wastewater irrigation increases abundance of potentially harmful
TRANSCRIPT
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Wastewater irrigation increases abundance of 1
potentially harmful Gammaproteobacteria in 2
soils from Mezquital Valley, Mexico 3
Melanie Broszat1,2, ‡, Heiko Nacke3, ‡, Ronja Blasi1,2, Christina Siebe4, Johannes Huebner1,5, 4
Rolf Daniel3, Elisabeth Grohmann1,2# 5
1 University Medical Centre Freiburg, Division of Infectious Diseases, Freiburg, Germany 6
2 Albert-Ludwigs University Freiburg, Institute for Biology II, Microbiology, Freiburg, 7
Germany 8
3 Georg-August University Göttingen, Institute of Microbiology and Genetics, Göttingen, 9
Germany 10
4 Universidad Nacional Autónoma de México, Instituto de Geología, Ciudad Universitaria, 11
Mexico City, Mexico 12
5 Hauner Children's Hospital, Division of Pediatric Infectious Diseases, Ludwig-Maximilians 13
University Munich, Munich, Germany 14
# E-Mail: [email protected] 15
‡ These authors contributed equally to this work. 16
17
Running title: Gammaproteobacteria in wastewater-irrigated soils 18
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AEM Accepts, published online ahead of print on 20 June 2014Appl. Environ. Microbiol. doi:10.1128/AEM.01295-14Copyright © 2014, American Society for Microbiology. All Rights Reserved.
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ABSTRACT 21
Wastewater contains a large amount of pharmaceuticals, pathogens, and antimicrobial 22
resistance determinants. Only little is known about the dissemination of resistance 23
determinants and changes in soil microbial communities affected by wastewater irrigation. 24
Community DNA from Mezquital Valley soils under irrigation with untreated wastewater for 25
0 to 100 years was analyzed by quantitative real time PCR for the presence of sul genes, 26
encoding resistance to sulfonamides. Amplicon sequencing of bacterial 16S rRNA genes from 27
community DNA from soils irrigated for 0, 8, 10, 85, and 100 years was performed revealing 28
a 14% increase of the relative abundance of Proteobacteria in rainy season and a 26.7% 29
increase in dry season soils irrigated for 100 years with wastewater. In particular, 30
Gammaproteobacteria, including potential pathogens like Pseudomonas, Stenotrophomonas 31
and Acinetobacter spp. were found in wastewater-irrigated fields. 16S rRNA gene sequencing 32
of 96 isolates from soils irrigated with wastewater for 100 years (48 from dry and 48 from 33
rainy season) revealed that 46% affiliated with Gammaproteobacteria (mainly potentially 34
pathogenic Stenotrophomonas strains) and 50% with Bacilli, whereas all 96 isolates from 35
rain-fed soils (48 from dry and 48 from rainy season) affiliated with Bacilli. Up to six 36
antibiotic resistances were found in isolates from wastewater-irrigated soils, sulfamethoxazole 37
resistance was the most abundant (33.3% of the isolates), followed by oxacillin resistance 38
(21.9% of the isolates). In summary, we detected an increase of potentially harmful bacteria 39
and larger incidence of resistance determinants in wastewater-irrigated soils which might 40
result in health risks for farmworkers and consumers of wastewater-irrigated crops. 41
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INTRODUCTION 43
Along with pharmaceuticals, wastewater can contain pathogenic microorganisms including 44
bacteria resistant to antimicrobial substances, and also antimicrobial resistance determinants 45
(1–6). In arid and semi-arid areas, wastewater is used for irrigation in agricultural production 46
to alleviate water shortages (7–10). The coexistence of antibiotics, pathogens and antibiotic 47
resistance determinants in wastewater raises concerns that antibiotic resistance genes are 48
mobilized from and disseminated into the environmental resistome and transferred to bacteria 49
that are potentially pathogenic to humans (11–13). The release of antibiotics together with 50
human-linked microbiota might be particularly important for the emergence of new evolving 51
antibiotic resistant pathogens (1,14). Environmental reservoirs for antibiotic resistances, 52
especially those impacted by anthropogenic activities (e.g., application of manure), can serve 53
as “hotspots” for the spread of antibiotic resistance genes and antibiotic resistant bacteria 54
through food and water, with unknown consequences for human health (14–16). D’Costa and 55
colleagues indicated that soil could serve as an underestimated reservoir for antibiotic 56
resistance that has already emerged or has the potential to emerge in clinically important 57
bacteria (17). The first report of a putative link between environmental and clinical antibiotic 58
resistance determinants was published in 1973 by Benveniste and Davies. They detected high 59
similarities between enzymes conferring gentamicin-resistance from soil-associated 60
Actinomycetes and enzymes that confer the same resistance in human pathogens such as 61
Escherichia coli and Pseudomonas aeruginosa (18). Recent studies have shown that the 62
CTX-M β-lactamases potentially originate from the environmental bacterium Kluyvera 63
ascorbata (19,20). Furthermore, the plasmid-encoded qnr genes encoding fluoroquinolone 64
resistance have originated from aquatic bacteria such as Shewanella algae (21–23). 65
Fluoroquinolones are a family of broad spectrum antibacterial agents that are active against a 66
wide range of Gram-positive and Gram-negative bacteria. They act by inhibition of type II 67
DNA toposisomerases (gyrases) that are required for bacterial DNA replication. Three 68
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mechanisms of resistance are known. Some types of efflux pumps act to decrease intracellular 69
quinolone concentration. In Gram-negative bacteria, plasmid-mediated resistance genes 70
produce proteins that can bind to DNA gyrase, protecting it from the action of quinolones. In 71
addition, mutations at key sites in DNA gyrase or topoisomerase IV can decrease their 72
binding affinity to quinolones, decreasing the effectiveness of the drug (24). 73
There are strong indications for a link between antibiotic resistance determinants from the 74
environment and those found in hospitals (13). Another problem is the release of 75
antimicrobials to the environment which might influence the composition of natural bacterial 76
communities and may as well change the physiology of environmental bacteria (25). Thus, 77
wastewater irrigation and other anthropogenic activities, e.g., application of manure, might 78
also change the composition of soil bacterial communities. Some studies have shown that a 79
shift of soil bacterial community structure towards a higher abundance of 80
Gammaproteobacteria (8,26) results from an input of organic carbon sources or irrigation 81
with treated wastewater. Gammaproteobacteria are a class of medically, ecologically and 82
scientifically important groups of bacteria, such as the Enterobacteriaceae (e.g. E. coli), 83
Vibrionaceae, Pseudomonadaceae and Xanthomonadaceae (e.g. Stenotrophomonas 84
maltophilia). An exceeding number of important pathogens belongs to this class, such as 85
Salmonella (enteritis and typhoid fever), Vibrio cholerae (cholera), Pseudomonas aeruginosa 86
(lung infections), and Klebsiella pneumoniae responsible for causing pneumonia. S. 87
maltophilia is found in various natural environments, such as soil, water and plants, but also 88
occurs in the hospital environment and may cause infections that affect the bloodstream, 89
respiratory tract, urinary tract and surgical-sites. 90
Frenk et al. (8) compared pyrosequencing data of bacterial 16S rRNA genes from soils 91
irrigated with treated wastewater with those from soils irrigated with freshwater. They 92
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observed an increase in the proportion of Gammaproteobacteria during the irrigation season 93
(dry season) and a return to the “baseline state” in the rainy season. 94
However, the influence of long-term irrigation with untreated wastewater on the bacterial soil 95
communities has not been studied so far. Here, we investigated the effect of wastewater 96
irrigation for different time-periods on the occurrence of pathogenic bacteria and antibiotic 97
resistance determinants in the affected Mezquital Valley soils and compared it with rain-fed 98
agriculture in the same area incorporating possible season effects by sampling the same soils 99
in the rainy and the dry season. In previous studies we detected an increase in the relative 100
abundance of sul resistance genes encoding resistance towards sulfonamides and an 101
accumulation of antibiotics during long-term wastewater irrigation in the Mezquital Valley 102
soils (27). Sulfonamides are bacteriostatic antibiotics that inhibit conversion of p-103
aminobenzoic acid to dihydropteroate, which bacteria need for folate synthesis and ultimately 104
purine and DNA synthesis. Resistance in Gram-negative enteric bacteria is plasmid-borne and 105
is mainly due to the presence of sul1 and sul2 genes encoding drug-resistance variants of the 106
dihydropteroate synthase enzyme in the folic acid pathway (28). 107
We hypothesize that irrigation with untreated wastewater changes the composition of soil 108
bacterial communities towards increased abundances of potentially harmful bacteria and, that 109
wastewater-derived pathogens can survive in the environment, which might pose risks to 110
people living in the area and consumers of agricultural products from wastewater-irrigation 111
fields. 112
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MATERIALS AND METHODS 114
Study sites and soil sampling 115
Over the past century the irrigated area in the Mezquital Valley increased due to the 116
expansion of the Mexico City Metropolitan Area (MCMA). We selected sites with different 117
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duration of irrigation with untreated wastewater (non-irrigated control, 8, 10, 85, and 100 118
years, further named soil chronosequence) for our study. All of them were either sampled in 119
August 2009 (rainy season) or in March 2011 (dry season). All soils have been irrigated with 120
MCMA wastewater, which has been well mixed especially over longer time periods because 121
of the extensive pumping and diversion of wastewater within the MCMA and the Mezquital 122
Valley irrigation system. From each field a sample composed of 48 subsamples distributed 123
equidistantly within the whole field was taken with an auger at a depth of 0–30 cm. Soil 124
samples were collected, transported to the laboratory at 4°C and stored at -20°C until DNA 125
extraction. Soil properties are given in Table 1. 126
127
Properties of the soil samples 128
To determine soil pH, 10 g of each soil sample were suspended at a soil-to-liquid ratio of 129
1:2.5 (soil/0.01 M CaCl2). Subsequently, pH was measured in the supernatant with a glass 130
electrode (31). For determination of the Total Organic Carbon content (TOC), the Total 131
Carbon content (TC) and the Total Nitrogen content (TN) 0.5 g of each composite soil sample 132
was suspended in 100 ml distilled water and homogenized with ULTRA-TURRAX® (T 10 133
basic, IKA-Werke GmbH & Co. KG, Staufen, Germany). The samples were measured with 134
the TOC analyzer (Shimadzu TOC-VCPN, Shimadzu Deutschland GmbH, Duisburg, 135
Germany). For the evaluation of TOC, TC and TN, standard curves were generated with serial 136
dilutions of the standards and measured five times. For TC measurement, a potassium 137
hydrogen phthalate solution (2.125 g/l potassium hydrogen phthalate, equivalent to 1 g carbon 138
per l) was used, for inorganic carbon, a sodium carbonate solution (4.100 g Na2CO3 and 3.500 139
g NaHCO3 per l, equivalent to 1 g inorganic carbon per l) and for TN measurement a 140
potassium nitrate solution (7.219 g potassium nitrate, equivalent to 1 g nitrogen per l) was 141
used following the manufacturer‘s instructions. 142
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Total DNA extraction from soils 144
Total DNA was extracted from 500 mg soil from fields irrigated for 0, 8, 10, 85, and 100 145
years with wastewater (triplicates of four soil samples from dry and four soil samples from 146
rainy season) using the NucleoSpin® Soil kit according to the manufacturer’s protocol 147
(Macherey-Nagel, Düren, Germany). Aliquots of total DNA from the soil samples were 148
analyzed by pyrosequencing of 16S rRNA genes. 149
150
Amplification of partial 16S rRNA genes and pyrosequencing 151
The V2-V3 region of 16S rRNA genes was amplified by PCR using total DNA from the 152
different soil samples as starting material. The PCR reaction mixture (50 µl) contained 10 µl 153
fivefold reaction buffer (Phusion HF buffer, Thermo Fisher Scientific, Inc., Waltham, MA, 154
USA), 200 µM of each of the four deoxynucleoside triphosphates, 5% DMSO, 0.5 U Phusion 155
hot start high fidelity DNA polymerase (Thermo Fisher Scientific, Inc.), 10 to 200 ng DNA as 156
template, and 4 µM of each of the primers. Primers used were 101F containing Roche 454 157
pyrosequencing adaptor B and 515R containing a sample-specific MID (Extended Multiplex 158
Identifier, size: ten nucleotides) and Roche 454 pyrosequencing adaptor A (Table 2). The 159
PCR reactions were initiated at 98°C (30 s), followed by 25 cycles of 98°C (10 s), 69°C (30 s) 160
and 72°C (20 s), and ended with incubation at 72°C for 10 min. All samples were amplified in 161
triplicate, purified using the peqGold gel extraction kit (Peqlab Biotechnologie GmbH, 162
Erlangen, Germany) as recommended by the manufacturer, and pooled in equal amounts. 163
Quantification of PCR products was performed using the Quant-iT dsDNA BR assay kit and a 164
Qubit fluorometer (Life Technologies, Darmstadt, Germany). The sequences of the partial 165
16S rRNA genes were determined using a Roche GS-FLX 454 pyrosequencer (Roche, 166
Mannheim, Germany) and Titanium chemistry as recommended by the manufacturer. All 167
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sequences have been deposited in the sequence read archive of the National Center for 168
Biotechnology Information under accession number SRP037963. 169
170
Pyrosequencing data processing and statistical analysis 171
Sequences shorter than 200 bp as well as those exhibiting an average quality value below 172
25, more than two primer mismatches or long homopolymers (> 8 bp) were removed from the 173
dataset by employing QIIME version 1.6 (38). All remaining primer sequences were truncated 174
using program cutadapt (39). Removal of potential chimeric sequences was performed by 175
applying Uchime (40) and Greengenes Gold dataset “gold_strains_gg16S_aligned.fasta” as 176
reference (41). The Acacia error-correction tool (42) was used to remove noise introduced by 177
amplicon pyrosequencing. Determination of operational taxonomic units (OTUs) was 178
performed using Uclust (43). To taxonomically classify OTUs, partial 16S rRNA gene 179
sequences were compared with the SILVA SSU Ref NR 115 database (44). A customized 180
script was used to remove all non-bacterial OTUs from the OTU table. Calculation of 181
rarefaction curves, Chao1 index (45), and the Shannon index (46) was conducted using 182
QIIME. 183
We used two sample t-test analyses and M-W-U-Test for non-parametric data to compare 184
relative abundances of bacterial groups, diversity and richness estimates between soils 185
collected during dry and rainy season as well as between wastewater-irrigated and rain-fed 186
soils using software package PAST (47). To compare bacterial community composition 187
across all samples based on weighted UniFrac (48) measures, principal coordinate analysis 188
was performed by using QIIME. For determination of the phylogenetic metric (weighted 189
UniFrac), a phylogenetic tree was calculated using a PyNAST (49) alignment. This alignment 190
was produced by aligning a representative sequence set (one sequence from each OTU at a 191
genetic distance of 3%) to Greengenes core set “core_set_aligned.fasta” (41). 192
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Isolation of soil bacteria 194
100 mg soil per analyzed sample were suspended in 900 µl sodium pyrophosphate (7.5 mM 195
with 0.05% Tween 80) and subsequently the bacteria were detached from the soil particles 196
through shaking at 1000 rpm for 45 min (50). After 5 min settling serial dilutions of the 197
bacteria suspensions were transferred onto TSA plates and incubated 24 h at 22°C. Single 198
colonies were picked and purified via two passages on TSA plates. 199
200
DNA extraction from bacterial soil isolates 201
DNA extraction from bacterial soil isolates was performed using MasterPure Gram Positive 202
DNA purification kit (Biozym Scientific GmbH, Hess. Oldendorf, Germany) according to the 203
manufacturer’s instruction from 1 ml overnight culture in TSB incubated at 22°C. The 204
isolated DNA was applied to amplify the 16S rRNA gene and antibiotic resistance genes by 205
PCR. 206
207
Amplification and sequencing of 16S rRNA genes of soil isolates 208
For the amplification of the 16S rRNA gene, each 50-μl PCR reaction contained 2.5 U Taq 209
polymerase and 1 × PCR buffer S (Peqlab Biotechnologie GmbH, Erlangen, Germany), 0.2 210
μM of each primer (27F and 1492R, Table 2), 0.2 mM of each of the four deoxynucleoside 211
triphosphates, 2 mM MgCl2, and 20 ng template DNA (genomic DNA of bacterial isolates). 212
DNA amplifications were carried out in an Eppendorf thermocycler (Eppendorf Mastercycler 213
for 96-well plates, Eppendorf AG, Hamburg, Germany). The temperature profile consisted of 214
an initial denaturation step at 95ºC for 2 min followed by 30 cycles of denaturation at 95°C 215
for 30 s, primer annealing at 58°C for 45 s and extension at 72°C for 1 min, followed by an 216
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additional 7-min elongation step at 72°C. PCR products were sequenced with the primer set 217
63F and 1387R (Table 2 by Beckman Coulter Genomics (Takeley, UK). Sequences were 218
analyzed by blastn using the 16S ribosomal RNA sequences reference data base for Bacteria 219
and Archaea (http://blast.ncbi.nlm.nih.gov/Blast.cgi) (51). 220
221
Assessment of antibiotic resistance genes by PCR 222
PCR assays specific for sul (32) and qnr (33) resistance genes were performed as follows: 223
each 25-μl PCR reaction mixture contained 12.5 µl KAPA2G Fast ReadyMix with dye 224
(Peqlab Biotechnologie GmbH, Erlangen, Germany), 2–3 mM MgCl2 and 20 ng genomic 225
DNA of bacterial isolates. DNA amplifications were carried out in an Eppendorf thermocycler 226
(Eppendorf Mastercycler for 96-well plates, Eppendorf AG, Hamburg, Germany). The 227
temperature profile consisted of an initial denaturation step at 95°C for 2 min followed by 30 228
cycles of denaturation at 95°C for 30 s, primer annealing at 57°C for 45 s for qnr genes and 229
65°C for 30 s for sul genes and extension at 72°C for 1 min, followed by an additional 7-min 230
elongation step at 72°C (only for qnr genes). Primers used are listed in Table 2. Absolute 231
quantifications of sul1 and sul2 genes were performed with serial diluted exogenous standards 232
that consisted of purified PCR products. Quantification of absolute target gene numbers was 233
carried out using the Light-Cycler 480 (Roche Diagnostics, Mannheim, Germany) as 234
described in (27). 235
236
Antimicrobial susceptibility testing of bacterial isolates 237
Resistance of the bacterial isolates to specific antibiotics was determined by the disc 238
diffusion method according to CLSI guidelines (52) with the following antibiotic discs 239
(Oxoid, Wesel, Germany): ampicillin (25 µg), chloramphenicol (30 µg), erythromycin (10 240
µg), gentamicin (10 µg), kanamycin (30 µg), oxacillin (5 µg), streptomycin (25 µg), 241
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ciprofloxacin (5 and 10 µg), doxycycline (30 µg), tetracycline (30 µg), vancomycin (30 µg), 242
and sulfamethoxazole (25 µg). Single colonies of bacterial soil isolates were diluted according 243
to McFarland to an OD630 of 0.16 and streaked-out with swabs according to DIN 58940-244
3:2007-10. Instead of Mueller-Hinton agar, TSA plates were used and incubated 24 h at 22°C. 245
246
RESULTS AND DISCUSSION 247
Characteristics of wastewater-irrigated and rain-fed soils 248
Irrigation with untreated wastewater releases organic carbon compounds and other nutrients 249
into soils. More nutrients and a higher humidity over the entire year provide better growth 250
conditions for indigenous bacteria and possibly also for wastewater-derived bacteria and thus 251
might change the composition of soil bacterial communities. The organic matter content of 252
the analyzed soils increased during long-term irrigation with wastewater (Table 1). In rain-fed 253
soils TOC ranged from 0.91 to 1.53% whereas in wastewater-irrigated soils TOC ranged from 254
1.06 to 3.35%. The total nitrogen content in the soils varied from 0.05 to 0.15% (rain-fed) and 255
0.10 to 0.30% (wastewater-irrigated). The soil pH values varied between 6.7 and 7.4. Increase 256
of soil organic matter content through wastewater irrigation has also been reported by others 257
(53–58). This results in rising microbial biomass and microbial activity (53,59–61). 258
Furthermore, increased water supply by wastewater irrigation in the dry season seems to 259
provide better conditions for microbial proliferation (53). This might also increase the 260
survival rate of wastewater-derived bacteria. 261
262
General analysis of the pyrosequencing-derived dataset and overall bacterial diversity and 263
richness 264
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Pyrosequencing of partial 16 S rRNA genes (V2-V3 region) yielded a total number of 265
452,999 sequences across all analyzed soil samples (n=24). After preprocessing including 266
quality-filtering, denoising and removal of non-bacterial or chimeric reads, 337,493 267
sequences with an average length of 353 bp were obtained for further analyses (Table S1). 268
Due to the fact that the number of analyzed sequences per sample has an effect on the 269
predicted number of operational taxonomic units (OTUs), OTU-based comparisons between 270
the analyzed 24 soils were performed at the same level of surveying effort (11,320 sequences 271
per sample) (62). 272
Rarefaction curve, richness and diversity analyses were based on OTUs determined at 3 and 273
20% genetic distance. Comparison of the rarefaction analyses with the number of OTUs 274
calculated by Chao1 richness estimator revealed that 72.6 to 86.8% (20% genetic distance) 275
and 31.0 to 48.2% (3% genetic distance) of the estimated richness were covered by the 276
sequencing effort (Table S2 and Fig. 1). (The Chao 1 nonparametric richness estimator was 277
employed to calculate the estimated true OTU diversity of the samples). Thus, we did not 278
survey the full extent of diversity, but particularly at 20% genetic distance (phylum level 279
according to Schloss and Handelsman (63), a substantial fraction of the bacterial diversity was 280
assessed within individual soil samples. Dry season samples exhibited significantly higher 281
OTU numbers, Chao1 richness estimates and bacterial diversity as assessed by Shannon index 282
(H’) than rainy season samples (3% genetic distance: P < 0.001; 20% genetic distance: P < 283
0.05) (Table S2 and Fig. 1), likely due to the larger input of wastewater-derived bacteria 284
during the irrigation season. Wastewater irrigation had no statistically significant impact on 285
overall bacterial diversity and richness (see Table S2 and Fig. 1) which is in agreement with 286
the studies of Frenk et al. (8). 287
288
Community composition in wastewater-irrigated and rain-fed soils 289
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Bacterial 16S rRNA gene sequences were affiliated to 23 phyla (Table S3) and 17 candidate 290
divisions (Table S4). The dominant phyla and proteobacterial classes across all 24 soil 291
samples were Actinobacteria (27.4%), Alphaproteobacteria (14.6%), Acidobacteria (14.0%), 292
Betaproteobacteria (9.5%), Chloroflexi (9.3%), Gammaproteobacteria (8.9%), Firmicutes 293
(5.2%), Deltaproteobacteria (2.7%), Gemmatimonadetes (2.5%), and Planctomycetes (1.9%). 294
These phyla and proteobacterial classes are typically encountered in soil and were also 295
reported in similar relative abundance in a meta-analysis of 32 soil-derived bacterial 16S 296
rRNA gene libraries (64) and recent metagenomic as well as metatranscriptomic microbial 297
community analyses (65,66). 298
The relative abundance of bacterial phyla and proteobacterial classes varied between 299
wastewater-irrigated and rain-fed soils (Fig. 2). A shift of the bacterial community towards a 300
higher relative abundance of Gammaproteobacteria was observed in both seasons (dry and 301
rainy season) in the wastewater-irrigated soils compared to the rain-fed soils (P = 0.002). 302
With respect to rain-fed soil samples, 3.2 to 5.5% (rainy season) and 3.4 to 4.2% (dry season) 303
of the bacterial sequences were affiliated to Gammaproteobacteria, whereas relative 304
abundances of gammaproteobacterial sequences determined for wastewater-irrigated soils 305
ranged from 5.8 to 10.3% (rainy season) and 8.5% to 17.7% (dry season) (Table S3). 306
Strikingly, more potentially harmful Gammaproteobacteria were detected in wastewater-307
irrigated than in rain-fed soils (Fig. 3). Up to 196, 28 and 20 fold higher relative abundances 308
of Acinetobacter, Stenotrophomonas and Pseudomonas, respectively, were determined in 309
wastewater-irrigated soil compared to rain-fed soil during the dry season (Fig. 3). Species 310
within these genera such as Pseudomonas aeruginosa and Acinetobacter baumanii are 311
representatives of the so called ESKAPE (Enterococcus faecium, Staphylococcus aureus, 312
Klebsiella species, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter 313
species) organisms that are frequently causing nosocomial infections (67,68). They are of 314
main concern due to the high abundance of multi-resistances. Another emerging organism 315
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that causes nosocomial and community-acquired infections is S. maltophilia (69–72). This 316
bacterium can be associated with respiratory tract infections (71,72), especially in 317
hospitalized patients on mechanical ventilation. The high prevalence of Stenotrophomonas, 318
Pseudomonas and Acinetobacter in the wastewater-irrigated soils indicates an adaptation of 319
wastewater-associated bacteria to the soil environment. These bacteria serve as carriers of 320
multiresistances and likely increase the dissemination of these resistance determinants in the 321
environment and to other potentially more dangerous bacteria. The high increase of the 322
relative abundance of Acinetobacter, Stenotrophomonas and Pseudomonas in wastewater-323
irrigated soil as determined for the dry season (P < 0.05) was not detected using statistical 324
analysis in the rainy season (Fig. 3). This result might be related to increased input of 325
wastewater-derived bacteria by wastewater irrigation in the dry season. These findings are in 326
agreement with the studies of Frenk et al. demonstrating that the relative abundance of 327
Gammaproteobacteria increases in the irrigation season and decreases in the rainy season (8). 328
Like Gammaproteobacteria, Betaproteobacteria were more abundant in all dry season 329
wastewater-irrigated soil samples than in dry season rain-fed soils (P < 0.001) (Fig. 2). 330
However, no medically relevant betaproteobacterial species were detected. Principal 331
coordinate analysis at 3% genetic distance indicated that rain-fed soil samples harbor 332
similarity in overall bacterial community composition since they tend to cluster (Fig. 4). An 333
effect of season on overall bacterial community composition was not revealed. 334
335
Characterization of soil isolates 336
Bacterial isolates from soils irrigated with untreated wastewater for 100 years and from soils 337
which have only received rainwater, both from dry and rainy reason, were obtained by 338
incubation on TSA plates for 24 to 48 hours at 23°C. The incubation temperature was chosen 339
because it was the mean soil temperature at the Mezquital in the dry season. TSA is a rich 340
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medium and proved to be appropriate for isolation of diverse environmental bacteria as 341
already shown by e.g., Krishnamurthi and Chakrabarti for soil and by Yashiro et al. for 342
phyllosphere (73, 74); the predominantly isolated soil bacteria in their studies were members 343
of the phyla Firmicutes (most of them Bacillus spp.), followed by Actinobacteria and 344
Proteobacteria. In our study, most bacterial isolates from wastewater-irrigated soils (48 345
isolates from soil samples collected in the dry season and 48 isolates from soil samples 346
collected in the rainy season) belonged to the Bacilli (50%) and Gammaproteobacteria 347
(46%). Only 3% of the isolates belonged to Actinobacteria and 1% to the class of 348
Alphaproteobacteria (Fig. 5A and Table S6). The most abundant genera were Bacillus (47%) 349
and Stenotrophomonas (39%), followed by Pseudomonas (5%) and Acinetobacter (2%) (Fig. 350
5B). In rain-fed soils all of the 96 isolates (48 isolates from soil samples collected in the dry 351
season and 48 isolates from soil samples collected in the rainy season) belonged to the Bacilli 352
and within this class to the genus Bacillus (Table S5). Bacillus is ubiquitous in soil 353
environments (75,76) and can persist under a variety of conditions due to the ability to form 354
endospores (77,78). 355
The taxonomic groups detected (Proteobacteria, Actinobacteria and Bacilli) are typical taxa 356
found in agricultural soils. A rise in the available soil nutrients and moisture in wastewater-357
irrigated soils leads to an increase in Proteobacteria, particularly Gammaproteobacteria, 358
which is in agreement with previous studies (8,26). Consistent with the amplicon data, which 359
revealed a higher relative abundance of Gammaproteobacteria in wastewater-irrigated soils 360
compared to rain-fed soils, more isolates belonging to the Gammaproteobacteria 361
(Stenotrophomonas, Pseudomonas and Acinetobacter) were obtained from wastewater-362
irrigated soils than from rain-fed soils (46% of the isolates from wastewater-impacted soils vs. 363
no isolate from rain-fed soils). The fact that these microorganisms were derived from samples 364
collected in the dry as well as in the rainy season indicates that they have adapted to the 365
wastewater-irrigated soil environment. Some crops, maize and several herbs such as Rumex 366
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sp., Malva sp., and Chenopodium mexicanum that grow in wastewater irrigated fields are 367
consumed by the people in the Mezquital Valley. In particular, the near-ground herbs are in 368
direct contact with wastewater and wastewater-irrigated soils. This might imply health risks 369
for consumers of insufficiently washed crops containing wastewater-derived bacteria and 370
resistance determinants. 371
372
Prevalence of multi-antibiotic resistant isolates from wastewater-irrigated soils 373
All isolates from wastewater-irrigated soils and from rain-fed soils were tested for 374
susceptibility to 12 different antibiotics. In addition, the isolates that were resistant to 375
sulfamethoxazole (SMX) or ciprofloxacin (CIP) were analyzed for the presence of sul and qnr 376
resistance genes that encode resistance to sulfonamides and fluoroquinolones, respectively. 377
These genes were detected in total DNA of the chronosequence soils. The genes sul1, sul2, 378
qnrA, qnrB and qnrS were not found in total DNA of the isolates. For the qnr genes, this is 379
not surprising, as these genes were rarely found in the chronosequence soils (27). Resistance 380
to fluoroquinolones is often the result of point mutations in target genes such as gyrA 381
encoding DNA gyrase and parC encoding a type IV topoisomerase (79). Only three out of 96 382
isolates from wastewater-irrigated soils (one Stenotrophomonas, one Bacillus and one 383
Exiguobacterium isolate) and none of the 96 isolates from rain-fed soils were resistant to low 384
concentrations of the fluoroquinolone ciprofloxacin (5 µg) (Table S5 and Table S6). Several 385
isolates, 32 from wastewater-irrigated soils and 18 from rain-fed soils, were resistant to the 386
sulfonamide SMX (25 µg). Interestingly, a considerable number of isolates belonging to the 387
Bacillaceae were resistant to SMX, 18 of the 96 Bacilli isolates from rain-fed soils and 21 of 388
the 46 Bacilli isolates from wastewater-irrigated soils. 389
In other studies, SMX-resistant Bacilli were isolated from different environments such as 390
wastewater, water and sediments on selective plates containing between 50 and 200 µg/ml 391
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SMX. But even when isolated under selective pressure not all resistant isolates contained 392
sul1, sul2 or sul3 genes (80). Sulfonamide resistance can also occur by other mechanisms, 393
such as modification of the antibiotic target, e.g., by mutations of the chromosomal 394
dihydropteroate synthase gene (81). Sulfonamide resistance (often also in combination with 395
trimethoprim) has been described for several Bacillus species (82). From wastewater-irrigated 396
soils 33% of the isolates (n=96) were resistant to SMX. Twenty-one of them were Bacillus 397
spp., the remaining 11 belonged to the genera Stenotrophomonas, Pseudomonas and 398
Acinetobacter. In wastewater irrigation fields more isolates (51%) were resistant to at least 399
one antibiotic than in rain-fed soils (34%). In particular the presence of multi-resistant 400
bacteria (resistance to ≥ 2 antibiotics) was more pronounced in wastewater-irrigated soils 401
(25%) compared to rain-fed soils (6%) (Table S5 and Table S6). 402
In the present study, resistance to oxacillin, erythromycin, vancomycin and ampicillin was 403
frequently found in isolates from wastewater-irrigated soils. Other resistances were less 404
frequent (< 10%) and no isolate resistant to doxycycline was found (summarized in Fig. 6, 405
Table S5 and Table S6). The higher abundance of multi-resistant isolates from wastewater- 406
irrigated fields is likely related to the different types of bacteria isolated from the two 407
irrigation regimes. In wastewater-irrigated soils three isolates were resistant to three 408
antibiotics, and nine were resistant to more than three antibiotics. For the isolates from rain-409
fed soils only two isolates showed resistance to three different antibiotics, none to more than 410
three antibiotics. The majority of multi-resistant bacteria belonged to the genus 411
Stenotrophomonas (Table S6). 16S rRNA gene sequences of the Stenotrophomonas isolates 412
showed highest identities (96 to 99%) to S. maltophilia, an opportunistic bacterial pathogen 413
with environmental origin, that is associated with several human diseases (69,70,72). 414
Treatment proves difficult due to the species’ intrinsic antibiotic resistance (69). An increase 415
of the relative abundance of Stenotrophomonas spp. in soils which have been treated by 416
sulfadiazine-amended manure was observed by Ding et al. (83). 417
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For the clinically relevant strains, S. maltophilia and P. aeruginosa, several studies reported 418
that multiple antibiotic resistances are due to the overexpression of multidrug efflux pumps 419
(e.g., SmeDEF, MexA-MexB-OprM) (84,85). For several environmental Pseudomonas 420
isolates intrinsic multi-drug resistance has been reported. Malik and co-workers have 421
demonstrated high prevalence of antibiotic resistances of Pseudomonas isolates from water 422
and soil (5). They have shown that 87.5% of the Pseudomonas isolates from wastewater-423
irrigated soils were resistant to the sulfonamide sulphadiazine. Furthermore, they revealed 424
that isolates from groundwater-irrigated soils were less resistant to antibiotics than isolates 425
from wastewater-irrigated soils, which is consistent with our data (5). 426
Finally, our data reveal a higher prevalence of Gammaproteobacteria, in particular of harmful 427
and multi-resistant bacteria like S. maltophilia in wastewater-impacted soil. To the best of our 428
knowledge, this is the first report on high incidence of Stenotrophomonas spp. in wastewater-429
irrigated soils. Most of the bacterial isolates from wastewater irrigated soils were resistant to 430
several antibiotics (up to five different antibiotic classes). The higher incidence of multiple 431
antibiotic resistant bacteria in wastewater-impacted soils indicates survival of wastewater-432
derived bacteria in the environment and thus represents an increased risk of antibiotic 433
resistance dissemination in the environment. A major health issue is related to the observation 434
that near-ground crops that are in direct contact with soil and wastewater are consumed raw 435
by the people in the Mezquital Valley. 436
437
ACKNOWLEDGMENTS 438
We thank R. Brämer and A. Henninger from the University of Applied Sciences Offenburg 439
for support with the measurement of the chemical soil parameters. This work was supported 440
by grants GR1792/4-1 and GR1792/4-2 from the German Research Foundation and the 441
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Mexican Consejo Nacional de Ciencia y Tecnología (CONACYT): grants CB 83767 and I 442
0110-193-10. 443
444
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FIGURE LEGENDS 686
Figure 1: Rarefaction curves indicating the observed number of operational taxonomic units 687
(OTUs) at a genetic distance of 3 and 20% within the analyzed soil samples. Sample 688
abbreviations: RS0, rainy season rain-fed soil; RS10, rainy season, 10 years wastewater 689
irrigation; RS85, rainy season, 85 years wastewater irrigation; RS100, rainy season, 100 years 690
wastewater irrigation; DS0, dry season rain-fed soil; DS8, dry season, 8 years wastewater 691
irrigation; DS85, dry season, 85 years wastewater irrigation; DS100, dry season, 100 years 692
wastewater irrigation. Triplicates were analyzed (indicated by a, b and c). 693
694
Figure 2: Relative abundances of dominant phyla and proteobacterial classes determined for 695
the analyzed soil samples. Sample abbreviations: RS0, rainy season rain-fed soil; RS10, rainy 696
season, 10 years wastewater irrigation; RS85, rainy season, 85 years wastewater irrigation; 697
RS100, rainy season, 100 years wastewater irrigation; DS0, dry season rain-fed soil; DS8, dry 698
season, 8 years wastewater irrigation; DS85, dry season, 85 years wastewater irrigation; 699
DS100, dry season, 100 years wastewater irrigation. Analysis of triplicates has been 700
illustrated using error bars. 701
702
Figure 3: Heatmap showing relative abundances of gammaproteobacterial genera as affected 703
by wastewater irrigation during dry as well as rainy season. Triplicates were analyzed 704
(indicated by a, b and c). 705
706
Figure 4: Weighted UniFrac 2D Principal Coordinate Analysis plot for beta diversity 707
analysis. Sample abbreviations: RS0, rainy season rain-fed soil; RS10, rainy season, 10 years 708
wastewater irrigation; RS85, rainy season, 85 years wastewater irrigation; RS100, rainy 709
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30
season, 100 years wastewater irrigation; DS0, dry season rain-fed soil; DS8, dry season, 8 710
years wastewater irrigation; DS85, dry season, 85 years wastewater irrigation; DS100, dry 711
season, 100 years wastewater irrigation. 712
713
Figure 5: A: Abundance of bacterial classes in isolates from wastewater-irrigated soils, 714
B: Abundance of different bacterial genera in isolates from wastewater-irrigated soils. 715
716
Figure 6: Percentage of antibiotic resistant isolates from wastewater-irrigated soils and rain-717
fed soils. (CIP: Ciprofloxacin (5 µg), Kana: Kanamycin (30 µg), SMX: Sulfamethoxazole (25 718
µg), Tet: Tetracycline (30 µg), Doxy: Doxycycline (30 µg), Gm: Gentamycin (10 µg), Amp: 719
Ampicillin (25 µg), Sm: Streptomycin (25 µg), Oxa: Oxacillin (5 µg), Cm: Chloramphenicol 720
(30 µg), Van: Vancomycin (30 µg), Em: Erythromycin (10 µg)); ww: wastewater. 721
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Table 1: Characteristics of the analyzed soil samples.
Sample-
ID
Irrigation
time
[years]
season pH TOC % TC % TN % C/N
DS0 0 dry 6.3 0.91 0.95 0.05 17.8
RS0 0 rainy 7.3a 1.53a 1.62a 0.15a 10.8
DS8 8 dry 6.7 1.16 1.21 0.10 12.4
RS10 10 rainy 8.2 1.84 2.56 0.18 14.2
DS85 85 dry 6.7 2.06 2.15 0.19 11.3
RS85 85 rainy 6.4 2.26 2.30 0.29 7.9
DS100 100 dry 6.9 3.15 3.25 0.30 10.9
RS100 100 rainy 7.4a 2.43a 2.56a 0.25a 10.2
a: data from (29). (TOC, TC and TN measured according to (30)).
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Table 2: Primer sets used in this study.
Target Amplicon Oligonucleotide Sequence (5´ to 3´) Ta Reference
sul1 158 sul1-FW CACCGGAAACATCGCTGCA 65 (32)
sul1-RV AAGTTCCGCCGCAAGGCT
sul2 190 sul2-FW CTCCGATGGAGGCCGGTAT 65 (32)
sul2-RV GGGAATGCCATCTGCCTTGA
qnrA 543 qnrA-F GATAAAGTTTTTCAGCAAGAGG 56 (33)
qnrA-R ATCCAGATCGGCAAAGGTTA
qnrB 497 qnrB-F AGCGGCACTGAATTTAT 56 (33)
qnrB-R GTTTGCTGCTCGCCAGTC
qnrS 600 qnrS-F GGAAACCTACAATCATACATA 56 (33)
qnrS-R GTCAGGATAAACAACAATACC
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Bacteria 1465 27F GAGTTTGATCMTGGCTCAG 58 (34)
16S rDNA 1492R GGYTACCTTGTTACGACTT
Bacteria 1324 63fw CAGGCCTAACACATGCAAGTC 56 (35)
16S rDNA 1387rev GGGCGGWGTGTACAAGGC
Bacteria 414 101Fb CCTATCCCCTGTGTGCCTTGGCAGTCTCAG
AGTGGCGGACGGGTGAGTAA
69 (36, 37)
16S rDNAa 515Rc CCATCTCATCCCTGCGTGTCTCCGACTCAG-
MID-CCGCGGCTGCTGGCAC
Ta: annealing temperature; Y: C or T; a: primers for pyrosequencing; b: Roche 454 pyrosequencing adaptor B is underlined;
c: Roche 454 pyrosequencing adaptor A is underlined; MID, sample-specific Extended Multiplex Identifier (size: ten nucleotides).
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