Structural and functional characteristics of faecal, vaginal and colostrum microbiota in high-yielding sows

Abstract

Recent studies have highlighted the critical roles of the microbiota in faeces, vagina and colostrum in reproductive success and piglet development. Understanding the structural and functional dynamics of these microbial communities is essential for optimizing the health and productivity of high-yielding (HY) sows. This study aimed to characterize the structural and functional features of these microbial communities in HY sows (litter size>10 piglets) vs. low-yielding (LY) sows (≤10 piglets) and to explore their associations with sow performance and piglet health. Fifteen Yorkshire sows from each group were selected, and fresh faecal, vaginal mucus and colostrum samples were collected post-parturition. Microbial composition was analysed using 16S rRNA gene amplicon sequencing, and functional potential was predicted via PICRUSt2. Results showed that HY sows exhibited higher alpha diversity in vaginal and colostrum microbiota and greater community stability (higher neutral community model R² values) compared to LY sows. In faeces, HY sows were enriched with Terrisporobacter and Romboutsia, while depleted in Ruminococcus_torques_group. In the vagina, Campylobacter and Peptoniphilus were increased, whereas Eubacterium_nodatum_group decreased. In colostrum, Lactobacillus, Bifidobacterium and Romboutsia were enriched, with reduced Peptostreptococcus, indicating a more beneficial profile for neonatal health. Functional predictions revealed distinct metabolic profiles: HY faecal microbiota upregulated cysteine/methionine metabolism and porphyrin and chlorophyll metabolism; vaginal microbiota enhanced oxidative phosphorylation and thiamine metabolism; colostrum microbiota showed enrichment in monobactam and novobiocin biosynthesis, which is associated with antimicrobial activity and stress response. These findings demonstrate that HY sows harbour a more stable and functionally advantageous microbiome across multiple biological niches, which may contribute to superior reproductive performance and improved offspring health. The results provide a foundation for developing microbiome-targeted strategies to enhance productivity and welfare in commercial pig production.

Data Summary

All sequencing data have been deposited in the National Center for Biotechnology Information (NCBI) database, accessible through BioProject accession number PRJNA1285878, which contains the NCBI Biosamples (Table S1).

Impact Statement.

This study provides critical insights into the distinct microbial profiles of high-yielding (HY) and low-yielding sows, highlighting the significant roles of faecal, vaginal and colostrum microbiota in reproductive success and piglet development. By identifying higher microbial diversity and beneficial bacterial abundance in HY sows, particularly in colostrum samples, this research underscores the potential of microbiota optimization to enhance sow productivity and piglet health. The findings pave the way for targeted interventions, such as probiotic supplementation or microbial modulation strategies, to improve reproductive performance and neonatal outcomes in commercial pig production. Ultimately, this work contributes to advancing sustainable and efficient swine farming practices by leveraging the power of microbial ecosystems to support animal welfare and production efficiency.

Introduction

The microbiota residing within various body sites of sows is gaining increasing attention due to its profound impact on animal health and productivity. The gut, vaginal and colostrum microbiomes are particularly important in high-producing sows, as they directly influence crucial aspects of metabolic processing, reproductive health and neonatal development. Recent research highlights the intricate relationship between these microbial communities and the host’s physiological functions, particularly concerning reproduction, digestion and immunity [1,2]. As the swine industry continues to push boundaries on productivity, a deeper understanding of these microbial populations becomes crucial.

In the gastrointestinal tract, the faecal microbiota is predominantly responsible for the fermentation of dietary components, including fibres that are otherwise indigestible, thereby enhancing nutrient absorption and energy metabolism [3,4]. Additionally, a balanced faecal microbiome supports gut health by preventing the colonization of pathogenic bacteria, thus reducing incidences of gastrointestinal diseases that could impair sow productivity [5]. Previous studies have demonstrated significant differences in the faecal microbiota of sows with varying litter sizes, which are closely associated with maternal oxidative stress and blood biochemical indices [6,8]. These findings underscore the pivotal role of gut microbiota in regulating sow reproductive performance. Likewise, the vaginal microbiota plays a pivotal role in the reproductive health of sows. A well-balanced vaginal microbiota is crucial for maintaining a healthy pregnancy and reducing the incidence of infections that can compromise gestation success [9,10]. Disruption in this microbiota has been linked to increased rates of reproductive complications, highlighting its importance [11,12]. Colostrum represents a critical nexus between maternal and neonatal health. Colostrum not only provides newborn piglets with essential nutrients and antibodies necessary for early-life immunity but also serves as a primary source for initial gut microbiota colonization [13,14]. The microbial composition of colostrum, therefore, can determine the early development of the piglet’s own gut microbiome, affecting their growth trajectory and long-term health [15,16]. Crucially, hyperprolific sows face unique challenges: their extreme metabolic demands may alter colostrum microbiota composition [17], potentially compromising microbial transmission to offspring. Despite growing awareness of the importance of these microbiomes in sows [18,19], none have systematically compared microbial profiles between high-yielding (HY) and low-yielding (LY) sows, despite the fact that HY sows dominate modern commercial herds and face heightened physiological stressors. This knowledge gap limits the ability to optimize colostrum quality and neonatal outcomes in high-output production systems.

By integrating 16S rRNA gene amplicon sequencing and functional prediction tools, this study aims to (1) characterize the structural and functional differences in faecal, vaginal and colostrum microbiota between HY and LY sows and (2) identify microbial signatures associated with reproductive efficiency. These findings will provide a foundation for targeted interventions to enhance sow productivity in hyperprolific breeding systems.

Methods

Animal welfare statement

The samples of sows were collected from the Beijing Shunxin Agricultural Small Shop Pig Farm (Beijing, China). The experimental protocols involving animals in this research were formally sanctioned by the Institutional Animal Care and Use Committee at the Institute of Feed Research, Chinese Academy of Agricultural Sciences (Approval No. IFR-CAAS20230620).

Experimental animals and sample collection

Purebred Yorkshire pregnant sows with synchronized farrowing (within 72 h) were used in the experiments. They had no medical history within 30 days pre-partum. All sows were provided with the same commercial formula feed and feeding regimen. All sows were housed individually in a piggery with hard plastic slatted flooring and had free access to water through nipple drinkers.

After parturition, the number of piglets, total litter weight and number of live piglets were recorded. Sows were categorized into HY sows and LY sows based on their litter sizes: 15 HY sows with litter size>10 and 15 LY sows with litter size≤10. The HY sows had an average parity of 2.60±1.76 (range: 1~7) and the LY sow had an average parity of 2.86±2.42 (range: 1~7). The HY/LY grouping in the present study was justified by alignment with preliminary farm data showing that this threshold effectively distinguishes productivity differences.

Fresh faecal and vaginal samples were collected from both HY and LY sows using sterile cotton swabs within 24 h post-partum. Rectal swabs were obtained from the rectum. For vaginal sampling, the vaginal opening was first gently cleaned with sterile saline solution prior to sample collection, followed by opening the vulva with one hand and wiping the mucosa just inside the vagina with the swab. Colostrum samples (~5 ml) were collected in sterile centrifuge tubes within 2 h post-partum. For colostrum acquisition, the central teat was selected, and its surface was pre-cleaned with sterile saline solution before manual expression. Owing to the challenges associated with naturally collecting colostrum, colostrum samples were gathered from a total of 15 sows, including 7 samples from LY sows and 8 samples from HY sows. All samples were immediately added to a DNA preservation solution (Catalogue No. RE1051) (Beijing Kulaibo Technology Co., Ltd., Beijing, China). Subsequently, the samples were stored at −80 °C before further analysis.

16S rRNA gene amplicon sequencing and analysis of microbiota

Genomic DNA from the total microbial community in the faecal, vaginal and colostrum samples was extracted using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Germany). The V3-V4 hypervariable regions of the 16S rRNA gene were selectively amplified using the primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), following previously described protocols [20]. The resulting PCR products were purified, quantified and pooled in equimolar concentrations to prepare for paired-end (PE300) sequencing on the Illumina MiSeq platform (Illumina, San Diego, USA), adhering to the standardized methodologies of Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

Sequencing generated a total of 6,227,289 raw reads, with per-sample read counts ranging from 42,281 to 221,800 (Table S2, available in the online Supplementary Material). Raw sequencing data were processed using QIIME2 on the Majorbio I-Sanger Cloud Platform (https://www.i-sanger.com/). Quality control and denoising were performed with DADA2 using default parameters [21], yielding amplicon sequence variants (ASVs). To reduce noise from low-abundance or spurious sequences, only ASVs with a minimum abundance of two reads and detected in more than two samples were retained. Additionally, ASVs annotated as Chloroplast or Mitochondria were removed to exclude host- or plant-derived sequences. The cleaned ASV table was rarefied to a minimum depth of 24,512 sequences to eliminate bias from uneven sequencing depth; all samples met this threshold, resulting in 75 retained samples. This rarefied ASV table was used for all downstream analyses, including alpha diversity, beta diversity and differential abundance analysis. The final curated dataset comprised 1,838,400 high-quality sequences and 23,415 ASVs (Table S3).

Representative sequences of ASVs were taxonomically classified using the classify-sklearn algorithm against the SILVA 138 database. Alpha and beta diversity metrics were calculated using the vegan package (version 3.3.1). To illustrate the compositional differences among microbial communities, principal coordinates analysis (PCoA) was conducted based on Bray–Curtis distances, unweighted UniFrac and weighted UniFrac metrics at the ASV level, with separate analyses conducted for each sample type (faecal, vaginal and colostrum). Additionally, to statistically evaluate differences in microbial community composition between the LY and HY groups, permutational multivariate analysis of variance and analysis of similarities (ANOSIM) were employed, with 999 permutations for significance testing. Neutral community model (NCM) analysis was performed with the minpack.lm (version 1.2.3) and getopt (version 1.20.3) packages in R (version 3.3.1). Venn plot analysis at the genus level was performed using Python 2.7.10. Taxonomic differentiation among samples was identified using linear discriminant analysis effect size (LEfSe) and validated via blast searches against the National Center for Biotechnology Information 16S rRNA sequence database. The functional potential of the microbiota was predicted using PICRUSt2 (version 2.2.0-b). PICRUSt2 was run with default parameters, using the built-in IMG reference genome database to project 16S rRNA gene amplicon sequences to functional profiles. Predicted metagenomes were then annotated against the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology database for functional classification. Differences in the relative abundance of KEGG orthologs and enzyme genes between LY and HY groups were assessed using the Mann–Whitney U test [22].

Statistical analysis

Differences in parity, average litter size, average live litter size and litter weight were evaluated using Student’s t-test with IBM SPSS Statistics 20.0 software (SPSS Inc., Chicago, IL, USA). For microbial alpha diversity, differences in the Shannon index, abundance-based coverage estimator (ACE), Chao and Simpson indices were analysed using R version 3.3.1 with the Wilcoxon rank-sum test (two-tailed). Differences in microbial species (at the phylum and genus levels) were also analysed using R version 3.3.1 with the Wilcoxon rank-sum test (two-tailed), followed by false discovery rate adjustment to account for multiple comparisons. All results are presented as mean values±sem, and statistical significance was defined as P<0.05.

Results

Litter performance of sows

As illustrated in Fig. 1, HY sows had an average litter size of 14.73±1.22 piglets, significantly higher than the 8.33±1.11 piglets in the LY group (P<0.001). Similarly, HY sows averaged 11.73±1.75 live piglets, significantly more than the 7.33±1.35 live piglets in the LY group (P<0.001). The average litter weight for HY sows was 18.39±3.82 kg, considerably higher than the 12.28±3.12 kg for LY sows (P<0.001).

Fig. 1. The reproductive performance between LY and HY sows. (a) Experimental schematic diagram. (b) Average litter size, average alive litter size and litter weight. LY, sows with lower litter size; HY, sows with higher litter size. All data are presented as mean±sem. ***, P<0.001.

Diversity differences in faecal, vaginal and colostrum microbiota between HY and LY sows

The composition of faecal, vaginal and colostrum microbiota was analysed using 16S rRNA gene amplicon sequencing. The microbial rarefaction curves (Fig. S1) indicated sufficient sequencing depth, as all curves reached a plateau. Alpha diversity analysis revealed no significant inter-group differences in faecal microbiota between HY and LY groups (Fig. 2a). In contrast, vaginal microbiota showed significantly higher alpha diversity in the HY group compared to the LY group, evidenced by a significant increase in the Shannon index, with no notable effect on the ACE, Chao and Simpson indices (Fig. 2b). For colostrum microbiota, HY sows exhibited significantly higher alpha diversity compared to LY sows, marked by significant increases in ACE, Chao and Shannon indices and a significant decrease in the Simpson index (Fig. 2c). PCoA based on weighted UniFrac and Bray–Curtis distances revealed clear segregation at the ASV level for faecal microbiota between HY and LY groups (ANOSIM similarity<0.05) (Figs 3 and S2), with LY samples showing greater dispersion (higher intra-group variability) across all sampling sites compared to HY. Similarly, segregation was observed for vaginal and colostrum microbiota using weighted UniFrac, unweighted UniFrac and Bray–Curtis distances (ANOSIM similarity<0.05). The NCM reflects the stability of microbial communities. The results (Fig. 4) showed that HY sows had higher stability in both faecal (R²: HY 0.81 vs. LY 0.58) and vaginal (R²: HY 0.63 vs. LY 0.51) microbiota.

Fig. 2. The α-diversity (ACE, Chao, Shannon and Simpson index) for faecal, vaginal and colostrum microbiota. (a) Faecal microbiota; (b) vaginal microbiota; (c) colostrum microbiota. LY, sows with lower litter size; HY, sows with higher litter size. All data are presented as mean±sem. *, P<0.05; **, P<0.01.

Fig. 3. The PCoA plot based on the weighted UniFrac at ASVs level for faecal, vaginal and colostrum microbiota. (a) Faecal microbiota; (b) vaginal microbiota; (c) colostrum microbiota. LY, sows with lower litter size; HY, sows with higher litter size. Differential analysis between LY and HY groups using the ANOSIM similarity analysis.

Fig. 4. The microbial structure analysis based on the NCM. (a) Analysis results for faecal microbiota in LY group; (b) analysis results for faecal microbiota in HY group; (c) analysis results for vaginal microbiota in LY group; (d) analysis results for vaginal microbiota in HY group. In the plots, the solid line represents the fit of the neutral model, and the dashed lines represent the 95% confidence interval of the model’s predictions. R² represents the overall goodness of fit of the NCM, with higher R² indicating that the community structure is more influenced by random processes and less by deterministic processes. N describes the metacommunity size, which refers to the total abundance of all ASVs in each sample. m quantifies the migration rate at the community level, which is the same for each community member (species-independent). A lower m value indicates that species diffusion is more restricted in the community, while a higher m value indicates that species diffusion is less restricted. Nm is the product of metacommunity size (N) and migration rate (m) (Nm=N*m), which quantifies the estimated diffusion between communities and determines the correlation between occurrence frequency and regional relative abundance. LY, sows with lower litter size; HY, sows with higher litter size. Since the NCM analysis requires a total sample size of more than 10, the colostrum samples do not meet the analysis requirements. Therefore, the NCM analysis was not conducted on the colostrum microbial data.

Composition changes of faecal, vaginal and colostrum microbiota of HY sows

The Venn diagram revealed 69 shared genera across faecal, vaginal and colostrum microbiota in LY and HY sows, with HY sows exhibiting distinct unique genera across sample types: 12 in faeces, 7 in vaginal samples and 87 in colostrum (Fig. 5a); the pie charts (Fig. 5b–d) display the percentage of each unique genus relative to the total number of unique genera in that sample type for HY sows. Specifically, the 12 unique faecal genera in HY sows (Fig. 5b) were primarily composed of Mitsuokella (25.00%), Erysipelotrichaceae_UCG-002 (20.54%), Selenomonas (17.86%) and Schwartzia (16.96%), and these 12 unique genera collectively accounted for 0.033% of the total faecal microbial community in HY sows. The seven unique vaginal genera in HY sows (Fig. 5c) included unclassified_o_Rickettsiales (35.48%), Amnipila (19.35%), Fenollaria (12.90%) and Plesiomonas (12.90%), with their total relative abundance constituting 0.009% of the entire vaginal microbial community in HY sows. The 87 unique colostrum genera in HY sows (Fig. 5d) included Allobaculum (24.66%), Conchiformibius (3.65%), Fusicatenibacter (3.20%) and Faecalibaculum (2.28%), and the total relative abundance of these 87 unique colostrum genera accounted for 0.245% of the total colostrum microbial community in HY sows.

Fig. 5. Venn diagram analysis illustrating unique bacterial genera in faecal, vaginal and colostrum microbiota between LY and HY sows. (a) Venn diagram comparing all samples from LY and HY sows. HY sows exhibited 12 unique bacterial genera in faecal samples, 7 in vaginal samples and 87 in colostrum samples; (b) Pie chart showing the distribution of the 12 unique faecal bacterial genera identified in HY sows at the genus level; (c) Pie chart showing the distribution of the 7 unique vaginal bacterial genera identified in HY sows at the genus level; (d) Pie chart showing the distribution of the 87 unique colostrum bacterial genera identified in HY sows at the genus level. For panels (b–d), different colours represent distinct bacterial genera, and the area of each pie segment indicates the relative abundance (percentage) of each genus relative to the total number of unique genera in each respective sample type (i.e. 100% of each pie chart=sum of all unique genera in the corresponding sample type). LY, sows with lower litter size; HY, sows with higher litter size.

At the phylum level, faecal microbiota was predominantly composed of Firmicutes (65.27%), Bacteroidota (15.91%) and Spirochaetota (11.22%). Vaginal microbiota was mainly composed of Firmicutes (29.79%), Proteobacteria (27.72%) and Bacteroidota (17.50%), while colostrum microbiota consisted primarily of Proteobacteria (52.04%), Firmicutes (30.24%) and Actinobacteriota (8.03%) (Fig. 6a). Analysis of the abundance ratio of (Firmicutes+Actinobacteriota) to (Bacteroidota+Proteobacteria) revealed that, compared to the LY group, the HY group had numerically higher ratios in faecal samples (HY 4.60 vs. LY 3.55; P=0.237), vaginal samples (HY 1.63 vs. LY 0.70; P=0.137) and colostrum samples (HY 1.21 vs. LY 0.45; P=0.074). A Wilcoxon rank-sum test revealed no significant differences in faecal microbiota between HY and LY groups. For vaginal microbiota, the HY group exhibited significantly lower relative abundances of Proteobacteria but higher relative abundances of Bacteroidota and Campylobacterota compared to the LY group (Fig. 6c). In colostrum microbiota, HY sows showed significantly lower relative abundances of Proteobacteria but higher relative abundances of Actinobacteriota, Fusobacteriota and Verrucomicrobiota compared to LY sows (Fig. 6d).

Fig. 6. Microbial composition at the phylum level in faeces, vaginal and colostrum of LY and HY sows. (a) Community composition bar plot showing phylum-level microbial profiles; (b) abundance ratio of (Firmicutes+Actinobacteriota) to (Bacteroidota+Proteobacteria); (c) significantly differentially abundant phyla in vaginal samples; (d) significantly differentially abundant phyla in colostrum samples. LY, sows with lower litter size; HY, sows with higher litter size. Data are presented as means. *, P<0.05; **, P<0.01.

At the genus level, faecal microbiota was dominated by Christensenellaceae_R-7_group (10.99%), Treponema (10.86%) and Terrisporobacter (7.18%). Vaginal microbiota consisted primarily of Fusobacterium (13.28%), Escherichia-Shigella (9.87%) and Actinobacillus (9.43%), while colostrum microbiota included Escherichia-Shigella (14.31%), Streptococcus (13.05%) and Staphylococcus (8.88%) (Fig. 7a). LEfSe analysis indicated that, compared to LY sows, HY sows had significantly lower relative abundance of Ruminococcus_torques_group in faecal microbiota and significantly higher relative abundance of Terrisporobacter, Clostridium_sensu_stricto_1 and Romboutsia [linear discriminant analysis (LDA)>2.5] (Fig. 7b). The relative abundances of these differential genera in faecal samples are detailed in Fig. S3 and Table S4. For vaginal microbiota, compared to LY sows, HY sows had significantly lower relative abundance of Eubacterium_nodatum_group and significantly higher relative abundance of Campylobacter, Anaerococcus and Peptoniphilus (LDA>3) (Fig. 7c), with specific abundances provided in Fig. S4 and Table S5. For colostrum microbiota, compared to LY sows, HY sows had significantly lower relative abundance of Moraxella, Peptostreptococcus and unclassified_f_Pasteurellaceae and significantly higher relative abundance of Rohia, Clostridium_sensu_stricto_1 and Fusobacterium (LDA>3) (Fig. 7d). Detailed abundances are listed in Fig. S5 and Table S6.

Fig. 7. Microbial composition at the genus level in faeces, vaginal and colostrum of LY and HY sows. (a) Community bar plot of microbial composition at the genus level; (b) LEfSe analysis of differential enriched faecal bacteria (LDA>2.5); only the top 15 species with the highest LDA scores in each group are displayed; (c) LEfSe analysis of differential enriched vaginal bacteria (LDA>3); only the top 15 species with the highest LDA scores in each group are displayed; (d) LEfSe analysis of differential enriched colostrum bacteria (LDA>3); only the top 20 species with the highest LDA scores in each group are displayed. LY, sows with lower litter size; HY, sows with higher litter size.

Functional and enzyme changes of faecal, vaginal and colostrum microbiota of HY sows

Microbial functional predictions were performed using PICRUSt2. Based on relative abundance, the top KEGG metabolic pathways included ‘Biosynthesis of secondary metabolites’, ‘Microbial metabolism in diverse environments’ and ‘Biosynthesis of amino acids’ (Fig. 8a). Further differential analysis showed that for faecal microbiota, the HY group significantly upregulated ‘Cysteine and methionine metabolism’ and ‘Porphyrin and chlorophyll metabolism’ while significantly downregulating ‘Oxidative phosphorylation’, ‘Fatty acid degradation’ and ‘Tyrosine metabolism’ (Fig. 8b). For vaginal microbiota, the HY group significantly upregulated ‘Oxidative phosphorylation’ and ‘Thiamine metabolism’ while significantly downregulating ‘Pyruvate metabolism’, ‘2-Oxocarboxylic acid metabolism’ and ‘Propanoate metabolism’ (Fig. 8c). For colostrum microbiota, the HY group significantly upregulated ‘Monobactam biosynthesis’, ‘Novobiocin biosynthesis’ and ‘Glucosinolate biosynthesis’ while significantly downregulating ‘Arachidonic acid metabolism’ and ‘Linoleic acid metabolism’ (Fig. 8d).

Fig. 8. Predicted microbial KEGG pathways based on the PICRUSt2 functional prediction analysis in LY and HY sows. (a) Heatmap of KEGG pathways, showing only the top 30 metabolic pathways by abundance; colour intensity in the heatmap represents log-transformed abundance values; (b) significantly altered KEGG pathways in faecal microbiota; (c) significantly altered KEGG pathways in vaginal microbiota; (d) significantly altered KEGG pathways in colostrum microbiota. LY, sows with lower litter size; HY, sows with higher litter size. All data are presented as mean±sem. *, P<0.05; **, P<0.01; ***, P<0.001.

The primary enzymes identified were EC 2.7.7.7 (DNA-directed DNA polymerase), EC 3.6.4.12 (DNA helicase), EC 2.7.13.3 (histidine kinase) and EC 5.2.1.8 (peptidylprolyl isomerase) (Fig. 9a). Differential analysis results showed that for faecal microbiota, the HY group significantly upregulated EC 3.4.16.4 (d-alanyl-d-alanine carboxypeptidase), EC 3.5.1.28 (N-acetylmuramoyl-l-alanine amidase), EC 1.2.7.3 (2-oxoglutarate synthase) and EC 2.7.11.1 (ATP-protein transphosphorylase), while significantly downregulating EC 2.7.2.4 (aspartate kinase) (Fig. 9b). For vaginal microbiota, the HY group significantly upregulated EC 2.7.7.7 (DNA-directed DNA polymerase), EC 3.6.4.12 (DNA helicase), EC 5.99.1.2 (DNA topoisomerase) and EC 3.6.3.14 [H(+)-ATPase], while significantly downregulating EC 2.2.1.6 (acetolactate synthase) and EC 3.6.4.13 (RNA helicase) (Fig. 9c). For colostrum microbiota, the HY group significantly upregulated EC 2.7.11.1 (ATP-protein transphosphorylase), EC 1.8.1.9 (thioredoxin-disulphide reductase), EC 2.3.1.51 (1-acylglycerol-3-phosphate O-acyltransferase) and EC 3.6.1.1 (inorganic diphosphatase), while significantly downregulating EC 3.6.4.13 (RNA helicase) and EC 1.15.1.1 (superoxide dismutase) (Fig. 9d). These findings provide insights into the functional changes in the microbiota of different biological samples from HY sows, highlighting the potential metabolic and signalling pathways that are influenced by these changes.

Fig. 9. Predicted relative abundance of microbial enzymes based on PICRUSt2 functional prediction analysis in LY and HY sows. (a) Heatmap of microbial enzymes, showing only the top 30 enzymes by abundance; colour intensity in the heatmap represents log-transformed abundance values; (b) significantly altered microbial enzymes in faecal microbiota; (c) significantly altered microbial enzymes in vaginal microbiota; (d) significantly altered microbial enzymes in colostrum microbiota. LY, sows with lower litter size; HY, sows with higher litter size. All data are presented as mean±sem. *, P<0.05; **, P<0.01; ***, P<0.001.

Discussion

The present study aimed to characterize and compare the faecal, vaginal and colostrum microbiota of sows stratified into HY (>10 piglets) and LY (≤10 piglets) groups based on litter size, using 16S rRNA gene amplicon sequencing. By focusing on microbial differences between these pre-defined production groups, the findings provide insights into how structural and functional variations in these microbial communities may relate to reproductive performance. Notably, we identified distinct microbial signatures in HY vs. LY sows across all sample types, which may offer mechanistic links to the observed divergence in litter success. These microbial differences align with previous research highlighting that a balanced or functionally enriched microbiota can contribute to reproductive success [6,8], suggesting that the unique microbial features of HY sows might play a role in supporting their higher litter performance.

Faecal microbiota

Despite the lack of significant differences in alpha diversity between HY and LY sows, the composition of the faecal microbiota revealed distinct genera that were unique to HY sows, such as Mitsuokella, Erysipelotrichaceae_UCG-002, Selenomonas and Schwartzia. These genera are known for their roles in various metabolic processes. For instance, Mitsuokella has been associated with fibre degradation and energy metabolism [23], which may enhance energy harvest from feed and support nutrient utilization – key for meeting the high metabolic demands of prolific sows. Erysipelotrichaceae_UCG-002 has been linked to host health and metabolic syndrome [24,25]. Its enrichment in HY sows suggests potential roles in maintaining metabolic balance during gestation and lactation. Similarly, Selenomonas is an important genus that may contribute to the superior metabolic performance observed in HY sows. Selenomonas is known for fermenting carbohydrates to produce short-chain fatty acids (SCFAs) (e.g. propionate) [26] and has been associated with reduced inflammation [27]. SCFAs contribute to intestinal barrier integrity and energy metabolism, while reduced inflammation may lower stress on reproductive physiology. As for Schwartzia, limited research exists on this genus in swine, but its association with HY sows warrants further investigation into its potential roles in nutrient metabolism or microbial cross-talk. Collectively, these taxa may synergistically enhance metabolic efficiency, immune regulation and gut health in HY sows.

This study also highlighted significant differences in the relative abundance of certain microbial taxa in the faecal microbiota of HY and LY sows. Specifically, HY sows showed a significant reduction in the relative abundance of unclassified_f_Eggerthellaceae and Ruminococcus_torques_group, while increasing Terrisporobacter, Clostridium_sensu_stricto_1 and Romboutsia. Eggerthellaceae is a diverse bacterial family encompassing multiple genera with varied functions. While some members of this family have been linked to gastrointestinal inflammation or microbial dysbiosis in mammals, others may play neutral or beneficial roles [28,29]. The reduction in abundance of unclassified_f_Eggerthellaceae in HY sows suggests potential shifts towards a more stable gut microecology, which could alleviate subclinical inflammation and support overall gut health – factors relevant to sustaining high reproductive performance. Ruminococcus_torques_group is a key genus in porcine intestines responsible for carbohydrate degradation, particularly fermenting complex polysaccharides to harvest energy [30,31]. Literature confirms that lactating sows exhibit significantly higher relative abundances of this group than non-pregnant/non-lactating pigs, directly linked to increased dietary fibre intake, indicating its abundance is regulated by physiological state (e.g. lactation) and dietary fibre availability [32]. However, the decreased abundance of Ruminococcus_torques_group in HY sows is noteworthy. While lactation typically elevates its abundance due to increased fibre intake [32], HY sows may reduce reliance on fibre fermentation via dietary or metabolic adaptations: if HY sows are fed energy-dense (rather than high-fibre) diets to prioritize reproductive demands (e.g. lactation, foetal development), or if they possess enhanced efficiency in converting fermentable substrates, the need for fibre-degrading taxa may decrease. In either case, the lower abundance in HY sows likely reflects a shift towards optimized energy partitioning – prioritizing nutrient allocation to reproductive needs (e.g. foetal growth) over fibre fermentation – thereby supporting their sustained high productivity. Conversely, the higher relative abundance of Terrisporobacter, Clostridium_sensu_stricto_1 and Romboutsia in HY sows suggests a potential association with specific metabolic or physiological functions relevant to their high productivity. Terrisporobacter has been shown to produce SCFAs such as acetate and butyrate [33,34], which are crucial for maintaining gut health and improving intestinal barrier function [35]. Clostridium_sensu_stricto_1 is known for its involvement in the degradation of complex carbohydrates and the production of SCFAs, which can provide additional energy sources for the host [36,37]. Romboutsia is a genus that has been linked to improved gut health and reduced inflammation [38]. These changes in the microbial composition may reflect a more efficient utilization of dietary components, alongside potential modulation of energy homeostasis and reproductive hormone balance (e.g. progesterone, oestrogen and testosterone) via microbial metabolites, as previously reported in studies linking gut microbiota to reproductive hormone regulation through such mechanistic interactions [39].

Functionally, PICRUSt2 predictions based on the faecal microbiota revealed that the HY group exhibited a significant upregulation in bacterial pathways for cysteine and methionine metabolism and porphyrin and chlorophyll metabolism, while downregulating oxidative phosphorylation, fatty acid degradation and tyrosine metabolism. These changes suggest an altered bacterial metabolic potential in the HY group, which could indirectly influence host physiological processes through microbial metabolites. For instance, enhanced bacterial cysteine and methionine metabolism may lead to increased production of sulphur-containing metabolites (e.g. hydrogen sulphide, S-adenosylmethionine) that can indirectly influence host energy homeostasis and immune function [40,41]. Similarly, the predicted downregulation of bacterial fatty acid degradation might suggest a reduced generation of bacterial-derived SCFAs or other lipid metabolites, which could indirectly affect host energy metabolism. The upregulation of cysteine and methionine metabolism further suggests enhanced bacterial protein synthesis and amino acid turnover, which may support microbial community stability and potentially provide essential amino acids for the host, contributing to piglet development and health. It is important to note that while bacterial metabolism can influence the host, the PICRUSt2 tool predicts microbial gene families and cannot directly assess host-specific pathways such as the prolactin or peroxisome proliferator-activated receptors (PPAR) signalling pathways, which are governed by host gene expression. Therefore, any potential effects on host reproductive hormones or energy regulation would be indirect, mediated through shifts in microbial metabolite production.

Vaginal microbiota

The vaginal microbiota of HY sows showed significantly higher alpha diversity, as evidenced by the increased Shannon index, and a more stable microbial community. High diversity and stability in the vaginal microbiota are associated with a reduced risk of infections and a healthier reproductive environment [42,43]. This finding suggests that HY sows have a more robust vaginal microbial community, which can better resist pathogenic invasions and maintain a favourable environment for optimal reproductive health.

HY sows exhibited a significant reduction in Eubacterium_nodatum_group, alongside an increased abundance of Campylobacter, Anaerococcus and Peptoniphilus. The Campylobacter genus includes both pathogenic and commensal species [44]. While Campylobacter is widely recognized for its pathogenic potential in certain species (e.g. Campylobacter jejuni and Campylobacter coli) [44], this study did not distinguish between specific species within the genus. Notably, prior research has highlighted that the vaginal environment of beef heifers during early pregnancy shows a markedly higher prevalence of Campylobacter species compared to the pre-breeding stage [45], and a study reported low-abundance Campylobacter during the breeding phase of primiparous sows [18]. These findings suggest that Campylobacter presence (at varying abundances) may reflect normal physiological conditions rather than pathogenesis. Given that Campylobacter was detected in both groups and only its abundance was increased in HY sows, the functional implications of this genus-level change remain unclear and require further species-specific investigation. Anaerococcus and Peptoniphilus are anaerobic bacteria that are typically involved in the degradation of proteins and peptides, which can contribute to the maintenance of a stable and healthy vaginal environment [46]. The research showed that there was a significant positive correlation between the hydrogen sulphide (H₂S) concentration values and the abundance of Eubacterium nodatum [47]. Thus, the lower Eubacterium_nodatum_group levels in HY sows may indicate a reduction in the production of detrimental metabolites, such as H₂S, which can harm the reproductive tract.

Functionally, the vaginal microbiota of HY sows upregulated pathways such as oxidative phosphorylation and thiamine metabolism, while downregulating pyruvate metabolism, 2-oxocarboxylic acid metabolism and propanoate metabolism. The upregulation of oxidative phosphorylation and thiamine metabolism indicates enhanced energy production and vitamin synthesis, which are essential for a healthy pregnancy [48,49]. These pathways are critical for maintaining the high energy demands of the reproductive process, including ovulation, embryo implantation and foetal development. In contrast, the downregulation of pyruvate and propanoate metabolism in HY sows may reflect a more efficient use of metabolic intermediates, reducing waste and promoting a stable internal environment. Pyruvate metabolism is involved in the production of energy, and its downregulation could indicate a more balanced energy utilization, reducing the risk of metabolic disorders [50]. Propanoate metabolism, which is often associated with inflammation and immune responses, is downregulated in HY sows, suggesting a reduced inflammatory state and a more stable immune environment [51]. This distinct metabolic profile of the vaginal microbiota in HY sows likely represents a crucial microbial basis for their superior reproductive performance, reflecting a more harmonious metabolic interplay and functional adaptation between the host and its microbiota.

Colostrum microbiota

The colostrum microbiota of HY sows exhibited significantly higher alpha diversity, with increases in Actinobacteriota, Fusobacteriota and Verrucomicrobiota and a decrease in Proteobacteria. This shift in microbial composition is critical for neonatal development, as Proteobacteria can include pathogenic strains that may negatively impact piglet health [52]. A higher diversity of the colostrum microbiota can enhance the piglet’s ability to establish a balanced and healthy gut microbiota, which is essential for early immune system development and overall health.

The colostrum of HY sows harboured unique species such as Allobaculum, Conchiformibius and Fusicatenibacter. Allobaculum has been shown to produce lactate and butyrate and support gut homeostasis, which is vital for the piglet’s early immune system development and overall health [53]. Fusicatenibacter was subordinate to Lachnospiraceae, one of the main butyrate-producing bacteria in the human gut [54]. The significant reduction in Moraxella, Peptostreptococcus and Pasteurellaceae further supports the idea of a more beneficial and less pathogenic microbial profile in the colostrum of HY sows. Moraxella and Peptostreptococcus have been associated with respiratory and gastrointestinal infections, respectively, which can be detrimental to piglet health [55,56]. Pasteurellaceae are known pathogens that can cause pneumonia and other respiratory issues in young animals [57]. In addition, HY sows significantly increased the presence of potential probiotics in colostrum, such as Lactobacillus, Bifidobacterium and Romboutsia. A substantial body of research has demonstrated that these bacteria play crucial roles in enhancing gut health and modulating the immune system [38,58, 59]. This can contribute to the development of a strong immune response in neonates.

Functionally, the colostrum microbiota of HY sows showed PICRUSt-predicted enrichment in bacterial metabolic pathways such as monobactam biosynthesis, novobiocin biosynthesis and glucosinolate biosynthesis, while downregulating pathways related to arachidonic acid metabolism and linoleic acid metabolism. These changes primarily reflect the metabolic potential of the bacterial community itself, with potential indirect implications for host neonatal health. For example, bacterial monobactam and novobiocin biosynthesis pathways can produce antibiotics that help prevent infections. Monobactams are a class of β-lactam antibiotics that are effective against Gram-negative bacteria, while novobiocin is a coumarin antibiotic that inhibits DNA gyrase, a bacterial enzyme essential for DNA replication [60,61]. The upregulation of these pathways in HY sows’ colostrum can provide piglets with a natural defence against potential pathogens, thereby supporting their healthy development. Bacterial glucosinolate biosynthesis is involved in the production of compounds with anti-inflammatory and immunomodulatory properties. Glucosinolates are sulphur-containing compounds found in plants that can be converted into isothiocyanates, which have been shown to reduce inflammation and enhance immune responses [62]. The presence of these compounds in the colostrum can help protect piglets from inflammatory diseases and promote a healthy immune environment. Notably, arachidonic acid and linoleic acid are polyunsaturated fatty acids that are precursors to pro-inflammatory eicosanoids [63]. The downregulation of these pathways indicates a reduction in the production of these pro-inflammatory compounds, which can lead to a more favourable environment for neonatal development.

Microbiota stability

The NCM results indicate that the microbial communities in HY sows are more stable and less influenced by random factors, which aligns with the observation of greater dispersion of LY samples across all sampling sites – reflecting higher intra-group variability in microbial composition compared to HY samples. This stability is crucial for maintaining consistent health and productivity, as it reduces the likelihood of dysbiosis and associated health issues [64]. The higher stability observed in the faecal and vaginal microbiota of HY sows may contribute to their superior reproductive and metabolic performance. A stable microbial community can better support host functions, such as nutrient absorption, immune system development and resistance to pathogens.

Implications for sow management and production efficiency

The findings from this study may offer potential insights for the swine industry, though their practical application requires cautious interpretation due to study limitations. By identifying microbial signatures and functional pathways associated with high productivity, these results could inform future research into targeted microbial modulation strategies for sows. For example, the enrichment of genera such as Mitsuokella and Terrisporobacter in the gut, Campylobacter and Anaerococcus in the vagina and Allobaculum and Fusicatenibacter in the colostrum of HY sows suggests potential targets for dietary or management interventions. However, the efficacy of such interventions would need validation in larger, controlled studies, given the current study’s constraint.

Genera like Mitsuokella and Treponema, known for SCFA production, may play roles in gut barrier integrity, inflammation reduction and immune modulation [51]. Their enrichment in HY sows hints at a potential link to improved gut health, though the modest effect sizes observed in statistical analyses (consistent with relatively small differences between groups) warrant further investigation into their functional relevance. Similarly, upregulated enzymes involved in cysteine and methionine metabolism in faecal microbiota could support amino acid synthesis – critical for protein metabolism and piglet development [65], but causal relationships remain to be established. In the vagina, Anaerococcus is known for its ability to produce lactate and SCFAs [66], which may contribute to maintaining a low-pH, pathogenic-resistant environment, while thiamine metabolism could support pregnancy health. However, these associations are preliminary and require experimental validation to confirm microbial function in vivo. Colostrum microbial profiles in HY sows, characterized by enrichment of Allobaculum and Fusicatenibacter (linked to SCFA production and gut homeostasis) and reduction of potential pathogens like Moraxella, Peptostreptococcus and Pasteurellaceae, suggest a potential role of colostrum microbiota in neonatal health. Nevertheless, the relatively modest differences in microbial abundance between groups (as reflected in statistical analyses) indicate that these observations should be interpreted as hypothesis-generating rather than definitive evidence of functional impact.

Limitations and future directions

This study has notable limitations. First, the small sample size constrained statistical power to detect subtle microbial differences and reduced generalizability, while uncharacterized sequencing depth may have compromised resolution of low-abundance taxa or accuracy of functional pathway inference. Second, although some microbial/functional differences between HY and LY sows were statistically significant, effect sizes were modest, and causal links between microbial profiles and productivity outcomes remain unestablished. A critical limitation lies in functional inference: reliance on 16S rRNA sequencing with PICRUSt only predicts the potential metabolic capacity of bacteria and cannot capture host-specific processes (e.g. cell signalling pathways like prolactin or mitogen-activated protein (MAP) kinase signalling). This risks conflating bacterial metabolic potential with direct host physiological regulation, leading to overinterpretation of microbial contributions to host outcomes.

To address these, future research should do the following: (1) Validate microbial signatures in larger, well-characterized cohorts with standardized sequencing depth to enhance detection of low-abundance taxa and functional pathways, improving generalizability. (2) Conduct longitudinal studies tracking sows/piglets over time to clarify dynamic microbial shifts, identify critical intervention time points and assess long-term impacts on health/productivity. (3) Integrate multi-omics approaches: metagenomics to resolve the actual functional gene repertoire of the microbiota (beyond predicted potential); metatranscriptomics/metaproteomics to quantify active microbial gene expression/protein function; and host-focused omics (e.g. transcriptomics, metabolomics) to directly assess host metabolic pathways and signalling. This framework will distinguish indirect effects of bacterial metabolism (e.g. via SCFAs) from direct host regulation, avoiding overreliance on 16S-based predictions. (4) Use targeted functional studies (e.g. germ-free models or microbial transplants) to establish causal relationships between specific microbial taxa/pathways and productivity outcomes, moving beyond correlative observations.

Conclusion

This study reveals that HY sows exhibit distinct microbial structures and functions in faecal, vaginal and colostrum samples compared to LY sows. Notably, HY sows displayed greater microbial stability (as indicated by higher NCM R²) and clearer community segregation across sample types. Specifically, in faeces, HY sows were enriched in Terrisporobacter, Clostridium_sensu_stricto_1 and Romboutsia, while depleted in Ruminococcus_torques_group. In the vagina, Campylobacter, Anaerococcus and Peptoniphilus were increased, whereas Eubacterium_nodatum_group decreased. Importantly, in colostrum, Lactobacillus, Bifidobacterium and Romboutsia were enriched, with reduced Peptostreptococcus, indicating a more beneficial profile for neonatal health. Functionally, HY faecal microbiota upregulated cysteine/methionine metabolism and porphyrin and chlorophyll metabolism; vaginal microbiota enhanced oxidative phosphorylation and thiamine metabolism; colostrum microbiota showed enrichment in monobactam and novobiocin biosynthesis. These findings provide empirical evidence linking microbiome structure and function to reproductive performance, offering a foundation for microbiome-targeted interventions to improve sow productivity and piglet health.

Abbreviations

ACE

abundance-based coverage estimator

ANOSIM

analysis of similarities

ASVs

amplicon sequence variants

HY

high-yielding

KEGG

Kyoto encyclopedia of genes and genomes

LDA

linear discriminant analysis

LEfSe

linear discriminant analysis effect size

LY

low-yielding

NCM

neutral community model

PCoA

principal coordinates analysis

PPAR

peroxisome proliferator-activated receptors

SCFAs

short-chain fatty acids