Kinetics of cadmium accumulation and its effects on microtubule integrity and cell viability in the seagrass Cymodocea nodosa

Aquatic Toxicology 144–145 (2013) 257–264

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Aquatic Toxicology journal homepage: www.elsevier.com/locate/aquatox

Kinetics of cadmium accumulation and its effects on microtubule integrity and cell viability in the seagrass Cymodocea nodosa Paraskevi Malea a,∗ , Ioannis-Dimosthenis S. Adamakis a , Theodoros Kevrekidis b a b

Department of Botany, School of Biology, Aristotle University of Thessaloniki, GR-541 24 Thessaloniki, Greece Laboratory of Environmental Research and Education, Democritus University of Thrace, Nea Hili, GR-68100 Alexandroupolis, Greece

a r t i c l e

i n f o

Article history: Received 23 August 2013 Received in revised form 8 October 2013 Accepted 13 October 2013 Keywords: Trace metal uptake Kinetics Uptake rate Toxicity Biomarker Marine angiosperm

a b s t r a c t The kinetics of cadmium accumulation and its effects on microtubule cytoskeleton and cell viability in leaf blades of the seagrass Cymodocea nodosa were investigated under laboratory conditions in exposure concentrations ranging from 0.5 to 40 mg L−1 . An initial rapid accumulation of cadmium was followed by a steady state. The Michaelis–Menten model adequately described metal accumulation; equilibrium concentration and uptake velocity tended to increase, whereas bioconcentration factor at equilibrium to decrease, as the exposure concentration increased. Cadmium depolymerized microtubules after 3–9 d of exposure, depending on trace metal concentration, indicating that microtubules could be used as an early biomarker of cadmium stress; cell death, occurring at later time than microtubule disturbance, was also observed. Microtubule depolymerization expressed as percentage of reduction of fluorescence intensity and cell mortality expressed as percentage of live cells increased with time. The lowest experimental tissue concentration associated with the onset of microtubule depolymerization and cell death (98.5–128.9 ␮g g−1 dry wt, 0.5 mg L−1 treatment, 7th and 9th d) was within the wide range of reported cadmium concentrations in leaves of seagrass species from various geographical areas. This lowest tissue concentration was exceeded up to the 3rd d at higher exposure concentrations, but toxic effects were generally detected at later time. The time periods required for the onset of depolymerization and for 10 and 50% of cells to die tended to decrease as the uptake velocity increased; in particular, significant negative correlations were found between these variables. These results suggest that toxicity appears to be a function of cadmium uptake rate rather than of the total tissue metal concentration. Hence, tissue residues should be interpreted in relation to the time frame of the exposure, while the estimation of metal uptake velocity could be utilized for predicting toxic effects. The data presented provide insight on the relationship between metal bioaccumulation and toxic effects in seagrasses and, overall, contribute to a better understanding of the impact of metals on aquatic organisms. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The use of biological species constitutes a valuable tool in the assessment of metal contamination in marine and estuarine environments. The measurement of metal concentrations in selected organisms, termed biomonitors, allows the evaluation of biological available levels of these contaminants in the ecosystem (Rainbow, 2006). The use of biomarkers, defined as cellular, molecular and biochemical changes induced by chemical pollutants measurable in biological systems such tissues, cells and biological fluids, allows the early detection and estimation of ecotoxicologically significant metal contamination (Ferrat et al., 2003; Rainbow, 2006). Metal concentrations in key biomonitors, being correlated in some way with the occurrence of the ecotoxicological effects identified

∗ Corresponding author. Tel.: +30 2310998272. E-mail address: [email protected] (P. Malea). 0166-445X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.aquatox.2013.10.017

using biomarkers, have also the potential to be used for detecting the presence of ecotoxicologically significant metal contamination (Rainbow, 2006). Seagrasses along with seaweeds and mollusks are the organisms of choice in most of the programmes of biological monitoring of coastal waters (Water Framework Directive 2000/60, EC, 2000). Various seagrass species have been employed in the field as biomonitors of metal contamination (see synthesis in Ferrat et al., 2003). However, little attention has been paid to laboratory studies on seagrasses to support their use as biomonitors in field situations. In particular, only a number of laboratory studies have addressed the uptake of metals in a few seagrass species, most commonly Zostera marina, Posidonia oceanica and Halophila stipulacea; moreover, in most of these studies, no attempt has been made to describe the uptake patterns by kinetic models (see among others Faraday and Churchill, 1979; Fabris et al., 1982; Lyngby and Brix, 1984; Malea, 1994; Malea et al., 1995a,b; Warnau et al., 1996).

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Information on the suitability of a few seagrass species, most commonly P. oceanica, in monitoring of metal contamination through the use of biomarkers has been provided by a number of studies. “Measurable responses” in photosynthetic activity, photosynthetic pigment concentration, secondary metabolite synthesis, oxidative stress molecules and detoxification mechanisms have been principally tested as biomarkers, and most of them appeared to be early symptoms of metal contamination (see among others Ferrat et al., 2003 for synthesis; Bucalosi et al., 2006; AlvarezLegorreta et al., 2008). Microtubule cytoskeleton in leaf cells of a seagrass species (Cymodocea nodosa) has also proved to be sensitive to metal (copper, nickel, chromium) stress at early time (Malea et al., 2013a). These results underline the need for additional data on the sublethal toxic responses of seagrasses to metal stress, particularly on toxic effects on microtubules of several other metals for which such information is lacking. Data on the relationship between metal accumulation and ecotoxicological effects in aquatic organisms are still scarce, particularly in seagrasses (see review in Lewis and Devereux, 2009; Adams et al., 2010). In a number of studies, some relationships between metal levels in tissues of a few seagrass species (e.g. H. stipulacea, P. oceanica, Zostera capricorni) and physiological impacts have been reported (see among others Cristiani et al., 1980; Malea, 1994; Malea et al., 1995a, 1995b; Macinnis-Ng and Ralph, 2004). However, as far as we know, none of these studies has essentially addressed the linkage between kinetics of metal uptake and toxicity. Rainbow (2002), on the basis of a literature review of metal accumulation patterns in aquatic invertebrates, argued that the factor that determines the effects is not the whole-body concentration but the rate of metal uptake in relation to metabolic capacity for detoxification and storage. Adams et al. (2010) exploring the strengths and limitations of using metal tissue residues to assess the potential for biological effects in aquatic organisms, underlined the need for additional information, particularly on the relationship between bioaccumulation rate and effects. The main goal of the present study is to achieve a better understanding of the impact of metals on aquatic organisms. In particular, this study aims to provide insight on the relationship between metal uptake kinetics and toxicological effects identified using biomarkers, with a focus on cadmium in seagrasses. Cadmium was chosen as a contaminant, because it belongs to the more toxic metals for aquatic organisms (Kennish, 2000). Amongst seagrasses, C. nodosa (Ucria) Ascherson was chosen since this species along with P. oceanica are the most important and wide-spread seagrasses in the Mediterranean Sea; moreover, C. nodosa has been regarded as a good biomonitor of metals, the leaves being the best fraction for the determination of metal loads (see among others Sanchiz et al., 2000; Malea et al., 2013b; Malea and Kevrekidis, 2013). Integrity of microtubule cytoskeleton, which plays an essential role in higher plant morphogenesis and growth (Hasezawa and Kumagai, 2002) and represents one of the intracellular targets of metal ions (see review in Adamakis et al., 2012; Malea et al., 2013a), and cell viability in leaf blades were chosen as response parameters. We investigated under laboratory conditions in increasing concentrations of cadmium (a) the uptake kinetics of cadmium into intermediate- juvenile leaf blades of the seagrass C. nodosa, (b) the effects of this metal on the microtubule (MT) cytoskeleton organization and the viability of leaf blade cells of this seagrass species, and (c) the relationship between uptake kinetics and effects. 2. Materials and methods 2.1. Plant collection C. nodosa was collected from the eastern coast of the Gulf of Thessaloniki, Northern Aegean Sea at Viamyl site (site V, 40◦ 33 N,

22◦ 58 E). At this site, C. nodosa grows from 0.4 m to around 2 m depth, forming a continuous monospecific meadow. Leaf biomass and leaf blade length display an almost unimodal annual pattern; both markedly increase from March to July-August, attaining a maximum value of approx. 150 g DW m−2 and 542.2 mm, respectively (Malea and Zikidou, 2011; unpublished data). Plant collection was done at the site V at 0.7–1.0 m depth in July 2011 with a 20 cm diameter acrylic corer, which penetrated to a depth of 30 cm; all the above- and below-substrate plant material that was rooted within the 20 cm area was collected. All plants were rinsed in seawater at the collection site and transported to the laboratory in plastic containers containing seawater. 2.2. Treatments Fresh green plants without epiphytes were kept for 24 h in seawater under laboratory conditions in order to equilibrate. Seagrass plants were incubated in plastic aquaria containing 10 L solutions of CdSO4 5H2 O (Merck) in filtered seawater (Whatman GF/C) in the following dissolved metal concentrations: 500, 5000, 10,000, 20,000 and 40,000 ␮g L−1 . Lower exposure concentrations might have resulted in toxic effects only after several days of incubation and, thus, these effects would most probably have been obscured by leaf deterioration. A control treatment, with no added metal, was included in the experiments. The same set of exposure concentrations has been also previously used in a similar study (Malea et al., 2013a). The seawater used for the experiments was also collected from the site V. The characteristics of the seawater used in the experiments were: salinity 36.7 psu, pH 7.9, dissolved O2 5.88 mg L−1 , N-NO2 − 0.02 ␮M, N-NO3 − 0.37 ␮M and N-NH4 + 0.43 ␮M. The solutions in the aquaria were changed every two days in order to adjust to the original levels. The aquaria were aerated constantly using aquarium pumps and covered with plastic foil in order to prevent evaporation. The experiments were conducted under a constant 16 h day/8 h night regime at an ambient temperature of 21 ± 1 ◦ C with light intensity set at 120 ␮mоl m−2 s−1 . After 0, 3, 5, 7 and 9 days at least three seagrass shoots from each aquarium were randomly removed for cadmium determination and after 0, 3, 5, 7, 9, 11 and 13 days three shoots for effects detection. Cadmium determination was not realized at the day 11 and 13 since a potential extensive cellular damage might markedly affect accumulation patterns. Similar procedures have been also used in previous studies (e.g. Malea, 1994; Malea et al., 1995a,b). 2.3. Cadmium determination Seagrass leaves were characterized as adult or intermediatejuvenile, and blades of intermediate-juvenile leaves were used for cadmium analysis. Leaf age estimation was based on the morphological feature of the sheath; leaves with no visible sheath were classed as juvenile and leaves with a no well-developed sheath as intermediate (Orfanidis et al., 2010). Intermediate-juvenile leaf blades from a common aquarium collected at the same incubation day were pooled. The samples were washed in double-distilled water, dried to a constant weight (60 ◦ C) and ground in an agate mill. Three sub-samples of each powdered plant sample were wet digested in HNO3 /HClO4 (4/1) at 50 ◦ C for 1 h and subsequently at 130 ◦ C for 3 h. The residue was filtered through an acid-washed filter (Whatman GF/C) and the final volume was adjusted to 25 mL with double-distilled water. Similar methods have been frequently used in previous studies (see among others Malea, 1994). Cadmium concentrations were measured using graphite furnace Atomic Absorption Spectrophotometry (AAS, AANALYST 700 Perkin-Elmer). Pro-analysis grade reagents (Merck, Darmstand, Germany) were used and reagent blanks were run concurrently. Standards were prepared by serial dilution of stock solutions. The

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accuracy of the technique was checked by analysis of standard sea lettuce reference material (Ulva lactuca no 279, Community Bureau of Reference BCR, Brussels, Belgium); one sample of the standard reference material was included in each analytical batch. Results were in agreement with certified values (certified value, mean ± SD: 0.274 ± 0.032 ␮g g−1 dry wt; measured value, mean ± SD: 0.280 ± 0.020 ␮g g−1 dry wt; recovery: 102%). 2.4. Tubulin immunostaining Tubulin immustaining was applied on either the smallest juvenile leaf or the meristematic region of the next smallest juvenile leaf blade of each collected shoot. All chemicals and reagents were purchased from Sigma (Taufkirchen, Germany), Merck (Darmstadt, Germany) and Applichem (Darmstadt, Germany), unless otherwise stated. Whole mound tubulin immunostaining was conducted in leaf pieces, following the protocol of Katsaros and Galatis (1992) with both anti-˛-tubulin (YOL1/34, AbD Serotec, Kidlington, UK) and FITC-anti-rat secondary antibody (Invitrogen, Carlsbad, CA) diluted at 1:80. DNA was counterstained with 3 ␮g mL−1 propidium iodide in PBS and the leaf pieces were finally mounted in an anti-fade solution. Then the leaf pieces were examined with a Nikon D-Eclipse C1 confocal laser scanning microscope (CLSM) and image recording was done with proper software (EZ-C1 3.20) according to the manufacturer’s instructions. Special care was taken in order to retain the laser beam gain equal among the different treatments. 2.5. Fluorescence intensity measurements Fluorescence intensity measurements were done in single cortical CLSM sections using the Image J (http://rsbweb.nih.gov/ij/) software. Linear adjustments in pixel values were made when measuring signal intensities and the corrected total cell fluorescence (CTCF) was calculated using this formula: CTCF = integrated density − (area of selected cell × mean fluorescence of background readings). At each treatment in each incubation day, fluorescence intensity from 15 individual cells of one leaf per shoot (total three leaves) was measured, the reduction in fluorescence intensity in relation to control values was expressed as percentage (%), and average percentage of 15 cells per leaf and mean (±standard error) of average percentages of three leaves were calculated. 2.6. Evans Blue staining In order to check the viability of the variously treated leaf blade cells, an Evans Blue staining was conducted. Evans Blue staining was done following the protocol by Malea et al. (2013a) adapted from Chen et al. (2008). In short, intermediate-juvenile leaf blades of the randomly selected shoots were incubated in a 0.25% aqueous Evans Blue solution done in seawater collected from the collection site V for 15 min at room temperature. After several washes with seawater the leaf blade segments were observed under a Zeiss Axiostar Plus light microscope equipped with a Canon PowerShot A640 camera and photographed. The dead cells (stained blue) from a total number of 500 cells of one leaf per shoot (total three leaves) were measured at each treatment in each incubation day, the measurements were expressed as percentage (%), and mean percentage (±standard error) of three leaves was calculated.

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time taken to reach half of the value of Cmax (see among others Fernández et al., 2006; Costa et al., 2011; Diaz et al., 2012). C=

C

×t Km + t max



(1)

The fits were carried out with the aid of IBM Statistics SPSS® 19, by means of nonlinear regression, and with sequential quadratic programming as the estimation method (Diaz et al., 2012). In order for the information on uptake kinetics to be completed, the velocity of initial uptake (found by dividing half of the value of Cmax by Km ), the time required to reach equilibrium (Teq ), the equilibrium concentration (Ceq ) and the mean velocity of uptake (Vc ) were calculated; Teq was estimated from the Michaelis–Menten equation as the exposure time beyond which the daily increase in tissue concentrations was less than 1% of that of the previous day, Ceq was estimated as the predicted value of C at Teq and Vc by dividing Ceq by Ted (Fernández et al., 2006; Diaz et al., 2012). Bioconcentration factors (BCFs) at equilibria were also calculated (Eq. (2)), where Ceq is the equilibrium concentration, Ci the initial tissue metal concentration (at day 0) and Cw metal concentration in the water (Martins and Boaventura, 2002; Diaz et al., 2012). BCF =

(Ceq − Ci ) Cw

(2)

The time period required for the onset of MT depolymerization (ET) was checked. The time course of MT depolynerization expressed as percentage of fluorescence intensity reduction and of cells viability expressed as percentage of alive (not stained blue) cells were presented graphically. The time periods required for 10% and 50% of cells to die (LT10 and LT50 , with 95% confidence limits), and the 10% and 50% mortality effect concentrations (LC10 and LC50 , with 95% confidence limits) at the end of the treatments (day 13), were estimated by probit analysis with XLSTAT 2013. Spearman’s rank correlation coefficient () was calculated to identify correlations. 3. Results 3.1. Uptake kinetics The initial concentration of cadmium (day 0, mean ± standard error) in intermediate- juvenile leaf blades of C. nodosa was 1.806 ± 0.186 ␮g g−1 dry wt; this value is within the range of those previously reported for leaves of C. nodosa from various geographical areas (see Malea et al., 2013b and references therein). The uptake kinetics of cadmium into intermediate- juvenile leaf blades were rapid during the first days of exposure and, in general, reached an equilibrium state thereafter; a variability during the last incubation days was observed at the highest exposure concentration (Fig. 1). The fit to the Michaelis–Menten equation was adequate for all of the treatments (Fig. 1 and Table 1). The values of the maximum concentration (Cmax ), the equilibrium concentration (Ceq ), the velocity of initial uptake (Cmax /(2 × Km )) and the mean velocity of uptake (Vc ) tended to increase with the exposure concentration (Cw ); the opposite was found for the time taken to reach half of the value of Cmax (Km ), the equilibrium time (Teq ) and the boiconcentration factor (BCF) at equilibrium (Table 1). In particular, Cmax , Ceq , Cmax /(2 × Km ) and Vc correlated positively, while Km , Teq and BCF negatively, with Cw (p < 0.01 or 0.001, Table 4).

2.7. Data analysis 3.2. Effects on microtubule integrity The kinetics of cadmium uptake were fitted to the Michaelis–Menten equation (Eq. (1)) frequently used in enzyme, but as well as in metal, kinetic studies, where C represents the tissue metal concentration reached in a certain time, Cmax the maximum or saturation concentration, t the time and Km the

Cadmium affected MTs of C. nodosa cells. Generally, MTs were completely depolymerized by cadmium in all leaf blade cells studied and in all concentrations studied (Fig. 2). At shorter exposure times (3rd day), only fluorescence spots of tubulin were

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Fig. 1. Kinetics of cadmium uptake (mean tissue concentration ± standard error) and the time course of (a) microtubule disturbance expressed as percentage of reduction of fluorescence intensity (mean ± SE) and (b) cell viability expressed as percentage of alive cells (mean ± SE), in leaf blades of Cymodocea nodosa at different concentrations of cadmium in water. Bold lines are the calculated uptake kinetics using a Michaelis–Menten equation.

observed, while at longer exposure times, MTs were appeared completely destroyed and only an even fluorescence background was observed (data not shown). In untreated cells, interphase cortical MTs appeared to form a rather dense network and were variously

oriented (Fig. 2) like what has already been reported by Malea et al. (2013a). Higher exposure concentrations of cadmium (20,000–40,000 ␮g L−1 ) affected MTs more quickly and rigorously

Table 1 Kinetics of cadmium uptake in intermediate- juvenile leaf blades of Cymodocea nodosa exposed to different concentrations of cadmium in water. Exposure concentration (␮g L−1 ) 500

5000

10,000

20,000

40,000

Cmax Km Cmax /(2 × Km ) r2

174.19 4.92 17.70 0.879*

575.75 3.75 76.75 0.978**

811.43 3.72 109.18 0.966**

1177.97 3.20 183.89 0.972**

1563.32 0.63 1250.66 0.905**

Teq Ceq Vc BCF

21 141.12 6.72 278.6

19 480.83 25.31 95.8

19 678.69 35.72 67.7

17 991.22 58.31 49.5

9 1461.81 162.42 36.5

The fits correspond to a Michaelis–Menten equation: C = (Cmax × t)/(Km + t). C: tissue concentration (␮g g−1 dry wt) reached in a given time; Cmax : maximum tissue concentration; Km : time (in days) to reach half of the value of Cmax ; t: time (in days); Cmax /(2 × Km ): velocity of initial uptake (in concentration/day); Teq : time to reach equilibrium (in days); Ceq : equilibrium concentration (in ␮g g−1 dry wt); Vc : mean velocity of uptake (in concentration/day); BCF: bioconcentration factor at equilibrium. * p < 0.05. ** p < 0.01.

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Fig. 2. Cortical optical CLSM sections of interphase cells; control (a) or cadmium-treated (b–f). Control cells bare a dense MT network of variously oriented (a). Upon cadmium treatment, MTs were depolymerized in a concentration depended manner and in higher concentrations only a green fluorescence background is visible (e, f). Scale bar: 5 ␮m. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

Table 2 Time course of the microtubule integrity and the viability of leaf cells of Cymodocea nodosa up to thirteen days exposure to different cadmium concentration; –: no effect; MT: microtubule depolymerization; D: cell death. Exposure concentration (␮g L−1 )

Days 0

3

5

7

9

11

13

500 5000 10,000 20,000 40,000

– – – – –

– – – MT MT

– – MT MT MT

– MT, D MT, D MT, D MT, D

MT, D MT, D MT, D MT, D MT, D

MT, D MT, D MT, D MT, D MT, D

MT, D MT, D MT, D MT, D MT, D

Control

–

–

–

–

–

–

–

Table 3 Period of time required for the onset of microtubule depolymerization (ET) and estimated periods of time required for 10 and 50% of cells to die (LT10 and LT50 , with 95% confidence limits) in leaf blades of Cymodocea nodosa exposed to different concentrations of cadmium in water. Exposure concentration (␮g L−1 ) (in days) 500 5000 10,000 20,000 40,000

ET

LT10

LT50

9 7 5 3 3

10.1 (9.9–10.4) 8.4 (8.1–8.7) 7.2 (7.0–7.4) 6.8 (6.7–7.0) 6.6 (6.4–6.8)

17.2 (16.4–18.3) 19.4 (18.1–21.1) 11.3 (11.1–11.5) 10.9 (10.7–11.1) 10.5 (10.3–10.7)

(at the 3rd day and thereafter) than the lower ones (500–10,000 ␮g L−1 ) which required longer exposure times (5–9 days) to manifest their deleterious effect (Table 2). Thus, the period of time required for the onset of MT depolymerization (ET) tended to decrease with increasing exposure concentration (Table 3); in particular, a significant negative correlation was found between ET and Cw (p < 0.01, Table 4). Fluorescence intensity measurements (Fig. 1) further consolidated the above results clearly indicating the depolymerizing effect of cadmium; this intensity dropped upon increasing exposure concentration and incubation time. Table 4 Spearman’s rank correlation coefficient () values between exposure concentration, uptake parameters and time periods required for toxic effects; n = 5. For abbreviations, see Tables 1 and 2. Variables



Variables



Cmax − Cw Km − Cw Cmax /(2 × Km ) − Cw Ceq − Cw Teq − Cw Vc − Cw BCF − Cw ET − Cw

1.000*** −1.000*** 1.000*** 1.000*** −0.975** 1.000*** −1.000*** −0.975**

LT10 − Cw LT50 − Cw ET − Cmax /(2 × Km ) LT10 − Cmax /(2 × Km ) LT50 − Cmax /(2 × Km ) ET − Vc LT10 − Vc LT50 − Vc

−1.000*** −0.900* −0.975** −1.000*** −0.900* −0.975** −1.000*** −0.900*

* ** ***

p < 0.05. p < 0.01. p < 0.001.

ET was shorter than the time taken to reach the equilibrium concentration (Teq ) at all of the treatments (Tables 1 and 3). Thereby, the predicted values associated with the onset of MT depolymerization were lower than the equilibrium concentrations (Ceq ). The lowest predicted tissue concentration associated with the first occurrence of MT defects had a value of 102.3–112.6 ␮g g−1 dry wt (500 ␮g L−1 treatment, 7th and 9th incubation day), while the respective experimental concentration a value of 98.5–128.9 ␮g g−1 dry wt (Fig. 1). These lowest tissue concentrations were exceeded up to the 3rd day at the higher exposure concentrations (5000–40,000 ␮g L−1 ); however, MT disturbance was first detected at later times at the 5000 and 10,000 ␮g L−1 treatments (Fig. 1 and Table 2). ET tended to decrease as the rate of cadmium uptake increased (Tables 1 and 3); in particular, ET showed a significant negative correlation with the velocity of initial uptake and Vc (p < 0.01, Table 4). 3.3. Effects on cell viability Some of leaf blade cells died after metal treatment as confirmed by Evans Blue staining contrast to the control where no positive Evans Blue staining was ever noticed. Dead cells appeared at 5000–40,000 ␮g L−1 at the 7th day and thereafter, while the lowest concentration tested (500 ␮g L−1 ) resulted in dead cells only at the 9th day and thereafter (Table 2). The percentage of dead cells increased with time at all of the treatments (Fig. 1). The estimated periods of time required for 10% and 50% of cells to die (LT10 and LT50 respectively) tended to decrease as the exposure concentration increased (Table 3); a significant negative correlation was found between LT10 and Cw (p < 0.01) and LT50 and Cw (p < 0.05, Table 4). The 13-d LC10 and LC50 (with 95% confidence limits) had a value of 190.4 (140.1–248.9) and 6746.5 (6083.5–7469.8) ␮g L−1 respectively. The estimated LT10 was longer than Teq (Tables 1 and 3) and, thereby, the predicted tissue concentration associated with the first occurrence of cell death was lower than the equilibrium concentration (Ceq ), at all of the treatments. The lowest predicted tissue concentration associated with the onset of cell death had a value of 102.3–112.6 ␮g g−1 dry wt and the lowest experimental concentration a value of 98.5–128.9 ␮g g−1 dry wt (500 ␮g L−1 treatment, 7th

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and 9th incubation day, Fig. 1); these lowest concentrations were exceeded by the 3rd day at 5000–40,000 ␮g L−1 , but as in MTs, cell death started to occur at later time (Fig. 1 and Table 2). LT10 and LT50 tended to decrease with increasing rate of cadmium uptake (Tables 1 and 3); in particular, a significant negative correlation was found between these time periods and both the velocity of initial uptake and the mean velocity of uptake (p < 0.05 or 0.001, Table 4). 4. Discussion 4.1. Uptake kinetics The uptake kinetics of cadmium into intermediate- juvenile leaf blades of the seagrass C. nodosa displayed a similar pattern at all of the exposure concentrations: an initial rapid accumulation was followed by a steady state. This trend is consistent with previous observations concerning the uptake of cadmium into compartments of seagrass species (see Fabris et al., 1982; Lyngby and Brix, 1984; Malea, 1994). This pattern of cadmium uptake is compatible with the mechanism of metal uptake in three stages. The first stage (exchange adsorption) corresponds to a rapid uptake into the Donnan-free-space of the cell wall, the second stage represents the diffusion across the plasma membrane into the protoplasm and the third stage corresponds to the active accumulation of metal within the plant cell (e.g. Pickering and Puia, 1969; Malea, 1994; Martins and Boaventura, 2002). Thus, cadmium is expected to be accumulated in different cellular compartments. The largest amount of metal is probably taken up in the first stage (see among others Martins and Boaventura, 2002; Andrade et al., 2006; Fernández et al., 2006), while the steady state may correspond to an equilibrium attained between the metal in solution and the metal bound on the cell surface (Sunda and Huntstman, 1998; Kola and Wilkison, 2005). The Michaelis–Menten equation satisfactorily described cadmium uptake into intermediate- juvenile leaf blades of C. nodosa, permitting to calculate uptake parameters. The values of the maximum concentration (Cmax ), the equilibrium concentration (Ceq ), the velocity of initial uptake and the mean velocity of uptake (Vc ) tended to increase with the exposure concentration, indicating that cadmium in young leaf blades reflects cadmium in the surrounding medium over a wide range of exposure concentrations. On the other hand, the concentration factor at the steady-state phase tended to decrease as the exposure concentration increased suggesting that young leaf blades of C. nodosa display a lower sensitivity as regards the detection of very high levels of cadmium contamination (see also Diaz et al., 2012). 4.2. Effects on microtubule integrity and cell viability Earlier toxicity tests have demonstrated adverse effects of cadmium on seagrass growth and cell viability. For instance, leaf growth of Z. marina incubated in 5 and 50 ␮M Cd was inhibited after 12–8 days (Brix and Lyngby, 1984), growth rate of Z. capricorni was reduced by 10 mg L−1 Cd after a 10-day exposure period (Conroy et al., 1991) and leaf cell mortality in H. stipulacea exposed to 10−6 to 10−4 mol L−1 Cd (0.1124–11.24 mg L−1 Cd) occurred after 12–8 days, the first signs of toxicity noted in ‘teeth’ cells with the ‘epidermal’ ones following (Malea, 1994). On the other hand, a rather limited impact of cadmium on photosynthetic activity and photosynthetic pigment content in seagrasses (Halophila ovalis, Z. capricorni) has been reported (Ralph and Burchett, 1998; MacinnisNg and Ralph, 2002). Recently, in P. oceanica it was demonstrated that 50 ␮ of cadmium exposure caused genomic DNA hypermethylation and the epigenetic basis of the cadmium toxicity response was highlighted for the first time (Greco et al., 2012). The fact that

MTs besides DNA are also a target of cadmium toxicity could also help to understand the mechanism involved in cadmium toxicity in seagrasses. Seagrass MTs constitute one of the intracellular targets of cadmium toxicity, like it was also shown for copper, nickel and chromium (Malea et al., 2013a) extending the list of aquatic organisms in which MTs have been found to be a target of Cd (e.g. Spirogyra decinima; Pˇribyl et al., 2005). In particular, cadmium depolymerized MTs like copper and nickel, but it did not cause any MT bundling as was observed after chromium application (Malea et al., 2013a). Studies conducted mostly in higher land plants postulated that cadmium affected the mechanisms controlling the organization of MT cytoskeleton, as well as tubulin assembly/disassembly processes. In particular, in Allium cepa plants cadmium induced the formation of abnormal MT arrays, consisting of discontinuous wavy MTs or short MT fragments at the cell periphery (Xu et al., 2009), while in Pisum sativum cadmium application resulted in a complete MT destruction (Fusconi et al., 2007). The observed destruction/disturbance of microtubules may disrupt a number of microtubule-mediated events, including the transport, localization and scaffolding of signalling molecules, all of which are likely important in preparing the cell for mitosis and are necessary for the cell’s metabolic activities (Mikhailov and Rieder, 2002). Taking into account the above, the defects observed after cadmium application in C. nodosa cells could be a responsive signal that could be used as an early biomarker of cadmium induced toxicity. The time periods required for the onset of MT depolymerization (and for 10 and 50% of cells to die as well) were significantly correlated (negative correction) with the exposure concentration suggesting that MT integrity (and cell viability) could additionally provide a measure of cadmium exposure. Until now, the principal biomarkers of cadmium tested in seagrasses were measurable responses in photosynthetic activity, photosynthetic pigment concentration and oxidative stress (see Ralph and Burchett, 1998; Hamoutène et al., 1996; Macinnis-Ng and Ralph, 2002; AlvarezLegorreta et al., 2008). Given that no single biomarker can provide all the necessary information on the environmental quality, a multiparametric approach is highly recommended in ecotoxicology (Ferrat et al., 2003); thus, a suite of different biomarkers, including MT integrity and several other markers of specific and general stress (for example the expression patterns of stress response genes, see Serra et al., 2012) could constitute a rapid and costeffective tool in the effort of monitoring and protecting of seagrass meadows. 4.3. Relationship between uptake kinetics and effects The lowest experimental tissue concentration of cadmium associated with the onset of MT depolymerization and cell death (98.5–128.9 ␮g g−1 dry wt) is within the wide range of reported cadmium concentrations in leaves of seagrass species (0.1–266 ␮g g−1 dry wt) from various geographical areas (see review in Lewis and Devereux, 2009). This finding may imply that cadmium is a contributing factor to the worldwide deterioration of seagrass meadows (Hemminga and Duarte, 2000). However, the observation that this lowest tissue concentration was exceeded by the third day of incubation at the 5000–40,000 ␮g L−1 treatments but toxic effects were generally detected at later time seems not to be compatible with the hypothesis that toxicity occurs when a threshold total body (or tissue) concentration of accumulated metal is exceeded (McCarty and Mackay, 1993). As noted earlier, metal may be accumulated in different cellular compartments. Extracellular metal, namely metal that is bound as exchangeable forms to binding sites in plasma membrane or cell wall and intercellular metal are not in direct contact with the cytoplasm and thus cannot be regarded to have an immediate

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influence upon metabolism (Sidhu and Brown, 1996). Intracellular metal might be divided into two components-in metal that has been detoxified and is no longer available to play any role in metabolism and in metabolically available metal (see PergentMartini and Pergent, 2000; Rainbow, 2002). Metal toxicity may occur when biologically active metal in one or more target sites reaches a threshold concentration (see Rainbow, 2002). If and how fast this threshold concentration is exceeded may depend on the rate of intracellular metal uptake in relation to metabolic capacity for detoxification and storage (see Rainbow, 2002); the more rapid the metal uptake, the sooner the detoxification mechanisms are overwhelmed, a threshold concentration at the sites of action is reached and effects ensue. Considering that elevated rate of total metal uptake most probably involved both elevated rate of extracellular uptake and elevated rate of intracellular uptake (see Fernandez et al., 2006), the above interpretation is reinforced by the finding that the periods of time required for the onset of MT depolymerization and for 10 and 50% of cells to die tended to decrease as the velocity of initial uptake and the mean velocity of uptake increased. The above explanation is consistent with the arguments forwarded by Rainbow (2002) for aquatic invertebrates, namely that the factor that determines the toxic effects is not the total accumulated metal concentration per se, but the rate of metal uptake. Studies of aquatic invertebrates and fish have already demonstrated the importance of metal accumulation rate in determining toxic effects (see Adams et al., 2010). In addition, this interpretation is compatible with previous findings concerning metal, particularly cadmium, uptake and sequestration in leaf cells of hyperaccumulator land plants. The main detoxification strategy in hyperaccumulators has been found to be metal sequestration usually into epidermal vacuoles; this sequestration needs an active transport system and is the rate limiting step in metal uptake into protoplasm (see Küpper et al., 2007; Leitenmaier and Küpper, 2011). The above findings suggest that tissue residues should be interpreted in relation to the time frame of the exposure. In addition, the significant negative correlations found between the periods of time required for toxic effects and the velocity of cadmium uptake suggest that data on the uptake kinetics of cadmium in seagrass leaves could be utilized in biomonitoring programmes for predicting the onset and progress of MT defects and cell death and for identifying the occurrence of ecotoxicologically significant metal contamination. 5. Conclusions The Michaelis–Menten model adequately described cadmium uptake into young leaf blades of the seagrass C. nodosa, permitting to calculate uptake parameters. Microtubule cytoskeleton on leaf blade cells proved to be at early time sensitive to cadmium stress, indicating that microtubule disturbance could be used as an early warning signal of emerging cadmium contamination. Microtubule disturbance and cell mortality appeared to be a function of the rate of cadmium uptake rather than of the total tissue cadmium concentration, suggesting that tissue residues should be interpreted in relation to the time frame of the exposure. The periods of time required for the onset of microtubules depolymerization and for 10 and 50% of leaf cells to die significantly correlated (negative correlation) with the velocity of cadmium uptake, suggesting that the estimation of metal uptake rate could be utilized for predicting toxic effects. The data presented provide insight on the relationship between metal bioaccumulation and toxic effects in seagrasses and, overall, contribute to a better understanding of the impact of metals on aquatic organisms.

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Acknowledgements The authors thank Dr. A. Tsingotjidou (Faculty of Veterinary Medicine, Aristotle University of Thessaloniki, Greece) for generously proving access to the Nikon D-Eclipse C1 CLSM, Dr A. Mogias (Laboratory of Environmental Research and Education, Democritus University of Thrace, Greece) for his help in laboratory procedures and Dr A. Markos (Laboratory of Mathematics and Informatics, Democritus University of Thrace, Greece) for assistance with SPSS and XLSTAT. We also grateful for the comments of two anonymous reviewers, which helped to improve the original version of the manuscript.

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