Introduction
The atmosphere is an oxidizing medium where trace gases are
transformed/removed by oxidation including methane and other organic
compounds, carbon monoxide, nitrogen oxides, and sulfur gases. Evaluating the
oxidizing power of the atmosphere is crucial since it controls pollutant
formation and fate, aerosol production, and greenhouse radiative forcing
(Thompson, 1992).
In this context, hydroperoxides (ROOH) contribute to the oxidizing power of
the troposphere (Lee et al., 2000; Herrmann et al., 2015) by controlling the
cycling of HOx radicals (HO⚫, HO2⚫). They can
serve as temporary reservoirs of HOx radicals since, for example, their
photolysis and reactivity will regenerate HO⚫ radicals. Among
hydroperoxide, hydrogen peroxide is a key gas-phase atmospheric chemical
species (Vione et al., 2003), with concentrations in the gas phase at the
ppbv level or less. The atmospheric concentration of H2O2 is
impacted by a variety of meteorological parameters (e.g., actinic flux,
temperature, and relative humidity) and is affected by the levels of chemical
species such as VOCs, CO, O3, and NOx (Lee et al., 2000). One of
the significant parameters controlling the evolution of H2O2
concentration is the actinic flux intensity. Diurnal and seasonal variations
of hydrogen peroxide are shown by field measurements with higher
concentrations during the day and in summer than during the night and in
winter. This is linked to the atmospheric formation of H2O2 that
results from a series of photochemical reactions creating free radicals
followed by corresponding radical reactions with appropriate precursor
substances.
In the presence of atmospheric liquid water (cloud, fog, rain),
H2O2 is rapidly dissolved because of its high Henry law constant
(7.7×104 M atm-1 at 298 K; Sander,
2015). In this liquid phase, it is also produced by
aqueous-phase reactivity (Möller, 2009). Several field campaigns have
reported H2O2 concentrations in atmospheric water in the
µM range (Gunz and Hoffmann, 1990; Marinoni et al., 2011;
Deguillaume et al., 2014; Li et al., 2017). Hydrogen peroxide plays a central
role in various important chemical processes in clouds. First, H2O2
is considered the most important oxidant for the conversion of sulfite to
sulfate for pH lower than 5.5, thereby contributing significantly to the
acidification of clouds and precipitations (Deguillaume et al.,
2005; Shen et al., 2012). Second, the photolysis of
H2O2 will lead to an efficient production of the hydroxyl radical
HO⚫ (Arakaki et al., 2013), and recent studies have shown that
this can be a dominant aqueous source (Bianco et al., 2015). They can also
directly oxidize organic compounds in the aqueous phase (Schöne and
Herrmann, 2015). Finally, H2O2 is involved in redox processes
leading to the conversion of reactive free radicals and trace metals such as
iron (Kieber et al., 2001; Deguillaume et al., 2005; Hems et al., 2017).
Consequently, H2O2 is a key chemical compound controlling the
aqueous-phase oxidant capacity and leading to the transformation of inorganic
and organic compounds present in the atmospheric aqueous phase. The resulting
inorganic and organic products can contribute to the aerosol phase when the
cloud evaporates, leading to a climatic effect.
A few decades ago, living microorganisms were observed in cloud water
(Sattler et al., 2001; Amato et al., 2005, 2007a, b; Wei et al., 2017).
Particularly through measurements of adenosine triphosphate (ATP) and
anabolic precursors or nutrient incorporation rates, it has been shown that
cloud microorganisms are metabolically active and play an important role in
cloud chemical reactivity (Sattler et al., 2001; Amato et al., 2007a; Hill
et al., 2007; Vaïtilingom et al., 2012, 2013). Several studies
performed on simplified or real microcosms have demonstrated that cloud
microorganisms are able to degrade carbon compounds (Ariya et al., 2002;
Amato et al., 2005, 2007c; Husarova et al., 2011; Vaïtilingom et al.,
2010, 2011, 2013; Matulovà et al., 2014); recent studies have also shown
that this could be the case in the air (Krumins et al., 2014).
Microorganisms are also in direct interaction with oxidant species in clouds
(iron, hydroxyl radical, hydrogen peroxide, etc.). Vaïtilingom et
al. (2013) have demonstrated that microorganisms present in real cloud water
are able to efficiently degrade hydrogen peroxide. This suggests that cloud
microorganisms found strategies to survive and resist stresses encountered in
this medium, and in particular oxidative stress. In this context, Joly et
al. (2015) have conducted laboratory experiments to investigate the survival
of selected strains (bacteria and yeasts) isolated from cloud waters, in the
presence of various concentrations of hydrogen peroxide. The results showed
that the survival rates of the studied strains were not affected by
H2O2 exposure. In addition, the strains were exposed to artificial
UV-visible light mimicking the natural solar irradiation inside clouds. No
significant impact on the survival of the bacterial strains was observed.
These results have been confirmed in real cloud water, including the
microbial community and chemical complexity (iron, H2O2, etc.),
incubated in a photo-bioreactor designed to mimic cloud conditions
(Vaïtilingom et al., 2013). Thanks to ADP/ATP ratio measurements,
reflecting the energetic metabolism of microorganisms, exposed or not to
solar radiation, it has been shown that microorganisms were not impacted by
artificial light and consequently by the generation of radicals from
H2O2 photo-reactivity. In addition, H2O2 is
efficiently degraded by catalases and peroxidases involved in oxidative
metabolism. Solar light did not modify the degradation rates of
H2O2, demonstrating that the biological process was not inhibited
by UV radiations and radicals.
Indeed, solar light can indirectly impact the viability of cells by the
production of reactive oxygenated species (ROS), including HO⚫
and O2⚫- radicals. The main sources of these radicals are
H2O2 photolysis or Fenton and photo-Fenton reactions involving iron
(Fe) and H2O2. Most of these compounds can cross the cytoplasmic
membrane by diffusion. Aerobic microorganisms can also produce similar ROS
during respiration. These radicals can potentially damage major cellular
components such as proteins, DNA, and lipids and lead to cellular death.
Because microorganisms are usually protected against these ROS, they can
specifically modify their metabolism to face oxidative stress taking place in
clouds. Therefore, microorganisms utilize various mechanisms involved in
oxidative stress metabolism such as (i) the production of pigments and
antioxidant molecules (vitamins, glutathione, etc.) which can scavenge
radicals or (ii) the production of specific enzymes such as superoxide
dismutase which can transform O2⚫- into H2O2.
H2O2 can be dismutated or reduced, respectively, by catalases and
other peroxidases (Delort et al., 2017).
The studies from Vaïtilingom et al. (2013) and Joly et al. (2015)
highlighted the interactions between biological activity and oxidants in
clouds. It is crucial to consider all sinks and sources of H2O2,
especially in atmospheric chemistry models, since H2O2 impacts
many relevant processes in the atmosphere. In the present work, we
artificially reproduced cloud conditions in microcosms to study the biotic
and abiotic transformation of H2O2 and, conversely, the impact of
hydrogen peroxide on the metabolism of cloud microorganisms.
For this purpose, we decided to study individually the effect of parameters
interacting with H2O2: UV radiation, iron, and bacteria. Under
various experimental conditions, the degradation rates of H2O2 were
followed to highlight how individual parameters control its transformation.
Moreover, the impact of H2O2 on the energetic state of the
bacterial cells was evaluated by measuring the ATP concentration over time
when the cells were exposed or not to H2O2. In order to confirm our
laboratory results on the interaction between microorganisms and
H2O2, we performed a correlation analysis considering
bio-physico-chemical parameters measured in real cloud samples collected at
the PUY station. This work will lead to a better description of the
mechanisms linking biological activity and cloud reactivity.
Material and methods
Bacterial strains and growth conditions
Pseudomonas graminis, 13b-3, DQ512786, Pseudomonas syringae, 13b-2, DQ512785, and Sphingomonas sp., 14b-5, DQ512789
were grown in 10 mL of R2A medium (Reasoner and Geldreich, 1985) under
stirring (200 rpm) at 17 ∘C for approximately 17, 24, or 48 h,
depending on the strain. The three selected bacterial strains belonging to
the Gamma-Proteobacteria (Pseudomonas) and Alpha-Proteobacteria
classes (Sphingomonas) were isolated from cloud water and are
representative of the genera most frequently found in cloud water samples
(Vaïtilingom et al., 2012) collected at the PUY site.
Cells in the exponential growth phase were collected by centrifugation for
3 min at around 10 000 g. The supernatant was removed and the bacterial
pellet was suspended and washed twice with an artificial cloud solution and
incubated in microcosms to perform biodegradation experiments (see
Sect. 2.2). The bacterial cell concentration was estimated by optical density
at 575 nm to obtain a concentration close to 106 cell mL-1.
Finally, the concentration of cells was precisely determined by flow
cytometry analysis (BD Facscalibur – Becton-Dickinson;
λexc=488 nm; λem=530 nm) using a
method based on the addition of a fluorochrome (SYBR Green) for their
counting (Marie et al., 1999).
Incubations in microcosms
Microcosms were designed to simulate as much as possible the water phase of
cloud waters. They provide the opportunity to work under artificial solar
light conditions and also in the presence of microorganisms. The experiments
were performed under bulk conditions as cloud droplets cannot be reproduced
in these bioreactors (Infors HT Multitron II).
For irradiation conditions the bioreactor was equipped with lamps that emit
UV radiation (Sylvania Reptistar; 15 W; 6500 K) to mimic solar light
measured directly in clouds at the PUY station (Fig. S1 in the Supplement).
The incubation flasks were Pyrex crystallizers covered with a Pyrex filter
and equipped with Teflon tubes of 8 mm Ø plugged with sterile cotton,
letting air and light pass (see Vaïtilingom et al., 2013), while for
dark conditions they were amber Erlenmeyer flasks.
All incubation flasks contained 100 mL of artificial cloud solution under
agitation (130 rpm); its composition was first described in Vaïtilingom
et al. (2011). This solution was mimicking cloud chemical composition from
cloud samples classified as “marine” following the work from Deguillaume et
al. (2014) at the PUY station. The major part of the collected cloud samples
were classified as marine (52 %), supporting our choice for the
artificial cloud composition. Stock solutions of this artificial cloud medium
were prepared with the following concentrations: 200 µM for acetic
acid (CH3COOH; Acros organics), 145 µM for formic acid (HCOOH;
Fluka), 30 µM for oxalic acid (H2C2O4; Fluka),
15 µM for succinic acid (H6C4O4; Fluka),
800 µM for ammonium nitrate (H4N2O3; Fluka),
100 µM for magnesium chloride hexahydrate (MgCl2, 6H2O;
Sigma-Aldrich), 50 µM for potassium sulfate (K2SO4;
Fluka), 400 µM for calcium chloride dihydrate (CaCl2,
2H2O; Sigma-Aldrich), 2000 µM for sodium chloride (NaCl;
Sigma-Aldrich), 1100 µM for sodium hydroxide (NaOH; Merck), and
315 µM for sulfuric acid (H2SO4; Sigma-Aldrich). Finally,
the obtained solution was adjusted to pH 6 as necessary with a few drops of
the solutions of NaOH or H2SO4 used for the preparation of the
marine artificial cloud water solution and sterilized by filtration
(polyethersulfone membrane, 0.20 µm; Fisher Scientific) before use.
For biotic conditions, the flasks were inoculated at 106 bacterial cells
per mL. The artificial cloud water solution was 10 times more concentrated
than a real cloud water solution in order to stabilize the pH. This was also
the case for bacteria concentration because the bacteria / substrate
ratio should be kept identical to that of a real cloud. Indeed, it has been
demonstrated that if this ratio is maintained, the degradation rate remains
constant (Vaïtilingom et al., 2010). The equipment was sterilized by
autoclaving at 121 ∘C for 20 min and all manipulations were
performed under sterile conditions.
Depending on the conditions, hydrogen peroxide and an iron complex
(Fe-[EDDS]) were added or not to the solution in the incubators. These two
compounds are present in marine cloud water collected at the PUY station at
average concentrations of 7.5 µM (with a dispersion of mean values
ranging from 0.1 to 20.8 µM) for H2O2 and
0.5 µM (with a dispersion of mean values ranging from
BDL – below detection limit – to 4.9) for Fe(III)
(Deguillaume et al., 2014). In the cloud aqueous phase, Fe(III) may be
complexed by organic compounds. Recently, it has been hypothesized than iron
can be chelated by other organic ligands of biological origin (Herckes et
al., 2013; Herrmann et al., 2015), and in particular by siderophores
(Vinatier et al., 2016), which are ligands characterized by high complexing
constants (K>1020). Fe-[EDDS] was chosen as an iron(III)
complex model because this ligand has a complexing constant for iron very
close to the values for siderophores. Moreover, it is known to be stable at
the working pH of 6.0 because its chemistry has been studied in detail by Li
et al. (2010).
Hydrogen peroxide solution was prepared from a commercial solution
(H2O2, 30 %, not stabilized; Fluka Analytical); 1:1
stoichiometry iron complex solution was prepared from iron (III) chloride
hexahydrate (FeCl3, 6H2O; Sigma-Aldrich) and from
(S,S)-ethylenediamine-N,N'-disuccinic acid trisodium salt (EDDS, 35 % in
water). The hydrogen peroxide solution and the iron complex solution were
freshly prepared before each experiment and the final working concentrations
were fixed at 20 and 4 µM, respectively, in agreement with the real
concentrations detected in samples collected at the PUY station multiplied by
a factor of 10 when median values measured in marine cloud waters are
considered (Deguillaume et al., 2014).
In addition, the working temperature was fixed at 17 ∘C, which is
the average temperature of cloud samples in summer. For all the incubation
conditions, samples were taken at regular intervals, and stored at
-20 ∘C before analysis.
Analyses
Hydrogen peroxide was quantified with a miniaturized Lazrus fluorimetric
assay (Lazrus et al., 1985; Vaïtilingom et al., 2013). This method is
based on a reaction between hydrogen peroxide, horseradish peroxidase (HRP),
and 4-hydroxyphenylacetic acid that produces a fluorescent dimeric compound.
Fluorescence readings (Safire II TECAN©; λexc=320 nm, λem=390 nm) were made in a 96 well plate
format.
Bioluminescence was used to analyze adenosine triphosphate (ATP)
concentrations (Glomax® 20/20 single tube
luminometer from Promega). This technique is based on an enzymatic reaction
involving luciferin and luciferase. The protocol used was adapted from
Biothema© commercial kit instructions (Koutny et al., 2006).
In order to determine the survival rate of microorganisms in the presence of
hydrogen peroxide (20 µM), plate counts were performed on an R2A
agar medium at the beginning of each experiment and after 3, 7, and 24 h of
incubation. Plates were incubated for 3 days at 17 ∘C before CFU
counts.
Determination of the initial degradation rates of hydrogen
peroxide
The processing of data was done with the Origin 6.1 software.
The graphs representing the hydrogen peroxide concentration decrease as a
function of time were plotted. The degradation rates have been calculated
from the initial slopes (the first five time points, i.e., between 0 and
2 h) normalized with the concentrations of cells. During these two hours no
cell growth was observed.
Cloud sampling and statistical analysis
Cloud water sampling was performed at the summit of the PUY station (summit
of the Puy de Dôme, 1465 m a.s.l., France), which is part of
atmospheric survey networks EMEP, GAW, and ACTRIS. The detachable part of the
impactor was sterilized beforehand by autoclave at 121 ∘C for
20 min and the fixed part was rinsed with alcohol at 70∘ and then
with sterile water just before sampling.
Between 2004 and 2013, 89 cloud events were collected at the PUY station. The
origin of these clouds can be analyzed according to their back trajectories
in four sectors (northwest, southwest, west, and northeast). They can also be
considered in four different categories considering their chemical
composition (marine, continental, highly marine, and polluted) as described
in Deguillaume et al. (2014).
Various parameters were measured, including ATP, bacteria and fungi
concentration, inorganic and organic species concentration (H2O2,
SO42-, NO3-, Cl-, acetate, formate, oxalate, Na+,
NH4+, Mg2+, K+, Ca2+), temperature, and pH (see
Supplement Table S1 for details). More information about the cloud sample
collection is given in Deguillaume et al. (2014).
These data were used in this study to achieve statistical analyses. R
software 3.1.2 was used to carry out principal component analysis (PCA). The
data of 37 cloud events (of 89 in total) were selected after the constraints
related to this statistical analysis (e.g., the cloud events with more than
10 % of missing values (parameters) were not considered) were applied.
Initial rates of abiotic degradation (a) and biotic
degradation (b) of H2O2 measured in artificial cloud
water. Values are expressed in 10-9 mol L-1s-1.
Standard errors were calculated.
(a)
Light + Fe-[EDDS]
Fe-[EDDS]
Light
1.07 ± 0.18
0.22 ± 0.05
0.14 ± 0.08
(b)
Light + Fe-[EDDS] + bacteria
Fe-[EDDS] + bacteria
Light + bacteria
Bacteria
Pseudomonas graminis 13b-3
1.55 ± 0.25
1.93 ± 0.18
2.15 ± 0.02
2.07 ± 0.01
Pseudomonas syringae 13b-2
1.75 ± 0.15
1.27 ± 0.04
1.72 ± 0.14
1.18 ± 0.08
Sphingomonas sp. 14b-5
1.97 ± 0.06
1.01 ± 0.21
0.87 ± 0.04
0.76 ± 0.11
In addition, a statistical significance test was evaluated using the PAST
software (Hammer et al., 2001). The mean difference was considered to be
statistically significant for a p-value less than 0.05.
Results
The interactions between H2O2, which is one of the major oxidants
present in clouds, and microorganisms were investigated by performing
experiments in artificial cloud microcosms, but also by considering chemical
and biological parameters measured in real cloud samples over a long period
at the PUY station.
Experiments in artificial cloud water microcosms
Experiments were conducted in microcosms mimicking cloud conditions in which
all important parameters, including H2O2, iron, light, and the
presence of bacteria, could be studied individually or in complementarity.
Hydrogen peroxide degradation in artificial cloud water
H2O2 degradation was monitored periodically over a 8 h period. The
kinetic profiles were similar for the three strains. Results obtained with
Pseudomonas graminis (13b-3) are illustrated in Fig. 1, whereas the
results obtained with the other strains are presented for information in
Fig. S2.
Evolution of H2O2 concentration as a function of time
(min) under abiotic conditions: light + Fe-[EDDS] (orange diamond), light
(orange circle), Fe-[EDDS] (orange square with black cross), and biotic
conditions: light + Fe-[EDDS] + Pseudomonas graminis (13b-3)
(green diamond), Fe-[EDDS] + Pseudomonas graminis (13b-3) (green
square with black cross), light + Pseudomonas graminis (13b-3)
(green circle), and Pseudomonas graminis (13b-3) (green triangle).
Values shown are averages of triplicates plus/minus 1 standard deviation.
Where error bars are not visible, they are smaller than the symbol.
Under abiotic conditions (Fig. 1, orange traces), the degradation of hydrogen
peroxide is clearly effective in the presence of artificial solar light and
an Fe-[EDDS] complex, due to the photo-Fenton reaction, with an initial
degradation rate of 1.07×10-9 mol L-1 s-1
(Table 1a). After 150 min this degradation rate decreases in parallel with
EDDS by oxidation (Li et al., 2010). In the presence of the Fe-[EDDS] complex
alone and in the absence of light, hydrogen peroxide is almost not degraded.
Indeed, the degradation rate of H2O2 due to the Fenton reaction is
much lower (2.23×10-10 mol L-1 s-1) than the value
obtained with the photo-Fenton reaction. Exposing the microcosm only to our
light conditions, the photolysis reaction of H2O2 is extremely slow
(1.38×10-10 mol L-1 s-1) due to the low absorption
of H2O2 in the solar spectrum measured inside a cloud and that was
reproduced by the lamps used for these experiments (Fig. S1).
For the biotic conditions (Fig. 1, green traces), the initial biodegradation
rates are summarized in Table 1b. These results show that, under our
experimental conditions, hydrogen peroxide was degraded more efficiently in
the presence of bacteria even if the values obtained stay within the same
order of magnitude compared to the abiotic conditions with artificial light
and an Fe-[EDDS] complex. Pseudomonas graminis (13b-3) and
Pseudomonas syringae (13b-2) are the most active strains, followed
by Sphingomonas sp. (14b-5). For each strain, biodegradation rates
are within the same order of magnitude without wide variations depending on
the tested conditions, i.e., in the presence or absence of artificial light
and of the Fe-[EDDS] complex.
The selected strains all degrade H2O2 within the same order of
magnitude (average value for the three strains and for the condition with
iron and light 1.76×10-9 mol L-1 s-1 and with
iron without light 1.40×10-9 mol L-1 s-1). In
Vaïtilingom et al. (2013), the biodegradation rates of H2O2
were found within the same order of magnitude (average value for two distinct
clouds with light 0.98 × 10-9 mol L-1 s-1 and
without light 0.29 × 10-9 mol L-1 s-1). The
results obtained are within the same order of magnitude of values in a real
cloud environment, thereby validating our microcosm conditions. This
demonstrates that under our experimental conditions, the selected strains
degrade H2O2 like the microbiome of real clouds. In addition, it
validates our approach of separately analyzing the influence of each
parameter (Fe, H2O2, light, …) on the microbial energetic state
metabolism in an artificial marine cloud solution detailed in the next
section.
ATP concentration (µM) as a function of time (min) in the
presence (red square) or absence (navy circle) of H2O2 for the
three strains: (a) Pseudomonas graminis (13b-3),
(b) Pseudomonas syringae (13b-2), and (c)
Sphingomonas sp. (14b-5). The vertical bar illustrates the time
corresponding to the total degradation of H2O2.
Impact of the H2O2 on
the microbial energetic state in artificial marine
cloud solution
The impact of the presence of H2O2 on the energetic state of the
bacterial cells was evaluated by measuring the time evolution of ATP
concentration for the three strains (Fig. 2). The ATP concentration was
measured in the presence (Fig. 2a, b, c – black square) or absence (Fig. 2a,
b, c – white square) of H2O2. In the absence of H2O2, a
strong increase in ATP concentration was observed, reflecting an active
metabolism of the bacteria. In contrast, in the presence of H2O2,
the results were clearly different and can be described in two phases. In the
first phase, ATP concentration was decreasing, while in a second phase it was
progressively increasing (Pseudomonas graminis, 13b-3) or
stabilizing (Pseudomonas syringae, 13b-2, Sphingomonas sp.,
14b-5). The kinetics of ATP concentration evolution and H2O2
degradation are closely related. As discussed earlier (Fig. 1), the
H2O2 initially present (20 µM) was entirely degraded in
approximately 3 h (depending on the strain); this corresponds exactly to the
end of the ATP decrease. Complementary experiments were performed with
incubations of the cells in the presence or absence of light and/or an iron
complex (Fe-[EDDS]) under conditions similar to that described previously in
the presence of H2O2 alone. The results obtained for the three
strains are reported in Fig. SM3 (Pseudomonas graminis), Fig. S4
(Pseudomonas syringae), and Fig. S5 (Sphingomonas sp.).
The results show that light and iron complex have no impact on the ATP
concentration decrease. The measured ATP concentration in the presence or
absence of an artificial light and/or iron(III) complex is similar to that
observed in the presence of H2O2 alone. The ATP concentration is
thus only linked to the presence of H2O2.
Impact of H2O2 on the
survival of the microbial strains
We also checked that the decrease in ATP in the presence of H2O2
was not due to cell mortality. Results of the number of culturable bacteria
in the presence or absence of H2O2 are shown in Fig. 3. The
evolution of the cell concentration was not significantly different when
cells were incubated in the presence or absence of hydrogen peroxide. The
decrease in ATP is therefore not linked to a lower concentration of cells,
but to a modification of metabolic pathways due to H2O2 presence.
The total number of cells increased by a factor of 5 to 10 after 24 h,
showing that bacteria were also able to divide and grow.
Impact of H2O2 on the microbial energetic metabolism in real
cloud environment
In the previous section, we showed that H2O2 had a strong impact
on the energetic metabolism of cells under our microcosm conditions. To go
further, we looked at the potential impact of H2O2 on microbial
energetic states in real cloud samples by carrying out statistical analyses
based on data measured on real cloud water collected at the PUY station.
For this, principal component analysis (PCA) was used. In order to perform
this multivariate statistical analysis, Table S1 was built.
Bacterial cell numbers measured by plate-counting in the presence
(red) and absence (navy) of H2O2 at 20 µM for the three
strains: (a) Pseudomonas graminis (13b-3), (b)
Pseudomonas syringae (13b-2), and (c) Sphingomonas
sp. (14b5). Error bars represent the standard deviation from the means
(n=3).
Variables' factor map (PCA) of the 37 cloud events on the PC1-PC2
plane based on 17 variables.
The result of the PCA analysis is presented in Fig. 4. The first two
dimensions contain practically 50 % of the total inertia (total variance
of the data table), reflecting the validity and reliability of the result.
The PCA shows that if we consider all important parameters in the collected
cloud samples, a strong correlation appears between ATP and H2O2
concentrations (longer vectors and very close on the PCA). There is no
correlation between ATP concentration and the number of bacteria (vectors
practically orthogonal); this shows that H2O2 is linked to the
energetic state of the cells and not to their concentration. Also, there is
no correlation between ATP and markers of pollution such as the pH values,
the NO3-, SO42-, and NH4+ concentrations, or even
the temperature that could impact microbial metabolism.
In addition, a Spearman rank correlation test (non-parametric test) was
performed based on the 37 cloud samples to confirm the correlation between
H2O2 and ATP. The values used for this test are presented in
Table S1. A p-value of 0.0047 was obtained with a Spearman coefficient of
0.45 (Zar, 1972). This shows an extremely strong correlation between
H2O2 and ATP, as theoretically the Spearman coefficient must be
greater than 0.27 for 37 observations and the p-value less than 0.05
(significance threshold). To confirm that ATP depletion due to H2O2
impact was not linked with the mortality of cells, a Spearman rank
correlation test was also performed to evaluate the correlation between ATP
and total microorganism concentrations (sum of bacteria and fungi
concentrations in Table S1) (p-value superior to 0.37).
Figure 4 suggested that ATP or H2O2 could also be correlated with
formate and oxalate since the vectors were relatively close. A Spearman rank
correlation test (non-parametric test) was thus performed based on data
extracted from the 37 cloud samples (Table S1). A strong correlation was
obtained between ATP and formate (p-value = 0.0043, Spearman's
coefficient = 0.46), and between H2O2 and formate
(p-value = 0.00015, Spearman's coefficient = 0.58). The
ATP–oxalate correlation is rather weak (p-value = 0.030, Spearman's
coefficient = 0.36) and much lower than the ATP–H2O2
correlation; similar values were obtained for oxalate and H2O2
(p-value = 0.035, Spearman's coefficient = 0.35).
Discussion
Our objective was to study in detail the interactions between cloud
microorganisms and H2O2.
First, we looked at the mechanisms involved in H2O2 transformations
under laboratory conditions by isolating each parameter to determine its
impact on H2O2 (artificial light, Fe-[EDDS] complex and bacteria).
Degradation rates of hydrogen peroxide were precisely determined for
different microbial strains frequently found in cloud water samples collected
at the PUY site. The results show that all bacterial strains studied under
these conditions degrade H2O2 within the same order of magnitude as
abiotic conditions. The degradation rates of H2O2 by bacteria are
not impacted by the presence of light and Fe-[EDDS] and consequently by the
generation of HO⚫ radicals. On the opposite end, in these
laboratory experiments mimicking real cloud conditions, we have shown that
H2O2 has a strong impact on the microbial energetic state of the
cells. This strong decrease in ATP concentration is not linked to the number
of cells as bacteria are able to divide and grow in the presence of
H2O2. This reveals that microorganisms are able to manage the
stress induced by H2O2 through their metabolism. It is likely that
they could respond using enzymes involved in H2O2 degradation
(e.g., catalases, peroxidases) and other typical antioxidant molecules (e.g.,
glutathione).
Hypothetical mechanism that could explain the impact of H2O2 on cell metabolism and ATP concentration. Interconnection between ATP
synthesis and cellular redox potential (NAD+ / NADH,
NADP+ / NADPH ratios). NAD+ depletion related to the DNA repair
system. Adapted from Oka et al. (2012). - - - arrows: inhibition of ATP
synthase; –.–.– arrows: NAD+ depletion related to the DNA repair
system.
A few studies reported the decrease in ATP concentration in microorganisms
(Perricone et al., 2003), plants (Tiwari et al., 2002), or mammalian cells
(Spragg et al., 1985; Josephson et al., 1991; Sporn and Peters-Goldenwhen,
1988; Hyslop et al., 1988; Oka et al., 2012) exposed to H2O2.
Figure 5 illustrates how H2O2 could affect the concentration of ATP
in the cells. First, H2O2 could directly inhibit the ATP synthase,
a membrane protein synthesizing ATP from ADP (Tamarit et al., 1998). Second,
H2O2 could impact different metabolic pathways which are
interconnected, including glutathione metabolism, glycolysis, the TCA cycle,
and the DNA repair system. The functioning of the enzymes in these pathways
and also the activity of the ATP synthase are dependent on the redox
potential of the cells (NAD+/NADH, NADP+/NADPH ratios), and as a
consequence the ATP concentration is regulated by this redox potential
(Haddock and Jones, 1977; Singh et al., 2007; Oka et al., 2012). If for
instance NAD+ is depleted when the repair system is activated to avoid
potential DNA damages induced by H2O2, then ATP is depleted, and
finally all the metabolic pathways involving these compounds are impacted and
a complete change in the metabolome can be expected.
The measurements preformed in microcosms do not reproduce what is really
occurring in cloud droplets. First, incubations were performed with
artificial cloud water and model strains; nevertheless, the obtained results
were consistent with those obtained with real cloud water samples. Second,
the potential growth of microorganisms during a cloud event could also modify
transformation rates; this is only realistic for long cloud lifetimes
(> 24 h). Finally, experiments were performed under bulk
conditions and not with individual cloud droplets; only models can take into
account the complexity of cloud conditions, in particular the multiphase
aspect of cloud chemistry. To go further and integrate biodegradation rates
into atmospheric chemistry models, complementary experiments should be
performed and biodegradation rates should be expressed as
mol-1 cell-1 h-1.
However, the most important result of this work was to show the correlation
between H2O2 concentrations and ATP concentrations. This result
obtained under our microcosm conditions was confirmed using data measured in
real cloud samples that experienced multiphase and real cloud conditions.
Indeed, we have shown, thanks to statistical analyses, that there was also a
high correlation between H2O2 and ATP concentrations in real cloud
samples collected under various environmental conditions. We suggest thus
that hydrogen peroxide modulates the global metabolism of cloud
microorganisms.
Another interesting correlation was obtained between H2O2 and
formate as well as ATP with formate. This could result from different
concomitant processes. First, formate is the most oxidized carbon molecule
before CO2 generated from successive oxidations of the organic matter by
radicals issued from H2O2. Second, it could reveal the impact of
H2O2 on the C1 metabolism; it is known that C1 compounds can be
transformed by cloud microorganisms (Husàrovà et al., 2011;
Vaitilingom et al., 2010, 2011, 2013). In addition, Thomas et al. (2016)
report the overproduction of formate in a strain of Pseudomonas fluorescens exposed to H2O2. Indeed, formate contributes to the
anti-oxidant strategy of this bacterium to supply NADH, which is known to be
decreased under oxidative conditions. Formate thus helps to control the
cellular redox potential (see Fig. 5).
Interaction between H2O2 and cloud microorganisms and its
potential consequences for atmospheric chemistry. (1) Cloud microorganisms
degrade H2O2 thanks to their catalases and peroxidases (oxidative
stress metabolism); as a result, this impacts the oxidant capacity of clouds.
The concentration of radicals issued from H2O2 is decreased and
radical chemistry is less efficient at transforming the organic matter. (2)
H2O2 impacts the energetic metabolism of microorganisms that react
to this stress. The depletion of ATP modulates the global carbon metabolism
of the microorganisms, and consequently the transformation of the organic
matter. These processes are modulated by the H2O2 concentration
that varies depending on the atmospheric scenario.
Finally, this work brings new insights into the interactions between
H2O2 and the cloud microbiome and its potential consequences for
cloud chemistry (see Fig. 6).
First, it confirms that cloud microorganisms are able to efficiently degrade
hydrogen peroxide and potentially impact the global carbon budget and the
oxidant capacity of clouds as already shown in Vaïtilingom et
al. (2013). By decreasing H2O2 concentration, radical chemistry is
less efficient at degrading the organic matter. Second, we show here for the
first time that H2O2 impacts the energetic metabolism of the cloud
microbiome and thus potentially modulates its carbon metabolism. As a
consequence it can modify the final transformation of the organic matter in
clouds. This reciprocal interaction between H2O2 and microorganisms
and its subsequent impact on cloud chemistry is clearly dependent on
H2O2 concentration.
To go further in the understanding of the modulation of the metabolic
pathways (including carbon, nitrogen, amino acids, or sugars) induced by
H2O2, a combined metabolomic and transcriptomic approach could be
used.
The next step could be to integrate biological data into numerical
atmospheric models to better quantify the consequence of this modulation for
atmospheric chemistry.