The effects of different disease-resistant cultivars of banana on rhizosphere microbial communities and enzyme activities

Authors

  • Jianbo Sun,

    1. Key Laboratory of Biology and Genetic Resources of Tropical Crops, Ministry of Agriculture, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Long hua District, Hai kou, Hai nan Province, China
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  • Ming Peng,

    Corresponding author
    • Key Laboratory of Biology and Genetic Resources of Tropical Crops, Ministry of Agriculture, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Long hua District, Hai kou, Hai nan Province, China
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  • Yuguang Wang,

    1. Key Laboratory of Biology and Genetic Resources of Tropical Crops, Ministry of Agriculture, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Long hua District, Hai kou, Hai nan Province, China
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  • Wenbin Li,

    1. Key Laboratory of Biology and Genetic Resources of Tropical Crops, Ministry of Agriculture, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Long hua District, Hai kou, Hai nan Province, China
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  • Qiyu Xia

    1. Key Laboratory of Biology and Genetic Resources of Tropical Crops, Ministry of Agriculture, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, Long hua District, Hai kou, Hai nan Province, China
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Correspondence: Ming Peng, Key Laboratory of Biology and Genetic Resources of Torpical Crops, Ministry of Agriculture, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences, No. 4, Xue yuan Road, Long hua District, Hai kou, Post code 571101, Hai nan Province, China. Tel.: +86 0898 66890981; fax: +86 0898 66988081; e-mail: sunjb126@126.com

Abstract

To understand the mechanism of soil microbial ecosystem and biochemical properties in suppressing soilborne plant diseases, the relationship between the soil rhizosphere microbial communities, hydrolase activities, and different disease-resistant cultivars was investigated. There were statistically significant differences in microbial diversity in the rhizosphere soil between the disease-tolerant cultivar Fj01 and susceptible cultivar Baxi. The rhizosphere soil of Fj01 showed a trend of higher microbial diversity than that of Baxi. At the same growth stage, the similar trends of variation in microbial community diversity between the two different cultivars were observed. The bacterial community abundance in rhizosphere soil from the two banana cultivars was quantified by real-time PCR assays. The size of the rhizosphere bacterial population from the Fj01 was significantly larger than that from the Baxi during the growing stage from July to September. The activities of urease and phosphatase were analyzed to study the effects of the two banana cultivars to soil ecosystem functioning. Urease activity was significantly higher in the rhizosphere soil of Fj01 than that of Baxi in the period from July to September. However, phosphatase activity showed no significant difference between the two different rhizosphere soils.

Introduction

Fusarium wilt of banana is a serious and destructive disease worldwide. It is a vascular wilt disease caused by the soilborne fungus Fusarium oxysporum f.sp. cubense (Peng et al., 1999). There are no effective chemical control measures currently, and the resistance breeding is tedious. It is therefore necessary to find effective and integrated sustainable methods to control Fusarium wilt of banana. Soil microbial ecosystem and biochemical properties are seen to be critical to the maintenance of soil health and quality (Naeem et al., 1994; Garbeva et al., 2004), and good soil quality may be helpful to improve the capacity of the disease suppressiveness (Abawi & Widmer, 2000; Peters et al., 2003). However, little is known about the relationship between the rhizosphere soil microbial ecosystem, biochemical properties of different disease-resistant cultivars, and the suppressing of Fusarium wilt of banana.

The rhizosphere is a special micro-ecosystem, which includes plant, soil, and microorganism; meanwhile, it is the gateway through which soilborne pathogens enter the crop. Plant species and genotype are significant factors determining the structure and composition of microbial communities in the rhizosphere (Smalla et al., 2001; Garbeva et al., 2004; Mazzola, 2004).

Several studies have shown a correlation between the microbial communities in the rhizosphere and the disease caused by soilborne pathogens (Workneh & van Bruggen, 1994; Mazzola & Gu, 2002). One possible strategy is the management of the diversity and the structure of rhizosphere microbial communities and, as a result, enhances the antagonism to pathogens activity, leading to a decrease in plant disease caused by soilborne pathogens.

Various approaches have been developed to analyze microbial ecosystems in soil microbial communities. Traditional culture-based methods are based on isolation–cultivation approaches. However, only a small fraction of the microbial cells in soil are cultivable. Alternative approaches based molecular fingerprinting methods provide a complex view of the microbial community without the need for isolation and cultivation. Terminal restriction fragment length polymorphism (T-RFLP) analysis is one of the most rapid and powerful methods for comparing microbial communities. It has been shown to be an ideal technique for the rapid analysis of microbial diversity (Bankhead et al., 2004; Ausec et al., 2009). Terminal restriction fragments of T-RFLP analysis are detected by fluorescence, and the result of scanning can be compared with the data stored in databases. In this research, the microbial diversity was determined by T-RFLP profiles of the 16S rRNA gene obtained from different soil samples.

Real-time PCR has been used for the detection and quantification of bacteria in various environments (Becker et al., 2000). In this study, real-time PCR is used for the quantification of soil bacterial community.

Soil enzyme activities have been considered as indicators of soil quality (Bastida et al., 2008). Soil microbial communities are the primary source of soil enzymes. Here, we analyzed the enzyme activities of urease and phosphatase to access functional changes in the rhizosphere microbial community between two cultivars of banana.

The objective of this research was to compare the bacterial community structure and hydrolase activities in the rhizosphere of banana between two different disease-resistant cultivars against Fusarium wilt disease at different growth stages.

Materials and methods

Soil sampling

The samples were collected from the experimental field in Chinese Academy of Tropical Agricultural Sciences, Hainan Province, China. The soil type is tropical red loam.

The experiments were carried out during the 2010 growing season. Two cultivars of ‘Baxi’ and ‘Fj01’ were grown in a randomized plot design with three replications per cultivar, each containing four plants. The ‘Baxi’ and ‘Fj01’ were susceptible and disease-tolerant cultivars, respectively.

The plantlets of banana were transplanted into the field in February. After this, soil samples from the rhizosphere of banana were collected every month until September. Samples collected from the cultivars of Baxi and Fj01 at different month were named B1–B7 and F1–F7 in time order, respectively.

Soil cores were taken at c. 20 cm from the tree and at 30 cm depth. Each sample was collected at five cores per tree and three plants per plot. Pieces of root from the composite sample were picked out, and their tightly adherent soil was collected. The rhizosphere soil was homogenized and sieved to remove possible root fragments. Soil samples were stored at −20 °C for subsequent molecular analyses.

Soil DNA extraction and PCR amplification of 16S rRNA genes

Total DNA was extracted from 500 mg of soil sample using the Fast DNA SPIN kit for soil (Qbiogene). The purity and concentration of the DNA were determined using a spectrophotometer (Jasco, Tokyo, Japan).

For the T-RFLP analysis, the 16S rRNA gene was amplified with the primers 27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492R (5′-GGTTACCTTGTTACGACTT-3′; Culman et al., 2006). The 5′ end of the 27F primer was labeled with 6-carboxyfluorescein (6-FAM) for fluorescent detection.

The PCR mixtures contained 0.1 μM of each primer, 30 ng of template DNA, 1× PCR buffer, 2.0 mM MgCl2, 200 μM dNTP mix, and 2.5 U of Taq DNA polymerase (Sangon Biotech, China).

DNA amplification was performed in a thermocycler (Biometra, Germany) under the following conditions: initial denaturation at 95 °C for 5 min followed by 30 cycles of denaturation at 94 °C for 1 min, annealing at 52 °C for 1 min, and extension at 72 °C for 1.5 min, with a final extension at 72 °C for 7 min. Three independent PCRs were performed, combined for each, and purified using a Qiagen gel extraction kit (Qiagen, Germany).

Approximately 200 ng of fluorescently labeled PCR products was digested with the restriction enzyme HaeIII at 37 °C for 3 h. The reaction mixtures contained 8 μL of PCR product, 2.0 μL of 10× restriction enzyme buffer, 3 U of HaeIII, and ultrapure water to a final volume of 20 μL. Digestion products were verified on a 1.5% agarose gel in 1% TBE buffer. The digested DNA was purified using a gel extraction kit (Omega). The Three replicates of the digestion product from each sample were mixed.

T-RFLP analysis

Terminal restriction fragments (T-RFs) were separated on an ABI 373 sequencer (Applied Biosystems). The peak results were analyzed using GeneMapper software 4.0 (Applied Biosystem). In each sample, only peaks over a threshold of 100 fluorescence units were considered for further analysis. The relative abundance of each T-RF was calculated as a relative percentage by calculating the ratio of a given peak area to the total peak area within one sample. T-RFs with relative abundance below 2% were regarded as background noise and excluded from analysis. T-RFs smaller than 50 bp or larger than 600 bp were excluded from the analysis.

Quantification of bacteria by real-time PCR

Soil samples from the rhizosphere were collected every 2 months from March to September. Total soil DNA was extracted in the same way as described in Quantification of bacteria by real-time PCR and using 1 μL of 50-fold diluted extracted DNA (1–7 ng) as template.

Abundances of bacteria were determined by quantitative real-time PCR analysis of 16S rRNA gene. Each reaction was performed in a 25-μL volume containing 12.5 μL of SYBR® Green PCR Master Mix, 1 μL sample DNA, and 20 μmol of each primer. The primers used for the amplification were as follows: 338F: 5′-CCT ACG GGA GGC AGC AG-3′ and 518R: 5′-ATT ACC GCG GCT GCT GG-3′ (Seghers et al., 2003).

Real-time PCR was performed on a MX3000P real-time PCR machine (Stratagene, Cedar Creek, TX). Quantitative PCR were performed under the following conditions: 95 °C for 3 min and 40 cycles of 95 °C for 30 s, 53 °C for 40 s, and 72 °C for 1 min. Three independent quantitative PCRs were performed for each soil sample.

The bacterial 16S rRNA gene standard was amplified with the primers 27F: 5′-AGA GTT TGA TCC TGG CTC AG-3′ and 1492R: 5′-GGT TAC CTT GTT ACG ACT T-3′ (Ahn et al., 2009). The amplified product was then cloned into the pGEM-T Easy Vector (Promega). Standard curves were generated with serial dilution series of quantified plasmid DNA.

Soil enzyme activities in rhizosphere soil

Soil samples from the rhizosphere were collected as the methods described in Quantification of bacteria by real-time PCR. Phosphatase activity was determined using p-nitrophenyl phosphate disodium (0.115 M) as substrate, and the released p-nitrophenol (PNP) by phosphatase was measured spectrophotometrically at 410 nm (Ros et al., 2006). Urease activity was determined using phenol–sodium hypochlorite colorimetry (Wang et al., 2009). The released inline image was measured at a wavelength of 578 nm.

Data analysis

To evaluate the diversity indices of each community, each different OTU was treated as a different species, and the peak area for individual T-RFs was normalized to percent of the total fingerprint area as a relative abundance.

The Shannon–Wiener and Shannon evenness index were used to evaluate the richness and evenness of each sample. The Shannon–Wiener index was calculated from (H′) = −∑pi ln pi, where pi is the proportion of the peak area for each T-RF out of the total peak area for all T-RFs. The evenness index was calculated from (E′) = H/Hmax, where S is the number of T-RFs within a given profile, Hmax = ln S.

anova was used to analyze the statistical significance of the data, and multiple comparisons of significant differences were calculated using Tukey's t-test at the 5% level with spss 11.5 (spss for Windows, version 11.5).

Results

T-RFLP profiles

Bacterial community profiles of the rhizosphere microbial communities in two different cultivars were analyzed using the T-RFLP fingerprints of the 16S rRNA genes.

T-RFs with a relative abundance of more than 2% were considered. For this analysis, a similar trend in diversity between samples from the disease-tolerant (Fj01) and susceptible cultivars (Baxi) was observed. Two months after transplantation, both of the diversities in the rhizosphere of the two different cultivars were all increased. After this, the diversities decreased for both cultivars up to 5 months after transplantation and then increased slowly (Fig. 1). During the tested growing stage, the diversity of samples in Fj01 was significantly (< 0.05) higher than that in the cultivar of Baxi (Table 1).

Table 1. Diversity indices of the bacterial communities in the rhizosphere soil based on the data from T-RFLP analysis
 SamplesShannon indexEvenness
  1. Different superscript lowercase letters indicate significant (P < 0.05) differences (univariate analysis of variance). B1–B7 and F1–F7: samples collected from the cultivars of Baxi and Fj01 at different month, respectively.

Soil samples from the cultivar of BaxiaB12.630.97
B22.670.99
B32.500.98
B42.470.94
B52.280.99
B62.320.93
B72.350.91
Soil samples from the cultivar of Fj01bF12.580.98
F22.750.97
F32.640.97
F42.550.97
F52.350.95
F62.490.97
F72.520.98
Figure 1.

Comparison of the soil diversity in rhizosphere of Baxi and Fj01. The numbers 1–7 in x-axis indicate the different soil samples from the rhizosphere collected every month.

In the period from March to June, differences in diversities of the T-RF profiles in the two cultivars were also compared. Some T-RFs were present only at one specific cultivar. For example, the T-RFs of 73, 208, and 291 bp were present only in the samples of Fj01. In contrast, the T-RFs of 196 and 307 bp were only observed in the samples of Baxi.

Five months after transplantation, the lowest diversities of samples in two different cultivars were all observed. During this period, seven of the T-RF profiles were common to all the samples of Fj01 (64, 73, 193, 208, 231, 258, and 291 bp). However, only three T-RFs (72, 196, and 232 bp) were present in all the samples of Baxi.

In addition, the first five T-RFs and their relative abundance of each sample were analyzed according to the T-RF profile (Table 2). In the samples collected from the Fj01, the T-RFs of 73, 291, 193, 233, 64, 231, and 217 bp were detected as the dominant components with the relative abundance of 12.7%, 14.1%, 12.4%, 11.7%, 11.6%, 16.3%, and 14.6% in the different months, respectively. In contrast, samples collected from the susceptible cultivar in the different months were dominated by 231, 231, 231, 230, 72, 206, and 217 bp and with the relative abundance of 14.0%, 10.9%, 14.3%, 21.8%, 15.0%, 16.1%, and 25.3%, respectively.

Table 2. Distribution and relative abundance of the first five dominant T-RFs after restriction with HaeIII
SamplesT-RF size (bp) [relative abundance (%)]
  1. The relative abundance is the ratio of a given peak area to the sum of all the peak areas in one sample. B1–B7 and F1–F7: samples collected from the cultivars of Baxi and Fj01 at different month, respectively.

B1231 (14.0)196 (10.7)307 (8.9)328 (8.9)216 (7.8) 
B2231 (10.9)196 (8.4)329 (8.2)216 (8.1)291(8.1) 
B3231 (14.3)196 (11.7)307 (9.8)232 (8.9)291 (8.8) 
B4230 (21.8)330 (12.4)216 (8.8)196 (8.3)307 (7.2) 
B572 (15.0)188 (12.4)66 (11.7)90 (10.3)231 (10.3) 
B6206 (16.1)201 (14.3)205 (14.0)233 (13.5)193 (7.9) 
B7217 (25.3)231 (8.9)216 (8.5)291 (7.6)307 (7.5) 
F173 (12.7)208 (10.8)296 (10.0)231 (8.9)167 (8.0) 
F2291 (14.1)231 (9.9)216 (7.5)64 (7.3)234 (6.9) 
F3193 (12.4)73 (11.7)217 (8.7)208 (8.0)168 (7.6)296 (7.6)
F4233 (11.7)193 (10.5)216 (10.5)73 (9.8)208 (9.3) 
F564 (11.6)182 (10.5)146 (10.2)219 (9.7)138 (9.6) 
F6231 (16.3)197 (12.6)72 (8.4)74 (8.1)193 (7.9) 
F7217 (14.6)90 (10.1)221 (9.8)174 (8.5)170 (8.0) 

Abundance of bacteria in rhizosphere soil

Rhizosphere soil bacterial community abundance was detected by real-time PCR assays. The sizes were expressed as 16S rRNA gene copy numbers. Rhizosphere soil bacterial abundance of Fj01 was higher during the tested growing stage than that of Baxi. Especially in the period from July to September, The size of the rhizosphere bacterial population from the cultivar Fj01 was significantly larger than that from the cultivar Baxi (Fig. 2).

Figure 2.

Quantification of 16S rRNA gene copy numbers in samples from cultivars Fj01 and Baxi at different growth stages. Different letters in the same column indicate significant differences at < 0.05 (Tukey's test).

Soil enzyme activities

The activities of enzymes in the rhizosphere soil are shown in Table 3. From May to September, the activity of urease in the rhizosphere soil of Fj01 was higher than that of Baxi; furthermore, there were significant differences between Fj01 and Baxi during the growing stage from July to September. However, there were no statistically significant differences in the activities of phosphatase in rhizosphere soil between Fj01 and Baxi (Table 3).

Table 3. Enzyme activities in rhizosphere soil from Fj01 and Baxi at different growth stages
 CultivarMarchMayJulySeptember
  1. Different letters in the same column for each enzyme activity indicate significant differences at P < 0.05 (Tukey's test).

Urease activity (NH4–N mg g−1 soil 24 h−1)Fj012.13 ± 0.049a2.26 ± 0.012a2.35 ± 0.015a2.31 ± 0.009a
Baxi2.16 ± 0.044a2.21 ± 0.021a2.30 ± 0.006b2.25 ± 0.012b
Phosphatase activity (PNP mg g−1 soil 24 h−1)Fj010.51 ± 0.018a0.66 ± 0.034a0.76 ± 0.019a0.70 ± 0.012a
Baxi0.52 ± 0.037a0.65 ± 0.027a0.79 ± 0.009a0.66 ± 0.012a

Discussion

Rhizosphere is a special micro-ecosystem of plant–microorganism interactions. Rhizosphere bacterial communities play an important role in suppressing soilborne plant diseases and promoting plant growth.

Plant roots release a broad range of compounds into the surrounding soil. The composition of root exudates is affected by the plant species and developmental stage (Di Cello et al., 1997; Siciliano et al., 1998; Yang & Crowle, 2000; Dunfield & Germida, 2001). These root exudates create unique environments for the microorganisms living in the rhizosphere. There are differences in utilization of different compositions of root exudates by the microorganisms, and thus, different rhizosphere communities were formed (Rumberger et al., 2004; Orlando et al., 2007).

In this study, significant differences in rhizosphere bacterial community structure and diversity of banana in relation to different disease-resistant cultivars were observed. One possible explanation for the result might be due to the difference in root exudates released by the different cultivars. These compounds provided potential carbon source to promote microbial growth, thus leading to different composition and diversity in their rhizosphere.

Seasonal shifts in microbial diversity in the rhizosphere have also been observed in some studies. Rumberger et al. (2007) revealed that in the rhizosphere of apple trees, the composition of bacterial communities was highly variable in different seasons. In this study, the impact of different growth stage on bacterial diversity in the rhizosphere was also observed. With the development of growth stage, the diversities of the two cultivars in rhizosphere were decreased.

Several studies have also indicated the relationship between the plant diseases and the diversity of soil microbial communities. For example, Pérez-Piqueres et al. (2006) showed that differences in soil suppressiveness to Rhizoctonia solani disease were related to differences in microbial composition. Using the T-RFLP fingerprints, Rotenberg et al. (2007) revealed that the incorporating of paper mill residuals into soil may improve the bacterial communities in soil and thus increase the soil's ability to suppress root rot disease. In this study, the disease-tolerant cultivar (Fj01) showed a higher bacterial diversity in the rhizosphere than that of the susceptible cultivar (Baxi) during the tested growing stage in general. The results indicated the relationship between bacterial diversity and the suppressiveness to soilborne diseases in rhizosphere of banana.

Urease and phosphatase play important roles in soil organic nutrient cycling and are important indicators of soil quality (Dick et al., 2000). Soil enzyme activities are greatly affected by root secretion and soil microorganisms (Ros et al., 2006). In the present study, the difference of soil urease activity between the two banana cultivars could be attributed to the changes in soil microbial group composition and root secretion. The higher activity of urease increased the ability of organic nutrient cycling in soil and thus provided a better nutrition environment for banana growth.

The mechanisms of plant diseases caused by soilborne pathogens are multiple and complex. A higher biodiversity and good biochemical property have been associated with better soil quality and thus a greater capacity in suppressing soilborne diseases. In this study, the disease-tolerant cultivar (Fj01) showed a significant higher microbial diversity and urease activity compared with the susceptible cultivar (Baxi). This may be one of the important reasons why the fungus is more difficult to infect the disease-tolerant cultivar compared with the susceptible cultivar of banana.

Acknowledgement

This work was financially supported by the grant no. 200903049-2 and ITBB110305.

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