Modeling Physiological Processes That Relate Toxicant Exposure and Bacterial Population Dynamics

Quantifying effects of toxicant exposure on metabolic processes is crucial to predicting microbial growth patterns in different environments. Mechanistic models, such as those based on Dynamic Energy Budget (DEB) theory, can link physiological processes to microbial growth. Here we expand the DEB framework to include explicit consideration of the role of reactive oxygen species (ROS). Extensions considered are: (i) additional terms in the equation for the “hazard rate” that quantifies mortality risk; (ii) a variable representing environmental degradation; (iii) a mechanistic description of toxic effects linked to increase in ROS production and aging acceleration, and to non-competitive inhibition of transport channels; (iv) a new representation of the “lag time” based on energy required for acclimation. We estimate model parameters using calibrated Pseudomonas aeruginosa optical density growth data for seven levels of cadmium exposure. The model reproduces growth patterns for all treatments with a single common parameter set, and bacterial growth for treatments of up to 150 mg(Cd)/L can be predicted reasonably well using parameters estimated from cadmium treatments of 20 mg(Cd)/L and lower. Our approach is an important step towards connecting levels of biological organization in ecotoxicology. The presented model reveals possible connections between processes that are not obvious from purely empirical considerations, enables validation and hypothesis testing by creating testable predictions, and identifies research required to further develop the theory.

[1]  S. Silver,et al.  Cadmium uptake in Escherichia coli K-12 , 1985, Journal of bacteriology.

[2]  Z. Tynecka,et al.  Reduced cadmium transport determined by a resistance plasmid in Staphylococcus aureus , 1981, Journal of bacteriology.

[3]  A. Anderson,et al.  Copper and cadmium: responses in Pseudomonas putida KT2440 , 2009, Letters in applied microbiology.

[4]  J Baranyi,et al.  A dynamic approach to predicting bacterial growth in food. , 1994, International journal of food microbiology.

[5]  P. Fernández,et al.  Modeling the Lag Period and Exponential Growth of Listeria monocytogenes under Conditions of Fluctuating Temperature and Water Activity Values , 2010, Applied and Environmental Microbiology.

[6]  Jack A. Taylor,et al.  Cadmium is a mutagen that acts by inhibiting mismatch repair , 2003, Nature Genetics.

[7]  A. Lebert,et al.  Recommendations for calculating growth parameters by optical density measurements , 1996 .

[8]  O. B. Popova,et al.  Effects of copper and cadmium ions on the physicochemical properties of lipids of the marine bacterium Pseudomonas putida IB28 at different growth temperatures , 2008, Russian Journal of Marine Biology.

[9]  B. P. Hills,et al.  Multi-compartment kinetic models for injury, resuscitation, induced lag and growth in bacterial cell populations , 1995 .

[10]  A. Hassen,et al.  Effects of heavy metals on Pseudomonas aeruginosa and Bacillus thuringiensis , 1998 .

[11]  Bas Kooijman,et al.  Dynamic Energy Budget Theory for Metabolic Organisation , 2005 .

[12]  Tânia Sousa,et al.  From empirical patterns to theory: a formal metabolic theory of life , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[13]  D. Bandyopadhyay,et al.  Effect of cadmium on purified hepatic flavokinase: involvement of reactive -SH group(s) in the inactivation of flavokinase by cadmium. , 1997, Life sciences.

[14]  Sergey M Borisov,et al.  Multiplex bacterial growth monitoring in 24‐well microplates using a dual optical sensor for dissolved oxygen and pH , 2008, Biotechnology and bioengineering.

[15]  D. Wilson,et al.  Characterization of Cadmium Uptake in Lactobacillus plantarum and Isolation of Cadmium and Manganese Uptake Mutants , 1999, Applied and Environmental Microbiology.

[16]  P. Sadler,et al.  Cadmium-Binding Proteins in Pseu dom onas putida : Pseu do thioneins , 2006 .

[17]  Tjalling Jager,et al.  A biology-based approach for mixture toxicity of multiple endpoints over the life cycle , 2009, Ecotoxicology.

[18]  S. Silver,et al.  Bacterial heavy metal resistance: new surprises. , 1996, Annual review of microbiology.

[19]  N. J. Ouborg,et al.  Ecotoxicogenomics: bridging the gap between genes and populations. , 2010, Environmental science & technology.

[20]  S. Kooijman,et al.  From molecules to ecosystems through dynamic energy budget models. , 2000 .

[21]  R. Nisbet,et al.  Sublethal toxicant effects with dynamic energy budget theory: model formulation , 2009, Ecotoxicology.

[22]  K. Timmis.  Handbook of hydrocarbon and lipid microbiology , 2010 .

[23]  Z. Tynecka,et al.  Energy-dependent efflux of cadmium coded by a plasmid resistance determinant in Staphylococcus aureus , 1981, Journal of bacteriology.

[24]  S. Kooijman,et al.  Ecotoxicological Applications of Dynamic Energy Budget Theory , 2009 .

[25]  J. Tainer,et al.  Cancer, cadmium and genome integrity , 2003, Nature Genetics.

[26]  R. Ricklefs,et al.  Biological implications of the Weibull and Gompertz models of aging. , 2002, The journals of gerontology. Series A, Biological sciences and medical sciences.

[27]  Chris Klok,et al.  Extrapolating toxic effects on individuals to the population level: the role of dynamic energy budgets , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[28]  Bernd W Brandt,et al.  Modelling microbial adaptation to changing availability of substrates. , 2004, Water research.

[29]  S. Silver,et al.  Ion efflux systems involved in bacterial metal resistances , 1995, Journal of Industrial Microbiology.

[30]  L. Halverson,et al.  Influence of water limitation on endogenous oxidative stress and cell death within unsaturated Pseudomonas putida biofilms. , 2009, Environmental microbiology.

[31]  Patricia A Holden,et al.  Effects of soluble cadmium salts versus CdSe quantum dots on the growth of planktonic Pseudomonas aeruginosa. , 2009, Environmental science & technology.

[32]  J. Galceran,et al.  Modeling of Cd uptake and efflux kinetics in metal-resistant bacterium Cupriavidus metallidurans. , 2010, Environmental science & technology.

[33]  M. Noble,et al.  Chemically Diverse Toxicants Converge on Fyn and c-Cbl to Disrupt Precursor Cell Function , 2007, PLoS biology.

[34]  S. Holbrook,et al.  Sublethal toxicant effects with dynamic energy budget theory: application to mussel outplants , 2009, Ecotoxicology.

[35]  G. Denning,et al.  Phenazine-1-carboxylic acid, a secondary metabolite of Pseudomonas aeruginosa, alters expression of immunomodulatory proteins by human airway epithelial cells. , 2003, American journal of physiology. Lung cellular and molecular physiology.

[36]  E. Muller,et al.  The dynamics of the macromolecular composition of biomass. , 2001, Journal of theoretical biology.

[37]  Tjalling Jager,et al.  Understanding toxicity as processes in time. , 2010, The Science of the total environment.

[38]  D. Crowley,et al.  Global Gene Expression Responses to Cadmium Toxicity in Escherichia coli , 2005, Journal of bacteriology.

[39]  S. Mongkolsuk,et al.  Exposure to Cadmium Elevates Expression of Genes in the OxyR and OhrR Regulons and Induces Cross-Resistance to Peroxide Killing Treatment in Xanthomonas campestris , 2005, Applied and Environmental Microbiology.

[40]  X. Gidrol,et al.  Exploration of intraclonal adaptation mechanisms of Pseudomonas brassicacearum facing cadmium toxicity , 2007, Environmental microbiology.

[41]  S. Gibbons,et al.  Use of Microcalorimetry To Determine the Costs and Benefits to Pseudomonas putida Strain KT2440 of Harboring Cadmium Efflux Genes , 2010, Applied and Environmental Microbiology.

[42]  B. Fowler,et al.  Oxidative stress induced by lead, cadmium and arsenic mixtures: 30-day, 90-day, and 180-day drinking water studies in rats: An overview , 2004, Biometals.