Predictive analysis of gut microbial function
The functional pathway abundance of KEGG corresponding to the 16S rRNA
sequencing data was predicted using picrust2. The results show that the
KEGG metabolic pathways at the first level mainly consisted of the
following functional categories: metabolism (78.6%), genetic
information processing (8.0%), environmental information processing
(6.5%), cellular processes (3.0%), human diseases (2.5%), and organic
systems (1.4%).
In the differential analysis of KEGG metabolic pathways at the second
level, the carbohydrate metabolism, replication and repair, and drug
resistance pathways were significantly enhanced in the spring group
(P <0.05). Amino acid metabolism, metabolism of
terpenoids and polyketides, xenobiotic biodegradation and metabolism,
cell motility, signal transduction, and lipid metabolism were
significantly enhanced in the autumn group (P <0.05;
Figure 6A).
In the enrichment process of the third-level metabolic pathways,
glycolysis/gluconeogenesis, amino sugar and nucleotide sugar metabolism,
and metabolic pathways such as fructose and mannose metabolism,
galactose metabolism, and purine metabolism were significantly higher in
the spring group (P <0.05). The two-component system and
glycine, serine, and threonine metabolism were significantly enhanced in
the autumn group (P <0.05; Figure 6B).
Fecal metabolic profiling ofT. roborowskii
The fecal metabolic profiles of T. roborowskii were acquired by
LC-MS, and metabolism was detected with good intra-group
reproducibility. Fecal metabolites were significantly separated in the
first principal component (PC1: 82.9%) (Figure 7A). In the OPLS-DA
model, the metabolic curves were clearly different between the two
sample groups (Q2=0.99>0.9 indicates an excellent model),
and the results show that there are obvious differences in fecal
metabolites between the spring and autumn groups (Figure 7B).
The absolute values of log2FC (Fold Change, FC) were sorted to get the
top 10 metabolites in each group. In the spring group, the levels of
hexaethylene glycol, leucyl-tyrosine, scillipheosidin
3-[glucosy-(1->2)-rhamnoside],
1-O-caffeoyl-(b-D-glucose 6-O-sulfate), serinyl-gamma-glutamate,
bradykinin hydroxyproline, phaseolic acid, TR-saponin B, corepoxylone,
and ethylparaben were high. Methyl dihydrojasmonate, dodecanoic acid,
trans-2-hexyl-1-cyclopropaneacetic acid, cochliophilin A,
“PC(20:5(5Z,8Z,11Z,14Z,17Z)/24:0),” uridine diphosphate glucose,
grevillol, benzyl hexanoate, trans-cinnamic acid, etc. were higher in
the autumn group (Figure 8).
The differential metabolites were classified as carboxylic acids and
derivatives, fatty acids, organooxygen compounds, steroids and steroid
derivatives, glycerol glycerophospholipids, prenol lipids, and
glycerolipids.
Differential metabolites were screened based on the criteria ofP =0.05, VIP=1, and FC=1. Finally, 1264 differential metabolites
were detected in the two groups of samples, of which 668 were
upregulated (higher in the autumn group), and 596 were downregulated
(higher in the spring group). The relative contents of deoxycholic acid
glycine conjugate and 26-methyl nigranoate were relatively higher in the
spring group. Simulansine, 3-oxocholic acid, and asparaginyl-proline
were the three metabolites with the highest relative contents in the
autumn group (Figure 9).
KEGG functional classification of metabolites showed that metabolites
participate mainly in metabolism, organismal systems, human diseases,
environmental information processing, and other functional pathways.
Among these, metabolic pathways, biosynthesis of secondary metabolites,
and microbial metabolism in different environments are related to a
variety of metabolites.
The KEGG enrichment network map of differential metabolites showed that
lysine degradation, pantothenate and CoA biosynthesis, steroid hormone
biosynthesis, carbon fixation pathways in prokaryotes, and other
differential metabolites between different seasons had a close positive
or negative relationship (Figure 10).