Evaluation of Metal Biouptake from the Analysis of Bulk Metal

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Article

Evaluation of metal biouptake from the analysis of bulk metal depletion kinetics at various cell concentrations: theory and application Elise Rotureau, Patrick Billard, and Jerome F.L. Duval Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es505049f • Publication Date (Web): 19 Dec 2014 Downloaded from http://pubs.acs.org on December 24, 2014

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Environmental Science & Technology

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Evaluation of metal biouptake from the analysis of bulk metal depletion kinetics

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at various cell concentrations: theory and application

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Elise Rotureau,1,2* Patrick Billard,1,2 Jérôme F.L. Duval1,2

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1

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UMR7360, Vandoeuvre-lès-Nancy F-54501, France.

7

2

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France.

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CNRS, LIEC (Laboratoire Interdisciplinaire des Environnements Continentaux),

Université de Lorraine, LIEC, UMR7360, Vandoeuvre-lès-Nancy, F-54501,

* Corresponding author: [email protected]

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Abstract

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Bioavailability of trace metals is a key parameter for assessment of toxicity on living

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organisms. Proper evaluation of metal bioavailability requires monitoring the various

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interfacial processes that control metal partitioning dynamics at the biointerface,

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which includes metal transport from solution to cell membrane, adsorption at the

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biosurface, internalization and possible excretion. In this work, a methodology is

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proposed to quantitatively describe the dynamics of Cd(II) uptake by Pseudomonas

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putida. The analysis is based on the kinetic measurement of Cd(II) depletion from

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bulk solution at various initial cell concentrations using electroanalytical probes. On

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the basis of a recent formalism on dynamics of metal uptake by complex

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biointerphases, cell concentration-dependent depletion timescale and plateau value

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reached by metal concentration at long exposure times (>3 hrs) are successfully

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rationalized in terms of limiting metal uptake flux, rate of excretion and metal affinity

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to internalization sites. The analysis shows the limits of approximate depletion

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models valid in the extremes of high and weak metal affinities. The contribution of 1 ACS Paragon Plus Environment


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conductive diffusion transfer of metals from the solution to the cell membrane in

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governing the rate of Cd(II) uptake is further discussed on the basis of estimated

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resistances for metal membrane transfer and extracellular mass transport.

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1. Introduction

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Understanding the mechanisms governing the uptake of essential or toxic trace

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metals by microorganisms in natural aquatic media is a key requirement in

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environmental risk assessments. Toxicity assays generally relate the toxicity

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endpoints like microorganisms mortality or growth rate to exposed ambient

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concentrations of metals. While such data are useful for the definition of water quality

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standards by stakeholders, there is still a need for investigating the basic connections

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between dynamic partitioning of metals at biointerfaces and their toxic effects on

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microorganisms.

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The free ion activity model (FIAM) and the biotic ligand model (BLM) may

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provide sound evaluations of metal biouptake since they include metal speciation in

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solution and explicitly differentiate the fractions of metals adsorbed at the cell surface

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and internalized by the organism.1–3 The biotic ligand model, however, tacitly implies

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that the diffusive transport of metals from bulk solution to a metal-assimilating

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biomembrane is very fast as compared to the internalization step.4 The equilibrium-

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based BLM thus intrinsically ignores the dynamic interplay between the various

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interfacial processes controlling metal biouptake.5 There is a large data body from the

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literature that evidenced the failure and limits of BLM for predicting metal uptake by

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organisms like periphyton,6,7 roots8 and algae.9–12 More involved models for metal

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biouptake were then developed to include the dynamic dimension absent from the 2 ACS Paragon Plus Environment

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BLM framework. These models are generally based on the definition of the relevant

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metal fluxes at the organism surface and include the diffusive transfer of metal

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species from solution to the internalisation sites and the kinetics of metal uptake, with

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or without accounting for bulk metal depletion effects.13,14 Other models rationalize

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the partitioning of metals at the biointerface by considering metal adsorption at the

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cell surface, metal excretion from the intracellular volume and metal depletion from

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bulk solution. However, the latter models ignore possible kinetic limitation of uptake

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by extracellular metal transport.15

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So far available formalisms thus differ with respect to their degree of

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sophistication in terms of accounting for or neglecting either extracellular metal

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transport or metal excretion processes, which possibly biases data interpretation. They

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however generally have in common that they are valid under the strict conditions

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where affinity of metal ions for internalization sites are so weak that a linearization of

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the metal uptake flux expressions is legitimate, which leads to explicit analytical

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solution for the time-dependent metal concentration in bulk solution. It is only very

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recently that a theory on dynamics of metal biouptake was elaborated with a full

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integration of the various interfacial processes that likely occur at complex metal-

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assimilating biointerphases, i.e. the metal transport from the solution to active

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biomembranes, metal depletion from bulk solution, metal adsorption on non-

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transporter sites, metal internalization and metal excretion.16,17 As detailed by Duval

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et al.,17 the theory applies over the entire spectrum of metal affinities to

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internalization sites and thus remains valid beyond the commonly adopted linear

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Henry regime of metal adsorption on the membrane. It further explicitly integrates

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electrostatic cell-metal interactions (overlooked in e.g. 15) and the possible role played

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by soft surface structures such as lipopolysaccharides (LPS) protruding from the 3 ACS Paragon Plus Environment


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membrane and that possibly hinder the accessibility of metals to the membrane

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internalization sites. In line with previous work,14 the formalism further suggests that

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adequate interpretation of the kinetics of metal depletion from bulk solution may lead

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to the evaluation of key biouptake parameters like limiting metal uptake flux and

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timescale for metal membrane transfer. Such interpretation, however, necessarily

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requires the analysis of kinetic data collected over a relevant range of operational

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parameters like cell volume fraction.17 Relaxing the latter condition leads to over-

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interpretation of data upon adjustment of too many unknown parameters.17

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In this work, we provide the first experimental data that confirm the basis of the

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above theory on metal biouptake dynamics. The kinetics of Cd(II) depletion from

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suspensions of Pseudomonas putida KT2440 containing or deficient in metal-efflux

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pumps, is measured in situ using equilibrium and dynamic electroanalytical methods

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for cell volume fraction in the range 10-4 to 2×10-3. In line with theoretical prediction,

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bulk metal concentration decreased over time in an exponential-like manner and

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reached an asymptotic plateau value at sufficiently long exposure times. On the basis

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of the theory detailed in

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quantitatively interpret metal depletion kinetic data and to unambiguously evaluate

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the limiting metal uptake flux, the kinetic constant of metal excretion, and the metal

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affinity to internalization sites. In addition, evidence is given for (i) the inapplicability

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of the commonly accepted linearized form of the Michaelis-Menten uptake flux

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expression and for (ii) the insignificant role played by the electrostatic cell-metal

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interactions under the medium conditions adopted in this work. Finally, the

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contribution of metal diffusion transport from the solution to the cell surface in the

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kinetic limitation of the overall uptake is rigorously quantified from evaluation of the

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Bosma number18 (also termed bioavailability number). The formalism proposed here

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, a consistent step-by-step methodology is proposed to

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offers a new route to interpret metal bioaccumulation dynamics from adequate

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analysis of metal partitioning at the cell/solution interface, a feature that has so far

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never been tackled at the level of rigor achieved here.

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2. Theory

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We consider the situation where a suspension of (charged) spherical cells depletes

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metal ions M (valence zM) from bulk solution as a result of metal adsorption at their

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biosurface and metal internalization processes. A list of the symbols/variables (with

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associated units) used in this section is given in Supporting Information (SI).

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Assuming that biosorption is much faster than internalization, a condition that applies

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for the system of interest in this study (see discussion section), Duval et al.17 recently

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showed that the decrease over time t of the metal concentration c∗M ( t ) (mol m-3) in

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a the bulk solution may be computed from the time-dependent concentration cM ( t ) of

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M (mol m-3) at the membrane surface according to a generalized form of the Best

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equation. The latter includes extracellular conductive diffusion transport of M from

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the solution to the membrane, Michaelis-Menten like-metal internalization and metal

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excretion by the cells.17 Denoting kint (s-1) and ke (s-1) the kinetic constants for M

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internalization and excretion, respectively, and starting from the original expression

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given in ref. [17] (eq 15 therein), this extended Best equation can be rewritten in the

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form

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t    1 e− keξ − ke t ∗ −1 a −1 -1    , (1) cM t = β c t − K β Bn − e 1 + k d ξ ( ) a M( ) M a e ∫ a a   1 + cM 1 + c ξ / K (t ) / KM ( ) M M 0  

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which holds for φu0 = 0 with φu0 the concentration of internalized metals (mol m-2) at t

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= 0.17 KM (mol m-3) is the affinity constant of M for the internalization sites, b a 5 ACS Paragon Plus Environment


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corresponds to the (dimensionless) Boltzmann factor b r = exp(- zM y (r )/ z) evaluated

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at the membrane surface positioned at r = a (r is the radial position from the cell

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center) with z the valence of the here-considered symmetrical electrolyte present in

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the solution in excess over metals, and y (r ) defines the (dimensionless) equilibrium

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electrostatic potential at the position r.17 The (dimensionless) Bosma number Bn

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involved in eq 1 compares the assimilation properties of the cell to the dynamic metal

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supplying potential of the medium according to Bn = RS / RT , with Rs = 1/ (kint K H b a )

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(m-1 s) the membrane transfer resistance reflecting the ability of M to cross the

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membrane surface barrier, and RT = 1/ DM,out f el a-

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extracellular compartment to conductive diffusion transport of M. The quantities K H

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(m) and DM,out (m2 s-1) correspond to the Henry coefficient for adsorption of M on

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the internalization sites and to the diffusion coefficient of M in the aqueous solution

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outside soft surface structures (e.g. LPS) possibly protruding from the cell

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membrane,19 respectively.17 f el is a factor that corrects the diffusion flux for the

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acceleration of (the positively charged) metals M due to the electric double layer field

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across the interphase between solution and (negatively charged) bacteria. Duval et al.

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a further demonstrated that cM ( t ) (involved in eq 1) is provided by the implicit

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equation17

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(

1

) (m-1 s) the resistance of the

p− (1+ c+ )   a  cM ( t ) − c−     a       cM ( 0 ) − c−  ln   = keτ L (1 + c+ )(1 + c− )( c+ − c− ) t p 1 + c τ − τ c − c ( ) ( )( ) + − E L + − a a   cM   1 + cM ( t ) − c+  (t )   a     a   cM ( 0 ) − c+   1 + cM ( 0 ) 

(2)

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a a , where we introduced the dimensionless M surface concentration cM ( t ) = cM (t ) / KM .

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The expressions for the variables p± (s) and c± = c± / K M (dimensionless) are detailed 6 ACS Paragon Plus Environment

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in 17, and involve the characteristic timescales τ L and τ E ( ≥ τ L ) (in s) for the transfer

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of M across the membrane and that from bulk solution to intracellular volume,

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respectively.17 The expressions for τ L and τ E depend on the (dimensionless) cell

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volume fraction ϕ in the dispersion and on the basic physicochemical descriptors for

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the electrostatic and structural properties of the M-assimilating biointerphase, as

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demonstrated in16,17 and briefly recalled in Supporting Information (SI). The volume

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fraction ϕ is defined as the ratio between the volume occupied by one bacterium

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times the number of bacteria present in solution over the total sample volume.

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Equation (2) describes how the metal concentration at the membrane surface depends

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on time. Equation 2 formulates, together with eq 1, how metal concentrations at the

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a ) and in bulk solution ( c∗M ) vary with time as a result of concomitant cell surface ( cM

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metal biouptake, excretion and depletion in solution. The magnitude of the ratio

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τ E / τ L basically indicates to what extent M biouptake is kinetically controlled by M

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transport to the membrane: in the extremes where τ E / τ L = 1 and τ E / τ L >> 1 , uptake

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kinetics is determined by the internalisation and transport steps, respectively, whereas

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both internalisation and transport are operational for intermediate values of τ E / τ L .

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Under conditions where electrostatic interactions between cells and metals are fully

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screened and cells are further devoid of any protruding soft surface structure, τ E / τ L

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simply reduces to τ E / τ L = 1 + Bn −1 1 − 3ϕ 1/ 3 / 2

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c+ = c+ / K M in eq 2 identifies with the (dimensionless) concentration of metals

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reached at t → ∞ where equilibrium between surface and bulk metal concentration is

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a ∗ necessarily achieved, i.e. cM ( ∞ ) = β a cM ( ∞ ) .17 Assuming that such equilibrium also

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a ∗ applies at t = 0 , i.e. cM ( 0 ) = β a cM ( 0 ) , and starting from the result given in ref. 17 (eq

(

)

(details in SI). The parameter



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23 therein), we obtain here for bacterial cells without protruding surface structure the

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original expression for the bulk metal concentration at t → ∞ (see details in the SI) 

∗ cM

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(∞) =

  K M β a−1  −  1 +    



2

1/ 2 



  ϕ  ϕ ϕ + x0 +   1 + ∗ + x0  − 4 ∗ x0  ∗      ϕ  ϕ  ϕ  



 /2 ,  

(3)

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∗ with x0 = βa cM ( 0) / KM (dimensionless) and the (dimensionless) critical cell volume

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fraction ϕ ∗ is formally defined by ϕ ∗ ≡ Vp ke / ( Sa β a K H kint ) where Vp and Sa are the

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volume (m3) and surface of the bacterial cells (m2), respectively. In the absence of

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∗ excretion, i.e. ke → 0 , we obtain cM ( ∞ ) → 0 ,17 and for biosystems with ke ≠ 0 , the

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∗ value of cM ( ∞ ) is non-zero. Detecting the presence of excretion may then be done

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from simple inspection of bulk M depletion kinetic data collected at sufficiently long

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exposure times.17 For ϕ > ϕ ∗ , eq 3 reduces to

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the following form (details in the SI) ∗ c∗M ( ∞ ) = cM ( 0)

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ϕ∗ . ϕ

(5)

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∗ Stated differently, cM ( ∞ ) depends linearly on ϕ for ϕ > ϕ ∗ . The searched concentration cM ( t ) of M in bulk solution may be rigorously

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computed from the numerical evaluation of the coupled eqs 1 and 2, as extensively

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detailed elsewhere.17 For that purpose, 3 independent variables pertaining to the

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uptake process are required: ke , K H kint and the limiting (maximum) uptake flux

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defined by J u* = KM KH kint (mol m-2 s-1).17 Explicit analytical expressions for c∗M ( t ) 8 ACS Paragon Plus Environment

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a a were derived from eqs 1-2 in the limits KM > cM ( t ) that

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correspond to the extremes of strong and weak affinity of M for the internalization

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sites.17 Specifying φu0 = 0 and considering the case of nude cell membranes,

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expressions of these limits may be simplified as follows (details in SI) ∗ cM (t ) =

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a K M > cM ( t ) : c∗M ( t ) =

KM ϕ

βa ϕ ∗

(e−k t − 1) + cM∗ (0) , e

∗ cM ( 0 ) k τ + 1 − k τ 1 + Bn −1 (1 + keτ L )  e −t /τ d  ,   e d (  e d ) 1 + Bn −1   1 + ke (τ L − τ E )  

(6)

(7)

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where τ d = τ E / (1 + keτ L ) is the characteristic ϕ -dependent timescale (in s) for bulk

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metal depletion from solution in the weak metal affinity limit (eq 7), and 1/ ke is the (

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ϕ -independent) depletion timescale in the strong metal affinity case (eq 6).

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Equations 6 and 7 apply to the situation where internalisation sites are all saturated

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and where the linear Henry adsorption regime is applicable, respectively. As

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previously discussed,17 eq 6 depends on the sole J u* and ke variables while eq 7

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involves the only K H kint and ke parameters. Equation 7 is based on the commonly

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adopted linearized form of the Michaelis-Menten flux expression valid for

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a K M >> cM (t ) .

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3. Materials and methods

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Two strains of Pseudomonas putida, a Gram negative soil bacterium colonizing

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roots, were adopted in this study. One of them is the wild type strain (WT), and the

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other is the mutant KT2440.2431 the transporter-deficient strain lacking four

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protective pumps used for detoxifying and expelling harmful divalent metal

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cations.20,21 For the sake of simplicity, we made the simplifying assumption that these 9 ACS Paragon Plus Environment


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bacteria were spherical (instead of rod-shaped, see image by Atomic Force

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Microscopy given in Figure S1 of the SI) with an equivalent radius a of 600 nm.

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AFM images collected for both strains analysed did not reveal soft surface structures

212

significantly protruding from the membrane surface.

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3.1. Preparation of bacterial cell suspensions

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Single colonies of Pseudomonas putida were transferred from agar plates of

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Luria-Bertani (LB) media to liquid LB media and were cultured aerobically

217

overnight. Cell cultures were agitated on a rotary shaker at 160 rpm and 30°C. 100 ml

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of LB were inoculated with the cells and left for 4h until exponential growth phase

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was reached. Then, cells were washed twice by gentle centrifugation with use of

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poorly metal-complexing Heavy Metal Medium (HMM) buffered at pH 6.8. HMM

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medium consisted of MOPS 40 mM, KNO3 50 mM, NH4Cl 1mM, Fe(III)NH4Citrate

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1 µM, Mg(SO4)2 0.5 mM, Na-β-glycerophosphate 0.5 mM and NaNO3 35 mM and

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no added carbon source. NaNO3 electrolyte was added to fix the ionic strength of the

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whole solution to 70 mM in order to warrant conditions where electrostatics are

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screened (see discussion later). Prior to each experiment, a fresh suspension of

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bacteria was systematically prepared. This suspension was diluted in a Cd(NO3)2

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solution (10-6 M) and the resulting cell volume fraction was evaluated from

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spectroscopic measurement at λ = 600 nm (OD600). We performed a series of

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suspension dilutions and optical density measurements that validated the linear

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dependence of the optical density on cell concentration for OD600 values up to unity.

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Assuming that this critical OD600 value corresponded to 109 cells/ml, as commonly

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done in literature,22 the cell number concentration cp (m-3) in suspensions with

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OD≤OD600 was straightforwardly estimated from the optical density value. For cell 10 ACS Paragon Plus Environment

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suspensions with OD600≥1, the corresponding cell volume fraction was estimated

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from the cubic spline interpolation of the calibration data points collected at large cell

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concentrations. From the known volume Vp of a bacterium assimilated -for the sake of

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simplification- to a sphere of radius 600 nm (Figure S1), the volume fraction ϕ

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involved in eqs 2-7 (and in the expressions of τ E,L ) is simply computed from the

239

relationship ϕ = Vp × cp .

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As expected for a medium devoid of carbon source, OD600 measurements revealed

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that the concentration of bacteria remained constant over time under all (initial) cell

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concentrations and Cd exposure conditions tested in this work. This excludes any

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significant impact of bacterial growth on metal depletion kinetic results reported in

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this work.

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3.2. Electrochemical set-up

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An Ecochemie Autolab type III potentiostat controlled by GPES 4.9 software

248

(Ecochemie, The Netherlands) was used in conjunction with a Metrohm 663VA stand.

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The auxiliary electrode was glassy carbon and the reference electrode was a Dri-Ref-

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5 electrode from WPI (Sarasota, USA). The working electrode was a thin mercury

251

film electrode (TMFE) plated onto a rotating glassy carbon (GC) disk of 2 mm

252

diameter (Metrohm). The preparation of TMFE was repeated daily for each set of

253

experiments. The conditioning procedure of the TMFE is described in detail in the SI.

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3.3. Kinetic measurements of metal depletion from cell suspensions

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Two electroanalytical methods were employed to determine the time-dependent

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concentration of metals in the bulk cell dispersions prepared as outlined in the

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preceding sections: Stripping Chronopotentiometry (SCP) and Absence of Gradient 11 ACS Paragon Plus Environment


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and Nernstian Stripping (AGNES). The former allows the measurement of the free

260

(not complexed) and labile metal complexes while the latter enables the detection of

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the only free metal ions. ‘Labile’ refers here to all metal complex species (i.e. formed

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between metals and ligands in solution) that may contribute to the SCP signal due to

263

their fast dissociation/association compared to the time-scale of the experiment.

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Analysis of the results obtained from these two techniques thus makes it possible to

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identify changes in metal speciation over time. Within the framework of this study,

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AGNES and SCP results were similar within analytical uncertainties, thus evidencing

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no significant metal speciation changes over time and excluding the possible

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excretion of metal complexing ligands by the bacteria. For that reason, only SCP

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results are hereafter reported. SCP and AGNES experiments were systematically

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performed in the same electrochemical cell thermostated at 30°C. SCP and AGNES

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electroanalytical techniques we adopted for measuring in situ over time the

272

concentration of metals in solution are -to the best of our knowledge- original. They

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differentiate with respect to e.g. traditional ICP-MS measurements done on samples

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collected at specific times during cell exposure to metals. Our measurement strategy,

275

while remaining accurate (detection at nM level), does not require considering

276

‘experiments in batch’ and, consequently, it provides a faster and more direct way to

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evaluate metal content in solution in the presence of microorganisms. The

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experimental protocol included the preparation of a 20 ml HMM solution buffered at

279

a pH close to 4. To remove oxygen, a chemical oxidant that can interfere with

280

measurements during the stripping step, the solution was left under nitrogen bubbling

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during a few minutes prior to the start of the experiments, and a nitrogen degassing of

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1 min was performed before each measurement. Metals were then first added in the

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form of Cd(NO3)2 certified standard solution (Fluka) (concentrations of 0.1, 0.5 and 12 ACS Paragon Plus Environment

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1.0 µM) for subsequent AGNES and SCP calibration measurements. The pH of the

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HMM media (around 4) ensured that added metals were in free form during

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calibration measurements. Once the calibration measurements were completed, a

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sample of HMM-medium containing Cd(II) in 1.0 µM concentration was prepared

288

and the pH adjusted to 6.8 by addition of NaOH. Decreases of SCP and AGNES

289

signals of 15% were observed following the formation of complexes between Cd(II)

290

and ions from the HMM medium, a value we confirmed from thermodynamic

291

speciation computation based on the freely available software V-Minteq.23 After the

292

addition of 2ml of bacterial suspension with known volume fraction, SCP and

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AGNES experiments were carried out alternatively every 10 min during 3h. After 3h,

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the TMFE electrode tended to be less effective due to mechanical degradation of the

295

mercury film. For some of the experiments, the electrode was renewed to collect data

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for 3h to 5h exposure conditions. For all initial cell concentrations tested, bulk metal

297

concentrations were measured (with renewed electrode) after exposure delays much

298

longer than 5 hrs in order to clearly address the asymptotic behavior of metal

299

depletion kinetics at long times (t→∞). The reader is referred to SI for additional

300

details on the SCP and AGNES measurements.

301 302 303

3.4. Determination of the amount of metals sorbed at the cell surface by ligand exchange technique

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A set of 40 ml HMM batch solutions containing 1.0 µM Cd(II) were prepared as

305

previously detailed and bacterial cells were then added to each solution. After 2, 10,

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30, 60 and 180 min exposure, half of the samples were 0.2µm filtered in order to

307

remove bacteria. For the other half, 2 ml of 0.01M ethylenediaminetetraacetic acid

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(EDTA) solution were added and vortexed for 1 min. After 10 min, solutions were 13 ACS Paragon Plus Environment


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then filtered and acidified. The Cd(II) content in all solution samples was

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subsequently determined by atomic absorption flame spectroscopy (Varian 220FS).

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Analysis of the first series of solutions provided the bulk concentration of Cd(II) and

312

the second one the sum of the concentration of metals adsorbed at the cell surface and

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that of metals in bulk solution, recalling here that EDTA is a suitable competing

314

ligand in the determination of the sorbed amount of metal to biosurfaces.15

315 316

3.5. Electrokinetics

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Cell electrophoretic mobility was measured at 24°C and neutral pH in solution of

318

NaNO3 according to the procedure detailed elsewhere.24 For each NaNO3 electrolyte

319

concentration tested, fresh suspensions of cells were prepared as described above.

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Figure S2 in the SI displays the electrophoretic mobility µ of Pseudomonas putida

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wild type and mutant strains at pH 7 for 1 mM to 200 mM NaNO3 solutions. The

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quantitative analysis of the electrokinetic features of the tested strains is detailed in SI

323

and it evidences the absence of electrostatic interactions between bacteria and metal

324

ions in the HMM solution adopted for metal uptake experiments. In turn, the position-

325

dependent Boltzmann function βr involved in eqs 1-6 is equal to unity and, in

326

particular, βa = 1. In addition, there is no acceleration of metal diffusion from the

327

solution to the membrane as a result of the electrostatic field at the cell-solution

328

interphase. This means that the parameter fel entering the definition of Bn is here

329

unity. The reader is referred to SI for further details on the electrokinetic analysis.

330 331

4. Results and discussion

332

4.1. Metal adsorption on non-internalisation sites at the cell surface


Page 14 of 32

Page 15 of 32


333

∗ The time-dependent concentration of metals in bulk solution, cM ( t ) , is reported

334

in Figure 1 for various volume fractions ϕ (10-4 ≤ ϕ ≤ 2×10-3) of Pseudomonas putida

335

KT2440.2431 lacking four metal efflux transporters. Examination of the data reveals

336

a fast initial decrease in c∗M ( t ) shortly after the addition of bacteria in solution. This

337

initial decrease likely corresponds to a rapid adsorption of metal ions on the

338

biosurface, a process believed to be faster than internalization.4,15 To demonstrate that

339

this fast decrease in c∗M ( t ) solely relates to the fraction of metals adsorbed on the

340

overall bacterial surface in the suspension, estimation of the amount of cell-surface

341

bound metals was achieved by the ligand exchange technique. Results collected for

342

selected values of ϕ are depicted in Figure 2. They confirm that the initial drop in

343

bulk metal concentration simply results from rapid biosorption process. This feature

344

holds also for the WT strain (data not shown). In addition, Figure 2 shows that the

345

amount of adsorbed metals remains constant over the duration of the measurement.

346

As a result, the contribution of this metal adsorption term in the mass balance

347

equation used for deriving metal partitioning at the biointerphase may be ignored.

348

This assumption is correct provided that the initial metal concentration involved in

349

eqs 1 and 2 is taken as the total concentration of metals initially present in bulk

350

solution and corrected by the amount of biosorbed metals. In the situation where there

351

is not an excess of internalization sites over metal ions in solution at any time t, then,

352

as soon as one adsorbed metal ion is internalized, it can be replaced by another metal

353

ion from solution via fast adsorption, therefore buffering the amount of adsorbed

354

metals over time. This is typically the situation met in our system (Figure 2), as later

355

demonstrated (see section 4.2) from the inapplicability of the linearized Michaelis-

356

Menten (M-M) uptake expression to interpret the data displayed in Figure 1. We 15 ACS Paragon Plus Environment


357

recall that this linearized M-M expression is derived from the assumption that

358

internalization sites are systematically in excess compared to metal ions in solution.14

359

Figure 2 thus highlights that the kinetics of metal depletion from solution is the result

360

of the sole internalization and possible excretion processes. As shown in SI (Figure

361

∗ S3), the initial concentration cM ( t → 0 ) corrected for the amount of rapidly adsorbed

362

metals decreases with increasing ϕ, which is in line with expectation. The intercept of

363

that plot at zero cell volume fraction provides a Cd(II) concentration of ca. 0.8 µM,

364

which is 80% of the total cadmium concentration (1 µM) considered for the

365

experiments. The origin of this discrepancy is found from the analysis of the

366

equilibrium speciation of cadmium in the HMM media adopted in this work. In line

367

with Figure S3, using V-Minteq code,22 we indeed determined that 85% of the total

368

cadmium is present in free form, the other part is mainly engaged in complexes

369

formed with nitrate and chloride anions. In the following, free metal ions are

370

therefore considered as the only bioactive species.

371 372 373

4.2 Methodology for quantifying biouptake dynamics from analysis of bulk metal depletion kinetics

374

After the adsorption process previously identified, bulk metal concentration

375

decreases with time according to an exponential-like dependence. The kinetic profiles

376

measured at different volume fractions ϕ of P. putida KT2440.2431 qualitatively

377

exhibit similar patterns. As intuitively anticipated, the larger ϕ, the more pronounced

378

is the depletion of cadmium from the solution. After ca. 3 hrs exposure time, c∗M ( t )

379

asymptotically converges to a constant (non-zero) plateau (hereafter denoted as

380

∗ cM ( ∞ ) ) whose value depends on ϕ. As briefly outlined in the theoretical section, the


Page 16 of 32

Page 17 of 32


381

presence of this plateau reflects the existence of one or several metal excretion

382

strategy(ies) developed by P. putida KT2440.2431 to regulate their internalized

383

amount of Cd(II). Mathematically, this translates into a non-zero kinetic constant ke

384

for metal efflux. This finding may be rather surprising because it is expected from

385

genetic construction that P. putida KT2440.2431 cells are devoid of cadmium efflux

386

system. Yet, this does not exclude that alternative excretion processes are operational,

387

e.g. the non specific out-pumping of cadmium by other metal transporters.25,26 Within

388

∗ the time window where bulk metal concentration reaches cM ( ∞ ) , there is no

389

limitation of the Cd(II) uptake by metal diffusion from medium to biomembrane, and

390

the thermodynamic Boltzmann relationship between bulk and surface metal

391

concentrations thus rigorously applies.16,17 The ϕ-dependent features of metal

392

depletion kinetics may be analyzed on the basis of eqs 1-2 in order to derive the

393

searched limiting metal uptake flux J u∗ , the excretion kinetic constant ke and the

394

metal affinity KM.

395

∗ To constrain the analysis, the dependence of cM ( ∞ ) on cell volume fraction ϕ is

396

first examined using eq 3. This implies hypothesizing the validity a priori of the

397

a ∗ a ∗ relationship cM ( 0) = βa cM ( 0) , or for that matter, cM ( 0 ) = cM ( 0)

398

investigation of cell electrokinetics led us to conclude that electrostatic effects are

399

a ∗ insignificant here (Figure S2 in SI). The applicability of cM ( 0) = cM ( 0 ) will be a

400

posteriori justified in the manuscript. The successful theoretical reconstruction of the

401

∗ cM ( ∞ ) versus ϕ data points by means of eq 3 is given in Figure 1. For that purpose,

402

the affinity constant KM and the critical cell volume fraction ϕ ∗ involved in eq 3 were

403

adjusted according to Levenberg-Marquardt algorithm (LMA). The analysis provides 17 ACS Paragon Plus Environment

since the


( )

404

-4 log ϕ ∗ = -3.4±0.2 and KM = 2.16×10 mM. In order to address the sensitivity of

405

parameter adjustment, Figure 3 further displays the theoretical curves corresponding

406

to the extremes of the (narrow) range of obtained ϕ ∗ values. Overall, the depletion

407

data collected at sufficiently long exposure time (plateau regime) correctly support

408

∗ the basis of eq 3. In particular, they confirm the linear dependence of cM ( ∞ ) on ϕ

409

∗ for ϕ > cM ( t ) and KM > cM ( t ) and KM > cM ( t ) is mathematically

418

∗ more constraining than K M >> cM ( t ) . In the current study, regardless the cell volume

419

fraction ϕ, KM is of the same order of magnitude as c∗M ( t ) that varies between 10-3

420

mM and 10-4 mM (Figure 1). Therefore, under the tested experimental conditions,

421

none of the eqs 6 and 7 can be used because the conditions underlying their strict

422

applicability are not satisfied. It is emphasized that the above methodology comes to

423

rigorously exclude the use of the linearized form of the Michaelis-Menten flux

424

expression. This expression is however often considered for interpretation of metal

425

biouptake, albeit without solid demonstration of its validity.14,15 18 ACS Paragon Plus Environment

Page 18 of 32

Page 19 of 32


426

Due to the inapplicability of the approximate eqs 6 and 7, reconstruction of the ϕ -

427

and time-dependent Cd(II) concentration in solution was achieved using the coupled

428

rigorous eqs 1 and 2 corresponding to the generalized Best equation and to the mass

429

conservation condition, as detailed elsewhere.17 Recalling that KM and the critical cell

430

volume fraction ϕ ∗ = Vpke / ( Sa K H kint ) have been evaluated from the analysis of the

431

∗ dependence of cM ( ∞ ) on ϕ , the numerical recovery of measured c∗M ( t ) can be

432

performed using eqs 1-2 upon adjustment of a single parameter, e.g. the excretion

433

kinetic constant ke. The results are provided in Figure 1 and the numerical analysis

434

∗ successfully reproduces the measured cM ( t ) over the entire range of ϕ values with

435

ke=(1.45 ± 0.25)×10-4 s-1 (∼ (2 hrs)–1). Using the above values of ke, KM and

436

ϕ ∗ = Vpke / ( Sa K H kint ) , we further estimate the limiting uptake flux defined by

437

J u* = K M K H kint and obtain J u* = (2.15±0.15)×10-11 mol.m-2.s-1 for ϕ < 1.9×10-3. This

438

value corresponds to a weak uptake of metal ions as compared to that reported for

439

other organisms, e.g. J u* : O 10−5

440

(algae) where O means ‘in the order of’, and it is comparable to that measured for the

441

uptake of Pb(II) by Chlorella kesslerii.4,5 Differences of uptake rates among

442

microorganisms may originate from numerous factors including the physiological

443

state of the cells (exponential versus stationary phases), the composition of the

444

medium used for the measurements (presence of nutrients, of complexing agents etc),

445

the cell wall composition, the metabolism demands, to quote only a few. It is

446

observed that the value derived for J u* at large ϕ (∼1.9×10-3) is ∼2-3 times lower than

447

that obtained for lower volume fractions. We do not have any robust explanation for

448

this discrepancy but it is worth mentioning here that the response of concentrated

( )

mol.m-2.s-1 for Chlamydomonas reinhardtii



449

bacterial suspension to metallic stress may significantly differ from that of diluted

450

samples as a result of e.g. competition effects or the occurrence of corum sensing. An

451

evidence for this is perhaps the quality of the fit to experimental data at large cell

452

volume fractions. Though acceptable, it is clearly poorer than that achieved at lower

453

ϕ.

454

From the above values obtained for KM and J u* , we can now determine whether

455

the overall uptake of Cd(II) by P. putida KT2440.2431 is kinetically limited by the

456

internalization step, by metal diffusion from solution to the membrane or whether

457

both processes are operational during uptake. For that purpose, the ratio between the

458

timescale for metal membrane transfer τ L and the timescale τ E for overall metal

459

transfer from bulk solution to intracellular volume, was estimated using

460

τ E / τ L = 1 + Bn−1 1 − 3ϕ1/3 / 2 with βa = 1 and DM ∼ 10-9 m2s-1. The result provides

461

τ L / τ E : 1 for all values of ϕ tested in this work with τ L varying between 45 min and

462

6 hrs at ϕ = 1.9×10-3 and 1.18×10-4, respectively. As a result, it is clear that the rate of

463

Cd(II) biouptake is controlled by the kinetics of internalization, which corresponds to

464

very large resistance RS for metal membrane transfer as compared to the resistance RT

465

for metal transfer from solution to the biomembrane. This leads in fine to Bosma

466

number Bn that well exceeds unity under the conditions of interest in this work (

467

Bn : O 104 ). This motivates a posteriori the use we made of eq 3 for analysing the

468

∗ dependence of cM ( ∞ ) on cell volume fraction ϕ (Figure 3). This expression is indeed

469

valid on the premise that initial metal surface concentration and bulk metal

470

a ∗ concentration are interrelated by the equilibrium relationship cM ( 0 ) = cM ( 0 ) . This

(

)

( )


Page 20 of 32

Page 21 of 32


471

equality is necessarily satisfied here because metal diffusion transport is much faster

472

than internalization as inferred from the ratio τ L / τ E or equivalently RS / RT .17

473

At this stage of the analysis, one may wonder whether a blind use of eqs 6 and 7

474

would still lead to an acceptable reconstruction of the depletion kinetic data of Figure

475

1 though the conditions validating their applicability are not satisfied, as detailed

476

above. This issue is important to tackle because it allows to appreciate the

477

appropriateness of a strategy that would consist in the only use of the approximate eqs

478

6 and 7 to discriminate between the extremes of weak and strong metal affinity

479

situations. It should be realized that eqs 6 and 7 are both independent of KM and they

480

involve the parameters ( J u* ; ke) and ( K H kint ; ke), respectively (see details in 17 and in

481

the theoretical section). For each equation, the two relevant parameters were thus

482

determined from experimental data fitted on the basis of Levenberg-Marquardt

483

adjustment algorithm. Results are given in the SI (Figure S4). Obviously, the (here

484

invalid) eqs 6 and 7 still lead to very satisfactory quantitative interpretation of the

485

experimental data and, even worse, the fitting curves resulting from application of

486

these equations are strictly identical. The latter feature is explained by the identical

487

mathematical form of eqs 6 and 7: both include indeed a time-dependent exponential

488

term, an exponential prefactor and a finite limit at t→∞. The results given in Figure

489

S4 suggest that it is impossible to correctly estimate -in an unambiguous way-

490

relevant quantitative information about metal biouptake with the sole use of eq 6 and

491

eq 7. For the sake of completeness, values obtained for ( J u* ; ke) and ( K H kint ; ke) are

492

collected in Figure S5. Comparison with the parameters derived from our rigorous

493

∗ treatment of cM ( ∞ ) (eq 3) and c∗M ( t ) (eq 1) (Figure S5) reveals that eq 6, valid for

494

strong metal affinities, is the most acceptable, at least at sufficiently low ϕ and short 21 ACS Paragon Plus Environment


495

∗ times where KM is ca. 2-3 times smaller than cM ( t ) (Figure 1). On the contrary, the

496

linearized Michaelis-Menten flux expression underestimates J u* = KM K H kint by a

497

factor of 5, as illustrated in Figure S5D. As a conclusion of this part, the analytical

498

eqs 6 and 7 should be cautiously employed because there is no way for apprehending

499

a priori their validity without information on the magnitude of KM. By no means, a

500

successful interpretation of M depletion kinetic data with these expressions implies

501

the correctness of the applied models. Our step-by-step analysis of the ϕ-dependent

502

kinetics of bulk metal depletion using eqs 1-3 offers a route to circumvent this

503

difficulty.

504

Finally, we performed measurements over time of Cd(II) depletion from solution in

505

the presence of P. putida wild-type strain KT2440 at a cell volume fraction of 4.09 ×

506

10-4. The results, given in the inset of Figure 1, are compared to those obtained for the

507

transporter-deficient strain under similar ϕ condition. After a fast adsorption process

508

∗ reflected by an abrupt decrease in cM ( t ) , bulk metal concentration remains constant

509

with time in the case of the WT strain. Supposing that the Michaelis-Menten uptake

510

parameters for this WT strain do not significantly differ from those previously

511

determined for the mutant, the absence of metal depletion in solution simply

512

corresponds to a larger ability of this strain to expel metals from intracellular

513

compartment, i.e. ke → ∞ , which is confirmed by data adjustment according to eqs 1-

514

2. This evidences the resistance mechanism against Cd(II) uptake that stems from the

515

presence of the metal efflux system. These findings are further in agreement with

516

metal detection sensitivity determined on the basis of the biosensing activity of

517

bacterial reporters constructed from our WT and mutant strains. P. putida


Page 22 of 32

Page 23 of 32


518

KT2440.2431 is indeed able to detect Cd at extracellular concentrations that are 10-

519

fold lower than those leading to the functioning of WT strain biosensor.20,21

520 521

4.3. Evaluation of other Cd(II) uptake mechanisms by Pseudomonas putida

522

In this section, we qualitatively discuss the relevance of considering uptake

523

mechanisms other than that implied by the here-adopted Michaelis-Menten

524

expression (i.e. fast Langmuirian adsorption of metals onto internalization sites

525

followed by a limiting first order kinetic internalization step). Indeed, microorganisms

526

possess several ions internalization paths via e.g. simple diffusion across the

527

membrane. In such a situation, the net uptake flux J of metals is simply defined by

528

the Fick’s law according to

(

)

membrane a in J ( t ) = DM cM ( t ) − cM (t ) / δ ,

529

(8)

530

where δ is the thickness of the membrane separating the intracellular volume from

531

a the external aqueous phase, cM ( t ) is the metal concentration at the biosurface as

532

in previously defined, cM ( t ) is the concentration inside the organism and DMmembrane the

533

membrane effective diffusion coefficient of metals through the membrane ( DM includes,

534

in particular, steric interactions between metals and membrane biocompounds27). It

535

could then be argued that the kinetics of Cd(II) depletion from bulk solution (Figure

536

a in 1) is simply the result of the suppression over time of the gradient cM ( t ) − cM (t ) / δ

537

a until the equilibrium situation is reached with J = 0 and dcM ( t ) / dt = 0 . This scheme

538

would not require arguing the existence of any metal excretion process. To decide

539

whether or not such uptake scenario makes sense, an analogy is formally drawn

540

below between eq 8 and the linearized form of the Michaelis-Menten expression

(


)


Page 24 of 32

541

which -from a pure numerical point of view (but not a physical one)- leads to a fit of

542

the experimental data (Figure S4). The linearized Michaelis-Menten equation for

543

a metal internalization flux reads J u ( t ) = K H kint cM ( t ) and the resulting net uptake flux

544

a including efflux contribution is then J ( t ) = K H kint cM ( t ) − keφu ( t ) 17 where φu ( t ) is the

545

time-dependent concentration of internalized metals expressed per surface area of

546

in organism.17 Rewriting φu ( t ) in terms of cM ( t ) , we obtain after straightforward

547

arrangements

(

a in J ( t ) = K H kint cM ( t ) − ϕ ∗cM (t )

548

)

(9)

549

membrane Equation 9 formally identifies with eq 8 provided that ϕ ∗ = 1 and DM / δ is

550

replaced by K H kint . The reconstruction of the M depletion kinetic data in Figure 1

551

using the linearized Michaelis-Menten expression led however to ϕ ∗ value much

552

lower than unity. Consequently, the transport of metals by simple diffusion through

553

the membrane can not adequately describe the biointernalization process for the

554

systems of interest in this study as a recovery of metal depletion kinetic data seems a

555

priori impossible. These results simply indicate, if needed, that simple diffusion is not

556

the adequate mechanism leading to metal accumulation within the intracellular cell

557

volume. Simple diffusion is a non-selective process according to which only small

558

and relatively hydrophobic molecules (e.g. O2, CO2, H2O, N2, glycerol, ethanol) can

559

dissolve in the lipid membrane and get transported. This process does not apply for

560

the system of interest in this work.

561

A gradual loss in the Cd(II)-assimilating activity of the bacteria could also explain

562

the presence of a non-zero plateau value c∗M ( t → ∞ ) without the need to invoke

563

excretion process. It is however unlikely that such picture applies to our system 24 ACS Paragon Plus Environment

Page 25 of 32


564

because introducing a kinetic loss of bioactivity on top of the internalization kinetic

565

∗ step comes to consider two distinct timescales that govern the decay of cM over time.

566

Our rigorous analysis shows however that there is an unique timescale for describing

567

metal depletion from solution over time.17 The above explanation is further

568

qualitatively supported by preliminary theoretical evaluations of metal partitioning

569

dynamics at biointerfaces in suspensions where the number density of active cells

570

depends on t. This work constitutes an extension of the formalism reported in 17 and it

571

will be the subject of a forthcoming publication.

572 573

5. Implications

574

A robust and quantitative description is given for the dynamics of Cd(II) uptake

575

by Pseudomonas putida from the analysis of Cd(II) bulk depletion kinetics measured

576

at various initial cell volume fractions. The formalism applies to cases where metal

577

transfer across biointerface occurs under conditions where concentration of cells (e.g.

578

algae, bacteria) remains constant over time and metal complexation in the

579

extracellular volume is insignificant. The interpretation is based on a theory recently

580

elaborated for the dynamics of metal biouptake and accounting for the effects of bulk

581

metal depletion and excretion.17 It is shown how the use of the analytical expression

582

derived for the dependence of c∗M ( t → ∞ ) on cell volume fraction is extremely

583

rewarding for data interpretation and evaluation of the relevant biouptake parameters.

584

The basic findings detailed in this work support the fundaments of the

585

aforementioned theory that further opens the route for (i) examining more complex

586

exposure scenarios where e.g. electrostatics comes into play, for (ii) studying more

587

involved cell biosurfaces that exhibit protruding surface structures (EPS, LPS), or for

588

(iii) analyzing uptake dynamics of essential metals as compared to that of toxic 25 ACS Paragon Plus Environment


589

elements (slower excretion kinetics and larger limiting uptake fluxes are expected for

590

the former). In addition, the proposed formalism remains appropriate for

591

interpretation of ‘spiking experiments’ where the operator adds metals over time, on

592

the premise that steady-state transport of metals from the solution to the cell

593

membrane is maintained. Other interesting perspectives consist in refining our

594

understanding of the effects of metal-complexing agents like colloids or nanoparticles

595

on the kinetics of metal depletion and the dynamics of metal biouptake. Last, the

596

extension of the theory to include the time-dependence of the cell number

597

concentration is currently in progress in our group. This time dependence may be

598

fixed by the operator who adds cells during exposure to metals, or it may be the result

599

of metal toxicity effects leading to cell growth inhibition and/or cell death.

600 601

Supporting information

602

Details on the derivations of eqs 1-7 and of the expression for τ E / τ L (section A), on

603

the electrochemical parameters used for SCP and AGNES techniques (section B), on

604

the analysis of electrokinetics of P. putida (section C and Figure S2). Section D

605

displays AFM images of Pseudomonas putida KT2440.2431 (Figure S1), the

606

dependence of the electrophoretic mobility of Pseudomonas putida KT 2440.2431

607

and that of the wild strain KT 2440 on NaNO3 concentration at pH=7 (Figure S2), the

608

dependence of the free Cd(II) concentration at short time as a function of ϕ (Figure

609

S3), the analysis of bulk metal depletion kinetics using eqs 6-7 (Figure S4) and the

610

corresponding obtained parameters ( J u* ; ke) and ( K H kint ; ke) that are further

611

compared to the values derived from application of eqs 1-3 (Figure S5).

612


Page 26 of 32

Page 27 of 32


613

Acknowledgements

614

We acknowledge the Region Lorraine and the program EC2CO (CNRS/INSU) for

615

financial support. E.R. thanks the Service d’Analyse des Roches et des Minéraux

616

(SARM, CRPG-CNRS UMR 7358, Vandoeuvre-les-Nancy, France) for metal titration

617

experiments by atomic absorption flame spectroscopy and A. Razafitianamaharavo

618

(LIEC) for AFM images.

619 620

References

621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654

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(11) Campbell, P. G. C.; Errécalde, O.; Fortin, C.; Hiriart-Baer, V. P.; Vigneault, B. Metal Bioavailability to Phytoplankton—applicability of the Biotic Ligand Model. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 2002, 133, 189– 206. (12) Fortin, C.; Campbell, P. G. C. Silver Uptake by the Green Alga Chlamydomonas Reinhardtii in Relation to Chemical Speciation: Influence of Chloride. Environ. Toxicol. Chem. 2000, 19, 2769–2778. (13) Jansen, S.; Blust, R.; Van Leeuwen, H. P. Metal Speciation Dynamics and Bioavailability: Zn(II) and Cd(II) Uptake by Mussel (Mytilus Edulis) and Carp (Cyprinus Carpio). Environ. Sci. Technol. 2002, 36, 2164–2170. (14) Pinheiro, J. P.; Galceran, J.; Van Leeuwen, H. P. Metal Speciation Dynamics and Bioavailability: Bulk Depletion Effects. Environ. Sci. Technol. 2004, 38, 2397– 2405. (15) Hajdu, R.; Pinheiro, J. P.; Galceran, J.; Slaveykova, V. I. Modeling of Cd Uptake and Efflux Kinetics in Metal-Resistant Bacterium Cupriavidus Metallidurans. Environ. Sci. Technol. 2010, 44, 4597–4602. (16) Duval, J. F. L. Dynamics of Metal Uptake by Charged Biointerphases: Bioavailability and Bulk Depletion. Phys. Chem. Chem. Phys. 2013, 15, 7873– 7888. (17) Duval, J. F. L.; Rotureau, E. Dynamics of Metal Uptake by Charged Soft Biointerphases: Impacts of Depletion, Internalisation, Adsorption and Excretion. Phys. Chem. Chem. Phys. 2014, 16, 7401–7416. (18) Bosma, T. N. P.; Middeldorp, P. J. M.; Schraa, G.; Zehnder, A. J. B. Mass Transfer Limitation of Biotransformation: Quantifying Bioavailability. Environ. Sci. Technol. 1997, 31, 248–252. (19) Francius, G.; Polyakov, P.; Merlin, J.; Abe, Y.; Ghigo, J.-M.; Merlin, C.; Beloin, C.; Duval, J. F. L. Bacterial Surface Appendages Strongly Impact Nanomechanical and Electrokinetic Properties of Escherichia Coli Cells Subjected to Osmotic Stress. PLoS ONE 2011, 6, e20066. (20) Leedjärv, A.; Ivask, A.; Virta, M. Interplay of Different Transporters in the Mediation of Divalent Heavy Metal Resistance in Pseudomonas Putida KT2440. J. Bacteriol. 2008, 190, 2680–2689. (21) Hynninen, A.; Tonismann, K.; Virta, M. Improving the Sensitivity of Bacterial Bioreporters for Heavy Metals. Bioeng. Bugs 2010, 1, 132–138. (22) Ramos-González, M. I.; Molin, S. Cloning, Sequencing, and Phenotypic Characterization of the rpoS Gene from Pseudomonas Putida KT2440. J. Bacteriol. 1998, 180, 3421–3431. (23) Gustafsson, J. P. Visual MINTEQ Version 3.0. KTH, Department of Land and Water Resources Engineering, Stockolm, Sweden, 2009. Available at Http://vminteq.lwr.kth.se/. (24) Dague, E.; Duval, J.; Jorand, F.; Thomas, F.; Gaboriaud, F. Probing Surface Structures of Shewanella Spp. by Microelectrophoresis. Biophys. J. 2006, 90, 2612–2621. (25) Nies, D. H.; Silver, S. Molecular Microbiology of Heavy Metals; Springer Science & Business Media, 2007. (26) Cánovas, D.; Cases, I.; De Lorenzo, V. Heavy Metal Tolerance and Metal Homeostasis in Pseudomonas Putida as Revealed by Complete Genome Analysis. Environ. Microbiol. 2003, 5, 1242–1256. 28 ACS Paragon Plus Environment

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708



709

Figures

710

Figure 1 0.8

ϕ1

0.7

ϕ2 ∗ ሺ‫ݐ‬ሻ ܿM (µM)

0.6 0.5

ϕ3

0.4

ϕ4

0.3

ϕ5

0.2

A

0.1

A

0.4 10

10

ϕ6

WS

0.6

2

10

2

3

10

10

4

3

104

105

t (s)

711 712 713 714

Figure 2 1 0.9

∗ ܿM∗ ሺ‫ݐ‬ሻ/ܿM, total

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.1 0 0

10

1

10

2

10

3

10

4

t (s)

715 716 717 718 719

Figure 3 30 ACS Paragon Plus Environment

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0.8 ϕ =ϕ*

ܿM∗ ሺ‫∞ → ݐ‬ሻ (µM)

0.7 0.6 0.5

-3.19

0.4 0.3

log (ϕ*) = -3.49

0.2 -3.58

0.1 0

0

0.5

1

1.5

2

߮ × 103

720 721 722

Figure captions

723

Figure 1. Time dependence of Cd(II) concentration in bulk solution measured by

724

electroanalytical technique for different cell volume fractions ϕ of P. putida KT2440-

725

2431. From top to bottom, ϕ = 1.18×10-4, 2.55×10-4, 4.09×10-4, 5.47×10-4, 8.06×10-4

726

and 1.91× 10-3 corresponding to ϕ1, …, ϕ6, respectively. The corresponding cell

727

number concentrations are 1.3×108, 2.8×108, 4.5×108, 6.0×108, 8.9×108 and 2.1×109

728

cells/ml. The points pertain to experimental data with accompanied analytical

729

uncertainties. The dotted lines correspond to the theoretical fit obtained from the

730

rigorous eqs 1 and 2 solved according to the numerical algorithm detailed in previous

731

work.17 In the inset A, a comparison of the depletion kinetics obtained for the WT

732

() and the mutant strain () is given for a similar ϕ of 4.09×10-4. The initial total

733

concentration of Cd(II) is 1µM. Due to the necessity to degas the solution and due to

734

the very delay required for data recording, measurements for t < 100s are not

735

possible. There is a drop in bulk metal concentration between 0 and ca. 100s

736

following fast metal adsorption on cells surface. Free metal concentration measured at 31 ACS Paragon Plus Environment


737

t=0 (prior to the introduction of the cells in solution) is reported as a function of cell

738

volume fraction in Figure S3 of the SI.

739 740

Figure 2. Time-dependent concentrations of Cd(II) adsorbed at the cells surface

741

normalized with respect to the total amount of Cd(II) for two selected values of cell

742

volume fractions ϕ = 2.55×10-4 () and ϕ = 5.47×10-4 (). Meaning of the symbols

743

: (,) measurements by electroanalytical technique in the bacterial suspension, and

744

by ligand exchange technique leading to estimation of the concentration of sorbed Cd

745

(,) and concentration of free Cd in bulk solution (,).

746 747

Figure 3. Dependence of the concentration of free Cd(II) in bulk solution measured at

748

sufficiently long exposure time on the volume fraction of Pseudomonas putida

749

KT2440. The points correspond to experimental data. Dotted and solid lines pertain to

750

theoretical fits obtained using eq 3 with KM = 2.16×10-4 mM and with the indicated

751

values of log ϕ ∗ .

( )

752 753

TOC/Abstract Art

754 755


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Evaluation of Metal Biouptake from the Analysis of Bulk Metal

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