Occurrence and abundance of antibiotic resistance genes in agricultural soil receiving dairy manure

Occurrence and abundance of antibiotic resistance genes in agricultural soil receiving dairy manure Abstract Animal manures are commonly used to enhance soil fertility, but there are growing concerns over the impact of this practice on the development and dissemination of antibiotic resistance. The aim of this field study was to determine the effect of annual dairy manure applications on the occurrence and abundance of antibiotic resistance genes (ARGs) in an agricultural soil during four years of crop production. Treatments included (i) control (no fertilizer or manure), (ii) inorganic fertilizer and (iii) dairy manure at three application rates. Quantitative PCR was used to determine absolute (per g dry soil) and relative (per 16S rRNA gene) abundances of ARGs in DNA extracted from soils. Six ARGs and one class 1 integron were targeted. This study found that (i) manure application increases ARG abundances above background soil levels; (ii) the higher the manure application rate, the higher the ARG abundance in soil; (iii) the amount of manure applied is more important than reoccurring annual applications of the same amount of manure; (iv) absolute abundance and occurrence of ARGs decreases with increasing soil depth, but relative abundances remained constant. This study demonstrated that dairy manure applications to soil significantly increase the abundance of clinically relevant ARGs when compared to control and inorganic fertilized plots. antibiotic resistance genes, dairy, manure, soil, class 1 integron, qPCR INTRODUCTION Antibiotics are commonly given to food-producing animals at therapeutic levels for treatment of infection and at subtherapeutic levels for prevention of disease and growth promotion. Enhanced selection for antibiotic-resistant bacteria (ARB) can occur in animal guts (Looft et al.2012) which are excreted through defecation (Inglis et al.2005; Alexander et al.2008). In addition, up to 80% of antibiotic administered to animals can be excreted as the parent compound or active metabolites in urine and feces (Elmund et al.1971; Winckler and Grafe 2001; Arikan et al.2007). While animal manures are regularly used as a soil conditioner and fertilizer for crop production, the land application of manure is a common route for the introduction of antibiotics, ARB and antibiotic resistance genes (ARGs) to enter the environment (Chee-Sanford et al.2009). Excreted antibiotics can continue exerting selection pressure in manure, soil, water/wastewater and sediment/sludge depending on their rate of degradation, hydrophobicity and sorption potential. Manure-amended soils have been documented to increase both ARB and ARGs, even in the absence of antibiotics (Heuer, Schmitt and Smalla 2011; Zhou et al.2013; Udikovic-Kolic et al.2014), but inconsistent results and limited data highlight the need for additional research on this topic (Franklin et al.2016). For example, Munir and Xagoraraki (2011) found that soil ARGs significantly increased in abundance above background levels after land application of manure and biosolids to one site, but did not significantly increase after land application of biosolids to another site. This study, like most determining the abundance of ARGs in soil, utilized quantitative real-time PCR (qPCR) in which ARGs of interest must be determined a priori and only a limited number of genes are generally targeted due to time and cost constraints (Luby et al.2016). ARGs have a tendency to be shared among bacteria (related and unrelated species) through horizontal gene transfer (HGT), which is of great concern and incredibly difficult to track in the environment (Luby et al.2016). The ARGs are often associated with mobile genetic elements (MGEs) such as plasmids, transposons and integrons that can be transferred via HGT mechanisms including conjugation (gene transfer through cell-to-cell contact), transduction (gene transfer mediated by bacteriophages) and transformation (uptake of extracellular DNA) (Levy and Marshall 2004). The reservoir of ARGs in soil has been termed the soil resistome (D’Costa et al.2006) and it has been determined that the antibiotic resistome is shared between soil bacteria and human pathogens (Forsberg et al.2012). The spread of ARGs in the environment is of significant interest, as disease-causing bacteria that acquire these genes can become resistant to medical treatment. Once in the environment, ARB and ARGs can be transmitted to humans through soil, water, air/dust, fresh produce, domesticated animals and wildlife (Huijbers et al.2015). Because of these reasons, it has been suggested that ARGs themselves are emerging contaminants for which mitigation strategies are needed to prevent their widespread dissemination (Pruden et al.2006). A thorough search of the literature indicated that studies investigating the long-term influence of dairy manure application on ARGs in soils are rare (Musovic et al.2014; Fang et al.2015; Peng et al.2015; Tang et al.2015; Graham et al.2016). The purpose of this study was to determine the occurrence and abundance of six ARGs [blaCTX-M-1, erm(B), sul1, tet(A), tet(W) and tet(X)] and a class 1 integron-integrase gene (intI1) in an agricultural soil that had received dairy manure at three rates on an annual basis for 4 years. These ARGs were chosen because they include resistance to a variety of antibiotics, utilize different resistance mechanisms and cover a wide range of bacterial hosts (Skold 2000; Canton and Coque 2006; Birkett et al.2007; Roberts 2008; Roberts and Schwarz 2016). Even though intI1 is not an ARG, it can be used as a proxy for ARG contamination because it is linked to antibiotic, disinfectant and metal resistance genes, resides in a diverse number of commensal and pathogenic bacteria, often located on MGEs, and many common forms are xenogenetic (i.e. assembled relatively recently under selection pressures brought upon by human activities) (Gillings et al.2015). Thus, throughout this paper, intI1 will be included in the group called ARGs. The comparisons in this study include treatment type (control, inorganic fertilizer and three manure application rates), soil depth (0 to 120 cm), and year (2012−2016). MATERIALS AND METHODS Field site and treatments The field site was located at the USDA-ARS Northwest Irrigation and Soils Research Laboratory in Kimberly, Idaho. This region has a semi-arid climate and consists of hot dry summers and cool wet winters, with a mean annual temperature of 8.9°C and precipitation of 229 mm (NRCS 2017). Soil at the site is a Portneuf silt loam (coarse-silty, mixed, superactive, mesic Durinodic Xeric Haplocalcids) that was surface-irrigated with Snake River water. The field was 1 ha and contained plots that were 12.2 m × 18.3 m. The experimental design was a randomized complete block with four replications. The treatments consisted of a control (Ctrl, no fertilizer or manure application), inorganic fertilizer (Fert) and manure at three application rates (17.2, 34.5 and 52.0 Mg [dry wt.] ha−1). The highest manure rate (i.e. 52 Mg ha−1) is representative of what is typically applied in this region. Stockpiled manure was obtained from anonymous dairies using a local manure hauler. The manure was analyzed for chlortetracycline, oxytetracycline, monensin, penicillin G, sulfamethazine, sulfadimethoxine, sulfamethoxazole, sulfathiazole and tetracycline, but only monensin and oxytetracycline were detected (concentration data is being withheld by collaborator until published). It was applied once per year (after crop harvest and soil sampling) to the same designated plots in the fall of 2012, 2013, 2014 and 2015. Inorganic fertilizer was applied using University of Idaho Extension recommendations based on spring soil test data (Brown et al.2010). If necessary, individual nutrients (N, P, K, S, Mn) were added to meet the requirements for the specific crop being grown. After broadcasting the fertilizer and manure treatments, they were incorporated into the soil using a tandem disk within 24 h (Ctrl plots were also disked) to a depth of ∼15 cm. The crops consisted of a wheat–potato–barley–sugar beet rotation from 2013 to 2016. Prior to this study, the crops at the field site were oats (2004), alfalfa (2005−2007), barley (2008−2009), corn (2011) and beans (2010, 2012). Soil collection Before treatment (BT), soils (i.e. background soils) were collected from the top 30 cm in the fall of 2012, prior to the first manure application. Pre-plant (spring) and post-harvest (fall) soil samples were collected in March and September, respectively. The pre-plant soil samples were collected every year from 2013 to 2016, but only from the top 30 cm of soil since they were also used to make fertilizer recommendations. Post-harvest samples were collected in 2014 and 2015 prior to manure addition (2013 samples collected, but were lost) and consisted of 120 cm long cores that were sectioned into five layers (0−15, 15−30, 30−60, 60−90 and 90−120 cm). Ten (0−30 cm) and two (0−120 cm) soil cores were collected per plot during the pre-plant and post-harvest sampling, respectively. The contents of each soil layer were composited and thoroughly mixed before a subsample was placed in clean sealable plastic bag and then frozen at −75°C. Soil DNA extraction Immediately after thawing at room temperature, one DNA extraction was performed per composited soil sample. The DNA was extracted from about 500 mg of soil (wet wt.) using the FastDNA Spin Kit for Soil and the FastPrep Instrument (MP Biomedicals, Santa Ana, CA) following the manufacturer's protocol. DNA was stored in DNase/pyrogen-free water at −20°C until qPCR was performed. Primers and probes Primers, probes, annealing temperatures, amplicon lengths, sequences and references can be found in Table 1. Due to the short sequences and low melting temperatures (TM), the blaCTX-M-1 and tet(A) probes were converted to locked nucleic acid (LNA) probes (Integrated DNA Technologies [IDT], Coralville, IA) to increase the TM of the probes to about 68°C–70°C. A search of the literature did not reveal a primer/probe combination for tet(X). The following GenBank accession numbers were used to design the tet(X) primers and probe: M37699, JQ990987, JQ990988 and EU864422. Only four sequences were used because these were the only verified tet(X) gene sequences in the Gene Database within GenBank at the time of primers/probe design. ClustalX 2.1 (Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland) was used to align the sequences to find the most conserved regions, followed by use of Primer3 (Whitehead Institute for Biomedical Research, Cambridge, MA) to select the best primer pair (18–30 bp, 60°C–64°C melting temperature and 35%–65% GC content) and probe (20–30 bp, melting temperature 6°C–8°C higher than the primers and 35%–65% GC content) combinations (amplicon length between 70 and 150 bp). The primers were then checked against the NCBI BLAST database to ensure that they would not amplify non-specific products. Table 1. Primers and probes used in this study. Gene  Type  Primer/probe name  Sequence 5΄ to 3΄  TA (°C)  Amplicon size (bp)  Reference  16S rRNA  Forward  BACT1369F  CGGTGAATACGTTCYCGG  56  ∼123  Suzuki, Taylor and    Reverse  PROK1492R  GGWTACCTTGTTACGACTT       DeLong (2000)    Probe  TM1389P  6-FAM-CTTGTACACACCGCCCGTC-BHQ-1        intI1  Forward  intI1-F  GCCTTGATGTTACCCGAGAG  60  196  Barraud et al. (2010)    Reverse  intI1-R  GATCGGTCGAATGCGTGT          Probe  intI1-P  6-FAM-ATTCCTGGCCGTGGTTCTGGGTTTT-BHQ-1        blaCTX-M-1  Forward  CTXM1-F  ACCAACGATATCGCGGTGAT  60  101  Colomer-Lluch, Jofre and    Reverse  CTXM1-R  ACATCGCGACGGCTTTCT       Muniesa (2011)    Probe (LNA)  CTXM1-P  6-FAM-TC + GTGCGC + CG + CTG-BHQ-1        erm(B)  Forward  ermB-F  GGATTCTACAAGCGTACCTTGGA  60  92  Bockelmann et al. (2009)    Reverse  ermB-R  GCTGGCAGCTTAAGCAATTGCT          Probe  ermB-P  6-FAM-CACTAGGGTTGCTCTTGCACACTCAAGTC-BHQ-1        sul1  Forward  sul1-F  CCGTTGGCCTTCCTGTAAAG  60  67  Heuer and Smalla (2007)    Reverse  sul1-R  TTGCCGATCGCGTGAAGT          Probe  sul1-P  6-FAM-CAGCGAGCCTTGCGGCGG-BHQ-1        tet(A)  Forward  tetA-F  CCGCGCTTTGGGTCATT  60  51  Guarddon et al. (2011)    Reverse  tetA-R  TGGTCGCGTCCCAGTGA          Probe (LNA)  tetA-P  6-FAM-TCG + GCG + AG + G + ATCG-BHQ-1        tet(W)  Forward  tetW-F  GCAGAGCGTGGTTCAGTCT  60  65  Smith et al. (2004)    Reverse  tetW-R  GACACCGTCTGCTTGATGATAAT          Probe  tetW-P  6-FAM-TTCGGGATAAGCTCTCCGCCGA-BHQ-1        tet(X)  Forward  tetX-F  GCAAGCGCCCATTACCCATAA  60  97  This study    Reverse  tetX-R  AAGGCATCCACCAACCCACT          Probe  tetX-P  6-FAM-CATTTGATGCCGCCTTTTGCAGGGC-BHQ-1        Gene  Type  Primer/probe name  Sequence 5΄ to 3΄  TA (°C)  Amplicon size (bp)  Reference  16S rRNA  Forward  BACT1369F  CGGTGAATACGTTCYCGG  56  ∼123  Suzuki, Taylor and    Reverse  PROK1492R  GGWTACCTTGTTACGACTT       DeLong (2000)    Probe  TM1389P  6-FAM-CTTGTACACACCGCCCGTC-BHQ-1        intI1  Forward  intI1-F  GCCTTGATGTTACCCGAGAG  60  196  Barraud et al. (2010)    Reverse  intI1-R  GATCGGTCGAATGCGTGT          Probe  intI1-P  6-FAM-ATTCCTGGCCGTGGTTCTGGGTTTT-BHQ-1        blaCTX-M-1  Forward  CTXM1-F  ACCAACGATATCGCGGTGAT  60  101  Colomer-Lluch, Jofre and    Reverse  CTXM1-R  ACATCGCGACGGCTTTCT       Muniesa (2011)    Probe (LNA)  CTXM1-P  6-FAM-TC + GTGCGC + CG + CTG-BHQ-1        erm(B)  Forward  ermB-F  GGATTCTACAAGCGTACCTTGGA  60  92  Bockelmann et al. (2009)    Reverse  ermB-R  GCTGGCAGCTTAAGCAATTGCT          Probe  ermB-P  6-FAM-CACTAGGGTTGCTCTTGCACACTCAAGTC-BHQ-1        sul1  Forward  sul1-F  CCGTTGGCCTTCCTGTAAAG  60  67  Heuer and Smalla (2007)    Reverse  sul1-R  TTGCCGATCGCGTGAAGT          Probe  sul1-P  6-FAM-CAGCGAGCCTTGCGGCGG-BHQ-1        tet(A)  Forward  tetA-F  CCGCGCTTTGGGTCATT  60  51  Guarddon et al. (2011)    Reverse  tetA-R  TGGTCGCGTCCCAGTGA          Probe (LNA)  tetA-P  6-FAM-TCG + GCG + AG + G + ATCG-BHQ-1        tet(W)  Forward  tetW-F  GCAGAGCGTGGTTCAGTCT  60  65  Smith et al. (2004)    Reverse  tetW-R  GACACCGTCTGCTTGATGATAAT          Probe  tetW-P  6-FAM-TTCGGGATAAGCTCTCCGCCGA-BHQ-1        tet(X)  Forward  tetX-F  GCAAGCGCCCATTACCCATAA  60  97  This study    Reverse  tetX-R  AAGGCATCCACCAACCCACT          Probe  tetX-P  6-FAM-CATTTGATGCCGCCTTTTGCAGGGC-BHQ-1        6-FAM = fluorescein; BHQ-1 = black hole quencher 1 (Suzuki, Taylor and DeLong 2000; Smith et al.2004; Heuer and Smalla 2007; Bockelmann et al.2009; Barraud et al.2010; Colomer-Lluch, Jofre and Muniesa 2011; Guarddon et al.2011). View Large Quantitative real-time PCR qPCR was performed on an iQ5 Real-Time PCR Detection System (Bio-Rad, Hercules, CA). Each individual reaction consisted of 12.5 μL of 2 × SsoAdvanced Universal Probes Supermix (Bio-Rad), 250 nM of forward and reverse primers and probes (IDT), 2 μL of DNA template and sterile DNase/pyrogen-free water to create a final volume of 25 μL. To help alleviate PCR inhibition, each DNA template was diluted by one order of magnitude. The temperature profile consisted of one cycle at 95°C for 3 min, 40 cycles at 95°C for 15 s and annealing temperature (TA, Table 1) for 30 s. All qPCR runs included a standard curve covering seven orders of magnitude, and each sample was analyzed in triplicate. Standards were created using gBlocks Gene Fragments (IDT). The standard curve r2 values were consistently >0.98, with an instrument limit of quantification (LOQ) of 102 gene copies μL−1. Since the DNA templates were diluted by an order of magnitude to prevent qPCR inhibition, the corrected LOQ was 103 gene copies μL−1. All ARG and intI1 qPCR results were normalized per g of dry soil (absolute abundance) and per 16S rRNA gene copies (relative abundance). To normalize the gene data per gram of dry soil, the moisture contents of the soils were determined gravimetrically. A spreadsheet of all the absolute and relative ARG and absolute 16S rRNA gene abundance data can be found in the Supporting Information. Statistical analysis All statistical tests were performed using SAS 9.4 (SAS Institute Inc., Cary, NC). The data were log (base 10) transformed to achieve homogeneity of variance, which was determined from visual analysis of predicted versus residual and quantile-quantile plots. Statements of statistical significance were defined as P ≤ 0.05. One-way analysis of variance (ANOVA, GLM procedure) model was used to generate population means and 95% confidence limits (CL). The least significant difference (LSD) test within the GLM procedure was used for comparison of population means for categorizing significance groups. Least squares means (lsmeans) was used to generate P-values to determine statistical significance for comparison of individual population means. Since standard deviations have no meaning for n equal to 1 or 2, 95% CL are not included. Pooled data refer to the systematic grouping of qPCR data to strengthen the ANOVA model due to similar trends; it do not refer to the mixing of unique soil samples or DNA extracts prior to analysis. Correlation analysis (CORR procedure) was used to calculate the Pearson correlation coefficient (r) and P-values between the various genes. Correlations were considered significant if the Pearson correlation coefficient (r) ≥0.60 and the P ≤ 0.05. Data were not pooled for the correlation analysis. RESULTS Gene occurrence Out of a total of 220 unique soil samples (all spring, fall and depth data included) analyzed, the intI1, blaCTX-M-1, erm(B), sul1, tet(A), tet(W) and tet(X) genes were detected 146, 4, 15, 176, 0, 128 and 59 times, respectively (Table 2). Due to limited detection of blaCTX-M-1, erm(B) and tet(A), these genes were not included in any quantitative or statistical analysis. blaCTX-M-1 was detected once in the Ctrl plots and three times in manured plots; erm(B) was only detected in soils with manure application; and tet(X) was not detected before treatment, but was detected twice in the Ctrl plots, once in the Fert plots and 56 times in the manured plots. All other ARGs, except for tet(A), were detected in BT, Ctrl, Fert and manured plots. Table 2 contains occurrence data for all ARGs broken down by treatment. Table 2. ARG occurrence by treatment. Treatment  n  intI1  blaCTX-M-1  erm(B)  sul1  tet(A)  tet(W)  tet(X)  BT  20  11  0  0  17  0  19  0  Ctrl  56  33  1  0  42  0  19  2  Fert  56  33  0  0  37  0  18  1  17.2M  16  16  1  4  16  0  16  13  34.5M  16  16  0  5  16  0  16  16  52.0M  56  37  2  6  48  0  40  27  Total  220  146  4  15  176  0  128  59  Treatment  n  intI1  blaCTX-M-1  erm(B)  sul1  tet(A)  tet(W)  tet(X)  BT  20  11  0  0  17  0  19  0  Ctrl  56  33  1  0  42  0  19  2  Fert  56  33  0  0  37  0  18  1  17.2M  16  16  1  4  16  0  16  13  34.5M  16  16  0  5  16  0  16  16  52.0M  56  37  2  6  48  0  40  27  Total  220  146  4  15  176  0  128  59  BT = before treatment; Ctrl = control; Fert = fertilizer; 17.2M = 17.2 Mg manure ha−1 yr−1; 34.5M = 34.5 Mg manure ha−1 yr−1; and 52.0M = 52.0 Mg manure ha−1 yr−1. View Large Treatment The general treatment effects indicate that manure application increases both ARG absolute and relative abundances (i.e. per gram of dry soil and per 16S rRNA gene copies, respectively). Also, manure application rate was positively correlated with ARG abundances, although not always significantly. Figure 1 presents the absolute (A) and relative (B) abundance results from fall 2012 and spring 2013–2016 (data pooled to strengthen the ANOVA model since similar trends occurred each year). Since the fall 2012 samples were obtained prior to any fertilizer or manure addition, they were not included in the pooled data analysis, but were used as a comparison for BT gene abundances. Figure 1. View largeDownload slide ARG absolute (A) and relative (B) abundances by treatment, fall 2012 (BT), spring 2013−2016 (pooled data) and soil depth = 0–30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. BT = Before Treatment; Ctrl = Control; Fert = Fertilizer; 17.2M = 17.2 Mg manure ha−1 yr−1; 34.5M = 34.5 Mg manure ha−1 yr−1; and 52.0M = 52.0 Mg manure ha−1 yr−1. 95% confidence level error bars are not included when n = 1 or 2. Figure 1. View largeDownload slide ARG absolute (A) and relative (B) abundances by treatment, fall 2012 (BT), spring 2013−2016 (pooled data) and soil depth = 0–30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. BT = Before Treatment; Ctrl = Control; Fert = Fertilizer; 17.2M = 17.2 Mg manure ha−1 yr−1; 34.5M = 34.5 Mg manure ha−1 yr−1; and 52.0M = 52.0 Mg manure ha−1 yr−1. 95% confidence level error bars are not included when n = 1 or 2. According to Fig. 1A, sul1 and tet(W) show the following statistically significant trends: 52.0M > 34.5M > 17.2M > Ctrl ≈ Fert ≈ BT. intI1 had a similar trend but the 17.2M was not statistically different from the Ctrl (P = 0.60) or Fert plots (P = 0.24). With tet(X), 34.5M and 52.0M were not significantly different according to LSD, but 52.0M was significantly greater than 34.5M using lsmeans (P = 0.0013). Even though 17.2M and 34.5M were not significantly different from each other or the Ctrl and Fert plots, tet(X) was only detected twice in the Ctrl and once in the Fert compared to 13 detections for 17.2M and 16 detections for 34.5M. The same trends occurred when normalizing to the 16S rRNA gene (Fig. 1B), except the 34.5M and 52.0M treatments were not significantly different for sul1 (P = 0.32) and tet(W) (P = 0.19). Soil depth Unlike the treatment analysis, ARG absolute abundances show a different trend than the relative abundances for the 52.0M treatments. According to Fig. 2A, the ARG absolute abundances show a general trend of decreasing with depth (though not always significant), except for tet(X). The ARG relative abundances show no general trend with very few significant differences between depths (Fig. 2B). One thing that is clear with both graphs is that the detection frequency of ARGs decreases with depth, including tet(X). A conclusion that can be drawn from this is that total ARG copy numbers decrease with depth, but the fraction of bacteria carrying the ARGs does not change significantly. Figure 2. View largeDownload slide ARG absolute (A) and relative (B) abundances by soil depth, fall 2014−2015 (pooled data) and 52.0 Mg manure ha−1 yr−1. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. 95% confidence limit error bars are not included when n = 1 or 2. Figure 2. View largeDownload slide ARG absolute (A) and relative (B) abundances by soil depth, fall 2014−2015 (pooled data) and 52.0 Mg manure ha−1 yr−1. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. 95% confidence limit error bars are not included when n = 1 or 2. Year For the 52.0M (Fig. 3A and B) manure treatments, the ARG abundances for the years 2013–2016 are significantly greater than 2012, for both normalizations, except for 2012 and 2015 relative intI1 abundances (P = 0.28). This makes sense because manure was applied after the fall 2012 sampling event. Both ARG abundances were quite variable when comparing individual years from 2013 to 2016. Figure 3. View largeDownload slide ARG absolute (A) and relative (B) abundances by year, fall 2012 (before treatment), spring 2013–16, 52.0 Mg manure ha−1 yr−1 and soil depth = 0−30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. Error bars are 95% confidence limits. Figure 3. View largeDownload slide ARG absolute (A) and relative (B) abundances by year, fall 2012 (before treatment), spring 2013–16, 52.0 Mg manure ha−1 yr−1 and soil depth = 0−30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. Error bars are 95% confidence limits. Correlation analysis Correlation analysis was performed to determine the relationship between ARGs and ARGs versus 16S rRNA genes (Tables 3 and 4). It should be noted that a significant correlation between two variables does not imply causation. All the ARGs [intI1, sul1, tet(W) and tet(X)] had a significant positive correlation with each other for both relative and absolute abundances. The strongest correlations were between tet(X) and sul1 (r = 0.88, P < 0.0001) for both abundances and between tet(X) and intI1 [r = 0.81 (absolute) and 0.85 (relative), P < 0.0001]. Table 3. Pearson correlation analysis of absolute ARG and 16S rRNA gene (16S) abundances.   intI1  sul1  tet(W)  tet(X)      gDS−1  gDS−1  gDS−1  gDS−1    16S gDS−1  0.3835  0.1845  0.3918  0.1383  r    <.0001  0.0143  <.0001  0.2964  P-value    146  176  128  59  n  intI1 gDS−1    0.6338  0.7065  0.8141  r      <.0001  <.0001  <.0001  P-value      127  106  59  n  sul1 gDS−1      0.7701  0.8788  r        <.0001  <.0001  P-value        121  59  n  tet(W) gDS−1        0.6707  r          <.0001  P-value          59  n    intI1  sul1  tet(W)  tet(X)      gDS−1  gDS−1  gDS−1  gDS−1    16S gDS−1  0.3835  0.1845  0.3918  0.1383  r    <.0001  0.0143  <.0001  0.2964  P-value    146  176  128  59  n  intI1 gDS−1    0.6338  0.7065  0.8141  r      <.0001  <.0001  <.0001  P-value      127  106  59  n  sul1 gDS−1      0.7701  0.8788  r        <.0001  <.0001  P-value        121  59  n  tet(W) gDS−1        0.6707  r          <.0001  P-value          59  n  gDS = gram of dry soil; r = Pearson correlation coefficient; n = number of observations; yellow boxes: 0.8000 ≤ r ≤ 0.8999; green boxes: 0.7000 ≤ r ≤ 0.7999; blue boxes: 0.6000 ≤ r ≤ 0.6999. View Large Table 4. Pearson correlation analysis of relative ARG abundances.   sul1 16S−1  tet(W) 16S−1  tet(X) 16S−1    intI1 16S−1  0.6617  0.6696  0.8464  r    <.0001  <.0001  <.0001  P-value    127  106  59  n  sul1 16S−1    0.7378  0.8824  r      <.0001  <.0001  P-value      121  59  n  tet(W) 16S−1      0.6870  r        <.0001  P-value        59  n    sul1 16S−1  tet(W) 16S−1  tet(X) 16S−1    intI1 16S−1  0.6617  0.6696  0.8464  r    <.0001  <.0001  <.0001  P-value    127  106  59  n  sul1 16S−1    0.7378  0.8824  r      <.0001  <.0001  P-value      121  59  n  tet(W) 16S−1      0.6870  r        <.0001  P-value        59  n  16S = 16S rRNA gene; r = Pearson correlation coefficient; n = number of observations; yellow boxes: 0.8000 ≤ r ≤ 0.8999; green boxes: 0.7000 ≤ r ≤ 0.7999; blue boxes: 0.6000 ≤ r ≤ 0.6999. View Large DISCUSSION Occurrence of ARGs in manure-amended soil This study was designed to determine the changes in absolute and relative abundances of various ARGs and a class 1 integron (intI1) in soil that had received inorganic fertilizer or dairy manure under a crop rotation that is typical for southern Idaho. While qPCR was used to track the ARGs, it should be noted that this technique cannot decipher between intracellular and extracellular DNA (Corinaldesi, Danovaro and Dell’Anno 2005), whether the DNA came from live or dead bacteria (Nocker, Cheung and Camper 2006) nor determine which bacteria carried these ARGs (Yang et al.2002) and if these genes were expressed (Smith and Osborn 2009). Of the six ARGs that were targeted in the soils, only three [sul1, tet(W) and tet(X)] and intI1 were quantified in enough samples to perform statistics and draw conclusions. Previous studies have found blaCTX-M (Marti et al.2013; Hu et al.2016; Nolvak et al.2016), erm(B) (Marti et al.2013, 2014; Sun et al.2015; Hu et al.2016), sul1 (Munir and Xagoraraki 2011; Marti et al.2013, 2014; Fahrenfeld et al.2014; Ross and Topp 2015; Sun et al.2015; Nolvak et al.2016; Ruuskanen et al.2016), tet(A) (Marti et al.2013; Hu et al.2016; Nolvak et al.2016; Sandberg and LaPara 2016), tet(W) (Munir and Xagoraraki 2011; Kyselkova et al.2013; Fahrenfeld et al.2014; Kyselkova et al.2015; Sandberg and LaPara 2016), tet(X) (Sandberg and LaPara 2016) and intI1 (Marti et al.2013, 2014; Kyselkova et al.2015; Sun et al.2015; Hu et al.2016; Nolvak et al.2016; Sandberg and LaPara 2016) in soil amended with dairy or beef cattle manure. All of these ARGs encode resistance to commonly used antibiotics in animal agriculture (Chee-Sanford et al.2009). Also, these ARGs are acquired from other bacteria through HGT, as opposed to intrinsically resistant. Acquired ARGs reside on MGEs such as plasmids or transposons which can be readily transferred between bacteria, even distantly related bacteria as long as they have the mechanisms to accept these MGEs (Van Hoek et al.2011). Manure application increases ARG abundances in soil Manure contains a considerable abundance of ARB and ARGs even if the producing animal has never received antibiotics (Heuer, Schmitt and Smalla 2011). This usually translates into increased ARG abundance in the manured soil as opposed to control or background levels. However, this increased ARG abundance in the manure-treated soil may be transient without selection pressure such as high concentrations of antibiotics or metals (Heuer, Schmitt and Smalla 2011). In a study in which dairy manure was applied to agricultural soils, a 77% and 36% increase in abundance from background soil levels was reported for tet(W) and sul1, respectively (Munir and Xagoraraki 2011). In a microcosm study where dairy cow feces [from an oxytetracycline-treated cow (OE) and non-treated cow (NE)] were applied to soils from three different farms, the tet(W) gene was not detected in the control soils, but it was detected in similar quantities in both OE- and NE-treated soils from all three farms (Kyselkova et al.2013). Fahrenfeld et al. (2014) conducted a field study in which dairy manure slurry was applied to a cornfield. There were significant increases in sul1 and tet(W) relative abundances in post- versus pre-manured soils, as well as post-manured versus downgradient non-manure treated soils. According to another study where dairy manure was applied to soil, sul1 and int1 relative abundances were significantly greater in manured soils than in soil that received inorganic fertilizer (Marti et al.2014). The sul1 genes significantly increased from ∼101 gene copies ng−1 DNA in control samples to ∼105 gene copies ng−1 DNA in soil amended with raw manure, according to a study by Ross and Topp (2015). In a microcosm study by Sandberg and LaPara (2016), tet(W) increased, and intI1 and tet(X) were similar in dairy manure-amended soils compared to control soils. In this study, intI1 and tet(X) were not significantly different between Ctrl and 17.2M manure treatment, but were significantly different between the Ctrl and both 34.5M and 52.0M manure treatments. Nolvak et al. (2016) conducted a field study to determine the fate of various ARGs in soil amended with no fertilizer (control), mineral fertilizer and cattle slurry. For intI1 and sul1, cattle slurry-amended soil was significantly greater than the control and mineral fertilized plots, while there were no significant differences between the control and inorganic fertilizer plots. These referenced studies, including our study, have found that applying dairy manure to soil increases the abundance of ARGs above background levels. The higher the manure application rate, the higher the ARG level To the knowledge of the authors, no previous paper sought to determine the effect of dairy manure application rate on the abundance of ARGs. There are many studies comparing different animal manures (i.e. dairy, beef, swine, chicken, etc.) (Chen et al.2007; Ghosh and LaPara 2007; Marti et al.2013, 2014; Hu et al.2016; Ruuskanen et al.2016; Sandberg and LaPara 2016), different manure types (composted, stockpiled, slurry, lagoon, etc.) (Munir and Xagoraraki 2011; Fahrenfeld et al.2014; Ross and Topp 2015; Nolvak et al.2016; Ruuskanen et al.2016) or application to different types of agricultural soils (Munir and Xagoraraki 2011; Kyselkova et al.2013, 2015; Sun et al.2015; Hu et al.2016; Ruuskanen et al.2016), but none exploring the difference between application rates for the same manure source and same soil type. When manure is applied to soil, it is either left on the surface of the soil or it is incorporated to a fixed depth (due to the limited penetration depth of the tillage equipment) no matter how much manure is applied. In this study, the manure was incorporated with a tandem disk to a fixed depth of ∼15 cm. Therefore, soil samples from the top 15 cm of the profile had a high ratio of manure to soil mass. Since manure usually has a higher ARG abundance relative to the soil (Munir and Xagoraraki 2011; Fahrenfeld et al.2014; Nolvak et al.2016; Ruuskanen et al.2016; Sandberg and LaPara 2016), the abundance of ARGs in the soil collected post-manure application will increase with each step increase of the manure application rate (see Fig. 1A and B). Manure application rate is more important than repeated annual applications of manure When comparing Figs 1A and 3A and Figs 1B and 3B, the trends clearly demonstrate that manure application rate increases ARG abundances while repeated annual applications of the same manure rate had a variable effect. The factors affecting the persistence of ARGs in manure-amended soils are numerous, including fitness effects to bacteria carrying ARGs, ability to transfer ARGs between closely and distantly related bacteria, half-lives and sorption potential of antibiotics to soil particles which can induce selection pressure, and co-selection of ARGs that reside on MGEs along with metal- and biocide-resistance genes in metal or biocide contaminated environments, to name a few (Chee-Sanford et al.2009). Other studies have investigated the persistence of ARGs in dairy manure-amended soil (Fahrenfeld et al.2014; Marti et al.2014; Kyselkova et al.2015; Nolvak et al.2016), but most studies collected soil samples and applied dairy manure much more frequently, with time frames much shorter than this study. Due to these reasons, it is hard to draw a conclusion as to why ARG abundances vary among the yearly spring sampling events. Absolute abundance and occurrence of ARGs decrease with increasing soil depth A search of the literature did not reveal any studies that assessed the difference in ARG abundances with soil depth after receiving dairy manure application, but there are studies that address swine manure applications (Huang et al.2013; Peng et al.2015; Tang et al.2015). The results of this study are in agreement with Huang et al. (2013) in which absolute abundances of tet(W) and tet(X) were higher in the surface soils, then generally decreased with increasing soil depth, while relative abundances stayed constant throughout the soil profile. Peng et al. (2015) determined the absolute and relative abundances of tet(W) at three soil depths after fresh swine manure application. A visual analysis of the graphs in Peng et al. (2015) indicates that the tet(W) abundances did not change with depth for both normalizations, which is not in agreement with the absolute abundances determined in our study. Tang et al. (2015) only provided data for relative abundances of tet(W) and sul1 in soil where swine manure was applied. The data from Tang et al. (2015) suggest that the relative abundances decreased with increasing soil depth, except for one of the sites where tet(W) increased with increasing soils depth, which is not consistent with our study as stated above. None of the referenced soil-depth studies performed statistical analyses to determine significant differences between different depths for a specific ARG, so all conclusions were drawn from visual inspection of graphs (Peng et al.2015) or tables (Huang et al.2013; Tang et al.2015). Also, none of these referenced studies included occurrence data of ARGs throughout the soil profile, as was determined in the present study. ARGs correlate with each other but not with 16S rRNA genes In two recent studies, a significant correlation was determined between the relative (Sun et al.2015; Nolvak et al.2016) and absolute (Nolvak et al.2016) abundance of sul1 and intI1. Both studies showed stronger correlations between these two genes than in our study (Tables 3 and 4). Nolvak et al. (2016) also found a significant correlation between the absolute abundances of 16S rRNA gene and sul1 (r = 0.65) in dairy cattle slurry-amended soil, as opposed to this study where no significant correlation was found (r = 0.18, Table 3). In conclusion, the results from this field study advance knowledge that dairy manure is an ARG source and applications to soil can enlarge the reservoir of clinically relevant ARGs when compared to soil that received inorganic fertilizer or no fertilizer/manure. While the ARGs may have been associated with dead bacteria or extracellular, an increase of the ARG reservoir is a potential cause for concern, as it could facilitate acquired resistance in bacteria that are pathogenic to humans and food-producing animals. However, our results cannot be used to verify the transfer of ARGs to pathogens, nor determine the level of resistance of those specific genes. Regardless of this limitation, dairy manure applications were shown to increase the absolute abundance of ARGs in soil. Although not investigated in this study, antibiotic residues and other constituents are also present in dairy manure; thus, their influence on the enrichment of ARGs in manure-amended soil should not be ruled out and should be a subject for continued investigation. SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. Acknowledgements Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. FUNDING This work was supported under USDA-ARS project number 2054-12000-011-00D. Conflicts of interest. None declared REFERENCES Alexander T, Yanke L, Topp E et al.   Effect of subtherapeutic administration of antibiotics on the prevalence of antibiotic-resistant Escherichia coli bacteria in feedlot cattle. Appl Environ Microb  2008; 74: 4405– 16. Google Scholar CrossRef Search ADS   Arikan OA, Sikora LJ, Mulbry W et al.   Composting rapidly reduces levels of extractable oxytetracycline in manure from therapeutically treated beef calves. Bioresource Technol  2007; 98: 169– 76. 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Occurrence and abundance of antibiotic resistance genes in agricultural soil receiving dairy manure

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Abstract

Abstract Animal manures are commonly used to enhance soil fertility, but there are growing concerns over the impact of this practice on the development and dissemination of antibiotic resistance. The aim of this field study was to determine the effect of annual dairy manure applications on the occurrence and abundance of antibiotic resistance genes (ARGs) in an agricultural soil during four years of crop production. Treatments included (i) control (no fertilizer or manure), (ii) inorganic fertilizer and (iii) dairy manure at three application rates. Quantitative PCR was used to determine absolute (per g dry soil) and relative (per 16S rRNA gene) abundances of ARGs in DNA extracted from soils. Six ARGs and one class 1 integron were targeted. This study found that (i) manure application increases ARG abundances above background soil levels; (ii) the higher the manure application rate, the higher the ARG abundance in soil; (iii) the amount of manure applied is more important than reoccurring annual applications of the same amount of manure; (iv) absolute abundance and occurrence of ARGs decreases with increasing soil depth, but relative abundances remained constant. This study demonstrated that dairy manure applications to soil significantly increase the abundance of clinically relevant ARGs when compared to control and inorganic fertilized plots. antibiotic resistance genes, dairy, manure, soil, class 1 integron, qPCR INTRODUCTION Antibiotics are commonly given to food-producing animals at therapeutic levels for treatment of infection and at subtherapeutic levels for prevention of disease and growth promotion. Enhanced selection for antibiotic-resistant bacteria (ARB) can occur in animal guts (Looft et al.2012) which are excreted through defecation (Inglis et al.2005; Alexander et al.2008). In addition, up to 80% of antibiotic administered to animals can be excreted as the parent compound or active metabolites in urine and feces (Elmund et al.1971; Winckler and Grafe 2001; Arikan et al.2007). While animal manures are regularly used as a soil conditioner and fertilizer for crop production, the land application of manure is a common route for the introduction of antibiotics, ARB and antibiotic resistance genes (ARGs) to enter the environment (Chee-Sanford et al.2009). Excreted antibiotics can continue exerting selection pressure in manure, soil, water/wastewater and sediment/sludge depending on their rate of degradation, hydrophobicity and sorption potential. Manure-amended soils have been documented to increase both ARB and ARGs, even in the absence of antibiotics (Heuer, Schmitt and Smalla 2011; Zhou et al.2013; Udikovic-Kolic et al.2014), but inconsistent results and limited data highlight the need for additional research on this topic (Franklin et al.2016). For example, Munir and Xagoraraki (2011) found that soil ARGs significantly increased in abundance above background levels after land application of manure and biosolids to one site, but did not significantly increase after land application of biosolids to another site. This study, like most determining the abundance of ARGs in soil, utilized quantitative real-time PCR (qPCR) in which ARGs of interest must be determined a priori and only a limited number of genes are generally targeted due to time and cost constraints (Luby et al.2016). ARGs have a tendency to be shared among bacteria (related and unrelated species) through horizontal gene transfer (HGT), which is of great concern and incredibly difficult to track in the environment (Luby et al.2016). The ARGs are often associated with mobile genetic elements (MGEs) such as plasmids, transposons and integrons that can be transferred via HGT mechanisms including conjugation (gene transfer through cell-to-cell contact), transduction (gene transfer mediated by bacteriophages) and transformation (uptake of extracellular DNA) (Levy and Marshall 2004). The reservoir of ARGs in soil has been termed the soil resistome (D’Costa et al.2006) and it has been determined that the antibiotic resistome is shared between soil bacteria and human pathogens (Forsberg et al.2012). The spread of ARGs in the environment is of significant interest, as disease-causing bacteria that acquire these genes can become resistant to medical treatment. Once in the environment, ARB and ARGs can be transmitted to humans through soil, water, air/dust, fresh produce, domesticated animals and wildlife (Huijbers et al.2015). Because of these reasons, it has been suggested that ARGs themselves are emerging contaminants for which mitigation strategies are needed to prevent their widespread dissemination (Pruden et al.2006). A thorough search of the literature indicated that studies investigating the long-term influence of dairy manure application on ARGs in soils are rare (Musovic et al.2014; Fang et al.2015; Peng et al.2015; Tang et al.2015; Graham et al.2016). The purpose of this study was to determine the occurrence and abundance of six ARGs [blaCTX-M-1, erm(B), sul1, tet(A), tet(W) and tet(X)] and a class 1 integron-integrase gene (intI1) in an agricultural soil that had received dairy manure at three rates on an annual basis for 4 years. These ARGs were chosen because they include resistance to a variety of antibiotics, utilize different resistance mechanisms and cover a wide range of bacterial hosts (Skold 2000; Canton and Coque 2006; Birkett et al.2007; Roberts 2008; Roberts and Schwarz 2016). Even though intI1 is not an ARG, it can be used as a proxy for ARG contamination because it is linked to antibiotic, disinfectant and metal resistance genes, resides in a diverse number of commensal and pathogenic bacteria, often located on MGEs, and many common forms are xenogenetic (i.e. assembled relatively recently under selection pressures brought upon by human activities) (Gillings et al.2015). Thus, throughout this paper, intI1 will be included in the group called ARGs. The comparisons in this study include treatment type (control, inorganic fertilizer and three manure application rates), soil depth (0 to 120 cm), and year (2012−2016). MATERIALS AND METHODS Field site and treatments The field site was located at the USDA-ARS Northwest Irrigation and Soils Research Laboratory in Kimberly, Idaho. This region has a semi-arid climate and consists of hot dry summers and cool wet winters, with a mean annual temperature of 8.9°C and precipitation of 229 mm (NRCS 2017). Soil at the site is a Portneuf silt loam (coarse-silty, mixed, superactive, mesic Durinodic Xeric Haplocalcids) that was surface-irrigated with Snake River water. The field was 1 ha and contained plots that were 12.2 m × 18.3 m. The experimental design was a randomized complete block with four replications. The treatments consisted of a control (Ctrl, no fertilizer or manure application), inorganic fertilizer (Fert) and manure at three application rates (17.2, 34.5 and 52.0 Mg [dry wt.] ha−1). The highest manure rate (i.e. 52 Mg ha−1) is representative of what is typically applied in this region. Stockpiled manure was obtained from anonymous dairies using a local manure hauler. The manure was analyzed for chlortetracycline, oxytetracycline, monensin, penicillin G, sulfamethazine, sulfadimethoxine, sulfamethoxazole, sulfathiazole and tetracycline, but only monensin and oxytetracycline were detected (concentration data is being withheld by collaborator until published). It was applied once per year (after crop harvest and soil sampling) to the same designated plots in the fall of 2012, 2013, 2014 and 2015. Inorganic fertilizer was applied using University of Idaho Extension recommendations based on spring soil test data (Brown et al.2010). If necessary, individual nutrients (N, P, K, S, Mn) were added to meet the requirements for the specific crop being grown. After broadcasting the fertilizer and manure treatments, they were incorporated into the soil using a tandem disk within 24 h (Ctrl plots were also disked) to a depth of ∼15 cm. The crops consisted of a wheat–potato–barley–sugar beet rotation from 2013 to 2016. Prior to this study, the crops at the field site were oats (2004), alfalfa (2005−2007), barley (2008−2009), corn (2011) and beans (2010, 2012). Soil collection Before treatment (BT), soils (i.e. background soils) were collected from the top 30 cm in the fall of 2012, prior to the first manure application. Pre-plant (spring) and post-harvest (fall) soil samples were collected in March and September, respectively. The pre-plant soil samples were collected every year from 2013 to 2016, but only from the top 30 cm of soil since they were also used to make fertilizer recommendations. Post-harvest samples were collected in 2014 and 2015 prior to manure addition (2013 samples collected, but were lost) and consisted of 120 cm long cores that were sectioned into five layers (0−15, 15−30, 30−60, 60−90 and 90−120 cm). Ten (0−30 cm) and two (0−120 cm) soil cores were collected per plot during the pre-plant and post-harvest sampling, respectively. The contents of each soil layer were composited and thoroughly mixed before a subsample was placed in clean sealable plastic bag and then frozen at −75°C. Soil DNA extraction Immediately after thawing at room temperature, one DNA extraction was performed per composited soil sample. The DNA was extracted from about 500 mg of soil (wet wt.) using the FastDNA Spin Kit for Soil and the FastPrep Instrument (MP Biomedicals, Santa Ana, CA) following the manufacturer's protocol. DNA was stored in DNase/pyrogen-free water at −20°C until qPCR was performed. Primers and probes Primers, probes, annealing temperatures, amplicon lengths, sequences and references can be found in Table 1. Due to the short sequences and low melting temperatures (TM), the blaCTX-M-1 and tet(A) probes were converted to locked nucleic acid (LNA) probes (Integrated DNA Technologies [IDT], Coralville, IA) to increase the TM of the probes to about 68°C–70°C. A search of the literature did not reveal a primer/probe combination for tet(X). The following GenBank accession numbers were used to design the tet(X) primers and probe: M37699, JQ990987, JQ990988 and EU864422. Only four sequences were used because these were the only verified tet(X) gene sequences in the Gene Database within GenBank at the time of primers/probe design. ClustalX 2.1 (Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland) was used to align the sequences to find the most conserved regions, followed by use of Primer3 (Whitehead Institute for Biomedical Research, Cambridge, MA) to select the best primer pair (18–30 bp, 60°C–64°C melting temperature and 35%–65% GC content) and probe (20–30 bp, melting temperature 6°C–8°C higher than the primers and 35%–65% GC content) combinations (amplicon length between 70 and 150 bp). The primers were then checked against the NCBI BLAST database to ensure that they would not amplify non-specific products. Table 1. Primers and probes used in this study. Gene  Type  Primer/probe name  Sequence 5΄ to 3΄  TA (°C)  Amplicon size (bp)  Reference  16S rRNA  Forward  BACT1369F  CGGTGAATACGTTCYCGG  56  ∼123  Suzuki, Taylor and    Reverse  PROK1492R  GGWTACCTTGTTACGACTT       DeLong (2000)    Probe  TM1389P  6-FAM-CTTGTACACACCGCCCGTC-BHQ-1        intI1  Forward  intI1-F  GCCTTGATGTTACCCGAGAG  60  196  Barraud et al. (2010)    Reverse  intI1-R  GATCGGTCGAATGCGTGT          Probe  intI1-P  6-FAM-ATTCCTGGCCGTGGTTCTGGGTTTT-BHQ-1        blaCTX-M-1  Forward  CTXM1-F  ACCAACGATATCGCGGTGAT  60  101  Colomer-Lluch, Jofre and    Reverse  CTXM1-R  ACATCGCGACGGCTTTCT       Muniesa (2011)    Probe (LNA)  CTXM1-P  6-FAM-TC + GTGCGC + CG + CTG-BHQ-1        erm(B)  Forward  ermB-F  GGATTCTACAAGCGTACCTTGGA  60  92  Bockelmann et al. (2009)    Reverse  ermB-R  GCTGGCAGCTTAAGCAATTGCT          Probe  ermB-P  6-FAM-CACTAGGGTTGCTCTTGCACACTCAAGTC-BHQ-1        sul1  Forward  sul1-F  CCGTTGGCCTTCCTGTAAAG  60  67  Heuer and Smalla (2007)    Reverse  sul1-R  TTGCCGATCGCGTGAAGT          Probe  sul1-P  6-FAM-CAGCGAGCCTTGCGGCGG-BHQ-1        tet(A)  Forward  tetA-F  CCGCGCTTTGGGTCATT  60  51  Guarddon et al. (2011)    Reverse  tetA-R  TGGTCGCGTCCCAGTGA          Probe (LNA)  tetA-P  6-FAM-TCG + GCG + AG + G + ATCG-BHQ-1        tet(W)  Forward  tetW-F  GCAGAGCGTGGTTCAGTCT  60  65  Smith et al. (2004)    Reverse  tetW-R  GACACCGTCTGCTTGATGATAAT          Probe  tetW-P  6-FAM-TTCGGGATAAGCTCTCCGCCGA-BHQ-1        tet(X)  Forward  tetX-F  GCAAGCGCCCATTACCCATAA  60  97  This study    Reverse  tetX-R  AAGGCATCCACCAACCCACT          Probe  tetX-P  6-FAM-CATTTGATGCCGCCTTTTGCAGGGC-BHQ-1        Gene  Type  Primer/probe name  Sequence 5΄ to 3΄  TA (°C)  Amplicon size (bp)  Reference  16S rRNA  Forward  BACT1369F  CGGTGAATACGTTCYCGG  56  ∼123  Suzuki, Taylor and    Reverse  PROK1492R  GGWTACCTTGTTACGACTT       DeLong (2000)    Probe  TM1389P  6-FAM-CTTGTACACACCGCCCGTC-BHQ-1        intI1  Forward  intI1-F  GCCTTGATGTTACCCGAGAG  60  196  Barraud et al. (2010)    Reverse  intI1-R  GATCGGTCGAATGCGTGT          Probe  intI1-P  6-FAM-ATTCCTGGCCGTGGTTCTGGGTTTT-BHQ-1        blaCTX-M-1  Forward  CTXM1-F  ACCAACGATATCGCGGTGAT  60  101  Colomer-Lluch, Jofre and    Reverse  CTXM1-R  ACATCGCGACGGCTTTCT       Muniesa (2011)    Probe (LNA)  CTXM1-P  6-FAM-TC + GTGCGC + CG + CTG-BHQ-1        erm(B)  Forward  ermB-F  GGATTCTACAAGCGTACCTTGGA  60  92  Bockelmann et al. (2009)    Reverse  ermB-R  GCTGGCAGCTTAAGCAATTGCT          Probe  ermB-P  6-FAM-CACTAGGGTTGCTCTTGCACACTCAAGTC-BHQ-1        sul1  Forward  sul1-F  CCGTTGGCCTTCCTGTAAAG  60  67  Heuer and Smalla (2007)    Reverse  sul1-R  TTGCCGATCGCGTGAAGT          Probe  sul1-P  6-FAM-CAGCGAGCCTTGCGGCGG-BHQ-1        tet(A)  Forward  tetA-F  CCGCGCTTTGGGTCATT  60  51  Guarddon et al. (2011)    Reverse  tetA-R  TGGTCGCGTCCCAGTGA          Probe (LNA)  tetA-P  6-FAM-TCG + GCG + AG + G + ATCG-BHQ-1        tet(W)  Forward  tetW-F  GCAGAGCGTGGTTCAGTCT  60  65  Smith et al. (2004)    Reverse  tetW-R  GACACCGTCTGCTTGATGATAAT          Probe  tetW-P  6-FAM-TTCGGGATAAGCTCTCCGCCGA-BHQ-1        tet(X)  Forward  tetX-F  GCAAGCGCCCATTACCCATAA  60  97  This study    Reverse  tetX-R  AAGGCATCCACCAACCCACT          Probe  tetX-P  6-FAM-CATTTGATGCCGCCTTTTGCAGGGC-BHQ-1        6-FAM = fluorescein; BHQ-1 = black hole quencher 1 (Suzuki, Taylor and DeLong 2000; Smith et al.2004; Heuer and Smalla 2007; Bockelmann et al.2009; Barraud et al.2010; Colomer-Lluch, Jofre and Muniesa 2011; Guarddon et al.2011). View Large Quantitative real-time PCR qPCR was performed on an iQ5 Real-Time PCR Detection System (Bio-Rad, Hercules, CA). Each individual reaction consisted of 12.5 μL of 2 × SsoAdvanced Universal Probes Supermix (Bio-Rad), 250 nM of forward and reverse primers and probes (IDT), 2 μL of DNA template and sterile DNase/pyrogen-free water to create a final volume of 25 μL. To help alleviate PCR inhibition, each DNA template was diluted by one order of magnitude. The temperature profile consisted of one cycle at 95°C for 3 min, 40 cycles at 95°C for 15 s and annealing temperature (TA, Table 1) for 30 s. All qPCR runs included a standard curve covering seven orders of magnitude, and each sample was analyzed in triplicate. Standards were created using gBlocks Gene Fragments (IDT). The standard curve r2 values were consistently >0.98, with an instrument limit of quantification (LOQ) of 102 gene copies μL−1. Since the DNA templates were diluted by an order of magnitude to prevent qPCR inhibition, the corrected LOQ was 103 gene copies μL−1. All ARG and intI1 qPCR results were normalized per g of dry soil (absolute abundance) and per 16S rRNA gene copies (relative abundance). To normalize the gene data per gram of dry soil, the moisture contents of the soils were determined gravimetrically. A spreadsheet of all the absolute and relative ARG and absolute 16S rRNA gene abundance data can be found in the Supporting Information. Statistical analysis All statistical tests were performed using SAS 9.4 (SAS Institute Inc., Cary, NC). The data were log (base 10) transformed to achieve homogeneity of variance, which was determined from visual analysis of predicted versus residual and quantile-quantile plots. Statements of statistical significance were defined as P ≤ 0.05. One-way analysis of variance (ANOVA, GLM procedure) model was used to generate population means and 95% confidence limits (CL). The least significant difference (LSD) test within the GLM procedure was used for comparison of population means for categorizing significance groups. Least squares means (lsmeans) was used to generate P-values to determine statistical significance for comparison of individual population means. Since standard deviations have no meaning for n equal to 1 or 2, 95% CL are not included. Pooled data refer to the systematic grouping of qPCR data to strengthen the ANOVA model due to similar trends; it do not refer to the mixing of unique soil samples or DNA extracts prior to analysis. Correlation analysis (CORR procedure) was used to calculate the Pearson correlation coefficient (r) and P-values between the various genes. Correlations were considered significant if the Pearson correlation coefficient (r) ≥0.60 and the P ≤ 0.05. Data were not pooled for the correlation analysis. RESULTS Gene occurrence Out of a total of 220 unique soil samples (all spring, fall and depth data included) analyzed, the intI1, blaCTX-M-1, erm(B), sul1, tet(A), tet(W) and tet(X) genes were detected 146, 4, 15, 176, 0, 128 and 59 times, respectively (Table 2). Due to limited detection of blaCTX-M-1, erm(B) and tet(A), these genes were not included in any quantitative or statistical analysis. blaCTX-M-1 was detected once in the Ctrl plots and three times in manured plots; erm(B) was only detected in soils with manure application; and tet(X) was not detected before treatment, but was detected twice in the Ctrl plots, once in the Fert plots and 56 times in the manured plots. All other ARGs, except for tet(A), were detected in BT, Ctrl, Fert and manured plots. Table 2 contains occurrence data for all ARGs broken down by treatment. Table 2. ARG occurrence by treatment. Treatment  n  intI1  blaCTX-M-1  erm(B)  sul1  tet(A)  tet(W)  tet(X)  BT  20  11  0  0  17  0  19  0  Ctrl  56  33  1  0  42  0  19  2  Fert  56  33  0  0  37  0  18  1  17.2M  16  16  1  4  16  0  16  13  34.5M  16  16  0  5  16  0  16  16  52.0M  56  37  2  6  48  0  40  27  Total  220  146  4  15  176  0  128  59  Treatment  n  intI1  blaCTX-M-1  erm(B)  sul1  tet(A)  tet(W)  tet(X)  BT  20  11  0  0  17  0  19  0  Ctrl  56  33  1  0  42  0  19  2  Fert  56  33  0  0  37  0  18  1  17.2M  16  16  1  4  16  0  16  13  34.5M  16  16  0  5  16  0  16  16  52.0M  56  37  2  6  48  0  40  27  Total  220  146  4  15  176  0  128  59  BT = before treatment; Ctrl = control; Fert = fertilizer; 17.2M = 17.2 Mg manure ha−1 yr−1; 34.5M = 34.5 Mg manure ha−1 yr−1; and 52.0M = 52.0 Mg manure ha−1 yr−1. View Large Treatment The general treatment effects indicate that manure application increases both ARG absolute and relative abundances (i.e. per gram of dry soil and per 16S rRNA gene copies, respectively). Also, manure application rate was positively correlated with ARG abundances, although not always significantly. Figure 1 presents the absolute (A) and relative (B) abundance results from fall 2012 and spring 2013–2016 (data pooled to strengthen the ANOVA model since similar trends occurred each year). Since the fall 2012 samples were obtained prior to any fertilizer or manure addition, they were not included in the pooled data analysis, but were used as a comparison for BT gene abundances. Figure 1. View largeDownload slide ARG absolute (A) and relative (B) abundances by treatment, fall 2012 (BT), spring 2013−2016 (pooled data) and soil depth = 0–30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. BT = Before Treatment; Ctrl = Control; Fert = Fertilizer; 17.2M = 17.2 Mg manure ha−1 yr−1; 34.5M = 34.5 Mg manure ha−1 yr−1; and 52.0M = 52.0 Mg manure ha−1 yr−1. 95% confidence level error bars are not included when n = 1 or 2. Figure 1. View largeDownload slide ARG absolute (A) and relative (B) abundances by treatment, fall 2012 (BT), spring 2013−2016 (pooled data) and soil depth = 0–30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. BT = Before Treatment; Ctrl = Control; Fert = Fertilizer; 17.2M = 17.2 Mg manure ha−1 yr−1; 34.5M = 34.5 Mg manure ha−1 yr−1; and 52.0M = 52.0 Mg manure ha−1 yr−1. 95% confidence level error bars are not included when n = 1 or 2. According to Fig. 1A, sul1 and tet(W) show the following statistically significant trends: 52.0M > 34.5M > 17.2M > Ctrl ≈ Fert ≈ BT. intI1 had a similar trend but the 17.2M was not statistically different from the Ctrl (P = 0.60) or Fert plots (P = 0.24). With tet(X), 34.5M and 52.0M were not significantly different according to LSD, but 52.0M was significantly greater than 34.5M using lsmeans (P = 0.0013). Even though 17.2M and 34.5M were not significantly different from each other or the Ctrl and Fert plots, tet(X) was only detected twice in the Ctrl and once in the Fert compared to 13 detections for 17.2M and 16 detections for 34.5M. The same trends occurred when normalizing to the 16S rRNA gene (Fig. 1B), except the 34.5M and 52.0M treatments were not significantly different for sul1 (P = 0.32) and tet(W) (P = 0.19). Soil depth Unlike the treatment analysis, ARG absolute abundances show a different trend than the relative abundances for the 52.0M treatments. According to Fig. 2A, the ARG absolute abundances show a general trend of decreasing with depth (though not always significant), except for tet(X). The ARG relative abundances show no general trend with very few significant differences between depths (Fig. 2B). One thing that is clear with both graphs is that the detection frequency of ARGs decreases with depth, including tet(X). A conclusion that can be drawn from this is that total ARG copy numbers decrease with depth, but the fraction of bacteria carrying the ARGs does not change significantly. Figure 2. View largeDownload slide ARG absolute (A) and relative (B) abundances by soil depth, fall 2014−2015 (pooled data) and 52.0 Mg manure ha−1 yr−1. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. 95% confidence limit error bars are not included when n = 1 or 2. Figure 2. View largeDownload slide ARG absolute (A) and relative (B) abundances by soil depth, fall 2014−2015 (pooled data) and 52.0 Mg manure ha−1 yr−1. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. 95% confidence limit error bars are not included when n = 1 or 2. Year For the 52.0M (Fig. 3A and B) manure treatments, the ARG abundances for the years 2013–2016 are significantly greater than 2012, for both normalizations, except for 2012 and 2015 relative intI1 abundances (P = 0.28). This makes sense because manure was applied after the fall 2012 sampling event. Both ARG abundances were quite variable when comparing individual years from 2013 to 2016. Figure 3. View largeDownload slide ARG absolute (A) and relative (B) abundances by year, fall 2012 (before treatment), spring 2013–16, 52.0 Mg manure ha−1 yr−1 and soil depth = 0−30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. Error bars are 95% confidence limits. Figure 3. View largeDownload slide ARG absolute (A) and relative (B) abundances by year, fall 2012 (before treatment), spring 2013–16, 52.0 Mg manure ha−1 yr−1 and soil depth = 0−30 cm. The letters above the bars are the least significant difference (LSD) category. The numbers at the base of the bars are the n detected out of the nmax displayed on the graph. Error bars are 95% confidence limits. Correlation analysis Correlation analysis was performed to determine the relationship between ARGs and ARGs versus 16S rRNA genes (Tables 3 and 4). It should be noted that a significant correlation between two variables does not imply causation. All the ARGs [intI1, sul1, tet(W) and tet(X)] had a significant positive correlation with each other for both relative and absolute abundances. The strongest correlations were between tet(X) and sul1 (r = 0.88, P < 0.0001) for both abundances and between tet(X) and intI1 [r = 0.81 (absolute) and 0.85 (relative), P < 0.0001]. Table 3. Pearson correlation analysis of absolute ARG and 16S rRNA gene (16S) abundances.   intI1  sul1  tet(W)  tet(X)      gDS−1  gDS−1  gDS−1  gDS−1    16S gDS−1  0.3835  0.1845  0.3918  0.1383  r    <.0001  0.0143  <.0001  0.2964  P-value    146  176  128  59  n  intI1 gDS−1    0.6338  0.7065  0.8141  r      <.0001  <.0001  <.0001  P-value      127  106  59  n  sul1 gDS−1      0.7701  0.8788  r        <.0001  <.0001  P-value        121  59  n  tet(W) gDS−1        0.6707  r          <.0001  P-value          59  n    intI1  sul1  tet(W)  tet(X)      gDS−1  gDS−1  gDS−1  gDS−1    16S gDS−1  0.3835  0.1845  0.3918  0.1383  r    <.0001  0.0143  <.0001  0.2964  P-value    146  176  128  59  n  intI1 gDS−1    0.6338  0.7065  0.8141  r      <.0001  <.0001  <.0001  P-value      127  106  59  n  sul1 gDS−1      0.7701  0.8788  r        <.0001  <.0001  P-value        121  59  n  tet(W) gDS−1        0.6707  r          <.0001  P-value          59  n  gDS = gram of dry soil; r = Pearson correlation coefficient; n = number of observations; yellow boxes: 0.8000 ≤ r ≤ 0.8999; green boxes: 0.7000 ≤ r ≤ 0.7999; blue boxes: 0.6000 ≤ r ≤ 0.6999. View Large Table 4. Pearson correlation analysis of relative ARG abundances.   sul1 16S−1  tet(W) 16S−1  tet(X) 16S−1    intI1 16S−1  0.6617  0.6696  0.8464  r    <.0001  <.0001  <.0001  P-value    127  106  59  n  sul1 16S−1    0.7378  0.8824  r      <.0001  <.0001  P-value      121  59  n  tet(W) 16S−1      0.6870  r        <.0001  P-value        59  n    sul1 16S−1  tet(W) 16S−1  tet(X) 16S−1    intI1 16S−1  0.6617  0.6696  0.8464  r    <.0001  <.0001  <.0001  P-value    127  106  59  n  sul1 16S−1    0.7378  0.8824  r      <.0001  <.0001  P-value      121  59  n  tet(W) 16S−1      0.6870  r        <.0001  P-value        59  n  16S = 16S rRNA gene; r = Pearson correlation coefficient; n = number of observations; yellow boxes: 0.8000 ≤ r ≤ 0.8999; green boxes: 0.7000 ≤ r ≤ 0.7999; blue boxes: 0.6000 ≤ r ≤ 0.6999. View Large DISCUSSION Occurrence of ARGs in manure-amended soil This study was designed to determine the changes in absolute and relative abundances of various ARGs and a class 1 integron (intI1) in soil that had received inorganic fertilizer or dairy manure under a crop rotation that is typical for southern Idaho. While qPCR was used to track the ARGs, it should be noted that this technique cannot decipher between intracellular and extracellular DNA (Corinaldesi, Danovaro and Dell’Anno 2005), whether the DNA came from live or dead bacteria (Nocker, Cheung and Camper 2006) nor determine which bacteria carried these ARGs (Yang et al.2002) and if these genes were expressed (Smith and Osborn 2009). Of the six ARGs that were targeted in the soils, only three [sul1, tet(W) and tet(X)] and intI1 were quantified in enough samples to perform statistics and draw conclusions. Previous studies have found blaCTX-M (Marti et al.2013; Hu et al.2016; Nolvak et al.2016), erm(B) (Marti et al.2013, 2014; Sun et al.2015; Hu et al.2016), sul1 (Munir and Xagoraraki 2011; Marti et al.2013, 2014; Fahrenfeld et al.2014; Ross and Topp 2015; Sun et al.2015; Nolvak et al.2016; Ruuskanen et al.2016), tet(A) (Marti et al.2013; Hu et al.2016; Nolvak et al.2016; Sandberg and LaPara 2016), tet(W) (Munir and Xagoraraki 2011; Kyselkova et al.2013; Fahrenfeld et al.2014; Kyselkova et al.2015; Sandberg and LaPara 2016), tet(X) (Sandberg and LaPara 2016) and intI1 (Marti et al.2013, 2014; Kyselkova et al.2015; Sun et al.2015; Hu et al.2016; Nolvak et al.2016; Sandberg and LaPara 2016) in soil amended with dairy or beef cattle manure. All of these ARGs encode resistance to commonly used antibiotics in animal agriculture (Chee-Sanford et al.2009). Also, these ARGs are acquired from other bacteria through HGT, as opposed to intrinsically resistant. Acquired ARGs reside on MGEs such as plasmids or transposons which can be readily transferred between bacteria, even distantly related bacteria as long as they have the mechanisms to accept these MGEs (Van Hoek et al.2011). Manure application increases ARG abundances in soil Manure contains a considerable abundance of ARB and ARGs even if the producing animal has never received antibiotics (Heuer, Schmitt and Smalla 2011). This usually translates into increased ARG abundance in the manured soil as opposed to control or background levels. However, this increased ARG abundance in the manure-treated soil may be transient without selection pressure such as high concentrations of antibiotics or metals (Heuer, Schmitt and Smalla 2011). In a study in which dairy manure was applied to agricultural soils, a 77% and 36% increase in abundance from background soil levels was reported for tet(W) and sul1, respectively (Munir and Xagoraraki 2011). In a microcosm study where dairy cow feces [from an oxytetracycline-treated cow (OE) and non-treated cow (NE)] were applied to soils from three different farms, the tet(W) gene was not detected in the control soils, but it was detected in similar quantities in both OE- and NE-treated soils from all three farms (Kyselkova et al.2013). Fahrenfeld et al. (2014) conducted a field study in which dairy manure slurry was applied to a cornfield. There were significant increases in sul1 and tet(W) relative abundances in post- versus pre-manured soils, as well as post-manured versus downgradient non-manure treated soils. According to another study where dairy manure was applied to soil, sul1 and int1 relative abundances were significantly greater in manured soils than in soil that received inorganic fertilizer (Marti et al.2014). The sul1 genes significantly increased from ∼101 gene copies ng−1 DNA in control samples to ∼105 gene copies ng−1 DNA in soil amended with raw manure, according to a study by Ross and Topp (2015). In a microcosm study by Sandberg and LaPara (2016), tet(W) increased, and intI1 and tet(X) were similar in dairy manure-amended soils compared to control soils. In this study, intI1 and tet(X) were not significantly different between Ctrl and 17.2M manure treatment, but were significantly different between the Ctrl and both 34.5M and 52.0M manure treatments. Nolvak et al. (2016) conducted a field study to determine the fate of various ARGs in soil amended with no fertilizer (control), mineral fertilizer and cattle slurry. For intI1 and sul1, cattle slurry-amended soil was significantly greater than the control and mineral fertilized plots, while there were no significant differences between the control and inorganic fertilizer plots. These referenced studies, including our study, have found that applying dairy manure to soil increases the abundance of ARGs above background levels. The higher the manure application rate, the higher the ARG level To the knowledge of the authors, no previous paper sought to determine the effect of dairy manure application rate on the abundance of ARGs. There are many studies comparing different animal manures (i.e. dairy, beef, swine, chicken, etc.) (Chen et al.2007; Ghosh and LaPara 2007; Marti et al.2013, 2014; Hu et al.2016; Ruuskanen et al.2016; Sandberg and LaPara 2016), different manure types (composted, stockpiled, slurry, lagoon, etc.) (Munir and Xagoraraki 2011; Fahrenfeld et al.2014; Ross and Topp 2015; Nolvak et al.2016; Ruuskanen et al.2016) or application to different types of agricultural soils (Munir and Xagoraraki 2011; Kyselkova et al.2013, 2015; Sun et al.2015; Hu et al.2016; Ruuskanen et al.2016), but none exploring the difference between application rates for the same manure source and same soil type. When manure is applied to soil, it is either left on the surface of the soil or it is incorporated to a fixed depth (due to the limited penetration depth of the tillage equipment) no matter how much manure is applied. In this study, the manure was incorporated with a tandem disk to a fixed depth of ∼15 cm. Therefore, soil samples from the top 15 cm of the profile had a high ratio of manure to soil mass. Since manure usually has a higher ARG abundance relative to the soil (Munir and Xagoraraki 2011; Fahrenfeld et al.2014; Nolvak et al.2016; Ruuskanen et al.2016; Sandberg and LaPara 2016), the abundance of ARGs in the soil collected post-manure application will increase with each step increase of the manure application rate (see Fig. 1A and B). Manure application rate is more important than repeated annual applications of manure When comparing Figs 1A and 3A and Figs 1B and 3B, the trends clearly demonstrate that manure application rate increases ARG abundances while repeated annual applications of the same manure rate had a variable effect. The factors affecting the persistence of ARGs in manure-amended soils are numerous, including fitness effects to bacteria carrying ARGs, ability to transfer ARGs between closely and distantly related bacteria, half-lives and sorption potential of antibiotics to soil particles which can induce selection pressure, and co-selection of ARGs that reside on MGEs along with metal- and biocide-resistance genes in metal or biocide contaminated environments, to name a few (Chee-Sanford et al.2009). Other studies have investigated the persistence of ARGs in dairy manure-amended soil (Fahrenfeld et al.2014; Marti et al.2014; Kyselkova et al.2015; Nolvak et al.2016), but most studies collected soil samples and applied dairy manure much more frequently, with time frames much shorter than this study. Due to these reasons, it is hard to draw a conclusion as to why ARG abundances vary among the yearly spring sampling events. Absolute abundance and occurrence of ARGs decrease with increasing soil depth A search of the literature did not reveal any studies that assessed the difference in ARG abundances with soil depth after receiving dairy manure application, but there are studies that address swine manure applications (Huang et al.2013; Peng et al.2015; Tang et al.2015). The results of this study are in agreement with Huang et al. (2013) in which absolute abundances of tet(W) and tet(X) were higher in the surface soils, then generally decreased with increasing soil depth, while relative abundances stayed constant throughout the soil profile. Peng et al. (2015) determined the absolute and relative abundances of tet(W) at three soil depths after fresh swine manure application. A visual analysis of the graphs in Peng et al. (2015) indicates that the tet(W) abundances did not change with depth for both normalizations, which is not in agreement with the absolute abundances determined in our study. Tang et al. (2015) only provided data for relative abundances of tet(W) and sul1 in soil where swine manure was applied. The data from Tang et al. (2015) suggest that the relative abundances decreased with increasing soil depth, except for one of the sites where tet(W) increased with increasing soils depth, which is not consistent with our study as stated above. None of the referenced soil-depth studies performed statistical analyses to determine significant differences between different depths for a specific ARG, so all conclusions were drawn from visual inspection of graphs (Peng et al.2015) or tables (Huang et al.2013; Tang et al.2015). Also, none of these referenced studies included occurrence data of ARGs throughout the soil profile, as was determined in the present study. ARGs correlate with each other but not with 16S rRNA genes In two recent studies, a significant correlation was determined between the relative (Sun et al.2015; Nolvak et al.2016) and absolute (Nolvak et al.2016) abundance of sul1 and intI1. Both studies showed stronger correlations between these two genes than in our study (Tables 3 and 4). Nolvak et al. (2016) also found a significant correlation between the absolute abundances of 16S rRNA gene and sul1 (r = 0.65) in dairy cattle slurry-amended soil, as opposed to this study where no significant correlation was found (r = 0.18, Table 3). In conclusion, the results from this field study advance knowledge that dairy manure is an ARG source and applications to soil can enlarge the reservoir of clinically relevant ARGs when compared to soil that received inorganic fertilizer or no fertilizer/manure. While the ARGs may have been associated with dead bacteria or extracellular, an increase of the ARG reservoir is a potential cause for concern, as it could facilitate acquired resistance in bacteria that are pathogenic to humans and food-producing animals. However, our results cannot be used to verify the transfer of ARGs to pathogens, nor determine the level of resistance of those specific genes. Regardless of this limitation, dairy manure applications were shown to increase the absolute abundance of ARGs in soil. Although not investigated in this study, antibiotic residues and other constituents are also present in dairy manure; thus, their influence on the enrichment of ARGs in manure-amended soil should not be ruled out and should be a subject for continued investigation. SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. 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Published: Mar 1, 2018

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