Effects of Kudzu-leaf Flavonoids supplement on Growth performances and Immune Organs Indexes
The differential analysis of KLF supplement on growth performances were first evaluated including ADFI, BWG, ADG, FCR. Just as shown in Table 2, the BWG in KLF treatment performed the highest among three treatments during the whole feeding phase, while the FCR in KLF and AGP were significantly lower than that in CON treatments(P < 0.05). No significant differences were observed of ADG and ADFI in all treatments, however chickens in CON treatment ate the most during the feeding phase.
Table 2
Effects of Kudzu-leaf Flavonoids supplement on the growth performances of yellow-feathered chicken.
Items | CON | KLF | AGP | SEM | P-value |
Growing Phase | BWG(g) | 484.1 | 483.1 | 466.5 | 14.57 | 0.505 |
ADFI(g) | 27.90 | 26.90 | 27.10 | 0.35 | 0.165 |
ADG(g) | 17.29 | 17.25 | 16.66 | 0.25 | 0.127 |
FCR | 1.86 | 1.93 | 2.02 | 0.02 | 0.059 |
Finishing Phase | BWG(g) | 1084.1 | 1160.8 | 1120.7 | 18.18 | 0.269 |
ADFI(g) | 50.97 | 47.72 | 45.40 | 1.11 | 0.526 |
ADG(g) | 38.72 | 41.46 | 40.03 | 0.54 | 0.205 |
FCR | 2.45 | 2.16 | 2.27 | 0.08 | 0.052 |
Whole Phase | BWG(g) | 1568.2 | 1643.9 | 1587.2 | 32.75 | 0.301 |
ADFI(g) | 78.87 | 74.62 | 72.50 | 1.46 | 0.421 |
ADG(g) | 28.00 | 29.36 | 28.34 | 0.37 | 0.264 |
FCR | 2.82 | 2.54 | 2.56 | 0.12 | 0.043 |
FI = feed intake, BWG = body weight gain, FCR = feed conversion ratio; CON = control treatment; KLF = kudzu-leaf flavonoids supplement treatment, and AGP = the antibiotic supplement (Aureomycin) treatments
Immune organs were collected and weighed at the end of feeding stage, and the immune organ indexes were calculated, subsequently. Based on the results shown in Table 3, no differences were found of all immune organ indexes in both growing and finishing phases.
Table 3
Effects of Kudzu-leaf Flavonoids supplement on the growth performances of yellow-feathered chicken.
Items | CON | KLF | AGP | SEM | P-value |
Growing Phase | Liver(g) | 10.99 | 11.15 | 10.85 | 0.219 | 0.866 |
Spleen (%) | 0.13 | 0.14 | 0.12 | 0.015 | 0.273 |
Bursa of Fabricius (%) | 0.25 | 0.2 | 0.23 | 0.042 | 0.213 |
Thymus (%) | 0.5 | 0.51 | 0.47 | 0.021 | 0.381 |
Finishing Phase | Liver(g) | 16.48 | 17.29 | 18.58 | 0.35 | 0.169 |
Spleen (%) | 0.14 | 0.12 | 0.14 | 0.015 | 0.736 |
Bursa of Fabricius (%) | 0.19 | 0.15 | 0.16 | 0.021 | 0.789 |
Thymus (%) | 0.39 | 0.36 | 0.39 | 0.027 | 0.846 |
FI = feed intake, BWG = body weight gain, FCR = feed conversion ratio; CON = control treatment; KLF = kudzu-leaf flavonoids supplement treatment, and AGP = the antibiotic supplement (Aureomycin) treatments
Effects of Kudzu-leaf Flavonoids Supplement on Gastrointestinal Bacteria community
Relative abundances and potential function analysis on cecal bacteria of each samples in different treatments were conducted based on the taxonomy results of all samples, and these results are shown in additional file 1. To simply state, 19 phyla and more than 250 genera were identified in the present study, and all these bacteria were used for further diversity analysis.
α-diversity. Alpha diversity was first applied in analyzing the internal complexity of species diversity of each treatment, and these results are shown in Table 4. In general, bacterial species in CON and KLF treatments showed a higher complexity than that in AGP, which indicated the anti-microbial functions of anti-biotics. Particularly, Shannon index performed a significant decrease in AGP treatment than those in CON and KLF treatments (P < 0.05). No changes were found between CON and KLF (P > 0.05). Besides, ACE, Chao1, and Observed species indexes showed the highest scores in KLF treatment than the other two treatments, though not significantly.
Table 4
Effects of Kudzu-leaf flavonoids supplement on α- diversity of cecal contents bacterial communities
Items | CON | KLF | AGP | SEM | P-value |
Shannon | 5.88a | 5.81a | 5.30b | 0.09 | 0.005 |
Simpson | 0.93 | 0.94 | 0.92 | 0.001 | 0.074 |
Ace | 877.1 | 912.7 | 742.7 | 37.7 | 0.152 |
Chao1 | 875.7 | 920.6 | 753.5 | 37.0 | 0.164 |
Observed_species | 742.5 | 752.3 | 613.7 | 30.6 | 0.114 |
a,b means within a row with different letters differed significantly (P < 0.05); SEM = standard error of the mean, CON = control treatment; KLF = kudzu-leaf flavonoids supplement treatment, and AGP = the antibiotic supplement (Aureomycin) treatments
β-diversity. Differential analyses on cecal bacteria of each treatment were subsequently applied and presented through PCoA. As shown in Fig. 2, PCoA axes 1 and 2 accounted for 49.91% and 26.38% of the total variation, respectively. Based on the results, bacteria communities in KLF, AGP and CON treatments could be clearly separated from each other by PCo1 and PCo2.
Differential analysis on the relative abundances of cecal bacteria at the phyla and genera levels were performed to investigate the effects of KLF supplement on gastrointestinal micro-ecosystem. Results are shown in Table 5 and Table 6, respectively. Among all phyla, Bacteroidetes, Firmicutes, and Proteobacteria accounted for the most 3 abundant phyla, which contributed to more than 95% of the total microbiota, and Bacteroidetes represented the dominant community. Relative abundance of Bacteroidetes in both CON and KLF, were significantly increased than that in AGP (P < 0.05). Besides, Proteobacteria showed a significantly proliferation after KLF supplement when compared with CON (P < 0.05). Whereas, the abundance was also significantly lower than in AGP treatment (P < 0.05). No significant changes were found on the other phyla among CON, KLF, and AGP treatments.
Table 5
Effects of kudzu-leaf flavonoids supplement on the relative abundances of cecal microbiota at the level of phyla
Phyla | CON | KLF | AGP | SEM | P-Value |
Bacteroidetes | 14.97a | 14.84a | 14.57b | 0.05 | 0.017 |
Firmicutes | 14.09 | 14.24 | 14.19 | 0.04 | 0.281 |
Proteobacteria | 11.09c | 11.76b | 12.46a | 0.13 | 0.001 |
Tenericutes | 7.44 | 7.02 | 6.95 | 0.16 | 0.253 |
Actinobacteria | 7.66 | 7.40 | 8.37 | 0.27 | 0.222 |
Elusimicrobia | 6.40 | 6.61 | 6.45 | 0.32 | 0.478 |
Synergistetes | 7.26 | 7.17 | 8.11 | 0.15 | 0.098 |
Verrucomicrobia | 5.83 | 5.59 | 6.00 | 0.21 | 0.871 |
others | 7.46 | 8.58 | 7.98 | 0.26 | 0.214 |
Sequences relative abundances were transformed using log2. a,b means within rows and with different letters differed significantly (P < 0.05); SEM = standard error of the mean, CON = control treatment; KLF = kudzu-leaf flavonoids supplement treatment, and AGP = the antibiotic supplement (Aureomycin) treatments
Table 6
Effects of kudzu-leaf flavonoids supplement on the relative abundances of cecal microbiota at the level of genera
Genera | CON | KLF | AGP | SEM | P-value |
Bacteroides | 14.27 | 14.11 | 14.00 | 0.06 | 0.179 |
Campylobacter | 9.50a | 5.52b | 4.74b | 0.66 | 0.001 |
Bifidobacterium | 4.74 | 6.13 | 4.30 | 0.34 | 0.066 |
Butyricimonas | 8.56 | 8.80 | 8.26 | 0.14 | 0.330 |
Coprococcus | 8.65 | 8.63 | 8.22 | 0.11 | 0.204 |
Clostridium | 5.08 | 5.55 | 4.74 | 0.20 | 0.278 |
Faecalibacterium | 10.36 | 9.46 | 10.24 | 0.22 | 0.203 |
Helicobacter | 6.75b | 9.42a | 10.30a | 0.49 | 0.002 |
Lactobacillus | 7.42 | 7.45 | 6.50 | 0.19 | 0.053 |
Megamonas | 8.31 | 9.76 | 7.46 | 0.47 | 0.127 |
Methanobrevibacter | 5.45 | 6.38 | 4.91 | 0.68 | 0.696 |
Oscillospira | 11.02 | 10.85 | 10.54 | 0.10 | 0.127 |
Parabacteroides | 10.88 | 12.00 | 11.65 | 0.23 | 0.120 |
Phascolarctobacterium | 9.37b | 9.59b | 11.05a | 0.23 | 0.001 |
Ruminococcus | 11.18 | 10.90 | 11.11 | 0.09 | 0.402 |
Sutterella | 10.16 | 9.62 | 10.30 | 0.13 | 0.068 |
Streptococcus | 3.51a | 4.34a | 2.47b | 0.27 | 0.008 |
others | 13.35 | 12.96 | 13.14 | 0.07 | 0.057 |
Sequences relative abundances were transformed using log2. a,b means within rows and with different letters differed significantly (P < 0.05); SEM = standard error of the mean, CON = control treatment; KLF = kudzu-leaf flavonoids supplement treatment, and AGP = the antibiotic supplement (Aureomycin) treatments
At the genera level, Bacteroides, Ruminococcus, Oscillospira, Faecalibacterium, and Parabacteroides accounted for the most 5 abundant genera in all the treatments. Compared with CON, KLF supplement significantly increased the abundance of Campylobacter, while significantly decreased Helicobacter(P < 0.05). Furtherly, KLF also showed a significant suppressing effect on Phascolarctobacterium, and a significant promoting effect on Streptococcus when compared with AGP(P < 0.05). No other significant changes were detected among other genera for the three treatments. Particularly, probiotics such as Bifidobacterium, Streptococcus, and Lactobacillus showed the highest abundance after KLF supplement compared with the other two treatments, which might give an evidential support for the antibiotic alternative functions of KLF.
Correlation analysis between production performances, immune organs indexes and bacteria communities
Correlation analysis between broiler production performance, immune organs and the most abundant bacteria communities were finally applied for investigating the effects of cecal bacteria on productions. Based on the results shown in Fig. 3, bacteria gathered into two big clusters. One was positively correlated with production performances while negatively correlated with immune organs, which included Bifidobacterium, Butyricimonas, Lactobacillus, and Streptococcus. The other cluster included ruminococcus, Sutterella, Faecalibacterium, and Phascolarctobacterium, which showed an inverse correlation with production performances and immune organs. To detailed state, Helicobacter was positively correlated with liver weight, while negatively correlated with ADFI, FCR and bursa of Fabrieius; Campylobacter showed an inverse correlation compared with Helicobacter, which was positively correlated ADFI, bursa of Fabrieius and FCR, and negative correlated with BWG, and liver weight. Phascolarctobacterium performed a negative correlation with ADFI, and a positive correlation with Liver. Sutterella was negatively correlated with ADG, while positively correlated with spleen. Particularly, probiotics including Bifidobacterium, Lactobacillus and Streptococcus showed positively correlated with ADG, while negatively correlated with immune organ indexes.