Effects of the long-term application of inorganic fertilizers on microbial community diversity in rice-planting red soil as studied by using PCR-DGGE

ACTA ECOLOGICA SINICA Volume 27, Issue 10, October 2007 Online English edition of the Chinese language journal Cite this article as: Acta Ecologica Sinica, 2007, 27(10), 4011−4018.

RESEARCH PAPER

Effects of the long-term application of inorganic fertilizers on microbial community diversity in rice-planting red soil as studied by using PCR-DGGE Zhong Wenhui1,2, Cai Zucong1,* , Yin Lichu3, Zhang He2 1 State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 21000, China 2 College of Chemistry and Environmental Science, Nanjing Normal University, Nanjing 210097, China 3 College of Resources and Environmental Science, Hunan Agricultural University, Changsha 410128, China

Abstract: The effects of the long-term application of inorganic fertilizers on microbial community diversity were investigated in a rice-planting soil derived from quaternary red clay in the Ecological Experimental Station of Red Soil, Chinese Academy of Sciences. After 13 years’ application of different inorganic fertilizers for double rice crops, the community structure of bacteria, archaea, actinomycetes and fungi in the soil changed greatly. The similarity of the SSU rDNA DGGE patterns of these four kinds of microorganisms between the soil without rice-planting and the soil with rice-planting reached only 33% and 66%, respectively. The microbial community structure among treatments with NP, PK and NPK types of P fertilizers were more similar; the similarity of the SSU rDNA DGGE patterns of the four kinds of microorganisms under these treatments reached a high range of 75%–81%. The microbial community structure between the treatment with N and K fertilizers (NK) and the treatment without inorganic fertilization (CK) with the treatments with P fertilizers differed greatly; the similarity of the SSU rDNA DGGE patterns of the four kinds of microorganisms under these treatments reached 69%–77% and 55%–77%, respectively. The results of this study provide a scientific basis for fertilizing and utilizing soil, protecting microbial diversity, and accomplishing the sustainable development of agroecology. Key Words: rice-planting red soil; microbial diversity; bacteria; actinomycetes; fungi; archaea; PCR-DGGE

Degraded red soils (Ultisols and Oxisols in US Soil Taxonomy) are widespread in the tropical and subtropical regions in southern and parts of central China, covering about 1.13 million km2 or 11.8% of the national land area. Rice planting is one of the main utilization manners of red soil. Red soils are heavily weathering and leaching soils, and are characterized by low pH and deficiencies in available nutrients, particularly P and N[1]. Crop growth is usually restricted by low pH and deficiencies in nutrition provision. There are many kinds of microorganisms in soil, which play important parts in the soil ecological system. Researchers pay more and more attention to the study on the diversity of soil microorganisms and their function in ecology and environmental science. Many kinds of techniques have been used to study soil microbial diversity nowadays, including roughly conventional culturing techniques, the community-level

physiological profiling (such as Biolog method), microbial biomarker analysis (such as PLFA analysis and ergosterol analysis), molecular fingerprint analysis and other methods (such as chloroform fumigation method, substrate induced respiration method, enzyme activity analysis and microorganism energy metabolism analysis). Conventional culturing techniques for studying soil microbial diversity have many defects and may not well reflect soil microbial diversity[2]. For example, the kinds of cultivable microorganisms only account for 1%–10% of total microorganisms in soil, and many “active but uncultivable” microorganisms could not be isolated and identified, which makes it necessary to adopt new techniques and methods. Nowadays, the community-level physiological profiling, microbial biomarker analysis and molecular fingerprint analysis are the most extensively used methods[3]. Molecular fingerprint analysis such as PCR-DGGE makes

Received date: 2006-08-15; Accepted date: 2007-03-28 *Corresponding author. E-mail: [email protected] Copyright © 2007, Ecological Society of China. Published by Elsevier BV. All rights reserved.

ZHONG Wenhui et al. / Acta Ecologica Sinica, 2007, 27(10): 4011–4018

researchers study soil microbial diversity on molecular level[4–6]. Even though information about microorganism activities may not be provided, PCR-DGGE has been used as a molecular tool to compare soil microbial population diversity and to monitor microbial population in many micrological laboratories since it was introduced into microbial ecology research since 1993. The approaches of PCR-DGGE are as follows[3,7]: after soil microbial genomic DNA was extracted from soil samples, small subunit ribosomal RNA genes (SSU rDNA, i.e. 16S rDNA or 18S rDNA) are PCR-amplified by using specific primers, and then PCR products are separated by using the denatured gradient gel electrophoresis (DGGE) technique. According to DNA band numbers and their location in the DGGE gel, combining sequencing of DNA bands, soil microbial diversity can be understood. By using the PCR-DGGE technique, adopting soil microbial genomic DNA extracted from long-term inorganic positioning soil samples in the Ecological Experimental Station of Red Soil, Chinese Academy of Sciences, as the research object, and comparing the 16S rRNA gene of prokaryotic microorganisms and the 18S rRNA gene of eucaryotic microorganisms, this study intends to study the changes of microbial community diversity under the conditions of the shifts of soil utilization manner and the long-term application of inorganic fertilizers. The objective of this study is to study the application of PCR-DGGE technique in the red soil microbial diversity, to deeply learn soil microbial community diversity and microbial resources, and to provide a scientific basis for fertilizing and utilizing soil, protecting microbial diversity, and accomplishing the sustainable development of agroecology.

1

Materials and methods

1.1 Description of the long-term experiment and soil sampling

The long-term field experiment was set up in the Ecological Experimental Station of Red Soil, Chinese Academy of Sciences, located in Yingtan, Jiangxi Province, China (28°15'30" N, 116°55'30"E) in 1990. This region has a typical subtropical monsoon climate with an annual precipitation of 1795 mm, annual evaporation of 1318 mm and a mean annual temperature of 17.6°C. The soil, red soil derived from the quaternary red clay, was planted with double rice (Oryza sativa) crops (i.e. early and late rice crops) and was in fallow in winter. There were five treatments with four replicates per treatment in the long-term experiment. They were the control (CK), NK, NP, PK and NPK. N, P and K were applied as urea, calcium superphosphate and KCl at annual rates of 333 kg N /hm2, 273 kg P/hm2 and 124 kg K/hm2, respectively. P and K were applied as basal fertilizers, while N was applied as both basal fertilizer and topdressing fertilizer, respectively (ratio = 250: 83). Both rice grains and stems were reaped. Soil samples were taken from the plough layer (0–20 cm deep) in March 2003 (the soil was in fallow then). A kind of degraded soil (CK’) was also sampled from the adjacent field, which remained uncultivated and used as a control. After removing visible root debris, the soil was sieved (2 mm) and kept at 4°C. All analyses were conducted within 2 months. 1.2 Soil DNA extraction and rDNA amplification Soil DNA was extracted with the FastDNA®SPIN Kit for Soil by using Fast PrepTMFP120. All PCR primers and PCR conditions are listed in Table 1. In order to increase sensitivity and to facilitate DGGE analysis, a nested PCR technique was used: in the first PCR round, different group-specific primers were used, each with their own corresponding PCR protocol. During the second PCR round, the obtained fragments were re-amplified by using universal primers P338F and P518r for Bacteria or specific primers for Archaea and fungi. Because actinomycetes belong to the domain of the Bacteria, Bacterial

Table 1 PCR condition used in this study PCR condition Target

Primer

Number of cycles

Denaturation

Annealing

Reference

Elongation

(ºC)

(min)

(ºC)

(min)

(ºC)

(min) [8]

Bacteria First PCR round

P63f, R1378r

30

95

1

53

1

72

2

Second PCR round

P338fGC, P518r

30

95

1

53

1

72

2

PRA46f, PRA1100r

30

92

1

55

1

72

1

30

92

1

55

1

72

1

[8]

Archaea First PCR round Second PCR round

PARCH340fGC, PARCH519r

Actinomycetes First PCR round

F243, R1378r

35

95

1

63

1

72

2

[9,10]

Second PCR round

P338fGC, P518r

30

95

1

53

1

72

2

[8]

First PCR round

NS1, NS2+10

30

95

1

55

1

72

1

Second PCR round

NS26, 518rGC

30

95

1

55

1

72

1

[11]

Fungi

ZHONG Wenhui et al. / Acta Ecologica Sinica, 2007, 27(10): 4011–4018

primers P338f and P518r were used to amplify rDNA of actinomycetes in the second PCR round. The final concentrations of different components in the mastermix were: 0.2 μmol/L of each primer, 200 μmol/L of each deoxynucleoside triphosphate, 1.5 mM MgCl2, 1×Taq DNA polymerase 10×reaction buffer (MgCl2-free) and 1.25 U/50μl of Taq DNA polymerase. During the first PCR round, 1 μl of extracted DNA was added to 24 μl of PCR mastermix, and in the second PCR round, 1 μl of amplified product from the first round was added to 49μl of PCR mixture. After each PCR amplification round, the fragments of the PCR product were verified with 1.5% agarose gel. PCR products were stored at 4ºC (within several hours) or –20ºC. 1.3 DGGE and gel photography DGGE, based on the protocol of Muyzer et al.[5], was performed using the Bio-Rad D Gene System (Bio-Rad Laboratories, Hercules, CA, USA). The PCR products of the second round were loaded onto 8% polyacrylamide gels in 0.5× TAE (20 mmol/L Tris, 10 mM acetate and 0.5 mM EDTA pH 7.4). The polyacrylamide gels were made by denaturing gradient ranging from 35% to 65%. The electrophoresis was run for 5 h at 60ºC and 200 V. After the electrophoresis, the gels were soaked for 30 min in SYBR Gold (1:10000 dilution, Bio Probe Products, Rockland, ME, USA). The stained gels were immediately photographed on a UV transillumination table with a Video Camera Module (Bio-Rad Laboratories, Hercules, CA, USA). 1.4 Analysis of DGGE patterns DNA bands in gels were identified and number of DNA bands was calculated with Imaging and Analysis Software (Windows/Macintosh, Bio-Rad Laboratories, Hercules, CA, USA). Clustering analysis for DGGE patterns was done with that software too. Principal component analysis was conducted with SPSS10.0.

2 2.1

Results and analyses PCR-DGGE of bacterial 16S rDNA

Expected DNA fragments of bacterial 16S rDNA were amplified by using the nested PCR amplification technique. The first and second PCR rounds produced 1339 bp and 237 bp of 16S rDNA fragments, respectively (Fig. 1). The DGGE pattern of the second PCR round amplification products was shown in Fig. 2. There were 30, 23, 23, 25, 25 and 22 DNA bands from treatment CK’, CK, NK, NP, PK, NPK, respectively. Clustering analysis showed that the similarity of DGGE pattern under treatments with P fertilizers (NP, NPK and PK) reached 74% (Fig. 3A), while there existed greater difference between treatment CK’ and other treatments with a similarity of 57% in the DGGE pattern. Principal component analysis for the DGGE pattern showed that the first, second and third principal components explained 47.23%, 19.49% and 16.90% of the variance, respectively and the three principal components explained 83.62% of the total variance. Loadings of the three principal components for DGGE pattern showed that treatment CK’ was distributed on the positive part of the first principal component (PCA1), while other treatments were distributed on the negative part of PCA1, and the treatments with P fertilizers were clustered together (Fig. 2). 2.2 PCR-DGGE of archaeal 16S rDNA The first and second PCR round amplifications for archaeal 16S rDNA produced 1072 bp and 237 bp rDNA fragments, respectively, and the DGGE pattern of the second PCR round amplification product were shown in Fig. 4. There were 17, 19, 20, 17, 19 and 17 DNA bands from treatments CK’, CK, NK, NP, PK, NPK, respectively. Clustering analysis showed that treatments NP and NPK had the highest similarity of 86% in DGGE pattern, and the similarity of DGGE pattern under treatments with P fertilizers (NP, NPK and PK) reached 75%, while there existed greater difference between treatment CK’ and other treatments with a similarity of 33% in DGGE pattern (Fig. 3B). Principal component analysis for DGGE pattern showed that the first, second and third principal components explained 58.92%, 14.59% and 12.44% of the variance, respectively, and the three principal components explained 85.95% of the total variance. Loadings of the three principal

Fig. 1 Bacterial 16S rDNA PCR amplification products of soil samples M: DNA Marker (100 bp ladder); A and B are the first and second round PCR amplification products

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Fig. 2 DGGE pattern and the PCA analysis result of bacterial 16S rDNA PCR amplification products of soil samples

Fig. 3 Clustering analysis results of the DGGE pattern of the soil samples (A: Bacteria; B: Archaea; C: Actinomycetes; D: Fungi)

components for DGGE pattern showed that treatment CK’ was distributed on the negative part of PCA1 and had the lowest ordinate on PCA1, while other treatments were distributed on the positive part of PCA1 and were clustered together. On the other hand, treatment NK was distributed on the positive part of the second principal component (PCA2) while other treatments were distributed on the negative part of PCA2 (Fig. 4). 2.3 PCR-DGGEof actinomycetes16SrDNA The first and second PCR round amplifications for archaeal 16S rDNA produced 1158 bp and 237 bp rDNA fragment, respectively, and the DGGE pattern of the second PCR round amplification products were shown in Fig. 5. There were 23,

25, 23, 25, 22 and 25 DNA bands from treatments CK’, CK, NK, NP, PK and NPK, respectively. Clustering analysis showed that the treatments with P fertilizers (NP, NPK and PK) had the highest similarity of 79% in DGGE pattern and there existed greater difference between treatment CK’ and other treatments with a similarity of 63% in DGGE pattern (Fig. 3C). Principal component analysis for DGGE pattern showed that the first, second and third principal components explained 43.27%, 25.75% and 17.71% of the variance, respectively, and the three principal components explained 86.73% of the total variance. Loadings of the three principal components for DGGE pattern showed that treatment CK’ was distributed on

ZHONG Wenhui et al. / Acta Ecologica Sinica, 2007, 27(10): 4011–4018

Fig. 4 DGGE pattern and the PCA analysis result of archaeal 16S rDNA PCR amplification products of soil samples

Fig. 5 DGGE pattern and the PCA analysis result of actinomycetes 16S rDNA PCR amplification products of soil samples

the positive part of PCA1 and had the highest ordinate on PCA1, while other treatments were distributed on the negative part of PCA1 and were clustered together (Fig. 5). 2.4 PCR-DGGE of fungal 18S r DNA The first and second PCR round amplifications for funal 18S rDNA produced 567bp and 317bp rDNA fragment, respectively, and the DGGE pattern of the second PCR round amplification products were shown in Fig. 6. There were 13, 11, 12, 11, 11 and 11 DNA bands from treatments CK’, CK, NK, NP, PK and NPK, respectively. Clustering analysis showed that the treatments with P fertilizers (NP, NPK and PK) had the highest similarity of 81% in DGGE pattern and there existed greater difference between treatment CK’ and other treatments with a similarity of 66% in DGGE pattern (Fig. 3D). Principal component analysis for DGGE pattern showed that the first, second and third principal components explained 49.92%, 20.62% and 11.23% of the variance, respectively, and

the three principal components explained 81.77% of the total variance. Loadings of the three principal components for DGGE pattern showed that treatment CK’ was distributed on the positive part of PCA1 and had the highest ordinate on PCA1, while other treatments were distributed on the negative part of PCA1 and were clustered together. On the other hand, the treatments without P fertilization (CK’, CK and NK) were distributed on the negative part of PCA2, while the treatments with P fertilization (NP, NPK and PK) were distributed on the positive part of PCA2 (Fig. 6).

3

Discussion

3.1 PCR-DGGE method and soil microbial diversity In DGGE pattern, each DNA band at different locations and its relative concentration (brightness) may represent a particular microbial species and its relative abundance/richness in microbial community[5]. Because the PCR template is the total

ZHONG Wenhui et al. / Acta Ecologica Sinica, 2007, 27(10): 4011–4018

Fig. 6 DGGE pattern and the PCA analysis of fungal 18S rDNA PCR amplification products of soil samples

soil DNA, which included the DNA of culturable and unculturable microorganisms, PCR-DGGE can reflect more microbial species than culturable microorganisms. On the other hand, PCR-DGGE can only detect the microbial population representing dominant species present in soil samples. If the number of microorganisms is too small, the DNA content will be low, so the yield of SSU rDNA will be low after PCR amplification and SSU rDNA will be probably unable to be distinguished in DGGE pattern. Muyzer et al.[5] reported, when the DNA template accounted for less than 1% of the total DNA, that DNA bands might still be distinguished in DGGE pattern, thus it was estimated that DGGE could detect those dominant species which accounted for over 1% of the microbial population present in the community. In this research, the DGGE pattern showed that there were 27–30, 17–20, 26–37 and 13–15 SSU rDNA bands of bacteria, archaea, actinomycetes and fungi, respectively, which might respectively reflect that there were as many microbial populations as the number of DNA bands. The band numbers of microbial species rDNA represented were more than all kinds of culturable microorganisms in red soil reported before[1]. In the research on the microbial diversity by using PCRDGGE, the key is how to effectively amplify 16S/18S rDNA genes of all microbial communities in samples. So the selection of suitable primers used in PCR is very important. On the one hand, the selected primers should be used to effectively amplify rDNA of most microbial groups in the sample and thus ensure that comprehensive result can be got. On the other hand, the design of primers should satisfy the request of the complete separation of the 16S rDNA fragments in the subsequent DGGE analysis and thus will be beneficial for the recovery and sequencing of the 16S/18S rDNA fragments of the microbial species and the qualitative analysis of category groups. In our research, the primers are commonly used ones

worldwide nowadays. In the PCR amplification, nested technique has not been always used[5], but we have found that the nested technique is better. Using the DNA extracted with FastDNA®SPIN Kit for Soil as template, we found that the amplification yield was low and the “smear” phenomenon might easily appear when primer set P338FfGC-P518r was directly used. The 16S rDNA DGGE fingerprinting technique has been widely used in monitoring the growth of bacteria and analyzing the bacteria community nowadays. However, 18S rDNA DGGE fingerprinting technique used in the analysis of the fungi community has not well established yet[11]. For different objects of the study, we may have to use different primers. Smit et al. made successful research on the fungi diversity in wheat rhizosphere by using the primer sets of EF4-EF3/ funga5 (the first PCR round) and EF4-NS3GC (the second PCR round)[12]. We successfully amplified 18S rDNA from the studied red soil by using the primer set of EF4-EF3/funga5. But in the second PCR round amplification by using the primer set of EF4-NS3GC, we found that several non-specific DNA bands appeared apart from the aimed DNA band in the amplification product. No matter how to optimize PCR conditions, these non-specific bands could not be eliminated. Clearly, the primer set was not suitable for the studied red soil. We successfully amplified 18S rDNA from the studied red soil by using the primer sets of NS1-NS2+10 and NS26-518rGC (Fig. 6). Theoretically, DGGE technique may distinguish DNA fragments that even differ in one base, which undoubtedly influence phylogenetic analysis of the sequence information to some extent as long as electrophoresis conditions such as denaturant gradient, electrophoresis time and voltage are meticulously chosen. However, this study showed that different DGGE experimental conditions could lead to different band

ZHONG Wenhui et al. / Acta Ecologica Sinica, 2007, 27(10): 4011–4018

patterns. The pretreatment process for tested samples is a key step in influencing the efficiency of DGGE and this step is also one of the main sources of the experimental error. Many factors may affect the extraction process of DNA samples such as whether the cells are fully broken and whether those materials inhibiting degradation of the DNA are completely removed. In addition, in the PCR amplification process, how to avoid priority amplification so as to enable all templates to be amplified with equal probability, as well as genome size, the design of primers and the selection of PCR procedure may greatly affect the quality and quantity of DNA fragments amplified. All these would indirectly affect DGGE analysis results. This study showed that in the DGGE electrophoresis technique, the 130 V/11h and 200 V/5h have equal electrophoresis effect. 3.2 Change in the microbial diversity after the rice was planted in degraded red soil and effect of long-term application of inorganic fertilizers on soil microbial diversity The fertility of degraded red soil (CK’) was very low. Soil organic carbon content was very low (2.64g kg–1), and soil microbial biomass carbon and nitrogen, the numbers of culturable microorganisms and microbial community functional diversity in CK’ were all lower than those in the treatments with rice-planting and application of N, P, K fertilizers (data not shown). However, this study showed that the total numbers of SSU rDNA of bacteria, actinomycete, archaea and fungi did not obviously change after the rice was planted in the degraded red soil, while the DGGE pattern changed greatly, with a similarity of 57%, 33%, 63% and 66% in SSU rDNA DGGE pattern for bacteria, archaea, actinomycete and fungi, respectively, between the degraded red soil and the rice-planting soil with or without inorganic fertilization, inferring that soil microbial community structure changed obviously. According to clustering analysis and principal component analysis for DGGE pattern, the difference between CK’ and CK as well as the treatments with different inorganic fertilizations (NK, NP, PK and NPK) was greater than that between treatments with different fertilizers. No matter what kind of microorganisms, there existed the greatest variation in DGGE pattern between CK’ and other treatments, suggesting that there were greater changes in soil microbial community structure when the rice was planted in the degraded red soil than when the red soil was in long-term application of different fertilizers. In terms of the effect of long-term application of inorganic fertilizers on the soil microbial diversity, according to clustering analysis and principal component analysis for DGGE pattern, no matter what kind of microorganisms were studied, there existed the highest similarity of DGGE pattern under the treatments with P fertilization of NP, NPK and PK, and DGGE pattern similarity for the four kinds of microorganisms reached 74%–81% (Fig. 3), showing that soil microbial com-

munity structure under the treatments with P fertilizers was similar. The similarity of SSU rDNA DGGE pattern of bacteria, archaea, actinomycetes and fungi was 69%, 70%, 76% and 77%, respectively, between the treatment CK and the treatments applied with P fertilizers (Fig. 3), suggesting that there existed greater difference in soil microbial community structure between the treatment CK and the treatments applied with P fertilizers. Microbial community structure also differed greatly between the treatment NK and the treatments applied with P fertilizers with the similarity of 66%, 55%, 75% and 77% in SSU rDNA DGGE pattern for bacteria, archaea, actinomycete and fungi, respectively (Fig. 3), which suggested that the long-term application of different inorganic fertilizers promoted certain changes in microbial community structure in the rice-planting red soil, although the reasons for these changes remained to be studied further.

Acknowledgements The project was financially supported by the National Natural Science Foundation of China (No. 40471065), the Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX2-YW-408) and the open fund of the State Key Laboratory of Soil and Sustainable Agriculture (No. 055122).

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