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Soil is a complex, dynamic and living habitat for a large number of organisms.

It shelters the most diverse biological communities on the planet, including a myriad of

micro flora and micro fauna, meso fauna and macro fauna as well as plant roots. These

include representatives of all groups of micro-organisms, algae and nearly all animal

phyla. Estimates of the number of species of some group include bacteria (30,000),

fungi (1,500,000), algae (60,000), protozoa (10,000), nematodes (500,000), and

earthworms (3000). One gram of soil may contain 109 bacteria, 107 Actinomycetes, 106

fungi, 104 algae and 105 protozoa (Pankhurst et al, 1997).

2.1 CLASSIFICATION OF SOIL FAUNA

The easiest and most widely used system for classifying soil organisms is by

using body size and dividing them into three main groups: macro, meso and micro biota

(Wallwork, 1970; Swift et al, 1979).

2.1.1 Macro biota

It comprises of organisms, generally more than 2mm in diameter and visible to

the naked eyes. These include vertebrates (snakes, lizards, mice, rabbits, foxes, badgers,

moles and others) that primarily dig within the soil for food or shelter, and invertebrates

that live in, feed in or upon the soil, the surface litter and their components (ants,

termites, millipedes, centipedes, earthworms, pillbugs and other crustaceans, caterpillars,

cicadas, ant lions, beetle larvae and adults, fly and wasp larvae, earwigs, silverfishes,

snails, spiders, harvestmen, scorpions, crickets and cockroaches).

2.1.2 Meso biota

It comprises of organisms generally ranging in size from 0.2–2mm in diameter.

These include mainly micro-arthropods, such as pseudo-scorpions, protura, diplura,

springtails, mites, small myriapods (pauropoda and symphyla) and the worm-like

enchytraeids.

2.1.3 Micro biota

It comprises of organisms measuring less than 0.2mm diameter. They are

extremely abundant, ubiquitous and diverse in soil. The micro flora includes algae,


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bacteria, archaea, Cyanobacteria, fungi, yeasts, Myxomycetes and Actinomycetes that

are able to decompose almost any existing natural material. The micro fauna includes

nematodes, protozoa, turbellarians, tardigrades and rotifers that generally live in the soil

water films and feed on micro flora, plant roots, other micro fauna and sometimes larger

organisms (e.g., entomopathogenic nematodes feed on insects and other larger

invertebrates).

2.2 ROLE OF SOIL ORGANISMS

Bacteria and fungi perform one of the major nutrient cycling processes in soil

(Coleman et al, 1992). Bacteria are responsible for most specific transformations in the

Nitrogen cycle. Role of bacteria in Phosphorous cycling is less specialized. Biological

transformations of sulphur are undertaken only by a few genera of bacteria. Fungi are a

major component of soil microbial biomass. Fungi have prominent role in plant litter

decomposition, and have a broad versatility in their metabolism. When the bacterial or

fungal component of the soil declines, more nutrients are lost into the ground and

surface water (Coleman et al, 1992). As climatic changes occur, bacterial populations in

the soil could be significantly impacted (Coleman et al, 1992).

Protozoa and nematodes feed on fungus and bacteria, so their contribution to

nutrient cycling is by their feeding on and assimilation of microbial tissue and excretion

of mineral nutrients. Micro-arthropods can be bacterivorous, fungivorous, predatory

(feeding on other fauna) or omnivorous, thus making a complete understanding of their

environment difficult. However, generally, micro-arthropods enhance nutrient

mineralization by feeding on micro flora and fauna. Collembolans are among the most

abundant soil arthropods and play an important role in the food webs (Butcher et al, 1971;

Petersen and Luxton, 1982; Petersen, 2002). Collembolans are known to be food

generalists (Hopkin, 1997; Scheu and Folger, 2004). The diet of most species is

composed of a mixture of detritus, algae, bacteria and fungi, and varies with season

(Wolters, 1985). Due to their feeding activity, collembolans affect decomposition

processes and the micro-structure of the soil (Seastedt, 1984; Cragg and Bardgett, 2001).

Nematodes are recognized as a major consumer group in soils, generally grouped

into four to five trophic categories based on the nature of their food, the structure of the


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stoma and oesophagus and methods of feeding (Yeates et al, 1993). Nematodes and

protozoa function as regulators of mineralization processes in soil (Coleman, 1985).

Bacterial and fungal feeding nematodes release a large percent of nitrogen (N) when

feeding on their prey groups and are thus responsible for much of the plant available N in

the majority of soils (Ingham et al, 1985). Nematode-feeding also selects for certain

species of bacteria, fungi and nematodes and thereby influences soil structure, carbon

utilization rates, and the types of substrates present in soil (Ingham, 1992).

2.3 SOIL MICRO-ORGANISMS

Soil micro-organisms are the most abundant of all the biota in soil and are

responsible for driving nutrients and organic matter cycling, soil fertility, soil

restoration, plant health and ecosystem primary production. Beneficial micro-organisms

include those that create symbiotic associations with plant roots (rhizobia, mycorrhizal

fungi, Actinomycetes, diazotrophic bacteria), promote nutrient mineralization and

availability, produce plant growth hormones, and are antagonists of plant pests,

parasites or diseases (bio-control agents). Commonly occurring microbes inhabiting soil

are bacteria, Azotobacter, Actinomycetes, fungi etc.

Fungi though lesser in number, form the major part of the microbial biomass.

Fungi may be divided into three groups: Yeasts, Moulds and Mushrooms. Only the last

two groups are considered important in soil, yeasts are rare in soil habitat. Fungi are

most versatile in decomposing organic residues.

Soil bacteria extensively participate in all the vital organic transactions to

support the higher forms of life. They occupy a significant position in the global cycling

of the nutrients. The unique metabolic feature of bacteria includes anaerobic respiration,

chemolithotrophic growth, nitrogen fixation and utilization of methane (Schlegel and

Jannasch, 1981). Bacteria are an important part of the soil micro flora because of their

abundance (up to 109 cells per gram of soil), their species diversity (at least 4000 to

7000 genomes per gram soil) (Torsvik et al, 1990a). They also have a potential to

reflect the past history of a given environment.

Azotobacter are free living Nitrogen fixers in soil and are very common in the

rhizosphere region of plants. They maintain themselves on root exudates. Besides


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nitrogen fixation, Azotobacter are also useful to the host plants through the production

of gibberellins and possibly other growth hormones, which results in an increased crop

yield (Brown, 1974). These bacteria not only fix nitrogen but also modify the shape and

increase the number of root hairs, helping the plants to acquire more nutrients.

2.4 SOIL HEALTH

Soil quality or soil health is defined as the, “continued capacity of soil to

function as a vital living system, within ecosystem and land use boundaries, to sustain

biological productivity, maintain environmental quality and promote plant, animal, and

human health (Doran et al, 1997). Quality is represented by a suite of physical,

chemical and biological properties that together: 1) provide a medium for plant growth

and biological activity, 2) regulate and partition water flow and storage in the

environment; and 3) serve as an environment buffer in the formation and destruction of

environmentally hazardous compounds (Doran, 1994).

Soil functions include life support processes, i.e. plant anchorage and nutrient

supply, water retention and conductivity, support of soil food webs, and environmental

regulatory functions, such as nutrient cycling, source of microbial diversity, remediation

of pollutants, and sequestration of heavy metals (Bezdicek, 1996). Soil health can be

considered a subset of ecosystem health. A healthy ecosystem is characterized by

integrity of nutrient cycles and energy flows, stability, and resilience to disturbance or

stress. Thus, soil health may be associated with biological diversity and stability. The

concept of soil health and soil quality has consistently evolved with an increase in the

understanding of soil and its quality attributes. There is need to study soil health so

there were many trends that were applied to study soil health.

Soil quality cannot be measured directly, but soil properties that are sensitive to

changes in management can be used as indicators. Biomarkers/ bio-indicator should

show a great degree of sensitivity to changes in the environment encompassing not only

soil physical and chemical factors but also microbial, biochemical and molecular

attributes. Biomarkers/ bio-indicators should show certain degree of stability by not

getting drastically affected, which leads to its eventual removal from the ecosystem.


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2.4.1 Soil health indicators

According to USDA soil quality indicators are classified into four categories

that include visual, physical, chemical and biological indicators.

2.4.1.1 Visual indicators

It can be obtained through field visits, perception of farmers, and local

knowledge. These are identified through observation or photographic interpretation,

subsoil exposure, erosion, presence of weeds, color, type of coverage which gives idea

whether the soil quality has been affected positively or negatively (USDA Definition).

2.4.1.2 Physical indicators

They are related to the organization of the particles and pores, reflecting effects

on root growth, speed of plant emergence and water infiltration; they include depth,

bulk density, porosity, aggregate stability, texture and compaction.

2.4.1.3 Chemical indicators

It includes pH, salinity, organic matter content, phosphorus availability, cation

exchange capacity, nutrient cycling, and the presence of contaminants such as heavy

metals, organic compounds, radioactive substances etc. These indicators determine the

presence of soil-plant-related organisms, nutrient availability, water for plants and other

organisms and mobility of contaminants.

2.4.1.4 Biological indicators

It includes measurements of micro-organisms and macro-organisms, their

activities or functions. Concentration or population of earthworms, nematodes, termites,

ants, as well as microbial biomass, fungi, actinomycetes, or lichens can be used as

indicators, because of their role in soil development and conservation, nutrient cycling

and specific soil fertility (Anderson, 2003). Biological indicators also include metabolic

processes such as respiration, chemical compounds or metabolic products of organisms,

particularly enzymes such as cellulases, arylsulfatase, phosphatases, related to specific

functions of substrate degradation or mineralization of organic nitrogen, sulfur or


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phosphorous. Soil enzymatic activity assays act as potential indicators of ecosystem

quality being operationally practical, sensitive, integrative, described as "biological

fingerprints" of past soil management, and relate to soil tillage and structure (Dick,

2000).

According to different authors (Doran, 2000; Cantu et al, 2007), indicators

should be limited and manageable in number by different types of users, simple and

easy to measure, cover the largest possible situations (soil types), including temporal

variation, and be highly sensitive to environmental changes and soil management (Dick,

2000). Several bioindicators of soil health and quality have been developed and

reviewed (Nielsen et al, 2002; Anderson, 2003).

2.4.2 Soil organic matter

Soil organic matter (SOM) is primarily plant residues, in different stages of

decomposition. Soil organic carbon (SOC) is a soil property considered one of the most

important indicators of soil quality; it has positive effects on soil physical properties and

promotes water infiltration, storage and drainage (Magdoff et al, 2004; Kowaljow,

2007). It is directly related to the maintenance of soil structure, presence of different

groups of micro-organisms, mineralization of organic matter, and nutrient availability.

However, SOM content varies with changes in climate, soil and crop

management, being higher in places with larger average annual precipitation, lower mean

annual temperature, and higher clay content (Nichols, 1984; Burke and Cole, 1995).

2.4.3 Soil respiration

Parameter of soil respiration is strongly affected by physiological state of micro-

organisms and nutrient availability. It depends on the physical and chemical properties

like temperature, soil moisture, density and pH. Nonetheless, respiration is considered

as sensitive soil microbial parameter. Determination of soil aerobic respiration is based

on measurement of soil CO2 release or O2 consumption.

The soil respiration without addition of any substrate or nutrients i.e. basal

respiration (BR) is elementary parameter of usual soil microbial assessment and


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monitoring. BR is limited by available organic matter. Decrease of respiration activity

could indicate decrease of decomposition by toxic compounds. On the other hand, some

organic chemicals could cause increase of BR (Eisentraeger et al, 2000).

2.4.4 Enzyme activity

Soil enzymes play biochemical functions in the overall process of organic

matter decomposition in the system; they are important in catalyzing several reactions,

necessary for the life processes of micro-organisms in soils, the stabilization of soil

structure, the decomposition of organic wastes, organic matter formation, and nutrient

cycling, providing an early indication of the history of a soil and its changes in

agricultural management (Ebersberger et al, 2003; Kandeler et al, 2006). Thus, they

have been studied as indicators of soil quality from the decade of the 80’s. Enzyme

activities have been associated with indicators of biogeochemical cycles, degradation of

organic matter and soil remediation processes, so they can determine the quality of a

soil (Gelsomino, 2006). Authors such as Dick (1996), Nielsen and Winding (2001), and

Eldor (2007), report enzymes as good indicators because: a) they are closely related to

organic matter, physical characteristics, microbial activity and biomass in the soil, b)

provide early information about changes in quality, and are more rapidly assessed.

2.4.4.1 Dehydrogenase

The dehydrogenase enzyme activity is commonly used as an indicator of

biological activity in soils. This enzyme is considered to exist as an integral part of

intact cells but does not accumulate extra cellularly in the soil. Dehydrogenase enzyme

is known to oxidize soil organic matter by transferring protons and electrons from

substrates to acceptors. With regard to soil air-water relationships, studies have shown

that dehydrogenase enzyme was greater in flooded compared to non-flooded soil

(Dkhar and Mishra, 1983; Baruah and Mishra, 1984; Benckiser et al, 1984; Tiwari et al,

1989). The increase in this enzyme after flooding was also related to decreased redox

potential (Okazaki et al, 1983; Pedrazzini and McKee, 1984). A study by Brzezinska et

al, (1998) suggested that soil water content and temperature influence dehydrogenase

activity indirectly by affecting the soil redox status. Additionally, dehydrogenase


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enzyme is often used as a measure of any disruption caused by pesticides, trace

elements or management practices to the soil (Wilke, 1991; Frank and Malkomes,

1993), as well as a direct measure of soil microbial activity (Skujins, 1978; Trevors,

1984; Garcia and Hernandez, 1997). It can also indicate the type and significance of

pollution in soils. For example, dehydrogenase enzyme is high in soils polluted with

pulp and paper mill effluents (McCarthy et al, 1994) but low in soils polluted with fly

ash (Pitchel and Hayes, 1990). Similarly, higher activities of dehydrogenases have been

reported at low doses of pesticides, and lower activities of the enzyme at higher doses

of pesticides (Baruah and Mishra, 1986).

2.4.4.2 Urease

These enzymes are involved in urea hydrolysis into CO2 and NH3 and

consequently with soil pH increase (Andrews et al, 1989; Byrnes and Amberger, 1989)

and N losses by NH3 volatilization (Fillery et al, 1984; Simpson et al, 1984, 1985,

Simpson and Freney, 1988). Due to the role of urea as a fertilizer, focus has been placed

on urease in order to evaluate N supply to plants, however, fertilization practices have

been reported as being very inefficient due to large N losses to the atmosphere by

volatilization mediated by these enzymes (Makoi and Ndakidemi, 2008). Urease has

been widely used to evaluate changes on soil quality related to management, since its

activity increases with organic fertilization and decreases with soil tillage (Saviozzi et

al, 2001). Urease is an extracellular enzyme representing up to 63 percent of total

activity in soil. It has been shown that its activity depends on microbial community,

physical, and chemical properties of soil (Corstanje et al, 2007) and its stability is

affected by several factors: organo-mineral complexes and humic substances make

them resistant to denaturing agents such as heat and proteolytic attack (Makoi and

Ndakidemi, 2008). Urease activity is used as a soil quality indicator because it is

influenced by soil factors such as cropping history, organic matter content, soil depth,

management practices, heavy metals and environmental factors like temperature and pH

(Tabatabai, 1977; Yang, 2006). The understanding of urease activity should provide

better ways to manage urea fertilizer, especially in warm high rainfall areas, flooded

soils and irrigated conditions (Makoi and Ndakidemi, 2008).


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2.5 METAGENOMICS

Most of our knowledge of microbiology is derived from cultured micro-

organisms. A new approach in microbiology, known as metagenomics, environmental

genomics, or community genomics, has been developed to access information about the

biology of the uncultured majority of micro-organisms. Metagenomics is the culture-

independent analysis of the metagenome, which refers to the collective genomes of the

organisms in an environment. The general approach is to extract DNA directly from an

environmental sample, clone the DNA into a plasmid vector, and introduce the cloned

DNA into a cultivable host.

2.6 SOIL BIO-DIVERSITY

In classical terms, bio-diversity is described as a function of two components: (i)

the total number of species present, i.e. specie richness or specie abundance; and (ii) the

distribution of individuals among those species, i.e. specie evenness or specie

equitability.

From molecular point of view, diversity often refers to the number of different

sequence types present in a habitat (Borneman et al, 1996; Dunbar et al, 1999; Ogram,

2000). Measuring this type of diversity can give valuable information about changes in

the community structure and species richness in response to changes in the physio-

chemical properties of soil, soil management practices and soil pollution. Therefore,

soil bio-diversity, including microbial diversity, could be used as an indicator of soil

quality (Pankhrust et al, 1997). Many Usher et al (1979), Fitter (1985), Usher (1985)

and Wardle (2002) have proposed that a better understanding of soil ecology and bio-

diversity would present new ecological theories.

2.7 APPROACHES TO MEASURE SOIL BIO-DIVERSITY

Methods to measure microbial diversity in soil can be categorized into two

groups: biochemical-based techniques and molecular-based techniques.


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2.7.1 Biochemical based techniques

2.7.1.1 Sole carbon source utilization patterns/community level physiological profiling

One of the more widely used culture dependent methods for analyzing soil

microbial communities has been that of community level physiological profiling

(CLPP) (Garland and Mills, 1991; Zak et al, 1994; Konopka et al, 1998). This technique

takes advantage of traditional methods of bacterial taxonomy in which bacterial species

are identified based on their utilization of different carbon sources. CLPP has 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 (Hill et al, 2000). Utilization of each substrate is detected by the reduction

of tetrazolium dye, which results in a colour change that can be quantified

spectrophotometrically.

This method has been used successfully to assess potential metabolic diversity

of microbial communities in contaminated sites (Derry et al, 1998; Konopka et al,

1998), plant rhizospheres (Ellis et al, 1995; Garland, 1996a; Grayston and Campbell,

1996; Grayston et al, 1998), arctic soils (Derry et al, 1999), soil treated with herbicides

(el Fantroussi et al, 1999) or inoculation of micro-organisms (Bej et al, 1991).

CLPPs can differentiate between microbial communities, are relatively easy to

use, reproducible and produce a large amount of data reflecting metabolic

characteristics of the communities (Zak et al, 1994). Limitations of metabolic profiling

are: the methods select for only cultivable micro-organisms capable of growing under

the experimental conditions (Garland and Mills, 1991), favours fast growing micro-

organisms (Yao et al, 2000), is sensitive to inoculums density (Garland, 1996) and

reflects the potential, and not the in situ, metabolic diversity (Garland and Mills, 1991).

2.7.1.2 Plate count

Traditionally, diversity was assessed using selective plating and direct viable

counts. These methods are fast, inexpensive and can provide information on the active,

heterotrophic component of the population. Limitations include the difficulty in

dislodging bacteria or spores from soil particles or bio-films, growth medium selections


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(Tabacchioni et al, 2000), growth conditions (temperature, pH, light), the inability to

culture a large number of bacterial and fungal species with current techniques and the

potential for colony–colony inhibition or of colony spreading (Trevors, 1998). Although

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 0.1-10 percent of the micro-organisms

found in typical agricultural soils are cultivable using current culture media

formulations (Torsvik et al, 1990; Atlas and Bartha, 1998). This is based on the

comparison between direct microscopic counts of microbes in soil samples and

recoverable colony forming units. In addition, plate growth favors those micro-

organisms with fast growth rates. All of these limitations can influence the apparent

diversity of the microbial community.

2.7.1.2a Limitation of culture based studies

Until the 1980s, the determination of microbial community structure and the

identification of micro-organisms in environmental samples depended on culture-based

studies. These can be both time-consuming and cumbersome and were already known

to be selective, as only a small part of a microbial community is accessed (Amann et al,

1995). The proportion of cells which can be cultured is estimated to be 0.1-10 percent

of the total population.

The phenomenon that only a small proportion of bacteria can form colonies

when traditional plating techniques are used (Amann et al, 1995) was first described by

Staley and Konopka (1985) as the great plate anomaly. A further limitation of the

cultivation-based studies of microbial communities is that under environmental stress

bacteria can enter a state termed ‘viable but non-cultivable’ (VBNC), and again these

bacteria would not be accessible to traditional cultivation techniques (Roszak, 1987;

Oliver, 2000).

2.7.1.3 Fatty acid methyl ester analysis

A biochemical method that does not rely on culturing of micro-organisms is

fatty acid methyl ester (FAME) analysis.


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Phospholipid fatty acids are potentially useful signature molecules due to their

presence in all living cells i.e. unique fatty acids are indicative of specific group of

organisms. In micro-organisms, phospholipids are found exclusively in cell membranes

and not in other parts of the cell as storage product. This is important because cell

membranes are rapidly degraded and the component phosholipid fatty acids are rapidly

metabolized following cell death. Consequently, phospholipids can serve as important

indicators of active microbial biomass as opposed to non living microbial biomass.

It has been used to study microbial community composition and population

changes due to cropping practices (Zelles et al, 1992; Zelles, 1999), chemical

contaminants (Siciliano and Germida, 1998; Kelly et al, 1999) and agricultural practices

(Bossio et al, 1998; Ibekwe and Kennedy, 1998).

For FAME analysis, fatty acids are extracted directly from soil, methylated and

analyzed by gas chromatography (Ibekwe and Kennedy, 1999).

Limitation to study include cellular fatty acid composition can be influenced by

factors such as temperature and nutrition, and the possibility exists that other organisms

can confound the FAME profiles (Graham et al, 1995). In addition, individual fatty

acids cannot be used to represent specific species because individuals can have

numerous fatty acids and the same fatty acids can occur in more than one species

(Bossio et al, 1998).

2.7.2 Molecular based techniques to study microbial diversity

There are two types of molecular techniques to study microbial diversity:

1. Total Community DNA analysis

2. Partial community DNA analysis

2.7.2.1 Total community DNA analysis

2.7.2.1.1 G+C Content

It is based on the knowledge that micro-organisms differ in their G+C content

and that taxonomically related groups only differ between 3 and 5 percent (Tiedje et al,


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1999). This method provides a coarse level of resolution as different taxonomic groups

may share the same G+C range. Advantages of G+C analysis are that it is not

influenced by PCR biases, it includes all DNA extracted, it is quantitative and it can

uncover rare members in the microbial populations. It does, however, require large

quantities of DNA (Tiedje et al, 1999).

2.7.2.1.2 Nucleic acid reassociation and hybridization

DNA reassociation is a measure of genetic complexity of the microbial

community and has been used to estimate diversity (Torsvik et al, 1990b, 1996). Total

DNA is extracted from environmental samples, purified, denatured and allowed to

reanneal. The rate of hybridization or reassociation will depend on the similarity of

sequences present. As the complexity or diversity of DNA sequences increases, the rate at

which DNA reassociates will decrease (Theron and Cloete, 2000). 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. PCR eliminates this problem.

2.7.2.1.3 DNA microarrays

More recently, DNA–DNA hybridization has been used together with DNA

microarrays to detect and identify 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 contain thousands of DNA sequences

(Cho and Tiedje, 2001) with high specificity.

Like DNA–DNA hybridization, microarrays have the advantage that it is not

confounded by PCR biases and microarrays can contain thousands of target gene

sequences. However, it only detects the most abundant species. In general, the species need

to be cultured, but in principle, cloned DNA fragments of unculturables could be used.

2.7.2.2 Partial community DNA analysis

These approaches consist of PCR amplified sequences. The most commonly

used target sequences are the genes of the ribosomal operon and particularly the 16S

rDNA, and the intergenic spacers IGS between 16S and 23S.


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2.7.2.2.1 PCR based analysis

Two major discoveries have revolutionized microbial ecology and have

permitted culture-independent characterization of microbial communities:

1. the recognition that phylogenetic relationships between micro-organisms

can be inferred from molecular sequences (Woese, 1987)

2. the ability to selectively amplify minute amounts of nucleic acids extracted

from environmental samples by the polymerase chain reaction (Saiki et al,

1998).

PCR based methods imply extracting nucleic acids and amplifying selected

molecular markers further by using specifically designed primer combination. The

structure of microbial communities is finally analyzed by taking advantage of the

polymorphism in the mixed pool of sequences by molecular fingerprinting techniques,

through cloning and sequencing, hybridization with probes or microarrays or using a

combination of these techniques. All PCR based analysis are subjected to some

limitations, generally related to nucleic acid extraction protocol used, the inherent

biases of the amplification reaction and efficiency in separating individual markers from

a complex and mixed population.

PCR allows the selective amplification of small amounts of DNA extracted

from natural samples. A PCR reaction consists of a buffered mixture containing at least

a thermostable DNA polymerase along with its buffer, oligonucleotide primers, free

deoxynucleoside triphosphates (dNTPs: dATP, dCTP, dGTP and dTTP), magnesium

ions and template DNA. The oligonucleotide primers are designed to hybridize to

regions of DNA flanking the desired gene sequence. The PCR process involves three

stages: the DNA is denatured to convert double-stranded DNA into single-stranded

DNA; oligonucleotide primers are annealed to complementary priming sites in the

target DNA; finally the DNA is extended from the primers by the addition of

nucleotides through DNA polymerase activity, resulting in double-stranded products.

Repetitive cycling through these three steps results in an exponential increase in the

DNA fragments of interest.


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The performance of PCR is influenced by the concentrations of the individual

components including the magnesium ion concentration, the type of DNA polymerase,

concentration and properties of primers and template in the PCR mixture, as well as the

length of the product (Steffan, 1991).

2.7.2.2.1.1 DNA extraction

Over 20 years ago, the first protocol titled ‘DNA extraction from soil’ was

published by Torsvik in 1980. However, only in the late 1980s/early 1990s, when

molecular tools such as nucleic acid hybridization, the PCR and DNA cloning and

sequencing became increasingly available, more attention was focused on the analysis

of DNA extracted from environmental bacteria without prior cultivation. Analysis of

nucleic acids extracted directly from environmental samples allows the researcher to

investigate microbial communities by obviating the limitations of cultivation

techniques.

Two principal approaches exist to extract DNA from soil, each with their own

advantages and limitations. The first approach pioneered by Ogram et al. (1987) is

based on direct or in situ lysis of microbial cells. This is by far the most frequently

utilized method. The advantage of the direct nucleic acid extraction approach is that it is

less time-consuming and that a much higher DNA yield is achieved. However, directly

extracted DNA often contains considerable amounts of co-extracted substances such as

humic acids that interfere with subsequent molecular analysis (Tebbe and Vahjen,

1993). Furthermore, a considerable proportion of directly extracted DNA might

originate from non-bacterial sources. In the second approach, the microbial fraction is

recovered from the environmental matrix prior to cell lysis and subsequent DNA

extraction and purification. The major concern with the so-called indirect or ex situ

DNA extraction approach is a differential recovery efficiency of surface-bound cells.

Dissociation of cells from surfaces is generally achieved by repeated blending/

homogenization steps and differential centrifugation. Thus the indirect method is more

time-consuming and prone to contamination. A clear advantage of the indirect approach

is that the nucleic acids recovered are less contaminated with co-extracted humic acids

and DNA of non-bacterial origin.


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Recently commercial kits for DNA extraction from soils (Ultra Clean™ Soil

DNA Kit, MoBio Laboratories Inc., Fast DNA® Spin® Kit for Soil) have become

available and these represent a major breakthrough in view of the simplification and

miniaturization of this crucial method for many cultivation-independent analysis

methods.

2.7.2.2.1.1.1 Pitfalls of PCR: artifacts and differential amplification

2.7.2.2.1.1.1.a PCR artifacts

PCR can give rise to a number of artifacts, such as mutations, deletions and

chimeras. DNA polymerase enzymes are not 100 percent accurate and introduce point

mutations due to intrinsic misincorporation of nucleotides during PCR. The frequency

of nucleotide misincorporation depends on the type of DNA polymerase used in the

PCR. The percentage of sequences with polymerase errors increases with the number of

PCR cycles and the length of the amplified fragment.

Another PCR artifact is the formation of chimeric PCR products (Wang and

Wang, 1997). Chimeric genes result from the incomplete synthesis of an rRNA gene

fragment during amplification. If the incomplete fragment anneals to a homologous

rRNA gene fragment forming a heteroduplex, it can be extended to full length. This

results in an rRNA gene fragment that has been replicated from different templates and

thus represents a complete rRNA sequence that does not exist naturally in a living

organism. Chimera formation can be diminished by increasing the elongation time and

decreasing the number of cycles (Wang and Wang, 1997). A number of computer

algorithms have been developed to identify chimeric sequences [e.g.

CHIMERA_CHECK (http://rdp.cme.msu.edu)]. Both misincorporation of nucleotides

and chimera formation lead to an overestimation of diversity.

2.7.2.2.1.1.1.b Differential amplification

Quantitative abundance of species from PCR can only be inferred when all

molecules are equally accessible to primer hybridization, when primer-template hybrids

are formed with the same efficiency, and DNA polymerase extends at equal efficiency

with all templates, throughout the whole PCR process. Unfortunately, these


21

assumptions often do not hold and several biases compromise the ability to draw

quantitative conclusions on species abundance from abundances of PCR-generated

fragments. These biases are related to the template and the PCR approach used.

Problems relating to template are:

• gene copy number, which leads to over representation of sequences from

organisms with higher numbers of rRNA operons during PCR (Farrelly et al,

1995).

• rRNA operon heterogeneity will complicate quantification and lead to an

overestimation of diversity (Clayton et al, 1995; Nubel et al, 1996).

• differences in G+C content. The 16S rRNA genes of different species differ

considerably in G+C content. Genes with a lower G+C content denature

with a higher efficiency and may therefore be preferentially amplified

(Reysenbach, 1992).

• presence of sequences outside the amplified sequence that inhibit

amplification (Hansen, 1998).

• modified template (eg DNA methylation) (von Wintzingerode et al, 1997)

and template concentration (Chandler, 1997).

Differential amplification as a consequence of PCR has been found to relate to

primer efficiency and selectivity and competition between primer annealing and

template re-annealing. Sub-optimal binding of the primer will result in less efficient

amplification of the respective DNA. When ‘universal’ primers are used, different

levels of mismatch between the primer and target sequences can result in preferential

amplification of certain rRNA gene sequences.

2.7.2.2.2 Cloning and sequencing

Cloning and sequencing protocols are considered to be the best exploratory

method in molecular ecology as it provides exhaustive and detailed phylogenetic

information and allows more accurate diversity estimates. Nevertheless, cloning

experiments are too laborious and time consuming. It consists of inserting the various

polymorphic sequences contained in a PCR product into vectors and transforming


22

competent cells to stably store genetic information, which can then be sequenced to

reveal the taxonomic affiliation of micro-organisms.

The current most popular kits exploit T-vectors (Marmchuk, 1991), which are

plasmids that when linearized have single deoxythymidine residues at their 3′ end.

These vectors allow sticky-end ligation of PCR products generated by non-proofreading

thermostable DNA polymerases without the need for restriction digestion (TA-

overhang cloning). Non-proofreading DNA polymerases that lack a 3′ to 5′

proofreading function (e.g. Taq DNA polymerase) have terminal deoxynucleotide

transferase activity and add a template-independent deoxyadenosine residue to the 3′

ends of the PCR product (Clark, 1988).

2.7.2.2.2.1 Screening of clone libraries

The cloned, correctly sized rRNA gene fragments can be sequenced from all, or

a selection, of the clones and a detailed picture of the sequence types present in a

particular environment can be gained. However, phylogenetic analysis of complex

communities is laborious, time-consuming and costly. The most costly element of the

analysis is sequencing and it is often desirable to cut down the number of clones to be

sequenced. Several screening methods allow the detection of similar or identical rRNA

sequences.

Some methods are based on detecting signature sequences in the cloned 16S

rDNA, such as colony hybridization procedures with oligonucleotide probes of defined

phylogenetic resolution (Rheims, 1996).

Recently, a PCR approach called ‘signature PCR’ (SIG-PCR) has been

described to classify 16S rDNA sequences into main taxa (Uphoff, 2001). SIG-PCR

employs a mixture of nine oligonucleotide primers and yields PCR products of taxon-

specific lengths.

A more common means of screening clones to diminish the number of clones

selected for sequencing is profiling of clones by one-dimensional electrophoresis

methods, comparing the patterns, and grouping clones that produce similar

electrophoretic profiles, followed by sequencing of representatives of each group.


23

Suitable profiling methods are amplified ribosomal DNA restriction analysis

(ARDRA), terminal restriction fragment length polymorphism (T-RLFP),

denaturing/thermal gradient gel electrophoresis (DGGE/TGGE) and single strand

conformation polymorphism (SSCP). ARDRA is most frequently used, since it offers

the ability of high speed screening of large numbers of clones in a simple, reproducible

way, at low cost.

2.7.2.2.2.2 Quantitative analysis of clone libraries: coverage and diversity indices

2.7.2.2.2.2.a Coverage

An important question in community analysis using clone libraries is how far the

actual species composition (richness) in a natural sample is captured in a clone library. A

very simple estimate can be obtained by calculating the coverage {C} (Good, 1953)

Cx=1−(nx/N)

where nx is the number of clone types (e.g. ARDRA types, sequence types) that

are encountered only once in library x and N is the total number of clones analyzed.

Hence if there is a large proportion of unique sequences recovered in a clone library,

nx/N tends towards unity and coverage is small.

An accumulation curve is a plot of the cumulative number of different clone

types observed versus total number of individuals (the clones). As all communities

contain a finite number of species, upon continued sampling an asymptote will be

reached that represents the number of types present: the richness. Thus, the shape of the

curve provides information on how well communities have been sampled.

For rank-abundance curves, the different clones are ordered from the most to

the least abundant on the x-axis, and the abundance of each clone type is plotted on the

y-axis.

2.7.2.2.2.2.b Diversity indices

Richness is an important parameter in the determination of diversity. Rarefaction

compares the observed richness among habitats that have been unequally sampled. More


24

suitable ways to estimate richness are probably nonparametric estimators, such as the

Chao1 estimator reviewed by Hughes et al. (2001). It should be noted that the chao1

estimator underestimates the richness at low sample sizes (<100 clones). A frequently

used index is the Shannon index of diversity (H′), which is calculated as

H’=−Σpi x log(pi)

where pi is the proportion of clones contributed by group i to the whole clone

library. The larger H′, the higher the diversity.

2.7.2.2.2.3 Phylogenetic inference

The primary goal of generating 16S rRNA gene clone libraries is to determine

the phylogenetic relationships of the organisms present, based on sequence comparisons

with cultured organisms or of sequences recovered from environmental samples.

The sequence obtained must be checked carefully for reading errors, after which

information on its identity can be obtained by comparing the sequence to one of the

online sequence databases (EMBL, GenBank, RDP) using BLASTN or FASTA

(Altschul, 1990; Pearson and Lipman, 1988). In Basic Local Alignment Search Tool

(BLAST) the highest scoring matches between the query sequence and a database are

searched. FASTA or Fast All tool for rapid identification of local sequence similarities.

The sequences to be compared are broken into short runs of consecutive characters

called words. Segments of nearby word hits are identified first, and scores (relative

values indicating the degree of similarity) are assigned to them. Multiple regions of

local similarities (segments) are then joined and scores calculated for the ensemble.

FASTA can be started at http://www.ebi.ac.uk/Tools/ and selecting ‘FASTA’.

ClustalW (Thompson et al, 1994) is a tool for establishing multiple sequence

alignments from scratch. Similar to FASTA and BLAST searches, alignments can be

generated for user-defined sets of database entries and again user-provided sequence

data can be included.

Many methods have been developed to infer phylogenetic relationships from

molecular sequences; three of these are widely used for analysis of 16S rRNA


25

sequences recovered from uncultivated micro-organisms: parsimony, corrected distance

and maximum likelihood analyses.

Parsimony methods select trees that minimize the tree length (i.e. the smallest

number of changes required to convert one sequence to another) required to explain a

set of data. Parsimony methods do not require a specific model of evolutionary change,

whereas the other two methods do. Parsimony methods underestimate the true amount

of change, because superimposed changes are not assessed.

The fundamental difference between parsimony and distance methods has been

explained in terms of a person arriving in a city from another place (Woese, 1987). The

distance method takes into account only how far the person has travelled, while the

parsimony analysis attempts to reconstruct the actual route taken.

Distance methods are perhaps easiest to understand. Pairwise comparisons of a

set of aligned sequences are used to construct a distance matrix. The distance matrix

expresses the divergence between pairs of sequences in terms of the fraction of sites

that are different. It is apparent that sequences that differ in 2 percent of the positions in

an alignment are more closely related than sequences that differ in 5 percent of the

positions. It is also logical to infer that, assuming constant rates of nucleotide

substitution, more time has passed from the point of divergence in a sequence that is 5

percent divergent from a given sequence than the divergence time of a sequence which

is 2 percent divergent from the same sequence. The calculation of distances often

includes a model of base substitution to account for multiple substitutions at a single

nucleotide position. Even so, distance methods tend to underestimate evolutionary

distances. Several different base substitution models exist and within the limitations of

this chapter it is only possible to briefly consider a small number of the models. The

Jukes and Cantor model (Jukes, 1969) is most commonly used. This model assumes

that there is independent change at all sites, with equal probability. A number of more

complicated models have been developed including the Kimura 2 parameter model

(Kimura, 1980) that distinguishes between transitions [mutation from a purine (A, G) to

purine, or a pyrimidine (C, T) to a pyrimidine] and transversion (mutation from a purine

to a pyrimidine or vice versa). Distance models that attempt to estimate different


26

evolutionary rates at different positions in a molecule have also been developed (Van de

Peer et al, 1993). The different models often, though not always, produce similar

results.

The matrix of pairwise distances between sequences in an alignment is used to

construct a tree by number of methods. UPGMA (unweighted pair group method with

arithmetic mean) and NJ (neighbor joining) are most commonly used. UPGMA is a

clustering method. There are a number of assumptions inherent in the construction of a

UPGMA tree. One is that the tree is additive (i.e. the distance between any two nodes is

equal to the sum of the branch lengths between them) and a second is that the tree is

ultra metric (all taxa are equally distant from the root). The second assumption is not

likely to be sustainable and consequently nowadays UPGMA is rarely used in

phylogenetic sequence analyses.

The NJ method (Saitou and Nei, 1987) is the most widely used algorithmic

method to generate trees from distance matrices. The NJ method is related to cluster

analysis in that it involves a sequential reduction in the size of a distance matrix, but

with the difference that it does not assume that all lineages have diverged an equal

amount from the common ancestor. Instead of producing clusters, NJ involves

calculation of distances from each taxon to internal nodes.

Although clearly imperfect from an evolutionary perspective, distance methods are

popular because of their conceptual simplicity, they are not computationally expensive and

for the needs of most microbial ecologists they provide results that fulfill their primary

requirement of determining the closest phylogenetic neighbor of an organism identified

from a nucleic acid sequence recovered from an environmental sample.

Maximum likelihood methods are a bridge between parsimony and distance

methods. They require a model of evolutionary change that accounts for the conversion

of one sequence into another, as in distance.

Bootstrap analysis: The statistical validity of a tree generated with the above

methods can be tested by bootstrapping. High bootstrap values are indicative of the

significance of the grouping to the right of the node. The description of phylogenetic


27

analysis methods above is far from exhaustive, and new approaches are being developed

continually, e.g. logdet methods (Lockhart et al, 1993) and Bayesian methods (Mau, 1999).

2.7.2.2.3 DNA fingerprinting methods

Genetic fingerprinting techniques were developed to overcome the main

drawbacks of cloning and sequencing, allowing faster analysis of community structure.

Therefore they are suited for studies of population dynamics, which generally entail the

simultaneous analysis of a large number of samples (Dunbar et al, 2000). These

approaches do not offer the real estimation of genetic diversity, yet greater simplicity of

these techniques compared to cloning analysis allows changes in microbial communities

to be monitored readily. Fingerprinting techniques provide a pattern which reflects the

structure of microbial communities (Muyzer, 1998).

Historically, the first DNA fingerprinting approach to be successfully applied to

microbial ecology was Denaturing Gradient Gel Electrophoresis (Muyzer et al, 1993;

Muyzer and Ramsing, 1995). The growing popularity of DGGE paved the way for a

number of other profiling approaches such as the related Temperature Gradient Gel

Electrophoresis (TGGE) (Rosenaum and Riesner, 1987; Felske et al, 1997), the

somewhat related Single Strand Conformation Polymorphism (SSCP) analysis (Oreta et

al, 1989; Lee et al, 1996; Schwieger and Tebbe, 1998 ) and two techniques: Terminal

Restriction Fragment Length Polymorphism (T-RFLP) (Avaniss et al, 1996; Liu et al,

1997) and Length Heterogeneity PCR (LH-PCR) (Suzuki et al, 1998). Whilst DGGE

and TGGE are based on the differential melting of GC-rich DNA stretches in the

amplified DNA molecules, SSCP separates on the basis of different melting behavior of

the secondary structures of single-stranded DNA. T-RFLP generates DNA fragment

length variations via the presence of restriction sites and the LH-PCR takes advantage

of the different length of DNA stretches in hyper-variable regions of the target gene and

in particular for ribosomal RNA (Van de Peer, 1996).

2.7.2.2.3.1 DGGE

The principle of DGGE is to separate various amplicons based on variation in

the melting behavior of the secondary structure of DNA sequence. DGGE is an efficient

technique for PCR fragments shorter than 500 base pairs (Myers et al, 1985).


28

Advantages of DGGE include being reliable, reproducible, rapid and somewhat

inexpensive. Multiple samples can also be analyzed concurrently, making it possible to

follow changes in microbial populations (Muyzer, 1999). DGGE enables, besides

mapping shifts in the community structure, further sequencing and phylogenetic

characterization of bands by gel excision and purification, followed by sequencing,

which is the great advantage of the DGGE method compared with other methods.

Limitations of DGGE include PCR biases (Wintzingerode et al, 1997),

laborious sample handling, as this could potentially influence the microbial community,

(Muyzer, 1999; Theron and Cloete, 2000), and variable DNA extraction efficiency

(Theron and Cloete, 2000). The major drawback of DGGE is that sequences amplified

from different organisms may have the same melting temperature and co-migrate to the

same position on the gel meaning that a single band can contain a mixture of genotypes

(Gelsomino et al, 1999). Also appearance of double bands caused by primer

degeneration or the formation of hetroduplex molecules is a major drawback of DGGE.

This method has been frequently used in microbial ecology to fingerprint

bacterial communities, most often based on the 16S rRNA genes (Schneegurt and

Kulpa, 1998; Kozdroj and van Elas, 2001; Niemi et al, 2001), 18S rDNA gene and

fungal diversity (Smit et al, 1999) and recently also for studies of the denitrifying

community composition (Hallin et al, 2006; Kjellin et al, 2007; Sharma et al, 2005;

Throbäck et al, 2007; Gelsomino et al, 1999; Maarit-Niemi et al, 2001).

2.7.2.2.3.2 TGGE

TGGE (Rosenbaum and Riesner, 1987) is a variant of DGGE in which the

increasing denaturing force applied across the gel is an increase in gel temperature

towards the anode. A high concentration of chemical denaturing agents is included in

the gel mix. The concentration of the chemical denaturation remains constant over the

entire gel, in contrast to DGGE, with these denaturants only included to reduce the

temperature that is required for full denaturation. This reduces the energy required and

also stops the gel from drying out. The theory behind the separation of DNA molecules

via TGGE is exactly the same as for DGGE, and its use in microbial ecology has been

widespread since its initial applications in microbial ecology in 1997 (Felske et al,

1997; Heuer et al, 1997).


29

2.7.2.2.3.3 SSCP

This method relies on the principle that the electrophoretic mobility of a single-

stranded DNA molecule in a non-denaturing gel is dependent on its structure and size

(Orita et al, 1989). A single nucleotide change may alter the conformation of a ssDNA

molecule and will allow two DNA fragments that differ in only one nucleotide to be

distinguished when electrophoresed in non-denaturing polyacrylamide gels due to

mobility difference between the molecules. Since no GC clamp primers, gradients, or

specific apparatus is required, SSCP appears at first to be more simple than

DGGE/TGGE. SSCP analysis requires uniform low temperature to maintain single

stranded DNA secondary structure.

SSCP has been used to analyze the community fingerprinting, microbial

diversity and structure in complex, non-cultivated bacterial and fungal populations from

various ecosystems (Peters et al, 2000; Stach et al, 2001)). Recently the molecular

identification of a broad panel of bacterial species by fluorescence-based SSCP analysis

of PCR amplified 16S rRNA gene was reported (Widjojoatmodjo et al, 1996). SSCP

has been used to measure succession of bacterial communities (Peters et al, 2000),

rhizosphere communities (Schwieger and Tebbe, 1998; Schmalenberger et al, 2001),

bacterial population changes in an anaerobic bioreactor (Zumstein et al, 2000) and

AMF species in roots (Simon et al, 1993; Kjoller and Rosendahl, 2000).

However, a significant limitation of SSCP is the formation of more than one

band from a single sequence. A second disadvantage of SSCP for the analysis of

communities is the high rate of DNA re-annealing during electrophoresis. This will

reduce signal intensity for the ssDNA bands. The greater the concentration of PCR

product that is loaded on a SSCP gel (i.e. which would be necessary for the analysis of a

highly diverse community), the more the effects of renaturation will spoil the display of

resolved products due to the formation of a large fraction of dsDNA molecules.

2.7.2.2.3.4 RFLP/ ARDRA

The potential of the method relies on sequence differences in primary DNA

structure of amplified fragments. Different digestion profiles are generated due to


30

variations in the position of selected sequences that act as cutting targets for restriction

endonucleases. This requires longer fragments (>500bp) than DGGE, and cannot be

used as a measure of real diversity since the number of bands obtained does not reflect

the number of different populations. However, it is a suitable method for screening

clone libraries (Pace, 1996) or isolates before DNA sequencing that has been used in

numerous studies concerning denitrifying and nitrifying diversity (Braker et al, 2001;

Cheneby et al, 2003; Enwall, 2005; Hallin et al, 2006; Horn et al, 2006; Liu, 2003;

Philippot et al, 2002; Scala, 1999; Sharma et al, 2005; Stres et al, 2004; Yan et al, 2003)

or used to measure bacterial community structure (Massol-Deya et al, 1995).

2.7.2.2.3.5 T-RFLP

Like RFLP, terminal restriction fragment length polymorphism (T-RFLP) is a

method based on sequence specific restriction digestion. However, when amplifying the

extracted DNA, one or both of the primers are fluorescently labelled with a fluorescent dye,

such as TET (4,7,2V,7V-tetrachloro-6-carboxyfluorescein) or 6-FAM (phosphoramidite

fluorochrome 5-carboxyfluorescein).

The PCR products are digested with restriction enzymes and it is common to

use more than one enzyme for each sample to obtain a more complex pattern. Due to

the sequence polymorphism, the location of the restriction sites varies and the terminal

fragments have different lengths. The terminal fragments are separated either on a gel

or capillary sequencer based on length. The resulting electropherogram gives a profile

of the microbial community composition and the terminal fragments, and their relative

abundance. This allows detection of only the labelled terminal restriction fragment (Liu

et al, 1997). 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 taxonomic unit or ribotype (Tiedje et al, 1999). This

procedure can be automated to allow sampling and analysis of a large number of soil

samples (Osborn et al, 2000). He also tested the reproducibility of the method and

found that banding patterns within and between samples were highly reproducible.

T-RFLP is limited not only by DNA extraction and PCR biases, but also by the

choice of universal primers. In addition, different enzymes will produce different


31

community fingerprints (Dunbar et al, 2000). Incomplete digestion by restriction

enzymes could also lead to an overestimation of diversity (Osborn et al, 2000).

Despite these limitations, some researchers are of the opinion that once

standardized, T-RFLP can be a useful tool to study microbial diversity in the

environment (Liu et al, 1997; Tiedje et al, 1999; Osborn et al, 2000). T-RFLP has also

been thought to be an excellent tool with which to compare the relationship between

different samples (Dunbar et al, 2000).

T-RFLP has been used to measure spatial and temporal changes in bacterial

communities (Acinas et al, 1997; Lukow et al, 2000), to study complex bacterial

communities (Clement et al, 1998; Moeseneder et al, 1999), to detect and monitor

populations (Tiedje et al, 1999) and to assess the diversity of AMF in the rhizosphere of

Viola calaminaria in a metal contaminated soil (Tonin et al, 2001). Tiedje et al (1999)

reported five times greater success at detecting and tracking specific ribotypes using T-

RFLP than DGGE. T-RFLP is widely used for analyzing the community structure of

micro-organisms in the environment (Clement et al, 1998; Liu et al, 1997).

In silico predictions of T-RFs can be generated from DNA sequences. This has

led to the development of programs such as TAP-T-RFLP (Marsh et al, 2000) and PAT

(Kent et al, 2003) which aim to allow predictive identification of micro-organisms

based on their T-RFs.

2.7.2.2.3.6 RISA/ARISA

Ribosomal intergenic spacer analysis (RISA) is based on the size polymorphism

of the 16S-23S rRNA intergenic region between strains (Borneman and Triplett, 1997;

Fisher and Triplett, 1999; Jensen et al, 1993). After amplification of this region, the

PCR products are separated on a polyacrylamide gel according to size to get a

fingerprint of the environmental community. RISA has been used to compare microbial

diversity (Martin-Laurent et al, 2001; Ranjard et al, 2001; Wertz et al, 2006), in soil and

rhizosphere of plants (Borneman and Triplett, 1997), in contaminated soil (Ranjard et

al, 2000) and in response to inoculation (Yu and Mohn, 2001). In RISA, the sequence

polymorphisms are detected using silver stain while in ARISA the forward primer is


32

fluorescently labelled and is automatically detected (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).

2.7.2.2.3.7 LH-PCR

This fingerprinting approach takes advantage of naturally occurring sequence

length variations. The typical protocol involves PCR amplification of a small part of the

target gene with a labeled primer and then electrophoresis of the labeled product on an

automated fluorescence-detection-based sequencing device. This approach was first

described by Suzuki et al, 1998 with amplification of bacterial 16S rDNA sequences with

universal primers between E. coli positions 8 and 355. This amplicon includes the highly

variable regions V1 and V2 and yielded fragments of 312–355 bp in length, and identified

up to 23 distinct length heterogeneity variants. The resolution limit is determined by the

variability of the covered DNA stretch. Comparison of 16S rDNA sequences in the

databases reveals that amplicon length in this region is not necessarily taxon specific, i.e.

completely different bacterial taxa may share the same fragment length, with certain taxa

having common amplicon lengths whilst other taxa yield amplicons that vary

considerably in length. Interpretation of such data is improved if sequence information

(i.e. from clones) is also available, not least so that primers can be chosen that will yield

more discriminatory profiles. Inclusion of only one variable region may not provide

sufficient resolution power but inclusion of a second hyper variable region may have the

adverse effect of nullifying length heterogeneity in the first hyper variable region. For

example, a certain taxon may include a specific insertion within the first variable region

of two base pairs, but in the second variable region a corresponding deletion of two bases.

Hence there will be no net change in amplicon length.

2.8 RIBOSOMAL GENE AS MOLECULAR MARKER

2.8.1 16S rDNA analysis

After more than 15 years of nucleic-acid-based analysis of natural microbial

communities, the 16S rRNA gene remains central in contemporary molecular microbial

ecology. This gene is a very suitable molecular marker (Woese, 1987).


33

Why?

1. This gene is universally present in all prokaryotes

2. Its product shows functional constancy: 16S rRNA is part of the ribosomes

that is required by all organisms to synthesize protein.

3. The gene is sufficiently long (~1.5 kb) to be used as a document of

evolutionary history,

4. Evidence for horizontal transfer of rRNA genes is limited.

5. These molecules are composed of both highly conserved regions and also of

regions with considerable sequence variation, because of these differential

rates of sequence evolution, phylogenetic relationships at several hierarchial

levels can be measured from comparative sequence analysis (Woese, 1987).

6. Finally, SSU rDNA can be easily amplified using polymerase chain reaction

and rapidly sequenced.

2.9 NITROGEN CYCLING

In soil, nitrogen is the most important nutrient for plant growth and therefore

primary production is often limited by nitrogen availability. The transformation of

nitrogen in the soil or other ecosystems is mediated by processes performed by different

groups of organisms.

The atmosphere contains 78 percent dinitrogen gas (N2), which has the potential

to enter the nitrogen cycle through the action of different groups of organisms that can

reduce nitrogen to ammonium. This biological process is called nitrogen fixation and is

carried out by the diazotrophic bacteria. These organisms contain the gene coding for

the enzyme nitrogenase, which can break the triple covalent bond of N2. The nitrogen

fixing bacteria are either free-living, such as Azotobacter and different Cyanobacteria,

or symbiotic and in association with plant roots, such as Rhizobium and Frankia

(Brill, 1980).

Ammonia is oxidized to nitrate (NO3-) in a two-step process called nitrification. In

the first step, NH3 is oxidized to nitrite (NO2-) via hydroxylamine (NH2OH) by ammonia

oxidizing bacteria (AOB) or ammonia oxidizing archaea (AOA). In the second step, NO2-


34

is then further oxidized to NO3- by nitrite oxidizing bacteria belong to the Proteobacteria

(NOB). The first step, ammonia oxidation, is often considered rate limiting. The nitrifiers

are aerobic and use oxygen as a terminal electron acceptor, NH3 and NO2- as energy

sources and CO2 as a carbon source (Könneke et al, 2005; Prosser, 1989).

The NO2- formed by the NOB can also be used in the oxidation of ammonium

(NH4+) to N2 (Jetten, 2001) by anaerobic ammonium oxidation (anammox).

Nitrate can be reduced to N2 via the denitrification process, or by dissimilatory

nitrate reduction to ammonium, a process abbreviated DNRA. DNRA is a strictly

anaerobic two-step process where NO3- is reduced to NH4

+ via NO2- (Tiedje, 1988).

Nitrogen fixation is a process that enables reduction of the atmospheric nitrogen

N2 to ammonium (NH+4) by nitrogenase, a universal enzyme. This process introduces

nitrogen into the biosphere. The natural fixing process is responsible for 65 percent of

annual fixation, while industrial processes represent only 25 percent (Newton’s

encyclopedia of chemical technology 4th edition).

Nitrogen fixation occurs in a wide range of bacterial phyla, from Archae to

bacteria (Young, 1992). Among bacteria the ability to fix nitrogen is seen in organisms

with various metabolisms such as anaerobes and aerobes, Cyanobacteria and

Actinomycetes. Biological Nitrogen Fixation (BNF) is the major natural process through

which atmospheric nitrogen is converted into forms that can be used by plants and

animals, contributing 100–290 T N per year to the biosphere (Cleveland et al, 1999).

Soil diazotrophs are the main source of nitrogen input in primary production

ecosystem. Progress in understanding the ecological significance of free living

diazotrophs has been limited because of the fact that many of these organisms are

recalcitrant to laboratory cultivation.

2.9.1 nifH gene as molecular marker

Most of the nitrogen fixing bacteria can express nitrogenase enzyme which

catalyzes nitrogen fixation. Nitrogenase is composed of two proteins: iron protein and


35

the molybdenum iron protein. Iron protein is encoded by the nifH gene, one of the

oldest existing functional genes in the history of gene evolution.

All N 2 fixers carry a nifH gene, which encodes the iron protein of the

nitrogenase enzyme. Henneke et al, 1985, Normand and Bousquet, 1989; Young, 1992,

Ueda, 1995; Borneman, 1996 reported that the phylogeny of the nifH gene is broadly

consistent with that based on 16S rDNA, showing that nifH could be considered a good

marker of diazotrophic community structure.

Surveys of nifH diversity in soil commonly reveal sequence types that

correspond to the diverse unidentified diazotrophs (Ueda et al, 1995; Widmer et al,

1999; Piceno and Lovell, 2000; Shaffer et al, 2000; Poly et al, 2001).

Several environmental factors have been suggested to influence N fixation in

soils including soil moisture, oxygen, pH, C quantity and quality, N availability and the

availability of trace elements, such as Mo, Fe and V, soil moisture, oxygen and pH have

fairly straightforward effects on N-fixation rates.

Increases in soil moisture (Brouzes et al, 1969; Sindhu et al, 1989) and

reductions of oxygen tension (Brouzes et al, 1969; O’Toole and Knowles, 1973; Kondo

and Yasuda, 2003) generally increase rates, whereas N fixation is not favored in soils of

low pH (Roper and Smith, 1991; Limmer and Drake, 1996; Nelson and Mele, 2006). N2

fixation is influenced by soil texture (Riffkin et al, 1999).

In contrast, the effects of C and N quantity and quality are less consistent.

Increases in the availability of labile C generally stimulate N fixation (O’Toole and

Knowles, 1973; Keeling et al, 1998; Burgmann et al, 2005; Kondo and Yasuda, 2003),

but in other cases have little or no effect (Brouzes et al, 1969; Roper and Smith, 1991;

Keeling et al, 1998). Likewise, N availability can have either stimulatory (Azam et al,

1988; Poly et al, 2001) or inhibitory (Koteva et al, 1992; Tan et al, 2003) effects.

Limmer and Drake (1998) suggested that N2 status of the soil may also influence N2

fixation by diazotrophs.

Traditional methods of analysis are useful but with the use of molecular

methods it is now possible to detect both cultivable and uncultivable microbial species.


36

Despite these advances, the link between microbial diversity and soil functions is still a

major challenge. Changes in the composition of soil micro flora can be crucial for the

functional integrity of soil (Insam, 2001).

Various techniques, such as PCR cloning (Zehr, 1995 and 1998), denaturing

gradient gel electrophoresis (Piceno 2000), PCR-restriction fragment length

polymorphism (RFLP), and fluorescently labeled terminal (T-RFLP) (Chelius and

Lepo, 1999; Noda, 1999; Ohkuma, 1999; Shaffer, 2000; Widmer, 1999), have been

used to analyze the composition of nifH gene pools in various environments.

These studies found that the nifH gene is present in diverse environments: forest

soil (Shaffer, 2000; Widmer, 1999), the rhizosphere of native wetland species, such as

Spartina (Chelius and Lepo, 1999; Piceno 2000), or of crop species, such as rice (Ueda,

1995), aquatic (Braun, 1999; Zehr, 1995 and 1998) or polar (Olson, 1998) Cyanobacteria,

and the bacteria found in termite guts (Noda, 1999; Ohkuma, 1999 and 1996)

Some nifH genes are characteristic of an ecological niche (Chelius and Lepo,

1999; Shaffer, 2000). Shaffer et al (2000) evoked the possible relationship between the

habitats of soil nitrogen-fixing bacteria and the structure of nifH gene pools.

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