Theoretical Determination of the pK a Values of Betalamic Acid Related to the Free Radical Scavenger Capacity: Comparison Between Empirical and Quantum Chemical Methods

Health benefits of dietary phytochemicals have been suggested in recent years. Among 1000s of different compounds, Betalains, which occur in vegetables of the Cariophyllalae order (cactus pear fruits and red beet), have been considered because of reducing power and potential to affect redox-modulated cellular processes. The antioxidant power of Betalains is strictly due to the dissociation rate of the acid moieties present in all the molecules of this family of phytochemicals. Experimentally, only the pK a values of betanin were determined. Recently, it was evidenced it was evidenced as the acid dissociation, at different environmental pHs, affects on its electron-donating capacity, and further on its free radical scavenging power. The identical correlation was studied on another Betalains family compound, Betalamic Acid. Experimental evidences showed that the free radical scavenging capacity of this compound drastically decreases at pH > 5, but pK a values were experimentally not measured. With the aim to justify the Betalamic Acid behavior as free radical scavenger, in this paper we tried to predict in silico the pK a values by means different approaches. Starting from the known experimental pK as of acid compounds, both phytochemicals and small organic, two empirical approaches and quantum-mechanical calculation were compared to give reliable prediction of the pK as of Betalamic Acid. Results by means these computational approaches are consistent with the experimental evidences. As shown herein, in silico, the totally dissociated species, at the experimental pH > 5 in solution, is predominant, exploiting the higher electron-donating capability (HOMO energy). Therefore, the computational estimated pK a values of Betalamic Acid resulted very reliable

Differential Virulence Gene Expression of Group A Streptococcus Serotype M3 in Response to Co-Culture with Moraxella catarrhalis

Streptococcus pyogenes (group A Streptococcus, GAS) and Moraxella catarrhalis are important colonizers and (opportunistic) pathogens of the human respiratory tract. However, current knowledge regarding colonization and pathogenic potential of these two pathogens is based on work involving single bacterial species, even though the interplay between respiratory bacterial species is increasingly important in niche occupation and the development of disease. Therefore, to further define and understand polymicrobial species interactions, we investigated whether gene expression (and hence virulence potential) of GAS would be affected upon co-culture with M. catarrhalis. For co-culture experiments, GAS and M. catarrhalis were cultured in Todd-Hewitt broth supplemented with 0.2% yeast extract (THY) at 37°C with 5% CO2aeration. Each strain was grown in triplicate so that triplicate experiments could be performed. Bacterial RNA was isolated, cDNA synthesized, and microarray transcriptome expression analysis performed. We observed significantly increased (≥4-fold) expression for genes playing a role in GAS virulence such as hyaluronan synthase (hasA), streptococcal mitogenic exotoxin Z (smeZ) and IgG endopeptidase (ideS). In contrast, significantly decreased (≥4-fold) expression was observed in genes involved in energy metabolism and in 12 conserved GAS two-component regulatory systems. This study provides the first evidence that M. catarrhalis increases GAS virulence gene expression during co-culture, and again shows the importance of polymicrobial infections in directing bacterial virulence

Predicting growth rates and growth boundary of Listeria monocytogenes - An international validation study with focus on processed and ready-to-eat meat and seafood

The performance of six predictive models for Listeria monocytogenes was evaluated using 1014 growth responses of the pathogen in meat, seafood, poultry and dairy products. The performance of the growth models was closely related to their complexity i.e. the number of environmental parameters they take into account. The most complex model included the effect of nine environmental parameters and it performed better than the other less complex models both for prediction of maximum specific growth rates (mu(max) values) and for the growth boundary of L. monocytogenes. For this model bias and accuracy factors for growth rate predictions were 1.0 and 1.5, respectively, and 89% of the growth/no-growth responses were correctly predicted. The performance of three other models, including the effect of five to seven environmental parameters, was considered acceptable with bias factors of 1.2 to 1.3. These models all included the effect of acetic acid/diacetate and lactic acid, one of the models also included the effect of CO2 and nitrite but none of these models included the effect of smoke components. Less complex models that did not include the effect of acetic acid/diacetate and lactic acid were unable to accurately predict growth responses of L. monocytogenes in the wide range of food evaluated in the present study. When complexity of L monocytogenes growth models matches the complexity of foods of interest. i.e. the number of hurdles to microbial growth, then predicted growth responses of the pathogen can be accurate. The successfully validated models are useful for assessment and management of L monocytogenes in processed and ready-to-eat (RTE) foods. (C) 2010 Elsevier B.V. All rights reserved

Retrospective Analysis of a Listeria monocytogenes Contamination Episode in Raw Milk Goat Cheese Using Quantitative Microbial Risk Assessment tools

In 2005, the Belgian authorities reported a Listeria monocytogenes contamination episode in cheese made from raw goat's milk. The presence of an asymptomatic shedder goat in the herd caused this contamination. On the basis of data collected at the time of the episode, a retrospective study was performed using an exposure assessment model covering the production chain from the milking of goats up to delivery of cheese to the market. Predictive microbiology models were used to simulate the growth of L. monocytogenes during the cheese process in relation with temperature, pH, and water activity. The model showed significant growth of L. monocytogenes during chilling and storage of the milk collected the day before the cheese production (median increase of 2.2 log CFU/ml) and during the addition of starter and rennet to milk (median increase of 1.2 log CFU/ml). The L. monocytogenes concentration in the fresh unripened cheese was estimated to be 3.8 log CFU/g (median). This result is consistent with the number of L. monocytogenes in the fresh cheese (3.6 log CFU/g) reported during the cheese contamination episode. A variance-based method sensitivity analysis identified the most important factors impacting the cheese contamination, and a scenario analysis then evaluated several options for risk mitigation. Thus, by using quantitative microbial risk assessment tools, this study provides reliable information to identify and control critical steps in a local production chain of cheese made from raw goat's milk