Microbial population dynamics and the role of sulfur reducing bacteria genes in stabilizing 1 Pb , Zn and Cd in the terrestrial subsurface 2 3

Microbial population dynamics and the role of sulfur reducing bacteria genes in stabilizing 1 Pb, Zn and Cd in the terrestrial subsurface 2 3 Ranju R. Karna,† Ganga M. Hettiarachchi†#, Joy D. Van Nostrand,‡ Tong Yuan,‡ 4 Charles W. Rice, § Y. M. Assefa,† and Jizhong Zhou ‡ 5 6 †Soil and Environmental Chemistry, Department of Agronomy, Kansas State University, 7 Manhattan, Kansas 66506, United States. 8 9 § Soil and Environmental Microbiology, Department of Agronomy, Kansas State University, 10 Manhattan, Kansas 66506, United States. 11 12 ‡ Institute of Environmental Genomics and Department of Microbiology and Plant Biology, 13 University of Oklahoma, Norman, OK73019, United States. 14 Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United 15 States. 16 School of Environment, Tsinghua University, Beijing 100084, China. 17 18 19 #Corresponding Author (ganga@ksu.edu) 20 21 Contribution no. 15-212-J from the Kansas Agric. Exp. Stn 22


Introduction
Generation of large amounts of mine waste containing several heavy metals is the main environmental concern associated with milling and mining activities (Baker et al., 2003, Bhattacharya et al., 2008).Heavy metals are dispersed via different pathways such as wind, surface water runoff, and metal-laden sediments are transported to neighboring water bodies (Almendras et al., 2009 andJohnson et al., 2005).The Tri-State mining district in parts of southeast Kansas, southwest Missouri, and northeast Oklahoma was one of the largest Pb and Zn ore-mining districts in the world for 120 years (until 1970).The movement of soluble metals and metal-laden sediments from the landscape into surface waters via surface runoff are the primary ecological concerns for both aquatic and terrestrial organisms (Pierzynski et al., 2006).The US Environmental Protection Agency (US EPA) has suggested wetland construction as a remediation strategy for soils highly contaminated by abandoned mine waste materials with the hypothesis that these metals could be transformed into their sulfide forms under reduced conditions in sulfate-rich environments, thus limiting their mobility and toxicity.
Several challenges are associated with this strategy.Mine waste material with low dissolved OC content could have significant effects on redox processes (Hayes et al., 2006, Stein et al., 2007and Zhang et al., 2005) because OC is the main driver of biogeochemical cycling of major and trace elements (Borch et al., 2009 andEvans et al., 2006).Limited S in mine waste could limit sulfide formation and promote carbonate precipitation, depending on pH and carbonate concentration (Toevs et al., 2006).Therefore, the addition of OC and S could facilitate these metals to be transformed back into their sulfide forms under reduced conditions, thereby limiting their mobility and toxicity.A generalized sulfate reduction reaction using organic matter (OM) as an electron donor is: SO4 2− + 2CH2O + 2H + → H2S + 2H2O + 2CO2 (pH<7.0):Stein (10) At high metal concentrations, metals tend to precipitate as metal sulfides around circumneutral pH because the rate of H2S formation increases at a pH of 7.0 to a maximum of 8.0 (Burton et al., 2008 andChen et al., 1997): The above-mentioned reaction is the result of dissimilatory sulfate metabolism that has been tested and successfully removed contaminants via biostimulation.Of all the metal sulfide minerals, iron sulfide mineralization is most often attributed to microbial activity (McLean et al., 2007), especially to the activity of dissimilatory sulfur-reducing bacteria (SRB).Environmentally important activities displayed by SRBs are the result of metabolic production of high levels of sulfides that are reactive and participate in subsequent mineral formation (Bazylinski et al., 2003 andLovely et al., 1995).
Using a culture-dependent technique would not be feasible to study the complex microbial community because 99% of microorganisms have not been cultured (Whiteman et al., 2004), therefore, culture-independent techniques such as functional gene arrays (FGA) are required (He et al., 2007 andVan Nostrand et al., 2011).GeoChip 4.2 is a functional gene array that contains 83,992 oligonucleotides (50-mer) probes targeting 152, 414 genes in 410 gene categories from more than 5200 microbial strains including bacteria, archaea, fungi, and viruses.
These genes are involved in the biogeochemical processes and functional activities of microbial communities important to human health, ecosystem management, agriculture, energy, global climate change, and environmental cleanup and restoration, including N, C, S and P cycling; metal reduction and resistance; and organic contaminant degradation (Tu et al., 2014).This technique enables detection, characterization, and quantification of microorganisms in mine waste and links microbial diversity to ecosystem processes and functions (He et al., 2007 andLoick et al., 2014).The approach has been used successfully to track the dynamics of metalreducing bacteria and associated communities for an in situ bioremediation study (Lu et al., 2012, Wu et al., 2001, Van Nostrand et al., 2009and Zhou et al., 2008).
Phospholipid fatty acid analysis (PLFA) is another rapid, inexpensive, and an efficient way to determine the structure, and the effect of treatments on microbial community (Frostegård et al., 2011).Certain PLFAs markers can serve as unique signatures for a particular group.
However, such biomarkers cannot detect individual microbial species due to overlapping PLFA patterns; nevertheless, whole PLFA pattern is used to elucidate the shift in community composition, and their relation to specific metabolic and environmental conditions (Olsson et al., 1999).
Few studies have combined microbial analysis with solution chemistry and microscopic and X-ray spectroscopic techniques to develop a complete molecular-scale understanding of complex biogeochemical processes affecting soil and water (Brantley et al., 2007 andBrown et al., 1999).This study attempted to explore the interplay between geochemical and biological processes in the transformation of Pb, Zn and Cd in natural subsurface environments biostimulated by the addition of OC and S. Stimulating the systems with OC and S would favor SRB growth and activities.We expect that OC-plus-S treatment would result in a higher abundance of SRB genes compared with natural, OC alone, or S alone treatments.Study objectives were to: a) characterize the microbial community playing a role in the biogeochemical transformation of Pb, Zn and Cd under reduced conditions; b) measure the change in microbial community structure with OC and/or S treatment over medium-and long-term incubation; and c) identify the most dominant genes and associated mechanisms involved in effective immobilization of Pb, Zn and Cd.

Sample collection and characterization
Contaminated mine waste materials were collected from a secured repository area in Baxter Springs, KS, a part of the Tri-State mining district that has a 120-year history of Pb-and Zn-ore mining related activities.The material was sieved to 2-mm size, and 0.5-g sample was digested in triplicate following the aqua-regia reflux tube soil-digestion method to determine the concentrations of selected elements (Zarcinas et al., 1996).Total N, and C content was measured using LECO TruSpec CN Carbon/Nitrogen combustion analyzer (LECO Corporation, St. Joseph, MI).The pH of a water extract (water: mine waste ratio of 2:1) was determined using Orion Ag/AgCl pH electrode.Particle-size distribution was determined using a modification of the pipet method of Kilmer et al. (Kilmer et al., 1949), and method 3A1 from the Soil Survey Laboratory Method Manual (1996).

Treatment application and experimental setup
For S-treatment application, sodium sulfate (Na2SO4) solution was added to the mine waste material to provide S at a ratio of 1:2 mM of sum of metals present in material: mM of S. The metal concentrations used for the summation were Pb, Zn, Cd, Fe, and Mn.The treated materials were equilibrated for 10 days at room temperature on a reciprocating shaker (6010, Eberbach Corporation, Ann Arbor, MI) at 192 reciprocates/min for 3 days, and at 92 reciprocates/min for the remaining 7 days.After equilibration, S-treated mine waste was leached with deionized (DI) water to reduce salinity until a target electrical conductivity of <2 mS cm -1 was achieved, then the material was air-dried.Both S-treated and untreated mine waste materials were inoculated with 0.5 g 100g -1 of soil slurry (Ivan, Kennebec, and Kahola silt loams) collected from the North Agronomy Farm at Kansas State University, Manhattan, KS.The serial dilution of soil slurry was cultured on a Petri dish using Postgate's medium and incubated overnight at 34 ºC in an anaerobic jar (AG0025A used with oxygen absorber; OXAN0025A, Fisher Scientific, Pittsburgh, PA).The black patches observed on the plate indirectly confirmed the presence of SRB in the soil slurry.The method used for SRB culturing was adapted from Luptakova et al. (Luptakova et al., 2005).The mine waste materials (non-treated or treated with S) were well mixed with soil slurry and used to pack Plexiglas columns (20 cm length, 3.2 cm ID with 3 windows milled at 2.8 cm, 9.84 cm, and 16.94 cm) to achieve a bulk density of about 1.7 g cm -3 .
The packed columns were saturated slowly with DI water using a Mariotte's bottle that delivered a constant flow rate before the eluent solution was supplied.The eluent consisted of a base of simulated groundwater (1 mM NaCl, 1mM MgCl2, 1 mM KCl, 1 mM CaCl2 adjusted to pH 7.2) with or without 10.7 mM Na-lactate (32 mM OC).This eluent provided four treatments for the columns designated as C0S0, C1S0, C0S1, and C1S1, where C0 and C1 designated simulated groundwater without OC and with OC, respectively; S0 designated simulated groundwater applied to columns without added S; and S1 designated simulated groundwater applied to columns with added S. Each treatment combination had two replicates due to limited space available in the glovebox.The eluent solution was supplied using a syringe pump (KD Scientific Inc., Holliston, MA) at the rate of 13 mm day -1 to simulate a slow groundwater discharge rate (Wan et al., 2005).Three series of column experiments, short (32-day), medium (119-day), and long-term (252-day), were conducted at room temperature ~25 °C at different times due to the lack of space in the anaerobic chamber to conduct them all simultaneously.All three series of experiments were conducted based on a completely randomized design with a two-way factorial experiment (factor 1: OC with two levels, 0 and 10.7 mM L -1 ; factor 2: S with two levels; 0 and 252.7 mg kg -1 ).Effluent samples were collected weekly for medium-term and biweekly for longterm submergence, and analyzed for pH, redox potential, total dissolved elements measurements for Pb, Cd, Zn, Fe, S, Mn, K, Ca, Mg, Na, anions including sulfate, nitrate, nitrite, chloride, phosphate, and dissolved organic carbon (DOC) measurements.At the end of each column experiment, samples (about 20 g) were collected from three windows located on the columns and frozen at -80 °C for DNA extraction, and x-ray absorption spectroscopy (XAS).More details on solution chemistry data collection, and approaches used in synchrotron-based X-ray analysis, and their outcomes can be found in Karna et al. (2016).

Phospholipid fatty acid (PLFA) analysis
The PLFA analysis was performed as an initial measurement to determine the microbial community changes with OC and S treatment prior to microarray analysis was performed.For this, PLFA extraction was done on the original mine waste materials, and submerged C0S0, and C1S1 treatments from medium term study only based on single phase extraction of lipids, which was then methylated to give fatty acids methyl esters (FAME) and analyzed by gas chromatograph.The PLFA extraction was performed by following the method of Bligh and Dyer (1959) as modified by White and Ringlberg (1998).The resulting FAMES were analyzed using a Thermo Scientific Trace GC-ISQ mass spectrometer (Thermo Scientific, Germany) with helium as a carrier gas.Analysis was conducted in the electron impact (70 eV) mode.Peaks were identified based on retention times of commercially available bacterial acid methyl esters (BAME; Matreya 1114) standard mix.The methyl ester peaks that were not present in the BAME mix were tentatively assigned through mass spectral interpretation by comparison with spectra from a library (Wiley 138K mass spectral database).Sample peaks were quantified based on comparison of the abundance with an internal standard -nonadecanoic acid methyl ester (19:0).The abundance was expressed as nmoles/gram.Fatty acids (FA) are designated a:b, where 'a' represents total number of carbons and b the number of double bonds.An 'ω' indicates the position of a double bond from the aliphatic end of the FA.The prefixes 'a' and 'i' refer to anteiso and iso branching, while the suffixes 'c' and 't' refers to cis and trans 33 isomers (conformations).Presence of methyl groups are indicated by aMe, where 'a' indicates the position of the methyl group.Fatty acids were grouped based on criteria by McKinley et al. (2005) whereby Gram positive bacteria were branched monounsaturated cyclopropane (i-15:0, a-15:0, i-16:0, i-17:0 and a-17:0).Gram negative biomarkers are cyclopropane PLFAs (2-OH 12:0, 3-OH 12:0, 2-OH 14:0, 3-OH 14:0, 2-OH 16:0, C16_1_9_cis, C16_0_2-OH, 16:1ω7c, 18:1ω7c, cy17:0, cy19:0), while actinomycetes is 10Me18:0.Fungi biomarkers are polyunsaturated PLFAs (18:1ω5c,18:2ω9,12C,18:2ω6,9,12), while arbuscular mycorrhizae fungi (AMF) is 16:1ω5c, and Desulfoviobrio biomarker is i_17_1 (McKinley et al., 2005).

GeoChip analysis
Microarray analysis was performed on all the treatment combinations; C0S0, C0S1, C1S0, C1S1 from medium term study, whereas only C0S0 and C1S1 treatment combinations were used from long-term study.The samples selection for long-term study was done based on the geochemical and spectroscopic results obtained from medium-term study.Due to lack of space issue in the glovebox chamber, we used two replicates for each treatment combination.Rather than running microbial analysis for those two replicates, we did generate an additional replicate by mixing equal portions materials of 1 and 2 column replicates and ran that as an independent confirmation sample/third replicate.

General characterization of mine waste materials
The mine waste material consisted of 85% sand (2000 to 50 µm), 11.3% silt (50 to 2 µm), and 3.4% clay (<2 µm).Total N and C were 0.03 g kg -1 and 1.56 g kg -1 , respectively.The pH of the water extract (DI water: geomaterial mass ratio, 2:1) was 7.2, and the electrical conductivity was 2.31 mS cm -1 .Selected total elemental concentrations of Pb, Zn, and Cd in the material were 5048, 23,468, and 67 mg kg -1 , respectively (Table S2).The standard reference material 2711a (National Institute of Standards and Technology, Gaithersburg, MD) was digested along with the geomaterial to ensure a recovery percentage of each element that ranged from 79 to 109%.

Preliminary microbial community characterization
The PLFA analysis results on starting original mine tailings, inoculum, non-amended control, and amended soils submerged for 119-day indicated the presence of biomarkers for various microbial groups (Gram-, Gram+, AMF, fungi, and Actinomycetes).Total PLFA in starting mine waste materials was 2.42 nmole/g, whereas it was 6.18 nmole/g in the submerged sediment that was used as inoculum (Table S1).Once the materials were inoculated and submerged, no significant increase in summed abundance of PLFA biomarkers was observed in non-amended control (C0S0), whereas it was significantly increased in the samples treated with both OC plus S (C1S1).Specifically, Gram-, and Gram+ biomarkers abundance was significantly increased in amended soil, with respect to starting mine waste materials, whereas there was no noticeable difference in non-amended soil.Fungi biomarker abundance was decreased with varied amount in both untreated and treated soils under submergence.AMF and Actinomycetes PLFA biomarkers were also decreased, however remains same in both non-amended and amended samples.More interestingly, total PLFA for Desulfovibrio biomarkers was significantly increased in OC plus S treated soil only (Table S1).

X-ray absorption spectroscopy
Multiple synchrotron-based techniques have been used to enhance quantitative mineral species identification (Heald et al., 2007 andManceau et al., 2002).Micro-, and bulk-XAS as well as µ-XRD techniques were used to identify the minerals in the original mine waste materials in this study.The results in agreement between µ-XRD and bulk XAFS techniques indicated presence of carbonates, sulfates, silicates, and oxides minerals, which are supported by other studies conducted on smelter-impacted soils (Manceau et al., 2000a, Nachtegaal et al., 2005and Scheinost et al., 2002).Bulk-XAFS speciation conducted for Pb, Zn and Cd in starting mine waste materials that was used in this study indicated none sulfide minerals, whereas it was dominant with silicates, carbonates, sulfates, phosphates, nitrates and hydroxides minerals (Fig. 1a, 1b and 1c).Speciation changed after the mine waste material was treated with OC and/or S, and submerged for different time period.Bulk XAS data indicated about 62% galena (PbS), 31% sphalerite (ZnS) and 39% Cd-sulfide formation in C1S1 compared to none in C0S0 (Fig. 1a, 1b and 1c), respectively under long-term incubation.Instead, more carbonates were formed in nonamended (C0S0) flooded materials (Karna et al., 2016).Functional gene diversity Functional gene richness, indicated by the total number of genes detected, was significantly increased in C1S1 compared to C0S0 under medium-term submergence (Fig. 2).In contrast, under long-term submergence, the total number of detected genes significantly decreased in both C0S0, and C1S1 treatment (Fig. 2).

Relationships among microbial communities
Detrended correspondence analysis (DCA) was used to examine the overall functional structure changes in microbial communities with the OC-plus-S treatment under medium-and long-term submergence.In the DCA ordination plot, similar samples cluster closely (Ramette et al., 2007).
The overall DCA ordination plot obtained from all detected genes resulted in clear clustering of samples from medium-and long-term submergence (Fig. 3).
When samples from medium-and long-term submergence were plotted individually, separate clusters for each treatment were formed (Fig. S1), indicating an overall effect of OC and/or S treatments and time on the community structure in relation to geochemistry dynamics and enhanced reduction (Fig. 3).DCA analysis with metal resistance genes showed a separate cluster for C1S1 but there was some overlap among the rest of the treatments under mediumterm submergence (Fig. S2), however clearer clusters were formed for both C0S0 and C1S1 under long-term submergence.Interestingly, the DCA ordination plot of C-cycling genes indicated clear cluster for C1S0 when only OC was added (Fig. S3).Similarly, the DCA plots of S-cycling category, and S-genes such as dsrA and dsrB segregated much clearly for C0S1 and C1S1 when S was added, whereas no overlapping was observed with rest of the other treatments (Fig. S4, S5, S6).Under longer submergence, both treatments, C0S0 and C1S1 samples made separate clusters under each category.Overall, DCA results for metal resistance and S-cycling genes showed clear clusters for the treatments submerged for both medium and long term, but the DCA ordination plot for C-cycling genes showed slight overlapping.The DCA of individual S-cycling genes: dsrA, dsrB (Fig. S5, S6) revealed clearer clusters with dsrB compared with dsrA genes.

Total abundance of functional gene categories
The shifts that were observed in the DCA ordination plots were likely the result of changes in total abundance of functional genes.Results from individual gene categories revealed that S-and C-cycling functional gene abundance was enhanced by 35% and 27%respectively, in C1S1 compared with C0S0 over time (Fig. 4a).On the other hand, metal resistance and organic remediation functional genes decreased by 26% (Fig. 4a) and 21% (Fig. 4b), respectively, in C1S1 compared with C0S0.
Thus, significant enrichment of S-(ρ = 0.01) and C-cycling genes (ρ = 0.01) and a large decrease in metal resistance (ρ = 0.001) and organic remediation genes by 50 to 60% (ρ = 0.001) within both treated and untreated samples over time could have resulted in community structure changes.Functional genes involved in S-and C-cycling were significantly enhanced in C1S1 despite the fact that the total number of detected genes decreased under long-term submergence, indicating direct involvement of S-and C-cycling genes in biogeochemical transformation processes.
Canonical correspondence analysis (CCA) was performed to examine the relationship between microbial community structure and geochemistry (Fig. 6) to correlate environmental variables with the functional community structure and determine the most significant variable causing the change in community structure.Environmental variables such as dissolved organic carbon (DOC), SO4 2-, total S, and NO3 -were used to perform CCA.
In CCA, environmental variables are represented as arrows starting at the origin and pointing outward.Our CCA results show that DOC and S are closer, with a small angle indicating these variables have a stronger correlation and have similar influence on microbial communities.Dissolved organic carbon and NO3 -had longer arrows with larger angles, indicating these variables have a stronger influence on the microbial community but in a different manner.The SO4 2-and total S vectors are in opposite directions, indicating that these factors are negatively correlated.This could be explained as the total difference between total sulfur and sulfate is sulfide indicating that under high S concentrations, sulfide formation has been favored.

Preliminary microbial community characterization
The contaminants effects on in situ microbiota are generally continuous, and may trigger the loss or emergence of a particular genera or species of microorganism (Smith et al., 1986).The higher abundance of gram+, and fungi biomarkers were present in the starting materials as these communities are more successful in resource limited situations like mine impacted soils with very less nutrients.On addition of inoculum followed by OC and S treatment, changes in PLFA composition and biomass was detected compared to non-amended soil in medium-term submergence.This suggests that the OC and S additions in this study favored microbial growth pattern and composition resulting in change of microbial community structure.Specifically, branched monounsaturated cyclopropane PLFAs, characteristic of Gram+ bacteria, and cyclopropane PLFAs, characteristic of Gram-bacteria abundance, and branched fatty acid, i17:1, characteristics of Desulfovibrio were increased on OC and S amendment indicating that these PLFA biomarkers could be the main contributors in microbial community structure change in amended soil.The increased abundance of those microbial communities could be due to added OC and S, their prior presence, and their capability to survive in adverse situation, and difference in substrate utilization (Bossio et al., 1998 andIbekwe et al., 1998).Another reason could be due to increased metal resistance genes.Several gram+, and gram-soil bacteria isolated from a Pbcontaminated sites have exhibited resistance to a range of metal ions such as Pb, Zn, Cu, Cd, Co, and Hg (Trajanovska et al., 1997).The significant increase in Desulfovibrio biomarker could be result of dissimilatory sulfate reduction happening in the system due to OC and S addition.

Relationships among microbial communities
Detrended correspondence analysis conducted based on time effect indicated that there was not very clear clustering of microbial community based on OC and/or S treatments under mediumterm submergence.Relatively more overlapping among the samples from C1S0, C0S1, and C0S0 systems were observed compared to C1S1 (Fig. S2), and that suggests some closer associated microorganisms from these treatments.The closely associated microbes could be due to common, and flexible substrate utilization preference.Comparatively, lesser or no overlap was observed among the samples from C1S1 and C0S0 under longer submergence.The segregation among the clusters from these two treatments under different category increased with time depending on their involvement in microbial community structure changes.This supports the fact that time was another dominant factor in determining the microbial community structure.
The positive effect of OC, S, and N via increase in corresponding functional genes abundance and the impact on change in microbial community structure has been observed by several studies (Fuhrman et al., 2009, Kleikemper et al., 2002and Tokunaga et al., 2003).Overall, DCA results indicated that the decreases in metal resistance and organic remediation functional genes and enrichment in S-and C-cycling functional genes were mainly involved in the observed community shift.

Functional gene diversity
The significant increase in microbial community abundance in C1S1 followed by a significant decline may indicate rapid oxidation of added OC coupled with a reduction in available terminal electron acceptors (TEAs) and a subsequent decline as suitable TEAs were exhausted.This result could be explained by the trend that was observed with DOC concentration in the current study.
Initial concentration of DOC in the eluent was 32 mM but was reduced to 30 mM in effluent at 7-day submergence and further decreased to <detection limit (DL) under long-term submergence in OC-added treatments.On the other hand, non-OC-treated columns showed <3 mM DOC, with no significant change during long-term submergence (Table 1).A similar result was reported by Brodie et al. (2006), in which initial enrichment in total functional genes was observed with OC addition and subsequently declined, but no such enhancement in functional gene richness was observed without OC addition.Therefore, we speculate that this results could be owing to decreased availability of OC (<3 mM) (Table 1).
Table 1.Chemical data for the effluent samples collected after medium-(119-day) and long-(252-day) term submergence.The soil samples collected at these time points were used for microarray analysis.
Previous studies revealed that the addition of OC stimulated biomass and microbial activity in these typically nutrient-poor environments and had a significant effect on microbial biomass, microbial community structure, and functional genes (Holmes et al., 2002, Martin et al., 2002and Yergeau et al., 2007).Sufficient labile OC must be available for sulfate reduction and is a key rate-limiting factor in metal sulfide formation (Ku et al., 2008 andMorse et al., 1999).
This process can be accelerated by the action of indigenous microorganisms fueled through the addition of exogenous carbon (Khan et al., 2010).The change in microbial community structure was observed because of direct and indirect involvement of certain functional genes that was also reported in the study, bioremediation of U using the microarray conducted by Van Nostrand et al. (2011).Several other studies conducted using other techniques, such as phospholipid fatty acid analysis (PLFA) and polymerase chain reactions-denaturing gradient gel electrophoresis (PCR-DGGE), reported changes in microbial community structure with the addition of OC as a substrate (Calbrix et al., 2007, Griffiths et al., 1998and Eiler et al., 2003).
As previously mentioned, some of these genes represent background populations, whereas others may be directly involved in bio-reduction (Van Nostrand et al., 2011).For example, if organic remediation genes are considered to represent background functional genes, their significant decrease (Fig. 4b) is probably owing to an increase in genes directly involved in bio-reduction (i.e., dsrA/B) rather than a true reduction in organic remediation genes, because they are likely not involved in bio-reduction; the similar result was also reported by Van Nostrand et al. (2011).

Total abundance of functional gene categories
The abundance of stress-related functional genes and metal resistance genes decreased during long-term submergence, with both C1S1 and C0S0 indicating that in addition to OC and S, submergence time played a role in decreasing toxicity in these systems.Heavy metals are predicted to represent a major stress on the microbial community, and adaptation to metal stress may be of particular importance in shaping microbial community structure (Hemme et al., 2010).
Several studies have indicated the impact of heavy metals on microbial activities and their community structure (Khan et al., 2010 andHemme et al., 2010).A study conducted on the effects of Pb and Cd on soil microbial activities and their community structure via denaturing gradient gel electrophoresis (DGGE) indicated that Pb and Cd together decreased the number of bacteria when no nutrients were supplied and revealed a significant impact on community structure dynamics, particularly at high Pb and Cd concentrations (Khan et al., 2010).Increased activity of S-cycling functional genes could be owing to readily available sulfate as TEA under more reduced conditions, thereby favoring dissimilatory sulfate reduction as reported before by Brodie et al. (2006) and Muyzer et al. (2008).In the current study, the relationship observed between enhanced dissimilatory sulfate reduction and increased S-cycling functional genes can be further supported by the decreased sulfate-S concentration in effluent samples (Table 1) and the increased metal sulfide formation (Fig. 1a, 1b and 1c).Direct involvement of C-cycling and S-cycling genes in dissimilatory S reduction via rapid consumption of OC followed by sulfate reduction were also reported before by Huerta-Diaz et al. (1998).The changes in microbial communities' result in changes in functional gene abundances.
Metal precipitation is one of the most significant processes involved in the long-term retention of metals in artificial and natural wetlands.Such processes may be accompanied by other indirect reductive metal precipitation (such as redox transformation), including dissimilatory sulfate reduction and the subsequent precipitation of metal sulfides (Huerta-Diaz et al., 1998).As reported in Karna et al. (2016), our results also suggest that appropriate microbial communities were stimulated by OC-and/or S-treatments and resulted in rapid immobilization of Pb, Zn, and Cd in both C1S0 and C1S1 under medium-and long-term submergence.The reduction of metals concentration in solution were most likely due to biogeochemical transformations of Pb, Zn and Cd under reduced conditions.This was supported by bulk XAS, indicating increasing galena (PbS), sphalerite (ZnS), and cadmium sulfide formations in C1S1over time (Karna et al., 2016).
Similar amount of galena formation was also observed in C1S0.Limited S concentration and enhanced pH in C1S0 treatment, however, could lead metal carbonates to be more stable in long run, which are not as stable as sulfide minerals, and controlling metal solubility.Therefore, treatment with both OC and S will be more promising as metal sulfides are more resistant to oxidation, and less sulfide formation is needed to maintain permissibly low metal concentrations in water for a longer period of time.
A handful of studies have examined non-redox-sensitive element removal via constructed wetland treatment systems (Almendras et al., 2009 andWhite et al., 2000).Earlier studies by Almendras et al. (6) tested Pb, Cu, and Zn stability via sulfide formations and showed that biostimulation plays a vital role in stabilizing Pb, Zn and Cd in the subsurface environment.The results from our study also suggest that wetland construction can be a better alternative for stabilizing non-redox-sensitive elements such as Pb, Zn and Cd in mine waste materials or similar geomaterial.Uniqueness of this study is that microbial analyses presented here in details are in agreements with molecular-scale synchrotron-based X-ray data (Karna et al., 2016).
Combining advanced microbiological techniques with synchrotron based speciation enhances

Figure 6 :
Figure 6: Canonical correspondence analysis (CCA) indicating the relationship between

Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 4 August 2018 doi:10.20944/preprints201808.0082.v1 2
Lu et al. (2012)n, labeling, hybridization, scanning, and data processing About 5 g of soil was used for genomic DNA extraction using the PowerMax soil DNA isolation kit (Mo Bio, Carlsbad, CA).Raw DNA extracts were purified using Wizard Plus SV Minipreps purification system (Promega Biosciences, San Luis Obispo, CA).Purified DNA was quantified using the Quant-iT PicoGreen dsDNA assay kit (Promega Biosciences, San Luis Obispo, CA).DNA was labeled then hybridized at 42 °C on GeoChip 4.2 as described inLu et al. (2012).The hybridized arrays were scanned with a NimbleGen MS 200 Microarray Scanner, and scanned images were extracted and quantified using Nimble Scan software (Roche NimbleGen, Madison, (Liang et al., 2011)a preprocessing(Lu et al., 2012).Positive and negative controls, including (i) 8 degenerate probes targeting 16S rRNA sequences for positive controls, (ii) 563 strainspecific probes targeting 7 hyper-thermophile genomes for negative controls, and (iii) a common oligonucleotide reference standard for data normalization and comparison was included for grid alignment and data normalization and comparison(Liang et al., 2011).Statistical analyses were performed using SAS for Windows version 9.2 (SAS Institute Inc., 2009).The data were analyzed using PROC ANOVA.Tukey's Honestly Significant Difference (HSD) test was used for means separation (α = 0.05).Dissimilarity test was also conducted by using the software available at Institute of Environmental genomics (IEG) website, OU, OK (TableS3).All hybridization data are available at http://www.agronomy.k-state.edu/research/soil-andenvironment/soil-environment-chem/Research%20Data.html.