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Advanced Environmental Biotechnology II


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Title: Advanced Environmental Biotechnology II

Advanced Environmental Biotechnology II
  • Week 12 - microbial ecology and genomics

  • 10 Lessons from the genomes microbial ecology
    and genomics
  • Andrew S.Whiteley, Mike Manefield, Sarah L.Turner
    and Mark J.Bailey

  • 10.1 Introduction
  • Microbial ecology is the study of interactions
    between microbes and their biotic and abiotic
    environments. The genes of microbes determine
    their responses to their environment and how they
    affect their environment.

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  • Microbes are constantly adapting to their
    environments through
  • - immediate responses already encoded on the
    genome, occurring through phenotypic changes
    mediated by the complex regulatory systems
    governing gene expression. This is fast yet
    relatively limited in what it can do.
  • - adaptive evolution, which involves changes in
    the genetic composition of an organism, as
    selected for by the environment. This is a slow,
    longer-term response but appears to be unlimited.

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  • Genomics can be divided into structural and
    functional aspects.
  • Structural genomics is the analysis of genome
    composition and architecture, which requires the
    sequencing of an organisms entire genetic
    material. The ecologically significant
    characteristics of genomes can be found by
    comparing the genome structure of organisms with
    different roles in different ecosystems.
    Structural genomics corresponds to an analysis of
    genetic adaptation to environmental change.

  • Functional genomics is the analysis of global
    gene expression from genomes. It looks at
    regulatory systems and their effect upon gene
    expression underlying physiological responses to
    environmental change.

10.2 Structural genomics
  • To understand the complete biology of an organism
    we must first find its entire genome sequence.
  • But genome sequences do not provide a complete
    biological understanding.
  • The complete ecology of any bacterium has to be
    fully described.

  • The first complete bacterial genome, of H.
    influenza, was reported to contain 1743 open
    reading frames (ORFs), of which 736 (42) had no
    role assignment.
  • Of these hypothetical ORFs, 347 matched other
    hypothetical proteins in the databases and 389
    were unique to the H. influenza genome.
  • Most of the predicted core metabolic functions
    necessary for free-living bacteria were found,
    except for those already predicted to be absent.

  • Bioinformatic analysis of the genome sequence
    showed functional grouping of genes and
    previously unidentified genes, under the
    regulation of well-described transcriptional

10.3 Comparative genomics
  • Complete genome sequences are available for over
    2370 Bacteria and 145 Archaea. http//www.ncbi.nlm
  • Comparative genomic studies help understand basic
    bacterial genome organization, ie. gene content,
    order, regulation and evolution.

10.3 Comparative genomics
  • This is done by intra-species/genus genome
    comparisons and can include species that are
    distantly related but occupy a similar ecological
    niche, eg. The former comparisons might include
    intracellular pathogens Mycobacteria, Chlamydia,
    Rikettsia) and/or symbionts (Buchnera) of
    eukaryotes, to identify shared genome traits.

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  • Comparisons can also be made to closely related
    bacterial species that occupy different
    ecological niches, eg. members of the
    Rhizobiaceae, which include the facultative
    intracellular pathogens Brucella spp. and
    plant/soil associated rhizobial species.
  • However, most of the genomes completed are of
    pathogenenic species, and so, the diversity and
    representation of environmental ecotypes for
    comparison is restricted.

  • Composition of bacterial communities based on the
    percentages of different bacterial phyla
    documented in clone libraries of the 16S rRNA gene

  • Phylogenetic tree of Alphaproteobacteria groups
    commonly found in dark ocean habitats.

10.3.1 Small genomes
  • The genomes of two Buchnera aphidicola spp.,
    isolated from different host aphids, give the
    simplest genomes for comparison. As obligate
    intracellular symbionts that are maternally
    transmitted between aphid generations, their
    ecology is relatively simple they only
    experience a single largely unchanging
    environment. The symbiosis may have originated
    150 million years ago (MYA) and the two host
    species to have diverged 50 MYA.

10.3.1 Small genomes
  • Ecologically, Buchnera provide the aphid host
    with metabolic pathways for the synthesis of
    essential amino acids, absent from phloem sap
    upon which the aphids feed. The two buchneral
    genomes are relatively small (641 kbp) and share
    526 of their 564 (Buchnera Ap) and 545 (Buchnera
    Sg) genes, all of which occur in the same order
    in each genome.

  • Comparisons show that most differences are point
    mutations that generate pseudogenes and gene
    losses, with Buchnera Ap containing 13 and
    Buchnera Sg 38 pseudogenes.

  • Some gene inactivations fit with known ecological
    differences. Five of the Buchnera Sg pseudogenes
    are parts of the cysteine biosynthetic pathway
    which correlates with the higher levels of
    cysteine in the phloem of grasses, compared with
    pea, the main food plants of S. graminum and A.
    pisum respectively. Random mutation and the
    reduction of selective pressures to keep the
    function have probably contributed to the
    inactivation of these genes.

  • Pseudogenes are relatively recent gene
    inactivations since, over time, useless DNA
    sequences will be deleted from the genome.
    Comparisons show that there are 14 gene sequences
    present only in Buchnera Ap and four in Buchnera
    Sg. All of these genes are similar to
    hypothetical proteins in E. coli, their closest
    free-living relative. All 18 were therefore
    likely to have been present in the last common
    ancestor of the Buchnera lineage Maybe
    differential loss or maintenance reflects
    host/niche adaptation.

  • Despite their small genome sizes, 14 of the
    open reading frames (ORFs) of the Buchnera
    genomes code for hypothetical proteins of
    unknown function. On average, at least 25 of the
    genes in each bacterial genome are
    hypothetical. This reaches a maximum in the
    archaeon, Aeropyrum pernix, within which 57 of
    the ORFs have no known function.
  • Even though the genetics of Escherichia coli K12
    and Bacillus subtilis have been studied
    intensively for gt50 years, they still dont have
    all their ORFs known.

  • Studies have looked at phenotypic differences, in
    laboratory assays, and may have overlooked genes
    that enable E. coli to survive in the mammalian
    gut or B. subtilis in soil.
  • eg. deleting up to 313 kbp of the E. coli genome
    doesnt affect the growth rate of the mutant in
    laboratory culture.
  • So there are high levels of functional redundancy
    and/or large numbers of genes which are only
    environmentally regulated.
  • This is a big problem when analyzing an
    organisms genome in the laboratory.
  • Environmentally regulated genes might be missed.

10.3.2 Large genomes
  • The natural environment is highly complex, due to
    physical, chemical and biological differences in
    different places and times.
  • All the larger genomes sequenced are from
    soil-associated bacteria. The largest examples
    include Mesorhizobium loti (7.04 Mbp),
    Bradyrhizobium japonicum (9.1 Mbp), Streptomyces
    coelicolor (8.67 Mbp), Pirellula sp. (7.14 Mbp)
    and Pseudomonas aeruginosa (6.26 Mbp).
  • P. aeruginosa is a common environmental

  • Other large genomes include the plant-associated
    bacteria Sinorhizobium meliloti (6.8 Mbp),
    Agrobacterium tumefaciens (5.6 Mbp), Bacillus
    anthracis (5.23 Mbp) and Bacillus cereus (5.43
    MBp) and Xanthomonas spp. (5.1 and 5.2 Mbp).
  • The large genome size of P. aeruginosa along with
    high numbers of regulators enable it to sense and
    respond to environmental fluctuations the same
    is possibly true for the genome of the S.
    meliloti, containing large numbers of ORFs with
    homology to membrane-associated transporter

10.3.3 The horizontal gene pool (HGP)
  • Comparison of benign and pathogenic strains have
    identified genes involved in pathogenicity.
  • These genes are often in pathogenicity islands
    and/or mobile genetic elements (e.g. phage,
    plasmids, transposons), responsible for gene
    transfers between strains and species.

  • Naked DNA uptake (transformation), viruses
    (transduction), and plasmids (conjugation) are
    why the genetic units of heredity need not be
    inherited in the usual sense

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  • Horizontally transferred genes should display
    patchy distributions across evolutionary
    lineages, within species and populations, and may
    be readily identified by GC contents that differ
    markedly from adjacent chromosomal sequences.
  • Such distributions have been described for
    pathogenicity traits in E. coli, Vibrio chloerae
    Streptococci, Helicobacter pylori and Shewanella
  • The acquisition of pathogenicity may be
    adaptation by niche expansion. Similar evidence
    exists for non-pathogenic bacteria isolated from
    different environments, where ecologically
    relevant genes are found on Ecoislands and

  • Gene loss seems to be a function of mutation and
    reduced selection, whilst the horizontal
    acquisition of advantageous genes enables niche
  • Buchnera spp. may have reduced numbers of
    transposons and other repeat sequences. Such
    repeat sequences are hot-spots for recombination,
    enabling gene acquisition, gene losses, gene
    duplications and genome rearrangements).

  • Organisms in more heterogeneous environments
    (variable in space and time) will have a greater
    genetic potential. This will be seen both
    quantitatively (more DNA) and qualitatively
    (greater potential for recombination) and that a
    large proportion of genomic information will be
    associated with the HGP. Horizontal gene
    transfers provide a means for rapid adaptation to
    new or changing environments.

10.3.4 Insights into phytogeny
  • Bacterial genomes are dynamic, undergoing gene
    losses and gene acquisition by horizontal
    transfer or gene duplication. This dynamism can
    result in different (non-congruent) tree
    topologies when phylogenies are constructed from
    several different genes for a defined group of

10.3.4 Insights into phytogeny
  • To construct better phylogenies is to combine
    data for several genes, reducing the contribution
    of strange or weak evolutionary signals. An
    alternative approach is to use whole genome gene
    content and/or gene order as a measure of
    phylogenetic relatedness.
  • These approaches produce phylogenies that largely
    agree with established single gene phylogenies,
    such as 16S ribosomal RNA, Horizontal gene
    transfers contribute significantly to short-term
    bacterial adaptation, but these are not enough to
    blur the evolutionary relationships of bacterial

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  • Comparative genomics says interesting things
    about bacterial evolution and patterns of
    adaptation, as a basis for understanding bacteria
    in an ecological context.
  • eg. the horizontal gene pool is an important
    component of bacterial adaptation and that the
    amount of horizontally transferred DNA increases
    with genome size.

10.4 Functional genomics in microbial ecology
  • Functional genomics (the genome-wide analysis of
    gene expression) is a powerful way to describe
    how microorganisms will respond to their
    environment. Global analyses of mRNA transcript,
    protein and metabolite production constitute the
    three levels of functional genomics, each with
    their own limitations and advantages.

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10.4.1 Characterizing microbial responses to
environmental change
  • The characterization of genome-wide gene
    expression profiles has been most useful in
    analyzing the response of a microbe to changes in
    environmental parameters under controlled
    laboratory conditions.

10.4.1 Characterizing microbial responses to
environmental change
  • For example, global changes in protein expression
    of E. coli K-12 in response to common
    environmental challenges such as starvation for
    carbon, nitrogen, phosphate and individual amino
    acids, oxidative stress, heat shock (42 and
    50C), shifts to and from anaerobic conditions
    and treatment with naladixic acid, quinone,
    2,4-dinitrophenol and cadmium chloride, have been

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  • The analysis of any given proteome and the
    ability to isolate proteins of interest via
    two-dimensional gel electrophoresis is limited to
    subsets of an organisms total protein content.
    This restricts the use of this analysis for the
    description of genome-wide gene expression.
  • The difficulties associated with the isolation of
    membrane fractions may necessitate the separate
    analysis of the membrane subproteome and
    cytosolic proteomes.

  • By contrast, microarrays enable the genome-wide
    assessment of gene expression in terms of mRNA
    transcript production (transcriptome analysis).
  • Eg. high-density microarrays of 4290 open reading
    frames from E. coli K-12 have been used to
    profile the global transcriptional response of
    this organism in minimal and rich media.
    Increasingly, transcriptome- and proteome-based
    analyses are now being used in combination to
    investigate global gene expression in various
    organisms under various conditions.

  • Isolating the entire transcriptome of an organism
    is a simpler task than isolating an entire
    proteome, but there are a number of limitations
    to transcriptomics.
  • The analyses of transcriptomes as a means of
    describing genome-wide gene expression ignores
    post-transcriptional and post-translational
    levels of control in gene expression.
  • DNA microarrays, until recently, have been
    relatively difficult to make but array
    developments help microarray production off the
    shelf for model organisms, or for those which
    are being sequenced independently by other

10.4.2 Relating specific genes to specific
  • It is hard to give a function to the large
    numbers of hypothetical genes in genome
  • Genome sequencing projects have shown that there
    are many hypothetical genes. Their individual
    roles in defining metabolic, physiological,
    structural and morphological diversity are still
  • A first goal in microbial ecology is to relate
    specific genes to particular ecological
  • Functional genomic analyses can be used to
    characterize the effects of mutations on gene
    expression and thereby relate the cellular role
    of a gene product to an ecologically relevant

  • One study compared the transcriptome and the
    proteome of a putative ferric uptake regulator
    (fur) mutant of Shewanella oneidensis with those
    of a wild type strain to confirm the role of fur
    in the regulation of siderophore-mediated iron
    assimilation. The results also showed fur is used
    in energy metabolism. There might be a regulatory
    framework underlying the ability of S. oneidensis
    to generate energy via the reduction of insoluble
    ferric iron. So this organism can live under
    oxygen-limited, iron-abundant conditions.

  • Another example is the characterization of genes
    regulated by intercellular signals in bacteria.
  • An E. coli DNA microarray was used to find which
    genes display altered expression levels during
    quorum sensing by investigating gene expression
    in a luxS mutant that was unable to synthesize
    the signalling pheromone AI-2.
  • If the AI-2 signal was added to growth medium,
    gt5 of E. coli ORFs were responsive to the
    presence of the signal, including genes involved
    in cell division, morphogenesis and cell surface

  • Saccharomyces cerevisiae was grown in
    glucose-limited chemostats over 250 generations
    and genome-wide transcription of the resulting
    strains was compared with that of the parent.
  • DNA microarrays revealed that all successful
    populations had alterations in their central
    metabolism to allow the complete oxidation of

  • Like proteins, metabolites have direct functional
    roles in cellular activities. Metabolomics,
    however, is complicated by the fact that in most
    cases there is no direct relationship between
    genes and metabolites, in contrast to the obvious
    link between mRNA transcripts and proteins.
  • But, the use of metabolomics to show the
    phenotypes of apparent silent mutations in S.
    cerevisiae has been demonstrated.

10.4.3 Expression of genes from components of a
  • Gene transfer is of great interest to the
    microbial ecologist.
  • Autonomous and mobile genetic elements such as
    plasmids and phages have genes which help the
    hosts in certain environments, so can help
    explain microbial population dynamics.
  • The expression and the induction or repression of
    environmentally responsive genes on mobile
    genetic elements can be assessed using the tools
    of functional genomics.

  • Microorganisms typically have a limited ability
    to move significant distances within their given
    environment and hence evade environmental
    challenges. Environmental changes will produce
    physiological responses by the microbe that may
    be controlled by environmental control of gene
    regulation in the cell. Consequently,
    investigation of the regulatory mechanisms
    controlling such physiological responses will
    contribute substantially to our understanding of
    microbial ecology.

  • However, the response of pure cultures of
    bacteria under controlled laboratory conditions
    to changes in specific environmental parameters
    may not represent the response of any given
    organism to the same change in situ.
  • Microbes do not notice individual environmental
    parameters but respond to the interaction beteen
    all such parameters, including the effects of
    other members in mixed microbial communities.
  • Recognize that it is not feasible to conduct
    comprehensive multifactorial functional genomic
    studies of microbial responses to all
    environmental parameters in vitro.

10.5 The application of genomic tools in situ
  • Can we study functional genomics in situ?

10.5.1 Organism diversity
  • Significant developments in microbial ecology are
    because of the use of 16S rRNA as a phylogenetic
  • 16S rRNA sequencing has revealed that the
    diversity of organisms in the natural environment
    is extensive, with prokaryotes occupying the
    major portion of the tree of life.
  • Molecular ecologists are interested in the
    microbial diversity in an environmental
    subsample, in terms of the numbers of species,
    their richness and evenness.

  • Array-based probing technologies are the first
    genomic tools to be applied to microbial ecology
    in situ.
  • A group of 16S rDNA probes was directed to key
    nitrifying genera immobilized in a simple array
    format. Successful detection of specific groups
    was demonstrated using reverse hybridization
    techniques using fluorescently labeled rRNA or in
    vitro-transcribed targets generated from cloned
    rDNA. A similar strategy was later used for
    PCR-amplified rDNA, or in vitro-transcribed rRNA
    targets, for successful biotin-label-based
    detection of Geobacter and Desulfovibrio groups.
  • Fragmented rRNA hybridized easier than intact
    rRNA and a minimum detection limit of 106 cells
    was determined. These data showed that soil
    extracts spiked into reactions inhibited the
    efficiency of rRNA hybridization, a major
    consideration for future environmental studies.

  • More recently, an array system termed the
    SRP-PhyloChip has been designed and applied to
    examine all known members of the sulfate-reducing
    prokaryotes, using 132 16S rRNA targeted 18-mer
    oligonucleotides. The work represented one of the
    first comprehensive attempts at array-based 16S
    analysis in the environment.
  • The chip was tested with DNA samples from a
    hypersaline microbial mat following PCR-based
    amplification of 16S rDNA prior to subsequent
    labeling and array analysis.
  • The chip accurately detected sulfate-reducing
    organisms in environmental samples that could be
    subsequently confirmed by array-independent
    technologies such as PCR amplification,
    sequencing and gene-specific analyses.

  • Key points for optimization within array formats
    include the quality of the probe design, the type
    and quality of nucleic acids that are hybridized
    with the array (purity and size of the DNA, RNA,
    or cDNA, and the strength of the fluorescent
    labels used), and finally the strategies for, and
    accuracy of, signal quantification. It is
    important to find the optimal hybridization
    stringency conditions required for accurate
    analyses. For arrays, if hybridization is to be
    effective the probe suite needs to be empirically
    analyzed under different stringency conditions
    (e.g. hybridization temperature and salt
    concentrations) with reference targets of known
    divergence from the probe sequence.

  • Establishment of hybridization conditions which
    encompass high stringency for exact match probes
    but also allow hybridization at moderate
    divergence (e.g. 20 sequence divergence) will be
    the most applicable to the analysis of
    environmental nucleic acids. This is due to the
    expectation of divergent targets within a given
    gene family in the environment and the need for
    this diversity to be accounted for. Specifically,
    using the dissociation temperature (Td),
    calculated by melting profiles of the probe is a
    good basis for assessing hybridization conditions
    and stringency in multi-probe formats.
  • Perhaps use nested probes of increasing
    phylogenetic resolution.
  • Including redundant probes to critically examine
    stringency during hybridizations could be a form
    of internal standardization.

10.5.2 Functional genes
  • Although a knowledge of the organisms present in
    any given environment is a prerequisite for many
    applications of microbial ecology, it is also
    fundamental to establish the function(s) of the
    constituents of the communities. Microbial
    functionality regulates the majority of
    biogeochemical cycles in the biosphere, serves as
    a reservoir for potential bioprospecting and
    provides potential solutions through green
    microbial biotechnologies. An understanding of
    the diversity and amount (gene dosage) of
    functional genes present and, in the future, the
    degree to which they are expressed at the mRNA
    level are central components to the understanding
    of microbial functionality. To examine the
    diversity of genes in the environment, two
    approaches have been taken utilizing genomic
    technology. The first takes the form of shotgun
    cloning and sequencing, the so-called
    metagenomics approach. The second approach is
    clearly an analogue of rRNA analysis in the
    environment, in that array formats have been used
    to look at the presence and/or expression of
    specific pathways.

10.5.3 Metagenomics
  • Metagenomics is the application of structural
    genomics to the environment, where gene libraries
    are constructed from the shotgun cloning of
    environmentally recovered DNA followed by
    sequence analyses. The technique provides a
    comprehensive way of isolating environmentally
    relevant genes, and as such is heavily utilized
    as a tool in the field of novel gene/pathway

  • However, within an ecological context
    metagenomics suffers from two pitfalls. First, it
    may be difficult to assign a taxonomic framework
    to the genes observed unless a 16S rRNA gene or
    robust phylogenetic marker is located within a
    sequenced fragment, or sequencing is restricted
    to taxonomically identified fragments. Second,
    the characterization of unknown genes is
    hampered in that a function for any given gene
    can only be assigned on the basis of similarity
    to known functional genes. This problem is
    exacerbated as most of the genomic datasets are
    from eukaryotes or clinically important bacteria,
    as opposed to prokaryotes from the environment.
    Despite these points, several major successes
    have been afforded by environmental metagenomic

  • The discovery that heterotrophic processes in the
    open ocean were contributed to by heterotrophs
    which contained light-harvesting systems for
    energy generation (proteorhodopsin) was a direct
    result of a metagenomics approach. This
    significant finding was found by sequencing
    oceanic-derived genomic libraries constructed in
    bacterial artificial chromosome (BAC)
    libraries. Later, proteorhodopsin homologues
    linked with a 16S rRNA gene representative of the
    SAR86 cluster in oceanic waters were observed.
    Homologies with the well-described bacterial
    pigments could be identified and the active
    nature of the pathway in the environments
    subsequently demonstrated.

  • Significantly, the metagenomic approach also led
    to an insight into the ecology of the organisms
    containing proteorhodopsin, through the
    elucidation of spectral variants occupying
    distinct geographical locations. Similar
    approaches using BAC library technology applied
    to soil have indicated the potential of
    metagenomics for accessing genes from uncultured
    soil organisms, such as the members of the
    Acidobacterium phyla, and the expression of their
    metabolites in a recombinant host. Applications
    of metagenomics are beginning to reveal the gene
    contents and differences between members of the
    Crenarcheota, an environmentally important group
    containing non-thermophilic members with
    widespread distributions in marine, freshwater
    and soil habitats.

  • On a larger scale, metagenomics has been employed
    to describe the genomic content of entire
    ecosystems. Tyson et al. assembled genome
    sequences of the dominant prokaryotes (Leptospira
    and Ferroplasma) together with three other
    partial genomes using shotgun sequencing of a
    small insert plasmid library derived from plasmid
    DNA isolated from an acid mine drainage biofilm.
    Venter et al. have applied similar methods to
    study the bacterial community present in surface
    waters from the Sargasso Sea. The authors
    generated a phenomenal 1.045 billion base pairs
    of non-redundant sequence that they assembled
    into near-complete or partial genome sequences
    from an estimated 1800 bacterial species. In
    particular, large sequence scaffolds were
    generated that showed strong similarities to
    Burkholderia, Shewanella, SAR86 and
    Prochlorococcus genomes, but in addition
    sequences were derived from an estimated 148
    novel phylotypes. These ecosystem level datasets,
    when combined with new projects aiming to
    generate environmental genomic datasets from both
    marine and terrestrial ecosystems, represent an
    outstanding opportunity to expand our knowledge
    and understanding of the microbial diversity in
    the natural environment.

10.5.4 Functional gene arrays
  • Metagenomic approaches reveal a large number of
    functional genes of interest in the environment.
    However, in order to assess the distribution and
    functioning of these genes, analyses other than
    the labour-intensive sequencing-based strategies
    are required, ideal candidates being those of the
    array technologies. The first attempt at a
    relatively comprehensive array-based detection of
    the genes present in environmental samples
    concentrated on the genes involved in nitrogen
    and methane cycling. Wu et al. used immobilized
    probes constructed by PCR amplification of 89
    nirS, nirK, amoA and amoP sequences on a glass
    slide array.

  • Hybridization with labeled targets from cultures
    and environmental samples provided some
    interesting insights into gene detection in the
    environment and, predictably, highlighted some of
    the pitfalls previously discussed. Under the
    hybridization conditions employed (65C), targets
    with 8085 identity could be efficiently
    detected (rising to 90 at 75C). Furthermore,
    they estimated that target abundances of 1 ng of
    pure genomic DNA and 25 ng of environmental DNA
    were required for nirS detection. These levels of
    detection, in principle, seem hopeful when
    considering potential applications in ecosystems
    such as soils and water. In terms of signal
    quantification, linear signal intensities were
    observed for pure genomic DNA in the range of 1
    to 100 ng, but the signal intensity was found to
    vary, depending upon the degree of target
    divergence, as would be theoretically predicted.
    The authors highlighted that a key requirement
    for future quantification of both 16S and
    functional gene arrays is the ability to
    differentiate signal intensities which arise from
    variations in target abundance from those that
    arise simply due to sequence divergence. As
    suggested, since signal intensity at lower
    stringencies of hybridization was relatively
    unaffected by sequence divergence (if lt20
    although the effect became more pronounced at
    higher stringencies), a sequential hybridization
    strategy could be employed to differentiate
    abundance/divergence signal variations.

  • Whilst signal intensity criteria are important
    issues to resolve in environmental applications
    of arrays, the issue of standardization and
    sample-to-sample comparison are also key areas
    for development. Single stringency hybridizations
    with exact match probes and targets have been
    employed to demonstrate the use of internal
    standardization as a tool for more robust
    quantification in array technologies. Internal
    array standardization allows multiple samples,
    with differing target concentrations, to be
    compared on a rational basis by correcting for
    uneven array printing and hybridization
    variation. Moreover, internal standardization
    affords the ability to quantify mRNA levels in
    the environment from multiple samples, a
    criterion currently limited to comparison of two
    environmental conditions or bacterial strains
    (e.g. wild type vs. mutant) using current
    competitive dual-color hybridizations.

  • Despite such challenges, microbial ecologists
    seem set to embrace array-based methods for
    investigating gene distribution, diversity and
    expression. Recent applications of array
    approaches have included development of small
    microarrays (68 probes) targeted at methane
    monooxygenase (pmoA) and ammonia monooyxgenase
    (amoA) genes for studying methanotrophs in
    landfills, and a macroarray (40 probes)
    consisting of PCR-amplified environmental nifH
    genes used to investigate spatial variability in
    marine diazotroph communities. Most recently,
    Rhee et al. reported the development, testing and
    application of a 1662 probe 50-mer microarray
    based on genes involved in biodegradation and
    metal resistance, and used this to show that
    degradation genes from members of the
    ß-proteobacteria were dominant in
    napthalene-amended microcosms.

10.5.5 Diversity, function and process
  • A primary concern for microbial ecologists is
    that the tools of genomics are problematic when
    analyzing organisms of interest in situ amidst a
    background of non-target microbes (e.g. within a
    complex soil community). Moreover, it could be
    said that the analyses tend to lack a context, in
    terms of directly linking the functional
    pathways/organisms with environmental processes,
    e.g. carbon cycling. In this respect we
    anticipate the development of complementary
    approaches to relate the genomic analyses of an
    organism to the process it performs within a
    mixed community in situ.

  • One way of using genomic technologies in an
    environmental context is the design and
    implementation of experimental techniques which
    can separate out the true signal of a target
    organism/group in mixed microbial communities
    from all of the organisms present within the
    sample. At present, cells or extracted rRNA for
    standard or genomic analyses can be recovered
    from environments with reference to a
    phylogenetic framework by oligonucleotide probe
    capture. One interesting facet of the rRNA
    hybridization capture method is the ability to
    link the phylogenetic analysis with a process the
    organism may be performing using stable isotope
    detection within their nucleic acids (e.g. 13C or

  • Quantification of isotopic signatures within
    recovered nucleic acids will, in principle,
    provide a very powerful way to determine the
    nature and rates of processes and link this with
    genomic detection strategies. Other applications
    include pulsing stable-isotope-labeled substrates
    into communities and recovering heavy DNA
    containing the genomes of the organisms that were
    involved in processing the substrate, and the
    recovery of RNA to access rRNA or the
    transcriptome. More recently, Adamczyk et al.
    have combined radioactive isotopes of carbon, in
    the form of 14C-bicarbonate, with a microarray
    analysis to investigate ammonia-oxidizing
    bacteria in activated sludge. This elegant work
    first detected the groups present by microarray
    hybridization and subsequently measured
    radioactive incorporation directly on the arrays,
    to give a combined phylogeny and function

  • The additional combination of the aforementioned
    approaches with the future application of
    isotope-labeled protein strategies such as ICAT
    (isotope-coded affinity tag) offers enormous
    potential for investigation of microbial
    community function at the level of the genome,
    metagenome, transcriptome and proteome.

  • There are several techniques that do not directly
    link molecular signatures to biogeochemistry
    through isotopic tracer studies, but which
    operationally define subgroups with microbial
    communities. Such methods include fluorescent in
    situ hybridization and/or activity measures
    coupled to flow cytometric cell sorting for the
    recovery of specific bacterial populations. We
    predict that these tools will become a central
    form of sample collection for in situ studies
    prior to genomic applications in the coming
    years. Techniques that allow directed genomic
    analyses will target the community in an
    effective way, could link genomics to microbially
    driven processes and substantially increase the
    success of genomic analyses in situ.

10.6 Lessons learned and future perspectives
  • The discipline of microbial ecology is firmly
    rooted in understanding the ecology of microbes
    in the real world. This encompasses microbial
    evolution, adaptation, community structure and
    function, together with their effects upon
    biogeochemical cycles at scales which span
    millimeters, kilometers, continents and oceans.
    Genomics is a tool which aids us in this
    endeavor. As we have stated in the preceding
    sections, certain obstacles need to be overcome
    prior to the widespread use of genomics in
    microbial ecology. These obstacles are by no
    means insurmountable, and we would formalize
    these as five lessons learned for future

  • (i) Progress will be directly proportional to
    database size. The current size of databases with
    fully annotated genomes and functional analyses
    for environmental organisms limits genomic
    application for microbial ecologists. A key
    requirement is the analysis of multiple
    representatives of the key environmental groups
    or lineages thus far identified. Specific tasks
    involved with this are assessing culture
    requirements, isolation of pure cultures,
    directed sequencing programs and effective
    functional analyses (both in silico and
    biochemically) of these isolates.

  • (ii) The horizontal gene pool. The importance of
    the HGP to the evolution of environmental
    microbes and their pathways cannot be
    understated. As such, intensive
    sequencing/functional analyses for mediators of
    the HGP (plasmids, transposons etc.) should be
    undertaken for a range of habitats and processes,
    and their results given equal significance to
    those placed upon chromosomal sequences.

  • (iii) Ex situ vs. in situ. Genomic ex situ
    studies using cultured isolates can be used to
    determine underlying principles of bacterial
    survival/adaptation/functionality in the
    environment. However, in order to extrapolate ex
    situ experiments to observations made in situ,
    great care must be required for the ex situ
    experimental design and execution. For example,
    microbes continuously experience multiple
    stresses and significant variation in both the
    type and concentrations of nutrient sources.
    Microbes rarely, if ever, exist in a batch
    culture scenario with optimal growth conditions.
    Experimental manipulations using genomic analyses
    should reflect the continuous and multifactoral
    lifestyle that microbes experience

  • (iv) Signals and noise. The successful
    application of genomic approaches in situ
    requires the development of methods that provide
    significant resolution of signal against noise.
    An example would be the requirements for the
    detection of the 16S rRNA genes of a specific
    species against a background of large numbers of
    co-habiting organisms. A large amount of research
    on standardization and calibration is still
    required prior to the widespread application of
    genomic technologies in environmental samples.

  • (v) Genomics and biogeochemistry. An important
    role of genomics in microbial ecology will be to
    relate features of microbial communities
    detectable by genomic strategies to environmental
    processes. As an example, the determination of
    abundances of methane oxidizers and their
    functional transcripts should be related to
    methane oxidation rates. The absence of a link
    between environmental process measurements and
    genomic detection of organism and transcript
    abundances will ultimately lead to an increase in
    information at the expense of ecological