Latitudinal Distribution of Ammonia-Oxidizing Bacteria and Archaea ...

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Latitudinal Distribution of Ammonia-Oxidizing Bacteria and Archaea in the 1

Agricultural Soils of Eastern China 2

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Running title: AOB and AOA in agricultural soils of eastern China 5

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Hongchen Jiang1^*, Liuqin Huang2^, Ye Deng3^, Shang Wang2, Yu Zhou4, Li Liu5, 8

and Hailiang Dong1,6* 9

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1. State Key Laboratory of Biogeology and Environmental Microbiology, China 11

University of Geosciences, Wuhan, 430074, China 12

2. State Key Laboratory of Biogeology and Environmental Microbiology, China 13

University of Geosciences, Beijing, 100083, China 14

3. Institute for Environmental Genomics and Department of Botany and 15

Microbiology, University of Oklahoma, Norman, OK 73019, USA; 16

4. Institute of Quality Standards for Agricultural Products, Zhejiang Academy of 17

Agricultural Sciences, Hangzhou, 310021, China 18

5. Information Center, Institute of microbiology, Chinese academy of sciences, 19

Beijing, 100101, China 20

6. Department of Geology and Environmental Earth Science, Miami University, 21

Oxford, OH45056, USA 22

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^These authors contributed equally 24

*Corresponding authors: 25

Hongchen Jiang: [email protected] or [email protected]; Tel: 26

86-27-67883452 27

Hailiang Dong: [email protected] or [email protected] 28

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May. 15, 2014 31

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Rvised for Applied and Environmental Microbiology 34

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AEM Accepts, published online ahead of print on 7 July 2014Appl. Environ. Microbiol. doi:10.1128/AEM.01617-14Copyright © 2014, American Society for Microbiology. All Rights Reserved.

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Abstract 36

The response of soil ammonia-oxidizing bacterial (AOB) and archaeal (AOA) 37

communities to individual environmental variables (e.g., pH, temperature, carbon- 38

and nitrogen-related soil nutrients) has been extensively studied, but how these 39

environmental conditions collectively shape AOB and AOA distributions in 40

unmanaged agricultural soils across a large latitudinal gradient remains poorly 41

known. In this study, the AOB and AOA community structure and diversity in 26 42

agricultural soils collected from eastern China were investigated by using 43

quantitative polymerase chain reaction and barcoded 454-pyrosequencing of the 44

amoA gene that encodes the alpha subunit of ammonia monooxygenase. The 45

sampling locations span over a 17o latitude gradient and cover a range of climatic 46

conditions. The Nitrosospira and Nitrososphaera were the dominant clusters of AOB 47

and AOA, respectively; but the subcluster-level composition of Nitrosospira-related 48

AOB and Nitrososphaera-related AOA varied across the latitudinal gradient. 49

Variance partitioning analysis showed that geography and climatic conditions (e.g. 50

mean annual temperature and precipitation) as well as carbon-/nitrogen-related soil 51

nutrients contributed more to the AOB and AOA community variations (~50% in 52

total) than soil pH (~10% in total). These results are important in furthering our 53

understanding of environmental conditions influencing AOB and AOA community 54

structure across a range of environmental gradients. 55

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INTRODUCTION 57

The eastern part of China is under East Asian monsoon climate. This region spans a 58

latitude gradient of 33o and covers a wide range of monsoonal climate zones, from 59

north to south by order, including mid temperate, warm temperate, northern 60

subtropic, mid subtropic, southern subtropic, and tropic zones (Fig. 1). Due to 61

variations in rainfall, solar irradiance, and mineralogical composition of parent rocks, 62

different types of soils are formed under these climatic conditions (64). The arable 63

lands in eastern China are responsible for more than 70% of gross grain yield within 64

China (43). In this region agricultural production relies heavily on inorganic 65

fertilizers (e.g. urea, N substrates) to sustain crop production for decades (45). 66

Ammonia-oxidizing bacteria (AOB) and archaea (AOA) are two main groups of 67

microorganisms affecting the utilization efficiency of inorganic nitrogen fertilizers 68

(59). Both AOB and AOA possess amoA genes encoding the alpha subunit of 69

ammonia monooxygenase. amoA gene-based phylogenetic analyses have shown that 70

AOB and AOA are widely distributed in various environments (58). 71

Nitrosospira and Nitrosomonas of the Betaproteobacteria are two dominant 72

genera of terrestrial AOB (34). The Nitrosospira-related AOB are abundant in 73

different soils and they can be grouped into clusters 0, 1, 2, 3a, 3b, 4, 9, 10, 11, and 74

12 (2, 3). The Nitrosomonas-related AOB are grouped into clusters 5, 6a, 6b, 7, and 75

8 (19), which are frequently found in freshwater environments (36), (hyper)saline 76

lakes (28, 32), estuarine environments (5, 51), sewage sludge (4, 62, 71), sediments 77

(19, 61), and flooded soils (13). Various environmental factors such as vegetation 78

type, nutrient level, microclimate (fertilizer, temperature, pH, water content, 79

elevation), and management practice can affect AOB distribution in soils (18). 80

All known AOA fall into the archaeal phylum Thaumarchaeota (47, 57). They 81

can be classified into Nitrosopumilus, Nitrososphaera, Nitrosocaldus, Nitrosotalea, 82

and Nitrososphaera sister cluster (46). The Nitrosocaldus cluster mainly consists of 83

sequences from hot springs, whereas Nitrosopumilus, Nitrosotalea, Nitrososphaera, 84


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and Nitrososphaera clusters are present in various environmental habitats (46). The 85

distributions of AOA in these environments are influenced by multiple 86

environmental conditions such as ammonium, dissolved oxygen, and organic carbon 87

levels, salinity, temperature, and pH (6, 8, 17, 22). 88

Investigations of the AOA and AOB distributions and their response to 89

environmental conditions are of great significance to the understanding of 90

bioavailability and microbial transformation of nitrogen nutrients in agricultural soils. 91

Previously, some amoA gene-based studies have been performed in manipulated 92

Chinese agricultural soils (using either experimental stations or artificially managed 93

soils) collected from the northern subtropic zone of central China (11-13, 23, 54, 65, 94

68). These studies showed that pH was a major factor affecting AOB and AOA 95

distributions: Nitrosospira clusters 10, 11 and 12 were the dominant AOB groups in 96

acidic soils, while Nitrosospira clusters 3a and Nitrosomonas clusters 6 and 7 were 97

dominant in alkaline and neutral soils; for AOA communities, Nitrosopumilus and 98

Nitrososphaera clusters were dominant AOA populations and their relative 99

abundances showed negative and positive correlations with soil pH, respectively 100

(53). 101

In addition to pH, climatic conditions (i.e. temperature and precipitation) and 102

soil properties have been shown to affect soil microbial communities under 103

controlled conditions (15, 20, 56), but their effects on AOB and AOA are generally 104

poorly known. Fierer and colleagues (18) showed that the AOB compositions in the 105

soils across North America were strongly influenced by temperature, which was 106

related to geographical locations, suggesting that biogeography controlled the AOB 107

distribution. However, this study did not evaluate the relative importance of 108

temperature and other environmental conditions in affecting AOB and AOA 109

abundance and distribution in agricultural soils. 110

The objective of this study was to quantitatively assess the relative importance 111

of multiple environmental conditions in shaping AOA and AOB community 112

structures in Chinese agricultural soils. We collected 26 agricultural soil samples 113


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from three different climatic zones from south to north in eastern China. AOB and 114

AOA abundance and diversity were investigated using amoA gene-based quantitative 115

polymerase chain reactions (qPCR) and bar-coded 454 sequencing techniques, 116

respectively. Statistical analyses were performed to assess correlation between the 117

AOB and AOA communities and environmental variables (e.g. geographic distance, 118

mean annual temperature, mean annual precipitation, pH, and soil nutrients). These 119

results are important to our understanding of the role of the microorganisms in 120

transforming nitrogen nutrients in agricultural soils. 121

MATERIALS AND METHODS 122

Site description and sampling. Soil samples were collected from arable lands 123

across six provinces of different climatic zones in Eastern China (see for details 124

Table S1). From north to south, the six provinces are Heilongjiang (in abbr. HLJ), 125

Jilin (in abbr. JL), Hebei (in abbr. HB), Shandong (in abbr. SD), Anhui (in abbr. AH), 126

and Jiangxi (in abbr. JX) (Fig. 1). Mid temperate climate dominates HLJ and JL 127

Provinces. The mean annual temperature (MAT) is 3.5-3.9 oC, and the mean annual 128

precipitation (MAP) 480-700 mm. In HB and SD provinces, warm temperate 129

prevails, and the MAT and MAP are 12.1-13.8 oC and ~620 mm, respectively. AH 130

and JX provinces are dominated by northern subtropic climate, and the MAT and 131

MAP are 15.5-17.2oC and ~800-1850 mm, respectively (Table S1). Due to different 132

climate and parent rock materials, different soil types developed in these provinces. 133

Black soil, dark brown soil, medium loam soil, dark brown soil, paddy soil, and red 134

soil are major soil types in HLJ, JL, HB, SD, AH, and JX Provinces, respectively. 135

These diverse soil types support many different crops (Table S1). 136

Field sampling was performed in October 2010 after crops were harvested. In 137

each of the provinces described above, soil samples cultivated with different crops 138

were collected (Table S1). At each chosen site, three separate soil subsamples (20 m 139

apart from each other) were taken at the 5-10 cm depth with sterile spatulas and 140

spoons. In the field, the three subsamples from each site were homogenized in a 141

pre-sterilized aluminum pan and were placed into 50 mL polypropylene tubes. The 142


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composite soil from one site was treated as one sample hereinafter, and all the 143

downstream analyses were performed on the composite samples. Sample tubes were 144

immediately stored in dry ice in the field and during transportation and then were 145

transferred to a -80oC freezer in the laboratory until further analysis. 146

Soil chemicals analysis. Soil pH was determined on a 1:2 soil: 0.01M CaCl2 147

suspension with a pH electrode (model 704 Mefrohm) (67). Contents of total organic 148

carbon (TOC), total nitrogen (TN), ammonium nitrogen (NH4+-N), nitrite nitrogen 149

(NO2--N), and nitrate-nitrogen (NO3

--N) were determined according to the 150

established methods (67). Microbial carbon (Micro-C) concentration was determined 151

by using a fumigation-incubation method described previously (63). 152

DNA extraction. In order to avoid DNA extraction bias, DNA was extracted in 153

triplicate from each composite soil sample using FastDNA SPIN Kit for Soil (MP 154

Biomedicals, LLC, Ohio, USA) according to the manufacturer’s protocol. DNA 155

quantity and quality were assessed by using a Nanodrops® ND-1000 UV-Vis 156

Spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The resulting 157

three DNA extractions per sample were pooled before further analysis. 158

Quantitative PCR (qPCR) of amoA and 16S rRNA genes. qPCR was 159

performed to determine the abundances of the bacterial and archaeal amoA and 16S 160

rRNA genes in the investigated samples according to Jiang et al. (31, 32). Briefly, 161

the primer sets of amoA-1F/amoA-2R, Arch-amoAF/Arch-amoAR, and 162

Bac331F/Bac797R and Arch349F/Arch806R were used for qPCR of AOB/AOA 163

amoA and bacterial/archaeal 16S rRNA genes, respectively (Table S2). qPCRs were 164

performed in a reaction volume of 20 µL, containing10μL of 2×SYBR® Premix Ex 165

TaqTM(Takara, Japan), 0.2μM of each primer, 0.4μL of ROX Reference Dye II (50×), 166

20μg of Bovine Serum Albumin(Takara,), and 1μL of soil DNA. qPCRs were 167

performed in triplicate on an ABI7500 real-time PCR system (Applied Biosystems, 168

Carlsbad, CA, USA). ddH2O was used as a negative control template. Purified 169

plasmids of AOB and AOA amoA and bacterial and archaeal 16S rRNA gene clones 170

from one soil sample were used as standard templates. The clone libraries of AOB 171


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and AOA amoA, and archaeal and bacteria 16S rRNA genes were constructed with 172

PCR products derived from the primer sets of amoA-1F/amoA-2R, 173

Arch-amoAF/Arch-amoAR, Arch21F/ Univ1492R, and Bac27F/ Univ1492R, 174

respectively (Table S2). Clone library construction and plasmids DNA extraction 175

were performed according to the procedures described elsewhere (31). Standard 176

curves were obtained by using serial dilutions of plasmids (pGEM-T) containing 177

cloned amoA and 16S rRNA genes. The data were used to create standard curves 178

correlating the CT values with the amoA gene copy numbers. Linear plots (not shown) 179

between the Ct value and log(copy numbers/reaction) for the AOB and AOA amoA 180

and 16S rRNA genes were obtained with correlation coefficients of R2>0.99. Melting 181

curve analysis was performed to determine the melting point of the amplification 182

products and to assess the reaction specificity. After the qPCR reaction was complete, 183

temperature ramped from 72 oC to 95 oC, rising by 0.1 degree per step, waiting for 45 184

s on the first step, and then 5 s for each subsequent step . The melting curve of each 185

run had only one peak, indicative of specific PCR amplification. The qPCR 186

amplification efficiencies were in the range of 96–100%. 187

PCR amplification of AOB and AOA amoA genes. The AOB and AOA amoA 188

genes were amplified with the primer sets of amoA-1F/amoA-2R and 189

Arch-amoAF/Arch-amoAR, respectively (Table S1). To pool multiple samples for 190

one run of 454 pyro-sequencing, a sample tagging approach was used (7). Special 191

5’-end tagged primers for each sample were designed by combining primers with 192

adaptors (‘TC’ and ‘CA’ for forward and reverse primers, respectively) and a unique 193

8-mer tag (i.e. barcodes)(7). The PCR amplification procedure was as follows: an 194

initial denaturation at 94oC for 5 min, 30 cycles of denaturation at 94oC for 30s, 195

annealing at 58oC for 40s and, extension at 72oC for 40s and 60s (for AOB and AOA 196

amoA genes, respectively), followed by a final extension step at 72oC for 10 min. 197

To obtain enough amplicons for pyrosequencing, four PCRs were run for each 198

sample. Each 25 μlPCR mix contained 10×PCR buffer (Mg2+ plus) (1× diluted; 199

Takara, Japan), dNTP Mixture (0.2 mM each; Takara), primers (0.4 μM each), 200


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TaKaRa Taq DNA polymerase (1.5 U), bovine serum albumin (20μg; Takara) and 1 201

μl template DNA. Reactions without template DNA were performed as negative 202

controls in parallel for each sample. Amplicons from four PCRs were pooled for 203

each sample. The PCR products were checked by agarose gel electrophoresis and 204

correct bands were incised. The incised PCR gels were then purified by using 205

AxyPrep DNA Gel extraction kit (Axygen Biosciences, Union City,CA, USA) 206

according to the manufacturer’s protocol. The quantity and quality of thepurified 207

PCR products were assessed by using a Nanodrops® ND-1000 UV-Vis 208

Spectrophotometer. Finally, the purified PCR amplicons were pooled in an 209

equimolar concentration for further 454 pyrosequencing. 210

Pyrosequencingand data analysis. The pyrosequencing was commercially 211

performed ona GS FLX sequencer (454 Life Sciences, USA) at the Chinese National 212

Human Genome Center in Shanghai.The obtained sequences were extracted, 213

trimmed, quality-screened, and aligned with the use of the Mothur 2.25.0 214

pipeline(52). Sequencing reads were assigned to each sample according to their 215

unique barcodes, low quality sequences (quality score < 25, length <150 bp, 216

ambiguous bases≥1, homopolymer≥6) were removed.The sequences from the 217

reverse primer (amoA-2R and Arch-amoAR for AOB and AOA, respectively)-end of 218

the amplicons were used for downstream data analysis. A total of 28211 and 219

97134high-quality raw sequence reads were obtained for AOB and AOA, 220

respectively. 221

The obtained high-quality raw sequences were clustered into operational 222

taxonomic units (OTUs) based on 97% sequence identity via the Qiime (quantitative 223

insights into microbial ecology) pipeline (9, 35). To minimize potential deviant 224

genotypes, OTU reads with relative abundance less than 0.5% were defined as rare 225

sequences and were removed from downstream analysis. The remaining reads were 226

checked against a local amoA gene database (downloaded from the Ribosomal 227

Database Project FunGene: http://fungene.cme.msu.edu/index.spron November 27th 228

2011) and the NCBI database (http://blast.ncbi.nlm.nih.gov/Blast.cgi), and then any 229


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chimera sequences (e.g., no hit in the database) were eliminated. The remaining 230

quality-screened sequence reads were deposited into the NCBI sequencing read 231

archive under accession number SRA082064. 232

One sequence read was randomly chosen as a representative sequence from each 233

OTU with the use of the Qiime scripts. Phylogenetic assignment of AOB and AOA 234

was made possible by aligning individual OTU sequences with those in the reference 235

databases of AOB (2, 12, 30, 55, 70) and AOA (46), respectively. The cluster 236

nomenclatures from the above references were adopted for the AOB and AOA amoA 237

gene sequences, respectively. 238

Statistical analyses. In order to calculate Chao1 (predicted number of OTUs), 239

Shannon, and evenness indices, three AOB amoA gene libraries with fewer than 240 240

reads were removed. The AOA and remaining AOB libraries were normalized to 610 241

and 240 reads (the number of the smallest libraries), respectively. Chao1 (predicted 242

number of OTUs), Shannon, and evenness indices were calculated at the 97% cutoff 243

level. Coverage was calculated as the ratio of the observed OTUs to Chao1 (27), and 244

this index was used to evaluate the sequencing depth. 245

Pearson correlation was performed to test any associations between the AOA 246

and AOB amoA gene abundances and the environmental variables. Significance 247

levels were adjusted by using the Holm correction for each environmental variable 248

(26). Any significance levels lower than 0.05 were chosen (25). Mantel tests were 249

performed to assess any correlation between AOA and AOB community structures 250

and the measured environmental variables (24). The measured environmental 251

variables were first normalized to zero means and unit standard deviations. 252

Euclidean distances were then calculated and used to construct dissimilarity matrices 253

of communities and environmental variables. All the analyses were performed by 254

functions in the vegan package (v.1.15-1) in the R software (16). 255

UPGMA clustering was performed to compare similarity of AOB (or AOA) 256

communities among the samples based on the Bray-Curtis matrix. Analysis of 257

similarity (ANOSIM) was performed to further confirm any significant similarity in 258

community composition between the samples as identified by the UPGMA 259


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clustering. SIMPER (similarity percentage) analysis was performed to rank the 260

relative contribution of AOB and AOA clusters or subclusters to the differences 261

revealed by the UPGMA cluster analysis. 262

To determine the relative importance of the geographic distance and the 263

measured environmental factors in shaping AOA and AOB communities, a canonical 264

correspondence analysis (CCA)-based variation partitioning analysis (VPA) was 265

implemented according to the methods described previously(72). First, a spatial 266

decomposition method, principal coordinates of neighbor matrices (PCNM), was 267

applied to the geographic coordinates of the samples (50). This method separates 268

sample geographic coordinates into multiple spatial variables. Then for each 269

community dataset, CCA test with 1,000 permutations was used to select the 270

significant (p<0.05) spatial variables, which were then retained for VPA. All these 271

analyses were carried out using the functions in the vegan package in the R software 272

(16). 273

RESULTS 274

Soil geochemical characteristics. Mean annual temperature and precipitation 275

(MAT & MAP) of the sampling sites varied latitudinally from north to south by 276

order: MAT ranged from 3.5 oC in HLJ (Heilongjiang Province) to 17.5 oC in JX 277

(Jiangxi Province); and MAP ranged from 481.5 mm in HLJ (Heilongjiang Province) 278

to 1852.1 mm in JX (Jiangxi Province). The contents of TOC, microbial carbon, and 279

total nitrogen in the samples of HLJ and JL (Jilin Province) were much higher than 280

most soil samples from HB (Hebei Province), SD (Shandong Province), AH (Anhui 281

Province) and JX. However, the contents of NH4-N, NO3-N, and NO2-N did not vary 282

significantly among the investigated samples (Table S2). In addition, the HB and SD 283

soils were slightly alkaline (pH 8.5-9.0), whereas those from other provinces were 284

slightly acidic to circumneutral (pH 5.9-7.7) (Table S2). 285

Quantification of archaeal and bacterial 16S rRNA and AOA and AOB 286

amoA genes. The archaeal and bacterial 16S rRNA gene abundances were 287


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2.9×107-4.5×108 and 1.5×108-7.5×109 copies per gram of the soils (dry weight), 288

respectively. The AOA and AOB amoA gene abundances were 2.9×106-9.9×107 and 289

1.2×106-4.0×107 copies per gram of the soils (dry weight), respectively. The ratio of 290

AOA amoA gene abundance to archaeal 16S rRNA gene abundance ranged from 291

0.006 to 0.735, whereas the ratio of AOB to bacterial 16S rRNA gene abundance 292

ranged from <0.001 to 0.067 (Table S3). The ratio of AOA amoA gene to AOB was 293

much greater than 1 (1.1-85.6) with the exception of the JL3 sample (0.3) (Fig. 2). 294

The Pearson correlation analysis showed that the AOA abundance was significantly 295

(P<0.05) correlated with climatic (e.g. altitude, MAT, MAP) and nutritional 296

(Micro-C and NH4-N) factors; in contrast, the AOB abundance did not show any 297

significant correlation with the measured environmental conditions (Table S4). 298

Diversity of AOB amoA gene and its correlation with environmental 299

conditions. A total of 23,451 AOB amoA gene sequence reads remained for 23 soil 300

samples (three samples that had fewer than 240 sequence reads were removed) after 301

removal of low-quality, chimeric, and low-abundance (<0.5% in all samples) OTU 302

sequences. These sequence reads were clustered into 20-77 observed and 21-98 303

predicted OTUs (based on Chao 1) at the 97% cutoff with coverage values ranging 304

from 77.3% to 100% (Table S5). The obtained AOB amoA gene OTUs could be 305

grouped into Nitrosospira (Clusters 1, 2, 3, 4, 9, 10, 11, and 12), Nitrosomonas 306

(Clusters 6 and 8), and Nitrosovibrio clusters with Nitrosospira being the dominant 307

(94.3%, 22122 out of 23451) (Fig. 3A). 308

Based on the Bray-Curtis similarity matrix, the amoA gene libraries were 309

classified into five significantly (ANOSIM, R-statistic >0.71, p <0.01) different 310

groups, and these groups were approximately distributed according to geographic 311

location (Fig. 3A). Group B1 included all SD and HB samples from central-eastern 312

China, and all sequences were mainly affiliated with Nitrosospira Cluster 3, within 313

which only 31 OTUs were shared between the SD and HB samples (accounting for 314

~40% and 25% of the Cluster 3 OTUs for the SD and HB samples, respectively) (Fig. 315

S1). This analysis indicated that even though both SD and HB samples were 316


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dominated by Cluster 3, the composition of Cluster 3 was very different in SD and 317

HB samples. Group B2 contained samples from JL (JL2, JL4) and HLJ (HLJ2, HLJ3, 318

and HLJ4) from northeastern China, and the sequences were predominantly affiliated 319

with Nitrosospira Cluster 3 and 9. AH2, AH3, JX1, JX2, and JX5 samples from 320

southeastern China constituted Group B3, which was mainly composed of 321

Nitrosospira Cluster 3, 4, 8, 10 and 11. Unlike the previous three groups, Group B4 322

was somewhat more diverse, and included samples from northeastern China (HLJ1 323

and JL3) and southeastern China (AH1 and AH5 samples), and the dominant AOB 324

were Nitrosospira Cluster 1, 2, 3, and 4. Two samples from JX (JX1 and JX4) 325

formed Group B5, but this small group was fairly diverse, consisting of Nitrosospira 326

Cluster 3, 10, 11, and 12 (Fig. 3A). 327

Among the five groups (B1-B5), some Nitrosospira clusters were present in 328

multiple groups but their compositions were greatly different among the groups. For 329

example, Cluster 3 was a major component in B1, B2 and B3 and was present in 330

some samples of the B4 and B5 groups (Fig. 3A). However, only 16 OTUs were 331

shared between the B1 and B2 groups (accounting for ~8% and ~21% of the OTUs 332

within Cluster 3, respectively) (Fig. S2); The samples in the B2 and B3 groups 333

shared 14 OTUs, accounting for ~18% and ~33% of the total Cluster 3 OTUs in 334

these two groups, respectively. The B1 and B3 groups shared 13 OTUs, which 335

accounted for ~7% and 3% of the Cluster 3 OTUs in these two groups (Fig. S2). 336

The CCA results showed that the AOB community structures were correlated 337

with soil pH, NO2--N and micro-C for the HLJ, JL, HB and SD samples, but NO3

--N, 338

NH4+-N and geography-related environmental variables (MAP, PCNM1, PCNM13, 339

and PCNM14) were important factors for the AH and JX samples (Fig. 4A). More 340

than a half (54.1%) of the observed variations in the AOB community structures (at 341

the OTU level) could be explained by the measured environmental variables, among 342

which the Grp3 of variables (e.g. TOC, Micro-C, Micro-C/TOC, Total N, NH4-N, 343

NO3-N, NO2-N) were more important (~30%, including direct and indirect) than the 344

other two groups of environmental variables (soil pH, geographic distance, MAT, 345


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MAP) (Fig. 5A). 346

Diversity of AOA amoA gene and its correlation with environmental factors. 347

A total of 85930 AOA amoA gene sequence reads remained for the 26 soil samples 348

after removal of low-quality, chimeric and low-abundance (<0.5% in all samples) 349

OTU sequences. The remaining sequence reads were clustered into 7-77 observed 350

and 8-86 predicted OTUs (based on Chao 1) at the 97% cutoff with coverage values 351

ranging from 83.7% to 99.3% (Table S5). The AOA amoA gene OTUs could be 352

grouped into Nitrososphaera (including subclusters 1.1a and 1.1b, subclusters 2-11), 353

Nitrosopulimus, Nitrosotalea subcluster1, and two unclassified clusters with 354

Nitrososphaera being the predominant (84.3%, 72047 out of 85930) (Fig. 3B). 355

Based on the Bray-Curtis similarity matrix, the AOA amoA gene libraries could 356

be classified into five significantly (ANOSIM, R-statistic >0.63, p <0.009) different 357

groups, again approximately according to geographic location (Fig. 3B). Similar to 358

Group B1 of AOB, all the SD and HB samples formed a tight distinct cluster that 359

was composed of Nitrososphaera subclusters 1.1a and 4. Within Group A1, the SD 360

and HB samples shared 21 OTUs, accounting for ~60% and ~40% of the SD and HB 361

OTUs that were affiliated with Nitrososphaera subcluster 4, respectively) (Fig. S3). 362

Similar to Group B2 of AOB, Group A2 consisted of all HLJ samples, some JL 363

samples (JL3 & JL4) and one AH sample. The sequences in this group were diverse 364

and mainly affiliated with Nitrososphaera subclusters 1.1b, 4, 5, 7 and 8, and 9. 365

Within Group A2, the HLJ and two JL samples (JL3&JL4) shared five OTUs, 366

accounting for ~24% and ~28% of the HLJ and JL OTUs that were affiliated with 367

Nitrososphaera subcluster 9 (Fig. S3). Group A3 consisted of three AH samples 368

(AH2, AH3, and AH5), which were mainly composed of Nitrososphaera subclusters 369

3, 8 and 9. Again similar to Group B4, Group A4 consisted of samples from JL (JL1, 370

JL2) and AH (AH1) but no HLJ samples, and the sequences in this group were 371

mainly composed of Nitrososphaera subclusters1.1a, 4, and 9, and Nitrosotalea 372

subclusters 1. Similar to Group B5, the JX3, JX4, and JX5 samples formed Group 373

A5, and they were mainly composed of Nitrososphaera subclusters 1.1b, 3, 7 and 8 374


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(Fig. 3B). Similar to the AOB groups, some AOA groups (A1-A5) were dominated 375

by the same Nitrososphaera subclusters, but the OTU compositions of these 376

subclusters were greatly different among the groups. For example, the 377

Nitrososphaera subcluster 8 only had one OTU in common (10%) between the A2 378

and A3 groups (Fig. S4). 379

The CCA results showed that the AOA community structures were correlated 380

with soil pH and NO2--N for the HB, SD, JL and JX samples, but with NH4

+-N and 381

geography-related environmental variables (MAP and PCNM1) for the AH and JX 382

samples (Fig. 4A). Approximately one half (50.2%) of the observed variations in the 383

AOA community structures (at the OTU level) could be explained by the measured 384

environmental variables, among which the Grp3 of variables (e.g. TOC, Micro-C, 385

Micro-C/TOC, Total N, NH4-N, NO3-N, NO2-N) contributed the most (~43.5%, 386

including direct and indirect) to the variation of AOA distribution patterns (Fig. 5B). 387

388

DISCUSSION 389

Predominance of AOA over AOB amoA genes in the eastern Chinese 390

agricultural soils. The predominance of AOA over AOB in the investigated soils 391

was consistent with previous observations in various agriculture soils (23, 38, 53). 392

The ratio of AOA to AOB amoA gene abundance was not correlated with latitude, 393

MAT and MAP, suggesting latitudinal variations (i.e. climatic change) might not be 394

important factors influencing the relative abundance between AOA and AOB in 395

agricultural soils. This was in contrast with one previous observation in North 396

American arid lands: AOA were more abundant than AOB in the warmer, more 397

southern deserts, but the relative abundance between AOA and AOB is reversed in 398

the samples from colder, more northern deserts (42). Such inconsistency may be 399

ascribed to the difference of sample types: non-agricultural desert soils (42) vs. 400

mature agricultural soils (this study). 401

Correlation between AOB and AOA community structure and 402


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environmental variables. Previous studies have shown that AOB and AOA 403

community structures in soils can be shaped by multiple environmental conditions, 404

including geography (18, 42), soil type (13), vegetation type (39), temperature (17, 405

22), pH (22, 44), and nutrient level (e.g. ammonium, dissolved oxygen, and organic 406

carbon levels) (22, 29, 33). However, little is known about the relative contribution 407

of these environmental conditions to the overall AOB and AOA community 408

structures. In this study, canonical correspondence analysis (CCA)-based VPA results 409

suggested that environmental factors influencing AOB and AOA distribution across 410

different soil types were complicated, and nearly half of AOB and AOA community 411

structure variability could not be explained by our measured environmental variables. 412

Thus geography, climatic conditions (i.e. MAT and MAP) and carbon/nitrogen 413

related soil nutrients were all important factors in shaping the AOB and AOA 414

community structures in the studied soils. The importance of geography and climatic 415

conditions were also apparent in the UPGMA clustering pattern (Fig. 3). In general, 416

soil samples from adjacent provinces were clustered together, but distinct from other 417

provinces. These observations were consistent with previous studies in showing 418

geographic distribution of AOB and AOA in North American soils (18, 42). Different 419

temperatures from distinct geographic locations were believed to lead to the 420

development of differential geographic niches and to result in distinct AOB and 421

AOA community structures (18, 42). In our case, in addition to temperature, MAP 422

and soil parent materials were also different latitudinally. In the JX Province, due to 423

high MAP and MAT, parent rocks undergo strong physical, chemical, and biological 424

weathering, and extensive leaching leads to enrichment of aluminum and iron in 425

acidic soils. Extensive leaching also results in lower levels of soil nutrients and 426

fertility. However, due to the local tradition of adding plant ash before each season, 427

the pH of the JX soils was not as acidic as other soils under similar climatic 428

conditions (40, 41). In AH, SD and HB provinces, soils are mainly developed from 429

loess and sandy aeolian sediments, and due to relatively lower temperature and 430

precipitation, chemical leaching is not important and erosion is unlikely. As a result 431

soils with moderate fertility and circumneutral to slightly alkaline pH have 432


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developed. In contrast, black or dark brown soils in JL and HLJ provinces develop 433

due to low MAT and plenty of organic input (69), leading to high levels of soil 434

nutrients and high soil fertility. These distinct climatic and lithological conditions 435

may have led to the formation of different soil types, which collectively contribute to 436

the development of “geographic niches” to host distinct AOB and AOA 437

communities. 438

In comparison with soil type, vegetation type appeared to be of less importance 439

to account for the observed differences in AOB and AOA communities in the studied 440

soils, in contrast to previous studies that have shown predominant control of 441

vegetation type on microbial distributions (14) and ammonia oxidizers (39) in soils. 442

Such inconsistency may be ascribed to our single-season samples. In the present 443

study, soils were sampled in one season (after all crops were harvested). Thus it is 444

reasonable to observe less importance of vegetation types to AOB and AOA 445

communities than other measured environmental factors.. 446

In comparison with geography, climatic conditions and soil nutrients, the 447

contribution of soil pH to AOB and AOA community structures was minor. 448

Theoretically, pH can affect the AOB and AOA communities through changing the 449

availability of ammonia (10, 22, 34, 60). In two previous studies, among the seven 450

measured environmental variables (i.e. pH, carbon, nitrogen, and organic matter 451

content, C:N ratio, soil moisture, and vegetation), pH was shown to be a determinant 452

in shaping the AOA community (21, 44). Such an inconsistency may be ascribed to 453

the different pH gradients of the soil samples between previous studies and this study. 454

The pH ranged from 4.5 to 7.5 while keeping other physicochemical conditions 455

nearly constant for the soils of Nicol et al. (44). The soils of Gubry-Rangin et al. (21) 456

included the samples used by Nicol et al. (44) and those collected from the 457

countryside of the United Kingdom, and the pH spanned from 3.5 to 8.7, much 458

larger than our soil pH range (pH 5.9-9.0). Any pH effect from a small pH range of 459

the soils in this study may be masked by other more important effects such as 460

climatic conditions, soil types, and nutrient availability. 461


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AOB and AOA community composition in Chinese agricultural soils. The 462

AOB and AOA communities were mainly composed of Nitrosospira and 463

Nitrososphaera in the studied agricultural soils, consistent with other studies that 464

have shown the dominance of these two groups of ammonia-oxidizing microbes in 465

soils (1, 2, 10, 12, 34, 46, 48, 66, 70). However, some samples contained different 466

AOB and AOA genera. For example, more than a half of AOB and AOA sequence 467

reads in JX1 were affiliated with the Nitrosomonas cluster 8 and Nitrosopumilus 468

subcluster, respectively. Previous studies have shown that the Nitrosomonas cluster 8 469

was composed of Nitrosomonas species and related sequences of miscellaneous 470

origins (including soils) (19, 49). So it was not unexpected to observe the dominance 471

of and Nitrosomonas-related AOB in the JX1 sample. In contrast, the Nitrosopumilus 472

subcluster has previously been observed in aquatic environments (46) and is usually 473

not abundant in soils (44, 46, 66). However, because only one sample (JX1) was 474

predominated by Nitrosopumilus-related sequences, the exact reason for its 475

predominance in this sample could not be determined at this time 476

It is notable to observe that the circumneutral-pH JL1 sample was almost 477

completely composed of Nitrosotalea-related AOA. In addition, Nitrosotalea-AOA 478

sequences were also one of the major AOA populations in two circumneutral-pH 479

samples (JL2 and AH1). Nitrosotalea-AOA were absent in the acidic sample (JX5 480

with pH 5.9). These data were in contrast to previous studies, in which 481

Nitrosotalea-related AOA have been observed predominant in acidic environments. 482

For example, the Nitrosotalea subcluster included the first obligate acidophilic AOA 483

isolate from one acidic soil (37) and was subsequently found to be dominant in 484

another acidic arable soil (46). So these data suggested that Nitrosotalea-related 485

AOA may not be limited to acidic soils. 486

In conclusion, this study represents a large effort to investigate the latitudinal 487

patterns of AOB and AOA in the agricultural soils of eastern China. We showed that 488

geographic distance, MAP, MAT, pH, and carbon- and nitrogen-related soil nutrients 489

were important factors in shaping the AOB and AOA community structures. This is 490


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first study to quantitatively assess the relative contributions of each measured 491

environmental parameter to the observed AOB and AOA distribution pattern. Our 492

measured environmental factors accounted for more than a half of observed 493

variations of the AOB and AOA community structures. Among the measured 494

environmental factors, geographic distance and climatic conditions (MAP and MAT) 495

and carbon- and nitrogen-related soil nutrients were important factors that influenced 496

the AOB and AOA distributions across the agricultural soils of eastern China. These 497

observations provide baseline data for future investigations of functional response of 498

microbial groups to climate-induced environment changes. 499

ACKNOWLEDGMENTS 500

This work was supported by the National Program on Key Basic Research Project 501

(973 Program) (2012CB822000), the Scientific Research Funds for the 1000 502

“Talents” Program Plan from China University of Geosciences-Beijing, the Program 503

for New Century Excellent Talents in University (NCET-12-0954), and the 504

Fundamental Research Funds for National University (China University of 505

Geosciences-Wuhan). Dr. Jizheng He from Research Center for Eco-Environmental 506

Sciences, Chinese Academy of Sciences provided helpful suggestions and comments 507

on this manuscript. The authors are grateful to three anonymous reviewers whose 508

constructive criticisms significantly improved the quality of the manuscript. 509

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Figure captions

Fig. 1. A geographic map showing the sampling locations of the studied agriculture soils collected

from eastern China

Fig. 2. Ratios of AOA to AOB amoA gene copies in the studied soils. The error bars were from three

analytical replicates.

Fig. 3. UPGMA cluster tree constructed based on the 97% cutoff level-based unweightedUnifrac

matrix. Those microbial groups with abundance greater than 2% were displayed. Those groups

with abundances lower than 2% were included as “Others”, which include Nitrosovibrio and

Nitrosomonas clusters 6 & 8. A and B panels indicate AOB and AOA, respectively. For AOB, the

previously published nomenclatures of Nitrosospira Clusters 0, 1, 2, 3a, 3b, 4, 9, 10, 11, and 12

(2, 3) and Nitrosomonas clusters 5, 6a, 6b, 7, and 8 (19) were employed; For AOA, the

previously published nomenclatures of Nitrosopumilus, Nitrososphaera, Nitrosocaldus, and

Nitrosotalea subclusters and Nitrososphaera sister cluster (46) were employed.

Fig. 4.Canonical correspondence analysis (CCA) of the AOB and AOA communities (at OTU level)

and measured environmental variables. A and B panels indicate AOB and AOA, respectively.

Fig. 5.Variation partition analysis of the effects geographic distance and environmental variables on

the phylogenetic structure (at the OTU level) of ammonia-oxidizing microbial populations.

“Grp1” contains geographic distance and climatic factors (mean annual temperature and

precipitation); “Grp2” denotes soil pH; “Grp3” contains TOC, Micro-C, Micro-C/TOC, Total N,

NH4-N, NO3-N, and NO2-N. A and B panels denote AOB and AOA, respectively.


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Fig. 1


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Fig. 2


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Fig. 3A


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Fig. 3B


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Fig. 4A

CCA1(19.4%)

-1 0 1 2 3 4

CC

A2(

20.

8%

)

-2

-1

0

1

2HLJJLHBSDAHJX

NO2-NSoil pH

NH4-N

Micro. C

MAP

PCNM13

PCNM1

NO3-N

Micro.C/TOC

PCNM14

Fig. 4B

CCA1(18.87%)

-1.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0

CC

A2

(17

.51

%)

-2

-1

0

1

2

3HLJJLHBSDAHJXNO2-N

Soil pH

NH4-N

Micro. CMAP

PCNM3

PCNM1

NO3-N

Micro.C/TOC

OTU-level


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Fig. 5A


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Fig. 5B


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Latitudinal Distribution of Ammonia-Oxidizing Bacteria and Archaea ...

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