RESEARCH ARTICLE
Do microbial numbers count? Quantifying the regulation of
biogeochemical £uxes by population size and cellular activity
Wilfred F. M. Röling
Department of Molecular Cell Physiology, Faculty of Earth and Life Sciences, Vrije Universiteit, De Boelelaan, Amsterdam, The Netherlands
Correspondence: Wilfred F.M. Röling,
Department of Molecular Cell Physiology,
Faculty of Earth and Life Sciences, Vrije
Universiteit, De Boelelaan 1087, 1081 HV
Amsterdam, The Netherlands. Tel.: 131 20
5987192; fax: 131 20 5987223; e-mail:
[email protected]
Received 30 December 2006; revised 25 April
2007; accepted 29 April 2007.
First published online 5 July 2007.
DOI:10.1111/j.1574-6941.2007.00350.x
Editor: Jim Prosser
Keywords
regulation analysis; biogeochemical rates; cell
numbers; qPCR; FISH; microbial adaptation.
Abstract
In order to enhance understanding of the interrelationships among community
members and between them and their environment, the concept of regulation
analysis is extended from biochemistry into microbial ecology. Ecological regulation analysis quantifies how biogeochemical fluxes are regulated by the microorganisms performing the process; the degree to which changes in fluxes are due to
changes in population size and to changes in activity cell1 (cellular activity).
Regulation analysis requires data on biogeochemical fluxes and the numbers of
cells through which these fluxes run. Its application to five biogeochemical
processes (aerobic methane oxidation, aerobic nitrite oxidation, methanogenesis,
sulfate reduction and reductive dehalogenation) revealed that in general, but not
always, flux was primarily regulated by cellular activity, i.e. by changes in the size
and properties of the enzyme pool and in the concentrations of substrates and
metabolites. Thus, it is often not sufficient to count the numbers of cells
performing a particular step in a biogeochemical process in order to estimate its
flux. Ecological regulation analysis can be extended to address which aspects of
cellular activity require quantification in order to describe biogeochemical fluxes
better. Its application is discussed in the context of the complexity of microbial
communities (e.g. functional redundancy) and their functioning.
Introduction
Microbial ecology has rapidly developed over the last two
decades by taking advantage of the advances in molecular
biology, materials (e.g. microarrays) and computing (Rittmann et al., 2006). This has considerably increased our
insight into microbial community structure and functioning
in many environmental settings. Yet we are still far from the
ultimate goal of microbial ecology, which is to understand the
interrelationships among community members and between
them and their environment (Rittmann et al., 2006).
To achieve the above goal, microbial ecologists can profit
from developments in other disciplines of biology. In
particular, theories on the ecology of macroorganisms are
currently being extended to and tested in microbial ecology
(e.g. Martiny-Hughes et al., 2006; Sloan et al., 2006).
Microbial ecologists have also started adopting mathematical approaches from biochemistry in order to describe
quantitatively the functioning of microbial communities in
relation to the properties of their individual members. For
example, Röling et al. (2007) and others (Allison et al., 1993)
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c
have extended Metabolic Control Analysis (MCA; Kacser
et al., 1995) to microbial ecology. MCA quantifies the
control exerted by properties of individual components
upon system variables such as fluxes (flows of material) and
metabolite concentrations. The components are enzymes in
biochemical pathways, or, in the case of the ecological variant
of MCA, species or functional groups (defined as the collection of microorganisms performing a similar major activity,
e.g. nitrite oxidation) in biogeochemical processes.
Control analysis does not address the question of how
living systems actually regulate their system properties when
challenged with an environmental change, but biochemists
have recently developed ‘regulation analysis’ to do so (ter
Kuile & Westerhoff, 2001). In biochemistry, this analysis
quantifies the extents to which a change in metabolic flux
through a particular enzyme in a biochemical pathway is
regulated by changes in gene expression (hierarchical regulation, due to changes in maximum enzyme activity) and
by changes in the interaction of the enzyme with the rest of
metabolism (metabolic regulation, due to changes in concentrations of metabolites and effectors). Application of
FEMS Microbiol Ecol 62 (2007) 202–210
203
Regulation analysis of biogeochemical fluxes
regulation analysis to glycolysis in nutrient-starved Saccharomyces cerevisiae falsified existing paradigms of flux regulation, such as single- and multisite modulation of enzymes,
in biochemistry (Rossell et al., 2006). Results of regulation
analysis also cast doubts on whether transcriptome and
proteome analysis will suffice to assess biological function
of a single species, since the biochemical flux through
glycolytic enzymes was rarely regulated by gene expression
alone (ter Kuile & Westerhoff, 2001; Rossell et al., 2006).
Culture-independent enumeration of a particular (group
of) species is increasingly applied in microbial ecology,
using such methods as FISH (Amann et al., 1995) and
quantitative PCR (Diviacco et al., 1992; Sykes et al., 1992;
Heid et al., 1996). We may ask ourselves what the actually
meaning of these cell numbers is. For example, is knowledge
of cell counts of a particular (group of) species sufficient to
describe the flux of material through this (group of) species?
Or do, instead, differences in fluxes relate mainly to adaptation at the level of the cell (e.g. by changes in enzyme
concentrations and/or substrate and metabolite concentrations or, in case of a flux through a group of species, by
selection for other species with similar functions)? These
questions resemble very much the questions biochemists
asked themselves on the relation between gene expression
and biochemical flux and subsequently answered using
hierarchical regulation analysis (ter Kuile & Westerhoff,
2001). The concept of regulation analysis was therefore
extended to microbial ecology in order to address the
questions above. While others (e.g. Krüger et al., 2005) have
previously stated that differences in fluxes appeared unrelated to differences in cell numbers in their research,
regulation analysis as developed here allows for unambiguous and quantitative statements on how fluxes are regulated. The analysis was applied to a number of studies in
which both cell numbers of a particular functional group of
microorganisms as well as the biogeochemical flux through
this group were quantified. The application of regulation
analysis is discussed in relation to the relatively larger
complexity of microbial community structure and functioning, in comparison to enzymatic pathways.
Materials and methods
Mathematical concept of ecological regulation
analysis
The concept of regulation analysis as used in biochemistry
(ter Kuile & Westerhoff, 2001; Rossell et al., 2005, 2006), will
be explained before deriving ecological regulation analysis in
a way comparable to biochemical regulation analysis. In
biochemical regulation analysis, the fluxes through a biochemical pathway and maximal enzyme activities in this
pathway are compared between at least two different condiFEMS Microbiol Ecol 62 (2007) 202–210
tions. The analysis is based on enzyme rate equations of the
kind:
vi ¼ vi ðei ; X; KÞ ¼ fi ðei Þ gi ðX; KÞ;
ð1Þ
in which vi is the rate and ei is the concentration of enzyme i,
while K is the vector of affinity and inhibition constants and
X is the vector of substrate, product and other effector (e.g.
NAD, ATP) concentrations that act on enzyme i. Equation 1
assumes that f and g do not depend on the same variables.
Then, by logarithmic transformation, equation 1 can be
dissected into a term that depends on the enzyme concentration and a term that depends on the concentrations of
metabolites and effectors:
ln vi ¼ ln fi ðei Þ þ ln gi ðX; KÞ:
ð2Þ
Logarithmic transformation simplifies comparing a relative
change in flux j and rate vi between two different states, since
1
d ln v ¼ dv
v
ð3aÞ
1
d ln j ¼ dj
j
ð3bÞ
At steady-state, the biochemical pathway flux j through
enzyme i equals the rate vi at which this enzyme catalyses
the reaction. Combined with equations 2 and 3, a summation theorem for the biochemical regulation of flux can then
be derived, describing the extent to which a relative change
in flux j is regulated by relative changes in the enzyme
concentration fi (ei) (hierarchical regulation) and in the
metabolic term gi (X, K) (metabolic regulation):
D ln vi Dðln fi ðei Þ þ ln gi ðX; KÞÞ
¼
D ln j
D ln j
D ln fi ðei Þ D ln gi ðX; KÞ
¼
þ
¼ rh þ rm ¼ 1
D ln j
D ln j
ð4Þ
in which rh is the hierarchical regulation coefficient and rm
is the metabolic regulation coefficient. Regulation coefficients can in principle take any value; a value of one
indicates proportional regulation, zero indicates no regulation. Enzyme kinetics are generally described by Michaelis–
Menten type equations, where fi (ei) in equation 1 equals the
maximum enzyme rate vmax,i. The hierarchical regulation
coefficient of a particular enzyme i can then be determined
from the slope of a double logarithmic plot of vmax,i vs. j,
after measuring vmax,i of and the flux j through that enzyme
under (at least) two different conditions.
The above mathematical concept is extended to microbial
ecology. The activity of a population of a particular species k
(or group k consisting of species which perform identical
activities) is the function of its number of cells, nk (population size), and the activity cell1, vk (cellular activity, relating
to concentrations and kinetic properties of all enzymes
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204
W.F.M. Röling
(Ek,, Kk) in the cell, and associated concentrations of
substrates, metabolites and products (Xk)):
Vk ¼ nk vk ðEk ; Xk ; Kk Þ
ð5Þ
When Vk equals J, a summation theorem for the ecological
regulation of flux J through population k is obtained in a
similar fashion as for biochemical regulation:
D ln V D ln nk D ln vk ðEk ; Xk ; Kk Þ
¼
¼ rP þ rC ¼ 1;
þ
D ln J
D ln J
D ln J
ð6Þ
in which rP is the population regulation coefficient and rC
the cellular regulation coefficient. Graphically, the slope in a
double logarithmic plot of population size nk vs. flux J, will
give rP . 1 rP then gives rC .
Calculation of ecological regulation coefficients
Experimental data on fluxes and cell numbers were taken
from the tables and figures in the publications mentioned in
Table 1. Samples were only included in the data analysis if
both their cell numbers and fluxes were reported, and their
values were not zero. Flux data were only used if determined
under situations closely corresponding to the in situ situation, by either reactive transport modeling or by measurements on incubated samples to which no major substrate
additions were made (only a small quantity of radioactive
labeled substrate added, at most). From the study by
Schramm et al. (1999) on nitrite oxidation, the cell numbers
[data on shell samples for cells staining with probes
NSR826/NSR1156 in their Table 5 were expressed cm3 in
stead of mm3 as used by Schramm et al. (1999)], while
fluxes (data reported for in situ conditions in Table 4 of
Schramm et al. (1999)) were expressed as mmol cm3 h1 in
stead of nmol mm3 h1. With respect to analyzing the data
of Llobet-Brossa et al. (2002), sulfate reduction rates determined at 20 1C were used, as this temperature was closest to
the in situ temperature (15.8–17.5 1C). Cell numbers were
obtained by multiplying the percentage of cells that hybri-
dized with SRB probes with the numbers stained with DAPI,
as reported in Table 3 of this publication. For analysis of the
Lu et al. (2006) data on reductive dechlorination, data on
sites LF-3 (year 2002), North Beach (2002), well A39L009PZ
in Area 2005 (1997) and well #23 in Area 2500 (2000) were
used, as only for these sites were both degradation rates and
cell numbers obtained.
Where data were obtained from figures, figures were first
enlarged and distances measured with a ruler. Double
logarithmic plots of cell numbers vs. flux were made in
KALEIDOGRAPH v. 3.09 (Synergy software) and linear regression was performed in MICROSOFT EXCEL 2002 to obtain rP .
Results
Ecological regulation analysis was applied to data from
publications reporting cell numbers and in situ fluxes
regarding five biogeochemical processes, i.e. aerobic
methane oxidation, aerobic nitrite oxidation, methanogenesis, sulfate reduction and reductive dehalogenation (Table
1). For convenience, and in relation to later discussion, a
short description on how fluxes and cell numbers were
determined in these studies is provided.
Regulation of aerobic methane oxidation
Carini et al. (2005) reported aerobic methane oxidation and
methanotroph community composition during seasonal
stratification of the water column in Mono Lake, California
(USA). Only slight shifts in community composition were
observed. Methane oxidation fluxes were measured at in situ
temperature using a 48-h-long tritiated CH4 radiotracer
technique, while methanotrophs were enumerated by FISH
with methanotroph-specific 16S rRNA gene probes. A
double logarithmic plot of cell numbers vs. flux revealed
that the slope, corresponding to the population regulation
coefficient rP , was indistinguishable from zero and much
lower than one (Fig. 1, Table 1). Thus, changes in methane
Table 1. Population (rP ) and cellular (rC ) regulation coefficients, with standard error of mean (SEM), for a particular biogeochemical flux going through
the microorganisms performing the biogeochemical process
Process
rP
SEM
4 0
o 1
rC
Origin of analyzed data
Aerobic methane oxidation
Aerobic nitrite oxidation
Hydrogenotrophic methanogenesis
Acetoclastic methanogenesis
Sulfate reduction
Sulfate reduction
Sulfate reduction
Sulfate reduction
Reductive dechlorination
0.03
0.54
0.12
0.41
2.06
0.20
1.26
1.68
0.22
0.03
0.16
0.07
0.08
1.56
0.14
1.15
0.66
0.10
1
1
1
1
1
1
1
1
1.03
1.54
0.88
0.59
1.06
0.80
0.26
0.68
0.78
Table 1 in Carini et al. (2005)
Table 4 and 5 in Schramm et al. (1999)
Figs. 1, 3C and 3D in Chan et al. (2005)
Figs. 1, 3C and 3D in Chan et al. (2005)
Table 3 in Leloup et al. (2004)
Figs. 2C and 3A in Leloup et al. (2007)
Fig. 2B and Table 3 in Llobet-Brossa et al. (2002)
Fig. 5 in Ravenschlag et al. (2000)
Table 2 and 3 in Lu et al. (2006)
Indicates whether r was significantly different (P o 0.05) from 0 (flux completely regulated by cellular activity) or 1 (flux completely regulated by
P
population size), with ‘1’ indicating a significant difference.
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FEMS Microbiol Ecol 62 (2007) 202–210
205
Regulation analysis of biogeochemical fluxes
was not significantly different from zero, suggesting that the
nitrite oxidation was primarily regulated at the cellular level
(Table 1).
Ln [Cell numbers] (L−1)
22
21
Regulation of methanogenesis
20
19
0
1
2
3
4
Ln [Methane oxidation] (nM day−1)
5
Fig. 1. Double logarithmic plot of numbers of aerobic methane oxidisers
in Mono Lake, California USA, against the flux that runs through them.
The slope of the regression line equals the population regulation
coefficient and is reported in Table 1.
Ln [cell numbers] (cm−3)
28
27.5
27
8.5
9
9.5
10
Ln [nitrite oxidation] (µmol cm−3 h−1)
Fig. 2. Double logarithmic plot of numbers of aerobic nitrite-oxidizing
Nitrospira cells in a fluidized bed reactor, against the flux that runs
through them. The slope of the regression line equals the population
regulation coefficient and is reported in Table 1.
oxidation rates in Mono Lake were primarily regulated at
the cellular level and were not due to changes in cell
numbers.
Regulation of aerobic nitrite oxidation
Changes in activity and abundance of aerobic nitrite oxidizing Nitrospira along a bulk water gradient in a nitrifying
fluidized bed reactor have been analyzed by a combination
of microsensor measurements and FISH (Schramm et al.,
1999). Volumetric conversion rates were calculated using a
diffusion-based mathematical model, fed with the measured
concentration profiles of oxygen, ammonia, nitrite and
nitrate. FISH was based on 16S rRNA gene probes targeting
Nitrospira spp., the only nitrite-oxidizing bacteria present in
the reactor. The double logarithmic plot of cell numbers
against flux revealed a negative slope (Fig. 2). However, due
to the low number of data points available (three), the slope
FEMS Microbiol Ecol 62 (2007) 202–210
The vertical distribution of structure and functioning of the
methanogenic archaeal community in Lake Dagow (Brandenburg, Germany) was described by Chan et al. (2005).
Methane production rates were measured on slices of
sediment cores, corresponding to 0–3, 3–6, 6–10, 10–15
and 15–20 cm depth, in unamended laboratory experiments
over a period of 18–24 days. Hydrogen and acetate are the
major sources of substrates for methanogens. Analysis of
carbon-isotope fractionation on the methane and carbon
dioxide produced allowed the authors to quantify the
fraction of methane formed from acetate at each depth. This
information was used to separate overall methane production into a methane flux resulting from the activity of
acetoclastic methanogens and a methane flux mediated by
hydrogenotrophic methanogens. Acetoclastic Methanosaetaceae and hydrogenotrophic Methanomicrobiales were
identified by cloning and sequencing as the major groups
of methanogens present. Chan et al. (2005) quantified the
relative abundance of these two groups at each depth
investigated using analysis of terminal restriction fragment
length polymorphism. As total archaeal numbers were
determined by real-time PCR, cell numbers of each group
of methanogens were calculated by multiplying per depth
the relative abundance of the two groups of methanogens
with the total number of Archaea. These calculations
allowed quantification of the regulation of the acetoclastic
methane flux through the acetoclastic methanogens, and
regulation of the hydrogenotrophic methane flux through
hydrogenotrophic methanogens. Double logarithmic plots
of cell numbers against flux (Fig. 3) revealed that cell
numbers were clearly not linearly related to flux for both
processes, as the population regulation coefficients rP were
significantly lower than one (Table 1). In the case of
acetoclastic methanogenesis, the flux was regulated by both
a change in population size and in cellular activity, as the rP
of 0.41 was also significantly higher than zero. Changes in
hydrogenotrophic methane fluxes mainly relate to changes
in cellular functioning of hydrogenotrophic methanogens;
rP was not significantly different from zero (Table 1).
Regulation of sulfate reduction
Combined measurements of sulfate reduction rates and cell
numbers of sulfate reducers have been reported for depth
profiles on marine (Ravenschlag et al., 2000; Llobet-Brossa
et al., 2002; Leloup et al., 2007) and estuarine (Leloup et al.,
2004) sediments. Sulfate reduction rates were determined by
measuring the amount of 35S-labelled sulfides produced
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206
W.F.M. Röling
22
Ln [cell numbers] (cm –3)
Ln [cell numbers] (g-dw–1)
19
18
17
16
15
0
1
2
3
4
5
6
20
18
16
14
−4
−2
0
2
4
6
Ln [sulfate reduction] (nmol cm–3 day–1)
Ln [methane flux] (nmol g-dw–1 h–1)
Fig. 3. Double logarithmic plot of numbers of methanogens in sediment
of Lake Dagow (Brandenburg, Germany), against the flux of methane
that runs through them. , methane flux running through hydrogenotrophic methanogens; m, methane flux running through acetoclastic
methanogens. The slopes of the regression lines equal the population
regulation coefficient and are reported in Table 1.
during a period of less than 24 h after injection of sediment
slices with 35SO2
4 . Sulfate reducers were enumerated by
either FISH targeting 16S rRNA (Ravenschlag et al., 2000;
Llobet-Brossa et al., 2002) or quantitative PCR targeting
the dsrAB gene encoding dissimilatory (bi)sulfite reductase,
the key enzyme in dissimilatory sulfate reduction (Leloup
et al., 2004, 2007). Many microorganisms can perform
more than one function. In the absence of sulfate, sulfate
reducers may switch to fermentation (Drzyzga et al., 2001).
Therefore, only data from depths at which sulfate reduction
was the major redox process were analyzed, as for these
depths it is most likely that sulfate reduction was the main
function performed by the sulfate reducers. Double logarithmic plots of cell numbers vs. fluxes revealed that cell
numbers and especially sulfate reduction rates were lowest
in the Black Sea sediments (Fig. 4; Leloup et al., 2007). The
rP value of 0.20 was significantly lower than one and not
significantly different from zero (Table 1), revealing the
importance of cellular regulation of biogeochemical fluxes.
Higher cell numbers and sulfate reduction rates were
observed in the other studies. The slopes were much steeper
than observed for the data from the study on Black Sea
sediments, and rP values were not significantly different
from one (Fig. 4, Table 1). However, rP values were also
associated with large SE and not significantly different from
zero either for the analysis on the studies by Llobet-Brossa
et al. (2002) and Leloup et al. (2004). The large values for rP
relate especially to the observed cell numbers at the lowest
sulfate reduction rates in these studies; these cell numbers
were relatively low (Fig. 4) and corresponded to the largest
depths investigated. When these values were omitted from
the analysis, rP values were much smaller than one (data not
shown).
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8
Fig. 4. Double logarithmic plot of numbers of sulfate reducers in marine
and estuarine sediments, against the flux of sulfate reduction that runs
through them. , data from a study on marine Artic sediment (Ravenschlag et al., 2000); &, data from a study on an intertidal mud flat in the
German Wadden Sea (Llobet-Brossa et al., 2002); m, data from a study
on Seine estuary sediments (Leloup et al., 2004); and ^, data from a
study on Black Sea marine sediments (Leloup et al., 2007). The slopes of
the regression lines equal the population regulation coefficient and are
reported in Table 1.
Regulation of reductive dehalogenation
The groundwater pollutant perchloroethylene can be degraded to ethylene under anaerobic conditions by reductive
dehalogenation, with trichloroethylene (TCE), 1,2-dichloroethylene (cis-DCE) and vinyl-chloride (VC) as intermediates. Each of these compounds accepts two electrons. While
a wide range of microorganisms is capable of the reduction
of perchloroethylene and TCE, only Dehalococcoides species
are known to reduce cis-DCE and VC (Smidt & de Vos,
2004). Their numbers were quantified for perchloroethylene- and TCE-contaminated aquifers in the USA by realtime PCR using Dehalococcoides 16S rRNA gene specific
primers (Lu et al., 2006). In parallel, first-order degradation
constants for cis-DCE and for VC were obtained from a
reactive transport model. Degradation rates were calculated
by multiplying the first-order constants with the corresponding concentrations of cis-DCE and VC in groundwater
as reported by Lu et al. (2006). The degradation rates of cisDCE and VC were summed, as both cis-DCE and VC are
solely reduced by Dehalococcoides spp. Figure 5 shows the
double logarithmic plot of cell numbers vs. flux of cis-DCE
plus VC degradation. The slope indicated clearly that
differences in flux were mainly regulated at the cellular level
and not by population size; rP was indistinguishable from
zero and significantly smaller than one (Table 1).
Discussion
Biochemical regulation analysis (ter Kuile & Westerhoff,
2001) was extended to microbial ecology, in order to
quantify the relative importance of changes in cell numbers
FEMS Microbiol Ecol 62 (2007) 202–210
207
Regulation analysis of biogeochemical fluxes
Ln [cell numbers] (L−1)
18
16
14
12
10
0
2
4
6
8
10
12
Ln [reductive dechlorination] (µg cis-DCE+VC L−1 year−1)
Fig. 5. Double logarithmic plot of numbers of Dehalococcoides in
aquifers in the USA, against the flux of cis-dichloroethylene plus
vinylchloride that runs through them. The slope of the regression line
equals the population regulation coefficient and is reported in Table 1.
and changes in activity cell1 (cellular activity) in regulating
biogeochemical fluxes. Its application to a number of
biogeochemical processes revealed that in these studies
regulation was mainly, but not always completely, due to
changes in cellular activity. The question remains how
general such cellular regulation of biogeochemical fluxes is.
Most studies that quantified both fluxes and cell numbers,
and the data of which are analyzed here, addressed depth
profiles in shallow sediment (Ravenschlag et al., 2000;
Llobet-Brossa et al., 2002; Leloup et al., 2004, 2007; Chan
et al., 2005) or water (Carini et al., 2005). Physical mixing
and bioturbation often occur in these settings and reactive
transport modeling has revealed that these perturbations
tend to homogenize biomass, leading to a loss of correlation
between biomass concentrations and process rates (Thullner
et al., 2005). Therefore, it is not surprising that flux
regulation is unrelated to changes in cell numbers in the
studies mentioned above. It is very possible that in other
environmental settings or for other biogeochemical processes fluxes are primarily regulated by changes in cell
numbers. The results of regulation analysis on sulfate
reducing sediments may suggest so, but the results were
inconclusive due to large SE. The analysis of data on
acetoclastic methanogenesis in Lake Dagow, Germany,
shows that flux is not always regulated by cellular activity
only; in this case the flux was cooperatively regulated by
changes in cell numbers and in cellular activity.
The most important implication of the analysis of regulation is that it is often not sufficient to measure the cell
counts of the (group of) species contributing to a particular
biogeochemical process in order to estimate its flux. Information on cell numbers needs to be integrated with information on cellular activity in order to do so. Cellular
activity depends on several factors; it is affected by changes
in environmental conditions (e.g. temperature, pH), by
FEMS Microbiol Ecol 62 (2007) 202–210
changes in the enzyme pool, and by changes in the interaction of the enzyme pool with the metabolic level (i.e.
substrate, metabolite and product concentrations that act
on these enzymes, and their affinity and inhibition constants). The cellular enzyme pool may change as a result of
changes in gene transcription, mRNA translation, posttranslational modification and degradation of mRNA and
protein. The enzyme pool may also change due to functional
redundancy; upon perturbation a change in community
structure may occur due to the selection of species with
similar functions, but different enzyme pools, which are
better adapted to the new conditions. Several (groups of)
species performing the same function are often present in a
single, small sample (e.g. Ravenschlag et al., 2000; LlobetBrossa et al., 2002; Carini et al., 2005; Chan et al., 2005;
Leloup et al., 2007). Enzymatic make-up (Ek) and kinetic
properties (Kk) can be quite different among species, for
example specific metabolic rates differed up to 40-fold
among sulfate-reducing species (Knoblauch et al., 1999).
Thus, a change in community composition may act on the
term vk (Ek, Xk, Kk) that indicates cellular activity (equation
6). Among the studies in which biogeochemical fluxes were
unambiguously regulated by cellular activities, only the
study by Carini et al. (2005) reported detailed information
on the community structure of the microorganisms performing the biogeochemical process. However, denaturing
gradient gel electrophoresis banding patterns of type I and
type II methanotroph communities revealed only slight
changes with depth and season (Carini et al., 2005), indicating that the observed cellular regulation did not appear to
relate to changes in community composition in this case.
Nevertheless, in parallel with measurements of fluxes and
cell numbers, preferably also community structure of the
microorganisms performing the biogeochemical process
should be determined to aid in interpretation of the results
of regulation analysis. Functional redundancy makes performing regulation analysis of ecosystem processes and
interpretation of its results more complicated than regulation analysis of biochemical fluxes. Ideally, one would like to
determine the flux through every individual species in order
to determine regulation on the species level in stead of on
the group level and avoid the impact of functional redundancy on the interpretation of the results of regulation
analysis. However, tools to accurately measure fluxes per
species in complex communities are not yet available.
Nevertheless, it should be noted that in enzymatic pathways
several isozymes, with different kinetic parameters, may be
expressed and perform the same function. This situation
resembles very much functional redundancy in ecosystems.
Rossell et al. (2005) showed that the usual regulation
analysis still can be performed on enzymatic pathways when
two or more isozymes are expressed. In a similar fashion it
can be shown that regulation analysis is possible despite
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208
W.F.M. Röling
functional redundancy. Consider the case of two species
with different kinetics that perform the same process,
equation 5 then becomes:
V ¼ n1 v1 ðE1 ; X1 ; K1 Þ þ n2 v2 ðE2 ; X2 ; K2 Þ
ð7Þ
This can be rearranged as:
V ¼ n0 v0 ðE; X; KÞ;
ð8Þ
with:
n0 ¼ n1 þ n2
ð9Þ
v0 ðE; X; KÞ ¼
þ
n1
v1 ðE1 ; X1 ; K1 Þ
n1 þ n2
n2
v2 ðE2 ; X2 ; K2 Þ:
n1 þ n2
ð10Þ
Equation 8 resembles equation 5, and thus the ecological
regulation analysis can still be applied. The population
regulation coefficient retains its meaning as the term expressing the dependence on total cell numbers of the functional
group. The cellular regulation coefficient now comprises not
only the changes within a species (changes in enzyme pool
or interaction of enzyme pool with the metabolic level), but
also regulation through a possible shift in kinetic properties
due to a shift in community structure.
Other experimental limitations make the analysis of
regulation of biogeochemical fluxes currently less straightforward than regulation analysis of biochemical fluxes.
Enzymes have in general a single and unique activity and it
is fairly well known which enzyme is responsible for what
biochemical conversion in a biochemical pathway. This aids
considerably in determining the regulation of biochemical
flux through an enzyme, as it is easy to identify which flux
and enzymatic activity one should attempt to measure. In
contrast, a microorganism might be able to perform multiple functions, with the expression of the functions depending on the environmental conditions, while many different
species can perform the same function. Furthermore, our
understanding of which species and how many different
functional groups consume or produce a certain compound
under particular environmental conditions might still be far
from complete. This is best illustrated for aerobic ammonia
oxidation, which until very recently was assumed to be
solely performed by some members of the Betaproteobacteria and Gammaproteobacteria (ammonia oxidizing bacteria;
AOB). This view has changed considerably with the isolation
of an ammonia oxidizing Archaeon (AOA) (Konneke et al.,
2005) and subsequent observation that AOA outnumbered
AOB in soils (Leininger et al., 2006) and oceans (Wuchter
et al., 2006); AOA might be more important than AOB in
aerobic nitrification. For this reason, ecological regulation
analysis was not performed on aerobic nitrification, as all
2007 Federation of European Microbiological Societies
Published by Blackwell Publishing Ltd. All rights reserved
c
current studies reporting fluxes and cell numbers have only
quantified AOB.
Technical limitations may also affect regulation analysis.
Biogeochemical fluxes should preferably be derived from
reactive transport models applied to in situ concentrations
of substrates and/or products. Otherwise, short-term incubations at conditions as close as possible to the in situ
situation should be used, in order to avoid changes in cell
numbers and activities during the measurement. With
respect to the publications analyzed in this study only that
of Chan et al. (2005) did not appear to fulfill these requirements, they used an 18- to 24-day long incubation to
measure methane production. However, the fact that production rates could be obtained by linear regression suggests
that no major changes in cell numbers and activities
occurred during the incubation. Cultivation in general
underestimates cell numbers (Amann et al., 1995), therefore
only studies that used culture-independent enumeration
methods were selected. Also these methods are not without
problems. Background fluorescence and masking by nonmicrobial particles complicate counting microorganisms
with FISH (Gough & Stahl, 2003). Quantitative PCR
requires DNA extraction, and while extraction is often not
complete and DNA yields depend on the extraction method
and sample matrix, extraction efficiency is often not determined (Mumy & Findlay, 2004). A detailed study on realtime PCR also revealed that absolute gene copy numbers
generated in independent determinations may not be
directly comparable (Smith et al., 2006). PCR itself is subject
to many pitfalls (von Wintzingerode et al., 1997). Yet,
methodological issues do not prevent the application of
regulation analysis, provided that the relative size of the
error is equal for all samples being compared (e.g. if all cell
numbers are underestimated by 50%, this will not affect the
values of regulation coefficients). The relative errors for
samples belonging to a single study were assumed to be
similar. No attempt was made to pool the data from the four
studies on sulfate reduction to derive a single population
regulation coefficient as it cannot be ignored that the relative
errors in these studies might have been different, for
example due to the use of different quantification methods.
This study indicates that cellular regulation appears to be
most important with respect to changes in flux. Cellular
activity can change by several processes; it is affected by
changes in the cellular enzyme pool, and by changes in the
interaction of the enzyme pool with the metabolic level
(substrate, metabolite and product concentrations that act
on these enzymes, and their affinity and inhibition constants). The enzyme pool is changed by hierarchical regulation (ter Kuile & Westerhoff, 2001), i.e. by changes in gene
transcription, mRNA translation, post-translational modification and degradation of mRNA and protein. The regulation of biogeochemical fluxes by changes in the enzyme pool
FEMS Microbiol Ecol 62 (2007) 202–210
209
Regulation analysis of biogeochemical fluxes
(hierarchical regulation) and metabolic regulation (all other
changes) can be integrated into the current mathematical
framework of ecological regulation analysis: cellular activity
can, like enzyme activities in biochemical regulation analysis
(equation 2), be described as a function of the maximum
activity of the cell, relating to its enzyme pool ( fk (Ek)) and
the interaction of this pool with the rest of metabolism
(gk (Xk, Kk)):
vk ¼ vk ðEk ; Xk ; Kk Þ fk ðEk Þ gk ðXk ; Kk Þ:
ð11Þ
Subsequently, the cellular regulation coefficient rC (see
equation 6) can be dissected into a hierarchical regulation
coefficient and a metabolic regulation coefficient (respectively rH and rM ; capitals H and M are used to distinguish
these coefficient from the coefficients as used in biochemical
regulation analysis):
rC ¼
¼
D ln vk ðEk ; Xk ; Kk Þ
D ln J
D ln f ðEk Þ D ln gðXk ; Kk Þ
þ
¼ rH þ rM :
D ln J
D ln J
ð12Þ
rH corresponds to the slope in a double logarithmic plot of
maximum cellular activity (fk (Ek)) vs. measured in situ
fluxes. Maximum potential activity (nk fk (Ek)) can be
measured on environmental samples to which electron
donors and electron acceptors have been added in excess.
While results of maximum potential measurements are
quite commonly reported in combination with cell numbers, especially for nitrification (e.g. Phillips et al., 2000), no
study was found that also reported in situ fluxes. Furthermore, the methods used to measure maximum potential
rates in these studies were not optimal for the purpose of
ecological regulation analysis: changes in enzyme activities
may have occurred during incubation, leading to a change in
maximum activity cell1. The cellular enzyme pool can be
fixed by the use of protein-synthesis inhibiting antibiotics
(Sokol, 1987). Maximum activities per cell are not constant
for a species, but dependent for example on growth rate
(Sokol, 1987). It is therefore very possible that cellular
regulation is contributed by both hierarchical and metabolic regulation. The above extension of ecological regulation analysis would aid in further determining which
biochemical aspects of cellular activity require quantification in order to better describe and predict biogeochemical
fluxes.
Acknowledgements
This work was financially supported by the Netherlands’
Bsik Ecogenomics program and by the TRIAS program
(TRIpartite Approach to Soil system processes), co-funded
by the Netherlands Organization of Scientific Research
FEMS Microbiol Ecol 62 (2007) 202–210
(NWO), the Netherlands Center for Soil Quality Management and Knowledge Transfer (SKB) and Delft Cluster. I
thank Frank Bruggeman and Maria Tobor-Kaplon for
critical reading a previous version of this manuscript.
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