Abstract
Recent advances have allowed for greater investigation into microbial regulation of mercury toxicity in the environment. In wetlands in particular, dissolved organic matter (DOM) may influence methylmercury (MeHg) production both through chemical interactions and through substrate effects on microbiomes. We conducted microcosm experiments in two disparate wetland environments (unvegetated and vegetated sediments) to examine the impacts of plant leachate and inorganic mercury loadings on microbiomes, DOM cycling, and MeHg production in the St. Louis River Estuary, which has a legacy of mercury contamination. Overall, our research reveals the greater relative capacity for mercury methylation in vegetated over unvegetated sediments in this environment. Further, oligotrophic unvegetated sediments receiving leachate produced more MeHg than unamended microcosms, pointing to the role of organic matter and vegetation patterns as an important control on MeHg production in these sediments. We also show that while leachate influenced the microbiome in both environment types, sediment with high organic carbon content was more resistant to change than oligotrophic sediment. Our work supports emerging research suggesting that Clostridia may be important methylators in oligotrophic environments. We demonstrate changes in community structure towards Clostridia and metagenomic shifts toward fermentation as well as degradation of complex DOM and MeHg production in unvegetated microcosms receiving leachate. Together, our work shows the importance of wetland vegetation in driving MeHg production in the Great Lakes region and provides evidence that this may be due to both enhanced microbial activity as well as differences in the composition of microbiomes associated with higher DOM levels.
Introduction
Mercury methylation in anoxic sediments is central to the bioaccumulation of mercury in plant and animal tissue (Benoit et al., 2003; Morel et al., 1998; Ullrich et al., 2001) and poses a significant environmental and human health concern in the freshwater wetlands of the Great Lakes region (Branfireun et al., 1999; Harmon et al., 2005; Jeremiason et al., 2006). Dissolved organic matter (DOM) has been a focus of geochemical investigations for decades, and both positive and negative interactions between DOM and mercury methylation – principally, a microbial transformation (Hsu-Kim et al., 2013) – have been demonstrated under contrasting environmental conditions (Graham et al., 2013; Hsu-Kim et al., 2013; Ravichandran, 2004). Yet, the role of sediment microbiomes that directly mediate mercury methylation have not been examined in the context of DOM quantity and quality.
Dissolved organic matter is comprised of various classes of organic compounds (primarily organic acids) with a wide range of molecular weights and aromaticities (Lambertsson and Nilsson, 2006; Wetzel, 1992). DOM concentrations are elevated in wetlands relative to other freshwater systems (>10 mg/L), and the humic fraction derived from plant leachate predominates. With respect to mercury cycling in wetlands, mercury methylation is impacted both by binding properties of the humic DOM fraction, resulting either in increased dissolution of inorganic mercury complexes or in physical inhibition of mercury bioavailability (Drexel et al., 2002; Haitzer et al., 2002; Waples et al., 2005), and by provisioning organic substrate for microbial activity (Hsu-Kim et al., 2013; King et al., 2000; Lambertsson and Nilsson, 2006).
These effects may also vary with ambient geochemistry, as Graham et al. (2013) have demonstrated that sulfide concentrations and DOM aromaticity interact to influence MeHg production. Further, interactive effects of sediment microbiomes and DOM biogeochemistry are less well-resolved than other aspects of linkages between environmental geochemistry and mercury toxicity. Since mercury methylation is so strongly impacted by DOM, environments such as the St. Louis River estuary, which contains areas of both vegetated and unvegetated sediments, may also show differences in the capacity for MeHg production across vegetation graidents. Numerous studies have shown regulation of freshwater microbial communities by DOM quantity or quality (Docherty et al., 2006; Forsström et al., 2013; Pernthaler, 2013), and such changes in environmental microbiomes may alter ecosystem biogeochemical cycling (Graham et al., 2016). The character of humic DOM (putatively most influential to mercury methylation) can be assessed at scales relevant to microbial activity with fluorescence spectroscopy, which correlates changes in humic fluorescence relative to other portions of the optically-active DOM pool (Fellman et al., 2010).
While the microbial mechanisms generating methylmercury are poorly understood, the recent discovery of the hgcAB gene cluster has allowed investigations into the microbial ecology of mercury cycling (Gilmour et al., 2013; Parks et al., 2013; Poulain and Barkay, 2013; Smith et al., 2015). In particular, interactions between environmental microbiomes, DOM quantity and quality, and mercury methylation in natural systems remain an uncertainty in predicting hotspots of mercury toxicity in the environment (Hsu-Kim et al., 2013; Podar et al., 2015). Nonetheless, recent work has increased knowledge on the microbiology of mercury methylation, expanding potential microorganisms mediating methylation beyond sulfate-reducing bacteria (Compeau and Bartha, 1985; Hsu-Kim et al., 2013), iron-reducing bacteria (Kerin et al., 2006) and methanogens (Hamelin et al., 2011). To date, all tested microorganisms containing the hgcAB gene cluster have been confirmed as methylators, and the gene appears to be highly conserved allowing it to serve as a genetic marker for methylating organisms (Gilmour et al., 2013; Hsu-Kim et al., 2013). Gilmour et al.12 have identified five clades of putative methylators, including new clades of syntrophic and Clostridial organisms. While research has provided insight into the abundance of these new organisms in mercury-contaminated landscapes (Bae et al., 2014; Hamelin et al., 2015; Liu et al., 2014a), many studies have continued to focus on the involvement of sulfate-(Liu et al., 2014b; Lu et al., 2016) and iron-reducing bacteria (Si et al., 2015) as well as methanogens (Yu et al., 2013) in mercury methylation. As such, the importance of organisms with alternative metabolisms in mercury methylation remain relatively unexplored. Resolving interactions between sediment microbiomes, environmental chemistry, and inorganic mercury complexes is thought to be central in understanding variation in methylation rates among natural systems (Gilmour et al., 2013; Hintelmann et al., 2000; Hsu-Kim et al., 2013).
Here, we examine the influence of DOM from plant leachate on net methylmercury (MeHg) production in a contaminated freshwater estuary at the base of Lake Superior. First, we describe MeHg production in environments associated with high (vegetated sediments) and low (unvegetated sediments) ambient DOM in building in understanding of conditions that underlie mercury methylation in the St. Louis River Estuary. We hypothesize that environmental biogeochemistry (in particular, DOM quantity and quality) influences mercury methylation both by regulating microbial activity and by shifting the abundance and metabolic diversity of mercury methylators. We test this hypothesis across chemically distinct sediments associated with unvegetated (oligotrophic) and vegetated (high-C) environments, using a microcosm experiment to monitor changes in sediment microbiomes, DOM chemical quality, and net MeHg production in response to additions of leachate from overlying plant material. In total, this work delineates a broad view of how vegetated vs. unvegetated sediments in the St. Louis River Estuary may have different capacities for the cycling of mercury. Our results show the importance of DOM availability as a control the production of MeHg in Lake Superior’s St. Louis River Estuary, an integral environment to human society and industries of the region. Further, our work provides evidence for the involvement of metabolisms that ferment recalcitrant organic matter in mercury methylation, particularly within oligotrophic unvegetated environments, an effect that may be imperative to understanding and mitigating human exposure to MeHg with increasing DOM deposition into aquatic environments (Regnier et al., 2013).
Methods
Field site
The St. Louis River Estuary is home to the largest U.S. port on the Great Lakes and covers roughly 12,000 acres of wetland habitat directly emptying into Lake Superior. Mining in the headwaters, industrial discharge in the port, and atmospheric deposition have left a legacy of mercury contamination in the sediment. We obtained sediment samples from vegetated (Zizania palustris (wild rice), 46° 40.855’ N, 91° 59.048’W) and unvegetated (46° 41.918’ N, 92° 0.123’ W) patches in Allouez Bay and wild rice plant matter from nearby Pokegama Bay (46.683448°N, 92.159261°W) to minimize sampling impacts. Both habitats are clay influenced embayments that drain an alluvial clay plain created by deposition during the retreat of the last glaciation approximately 10,000 years BP.
Experimental design
A total of 20 anoxic microcosms were constructed in September 2013 to investigate relationships between sediment microbiomes, DOM chemical quality, and mercury methylation. Sediment was obtained in 250-mL amber Nalgene bottles from the top 10 cm of sediment using a block sampling design described in the Supplemental Material. Leachate was extracted using 1 g dried, ground plant matter:20 mL of Nanopure water, filtered through Whatman 0.7 μm GFF filters (Whatman Incorporated, Florham Park, NJ, USA). Microcosms were constructed in 500-mL airtight glass mason jars and stored at room temperature in the dark in Mylar bags with oxygen-absorbing packets between subsampling. Our experiment was designed to promote microbial MeHg production by minimizing abiotic photo-methylation and -demethylation (Morel et al., 1998) and sustaining a low redox environment to inhibit demethylation (Compeau and Bartha, 1984). All experimental set up and sample processing was conducted in an anaerobic glovebox containing 85% N2, 5% CO2, and 10% H2 gas mix at the USGS in Boulder, CO. Jars were degassed in the glovebox for 48hr prior to experimentation to remove oxygen.
A full-factorial design was employed with two environments (vegetated and unvegetated sediment) and two treatments (plant leachate and Nanopure water). Sediments were homogenized via mixing but unsieved to maintain environment characteristics. Large roots (>1 cm) were infrequent and removed to lessen heterogeneity among replicates. Each microcosm received 100 g wet sediment, and 250 mL solution consisting either of leachate at 100 mg/L (∼5x natural concentrations to mimic a loading event) and HgCl2 at 20 mg/L (50 μg/g Nanopure water (leachate replicates) or solely of HgCl2 at 20 mg/L in nanopure water (no leachate replicates). The purpose of HgCl2 addition at high concentration was to negate initial differences in mercury, overcome HgCl2 inaccessibility due to abiotic organo-metal interactions, and provide substrate for the duration of the experiment. HgCl2 concentrations were comparable to microcosm experiments of similar design (Harris-Hellal et al., 2009; Poulain et al., 2006; Ruggiero et al., 2011; Zhou et al., 2012), and we estimate minimal dosage effects as communities without leachate did not change through time in unvegetated microcosms and only slightly changed through time in vegetated microcosms (R2 = 0.19, see results and Fig S1). Microcosms were incubated for 28 days, and subsamples of sediment and water were taken every seven days for analysis of sediment microbiomes and DOM characteristics.
Sediment chemistry, extracellular enzyme activity, and mercury methylation
Percent carbon and nitrogen, NO3−/NO2−, NH4+ total particulate organic carbon (TPOC), total dissolved nitrogen (TDN), pH, and extracellular enzyme activities of β-1,4-glucosidase, β-1,4-N-acetylglucosaminidase, and acid phosphatase were determined on pre-incubation sediments, as described in the Supplemental Material. For total- and methylmercury analysis, initial (day 0) and final (day 28) subsamples were frozen at -70°C, freeze-dried, and sent on dry ice to the USGS Mercury Lab in Middleton, WI for analysis by aqueous phase ethylation, followed by gas chromatographic separation with cold vapor atomic fluorescence detection (Method 5A-8), acid digestion (Method 5A-7), and QA/QC. Mercury analyses were performed on 3 of 5 replicates for each environment and microcosm type. All other analyses were performed on 5 replicates, except for no unvegetated microcosms without leachate beyond day 0 (n = 4, one replicate destroyed during experiment).
Dissolved organic matter characteristics
Water subsamples were collected at 7-day intervals (days 0, 7, 14, 21, and 28) to determine non-purgeable organic carbon (NPOC) concentration and specific UV absorbance at 254 nm (SUVA254) as well characteristics of the optically active DOM pool (mostly associated with humic DOM fraction), as described in the Supplemental Material. We calculated the fluorescence index (FI) to determine the relative contribution of microbial vs. terrestrial matter to the DOM pool, the humic index (HIX) to identify large aromatic compounds consistent with humic material, and the freshness index to determine the availability of labile carbon (Fellman et al., 2010; Gabor et al., 2014a) using MATLAB software (2013a, The MathWorks, Natick, MA) according to Gabor et al. (2014b).
Microbial DNA extraction, 16S rRNA amplicon, and metagenomic shotgun sequencing
DNA from each sediment subsample was extracted using the MO Bio Power Soil DNA Extraction kit (MO BIO Laboratories, Carlsbad, CA, USA), as described in Knelman et al. (2012). The region encoding the V4 fragment of the 16S rRNA gene was amplified with the primers 515F/806R, using the PCR protocol described by the Earth Microbiome Project (Caporaso et al., 2012) (Supplemental Material). The final multiplexed DNA samples were sequenced at CU-Boulder (BioFrontiers Institute, Boulder, CO) on an Illumina MiSeq with the MiSeq Reagent Kit v2, 300 cycles (Illumina, Cat. # MS-102-2002) to generate 2 x 150-bp paired-end reads. Sequences are available at XXXXXX. In addition, 3 unvegetated leachate replicates at day 0 (before leachate addition) and day 28 were sent to the Joint Genome Institute (JGI) for shotgun metagenomic sequencing on the Illumina HiSeq platform. Sequences are available at XXXXX.
Sequence analysis
Partial 16S rRNA gene were filtered for sequence length and minimum quality score in the UPARSE pipeline (Edgar, 2013) and OTUs were assigned using QIIME (Caporaso et al., 2010) (Supplemental Material). Metagenomic shotgun sequences were assembled and classified against the protein families database (Pfam) (Finn, 2012), Clusters of Orthologous Groups of proteins (COG) (Tatusov et al., 2003), and Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000) by JGI via the IMG database pipeline (Markowitz et al., 2012).
In addition, 46 of 52 genomes identified by Parks et al. (2013) were represented by complete or partial 16S rRNA gene sequences in the NCBI GenBank database (Benson et al., 2013), spanning all clades of methylators. We used two different approaches to determine methylator relative abundance and community structure. To determine relative abundance, we combined available methylating sequences with generated sequences and re-preformed de novo OTU-picking. We then identified OTUs containing known methylator sequences as potential methylators. Because of high Deltaproteobacteria abundance, many closely-related methylator sequences may have clustered with non-methylating Deltaproteobacteria in this approach. Thus, to examine methylator community structure at finer resolution, we created a database of known methylator sequences and performed closed-reference OTU-picking in QIIME against this database. In addition, a BLAST database was constructed from all hgcA and hgcB gene sequences available in GenBank. A BLASTX search was conducted against this database to identify taxonomic affiliation of methylators in our samples; however, our query resulted in no matches, likely due to inadequate sequencing depth.
Statistical analysis
All analyses, unless otherwise noted, were conducted using the R software platform. Shapiro-Wilk tests were used to verify normality and assess the appropriateness of parametric vs. non-parametric tests. Multivariate sediment properties (e.g., sediment geochemistry, extracellular enzyme activity, and DNA quantity) were compared across environments at day 0 with Hotelling’s T-square Test and post hoc Student’s t-tests. MeHg production was calculated by subtracting day 0 from day 28 MeHg concentrations; values below detection limit were assigned the detection limit as a value for a conservative estimate of change. MeHg production was compared across groups using ANOVA. Changes in DOM indices (FI, freshness, HIX) through time (days 0, 7, 14, 21, and 28) in each sample group were assessed with linear and quadratic regressions. DOM samples with SUVA254 >7 were removed due to fluorescence interference from inorganic molecules. Comparisons of DOM indices between data subsets were conducted with ANOVA and post hoc Tukey HSD.
Microbial community dissimilarity matrices based on 16S rRNA sequences were constructed using the weighted UniFrac method (Lozupone et al., 2011) in QIIME. We preformed analysis using the full community and within the methylating community. To examine the relative abundance of our methylating OTUs, we removed OTUs with less than eight total occurrences (bottom quartile) in our 91 subsamples to limit artifacts from sequencing errors among rare organisms (methylating OTUs were <1% of sequences). Alpha diversity for each sample was assessed using the PD whole tree metric in QIIME. The relative abundance of methylators was compared within each environment at days 0 and 28 (leachate vs. no leachate) using unpaired one-way Student’s t-tests.
Changes in community structure through time (days 0, 7, 14, 21, 28) were assessed with ANOSIM in QIIME. Differences in alpha diversity at day 0 were assessed using unpaired one-way Student’s t-tests. Relative abundances of major clades were assessed between vegetated and unvegetated environments at day 0 and changes in clades through time (days 0, 7, 14, 21, 28) were assessed using non-parametric Kruskal-Wallis tests with FDR-correct P values. SIMPER analysis was conducted using the ‘vegan package’ to identify OTUs associated with community dissimilarity between days 0 and 28 in microcosms receiving leachate. Correlations between methylating clades that exhibited significant changes (Kruskal-Wallis, days 0, 7, 14, 21, 28), HIX, and MeHg production were assessed at day 28 using the Pearson product-momentum correlation coefficient, grouping leachate and no leachate microcosms within each environment in a single analysis to provide sufficient variation.
Increases in the frequency of COGs, Pfams, and KEGG pathways at day 28 relative to day 0 were evaluated using binomial tests. Targets more abundant at day 28 (FDR-corrected P < 0.01) were examined for correlations with HIX and MeHg production at day 28 with the Pearson product-momentum correlation coefficient.
Results
Ambient geochemistry and microbiology
Physicochemical and biological properties of vegetated and unvegetated environments significantly differed (Hotelling P = 0.004, Table 1). The unvegetated environment was extremely oligotrophic, with low concentrations of sediment C and N, and both vegetated and unvegetated environments appeared to be N-limited (C:N 16.43 and 20.06). DNA concentration, enzyme activities, and mercury concentrations were an order of magnitude higher within the vegetated environment (Table 1). In addition, methylmercury production in sediments without leachate addition was significantly higher in vegetated sediment than unvegetated sediment, by nearly two orders of magnitude (Figure 1).
Microbial community structure and alpha diversity were significantly different between the two environments (ANOSIM, P = 0.001, R= 1.00, t-test, P = 0.01), though major phyla were similar (Table 1) and both environments were bacteria (vs. archaea) dominated. The relative abundance of methylators was higher in the vegetated environment (t-test, P = 0.005), and the structure of potential methylators within microbial communities also differed between environments (ANOSIM, P = 0.001, R = 0.99).
Microbiome response to HgCl2 and leachate addition
Over the course of the incubation, microcosms with vegetated, high-C sediment produced over ten times more MeHg than unvegetated sediment microcosms, regardless of leachate amendment (ANOVA P = 0.002, Figure 1). Mercury methylation was enhanced by leachate within the unvegetated nutrient-poor environment with roughly two to four times more production in microcosms receiving leachate as compared to those without leachate. However, leachate did not stimulate MeHg production in the vegetated environment.
Community structure changed through time in vegetated and unvegetated environments with leachate (ANOSIM across days 0, 7, 14, 21, 28, veg.: P = 0.001 R = 0.40, unveg.: P = 0.001 R = 0.43, Figure S1A and B), but not without leachate (veg.: P = 0.02, R = 0.19, unveg.: P > 0.05, Figure S1A and B), indicating no substantial effect from high concentrations of added mercury. At day 28, communities in unvegetated microcosms with leachate were different than those without leachate (ANOSIM, P = 0.01, R = 0.54), while structure in vegetated sediment microcosms only weakly differed between leachate and no leachate groups (P = 0.04, R = 0.22). When examining only potential methylators, the relative abundance of methylators was significantly greater in lechate microcosms versus no leachate for each environment at day 28 (Figure 2A, t-test, veg.: P = 0.04, unveg.: P = 0.04). However, for both environments, there were no significant changes in community structure within methylating clades through time (ANOSIM across days 0, 7, 14, 21, 28, P > 0.05). This result was not unexpected given our small sample sizes (methylator OTUs contained less than 1% of sequences).
Changes in community structure in response to leachate was partially generated by an increase in Clostridia in both environments (Kruskal-Wallis, veg.: FDR-corrected P = 0.003, unveg.: P = 0.018, Figure 2B, Table S3) and a decrease in Deltaproteobacteria in unvegetated sediment (Kruskal-Wallis, veg.: FDR-corrected P = 0.36, unveg.: FDR-corrected P = 0.015, Figure 2B). In particular, Clostridia abundances increased by 3-fold (1.1% to 3.8% of the microbiome) and 10-fold (1.5% to 10.5% of the microbiome), respectively in vegetated and unvegetated environments, driven by increases in nearly all families of Clostridia. These shifts were mirrored within our subset of data containing only suspected methylators (Figure 2C), which showed distinct (non-significant) trends for increases in Clostridia and decreases in Deltaproteobacteria in response to leachate in both environments.
Changes in the methylating community were more evident at finer taxonomic levels. One family of Clostridia (Peptococcaceae), sharply increased with leachate in unvegetated sediment and displayed a similar trend in vegetated sediment (Kruskal-Wallis, veg.: FDR-corrected P = 0.18, unveg.: FDR-corrected P = 0.04, Figure 2D). These changes were due to increases in two closely related methylating OTUs (Kruskal-Wallis, Dehalobacter restrictus veg.: FDR-corrected P = 0.24 (uncorrected P = 0.04), and Syntrophobotulus glycolicus, unveg.: FDR-corrected P = 0.006) grouped in a single genus by our classification system (Dehalobacter_Syntrophobotulus, Kruskal-Wallis, veg.: FDR-corrected P = 0.09, unveg.: FDR-corrected P = 0.0027, Figure S2). Increases in Clostridia (t-test, FDR-corrected P = 0.006), Peptococcaceae (t-test, FDR-corrected P = 0.018), Dehalobacter restrictus (t-test, FDR-corrected P = 0.024), and Syntrophobotulus glycolicus (t-test, FDR-corrected P = 0.042) as well as a possible trend for decreases in Deltaproteobacteria (t-test, FDR-corrected P = 0.18) were also reflected in metagenomic data (Figure 3D).
SIMPER analysis of 16S rRNA genes associated with methylator taxonomy in unvegetated leachate microcosms indicated that two OTUs, in D. restrictus (increase) and in Geobacter (decrease), significantly contributed to community differences between day 0 and day 28 (P < 0.05, Table S1). This was reflective of broader changes in the full community, in which 22.9% of 175 SIMPER-identified OTUs belonged to Clostridia (increased from avg. 0.78 OTUs/sample to avg. 17.20 OTUs/sample, Table S3) while 8% belonged to Deltaproteobacteria (decreased from avg. 8.5 OTUs/sample to 7.4 OTUs/sample, Table S2).
In total, 7,150 KEGG pathways, 84 COGs, and 79 Pfams were significantly more abundant at day 28 relative to day 0 in unvegetated leachate microcosms (Figure 3A-C). All classfication systems revealed metabolic shifts towards glycosyltranseferases, among other pathways involved in DOM oxidation and in iron and nitrate reduction.
Changes in DOM chemistry
Details of DOM quantity and quality changes are presented in the Supplemental Material (Figure S3) and regression statistics are presented in Table 2.
DOM fluorescence indices displayed notable changes through time. In the vegetated environment, FI remained stable at a low value in leachate microcosms, indicating plant-derived DOM, and rose in microcosms without leachate indicating greater relative contribution of microbial vs. abiotic processing (Figure 4A and B). In contrast, in the vegetated environment, HIX increased in both leachate and no leachate microcosms indicating processing of more labile vs. recalcitrant DOM (Figure 4C and D). This increase in HIX corresponded with decrease in freshness (Figure 4E and F), further supporting our interpretation. In the unvegetated environment, leachate microcosms (but not microcosms without leachate) increased in FI (Figure 4A and B) denoting progressively microbial DOM sources. There was no change in HIX (Figure 4C and D) suggesting equal processing of labile vs. recalcitrant DOM. Freshness varied non-linearly in leachate microcosms but not those without leachate (Figure 4E and F).
Across environment types, HIX was significantly higher in vegetated microcosms (ANOVA P < 0.0001, Tukey HSD, leachate: P < 0.0001, no leachate: P = 0.004). FI and freshness were higher in unvegetated leachate microcosms than in vegetated DOM-amended microcosms (Tukey HSD, FI: P = 0.003, freshness: P = 0.03) but did not differ across microcosms without leachate (Tukey HSD, FI: P = 0.89, freshness: P = 0.40).
Correlation of microbiome, DOM characteristics, and MeHg production
Given the apparent shift in community structure towards Clostridia, and (chemoorganotrophic) Peptococcaceae in particular, we examined correlations of this family with the proportion of complex organic matter (HIX) and MeHg production within each environment. We focused on HIX because this index changed consistently and reflected portions of recalcitrant carbon substrate pools utilized by the organisms we identified. Because we only calculated net MeHg production at the conclusion of the incubation, we analyzed these correlations at day 28 and grouped leachate and no leachate replicates within each environment to provide sufficient variation and sample size. Peptococcaceae was negatively correlated with HIX and positively correlated with MeHg production in unvegetated microcosms (Pearson’s r (n = 6), HIX: P = 0.001, r = -0.96, MeHg: P = 0.03, r = 0.81). Peptococcaceae abundance in vegetated microcosms did not correlate with HIX (P = 0.20) or MeHg production (P = 0.45).
Finally, despite low statistical power (n = 3), we observed marginally significant trends (P < 0.10) between key metabolic pathways and HIX (Table 3). In particular, COGs classified as: Glycosyltransferase, Glycosyltransferases involved in cell wall biogenesis, Glycosyltransferases - probably involved in cell wall biogenesis, and Beta-galactosidase/beta-glucuronidase; and Pfams classified as: Glycosyl transferase family 2, Radical SAM superfamily, and SusD family displayed significant correlations with HIX at the P < 0.10 level. Only Pfam PF00593, TonB dependent receptor, correlated with MeHg production (P < 0.001, r = -1.00, Table 2).
Discussion
Mercury methylation across environments
Geochemical and microbial characteristics varied across environments, resulting in differential patterns of net MeHg production. First, our work indicated a strongly different capacity of vegetated vs. unvegetated wetland sediments to cycle mercury. Without leachate addition, MeHg production in vegetated sediments was two orders of magnitude higher than in unvegetated sediments (Figure 1). As such, vegetated sediments may be considered potentially important locations for mercury methylation when mercury is present in the environment. Such a dynamic may be due to either higher overall activity of microorganisms or the unique microbiomes contained within these sediments. Within the high-C vegetated environment, leachate did not influence the sediment microbiome or net MeHg production to the same extent as within the more oligotrophic unvegetated environment (Figure 1, Figure S1). Given high ratios of C:N, high OC content, and low NO3− concentrations in our vegetated sediment (Table 1), N-limitation may have mitigated net MeHg production in vegetated environments relative to the unvegetated environment (Taylor and Townsend, 2010), which had substantially lower concentrations of all measured C and nutrient concentrations. Both ambient MeHg levels and net MeHg production were dramatically higher in the vegetated environment, supporting other findings that plant-microbe interactions facilitate MeHg production (Roy et al., 2009; Windham-Myers et al., 2014; Windham - Myers et al., 2009). Indeed, the vegetated environment displayed higher ambient DNA concentration, enzyme activities, and methylator abundance, underlying a higher in situ rate of biological activity.
By contrast, the unvegetated environment experienced a dramatic increase in MeHg (Figure 1) in response to leachate that correlated with changes in the sediment microbiome (Figure 2 and 3, Figure S1). Carbon limitation has been widely demonstrated as a constraint on microbial activity (Bradley et al., 1992; Brooks et al., 2005; Wett and Rauch, 2003); thus, leachate may bolster MeHg production in C-limited ecosystems via impacts on microbial activity. In our system, net MeHg production in the unvegetated environment was possibly also constrained by low in situ rates of microbial activity and by low N concentration, and net MeHg production in response to leachate stimulus never increased to vegetated levels. Importantly, leachate enhanced the relative abundance of putative methylators within the microbiome in both environments, indicating that mercury methylation rates may be dually influenced by the sediment microbiome and by organic matter (Aiken et al., 2011; Hsu-Kim et al., 2013).
Microbiome response to leachate addition
Our results bolster support for recent work demonstrating preferential organic degradation by Clostridial fermentation over oxidation by Deltaproteobacteria (Reimers et al., 2013) and provide support for the involvement of this clade in MeHg production. Within both environments, leachate altered the sediment microbiome, with structural shifts denoting an increase in Clostridia and decrease in Deltaproteobacteria. Unvegetated microcosms displayed greater changes in these clades, supporting a greater role for environmental filtering by DOM within oligotrophic environments (Barberán et al., 2012; Stegen et al., 2012). Clostridia are obligate anaerobes with the ability to produce labile carbon compounds via fermentation of recalcitrant organic matter (Reimers et al., 2013; Ueno et al., 2016). Recent work has shown organic carbon degradation via Clostridial fermentation to operate at comparable rates to more energetically favorable carbon processing pathways62. Moreover, organic acids (e.g., lactate and acetate) produced through these pathways can be subsequently utilized as a carbon source by sulfate- and iron-reducing Deltaproteobacteria (Guerrero-Barajas et al., 2011; Reimers et al., 2013; Zhao et al., 2008). Importantly, microbiome changes were mirrored when examining putative methylators independently. Specifically, Deltaproteobacteria and Clostridia, respectively, were the most abundant methylating organisms at the end of the incubation in all experimental groups except no leachate vegetated microcosms.
In unvegetated sediments, although no methylating pathways were identified, metagenomic analyses indicated an increase in carbon, and secondarily, iron metabolisms, supporting a role for microbial carbon and iron cycling in mercury methylation (Gilmour et al., 2013; Hamelin et al., 2011; Kerin et al., 2006; Podar et al., 2015). Carbon metabolisms were the primary KEGG category increasing in abundance within metagenomes (Figure 3A), and several COG pathways and Pfams indicated a possible metabolic shift favoring glycosyltransferases that convert starches, sugars, and nitroaromatics into a wide range of compounds (Bowles et al., 2005; Ramli et al., 2015) (Figure 3B and C). Further, metagenomic increases in Beta-galactosidase/beta-glucoronidase (lactose to galactose/glucose) (Martini et al., 1987), sugar phosphate isomerase/epimerases (sugar metabolism) (Yeom et al., 2013), and lactoylglutathione lyase (detoxification for methyglyoxal fermentation byproduct) (Inoue and Kimura, 1995) and the SusD family (glycan binding) (Martens et al., 2009) provide additional evidence increases in fermentation processes in response to leachate. Increases in TonB dependent receptors (Moeck and Coulton, 1998), amidohydrolase (Seibert and Raushel, 2005), and NRAMP (Cellier et al., 1995) suggest a secondary importance of iron processing and/or transport of large organic compounds across cellular membranes. Finally, our results provide a possible genetic mechanism connecting iron, sulfur, carbon, and mercury cycling, as the radical SAM superfamily, which facilitates methyl transfers via the use of a [4Fe-S]+ cluster (Booker and Grove, 2010), increased in concert with net MeHg production. In total, the metabolic potential of the sediment microbiome indicates changes in carbon and iron metabolisms within microcosms experiencing higher net MeHg production in response to leachate, supporting past work that suggests a linkage between mercury methylation and these factors (Gilmour et al., 2013; Hamelin et al., 2011; Hsu-Kim et al., 2013; Kerin et al., 2006; Podar et al., 2015).
Lastly, at high taxonomic resolution in both environments, leachate increased the proportion of methylating organisms classified as Peptococcaceae within Clostridia, despite drastic differences in sediment chemistry (Figure 2D). Specifically, the two OTUs displaying the greatest change are thought to generate energy via organohalide respiration (D. restrictus) and fermentative oxidation of organic matter (S. glycolicus, also capable of syntrophy) (Han et al., 2011; Stackebrandt, 2014). The relative abundance of Peptococcaceae was positively correlated with MeHg production in the unvegetated environment, and other methylating organisms did not increase in abundance, as would be expected if the activity of these organisms was enhanced by leachate.
Associations between microbiology, DOM processing, and net MeHg production
The processing of proportionally more labile (microbe-preferred) organic matter would be expected to result in decreases in DOM freshness and increases in HIX. However, our results suggest substantial contributions of recalcitrant organic matter processing within the unvegetated environment (but not the vegetated environment which followed expectations). In unvegetated microcosms (both leachate and no leachate), HIX did not rise through time indicating relcatritant matter processing (Figure 4C and D). Further, leachate unvegetated microcosms, which experienced pronounced changes in the sediment microbiome and high MeHg production, HIX was significantly lower than in all other experimental groups (ANOVA, P < 0.0001, all Tukey HSD P < 0.0001). While most microorganisms preferentially degrade labile C sources, the degradation of recalcitrant organic matter can contribute substantially to aquatic carbon cycling (Mcleod et al., 2011). Leachate unvegetated microcosms also exhibited large increases in microbially-derived DOM (FI) through time, demonstrating a noticeable contribution of microbial activity to the DOM pool (Figure 4A).
The abundance of methylating Peptococcaceae in unvegetated microcosms negatively correlated with HIX, denoting an apparent contribution of these members or co-occuring community members to DOM processing, but the mechanisms behind these shifts remain unclear. Metabolism of recalcitrant organic matter by fermenting organisms may influence mercury methylation via direct and indirect mechanisms. Members of Clostridia can generate MeHg themselves, and Clostridial degradation of recalcitrant organic matter can also produce bioavailable carbon substrates for sulfate- and iron-reducing organisms that produce MeHg (Reimers et al., 2013).
Changes in metagenomes in responses to leachate elucidate metabolic pathways that may be involved in recalcitrant organic matter processing and MeHg production. For example, both COG and Pfam glycosyltransferases were negatively correlated with HIX, suggesting a role for starch, sugar, and nitroaromatic fermentation in response to DOM loading. As well, a negative correlation between HIX, and the radical SAM superfamily provides a possible mechanistic linkage between methyl transfers and recalcitrant organic matter processing. Conversely, Beta-galactosidase/beta-glucuronidase, and the SusD family were positively correlated with HIX, indicating a co-association with labile C processing rather than recalcitrant organic matter. Only one abundant Pfam – a TonB dependent receptor, signaling enzyme that may be involved in iron cycling (Moeck and Coulton, 1998) – and no COGs was correlated with MeHg production. Although our results do not provide a direct linkage between metabolic pathways and mercury methylation, it is notable that no pathways associations involve sulfate-reducing or methanogenic methylators.
Conclusions
Our work shows clearly distinct mercury cycling dynamics between the vegetated and unvegetated sediments of the St. Louis River Estuary environment. While substantially greater MeHg production is observed in vegetated sediments, unvegetated sediments stand to respond more strongly to DOM additions in driving increases in MeHg production. While we observed evidence for changes in the microbiome of both high-C and nutrient-poor sediment, the more oligotrophic environment showed greater responses in the sediment microbiome and in mercury methylation to the addition of DOM, an important insight given increasing risks of anthropogenic eutrophication. Microbiome shifts towards fermentation pathways, increases in chemoorganotrophic Clostridia, degradation of recalcitrant organic matter, and increases in MeHg within oligotrophic environments begin to elucidate the microbial ecology of mercury methylation. Importantly, our results provide evidence for organisms not historically considered in MeHg production and suggest future work into the environmental relevance of these organisms in mercury methylation. Clostridia thrive in a variety of anoxic environments from wastewater effluent (Wang et al., 2003) to the human gut (Mahowald et al., 2009), and our work supports the potential for mercury methylation across a broad range of ecological niches (Gilmour et al., 2013; Podar et al., 2015). Taken together, our research provides new insights into microorganisms impacting MeHg production in natural settings in the Great Lakes region and emphasizes the importance of exploring microbial physiology not typically associated with methylating organisms in enhancing mercury toxicity.
Conflict of Interest
The authors declare no conflict of interest.
Acknowledgements
This work was supported by EPA STAR and NOAA NERRS fellowships to EBG and a JGI CSP grant to DRN. We also acknowledge support from the US Department of Energy (DOE), Office of Biological and Environmental Research (BER), as part of Subsurface Biogeochemical Research Program’s Scientific Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL). PNNL is operated for DOE by Battelle under contract DE-AC06-76RLO 1830. We thank Alan Townsend, Teresa Bilinski, Deb Repert, Dick Smith, Steve Schmidt, Sharon Collinge, Garrett Rue, Jess Ebert, Alexis Templeton, and the LSNERR staff for valuable support and feedback during this project. We also thank Axios Review Service for valuable feedback on this manuscript.