SEARCH

SEARCH BY CITATION

Keywords:

  • protozoa;
  • energy transfer;
  • trophic interactions;
  • chronic stress;
  • Acanthamoeba castellanii

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information

Predation rates were measured for two Acanthamoeba castellanii strains feeding on metal-tolerant and metal-sensitive strains of Pseudomonas putida and compared with cellular thermodynamic data. Predation rates by A. castellanii strain ATCC 30010 correlated with cell volume of the prey. To explore whether this observation could be environmentally relevant, pseudomonad species were isolated from a pristine and a metal-contaminated river and were paired based on phylogenetic and physiological relatedness. Then, cellular thermodynamics and predation rates were measured on the most similar pseudomonad pair. Under cadmium stress, the strain from contaminated river sediments, Pseudomonas sp. CF150, exited metabolic dormancy faster than its pair from pristine sediments, Pseudomonas sp. N9, but consumed available resources less efficiently (more energy was lost as heat). Predation rates by both strains of ameba were greater on Pseudomonas sp. CF150 than on Pseudomonas sp. N9 at the highest cadmium concentration.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information

Chronic heavy metal contamination suppresses the functioning of ecosystems worldwide (Helgen & Moore, 1996). Many species of bacteria in these systems adapt to resist toxic effects of metals, so microbial diversity remains high (Gough & Stahl, 2011; Berg et al., 2012). Yet, the stressor suppresses cell functions such as respiration and metabolism (Kandeler et al., 2000; Ramsey et al., 2005; Stanaway et al., 2012) and the flow of energy to higher trophic levels (Pratt et al., 1997; Iwasaki et al., 2009). A possible explanation for these observations is that over time, genes for tolerance mechanisms become widespread and abundant (Coombs & Barkay, 2004; Martinez et al., 2006; Sobecky & Coombs, 2009; Nongkhlaw et al., 2012), but impose a continual energetic cost (Gibbons et al., 2011). The genetic mechanisms of heavy metal resistance in bacteria are well documented (Silver & Phung, 2005; Monsieurs et al., 2011; Ray et al., 2013), yet there is little knowledge of how these genes influence energy flow and trophic interactions.

Microbial communities in river sediments grow on particles coated with metal oxides and oxyhydroxides. Heavy metals such as cadmium and copper often replace iron and manganese in the metal oxide coatings in contaminated rivers (Nimick & Moore, 1994). Bacteria that grow on sediments are therefore chronically exposed to toxic metals (Brown et al., 1999). To tolerate chronic heavy metal stress, bacteria express genes to expel, bind, or reduce the metal (Silver & Phung, 1996; Nies, 1999, 2003). Microcalorimetry was previously used to quantify the energetic cost of heavy metal tolerance genes on growth yield and metabolism (Gibbons et al., 2011). Cellular thermodynamics were compared between isogenic strains of Pseudomonas putida KT2440, each with different single tolerance gene deletions. The study demonstrated that under cadmium stress, metal efflux transporters allow for enhanced competition (faster exit from metabolic dormancy) at the cost of depressed growth. This study appears to provide the first evidence that tolerance genes may limit biomass availability for higher trophic levels.

Protozoa, the primary consumers of bacteria in freshwater systems (Finlay et al., 1988), differentiate among prey based on size (Kinner et al., 1998; Rønn et al., 2002), C/N/P ratios (Gruber et al., 2009), and even extracellular polysaccharides. For example, Wildschutte et al. (2004) observed ameba discriminating among prey species that differed only in O-antigen compositions. Some tolerance gene proteins extend beyond the cell wall and can induce morphological and physiological changes in bacteria (Nies, 1999; Gibbons et al., 2011). Because ameba are highly selective, they may discriminate between prey based on cellular effects related to the presence or absence of tolerance genes.

In the present study, amebal predation and microcalorimetric assays were used to explore mechanisms by which tolerance genes may limit energy flow in an ecosystem suppressed by heavy metal mine waste contamination (Fig. 1). Two strains of Acanthamoeba castellanii were studied: a culture-collected species and an environmentally derived species. With these strains, amebal predation rates on P. putida KT2440 and two new metal-sensitive environmental isolates were measured under varying concentrations of cadmium. This controlled approach allowed us to determine whether tolerance genes affect predation in the experimental system. Environmentally relevant Pseudomonas spp. were isolated from contaminated and uncontaminated river sediments. Next, the isolates were screened to find a pair of organisms, one from each river, with highly similar 16S rRNA gene sequences and strong phylogenetic similarity as determined by multigene phylogeny (Supporting Information, Fig. S1), but with differing levels of cadmium tolerance. Microcalorimetry was used to measure cellular thermodynamics under varied cadmium concentrations. Lastly, amebal predation rates were measured on these environmental pseudomonads. Our hypothesis was that bacterial cellular changes that accompany cadmium stress would influence amebal predation rates. Our next hypothesis was that under cadmium stress, metal-tolerant bacteria from contaminated sediments would grow rapidly, but produce less biomass than the bacteria from pristine sediments.

image

Figure 1. Flow chart of the experimental approach and test species. For each test, organisms were exposed to varying concentrations of cadmium. Roman numerals correspond to specific Results and discussion section headings. *These data were obtained by Gibbons et al. (2011).

Download figure to PowerPoint

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information

Pseudomonas putida KT2440

Three metal-tolerant P. putida KT2440 strains were subjected to predation assays: KT2440 (wild type), the CzcCBA1 efflux transporter (∆czcA1) deletion mutant (KT1), and the P-type ATPase ion pump (∆cadA2) deletion mutant (KT4). Previous work determined the maximum Cd2+ tolerance of each organism as 380, 190, and 23 μM for the wild-type, KT1, and KT4 strains, respectively. These genes were shown to be partly responsible for cadmium resistance in these organisms (Leedjarv et al., 2008) and represent the sole difference between the strains. For predation assays with A. castellanii strain ATCC 30010, strains were cultivated on Bacto R2A solid media (20% nutrient strength). For assays with A. castellanii strain Clark Fork, strains were grown on solid defined media (15.5 mM KxPO4, pH 7.5/0.2% peptone/0.2% glucose/2.0% agar).

Predator isolation, propagation, and preparation

Two ameba were studied. A. castellanii strain ATCC 30010 was axenically cultured in ATCC medium 354 for 14 days and then diluted to 5 × 105 cells mL−1 as determined by hemocytometry. A second ameba was isolated from sediments of the heavy metal-contaminated Clark Fork River at the headwaters near Silver Bow Creek in Montana. Sediment samples were added to sterile tubes and filled with river water. The tubes were gently agitated on a vortex mixer, and 20 μL aliquots were dispensed onto the middle of a diluted Bacto R2A plate (20% nutrient strength) previously streaked with Escherichia coli ATCC 8739 bacterial cells. To avoid conditioning the ameba to prefer metal-tolerant organisms, cadmium was not incorporated into the agar. Amebic cysts were scraped from areas of clearing, suspended in phosphate-buffered saline (PBS; pH 7.2), and re-inoculated onto plates. Over several months of repeated subculture, an A. castellanii isolate (identified following Jahn et al., 1979, and named ‘A. castellanii strain Clark Fork’) outcompeted other protozoa and was the only species present. To harvest the ameba, cysts were identified via microscopy, collected from the plates, suspended in PBS, centrifuged five times at a Relative Centrifugal Force of 100, and diluted to 5 × 105 cells mL−1.

Predation measurements

Bacterial cells in late log phase growth were diluted to 4 × 107 cells mL−1. Assay plates were composed of agar (Difco 281230) containing 0, 1, or 10 μM CdCl2. Plates amended with 100 μM CdCl2 were prepared, but not used in this study because bacteria grew nonuniformly. Whatman 1001–6508 filter pads (10-mm diameter) were placed in the center of the agar plates. Using sterile 10 μL inoculation loops, bacterial strains were streaked outward from the filter pad (adapted from Wildschutte et al., 2004; The environmental bacteria were alternately streaked for a total of eight streaks per plate. The P. putida strains were streaked in the same manner. Then, the filter pad was inoculated with 20 μL of the 5 × 105 cells mL−1 A. castellanii suspension. Clearing was measured from the center of the filter pad to the end of the cleared zone every 2 days at 20 °C until a streak line was consumed (6–8 days). Bacterial cell density on control assay plates was determined by collecting three core samples (1.1 mm diameter) from streak lines during stationary phase, suspending them in 9-mL PBS tubes, vortexing them for 10 s, and counting cells in a Petroff–Hausser chamber (Hausser Scientific, Horsham, PA). Bacterial length and width were measured on at least 20 cells using ImageJ software (Abramoff et al., 2004). The mean cell volume was calculated as described by Montesinos et al. (1983). The amebal consumption rate was determined by dividing the total cell number consumed at each time interval by the elapsed time. Statistical analysis between groups was performed using one-way analysis of variance (anova) with a Tukey's post hoc test. Pearson's correlation tests assessed relationships between cell size and feeding rates.

Isolation of bacterial strains

Pseudomonads were isolated from the pristine Middle Fork of the Flathead River, near the Nyack floodplain in Montana. Sediment (2 g) was added to filter-sterilized subsurface water (8 mL) and tetrasodium pyrophosphate (2 mL; 0.1 M) in a 15-mL centrifuge tube. Tubes were vortex-mixed for 30 s, shaken, and vortexed again for 30 s. Diluted Alcoclear CCP-II cationic dispersant (200 μL; 1 g of 50% CCP-II into 99 g filter-sterilized deionized water) was added, and the tube was sonicated for 10 min (Branson 3210R-DTH, 50/60 Hz, 117V). After sonication, samples were vortex-mixed for 1 min and allowed to settle for 15 min. Suspensions were serially diluted (1:10, 1:100, and 1:1000) in filter-sterilized subsurface water and plated on dilute R2A agar (Difco) plates (10% nutrient strength). Plates were incubated aerobically for 2–4 weeks at 10 °C in the dark (in situ temperature ranged from 4 °C to 12 °C seasonally). Visible bacterial colonies were selected among culturable heterotrophs for subculturing. All isolates were subsequently cultivated on dilute R2A agar (20% nutrient strength) at 10 °C.

Metal-tolerant bacteria were isolated from sediments obtained from the headwaters of the heavy metal-contaminated Clark Fork River at Silver Bow Creek in Montana. Sediment (10 g) was added to sterile tubes containing sterile PBS. The tubes were mildly sonicated for 10 min (see above), and suspensions were decanted into new sterile tubes followed by serial dilution. To select for Cd-tolerant bacteria, 100 μL CdCl2 solutions with concentrations of up to 3.5 mM CdCl2 were added to the surface of the plates and let dry. Aliquots (100 μL) from the sediment dilutions were spread on the CdCl2-amended plates. Individual colonies were streaked on agar plates to obtain pseudomonad monocultures. All strains were identified using conventional molecular techniques (described in the Supplementary Information).

Montana strain comparisons

One metal-tolerant isolate (Pseudomonas sp. CF150) from contaminated sediments was paired with one metal-sensitive isolate (Pseudomonas sp. N9) from pristine sediments. Based on 16S rRNA gene, both bacterial strains were 100.0% identical at 795 base pairs to Pseudomonas reactans. To further determine phylogenetic relatedness among these two strains, a multigene phylogeny was generated using 8 functional genes: gatA: aspartyl/glutamyl-tRNA aminotransferase subunit A; ubiD: aromatic acid decarboxylase; trpD: anthranilate phosphoribosyltransferase; mqo1: malate quinone oxidoreductase; cafA: ribonuclease G; rpl1: 50S ribosomal subunit protein L9; trmD: tRNA (guanine-N(1)-)-methyltransferase; and rpsR: 30S ribosomal subunit protein S18. The multigene phylogeny supported the high degree of relatedness between the Montana Pseudomonas spp. (Fig. S1; for more detail on the multigene phylogenetic analysis, see the Supplemental Information). To compare phenotypic traits between the strains, each strain was inoculated in test kits (API 50 CH, bioMérieux). The strains were 96% similar on physiological tests. The only difference was that Pseudomonas sp. CF150 metabolized (l)-arabitol and erythritol, whereas Pseudomonas sp. N9 did not. The similarity between the strains as measured by additional phenotype tests (API 20 NE, bioMérieux) and enzyme assays (API ZYM, bioMérieux) was 86% and 75%, respectively. In lysogeny broth, Pseudomonas sp. CF150 grew up to 1.75 mM cadmium, and Pseudomonas sp. N9 grew up to 1.5 mM cadmium. Each isolate was also surveyed for the presence of heavy metal tolerance genes. The genomes of both organisms were sequenced using an Illumina sequencing platform, and the resulting genome scaffolds were analyzed and annotated as described in the Supplementary Information. Two heavy metal tolerance genes were detected in Pseudomonas sp. CF150, and 5 were detected in Pseudomonas sp. N9. Efflux genes were characterized in each strain (Supplementary Information). CzcA family efflux protein (a RND protein) was present in both strains; this gene enables organisms to tolerate high levels of metal stress (Leedjarv et al., 2008). Cell sizes were determined for all isolates under differing cadmium levels (Table 1).

Table 1. Cell sizes: dimensions of the Montana Pseudomonas spp. and P. putida KT2440
DimensionMontana Pseudomonas spp.P. putida KT2440
CF150N9Wild-typeKT1KT4
  1. n = 20–28 for length and width measurements. Numbers are given as mean ± SE.

0 Cd (μM)
Length (μm)1.61 ± 0.081.60 ± 0.060.97 ± 0.050.97 ± 0.061.18 ± 0.05
Width (μm)0.62 ± 0.030.53 ± 0.020.74 ± 0.040.71 ± 0.040.63 ± 0.02
Volume (μm3)0.430.310.310.290.31
1 Cd (μM)
Length (μm)1.64 ± 0.091.51 ± 0.070.95 ± 0.040.85 ± 0.061.15 ± 0.06
Width (μm)0.62 ± 0.030.58 ± 0.030.57 ± 0.020.48 ± 0.030.62 ± 0.02
Volume (μm3)0.430.350.190.120.29
10 Cd (μM)
Length (μm)1.61 ± 0.061.49 ± 0.070.95  ± 0.090.93 ± 0.071.22 ± 0.05
Width (μm)0.67 ± 0.030.51 ± 0.020.58 ± 0.030.42 ± 0.020.62 ± 0.02
Volume (μm3)0.480.260.200.110.31

Microcalorimetry measurements

Calorimetry measures differences in metabolic output caused by variations in metal stress and individual resistance mechanisms. Thermogram peaks begin as the organism responds to the addition of a limiting resource (glucose). Heat output increases until all the resource is consumed and then drops rapidly. The thermograms allow the calculation of peak height (the maximum metabolic heat flow rate during growth), peak time (time to reach peak output, a combination of both lag phase and metabolic rate), metabolic rate (rate of heat output during log growth phase), and total heat produced by each strain (an efficiency measure, as more heat output results in less biomass).

Strains grown from stock on Bacto R2A plates (0.2% nutrient strength) for 48 h (25 °C) were transferred into a modified M1 minimal growth medium composed of 0.1 g L−1 KH2PO4, 0.1 g L−1 MgSO4·7 H2O, 0.25 g L−1 KNO3, 0.02 g L−1 anhydrous CaCl2, and 0.6% (wt/vol) d-glucose. For growth in the calorimeter, M1 medium was amended with CdCl2 to make 0, 0.01, 0.1, and 1 mM cadmium concentrations. Calorimetry assays were run in a VP-ITC instrument (Microcal VP-ITC user's manuals, 2002; MicroCal LLC, Northampton, MA) and analyzed using VP-Viewer2000 software. Each experiment started with the same amount of glucose (101 ± 5 nmol) so that there would be a fixed amount of carbon and energy (∆Ggluc = −2870 kJ mol−1). Initial and final glucose concentrations were measured with fluorometric glucose assay kits (K606-100; BioVision, Mountain View, CA) according to the manufacturer's instructions. The metabolic rate was obtained by calculating the rate of the heat output. Detailed methods are described in Gibbons et al. (2011).

Results and discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information

Amebal predation of P. putida KT2440

In the absence of cadmium, A. castellanii strain ATCC 30010 consumed the wild-type P. putida KT2440 and P. putida strain KT1 (czcA1 gene deletion) at a faster rate than P. putida strain KT4 (cadA2 gene deletion; P < 0.001; Fig. 2a; Table 2). As the metal concentration increased to 1 and 10 μM cadmium, the wild type was consumed faster than both KT1 and KT4. This feeding behavior may be explained by the fact that ameba have been observed to consume smaller bacteria faster than larger bacteria (Dillon & Parry, 2009). Also, nanoflagellates, which can feed similarly to ameba (Xinyao et al., 2006), were shown to preferentially graze smaller P. putida cells faster than larger cells (Fu et al., 2003). The volumes of the wild type and KT1 were smaller than KT4 at 1 and 10 μM cadmium, and they were consumed faster than KT4. The feeding rate on KT1 positively correlated with its volume (Pearson's r = 0.217; P = 0.10), whereas the feeding rate on KT4 negatively correlated with its volume (Pearson's r = −0.301; P < 0.001).

Table 2. Mean consumption rates (cells h−1) of Montana Pseudomonas spp. and P. putida strain KT2440
A. castellanii strainCd (μM)Monlnna Pseudomonas spp.PP. putida strain KT2440 P
CF 150N9 F KT2440KT1KT4 F
  1. Numbers are given as mean ± SE. n = 8–80 for consumption rate measures. A one-way analysis of variance (anova) with a Tukey's post hoc test was performed to test the probability of a significant difference in measured values between strains and obtain F and P values. Subscripted symbols α, β, and χ indicate statistical groupings from the post hoc test.

ATCC01.2 × 104 ± 1 × 1031.1 × 104 ± 1 × 1032.230.140 inline image inline image inline image 42.86< 0.001
19.7 × 103 ± 6 × 1029.2 × 103 ± 6 × 1020.330.569 inline image inline image inline image 91.34< 0.001
101.3 × 104 ± 1 × 1038.7 × 103 ± 6 × 10224.5< 0.001 inline image inline image inline image 34.13< 0.001
Clark Fork01.9 × 104 ± 1 × 1031.9 × 104 ± 1 × 1030.040.851 inline image inline image inline image 31.24< 0.001
11.4 × 104 ± 1 × 1021.4 × 104 ± 1 × 1030.450.5131.7 × 104 ± 1 × 1031.5 × 104 ± 1 × 1031.6 × 104 ± 1 × 1031.2970.280
102.0 × 104 ± 1 × 1031.8 × 104 ± 1 × 1035.190.039 inline image inline image inline image 17.43< 0.001
image

Figure 2. (a) ATCC Acanthamoeba castellanii (b) A. castellanii strain Clark Fork consumption rates of Pseudomonas putida KT2440. White bars represent KT2440, gray bars represent KT1, and black bars represent KT4. (c) ATCC A. castellanii and (d) A. castellanii strain Clark Fork consumption rates of the Montana Pseudomonas spp. CF150 (white bars) and N9 (black bars). Error bars represent standard error.

Download figure to PowerPoint

Similar to the A. castellanii strain ATCC 30010, A. castellanii strain Clark Fork consumed more wild-type and KT1 cells than KT4 cells at 0 μM cadmium (P < 0.001; Fig. 2b). These results correspond to the trends observed with the ATCC A. castellanii in the absence of cadmium. However, the relationship between prey volume and feeding rate did not exist under cadmium exposure with A. castellanii strain Clark Fork. At 1 μM Cd, there was no difference in bacterial consumption rate between the two ameba strains, but at 10 μM cadmium, A. castellanii strain Clark Fork consumed more KT4 cells than the wild type. In these experiments, the only difference in the prey organisms is the presence and/or absence of one of two metal tolerance genes (czcA or cadA2). In the wild-type P. putida, the cadA2 gene is constitutively expressed and serves as a housekeeping metal resistance mechanism. When cadA2 is deleted, P. putida strain KT4 (the cadA2 knockout mutant) becomes 16 times more sensitive to cadmium exposure than the wild type. Strains that lack the cadA gene accumulate intracellular cadmium upon exposure (Leedjarv et al., 2008), which may deter predators. However, our data suggest that in the case of A. castellanii strain Clark Fork that was isolated from contaminated sediments, the predator was not deterred by an increase in its prey's intracellular cadmium levels.

Thermodynamic measures of environmental pseudomonads

At a moderate cadmium concentration (0.10 mM), the peak height (maximum metabolic heat flow rate) was higher in Pseudomonas sp. CF150 than in Pseudomonas sp. N9 (F = 19.24; P = 0.012; Fig. 3a; Table 3). The peak heights of both strains dropped at elevated cadmium concentrations (1.0 mM). At 1.0 mM cadmium, the performance (peak time) of Pseudomonas sp. N9 halved relative to peak times of both strains at each cadmium concentration (F = 51.4; P < 0.001; Fig. 3b). Exposure to high cadmium concentrations reduced the performance of Pseudomonas sp. N9. Because both strains tolerate at least 1.5 mM cadmium, a divergence in performance at only the highest cadmium level was expected. These results correspond to our previous finding with P. putida KT2440, in which the strains that were more susceptible to Cd toxicity, KT1 and KT4 (Leedjarv et al., 2008), performed poorly when exposed to cadmium relative to the tolerant wild-type strain (Gibbons et al., 2011). The performance between the environmental strains remained unchanged from 0.00 mM to 0.10 mM cadmium. This is different from the previous study in which the wild-type strain was less competitive in the absence of cadmium.

Table 3. Principal calorimetric parameters obtained for Pseudomonas spp. CF150 and N9 in the presence of CdCl2 at 25 °C
Cadmium (mmol)Thermokinctic parametersMontana Pseudomonas spp.
CF150N9 F P
  1. a

    Total heat released by the microbial growth reaction as measured by the VP-ITC (mean ± SE, n = 3).

  2. b

    Maximum heat flow rate of the microbial growth reaction (mean ± SE, n = 3).

  3. c

    Time to reach peak of power/time curve (mean ± SE, n = 3).

  4. d

    Carbon moles of biomass divided by the carbon moles of glucose consumed (mean ± SE, n = 3).

  5. e

    Total heat released per carbon mole of biomass (μcal/C-mol × 10−11) (mean ± SE, n = 3). A one-way analysis of variance (anova) was performed to test the probability of a significant difference in measured values between strains and obtain F and P values.

0.00QT (mcal)a42.5 ± 1.543.6 ± 0.40.440.543
Pmax (μcal s−1)b3.20 ± 0.262.75 ± 0.311.280.319
tmax (h−1)c0.057 ± 0.0050.067 ± 0.0042.500.189
Yieldd0.48 ± 0.070.13 ± 0.0221.550.010
QT/C-mol of Biomass (10−11)e3.04 ± 0.5411.84 ± 2.0317.520.013
0.01QT (mcal)a49.8 ± 0.451.1 ± 0.92.160.216
Pmax (μcal s−1)b3.30 ± 0.193.40 ± 0.060.370.576
tmax (h−1)c0.049 ± 0.0010.052 ± 0.0013.010.158
Yieldd0.48 ± 0.010.20 ± 0.0488.350.001
QT/C-mol of boimass (10−11)e3.34 ± 0.038.84 ± 2.3414.560.019
0.10QT (mcal)a50.9 ± 0.647.3 ± 1.36.330.066
Pmax (μcal s−1)b3.37 ± 0.062.71 ± 0.0819.240.012
tmax (h−1)c0.047 ± 0.0010.049 ± 0.0050.030.873
Yieldd0.40 ± 0.060.32 ± 0.031.100.353
QT/C-mol of boimass (10−11)e4.36 ± 0.654.88 ± 0.590.360.582
1.00QT (mcal)a46.7 ± 0.241.4 ± 0.2362.650.001
Pmax (μcal s−1)b2.10 ± 0.182.04 ± 0.000.510.527
tmax (h−1)c0.048 ± 0.0010.024 ± 0.000519.290.001
Yieldd0.08 ± 0.010.31 ± 0.02127.650.001
QT/C-mol of boimass (10−11)e19.07 ± 2.674.33 ± 0.1918.200.024
image

Figure 3. Thermodynamic measures. (a) Peak height (b) performance and (c) percent biomass yield of the Pseudomonas spp. strains CF150 (black bar) and N9 (white bar) in the presence of different cadmium concentrations. Error bars represent the standard error.

Download figure to PowerPoint

A faster transition from metabolic dormancy to maximum resource consumption could offer an advantage over competitors in highly contaminated sites. However, such a response would reduce biomass production and potential energy flow to higher trophic levels. In contrast, the organisms without enhanced metal resistance produced more biomass, but are unable to process available glucose as soon as their metal-tolerant counterpart strains. These trends are supported by the energetics of growth of both the P. putida KT2440 wild type and mutants (KT1 and KT4) (Gibbons et al., 2011) and the Montana isolates. Pseudomonas sp. CF150 produced more biomass than Pseudomonas sp. N9 at 0.00 and 0.01 mM cadmium (269% and 140%, respectively; Fig. 3c). However, at 1.0 mM cadmium, biomass production by Pseudomonas sp. CF150 decreased (while the overall speed of glucose consumption by the Pseudomonas sp. CF150 strain increased relative to Pseudomonas sp. N9), and Pseudomonas sp. N9 yielded 288% more biomass (F = 11.42; P = 0.004). The KT1 strain also yielded more biomass (400% relative to the wild type) at 1.0 mM cadmium, but took longer to consume the available glucose than the wild type.

Amebal predation of Montana pseudomonads

Acanthamoeba castellanii strain ATCC 30010 consumed Pseudomonas spp. CF150 and N9 at the same rate at 0 and 1 μM cadmium (Fig. 2c). When the cadmium level increased to 10 μM, Pseudomonas sp. CF150 was consumed at a rate of 49% more cells per hour than Pseudomonas sp. N9. Furthermore, Pseudomonas sp. CF150 was larger than Pseudomonas sp. N9, so the predator consumed more volume per cell. Similar to the ATCC ameba strain, the consumption rate of these Pseudomonas strains by A. castellanii strain Clark Fork was the same at 0 and 1 μM (Fig. 2d). Further, as with the other ameba strain, more Pseudomonas sp. CF150 cells were consumed than Pseudomonas sp. N9 at 10 μM, which may be related to the greater biomass yield of Pseudomonas sp. CF150 at 0.01 mM cadmium (Fig. 3c).

Conclusion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information

This work reveals a possible factor for altered energy flow between trophic levels in contaminated ecosystems. The genes enabling an organism to survive in contaminated systems alter cellular metabolism, biomass yield, and predation rates. Under metal stress, metal-tolerant bacterial strains can exit metabolic dormancy faster than susceptible strains, but at the cost of a decreased growth yield. This corroborates work by von Stockar et al. (2006), which outlines theoretical and empirical support for the idea that an inverse relationship exists between an organism's growth rate and its biomass yield. In freshwater systems, many cells remain inactive, and the flux of nutrients is constant (Lennon & Jones, 2011). The ability to quickly ramp up metabolic activity during a pulse of nutrients and also withstand a stressor by employing tolerance genes would confer a strong selective advantage. Our results, coupled with the fact that stressed organisms can acquire an abundance of tolerance genes (Hemme et al., 2010; Zhang et al., 2013), may provide a starting point toward understanding the observed biomass and respiration suppression in contaminated systems (Kandeler et al., 2000; Ramsey et al., 2005; Gough et al., 2008; Stanaway et al., 2012).

Much ecotoxicology research seeks to determine lethal doses of pollutants to single species. These tests ensure that contaminant concentrations in the environment will be kept lower than what is required to kill a test species. This approach misses the important factor of how sublethal concentrations of pollution affect interactions between species, energy flow, and ecosystem health. Further investigation into the abundance and energetic costs of tolerance genes as they relate to energy flow and predator–prey interactions will improve our understanding of ecosystem responses to chronic stress.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information

The research was funded by awards from the National Science Foundation (DEB-0717449; MCB 0348773; EPS-0701906) and Montana Integrative Learning Experience for Students, a Howard Hughes Medical Institute-sponsored program (#52005905). We thank Crystal Munger for assisting in plate measurements, Anu Hynninen for providing the P. putida KT2440 strains, and Sergio E. Morales and Ylva Lekberg for reviewing this manuscript. We also acknowledge Cedric Jacobson, Callie Cole, Anne Larcom, Ruth Guthrie, and Mollie Riordan for their assistance with the Montana isolates and their help around the laboratory. S.G. is supported by an EPA STAR Fellowship.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Abramoff MD, Magalhaes PJ & Ram SJ (2004) Image processing with ImageJ. Biophoton Int 11: 3642.
  • Berg J, Brandt KK, Al-Soud WA, Holm PE, Hansen LH, Sørensen SJ & Nybroe O (2012) Long-term Cu exposure selects for Cu-tolerant bacterial communities with altered composition, but unaltered richness. Appl Environ Microbiol 78: 74387446.
  • Brown GE Jr, Foster AL & Ostergren JD (1999) Mineral surfaces and bioavailability of heavy metals: a molecular-scale perspective. P Nat Acad Sci USA 96: 33883395.
  • Cole JR, Wang Q, Cardenas E et al. (2009) The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 37: (Database issue): D141D145.
  • Coombs JM & Barkay T (2004) Molecular evidence for the evolution of metal homeostasis genes by lateral gene transfer in bacteria from the deep terrestrial subsurface. Appl Environ Microbiol 70: 16981707.
  • Dillon A & Parry DP (2009) Amoebic grazing of freshwater Synechococcus strains rich in phycocyanin. FEMS Microbiol Ecol 69: 106112.
  • Finlay BJ, Clarke KJ, Cowling AJ, Hindle RM & Rogerson A (1988) On the abundance and distribution of protozoa and their food in a productive freshwater pond. Eur J Protistol 23: 205217.
  • Fu Y, O'Kelly C, Sieracki M & Distel D (2003) Protistan grazing analysis by flow cytometry using prey labeled by in vivo expression of fluorescent proteins. Appl Environ Microbiol 69: 68486855.
  • Gibbons SM, Feris K, McGuirl MA, Morales SE, Hynninen A, Ramsey PW & Gannon JE (2011) Use of microcalorimetry to determine the costs and benefits to Pseudomonas putida strain KT2440 of harboring cadmium efflux genes. Appl Environ Microbiol 77: 108113.
  • Gough HL & Stahl DA (2011) Microbial community structures in anoxic freshwater lake sediment along a metal contamination gradient. ISME J 5: 543558.
  • Gough HL, Dahl AL, Nolan MA, Gaillard JF & Stahl DA (2008) Metal impacts on microbial biomass in the anoxic sediments of a contaminated lake. J Geophys Res 113: G02017.
  • Gruber DF, Tuorto S & Taghon GL (2009) Growth phase and elemental stoichiometry of bacterial prey influences ciliate grazing selectivity. J Eukaryot Microbiol 56: 466471.
  • Helgen SO & Moore JN (1996) Natural background determination and impact quantification in trace metal-contaminated river sediments. Environ Sci Technol 30: 129135.
  • Hemme CL, Deng Y, Gentry TJ et al. (2010) Metagenomic insights into evolution of a heavy metal-contaminated groundwater microbial community. ISME J 4: 660672.
  • Holben WE, Williams P, Saarinen M, Särkilahti LK & Apajalahti JHA (2002) Phylogenetic analysis of intestinal microflora indicates a novel Mycoplasma phylotype in farmed and wild salmon. Microb Ecol 44: 175185.
  • Iwasaki U, Kagaya T, Miyamoto K & Matsuda H (2009) Effects of heavy metals on riverine benthic macroinvertebrate assemblages with reference to potential food availability for drift-feeding fishes. Environ Toxicol Chem 28: 354363.
  • Jahn TL, Bovee EC & Jahn FF (1979) How to Know the Protozoa, Vol. 4, 2nd Edn. pp. 148. WM.C. Brown Company Publishers, Dubuque, IA
  • Kandeler E, Tscherko D, Bruce KD, Stemmer M, Hobbs PJ, Bardgett RD & Amelung W (2000) Structure and function of the soil microbial community in microhabitats of a heavy metal polluted soil. Biol Fertil Soil 32: 390400.
  • Kinner NE, Harvey RW, Blakeslee K, Novarino KG & Meeker LD (1998) Size-selective predation on groundwater bacteria by nanoflagellates in an organic-contaminated aquifer. Appl Environ Microbiol 64: 618625.
  • Lane DJ (1991) 16S/23S rRNA sequencing. Nucleic Acid Techniques in Bacterial Systematics, (Stackebrandt E & Goodfellow M, eds), pp. 115148. Wiley, Chichester, UK.
  • Leedjarv A, Ivask A & Virta M (2008) Interplay of different transporters in the mediation of divalent heavy metal resistance in Pseudomonas putida KT2440. J Bacteriol 190: 26802689.
  • Lennon J & Jones SE (2011) Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat Rev Microbiol 9: 119130.
  • Martinez RJ, Wang Y, Raimondo MA, Coombs JM, Barkay T & Sobecky PA (2006) Horizontal gene transfer of PIB-type ATPases among bacteria isolated from radionuclide-and metal-contaminated subsurface soils. Appl Environ Microbiol 72: 31113118.
  • Microcal VP-ITC user's manual (2002) Northampton, MA.
  • Monsieurs P, Moors H, Van Houdt R, Janssen PJ, Coninx I, Mergeay M & Leys N (2011) Heavy metal resistance in Cupriavidus metallidurans CH34 is governed by an intricate transcriptional network. Biometals 24: 11331151.
  • Montesinos E, Esteve I & Guerrero R (1983) Comparison between direct methods for determination of microbial cell volume: electron microscopy and electronic particle sizing. Appl Environ Microbiol 45: 16511658.
  • Nies DH (1999) Microbial heavy-metal resistance. Appl Microbiol Biotechnol 51: 730750.
  • Nies DH (2003) Efflux-mediated heavy metal resistance in prokaryotes. FEMS Microbiol Rev 27: 313339.
  • Nimick DA & Moore JN (1994) Stratigraphy and chemistry of sulfidic flood-plain sediments in the Upper Clark Fork Valley, Montana. Environmental Geochemistry of Sulfide Oxidation. pp. 276288. American Chemical Society, Washington, DC.
  • Nongkhlaw M, Kumar R, Acharya C & Joshi SR (2012) Occurrence of horizontal gene transfer of PIB-type ATPase genes among bacteria isolated from the uranium rich deposit of Domiasiat in North East India. PLoS one 7: e48199.
  • Pratt J, Melendez A, Barreiro R & Bowers N (1997) Predicting the ecological effects of herbicides. Ecol Appl 7: 11171124.
  • R Development Core Team (2011) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org/.
  • Ramsey PW, Rillig MC, Feris KP, Gordon NS, Moore JN, Holben WE & Gannon JE (2005) Relationship between communities and processes; new insights from a field study of a contaminated ecosystem. Ecol Lett 8: 12011210.
  • Ray P, Girard V, Gault M, Job C, Bonneu M, Mandrand-Berthelot MA, Singh SS, Job D & Rodrigue A (2013) Pseudomonas putida KT2440 response to nickel or cobalt induced stress by quantitative proteomics. Metallomics 5: 6879.
  • Rønn R, McCaig AE, Griffiths B & Prosser J (2002) Impact of protozoan grazing on bacterial community structure in soil microcosms. Appl Environ Microbiol 68: 60946105.
  • Silver S & Phung LT (1996) Bacterial heavy metal resistance: new surprises. Annu Rev Microbiol 50: 753789.
  • Silver S & Phung LT (2005) A bacterial view of the periodic table: genes and proteins for toxic inorganic ions. J Ind Microbiol Biotechnol 32: 587605.
  • Sobecky PA & Coombs JM (2009) Horizontal gene transfer in metal and radionuclide contaminated soils. Methods Mol Biol Clifton, NJ 532: 455472.
  • Stanaway D, Haggerty R, Benner S, Flores A & Feris K (2012) Persistent metal contamination limits lotic ecosystem heterotrophic metabolism after more than 100 years of exposure: a novel application of the resazurin resorufin smart tracer. Environ Sci Technol 46: 98629871.
  • von Stockar U, Maskow T, Liu J, Marison IW & Patiño R (2006) Thermodynamics of microbial growth and metabolism: an analysis of the current situation. J Biotechnol 121: 517533.
  • Wildschutte H, Wolfe D, Tamewitz A & Lawrence J (2004) Protozoan predation, diversifying selection, and evolution of antigenic diversity of salmonella. P Nat Acad Sci USA 101: 1064410649.
  • Xinyao L, Miao S, Yonghong L, Yin G, Zhongkai Donghui W, Weizhong W & Chencai A (2006) Feeding characteristics of an amoeba (Lobosea: Naegleria) grazing upon cyanobacteria: food selection, ingestion and digestion progress. Microb Ecol 51: 315325.
  • Zhang Z, Zhao X, Liang Y, Li G & Zhou J (2013) Microbial functional genes reveal selection of microbial community by PAHs in polluted soils. Environ Chem Lett 11: 1117.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusion
  7. Acknowledgements
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
fml12226-sup-0001-SupplementaryInformation.docxWord document225K

Data S1. Identification of bacterial strains.

Table S1. Table of NCBI prokaryotic genome annotation pipeline outputs for isolates Pseudomonas sp. CF150 and Pseudomonas sp. N9.

Fig. S1. Neighbor joining tree of multi-gene phylogenetic analysis among the metal tolerance and metal sensitive microbial isolates and known Pseudomonad species.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.