3.1. Prebiotic supplementation slightly improves growth performance of broiler chickens
As depicted in Table 2, the GOS had a growth promoting effect on the average body weight of chickens, while there were no significant differences between XOS treatment group and control group (P>0.05). No statistical differences in performance metric of feed conversion ratio. Additionally, the breast muscle percentage and thigh muscle percentage were slightly increased in prebiotics group relative to the control group (P>0.05). The abdominal fat percentage and subcutaneous fat thickness (cm) showed slight decrease in prebiotic treatment group (P >0.05). Feed conversion ratio of the prebiotic treatment group was lower that of control group (P>0.05). Thus, considering the growth performance, the prebiotic group was slightly superior to the control group.
Table 2
Growth and meat production of 70-day-age chickens in different prebiotics-treated groups.
Performance Metric
|
Control group (n=10)
|
XOS group (n=10)
|
GOS group (n=9)
|
Average body weight (g)
|
1768±163
|
1773±165
|
1837±171
|
Breast muscle percentage (%)
|
15.66±1.00
|
16.03±1.29
|
16.43±1.96
|
Thigh muscle percentage (%)
|
19.31±0.94
|
19.87±1.03
|
20.02±6.36
|
Abdominal fat percentage (%)
|
3.26±1.5
|
3.59±1.3
|
3.35±1.2
|
Subcutaneous fat thickness (cm)
|
3.75
|
3.38
|
3.61
|
Feed conversion ratio
|
2.4:1
|
2.35:1
|
2.30:1
|
Morbidity and mortality cases
|
5.2%
|
5.0%
|
4.3%
|
Notes: There are no significant difference between the prebiotics group and the control group. |
3.2. Prebiotic intervention alters microbiome composition
In our present microbiome investigation, the caecal microbial communities were compared between prebiotics (GOS and XOS) treatment group and control group. A total number of 1513565 valid reads and 795 OTUs at 97% sequence similarity were obtained from all samples, respectively. The rank abundance analysis on the OUT level revealed that the richness and evenness of the microbiota composition was the similar among three groups (Figure 1A). The extent of the similarity of gut microbial communities between the three groups was measured PCoA at the OTU level, the results showed that the gut microbial communities was significantly separated between GOS, XOS and control group (Figure 1B). Moreover, we found that the diversity of the microbial community significantly decreased in the prebiotics treatment group, especially in XOS group, as showed by abundance-based coverage estimators (ACE) and Chao1 indices (Figure 1C and 1D).
Then, we displayed the composition of caecal microbiota at family and genus levels. Microbiota of the 30 samples from the prebiotics treatment group and control group were altered at family level. As shown in Figure 1E, the abundance of Ruminococcaceae, Barnesiellaceae, and Acidaminococcaceae were increased in the prebiotics treatment group. Unlike these bacteria, the prebiotics treatment group had a lower average relative abundance of Bacteroidaceae and Lactobacillaceae compared with control (Figure 1E). At the Top10 genus level, the increased proportions of Alistipes, Faecalibacterium, unclassified_f_Barnesiellaceae, and Phascolarctobacterium, decreased proportions of Bacteroides and Lactobacillus were observed in prebiotics treatment group in comparison to control (Figure 1F).
Consistent with above results, the circos plot at genus level revealed that Alistipes was the dominant flora in GOS group, accounting for 19.86% (Figure 2A). Faecalibacterium and Bacteroides were the dominant flora in XOS group, accounting for 18.15% and 17.73%, respectively (Figure 2A). Bacteroides was the only dominant flora in control group, accounting for about 20.82% (Figure 2A). Importantly, Alistipes and Bacteroides are a genus in the phylum Bacteroidetes, and Faecalibacterium is a genus in the phylum Firmicutes, indicating that prebiotics treatment may affect the quality of chicken mainly by changing the composition of Bacteroidetes and Firmicutes. Subsequently, LEfSe analysis revealed specific bacteria that were associated with prebiotics (Figure 2B). Several microbiota including f_ Porphyromonadaceae_g_Barnesiella_OTU436, f_Bacteroidaceae_g_Bacteroides_ OTU370, and s_Bacteroides_ caecigallinarum were all significantly over-represented (all LDA scores (log10) > 3) in the feces of chickens fed with XOS. Interestingly, the Porphyromonadaceae, Bacteroidaceae, and Bacteroides are belong to the phylum Bacteroidetes. The c_Clostridia_o_Oscillospirales_OTU445, belong to Firmicutes phylum, was identified as specific taxa in chickens treated with GOS (all LDA scores (log10) > 2). Therefore, these results again suggested that prebiotics treatment primarily altered the abundance of Bacteroidetes and Firmicutes.
3.3. Prebiotic changes global metabolome of caecum
Since signature of microbiota related with prebiotics in the chickens was demonstrated, we speculated that alterations in metabolic pathways may be at least partially influenced by prebiotic-driven gut microbiota in chickens. Thus, we subsequently analyzed intestinal metabolites in caecal feces of chickens, using a nontargeted LC-MS technology. As shown in Fig. S1A-1D, the score plots of PLS-DA confirmed that the metabolic profiles had significant differences between prebiotics treatment group and control. Permutation test for the OPLS-DA model to further visualized group separation that XOS group and control group generated intercepts of R2 = 0.9848 and Q2 = -0.1713 (Fig. S1E), and that of GOS group and E group produced intercepts of R2 = 0.9851 and Q2 = -0.1205 (Fig. S1F), which revealed OPLS-DA had well fitted-effect.
Subsequently, we summarized the distribution of differential metabolites separating the two groups. Overall, 135 differential metabolites were identified in comparison of prebiotics treatment group relative to control (VIP > 1, P < 0.05). There were 79 and 92 differential metabolites respectively found in XOS vs control and GOS vs control, respectively (Table S1 and Table S2). A heat map was utilized to visualize the abundance of the differential metabolites (Figure 3A and 3B). In total, two clusters were respectively generated in XOS vs control and GOS vs control. Among these, 36 metabolites were shared (Table S3), including seven type of lysophosphatidylcholine (LysoPC), such as PC(16:0/0:0)[U], LysoPC(16:1(9Z)/0:0), LysoPC(18:1(11Z)), LysoPC(16:1(9Z)), LysoPC(18:1(9Z)), LysoPC(16:0), and LysoPC(P-16:0), and two metabolites of the shikimic acid pathway (such as shikimic acid, cinnamic acid). Together, these pieces of evidence indicated that production of intestinal metabolites regulated by different prebiotic, especially LysoPC and shikimic acid pathway.
Moreover, we identified several altering metabolic pathways that specific related with prebiotics treatment. As depicted in Figure 3C and 3D, the Phenylalanine, tyrosine and tryptophan biosynthesis, Purine metabolism, and Glutathione metabolism were observed to be strikingly disturbed in response to XOS. Interestingly, the biosynthesis of these amino acids depend on shikimic acid pathway. The perturbations of D-Glutamine and D-glutamate metabolism, Glutathione metabolism and Glycerophospholipid metabolism (the enriched pathway of LysoPC -like metabolites) were discovered in response to GOS treatment. These results suggested that prebiotics feeding may induce changes in intestinal microbial LysoPC and shikimic acid metabolism in broilers chicken.
3.4. Alteration of meat metabolites
Subsequently, we further detected the concentration of common nutrients, including the proteins, amino acids, fatty acids in the chicken breast. As presented in Table S4, overall, prebiotics treatment significantly decreased the content of histidine compared to control samples but had no significant effect on other amino acids in chicken breast. Although administration of prebiotics significantly decreased the proportion of some fatty acids, such as myristic acid (C14) and palmitoleic acid (C16), the proportion of IMF remarkably increased in prebiotics group. Notably, although concentration of these metabolites such as oleic acid and polyunsaturated fatty acids (linoleic acid and arachidonic acid) had no significant alteration, that of them had a slight increase in chicken breast and viscera samples from XOS or GOS treatment group relative to control group. Accumulating studies have confirmed that juiciness, flavor, and tenderness correlate positively with the muscle total fat content [22, 23]. Polyunsaturated fatty acids are heated and oxidized to generate volatile components, such as 2, 4-decaldehyde, which improve the flavor of meat [24]. Together, the addition of prebiotics had an effect on meat quality and flavor of chicken.
3.5. Profiling of genes expressed in the chicken breast of the prebiotics treatment group and control group
We further investigated the metabolite gene response. In total, 599 genes that were significantly altered by transcriptome sequencing identified. There were 381 and 298 DEGs genes respectively obtained in XOS vs control and GOS vs control (Table S5). Among these, 80 DEGs were shared, including 36 up-regulated DEGs and 42 down-regulated DEGs (data not shown). We further analyzed the expression tendency of these common genes, the trend for 10 genes of subcluster_5, such as LRRTM3, LRRC10B, and ENSGALG00000046353 was similar; these genes all highly expressed in prebiotics group relative to control group. The trend for 4 genes of subcluster_9, such as CFAP44 and CCL21 presented same result as that for subcluster_5 (Figure 4A). Subsequently, corresponding functions of the 80 common genes were determined (Figure 4B), which were mainly found to function as biological process in the “regulation of lipid kinase activity”, “regulation of lipid metabolic process”, “regulation of phospholipid metabolic process”, and “regulation of hormone biosynthetic process. KEGG annotation analysis showed these genes were mainly involved in organismal systems of “endocrine system”, “immune system”, “immune system”, “digestive system”, and various metabolism (e.g. nucleotide, carbohydrate, biosynthesis of other secondary, amino acid metabolism) (Figure 5A). Subsequently, KEGG enrichment analysis delineated 80 genes primarily implicated in metabolism pathway of “regulationof lipolysis inadipocytes”, “adipocytokine signaling pathway”, “protein digestion and absorption”, “cytokine-cytokine receptor interaction” and “Jak-STAT signaling pathway” (Figure 5B). These pathways were related with lipid and protein metabolism and may affect meat quality and flavor.
3.6. Caecal microbiota correlated to growth performance of broiler chickens
To investigate the correlation between caecal microbiota alteration and growth performance, we analyzed the correlation between species abundance (Bacteroidetes and Firmicutes, and other taxa at family level mentioned above) and growth performance by Pearson correlation coefficient analysis. At the phylum level, only Bacteroidetes showed significantly positively correlation with average body weight (Figure 6). At the family level, caecal microbiota of Barnesiellaceae and Ruminococcaceae were significantly positively correlation with breast muscle percentage and average body weight, respectively (Figure 6). No significant correlation was detected between other caecal microbiota and growth performance.
3.7. Correlations between the prebiotics-induced gut microbiome and metabolome
A pearman correlation analysis of differential metabolites and top40 OTUs in abundance (microbes) was performed. As depicted in Figure 7A, based on differential metabolites in XOS group, the f_Acidaminococcaceae_g_Phascolarctobacterium_OTU754 was significantly positively related to retapamulin, N-Methyl-14-O-demethylepiporphyroxine, mandelonitrile rutinoside, N-Arachidonoyl tyrosine, benzaldehyde and L-Tryptophan.
F_Rikenellaceae_g_Alistipes_OTU714 and F_Rikenellaceae_g_Alistipes_OTU326 were significantly positively correlated with LysoPC such as LysoPC (P-16:0), LysoPC (16:0). F_Lactobacillaceae_g_Lactobacillus_OTU4 was remarkably negatively related to mandelonitrile rutinoside, alliosterol 1-rhamnoside 16-galactoside, halocins, benzaldehyde and 3-Buten-1-amine. The indoleacetic acid and hypoxanthine were also found to be dramatically negatively related with f_Lachnospiraceae_g_CHKCI001_OTU416.
Based on differential metabolites in GOS group (Figure S2), F_Rikenellacea_g_Alistipes_OTU756 was closely positively correlated with cucurbic acid, cerebronic acid, 3''-O-Caffeoylcosmosiin and choline except for LysoPC. The F_Rikenellacea_g_Alistipes_OTU714, f_Barnesiellaceae_g_Barnesiella_OUT78, and f_Barnesiellaceae_g_Barnesiella_OTU155 were positively correlated with shikimic acid. The 1, 25-Dihydroxyvitamin D3-26, 23-lactone and choline were found to present remarkably positive and negative correlation with f_Barnesiellaceae_g_Barnesiella_OTU436, respectively. L-Glutamate was dramatically positively associated with f_Barnesiellaceae_g_unclassified_f_OTU716. Together, these results revealed above dominant microbiota caused the differences in gut metabolites in the chickens fed with prebiotics.
Moreover, we also investigate the correlation between species abundance (Bacteroidetes and Firmicutes) and seven type of LysoPC by Pearson correlation coefficient analysis. As depicted in Figure 8A-8B, Bacteroidetes was significantly positive correlated with LysoPC(18:1(11Z)) and LysoPC(16:0). While Firmicutes was significantly negative correlated with LysoPC(16:0), LysoPC(16:1(9Z)/0:0), LysoPC(P-16:0), LysoPC(16:1(9Z)), and LysoPC(18:1(9Z)) (Figure 8C-8G). These results suggest that the change of caecal microflora Bacteroidetes and Firmicutes are related to the metabolism of differential metabolite LysoPC.
3.8. Metabolite-gene network analysis
To extract the interactions among the all differential metabolites and gene expression in prebiotics-supplemented chickens, a network diagram was constructed. Among the differential metabolites, a total of twelve metabolites interacted with differentially expressed genes (Figure 7B). Particularly, the AMD1(S-adenosylmethionine decarboxylase)-cadaverine or omithine pairs, and PLA2G1B (phospholipase A2 group 1B) - indoleacetic acid pairs were discovered in the network. These genes were likely hubs of the prebiotics treatment on meat quality of chickens.