D). Through α diversity analysis, we discerned no noteworthy differences in VF abundance or diversity concerning fiber levels or breeds (Figures 1E,F). At level 2, VFs were segregated into 16 categories based on biological characteristics. The most abundant factors included adherence, antiphagocytosis, iron uptake system, and others (Figure 2A; Supplementary Figure S1). The high-fiber diet notably diminished the abundance of the iron uptake system and Ig protease (Supplementary Figure S1; Supplementary Table S2). Between breeds, Taoyuan pigs exhibited significantly higher abundance levels of the six virulence factor categories compared to Duroc pigs (Supplementary Figure S1; Supplementary Table S2). To overcome the shortcomings of linear models and better reflect the non-linear structure, we evaluated the accuracy of the model with NMDS stress values. We confirmed that the stress values less than 0.2, which ensured the reliability of the model. The NMDS analysis showed that in the low-fiber group, Duroc and Taoyuan pigs formed clearly differentiated clusters, whereas Xiangcun pigs showed some degree of similarity with both Duroc and Taoyuan pigs. In the high-fiber group, clusters of different breeds were more tightly clustered together (Figures 2B,C).

Expanding our study into the influences of breed and fiber on pig intestinal VFs, we examined the abundances of the top 40 VFs. The analysis revealed inter-breed differences in 18 of the top 40 VFs, with Taoyuan pigs exhibiting higher abundances compared to Duroc pigs (Figure 2D; Supplementary Table S3). These differences including LOS (CVF494), Capsule (VF0543), Beta-hemolysin/cytolysin (CVF171), LPS (VF0542), HitABC (VF0268), FbpABC (VF0272) Capsule (VF0274), Polysaccharide capsule (CVF567), Fibronectin-binding protein (AI238), Cytolysin (VF0356), Pyrimidine biosynthesis (VF0558), alpha-Hemolysin (VF0225), T4SS effectors (CVF803), Capsule (VF0003), The repeat in toxin (RTX) (CVF795), EF-Tu (VF0460), Acinetobactin (VF0467) and Capsule (VF0560). In an analysis of different fiber levels, a high-fiber diet significantly reduced 7 of the top 40 enriched VFs (HitABC (VF0268), FbpABC (VF0272), alginate conditioning (CVF523), capsule (VF0144), encapsulant (VF0003), RTX (CVF795), and copper exporter (CVF658)) (Figure 2D; Supplementary Table S3). The results showed that the abundance of VFs was higher in the colonic contents of local pigs under the same scale feeding pattern, while the high-fiber diet reduced the abundance of some VFs.

Intestinal VFs predominantly originate from gut microbiota. These were analyzed through a comparative assessment of metagenomic microbial data against the VFDB database. The top 10 genus-level microorganisms present in the porcine colon, constituting over 40% of the population, included Lactobacillus, Clostridium, Prevotella, Treponema, Oscillibacter, Ruminococcus, unclassified_f_Lachnospiraceae, unclassified_o_Clostridiales, unclassified_p_Firmicutes, and unclassified_f_Ruminococcaceae (Figure 3A). Alpha diversity analysis indicated that a high-fiber diet substantially enhanced microbial diversity, with no marked disparities across species. A Procrustes analysis revealed a significant correlation between VFs and bacterial taxa, showing that VFs composition aligned closely with bacterial composition (m² = 0.1132, p = 0.001) (Figure 3B). VFs were widely distributed among a variety of bacteria, mainly from the phylum Firmicutes (Clostridium and Bacillus), the phylum Bacteroidetes (mainly Bacteroidia and Flavobacteriia), the phylum Proteobacteria (mainly Gammaproteobacteria and Betaproteobacteria), and the phylum Actinobacteria (mainly Actinobacteria and Coriobacteriia) (Figure 3C). Correlation analysis between species abundance and functional abundance was performed based on the relative species and functional abundance of the samples. We identified species-level microorganisms that were major contributors in the abundance of VFs, which were mainly s_Lactobacillus_johnsonii, s_Clostridiales_bacterium, s_Oscillibacter_sp., s_Lachnospiraceae_bacterium, s_Lactobacillus_reuteri, s_uncultured_Clostridium_sp., s_Phascolarctobacterium_succinatutens, s_Lactobacillus_amylovorus, s_Faecalibacterium_prausnitzii, and s_Alistipes_sp._CAG:435 (Figure 3D).

To elucidate the relationship between gut flora and VFs, we conducted an interaction network analysis using Network software. This analysis aimed to assess the interactions and correlations between the top 100 genera of microorganisms and VFs within the same sample. In the node centrality plot, the numbers on the x-axis represent different nodes. Each node corresponds to the interaction of a single genus with other genera. When a peak appears simultaneously on all three curves between Degree_Centrality, Closeness_Centrality and Betweenness_Centrality, we consider that the genus corresponding to that node may be important for the whole network. Based on this principle, we identified 6 genera, including g_Lactobacillus, g_unclassified_f_Lachnospiraceae, g_Roseburia, g_unclassified_c_Clostridia, g_ Mediterraneibacter, and g_Acetivibrio, as well as three VFs, including LPS (VF0542), Trehalose-recycling ABC transporter (CVF651), and BfmRS (VF0463). This result further confirmed the important influence of gut flora on the distribution of VFs (Figures 4A,B; Supplementary Table S4).

We examined the disparities in the abundance of functional classes of MGEs in the colonic contents of different porcine breeds subjected to varying fiber intake levels. Our findings revealed that a high-fiber diet markedly diminished the relative abundance of plasmids (p < 0.05) without affecting isfinder, integrall, or iceberg. Interestingly, the MGEs categories within the porcine colonic contents showed no significant variations across breeds (Figure 5A). Linear correlation analysis indicated a notable positive association (p < 0.05) between the abundances of MGEs and VFs (Figure 5B). To determine the correlation of microbial diversity and MGEs with VFs, we assessed the relationship between microbial diversity and abundance of MGEs and different classifications of VFs. The results of the Mantel assay showed that MGEs were associated with Antiphagocytosis, Iron uptake system, Regulation, Toxin, Serum resistance, Exoenzyme and Anti-proteolysis, Bacteria richness was significantly positively correlated with Adherence, Secretion system and Stress protein, bacteria diversity was significantly positively correlated with Antiphagocytosis, Iron uptake system, Serum resistance, Phase variation, Magnesium uptake system and Actin-based motility (Figure 5C).

We detected 46 subtypes of VFs in the feces of different breeds of pigs by absolute quantification. These VFs were mainly classified into 22 categories by function, including Adherence, Antivirulence, Autotransporter, Biofilm formation, Capsule, Fimbrial adherence determinants, Immune evasion, Invasion, Iron uptake, LEE-encoded TTSS effectors, Macrophage inducible genes, Magnesium uptake, Nonfimbrial adherence determinants, Non-LEE encoded TTSS effectors, Nutritional factor, Protease, Regulation, Secretion system, Serum resistance, Spv locus, Stress adaptation and Toxin (Figure 6A). The results showed that high fiber diets significantly reduced the normalized abundance of total VFs in swine feces, and there was no significant difference in the abundance of VFs between breeds (Figures 6A,C). Previous laboratory studies have shown that high-fiber diets resulted in a significant reduction in the normalized abundance of the integrase gene intI1 in feces (19). After examining the amount of SCFAs in feces we found that the high fiber diet significantly elevated the amount of total SCFAs in swine feces, but there were no differences between breeds (Figure 6B). We used correlation analysis to further research these relationships. We found significant positive correlations between the normalized abundance of intI1 and the categories of Fimbrial adherence determinants, Immune evasion, Serum resistance, and Total VFs Abundance, whereas the fecal concentration of Total SCFAs was significantly correlated with the concentrations of Immune evasion, LEE-encoded TTSS effectors, Macrophage inducible genes and Total VFs Abundance categories showed significant negative correlations with each other (Figure 6D). In contrast, high-fiber dietary interventions may effectively modulate fecal excretion of VFs through the intermediate effects of their metabolic byproducts, SCFAs and MGEs. To confirm the association between SCFAs and VFs, we evaluated the effects of BA, AA, and PA on VFs carried by microorganisms after fecal flora fermentation using an in vitro fermentation model. The results showed that AA tended to reduce the normalized abundance of VFs in swine fecal fermentation broth (p = 0.051) (Figure 6E). In conclusion, our results suggest that fiber diets can influence the excretion of VFs in different pig breeds through the intermediate action of their metabolite AA. In addition, this effect can inhibit the spread of VFs by reducing MGEs in feces.