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. 2024 Oct 10;20:458. doi: 10.1186/s12917-024-04308-0

Effect of host breeds on gut microbiome and fecal metabolome in commercial pigs

Sui Liufu 1, Kaiming Wang 1, Bohe Chen 1, Wenwu Chen 1, Xiaolin Liu 1, Sheng Wen 1, Xintong Li 1, Dong Xu 3, Haiming Ma 1,2,4,✉
PMCID: PMC11465751  PMID: 39390513

Abstract

Background

Gut microbial composition and its metabolites are crucial for livestock production performance. Metabolite profiles from autopsied biospecimens provide vital information on the basic mechanisms that affect the overall health and production traits in livestock animals. However, the role of the host breed in the gut microbiome and fecal metabolome of commercial pigs remains unclear. In this work, differences in microbiota composition among three commercial pig breeds Duroc, Yorkshire, and Landrace were measured by 16S rRNA gene sequencing. Fecal metabolite compositions of the three pig breeds were detected using untargeted metabolomics.

Results

There were significant differences in the gut microbiomes of the three species, indicating that host breed affects the diversity and structure of gut microbiota. Several breed-associated microorganisms were identified at different taxonomic levels. Notely, most microbial taxa were annotated as Lactobacillacea, Muribaculaceae, and Oscillospiraceae. Several bacteria, including Lactobacillus, Subdoligranulum, Faecalibacterium, Oscillospira, Oscillospiraceae_UCG-002, and Christensenellaceae_R-7_group, could be considered as biomarkers for improving the backfat thickness (BF) for commercial pigs. Additionally, KEGG analysis of gut microbiota further revealed that arginine biosynthesis, pyruvate metabolism, and fatty acid biosynthesis varied greatly among pig breeds. Multiple gut bacterial metabolites (e.g., spermidine, estradiol, and palmitic acid) were identified as breed-associated. Mediation analysis ultimately revealed the cross-talk among gut microbiota, metabolites, and BF thickness, proclaiming that the microbial and metabolic biomarkers identified in this study could be used as biomarkers for improving BF phenotype.

Conclusions

This work provides vital insights into breed effects on gut microbiota and metabolite compositions of commercial pigs and uncovers potential biomarkers that are significant for pig breed improvement.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-024-04308-0.

Keywords: Host breeds, Gut microbiota, Fecal metabolites, Commercial pigs

Background

At present, the main supporting pig strains in the world are basically Duroc, Landrace, Yorkshire, and other pigs as the maternal or paternal parents, and parent-breeding pigs and commercial generation pigs are bred through crossbreeding [1, 2]. Duroc pigs were found be well suited as a crossbreeding boar owing to their fast growth rates, high feed-to-meat ratios, and excellent carcass meat quality [3]. The Landrace pigs have obvious crossbreeding advantages, which can significantly improve the growth rate, slaughter rate and leanness of crossbred progeny [4]. The Yorkshire pigs are full-bodied, distinguished by high leanness, fast growth and excellent feed utilization [5]. Additionally, BF thickness, as an important indicator in animal husbandry, is strongly related to economic efficiency [6, 7]. The BF thickness of animals is influenced by a combination of genetics, environment and other factors [6]. Owing to their unique intestinal flora, different pig breeds may show different nutrient absorption capabilities, which may affect feed efficiency and base fertilizer thickness [7].

Gut microbes play an important role in maintaining the host's digestion, energy metabolism, and immune function [8]. Recent studies have demonstrated that changes in the composition and function of the gut microbiota are closely related to the health status and productive performance of domestic animals [9]. Accordingly, a deeper understanding of the interaction between the intestinal microbiota and the host is crucial to better discover its functional mechanism. Both environment and host factors have been reported that could affect the community composition of gut microbiota [10, 11]. In research conducted by Pandit et al. [12], it was found that the gut microbiota of different broiler breeds contained breed-associated bacteria including Clostridium, Butyrivibrio, Ruminococcus, etc. Meanwhile, in recent years, there has been increasing evidence that gut microbiota-derived metabolites (e.g., fatty acids and short-chain fatty acids) were vital in regulating livestock growth performance [13]. The Rikenellaceae_RC9_gut_group had a positive correlation with palmitic acid in muscle and a negative correlation with stearic acid in BF. The genus Faecalibacterium, and family Ruminococcaceae were involved in the digestion of dietary fiber, affecting the production of short-chain fatty acids (SCFAs), and playing a vital role in regulating pig body weight and BF thickness [14]. In addition, it was worth noting that Rikenellaceae and Ruminococcus were the main genera responsible for fermenting carbohydrates and producing acetate and propionate [15].

Metabolomics discovers the types or quantities of metabolites and their patterns of change by conducting and quantifying all the small-molecule metabolites in an organism [16]. By characterizing the metabolic profile of an individual, it can fully reflect the result of a complex biological interaction of genetic and environmental factors [17]. Fecal metabolic biomarkers are critical for the production performances of farm animals because they deliver information on the net outcome of nutrient digestion and absorption [18]. Some studies have revealed breed-related metabolic molecules in different animal species. For instance, Italian Large White pigs are distinguished from Italian Duroc pigs by plasma sphingomyelin and biogenic amine levels [19]. Several serum fatty acids (e.g., oleic and linoleic acids), amino acids (e.g., glutamine and asparagine), and organic acids (e.g., citric and fumaric acids) have been suggested as breed-specific biomarkers for different beef cattle breeds [17]. However, the above studies have mainly focused on the effect of host breeds on serum metabolome rather than the fecal metabolome in farm animals. Accordingly, it is necessary to investigate the breed-associated impacts on the composition of fecal metabolites.

There is a strenuous interrelationship between the gut microbiota and fecal metabolites [20, 21]. Thus, this paper analyzed the composition of the gut microbiota and fecal metabolites of three pig breeds (Duroc, Landrace, and Large White) and explored the associations between gut bacteria, fecal metabolites, and BF thickness. The results not only help to understand the contribution of intestinal microorganisms to animal metabolism, but also reveal potential biomarkers with practical application value in promoting the health status and production performance of different pig breeds.

Materials and methods

Experimental pigs and sample collection

The Duroc, Landrace, and Yorkshire pigs were randomly selected from a large-scale commercial pig farm (Xiangtan County, Changsha City, Hunan Province, China). They were raised under the same nutritional and management conditions (160 ± 3 days of age, 5 sows and 5 boars in each group). All pigs were fed the same ration twice a day containing 14–16% crude protein, 0.25–0.60% sodium chloride, 0.60 – 1.50% calcium, 8% coarse fiber, 3,100–3,200 kJ of digestible energy, and 0.80% of lysine, and offered an ad libitum water. All pigs were healthy and were not fed probiotics, prebiotics, antibiotics, or anticoccidials throughout the trial. BF thickness in the three pig breeds was determined and recorded using B-scan ultrasonography (DAWEI, Jiangsu, China). Meanwhile, Fresh faeces from Duroc, Landrace, and Yorkshire pigs were collected directly from the rectum. All samples were kept in 2 mL freezing tubes and then immediately immersed in liquid nitrogen. The fecal samples were used for 16S rRNA gene sequencing and metabolome analysis. All animal experiments in this study were approved by the Institutional Animal Care and Use Committee of Hunan Agricultural University, Changsha, Hunan Province, China, with approval number CACAHU 20230601.

DNA extraction and 16S rRNA gene sequencing

The QIAamp fast DNA stool Mini Kit (QIAGEN, Germany) was selected for extraction of microbial DNA according to the manufacturer's guidelines. The concentration and quality of stool DNA were determined using a spectrophotometer Nanodrop 2000 (Thermo Scientific, USA) and 1.5% agarose gel electrophoresis, respectively. The specific primers 338F (5′-ACTCCTACGGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGGTWTCTAAT-3′) amplified the hypervariable regions (V3-V4) of the 16S rRNA gene. The PCR amplification was performed by the following steps: an initial 94 °C denaturation step for 5 min, with subsequent 30 cycles of 95 °C for 30 s, 55 °C for 25 s, and 72 °C for 25 s, and a final prolongation step for 10 min at 72 °C. All the amplicons were sequenced on a MiSeq platform (Illumina, USA) using the paired-end method according to standard protocols. The raw data were quality first processed using QIIME (v11.0) [22] to remove the primers, barcodes, and low-quality sequences with the parameter “cutadapt”. FLASH (v.1.2.11) [23] was used to merge high-quality pairwise reads into raw tags based on The SILVA database (v.138.1). To standardize the sequencing depth, the sequence library size was streamlined to 40,000 tags per sample before further analysis. Fragmentation was performed using the function “uclust” in QIIME (v11.0) to match tags into operational taxonomic units (OTUs) based on 97% sequence identity. We filtered low-quality OTUs according to the following criteria: relative abundance was less than 0.05% and OTUs appeared in 1% of the samples from test pigs. QIIME with the function “diversity” was used for the exploration of alpha and beta diversity indices, respectively. PICRUSt was used to predict KEGG metabolic pathways of gut bacterial communities [24].

Biomarker discovery in 16S rRNA data

Microbial biomarkers detection among the three groups of Duroc, Landrace and Yorkshire pigs were analyzed using linear discriminant analysis of effect sizes (LEfSe) [25] based on the standardized OTU relative abundance matrix. As a high-dimensional biomarker identification and interpretation algorithm, the LEfSe algorithm identified features that were significantly different using the non-parametric factoria Kruskal–Wallis test, and used LDA to estimate the effect size for each feature. The LDA threshold in the current study was 2.0 with a significance level of p-value smaller than 0.05.

Extraction of metabolite and LC-MS/MS profiling

The instrument platform for LC–MS analysis in this study was the UHPLC-Q Exactive HF-X system of Thermo Fisher Scientific. 50 mg solid samples were accurately weighed, and the metabolites were extracted using a 400 µL methanol: water (4:1, v/v) solution with 0.02 mg/mL L-2-chlorophenylalanin as internal standard. The mixture was allowed to settle at -10 ℃ and treated by High throughput tissue crusher Wonbio-96c (Shanghai wanbo biotechnology co., LTD) at 50 Hz for 6 min, then followed by ultrasound at 40 kHz for 30 min at 5 ℃. The samples were placed at -20 ℃ for 30 min to precipitate proteins. After centrifugation at 13,000 g at 4 ℃ for 15 min, the supernatant was carefully transferred to sample vials for LC–MS/MS analysis. As a part of the system conditioning and quality control process, a pooled quality control sample (QC) was prepared by mixing equal volumes of all samples. The QC samples were disposed of, and tested in the same manner as the analytic samples. It helped to represent the whole sample set, which would be injected at regular intervals (every 6 samples) to monitor the stability of the analysis. Simultaneously, unwanted information was dynamically removed from the MS/MS spectra.

Qualitative, quantitative and statistical analysis for metabolites

The accuracy of metabolite identification largely depended on accurate mass numbers, mass spectrometry spectra, ion fragmentation patterns, retention times, and other information. The main reference databases included MoNA (https://mona.fiehnlab.ucdavis.edu//), Metlin (https://metlin.scripps.edu/), and the self-owned standard library of Bionovogene Company (Suzhou, Jiangsu, China). To identify significantly changed metabolites (SCMs), statistical methods such as univariate and multivariate data analysis were used, and metabolites that satisfied the criteria below were considered significantly different: variable importance in projection (VIP) ≥ 1 and |fold change| (FC) ≥ 1. To probe the relationships between the metabolomic profiles of the Duroc, Landrace and Yorkshire pigs, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were estimated using the R package "ropls".

Statistical analysis

One-way analysis of variance (ANOVA) was performed using SPSS 25.0 to analyze the differences in diversity indexes, relative abundances of bacteria, and phenotype values among the Duroc, Landrace, and Yorkshire groups. The box plots were visualized with the median and standard error. Linear discriminant analysis (LDA) was performed in R using the "LEfSe" package. Spearman correlation analysis was performed using the R package “psych” to explore the correlation between different OTUs. The major modules in the interaction network for each pig breed were calculated and visualized by using Gephi (v.0.9.2; https://gephi.org/). Mediation analysis was performed using the "mediation" package in the R software to evaluate the interactions among microbial biomarkers, key metabolites, and BF thickness. The p-value of less than 0.05 was considered statistically significant.

Results

Structure and diversity of gut microbiota in Duroc, Landrace, and Yorkshire pigs

An average of 97,576.7 ± 8899.4 16S rRNA gene sequences per sample (min. = 83,542; max. = 115,270) were achieved from 30 fecal samples (Table S1). Microbial diversity indices (Shannon and Chao1) were used to assess the differences in gut microbiome across three pig breeds. As shown in Fig. 1A, the Shannon index was higher in Yorkshire than in Duroc and Landrace pigs, but there is no statistical difference (p > 0.05). While the Chao1 index of Duroc was significantly lower than that in Yorkshire and Landrace pigs (Fig. 1B, p < 0.05, Table S2).

Fig. 1.

Fig. 1

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Differences in diversity and structure of gut microbiota among Duroc, Landrace and Yorkshire pigs. A-B Comparison of Shannon and Chao1 indices for three groups. C The comparison of backfat (BF) thickness for three pig breeds. D Redundancy Analysis (RDA) exhibited the effect of host breeds on gut microbial community structure. E, F The gut microbial composition for Duroc, Landrace, and Yorkshire pigs in phylum (E) and genus (F) levels. G The difference in the relative abundance of Firmicutes, Bacteroidota and Spirochaetota in the phylum level among the three groups. H The difference in the relative abundance of Lactobacillus, Streptococcus and Christenellaceae in the genus level among the three groups. The box meant the interquartile range, and the box with the midline represented the median (n = 10 for each group)

BF thickness, as an important phenotype for commercial pig breeds, has been always the focus for breeders. The results demonstrated that BF thickness was significantly higher in Landrace and Yorkshire pigs than in Duroc pigs, respectively (Fig. 1C, p < 0.05). Although BF thickness was significantly higher in Yorkshire than in Landrace pigs, there was no statistical difference (Fig. 1C, p > 0.05). Next, to explore whether breed effects affect gut microbial community structure, the RDA analysis of each group was performed (Fig. 1D). The findings revealed that the breed exerted a significant effect on intestinal microbial communities.

The results of 16S rRNA gene sequencing indicated that the intestinal flora of the three pig breeds were similar, but there were still subtle differences at the phylum and genus levels. At the phylum level (Fig. 1E), Firmicutes, Bacteroidota, and Spirochaetota ranked in the top three relative abundances of the three pig breeds. The abundance of Firmicutes was highest in Duroc pigs, and the abundance of Bacteroidota was highest in Yorkshire pigs (Fig. 1G, p < 0.05). At the genus level (Fig. 1F), Lactobacillus, Streptococcus and Christenellaceae were the top three genera for three breeds, with Duroc pigs having higher levels of Lactobacillus than Landrace and Yorkshire pigs. About the genus Streptococcus, it was significantly higher in Yorkshire pigs compared to Duroc pigs. Christensenellaceae_R-7_group was significantly higher in Landrace pig in comparison with Duroc pig (Fig. 1H, p < 0.05).

Microbial biomarkers for Duroc, Landrace and Yorkshire pigs

To identify the microbial biomarkers, LEfSe analysis was performed based on the relative abundance of OTUs among three pig breeds. The results showed that bacteria such as Lactobacillus, Rikenellaceae_RC9_gut_group, UCG-005, Subdoligranulum, and Faecalibacterium were significantly enriched in Duroc pigs (Fig. 2A). While OTUs including Oscillospira, Bacteroidales_RF16_group, and Ralstonia were abundant in Landrace pigs (Fig. 2B). Regarding the Yorkshire pigs, we found that Muribaculaceae, Clostridium_sensu_stricto_1, Terrisporobacter, Oscillospiraceae_UCG-002, and NK4A214_group were enriched in it (Fig. 2C).

Fig. 2.

Fig. 2

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The identified significantly different OTUs in Duroc, Landrace and Yorkshire pigs. A, the Duroc group; B, the Landrace group; C, the Yorkshire group

Interactions of gut microbiota and their associations with BF thickness

To determine whether these significantly enriched bacteria interact and influence on intestinal wall thickness in the intestines of Duroc, Landrace and Large White pigs, the Spearman method was used to estimate the correlation coefficient between them.

The interaction network for each pig breed mainly consisted of two modules. We found that in the Duroc pigs (Fig. 3A), dominant genera such as Lactobacillus, Subdoligranulum, and Faecalibacterium were clustered in module 1 through negative internal interactions with other bacteria, and module 1 was negatively correlated with BF thickness (p < 0.01). In contrast, OTUs such as Rikenellaceae_RC9_gut_group and UCG-005 aggregated in module 2 through positive internal interactions, and module 2 was positively correlated with the thickness of BF (p < 0.01, Fig. 3D). In Landrace pigs (Fig. 3B), dominant OTUs such as Oscillospira, norank_f_Bacteroidales_RF16_group, and Christensenellaceae_R-7_group were clustered in module 1 through negative internal interactions. Module 2 mainly consisted of OTUs including unclassified_o_Lactabacillales, Streptococcus, and UCG-005 through positive internal interactions (Fig. 3E). In Yorkshire pigs (Fig. 3C), dominant bacteria such as Muribaculaceae, Oscillospiraceae_UCG-002, and Streptococcus were agglomerated in module 1 through negative internal interactions. While module 2 was dominated by genera such as Terrisporobacter, Clostridium_sensu_stricto_1, and UCG-005, which dominate through positive intra-interactions. However, in Landrace pigs and Yorkshire pigs, the interaction of each OTU in each module did not show significant associations (p > 0.05) with BF thickness (Fig. 3F).

Fig. 3.

Fig. 3

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Interactions among the bacteria enriched in each pig breed and their associations with BF thickness. A-C The clustering analysis for OTUs in each group; the red and green lines represented the positive and negative correlations, respectively. Each line linked to nodes represents significant correlations (FDR adjusted p < 0.05, |r|> 0.80). The node size was drawn according to the relative abundance of each OTU. D-F The correlation analysis between each module and BF thickness among the Duroc, Landrace, and Yorkshire pig breeds

Functional analysis of the gut microbiome among three pig breeds

Functional capacity of gut microbiome can reflect the metabolize status of the host at certain stages. Here, a total of 29 differentially enriched KEGG pathways were detected in the gut microbiota among three pig breeds (Fig. 4). We identified that arginine biosynthesis, glycerophospholipid metabolism and D-Alanine metabolism were more active in the gut microbiome of the Duroc pigs. Simultaneously, in Landrace pigs, the gut microbiota was more capable of biosynthesis of pyruvate metabolism, lysine biosynthesis, glycerolipid metabolism and HIF-1 signaling pathway. In Yorkshire pigs, it was mainly enriched in the pathways of phenylalanine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, sulfur metabolism and vitamin B6 metabolism.

Fig. 4.

Fig. 4

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The differential KEGG pathways were identified among Duroc, Landrace and Yorkshire pigs. The bar-plot (plink) represents the LDA value for KEGG pathways enriched in Duroc pigs. The bar-plot (cyan) represents LDA value for the KEGG pathways enriched in Landrace pigs. The bar-plot (violet) represents LDA value for the KEGG pathways enriched in Yorkshire pigs. LDA value > 2.0 and p < 0.05 was the significant threshold

Association analysis of gut microbiota with KEGG functions

As shown in Fig. 5, we found that in Duroc pigs, Lactobacillus was the only genus positively associated with arginine biosynthesis. Rikenellaceae_RC9_gut_group had the most correlation relationships with KEGG pathways including glycerophospholipid and galactose metabolisms. (p < 0.05). In Landrace pigs, Oscillospira was negatively correlated with glycerolipid metabolism (p < 0.05), while Bacteroidales_RF16_group was positively correlated with the HIF-1 signaling pathway, purine, and glycerolipid metabolism (p < 0.05). In Yorkshire pigs, Muribaculaceae was positively correlated (p < 0.05) with phenylalanine metabolism and citrate cycle (TCA cycle). Importantly, we found that Prevotellaceae_NK3B31_group, an important genus associated with persistent inflammation in the host [26], had the greatest correlation with pathways, especially fatty acid metabolism and biosynthesis (p < 0.05).

Fig. 5.

Fig. 5

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Heatmap showing the correlations between breed-associated OTUs and KEGG pathways. “*” FDR adjusted p < 0.05, “**” FDR adjusted p < 0.01, and “***” FDR adjusted p < 0.001

Overview of metabolome data and SCMs identification

LCMS/MS was performed to investigate the differential composition of metabolites in feces from Duroc, Landrace and Yorkshire pigs. The PCA plot corresponding to the three different pig breeds showed significant differences among each pig breed (Fig. 6A). Similarly, OPLS-DA analysis demonstrated significant differences between each pig species (Fig. 6B). Overall, a total of 415 SCMs (169 up-regulated, 246 down-regulated) were filtered in Duroc vs. Landrace (Fig. 6C, Table S3); 319 SCMs (238 up-regulated, 81 down-regulated) were identified in Landrace vs. Yorkshire (Fig. 6D, Table S4); and 508 SCMs (241 up-regulated, 267 down-regulated) were screened in Duroc vs. Yorkshire (Fig. 6E, Table S5). Finally, the heatmap revealed the expression pattern of metabolites among different pig breeds (Fig. 6F). A total of 37 (Table S6), 15 (Table S7), and 33 (Table S8) SCMs were upregulated at Duroc, Landrace, and Yorkshire pigs. respectively.

Fig. 6.

Fig. 6

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The faecal metabolome analysis among three commercial pig breeds. A, PCA plot of metabolomic profiles among three groups (Duroc, Landrace, and Yorkshire). B, OPLS-DA plot for metabolomic profiles from three groups (Duroc, Landrace, and Yorkshire). C-E, Volcano plots of significantly changed metabolites (SCMs) among the three comparison groups (Duroc vs. Landrace, Landrace vs. Yorkshire, and Duroc vs. Yorkshire). F, Cluster heatmap of shared SCMs identified from three pig breeds. Colors with plink, cyan, and violet represent Duroc, Landrace, and Yorkshire, respectively

KEGG enrichment analysis of SCMs

We found that among the three pig breeds, the breed-related monomers of Duroc pigs mainly belonged to fatty acyl (31.4%) and isochromoquinone (22.9%) (Fig. 7A). In Landrace pigs, were mainly annotated to organooxygen compounds (20.0%) and purine nucleosides (20.0%) (Fig. 7B). In Yorkshire pigs, it was mainly belonged to fatty acyls (30.3%), furans (12.1%), isochromanequinones (12.1%), organooxygen compounds (12.1%), and benzene and substituted derivatives (12.1%) (Fig. 7C).

Fig. 7.

Fig. 7

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The classification and functional analysis of breed-associated SCM among Duroc, Landrace and Yorkshire pigs. A-C, Overview for the classification of breed-associated SCMs in three groups (Duroc, Landrace, and Yorkshire). D-F The KEGG pathway analysis among Duroc, Landrace and Yorkshire pigs based on breed-associated SCMs

Next, we performed KEGG functional enrichment analysis based on breeds-associated SCMs. In Duroc pigs, SCMs were mainly engaged in the cGMP signaling pathway, arginine and proline metabolism, and mTOR signaling pathway (Fig. 7D, p < 0.05). In Landrace pigs, SCMs have participated in fatty acid elongation, degradation and biosynthesis (Fig. 7E, p > 0.05). In Yorkshire pigs, SCMs were annotated in the estrogen signaling pathway, GnRH secretion, and prolactin signaling pathway (Fig. 7F, p < 0.05).

The mediation analysis for gut microbiota, fecal metabolites and BF thickness

Mediation analyses were used to assess potential associations of gut microbiota affecting BF thickness by the mediation roles of metabolites. In Duroc pigs (Fig. 8A), Lactobacillus and Subdoligranulum were associated with BF thickness via spermidine and both showed negative correlations (p < 0.01). In Landrace pigs (Fig. 8B), there were no significant associations between Oscillospira and estradiol (p > 0.05), but the total effect showed a positive correlation (p < 0.05); Lactobacillales significantly affected the BF thickness with the existence of estradiol, the total effect showed a positive correlation (p < 0.01). In Yorkshire pigs (Fig. 8C), with the presence of palmitic acid, Muribaculaceae highly significantly affected the BF thickness (p < 0.01). In contrast, for Oscillospiraceae_UCG-002, although there was no significant association between palmitic acid, the total effect showed a positive association (p < 0.01).

Fig. 8.

Fig. 8

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The mediation analysis identifies the linkages in the microbial biomarkers, key metabolites, and BF thickness. The arrows represent the associations between the factors, and the total effect is a direct estimation of the effect on BF through gut microbiota, with Spearman coefficients and p values shown

Discussion

The important roles of gut microbiota and metabolites in livestock are constantly being confirmed [27], particularly concerning energy metabolism, gut barrier function, and immune system, which are closely associated with host health. As globally popular pig breeds, Duroc, Landrace, and Yorkshire pigs possess different excellent phenotypic traits, particularly in growth rate, BF thickness, and litter size. Insights into breed-associated effects in the gut microbiota and metabolites might contribute to exploring the potential probiotics and improving animal health and productivity [27]. Therefore, we conducted a study of the gut microbiome and fecal metabolome in three breeds of Duroc, Landrace, and Yorkshire pigs to explore what gut microbial biomarkers and fecal metabolites play important roles in affecting BF thickness.

We evaluated whether breed factors would alter the gut microbiota structure, and we found that the host breed had a significant impact on the gut microbiota structure, similar to the study by Kylie et al. [28]. The Chao1 index and BF thickness of Landrace and Yorkshire pigs were significantly higher than those of Duroc pigs. Previous studies have shown significant differences in these microbial diversity indices among different chicken and horse breeds [12, 29]. Similar to a previous study, our work showed that Lactobacillus was the dominant biomarker in Duroc pigs compared with Landrace and Yorkshire pigs, indicating that it might play important roles in the growth and development of Duroc pigs [30]. Furthermore, this study indicated that in Duroc pigs, dominant species such as Lactobacillus, Subdoligranulum, and Faecalibacterium were negatively correlated with BF thickness, while Rikenellaceae_RC9_gut_group and UCG-005 were positively correlated with it. A previous study showed that the proportion of Lactobacillus in the GI tract reflects the growth performance of pigs [31]. Some bacterial species, such as Lactobacillus johnsonii [32] and Lactobacillus plantarum [33] do belong to Lactobacillus indeed and are closely associated with lipid balance and body weight. Besides, Faecalibacterium, a butyrate-producing genus, has been reported to be crucial in improving insulin resistance and lipid metabolism disorder [34]. Interestingly, a recent study indicated that Subdoligranulum, a strictly anaerobic gram-negative microorganism and SCFA producer, was negatively correlated with insulin resistance, fat mass, and adipocyte diameter [35, 36]. All of these data indicated that the genera Lactobacillus, Subdoligranulum, and Faecalibacterium might the potential biomarkers for improving the BF thickness. As for the Rikenellaceae RC9 gut group, a genus related to severe intestinal inflammation, fatty acids production, and abnormal lipid metabolism [37–40]. Oscillospiraceae_UCG-005 has been revealed that positively associated with fatty acids such as palmitic acid and pentadecanoic acid [41].

KEGG predictions analysis using PICRUSt revealed that genes involved in arginine biosynthesis metabolism were more abundant in Duroc pig, and Lactobacillus were positively correlated with arginine biosynthesis metabolism. Indeed, previous studies suggested that lactic acid bacteria (e.g., Lactobacillus plantarum and Lactobacillus buchneri) were capable of producing arginine [42, 43]. Additionally, dietary L-arginine supplementation reduced white adipose tissue and abdominal fat content of pig and broiler chickens, respectively [44, 45]. Herein, our data revealed a negative correlation between Lactobacillus and BF thickness, and speculated that Lactobacillus may produce arginine which in turn affects BF thickness, nevertheless, more verification is needed. In Landrace pigs, Oscillospira showed a negative correlation with glycerolipid metabolism, while Bacteroidales_RF16_group showed a positive correlation with purine and glycerolipid metabolism. A recent study discovered that Oscillospira was inversely correlated with triglyceride and fasting blood glucose, indicating its potential functions in improving obesity [46]. While for Bacteroidales_RF16_group, it was common in a range of ruminants [47], and positively associated with propionate and acetate [48]. A recent study found that it was enriched in high-finishing weight pigs [49]. However, the mechanism of Bacteroidales_RF16_group metabolism is not yet clear. In Yorkshire pigs, Muribaculaceae was positively correlated with phenylalanine, tyrosine, and tryptophan biosynthesis. Consistent with our study, a recent study found that metabolites including l-phenylalanine, l-tyrosine, and l-tryptophan were positively correlated with Muribaculacea [50], which further indicated that Muribaculacea might play a vital role in the biosynthesis of above-related metabolites. In conclusion, there are obvious differences in the functional characteristics of the intestinal flora among the three pig breeds. This result provides important information for promoting the health and production performance of different pig breeds by clarifying the functions of the intestinal flora.

To determine the breed-related microbial metabolites and further understand the potential interaction of microbiota, metabolites, and host phenotypes. We investigated the characteristics of fecal metabolomics. We found that most breed-associated SCMs mainly belonged to fatty acids, isochromanequinones, and purine nucleosides. Similarly, the KEGG analysis of fecal metabolites showed that arginine metabolism enriched in the Duroc pig. Spermidine is an arginine metabolite [51], and Lactobacillus has also been proven to be one of the main producers of spermidine [52]. The mediation analysis indicated that Lactobacillus and Subdoligranulum in Duroc pigs showed a negative correlation with BF thickness with the presence of spermidine. As we discussed above, Lactobacillus was a main spermidine producer and exerted beneficial roles in domestic animals, especially in lipid metabolism. These results indicated that certain bacterial strains from Lactobacillus could be important in reducing the BF thickness. In the Landrace pigs, Oscillospira and Lactobacillales exhibited a positive correlation with BF thickness under the mediation role of estradiol. In Yorkshire pigs, Muribaculaceae and Oscillospiraceae_UCG-002 could affect BF thickness with the existence of palmitic acid. Previous studies have found that estradiol is positively correlated with fat mass and BMI, suggesting that it plays a key role in promoting obesity [53]. A higher abundance of Oscillospira was detected in mice with gut microbial dysbiosis and was highly associated with obesity [54]. The relative abundance of Lactobacillales was significantly correlated with growth performance [55], and when the concentration of estradiol was high, lactobacillus was predominant [56, 57]. In rats, an increased abundance of intestinal Muribaculaceae led to a decrease in the levels of palmitic acid in the serum [58]. A decrease in the content of intestinal Oscillospiraceae_UCG-002 promoted lipid metabolism and reduced fat deposition [59]. These results suggested that breed effects can influence the composition of gut microbiota and fecal metabolites and that interactions between gut microbiota and metabolites can influence host phenotypes. However, further studies would warrant confirming the function roles of the above bacteria such as Lactobacillus, Subdoligranulum, and Oscillospira in the production performance of domestic livestock.

Our study has limitations, as we only analyzed three breeds of pigs, but it provides important evidence for the influence of host breeds on the gut microbiota and fecal metabolome of pigs. It also revealed certain gut bacteria and fecal metabolites that could be considered as candidate biomarkers for pig breed improvement. In this context, further studies are needed to elucidate the potential mechanisms by which host species modulate the gut microbiota and fecal metabolome. In addition, future studies should use metagenomics and metatranscriptomics to explore more bacterial species and functional capabilities that may affect livestock growth performance.

Conclusion

In summary, this work showed that commercial pig breeds, including Duroc, Yorkshire and Landrace pigs, possessed disparate gut microbiota profiles, which might be greatly influenced by their genotype and phenotype. Similarly, further metabolomics studies also revealed the distinct metabolites profile between the three breeds, which play vital roles in regulating lipid metabolism homeostasis. The mediation analysis between microbial biomarkers, key metabolites, and BF thickness showed that there was a close relationship between the effects of microbial-derived products on host phenotypes. Despite this study has some limitations, the findings have important enlightenments for commercial pig breed improvement.

Supplementary Information

12917_2024_4308_MOESM1_ESM.xlsx (259.4KB, xlsx)

Additional file 1: Table S1. Summary of 16S rRNA gene sequencing data in faecal samples for Duroc, Landrace, and Yorkshire pigs. Table S2. Comparison of Shannon and Chao1 indices for three groups. Figure S3. Significantly changed metabolites (SCMs) identified from the comparison group Duroc vs. Landrace. Figure S4. Significantly changed metabolites (SCMs) identified from the comparison group Landrace vs. Yorkshire. Figure S5. Significantly changed metabolites (SCMs) identified from the comparison group Duroc vs. Yorkshire. Figure S6. Duroc-associated significantly changed metabolites (SCMs) identified by cluster heatmap. Figure S7. Landrace-associated significantly changed metabolites (SCMs) identified by cluster heatmap. Figure S8. Yorkshire-associated significantly changed metabolites (SCMs) identified by cluster heatmap.

Abbreviations

16S rRNA

16S ribosomal ribonucleic acid

OTU

Operational taxonomic unit

RDA

Redundancy analysis

LEfSe

Linear discriminant analysis effect size

KEGG

Kyoto Encyclopedia of Genes and Genomes

FDR

False discovery rate

QC

Quality control

PLS-DA

Partial least squares discriminant analysis

SCMs

Significantly changed metabolites

Authors’ contributions

Conceptualization, H.M.; data curation, S.LF.; formal analysis, K.W., B.C.; and X.L.(Xiaolin Liu).; investigation, W.C., X.L.(Xintong Li)., and S.W.; methodology, S.LF.; writing— original draft, S.LF.; writing—review and editing: D.X., H.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the design and cultivation of the new breed in pigs with high-quality meat (2023ZD04046-05), Major Science and Technology Special Plan of Yunnan Province (202202AE090032 and 202102AE090046-3), Postgraduate Scientific Research Innovation Project of Hunan Province (CX20230713), Postgraduate Scientific Research Innovation Project of Hunan Agriculture University (2023XC006), and the Hunan Provincial Department of Education Scientific Research Project (23B1097).

Availability of data and materials

Amplicon sequence data in this study was deposited to China National GeneBank (CNGB) under number CNP0005692 (http://db.cngb.org/cnsa/project/CNP0005692_0b98c629/reviewlink/).

Declarations

Ethics approval and consent to participate

All animal experiments in this study were approved by the Institutional Animal Care and Use Committee of Hunan Agricultural University, Changsha, Hunan Province, China, with approval number CACAHU 20230601. Written informed consent was obtained before sample collection from the commercial farm.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

12917_2024_4308_MOESM1_ESM.xlsx (259.4KB, xlsx)

Additional file 1: Table S1. Summary of 16S rRNA gene sequencing data in faecal samples for Duroc, Landrace, and Yorkshire pigs. Table S2. Comparison of Shannon and Chao1 indices for three groups. Figure S3. Significantly changed metabolites (SCMs) identified from the comparison group Duroc vs. Landrace. Figure S4. Significantly changed metabolites (SCMs) identified from the comparison group Landrace vs. Yorkshire. Figure S5. Significantly changed metabolites (SCMs) identified from the comparison group Duroc vs. Yorkshire. Figure S6. Duroc-associated significantly changed metabolites (SCMs) identified by cluster heatmap. Figure S7. Landrace-associated significantly changed metabolites (SCMs) identified by cluster heatmap. Figure S8. Yorkshire-associated significantly changed metabolites (SCMs) identified by cluster heatmap.

Data Availability Statement

Amplicon sequence data in this study was deposited to China National GeneBank (CNGB) under number CNP0005692 (http://db.cngb.org/cnsa/project/CNP0005692_0b98c629/reviewlink/).

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