Species-Level Variability in Extracellular Production Rates of Reactive Oxygen Species by Diatoms

Introduction

The reactive oxygen species (ROS), superoxide radical (O${}_2^{-}$), hydrogen peroxide (H₂O₂), and hydroxyl radical (OH) are intermediates in the sequential one-electron reduction of oxygen to water, and are important to the biogeochemical cycling of trace metals and carbon.

Photochemical production of O${}_2^{-}$ in the marine environment has been well-studied, and occurs when photo-excited chromophoric dissolved organic matter (CDOM) transfers an electron to dissolved O₂ to generate O${}_2^{-}$ (Cooper et al., 1988; Shaked et al., 2010). Biological production of O${}_2^{-}$ also occurs in marine environments, but is less well-understood than photochemical production (Rose et al., 2010). The typical removal pathways for O${}_2^{-}$ are by a dismutation reaction (Cooper and Zika, 1983; Zafiriou, 1990) and by redox reactions with trace metals and organic matter (Goldstone and Voelker, 2000; Wuttig et al., 2013).

H₂O₂ is produced through dismutation and reduction of O${}_2^{-}$; it therefore has the same photochemical and biological sources as O${}_2^{-}$ (Zhang et al., 2012). In addition, H₂O₂ can be produced biologically without O${}_2^{-}$ as a precursor (Palenik et al., 1987). H₂O₂ can decompose through reaction with reduced metals to form OH; however, in marine environments, the predominant method of decay is likely to be enzymatic destruction (Petasne and Zika, 1997; Herut et al., 1998; Yuan and Shiller, 2001).

Field studies have shown that particle-associated production of ROS occurs in the ocean (Avery et al., 2005; Rose et al., 2008; Hansard et al., 2010) and that this production can be slowed by biological inhibitors (Moffett and Zafiriou, 1990; Rose et al., 2010), indicating that it is of biological origin. Recent studies by Vermilyea et al. (2010) and Roe et al. (2016) show that dark production of H₂O₂ in the Gulf of Alaska and at Station ALOHA is significant compared to photochemical production, indicating that biological ROS production may impact biogeochemical cycles in the ocean. Thus, it is important to consider which organisms produce ROS, how they do so, and why.

Most culture studies of biological extracellular ROS production have been performed on ichthyotoxic organisms that negatively impact the fishing industry. Chattonella marina, in particular, along with other raphidophyte species, have been studied intensively (see list in Marshall et al., 2002). However, from a global perspective, it is interesting to consider the more common phytoplankton—diatoms, coccolithophores, and cyanobacteria—which are more likely to influence biological production of ROS in the majority of the ocean. Extracellular production rates have been quantified in only a few species to date (see Table 1 and references cited therein): the diatoms Thalassiosira pseudonana and T. weissflogii, coccolithophore Pleurochrysis carterae, and cyanobacteria Synechococcus sp., Lyngbya majuscula and Anacystis nidulans. Compared to raphidophytes, these species of phytoplankton have cell normalized O${}_2^{-}$ and H₂O₂ production rates that are up to five orders of magnitude lower (Table 1).

TABLE 1

Table 1. Previously published phytoplankton studies showing cell-normalized production of superoxide (P_{O2−, cell}) and hydrogen peroxide (P_{H22, cell}).

Previous studies suggest that ROS production may occur for different reasons in different organisms. Palenik and Morel (1990) showed that H. carterae produced H₂O₂ as a byproduct of uptake of organic nitrogen sources. Two organisms, Trichodesmium (Roe and Barbeau, 2014) and Lyngbya majuscula (Rose et al., 2005) have been postulated to use O${}_2^{-}$ as a reductant to facilitate biological uptake of iron. In contrast, production of superoxide was not beneficial for iron uptake by T. weissflogii (Kustka et al., 2005); an alternative explanation for superoxide production by T. weissflogii has not been proposed. Alternatively, O${}_2^{-}$ has also been proposed as a cell signal and autocrine growth promoter in C. marina and Prymnesium parum that is required for cell proliferation (Oda et al., 1995; Marshall et al., 2005).

Extracellular H₂O₂ could be produced simply via dismutation or reduction of biologically produced O${}_2^{-}$, or due to direct production by a separate mechanism. Thus, one way to gain a better understanding of H₂O₂ production mechanisms is to determine the relationship between extracellular O${}_2^{-}$ and H₂O₂ production rates. Of the previous studies on ROS production by non-raphidophytes, only two (Palenik et al., 1987; Milne et al., 2009) measured both species directly. In the first study, P. carterae produced H₂O₂ without measurable O${}_2^{-}$ during uptake of organic nitrogen (Palenik et al., 1987). By contrast, the ratio between O${}_2^{-}$ and H₂O₂ production by T. weissflogii under high light conditions was around the 2:1 ratio expected for production of H₂O₂ via the superoxide dismutation pathway (Milne et al., 2009).

Direct comparisons of rates of biological production of both O${}_2^{-}$ and H₂O₂ under light and dark conditions are important for better understanding factors that stimulate production, determining links between production and photosynthesis, and identifying possible sources of ROS to the dark ocean. Milne et al. (2009) showed increased extracellular production of O${}_2^{-}$ by T. weissflogii under high light conditions (150–500 μmol photons m⁻² s⁻¹), and Waring et al. (2010) showed increased H₂O₂ production by T. pseudonana under high light (1000 μmol photons m⁻² s⁻¹). Nevertheless, enhanced O${}_2^{-}$ production cannot be a direct byproduct of photosynthesis, as O${}_2^{-}$ cannot pass the cell membrane (Seaver and Imlay, 2001). Instead, stimulated photosynthetic activity would lead to increased intracellular NADPH pools that could serve as a source of reducing equivalents to transmembrane NADPH oxidoreductases proposed to be involved in extracellular superoxide production (see for instance, Kawano et al., 1996). Another possibility that has not been examined is that light-induced ROS production is due to a passive biological mechanism (e.g., outer membrane proteins and/or pigments) on the cell surface; if so, ROS production in the light could continue even after cell death.

Finally, ROS production rates are typically reported as net production rates, which do not take into account the contribution of decay to ROS concentrations. Diaz et al. (2013) showed that net O${}_2^{-}$ production rates by heterotrophic bacteria could be significantly affected by decay. Thus, to obtain gross production rates, decay, and production rates should be addressed in conjunction with each other. In addition, although Wong et al. (2003) found that killed organisms decompose substantially less H₂O₂ than live ones, no study has yet determined whether there is a similar effect on O${}_2^{-}$ decomposition.

The goals of this study were to measure O${}_2^{-}$ and H₂O₂ production by common phytoplankton, to examine whether there is a link between the two ROS, and to determine the influence of light and active metabolism on their production and decay rates. We focused on five species of diatoms, three of which were in the same genus. Production of both ROS was measured in parallel by both live and killed cultures under light and dark conditions. In addition to measuring production rates, we also determined recoveries of the ROS during measurement. This not only allowed us to quantify gross, rather than net, production, but also served as a measurement of the relative ability of these organisms to break down ROS under different conditions.

Materials and Methods

Organisms: Growth and Experimental Conditions

Five marine diatom species were used in this study: Thalassiosira pseudonana (CCMP 1335), Phaeodactylum tricornutum, Cyclotella cryptica (CCMP 332), Thalassiosira oceanica (CCMP 1005), and Thalassiosira weissflogii (CCMP 1336). Axenic cultures were obtained from the National Center for Marine Algae (NCMA) and from the Hildebrand lab at Scripps Institute of Oceanography. Maintenance cultures were grown in acid-washed polycarbonate flasks in Guillard's F/2 medium (Sigma) under grow lights at 20°C at 100 μmol photons m⁻²s⁻¹ (as measured by a LI-COR LI-250 light meter and LI-190 Quantum/PAR sensor) with a 12 h light:dark cycle. Maintenance cultures were transferred to fresh medium every 2 weeks. An aliquot was taken from the maintenance culture during exponential growth to start the experimental culture, which was grown to mid-exponential phase under the same conditions in F/2 medium before being harvested for the experiment. On the day of the experiment, an aliquot of experimental culture was removed for measuring cell density on a Beckman Z2 Coulter counter, and all cultures were checked for purity with marine purity broth (Saito et al., 2002) or, for P. tricornutum, by making slides of DAPI-stained cultures.

Cells were harvested for each experimental run by removing an aliquot (~5 mL to get ~10⁶ cells) of the experimental culture from the light and loading it directly onto an acid-washed (0.1 M HCl) 25-mm 0.45-μm cellulose acetate syringe filter (VWR) as described below. The total number of cells on each filter was estimated based on multiplying the cell density per mL by the volume of culture loaded. The filter disk was positioned parallel to the floor for the whole experiment. For H₂O₂ measurements, cells were generally loaded onto the filter using a peristaltic pump (0.6 mL min⁻¹). In a few experiments, cells were gently loaded using a syringe (~5 mL/min). For O${}_2^{-}$ measurements, the culture was loaded onto the filter using a peristaltic pump at 3 mL min⁻¹, and the pump was briefly stopped (<2 s) while the tubing was moved from the ASW bottle to the culture bottle to avoid trapping air on the filter.

For both O${}_2^{-}$ and H₂O₂ measurements, the entire experimental run was either conducted under light conditions (from a small grow light emitting 75 μmol photons m⁻² s⁻¹ as measured by a LI-COR LI-250 light meter and LI-190 Quantum/PAR sensor held directly adjacent to the filter) or in the dark (wrapped in foil). The analytical medium was an artificial seawater (hereafter referred to as ASW) based on a modified Aquil medium (Price et al., 1989) where only the major salts were added and chelexed, adjusted to pH 8, and amended with 10 μM of the chelator diethylenetriaminepentaacetic acid (DTPA) (Sigma), which was added to prevent rapid loss of O${}_2^{-}$ via reactions with trace metals.

A 4% formaldehyde killed control was allowed to incubate for ~2 h before the cells were rinsed on a 2.0 μm filter and resuspended in fresh F/2 medium. The killed culture was then placed back in the growing conditions for ~1 h before being harvested for the experiment. A second killed control was maintained in the growth conditions for a week to ensure that this procedure successfully killed each organism.

Each organism was analyzed on two separate days, and on each day, six experimental runs were performed: two runs with live organisms kept in the dark; two runs with live organisms in the light; and one run each with killed organisms in the light and in the dark.

O${}_2^{-}$ Detection

O${}_2^{-}$ was detected with a flow injection system (FeLume Mini, Waterville Analytical) with the use of the MCLA chemiluminescence probe (Godrant et al., 2009) using the experimental set-up described in Section S1.1 (Figure S1).

Calibration of the instrument was completed at the start of each experimental day using ASW. An acid-washed 25-mm 0.45-μm cellulose acetate filter was used during calibration to mimic runs in the presence of cells as closely as possible. Briefly, primary O${}_2^{-}$ stock solutions were prepared with KO₂ and calibrated spectrophotometrically. These were then diluted to make working stock solutions, which were added to the ASW (see Section S1.2 for details). The output signal was then monitored as a function of time and fit to:

$\begin{array}{cc}{R}_t\text{}=\text{}{R}_{BL}+R_{t=0}ex{p}^{(-k_{loss,O2-}t)}& \left(1\right)\end{array}$

where R_t is the measured response at time t, R_BL is the baseline response, R_{t = 0} is the baseline corrected response at t = 0, and k_{loss, O2−} is the pseudo-first order decay constant in the analytical medium. The sensitivity (S, counts nM⁻¹) of the response was determined as:

$\begin{array}{cc}S=\frac{R_{t=0}}{{[O_2^{-}]}_{spike}}& \left(2\right)\end{array}$

An average of four S measurements was used for each calibration. [O${}_2^{-}$]_spike generally ranged from 0.207 to 1.079 nM. No dependence of S on [O${}_2^{-}$]_spike was observed and all of the decay curves were well-described by first-order kinetics.

Calculation of O${}_2^{-}$ Production and Percent Recovery from Cultures

An experimental run consisted of the following steps: measuring the steady-state signal R_ASW from running ASW past an empty filter, loading cells on the filter, measuring the steady-state signal R_cell from running ASW past the cells on the filter, observing the signal response to a known O${}_2^{-}$ spike to determine the recovery of O${}_2^{-}$ in the presence of cells, and finally measuring the steady-state background signal from MCLA autooxidation, R_SOD, after addition of ~0.24 U mL ⁻¹ (~1.6 nM) SOD to the ASW (Section S1.3, Figure S2).

Recovery Rec_O2− was determined by fitting the spike data to Equation (1), with R_BLfixed to the measured value of R_cell. The recovery (Rec_O2−) was then calculated from:

$\begin{array}{cc}Re{c}_{O2-}=\left(\frac{R_{t=0}}{Expected{R}_{t=0}}\right)& \left(3\right)\end{array}$

where Expected R_{t = 0} was calculated as the product of sensitivity S and the concentration of the added O${}_2^{-}$ spike.

The increase in O${}_2^{-}$ concentration due to production by the cells, [O${}_2^{-}$]_cell (nM), was calculated from:

$\begin{array}{cc}{[O_2^{-}]}_{cell}=\left(\frac{R_{cell}-R_{SOD}}{\text{S}\times \text{RecO2}-}\right)-{[O_2^{-}]}_{ASW}& \left(4\right)\end{array}$

where the first term represents the O${}_2^{-}$ concentration measured in the presence of cells and the second term, [O${}_2^{-}$]_ASW, is the background concentration of O${}_2^{-}$ in the ASW. [O${}_2^{-}$]_ASW was calculated according to:

$\begin{array}{cc}{[O_2^{-}]}_{ASW}=(\frac{{\text{R}}_{\text{ASW}}-{\text{R}}_{\text{SOD}}}{S})& \left(5\right)\end{array}$

The cell-normalized production rate P_{O2−, cell} (nmol cell⁻¹ h⁻¹) was determined by:

$\begin{array}{cc}{P}_{O2-}=\frac{{\left[O_2^{-}\right]}_{cell}\times Q}{N}\times (3600sh{r}^{-1})& \left(6\right)\end{array}$

where Q is the flow rate (L s⁻¹) and N is the total number of cells on the filter.

Our calculations of [O${}_2^{-}$]_cell and [O${}_2^{-}$]_ASW above assume that the difference between R_ASW and R_SOD is due only to a background concentration of O${}_2^{-}$ in the ASW. We have tested this assumption in another work (Roe, unpublished data), where we observed that small (<0.1 nM) additions of SOD to the ASW bottle decreased R_ASW proportionally to the resulting increase in O${}_2^{-}$ decay rate. This gradual decrease in R_ASW-R_SOD with increasing SOD concentration can therefore be attributed to a “bottle blank” created by a constant background production rate of O${}_2^{-}$ in the ASW bottle, which could be due to compounds entering from the atmosphere or to redox reactions occurring at the bottle wall or in the ASW. The final large addition of SOD to the ASW bottle to establish R_SOD caused an additional small decrease in signal, indicating that there is another effect of SOD beyond eliminating the “bottle blank” O${}_2^{-}$, which we will call a method blank. By not accounting for the method blank separately, we are essentially assuming that it will be affected by the decreased recovery in the presence of cells the same way that the O${}_2^{-}$ bottle blank is affected. However, because the method blank is small, this assumption does not add much uncertainty to our reported measurements. The method blank in DTPA-amended ASW corresponded to a contribution to [O${}_2^{-}$]_ASW (as defined by Equation 5) of 0.015 ± 0.006 nM (n = 7) (Roe, unpublished data). If the method blank in the present study is similar to that measured by Roe et al., but not affected by recovery, our reported values of [O${}_2^{-}$]_cell would represent an overestimate of 6 ± 4%.

We define the detection limit for each run as five times the standard deviation of the baseline signal. Detection limits converted to concentration by dividing by S ranged from 0.013 to 0.092 nM. In several samples, the signal from the spike did not give a detectable offset from R_cell. In these cases, Rec_O2−, [O${}_2^{-}$]_cell, and P_O2− could not be determined.

H₂O₂ Detection and Calibration

H₂O₂ was measured by flow injection analysis using a Waterville Analytical FeLume system and the base-catalyzed chemiluminescent reaction with acridinium ester as described by Cooper et al. (2000) and King et al. (2007). Briefly, a slug (~0.5 mL) of sample was pushed into the system by a stream consisting of an artificial seawater (ASW) (Price et al., 1989) treated with 10 U/mL catalase. The sample then combined with the acridinium ester reagent (10 μM, pH 3) in a mixing tee. Next the sample/acridinium ester mixture entered a flow cell, where it mixed with carbonate buffer (0.02 M, pH 11.2). The photons produced by the reaction were measured by a photomultiplier tube (Section S2, Figure S3).

A calibration curve was created at the beginning of each experimental day by standard additions of H₂O₂ stock to aliquots of ASW. H₂O₂ stock solutions were prepared immediately before calibration by dilution of a ~3 mM primary stock solution that was stored in the refrigerator. The concentration of the primary stock solution was determined approximately every 2 months by measuring its absorbance at 240 nM, at which the molar absorptivity of H₂O₂ is 38.1 ± 1.4 M⁻¹ cm⁻¹ (Miller and Kester, 1988). The loss of H₂O₂ in the primary stock solution over the course of the study was less than 1%.

Calculation of Production Rates and Percent Recovery for H₂O₂

For each experimental run, filter-sterilized ASW flowed through a peristaltic pump at 0.6 mL min⁻¹ over an empty acid-washed (0.1 M HCl) filter and directly into the FeLume system until a steady-state concentration of H₂O₂, representing a background concentration, was detected ([H₂O₂]_{unspiked, direct}). Next, the cells were loaded onto the filter as described above, and the H₂O₂ was again monitored until a steady state was reached ([H₂O₂]_{unspiked, cells}). Then the ASW was spiked with additional H₂O₂ and flowed over the cells and measured ([H₂O₂]_{spiked, direct}). Finally, the filter was disconnected from the ASW stream and the H₂O₂ of the spiked ASW was quantified ([H₂O₂]_{spiked, direct}). An example of a typical experimental run is shown in Figure S4.

Recovery (Rec_H2O2) for each experimental run was calculated by dividing the increase in [H₂O₂] in the medium due to the addition of the H₂O₂ spike by the measured increase in the presence of cells:

$\begin{array}{cc}Re{c}_{H2O2}=\frac{{\left[H_2{O}_2\right]}_{spiked,cells}-{\left[H_2{O}_2\right]}_{unspiked,cells}}{{\left[H_2{O}_2\right]}_{spiked,direct}-{\left[H_2{O}_2\right]}_{unspiked,direct}}& \left(7\right)\end{array}$

Calculation of recovery in this manner assumes that the cells on the filter break down the same fraction of H₂O₂ from both the spiked and unspiked medium, i.e., that the decomposition is first order in H₂O₂.

The increase in [H₂O₂] due to cell production, [H₂O₂]_cell (nmol L⁻¹) for a given experimental run was then calculated with the equation:

$\begin{array}{cc}{\left[H_2{O}_2\right]}_{cell}=\frac{{\left[H_2{O}_2\right]}_{unspiked,cells}}{Re{c}_{H2O2}}-{\left[H_2{O}_2\right]}_{unspiked,direct}& \left(8\right)\end{array}$

A cell-normalized production rate P_H2O2 (nmol cell⁻¹ h⁻¹) was then calculated with the equation:

$\begin{array}{cc}{P}_{H2O2}=\frac{{\left[H_2{O}_2\right]}_{cell}\times Q}{N}& \left(9\right)\end{array}$

where Q is the flow rate (L h⁻¹) and N is the number of cells on the filter, calculated from the measured cell density in the experimental culture and the volume of culture loaded onto the filter.

Spiked Batch Incubations with Spent Culture Medium

The ability of spent culture medium from P. tricornutum to produce and break down ROS was assessed by using spiked batch incubation methodology, as described in Vermilyea et al. (2010). Briefly, mid-exponential P. tricornutum cultures were filtered using 0.22 μm PES syringe filters (Millipore) at approximately 10 mL min⁻¹, changing the syringe filters after every 10 mL of culture. The filtrate was collected in two 60-mL syringes (Kendell Mono-ject), one of which was then spiked with additional H₂O₂ to determine whether simultaneous H₂O₂ decay took place. The syringes were then incubated at room temperature in the dark and [H₂O₂] was monitored over a period of several hours to determine production and decay rates.

If the production rate in the medium, P'_H2O2 (nM h⁻¹) is constant per unit volume, and decay is first-order with respect to [H₂O₂], the change in [H₂O₂] over time is given by the equation:

$\begin{array}{cc}\frac{d\left[H_2{O}_2\right]}{dt}=P_{H2O2}^{\prime }-k_{loss,H2O2}^{\prime }\left[H_2{O}_2\right]& \left(10\right)\end{array}$

where k'_{loss, H2O2} (h⁻¹) is the pseudo-first order rate coefficient of H₂O₂ decay. Time and concentration data from each spiked batch incubation were fitted to the solution to differential Equation 10:

$\begin{array}{cc}\left[H_2{O}_2\right]=\frac{P_{H2O2}^{\prime }}{k_{loss,H2O2}^{\prime }}-\left\{\left(\frac{P_{H2O2}^{\prime }}{k_{loss,H2O2}^{\prime }}-{\left[H_2{O}_2\right]}_0\right)e^{-k_{loss,H2O{2}^t}^{\prime }}\right\}& \left(11\right)\end{array}$

using the Microsoft Excel Solver function with the initial [H₂O₂], [H₂O₂]₀, of each incubation and global P${}_{H2O2}^{\prime }$ and k'_{loss, H2O2} values for both incubations as fitting parameters.

Replicates and Measurement Uncertainties

Dark and light live production rates for O${}_2^{-}$ and H₂O₂ were measured in duplicate on a given experimental day, while single measurements of killed dark and light production rates were measured each day. Data for each condition from two separate experimental cultures, examined on different days, were pooled for each organism except as noted.

Statistical significance for all results was assessed using a two-tailed t-test with the minimum level for significance at p = 0.05. Reported uncertainties in figures and text represent one standard deviation. Detection limits for P_O2 and P_H2O2 are a function of the size of the analytical and background signals, the recovery, and the cell density, and therefore varied from run to run. For the purposes of this work, we treat any set of replicate results whose average was not significantly greater than zero (using a t-test and p = 0.05) as below detection limit.

Results

Thalassiosira Culture ROS Production

O${}_2^{-}$

Successful measurements of P_O2− were performed in cultures of all three Thalassiosira spp. under all four conditions (Figure 1). T. oceanica and T. pseudonana had similar live P_O2−, with both dark and light live conditions giving production rates significantly greater than zero. T. oceanica had an average P_O2− of 18.0 ± 3.9 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the light and 6.0 ± 0.8 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the dark (n = 4). T. pseudonana had an average P_O2− of 13.9 ± 5.1 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the light and 7.5 ± 1.5 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the dark (n = 4). For both of these organisms, the live P_O2− values were also considerably greater (~2–5 times) than the corresponding killed production rates, with the difference being statistically significant for the dark values. T. weissflogii had much greater production rates than the other two species, 72.7 ± 2.3 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the light and 25.2 ± 7.6 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the dark (n = 2). However, T. weissflogii is also the largest of the three organisms, and its production rates look similar to those of T. oceanica and T. pseudonana when normalized to surface area (Section S4, Figure S6). Because of the lower number of replicates for T. weissflogii analyses, the measurement uncertainties for this species were high (the spike signal was undetectable in the replicate analysis, so Rec_O2− and P_O2− could not be determined). As a result, only the light live P_O2− is significantly greater than zero for this organism.

FIGURE 1

Figure 1. Calculated O${}_2^{-}$ production rates in genus Thalassiosira. Error bars represent one standard deviation. Letters designate rates significantly different from each other within each panel. Asterisks designate P_O2− values significantly different from zero.

The light treatments had a greater P_O2− than the dark treatments (~2–8 times more) for all three organisms, and the differences were statistically significant for both live and killed T. oceanica and T. pseudonana, and live T. weissflogii. The light-induced increase in live cultures did not differ significantly from the light-induced increase in killed cultures for any of the three organisms (Figure 2). These findings are summarized in Table 2.

FIGURE 2

Figure 2. Differences between light and dark P_O2− for both live (L) and killed (K) cultures in Thalassiosira spp. Error bars represent one standard deviation.

TABLE 2

Table 2. Summary of light vs. dark and live vs. killed comparison for H₂O₂ and O${}_2^{-}$ production rates.

H₂O₂

Of the three species of Thalassiosira studied, only live cultures of T. oceanica produced significantly more H₂O₂ in light than in dark conditions, 10.5 ± 4.6 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ (n = 4) as opposed to 2.1 ± 2.1 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ (n = 4) (Figure 3). T. pseudonana appeared to produce slightly, but not significantly, more H₂O₂ in light than in dark conditions: 22.4 ± 11.9 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ (n = 4) vs. 18.9 ± 7.2 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ (n = 4). By contrast, live cultures of T. weissflogii had H₂O₂ production rates statistically indistinguishable from zero in both dark (n = 3 with one statistical outlier, with a large negative production rate, removed) and light (n = 4) experiments. Killed cultures of both T. pseudonana and T. oceanica had H₂O₂ production rates indistinguishable from zero, whereas H₂O₂ production by killed cultures of T. weissflogii was relatively high, 34.5 ± 29.8 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the dark (n = 2) and 26.1 ± 7.6 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ in the light (Figure 3). Normalized to cell surface area, T. pseudonana produced much more H₂O₂ than the other two organisms (Section S4, Figure S7).

FIGURE 3

Figure 3. Calculated H₂O₂ production rates in Thalassiosira spp. Error bars represent one standard deviation. Letters designate rates significantly different from each other within each panel. Asterisks represent H₂O₂ production rates that are significantly different from zero.

ROS Production in Other Axenic Diatom Cultures

O${}_2^{-}$

Live cultures of both Phaeodactylum tricornutum and Cyclotella cryptica were observed to produce a O${}_2^{-}$ signal that could be detected above the ASW medium. However, a P_O2− could not be calculated since Rec_O2 could not be determined. For P. tricornutum the detection limit was 0.054 ± 0.024 nM or a Rec_O2− <12%. For C. cryptica the detection limit was 0.033 ± 0.012 nM or a Rec_O2− <5%.

H₂O₂

In both live and killed cultures of both organisms, H₂O₂ production rates were generally indistinguishable from zero (Section S3, Figure S5).

Although P. tricornutum cells did not appear to make much H₂O₂ on the filter, high [H₂O₂] in the growth medium was observed when cells were first loaded onto the filter, suggesting that the cells secreted ROS-producing small molecules or enzymes. Using the methodology described in the Section titled Spiked Batch Incubations with Spent Culture Medium, we observed that cell-free spent culture medium had a H₂O₂ decay rate below detection limit and an H₂O₂ production rate P'_H2O2 that remained constant at 36.9 ± 9.1 nM h⁻¹ (n = 2) over the course of 5 h. Taking into account the original cell densities, this would equate to a cell-normalized production rate, P_H2O2, of 50 × 10⁻¹⁷ mol cell⁻¹ h⁻¹. By contrast, sterile F/2 medium had decay of 0.2 h⁻¹ and production of 0.3 nM h⁻¹ (n = 1) (Figure 4). Although high [O${}_2^{-}$] in the medium was also observed during cell loading, it was not possible to quantify O${}_2^{-}$ production rates in the spent medium with a similar methodology, due to the fast O${}_2^{-}$ decay rates.

FIGURE 4

Figure 4. Spiked batch incubations for (top) spent growth medium from P. tricornutum and (bottom) sterile F/2 medium. The circles indicate unaltered medium, while the squares indicate medium that has been spiked with additional H₂O₂.

ROS Recoveries

O${}_2^{-}$ Recoveries

The O${}_2^{-}$ standard addition spike could be detected in all Thalassiosira cultures except for one of the replicate live T. weissflogii cultures. Each Thalassiosira species decreased the signal observed from the spike, with all the live cultures degrading significantly more O${}_2^{-}$ (indicated by lower Rec_O2−) than the killed cultures (P < 0.05), except for T. weissflogii. The average live Rec_O2− were 18.2 ± 5.4% (n = 8), 21.3 ± 10.8% (n = 8), and 9.1 ± 1.3% (n = 4) for T. pseudonana, T. oceanica, and T. weissflogii, respectively. The average killed Rec_O2− were 43.4 ± 5.0% (n = 4), 46.8 ± 21.7% (n = 4), and 16.9 ± 6.9% (n = 4) for T. pseudonana, T. oceanica, and T. weissflogii, respectively. There were no significant differences in the Rec_O2− for the light and dark treatments for both T. weissflogii cultures and the live T. pseudonana cultures (P > 0.05) but differences were observed for killed T. pseudonana cultures and both T. oceanica cultures (P < 0.05) (Figure 5). Recovery values can be converted to surface-area-normalized O${}_2^{-}$ decay coefficients for a more direct comparison of these different organisms' ability to degrade O${}_2^{-}$. These calculations show that even on a surface-area normalized basis, T. weissflogii will degrade O${}_2^{-}$ more rapidly than the other two organisms (Section S5, Figure S8). The O${}_2^{-}$ spikes could not be detected in the presence of C. cryptica and P. tricornutum, meaning that Rec_O2− could not be determined.

FIGURE 5

Figure 5. O${}_2^{-}$ recoveries (%) for Thalassiosira cultures examined. Live values shown are averages and standard deviations from duplicate measurements on the same day. The line represents a 1:1 relationship.

H₂O₂ Recoveries

All of the species in the present study decayed H₂O₂ (Figure 6), though the recoveries were always higher than for O${}_2^{-}$ (indicating less decay) under the same conditions (compare Figure 5 and Figure 6). Live T. weissflogii degraded significantly more H₂O₂ than the other organisms, with recovery rates of 66 ± 3% (n = 7), as opposed to rates ranging from 84 ± 5% in P. tricornutum (n = 8) to 93 ± 2% in C. cryptica (n = 8). Even on a cell-surface area normalized basis, T. weissflogii's ability to degrade H₂O₂ was greater than those of the other organisms (Figure S9). H₂O₂ recoveries were statistically indistinguishable from 100% in killed organisms. Of the cultures studied, only C. cryptica had a value of Rec_H2O2 for killed cultures (99 ± 5%, n = 4) that was not significantly different from that for live cultures. H₂O₂ recovery rates were not determined for killed cultures of P. tricornutum.

FIGURE 6

Figure 6. H₂O₂ recovery percentages for live vs. killed organisms for T. weissflogii, T. pseudonana, T. oceanica, and C. cryptica. The line represents a 1:1 relationship.

H₂O₂ Production in Cultures Loaded on the Filter by Syringe

In early H₂O₂ experiments, aliquots of culture were loaded on the filter using a syringe. Differences between the syringe-loaded and pump-loaded live cultures were compared in two diatom species, T. oceanica and T. weissflogii (Figure 7). For purposes of this section, both light and dark measurements are pooled.

FIGURE 7

Figure 7. H₂O₂ production for pump-loaded vs. syringe loaded cultures of T. weissflogii.

There was no significant difference between syringe-loaded cultures and pump-loaded cultures for T. oceanica. P_H2O2 was 7.1 ± 5.5 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ for pump-loaded cultures (n = 8) and 10.7 ± 4.0 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ for syringe-loaded cultures (n = 9); recovery percentages were 88 ± 5 and 86 ± 3%, respectively. While recovery rates were also statistically indistinguishable for T. weissflogii (66 ± 4% and 63 ± 6% for pump-loaded cultures vs. syringe loaded cultures, respectively), production rates were substantially different: in pump-loaded cultures (n = 7), H₂O₂ production rates were statistically indistinguishable from zero, while in syringe-loaded cultures (n = 4), they were significantly higher at 80.3 ± 22.1 × 10⁻¹⁷ mol cell⁻¹ h⁻¹.

Discussion

Diatom O${}_2^{-}$ Production

The O${}_2^{-}$ values reported in the present study compare well to the previously published values (Kustka et al., 2005; Rose et al., 2008; Milne et al., 2009) for T. weissflogii (Table 3). Both Milne et al. (2009) and the present study found that light enhances P_O2−, though killed cultures were not examined in the former. It appears that the light response can also occur in low light since Milne et al. (2009) found increased P_O2− with as little as 30 μmol photons m⁻² s⁻¹ light intensity while 75 μmol photons m⁻² s⁻¹ was used in the present study. In the case of T. pseudonana, the present study reports values ~six times lower than Rose et al. (2008) who examined P_O2− under different conditions (Fe stress and different growth phases) than tested here.

TABLE 3

Table 3. Comparison of published diatom P_O2− and methods.

Despite the similar results between the present study and the other three studies listed in Table 3, especially for T. weissflogii, there were several important methodological differences. Milne et al. (2009) and Rose et al. (2008) used the xanthine:xanthine oxidase system to generate O${}_2^{-}$ for calibration, while the present study used KO₂ and Kustka et al. (2005) used photochemically generated O${}_2^{-}$ stock solutions. The poor stability of the xanthine:xanthine oxidase system, as well as its tendency to generate less O${}_2^{-}$ than the manufacturer's specifications of the enzyme's activity, have been noted in a previous study (Rose et al., 2008; Rose, 2012). Both Milne et al. (2009) and Rose et al. (2008) took these issues into account. However, Milne et al. (2009) also based their calibration on an assumed half-life of 100 s for O${}_2^{-}$ in seawater instead of directly measuring the half-life of O${}_2^{-}$ in their assay medium (a natural seawater sampled using a CTD). Because decay rates can vary widely depending on where the water was sampled (Rose et al., 2008, 2010; Hansard et al., 2010; Heller and Croot, 2010) and are also highly sensitive to trace metal contamination, the actual half-life of O${}_2^{-}$ could have been much lower than the value they assumed.

Another consideration is how the cells are influencing the O${}_2^{-}$ signal detected by the photomultiplier tube. The present study examined this effect using a known O${}_2^{-}$ spike and determined that the diatom cells which were immobilized on the filter (both live and killed) drastically reduced the O${}_2^{-}$ signal. Milne et al. (2009) and Kustka et al. (2005) also measured O${}_2^{-}$ production by cells immobilized on a filter, but did not correct the signal for this effect. In contrast, Rose et al. (2008) added MCLA directly to cell cultures and compared the resulting chemiluminescence signal to the signal from cultures with xanthine/xanthine oxidase added in addition to MCLA. While this technique did not explicitly measure “recovery” values, it would have automatically corrected for the effect of cells on the signal. However, their reported signal would have also included any O${}_2^{-}$ generated by cell exudates in the medium.

A final difference in these studies is the value assigned as the “baseline” signal. In Rose et al. (2008) the baseline was defined as a cell culture with MCLA and SOD added. Kustka et al. (2005) did not directly state what they used as a baseline signal, but implied that it is also the SOD-added signal. This means any background superoxide generated by the medium or the container would have also been included in both of these studies' reported measurements. Milne et al. (2009) used the signal measured in the absence of light as their baseline, so their reported values only measure the enhancement caused by light.

Given these substantial methodological differences, the correspondence of the results of previous studies with each other and the present study could be fortuitous. However, the present study is the only study that attempted to correct for both recovery effects and background signal.

H₂O₂ Production

A previous investigation of H₂O₂ production rates (Milne et al., 2009) by T. weissflogii found a range of P_H2O2 between 11 and 66 × 10⁻¹⁷ mol cell⁻¹ h⁻¹ for cultures loaded onto the filter by syringe, under a range of light intensity (30–500 μmol photons m⁻² s⁻¹) and quantified by a similar method to that employed in the present study, but without the correction for simultaneous decay (Milne et al., 2009). P_H2O2 values for syringe-loaded cultures in the present study were similar in magnitude (80.3 ± 22.1 × 10⁻¹⁷ mol cell⁻¹ h⁻¹); however, T. weissflogii cultures loaded onto the filter by peristaltic pump had P_H2O2 indistinguishable from zero; these values were therefore significantly lower than those for syringe-loaded cultures. Even gentle syringe loading results in considerably higher flow rates (~5 mL min⁻¹) than loading by peristaltic pump (0.6 mL min⁻¹). We suspect that suspending cells on a filter under the higher pressure that accompanies syringe loading induced stress-related H₂O₂ production, which would suggest that T. weissflogii only produces substantial H₂O₂ under stress conditions.

A previous investigation of H₂O₂ production by T. pseudonana (Waring et al., 2010) used the Amplex Red™ method, which quantifies gross H₂O₂ production, and therefore those rates were expected to be comparable to the present study. Although production was not reported numerically, it can be inferred from Figure 3D in Waring that low-light production is 7 × 10⁻⁷ mol μg chl a⁻¹ over a 30 min period. Given the value of 4.45 × 10⁻⁸ μg chl a cell⁻¹ (Table 2 in Waring), we calculated a much higher value of P_H2O2 than seen in the present study, 7 × 10⁻¹⁴ mol cell⁻¹ h⁻¹. However, this discrepancy is the result of a calculation error (J. Waring, pers. comm.). The corrected H₂O₂ production rate is three orders of magnitude lower, meaning that their calculated value is not statistically different from the P_H2O2 measured under light conditions in the present study.

H₂O₂ production by the other three species—T. oceanica, C. cryptica, and P. tricornutum—has not previously been studied.

O${}_2^{-}$ and H₂O₂ Recoveries

O${}_2^{-}$ recoveries in the presence of cells have only been quantified in one previous study. Diaz et al. (2013) observed that heterotrophic bacteria exhibited a range of O${}_2^{-}$ recovery, with values ranging from 1 to 100% depending on the bacterial species. By contrast, the present study shows that diatoms fall at the low end of this scale, with recoveries for live organisms ranging from < 5 to 50%. While the low O${}_2^{-}$ recoveries in the present study suggest that O${}_2^{-}$ decay by phytoplankton could potentially contribute to O${}_2^{-}$ decay in the environment, a recent field study saw no significant effect of filtering on decay rates in water samples from Station ALOHA and the California Current (Roe et al., 2016).

The low recoveries shown in the present study indicate that phytoplankton break down H₂O₂, which is consistent with previous findings (Wong et al., 2003). In addition, Wong et al. found that neither killed phytoplankton nor spent cell medium decayed H₂O₂. This aligns with the findings of the present study, in which recovery percentages for H₂O₂ were generally 100% for killed organisms, and in which very little decay occurred in spent growth medium from P. tricornutum. Overall this suggests that H₂O₂ decay occurs through an active cell process, whereby organisms ultimately control the H₂O₂ levels in their vicinity. Interestingly, H₂O₂ decay is not necessarily an absolute characteristic of all microorganisms; in fact, some symbiotic relationships between microbial consortia may be based, in part, on the need for catalase-deficient hosts to acquire H₂O₂-degrading symbionts (Morris et al., 2011).

While active cell processes seemed to be responsible for degradation of H₂O₂, the same cannot be said for decay of O${}_2^{-}$. Although recovery rates for killed organisms were higher in live organisms (Figure 5), in no case did they ever reach 100%. Thus, while decomposition of O${}_2^{-}$ is likely to be mediated in part by active cell processes, at least some of it occurs through a passive process.

Controls on Biological ROS Production

Live T. pseudonana and T. oceanica appear to be actively producing O${}_2^{-}$ in the dark, since killing these organisms results in a significant decrease in P_O2− (results for T. weissflogii are not definitive because of the uncertainties in the measured P_O2− values for this species) (Table 2). This dark production corresponds with previous studies that link P_O2− to light-independent activity of outer membrane and/or transmembrane NADPH oxidases (Kim et al., 2000; Kustka et al., 2005; Saragosti et al., 2010). Similar transmembrane NADPH oxidases are known to mediate a number of essential physiological processes, such as cell development, signaling, and defense, in various eukaryotes, including fungi, plants, and mammalian cells (Saran, 2003; Bedard et al., 2007; Tsukagoshi et al., 2010). The potential for a physiological role for superoxide in diatoms, including Thalassiosira, is currently unknown and requires further exploration.

As observed previously (Kim et al., 2004; Kustka et al., 2005; Saragosti et al., 2010), P_O2− was enhanced for all three Thalassiosira species in the presence of light (Table 2). However, the magnitude of the enhancement appeared similar in live and killed cultures (Figure 2), so a passive photochemical source such as photo-oxidation of pigments present in the cell membrane cannot be ruled out as the additional source of superoxide. However, this is somewhat surprising given the low light levels used.

For the Thalassiosira spp, it is possible to compare the ratio of P_H2O2 to P_O2− to determine how much, if any, of the H₂O₂ could be produced via O${}_2^{-}$. The ratio of H₂O₂ formation from O${}_2^{-}$ can vary anywhere from 0, if all of the O${}_2^{-}$ is oxidized, to 0.5, if dismutation occurs, to 1, if all of the O${}_2^{-}$ is reduced. The relationship between P_O2− and P_H2O2 is shown in Figure 8. For T. oceanica, the data cluster around the 2:1 P_O2−:P_H2O2 line, suggesting that most or all of the H₂O₂ is produced via O${}_2^{-}$ dismutation. By contrast, P_H2O2:P_O2− data for T. pseudonana plot far under the same line, signifying H₂O₂ production in excess of what could be produced from O${}_2^{-}$ dismutation, or even reduction, alone.

FIGURE 8

Figure 8. Representation of the P_O2−:P_H2O2 ratio. The diagonal line indicates the 2:1 ratio that would be obtained if all H₂O₂ was produced through dismutation of O${}_2^{-}$.

One source of this surplus H₂O₂ might be diffusion of intracellular H₂O₂ through the cell membrane. H₂O₂ can diffuse across the lipid bilayer to a small extent (Seaver and Imlay, 2001), but a larger quantity simply passes through aquaporins (Halliwell and Gutteridge, 2007). Extracellular H₂O₂ production is often thought to represent cell leakage of photosynthetically reduced oxygen under high light conditions (Suggett et al., 2008; Waring et al., 2010). In the absence of light, leakage of intracellular hydrogen peroxide formed from oxygen reduction at other electron transport chains is also probable (Forman and and Boveris, 1982). Indeed, most H₂O₂ production by C. marina is believed to originate from intracellular processes, which is magnified under cell stress and loss of membrane integrity (Kim et al., 2007). However, H₂O₂ can also be produced extracellularly. A previous study found that proteins on the cell surface of the coccolithophore P. carterae produced H₂O₂ without simultaneously producing O${}_2^{-}$ (Palenik et al., 1987) using a two-electron reduction of oxygen for nitrogen metabolism (Palenik and Morel, 1990). At present it is not clear whether the excess H₂O₂ produced by T. pseudonana originates from extracellular and/or intracellular sources.

By contrast with T. pseudonana, T. weissflogii produces far more than twice as much O${}_2^{-}$ as H₂O₂, indicating that not all O${}_2^{-}$ undergoes dismutation to form H₂O₂ but rather is destroyed by a different process, e.g., oxidation by organic matter. Alternatively, the higher flow rates in the O${}_2^{-}$ determination as compared to those in the H₂O₂ experiment might lead to higher ROS production overall; the present study clearly shows that T. weissflogii cells produce more H₂O₂ when subjected to higher flow rates, and this may also be true for O${}_2^{-}$ production.

This study shows that diatoms have a wide range of values for P_H2O2 that hint at a diversity of biological pathways involved in production. Neither C. cryptica nor P. tricornutum produced measurable extracellular H₂O₂ under the tested conditions. Likewise, we cannot rule out that they produce O${}_2^{-}$, but if they do, they decompose it so effectively that the present study could not quantify it. While significant production of ROS was not detected from P. tricornutum cells, they do exude substances into the growth medium that produce ROS. H₂O₂ production by cell exudates was not quantified for the other species because P. tricornutum is the only species for which unusually high ROS signals were observed during loading, when spent medium was passing through the filter. It is likely that P. tricornutum secretes enzymes that produce H₂O₂ and, possibly O${}_2^{-}$ (though the present study was unable to test the latter). In fact, secretion of extracellular enzymes that produce O${}_2^{-}$ has previously been observed for C. marina (Kim et al., 2000) and a bacteria within the Roseobacter clade (Learman et al., 2011). These enzymes have been identified as an animal heme peroxidase for the Roseobacter bacterium (Andeer et al., 2015) and a protein analogous to the neutrophil NADPH oxidase in C. marina (Kim et al., 2000).

Concluding Remarks

Previous field studies (Rose et al., 2008; Vermilyea et al., 2010; Roe et al., 2016) showed that dark biological production of ROS is significant in comparison with photochemical production. Assuming all large phytoplankton, such as diatoms, have ROS production rates of the same approximate magnitude as those in this study, and assuming cell counts of 10⁴ (in oligotrophic waters such as Station ALOHA; Venrick, 1997) to 10⁶ (in the Gulf of Alaska; Paul et al., 1991) cells L⁻¹, the phytoplankton contribution to this dark biological production would be 1–100 pM h⁻¹, a small fraction of the observed 1–8 nM h⁻¹. It is likely that dark biological ROS production is usually driven by the more numerous cyanobacteria (as in Rose et al., 2008) and heterotrophic bacteria (Diaz et al., 2013). However, phytoplankton such as diatoms may make major contributions to steady state concentrations of O${}_2^{-}$ and H₂O₂ during blooms, when their abundance can increase 10-fold (Villareal et al., 2012); this corresponds to observations showing higher O${}_2^{-}$ concentrations in Trichodesmium blooms (Rose et al., 2010).

Although diatoms may not be the primary influence on ROS concentrations in the ocean, their ROS production is likely an essential physiological process. In fact, previous studies have shown that O${}_2^{-}$ production is linked to Fe uptake in Trichodesmium (Roe and Barbeau, 2014) and Lyngbya majuscula (Rose et al., 2008) and cell growth/signaling in C. marina (Oda et al., 1995; Marshall et al., 2005), and that H₂O₂ may be a byproduct of nitrogen metabolism in P. carterae (Palenik et al., 1987). Given that similar species of diatoms produce ROS via different pathways, it is likely a suite of physiological benefits are conferred through ROS production that vary with species and, likely, environmental conditions.

Author Contributions

BV: Directed the research. RS: Designed experiments, cultured algae, conducted hydrogen peroxide measurements. KR: Designed experiments, cultured algae, conducted superoxide measurements. CH: Aided in design and interpretation of experiments.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank Mak Saito for his input on culture purity and undergraduates Lauren Leavitt and Shannon Pollmiller for help with methods testing. We would like to thank the Hildebrand lab at Scripps Institute of Oceanography for the P. tricornutum. This research was supported by NSF grant OCE-1131734/1246174 to BV and CH.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fchem.2016.00005

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