Environmental factors influencing the composition of phyllosphere bacterial communities in bamboo: A staple food source of giant pandas

doi:10.21203/rs.3.rs-652611/v1

Download PDF

Research Article

Environmental factors influencing the composition of phyllosphere bacterial communities in bamboo: A staple food source of giant pandas

https://doi.org/10.21203/rs.3.rs-652611/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

The giant panda has developed a series of evolutionary strategies to adapt to a bamboo diet. The abundance and diversity of the phyllosphere microbiome change dramatically depending on the season, host species, location etc., which may, in turn, affect the growth and health of host plants. However, few studies have investigated the factors that influence phyllosphere bacteria in bamboo, a staple food source of the giant panda.

Methods

Amplicon sequencing of the 16S rRNA gene of rRNA genomic loci was used to explore the abundance and diversity of phyllosphere bacteria in three bamboo species (Arundinaria spanostachya, Yushania lineolate and Fargesia ferax) over different seasons (spring vs. autumn), elevation, distance from water, etc. in Liziping National Nature Reserve (Liziping NR), China.

Results

The results show that a total of 2,562 operational taxonomic units (OTUs) were obtained from all 101 samples, which belonged to 24 phyla and 608 genera. Proteobacteria was the dominant phyla, followed by Acidobacteria and Actinobacteria. The Sobs index and Shannon index of F. ferax phyllosphere bacteria were greater than that of the other two bamboo species in both seasons. The Sobs index and Shannon index of phyllosphere bacteria in all three bamboo species in autumn were significantly higher than in spring. Season was a stronger driver of community structure of phyllosphere bacteria than host bamboo species based on the (un)weighted UniFrac distance matrix. Many bacteria phyla were negatively correlated with elevation and distance from water, but positively related to mean height of bamboo and mean base diameter of bamboo. Function prediction of PICRUSt revealed the relative abundance of transporters function was highest in all three bamboo species, followed by ABC transporters. There were nine relative abundance pathways with significant differences in the 3-level KEGG pathway. The genes related to membrane transport, signal transduction and porphyrin transport in phyllosphere bacteria of F. ferax were significantly lower than in the other two species.

Conclusions

The composition, diversity and community structure of phyllosphere bacteria in bamboo, a staple food source of giant pandas, were primarily affected by the season, species, altitude, tree layer and shrub layer. The better the growth of bamboo forests, the richer the bacterial phyla in the bamboo phyllosphere. Our study presented a deeper understanding of factors influencing the bacterial community in the bamboo phyllosphere. These findings could provide a reference for the restoration and management of giant panda habitat and food resources in this area, especially for those small isolated populations of giant pandas in Xiaoxiangling mountains.

Forestry

Bacteriology

giant panda

phyllosphere bacteria

influencing factors

microbial diversity

function prediction

bamboo

Large numbers of different kinds of microorganisms within the phyllosphere constitute a stable and complex micro-ecosystem (Mei et al. 1991; Li et al. 1998; Shi et al. 2007; Vorholt et al. 2012). Different hosts have different microbial communities (Yadav et al. 2008; Leveau et al. 2015) and may include bacteria, archaea, fungi and protists (Newton et al., 2010). Bacteria is the most abundant microorganism in the phyllosphere (Lindow et al., 2003) and Proteobacteria are the dominant phylum (Delmotte et al. 2009).

Numerous environmental factors determine phyllosphere community composition, such as plant species, temperature, humidity, nutrient availability (on the plant surface), sun/UV exposure levels, and even the underlying soil geochemistry (Lindow et al. 2003; Leveau et al. 2015). Ecological factors and plant attributes e.g., wood density, leaf mass per unit area, and leaf nitrogen and phosphorus concentration, may also affect the microbial community structure in the phyllosphere (Steven et al. 2014).

The phyllosphere microbial community represents a wide range of primitive symbiotic relationships (Fedorov et al. 2011; Partida et al. 2011). Phyllosphere microbiota can affect plant yield (e.g., via nutrients or hormones) (Gourion et al. 2006; Reed et al. 2010) and plant health in a variety of ways (Balint et al. 2010; Innerebner et al. 2011). For example, Beauveria fungus on rice leaf can protect rice enzyme activity and is an environmentally friendly microbe (Du et al. 2014). Some phyllosphere microbes are beneficial to plants via nitrogen fixation (Leveau et al. 2015), growth promotion (Bringel et al. 2015), acting as barriers against pathogen invasion, and decomposition of residual pesticides (Venkatachalam et al. 2016).

Bamboo is a perennial evergreen plant belonging to the Gramineae family and Bambusoideae subfamily and is an important forest resource all over the world. It has the characteristics of wide-distributing, fast-growing and high-yield, and strong regeneration ability. Therefore, it has considerable economic, ecological and social benefits (Shanmughavel et al. 1997; Anu et al. 2008; Chang et al. 2015; Zhang and Xue, 2018). Bamboo leaves are an important habitat for many microorganisms, with microbial abundance and diversity acting as an indicator of forest health. However, few studies have investigated the environmental factors that influence phyllosphere bacterial composition in bamboo. Using the traditional cultivation method, Zhang and colleagues (2014) found that the composition of the phyllosphere microbial community differed between bamboo species and seasons. The species composition and frequency of leaf endophytic bacteria and fungi have also been found to differ between bamboo species (Helander et al. 2013), while endophytic fungi from fish-scale bamboo (Phyllostachys heteroclada) differs between the branch and leaf tissue (Zhou et al. 2017). Significant differences in bacterial richness and diversity were also observed between different bamboo species using high throughput amplicon sequencing (Jin et al. 2020). However, there is a lack of research on what factors influence the composition and diversity in the bamboo phyllosphere microbiome.

The giant panda (Ailuropoda melanoleuc) belongs to a carnivorous clade (Wei et al. 1999), yet has an exclusively herbivorous diet and specialises in the consumption of bamboo leaves throughout the year (Hu et al. 1985; Zhao et al. 2013). In the long evolutionary process, the giant panda developed a series of foraging strategies to adapt to a bamboo diet, such as seasonal vertical migration, selection of habitat and feeding point (Hong et al. 2015, 2016). However, in the different seasons of different mountain systems, the diet of giant pandas does vary. For example, in the Qinling mountains, giant pandas mainly feed on bamboo leaves during the non-bamboo shoot seasons (Pan, 2001; Wu et al. 2017), while bamboo leaves of the Xiaoxiangling mountains account for more than half of the panda faeces in summer and autumn (Wei et al. 1999). Although bamboo is an important food source, its leaves also contribute to intestinal diseases of giant pandas in captivity. Previous research shows that Escherichia coli and Klebsiella pneumoniae could cause diarrhoea and septicaemia for giant pandas (Zhang et al. 1997; Xiong et al. 1999). However, there are few studies into the phyllosphere microbiome in the bamboos pecies foraged by wild giant pandas.

In this study, we investigate the phyllosphere bacterial community of bamboo species frequently used as a food source by giant pandas, using Next Generation Sequencing Technology (NGS) in Liziping National Nature Reserve (Liziping NR), Sichuan, China. The specific goals include: (i) comparing differences in microbial composition and diversity between three bamboo species and two seasons; (ii) exploring the ecological factors that influence the phyllosphere bacteria community changes; and (iii) predicting the functional differences of phyllosphere bacterial communities in different bamboo species.

Study area

Liziping NR is located in Shimian County, Sichuan Province, China, and is in the middle and upper reaches of the Dadu River, on the southwestern edge of the Sichuan Basin and southeast of Gongga Mountain (E102°10'33″-E102°29'07″, N28°51'02″ -N29°08'42″) (Hong et al., 2015). The reserve covers an area of 47940 km² and ranges from 1330 m to 4550 m above sea level, with uneven ridges and narrow valleys. The annual average temperature and rainfall are 11.7–14.4°C and 800–1250 mm respectively (Xie et al., 2020). As the altitude increases, the vegetation in the reserve transitions from evergreen broad-leaved forest to deciduous broad-leaved forest, then coniferous and broad-leaved mixed forest, coniferous forest to alpine thickets and alpine rocky beech. According to the Fourth National Survey of giant pandas, there were 22 giant pandas in the reserve in 2015 (Sichuan Forestry Bureau. 2015). To strengthen the small isolated populations of giant pandas, the Chinese government initiated the Captive Giant Panda Release Program (CGPRP). To date, one giant panda rescued from the wild and nine giant pandas from captivity have been released into this nature reserve (Hong et al., 2012). There are seven bamboo species (three genera) consumed by the giant panda within the Xiaoxiangling Mountains. The species with the largest distribution is Arundinaria spanostachya, which accounts for 38.08% of the total area of giant panda feeding bamboos in these mountains. Next is Yushania lineolate and Fargesia ferax, which account for 28.02% and 12.48% of the giant panda's feeding bamboo respectively (Sichuan Forestry Bureau. 2015). A. spanostachya mainly grows above 2500 m a.s.l, while Y. lineolate and F. ferax occur below 2800 m a.s.l. Giant pandas in this nature reserve prefer to eat A. spanostachya throughout the whole year, some Y. lineolate in winter, and occasionally F. ferax (Hong et al., 2015, 2016; Xie et al., 2020).

Experimental design

We conducted surveys and sampling during May (Spring) and October (Autumn) in 2020. First, we set up four transects in A. spanostachya, Y. lineolate and F. ferax bamboo forests respectively. The transects were set from low altitude to high altitude, and the distance between them was no less than 200 m. Secondly, we set up 3–5 survey plots (20×20 m²) in each transect, with the altitude distance of adjacent survey plots on the same transect no less than 50 m. We then recorded and measured bamboo species, latitude and longitude, altitude, and other related variables in the tree and shrub layer (Table S1). Moreover, one bamboo plot (1×1 m²) was set up in the centre of each survey plot, and another two bamboo plots (1×1 m²) were set up east and south, 5 m from the centre point of each survey plot. Finally, the related variables within the bamboo layer were also measured and recorded (Table S1).

Sample collection and DNA extraction

In each survey plot, one mixed bamboo leaf sample (not less than 200g) was collected with sterile gloves, immediately transported to the laboratory (less than 2 hours) and stored at -20°C for further processing within 48 h. Each 200g sample was aseptically transferred into a Ziplock bag (24 cm × 35 cm) containing 200 ml sterile precooled TE-buffer (10 mM Tris, 1 mM EDTA, pH 7.5) supplemented with 0.05% Tween-80 (Hong et al. 2017). Leaf surfaces were washed to collect the microbial population by 5 min of shaking, vortexing and sonication of each sample in the TE-buffer, with the Ziplock bag kept in ice water (~ 4°C) for each processing step (Hong et al. 2017). The cell suspension was separated from the leaf material by filtration through three-layer sterile nylon mesh. Sonication was performed at a frequency of 40 kHz in an ultrasonic cleaning bath (Shanghai Kudos Instrument Co. Shanghai, China) to dislodge the microbes from the leaf surface. Following filtration, cell suspensions were placed in four 50 ml tubes/sample, and cells were pelleted using centrifugation at 2000×g for 15 min at 4°C. Cell pellets from multiple tubes were pooled into 2.0-ml reaction tubes and washed twice with TE-buffer with Tween-80. Cell pellets were immediately frozen at − 80°C until DNA extraction.

DNA extraction was performed using the E.Z.N.A.™ Soil DNA Kit (Omega, Norcross, GA) as described with slight modifications. Frozen cell pellets were resuspended in 1 ml of kit-supplied SLX Mlus buffer with 500 mg of glass beads, and cell lysis was performed at 65 Hz for 90 s. The cell debris suspension was immediately processed following the instructions in the kit manual. Finally, total DNA was obtained from the column by two sequential elutions with 50 µl elution buffer.

16S rRNA gene V3-V4 amplification, quantification and sequencing

Sequencing of the V3-V4 hypervariable region of the 16S rRNA gene was performed for bacterial identification (Caporaso et al. 2011; Lundberg et al. 2013). Thirty-five cycles of polymerase chain reaction (PCR) amplification of the target marker genes were performed. Error-correcting 12-bp barcoded primers specific to each sample were used to permit multiplexing of samples. Polymerase chain reaction products from all samples were quantified using the PicoGreen dsDNA assay and pooled together in equimolar concentrations. Each library was submitted to Mega Biotech on the Illumina MiSeq PE300 platform. The raw reads were deposited in the NCBI Sequence Read Archive (SRA) database with accession number PRJNA718425.

Sequence data analysis

The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by fastp version 0.20.0 (Chen et al.,2018) and merged by FLASH version 1.2.7 (Magoč et al.,2011) with the following criteria: (i) the 300 bp reads were truncated at any site receiving an average quality score of < 20 over a 50 bp sliding window, and the truncated reads shorter than 50 bp were discarded. Reads containing ambiguous characters were also discarded; (ii) only overlapping sequences longer than 10 bp were assembled according to their overlapped sequence. The maximum mismatch ratio of the overlap region was 0.2. Reads that could not be assembled were discarded; (iii) samples were distinguished according to the barcode and primers. The sequence direction was adjusted using exact barcode matching with two nucleotide mismatch in primer matching.

Operational taxonomic units (OTUs) with 97% similarity cut-off (Edgar,2013; Stackebrandt et al.,1994) were clustered using UPARSE version 7.1 and chimeric sequences were identified and removed. The taxonomy of each OTU representative sequence was analysed by RDP Classifier version 2.2 (Wang et al., 2007) against the 16S rRNA database (e.g., Silva v138) using a confidence threshold of 0.7. Non-target sequences, including mitochondrial and chloroplast sequences, were also removed by QIIME from the final OTU data set. To better convey the biological information in these samples, the average relative abundance of the bacterial community was visualized by bar at the level of phylum and genus.

Statistical analysis

First, Kruskal-Wallis H tests and Wilcoxon rank-sum tests were used to evaluate the differences between dominant bacteria abundance at both phylum and genus levels, for each combination of bamboo species and season. We used mothur software (version v.1.30.1) to calculate the Sobs and Shannon indices for each sample, to estimate the species abundance and diversity of phyllosphere bacteria between the different bamboo species and seasons and used a Student's t-test to identify any significant differences. We then used PCoA based on the weighted UniFrac and unweighted UniFrac method distance matrix to evaluate the differences in the microbial community structure between the bamboo species and seasons. We also used the permutational MANOVA (PERMANOVA) to analyse the effect of different bamboo species and seasons on the phyllosphere bacterial community, based on Bray-Curtis distance matrices with a substitution test to analyse the statistical significance of the division.

Second, Spearman’s correlation heatmap analysis was performed to examine the relationship between the relative abundance of bacterial taxa and the environmental factors described in Table S1. Linear regression was used to evaluate the relationship between environmental factors and the results of either Alpha (Sobs and Shannon indices) or Beta diversity analysis (Bray-Curtis distance). The Mantel test was used to test the correlation between the UniFrac distance matrix and the environmental variable distance matrix.

Finally, the PICRUSt prediction was used to predict the functional composition of all phyllosphere bacteria in the different bamboo species. The greengene id corresponding to each OTU, the COG and KEGG functions of the OTU were annoted to obtain the function level of COG and KEGG, and the abundance information for each function in different samples.

Samples, sequences and OTUs between different seasons and bamboo species

Up to 19 samples of each bamboo species were collected in each study season (Table 1). After removing the mitochondrial and chloroplast sequences, a total of 1,233,917 effective target 16S rRNA reads were obtained. The average sequence of each sample was 12,217 ± 3,037. Clustered by 97% similarity, all samples had a total of 2,564 OTUs. 1378 OTUs were shared between the three bamboo species, and the bacterial OTUs of each in autumn were significantly higher than in spring (Fig. 1 and S1). Fargesia ferax had a higher number of OTUs than the other two species in both seasons (Fig. 1a).

Table 1

Statistics table of each sample and sequence (AS: *A. spanostachya*; YL: *Y. lineolate*; FF: *F. ferax*) (Mean ± SD).
Season	Spring			Autumn
Banboo specie	AS	YL	FF	AS	YL	FF
Samples	17	17	16	19	16	16
Total number of sequences	41,426 ± 5,012	45,313 ± 5,317	43,209 ± 4,450	53,715 ± 4,620	51,920 ± 2,923	52,921 ± 6,892
Total number of effective sequences	23,149 ± 2,291	24,619 ± 3,70125	20,013 ± 3,572	24,799 ± 1,453	25,286 ± 797	24,098 ± 2,170

The influence of season and bamboo species on phyllosphere bacterial composition

After the clustered OTU representative sequence was annotated for species, all bacterial OTUs belonged to 24 phyla and 614 genera. The most dominant phylum was Proteobacteria, comprising ~ 70% of the bacterial diversity observed across all samples (Fig. 2a). The remaining bacteria were distributed among the phyla Acidobacteria, Bacteroidota, Actinobacteria, Planctomycetota, Myxococcota and others. In spring, the relative abundance of phyla Bacteroidota, Actinobacteriota and Myxococcota in the F. ferax phyllosphere were significantly higher than that of A. spanostachya and Y. lineolate. However, the relative abundance of phylum Acidobacteriota in the F. ferax phyllosphere was significantly lower than that of A. spanostachya and Y. lineolate (Fig. 2a; Table S2). In autumn, the relative abundance of phyla Actinobacteriota and Myxococcota in the F. ferax phyllosphere were significantly higher than that of A. spanostachya and Y. lineolate. The relative abundance of phyla Bacteroidota in Y. lineolate and Planctomycetota in A. spanostachya phyllospheres were significantly lower than that of the other two bamboo species (Fig. 2a; Table S2). Not difference was found in the relative abundance of dominant phyla in the F. ferax phyllosphere between seasons, except Planctomycetota (Table S2). The relative abundance of phylum Acidobacteriota in the A. spanostachya and Y. lineolate phyllospheres were lower in autumn than spring, and the relative abundance of phyla Bacteroidota, Actinobacteriota and Myxococcota all increased from spring to autumn (Table S2).

The relative abundances of genera 1174-901-12, Acidiphilium, Sphingomonas, unclassified_f__Acetobacteraceae, Terriglobus, Granulicella, Pseudomonas and Hymenobacter revealed them to be the dominant bacteria in the phyllosphere of all three bamboo species, and the differences among the top eight total abundances of genera were calculated. In spring, the relative abundance of 1174-901-12, Acidiphilium and Terriglobus were highest in the Y. lineolate phyllosphere. The relative abundance of Hymenobacter and Sphingomonas were highest in the F. ferax phyllosphere, but the opposite pattern was observed for Granulicella (Fig. 2b; Table S2). In autumn, the lowest relative abundance of Acidiphilium and Granulicella existed in the F. ferax phyllosphere, and that of Hymenobacter and Sphingomonas existed in the Y. lineolate phyllosphere (Fig. 2b; Table S2). For all three bamboo species, similar relative abundance patterns were found for the seven most dominant bacteria genus in each phyllosphere. The relative abundance of 1174-901-12, Acidiphilium, Hymenobacter, Granulicella and Terriglobus decreased from spring to autumn but increased for Sphingomonas and Pseudomonas (Fig. 2b; Table S2).

The influence of season and bamboo species on phyllosphere bacterial diversity. The Sobs and Shannon indices for the bacterial community in the F. ferax phyllosphere were higher than that of A. spanostachya and Y. lineolate in both seasons (Fig. 3; Table S3). However, in spring the Sobs and Shannon indices in Y. lineolate was lower than that of F. ferax and A. spanostachya. The phyllosphere bacterial community of A. spanostachya showed the lowest Sobs and Shannon indices in autumn (Fig. 3; Table S3). These indices were also found to increase significantly from spring to autumn in all bamboo species (Fig. 3).

The results of the PCoA revealed that samples were clustered by species and season. Samples of A. spanostachya and Y. lineolate were also clustered together away from that of F. ferax in the same season (Fig. 4). Principle Coordinate Analyses based on unweighted UniFrac differentiated better the samples based on weighted UniFrac (Fig. 4). The PERMANOVA revealed significant differences in phyllosphere bacterial community based on Bray-Curtis distance matrixes between the three bamboo species in spring and autumn (Table S4).

The influence of ecological factors on bamboo phyllosphere bacterial community

The results of Mantel analysis revealed that elevation had the greatest impact on the phyllosphere bacterial community (R = 0.313, P = 0.001). And elevation; distance from water; trees height; trees diameter at breast height; shrubs coverage; shrubs numbers; mean height of bamboo and mean base diameter of bamboo also had significant impacts on the bacterial community (Table S5).

Spearman’s correlation heatmap analysis revealed that almost all bacterial phyla abundance were positively correlated with the base diameter of bamboo except Proteobacteria and Actinobacteria, and almost all of the bacterial phyla abundance were negatively correlated with elevation except Proteobacteria, Acidobacteria and WPS-2 (Fig. 5a). Moreover, distance from water was negatively correlated with abundance of Abditibacteriota, Actinobacteria, Bacteroidota and Deinococcota, but positively correlated with Acidobacteria and WPS-2. Abundance of Bdellovibrionota was positively correlated with tree height, trees diameter at breast height, shrub coverage and the number of shrubs. Mean height of bamboo was positively correlated with Chloroflexi, Fusobacteriota, Myxococcota, SAR324_cladeMarine_group_B, unclassified_k_norank_d_Bacteria and Verrucomicrobiota, but negatively correlated with WPS-2. Trees diameter at breast height was negatively correlated with unclassified_k_norank_d_Bacteria and positively correlated with WPS-2. At the genus level, 1174-901-12 and Acidiphilium were both negatively correlated with tree height, trees diameter at breast height, shrub coverage and shrubs number (Fig. 5b). A positive correlation was observed between Sphingomonas and shrubs number, shrubs number, tree height and mean base diameter of bamboo. Granulicella showed significant correlations with elevation and water source distance, but negative correlations with mean base diameter of bamboo (Fig. 5b).

Further linear regression found that elevation, bamboo deaths, mean base diameter of bamboo, bamboo coverage and tree height had a significant impact on the Sobs and Shannon indices (Table S6). Elevation had the highest explanation for the Sobs index, the highest explanation ecological factor for the Shannon index, and Bray-Curtis distance was death bamboo number (Table S6). Elevation, death bamboo number, mean base diameter of bamboo, bamboo coverage and tree height, shrub coverage and shrubs number all had significant relationships with bacterial community structure in bamboo phyllosphere based on Bray-Curtis distance matrixes (Table S6).

PICRUSt gene function estimation

All three bamboo phyllosphere bacterial communities had similar COG function classification patterns as generated by PICRUSt in both spring and autumn (Fig. S2). There were higher relative abundance sequences related to cell wall/membrane/envelope biogenesis, amino acid transport and metabolism. Interestingly, the relative abundance of transporter function (4.17%) was the highest in all three bamboo species, followed by ABC transporters (2.55%), DNA repair and recombination proteins (2.37%) and two-component systems (2.23%). Prediction software PICRUSt enriched 22 categorizable dominant pathways (relative abundance > 1%) in the level 3 KEGG pathway (Fig. 6; Table S7). Among them, nine pathways had significant differences between bamboo species (P < 0.05). It is worth noting that there were significant differences in the relative abundance of the bacterial secretion system, the secretion system and the two-component system. Oxidative phosphorylation in energy metabolism, porphyrin and chlorophyll metabolism were also different, and the gene relative abundance for these functions for F. ferax was significantly lower than that of the other two species (Fig. 6; Table S7).

This study revealed that the bacterial OTU richness of the three bamboo species in autumn was significantly higher than that in spring. The Phyllosphere of F. ferax had a greater diversity of bacterial OTUs than that of A. spanostachya and Y. lineolate. The dominant phyllosphere bacteria of the three bamboo species are Proteobacteria, Acidobacteria, Bacteroides and Actinomycetes. In a warm and humid climate, the diversity and richness of phyllosphere bacteria in the three bamboo species in spring were significantly higher than in autumn. The overall importance of seasonality to the structure and composition of the phyllosphere microbial community has been confirmed by many studies (Thompson et al., 1993; Copeland et al., 2015). Proteobacteria, Acidobacteria, Bacteroides and Actinomycetes are often detected in a variety of forests, indicating that these organisms have a wide ecological range and an ability to adapt to many environments (Isabelle et al., 2016; Feng et al., 2019). In this study, the relative abundance of the Proteobacteria in the three bamboo species were all above 60%, and the differences between the bamboo species were not significant, indicating that Proteobacteria played a dominant role in the phyllosphere microbial community. Its changing in turn may impact health of the staple food bamboo foraged by giant panda around Xiaoxiangling mountains. In spring, the relative abundance (15.29%) of Acidobacteria in F. ferax was significantly lower than that of A. spanostachya (21.16%) and Y. lineolate (25.67%). Actinomycetes are gram-positive bacteria that can decompose cellulose and lignin (Taibi et al., 2012).

The bacterial diversity and abundance of all three bamboo species in autumn were significantly higher than that in spring, which was similar to the result of Zheng and colleagues (2011), who found that the number of phyllosphere microbial community of Pinus tabulaeformis varied significantly between different seasons, with the largest diversity and abundance in autumn, followed by summer and the least in spring. The higher temperature and humidity in summer and autumn contributed to the higher diversity and richness than that in spring, while the higher altitude and longer low temperature in winter lead to the lower diversity and abundance (Isabelle et al, 2016). Airborne microorganisms can settle directly onto the leaf layer and their diversity and density may vary with time (daily and seasonal patterns)and other environmental events (Liu et al., 2020). In addition, agricultural practices such as harvesting and planting also have an impact on the movement of airborne microorganisms via disturbances to the leaf surface, precipitation or rain splash, and soil pollution (Redford et al., 2009 ).

The Mantel test found that elevation, distance from water, tree diameter at breast height, mean height of bamboo, mean base diameter of bamboo, tree height, shrub coverage and the number of shrubs, all significantly affected phyllosphere microbial community (Table S5). Jackson and colleagues (2006) found that the changes in the phyllosphere bacterial community in resurgent ferns were related to rainfall and humidity. Similarly, Laforest and colleagues (2016) found that the host species, habitat and climate (average summer temperature and precipitation) drove the phyllosphere bacterial community structure in temperate trees. In this study, elevation had the strongest relationship with phyllosphere microbial community. Elevation was significantly and positively correlated to many bacteria phyla abundance, such as Proteobacteria (Fig. 5). However, with an increase in elevation, both the Shannon and Sobs indices declined (Fig. S6). Higher elevations generally have fewer and more widely distributed water sources, and lower temperatures can limit the fluidity of microbial cell membranes and proteins, which are not conducive to microbial reproduction and growth (Zhang et al., 2014).

Changes to the phyllosphere microbial community could impact the degradation and absorption of plant nutrients and the metabolism of enzymes (Fazal et al., 2021). In this study, we used PICRUSt to predict the function of phyllosphere bacteria of three bamboo species foraged by giant pandas. Our data shows that the gene function spectrum of phyllosphere bacteria of F. ferax was significantly different from the other two bamboo species. The relative abundance of gene types in the membrane transport secretion system, signal transduction and oxidative phosphorylation metabolism in the third level of the KEGG pathway for F. ferax were lower than for the other two bamboo species (Fig. 6). This indicated that the low-altitude F. ferax may need less energy and protein to maintain the physiological activities of some phyllosphere bacteria. While the high-altitude A. spanostachya and Y. lineolate may require more material and energy to adapt to the colder, lower oxygen environment. It is worth noting that the relative abundance of gene types in transporters and ABC transporters were the most expressed pathways in membrane transport at level 2. Hamana and colleagues (2012) revealed that ABC transporters can protect animals from the barrier of toxic substances. In addition, genes related to replication and repair may help reduce damage to biomolecules and may help bamboo adapt to high altitude environments. However, our results are only based on predicted metagenomics, and do not represent the actual function of leaf-peripheral bacteria.

Food microbes can affect the gut microbes of animals (Kohl and Dearing 2014;Kohl et al. 2016). Lei and colleagues (2020) found significant associations of certain bacteria and fungi between bamboo and the gut of giant panda. The diversity of bamboo bacteria was also positively correlated with that of gut bacteria in giant panda. Giant pandas prefer to consume bamboo that grows naturally at high altitudes, probably because the total number of endophytic bacteria in high altitudes tends to be lower (Helander et al., 2013). There's a lot of work needed to fully understand the relationship between the food microbes and gut microbes of pandas. To explore the relevance of these genes in the environmental adaptability of giant pandas and bamboo, further research is needed to directly sequence the metagenomics of the phyllosphere microbial community and determine whether there are specific enzymes related to digestion in giant pandas.

In this study, high-throughput sequencing was used to explore the factors that influence phyllosphere bacterial composition in three bamboo species (A. spanostachya, Y. lineolate and F. ferax) foraged by giant panda in different seasons (spring vs. autumn), in Liziping National Nature Reserve (Liziping NR), China. Our findings suggested that the diversity of F. ferax phyllosphere bacterial species was greater than that of the other two bamboo species in both seasons, indicating that low altitude was a promoter of bamboo phyllosphere microbial richness and diversity. Besides, phyllosphere bacterial diversity was also significantly higher in autumn than in spring, implying that season-related changes in environmental factors (e.g.,temperature and moisture) may influence bacterial communities. In autumn, giant pandas prefer bamboo at higher altitudes, which has lower abundance and diversity of phyllosphere bacteria. Lower abundance and diversity of phyllosphere bacteria also were found at lower altitudes in spring than in autumn. Therefor, giant pandas prefer to lower abundance and diversity of phyllosphere bacteria may attribute to nutrient-rich bamboo and adapted climate, these may be also considered as panda adaptation to partially meet the higher energy requirements needed for survival in the harsh cold and hypoxic environment. All these findings could provide a reference for the restoration and management of giant panda habitat and food resources in this area, especially for those small isolated populations of giant pandas in Xiaoxiangling mountains.

Acknowledgements

Thanks to all the students who assisted with data collection and the experiments.

Authors’ contributions

Conceived and designed the study: MS H, ZJ Z, JJ L. Collected data and samples in the field: JJ L, L W, JM X, Y Y, J W, LW K, Y L. Processed samples in the lab: MS H, JJ L, JM X, Y Y, J W. Analysed the data: MS H, JJ L, Y Y. Wrote the paper: MS H, ZJ Z, JJ L, L W. All authors read and approved the final manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China (No. 31900337), and Fundamental Research Funds of China West Normal University (18B025, 18Q040, 20B006).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

²Liziping National Nature Reserve Administration, Shimian County 625400, China

Gupta A, Kumar A. Potential of Bamboo in Sustainable Development[J]. Asia Pacific Business Review,2008,4(3)
Balint-Kurti P, Simmons SJ, Blum JE et al (2010) Maize leaf epiphytic bacteria diversity patterns are genetically correlated with resistance to fungal pathogen infection.[J]. Mol Plant Microbe Interact 23(4):473–484
Bringel F, Ivan C (2015) Pivotal roles of phyllosphere microorganisms at the interface between plant functioning and atmospheric trace gas dynamics[J]. Front Microbiol 6:486
Chang EH, Chiu CY (2015) Changes in soil microbial community structure and activity in a cedar plantation invaded by moso bamboo[J]. Appl Soil Ecol 91:1–7
Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884–i890
Copeland JK, Yuan L, Layeghifard M et al (2015) Seasonal community succession of the phyllosphere microbiome.[J]. Mol Plant Microbe Interact 28(3):274–285
Delmotte N, Knief C, Chaffron S et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria[J]. Proceedings of the National Academy of Sciences, 2009, 106(38)
Du W, Jiang P, Wang YS, Lü LX, Wang HW, Bu YQ, Liu CH, Dai CC (2014) Effects of Beauveria bassiana on paddy antioxidant enzymes activities and phyllosphere microbial diversity[J]. Acta Ecol Sin 34(23):6975–6984
Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10(10):996–998. doi:10.1038/nmeth.2604
Fazal ur Rehman, Sultan A, Maria Kalsoom. Chemistry of Microbial Life in Phyllosphere: A Review[J]. Green Reports, 2021, 2(4), 1–8
Fedorov DN, Doronina NV, Trotsenko IA (2011) Phytosymbiosis of aerobic methylobacteria: New facts and views[J]. Mikrobiologiia 80(4):435–446
Feng XC, Cao XG, Ju WH, Liu KM. A study on community characteristics of forest soil bacteria in Lushan National Nature Reserve[J]. Forest Resources Management, 2019(6): 101–107 (in Chinese)
Gourion B, Rossignol M, Vorholt JA. A proteomic study of Methylobacterium extorquens reveals a response regulator essential for epiphytic growth[J]. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(35)
Hamana K, Itoh T, Sakamoto M et al (2012) Covalently linked polyamines in the cell wall peptidoglycan of the anaerobes belonging to the order Selenomonadales[J]. The Journal of General Applied Microbiology 58(4):339–347
Helander M, Jia R, Huitu O et al (2013) Endophytic fungi and silica content of different bamboo species in giant panda diet[J]. Symbiosis 61(1):13–22
Hong M, Wei W, Yang Z et al (2016) Effects of timber harvesting on Arundinaria spanostachya bamboo and feeding-site selection by giant pandas in Liziping Nature Reserve, China[J], 373. Forest Ecology & Management, pp 74–80
Hong M, Yuan S, Yang Z et al (2015) Comparison of microhabitat selection and trace abundance of giant pandas between primary and secondary forests in Liziping Nature Reserve, China: Effects of selective logging[J]. Mammalian Biology - Zeitschrift für Säugetierkunde
Hong MS, Wang JC, Yang XY et al. Comparison of microhabitat structure of giant panda in primary forest and secondary forest [J]. Journal of China West Normal University (Natural Science Edition), 2012(04):356–361
Hu JC, Schaller GB, Pan WS, Zhu J (1985) The Giant Pandas of Wolong. Sichuan Science and Technology Press, Chengdu (in Chinese)
Innerebner G, Knief C, Vorholt JA (2011) Protection of Arabidopsis thaliana against Leaf-Pathogenic Pseudomonas syringae by Sphingomonas Strains in a Controlled Model System[J]. Appl Environ Microbiol 77(10):3202–3210
Jackson EF, Echlin HL, Jackson CR. Changes in the phyllosphere community of the resurrection fern, Polypodium polypodioides, associated with rainfall and wetting[J]. Fems Microbiology Ecology, 2010(2):236–246
Jin L, Wu D, Li C et al. Bamboo nutrients and microbiome affect gut microbiome of giant panda[J]. Symbiosis, 2020(16)
Kembel SW, O'Connor TK, Arnold HK et al. Relationships between phyllosphere bacterial communities and plant functional traits in a neotropical forest[J]. Proceedings of the National Academy of Sciences, 2014, 111(38)
Kohl KD, Dearing MD (2014) Wild-caught rodents retain a majority of their natural gut microbiota upon entrance into captivity. Environmental Microbiology Reports 6:191–195
Kohl KD, Brun A, Magallanes M, Brinkerhoff J, Laspiur A, Acosta JC, Caviedes-Vida E, Bordenstein SR (2016) Gut microbial ecology of lizards: Insights into diversity in the wild, effects of captivity, variation across gut regions, and transmission. Mol Ecol 26:1175–1189
Laforest-Lapointe I, Messier C, Kembel SW (2016) Host species identity, site and time drive temperate tree phyllosphere bacterial community structure[J]. Microbiome 4(1):27
Leveau JH. Life of Microbes on Aerial Plant Parts[M].Principles of Plant-Microbe Interactions.Springer,2015:17ï¼࿽24
Li XJ. Relationship between microorganisms and higher plants [J]. Journal of Hebei North University: Natural Science Edition, 1998
Lindow SE, Bandl MT. Microbiology of the Phyllosphere[J].Appl Environ Microbiol,2003,69(4): 1875–1883
Liu H, Brettell LE, Singh B. Linking the Phyllosphere Microbiome to Plant Health[J]. Trends in Plant Science, 2020, 25(9)
Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27(21):2957–2963. doi:10.1093/bioinformatics/btr507
Mei RH (1991) The study of plant microecology[J]. World Agriculture 000(007):22–23
Newton AC, Gravouil C, Fountaine JM (2010) Managing the ecology of foliar pathogens: ecological tolerance in crops[J]. Ann Appl Biol 157(3):343–359
Pan WS (2001) The chance to continue to survive [M]. Peking University Press
Partida-Martinez LPP, Heil M (2011) The Microbe-Free Plant: Fact or Artifact?[J]. Front Plant Sci 2(100):100
Redford AJ, Fierer N (2009) Bacterial Succession on the Leaf Surface: A Novel System for Studying Successional Dynamics[J]. Microb Ecol 58(1):189–198
Reed SC, Townsend AR, Cleveland CC et al (2010) Microbial community shifts influence patterns in tropical forest nitrogen fixation[J]. Oecologia 164(2):521–531
Shanmughavel P, Francis K. Balance and turnover of nutrients in a bamboo plantation (Bambusa bambos) of different ages[J]. Biology & Fertility of Soils, 1997, 25(1):pp 69–74
Shi W, Zhang HB. Leaf microenvironment and microbial community [J]. Microbiology Bulletin,2007(04):761–764
Sichuan Forestry Bureau (2015) The Giant Panda of Sichuan: Report of the Fourth National Giant Panda Survey. Sichuan Science and Technology Press
Stackebrandt E, Goebel BM (1994) Taxonomic Note: A Place for DNA-DNA Reassociation and 16S rRNA Sequence Analysis in the Present Species Definition in Bacteriology[J]. Int J Syst Bacteriol 44(4):846–849
Taibi Z, Saoudi B, Boudelaa M et al. Purification and Biochemical Characterization of a Highly Thermostable Xylanase fromActinomadurasp. Strain Cpt20 Isolated from Poultry Compost[J]. 2012, 166(3):663–679
Thompson IP, Bailey MJ, Fenlon JS et al. Quantitative and qualitative seasonal changes in the microbial community from the phyllosphere of sugar beet (Beta vulgaris)[J]. Plant & Soil, 1993, 150(2):pp 177–191
Venkatachalam S, Ranjan K, Prasanna R et al (2016) Diversity and functional traits of culturable microbiome members, including cyanobacteria in the rice phyllosphere[J]. Plant Biol 18(4):627–637
Vorholt JA. Microbial life in the phyllosphere[J]. Nature Reviews Microbiology, 2012
Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73(16):5261–5267. doi:10.1128/AEM.00062-07
Wei FW, Feng ZJ, Wang ZW et al (1999) Habitat selection of giant panda and red panda in Xiangling Mountains [J]. Acta Zool Sin 45(1):57–63 :(in Chinese .
Wu Q, Wang X, Ding Y et al. Seasonal variation in nutrient utilization shapes gut microbiome structure and function in wild giant pandas[J]. Proceedings of the Royal Society B Biological Sciences, 2017, 284(1862):20170955
Xie J, Hong M (2020) Effects of ecological factors on growth of Arundinaria spanostachya shoots in Liziping National Nature Reserve, China[J], 23. Global Ecology and Conservation, p e01121
Xiong Y, Peng GN, Zhang GQ et al (1999) Detection of enterotoxin of Escherichia coli EL-1 strain from giant panda by dot enzyme immunobinding assay [J], 05. Chinese Veterinary Science and Technology, pp 28–29
Yadav RKP, Papatheodorou EM, Karamanoli K et al (2008) Abundance and diversity of the phyllosphere bacterial communities of Mediterranean perennial plants that differ in leaf chemistry[J]. Chemoecology 18(4):217–226
Zhang CL, Zhu FB, Zhang JG et al (1997) Pathogenicity of Klebsiella to giant panda [J]. Chinese Veterinary Science Technology 04:40–41
Zhang H, Xue J. Spatial Pattern and Competitive Relationships of Moso Bamboo in a Native Subtropical Rainforest Community[J]. Forests, 2018, 9(12)
Zhang X, Xu S, Li C et al (2014) The soil carbon/nitrogen ratio and moisture affect microbial community structures in alkaline permafrost-affected soils with different vegetation types on the Tibetan plateau[J]. Res Microbiol 165(2):128–139
Zhao S, Zheng P, Dong S et al (2013) Whole-genome sequencing of giant pandas provides insights into demographic history and local adaptation[J]. Nat Genet 45(1):67–99
Zheng Y (2011) Study on microorganism and bacteriostasis of leaf circumference of Pinus tabulaeformis [J]. Jilin agriculture 10:65–65
Zhou H, Yuan SB, Yang ZS et al (2014) Relationship between feeding habits of giant pandas and biomass of main food bamboo in summer in Liziping Nature Reserve, Sichuan Province [J]. Acta Theriologica Sinica 34(001):93–99 :(in Chinese .
Zhou YK, Shen XY, Hou CL (2017) Diversity and antimicrobial activity of culturable fungi from fishscale bamboo (Phyllostachys heteroclada) in China. World J Microbiol Biotechnol 33:104

Download PDF

Version 1

posted

You are reading this latest preprint version

Environmental factors influencing the composition of phyllosphere bacterial communities in bamboo: A staple food source of giant pandas

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Background

Materials And Methods

Study area

Experimental design

Sample collection and DNA extraction

16S rRNA gene V3-V4 amplification, quantification and sequencing

Sequence data analysis

Statistical analysis

Results

Samples, sequences and OTUs between different seasons and bamboo species

The influence of season and bamboo species on phyllosphere bacterial composition

The influence of ecological factors on bamboo phyllosphere bacterial community

PICRUSt gene function estimation

Discussions

Conclusions

Declarations

References

Supplementary Files

Status:

Version 1