Introduction
Agricultural production of food and energy has required a 10-fold increase
(i.e. from 10 to 100 TGN yr-1) in the application of synthetic
fertilizer since 1950 (Robertson and Vitousek, 2009). Moreover, to maximize
crop yields, nitrogen (N) is often applied at rates in excess of a crop's
yield response, the average maximum crop yield as a function of fertilizer
application rate. This results in a residual N pool (Sebilo et al., 2013).
While some of the excess N may be incorporated into the soil, much of it is
either transported out of the system via runoff as NO2- or
NO3- (Zhou and Butterbach-Bahl, 2014), volatilized as
NH3 (Pan et al., 2016) or converted to N2 and/or nitrous
oxide (N2O) via oxidative and reductive microbial processes
(Schreiber et al., 2012; Venterea et al., 2012) such as nitrification and
denitrification, respectively. Stimulated by agricultural practices, these
microbial processes account for 90 % of anthropogenic N2O
(Denman, 2007; Reay et al., 2012). Losses of N from soils in the form of
N2O are of particular concern because this greenhouse gas
contributes to stratospheric ozone depletion (Portmann et al., 2012;
Ramanathan et al., 1985; Ravishankara et al., 2009) and has a 100-year global
warming potential that is approximately 300 times that of CO2(IPCC,
2014). Moreover, the relationship between N application rate and
N2O emissions from agricultural soils is non-linear (McSwiney and
Robertson, 2005; Shcherbak et al., 2014), with N2O emissions
dramatically increasing with moderate increases in fertilization. To mitigate
the N2O flux without compromising crop yield, several systems have been
developed that include the maximum return to nitrogen system (Nafziger et
al., 2004) and the variable rate nitrogen application system (Scharf et al.,
2011). These strategies provide recommendations of fertilization rates that
minimize reductions in crop yield while simultaneously decreasing the amount
of residual N available for N2O production, thereby lowering the soil
N2O flux. The identification of how to manage the microbial processes
contributing to N2O flux from agricultural systems would be an
additional mechanism to mitigate atmospheric N2O additions
(Paustian et al., 2016; Reay et al., 2012; Venterea et al., 2012). Because
nitrification and denitrification require aerobic and anaerobic conditions,
respectively, strategies directed at controlling soil oxygen saturation could
become part of a management strategy (Kravchenko et al., 2017). This,
however, requires identification of the relative importance of nitrification and
denitrification to the N2O flux spatially and temporally across
different agricultural landscapes.
The stable isotope ratios of δ15N and δ18O have been used
to apportion N2O flux between nitrification and denitrification
(Davidson and Keller, 2000; Park et al., 2011; Yamagishi et al., 2007).
Apportionment approaches require that the isotope values of N2O
differ between the two production processes and remain constant throughout
the course of a reaction (Jinuntuya-Nortman et al., 2008; Ostrom and Ostrom,
2017). However, shifts or fractionation in the isotope values of
N2O produced during either nitrification or denitrification can
compromise source apportionment (Barford et al., 1999; Perez et al., 2000;
Sutka et al., 2008; Yoshida, 1988), and reduction of N2O by
denitrification may further alter isotope values (Jinuntuya-Nortman et al.,
2008). Nevertheless, δ15N and δ18O can still be a useful
tool if environmental conditions constrain processes, such as anoxic
conditions prohibiting nitrification.
Site preference (SP), the difference in 15N abundance between the
central N (δ15Nα) and terminal N (δ15Nβ) of N2O, offers an alternative tool for
apportionment of N2O production (Yoshida and Toyoda, 2000). The
large difference in SP of N2O produced from nitrification and
denitrification (ca. 30 ‰) paired with the observations that SP is
constant during N2O production, and is independent of the isotopic
composition of the nitrogen substrates of nitrification and denitrification,
has prompted the use of SP for N2O source apportionment (Sutka et
al., 2006; Toyoda et al., 2005). However, SP is not without variation (Toyoda
et al., 2017). In addition to the production pathway, numerous factors could
theoretically control the degree of variation in SP including differences in
bacterial species, the specific enzyme involved in its production (Yang et
al., 2014), and, for denitrification, the carbon source. The accuracy of
apportionment estimates using isotope values, including SP, will be improved
by understanding sources of variation.
This study investigated the effect of the carbon source (electron donor) and
carbon source concentration on δ15N and δ18O of
N2O produced by two denitrifier species in vitro, as well the
effect of these factors on SP values of N2O. We conducted our study
with Pseudomonas chlororaphis subsp. chlororaphis and P. chlororaphis subsp. aureofaciens because they are highly related
denitrifiers that lack N2O reductase but encode different types of nitrite
reductase (NIR).
Materials and methods
Organisms and culture conditions
Cultures of Pseudomonas chlororaphis subsp. chlororaphis (ATCC
43928; P. chlororaphis) and Pseudomonas chlororaphis subsp. aureofaciens (ATCC 13985; P. aureofaciens) were cryogenically
stored (-80 ∘C) in tryptic soy broth (TSB; Caisson Labs,
Smithfield, UT) and sterile glycerol 1:1 (v/v). Stock cultures were
re-established in 5 mL TSB amended with sodium nitrate (NaNO3, 10 mM;
Sigma-Aldrich, St. Louis, MO) under aerobic conditions at a constant
temperature with continuous agitation (18 h, 25 ∘C). Individual
colonies were obtained from re-established stock cultures by the streak-plate
technique on tryptic soy agar (TSA; Caisson Labs, Smithfield, UT) amended
with NaNO3 (10 mM). Tryptic soy agar plates of stock cultures were
sealed with parafilm and incubated (aerobic, 25 ∘C). The plates were
stored at 4 ∘C for up to 2 weeks prior to establishment in liquid
media for denitrification experiments.
Preparation of cultures for denitrification experiments
Starter cultures of each species were established in 5 mL TSB amended with
NaNO3 (10 mM) with one colony from stored stock culture plates (Thermo
Fisher Scientific, Waltham, MA). Cultures were then grown aerobically with
agitation (25 ∘C, 18 h) to the late exponential phase (the optical
density at 600 nm (OD600) was 0.3). Optical density was determined with
a Spectronic 20 spectrophotometer (Bausch and Lomb, Rochester, NY). Two
160 mL sterile serum bottles containing 50 mL of carbon minimal media (CMM)
(Anderson et al., 1993) amended with 10 mM NaNO3 and 10 mM sodium
succinate (Sigma-Aldrich, St. Louis, MO) were each inoculated with
200 µL of the aerobic culture. The bottles were stoppered
(Geomicrobial Technologies, Inc.) and crimp-sealed, and the headspace was
sparged with ultra-high purity (UHP) N2 for 15 min. Cultures were
incubated (25 ∘C, 18 h) with agitation. Following 18 h, the cells
were transferred to 50 mL conical Falcon™
tubes (Corning, Corning, NY) and centrifuged (3000 × g, 30 min,
25 ∘C) to pellet the cells. The supernatant was decanted and the
cells dispersed in CMM lacking a carbon or nitrogen source (OD600=0.2). The cells were aliquoted (2 mL) into sterile 35 mL serum bottles,
which were then stoppered (Geomicrobial Technologies, Inc.) and crimp-sealed.
An anaerobic environment was created by sparging the cells with UHP N2
for 20 min. Sparging was accomplished by inserting one sterile
stainless-steel needle (no. 20 Thomas Scientific, Swedesboro, NJ, USA)
carrying N2 through the stopper into the media while a second sterile
stainless-steel needle was inserted through the stopper and into the
headspace to allow gas to exit. Following sparging, the bottles were allowed
to reach atmospheric pressure, and reactions were then initiated by injecting
20 µL of the carbon source (anaerobic) to reach a final
concentration of 0.01, 0.1, 1 or 10 mM. Treatments with citrate and
succinate concentrations of 1 and 10 mM were conducted for both bacterial
taxa. Treatments of citrate and succinate at 0.1 mM were only conducted for
P. chlororaphis. Treatments with a carbon source concentration of
0.01 mM were only conducted with succinate but were done so for both taxa.
The addition of the carbon source was followed by adding 26 µL of
0.1 M NaNO3 (anaerobic) to reach a final NO3-
concentration of 1.3 mM. The δ15N and δ18O of the
NO3- source was 5.4 and 24.5 ‰, respectively.
Isotope analysis and modelling isotope behaviour
Each treatment consisted of four denitrification cultures. Headspace samples
were obtained from each culture with a gas-tight syringe (Hamilton; Reno,
NV). For one of the four cultures, a 100 µL headspace sample was
obtained every 15 min for analysis of N2O concentration. Headspace
N2O concentration of this culture was determined with a Shimadzu
Greenhouse Gas Analyzer gas chromatograph equipped with an electron capture
detector (ECD) (model GC-2014, Shimadzu Scientific Instruments; Columbio, MD,
USA). For details regarding this method see Yang et al. (2014). These data
were used to determine when the N2O concentration was sufficient
for isotope analysis and to estimate the volume of headspace required for
isotope analysis over the course of the reaction. Headspace sampling of the
remaining three cultures was initiated when the N2O concentration
determined by ECD was above ca. 0.4 ppm. Headspace samples between 200 and
500 µL of each of the three cultures were injected into 60 mL
serum bottles (one per culture) that had been sparged with UHP N2 for
15 min, and stored for isotope analysis. Each bottle contained between 5 and
15 nmols of N2O for isotopic analysis. Samples were analysed on an
IsoPrime100 stable isotope ratio mass spectrometer (IRMS) interfaced to a
TraceGas inlet system (Elementar; Mt. Laurel, NJ) (Sutka et al., 2003). The
inlet system used He as the carrier gas and removed both water and
CO2 with separate magnesium perchlorate (Costech; Valencia, CA,
USA) and CO2 absorbent traps (Carbosorb, 8–14 mesh, Costech;
Valencia, CA, USA), respectively, prior to concentrating N2O within
a cryofocusing trap. Chromatographic separation of N2O was achieved
with a Porplot Q column prior to isotopic analysis. Mass overlap and related
corrections followed the protocol outlined in Toyoda and Yoshida (2000). Our
internal laboratory pure N2O tank standard (MSU Tank B) was
isotopically characterized by analysis relative to the USGS51 and USGS52
reference materials
(https://isotopes.usgs.gov/lab/referencematerials.html; last access: 13
June 2018). Following the guidelines proposed by Coplen (2011) we report here
the isotope values of the reference materials as well as our internal
laboratory standard. The δ15N, δ18O, δ15Nα, δ15Nβ and SP values of USGS51 and
USGS52 are 1.3, 41.2, 0.5, 2.2 and -1.7 ‰ and 0.4, 40.6, 13.5,
-12.6 and 26.2 ‰, respectively. The δ15N,
δ18O, δ15Nα, δ15Nβ and SP
values of reference MSU Tank C are -0.9, 0.7, -2.6, 39.6 and
3.4 ‰, respectively. The δ15N, δ18O,
δ15Nα, δ15Nβ and SP values of the
isotope standard MSU Tank B are -0.5, 11.13, -12.2, 40.8 and
23.3 ‰, respectively. All nitrogen isotope values are reported with
respect to the international air N2 standard
and with respect to VSMOW for δ18O. The mean precision of replicate
N2O standards were 0.1 ± 0.1, 0.3 ± 0.2,
0.3 ± 0.2, 0.2 ± 0.1 and 0.6 ± 0.3 ‰ composition
for δ15N, δ15Nα, δ15Nβ,
δ18O and SP, respectively.
The δ15N and δ18O values are reported as
δ=RsampleRstandard-1×1000,
where R is the ratio of the trace to the abundant isotope of N or O, and
air and VSMOW are the standards for N and O, respectively. Site preference is
defined as
SP=δ15Nα-δ15Nβ,
where δ15Nα and δ15Nβ are the
isotope values at the central and peripheral N atom of the linear
N2O molecule, respectively. The changes in δ15N,
δ18O and SP of N2O during the course of the reaction
were investigated using the Rayleigh equation by plotting each isotope value
vs. [-flnf/(1-f)], where f is the fraction of substrate remaining
(Mariotti et al., 1981). According to convention (Mariotti et al., 1981), the
magnitude of the isotopic fractionation factor (α) for a single
unidirectional reaction is defined by the rate constants of the light
(k1) and heavy (k2) isotopically substituted compounds:
α=k2/k1.
Further, the isotopic enrichment factor, ε, is defined as
ε=α-1×1000,
and can be estimated from the slope of the linear relationship described by
the Rayleigh model:
δ15Np=δ15Nso-εpsflnf/1-f,
where δ15Np is the isotope value of the accumulated
product, δ15Nso is the isotope value of the initial
substrate, ε is the fractionation factor and f is the
fraction of substrate remaining (Mariotti et al., 1981). The fraction of
substrate remaining was determined by dividing twice the amount of
N2O produced by the total amount of nitrate added, and then
subtracting this quantity from 1. Generalized additive modelling of the
relationship between the isotope value of N2O and [-flnf/(1-f)] indicated asymptotic curvilinear behaviour. Therefore, we performed
non-linear least squares regression starting with a three-parameter
exponential function of the form
y=a+cebx.
Model reduction and selection were performed following the methods of Baty et
al. (2015). A non-linear model fit was also compared to a linear model fit.
Models with the lowest residual standard error, fewest iterations to
convergence (< 10), lowest parameter confidence intervals and
lowest collinearity of variables were deemed to have the best fit. The
goodness of fit for each model was also assessed visually from residual
plots. Model residuals that displayed patterns were also deemed poor. This
process produced an exponential function with the generalized form
y=a+ebx,
where y is the isotope value of the accumulated product, x is [-flnf/(1-f)] and a and b are coefficients estimated by the model. Values of
a affect the y intercept, with larger values contributing to increased
prediction of the final isotope value of the reaction. Values of b
affect the rate of change of the isotope values particularly at the beginning
of the reaction. Larger values of b result in a more gradual rate of
change, whereas lower values of b increase the initial slope. The
starting values supplied to the function were a=7 and b=5 for
δ15N and a=75 and b=10 for δ18O. These starting
values were selected because they are greater than the expected coefficients,
which aids in model convergence (Baty et al., 2015). The derivative of
Eq. (7),
y′=bebx,
can be used to predict the slope at any extent of the reaction. The term net
isotope effect (η) has been used to describe isotopic discrimination,
the change in isotope value, observed during a multi-step reaction
(Jinuntuya-Nortman et al., 2008). Therefore, η is equivalent to y′ in
Eq. (8).
We used kernel density estimation to illustrate the density distribution (DD)
of η across the extent of the reaction observed. Kernel density
estimation is a non-parametric method of determining the probability density
function of a random continuous variable. Probability density functions were
determined with a Gaussian smoothing kernel from 50 equally spaced estimates
of η, spanning the complete extent of the reaction (i.e. f=0 to 1).
The bandwidth was set to 1 for each density estimate.
Modelling was performed with R statistical software (R Team, 2013), and all
figures were produced with ggplot2 (Wickham, 2009, 2011).
Statistical analysis of SP data
We used a linear model to determine if SP changed as a function of [-flnf/(1-f)]. Significant relationships were not observed and therefore the
effect of taxa, carbon source and carbon source concentration on mean SP was
examined with the analysis of variance (ANOVA). Tukey's honest significant
difference (HSD) test was used to identify significant differences between
and among groups. Normality of the data was assessed with Q–Q plots and the
Shapiro–Wilk test.
Statistical analyses were performed with R statistical software (R Team,
2013), and all figures were produced with ggplot2 within that software
platform (Wickham, 2009, 2011).
δ15N, δ18O and site preference (SP) of
N2O produced during denitrification of NO3- by
Pseudomonas chlororaphis subsp. aureofaciens and Pseudomonas chlororaphis subsp. chlororaphis with different electron donor sources and
concentrations. A larger value of [-flnf/(1-f)], where f is the
fraction of substrate remaining, represents earlier points in the reaction.
The curved relationships are of the form y=a+ebx, where y is the isotope value, x is [-flnf/(1-f)] and a
and b are the estimated coefficients that affect the y intercept and
curvilinear shape, respectively.
Density distributions (DDs) of δ15N net isotope effects
(η) derived from the derivative of the exponential function (Eq. 4)
describing the relationship between δ15N and [-flnf/(1-f)] for
Pseudomonas aureofaciens (orange) and Pseudomonas chlororaphis (blue). Estimates of η were produced over the entire
extent of the reaction (i.e. f=0 to 1). The left panel displays the DDs for citrate treatments and the right panel
displays the DDs for succinate treatments. Positive values of η were
not observed during reactions. Tally marks at the base of each panel indicate
the actual distribution of calculated values.
Discussion
This study investigated the effect of the denitrifier species, the carbon source
(electron donor) and the electron donor concentration on δ15N,
δ18O and SP isotope values of N2O produced during
denitrification in pure cultures. We observed isotopic discrimination against
15N and 18O but no change in SP during the reduction of
NO3- to N2O by P. aureofaciens or P. chlororaphis, and these observations held, regardless of carbon source and
electron donor concentration.
In contrast to the expectation of the Rayleigh model, the reduction of
NO3- to N2O by P. aureofaciens and P. chlororaphis displayed a non-linear exponential relationship between
δ15N vs. [-flnf/(1-f)] and δ18O vs. [-flnf/(1-f)]. This curvilinear isotopic behaviour was evident for
denitrification metabolizing both carbon substrates (citrate or succinate)
and at all substrate concentrations (Fig. 2, Table S1). The non-linear
behaviour indicates that the fractionation factor, ε, is not
constant, a phenomenon not unexpected for multi-step reactions in which more
than one enzymatic step and diffusion of products and/or substrates into and
out of the cell can result in variation in isotopic discrimination (Granger
et al., 2008; Sutka et al., 2008). Because the fractionation factor varies
during multi-step reactions, it is best considered a net isotope effect
(η) (Jinuntuya-Nortman et al., 2008). The reduction of
NO3- to N2O during denitrification involves three
enzymes and multiple opportunities for diffusion, all cases where isotope
discrimination can occur (Fig. 5). Similar to other studies, our previous
work on denitrification estimated η from a Rayleigh model (Barford et
al., 1999; Lewicka-Szczebak et al., 2014; Sutka et al., 2006; Toyoda et al.,
2005; Yano et al., 2014). However, the Rayleigh model assumes a
unidirectional single-step reaction with linear behaviour, assumptions that
are clearly not valid for N2O production from nitrate during
denitrification. Thus, here we developed estimates of η from the
derivative of the exponential relationship between the isotope value of the
accumulated product, N2O, and the extent of the reaction [-flnf/(1-f)]. This allowed us to quantify changes in η over the course of
the denitrification reaction.
Density distributions (DDs) of δ18O net isotope effects
(η) estimated from the derivative of an exponential function describing
the relationship between δ18O and [-flnf/(1-f)] for
Pseudomonas aureofaciens (orange) and Pseudomonas chlororaphis (blue). The estimates of η are extrapolated to include
the complete extent of the reaction. The left panel displays the
DDs for citrate treatments and the
right panel displays DDs for
succinate treatments. Positive values of η were not observed during
reactions. Tally marks at the base of each panel indicate the actual
distribution of calculated values.
The mean site preference (SP) of N2O produced during the
reduction of NO3- by Pseudomonas aureofaciens (orange)
and Pseudomonas chlororaphis (blue) with different concentrations of
electron donors: citrate and succinate. Error bars indicate 1 SD.
For our entire data set, η15N and η18O varied by as much as
ca. 100 ‰ within a single experiment (Figs. 2, 3). Note, however,
that during N2O production, both δ18O and η18O
can be influenced by oxygen exchange between water and nitrogen oxides (Kool
et al., 2009, 2011). These oxygen exchange effects are difficult to quantify,
making interpretation of η18O data difficult. Additionally, visual
inspection of the covariation between δ18O and δ15N
indicated similar trends among treatments and species, and the observed
kinetic isotope effect for δ18O suggests that there is little
exchange with H2O in the reaction vessels (Fig. 1). Thus, we limit our
discussion of fractionation to η15N. Values of η15N
previously reported for reduction of NO3- to N2O in
pure cultures (-43 to -9 ‰) fall within the range we observed
(Sutka et al., 2006, 2008; Toyoda et al., 2005). However, some of our values
are much greater in magnitude than those previously reported (e.g.
-119 ‰). Values of such magnitude occurred near the onset of the
reaction (i.e. high values of [-flnf/(1-f)]), most notably when no more
than 10 % of the NO3- had been reduced. The occurrence of
high magnitude η values near the beginning of the reaction is likely
related to the relative importance of diffusion and enzymatic fractionation
in controlling η. Fractionation associated with enzymes is often much
larger than that associated with diffusion, and enzymatic fractionation is
fully expressed when diffusion does not limit substrate supply to the enzyme
(Jinuntuya-Nortman et al., 2008; Ostrom and Ostrom, 2012). Thus, the largest
η is expected at the beginning of the reaction, consistent with what we
observed. Large magnitude values for η can be easily missed if the
isotope value of the accumulated product is used to estimate η. Without
knowledge of the production rate, it can be difficult to know when there is
sufficient product for isotopic measurement. By characterizing production
rates before initiating experiments to estimate η, we were able to
capture isotope values for N2O close to the onset of the reaction.
A schematic representation of the multi-step reduction of nitrate to
nitrous oxide during denitrification specific to Pseudomonas aureofaciens and Pseudomonas chlororaphis, which lack the enzyme
nitrous oxide reductase. The enzymes responsible for the reduction of
nitrogen species appear in boxes with rounded corners and are indicated by
three letter sequences: nitrate reductase (NAR), nitrite reductase (NIR) and
nitric oxide reductase (NOR). A nitrate/nitrite transporter protein is
presented as a hexagon. The position of the enzymes with respect to the
periplasm, membrane or cytoplasm identifies the location of the enzymes in the
cell. Vertical dashed arrows indicate diffusion of various nitrogen species
into and out of the cell, and curved dashed arrows represent transport across
the membrane. Solid arrows represent enzyme-catalyzed reduction steps.
There are important reasons why published discrimination factors might be
less negative and therefore of lower magnitude than ours. Prior estimates
were derived from a single slope from a Rayleigh model and, therefore, do not
produce estimates of η over the course of the reaction. Importantly,
they may not characterize the large fractionation occurring at the onset of a
reaction. Even so, our highly negative values for η might, initially,
seem remarkable. Considering variation in η in the context of a
multi-step model provides insight into how these values might arise,
particularly in the early stages of a culture when the substrate
concentration is high. The reduction of NO3- to N2O
includes three enzymatic steps in which substantive fractionation may occur
(Fig. 5). As a consequence, we would expect the products of each successive
reaction to become progressively depleted in the heavy isotope, assuming
normal ε. If, for example, the ε for each of the
three steps was -40 ‰, then reduction of nitrate with a
δ15N of 0 ‰ could yield N2O of
-120 ‰. Thus, denitrification has the potential to produce
N2O that is greatly depleted in 15N, resulting in highly
negative values for η. As the reaction proceeds, each enzyme is likely
to be limited by the supply of substrate from diffusion. This has a tendency
to reduce the expression of fractionation, and η is therefore reduced to
less negative values.
Probability density distributions indicate that markedly low η values
associated with one endpoint of the range in η are not common (Fig. 3).
They also illustrate the range in η that would be expected for the
reaction, and their shape emphasizes important changes in η during the
course of a reaction. For example, several of the distributions show a marked
change in slope on the left side of the distribution (e.g. 10 mM citrate
η15N, both species) that is a consequence of a significant change in
slope along the curve of δ15N vs. [-flnf/(1-f)] (Figs. 1, 3).
While we cannot ascribe a specific event to this change, future studies aimed
at investigating specific enzymes may provide a better understanding of the
behaviour of η during denitrification. Perhaps most importantly, these
distributions emphasize that assessments of net isotope effects for
multi-step reactions will not be complete without consideration of isotopic
behaviour over a wide extent of the reaction and the development of models
that describe isotope behaviour that does not fit a linear Rayleigh model.
In contrast to the results we observed for δ15N and δ18O, isotopic discrimination was not evident for SP, regardless of
treatment (Fig. 2). Instead, SP was constant during the course of the
reaction. This finding is consistent with pure culture studies of
nitrification and denitrification across multiple species (Frame and
Casciotti, 2010; Sutka et al., 2003, 2006; Toyoda et al., 2005). The
differences we observed in SP between species, however, is likely to relate
to the factors that control SP. Unlike the case for bulk isotopes, SP is
determined during a single reaction, the reduction of NO to N2O
(Toyoda et al., 2005). Thus, as N2O reduction does not occur in
P. aureofaciens or P. chlororaphis, SP is only influenced
by nitric oxide reductase (NOR) activity and diffusion of NO or N2O
into or out of the cell. As SP is the difference between the δ15N
value of two N atoms that rely on the same NO substrate, SP is not dependent
upon the isotopic composition of the initial substrate (Toyoda et al., 2005;
Sutka et al., 2006). The observation that SP remained constant during
bacterial denitrification, even though the extent of the reaction varied,
(e.g. Sutka et al., 2006) suggests that the expressed fractionation for the
α and β N atoms during NO reduction was the same. If so, then
one hypothesis is that f can vary markedly and SP will be constant.
However, during production of N2O by the pure fungal cytochrome
P450 NOR enzyme, distinct fractionation factors for the α and β N atoms were observed and it was proposed that observations of constant SP
values during production by fungi were the result of f, or the internal pool
size of NO, being held relatively constant during cellular metabolism (Yang
et al., 2014). We observed a minor but significant different in SP between
two species of Pseudomonas sp. during N2O production that
is consistent with a difference in the internal pool size of NO within the
cell. The abundance of NO within the cell will depend on its production,
reduction and losses due to diffusion into or out of the cell, all of which
could vary between species. We do not know, for example, the degree to which
the rate of NO production intrinsically differs between the cd1-type
NIR of P. chlororaphis and copper-containing NIR of P. aureofaciens or how gene expression may alter these rates. We posit that
small differences in SP between and even within species in our study and
others may relate to the size of the NO pool available to NOR.
Nitrous oxide is the third most abundant greenhouse gas in the atmosphere and
is the greatest source of stratospheric ozone depletion (Ravishankara et al.,
2009). Moreover, efforts to balance the N2O budget have been
challenged by the episodic nature of the N2O flux (Nishimura et
al., 2005) and, historically, identifying the pathway of N2O
production has been enigmatic (Schreiber et al., 2012). Here we emphasize
that within our toolbox, SP remains a robust indicator of N2O
derived from denitrification, regardless of carbon source or concentration,
and we identify that a component of the variation in SP can be ascribed to
species differences. Our ability to understand factors that control variation
in SP is important for the refining of estimates of the relative importance
of N2O production pathways, something that is necessary for the
mitigation of fluxes of this important greenhouse gas from aquatic and
terrestrial environments.