GCW used in this experiment was obtained by mixing 85.7% pregelatinized waxy maize starch (Hangzhou, China) with 14.3% guar gum (Yunzhou, China). A total of 61 multiparous sows (large white, parity 2–6) were randomly grouped into two dietary treatments using a complete block design. During gestation, sows were fed with corresponding experimental diets: the control group was fed with basal diet (control, n = 33) whereas the GCW group was fed with basal diet supplemented with 2% GCW to replace rice bran meal (GCW, n = 28). All experimental diets were formulated to meet nutrient requirements of gestating sows according to recommendations by NRC (20). Treatments were isonitrogenous and isoenergetic diets. Diet compositions and nutrient levels are presented in Supplementary Table 1. Animals were fed twice a day (07:00 and 14:30 h). Blood samples (6~8 mL) were collected from the marginal ear vein of sow with disposable vacuum blood collection tubes 4 h after the morning meal (7~10 sows per group) on days 30, 60, 90, 109 of gestation (G30, G60, G90, and G109). Plasma was extracted by centrifugation of blood samples at 3,000 × g for 5 min at 4°C and stored at −80°C until analysis. Fresh feces were collected on day 109 of gestation and stored in a −80°C freezer. All sows did not present any signs of disease before sampling.

Plasma TBA, alanine aminotransferase (ALT), and alanine aminotransferase (AST), were determined through standard routine procedures using a Mindray BS-240 automatic biochemical analyzer (Mindray biomedical electronic co. LTD, Shenzhen, China).

Concentrations of fibroblast growth factor 19 (FGF19) and 7α-hydroxy-4-cholesten-3-one (C4) in plasma were determined using porcine enzyme-linked immunosorbent assay kits (Nanjing Camilo biological engineering co. LTD, Nanjing, China), following the manufacturer's instructions.

Total fecal microbial DNA was extracted using TIANamp Stool DNA Kit (Tiangen Biotech co. LTD, Beijing, China), following the manufacturer's instructions. Expression levels of bacterial bile acid metabolism-related genes (baij, bsh1, and bsh2) were determined by quantitative real-time PCR (Q-PCR). Expression levels of baij, bsh1, and bsh2 genes were normalized using 16s rDNA. The reaction system and conditions used for Q-PCR were as described in a previous study (21). Primer sequences used in this study are presented in Supplementary Table 2.

All bile acid standards were purchased from Olchemim Ltd. (Olomouc, Czech Republic) and Sigma (St. Louis, MO, USA). For plasma samples, 200 μL methanol was added to 50 μL plasma-spiked with 10 μL internal standard (IS), vortexed, and centrifuged at 12,000 r/min for 10 min. For feces samples, 20 mg of feces were spiked with 10 μL IS and mixed with 200 μL methanol, vortexed, and centrifuged at 12,000 r/min for 10 min. The supernatant were evaporated to dryness, reconstituted in 100 μL 50% aqueous methanol (V/V) for further LC-MS analysis.

Bile acids were quantified using ultra-performance liquid chromatography (UPLC, Shim-pack UFLC SHIMADZU CBM30A system) coupled to a tandem mass spectrometry system (MS/MS, Applied Biosystems 6500 QTRAP). Chromatographic separations were performed with an ACQUITY UPLC HSS C18 column (1.8 μm, 2.1 mm 100 mm, Waters Corp., Milford, MA, USA). The mobile phase was water (1,000 mL) + formicacid (100 μL) + ammonium acetate (5 mM; A) and formicacid (100 μL) + acetonitrile (1,000 mL; B). The gradient conditions were as follows: 0–0.5 min (5% B), 0.5–4.5 min (40–50% B), 4.5–7.5 min (50–80% B), 7.5–10 min (80–95% B), and 10–12 min (5% B). The total analysis time was 12 min and the injection volume was 3 μL. The column temperature was maintained at 40°C with the flow rate set at 0.35 mL/min. The mass spectrometer was operated with an electrospray ionization interface in negative ionization (ESI–) mode. The UPLC-MS raw data were collected using Multiple Reaction Monitoring (MRM) mode. The qualitative analysis of mass spectrometry data was performed based on the self-built database MWDB (Metware Biotechnology Co., Ltd., Wuhan, China), and the quantitative analysis were performed using the Multiquant 3.0 software (Sciex, Framingham, MA, USA).

Total bacterial DNA was extracted from fecal samples using a QIAamp DNA Stool Mini Kit (Qiagen, Germany) following the manufacturer's protocol. Bacterial 16S rRNA gene V3-V4 region of each sample was then amplified using 16S rRNA universal primers. PCR products were mixed then sequenced on an Illumina MiSeq platform (2 × 250 bp) (Illumina, United States). Microbiome bioinformatics were performed with QIIME2 2019.4 (22) with slight modification according to the official tutorials. Briefly, raw sequence data were demultiplexed using the demux plugin following by primers cutting with cutadapt plugin (23). Sequences were then quality filtered, denoised, merged, and chimera removed using the DADA2 plugin (24). Non-singleton amplicon sequence variants (ASVs) were aligned with mafft (25) and used to construct a phylogeny with fasttree2 (26). Alpha-diversity values of the samples were determined based on the Observed species, Chao1, Shannon, and Simpson index. Beta-diversity measures were calculated using weighted-UniFrac distance. Linear discriminant analysis (LDA) effect size analysis (LEfSe) was used to identify key gut microbiota of the two groups (p < 0.05, Wilcoxon rank-sum test; log LDA > 2).

Data were analyzed with SPSS (SPSS 23.0, SPSS Inc., Chicago, IL). Categorical variables were analyzed using χ²-tests. Normally distributed data were analyzed using student's unpaired t-test; whereas data that was not normally distributed was analyzed using non-parametric Mann Whitney U-test. Spearman correlation analyses were performed using Prism GraphPad 8.0.1 (GraphPad Software, San Diego, CA, United States). P < 0.05 were considered significant.

Effects of GCW in the gestation diet on reproductive performance of sows are presented in Table 1. GCW showed no significant effect on total number of born piglets and number of alive piglets. GCW significantly increased birth weight of piglets compared with the weight of piglets in the control group. Moreover, the percentage of IUGR piglets (< 800 g) in the total alive piglets for the GCW group was 0.91%, which was significantly lower compared with that of the control group (3.89%). However, no significant differences were observed for the number of piglets with birth weights ranging between 800–1,000, 1,000–1,200, 1,200–1,400, and more than 1,400 g between the two groups.

To explore the composition of gut microbiota for the two groups on day 109 of gestation, 16S ribosomal RNA gene (V3-V4 gene regions) sequencing was conducted. Analysis of Alpha-diversity indexes showed that the number of observed species, Chao1, Shannon, and Simpson index were significantly lower in the GCW group compared with the control group (Figures 1A–D). Nonmetric multidimensional scaling (NMDS) analysis for beta density showed that the control group was separately clustered from the GCW group (Figure 1E). Hierarchical Cluster Analysis (HCA) based on the weighted-UniFrac distance showed that control samples clustered together whereas GCW samples clustered together (Figure 1F). This finding validated the results obtained by NMDS analysis.

Firmicutes, Bacteroidetes, Spirochaetes, and Proteobacteria were the four most dominant phyla (Figure 2A). At the genus level, Oscillospira, Prevotella, YRC22, and Treponema were the four most abundant genera (Figure 2B). The GCW group showed higher abundances of Firmicutes and lower abundances of Bacteroidetes and Proteobacteria compared with the control group (Figure 2C). The GCW group had lower abundances of Lactobacillus, Bacteroides, Parabacteroides, Streptococcus, Dorea, Lachnospira, Bulleidia, and L7A_E11 and greater abundances of Unidentified_Ruminococcaceae compared with the control group (Figure 2D). In addition, GCW treatment tended to reduce the relative abundance of Clostridium and CF231 compared with the levels in the control group (Figure 2D). At the family level, the GCW group showed higher abundances of Ruminococcaceae and lower abundances of Lactobacillaceae, Bacteroidaceae, and RF16 compared with the levels in the control group (Supplementary Figure 1). Notably, dietary GCW supplementation significantly reduced expression level of baij gene for Clostridium scinden, bsh1 gene for Lactobacillus plantarum, and bsh2 gene for Bacteroides ovatus compared with the levels in the control group (Figure 2E). LEfSe analysis showed similar changes in composition of gut microbiota at the phyla, family, and genus taxonomic levels (Supplementary Figure 2).

To explore the effect of GCW on bile acid homeostasis, levels of several related plasma biological parameters were determined. Plasma TBA levels in the GCW group on day 90 of gestation were significantly reduced by 45.04% compared with the levels in the control group (Figure 3A). Moreover, GCW treatment tended to decrease plasma TBA level on day 109 of gestation compared with the level in the control group (Figure 3A). However, the two groups showed no significant difference in the levels of plasma ALT, AST, and FGF19 during late gestation (days 90 and 109) (Figures 3B–D). Notably, GCW diet significantly reduced plasma C4 concentration during late gestation compared with the control diet (Figure 3E).

To further characterize the effect of GCW feeding on bile acid homeostasis of sows, we analyzed bile acids composition in plasma samples obtained from control and GCW groups. Most plasma bile acids were unconjugated, followed by glycine-conjugated bile acids and taurine-conjugated bile acids (Figures 4A,B). Furthermore, analysis of individual bile acids showed high abundance of HDCA and CDCA and GUDCA bile acids in plasma (Figures 4C,D). In addition, the concentrations of plasma total bile acids, total glycine-conjugated bile acids and total taurine-conjugated bile acids in the GCW group on day 90 of gestation were significantly lower compared with the levels in the control group (Figure 4A). Moreover, concentrations of total taurine-conjugated bile acids were significantly reduced by GCW treatment, and level of total bile acids and total glycine-conjugated bile acids tended to decrease on day 109 of gestation in the GCW group compared with the control group (Figure 4B). Plasma levels of GUDCA, GCDCA, TUDCA, GLCA, TCDCA, and CDCA-3Gln were significantly decreased in GCW group on day 90 of gestation compared with the control group (Figure 4C). In addition, GCW intervention induced lower levels of GCDCA, HCA, GHCA, 7-KLCA, and TCDCA in plasma of sows on day 109 of gestation (Figure 4D).

Alterations in gut microbial flora induced by GCW treatment affected fecal bile acids composition. Predominant bile acids in fecal samples were unconjugated, followed by glycine-conjugated bile acids and taurine-conjugated bile acids (Figure 5A). Notably, LCA and ILCA and HDCA and IALCA were the most abundant fecal bile acids (Figure 5B). GCW had no significant effect on fecal total bile acids, total unconjugated bile acids, total glycine-conjugated bile acids or total taurine-conjugated bile acids (Figure 5A). In addition, GCW treatment upregulated the levels of fecal HDCA, 3β-HDCA, MDCA, 7-KLCA, HCA, DCA, and CDCA, with higher levels observed for DCA (Figure 5B). However, the levels of fecal DLCA decreased in GCW group compared with the control (Figure 5B).

Spearman correlation analysis was conducted to explore the potential link between microbiome and parameters of bile acid metabolism and reproductive performance of sows (Figure 6). Unidentified_Ruminococcaceae was positively correlated with birth weight of piglets, meanwhile negatively correlated with plasma TUDCA, plasma α-MCA, and fecal DLCA. Bacteroides was negatively correlated with fecal DCA, but positively correlated with plasma GHCA, plasma 7-KLCA and fecal DLCA. Additionally, Clostridium was positively correlated with plasma α-MCA, Parabacteroides was positively correlated with fecal DLCA, and Streptococcus was negatively correlated with fecal DCA.