Seasonal dynamics, dietary patterns, and bamboo leaf nutrition shape the phyllosphere-associated gut microbiota of red pandas

Abstract

Background

The gut microbiota of bamboo-eating red pandas (Ailurus fulgens) comprises a intricate and multifaceted ecosystem influenced by numerous factors. Despite considerable research dedicated to captive red pandas, the microbial dynamics observed in wild populations are still not well understood. To address this research gap, our study employed advanced techniques such as high-throughput sequencing and metagenomic analysis to characterize the microbial communities and their functional profiles in fresh fecal samples from wild red pandas and in samples of their primary food source. Our objective was to conduct a thorough examination of how seasonality, diet, bamboo leaf nutrition, and phyllosphere-associated microorganisms affect the gut microbiota of red pandas.

Results

Our findings reveal that seasonal variations have a notable impact on the composition, structure, and functionalities of red pandas’ gut microbiota. Specifically, autumn and winter exhibit heightened microbial diversity and richness. Moreover, during different feeding phases (leaf-feeding, shoot-feeding, and mixed-feeding), the gut microbiota displays varied cellulose-digesting abilities, marked by increased expression of key enzymes during high-fiber dietary phases. Our analysis reveals robust correlations between bamboo nutrients and microbial communities in both bamboo and red panda guts. Notably, bamboo’s crude protein and phosphorus content are pivotal in shaping the phyllosphere and gut microbial communities, while crude fat, crude protein, and phosphorus emerge as key drivers of microbial structure. Seasonal fluctuations in microbial populations of both bamboo and red panda guts with shared genera, underscore their tight linkage and interconnected seasonal adaptations.

Conclusions

In conclusion, our study provides a comprehensive understanding of how seasonality, diet, and bamboo leaf nutrition shape the gut microbiota of red panda connected to bamboo microbiome. It underscores the gut microbes’ indispensable role in facilitating red pandas’ adaptation to their bamboo-based diet, crucial for their survival in natural habitats.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-025-00474-0.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-025-00474-0.

Introduction

Animals harbor a diverse array of microorganisms, predominantly in their guts, where complex symbiotic relationships underpin essential biological functions, including immunity, metabolism, and resilience to disease [1–3]. Among these, the gut microbiota not only fosters intestinal development [4] and strengthens host defenses [5–7] but also plays a pivotal role in processing dietary components [8, 9]. Over evolutionary timescales, hosts have developed gut microbiota compositions that reflect and support their characteristic diets. Herbivores, omnivores, and carnivores each harbor gut microbiota tailored to their nutritional niches, from cellulose-degrading consortia in herbivores to fat-specialized and protein-specialized microbes in carnivores [10–12].

For many herbivorous and omnivorous species, fresh plant tissues—particularly those comprising the phyllosphere (the above-ground surfaces of plants)—are a major food source. These tissues carry their own complex microbial communities, which can influence, and be influenced by, the consumer’s gut microbiota [13, 14]. Phyllosphere microbes may contribute beneficial taxa, facilitate digestion, or shape microbial succession in the gut, but they can also act as vectors for pathogens, including Salmonella enterica and Escherichia coli, implicated in disease outbreaks [15, 16]. Seasonal changes in plant availability and microbial load further modulate this interaction, as shown in giant pandas, where shifts in bamboo phenology drive changes in gut microbiota composition, diversity, and host physiology [11, 17–19]. Given the dual nutritional and microbial inputs from phyllosphere tissues, understanding how these plant-associated microbial communities interact with animal gut microbiota is critical for elucidating diet–microbiome–health relationships. This perspective can provide a more integrated view of host ecology, pathogen risk, and the evolutionary dynamics of diet–microbe coadaptation [20].

The red panda (Ailurus fulgens), despite its carnivorous classification, has a predominantly herbivorous diet focused on bamboo [21]. Because they lack enzymes for cellulose/hemicellulose digestion, they rely heavily on gut microbiota to process their high-fiber bamboo diet [22–25]. Bamboo plays a vital role in the red panda’s environment, with its nutritional components and associated microbiota changing across seasons [26–28]. However, little is known about the relationship between bamboo nutrition, phyllosphere microorganisms, and the gut microbiota of red pandas. To address this knowledge gap, our study examined seasonal variations in red panda gut microbiota through monthly fecal and bamboo leaf sampling from their Sichuan habitat over a year. Additionally, we explored the complicated relationship between red pandas’ diet, bamboo leaf nutrition, leaf-associated microbes, and gut microbiota. By analyzing these interactions, we aimed to gain deeper insights into the ecological and evolutionary strategies enabling red pandas to thrive on their specialized bamboo-based diet.

Materials and methods

Study area

The Meigu Dafengding National Nature Reserve, situated in northeastern Meigu County, Sichuan Province, China, spans 50,700 hectares across latitudes 28°32′N to 28°50′N and longitudes 102°58′E to 103°20′E, with elevations from 1,444 to 4,012 m. As a forest ecosystem reserve, it prioritizes the conservation of rare species, such as giant pandas and red pandas. and their habitats. Bamboo, which thrives between 1,356 and 3,770 m, is a vital component, with Yushania ailuropodina prevalent, covering 16,988 hectares in Dafengding and Weikeluo, sustaining red pandas all year round. The local climate divides the year into spring (March–May), summer (June–August), autumn (September–November), and winter (December–February).

Sample collection

From September 2021 to August 2022, monthly collections of fresh wild red panda fecal samples (≥ 50 g each, at least 10 per month) were conducted in Meigu Dafengding National Nature Reserve, Sichuan, China. Samples were stored in sterile 100 mL centrifuge tubes and collected using sterile gloves, with soil contamination removed.

To assess the diet of the red pandas from which fecal samples were collected, each fecal sample was placed in a thermostatic drying oven at 105 °C and dried to a constant weight. The dry weight of the fecal sample (in grams, to three decimal places) was recorded as the total weight. The dried material was then separated into components such as bamboo leaves, bamboo shoots, berries, and hair, and each component was weighed individually. The proportion of each component relative to the total weight was calculated, allowing us to analyze annual variations in the diet of red pandas. Three phases of foraging were identified: leaf-feeding (excluding June and October), shoot-feeding (June, focused on Yushania ailuropodina shoots), and mixed-feeding (October, incorporating berries and leaves). Simultaneously, ≥ 200 g bamboo leaf samples were collected near fecal sites using sterile gloves and stored at − 20 °C in the research station before being transported on dry ice to the laboratory for − 80 °C storage. Fecal samples were subjected to genomic DNA extraction within 24 h, while bamboo leaves were subjected to fungal residue removal, triple ultrapure water washes, 70 °C drying, grinding into powder, and storage in labeled sealed bags for nutritional analysis.

A total of 123 fecal samples from wild red pandas were collected opportunistically in the field, without the possibility of individual identification; among them, 101 fresh samples (excluding those from May due to insufficient microbial DNA) were used for microbial analysis. In parallel, 101 bamboo leaf samples corresponding to these fecal samples were also collected for microbial analysis.

Fecal DNA extraction

Microbial DNA from red panda fecal samples was extracted using the QIAGEN Stool DNA Kit, with a modification: Samples were ground for 1 min (40 Hz) instead of vortexing for thorough homogenization. In the last step, 100 µl of buffer ATE was directly pipetted onto the QIAamp membrane, otherwise adhering to the manufacturer’s protocol.

Nutrient analysis of bamboo leaves

Crude protein (CP) was analyzed by the Kjeldahl method, crude fiber (CF) through acid-alkali digestion, crude fat (EE) by Soxhlet extraction [11], calcium (Ca) levels via flame atomic absorption spectroscopy, and phosphorus (P) content using the molybdenum blue spectrophotometric method [29].

Leaf-associated microbial DNA extraction

For the extraction of microbial DNA associated with bamboo leaves, a previously established protocol was followed [30, 31]. Briefly, microbial biomass was enriched from bamboo leaves, and total genomic DNA of the microbial community was extracted using the OMEGA Stool DNA Kit (Omega Bio-tek, Norcross, GA, USA) according to the manufacturer’s instructions.

Amplification and sequencing of 16 S rRNA from leaf-associated microbiota and red panda gut microbes

DNA amplification and sequencing were performed by Shanghai Majorbio Bio-Pharm Technology Co., Ltd., using primers 27 F and 533R to target the V1-V3 region of the 16 S rRNA gene [30]. The amplification protocol included initial denaturation, followed by 27 cycles of denaturation, annealing, and extension, and a final extension. The samples were replicated thrice. PCR products were pooled, obtained via gel electrophoresis, purified using the AxyPrep Kit, quality-verified by gel electrophoresis, and quantified using the Quantus™ Fluorometer. Sequencing was conducted on the Illumina Miseq PE300 platform [31].

Metagenomic sequencing

Seventeen fecal samples (four to five per season) were randomly selected for metagenomic sequencing to investigate seasonal and dietary variations in the gut microbial composition and functions of red pandas. DNA previously extracted from the fecal microbiota was used to construct sequencing libraries using the NEXTFLEX Rapid DNA-Seq Kit (Bioo Scientific, USA), and sequencing was performed on the Illumina NovaSeq platform (Illumina, USA) to generate raw reads. Quality control of the sequencing data was conducted with fastp, and high-quality reads were assembled into contigs using MEGAHIT [32]. Open reading frames (ORFs) were predicted from the contigs using MetaGene [33], and genes with lengths ≥ 100 bp were translated into amino acid sequences to generate a gene prediction table. The predicted gene sequences from all samples were clustered using CD-HIT (v4.6.1) at 90% identity and 90% coverage to establish a non-redundant gene set, for which nucleotide sequences were identified. High-quality reads were then aligned to this non-redundant gene set using SOAPaligner (v2.21) at 95% identity to quantify gene abundance [27]. For taxonomic annotation, the non-redundant gene set was compared against the NCBI NR database using DIAMOND (v0.8.35), and for functional annotation, genes were compared against the KEGG database via DIAMOND [34]. In addition, hmmscan was used to analyze the gene set against the CAZy database (blastp; E-value ≤ 1e − 5) to annotate carbohydrate-active enzymes [29].

Data analysis

Raw sequencing data by amplicon sequencing were filtered to exclude host, chloroplast, and mitochondrial sequences, yielding operational taxonomic units (OTUs). Sequences were standardized to the minimum count for effective analysis. UPARSE (v11.0) was employed to group sequences into OTUs with 97% similarity [35]. The RDP classifier using the Bayesian algorithm and Silva 16 S rRNA database assigned taxonomic identities to OTUs representatives. Gut microbial composition across seasons and diets was evaluated at the phylum and genus levels. Alpha diversity (Sobs and Shannon indices) was analyzed using Mothur (v1.30.2) [36], visualized in GraphPad Prism (v8.0) boxplots. Statistical significance was assessed using one-way ANOVA for normal data and the Kruskal–Wallis H test for non-normal data, comparing microbial variations across seasons and diets. UniFrac distances (unweighted/weighted) were calculated using statistical comparisons reflecting the above approach. NMDS based on Bray–Curtis distances visualized microbiota differences [11]. LefSe identified significant compositional changes in gut microbiota among seasons. The unifrac distance matrix of microbial communities (bamboo leaf microbiota and red panda gut microbiota) and bamboo leaf nutrition components were constructed and analyzed using db-RDA to investigate the relationship between microbial community structure and bamboo leaf nutrition.

For metagenomic data, We assessed the correlations between gut microbiota’s KEGG Level 2, CAZy relative abundances, and bamboo leaf nutrition, by applying Pearson or Spearman correlation analysis based on the data distribution. Finally, LEfSe identified seasonal biomarker features in KEGG Level 2 and CAZy abundances [37, 38].

Co-occurrence network analysis was conducted using Gephi software (Version 0.9.2) to establish co-occurrence patterns of gut microbiota in the pandas and microbiota surrounding bamboo leaves during different seasons. A Spearman correlation coefficient of |r| ≥ 0.8, with a P < 0.05, was deemed statistically significant, indicating robust correlations between microbial taxa [28, 39].

Results

Seasonality affects gut microbes of red pandas

Proteobacteria and Bacteroidota were the dominant phyla in red pandas’ gut microbes in all four seasons (Fig. 1A; Supplementary Table 1). In particular, Bacteroidota was significantly more dominant in spring (42.5%) than in the other seasons (8.5%, 12.2%, and 20.6% in autumn, winter, and summer, respectively). Proteobacteria dominated in autumn (63.2%), winter (55.2%), and summer (61.6%) (Fig. 1A; Supplementary Table 1). At the genus level, Pseudomonas and Pedobacter were predominantly dominant across all seasons. Pedobacter was significantly more dominant in spring (26.7%) than in the other seasons (1.1%, 1.9%, and 5.1% in autumn, winter, and summer, respectively). Pseudomonas dominated in autumn (31.9%), winter (29.6%), and summer (27.5%) (Fig. 1B; Supplementary Table 2). Significant differences in gut bacterial richness (Sobs index) and diversity (Shannon index) were observed among the four seasons (Fig. 1C and D). Both indices were significantly higher in autumn and winter than in spring and summer (P < 0.05, ANOVA) (Fig. 1C and D).

Fig. 1.

The gut microbes of red pandas among different seasons. A: Relative abundance of gut microbes phyla; B: Relative abundance of gut microbes genus; C: Sobs index of gut microbes; D: Shannon index of gut microbes; E: NMDS of gut microbes at the OTU level based on Bray-Curtis distance; F: LEfSe analysis of gut microbes. LDA ≥ 4.5. (*<0.05; **<0.01; ***<0.001)

NMDS analysis based on the Bray-Curtis distance showed that the spring and winter samples were grouped according to season. In contrast, the summer and autumn samples were more dispersed (Fig. 1E). Further LEfSe analysis revealed significant differences in red pandas’ gut microbes across seasons (P < 0.05; Fig. 1F).

To compare red pandas’ gut microbe functions, we sequenced 17 samples (four to five samples each season) for whole-genome shotgun sequencing. After quality control, 768,232,618 sequences were obtained from the original 786,700,514 sequences. Sequence splicing and assembly resulted in 88,246,563 contigs, with an average contig N50 of 723 bp. Compared to other seasons, the relative abundance of global and overview maps (P < 0.05), cardiovascular diseases (P < 0.05), and endocrine and metabolic diseases (P < 0.005) in the autumn had a significantly higher impact (Fig. 2A). In winter, the higher influencing functions were cell growth and death (P < 0.05), aging (P < 0.05), and signaling molecules and interaction (P < 0.05) (Fig. 2A). In spring, carbohydrate metabolism (P < 0.05), lipid metabolism (P < 0.05), biosynthesis of other secondary metabolites (P < 0.05), and immune disease (P < 0.05) had a significantly higher influence (Fig. 2A). In summer, the metabolism of cofactors and vitamins (P < 0.05) and infectious diseases (P < 0.05) were also significantly higher (Fig. 2A).

Fig. 2.

Gut microbe functions (A: KEGG Level 2 categories; B: CAZyme families) of gut microbes by LEfSe analysis. GT, glycosyl transferases; GH, glycoside hydrolases; CE, carbohydrate esterases; CBM, Carbohydrate-Binding Modules; PL, Polysaccharide Lyases; AA, auxiliary activities. LDA ≥ 3

The relative abundance of GT9 (P < 0.05), GH5_8 (P < 0.005), and CT9 (P < 0.05) were significantly higher in autumn, with a greater influence than in other seasons (Fig. 2B). GH2, GH31, GH89, GT27, and CBM48 were more abundant in winter. In spring, the glycoside hydrolase family (GH42, GH78, GH3, GH35, etc.), GT2, GT76, AA7, PL21, and PL29 were significantly more abundant than in other seasons (P < 0.05). Glycoside hydrolase family (GH23, GH103, GH19, etc.), glycosyltransferase family (GT30, GT107), carbohydrate esterase family (CE1, CE3, CE11), AA10, and PL17 had a significantly higher relative abundance in summer (P < 0.05) (Fig. 2B).

Diet pattern affects gut microbes of red pandas

Proteobacteria were the dominant phylum during the leaf-feeding, shoot-feeding, and mixed periods (Fig. 3A). Bacteroidota and Firmicutes were dominant during the leaf-feeding period, Firmicutes were dominant during the shoot-feeding period, and Campilobacterota and Bacteroidota were dominant during the mixed period (Fig. 3A). At the genus level, Pseudomonas was dominant during the leaf-feeding period, followed by Pedobacter (Fig. 3B). Acinetobacter, Streptococcus, and Escherichia-Shigella were dominant during the shoot-feeding period. During the mixed period, Helicobacter was the dominant genus, followed by Pseudomonas (Fig. 3B). The Sobs index was highest in the mixed period and lowest in the shoot-feeding period (P < 0.01; Fig. 3C). The Shannon index was highest in the leaf-feeding period and lowest in the mixed-feeding period (Fig. 3D).

Fig. 3.

The gut microbes of red pandas among different diets. A: Relative abundance of gut microbes phyla; B: Relative abundance of gut microbes genus; C: Sobs index of gut microbes; D: Shannon index of gut microbes; E: NMDS of red pandas gut microbes at the OTU level based on Bray-Curtis distance; F: LEfSe analysis of red pandas gut microbes. LDA ≥ 4.5;

Based on the Bray-Curtis distance analysis, the leaf-feeding period, the shoot-feeding period and the mixed period samples were clustered together respectively according to their diet pattern (Fig. 3E). LEfSe analyses revealed significant differences in the gut microbial taxa of different food compositions (Fig. 3F).

Cellulose is broken down into glucose by the combined action of cellulase (EC 3.2.1.4) and β-glucosidase (EC 3.2.1.21) (Fig. 4A). Xylan, the primary component of hemicellulose, is degraded to pentose or glucuronide by xylan 1,4-β-xylosidase (EC 3.2.1.37) (Fig. 4A). The expression of the three carbohydrate-active enzymes (cellulase, β-glucosidase, and 1,4-β-xylosidase) was significantly lower in the shoot-feeding period compared to the leaf-feeding and mixed periods (Fig. 4B, C, D). The expression of cellulase and 1,4-β-xylosidase was highest in the leaf-feeding period, followed by the mixed period (Fig. 4B, D). However, β-glucosidase expression was highest in the mixed period, followed by the leaf-feeding period (Fig. 4C).

Fig. 4.

Pathways involved in (A) starch and sucrose metabolism, and amino sugar and nucleotide sugar metabolism; Expression of (B) cellulase, (C) β-glucosidase, and (D) xylan 1,4-β-xylosidase among leaf feeding, during shoot feeding and during mixed feeding periods (*<0.05; ***<0.001)

Leaf nutrition affects bamboo phyllosphere microbe

Bamboo leaf samples collected over all four seasons revealed that crude fiber (CF) had the highest percentage of nutrient content, ranging from 27.53% ± 1.97% to 28.63% ± 1.37%, followed by crude protein (CP), crude fat (EE), calcium (Ca), and phosphorus (P), respectively (Supplementary Table 3). Significant seasonal variations (P < 0.001, Kruskal–Wallis H test) were observed in the nutrient content of bamboo leaves. In particular, the CP content was significantly higher in autumn (Z = − 3.235, P < 0.01), spring (Z = − 3.836, P < 0.001), and summer (Z = 6.202, P < 0.001) than in winter. The CF content also varied significantly across seasons (P < 0.05, Kruskal-Wallis H test), with the highest content in autumn (Z = − 3.547, P < 0.05) and lowest in spring (Z = − 2.547, P < 0.05). Furthermore, seasonal differences in P content were evident (P < 0.001, Kruskal-Wallis H test), with the highest P content found in winter, which was significantly higher than in spring (Z = − 3.093, P < 0.01) and summer (Z = − 4.306, P < 0.001) (Supplementary Table 3).

Spearman correlation analysis was conducted to investigate the relationship between nutrient composition and phyllosphere microbes in bamboo. CP and P were significantly correlated with a variety of microbes, indicating that they had the greatest influence on phyllosphere microbes (Fig. 5A, B), suggesting that nutrient dynamics can plays a crucial role in shaping the microbial community structure and potentially impact the overall health and functioning of the bamboo ecosystem. The impact of bamboo leaf nutrition on the microbial structure within the phyllosphere was further investigated using db-RDA analysis, considering seasonal variations. The study found that the community structure was primarily affected by CP and EE, as shown by the redundancy analysis of unweighted unifrac distances. CP had a positive correlation with phyllosphere microbes in winter and summer and a negative correlation with phyllosphere microbes in autumn and spring. Similarly, EE had a positive correlation with phyllosphere microbes in autumn and winter and a negative correlation with phyllosphere microbes in spring and summer (Fig. 5C). Redundancy analysis based on weighted unifrac distances indicated that the community structure was primarily influenced by EE and P (Fig. 5D). EE and P were negatively correlated with summer phyllosphere microbes and positively correlated with phyllosphere microbes in the other seasons (spring, autumn, and winter) (Fig. 5D).

Fig. 5.

Leaf nutrition affects bamboo phyllosphere microbes. Correlation heatmap showed the relative abundance of phyllosphere microbes of phylum (A) and genus (B) with leaf nutrient; The db-RDA analysis based on Unweighted-unifrac distances (C) and weighted-unifrac distances (D) of leaf nutrient and phyllosphere microbes in different season. CP: crude protein; CF: crude fibre; P: phosphorus; EE: crude fat; Ca: calcium (*<0.05; **<0.01; ***<0.001)

Bamboo leaf nutrition affects red pandas’ gut microbes

Spearman correlation analysis was used to examine the relationship between bamboo leaf nutrients and red pandas’ gut microbes. CP and P were significantly correlated with a variety of microbes, indicating that they had the greatest influence on gut microbes (Fig. 6A, B). The impact of bamboo leaf nutrition on gut microbial structure during seasonal changes was explored using additional db-RDA analysis. The results indicated that community structure was primarily influenced by P and EE, as demonstrated by redundancy analysis with unweighted unifrac distance (Fig. 6C). P exhibited a positive correlation with gut microbes in autumn and winter but a negative correlation in spring and summer. EE displayed a similar pattern to P (Fig. 6C). Redundancy analysis based on weighted unifrac distances indicated that the community structure was primarily influenced by EE and CP (Fig. 6D). EE exhibited a positive correlation with gut microbes in both autumn and spring. In contrast, CP exhibited a positive correlation with gut microbes in autumn and winter and a negative correlation with gut microbes in spring (Fig. 6D).

Fig. 6.

Bamboo leaf nutrition affects gut microbes of red pandas. Correlation heatmap showed the relative abundance of gut microbes phylum (A) and genus (B) of red pandas with leaf nutrient; The db-RDA analysis based on Unweighted-unifrac distances (C) and weighted-unifrac distances (D) of leaf nutrient and gut microbes of red pandas in different seasons; Correlation heatmap showed the relative abundance of gut microbes functions (E: KEGG Level 1 categories; F: CAZyme families) of red panda with leaf nutrient. CP: crude protein; CF: crude fibre; P: phosphorus; EE: crude fat; Ca: calcium. GT, glycosyl transferases; CE, carbohydrate esterases; GH, glycoside hydrolases; AA, auxiliary activities. (*<0.05; **<0.01; ***<0.001)

To assess the influence of bamboo leaf nutrition on red panda gut microbial function, we correlated nutritional components with functional gene sets. Our analysis indicated that CP had a significant negative correlation with amino acid metabolism, biosynthesis of secondary metabolites, and carbohydrate metabolism at the KEGG Level 2 categories (Fig. 6E). P also showed a significant negative correlation with amino acid metabolism, prokaryotic cellular communities, and antimicrobial drug resistance. At the CAZy level, CP exhibited a significant negative correlation with multiple glycosyltransferase families, including GT2, GT4, and CE1 (Fig. 6F). Ether extract (EE) showed a significant negative correlation with AA7, while cellulose (CF) demonstrated a significant positive correlation with GT41 (Fig. 6F). These findings highlight the key roles of bamboo leaf nutrients in shaping the functional characteristics of red panda gut microbiota.

Bamboo phyllosphere microbes affect red pandas’ gut microbes

Our analysis identified 30, 22, 11, and 14 common genera (> 0.1%) between bamboo phyllosphere microbes and gut microbes in autumn, winter, spring, and summer, respectively (Supplementary Table 4). These included genera such as 1174-901-12, Pseudomonas, and Sphingomonas, which were in varying proportions in both the bamboo phyllosphere and red pandas’ gut microbes (Supplementary Table 4). To further investigate the interaction pattern between these two microbial communities, we constructed co-occurrence network models at the genus level, focusing on the nine major genera of bamboo phyllosphere microbes and the 10 major genera of red pandas’ gut microbes. Our results revealed 3–10 significant associations between these microbial communities in autumn, winter, and spring (Fig. 7). For example, Pseudomonas in red pandas’ gut was significantly correlated with Hymenobacter in the bamboo phyllosphere. In contrast, Acidiphilium in the bamboo phyllosphere was correlated significantly with Pseudomonas, Flavobacterium, and Sphingobacterium in the red pandas’ gut (Fig. 7A). Notably, in summer, only one significant association was observed between these microbial communities. Acinetobacter in red pandas’ gut was significantly correlated with 1174-901-12 in the bamboo phyllosphere.

Fig. 7.

Co-occurrence network analysis at genus level showing the bamboo phyllosphere microbes of bamboo interactions with gut microbes of red panda in different seasons. A: autumn; B: winter; C: spring; D: summer. (Green lines represents the interaction between phyllosphere microbes; Red lines represents the interaction between gut microbes; Gray lines represents the interaction between the phyllosphere microbes and the gut microbes)

Discussion

Seasonal changes exert a significant influence on red pandas’ gut microbiota, as seen at phylum (Fig. 1A) and genus (Fig. 1B) taxonomic levels. Notably, Firmicutes exhibit peak abundance in winter, whereas Bacteroidetes demonstrate reduced prevalence in autumn and winter, contrasting with their increased presence in spring and summer. Firmicutes play a crucial role in modulating energy storage genes and enable the host to thrive in cold environments by efficiently using dietary heat and gradually converting it into fat reserves [4, 40]. Conversely, Bacteroidetes specialize in carbohydrate degradation, resulting in diminished fat accumulation and a lower risk of obesity in the host [40, 41]. The increased Firmicutes-to-Bacteroidetes ratio observed in red pandas during colder seasons highlights the adaptive capabilities of gut microbes to increase the host’s resilience to extreme environmental challenges [42, 43].

The gut microbial richness and diversity of wild red pandas are elevated in autumn and winter, contrasting with their lower levels in spring and summer. Notably, during shoot-feeding, these indices significantly decrease compared to leaf-feeding. In stark contrast, giant pandas display higher gut microbial abundance in spring and summer than in autumn [44], and their diversity markedly increases during shoot-feeding over leaf-feeding [11]. These observations underscore the impact of seasonal dietary shifts on the gut microbiota of both panda species. Despite their shared herbivorous nature within the Carnivora order, the gut microbial patterns of red and giant pandas diverge, exhibiting contrasting trends in richness and diversity according to seasonal and dietary changes. Further exploration is vital to unravel this intriguing contrast.

NMDS analysis of red panda gut microbiota reveals seasonal clustering in spring and winter, indicating similarity in microbial communities during these periods. Conversely, summer and autumn exhibit greater variability. Dietary analysis further segregates communities into shoot-feeding, leaf-feeding, and mixed-feeding clusters, with minimal variation during shoot-feeding. The convergence of spring and winter microbial structures is attributed to exclusive bamboo leaf consumption, whereas the inclusion of shoots and berries in summer and autumn leads to a diversification of microbial communities. Notably, diet emerges as a dominant factor influencing gut microbiota, surpassing seasonal effects observed in red pandas, which is similar with the giant pandas [11].

At KEGG Level 2, autumn microbial communities display elevated pathways linked to human diseases, yet spring exhibits enhanced carbohydrate metabolism and secondary metabolite biosynthesis, potentially mitigating these adverse effects [27, 28]. Robust xenobiotic degradation and metabolism capabilities enhance nutrient absorption [45, 46]. Winter microbial communities exhibit increased pathways related to cell growth, death, aging, and signal transduction, signifying heightened cellular vitality and proliferative potential [27].

The LEfSe analysis of CAZy families underscores the pivotal role of carbohydrate metabolism in sustaining gut microbial survival [47–49]. Glycoside hydrolases, notably diverse enzymes, facilitate complex carbohydrate breakdown, which is critical for nutrient extraction from low-quality bamboo leaves consumed by red pandas [50, 51]. The differential expression of cellulase, β-glucosidase, and 1,4-β-xylosidase reflects the gut microbes’ adaptability to varying feeding stages. During leaf-feeding, elevated expression of cellulase and 1,4-β-xylosidase enhances polysaccharide degradation, releasing glucose and pentose for energy [22]. In the mixed-feeding phase, β-glucosidase modulates to further process cellobiose, optimizing energy utilization [22]. Conversely, shoot-feeding sees a decline in these enzymes’ expression, potentially due to altered polysaccharide composition or microbial metabolic adjustments to the host’s nutritional requirements [52]. This enzyme regulation reflects the gut microbial adaptation observed in giant pandas, emphasizing the functional convergence of microbial communities among bamboo-feeding panda species due to their shared diet [22, 53].

Our study confirmed the stability of bamboo’s fat content across seasons, aligning with previous findings by Jin et al. [54]. Notably, a positive correlation between crude fat and blastocladiomycota suggests that this fungal class may indirectly support plant growth through its involvement in ecological processes [55]. Analyzing phyllosphere microbial communities in bamboo, we uncovered seasonal variations in community structure, which were significantly associated with higher P levels in autumn leaves compared to summer leaves. This finding echoes previous observations linking leaf P concentrations to bacterial community shifts in neotropical forests [56, 57]. P is a key element in several important physiological activities in leaves, including photosynthesis, energy metabolism, and signaling [58].

The study identified a robust correlation between bamboo leaf nutrients and gut microbiota in red pandas. These nutrients are essential substrates, driving microbial growth and reproduction and ultimately shaping the gut microbial landscape [59]. Furthermore, microbial metabolism of these nutrients generates beneficial metabolites and signaling molecules that enhance red pandas’ physiology and health [60]. Notably, CP and P emerged as pivotal factors governing the abundance of the phyllosphere and gut microbiota, while EE, CP, and P jointly dictate their community structure. EE reserves are vital for red pandas, particularly during dietary challenges and life transitions, offering crucial calories, vitamins, and fatty acids [61, 62]. In giant pandas, seasonal nutrient fluctuations similarly regulate gut microbial dynamics [11], reinforcing the parallel shifts observed in red pandas. Huang et al. highlighted the role of gut microbes in optimizing bamboo nutrient uptake for giant pandas [19], underscoring their significance in red panda nutrition.

Phyllosphere microbes exert a profound influence on gut microbiota, modulating chemical composition of food and subsequently impacting host’s gut microbes, fitness, and behavior [23]. Seasonal analysis reveals shared microbial genera between bamboo phyllosphere and red panda gut microbiota, with their prevalence fluctuating throughout the year. These shared genera demonstrate remarkable adaptability and resilience across diverse environments.

The co-occurrence network analysis underscores the intricate interplay between the bamboo phyllosphere and red panda gut microbes, indicating potential interdependence or mutual population dynamics. Notably, this network exhibits pronounced seasonal shifts, reflecting the dynamic nature of microbial communities in response to varying bamboo leaf nutrition, environmental factors, and red panda dietary preferences.

Minimal interactions are observed in summer with a solitary Acinetobacter genus in red panda guts, significantly correlated to a specific strain in the bamboo phyllosphere. This finding aligns with red pandas’ dietary shift toward bamboo shoots, minimizing direct exposure to phyllosphere microbes. This seasonal variation underscores the importance of environmental cues in shaping microbial community structures and functions, revealing intricate mechanisms of microbial adaptation and resilience.

Conclusion

Our study comprehensively examines the intricate interplay between seasonality, diet, bamboo nutrient content, phyllosphere microbes, and the gut microbiota of red pandas. We reveal that seasonal changes and dietary shifts significantly impact the composition, structure, and functionality of red pandas’ gut microbes. Specifically, autumn and winter, as well as the leaf-feeding period, exhibit heightened microbial diversity and richness. Distinct microbial clusters correlate with distinct dietary patterns, with notable variations in cellulose-digesting enzyme expression across feeding stages. Notably, bamboo’s CP and P content are pivotal in shaping the phyllosphere and gut microbial communities, while EE, CP, and P emerge as key drivers of microbial structure. The substantial overlapping of microbial genera between the bamboo phyllosphere and red panda guts underscores their interconnectedness, with clear seasonal dynamics observed. Our study highlights the crucial role of microbial communities’ adaptation to seasonal and dietary changes in red panda gut associated with bamboo phyllosphere microbes. The results of this study can provide guidance for the wild release of captive species. Specifically, the nutrient content and phyllosphere microbes of bamboo in the wild training area are tested to select suitable areas, helping the gut microbiota of captive species to approach the wild population, and some strains can help the host adapt to diet in the wild after being released in captivity. This will further help protect and restore the endangered red panda population in its natural environment.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Liwen Kang, Yi Li: Conceptualization, Methodology, Investigation, Software, Resources, Formal analysis, Writing–original draft. Jinghui Fu, Qiuxian Li, Qingsong Jiang, Huaqiang Zhou, Hangshu Xiao, Zejun Zhang: Investigation, Validation, Data curation. Mingsheng Hong: Conceptualization, Supervision, Funding acquisition, Writing–original draft, review & editing.

Funding

This study was supported by the National Natural Science Foundation of China (grant no. 32470516 and 31900337), Sichuan Natural Science Foundation (grant no. 2024NSFSC2082), the Giant Pandas International Cooperation Foundation of State Forestry Administration (2023), and the Innovation Team Funds of China West Normal University (grant no. KCXTD2022-7). We also thank the anonymous reviewers for their constructive comments.

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA1162457.

Declarations

Competing interests

The authors declare no competing interests.