Gut microbiota in different age groups. Among the fecal samples of 56 pandas, there were 4,066,208 optimized 16S rRNA gene sequences, involving 1,736,583,084 optimized bp. A mean ± SD of 72610.86 ± 11,679.57 sequences (range: 42,970–93,193) was obtained per sample.
The Good’s estimates for the 56 samples were > 98% (Figure S1), suggesting that > 98% of the diversity estimated in the samples was recovered. The results of the species-accumulation curves indicate that the number of OTUs leveled off as the sample size increased and the sample size was adequate (Figure S2). There were 1,140 OTUs (97% similarity threshold) with the minimum number of sample sequences. The mean number of OTUs per sample was 105 (range: 35–759). A total of 375 OTUs were found in the cub group, 304 in the juvenile group, 347 in the adult group, and 343 in the geriatric group. However, there were 759 OTUs in sample C4 in the cub group, which was much higher than in the other 55 samples. Therefore, in the subsequent analysis, C4 was removed. Rarefaction curves of Shannon index values indicated that the bacterial diversity of each sample was fully measured at the sequencing depth used (Figure S3). Rank abundance curves (indicating species richness and evenness), which tend to be horizontal if there is high evenness, indicated that the sequencing depth was sufficient to represent the gut microbiota diversity in the four age groups (Figure S4).
The OTUs were divided into 27 phyla and 374 genera. At the phylum level (Table S2), Firmicutes and Proteobacteria were dominant, accounting for 65.45 ± 30.21% (0.91–99.62%) and 31.49 ± 27.99% (0.26–85.35%) of the total sequences, respectively. The subdominant phyla were Bacteroidetes (1.59 ± 6.91%; 0–46.43%), Cyanobacteria (0.79 ± 1.84%; 0–12.8%), and Actinobacteria (0.63 ± 1.71%; 0–8.49%). At the genus level (Table S3), the top two were Streptococcus (47.43 ± 40.03%; 0.02–47.43%) and Escherichia-Shigella (27.22 ± 26.86%; 0.03–27.22%). Other genera with a relative abundance > 1% were Lactobacillus (7.98 ± 16.73%; 0–7.98%), Clostridium_sensu_stricto_1 (4.2 ± 7.88%; 0–4.2%), Pseudomonas (1.49 ± 8.04%; 0–1.49%), Turicibacter (1.27 ± 3.39%; 0–1.27%), and Megasphaera (1.26 ± 3.8%; 0–1.26%).
There were differences in gut microbiota composition among the four age groups (Table S4). The cubs had the most OTUs (383) and the juveniles had the fewest OTUs (337) (Figs. 1A and 1B). The cubs’ and juveniles’ dominant phyla were Proteobacteria (cubs: 51.83 ± 22.6%; juveniles: 51.83 ± 22.6% ) and Firmicutes (cubs: 42.51 ± 24.82%; juveniles: 42.51 ± 24.82%). The cubs’ dominant genera were Escherichia-Shigella (46.8 ± 25.4%) and Lactobacillus (24.8 ± 22.4%). The juveniles’ dominant genera were Streptococcus (63.2 ± 30%) and Escherichia-Shigella (18.8 ± 18%). The adults had 381 OTUs; the dominant phyla were Firmicutes (85.32 ± 22.26%) and Proteobacteria (12.6 ± 21.78%) and the dominant genera were Streptococcus (79.1 ± 25.4%) and Escherichia-Shigella (11.5 ± 21.5%). The geriatrics had 386 OTUs; the dominant phyla were Firmicutes (71.53 ± 29.11%) and Proteobacteria (27.37 ± 29.07%) and the dominant genera were Streptococcus (62.7 ± 34.1%) and Escherichia-Shigella (24.2 ± 27.1%).
At the phylum level(Table S5), the differences in the relative abundance of Firmicutes, Cyanobacteria, Proteobacteria, and Actinobacteria among the four groups were extremely significant (P ≤ 0.001) (Figs. 1C–F). The relative abundances of Firmicutes and Cyanobacteria were highest in the adults, while the relative abundances of Proteobacteria and Actinobacteria were highest in the cubs. At the genus level(Table S6), the relative abundance of Streptococcus was highest in the adults, while Escherichia-Shigella and Lactobacillus were highest in the cubs (Figs. 1G–I).
The alpha diversity indexes differed among the age groups(Table S7). The Shannon index was significantly higher in the cubs (indicating low diversity) than the others (P ≤ 0.01), while the adults had the lowest value (Figs. 1J–N). The Simpson index was significantly higher in the adults (indicating high diversity) than the others (P ≤ 0.01), while the cubs had the lowest value. The ACE index and Chao 1 index were also significantly different among the age groups (P ≤ 0.01), with the adults having the highest and the cubs having the lowest values.
Core gut microbiota composition in different age groups. The graph showing the pan analysis curves (Figure S5) exhibited an upward trend for each age group, but the graph showing the core analysis curves exhibited a horizontal line for each age group (between 10 to 20). This indicates that increasing the sample size may increase the total number of OTUs, but not the number of core OTUs. The number of OTUs shared by all pandas in each group was 124 (Fig. 2A). These core OTUs which are shared by all giant pandas belonged to 4 phyla and 7 genera, mainly including Firmicutes (61.96%), Proteobacteria (35.99%), Streptococcus (47.58%), and Escherichia-Shigella (31.48%) (Figs. 2B and 2C).
The numbers of OTUs (relative abundance ≥ 1%) unique to all pandas in the cub, juvenile, adult, and geriatric groups were 117, 44, 52, and 61, respectively. In the cubs, the unique OTUs belonged to 12 phyla and 13 genera, mainly including Firmicutes (66.18%), Actinobacteria (25%), Bacteroidetes (8.82%), Megasphaera (39.71%), and Lactobacillus (16.18%) (Figs. 2D and 2H). In the juveniles, the unique OTUs belonged to 6 phyla and 11 genera, mainly including Bacteroidetes (41.67%), Firmicutes (25%), Parabacteroides (16.67%), and norank_f_Saprospiraceae (8.33%) (Figs. 2E and 2I). In the adults, the unique OTUs belonged to 10 phyla and 19 genera, mainly including Firmicutes (53.66%), Bacteroidetes (19.5%), Ezakiella (15.61%), and Dialister (13.66%) (Figs. 2F and 2J). In the geriatrics, the unique OTUs belonged to 11 phyla and 7 genera, mainly including Parabacteroides (28.57%), Firmicutes (21.43%), Alteromonas (14.29%), and Shuttleworthia (14.29%) (Figs. 2G and 2K).
Age-related differences in gut microbiota structure. The heatmaps of the gut microbiota in the four age groups indicated that the cubs significantly differed from other groups. The juveniles and geriatrics were clustered together. The significant differences in the relative abundances of the gut microbiota phylum- and genus-level communities were consistent with the overall findings shown in the heatmaps (Figs. 3A and 3B).
Using LEfSe analysis, we were able to identify the gut bacteria that significantly (LDA > 4.0 and p ≤ 0.05) characterized the four groups (Figs. 3C and 3D). Based on the LDA scores and cladogram assay, the gut bacteria that significantly characterized the cubs were Proteobacteria at the phylum level, Gammaproteobacteria, Negativicutes, and Sphingobacteriia at the class level, Enterobacteriales, Selenomonadales, and Sphingobacteriales at the order level, Enterobacteriaceae, Veillonellaceae, and Lactobacillaceae at the family level, and Escherichia-Shigella, Megasphaera, Sarcina, and Lactobacillus at the genus level (LDA > 4.0 and p ≤ 0.05). The corresponding taxa for juveniles were Pseudomonadaceae (family), and Pseudomonas (genus) (LDA > 4.0 and p ≤ 0.05). The corresponding taxa for adults were Firmicutes (phylum), Bacilli (class), Lactobacillales (order), Streptococcaceae (family), and Streptococcus (genus) (LDA > 4.0 and p ≤ 0.05). The corresponding taxon for geriatrics was vadinBC27_wastewater_sludge_group (genus belonging to the phylum Bacteroidetes) (LDA > 4.0 and p ≤ 0.05).
The NMDS analysis indicated that the juveniles, adults, and geriatrics partially overlapped, while the cubs were completely separated from the other groups (R = 0.4504, P = 0.001) (Fig. 3E). ANOSIM showed that the inter-group differences were greater than the intra-group differences (P = 0.4504, R = 0.001). Adonis (PERMANOVA) showed that the different grouping factors could explain the differences among samples to a high degree and with reliability (R2 = 0.05, P = 0.04). Partial least squares discriminant analysis (PLS-DA) showed that comp1 and comp2 could explain the results with a weight ratio of 21.5%. The cubs and adults were separated into two subgroups by comp1, while the juveniles and the other three groups were distinguished by comp2 (Fig. 3F). PERMANOVA also explained the relationship between the age groups (R = 0.43398, P = 0.001).
Prediction of gut microbial functions in the different age groups. The gut microbiota played important roles in amino acid transport and metabolism, carbohydrate transport and metabolism, translation, ribosomal structure, and biogenesis (Fig. 4A). One-way ANOVA analysis indicated that the results of KEGG functional enrichment analysis of gut microbes in giant pandas differed among age groups, especially for Human diseases, Cellular processes, Genetic information processing, and Environmental information processing was highly significant (P ≤ 0.001)(Fig. 4B). The heatmaps of the Enzyme in the four age groups indicated that the cubs significantly differed from other groups (Fig. 4C). To assess gut microbial functions, all quantified microbial proteins were annotated using the COG database. Among the 10 functions with the highest relative abundance, the differences were highly significant (P ≤ 0.001) for Function unknown, Carbohydrate transport and metabolism, Translation, ribosomal structure and biogenesis, Inorganic ion transport and metabolism, Cell wall/membrane/envelope biogenesis, Energy production and conversion, Nucleotide transport and metabolism in four age groups, and significant (P ≤ 0.01) for Carbohydrate transport and metabolism (Fig. 4D). Functional analysis of four groups of giant panda gut microbiome was performed with the MetaCyc metabolic pathway database, and the results showed that among the top ten metabolic pathways in terms of enrichment abundance, pyruvate fermentation to isobutanol (engineered), sucrose degradation III (sucrose invertase), peptidoglycan maturation (meso-diaminopimelate containing) and pentose phosphate pathway (non-oxidative branch) were highly significant (P ≤ 0.001), while CDP-diacylglycerol biosynthesis I, CDP-diacylglycerol biosynthesis II, superpathway of pyrimidine nucleobases salvage, adenosine deoxyribonucleotides de novo biosynthesis II, guanosine deoxyribonucleotides de novo biosynthesis II, and acetylene degradation were significant (0.01 ≤ P < 0.001) (Fig. 4E).