Environmental Toxicology and Chemistry, Vol. 29, No. 7, pp. 1503–1511, 2010# 2010 SETAC

Printed in the USADOI: 10.1002/etc.186

EVALUATING THE BIOTIC LIGAND MODEL FOR TOXICITY AND THE ALLEVIATION OF

TOXICITY IN TERMS OF CELL MEMBRANE SURFACE POTENTIAL

PENG WANG,yz DONG-MEI ZHOU,*y LIAN-ZHEN LI,yz and XIAO-SAN LUO§yState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, No. 71, East Beijing Road, Nanjing 210008,

China

zGraduate School of Chinese Academy of Sciences, Beijing 100049, China

§Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

(Submitted 2 July 2009; Returned for Revision 2 November 2009; Accepted 29 January 2010)

All* To

(dmzhoPub

(www.

Abstract—The electrostatic nature of plant cell membrane (CM) plays significant roles in ionic interactions at the CM surface and hencein the biotic effects of metal ions. Increases in major cations (commonly Ca2þ, Mg2þ, Hþ, Naþ, Kþ, etc.) in bulk-phase medium reducethe negativity of CM surface electrical potential (c0), but these slightly increase the driving force of a metal ion crossing CMs (surface-to-surface transmembrane potential difference, Em,surf). Toxicologists commonly attributes the interactions between heavy metals andcommon cations (e.g., Hþ, Ca2þ, and Mg2þ) to competitions for binding sites at a hypothetical CM surface ligand. The c0 effects arelikely to be more important to metal toxicity and the alleviation of toxicity than site-specific competition. Models that do not consider c0,such as the biotic ligand model (BLM) and the free ion activity model (FIAM), as usually employed are likely to lead to false conclusionsabout competition for binding at CM surface ligands. In the present study a model incorporating c0 effects and site-specific competitioneffects was developed to evaluate metal (Cu2þ, Co2þ, and Ni2þ) toxicities threshold (EA50, causing 50% inhibition) for higher plants. Inaddition, the mechanisms for the effects of common cations on toxicity of metals were also explored in terms of CM surface electricalpotential. Environ. Toxicol. Chem. 2010;29:1503–1511. # 2010 SETAC

Keywords—Surface electrical potential Biotic ligand model Copper Cobalt Nickel

INTRODUCTION

Soil and water contamination with metals (commonly heavymetals) has been a worldwide problem that could cause environ-mental risk and threaten the essential functions of soil and water[1]. Proper environmental quality criteria and standards areurgently needed to assess the level of risk. Current environ-mental quality criteria and risk assessment procedures formetals are predominantly based on total or dissolved metalconcentrations [2]. However, extensive evidence indicates thatboth total and dissolved concentrations are poor predictors ofmetal bioavailability and toxicity. The physicochemical char-acteristics of soil and water, such as pH [3], coexistent majorcations (commonly Ca2þ, Mg2þ, etc.) [4], and organic mattercontent [5] exert important influences on metal bioavailabilityand toxicity. Such dependencies suggest the need to take thesemodifying factors into account in the regulatory frameworks.

The alleviation of metal toxicity by protons and commoncations, such as Ca2þ, Mg2þ, Naþ, and Kþ, has been widelyreported in aquatic [2,3,6,7] and terrestrial organisms [8–10].Toxicologists and environmentalists commonly interpret theinteractions between toxic metals and these alleviative cationsas a site-specific competition for binding sites of a hypotheticalbiotic ligand (BL) [2,7,11,12]. This assumption has beenincorporated into the framework of the biotic ligand model(BLM). As an extension of the free ion activity model (FIAM),

Supplemental Data may be found in the online version of this article.whom correspondence may be addressed

u@issas.ac.cn).lished online 26 March 2010 in Wiley InterScienceinterscience.wiley.com).

1503

the BLM has been suggested as a useful construct for assessingthe effects of metals on both aquatic organisms (aBLM) [11,13]and terrestrial organisms (tBLM) [1,8,9,14]. The U.S. Environ-mental Protection Agency (U.S. EPA) has incorporated theBLM into its regulatory framework and other countries areconsidering the implications of following suit [15].

The BLM invokes a site-specific competition between toxicmetals and alleviative cations for binding sites of BL, meaningthat the toxicity by toxic metal requires binding to the BL andthat the alleviation of the toxicity by alleviative cations alsorequires binding to the same BL sites. The assumption may bearbitrary and, if true, difficult to verify. Kinraide [14] and Wanget al. [16] reported an enhancement of selenate (SeO4

2�) andarsenate (H2AsO3

�) rhizotoxicity with increases in CaCl2 andMgCl2. This enhancement of toxicity of metalloid anions wasjust the opposite of their effects on toxicity of cationic toxicants.Site-specific competition fails to interpret the enhancement oftoxicity of metalloid anions by treatments that reduce thetoxicity of cationic toxicants.

The cell surfaces of virtually all taxa are negatively charged.Generally neglected is the significant role of cell membrane(CM) electrical features, especially the CM surface electricalpotential (c0) [17]. A mechanistic description of the results interms of c0, such as the alleviation of cationic toxicity(Al3þ, La3þ, Cu2þ) or the enhancement of anion toxicity(SeO4

2�, H2AsO4�) by treatments that reduce CM surface

negativity (e.g., increases in common cations or decreases inthe pH of bulk medium) has provided a unified interpretation ofboth phenomena and has gained recent recognition. In nearly allcases, electrostatic enhancement or depletion of cationic oranionic toxicants at the CM surface could be sufficient toexplain most ion–toxicant interactions [14,16,18–22]. Although

Fig. 1. Profile of the electrical potentials and ion distributions at the cellmembrane (CM) surface. The top portion of the figure represents CM batheddirectly in bulk-phase medium (BM). In the bottom portion, the CM is bathedby the Donnan-phase solution of the cell wall (CW). Line (1) illustrates thepotential profile through the CM and electric double layer. Line (2) representsthe potential profile after the addition of common cations to BM thatdepolarize the surface potential. Em¼ the transmembrane electrical potentialdifference from the BM to cell interior; c0¼ the CM surface electricalpotential which is the electrical potential difference between the BM and theCM surface; Em,surf¼ the electrical potential difference through the CM fromsurface to surface;ccm,cw¼ the electrical potential of the CM surface relativeto the CW. Lines 3 and 4 represent the profile of activities of cations andanions at the CM surface, respectively. [Color figure can be seen in the onlineversion of this article, available at www.interscience.wiley.com.]

1504 Environ. Toxicol. Chem. 29, 2010 P. Wang et al.

site-specific competition between ions might occur, the c0

effects should not be negligible, and are likely to be moreimportant than the effects of site-specific competition in somecases.

The BLM has gained increasing attention from both aca-demic scientists and regulators. Some reviews and researcharticles have been published [1,9,15,23,24], but there is onlylimited discussion of the key alleviative mechanisms by coex-istent major cations [17]. Therefore, this study aims to evaluatethe relative effectiveness of c0 effects and competition effectsand to incorporate the electrostatic mechanism into the BLMequations for predicting toxicity threshold of metals. In thisstudy, we chose the EA50 (metal activity in the bulk mediumproducing 50% inhibition of biological response) as the toxicitythreshold of metals. Several practical examples of metal phy-totoxicities, i.e., Cu toxicity to wheat (Triticum aestivum), Cu,Co, and Ni toxicities to barley (Hordeum vulgare), are given toillustrate the modified BLM. Also, this study attempts to presentthe mechanisms for the interactions between the metals con-cerned and coexistent major cations.

MATERIALS AND METHODS

Cell membrane surface electrical potential (c0)

Generally, three electrical properties of CMs (Fig. 1) havebeen recognized. The first is the transmembrane electrical poten-tial (Em), which presents the electrical potential differencebetween the cell interior and bathing medium and can be directlymeasured relatively easily by insertion of a microelectrode intocells [25]. Another is the negative electrical potential (c0) on theCM exterior surface, which is due to the residues of acidic aminoacids in membrane proteins and the phosphate groups of mem-brane phospholipids [25]. The last is the electrical potentialdifference through the CM from surface to surface (Em,surf),which is the driving force of a metal ion across CMs and affectssome other membrane phenomena such as voltage gating. Bothc0

and Em,surf are physiologically important. The c0 controls iondistribution between at the CM surface and in the bulk-phasemedium (BM), i.e., concentrates cations and depletes anions at theCM surface. The magnitude of concentration or depletion can bequantified with the Nernst equation. The Em,surf exerts an impor-tant influence on ion transport across CMs. Increases in commoncation concentrations or decreases in pH reduce the negativity ofc0 by ionic binding and charge screening [26]. The order ofdepolarizing effectiveness on reducing the negativity of c0 isAl3þ > Hþ > Ca2þ ˜

Mg2þ > Naþ˜Kþ [16,27]. The depolarizing

effectiveness depends on ion charge and strength of binding to thecell membrane [28]. The changes in c0 influence the Em,surf, theelectrical component of the driving force for ion uptake. Changesin c0 have little effect on Em, but do affect the Em,surf. Con-sequently, changes in c0 are offset almost entirely by changes inEm,surf (compare solid line 1 and dashed line 2 in Fig. 1) [29].

Gouy–Chapman–Stern (GCS) model and computation of c0

The modified GCS model combines a classical electrostatictheory (Gouy–Chapman theory) with ion binding (Stern por-tion) [28,30–33]. The Gouy–Chapman portion of the model canbe expressed in the Muller (Grahame) equation (derivationpresented in [34]). The equation describes the relationshipsamong the CM surface charge density (sCMS), the concentrationof the ith ion at infinite distance from the membrane (i.e., in thebulk-phase medium, [IZ]b) and the electrical potential at the

membrane exterior surface (c0).

s2CMS ¼ 2�r�0RT

Xi

IZ� �

b� exp �ZiFc0=RT½ � � 1ð Þ (1)

where sCMS is expressed in coulombs per square meter (C/m2);2ere0RT¼ 0.00345 at 258C for concentrations expressed in M (er

is the dielectric constant for water, e0 is the permittivity of avacuum, R is the gas constant, and T is temperature); Zi is thecharge on the ith ion; and F is the Faraday constant.

For the Stern portion of the GCS model, the CM surface wasassumed to be composed of two classes of binding sites: one

Toxicity of metal ions and the alleviation of toxicity Environ. Toxicol. Chem. 29, 2010 1505

negatively charged (R�) and one neutral ( P0). Ions may bind toboth sites according to the reactions R� þ IZ $ RIZ�1 and P0 þIZ $ PIZ. Binding constants can be expressed as KR,I¼ [RIZ�1]/([R�][IZ]0) and KP,I¼ [PIZ]/([P0][IZ]0). [R�], [P0], [RIZ–1], and[PIZ] denote CM surface densities (mol m�2), and [IZ]0 denotesthe concentration of an unbound ion at the CM surface. Thecontingent surface charge density (sCMS) can be expressed asthe sum of the products of the surface density of each speciesand the charge of each species all times F (the Faraday con-stant):

sCMS ¼ � R�½ � þX

RI

ðZ � 1Þ RIZ�1� �

þX

PI

Z PIZ� � !

F (2)

The computation ofc0 requires both binding constants andsCMS

by combining Equations 1 and 2. (See [28,30,31,33] andSupplemental Data for a more detailed description.)

Computation of ion activities at the CM surface

The activity of ion IZ at the CM surface ({IZ}0) can becomputed from the activity of IZ in the BM ({IZ}b) according tothe Nernst equation:

IZ� �

0¼ IZ� �

bexp �ZFc0=ðRTÞ½ � (3)

The equation can be simplified as {IZ}0¼ {IZ}bexp(�Zc0/25.7)whenc0 is expressed in mV at 258C. Thec0 concentrates cationsand depletes anions at CM surface. For instance, for a c0 of�45.0 mV, mono-, di-, and tri-valent cations will be concen-trated, and anions of corresponding negative charges will bedepleted by 6-, 33-, and 191-fold, respectively. The activity ofion at the cell wall (CW) surface can also be computed with theNernst equation.

Effect of CWs on ion activity at the CM surface

Figure 1 also illustrates the possible electrostatic interactionsbetween CWs and CMs. In the top panel of Figure 1, the CM isnot affected by the CW due to physical separation or some otherreason and thus the ion activity at the CM surface is notinfluenced by the CWs. In the bottom panel the CM is directlybathed by Donnan-phase solution of the CW. The CWs behaveas an ion exchanger in which the fixed CW charges interact withexchanger ions in the surrounding solution. Its net chargeis negative and results from weakly dissociating acidicgroups. Shomer et al. [35] monitored the electrical potentials(ccw,medium) at the outer and inner surfaces of the CWs frompotato tubers and wheat roots in response to ionic changes(20 ions including a vide ranging ion charge and size) by placingglass microelectrodes, and also considered the ccw,medium interms of a model composed of Donnan theory and ion bindingmodels. Although the CWs (a meshwork of unevenly distrib-uted and stationary charges) is not an ideal Donnan phase(homogeneous phase and all charges behave as freely mobilepoint sources of charge), the authors nevertheless successfullysimulated the ccw,medium with the Donan-plus-binding model inresponse to ionic environment by directly measuring theccw,medium. Kinraide [36] further studied the effect of CWson ion activity at the CM surface directly bathed with theDonnan solution of CW. The activity of ion at the CM sur-face can be computed from {IZ}0¼ {IZ}b exp(�ZF[ccw,mediumþccm,cw]/RT). The ccm,cw is the electrical potential differencebetween CM and CW. The simulated results indicated that aclose association of CWs and CMs results in a slight increase incation concentrations or activities and a slight decrease in anion

concentrations or activities at the CM surface compared withconcentrations when the CW is separated or has no effect.Therefore, the CWs appear to have only slight effects on ionactivities at the CM surface.

BLM equations incorporating electrostatic mechanism

Biological response (i.e., growth inhibition) may be plottedagainst terms of toxicant intensity T, and the resulting curvesare often negatively sigmoidal and can be expressed by avariation of the Weibull equation that has been used previouslyto describe growth responses to toxicants [37,38]. The toxicantintensity T can be expressed as the metal ion activity in the BM({Mnþ}b), metal ion activity at the CM surface ({Mnþ}0), or thefraction ( fM) of ligand occupied by metal ion (¼[MBL(n�1)þ]/[LL]total, [MBL(n�1)þ] and [BL]total denote concentrations ofmetal-BL complex and complexation capacity of the BL,respectively. (See [2,11] for more details about the BLMequation.) If BR is limited only by {Mnþ}b, then:

BR ¼ 100= exp a � Mnþf gb

� b(4)

where BR represents biological response expressed as percentrelative to the control, a is a strength coefficient (M�1) and b ashape coefficient, and a increases with the strength of the metaltoxicity. This versatile equation adequately represents doseresponse to most toxicants. Given that there are no electrostaticeffects, competition effects, or other effects (invoked by the freeion activity model), the value of {Mnþ}b at a given BR(commonly BR¼ 50%) will be a constant for a metal ionconcerned independent of the ionic composition in bulk medium.The corresponding value of {Mnþ}b at BR¼ 50% is expressed asEA50{Mnþ}b (i.e., metal ion activity in the BM causing 50%inhibition).

Root responses to metal ion (e.g., elongation, intoxication,alleviation of intoxication, transport across CMs, and CMenzyme activity) often correlate poorly with {Mnþ}b but oftencorrelate better with {Mnþ}0. Therefore, initially expressingtoxicity intensity T as {Mnþ}0 (computed from Eqn. 3) insteadof {Mnþ}b takes an electrostatic mechanism (i.e., the c0 effects)into account, thus the equation becomes:

BR ¼ 100= exp a � Mnþf g0

� b(5)

Again, the {Mnþ}0 at BR¼ 50% will be a constant irrespective ofthe solution ionic environment (denoted as EA50{Mnþ}0, thecorresponding activity of metal ion at the CM surface producing50% inhibition) in case there are no competition effects or othereffects. The values of EA50 {Mnþ}0 are introduced here topossibly distinguish c0 effects from competition effects or othereffects. In addition, the values of EA50 {Mnþ}0 are regarded asthe intrinsic toxicity of metals to plants.

Toxicant intensity T in Equation 4 would be replaced with fM(¼[MBL(n�1)þ]/[BL]total) and its equivalent from [2] when boththe c0 effects and the competition effects of major cation andproton (e.g., Ca2þ, Mg2þ, Naþ, Kþ, and Hþ, denoted as XZþ inthe equation) are taken into account. It is assumed in the BLMthat the 50% effect (i.e., BR¼ 50%) is always occurring at theconstant relative coverage ( f50) of the BL sites independent ofionic characteristics and competition effects. Thus, Equation 4can be rewritten as:

BR ¼ 100= exp a � fMð Þb

¼ 100

expa�KMBL Mnþf g0

1þKMBL Mnþf g0þP

KXBL XZþf g0

� �b(6)

Fig. 2. Cell membrane (CM) surface activities of Ca2þ, sum of Ca2þ

and Mg2þ and Hþ in response to Ca2þ addition to the bulk medium. Subscriptb refers to variables in the bulk-phase medium. Subscript 0 refers to variablesat the CM surface. The surface activities were computed with the Nernstequation. The bulk phase medium (BM) contained 0.05 mM MgCl2, 3.20 mMNaCl, 0.08 mM KCl and variable CaCl2 at pH 6.0.

1506 Environ. Toxicol. Chem. 29, 2010 P. Wang et al.

where KMBL and KXBL are the stability constants for metal ionand companion cations binding to BL sites, respectively.They are written as KMBL¼ [MBL(n�1)þ]/[BL�]{Mnþ}0 andKXBL¼ [XBL(Z�1)þ]/[BL]{XZþ}0, where [BL�]¼ [BL] total �[MBL(n�1)þ] þS[XBL(Z�1)þ]. It is important to note that thestability constants of specific sites here are different from thebinding constants (KR,I and KP,I) used in the GCS model (Table S-1, Supplemental Data). The parameters KR,I and KP,I areequilibrium constants of ion binding to the CM surfaces. Thebinding sites are not necessarily the same as the sites of action oftoxicity (invoked by the BLM). The occupancy of the bindingsites reduce the negativity of CM surface, but could have little orno toxic effect.

A combination of Equations 3 and 6 above may be consid-ered a rendition of the calculation of EA50 {Mnþ}b as follows:

EA50 Mnþf g0 ¼ a 1 þP

KXBL XZþf g0

� EA50 Mnþf g0 ¼ a 1 þ

PKXBL XZþf gbexp �ZFc0=ðRTÞ½ �

� EA50 Mnþf gb ¼ a 1 þ

PKXBL XZþf g0

� �exp nFc0=ðRTÞ½ �

(7)

EA50 Mnþf gb ¼ a 1 þX

KXBL XZþ� �bexp �ZFc0=ðRTÞ½ �

�� exp nFc0=ðRTÞ½ �

(8)

The constants a(¼f50/(1� f50)KMBL), KXBL can be determined bymultiple nonlinear analysis. The coefficient f50 appears twice andtherefore it might be difficult to evaluate in the statisticaltreatment. If KMBL and f50 are combined into a single coefficienta¼ f50/(1 � f50)KMBL, that coefficient a could evaluatesignificantly, whereas the KMBL might evaluate nonsignificantly.

Data treatment and statistical analyses

To validate the model described above and explore themechanisms for the interactions between common cationsand toxicant ions, data were pooled from our some previousexperiments and three publications [39–41] (Tables S-2 to S-5,Supplemental Data). Previous experiments studied the mecha-nisms of major cations on Cu2þ toxicity to wheat (T. aestivum)in terms of CM surface potential. In addition, Lock et al. [39–41] studied the influences of major cations on Ni2þ, Co2þ,and Cu2þ toxicities to H. vulgare and attempted to developmetal-BLMs for predicting metal toxicities based on the presentBLM framework. For all experiments, the experimental con-ditions and treatments were very similar. Additional details forthe bioassay conditions including solutes are presented below.The multiple nonlinear regression analyses with Equation 8were performed using a statistics program (DPS, v. 7.05,Zhejiang University, China). All values for regression coeffi-cients presented without specially stated below are significantlydifferent from zero at the 5% level ( p< 0.05).

RESULTS AND DISCUSSION

Copper toxicity to wheat (Triticum aestivum)

For toxicity assays, 2-d-old seedlings with uniform rootlength (1 to 2 cm) were cultured in darkness for 48 h at 258Cin 500 ml of test medium. Five sets of Cu bioassays wereperformed with rooting media variously composed of 0.2–14.0 mmol L�1 CaCl2, 0.05–6.0 mmol L�1 MgCl2, 0.08–10.0 mmol L�1 KCl, 3.2–25.6 mmol L�1 NaCl, 0–4.0mmol L�1 CuCl2, 2.0 mmol L�1 MES (2-[N-morpholino]ethane sulfonic acid), and NaOH to achieve pH values ranging

from 5.0 to 6.8 (see Table S-2 in the Supplemental Data forchemical characteristics of test medium). After 2 d in the testsolutions the two longest roots of each seedling were measuredand the mean value of 12 measured values per replicate wasrecorded. The 48-h EA50 values expressed as the cupric ionactivity in BM or at CM surface, denoted EA50{Cu2þ}b orEA50{Cu2þ}0, were then calculated from the observed rootgrowth at each calculated free cupric activity in BM ({Cu2þ}b)or at the CM surface ({Cu2þ}0). The values of EA50s werecalculated by fitting a sigmoid curve to the dose-effect relation-ship according to the model of Haanstra et al. [42]. Ion speciesand activities were calculated using visual MINTEQ (v. 2.51)chemical equilibrium program (U.S. EPA, Athens, GA). Theequilibrium phases in the speciation calculation includedatmospheric CO2 (pCO2¼ 10�3.5 atm.). The value of c0 ineach test media was computed with the GCS model. The ionactivities at the CM surface were calculated with Equation 3.

As Ca2þ concentration in the BM increased from 0.20 to14.0 mmol L�1 in the bulk medium, the negativity of c0

significantly declined from �50.29 to �5.84 mV. The Ca2þ

surface activity of ({Ca2þ}0) increased initially and reached apeak value with increasing of Ca2þ from 0.25 to 1.0 mM in theBM, and then slightly declined with further increases in Ca2þ

(Fig. 2). In contrast, the CM surface activities of other coex-istent cations, especially Mg2þ and Hþ, declined markedlydespite their constant concentrations in the BM. The sum ofCM surface activities Ca2þ and Mg2þ showed a consistent trendwith the variation of {Ca2þ}0 (Fig. 2). It was indicated that thecompetition effect from only surface Ca2þ may take place atlow Ca2þ bulk concentrations (i.e., lower than 1.0 mM in thiscase) and that the overall competition effects fromcations Ca2þ, Mg2þ, and Hþ may be weakened with the additionof Ca2þ to the BM. The enrichment factor of Cu2þ activity at theCM surface ({Cu2þ}0/{Cu2þ}b) decreased over time from 51 to1.6. The extrinsic Cu toxicity (expressed as 48-h EA50{Cu2þ}b)was significantly alleviated initially and then elevated, causedby a reduction in the negativity of c0 that accompanied anincrease in CaCl2 (Fig. 3a; and Table S-1 in SupplementalData). The initial alleviation of Cu2þ toxicity could beexplained by decreases in {Cu2þ}0 caused by a reduction inthe negativity of c0 that accompanied an increase in CaCl2. Thesubsequent enhancement of toxicity was likely due to the

Fig. 3. The 48-h EA50{Cu2þ}b (expressed as cupric ion activity in bulk-phase medium) for wheat (Triticum aestivum) as a function of the cellmembrane (CM) surface potential computed with the GCS model (a).Comparison of the predicted and observed 48-h EA50s (b). Predicted EC50swere calculated with Equation 8 using the regression coefficients obtained inthe present study (Table 1). The solid line in (b) indicates the 1:1 ratio; thedashed lines represent a factor of 2 variations above and below the 1:1 lines.

Toxicity of metal ions and the alleviation of toxicity Environ. Toxicol. Chem. 29, 2010 1507

decrease of other competitive cations such as Mg2þ and Hþ atthe CM surface while {Ca2þ}0 remained constant, and/or theincreases in surface-to-surface transmembrane potential differ-ence (Em,surf) (discussed below). Similar results were alsoobserved in Mg-Cu interactions (Fig. 3a).

Table 1. Model fit parameters for both models considered: the biotic ligand modeleffects

Metal Model a KCaBL KMgBL

Cua mBLM 7.60E-07 101.82 101.99

BLM 3.89E-05 101.48 101.85

Nib mBLM 9.46E-06 103.28

BLM 7.12E-06 103.47

Coc mBLM 2.54E-05 103.57

BLM 1.13E-05 103.85

Cud mBLM 2.18E-07 �101.51 101.66

BLM

The parameters in the mBLM were derived by multiple nonlinear regression analyfrom zero at the 5% level. Blank spaces indicate values not significantly differena Cu toxicity to T. aestivum and original data from previous studies. The param

Schamphelaere et al. [2].b,c Ni and Co toxicity to barley H. vulgare and original data from Lock et al. [40d Cu toxicity to barley H. vulgare and original data from Lock et al. [39].

In fact, the c0 effects could give a false appearance ofcompetition in cases where it did not occur or it was weakenedwith the increases of Ca2þ or Mg2þ in the BM, as demonstratedabove. Multiple nonlinear regression analyses with Equation 8yielded these values for the coefficients: a¼ 7.76E-07,KCaBL¼ 101.82, KMgBL¼ 101.99; n¼ 25; R2¼ 0.84;p< 0.001. Thus Ca2þ alleviated Cu2þ toxicity somewhat sim-ilarly to Mg2þ because the 95% confidence intervals for KCaBL

and KMgBL overlapped (Table 1). In this case, the terms of(KCaBL{Ca2þ}b þKMgBL{Mg2þ}b] exp(-2Fy0/RT) in Equation8 were slightly decreased with increases of Ca2þ or Mg2þ.Therefore, the values of 48-h EA50{Cu2þ}b were mainlyinfluenced by both the c0 effects and the decreased competitioneffects at the CM surface. Inclusion of Naþ, Kþ, and Hþ in themultiple regression had a negligible effect on the regressioncoefficients and R2 values. We also derived these BLM-param-eters through the methodology described previously [2]. TheBLM-parameter KCaBL and KMgBL were somewhat smaller thanthe corresponding coefficients in Equation 7 and the value of R2

was 0.73 (Table 1). The predicted EC50s calculated withEquation 8 using these regression coefficients differed fromthe observed EA50s with a factor of less than 2 (Fig. 3b). Itshould be recognized that the model considering the c0 effectsand competition effects fits the observed EA50 data better than aCu-BLM (Table 1).

Nickel toxicity to barley (Hordeum vulgare)

Lock et al. [41] attempted to develop an Ni-BLM toH. vulgare based on the BLM framework. Five sets of Nibioassays were performed with rooting media variously com-posed of [CaCl2]¼ 0.2 and 15 mmol L�1, [MgSO4]¼ 0.05–3.9 mmol L�1, [KCl]¼ 0.078–10.0 mmol L�1, [NaCl]¼ 2.6,24 mmol L�1. Solution pH values except for the pH-set werealways adjusted at 7.0 using MOPS (3-[N-morpholino] propanesulfonic acid). Solution pH values at 5.0 to 6.0 were adjusted byMES-buffering (2-[N-morpholino] ethane sulfonic acid) andNaOH. Solution pH values at >7.0 were adjusted by NaHCO3

(see Table S-3 in Supplemental Data for chemical character-istics of test medium). The 4-d EC50{Ni2þ}0 (expressed asnickel ion activity at the CM surface) was computed from the4-d EC50{Ni2þ}b (expressed as nickel ion activity in the BM)according to Equation 3.

The results of Lock et al. [41] demonstrated that onlyincreases in Mg2þ activity caused a significant increase in 4-d EA50{Ni2þ}b, and increases in Kþ and Hþ activities didnot significantly affect the 4-d EA50{Ni2þ}b. The authors

(BLM) and the modified BLM model (mBLM) incorporating the electrostatic

KHBL KNaBL KKBL n r2

25 0.8425 0.7322 0.9822 0.6533 0.92

102.50 33 0.70101.55 32 0.39

32

ses according to Equation 8. All presented values are significantly differentt from zero.eters in BLM were obtained by the BLM methodology described in De

,41].

1508 Environ. Toxicol. Chem. 29, 2010 P. Wang et al.

attributed the alleviative effect of Mg2þ to its competitionwith Ni2þ for binding sites. Therefore, only Mg2þ was incor-porated in the Ni-BLM framework and the BLM-parameters arelisted in Table 1.

The Ni toxicity (expressed as 4-d EA50{Ni2þ}b) was alle-viated as the negativity of c0 reduced except for three points(Fig. 4a), for which their intrinsic toxicity (expressed as 4-dEA50{Ni2þ}0) were the highest (Fig. 4b). When the Mg-set wasdisregarded, then the intrinsic toxicity of Ni2þ was enhanced asthe negativity of c0 declined (discussed in more detail below).The variation for Mg2þ was explored in other studies and is notpresented here. Multiple nonlinear regression analyses withEquation 8 yielded the following coefficients: a¼ 9.46E-06,KMgBL¼ 103.28; n¼ 22; R2¼ 0.98; p< 0.001. The predictedEA50s calculated with Equation 8 using these regression coef-

Fig. 4. 4-d EA50{Ni2þ}b (expressed as nickel ion activity in bulk-phasemedium) (a) and 4-d EA50{Ni2þ}0 (expressed as nickel ion activity at cellmembrane surface) (b) for barley (Hordeum vulgare) as a function of the CMsurface potential computed with the GCS model. Comparison of the predictedand observed 48-h EA50s (c). Predicted EA50s were calculated withEquation 8 using the regression coefficients obtained in the present study(Table 1). The solid line in the bottom of (c) indicates the 1:1 ratio; the dashedlines represent a factor of two variations above and below the 1:1 lines. Dataare from Lock et al. [41].

ficients differed from the observed EA50s by a factor of lessthan 2 (Fig. 4c).

Cobalt toxicity to barley (Hordeum vulgare)

Lock et al. [10] attempted to develop a Co-BLM toH. vulgare based on the BLM framework. Five sets of Co2þ

bioassays were performed with rooting media variously com-posed of [CaCl2]¼ 0.20–15 mmol L�1, [MgSO4]¼ 0.05–6.2 mmol L�1, [KCl]¼ 0.078–10.0 mmol L�1, [NaCl]¼ 2.1–24.0 mmol L�1, and MES, MOPS, or NaHCO3 used for buffer-ing according to the end pH values (see Table S-4 in Supple-mental Data for chemical characteristics of test medium). The4-d EA50{Co2þ}0 (expressed as cobalt ion activity at CMsurface) was computed from the 4-d EA50{Co2þ}b (expressedas cobalt ion activity in BM) according to Equation 3.

The results of Lock et al. [10] showed that the 4-dEA50{Co2þ}b significantly increased as Mg2þ and Kþ activityin the BM increased. However, increases in Ca2þ activityresulted in a decreased 4-d EA50{Co2þ}b. The authors attrib-uted the alleviative effect of Mg2þ and Kþ to competition ofthese cations with Co2þ for binding sites and did not explain theenhanced Co2þ toxicity with increasing Ca2þ activity. There-fore, the Co-BLM framework incorporated Mg and K and theBLM parameters are also listed in Table 1.

Similarly, the Co toxicity (expressed as 4-d EA50{Co2þ}b)was alleviated as the negativity of c0 reduced except for Ca-set(Fig. 5a), and the intrinsic toxicity (expressed as 4-dEA50{Co2þ}0) in Ca-set was decreased as the negativity ofc0 declined (Fig. 5b). In Ca-set, the Mg2þ surface activity at theCM surface was significantly decreased as increases in Ca2þ

bulk activity and the 4-d EA50{Co2þ}0 increased as theincreases in Mg2þ surface activity (Fig. 5c). Theenhanced Co2þ extrinsic toxicity with increasing Ca2þ activitywas likely due to the balance of the reduced electrostatic effectsand the decreased competition by declined Mg2þ activity at theCM surface (discussed in more detail below). When Mg-set wasalso disregarded, then the intrinsic toxicity of Co2þ was alsoenhanced all the time as the negativity of c0 declined (Fig. 5b).Multiple nonlinear regression analyses with Equation 8 yieldedthe following coefficients: a¼ 2.54E-06, KMgBL¼ 103.57;n¼ 33; R2¼ 0.92; p< 0.001. Almost all the predicted EA50sby Equation 8 using these regression coefficients fit well withthe observed EC50s within a factor of less than 2 (Fig. 5d).

Copper toxicity to barley (Hordeum vulgare)

Lock et al. [39] demonstrated the influence ofCa2þ, Mg2þ, Naþ, Kþ, and pH on Cu2þ toxicity to H. vulgare.The experimental conditions and statistical treatments werevery similar to the experiment described for Cu2þ toxicity toT. aestivum. The chemical characteristics of the test media arelisted in Table S-5 in the Supplemental Data. Similar to Cotoxicity to H. vulgare, increased Mg2þ activity resulted in atwofold increase in 4-d EA50{Cu2þ}b values, whileincreased Ca2þ activity caused a sixfold decrease in 4-dEA50{Cu2þ}b values. The authors thought that the competi-tions for binding sites between Cu2þ and other cations, suchas Ca2þ and Mg2þ, were not an important factor indetermining Cu2þ toxicity to H. vulgare. Thus, a Cu-BLMfor predicting Cu2þ toxicity to H. vulgare was not established.

The data were also reanalyzed in terms of c0. A similarrelationship between the 4-d EA50{Cu2þ}b and the negativityof c0 was not observed (Fig. 6a). However, similar to Cu2þ

toxicity to T. aestivum, the intrinsic toxicity (expressed as 4-dEA50{Cu2þ}0) in all sets was decreased as the negativity of c0

Fig. 5. The 4-d EA50{Co2þ}b (expressed as cobalt ion activity in bulk-phasemedium) (a) and 4-d EA50{Co2þ}0 (expressed as cobalt ion activity at cellmembrane surface) (b) for barley (Hordeum vulgare) as a function of the CMsurface potential computed with the GCS model. The 4-d EA50{Co2þ}0 as afunction of the Mg2þ surface activity (c). Comparison of the predicted andobserved 48-h EA50s (d). Predicted EA50s were calculated with Equation 8using the regression coefficients obtained in the present study (Table 1). Thesolid line in the bottom of (d) indicates the 1:1 ratio; the dashed lines representa factor of two variations above and below the 1:1 lines. Data are from Locket al. [10].

Toxicity of metal ions and the alleviation of toxicity Environ. Toxicol. Chem. 29, 2010 1509

declined (Fig. 6b). Multiple nonlinear regression analyses withEquation 8 yielded the following coefficient values: a¼ 2.18E-06, KCaBL¼�101.51, KMgBL¼ 101.66, KHBL¼ 101.55; n¼ 32;R2¼ 0.39; p¼ 0.009. Readers may note a potential problem inthe coefficient of K in this case. The positive coefficient valuesindicate that Mg2þ and Hþ alleviated the toxicity of Cu2þ, butthe negative coefficient value for Ca2þ coefficient indicatesthat Ca2þ appears to have enhanced the toxicity of Cu2þ. Thiscould mean that perhaps only invoking the c0 effect and thecompetition effect between Cu2þ and Ca2þ were not the truemechanisms of how Ca2þ impacted Cu2þ toxicity in this case,or it might mean that Mg2þ or Hþ could play more importantroles than Ca2þ (increases in Ca2þ resulted in greatly decreasing

Fig. 6. The4-d EA50{Cu2þ}b (expressedas cupric ion activity in bulk-phasemedium) (a) and 4-d EA50{Cu2þ}0 (expressed as cupric ion activity at cellmembrane surface) (b) for barley (Hordeum vulgare) as a function of the CMsurface potential computed with the Gouy-Chapman-Stern (GCS) model.Comparison of the predicted and observed 48-h EA50s (c). Predicted EA50swere calculated with Equation 8 using the regression coefficients obtained inthe present study (Table 1). The solid line in the bottom of (c) indicates the 1:1ratio; the dashed lines represent a factor of two variations above and below the1:1 lines. Data are from Lock et al. [39].

1510 Environ. Toxicol. Chem. 29, 2010 P. Wang et al.

of CM surface Mg2þ and/or Hþ), as in the case for regressionanalyses of barley toxicity by Co2þ and Ni2þ, which revealed nosignificant value for coefficient KCaBL. Pursuing an interpreta-tion of a negative value for the coefficient would requirededicated study. The predicted EC50s calculated with theequation using these regression coefficients were, with twoexceptions, within a factor of less than two (Fig. 6c).

Mechanisms for the effects of cations on metal toxicity

The mechanisms of metal toxicity are generally very poorlyunderstood, but the interactions between toxic ion and commoncations seem better understood. The present study and previousstudies indicate at least three mechanisms for ion-toxicantinteractions. These mechanisms are intended to account forionic alleviation or enhancement of metal toxicity. Mechanism Iis the electrostatic displacement of metal ions from the CMsurface. Additions of Ca2þ or Mg2þ in the BM cause a reductionof the negative electrical potential at the CM surface and hencethe electrostatic attraction of metal ion is reduced. Ions Ca2þ

and Mg2þ are equally effective in the electrostatic mechanismbecause of their identical depolarizing effectiveness. Ions Naþ

and Kþ also have similar electrostatic effectiveness but muchweaker than Ca2þ and Mg2þ. The depolarizing effectiveness ofcommon cations depends on charge and strength of binding tothe CM surface. In some cases, c0 effects appear to play adominant role in ion-toxicant interactions ([16,17,33] andresults in the present study). Mechanism II is site-specificcompetition. This mechanism accounts for ion-toxicant inter-actions according to the BLM. The operation of mechanism Iabove does not negate the operation of site-specific competition.Surface Ca2þ, Mg2þ, and/or Hþ could displace metal ion fromsurface ligands through direct competition. However, the com-petition effects could take place at the low cation concentrationsor may be weakened with the addition of Ca2þ in the BM,caused by a reduction of other cations at the CM surface thataccompanied an increase in CaCl2 or MgCl2 in the BM (Fig. 2).Mechanism III may be related to the surface-to-surface trans-membrane potential difference (Em,surf). Increasing Ca2þ

or Mg2þ in the BM results in an increase in the Em,surf, whichoffers a bigger force to transport metal ions across the cellmembrane and results in an enhanced uptake. Similarly,ions Naþ and Kþ also have similar effectiveness but muchweaker than that of Ca2þ and Mg2þ.

As for Cu2þ toxicity to T. aestivum, the 48-h EA50{Cu2þ}b

was significantly alleviated initially and then elevated with theincreases in Ca2þ or Mg2þ bulk activities (Fig. 3a). The intrinsictoxicity was enhanced as Ca2þ or Mg2þ bulk activitiesincreased. This indicates that mechanism I is the principalmechanism initially and then the alleviative effect by mecha-nism I is not enough to compensate for the enhanced toxicitythrough mechanisms II and/or III. Due to their lower depola-rizing effectiveness, increases in Naþ or Kþ activities in BM didnot significantly influence Cu2þ toxicity. It is likely that a feeblealleviate effect resulted from a decrease in attraction of Cu2þ toCM surface (Mechanism I) compensates for a slightly enhancedtoxicity by weakened competition (Mechanism II) or anincreased driving force (mechanism III). As a result, no obviousinfluence caused by Naþ or Kþ on metal toxicity was observed.

For Co2þ and Cu2þ toxicities to H. vulgare, increasing Ca2þ

activity in the BM resulted in a decreased 4-d EA50{Mnþ}b.This indicates that mechanisms II and/or III predominate allalong. Except for Mg-sets in Co2þ and Ni2þ toxicities toH. vulgare, the influences of other common cations onEA50{Mnþ}b could be interpreted similarly by Mechanisms

I and III. The Co2þ and Ni2þ toxicities are especially alleviatedby Mg2þ (Figs. 4a, 5a). When Mg is disregarded in the caseof Co2þ and Ni2þ, then the intrinsic toxicities of Co2þ, Ni2þ,and Cu2þ are enhanced as CM surface negativities declined.This suggests that the transport mechanisms for Co2þ and Ni2þ

across membrane are related to Mg2þ and different from thatfor Cu2þ. The radius of Mg2þ (0.72 pm) is similar to that of Ni2þ

(0.69 pm) and Co2þ (0.65 pm) and the chemical featuresof Co2þ and Ni2þ are also very similar to each other. Snavelyet al. [43] reported that Ni2þ was transported into the cell by allthree Mg2þ transport systems, and uptake of Ni2þ was com-pletely inhibited in a strain deficient in all three systems.Thus, Mg2þ will compete with Ni2þ for binding sites onthe Mg2þ transporters and as a result less Ni2þ will betaken up. In addition, high Mg2þ concentrations candownregulate Mg2þ transporters [43] and also reducethe Ni2þ uptake. Therefore, the alleviative Co and Ni toxicitiesby Mg2þ could be mainly due to the alleviation throughMechanisms I, II, and regulation by Mg2þ.

In conclusion, the electrical properties of CMs not onlyprovide a new insight into exploring the mechanisms of ioninteraction and biotic effects of metal ions, but also can bedeveloped to assess the phytotoxicity of metal ions. A study toquantify the effects by the three mechanisms and incorporatingthem into a comprehensive model for predicting metal toxicityis under way.

SUPPLEMENTAL DATA

The Supplemental Data includes a simple description of theBLM, the GCS model, the calculation of c0, parameter valuesused for the calculation of c0 in the GCS model (Table S-1),data of Cu toxicity to T. aestivum (Table S-2), and Ni, Co, andCu toxicities to H. vulgare (Tables S-3, S-4, and S-5). (165 KBDOC)

Acknowledgement—The authors are grateful for the help and advice ofDr. Thomas Kinraide of the U.S. Department of Agriculture and forthoughtful and helpful reviews from three anonymous reviewers. This workwas supported financially by the National Natural Science Foundation ofChina (Grant No. 40671095; 40871115), the Natural Science Foundation ofJiangsu Province, China (Grant No. BK 2009339) and the GraduateInnovative Program of Graduate School of Chinese Academy of Sciences.

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