Study Population
Table 1 shows the baseline of the study cohorts. In detail, we enrolled 50 infants with EUGR and 23 infants without EUGR between December 2017 and July 2018.
Overview of the different intestinal bacterial species between the EUGR and non-EUGR groups
The microbial composition was detected by metagenome sequencing [12]. As shown in Figure 1, the intestinal microbiota of the EUGR group exhibited a lower level of Bacteroides vulgatus (p < 0.05), Dorea unclassified (p < 0.05), Lachnospiraceae bacterium 1_1_57FAA (p < 0.001), and Roseburia unclassified (p < 0.05) than that of the non-EUGR group. In fact, L. bacterium 1_1_57FAA was absent in the EUGR group. Meanwhile, the EUGR group showed a higher level of Streptococcus mitis_oralis_pneumoniae (p < 0.05). These results indicate that L. bacterium 1_1_57FAA could distinguish infants with or without EUGR.
Overview of the different KEGG pathways between the EUGR and non-EUGR groups
To investigate the potentially influenced metabolic pathways induced by EUGR status, we further analyzed the metagenome sequencing readings with KEGG. As shown in Figure 2, there were seven different enriched KEGG pathways between the non-EUGR group and EUGR group. Among these pathways, the non-EUGR group exhibited significantly higher levels of bile secretion (p < 0.001), galactose metabolism (p < 0.05), glycine, serine, and threonine metabolism (p < 0.05), and the renin–angiotensin system (p < 0.05) compared with the EUGR group. However, the EUGR group exhibited significantly higher levels of the cAMP signaling pathway (p < 0.05), yeast meiosis (p < 0.05), and oxidative phosphorylation (p < 0.05) in comparison to the non-EUGR group.
Overview of the different metabolites between the EUGR and non-EUGR groups
The metabolites generated by the intestinal microbiota affect the biological processes of the hosts, including appetite control and weight management. As infants with EUGR exhibit a lower growth velocity, we sought to examine the metabolites in the feces and blood of the infants of both groups. A non-targeted analysis of the metabolite composition was carried out to analyze the different metabolites between the infants with EUGR and HC. Figure 3 and Figure 4 show the different metabolites in the blood and feces, respectively.
Correlation between the different microbial species and different KEGG pathways
To investigate the association between the different species and KEGG pathways, Spearman’s correlation analysis was used. As presented in Figure 5, among the KEGG pathways higher in the non-EUGR group, bile secretion was positively correlated with B. vulgatus (r = 0.43, p < 0.001), D. unclassified (r = 0.31, p < 0.05), and R. unclassified (r = 0.33, p < 0.05). Galactose metabolism was positively correlated with D. unclassified ( r =0.14, p < 0.05). Glycine, serine, and threonine metabolism was positively correlated with D. unclassified (r = 0.30, p < 0.05), L. bacterium 1_1_57FAA (r = 0.30, p < 0.05), and R. unclassified (r = 0.40, p < 0.05). Among the KEGG pathways higher in the EUGR group, the cAMP signaling pathway was positively correlated with B. vulgatus (r = 0.31, p < 0.05) and L. bacterium 1_1_57FAA (r = 0.33, p < 0.05). Yeast meiosis was positively correlated with D. unclassified (r = 0.31, p < 0.05). In brief, the different intestinal microbial species were significantly correlated with the metabolic pathways. These results indicate that these intestinal microbial species may play a role in the metabolism of the hosts.
Correlation between the blood metabolome and intestinal microbiota
To evaluate the association between the different blood metabolites and different Microbial species, Spearman’s correlation was analyzed for the five different species and 48 different serum metabolites; the results are graphically presented in Figure 6. In general, a total of 30 significant interactions were found. Among the total different serum metabolites, 16 of them were significantly correlated with different Microbial species, half of which were amino acids and the rest other metabolites. Among the microbial species enriched in the non-EUGR group, B. vulgatus was negatively correlated with indole (r = –0.43, p < 0.001) and L-tryptophan (r = –0.43, p < 0.001), which were much higher in the EUGR group. Meanwhile, it was positively correlated with sphingosine (r = 0.41, p < 0.05), which was higher in the non-EUGR group. Additionally, D. unclassified was negatively correlated with indole (r = –0.37, p < 0.05), L-glutamine (r = –0.38, p < 0.05), pyroglutamic acid (r = –0.32, p < 0.05), 4-hydroxyisoleucine (r = –0.40, p < 0.05), methionine sulfoxide (r = –0.37, p < 0.05), p-chlorophenylalanine (r = –0.33, p < 0.05), 2-butynedioic acid (r = –0.37, p < 0.05), oxaloacetic acid (r = –0.39, p < 0.05), and galactitol (r = –0.40, p < 0.05), which were all more abundant in the EUGR group. Moreover, it was positively correlated with N6-carbamoyl-DL-lysine (r = 0.32, p < 0.05), which was more abundant in the non-EUGR group. Furthermore, L. bacterium 1_1_57FAA was negatively correlated with L-glutamine ( r = –0.36, p < 0.05), p-chlorophenylalanine (r = –0.35, p < 0.05), 2-butynedioic acid (r = –0.35, p < 0.05), 3-hydroxyanthranilic acid (r = –0.37, p < 0.05), and galactitol (r = –0.37, p < 0.05), which were more abundant in the EUGR group. In addition, R. unclassified was negatively correlated with indole (r = –0.54, p < 0.001), L-glutamine (r = –0.37, p < 0.05), diphenylamine (r = –0.36, p < 0.05), p-chlorophenylalanine (r = –0.42, p < 0.001), L-tryptophan (r = –0.41, p < 0.001), 2-butynedioic acid (r = –0.38, p < 0.05), oxaloacetic acid ( r = –0.38, p < 0.05), and galactitol (r = –0.40, p < 0.05), which were more abundant in the EUGR group. Meanwhile, it was positively correlated with p-tolyl sulfate (r = 0.41, p < 0.001) and N6-carbamoyl-DL-lysine (r = 0.36, p < 0.05), which were more abundant in the non-EUGR group. Finally, S. mitis_oralis_pneumoniae, which exhibited much higher levels in the EUGR group, was positively correlated with citrulline (r = 0.36, p < 0.05) and negatively correlated with p-tolyl sulfate (r = –0.35, p < 0.05).
Correlation between the fecal metabolome and gut microbiota
To address the correlations between the different fecal metabolites and the different gut microbiota, Spearman’s correlation was calculated for the five different microbial species and 24 different fecal metabolites. As shown in Figure 7, a total of seven interactions with statistical significance were found. For instance, R. unclassified was positively correlated with pipecolic acid (r = 0.45, p < 0.05) and L-lysine (r = 0.45, p < 0.05), both of which were much higher in the non-EUGR group. Moreover, B. vulgatus was negatively correlated with phosphatidylinositol lyso 20:4 (r = –0.46, p < 0.05), and D. unclassified was negatively correlated with myristoleic acid (r = –0.42, p < 0.05). However, S. mitis_oralis_pneumoniae, which exhibited much higher levels in the EUGR group, was positively correlated with phosphatidylinositol lyso 16:0 (r = 0.42, p < 0.05), phosphatidylinositol lyso 18:1 (r = 0.53, p < 0.05), and phosphatidylinositol lyso 18:0 (r = 0.44, p < 0.05), which were higher in the fecal from the EUGR group.