**Molecular Analyses of Soil Fungal Community – Methods and Applications**

Yuko Takada Hoshino

*National Institute for Agro-Environmental Sciences Japan* 

## **1. Introduction**

278 Soil Health and Land Use Management

Program of small retention of the Małopolski District, (2004). Project of Marshal Office of

Roehl, J. (1962). Sediment source area, delivery rations and influencing morphological

Sawunyama, T. (2005). Estimation of small reservoirs storage capacities in Limpopo river

Singh, M.; Müller, G.; Singh, I. B. (2002). Heavy metals in freshly deposited stream

Soler-López, L. R. (2001). Sedimentation survey results of the principal water supply

White, P.; Labadz, J. C.; Buchter, D. P. (1996). Sediment yield estimation from reservoir

Wischmeier, H. W.; Smith, D. D. (1965). Predicting rainfall erosion losses-aquide from cropland east of the Rocky Mountains. USDA, *Agriculture Handbook*, No. 282, p. 47 Wiśniewski, B.; Kutrowski, M. (1973). Special constructions in water managements*. Water* 

Yau, H; Gray, N. F. (2005). Riverine sediment metal concentration of the Avoca-Avonmore

Proceedings of the Royal Irish Academy, Vol.105B, No.2, pp. 95-106

(unpaginated CD), http://pr.water.usgs.gov/public/reports/soler.html Tarnawski, M. (2009). Appraise of chemical quality of bottom sediments of chosen small water reservoirs. *Environmental Protection and Natural Resources,* No.38, pp. 372-379 Wedepohl, K. H. (1995). The composition of the continental crust. *Geochimica et Cosmochimica* 

Provincie in Cracow, Cracow, ersion for CD, pp. 47 (in Polish)

factors. *IAHS Publication,* No.59, pp. 202-213

*Air, and Soil Pollution,* No.141, pp. 35–54

Office "Hydroprojekt", Warsaw, pp. 55 (in Polish)

University of Zimbabwe, pp. 68

*Acta*, No.59, pp. 1217–1239

173

Małopolski District and Land Melioration and Water Units Board of Małopolski

basin using GIS and remotely sensed surface areas. Dep. of Civil Engineering,

sediments of rivers associated with urbanization of the Ganga Plain, India. *Water,* 

reservoirs of Puerto Rico. *Proc. of 6th Caribbean Islands Water Res. Congr.* Puerto Rico

studies: An appraisal of variability in the southern Pennines of the UK. In: *Erosion and sediment yield: global and regional perspectives,* IAHS publication No. 236, pp. 163-

*reservoirs. Predicting silting rate. Manual.* Water Management Study and Design

catchment, south-east Ireland: a baseline assessment. *Biology and Environment*:

Fungi play important and diverse roles in soil ecosystems. They act as plant pathogens, mycorrhizal symbionts and most importantly, as the principal decomposers of organic materials (Christensen, 1989; Thorn, 1997). Fungi also represent a dominant component of the soil microflora in terms of biomass (Thorn, 1997). Compared with bacterial communities, however, knowledge regarding the diversity and functions of soil fungal communities remains limited.

Culture-independent molecular techniques, comprising of direct DNA extraction from soil followed by PCR and electrophoresis or cloning, have been introduced to investigate soil fungal communities (Anderson & Cairney, 2004). These techniques facilitate the detection of fungi, including fastidious or non-culturable strains, and an understanding of the fungal community structures and dynamics in soil (Hoshino & Matsumoto, 2007; Vandenkoornhuyse et al., 2002).

Molecular techniques have provided novel insights and significant advances in research on soil fungal ecology and have been applied to various soils in different ecosystems, such as forests (Perkiomaki et al., 2003), grasslands (Brodie et al., 2003), dunes (Kowalchuk et al., 1997), stream sediments (Nikolcheva et al., 2003) and agricultural fields (Gomes et al., 2003). For example, in agricultural soils, a fungal community is affected by plant growth (Gomes et al., 2003) and cultural practices, such as application of fertilizers and pesticides (Girvan et al., 2004).

With the development of new technologies, accumulating molecular data has contributed to the establishment of database combined with other environmental data and facilitated metaanalysis on a large scale. In agricultural soils, fungal communities are directly and indirectly related to crop production. Technological advances in molecular methods would help elucidate such a complicated relationship. Here I present molecular techniques applied to soil fungal community analyses, particularly in agricultural soils and discuss their limitations and future applications.

#### **2. Molecular analysis techniques**

In culture-independent molecular analysis of microbial community, DNA directly extracted from soil may be analyzed by PCR-based techniques targeting specific genes and by metagenomic approach using direct sequencing (Suenaga, 2011). For fungal community

Molecular Analyses of Soil Fungal Community – Methods and Applications 281

extraction, using commercially available kit (Fig. 3B), while such variation was not detected in bacterial community profiles (Fig. 3A), using denaturing gradient gel electrophoresis

(B)

23.1 9.4 6.7 2.3 2.0

4.4

0.5

kb

23S rRNA 16S rRNA

MF 12345 MF 12345 12345 MF MF

Fig. 2. Improvement of DNA and RNA extraction from Andisols, using adsorption

ABC (A) (B)

Fig. 3. Variation in bacterial (A) and fungal DGGE profiles (B) among replicates (1-5) from 0.4g of soil from a single homogenized sample. Soils A and B were taken from upland fields, and soil C from a paddy field. MB: Marker for bacterial DGGE, MF: Marker for fungal DGGE Increasing sample size and pre-treatments for homogenization of soil samples decreased variation in fungal DGGE profiles among replicates (Fig. 4). Sample size increased by mixing DNA extracts from 0.4g soil to rule out the influence of sample size on the efficacy of DNA extraction (Fig. 4A). With regard to pre-treatment for soil homogenization, we found that grinding in liquid nitrogen was suitable for upland field soils while adding buffer to soil to obtain homogeneous soil suspension was suitable for paddy filed soils. These pretreatments did not significantly affect fungal DGGE profiles under the experimental

(DGGE), a community profiling method (see Section 2.3).

MB 12345 MB 12345 12345 MB MB ABC

M M

Genomic DNA

competitors

0.5

(A)

23.1 9.4 6.6 2.3 2.0

4.4

kb

conditions (Fig. 4B).

analyses, PCR-based techniques have been widely and generally used (Anderson & Cairney, 2004; Hoshino & Matsumoto, 2007). Fig. 1 showed the experimental scheme of PCR-based molecular analyses for soil fungal community, which consist of three steps: (i) direct extraction of DNA or RNA from soil, (ii) polymerase chain reaction (PCR) amplification of the 18S rRNA gene (rDNA) and internal transcribed spacer (ITS) region using fungal specific primers, and (iii) community profiling, including some electrophoresis techniques and sequence based techniques.

Fig. 1. Experimental scheme: molecular analyses of soil fungal community

#### **2.1 DNA/RNA extraction**

Many protocols for DNA/RNA extraction from soil have been developed (Robe et al., 2003) and used to extract fungal genomic DNA and fungal RNA. The majority of the direct extraction is the combination of chemical and/or enzymatic treatments and physical procedures. Bead-beating is most effective in cell disruption (Miller et al., 1999) and commercially available kits include this step (Borneman et al., 1996). In these procedures, soil samples are shaken vigorously with small glass beads in buffer including detergent. Microbial cells are disrupted within the soil matrix, and nucleic acids are released from lysed cells. DNA or RNA is, then, recovered and purified.

Because of soil diversity in terms of property and composition, extraction protocol of nucleic acids needs to be optimized for each soil type. For example, it was difficult to extract nucleic acids from Andisol, volcanic ash soils, which strongly adsorbed nucleic acids. The addition of adsorption competitors to the extraction buffer enabled to extract DNA and RNA, and increased the yield of DNA and RNA. From a variety of Andisols, we successfully extracted DNA and RNA for molecular analyses by using skim milk or RNA for DNA extraction (Fig.2A) (Hoshino & Matsumoto, 2004) and DNA for RNA extraction as adsorption competitors (Fig.2B) (Hoshino & Matsumoto, 2007).

The DNA extraction protocols, especially with different conditions of cell disruption, can affect the result: Martin-Laurent et al. (Martin-Laurent et al., 2001) showed that microbial community profiles were variable, according to DNA recovery methods used, both in terms of phylotype abundance and the composition of indigenous bacterial community. Soil sample size may also influence analysis results targeting fungal community. Ranjard et al. (Ranjard et al., 2003) reported that analytical results of fungal community structure varied among replicates from a single homogenized soil sample when sample size was less than 1 g, while sample range between 0.125 - 4 g had no effect on the assessment of bacterial community structure.

We also observed significant variation in fungal community profiles among replicates of the conventional sample size of 0.4 g soil from a single homogenized sample for DNA

analyses, PCR-based techniques have been widely and generally used (Anderson & Cairney, 2004; Hoshino & Matsumoto, 2007). Fig. 1 showed the experimental scheme of PCR-based molecular analyses for soil fungal community, which consist of three steps: (i) direct extraction of DNA or RNA from soil, (ii) polymerase chain reaction (PCR) amplification of the 18S rRNA gene (rDNA) and internal transcribed spacer (ITS) region using fungal specific primers, and (iii) community profiling, including some electrophoresis techniques

Many protocols for DNA/RNA extraction from soil have been developed (Robe et al., 2003) and used to extract fungal genomic DNA and fungal RNA. The majority of the direct extraction is the combination of chemical and/or enzymatic treatments and physical procedures. Bead-beating is most effective in cell disruption (Miller et al., 1999) and commercially available kits include this step (Borneman et al., 1996). In these procedures, soil samples are shaken vigorously with small glass beads in buffer including detergent. Microbial cells are disrupted within the soil matrix, and nucleic acids are released from

Because of soil diversity in terms of property and composition, extraction protocol of nucleic acids needs to be optimized for each soil type. For example, it was difficult to extract nucleic acids from Andisol, volcanic ash soils, which strongly adsorbed nucleic acids. The addition of adsorption competitors to the extraction buffer enabled to extract DNA and RNA, and increased the yield of DNA and RNA. From a variety of Andisols, we successfully extracted DNA and RNA for molecular analyses by using skim milk or RNA for DNA extraction (Fig.2A) (Hoshino & Matsumoto, 2004) and DNA for RNA extraction as adsorption

The DNA extraction protocols, especially with different conditions of cell disruption, can affect the result: Martin-Laurent et al. (Martin-Laurent et al., 2001) showed that microbial community profiles were variable, according to DNA recovery methods used, both in terms of phylotype abundance and the composition of indigenous bacterial community. Soil sample size may also influence analysis results targeting fungal community. Ranjard et al. (Ranjard et al., 2003) reported that analytical results of fungal community structure varied among replicates from a single homogenized soil sample when sample size was less than 1 g, while sample range between 0.125 - 4 g had no effect on the assessment of bacterial

We also observed significant variation in fungal community profiles among replicates of the conventional sample size of 0.4 g soil from a single homogenized sample for DNA

Fig. 1. Experimental scheme: molecular analyses of soil fungal community

lysed cells. DNA or RNA is, then, recovered and purified.

competitors (Fig.2B) (Hoshino & Matsumoto, 2007).

and sequence based techniques.

**2.1 DNA/RNA extraction** 

community structure.

extraction, using commercially available kit (Fig. 3B), while such variation was not detected in bacterial community profiles (Fig. 3A), using denaturing gradient gel electrophoresis (DGGE), a community profiling method (see Section 2.3).

Fig. 2. Improvement of DNA and RNA extraction from Andisols, using adsorption competitors

Fig. 3. Variation in bacterial (A) and fungal DGGE profiles (B) among replicates (1-5) from 0.4g of soil from a single homogenized sample. Soils A and B were taken from upland fields, and soil C from a paddy field. MB: Marker for bacterial DGGE, MF: Marker for fungal DGGE

Increasing sample size and pre-treatments for homogenization of soil samples decreased variation in fungal DGGE profiles among replicates (Fig. 4). Sample size increased by mixing DNA extracts from 0.4g soil to rule out the influence of sample size on the efficacy of DNA extraction (Fig. 4A). With regard to pre-treatment for soil homogenization, we found that grinding in liquid nitrogen was suitable for upland field soils while adding buffer to soil to obtain homogeneous soil suspension was suitable for paddy filed soils. These pretreatments did not significantly affect fungal DGGE profiles under the experimental conditions (Fig. 4B).

Molecular Analyses of Soil Fungal Community – Methods and Applications 283

achieved on the basis of the nucleotide composition. The compositions of these PCR products were analyzed by community profiling methods, including some electrophoresis techniques, such as denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), terminal restriction fragment length polymorphism analysis (T-RFLP), and automated ribosomal intergenic spacer analysis (ARISA), and sequence based techniques, such as cloning and sequencing and second-generation sequencing technologies (Nocker et al., 2007). Here we introduce the principles and the properties of the commonly

Electrophoresis techniques are suitable for obtaining an overview of the total genetic diversity of a soil microbial community. PCR products are separated by electrophoresis based on the nucleotide composition. The data of electrophoretic profiles, i.e., the position and the relative intensity of different bands or peaks, could be transferred to numerical data which is applicable for calculation of diversity indices and several statistical analyses and

Currently, three electrophoresis methods have been mainly used: denaturing gradient gel electrophoresis (DGGE) (Muyzer et al., 1993), terminal restriction fragment length polymorphism analysis (T-RFLP) (Dunbar et al., 2000), and automated ribosomal intergenic spacer analysis (ARISA) (Ranjard et al., 2001). These fingerprinting approaches are based on

DGGE separates DNA fragments of the same size but of different sequence based on the melting behaviour of DNA: double strands of the AT base pair more easily disassociate than those of the GC base pair. During electrophoresis in a denaturing gradient acrylamide gel, an increasing denaturing environment, partially dissociates DNA double strands, creating diverse, branched molecules. The partial melting of DNA strands reduces mobility. Because the melting concentration of the denaturant is sequence-specific, different sequences of DNA fragments have different mobility in denaturing gels, and each DNA fragment can be seen as a distinct band in the gel. T-RFLP is modified from the conventional RFLP approach using fluorescently labelled PCR primers before restriction digestion and size detection of fluorescently labelled terminal restriction fragments using a DNA sequencer. ARISA is simple and discriminates the length of whole PCR amplicons, generally targeting highly variable ITS

regions. Automated DNA sequencer technology is applied for T-RFLP and ARISA.

The three fingerprinting techniques have both advantages and disadvantages. One of the main advantages of gel-based community profiling techniques like DGGE enables sequence analysis of each band in a gel, and therefore, facilitates more detailed phylogenetic analysis. In T-RFLP, each T-RFLP peak can be identified by using database of T-RF length in various microbial groups. However, the inability to get sequence data from T-RFLP peaks makes it

The use of an automated DNA sequencer significantly increases throughput of T-RFLP and ARISA compared with gel-based techniques. It also improves the accuracy in sizing general fragments through the inclusion of an internal standard in each sample. On the other hand, reproducibility between gels has been highlighted as one of main pitfalls of DGGE (Fromin et al., 2002). As comparison between several different gels is required when dealing with large sample numbers, it is critical to standardize the resolution and quality of gels and to

used community profiling methods.

**2.3.1 Electrophoresis techniques** 

enable comparison of numerous samples.

difficult to identify unknown species.

use suitable internal standards for the accuracy of analyses.

different principles (Fig. 5).

Fig. 4. Multi-dimensional scaling (MDS) map based on the similarities of fungal DGGE profile among replicates when increasing soil sample size from 0.4 to 2.0g (A) and when grinding in liquid nitrogen or suspending after buffer addition was done as a pre-treatment (B). In MDS map the closer the points to each other, the more similar the DGGE banding patterns represented by the points

#### **2.2 PCR amplification of fungal genetic markers**

Ribosomal RNA genes (rDNA), especially the small subunit ribosomal RNA genes, i.e., 18S rRNA genes (18S rDNA) in the case of eukaryotes, have been predominant target for the assessment of microbial community (Kowalchuk et al., 2006). The large subunit ribosomal RNA genes, 28S rDNAs, have been also targeted (Möhlenhoff et al., 2001) but been used less frequently than 18S rDNAs. The following properties of rDNAs are suitable for taxonomic identification: (i) ubiquitous presence in all known organisms; (ii) presence of both conserved and variable regions; (iii) the exponentially expanding database of their sequences available for comparison. In community analysis of environmental samples, the conserved regions serve as annealing sites for the corresponding universal PCR primers, whereas the variable regions can be used for phylogenetic differentiation. In addition, the high copy number of rDNA in the cells facilitates detection from environmental samples.

However, the lack of relative variation within 18S rDNA genes among closely related fungal species results in taxonomic identification commonly limited to genus or family level. For higher resolution in taxonomic identification, the internal transcribed spacer (ITS) region, which located between the 18S rDNA and 28S rDNA has been targeted (White et al. 1990; Gardes & Bruns, 1993). The ITSs, non-coding regions, have greater sequence variation among closely related species than the coding regions of rRNA genes because of their fast late of evolution. Protein-coding functional genes have been also employed as genetic markers to target a specific functional group (Ascomycetous laccase; Lyons et al., 2003) or to get higher resolution in specific fungal taxa (Fusarium elongation factors; Yergeau et al., 2005).

These genetic markers were amplified from soil DNA or RNA by PCR using fungal specific primers. Various PCR primer sets targeting fungal sequences are now available (Anderson & Cairney, 2004). Selection of PCR primer is one of the most important factors affecting outcome in fungal community analysis (Jumpponen, 2007; Hoshino & Morimoto, 2010). The properties of PCR primers will be described in Section 3.

#### **2.3 Community profiling methods**

PCR products are the mixtures of target genes, such as the rDNA and ITS region, derived from various kinds of fungi and are often of very similar size, differentiation must be achieved on the basis of the nucleotide composition. The compositions of these PCR products were analyzed by community profiling methods, including some electrophoresis techniques, such as denaturing gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), terminal restriction fragment length polymorphism analysis (T-RFLP), and automated ribosomal intergenic spacer analysis (ARISA), and sequence based techniques, such as cloning and sequencing and second-generation sequencing technologies (Nocker et al., 2007). Here we introduce the principles and the properties of the commonly used community profiling methods.

#### **2.3.1 Electrophoresis techniques**

282 Soil Health and Land Use Management


grinding in liquid nitrogen

grinding in liquid nitrogen

Soil B without pretreatments



suspending after buffer addition

PCR replicates from same DNA extracts

without pretreatments

Soil C

without pretreatments suspending after buffer addition

Fig. 4. Multi-dimensional scaling (MDS) map based on the similarities of fungal DGGE profile among replicates when increasing soil sample size from 0.4 to 2.0g (A) and when grinding in liquid nitrogen or suspending after buffer addition was done as a pre-treatment (B). In MDS map the closer the points to each other, the more similar the DGGE banding


Ribosomal RNA genes (rDNA), especially the small subunit ribosomal RNA genes, i.e., 18S rRNA genes (18S rDNA) in the case of eukaryotes, have been predominant target for the assessment of microbial community (Kowalchuk et al., 2006). The large subunit ribosomal RNA genes, 28S rDNAs, have been also targeted (Möhlenhoff et al., 2001) but been used less frequently than 18S rDNAs. The following properties of rDNAs are suitable for taxonomic identification: (i) ubiquitous presence in all known organisms; (ii) presence of both conserved and variable regions; (iii) the exponentially expanding database of their sequences available for comparison. In community analysis of environmental samples, the conserved regions serve as annealing sites for the corresponding universal PCR primers, whereas the variable regions can be used for phylogenetic differentiation. In addition, the high copy number of rDNA in the cells facilitates detection from environmental samples. However, the lack of relative variation within 18S rDNA genes among closely related fungal species results in taxonomic identification commonly limited to genus or family level. For higher resolution in taxonomic identification, the internal transcribed spacer (ITS) region, which located between the 18S rDNA and 28S rDNA has been targeted (White et al. 1990; Gardes & Bruns, 1993). The ITSs, non-coding regions, have greater sequence variation among closely related species than the coding regions of rRNA genes because of their fast late of evolution. Protein-coding functional genes have been also employed as genetic markers to target a specific functional group (Ascomycetous laccase; Lyons et al., 2003) or to get higher resolution in specific fungal taxa (Fusarium elongation factors; Yergeau et al.,

These genetic markers were amplified from soil DNA or RNA by PCR using fungal specific primers. Various PCR primer sets targeting fungal sequences are now available (Anderson & Cairney, 2004). Selection of PCR primer is one of the most important factors affecting outcome in fungal community analysis (Jumpponen, 2007; Hoshino & Morimoto, 2010). The

PCR products are the mixtures of target genes, such as the rDNA and ITS region, derived from various kinds of fungi and are often of very similar size, differentiation must be

patterns represented by the points

(A) (B)


1.6 g

0.8 g 1.2 g 1.6 g


0.4 g 0.8 g 2.0 g 1.2 g

2.0 g

2005).

**2.2 PCR amplification of fungal genetic markers** 

2 3

0.4 g

sample size pre-treatment

properties of PCR primers will be described in Section 3.

**2.3 Community profiling methods** 

Electrophoresis techniques are suitable for obtaining an overview of the total genetic diversity of a soil microbial community. PCR products are separated by electrophoresis based on the nucleotide composition. The data of electrophoretic profiles, i.e., the position and the relative intensity of different bands or peaks, could be transferred to numerical data which is applicable for calculation of diversity indices and several statistical analyses and enable comparison of numerous samples.

Currently, three electrophoresis methods have been mainly used: denaturing gradient gel electrophoresis (DGGE) (Muyzer et al., 1993), terminal restriction fragment length polymorphism analysis (T-RFLP) (Dunbar et al., 2000), and automated ribosomal intergenic spacer analysis (ARISA) (Ranjard et al., 2001). These fingerprinting approaches are based on different principles (Fig. 5).

DGGE separates DNA fragments of the same size but of different sequence based on the melting behaviour of DNA: double strands of the AT base pair more easily disassociate than those of the GC base pair. During electrophoresis in a denaturing gradient acrylamide gel, an increasing denaturing environment, partially dissociates DNA double strands, creating diverse, branched molecules. The partial melting of DNA strands reduces mobility. Because the melting concentration of the denaturant is sequence-specific, different sequences of DNA fragments have different mobility in denaturing gels, and each DNA fragment can be seen as a distinct band in the gel. T-RFLP is modified from the conventional RFLP approach using fluorescently labelled PCR primers before restriction digestion and size detection of fluorescently labelled terminal restriction fragments using a DNA sequencer. ARISA is simple and discriminates the length of whole PCR amplicons, generally targeting highly variable ITS regions. Automated DNA sequencer technology is applied for T-RFLP and ARISA.

The three fingerprinting techniques have both advantages and disadvantages. One of the main advantages of gel-based community profiling techniques like DGGE enables sequence analysis of each band in a gel, and therefore, facilitates more detailed phylogenetic analysis. In T-RFLP, each T-RFLP peak can be identified by using database of T-RF length in various microbial groups. However, the inability to get sequence data from T-RFLP peaks makes it difficult to identify unknown species.

The use of an automated DNA sequencer significantly increases throughput of T-RFLP and ARISA compared with gel-based techniques. It also improves the accuracy in sizing general fragments through the inclusion of an internal standard in each sample. On the other hand, reproducibility between gels has been highlighted as one of main pitfalls of DGGE (Fromin et al., 2002). As comparison between several different gels is required when dealing with large sample numbers, it is critical to standardize the resolution and quality of gels and to use suitable internal standards for the accuracy of analyses.

Molecular Analyses of Soil Fungal Community – Methods and Applications 285

referred to as dideoxy chain termination sequencing (Sanger & Coulson, 1975). During the current decade, high-throughput second-generation sequencing technologies, such as pyrosequencing, have been developed and introduced to the research of microbial ecology (Petrosino et al., 2009; Roesch et al., 2007), including fungal community analyses (Buée et al., 2009; Lim et al., 2010; Lumini et al., 2010). Buée et al. assessed the fungal diversity in six different forest soils using 454 pyrosequencing (Buée et al., 2009). No less than 166350 reads were obtained from all samples. It enables reading of hundreds, thousands of PCR

DNA pyrosequencing, sequencing by synthesis, was developed in the mid 1990s as a fundamentally different approach to DNA sequencing (Ronaghi et al., 1996). Sequencing by synthesis occurs by a DNA polymerase-driven generation of inorganic pyrophosphate, with the formation of ATP and ATP-depending conversion of luciferin to oxyluciferin. The generation of oxyluciferin causes the emission of light pulses, and the amplitude of each signal is directly related to the presence of one or more nucleotides. Pyrosequencing can eliminate time and labours for cloning and has the 10-fold cost advantage per base pair over Sanger sequencing. The use of primer barcoding techniques enables to characterize many environmental samples in parallel on a single sequencing run. One important limitation of pyrosequencing is its relative inability to sequence longer stretches of DNA. With first- and second-generation pyrosequenceing chemistries, sequences rarely exceed 100-200 bases. Because of this limitation, cloning and Sanger sequencing are applied for the accurate

For fungal community analyses, PCR-based techniques are most powerful and generally used. The 18S rRNA gene (rDNA) and internal transcribed spacer (ITS) region are used widely as molecular markers for fungi, through the exploitation of both conserved and variable regions, and a large number of sequences are available in the data bank (Anderson & Cairney, 2004). Various PCR primer sets targeting 18S rDNA and ITS region are available for assessing fungal diversity in soil DNA samples (Table 1). Selection of PCR primer is one of the most important factors affecting outcome. Here, I will summerize their properties

Although PCR-based strategies are the most powerful tools for the investigation of microbial diversity, they have a number of recognized limitations, perhaps the most insidious of which is the formation of recombinant or chimeric sequences during PCR amplification. Recombination can occur during PCR to jump from one template to another. Thus, whenever a heterogeneous pool of similar sequences, like rDNA and ITS regions, is amplified, chimera formation should be taken into account. The problem of chimeras in mixed DNAs from environmental samples has been highlighted several times in the literature (Kopczynski et al., 1994; Liesack et al., 1991; Wang & Wang, 1997). The existence of chimeras in PCR products may result in the overestimation of community diversity (Wintzingerode et al., 1997) and the occurrence of artificial novel taxa (Jumpponen, 2007). Chimeras seem to comprise a large proportion of the environmental sequence data in public databases (Ashelford et al., 2005; Hugenholtz & Huber, 2003). Jumppone (Jumpponen, 2007)

amplicons per 1 run.

recovery of longer sequence data at this stage.

from our results and previous data.

**3.1 PCR amplification and chimera formation** 

**3. Properties of PCR primers for fungal sequences** 

Fig. 5. Principles of three molecular fingerprinting methods

Okubo & Sugiyama (Okubo & Sugiyama, 2009) compared these fingerprinting methods, i.e., DGGE, T-RFLP and ARISA, by analyzing soil fungal communities. They reported that DGGE showed higher discrimination ability for soil fungal community rather than T-RFLP and ARISA, while ARISA exhibited the highest resolution ability.

From these properties, DGGE is suitable for analyses of highly heterogeneous communities including unknown members such as soil microbial community. T-RFLP is suitable for analyses of communities including known members and for specific taxonomic groups. ARISA appeared to be suitable for diversity analysis.

#### **2.3.2 Cloning and high-throughput sequencing technology**

Sequence-based community analyses can reveal fungal community structures with higher resolutions; fungal sequence data obtained can be identified or determined similarity to already known species through the use of extensive and rapidly growing sequence database (Nocker et al., 2007). Sequencing is the basis for construction of phylogenetic trees and for other comparative studies. Conversely, the sequence-based techniques are relatively time-consuming and costly. It depends on samples or target genes how many PCR amplicons were required to have fully analyzed the diversity contained within a single sample. Fierer et al. (2007) estimated that fungal 18S rDNA richness at the 97% sequence similarity level is likely to exceed 106 in 1.0 g of prairie soil, approximately 2 x 103 in rainforest soil and 2 x 104 in desert soil. Buée et al. (2009) reported that predicted richness of fungal ITS regions at the 97% sequence similarity level was approximately 2 x 103 in 4 g of forest soil.

Until recently, cloning and sequencing were primarily used to generate sequence data of environmental microbial community. PCR amplicons of rDNA and ITS region were cloned into an appropriate vector and clone libraries were sequenced by Sanger methods, also

Fluorescence label

Digestion with restriction enzyme

Okubo & Sugiyama (Okubo & Sugiyama, 2009) compared these fingerprinting methods, i.e., DGGE, T-RFLP and ARISA, by analyzing soil fungal communities. They reported that DGGE showed higher discrimination ability for soil fungal community rather than T-RFLP

size of T-RFs

Separation by

Fluorescence label

size of ITS region Separation by

T-RFLP ARISA

From these properties, DGGE is suitable for analyses of highly heterogeneous communities including unknown members such as soil microbial community. T-RFLP is suitable for analyses of communities including known members and for specific taxonomic groups.

Sequence-based community analyses can reveal fungal community structures with higher resolutions; fungal sequence data obtained can be identified or determined similarity to already known species through the use of extensive and rapidly growing sequence database (Nocker et al., 2007). Sequencing is the basis for construction of phylogenetic trees and for other comparative studies. Conversely, the sequence-based techniques are relatively time-consuming and costly. It depends on samples or target genes how many PCR amplicons were required to have fully analyzed the diversity contained within a single sample. Fierer et al. (2007) estimated that fungal 18S rDNA richness at the 97% sequence similarity level is likely to exceed 106 in 1.0 g of prairie soil, approximately 2 x 103 in rainforest soil and 2 x 104 in desert soil. Buée et al. (2009) reported that predicted richness of fungal ITS regions at the 97% sequence similarity level was approximately 2 x

Until recently, cloning and sequencing were primarily used to generate sequence data of environmental microbial community. PCR amplicons of rDNA and ITS region were cloned into an appropriate vector and clone libraries were sequenced by Sanger methods, also

Fig. 5. Principles of three molecular fingerprinting methods

DGGE

GC clump

Separation by sequence of GC/AT

denatuarant

Gradient of

30

(%)

50

70

ARISA appeared to be suitable for diversity analysis.

103 in 4 g of forest soil.

and ARISA, while ARISA exhibited the highest resolution ability.

**2.3.2 Cloning and high-throughput sequencing technology** 

referred to as dideoxy chain termination sequencing (Sanger & Coulson, 1975). During the current decade, high-throughput second-generation sequencing technologies, such as pyrosequencing, have been developed and introduced to the research of microbial ecology (Petrosino et al., 2009; Roesch et al., 2007), including fungal community analyses (Buée et al., 2009; Lim et al., 2010; Lumini et al., 2010). Buée et al. assessed the fungal diversity in six different forest soils using 454 pyrosequencing (Buée et al., 2009). No less than 166350 reads were obtained from all samples. It enables reading of hundreds, thousands of PCR amplicons per 1 run.

DNA pyrosequencing, sequencing by synthesis, was developed in the mid 1990s as a fundamentally different approach to DNA sequencing (Ronaghi et al., 1996). Sequencing by synthesis occurs by a DNA polymerase-driven generation of inorganic pyrophosphate, with the formation of ATP and ATP-depending conversion of luciferin to oxyluciferin. The generation of oxyluciferin causes the emission of light pulses, and the amplitude of each signal is directly related to the presence of one or more nucleotides. Pyrosequencing can eliminate time and labours for cloning and has the 10-fold cost advantage per base pair over Sanger sequencing. The use of primer barcoding techniques enables to characterize many environmental samples in parallel on a single sequencing run. One important limitation of pyrosequencing is its relative inability to sequence longer stretches of DNA. With first- and second-generation pyrosequenceing chemistries, sequences rarely exceed 100-200 bases. Because of this limitation, cloning and Sanger sequencing are applied for the accurate recovery of longer sequence data at this stage.

## **3. Properties of PCR primers for fungal sequences**

For fungal community analyses, PCR-based techniques are most powerful and generally used. The 18S rRNA gene (rDNA) and internal transcribed spacer (ITS) region are used widely as molecular markers for fungi, through the exploitation of both conserved and variable regions, and a large number of sequences are available in the data bank (Anderson & Cairney, 2004). Various PCR primer sets targeting 18S rDNA and ITS region are available for assessing fungal diversity in soil DNA samples (Table 1). Selection of PCR primer is one of the most important factors affecting outcome. Here, I will summerize their properties from our results and previous data.

### **3.1 PCR amplification and chimera formation**

Although PCR-based strategies are the most powerful tools for the investigation of microbial diversity, they have a number of recognized limitations, perhaps the most insidious of which is the formation of recombinant or chimeric sequences during PCR amplification. Recombination can occur during PCR to jump from one template to another. Thus, whenever a heterogeneous pool of similar sequences, like rDNA and ITS regions, is amplified, chimera formation should be taken into account. The problem of chimeras in mixed DNAs from environmental samples has been highlighted several times in the literature (Kopczynski et al., 1994; Liesack et al., 1991; Wang & Wang, 1997). The existence of chimeras in PCR products may result in the overestimation of community diversity (Wintzingerode et al., 1997) and the occurrence of artificial novel taxa (Jumpponen, 2007). Chimeras seem to comprise a large proportion of the environmental sequence data in public databases (Ashelford et al., 2005; Hugenholtz & Huber, 2003). Jumppone (Jumpponen, 2007)

Molecular Analyses of Soil Fungal Community – Methods and Applications 287

The frequency of chimera sequences was related to the length of target regions of each primer set (Table 2). Long amplicons (targeted by primer sets NS1/FR1(N)-GC and NS1/EF3 & NS1/FR1(N)-GC) were more liable to produce chimeras than short amplicons (targeted by primer sets NS1/GCFung and FF390/FR1(N)-GC). Incomplete, prematurely terminated 18S rDNA sequences were also more frequent in the libraries obtained with primer sets NS1/FR1(N)-GC and NS1/EF3 & NS1/FR1(N)-GC. The concentration of amplified DNA initially increases exponentially and then gradually approaches a plateau when the depletion of reagents results in the generation of prematurely terminated strands. Such fragments seldom anneals with DNA strands of the same species among many homologous sequences of soil DNA (Torsvik et al., 1990), and the recombination events

V1 V2 V3 V4 V5 V7 V8 V9

**Fung FR1**

**SSU-nu-0817 SSU-nu-1196 SSU-nu-1536**

**EF3**

Fig. 6. Positions of PCR primers for fungal 18S rDNA. The variable regions (V) are

**NS1 FF390**

**EF4 fung5**

could be maximally expressed as chimeric molecules (Wang & Wang, 1996).

mature 18S rDNA

Table 2. Composition of clone libraries obtained from upland and paddy field soils using primer sets 1. NS1/GCFung, 2. FF390/FR1(N)-GC, and 3. NS1/FR1(N)-GC; and for nested PCR, 4. NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR. Figures indicated the percentage of the clones within each library (Hoshino & Morimoto, 2010)

1 NS1/GCFung 350 30 94.6 0.0 5.4 0.0 95.5 1.1 3.4 0.0 2 FF390/FR1-GC 390 40 89.0 0.0 9.9 1.1 89.9 2.2 7.9 0.0 3 NS1/FR1-GC 1650 40 62.5 11.4 18.2 8.0 79.1 4.4 8.8 7.7

immature 18S rDNA

chimeric 18S rDNA

1650 29.5 17.9 48.4 4.2 41.1 22.1 34.7 2.1

non-18S rDNA

upland fiield soil paddy fiield soil

mature 18S rDNA

immature 18S rDNA

chimeric 18S rDNA

non-18S rDNA

Our results indicated that the numbers of PCR cycle was also related to chimera formation (Table 2). Because the efficacy of PCR amplification differed among primer sets, we used 30, 40, and 40 cycles for primer sets NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC, respectively, to produce a sufficient amount of PCR products for DGGE. In the case of nested PCR, 25 cycles were made for first PCR using NS1/EF3, and 20 cycles for second PCR using NS1/FR1(N)-GC. The higher the number of PCR cycle, the higher the frequency of chimeras. These results suggested that a smaller number of PCR cycles worked better. When using primer sets FF390/FR1(N)-GC and NS1/FR1(N)-GC; however, high PCR cycle numbers are needed to obtain enough product, because the efficacy of the amplification is

Reducing chimera formation is required to provide a more accurate estimation of community diversity. Although the sequences with the potentiality of chimera could be identified and eliminated from data set, this procedure is often difficult and largely depends on personal judgement (Anderson & Cairney, 2004). The existence of chimera sequences also reduced available data in clone libraries (Table 2). Our results showed that properties of

highlighted in blue

low (Hoshino & Matsumoto, 2008).

4 NS1/EF3 25 NS1/FR1-GC 30

Expected products size (bp)

Number of PCR cycle

Primer set


reported that a large proportion (40 or 31%) was chimeric in clone libraries obtained from soil fungal analyses.

Table 1. Sequences of PCR primers used for assessing soil fungal diversity

PCR protocol was reported to affect the frequency of chimera formation (Qiu et al., 2001; Wang & Wang, 1997; Wintzingerode et al., 1997). To evaluate the significance of primer selection, we compared the compositions of 18S rDNA libraries amplified from upland and paddy field soils using four primer sets: for single PCR, NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC; and for nested PCR, NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR (Fig. 6) (Hoshino & Morimoto, 2010).

reported that a large proportion (40 or 31%) was chimeric in clone libraries obtained from

EF3 TCCTCTAAATGACCAAGTTTG (Smit et al., 1999)

nu-SSU-0817 TTAGCATGGAATAATRRAATAGGA (Borneman & Hartin, 2000)

FR1 AICCATTCAATCGGTAIT (Vainio & Hantula, 2000)

Fun18S1 CCATGCATGTCTAAGTWTAA (Lord et al., 2002)

Fung ATTCCCCGTTACCCGTTG (May et al., 2001)

ITS1F CTTGGTCATTTAGAGGAAGTAA (Gardes & Bruns, 1993)

ITS4A CGCCGTTACTGGGGCAATCCCTG (Larena et al., 1999) 2234C GTTTCCGTAGGTGAACCTGC (Sequerra et al., 1997)

PN3 CCGTTGGTGAACCAGCGGAGGGATC (Viaud et al., 2000)

PCR protocol was reported to affect the frequency of chimera formation (Qiu et al., 2001; Wang & Wang, 1997; Wintzingerode et al., 1997). To evaluate the significance of primer selection, we compared the compositions of 18S rDNA libraries amplified from upland and paddy field soils using four primer sets: for single PCR, NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC; and for nested PCR, NS1/EF3 for the first PCR and NS1/FR1(N)-GC

ITS ITS1 TCCGTAGGTGAACCTGCGG (White et al., 1990)

target PCR primer Primer sequence (5'-3') reference

18S rDNA NS1 GTAGTCATATGCTTGTCTC (White et al., 1990)

NS2 GGCTGCTGGCACCAGACTTGC NS3 GCAAGTCTGGTGCCAGCAGCC NS8 TCCGCAGGTTCACCTACGGA

EF4 GGAAGGGRTGTATTTATTAG Fung5 GTAAAAGTCCTGGTTCCCC

nu-SSU-1196 TCTGGACCTGGTGAGTTTCC nu-SSU-1536 ATTGCAATGCYCTATCCCCA

FF390 CGATAACGAACGAGACCT

Fun18S2 GCTGGCACCAGACTTGCCCTCC

ITS2 GCTGCGTTCTTCATCGATGC ITS4 TCCTCCGCTTATTGATATGC

ITS4B CAGGAGACTTGTACACGGTCCAG

3126T ATATGCTTAAGTTCAGCGGGT

Table 1. Sequences of PCR primers used for assessing soil fungal diversity

PN34 TTGCCGCTTCACTCGCCGTT

for the second PCR (Fig. 6) (Hoshino & Morimoto, 2010).

soil fungal analyses.

Genomic

Fig. 6. Positions of PCR primers for fungal 18S rDNA. The variable regions (V) are highlighted in blue

The frequency of chimera sequences was related to the length of target regions of each primer set (Table 2). Long amplicons (targeted by primer sets NS1/FR1(N)-GC and NS1/EF3 & NS1/FR1(N)-GC) were more liable to produce chimeras than short amplicons (targeted by primer sets NS1/GCFung and FF390/FR1(N)-GC). Incomplete, prematurely terminated 18S rDNA sequences were also more frequent in the libraries obtained with primer sets NS1/FR1(N)-GC and NS1/EF3 & NS1/FR1(N)-GC. The concentration of amplified DNA initially increases exponentially and then gradually approaches a plateau when the depletion of reagents results in the generation of prematurely terminated strands. Such fragments seldom anneals with DNA strands of the same species among many homologous sequences of soil DNA (Torsvik et al., 1990), and the recombination events could be maximally expressed as chimeric molecules (Wang & Wang, 1996).


Table 2. Composition of clone libraries obtained from upland and paddy field soils using primer sets 1. NS1/GCFung, 2. FF390/FR1(N)-GC, and 3. NS1/FR1(N)-GC; and for nested PCR, 4. NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR. Figures indicated the percentage of the clones within each library (Hoshino & Morimoto, 2010)

Our results indicated that the numbers of PCR cycle was also related to chimera formation (Table 2). Because the efficacy of PCR amplification differed among primer sets, we used 30, 40, and 40 cycles for primer sets NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC, respectively, to produce a sufficient amount of PCR products for DGGE. In the case of nested PCR, 25 cycles were made for first PCR using NS1/EF3, and 20 cycles for second PCR using NS1/FR1(N)-GC. The higher the number of PCR cycle, the higher the frequency of chimeras. These results suggested that a smaller number of PCR cycles worked better. When using primer sets FF390/FR1(N)-GC and NS1/FR1(N)-GC; however, high PCR cycle numbers are needed to obtain enough product, because the efficacy of the amplification is low (Hoshino & Matsumoto, 2008).

Reducing chimera formation is required to provide a more accurate estimation of community diversity. Although the sequences with the potentiality of chimera could be identified and eliminated from data set, this procedure is often difficult and largely depends on personal judgement (Anderson & Cairney, 2004). The existence of chimera sequences also reduced available data in clone libraries (Table 2). Our results showed that properties of

Molecular Analyses of Soil Fungal Community – Methods and Applications 289

Anderson et al. (Anderson et al., 2003) reported that the relative proportion of sequences representing the four main fungal phyla was similar in clone libraries from grassland soil with primer sets nu-SSU-817/nu-SSU-1196, nu-SSU-817/nu-SSU-1536, EF4/EF3 and ITS1F/ITS4. On the other hand, Jumpponen (Jumpponen, 2007) reported that EF4/EF3 biased toward Basidiomycota as predicted (Smit et al., 1999) and that nu-SSU-817/nu-SSU-1536 mainly amplified Ascomycota from soil samples of underneath willow canopies. We found that fungal 18S rDNA fragments showed a similar distribution at the phylum level in the upland and paddy soil libraries amplified with primer sets NS1/GCFung, FF390/FR1(N)-GC and NS1/FR1(N)-GC (Hoshino & Morimoto, 2010). The detection frequency of the Chytridiomycota, however, differed among these primer sets. NS1/GCFung failed to detect the Chytridiomycota, while FF390/FR1(N)-GC amplified it more efficiently (Table 3). At the class level, especially in the paddy soil libraries, the difference was evident in the distribution of fungal taxa inferred from 18S rDNA with these primer sets (Table 3). Primer selection has a pivotal importance on the community structure to be investigated although primer bias may not be as significant as previously thought, as

kingdom subkingdom phylum subphylum class 1234 1234 fungi Dikarya Ascomycota Pezizomycotina Dothideomycetes 19 11 9.1 11 9.5 10 4.2 5.1

incertae sedis Chytridiomycota Chytridiomycetes 8.6 7.3 1.2 6.3 1.4

Kickxellomycotina 1.1

Eurotiomycetes 3.4 3.7 5.5 8.3 Leotiomycetes 25 14 4.2 2.6 Orbiliomycetes 3.7 2.5

Tremellomycetes 1.8 2.4 2.5 1.4

Microbotryomycetes 2.5 1.4

Monoblepharidomycetes 1.4

Sordariomycetes 27 35 42 29 2.4 8.8 8.3 5.1 unindentified 1.3

upland field soil paddy field soil

Pezizomycetes 4.5 3.7 1.8

Basidiomycota Basidiomycota Agricomycets 6.8 9.9 7.3 3.6 21 16 26 67

Pucciniomycotina Atractiellomycetes 1.2

Blastocladiomycota Blastocladiomycetes 1.4 2.6 incertae sedis Mucoromycotina Mucoromycetes 22 15 18 57 6 16 11 15

Ochrophyta 1.2 4.2 incertae sedis 1.8 1.4

Zoopagomycotina 1.2 1.4

Table 3. Distribution of 18S r RNA gene sequenses in clone libraries from upland and paddy

Heliozoa 1.2

(=Metazoa) Bilateralia Annelida 25 13 Arthropoda 1.1 Rhizaria Cercozoa 6.8 2.5 3.6 1.2 19 9.7 2.6

field soils using different primer sets for single PCR (1-3) and nested PCR (4), i.e., 1, NS1/GCFung; 2, FF390/FR1-GC; 3, NS1/FR1-GC; and 4, NS1/EF3 for first PCR and NS1/FR1-GC for second PCR. Figures indicated the percentage of the clones within each

Plantae Viridiplantae 1.3

Alveolata Apicomplexa 3.6 stramenopiles Oomycota 8 1.2 1.8 1.4

Amoebabiota Amoebazoa 1.2 Animalia Amoebidiobiotina Amoebidiozoa 1.2

incertae sedis Apusozoa 1.2

Anderson et al. (Anderson et al., 2003) concluded.

library

primer sets affected the frequency of chimera formation and that PCR protocol may be modified to decrease PCR cycles and to extend elongation time so that chimera contamination may minimized. Thus, PCR efficacy is an important factor, as well as the length of target fragment.

#### **3.2 PCR primers specificity and bias in detection of fungal sequence from environmental samples**

For accurate fungal community analyses, desirable PCR primer sets could exhaustively amplify fungal sequences without bias and strictly avoid the amplification non-fungal sequences from DNA pools extracted from environmental samples. However, primer sets targeting fungal 18S rDNA or ITS regions were designed for a broad range of fungi and consequently amplify genes of non-fungal organisms because of the high level of sequence similarity between 18S rDNAs of fungi and some closely related eukaryotes (Anderson & Cairney, 2004). Conversely, when increasing the specificity of primers for fungal genes, they may preferentially amplify a certain group of fungi, resulting in bias (Anderson & Cairney, 2004).

We evaluated single and nested PCR systems in terms of the frequency of non-fungal sequences and the diversity of fungal sequences in clone libraries (Hoshino & Morimoto, 2010). Four primer sets, i.e., for single PCR: NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC; and for nested PCR: NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR, were compared using soil samples from upland and paddy fields. The rate of non-fungal eukaryotic 18S rDNAs amplified by single PCR ranged between 7 to 16 % for upland soil and between 20 to 31% for paddy field soil, whereas nested PCR produced a single eukaryotic clone in each library. The difference indicates that nested PCR increased the specificity to fungal sequences. Although the detection range of fungal taxa by 18S rDNA was generally similar among primer sets for single PCR, the fungal community detected by nested PCR was biased to specific sequences: diversity indices were significantly lower than those from single PCR in both libraries. These differences indicate that nested PCR system using primer set NS1/EF3 & NS1/FR1(N)-GC is not appropriate for diversity analysis on a wide range of taxonomic groups (i.e., total fungi).

The specificity of PCR primers varies depending on the composition of eukaryotic DNA contained in the extracted DNA pool. For example, although primer sets of EF4/EF3 and EF4/fung5 exclusively amplified fungal sequences from DNA extracted from wheat rhizosphere soil (Smit et al., 1999), they also amplified some non-fungal sequences from cultured organisms and avocado grove soil (Borneman & Hartin, 2000). Single PCR primer sets, NS1/GCFung, FF390/FR1(N)-GC and NS1/FR1(N)-GC, amplified more clones of nonfungal eukaryotic from the paddy soil than from the upland soil (Table 3)(Hoshino & Morimoto, 2010). These results may reflect the actual ratio of non-fungal related eukaryotic DNA to fungal DNA. In addition, phylogenetic groups of non-fungal eukaryotes detected were variable according to primer sets used (Table 3). The specificity of set NS1/FR1(N)-GC for fungi was higher than that of the other sets in the upland soil library but lower in the paddy soil library. The ratio of non-fungal gene preferentially detected by NS1/FR1(N)-GC was assumed to be higher in the DNA pool of paddy field soil than in that of upland field soil. Primer specificity is also affected by compositions of non-fungal eukaryotic sequences in samples. It showed that it is critical to check the specificity of primer sets for environmental samples to be studied.

primer sets affected the frequency of chimera formation and that PCR protocol may be modified to decrease PCR cycles and to extend elongation time so that chimera contamination may minimized. Thus, PCR efficacy is an important factor, as well as the

For accurate fungal community analyses, desirable PCR primer sets could exhaustively amplify fungal sequences without bias and strictly avoid the amplification non-fungal sequences from DNA pools extracted from environmental samples. However, primer sets targeting fungal 18S rDNA or ITS regions were designed for a broad range of fungi and consequently amplify genes of non-fungal organisms because of the high level of sequence similarity between 18S rDNAs of fungi and some closely related eukaryotes (Anderson & Cairney, 2004). Conversely, when increasing the specificity of primers for fungal genes, they may preferentially amplify a certain group of fungi, resulting in bias (Anderson & Cairney,

We evaluated single and nested PCR systems in terms of the frequency of non-fungal sequences and the diversity of fungal sequences in clone libraries (Hoshino & Morimoto, 2010). Four primer sets, i.e., for single PCR: NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC; and for nested PCR: NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR, were compared using soil samples from upland and paddy fields. The rate of non-fungal eukaryotic 18S rDNAs amplified by single PCR ranged between 7 to 16 % for upland soil and between 20 to 31% for paddy field soil, whereas nested PCR produced a single eukaryotic clone in each library. The difference indicates that nested PCR increased the specificity to fungal sequences. Although the detection range of fungal taxa by 18S rDNA was generally similar among primer sets for single PCR, the fungal community detected by nested PCR was biased to specific sequences: diversity indices were significantly lower than those from single PCR in both libraries. These differences indicate that nested PCR system using primer set NS1/EF3 & NS1/FR1(N)-GC is not appropriate for

The specificity of PCR primers varies depending on the composition of eukaryotic DNA contained in the extracted DNA pool. For example, although primer sets of EF4/EF3 and EF4/fung5 exclusively amplified fungal sequences from DNA extracted from wheat rhizosphere soil (Smit et al., 1999), they also amplified some non-fungal sequences from cultured organisms and avocado grove soil (Borneman & Hartin, 2000). Single PCR primer sets, NS1/GCFung, FF390/FR1(N)-GC and NS1/FR1(N)-GC, amplified more clones of nonfungal eukaryotic from the paddy soil than from the upland soil (Table 3)(Hoshino & Morimoto, 2010). These results may reflect the actual ratio of non-fungal related eukaryotic DNA to fungal DNA. In addition, phylogenetic groups of non-fungal eukaryotes detected were variable according to primer sets used (Table 3). The specificity of set NS1/FR1(N)-GC for fungi was higher than that of the other sets in the upland soil library but lower in the paddy soil library. The ratio of non-fungal gene preferentially detected by NS1/FR1(N)-GC was assumed to be higher in the DNA pool of paddy field soil than in that of upland field soil. Primer specificity is also affected by compositions of non-fungal eukaryotic sequences in samples. It showed that it is critical to check the specificity of primer sets for

**3.2 PCR primers specificity and bias in detection of fungal sequence from** 

diversity analysis on a wide range of taxonomic groups (i.e., total fungi).

environmental samples to be studied.

length of target fragment.

**environmental samples** 

2004).

Anderson et al. (Anderson et al., 2003) reported that the relative proportion of sequences representing the four main fungal phyla was similar in clone libraries from grassland soil with primer sets nu-SSU-817/nu-SSU-1196, nu-SSU-817/nu-SSU-1536, EF4/EF3 and ITS1F/ITS4. On the other hand, Jumpponen (Jumpponen, 2007) reported that EF4/EF3 biased toward Basidiomycota as predicted (Smit et al., 1999) and that nu-SSU-817/nu-SSU-1536 mainly amplified Ascomycota from soil samples of underneath willow canopies. We found that fungal 18S rDNA fragments showed a similar distribution at the phylum level in the upland and paddy soil libraries amplified with primer sets NS1/GCFung, FF390/FR1(N)-GC and NS1/FR1(N)-GC (Hoshino & Morimoto, 2010). The detection frequency of the Chytridiomycota, however, differed among these primer sets. NS1/GCFung failed to detect the Chytridiomycota, while FF390/FR1(N)-GC amplified it more efficiently (Table 3). At the class level, especially in the paddy soil libraries, the difference was evident in the distribution of fungal taxa inferred from 18S rDNA with these primer sets (Table 3). Primer selection has a pivotal importance on the community structure to be investigated although primer bias may not be as significant as previously thought, as Anderson et al. (Anderson et al., 2003) concluded.


Table 3. Distribution of 18S r RNA gene sequenses in clone libraries from upland and paddy field soils using different primer sets for single PCR (1-3) and nested PCR (4), i.e., 1, NS1/GCFung; 2, FF390/FR1-GC; 3, NS1/FR1-GC; and 4, NS1/EF3 for first PCR and NS1/FR1-GC for second PCR. Figures indicated the percentage of the clones within each library

Molecular Analyses of Soil Fungal Community – Methods and Applications 291

(A) (B)

1234




0


1

2

0


1

2 2.5

Fig. 7. Number of bands in DGGE gel (A), Shannon diversity index (B) of 18S rDNA DGGE profiles of upland field soils (F1, F2, F3, F4) and paddy field soils (P1, P2) using primer sets: 1. NS1/GCFung, 2. FF390/FR1(N)-GC, 3. NS1/FR1(N)-GC, and 4. NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR (Modified from Fig. 4 in (Hoshino & Matsumoto,

Primer set 1: NS1/GCFung Primer set 2: FF390/FR1(N)-GC

Primer set 3: NS1/FR1(N)-GC Primer set 4:NS1/EF3 &

Shannon diversity

index, *H'*

Primer sets Primer sets

2.4 2.7 3 3.3 3.6

1234

F1 F2 F3 F4 P1 P2

F1 F2 F3 F4 P1 P2

Fig. 8. Multidimensional scaling (MDS) map based on the squared distance of similarity of the 18S rDNA DGGE profiles of upland field soils (F1, F2, F3, F4) and paddy field soils (P1, P2) using primer sets: 1. NS1/GCFung, 2. FF390/FR1(N)-GC, 3. NS1/FR1(N)-GC, and 4. NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR (Modified from Fig. 5 in



0


NS1/FR1(N)-GC



0


1

2

1

2

2008))

20

30

Number of bands

in DGGE gel

40

50

(Hoshino & Matsumoto, 2008))

These results indicate that appropriate primers should be selected according to the aims and the origin of samples and/or that more than two primer sets with different properties should be used to obtain a more comprehensive view of the fungal communities.

## **3.3 Applicability for DGGE**

Community analysis by DGGE is sensitive to choice of primer sets because the separation of each DNA fragment in denetuaring gradient gels largely depends on the sequences of target regions. Okubo & Sugiyama (Okubo & Sugiyama, 2009) compared five fungal primer sets in terms of band separation of four fungal species in DGGE gels; when using EF4/GCFung, bands of the four species showed the same mobility in DGGE gels and were not separable, while they separated but smeared with EF4/Fung5 or ITS1F/ITS2-GC. On the other hands, NS1/GCFung and FF390/FR1-GC produced separate and single bands.

We evaluated primer sets for fungal 18S rDNA DGGE using agricultural soils in terms of the following features: detection and reproducibility of DGGE banding profiles, obtained diversity indices, and ability to discriminate fungal communities by DGGE (Hoshino & Matsumoto, 2008). Four primer sets, i.e., for single PCR, NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC; and for nested PCR, NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR, were compared using six soil samples from upland (F1, F2, F3 and F4) and paddy fields (P1 and P2) in Japan (Fig. 6).

PCR products with different primer sets under the appropriate experimental regimes showed clear band separation in DGGE analysis, as reported previously (May et al., 2001; Oros-Sichler et al., 2006; Vainio & Hantula, 2000). In addition, repeated trials with the same samples produced virtually identical profiles in the same DGGE gels with primer sets NS1/GCFung and FF390/FR1(N)-GC. However, when primer set NS1/FR1(N)-GC was used, aggregates present in the middle of DGGE gel that sometimes interfered with the detection of target bands. We also detected smiling and distortion of banding patterns in DGGE with these primer sets. The presence of nonspecific aggregates and the distortion of banding pattern reduced reproducibility especially between different gels.

Although there was no significant difference in the number of bands that appeared in each sample among primer sets (Fig. 7A), the Shannon diversity indices, used to measure diversity in categorical data (Krebs, 1989), were lowest for primer set FF390/FR1(N)-GC, and tended to be higher for primer sets NS1/FR1(N)-GC and NS1/EF3 & NS1/FR1(N)-GC (Fig. 7B). Two main bands were highly dominant in DGGE profiles of these six samples with primer set FF390/FR1(N)-GC. However, sequence diversity in clone library with primer set FF390/FR1(N)-GC was the higher than libraries with other primer sets (Hoshino & Morimoto, 2010). The main reason for the difference may be ascribed to the low band separability in DGGE.

To evaluate the ability to discriminate fungal communities, multidimensional scaling (MDS) maps were generated from DGGE profiles for each primer set. Each MDS map showed a similar tendency (Fig. 8). Samples from upland and paddy field soils were positioned separately, with the exception of sample F4, which was always distant from other samples in the MDS maps. The MDS map with primer set NS1/GCFung showed the highest differentiation, with samples distantly located from one another, whereas with primer set FF390/FR1(N)-GC, except F4 differentiation among samples was lower in the MDS map. With primer set NS1/FR1(N)-GC, samples F1 and F3 and samples P1 and P2 were plotted close together.

These results indicate that appropriate primers should be selected according to the aims and the origin of samples and/or that more than two primer sets with different properties

Community analysis by DGGE is sensitive to choice of primer sets because the separation of each DNA fragment in denetuaring gradient gels largely depends on the sequences of target regions. Okubo & Sugiyama (Okubo & Sugiyama, 2009) compared five fungal primer sets in terms of band separation of four fungal species in DGGE gels; when using EF4/GCFung, bands of the four species showed the same mobility in DGGE gels and were not separable, while they separated but smeared with EF4/Fung5 or ITS1F/ITS2-GC. On the other hands,

We evaluated primer sets for fungal 18S rDNA DGGE using agricultural soils in terms of the following features: detection and reproducibility of DGGE banding profiles, obtained diversity indices, and ability to discriminate fungal communities by DGGE (Hoshino & Matsumoto, 2008). Four primer sets, i.e., for single PCR, NS1/GCFung, FF390/FR1(N)-GC, and NS1/FR1(N)-GC; and for nested PCR, NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR, were compared using six soil samples from upland (F1, F2, F3 and F4)

PCR products with different primer sets under the appropriate experimental regimes showed clear band separation in DGGE analysis, as reported previously (May et al., 2001; Oros-Sichler et al., 2006; Vainio & Hantula, 2000). In addition, repeated trials with the same samples produced virtually identical profiles in the same DGGE gels with primer sets NS1/GCFung and FF390/FR1(N)-GC. However, when primer set NS1/FR1(N)-GC was used, aggregates present in the middle of DGGE gel that sometimes interfered with the detection of target bands. We also detected smiling and distortion of banding patterns in DGGE with these primer sets. The presence of nonspecific aggregates and the distortion of

Although there was no significant difference in the number of bands that appeared in each sample among primer sets (Fig. 7A), the Shannon diversity indices, used to measure diversity in categorical data (Krebs, 1989), were lowest for primer set FF390/FR1(N)-GC, and tended to be higher for primer sets NS1/FR1(N)-GC and NS1/EF3 & NS1/FR1(N)-GC (Fig. 7B). Two main bands were highly dominant in DGGE profiles of these six samples with primer set FF390/FR1(N)-GC. However, sequence diversity in clone library with primer set FF390/FR1(N)-GC was the higher than libraries with other primer sets (Hoshino & Morimoto, 2010). The main reason for the difference may be ascribed to the low band

To evaluate the ability to discriminate fungal communities, multidimensional scaling (MDS) maps were generated from DGGE profiles for each primer set. Each MDS map showed a similar tendency (Fig. 8). Samples from upland and paddy field soils were positioned separately, with the exception of sample F4, which was always distant from other samples in the MDS maps. The MDS map with primer set NS1/GCFung showed the highest differentiation, with samples distantly located from one another, whereas with primer set FF390/FR1(N)-GC, except F4 differentiation among samples was lower in the MDS map. With primer set NS1/FR1(N)-GC, samples F1 and F3 and samples P1 and P2 were plotted

should be used to obtain a more comprehensive view of the fungal communities.

NS1/GCFung and FF390/FR1-GC produced separate and single bands.

banding pattern reduced reproducibility especially between different gels.

and paddy fields (P1 and P2) in Japan (Fig. 6).

**3.3 Applicability for DGGE** 

separability in DGGE.

close together.

Fig. 7. Number of bands in DGGE gel (A), Shannon diversity index (B) of 18S rDNA DGGE profiles of upland field soils (F1, F2, F3, F4) and paddy field soils (P1, P2) using primer sets: 1. NS1/GCFung, 2. FF390/FR1(N)-GC, 3. NS1/FR1(N)-GC, and 4. NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR (Modified from Fig. 4 in (Hoshino & Matsumoto, 2008))

Fig. 8. Multidimensional scaling (MDS) map based on the squared distance of similarity of the 18S rDNA DGGE profiles of upland field soils (F1, F2, F3, F4) and paddy field soils (P1, P2) using primer sets: 1. NS1/GCFung, 2. FF390/FR1(N)-GC, 3. NS1/FR1(N)-GC, and 4. NS1/EF3 for the first PCR and NS1/FR1(N)-GC for the second PCR (Modified from Fig. 5 in (Hoshino & Matsumoto, 2008))

Molecular Analyses of Soil Fungal Community – Methods and Applications 293

two chemical fumigants (CP and 1,3-D) and spinach growth on fungal community structure

Experiments were performed in an experimental field in Tsukuba, Japan. Annual cropping system consisted of soil fumigation in September followed by two consecutive spinach cultivations. Soil was treated with fumigants (CP at 20 ml m-2 or 1,3-D at 32 ml m-2) and covered with polyethylene film for about two weeks. Bulk soil and rhizosphere samples were taken periodically during the three fumigation trials. DNA was extracted directly from soil samples and fungal 18S rRNA genes were amplified by nested PCR with primer pairs AU2/AU4 and GC-FR1/FF390 for DGGE analyses. Dezitized data of DGGE profiles were analyzed by (i) diversity indices and (ii) multivariate statistical technique. Dominant bands in DGGE gels were excised for sequencing. Sequences of DGGE bands were identified with the FASTA search from the database of the DNA Data Bank of Japan

Fig. 9. Quantitative analysis of the 18S rDNA DGGE profiles 2 months after fumigation in the second trial (year 2). (A) Shannon's diversity index and (B) multi-dimensional scaling (MDS) map based on the squared distance of similarity. (Modified from Fig. 3 in (Hoshino &

We compared the fungal 18S rDNA DGGE profiles among each treatment two month after fumigation both in bulk soil and rhizosphere. The Shannon diversity index *H*' (Fig. 9A) was calculated from these profiles. The index for bulk soil in the CP plots was significantly lower than that in the control plots (P<0.05). The index for the rhizosphere soil in the CP plots also tended to be lower than that of control plots, but the difference was not significant. These

These DGGE profiles were also analyzed by multi-dimensional scaling (MDS) (Fig. 9B). The MDS map shows every band pattern on one plot, where relative changes in community structure can be visualized and interpreted as the distances between the points (Araya et al., 2003). The closer the points with each other, the more similar the DGGE banding patterns represented by the points are. In the MDS map, samples from the bulk soil and the

values of the 1,3-D plots were almost equivalent to those of the control plots.

in a field using molecular techniques (Hoshino & Matsumoto, 2007).

(DDBJ).

Matsumoto, 2007))

These data suggested that primer sets NS1/GCFung and FF390/FR1-GC were applicable for soil fungal community DGGE analysis and that primer set NS1/GCFung was the most suitable, considering all the various factors together. Comparison of DGGE profiles among each study is required to standardize experimental conditions, especially PCR primers. We selected primer set NS1/GCFung for this purpose and established DGGE experimental conditions using this primer set to prepare experimental protocols and technical reports of bacterial and fungal DGGE analyses (Morimoto & Hoshino, 2010).

## **4. Application examples of molecular techniques to fungal community analyses in agricultural field soils**

Molecular analyses of fungal community have been reported for various soils in different ecosystems, such as forests (Perkiömäki et al., 2003), grasslands (Brodie et al., 2003), dunes (Kowalchuk et al., 1997), and stream sediments (Nikolcheva et al., 2003), as well as agricultural fields (Gomes et al., 2003). In agricultural soils, many field trials have shown the effect of plant cultivation (Gomes et al., 2003), fertilizer and pesticide application on fungal community (Girvan et al., 2004). Here, I will show two examples of the application of molecular techniques to fungal community analyses in Japanese agricultural soils. We analyzed the impact of chemical fumigation on fungal community structure of bulk soil and spinach rhizosphere in a field and monitored their recovery from the drastic change (Hoshino & Matsumoto, 2007). The results suggested that the effects were different among the chemicals and between bulk soil and rhizosphere. In addition, it was reported that fungal communities were most obviously affected by fertilizer treatment, i.e., changes in soil nutrient status, rather than edaphic factors such as soil type (Suzuki et al., 2009).

#### **4.1 Effect of chemical fumigants**

Pre-planting soil fumigation is used widely around the world in high-value crops and has been shown to be effective to control soil-borne pathogens, weeds, and plant-parasitic nematodes. In Japan, especially in areas that produce vegetables such as spinach, lettuce, and tomato, continuous monoculture is widely adopted to increase profit, often resulting in outbreak of pests. Many areas utilize soil chemical fumigation for consistent production. Most of chemical fumigants have a broad range of biocidal activity and can potentially harm beneficial organisms, in addition to target pests. Although methyl bromide (MeBr) had been widely used in the past, the use of MeBr in soil fumigation was banned since 2005 because of its environmental risk. Therefore, the use of alternatives, such as chloropicrin (CP) and 1,3-dichloropropene (1,3-D), has been increasing (Dungan et al., 2003). Their effect on nontarget organisms was also of concern and should be evaluated.

However, there are relatively few studies on the effect of chemical fumigants on non-target soil fungal community as compared with a number of studies reporting the effect on specific plant pathogens (Browning et al., 2006; Hamm, 2003; Takehara et al., 2003) and on bacterial community (Dungan et al., 2003; Ibekwe et al., 2001). Itoh et al. (Itoh et al., 2000) and Tanaka et al. (Tanaka et al., 2003) reported that the count of viable fungi decreased after CP fumigation. De Cal et al. (De Cal et al., 2005) used a culture-dependent method with selective media to show that chemical fumigants reduced certain members of soil fungi, such as *Fusarium* spp., *Pythium* spp., and *Verticillium* spp. We aimed to analyze the effect of

These data suggested that primer sets NS1/GCFung and FF390/FR1-GC were applicable for soil fungal community DGGE analysis and that primer set NS1/GCFung was the most suitable, considering all the various factors together. Comparison of DGGE profiles among each study is required to standardize experimental conditions, especially PCR primers. We selected primer set NS1/GCFung for this purpose and established DGGE experimental conditions using this primer set to prepare experimental protocols and technical reports of

**4. Application examples of molecular techniques to fungal community** 

nutrient status, rather than edaphic factors such as soil type (Suzuki et al., 2009).

target organisms was also of concern and should be evaluated.

Pre-planting soil fumigation is used widely around the world in high-value crops and has been shown to be effective to control soil-borne pathogens, weeds, and plant-parasitic nematodes. In Japan, especially in areas that produce vegetables such as spinach, lettuce, and tomato, continuous monoculture is widely adopted to increase profit, often resulting in outbreak of pests. Many areas utilize soil chemical fumigation for consistent production. Most of chemical fumigants have a broad range of biocidal activity and can potentially harm beneficial organisms, in addition to target pests. Although methyl bromide (MeBr) had been widely used in the past, the use of MeBr in soil fumigation was banned since 2005 because of its environmental risk. Therefore, the use of alternatives, such as chloropicrin (CP) and 1,3-dichloropropene (1,3-D), has been increasing (Dungan et al., 2003). Their effect on non-

However, there are relatively few studies on the effect of chemical fumigants on non-target soil fungal community as compared with a number of studies reporting the effect on specific plant pathogens (Browning et al., 2006; Hamm, 2003; Takehara et al., 2003) and on bacterial community (Dungan et al., 2003; Ibekwe et al., 2001). Itoh et al. (Itoh et al., 2000) and Tanaka et al. (Tanaka et al., 2003) reported that the count of viable fungi decreased after CP fumigation. De Cal et al. (De Cal et al., 2005) used a culture-dependent method with selective media to show that chemical fumigants reduced certain members of soil fungi, such as *Fusarium* spp., *Pythium* spp., and *Verticillium* spp. We aimed to analyze the effect of

Molecular analyses of fungal community have been reported for various soils in different ecosystems, such as forests (Perkiömäki et al., 2003), grasslands (Brodie et al., 2003), dunes (Kowalchuk et al., 1997), and stream sediments (Nikolcheva et al., 2003), as well as agricultural fields (Gomes et al., 2003). In agricultural soils, many field trials have shown the effect of plant cultivation (Gomes et al., 2003), fertilizer and pesticide application on fungal community (Girvan et al., 2004). Here, I will show two examples of the application of molecular techniques to fungal community analyses in Japanese agricultural soils. We analyzed the impact of chemical fumigation on fungal community structure of bulk soil and spinach rhizosphere in a field and monitored their recovery from the drastic change (Hoshino & Matsumoto, 2007). The results suggested that the effects were different among the chemicals and between bulk soil and rhizosphere. In addition, it was reported that fungal communities were most obviously affected by fertilizer treatment, i.e., changes in soil

bacterial and fungal DGGE analyses (Morimoto & Hoshino, 2010).

**analyses in agricultural field soils** 

**4.1 Effect of chemical fumigants** 

two chemical fumigants (CP and 1,3-D) and spinach growth on fungal community structure in a field using molecular techniques (Hoshino & Matsumoto, 2007).

Experiments were performed in an experimental field in Tsukuba, Japan. Annual cropping system consisted of soil fumigation in September followed by two consecutive spinach cultivations. Soil was treated with fumigants (CP at 20 ml m-2 or 1,3-D at 32 ml m-2) and covered with polyethylene film for about two weeks. Bulk soil and rhizosphere samples were taken periodically during the three fumigation trials. DNA was extracted directly from soil samples and fungal 18S rRNA genes were amplified by nested PCR with primer pairs AU2/AU4 and GC-FR1/FF390 for DGGE analyses. Dezitized data of DGGE profiles were analyzed by (i) diversity indices and (ii) multivariate statistical technique. Dominant bands in DGGE gels were excised for sequencing. Sequences of DGGE bands were identified with the FASTA search from the database of the DNA Data Bank of Japan (DDBJ).

Fig. 9. Quantitative analysis of the 18S rDNA DGGE profiles 2 months after fumigation in the second trial (year 2). (A) Shannon's diversity index and (B) multi-dimensional scaling (MDS) map based on the squared distance of similarity. (Modified from Fig. 3 in (Hoshino & Matsumoto, 2007))

We compared the fungal 18S rDNA DGGE profiles among each treatment two month after fumigation both in bulk soil and rhizosphere. The Shannon diversity index *H*' (Fig. 9A) was calculated from these profiles. The index for bulk soil in the CP plots was significantly lower than that in the control plots (P<0.05). The index for the rhizosphere soil in the CP plots also tended to be lower than that of control plots, but the difference was not significant. These values of the 1,3-D plots were almost equivalent to those of the control plots.

These DGGE profiles were also analyzed by multi-dimensional scaling (MDS) (Fig. 9B). The MDS map shows every band pattern on one plot, where relative changes in community structure can be visualized and interpreted as the distances between the points (Araya et al., 2003). The closer the points with each other, the more similar the DGGE banding patterns represented by the points are. In the MDS map, samples from the bulk soil and the

Molecular Analyses of Soil Fungal Community – Methods and Applications 295

the other hand, band intensity decreased in *Basidiobolus microsporus* (83.7%, Basidiomycota)

After CP treatment, bands inferred to represent chytridiomycota became dominant (Fig.10). Chytrids can rapidly reproduce and increase their populations in response to disturbance (Lozupone & Klein, 2002). These characteristics could allow them to quickly exploit nutrients released after soil disturbance, such as fumigation, increasing their overall population. Chytridiomycota cannot be detected by conventional dilution-plate counting, and are usually studied using culture and microscopic protocols based on baiting techniques, using a "bait" substrate to attract chytrids under flooded conditions (Lozupone & Klein, 2002). Our results indicated the advantages of molecular techniques to detect whole

Soil microbial communities are influenced by various factors such as cropping system (Kuske et al., 2002), tillage (Peixoto et al., 2006), fertilization (Marschner et al., 2003) and application of pesticide and herbicide (Yang et al., 2000). On the other hand, environmental factors, including soil characteristics, also affect microbial communities, e.g., soil type (Girvan et al., 2003), soil particle size (Sessitsch et al., 2001), soil air composition (Øvreås et al., 1998) and season (Girvan et al., 2004). Bacteria have been well documented for agricultural soils; many field trials have shown that the composition of the entire bacterial community is determined primarily by soil type (Girvan et al., 2003; Xu et al., 2009), emphasizing the effect of soil chemistry and structure, especially pH and soil texture (Fierer & Jackson, 2006; Lauber et al., 2008), rather than cultural practices. However, little has so far

Suzuki et al. (Suzuki et al., 2009) studied the effect of soil type and fertilizer type on bacterial and fungal communities in a long-term experimental field in Tsukuba, Japan. Upland field plots containing four different soil types, i.e., Gleyic Mollic-Umbric Andosols (Cumulic Andosol), Gleyic Haplic Andosols (Low-humic Andosol), Gleyic Haplic Alisols (Yellow Soil), and Entric Fluvisols (Gray Lowland Soil) were maintained under three different fertilizer management systems (chemical fertilizer rice husks plus cow manure, and pig manure) for 5 years. Carrot and maize were annually cropped in the fields once every summer. Bulk soil samples were taken in May prior to fertilization and cultivation. From directly-extracted soil DNA, bacterial 16S rDNA and fungal 18S rDNA were amplified using primer pairs 968g-GC/1378r and NS1/GCFung, respectively and subjected to DGGE

Fungal DGGE profiles based on the 18S rRNA gene were analyzed by principal component analysis (PCA) to separate plots based on fertilization practices. This result showed that fungal community composition was more directly related to fertilization than soil type. On the other hand, PCA of bacterial DGGE profiles indicated that the plots were separated by soil type. Lauber et al. (Lauber et al., 2008) reported that fungal community composition was most closely associated with changes in soil nutrient status, i.e., concentration of total nitrogen and extractable phosphate, and the ratio of total carbon and nitrogen concentration. Suzuki et al. (Suzuki et al., 2009) described that fungi may be more suitable as microbial indicators of soil quality because the dynamics of fungal community were more reflected

and *Bensingtonia ciliate* (88.9%, Basidiomycota) in 1,3-D treated plots.

been known about factors affecting fungal community structure.

soil nutrient status than that of bacterial community.

fungal community including such fungal groups.

**4.2 Effect of soil types and fertilizers** 

analyses.

rhizosphere soil were positioned separately, indicating that spinach cultivation affected soil fungal community structure, too (Fig. 9B). The MDS map also showed that the difference in DGGE profiles was greater between CP and control plots than between 1,3-D and control plots, both in bulk soil and rhizosphere soil (Fig. 9B). When the magnitude of the impact was compared between samples of bulk soil and rhizosphere soil, the differences in DGGE profiles between control and chloropicrin plots were smaller in rhizosphere soil than in bulk soil.

We monitored changes in fungal DGGE profiles in bulk soil after chemical fumigation of this field over three years. Fig. 10 shows the change in DGGE profiles of plots fumigated with chloropicrin or 1,3-D before fumigation, two months after fumigation, and six months after fumigation for each year. DGGE profiles drastically changed after CP treatment and did not recover completely 1 year after, e.g., before treatments in years 2 and 3 (Fig.10). In contrast, DGGE profiles of 1,3-D plots revealed a smaller change 2 months after fumigation but became indistinguishable from those of control plots after 6 months. These results indicated that the impact of fumigation on the soil fungal community was greater in the CP treatment than in the 1,3-D treatment both in terms of the magnitude of the effect after 2 months and the extent of recovery 1 year after.

Fig. 10. Temporal change after fumigation in fungal 18S rDNA DGGE profiles from bulk soil samples in (A) untreated control plot, (B) CP plot, and (C) 1,3-D plot. Samples were taken before (B) and 2 and 6 months (2M and 6M, respectively) after fumigation for each year (1Y, 2Y and 3Y) in three-year trials

Between treatments of CP and 1,3-D, there are differences in fungal species affected. In CPtreated plots, bands with high sequence similarity to *Myrothecium cinctum* (100%, Ascomycota), *Bionectria ochroleuca* (99.7%, Ascomycota), *Metarhizium anisopliae* (100%, Ascomycota), *Dentipellisseparans* (96.7%, Basidiomycota), *Verticillium dahliae* (98.4%, Ascomycota) and *Exophiala dermatitidis* (100%, Ascomycota) decreased in band intensity. On the other hand, band intensity decreased in *Basidiobolus microsporus* (83.7%, Basidiomycota) and *Bensingtonia ciliate* (88.9%, Basidiomycota) in 1,3-D treated plots.

After CP treatment, bands inferred to represent chytridiomycota became dominant (Fig.10). Chytrids can rapidly reproduce and increase their populations in response to disturbance (Lozupone & Klein, 2002). These characteristics could allow them to quickly exploit nutrients released after soil disturbance, such as fumigation, increasing their overall population. Chytridiomycota cannot be detected by conventional dilution-plate counting, and are usually studied using culture and microscopic protocols based on baiting techniques, using a "bait" substrate to attract chytrids under flooded conditions (Lozupone & Klein, 2002). Our results indicated the advantages of molecular techniques to detect whole fungal community including such fungal groups.

#### **4.2 Effect of soil types and fertilizers**

294 Soil Health and Land Use Management

rhizosphere soil were positioned separately, indicating that spinach cultivation affected soil fungal community structure, too (Fig. 9B). The MDS map also showed that the difference in DGGE profiles was greater between CP and control plots than between 1,3-D and control plots, both in bulk soil and rhizosphere soil (Fig. 9B). When the magnitude of the impact was compared between samples of bulk soil and rhizosphere soil, the differences in DGGE profiles between control and chloropicrin plots were smaller in rhizosphere soil than in bulk

We monitored changes in fungal DGGE profiles in bulk soil after chemical fumigation of this field over three years. Fig. 10 shows the change in DGGE profiles of plots fumigated with chloropicrin or 1,3-D before fumigation, two months after fumigation, and six months after fumigation for each year. DGGE profiles drastically changed after CP treatment and did not recover completely 1 year after, e.g., before treatments in years 2 and 3 (Fig.10). In contrast, DGGE profiles of 1,3-D plots revealed a smaller change 2 months after fumigation but became indistinguishable from those of control plots after 6 months. These results indicated that the impact of fumigation on the soil fungal community was greater in the CP treatment than in the 1,3-D treatment both in terms of the magnitude of the effect after 2

1,3-D

B 2M 6M 2Y

B 2M 6M 3Y

Fig. 10. Temporal change after fumigation in fungal 18S rDNA DGGE profiles from bulk soil samples in (A) untreated control plot, (B) CP plot, and (C) 1,3-D plot. Samples were taken before (B) and 2 and 6 months (2M and 6M, respectively) after fumigation for each year (1Y,

**Chytridiomycota dominate**

B 2M 6M 2Y

B 2M 6M 3Y

B 2M 6M 1Y

CP

**Ascomycota decline**

Between treatments of CP and 1,3-D, there are differences in fungal species affected. In CPtreated plots, bands with high sequence similarity to *Myrothecium cinctum* (100%, Ascomycota), *Bionectria ochroleuca* (99.7%, Ascomycota), *Metarhizium anisopliae* (100%, Ascomycota), *Dentipellisseparans* (96.7%, Basidiomycota), *Verticillium dahliae* (98.4%, Ascomycota) and *Exophiala dermatitidis* (100%, Ascomycota) decreased in band intensity. On

soil.

months and the extent of recovery 1 year after.

B 2M 6M 3Y

B 2M 6M 1Y

control

B 2M 6M 2Y

B 2M 6M 1Y

2Y and 3Y) in three-year trials

Soil microbial communities are influenced by various factors such as cropping system (Kuske et al., 2002), tillage (Peixoto et al., 2006), fertilization (Marschner et al., 2003) and application of pesticide and herbicide (Yang et al., 2000). On the other hand, environmental factors, including soil characteristics, also affect microbial communities, e.g., soil type (Girvan et al., 2003), soil particle size (Sessitsch et al., 2001), soil air composition (Øvreås et al., 1998) and season (Girvan et al., 2004). Bacteria have been well documented for agricultural soils; many field trials have shown that the composition of the entire bacterial community is determined primarily by soil type (Girvan et al., 2003; Xu et al., 2009), emphasizing the effect of soil chemistry and structure, especially pH and soil texture (Fierer & Jackson, 2006; Lauber et al., 2008), rather than cultural practices. However, little has so far been known about factors affecting fungal community structure.

Suzuki et al. (Suzuki et al., 2009) studied the effect of soil type and fertilizer type on bacterial and fungal communities in a long-term experimental field in Tsukuba, Japan. Upland field plots containing four different soil types, i.e., Gleyic Mollic-Umbric Andosols (Cumulic Andosol), Gleyic Haplic Andosols (Low-humic Andosol), Gleyic Haplic Alisols (Yellow Soil), and Entric Fluvisols (Gray Lowland Soil) were maintained under three different fertilizer management systems (chemical fertilizer rice husks plus cow manure, and pig manure) for 5 years. Carrot and maize were annually cropped in the fields once every summer. Bulk soil samples were taken in May prior to fertilization and cultivation. From directly-extracted soil DNA, bacterial 16S rDNA and fungal 18S rDNA were amplified using primer pairs 968g-GC/1378r and NS1/GCFung, respectively and subjected to DGGE analyses.

Fungal DGGE profiles based on the 18S rRNA gene were analyzed by principal component analysis (PCA) to separate plots based on fertilization practices. This result showed that fungal community composition was more directly related to fertilization than soil type. On the other hand, PCA of bacterial DGGE profiles indicated that the plots were separated by soil type. Lauber et al. (Lauber et al., 2008) reported that fungal community composition was most closely associated with changes in soil nutrient status, i.e., concentration of total nitrogen and extractable phosphate, and the ratio of total carbon and nitrogen concentration. Suzuki et al. (Suzuki et al., 2009) described that fungi may be more suitable as microbial indicators of soil quality because the dynamics of fungal community were more reflected soil nutrient status than that of bacterial community.

Molecular Analyses of Soil Fungal Community – Methods and Applications 297

techniques are limited. One reason for these limitations is the lack of sufficient information, in the case of fungi, about the influence of various experimental parameters, particularly

We evaluated various PCR primer sets targeting the 18S rRNA gene (rDNA), a widely used molecular marker for fungi, as well as other experimental parameters and established a standard DGGE protocol for soil fungal community analysis. Molecular methods revealed that soil fungal communities were affected by cultural practices, such as chemical fumigation and fertilization, in agricultural fields. These techniques undergo constant improvement and should continue to promote research based on fungal ecology in soil

We thank Dr. Naoyuki Matsumoto (HokkaidoUniversity) for useful suggestions regarding an early draft of this manuscript. This work was partially supported by a Grant-in-Aid (Soil eDNA) from the Ministry of Agriculture, Forestry and Fisheries of Japan (eDNA-07-101-3).

Anderson IC & Cairney JWG (2004). Diversity and ecology of soil fungal communities:

Anderson IC, Campbell CD & Prosser JI (2003). Potential bias of fungal 18S rDNA and

Araya R, Tani K, Takagi T, Yamaguchi N & Nasu M (2003). Bacterial activity and

Borneman J & Hartin RJ (2000). PCR primers that amplify fungal rRNA genes from

Borneman J, Skroch PW, O'Sullivan KM , Palus JA, Rumjanek NG, Jansen JL, Nienhuis J &

Brodie E, Edwards S & Clipson N (2003). Soil fungal community structure in a temperate

Browning M, Wallace DB, Dawson C, Alm SR & Amador JA (2006). Potential of butyric acid

No.12, (December 2005), pp. 7724-7736, ISSN 0099-2240

(October 2000), pp. 4356-4360, ISSN 0099-2240

1935-1943, ISSN 0099-2240

114, ISSN 0168-6496

increased understanding through the application of molecular techniques. *Environmental Microbiology,* Vol.6**,** No.8, (August 2004), pp. 769-779, ISSN 1462-

internal transcribed spacer polymerase chain reaction primers for estimating fungal biodiversity in soil. *Environmental Microbiology,* Vol.5**,** No.1, (January 2003), pp. 36-

community composition in stream water and biofilm from an urban river determined by fluorescent in situ hybridization and DGGE analysis. *FEMS Microbiology Ecology,* Vol.43**,** No.1, (February 2003), pp. 111-119, ISSN 0168-6496 Ashelford KE, Chuzhanova NA, Fry JC, Jones AJ & Weightman AJ (2005). At least 1 in 20

16S rRNA sequence records currently held in public repositories is estimated to contain substantial anomalies. *Applied and Environmental Microbiology,* Vol.71**,**

environmental samples. *Applied and Environmental Microbiology,* Vol.66**,** No.10,

Triplett EW (1996). Molecular microbial diversity of an agricultural soil in Wisconsin. *Applied and Environmental Microbiology,* Vol.62**,** No.6, (June 1996), pp.

upland grassland soil. *FEMS Microbiology Ecology,* Vol.45**,** No.2, (July 2003), pp. 105-

for control of soil-borne fungal pathogens and nematodes affecting strawberries.

PCR primer selection, on the results of diversity studies.

ecosystems.

**7. Acknowledgment** 

**8. References** 

2912

47, ISSN 1462-2912

## **5. Future perspectives**

PCR-based techniques targeting 18S rDNA are powerful tools for fungal community analysis and have revealed phylogenetic compositions and dynamics of fungal communities in the environment. The accumulating molecular data has facilitated fungal community analyses on a large scale. Several previous reports, including examples shown in Section 4, have indicated that the fungal community could be substantially altered by cultural practices. However, most results were obtained from a few experimental fields. Large-scale and comprehensive analyses using enormous amounts of data on soils from various regions are required to determine whether the results presented in those reports are universally applicable or represent specific examples.

In Japan, the Environmental DNA database for agriculture soils (eDDASs) was established, which included not only DGGE profiles of bacteria, fungi and nematodes but also relevant information on soil, cultural practices, crop yield, etc. eDDASs facilitates large-scale analyses of the relationships between soil microbial communities and various environmental factors and may facilitate resolution of problems, such as disease forecasting, soil fertility evaluation, etc, in agricultural fields (Tsushima et al., 2011). The introduction of the next generation of sequencers combined with the development of bioinformatics tools will accelerate such large-scale analyses.

The abovementioned molecular techniques have limitations for the analyses of environmental fungal communities. The sequences of 18S rDNA or ITS regions only reflect the phylogenetic positions of target microbes but not necessarily their metabolic functions. The existence of DNA in soil, even functional genes, only demonstrates the potential of fungal activity not a confirmation of its actual presence. Analyses based on the utilisation of soil RNA and/or other genetic markers associated with metabolic function should fortify fungal community analyses. The next step should focus on the functional aspects of fungal communities.

PCR can cause biased detections that prevent the complete recognition of microbial diversity through primer specificity and simultaneous amplification of different targets. New approaches that do not depend on PCR, such as metagenomic or metatranscriptomic analysis, can provide less biased data on fungal community structures and functional aspects, although some problems remain, particularly in data analyses (Suenaga, 2001). Currently, it is difficult to directly assign individual sequences that were directly recovered from soils or to construct contigs from them because a single soil sample may contain several thousand microbial genotypes, whereas most of their genomic sequences are still unrevealed.

The development of new molecular technologies should alleviate the problems associated with rDNA-based methods and PCR amplification and promote the investigation of current topics, such as the effect of pollution and global warming on fungal communities and their functions and roles in soil ecosystems.

## **6. Conclusion**

Culture-independent molecular techniques, such as direct DNA extraction from soil followed by PCR-based community analysis techniques, provide novel insights and significant research advances in soil microbial ecology. Compared with bacterial communities, however, the results of soil fungal community analyses using molecular techniques are limited. One reason for these limitations is the lack of sufficient information, in the case of fungi, about the influence of various experimental parameters, particularly PCR primer selection, on the results of diversity studies.

We evaluated various PCR primer sets targeting the 18S rRNA gene (rDNA), a widely used molecular marker for fungi, as well as other experimental parameters and established a standard DGGE protocol for soil fungal community analysis. Molecular methods revealed that soil fungal communities were affected by cultural practices, such as chemical fumigation and fertilization, in agricultural fields. These techniques undergo constant improvement and should continue to promote research based on fungal ecology in soil ecosystems.

## **7. Acknowledgment**

We thank Dr. Naoyuki Matsumoto (HokkaidoUniversity) for useful suggestions regarding an early draft of this manuscript. This work was partially supported by a Grant-in-Aid (Soil eDNA) from the Ministry of Agriculture, Forestry and Fisheries of Japan (eDNA-07-101-3).

## **8. References**

296 Soil Health and Land Use Management

PCR-based techniques targeting 18S rDNA are powerful tools for fungal community analysis and have revealed phylogenetic compositions and dynamics of fungal communities in the environment. The accumulating molecular data has facilitated fungal community analyses on a large scale. Several previous reports, including examples shown in Section 4, have indicated that the fungal community could be substantially altered by cultural practices. However, most results were obtained from a few experimental fields. Large-scale and comprehensive analyses using enormous amounts of data on soils from various regions are required to determine whether the results presented in those reports are universally

In Japan, the Environmental DNA database for agriculture soils (eDDASs) was established, which included not only DGGE profiles of bacteria, fungi and nematodes but also relevant information on soil, cultural practices, crop yield, etc. eDDASs facilitates large-scale analyses of the relationships between soil microbial communities and various environmental factors and may facilitate resolution of problems, such as disease forecasting, soil fertility evaluation, etc, in agricultural fields (Tsushima et al., 2011). The introduction of the next generation of sequencers combined with the development of bioinformatics tools will

The abovementioned molecular techniques have limitations for the analyses of environmental fungal communities. The sequences of 18S rDNA or ITS regions only reflect the phylogenetic positions of target microbes but not necessarily their metabolic functions. The existence of DNA in soil, even functional genes, only demonstrates the potential of fungal activity not a confirmation of its actual presence. Analyses based on the utilisation of soil RNA and/or other genetic markers associated with metabolic function should fortify fungal community analyses. The next step should focus on the functional aspects of fungal

PCR can cause biased detections that prevent the complete recognition of microbial diversity through primer specificity and simultaneous amplification of different targets. New approaches that do not depend on PCR, such as metagenomic or metatranscriptomic analysis, can provide less biased data on fungal community structures and functional aspects, although some problems remain, particularly in data analyses (Suenaga, 2001). Currently, it is difficult to directly assign individual sequences that were directly recovered from soils or to construct contigs from them because a single soil sample may contain several thousand microbial genotypes, whereas most of their genomic sequences are still

The development of new molecular technologies should alleviate the problems associated with rDNA-based methods and PCR amplification and promote the investigation of current topics, such as the effect of pollution and global warming on fungal communities and their

Culture-independent molecular techniques, such as direct DNA extraction from soil followed by PCR-based community analysis techniques, provide novel insights and significant research advances in soil microbial ecology. Compared with bacterial communities, however, the results of soil fungal community analyses using molecular

**5. Future perspectives** 

applicable or represent specific examples.

accelerate such large-scale analyses.

functions and roles in soil ecosystems.

communities.

unrevealed.

**6. Conclusion** 


Molecular Analyses of Soil Fungal Community – Methods and Applications 299

Hamm PB, Ingham RE, Jaeger JR, Swanson WH & Volker KC (2003). Soil fumigant effects on

Hoshino YT & Matsumoto N (2004). An Improved DNA Extraction Method Using Skim

Hoshino YT & Matsumoto N (2007). Changes in fungal community structure in bulk soil

Hoshino YT & Matsumoto N (2008). Comparison of 18S rDNA primers for estimating fungal

Hoshino YT & Morimoto S (2010). Soil clone library analyses to evaluate specificity and

Hugenholtz P & Huber T (2003). Chimeric 16S rDNA sequences of diverse origin are

Ibekwe AM, Papiernik SK, Gan J, Yates SR, Yang C-H & Crowley DE (2001). Impact of

Itoh K, Takahashi M, Tanaka R, Suyama K & Yamamoto H (2000). Effect of fumigants on soil

Jumpponen A (2007). Soil fungal communities underneath willow canopies on a primary

Kopczynski ED, Bateson MM & Ward DM (1994). Recognition of chimeric small-subunit

Kowalchuk GA, Gerards S & Woldendorp JW (1997). Detection and characterization of

Kowalchuk GA, Drigo B, Yergeau E & van Veen JA (2006): Assessing bacterial and fungal

188. Springer Berlin Heidelberg, ISBN 978-3-540-29448-1, Germany

Vol.67**,** No.7, (July 2001), pp. 3245-3257, ISSN 0099-2240

1449-1456, ISSN 0191-2917

55, ISSN 0038-0768

701-710, ISSN 0038-0768

pp. 281-287, ISSN 1342-6311

ISSN 1348-589X

0099-2240

2240

233-246, ISSN 0095-3628

No.1, (March 2004), pp. 13-19, ISSN 1342-6311

three genera of potential soilborne pathogenic fungi and their effect on potato yield in the columbia basin of oregon. *Plant Disease,* Vol.87**,** No.12, (December 2003), pp.

Milk from Soils That Strongly Adsorb DNA. *Microbes and Environments,* Vol.19**,**

and spinach rhizosphere soil after chemical fumigation as revealed by 18S rDNA PCR-DGGE. *Soil Science and Plant Nutrition,* Vol.53**,** No.1, (February 2007), pp. 40-

diversity in agricultural soils using polymerase chain reaction-denaturing gradient gel electrophoresis *Soil Science and Plant Nutrition,* Vol.54**,** No.5, (October 2008), pp.

selectivity of PCR primers targeting fungal 18S rDNA for denaturing-gradient gel electrophoresis (DGGE). *Microbes and Environments,* Vol.25**,** No.4, (December 2010),

accumulating in the public databases. *International Journal of Systematic and Evolutionary Microbiology,* Vol.53**,** No.1, (January 2003), pp. 289-293, ISSN 1466-5026

fumigants on soil microbial communities. *Applied and Environmental Microbiology,*

microbial population and proliferation of *Fusarium oxysporum* inoculated into fumigated soil. *Journal of Pesticide Science,* Vol.25**,** No.2, (August 2000), pp. 147-149,

successional glacier forefront: rDNA sequence results can be affected by primer selection and chimeric data. *Microbial Ecology,* Vol.53**,** No.2, (February 2007), pp.

ribosomal DNAs composed of genes from uncultivated microorganisms. *Applied and Environmental Microbiology,* Vol.60**,** No.2, (February 1994), pp. 746-748, ISSN

fungal infections of *Ammophila arenaria* (marram grass) roots by denaturing gradient gel electrophoresis of specifically amplified 18S rDNA. *Applied and Environmental Microbiology,* Vol.63**,** No.10, (October 1997), pp. 3858-3865, ISSN 0099-

community structure in soil using ribosomal RNA and other structural gene markers. In: *Nucleic Acids and Proteins in Soil*, Nannipieri P & Smalla K (Eds), 159-

*Soil Biology and Biochemistry,* Vol.38**,** No.2, (February 2006), pp. 401-404, ISSN 0038- 0717


Buée M, Reich M, Murat C, Morin E, Nillson RH, Uroz S & Martin F (2009). 454

Christensen M (1989). A view of fungal ecology. *Mycologia,* Vol.81**,** No.1, (March 1989), pp.

De Cal A, Martinez-Treceno A, Salto T, Lopez-Aranda JM & Melgarejo P (2005). Effect of

*Applied Soil Ecology,* Vol.28**,** No.1, (January 2005), pp. 47-56, ISSN 0929-1393 Dunbar J, Ticknor LO & Kuske CR (2000). Assessment of Microbial Diversity in Four

Dungan RS, Ibekwe AM & Yates SR (2003). Effect of propargyl bromide and 1,3-

Fierer N, Breitbart M, Nulton J, Salamon P, Lozupone C, Jones R, Robeson M, Edwards RA,

Fromin N, Hamelin J, Tarnawski S, Roesti D, Jourdain-Miserez K, Forestier N, Teyssier-

Gardes M & Bruns TD (1993). ITS primers with enhanced specificity for basidiomycetes -

Girvan MS, Bullimore J, Ball AS, Pretty JN & Osborn AM (2004). Responses of Active

Girvan MS, Bullimore J, Pretty JN, Osborn AM & Ball AS (2003). Soil type Is the primary

Gomes NCM, Fagbola O, Costa R, Rumjanek NG, Buchner A, Mendona-Hagler L & Smalla

No.3, (January 2006), pp. 626-631, ISSN 0027 -8424

(November 2007), pp. 7059-7066, ISSN 0099-2240

No.2, (April 1993), pp. 113-118, ISSN 0962-1083

No.5, (May 2004), pp. 2692-2701, ISSN 0099-2240

1800-1809, ISSN 0099-2240

3758-3766, ISSN 0099-2240

No.11, (November 2002), pp. 634-643, ISSN 1462-2912

0717

8137

1-19, ISSN 0027-5514

2950, ISSN 0099-2240

*Soil Biology and Biochemistry,* Vol.38**,** No.2, (February 2006), pp. 401-404, ISSN 0038-

Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. *New Phytologist,* Vol.184**,** No.2, (October 2009), pp. 449-456, ISSN 1469-

chemical fumigation on soil fungal communities in Spanish strawberry nurseries.

Southwestern United States Soils by 16S rRNA Gene Terminal Restriction Fragment Analysis. *Applied and Environmental Microbiology,* Vol.66**,** No.7, (July 2000), pp. 2943-

dichloropropene on microbial communities in an organically amended soil. *FEMS Microbiology Ecology,* Vol.43**,** No.1, (February 2003), pp. 75-87, ISSN 0168-6496 Fierer N & Jackson RB (2006). The diversity and biogeography of soil bacterial communities.

*Proceedings of the National Academy of Sciences of the United States of America,* Vol.103**,**

Felts B, Rayhawk S, Knight R, Rohwer F & Jackson RB (2007). Metagenomic and Small-Subunit rRNA Analyses Reveal the Genetic Diversity of Bacteria, Archaea, Fungi, and Viruses in Soil. *Applied and Environmental Microbiology*, Vol.73, No.21,

Cuvelle S, Gillet F, Aragno M & Rossi P (2002). Statistical analysis of denaturing gel electrophoresis (DGE) fingerprinting patterns. *Environmental Microbiology,* Vol.4**,**

application to the identification of mycorrhizae and rusts. *Molecular Ecology,* Vol.2**,**

Bacterial and Fungal Communities in Soils under Winter Wheat to Different Fertilizer and Pesticide Regimens. *Applied and Environmental Microbiology,* Vol.70**,**

determinant of the composition of the total and active bacterial communities in arable soils. *Applied and Environmental Microbiology,* Vol.69**,** No.3, (March 2003), pp.

K (2003). Dynamics of fungal communities in bulk and maize rhizosphere soil in the tropics. *Applied and Environmental Microbiology,* Vol.69**,** No.7, (July 2003), pp.


Molecular Analyses of Soil Fungal Community – Methods and Applications 301

Miller DN, Bryant JE, Madsen EL & Ghiorse WC (1999). Evaluation and optimization of

Möhlenhoff P, Müller L, Gorbushina AA & Petersen K (2001). Molecular approach to the

Morimoto S & Hoshino YT (November 2010). Technical Report on the PCR-DGGE Analysis

*Microbiology,* Vol.59**,** No.3, (March 1993), pp. 695-700, ISSN 0099-2240 Nikolcheva LG, Cockshutt AM & Bärlocher F (2003). Determining diversity of freshwater

Nocker A, Burr M & Camper A (2007). Genotypic Microbial Community Profiling: A Critical

Okubo A & Sugiyama S (2009). Comparison of molecular fingerprinting methods for

Oros-Sichler M, Gomes NCM, Neuber G & Smalla K (2006). A new semi-nested PCR

Øvreås L, Jensen S, Daae FL & Torsvik V (1998). Microbial Community Changes in a

Peixoto RS, Coutinho HLC, Madari B, Machado PLOA, Rumjanek NG, Van Elsas JD, Seldin

Perkiömäki J, Tom-Petersen A, Nybroe O & Fritze H (2003). Boreal forest microbial

Petrosino JF, Highlander S, Luna RA, Gibbs RA & Versalovic J (2009). Metagenomic

Vol.195, No.2, (February 2001), pp. 169-173, ISSN 0378-1097

*Environmental Sciences*, 02.09.2011, Available from

(November 2009), pp. 1399-1405, ISSN 0912-3814

*Research,* Vol.90**,** No.1-2, pp. 16-28, ISSN 0167-1987

No.11, (November 2003), pp. 1517-1526, ISSN 0038-0717

ISSN 0099-2240

ISSN 0099-2240

pp. 63-75, ISSN 0167-7012

2739-2742, ISSN 0099-2240

2009), pp. 856-866, ISSN 0009-9147

0095-3628

DNA extraction and purification procedures for soil and sediment samples. *Applied and Environmental Microbiology,* Vol.65**,** No.11, (November 1999), pp. 4715-4724,

characterisation of fungal communities: methods for DNA extraction, PCR amplification and DGGE analysis of painted art objects. *FEMS Microbiology Letters*,

of Bacterial and Fungal Soil Communities, In: *National Institute for Agro-*

populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. *Applied and Environmental* 

fungi on decaying leaves: comparison of traditional and molecular approaches. *Applied and Environmental Microbiology,* Vol.69**,** No.5, (May 2003), pp. 2548-2554,

Technical Review. *Microbial Ecology,* Vol.54**,** No.2, (August 2007), pp. 276-289, ISSN

analysis of soil microbial community structure. *Ecological Research,* Vol.24**,** No.6,

protocol to amplify large 18S rRNA gene fragments for PCR-DGGE analysis of soil fungal communities. *Journal of Microbiological Methods,* Vol.65**,** No.1, (April 2006),

Perturbed Agricultural Soil Investigated by Molecular and Physiological Approaches. *Applied and Environmental Microbiology,* Vol.64**,** No.7, (July 1998), pp.

L & Rosado AS (2006). Soil aggregation and bacterial community structure as affected by tillage and cover cropping in the Brazilian Cerrados. *Soil and Tillage* 

community after long-term field exposure to acid and metal pollution and its potential remediation by using wood ash. *Soil Biology and Biochemistry,* Vol.35**,**

Pyrosequencing and Microbial Identification. *Clinical Chemistry,* Vol.55**,** No.5, (May

http://www.niaes.affrc.go.jp/project/edna/edna\_jp/manual\_bacterium\_e.pdf Muyzer G, de Waal EC & Uitterlinden AG (1993). Profiling of complex microbial


Krebs CJ (1989). (January 1989). *Ecological Methodology*, Harpercollins, ISBN 978-0060437848,

Kuske CR, Ticknor LO, Miller ME, Dunbar JM, Davis JA, Barns SM & Belnap J (2002).

Larena I, Salazar O, González V, Julián MC & Rubio V (1999). Design of a primer for

Lauber CL, Strickland MS, Bradford MA & Fierer N (2008). The influence of soil properties

Liesack W, Weyland H & Stackebrandt E (1991). Potential risks of gene amplification by

Lim Y, Kim B, Kim C, Jung HS, Kim BS, Lee JH & Chun J (2010). Assessment of soil fungal

Lord NS, Kaplan CW, Shank P, Kitts CL & Elrod SL (2002). Assessment of fungal diversity

Lozupone CA & Klein DA (2002). Molecular and cultural assessment of chytrid and

Lumini E, Orgiazzi A, Borriello R, Bonfante P & Bianciotto V (2010). Disclosing arbuscular

Lyons JI, Newell SY, Buchan A & Moran MA (2003). Diversity of Ascomycete laccase gene

Marschner P, Kandeler E & Marschner B (2003). Structure and function of the soil microbial

Martin-Laurent F, Philippot L, Hallet S, Chaussod R, Germon JC, Soulas G & Catroux G

May LA, Smiley B & Schmidt MG (2001). Comparative denaturing gradient gel

Vol.35**,** No.3, (March 2003), pp. 453-461, ISSN 0038-0717

Vol.68**,** No.4, (April 2002), pp. 1854-1863, ISSN 0099-2240

Comparison of Soil Bacterial Communities in Rhizospheres of Three Plant Species and the Interspaces in an Arid Grassland. *Applied and Environmental Microbiology,*

ribosomal DNA internal transcribed spacer with enhanced specificity for ascomycetes. *Journal of Biotechnology,* Vol.75**,** No.2-3, (October 1999), pp. 187-194,

on the structure of bacterial and fungal communities across land-use types. *Soil Biology and Biochemistry,* Vol.40**,** No.9, (September 2008), pp. 2407-2415, ISSN 0038-

PCR as determined by 16S rDNA analysis of a mixed-culture of strict barophilic bacteria. *Microbial Ecology,* Vol.21**,** No.1, (December 1991), pp. 191-198, ISSN 0095-

communities using pyrosequencing. *The Journal of Microbiology,* Vol.48**,** No.3, (June

using terminal restriction fragment (TRF) pattern analysis: comparison of 18S and ITS ribosomal regions. *FEMS Microbiology Ecology,* Vol.42**,** No.3, (December 2002),

Spizellomyces populations in grassland soils. *Mycologia,* Vol.94**,** No.3, (May – June

mycorrhizal fungal biodiversity in soil through a land-use gradient using a pyrosequencing approach. *Environmental Microbiology,* Vol.12**,** No.8, (August 2010),

sequences in a southeastern US salt marsh. *Microbial Ecology*, Vol.45, No.3, (April

community in a long-term fertilizer experiment. *Soil Biology and Biochemistry,*

(2001). DNA extraction from soils: old bias for new microbial diversity analysis methods. *Applied and Environmental Microbiology,* Vol.67**,** No.5, (May 2001), pp.

electrophoresis analysis of fungal communities associated with whole plant corn silage. *Canadian Journal of Microbiology,* Vol.47(September 2001), pp. 829-841, ISSN

New York

ISSN 0168-1656

2010), pp. 284-289, ISSN 1225-8873

2002), pp. 411-420, ISSN 0027-5514

pp. 2165-2179, ISSN 1462-2912

2354-2359, ISSN 0099-2240

0008-4166

2003 ), pp. 270-281, ISSN 0095-3628

pp. 327-337, ISSN 0168-6496

0717

3628


http://www.niaes.affrc.go.jp/project/edna/edna\_jp/manual\_bacterium\_e.pdf


Molecular Analyses of Soil Fungal Community – Methods and Applications 303

Takehara T, Kuniyasu K, Mori M & Hagiwara H (2003). Use of a nitrate-nonutilizing mutant

Tanaka S, Kobayashi T, Iwasaki K, Yamane S, Maeda K & Sakurai K (2003). Properties and

Thorn G (1997). The fungi in soil. In: *Modern Soil Microbiology*, van Elsas JD, Trevors JT &

Tsushima S (March 2011). eDNA Database for Agricultural Soils, In: *National Institute for* 

Torsvik V, Goksøyr J & Daae FL (1990). High diversity in DNA of soil bacteria. *Applied and* 

Vainio EJ & Hantula J (2000). Direct analysis of wood-inhabiting fungi using denaturing

Vandenkoornhuyse P, Baldauf SL, Leyval C, Straczek J & Yong JPW (2002). Extensive fungal

Viaud M, Pasquier A & Brygoo Y (2000). Diversity of soil fungi studied by PCR-RFLP of ITS.

Wang GC & Wang Y (1996). The frequency of chimeric molecules as a consequence of PCR

Wang GC & Wang Y (1997). Frequency of formation of chimeric molecules as a consequence

White TJ, Bruns T, Lee S & Taylor J (1990) Amplification and direct sequencing of fungal

Wintzingerode FV, Göbel UB & Stackebrandt E (1997). Determination of microbial diversity

*Reviews,* Vol.21**,** No.3, (November 1997), pp. 213-229, ISSN 0168-6445 Xu Y, Wang G, Jin J, Liu J, Zhang Q & Liu X (2009). Bacterial communities in soybean

*Agro-Environmental Sciences*, 02.09.2011, Available from http://eddass.niaes3.affrc.go.jp/hp/index.html

Vol.104**,** No.8, (August 2000), pp. 927-936, ISSN 0953-7562

Vol.142**,** No.5, (May 1996), pp. 1107-1114, ISSN 1350-0872

Academic Press, ISBN 978-0123721808, New York, USA

90, ISSN 1747-0765

ISSN 0031-949X

USA

2240

0036-8075

ISSN 0099-2240

7562

0717

communities. *Soil Science and Plant Nutrition,* Vol.55**,** No1, (February 2009), pp. 80-

and selective media to examine population dynamics of *Fusarium oxysporum* f. sp. spinaciae in soil. *Phytopathology,* Vol.93**,** No.9, (September 2003), pp. 1173-1181,

metabolic diversity of microbial communities in soils treated with steam sterilization compared with methyl bromide and chloropicrin fumigations. *Soil Science and Plant Nutrition,* Vol.49**,** No.4, (August 2003), pp. 603-610, ISSN 0038-0768

Wellington EMH (Eds)*,* 63-127. Marcel Decker, ISBN 978-0824794361, New York,

*Environmental Microbiology,* Vol.56**,** No.3, (March 1990), pp. 782-787, ISSN 0099-

gradient gel electrophoresis of amplified ribosomal DNA. *Mycological Research,*

diversity in plant roots. *Science,* Vol.295**,** No.5562, (March 2002), pp. 2051, ISSN

*Mycological Research,* Vol.104**,** No.9, (September 2000), pp. 1027-1032, ISSN 0953-

co-amplification of 16S rRNA genes from different bacterial species. *Microbiology,*

of PCR coamplification of 16S rRNA genes from mixed bacterial genomes. *Applied and Environmental Microbiology,* Vol.63**,** No.12, (December 1997), pp. 4645-4650,

ribosomal RNA genes for phylogenetic In: *PCR Protocols: A Guide to Methods and Applications*, Innis MA, Gelfand DH, Sninsky JJ & White TJ (Eds)*,* 315-322.

in environmental samples: pitfalls of PCR-based rRNA analysis. *FEMS Microbiology* 

rhizosphere in response to soil type, soybean genotype, and their growth stage. *Soil Biology and Biochemistry,* Vol.41**,** No.5, (May 2009), pp. 919-925, ISSN 0038-


Qiu X, Wu L, Huang H, McDonel PE, Palumbo AV, Tiedje JM & Zhou J (2001). Evaluation of

Ranjard L, Lejon DPH, Mougel C, Schehrer L, Merdinoglu D & Chaussod R (2003).

Ranjard L, Poly F, Lata JC, Mougel C, Thioulouse J & Nazaret S (2001). Characterization of

Robe P, Nalin R, Capellano C, Vogel TM & Simonet P (2003). Extraction of DNA from soil.

Roesch LFW, Fulthorpe RR, Riva A, Casella G, Hadwin AKM, Kent AD, Daroub SH,

Ronaghi M, Karamohamed S, Pettersson B, Uhlén M & Nyrén P (1996). Real-Time DNA

Sanger F & Coulson AR (1975). A rapid method for determining sequences in DNA by

Sequerra J, Marmeisse R, Valla G, Normand P, Capellano A & Moiroud A (1997). Taxonomic

Sessitsch A, Weilharter A, Gerzabek MH, Kirchmann H & Kandeler E (2001). Microbial

Smit E, Leeflang P, Glandorf B, Dirk van Elsas J & Wernars K (1999). Analysis of fungal

Suenaga H (2011). Targeted metagenomics: a high-resolution metagenomics approach for

Suzuki C, Nagaoka K, Shimada A & Takenaka M (2009). Bacterial communities are more

*Microbiology,* (March 2011), pp. 1-10, ISSN 1462-2920

Vol.242**,** No1, (November 1996), pp. 84-89, ISSN 0003-2697

2001), pp. 880-887, ISSN 0099-2240

1462-2912

2240

2240

190, ISSN 1164-5563

pp. 283-290, ISSN 1751-7362

(May 1975), pp. 441-446, ISSN 0022-2836

1997), pp. 465-472, ISSN 0953-7562

pp. 4215-4224, ISSN 0099-2240

PCR-generated chimeras, mutations, and heteroduplexes with 16S rRNA genebased cloning. *Applied and Environmental Microbiology,* Vol.67**,** No.2, (February

Sampling strategy in molecular microbial ecology: influence of soil sample size on DNA fingerprinting analysis of fungal and bacterial communities. *Environmental Microbiology,* Vol.5**,** No.11, (November 2003), pp. 1111-1120, ISSN

Bacterial and Fungal Soil Communities by Automated Ribosomal Intergenic Spacer Analysis Fingerprints: Biological and Methodological Variability. *Applied and Environmental Microbiology,* Vol.67**,** No.10, (October 2001), pp. 4479-4487, ISSN 0099-

*European Journal of Soil Biology,* Vol.39**,** No.4, (October - December 2003), pp. 183-

Camargo FAO, Farmerie WG & Triplett EW (2007). Pyrosequencing enumerates and contrasts soil microbial diversity. *The ISME Journal,* Vol.1**,** No.4, (August 2007),

Sequencing Using Detection of Pyrophosphate Release. *Analytical Biochemistry,*

primed synthesis with DNA polymerase. *Journal of Molecular Biology*, Vol.94, No.3,

position and intraspecific variability of the nodule forming Penicillium nodositatum inferred from RFLP analysis of the ribosomal intergenic spacer and Random Amplified Polymorphic DNA. *Mycological Research,* Vol.101**,** No.4, (April

Population Structures in Soil Particle Size Fractions of a Long-Term Fertilizer Field Experiment. *Applied and Environmental Microbiology,* Vol.67**,** No.9, (September 2001),

diversity in the wheat rhizosphere by sequencing of cloned PCR-amplified genes encoding 18S rRNA and temperature gradient gel electrophoresis. *Applied and Environmental Microbiology,* Vol.65**,** No.6, (June 1999), pp. 2614-2621, ISSN 0099-

specific gene clusters in complex microbial communities. *Environmental* 

dependent on soil type than fertilizer type, but the reverse is true for fungal

communities. *Soil Science and Plant Nutrition,* Vol.55**,** No1, (February 2009), pp. 80- 90, ISSN 1747-0765


**16** 

*Italy* 

**Earthworm Biomarkers as Tools** 

Maria Giulia Lionetto, Antonio Calisi and Trifone Schettino

*University of Salento - Dept. of Biological and Environmental Sciences and Technologies* 

Soil pollution has enormously increased during the last decades due to the intensive use of biocides and fertilizers in agriculture, industrial activities, urban waste and atmospheric deposition. Its occurrence is related to the degree of industrialization and intensity of chemical usage. Soil pollution causes decrease in soil fertility, alteration of soil structure, disturbance of the balance between flora and fauna residing in the soil, contamination of the crops, and contamination of groundwater, constituting a threat for

The most diffusive chemicals occurring in soil are heavy metals, pesticides, petroleum hydrocarbons, polychlorobiphenyl (PCBs), dibenzo-p-dioxins/dibenzofurans (PCDD/Fs). Heavy metals from anthropogenic sources are widely spread in the environment and most of them finally reach the surface soil layers. Heavy metals can enter the soil from different sources, such as pesticides, fertilizers, organic and inorganic amendants, mining, wastes and sludge residues (Capri & Trevisan, 2002). In contrast to harmful organic compounds, heavy metals do not decompose and do not disappear from soil even if their release to the environment can be restricted (Brusseau, 1997). Therefore, the effects of heavy metal contamination on soil organisms and decomposition processes persist for many years. Pesticides are widely used in agriculture for counteracting insects, fungi, rodents or other animals living in or on the crops. They are either directly applied to soil to control soil borne pests or deposited on soil as run off from foliar applications and their concentrations are high enough to affect the soil macro-organisms (Bezchlebova et al., 2007). The pesticides most widely used in the past have been organochlorine pesticides, characterized by high hydrophobicity and persistence. Currently, they have been replaced by less persistent compounds. Organophosphates have become the most widely used pesticides today. They are used for pest control on crops in agriculture and on livestock, for other commercial purposes, and for domestic use. Due to their water solubility, the organophosphate residues in agricultural practices are capable of infiltrating through soil into surface water. As a consequence of their wide diffusion they have been detected in food, ground and drinking water, and natural surface waters (Dogheim et al., 1996; Garrido et al., 2000). Soil pollution by petroleum hydrocarbons usually originates from spills or leaks of storage tanks during fuel supply and discharge operations. Petroleum hydrocarbons include aliphatic and aromatic compounds; some of them are known or suspected human carcinogens, and are classified as priority pollutants. PCBs are persistent soil contaminants due to their

**1. Introduction** 

living organisms.

**for Soil Pollution Assessment** 

