3.1 Piglet Performance
Piglet birth BW (day 0) was greater for sows fed LP compared with piglet birth BW for sows fed CON, while 21 d piglet BW tended (P < 0.07) to be greater for piglets from sows fed LP compared with sows fed CON (Table 2). However, these initial and final piglet BW differences did not affect piglet ADG, which was similar (P > 0.36) among both treatments.
3.2 Jejunal Morphology
Intestinal HE staining demonstrated that piglets nursing sows fed a maternal LP diet demonstrated reduced (P <0.05) villus height and ratio of villus height to crypt depth, while jejunum relative weight, villus width, crypt depth, and muscle thickness were increased (P < 0.05) compared with piglets from sows fed the maternal CON diet (Table 3).
3.3 The Diversity and Composition of Jejunal Microbiota
The 16S RNA jejunal microbiota samples after data filtering, quality control, and low-confidence singletons removal resulted in an average of 42,718 V3-V4 16S rRNA gene sequence reads being obtained for the 21 d samples (two piglet litters were not yet weaned due to late farrowing). The sequence lengths ranged from 415 up to 429 bp. The rarefaction curves resulted in new OTU diminishing identification rates with increasing number of reads per sample. This implies that the jejunum bacterial community has adequate sampling depth for identifying dominant members. Similarly, the Good’s coverages exceeded 99% demonstrating excellent sequence accuracy and reproducibility (Table 4). Of the 482 total OTU numbers, 452 OTU were detected in both groups. Based on the Shannon (P < 0.001), and Simpson (P = 0.001) indices piglets from the maternal fed LP diet demonstrated more diversity and greater evenness compared with piglets from the material fed CON diet (Table 4). The Chaol (P = 0.519) and Ace (P = 0.435) indices were similar for piglets from the maternal fed LP compared with the maternal fed CON. Taxonomic analysis revealed the predominant phyla Firmicutes and Proteobacteria being 67.21% and 24.97%, respectively of total reads identifying 16 bacterial phyla. (Figure 1A). At the genus level, 232 genera were identified in the jejunal samples. The predominant genera were Lactobacillus (51.11%), Escherichia-Shigella (9.00%), Actinobacillus (7.41%), Clostridium_sensu_stricto_1 (5.60%), Romboutsia (4.35%), and Buchnera (3.54%), respectively (Figure 1B).
Furthermore, using a PCoA plot illustrated microbial community dissimilarity and revealed distinct structures between piglets from the maternal fed LP compared with maternal fed CON (Figure 1C). The PCoA plot uses a weighted method for unifrac similarity, which revealed PC1 and PC2 explained 55.61% and 13.98% of sample variation, respectively. Similarly, the jackknifed beta diversity and hierarchical clustering analysis via the Unweighted Pair-group Method with Arithmetic Mean (UPGMA) demonstrated that different piglets fed different maternal CP diets were clustered in their individual groups (Figure 1D). In addition, piglets from maternal fed CON diets in the PCoA plot were clustered into two subgroups (Figure 1C) and UPGMA hierarchical clustering analysis (Figure 1D), which was attributed to individual variations of jejunum microbiome profiles.
3.3 Differences in Jejunal Bacterial Community Composition
Relative phylum abundances of Firmicutes, Proteobacteria, Bacteroidetes, and unknown were > 1% for both treatments (Table 5). Firmicutes relative abundance was decreased (P = 0.002) and Proteobacteria (P = 0.001) was increased for piglets from the maternal LP treatment compared with piglets from the sows fed maternal CON. Thirty-two (32) specific genera demonstrated relative abundances > 0.1%. The relative bacterial community abundances of Escherichia-Shigella (P = 0.050), Actinobacillus (P = 0.050), Clostridium_sensu_stricto_1 (P = 0.003), Veillonella (P = 0.015), and Turicibacter (P = 0.011) were higher and Lactobacillus was lower (P < 0.001) for piglets from the maternal fed LP treatment compared with piglts from the maternal fed CON treatment (genus level; Table 6).
The receiver operating characteristic curve (ROC) predicted different microorganisms for piglets from maternal fed LP compared to maternal fed CON piglets for inducing jejunal development. The area under the curve (AUC) judged via diagnosis test (Xia et al., 2013) that Lactobacillus is the most likely biomarker (0.9 < AUC < 1.0) for piglets from both treatments, while Clostridium_sensu_stricto_1 and Turicibacter are more likely biomarkers (0.8 < AUC < 0.9) for piglets from maternal fed LP sows.
3.5 Predicted Function of Jejunal Microbiota
The PICRUSt analyzed pathway compositions for evaluating jejunal bacterial community functional capacity is a functional-gene-count matrix. Second level KEGG (levels) metabolism pathway analysis via global and overview maps demonstrated that biosynthesis of other secondary metabolites were enriching amino acid, cofactors, and vitamins metabolism (P < 0.05), while lipid and nucleotide metabolism were decreased (P < 0.05) for piglets when maternal sows were fed LP diet compared with piglets from the maternal fed CON (Figure 2).
3.6 Correlations between Intestinal Microbial Species and Jejunum Morphological Traits
Numerous correlations via Spearman’s correlation analyses (correlation coefficient | > or < 0.4, P < 0.05, Figure 3) were investigated between the different genera (n=6) relative abundances and morphological parameters (n = 7). Clostridium_sensu_stricto_1 was positively correlated with villus width, crypt depth, and muscular thickness, while being negatively correlated with villus height, and ratio of villus height: crypt depth. Escherichia-Shigella was positively correlated with muscular thickness and negatively correlated with villus height. Turicibacter was positively correlated with crypt depth and muscular thickness, while Veillonella was positively correlated with villus width. Lactobacillus was positively correlated with villus height, and villus height: crypt depth, and negatively correlated with jejunum weight, villus width, crypt depth, and muscular thickness.
3.7 Jejunum Metabolites and Metabolic Pathways
The 3D-PCA and OPLS-DA multivariate statistical analysis models were applied to evaluate the different group classifications via Score plots (Figure 4). The 3D-PCA Score plots were derived from the LC-TOF/MS jejunal metabolic profiles demonstrated separation between the LP and CON fed diets. A clear separation and discrimination were observed between the two groups, which indicated that the OPLS-DA model could be used to identify piglet differences between maternal fed LP and CON diets. In addition, the volcano plots highlight the 44 metabolites being altered (VIP > 1.0 and P < 0.05) for piglets from the maternal fed LP fed treatment (Table 7). Thirty-four (34) metabolites were increased and 10 metabolites were decreased in piglets from the maternal fed LP diet compared with piglets from the maternal fed CON (Table 7). These amino acids, nucleotides, lipids, organic acids, and numerous metabolites are involved in multiple jejunum biochemical processes of the Bamei piglet. The hierarchical clustering analysis (HCA) with a heat map was performed to visualize the Bamei piglet jejunum metabolome differences associated with two maternal CP concentrations. The positive ionization data (Figure 5) and negative ionization data (Figure 6) clearly demonstrate similar clustering patterns of molecular features within each treatment. The maternal CP concentration demonstrated an impact (P < 0.05) on jejunum metabolome, while cluster differences were clearly observable in the HCA generated heatmap plot. The AUC value for each metabolite was calculated, such that metabolites with an AUC > or equal to 0.85 were selected as potential signatures. Six metabolites determine via the AUC elimination step (Figure 7A) were: L-Methionine (AUC = 0.875), Taurochenodeoxycholate (AUC = 0.882), Taurodeoxycholic acid (AUC = 0.875), Tauroursodeoxycholic acid (AUC = 0.896), 4-Androsten-17 beta-ol-3-one glucosiduronate (AUC = 0.931), and Cholic acid (AUC = 0.882). Furthermore, the metabolic pathway enrichment analysis (Figure 7B) demonstrated that feeding a maternal LP diet altered (P < 0.05; rich factor > 0.10) Arg, Cys, His, Met, Phe, Try, Tyr, and linoleic acid metabolism, biosynthesis of Phe, Tyr, and Try, and Lys degradation. Consistent with the PICRUSt function prediction pathway, amino acid metabolism was enriched for piglets from sows fed a maternal LP concentration diet compared with piglets from sows fed a maternal CON.
3.8 Correlations between Differential Genera and Metabolites
The functional correlation between intestinal microbiome changes and metabolite perturbations (VIP > 2, P < 0.05) was evaluated using a correlation matrix generated by calculating the Spearman’s correlation coefficient. Clear identifiable correlations between perturbed intestinal microbiome and altered metabolite profiles were found (r > 0.5 or < -0.5, P < 0.05). Clostridium_sensu_stricto_1 was negatively correlated with L-His; Lactobacillus was positively correlated with chenodeoxycholate, cholic acid, glycocholic acid, L-His, L-Leu, L-Met, L-Try, L-Val, taurochenodeoxycholate, taurodeoxycholic acid, and tauroursodeoxycholic acid, but was negatively correlated with hypoxanthine, linoleic acid, palmitoyl ethanolamide, PC (16:0/16:0), and uracil; Turicibacter was negatively correlated with the D-Pro; Veillonella was positively correlated with uracil (Figure 8). In summary, dietary maternal CP concentrations induced a piglet intestinal microbiome taxonomic perturbation, which in turn substantially alters the intestinal metabolomic profile as observed due to changes in the diverse intestinal microbiota-related metabolites.