Impacts of Long-Term Irrigation of Domestic Treated Wastewater on Soil Biogeochemistry and Bacterial Community Structure.

Impacts of Long-Term Irrigation of Domestic Treated Wastewater on Soil Biogeochemistry and Bacterial Community Structure Denis Wafula,a* John R. White,b Andy Canion,c* Charles Jagoe,a,d Ashish Pathak,a Ashvini Chauhana Environmental Biotechnology and Genomics Laboratory, School of the Environment, Florida A&M University, Tallahassee, Florida, USAa; Wetland and Aquatic Biogeochemistry Laboratory, Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, Louisiana, USAb; Earth, Ocean, and Atmospheric Science Department, Florida State University, Tallahassee, Florida, USAc; NOAA Environmental Cooperative Science Center, Florida A&M University, Tallahassee, Florida, USAd

Freshwater scarcity and regulations on wastewater disposal have necessitated the reuse of treated wastewater (TWW) for soil irrigation, which has several environmental and economic benefits. However, TWW irrigation can cause nutrient loading to the receiving environments. We assessed bacterial community structure and associated biogeochemical changes in soil plots irrigated with nitrate-rich TWW (referred to as pivots) for periods ranging from 13 to 30 years. Soil cores (0 to 40 cm) were collected in summer and winter from five irrigated pivots and three adjacently located nonirrigated plots. Total bacterial and denitrifier gene abundances were estimated by quantitative PCR (qPCR), and community structure was assessed by 454 massively parallel tag sequencing (MPTS) of small-subunit (SSU) rRNA genes along with terminal restriction fragment length polymorphism (TRFLP) analysis of nirK, nirS, and nosZ functional genes responsible for denitrification of the TWW-associated nitrate. Soil physicochemical analyses showed that, regardless of the seasons, pH and moisture contents (MC) were higher in the irrigated (IR) pivots than in the nonirrigated (NIR) plots; organic matter (OM) and microbial biomass carbon (MBC) were higher as a function of season but not of irrigation treatment. MPTS analysis showed that TWW loading resulted in the following: (i) an increase in the relative abundance of Proteobacteria, especially Betaproteobacteria and Gammaproteobacteria; (ii) a decrease in the relative abundance of Actinobacteria; (iii) shifts in the communities of acidobacterial groups, along with a shift in the nirK and nirS denitrifier guilds as shown by T-RFLP analysis. Additionally, bacterial biomass estimated by genus/group-specific real-time qPCR analyses revealed that higher numbers of total bacteria, Acidobacteria, Actinobacteria, Alphaproteobacteria, and the nirS denitrifier guilds were present in the IR pivots than in the NIR plots. Identification of the nirK-containing microbiota as a proxy for the denitrifier community indicated that bacteria belonged to alphaproteobacteria from the Rhizobiaceae family within the agroecosystem studied. Multivariate statistical analyses further confirmed some of the above soil physicochemical and bacterial community structure changes as a function of long-term TWW application within this agroecosystem.

R

apid population growth across the globe, an increase in per capita water consumption, and, in part, global climate change have resulted in increased demands on available freshwater resources (1–3). Many countries are turning to wastewater recycling in order to meet these increased freshwater demands (3–5). Therefore, planned and managed reuse of wastewater is increasingly practiced not only in arid or semiarid regions but also in temperate and subtropical regions that do not routinely face water shortages (6–9). Regardless of the motivation, large-scale reuse of treated wastewater (TWW) is now becoming increasingly common worldwide. With proper planning, implementation, and management, land application of treated wastewater can benefit agriculture, water resource management, and the environment (10–13). Therefore, in 1992, the U.S. Environmental Protection Agency (U.S. EPA) developed guidelines for the reuse of TWW (8) intended for the irrigation of residential landscapes, parks, school yards, highway medians, fodder, and fiber crops, as well as for environmental purposes such as creating artificial wetlands, and sustaining stream flows. However, reuse or disposal of TWW is not totally free of undesirable impacts. Most notably, land application of TWW has the potential to transfer heavy metals (14), pharmaceuticals (15), and even pathogens (16) in the environment and into the food chain (17). In fact, several studies have shown that nutrients, including total carbon (TC), total nitrogen (TN), and soil microbial quotient (the ratio of microbial biomass carbon [MBC] to soil total

October 2015 Volume 81 Number 20

organic C) remain higher in soils irrigated (IR) with TWW (4, 5, 9, 18, 19). Among the nutrients originating from land application of wastewater, nitrate (NO3⫺) is considered to be a ubiquitous contaminant worldwide (20, 21), threatening aquatic ecosystems and subsurface aquifers, which are often the major source of potable water. Thus, it comes as no surprise that over 20% of rural wells in some parts of the United States contain NO3⫺ concentrations above the drinking water limit of 10 mg/liter (22); inputs of even a

Received 7 July 2015 Accepted 23 July 2015 Accepted manuscript posted online 7 August 2015 Citation Wafula D, White JR, Canion A, Jagoe C, Pathak A, Chauhan A. 2015. Impacts of long-term irrigation of domestic treated wastewater on soil biogeochemistry and bacterial community structure. Appl Environ Microbiol 81:7143–7158. doi:10.1128/AEM.02188-15. Editor: J. E. Kostka Address correspondence to Ashvini Chauhan, [email protected]. * Present address: Denis Wafula, Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, USA; Andy Canion, St. Johns River Water Management District, Palatka, Florida, USA. Supplemental material for this article may be found at http://dx.doi.org/10.1128 /AEM.02188-15. Copyright © 2015, American Society for Microbiology. All Rights Reserved. doi:10.1128/AEM.02188-15

Applied and Environmental Microbiology

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FIG 1 Location of the spray-field agroecosystem in Tallahassee, FL. Groundwater in the area flows in a southerly direction toward the Gulf of Mexico. The right panel shows an aerial image of the wastewater-receiving plots (pivots) and the nonirrigated control sites sampled for this study. Aerial image data are from the U.S. Department of Agriculture, Farm Service Agency; the map was created using ArcGIS, version 10.1.

few milligrams/liter of NO3⫺ can have significant and long-lasting environmental impacts (23). Recent reports from Florida (Tallahassee) and Colorado (Denver and Fort Collins) have shown that groundwater sampled downgradient from farmlands receiving spray-irrigated TWW contained elevated levels of sodium (Na), boron (B), phosphorus (P), NO3⫺-N, chloride, and even pharmaceutical and personal care products (PPCPs) (6, 24–27). Studies conducted by the U.S. Geological Survey (USGS) on the same location used to conduct this study showed that land application of TWW is a major contributor to eutrophication in Wakulla Springs, FL (28), the world’s largest freshwater spring, located downgradient from the wastewater-receiving agroecosystem (Fig. 1). In fact, the TWW applied to the study site contains approximately 16 mg/liter total dissolved N, with a further breakdown of 8.8 mg/liter of ammonia plus organic N and 7.6 mg/liter NO3⫺-N (27). However, NO3⫺ is the only dissolved N species found in water from wells monitored at the southern boundary of the spray field under study, with concentrations ranging from 3.4 to 4.8 mg/liter N (27). This is in line with previous reports of the conversion of ammonium and organic nitrogen to nitrate in the upper part of the unsaturated zone beneath the root zone (28). Because TWW-associated NO3⫺-N has been identified as a major pollutant to the receiving agroecosystem soils and groundwater (23) and is known to persist in the environment for decades (29), our main objective in this study was to investigate the fate of applied TWW-associated nitrate, which is known to recycle in the soils and groundwater primarily via denitrification.

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Despite the above concerns about the land application of TWW, previous studies have largely focused on the assessment of soil biogeochemistry, but the impacts on soil microbiota that underpin nutrient biogeochemical cycling have remained largely ignored. Even the studies that have attempted to assess the impact of land application of wastewater have primarily focused on the enzymatic activity of soil microorganisms, the fate and transport of pathogenic microorganisms from the irrigated wastewater into the soils, or the characterization of antibiotic resistance in bacteria isolated from the irrigated soils (17, 30–32). To address the impact of TWW irrigation on soil microorganisms, Hidri and colleagues (33) utilized automated ribosomal intergenic spacer analysis (ARISA), and more recently Frenk et al. (31) and Broszat et al. (17) used 454 massively parallel tag sequencing (MPTS) of small-subunit (SSU) rRNA genes to show that the soil bacterial communities were indeed impacted by wastewater irrigation such that irrigation caused an increase in the relative abundances of potentially pathogenic gammaproteobacterial assemblages within the TWWirrigated soils. However, no attempts were made to correlate the biogeochemical impacts, such as those from the TWW-associated nitrate (NO3⫺) on the soil microbiota, in particular, the bacterial NO3⫺ reducers, which are likely to be the main sink of N sequestration in such TWW-impacted environments. Therefore, we sought (i) to investigate the impacts of land application of TWW on soil biogeochemistry along with bacterial abundances, community composition, and diversity and (ii) to obtain a better understanding of the impact of TWW-associated NO3⫺-N on the soil denitrifier microbiota.



Impacts of Wastewater Reuse

Denitrification is a microbially mediated process in which NO3⫺ is reduced sequentially, producing N2 gas (34). It is a major cause of N loss from agricultural soils, and, moreover, the process also has the potential to produce nitrous oxide (N2O), a potent greenhouse gas. Although soil abiotic conditions can affect denitrification (35–37), microbial factors such as the diversity, composition, and abundance of bacterial as well as fungal denitrifiers are also critical in regulating the fate of environmental N (38–40). Therefore, it is necessary to obtain a holistic understanding of the community dynamics of soil bacterial denitrifiers as well as of their response(s) to human activities, such as the reuse of TWW via land application. Such studies help further our understanding of the impacts of human managed systems on ecosystem services, which are largely controlled by environmental microorganisms. To understand the structure and diversity of bacterial denitrifier communities in the environment, functional genes coding for denitrification enzymes have been extensively used; these include the nitrite reductases (nirK and nirS) and the nitrous oxide reductase (nosZ) (38–41). Using these genes, previous studies have shown that long-term fertilization with different treatments influenced the size, composition, and functioning of bacterial denitrifiers (42). In fact, Zhou et al. (41) demonstrated that, compared to irrigation with clean water, TWW application on agricultural soils resulted in an increase in the numbers of nirK-containing communities and also shifted the composition of the nirS and nirK denitrifier assemblages. This study was performed to further enhance our understanding of soil biogeochemistry and the response(s) of total bacteria and the denitrifier bacterial guilds to the long-term application of secondary treated wastewater (TWW). Our results show that TWW irrigation influenced the composition and abundance of total bacteria along with shifts in the nirS and nirK denitrifier guilds. Additionally, some of the microbial changes also strongly correlated with changes in the irrigated-soil physicochemical status, most likely brought about by the land application of treated wastewater. MATERIALS AND METHODS Site description and sample collection. Since 1982, the City of Tallahassee, FL, has been reusing secondary treated wastewater by spray irrigation on an approximately 890-ha farm to grow crops (Fig. 1). On average, about 64.5 million liters (approximately 83 thousand liters/ha) of TWW is discharged daily into these fields by the use of 16 computer-controlled irrigators. TWW-receiving areas (termed pivots) range from 14 ha to 80 ha although the majority are approximately 59 ha. The irrigated soils are managed through no-till farming and do not receive any synthetic or natural fertilizers. Thus, the spray-irrigated crops, which include canola, corn, soybeans, hay, and sorghum, are sustained solely by the nutrients contained in the treated wastewater and, upon harvesting, are mainly used to feed farm animals. Soil samples used for this study were part of a larger ongoing study and were collected in December 2010 (winter, W) and August 2011 (summer, S). Triplicate samples were collected from a depth of 0 to 40 cm by a soil auger from five pivots and three adjacent plots, serving as controls. The plots serving as controls for this study have never been used for agriculture, nor have these soils ever received direct application of irrigated wastewater. An additional nonirrigated (NIR) site (control D) was also sampled because measurements at the initial nonirrigated site (control C) mirrored the pivots in our preliminary soil biogeochemical analysis, suggesting that this site was likely indirectly impacted by the hydrological flow of irrigated TWW (Fig. 1), as also shown previously (6). The five pivots have different TWW exposure histories: pivot 1 and pivot 6 have


received TWW since 1982, pivot 13 was established in 1987, and pivot 15 and pivot 16 began receiving TWW in 1999. Plant debris and roots, if any, were removed, and samples were homogenized in Ziplock bags and stored over ice for return to the laboratory at Florida A&M University, Tallahassee, where they were preserved at ⫺80°C until further analysis. Soil biogeochemical measurements and statistical analysis. For biogeochemical analyses, triplicate cores collected from the five pivots and four control plots were processed and analyzed independently. Soil moisture content (MC) was determined using the standard gravimetric method (43). Total carbon (TC) and total nitrogen (TN) were determined on dried, ground subsamples using a Carlo-Erba NA-1500 CNS analyzer (Haak-Buchler Instruments, Saddlebrook, NJ). For total phosphorus (TP), 0.5 g of dried, ground subsample was combusted at 550°C for 4 h in a muffle furnace, followed by dissolution of the ash in 6 M HCl on a hot plate. TP was analyzed in the digested solution using an automated ascorbic acid method on a Seal AQII discrete analyzer, according to U.S. EPA method 365.4 (44). Microbial biomass carbon (MBC) was determined using a fumigation and extraction technique (45). The soil biogeochemical data were analyzed by two-way analysis of variance (ANOVA) using SAS JMP, version 10. Shapiro-Wilk W tests were used to determine whether data were normally distributed, and log transformations were applied where necessary to meet the assumptions of analysis of variance. For this analysis, classification variables were land use (irrigated sites [n ⫽ 5] or nonirrigated sites [n ⫽ 3]) and season (winter or summer). Statistical analysis was performed by averaging replicate samples obtained from winter and summer from the irrigated and nonirrigated samples separately, and the mean values for each plot were used for the two-way ANOVA. Extraction of soil gDNA. Genomic DNA (gDNA) was extracted from each of the triplicate cores collected from five pivots and four control plots using a PowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, CA) according to the manufacturer’s instructions. The extracted DNA from five irrigated soils and four nonirrigated soils, respectively, were quantified using an EON Microplate spectrophotometer equipped with a Take 3 microvolume plate (BioTek Instruments, Winooski, VT). Soils are known to be extremely variable, and the best approach is to analyze replicates independent of each other. However, to avoid pseudoreplication, we combined the gDNA extracts based on exposure histories of the pivots to wastewater application. Specifically, triplicate gDNA extracts from each of the irrigated and nonirrigated samples were pooled to obtain three different pivot samples with variable periods of TWW exposure and four different control plot samples. Samples were pooled based on the TWW exposure time such that pivots 1 and 6 had received TWW since 1982, pivot 13 was established in 1987, and pivots 15 and 16 began receiving TWW in 1999. Thus, gDNA extracts from pivots 1 and 6 were pooled, pivot 15 was analyzed without pooling, and extracts from pivots 15 and 16 were pooled. This way, we obtained replicated but pooled samples from three irrigated and four nonirrigated sites in both summer and winter, respectively, which were further processed for the quantitative PCR (qPCR), terminal restriction fragment length polymorphism (T-RFLP), PCR cloning, pyrosequencing, and diversity analyses. Statistical analysis conducted on total bacterial gene copy numbers (see Fig. S1 in the supplemental material) and denitrifier genes (see Fig. S2) showed that, despite pooling, control measurements were distinctly different from those of the pivots. Total bacterial and denitrifier gene abundance measurements by qPCR. Genes of interest from the irrigated and nonirrigated soil samples were quantified by generating calibration curves using SsoAdvanced SYBR green Supermix and a Bio-Rad C1000 thermocycler equipped with a CFX 96 real-time system (Bio-Rad Laboratories, Hercules, CA). The primers used for estimating gene copy numbers were from Fierer et al. (46) for total bacteria, Acidobacteria, Actinobacteria, and alphaproteobacteria along with primers for nirK (47) nirS, and nosZ (48). Further details including the thermocycling conditions are listed in Table S1 in the supplemental material. The qPCR mixtures (20 ␮l) contained 10 ␮l of the


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master mix, 1 ␮l each of 500 nM forward and reverse primers, 20 to 40 ng of template DNA, and nuclease-free water. The cycling conditions included an initial denaturing step at 98°C for 5 min, followed by 40 cycles of 98°C for 30 s, annealing at a target-specific temperature for 30 s, and an elongation step at 72°C for 36 s, which included a fluorescent data collection step if appropriate; otherwise the data were collected at 82°C. The specificity of our reaction was examined by performing a melt curve analysis at 55°C to 95°C with 0.2°C increments. To ensure robustness of the quantification data, all applicable guidelines for the minimum information for publication of quantitative real-time PCR experiments (MIQE) were followed (49). For example, a no-template control and a positive control consisting of plasmid DNA with the gene of interest were included with each run. Additionally, we also performed inhibition tests using a known amount of DNA template spiked with plasmid DNA containing the target gene of interest as a positive control to assess if templates contained inhibitors that could potentially affect the quantification process, as shown previously (50). For each calibration curve generated, DNA standards consisted of plasmid mixtures from 10 to 20 transformants containing the target gene of interest as a PCR amplicon. The cloning reaction to obtain plasmids was performed using a TOPO TA cloning kit (Life Technologies, Carlsbad, CA) according to the manufacturer’s instructions. qPCRs on each of the irrigated and nonirrigated samples were performed separately, and the acquired data were initially visualized and analyzed using CFX Manager, version 2.1 (Bio-Rad Laboratories, Hercules, CA). All of the qPCR assays run were shown to possess efficiencies between 90 and 110%. Gene copy numbers obtained were analyzed using two-way analysis of variance (ANOVA) using SAS JMP, version 10; Shapiro-Wilk W tests were used to determine whether variables were normally distributed, and log transformations were applied where necessary to meet the assumptions of analysis of variance. Classification variables used for the ANOVA were land use (irrigated sites [n ⫽ 5] or nonirrigated sites [n ⫽ 3]) and season (winter or summer). As was done for the biogeochemical data, replicate qPCR gene copy numbers obtained from the irrigated and nonirrigated samples were averaged, and the mean values for each plot were used for the two-way ANOVA. Comparison of the ratios of denitrifying gene (nirS, nirK, and nosZ) to 16S rRNA gene copy numbers representing the total bacterial population size was also performed using Microsoft Excel. MPTS, heat maps, and statistical analyses. The gDNA was subjected to massively parallel tag sequencing (MPTS; 454 pyrosequencing) using previously described methods (51). The bar-coding and pyrosequencing analyses were performed by Research and Testing Laboratories (Lubbock, TX), using forward primer Gray28F (5=-GAGTTTGATCNTGGCTCAG) and reverse primer Gray 519r (5=-GTNTTACNGCGGCKGCTG). The sequencing reactions were performed using a Roche 454 FLX instrument (Roche, Indianapolis, IN, USA) with titanium reagents, according to the manufacturer’s recommended procedures. After sequencing was completed, sequences that passed the quality controls were uploaded into mothur (52), where tags were removed before the sequences were denoised, along with the removal of low-quality sequence reads and chimeras. Sequences that were below 150 bp were discarded from further analysis. We used a combination of taxonomy-based and taxonomy-independent approaches to analyze the MPTS data. For the taxonomy-based approach, sequences were analyzed using the Ribosomal Database Project (RDP) Classifier at an 80% confidence level (53). The data obtained in this manner were analyzed at the bacterial class level. To test for statistical differences in the bacterial community composition, the data were normalized for each site using a modification of the procedure applied by Wu et al. (54). Data were transformed by log(x ⫹ 1), and Bray-Curtis similarities were calculated using Primer, version 6 software (Primer-E, Plymouth, United Kingdom). The similarity data were then analyzed by cluster analysis, and nonmetric multidimensional scaling (NMDS) plots were generated. In addition, significance of the data was tested using permuta-

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tional multivariate analysis of variance (PERMANOVA) with 999 permutations, treating land use and season as the two main factors (55, 56). For the taxonomy-independent analysis, 16S rRNA gene sequences were analyzed using the QIIME platform (57). Briefly, all the sequences were trimmed to 300 bp, and the sequences were clustered at 97% and 95% similarity levels. The 97% similarity clustering data were used to calculate UniFrac distances (58), while the 95% clustering data were used for the canonical correspondence analysis (CCA). Random subsampling was conducted to normalize sequence numbers in each sample prior to calculating the UniFrac distances. The site differences were assessed based on season and land application and visualized using principal coordinate analysis (PCoA). To correlate the measured biogeochemical parameters with the microbial communities, CCA was performed using XLSTAT (Addinsoft, New York, NY), which is a statistical software suite for Microsoft Excel, and/or PAST (PAleontological STatistics) (O. Hammer, D. A. T. Harper, and P. D. Ryan, University of Oslo, Oslo, Norway [http: //folk.uio.no/ohammer/past]). In addition, we analyzed the taxonomic differences between the unassembled pyrosequences (environmental gene tags) which were annotated using the MG-RAST (Metagenomics Rapid Annotation using Subsystem Technology) pipeline, version 3.2.5 (http://metagenomics.anl.gov/) (59), with a maximum BLAST E value cutoff of ⬍1 ⫻ 10⫺5 and a minimum alignment length of 15 bp. Bacterial taxonomic profiles at the phylum and class levels were generated within MG-RAST using the normalized abundance of phylogenetic identity of sequence matches to the Ribosomal Database Project and Greengenes, both at a BLAST E value cutoff of ⬍1 ⫻ 10⫺5 and a minimum alignment length of 15 bp (60). Heat maps of the frequency of MG-RAST hits to individual taxa across soils were created after data were normalized (dividing by the total number of hits) to remove sequencing bias or differences in the read lengths. Rarefaction analysis of obtained pyrosequences using both RDP and MG-RAST showed that enough numbers of sequences were obtained from each site because the curves from each sample reached the near-plateau phase at 95% confidence of upper and lower limits for each distance, representing good sampling depth (data not shown). T-RFLP. For nirS amplification, each of the triplicate 20-␮l reaction mixtures from the irrigated and nonirrigated samples contained 10 ␮l of SsoAdvanced SYBR green master mix (Bio-Rad Laboratories, Hercules, CA), molecular-grade water, approximately 50 ng of template, 500 nM (each) phosphoramidite dye (6-carboxyfluorescein [FAM])-labeled forward primer cd3aF and reverse primer R3cd. PCR was carried out in a Bio-Rad C1000 Touch thermal cycler (Bio-Rad Laboratories, Hercules, CA) with an initial denaturation at 98°C for 5 min, followed by 34 cycles of 98°C for 45 s, 64.2°C for 45 s, and 72°C for 60 s, with a final extension at 72°C for 5 min. The enzyme used in this reaction is normally used for qPCR; however, during PCR optimization, we found satisfactory products (sharp bands and no smearing) with only the SYBR green master mix. For nirK amplification, triplicate 25-␮l PCR mixtures were set up consisting of 12.5 ␮l of GoTaq Hot Start Green master mix (Promega, Madison, WI) with the FAM-labeled forward primer nirK1F and reverse primer nirK5R; a denaturing temperature of 95°C along with an annealing temperature of 64.5°C were used. For nosZ amplification, each of the triplicate 25-␮l PCR mixtures contained 12.5 ␮l of GoTaq Hot Start Green master mix (Promega, Madison, WI), along with forward hexachlorofluorescein (HEX)-labeled nosZ-F primer and nosZ1622R reverse primer; the cycling conditions were similar to those of the nirK amplification program. PCR amplicons were purified using an UltraClean PCR cleanup kit (MoBio), and DNA quantities were spectrophotometrically adjusted using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Inc., Wilmington, DE, USA). Approximately 30 ng of each reaction product was digested using MspI (Promega, Madison, WI) for 4 h at 37°C. The digested DNA was cleaned by ethanol precipitation and dried using a Savant DNA 120 Speedvac (Thermo Scientific). The amplified DNA was




FIG 2 Box-and-whisker plots are shown for the soil biogeochemical parameters measured from the spray-field agroecosystem soils in Tallahassee, FL, analyzed as a function of land use and season. Box-and-whisker plots give the medians (horizontal lines inside the boxes), interquartile ranges (boxes), and outliers (small black dots). †, significant difference in values between seasons; *, significant difference in values between land use types. IR and NIR indicate data obtained from irrigated pivots and nonirrigated plots, respectively.

then suspended along with a 6-carboxy-X-rhodamine (ROX)-labeled standard in formamide and run on an ABI 310 genetic analyzer (Applied Biosystems, Foster City, CA). The digested samples were evaluated by GeneScan analytical software (Applied Biosystems, Foster City, CA), and the resulting T-RFLP peaks were transformed by using the log(x ⫹ 1) value to reduce the effect of larger peaks. A resemblance matrix was created using the Bray-Curtis method in Primer, version 6, software (Primer-E, Plymouth, United Kingdom). To test for differences and visualize the relationships between different sites, PERMANOVA followed by canonical analysis of principal components (CAP) was performed as shown previously (61, 62). Since PERMANOVA is nonparametric, significance is determined through permutations and does not require distributional assumptions such as normality (55). Identification of the nirK-containing microbiota as a proxy for the denitrifier communities using PCR cloning and sequencing. PCR was performed using the nirK-specific primers and conditions described by Hallin and Lingren (63), with a slight modification which involved increasing the annealing temperature from 57°C to 60°C, which yielded the best PCR products from the soils under investigation. PCR amplicons of nirK genes were visualized to determine correct product size on 2% aga-


rose and stained with GelStar nucleic acid stain (Lonza, MD). The PCR products were then purified using an UltraClean PCR purification kit (MoBio) and cloned into TOPO TA cloning vector pCR4 according to the manufacturer’s protocol (Invitrogen, Carlsbad, CA). From each of the IR and NIR clone libraries, 96 clones were screened by PCR amplification of the nirK gene. In spite of the use of the nirK gene-specific primers, a sizeable number of gene sequences turned out to be non-nirK sequences after a BLAST search, especially those from control plot samples and most of the winter samples. The application of a temperature gradient during PCR could also not resolve this problem. Thus, several additional libraries were generated to obtain at least 96 clones each from the IR and NIR soils, and clones were amplified for the nirK gene, digested with HaeIII restriction endonuclease enzyme (New England BioLabs, Beverly, MA), and resolved in a 3% agarose gel. Clones were grouped into different operational taxonomic units (OTUs) according to restriction fragment length polymorphism (RFLP) banding patterns. Two representatives from each OTU were sequenced using a BigDye Terminator sequencing kit (Applied Biosystems, Foster City, CA) on an Applied Biosystems 3100 genetic analyzer prior to phylogenetic analysis. Vector sequences flanking the nirK gene sequences were removed using FinchTV (Geospiza) sequence view-


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FIG 3 Box-and-whisker plots are shown for the gene copy numbers measured from the spray-field agroecosystem soils in Tallahassee, FL, analyzed as a function of land use and season. Box-and-whisker plots give the medians (horizontal lines inside the boxes), interquartile ranges (boxes), and outliers (small black dots). Differences between seasons were not significant. *, significant difference between values for land use types. IR and NIR indicate data obtained from irrigated pivots or nonirrigated plots, respectively. ing and editing software. The BLAST algorithm and GenBank database (64) were used to acquire the nearest known phylogenetic relative of each sequence, which was aligned using MEGA4 (65). A neighbor-joining tree with a Jukes-Cantor correction with 1,000 bootstrap sampling was generated. Nucleotide sequence accession numbers. Clone library sequences generated in this study are available in GenBank under accession numbers

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KF235417 to KF235419 (nirK), KF235420 to KF235422 (nosZ), and KF235423 to KF235426 (nirS), respectively. Standard sequences generated for qPCR are listed in GenBank under accession numbers KF235427 to KF235436. Bar-coded pyrosequences generated in this study are available in the MG-RAST database under the following accession numbers: for IR (S), 4532400.3; IR (W), 4532401.3; NIR (S), 4532554.3, and NIR (W), 4532555.3.



FIG 4 (A) Bar plot showing abundances of the predominant phyla identified from the spray-field agroecosystem soils in Tallahassee, FL, as a function of land application of domestic TWW relative to those of nonirrigated soils. Identified taxa were categorized at the class level, with the exception of only Acidobacteria, which is shown at the phylum level. (B) Double-hierarchical dendrogram showing distribution at the class level of identified taxa from the spray-field agroecosystem soils in Tallahassee, FL. The phylogenetic tree was calculated using the neighbor-joining method, and the relationship among samples was determined by Bray-Curtis distance and the complete clustering method. The heat map depicts the relative percentage of each identified class (variables clustering on the y axis) within each sample (x-axis clustering). The relative Euclidean distance values for the bacterial classes identified are depicted by red and green, indicating low and high abundance, respectively, correlating with the legend at the bottom of the figure. Clusters based on the distance of samples along the x axis and the bacterial classes along the y axis are indicated in the top and left of the figure, respectively. Arrows point to the phyla/taxa that were clearly different in the irrigated pivots relative to those in the nonirrigated plots based on Euclidean distances.



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TABLE 1 Bacterial diversity measures obtained between irrigated and nonirrigated soil samples used in this studya Site (season)

No. of sequences retrieved

No. of OTUs calculated

Chao1

Coverage (%)

Shannon diversity (H=)

Shannon evenness (E)

NIR (S) NIR (W) IR (S) IR (W)

7,946 9,389 59,374 14,022

62 66 86 73

63 70 91 76

99.9 99.9 100 100

2.81 2.82 2.81 2.86

0.68 0.67 0.67 0.63

a

Alpha diversity measures between different site locations based on the taxonomy-dependent data at the class level.

RESULTS

Soil biogeochemistry. To achieve a meaningful statistical comparison between 15 irrigated and 9 nonirrigated soil samples, we combined the data obtained into irrigated summer [IR (S)], irrigated winter [IR (W)], nonirrigated summer [NIR (S)], and nonirrigated winter [NIR (W)] groups and assessed the effect of irrigation treatment and seasons on soil properties by a two-way ANOVA; differences were considered significant at a P value of ⬍0.05. As shown in Fig. 2 and Table S2 in the supplemental material, pH and moisture were significantly higher in irrigated soils than in the nonirrigated soils. Additionally, organic matter (OM) and MBC were significantly higher in summer but did not differ between irrigation treatments. Other measured nutrients, including carbon, nitrogen, and phosphorous, did not differ between seasons or irrigation treatments. Quantification of bacterial 16S rRNA genes and bar-coded pyrosequence analyses. The standard curves generated for bacterial 16S rRNA gene analysis were linear across a scale of 8 orders of magnitude (from 100 to 108 copies per reaction), indicating that the test reactions were devoid of inhibitors that could have compromised the quantification process (data not shown). Additionally, as is typical, we did not use one cloned cell containing the gene of interest to generate the standard curves; rather, we used a pool of plasmid DNA from several cloned cells containing the gene of interest so as to minimize any PCR bias potentially associated with using DNA from a single clone. Using this approach, the bacterial biomass was estimated using the 16S rRNA genes as tracers; a two-way ANOVA showed that the total 16S gene copy numbers were significantly higher in the irrigated pivots, and, by inference, bacterial abundance was higher in the irrigated soils than in the nonirrigated plots (Fig. 3; see also Table S3 in the supplemental material). However, bacterial numbers did not vary significantly between the two seasons. Additionally, 454 massively parallel tag sequencing (MPTS) analysis was performed to assess differences in overall community structure between the irrigated pivots and the nonirrigated control plots. We found that Proteobacteria, especially Alphaproteobacteria, predominated in these soils regardless of season or irri-

gation status, with a relative abundance between 30.8% and 40.7%. Proteobacteria were closely followed by Acidobacteria (13% to 31%) and Actinobacteria (6.5% to 25.5%) (Fig. 4A). In addition, specific bacterial responses shown as a function of TWW loading included the following:(i) an increase in the relative abundance of Proteobacteria, especially Betaproteobacteria and Gammaproteobacteria, (ii) a decrease in the relative abundance of Actinobacteria, (iii) shifts in the communities of acidobacterial groups, and (iv) no consistent trends in the less abundant phyla. Furthermore, the OTUs and Chao1 species richness estimates calculated from the pyrosequencing data also suggested that the bacterial communities in the TWW-receiving soils were generally more similar to each other than those in the nonirrigated soils (Table 1). Additionally, estimation of the 16S gene copy numbers of the predominant phyla identified by MPTS analyses across the wastewater-receiving agroecosystem were also performed (Fig. 3; see also Table S3 in the supplemental material). Two-way ANOVA to test for the effects of land use (irrigated or nonirrigated) and season (winter or summer) on the bacterial gene copy numbers showed that Acidobacteria (P ⬍ 0.002), Actinobacteria (P ⬍ 0.03), and Alphaproteobacteria (P ⬍ 0.001) abundances were significantly higher in the irrigated pivots than in the nonirrigated plots and that seasons did not impact the numbers. Heat map analyses of bacterial communities. We also compared the taxonomic affiliations of the bacterial communities obtained by pyrosequencing analyses of the irrigated versus the nonirrigated soils at the class, order, and genus levels, using a double-hierarchical dendrogram (Fig. 4B). Overall, we found that bacterial communities from the nonirrigated samples clustered together from both of the seasons tested; conversely, the bacterial communities from the irrigated sites formed a distinctly separate cluster (Fig. 4B). Moreover, the acidobacterial groups in the irrigated soils showed Euclidean distances of 0.41 to 0.47 in the irrigated summer and winter soils, whereas the nonirrigated soils were distinctly separate, with Euclidean distances in the range of 0.69 to 0.8 in the summer and winter seasons, respectively (Fig. 4B). Other taxa that were visibly different in the heat map belonged to Chlorophyta, with Euclidean distances of 0.29 in the IR soils and 0.55 in the NIR soils,

TABLE 2 Relative abundance of denitrifying gene copy numbers compared to 16S RNA gene copy numbers Relative abundance of the indicated gene (no. of gene copies/no. of 16S rRNA gene copies) a

Site (season) NIR (S) NIR (W) IR (S) IR (W) a b

nirK ⫺4

⫺4

4.49 ⫻ 10 ⫾ 4.28 ⫻ 10 5.30 ⫻ 10⫺4 ⫾ 1.82 ⫻ 10⫺4 1.12 ⫻ 10⫺3 ⫾ 1.32 ⫻ 10⫺3 6.05 ⫻ 10⫺4 ⫾ 5.26 ⫻ 10⫺4

nirSb

nosZ

5.04 ⫻ 10⫺7 ⫾ 5.97 ⫻ 10⫺7 1.08 ⫻ 10⫺7 ⫾ 1.53 ⫻ 10⫺7 6.68 ⫻ 10⫺6 ⫾ 8.72 ⫻ 10⫺6 1.78 ⫻ 10⫺5 ⫾ 1.66 ⫻ 10⫺5

1.07 ⫻ 10⫺3 ⫾ 9.41 ⫻ 10⫺4 1.17 ⫻ 10⫺3 ⫾ 4.66 ⫻ 10⫺4 1.26 ⫻ 10⫺3 ⫾ 1.50 ⫻ 10⫺3 1.37 ⫻ 10⫺3 ⫾ 1.02 ⫻ 10⫺3

S and W, summer and winter seasons, respectively. It is noteworthy that nirS gene copy numbers were lowest across the control and pivot soils (gene copy number values are reported as the negative powers of the exponent).

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Nitrospira, with Euclidean distances of 0.42 in the IR soils and 0.7 in the NIR soils, Bacteroidetes, with Euclidean distances of 0.36 in IR and 0.53 in NIR soils, and Actinomycetes, with Euclidean distances of 0.3 in IR and 0.08 in NIR soils. Quantification of the denitrifier-associated functional genes. Because the irrigated sites are continuously spray-irrigated with nutrient-rich (mainly NO3⫺) wastewater, we hypothesized that the wastewater NO3⫺ would potentially impact the soil denitrifier microbiota. To test this, we quantified and compared the abundances of denitrifier-associated bacterial nirS, nirK, and nosZ genes at the five irrigated pivots and four nonirrigated sites. Using two-way ANOVA, we found that the nirS gene abundances were significantly higher at irrigated sites (P ⬍ 0.03) (Fig. 3). Conversely, abundances of nirK and nosZ genes did not differ significantly between the irrigated and nonirrigated sites although their numbers appeared to be relatively higher in the irrigated soils (Fig. 3; see also Table S3 in the supplemental material). In addition, we found no significant differences in the denitrifier-associated genes between the two seasons. Thus, overall, the denitrifier communities mirrored the pattern observed for the total bacterial communities such that the bacterial population sizes, as inferred by their gene copy numbers, were higher in the irrigated sites than in the nonirrigated plots. Moreover, when the ratios of denitrifying genes were compared with 16S rRNA gene abundances, we found that the contributions of the nirS gene relative to total bacteria (16S rRNA genes), at all locations, were the highest such that their gene copy numbers ranged between 10⫺5 and 10⫺7, which are 2- to 3-fold greater than those of nirK (10⫺3 to 10⫺4) or of nosZ (10⫺3 to 10⫺4), respectively (Table 2). This observation is in line with a previous study (66). Studies on the denitrifier bacterial community structure by terminal restriction fragment length polymorphism. T-RFLP analyses were used to assess the community structure of bacterial denitrifier guilds, which showed trends similar to those of the total bacterial communities. More specifically, we found that the nirK and the nirS guilds from the irrigated pivots clustered together and away from the nonirrigated control plot communities (Fig. 5A and B). Moreover, in the irrigated pivots, the nirS guilds showed the strongest similarities at 40%, 60%, and 80% resemblance levels while nirK communities had only 60% and 80% levels (Fig. 5A and B). Conversely, the nosZ guilds did not correlate with either the irrigation status or the tested seasons (Fig. 5C). Additionally, PERMANOVA also showed that a significant relationship exists between the irrigation status and both the nirS (P ⬍ 0.05) and nirK (P ⬍ 0.004) assemblages, but neither irrigation nor season had any significant impact on the nosZ guilds. Identification of the nirK-containing soil microbiota as a proxy for denitrifier communities using PCR cloning and sequencing. In order to determine the diversity of the bacterial denitrifier community in the IR and NIR soils, the nirK gene was used as a proxy, mainly because several previous reports indicated that nirK is more amenable than nirS to PCR amplification in soils (67–69). Using PCR cloning, a total of 95 nirK-type gene sequences were recovered from the IR and NIR samples. Phylogenetic analysis revealed that many of the nirK-containing microorganisms from this agroecosystem were similar to environmental clones and alphaproteobacteria from the Rhizobiaceae family native to agricultural soils (Fig. 6). These results are in accordance with previous studies that found numerous functional marker


FIG 5 Nonmetric multidimensional scaling ordination of the T-RFLP data for the nirK gene (A), nirS gene (B), and nosZ gene (C) obtained from the spray-field agroecosystem soils in Tallahassee, FL. Each data point is a mean of triplicate runs; the data are based on a Bray-Curtis similarity matrix. Open and filled blue squares and red circles represent the summer (S) and winter (W) seasons, respectively, for the IR and NIR sites, as indicated. Bray-Curtis similarity values between irrigated and nonirrigated sample bacterial communities are shown at the 20%, 40%, 60%, and 80% levels in the summer (S) and winter (W) seasons.

genes for denitrification in soils clustering with denitrification genes of Rhizobiales (67, 70–72). Correlation between soil biogeochemistry and bacterial community structure using CCA. To further understand which of the environmental and biogeochemical factors likely caused the


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differences observed between the bacterial communities of irrigated and nonirrigated soils, canonical correspondence analysis (CCA) was performed by both taxonomy-dependent and -independent approaches. As shown in Fig. 7, the first two CCA axes represented over 88% of the variance, with Actinobacteria and Bacilli in the nonirrigated-site summer samples showing positive correlations with TP; conversely, cyanobacteria were negatively correlated with the irrigated summer samples. On the second axis, we found positive correlations of Acidobacteria and Proteobacteria from the irrigated winter samples with organic matter and total nitrogen concentrations; moisture content and pH were present almost at the borderline of axes 1 and 2, respectively. CCA also showed that nonirrigated winter samples correlated negatively with total carbon and the microbial biomass carbon (Fig. 7). Correlation of soil properties with bacterial OTUs from taxonomyindependent analysis revealed a pattern similar to that observed for the taxonomy-dependent analysis, albeit with the first and second axes describing a reduced (slightly over 50%) source of the community structure variation (data not shown). In addition, pyrosequencing data were analyzed by principal coordinate analysis (PCoA) of the weighted UniFrac resemblance matrix, which supported the observations described above, such that the wastewater-receiving pivots clustered away from the control sites on the first principal axis, which accounted for 45.12% of the variation (data not shown). Additionally, the second axis, which accounted for 21.90% of the variation, was able to separate the bacterial community of the irrigated sites by season, with winter samples clustering together and away from the summer samples. This also suggests seasonal impacts to the soil microbial community within this wastewater-receiving agroecosystem. Additional ordination of the pyrosequencing data confirmed strong clustering of the samples as a function of both land use and seasons at an 80% similarity level (Fig. 8A and B). Finally, PERMANOVA of the class-level data also revealed significant differences (P ⬍ 0.05) in the bacterial communities as a function of wastewater irrigation. DISCUSSION

Historically, environmental controls of microbial community structure and their associated functions have been under studied and hence poorly understood (73–75). However, an increasing body of evidence garnered from recent studies has shown that soil properties, vegetation, land use, and even climate change exert a significant control on the abundance, activities, structure, and functions of soil microbial communities (76–79). Using a suite of approaches that included measurements of major soil nutrients (TC, TN, and TP), quantitative estimation of total bacterial gene abundances and denitrifier gene abundances, and a detailed assessment of the bacterial community shifts using 454 massively parallel tag sequencing (MPTS) analyses, T-RFLP, and PCR cloning, we depict a holistic understanding of the impact of domestic

FIG 6 Phylogenetic analysis of the denitrifying bacteria obtained from the agroecosystem soils in Tallahassee, FL, based on nirK-type gene sequences. The neighbor-joining method was used to construct the phylogenetic tree with a bootstrap value of 1,000 iterations; only values above that were above 50 are shown at branch points. Accession numbers of the retrieved nirK-type gene sequences along with their closest phylogenetic relatives are shown in parentheses. The Escherichia coli formate dehydrogenase gene (fdoG) (GenBank accession number X87583) was used as an outgroup.

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FIG 7 Biplot derived from canonical correspondence analysis (CCA) of the bacterial abundances, correlated with soil biogeochemical and environmental properties obtained from the spray-field agroecosystem in Tallahassee, FL. Percentages of variation are shown in parentheses on the x and y axes. Nonirrigated and irrigated sites and sampling seasons are indicated. The filled blue circles represent bacterial taxa/phyla identified over two seasons in IR and NIR soils.

treated wastewater (TWW) irrigation on soil microbiota that underpin biogeochemical cycling, especially of nitrogen, an area about which currently little is known. We found that regardless of seasons, pH and moisture content (MC) were higher in the irrigated (IR) pivots than in the nonirrigated plots; these findings are in line with previous studies conducted by the U.S. Geological Survey (USGS) on the same location investigated in this study (6, 24–26). However, a detailed analysis of bacterial response(s) associated with TWW irrigation was not attempted previously, a knowledge gap which is filled by this study. Note that despite the pooling of irrigated samples for the microbial analysis based on the period of exposure to TWW application, we found that excess NO3⫺ from the TWW irrigation positively correlated to enhanced bacterial abundances and likely caused a shift in the community structure of total bacteria as well as the nirK- and nirS-containing bacterial assemblages associated with denitrification of TWW NO3. However, the gene copy numbers of nosZ guilds did not differ significantly with respect to the variables of land use and season, suggesting that the nosZ-containing complete denitrifiers were either under different environmental controls than nirK and nirS or that the nosZ assemblages are insensitive to the TWW application. This is an important finding because denitrifier bacterial assemblages lacking the terminal nosZ gene produce N2O as a metabolic end product, which is an important greenhouse gas known to exacerbate global warming processes. However, the nosZ data obtained in this study should be interpreted with caution because it is possible that we underestimated the population of nosZ-containing denitrifiers. Specifically, after we conducted our study, Sanford et al. used bioinformatics to identify phylogenetically distinct, atypical nosZ genes in microbial taxa from both terrestrial and marine environments (80). It was established that due to the conserved sequence features that distinguish the atypical and typical nosZ genes, previously reported


primers for nosZ abundance and diversity analysis, including those that we used in this study, are likely to not target the newly discovered atypical nosZ genes. In fact, gene copy numbers for all denitrifiers from this study should be interpreted with caution because the taxonomy of denitrification genes in complex environmental samples is still not fully known, and thus existing primers may bind to and amplify nonspecific genomic regions, which is likely the reason for the non-nirK-type genes that were found in our study during the clone library sequencing. It can also be that some denitrifier members have duplications of the nirK gene. Moreover, hardly any primers are known to amplify the nirS and nosZ genes of Gram-positive denitrifiers; only very recently have newer primers been developed to target distinctly different nirK and nosZ gene sequences of Gram-positive denitrifiers (81, 82). To address these issues, we performed qPCR assays using SYBR green, which is well known to be much more robust than Taq; melting curve analysis was also performed to ensure that qPCR assays produced a single, specific product, as expected for each of the denitrifier genes. We also performed denitrification enzyme assays (DEA) but only in samples collected in winter that formed part of an earlier analysis related to this study. DEA rates can serve as an effective indicator of in situ denitrification activity (83, 84), and our analyses showed that DEA rates were below the detection limit in the nonirrigated plots apart from control C, which is located downgradient from the spray-field agroecosystem and shows the indirect impact from the TWW application; conversely, the irrigated soils showed higher rates, ranging between 0.15 and 0.67 ␮g kg⫺1 dry soil h⫺1 (see Fig. S3 in the supplemental material). Thus, taken together, our results demonstrate that NO3⫺-rich wastewater irrigation can affect the abundances, diversity, and activities of Ncycling bacterial communities. It is imperative to note that despite the recent interest in the impacts of TWW irrigation on soils (17, 31), rarely has the impact of land application of wastewater been correlated to both the biogeochemistry and associated soil microbiota that underpin biogeochemical processes. This is especially true for the bacterial NO3⫺ reducers, which likely are one of the main guilds mediating N sequestration in such TWW-impacted environments. To this end, our study showed that, similar to the nirK- and nirS-containing microbiota, total soil microorganisms also responded to the TWW irrigation, indicated especially by the increase in the relative abundances of Betaproteobacteria and Gammaproteobacteria (Fig. 4). Moreover, an increase of as much as 50% in gammaproteobacterial assemblages from the irrigated soils in this study is similar to findings from Frenk et al. (31) and Broszat et al. (17) from Israel and Mexico, respectively. Thus, overall, given the cooccurrence and predominance of Proteobacteria, Acidobacteria, and Actinobacteria in the TWW-irrigated soils investigated in this study, we recommend that future studies specifically focus on these groups so that a better understanding of their functional roles in soil and plant productivity can be obtained and demonstrated. We observed conflicting results on the impact of wastewater on Actinobacteria; i.e., we documented a decrease in relative abundance by using MPTS analysis but an increase in absolute abundance by using qPCR analysis. As discussed by Fierer et al. (46), this discrepancy can potentially be due to DNA extraction bias that can likely change the estimated abundances of certain bacterial groups; heterogeneity observed in


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FIG 8 (A) Nonmetric multidimensional scaling ordination plots of the bar-coded pyrosequences showing the bacterial community structure at the class level. Bray-Curtis similarity values between irrigated and nonirrigated sample bacterial communities are shown at the 50%, 60%, 70%, and 80% levels in the summer (S) and winter (W) seasons. (B) Dendrogram based on cluster analysis of the NMDS of the total bacterial community analyzed by 454 massively parallel tag sequencing. The relative abundance data were transformed by log(x ⫹ 1), and the bacterial communities were grouped using the complete linkage option.

the bacterial ribosomal operon numbers may also affect relative estimates of specific group abundances, and, moreover, the qPCR assays may not amplify rRNA genes from all the members of each targeted group present in the tested soils. In contrast to results with the irrigated soils, we observed that the nonirrigated soils generally contained a higher diversity of Acidobacteria, with the exception of only control C site, where Acidobacteria group 1 represented more than 50% of the total bacterial assemblages. Irrigated sites showed two distinct trends for Acidobacteria; in summer, approximately 50% of all Acidobacteria were members of group 1, but in winter, this

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group was only 10% of Acidobacteria communities, with groups 4 and 6 representing at 80% to 90% of this community. These two groups were generally predominant in winter, and they also made up 20% of the total bacterial communities in the irrigated sites. Acidobacteria ecology continues to be poorly understood even though these bacteria can represent as much as 80% of the total microbial communities in a variety of different soil types (85–87). Recently, 26 Acidobacteria subdivisions were proposed, and whole-genome sequencing of some Acidobacteria phylotypes has shed light on their physiological and metabolic superiority, which likely facilitates their survival in dry, low-




pH, and k-type or oligotrophic environments (88), such as the nonirrigated sites from this study. In a previous study conducted by our group to assess the effects of bauxite mining on soil microbial communities in Jamaica, we found Acidobacteria group 6 to predominate, followed by groups 4, 5, 7, and 17(89), suggesting the ubiquitous presence of Acidobacteria in a variety of differently managed soils. Some of the bacterial shifts described above strongly suggest the possibility of changes in soil properties brought about by the TWW irrigation. Specifically, previous studies performed by the City of Tallahassee have shown pH shifts in the top 0 to 30 cm of soils irrigated with TWW. The background pH of these soils is typically 5.20 ⫾ 0.20 (0 yr). The pH increased to 6.7 ⫾ 0.1 after 3.5 years of TWW application, 7.1 ⫾ 0.1 after 7.9 years, and 7.3 ⫾ 0.2 after 17.6 years (90). We are tempted to speculate that this increase in soil pH might have caused suppression of some acidobacterial groups (Fig. 4; see also Table S3 in the supplemental material) because these bacteria thrive at pH optima of 5.0 to 6.5 (91). In addition, the nutrient-rich wastewater likely caused some groups of Acidobacteria to be outcompeted in the TWW-irrigated soils because most Acidobacteria perform better in oligotrophic k-type environments (73, 88). Therefore, in all likelihood, the irrigation of nutrient-rich TWW favored the r-type microbial communities in these soils. In fact, an overall increase in the proportion of Proteobacteria and, more specifically, the 2-fold increase of betaproteobacterial populations in the TWW-irrigated soils are a clear indication of an increased r-type behavior or copiotrophy because these bacteria prefer high-nutrient environmental conditions (73). When CCA was used to identify the environmental parameters having significant linkages to the soil microbial community at the taxonomic or functional gene level, we observed a positive correlation of total nitrogen, organic matter, moisture concentration, and pH with Proteobacteria and Acidobacteria, communities that jointly constituted approximately 50% of the total bacteria in the irrigated sites (Fig. 7). This is strong evidence that changes in soil biogeochemistry occurred due to TWW irrigation, which, in turn, likely influenced the soil microbial assemblages. However, these findings should be interpreted with caution. For example, it can be argued that the biogeochemical and microbial differences observed in the wastewater receiving soils can also be due to the inherent properties of the supplied wastewater. In fact, some of the microorganisms identified from the irrigated pivots are likely native to the wastewater holding tanks where TWW is stored prior to being used in irrigation and so were likely transported into the soils from the wastewater. This is a critical point, given that the survival of wastewater microorganisms under environmental conditions can vary from a few days to 3 months (92, 93). However, a detailed examination of our data clearly shows that most of the predominant bacteria from the irrigated soils are, in fact, native to soils rather than wastewater, with the exception of only cyanobacteria (Fig. 4B) and some members of Gammaproteobacteria, which made up only a minor fraction of the total bacterial communities identified in the TWW-receiving soils. Hence, we believe that the contributions of the wastewater-native communities to the overall bacterial shifts observed in the irrigated soils were minimal, if any. One can also argue that the differences between the irrigated and nonirrigated soils were due to the farming practices being used in the


agroecosystem. However, it seems unlikely that the farming practices, which include no-till farming and no application of fertilizers or pesticides, would have caused the irrigated soils to differ in their biogeochemical and microbial properties relative to those of the nonirrigated soils. Finally, pH and moisture were significantly higher in the irrigated soils, likely brought about by the TWW irrigation. Specifically, moisture content significantly differed in only the nonirrigated winter soil samples. Therefore, soil moisture was not a significant driver of the observed bacterial trends in the TWW-receiving sites. If higher pH and moisture were not brought about by irrigation, then, clearly, these two inherent soil parameters alone could have been responsible for the bacterial shifts observed. This, however, does not seem to be the case, and most likely, TWW application brought about the soil changes we demonstrate in this study. In conclusion, multiple lines of evidence clearly showed that the soil microbial community dynamics and their functional roles, such as denitrification, are influenced by the land application of domestic treated wastewater. Specifically, TWW irrigation influenced the composition and abundance of total bacteria, along with shifts in the nirS and nirK denitrifier guilds; statistically, some of these changes also strongly correlated with physicochemical differences measured within the irrigated soils. To our knowledge, such a comprehensive evaluation of TWW-irrigated soils has not been presented before. An extension of this study would be to investigate whether microbial changes brought about by the TWW irrigation are advantageous or detrimental to the maintenance of soil productivity and associated microbially mediated ecosystem services such that environmental risk analysis of wastewater reuse by land application can be properly evaluated. ACKNOWLEDGMENTS We acknowledge the financial support provided by the School of Graduate Studies, Florida A&M University, and by partial funding obtained from Department of Defense (DoD) grants W911NF-10-1-0146 and W911NF-10-R-0006. Support provided by Jamie Shakar, Water Quality Manager, and City of Tallahassee Underground Utilities for access to the spray-field site is greatly appreciated. We also thank Caitlin Van Dyke for help with sample collection, Anthony Nguyen and Nathan Nguyen for assistance with biogeochemical analyses, and Drew Seminara for help with the geographic information systems mapping of the sampled site locations. We also extend our sincere appreciation to the reviewers for providing critical input which greatly facilitated better presentation of this study.

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Assessment of long-term wastewater irrigation impacts on the soil geochemical properties and the bioaccumulation of heavy metals to the agricultural products.

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Assessing the ecological long-term impact of wastewater irrigation on soil and water based on bioassays and chemical analyses.

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The geostatistic-based spatial distribution variations of soil salts under long-term wastewater irrigation.

Soil bacterial community structure remains stable over a 5-year chronosequence of insect-induced tree mortality.

Treated wastewater irrigation: uptake of pharmaceutical and personal care products by common vegetables under field conditions.