1 Beyond metagenomics: Integration of complementary approaches for the study of microbial communities 1,2 Andrés Cubillos-Ruiz, 1,2 Howard Junca, 2,3 Sandra Baena, 2,4 Ivonne Venegas and 1, 2 María Mercedes Zambrano 1 Corpogen Research Center, Carrera 5 No. 66A – 34, Bogotá, Colombia. 2 Colombian Center for Genomics and Bionformatics of Extreme Environments - Gebix, Carrera 5 No. 66A – 34, Bogotá, Colombia 3 Department de Biology, Pontificia Universidad Javeriana, POB 56710, Bogotá, Colombia 4 Department de Microbiology, Pontificia Universidad Javeriana, POB 56710, Bogotá, Colombia Abstract Advances in genomics have had a great impact on the field of microbial ecology. Metagenomics in particular holds great promise for accessing and characterizing microbial communities. However, the high diversity and level of complexity present in microbial communities represent an obstacle to understanding these assemblages given the current approaches. The integration of microbial community structure with function, taking into account uncultured microbes in diverse environments, remains particularly challenging. The anticipated increase in metagenomic data available in the future will require high-throughput methods for data management and analysis of these large and complex microbial communities. Integration of complementing technologies like microarrays, high throughput sequencing and bioinformatics and of novel tools and “meta” approaches, such as metaproteomics, metatranscriptomics and meta-metabolomics, will be required to understand the role of microbes in different ecological habitats. In spite of the many challenges, the field offers promising perspectives for achieving a more comprehensive view of microbial communities and how microorganisms adapt to and function within their ecosystems. Introduction The field of genomics has led to a conceptual shift in the way we approach biological systems by enabling researchers to go beyond studies of isolated components and address global functions and complex ecosystem interactions (Bertin et al., 2008). Recent technological advances have also paved the way for novel experimental approaches to the study of microbial communities that seemed largely implausible less than a decade ago. The rapidly growing area of metagenomics has applied the tools of genomics to analyze complex microbial assemblages and has become a powerful strategy for exploring and characterizing microbial communities in diverse settings. The appeal behind the metagenomics approach lies largely in its ability to bypass cultivation and offer a unique opportunity to directly sample and gain new insights regarding natural microbial assemblages. Metagenomic explorations therefore enable examination of complex communities and microorganisms of difficult access, providing a more comprehensive view of the populations present that can go from more extensive phylogenetic descriptions to valuable information regarding metabolic potential (Xu, 2006). One of the major challenges in the field of microbial ecology is to understand how microorganisms in a community interact with each other and how the community structure is related to ecosystem function. Research in microbial diversity and technological advances over the last decades have led to a new appreciation of the diversity of microbiological life in our planet and provided tools for accessing a broad spectrum of microbial communities. The use of culture-independent methods has been crucial to our understanding and estimates of microbial diversity, which now greatly surpass original calculations that were limited by culture-dependent methods. Modern molecular tools have therefore been fundamental to our growing recognition of the extent of microbial diversity and the capacity of microorganisms to influence global ecosystem functioning (Schmidt, 2006). However, much remains to be learned regarding microorganisms and their roles in
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Beyond Metagenomics- Integration Of Complementary Approaches For The Study Of Microbial Communities
Cubillos-Ruiz A, Junca H, Baena S, Venegas I, Zambrano MM. 2009. Beyond Metagenomics: Integration of complementary approaches for the study of microbial communities. In Metagenomics: Theory, Methods and Applications - Editor: Diana Marcos. Horizon Scientific Press. ISBN: 978-1-904455-54-7
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Beyond metagenomics: Integration of complementary approaches for the study of microbial communities
1Corpogen Research Center, Carrera 5 No. 66A – 34, Bogotá, Colombia. 2Colombian Center for Genomics and Bionformatics of Extreme Environments - Gebix, Carrera 5 No. 66A – 34, Bogotá, Colombia 3Department de Biology, Pontificia Universidad Javeriana, POB 56710, Bogotá, Colombia 4Department de Microbiology, Pontificia Universidad Javeriana, POB 56710, Bogotá, Colombia
Abstract
Advances in genomics have had a great impact on the field of microbial ecology. Metagenomics in particular holds great promise for accessing and characterizing microbial communities. However, the high diversity and level of complexity present in microbial communities represent an obstacle to understanding these assemblages given the current approaches. The integration of microbial community structure with function, taking into account uncultured microbes in diverse environments, remains particularly challenging. The anticipated increase in metagenomic data available in the future will require high-throughput methods for data management and analysis of these large and complex microbial communities. Integration of complementing technologies like microarrays, high throughput sequencing and bioinformatics and of novel tools and “meta” approaches, such as metaproteomics, metatranscriptomics and meta-metabolomics, will be required to understand the role of microbes in different ecological habitats. In spite of the many challenges, the field offers promising perspectives for achieving a more comprehensive view of microbial communities and how microorganisms adapt to and function within their ecosystems.
Introduction The field of genomics has led to a conceptual shift in the way we
approach biological systems by enabling researchers to go beyond
studies of isolated components and address global functions and
complex ecosystem interactions (Bertin et al., 2008). Recent
technological advances have also paved the way for novel
experimental approaches to the study of microbial communities
that seemed largely implausible less than a decade ago. The rapidly
growing area of metagenomics has applied the tools of genomics to
analyze complex microbial assemblages and has become a powerful
strategy for exploring and characterizing microbial communities in
diverse settings. The appeal behind the metagenomics approach lies
largely in its ability to bypass cultivation and offer a unique
opportunity to directly sample and gain new insights regarding
enable examination of complex communities and microorganisms
of difficult access, providing a more comprehensive view of the
populations present that can go from more extensive phylogenetic
descriptions to valuable information regarding metabolic potential
(Xu, 2006).
One of the major challenges in the field of microbial
ecology is to understand how microorganisms in a community
interact with each other and how the community structure is
related to ecosystem function. Research in microbial diversity and
technological advances over the last decades have led to a new
appreciation of the diversity of microbiological life in our planet
and provided tools for accessing a broad spectrum of microbial
communities. The use of culture-independent methods has been
crucial to our understanding and estimates of microbial diversity,
which now greatly surpass original calculations that were limited by
culture-dependent methods. Modern molecular tools have therefore
been fundamental to our growing recognition of the extent of
microbial diversity and the capacity of microorganisms to influence
global ecosystem functioning (Schmidt, 2006). However, much
remains to be learned regarding microorganisms and their roles in
2
particular environments. Studies aimed at understanding complex
communities require novel and more holistic approaches as well as
integration of methodologies in order to understand the ecology of
populations and factors that control their activities. In this respect
metagenomics, coupled to complementing high-throughput
strategies for studying expression profiles and microbial metabolic
potential, offers a unique opportunity for examining uncultured
microbes and assessing their role in an ecosystem (Turnbaugh and
Gordon, 2008).
Metagenomics holds an undisputed advantage in terms of
accessing and examining complex and difficult to study natural
microbial communities. However, the metagenomic approach that
studies the entire DNA content of a community is still limited in
its scope and capacity to derive ecologically meaningful information
regarding the complex interactions that drive and shape
communities. Difficulties inherent to this strategy, from problems
associated with extraction of genomic material to loss of relevant
information regarding the microorganisms and the ecosystem,
necessarily limit the information that can be obtained from a
particular study. Problems related to limited recovery of DNA have
been addressed recently by amplification of the isolated material
using multiple strand displacement (MDA), a strategy that can also
be applied to single cells (Lasken, 2007). This is done by means of
the isothermal proof reading multiple displacement amplification
activity of phi29 DNA polymerase, an enzyme discovered almost
30 years ago that has now been recognized as a powerful means for
obtaining up to micrograms of DNA from minute amounts of
starting material (Binga et al., 2008). This enzyme has been used
for amplification of metagenomic DNA and tested on soil DNA
templates probed against microarrays (Gonzalez et al., 2005; Wu et
al., 2006). Since metagenomics involves direct isolation of DNA
from the environment, information regarding particular phenotypic
traits is lost together with the capacity to carry out additional
analyses regarding the physiology of specific microbes. Depending
on the questions being addressed, simplification of the microbial
community might be a viable alternative in order to facilitate
interpretation of the data and the reconstruction of genomic
information. This could be achieved either through enrichment of
certain populations or by following diverse cultivation strategies
aimed at recovering microorganisms that can be further analyzed in
the lab. The study of isolates or the reconstruction of genomes from
simplified communities could provide relevant information in
terms of understanding the role of microbes within their particular
niche (Steward and Rappe, 2007; Tyson et al., 2004). More
sophisticated approaches, such as cell sorting and microfluidics have
also been tried (Cardenas and Tiedje, 2008; Warnecke and
Hugenholtz, 2007). Another major drawback of metagenomics is
that gene discovery is carried out at the expense of genomic context
and in the absence of information regarding the organisms
themselves. Deriving useful genomic data thus relies on the capacity
of bioinformatics to reassemble and make sense of the massive
amount of sequence information generated. The taxonomic
classification of metagenomic sequences, which could greatly help
in assessing community composition and dynamics and assignment
of roles to encoded proteins, depends on available information
stored in the databases. Thus our capacity to derive information
from metagenomic samples is also constrained by our current
knowledge regarding gene sequences and proteins, most of which
comes from sequenced genomes (Pignatelli et al., 2008). One of the
most substantial technical improvements is perhaps the recent
introduction of massively parallel sequencing technologies that
generate large amounts of sequence information at reduced costs.
The use of high-throughput approaches will, no doubt, lead to an
increase in the generation of metagenomic data that will in turn
require additional and more sophisticated bioinformatics tools to
manage this information and carry out processes such as assembly,
gene prediction, annotation, and metabolic reconstruction (Steward
and Rappe, 2007).
Metagenomics is therefore at the point where scientific
questions focused on understanding the interaction among
microorganisms and their roles in the environment can start to be
addressed. This will require coupling genotypic and phenotypic
analyses through the implementation of novel, powerful and
innovative tools and the concerted integration of other “omic”
approaches such as proteomics and transcriptomics (see Figure 1).
The formidable plasticity displayed by microorganisms is related to
their metabolic versatility, the interaction of complex regulatory
networks and their capacity to trigger differential responses that
become evident in the expressed metabolic potential. Focusing on
the global analysis of all genes and expression profiles, can therefore
reveal information beyond what can be gathered from studies of
individual genes, contributing substantially to our understanding of
the physiology and the strategies involved in microbial adaptation
to changing environmental conditions (Schweder et al., 2008). The
major challenge in the future will be to integrate experimental
approaches and formulate questions aimed at deriving relevant
ecological information, questions that can only be addressed in the
context of intact communities where population requirements and
interactions are at work (Turnbaugh and Gordon, 2008).
Figure 1. “Omics” approach to the study of microbial ecology. Microbial communities are influenced and shaped by both biotic and abiotic factors. The “omic” strategies target different levels of the information flux, starting with the metagenome and increasing in complexity. The integration of these approaches can provide a more comprehensive of view of a community structure and function in a defined spatial and temporal setting.
3
Metatranscriptomics Definition and origins Metatranscriptomics is the high-throughput detection and analysis,
in sequence diversity and associated functions, of the transcripts
(RNA molecules) extracted from samples where more than one
microbial genome type is present. It is essentially a transcriptomic
study in samples containing multiple cell types, species or
operational taxonomic units (OTUs). The word
“metatranscriptomic” is derived by analogy with “metagenomic”.
In the strict sense of the definition, metatranscriptomics could
include all the work involving direct extraction and detection of
RNA sequences from environmental samples, i.e. those involving
reverse transcription, target amplification, sequencing and analyses
of 16S rRNA gene transcripts (Felske et al., 1996a; Nogales et al.,
2001b; Small et al., 2001a; Weinbauer et al., 2002). However, if
one considers metagenomics mostly as a sequence-based approach
(excluding function-based screenings), metatranscriptomics could
be restricted to analyses that have a broader scope and encompass
total mRNA and/or rRNA transcripts in a sample. This approach is
made possible by massive sequencing efforts and ideally does not
involve cloning procedures or targeted PCR amplifications.
However, the widespread use of 16S rRNA gene amplifications to
characterize microbial communities could be considered as a special
case since this gene is still extremely useful for exploring diversity
and complexity in microbial communities (Tringe and Hugenholtz,
2008). Metatranscriptomics complements the metagenomic
approach by focusing on the expressed subset of genes
(metatranscriptome), thus reducing the complexity of the data to be
analyzed. This allows, for example, detection of sequences
associated with a particular environmental condition that may not
be so readily identified in metagenomic studies and increases the
chance of detecting ecologically relevant active functions. The
discovery of functions being induced in a sample as a response to a
certain environmental condition (exerted pressure) also gives insight
into processes of adaptation and enriches our understanding of
communities previously captured through metagenomic sequence
surveys. Thus, this approach gives a composite view of the
transcriptionally active subset of the genomes present in a
community under the environmental condition sampled. As we will
describe below, metatranscriptomics is now possible thanks to the
recent integration of various developments in different technical
and theoretical fields such as nucleic acids sequencing technologies,
hybridization-based (array) transcriptomics, new molecular biology
applications of well-characterized enzymes, microbial ecology
techniques to improve quantities, stability and detection of RNA
molecules, and the emergence of bacterial phylogenomics and
related bioinformatics tools customized for metagenomic datasets,
among others.
Limitations in analyzing the metatranscriptome The exploitation of transcriptomics to assess the active subset of
genes in a given environmental microbial community metagenome
is very recent, with reports appearing only in the last five years. A
search carried out in February 2009 for key terms in PubMed, such
as metatranscriptomics and related words, retrieved only 10
citations starting in 2006. While this raw search can miss some
relevant publications on metatranscriptomic studies, it does suggest
that this is a new and emerging field. Reasons for the apparent
delay in reports of research in this field, with respect to research in
the general area of metagenomics, are essentially related to technical
difficulties and previously identified limitations inherent to
performing studies using environmental RNA.
The inherent instability of RNA molecules has been one
of the most limiting factors for the development of
metatranscriptomics. Transcriptional studies had already revealed
the complexity of working with RNA, an unstable molecule of
rapid turnover and short cellular half-life (seconds to minutes)
when compared to the informative and more stable molecules of
DNA. The lability of RNA molecules can also contrast with the
proteome, which can have variable protein half-lives that are
dependent on the specific protein’s biochemical nature and
localization. The transient nature of a given RNA population will
therefore influence the expression profiles observed, providing at
best a snapshot of what are probably highly dynamic patterns of
expression (Velculescu et al., 1995). Another factor limiting the
capacity for deep sequence-based transcriptomic analyses of
metagenomes is the low quantities of transcripts inherently present
and/or recovered from environmental samples. This is due to the
substantially lower biomass content found in these samples when
compared with a pure bacterial culture (Amann et al., 1995). In
addition, components that contaminate samples and are co-
extracted with the nucleic acids (Griffiths et al., 2000), such as
humic acids in soils, can interfere with additional steps in sample
processing like quantification, enzymatic amplification,
modification or hybridization (Alm et al., 2000; Roh et al., 2006).
These problems, despite being shared with metagenomics, are
particularly critical for the demanding methodological steps
involved in metatranscriptomic studies. However, improvements in
sample recovery and purification over the last years have opened the
way for global analyses that involve detection and identification of
transcripts from environmental samples.
From 16S rRNA transcript sequencing to total metatranscriptome pyrosequencing In many cases, the first approach to characterizing an
environmental microbial community still relies on a description of
the taxonomical composition of the sample, usually based on 16S
rRNA gene amplification and sequencing. In the late 90s, some
reports described the so-called “active fraction” of the microbial
community by extracting RNA, generating cDNA and then
determining the sequence complexity in ribosomal genes (Felske et
al., 1996b; Nogales et al., 2001a). The community composition
differed depending on whether DNA or RNA was used for 16S
rRNA gene amplification, with some phylogenetic groups found
only in one of the two clone libraries from the same sample. In
addition, predominant 16S rRNA types were more evident when
RNA was used as template, a reflection of a dominant
transcriptionally active species that did not necessarily correlate
4
with the most abundant type detected using DNA (Nogales et al.,
2001b). These studies revealed the discrepancy between observed
predominant species or genome types and the transient expression
profile of particular microbes within a community. This transient
expression is reflected by the amount of rRNA transcripts recovered
and is influenced by the conditions at the time of sampling. These
initial studies struggled with the technical difficulty of extracting
RNA from environmental samples and paved the way for
improvements required for the analyses of transcripts from
environmental samples (Hurt et al., 2001). Superior protocols and
commercial kits thus became available, improving the
reproducibility, quality and quantity of nucleic acids being
extracted from various environmental sources. Despite these
advances, there are still problems inherent to these procedures that
require experimental fine-tuning in order to optimize procedures
for diverse environmental samples.
The recently developed high-throughput sequencing
technologies have obvious advantages in terms of exploring the
metatranscriptome. Pyrosequencing, which is based on the
detection of the released pyrophosphate, represents a turning point
because it dispenses with cloning and provides a fast and
economical alternative for obtaining large-scale sequence
information. The basic steps involved in the pyrosequencing-based
metatranscriptomic approach are: isolation of environmental RNA
(eRNA), generation of complementary ecDNAs by random-primed
reverse transcription that are then treated to produce double
stranded DNA fragments of the environmental cDNAs (ds
ecDNA). These ds ecDNAs are then ligated to adaptors, emulsified,
and subjected to the 454-sequencing process (Leininger et al.,
2006). These DNAs contain information of the expressed
including PKS, NRPS, and hybrid (PKS/NRPS) enzymes, and is
also able to predict some chemical structures and make inferences
about domain specificities and function of the predicted small-
molecule products (Starcevic et al., 2008). However, information
based merely on gene clusters is limited and does not yet faithfully
predict end product structures. This can be particularly true for
clusters with multiple tailoring enzymes, hidden biosynthetic genes
or genes for novel small molecules produced by assembly line
enzymes that operate in an unconventional way (Sattely et al.,
2008).
The prediction of the biosynthetic pathways and the
hypothetical structure of secondary metabolites is the first step
towards the identification and understanding of natural products in
the ecosystem. Once a comprehensive list is made of the gene
clusters found in the microbial community, a metatranscriptomic
analysis of the ecosystem can then be carried out to analyze the
expression dynamics of the genes making up the predicted clusters.
This analysis can shed light on how spatial and temporal conditions
influence differential expression of secondary pathways (Raes and
Bork, 2008). Subsequent linking of identified gene clusters and
expression profiles to microbial species within an ecosystem is an
important but difficult task that has nevertheless been achieved by
co-cloning of a phylogenetic marker (Beja et al., 2000). Nowadays,
the use of single-cell isolation and sequencing technologies provide
promising alternatives to this seemingly daunting endeavor (Walker
and Parkhill, 2008). Thus the identification of actively transcribed
gene clusters encoding small molecules uses both metagenomics
and metatranscriptomic approaches and is based on bioinformatic
tools to predict metabolite scaffold structure and reveal information
regarding physicochemical properties. Using this data the
metabolomics approach can be maneuvered to identify a fraction of
the molecules known to be expressed from gene clusters in a
defined spatial and temporal environmental setting. Additional
information regarding hypothetical chemical properties also
narrows the search space in the overall metabolite profile of the
community. This type of identification will require specialized
extraction protocols for the meta-metabolome and extremely
sensitive analytical tools in order to deconvolute the hundreds of
similar low-concentration metabolites found in such a complex
chemical background. Much hope is held on the application of the
ultrahigh-field Fourier transform ion cyclotron resonance mass
spectrometry (FTICR-MS) that has been useful to profile over 400
metabolites in a short period of time (Han et al., 2008). The
combination of all of these eco-systems biology approaches will
help us to mine and understand the metabolic potential concealed
in microbial populations (Raes and Bork, 2008).
Microarrays Microarrays are a powerful high-throughput technique for the
simultaneous analysis of thousands of target molecules that has
incredible potential for the detection of activities and monitoring
the dynamics of microbial communities. Microarrays, which have
been used extensively for analysis of gene expression, are being
adapted for use in environmental samples (Gentry et al., 2006).
They have the advantage of providing rapid information on a great
number of genes and supplying quantification data without having
to clone DNA. There have been spectacular advances in microarray
design and commercial availability, improving the coverage, density
and limit of detection of gene or transcript copies (Bouchie, 2002).
In environmental setups, microarray technology has not been as
extensively used as for genomic or transcriptomic comparisons of
single organisms. This is due to the relatively high amounts of
nucleic acids needed to detect a signal and to the complexity
underlying the design of multiple probes to target and cover an
uncharacterized diversity. Arrays designed for environmental
applications therefore contain probes for detection of well-defined
gene families of known environmental bacterial functions (Iwai et
al., 2008; Taroncher-Oldenburg et al., 2003; Wu et al., 2006). Due
to the difficulty in recovering large amounts of environmental
DNA, these arrays in many cases require PCR amplification of
specific genes prior to hybridization, a step that can introduce
biases. Alternatives to avoid biases associated with PCR
amplification include either extraction from larger amounts of
sample or the amplification of genomic material using the phi29
polymerase (Binga et al., 2008). In the case of protein coding genes,
the use of arrays can substantially increase our capacity to detect
small variants within the context of a particular gene family since all
known possible variations can be targeted simultaneously.
However, the detection of environmental mRNA is particularly
cumbersome due to the low amount of single gene transcripts,
which even for highly expressed genes can still be 100 times less
when compared to the more abundant rRNAs. Various arrays have
been developed for the study of microbial communities and these
include: 1) phylogenetic arrays based on 16S rRNA, 2) community
arrays with signature genes and 3) functional gene arrays with
information for genes involved in metabolic pathways.
The most extensively used phylogenetic marker in
microbial ecology is undoubtedly the 16S rRNA gene. This is an
ideal marker for community profiling given the large amount of
sequence data, coupled to the intrinsic characteristics of this
molecule. Phylogenetic arrays have only recently begun to be used
to study microbial communities in diverse settings, with some of
the first reports appearing in recent years (Loy et al., 2002) and
further extended to include analysis of either DNA or RNA
obtained from the environment (Adamczyk et al., 2003; El
Fantroussi et al., 2003; Gentry et al., 2006). A recently developed
high density 16S rRNA PhyloChip that targets 8741 bacterial and
archaeal taxa has been used to compare coverage with respect to
clone libraries and to inspect diversity in environmental
communities (DeSantis et al., 2007; Yergeau et al., 2007; Yergeau
et al., 2009). Functional arrays that contain genes involved in key
biogeochemical process, including a comprehensive array called
GeoChip, have also been developed and used for detecting activities
12
in microbial communities (He et al., 2007; Leigh et al., 2007; Rhee
et al., 2004; Steward et al., 2004; Yergeau et al., 2007).
Despite the great potential of applying microarray
technology for the specific, quantitative and rapid assessment of
microbial communities, the analysis of environmental samples
represents several challenges. As occurs with other strategies,
microarrays detect the most abundant organisms or molecules
present in a given ecosystem and can therefore have problems
associated with low sensitivity. There are also difficulties related
with recovery of genetic material due to low biomass present in the
sample or problems with extraction procedures. In addition, the
results can be difficult to interpret due to the large amount of array
data generated, information which can occasionally also be
misleading due to signals generated by cross-hybridization with
related sequences. Finally, and perhaps most importantly, is the fact
that microarrays rely on previously gathered information for probe
design and will therefore miss any novel genes found in the
community that are not represented in the array (Gentry et al.,
2006; Wagner et al., 2007). Thus exploratory studies using
microarrays may overlook functions residing in environmental
populations that have not yet been described and which might very
likely represent a large fraction of the community (Pignatelli et al.,
2008).
Future perspectives The field of microbial ecology has made substantial progress thanks
to novel molecular and genomic approaches that allow estimations
and explorations of the vast majority of uncultured microorganisms
in our planet. Metagenomics is now facing new challenges
precipitated by ongoing developments and novel tools for research
of complex microbial communities. As evidenced by recent reports,
the focus of these studies has started to shift from mere descriptions
of ecosystems to the generation of more comprehensive and
complex datasets aimed at deriving relevant ecological information.
Technological innovations, the development of more economical,
efficient and high-throughput strategies and modifications to
existing methodologies will most probably continue to flourish in
the near future. This will probably lead to increased access and
application of these technologies, prompting research into a
broader spectrum of environments. We will probably see “meta”
strategies being used successfully for investigating diverse microbial
consortia and addressing the role of uncultured microbes in their
natural settings. Tackling some of the fundamental and interesting
questions driving research in microbial ecology will however require
the integration of diverse fields of study, such as geochemistry,
biochemistry, and genetics, among others, and techniques that
expand on the basic metagenomics strategy and move beyond
towards a more integrative eco-systems biology approach. Thus
multidisciplinary teams and complementation with additional
“meta” approaches, such as metaproteomics, transcriptomics and
metabolomics to capture the expressed potential of microbial
populations, will surely lead to a more global and comprehensive
picture of the evolution, complexity and functionality of
environmental microbial communities (Maron et al., 2007b; Raes
and Bork, 2008). The incorporation of additional technologies like
cell sorting and microfluidics, together with advances in isolation
techniques, will prove extremely useful for complementing these
studies using isolates or more simplified communities. Thus
multifaceted approaches will probably become more extensively
used when engaging in comprehensive explorations of in situ
communities. In addition to providing novel genomic and
physiological information, these novel approaches will also prove to
be fundamental for the search and discovery of novel bacterial
functions for biotechnological or clinical applications. All together
the field promises stimulating new developments that will very
likely reshape our vision of microbial interactions and communities
in their natural settings.
Despite these exciting prospects, some of the inherent
difficulties associated with “omic” approaches to study whole
communities, such as efficient isolation of nucleic acids and
proteins from environmental samples, still hamper progress and
thus need to be overcome for the efficient integration of various
disciplines. It is anticipated, however, that the involvement of more
research groups will precipitate innovations and the capacity to
overcome many of these difficulties, paving the way for more in-
depth studies of microbial communities and diversity. One of the
key concerns for the future on any “meta” and “omic” approach is
how to handle and make sense of the vast amount of sequence data
that will be generated from such explorations (Chen and Pachter,
2005). The use of massively parallel sequencing technologies,
coupled to reduced costs, are expected to expand our capacity to
generate data. Therefore, the development of novel and
sophisticated bioinformatics tools will become essential for data
management and analysis of metagenomic data involving assembly,
identification and assignment of functions to expressed proteins
and phylogenetic affiliation to sequence reads. Another aspect of
importance in the field should involve reproducibility of results and
functional experimental validation of sequence-derived
information, an important point that has been largely neglected in
the post-genomic era, given the experimental challenges involved.
The capacity to explore ecosystems at an unprecedented
depth will undoubtedly lead to improvements on our actual survey
of microbial diversity. The deeper resolution obtained by the new
sequencing technologies, coupled to explorations using “omic”
approaches, will not only allow us to assess less abundant organisms
and yield clues regarding the prevalence and distribution of
particular groups of organisms, but will also lead to key
information about niche adaptation. One especially interesting
development in the last years has been the unprecedented capacity
of metagenomics to reveal viral diversity. Viruses, which are
abundant and harbor an immense genetic diversity, affect microbial
community dynamics and are therefore an integral part of
microbial ecology. It is expected that in the future the application
of “meta” approaches will broaden our view of this viral diversity
and include analyses regarding their ecological role (Allen and
Wilson, 2008). Thus as has occurred in the recent past, the
13
development of new technologies will open the way for more in-
depth and large-scale environmental explorations. The integration
of strategies and methodologies will add new dimensions to the
study of microbial communities, expand our appreciation of
microbial diversity and allow us to answer more sophisticated
questions regarding the role of microorganisms within a
community. These composite explorations will therefore prove to
be pivotal in our search for a more comprehensive understanding of
microbial community dynamics and function.
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