Methods for assessing the composition and diversity of
soil microbial communities
G.T. Hill
a,∗, N.A. Mitkowski
a, L. Aldrich-Wolfe
b, L.R. Emele
a, D.D. Jurkonie
a, A. Ficke
a,
S. Maldonado-Ramirez
a, S.T. Lynch
a, E.B. Nelson
aaDepartment of Plant Pathology, Cornell University, Ithaca, NY 14853, USA bDepartment of Ecology and Systematics, Cornell University, Ithaca, NY 14853, USA
Abstract
Soil microorganisms play important roles in soil quality and plant productivity. The development of effective methods for studying the diversity, distribution, and behavior of microorganisms in soil habitats is essential for a broader understanding of soil health. Traditionally, the analysis of soil microbial communities has relied on culturing techniques using a variety of culture media designed to maximize the recovery of diverse microbial populations. However, only a small fraction (<0.1%) of the soil microbial community has been accessible with this approach. To overcome these problems, other methods such as the analysis of phospholipid fatty acids and community-level physiological profiles have been utilized in an attempt to access a greater proportion of the soil microbial community. In recent years, molecular methods for soil microbial community analysis have provided a new understanding of the phylogenetic diversity of microbial communities in soil. Among the most useful of these methods are those in which small subunit rRNA genes are amplified from soil-extracted nucleic acids. Using these techniques, it is possible to characterize and study soil microbes that currently cannot be cultured. Microbial rRNA genes can be detected directly from soil samples and sequenced. These sequences can then be compared with those from other known microorganisms. Additionally, group- and taxon-specific oligonucleotide probes can be developed from these sequences making direct visualization of microorganisms in soil habitats possible. The use of these techniques provides new ways of assessing soil microbial diversity and ultimately, a more complete understanding of the potential impacts of environmental processes and human activities on responses of soil microorganisms. Information gained from such studies will have direct impacts on our understanding of the role of microbial processes in soil health. © 2000 Published by Elsevier Science B.V.
Keywords:Soil ecology; Molecular microbial ecology; Soil health; Soil quality; PLFA analysis; Community-level physiological profiles; FISH; SSU rRNA; rDNA
1. Introduction
Microbial characteristics of soils are being evalu-ated increasingly as sensitive indicators of soil health because of the clear relationships between microbial diversity, soil and plant quality, and ecosystem sus-tainability (Doran et al., 1994). While the
understand-∗Corresponding author.
ing of microbial properties such as biomass, activity, and diversity are important to scientists in furthering knowledge of the factors contributing to soil health, results of such analyses may also be useful to exten-sion personnel and farmers in devising practical mea-sures of soil quality.
Studies of soil microbial properties have been com-monly conducted at the process level, where biomass, respiration rates, and enzyme activities have been
examined. Less attention has been given to community-level or organism-community-level responses to changes in soil properties or management. Although these process-level measurements provide an important understand-ing of gross microbial processes and their potential role in soil health, they tell us little about quali-tative community-level changes because any given microbial process may be carried out by diverse taxa. Furthermore, these process-level measurements are limited in their ability to describe a particular microbial ecosystem.
Community-level microbial interactions are com-plex, with individual species relying on the presence, function, and interaction of many other species. There-fore, quantitative and qualitative changes in the com-position of soil microbial communities may serve as important and sensitive indicators of both short and long-term changes in soil health. The analysis of soil microbial communities should involve not only deter-minations of microbial biomass and diversity, but also determinations of microbial growth, distribution, func-tion, and, if possible, the nature of interactions among species.
Two of the longstanding challenges in soil mi-crobiology have been the development of effective methods to (1) determine which microorganisms are present in soil and (2) determine microbial function in situ. These challenges have been exacerbated by the difficulties of separating microorganisms from the soil matrix and from plant tissues, the morphological similarities among many organisms found in soils, and changing microbial taxonomies. Furthermore, the microscopic size of soil microorganisms has made direct visualization more difficult than with macro-organisms.
Over the past 10 years, the approach to analyzing soil microbial communities has changed dramatically. Many new methods and approaches are now avail-able, allowing soil microbiologists to gain access to more of the microorganisms residing in soil and allowing for better assessments of microbial diver-sity. In this review, we briefly discuss some of the more important approaches for studying soil micro-bial communities, describing their strengths as well as their weaknesses. Our goal is to place the newer culture-independent methods in perspective with the traditional culture-based approaches for assessing microbial diversity.
2. Culture-dependent methods of community analysis
2.1. Dilution plating and culturing methods
Traditionally, the analysis of soil microbial com-munities has relied on culturing techniques using a variety of culture media designed to maximize the recovery of different microbial species. This is par-ticularly the case for soil health studies. There are numerous examples where these techniques have re-vealed a diversity of microorganisms associated with various soil quality parameters such as disease sup-pression and organic matter decomposition (Tunlid et al., 1989; Boehm et al., 1993, 1997; de Leij et al., 1993; Workneh et al., 1993; Alvarez et al., 1995; Hu and van Bruggen, 1997; Maloney et al., 1997). Al-though there have been recent attempts to devise suites of culture media to maximize the recovery of diverse microbial groups from soils (Balestra and Misaghi, 1997; Mitsui et al., 1997), it has been estimated that less than 0.1% of the microorganisms found in typical agricultural soils are culturable using current culture media formulations (Torsvik et al., 1990a; Atlas and Bartha, 1998). This is based on comparisons between direct microscopic counts of microbes in soil samples and recoverable colony forming units.
2.2. Community-level physiological profiles
One of the more widely used culture-dependent methods for analyzing soil microbial communities has been that of community-level physiological pro-files (Garland and Mills, 1991; Winding, 1994; Zak et al., 1994; Konopka et al., 1998). This technique takes advantage of the traditional methods of bacterial taxonomy in which bacterial species are identified based on their utilization of different carbon sources. Community-level physiological profiles have been facilitated by the use of a commercial taxonomic system, known as the BIOLOG® system, which is currently available and has been used extensively for the analysis of soil microbial communities (Winding, 1994; Lehman et al., 1995; Garland, 1996b).
Utilization of each substrate is detected by the reduc-tion of a tetrazolium dye, which results in a color change that can be quantified spectrophotometrically. The pattern of substrates that are oxidized can be com-pared among different soil samples from a series of times or locations as an indication of differences in the physiological functions of microbial communities. Most commonly, multivariate statistical techniques are necessary to analyze the substrate utilization profile data (Hackett and Griffiths, 1997; Hitzl et al., 1997). For example, in these analyses, communities are con-sidered to be functionally similar if the utilization pro-file of the 95 different carbon sources from one com-munity clusters with that from another comcom-munity. If the profiles segregate, communities would be consid-ered functionally different. As such, community-level physiological profiles can be useful in assessing gross functional diversity (Zak et al., 1994; Garland, 1996b; Campbell et al., 1997).
There are a number of important considerations in the use of this method for community analysis. First, the density of the initial inoculum must be standard-ized because it affects the rate at which color develops in the wells and thus the time at which color develop-ment should be measured (Garland and Mills, 1991; Haack et al., 1995). Visible color will not develop within a well until the total number of cells able to
uti-lize that substrate reaches approximately 108cells/ml
(Haack et al., 1995). Because the number of cells directly inoculated into the wells may be well
be-low 108cells/ml, there can be a substantial lag phase
while the cells grow within the well. This may lead to false negatives if wells are read too soon. Inaccurate physiological profiles may also result if samples are dominated by only a few species capable of growing on particular substrates. Furthermore, the period of microbial growth within the well may also lead to competition effects which again may bias the substrate utilization profile (Haack et al., 1995). Perhaps the best way to standardize inoculum levels is to employ vital stains combined with epifluorescence microscopy as a means to quantify actively-respiring cells (Gar-land, 1996a). This way, a standard population of metabolically active cells can be introduced into each well.
A second methodological consideration is that an analysis of functional diversity is based on the assump-tion that color development in each well is solely a
function of the proportion of organisms present in the sample which are able to utilize a particular substrate (Garland, 1997). However, this may not be valid given that some strains may utilize certain substrates more efficiently than others in the guild, predominating in the well and resulting in proportions of strains that differ from the original sample (Smalla et al., 1998). Furthermore, the ability of different taxa in the sam-ple to utilize the same carbon sources is generally unknown.
A third problem is that the substrates found in com-mercially available BIOLOG® plates are not necessar-ily ecologically relevant and most likely do not reflect the diversity of substrates found in the environment (Konopka et al., 1998). This is supported by the recent study of Campbell et al. (1997) in which plant root exudate compounds were included as carbon sources in a functional analysis of nine upland grassland sites. The carbon sources most useful in differentiating the different sites were predominantly these plant root ex-udates. All of these compounds had particularly low utilization rates suggesting they were utilized by or-ganisms that were present in the soil in low numbers. Campbell et al. (1997) hypothesized that these com-pounds have greater differentiating ability both cause they are biochemically more diverse and be-cause they select for the more slow growing organ-isms that are usually present in the sample in smaller numbers.
Table 1
Common fatty acid signatures
Common bacterial signatures i15:0, a15:0, 15:0, 16:0, 16:1v5, 16:1v9, i17:0, a17:0, 17:0, 18:1v7t, 18:1v5, i19:0, a19:0
Aerobes 16:1v7, 16:1v7t, 18:1v7t
Anaerobes cy17:0, cy19:0
Sulfate-reducing bacteria 10Me16:0, i17:1v7, 17:1v6
Methane-oxidizing bacteria 16:1v8c, 16:1v8t, 16:1v5c, 18:1v8c, 18:1v8t, 18:1v6c
Barophilic/psychrophilic bacteria 20:5, 22:6
Cyanobacteria 18:2v6
Protozoa 20:3v6, 20:4v6
Fungi 18:1v9, 18:2v6, 18:3v6, 18:3v3
Actinobacteria 10Me18:0
Microalgae 16:3v3
Flavobacterium balustinum i17:1v7, Br 2OH-15:0
Bacillusspp. Various branched chain fatty acids
3. Culture-independent methods of community analysis
Because of the inherent limitations of culture-based methods, soil microbial ecologists are turning increas-ingly to culture-independent methods of community analysis. Using culture-independent methods, the composition of communities can be inferred based on (1) the extraction, quantification, and identifi-cation of molecules from soil that are specific to certain microorganisms or microbial groups; or (2) advanced fluorescence microscopic techniques. Use-ful molecules for such studies include phospholipid fatty acids and nucleic acids (Morgan and Winstanley, 1997) whereas the microscopic techniques involve either the hybridization of fluorescent-labeled nucleic acid probes with total RNA extracted from soils or hybridizations with cells in situ.
3.1. Phospholipid fatty acid analysis
Phospholipid fatty acid (PLFA) analysis has been used as a culture-independent method of assessing the structure of soil microbial communities and determin-ing gross changes that accompany soil disturbances such as cropping practices (Zelles et al., 1992, 1995), pollution (Frostegard et al., 1993), fumigation (Macal-ady et al., 1998), and changes in soil quality (Bardgett et al., 1996; Reichardt et al., 1997; Bossio et al., 1998; Petersen et al., 1998). Phospholipid fatty acids are potentially useful signature molecules due to their presence in all living cells. In microorganisms, phos-pholipids are found exclusively in cell membranes
and not in other parts of the cell as storage products. This is important because cell membranes are rapidly degraded and the component phospholipid fatty acids are rapidly metabolized following cell death. Con-sequently, phospholipids can serve as important in-dicators of active microbial biomass as opposed to non-living microbial biomass.
An essential consideration in the use of these molecules to describe microbial communities is that unique fatty acids are indicative of specific groups of organisms (Table 1). Our knowledge of such signature molecules comes from the use of fatty acid analysis for bacterial taxonomy, in which specific fatty acid methyl esters (FAMEs) have been used as an accepted taxonomic discriminator for species identification. Furthermore, phospholipid fatty acids are easily ex-tracted from microbial cells in soil (Tunlid and White, 1992; Zelles and Bai, 1993) allowing access to a greater proportion of the microbial community resi-dent in soil than would otherwise be accessed during culture-dependent methods of analysis.
The presence and abundance of these signature fatty acids in soil reveals the presence and abundance of particular organisms or groups of organisms in which those signatures can be found. For example, Tunlid et al. (1989) were able to use PLFA analysis to demon-strate differences in microbial communities associated with Rhizoctonia damping-off. They were further able to monitor the presence of the biological control
or-ganismFlavobacterium balustinumstrain 299 on
(i.e. those receiving no synthetic fertilizers and pes-ticides) gave rise to PLFA profiles that were signifi-cantly different from those from conventionally man-aged soils (i.e. those receiving synthetic fertilizers and pesticides). Profiles from organically-managed soils
were enriched with i14:0, a15:0, 16:1v7c, 16:1v5c,
14:0, and 18:2v6c fatty acids indicating a greater
di-versity of aerobic bacteria as well as populations of cyanobacteria and methane-oxidizing bacteria. These studies clearly demonstrate the utility of this method in determining gross community changes associated with soil management practices.
Despite the usefulness of this method, there are some important limitations (Haack et al., 1994). First, appropriate signature molecules are not known for all organisms in a soil sample and, in a number of cases, a specific fatty acid present in a soil sample cannot be linked with a specific microorganisms or group of mi-croorganisms. In general, the method cannot be used to characterize microorganisms to species. Second, since the method relies heavily on signature fatty acids to determine gross community structure, any variation in these signatures would give rise to false community estimates created by artifacts in the methods. Third, bacteria and fungi produce widely different amounts of PLFA and the types of fatty acids vary with growth conditions and environmental stresses. Although sig-nature PLFAs can be correlated with the presence of some groups of organisms, they may not necessarily be unique to only those groups under all conditions. Consequently, this could give rise to false community signatures.
3.2. Nucleic acid techniques
Of all the cell component molecules tested to date, nucleic acids have been the most useful in providing a new understanding of the structure of microbial com-munities. For example, in recent studies of soil micro-bial diversity, Torsvik and colleagues (Torsvik et al., 1990a,b, 1996; Ovreas and Torsvik, 1998) compared the re-association kinetics of DNA isolated from soil with that of pure cultures of microorganisms. They reasoned that the greater the sequence diversity of the DNA (and hence the microbial diversity), the greater the DNA reannealing time. Based on these studies, they estimated that the genetic diversity of soil was 200 times greater than the diversity among bacteria
cultured from the same soil. This indicates that soil microbial communities are much more complex than we currently recognize and that the analysis of DNA sequences may provide a greater understanding of the microbial diversity that exists in soil than could be gained from culture-dependent methods.
Of the various nucleic acid techniques used to esti-mate microbial community composition and diversity in complex habitats, the most useful is the determina-tion of the sequences of 16S ribosomal RNA (rRNA) genes (i.e. encoded by rDNA) in prokaryotes and 5S or 18S rRNA genes in eukaryotes (Ward et al., 1992). These small subunit (SSU) rDNA molecules are par-ticularly suited for such studies for a number of rea-sons. First, they are found universally in all three forms of life: the domains Bacteria, Archaea, and Eucarya (Woese et al., 1990). Second, these molecules are com-posed both of highly conserved regions and also of regions with considerable sequence variation (Woese, 1987). Because of these differential rates of sequence evolution, phylogenetic relationships at several hier-archical levels can be measured from comparative se-quence analyses. Third, the phylogenetic information held in the SSU rDNA molecule is further enhanced by
its relatively large size (e.g.∼1.5 kb for the 16S rDNA
molecule) and the presence of many secondary struc-tural domains. Consequently, evolutionary changes in one domain do not affect the rate of change in other domains. Finally, SSU rDNA can be easily amplified using polymerase chain reaction (PCR) and rapidly sequenced.
Perhaps the greatest advantage of the analysis of SSU rDNA is that is that microorganisms from natu-ral habitats can be studied and characterized without culturing. Various studies have shown that rDNA from over 90% of the microorganisms that can be observed microscopically in situ can be extracted and analyzed (Steffan and Atlas, 1988; Steffan et al., 1988; Tsai and Olsen, 1992; More et al., 1994; Zhou et al., 1996; Porteous et al., 1997) as compared with less than 0.1% of the microorganisms observed in soil that can be recovered on culture media.
Heuer and Smalla, 1997; Kuske et al., 1997; Smith et al., 1997; Duineveld et al., 1998; Grosskopf et al., 1998). In nearly all of these studies, novel microbial lineages have been discovered, confirming our lack of understanding of the microbial species that inhabit soils and their potentially important roles in ecosys-tem function. For example, studies have shown that agricultural soils contain a diversity of Archaea, or-ganisms previously thought to exist only in extreme environments (Ueda et al., 1995; Bintrim et al., 1997; Buckley et al., 1998). Other studies have shown that some soil microbes, which have previously not been cultured and described, are global in their distribution and may play important roles in soils worldwide (Lie-sack and Stackenbrandt, 1992; Felske et al., 1997; Kuske et al., 1997).
All DNA extraction techniques are based on meth-ods developed over the past 20 years. Once the micro-bial community rDNA is amplified from soil samples using PCR, individual amplicons must be separated prior to sequence analysis. Methods used most com-monly for the separation of individual amplicons have been standard cloning procedures using a variety of Escherichia colivectors.
Recently, as a complement to cloning procedures, the use of denaturing gradient and temperature gra-dient gel electrophoresis (DGGE/TGGE) for separat-ing individual amplicons has been described (Muyzer et al., 1993; Ferris and Ward, 1997; Heuer et al., 1997; Muyzer and Smalla, 1998). This technique allows one to separate mixtures of PCR products that are of the same length but differ only in sequence. The sepa-ration power of this technique rests with the melting behavior of the double stranded DNA molecule. As DNA molecules are electrophoresed in an increasing gradient of denaturant or in an increasing temperature gradient, it remains double-stranded until it reaches the denaturant concentration or temperature that melts the double-stranded molecule. As the DNA melts, it branches, thus reducing the mobility in the gel. Since the melting behavior is largely dictated by the nu-cleotide sequence, the separation will resolve individ-ual bands, each corresponding to a unique sequence. Theoretically, any SSU rRNA gene found in the mixed template DNA extracted from soils could be specifi-cally amplified and resolved on a DGGE gel.
Once rDNA amplicons have been cloned or sepa-rated by DGGE or TGGE, they can be sequenced and
analyzed for similarity to other known sequences in public-domain databases (e.g. the NCBI GeneBank database [http://www.ncbi.nlm.nih.gov], the Riboso-mal Database Project [http://www.cme.msu.edu/RDP/] (Maidak et al., 1997) and the Antwerp SSU rRNA database [http://rrna.uia.ac.be/] (Van de Peer et al., 1997)). By estimating phylogenetic relatedness to other sequences in the databases, the identity of the microorganism from which the SSU rRNA gene was derived can be determined. It is hoped that the potentially close phylogenetic relationships of non-culturable microorganisms with known species can be utilized to devise culturing techniques for many of these microorganisms.
In recent years, a number of analyses have focused on the characterization of soil microbial communities based on rRNA as opposed to rRNA genes encoded by rDNA (e.g. Felske and Akkermans, 1998b; Hahn et al., 1990; Moran et al., 1993; Felske et al., 1996; Purdy et al., 1996; Duarte et al., 1998). Like rDNA, rRNA has both conserved and highly variable regions that permit the discrimination of taxa at multiple tax-onomic levels. In addition, use of rRNA offers three principle advantages over rDNA techniques:
1. Because ribosomes are the sites of protein synthe-sis, cellular ribosome content (and thus rRNA con-tent) are directly correlated with metabolic activ-ity and growth rate (Wagner, 1994). Therefore, a high proportion of the rRNA sequences detected in soil samples should correspond to metabolically active and growing microorganisms (Felske et al., 1996). Results with rRNA can be readily compared with those for simultaneously-extracted DNA (e.g. Felske et al., 1996) to estimate both the dormant and metabolically-active community.
2. Because rRNA sequences are typically present in cells in higher copy number than rDNA sequences, they should be easier to detect (Moran et al., 1993). 3. When ribosomes are extracted directly from soil samples, free nucleic acids and many dormant mi-croorganisms are excluded and only rRNA from active cells is detected (Felske et al., 1997; Felske and Akkermans, 1998a).
in the soil community (Felske et al., 1998b). A com-plicating factor is that the number of rRNA operons is known to vary among taxonomic groups (Rosado et al., 1997), so that rRNA sequence heterogeneity can and does occur within cells of the same species. Con-sequently, each amplification product on a gel cannot be assumed to correspond to a different organism, and a single organism may be represented by several am-plification products.
A taxon may contribute rRNA to a soil community in two ways: (1) by being represented by many active cells; and (2) by being represented by cells containing many ribosomes (Felske et al., 1997). These scenarios are only reliably distinguishable by in situ hybridiza-tion (e.g. Binder and Liu, 1998). However, a compar-ison of probe signal intensity for rRNA and rDNA in DGGE or TGGE gels can provide evidence for which of the scenarios is more likely. For example, simi-lar signal intensities for rRNA and for rDNA suggest that activity is due to cell abundance rather than high ribosome copy number per cell (Felske et al., 1997).
Despite the usefulness of these nucleic acid tech-niques for characterizing soil microbial communities, there are a number of limitations. As with most tech-niques that measure metabolic activity, storage of samples prior to processing can bias results. Shifts in active functional groups of prokaryotes have been observed when samples are stored aerobically or left at room temperature (reviewed in van Winzingerode et al., 1997). However, presumably this bias could be removed easily by immediate processing or freezing. Another limitation is that comparisons of activity among organisms or soil samples may be confounded by several factors. Extraction efficiency differs among soils and microorganisms, so that apparent differences in activity of particular organisms across soil samples can be an artifact of the extraction procedure (Moran et al., 1993). Some prokaryotic cells are more easily lysed than others, so that incomplete lysis of some species could result in underestimates of activity (van Winzingerode et al., 1997). Sequence amplification and detection are only as good as the probes used; or-ganisms with different affinities for the probes used will differ in their apparent activities (Zheng et al., 1996). Amplification bias has also been shown to oc-cur for templates that differ substantially in abundance, with preferential amplification of more abundant se-quences (Suzuki and Giovannoni, 1996). However,
Felske and Akkermans (1998a) found no evidence of amplification bias for the universal bacterial primers they have used to amplify 16S rDNA. For a detailed review of possible sources of amplification bias, see van Winzingerode et al. (1997).
Another important limitation to this approach is that it has been applied largely to investigations of prokary-otes. Theoretically, detection of rRNA could be used to determine active eukaryotes, as well as prokary-otes, in soils. However, eukaryote ribosomes have not been well studied and their encoding genes and reg-ulation are far more complex. Furthermore, sequence representation in public databases is not as extensive, making identifications of known eukaryotes from soil samples problematic.
3.3. Phylogenetic analysis
The success of any of the preceding methods for community characterization relies on a suitable phy-logenetic analysis because many of the organisms that are likely to be described from soil communities have not been studied previously. A number of phylogenetic methods have been utilized in studies of microbial ecology (Woese, 1987). While rDNA and rRNA are commonly used as characters in phylogenetic analysis, the list of characters is extensive and can range from molecular to morphological traits (Olsen and Woese, 1993). For microorganisms, molecular data often pro-vide the greatest wealth of information because mi-croorganisms such as bacteria simply do not have the diversity of form to make morphological characteris-tics useful in establishing phylogenies.
interpreted with caution when used in soil microbial community analyses.
3.4. Fluorescent in situ hybridization (FISH)
Fluorescent in situ hybridization (FISH) has been used primarily with prokaryotic communities and allows the direct identification and quantification of specific and/or general taxonomic groups of microor-ganisms within their natural microhabitat (Amann et al., 1995; Assmus et al., 1995; MacNaughton et al., 1996; Kenzaka et al., 1998). In FISH, whole cells are fixed, their 16S or 23S rRNA is hybridized with fluorescently-labeled taxon-specific oligonucleotide probes, and then the labeled cells are viewed by scan-ning confocal laser microscopy (SCLM). Because whole cells are hybridized, artifacts arising from biases in DNA extraction, PCR amplification, and cloning are avoided (Ludwig et al., 1997; Wallner et al., 1997; Felske et al., 1998a). FISH has two ad-vantages over immunofluorescence techniques. First, FISH can detect microorganisms across all phyloge-netic levels, whereas immunofluorescence techniques are limited to the species and sub-species levels. Second, FISH is more sensitive than immunofluores-cence because non-specific binding to soil particles does not typically occur (Amann et al., 1995). FISH probes can be generated without prior isolation of the microorganism, whereas pure cultures are needed in immunofluorescence studies for generating specific antibodies (Hahn et al., 1992). Scanning confocal laser microscopy (SCLM) surpasses epifluorescence microscopy in sensitivity and has the ability to view the distribution of several taxonomic groups simulta-neously as a three-dimensional image (Assmus et al., 1995; Kirchhof et al., 1997). Use of distinctive flu-orescent dyes and corresponding filter sets allows the observer to differentiate fluorescing microbes from autofluorescent soil particles and plant debris (Assmus et al., 1995; MacNaughton et al., 1996). FISH provides a more accurate quantification of cells as compared to the rough estimates obtained from dot-blot assays (Amann et al., 1995) in which micro-bial DNA is blotted onto a membrane than probed with the fluorescent oligonucleotide probe.
The sensitivity of FISH has been greatly improved to afford the detection of single cells within com-plex environments such as rhizosphere and bulk soils
(Christensen and Poulsen, 1994; Fischer et al., 1995; MacNaughton et al., 1996; Zarda et al., 1997; Felske et al., 1998a). Strongly fluorescing dyes can be used or multiple probes can be designed to target different regions of the same 16S or 23S rRNA molecule, thus increasing the strength of the signal (Amann et al., 1995; Ludwig et al., 1997). Probes for kingdoms (Eu-bacteria, Archaea, Eucarya), families, genera, species, or sub-species can be differentially labeled and used in combination to view the occurrence and distribution of several taxonomic groups simultaneously within a single soil sample (Manz et al., 1992; Amann et al., 1995; Zarda et al., 1997). To be detected, soil mi-crobes must be metabolically active and possess cell walls sufficiently permeable to allow penetration of the probe (Christensen and Poulsen, 1994; Amann et al., 1995). Penetration of cells with such probes is a prob-lem in nutrient-poor soils and in soils where microor-ganisms are dormant or quiescent (Hahn et al., 1992; Fischer et al., 1995) because cells are generally smaller and cell walls relatively thicker under these condi-tions. However, progress is being made to overcome these problems with groups such as actinobacteria and
Bacillus spores (MacNaughton et al., 1994; Fischer
et al., 1995). To address the problem of low metabolic activity in soil, some researchers have added nutri-ents to stimulate microbial activity (Hahn et al., 1992). However, so as not to bias the community profile, the amendments should equally stimulate all members of the community.
FISH can be used to visualize soil microorgan-isms that have not yet been cultured, and is useful in studying the ecological distribution of microorgan-isms throughout diverse habitats (Ludwig et al., 1997; Zarda et al., 1997; Wullings et al., 1998). When using FISH to examine all members within a given taxon, one must keep in mind that the probe being used is only as good as the representative members that were used to generate it (Amann et al., 1995). Other, non-cultured organisms may not be detected with this probe or cross-hybridization to related organisms may occur (Hahn et al., 1992; MacNaughton et al., 1996; Felske et al., 1998a).
a complex microbial community (Amann and Kuhl, 1998). FISH is a powerful tool that can be used not only for studying individuals within a population, but also has potential uses for studying population dynamics, tracking microorganisms released into the environment (e.g. for biological control or biore-mediation), epidemiology, and microbial ecology of economically important plant pathogens in agricul-tural soils (Hahn et al., 1992; Kirchhof et al., 1997; Wullings et al., 1998).
4. Summary
A number of methods are currently available for studies on soil microbial communities. The use of molecular techniques for investigating microbial di-versity in soil communities continues to provide new understanding of the distribution and diver-sity of organisms in soil habitats. The use of SSU rRNA or rDNA sequences, combined with fluorescent oligonucleotide probes provides a powerful approach for studying soil microorganisms that may not be amenable to current culturing techniques.
Despite the utility of culture-independent tech-niques such as SSU rRNA or rDNA analyses, there remains a general need to cultivate microorganisms from soil habitats to better understand their role in soil processes. Future studies of soil microbial communi-ties must necessarily rely on a combination of both culture-dependent and culture-independent methods and approaches. Only then will we be able to develop a more complete picture of the contribution of spe-cific microbial communities to the overall quality and health of agricultural soils.
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