Latitudinal Distribution of Ammonia-Oxidizing Bacteria and Archaea in the Agricultural Soils of ...

35 Pages·2014·0.87 MB·English

AEM Accepts, published online ahead of print on 7 July 2014
Appl. Environ. Microbiol. doi:10.1128/AEM.01617-14
Copyright © 2014, American Society for Microbiology. All Rights Reserved.
1  Latitudinal Distribution of Ammonia-Oxidizing Bacteria and Archaea in the
2  Agricultural Soils of Eastern China
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5  Running title: AOB and AOA in agricultural soils of eastern China
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7 
8  Hongchen Jiang1^*, Liuqin Huang2^, Ye Deng3^, Shang Wang2, Yu Zhou4, Li Liu5,
9  and Hailiang Dong1,6*
10 
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11  1. State Key Laboratory of Biogeology and Environmental Microbiology, China o
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12  University of Geosciences, Wuhan, 430074, China n
13  2. State Key Laboratory of Biogeology and Environmental Microbiology, China lo
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14  University of Geosciences, Beijing, 100083, China d
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15  3. Institute for Environmental Genomics and Department of Botany and d

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16  Microbiology, University of Oklahoma, Norman, OK 73019, USA; ro
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17  4. Institute of Quality Standards for Agricultural Products, Zhejiang Academy of

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18  Agricultural Sciences, Hangzhou, 310021, China t
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19  5. Information Center, Institute of microbiology, Chinese academy of sciences, :
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20  Beijing, 100101, China a
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21  6. Department of Geology and Environmental Earth Science, Miami University, m
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22  Oxford, OH45056, USA a
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23  m
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24  ^These authors contributed equally rg
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25  *Corresponding authors:
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26  Hongchen Jiang: [email protected] or [email protected]; Tel: ril
27  86-27-67883452 1
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28  Hailiang Dong: [email protected] or [email protected] 2
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29  b
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31  May. 15, 2014 es
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33 
34  Rvised for Applied and Environmental Microbiology
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36  Abstract
37  The response of soil ammonia-oxidizing bacterial (AOB) and archaeal (AOA)
38  communities to individual environmental variables (e.g., pH, temperature, carbon-
39  and nitrogen-related soil nutrients) has been extensively studied, but how these
40  environmental conditions collectively shape AOB and AOA distributions in
41  unmanaged agricultural soils across a large latitudinal gradient remains poorly
42  known. In this study, the AOB and AOA community structure and diversity in 26 D
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43  agricultural soils collected from eastern China were investigated by using w
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44  quantitative polymerase chain reaction and barcoded 454-pyrosequencing of the a
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45  amoA gene that encodes the alpha subunit of ammonia monooxygenase. The d

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46  sampling locations span over a 17o latitude gradient and cover a range of climatic o
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47  conditions. The Nitrosospira and Nitrososphaera were the dominant clusters of AOB h
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48  and AOA, respectively; but the subcluster-level composition of Nitrosospira-related :/
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49  AOB and Nitrososphaera-related AOA varied across the latitudinal gradient. m
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50  Variance partitioning analysis showed that geography and climatic conditions (e.g. s
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51  mean annual temperature and precipitation) as well as carbon-/nitrogen-related soil .o
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52  nutrients contributed more to the AOB and AOA community variations (~50% in /
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53  total) than soil pH (~10% in total). These results are important in furthering our A
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54  understanding of environmental conditions influencing AOB and AOA community il
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55  structure across a range of environmental gradients. ,

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56  9

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57  INTRODUCTION
58  The eastern part of China is under East Asian monsoon climate. This region spans a
59  latitude gradient of 33o and covers a wide range of monsoonal climate zones, from
60  north to south by order, including mid temperate, warm temperate, northern
61  subtropic, mid subtropic, southern subtropic, and tropic zones (Fig. 1). Due to
62  variations in rainfall, solar irradiance, and mineralogical composition of parent rocks,
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63  different types of soils are formed under these climatic conditions (64). The arable o
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64  lands in eastern China are responsible for more than 70% of gross grain yield within n
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65  China (43). In this region agricultural production relies heavily on inorganic d
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66  fertilizers (e.g. urea, N substrates) to sustain crop production for decades (45).
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67  Ammonia-oxidizing bacteria (AOB) and archaea (AOA) are two main groups of
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68  microorganisms affecting the utilization efficiency of inorganic nitrogen fertilizers p
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69  (59). Both AOB and AOA possess amoA genes encoding the alpha subunit of e
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70  ammonia monooxygenase. amoA gene-based phylogenetic analyses have shown that .a
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71  AOB and AOA are widely distributed in various environments (58).
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72  Nitrosospira and Nitrosomonas of the Betaproteobacteria are two dominant /
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73  genera of terrestrial AOB (34). The Nitrosospira-related AOB are abundant in A
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74  different soils and they can be grouped into clusters 0, 1, 2, 3a, 3b, 4, 9, 10, 11, and il
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75  12 (2, 3). The Nitrosomonas-related AOB are grouped into clusters 5, 6a, 6b, 7, and ,

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76  8 (19), which are frequently found in freshwater environments (36), (hyper)saline 1
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77  lakes (28, 32), estuarine environments (5, 51), sewage sludge (4, 62, 71), sediments b
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78  (19, 61), and flooded soils (13). Various environmental factors such as vegetation u
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79  type, nutrient level, microclimate (fertilizer, temperature, pH, water content, t
80  elevation), and management practice can affect AOB distribution in soils (18).
81  All known AOA fall into the archaeal phylum Thaumarchaeota (47, 57). They
82  can be classified into Nitrosopumilus, Nitrososphaera, Nitrosocaldus, Nitrosotalea,
83  and Nitrososphaera sister cluster (46). The Nitrosocaldus cluster mainly consists of
84  sequences from hot springs, whereas Nitrosopumilus, Nitrosotalea, Nitrososphaera,
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85  and Nitrososphaera clusters are present in various environmental habitats (46). The
86  distributions of AOA in these environments are influenced by multiple
87  environmental conditions such as ammonium, dissolved oxygen, and organic carbon
88  levels, salinity, temperature, and pH (6, 8, 17, 22).
89  Investigations of the AOA and AOB distributions and their response to
90  environmental conditions are of great significance to the understanding of
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91  bioavailability and microbial transformation of nitrogen nutrients in agricultural soils. o
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92  Previously, some amoA gene-based studies have been performed in manipulated n
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93  Chinese agricultural soils (using either experimental stations or artificially managed d
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94  soils) collected from the northern subtropic zone of central China (11-13, 23, 54, 65,
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95  68). These studies showed that pH was a major factor affecting AOB and AOA m

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96  distributions: Nitrosospira clusters 10, 11 and 12 were the dominant AOB groups in tt
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97  acidic soils, while Nitrosospira clusters 3a and Nitrosomonas clusters 6 and 7 were /a
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98  dominant in alkaline and neutral soils; for AOA communities, Nitrosopumilus and
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99  Nitrososphaera clusters were dominant AOA populations and their relative m
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100  abundances showed negative and positive correlations with soil pH, respectively r
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101  (53). o
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102  In addition to pH, climatic conditions (i.e. temperature and precipitation) and r
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103  soil properties have been shown to affect soil microbial communities under 0
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104  controlled conditions (15, 20, 56), but their effects on AOB and AOA are generally 0
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105  poorly known. Fierer and colleagues (18) showed that the AOB compositions in the
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106  soils across North America were strongly influenced by temperature, which was g
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107  related to geographical locations, suggesting that biogeography controlled the AOB s
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108  distribution. However, this study did not evaluate the relative importance of
109  temperature and other environmental conditions in affecting AOB and AOA
110  abundance and distribution in agricultural soils.
111  The objective of this study was to quantitatively assess the relative importance
112  of multiple environmental conditions in shaping AOA and AOB community
113  structures in Chinese agricultural soils. We collected 26 agricultural soil samples
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114  from three different climatic zones from south to north in eastern China. AOB and
115  AOA abundance and diversity were investigated using amoA gene-based quantitative
116  polymerase chain reactions (qPCR) and bar-coded 454 sequencing techniques,
117  respectively. Statistical analyses were performed to assess correlation between the
118  AOB and AOA communities and environmental variables (e.g. geographic distance,
119  mean annual temperature, mean annual precipitation, pH, and soil nutrients). These
120  results are important to our understanding of the role of the microorganisms in D
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121  transforming nitrogen nutrients in agricultural soils. w
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122  MATERIALS AND METHODS d
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123  Site description and sampling. Soil samples were collected from arable lands fr
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124  across six provinces of different climatic zones in Eastern China (see for details
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125  Table S1). From north to south, the six provinces are Heilongjiang (in abbr. HLJ), p
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126  Jilin (in abbr. JL), Hebei (in abbr. HB), Shandong (in abbr. SD), Anhui (in abbr. AH), e
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127  and Jiangxi (in abbr. JX) (Fig. 1). Mid temperate climate dominates HLJ and JL .a
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128  Provinces. The mean annual temperature (MAT) is 3.5-3.9 oC, and the mean annual
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129  precipitation (MAP) 480-700 mm. In HB and SD provinces, warm temperate g
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130  prevails, and the MAT and MAP are 12.1-13.8 oC and ~620 mm, respectively. AH n

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131  and JX provinces are dominated by northern subtropic climate, and the MAT and r
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132  MAP are 15.5-17.2oC and ~800-1850 mm, respectively (Table S1). Due to different 10
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133  climate and parent rock materials, different soil types developed in these provinces. 0
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134  Black soil, dark brown soil, medium loam soil, dark brown soil, paddy soil, and red
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135  soil are major soil types in HLJ, JL, HB, SD, AH, and JX Provinces, respectively. g
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136  These diverse soil types support many different crops (Table S1). s
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137  Field sampling was performed in October 2010 after crops were harvested. In
138  each of the provinces described above, soil samples cultivated with different crops
139  were collected (Table S1). At each chosen site, three separate soil subsamples (20 m
140  apart from each other) were taken at the 5-10 cm depth with sterile spatulas and
141  spoons. In the field, the three subsamples from each site were homogenized in a
142  pre-sterilized aluminum pan and were placed into 50 mL polypropylene tubes. The
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143  composite soil from one site was treated as one sample hereinafter, and all the
144  downstream analyses were performed on the composite samples. Sample tubes were
145  immediately stored in dry ice in the field and during transportation and then were
146  transferred to a -80oC freezer in the laboratory until further analysis.
147  Soil chemicals analysis. Soil pH was determined on a 1:2 soil: 0.01M CaCl2
148  suspension with a pH electrode (model 704 Mefrohm) (67). Contents of total organic
149  carbon (TOC), total nitrogen (TN), ammonium nitrogen (NH4+-N), nitrite nitrogen Do
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150  (NO2--N), and nitrate-nitrogen (NO3--N) were determined according to the nlo
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151  established methods (67). Microbial carbon (Micro-C) concentration was determined d
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152  by using a fumigation-incubation method described previously (63).
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153  DNA extraction. In order to avoid DNA extraction bias, DNA was extracted in
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154  triplicate from each composite soil sample using FastDNA SPIN Kit for Soil (MP p
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155  Biomedicals, LLC, Ohio, USA) according to the manufacturer’s protocol. DNA e
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156  quantity and quality were assessed by using a Nanodrops® ND-1000 UV-Vis .a
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157  Spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The resulting
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158  three DNA extractions per sample were pooled before further analysis. g
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159  Quantitative PCR (qPCR) of amoA and 16S rRNA genes. qPCR was A
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160  performed to determine the abundances of the bacterial and archaeal amoA and 16S il
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161  rRNA genes in the investigated samples according to Jiang et al. (31, 32). Briefly, ,

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162  the primer sets of amoA-1F/amoA-2R, Arch-amoAF/Arch-amoAR, and 1
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163  Bac331F/Bac797R and Arch349F/Arch806R were used for qPCR of AOB/AOA b
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164  amoA and bacterial/archaeal 16S rRNA genes, respectively (Table S2). qPCRs were u
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165  performed in a reaction volume of 20 µL, containing10μL of 2×SYBR® Premix Ex t
166  TaqTM(Takara, Japan), 0.2μM of each primer, 0.4μL of ROX Reference Dye II (50×),
167  20μg of Bovine Serum Albumin(Takara,), and 1μL of soil DNA. qPCRs were
168  performed in triplicate on an ABI7500 real-time PCR system (Applied Biosystems,
169  Carlsbad, CA, USA). ddH2O was used as a negative control template. Purified
170  plasmids of AOB and AOA amoA and bacterial and archaeal 16S rRNA gene clones
171  from one soil sample were used as standard templates. The clone libraries of AOB
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172  and AOA amoA, and archaeal and bacteria 16S rRNA genes were constructed with
173  PCR products derived from the primer sets of amoA-1F/amoA-2R,
174  Arch-amoAF/Arch-amoAR, Arch21F/ Univ1492R, and Bac27F/ Univ1492R,
175  respectively (Table S2). Clone library construction and plasmids DNA extraction
176  were performed according to the procedures described elsewhere (31). Standard
177  curves were obtained by using serial dilutions of plasmids (pGEM-T) containing
178  cloned amoA and 16S rRNA genes. The data were used to create standard curves D
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179  correlating the CT values with the amoA gene copy numbers. Linear plots (not shown) wn
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180  between the Ct value and log(copy numbers/reaction) for the AOB and AOA amoA a
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181  and 16S rRNA genes were obtained with correlation coefficients of R2>0.99. Melting ed

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182  curve analysis was performed to determine the melting point of the amplification o
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183  products and to assess the reaction specificity. After the qPCR reaction was complete, h
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184  temperature ramped from 72 oC to 95 oC, rising by 0.1 degree per step, waiting for 45 :/
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185  s on the first step, and then 5 s for each subsequent step . The melting curve of each m
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186  run had only one peak, indicative of specific PCR amplification. The qPCR s
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187  amplification efficiencies were in the range of 96–100%. .o
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188  PCR amplification of AOB and AOA amoA genes. The AOB and AOA amoA o
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189  genes were amplified with the primer sets of amoA-1F/amoA-2R and p
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190  Arch-amoAF/Arch-amoAR, respectively (Table S1). To pool multiple samples for il 1
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191  one run of 454 pyro-sequencing, a sample tagging approach was used (7). Special 2
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192  5’-end tagged primers for each sample were designed by combining primers with 9

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193  adaptors (‘TC’ and ‘CA’ for forward and reverse primers, respectively) and a unique y
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194  8-mer tag (i.e. barcodes)(7). The PCR amplification procedure was as follows: an e
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195  initial denaturation at 94oC for 5 min, 30 cycles of denaturation at 94oC for 30s,
196  annealing at 58oC for 40s and, extension at 72oC for 40s and 60s (for AOB and AOA
197  amoA genes, respectively), followed by a final extension step at 72oC for 10 min.
198  To obtain enough amplicons for pyrosequencing, four PCRs were run for each
199  sample. Each 25 μlPCR mix contained 10×PCR buffer (Mg2+ plus) (1× diluted;
200  Takara, Japan), dNTP Mixture (0.2 mM each; Takara), primers (0.4 μM each),
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201  TaKaRa Taq DNA polymerase (1.5 U), bovine serum albumin (20μg; Takara) and 1
202  μl template DNA. Reactions without template DNA were performed as negative
203  controls in parallel for each sample. Amplicons from four PCRs were pooled for
204  each sample. The PCR products were checked by agarose gel electrophoresis and
205  correct bands were incised. The incised PCR gels were then purified by using
206  AxyPrep DNA Gel extraction kit (Axygen Biosciences, Union City,CA, USA)
207  according to the manufacturer’s protocol. The quantity and quality of thepurified D
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208  PCR products were assessed by using a Nanodrops® ND-1000 UV-Vis w
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209  Spectrophotometer. Finally, the purified PCR amplicons were pooled in an a
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210  equimolar concentration for further 454 pyrosequencing. d

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211  Pyrosequencingand data analysis. The pyrosequencing was commercially m

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212  performed ona GS FLX sequencer (454 Life Sciences, USA) at the Chinese National tt
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213  Human Genome Center in Shanghai.The obtained sequences were extracted, /a
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214  trimmed, quality-screened, and aligned with the use of the Mothur 2.25.0
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215  pipeline(52). Sequencing reads were assigned to each sample according to their m
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216  unique barcodes, low quality sequences (quality score < 25, length <150 bp, r
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217  ambiguous bases≥1, homopolymer≥6) were removed.The sequences from the o
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218  reverse primer (amoA-2R and Arch-amoAR for AOB and AOA, respectively)-end of p
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219  the amplicons were used for downstream data analysis. A total of 28211 and il 1
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220  97134high-quality raw sequence reads were obtained for AOB and AOA, 2
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221  respectively. 9

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222  The obtained high-quality raw sequences were clustered into operational g
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223  taxonomic units (OTUs) based on 97% sequence identity via the Qiime (quantitative s
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224  insights into microbial ecology) pipeline (9, 35). To minimize potential deviant
225  genotypes, OTU reads with relative abundance less than 0.5% were defined as rare
226  sequences and were removed from downstream analysis. The remaining reads were
227  checked against a local amoA gene database (downloaded from the Ribosomal
228  Database Project FunGene: http://fungene.cme.msu.edu/index.spron November 27th
229  2011) and the NCBI database (http://blast.ncbi.nlm.nih.gov/Blast.cgi), and then any
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230  chimera sequences (e.g., no hit in the database) were eliminated. The remaining
231  quality-screened sequence reads were deposited into the NCBI sequencing read
232  archive under accession number SRA082064.
233  One sequence read was randomly chosen as a representative sequence from each
234  OTU with the use of the Qiime scripts. Phylogenetic assignment of AOB and AOA
235  was made possible by aligning individual OTU sequences with those in the reference
236  databases of AOB (2, 12, 30, 55, 70) and AOA (46), respectively. The cluster
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237  nomenclatures from the above references were adopted for the AOB and AOA amoA ow
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238  gene sequences, respectively. lo
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239  Statistical analyses. In order to calculate Chao1 (predicted number of OTUs), e
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240  Shannon, and evenness indices, three AOB amoA gene libraries with fewer than 240 fr
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241  reads were removed. The AOA and remaining AOB libraries were normalized to 610

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242  and 240 reads (the number of the smallest libraries), respectively. Chao1 (predicted ttp
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243  number of OTUs), Shannon, and evenness indices were calculated at the 97% cutoff /a
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244  level. Coverage was calculated as the ratio of the observed OTUs to Chao1 (27), and m
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245  this index was used to evaluate the sequencing depth. s
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246  Pearson correlation was performed to test any associations between the AOA rg
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247  and AOB amoA gene abundances and the environmental variables. Significance n

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248  levels were adjusted by using the Holm correction for each environmental variable p
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249  (26). Any significance levels lower than 0.05 were chosen (25). Mantel tests were 1
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250  performed to assess any correlation between AOA and AOB community structures 2
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251  and the measured environmental variables (24). The measured environmental 9

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252  variables were first normalized to zero means and unit standard deviations. g
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253  Euclidean distances were then calculated and used to construct dissimilarity matrices es
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254  of communities and environmental variables. All the analyses were performed by
255  functions in the vegan package (v.1.15-1) in the R software (16).
256  UPGMA clustering was performed to compare similarity of AOB (or AOA)
257  communities among the samples based on the Bray-Curtis matrix. Analysis of
258  similarity (ANOSIM) was performed to further confirm any significant similarity in
259  community composition between the samples as identified by the UPGMA
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260  clustering. SIMPER (similarity percentage) analysis was performed to rank the
261  relative contribution of AOB and AOA clusters or subclusters to the differences
262  revealed by the UPGMA cluster analysis. 
263  To determine the relative importance of the geographic distance and the
264  measured environmental factors in shaping AOA and AOB communities, a canonical
265  correspondence analysis (CCA)-based variation partitioning analysis (VPA) was
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266  implemented according to the methods described previously(72). First, a spatial o
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267  decomposition method, principal coordinates of neighbor matrices (PCNM), was n
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268  applied to the geographic coordinates of the samples (50). This method separates d
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269  sample geographic coordinates into multiple spatial variables. Then for each
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270  community dataset, CCA test with 1,000 permutations was used to select the m

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271  significant (p<0.05) spatial variables, which were then retained for VPA. All these tt
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272  analyses were carried out using the functions in the vegan package in the R software /a
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273  (16).
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274  RESULTS .o
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275  Soil geochemical characteristics. Mean annual temperature and precipitation o
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276  (MAT & MAP) of the sampling sites varied latitudinally from north to south by p
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277  order: MAT ranged from 3.5 oC in HLJ (Heilongjiang Province) to 17.5 oC in JX il 1
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278  (Jiangxi Province); and MAP ranged from 481.5 mm in HLJ (Heilongjiang Province) 2
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279  to 1852.1 mm in JX (Jiangxi Province). The contents of TOC, microbial carbon, and 9

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280  total nitrogen in the samples of HLJ and JL (Jilin Province) were much higher than y
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281  most soil samples from HB (Hebei Province), SD (Shandong Province), AH (Anhui e
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282  Province) and JX. However, the contents of NH4-N, NO3-N, and NO2-N did not vary
283  significantly among the investigated samples (Table S2). In addition, the HB and SD
284  soils were slightly alkaline (pH 8.5-9.0), whereas those from other provinces were
285  slightly acidic to circumneutral (pH 5.9-7.7) (Table S2).
286  Quantification of archaeal and bacterial 16S rRNA and AOA and AOB
287  amoA genes. The archaeal and bacterial 16S rRNA gene abundances were
10