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Central role of the cell in microbial ecology
1. Central Role of the Cell in Microbial
Ecology
Karsten Zengler
Microbiol. Mol. Biol. Rev. 2009, 73(4):712. DOI:
10.1128/MMBR.00027-09.
These include:
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3. VOL. 73, 2009
CENTRAL ROLE OF THE CELL IN MICROBIAL ECOLOGY
When using cells as a kind of stepping-stone to move from
molecular biology data to natural populations and whole environments, it is crucial to evaluate the robustness of this process. This means that we have to carefully estimate the implications that can be drawn from our data. The following will
therefore cover a wide range of subjects, from biological processes on a molecular level to individual microorganisms, from
individual organisms to populations, and from populations to
the environment.
Since the terms isolation, growth, and cultivation are often
used synonymously, it will be beneficial to briefly define them
here so that they can be distinguished throughout this review.
“Isolation” of an organism (or multiple organisms at a time)
describes the process by which individual cells are physically
separated from each other and/or from matrix material, such
as water, air, soil particles, or eukaryotic tissues. Isolation
therefore represents the most crucial step during the process of
obtaining pure cultures. Isolation also includes the process by
which defined cocultures are obtained for further cultivation.
For microorganisms, “growth” implies the division of a bacterial cell, resulting in duplication of the cell number. Measuring and observing bacterial growth, especially in the environment, can be challenging since the rates of growth and of death
(e.g., due to apoptosis, grazing by eukaryotic predators, or cell
lysis by phages) can be identical, resulting in net growth that
will be zero.
Traditionally the terms “culture” and “cultivation” are used
to describe a defined bacterial population that can be grown
and maintained in the laboratory, usually at a scale that involves billions of cells at a time. Cultivation is not exclusive to
pure cultures but can include mixed populations and stable
consortia that are propagated in the laboratory for a prolonged
period of time.
Microorganisms are isolated, grown, and cultivated in the
laboratory for many reasons. Examples are the enumeration of
bacteria with a certain function or role in nutrient cycling, in
formation and degradation of organic and inorganic molecules,
or in bioremediation and energy production. Other examples
include the testing of Koch’s postulates, identification of organisms that carry specific genetic information (gene or pathway), evaluation of phylogeny and physiology, and discovery of
novel enzymes and chemical entities (e.g., anti-infectives) for
industrial and pharmaceutical applications. As broad as the
scientific goals are the cultivation methods used to accomplish
them. Depending on whether a defined group of microorganisms is targeted (e.g., new bacterial or algal strains for biofuel
production) or whether “as many as possible” different strains
should be isolated (e.g., for diversity assessment or to accompany metagenomic studies), the most suitable methods and
their refinement will differ substantially. However, having microorganisms in culture allows for the direct study of morphology, physiology, genetics, and pathogenicity in great detail,
tasks which are difficult to accomplish when solely molecular
tools are used.
Advances in molecular biological techniques over the last
three decades have spurred cultivation-independent developments. In medical diagnostics, for example, isolation and cultivation have been replaced by advances in molecular methods
that can identify specific microbes or genetic markers more
accurately, often faster, and more cost-effectively. However,
713
one has to keep in mind that these markers were initially linked
to a certain disease by work that was performed with microbial
pure cultures.
ISOLATION TECHNIQUES
Physical separation of individual cells (or groups of cells) is
essential to cultivation efforts. This isolation step can take
place before or after cells are grown (see below). There are
several methods to physically separate cells, probably the most
common of which is separation of cells by spreading them onto
a solid medium. This method was introduced by Robert Koch
over a century ago (118) to visualize, isolate, and ultimately
cultivate microorganisms. Although several advances have
been made in isolating bacteria on solidified medium since
Koch first used agar-solidified medium (108), the basic principle of isolating bacteria by spreading them on plates and “picking” colonies remains unchanged. The underlying concept is
that a single bacterial cell, spread on an agar plate (or solid
medium made with other gelling agents), will start to divide
and consequently form a colony that is visible by the naked eye
or by microscopy. These colonies can then be separated from
each other using various tools, e.g., a loop or toothpick, depending on the colony size. The process is defined by a separation step (spreading cells onto a plate), a growth step (colony
formation), and the actual isolation step (colony picking). The
most critical step here is the colony formation. It was recognized early on that the majority of cells observed under the
microscope will not form colonies on solid media (38), a phenomenon that over half a century later became known as “the
great plate count anomaly” (201). However, it is important to
note that bacterial cultures can undergo certain adaptations
during these isolation and growth procedures. For example,
some strains that were not able to grow on solid media before
were adapted to form colonies on agar plates after several
attempts (35) (different forms of adaptation will be discussed
in more detail below). Other microbes (e.g., some strictly anaerobic microorganisms) will not form colonies on surfaces but
instead can be grown inside solid media, a phenomenon that
resulted in the use of agar shakes or agar dilution series for
isolation purposes (233). Conversely, there are bacteria that
require surfaces to grow on (e.g., gliding bacteria), and isolation and cultivation of these organisms is hindered by the use
of liquid media (189).
Methods by which cells are isolated before growth takes
place include the use of flow cytometry (61, 165), microfluidics
(141, 211), or micromanipulation using focused laser beams
(so-called optical tweezers) (69, 105). These techniques are all
sensitive enough to detect and subsequently separate individual cells (Fig. 1). An approach that does not require single-cell
detection for separation is the isolation of bacteria by microencapsulation (249). A more commonly used technique is the
isolation of bacteria via liquid serial dilution (27, 39, 49, 189).
This technique is applied especially in cases where bacteria do
not form colonies on solid surfaces or where media cannot be
adequately solidified with agar, e.g., due to low pH (221).
Recently a method that uses nanofibrous cellulose to solidify
media even at low pH and therefore can support growth of
acidophiles has been described (46, 220).
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MICROBIOL. MOL. BIOL. REV.
FIG. 1. Microbial growth can be directly determined without the use of molecular biology techniques. Methods used to determine optical
density or cell numbers vary in their sensitivity. (A) Visualizing the turbidity of a culture with the naked eye allows detection of ϳ105 cells/ml.
(B) Observing cells under the microscope allows detection of ϳ103 cells/ml. (C) The use of a flow cytometer in combination with encapsulation
of cells detects up to 101 cells total. (D) Growth (division) of single microbial cells can be monitored by microscopy, and cells can subsequently
be isolated using microfluidic and micromanipulation devices. In addition to microscopy, flow cytometry also allows for detection and isolation of
individual cells.
OBSERVING MICROBIAL GROWTH
There are several methods with various sensitivities to measure and describe bacterial growth (Fig. 1). Most often, growth
is observed by turbidity, using a photometer, or just by looking
at a culture; these methods are suitable if Ͼ105 cells per milliliter are present. Detecting microbial growth, qualitatively or
quantitatively, sounds trivial, but not all bacterial cells are able
to form visible colonies on solid medium plates, and therefore
growth cannot be detected conveniently by the naked eye.
Even within pure cultures, it is known that the rate of colony
formation is not uniform and that several cells might form only
microcolonies which cannot be detected by the naked eye (104,
235). Therefore, several researchers applied microscopy to observe colony formation on solid agar (112) or membrane systems (60, 114). Even after prolonged incubation of 1 to 6
months, many colonies of soil bacteria can still be present as
microcolonies and might never grow larger (108, 228). Similar
“self-limiting” growth behavior has been observed for oligotrophic marine bacteria (35, 170). Detecting growth by microscopy, which normally allows a detection of ϳ103cells/ml, increases the sensitivity compared to that of turbidity
measurements, which require higher cell densities (Fig. 1). A
method that allows for the detection of even fewer cells is the
combination of isolating cells by encapsulation in microcapsules and sorting by flow cytometry (250). Instead of a flow
cytometer, this encapsulation technique can also be combined
with a microfluidic approach to monitor division of cells (122).
Microfluidics can also be applied to observe the division of
single cells directly. Advanced methods have been developed
that allow monitoring and screening of large numbers of organisms at the same time. Flow cytometry, for example, allows
the screening of 5,000 to 50,000 events per second (249), and
high-throughput approaches using new technologies, such as a
GigaMatrix (128) and a microdish (102), have been developed
to be applied for miniaturized culture volumes, allowing the
growth and screening of millions of cells in a highly compartmentalized format. An advantage of these high-throughput
methods is that they are capable of growing and screening
many organisms at a time so that cultivation of previously
uncultivated ones becomes more likely (102, 249). Furthermore, there are a variety molecular biology tools that measure
various cellular components and infer growth from these data
(see below).
WHY ARE MOST BACTERIA CURRENTLY
NOT CULTIVATED?
The Legend of the Unculturable Bacteria
Over the last decade there has been some discussion about
the general culturability of microorganisms. Several authors
referred to organisms that were known by their molecular
fingerprints (mainly 16S rRNA gene sequences) but could not
be brought into culture at that time as they were “unculturable.” Clearly, this word has been used in an imprecise way,
since many formerly “unculturable” organisms later became
part of our culture collections (72). However, the majority of
microorganisms in any given environment have not been cultivated (yet) even when sophisticated media and new cultivation and isolation methods are applied. One possible reason is
that researchers tend to stick to a handful of different media
(at most) and do not spend time and effort to optimize nutritional needs, i.e., medium compositions as well as physicochemical parameters such as temperature, pH, salinity, and
growth atmosphere. The notion that cultivation attempts fail
because exotic compounds serve as exclusive carbon sources
for growth is probably not correct. There are many success
stories where former “unculturable” microorganisms (in some
cases known for decades) have been cultivated in the laboratory using common nutrients (25, 204, 226); to rely exclusively
on exotic substrates (140) is therefore more likely the exception than the norm. However, we should note that various
signal molecules, as discussed later in this review, seem to play
an important role during cultivation and that isolation procedures likely disrupt this signaling. Cultivation success is probably not hampered exclusively by what is offered to the microorganisms for growth but, importantly, on how much of these
nutrients is provided—plenty can often be too much. Media
containing high concentrations of nutrients, often a billion to a
trillion times more than what microbes encounter in their
natural environment, can have inhibitory effects (1, 158). Bacteria seem to have developed different strategies to adapt to
changing nutrient concentrations in the environment, which
consequently will determine if they are able to form colonies
on nutrient-rich agar plates (8). Therefore, microorganisms
that have the capability to adapt and cope with high concentrations of nutrients are often overrepresented in cultivationbased studies (107) and ultimately in our culture collections
5. VOL. 73, 2009
CENTRAL ROLE OF THE CELL IN MICROBIAL ECOLOGY
(117). As a response, several techniques that use low concentrations of nutrients for isolation and cultivation efforts have
emerged (10, 59, 83, 103, 112, 114, 170, 249). However, growing bacteria at a low concentration of nutrients will logically
result in low biomass gains. Large culture volumes become
necessary (170) to obtain enough biomaterial for physiological
and genetic studies, and techniques to observe growth (Fig. 1)
have to be adapted accordingly (see above). Decades ago, an
elegant way to circumvent the necessity of large culture volumes was introduced by Novick and Szilard (156), who were
the first to use flowthrough setups for growing cells under a
constant supply of nutrients (a chemostat). Since then, chemostats have been an excellent tool in microbiology for various
approaches, such the study of pure cultures, competition experiments (125), and the study of mixed cultures and microbial
consortia (57, 205). Another benefit of flowthrough setups is
that inhibitory (by)products which tend to accumulate in
closed cultures are diluted or removed from the system. However, using chemostats for enrichment and isolation of microorganisms from environmental samples has often not been
possible without thoughtful modifications, since many organisms tend to attach to surfaces of the setup, form biofilms, and
potentially out-compete their planktonic counterparts (116). A
modified flowthrough setup for cultivation purposes, which
provides a constant nutrient flow at low concentrations but
eliminates the formation of biofilms, has been the incubation
and growth of separated cells in microcapsules (117, 250).
The Medium: So Many Choices, So Little Time
I have touched already on some reasons and theories why
cultivation of the majority of microorganisms in the laboratory
has failed so far. In the following I will emphasize and discuss
that the selection of the microorganism targeted for cultivation
defines all of the following growth and cultivation steps. First,
there is the choice of separation and isolation methods (see
above), which consequently determines the selection of liquid
or solid medium for growing cells. Second, it is fundamental to
determine if any contact of cell to cell or cell to substrate is
essential for the targeted microorganism to grow. An encapsulation method (249), for example, is of limited use if direct
cell-to-substrate contact is required for oxidation and reduction reactions and subsequent growth. Such substrates are, for
example, long-chain hydrocarbons and other crude oil components that serve as electron donors or an electrode in a fuel-cell
that serves as an electron acceptor. Third, the composition of
the medium is also critical for the cultivation success. However,
we tend to use vitamin and trace element solutions that have
been developed decades ago without rethinking and redesigning their composition. Medium components are known to have
an effect on cultivation efficiency, including carbon and energy
sources (44), various inorganic chemicals and salts (52, 231),
signal compounds (22, 31, 80), and trace elements, vitamins,
and amino acids (87, 88, 134). Basically, every component of
the medium other than water has been demonstrated to have
an inhibitory effect on certain microorganisms. Accounting for
all these inhibitory effects by varying every component of a
standard medium can be a daunting task (Fig. 2). To illustrate
this, I picked a medium which is commonly used to grow
anaerobic bacteria and that contains 33 different components
715
(29). By changing the concentration of a single component at
a time, one would need to generate and test 33 different media;
accounting for increasing as well as decreasing concentrations
of this component would result in 66 different medium combinations to be evaluated. Changing any two components of
the medium at the same time (increasing and decreasing their
concentrations) would result in 2,112 different medium combinations to study. Variation of any 22 components at a time
would require an inconceivable ϳ1015 (1 quadrillion ϭ 1,000
billion) medium combinations, clearly an unrealistic effort
(Fig. 2). Furthermore, these are only variations of an existing
medium, reflecting a medium “optimization” effort. This effort
would neither include other electron acceptors nor account for
additional potential electron donors (e.g., carbon sources),
and, more importantly, it does not include differing environmental conditions (pH, temperature, salinity, buffering capacity, pressure, and gas atmospheres such as different carbon
dioxide or oxygen concentrations). It has often been cited that
the media which are routinely used for most cultivation efforts
do not allow growth of most microorganisms in the laboratory.
It is surprising that not more attention has been paid to the
improvement of cultivation methods but that instead the majority of microorganisms are categorized as “unculturable.”
This might be the case for some organisms, but there is clearly
(a lot of) room for rational optimization of just medium compositions, which has been documented by a number of studies
(44, 54).
The recent success of new cultivation techniques and the use
of modified media to gain access to previously noncultivated
microorganisms demonstrates that many organisms can be isolated and maintained in culture in the laboratory (131). In
addition, it was realized that cultivation of “new” microorganisms might be nominal when nutrient-rich, “off-the-shelf media” are being used. When cultivating any kind of microorganism, conditions should be adapted to natural environmental
conditions, by at least adjusting the pH, salinity, and temperature (and in some cases pressure [247]) to simulate environmental conditions. While this effort has increasingly become a
routine, only few studies consider variations in atmospheric
pressures. Many cultivation attempts are performed exclusively
under oxic (ϳ20% O2) or anoxic conditions, but only a few
studies account for low oxygen requirements of microaerophilic microorganisms. Molecular oxygen, however, represents
one of the most reactive elements on our planet; only fluorine
exhibits a greater electronegativity (the ability of an atom to
draw electrons) (162). The reactivity of oxygen and reactive
oxygen compounds such as hydrogen peroxide, superoxide,
and hydroxyl radical has been well described in the literature
(3, 124). Bacterial life existed on this planet before elemental
oxygen was introduced into the atmosphere and with it an
increase of oxidized compounds, such as the common electron
acceptors nitrate and sulfate (166). During the time of the slow
oxidation event on our planet, microorganisms had billions of
years to adapt to various oxygen concentrations and ultimately
develop fully aerobic metabolisms, utilizing oxygen at atmospheric concentrations. There is a broad range of requirements
and tolerances toward oxygen among microorganisms between
the strictly anaerobic and fully aerobic bacteria. Some anaerobic microbes do not tolerate any level of oxygen; others tolerate various concentrations and have different levels of inhi-
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MICROBIOL. MOL. BIOL. REV.
FIG. 2. A combinatorics example illustrates the vast number of medium combinations possible by variation of its components. Starting with a
standard medium containing 33 components (plus water) and changing one component at a time (gray line) led to 33 different media. Accounting
for variants in concentrations (increasing or decreasing concentration for each component [black line]) resulted in 66 different medium combinations. Depending on the total number of components in the medium that will be varied to account for inhibitory effects, the potential number
of combinations will reach over 109 (for one component at a time [gray line]) and 1014 (to account for two variations in concentration).
bition (42, 135). Other bacteria require oxygen for their energy
metabolism but are extremely sensitive to higher oxygen concentrations (139, 161). These fundamental differences in oxygen requirement and tolerance had already been described by
Winogradsky in the 1880s (236). The degree of flexibility in
regard to oxygen not only defines the ecological niches of
specific organisms but largely affects cultivation success (202).
Recently, several microaerophilic organisms that thrive strictly
at the oxic-anoxic interface and tolerate oxygen only at specific
and low concentrations have been isolated (55, 153, 193, 216).
To maximize the cultivation success, the simulation of the
natural environmental conditions is critical, and the degree of
specificity increases with the specificity of the requirements
and tolerances of the organism. The smaller the ecological
niche where the microorganism can thrive is defined, the more
specifically the medium has to be prepared and the environmental conditions have to be simulated to allow cultivation.
Unfortunately, thorough investigations of the ecological niche
on scales relevant to microorganisms are sparse. Due to the
lack of detailed and high-resolution measurements, researchers tend to simplify what the ecological niche of an organism
consist of. However, only such high-resolution measurement of
varying environmental parameters (17) offers a peek into the
“living room” of a microbe—a necessary insight if we want the
organisms to feel at home in the laboratory.
“Dos and Don’ts” in Cultivation
Growth of microbes in the laboratory is dependent on the
medium and the cultivation conditions that are applied. This
includes the equipment and materials that are being used for
cultivation. It has been known for many years that chemicals
leaking out of plastic and rubber laboratory supplies (pipettes,
cultivation plates and trays, rubber stoppers, and tubing) can
have inhibitory effects on bacterial growth (204), and several
additional bioactive contaminants have been identified (144).
The release of substances into incubation setups changes the
composition of the medium and the environmental conditions.
For example, if organic solvents are used as growth substrates,
concentration of bioactive compounds leaching out of plastic
containers can have inhibitory effects. Even filter material used
for sterile filtration may have negative effects on bacterial
growth. Therefore, it is desirable that contact with plastic and
rubber materials be kept to a minimum during cultivation. A
pretreatment of the lab equipment, e.g., by boiling rubber
stoppers and tubing with 1 N NaOH followed by additional
boiling in ultrapure H2O prior to use, can also improve cultivation efficiency (K. Zengler, unpublished data). Inhibitory
effects have also been linked to certain types of glassware as
well as in general to the use of new glassware. To my knowledge, it has never been determined what actually causes the
inhibitory effect of certain glassware, but boron nitrides and
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CENTRAL ROLE OF THE CELL IN MICROBIAL ECOLOGY
FIG. 3. Two growth curves of E. coli (optical density [OD] at 660
nm [red line]) and a methanogenic consortium (gas production in ml
[black line]) plotted in the same graph over time (days) illustrate two
extremes in microbial growth rates. Successful cultivation of microorganisms depends on the growth stage of the organisms and the correct
timing of isolation. After 2 years of cultivation, the methanogenic
consortium exhibited exponential growth, while viable cells from the E.
coli culture could no longer be recovered. (Adapted from reference
248 by permission from Macmillan Publishers Ltd., copyright 1999.)
other chemicals have been suspected to be slowly released
from the glass and to be responsible for inhibitory effects (108).
A thorough washing step, especially for new glassware, can
reduce these inhibitions. Also, the quality of water that is used
to prepare the medium is of great importance. Chemical impurities of water and gelling agents (such as agar or gellan) not
only can affect cultivation success but are known to change the
phenotypic behavior of microbes (109, 232). As an example, it
was found that in members of the order Actinomycetales the
expression of pathways encoding certain secondary metabolites
is favored when media are prepared with tap water versus
double-distilled water. Autoclaving time and its negative side
effects (i.e., formation of reactive oxygen species or Maillard
reaction products) can also have an impact on the cultures (76,
110, 195). Physical stress may also have an effect on cultivation
efficiency. Certain microorganisms may disagree with James
Bond’s “shaken, not stirred” when it comes to their preferred
growth environment and grow much better without any agitation.
Watching the Grass Grow: Slow-Growing Microorganisms
Even if all the steps mentioned so far are carefully considered, many cultivation attempts will still fail, because the researcher has not been patient enough. This is something that
has to be especially taken into account when assigning a cultivation project to a graduate student, who has only a limited
time frame available to complete his or her projects. Part of
this has to do with the way bacterial growth is determined and
measured (see above), but it also has to do with the fact that
growth rates of most microorganisms in the environment are
much lower than what we are used to from very common
laboratory bacteria such as Escherichia coli (Fig. 3). Incubation
times can reach several months before formation of colonies or
even microcolonies can be observed (109, 184, 249). Some
cultures and consortia grow so slowly that it takes years before
visible biomass is observed, with an anaerobic hydrocarbondegrading methanogenic consortium (248) being one of the
717
slowest-growing laboratory cultures reported (Fig. 3). In this
case, microbial activity during the first year of cultivation of
this consortium had to be inferred by measuring production of
methane, and slight changes in turbidity (optical density at 600
nm of ϳ0.1) could be observed only after 3 years of incubation
(248). The success of cultivation of organisms with such low
growth rates depends not only on the patience of the researcher but also, even more importantly, on “good timing” for
the cultivation steps (Fig. 3). While some microbes can be
isolated and grown in a matter of hours or days, others require
incubation times that range from months to years. It is known
that certain microorganisms are specifically adapted to slow
growth (8) and have developed an advantage for, for example,
avoidance of lysis by phages (215). Microbial growth is largely
dependent on the Gibbs free energy available and maintenance energy requirements of the organism (185). However,
syntrophic cultures, which survive on maintenance Gibbs free
energies that are much lower than the theoretical values, have
been studied in the laboratory (190). There are even reports of
microorganisms in subsurface sediments that make a living
with maintenance energies that are orders of magnitude lower
than minimum values obtained from laboratory-derived experiments. Estimates of doubling times and resulting community
turnover for subsurface microorganisms are between 100 and
2,000 years (18). Clearly, microorganisms with such growth
rates are not suitable for any classical kind of cultivation experiment. So far it is not known whether all members of these
communities can adapt to more rapid growth if provided with
sufficient nutrients in the laboratory.
ROLE OF CULTIVATION IN MICROBIAL ECOLOGY
Multiscale Measurements
It is important to be aware that comprehensive studies in
microbial ecology are not restricted to members of the Bacteria
and Archaea alone but have to include all members of the
microbial community, such as fungi, protists, and viruses. Although not discussed in this review, it has been recognized that
viruses can substantially influence bacterial and eukaryotic
(protist and metazoan) host metabolisms, which consequently
has broad implications for the environmental “fitness” of these
populations (21, 234). Viruses are the most abundant biological entity in nature, and several sequence-based surveys have
revealed an enormous viral and phage diversity (180). However, most diversity surveys have focused exclusively on bacteria and/or archaea. A comprehensive study comparing the
ratios of microbial and viral populations based on communitywide data has only recently been performed (50). Given that
studies of viral and bacterial diversity are already challenging
(50, 98, 180), eukaryotic microorganisms (fungi as well as protists) are often ignored as members of the microbial community in most surveys. Fungi, which play an important ecological
role particularly in soil environments, are key players for the
decomposition and cycling of nutrients, and therefore their
activity is directly linked to bacterial nutrient cycles. Although
mycologists have been collecting and growing fungi for several
hundred years, the ϳ80,000 fungal species described so far
represent only about 5% of the estimated total diversity (89).
Another group of microorganisms that was described by
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Hooke and van Leeuwenhoek in the 17th century are the
protists, which represent highly diverse taxa of single-cell eukaryotic organisms that play an essential ecological role in any
aquatic environment (33, 111).
Environments are characterized by both their biological and
physicochemcial parameters. Ideally, a comprehensive analysis
of any environment includes the spatial and temporal assessment of the composition of its viral, prokaryotic, and eukaryotic communities and their interactions (63) along with highresolution measurements of the environmental parameters
shaping these communities—a daunting task. To make it even
more complicated, we know that the phenotype of a cell, for
example, is determined by subcellular components. To understand the various forms of interactions between organisms and
between living cells and their abiotic environment, it becomes
necessary to elucidate parts of their subcellular (i.e., molecular) diversity. Such a comprehensive analysis bridges 17 orders
of magnitude (147), from molecular interaction at the DNA
level (10Ϫ10 meter) to single cells (10Ϫ6 meter) to whole environments (from local [101 meters] to global [107 meters]
scales). In addition to spatial scales, temporal scales of similar
orders of magnitude have to be accounted for as well. Processes range from molecular events that happen in microseconds (10Ϫ6 s) to growth rates on the order of thousands of
years (1011 s). Data obtained at the lowest level (DNA interaction) can often not directly be used to explain processes at
the highest level (environment) because there is insufficient
information available on how various data sets can be linked
and integrated quantitatively as well as qualitatively. For example, a quantitative, genome-wide correlation between transcription and translation in vivo does not yet exist because
many of the underlying principles, such as transcription and
translation efficiency, are yet not fully understood. It is also
unclear at the moment to what extent this correlation varies
between different organisms (43). As a result, quantitative interpretation of processes on a community level using transcriptomic and proteomic data remains challenging. The cell as the
defining entity in these studies can therefore provide a valuable
stepping-stone to bridge this gap (see below). This steppingstone becomes especially useful when multiple data sets are
being integrated in a rational manner.
Listening Carefully: Bacterial Communications
One example of molecular diversity that can define and
shape microbial diversity is bacterial communication. When
microorganisms are physically separated from each other, this
communication can be hindered, resulting in unsuccessful cultivation attempts. Any cultivation technique that excludes cellto-cell communication is eliminating a part of molecular diversity that might be critical to the growth of a particular
microorganism. Efforts have been made to simulate this kind
of communication in the laboratory, for example, by adding
signal compounds (22, 23, 26, 79, 80) or by keeping the microbial community as a whole intact (114, 249). Intercelluar communication is widespread within microorganisms and represents the foundation for several aspects of growth and
physiology, cell cycling, molecular clocks, and oscillation. Microorganisms “speak a variety of languages” using molecules
which have distinct molecular structures, such as acyl-homo-
MICROBIOL. MOL. BIOL. REV.
serine lactones, ␥-butyrolactones, 3-hydroxy palmitic acid
methyl ester, quinolones, autoinducer-2, and cyclic dipeptides
and peptides of various lengths (for a review, see reference
227). Communication can have multiple effects, e.g., “silencing” of competitors (132) and induction of growth as well as
death (143, 154). The various signals can promote one-way,
two-way, and multiple-way communications and are not limited to intraspecies communication (e.g., formation of fruiting
bodies or growth induction) but also take place between bacteria of different phylogenies as well as between members of
different kingdoms (for example, between bacteria and eukaryotes) (15, 172, 174). Crucial to all kinds of communication
is that the signals are easily perceived. It makes perfect sense
that organisms react not only to environmental signals such as
nutrient supply (e.g., by two-component systems) but also to
signals from “friend or foe” to gain a competitive advantage. In
order to screen for suitable growth conditions, bacteria have to
monitor their environment closely in order to switch from a
dormant to an active growth state. This is also highly relevant
for the cultivation of microorganisms in the laboratory. Signals
that trigger this change of state in microorganisms can include
the availability of nutrients as well as substances released by
other growing organisms that function as signals, such as peptidoglycan fragments or proteins (148, 191). Recently, another
hypothesis has been formulated by Slava Epstein (56), which
proposes a stochastic awakening of cells. In contrast to previously described processes, this change from a state of dormancy to a state of active growth would not require any signal
molecule but rather would be stochastic. An awakening of
dormant cells is assumed to result from random bursts (noise)
in transcription or translation (171). Such stochastic events
that trigger changes in the phenotype have been recently described at a single-molecule/single-cell level for E. coli (30, 36,
53, 75, 245). It has been shown that even a single mRNA copy
within a single cell can lead to bursts in protein expression and
that therefore not only transcription but also posttranscriptional effects are responsible for stochastic protein expression
profiles (34). It has been demonstrated that the random dissociation of a single protein molecule (repressor) from the
DNA can result in large bursts of protein expression in E. coli,
ultimately determining the cell’s phenotype (36). In principle,
the awakening of dormant cells by random molecular events
therefore seems possible. However, although this random
switch from a dormant into an active state is intriguing, this
theory might have limited use for K-strategists (bacteria that
are adapted to slow growth in nutrient-sparse environments
[8]), as pointed out by Peter Janssen (106). It is also important
to keep in mind that not every cell of a clonal population,
especially in a heterogeneous environment, will encounter
identical conditions. Natural environments cannot be compared to our usual laboratory setups where nutrients are being
kept evenly distributed by shaking or stirring. A single molecule of some kind (a signal molecule as well as an electron
acceptor or donor) might be absorbed by a single cell and
initiate a transcriptional cascade, yet the genetically identical
cell next to it will not be exposed to this molecule and therefore
will not set off a similar transcriptional response eventually
resulting in cell division and growth. Natural (clonal) microbial
populations are not synchronized (see below), which represents a huge ecological advantage. Concentrations of nutrients
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CENTRAL ROLE OF THE CELL IN MICROBIAL ECOLOGY
as well as signal molecules can be significantly different on a
single cell-level, especially at ultralow (single-molecule) concentrations. The awakening of single cells in their natural environment could therefore still be a response to an environmental trigger, e.g., a few nutrient molecules that are sufficient
enough to initiate a transcriptional response. Evaluating the
difference and effects of random and nonrandom responses in
a natural population will be essential to our understanding of
dynamic microbial processes. This also reemphasizes the necessity for high-resolution and high-sensitivity measurements
of environmental parameters to accompany microbiological
studies.
The 16S rRNA Is Dead; Long Live the 16S rRNA
Estimates about microbial diversity have changed drastically
over the last decade, mainly due to advances in detection,
computational modeling methods, and algorithms applied and
the changing concept of what defines a microbial species (100,
138, 168, 196, 223). For example, the numbers of different
species or operational taxonomic units (182) within a soil sample vary from below 500 (99) to 2,000 (186), 10,000 (218),
21,000 (179), and half a million (51) and even up to nearly 107
cells (24, 71). Part of this discrepancy is due to the diversity of
the samples themselves (70, 129, 179); another part is likely
due to the varying approaches used to estimate the diversity
(167, 168). When analyzing diversity over temporal and spatial
scales (see above), it is essential that a marker that is relatively
stable and is not mutating rapidly under perturbation or environmental pressure will be used. Some molecules at the very
center of biology, which, for example, translate genetic information into proteins or convert energy in the form of ATP, are
less likely to be subject to rapid mutations and therefore could
serve as an evolutionary marker molecule (177). This concept
was first perceived by George Fox and Carl Woese and consequently resulted in the use of sequence information (rRNA
gene sequences) to study phylogeny and evolution (66, 238,
240). Their discovery was paramount to our current understanding of phylogenetic relationships and the evolution of life
on our planet and led to the description of the three domains
of life (239).
The use of a phylogenetic marker enabled the discovery of
thus-far-undiscovered forms of microbial life. Early studies by
Norman Pace and coworkers paved the way for the discovery
of the endless microbial diversity that we know today (48, 73,
199, 200). Since then diversity studies have been carried out in
almost every imaginable environment, leading to the discovery
of a microbial world that dominates the biosphere but is (in
most cases) impossible to sample properly (167, 241). The 16S
rRNA gene, which today represents the basis for microbial
ecology studies, is hence a perfect molecule to study phylogeny, evolution, and molecular diversity. It also allows for insightful comparisons of different environments and ecological
niches (78). The use of molecular surveys in microbial ecology
is yet another example where data from the molecular level are
utilized and extrapolated to the cell level. However, without
detailed physicochemical parameters from the microenvironment, ideally on the cell level, these data will likely allow only
superficial interpretation (137). There currently exists a comprehensive yet still rapidly growing 16S rRNA gene sequence
719
database; however, reports from metagenomic surveys indicate
that current 16S rRNA gene primers are not “universal” and
that some organisms might be missed by approaches targeting
the gene directly (14, 97).
The rRNA is, however, not a good marker when it comes to
physiology, since physiology and phylogeny are not necessarily
correlated. There are some phylogenetic groups (e.g., methanogenic archaea) where this correlation still holds true, but
there are many examples where phylogeny and physiology do
not match. An rRNA gene sequence is therefore not well
suited to predict the function, i.e., phenotype, of an organism.
For identification purposes, new cultures and environmental
genome sequences are generally put into context with their
ribosomal sequence. At times the phylogenetic context is used
to imply that organisms with similar 16S rRNA gene sequences
carry out similar functions or have an identical metabolism, but
identical 16S rRNA genes do not automatically translate into
identical physiologies, identical phenotypes, or identical pathogenicities and similar functions. The phylogenetic context of an
organism can only provide a prediction. To what extent the
phylogeny matches the predicted physiology has to be ultimately confirmed through experiments. In addition, it is important to keep in mind that the 16S rRNA gene commonly
used for these analyses lacks the resolution at the species level
(121, 181). I am avoiding here the discussion about how to
define a microbial species (146, 176), but in any case it is
essential to recognize that even if diversity surveys at the species level become abundant in the future, different strains and
subpopulations of the same species can have very different
properties (see below). High-resolution diversity surveys therefore would not solve the current dilemma but would move it to
another level (from genus to species). This intraspecies diversity has traditionally been assessed by cumbersome DNA-DNA
hybridization, multilocus sequence typing, or average nucleotide identity methods (120) requiring large DNA quantities or
substantial genomic information. However, novel sequencing
techniques, in combination with DNA amplification methods
using miniscule amounts of DNA, have already started to replace these efforts (6, 164). Whole-genome sequencing and
resequencing recently became broadly affordable and subsequently paved the way for comprehensive comparative
genomic studies (244). It is now possible to analyze several
genomes of one particular group of organisms simultaneously
by comparing whole genomes and identifying shared genes and
nonoverlapping sequences—a so-called pan-genome (130, 173,
214). This analysis has revealed a vast genomic diversity within
subpopulations (see below). This diversity of course increases
substantially when organisms with “almost” identical 16S
rRNA genes are included in the study, e.g., E. coli and Shigella.
Konstantinidis and coworkers estimated that the pan-genome
of E. coli-Shigella spp. would increase by ϳ300 new genes
(equaling ϳ5% of all genes in E. coli) for every new genome
that is sequenced and added to the study (119). If the genomic
diversity within a population is expanding, it is likely that the
phenotypic diversity increases as well. In addition to adaptation and evolution through mutation (see below), which can
have profound impacts on microbial physiology, genetic material is transferred by horizontal gene transfer (HGT) between
closely related organisms as well between different kingdoms
of life. HGT can be mediated by the exchange of plasmids,
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ZENGLER
transformation, and transduction (68, 82, 188, 208). Since adaptation and HGT take place at different rates and affect
different genes in different organisms (67, 177), a direct correlation between phylogeny and physiology (phenotype) is extremely difficult to determine. Although this direct link between phylogeny and physiology is not achievable in most
cases, the rRNA gene(s) still represents one of the few markers
that allows for cross-kingdom and cross-phylum comparison,
which makes it so ideal for diversity assessments and comparison studies. The common question in microbial ecology, “who
are out there and what are they doing?,” however, still awaits
an answer in most cases. Fundamental steps toward this goal
have been made, and recent developments include functional
metagenomic approaches (115, 207) and the combination of in
situ hybridization techniques with microautoradiography, secondary ion mass spectrometry, or Raman spectroscopy (85, 96,
133, 150, 159, 160).
Implications of Genome Heterogeneity and Plasticity
After microbial strains have been identified in the environment and successfully propagated in the laboratory, the question arises as to how and at what level these strains (and data
obtained from them) can be compared to their counterparts in
the original environment. Genomes of different strains belonging to the same species can vary substantially (5, 37, 145, 163,
229). A well-studied case is the virulent E. coli O157:H7 strain,
for which it is known that the genome not only is around 25%
larger than that of the laboratory strain K-12 but also encodes
1,632 proteins and 20 tRNAs that are not present in K-12 (90).
Of these proteins, only 10% are assumed to have virulencespecific functions, leaving around 1,500 proteins being strain
specific independent of virulence (90). Clearly, this high number of proteins can potentially lead to various differences in cell
composition, physiology, and metabolism (12, 81). Sequence
diversity within a whole group of strains was first described for
the pathogen Streptococcus agalactiae (213). When six strains
were analyzed, they shared ϳ80% of any single genome, resulting in around 20% of each genome consisting of partially
shared as well as strain-specific genes (213). Similar trends
have been shown for other organisms such as Haemophilus
influenzae (94), Helicobacter pylori (77), Prochlorococcus (37),
and Sulfolobus islandicus (175). Intraspecies variations (Ͻ1%
divergent 16S rRNA gene sequences) have also been demonstrated for natural populations of Ferroplasma acidarmanus
(4), Prochlorococcus (37), and Vibrio splendidus (217). In addition, vast differences in the locations of mobile genetic elements and clustered regularly interspaced short palindromic
repeats within individual organisms and populations have been
reported, representing a glimpse into the history of viral encounters for these cells (7, 126, 187). Potential mechanisms
that will lead to these variants (ecotypes) in natural populations have been debated in the past. Evidence for both clonal
populations (206) and recombining populations (194) has been
described (for a review, see reference 230).
Overall, these examples highlight the vast genetic diversity of
subpopulations within a single defined species. It was mentioned earlier that part of this genetic diversity can be virulence
specific, but how significant are small genetic variations for the
physiology and phenotype of different organisms? It has been
MICROBIOL. MOL. BIOL. REV.
demonstrated that even relatively small changes in the genome
(single-nucleotide polymorphisms [SNPs] as well as insertions
and deletions [indels]) can have substantial effects on the phenotype and fitness of the strains (13, 169). Mutations can take
place in structural genes influencing membrane fluidity (246);
in metabolic enzymes they can reroute carbon and energy
fluxes and increase metabolic efficiency (65, 101); and in regulatory elements or in the transcription machinery (e.g., transcription factors and the RNA polymerase or promoter region)
they can have effects on transcription/translation speed, transcript stability, and strength of induction and repression of
genes (40). It has been demonstrated that organisms adapt
rapidly on the molecular level to changing environmental conditions by increasing mRNA expression levels (11, 41, 47, 93).
Organisms can also adapt their metabolic capabilities, e.g.,
utilizing substrates that previously have not supported growth.
E. coli, which normally does not grow aerobically with citrate,
was able to do so after 31,500 generations, suggesting that the
inability to transport citrate aerobically had been resolved (19).
What are the consequences of these findings for studies in
microbial ecology? How will this affect the interpretation of
data obtained from laboratory cultures as well as environmental genomic surveys? It has been suggested that evolution experiments carried out in the laboratory reflect adaptation and
selection patterns found in natural populations (197). One
limitation of these laboratory experiments, however, is that
environmental changes and selection pressures are manifold
and cannot, or can only insufficiently, be simulated in the
laboratory. Another limitation is that evolution is communal
(224), and therefore experiments with single organisms can
only glance at the potential genetic diversity that has been
evolved in nature (183). Nevertheless, these laboratory experiments are essential in order to understand the general principles and mechanisms underlying adaptation and evolution.
Pure cultures. The immense genome plasticity observed in
pure culture experiments also raises some questions about
isolation and cultivation efforts. If organisms readily adapt on
a genome level to conditions provided in the laboratory, what
“kind” of organisms will then be isolated and propagated in the
laboratory? While microorganisms are cultivated in the laboratory (e.g., by enrichment cultivation), it is often observed that
the culture grows faster and faster after each transfer: the
culture has adapted to the conditions provided (41). There are
also several reports in the literature where isolated strains
could be adapted over time to grow on certain media; e.g.,
isolates could be propagated to form colonies on agar plates on
which the original isolate was unable to grow (35, 44). Adaptation to culture conditions can be due to changes in the
transcriptional machinery, but it also can be due to activation
of cryptic pathways (84, 123, 149). It is also known that organisms undergo genetic changes over time even when cultivated
under the same conditions, resulting in genetically diverse subpopulations (64, 169, 183, 242). In most cases this “genetic
drift” will remain unrecognized by the researcher. Does this
mean that all the organisms in our culture collections represent
genetically altered strains that are well adapted to conditions
provided by the researcher in the laboratory but have no direct
(genetic and/or phenotypic) counterpart in their natural habitat? It also raises the question of how genetically “pure” the
organisms in our laboratories really are, since they could the-
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CENTRAL ROLE OF THE CELL IN MICROBIAL ECOLOGY
oretically consist of multiple members of a subpopulation. An
effective preservation of laboratory strains at various time
points after isolation therefore is crucial for the investigations.
Besides differences in individual cells that are based on the
genome or on stochastic bursts in transcription (see above), it
has also been known since the late 1950s that cells can inherit
different traits on a nongenetic basis just by asymmetric cell
division (157). This cell individuality is based on a random
distribution of proteins, such as enzymes and efflux pumps,
before and after cell division and will lead to cells that are
genetically identical but that exhibit different phenotypes because of differences in their cell composition (136, 203, 222).
Based on the number of these proteins per cell, these differences in phenotype can be passed on to the following generation(s) and can be regarded as a “molecular memory,” which is
different for each individual cell.
Natural populations. Assuming that natural microbial populations are characterized by cell individuality (198) and vast
genetic heterogeneity, would a selective pressure applied by
cultivation as well as adaptation to laboratory conditions consequently result in the isolation of very similar cultures (on the
genome level)? In other words, would cultivation by itself represent such a strong selection that only one or two individuals
would dominate all the isolates obtained from one sample?
Exactly the opposite seems to be the case. Thompson and
coworkers obtained 232 Vibrio splendidus isolates from marine
water samples (out of 333 total isolates), which exhibited large
heterogeneity throughout their genomes (217). Similar trends
have been reported for bacteria from low-diversity environments (4, 9). However, isolation of strains at different times,
even from these low-diversity environments, will not necessarily result in identical strains. For example, two strains of Salinibacter ruber (M8 and M31), which were used for describing this
species, have not yet been reisolated from the same environment (J. Anton, personal communication). The natural popu´
lation, on the other hand, can be quite stable; almost identical
environmental sequence types were found at the same site
even after a greater-than-4-year time interval in sampling (4).
Targeted reisolation of identical strains (ecotypes) can be difficult, and it remains to be seen if this is due to biases in
culturability, unnoticed changes of the habitat, or just the extreme genome heterogeneity existing in these populations.
Analyzing genome heterogeneity in microbial populations
involves studying whether substitutions in the genome are synonymous or nonsynonymous, meaning whether the mutation
influences coding of the amino acid, thus affecting translation
and protein structure (155, 178). It is important to keep in
mind that although it is often assumed, synonymous or silent
mutations do not always have to be neutral. There are indications that some synonymous mutations can affect the fitness of
the organism (113). A reason for this may be changes affecting
regulatory elements and transcription. For example, can
synonymous mutations have an impact on the promoter
strength, resulting in subsequent changes of expression level?
Another effect on transcription levels by synonymous mutations is due to changes in codon usage. Also, mutations can
be condition specific, meaning that they can be silent under
one condition but can have an effect on fitness under another
condition. When studying causal effects of genetic heterogeneity in natural populations, it would in principle be possible to
721
specifically target genes of interest from the natural community, study them in greater detail, and by doing so link the
genetic diversity to phenotypic diversity. Unfortunately, in
most cases it will not be possible to a priori predict what gene
mutations will result in what kind of phenotype (101). In addition, changes in the genomes (SNPs and indels) that lead to
the described phenotypic differences can be so subtle that they
might be undetected by certain sequence approaches, due to
error rates in sequencing and insufficient coverage which prevent the identification of these SNPs/indels, especially in the
context of a genetically diverse population.
ORDERS OF MAGNITUDE IN MICROBIOLOGY: FROM
TRILLIONS TO A SINGLE CELL
Synchronization
Most studies in microbiology are traditionally performed on
a community level. Since the sensitivity levels of those techniques are seldom suitable to work at a single-cell level, millions of cells are needed to perform most experiments (Fig. 1).
However, this also means that data are generated from millions, billions, or often even trillions of individual cells. The
data therefore represent an average of results obtained from
large numbers of individual cells. All these individual cells exist
in different stages of their life cycle, since bacterial cultures are
generally not synchronized—they are not doing exactly the
same thing at any given moment. For most questions, these
conditions are suitable and synchronized cultures are not a
necessity. Although continuous cultivation in a chemostat
(156) provides more constant conditions than batch cultures,
the cells are still not in a synchronized stage. Truly synchronized cultures are obtained, for example, by the use of a temperature-sensitive allele of the essential DNA replication protein DnaC (32), which allows the initiation to be synchronized
after heat shock treatment (237). Another method is the use of
the amino acid analog DL-serine hydroxamate, which induces a
stringent response (62). Following the release of the stringent
response, the cells initiate replication in synchrony. A method
that allows for synchronized cells without genetic (237) or
chemical (62) perturbation, is the so-called “baby machine”
described by Helmstetter and Cummings (91, 92). This method
relies on cells that are affixed to a membrane. When medium
flows through the membrane, newly divided daughter cells are
released, whereas the parents remain bound to the membrane
and produce other cells. All cells released at the same time are
at the same growth stage (division stage) (91, 92). These approaches are intriguing, yet they have so far been described
only for E. coli, and synchronized studies of other organisms
are limited. For most experiments, synchronized cultures will
not be available, and we have to be aware not only that our
data represent average results from many individual cells but
also that these cells exist in different growth stages and that
they can differ in their cellular composition (e.g., have different
proteins).
Single-Cell Techniques
In order to circumvent issues of culturability, cell individuality, and genome heterogeneity and plasticity, as well as dif-
12. 722
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ferent growth stages, it can be greatly beneficial to study single
cells. However, work with single cells of microorganisms beyond visualization by microscopy is still in its infancy. Individual components of single cells, such as nucleic acids, proteins,
fatty acids, and lipids, are present in such low quantities that in
most cases a direct measurement is not possible. For example,
a single cell contains only a few femtograms of DNA. However,
due to recent developments in DNA amplification techniques,
it became possible to amplify these few femtograms of DNA
from a single cell to quantities usable by standard techniques
(45, 212, 252). This allowed for the sequencing of genomes of
organisms that had not been cultivated before or from which
genomic information was limited to the information obtained
by metagenomic surveys (2, 95, 141, 151, 164, 243, 251). Elucidation of molecule classes other than DNA which are not as
stable (e.g., mRNA) or which cannot be amplified is still challenging for prokaryotes at a single-cell level (209, 210). Since a
genome sequence by itself does not provide information about
the current metabolic state of an individual cell, methods to
address this question have been developed. Recent studies
combined single-cell measurements of non-DNA molecules
with time-lapse microscopy techniques to elucidate dynamic
capabilities (136). Great progress has been made in elucidating
single-cell individuality for pure cultures, since fluorescent tags
can be introduced into the genome, allowing for dynamic monitoring of molecules such as enzymes, regulators, and RNA
polymerase (28, 30, 203, 222). Elucidation of cell individuality
in natural populations or in cultures for which a genetic system
does not exist is still much more challenging. Combinations of
fluorescence in situ hybridization or halogen in situ hybridization with microautoradiography (133, 160), secondary ion mass
spectrometry (85, 150, 159), or Raman spectroscopy (96) are
currently being applied successfully to obtain information
about metabolism and cell composition in natural populations.
An advantage of noninvasive methods, such as secondary ion
mass spectrometry, Raman spectroscopy, and Fourier transform infrared spectroscopy (152, 253), is that they allow for
downstream processing of the cells (e.g., DNA amplification).
Interpretation of single-cell studies for natural populations can
be challenging not only because of environmental heterogeneity but also because of the genetically heterogeneous background of targeted cells. Nongenetic differences in cell composition, the nonsynchronized stage of genetically identical
cells, and the lack of measurements over various time scales
further complicate a comprehensive understanding in many
cases. However, it is anticipated that further advancements of
these innovative single-cell methods in combination with highresolution measurements of environmental parameters will allow us to gain detailed insights into microbial communities—
one cell at a time.
TOP-DOWN AND BOTTOM-UP APPROACHES IN
MICROBIAL ECOLOGY
In general, various methods used in microbial ecology can be
grouped into bottom-up and top-down approaches (Fig. 4).
Depending on the specific question, different methods allow
different avenues to be used to obtain the answers. The overarching goal of all these methods is to understand the role of
microorganisms in the environment, meaning microbial inter-
MICROBIOL. MOL. BIOL. REV.
FIG. 4. Top-down and bottom-up approaches in microbial ecology,
spanning orders of magnitude in spatial resolution. Top-down approaches (including but not limited to biodiversity assessments, rate
measurements, isotope signature determination, and various “-omics”
studies) utilize data sets which are in general not organism (individual)
specific. Interpretation of these data often relies on previous knowledge (e.g., in the form of a molecular biology database). Bottom-up
approaches (e.g., cultivation or single-cell techniques and various
“-omics” methods) focus on single organisms. Knowledge gained by
studying individual organisms or defined communities is consequently
extrapolated to larger communities and the environment. Both concepts have advantages and limitations (see text) and are clearly dependent on the scientific goal.
actions and the mutual influence of microbial cells with their
biotic and abiotic environments (where the environment could
be another organism). For many questions it is suitable to
consider the microbial community as well as the environment
as a “black box” where physical, chemical, and biological parameters can be analyzed as “bulk,” e.g., by microelectrodes,
automated remote sensing, rate measurements, labeling studies, and gene surveys (Fig. 4). These methods have the great
advantage that parameters can be measured in situ, which is
indispensable if perturbations to the environment are being
studied. These “black box” approaches in general do not intend to link specific organisms or individual cells with a specific
processes measured. Many of the meta-omics methods, which
can be considered to be a kind of “molecular black box” approach, set out to make this link (86, 142, 192). Progress toward this goal has recently been made by combining a metagenomic approach with stable isotope probing, resulting in
functional active community data (115, 207). In-depth knowledge about phenotype, metabolism, and transcription and
translation on a cellular level can only be inferred by top-down
approaches, since direct measurement are so far lacking, but
these are likely to become available in the future. In contrast,
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CENTRAL ROLE OF THE CELL IN MICROBIAL ECOLOGY
bottom-up approaches utilize direct measurements performed
at the cell level (Fig. 4). These approaches include isolation
and cultivation techniques as well as various single-cell techniques, which allow for direct phenotype determination.
Top-down and bottom-up approaches should not be regarded as isolated approaches but instead should be integrated. For example, genome information obtained by a metagenomic approach can help guide cultivation (221), or vice
versa, data derived from pure culture experiments can be utilized for comparative genomics to gain insights into the physiology of environmental populations (219). These studies have
in common that they used the cell as a stepping-stone (see
Introduction) to integrate data that originated from various
levels of complexity and at various temporal and spatial scales
(Fig. 4). To understand and ultimately predict functions of
complex biological systems, large data sets generated by various methods have to be analyzed, an approach that now is
referred to as “systems biology.” This analysis undoubtedly
involves computational methods for data integration as well as
for model building. Computational systems analysis has been
successfully implemented for various single microorganisms by
building functional biological networks using computational
bottom-up (58) as well as top-down (20) approaches, and efforts at integrating both approaches as well as utilizing them
for microbial communities are currently being pursued.
CONCLUSION
In light of the genome-level diversity which trumps phylogenetic diversity (determined by rRNA gene sequences) by
orders of magnitude, Nigel Goldenfeld and Carl Woese rhetorically asked “…how valid is the very concept of an organism
in isolation?” (74). It is unlikely that we will achieve an indepth understanding of microbial ecology by cultivating all
members of the microbial community or by making inferences
about their function in the environment from the information
on their phenotypes displayed in the laboratory. Evidently, in
most cases, we are not able to cultivate them in the first place.
If the cultivation hurdle has been overcome, we often fail to
accurately assess what role this particular isolate plays in the
environment or even if this microbe has any identical counterpart in the environment. This is due in part to the limited
resolution of the 16S rRNA gene and the tremendous genetic
diversity of subpopulations of different organisms. It is also
unrealistic to assume that we will be able to solve questions in
environmental microbiology solely by applying various metaomics approaches without having comprehensive and experimentally validated databases with which to map and compare
them. With next-generation sequencing and automated annotation readily available, we are now in a situation where it can
be faster to obtain a complete genome sequence rather than a
growth curve (127). Does this mean that we have to describe all
biodiversity, cultivate and study all variants, and treat each cell
as an individual before comprehensive understanding can be
achieved? Not at all—it depends on the level of understanding
that is required to answer our research questions. For example,
we know that all human physiology is identical: we all use
various carbon sources as electron donors and oxygen as a
terminal electron acceptor. Sequence information tells us that
there are differences within the genomes of individuals, but
723
they do not affect basic physiology. However, we know that
these variations can effect pigmentation, tolerance toward certain foods, and susceptibility to diseases and drugs, which explains why different groups of people get easily sunburned, are
lactose tolerant, or develop a certain disease that can be cured
by a certain drug. Understanding the traits of a group of individuals therefore allows us to recognize the niche of this group
and ultimately understand human biology as a whole. This
should also be the case for microorganisms; depending on the
questions asked, we have to consider common traits of groups
of individual cells.
I believe that a comprehensive understanding of microbial
communities can be achieved only by the synergy between
top-down and bottom-up approaches, with the cell as a junction between them (Fig. 4). The central unit in microbial ecology therefore has to be the microbial cell (Fig. 4). A census
alone, no matter how detailed and on what level of complexity
it is performed, will not allow for a comprehensive view of a
given environment. Counting, even sequencing, all individuals
of a certain group of animals (as an example from macroecology) will not give us detailed information about their behavior or physiology. Additional information will be needed;
e.g., what do these animals live on, who might live on them,
and how is their habitat defined? For a microorganism this
would mean acquiring detailed knowledge about other microorganisms (bacteria and archaea but also protists, fungi, and
viruses) present in that environment as well as performing
comprehensive analyses of the ecological niche. Studying the
organism in captivity (as a culture in the laboratory) will also
not allow us to really understand its role in the environment,
but it will enable us to formulate hypotheses and theories,
originated from direct measurements, which can be, and
should be, tested “in the wild.” Microorganisms cannot be
regarded as just the sum of their parts (genome, proteome, and
metabolome). Only the rational integration of different data
sets (their “parts list”) will advance our knowledge of various
microbial phenotypes in the environment. The knowledge of
the genome, transcriptome, proteome, and metabolome of an
organism does not consequently lead to a systems-level understanding of this microbe; these data have to be assessed in a
timely and condition-specific manner and rationally integrated
in order to fathom the dynamics of microbial life.
Understanding and predicting bacterial phenotypes involves
knowledge about how genetic information is transcribed and
translated into proteins. The regulation of this information
flow is essential to generate a systems-level understanding,
both for a single organism and ultimately on the community
level. When studying microbial ecology, it is important to use
the cell as a central unit, a kind of stepping-stone, to overcome
limitations of individual data represented at various scales of
resolution, spatial as well as temporal. A similar concept has
been perceived for human biology, where in analogy to microbial ecology, data from various cell types have to be integrated
into a whole (the human body). Sydney Brenner, Nobel laureate in medicine, stated, “I believe very strongly that the fundamental unit, the correct level of abstraction, is the cell and
not the genome” (unpublished lecture, Columbia University,
2003). In the same way we use molecular data in human biology to understand processes on the cellular level, the organ
level, and finally on the level of human physiology, we can use
14. 724
ZENGLER
MICROBIOL. MOL. BIOL. REV.
the microbial cell as a central unit for understanding of processes on a community level and finally an environmental level.
Assuming that microbial cells are central to completing the
link between various forms of diversity for subsequently understanding complex systems, then it is most beneficial to obtain as much information as possible about the cell as a whole.
Cultivation of microorganisms, when possible, enables these
detailed studies under dynamic conditions and allows us to
formulate biological principles and generate a knowledge base,
onto which “-omics” data can be mapped and linked to. The
rational integration of various data sets obtained by top-down
and bottom-up approaches is therefore crucial for any systemslevel approach on which we are embarking, from a single cell
in the laboratory to whole microbial communities in the environment. Around the turn of the last century, tremendous
advances in environmental microbiology that still shape and
define our current research were made (16, 236). Today, 120
years later, we are again at a point in time where enormous
breakthroughs are being made. These advances are possible
not only because of novel technology and methods available
but mainly because of interdisciplinary research that bridges
the gap between molecules, single cells, and microbial communities. What better time than now is there to be a microbiologist?
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
ACKNOWLEDGMENTS
I am tremendously thankful to Wiebke Ziebis for fruitful discussions, valuable insights, and critical review of the manuscript. I also
thank Marc Abrams and Kenyon Applebee for editorial help.
This work was supported in part by the Office of Science (BER),
U.S. Department of Energy, grants DE-FC02-02ER63446 and DEFG02-08ER64686.
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