Introduction

Gestation, lactation, and newborn periods are the core stages for management in the large-scale production of sows and piglets1. From gestation to and during lactation, sows undergo dramatic changes in their physiology, metabolism, and immunity to meet the nutritional and energy requirements2. However, imbalanced nutrition during pregnancy and lactation not only causes a range of health issues in sows, including constipation, abortion, and intrauterine growth retardation but also increases the risk of problems like low weaning weight and high rates of diarrhea in offspring3. Therefore, reducing inflammation, maintaining a healthy metabolism, and relieving constipation during late gestation and lactation of sows are extremely important in ensuring sow reproductive performance and the growth of offspring4,5. Various efforts have been put to improve the health of gestating sows. For example, the supplementation of dietary fiber and L-glutamine to relieve constipation in gestation sows, but inferences drawn from different studies are largely inconsistent6,7. Antibiotics have been used in the swine industry to reduce inflammation, but the potential risks to human health, spread of antibiotic resistance, and disruption of gut microbiota homeostasis are alarming concerns8. The gut microbiota plays an important role in maintaining gut homeostasis, acquiring and assimilating nutrients, regulating inflammation, and guarding against pathogens in both sows and piglets9,10. The metabolites produced by the sow gut microbiota can be transferred to the offspring, promoting the maturation and development of the immune system in newborn piglets11,12.

In the past few years, the swine industry has benefited from probiotic research as it is increasingly recognized that, when applied in pig farming, probiotics can serve as health and/or growth promoters by improving the gut microbiome and the bioactive metabolome of the animals13. Probiotics have been evaluated in sows for their beneficial effects on their well-being, health promotion, and reproductive performance14,15. Supplementing probiotics to gestating sows during late pregnancy or lactation stages has been shown to exert desirable effects both on the sows and the newborns, e.g., improving colostrum quality, shortening estrus intervals, relieving constipation, reducing serum inflammatory factors, increasing piglet weaning weight, regulating host immunity, and reducing diarrhea rate and mortality16. Moreover, other studies showed that probiotic supplementation could modulate the gut microbiota diversity and community structure in gestating sows and piglets, meanwhile reducing the abundance of pathogenic bacteria, such as Salmonella, Clostridium, and Escherichia coli17,37. The gastrointestinal microbiota plays an important role in gut motility. For example, germ-free mice have increased gastric emptying and gut transit time compared with wild-type mice38. Our data suggested that probiotic administration could maintain intestinal homeostasis by modulating gut microbes to reduce inflammation, thereby enhancing epithelial barrier integrity, and possibly regulating gastrointestinal motility-related endocrine responses to relieve constipation39,40.

Generally, a high gut microbiota diversity is considered to be desirable for maintaining a healthy physiological state41,42. It is thought that supplementing probiotics to sows is health-promoting, partly via building a robust gut microbiome8. Our study revealed that the gut bacterial microbiota of the probiotic group had a significantly higher alpha diversity compared with the control group at 113 days of gestation, and an opposite trend was observed in the diversity of gut phages. The diversity measures are the outcome of ecological processes but not the ecological process itself, and Shade et al.43. proposed that diversity is only “a starting point for further exploration of ecological mechanisms, rather than an ‘answer’ to community outcomes”. Therefore, although contrasting trends were observed in the alpha diversity of gut bacteria and phages, the biological meaning of such a phenomenon remains to be clarified. To explore the role of the gut bacteriome and bacteriophage, we further analyzed the structure changes of gut bacteriome and bacteriophage.

Both the gut bacteriome and phageome structure of sows however have surprisingly shown significant intra-group differences, suggesting a relatively strong effect of the probiotic intervention in shifting the overall gut microbial community structure of sows. Wang et al.34. supplemented Bacillus subtilis and Enterococcus faecium to sows and effectively reshaped their gut microbiota structure, which is consistent with the current results. Although the microbiota-modulating effect was more obvious on the bacteriome and less to the phageome community, one limitation to note is the lack of annotation of most bacteriophage elements in the current dataset due to insufficient knowledge and taxonomic annotation tools for gut viruses, especially in non-human subjects44. Nevertheless, our observation of the overall intergroup differences in the phageome structure supported that it is also part of the probiotic responsive elements and should be considered when evaluating the action of probiotic application45.

We further tracked significant changes in the abundance of major SGBs and bacteriophages identified in our dataset to reveal probiotic-responsive gut taxa. Our analysis revealed that the probiotic group had significantly more Parabacteroides distasonis, Prevotella tannerae, Alloprevotella sp., Paraprevotella xylaniphila, and Rikenellaceae bacterium, while significantly fewer Clostridium sartagoforme, Oscillibacter sp., Treponema sp., and Siphoviridae. Some of these taxa are associated with host health. For example, oral administration of Parabacteroides distasonis could attenuate experimental colitis in mice by modulating immunity and microbiota composition46. Alloprevotella has been considered to be beneficial bacteria as they produce succinate and acetate, which could improve the intestinal barrier and exhibit anti-inflammatory function47. We also found a significant positive correlation between Alloprevotella sp. and the sow fecal score as well as the viral family of Microviridae, but a significantly negative correlation was identified between Alloprevotella sp. and Podoviridae. Another taxon that correlated positively with the sow fecal score was Paraprevotella xylaniphila. This species has been reported to produce succinate by fermentation48, and its assembled genome contained multiple xylan- and pectin-hydrolyzing enzymes, which is consistent with the report of Sabater et al.49. identifying similar enzymes in the genome of the strain Paraprevotella xylaniphila YIT 11841. This strain might be associated with the alleviation of Crohn’s disease by establishing gut microbial metabolic interaction. Siphoviridae is a family of double-stranded DNA viruses in the order Caudovirales. In this study, a significant negative correlation was found between Siphoviridae and the sow fecal score, and Mihindukulasuriya et al.50. reported that subjects with constipation-predominant irritable bowel syndrome (IBS-C) had significantly more Siphoviridae than healthy subjects, suggesting a high gut Siphoviridae content might be linked to constipation. In addition, several responsive SGBs detected in the sow fecal microbiota were found to be significantly associated with some serum immune factors. Oscillibacter sp. had a significant positive correlation with the pro-inflammatory factors, IL-12p40 and IFN-α, and its abundance was significantly lower in the probiotic group compared with the control group at 113 days of gestation. Recent studies have reported that the alleviation of constipation was associated with increased abundances of beneficial bacteria, such as Bifidobacterium and Alistipes, and decreased levels of Oscillopira and Odoribacter, which are related to methanogenesis and colonic transit51. The results of our comparative analysis suggested that the relief of constipation and anti-inflammatory effects of compound probiotic treatment during late gestation were associated with desirable changes in the sow gut bacteriome and phageome. To quantify Probio-M8 and Probio-M9 in both control and probiotic groups, reads highly similar (>97% homology) to these strains were counted. However, we failed to detect any Probio-M8 and Probio-M9 genome across all samples using 40% breadth genome coverage, which could be due to the relatively shallow sequencing depth and thus detection power52. In general, at least 5-fold sequencing coverage is required for tracking a specific bacterial strain in a fecal metagenomic sample. It is interesting that although the probiotics did not engrain themselves in the microbiome at levels that were detectable via the read depth of the sequencing efforts used, it does not mean that they were not present. Particularly, it still caused detectable perturbations in other species. Comparable changes are also seen in similar gut metagenomic studies53.

Probiotics not only modulate host gut microbiota but also cause changes in health-related microbial metabolites54,55. The predicted gut bioactive metabolite profiles of the probiotic and control groups showed obvious differences at 113 days of gestation, and the abundance of several bioactive metabolites, including chenodeoxycholate, arachidonic acid, C18:0 sphingomyelin, and ceramide, increased significantly in the probiotic group. Chenodeoxycholic acid is one of the main bile acids, and two previous studies found that administering 500 to 1000 mg chenodeoxycholate in delayed-release capsules significantly accelerated colonic transit and increased stool frequency in both healthy volunteers and female patients with IBS-C56,57. In our study, chenodeoxycholate had a significant negative correlation with Odoribacter, which is known to affect colonic transit. Another potentially interesting treatment-responsive metabolite is arachidonic acid, an n-6 polyunsaturated 20-carbon fatty acid, which is beneficial to the central nervous system and the growth performance of piglets58. Constipation is usually accompanied by mild inflammation and damage to the intestinal barrier, and previous studies reported an important role of prostaglandins in restoring intestinal barrier function in ischemic-injured porcine ileum by converting arachidonic acid to PGH259,60. In addition, our study found that arachidonic acid, sphingomyelin, and ceramide had a significant negative correlation with Clostridiales bacterium while significantly and positively correlating with Prevotella copri and Prevotellaceae bacterium. Sphingomyelins and ceramides are structural components of cellular membranes, playing critical roles in cellular signaling events61,62. Moreover, sphingomyelin protects against LPS-induced gut inflammation, while some very long-chain ceramides have been shown to enhance the gut barrier63. Our knowledge concerning specific mechanisms of the wide spectrum of lipid species in developmental programming remains extremely limited; nevertheless, our findings support that the current probiotic intervention relieved inflammation, accelerated colonic transit, and effectively relieved constipation in sows at late gestation by regulating their levels of bioactive metabolites.

The genomes of mammalian gut microbiota encode a large number of CAZymes, which are necessary for the digestion of complex polysaccharides through fermentation64. Our study found that the cumulative abundance of 78 of the significant differential subfamilies of CAZymes was enriched in the probiotic group compared with the control group at G113d. Many of these enzymes belonged to the families of GHs, PLs, and CEs, which are key enzymes responsible for breaking down complex carbohydrates65. Our study also found that the probiotic group had a significantly higher cumulative abundance of CAZyme-encoding genes involved in the metabolism of pectin and starch than in the control group, which likely enhanced the capacity of gut microbes of the probiotic group to utilize dietary complex carbohydrates directly and convert them into SCFAs or indirectly through a cross-feeding mechanism66. Consistently, genes encoding several SCFA production pathways were enriched in the probiotic group, accompanied by significantly more fecal butyrate and acetate levels in the probiotic group than in the control group at G113d. Subsequent increases in the intestinal content of SCFAs would further stimulate the growth of anti-inflammatory bacteria, meanwhile suppressing the pro-inflammatory bacteria in the colon27. These changes together could help reduce physiological inflammatory responses and improve gut health in late gestation sows.

The modulation of the gut microbiota composition would naturally accompany by changes in the colonic metabolite content, and some colonic metabolites would be absorbed and transported into and through the circulatory system to exert systemic host physiological effects67. Our study found interesting changes in the serum metabolome of sows receiving the probiotic treatment, i.e., enrichment in metabolites relating to vitamin metabolism (pyridoxamine, lutein, and vitamin E) and amino acid metabolism (lysine, isoleucine, tryptophan). Low levels of pyridoxamine are associated with inflammation, which is also thought to be a cause of IBS68. Lutein and vitamin E are powerful antioxidants that could reduce intestinal oxidation and prevent mucosal damage69,70. Our study observed a continuous decrease in serum lutein levels in sows from late gestation to lactation, but such a drop was slowed down with probiotic intervention, and the serum lutein levels in the probiotic recipients were significantly higher than the control sows. Moreover, recent studies found that consuming higher levels of lutein may lower the incidence of constipation71, and lutein has also been found to have significant anti-inflammatory effects in animal models72. Patients with IBS have been found to have a significantly lower serum lutein level than healthy participants, further supporting a gut health-promoting role of lutein73. Vitamin E is another anti-inflammatory compound that relieves constipation74. Our previous study found that the intake of probiotic-containing fermented milk alleviated constipation symptoms by regulating the gut microbiota, host inflammation status, and vitamin E metabolic pathways in patients, which is in line with current findings75. Apart from specific vitamin-associated metabolites, the serum metabolome of sows receiving the probiotic treatment had higher levels of lysine and isoleucine, tryptophan compared with those in the control group, suggesting increases in the metabolism of these amino acids. Several amino acids, including lysine, threonine, and isoleucine, have shown potential therapeutic effects on gut-related diseases and have a strong relationship with laxative effects in constipation patients76,77. On the other hand, tryptophan and its metabolites are mainly involved in regulating peristalsis; and probiotics play an important role in converting tryptophan to tryptamine, one of its major metabolites, that accelerates metabolism78. Our study found that sows in the control group exhibited a persistent decrease in serum tryptophan levels during late gestation and suffered from more severe constipation, while those in the probiotic group showed an opposite trend of change in serum tryptophan levels and did not suffer from severe constipation. Furthermore, a significant negative correlation was identified between tryptophan and Oscillibacter sp., and members of this genus have been shown to affect colonic transit. Thus, it is likely that the elevated levels of tryptophan helped accelerate intestinal transit in late pregnant sows. The serum tryptophan levels in patients with IBS were significantly lower than in healthy individuals, suggesting a potential role of tryptophan in maintaining colonic health79. Overall, the vitamin and amino acid metabolism of late gestation sows showed obvious and interesting responses after probiotic treatment, which is a key finding of the current study. We found that the addition of complex probiotics to drinking water during late gestation and lactation influenced the reproductive/growth performance of sows and piglets. In particular, the probiotic treatment significantly improved sow constipation and relieved systemic inflammation. The probiotic effects were likely achieved through orchestrated responses of the gut bacteriome, phageome, and bioactive metabolites, as well as colonic and systemic metabolism in the late gestation sows. This study provides scientific knowledge and practical information for both the academic community and the pig farming industry, expanding the scope of probiotic application in animal husbandry.

Methods

Experimental design

Based on similar expected dates of confinement and backfat thickness (Supplementary Table 3), 76 sows (Large × Landrace; at 100 days of gestation) were randomly allocated to the control (n = 38) and probiotic (n = 38) groups. However, two sows (sow number 7 and 13) in the probiotic group were not included in the final analysis because of the physical damage to the hind legs of sow number 7 during transfer and the unexpectedly longer farrowing period of sow number 13. All experimental diets met or exceeded the nutrient requirements of gestation and lactating sows recommended by the National Research Council (Supplementary Table 8)80. All sows were fed twice a day during gestation, and the amount per feeding was evaluated by a professional nutritionist on the farm, and the sows had free access to food after delivery.

The sows in the control and probiotic groups were reared in different pens in the same pig house. The drinking water of the two groups came from separate systems with the same management and water source, except that a compound probiotic formulation was added to the drinking water of the probiotic group from 100 days of gestation to 23 days of lactation. An individual meter has been installed in each sow pen to collect the sows’ daily water intake. The dosage level of probiotics was 40 grams per ton of water, comprising two bacterial strains, i.e., Probio-M8 and Probio-M9 (a total of 1 × 1011 CFU/g; 1:1). The compound probiotics were supplied by **Hua YinHe Biological Technology Co., Ltd. (Zhejiang, China), prepared under ISO9001 and HALAL standards in the form of dried powder.

Isolation, cultivation, and freeze-dried powder production of probiotics

The Probio-M8 and Probio-M9 strains were isolated from breast milk samples of healthy women in Inner Mongolia in 201781. Briefly, the milk samples were plated on an appropriate agar medium for isolating lactic acid bacteria, and colonies showing the morphology of lactic acid bacteria were picked and purified. The Probio-M8 and Probio-M9 strains were selected based on physiological and biochemical characterization, and their taxonomic identity was confirmed with 16 S rRNA sequencing. The strains were stored at −80 °C in the presence of a cryoprotectant (10% non-fat milk, 0.1% sodium glutamate, and 0.05% L-cysteine) and reactivated when required.

The strain reactivation was achieved by inoculating the frozen bacteria in MRS broth, incubated anaerobically at 37 °C for 24 h. The subculture was repeated twice, and the third-generation seed solution was inoculated in the same culture medium at a high-volume fraction of 7% for high-density fermentation. After high-density fermentation, the resulting culture broth was centrifuged to collect the cell pellet, which was then freeze-dried in a vacuum freeze dryer in the presence of a freeze-drying protectant to obtain the bacterial powder.

Determination of viable counts of probiotics

The levels of live probiotics in the probiotic powder, pre-solution, diluted pre-solution, and samples collected from the drinking water system were enumerated by the pour plate method. Samples were serially diluted appropriately for pour plate counting. Inoculated plates were incubated anaerobically at 37 °C for 72 h before counting the number of colonies. A modified culture medium, MRSC modified agar (regular MRS medium supplemented with 0.5 g/L L-cysteine hydrochloride and 5.0 mg/100 mL lithium mupirocin), was used for cultivating Probio-M8; and MRS agar supplemented with 0.01% vancomycin (w/v, 10 mg/L) was used for cultivating Probio-M9.

Feeding management

The field experiment was performed at the pig breeding site under the Zhengye Project of Inner Mongolia Zhengda Food Co., Ltd. At 110/111 days of gestation, sows were moved to a 2.2 × 1.8 m2 delivery bed with plastic slatted flooring, equipped with stainless steel adjustable troughs and nipples drinking fountains for sows. Each farrowing bed was equipped with a heating lamp to maintain a stable temperature (about 30 °C) in the piglet activity area. At parturition, the total number of litters, live-born, mummies, and weak piglets were recorded, and piglet birth weight was measured 12 h after farrowing.

Fecal scores, constipation, and sample collection

The intestinal activity of the sows was monitored at six different time points, i.e., between 100 days of gestation (G100d, G106d, and G113d) and 23 days of lactation (L6d, L13d, and L19d). Sows’ feces were qualitatively evaluated before the daily morning cleaning session according to the scheme proposed by Oliviero et al. (i.e., 0 = absence of feces, 1 = dry and pellet-shaped feces, 2 = between dry and normal feces, 3 = normal and soft, but firm and well-formed feces, 4 = between normal and wet, still formed but not firm feces, and 5 = very wet, unformed, and liquid feces)6.

Blood and fecal samples of sows were collected at G100d, G113d, and L23d. Blood samples (10 mL) were collected in heparinized tubes from the vena jugulars of the sows, and serum samples were obtained after centrifugation at 3500 × g at 4 °C for 10 min, which were immediately stored at −80 °C for further analysis. Fresh fecal samples were collected from each pig using sterile 20 mL centrifuge tubes, and collected samples were stored at −80 °C for microbial composition analyses. All samples were collected by experienced veterinary personnel to avoid frightening or causing any physical harm to the sows. The lactation 23d is the experimental endpoint, and all animals survived in good health after the end of the experiment.

Measurement of serum inflammatory factors

The serum concentrations of IFN-α, IFN-γ, IL-1β, IL-4, IL-6, IL-8, IL-10, TNF-α, and IL-12p40 were determined using sandwich enzyme-linked immunosorbent assay kits (Meimian Biotechnology, Jiangsu, China) and corresponding protocols suggested by the manufacturer.

Metagenomic sequencing, binning, genome dereplication

Firstly, the DNA was extracted from the feces of the sows with the QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany), and metagenomic sequencing was performed on all samples on the Illumina HiSeq X Ten system. Libraries were constructed using NEBNext® Ultra™ DNA Library Prep Kit (New England Biolabs, Inc., Ipswich, MA, USA) for Illumina to produce DNA fragments of ~300 bp length. Then, reads from each sample were assembled into contigs using MEGAHIT82, and contigs greater than 2000 bp were selected for binning using MetaBAT2, VAMB, and DAS Tool with default parameters83,84,85. Finally, all bins were combined to obtain metagenome-assembled genomes (MAGs) using in-house scripts.

The MAGs were evaluated by CheckM and classified into MAGs of high-quality (completeness ≥80%, contamination ≤5%), medium-quality (completeness ≥70%, contamination ≤5%), and partial-quality (completeness ≥50%, contamination ≤5%)86. The high-quality genomes were clustered and selected by dRep to obtain SGBs, using the options -pa 0.95 and -sa 0.9587.

SGBs annotation and identification of bioactive metabolites and CAZymes

The SGBs were annotated by Kraken2 against NCBI nonredundant Nucleotide Sequence Database (released in 2020.11). The relative abundance of each SGB was calculated by CoverM using the options “--min-read-percent-identity 0.95 --min-covered-fraction 0.4” (https://github.com/wwood/CoverM). The distribution of gut bioactive metabolic compounds was predicted based on the MelonnPan-predict pipeline according to the method described in our previous work88.

Genome function analysis

We further analyzed the functional genome, focusing on carbohydrate degradation pathways, by implementing a module-based analytical framework (described by Valles-Colomer89) in which the MetaCyc metabolic database was used to predict SGBs that encoded GMMs of polysaccharide metabolism and SCFA biosynthesis pathways. The open reading frames of each SGB were predicted using with default parameters, and several methods were employed for functional annotation. Polysaccharide metabolism and SCFA biosynthesis pathways were identified based on key reactions in the Kyoto Encyclopedia of Genes and Genomes Orthologies database. Omixer-RPM (parameter: -c 0.66)90 was used to identify SGBs containing the related genes. dbCAN291 was further used to detect CAZyme-encoding genes in the sow fecal microbiome.

Taxonomic annotation and abundance analysis of phageome

After assembly by MEGAHIT, contigs were selected and potential viral features were identified by VIBRANT and CheckV92. The results recovered from these two tools were combined, and contigs greater than 5000 bp were further clustered into viral operational taxonomic units (vOTUs) with 95% nucleotide identity and 80% sequence coverage using CD-HIT (https://github.com/weizhongli/cdhit). To evaluate the novelty of vOTUs in the current dataset, 145,589 vOTUs were cross-compared with the Metagenomic Gut Virus catalog (comprised of 189,680 viral genomes from 11,810 publicly available human stool metagenomes)93. The average abundance of vOTUs was calculated using the CoverM-contig pipeline (https://github.com/wwood/CoverM) with the options “--min-read-percent-identity 0.95, --min-read-aligned-percent 0.5, --proper-pairs-only, and –exclude-supplementary”.

Measurement of serum metabolites by liquid chromatography-mass spectrometry (LC-MS)

Serum samples were extracted according to Wu et al.94. Briefly, serum samples were thawed at 4 °C and mixed by vortexing for 10 s, and 50 μL of each sample was transferred into a fresh centrifuge tube. Three hundred microliters of acetonitrile methanol extraction solution (20%) was added and vortexed for 3 min, centrifuged at 12,000 r/min for 10 min at 4 °C. After centrifugation, 200 μL of the supernatant was transferred to another correspondingly numbered centrifuge tube and placed in a −20 °C refrigerator for 30 min, followed by a final centrifugation step at 12,000 r/min for 3 min at 4 °C. Then, 180 μL of the supernatant was pipetted into autosampler vials for analysis by mass spectrometry using AB Sciex TripleTOF 6600 (SCIEX, Framingham, MA, USA) in both positive and negative ion modes. The quality control (QC) sample was prepared by mixing the same amount of each sample, and the QC sample was injected five times before the actual analysis to evaluate the stability of the instrument.

Peak areas were corrected using the support vector machine regression method, and features with missing values of more than 50% were filtered. The metabolomic data were analyzed by PLS-DA to identify differential markers based on peak shape and signal-to-noise ratio. The marker features were cross-compared against the blood exposure database (https://bloodexposome.org) to determine the best annotation results.

Detection of fecal SCFAs

The concentrations of fecal SCFAs were determined using a GC-MS system (TRACE 1300 Series GC System, Thermo Fisher Scientific Inc., Waltham, MA, USA) which was fitted with a capillary column Agilent HP-INNOWAX (30 m, 0.25 mm, 0.25 μm). Mass spectrometric detection of metabolites was performed on a Thermo Scientific™ ISQ™ 7000 GC-MS system. Single ion monitoring mode was used, with an electron energy of 70 eV. Fecal samples (0.1 g) were thawed, added in 2 ml of distilled water, and ultrasonically mixed for 20 min. Samples were extracted in 50 μL of 15% phosphoric acid with 10 μL of 75 μg/mL 4-methylvaleric acid solution and 140 μL ether, before centrifugation at 4 °C for 10 min at 12,000 × g. The supernatants were transferred into fresh sample vials before GC-MS analysis.

Statistical analysis

All statistical analyses and data visualization were performed using the R software (v.4.1.0) and Adobe Illustrator. The species diversity, principal coordinate analysis (PCoA), PLS-DA, Adonis test, and Procrustes analysis were executed by using R packages (vegan, optparse, mixOmics, and ggpubr). Wilcoxon test and t-test were used to evaluate differences in various variables between groups, and P values were corrected using the Benjamini-Hochberg procedure. The cumulative abundances of GMMs modules and CAZymes were calculated by the dplyr R package with the formula: cumulative abundance = number of metabolic modules/ CAZymes encoded in the genomes × genome abundance. Correlation analyses (cut-off: r > 0.4 or < −0.4) of fecal scores, immune factors, differential bacterial species and bacteriophages, bioactive metabolites, and serum metabolites data were performed using the Spearman correlation coefficient. Third-party material in the figure legends was created with BioRender.com.