Modelling metal–metal interactions and metal toxicity to lettuce Lactuca sativa following mixture exposure (Cu2+–Zn2+ and Cu2+–Ag+)

Le, T.T. Yen; Vijver, Martina G.; Kinraide, Thomas B.; Peijnenburg, Willie J.G.M.; Hendriks, A. Jan

doi:10.1016/j.envpol.2013.01.017

Cited by 36 publications

(23 citation statements)

References 73 publications

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“…These and our previous results , together with those reported by others in connection with the MMME project , have improved our understanding of mixture effects with cations, but further measurements and modeling are needed to provide a full picture. Data on a wider range of metals and at different pH values are desirable, and there is a need to follow the approach of Meyer et al in designing metal mixture experiments to challenge the models.…”

Section: Discussionsupporting

confidence: 73%

“…Observed (points) and fitted metal mixture toxicity to Lactuca sativa (data of Le et al ). In each case the (constant) free‐ion concentration of 1 of the metals increases in the order open circles < closed circles < open squares.…”

Section: Resultsmentioning

confidence: 99%

“…These data were also analyzed by Le et al , using both the BLM and an extended empirical concentration‐addition toxicity model. To apply the BLM, they assumed that Cu and Zn compete for binding to their respective biotic ligands, whereas Cu and Ag do not.…”

Section: Resultsmentioning

confidence: 99%

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Testing WHAM‐F_TOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Tipping

Lofts

2015

Enviro Toxic and Chemistry

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The Windermere humic aqueous model using the toxicity function (WHAM‐FTOX) describes cation toxicity to aquatic organisms in terms of 1) accumulation by the organism of metabolically active protons and metals at reversible binding sites, and 2) differing toxic potencies of the bound cations. Cation accumulation (νi, in mol g−1) is estimated through calculations with the WHAM chemical speciation model by assuming that organism binding sites can be represented by those of humic acid. Toxicity coefficients (αi) are combined with νi to obtain the variable FTOX (= Σ αiνi) which, between lower and upper thresholds (FTOX,LT, FTOX,UT), is linearly related to toxic effect. Values of αi, FTOX,LT, and FTOX,LT are obtained by fitting toxicity data. Reasonable fits (72% of variance in toxic effect explained overall) were obtained for 4 large metal mixture acute toxicity experiments involving daphnids (Cu, Zn, Cd), lettuce (Cu, Zn, Ag), and trout (Zn, Cd, Pb). Strong nonadditive effects, most apparent in results for tests involving Cd, could be explained approximately by purely chemical competition for metal accumulation. Tentative interpretation of parameter values obtained from these and other experimental data suggests the following order of bound cation toxicity: H < Al < (Cu Zn Pb UO2) < (Cd Ag). Another trend is a strong increase in Cd toxicity relative to that of Zn as organism complexity increases (from bacteria to fish). Environ Toxicol Chem 2015;34:788–798. © 2014 SETAC

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Section: Discussionsupporting

confidence: 73%

Section: Resultsmentioning

confidence: 99%

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Testing WHAM‐F_TOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Tipping

Lofts

2015

Enviro Toxic and Chemistry

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“…Toxicity of single metals was found to follow the Weibull Equation . Accordingly, the response of lettuce exposed to single metals expressed by the root growth (growth; mm) can be related to {M n+ } b (Equation 2) or {M n+ } 0 (Equation 3) :

Growth = \frac{b}{\exp [{(c \times {{normalM}^{n +}}_{normalb})}^{d}]}

Growth = \frac{b}{\exp [{(c \times {{normalM}^{n +}}_{0})}^{d}]}

where coefficient b (mm) is the growth of lettuce roots when the metal ion is not present in the solution (i.e., {M n+ } b = 0) or at the plasma membrane surface (i.e., {M n+ } 0 = 0). Coefficient c (µM −1 ) reflects the metal‐specific strength of toxicity.…”

Section: Methodsmentioning

confidence: 99%

“…According to the concentration addition model, mixture substances are supposed to act by the same mechanism . Subsequently, the growth of lettuce roots (growth; mm), after exposure to a noninteractive mixture of Cu 2+ , Zn 2+ , and Ag + , can be determined based on the free ion activity of these metals at the plasma membrane surface according to the following equation:

Growth = \frac{b}{\exp [{(c_{1} \times {C {normalu}^{2 +}}_{0} + c_{2} \times {Z {normaln}^{2 +}}_{0} + c_{3} \times {A {normalg}^{+}}_{0})}^{d}]}

where c 1 , c 2 , and c 3 (µM −1 ) are the strength coefficients of toxicity of Cu 2+ , Zn 2+ , and Ag + in their noninteractive mixtures, respectively; d (dimensionless) is the shape coefficient of the dose–response curve describing toxicity of these metal ions according to the concentration addition model .…”

Section: Methodsmentioning

confidence: 99%

Delineating ion‐ion interactions by electrostatic modeling for predicting rhizotoxicity of metal mixtures to lettuce Lactuca sativa

Wang²,

Vijver

et al. 2014

Enviro Toxic and Chemistry

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Effects of ion-ion interactions on metal toxicity to lettuce Lactuca sativa were studied based on the electrical potential at the plasma membrane surface (ψ0 ). Surface interactions at the proximate outside of the membrane influenced ion activities at the plasma membrane surface ({M(n+)}0). At a given free Cu(2+) activity in the bulk medium ({Cu(2+)}b), additions of Na(+), K(+), Ca(2+), and Mg(2+) resulted in substantial decreases in {Cu(2+)}0. Additions of Zn(2+) led to declines in {Cu(2+)}0, but Cu(2+) and Ag(+) at the exposure levels tested had negligible effects on the plasma membrane surface activity of each other. Metal toxicity was expressed by the {M(n+)}0 -based strength coefficient, indicating a decrease of toxicity in the order: Ag(+) > Cu(2+) > Zn(2+). Adsorbed Na(+), K(+), Ca(2+), and Mg(2+) had significant and dose-dependent effects on Cu(2+) toxicity in terms of osmolarity. Internal interactions between Cu(2+) and Zn(2+) and between Cu(2+) and Ag(+) were modeled by expanding the strength coefficients in concentration addition and response multiplication models. These extended models consistently indicated that Zn(2+) significantly alleviated Cu(2+) toxicity. According to the extended concentration addition model, Ag(+) significantly enhanced Cu(2+) toxicity whereas Cu(2+) reduced Ag(+) toxicity. By contrast, the response multiplication model predicted insignificant effects of adsorbed Cu(2+) and Ag(+) on the toxicity of each other. These interactions were interpreted using ψ0, demonstrating its influence on metal toxicity.

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Neurotoxicity of Metal Mixtures

Andrade

Aschner

Santos

2017

Advances in Neurobiology

131

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Metals are the oldest toxins known to humans. Metals differ from other toxic substances in that they are neither created nor destroyed by humans (Casarett and Doull's, Toxicology: the basic science of poisons, 8th edn. McGraw-Hill, London, 2013). Metals are of great importance in our daily life and their frequent use makes their omnipresence and a constant source of human exposure. Metals such as arsenic [As], lead [Pb], mercury [Hg], aluminum [Al] and cadmium [Cd] do not have any specific role in an organism and can be toxic even at low levels. The Substance Priority List of Agency for Toxic Substances and Disease Registry (ATSDR) ranked substances based on a combination of their frequency, toxicity, and potential for human exposure. In this list, As, Pb, Hg, and Cd occupy the first, second, third, and seventh positions, respectively (ATSDR, Priority list of hazardous substances. U.S. Department of Health and Human Services, Public Health Service, Atlanta, 2016). Besides existing individually, these metals are also (or mainly) found as mixtures in various parts of the ecosystem (Cobbina SJ, Chen Y, Zhou Z, Wub X, Feng W, Wang W, Mao G, Xu H, Zhang Z, Wua X, Yang L, Chemosphere 132:79-86, 2015). Interactions among components of a mixture may change toxicokinetics and toxicodynamics (Spurgeon DJ, Jones OAH, Dorne J-L, Svendsen C, Swain S, Stürzenbaum SR, Sci Total Environ 408:3725-3734, 2010) and may result in greater (synergistic) toxicity (Lister LJ, Svendsen C, Wright J, Hooper HL, Spurgeon DJ, Environ Int 37:663-670, 2011). This is particularly worrisome when the components of the mixture individually attack the same organs. On the other hand, metals such as manganese [Mn], iron [Fe], copper [Cu], and zinc [Zn] are essential metals, and their presence in the body below or above homeostatic levels can also lead to disease states (Annangi B, Bonassi S, Marcos R, Hernández A, Mutat Res 770(Pt A):140-161, 2016). Pb, As, Cd, and Hg can induce Fe, Cu, and Zn dyshomeostasis, potentially triggering neurodegenerative disorders, such as Alzheimer's disease (AD) and Parkinson's disease (PD). Additionally, changes in heme synthesis have been associated with neurodegeneration, supported by evidence that a decline in heme levels might explain the age-associated loss of Fe homeostasis (Atamna H, Killile DK, Killile NB, Ames BN, Proc Natl Acad Sci U S A 99(23):14807-14812, 2002).The sources, disposition, transport to the brain, mechanisms of toxicity, and effects in the central nervous system (CNS) and in the hematopoietic system of each one of these metals will be described. More detailed information on Pb, Mn, Al, Hg, Cu, and Zn is available in other chapters. A major focus of the chapter will be on Pb toxicity and its interaction with other metals.

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Modelling metal–metal interactions and metal toxicity to lettuce Lactuca sativa following mixture exposure (Cu2+–Zn2+ and Cu2+–Ag+)

Cited by 36 publications

References 73 publications

Testing WHAM‐F_TOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Testing WHAM‐F_TOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Delineating ion‐ion interactions by electrostatic modeling for predicting rhizotoxicity of metal mixtures to lettuce Lactuca sativa

Neurotoxicity of Metal Mixtures

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Modelling metal–metal interactions and metal toxicity to lettuce Lactuca sativa following mixture exposure (Cu2+–Zn2+ and Cu2+–Ag+)

Cited by 36 publications

References 73 publications

Testing WHAM‐FTOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Testing WHAM‐FTOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Delineating ion‐ion interactions by electrostatic modeling for predicting rhizotoxicity of metal mixtures to lettuce Lactuca sativa

Neurotoxicity of Metal Mixtures

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Product

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Testing WHAM‐F_TOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)

Testing WHAM‐F_TOX with laboratory toxicity data for mixtures of metals (Cu, Zn, Cd, Ag, Pb)