The presence of charcoal in soil triggers a range of biological effects that are not yet predictable, in part because it interferes with the functioning of chemical signals that microbes release into their environment to communicate. We do not fully understand the mechanisms by which charcoal alters the biologically available concentrations of these intercellular signals. Recently, charcoal has been shown to sorb the signaling molecules that microbes release, rendering them ineffective for intercellular communication. Here, we investigate a second, potentially more important mechanism of interference: signaling-molecule hydrolysis driven by charcoal-induced soil pH changes. We examined the effects of 10 charcoals on the bioavailable concentration of an acyl-homoserine lactone (AHL) used by many Gram-negative bacteria for cell-cell communication. We show that charcoals decrease the level of bioavailable AHL through sorption and pH-dependent hydrolysis of the lactone ring. We then built a quantitative model that predicts the half-lives of different microbial signaling compounds in the presence of charcoals varying in pH and surface area. Our model results suggest that the chemical effects of charcoal on pH-sensitive bacterial AHL signals will be fundamentally distinct from effects on pH-insensitive fungal signals, potentially leading to shifts in microbial community structures.
The presence of charcoal in soil triggers a range of biological effects that are not yet predictable, in part because it interferes with the functioning of chemical signals that microbes release into their environment to communicate. We do not fully understand the mechanisms by which charcoal alters the biologically available concentrations of these intercellular signals. Recently, charcoal has been shown to sorb the signaling molecules that microbes release, rendering them ineffective for intercellular communication. Here, we investigate a second, potentially more important mechanism of interference: signaling-molecule hydrolysis driven by charcoal-induced soil pH changes. We examined the effects of 10 charcoals on the bioavailable concentration of an acyl-homoserine lactone (AHL) used by many Gram-negative bacteria for cell-cell communication. We show that charcoals decrease the level of bioavailable AHL through sorption and pH-dependent hydrolysis of thelactone ring. We then built a quantitative model that predicts the half-lives of different microbial signaling compounds in the presence of charcoals varying in pH and surface area. Our model results suggest that the chemical effects of charcoal on pH-sensitive bacterial AHL signals will be fundamentally distinct from effects on pH-insensitive fungal signals, potentially leading to shifts in microbial community structures.
Charcoal intentionally
added to soil (called biochar) can trigger a wide
range of biological effects such as changing
microbial community structure,[1,2] soil nitrogen cycling,[3,4] and plant−microbe symbiosis.[5,6] Charcoal has
also been shown to alter the rate at which microbes decompose soil
organic carbon,[7−9] inducing both increases and decreases in soil organic
carbon mineralization. Interference with microbial cell–cell
signaling (e.g., quorum sensing) is likely to be one of the mechanisms
driving charcoal-induced microbial responses.[10,11] In quorum sensing, microbes regulate physiological activities based
on their population density.[12] To sense
the population density, microbes synthesize chemical signals (called autoinducers) that diffuse across cell membranes and use
the accumulation of these molecules to activate receptors that control
gene expression.[12] Early studies of quorum
sensing linked this community-level decision making to bioluminescence,
biofilm formation, virulence, plasmid transfer, and antibiotic synthesis.[12] Increasing evidence points to the role of quorum
sensing in the regulation of key processes in the biogeochemical cycling
of carbon and nitrogen such as development of the microbe–plant
symbioses critical for nitrogen fixation,[13] secretion of enzymes that destabilize soil organic matter,[14] and production of methane by some archaea.[15] Cell–cell signaling has also been shown
to regulate the growth of ammonia-oxidizing bacteria and the production
of N2O after starvation,[16] and
it has been shown to downregulate denitrification.[17,18] Although charcoal has been proposed to alter microbial behaviors
by interfering with quorum sensing,[11] our
fundamental understanding of the chemical reactivity of charcoal with
signaling molecules remains too limited to predict how the physicochemical
properties of a given charcoal will impact signaling within a single
microbial species or how charcoal will differentially affect bacteria
and fungi that use distinct chemical signals for communication.The processes that decrease the concentration of intercellular
signaling molecules increase the population density required to trigger
signal-dependent behaviors, such as N2O production, N fixation,
or production of some enzymes that decompose soil organic carbon.[13,16−18] The concentration of signaling molecules can be diminished
(“quenched”) by enzymatic degradation,[19] sorption to abiotic materials,[20] abiotic degradation reactions,[21,22] and interference
by other chemicals.[23] Charcoal has recently
been proposed to contribute to the quenching of cell–cell communication
in the environment,[5,24] displaying quenching activities
that correlate with amendment amounts and production conditions.[11] The strong sorptive affinity that charcoal has
for apolar organic compounds[25−28] suggests that it will efficiently sorb the diverse
types of apolar signaling molecules synthesized by archaea, bacteria,
fungi, and plants. Indeed, recent work demonstrated that charcoal
disrupts the cell–cell signaling mediated by N-3-oxo-dodecanoyl-l-homoserine lactone,[11] an AHL intercellular signaling molecule used by many Gram-negative
soil bacteria to regulate gene expression. In that study, charcoal
quenching of cell–cell communication correlated with thecharcoal
surface area (SA), which also covaried with thecharcoal production
temperature.[11]Although previous
work showed a role for SA in charcoal quenching
of quorum sensing, charcoal has many physicochemical properties that
can vary with pyrolysis temperature and feedstock, including alkalinity,
aromaticity, density, hydrophobicity, and porosity.[29−32] We hypothesized that, among these
properties, alkalinity would also influence microbial quorum sensing
because some autoinducers have structures that are sensitive to elevated
pH.[22] In the case of AHLs, alkalinity causes
hydrolysis of thelactone ring, yielding an acyl-homoserine (Acyl-HS)
product that can be inactive for cell–cell signaling.[21,22] This susceptibility to hydrolysis at elevated pH suggests that the
alkalinity of some charcoals may decrease AHL levels, perhaps even
more efficiently than surface sorption. The effects of charcoal on
AHL hydrolysis kinetics are expected to vary, because ash produced
during pyrolysis can lead to a wide range of charcoal pH values (at
least 5–10).[33] The extent to which
alkalinity contributes to thecharcoal quenching of AHL signaling
has not been evaluated, and it is unclear how the rates of AHL inactivation
through sorption and hydrolysis relate to one another.The objective
of this study was to determine the effect of charcoal
alkalinity on microbial AHL signaling. We compared the effects of
10 charcoals on AHL stability and used our results to build a model
that explores how charcoals with different SAs and pH values will
affect the half-life of an AHL signaling molecule. We used N-3-oxo-dodecanoyl-l-homoserine lactone as a representative
AHL because charcoal is known to influence the bioavailable levels
of this signaling molecule.[11] We provide
evidence that the rate of AHL quenching varies by more than a factor
of 104 over a range of pH and SA values found in charcoals
created from five different feedstocks. Our model also allowed us
to compare the relative effects of different charcoals on an AHL with
their effects on a representative fungal signaling molecule, farnesol.
Materials
and Methods
Materials
Escherichia coli XL1-Blue was purchased from Stratagene, AHL (N-3-oxo-dodecanoyl-l-homoserine, N-3-oxo-C12 HSL) was purchased
from Cayman Chemical, and all other reagents were from Sigma-Aldrich,
VWR, or BD Biosciences. The Luria–Bertani (LB) growth medium
contained 100 mM 3-(N-morpholino) propanesulfonic
acid (MOPS) pH 7. MOPS was included to buffer the growth medium against
pH changes arising from reaction with thecharcoal-treated solutions.
Charcoal Production
Charcoals prepared from oilseed
rape, wheat straw, miscanthus straw, mixed softwood, and rice husk
feedstocks were obtained from the UK Biochar Research Center (UKBRC,
University of Edinburgh, UK). Each feedstock was subjected to slow
pyrolysis at 550 and 700 °C under N2 in a pilot-scale
pyrolysis unit with a continuous-feed rotary kiln. All feedstock materials
were pelletized to a size of 5 × 20 mm before pyrolysis except
for therice husk. Thecharcoals were gently crushed by mortar and
pestle and sieved to obtain charcoals of size <1.4 mm.
Charcoal Characterization
Thesamples were degassed
in glass cells, vacuum-dried overnight at 200 °C, and analyzed
using a Quantachrome Autosorb-3b Surface Analyzer. N2 adsorption/desorption
isotherms were obtained at 77 K by a 26-point analysis for relative
pressures P/P0 ranging
from 1.21 × 10–4 to 0.99, where P is the adsorption equilibrium pressure and P0 is the vapor pressure of bulk liquid N2. Specific
SA was calculated using Brunauer–Emmett–Teller (BET)
theory as previously described.[11] Charcoal
pH was measured using a 1:20 charcoal/water mixture (w/w) after a
1.5 h reaction while shaking. The UKBRC charcoals exhibited a large
variation in SA (1.9–145.0 m2 g–1) and pH (7.88–10.69) (Figure S1). Those with the highest SA (softwood) displayed the lowest pH,
whereas those with the lowest SA (oilseed rape) exhibited high pH.
Microbial Assay
Physiologically and environmentally
relevant AHL concentrations are on the order of 100× lower than
the detection limit of commonly available forms of analytical instrumentation,
for example, gas chromatography–mass spectrometry (GC–MS).[34] We therefore used a microbial biosensor to detect
the presence of biologically active AHL, using an approach previously
described by our group.[11] This biosensor
has a detection threshold of 100 pM, whereas the detection threshold
of GC–MS was ∼5 μM, similar to previous studies.[35] We added varying concentrations of the UKBRC
charcoals (0, 1, 5, 10, 25, and 50 mg/mL) to water containing 2 μM
AHL for 60 min at 23 °C while shaking at 100 rpm. Thecharcoal
was removed through centrifugation (13000g, 1 min),
the pH of the supernatant fraction was analyzed using a micro pH electrode,
and aliquots of the supernatant were mixed in a 96-well plate (Corning
Costar) with identical volumes of LB-MOPS containing E. coli (at an OD600 = 0.05) transformed
with the receiver plasmid and 50 μg/mL kanamycin. The receiver
plasmid encoded a green fluorescent protein (GFP) that is expressed
only in the presence of AHL.[11] Cells were
incubated at 30 °C while shaking at 250 rpm for 18 h; whole cell
fluorescence (λex = 488; λem = 509)
and absorbance (600 nm) were measured using a Tecan M1000 plate reader.
This long incubation was used to ensure that our assay was able to
report on low levels of AHL remaining in solution after charcoal incubations.
In all experiments, cultures grew to a similar maximal density as
observed previously when this biosensor was used to study the effects
of charcoal on AHL bioavailability.[11] To
account for well-to-well variation in cell density, fluorescence was
normalized to absorbance. Data were reported as a fraction of the
signal obtained with AHL that had not been incubated with charcoal
(Figure S2). All data represent the average
of three or more independent incubation experiments. For each incubation,
three replicate measurements were performed in parallel.
Acidified Charcoal
We arrayed 550 °C miscanthus
straw charcoal in 24-well plates in 2 mL of sterile water at different
concentrations (0, 1, 5, 10, 25, and 50 mg/mL). Each well was acidified
with HCl to pH 3 and shaken at 100 rpm overnight at 23 °C. Depending
on thecharcoal concentration, different HCl concentrations were used
to minimize changes in the liquid volume. The pH was measured a second
time, and thesamples were acidified again to pH 3 using HCl. AHL
was then added to each sample at a concentration of 2 μM and
held at 23 °C for 60 min while shaking. At the end of these reactions,
we measured the concentration of soluble AHL and solution pH as described
above.
AHL Hydrolysis Kinetics
To develop a model that predicts
the rates of lactone hydrolysis in the presence of charcoals having
a range of pH and SA properties, we needed to measure AHL hydrolysis
kinetic parameters so that we could constrain our model. We measured
the pH-dependent hydrolysis of AHL using GC–MS, which necessitated
the use of a high AHL concentration (0.05 mM). Hydrolysis rates were
determined at 25 °C by adjusting solutions containing AHL concentrations
suitable for GC–MS analyses to different pH values (6.7, 8.1,
9.6, and 10.5) using 0.1 NNaOH. At different times after pH adjustment
(1 min, 15 min, 1 h, 2 h, and 24 h), each sample was split into two
fractions. One fraction was mixed with an equal volume of chloroform
to extract the soluble AHL, and the concentration of AHL ([AHL]soluble) was analyzed using GC–MS. The other fraction
was acidified to pH 3 using HCl, shaken for 15 min to convert Acyl-HS
that formed during the reaction into AHL, and analyzed for AHL content
using GC–MS. This 15 min reaction time was chosen because we
found that it was consistently sufficient to recover >85% of AHL
after
24 h reactions (Figure S3). TheAHL measured
in the latter fraction represents the sum of theAHL and Acyl-HS at
the end of each reaction, that is, [AHL]soluble + [Acyl-HS]soluble. The fraction of nonhydrolyzed AHL at each time point
was calculated by dividing the GC–MS signal obtained from the
untreated sample by the signal obtained with the acid-treated sample.
Gas Chromatography–Mass Spectrometry
We measured
the rate of lactone hydrolysis using GC–MS. Thechloroform
extractions were transferred to 2 mL amber vials and analyzed using
an Agilent 6890 gas chromatograph equipped with a capillary column
(30 m × 250 μm ID and 0.25 μm film thickness) coated
with 5% Ph Me siloxane in selective ion monitoring mode.[36] We injected each sample at splitless mode using
He as a carrier gas. The injector temperature was set at 200 °C,
and the oven was set to hold at 150 °C for 1 min, and then to
increase by 20 °C min–1 to 230 °C, 10
°C min–1 from 230 to 260 °C, and 20 °C
min–1 from 260 to 300 °C. We used a solvent
delay (3 min) to prevent interference from the extraction solvent.
Accumulation of Acyl-HS
To establish whether Acyl-HS
accumulates in the aqueous phase after exposing AHL to charcoals,
oilseed rape and mixed softwood 550 °C charcoals (5 mg/mL) were
placed in petri dishes (60 × 15 mm) containing 2 μM AHL
and 5 mL water. After 1 h at 23 °C while shaking at 50 rpm, the
liquid in the petri dishes was transferred to a sterile 2 mL eppendorf
tube, and centrifugation was used to remove thecharcoal. One supernatant
fraction was used to measure the pH, a second fraction was analyzed
using our microbial bioassay to determine theAHL concentrations,
and a third fraction was acidified to pH < 3 using HCl and reacted
for 90 and 135 min before analysis for AHL content. In each experiment,
AHL concentration was measured using serial dilutions (1, 5, 25, 125,
and 625×) of the acidified and untreated fractions. To calculate
theAHL concentration, we compared the signal from each experiment
with the signal in a standard curve generated by growing cells in
the presence of different AHL concentrations and fitting the data
to the Michaelis–Menten equation. The two dilutions that yielded
signals within the most sensitive range of the microbial assay (0.4–40
nM) were used to calculate theAHL concentrations. This approach minimized
errors in estimates by avoiding GFP signals that were too near saturation
or background.
Modeling
The model consisted of
five first-order ordinary
differential equations that described the time-dependent concentrations
of the different molecular species that can arise: (i) aqueous soluble
AHL [AHL], (ii) aqueous soluble Acyl-HS [HS], (iii) insoluble charcoal-bound
[AHL-BC], (iv) insoluble charcoal-bound Acyl-HS [HS-BC], and (v) charcoal
binding sites for AHL and Acyl-HS [BC]. The time-dependent AHL and
Acyl-HS concentrations are described byIn this model, khyd1 and khyd2 represent
thehydroxide-dependent
and hydroxide-independent rates of hydrolysis, respectively.[37] Thedehydration of Acyl-HS is described with
thehydrogen-ion-dependent term kdehyd1 and the independent term kdehyd2. Because
of the chemical and structural similarities between AHL and Acyl-HS,
their rates of association with charcoal were represented by a single
rate constant, ks. This binding reaction
was assumed to be irreversible. The concentrations of thecharcoal-bound
AHL [AHL-BC] and Acyl-HS [HS-BC] are described by eqs and 4The concentration of thecharcoal binding
sites [BC] is described by eqWe obtained the rate constants for
AHL hydrolysis (khyd1, khyd2) and Acyl-HS dehydration
(kdehyd1 and kdehyd2) by globally fitting our kinetic AHL hydrolysis data from GC–MS
analysis (Figure S4) to this model; ks was estimated by fitting the model to the
results of the microbial assay. TheAHL half-life was calculated by
fitting the time-dependent [AHL] for each SA and pH combination to
the exponential decay equation (eq )where [AHL] is the initial concentration and t1/2 represents its half-life. The model predictions were generated
using
a defined range of SA (0.01–7.2 m2 mL–1) and pH (6.25–10.5) values.
Results
and Discussion
Charcoal Effects on AHL and pH
Prior
studies have reported
that alkaline conditions accelerate hydrolysis of theAHLlactone
ring,[21,22] suggesting that some alkaline charcoals
may destabilize these cell–cell signaling molecules through
hydrolysis. To test this idea, we examined the relationship between
charcoal-induced pH changes and the bioavailable level of AHL after
1 h of exposure to charcoal. For this analysis, we measured the pH
of thecharcoal-treated AHL solutions at the end of each exposure
and the ability of the solutions to induce a GFP signal in a microbial
assay. In all cases, we found that increasing thecharcoal concentrations
led to an elevated pH (Figure S5). To determine
how pH relates to quenching of theAHL-dependent GFP signal across
different feedstocks, we compared the pH and GFP signal from each
experiment (Figure ). Below pH 8, the GFP signal was similar to that observed in the
absence of charcoals, whereas the GFP signal decreased above pH 8.
A linear fit to the data acquired using all of thecharcoals yielded
a strong correlation coefficient (R = 0.95) for the
inverse relationship between thecharcoal pH and the GFP signal. Strong
linear correlations were also obtained when we analyzed thecharcoals
from each pyrolysis temperature separately (Figure S6).
Figure 1
Charcoal-induced pH increases are inversely correlated with bioavailable
AHL. The relative fluorescence of E. coli mixed with charcoal-treated AHL is compared with the pH value after
exposure to 1, 5, 10, 25, and 50 mg/mL of wheat straw, oilseed rape,
softwood, miscanthus straw, and rice husk charcoals for 1 h. The measurements
were performed using 550 °C (open symbols) and 700 °C (closed
symbols) charcoals in triplicate and are reported as mean ±1σ. R = 0.95 was obtained from a linear fit (y = 9.9865 – 2.5809x) to the data.
Charcoal-induced pH increases are inversely correlated with bioavailable
AHL. The relative fluorescence of E. coli mixed with charcoal-treated AHL is compared with the pH value after
exposure to 1, 5, 10, 25, and 50 mg/mL of wheat straw, oilseed rape,
softwood, miscanthus straw, and rice husk charcoals for 1 h. The measurements
were performed using 550 °C (open symbols) and 700 °C (closed
symbols) charcoals in triplicate and are reported as mean ±1σ. R = 0.95 was obtained from a linear fit (y = 9.9865 – 2.5809x) to the data.The inverse correlation between thecharcoal pH
and the GFP signal
suggested that theAHL hydrolysis might at times account for a significant
fraction of the decrease in the bioavailable AHL. To evaluate the
relative contributions of AHL surface sorption and hydrolysis under
the conditions of our experiment, we tested the ability of our biosensor
to detect AHL after theAHL had been exposed to acidified charcoal.
Because theAHL hydrolysis rate becomes significant above pH 7,[22] we investigated whether charcoals have smaller effects
on bioavailable AHL when adjusted to a lower pH value. For this experiment,
we analyzed the effect of pH-adjusted and untreated miscanthus straw
charcoal (550 °C) on the GFP signal in our bioassay. We found
that the miscanthus straw charcoal had a very strong buffer capacity,
presenting elevated pH values even after initial pH adjustment (Figure ). The acid-treated miscanthus
straw charcoal had pH values that ranged from 4 to 7, whereas the
untreated miscanthus straw charcoal ranged from 6 to 10. Without pH
adjustment, treatment of an AHL solution with 10 mg/mL charcoal decreased
the GFP signal to a half-maximal value and yielded a pH of ∼9.
In contrast, with pH adjustment, the GFP signal only decreased by
∼20% at the highest charcoal concentration analyzed, that is,
50 mg/mL. This charcoal-treated solution had a near-neutral pH. These
results suggest that the large decrease in bioavailable AHL observed
with the untreated miscanthus straw charcoal arises to a large extent
because thecharcoal-induced pH changes accelerate AHL hydrolysis.
The smaller loss of bioavailable AHL with theacidified charcoal is
interpreted as arising from sorption because this analysis was performed
under pH conditions that are not predicted to hydrolyze AHL.
Figure 2
Charcoal acidification increases bioavailable AHL. (A)
Varying
concentrations of 550 °C miscanthus straw charcoal were acidified
to pH 3 and exposed to AHL. After 1 h, the pH and the level of GFP
expression induced by AHL remaining in the solution were measured.
(B) Effects of untreated miscanthus straw charcoal (550 °C) on
GFP expression and pH. The error bars represent ±1σ calculated
using three independent measurements.
Charcoal acidification increases bioavailable AHL. (A)
Varying
concentrations of 550 °C miscanthus straw charcoal were acidified
to pH 3 and exposed to AHL. After 1 h, the pH and the level of GFP
expression induced by AHL remaining in the solution were measured.
(B) Effects of untreated miscanthus straw charcoal (550 °C) on
GFP expression and pH. The error bars represent ±1σ calculated
using three independent measurements.Our experiments suggested that charcoals decrease the bioavailable
AHL on the time course of our experiments through two mechanisms.
As previously suggested, charcoals decrease the bioavailable concentration
of AHL through sorption. In addition, charcoals convert biologically
active AHL into soluble Acyl-HS, which is no longer biologically active.
To directly investigate whether AHL is converted into Acyl-HS before
charcoal sorption as predicted from the second mechanism (Figure A), we exposed low-
and high-pH charcoals to AHL for 1 h, split the aqueous portion of
each sample, acidified one fraction while leaving the other sample
untreated, and measured the concentration of bioavailable AHL in each
sample. When this analysis was performed with theoilseed rape 550
°C charcoal, one of the more-alkaline charcoals, the acid-treated
fractions yielded AHL concentrations that were significantly higher
than those observed in the untreated fraction (Figure B). Because acidic conditions promote thedehydration of Acyl-HS back to AHL, the increased yield of AHL with
acid-treated fractions provides evidence for Acyl-HS accumulation
in the presence of this charcoal. When this analysis was performed
with thecharcoal having the lowest pH (softwood 550 °C), the
acid-treated fractions yielded AHL concentrations that were not significantly
different from the untreated fraction. Together, these results lead
us to conclude that charcoal alkalinity contributes to the loss of
AHL by hydrolyzing this signaling chemical.
Figure 3
Charcoals can convert
AHL into soluble Acyl-HS before sorption.
(A) AHL can be inactivated through pH-dependent hydrolysis (khyd1), pH-independent hydrolysis (khyd2), and sorption to charcoal (ks). AHL can also be generated by dehydrating Acyl-HS (kdehyd1 and kdehyd2). (B) Oilseed rape and softwood 550 °C charcoals were reacted
with AHL for 1 h, and the fraction of soluble AHL was measured (untreated)
as well as the total concentration of AHL and Acyl-HS. The latter
was quantified by acidifying the samples for 90 and 135 min (acid-90m
and acid-135m) before analyzing the AHL levels. The AHL in the acidified
samples from the oilseed rape reactions was significantly higher than
in the untreated fraction (two-tailed t-test; p < 0.01), whereas no significant difference was observed
between the untreated and acidified samples from softwood. The error
bars represent ±1σ calculated using three independent measurements.
Charcoals can convert
AHL into soluble Acyl-HS before sorption.
(A) AHL can be inactivated through pH-dependent hydrolysis (khyd1), pH-independent hydrolysis (khyd2), and sorption to charcoal (ks). AHL can also be generated by dehydrating Acyl-HS (kdehyd1 and kdehyd2). (B) Oilseed rape and softwood 550 °C charcoals were reacted
with AHL for 1 h, and the fraction of soluble AHL was measured (untreated)
as well as the total concentration of AHL and Acyl-HS. The latter
was quantified by acidifying thesamples for 90 and 135 min (acid-90m
and acid-135m) before analyzing theAHL levels. TheAHL in the acidified
samples from theoilseed rape reactions was significantly higher than
in the untreated fraction (two-tailed t-test; p < 0.01), whereas no significant difference was observed
between the untreated and acidified samples from softwood. The error
bars represent ±1σ calculated using three independent measurements.
Modeling Charcoal Effects
on AHL
To predict how charcoals
with different SAs and alkalinities affect AHL concentrations, we
built a kinetic model that reports how AHL levels change with time
upon exposure to charcoals having different physicochemical properties.
In this model, the concentration of biologically available AHL depends
upon pH-dependent and pH-independent AHL hydrolysis reactions (khyd1 and khyd2),
pH-dependent and pH-independent Acyl-HS dehydrations reactions (kdehyd1 and kdehyd2), and AHL and Acyl-HS sorption reactions (ks). Our model also considers the concentrations of soluble
[AHL] and [Acyl-HS], pH, and thecharcoalSA. The rate constants describing
the hydrolysis and dehydration reactions were obtained by globally
fitting the kinetics of AHL hydrolysis to our model (Figure S4).As a frame of reference for our model, we
analyzed the relative influence of pH and SA of the UKBRC charcoals
on theAHL-dependent GFP signal in our microbial assay (Figure S7A). At the 1 h time point when we performed
our measurements, the GFP signal displayed a stronger correlation
with pH compared with SA. When we fit our kinetic model to the data
from this time point, using kinetic values for hydrolysis and dehydration
measured in the absence of charcoal, we obtained a sorption rate constant ks = 0.0039 h–1mM–1 that allowed us to estimate the effects of charcoals on AHL activity
over a greater set of pH and SA values (Figure S7B). We used our model with this ks value to calculate the time-dependent changes in AHL over thesame
range of physicochemical charcoal parameters (Figure A). This analysis revealed that theAHL half-life
varies up to 5620 times across the different pH and SA values analyzed
(Figure B). For these
calculations, we used charcoalSAs that overlap with the range that
has been used for soil amendment.[3] A smaller
half-life range (273-fold) was observed when calculations were performed
with farnesol (Figure C), a fungal autoinducer that lacks the pH-sensitive lactone found
in AHLs.[38]
Figure 4
Modeling the effect of charcoals on AHL
availability. The kinetic
model suggested that the charcoal pH has a more immediate effect on
the half-life of AHL compared with charcoal SA. (A) AHL concentration
changes over time with different physiochemical charcoal parameters.
The color gradient represents the AHL remaining after mixing with
charcoal. The calculated half-lives of (B) AHL and (C) farnesol when
reacted with charcoals having a range of SA and pH values. The colors
represent the logarithm of the calculated half-lives in hours.
Modeling the effect of charcoals on AHL
availability. The kinetic
model suggested that thecharcoal pH has a more immediate effect on
the half-life of AHL compared with charcoalSA. (A) AHL concentration
changes over time with different physiochemical charcoal parameters.
The color gradient represents theAHL remaining after mixing with
charcoal. The calculated half-lives of (B) AHL and (C) farnesol when
reacted with charcoals having a range of SA and pH values. The colors
represent the logarithm of the calculated half-lives in hours.
Environmental Implications
Our results provide evidence
that charcoals can decrease the concentrations of autoinducers used
for cell–cell signaling through multiple chemical mechanisms,
including sorption and hydrolysis. Because these reactions occur through
distinct mechanisms, their rates are controlled by distinct physicochemical
properties of charcoals. With sorption, thecharcoalSA will control
AHL sorption as observed with apolar chemicals.[25−28] In contrast, hydrolysis is predicted
to depend upon charcoal alkalinity, which varies with feedstock and
pyrolysis conditions,[30,31] and does not always correlate
with SA. Microbes will likely respond to charcoal-induced AHL depletion,
and this response will depend on the soil type, ecosystem conditions,
and charcoal amendment rate. Some soils may buffer the pH effects
arising from charcoal addition, particularly those soils rich in organic
matter and metal oxides (iron and aluminum) that have a strong buffering
capacity.[39] In these soils, our results
predict that charcoals will quench AHL signaling primarily through
sorption. In soils having limited buffering capacities, charcoals
will quench AHL signaling through both hydrolysis and sorption. TheAHL concentrations observed in a given environment may be influenced
by the presence of lactonases, enzymes synthesized by soil microbes
that catalyze AHL hydrolysis.[19] Whether
or not the activity of these enzymes is modulated by charcoal addition
is not known.The relative contributions that sorption and hydrolysis
make to charcoal quenching of microbial communication are expected
to vary across species because of the structural and chemical diversity
in signaling molecules.[23] Our findings
suggest that within a single soil, the rates of charcoal quenching
of different cell–cell communication reactions will vary. We
expect charcoal to sorb (and quench) all types of signaling compounds
used for cell–cell communication as previously proposed,[11] although the extent and rate of sorption may
vary with signaling molecule structure. However, only a subset of signaling compounds will be hydrolyzed
by thecharcoal-driven pH shifts. In the case of farnesol, a fungal
autoinducer,[40] we predict sorption to be
the dominant mechanism by which charcoal could decrease the bioavailable
concentration because this chemical does not contain functional groups
whose stability varies with pH like AHL. However, other classes of
signals may be sensitive to charcoal-induced pH changes like AHL.
For example, oligopeptide autoinducers display a charge that depends
upon soil pH because they contain functional groups whose protonation
can change.[23] This protonation is not expected
to promote hydrolysis as observed with AHL but could alter charcoal
sorption by changing the oligopeptide charge and altering the ionic
interactions as observed with other chemicals whose protonation changes
with pH.[41]Our modeling results suggest
that the complex biological effects
of charcoal in the environment, such as changes in microbial community
structure,[42,43] could arise because charcoals
differentially affect microbial species by altering cell–cell
signaling that enhances or inhibits growth. Indeed, both inhibitory
and stimulatory effects on microbial physiological activities and
population induced by charcoal have been reported. In one incubation
study, the biomass of Gram-negative bacteria was significantly increased
as a result of soil charcoal addition, whereas fungal and Gram-positive
bacterial biomasses were less affected.[1] In another field study, charcoal increased both bacterial and fungal
populations. However, in this case, charcoal shifted the microbial
community toward a greater relative amount of bacteria.[2] In both studies, no clear mechanism was established
for thecharcoal-mediated microbial responses. Additional research
is needed to establish how charcoal quenching of cell–cell
communication relates to dynamic community-level changes in situ.
The recent development of biosensors that provide dynamic information
on gene expression in a soil should aid in future studies examining
charcoal effects on cell–cell signaling.[44]
Authors: Hsiao-Ying Cheng; Caroline A Masiello; George N Bennett; Jonathan J Silberg Journal: Environ Sci Technol Date: 2016-08-01 Impact factor: 9.028
Authors: J M Hornby; E C Jensen; A D Lisec; J J Tasto; B Jahnke; R Shoemaker; P Dussault; K W Nickerson Journal: Appl Environ Microbiol Date: 2001-07 Impact factor: 4.792
Authors: Edwin A Yates; Bodo Philipp; Catherine Buckley; Steve Atkinson; Siri Ram Chhabra; R Elizabeth Sockett; Morris Goldner; Yves Dessaux; Miguel Cámara; Harry Smith; Paul Williams Journal: Infect Immun Date: 2002-10 Impact factor: 3.441
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Authors: Christopher A Vaiana; Hyungseok Kim; Jonathan Cottet; Keiko Oai; Zhifei Ge; Kameron Conforti; Andrew M King; Adam J Meyer; Haorong Chen; Christopher A Voigt; Cullen R Buie Journal: Mol Syst Biol Date: 2022-03 Impact factor: 11.429
Authors: Ilenne Del Valle; Tara M Webster; Hsiao-Ying Cheng; Janice E Thies; André Kessler; Mary Kaitlyn Miller; Zachary T Ball; Kevin R MacKenzie; Caroline A Masiello; Jonathan J Silberg; Johannes Lehmann Journal: Sci Adv Date: 2020-01-29 Impact factor: 14.136