Enhalus Classification Essay

1. Introduction

Seagrasses are the only true flowering plants that grow and reproduce underwater in shallow coastal waters, and they are widely distributed. There are about 72 species of 12 genera of seagrasses throughout the world in a wide variety of habitats [1,2]. Seagrass meadows are characterized by high marine biodiversity and productivity and play a critical role in the equilibrium of coastal ecosystems and human livelihoods [2]. They can stabilize sediments, purify waters and provide food and habitats for marine creatures. It is estimated that 70% to 90% of commercial fish depend on seagrass meadows for a part of their life period [3]. Seagrass plants can also photosynthesize by taking in carbon dioxide and releasing oxygen, so they can potentially help to alleviate rising carbon dioxide levels and contribute to global climate change [4]. They can also exert great importance in the nitrogen and carbon biogeochemical cycles [5,6]. However, seagrass meadows have been at risk over the past few decades.

Previous investigations indicated that the seagrass habitat is declining worldwide. Waycott et al. [7] reported that the disappearance rate of seagrass has accelerated from less than one percent per year before 1940 to seven percent per year since 1990, and from the year 1979, 29% of the recorded seagrass areas have disappeared. Seagrass declines may be caused by natural causes, such as grazing, global climate change, sedimentation, erosion and disease and by anthropogenic factors, such as prop scarring, dredging, eutrophication, siltation and toxic chemicals [2]. Among the toxic pollutants, polycyclic aromatic hydrocarbons (PAHs) are thought to be the most ubiquitous pollutants in that they are of great environmental and human health concerns due to their widespread occurrence in marine environment, persistence and carcinogenic properties [3,8]. PAHs have been precisely classified by groups according to the temperature at which they form or their origin, and anthropogenic PAHs can be pyrogenic or petrogenic in origin [8,9]. Pyrogenic PAHs are mainly caused by incomplete combustion of organic matter (e.g., coal, petroleum and wood), while the petrogenic PAHs are derived mostly from crude oil and petroleum products (e.g., kerosene, gasoline, diesel, lubricating oil and asphalt) [8,9]. A comparative survey for chemical quality conducted in Florida seagrass beds suggested that toxic substances accumulated in seagrass-rooted sediments, the concentrations of which were significantly greater than those in adjacent non-vegetated sediments [3]. Furthermore, PAHs tend to be accumulated in sediments and plant tissue, which may lead to adverse results, and are transferred along the food chain [8].

Microbes, including fungi, play an important role in sustaining ecosystem health through functioning both as important contributors and transformers. They can act as plant pathogens and mycorrhizal symbionts. Previous studies showed that fungi could degrade PAHs by co-metabolizing PAHs to a wide variety of oxidized products and, in some cases, to CO2. Additionally, both ligninolytic fungi and non-ligninolytic fungi can contribute to the degradation of PAHs [10]. Seagrass Posidonia oceanica harbored rich fungal assemblages and plays a central role in the element biogeochemical cycle by decomposing organic matter [11,12].

The report of Apostolopoulou et al. (2012) [9] showed that seagrass Posidonia oceanic could be used as a recorder of ambient trace metal pollution and a bioindicator of spatiotemporal organic pollution trends. However, scarce information is available regarding the response of fungal communities in seagrass sediments to PAHs [13]. Hence, the present study was therefore undertaken with the aim of: (1) investigating the fungal community in Enhalus acoroides sediment by constructing a clone library, and the clone library sequencing results reveal the predominant fungal species and phylogenetic community structure; (2) determining the effects of a mixture of three PAHs, naphthalene (Nap), fluorene (Flu) and pyrene (Pyr), on the fungal community of seagrass sediments through polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and quantitative PCR assay (qPCR) through a 28-day laboratory incubation experiment and distinguishing the effects of the different concentrations of PAHs on fungal communities, as well as the temporal shift of fungal communities as determined from the DGGE bands and fungal 18S rRNA genes copies; (3) analyzing which parameters would be the critical one(s) affecting the fungal distribution during the incubation period.

2. Results and Discussion

2.1. Sediment Characteristics

Total phosphorus (TP), total nitrogen (TN) and total carbon (TC) contents and the C/N ratio for the sediment sample are summarized in Table 1. TP, TC and TN contents were 0.0035, 0.021 and 0.0384 by dry weight, respectively. Previous investigations suggested that total organic carbon is a very important variable related to the adsorption and persistence of PAHs in marine sediments [14]. The typical C/N values reported for most coastal sediments (C/N = 6 to 10), with the C/N ratios being much higher than the typical C/N values in Xincun Bay sediments, indicated that many inputs of organic carbon were received and that this was highly influenced by terrestrial and anthropogenic sources [15]. Northcott and Kevin [16] reported that organic pollutants in sediments would be controlled by organic matter when the TC content was above 0.1 percent.

Table 1. The original organic matter content of the sediment. TP, total phosphorus; TN, total nitrogen; TC, total carbon.

SampleTP (%)TN (%)TC (%)C/N Ratio
In situ Sediment0.00350.0210.38418.29

2.2. DGGE (Denaturing Gradient Gel Electrophoresis) Patterns the Fungal Communities under PAH (Polycyclic Aromatic Hydrocarbon) Stress

The investigation conducted by Sakayaroj et al. [17] confirmed that the fungal assemblages of seagrass E. acoroides were very rich. The three experimental groups, each in triplicate, were carried out by spiking a mixture of Nap, Flu and Pyr into the fresh seagrass sediment slurries from Day 0 (the beginning) to Day 28 (the end of the experiment). Group Control Check (CK) received no PAH addition, while the PAH concentrations of Group 1 and Group 2 were 100 and 1000 mg·kg−1, respectively. Samples were harvested on five different dates, namely S (Day 0), A (Day 2), B (Day 7), C (Day 14) and D (Day 28). In this study, the DGGE profile of 15 samples collected at five different incubation phases are shown in Figure 1A. Differences in the compositions of fungal communities were observed in the same group with the different incubation periods, with some bands obtained at a certain stage, whereas some bands were present through the whole incubation time. For instance, Band 14 could be detected through the 28-day incubation time, while Bands 25 and 26 only can be found at the early stages. By comparison, some bands, such as Bands 2, 4, 5, 19 and 21, could only be detected in Group 1 and/or 2, which indicated that such species can adapt to the PAH-contaminated environment. They may have high endurance with respect PAHs. Previous studies have proven that fungi would be necessary to efficiently remediate multiple polluted sites, and fungi obtained from mangrove sediments have high pyrene-degrading activity, even at an acidic pH of approximately 4 [10,18]. The result is consistent with the report of Wu et al. [19] that fungi could degrade the PAHs and use them as carbon sources for growth.

The DGGE bands were labeled “Seagrass Fungi DGGE (SFD)” in the phylogenic tree construction (Figure 1B). Twenty-six bands were excised from the DGGE gel and sequenced. Taxonomic analysis based on top BLAST hits in GenBank showed that all of the DGGE bands sequenced were identified as related to phyla Ascomycota and Basidiomycota and unidentified species (Figure 1B). For instance, Band 2 was more intensive in Group 1 than Group CK, and this may illustrate that the PAH addition stimulated its growth. Sequencing results showed the closest relative of Band 2 in the NCBI database is Saccharomyces cerevisiae (LK021686). This fungus also showed high similarity with Saccharomyces cerevisiae F-6 (99%) (Figure 1B), and Sutherland [20] and Deng et al. [21] have already proven that S. cerevisiae had the ability to oxidize PAHs. Likewise, species SFD2 detected in this study might also own the ability to degrade PAHs. Many of the band sequence results were related to uncultured fungi, and they suggest that seagrass sediment harbored a surprisingly rich fungal diversity. Salvo et al. [22] have found that there exists a correlation between the abundance of cultivable fungi and the concentrations of PAHs in harbor sediments. Fungi have already been applied to degrade hazardous organic waste, such as Aspergillus oryza, which has been reported to accumulate heavy metal ions in its mycelial mass [23]. The BLAST results showed that the species SFD12 had 100% similarity with A. oryza. In addition, species SFD12 was intense in Lane B2 after seven days of incubation, which indicated that this fungus could survive and grow under stress induced by high levels of PAHs.

Investigations on the fungal diversity of seagrass meadow recently have been mainly conducted by a culture-dependent method and focus on the fungal endophytes [24,25,26]. The results of Panno et al. [25] demonstrated that the fungal assemblage had habitat specificity, with only two species (Penicillium chrysogenum and P. janczewskii) distributed in four parts: leaves, rhizomes, roots and matte. Many fungal isolates derived from seagrass meadow exhibited antimicrobial potential due to their novel bioactive metabolites [26].

All of the sequences obtained in DGGE have been submitted to the GenBank database and are available under the following accession numbers: KP998709 to KP998734.

Figure 1. DGGE (denaturing gradient gel electrophoresis) profiles of sediment fungal communities exposed to different concentrations of PAH (polycyclic aromatic hydrocarbon) contamination at different incubation stages (S: Day 0; A: Day 2, B: Day 7, C: Day 14, D: Day 28; 0: control without PAH addition; 1: 100 mg/kg; 2: 1000 mg/kg) (A); neighbor-joining phylogenetic tree based on 18S rRNA gene sequences from DGGE bands. Bootstrap analysis was based on 1000 replicates. Bootstrap values from distance analysis are depicted. Bootstrap values less than 50% are not shown (B).

Figure 1. DGGE (denaturing gradient gel electrophoresis) profiles of sediment fungal communities exposed to different concentrations of PAH (polycyclic aromatic hydrocarbon) contamination at different incubation stages (S: Day 0; A: Day 2, B: Day 7, C: Day 14, D: Day 28; 0: control without PAH addition; 1: 100 mg/kg; 2: 1000 mg/kg) (A); neighbor-joining phylogenetic tree based on 18S rRNA gene sequences from DGGE bands. Bootstrap analysis was based on 1000 replicates. Bootstrap values from distance analysis are depicted. Bootstrap values less than 50% are not shown (B).

2.3. Dynamics of Shannon Index, Fungal Abundance Analysis

The patterns of the Shannon index and the abundance of the fungal communities over the whole incubation time presented different tendencies. The Shannon index determined by the DGGE band numbers of all groups changed rapidly though the 28-day incubation period (Figure 2A). In Group CK, it firstly increased from 2.58 to 2.70 in the first two days incubation and then decreased to 2.0 in the end. Group 1 showed a similar tendency of the Shannon index with the CK group in the first 14 days of incubation that firstly slowly increased from Day 0 to Day 2 and then decreased until Day 14. However, the Shannon index of Group 2 dropped sharply from 2.63 to 1.09 through the whole incubation time. The biggest difference among the three groups was that the Shannon index of Group CK increased slowly from Day 14 to Day 28, while Group 1 and Group 2 decreased from the beginning to the end of the incubation. This phenomenon could be explained by the fungi’s capability of utilizing or enduring PAHs to survive in environments with the addition of PAHs, while the others were suppressed or inhibited. This could lead to the reduction of the Shannon index. The results of this study agreed with the microbe of mangrove sediments under contamination by PAH [27

2.2. Protein Identification

To explore the correlation between the proteomic and metabolite profiles of buds and young expanding leaves, samples were analyzed by iTRAQ proteomics coupled with LC-MS/MS. A total of 60,820 spectra were generated from the iTRAQ experiment and the data were analyzed using Mascot software. A total of 8015 spectra were matched to known spectra, 6974 spectra were matched to unique spectra, 4746 were matched to peptides, 4260 were matched to unique peptides and 2507 were matched to proteins (Figure 2A). The distribution of the number of peptides defining each protein is shown in Figure 2B; over 55% of the proteins were represented by at least two peptides.

Figure 2. The spectra, peptides, and proteins, as well as the number of peptides in the iTRAQ proteomic analysis identified as matching proteins. The spectra, peptides and proteins were identified by searching against a database (A); and The number of peptides matched to proteins using MASCOT (B).

Figure 2. The spectra, peptides, and proteins, as well as the number of peptides in the iTRAQ proteomic analysis identified as matching proteins. The spectra, peptides and proteins were identified by searching against a database (A); and The number of peptides matched to proteins using MASCOT (B).

2.3. Functional Classification of the Differentially Expressed Proteins

The proteins whose levels changed more than 1.5-fold and had a p-values of less than 0.05 were considered differentially expressed. Based on these two criteria, 233 proteins were differentially expressed between the buds and the young expanding leaves, and these proteins were isolated and quantified using comparative proteomics via iTRAQ. Of the 233 differentially expressed proteins, 116 were more abundant and 117 were less abundant in the young expanding leaves compared with the buds. GO analysis revealed that the differentially expressed proteins participated in several biological processes (p < 0.05), as shown in Table S1. KEGG enrichment analysis suggested that the differentially expressed proteins are involved in several pathways (p < 0.05), including phenylalanine metabolism (Table S2).

The proteins were classified into seven functional categories based on their functional biological properties and pathways: metabolism (58, 25.11%), nucleic acid metabolism (33, 14.04%), protein metabolism (59, 25.11%), biological regulation and signal transduction (24, 10.21%), stress/defense/detoxification (19, 8.09%), transport (7, 2.55%), and unknown function (35, 14.89%) (Figure 3A). Of the up-regulated proteins, 25.00% (29 proteins) function in metabolism, 16.38% (19 proteins) function in nucleic acid metabolism, 16.38% (19 proteins) are involved in protein metabolism, 7.76% (nine proteins) have biological regulation and signal transduction function, 9.58% (11 proteins) function in stress/defense/detoxification, 4.31% (5 proteins) are involved in transport and 20.69% of them (24 proteins) were of unknown function (Figure 3B). Among the down-regulated proteins, 24.37% (29 proteins) function in metabolism, 11.76% (14 proteins) function in nucleic acid metabolism, 33.61% (40 proteins) have a role in protein metabolism, 12.61% (15 proteins) are involved in biological regulation and signal transduction, 6.72% (8 proteins) are involved in stress/defense/detoxification, 1.68% (two proteins) function in transport and 9.24% (11 proteins) were of unknown function (Figure 3C). More detailed information can be found in Table 1.

Figure 3. Functional classification of the differentially expressed proteins. Functional groups and the numbers of proteins of all 233 differentially expressed proteins that fall into each group (A); categorization of the 116 up-regulated proteins (B); and categorization of the 117 down-regulated proteins (C). The number in each functional category represents the number of proteins in that category.

Figure 3. Functional classification of the differentially expressed proteins. Functional groups and the numbers of proteins of all 233 differentially expressed proteins that fall into each group (A); categorization of the 116 up-regulated proteins (B); and categorization of the 117 down-regulated proteins (C). The number in each functional category represents the number of proteins in that category.

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