 majority of the genes were associated with membrane transport (12.4%), amino acid metabolism (9.9%), carbohydrate metabolism (9.8%), replication and repair (8.7%), translation (5.7%), and energy metabolism (5.6%) (Figure 5A). In addition, we further analyzed the composition of gene families at level 3. A total of 328 KEGG pathways were identified, and 27 predominant metabolic FIGURE 4 | The network of co-occurring predominant genera within the milk samples (n = 130). The nodes represent the predominant genera, and the size of each node is proportional to the degree (the number of connections). The edges represent strong and significantly positive (red) or negative (green) correlations between predominant genera. The nodes are colored based on module structure. pathways had relative abundances greater than 1% in our samples (Supplementary Figure S6). These profiles revealed that most pathways at level 3 were related to transporters (6.2%).
Principal coordinate analysis based on the relative abundances of the KEGG pathways of the milk microbiota identified close clustering of pathways at 0, 1, 3, 7, 10, and 14 days, and a relative divergence of pathways at 5 and 21 days ( Figure 5B). Next, we analyzed KEGG pathways that had been annotated at level 2. The relative abundances of 28 of the 36 gene families in the milk microbiota differed significantly with lactation time (Figure 5C, p < 0.01). The proportions of genes related to infectious diseases, signal transduction and transport and catabolism significantly decreased with lactation time (Figure 5C, p < 0.01). The proportions of genes for amino acid metabolism, lipid metabolism, metabolism of other amino acids and neurodegenerative diseases significantly increased from day 0 to day 5 and then decreased with lactation time (Figure 5C). In contrast, the relative abundances of genes involved in nucleotide metabolism, transcription, enzyme families and signaling molecules and interaction significantly decreased for the first 5 days and then increased with lactation time (Figure 5C). In addition, the relative abundances of other gene families dramatically varied with lactation time. To further characterize the effects of lactation time on the proportions of functional genes, we analyzed the variations in gene families at level 3. According to our results, all the predominant pathways were affected by lactation time (Supplementary Figure S6).

Relationships Between Predominant Bacteria and the Components of Sow Milk
Spearman rank-order correlations between nutrition content (Supplementary Table S6) and the relative abundances of the predominant bacteria in milk were analyzed to determine the relationships between components (Supplementary Table S7) and the microbiota of sow milk. The abundance of 29 bacterial taxa significantly correlated with the fat content in the milk samples: 14 of these bacterial taxon abundances were significantly positively correlated (r s ≥ 0.3 and p ≤ 0.05) with milk fat content, while the others were negatively correlated (r s ≤ 0.3 and p ≤ 0.05) (Figure 6). In our study, the abundances of 20 and 21 bacterial taxa were significantly positively correlated with lactose and protein content in the milk samples, respectively, while the abundances of 17 bacterial taxa were significantly negatively correlated with both lactose and protein content (r s ≤ 0.3 and p ≤ 0.05) (Supplementary Table S7). The abundances of 27 bacterial taxa were all significantly correlated with fat, lactose and protein content in milk. The bacterial taxa abundances that were positively correlated with fat content were negatively correlated with protein and lactose content in the milk samples. The relative abundances of Prevotella and Leptotrichia were the most positively correlated with fat content (r s = 0.58, r s = 0.50; p ≤ 0.01) of all the genera. Conversely, the fat content was most negatively correlated with the proportion of unclassified Comamonadaceae (r s = −0.57; p ≤ 0.01). In addition, the relative abundances of Staphylococcus and unclassified Comamonadaceae were the most positively correlated of the genera with lactose (r s = 0.60, r s = 0.69; p ≤ 0.01) and protein content (r s = 0.62, r s = 0.68; p ≤ 0.01).

DISCUSSION
In this present study, alpha and beta diversity analyses indicated that lactation stage plays crucial roles in shaping the composition of the sow milk microbiota. Our findings were generally similar to those previously described for human milk (Hunt et al., 2011;Cabrera-Rubio et al., 2012;Drago et al., 2017), cow milk (Li et al., 2018) and goat milk samples (McInnis et al., 2015). However, changes in the alpha and beta diversity mainly appeared in the colostrum, with no further changes in transitional and mature milk in our study. One plausible explanation is that the proportion of nutrient volatility changes in the colostrum and is generally stable in transitional and mature milk.
To our knowledge, this is the first study to conduct a comprehensive investigation of microbial diversity and composition in sow milk. The most predominant phyla identified in sow milk were Firmicutes and Proteobacteria, and the six most predominant genera were Ruminococcaceae, Streptococcus, unclassified Clostridiales, Lactobacillus, Corynebacterium, and unclassified Lachnospiraceae. At the phylum level, the composition of the bacterial communities detected in the present study was similar to that described in previous studies of human milk (Urbaniak et al., 2016). Similar counter-balanced relationships between Firmicutes and Proteobacteria that were reported in cow milk (Li et al., 2018) were also observed in our samples. Although the relative abundances of these two bacteria significantly fluctuated throughout lactation, total proportions remained at a certain level (75.9-80.9%), indicating that these two microorganisms compete with each other for ecological niches. Staphylococcus, Streptococcus, and Propionibacterium are generally the predominant genera reported in human milk studies (Hunt et al., 2011;Fitzstevens et al., 2017). However, Propionibacterium was not a predominant genus in our samples. In addition, certain core genera, such as Bacteroides, Ruminococcus, and Corynebacterium, previously detected in human milk were also found in our samples, but more of the core genera in sow milk were not found in core human milk microbiomes. Clear differences in the composition of the microbiota in sow and human milk may be due to genetic, dietary, rearing and environmental differences among subjects (Hunt et al., 2011).
Our results showed that lactation stage had a significant effect on the relative abundances of predominant phyla and genera (except unclassified Pseudomonadaceae and Micrococcaceae), although the degree of change in the relative abundances of predominant taxa was small. Lower relative abundances at the genus level might explain these inconspicuous changes. The most predominant genus in the colostrum was Streptococcus, while transitional and mature milk samples were dominated by unclassified Ruminococcaceae. Bifidobacterium, Staphylococcus, and Acinetobacter, which are lactose-utilizing genera (Zhang et al., 2017), were more abundant in colostrum and generally decreased with the decreased lactose content in transitional and mature milk samples. Prevotella demonstrated the most positive correlation with fat content in our samples (r s = 0.58; p ≤ 0.01) and with the intestinal microbiota of growing pigs in a previous study (Feng et al., 2015). The relative abundance of Prevotella increased with increasing fat content throughout lactation. Peptoniphilus and Anaerococcus metabolized peptone and amino acids to produce butyrate as a major energy source (Ezaki et al., 2001). In our study, the proportions of Peptoniphilus and Anaerococcus showed positive correlations with the protein content (r s = 0.60, r s = 0.60; p ≤ 0.01) and significantly decreased as proteins content decreased. This result was consistent with other studies (Boix-Amoros et al., 2016), supporting a correlation between nutritional components and specific components of the microbiome Zhang et al., 2017). Major differences in the relative abundances of the most predominant genera were mostly apparent during the first 5 days of lactation, which might be due to changes in nutritional components in the colostrum and the relative stability of nutrients in transitional and mature milk. Similar patterns of change were also observed at the species level. Lactobacillus reuteri, Lactobacillus mucosae, and Akkermansia muciniphila are potential probiotic bacteria (Urbańska and Szajewska, 2014;Pajarillo et al., 2017;Chelakkot et al., 2018). The relative abundances of these probiotics were shown to significantly increase with lactation time in sow milk and the piglet gut (Saraf et al., 2017), while the potentially pathogenic Staphylococcus epidermidis generally decreased in our samples (Sabaté Brescó et al., 2017). Studies investigating milk-associated probiotics might be beneficial to the development of probiotics and piglet health.
Network analysis is a powerful tool for investigating microbial interactions in complex environments. In our network analysis, Lachnospiraceae, Ruminococcaceae, and Bacteroidetes S24-7 demonstrated positive correlation, potentially because all of them are short chain fatty acid (SCFA)-producing strains (Biddle et al., 2013;Ormerod et al., 2016). Growing evidence suggests that Helcococcus is involved in a wide spectrum of animal diseases, such as subclinical mastitis and puerperal metritis (Oikonomou et al., 2012;Locatelli et al., 2013). In our study, Helcococcus negatively interacted with many potential probiotics