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Review Article
Emerging Trends in Genomic Approaches
for Microbial Bioprospecting
K.B. Akondi and V.V. Lakshmi
Abstract
Microorganisms constitute two out of the three domains of life on earth. They exhibit vast biodiversity and
metabolic versatility. This enables the microorganisms to inhabit and thrive in even the most extreme envi-
ronmental conditions, making them all pervading. The magnitude of biodiversity observed among micro-
organisms substantially supersedes that exhibited by the eukaryotes. These characteristics make the microbial
world a very lucrative and inexhaustible resource for prospecting novel bioactive molecules. Despite their vast
potential, over 99% of the microbial world still remains to be explored. The primary reason for this is that the
culture-dependent methods used in the laboratories are grossly insufficient, as they support the growth of under
1% of the microorganisms found in nature. This limitation necessitated the development of techniques to
circumvent culture dependency and gain access to the outstanding majority of the microorganisms. The de-
velopment of culture-independent techniques has essentially reshaped the study of microbial diversity and
community dynamics. Application of genomic and metagenomic approaches is contributing substantially to-
wards characterization of the real microbial diversity. The amenability of these techniques to high throughput
has opened the doors to explore the vast number of ‘‘uncultivable’’ microbial forms in substantially lesser time.
The present article provides an update on the recent technological advances and emerging trends in exploring
microbial community.
Introduction
Microorganisms occupy every habitat on earth and
determine the biogeochemistry of the planet. For more
than a century, the identification and characterization of micro-
organisms has been carried out exclusively via traditional/
axenic culturing techniques. These studies contributed sub-
stantially towards the establishment and development of
public healthcare practices. They also revealed the key roles
that microorganisms play in geochemical cycles and biore-
mediation, thus offering insights into the vast potential of the
microbial world. As rich sources of novel bioactive molecules,
the microorganisms continue to attract substantial interest for
their pharmaceutical and therapeutic applications ( Joint et al.,
2010; Van Hamme et al., 2003; Widada et al., 2002). Despite
providing considerable insights into the microbial world,
culture-dependent techniques have some major limitations.
They show a considerable bias towards organisms that are
best adapted to laboratory conditions. Microorganisms co-
exist as mixed communities in nature. The composition and
dynamics of each community are determined by biotic and
abiotic factors. Environmental factors such as pH, tempera-
ture, and salinity play a vital role in determining the type of
microflora. Numerous studies have found that the cultivable
microorganisms constitute only a fraction of the overall
community diversity. For example, the microorganisms ini-
tially identified to play a predominant role in bioremediation
via culture techniques were later realized to contribute mar-
ginally. Further, when the gene pools of various xenobiotics
degrading microbial communities were compared, the ‘un-
cultivable organisms’ were found to constitute a significant
fraction of total diversity (Ferrer et al., 2009; Handelsman,
2004, 2005). Vast disparities were also apparent between the
number and range of cultivable microorganisms and the
overall estimate of microbial diversity in marine biopros-
pecting studies. Characterizing the biodiversity of marine
microflora has been especially challenging due to the com-
plexity and dynamic nature of the marine environments ( Joint
et al., 2010). These and many other studies have revealed that
preferential growth of specific microorganisms under labo-
ratory culturing conditions gave a distorted/erroneous pic-
tures of the constitution of microbial communities (Eyers
et al., 2004; Malik et al., 2008; Torsvik et al., 2002). Hence,
investigations employing traditional culturing approaches
alone are becoming unacceptable for bioprospecting and
cannot be relied upon to determine the community
Department of Applied Microbiology, Sri Padmavati Women’s University, Tirupati, India.
OMICS A Journal of Integrative Biology
Volume 17, Number 2, 2013
ªMary Ann Liebert, Inc.
DOI: 10.1089/omi.2012.0082
61
composition accurately either qualitatively or quantitatively
(Daniel, 2005; Malik et al., 2008; Torsvik et al., 2002).
To address this issue, novel culturing techniques to simu-
late the natural habitat of various organisms in laboratory are
being developed and optimized to enhance microbial growth
(Widada et al., 2002). In addition, culture-independent meth-
ods (CIMs) employing current molecular approaches, are also
significantly contributing towards improving our under-
standing of the biodiversity of microorganisms (Malik et al.,
2008; Stenuit et al., 2008). The various strategies being adopted
for studying bacterial bioprospecting and biodiversity are
summarized in Figure 1. Collectively, these approaches serve
as powerful tools to tap into the biotechnological applications
of the vast resource of uncultured microorganisms.
Emergence of Culture-Independent Methods
The most significant surge in application of molecular tools
to study the microbial ecology and biodiversity began in 1995.
Analysis of specific cell constituents such as nucleic acids,
proteins, and lipids extracted directly from the environment
samples allow simultaneous study of both the cultivable and
noncultivable microorganisms (Greene and Voordouw, 2003;
Neufeld et al., 2004). Application of these strategies revealed a
startling diversity of the ‘uncultivable’ microbiota. Their
abundance renewed the excitement towards exploring these
unknown microorganisms. The initial CIMs were based on
PCR sequencing of clones from 5S rRNA cDNA library, which
were found to be unreliable. Analyzing the 1500 bp long 16S
FIG. 1. Current approaches in investigating biodiversity and bioprospection of microorganism. Microorganisms from
environmental samples can be isolated using traditional plate culture methods, but only a limited number of microorganisms
are able to adapt to the culture conditions. Culture enrichment techniques enhance the possibility of isolating organisms, if
selective conditions are provided to encourage growth of desired organisms. The high throughput methods allow simulta-
neous testing of a wide range of conditions and allow isolation of novel organisms of biological interest. The culture-
independent approaches are based on DNA isolation and generation of genomic or metagenomic libraries to explore the vast
diversity of uncultivable, as well as cultivable, microflora. These libraries can be further screened for novel bioactive mol-
ecules and functional characteristics.
62 AKONDI AND LAKSHMI
rRNA gene, however, was much more successful. This is be-
cause these sequences are ubiquitously distributed and show
high levels of conservation even among evolutionarily distant
species. 16S rRNA typing is widely applied for taxonomic
grouping of bacteria. The well-accepted criteria for defining a
new bacterial species is a less than 97% similarity in the 16S
rRNA gene sequence compared to the closest known bacterial
species (Lane et al., 1985; Nocker et al., 2007; Pace et al., 1985).
16S rRNA typing also allows comparison of microbial distri-
bution among different samples, in addition to quantifying
the relative abundance of each taxonomic group. The ro-
bustness and versatility of this technique makes it a good tool
to carry out phylogenetic inferences and gain insights into the
metabolic diversity of microorganisms. This technique has
provided the most convincing evidence of the vastness of
microbial diversity (Handelsman, 2004; Ludwig, 2010; Sanz
and Kochling, 2007).
In spite of the above advantages, relying solely on 16S
rRNA gene sequences for phylogenetic analyses does have
some limitations. First, the differentiation of closely-related
bacterial species can become ambiguous at times, since the
16S rRNA genes are highly conserved (Gu
¨rtler and Stanisich,
1996; Kolbert and Persing, 1999). The presence of multiple 16S
rRNA gene copies among different organisms (varying from 1
to 15), heterogeneity due to paralogous copies, occurrence of
lateral gene transfer in some cases can also result in incon-
clusive phylogenity (Klappenbach et al., 2001). To address
some of these challenges, Multi-Locus Sequence Analysis
(MLSA) has been developed. This technique in principle is
similar to Multi-Locus Sequence Typing (MLST), which is
widely used for bacterial typing in epidemiological studies
(Enright and Spratt, 1999; Platonov et al., 2000). MLSA utilizes
ubiquitous housekeeping and other functional genes present
as single copies, to complement 16SrRNA data. Some of the
genes targeted for MLSA genotyping approach include 23S
rRNA,RpoB, gyrB, and pheS. recA, (DNA repair protein) dnak,
atpD EF-Tu, EF-G hsp70, and hsp60, in addition to 16SrRNA
sequence (Lee and Cote, 2006; Ludwig, 2010). Additional
specific functional genes like pmoA, mxaF, and nod are helpful
when analyzing environmental samples to deduce correlation
between the extent of diversity and the metabolic function
(Horz et al., 2001). To strike a balance between the acceptable
identification power and time/cost for strain typing, internal
fragments (450–500 bases length) of multiple housekeeping/
functional genes (about 7–8 genes) are commonly used in the
laboratories (Ludwig, 2010). Genetic fingerprinting tech-
niques based on the separation of the phylogenetic markers
provide a reliable and specific profiling pattern for a given
microbial community. The application potential, advantages,
and limitations of the various finger printing techniques
available have been extensively reviewed earlier (Cardenas
and Tiedje, 2008; Fromin et al., 2002; Gabor et al., 2007; Nocker
et al., 2007). The application of these approaches has signifi-
cantly widened the horizon of phylogenetic and taxonomical
characterization of uncultured bacteria from diverse envi-
ronments (Ferrer et al., 2009).
The ability to conduct genome-wide analysis of large
communities of microbial phyla in the post-genomic era has
substantiated the complete dependence of the biosphere on
the metabolic activities of microorganisms. The total number
of characterized bacterial species to-date is limited to around
only 6000 (Rappe and Giovannoni, 2003; Vinuesa,2010).
However, the genome representation per g of soil is estimated
to be about 4000–7000 and total prokaryotic cell diversity
predicted to be an incredible 4–6 ·10
30
!( Joint et al, 2010;
Kaeberlein et al, 2002).The advancements in r-RNA based
phylogenetic approaches are currently allowing monitoring
of 50–200 microbial forms/g (Pontes et al., 2007; Malik et al.,
2008).Even at this stage, the current Gene Bank entries of 16S
RNA genes from uncultured prokaryotes significantly out-
numbering (over twice) those identified via culturing tech-
niques. Several new divisions of bacteria with little or no
affiliation to the known organisms have been characterized
from the fraction of uncultured organisms using CMI (Hallam
et al., 2006; Kunin et al., 2008b). Thus, the bulk of the micro-
biota still remains as a vast untapped resource for application
(Handelsman, 2004, 2005; Lovely, 2003; Vinuesa,2010). Sev-
eral studies have further revealed presence of huge genomic
diversity even within a single bacterial species (Malik et al.,
2008; Mira et al., 2010; Nocker et al., 2007; Tettelin et al., 2008).
Advances in sequencing technologies and subsequent re-
duction in cost are permitting complete genome sequencing of
several bacterial strains of individual species. Comparative
analysis of data from genomes of multiple strains/species of a
single bacterium is collectively termed as a pan-genome. The
pan-genomic repertoire is larger in magnitude by many or-
ders than any single genome. It comprises mainly of a ‘‘core
genome’’ containing the genes which are present in all char-
acterized strains. Other ‘‘dispensable genome’’ sets contain
genes that are present in two or more strains or genes that are
unique to a specific strain (Mira et al., 2010; Tettelin et al.,
2008). Thus, CIMs have become indispensable tools for in-
vestigating bacterial genetic diversity, population structures,
and understanding their ecological role in various habitats.
DNA Microarrays
Microarray technology is yet another important taxo-
nomical and functional tool that is widely used for genome
and proteome analysis of mixed microbial communities. The
property of a single-stranded DNA or RNA molecule to hy-
bridize with complementary probe molecules attached to a
solid support forms the basic principle of microarrays.
Readouts are detected as signals given off by the fluorescent
dyes incorporated in the sample (Zhou, 2003). The microarray
slides/chips are prepared by spotting an ex situ synthesized
probe or by directly assembling the probe in situ. In the latter
approach, thousands of probes are spotted on a single slide
using photolithographic masks and electrochemical reactions
(e.g., ‘‘Gene Chip’’ arrays). The microchip types differ based
on immobilization technology used, length, and nature of the
probes, as well labeling of the targets. Factors such as probe
density, specificity, sensitivity, quantification, and cost decide
the technique selected for a study. Compared to traditional
nucleic acid hybridization techniques, microarrays provide a
rapid and sensitive detection system capable of detecting upto
a single mismatch (Gao et al., 2007; Liu and Zhu. 2005; Zhou,
2003).
Three major classes of environmental microarray formats
are used for bioprospecting microbial community. These are
the community genome arrays (CGA), the phylogenetic
oligonucleotide arrays (POA), and functional gene arrays
(FGA) (Chandler et al., 2006; Rhee et al., 2004; Wu et al., 2001,
2004, 2006). Combination of microarrays types increases the
MICROBIAL BIOPROSPECTING 63
versatility to probe complex microbial communities. For ex-
ample, comprehensive FGA or Geochips that also have sev-
eral phylogenetic probes, facilitated study of microbial
communities dynamics in situ (He et al., 2007; Wu et al., 2001).
Coupling of whole community RNA amplification (WCRA)
with CGA allowed monitoring of the functional activities of
microbial communities in environments contaminated with
organic solvents, hydrocarbons, and uranium (Gao et al.,
2007; Wu et al., 2006). Another prominent step forward in
application of DNA arrays to community analysis is the use of
probes produced directly from environmental DNA without
any cultivation steps. Such metagenomic arrays (MGA) hold
potential for high-throughput screening and have been suc-
cessfully applied to characterize microbial community of a
groundwater microcosm and other natural environments
(Gentry et al., 2006; Sebat et al., 2003). These strategies reveal
many direct linkages between biogeochemical processes and
functional activities among microbial communities of the
environments. Thus, microarrays present the advantage of
miniaturization, for simultaneous gene function analysis in
real time (Chandler et al., 2006; Rhee et al., 2004).
Real-Time PCR
Real-time PCR (rt-PCR) is a robust and powerful tool
widely employed in microbial ecology for profiling and bio-
prospecting of environmental samples. This technique allows
real-time quantification of amplicons during or at end of each
cycle using fluorescent markers. In early exponential phase of
PCR, the amplification of targets is directly proportional to the
initial template concentration. This allows reliable quantifi-
cation, as the observed intensity of fluorescence is directly
proportional to the product concentration. rt-PCR is found to
be 100- to 10,000-fold more sensitive than the microarray-
based methods, with quantification limit of 1–2 genome
copies (Eyers et al., 2004; Inglis and Kalischuk, 2004). Further,
its real-time monitoring ability avoids all the time-consuming
post-PCR quantification steps. The high analytical sensitivity
for identification of specific genes in complex DNA mixtures
also makes rt- PCR highly suitable for analysis of environ-
mental samples (Powell et al., 2006; Ritalahti et al., 2006). Its
amenability to high-throughput allows scale up for genomic
and metagenomic level analyses. Community profiling of
challenging samples such as hydrocarbon-contaminated
Antarctic soil has been achieved using this approach. In this
study, real time changes in gene expression and influence of
biotic and abiotic factors were successfully recorded across
both spatial and temporal levels (Ritalahti et al., 2006).
Numerous variants of PCR have been used for DNA am-
plification from complex environmental samples to address
microbial communities profiling from diverse facets including
biodegradation of organic contaminants (Gabor et al., 2007;
Gilbride et al., 2006; Stenuit et al., 2006, 2008). These tech-
niques adopted in conjunction with real time analysis increase
their versatility and allow quantification. Multiplex rt-PCR
technique is one such variant that utilizes multiple primer sets
within a single PCR mix. As multiple genes are targeted at
once, several amplicons are formed simultaneously. Each
primer set is labeled with distinct fluorescent dyes having
nonoverlapping excitation ranges. The resulting spectra per-
mit independent detection of target amplification rates with
good correlation. Thus, careful design of primers sets, coupled
with use of optimized annealing conditions can provide a lot
of additional information in a single test run. This helps to
conserve the sample material and avoids well-to-well varia-
tion. Another important benefit of multiplex rt-PCR is the ease
with which normalization can be carried out to increase the
reliability of the results. To achieve this, reference genes (like
house-keeping genes) are amplified along with target genes as
internal controls to get more accurate quantification of target
genes. Normalization also permits reliable comparison be-
tween results obtained from different experiments.
Multiplex PCR has been widely used to save time and
resources in several bioprospecting and bioremediation
studies. Some of these include detection of mono- or dioxy-
genase enzymes attacking polycyclic aromatic hydrocarbon
(PAH) (Baldwin et al., 2003; Dionisi et al., 2004; Gilbride
et al., 2006; Harms et al., 2003; Wilson et al., 1999). Targeting
gene encoding Rieseke iron sulfate center (which is common
in dioxygenase enzyme) using generic primers and rt- PCR
could track the population shifts of PAH degrading micro-
organisms (Ce
´bron et al., 2008; Chandain et al., 2006). This
approach was also used to characterize microbial heteroge-
neity and functionality of the Anaeromyxobacter community at
the Oak Ridge IFC uranium-contaminated sub-surface envi-
ronment (Thomas et al., 2009). In another study, good cor-
relation was observed between data obtained from multiplex
rt-PCR and dot blot hybridization (validation test), as well as
C
14
-mineralization (direct indicator) for naphthalene degra-
dation by Proteobacteria sp. showing validation of the former
rt-PCR results (Nyyssonen et al., 2006). These studies illus-
trate the use of rt-PCR and its variants as powerful and re-
liable tools for assessing bioremediation potential in an
environment, as well as for bioprospecting of novel enzymes
without isolation/cultivation of bacteria. Thus speed, sensi-
tivity, accuracy, and amenability to robotic automation
makes rt- PCR occupy a prominent position among the mo-
lecular tools (Cardenas and Tiedje, 2008; Lerat et al., 2005;
Powell et al., 2006).
PCR-Independent Amplification Techniques
New PCR-independent amplification techniques are
emerging as popular tools to access genomic information
from very low abundance microbial sources that otherwise
remain inaccessible This approach avoids generic biases as-
sociated with the PCR-dependent methods such as artifacts/
errors resulting from PCR or skewing due to unequal am-
plification. Whole genome amplification using multiple
displacement amplification (MDA) techniques exhibited re-
markably uniform amplification across the genomic targets in
comparison to PCR-based whole genome amplification. As
MDA used ø29 DNA polymerase and random exonuclease
resistant primers, there was no need for thermal cycling
(Abulencia et al., 2006; Binga et al., 2008; Dean et al., 2002).
This method generated larger sized products with lower error
frequency and was amicable to single cell genome sequencing
as well. Combination of MDA and CGA technique was suc-
cessful in analyzing oligotrophic microbial communities in
groundwater contaminated with uranium and other metals
(Wu et al., 2006). T7 polymerase-based linear amplification
approach using fusion primers has been utilized for mRNA-
based metatranscriptome analyses (Gao et al., 2007). These
and other emerging techniques overcome the various
64 AKONDI AND LAKSHMI
limitations of PCR and open vistas to explore low density/
uncultivable organisms present in a microbial community.
Innovations in Culturing Techniques
In an effort to enhance cultivability of more microbial types
in laboratory, novel culturing techniques are being actively
developed. These techniques are based on mimicking the
natural habitat in which the microorganisms of interest grow
and thrive. Techniques such as dilution-to-extinction, cultur-
ing in arrays, diffusion chambers, and micro-droplet encap-
sulation are being successfully applied to significantly
improve the cultivability of as-yet uncultivable marine or-
ganisms in low-nutrient media (Connon and Giovannoni,
2002; Dionisi et al., 2012; Kaeberlein et al., 2002; Nicholas et al.,
2008; Zengler et al., 2002, 2005). Providing a nutrient-poor
media increased the recovery percentage of the cultured
forms by several orders of magnitude compared to what was
achieved earlier using nutrient-rich media. The studies re-
sulted in isolation of several previously uncultured marine
bacteria and bacterio-planktons ( Joint et al., 2010; Penesyan
et al., 2009). Further, the novel cultivation methods signifi-
cantly increased the proportion of recovered microorganisms
from marine environments from 10% to 25%, just in the last
decade (Connon and Giovannoni, 2002; Lee et al., 2010).
Optimizing the physical parameters such as temperature, pH,
and pressure has permitted the isolation of several ex-
tremophiles in laboratory that produce novel cold/heat
adaptive enzymes (Cowan et al., 2005; Dionisi et al., 2012).
More recently, the availability of information regarding the
cell attachment characteristics, cellular signaling pathways,
and alternate electron acceptor requirements is also aiding in
further optimization of the culture parameters ( Joint et al.,
2010; Lee et al., 2009; Maldonado et al., 2005). Second gener-
ation automated high throughput systems like isolation chips
(ichips) and micro-petridishes available, allow enhanced
cultivability. They have few hundreds to million growth
compartments that can be inoculated even with single cells.
Integration of these novel culturing techniques with fluores-
cence microscopy allows high-throughput screening of mul-
tiple cell arrays on a large scale (Lee et al., 2010). Another
approach uses gel micro-droplets to encapsulate single cells
for large-scale parallel microbial cultivation under low-
nutrient flux conditions (Keller and Zengler, 2004). As the
micro-colonies are formed in agarose, its porous nature fa-
cilitates easy diffusion of nutrients, signaling molecules, and
waste metabolites. Growth within these microcapsules is de-
tected by flow cytometry (Toledo et al., 2002; Zengler et al.,
2002, 2005). The advantage of the micro-droplet and microbial
trap methods is that the beads formed are easier to handle due
to being physically distinct and much larger in size than
bacterial cells. The studies of Zengler and colleagues (2005)
efficiently employed micro-droplet and encapsulation proce-
dure to enrich actinomycetes by allowing their efficient colo-
nization. The study identified several new clades of marine
actinomycetes, which could not be detected previously in the
environmental gene library. These approaches have sub-
stantially widened the scope of microbial bioprospecting
(Ingham et al., 2007, Nicholas et al., 2010; Sprenkels et al.,
2007). Although novel culturing techniques allow isolation of
several novel organisms, many of them are found to undergo
only limited number of divisions in the laboratory (Doinisi
et al., 2012; Joint et al., 2010). Adaptability/domestication of
the recovered strain to laboratory conditions are a bottleneck
that needs to be addressed for the successful large scale cul-
tivation. Co-culturing with helper organisms and/or identi-
fying signal peptides has been found to aid culturing of
hitherto uncultivable strains (Lewis et al., 2010; Nicholas et al.,
2008).
The Metagenomic Shift
All the genomic approaches described thus far focus on
deciphering the complete genetic complement of a single or-
ganism. However, microorganisms exist in nature as com-
munities of varying complexity. The role of an individual
organism in an ecosystem is dictated by the composition of its
surrounding microbial community. Metagenomics focuses on
microbial community profiling and transcends the limitations
of studying individual organisms. The term ‘‘metagenomics’’
was coined by Handelsman and associates in 1998 and is also
known as community genomics, ecogenomics, or environ-
mental genomics. In metagenomics, genome sequences from
an entire community of organisms inhabiting a common en-
vironment are sampled. The process requires no prior sepa-
ration of organisms from their habitat or maintenance of the
microorganisms as pure or mixed cultures in laboratory.
Metagenomic strategies also circumvent several limitations
occurring due to direct DNA cloning. The approach mini-
mizes improper representations of the microbial community
as observed while screening a finite number of clones (Cowan
et al., 2005). As nucleic acids are extracted directly from the
environmental sample, the genomic data obtained includes
both characterized and novel microbial forms of the com-
munity. In principle, any environment is amenable to meta-
genomic analysis, provided good quality nucleic acids can be
extracted from the sample material. The advancements in
sequencing technology are now catering for massive scale
sequencing of the vast metagenomic repertoire.
The strength of metagenomics lies in its potential for ser-
endipitous discovery as the approach by-passes the major
limitations of classical approaches in microbiology (Binga
et al., 2008; Cowan et al., 2005; Ferrer et al., 2009). This field
area has gained a lot of significance in the last decade by
becoming the center of focus for several studies. Numerous
metagenomics projects have been initiated for analysis of
microbial communities in diverse environments, including
oceans, soils, thermal vents, hot springs, and the human
micro-biome (Sebat et al., 2003; Hugenholtz and Tyson, 2008;
Langer et al., 2006). Metagenomics studies can be conducted
on three different scales. Small-scale studies selectively inves-
tigate a function of interest in a given microbial community.
These projects are mainly initiated by a single investigator or
laboratory. Middle-scale projects are collaborative efforts em-
ploying multidisciplinary approaches to thoroughly investi-
gate the community of interest. Large-scale projects, on the
other hand, are global initiatives undertaken to understand
select microbial communities in depth and to obtain detailed
profiles (Ferrer et al., 2009; Langer et al., 2006).
The design of metagenomics projects is crucial, as the re-
sults obtained largely depend on the design strength of the
study. The design process is broadly categorized into pre-
sequencing, sampling, data generation, sequence processing,
gene prediction, annotation, and data analysis stages (Kunin
MICROBIAL BIOPROSPECTING 65
et al., 2008a, b). Prior knowledge about the dominant popu-
lation in the community of interest is helpful for optimizing
the design selection, as well as during subsequent interpre-
tation of the results. A crucial factor that needs to be ascer-
tained right at the beginning itself is the sequence coverage
requirement for the proposed study. This is because, unlike
complete genome sequencing studies where the genome size
is already known, metagenomic analyses do not have a fixed
end point. Determining the quantum of genome to be se-
quenced is especially challenging when studying diverse
microbial communities as the abundance of different organ-
isms is not uniform within the community. Further, the gene
coding densities also vary significantly among different spe-
cies. In view of these issues, to ensure a decent representation
of the genomes of the community, coverage of 6 ·to 8 ·fold
is taken as the standard (Dinsdale et al., 2008; Ferrer et al.,
2009; Kunin et al., 2008a). Certain studies have shown that
even extremely low coverage of <0.01 ·was sufficient to de-
tect genetic gradients in case of dominant population of
stratified hyper saline mat community (Goldberg et al., 2006;
Kunin et al., 2008a, b). Ultimately, the objectives of the study
guide the decisions made on coverage parameters, with an
average genome sizes reaching over 100 Mb for moderate
metagenomics libraries. The Sanger (dye terminator) se-
quencing technique has been employed as the method of
choice for obtaining metagenomic sequence data for many
years. However in recent times, pyrosequencing is also
gaining wider applicability. The advantages of this method
over the Sanger method is that it requires no prior cloning,
which avoids cloning bias. Further, the sequencing cost per
base is much lower in pyrosequencing, thus permitting se-
quencing of large repertoires at affordable cost (Edwards
et al., 2006; Shendure and Ji, 2008). The major limitation of
pyrosequencing is its short average read length. Due to this,
the approach relies heavily on similarity searches against
reference databases as gene calling or assemblies are usually
not feasible (Wommack et al., 2008). Combining both the se-
quencing technologies together has also being explored for
producing high-quality draft assemblies (Cardenas and
Tiedje, 2008; Gabor et al., 2007; Goldberg et al., 2006).
The metagenomics data sets obtained are annotated by
mapping the genes and gene fragments into families. This
provides an estimate of their relative representation. Assign-
ing roles to the proteins encoded by the sequenced genes is the
next crucial step. The two principal strategies currently used
for gene prediction are evidence-based gene calling method
and ‘‘ab initio’’ approach (Kunin et al., 2008a; Raes et al., 2007).
Gene prediction is generally followed by functional annota-
tion, which is similar to that in genomic annotation. To en-
hance the function assignment of the sequenced data,
gene-centric trends can also be adopted in metagenomics
(Tringe and Rubin, 2005). The annotated data is further sup-
plemented with collateral nonsequence data (i.e., ‘‘metada-
ta’’). For environmental studies, this information generally
includes geographical data such as global positioning system
coordinates, depth/height from where samples are collected,
and environmental parameters such as pH, temperature, and
salinity of the site (Kunin et al., 2008a, b; Urich et al., 2008;
Venter et al., 2004). It is crucial to record the meta information
during the initial sampling process itself, as re-sampling is not
always directly comparable for analysis. The metadata sig-
nificantly aids in interpreting the sequence data and is par-
ticularly useful during comparative analysis of temporal or
spatial distribution. As mentioned earlier, genome closure is
not possible for most metagenomes due to the vast and un-
known size. Hence, finishing becomes a viable option only for
those data sets pertaining to dominant populations within the
metagenome (Hallam et al., 2006). Some of the notable studies
that obtained complete or near-complete draft-level coverage
of dominant genome assemblies include biofilms in acid mine
drainage, activated sludge, and hypersaline environments
(Hugenholtz and Tyson, 2008; Kunin et al., 2008b).
Metagenomics is being extensively applied to explore bio-
synthetic diversity of microorganisms from varied environ-
ments. The studies have led to identification of new genes,
novel biosynthetic pathways, and bioremediation mecha-
nisms of important xenobiotic compounds (Baldwin et al.,
2003; Cowan et al., 2005; Dionisi et al., 2012; Lewis et al., 2010).
Light-driven proton pump mediated by proteorhodopsin was
first identified in bacterioplanktons from environmental DNA
samples using the metagenomics approach (Harms et al.,
2003; Robertson and Steer, 2004; Sanz and Kochling, 2007). A
more recent discovery pertains to the implication of Archaea
as one of the main ammonia oxidizers. Using bioinformatics
tools, an ammonia mono-oxygenase gene was initially iden-
tified next to the gene encoding small subunit ribosomal RNA
and their role was later confirmed experimentally both in
marine and terrestrial ecosystems (Baldwin et al., 2003; Daniel
2005; Schneiker et al., 2006). Co-localization studies combin-
ing FISH and digital image analysis are providing compara-
tive analysis of temporal or spatial information in structured
ecosystems in metagenome analysis (Handelsman, 2005;
Malik 2008; Sanz and Kochling, 2007; Wagner et al., 2006).
Intracellular fluorescent biosensors or whole-cell-based bio-
sensors are also being widely applied for environmental
sensing and detecting bioremediation of specific contami-
nants (Bhattacharyyaa et al., 2005; Williamson et al., 2005).
These studies show the presence of extensive metabolic in-
teractions and high interdependency between members of the
community. Further, they provide means for assessing the
‘true’ biodiversity of the microbial communities by cir-
cumventing the limitations imposed by the low cultivability
of the microorganisms (Cowan et al., 2005; Daniel, 2005;
Dinsdale et al., 2008; Ferrer et al., 2009). Metagenomics as-
semblages of microorganisms are also providing answers to
several fundamental questions in microbial ecology by aiding
in assessing the diversity and functionality of microorganism
objectively and quantitatively in situ.
Constructing metagenomic libraries from complex envi-
ronmental sample though is conceptually simple; it can be
technically very challenging. Co-extraction of inhibitory
substances such as humic acids, organic matter, and clay
particles can significantly interfere in the amplification step
(Gabor et al., 2007; Pontes et al., 2007). As discussed earlier,
determining the minimum size of the metagenomic library is a
major challenge. The high sequencing cost associated with a
large metagenome repertoire from complex environments can
still be prohibitive (Malik et al., 2008; Stenuit et al., 2006, 2008;
Wommack et al., 2008). In addition, improvements in the
three aspects namely, resolution, classification of short me-
tagenomic fragments, and better means for robust functional
assignment and verification can further improve the appli-
cation potential of metagenomics (Hendelsman, 2004, 2005;
Kunin et al., 2008a, b; Lewis et al., 2010). Thus, metagenomics
66 AKONDI AND LAKSHMI
has emerged as a valuable tool with applications in diverse
fields including medicine, alternative energy, environmental
remediation, biotechnology, agriculture, biodefense, and fo-
rensics (Cowan et al., 2005; Dinsdale et al., 2008; Dionisi et al.,
2012).
Integration of ‘‘Omic’’ Approaches for Microbial
Bioprospecting
Elucidating the phylogenetic diversity of microorganisms
is only the first step in understanding the biodiversity. It is
vital to determine the function of each gene at protein or
metabolic levels in addition to gathering sequencing data.
Around 30%–40% of the fully sequenced genomes from var-
ious organisms contain genes with no assigned function, as
they are unrelated to any known gene. Thus, the greater
challenge lies in assigning functions at the biochemical level to
the newly identified genes. Bridging this void is the focus of
the rapidly emerging discipline of functional genomics and
proteomics (Ferrer et al., 2009; Langer et al., 2006; Valenzuela
et al., 2006). Three different types of function-driven ap-
proaches are widely recognized. These are the phenotypic
detection of gene activity, heterologous complementation of
host strains, and induced gene expression (Ferrer et al., 2009).
At the genomic level, these functional approaches have led to
discovery of several new enzymes, antibiotics, and other
biomolecules with therapeutic and biotechnological applica-
tions (Eyers et al., 2004; Langer et al., 2006; Lee et al., 2010). As
function-driven approaches typically involve low-throughput
screens based on visual detection, there were initial difficul-
ties in adopting them to a metagenomic scale. However, au-
tomated colony picking, pipetting robotics, use of microtiter
plates, availability of sensitive activity assays, targeted bio-
molecules screening, and informatics assisted data manage-
ment have aided in automation of the functional screens,
thereby making them viable for application at metagenomic
levels (Dionisi et al., 2012; Gentry et al., 2006; Kunin et al.,
2008a). The capacity for high-throughput is vital as the
number of ‘‘hits’’ obtained during metagenome screening is
typically very low (<2 out of 10,000 clones screened). Other
high throughput techniques like microarrays, real time anal-
ysis of expressed gene, and PCR independent analysis
already discussed above are also employed in functional
metagenome analyses. As an alternative to elaborate func-
tional screens, the induced gene expression approach uses
diverse strategies to enrich/select community genomes with
desired traits through promoter activity rather than via phe-
notypic expression (Lorenz and Eck, 2005). Substrate-induced
gene expression (SIGEX) and its variants utilizing promoter-
trap gfp-expression vector, in combination with fluorescence-
activated cell sorting, have been highly successful in liquid
cultures for efficient, large scale selection of clones (Uchiyama
and Miyazaki, 2010; Yun and Ryu, 2005).
Metagenomic libraries have thus become good sources for
bioprospecting of novel biocatalyst and biomolecules, even
from yet to be cultivable organisms. Combination of the meta-
genomic approaches with heterologous expression systems aid
in substantial utilization of the microbial biodiversity for
human welfare. However, a single gene can generate a
number of distinguishable functional entities at protein level
as a result of differential splicing (Benndorf et al., 2007). The
entire sets of metabolites produced by cellular proteins in
response to various environmental stimuli are examined in
metabolomics. As all the metabolites present in a system are
targeted, there is little scope for bias related to the metabolites
examined. Thus, in addition to gene profiling, global protein
and metabolite profiling is crucial, especially while investi-
gating mechanisms of complex pathways such as bioreme-
diation (Malik et al., 2008; Stenuit et al., 2008). This makes
comprehensive characterization of the physiological func-
tions and elucidating the ecological roles of the novel organ-
isms a challenging task. Progressive integration of various
high-throughput approaches is particularly fruitful in the
field of environmental microbiology. They offer advantage of
miniaturization, automation, massive parallelization of time
consuming steps, along with ‘‘real-time’’ analysis (Dinsdale
et al., 2008; Paul et al., 2005; Zhao and Poh, 2008).
Achieving synergy between the emerging –omic ap-
proaches in the long run can offer comprehensive character-
ization of the biological and ecological function of microbial
communities in an environment. Integration of complemen-
tary ‘‘omics’’ techniques is envisaged to provide greater in-
sight into genome structure, micro-heterogeneity, lateral gene
transfer, and nutrient cycling among the members of micro-
bial community of a region. This ‘systems biology’ outlook in
a way holds promise to provide the holistic overview required
to understand the microbial ecosystems and resolve several
fundamental questions in microbial ecology and evolution.
Author Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Prof. V.V. Lakshmi
Department of Applied Microbiology
Sri Padmavati Women’s University
Tirupati 517502
India
E-mail: vedula_lak28@yahoo.co.in
70 AKONDI AND LAKSHMI
