Rapid economic development in China has led to serious contamination and eutrophication in many inland lakes and rivers. For the safety of the water supplies in cities, numerous large artificial reservoirs have been built in the suburban areas. Finding ways to conserve and sustainably use these reservoirs is one of the most urgent research tasks faced by local governments. Huguangyan Maar Lake (21°09′N, 110°17′E) lies in the north of Leizhou Peninsula in China, close to Zhanjiang City, Guangdong Province. It is one of well-preserved maar lakes in the world and the swales of volcanic crater form the lake's catchment area. This lake is heart-shaped, has a surface area of 2.25 km2, and is encircled by a high tephra wall. With no river inputs, which makes it similar to a reservoir, its water sources are mainly derived from precipitation and groundwater. The depth of the lake sediment is up to 50 m. It has been found that these sediments are made up of fossil algae, which can reflect past climatic and environmental variation (Wolfe et al., 2006). These characteristics make the lake an ideal site for studying ancient global changes. For example, by studying biogenic silica and magnetic susceptibility in the sediments of Huguangyan Maar Lake, along with radiocarbon dating, it has been suggested that the summer monsoon was stronger in the early Holocene but began to decline at 6 080 a BP and has weakened substantially since 3 600 a BP (Yancheva et al., 2007; Wu et al., 2012). The decreasing summer solar radiation at 30°N through the Holocene was probably responsible for the gradual weakening of the summer monsoon (Wu et al., 2012).
Bacteria and Archaea are known to play important roles in the transformation of organic matter and in the biogeochemical cycling of nitrogen, sulfur, and phosphorus (Carlsson and Caron, 2001; Salcher et al., 2010). The diversity and structure of microbial communities in lake water and sediment have been extensively investigated, and many unique microbial lineages associated with hypersaline, hyper alkaline, high temperature, acidic, high altitude or strictly anoxic habitats have been identified (Vila-Costa et al., 2013; Zhang et al., 2015; Aguirre-Garrido et al., 2016; Kan et al., 2016; Paul et al., 2016; Sirisena et al., 2018). The diversity and structure of the microbial populations are controlled largely by environmental factors. For instance, major vent-associated geochemical constituents including CH4, CO2, H2, SO42-, and metals were likely the main drivers for shaping microbial populations in Yellowstone Lake (Kan et al., 2016). In Lonar Lake, a hypersaline meteorite crater lake, a distinctive microbial community that includes methylotrophs and purple sulfur and non-sulfur photosynthetic bacteria has been reported (Paul et al., 2016). In Tibetan lakes, salinity and altitude have been shown to play important roles in the selection of bacterial taxa (Zhang et al., 2013). In the Lake Lucero playa, microbial communities could be used to characterize unique geochemical microenvironments (Sirisena et al., 2018).
Huguangyan Maar Lake is a crater lake, created by basaltic phreatomagmatic eruptions, whose unique habitats and chronic geographical isolation may have shaped novel phylogenetic lineages. However, little is known about the diversity and structure of the microbial populations in the lake to date. Surveying this lake may contribute to unraveling which types of microorganisms could inhabit similar sediments in maar lakes. Our data will provide clues for deducing ancient biological traces or biomarkers in similar environments.2 MATERIAL AND METHOD 2.1 Sample collection and analysis of chemical parameters
Water and sediment samples were taken from Huguangyan Maar Lake, which is located in Zhanjiang in Guangdong Province (Fig. 1 and Table 1), in October 2015. Water samples were collected from the surface (0 m) and bottom (close to the sediment) layers of the lake with a 2-L bottle. Microbial cells were obtained by filtering 2 L of lake water on 0.22-μm membrane filters (Whatman). The membranes were stored in liquid nitrogen until the DNA was extracted. Surface sediment samples (0 to 5 cm) were collected using a stainless steel grab sampler, placed in clean plastic bags, and then stored in a freezer at -80℃. Analysis of chemical parameters was performed as previously described (Lovalvo et al., 2010; Clingenpeel et al., 2011; Inskeep et al., 2015). pH was measured on site using a multi-parameter meter (Thermo Fisher Scientific, USA). Dissolved oxygen (DO) was determined through the Winkler titration method. Chlorophyll a (Chl-a) concentrations were determined fluorometrically after water samples were filtered on a GF/F filter and extracted with 90% acetone. Nutrients including nitrite (NO2-), nitrate (NO3-), ammonium (NH4+), silicate (SiO32-), and phosphate (PO43-) were measured on a San++ continuous flow analyzer (Skalar, Netherlands). Nutrients in sediments were determined after obtaining pore waters by centrifugation.2.2 DNA extraction, PCR amplification, and highthroughput sequencing
The microbial genomic DNA of the water samples was extracted from the filters by using the E.Z.N.A.® Water DNA Kit (Omega Bio-Tek, USA) and following the manufacturer's instructions. For the sediment samples, 0.5 g (weight wet) of each sediment fraction was used for DNA extraction using the E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, USA). The purity and concentration of DNA were determined with the NanoDrop ND-2000 spectrophotometer (Thermo Scientific, USA).
The V3–V4 region of the bacterial and archaeal 16S rRNA gene was amplified by PCR using barcoded primers (Bacteria: 338F 5′-barcode- ACTCCTACGGGAGGCAGCAG-3′ and 806R 5′-GGACTACHVGGGTWTCTAAT-3′; Archaea: 524F_10_ext 5′-barcode-TGYCAGCCGCCGCGGTAA-3′ and Arch958R 5′-YCCGGCGTTGAVTCCAATT-3′). For the bacterial amplification, the following PCR parameters were used: 95℃ for 3 min; 27 cycles of 95℃ for 30 s, 55℃ for 30 s, and 72℃ for 45 s; and a final extension at 72℃ for 10 min. The Archaea PCR was performed as follows: 95℃ for 3 min; 35 cycles at 96℃ for 30 s, 55℃ for 30 s, and 72℃ for 45 s; and finally, 72℃ for 10 min.
Triplicate PCR products were put together per sample and then clarified by the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA). The resultant products were paired-end sequenced on an Illumina MiSeq platform following the manufacturer's manual. The raw reads were stored into the NCBI Sequence Read Archive database with SRA number SUB3050422.2.3 Processing of sequencing data
Raw FASTQ files were demultiplexed, quality-filtered by Trimmomatic software, and merged by FLASH with the following criteria: (ⅰ) reads with shorter than 50 bp were discarded; (ⅱ) exact barcode matching and 2 nucleotide mismatch in primer matching were adopted, and reads containing ambiguous characters were removed; and (ⅲ) paired reads that overlapped by at least 10 bp were merged according to their overlap sequence, and reads that could not be merged were discarded.
UPARSE (version 7.1, http://drive5.com/uparse/) was used to assemble Operational Taxonomic Units (OTUs) based on 97% similarity cutoff. UCHIME was applied to discern and get rid of chimeric sequences. Each 16S rRNA gene sequence was searched against the Silva (SSU123) 16S rRNA database using RDP Classifier (http://rdp.cme.msu.edu/) and the taxonomic information with a confidence threshold of 70% was retrieved (Amato et al., 2013).
To determine whether the amount of pro sequencing was reasonable, the rarefaction curve for each sample was calculated using the Mothur online software (http://mothur.org; Schloss et al., 2011). To compare the community richness and diversity of Bacteria and Archaea in the water and sediment, richness indices (Chao and ACE) and diversity indices (Shannon and Simpson) were computed with Mothur (Schloss et al., 2011); coverage for each sample was also calculated in Mothur at the same time. Bar plots of community structure at the phylum and class levels were done with QIIME software. The microbial networks were constructed using NetworkX packages in Python; their topologies were based on the number of links and correlation coefficients between neighboring nodes. Principal coordinates analysis (PCoA) were performed with UniFrac (Lozupone and Knight, 2005) using clustering at 97% sequence identity. Redundancy analysis (RDA) was performed using CANOCO software. Phylogenetic trees were constructed using the neighbor-joining algorithms of MEGA 5.0.3 RESULT 3.1 Environmental parameters
Huguangyan Maar Lake is largely unaffected by allochthonous riverine inputs. Chemical data are shown in Table 1. Chl-a measurements ranged from 1.82 to 3.4 μg/L in the surface waters. Nitrate and nitrite concentrations were slightly higher in the nearshore areas of the lake (H06, H07, H08, H09), likely due to surface runoff inputs. N:P ratios ranged from 70 to 170 in the surface waters. Ammonium concentrations in the bottom waters were generally higher than in the surface waters. Meanwhile, DO was higher near the surface (on average 9.6 mg/L) than at the bottom (on average 7.9 mg/L). Finally, high concentrations of SiO32-, PO43-, NO2-, and NO3- were detected in the pore waters of the surface sediments.3.2 Microbial community composition
A total of 942 402 high-quality 16S rRNA gene sequences were obtained from the lake water and sediment samples. Of these, 678 453 came from the primers for Bacteria, with an average of 45 230 reads per sample. There were 263 949 reads for Archaea, averaging 29 327 reads per sample. For Bacteria and Archaea, the number of OTU from the sediment samples was much higher than that of OTU from the water samples (Table 2). Estimated OTU richness and diversity indices indicated that the sediment samples had higher diversity for both Bacteria and Archaea than the water samples (Table 2).
Actinobacteria, Betaproteobacteria, Cyanobacteria, Deltaproteobacteria, Gammaproteobacteria, Sphingobacteria, Alphaproteobacteria, and Verrucomicrobia were the major bacterial groups in all of the water samples (Fig. 2). Actinobacteria was the most dominant group in the lake, ranging from 39.7% (H09_0m) to 61% (H06_0m) of the total reads except for H02_2m. Betaproteobacteria was the second most abundant group in the sampled waters, in the abundance ranging from 6.3% (H02_2m) to 21.7% (H07_0m). Cyanobacteria was widely found in both the surface and bottom waters, peaking at the 2 m water depth of H02_2m (33% of the reads). Sphingobacteria and Verrucomicrobia were detected at significant levels in all the water samples, in an average abundance of 5.1% and 4.1% of the total reads, respectively. For the sediment samples, a distinct microbial community composition was observed. Deltaproteobacteria, Anaerolineae, Nitrospira, Dehalococcoidia, Acidobacteria, Chloroflexi, and TA06_norank were the major groups in the sediments. Deltaproteobacteria was the most abundant group, making up 16.1% to 23.9% of the total reads in each sediment sample. Anaerolineae and Nitrospira were detected at significant levels in all of the sediment samples, in an average abundance of 10% and 8.9% of the total reads, respectively. Meanwhile, Anaerolineae was seen at very low levels, and Nitrospira could barely be detected in the water column. Finally, Dehalococcoidia, TA06_norank, Ignavibacteria, Latescibacteria, and Aminicenantes were only detected in sediments, and their average abundance made up 2.2% to 6.8% of the total reads.
The archaeal reads were found in nine of 15 samples (Fig. 3). Among them, the three were water samples and six were sediment samples. Woesearchaeota (DHVEG-6) was the most dominant archaeal group in the three water samples, ranging from 14.3% to 19.8% of the reads. Unclassified Archaea was the second most abundant group in water samples, accounting for 10.2% to 18.4% of the reads of each sample. Deep Sea Euryarchaeotic Group (DSEG), Parvarchaeota, and Marine Group Ⅰ were also detected at significant levels in the water. Parvarchaeota was the most abundant group and was found at the 8 m water depth in 18.9% of the reads (H04_8m). Marine Group Ⅰ made up a significant portion (34.6%) of the reads in the surface waters at site H08. Low levels of Methanobacteria were also found in the water samples (0.8% to 4.6%). The archaeal composition in the sediments was very distinct from that of the water. Miscellaneous Crenarchaeotic Group (MCG), Thermoplasmata, and Methanomicrobia dominated in the sediment samples, with their average abundances accounting for 39.9%, 23.6%, and 16.1% of the total reads, respectively. MCG, the most abundant group, was found in the sediments at site H04.3.3 Microbial interaction
To discriminate interactions between different microbial groups at the family level, co-occurrence analysis was applied. The parameters of the networks were provided in Supplementary Table 1. For the bacterial groups, 42 and 34 interactions were detected in the water and sediment samples, respectively (Fig. 4a, b). In addition, we detected 23 archaeal interactions in the sediment samples (Fig. 5). Archaeal interactions were not analyzed in water samples because Archaea were only detected in three water samples. We showed the co-presence of Moraxellaceae, Enterococcaceae, Streptococcaceae, Bacillaceae, Xanthomonadaceae, Caulobacteraceae, Pseudomonadaceae, Acetobacteraceae, and Rhodocyclaceae in the water column. Interestingly, the bacterial families Desulfobacteraceae, Methylococcaceae, and Syntrophaceae were correlated positively with one another in the sediments. Negative correlations were observed in the sediments for the bacterial families Desulfobacteraceae and Cystobacteraceae, 43F-1404R and TA06, Microgenomates and MSBL5, and Gemmatimonadaceae and B1-7BS. For Archaea in the sediments, the family Methanocellaceae was positively correlated with MCG (Fig. 5); Marine Benthic Group B, Marine Benthic Group D, and Group C3 were positively correlated with one another; and MKCST-A3 was negatively correlated with the terrestrial group, Diapherotrites, and AMOS1A- 4113-D04.3.4 Spatial distribution of microbial communities and their relationship to environmental parameters
The PCoA analysis showed that the bacterial populations from water samples and sediment samples were grouped separately, but that they were not separate in the surface and bottom water samples (Fig. 6a). The archaeal PCoA analysis revealed that the sediment samples were clustered together and separated from the water samples (Fig. 6b). RDA analyses showed that the surface water bacterial communities from three sites were related to increasing Chl-a, DO, pH, NO3-, and PO43-, while bottom water bacterial communities from three sites were correlated with increasing SiO32- and NH4+ (Fig. 7).4 DISCUSSION
We observed that both Bacteria and Archaea were more diverse in the sediment samples than in the water samples from Huguangyan Maar Lake. Although the organic matter was not measured in the sediments, the concentrations of inorganic nutrients (Si, PO43-, NO2-, NO3-) in the sediment pore water were much higher than those in the water column. It has been shown that loads of increasing phosphorus can enhance bacterial diversity and abundance (Kou et al., 2016). In addition, high levels of organic matter in the sediment can markedly elevate prokaryotic activity and the diversity of microbial populations (Parkes et al., 2005). However, the bacterial cooccurrence analysis indicated a closer relationship among bacterial community members in the water samples than in the sediment samples, which suggests that the flow of water may prompt metabolite exchanges and bacterial interactions. This phenomenon is inconsistent with previous findings in other lakes (Paul et al., 2016).
In accordance with other lakes around the world, Actinobacteria, Betaproteobacteria, and Cyanobacteria were the dominant groups in water samples of Huguangyan Maar Lake (Xing et al., 2009; Ye et al., 2009). Actinobacteria have been shown to be predominant in many lakes and are considered ecological generalists in freshwater systems (Percent et al., 2008). In our study, the most abundant group within the Actinobacteria was the hgcI clade, also called acI, followed by CL500-29 marine group. The hgcI clade is prevalent in various lakes around the world (Warnecke et al., 2004). A recent genomic analysis has indicated that members of the hgcI clade have the capacity to utilize organic compounds containing nitrogen and carbohydrates (Ghylin et al., 2014). In addition, the actinorhodopsin gene has been found in bacteria of this clade (Ghylin et al., 2014), suggesting that they may have the potential to make use of light energy. The CL500-29 clade has previously been found to be a generalist and can utilize different carbon sources (Lindh et al., 2015). Our results suggest that these generalists may play important roles in the self-cleaning of Huguangyan Maar Lake. In the water samples, 57 OTUs were affiliated with Cyanobacteria. Among them, five were affiliated with Synechococcus of Cyanobacteria (Fig. 8), and reads from Synechococcus accounted for 47.6% of total reads from Cyanobacteria, suggesting that Synechococcus was one of the important primary producers in this lake.
Deltaproteobacteria were the most abundant group in the sediment, followed by Anaerolineae and Nitrospira. Deltaproteobacteria have been shown to be prevalent in the sediments of lakes ranging from oligotrophic to eutrophic (Tamaki et al., 2005; Schwarz et al., 2007; Ye et al., 2009). Yet the representative Deltaproteobacteria lineages may be different in different lake systems. In the anoxic sediment of Lake Kinneret in Israel, for example, Syntrophobacterales and Desulfobacterales dominated (Schwarz et al., 2007). Meanwhile, in contaminated Lake Geneva, Switzerland, sulfatereducing bacteria and Fe(Ⅲ)-reducing bacteria affiliated with Deltaproteobacteria were very abundant in the sediments (Haller et al., 2011). In this study, the dominant phylogenetic groups within the Deltaproteobacteria were 43F-1404R, Sva0485, and Desulfobacteraceae. A single-cell genome analysis showed that members of the 43F-1404R group might be heterotrophs with a canonical electron transport chain and that they can probably reduce nitrate and nitrite to ammonium (Hug et al., 2016). In marine sediments with active sulfur cycling, the 43F-1404R group was frequently detected (Asami et al., 2005). The functions of the members of the Sva0485 group are still unclear at present. But Pelobacter carbinolicus, which is affiliated with the Sva0485 group, has been reported to be able to reduce Fe(Ⅲ) and sulfur (Lovley et al., 1995). The members of Desulfobacterales can reduce sulfates to sulfides to obtain energy. Our findings suggest that Huguangyan Maar Lake sediments were enriched with sulfurcontaining compounds, which could be a result of the soils being derived from weathered volcanic rocks. It has been found that all cultured members of the class Anaerolineae, which belongs to the phylum Chloroflexi, are neutrophilic, are strictly anaerobic chemoorganotrophs, and feed on sugars and polysaccharides. To date, reports of Anaerolineae in lakes around the world are rare. Interestingly, we observed that more than 90% of the Anaerolineae were novel 16S rRNA gene sequences (< 97% similarity to the closest sequences in the SILVA database), which suggests that this maar lake provides a unique habitat for these organisms.
Woesearchaeota (DHVEG-6) and unclassified Archaea dominated the archaeal data set of the water samples, comprising up to 24.5% to 38.2% of the archaeal sequences. All of the Woesearchaeota were novel 16S rRNA gene sequences at the class, order, and family levels. Woesearchaeota has been found in several inland saline lakes of the Monegros Desert (Casamayor et al., 2013). Single-cell genome analysis showed that members of Woesearchaeota had small genome sizes and lack important metabolic pathways (Castelle et al., 2015), meaning that they might be symbiotic or parasitic Archaea. In all sediment samples, the most dominant archaeal group was the MCG. The MCG has been shown to be particularly abundant and widespread in marine sediments (Durbin and Teske, 2012; Lazar et al., 2015). The widespread distribution of MCG could signify that they are a generalist group in the sediment realm. Recent metagenomic and single-cell genomic analyses have demonstrated that members of the MCG lineage may be involved in the degradation of aromatic compounds and detrital proteins, respectively (Meng et al., 2014). In this study, all detected MCG were novel 16S rRNA gene sequences, suggesting that distinct evolutionary MCG subgroups have occurred in the lake sediments. This may be due to long-term geographic segregation. These MCG would provide abundant labile compounds for other microorganisms as primary degraders of detrital proteins in the sediments. Thermoplasmata was the second most abundant archaeal lineage in the Huguangyan sediments. Both saline and freshwater MCG have been shown to recurrently co-occur with Archaea of the class Thermoplasmata in sediments, possibly indicating a relevant trophic connection between the two clades (Fillol et al., 2016). In our study, Marine Benthic Group D dominated the class Thermoplasmata. Single-cell genomics have revealed that members of the MCG and Marine Benthic Group D lineages may be involved in the degradation of detrital proteins in both marine and continental sediments (Lloyd et al., 2013). We speculate that the two clades may work cooperatively to degrade detrital proteins. Methanomicrobia was also detected as an abundant group in the sediment. This group was mainly divided into Methanosaetaceae and the Methanomicrobiaceae family. Methanomicrobia have frequently been detected in high abundances in mesoand eutrophic freshwater lakes (Glissman et al., 2004; Schwarz et al., 2007; Ye et al., 2009). They have been found to have the ability to use hydrogen and acetate to produce methane (Schwarz et al., 2007), and have thus played key roles in the complete degradation of organic biomass in freshwater sediments.5 CONCLUSION
We present here a diverse and distinct picture of bacterial and archaeal fauna in both the water and sediments of Huguangyan Maar Lake using high-throughput sequencing of the 16S rRNA gene. We found that the dominant microbial groups in the lake were similar to those in many other lakes at higher phylogenetic levels (phylum or class), even though this lake has been segregated since 160 000 to 140 000 a BP. At lower phylogenetic levels, some novel archaeal clades or lineages were found, and they may play key roles in the degradation of various kinds of organic matter in this lake. Further studies on these archaeal groups will provide new insights into the self-cleaning of ponds or reservoirs in which they reside.6 DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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