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Article

Discovering the Characteristics of Community Structures and Functional Properties of Epiphytic Bacteria on Spartina alterniflora in the Coastal Salt Marsh Area

by
Zenglei Song
1,2,
Yanyan Huang
1,2,
Qing Liu
1,2 and
Xiaoke Hu
1,3,4,5,*
1
Key Laboratory of Coastal Biology and Bioresource Utilization, Yantai Institute of Costal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Laboratory for Marine Biology and Biotechnology, Qingdao 266237, China
4
Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
5
Marine Science Research Institute of Shandong Province, National Oceanographic Center of Qingdao, Qingdao 266237, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2022, 10(12), 1981; https://doi.org/10.3390/jmse10121981
Submission received: 22 November 2022 / Revised: 7 December 2022 / Accepted: 8 December 2022 / Published: 12 December 2022
(This article belongs to the Section Marine Ecology)
Browse Figures
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Abstract

:
The invasive submerged Spartina alterniflora is dominant in the coastal Yellow River Delta wetland. Although sediment microorganisms have been found to mediate the nutrient cycle in wetlands, the role of epiphytic bacteria on submerged S. alterniflora has rarely drawn attention. In the present study, we analyzed the characteristics of epiphytic microbial community diversity and functional properties related to S. alterniflora in summer and winter by Illumina MiSeq sequencing and functional prediction. Marked high abundances of Proteobacteria, Actinobacteriota, Planctomycetota, Cyanobacteria and Desulfobacterota were found in S. alterniflora epiphytic microbiome. Beta diversity based on NMDS and LDA analysis revealed that the distribution of these epiphytic microbial communities clustered according to the leaf locations and variation in seasons. Environmental factors, including temperature, salinity, DO and total organic matter, exert important roles in impacting the microbial community. Significantly higher abundances of chemoheterotrophy, aerobic_chemoheterotrophy, hydrocarbon degradation, fermentation, nitrate reduction and nitrate respiration were correlated with the submerged S. alterniflora epiphytic microbiome. Collectively, the results indicated that S. alterniflora epiphytic bacterial community diversity and functional guilds varied greatly with variations in leaf locations and seasons. These results will also provide guidance for the isolation of functional bacteria in controlling plant spread.

1. Introduction

Spartina alterniflora originally lived on the Atlantic coast of North America, and mainly provided food sources and nursery areas for animals with multiple ecological functions, such as beach protection, dike protection, siltation promotion and land reclamation [1]. It was first introduced to China in 1979 as a material for the ecological engineering of coastal tidal flats and for achieving ideal treatment results [2]. S. alterniflora spread widely from being absent in 1981 to an area of 54,580 ha by 2015 from Hebei Province to Guangxi Province. The coverage area of S. alterniflora accounts for nearly 94% of the coastal area of Jiangsu, Shanghai, Zhejiang, and Fujian Provinces, representing the most highly concentrated areas in China [3]. However, the invasion of this exotic species has caused vast negative consequences, including threatening native wetland plants and waterfowl, and imposing negative effects on fishing, water transportation, mariculture activities, and tourism development [4]. Therefore, it has been categorized as one of the most serious invasive plants by the State Environmental Protection Administration of China [3,5]. At present, research on S. alterniflora mainly focuses on the following three aspects: invasion mechanism [6], impact on local ecosystems [7] and control of S. alterniflora [8]. Microorganisms play a key role in maintaining plant health and promoting plant growth. In particular, epiphytic bacteria, live on the surface of plant organs or attach to their surfaces [9], which plays a key role in influencing the growth of some submerged plants.
The phyllosphere includes the surface parts of leaves, stems, flowers, fruits and other tissues in contact with the air, providing multiple niches for the colonization of microorganisms, especially epiphytic bacteria [10,11,12]. Leaves are the most important part of the phyllosphere, where a large number of microorganisms colonize and evolve adaptability [11,13]. Many studies have shown that the microbial community structure in the plant phyllosphere is dynamic, and the host characteristics, plant geographical distribution pattern, time/seasonality, environmental changes and other factors directly or indirectly affect the diversity and composition of the microbial community in the phyllosphere [14,15]. Among them, the seasonal dynamic changes in bacterial and fungal communities in the phyllosphere have been investigated in studies of Populus deltoids, Ginkgo biloba, Pinus bungeana, Cunninghamia lanceolata and other plants [14,16,17,18,19]. According to previous research, changes in the structure and composition of phyllospheric microorganisms are regulated by temporal/seasonal variations in temperature, humidity and solar radiation levels [20,21]. Other studies have found that the increase in salinity and seasonal variation had a greater impact on the composition and function of the spinach phyllospheric bacterial community, and it has been confirmed that the seasonal effect presented an obvious influencing trend [22].
The Yellow River Delta is located near the northeast of the Bohai Sea, which is dominated by a large amount of submerged S. alterniflora in the coastal wetland [23]. The Yellow River Delta wetland plays an indispensable role in maintaining the ecological balance. It is estimated that S. alterniflora has reached an area of 3000 hectares near the Yellow River Delta wetland, which has posed a serious threat to biodiversity and bird habitat quality in the Yellow River Delta coastal tidal wetland and intertidal zone [24]. Many measures have been adopted to control S. alterniflora invasion in China in recent decades [25]. Epiphytic bacteria are mainly harboured by the leaves of plants and exert important roles during the process of plant growth [26]. Therefore, determining the variation in the ecological and functional properties of epiphytic bacteria will be of great help in monitoring the growth state of invasive S. alterniflora. However, research on the microbial community of S. alterniflora has mainly focused on the comparison of the rhizospheric soil microbial community with native species [23,27,28]. Few studies have been conducted to explore epiphytic microbial community variation related to S. alterniflora [29].
In this study, S. alterniflora was taken as the research object, and the characteristics of the bacterial community structure and functional properties in the phyllosphere of S. alterniflora were analyzed in summer and winter. We aimed to study (i) the seasonal distribution pattern of the epiphytic bacterial community on S. alterniflora. (ii) The variation in epiphytic bacterial community composition among different sampling locations with the change in environmental conditions. (iii) Functional characteristics of epiphytic bacteria in summer and winter. Uncovering the structure and function of submerged S. alterniflora epiphytic bacterial communities can not only enrich the understanding of their biodiversity but also provide novel insight into the control of S. alterniflora invasion.

2. Materials and Methods

2.1. Study Site and Sampling

The sampling site is located in the Yellow River Estuary of Shandong Province (37°47′ N, 119°7′ E). This area is adjacent to the northeastern Bohai Sea and eastern Laizhou Bay, facing the Liaoning Peninsula across the sea. The sampling area has a temperate continental monsoon climate, with an average annual temperature of 12.88 °C, and precipitation is mostly concentrated in summer, accounting for 65% of the annual precipitation, with an average annual precipitation of 537.3 mm, and the evaporation is over 1500 mm [30]. The Yellow River Estuary is an irregular semidiurnal tide with an average tidal height of approximately 1.3 m, which is characterized by tidal asymmetry in most estuaries in the world and has obvious diurnal unequal tidal height [31]. In this study, phyllospheric samples of Spartina alterniflora were collected at low tide in August 2020 and January 2021. The sampling seawater temperatures were 29.8 °C and −3.3 °C, respectively (Table S2). According to the position of Spartina alterniflora leaves, the experiment of each season was divided into three groups: upper leaves (leaves never touch seawater), middle leaves (some leaves intermittently touch seawater with tides) and lower leaves (leaves submerged in seawater with tides). Thus, we sampled 18 samples in summer and winter, which were divided into 6 groups, namely, summer samples of upper leaves (LUS, 3 samples), summer samples of middle leaves (LMS, 3 samples), summer samples of lower leaves (LDS, 3 samples), winter samples of upper leaves (LUW, 3 samples), winter samples of middle leaves (LMW, 3 samples), and winter samples of lower leaves (LDW, 3 samples) (Table S1). All the samples were immediately placed on ice and transported to the laboratory, where they were stored at −80 °C until physicochemical property measurement and DNA extraction.

2.2. Physicochemical Properties of Sampling Water

Total carbon (TC), total nitrogen (TN), total organic carbon (TOC) and total organic nitrogen (TON) in the sampled seawater were measured by using a CHNS Vario EL III elemental analyzer (Varion EL, Elementar Analyzer system GmbH, Hanau, Germany). Environmental factors related to temperature, salinity, DO (dissolved oxygen), and pH was analyzed by a YSI 556 multi-probe system (YSI, Ohio, USA) in an in situ sampling environment. All results obtained in this study were performed in triplicate.

2.3. DNA Extraction and Sequencing

DNA extraction of phyllospheric microorganisms followed Nasanit’s method [32] with some modifications. First, 10 g of leaf samples were cut into pieces and placed into a sterile triangle flask, and 1:20 (leaf weight/volume TE buffer = 1:20) sterile TE buffer (10 mmol/L Tris-HCl, 1 mmol/L EDTA, pH 8.0) was added. After being sealed with a sterilizing film, the samples were shaken on a shaking table (Zhi cheng Inc., Shanghai, China) (200 r/min, room temperature, 30 min). Microbial cells were separated from the leaf surface. The leaves were subjected to 40 kHz ultrasound for 15 min, and the microbes in the oscillating solution were collected on a 0.22 μm filter membrane (Merck Millipore Ltd., Tullagreen, Carrigtwohill Co., Cork, Ireland) in a sterile environment using a vacuum filtration device. The total DNA on the filter membrane was extracted using the Fast DNA Spin Kit (Qbiogene, Irvine, CA, USA) with the manufacturer’s specifications. DNA samples were amplified using the 515F (5′-GTGCCAGCMGCCGCGG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primer set that amplified the V4–V5 region of the 16S rRNA gene as described previously [33]. The PCR was performed with a T100 Thermal Cycler (Bio-Rad, Hercules, CA, USA) with an initial denaturation at 95 °C for 3 min, followed by 27 cycles of denaturation at 95 °C for 30 s, annealing at 55 °C for 30 s, and elongation at 72 °C for 45 s, with the final extension at 72 °C for 10 min. The PCR products were analyzed by using the Illumina MiSeq platform (Majorbio Bio-Pharm Technology Co., Ltd., Shanghai, China).

2.4. Sequence Processing and Analysis

The raw sequencing data were processed in QIIME2 [34]. The adaptor and primer sequences were trimmed using the cutadapt plugin. The DADA2 plugin was used for quality control and identification of amplicon sequence variants (ASVs) [35]. SILVA release 132 (Ref NR 99) taxonomy [36] and q2-feature-classifier [37] were used for classifying the 16S rRNA gene sequences.

2.5. Statistical Analyses

The data were analyzed by using IBM SPSS 23.0 (IBM Corporation, USA). Physicochemical properties were calculated and statistically examined by independent t-test. Spearman correlation analysis was used to evaluate the absolute abundances of bacterial phyla with physicochemical properties. Non-metric multidimensional scaling (NMDS), Spearman correlation and network analysis were implemented by R using the vegan package (version 2.4.5). To identify potentially discriminating taxa among the six groups, LEfSe was applied to describe differences between groups [38]. First, the nonparametric Kruskal-Wallis sum rank test was used to detect the differentially abundant features (genera, families, classes, phyla) among the four groups. Then, based on the significantly different species above, a paired Wilcoxon rank sum test was used to analyze the difference between subgroups. Finally, the effective size of each differentially abundant feature was estimated using linear discriminant analysis. All-against-all classes were compared (most stringent), and a value of 4.0 of the logarithmic linear discriminant analysis score was chosen as the threshold for discriminative features. FAPROTAX analysis was used to predict the distribution of functional properties [39]. First, we read an annotated ASV table of the Greengenes or Silva database, match the annotated information of ASV with the species information in the database through the python program, and output the predicted result of the function (http://www.zoology.ubc.ca/louca/FAPROTAX/lib/php/index.php?section=Home, accessed on 9 August 2022).

3. Results

3.1. Physicochemical Properties of Sampling Water in Different Seasons

The physicochemical properties of seawater in the sampling locations in summer and winter are shown in Table S2. The contents of TON, TOC and TN in summer were significantly (p < 0.05) higher than those in winter (Table S2). Salinity, DO and TC content was much lower in summer than in winter (Table S2). In winter, there is a higher DO level in the Yellow River Delta wetland, which may be due to the billowy waves in this area [40].

3.2. Diversity of the Prokaryotic Community

A total of 960,149 raw sequences for 18 samples (6 groups) were obtained. After data quality filtering, noise reduction, splicing, chimerism and pumping, a total of 104,868 sequences were screened for subsequent analysis. The number of generated ASVs in each group varied from 36 to 1282. Rarefaction curves (Figure S1) showed that nearly complete bacterial diversity had been covered.
Alpha diversity estimators of prokaryotic communities varied greatly among the six groups, with Sobs indices ranging from 17~1283, Chao indices from 18~1620, Shannon indices from 1.3~6.5, and Simpson indices from 0.003~0.349 (Figure 1). The highest levels of the Shannon and Sobs indices were observed in the LUW group, which were significantly different from those in the other locations of the leaf groups (p < 0.05). Nevertheless, there were no significant differences in alpha diversity (Sobs index, Shannon index, Chao index and Simpson index) among seasons in all six groups (Figure S2).
Beta diversity analysis was performed on six different groups in summer and winter. The results of NMDS analysis based on Bray-Curtis distance and Adonis analysis showed that the samples in different groups were significantly different in summer (p = 0.001, R = 0.78) and winter (p = 0.001, R = 0.54). In the summer, the LUS group was significantly different from the other two groups (p < 0.05). In the winter, the LMW group varied greatly from the LDW and LUW groups (Figure 2).

3.3. Structure and Distribution Patterns of Prokaryotic Communities

Overall, 54 bacterial and 9 archaeal phyla were detected in 18 samples. Distribution patterns of species at the phylum level and family level are shown in Figure 3. There were great differences in community composition between different locations of leaves. The dominant phyla varied greatly across all the samples, but the predominant phyla in all groups mainly included Proteobacteria and Bacteroidota. In the summer group, the bacterial community of leaves submerged in the seawater group (LDS) presented high diversity, and the relative abundance of Euryarchaeota (approximately 8.44%) was highest in all groups. In winter, the LUW group with upper leaf locations showed the highest diversity, and Desulfobacterota (approximately 3.58%) was the predominant phylum in the winter group. The bacterial community composition of phyllospheric samples showed obvious differences in both leaf position and season (Figure 3A).
At the family level, Moraxellaceae, Rhodobacteraceae, Sphingomonadaceae, Flavobacteriaceae, Weeksellaceae, Rhizobiaceae, Sarospiraceae and Halomonadaceae were the dominant families in phyllospheric samples. It is worth noting that only Moraxellaceae and Weeksellaceae were the dominant groups in the LUS group, which was significantly different from the other groups. In addition, Flavobacteriaceae and Rhodobacteraceae were the dominant families in all groups except the LUS group. Interestingly, Flavobacteriaceae and Rhodobacteraceae were also considered the main dominant groups of this ecosystem in our previous studies (Figure 3B). The Venn diagram of the generated ASVs from different groups in summer (Figure 3C) and in winter (Figure 3D) showed that there were 11 shared ASVs in different locations of leaves in summer, and there were 82 shared ASVs in different locations of leaves in winter.
To describe the relationship between samples and species at the genus level, we used a Circo map to exhibit the distribution patterns of the key genus in different groups (Figure 4). Psychrobacter was the most abundant genus in winter, Acinetobacter was predominant in the upper leaves of the summer group, and Erythrobacter was the dominant genus in the middle leaves and lower leaves in summer. Empedobacter was present in all groups of leaves, and it occupied the highest relative abundance in the upper leaves of the summer group. Gillisia was found to be predominant in the lower leaves of the winter group. Aureispira was found to be most abundant in the lower leaves of the summer group.
LEfSe analysis (Figure 5B) was used to exhibit the distribution of key taxa that contribute to the differences between groups from the phylum level to the genus level. The LDA score bar plot was constructed for the taxonomic groups with a threshold value of 4 to find the unique species in each season (Figure 5A). 35 phylogenetic units were identified as statistically significant (p < 0.05) discriminative for the 6 groups. The prokaryotic communities enriched in the LUS group mainly included Acinetobacter and Gamaproteobacteria. Cyanobacteria at the phylum level; Alphaproteobacteria and Cyanobacteriia at the class level; Rhodobacterales, Phormidesmiales, and Phormidesmiales at the order level; Rhodobacteraceae, Phormidesmiaceae, and Altermonadaceae at the family level had the highest LDA scores and represented the leading prokaryotic members in the LMS group. Chitinophagales, Burkholderiales and Saprospiraceae were characteristic community members of the LDS group. Chloroflexi was enriched in the LUW group. The LMW group mainly enriched Actinobacteriota, Micrococcales, Corynebacteriales and Propionibacteriales. Flavobacteriaceae, Rhizobiales, and Sulfitobacter were characteristic community members of the LDW group (Figure 5A).

3.4. Potential Factors Influencing the Prokaryotic Communities

Correlation analysis among prokaryotic communities and physicochemical properties was completed by heatmap correlation analysis at the phylum level (Figure 6A). From the results, all physicochemical properties, including temperature, salinity, DO, pH, TN, TC, TON and TOC, were key factors that affected the prokaryotic community. Some crucial phyla, such as Proteobacteria, Chloroflexi and Desulfobacterota were significantly correlated with these physicochemical factors (p < 0.05).
To further explore the relationship between bacterial species and environmental factors, we conducted network analysis at the class level to show a visual presentation (Figure 6B). According to the results, we found that Rhodothermia, Cyanobacteriia, Desulfuromonadia, Bdellovibrionia and Anaerolineae were positively correlated with temperature, TON, TN and TOC, while they were negatively correlated with salinity, pH, DO and TC. In contrast, Gammaproteobacteria, Campvlobacteria, Vampirivibrionia and Thermoanaerobaculia were negatively correlated with temperature, TON, TN and TOC, while they were positively correlated with salinity, pH, DO and TC.

3.5. Functional Characteristics of the Bacterial Community

FAPROTAX analysis was used to predict the distribution of functional properties (Figure 7). The most abundant functional properties mainly included chemoheterotrophy, aerobic_chemoheterotrophy, hydrocarbon degradation, fermentation, nitrate reduction and nitrate respiration. It was demonstrated that the highest relative abundance of function in the epiphytic bacterial community on Spartina alterniflora was chemoheterotrophy. The functional distribution of the bacterial community in the summer group and in the winter group was roughly different (Figure 8). The relative abundance of functions involved in the nitrogen cycle was much higher in winter than in summer. From the results of the comparative analysis of functional differences, we found that there were significant differences between the summer group and winter group in functions related to chemoheterotrophy (p < 0.01), aerobic_chemoheterotrophy (p < 0.01), aromatic_compound_degradation (p < 0.01), nitrate reduction (p < 0.01) and nitrate respiration (p < 0.01). Among all of them, the relative abundances of functions related to human_pathogens (p < 0.001) and animal_parasites_or_symbionts (p < 0.001) in summer were significantly higher than those in winter.

4. Discussion

Plants host distinct epiphytic bacterial communities in the phyllosphere, and these bacteria play a crucial role in maintaining plant growth and health [41]. Wetland-submerged plant microorganisms are an important part of wetland ecosystems and play an important role in promoting material circulation, energy flow, and maintaining ecosystem stability [42]. The results of this study showed that the invasion of S. alterniflora could significantly affect the physical and chemical properties of wetland soil and the soil microbial community [23,43]. S. alterniflora is small in number but ecologically significant, and the phyllosphere of S. alterniflora is an efficient producer of DMSP, which can be metabolized to acrylate and DMS by plant-associated microbes possessing DMSP lyase [29]. It is therefore highly likely that phyllospheric microbiota plays major roles in carbon, nitrogen and sulfur biogeochemical cycles, in ecosystemic signalling and in climate regulation through their action on plant-related volatile compounds. Thus, it is imperative to clarify the functional ecology of the epiphytic bacterial community in the phyllosphere, especially in species of submerged S. alterniflora in the Yellow River Delta wetland. In the present study, we examined and compared the epiphytic bacterial communities and functional properties by Illumina amplicon sequencing in the phyllosphere of submerged S. alterniflora in different seasons and different environmental conditions, which might provide the potential mechanisms of bacteria-plant interactions.
Based on the Chao and Shannon diversity index results, the richness and diversity of epiphytic bacteria in different groups were compared. In general, the richness and diversity of the middle leaves of samples in summer were significantly higher than those of the other locations of phyllospheric samples (p < 0.05). In winter, the highest richness and diversity in the phyllospheric samples were located in the upper leaves of the samples (Figure 1 and Figure S2). These findings were consistent with previous reports on other plant species, including Arabidopsis, soybean, rice, Agave, tomato and their related plants [44,45,46]. However, there were no significant differences in richness and diversity between samples in different seasons and among phyllospheric samples in different positions (Figure S2). Therefore, we inferred that the source of bacterial inoculum might be the main factor affecting alpha diversity. Phyllospheric bacteria are directly exposed to high UV radiation, higher temperature gradients and antimicrobial pesticides, and the locations of leaves determine the degree of being affected by environmental stimulation; thus, only bacteria with high resistance can survive in the phyllosphere [47]. This might explain the variation in the richness and diversity of the phyllospheric microbial community related to plants [9]. In addition, we found differences in the beta diversity among different locations of phyllospheric associated bacterial communities (Figure 2), which revealed differences in microbial composition related to their locations in plant leaves [48]. It has been revealed that there are differences in the composition of bacterial communities in different organs of Arabidopsis thaliana and potato [48,49]. These differences in composition might be due to different bacterial population sizes carried by different plant tissues. Bacterial diversity was proven to be positively correlated with the total community size in a previous study [50]. In the phyllosphere, the abundance of bacteria was estimated to be 107 cells/cm2 [51] or approximately 106 cells/g [18], which might explain the variation in the phyllospheric associated bacterial communities in different locations of leaves.
A comparison of the bacterial communities associated with S. alterniflora plants revealed both ubiquitous and specific community members in different groups. Proteobacteria, Bacteroidota, Actinobacteriota, Chloroflexi and Planctomycetota were the most abundant phyla in the phyllospheric samples both in summer and in winter, which was consistent with other studies on S. alterniflora [23]. Although the dominant epiphytic bacterial community composition did not change significantly at the phylum level in different seasons, there were some specific bacterial community members in the phyllospheric samples of the two seasons. Euryarchaeota was highly abundant in the lower leaves of summer samples. Cyanobacteria accounted for a large proportion of the middle and lower leaves in summer, and it has been proven that Cyanobacteria can inhabit all possible habitats [52]. Halobacterota was found to have extremophilic properties and has been proven to be involved in the process of dissimilatory sulfate reduction [53]. In our study, the relative abundance of Halobacterota was highest in the lower leaves in summer, which indicated the specific environment inhabited by these prokaryotes. Myxococcota was predominant in the middle leaves of summer samples. High microbial diversity in the lower leaves in summer also indicated that epiphytic bacteria located in submerged leaves were more susceptible to environmental factors [54]. Proteobacteria, Chloroflexi and Desulfobacterota were highly correlated with the variation in environmental factors, which verified their sensitivity to environmental changes [55]. In summer, high temperatures, and high levels of TON and TOC contributed to the variation in the microbial community and diversity [56].
For phyllospheric microorganisms, plant species, seasonal changes, host plant genotype and environment could affect the structure of the phyllosphere microbial community [57]. However, there has never been any research on the special environment where the leaves are in different positions, especially in the intertidal zone, which allows the leaves to be in continuous circulation with or without contact with the seawater. In this study, for the first time, we compared the bacterial communities in the phyllosphere of S. alterniflora at different locations, ingeniously revealing the impact of the tidal environment on the microbial communities in the phyllosphere. This study found that the diversity and richness of the upper layer of the leaves, that is, the land not in contact with seawater, were lower than those in the lower layer of the leaves, especially in the LUS group, which was significantly (p < 0.05) lower than other groups (Figure 1). In addition, we found that except for the LUS group, other groups had enriched Sphingomonadaceae (Figure 3) with high abundance, and some strains of Sphingomonadaceae had protective effects on plant pathogens [58]. For epiphytic microorganisms living on the surface of plant tissues, the phyllosphere is an extreme and unstable habitat, with oligotrophic characteristics such as carbon and nitrogen nutrient constraints, and multiple and highly fluctuating physical and chemical constraints (high intensity of light, ultraviolet radiation, temperature and desiccation) [29]. Compared with other groups, the upper leaves in summer suffered from a more severe environment, which led to the inhibition of many probiotics such as Sphingomonadaceae and even some plant pathogens, leading to lower diversity and richness. Through LEfSe analysis, we found that, as the lower leaves (LDS group and LDW group) can continuously contact seawater, the samples in summer and winter were characterized by Roseobacter and Flavobacteria, respectively (Figure 5). This is understandable because Roseobacter and Flavobacteria have been proven to be highly adaptable and can widely inhabit coastal environments and marine environments [59,60]. Unexpectedly, a large relative abundance of Rhizobiaceae, a soil and rhizosphere bacterium, was detected in the leaves of the winter sample (Figure 5). Since the wind is very strong in winter in this area, the leaves are close to the ground, and the bacteria in the leaves may come from the soil splashed by rain or sediment stirred by tidal action. In fact, some sediment particles could be observed on the leaves of S. alterniflora during sampling which also explained this phenomenon.
FAPROTAX functional prediction was performed to analyze key functions related to epiphytic bacterial communities on Spartina alterniflora. The predominant high relative abundance of key functional properties mainly included chemoheterotrophy, aerobic_chemoheterotrophy, hydrocarbon degradation, fermentation, nitrate reduction and nitrate respiration, which was consistent with previous studies involving the bacterial community on Myriophyllum spicatum [61]. Chemoheterotrophy is involved in the process of assimilation of any organic material that can be metabolized by fermentation or anaerobic oxidation processes under dark conditions [62]. These epiphytic bacteria on the surface of the leaves were responsible for the degradation of organic matter [63]. Nitrate reduction is an important process in the nitrogen cycle [64]. Many epiphytic bacteria involved in submerged plants have been found to participate in the process of nitrogen cycle and metabolism [65]. Many microorganisms have been found to be involved in the process of nitrogen transformation in the environment with available nitrate and low oxygen, such as Proteobacteria, Bacteroidetes and ammonium-oxidizing bacteria [66]. In the present study, Proteobacteria and Bacteroidetes were the predominant phyla in both summer and winter, which was consistent with the results from the functional prediction. The discovery of a large number of bacterial groups involved in nitrogen cycling in epiphytic bacterial communities on Spartina alterniflora suggested the importance of submerged plants because of their potentially important role in nitrogen biogeochemistry in wetland ecosystems.

5. Conclusions

In summary, the present study provides a holistic perspective on the composition, diversity, functional properties and influencing factors shaping the phyllospheric bacterial community associated with the invasive species S. alterniflora. We found that there were differences in microbial communities in different sample types and seasons, and there were characteristic bacterial communities suitable for the environment in different groups of samples. In addition, we found that all these environmental factors, including temperature, salinity, DO and the contents of organic carbon, and nitrogen, had close correlations with the epiphytic bacterial community in the two seasons. More importantly, in addition to comparing the effects of different seasons on the phyllospheric bacterial community, we compared the effects of tidal power on the phyllospheric bacterial community for the first time. This study provides information for a comprehensive understanding of the epiphytic bacterial community of S. alterniflora., which is helpful for clarifying the potential invasion mechanism of S. alterniflora from a microbial perspective.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/jmse10121981/s1, Figures S1–S2, Tables S1–S2. Figure S1: Rarefaction curves of the samples. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves; Figure S2 Comparative analysis of alpha diversity estimators of epiphytic bacteria communities on Spartina alterniflora among different groups. Significant differences between the two groups were given by using Kruskal Wallis Rank Sum Test. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves; Table S1 Description of the samples grouping; Table S2 Seasonal differences in water physicochemical properties of sampling sites.

Author Contributions

Z.S. and X.H. designed the study; Z.S., Y.H. and Q.L. performed the research; Z.S. and Y.H. performed the analysis; Z.S. wrote the manuscript; X.H. revised the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China (No. 92051119; 32070112); the Key Research Project of Frontier Science of Chinese Academy of Sciences (QYZDB-SSWDQC041); the Special Foundation of Science and Technology Resources Survey (2019FY100700); Bureau of International Cooperation, the Chinese Academy of Sciences (133337KYSB20180015); the Key project of Center for Ocean Mega-Science, Chinese Academy of Sciences (COMS2020J05); and the Research Fund for the Taishan Scholar Project of Shandong Province of China (NO.tspd20210317).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are openly available in NCBI Sequence Read Archive (SRA) database under accession number “PRJNA884545” (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA884545, accessed on 18 November 2022). GenBank accessions: SRR21779574-SRR21779580, SRR21779583, SRR21779592-SRR21779595, SRR22142624-SRR22142627, SRR22142634 and SRR22142635.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Distribution of alpha diversity estimators of epiphytic bacterial communities on Spartina alterniflora among different groups. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
Figure 1. Distribution of alpha diversity estimators of epiphytic bacterial communities on Spartina alterniflora among different groups. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
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Figure 2. NMDS of Spartina alterniflora-associated samples based on Bray-Curtis matrices at the ASV level. (A) indicates the summer group and (B) indicates the winter group. The centroid of each ellipse represents the group mean, and the shape was defined by the covariance within each group. The data were analyzed by the ANOSIM group difference test based on the Bray-Curtis distance algorithm for multiple comparisons. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
Figure 2. NMDS of Spartina alterniflora-associated samples based on Bray-Curtis matrices at the ASV level. (A) indicates the summer group and (B) indicates the winter group. The centroid of each ellipse represents the group mean, and the shape was defined by the covariance within each group. The data were analyzed by the ANOSIM group difference test based on the Bray-Curtis distance algorithm for multiple comparisons. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
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Figure 3. Relative abundance of epiphytic bacterial taxa associated with Spartina alterniflora at the phylum level (A) and family level (B). Venn map of different groups based on the ASVs in summer (C) and winter (D). LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
Figure 3. Relative abundance of epiphytic bacterial taxa associated with Spartina alterniflora at the phylum level (A) and family level (B). Venn map of different groups based on the ASVs in summer (C) and winter (D). LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
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Figure 4. Circos map was used to describe the relationship between samples and species at the genus level. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
Figure 4. Circos map was used to describe the relationship between samples and species at the genus level. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
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Figure 5. (A), Linear discriminate analysis (LDA) was used to compare differences between six groups (LDA score > 4). (B), LEfSe analysis was used to show species characteristics that explain differences among multiple groups. Different colour nodes indicate the microbial groups that are significantly enriched in the corresponding groups and have a significant impact on the differences between groups. The yellow nodes indicate the microbial groups that were not significantly different in different groups or had no significant effect on the differences between groups. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
Figure 5. (A), Linear discriminate analysis (LDA) was used to compare differences between six groups (LDA score > 4). (B), LEfSe analysis was used to show species characteristics that explain differences among multiple groups. Different colour nodes indicate the microbial groups that are significantly enriched in the corresponding groups and have a significant impact on the differences between groups. The yellow nodes indicate the microbial groups that were not significantly different in different groups or had no significant effect on the differences between groups. LUS: summer samples of upper leaves; LMS: summer samples of middle leaves; LDS: summer samples of lower leaves; LUW: winter samples of upper leaves; LMW: winter samples of middle leaves; LDW: winter samples of lower leaves.
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Figure 6. (A), Correlation heatmap of the top ten phyla and physicochemical properties. The X and Y axes are physicochemical properties and phyla. The correlation coefficient is shown in different colours, and the right side of the legend is the colour range of varying R values, with p < 0.05 *, respectively. (B), Network analysis of the relationships between key species at the class level and environmental factors. Node size represents the relative abundance of different species. The red line represents a positive relationship between species and environmental factors. The green line represents negative relationships between species and environmental factors. Temper: temperature, Salin: salinity, DO: dissolved oxygen, pH, TN, total nitrogen; TOC, total organic carbon; TON, total organic nitrogen.
Figure 6. (A), Correlation heatmap of the top ten phyla and physicochemical properties. The X and Y axes are physicochemical properties and phyla. The correlation coefficient is shown in different colours, and the right side of the legend is the colour range of varying R values, with p < 0.05 *, respectively. (B), Network analysis of the relationships between key species at the class level and environmental factors. Node size represents the relative abundance of different species. The red line represents a positive relationship between species and environmental factors. The green line represents negative relationships between species and environmental factors. Temper: temperature, Salin: salinity, DO: dissolved oxygen, pH, TN, total nitrogen; TOC, total organic carbon; TON, total organic nitrogen.
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Figure 7. Relative abundance of predictive functions of epiphytic bacteria on submerged Spartina alterniflora in the coastal salt marsh area. Each group included three parallel samples.
Figure 7. Relative abundance of predictive functions of epiphytic bacteria on submerged Spartina alterniflora in the coastal salt marsh area. Each group included three parallel samples.
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Figure 8. Results of the difference between function properties in two groups of summer and winter. The abscissa represents the name of the function, the ordinate represents the percentage value of the abundance of a function in the sample, and different colours represent different groups. * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001.
Figure 8. Results of the difference between function properties in two groups of summer and winter. The abscissa represents the name of the function, the ordinate represents the percentage value of the abundance of a function in the sample, and different colours represent different groups. * 0.01 < p ≤ 0.05, ** 0.001 < p ≤ 0.01, *** p ≤ 0.001.
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Song, Z.; Huang, Y.; Liu, Q.; Hu, X. Discovering the Characteristics of Community Structures and Functional Properties of Epiphytic Bacteria on Spartina alterniflora in the Coastal Salt Marsh Area. J. Mar. Sci. Eng. 2022, 10, 1981. https://doi.org/10.3390/jmse10121981

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Song Z, Huang Y, Liu Q, Hu X. Discovering the Characteristics of Community Structures and Functional Properties of Epiphytic Bacteria on Spartina alterniflora in the Coastal Salt Marsh Area. Journal of Marine Science and Engineering. 2022; 10(12):1981. https://doi.org/10.3390/jmse10121981

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Song, Zenglei, Yanyan Huang, Qing Liu, and Xiaoke Hu. 2022. "Discovering the Characteristics of Community Structures and Functional Properties of Epiphytic Bacteria on Spartina alterniflora in the Coastal Salt Marsh Area" Journal of Marine Science and Engineering 10, no. 12: 1981. https://doi.org/10.3390/jmse10121981

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