Alteration of cecal microbiome after S. Enteritidis infection
16S rRNA gene sequencing was performed on 41 samples of cecal contents (n = 12, 11, 9, 9 of CC, CT, RC and RT, respectively) and generated 2,439,579 high-quality sequence reads. The sequences were clustered into 942 operational taxonomic units (OTUs) (Additional file 1: Supplementary Table S1) with the threshold of 99% sequence identity. All of OTUs were taxonomically grouped into 10 phyla, 15 classes, 41 orders, 68 families, 125 genera and 201 species. Of phyla, Firmicutes had the most OTUs (794, 84.3%), the next three were Proteobacteria (88, 9.3%), Bacteroidota (28, 3.0%) and Actinobacteria (12, 1.3%). The sole Salmonella strain was OTU607 of 942 OTUs and was only detected in CT and RT.
The rarefaction curve (Fig. 1) and Principal Co-ordinates Analysis (PCoA) (Fig. 2) showed that the amount of sequencing data was reasonable and the samples were gathered in groups. In comparison of CC and RC, there were 73 common species between CC and RC (Fig. 3; Additional file 2: Supplementary Table S2), which contributed to 97.5 (± 2.2 SD, n = 12) % and 97.3 (± 3.2 SD, n = 9) % of total abundance in CC and RC, respectively. The community diversity in Simpson and Shannon index had no significant difference (Figs. 4 and 5), though the community richness in Chao1 index of CC was higher than that of RC (P༜0.01) (Fig. 6). Additionally, some dominant genus showed significant differences between CC and RC (P < 0.05), such as Flavonifractor, Erysipelatoclostridium, Streptococcus, Klebsiella, Lachnospiraceae_NK4A136_group, Blautia and Lachnoclostridium (Fig. 7). To prevent the batch effect, next analyses were performed within each cross.
LEfSe analysis can discover multi-dimensional biomarkers and omics features. As shown, 25 genera (23.4%) and 15 genera (17.2%) were altered (P < 0.05) compared to control in the Cross and in the Reverse-cross, respectively (Fig. 8A and B). Obvious differences could be seen between the reciprocal crosses. On family level, there were three co-differential families (P < 0.05), Erysipelotrichaceae, Lactobacillaceae and Eggerthellaceae. Only Erysipelotrichaceae altered in the same trend, and the other two altered in the opposite trend. On genus level, there were four co-differential genera (P < 0.05) Tyzzerella, Negativibacillus, Lactobacillus and Salmonella. Only Salmonella was consistent in trend, and the other three were contrary. Considering that Salmonella was the bacteria inoculated, that was to say, the alteration of cecal microbiome after S. Enteritidis infection was completely different. For the next correlation analysis, we merged the two parts of differential genera and obtained 36 genera (4 genera were overlapped) (Table 1). Among them, 11 genera (30.6%) were consistent in trend and 25 genera (69.4%) were contrary though some genera were insignificant in another cross.
Alteration of cecal metabolome after S. Enteritidis infection
Liquid chromatography-tandem mass spectrometry (LC-MS) was performed on 24 samples of cecal contents (n = 4, 7, 6, 7 of CC, CT, RC and RT, respectively, overlapped with the microbiome samples) and generated 22,358 mass spectrum peaks (Additional file 3: Supplementary Table S3). Therein, 1,468 peaks were annotated and used for analysis. Principal Component Analysis (PCA) (Fig. 9) and Venn analysis (Fig. 10) showed that the samples were gathered in groups and 1,306 metabolites were common in CC and RC, which accounted for 91.2% and 97.6% of their respective total number. The similarity within the Cross (1422/1450, 98.1%) or the Reverse-cross (1324/1381, 95.9%) was also high. According to the standards of Variable Importance in the Projection (VIP) > 1 and P < 0.05, 168 metabolites (11.6%) (Additional file 4: Supplementary Table S4) and 346 metabolites (25.1%) (Additional file 5: Supplementary Table S5) were screened in the Cross and in the Reverse-cross, respectively. There were 52 co-differential metabolites between the reciprocal crosses (Additional file 6: Supplementary Table S6). Of these, 31 and 21 metabolites were up- and down-regulated compared to control in the Cross, respectively; it was 29 and 23 in the Reverse-cross. From another perspective, 14 metabolites (26.9%) altered in the same trend and 38 metabolites (73.1%) altered in the opposite trend.
Correlation between metabolites and microbes during S. Enteritidis infection
For further relationship between metabolites and microbes, the matrix was established based on the screened 52 metabolites and 36 bacterial genera (Fig. 11). The metabolites were divided into 4 classes (①②③④) according to the unsupervised clustering result and were labeled the up- or down-regulation with different colors. Notably, all the 11 co-upregulated metabolites (red) had positive correlation with Salmonella (P < 0.05), while all the 3 co-downregulated metabolites (blue) had negative correlation with it (P < 0.01). Another remarkable feature of Fig. 11 was that the correlation of Class ② was almost completely contrary to Class ③. For example, most metabolites of Class ③ were correlated positively with Lactobacillus and Negativibacillus (P < 0.05), while it was contrary in terms of Class ②.
Phenotypic difference of phenylpropanoids between the reciprocal crosses after S. Enteritidis infection
Among the 52 co-differential metabolites, there were 11 phenylpropanoid metabolites (labeled ☆). Ten were upregulated and only 1 was downregulated compared to control in the Cross. On the contrary, it was 1 and 10 in the Reverse-cross, respectively. They were consistent when the search range was expanded to all the differential metabolites in each cross (not displayed). In a word, the phenylpropanoid metabolites were more accumulated in the Cross than the Reverse-cross.
For further information of a community’s functional capabilities, we herein utilized PICRUSt approach to predict the functional composition of cecal microbiome (Additional file 7: Supplementary Table S7), and found it was not quite in line with the fact. For instance, there were three predicted functions related to phenylpropanoids biosynthesis, including the flavonoid biosynthesis, the flavone and flavonol biosynthesis and the phenylpropanoid biosynthesis (Fig. 12). They were enhanced by 2.7 folds (P = 1.2E-03), 4.2 folds (P = 4.9E-04) and 1.4 folds (P = 0.009) in the Cross compared to control, respectively, which was consisted with the actual result of the Cross. However, it was contradictory in the Reverse-cross. Most of the phenylpropanoid metabolites were decreased significantly in fact, while PICRUSt showed that the three pathways of phenylpropanoids biosynthesis were matched or enhanced (P = 3.0E-05) compared to control.
Phenotypic difference of lipids between the reciprocal crosses after S. Enteritidis infection
Furthermore, we noticed there were 23 lipid metabolites in the 52 co-differential metabolites. Ten were upregulated and 13 were downregulated compared to control in the Cross. It was 15 and 8 in the Reverse-cross, respectively. They were consistent when the search range was expanded to all the differential metabolites in each cross (not displayed). Thus the lipid metabolites were more accumulated in the Reverse-cross than the Cross. It was worth noting that there were two classes of bioactive lipids in them. The first class was lysophosphatidylcholines (lysoPCs), also named as 1-acyl-sn-glycero-3-phosphocholines, including lysoPC(17:0), lyso-PAF C-16 and lysoPC(0:0/18:0). All of them were increased (P < 0.05) in the Reverse-cross, but only 1 was increased (P < 0.05) and 2 were decreased (P < 0.05) in the Cross (Fig. 13). The second class was eicosanoids, including unoprostone, △17-U-46619, 15(S)-HETrE and O-arachidonoyl ethanolamine (O-AEA). Three of them were increased (P༜0.05) and 1 was decreased (P < 0.05) in the Reverse-cross, but on the contrary, 1 was increased (P༜0.05) and 3 were decreased (P < 0.05) in the Cross (Fig. 14).