Home » Unraveling Soil Microbial Biodiversity using Recent Developments in Molecular Tools

Unraveling Soil Microbial Biodiversity using Recent Developments in Molecular Tools


Biodiversity is generally defined as the variety and variability of living organisms and the ecosystems in which this occurs. The variability of life in the soil encompasses not only plants and animals but also the invertebrates and microorganisms that are interdependent on one another and the higher plants they support. Biodiversity is composed of three interrelated elements: genetic, functional and taxonomic diversity Taxonomic diversity i.e. the number of species forms an important part of an ecosystem’s diversity and is controlled by the genetic diversity. Genetic diversity can be much more than the number of recognized species. Hence, several species may have the same functions, resulting in functional redundancy. Some species may also interact to perform functions not possible by any single species. Therefore, biodiversity is the interaction of all these elements.

Soil biodiversity is more extensive than any other environment on the globe when all living forms are considered. The soil biota contains representations of all groups of microorganisms, fungi, bacteria, algae and viruses, as well as the microfauna such as protozoa and nematodes. Soil algae and protozoa, like higher plants and animals, can be identified by their morphology. Fungi and bacteria, however, require more extensive biochemical and genetic analysis to enable identification.

It has been estimated that only between 1 and 5% of all microorganisms on the earth have been named and classified. A large proportion of these unknown species is thought to reside in the soil. The possible numbers of existing species of different groups are 1.5 million species of fungi, 300,000 species of bacteria, 400,000 species of nematodes and 40,000 species of protozoa. New molecular techniques have been used to estimate that single gram of soil probably contains several thousand bacterial species.

Bacteria have diverse metabolic capacities that allow them to exhibit the wide range of energy sources in soil. Along with fungi, they are the primary agents of biogeochemical transformations and the decomposition of dead plant and animal remains which recycle nutrients for plant growth. Practically every chemical compounds, both natural and manufactured, ends up in the soil and we rely on the soil to retain and transform these compounds, thereby protecting surface and ground waters. Soil contributes to the regulation of the global atmosphere. The gaseous end products of microbial activity include carbon dioxide, methane, nitrous oxide, some of which contribute to the greenhouse effect responsible for global warming.

Some bacteria and fungi form important symbiotic associations with plants, for instance, Rhizobium bacteria form nitrogen-fixing nodules in legumes in mycorrhiza (root dwelling fungi) live in symbiosis with nearly all plants in the natural environment. Mycorrhiza helps plants in the uptake of essential nutrients. They also protect the plant from pollutants and plant pathogens.

Soil microbial diversity and functioning is a product of soil, climate and plant factors. Soils exhibit a high degree of spatial heterogeneity both vertically and horizontally. Soil properties can vary within fields and is a complex network of pores ranging in size from 0.2 micron to 2 millimeter. Thus, many of the smallest organisms are protected from predation in those pores. Another reason for biodiversity can be variation in many of the important chemical and physical properties of soil such as pH and oxygen status over a short distance e.g. from rhizosphere to bulk soil and from inner to outer surfaces of a soil crumb. The spatial heterogeneity of potential living spaces, the diversity of food and energy sources available and temporal variation in moisture, all lead to cause the biodiversity.

Cultivation independent methods to study Soil Microbial Biodiversity

The measuring of soil microbial diversity has traditionally been problematic due to the vast numbers of potential species and the difficulties of culturing organisms in the lab using traditional techniques. In microorganisms, especially bacteria, there is an additional problem with taxonomy because of continuous exchange of DNA from one species to another, so that the species concept may be invalid.

Biodiversity in soil is not simply about evaluating species numbers but includes evaluating the way species interact with one another and their environment. In order to measure the impact of changing community structure, biodiversity must be linked with measures of ecosystem function. Chemical compounds found in microbial cell walls, such as phospholipids fatty acids, can be extracted. These compounds are specific to certain types of microbes and can provide information regarding broad changes in community structure.

There is an enormous reservoir of uncultured bacteria, many of which are only discovered and described by their DNA sequences. The bacteria that can be cultured are only a small fraction (1-10%) of the numbers actually contained in soil. Many cells in soil are in a viable but non-culturable state. Microorganisms have a long evolutionary history and are present in very complex communities. These microorganisms can be accessed by a classical approach, involving culturing the microorganism by solid or liquid growth medium containing appropriate carbon and electron acceptor sources and a range of other physiological conditions to promote microbiological growth. However, general culture conditions impose a selective pressure, preventing the growth of many uncultivable microorganisms. Only a small fraction (1-15%) of total bacteria can be cultured, thus, depicting a narrow vision of the actual microbial communities.

The application of molecular tools to study microbial ecology in natural environments has been practices since the mid-1980s and many new insights into the composition of uncultivated microbial communities have acquired.

Nowadays, the use of molecular techniques in molecular ecology is taken as essential and classic microbiology and molecular microbiology are longer easily considered as separated sciences (Pexixoto et al. 2008). New molecular techniques (Fig.?) can be used to measure genetic diversity of soil organisms without the need to isolate and culture them in the lab.

Molecular techniques to measure soil microbial diversity

Culture based studies, which require the growth of microorganism in the laboratory, are severely limited by the fact that less than 1% of soil microorganisms can be cultured. Culture-based studies thus provide an extremely restricted view of the diversity of the natural microflora. Advances in molecular techniques  have led to significant advances in the study of soil microbial communities. The molecular approaches are mainly based on the RNA of the small ribosomal subunit (16S rRNA for prokaryotices) or their corresponding genes, considering it as a “molecular clock”. This molecule was chosen because it presents some specific features, such as its universal distribution among all organisms, and some highly conserved and other highly variable regions. This allows comparison of organism within the same domain, as well as differentiation of strain of same species. Profiles for microbial communities based on differences in the 16S rRNA gene sequences of their constituents provide insight into differences in microbial population structure over time or with different experimental treatments.

Therefore, it is possible to make a comprehensive survey of the microbial diversity of a natural habitat in a relatively simple and more extensive way than provided by cultivation techniques. The molecular techniques that are commonly used to assess soil microbial communities are described here.

The 16S ribosomal RNA molecule

This approach based on small subunit ribosomal RNA (SSU rRNA) has provided a comprehensive phylogenetic framework for the analysis of microbial communities. The 16S  rRNA gene is a useful phylogenetic marker because

  • It is present in all organisms from bacteria to higher organism,  and its sequence is fairly well conserved across phylogenetic groups.
  • Its sequences differ between species.
  • Some sections of the sequence are highly conserved ,whereas others differ greatly among organisms, showing evolutionary relationship between organism.
  • Its sequences are available on databases to use for identification purposes.

The 16S Ribosomal RNA Molecule: The 16S rRNA molecule is a major component of the small ribosomal subunit. It has approximately 1500 ribonucleotides. This single-stranded rRNA molecule has an intricate secondary structure with extensive intrachain base pairing. The 16S rRNA forms a part of the ribosomal structure that is the site of protein biosynthesis resulting in the translation of messenger RNA. The 3′ end of the bacterial 16S rRNA base-pairs with the Shine-Dalgarno sequence located upstream of the AUG initiation codon in mRNA during the initiation step of the translation process. This allows the mRNA to position itself on the ribosome. There is also evidence that 16S rRNA is directly involved in the interactions between the large and small ribosomal subunits.

The sequence of 16S rRNA is highly conserved among all organisms due to the antiquity of the protein-synthesizing process. Thus ribosomal RNA is an excellent molecule for discerning evolutionary relationships among prokaryotic organismsms. Ribosomal RNAs are ancient molecules, functionally constant, universally distributed, and moderately well conserved across broad phylogenetic distances. Various regions within the rRNA genes evolve at slightly different rates due to the fact that 16S rRNA is functionally involved in the protein biosynthesis process and involved in different interactions in the ribosome. This results in alternating regions in the rRNA sequences of nucleotide conservation and variability.

The 16S rRNA of most major phylogenetic groups has one or more characteristic nucleotide sequences called oligonucleotide signatures. Oligonucleotide signature sequences are specific sequences that occur in most or all members of a particular phylogenetic group. Because the number of different possible sequences is so large, similarity in two sequences always indicate some phylogenetic relationship. However, it is the degree of similarity in the sequences between two organisms that indicates their relative evolutionary relatedness. Thus signature sequences can be used to place microorganisms in the proper group. Molecular techniques that analyse prokaryotic SSU rRNA (16S rRNA) have been applied widely to the study of microbial community structure in soils. A variety of methods can be used for the analysis of soil microbial communities via 16S rRNA.

Exploring the uncultivable bacterial diversity 

The first and fundamental step to explore molecular microbial ecology is to obtain the nucleic acids (DNA or RNA). There are different protocols available in literature (Courtois et al. 2003). The choice of which protocol to use for each approach will depend on many variable such as type of sample, type of subsequent analysis, and the quality and quality of nucleic acids recovered from environmental samples will be critical for the success of any molecular study (Lopes et al. 2009).

Subsequent molecular biology techniques most often applied to study soil microbial biodiversity and ecology would include: Denaturing Gradient Gel Electrophoresis (DGGE), Terminal Restriction Fragment Length Polymorphism (T-RFLP), Fluorescent In situ Hybridization (FISH), DNA Microarrays, cloning of 16S rDNA, Pyrosequencing.

The Polymerase Chain Reaction based techniques

The Polymerase Chain Reaction (PCR) is used to amplify all of the 16S rRNA genes within a sample and a phylogenetic inventory of the prokaryotic component of a soil microbial community can be assembled. This collection of amplified genes subsequently cloned and sequenced. Universal primers designed to amplify all of the 16S rRNA genes within a sample or primers designed to amplify the 16S rRNA genes of a specific group e.g. ammonia oxidizing bacteria. Phylogenetic inventories produced by such method have revealed much greater diversity in soil microbial communities than was detected by classical culture-based studies and thus significantly increased our understanding of soil biological diversity.

Denaturing Gradient Gel Electrophoresis (DGGE): Denaturing Gradient Gel Electrophoresis (DGGE) is a molecular fingerprinting techniques which separates PCR products based on the variation in the electrophoretic mobility profile of 16S rDNA molecule under denaturing gradient according to its sequence of base pairs, generating band pattern that an reflect the genetic biodiversity of a given sample (Muyzer et al. 1993). This profiling technique is based on the melting behaviour of the 16S rRNA gene on a polyacrylamide gel containing a urea gradient, which is dependent on the G+C content and the nucleotide sequence. The primers used to amplify the 16S rRNA genes in the community contain a GC clamp; as the DNA fragment moves down the gradient it will denature except for the terminal GC clamp. The denaturation or melting dramatically reduces the mobility of the DNA fragment in the gradient gel. Amplification of the 16S rRNA genes within a community and subsequent analysis by DGGE gives rise to a banding pattern in which each band corresponds to a single species.

The advantage of this technique is that the band may be recovered from the gel for further analysis such as sequencing to identify representatives of the microbial communities, so it not only a comparative tool but also allows for downstream community member identification.

Since the first report which applied the DGGE technique to analyse complex microbial populations, many researcher reported the use of DGGE in a wide range of habitats, and is possible to observe a increasing number of works that have used the DGGE in environmental microbiology (Alves et al. 2009, Hardoim et al. 2009).

Terminal Restriction Fragment Length Polymorphism (T-RFLP): Terminal restriction fragment length polymorphism (T-RFLP) analysis is a method for rapid profiling of mixed populations of a homologous amplicon (i.e., diverse sequences of a single gene). It involves tagging one end of PCR amplicons through the use of a fluorescent molecule attached to a primer. The amplified product is then cut with a restriction enzyme. Terminal restriction fragments (TRFs) are separated by electrophoresis and visualized by excitation of the fluor. T-RFLP analysis provides quantitative data about each TRF detected, including size in base pairs and intensity of fluorescence (peak height). TRF sizes can be compared to a database of theoretical T-RFs derived from sequence information. T-RFLP profiles have been shown to be relatively stable to variability in PCR conditions. In most cases, both the sizes and relative signal intensities of the individual TRFs in a sample are highly reproducible. Consequently, T-RFLP analysis is an excellent tool for rapidly comparing microbial communities.

Assessment of the diversity, structure, and dynamics of complex microbial communities with T-RFLP has mainly been based on PCR-amplified 16S rRNA genes (16S rDNA). Being a community profiling method based on the 16s rRNA gene, can be used with universal down to species level primers depending on the resolution required. This method however does require amplification of the 16s gene with specific primers and is thus more susceptible to biases and skewing of the native community. The technique itself depends on the amplification of the DNA with a primer set, one with a fluorescently end labeled primer and restriction of the resulting product with frequently cutting enzymes. Due to sequence variations the terminal restriction site for each species in the community should be different. The output is digital and provides information on the size of the product in base pairs (i.e. species) and the intensity of fluorescence or relative abundance of the various community members.

Probe-based techniques

In additions to PCR-based techniques, probe based methods can be used to detect specific phylogenetic groups of bacteria in soil. DNA probes are composed of short segments of DNA which can be designed to target regions of the 16S rRNA gene that are unique to a particular phylogenetic group and can be synthesized in the laboratory. These probes then can be hybridized to RNA extracted from a soil sample and provide information on the presence or absence, as well as relative abundance of that group targeted by that probe. Since active cells contain thousands to tens of thousands of copies of ribosomal RNA per cell. Therefore, ribosomal RNA is a good target for probing technology, thus making it a naturally amplified target. The application of DNA probes is generally carried out using membrane-based or in situ hybridization techniques. The FISH uses in situ hybridization of probes.

Fluorescent In Situ Hybridization (FISH): The 16s rRNA is targeted in situ by a probe that has been labeled with a dye that fluoresces under a particular wavelength of light e.g. TAMRA which fluoresces red at 575nm. The probe may be general such as the universal bacterial probe EUB 338 or may be specific to family, genus or even species level depending on the questions being asked. The bacteria are then visualised under a fluorescent microscope or if more information is required, for example the construction of a 3D image, confocal scanning electron microscopy may be used.

16S rRNA-based approaches have provided a wealth of information on soil microbial composition, but these approaches have some limitations. Although these approaches can indicate which phylogenetic groups of microbes are present in a soil sample, but they provide no information on the metabolic processes being carried out by those groups. In addition, group-specific 16S rRNA approaches may not be suitable for analysis of specific functional guilds if the function is distributed widely over the phylogenetic tree.

Stable Isotope Probing : Stable isotope technique is a technique that is used to identify the microorganism in environmental samples that use a particular growth substrate (Radajewski et al. 2000). This method is based on the incorporation of an enriched substrate with a stable isotope, such as 13C, and the following selective recovery and identification of active microorganism by isotope-enriched cellular component analysis. DNA  and rRNA 13C-labelled molecules can be separated from unlabelled nuclei acid by density-gradient centrifugation (Radajewski et al. 2000). The major disadvantage in this technique is the dependence on the commercial availability of compounds that are highly enriched in 13C. But this technique coupled with 16S rDNA cloning library or DGGE, and T-RFLP fingerprinting analysis can be used to find out which organism us a specific substrate (Dumont & Murell 2005).

DNA Microarrays: DNA microarrays offer a much higher probe capacity. DNA microarrays generally consist of a set of nucleic acids that are spotted and covalently bound to some solid support, such as a glass slide. On a typical DNA microarrays, hundreds to tens of thousands of nucleic acids can be spotted within a very small surface area, can be hybridized simultaneously.

In addition to higher probe capacity, microarrays also offer the advantages of increased speed of detection, low cost and the potential for automation. This technology has been used for characterizing entire genomes as well as for measuring gene expression.

This technology has great potential for characterizing microbial communities and their function in the environment. However, the application of DNA microarrays to the assessment of microbes in the environment poses a number of technical challenges, including optimization of probe-target specificity and quantification of target genes. Recent work has demonstrated that these technical challenges can be overcome.

Based on the potential advantages of DNA microarrays and recent technical advances, it is likely that the application of 16S rRNA targeted DNA microarrays environmental systems will increase dramatically in the near future.

Soil microbial diversity can be explored on the basis of functional genes or functional gene expression or genome sequencing using recent developments in molecular techniques. Some approaches for studying microbial diversity in soil communities are described here.

Approach based on Functional genes

The analysis of genes encoding enzymes involved in specific metabolic functions (functional genes) can overcome some of the limitations of 16S rRNA-based techniques. Several functional genes have been discovered that are well conserved across phylogenetic groups e.g. the genes coding for nitrogenase (the enzyme that catalyses biological nitrogen fixation), ammonia monoxygenase (the enzyme that involves in nitrification) and nitrite reductase (the enzyme that involves in denitrification).

Because these genes are conserved across phylogenetic groups, so it more practical choice for the study of these groups than 16S rRNA. In addition, functional genes often provide a level of resolution below specifies because functional molecules may experience higher rates of evolutionary changes than the 16S rRNA molecule. Functional genes can be used to assess the diversity of functional guilds in several ways. Once a functional gene has been identified, the diversity of this gene in different microbial species can be analysed by PCR-based approaches. The diversity of functional genes can be assessed directly from the environment. Specific PCR primers can be used to amplify the target gene from DNA extracted from environmental samples. This mixture of functional gene amplicons can be used to create a clone library, and individual clones can be sequences and compared.

Similarly, the diversity of environmentally derived functional gene amplicons can also be assessed by profiling techniques such as T-RFLP and DGGE. The T-RFLP profiles obtained from the environmental samples enabled the researchers to rapidly assess the complexities of ammonia oxidizing community in the environmental samples. There has also been development of DNA probes targeting functional genes e.g. nir gene (nitrite reductase- the enzyme which involves in denitrification).

The application of DNA probes is generally carried out using membrane-based or in situ hybridization techniques. These techniques can be used for probes targeted to various functional genes. However, because these hybridization techniques severely limit the number of probes that can be applied simultaneously. So the amount of acquired information is limited but DNA microarrays offer a much higher probe capacity.

Approach based on Functional Gene Expression

The presence of functional genes by PCR-based techniques can be assessed as described above, but it is possible now to assess the expression of functional genes in the environmental samples by the detection of mRNA. Since mRNA is critical intermediate in the gene expression and has a short half-life. Detection of mRNA specific for a gene is a strong indicator that the gene is being actively converted to protein. In addition, the number of mRNA transcripts is correlated with level of activity. Several different techniques have been employed to assess functional gene expression by detecting mRNA.

Reverse Transcriptase-PCR is an effective tool for mRNA detection. In this, mRNA is used as a template to synthesize a complementary strand of DNA (cDNA). This cDNA is then amplified by PCR. The amplicons can be cloned and sequenced. This powerful analysis method could be an excellent technique for the analysis of functional guilds in soils. Detection of mRNA can also be done via DNA probes and conventional hybridization techniques. Probes targeted to mRNA can also be applied in a DNA microarrays format.

So far the use of microarrays to monitor gene expression has been confined largely to analysis of expression patterns in a single organism (usually E. coli). But recently, DNA microarrays for the detection of expression in complex microbial communities has been employed. Dennis et al (2003) built a DNA microarrays that included 64 functional genes from a variety of organism. This technique shows great promise for the monitoring of gene expression in soils.

Approach based on sequencing technologies

Since Frederick Sanger began sequencing by electrophoretic size separation, there are many improvements in DNA sequencing such as use of fluorescent tags instead of radioactive labels to detect the terminal ladders, the use of capillary electrophoresis, and the development of paired-end sequencing protocols.

The concept of sequencing by synthesis (pyrosquencing) was presented first time by Nyren et al (1993). In this approach, sequencing is performed by detecting pyrophosphate release with enzymatic cascade ending in luciferase and detection of emitted light (Margulies et al. 2005). This is based on the detection of light produced whenever a nucleotide is incorporated.

The sample preparation start with fragmentation of the genomic DNA or amplification of desire gene, followed by the attachment of adaptor sequences to the ends of the DNA pieces. DNA strand are denatured DNA-template-carrying beads are loaded into the picolitre reactor wells of a fibre-optic slide (each well having space for just one bead) and then smaller beads carrying immobilized enzymes required for the pyrophosphate sequencing reaction are deposited into each well. The incorporation of the complementary base at the growing DNA chain generates inorganic pyrophophate (PPi), which is converted to ATP by the sulfurylase. This ATP is used by luciferase to convert luciferin to oxyluciferin, producing light.Sequential washed of each of the four nucleotides are run over the plate, and a detector senses which of the well emit light with each wash to determine the sequence of the growing strand. (Nyren et al. 1993, Margulies et al. 2005). There are some next-generation technologies available these days. The main advantage of the high –throughput sequencing is the substantial reduction of cost and is especially useful for the study of microbial communities by 16S rRNA gene analysis. Current approaches to understand microbial communites with high throughput sequencing are limited, since a large number of sequences are necessary to minimize the underestimation of richness due to insufficient sampling and because the available database is still poor (Schloss, 2008).


Metagenomics is the culture-independent analysis of a mixture of microbial genomes (metagenome) using an approach based either on expression or on sequencing (Riesenfield et al., 2004; Schloss et al. 2003), Susannah et al. 2005; Patrick et al 2005). The term is derived and coined from the statistical concept of meta-analysis and genomics to capture the notion of analysis of a collection of similar but not identical items. The meta-analysis is a process of statistically combining separate analyses, that is, analysis of analysis (Glass, 1976). In Greek, meta means “transcendent” so metagenomics transcends the individual organism to the “meta level” of the community. Genomics is the comprehensive analysis of an organism’s genetic material i.e. the analysis of all the DNA in an organism. Genomics, a field of study concerned with the sequencing and analysis of whole genomes, has traditionally advanced through the accumulation of data from individual sequencing projects, each being devoted to completing the genome of a single stain or an individual. So, metagenomics is the application of the methods of genomics to microbial assemblages. It involves studying the genetic makeup pf many microbes in an environment simultaneously, and makes accessible the many types of microbes that cannot be grown in the lab and therefore cannot be studies using the central tool of classical microbiology. Metagenomics also enables the study of entire microbial communities, offering a window to intact microbial system. The emerging field of metagenomics presents the greatest opportunity to revolutionize understanding of the living world.

Metagenomic Analysis

Metagenomics, also known as microbial ecogenomics or environmental genomics (Handelsman, 2004; M.D. Zwdinki, 2007) is the analysis of all of the microbial genomes from our environment. Metagenomics is employed as a means of systemically investigating, classifying and manipulating the entire genetic material isolated from the environmental samples.  It requires neither prior cultivation of the organism present, nor prior knowledge of the community inhabitants or target sequences.

This involves isolating DNA from an environmental sample, cloning the DNA into a suitable vector, transforming the clones into a host bacterium and screening the resulting transformants. The multi-step process consists of four steps namely, isolation of genetic material, manipulation of the genetic material, library construction, analysis of genetic material in the metagenomics library. Soil is considered as a complex environment, which appears to be a major reservoir of microbial genetic diversity (Robe et al, 2003). There are many published methods for extracting DNA from soil samples. Methods described for metagenomics DNA isolation from soil samples can be broadly classified into direct and indirect extraction procedures.The idea of cloning  DNA directly from environmental samples was first proposed by Pace (Pace et al., 1985) and in 1991, the first such cloning in phage vector was reported (Schmidt et al. 1991).

The next advance was the construction of a metagenomics library with DNA derived from a mixture of organisms (Healy et al. 1985). Metagenomics libraries are created by shotgun cloning DNA fragment from an environment samples. Metagenomics libraries were constructed from prokaryotes from seawater by the DeLong’s group (Stein et al., 1990) and they identified a 40 kb clone that contained a 16S rRNA gene indicating that the clone was derived from an Archae which has never been cultured.

Finally, metagenomics analysis involves library screening or assay that is required to identify clones that express a function. This requires faithful transcription and translation of the gene or genes of interested and secretion of the gene product.

The clones can be screened for phylogentic markers such as 16S rRNA or for other conserved genes by hybridization or multiplex PCR (Stein et al., 1996) or for expression of specific traits such as enzyme activity or antibiotic production (Coutries et al., 2003; Knietsch et al., 2003; Loresnz et al. 2002; Schloss et al., 2003) or they can be sequenced randomly (Venter et al, 2004). Each approach has strength and limitation. These approaches have enriched our understanding of the non-cultivable soil bacterial.

Broadly two types of screening have been used to identify clones carrying desired traits from metagenomics libraries: Function-based screening and sequenced-based screening. Both screenings have individual advantages and disadvantages. Both screening are laborious and tedious because of the low frequency of screening hits. Recently, a high throughput screening strategy, termed substrate-induced gene expressing screening (SIGEX) has been introduced (Jiae Yun and Sangreueol Ryu, 2005). It is designed to select the clones harboring catabolic genes induces by various substrates in concert with FACS.

Sequenced based analysis

This can involve complete sequencing of clones containing phylogenetic anchors that indicate the taxonomic group. Random sequencing can also be conducted. Once a gene of interest is identified, phylogenetic anchors can be sought in the flanking DNA to provide a link of phylogeny with a functional gene. This approach produced the first genomic sequence linked to a 16S rRNA gene of an uncultured Archaeon (Stein et al, 1996). Similarly, the sequence of flanking DNA revealed a bacteriorhodopsin-like gene, which was shown to produce a photoreceptor, in the seawater bacteria that affiliated with the gamma-proteobacteria. This led to the insight that bacteriorhodopsin genes are not limited to Archaea but is abundant among the Protobacteria of the ocean (Beja et al., 2001).

This has been initiated with clones from diverse soils carrying 16S rRNA genes that affiliate with the Acidobacterium phylum, abundant in soil and highly diverse (Barns et al., 1999; Buckley et al., 2003) and about which little is known ( Liles et al., 2003; Quasises et al., 2003). Complete sequence of the estimate about 500 kb of Acidobacterium DNA in metagenomics libraries may provide insight into the subgroup of bacteria in this phylum that have never been cultured. Random sequencing is an alternative approach in which random clones are sequenced. This has produced dramatic insights. The distribution and redunctancy   of functions in a community, linkage of traits, genomic organization and horizontal gene transfer can be inferred from sequence-based analysis.

Functional based analysis

The frequency of metagenomic clones that express any given activity is very low. The scarcity of active clones, therefore, necessitates development of efficient screen and selections for discovery of new activities or molecules. Soil microbial diversity is immense (Gans et al., 2005) and the vast majority of that diversity remains uncultured. The soil metagenome may be too large to sequence completely in the near future. This microbial diversity of soil contains vast untapped resources of microbial processes that may have significant scientific, practical or profitable potential. Soil metagenome could be a source for novel microbial products without cultivating or identifying the organism responsible. For this we have to look for specific functions with selective media and to screen many clones. Deciding what activities to look for and how many clones to screen are significant decisions that may depend on several factors including the size of the fragment inserts and the activities of interest. Advances in the   availability of vectors (Voget et al., 2003; Martinz et al., 2004) and host strains (Williamson et al., 2005) are increasing the potential for finding novel bioproducts and catalytic enzymes from soil metagenome. Other promising applications that may come from soil metagenome libraries are antitumor agents (Pettit, 2004) and novel biodegradive pathways for xenobiotics (Eyers et al., 2004). Many companies such as Diversa Corporation (San Diego) involved in mining microbial genomes from unique environments for economically viable enzyme, biochemical pathways, agricultural products, new pharmaceuticals and other products (Handelsman, 2004; Schloss and Handelsman, 2005a).

In future, soil metagenomics research will involves combining techniques that can link microorganism and their functions. It is performed either by labeling or quantifying target genes (RT-PCR, stable isotope probing or FISH). It will facilitated by the new functional or phylogenetic genetic targets obtained from metagenomics libraries (Wellington et al., 2003).

This will also help in understanding the role of lateral gene transfer in microbial evolution, monitoring the dynamic interactions between environmental influences and microbial community changes, finding pattern in microbial processes, understanding the importance of species diversity and functional redundancy in soil, and predicting biogeochemical cycles (Wellington et al., 2003; Delong, 2004; Handelsman. 2004; Streit and Schnitz, 2004).

Challenges ahead in metagenomics

Much has been learned from early metagenomics studies and new researches are in process to know which steps in the process commonly present, difficulties and obstacles. Large sequencing studies still face significant challenges. Foremost among the challenges will be evaluating the tremendous amount of information that will be generated (Nelson, 2003).

It is the daunting task of understanding the genomics of uncultivated microorganisms or whole environmental genomes with respect to identifying the functions of genes as compared with a well-studied and easily cultivated microorganism.

In mixed microbial communities, it will be difficult to separate microbial genomes and organize, assemble and annotate the genomics. Most of the ORFs that have been found in metagenomic studies have no homologous representatives in the ?? databases (Schloss and Handelsman, 2005a)

Metagemomics produces a snapshot of the microbial community genome at a specific point in time and space (Schloss and Handelsman, 2005a). Metagenomics studies alone count describe how or when the genes found are expressed, therefore, analyses of the proteomics or specific assays for microbial activities are also needed.

The most abundant DNA will come from the most abundant or most readily lysed cells, not necessarily the most environmentally important or interesting soil organisms. The lysis procedures used are relatively gentle and may introduce biases against lysis-resistant organism in the genome collection. Streptomycetes, a group of microorganisms from soil which produce bioactive molecule, may not lyse under detergent-based methods. Therefore, these organisms, although abundant and important, may be excluded from soil metagenomics libraries.




This is one of most powerful tools for understanding gene structure and RNA-based regulation in any organism. This also helps in understanding the timing and regulation of complex microbial processes within communities. In metatranscriptomics total RNA is extracted from a microbial community, converted into cDNA and sequenced. Neither primers nor probes are needed and transcripts from microbial assemblages are sequenced with little bias. Random transcripts sequencing has been used to capture organism-specific wild transcriptomes, obtaining insight into activities of ecologically important microbes where biology is poorly known (Urich et al, 2008). This provides a revolutionary means of discovering functional non-coding RNAs in some of uncultured organism.

Gene expression of a given microbial community and analysis of transcriptome (all the mRNA molecules transcribed from the genome or microbial community) has been successfully applied through microarray technology and random cloning methodologies improved by new sequencing technologies and massively parallel direct sequencing of cDNA followed by mapping, generating the so called “Metatranscriptomics”. Metatranscriptomics studies have the potential to identify large numbers of new, niche-specific non-coding RNA family among localized microbial community.


Interpretation of data


Statistical approaches developed by ecologist to work on distribution and diversity patterns of plants and animals can also be applied to soil microbial community analysis. For example, the number of species or operation taxonomic units (OTU) and gene presence or absence or polymorphism can be measured (Lopes et al. 2009). In DGGE fingerprinting, one band refers to a unique sequence type or phylotype for a bacterial population present in the sample and band intensity is a consequence of the density of corresponding bacterial phylotypes within the sample (Murray et al. 1996).  Thus the total number of bands and their relative intensities in each sample can be used to calculate well-known diversity indices such as the Shannon-Weaver index (Nubel et al. 1999). Similarly, coefficients such as the Euclidean measure (McSpadden Garderner & Lilley, 1997) or the Pearson correlation (Rolling et al. 2001, Smalla et al. 2001) can be measured. Similarity and distance matrices can be visualized as a dendrogram and can be extended to clustering and ordination methods. Clustering techniques such as unweighed pairing using arithmetic averages (UPGMA) offer a simple way to view the DGGE profiles. Ordination methods such as cluster analysis, principal component analysis (PCA), can be used to correlate diversity pattern to environmental parameters.

A growing number of statistical approaches have been successful in describing and comparing microbial communities that can be sequenced (Schloss 2008). The Ribosomal Data Project (RDP) has reported accuracy in classification of partial sequences to the genus level for 400-base reads and to the family level for 200-base reads (Cardenas & Tiedje 2008). The new sequences technologies create a scenario where the limitation is not the ability to produce sequence data but the ability to store and analyse it in new revealing ways. Databases and software tools are essential to deal with the growing of metagenomic and metatranscriptomic data (Shendure J, 2008).

Significance of Soil Microbial Biodiversity

Soil biodiversity is probably the largest component of total biodiversity. It is highly probable that many habitats of nature conservation value are sustained and maintained by soil biodiversity. Although we are still unsure of the links to ecosystem functioning and above ground biodiversity.

Soil microorganisms are vital to sustaining the biosphere and functioning of ecosystems and consequently, can be used for maintaining and predicting environmental change and pollutant impacts. Since soil functioning depends on the activity of microorganisms, measures of the activity, biomass and diversity of soil microorganisms have been proposed as indicators of soil quality. Before doing this, we should be able to link biological and chemical parameters to predict changes in soil functioning as microbial biomass is usually proportional to levels of soil organic matter. Therefore, using the ratio of biomass to soil organic matter or to the rate of decomposition may provide more sensitive and meaningful ways to assess change.

The untapped diversity of microorganisms is a resource for new genes and species that may have value to biotechnology and medicines. Microorganisms have been the main source of medicinal compounds such as antibiotics, immunosuppressants, anti-tumor agents and pesticides, vitamins, enzymes etc. Hence, the vast unexplored biodiversity of soil has potential for commercial exploitations in biotechnology especially in areas of medicines, agriculture, industrial processes and bioremediation of polluted wastes, waters and land.

Thus, by understanding the complexity and limits of life, especially in extreme environment, we can expand our knowledge regarding the diversity and extent of life in general and the possibilities of life on other planets such as Mars.




Little is known about soil microbial biodiversity as compared to other environments, but now it is possible to unlock some of the secrets of the soil which is not possible earlier. Molecular techniques have revolutionized the field of soil microbiology especially soil microbial ecology  over the last 25 years. Application of these techniques to study of 16S rRNA genes has created a comprehensive framework for microbial phylogeny and has dramatically expanded our understanding of soil microbial diversity. Recent functional gene studies have already contributed to our understanding of functional guild diversity. Functional gene analysis techniques will ultimately allow for effective monitoring of the expression of these genes in soil. An exciting new technology, DNA microarrays, has already demonstrated its utility as format for the application of both 16S rRNA and functional gene targeted DNA probes.

Because of these high probe capacity, DNA microarrays offers the potential to build complex array that integrate monitoring of both 16S rRNA and functional genes, which will enables us to examine both phylogenetic and functional components of soil microbial communities. Therefore, molecular techniques such as DGGE, T-RFLP, and DNA microarrays in particular as well as soil metagenomics study, should help to revolutionize our understanding of the complex role that microorganisms play in soil processes.

Soil microbial biodiversity and community analysis lives now in the era of “Meta” where massive sequencing, metagenomics, metaproteomics and metatranscriptomics will allow us to survey a comprehensive range of data about soil microbial diversity, ecology and function. The speed of data generation will soon outstrip the capacity of data analysis unless radically new approaches are implemented. So our next limitations will be how to analyse and integrate all the generated data and how to use all the information to improve biotechnological development as well as environmental maintenance.


  1. Alves FR. Siqueza JF and Carmo FL (2009). Bacterial Community profiling of cryogenically ground samples from the aptical and coronal root segments of teeth with apical periodonties. J. Endodoutics 35:486-492.
  2. Amann I., Ludwig W., and Schleifer K.H. (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation, Microbiol.Rev., 59:143-169.
  3. Barns S.M., Takata S.L. and Kuske C.R. (1999). Wide distribution and diversity of members of the bacterial Kingdome Acidobacterium in the environment. Appl. Envion. Microbiol. 65:1731-1737.
  4. Beja O., Spudich E.N., Spudich J.L., Leclerc M. Delong E.F. (2001) Proteorhodopsin phototrophy in the ocean. Nature 411:786-789.
  5. Borneman J., Skroch P.W., O’Sullivan K.M., Palus J.a., Rumjanek N.G., Jansen J.L., Niehuis J., and Triplett E.W. (1996), Mlecular microbial diversity of an agricultural soil in Wisconsin., Environ. Microbiol, 62:1932-1943.
  6. Braker G., Fesefeldt and Witzell KP (1998) Development of PCR primer systems for amplification of nitrite reductase gene (nirK and nosS) to detect denitrifying bacteria in environmental samples; 64:3769-3775.
  7. Braker G., Ayala-del-Rio H.L., Devol A.H., Fesefeldt A., Tiedje J.M., (2001), Community structure of denitrifiers, bacteria and Archae along redox gradients in Pacific northwest marine sediments by TRFL polymorphism analysis of amplified nitrite reductase (nirS) and 16S rRNA genes; 67:1893-1901.
  8. Buckley D.H. and Schmidt T.M. (2003). Diversity and dynamics of microbial communities in soils from agro-ecosytems, Envion. Microbiol. 5:441-452.
  9. Cardenas and Tiedje (2008) New tool for discovering and characterizing microbial diversity. Opin. Biotech 19:544-549.
  10. Courtois S., Cappellano C.M., Ball M., Francou F.X., Normand P., Helynck G. Martinez A., Kolvek S.J., Hopke J.,Osburne M.S. et al. (2003). Recombinant environmental libraries provide access to microbial diversity for drug discovering from natural products. Environ. Microbiol. 69:49-55
  11. Delong E.F. (2004) Microbial population genomics and ecology: The road ahead. Microbiol 6:875-878.
  12. Delong E.F. (2005) Microbial community genomics in the ocean, Nature Review Microbiol, 3:459-469.
  13. Dumont M.G. and Murell J.C. (2005), Stable isotope probing-linking microbial identity to function. Nature Rev. Microbiol. 3:499-504.
  14. Eyers I, George I., Schuler I., Stenuit B., Aathos S.N., and Fontrosussi S.E. (2004). Envioenmental genomics: Exploring the unmind richness of microbes to degrade xenobiotics. Envion. Microbiol. 66:123-130.
  15. Gans J., Wolinsky M., Dunbar J., (2005) Computational improvements reveal great bacterial diversity and high metal toxicity in soil, Science 309:1687-1390.
  16. Glass G.V. (1976). Primary, secondary and meta-analysis of research, Res. 5:3-8.
  17. Ghazanfar S, Azim A, Ghazanfar MA, Anjum MI and Begum I (2010) Matagenomics and its application in Soil microbial community studies: Biotechnological prospects. Animal Pl. Sci, 6:611-622.
  18. Hardoin CC, Costa R. and Araujo FV (2009) Diversity of bacteria in the marine sponge Aplysina fulva in Brazilian coastal waters, Environ Microbiol. 75:3331-3343.
  19. Henckel T, Roslev P and Contrad R. (2000) Molecular analysis of novel methanotrophic communities in forest soil that oxidize atomspheric methane, Envior. Microbiol, 2:666-679.
  20. Handelsman J. (2004) Metagenomics: application of genomics to uncultured microorganisms. Mol. Biol. Rev. 68:669-685.
  21. Healy F.G., Ray R.M., Aldrich H.C., Wilkie A.C., IL, and Shanmugam K.T. (1995). Direct isolation of functional genes encoding cellulases from the microbial consortia in a thermophilic, anaerobic digestion maintained on ligncellulose. Appl. Microbiol. Biotechnol. 43:667-474.
  22. Jiae Yun and Sangreued Ryu (2005) Screening for novel enzymes from metagenome and SIGEX, as way to improve it. Microial Cell Factories 2005, 4:8 doi:10.1186/1475-2859-4-8
  23. Kuske C.R., Barns S.M., Busch J.D. (1997), Diverse uncultivated bacterial roups ffrom soils of the arid Southwestern Univted States that are present in many geographic regions, Environ. Microbiol., 63:3614-3621.
  24. Knietsch A., Bowien S., Whited G., Gott Schalk G. and Darriel R. (2003) Identification and characterization of eoenzymes B12- dependent glycerol dehygrogense- and diol dehydrogenase-encoding genes from metagenomic DNA libraries derived from enrichment culture. Environ. Microniol. 69:3048-3060.
  25. Kowalchuk G.A., Bodelier P.L.E., Heilig G.H.J., Stephen J.R., and Laanbroek H.J., (1998), FEMS Microbiol Ecol. 27:339-350.
  26. Lopes dos Santos A., PeixotoR. And Rosado A.S. (2009), New approaches to understanding microbial diversity in wastewater landfills and leachate treatment, Oecologia Brasilienris. 13:631-648.
  27. Liles M.R., Manske B.F., Bintrim S.B., Handelsman J., Gaztman R.M. (2003) A census of rRNA genes and linked genomic sequences within a soil metagenomic library, Environ. Microbiol. 69:2684-2691.
  28. Lorensnz P., and Schleper (2002) Metagenome- a chalengiong source of enzymes discovery. Mol. Catal. B. Enzym. 19:13-19.
  29. Martinz A., Kolvek S.J., Yip C., Hopke J., Brown K.A., MacNeil IA, and Osburne M.S. (2004) Genetically modified bacterial strains and novel bacterial artificial chromosomes shuttle vectors for constructing environmental libraries and detecting heterologuous natural products in multiple expression hosts. Envion. Microbiol. 70:2452-2463.
  30. Margulies M., Egholm M. Altman W.E., Attiya s., Bader J.S., Bemben L.A. et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376-380.
  31. Muyzer G., Smalla K. (1998). Application of DGGE and TGGE in microbial ecology. Antonie Van Leeuwenhoek 73:127-141.
  32. Murray AE, Hoolibaugh JT and Orego C. (1996) Phylogentic composition of bacterioplankton from two California estuaries compared by denaturing gradient gel electrophoresis of 16S rDNA fragments. Environ. Microbiol. 62:2676-2680.
  33. Mc Spadden Gardernes BB and Lilley AK (1997), Application of common statistical tools. Modern Soil Microbiology; 501-523.
  34. Nubel , Garcia-Pichel, Kuhl F. and Muyzer G. (1999) Quantifying microbial diversity: morphotypes, 16S rRNA genes, and carotenoids of oxygenic phototrophs in microbial mats. Appl. Environ.Microbio. 65:422-430.
  35. Nyren P., Pettersson B. and Uhlen M (1993) Soild phase DNA minisequencing by an enzymatic luminometric inorganic pyrophosphate detection assay. Analytical Biochemistry 208:171-175.
  36. Pace N.R., Stahl D.A., Lane D.J. and Olsen G.J. (1985), Analying natural microbial populations by rRNA sequences, ASM News, 51:4-12.
  37. Patrick D.S. and Handelsman J. (2005). Metagenomics for studying unculturable microorganism:cutting the Gordian Knot. Genome Biol. 6:229
  38. Peixoto R.S., Costa Coutinho D.A., Rumjamek N.G., Macrae A. and Rosado A.S. (2002). Use of rpo B and 16S rRNA genes to analyse bacterial diversity of a tropical soil using polymerase chain reaction and denaturing gel gradient electrophoresis. Letters in applied Microbiol. 35:316-320.
  39. Pettit R.K. (2004). Soil DNA libraries for anticancer drug discovery. Cancer Chemo. Pharmaco 54:1-6.
  40. Quasises A., Ochsenareiter T., Lanz C., Schuster S.C. Treusch A.H. Eck J., and Schleper C. (2003), Acidobacteria from a coherent but highly diverse group within the bacterial domain: evidence from environmental genomics. Microbiol. 50:563-575.
  41. Robe P., Nalin R., Capellano C. and Vogel T.M. (2003) Extraction of DNA from soil, J. Soil Biol. 39:183-190.
  42. Radajewski S., Inesen P, Pareskh N.R., Murerell J.C. (2000). Stable-isotope probing as a tool in microbial ecology. Nature, 403:646-649.
  43. Riesenfield C.S., Schloss P.D., Handelsman J. (2004). Metagenomics: genomic analysis of microbial community. Rev. Genet, 38:525-552.
  44. Rotthauwe JH Witzel KP and Liesack W. (1997) The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammoniaoxidizing populations;  Environ. Microbiol. 63:4704-4712.
  45. Rolling W.F.M., van-Breukelen B.M., Braster M., Goeltom M.T., Groen J. (2001). Analysis of microbial communities in a landfill leachate polutted acquifer using a new method for anaerobic physiolical profiling and 16S rDNA based fingerprinting. Ecol, 48:177-188.
  46. Sanger F, Nicklen S, Coulson AR (1977), DNA sequencing with chain-terminating inhibitors, Natl. Acad. Sc., USA, 74:5463-5467.
  47. Sanger F and Coulson AR (1975) A rapid method for determing sequencing in DNA by primed synthesis with DNA polyersase J. Mol. Biol. 94:441-448.
  48. Scala DJ. And Kerkhoff L.J. (2000); Horizontal heterogeneity of denitrifying bacterial community in marine; 66:1980-1986.
  49. Schloss P.D. and Handelsman J. (2003) Biotechnological prospects from metagenomics. Opin. Biotechnol. 14:303-310.
  50. Schloss P.D., Handelsman J. (2005) Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness, Environ. Microbiol. 71:15011506
  51. Schloss PD and handelsman J (2005a). Metagenomics for studying unculturable microorganism:cutting the Gordian knot; Genome Biol. 6:229,doi:10.1186/gb-2005-6-8-229.
  52. Schloss P.D. (2008) Evaluating different approaches that test whether the microbial communities have the same structure. ISME Journal, 2:265-275.
  53. Schmidt T.M., Delong E.F. and Pace N.R. (1991) Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing. J Bacteriol. 173:4371-4378.
  54. Shalon DS, Smith J and Brown PO (1996) A DNA microarray system for analyzing complex DNA samples using two-colour fluorescent probe hybridization., High-density microarray system for analyzing complex DNA samples using two-colour fluorescent probe hybridization, Genome Res. 6:639-645.
  55. Shendure J.(2008) The beginning of the end for microarrays? Nature Methods 5:585-587.
  56. Smalla K., Wieland G., Buchner A., Zock A., Parzy J.,Kaiser S., Roskot R., Hener H., Berg G. (2001), Bulk and rhizosphere soil bacterial communities studied by denaturing gradient gel electrophoresis: plant dependent enrichment and seasonal shifts revealed. Envion. Micriol. 67:4742-4751.
  57. Smith GB and Tiedje JM (1992), Isolation and characterization of a nitrite reductase gene and its use as a probe for denitrifying bacteria, Environ. Microbiol., 58:376-384.
  58. Stein J.L., Marsh T.L., Wu K.Y., Shizuya H., and Delong E.F. (1996) Characterization of uncultured prokaryotes: isolation and analysis of a 40-kilobase pair genome fragment from a planktonic marine archaeon. Bacteriol. 178:591-599.
  59. Stahl D.A. (1995) Application of phylogenetically based hybridization probes to microbial ecology. Ecol, 4:535-542.
  60. Streit W.R. and Schnitz R.A. (2004) Metagenomics: The key to the uncultured microbes. Opin. Microbiol. 7:492-498.
  61. Susannah G.T., and Edward M.R. (2005) Metagenomics: DNA sequencing of environemental samples, Nature Rev. Gen. 6:805-814.
  62. Urich T., Lanzen A., Qi J., Huson D.H.,Schleper C., Shuster S.C.; (2008). Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE 3,:e2527
  63. Venter J.C., Remington K., Heidelberg J.F., Halpern A.L., Rusch D.,Eisen J.A., Wu D., Paulsen I., nelson K.e., Neson W. et al. (2004) Envionmental genome shotgun sequencing of Sar gass Sea, Science, 304:66-74.
  64. Voget S., Leggewie C., Uesbek A., Raasch C., Jaeger K.E. and Steit W.R. (2003) Prospecting for novel biocatalyst in a soil metagenome. Envion. Microbiol. 69:6235-6242.
  65. Wellington EM., Berry A., and Kresek M. (2003) Resolving functional diversity in relation to microbial community structure in soil:Explring genomics and stable isotope probing, opin. Microbiol. 6:295-301
  66. Williamson LL, Borlee BR, Schloss PD, Guan C, Allen HK and Handelsman J. (2005), Intracellular screen to identify metagenomic clones that induce or inhibit a quorum-sensing biosensor. Envion. Microbiol., 71:6335-6344.
  67. Wilson KH, Wilson WJ, Radosevich JL, De-Santis TZ, Viswanthan VS, Kuczmarski TA and Andersen GL (2002), High-density microarray of small-subunit ribosomal DNA probes, Environ. Microbiol. 68:2535-2541.
  68. Zehr J.P., Mellon M., Braun S., Litaker W. , Steppe T. and Paerl H.W. (1995) Enviorn. Microbiol, 61:2527-2532
  69. Zwdinki M.D. (2007) DNA sequencing: strategies for soil microbiology. SSAJ. 71:592-600.


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: