Introduction
The peach [Prunus
persica (L.) Batsch] is one of the most widely cultivated fruit
trees throughout the world (Allison 1976) and it has high economic value. Peach
trees are cultivated using a variety of methods. Many experimental studies have
shown that root-limited cultivation can promote the production of
lateral roots by fruit trees (Peren 2020), inhibit
the growth of new shoots, promote flower formation, increase the
fruit set rate, enhance the yield, and improve the seedling quality (Smith and
Graber 1948). Root restriction cultivation is a new
cultivation technique where physical or ecological methods are used to constrain the
plant root system within a certain volume, and to control plant root growth in
order to regulate the vegetative and reproductive growth of the aerial parts (Mukti and Ashikeen 2005). Root
growth and the external environment can vary under different root-restricted
cultivation methods, thereby resulting in corresponding root function changes
that can affect the external growth of plants as well as internal physiological
and biochemical reactions (Guo et al. 2017).
This method can promote root regeneration and new root growth, and inhibit
the growth of shoots above ground.
The high salt content of saline–alkali land can cause water deficiencies in plants.
Saline–alkali soil is readily degraded and the water-holding capacity and water
permeability are poor. The soil enzyme activities are inhibited in
saline–alkali land, which affects the soil organic matter conversion process
and the fertility declines as a consequence (Sun et al. 2012). Soil
salinization has become an important factor that limits the development of
agricultural production. Soil salinization directly affects the physiology of
plants to reduce crop yields. The development of new salt-tolerant crop
varieties is a promising strategy but the productivity of plants is still hindered
by salinization. Thus, the increasing salinity of soils means
that it is necessary to find new solutions for improving the plant growth
conditions (Paul and Lade 2014).
The microorganisms in farmland
soil play important roles in matter cycling and they can affect the soil
biochemistry (Jacoby et al. 2017). These microorganisms are able to
adapt to abiotic stress conditions, such as soil salinization and osmotic
stress (Xu and Fujiyama 2013). Previous studies have shown that these
microbes contribute significantly to plant growth by maintaining the cycling of
soil nutrients and promoting soil–plant interactions (Zhang et al. 2016).
Bacterial and fungal communities have been applied as indicators
to evaluate the effects of various cultivation methods on the soil ecological environment (Zornoza et al.
2009). The diversities of bacteria and fungi are closely related to the
complexity and variability of the soil ecological environment. Thus, the
microbial complexity can greatly influence the sustainable development of
farmland soil (Parausic et al. 2013), and high
microbial diversity will have beneficial effects on the quality and fertility
of the soil. However, degradation of the soil ecosystem will reduce the
microbial diversity (Teketay 2001). Therefore, high
bacterial and fungal diversities may facilitate the development of
saline–alkali land and improve the physical and chemical properties of the
soil. Soil salinization is an important issue that has affected agricultural
production and the soil quality for many years in northern China. Root
restriction cultivation has been applied widely in recent years for fruit tree
cultivation in this area. However, the effects of different root cultivation
modes on diversities and abundances of soil bacterial and fungal in land remain
unclear.
Due to the
development of next generation sequencing, the compositions of the bacterial
and fungal communities in soil can be determined readily by 16S rRNA and
internal transcribed spacer (ITS) sequencing. Some of these organisms can cause
plant diseases. However, few studies have investigated the effects of different
cultivation methods on the composition and function of the microbial
communities in soil. Mechanistic details of the effects of soil microorganisms
and different cultivation patterns on plant growth are still lacking. In the
present study, we determined the changes in the microbial community in response
to cultivation for several years under root-limited or normal conditions, and
the microbial populations that exhibited significant increases or decreases. In
order to study the response of the soil microbial community to soil
salinization under root-limited cultivation conditions, the bacterial V4–V5 16S
rRNA and fungal ITS gene regions were sequenced, and the physical and chemical
properties of the soils were determined using experimental methods.
Materials and Methods
Soil sampling and experimental treatments
The field test site was established in 2015 at
Xiaobotouzhen, Wudi County, Binzhou City, China (117.12°E, 37.15°N). The 3 years old peach
trees were in the same growth condition. Soil samples were collected from
January 18, 2016 to August 7, 2019. The experiment tested two treatments: (1)
root restriction cultivation (XG group) and (2) normal cultivation without root
restriction (BXG group), as well as a normal field without any trees as the
control (NC group). Each field was treated with the same amount of fertilizer
(urea: 30 kg 667 m-2, K2SO4: 12 kg 667 m-2
and Ca(H2PO4)2·H2O: 1 kg/667 m2).
Three replicates were conducted for each treatment. Table 1 shows the detailed
location information. In total, 21 rhizospheric samples were collected from
seven points with three replicates for each treatment. The samples were sieved
through a 2-mm mesh, before removing any impurities, roots, and stones. Each
sample was then divided into two portions, where one was used for the chemical
analyses the other one was stored at −80°C for DNA extraction.
Determination of soil properties
The soil pH was measured with a glass electrode
using a soil to water mixture at a ratio of 1:2.5 (Islam and Weil 2000). The
SOM contents were measured using Chinese Standard Method
GB9834-88. The Kjeldahl method was used to measure the soil available
nitrogen. The available phosphorus contents were determined with the Olsen
method (Li et al. 2004). Flame atomic absorption spectrophotometry was
conducted to determine the soil available potassium contents. Soil mineral
elements (sodium, Na; calcium, Ca; magnesium, Mg) were determined by the
methods of nitric acid (HNO3) digestion and then measured by
inductively coupled plasma atomic emission spectroscopy (Thermo Electron
ICP-6000) (Shaheen et al. 2016).
Soil extracellular enzyme activities
The activities of a-glucosidase, b-glucosidase, chitinase, cellobiase, urease and
phosphatase were determined as described previously (Corbel
and Hendry 1985) using 10 g of each fresh soil sample. Each 10 g soil
sample was suspended in 25 mL of MilliQ water and the mixture was incubated for
10 min at 25°C with stirring at 250 rpm.
Next, 100 mL of sterile Milli-Q water was added to dilute the enzymes.
The a-glucosidase and b-glucosidase activities were
measured using Soil a-Glucosidase (5-a-GC) and Soil b-Glucosidase (s-b-GC) Activity Detection Kits (Solarbio Life Science Co. Ltd., Beijing,
China). The urease activity was determined by toluene titration. The chitinase
activity was determined according to Chinese Standard GB T 34799-2017.
Cellobiase was determined by 3,5-dinitrosalicylic acid
titration.
Soil DNA extraction and high-throughput sequencing
DNA was extracted from 1.0 g of each fresh soil
sample in triplicate using a Tiangen DP336-02 Soil DNA Extraction Kit (Tiangen
Biotech Co. Ltd., Beijing, China) according to the manufacturer’s instructions.
The quality and quantity of the DNA were determined with a Nano Drop™2000
spectrophotometer (Nano Drop Technologies, Wilmington, DE, USA). 16S rRNA and
ITS1 were amplified for high-throughput sequencing as described previously
(Shen et al. 2013). Primers F515 (5¢-GTGCCAGCMGCCGCGG-3¢)/R907 (5¢-CCGTCAATTCMTTTRAGTTT-3¢) and ITS1 (5¢-CTTGGTCATTTAGAGGAAGTAA-3¢)/ITS2 (5¢-GCTGCGTTCTTCATCGATGC-3¢) were used to amplify the V4–V5 regions of the
bacterial 16S rRNA and fungal ITS1, respectively. PCR was then performed as
described previously (Sun et al. 2015). The PCR products were purified
and analyzed with an Illumina HiSeqXten sequencer (Illumina Inc., CA, USA) to
obtain the raw sequencing reads.
Bioinformatics analysis
In order to
integrate the original double-end sequencing data, we first used the sliding
window method to perform quality screening for the double-end sequences in
FASTQ format, where the window size was 10 bp, the step size was 1 bp, and the
movement started from the first base position at the 5¢ end. The average quality of bases in the window was
required to be ≥ Q20 (the average sequencing accuracy of the bases was
over 99%). The sequence was truncated from the first window when the average
quality value was lower than Q20, while the length of the sequence after
truncation was required to be ≥ 150 bp, and an ambiguous base N was not
allowed. Subsequently, the double-ended sequences that passed the initial
quality screening were paired and connected according to the overlapping bases
with FLASH software (v. 1.2.7, http://ccb.jhu.edu/software/FLASH/) (Magoc and
Salzberg 2011). The overlapping base length for the two sequences in Read 1 and
Read 2 had to be ≥ 10 bp, and the number of base mismatches was less than
10% of the overlapped base length. Finally, according to the index information Table 1: Soil samples cultivation years and pattern
|
Sample name |
Cultivation years |
Cultivation pattern |
|
XG11 |
1 |
root limited cultivation |
|
XG12 |
1 |
root limited |
|
XG13 |
1 |
root limited |
|
XG21 |
2 |
root limited |
|
XG22 |
2 |
root limited |
|
XG23 |
2 |
root limited |
|
XG01 |
3 |
root limited |
|
XG02 |
3 |
root limited |
|
XG03 |
3 |
root limited |
|
BXG11 |
1 |
not limited |
|
BXG12 |
1 |
not limited |
|
BXG13 |
1 |
not limited |
|
BXG21 |
2 |
not limited |
|
BXG22 |
2 |
not limited |
|
BXG23 |
3 |
not limited |
|
BXG31 |
3 |
not limited |
|
BXG32 |
3 |
not limited |
|
BXG33 |
3 |
not limited |
|
NC1 |
0 |
Soil without plant |
|
NC2 |
0 |
Soil without plant |
|
NC3 |
0 |
Soil without plant |
(the barcode sequence comprising a small base sequence used
to identify the sample at the beginning of the sequence) corresponding to each
sample, the connected sequence identification was assigned to the corresponding
sample to obtain the valid sequence for each sample.
The UCLUST sequence
comparison tool (Edgar et al. 2015)
in QIIME software was used to merge and divide the previously obtained
sequences according to sequence similarity of 97% in order to obtain operational
taxonomic units (OTUs). The most abundant sequence for each OTU was selected as
the representative OTU sequence. According to the number of OTU sequences
present in each sample, we constructed a matrix file for the OTU abundances in
each sample (OTU table). The QIIME OTU Classifier was used to identify the
taxonomy of each 16S rRNA and ITS1 gene sequence against the RDP (Release 11.1,
http://rdp.cme.msu.edu/) (Maidak et al.
2001) and Silva (Release132, http://www.arb-silva.de) (Pruesse et al. 2007) databases,
respectively. Alpha-diversity analyses based on the Chao1, ACE, Shannon, and
Simpson indices were conducted using Mothur software
(Schloss et al. 2009). Redundancy analysis
(RDA) was performed to determine the variations in the microbial communities
with potential effects on the soil properties (Zuur et al. 2010).
Results
Soil properties
The SOM, AN, AP, and AK contents in each test group
are shown in Table 2. The pH of the soil decreased by 0.5 units compared with
the NC group after peach cultivation for 3 years. The pH of the soil in each
field decreased gradually as the number of years under cultivation increased.
The SOM contents gradually decreased in all samples with the number of years
under cultivation. After root-restricted cultivation for 3 years, the SOM
content decreased more significantly than that under the normal cultivation
pattern, thereby suggesting that root restriction may be beneficial for SOM
utilization.
The AN, AP, and AK
concentrations tended to decrease significantly in the soil under
root-restricted cultivation (XG group) compared with the normal cultivation
pattern. No clear patterns were found in the concentrations of sodium, calcium,
and magnesium ions due to their low variation.
Soil enzyme activities
As shown in Table 2, the a-glucosidase activity decreased significantly in the
NC soil (79.24 mg g–1) and it was significantly lower under XG compared
with NC. The a-glucosidase activity
increased gradually to 85.25, 112.67, and 148.65 mmol g−1 after cultivation for 1, 2,
and 3 years under XG, respectively. The a-glucosidase activity was
significantly higher under XG after 3 years (148.65 mg g–1) compared
with BXG, and the a-glucosidase activity did
not increase significantly in BXG. The b-glucosidase activity was
significantly higher under XG compared with those in BXG and NC after the same
number of years of cultivation. The cellobiase and chitinase activities were
4.19 and 73.05 in XG, respectively, and they were significantly higher than
those in BXG (46.50%) and NC (55.49%).
Table 2: Soil chemical properties
|
|
pH |
SOM (g/kg) |
AN (g/kg) |
AP (g/kg) |
AK (g/kg) |
Na (g/kg) |
Ca (g/kg) |
Mg (g/kg) |
|
XG1 |
8.85 ± 0.09ab |
9.59 ± 0.19 |
168 ± 3a |
7.29 ± 0.06ab |
124.00 ± 3ab |
14.56 ± 1.56ab |
11.46 ± 1.71a |
7.09 ± 0.76a |
|
XG2 |
8.61 ± 0.2bc |
7.11 ± 0.34 |
150.67 ± 5.69ab |
6.08 ± 0.05b |
95.33 ± 2.08b |
13.67 ± 2.06b |
10.34 ± 1.41a |
6.09 ± 0.76a |
|
XG3 |
8.16 ± 0.08d |
5.26 ± 0.17 |
113.33 ± 9.45c |
5.29 ± 0.16c |
69.67 ± 1.53c |
14.37 ± 0.87ab |
11.41 ± 0.96a |
6.09 ± 0.76a |
|
BXG1 |
8.82 ± 0.06ab |
9.63 ± 0.11 |
165.67 ± 5.13a |
7.49 ± 0.16a |
127.33 ± 1.53ab |
15.96 ± 1.34ab |
12.19 ± 2.36a |
6.09 ± 0.76a |
|
BXG2 |
8.71 ± 0.06bc |
8.02 ± 0.07 |
147 ± 6.08ab |
6.61 ± 0.22ab |
101.67 ± 2.08bc |
16.22 ± 1.43ab |
10.86 ± 1.74a |
6.09 ± 0.76a |
|
BXG3 |
8.52 ± 0.03c |
7.46 ± 0.15 |
116 ± 4c |
5.95 ± 0.07bc |
84.67 ± 3.06c |
13.85 ± 2.06b |
9.92 ± 0.76a |
7.09 ± 0.76a |
|
NC |
8.86 ± 0.07a |
8.79 ± 0.22 |
170.33 ± 10.6a |
7.69 ± 0.15a |
135.33 ± 3.51a |
17.32 ± 1.76a |
10.32 ± 0.95a |
6.43 ± 0.76a |
SOM, Soil organic matter; AN,
Active nitrogen; AP, Active phosphorus; AK, Active potassium; Values are
presented as mean ± SD (n ≥ 3)
The phosphatase activity
decreased gradually over the years under each cultivation pattern. The highest
phosphatase activity was determined in NC (364.49 mg g–1). The urease activities were
significantly higher under XG and BXG than NC (709.29 mg g–1). The highest urease activity was
found in XG2 (1393.79 mg g–1).
Diversities of bacterial and fungal communities
Alpha diversity analysis was conducted to determine
the effects of XG cultivation on the richness and diversity of the soil
bacteria and fungi. In total, 1,482,439 paired raw reads of V3–V4 16S rRNA
sequences were obtained to assess the soil bacterial communities. After quality
filtering and merging, 1,365,030 clean tags were used for subsequent analyses.
On average, we obtained at least 34,843 high-quality bacterial 16S rRNA gene
reads from each soil sample in seven groups from the total of 21 samples. In
total, 2,566,235 paired raw reads and 2,460,227 valid reads of ITS1 sequences
were obtained after Illumina sequencing for assessing the fungal diversity.
The numbers of OTUs in each
group based on sequence similarity greater than 97% are shown in Table 3. All
of the high-quality sequences were clustered into 4270 OTUs, with 230 to 534 in
each sample. The species richness at the OTU level was represented using
Shannon, Simpson, ACE, and Chao1 indices (Table 4). In terms of the soil
bacteria, the Shannon indices ranged from 4.11 to 6.05, with the highest in XG1
and the lowest in NC. The community richness and diversity values were higher
in XG2 than those in NC. In term of the fungal diversity, the Shannon indices
ranged from 2.20 for NC to 3.61 for XG2, where the trends were similar to the
bacterial diversity indices (Table 4). The Shannon and Chao1 values for fungi
were lower in NC and BXG1 compared with XG2 and XG3. The richness values for
the bacterial and fungal communities were significantly lower in NC. The highest
fungal diversity values were found in BXG2 and XG, and the lowest in BXG3 and
XG3.
Compositions of bacterial and fungal communities
The relative abundances of the main bacterial and
fungal taxa in the different soil samples at the phylum and genus levels are
shown in Fig. 1. The bacterial community composition changed significantly in
NC, thereby demonstrating that cultivating plants could help to improve the
soil bacterial community. At the phylum level, Proteobacteria dominated
the communities in all of the samples, with the highest proportion of 54.3% in
NC and the lowest of 21.62% in BXG1, followed by Actinobacteria, Acidobacteria,
Chloroflexi, Gemmatimonadetes,
and Bacteroidetes in decreasing
order. These six common phyla dominated comprised more than 90% of all of the
bacterial communities under NC, XG, and BXG. Proteobacteria, Actinobacteria,
Acidobacteria, Chloroflexi, and Gemmatimonadetes
comprised 82.4–89.1% and 82.3–87.5% of the total bacterial taxa in XG and
BXG, respectively (Fig. 1). Within Proteobacteria,
the class Gammaproteobacteria was most abundant in NC (47.2%) but the
relative abundance of this class was lowest in BXG1 (0.09%). Actinobacteria comprised the second most
dominant phylum in all samples, with 14.9% in BXG2.
The least abundant of the
top 15 classes was Deltaproteobacteria with
a highest proportion of 2.8% in BXG2 (data not shown). At the genus level, the
most abundant members of the bacterial community were Arthrobacter, Lysobacter,
uncultured bacterium Subgroup 6, and uncultured bacterium Gemmatimonadaceae, which comprised 7.3, 6.8, 5.6 and
5.4%, respectively, of the genera in XG1, XG2, and XG3 (Fig. 2). By contrast,
in BXG1, BXG2, and BXG3, Lysobacter, uncultured bacterium Subgroup 6, Arthrobacter,
uncultured bacterium Gemmatimonadaceae,
and RB41 were the dominant genera, where they comprised 22.50, 25.90, 14.90,
18.50 and 12.20% of the total genera, respectively. In addition, Lysobacter
(0.34%) and Cryobacterium (8.7%) were the dominant genera in NC (Fig.
2).
Table 3: Activity of five extracellular soil enzymes
|
|
α-Glucosidase |
β-Glucosidase |
Cellobiase |
Chitinase |
Phosphatase |
Urease |
|
XG1 |
76.08±6.82c |
100.51±8.22c |
3.68±0.84b |
37.06±4.83c |
340.81±98.41a |
963.91±183.59c |
|
XG2 |
93.34±8.96bc |
130.03±12.65b |
3.75±0.37ab |
49.02±6.09b |
325.57±80.01bc |
1393.79±277.86a |
|
XG3 |
148.65±7.97a |
143.79±15.72a |
4.19±0.41a |
73.05±5.37a |
288.87±37.44d |
1226.91±222.42ab |
|
BXG1 |
72.34±9.21bc |
125.68±8.9b |
3.00±0.11c |
51.81±7.88b |
319.55±59.41b |
1131.86±219.33b |
|
BXG2 |
85.25±8.34b |
130±8.35a |
3.6±0.43b |
54.70±4.98b |
313.99±36.39c |
817.32±43.55cd |
|
BXG3 |
112.67±5.7b |
113.65±10.66b |
3.43±0.2b |
57.94±7.53b |
231.31±56.47e |
958.34±67.23c |
|
NC |
59.24±9.17d |
83.48±6.94c |
2.86±0.42d |
46.98±4.63b |
364.49±26.07a |
709.29±106.84d |
XG1,2,3
and BXG1,2,3 were soils from root restriction cultivation method and normal
cultivation pattern for 1,2,3 years respectively. NC was soils samples without plant.Values are the means ±standard deviations (n ≥
3).
Table 4: Comparisons
of OTUs and alpha-diversity index in each group
|
|
|
Shannon |
Simpson |
Ace |
Chao |
|
NC |
Bacteria |
4.1147±0.0479 |
0.095±0.0041 |
970.0997±38.3115 |
996.6788±38.9847 |
|
Fungi |
3.606±0.2204 |
0.1348±0.0334 |
258.7147±11.7913 |
265.6667±14.6072 |
|
|
BXG1 |
Bacteria |
5.8626±0.0181 |
0.0067±0.0004 |
1084.2928±2.7284 |
1117.0611±8.4919 |
|
Fungi |
2.2007±0.0929 |
0.2148±0.02 |
172.6625±3.5284 |
173.0129±5.4133 |
|
|
BXG2 |
Bacteria |
5.3637±0.0634 |
0.0245±0.002 |
1418.5527±6.928 |
1431.6483±8.186 |
|
Fungi |
2.2168±0.0249 |
0.1938±0.0148 |
107.4197±6.0566 |
108.4375±5.9432 |
|
|
BXG3 |
Bacteria |
5.8457±0.0078 |
0.0128±0.0005 |
1455.3623±0.4069 |
1466.6698±5.6031 |
|
Fungi |
3.398±0.0178 |
0.0945±0.0035 |
240.3863±3.4438 |
245.1667±4.4729 |
|
|
XG1 |
Bacteria |
6.0535±0.0554 |
0.009±0.0005 |
1436.0238±6.6797 |
1473.9288±4.0093 |
|
Fungi |
3.1857±0.3171 |
0.1585±0.0532 |
164.348±7.434 |
174.0667±2.0342 |
|
|
XG2 |
Bacteria |
5.9367±0.0459 |
0.0141±0.0019 |
1521.5527±13.2345 |
1546.3528±19.6163 |
|
Fungi |
2.9283±0.0424 |
0.0864±0.0043 |
153.9547±5.0252 |
156.183±5.3236 |
|
|
XG3 |
Bacteria |
5.6952±0.0554 |
0.0153±0.0022 |
1421.7694±18.4288 |
1443.4586±18.3478 |
|
Fungi |
3.3082±0.3974 |
0.132±0.0328 |
191.0006±25.5467 |
192.8±23.4813 |
%201325-1334_files/image002.png)
Fig. 1: Microbial community bar plot of relative
abundances of bacterial on phyla
The fungal community
compositions were significantly affected by cultivation with plants (Fig. 3 and
4). Ascomycota was the dominant phylum and it accounted for 54.78% of
the total sequences on average, while Mortierellomycota
and Basidiomycota comprised 36.55 and 3.16%, respectively
(Fig. 3). The proportions of Zoopagomycota
and Glomeromycota were less than
0.1% in each treatment. BXG2, BXG3, XG2, and XG3 contained lower relative
abundances of Ascomycota than BXG1,
XG1, and NC. Compared with the other treatments and times, the relative
abundances of the phylum Mortierellomycota
were higher in BXG2, BXG3, XG2, and XG3, whereas the relative abundances of
Ascomycota were lower. The abundances of Basidiomycota increased significantly after 2 and 3 years under XG
and BXG compared with those after 1 year and NC. The dominant genera comprised Mortierella,
Pseudogymnoascus, and Botryotrichum
with average relative abundances of 36.37, 11.70 and 7.65%, respectively
(Fig. 4).
Multivariate analysis of microbial community
compositions and environmental factors
RDA was performed to evaluate the correlations
between the microbial communities at the phylum level and the enzyme activities (Fig. 5).
The correlations between the 10 most dominant phyla and environmental
parameters were determined by RDA. RDA1 and RDA2 explained 29.59% of the total
variance in the data. XG1, XG2, and XG3 were positively correlated with the
activities of a-glucosidase, b-glucosidase, cellobiase, phosphatase, and urease, whereas BXG1, BXG2 and BXG3
had negative correlations (Fig. 5a). The fungal enzyme activities exhibited
different patterns in the cultivation groups. XG3 was positively correlated
with the phosphatase and cellobiase activities, whereas BXG3 had negative
correlations (Fig. 5b). SOM, pH, AP, AN, and AK were strongly negatively linked
with Nitrospirae, whereas Proteobacteria had positive correlations
with pH, AK and AN.
Among the five soil chemical
parameters, AN, AK, and pH were correlated with specific bacterial phyla (Fig.
6a). Chloroflexi was negatively
correlated with SOM, pH, AK, and AN but positively
correlated with AP. Among the fungal phyla, Ascomycota
had the strongest positive relationships with SOM, AN, AK, pH, and AP. Basidiomycota
had negative relationships with SOM, AK, pH, and AP (Fig. 6b).
%201325-1334_files/image004.png)
Fig. 2: Microbial community bar plot of relative
abundances of bacterial on genus
%201325-1334_files/image006.png)
Fig. 3: Microbial community bar plot of relative
abundances of fungal phyla
%201325-1334_files/image008.png)
Fig. 4: Microbial community bar plot of relative
abundances of fungal genus
%201325-1334_files/image010.png)
Fig. 5: Redundancy analysis of the microbial communities
and enzyme activity
The RDA of bacterial communities
and enzyme activity
The RDA of fungal communities and
enzyme activity
The relationship between a parameter
and the microbial communities and samples was provided by the length of the
physicochemical parameter arrow in the ordination plot
aGlu, α-glucosidase;
bGlu, β-glucosidase;
Cell, cellobiase; Phos,
phosphatase; Chit, Chitinase; Ure,
urease
%201325-1334_files/image012.png)
Fig. 6: Relationship of microbial composition and soil
chemical property
Relationship of bacterial composition and soil
chemical property
Relationship of fungal composition and soil
chemical property
OM, organic materials; AN,
available nitrogen; AP, available phosphorus; AK, available potassium
%201325-1334_files/image014.png)
Fig. 7: PICRUSt analysis
results of predicted functional pathways in soil microbiota
%201325-1334_files/image016.png)
Fig. 8: Multiple-sample PCA analysis of bacteria (a) and
fungi (b) in different cultivation patterns and years
KEGG functional predictions based on 16S rRNA
The metabolic processes related to the soil
microbiota were predicted based on comparisons with previously published 16S
rRNA data using the PICRUSt method. Carbohydrate metabolism, amino acid metabolism,
energy metabolism, and metabolism of cofactors and vitamins were affected mainly
by soil bacteria (Fig. 7). Changes in the metabolic processes were found in BXG
compared with XG, including an increased carbohydrate metabolism rate and
decreased nucleotide metabolism rate.
Principal component analysis (PCA) and linear
discriminant analysis
PCA showed that the bacterial community compositions
were separated mainly by the cultivation pattern, but not significantly by the
cultivation time (Fig. 6a). The bacterial samples were clustered under the XG
cultivation pattern (Fig. 8a). The fungal communities were clustered by both
the cultivation year and method (Fig. 8b).
Linear discriminant analysis effect size (LEfSe) analysis was
conducted to further discriminate the bacterial and fungal taxa in different
groups at the phylum to genus levels (Fig. 9). The results obtained by LEfSe
analysis showed that the bacterial phyla comprising Chloroflexi, Proteobacteria,
Actinobacteria, and Gemmatimonadetes were
affected most by the treatments (Fig. 9a), where Ascomycota and Mortierellomycota varied most significantly
in NC and BXG, respectively (Fig. 9b).
%201325-1334_files/image018.png)
Fig. 9: The LDA scores (log 10) of the significantly
differential soil groups provided by LEfSe in
bacteria (a) and fungi (b). The color of each bar corresponds to the group with
the highest averaged abundance
Discussion
After cultivation for 3 years, the pH decreased by
about 1 unit under NC. In addition, cultivation with plants (XG and BXG)
gradually decreased the soil pH over time, as also shown in a previous study
(Sun et al. 2015). The AK, AN, and AP
levels decreased gradually over time under peach cultivation. The N, P, and K
concentrations were lower under XG than BXG, possibly due to the greater
fertilizer utilization efficiency. Similar studies showed that root restriction
cultivation increased the nutrient utilization efficiency (Xue
et al. 2011).
The SOM content was
significantly higher under NC than XG and the SOM content decreased with the
cultivation time. Previous studies have demonstrated that the nutrients
required for plant growth are mainly stored as SOM. The availability of
nutrients to the plant roots also depends partly on their transformation by
microbial communities (Walker and Bernal 2006). Our results showed that the SOM
contents clearly increased with the number of years of cultivation, but there
were no significant differences under the different cultivation patterns in the
same year of cultivation, thereby indicating that the different cultivation
patterns had no significant effects on the soil nutrient levels. The soil
microbial community has close relationships with the soil enzyme activity levels
(Zhang et al. 2013). In the present study, the a-glucosidase activity was significantly higher in XG
than BXG. The results also indicated that the a-glucosidase activity
increased with the number of years of cultivation. The results obtained by RDA
and the correlation heatmap showed that a-glucosidase had important
links with the bacterial communities. A previous study showed that the soil pH
decreased significantly with the a-glucosidase
level (Acosta-Martínez and Tabatabai 2000), which agrees with our results. The cellulase
activity in the same field increased gradually with number of years of
cultivation and the cellulase activity was higher in XG3 compared with BXG3.
The mineralization of organic phosphorus is usually assessed based on the
phosphatase activity (Krishnamoorthy 1990). We found that the
phosphatase activity in the same field decreased gradually with the number of
years of cultivation and the highest phosphatase activity was found in NC. The
chitinase and cellulase activities exhibited similar trends with the highest
levels in XG3 and the lowest in NC. Urea is hydrolyzed into
carbon dioxide and ammonia is transformed by urease (Corbel and Hendry 1985). The urease
activities were significantly lower in NC and BXG than XG2. The soil enzyme
activities are stimulated by plant roots (Touceda et al. 2017; Benbi et al. 2017).
Our results indicated that the urease activities were significantly lower under
BXG and NC than XG, thereby suggesting that XG could improve the urease status.
The soil enzyme activities and root structure are also related. The
effects of the cultivation time on the enzyme activities were determined based on heatmaps
and RDA in our study. Major differences in the bacterial compositions were
found compared with NC, where XG and BXG had different effects on the bacterial
communities. Open root and root-limited cultivation are both
applied as common cultivation methods in China, but few previous studies have
investigated the bacterial and fungal communities under normal cultivation
(root unlimited) and root-limited cultivation patterns. The
variations in the bacterial communities under different fertilizer conditions
were explored in previous studies, which indicated that Bacteroidetes, Proteobacteria,
Firmicutes, Chloroflexi, and Proteobacteria
were the dominant phyla. Members of Proteobacteria
play significant roles in the biotransformation and biodegradation of SOM,
especially the genus Pseudomonas. Previous studies have shown that
carbon, nitrogen, and phosphorus cycling are mediated partly by members of the
genus Pseudomonas. Thus, Proteobacteria
might be highly involved in the utilization of SOM under root-limited
planting. We found that the abundance of Proteobacteria
decreased after cultivation for 1–3 years, whereas that of Gemmatimonadetes increased. Betaproteobacteria also participate in
nitrogen fixation and the oxidization of ammonium to produce nitrite, as found
in waste water and farmland soil.
Reducing the accumulation of
N, P, and K in planted soil could alleviate the acidification and salinization
of soil. A previous study found that increased soil acidity was caused by
excess nitrogen fertilization (Han et al. 2015), and we obtained similar
findings in the present study. The soil bacteria and fungi compositions in XG3
had the most negative associations with the high pH, P, N, K and SOM levels.
The communities in BXG1, BXG2, XG1 and XG2 were positively correlated with all
five soil parameters, whereas negative relationships were found under BXG3 and
XG3. The changes in the patterns were similar for both fungi and bacteria. The
bacterial and fungal community compositions were separated by the cultivation
pattern and number of years of cultivation by PCA. The bacterial and fungal
samples were clustered by the same cultivation method, but the NC samples were
clustered separately from XG and BXG.
To elucidate the
distinctions among the bacterial and fungal communities under different soil treatments,
LEfSe was conducted to analyze the differences in the bacterial and fungal taxa
at the phylum to genus levels. Chloroflexi,
Proteobacteria, Actinobacteria, and
Gemmatimonadetes were identified as
phyla that changed greatly in abundance by LEfSe analysis. The abundances of Ascomycota and Mortierellomycota differed significantly in NC and BXG. Ascomycota
and Mortierellomycota are common
and abundant in planted soils. In the present study, the abundance of Mortierellomycota increased in the
fields cultivated with Prunus persica, and it may have improved the
soil’s alkalinity.
The genus Lysobacter in
the phylum Proteobacteria contains
important biocontrol bacteria, which have beneficial antagonistic effects
against nematodes. The increased abundance of Lysobacter in XG3 may have
been beneficial for the growth of the fruit trees. Finally, the results
obtained based on the correlation heatmaps and RDA showed that pH, AN, and AP
contributed to the different effects of the microbial community under XG compared
with BXG.
Conclusion
In this study, specific bacterial and fungal
community compositions were obtained under different cultivation patterns. The
bacterial and fungal genera mainly belonged to the phyla Proteobacteria,
Actinobacteria, and Acidobacteria, and Ascomycota, Mortierellomycota, and Basidiomycota.
The environmental parameters and abundances of various phyla were affected by
the different cultivation methods according to multivariate analysis. The
result suggested that XG had beneficial effects on improving the soil’s
alkalinity and changing the microbial populations. The results obtained in this
study provide fundamental insights into the microbial ecology in fields
cultivated under root restriction.
Author Contributions
Wu Chong planned the experiments. Han Zhen and Zhang
Qingtian interpreted the results. Wu Chong, Tao Jihan, and Zhang Qingtian wrote
the manuscript. Wu Chong conducted statistical analyses of the data and
prepared the illustrations. We also wish to thank the timely help given by Dr.
Zhu Chenxi in analyzing data and writing manuscript. Funding:
This research was supported by Shandong Major Science and Technology Project
(No.2019JZZY010727).
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