Carbonyl sulfide (OCS) plays an important role in the global sulfur cycle
and is relevant for climate change due to its role as a greenhouse gas, in
aerosol formation and atmospheric chemistry. The similarities of the carbon
dioxide (CO2) and OCS molecules within chemical and plant metabolic
pathways have led to the use of OCS as a proxy for global gross CO2
fixation by plants (gross primary production, GPP). However, unknowns such
as the OCS exchange from soils, where simultaneous OCS production
(POCS) and consumption (UOCS) occur, currently limits the use of
OCS as a GPP proxy. We estimated POCS and UOCS by measuring net
fluxes of OCS, carbon monoxide (CO), and nitric oxide (NO) in a dynamic
chamber system fumigated with air containing different mixing ratios [OCS].
Nine soils with different land use were rewetted and soil–air exchange was
monitored as soils dried out to assess responses to changing moisture. A
major control of OCS exchange was the total amount of available sulfur in
the soil. POCS production rates were highest for soils at WFPS (water-filled pore space)
>60 % and rates were negatively related to thiosulfate
concentrations. These moist soils switched from a net source to a net sink
activity at moderate moisture levels (WFPS 15 % to 37 %). For three soils we
measured NO and CO mixing ratios at different mixing ratios of OCS and
revealed that NO and potentially CO exchange rates are linked to UOCS
at moderate soil moisture. High nitrate concentrations correlated with
maximum OCS release rates at high soil moisture. For one of the investigated
soils, the moisture and OCS mixing ratio was correlated with different microbial
activity (bacterial 16S rRNA, fungal ITS RNA relative abundance) and gene
transcripts of red-like cbbL and amoA.
Introduction
Carbonyl sulfide (OCS) is the most abundant sulfur-containing trace gas in
the troposphere with a lifetime in the order of years. OCS contributes to
warming of the troposphere and cooling of the stratosphere, and both
processes are considered balanced (Brühl et al., 2012). Plants
simultaneously take up carbon dioxide (CO2) and OCS by the enzymes
ribulose-1,5-bisphosphate carboxylase (rubisco) and
phosphoenolpyruvate carboxylase (PEPCO). Carbonic anhydrase (CA) enhances
this uptake process, since it accumulates CO2 intracellularly
(Protoschill-Krebs and Kesselmeier, 1992; Protoschill-Krebs et al., 1996).
Photosynthesis represents the largest global OCS, i.e., 0.73 to
1.5 Tg S a-1 (Sandoval-Soto et al., 2005). Thus, fluxes of OCS are
closely related to gross CO2 uptake during photosynthesis.
Soils can act as both sources and sinks of OCS. While anoxic soils and
wetlands are considered a global source for OCS of about 0.05 Tg a-1
(Watts, 2000), oxic soils are considered to be a sink for OCS of about
0.36 Tg a-1 (Berry et al., 2013). OCS uptake in soils has been
considered to be predominantly performed by CA (Wingate et al., 2009).
However, there is some evidence that rubisco of soil microorganisms might
also play a role (Whelan et al., 2018; Kesselmeier et al., 1999; Meredith et
al., 2019). The microbial mechanisms underlying OCS production
(POCS) and consumption (UOCS) in soil, however, are
not resolved and are a topic to recent research (Kaisermann et al., 2018;
Meredith et al., 2019; Ogée et al., 2016). In fact, current studies
report that soils can switch between net OCS uptake and emission related
either to soil moisture and/or soil temperature (Bunk et al., 2017; Whelan et
al., 2016; Maseyk et al., 2014). Thus, an understanding of environmental
factors controlling the soil microbial community is required for the
prediction of net soil OCS fluxes from the ecosystem to global scale.
OCS can be produced by microbial decomposition of organic S compounds via
thiosulfate (with minor amounts of CS2; Smith and Kelly, 1988) and
thiocyanate hydrolysis (Katayama et al., 1992). Nonetheless, alternative
metabolic pathways for OCS production might occur in soil (Conrad, 1996). A
recent study suggests that S-containing amino acids act as important OCS
precursors (Meredith et al., 2018). There is indication that also archaea are
capable of OCS production via CS2 hydrolase (Smeulders et al.,
2011). Previous studies suggest thiocyanate as important precursor in
microbial OCS production. However, there is no clear evidence if it is the
only or main precursor in soil since it can also inhibit microbial OCS
production (Katayama et al., 1992). S-oxidizing bacteria that utilize this
pathway are Thiobacillus thioparus, Thiohalophilus thiocyanatoxydans, Acinetobacter junii, Geodermatophilus obscurus, and Amycolatopsis orientalis (Katayama et al., 1992; Sorokin
et al., 2006; Mason et al., 1994; Ogawa et al., 2016). Sulfate (Banwart and
Bremner, 1976), S-containing amino acids (Meredith et al., 2018; Banwart and
Bremner, 1975), and other S compounds (Flöck et al., 1997; Lehmann and
Conrad, 1996) can therefore act as precursors for microbial OCS formation.
Additionally, an abiotic process in which organic matter is degraded
depending on temperature and/or light might be of importance for
POCS (Whelan and Rhew, 2015).
Consumption of OCS can be linked to microbial pathways in soils that utilize
either CO2 or bicarbonate (HCO3-) by various microbial
carboxylases (Erb, 2011). CA reversibly catalyzes the hydration of gaseous
CO2 to bicarbonate (HCO3-) under neutral pH (Smith and
Ferry, 2000). As a ubiquitous enzyme for exchanging and equilibrating
CO2, CA does not only occur in higher plants but also in
microscopic algae and lichens which may assimilate S from the atmosphere
(Kuhn and Kesselmeier, 2000). CA also irreversibly catalyzes OCS to
H2S and CO2 (Ogawa et al., 2016; Protoschill-Krebs et al.,
1995; Blezinger et al., 2000; Notni et al., 2007). A recent study found a
correlation of OCS exchange rates and CO18O with different forms of
CA (Meredith et al., 2019).
Rubisco occurs in plants and other photoautotrophs, as well as in soil
microbial chemolithoautotrophs (Badger and Bek, 2008). Thus, rubisco is also
a candidate for OCS consumption. In plant leaves, stomatal control is the
main regulator of OCS uptake (Sandoval-Soto et al., 2012). In soils,
accumulating CO2 mixing ratios may have the potential for inhibition of
rubisco but not for the alternative enzymes by which soil organisms may
uptake CO2, such as CA or PEPCO (Bunk et al., 2017; Cousins et al.,
2007).
In addition to its co-metabolism due to its similarity with CO2, OCS
can be a direct source of sulfur and/or energy for some autotrophs and
heterotrophs. Based on pure culture studies, Thiobacillus thioparus
(Smith and Kelly, 1988; Kamezaki et al., 2016), fungal and bacterial strains
belonging to Trichoderma (Masaki et al., 2016), and
Actinomycetales (Ogawa et al., 2016) may degrade OCS. It has been
shown by Laing and Christeller (1980) that OCS acts as a competitive
inhibitor for CO2 uptake by rubisco, where CO2 and OCS
compete for the active center of the enzyme as alternative substrates
(Lorimer and Pierce, 1989).
The OCS production process which has been found to correlate with the amount
of nitrogen fertilizer (Kaisermann et al., 2018; Melillo and Steudler, 1989) is
still not understood and thus it is still unknown if OCS consumption might
be linked to the nitrogen cycle as well. In aerobic soils NO is predominantly
produced by nitrifiers (e.g., Placella and Firestone, 2013). In addition, some
methanotroph species fix carbon via rubisco (Rasigraf et al., 2014, and
references therein). Instead of rubisco, ammonia-oxidizing archaea utilize
the hydroxypropionate–hydroxybutyrate cycle for aerobic CO2 fixation
(Könneke et al., 2014; Pratscher et al., 2011). Thus, there is evidence
that the microbial NO (and potentially CO) exchange might be linked to each
other. Ammonia-oxidizing bacteria and methanotrophs may co-oxidize CO via
ammonia monooxygenase (AMO) and methane monooxygenase (MMO), which likely is
stoichiometrically correlated to ammonia oxidation (Jones et al., 1984).
There is some evidence that the CO and OCS consumption is coupled since
various carboxydotrophic soil microorganisms exist. Soil ammonia oxidizers
and methanotrophs are capable of CO co-oxidation via ammonia and methane
monooxygenase (Bédard and Knowles, 1989; Jones and Morita, 1983; Jones et
al., 1984; Bender and Conrad, 1994). Aerobic carboxydotrophic bacteria and
fungi can consume CO (King and Weber, 2007; Inman and Ingersoll, 1971).
Inhibition experiments indicate that fungi might utilize CA for OCS
consumption (Bunk et al., 2017). Archaeal carboxydotrophs are typically
hyperthermophilic aerobes that are not common in temperate soils (King and
Weber, 2007; Sokolova et al., 2017). The energy conserved from the oxidation
of CO can be utilized for CO2 fixation within the
Calvin–Benson–Bassham (CBB) cycle via rubisco (Ragsdale, 2004). Anaerobes,
such as acetogens, methanogens, and sulfate reducers that harbor the
Wood–Ljungdahl pathway might also be capable of oxidizing CO via carbon
monoxide dehydrogenase, CODH (Davidova et al., 1993; Ragsdale, 2004; Alber,
2009). CO dehydrogenase can reduce OCS to CO and H2S, and the
substrate affinity Km is about 2.2 mM for OCS (Conrad, 1996). While some enzymes
consume only OCS (e.g., CA), others consume OCS and produce CO (e.g., CODH).
Consistently, OCS:CO2 ratios are correlated with CO:CO2
ratios in a boreal forest (Sun et al., 2018). CO production from abiotic
photodecomposition of organic matter (Conrad and Seiler, 1985) might be
negligible in soil.
A key goal of our study work was to explore whether simultaneous measurements
of, for example, NO and CO and microbial activity by RNA-based approaches have the
potential to indicate active metabolic pathways (e.g., CO2 fixation
via different enzymes). In turn, this information may provide insights into
pathways of POCS and UOCS in a way that allows
prediction of net OCS fluxes across a range of soils and moisture contents.
Ultimately the ability to understand the role of soils in net ecosystem
exchange of OCS is relevant to enabling the estimation of canopy fluxes of OCS
and their interpretation as a proxy for gross primary production, GPP
(Campbell et al., 2017, 2008; Blonquist et al., 2011; Berry
et al., 2013).
Based on this approach, we investigated whether NO and CO exchange rates
measured over a range of different moisture conditions and in different
soils reveal the influence of soil moisture on the underlying microbial
metabolisms of the net soil OCS exchange. For one of the investigated soils
(an agricultural soil from Germany), gas exchange rates were linked to
microbial activity of archaeal and bacterial ammonia oxidizers (AOA, AOB)
and fungal activity based on RNA relative abundance of internal transcribed
spacer (ITS). ITS RNA's half-time is low since it is functionally not needed
for the establishment of ribosomes but can be considered to be a general proxy
for fungal protein biosynthesis (Žifčáková et al., 2016;
Baldrian et al., 2012). Additionally, quantitative real-time polymerase
chain reaction (qPCR) was applied for detection of the functional red-like
cbbL gene encoding rubisco (in nongreen algae and α- and β-Proteobacteria, Selesi et al., 2005) and the archaeal and bacterial amoA gene
encoding ammonia monooxygenase. This study is based on the assumption that
an increase in the numbers of rRNA and ITS RNA relative abundance reflects
increased metabolic activity (Blazewicz et al., 2013; Rocca et al., 2015).
Nonetheless, rRNA content is not always directly related to activity since
it is relatively stable.
Materials and methodsSoil analysis
In total 11 samples of topsoil (integrating a depth between 0 and 5 cm) were
used, representing different soil types and land uses (see Table 1). To make
a representative sample for each site, nine individual subsamples were taken on
a grid from within a 10m×10m area and homogenized. Samples were
sieved to <2 mm, hand-picked to remove roots, and stored at 4 ∘C
(for up to 6 months) prior to incubations. The field moist soil used for the
incubations was analyzed for total sulfur and thiocyanate
(SCN-) to link OCS production to substrate availability at the
start of the incubation experiments. Bulk soil sulfur content was determined
on an elemental analyzer (Vario EL, Elementar Analysensysteme GmbH, Germany).
For thiocyanate measurement, about 8 g of soil was extracted in 1 M sodium
hydroxide (NaOH) solution, centrifuged, and filtered to remove particulates.
Thiocyanate concentrations (reported per gram dry soil) were determined
colorimetrically using 50 mm cuvettes and adding chloramine-T, isonicotinic
acid as well as 1,3-dimethylbarbituric acid
(Environment Agency, 2011). Absorption measurements
were made at 600 nm using a photometer (DR3900, Hach Lange GmbH, Germany),
calibrated based on a standard curve of diluted potassium thiocyanate from
1 to 5 mg L-1. The blank for photometry analysis was subjected to the
same color reactions as the samples using 1 M NaOH instead of sample
extract. For ammonium (NH4+) and nitrate (NO3-)
quantification 5 g soil was extracted in 50 mL of 2 M KCl for
60 min and was filtered through a 604
1/2 Whatman™ filter paper (GE Healthcare,
Chicago, Illinois, USA). The filtered extracts were frozen at
-20∘C until analysis with a flow injection analyzer (Quickchem
QC85S5, Lachat Instruments, Hach Company, Loveland, CO, USA).
Soil properties and experimental conditions used for analysis. Note
that NO and CO exchange rates were measured only for A1, A1dry, and F3 soils at
50 and 1000 ppt OCS, respectively. Temperature for all experiments was
25 ∘C.
Note that OCS fluxes for F3, A1, and A1dry are
presented in Bunk et al. (2018).
a Data adopted from Bunk et al. (2017); b data
adopted from Oswald et al. (2013); n.d. means not determined.
Incubations
An automated dynamic chamber system was used to incubate soil at
25 ∘C in the dark (Behrendt et al., 2014). The system has
six chambers, switching so that it is measuring one while flushing the other
five. It also includes a soil-free reference chamber. Experiments of
pseudo-replicates, which were representative for a 10m×10m area,
were run in series, with three technical replicates at any given time, for a
given soil moisture. Each chamber was measured for 15 min and then flushed
at a rate of 2.5 L min-1.
At the start of each experiment/run (for overview see Table 1), soil (∼60 g) was moistened to saturation (100 % water-filled pore space, WFPS)
for most soils; 80 % WFPS for desert soils (D1 and D2), and placed into
Plexiglas incubation chambers (inner diameter 0.092 m, height 0.136 m). The
composition of air entering the chambers (flow 500 mL min-1) was
adjusted by adding (i) CO2 (Westphalia, Germany) to a CO2-free air
stream using soda lime to reach ∼400±8 ppm and (ii) a variable amount
of OCS to “zero” air produced by a pure air generator (PAG 003, Eco Physics
AG, Switzerland). For practical reasons, different experiments were performed
to test various controls on OCS fluxes. First, OCS fluxes were compared using
soils from agricultural sites – corn (A1 and A2), sugar beet (A3), and wheat
(A4), as well as from a grassland site (A5), from sand deserts (D1 and D2),
and from a natural and previously burned rainforest (F1 and F2) under ambient
OCS mixing ratios (about 500 ppt). Second, for three soils NO and CO exchange
rates were compared under 50 and 1000 ppt OCS fumigation using the fresh and
40 ∘C dried midlatitude cornfield soil (A1), Mainz, Germany, and a
soil sample originated from a spruce forest (F3), Sparneck, Germany. Data for
OCS exchange for A1 are shown in the Supplement. Third, for only one site, a
fresh midlatitude cornfield soil (A1) also previously investigated for OCS
exchange (Kesselmeier et al., 1999; Van Diest and Kesselmeier, 2008; Bunk et
al., 2017), we stopped the incubation at selected moisture contents and inlet
OCS concentrations and subsampled for molecular analysis. During the
incubations, subsamples of this soil were taken at four different soil
moistures flushed with OCS-free air (50 ppt OCS). In addition, one sample at
the moisture representing maximum OCS consumption under 1000 ppt OCS
fumigation was taken for microbial analysis.
For OCS, comparison of net fluxes measured using different levels of OCS in
inlet air allows separate quantification of OCS production and consumption
contributions to the net flux (Kaisermann et al., 2018). As the air entering
the chamber is moisture-free, the soils dry out over time, allowing us to see
how gas production and consumption changed with soil moisture. At the start
and end of each experiment the gravimetric soil moisture (θg) was determined. Over the course of the experiment gravimetric
soil moisture was determined by calculating the mass balance of evaporated
water vapor (Behrendt et al., 2014).
For the comparison of results of soils that differ in texture, the
gravimetric soil moisture was converted into the percent of water-filled pore
space, WFPSlab, as
WFPSlabti=msoilti-msoiltsmsoilts⋅100θsat,
where θsat is the gravimetric soil moisture at saturation,
which was estimated by rewetting the soil until the surface of the particles
was covered by a tiny film of water. Msoil(ti) equals the dry
mass of soil plus water at any given time point ti and
msoil(ts) equals the dry mass of soil plus the residual
mass of water at the end of the experiment.
OCS, NO and CO exchange rates
The selected outflow from the six soil chambers of the automated incubation
system was connected to a commercial OCS,CO2, CO, and H2O
analyzer (907-0028, Los Gatos Research Inc., USA). Absorption peaks were
detected at gas-specific spectral lines (OCS at 2050.40 cm-1 and CO at
2050.86 cm-1). The instrument performs an off-axis integrated cavity
output spectroscopy (OA-ICOS), a type of cavity enhanced absorption
spectroscopy. In principle the absorption of a quantum cascade laser light by
a trace gas is measured according to the Bouguer–Lambert–Beer's law. For
incubations of the agricultural soil (A1 fresh and A1 dried at
40 ∘C) and a soil sample from a spruce forest (F3), a
NOx analyzer was also connected to the collection line
(42i-TL, Thermo Scientific, USA), and NO was detected via chemiluminescence.
NO standard gas (5 ppm, Air Liquide, Germany) was used for the calibration
of the NOx analyzer. The limit of detection (LOD) was estimated
based on the 3σ of the noise from the soil-free chamber
(LODNO=0.15 ppb NO, LODOCS<15 ppt, and LODCO<0.3 ppb). The precision and
accuracy of laser spectrometers have been evaluated in detail elsewhere
(Kooijmans et al., 2016).
In front of the inlet of both analyzers a Nafion dryer (MD-110,
Perma Pure LLC, USA) was installed. The exchange rate of each trace gas,
JTG, OCS, NO, and CO was calculated as
JTGcref,Tconst,WFPS=Q⋅(cout-cref)Msoil,
where Q is the flow rate through the chamber (2.5 L min-1), and
cout and cref are the concentrations of each trace
gas at the outlet of the soil chamber and soil-free chamber (ng m-3),
respectively (Behrendt et al., 2014). Msoil equals the dry mass
of soil after drying for 48 h at 105 ∘C. The average and standard
deviation of the fluxes were calculated for the last five points of each 15 min
interval that the air exiting the soil was analyzed, over the entire time period
during which the soil dried out. While the OCS mixing ratios measured were
all above the limit of detection, the difference between the OCS mixing ratio of
incoming and outgoing air, especially under moderate to low soil moisture,
was generally only a few parts per trillion. Therefore, it seems reasonable
to set a threshold of detection (i.e., the minimum detectable rate of
production or consumption based on the noise of the instrument). Similar to
the definition of a limit of detection, we used 3 times the deviation of OCS
mixing ratios measured from one soil chamber to define this threshold and
converted it into an OCS exchange rate of about ±1.09 pmol g-1 h-1.
Extraction of RNA and amplicon sequencing
A commercial RNA extraction kit (RNA PowerSoil, MOBIO, USA), involving bead
beating at 6 m s-1 for 30 s for cell disruption (FastPrep, MOBIO,
USA), was used for RNA extraction of about 1 g soil. RNA was eluted in
100 µL nuclease-free water and further cleaned with a commercial
kit for RNA (RNeasy PowerClean Pro CleanUp Kit, MOBIO, USA). Quality and
quantity of purified nucleic acids were analyzed by agarose gel
electrophoresis (1 % w/v), nanodrop (ND-2000, Thermo Fisher
Scientific, USA), and Qubit fluorometer (Thermo Fisher Scientific, USA). RNA
integrity and quantity were analyzed by agarose gel electrophoresis
(0.5 % w/v) and Qubit analysis, after DNase treatment (DNase Max Kit,
MOBIO, USA). Subsequently, cDNA was produced with random hexamer primers
(Roche) and SuperScript III Reverse Transcriptase (Invitrogen, Karlsruhe,
Germany). Ribosomal 16S rRNA and ITS genes were amplified for the V3–V4
region (Klindworth et al., 2013) and ITS3F-4R region (White et al., 1990),
respectively, from cDNA. Amplification and sequencing-library preparation
were performed for MiSeq Illumina platform in Macrogen Inc. (Seoul, South
Korea).
qPCR for archaeal and bacterial <italic>amoA</italic> gene and for red-like
<italic>cbbL</italic> gene
The abundance of the archaeal and bacterial amoA functional marker gene
encoding ammonia monooxygenase (AMO) was measured by real-time polymerase
chain reaction (qPCR), with the crenamo23f/crenamo616r (Tourna et al., 2008)
and amoA1F/amoA2R primers (Rotthauwe et al., 1997), respectively. The
red-like cbbL functional marker gene encoding rubisco large subunit
type IA was quantified with cbbLR1F and cbbLR1intR primers (Selesi et al.,
2007). The total reaction volume of 20 µL consisted of
2 µL DNA (1 ng µL-1) or cDNA (diluted 1/50), 0.4
or 0.6 µM of primer (archaeal and bacterial amoA,
respectively), and 1× Power SYBR Green PCR Master Mix (Invitrogen,
Karlsruhe, Germany), performed in a qPCR cycler
(StepOnePlus™, Applied Biosystems, USA).
Reactions were performed in triplicate, and cycling parameters were set to
10 min at 95 ∘C for initialization; 40 cycles of denaturation
at 95 ∘C for 30 s; annealing for 30 s at either 54 ∘C (archaeal
amoA), 60 s at 55 ∘C (bacterial amoA), or 30 s
at 55 ∘C (cbbL); and 30 s at 72 ∘C for
elongation, followed by fluorescence measurement. Melting curves were
measured in the range of 60 to 95 ∘C in 0.3 ∘C increments.
Standard curves were created from 10-fold dilutions of purified plasmids
containing the respective gene of interest as described previously (Catão
et al., 2016). Archaea and bacterial amoA standard curves had
87.5 % and 67.1 % efficiency and 0.93 and 0.97 coefficient of
determination (R2), respectively. The abundance of the red-like form of
rubisco was calculated from the 10-fold dilutions standard curve produced from
purified DNA of Sinorhizobium meliloti obtained from DSMZ (number
30135), with 84.8 % efficiency and a 0.99 coefficient of determination
(R2).
Sequence analysis
The RNA relative abundance was used as proxy for microbial activity in this
study. Before sequence analysis was performed with a standard QIIME pipeline,
paired-end reads of 300 bp were merged with PEAR (Zhang et al., 2014), with
maximum lengths of 500 or 550 bp for 16S rRNA and ITS, respectively, and
cleaned with PrinSeq (Schmieder and Edwards, 2011). Specific criteria were
used to proceed with the analysis only with high-quality reads in terms of
sequence confidence: mean phred over 25 (probability that the base assigned
by the sequencer is at least 99 %); trim quality window of 50 (space of
nucleotides scanned for quality at each time); minimum length of 200 bp;
removal of artificial duplicates obtained during sequencing; and only 1 %
of bases, which were not recognized as ATGC, were allowed (Schmieder and
Edwards, 2011). Pre-cleaned sequences were analyzed with QIIME version 1.9.1
(Caporaso et al., 2010), following usearch61 chimera (sequences that
can be artificially created during amplification of DNA molecules for the
sequencing) screening, and the operational taxonomic units (OTUs) picking
process was performed using the uclust_ref method. Chimera
checking, OTU picking, and OTU taxonomy assignment of representative OTUs
were based on Greengenes taxonomy database 13.8 version for 16S rRNA
(McDonald et al., 2012) and ITS UNITE 12.11 version for ITS (Abarenkov et
al., 2010). The biome table was exported using the .tsv file format
and was used for calculations in R (version 3.3.1) or Igor Pro 7. For
graphical representation, overall description of taxa is presented as the
normalized relative abundance of the counts (from QIIME pipeline) of
sequences assigned to that taxa divided by the total amount of sequences
obtained after cleaning steps for each sample. Similarly, only the first hit
of classification (from the blast approach) with the highest bit score and
lowest e value was considered. The count of reads classified per species
above was normalized per the total of cleaned reads and expressed per million
reads.
ResultsOCS exchange for rewetted and dried-out soils under ambient
(500 ppt) OCS
After wetting stored soils to 80 %–100 % WFPS, all agricultural
soils (A1 to A5) produced OCS, with rates of production declining as the soil
dried out. At ∼37 % WFPSlab, these soils switched to a
state of net OCS consumption (Fig. 1a). Around 15 % WFPSlab,
OCS exchange rates increased again to a local maximum (in some cases again
net-producing OCS) at about 10 % WFPSlab before they finally
declined to zero exchange under completely dry conditions. The cornfield soil
(A2) produced 13 pmol g-1 h-1, followed by the
4.7 pmol g-1 h-1 from the grassland soil (A5) and
3.8 pmol g-1 h-1 OCS from the wheat field soil (A4),
respectively. For the sugar beet soil (A3) OCS fluxes were less than
1.09 pmol g-1 h-1 (undetectable) or negative (net OCS uptake) in
the range from 65 % to 15 % WFPSlab but increased to a
production of 1.5 pmol g-1 h-1 at about 10 %
WFPSlab. The soil from a wheat field (A4) had an almost identical
OCS exchange profile to the cornfield soil (A1). The grassland soil (A5)
produced up to 5 pmol g-1 h-1 OCS and was the only agricultural
soil that emitted OCS > 1.09 pmol g-1 h-1 within the range
of moderate soil moisture. Both rainforest soil samples (F1 and F2)
exchanged OCS above detection levels only at very high and low soil moisture;
both acted as small net sinks for OCS in between (Fig. 1b). The two desert
soils (D1 and D2, sand content ≥90 % determined according to
ISO 11277) produced up to 3.3 to 9.56 pmol g-1 h-1 at high soil
moisture, with fluxes declining as the soil dried out (Fig. 1c).
OCS exchange rates from soil samples originated from agriculture
(a) A1 to A5: cornfield (light blue), cornfield (blue), sugar beet
(dark blue), wheat field (red), and grassland (pink); (b) F1, F2:
natural rainforest (orange) and annual burned rainforest (brown); and
(c) D1, D2: sand desert (blue) and sand desert (red) measured at
500 ppt OCS mixing ratio and 400 ppmCO2 mixing ratio. According
to Bunk et al. (2017) OCS release rates are classified into high-moisture
(HM), moderate-moisture (MM), and low-moisture (LM) regime. The y axis has
different scales in subfigures. Data of A1, A2, and A3 are adapted from Bunk
et al. (2019). Grey shaded area represents the threshold of 1.09 to
-1.09 pmol g-1 h-1 where no significant OCS exchange could be
detected.
We measured thiocyanate in soil extracts at the start of the dry-out experiments
where high POCS was observed, because a pathway of thiocyanate
hydrolase has been proposed for OCS production (POCS).
Thiocyanate concentrations for the desert soils were very low, below
detection for D1 (<0.5 mg kg-1; grey point in Fig. 2) and only
0.65 mg SCN- kg-1 for D2. For all other soils, thiocyanate
concentrations ranged between 0.87 and 12.04 mg SCN- kg-1. For
all soils except the agricultural soil (A2, not used in curve fitting), an
increase in thiocyanate concentration coincided with a decrease in the
maximum observed OCS exchange rate at WFPS > 37 %,
OCSmax,HM (see Fig. 2). The maximum OCS exchange rate and
thiocyanate concentration for the agricultural soil (A2, green circle) are
considered outliers, possibly due to the release of thiocyanate from fine
roots during the sieving procedure.
Correlation between OCS exchange rate, OCSmax,HM, and
thiocyanate (SCN-) at high soil moisture for samples F1, F2, A3, A4, and A5
(green). Thiocyanate was below limit of detection (LOD of 0.5 mg kg-1)
for D1 soil (grey).
While the agricultural soils (A) and forest soils (F) showed similar patterns
that included a second increase in OCS production at below 10 %
WFPSlab, desert soils (D) only produced OCS. The different
behavior for OCS exchange from desert soils may be related to differences in
soil properties: desert soils (D) are characterized by high pH (carbonate
contents of 1.89 % to 0.55 % for D1 and D2 soils, respectively) and
high amount of total sulfur (0.13 % to 3.74 %). The highest
NO3- concentrations from a desert soil (D2) and a cornfield soil
(A2) correlated with the largest net OCS exchange rates (see Table 1). The high
NH4+ correlated with low maximum OCS exchange rate at the start of
the experiment.
Fungal activity correlated with <inline-formula><mml:math id="M235" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">OCS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M236" display="inline"><mml:mrow><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="normal">OCS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> from a
midlatitude cornfield soil (A1) over the range of soil moisture
The highly conserved 16S rRNA gene reflects differences in bacterial and
archaeal populations. Overall, the sequencing approach did not result in
significant differences in 16S rRNA relative abundance for bacterial classes
for the cornfield soil (A1) fumigated at 50 versus 1000 ppt OCS at moderate
soil moisture (Fig. 3). In contrast, for ITS RNA relative abundance of
Ascomycota (p value = 0.006) indicated these were significantly more
active under 1000 ppt OCS compared to 50 ppt OCS, which could suggest their
importance for OCS exchange (Fig. 4). Within the phylum Ascomycota the
largest difference in RNA relative abundance from 50 to 1000 ppt OCS
resulted for the class Sordariomycetes (p value = 0.029). Within the
phylum Basidiomycota (p value = 0.034) the largest difference in RNA
relative abundance from 50 to 1000 ppt OCS was observed for the class
Cystobasidiomycetes (p value = 0.009), which is also significantly more
abundant in the OCS 1000 ppt samples. For the phylum Zygomycota the RNA
relative abundance decreased from 50 to 1000 ppt OCS
(p value = 0.035).
Taxonomic composition of the midlatitude corn field soil in Mainz,
Germany, at 22 % WFPSlab of the samples under 1000 or 50 ppt OCS.
The 16S rRNA relative abundance for selected bacterial classes was
normalized by the total number of assigned reads per sample. Classes with RNA
relative abundance <5×10-4 did not show significant
differences and were not plotted. Error bars represent standard deviation.
Asterisks represent statistically different values (p value < 0.05).
Taxonomic composition of the midlatitude corn field soil in Mainz,
Germany, at 22 % WFPSlab of the samples under 1000 or 50 ppt OCS.
RNA relative abundance of internal transcribed spacer (ITS) for
fungal classes was normalized by the total number of assigned reads per
sample. Classes with RNA relative abundance <5×10-4 did not
show significant differences and were not plotted. Error bars represent
standard deviation. Asterisks represent statistically different values
(p value < 0.05). “Other” is identified by the QIIME pipeline,
however with no known classification in the database, under the used
threshold of sequence similarity (90 %).
Effect of [OCS] on NO release rate
For the investigation of the microbial groups involved in OCS production and
consumption, we studied simultaneous OCS, NO (as a proxy for nitrification; see Fig. 5),
and CO exchange for a fresh and 40 ∘C dried cornfield soil (A1) and
a soil originated from a spruce forest (F3). The maximum NO
release rate for the 40 ∘C dried cornfield sample (Fig. 5a) was
726.9 pmol g-1 h-1 at 50 ppt OCS and
1102.7 pmol g-1 h-1 at 1000 ppt OCS at 37 %
WFPSlab, whereas for the fresh sample, NO rates were
substantially lower (Fig. 6d). The soil sample from the spruce forest in
Sparneck, Germany (F3), released maximal NO of
5579.5 pmol g-1 h-1 at 50 ppt OCS and
7159.4 pmol g-1 h-1 at 1000 ppt OCS and 41 %
WFPSlab (Fig. 5b). The observed increase in NO release rate at
1000 ppt OCS compared to 50 ppt OCS suggests that microbial groups
involved in the nitrogen cycle (e.g., nitrifiers), which utilize CA and
rubisco, might have contributed to the simultaneous exchange of NO and OCS
under moderate soil moisture. Interestingly, at 1000 ppt OCS its OCS
release rate was lower (indicating OCS consumption increased) and coincided
with low CO release compared to 50 ppt OCS under moderate soil moisture
regime (see Fig. S2 in the Supplement).
NO exchange rates (a) are shown for a midlatitude cornfield soil
sample 40 ∘C dried from Mainz, Germany (A1), and a soil sample
originated from a spruce forest in Sparneck, Germany, at OCS mixing ratio of 50 ppt (blue) and 1000 ppt (red).
Effect of OCS fumigation on the archaeal and bacterial
<italic>amoA</italic> and red-like <italic>cbbL</italic> gene transcripts (qPCR) from a
midlatitude cornfield soil (A1)
The change in 16S rRNA relative abundance for bacteria (sequencing) could not
resolve significant differences for a cornfield soil (A1) fumigated at 50
versus 1000 ppt OCS at moderate soil moisture (see Sect. 3.1). Hence, qPCR
assays were used for the specific quantification of the AOB and AOA
amoA and red-like cbbL gene transcripts. For the fresh soil
sample from a cornfield (A1) 34 AOB amoA transcripts per nanogram
extracted DNA were detected at 95 % WFPSlab with a
continuous increase up to 221 transcripts per nanogram extracted DNA at
7 % WFPSlab, all at 50 ppt OCS (see Fig. 6).
A total of 2193 AOA amoA transcripts per nanogram extracted DNA were
detected at 33 % WFPSlab with a continuous increase up to
39 494 transcripts at 7 % WFPSlab at 50 ppt OCS (see
Fig. 6). For red-like cbbL (rubisco) 13 463 transcripts per nanogram
extracted DNA were detected at 95 % WFPSlab with a
continuous increase up to 45 033 transcripts per nanogram extracted DNA at
7 % WFPSlab, all at 50 ppt OCS (see Fig. 6).
RNA relative abundance of the cbbL functional gene, encoding
ribulose-1,5-bisphosphate carboxylase (rubisco) large subunit type IA,
measured over dry-out under 50 ppt OCS (blue diamonds, a). RNA relative
abundance of the amoA functional gene for ammonia-oxidizing archaea (AOA; blue
squares, b) and ammonia-oxidizing bacteria (AOB; blue points, c) measured over
dry-out under 50 ppt OCS. NO exchange rates at 50 ppt (dark blue, d)
and 1000 ppt (light blue, d) OCS mixing ratio are shown for the A1 soil sample from a
midlatitude corn field, Mainz, Germany.
DiscussionInterpretation of patterns of OCS exchange for rewetted and
dried-out soils under ambient (500 ppt) OCS
OCS is produced by the degradation of various S compounds. Thiocyanate has
been considered to be an important precursor for OCS (e.g., Conrad, 1996). Thus,
it is likely that the OCS production rate is correlated with the
concentration of thiocyanate as a dominant intermediate of organic S compound
degradation. Lehmann and Conrad (1996) added sodium thiocyanate to soil
samples and found an increase in OCS production. Nonetheless, there is
indication that also organic compounds might be precursors of OCS in soil
(Smith and Kelly, 1988; Kelly et al., 1993). In our study, all vegetated
soils (i.e., not D1 and D2 desert soils) contained significant amounts of
thiocyanate that likely were produced during decomposition of plant material
(Bunk et al., 2017; Kelly et al., 1993). In the two tropical forest soils,
thiocyanate and OCS fluxes were at or close to detection limits. Over a range
of moisture conditions, these soils consume any OCS produced and provide a
(barely detectable) sink for OCS from the atmosphere (Whelan et al., 2016;
Sun et al., 2018).
The desert soils (D1 and D2), although exhibiting low thiocyanate
concentrations, contained high bulk S, likely in the form of inorganic S
compounds. In deserts such enrichments of inorganic salts are the result of
long-term dry deposition (Michalski et al., 2004). Microorganisms might be
able to produce OCS from sulfate (Meredith et al., 2018; Banwart and Bremner,
1976) or other S-containing precursors (Banwart and Bremner, 1975; Flöck
et al., 1997; Lehmann and Conrad, 1996) and thus may have high rates of OCS
production that do not depend on organic S availability. The positive OCS net
fluxes from desert soils (D1 and D2) at 500 ppt OCS suggest that OCS
consumption in these soils is, if at all present, only low. Low amounts of
organic matter in these soils might limit the abundance and activity of
heterotrophs, such as Actinobacteria (Ogawa et al., 2016). An alternative
explanation is the inhibition of rubisco through high pH and the presence of
carbonate (Lorimer and Pierce, 1989). Both inorganic and organic S
availability control OCS production rates in general (e.g., Meredith et al.,
2018; Banwart and Bremner, 1976; Banwart and Bremner, 1975; Flöck et al.,
1997; Lehmann and Conrad, 1996), but rates of OCS consumption are controlled
by different parameters (e.g., Kaisermann et al., 2018). And thus net soil
OCS exchange and its relation to moisture is not linearly dependent on further
controls.
There is already evidence that OCS exchange correlates with total nitrogen
content (Kaisermann et al., 2018). In our study the highest nitrate
concentrations correspond to maximum OCS net exchange under high soil
moisture. This is in agreement with a nitrate fertilization study in which
substantial increase in OCS net fluxes from forest soils was the consequence
(Melillo and Steudler, 1989). The correlations of NO3- and
NH4+ concentration with OCS net release rate at the start of the
experiment suggest that microbial nitrogen cycling is connected to OCS
exchange.
Fungal activity correlated with <inline-formula><mml:math id="M276" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">OCS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M277" display="inline"><mml:mrow><mml:msub><mml:mi>U</mml:mi><mml:mi mathvariant="normal">OCS</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> from a
midlatitude cornfield soil (A1) over the range of soil moisture
Carbonic anhydrase is thought to be the most important enzyme involved in OCS
uptake (Bunk et al., 2017). Masaki et al. (2016) concluded that fungal
species may contribute differently to OCS exchange in soils; although some
were net consumers of OCS, pure cultures from strains of
Umbelopsis/Mortierella sp. according to Masaki et al. (2016) were net
producers of OCS.
In our study, we found a significant difference in ITS RNA relative abundance for several
fungi when OCS in ambient air was changed from 50 to 1000 ppt, indicating
fungal sensitivity to OCS. Recent studies suggest that fungi containing CA
might be responsible for OCS uptake (Ogawa et al., 2016; Bunk et al., 2017).
In addition, enzymes involved in different CO2 fixation pathways,
including the CBB cycle, hydroxypropionate–hydroxybutyrate cycle (HP–HB),
anaplerotic reactions of heterotrophic microorganisms (PEPCO), or the Wood–Ljungdahl
pathway, might play a role for OCS. For example, using a specific
inhibitor for CA leads to changed OCS flux (Kesselmeier et al., 1999). A
possible explanation for the large differences in OCS exchange among the
various soils investigated here might be a niche separation (here soil
moisture) of gene expression and activity maxima under different moisture
conditions for different OCS-converting enzymes: at high soil moisture the
OCS production by hydrolysis of organic S compounds might be the dominant
process, while at moderate soil moisture consumption of OCS by CO2
assimilation might be the predominant process. Under moderate soil moisture
we found a lower activity of Zygomycota and Tremellomycetes at 1000 ppt
compared to 50 ppt OCS, whereas both Sordariomycetes (Ascomycota showed
the highest RNA relative abundance of overall fungi in the midlatitude cornfield
soil (A1) and Cystobasidiomycetes exhibited an increased metabolic activity
(see Fig. 4).
Carbonic anhydrase is not a single enzyme but rather a group of five different
families (α, β, γ, δ, and ζ). A recent
study suggest that Actinobacteria contain a CA-like gene, to which also OCS
hydrolases are similar (Ogawa et al., 2016). Thus, these bacteria may contain
a hydrolase that might be specialized to uptake of OCS. The importance of
phototrophs (eukaryotic algae) for OCS exchange was already demonstrated
(Sauze et al., 2017). There is evidence that different CAs and likely other
enzymes are involved in the OCS exchange (Meredith et al., 2019). CA is well
known to act as an “upstream amplifier” for rubisco and PEPCO. Due to
the similarity of the OCS and CO2 molecules, it seems reasonable that for
OCS consumption the roles of rubisco and PEPCO might have been
underestimated. There might be not only a bulk kcat and
KM (Ogée, et al.,
2016) but rather multiple parameters for diverse types of CA (Meredith et
al., 2019) and maybe even for other enzymes such as rubisco (this study)
necessary to fully understand and model the microbial OCS production and
consumption from soils.
Effect of [OCS] on NO release rate
While in other studies the OCS production and consumption are disentangled by
utilizing different inlet mixing ratios (Kaisermann et al., 2018), we
introduce a new concept of measuring different gases, such as NO release rate
(as a proxy for nitrification), simultaneously to OCS exchange rates to better
understand which microbial groups are involved in OCS production and
consumption. Interestingly under moderate soil moisture conditions, where
the lowest OCS net release at 1000 ppt OCS occurred (see Fig. S1 in the Supplement), maximum NO release rates were detected. Under moderate to low
soil moisture, NO net production is predominantly accepted to originate from
nitrification (e.g., Oswald et al., 2013). NO release rates increased under
elevated OCS fumigation, which agrees with our results. Based on the
correlations with NH4+ and NO3- concentrations
(Sect. 4.1), we hypothesize that microbial groups involved in the nitrogen
cycle (e.g., nitrifiers and potentially denitrifiers) are involved in the OCS
exchange. Interestingly, at 1000 ppt OCS its release was lower (indicating
OCS consumption increased) and coincided with low CO release compared to
50 ppt OCS under moderate soil moisture (see Fig. S2 in the Supplement). It is worth noting the correlation of OCS and CO exchange rates
(see Figs. S1 and S2 in the Supplement), but, given the lack
of CO ambient mixing ratios at the inlet and the lack of CO dehydrogenase
activity measurement, we cannot fully explain that result.
Effect of OCS fumigation on the 16S rRNA relative abundance of
archaeal and bacterial <italic>amoA</italic> and red-like <italic>cbbL</italic> gene
transcripts (qPCR)
Despite the evidence of nitrogen-dependent OCS exchange, the mechanisms are
not understood (Kaisermann et al., 2018; Melillo and Steudler, 1989). Fungi
are considered to be relevant OCS consumers utilizing CA over the whole range of
soil moisture (Bunk et al., 2017). However, there is increasing evidence that
OCS consumption is not performed by a single metabolic process (Sauze et al.,
2017; Meredith et al., 2019; our study). Our data suggest that indeed CA
plays an important role for OCS exchange, but also for further enzymes (e.g.,
rubisco) being involved in CO2 assimilation. At high soil moisture,
anaerobes such as acetogens, methanogens, and sulfate reducers might have
been active and capable of catalyzing the oxidation of CO via
CODH via the Wood–Ljungdahl pathway to fix CO2 (Davidova et al.,
1993; Ragsdale, 2004). Since the incubations were performed under oxic
conditions and CO production was observed from the soil (inlet air was free
of CO), the contribution of CO consumption via the Wood–Ljungdahl pathway
from anaerobic pockets at elevated soil moisture range might have been
underestimated. Under moderate soil moisture, reduced CO production may be
mainly attributed to the activity of aerobic CO2-assimilating
microorganisms (Bédard and Knowles, 1989; Jones and Morita, 1983; Jones
et al., 1984; Bender and Conrad, 1994) with minor importance of the aerobic
CODH pathway (Conrad et al., 1981). Our study suggests that under moderate
soil moisture prokaryotic autotrophs, Sordariomycetes (Ascomycota), and
Cystobasidomycetes were dominant OCS consumers in the midlatitude
agricultural soil (A1). Our study highlights how gene expression information
of enzymes involved in CO2 fixation combined with the simultaneous
assessment of NO and CO as well as OCS exchange is useful for understanding
the complex microbial controls on net OCS exchange from soils.
We restricted the discussion of the microbial groups involved in OCS
consumption to fungi since the involvement of bacterial groups would have
required a more specific approach such as stable isotope probing to prove
their involvement. The strength of our study is the proven correlations of
OCS net exchange to NH4+, NO3-, and NO exchange and
functional genes (AOB, AOA amoA and red-like cbbL rubisco
over drying out at 50 ppt OCS).
Conclusions
Fungi are considered to be dominant microbial OCS consumers in the literature, which
may utilize CA over the whole range of soil moisture (Bunk et al., 2017).
However, there is increasing evidence that OCS consumption is not performed
by a single metabolic process (Kaisermann et al., 2018; Sauze et al., 2017;
Meredith et al., 2019; this study). Our data suggest that indeed CA plays an
important role for OCS exchange, but the role of other enzymes involved in
CO2 fixation might have been underestimated. At high soil moisture
creating anoxia, acetogens, methanogens, and sulfate reducers are capable of
catalyzing the oxidation of CO (Davidova et al., 1993; Ragsdale, 2004). Our
study suggests that under moderate soil moisture autotrophs (e.g., AOB),
Sordariomycetes (Ascomycota), and Cystobasidomycetes are likely the dominant
OCS consumers in the midlatitude agricultural soil (A1). Our study
highlights that simultaneous assessment of enzymes involved in CO2
assimilation and simultaneous assessment of NO and potentially CO as well as
OCS exchange are useful for disentangling the complex microbial controls of
net OCS exchange from soils. Our study is the first assessment of the
environmental significance of different microbial groups producing and
consuming OCS by various enzymes other than CA. A combination of stable
isotope probing (e.g., Eyice et al., 2015) with 32S-labeled OCS
plus metagenomics is required to prove our conclusions that further enzymes
beyond CA are involved in OCS conversion. Our study is a first important step
towards the understanding of the mechanism of microbial OCS consumption and
production in soils.
Data availability
Raw sequencing data were deposited in the NCBI SRA
accession number SRP121207, BioProjectID PRJNA415548. Data for trace gas
release are stored in a database (http://bexis2.uni-jena.de/, last
access: 28 February 2019) and are available on
request.
The supplement related to this article is available online at: https://doi.org/10.5194/soil-5-121-2019-supplement.
Author contributions
TB was responsible for sample collection and performed the laboratory
experiments with RB. TB extracted the nucleic acids and analyzed the data together with ZY.
ECPC and ES performed qPCR analysis, and ECPC evaluated the qPCR results and
sequencing results. TB wrote the paper. JK and ST supervised
the project and commented on the paper. SK contributed to the evaluation of qPCR results and writing the paper.
All authors have contributed to writing and editing the
manuscript.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The work has been funded by the Max Planck Society and Deutsche
Forschungsgemeinschaft (DFG) CRC 1076 “AquaDiva”. We thank
Alexander Moravek, Dianming Wu, and Moawad Badawy for support in collecting
the soil samples.The article processing
charges for this open-access publication were covered by the
Max Planck Society. Edited by: Asmeret Asefaw
Berhe Reviewed by: three anonymous referees
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