Acid-Base Titrations of Functional Groups on the Surface of the Thermophilic Bacterium Anoxybacillus flaWithermus: Comparing a Chemical Equilibrium Model with ATR-IR Spectroscopic Data Hannah T. M. Heinrich, †,‡ Phil J. Bremer, † Christopher J. Daughney, § and A. James McQuillan* ,‡ Departments of Chemistry and Food Science, UniVersity of Otago, P.O. Box 56, Dunedin, New Zealand, and Institute of Geological and Nuclear Sciences, Lower Hutt, New Zealand ReceiVed August 15, 2006. In Final Form: NoVember 20, 2006 Acid-base functional groups at the surface of Anoxybacillus flaVithermus (AF) were assigned from the modeling of batch titration data of bacterial suspensions and compared with those determined from in situ infrared spectroscopic titration analysis. The computer program FITMOD was used to generate a two-site Donnan model (site 1: pK a ) 3.26, wet concn ) 2.46 × 10 -4 mol g -1 ; site 2: pK a ) 6.12, wet concn ) 6.55 × 10 -5 mol g -1 ), which was able to describe data for whole exponential phase cells from both batch acid-base titrations at 0.01 M ionic strength and electrophoretic mobility measurements over a range of different pH values and ionic strengths. In agreement with information on the composition of bacterial cell walls and a considerable body of modeling literature, site 1 of the model was assigned to carboxyl groups, and site 2 was assigned to amino groups. pH difference IR spectra acquired by in situ attenuated total reflection infrared (ATR-IR) spectroscopy confirmed the presence of carboxyl groups. The spectra appear to show a carboxyl pK a in the 3.3-4.0 range. Further peaks were assigned to phosphodiester groups, which deprotonated at slightly lower pH. The presence of amino groups could not be confirmed or discounted by IR spectroscopy, but a positively charged group corresponding to site 2 was implicated by electrophoretic mobility data. Carboxyl group speciation over a pH range of 2.3-10.3 at two different ionic strengths was further compared to modeling predictions. While model predictions were strongly influenced by the ionic strength change, pH difference IR data showed no significant change. This meant that modeling predictions agreed reasonably well with the IR data for 0.5 M ionic strength but not for 0.01 M ionic strength. Introduction Bacteria are unicellular organisms that have a high surface area 1 and usually an overall negative surface charge in their natural environment, making them potent binding agents for cations such as dissolved heavy metals. 2 Microbial communities are ubiquitous in near-surface fluid-rock systems, 3 and it is recognized today that they can have a significant impact upon the mobility and distribution of metal ions in the environment. 1,4 This has created an interest in the use of bacteria for the remediation of heavy-metal-contaminated wastewaters. 5 Binding of metal ions to bacteria is believed to be mediated by functional groups on the bacterial surface. 6 The cell wall of gram-positive bacteria consists of polymeric substances. The most abundant of these is peptidoglycan, which usually accounts for ca. 50% of the dry weight. Other common cell wall components are teichoic acid, teichuronic acid, and proteins. 7 Figure 1 shows model chemical structures of peptidoglycan and teichoic acid, the two main components of a typical gram-positive cell wall. Free carboxyl and amino groups located in the side chains of peptidoglycan and phosphodiester groups of teichoic acids can be protonated or deprotonated depending on the pH of the suspending medium. Teichoic acids can further contain alanyl esters with ionizable amino groups. All these groups are considered to be involved in the interactions between cell walls and metal ions. 8 Previous attempts to quantitatively assess and predict metal binding to bacterial cell walls have used a two-step approach. First, acid-base titrations of bacterial suspensions have been conducted to identify surface functional groups. Since all titratable functional groups present contribute to the titration curve, software has been developed to calculate the pK a ’s and concentrations of contributing groups based on chemical equilibrium models. 9 The obtained values have subsequently been used to model metal uptake data from batch adsorption experiments by assuming the binding of metal cations to one or two of the identified groups. 3,10,11 In several publications, it has been reported that bacterial acid- base titration curves can be modeled assuming three types of sites. On the basis of the obtained pK a values and the known composition of cell walls, these sites are most frequently assigned as carboxyl, “phosphoryl”, and amino groups. 3,10-12 It is important to be aware, however, that different sets of model parameters can provide reasonable fits to the acid-base titration data. One way to further constrain a model is to use the obtained concentration * Corresponding author. E-mail: jmcquillan@chemistry.otago.ac.nz. Tel: +64 3 479 7928. Fax: +64 3 479 7906. † Department of Food Science, University of Otago. ‡ Department of Chemistry, University of Otago. § Institute of Geological and Nuclear Sciences (GNS). (1) Beveridge, T. J. Annu. ReV. Microbiol. 1989, 43, 147-171. (2) van der Wal, A.; Norde, W.; Zehnder, A. J. B.; Lyklema, J. Colloids Surf., B 1997, 9, 81-100. (3) Fein, J. B.; Daughney, C. J.; Yee, N.; Davis, T. A. Geochim. Cosmochim. Acta 1997, 61, 3319-3328. (4) Pang, L.; Close, M. E.; Noonan, M. J.; Flintoft, M. J.; van den Brink, P. J. EnViron. Qual. 2005, 34, 237-247. (5) Schiewer, S.; Volesky, B. In EnVironmental Microbe-Metal Interactions; Lovley, D. R., Eds.; ASM Press: Washington, DC, 2000; pp 329-362. (6) Kulczycki, E.; Ferris, F. G.; Fortin, D. Geomicrobiol. J. 2002, 19, 553- 565. (7) Hammond, S. M.; Lambert, P. A.; Rycroft, A. N. The Bacterial Cell Surface; Croom Helm Ltd.: Beckenham, Kent, 1984. (8) Doyle, R. In Metal Ions and Bacteria; Beveridge, T. J., Doyle, R., Eds.; John Wiley & Sons: New York, 1989; pp 275-293. (9) Westall, J. C., FITEQL: A Computer Program for Determination of Chemical Equilibrium Constants from Expermimental Data, version 2.0; Report 82-02; Department of Chemistry, Oregon State University: Corvallis, OR, 1982. (10) Daughney, C. J.; Fein, J. B. J. Colloid Interface Sci. 1998, 198, 53-77. (11) Daughney, C. J.; Fowle, D. A.; Fortin, D. E. Geochim. Cosmochim. Acta 2001, 65, 1025-1035. (12) Wightman, P. G.; Fein, J. B.; Wesolowski, D. J.; Phelps, T. J.; Benezeth, P.; Palmer, D. A. Geochim. Cosmochim. Acta 2001, 65, 3657-3669. 2731 Langmuir 2007, 23, 2731-2740 10.1021/la062401j CCC: $37.00 © 2007 American Chemical Society Published on Web 01/23/2007