No abstract

beta-Galactosidase was isolated from the cell-free extracts of Lactobacillus crispatus strain ATCC 33820 and the effects of temperature, pH, sugars and monovalent and divalent cations on the activity of the enzyme were examined. L. crispatus produced the maximum amount of enzyme when grown in MRS medium containing galactose (as carbon source) at 37 degrees C and pH 6.5 for 2 d, addition of glucose repressing enzyme production. Addition of lactose to the growth medium containing galactose inhibited the enzyme synthesis. The enzyme was active between 20 and 60 degrees C and in the pH range of 4-9. However, the optimum enzyme activity was at 45 degrees C and pH 6.5. The enzyme was stable up to 45 degrees C when incubated at various temperatures for 15 min at pH 6.5. When the enzyme was exposed to various pH values at 45 degrees C for 1 h, it retained the original activity over the pH range of 6.0-7.0. Presence of divalent cations, such as Fe2+ and Mn2+, in the reaction mixture increased enzyme activity, whereas Zn2+ was inhibitory. The Km was 1.16 mmol/L for 2-nitrophenyl-beta-D-galactopyranose and 14.2 mmol/L for lactose.

OqxB is an RND (Resistance-Nodulation-Division) efflux pump that has emerged as a factor contributing to the antibiotic resistance in Klebsiella pneumoniae. OqxB underwent horizontal gene transfer and is now seen in other Gram-negative bacterial pathogens including Escherichia coli, Enterobacter cloacae and Salmonella spp., further disseminating multi-drug resistance. In this study, we describe crystal structure of OqxB with n-dodecyl-β-D-maltoside (DDM) molecules bound in its substrate-binding pocket, at 1.85 Å resolution. We utilize this structure in computational studies to predict the key amino acids contributing to the efflux of fluoroquinolones by OqxB, distinct from analogous residues in related transporters AcrB and MexB. Finally, our complementation assays with mutated OqxB and minimum inhibitory concentration (MIC) experiments with clinical isolates of E. coli provide further evidence that the predicted structural features are indeed involved in ciprofloxacin efflux.

Steaks of 4 types collected from 113 retails stores in 4 Canadian cities were frozen for storage. Swab samples collected from approximately 100 cm² of each of 598 thawed steaks were processed for enumeration of bacteria. The fraction of steaks from which total aerobic counts (AER), psychrotrophs (PSY), lactic acid bacteria (LAB), pseudomonads(PSE) and Brochothrix thermosphacta (BRO) were not recovered at > 2 log cfu/100 cm² were 3, 12, 8, 25 and 51%, respectively. The fractions of steaks from which coliforms (COL) and Escherichia coli (ECO) were not recovered at > 0 log cfu/ 100 cm² were 56 and 92%, respectively. The log number per 100 cm² recovered from > 90% of steaks were < 6 for AER, PSY and LAB, < 5 for PSE, <4 for BRO, and < 2 for COL. The microbiological conditions of groups of steaks of different types, from different cities or from different groups of stores were not substantially different.

Using Escherichia coli as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic E. coli were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.