Published online 12 March 2007
Published in Soil Sci Soc Am J 71:601-610 (2007)
DOI: 10.2136/sssaj2006.0115
© 2007 Soil Science Society of America
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MOLECULAR-BASED APPROACHES TO SOIL MICROBIOLOGY
Introduction to Molecular Analysis of Ectomycorrhizal Communities
Kendall J. Martin*
Department of Biology, William Paterson Univ., 300 Pompton Rd., Wayne, NJ 07470
* Corresponding author (MartinK31{at}wpunj.edu).
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ABSTRACT
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A number of methods are available for those researchers considering the addition of molecular analyses of ectomycorrhizal (EcM) fungi to their research projects and weighing the various approaches they might take. Analyzing natural EcM fungal communities has traditionally been a highly skilled, time-consuming process relying heavily on exacting morphological characterization of EcM root tips. Increasingly powerful molecular methods for analyzing EcM communities make this area of research available to a much wider range of researchers. Ecologists can gain from the body of work characterizing EcM while avoiding the requirement for exceptional expertise by carefully combining elements of traditional methods with the more recent molecular approaches. A cursory morphological analysis can yield a traditional quantification of EcM fungi based on tip numbers, a unit with functional and historical significance. Ectomycorrhizal root DNA extracts may then be analyzed with molecular methods widely used for characterizing microbiota. These range from methods applicable only to the simple mixes resulting from careful morphotyping, to community-oriented methods that identify many types in mixed samples as well as provide an estimate of their relative abundances. Extramatrical hyphae in bulk soil can also be more effectively studied, extending characterization of EcM fungal communities beyond the rhizoplane. The trend toward techniques permitting larger sample sets without prohibitive labor and time requirements will also permit us to more frequently address the issues of spatial and temporal variability and better characterize the roles of EcM fungi at multiple scales.
Abbreviations: AM, arbuscular mycorrhizal • DGGE, denaturing gradient gel electrophoresis • DHPLC, denaturing high-performance liquid chromatography • EcM, ectomycorrhizal • FRET, fluorescence resonance energy transfer • ITS, internal transcribed sequence • LH-PCR, length heterogeneity–polymerase chain reaction • PCR, polymerase chain reaction • PCR-RFLP, polymerase chain reaction–restriction fragment length polymorphism • PLFA, phospholipid fatty acid • rDNA, ribosome-coding deoxyribonucleic acid • rtqPCR, real-time, quantitative, polymerase chain reaction • SSU, small subunit ribosome-coding sequence • TGGE, thermal gradient gel electrophoresis • T-RFLP, terminal restriction fragment length polymorphism
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INTRODUCTION
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Ectomycorrhizal fungi play an important role in the health and nutrition of the plants that dominate many terrestrial ecosystems (Smith and Read, 1997). Any community analysis research on such ecosystems, particularly those dominated by tree species, would benefit from including analyses of EcM fungal populations to add depth to patterns seen aboveground. There have been a number of very good reviews recently regarding the molecular analysis of EcM fungi and soil fungi. Horton and Bruns (2001) published perhaps the most comprehensive discussion of the issues related to ecological analyses of EcM. Dahlberg (2001) expanded on the themes of EcM community analysis. Anderson and Cairney (2004) more recently addressed analyses of fungi through DNA recovered from the bulk soil, an area also addressed by Kennedy and Clipson (2003). The purpose of this review is not to repeat those notable efforts but to present an introductory review of methods for those researchers considering the addition of molecular analyses of EcM fungi to their research projects, and to weigh the various approaches they might take. This review will focus on EcM fungi, but for those interested in arbuscular mycorrhizal (AM) fungi, the greatest difference between these groups is that they require different methods for visual identification because AM root tips are not as readily distinguished with the naked eye. Beyond that, the AM fungi of the group Glomeromycota are quite distinct from the taxonomic divisions involved in the EcM symbioses. This dictates that research on AM fungi requires different primer sets than those discussed here. Otherwise, many of the general concepts discussed here apply equally to AM and EcM fungal ecology.
Perhaps the most notable characteristic of natural populations of EcM is that the root tip is a visible and physiologically important ecological unit (Taylor, 2002). The EcM fungi typically induce a fairly dramatic change in the morphology of colonized plant root tips, often inducing much greater rates of branching that result in high numbers of tips. This availability of an easily visualized and discrete unit of biomass is quite distinct from much of microbial ecology. Ectomycorrhizal root tips can be readily weighed (or counted) once they are distinguished from non-EcM and dead root tips. Note that these units are directly (weight) or indirectly (counts) a measure of biomass rather than an estimate of the numbers of individuals (or genets). Indeed, many root tips on different parts of the root system may be colonized by a single EcM genet. The spatial structure of the EcM genet can be quite large and complex (Lilleskov et al., 2004). Aspects of this structure may be indicated by aboveground fungal fruiting bodies but these do not correspond well with belowground fungal tissues such as EcM root tips (Horton, 2002).
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MORPHOLOGICAL CHARACTERIZATION OF ECTOMYCORRHIZAL FUNGI ROOT TIPS
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The primary difficulty in implementing classical mycorrhizal analysis as a component of an ecological study lies in the visual classification of the root tips. Fungi forming EcM symbioses number as many as 6000 species (Molina et al., 2002) spanning all the phyla of true fungi. As a result, studies of EcM fungi typically require analyses of fungi covering a broad taxonomic range. Analysis of natural EcM fungal communities has traditionally been a labor-intensive, highly skilled process with heavy reliance on gross morphological characterization of the EcM root tips. The techniques for morphological characterization of EcM root tips have been described in Agerer (1987–1998) and Goodman et al. (1996). An online database is also being developed (Goodman et al., 2000). Still, the complexity of visual classification of root tips makes an extensive analysis of natural populations of EcM particularly daunting. This is because a high rate of sampling is required to characterize EcM at the landscape scale. A large number of fungal species are typically involved in natural systems, and the high spatial and temporal variability of each species distribution makes characterization of the EcM fungi a challenge (Lilleskov et al., 2004; Goodman and Trofymow, 1998; Jonsson et al., 1999; Taylor, 2002). Both the spatial and temporal variation in EcM community composition may be informative rather than merely obstacles to be overcome (Lilleskov et al., 2004; Rygiewicz et al., 2000), but the highly labor-intensive microscopic analysis and identification can be prohibitive.
This conundrum has been the driving force behind the development of methods for molecular verification of identified morphotypes (Gardes and Bruns, 1993; Horton and Bruns, 2001). Morphotypic analysis of EcM root tips, even using only a cursory dissecting-microscope-level evaluation, requires significant training but is manageable when buttressed by molecular fingerprinting. Thus, molecular characterization of EcM root tips serves as a safety net in verifying the morphotyping of EcM fungi, allowing a much wider range of researchers and staff to approach this component of ecosystem analysis with a reasonable certainty of success.
It has been my experience that, with the involvement of a motivated and observant research team, only a couple of days of training are required to implement a manageable scheme for classifying large numbers of EcM root tips at the gross-morphology level with good reliability (Burke et al., 2005). There is a balance that must be struck between over- and under-analyzing the morphology of the root tips. Multiple genetic types may be included in a morphotype (lumping) or a single genetic type may be divided across multiple morphotypes (splitting). The tendency is justifiably toward splitting, in most morphotyping efforts, since morphotyping is confounded by natural gradations in morphological characteristics (Izzo et al., 2005). These gradations result from differences in environmental conditions, tip age, and phenotypic expression. As a result, morphotyping tends to overestimate EcM richness (Burke et al., 2005). Molecular methods are necessary to balance this tendency with accurate identification of fungal species (Horton and Bruns, 2001). When morphotyping is coupled with molecular analysis, the tendency toward splitting is easily remedied. It is easy to mathematically combine numbers of root tips assigned to genetically indistinguishable morphotypes. This is much more straightforward than the process of subdividing a genetically diverse morphotype set. Mathematically dividing a collection of root tips into subtypes (defined by molecular methods) requires analysis of a much larger number of tips within a type to obtain a reasonable estimate of the proportion in each subtype. Researchers should also be aware, however, that individual root tip clusters may have multiple EcM fungi, as mixed symbioses, remnants from succession, or contamination with saprobic fungi. The goal, then, in morphotyping EcM root tips is to isolate reasonably pure and representative categories of root tips so that verification by molecular analysis readily leads to ecologically useful numbers.
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RECOVERING USEFUL DNA FROM SAMPLES
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It is important to choose the range of root tips pooled in a sample to match the objectives of the study. DNA may be extracted, with decreasing degrees of preparation, from single root tips, a few identical root tips, whole morphotype groups, bulk live roots, or total root material. Extracting single root tips gives the greatest likelihood of obtaining a single-fungus DNA extract, which allows for polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) analysis supported by sequencing to gain a high certainty of identification. But it may be that this approach, on its own, is uninformative in the face of high spatial and temporal variation in EcM populations. The PCR-RFLP technique yields ambiguous results for mixed-species DNA extracts where multiple, overlapped banding patterns make it difficult to determine the patterns for the individual species involved. Extracting pooled root tips and applying community analysis techniques that simultaneously characterize many of the dominant species present can greatly decrease the labor per sample and allow much larger numbers of samples to be analyzed (Zhou and Hogetsu, 2002; Burke et al., 2005). These methods include amplicon length heterogeneity PCR (LH-PCR; Suzuki et al., 1998), terminal restriction fragment length polymorphism (T-RFLP; Liu et al., 1997), and denaturing gradient gel electrophoresis (DGGE) or thermal gradient gel electrophoresis (TGGE) (Muyzer et al., 1993). The combined DNA in these extracts may make it difficult, however, to detect relatively rare types because the limited dynamic range of the methods obscures such information. If dead or senescent root material is included, then the presence of saprobic fungi may interfere more strongly with the analysis. It is likely that, for most studies of EcM in natural environments, a relatively low proportion of samples can be reserved for detailed analysis by morphotyping or sequencing, while a much higher proportion of samples can be more economically evaluated using a community analysis approach. This could yield the highest quality information on the identities of the fungi involved (including rare types) in a way that directly supports an analysis of the distribution of the species on an ecologically relevant scale.
Other methods that can help increase a research team's sample-handling capacity concern the preservation and extraction of the DNA. To maintain the DNA sequence information, it is just as important to minimize the storage time for, and degradation of, fresh roots before extraction as it is to choose a proper extraction method. Keeping the samples cold is the primary means to slowing down any changes, but more than a few days' refrigeration will allow extensive fungal growth. If morphotyping is involved, there will be a considerable amount of time involved in handling the roots in solution (so that the hyphal material extends naturally from the surfaces); it is critical to preserve the DNA while roots are sorted. Ethanol may be applied in incremental steps to 100% ethanol (Lilleskov et al., 2002) to decrease DNA solubility and slow degradation. For larger sample sets it is possible to delay DNA extraction by storage in detergents (Gardes and Bruns, 1993). Roots that are being stored before DNA extraction may be preserved by lyophilization and storage at –80°C (Rygiewicz et al., 2000). Any DNA preservation procedure should be tested for effectiveness for the system in question.
A cetyl trimethyl ammonium bromide based DNA extraction procedure with light grinding is often used for extracting EcM root tips (Gardes and Bruns, 1993). Many other extraction methods, including some where reagents are bundled into easily used, commercially available kits, can be used. A Xanthogenate/Tween method is available that can rapidly extract surface tissues, such as the EcM mantle, for single or a few root tips, but may underrepresent EcM that situate the bulk of their DNA inside the root (Martin and Rygiewicz, 2005).
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CHOICE OF POLYMERASE CHAIN REACTION PRIMERS
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The most commonly used target sequence for PCR analysis of EcM is the fungal internal transcribed sequence (ITS). This region consists of hypervariable sections flanked by conserved, ribosomal 18S, 5.8S, and 28S sequences suitable for primer sites (Fig. 1
). The earliest PCR primers to gain wide acceptance for work with this ITS region were ITS1 and ITS4, which amplify the highly variable ITS1 and ITS2 sequences surrounding the 5.8S coding sequence situated between the small subunit coding sequence and the large subunit coding sequence of the ribosomal operon (White et al., 1990). These primers amplify a wide range of fungal targets and work well to analyze DNA isolated from individual organisms, but do not exclude the plant host sequences effectively in the mixed, phytosphere DNA extracts typical of studies of plant-associated microbiota. Subsequently, the plant-excluding primers ITS1-F and ITS4-B came into wide use for analysis of fungal ITS sequences. These primers were "intended to be specific to fungi and basidiomycetes, respectively" (Gardes and Bruns, 1993). They have been the most widely used primers in the analysis of fungal ITS sequences, although more recently many other primer sets have been developed targeting the ITS region with different ranges of specificity (Fig. 1).
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Fig. 1. Diagram of primer locations in the ribosomal cassette (modified from Martin and Rygiewicz, 2005). Primers are positioned above (forward primers) or below (reverse) their sequence positions in the ribosomal cassette, consisting of SSU, ITS1, 5.8S, ITS2, and LSU rDNA. Primers ITS1, ITS2, ITS3, ITS4, and TW13 from White et al. (1990); Primers ITS8mun, ITS9mun, ITS10mun, NL5mun, NL6Amun, NL6Bmun, and NL8mun from Egger (1995); NL6C from Kernaghan et al. (2003); Primers ITS1-F, ITS4-B from Gardes and Bruns (1993); and the remaining primers (NSA3, NSI1, 58A1F, 58A2F, 58A2R, NLB4, NLC2) from Martin and Rygiewicz (2005).
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Martin and Rygiewicz (2005) recently published a set of primers for use in nested amplifications of fungal ITS sequences. Nested PCR uses the selectivity of four different primers by "nesting" a pair of primers for a second round of amplification within the target sequence of the first-round primers. These primers were developed with an emphasis on discrimination between plant and fungal sequences and should be particularly useful for studies of fungi where samples also contain high levels of background plant DNA. They provide a wide range of compatibility across Basidiomycota and Ascomycota (Wu et al., 2007). Nested PCR can give a high degree of flexibility in the nature of the DNA templates and the target sequences for amplification by allowing for somewhat less stringent conditions in each step and relying on the combined stringency and sensitivity of the two nested steps to provide a highly efficient amplification.
One problem encountered for PCR amplification of the ITS region and for subsequent electrophoretic or sequence analysis is the inclusion of inserted sequences called introns in the PCR product. These inserted sequences can give a false estimate of species richness. Group I introns may occur at the 3' end of the 18S subunit, particularly in ascomycetes, and may add hundreds of bases to the amplicon length for PCR reactions that encompass the site of an intron. Because they are not monophyletic (i.e., not occurring in a taxonomically continuous pattern), group I introns in the nuclear-encoded ribosomal RNA genes (rDNA) of fungi such as Hymenoscyphus ericae (Egger et al., 1995) and Cenococcum (Shinohara et al., 1999) may give inflated estimates of diversity by producing multiple results for a single genetic type. Indeed, these introns may give distinct PCR products for the multiple rDNA repeats within the genome of an individual fungus (Hibbett, 1996; Avis et al., 2005). One strategy to avoid this is to use a forward primer such as ITS1 (situated at the 3' end of 18S) that will not typically include an intron in amplification of the ITS region. In some cases, however, such as for the Helotiales, this ITS1 primer may not produce an amplicon due to a large insert within the ITS1 primer site itself (Vrålstad et al., 2002). Another approach would be to support any particular PCR-based EcM community analysis with a significant sequencing effort designed to detect cases where an intron is causing a single genetic type to be counted as two or more types.
Other common PCR target sequences for analyzing EcM fungal DNA include nuclear 18S or 28S rDNA (Borneman and Hartin, 2000) and mitochondrial 16S rDNA (Bruns et al., 1998). The fungal nuclear rDNA sequences are much more strongly conserved than the ITS regions and therefore less taxonomically discriminatory. They are often used for approaches such as sequencing or real time quantitative PCR (rtqPCR) where the lesser degree of variation adds to the power of the analyses (Borneman and Hartin, 2000). Similarly, sequencing of a well-characterized segment of the mitochondrial 16S rDNA for EcM fungi can be used to effectively place fungi taxonomically (Bruns et al., 1998). Unfortunately, amplification of these sequences also suffers from the occurrence of introns that interfere with amplification of sequences from some species (Horton and Bruns, 2001).
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MOLECULAR METHODOLOGIES FOR ANALYZING ECTOMYCORRHIZAL FUNGAL COMMUNITIES
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The most widely used molecular analysis for typing EcM fungi, PCR-RFLP, typically involves the PCR amplification and subsequent restriction digestion of the ITS region of the ribosome-coding DNA to produce a diagnostic pattern of fragment sizes. It is possible to analyze ITS1 and ITS2 regions together or separately by choosing appropriate primers. The combined region is large enough to give good resolution for PCR-RFLP analyses. Individual ITS sequences are more suited to fragment analysis as the capillary electrophoresis instrumentation has a limited range covering fragments smaller than 500 or 1000 bases, depending on the accuracy needed. The PCR-RFLP technique is the most easily adopted molecular analysis of EcM fungal DNA. It is an agarose-gel-based analysis, which makes it relatively low in cost and simple to implement; however, PCR-RFLP is generally applicable only to relatively pure, single-genotype DNA samples because of ambiguities in interpreting multiple combined patterns. There are a number of software packages available for processing and matching RFLP pattern information. One popular solution, available at no cost, is the "good enough RFLP matcher" (GERM) spreadsheet-based program, which can match unknown RFLP patterns to a database of known samples (Dickie et al., 2003).
In addition to well-established methods such as PCR-RFLP analyses of single fungal species (Gardes et al., 1991), newer molecular methods that have been adopted for characterization of microbial communities are being explored as approaches to study EcM ecology. These methods include: LH-PCR, more recently applied as fungal automated ribosomal intergenic spacer analysis (Ranjard et al., 2001), T-RFLP (Burke et al., 2006), DGGE (Landeweert et al., 2005), and high-throughput sequencing (Landeweert et al., 2003a). The LH-PCR technique distinguishes types based on the variation among taxa of the amplicon sequence length in PCR amplifications of mixed DNA. It is a reliable and effective approach to analysis of targets, such as fungal ITS, with high variability in overall length (Ranjard et al., 2001). In both T-RFLP and LH-PCR, a fluorescent label on the PCR primer is used for detection and quantification of the electrophoretically separated DNA; a fluor of a different color is used to distinguish a size standard that runs in the same capillary (or lane) as the sample. In LH-PCR, the whole PCR product is detected, separated by size. In T-RFLP, only the terminal fragments of restriction-digested PCR products that contain the labeled primer are detected. These terminal restriction fragments (TRFs) contain the labeled primer and extend in length to the first instance of a restriction site for the enzyme(s) used. With the advent of automated, capillary-electrophoresis instruments capable of high-resolution discrimination of oligonucleotide lengths, these methods have become both rapid and reliable. Such methods enable rapid analysis of environmental samples and can provide extensive data on microbial communities as defined or restricted by the specificity range of the primers used. These data include both relative abundance information for dominant microbial phylotypes and characteristic PCR product or TRF sizes for these phylotypes. For comparison of fungal communities, either method provides a relatively complete, culture-independent analysis, with each fragment representing one (or more) phylotype while avoiding the complexity of untangling data from methods where each individual type is represented by multiple fragments, as in PCR-RFLP. In most studies, phylotypes identified as dominant or responsive to ecological factors of interest should subsequently be taxonomically characterized (by applying sequence analysis to amplified targets). The expense of sequencing every phylotype is often prohibitive; using the community analyses to prioritize the sequencing effort allows resources to be focused on the phylotypes that are most important to the study at hand. The identity of the fungi can give an indication of the ecological role that would be expected (Agerer, 2001; Dickie et al., 2002), allowing greater depth of interpretation than possible with community analysis alone.
The T-RFLP and LH-PCR techniques are capillary electrophoresis methods and this technology entails a relatively high cost for both equipment and operation, but capillary electrophoresis produces large amounts of useful information in a short time with good reproducibility. Of these two approaches to community analysis, T-RFLP is more artifact prone because of difficulties inherent in restriction analysis of PCR products (Egert and Friedrich, 2003; Avis et al., 2005). The T-RFLP techique can be performed with many different restriction enzymes for a single PCR product, however, which means that T-RFLP methods can be optimized to provide the greatest resolution for a specific EcM fungal community. The LH-PCR technique does not require the restriction step, which makes it more robust and less error prone (Ranjard et al., 2001). Because of the limited range of PCR product sizes that can result from any particular amplification, a lower threshold can be set for an LH-PCR analysis to exclude the material from nonspecific amplification typically seen below 100 base pairs in size. So, while LH-PCR inherently produces fewer artifacts and is much easier to interpret, it has fewer options for analysis than T-RFLP, where the choice of restriction enzymes or which primer to analyze can be varied.
One of the advantages of T-RFLP and LH-PCR is the increasingly sophisticated automation of the techniques and the more consistent and precise cross-comparisons of runs due to the greater accuracy of in-lane comparisons using a range of available internal size standards. Both of these methods use automated detection of fluorescence as the fragments pass a detector during electrophoresis. This technology does, however, have problems with over-scale peaks that have too much fluorescence for the detector, in which case the center of the peak cannot be read and the fragment cannot then be accurately sized. Appropriate quantification of the labeled DNA to be analyzed is a major concern in community analysis by automated fluorescence detection. In these methods, variation in total fluorescence can be compared to different sampling intensities in most ecological studies. That is, if total fluorescence differs significantly between two samples, then rare types will be more likely to be detected in the samples with greater overall fluorescence. This can have important effects on measurement of species richness as well as more refined measures of community composition (Gotelli and Colwell, 2001; Brose et al., 2003). If there were a direct correlation between the amount of product from a standard PCR amplification and the amount of target in the sample, then this effect of variable sensitivity to rare types would be less important; low fungal biomass would be appropriately measured as having fewer types that rise above a threshold. As it is, we have no well-demonstrated solution to this problem. Ideally, some algorithm using a rtqPCR measure of total biomass that would provide a lower threshold on the distribution of types would improve the legitimacy of comparisons between communities. In practice, the precision of a SYBR green based rtqPCR (Martin and Rygiewicz, 2005) may not be sufficient to do this, but a fluorescence resonance energy transfer (FRET, see discussion below) approach might have the precision to support such an analysis for smaller taxonomic ranges (Landeweert et al., 2003b). The consequence of this ambiguity in measuring populations in different samples to equal depths is that simple indices of richness or diversity are less effective vehicles for comparison of communities characterized by PCR-based methods. Comparisons can be made using multivariate analyses that give more weight to the type and relative abundance of the dominant fungi. Martiny et al. (2006) discussed an application of multivariate analysis of microbial communities in a recent review.
The DGGE technique has also been used effectively in characterizing EcM communities (Landeweert et al., 2005; Anderson and Cairney, 2004). Denaturing (and thermal) gradient gel electrophoresis analyses produce a distribution of fragments based on the melting point, which is related to guanine (G) + cytosine (C) content and length. Because DGGE uses detection of fragments in a polyacrylamide, slab-gel format, the gel image exposure can be adjusted and consequently does not have as many problems with over-scale peaks as the methods using automated fluorescence detection. The DGGE technique has been shown to be less effective at discriminating between fungal types than automated methods (Brodie et al., 2003; Kirk et al., 2004; Singh et al., 2006; Tiedje et al., 1999). To get good discrimination in DGGE, a GC-rich sequence, or "GC clamp," is often synthesized at the 5' end of one primer to inhibit complete denaturation of the PCR product and increase the specific affinity of the melting-point position on the DGGE gel. This alteration of the primer will increase both its length and the annealing temperature and may change the dynamics of the amplification process (Muyzer and Smalla, 1998). The altered primer should be evaluated for any effects the GC clamp may have on the amplification and final product distributions. It is possible that a short reamplification with these altered primers may give the best fidelity to the original distribution of types in the fungal community (Suzuki et al., 1998).
It is useful to be able to excise bands containing DNA sequences of interest directly from a DGGE gel and clone the DNA for sequencing, but often these fragments are too short to provide useful information, typically well short of 500 base pairs (Muyzer and Smalla, 1998). In the cases of T-RFLP or LH-PCR, the entire mixed PCR product must be cloned and screened by fragment length analysis to identify candidate clones representing types of interest. (Sequencing is based on the full cloned sequence rather than the fragment used for screening.) For a taxonomic analysis of a genotype that represents only 1% of the total population, successful sequencing may require the screening of many hundreds of clones.
It is also possible to directly apply sequencing as a community analysis tool on its own (O'Brien et al., 2005). As the cost and effort involved in high-throughput sequencing projects have fallen, this is becoming a more widely adopted approach. The cost is still quite high, however, and the majority of clones sequenced without screening are going to be redundant. In most cases, sequencing efforts are still economical only as a complement to other methods of community analysis that allow the researcher to identify the types of interest and screen for corresponding clones to focus the sequencing effort. A highly effective approach is to screen clones for similarity by any of the previously mentioned molecular characterization methods and sequence the clones that represent each identified type. This can reduce the cost of sequence analysis while providing a database for translation between cloning results and other methods. Often, sequence length for a fragment differs from its apparent length by electrophoresis, and that error can be much higher for some fragments than for others. Purine bases are sufficiently large compared with pyrimidine bases that a difference in composition between the standard and the sample fragments leads to a difference in electrophoretic mobility (Kaplan and Kitts, 2003). More predictable errors can also be caused by differences in the fluorophore labels.
All studies of fungal communities should include sequencing to identify the types discovered, but as stated above, the connection between types seen in community analysis and by sequencing can be hard to make (Wu et al., 2007). A relatively untested but very promising technology being investigated as a tool for characterizing soil fungi (Waldrop et al., 2005), also bears mention here. Denaturing high-performance liquid chromatography (DHPLC) and related DNA chromatography technologies (Evans et al., 2004) may have the potential to enable community analyses at resolutions similar to the capillary electrophoresis or DGGE with the important additional benefit of being able to collect the eluting DNA in an automated fraction collector (Barlaan et al., 2005). As mentioned for DGGE, a GC clamp may be necessary for good discrimination of types (Barlaan et al., 2005); strategies should be used to account for the resultant changes in primer activity. A recent study showed that fungus-specific primers for amplification of ITS2 could be used effectively with DHPLC (Goldenberg et al., 2005). The DHPLC technique has been successfully optimized to discriminate large numbers of types in samples of complex communities using 16S, a much more conserved region than the fungal ITS (Goldenberg et al., 2006).
Cloning and sequencing DNA fragments from PCR of EcM communities or root tips typically allows reliable taxonomic placement of the corresponding fungi through comparison of the sequences with those in the international databases. Because of the ease of cloning with commercial kits and sequencing through a variety of services, very few molecular studies of EcM are performed without sequence information for at least a few representative types. In particular, databases for the ITS sequences of fungi are rapidly growing and provide an increasingly powerful basis for taxonomic identification of cloned sequences (Horton and Bruns, 2001). A number of approaches have been suggested to improve the effectiveness of sequencing efforts. Kieleczawa (2006) performed a systematic analysis of some popular techniques and found that some of the most common recommendations, such as using larger amounts of template produced by PCR or by in vitro plasmid amplification, did not improve the outcome. Once sequences of clones have been determined, it is important to check that none of the sequences are chimeric (i.e., a combination of two sequences caused by premature termination of extension in one cycle leaving a fragment that can act as a primer on an unrelated sequence in a subsequent cycle). Jumpponen (2003) found that failure to detect chimeras could affect data interpretation. Such a sequence can be identified by its match to different taxons when end portions of the sequence are aligned independently with the international sequence databases. Tools such as the chimera detection program at the Ribosomal Database Project (Cole et al., 2003), Mallard (Ashelford et al., 2006), or Bellerophon (Huber et al., 2004) can help with this effort, although effectiveness on fungal ITS sequences rather than 16S needs to be further validated. A threshold must be chosen for the percentage of similarity above which fungi are assigned to the same species. This value will affect the interpretation of the data, particularly where indices based in richness are used. This threshold commonly ranges from 95 to 99%. Once a database of sequences has been developed from the microbial communities in a set of samples, the difference between the communities can be analyzed using taxonomic distances of the members rather than just the relative abundances; LIBSHUFF is an example of such an analytical tool (Schloss et al., 2004). Jones and Martin (2006) recently reviewed the range of approaches to phylogenetic comparisons of microbial communities.
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PRECAUTIONS FOR COMMUNITY ANALYSIS
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Community analysis methods such as cloning, DGGE, LHPCR, T-RFLP, and DHPLC can all, to various degrees, give estimates of the relative abundance of phylotypes in a community; however, a few caveats must be observed to ensure data quality. When measuring population distributions, it is important to avoid late-phase PCR where the more rare types are preferentially amplified due to the greater time to encounter a complementary strand in the DNA mix, which allows for more efficient primer annealing (Suzuki and Giovannoni, 1996). Faster ramp times from melting to annealing apparently improve fidelity and can alleviate the problem of late-phase PCR (Kurata et al., 2004).
It is also important to take steps to avoid PCR product carryover. The PCR product resulting from a completed amplification can easily exceed a trillion copies in a microliter, a volume that can readily become an aerosol droplet when the container is opened. The greatest obstacle to reliable identification of DNA sequences in environmental extracts, then, is the danger of back-contamination of original samples with PCR products. This contamination can result in false positives for sequences that have been previously amplified in the lab but were not present in a specific sample. The remedies for this problem are relatively simple (Kwok, 1990). The areas where samples are handled and PCR reaction mixtures are prepared need to be separated from the areas where amplified PCR product is present. Monitor for contamination with multiple no-template controls (reagent-only reactions) in each run. It is also worthwhile to aliquot reagents into small volumes so that when no-template controls do show contamination, the cost of discarding all current reagents is minimized. In labs where the research has stalled because of unreliable PCR results, these three measures have successfully resolved the problems.
Positive controls are also necessary to distinguish between failed PCR reactions and a set of negative results. For these controls, the template concentration should be at the lowest level that reliably amplifies, so that the positive control will fail before many of the samples do when degraded reagents produce an inefficient amplification. The template for positive controls can be a DNA source that is readily obtained commercially, or an otherwise easily reproduced DNA source, so that the resulting protocol is more consistent between labs.
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QUANTIFICATION OF ECTOMYCORRHIZAL FUNGI
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In addition to numbers or weight of EcM root tips discussed above, classical methods for measuring EcM fungi in the soil include hyphal length measurements and measurements of the amounts of indicator compounds specific to fungi, such as ergosterol and certain phospholipid fatty acids (PLFAs). Landeweert et al. (2003b) compared hyphal length and PLFA with the molecular methods: DGGE, rtqPCR, and cloning. Using artificial syntheses of EcM fungi on seedlings, they found that the cloning approach supported the relative measurements of DGGE. The rtqPCR approach provided absolute quantification of the fungi, but may have been affected by PCR inhibitory substances in the DNA extracts. As with the variation in concentrations of PLFAs according to the fungal species encountered, the ribosomal DNA copy number can vary between species and may affect estimated biomass.
Analysis of DNA copy number with rtqPCR requires a positive control template for the concentration standard, which will be used to calibrate the quantifications. This standard should have a balance of similarity to the target populations and availability to other researchers so that results can be compared. In EcM applications where a wide range of fungi are being analyzed, rtqPCR gives an overall quantification for the target sequences. This can then be subdivided mathematically using relative-abundance values from community analysis to provide an estimate of absolute population levels for individual phylotypes. Higuchi et al. (1992, 1993) developed a method for real-time detection of PCR products for quantitation of sample target sequences based on fluorescence of the intercalating dye ethidium bromide, and numerous companies now offer kits for such quantification using SYBR Green I, a fluorescent nucleic acid stain available from Invitrogen Life Technologies (Carlsbad, CA), as the intercalating dye. Most of these instruments have since incorporated the ability to perform a melting curve analysis on the PCR product after quantification (Ririe et al., 1997), which partially ameliorates the uncertainty that arises from the inability of the dye to distinguish target amplicon from nontarget PCR products. The FRET methods of implementing rtqPCR, which use changes in fluorescence of DNA-bound label to monitor the amount of PCR product produced each cycle, have also been applied to smaller taxonomic ranges of fungi where variation within the amplicon sequence is less of an issue (Landeweert et al., 2003b). In FRET analyses, a thermal cycler equipped with the appropriate optic instrumentation monitors the increasing numbers of amplicons during amplification by detecting changes in fluorescence resulting from effects such as degradation or displacement of labeled probes. The interaction between fluorophores based on their proximity changes when the new copies of the product are synthesized and the conformation of the DNA– fluorophore complex is altered. There are inherent difficulties in the requirement for probe sequences that anneal to the DNA between the PCR primers such that most of the length of the target sequence must be conserved across the target group. In the case of amplifications of broad taxonomic groups such as fungi, the sequence similarity needed for consistent annealing characteristics for the primers and probes has to be balanced against exclusion of nonfungal sequences. That is, it is difficult to find such large sequences that are both conserved within fungi and specific to fungi.
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SPATIAL AND TEMPORAL PATTERNS IN ECTOMYCORRHIZAL DISTRIBUTIONS
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Sampling schemes for EcM studies need to be extensive. The high spatial variability in EcM fungal populations requires large numbers of samples to characterize the correspondence between EcM fungal community composition and other ecological factors (Horton and Bruns, 2001). Because EcM fungi are patchy in space and time, chance selection of sampling sites may obscure the role of important EcM fungi. It is particularly difficult to characterize rare types without extensive sampling (Taylor, 2002). Spatial statistics can be used to characterize the sizes of patches of the EcM species or communities. Multivariate analyses of community similarity such as clustering, principle component analysis, or Mantel tests can be collapsed into variables that are appropriate for spatial analysis (Lilleskov et al., 2004; Martiny et al., 2006). Community similarity can be calculated by any of a number of multivariate approaches that produce distance matrices based on relative abundance (Martiny et al., 2006; Wu et al., 2007). The variation among samples can be separated into components using geostatistical analysis in which sample variance for pairs of samples grouped by their distance apart (the lag) reveals to what degree samples are more similar within a certain distance (the range) beyond which variance ideally levels off at a maximum value (the sill). Analysis of such variograms provides information on appropriate sampling distances to reliably sample different communities, avoiding repeated sampling of any single fungal community patch. Spatial analysis also provides an integrative measure of the state of fungal communities with regard to patch or genet size in response to factors such as the distribution of host plant species or nutrient sources (Lilleskov et al., 2004; Guidot et al., 2005). Spatial distributions of EcM fungi are significant both laterally across the landscape and vertically in the soil profile. Vertical distribution of EcM fungi reflects the nature of the humus and mineral soil layers and the adaptation of specific fungi to these locations (Baier et al., 2006). Temporal variation in distributions of EcM species may reflect differences among the strategies fungi use in response to events such as drought (Izzo et al., 2005). The temporal correlation between community composition changes and such factors as season, moisture status, or host physiology using statistical approaches such as "repeated measures" will reveal much more about EcM communities than condensed indices of community composition (Rygiewicz et al., 2000). Issues that can be addressed by spatial analysis of fungi include: (i) the relative importance of limits to dispersal or "history" vs. selection by habitat or "environment," and (ii) the strategies of stable growth creating large genets vs. rapid, transient colonization of root systems (Martiny et al., 2006). Patterns of succession may similarly be expressed in patch size as well as species distributions.
The need to address the spatial distribution of EcM fungi also raises the issue of their distribution in the bulk soil beyond the root (Guidot et al., 2005). Indeed, many questions regarding the ecology of EcM fungi would benefit from extending the analysis into the soil (Anderson and Cairney, 2004; Landeweert et al., 2005). Another extension of analyses of EcM roots is to use the plant DNA in the extract to identify the host plant species (Martin and Rygiewicz, 2005). The methods of plant genome mapping are becoming increasingly sophisticated and offer the possibility of not only identifying the plant host species but also linking this information with the community of EcM fungi associated with individual trees (Saari et al., 2005).
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SORTING THROUGH THE OPTIONS FOR ANALYSIS OF ECTOMYCORRHIZAL COMMUNITIES
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Morphotyping is useful for analyzing EcM communities but is labor intensive and may be performed at different levels of rigor with trade-offs at each level. The easiest approach to adopt with widely available lab resources is morphotyping checked by PCR-RFLP and sequencing. The DGGE, LH-PCR, and T-RFLP techniques may be applied to pooled root tips to scale up the rate of sampling, and may be applied to roots that have either been morphotyped or merely sorted from the soil (Table 1). These community analysis approaches are less well characterized but are very efficient, particularly when applied to pooled samples of all roots. Such community composition data can also be readily compared with bulk soil analyses. One drawback is that community analysis by these methods is restricted to the dynamic range of the detection technology. Rare species below a threshold of around 1% of total biomass will be indistinguishable from noise (personal observation). Performing community analysis on roots pooled by morphotype group will overcome the potential for loss of rare types but will not include the relative abundance data available from total root extracts, meaning that quantification will rely solely on root tip counts or weight. Live roots may be separated from dead or senescent roots to limit the inclusion of fungal saprobes that may be more abundant on dead roots. But work by Landeweert et al. (2005) showed that dead or senescing roots may not yield amplifiable DNA. Therefore, it may be worthwhile to test whether sorting live from dead roots is worth the effort.
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CONCLUSIONS
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As the technologies available for rapid analysis of microbial communities have been developed and improved, there has been a trend toward convergence of methods used for analysis of EcM communities and those used for other microbial ecology studies. This has made the study of EcM communities much more efficient and more easily adopted by those outside of those fields with traditionally strong interest in the study of the EcM symbiosis. Still, the existence of the EcM root tips as visible units is an important distinction between the study of EcM communities and other microbial communities. Even in the case of analysis of unsorted roots, it is possible to randomly select a subset of root tips for more in-depth analysis to balance the sometimes poor resolution of bulk analyses. In any molecular analysis of EcM communities, it is very important to support the typing method with a sequencing effort to achieve taxonomic placement at least of the types that appear to be most ecologically important. This verifies the degree of genetic unity of the types identified as well as allowing discussion of the traits that may be expected for the related species. With these factors in mind, any suitably equipped lab could augment an ecological study by including analyses of EcM communities to the benefit of both their understanding of the ecosystem at hand and the general understanding of the role of EcM fungi in terrestrial ecosystems.
The trends toward faster and more efficient methods for analyses of EcM communities are allowing much more powerful studies to be performed. Extending the analyses across space and time as well as connecting them to the bulk soil and the host genotype will provide a much more sophisticated array of questions that can be asked. At the same time, the increasing ease of implementing analyses of EcM communities will allow these important relationships to be studied as part of any research involving plant communities, bridging a gap that has been an obstacle to fully integrating mycorrhizal research with the broader field of ecology.
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NOTES
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All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.
Received for publication March 13, 2006.
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REFERENCES
|
|---|
- Agerer, R. (ed.). 1987–1998. Colour atlas of ectomycorrhizae. 1st–11th ed. Schwäbisch Gmünd, Einhorn, Germany.
- Agerer, R. 2001. Exploration types of ectomycorrhizae: A proposal to classify ectomycorrhizal mycelial systems according to their patterns of differentiation and putative ecological importance. Mycorrhiza 11:107–114.
- Anderson, I.C., and J.W.G. Cairney. 2004. Diversity and ecology of soil fungal communities: Increased understanding through the application of molecular techniques. Environ. Microbiol. 6:769–779.[CrossRef][Medline]
- Ashelford, K.E., N.A. Chuzhanova, J.C. Fry, A.J. Jones, and A.J. Weightman. 2006. New screening software shows that most recent large 16S rRNA gene clone libraries contain chimeras. Appl. Environ. Microbiol. 72:5734–5741.[Abstract/Free Full Text]
- Avis, P.G., I.A. Dickie, and G.M. Mueller. 2005. A ‘dirty’ business: Testing the limitations of terminal restriction fragment length polymorphism (TRFLP) analysis of soil fungi. Mol. Ecol. 15:873–882.
- Baier, R., J. Ingenhaag, H. Blaschke, A. Göttlein, and R. Agerer. 2006. Vertical distribution of an ectomycorrhizal community in upper soil horizons of a young Norway spruce (Picea abies [L.] Karst.) stand of the Bavarian Limestone Alps. Mycorrhiza 16:197–206.[CrossRef][Web of Science][Medline]
- Barlaan, E.A., M. Sugimori, S. Furukawa, and K. Takeuchi. 2005. Profiling and monitoring of microbial populations by denaturing high-performance liquid chromatography. J. Microbiol. Methods 61:399–412.[CrossRef][Web of Science][Medline]
- Borneman, J., and R.J. Hartin. 2000. PCR primers that amplify fungal rRNA genes from environmental samples. Appl. Environ. Microbiol. 66:4356–4360.[Abstract/Free Full Text]
- Brodie, E., S. Edwards, and N. Clipson. 2003. Soil fungal community structure in a temperate upland grassland soil. FEMS Microbiol. Ecol. 45:105–114.
- Brose, U., N.D. Martinez, and R.J. Williams. 2003. Estimating species richness: Sensitivity to sample coverage and insensitivity to spatial patterns. Ecology 84:2364–2377.
- Bruns, T.D., T.M. Szaro, and M. Gardes. 1998. A sequence database for the identification of ectomycorrhizal basidiomycetes by phylogenetic analysis. Mol. Ecol. 7:257–272.
- Burke, D., K. Martin, P. Rygiewicz, and M. Topa. 2005. Characterization of ectomycorrhizal fungal communities using TRFLP. Soil Biol. Biochem. 37:1683–1694.
- Burke, D., K. Martin, P. Rygiewicz, and M. Topa. 2006. Relative abundance of ectomycorrhizas in a managed loblolly pine (Pinus taeda) genetics plantation as determined through terminal restriction fragment length polymorphism profiles. Can. J. Bot. 84:924–932.
- Cole, J.R., B. Chai, T.L. Marsh, R.J. Farris, Q. Wang, S.A. Kulam, S. Chandra, D.M. McGarrell, T.M. Schmidt, G.M. Garrity, and J.M. Tiedje. 2003. The Ribosomal Database Project (RDP-II): Previewing a new autoaligner that allows regular updates and the new prokaryotic taxonomy. Nucleic Acids Res. 31:442–443.[Abstract/Free Full Text]
- Dahlberg, A. 2001. Community ecology of ectomycorrhizal fungi: An advancing interdisciplinary field. New Phytol. 150:555–562.
- Dickie, I.A., P.G. Avis, D.J. McLaughlin, and P.B. Riech. 2003. Good enough RFLP matcher (GERM) program. Mycorrhiza 13:171–172.[CrossRef][Web of Science][Medline]
- Dickie, I.A., B. Xu, and R.T. Koide. 2002. Vertical niche differentiation of ectomycorrhizal hyphae in soil as shown by T-RFLP analysis. New Phytol. 156:527–535.
- Egert, M., and M.W. Friedrich. 2003. Formation of pseudoterminal restriction fragments, a PCR-related bias affecting terminal restriction fragment length polymorphism analysis of microbial community structure. Appl. Environ. Microbiol. 69:2555–2562.[Abstract/Free Full Text]
- Egger, K.N. 1995. Molecular analysis of ectomycorrhizal fungal communities. Can. J. Bot. 73:S1415–S1422.
- Egger, K.N., G. Osmond, and J.L. Goodier. 1995. Sequence and putative secondary structure of group I introns in the nuclear-encoded ribosomal RNA genes of the fungus Hymenoscyphus ericae. Biochim. Biophys. Acta 1261:275–278.[Medline]
- Evans, J.T., P.M. Hawkey, E.G. Smith, K.A. Boese, R.E. Warren, and G. Hong. 2004. Automated high-throughput mycobacterial interspersed repetitive unit typing of Mycobacterium tuberculosis strains by a combination of PCR and nondenaturing high-performance liquid chromatography. J. Clin. Microbiol. 42:4175–4180.[Abstract/Free Full Text]
- Gardes, M., and T.D. Bruns. 1993. ITS primers with enhanced specificity for basidiomycetes: Application to the identification of mycorrhiza and rusts. Mol. Ecol. 2:113–118.[Medline]
- Gardes, M., J. Fortin, T.J. White, T.D. Bruns, and J.W. Taylor. 1991. Identification of indigenous and introduced symbiotic fungi in mycorrhizae by amplification of the internal transcribed spacer. Can. J. Bot. 69:180–190.
- Goldenberg, O., S. Herrmann, T. Adam, G. Marjoram, G. Hong, U.B. Göbel, and B. Graf. 2005. Use of denaturing high-performance liquid chromatography for rapid detection and identification of seven Candida species. J. Clin. Microbiol. 43:5912–5915.[Abstract/Free Full Text]
- Goldenberg, O., S. Herrmann, G. Marjoram, M. Noyer-Weidner, G. Hong, S. Bereswill, and U.B. Göbel. 2006. Molecular monitoring of the intestinal flora by denaturing high performance liquid chromatography. J. Microbiol. Methods (in press), doi:10.1016/j.mimet.2006.06.009.
- Goodman, D.M., D.M. Durall, and J.A. Trofymow. 1996. Describing morphology and anatomy. 3A.1–3A.5. In D.M. Goodman et al. (ed.) Concise descriptions of North American ectomycorrhizae. Mycologue Publ., Victoria, BC.
- Goodman, D.M., and J.A. Trofymow. 1998. Distribution of ectomycorrhizas in micro-habitats in mature and old-growth stands of Douglas-fir on southeastern Vancouver Island. Soil Biol. Biochem. 30:2127–2138.
- Goodman, D.M., J.A. Trofymow, and A.J. Thomson. 2000. Developing an online database of descriptions of ectomycorrhizae [Online]. Available at http://www.forrex.org/jem/ISS1/vol1_no1_art2.pdf (verified 22 Dec. 2006). BC J. Ecosyst. Manage. 1(1).
- Gotelli, N.J., and R.K. Colwell. 2001. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4:379–391.
- Guidot, A., M.-C. Verner, J.-C. Debaud, and R. Marmeisse. 2005. Intraspecific variation in use of different organic nitrogen sources by the ectomycorrhizal fungus Hebeloma cylindrosporum. Mycorrhiza 15:167–177.[CrossRef][Web of Science][Medline]
- Hibbett, D.S. 1996. Phylogenetic evidence for horizontal transmission of group I introns in the nuclear ribosomal DNA of mushroom-forming fungi. Mol. Biol. Evol. 13:903–917.[Abstract]
- Higuchi, R., G. Dollinger, P.S. Walsh, and R. Griffith. 1992. Simultaneous amplification and detection of specific DNA sequences. Biotechnology 10:413–417.
- Higuchi, R., C. Fockler, G. Dollinger, and R. Watson. 1993. Kinetic PCR analysis: Real-time monitoring of DNA amplification reactions. Biotechnology 11:1026–1030.
- Horton, T.R. 2002. Molecular approaches to ectomycorrhizal diversity studies: Variation in ITS at a local scale. Plant Soil 244:29–39.
- Horton, T.R., and T.D. Bruns. 2001. The molecular revolution in ectomycorrhizal ecology: Peeking into the black box. Mol. Ecol. 10:1855–1871.[CrossRef][Medline]
- Huber, T., G. Faulkner, and P. Hugenholtz. 2004. Bellerophon: A program to detect chimeric sequences in multiple sequence alignments. Bioinformatics 20:2317–2319.[Abstract/Free Full Text]
- Izzo, A., J. Agbowo, and T.D. Bruns. 2005. Detection of plot-level changes in ectomycorrhizal communities across years in an old-growth mixed-conifer forest. New Phytol. 166:619–630.[CrossRef][Web of Science][Medline]
- Jones, R.T., and A.P. Martin. 2006. Testing for differentiation of microbial communities using phylogenetic methods: Accounting for uncertainty of phylogenetic inference and character state mapping. Microb. Ecol. 52:408–417.[CrossRef][Web of Science][Medline]
- Jonsson, L., A. Dahlberg, M.-C. Nilsson, O. Zackrisson, and O. Kårén. 1999. Ectomycorrhizal fungal communities in late-successional Swedish boreal forests, and their composition following wildfire. Mol. Ecol. 8:205–215.
- Jumpponen, A. 2003. Soil fungal community assembly in a primary successional glacier forefront ecosystem as inferred from rDNA sequence analyses. New Phytol. 158:569–578.
- Kaplan, C.W., and C.L. Kitts. 2003. Variation between observed and true terminal restriction fragment length is dependent on true TRF length and purine content. J. Microbiol. Methods 54:121–125.[CrossRef][Web of Science][Medline]
- Kennedy, N., and N. Clipson. 2003. Fingerprinting the fungal community. Mycologist 7:158–164.
- Kernaghan, G., L. Sigler, and D. Khasa. 2003. Mycorrhizal and root endophytic fungi of containerized Picea glauca seedlings assessed by rDNA sequence analysis. Microb. Ecol. 45:128–136.[CrossRef][Web of Science][Medline]
- Kieleczawa, J. 2006. Fundamentals of sequencing of difficult templates: An overview. J. Biomol. Technol. 17:207–217.[Medline]
- Kirk, J.L., L.A. Beaudette, M. Hart, P. Moutoglis, J.N. Klironomos, H. Lee, and J.T. Trevors. 2004. Methods of studying soil microbial diversity. J. Microbiol. Methods 58:169–188.[CrossRef][Web of Science][Medline]
- Kurata, S., T. Kanagawa, Y. Magariyama, K. Takatsu, K. Yamada, T. Yokomaku, and Y. Kamagata. 2004. Reevaluation and reduction of a PCR bias caused by reannealing of templates. Appl. Environ. Microbiol. 70:7545–7549.[Abstract/Free Full Text]
- Kwok, S. 1990. Procedures to minimize PCR product carry-over. p. 142–145. In M.A. Innis et al. (ed.) PCR protocols: A guide to methods and applications. Academic Press, San Diego.
- Landeweert, R., P. Leeflang, T.W. Kuyper, E. Hoffland, A. Rosling, K. Wernars, and E. Smit. 2003a. Molecular identification of ectomycorrhizal mycelium in soil horizons. Appl. Environ. Microbiol. 69:327–333.[Abstract/Free Full Text]
- Landeweert, R., P. Leeflang, E. Smit, and T.W. Kuyper. 2005. Diversity of an ectomycorrhizal fungal community studied by a root tip and total soil DNA approach. Mycorrhiza 15:1–6.[CrossRef][Web of Science][Medline]
- Landeweert, R., C. Veenman, T.W. Kuyper, H. Fritze, K. Wernars, and E. Smit. 2003b. Quantification of ectomycorrhizal mycelium in soil by real-time PCR compared to conventional quantification techniques. FEMS Microbiol. Ecol. 45:283–292.
- Lilleskov, E., T. Bruns, T. Horton, D. Taylor, and P. Grogan. 2004. Detection of forest stand-level spatial structure in ectomycorrhizal fungal communities. FEMS Microbiol. Ecol. 49:319–332.
- Lilleskov, E.A., T.J. Fahey, T.R. Horton, and G.M. Lovett. 2002. Belowground ectomycorrhizal fungal community change over a nitrogen deposition gradient in Alaska. Ecology 83:104–115.
- Liu, W.-T., T.L. Marsh, H. Cheng, and L.J. Forney. 1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl. Environ. Microbiol. 63:4516–4522.[Abstract]
- Martin, K.J., and P. Rygiewicz. 2005. Fungal-specific PCR primers developed for analysis of the ITS region of environmental DNA extracts. BMC Biol. 5:28, doi:10.1186/1471-2180-5-28.
- Martiny, J.B.H., B.J.M. Bohannan, J.H. Brown, R.K. Colwell, J.A. Fuhrman, J.L. Green et al. 2006. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. 4:102–112.[CrossRef][Web of Science][Medline]
- Molina, R., B.A. Caldwell, M.A. Castellano, T. Horton, and J.E. Smith. 2002. Mycorrhizae: Ectomycorrhizal fungi. p. 2124–2132. In G. Bitton (ed.) Encyclopedia of environmental microbiology. Wiley Interscience, Hoboken, NJ.
- Muyzer, G., E.C. de Waal, and A.G. Uitterlinden. 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59:695–700.[Abstract/Free Full Text]
- Muyzer, G., and K. Smalla. 1998. Application of denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE) in microbial ecology. Antonie van Leeuwenhoek 73:127–141.[CrossRef][Web of Science][Medline]
- O'Brien, H.E., J.L. Parrent, J.A. Jackson, J.-M. Moncalvo, and R. Vilgalys. 2005. Fungal community analysis by large-scale sequencing of environmental samples. Appl. Environ. Microbiol. 71:5544–5550.[Abstract/Free Full Text]
- Ranjard, L., F. Poly, J.C. Lata, C. Mougel, J. Thioulouse, and S. Nazaret. 2001. Characterisation of bacterial and fungal soil communities by automated ribosomal intergenic spacer analysis fingerprints: Biological and methodological variability. Appl. Environ. Microbiol. 67:4479–4487.[Abstract/Free Full Text]
- Ririe, K.M., R.P. Rasmussen, and C.T. Wittwer. 1997. Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Anal. Biochem. 245:154–160.[CrossRef][Web of Science][Medline]
- Rygiewicz, P.T., K.J. Martin, and A.R. Tuininga. 2000. Morphological diversity of ectomycorrhizas on Douglas-fir (Pseudotsuga menziesii Mirb. Franco) seedlings grown under elevated atmospheric CO2 and temperature. Oecologia 124:299–308.
- Saari, S.K., C.D. Campbell, J. Russell, I.J. Alexander, and I.C. Anderson. 2005. Pine microsatellite markers allow roots and ectomycorrhizas to be linked to individual trees. New Phytol. 165:295–304.[CrossRef][Web of Science][Medline]
- Schloss, P.D., B.R. Larget, and J. Handelsman. 2004. Integration of microbial ecology and statistics: A test to compare gene libraries. Appl. Environ. Microbiol. 70:5485–5492.[Abstract/Free Full Text]
- Shinohara, M.L., K.F. LoBuglio, and S.O. Rogers. 1999. Comparison of ribosomal DNA ITS regions among geographic isolates of Cenococcum geophilum. Curr. Genet. 35:527–535.[CrossRef][Web of Science][Medline]
- Singh, B.K., S. Munro, E. Reid, B. Ord, J.M. Potts, E. Paterson, and P. Millard. 2006. Investigating microbial community structure in soils by physiological, biochemical and molecular fingerprinting methods. Eur. J. Soil Sci. 57:72–82.
- Smith, S.E., and D.J. Read. 1997. Mycorrhizal symbiosis. 2nd ed. Academic Press, London.
- Suzuki, M., M.S. Rappé, and S.J. Giovannoni. 1998. Kinetic bias in estimates of coastal picoplankton community structure obtained by measurements of small-subunit rRNA gene PCR amplicon length heterogeneity. Appl. Environ. Microbiol. 64:4522–4529.[Abstract/Free Full Text]
- Suzuki, M.T., and S.J. Giovannoni. 1996. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl. Environ. Microbiol. 62:625–630.[Abstract]
- Taylor, A.F.S. 2002. Fungal diversity in ectomycorrhizal communities: Sampling effort and species detection. Plant Soil 244:19–28.
- Tiedje, J.M., S. Asuming-Brempong, K. Nusslein, T.L. Marsh, and S.J. Flynn. 1999. Opening the black box of soil microbial diversity. Appl. Soil Ecol. 13:109–122.
- Vrålstad, T., E. Myhre, and T. Schumacher. 2002. Molecular diversity and phylogenetic affinities of symbiotic root associated ascomycetes of the Helotiales in burnt and metal polluted habitats. New Phytol. 155:131–148.
- Waldrop, M., J. Harden, and K. Manies. 2005. Fungal community composition and function in boreal forest soils: Landscape controls and feedback to ecosystem processes. Available at abstracts.co.allenpress.com/pweb/esa2005/document/?ID=51303 (verified 22 Dec. 2006). Annu. Meet. Ecol. Soc. Am., Montreal. 4–12 Aug. 2005. Ecol. Soc. Am., Washington, DC.
- White, T.J., T.D. Bruns, S. Lee, and J. Taylor. 1990. Analysis of phylogenetic relationships by amplification and direct sequencing of ribosomal RNA genes. p. 315–322. In M.A. Innis et al. (ed.) PCR protocols: A guide to methods and applications. Academic Press, New York.
- Wu, T., D.O. Chellemi, K.J. Martin, J.H. Graham, and E.N. Rosskopf. 2007. Discriminating the effects of agricultural land management practices on soil fungal communities. Soil Biol. Biochem. (in press).
- Zhou, Z., and T. Hogetsu. 2002. Subterranean community structure of ectomycorrhizal fungi under Suillus grevillei sporocarps in a Larix kaempferi forest. New Phytol. 154:529–539.
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ABSTRACT
INTRODUCTION