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Soil microbial diversity analysis using molecular approaches
R. Dinesh
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Introduction
Understanding the dynamics of soil microbial communities is atthe heart of contemporary microbial ecology, and understanding
of the soil microbial community is probably the most challenging
because of the exceptionally high microbial diversity in soil
(Borneman et al., 1996; Nsslein and Tiedje,1998)
Torsvik et al. (1990a, b) estimated that in 1 g of soil there are
4000 different bacterial genomic units based on DNADNAreassociation.
It has also been estimated that about 5000 bacterial species have
been described (Pace, 1999).
Approximately only 1% of the soil bacterial population can be
cultured by standard laboratory practices (van Elsas et al., 2000).
An estimated 1,500,000 species of fungi exist in the world (Giller
et al., 1997). But unlike bacteria, many fungi cannot be cultured
by current standard laboratory methods (Thorn, 1997; van Elsas
et al., 2000).
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It is important to study microbial diversity not only for basic
scientific research, but also to understand the link between
diversity and community structure and function.
Human influences such as pollution, agriculture and chemical
applications could adversely affect microbial diversity, and
perhaps also above and below-ground ecosystem functioning.
It is generally thought that a diverse population of organisms
will be more resilient to stress and more capable of adaptingwith environmental changes.
Bacterial and fungal diversity increases soil quality by affecting
soil agglomeration and increasing soil fertility.
They are both important in nutrient cycling and in enhancing
plant health through direct or indirect means.
In addition, a healthy rhizosphere population can help plants
deal with biotic and abiotic stresses such as pathogens,
drought and soil contamination.
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Approaches to study molecular microbial diversity
DNA reassociation
DNA-DNA and mRNA: DNA hybridization
DNA cloning and sequencing
PCR-based methods such as
Denaturing gradient gel electrophoresis (DGGE)
Temperature gradient gel electrophoresis (TGGE)
Single strand conformation polymorphism (SSCP)
Restriction fragment length polymorphism (RFLP)/amplified ribosomal DNA
restriction analysis (ARDRA)
Ribosomal intergenic spacer analysis (RISA)
Automated ribosomal intergenic spacer analysis (ARISA)
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G + C analysis
This technique is based on the fact that prokaryotic DNA varies in G + C contentfrom ~24% - 76% G + C vs. A + T, and that particular taxonomic groups only
include organisms that vary in G + C content by no more than 3 - 5%
(Vandamme et al., 1996). Hence, G + C can be related to taxonomy.
The G + C method fills a gap in the tool box by providing one of the few coarse -
level methods for microbial community analysis.
It is comprehensive for all DNA extracted;
it is not subject to the biases of PCR-based methods
Disadvantage:
This method requires a reasonably
large amount of DNA, e.g. 50 g,
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Hybridization can also be conducted at the
cellular level and can be done in situ.
Fluorescently labelled primers are addedand allowed to hybridize, excess is washed
away and the hybridized cells detected
(Head et al., 1998).
The method, known as fluorescent in situ
hybridization or FISH has been used
successfully to study the spatial distribution
of bacteria in biofilms (Schramm et al.,
1996).
One limitation of in situ hybridization or
hybridization of nucleic acids extracted
directly from environmental samples is the
lack of sensitivity.
Unless sequences are present in high copy
number, i.e. from dominant species, they
probably will not be detected.
Structures of different types of methanogenic granules
analyzed by scanning electron microscopy (upper part)
and confocal laser microscopy of fluorescent in situ-
hybridized granules (lower part). (A) Black granule. (B)
Gray granule. (C) Brown granule. FISH Eub338-fluorescein, green, universal probe forBacteria; Arch915-
Cy3, red, universal probe forArchaea (Daz et al. 2006).
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More recently, DNADNA hybridization has been
used together with DNA microarrays to detect andidentify bacterial species (Cho and Tiedje, 2001)
or to assess microbial diversity (Greene and
Voordouw, 2003).
This tool could be valuable in bacterial diversity
studies since a single array can containthousands of DNA sequences (Cho and Tiedje,
2001) with high specificity.
The microarray can either contain specific target
genes such as nitrate reductase, nitrogenase or
naphthalene dioxygenase to provide functionaldiversity information.
This method has been used to analyze microbial
communities in oil fields (Voordouw et al., 1993),
and in contaminated soils (Greene et al., 2000).
DNA microarrays
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Relative profiles for 25 Salmonella isolates. Black bands (or
probes) differentiate the isolate from the average of all otherisolates. These are probes for which the difference between
the isolate and the average of all other isolates issignificantly larger than 0 ( = 0.01). Probes falling into the
high intensity group from the ANOVA-based clustering are
shaded gray (if they have not already been shaded black).
This signature is useful for making relative comparisons, but
it is not the signature used for classification. (B) REPPCR
agarose gel fingerprints from the same 25 isolates (Willse et
al. 2004)
Advantages:
It is not confounded by PCR
biases and microarrays cancontain thousands of target gene
sequences.
Disadvantages:
It only detects the most abundant
species.
The diversity has to be minimal,
otherwise cross-hybridization
can become problematic
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PCR targeting the 16S rDNA has been used extensively to study
prokaryote diversity and allows identification of prokaryotes aswell as the prediction of phylogenetic relationships (Pace, 1996,
1997, 1999).
18S rDNA and internal transcribed spacer (ITS) regions are
increasingly used to study fungal communities.
Although sequencing has become routine, sequencing thousands
of clones is cumbersome (Tiedje et al., 1999).
Therefore, many other techniques have been developed to assess
microbial community diversity.
In these methods, DNA is extracted from the environmental
sample and purified.
Target DNA (16S, 18S or ITS) is amplified using universal or
specific primers and the resulting products are separated in
different ways.
PCR-based approaches
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1. Denaturing gradient gel electrophoresis (DGGE)/temperature gradient gel
electrophoresis (TGGE)
DNA is extracted from soil samples and amplified using PCR with universal primers targeting part ofthe 16S or 18S rRNA sequences.
The 5V-end of the forward primer contains a 3540 base pair GC clamp to ensure that at least part of
the DNA remains double stranded.
This is necessary so that separation on a polyacrylamide gel with a gradient of increasingconcentration of denaturants (formamide and urea) will occur based on melting behaviour of the
double-stranded DNA.
If the GC-clamp is absent, the DNA would denature into single strands.
On denaturation, DNA melts in domains, which are sequence specific and will migrate differentially
through the polyacrylamide gel (Muyzer, 1999).
Theoretically, DGGE can separate DNA with one base-pair difference (Miller et al., 1999).
TGGE uses the same principle as DGGE except the gradient is temperature rather than chemical
denaturants. DGGE/TGGE have the advantages of being reliable, reproducible, rapid and
somewhat inexpensive.
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Specific DGGE/TGGE bands can also be
excised from gels, re-amplified and
sequenced or transferred to membranes and
hybridized with specific primers to provide
more structural or functional diversityinformation (Theron and Cloete, 2000).
By sequencing bands, one can obtain
information about the specific
microorganisms in the community.
Multiple samples can also be analyzed
concurrently, making it possible to follow
changes in microbial populations (Muyzer,1999).
Typical DGGE profiles of 16S rRNA genes amplified from DNA
extracted from rhizoplanes of each of the plants colonizing the
study site (Nunan et al. 2005).
Limitations of DGGE/ TGGE include
PCR biases (Wintzingerode et al., 1997),
Laborious sample handling, as this could
potentially influence the microbialcommunity, (Muyzer, 1999; Theron and
Cloete, 2000)
Variable DNA extraction efficiency (Theron
and Cloete, 2000).
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2. Single strand conformation polymorphism (SSCP)
Another technique that relies on electrophoretic separation based on differences inDNA sequences is single strand conformation polymorphism (SSCP).
Like DGGE/TGGE, this technique was originally developed to detect known or novelpolymorphisms or point mutations in DNA (Orita et al., 1989).
Single-stranded DNA is separated on a polyacrylamide gel based on differences inmobility caused by their folded secondary structure (Lee et al., 1996).
When DNA fragments are of equal size and no denaturant is present, folding andhence mobility, will be dependent on the DNA sequences.
SSCP has all the same limitations of DGGE
SSCP has been used to measure succession ofbacterial communities (Peters et al., 2000),
rhizosphere communities (Schmalenberger etal., 2001), bacterial population changes in ananaerobic bioreactor (Zumstein et al., 2000) andAMF species in roots (Kjoller and Rosendahl,2000).
SSCP analysis of Clostridium spp after PCR
amplification of the bp 1175 to 1390 fragment
of the 16s rRNA(Widjojoatmodjo et al. 2004)
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3. Restriction fragment length polymorphism (RFLP)/amplified ribosomal DNA restriction
analysis (ARDRA)
This is another tool used to study microbial diversity that relies on DNA polymorphisms.
PCR amplified rDNA is digested with a 4-base pair cutting restriction enzyme.
Different fragment lengths are detected using agarose or non-denaturing polyacrylamide gelelectrophoresis in the case of community analysis (Liu et al., 1997; Tiedje et al., 1999).
RFLP banding patterns can be used to screen clones (Pace, 1996) or used to measure
bacterial community structure (Massol-Deya et al., 1995).
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This method is useful for detecting structural changes inmicrobial communities but not as a measure of diversity
or detection of specific phylogenetic groups (Liu et al.,
1997).
The major drawback is that the banding patterns in
diverse communities become too complex to analyze
using RFLP since a single species could have four to sixrestriction fragments (Tiedje et al., 1999).
Gel electrophoresis showing length polymorphism of PCR-amplified 16S-23SrDNA ITS regions from fluorescent Pseudomonas genotypes (A) and restrictionpatterns of PCR-amplified 16S-23S rDNA ITS regions digested with DdeI (B),HaeIII (C), and HhaI (D). Lane S, 100-bp DNA size marker (Gibco-BRL); lanes1, 2, 3, 4, 5, 6, 7, and 8, genotypes 19, 20, 11, 12, 4, 28, 27, and 78, respectively
(Cho et al. 2000).
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Terminal restriction fragment length polymorphism(T-RFLP) is a technique that addresses some of the
limitations of RFLP (Tiedje et al., 1999).
It follows the same principle as RFLP except that
one PCR primer is labeled with a fluorescent dye,
such as TET (4,7,2V,7V-tetrachloro-6-
carboxyfluorescein) or 6-FAM (phosphoramidite
fluorochrome 5-carboxyfluorescein).
This allows detection of only the labeled terminal
restriction fragment (Liu et al., 1997).
4. Terminal restriction fragment length polymorphism (T-RFLP)
Electrophoretic gel images generated by T-RFLP and RT-T-RFLP (Rogers et
al. 2005).
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This simplifies the banding pattern, thus allowing
the analysis of complex communities as well as
providing information on diversity as each visible
band represents a single operational taxonomicunit or ribotype (Tiedje et al., 1999).
The banding pattern can be used to measure
species richness and evenness as well as
similarities between samples (Liu et al., 1997).
This procedure can be automated to allow
sampling and analysis of a large number of soil
samples (Osborn et al., 2000).
T-RFLP has been used to measure spatial and
temporal changes in bacterial communities (Lukow
et al., 2000), to study complex bacterialcommunities (Moeseneder et al., 1999) and to
detect and monitor populations (Tiedje et al.,
1999). Dendrogram constructed using T-RFLP andRT-T-RFLP profiles generated from the
sample set (Rogers et al. 2005).
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5. Ribosomal intergenic spacer analysis (RISA)/ automated ribosomal intergenic spacer
analysis (ARISA)
RISA and ARISA provide ribosomal-based fingerprinting of the microbial community.
In RISA and ARISA, the intergenic spacer (IGS) region between the 16S and 23S ribosomal
subunits is amplified by PCR, denatured and separated on a polyacrlyamide gel underdenaturing conditions.
This region may encode tRNAs and is useful for differentiating between bacterial strains and
closely related species because of heterogeneity of the IGS length and sequence (Fisher and
Triplett, 1999).
In RISA, the sequence polymorphisms are detected using silver stain while in ARISA the
forward primer is fluorescently labeled and is automatically detected.
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Partial ARISA profiles of the bacterial communities inCrystal Bog Lake. (A) Red and black electropherograms
representing samples collected from depths of 1 and 2 m,
respectively, in June 1998. (B) Red and black profiles
representing samples collected from a depth of 1 m in
September and June 1998, respectively (Triplett et al. 2003).
Both methods provide highly reproducible
bacterial community profiles but RISA requires
large quantities of DNA, is more time consuming,
silver staining is somewhat insensitive andresolution tends to be low (Fisher and Triplett,
1999).
ARISA increases the sensitivity of the method and
reduces the time but is still subject to the
traditional limitations of PCR (Fisher and Triplett,
1999).
RISA has been used to compare microbial
diversity in soil (Borneman and Triplett, 1997), in
the rhizosphere of plants (Borneman and Triplett,
1997), in contaminated soil (Ranjard et al., 2000)
and in response to inoculation (Yu and Mohn,
2001).
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Future perspectives
Given the current state of knowledge, the best
way to study soil microbial diversity would be to
use a variety of tests with different endpoints anddegrees of resolution to obtain the broadest
picture possible and the most information
regarding the microbial community.
In addition, methods to understand the link
between structural diversity and functioning of
below- and above-ground ecosystems need to
be developed so that the question of how
diversity influences function can be addressed.
Our knowledge of plantmicrobesoil interactions
is increasing, but the complexity of interacting
biological, chemical and physical factors means
that much remains to be understood.
DGGE gels of the 5 end of 18S rDNA amplified with fungus-specific primers and run on a 20 to 55%
gradient gel. Amplification products from pure cultures of aquatic hyphomycetes were run as standards.
Bands: 1, Anguillospora furtiva; 2, H. lugdunensis; 3, T. marchalianum; 4, Articulospora tetracladia; and 5,
T. aquatica (Nikolcheva et al. 2003).
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The major problem is the difficulty in
comprehensive recovery of DNA that was
sufficiently pure for reliable PCR, cloning,hybridization, and restriction analysis.
This problem has now largely been solved.
Now, the rate of discovery of new knowledge
using molecular techniques is growing rapidly
and the rate is expected to continue to
increase because of the further
improvement of methods particularly
tuned for environmental work and theextent of the unknown character of the
soil community
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Berkeley Labs PhyloChip Enables Tracking of Bacterial Dangers
Researchers at Lawrence Berkeley
National Laboratory (LBNL) are able to
accurately and quickly test for over
8,000 bacterial species with a device
that fits into a persons hand a
microbial detection power previously
unknown.
This new technology, called PhyloChip, enables
scientists to study bacterial communities, their
interactions, and how they change over time. This
capability is important because deep, sudden changes
in the structure of a bacterial community could
represent dangers in the form of an airborne biological
terrorist attack, an epidemic caused by contaminated
water or soil, or hazardous atmospheric alterations
caused by climate change.
Recently, LBNLs PhyloChip was tested by
cataloging the bacteria in air samples taken
from San Antonio and Austin, Texas. Over
1,800 types of bacteria were found! Before this
study, no one comprehended the diversity of
airborne microbes. By identifying microbial
communities typically inhaled by inhabitants
of large U.S. cities, PhyloChip can help monitor
air quality. The bacterial census from this study
will help the Department of Homeland
Security differentiate between normal and
suspicious fluctuations in airborne microbes.
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Invented by Gary Andersen, Todd DeSantis,
and colleagues at LBNL, PhyloChip is a DNA
microarray unique in its ability to identifymultiple bacterial species and organisms
from complex microbial samples. Because
PhyloChip produces results within hours,
numerous samplings of a specific
environment can be conducted on a daily
basis, enabling scientists to track the
progress of a certain microorganism over ashort period of time.
Gary Andersen, Todd DeSantis,Eoin Brodie, and Yvette Piceno
Formerly, microbiologists have relied on bacterial cultures to identify the microbes present inan environmental or medical sample, but most organismsup to 99% of the bacteria in a
sampledont survive in a culture. PhyloChip is a much more rapid, comprehensive, and
accurate means for sample testing without culturing. As reported in the Journal of Clinical
Microbiology (2007), PhyloChip was key to discovering that a loss of bacterial diversity due to
antibiotic treatments was directly associated with the development of pneumonia in
ventilated patients exposed to a certain common strain of bacteria.
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APPLICATIONS OF PHYLOCHIP TECHNOLOGY:
Monitoring airborne bacteria for bioterrorism surveillance
Tracking microbial changes in the atmosphere due to climate change
Detecting harmful and beneficial organisms in water or soil samples
Monitoring bacterial populations during environmental bioremediation
Identifying bacteria and archaea in medical samples without culturing
Enabling source tracking of pathogens by public health agencies
ADVANTAGES:
Quickly and accurately identifies microbes in complex samples
Analyzes microbial DNA samples from any environmental source
Simultaneously detects most known microorganisms (>8000 strains tested in
parallel)Offers the option to analyze the rRNA to determine the most metabolically
active organisms in a sample
Detects low-abundance organisms that would be missed by conventional
culturing or sequencing
Identifies microorganisms most responsive to changes in environmental
parameters
Reduces the chances of misidentifying a specific microorganism
phylochip.com/biodiversity.html
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thank you
Acknowledgements:
To Google for the images used in this presentation