Journal of Integrative Agriculture
Advanced Online Publication: 2015
Doi: 10.1016/S2095-3119(15)61147-9
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Wheat, maize and sunflower cropping systems selectively influence bacteria community
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structure and diversity in their and succeeding crop’s rhizosphere1
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WEN Xin-ya1, 2, Eric Dubinsky3, WU Yao1, 2, Yu Rong4, CHEN Fu1, 2
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College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, P.R.China
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Key Laboratory of Farming System, Ministry of Agriculture, Beijing 100193, P.R.China
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Lawrence Berkeley National Laboratory, Earth Sciences Division, Berkeley, California 94720, USA
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Institute for the Control of Agrochemicals, Ministry of Agriculture, Beijing 100125, P.R.China
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Abstract
Wheat and maize are increasingly used as alternative crops to sunflower monocultures that
dominate the Hetao Irrigation District in China. Shifts from sunflower monocultures to alternate
cropping systems may have significant effects on belowground microbial communities which
control nutrient cycling and influence plant productivity. In this research, rhizosphere bacterial
communities were compared among sunflower, wheat and maize cropping systems by 454
pyrosequencing. These cropping systems included 2 years wheat (cultivar Yongliang 4) and maize
(cultivar Sidan 19) monoculture, more than 20 years sunflower (cultivar 5009) monoculture, and
wheat-sunflower and maize-sunflower rotation. In addition, we investigated rhizosphere bacterial
communities of healthy and diseased plants at maturity to determine the relationship between plant
health and rotation effect. The results revealed taxonomic information about the overall bacterial
community. And significant differences in bacterial community structure were detected among these
cropping systems. Eight of the most abundant groups including Proteobacteria, Bacteroidetes,
Acidobacteria, Gemmatimonadetes, Chloroflexi, Actinobacteria, Planctomycetes and Firmicutes
accounted for more than 85% of the sequences in each treatment. The wheat-wheat rhizosphere had
the highest proportion of Acidobacteria, Bacteroidetes and the lowest proportion of unclassified
bacteria. Wheat-sunflower cropping system showed more abundant Acidobacteria than
maize-sunflower and sunflower monoculture, exhibiting some influences of wheat on the
succeeding crop. Maize-maize rhizosphere had the highest proportion of γ-Proteobacteria,
Pseudomonadales and lowest proportion of Acidobacteria. Sunflower rotation with wheat and
maize could increase the relative abundance of the Acidobacteria while decrease the relative
abundance of the unclassified phyla, as was similar with the health plants. This suggests some
positive impacts of rotation with wheat and maize on the bacterial communities within a single field.
These results demonstrate that different crop rotation systems can have significant effects on
WEN Xin-ya, Mobile: +86-18611620182, E-mail: [email protected], Correspondence CHEN Fu, Tel/Fax: +86-10-62733316, E-mail:
[email protected]
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rhizosphere microbiomes that potentially alter plant productivities in agricultural systems.
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Keywords: bacterial community structure and diversity, rhizosphere, cropping system, 454
pyrosequencing
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内蒙古河套灌区向日葵长期连作导致病害流行、产量降低，与小麦和玉米轮作是减轻连作障
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碍的重要农业措施之一。研究农业管理措施对土壤微生物群落的影响是农业系统生态稳定和
产量提高的重要内容。本研究采用 454 高通量测序比较不同作物种植系统根际土壤细菌群落
的变化，并比较当地主栽作物向日葵成熟后健康植株和发病植株根际细菌群落的差异以探究
健康的种植系统。不同作物种植系统包括 2 年小麦连作（品种“永良 4 号”）、2 年玉米连
作（品种“四单 19”）、20 年向日葵连作（品种“5009”）以及小麦-向日葵轮作和玉米-向
日葵轮作。研究结果表明 454 测序可以获得不同作物根际细菌的整体群落构成，并揭示了不
同种植系统的细菌多样性和组成存在显著差异。小麦、玉米和向日葵根际主要由 8 个细菌门
组成，占全部细菌总量的 85%以上，包括变形菌、拟杆菌、酸杆菌、芽单胞菌、绿弯菌、放
线菌、浮霉菌和厚壁菌。小麦根际酸杆菌和拟杆菌比例显著高于玉米和向日葵。玉米根际γ变形菌纲和假单胞菌属比例显著高于小麦和向日葵。与小麦轮作后，向日葵根际酸杆菌比例
高于玉米-向日葵轮作和向日葵连作，反映了小麦对后茬作物的影响。与向日葵连作相比，小
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麦、玉米与向日葵轮作能够提高酸杆菌比例、降低未分类菌比例，与健康植株根际相似，反
映出轮作的积极效应。
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关键词 细菌群落结构和多样性，根际，种植系统，454 高通量测序
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1. Introduction
In the last decades, many studies have provided evidence that plant-microbe interactions are
not only crucial for better understanding of plant growth and health, but also for sustainable
agriculture and nature conservation (Philippot et al. 2013). Ecologists have been traditionally more
concerned about aboveground ecosystem processes, which are much better understood than
belowground processes. However, recent advances in belowground processes have suggested that
understanding the ecology and evolution of rhizosphere has an important significance for enhancing
plant productivity and ecosystem functioning (Philippot et al. 2013). However, current
understanding of the complicated plant–microbe interactions in the rhizosphere is still in its primary
stage (Berendsen et al. 2012).
Rhizodeposits account for around 11% of net photosynthetically fixed carbon and 10∼16% of
total plant nitrogen (Bulgarelli et al. 2013). The nutrient allocation vary greatly depending on plant
species and age, location along the root system and soil type (Yang and Crowley 2000;
Hertenberger et al. 2002). Stable isotope probing in combination with microbiota DNA profiling
(DNA-SIP) of 13CO2-labelled plants identified that rhizosphere bacteria assimilate root exudates
(Haichar et al. 2008). Thus, plants are able to shape the rhizosphere microbiota by active secretion
of compounds that facilitate or suppress specific members of the microbial community, establishing
a habitat which is favorable for the plant (Doornbos et al. 2012). Some plants have their own
species-specific organisms in rhizosphere (Lemanceau et al. 1995; Berg et al. 2002, 2006; Weller et
al. 2002; Picard and Bosco, 2008), bacteria as well as fungi, showing a high degree of host
specificity and coevolving with plants (Raaijmakers et al. 2009). Some plant species can produce
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similar communities in different soils (Miethling et al. 2000). Different plant species often exhibit
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different rhizosphere microflorae even if they are growing in the same soil (Viebahn et al. 2005;
Berg et al. 2006; Garbeva et al. 2008). As numerous bacterial and fungal groups are found to
promote the plant growth and boost their defensive capacity (Zamioudis and Pieterse, 2012), plants
have even been postulated to actively recruit these beneficial microorganisms in their rhizospheres,
for example, to counteract pathogen assault (Cook et al. 1995). A recent study further demonstrated
that one kind of plant-beneficial rhizobacteria, Pseudomonas putida, was recruited to plant roots by
benzoxazinoid secondary metabolites (Neal et al. 2012).
With the exploration of the belowground processes, increasing attention is drawn to deep
understanding of the structure and the functions of the bacterial communities. Several studies
indicate that plants showed strong influence on rhizosphere bacterial community composition
(Smalla et al. 2001; Kowalchuk et al. 2002; Costa et al. 2006). In these researches, cultivation and
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some newer biochemical and molecular analytical tools are used to assay the dominant or special
rhizosphere bacteria populations (Smit et al. 2001). The new methods include signature fatty acids
in cell membranes for phospholipid fatty acid (PLFA) (Nelson et al. 2011), fatty acid methyl ester
(FAME) profiles (Zelles et al. 1992, 1995; Hamel et al. 2006), 16S rDNA for denaturing gradient
gel electrophoresis (DGGE) (Gu et al. 2009) or terminal restriction fragment length polymorphism
(T-RFLP) (de Oliveira et al. 2006; Donn et al. 2014). However, these methods do not provide high
resolution taxonomic information to characterize complex soil microbial communities and identify
bacteria among different crops (Jansson et al. 2012). In the past five years, a paradigm shift in
metagenomics have been seen that advances in DNA sequencing, such as pyrosequencing, and
high-performance computing enable the application of cross-sectional and longitudinal studies
(Knight et al. 2012). 454 pyrosequencing has been used to evaluate differences in bacterial
communities for its sufficiently sensitive to taxonomic information and highly efficient for mixed
samples to be run on the same sequencing run and later binned in a cost-effective and timely
manner (Quince et al. 2009).
Determining how rhizosphere microbial communities differ among cropping systems is an
important step toward understanding how different agricultural strategies can promote beneficial
interactions in the rhizosphere. The objective of this study was to characterize the structure and
diversity of soil bacterial communities in rhizosphere of wheat, maize, sunflower, and different
sunflower rotations in the Hetao Irrigation District in China. We applied 454 pyrosequencing to
evaluate differences among these crop rotation systems and provide information about belowground
effects on microbial communities that may enhance crop productivity.
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2. Materials and methods
2.1 Site and sampling
This study was carried out from March 2011 to September 2012 at Yichang Experimental
Station, located in Hetao Irrigation District (40°19´ to 41°18´N, 106°20´ to 109°19´E) in the
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western part of Inner Mongolia Autonomous Region in China. Hetao Irrigation District has a
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typically arid and semi-arid continental climate, where the soil freezing-thawing period typically
occurs between mid-November and mid-May, and salt accumulates heavily to the surface in spring
(Gao and Yu, 1986; Wang et al. 2011; Tong et al. 2015). This district has an average evaporation of
2100-2300 mm and rainfall of 130-222 mm (Li et al. 2010). Groundwater depth varies annually
between 0.8 and 2.6 m and air temperature measured from 1990 to 2012 averaged 8.1°C. The soil
type at the site is salty clay loam with the average organic matter content of 7.9 g kg-1, total nitrogen
content of 574.8 mg kg-1, available phosphorus content of 472.7 mg kg-1, available potassium
content of 683.5 mg kg-1, average pH of 8.94, and total salt content of 2.2‰. Before establishing
this experiment, sunflower had been continuously grown for more than 20 years in this field.
The experiment was conducted for 2 years. First, three crops including wheat (cultivar
Yongliang 4), maize (cultivar Sidan 19) and sunflower (cultivar 5009) were planted in 2011. Next
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year, half of the wheat and maize fields were changed to grow sunflower in 2012. Thus, there were
six treatments: (1) wheat-wheat (WW), (2) maize-maize (MM), (3) sunflower-sunflower (SS), (4)
wheat-sunflower (WS), (5) maize-sunflower (MS), and (6) fallow-sunflower (FS). Crop
managements were the same as those adopted by local farmers.
All the crop soil samples were collected at anthesis in 2012. And at harvest, sunflower soil
samples were collected again meanwhile one health (Sh) and one disease (Sd) sunflower were
selected respectively to collect the rhizosphere soils. The crops roots were shaken gently to collect
the rhizosphere soils. Soil samples collected from three plants was mixed, sieved through a 2 mm
mesh sieve, and stored at -20°C until use.
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2.2 DNA extraction, amplification of 16S rDNA variable region
Total DNA was extracted from 1gram of per soil sample using the E.Z.N.A. Soil DNA Kit
(OMEGA Bio-tek, USA) according to the manufacturer’s instruction.
For the V1-V3 region of bacterial 16S-rDNA amplicon sequencing, each DNA was amplified
with bar-coded universal primers, primers 27F and 533R containing the A and B sequencing
adaptors.
The
forward
primer
(B-27F)
was
5’-CCTATCCCCTGTGTGCCTTGGCAGTCTCAGAGAGTTTGATCCTGGCTCAG-3’, where the
sequence of the B adaptor is shown in italics and underlined. The reverse primer (A-533R) was
5’-CCATCTCATCCCTGCGTGTCTCCGACTCAGNNNNNNNNNNTTACCGCGGCTGCTGGC
AC-3 (Kumar et al. 2011), where the sequence of the A adaptor is shown in italics and underlined
and the Ns represent an eight-base sample specific barcode sequence.
All polymerase chain reaction (PCR) reactions were carried out in 20 μL volumes,
containing 10 ng of template DNA, 2 μL of 2.5 mmol L-1 dNTPs, 0.4 μL of each primer (5 μmol
L-1), 4 μL of 5× FastPfu Buffer [200 mmol L-1 KCl; 100 mmol L-1 Tris-H2SO4 , pH 9.2; 10 mmol
L-1 MgSO4 and 50 mmol L-1 (NH4)2SO4] (Transgen Biotech, China), and 0.4 μL of FastPfu
Polymerase (2.5 U μL−1) (Transgene Biotech, China); the volume was brought up to 20 μL with
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double distilled water.
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The conditions of the touchdown PCR for the amplification of fragments in the hypervariable
V3 region of the 16S rRNA genes were as follows: 2 min initial denaturation at 95°C; 25 cycles of
denaturation at 95°C (30 s), annealing at 55°C (30 s), elongation at 72°C (30 s); and final extension
at 72°C for 5 min.
Amplifications were performed using ABI GeneAmp® 9700. The presence of PCR products
was determined by analyzing 3 μL of the product on a 2% agarose gel. Next, all PCR products of
the same sample were pooled, and the PCR products of an approximate size of 530 base pairs (bp)
were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Bio, USA). The DNA
concentration of each PCR product was determined using a QuantiFluor™-ST Pico Green double
stranded DNA Quantitation Reagent (Promega, USA), and then was pooled in equimolar ratios and
subjected to emulsion PCR to generate amplicon libraries, as per the manufacturer’s
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recommendations. The products of EmPCR (emulsion-based clonal amplification) were prepared
using Roche emPCR_Amp-Lib_L Kit and then sequenced by Roche Genome Sequencer FLX+.
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2.3 Data analysis
Sequence analysis was based on the software Mothur (v.1.28.0), as described previously
(Schloss et al. 2009). All pyrosequencing reads were filtered according to barcode and primer
sequences. The resulting sequences were further screened and filtered for quality and length.
Sequences that were less than 200 bp, contained ambiguous characters, contained over two
mismatches to the primers, or contained mononucleotide repeats of over seven bp were removed.
After that, the sequences were aligned against the reference sequence database (Silva database).
Operational taxonomic units (OTUs) were clustered at a dissimilarity of 0.03, and rarefaction waves
of all the samples at different levels were generated. Richness and diversity indexes were calculated
(Ace, Chao and Shannon). Lastly, all the OTUs at the dissimilarity of 0.03 were classified by
Shanghai Majorbio Bio-pharm Technology Co., Ltd (China).
Principal component analysis (PCA) was used to determine overall structure changes at species
level in the microbial communities. Bray-Curtis distance was used to obtain dissimilarity matrices
in the adonis algorithm of the dissimilarity test for comparing 454 pyrosequencing data of three
cropping systems. All the analyses were performed by functions in the Vegan package (v.1.15-1) in
R v. 2.8.1 (Team 2006).
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3. Results
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3.1 Bacterial diversity and richness among different crop systems
A total of 249716 valid reads and 83628 OTUs were obtained from the nineteenth samples
through 454 pyrosequencing analysis. These OTUs were assigned to 44 different phyla or groups.
Each of the nine communities contained between 8001 and 17116 reads, with OTU richness ranging
from 3064 to 6439. Rarefaction curves did not approach a saturation plateau by increasing sample
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size demonstrating even higher numbers of taxa in these samples (Appendix A).
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Wheat rhizosphere had the highest Shannon diversity and Chao1 richness indices (Table 1), but
no significant differences were detected except that the Shannon diversity index of maize was
significantly lower than wheat and sunflower (P<0.05). The results revealed that maize rhizosphere
had the lowest bacterial diversity.
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3.2 Bacterial taxonomic comparison among different cropping systems
The PCA showed that the microbial communities in wheat rhizosphere, maize and sunflower
were well separated from each other (Fig. 1), which was supported by the dissimilarity test using
adonis algorithm (Appendix B).
The relative abundances of these phyla at Phylum to Order levels were compared among three
crops (Fig. 2). The majority groups belonged to the eight most abundant phylum Proteobacteria,
Bacteroidetes, Acidobacteria, Gemmatimonadetes, Chloroflexi, Actinobacteria, Planctomycetes and
Firmicutes, making up from 1 to 36% of classified sequences. These eight phyla accounted for
more than 85% of the sequences in each cropping system. The unclassified sequences were between
2 and 4% in all treatments. The other phyla were less abundant and most of their proportions were
less than 1%. The results revealed significant differences (P<0.05) in proportion of dominant phyla
Bacteroidetes, Acidobacteria, Gemmatimonadetes, Planctomycetes and Firmicutes among wheat,
maize and sunflower cropping systems.
Results showed that three cropping systems had no significant effects on the proportion of
Proteobacteria but had significant effects on the proportion of Proteobacteria subgroups. The
Proteobacteria was the most dominant phylum that made up 30.18% for wheat, 35.34% for maize
and 36.18% for sunflower in the total bacterial community. No significant differences were
observed among crops at the phylum level (Fig. 2). Significant differences within Proteobacteria
community were observed (Figs. 3-A and 4). The maize rhizosphere had significantly high
γ-Proteobacteria and significantly low β- and δ-Proteobacteria (P<0.05). The sunflower
rhizosphere had lowest γ-Proteobacteria. Within α-Proteobacteria, the sunflower rhizosphere had
significantly high Rhodospirillales and Sphingomonadales (P<0.05) (Fig. 4-A). Within
β-Proteobacteria, the wheat rhizosphere had significantly high Nitrosomonadales and significantly
low Rhodocyclales (P<0.05) (Fig. 4-B). Within δ-Proteobacteria, the sunflower rhizosphere had
significantly high Desulfuromonadales and significantly low Myxococcales (P<0.05) which was
most abundant in wheat rhizosphere (Fig. 4-C). Within γ-Proteobacteria, the wheat rhizosphere had
significantly high Xanthomonadales and significantly low Pseudomonadales (P<0.05) which was
most abundant in maize rhizosphere, while the sunflower rhizosphere had significantly high
unclassified order (P<0.05) (Fig. 4-D).
Crop type had significant effects on the proportion of Bacteroidetes and its subgroups. The
Bacteroidetes made up 13.69% for wheat, 10.89% for maize and 6.43% for sunflower in the total
bacterial community. The wheat rhizosphere had significantly high relative abundance of this
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phylum (P<0.05) (Fig. 2). Within the Bacteroidetes, two of the most dominant subgroups
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Flavobacteria and Sphingobacteria were significantly different among crops (Fig. 3-B). The wheat
rhizosphere had significantly high proportion of Sphingobacteria (P<0.05). The maize rhizosphere
had significantly high proportion of Flavobacteria (P<0.05).
Crop type had significant effects on the proportion of Acidobacteria but had no significant
effects on the proportion of Acidobacteria subgroup. The Acidobacteria accounted for 12.74% for
wheat, 5.81% for maize and 9.47% for sunflower of the total bacterial community. The wheat
rhizosphere had significantly high proportion of this phylum and the maize rhizosphere had
significantly low proportion of this phylum (P<0.05) (Fig. 2). Within the Acidobacteria, the most
abundant subgroup was Acidobacteria, accounting for more than 82% of the sequences in each crop,
which showed no significant differences among three crops (Fig. 3-C).
Crop type had significant effects on the proportion of Firmicutes and its subgroup. The
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Firmicutes was 1.07% for wheat, 8.49% for maize and 8.38% for sunflower. The wheat rhizosphere
had significantly low proportion of this phylum (P<0.05) (Fig. 2). Within the Firmicutes, the most
dominant subgroups were the Bacilli and Clostridia whose relative abundances showed significant
differences (Fig. 3-D). The maize rhizosphere had significantly high Bacilli (82.43%) which was
significantly low (P<0.05) in the sunflower rhizosphere (36.67%). The sunflower rhizosphere had
significantly high Clostridia (61.48%) which was significantly low (P<0.05) in the maize
rhizosphere (15.72%).
The Planctomycetes made up 5.53% for wheat, 4.38% for maize and 5.94% for sunflower in
the total bacterial community. The relative abundance of this phylum in sunflower rhizosphere was
significantly higher (P<0.05) than in maize (Fig. 2).
The Gemmatimonadetes made up 8.50% for wheat, 8.53% for maize and 5.57% for sunflower
in the total bacterial community. The maize rhizosphere had significantly low proportion (P<0.05)
of this phylum (Fig. 2).
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3.3 Different cropping systems had different structure of specific species
To compare the relationships among these bacterial communities in detail, the shared and
specific species were determined via a Venn diagram (Fig. 5-A). The results showed that the
number of species shared among three crops was only small proportion of each bacterial community
which was 14.61% for wheat, 20.43% for maize and 15.86% for sunflower. But the specific species
were dominant that respectively make up 68.09% for wheat, 49.14% for maize, and 62.27% for
sunflower of total community. The structure of specific species was different (Fig. 5-B). The wheat
rhizosphere had distinctly high proportion of specific Bacteroidetes, Actinobacteria and
Acidobacteria and the low proportion of specific Proteobacteria and Firmicutes. The sunflower
rhizosphere had distinctly high proportion of specific unclassified bacteria and low proportion of
specific Bacteroidetes and Gemmatimonadetes. Our results revealed that three cropping systems
had distinctly different OTU compositions in their rhizospheres.
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4. Discussion
4.1 Sunflower monoculture had high bacterial richness and diversity
The bacterial community in sunflower rhizospheres showed no decrease in richness and
diversity compared with other cropping systems in spite of more than 20 years of monoculture
cropping. In fact, the results showed higher population and diversity of bacterial communities in the
monoculture compared to the rotation systems (Appendix C). In addition, the richness of bacterial
population was higher in diseased plants than in healthy plants (Appendix C). These findings were
similar to the significant increase of the total bacteria community in TAD (take-all disease) outbreak
stage after wheat monoculture (Sanguin et al. 2009). The diversity of diseased plant samples was
high, which is similar to the results of Phytophthora-infected avocado roots (Yang et al. 2001). This
directly contradicts the hypothesis that plant pathogens have difficulty establishing in a diversified
rhizobacterial community (Shiomi et al. 1999) and that a greater biodiversity is synonymous with
better soil quality having good stability and normal function (Janvier et al. 2007). An interpretation
of these findings is that more nutrients associated with the diseased root decay are probably released
into the rhizosphere. Thus, the microenviroment could favor a larger bacterial population and attract
more bacteria that otherwise could not compete in the normal rhizosphere before or during the early
stages of heavy disruption of microbiome activity and composition (Kent and Triplett, 2002).
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4.2 A trend of more α-Proteobacteria and less γ-Proteobacteria in sunflower monoculture
As in many other soils (Janssen, 2006; Yu et al. 2012), the Proteobacteria comprise the largest
fraction of the bacterial community in this study. Although there weren’t significant differences
among cropping systems, the proportion of its subgroup showed some trends in monoculture.
Sunflower monoculture had relative high α-Proteobacteria and low γ-Proteobacteria at both
anthesis and maturity, as was similar with the diseased plants (Appendix D). Meanwhile
maize-sunflower had distinctly high γ-Proteobacteria at both stages, which reflected the influence
of maize on the succeeding crops.
In addition, this phylum was more abundant in diseased plants than in healthy plants
(Appendix E), which was similar to the important increase of rhizosphere Proteobacteria
communities in TAD (take-all disease) outbreak stage after wheat monoculture (Sanguin et al.
2009). This result may implicate that upon attack by a fungal root pathogen, plants can exploit
microbial consortia from soil, for example, the beneficial groups such as Proteobacteria, to protect
against infections (Cook et al. 1995; Mendes et al. 2011).
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4.3 Wheat-sunflower had high proportion of Acidobacteria
The Acidobacteria is ubiquitous and among the most abundant bacterial phyla in soil (Janssen,
2006). Across 87 unique soils throughout North and South America, the Acidobacteria represented
30.9% of all classified bacterial sequences detected using pyrosequencing (Lauber et al. 2009). This
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phylum was less abundant in the arable land than in bulk soil (Kielak et al. 2008). It represented
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28% of the sequences from forest soils, 16% from cropland soils, and 17% from soybean
rhizosphere (Navarrete et al. 2013). The relative abundance of soil Acidobacteria was strongly
correlated with soil pH (Rousk et al. 2010). In this study it was less than 20% (Appendix E) since
the soil pH was above 8, which is consistent with the proportional range obtained from eleven soil
samples with higher pH across North and South America (Lauber et al. 2009).
The Acidobacteria is an important group deserving to be mentioned in this study. It was
significantly high abundance in wheat-wheat and also obviously high in wheat-sunflower cropping
systems at both anthesis and maturity (Fig. 2, Appendix E). Meanwhile its proportion was higher in
healthy plant than in diseased plant rhizosphere (Appendix E). In addition, it also accounted for
high relative abundance in specific groups of wheat-sunflower at both growing periods, as was
similar with health plants (Appendix F，G). The Acidobacteria has been found to associate with the
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suppressive stages of take-all decline (Sanguin et al. 2009). Our results showed that wheat rotation
could increase its abundance while monoculture could decreas its proportion. This suggests that
these changes may be involved in sunflower monoculture obstacle and wheat rotation function in
this district.
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4.4 The relative abundance of Firmicutes decreased sharply from anthesis to maturity
The proportion of Firmicutes was significantly lower in wheat-wheat at anthesis than other
cropping systems at anthesis, and decreased sharply to around 1% in rotation systems at maturity
(Appendix E). This phylum may be locally abundant. In a grassland soil in The Netherlands about
65% of all the bacterial ribosomes originated from Firmicutes (Felske et al. 1998). It was also the
most abundant phylum in all rhizosphere soil having no anthropogenic impacts from Antarctic
vascular plants of Admiralty Bay, maritime Antarctica (Teixeira et al. 2010). This phylum is
composed of spore forming groups such as Bacilli and Clostridia. One of the primary functions of
spores is to ensure the survival of bacteria through periods of environmental stress. In this study
the high relative abundance may be related to the high moisture at anthesis when rain was
obviously more abundant and frequent than usual in this district.
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4.5 The relative abundance of bacterial taxa could be bioindicator of soil health
It has been reported that the relative abundance of bacterial taxa is a more important indicator
of disease suppression than the exclusive presence of specific bacterial taxa (Mendes et al. 2011).
Differences in bacterial phyla between crop rotations and plant health suggest crop rotation
strategies could be used to improve the soil biology environment, increase plant productivity and
reduce disease. Further research is necessary to establish mechanisms that explain these correlations
between crop rotation and rhizosphere microbial community differences, and clarify the role of
these bacteria in plant growth promotion or disease suppression in sunflower rotation treatments.
Our study did not analyze the beneficial and functional bacterial populations that have close
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relationships with soil nutrient translocation and utilization. Further research is needed to clarify
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microbial community structures and the ways in which they change, the relationships between crop
yields and utilization of nutrients in the crops’ rhizospheres in rotation systems, and the sustainable
development of rotation cropping systems.
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5. Conclusion
The Proteobacteria, Bacteroidetes, Acidobacteria, Gemmatimonadetes, Chloroflexi,
Actinobacteria, Planctomycetes and Firmicutes dominated in all cropping systems. There were
significant differences (P<0.05) in proportions of dominant phyla Bacteroidetes, Acidobacteria,
Gemmatimonadetes, Planctomycetes and Firmicutes among three cropping systems in the middle of
growing stages. The wheat-wheat rhizosphere had the highest proportion of Acidobacteria,
Bacteroidetes and the lowest proportion of unclassified bacteria. The maize-maize rhizosphere had
the highest proportion of γ-Proteobacteria and Pseudomonadales and lowest proportion of
Acidobacteria. The sunflower monoculture rhizosphere had the highest proportion of the
unclassified bacterial. Furthermore, phylum Proteobacteria, Acidobacteria and the unclassified
bacterial exhibited the influences of the previous crops on the succeeding crops. Rotation of
sunflower with wheat and maize increases the relative abundance of the Acidobacteria and
decreases the relative abundance of the unclassified phyla. Likewise, Acidobacteria are relatively
more abundant in the rhizospheres of healthy plants. These variations may suggest that cultivation
of sunflower in rotation with wheat or maize had some positive impact on soil health within a single
field.
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Acknowledgements
This work was supported the Special Fund for Agro-Scientific Research in the Public Interest in
China (201103001).
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Fig. 1 Principle component analysis (PCA) of 454 pyrosequencing data. The values of PCA 1 and 2 are
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percentages of total variations that can be attributed to the corresponding axis.
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Fig. 2 Relative read abundance of different bacterial phyla within the different communities. Sequences that could
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not be classified into any known group were assigned as ‘Unclassified’ bacteria.
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Fig. 3 Relative read abundance of four bacterial phyla within the different communities. Sequences that could not
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be classified into any known group were assigned as ‘Unclassified’ bacteria; a, b, and c indicate significant
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differences among the three crops within one field by Tukey’s test (P＜0.05).
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Fig. 4 Relative read abundance of the Proteobacteria subgroup within the different communities. Sequences that
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could not be classified into any known group were assigned as ‘Unclassified’ bacteria; a, b, and c indicate
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significant differences among the three crops within one field by Tukey’s test (P＜0.05).
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Fig. 5 Specific OTUs analysis of the different cropping system libraries: a for Venn diagram showing the unique
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and shared OTUs (3% distance level), b for special species existing in each cropping system.
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Table 1 Estimated OTUs richness and diversity indices in rhizosphere samples
Wheat
Maize
Sunflower
OTUa
5432
3665
4499
Chaob
12612.29±4661.71 a
8649.59±2362.34 a
11856.50±3894.86 a
8.12±0.19 a
7.28±0.25 b
7.88±0.27 a
Shannonc
532
a
533
b
534
c
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Significant differences among cropping systems are indicated by alphabetic letters.
Operational taxonomic unit. Calculated with Furthest neighbor at the 3% distance level.
Chao 1 richness index. Higher number represents higher relative abundance.
Shannon diversity index. Higher number represents higher diversity.
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19