Jekaterina Zyuzin, John Tuthill, and Pedro Verdugo

The role of assembled polymer gels in marine bacterial growth: an outlet for refractory carbon?

Jekaterina Zyuzin, John Tuthill, and Pedro Verdugo August 17, 2006

Abstract

The cycling of carbon in the ocean is critical for the transport of organic nutrients from the surface to the seafloor. Carbon from the atmosphere is fixed by photosynthetic plankton and stored as refractory dissolved organic material (DOM) in the ocean. Discrete biopolymers from this DOM pool can spontaneously assemble via ionic interactions to form microscopic polymer gels; approximately 10% of DOM is stored as assembled gel. This estimate predicts that 1016 g of reduced carbon is stored as microscopic polymer gel, a tremendous sink of particulate organic material. We tested the hypothesis that marine bacteria rely on these assembled polymer gels for growth and reproduction by monitoring microbial concentration and distribution in the presence of polymer gels. Our results indicate that bacteria are present inside gels in much higher concentrations than in the ambient seawater, and that bacterial growth is enhanced in the presence of polymer gels. Bacteria may also actively colonize assembled polymer gels. The pool of DOM that was previously considered to be refractory and vital for carbon sequestration may in fact be accessible to microbial consumption in the form of assembled polymer gels, resulting in significant release of carbon back into the atmosphere.

Introduction

The cycling of biological macromolecules in the ocean is critical for the transport of organic nutrients from the surface euphotic zone to interior regions inaccessible to the atmosphere. A “biological pump” is responsible for transporting organic carbon fixed by photosynthetic plankton from the surface of the ocean to its depths. This is achieved primarily by the sinking of particulate organic matter. Large macromolecules from dead organisms are dismantled via microbial degradation, and while some of the organic nutrients are then converted back to a biologically useful form, the majority of organic currency is stored as dissolved organic material (DOM). Understanding how patterns of carbon flux operate and may respond to global changes due to anthropogenic influence is a primary motive for the study of carbon cycling in the ocean. Dissolved organic material between 1 and 1000 nm comprise the most abundant fraction of biomass in the ocean (Hedges and Oades, 1997). DOM removal depends on either degradation to soluble chemical species smaller than 600 daltons or assembly to form larger sinking particles (Amon and Benner, 1994). Discrete natural biopolymers from this pool may then spontaneously assemble via nonspecific surface interactions to form large particulate microgels (Chin et al., 1998) that play an important role in the formation of the sinking particles that act as the deposition mechanism in the biological pump (Alldredge et al., 1993; Logan et al., 1995). It has been estimated that 10% of the total DOM in the ocean is in the form of assembled microgels (Chin et al., 1998). If 97% of the ocean’s organic carbon is stored as DOM (Hedges, 1992; Guo and Santschi, 1997), this would amount to 70 gigatons of polymer gel, a substantial sink of potentially bioactive substrate that exceeds the global biomass of marine organisms by a factor of 50

(Verdugo et al., 2004).

The assembly of hydrogels consists of the formation of a three-dimensional polymer network in water, stabilized by ion-bridging between polymers (Chin et al.,

1998). Gels are large enough to form a distinct interface. Unique properties emerge because the entrapment of water in the network maintains thermodynamic equilibrium between the interior of the gel and the surrounding aqueous medium. The polymers that compose hydrogel networks consist of polysaccharides, proteins, and nucleic acids, whose chemical and physical properties determine the architecture and reactivity of the network. Their spontaneous assembly follows a second-order kinetics, which appears to reach equilibrium after several days under abiotic conditions (Chin et al., 1998). DOM responsible for the removal of carbon from the euphotic zone may also recycle dissolved carbon to a form accessible to microorganisms as high molecular weight marine gels (Amon and Benner. 1996). Surveys of bacterial populations have found clusters of bacteria around transparent organic particles (Alldredge et al., 1993).

Distribution of particulate organic matter may also regulate bacterial species richness

(Long and Azam, 2001). Gels may serve as nutrient sources or attachment surfaces for bacteria, providing regions of high substrate concentration (Azam 1998; Wells 1998).

Studies of the complexity of an organic matter continuum in the ocean (Azam, 1993) and active behavior of scavenging microbes (Mitchell et al., 1995) also suggest that bacteria may substantially modify the biogeochemical properties of organic matter (Azam, 1998).

If bacteria are directly interacting with assembled polymer gels from the DOM pool, this will have profound implications for not only the regulation of microbial diversity and abundance, but also the reactivity of what is often considered an inaccessible sink of carbon.

We tested the hypothesis that bacteria in the ocean rely on assembled polymer gels for growth and reproduction using two complementary experiments. First, we examined microbial colonization of assembled gels by measuring the concentration of bacteria associated with polymer gels as a function of time. Second, microbial growth was monitored in the presence of assembled and dispersed polymer networks to test how the assembly state of the organic matter field affects bacterial population concentration.

Materials and Methods

Experiment 1 Seawater (SW) samples were collected from the dock of Friday Harbor

Laboratories on July 21, 2006. Five hundred-fifty ml of SW was filtered at 0.22 m and stored in the dark at 12° C for 240 hours to allow gel assembly (Orellana et al., 2003).

On July 31, another SW sample was collected from the same site and 500 ml was filtered at 3 m to serve as a bacterial inoculant. Bacterial population was measured by drawing a 50 ml sample of the inoculant and halting bacterial growth with 50 l of 0.02%

NaN3. A 50 ml control sample was also taken from the pool of assembling gels and

treated with 50 l 0.02% NaN3.

The remaining 450 ml of bacterial inoculant was then added to the 500 ml of assembled gels. We measured the bacterial colonization of the assembled gels by sampling at 9 time points after gel infection: 5, 15, 30, 60, 120, 360, 720, 1440, and 2880 minutes. At each point, a 100 ml aliquot was drawn from the sample and bacterial

growth was arrested with 100 l of 0.02% NaN3. The sample was then filtered through a

0.22 m black filter at 20 cmHg vacuum pressure. Gels and bacteria retained on the filter were stained with 10 ml of 1 mM chlorotetracycline, (ex = 390 nm, em = 530 nm;

Chin et al., 1998) to label gels and 10 ml of 0.6 M BacLight Red (ex = 581 nm, em =

644 nm) to stain bacteria.

Stained gels and bacteria were imaged using an Olympus IX71 inverted fluorescent microscope and Deltavision RT Restoration Imaging System. Gel concentration was determined by random sampling of filters at 40X magnification for both controls and experimental treatments. Distributions of free bacteria (outside of gels) were estimated by random sampling of the filter at 100X magnification. We quantified the number of bacteria within assembled gels by locating individual gels on the filter and creating z-section photograph series with multiple filters

(ex = 360/40 nm, em = 528/38 nm; ex = 555/38 nm, em = 617/73 nm). This allowed us to resolve both the volume of the gel and the number of bacteria inside. Gel area in the x-y plane was estimated using 2D-polygon finder software (Softworx 3.5.0). We measured thickness in the z-direction by defining the upper and lower planes where intensity was half maximal as the top and bottom (3D Object Builder, softWoRx 3.5.0).

Due to the small sample size, the data was fit to a normal distribution using the

Shapiro-Wilks test. Analysis of variance of nonparametric data was calculated using the

Kruskal-Wallis test, and t-tests were used to test for individual differences between means. Statistical tests and logarithmic regression were performed with JMP software.

Experiment 2

Surface seawater (SW) samples were collected from the Friday Harbor

Laboratory dock over a 5-day period from July 10 to 14, 2006. One-liter samples were pre-filtered with 5 m Millipore filters followed by filtration through 0.22 m Millipore

Stericup filter system under 20 cmHg vacuum pressure. Sample containers were stored in the dark at 12 °C. On July 13, artificial seawater (ASW: Sigma Sea Salt, 40 g/l in

MilleQ) was prepared and filtered at 0.22 m kept in the dark together with other gels as a control sample.

To obtain bacterial concentrate, 2 liters of SW were collected and centrifuged at

7,500 RPM for 15 min. The supernatant was discarded except for 400 mL of bacterial concentrate. This concentrate was filtered at 3 m into a single container and thoroughly mixed before inoculation. From this solution 10 mL was drawn, filtered and stained as a control sample.

Samples of filtered SW were prepared that differed only in the interval between filtration and inoculation with bacterial concentrate. Five time intervals were tested: 2,

23.5, 48.5, 72, and 97 hours. To ensure adequate and uncontaminated concentration of gels, 100 ml of ASW and five 100 ml filtered SW samples were prepared. Addition of

0.02% NaN3 was used to halt metabolic activity.

Another 100 ml was then drawn from each sample and inoculated with 10 mL of bacterial concentrate. This solution was stored in the dark at 12 °C for two hours, at which point microbial activity was halted by addition of 0.02% NaN3. Samples were filtered with a 0.22 m black Millipore filter at 20 cmHg vacuum pressure. The filter was then stained with 10 mL of 1 mM chlorotetracycline and 5 ml of 1.2 M BacLight

A similar protocol was used for samples collected between July 30 and August 2,

2006 except for the following modifications. SW and ASW samples were filtered with

0.45 m filter systems instead of 0.22 m. The intervals between filtration and seizure of metabolic activity were 2.5, 11.5, 28.5, and 44.5 hours. As a control for the volume of bacteria and assembled gel in each of the samples, 0.02% NaN3 was added to 110 ml samples immediately after inoculation with bacteria.

Gels in the control samples were imaged at 40X magnification using a Nikon

Inverted Fluorescent Microscope (DIAPHOT). An MRC-600 Series Laser Scanning

Confocal Imaging System (BioRad) and COMOS software (BioRad) were used for bacterial imaging. Images were taken at random portions of each filter, using 100X magnification. Concentration of bacteria per milliliter of SW was calculated by taking random images of twenty fields per filter (with active area of 2x108 m2), hand-counting bacteria in each image (with area of 10,411 m2), and calculating total number of bacteria per milliliter of SW. Mean numbers of cells per milliliter were compared by a one-way analysis of variance (ANOVA).

Results Experiment 1

The total concentration of bacteria in ambient seawater changed significantly throughout the experiment (Figure 1; F[9,100] = 8.60, p = .0001). There was a pronounced drop in bacterial population between 15 and 60 minutes into the experiment.

A similar trend was observed for the average area of polymer gels in the sample, although this trend was not significant (Figure 2). At 15 minutes, the concentration of gels dropped then recovered slightly as the experiment progressed. The concentration of both bacteria and polymer gels is an important factor to consider while interpreting the remainder of the data.

16000000 14000000 12000000 10000000 8000000 6000000 4000000 2000000 0

Figure 1. Concentration of bacteria in ambient seawater as a function of time. Error bars represent standard error from the mean. 4000 3500 3000 2500 2000 1500 1000 500 0

Figure 2. Area of assembled polymer gels in SW as a function of time. Error bars represent standard error from the mean.

The number of bacteria/ml of gel increased steadily over time (Figure 3; F[8,129]

= 5.60, p = 0.0001). A regression was fit to the data, indicating a strong positive correlation between bacterial concentration and time (R2=0.86), which was highly significant (p= 0.0003) . The ratio of bacteria inside of to outside of gels increased over time, although the regression was not statistically significant (Figure 4). Together these data indicate that the number of bacteria inside of gels increased over time relative to the total number of bacteria.

3.5E+09

3.0E+09

2.5E+09

2.0E+09

1.5E+09

1.0E+09

5.0E+08

0.0E+00 1 10 100 1000 10000

Figure 3. Mean bacterial count inside assembled polymer gels, represented as number of bacteria per ml gel, as a function of time. Error bars represent standard error. 700 600 500 400 300

200 100 0 1 10 100 1000 10000

Figure 4. Mean values of the ratio of bacteria inside gels to bacteria outside gels as a function of time. An average gel thickness of 8.33 m is used to calculate volume of gel. Error bars represent standard error from the mean.

The concentration of bacteria inside of gels was approximately two orders of magnitude greater than that in the ambient seawater (Figure 5). Bacterial concentration both inside and outside of gels followed the trend in bacterial population indicated in

Figure 3.

1.0E+10 1.0E+09 1.0E+08 1.0E+07 1.0E+06 1.0E+05 In seawater 1.0E+04 Inside polymer gels 1.0E+03 1 10 100 1000 10000

Figure 5. Mean concentration of bacteria as a function of time in ambient seawater and inside assembled polymer gels. Error bars represent standard error.

Experiment 2

Control samples in which bacterial growth was stopped immediately after inoculation indicate that initial bacterial count was uniform throughout the series. There was also a significant difference (p < 0.0001) between the control and experimental treatments with 2-hour bacterial growth (Figure 6). Paired student t-tests demonstrate that 11.5 hrs and ASW samples were significantly different from the rest. It should be noted that the quality of the images obtained for those two samples are lower than for the rest of the series due to non-optimal laser module settings.

100,000 In SW with gels at 0 hrs In SW with gels after 2 hrs 80,000 In ASW at 0 hrs In ASW after 2 hrs 60,000

40,000

20,000

0 0 2.5 11.5 28.5 44.5

Figure 6: Bacterial concentration for SW samples filtered at 0.45 m after two-hours of inoculation (red). The control was inoculated at time = 0 after (blue). Amount of bacteria in ASW was roughly the same at the time of inoculation (yellow) and at two-hours later (green).

The number of bacteria/ml of SW was higher for the 0.45 m treatment then the

0.22 m treatment for assembly times longer than 2.5 hours (Figure 7). Larger pore size permits more and bigger polymers to pass through the filter, and is consistent with the leftward shift of the curve for bacterial concentration in samples filtered at 0.45 m versus 0.22 m. 100,000

Filtered at 0.22 um 80,000 Filtered at 0.45 um

60,000

40,000

20,000

0 0 20 40 60 80 100

Figure 7: Bacterial concentration in two samples; one is filtered at 0.22 m (blue) and the other is filtered at 0.45m (red). Both samples experience an increase in the total amount of bacteria per milliliter as the time of gel assembly increases.

The concentration of bacteria in the ASW control sample was similar to that of the 2 and 23.5 hrs SW samples from the 0.22 m filtered treatment (Figure 8a) as well as the 2.5 hrs SW sample from the 0.45 m filtered series (Figure 8b; p < 0.118). The amount of bacteria in the ASW control sample was consistent with the amount obtained from the SW filtered samples at the early phase of the gel formation.

Density of bacteria in SW with gels Density of baceria in ASW 50,000

40,000

30,000

20,000

10,000

0 0 2 24 49 72 97 100,000

80,000

60,000

40,000

20,000

0 0 2.5 11.5 28.5 44.5

Figure 8: Bacterial concentration for SW and ASW samples. a. (top) Samples filtered at 0.22 m. b. (bottom) Samples filtered at 0.45 m.

We observed a gradual increase in bacterial concentration within the 0.22 m treatment up to 48.5 hrs and up to 28.5 hrs for the samples in 0.45 m series. Although differences in the quantity of bacteria between 2 hr and 23.5 hr samples in 0.22 m filtration series were not statistically different (p < 0.1177), a positive trend was clear. At assembly times of 48.5 hrs for 0.22 m filtration and 28.5 hrs for 0.45 m filtration, we saw the highest count of bacteria (in comparison to control, p < 0.0001). At longer assembly times, there was a slight decrease in bacterial concentration in both treatments.

For the samples filtered at 0.22 m at 72 hrs and 97 hrs, there was no significant difference (p < 0.3773). At such long assembly times, the polymer network should have already been in the assembly/dispersion equilibrium phase with no difference in the concentration of gels between these samples.

Discussion

The concentration of bacteria inside assembled polymer gels was several orders of magnitude greater than the concentration of bacteria in ambient seawater (Figure 1). This result indicates that gels provide a substrate for bacterial growth, and may explain the observation that marine bacterial abundance varies at a very fine spatial scale (Long and

Azam, 2001). Microscale “hotspots” of bacterial distribution have been observed as maxima within what was previously considered a continuously variable distribution of marine bacteria (Seymour et al., 2000). The anisotropic distribution of organic matter at this scale, including the assembly of organic matter into ordered polymer gels, may be responsible for the patchiness of bacterial distribution (Long and Azam, 1996).

Bacterial growth was significantly greater in environments that contained assembled polymer gels than those that contained organic carbon in a dissolved form

(Figure 8a. and 8b). The obvious trends in this data indicate that marine bacteria exhibit enhanced growth in the presence of assembled gels compared to unassembled gels. This result is consistent with the hypothesis that bacteria interact directly with the polymer gel matrix, and that microhabitats of organic matter provide a more valuable growth substrate than seawater containing dissolved organic matter. Gels may act as nutrient sources for microbes, or may provide surfaces for microbial attachment and interaction (Verdugo,

2004). There were two peaks in bacterial population: one at 48.5 hrs for 0.22 m filtration and the other at 28.5 hrs for 0.45 m filtration (Figure 7). At these times, gel formation had not yet reached equilibrium. On the other hand, polymeric material may have already grown to a size that, at least initially, would be easily accessed and consumed by bacteria. This could explain the dramatic increase in the number of bacteria. At later times, samples of SW presumably reached an equilibrium stage at which polymers assemble and disassemble at the same rate; this may explain why bacteria count remained approximately static (Figure 8b). The measurement of bacterial interaction with assembled gels suggests that bacteria actively colonize gels in seawater (Figure 3). Over the course of two days, significant increases in the number of bacteria per volume gel were observed. It is unclear whether bacteria respond behaviorally to the presence of microgels by actively seeking them, or whether they exhibit enhanced growth when associated with gels.

Increases in the percent of bacteria that occurred within gels compared to those in ambient seawater (Figure 4) indicate that over time microbes migrate to assembled gels; our data are unable to distinguish whether this result is due to microbial behavior or reproduction.

Total bacterial population exhibited a distinct decline and subsequent increase after 30 minutes following introduction to gels (Figure 1). This pattern may be caused by the growth cycle of bacteria, or could reflect an unknown interaction with the gel substrate. Similar decreases in gel volume were observed, followed by a distinct rebound

(Figure 2). A complex relationship between microbial growth, destruction of existing microgels, and microbial polymer production may be responsible for this trend.

Exoenzymes excreted by bacteria are able to hydrolyze assembled gels by breaking bonds between polymer strands (Verdugo et al., 2004). The interaction between gels and bacteria at this level is poorly understood. If bacteria do make use of microgels as readily as our data indicate, it will be valuable to investigate the complex interface between microbes and gel substrates.

Our results indicate that bacteria are not isotropically distributed throughout the ocean, but are concentrated in microscale patches based on the organic gel particle matrix. Furthermore, bacteria exhibit enhanced growth in the presence of polymer gels and are even capable of colonizing existing microgels. We propose a model for bacterial distribution in the ocean where assemblages of dissolved organic matter serve as substrates for bacterial growth and reproduction.

This finding may have profound implications for understanding the interaction of heterotrophic marine bacteria with particulate organic matter and nutrients, and how this interaction affects the biogeochemistry of the ocean. The existence of distinct gel microhabitats may be responsible for the microscale patchiness of bacterial diversity

(Long and Azam, 2001). Changes in the state of the aquatic gel phase would therefore have profound ecological implications for bacterial diversity and distribution in the ocean. If marine polymer gels do serve an important role in the microbial loop, the state of gels might exert effects that scale to higher trophic levels.

Furthermore, these data suggest that DOM is not as impervious as once thought.

Spontaneous assembly of DOM into polymer gels may render this vital pool of organic carbon susceptible to microbial consumption. If confirmed, this finding will require significant revisions of our model of how atmospheric carbon is sequestered in the ocean via the carbon cycle.

Acknowledgements

We would like to thank Michelle Herko, Dr. Yongxue Ding, Anthony Chi,

Paulette Brunner, the Center for Cell Dynamics, and the other members of the 2006 Gel

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