An Electronic-structure Informatics Study on the Toxicity of Alkylphenols to Tetrahymena pyriformis.

Alkylphenols (APs) dissolved in water are known to be toxic to animals including humans. In this study, regression models describing the toxicity of the 33 AP molecules were investigated for reproducing and thereby making it possible to predict a quantitative structure-activity relationship (QSAR). For this purpose, we tried to derive regression models for the experimental IGC50 (growth inhibition concentration at 50 %) to Tetrahymena pyriformis by using various descriptor sets consisting of electronic and shape descriptors. By applying the multiple linear regression (MLR) analysis, it was successful to derive a quantitative regression model for the IGC50 values. In this analysis, the size parameters of the molecules were found important, suggesting that bulky molecules should be less toxic. We could also obtain, when the size descriptors were excluded, an MLR model indicating that the electron affinity (EA) should be important, which is consistent with the previous QSAR studies. Through the correlation analysis among the descriptors, it was shown that, in the present set of molecules, EA and a size parameter are highly correlated. Since EA was calculated to be negative, indicating that the related process would be energetically unfavourable, it was concluded that the size of the molecules should be a dominant factor determining IGC50 . This implies that a molecular recognition process would play a critical role in the mode of action for the toxicity.

Prediction of Aquatic Toxicity of Benzene Derivatives to Tetrahymena pyriformis According to OECD Principles.

BACKGROUND: Many QSAR studies have been developed to predict acute toxicity over several biomarkers like Pimephales promelas, Daphnia magna and Tetrahymena pyriformis. Regardless of the progress made in this field there are still some gaps to be resolved such as the prediction of aquatic toxicity over the protozoan T. pyriformis still lack a QSAR study focused in accomplish the OECD principles. METHODS: Atom-based quadratic indices are used to obtain quantitative structure-activity relationship (QSAR) models for the prediction of aquatic toxicity. Our models agree with the principles required by the OECD for QSAR models to regulatory purposes. The database employed consists of 392 substituted benzenes with toxicity values measured in T. pyriformis (defined endpoint), was divided using cluster analysis in two series (training and test sets). RESULTS: We obtain (with an unambiguous algorithm) two good multiple linear regression models for non-stochastic (R2=0.807 and s=0.334) and stochastic (R2=0.817 and s=0.321), quadratic indices. The models were internally validated using leave-one-out, bootstrapping as well as Y-scrambling experiments. We also perform an external validation using the test set, achieving values of R2 pred values of 0.754 and 0.760, showing that our models have appropriate measures of goodness- of-fit, robustness and predictivity. Moreover, we define a domain of applicability for our best models. CONCLUSION: The achieved results demonstrated that, the atom-based quadratic indices could provide an attractive alternative to the experiments currently used for determining toxicity, which are costly and time-consuming.

Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from Toxtree ver. 2.6.

Assessment of the potential of compounds to cause harm to the aquatic environment is an integral part of the REACH legislation. To reduce the number of vertebrate and invertebrate animals required for this analysis alternative approaches have been promoted. Category formation and read-across have been applied widely to predict toxicity. A key approach to grouping for environmental toxicity is the Verhaar scheme which uses rules to classify compounds into one of four mechanistic categories. These categories provide a mechanistic basis for grouping and any further predictive modelling. A computational implementation of the Verhaar scheme is available in Toxtree v2.6. The work presented herein demonstrates how modifications to the implementation of Verhaar between version 1.5 and 2.6 of Toxtree have improved performance by reducing the number of incorrectly classified compounds. However, for the datasets used in this analysis, version 2.6 classifies more compounds as outside of the domain of the model. Further amendments to the classification rules have been implemented here using a post-processing filter encoded as a KNIME workflow. This results in fewer compounds being classified as outside of the model domain, further improving the predictivity of the scheme. The utility of the modification described herein is demonstrated through building quality, mechanism-specific Quantitative Structure Activity Relationship (QSAR) models for the compounds within specific mechanistic categories.

[The detection of activity of components and factors of complement according its effect on infusorians].

The article considers the developed techniques of detection of functional activity of components C1, C2, C3, C4 and membrane attacking complex of classical pathway and also factors B and D of alternative pathway of human complement using automated device measurement of immobilizing effect on infusorians Tetrahymena pyriformis. The techniques are based on application of artificially produced reagents containing all necessary components of compliment besides testing one. The mathematical mode of activities calculation is developed. The linear dependency of velocity of infusorians immobilizing from functional activities of testing component. The relevance of techniques is demonstrated by correlation of results derived using the proposed mode with the developed earlier hemolytic mode.

Discrimination of excess toxicity from narcotic effect: influence of species sensitivity and bioconcentration on the classification of modes of action.

The toxicity data of 2624 chemicals to fish, Daphniamagna, Tetrahymenapyriformis and Vibriofischeri were used to investigate the effects of species sensitivity and bioconcentration on excess toxicity. The results showed that 47 chemical classes were identified as having the same modes of action (MOAs) to all four species, but more than half of the classes were identified as having different MOAs. Difference in chemical MOAs is one of the reasons resulting in the difference in toxic effect to these four species. Other important reasons are the difference in sensitivity and bioconcentration of species. Among the four species, V. fischeri has the most compounds identified as reactive MOA. This may be due to some compounds can be easily absorbed into the bacteria, react with the DNA or proteins, disrupt the normal function of the cell and exhibit significantly greater toxicity to the bacteria. On the other hand, the skin and lipid content of aqueous organisms can strongly inhibit the bio-uptake for some reactive compounds, resulting in a less toxic effect than expected. D. magna is the most sensitive species and T. pyriformis is the least sensitive species of the four species. For a comparison of interspecies toxicity, we need to use the same reference threshold of excess toxicity. However, some reactive compounds may be identified as baseline or less inert compounds for low sensitive species from the threshold developed from high sensitive species. The difference in the discrimination of excess toxicity to different species is not only because of the difference in MOAs for some compounds, but also due to the difference in sensitivity and bioconcentration.

Experimental verification of structural alerts for the protein binding of sulfur-containing compounds.

As often noted by Dr. Gilman Veith, a major barrier to advancing any model is defining its applicability domain. Sulfur-containing industrial organic chemicals can be grouped into several chemical classes including mercaptans (RSH), sulfides (RSR'), disulfides (RSSR'), sulfoxides (RS(=O)R'), sulfones (RS(=O)(=O)R'), sulfonates (ROS(=O)(=O)R') and sulfates (ROS(=O)(=O)OR'). In silico expert systems that predict protein binding reactions from 2D structure sub-divide these chemical classes into a variety of chemical reactive mechanisms and reactions which have toxic consequences. Using the protein binding profilers in version 3.1 of the OECD QSAR Toolbox, a series of sulfur-containing chemicals were profiled for protein binding potential. From these results it was hypothesized which sulfur-containing chemicals would be reactive or non-reactive in an in chemico glutathione assay and whether if reactive they would exhibit toxicity in excess of baseline in the Tetrahymena pyriformis population growth impairment assay. Subsequently, these hypotheses were tested experimentally. The in chemico data show that the in silico profiler predictions were generally correct for all chemical categories, where testing was possible. Mercaptans could not be assessed for GSH reactivity because they react directly with the chromophore 5,5'-dithiobis-(2-nitrobenzoic acid). With some exceptions, the major being disulfides, the in vitro toxicity data supported the in chemico findings.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches.

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals.

Vacuum ultraviolet photolysis of diclofenac and the effects of its treated aqueous solutions on the proliferation and migratory responses of Tetrahymena pyriformis.

The effects of dissolved O2, phosphate buffer and the initial concentration of diclofenac on the vacuum ultraviolet photolysis of this contaminant molecule were studied. Besides kinetic measurements, the irradiated, multicomponent samples were characterized via the proliferation and migratory responses (in sublethal concentrations) of the bioindicator eukaryotic ciliate Tetrahymena pyriformis. The results suggest that hydroxyl radicals, hydrogen atoms and hydroperoxyl radicals may all contribute to the degradation of diclofenac. The aromatic by-products of diclofenac were presumed to include a hydroxylated derivative, 1-(8-chlorocarbazolyl)acetic acid and 1-(8-hydroxycarbazolyl)acetic acid. The biological activity of photoexposed samples reflected the chemical transformation of diclofenac and was also dependent on the level of dissolved O2. The increase in toxicity of samples taken after different irradiation times did not exceed a factor of two. Our results suggest that the combination of vacuum ultraviolet photolysis with toxicity and chemotactic measurements can be a valuable method for the investigation of the elimination of micropollutants.

Joint toxicity of heavy metals and chlorobenzenes to pyriformis Tetrahymena.

Chlorobenzens and heavy metals are frequently detected in the environment, but few studies have assessed the joint toxicity of organic and inorganic contaminants. The joint toxicity of heavy metals and chlorobenzenes was evaluated in the present study. Growth metabolism of the joint toxicity was studied by microcalorimetry at 28°C, the growth constant (k) and inhibitory ratio (I) were calculated. Toxic unit (TU) and additional index (AI) were introduced to determine the outcome in combined tests, and the coexistence of Cu, Cd, Cr(III) and p-chlorobenzene was antagonism, and the effect of Cu, Cd, Cr(III) and o-chlorobenzene, Cu and 1,2,4-trichlorobenzene were synergism. In addition, micro-situation of the cell membrane surface of pyriformis Tetrahymena was observed by SEM. The cells suffered serious damage after sufficient acting time. ATR-FTIR spectra revealed that amide groups and PO2(-) of the phospholipid phospho-diester, both in the hydrophobic end exposed to the outer layer, were the easiest to be damaged.

Microcalorimetry: a powerful and original tool for tracking the toxicity of a xenobiotic on Tetrahymena pyriformis.

Fighting against water pollution requires the ability to evaluate the toxicity of pollutants such as herbicides. Tetrahymena pyriformis are ubiquitous ciliated protozoans commonly used in ecotoxicological research. Microcalorimetry can be used in many biological investigations as a universal, non-destructive and highly sensitive tool that provides a continuous real-time monitoring of the metabolic activity. This technique based on the thermal power output was applied to evaluate the influence of herbicide diuron on cultures of T. pyriformis. The heat flux produced upon addition of 0, 3.5, 7.0, 14.0, 28.0, and 56.0 µg mL⁻¹ of diuron was monitored. The biomass change during the growth was also determined by flow cytometry. The results confirmed that the growth of T. pyriformis is progressively inhibited as the concentration of diuron increases and revealed that the state of the living system is severely altered at a concentration of 56.0 µg mL⁻¹. The IC50 was estimated at 13.8 µg mL⁻¹ by microcalorimetry and at 18.6 µg mL⁻¹ by flow cytometry. It was shown that microcalorimetry is not only a very effective tool for the determination of the growth rate constant but that it is also a valuable probe for a rapid detection of the metabolic perturbations and, in ultimate cases, of the critical alterations of the living system under the action of a toxic agent.

Individual and combined effects of mycotoxins from typical indoor moulds.

The mycotoxins patulin, gliotoxin and sterigmatocystin can be produced by common indoor moulds and enter the human body via inhalation of mycotoxin containing spores and particulates. There are various studies about the individual effects of these mycotoxins, but a lack of knowledge about their effects in mixtures. The aim of this study was to evaluate combined effects on the singe celled organism Tetrahymena pyriformis. Using the MIXTOX model (EU project NOMIRACLE) synergistic or antagonistic effects with dose level deviation or dose ratio dependent deviation were analyzed. The most toxic compound gliotoxin (EC50 0.37 µM) and patulin (EC50 9.3 µM) as shown by the MIXTOX model acted synergistic, caused by similar mode of actions. Within the combination with sterigmatocystin (maximum inhibition of 45% at 12.5 µM) antagonistic effects were observed with switch to synergism if the toxicity of the mixture is mainly caused by sterigmatocystin. In the end the MIXTOX model was applicable for the prediction of combined effects of toxic compounds.

Experimental verification, and domain definition, of structural alerts for protein binding: epoxides, lactones, nitroso, nitros, aldehydes and ketones.

This study outlines how a combination of in chemico and Tetrahymena pyriformis data can be used to define the applicability domain of selected structural alerts within the profilers of the OECD QSAR Toolbox. Thirty-three chemicals were profiled using the OECD and OASIS profilers, enabling the applicability domain of six structural alerts to be defined, the alerts being: epoxides, lactones, nitrosos, nitros, aldehydes and ketones. Analysis of the experimental data showed the applicability domains for the epoxide, nitroso, aldehyde and ketone structural alerts to be well defined. In contrast, the data showed the applicability domains for the lactone and nitro structural alerts needed modifying. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in the chemical category in predictive and regulatory toxicology. This study highlights the importance of utilizing multiple profilers in category formation.

Combined toxicity of ferroferric oxide nanoparticles and arsenic to the ciliated protozoa Tetrahymena pyriformis.

Fe3O4 nanoparticles (NPs) have a high affinity for arsenic. As a result of this association, Fe3O4 NPs loaded with high concentration of arsenic can enter into organisms and produce locally high concentrations of arsenic, which may lead to some unexpected toxicity to aquatic organisms. The objectives of this research were to investigate the toxic effect of Fe3O4 NPs in combination with As(V). Cultured Tetrahymena pyriformis was chosen as a research model organism to evaluate the toxic effects of the combined agents. The results showed that after 24 h of As(V) exposure, the median effective concentration of As(V) to T. pyriformis was 1.29 mg/L. Fe3O4 NPs alone were not only non-toxic, but actually promoted the growth of T. pyriformis at the experimental doses. After 24 h exposure, the cell number increased by 32.2% at an exposure level of 3mg/L Fe3O4 NPs. After 24h exposure to 1.0 mg/L As(V), the survival rate increased from 60.5% in the absence of Fe3O4 NPs to 73.8% and 83.8% in the presence of 13 mg/L and 19 mg/L Fe3O4 NPs, respectively. However, after 30 h, the combined toxic effect of As(V) and Fe3O4 NPs on T. pyriformis was significantly enhanced and the survival rates for co-exposure to 1.5 mg/L As(V) and 13 mg/L Fe3O4 NPs decreased from 92.3% after 18 h to 45.3% after 30 h. After 18 h of exposure to Fe3O4 NPs alone, the intracellular ROS levels were markedly increased and achieved steady state. Compared with the control group, the intracellular ROS levels were significantly increased (2.56-fold) by the combination of 19 mg/L Fe3O4 NPs and 1.0 mg/L As(V). Accumulation of As(V) in T. pyriformis led to an increase in trivalent arsenics as a result of the saturation of the cellular arsenic methylation capability or/and redox reactions. These exposures also resulted in an imbalance between oxidants and antioxidants, resulting in oxidative damage and cell death.

DNA content alterations in Tetrahymena pyriformis macronucleus after exposure to food preservatives sodium nitrate and sodium benzoate.

The toxicity, in terms of changes in the DNA content, of two food preservatives, sodium nitrate and sodium benzoate was studied on the protozoan Tetrahymena pyriformis using DNA image analysis technology. For this purpose, selected doses of both food additives were administered for 2 h to protozoa cultures and DNA image analysis of T. pyriformis nuclei was performed. The analysis was based on the measurement of the Mean Optical Density which represents the cellular DNA content. The results have shown that after exposure of the protozoan cultures to doses equivalent to ADI, a statistically significant increase in the macronuclear DNA content compared to the unexposed control samples was observed. The observed increase in the macronuclear DNA content is indicative of the stimulation of the mitotic process and the observed increase in MOD, accompanied by a stimulation of the protozoan proliferation activity is in consistence with this assumption. Since alterations at the DNA level such as DNA content and uncontrolled mitogenic stimulation have been linked with chemical carcinogenesis, the results of the present study add information on the toxicogenomic profile of the selected chemicals and may potentially lead to reconsideration of the excessive use of nitrates aiming to protect public health.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

PLS-optimal: a stepwise D-optimal design based on latent variables.

Several applications, such as risk assessment within REACH or drug discovery, require reliable methods for the design of experiments and efficient testing strategies. Keeping the number of experiments as low as possible is important from both a financial and an ethical point of view, as exhaustive testing of compounds requires significant financial resources and animal lives. With a large initial set of compounds, experimental design techniques can be used to select a representative subset for testing. Once measured, these compounds can be used to develop quantitative structure-activity relationship models to predict properties of the remaining compounds. This reduces the required resources and time. D-Optimal design is frequently used to select an optimal set of compounds by analyzing data variance. We developed a new sequential approach to apply a D-Optimal design to latent variables derived from a partial least squares (PLS) model instead of principal components. The stepwise procedure selects a new set of molecules to be measured after each previous measurement cycle. We show that application of the D-Optimal selection generates models with a significantly improved performance on four different data sets with end points relevant for REACH. Compared to those derived from principal components, PLS models derived from the selection on latent variables had a lower root-mean-square error and a higher Q2 and R2. This improvement is statistically significant, especially for the small number of compounds selected.

Predicting Michael-acceptor reactivity and toxicity through quantum chemical transition-state calculations.

The electrophilic reactivity of Michael acceptors is an important determinant of their toxicity. For a set of 35 α,ß-unsaturated aldehydes, ketones and esters with experimental rate constants of their reaction with glutathione (GSH), k(GSH), quantum chemical transition-state calculations of the corresponding Michael addition of the model nucleophile methane thiol (CH(3)SH) have been performed at the B3LYP/6-31G** level, focusing on the 1,2-olefin addition pathway without and with initial protonation. Inclusion of Boltzmann-weighting of conformational flexibility yields intrinsic reaction barriers ΔE(‡) that for the case of initial protonation correctly reflect the structural variation of k(GSH) across all three compound classes, except that they fail to account for a systematic (essentially incremental) decrease in reactivity upon α-substitution. By contrast, the reduction in k(GSH) through ß-substitution is well captured by ΔE(‡). Empirical correction for the α-substitution effect yields a high squared correlation coefficient (r(2) = 0.96) for the quantum chemical prediction of log k(GSH), thus enabling an in silico screening of the toxicity-relevant electrophilicity of α,ß-unsaturated carbonyls. The latter is demonstrated through application of the calculation scheme for a larger set of 46 Michael-acceptor aldehydes, ketones and esters with experimental values for their toxicity toward the ciliates Tetrahymena pyriformis in terms of 50% growth inhibition values after 48 h exposure (EC(50)). The developed approach may add in the predictive hazard evaluation of α,ß-unsaturated carbonyls such as for the European REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) Directive, enabling in particular an early identification of toxicity-relevant Michael-acceptor reactivity.

Epoxide and thiirane toxicity in vitro with the ciliates Tetrahymena pyriformis: structural alerts indicating excess toxicity.

The 48 h toxicity of 18 organic narcotics, 13 epoxides, and 2 thiiranes toward the ciliates Tetrahymena pyriformis was determined in terms of 50% growth inhibition EC(50). Nominal EC(50) was corrected for volatilization and sorption to quantify the freely dissolved compound fraction in solution. The derived baseline narcosis model served to evaluate toxicity enhancements T(e) as ratios of narcosis-predicted over experimental EC(50) values. Among the nine heterocycles with aliphatic side chains that include two thiiranes, three compounds yielded T(e) > 10, suggesting their covalent binding at nucleophilic protein sites such as -OH, -NHR, and -SH through S(N)2-type ring-opening. As a general trend of this group, T(e) decreases with increasing alkyl group size. Moreover, four of the six nonaliphatic epoxides exerted substantial excess toxicities with T(e) > 10, which could be rationalized by ring-opening activation through negative inductive effect, benzylic stabilization, and phenyl ring H-bonding. By contrast, 1,2 substituted epoxides showed narcosis-level toxicity, despite the opportunity of side-chain Schiff-base formation with protein amino groups. The resulting structural alerts enable an in silico screening of epoxides and thiiranes for their potential to exert excess toxicity. Note that observed differences in T(e) sensitivity between ciliates, bacteria and fish should be taken into account when designing in vitro alternatives to fish toxicity studies.

Listeria monocytogenes virulence factor Listeriolysin O favors bacterial growth in co-culture with the ciliate Tetrahymena pyriformis, causes protozoan encystment and promotes bacterial survival inside cysts.

BACKGROUND: The gram-positive pathogenic bacterium Listeria monocytogenes is widely spread in the nature. L. monocytogenes was reported to be isolated from soil, water, sewage and sludge. Listeriolysin O (LLO) is a L. monocytogenes major virulence factor. In the course of infection in mammals, LLO is required for intracellular survival and apoptosis induction in lymphocytes. In this study, we explored the potential of LLO to promote interactions between L. monocytogenes and the ubiquitous inhabitant of natural ecosystems bacteriovorous free-living ciliate Tetrahymena pyriformis. RESULTS: Wild type L. monocytogenes reduced T. pyriformis trophozoite counts and stimulated encystment. The effects were observed starting from 48 h of co-incubation. On the day 14, trophozoites were eliminated from the co-culture while about 5 x 104 cells/ml remained in the axenic T. pyriformis culture. The deficient in the LLO-encoding hly gene L. monocytogenes strain failed to cause mortality among protozoa and to trigger protozoan encystment. Replenishment of the hly gene in the mutant strain restored toxicity towards protozoa and induction of protozoan encystment. The saprophytic non-haemolytic species L. innocua transformed with the LLO-expressing plasmid caused extensive mortality and encystment in ciliates. During the first week of co-incubation, LLO-producing L. monocytogenes demonstrated higher growth rates in association with T. pyriformis than the LLO-deficient isogenic strain. At latter stages of co-incubation bacterial counts were similar for both strains. T. pyriformis cysts infected with wild type L. monocytogenes caused listerial infection in guinea pigs upon ocular and oral inoculation. The infection was proved by bacterial plating from the internal organs. CONCLUSIONS: The L. monocytogenes virulence factor LLO promotes bacterial survival and growth in the presence of bacteriovorous ciliate T. pyriformis. LLO is responsible for L. monocytogenes toxicity for protozoa and induction of protozoan encystment. L. monocytogenes entrapped in cysts remained viable and virulent. In whole, LLO activity seems to support bacterial survival in the natural habitat outside of a host.

Metabolic rate links density to demography in Tetrahymena pyriformis.

Many populations show density-dependent growth rates. We suggest that population growth rate may be connected to density through the density dependence of metabolic rate. If metabolic rate is an index of the total biochemical work being done by an organism, then as populations grow and density suppresses metabolic rate, the rate of reproduction should slow and the ability to avoid death should diminish. To test this idea, we grew axenic populations of a single-celled protist, Tetrahymena pyriformis, in laboratory microcosms, and measured metabolic rate and density. We also estimated division (birth) and death rates. The proposed connection was supported by two observations. First, increasing population density suppressed per-capita metabolic rate in accordance with the predictions of a resource-division model. The same pattern was shown with experimentally altered densities. Second, per-capita metabolic rate was positively related to per-capita division rate and negatively related to per-capita death rate. Thus, the particular pattern of population growth and regulation depends on exactly how individuals respond energetically to the density of conspecifics, and how they allocate metabolism to maintenance and production. The physiological basis of population regulation in this system is based on the constraints imposed on the total metabolic work that individual cells can perform.