The in situ physiology of "Nostocoida limicola" II, a filamentous bacterial morphotype in bulking activated sludge, using fluorescence in situ hybridization and microautoradiography

The in situ physiology of the actinobacterial bulking and foaming filamentous bacterium "Nostocoida limicola" II was studied by fluorescence in situ hybridization/microautoradiography. Substrate assimilation patterns of pure cultures of this bacterium were different to those seen in activated sludge biomass samples. There was no evidence to suggest that "N. limicola" II preferred hydrophobic substrates, but evidence was produced to support the view that it is metabolically active under anaerobic conditions in activated sludge.

Bacterial membrane vesicles deliver peptidoglycan to NOD1 in epithelial cells

Gram-negative bacterial peptidoglycan is specifically recognized by the host intracellular sensor NOD1, resulting in the generation of innate immune responses. Although epithelial cells are normally refractory to external stimulation with peptidoglycan, these cells have been shown to respond in a NOD1-dependent manner to Gram-negative pathogens that can either invade or secrete factors into host cells. In the present work, we report that Gram-negative bacteria can deliver peptidoglycan to cytosolic NOD1 in host cells via a novel mechanism involving outer membrane vesicles (OMVs). We purified OMVs from the Gram-negative mucosal pathogens: Helicobacter pylori, Pseudomonas aeruginosa and Neisseria gonorrhoea and demonstrated that these peptidoglycan containing OMVs upregulated NF-κB and NOD1-dependent responses in vitro. These OMVs entered epithelial cells through lipid rafts thereby inducing NOD1-dependent responses in vitro. Moreover, OMVs delivered intragastrically to mice-induced innate and adaptive immune responses via a NOD1-dependent but TLR-independent mechanism. Collectively, our findings identify OMVs as a generalized mechanism whereby Gram-negative bacteria deliver peptidoglycan to cytosolic NOD1. We propose that OMVs released by bacteria in vivo may promote inflammation and pathology in infected hosts.

Lipid membrane : a novel target for viral and bacterial pathogens

Abstracts: Lipid rafts are defined as specialized, dynamic microdomains that can be found in plasma membrane, and they are enriched with cholesterol and sphingolipids. Since lipid rafts’ first debut in the mid 1990’s, their existence, function and biological relevance have been a subject of intense scrutiny within the scientific community. Throughout this debate, we have learned a great deal regarding how cargos (both pathogens and cellular factors) are transported into and out of the cell through raft-dependent or raft-independent pathways. It is now apparent that a number of toxins, bacterial-, and viral-pathogens are able to exploit cholesterol and/or lipid rafts to gain a foot hold in their target hosts. The objective of this review is to describe our current appreciation on how selected pathogens utilise cholesterol and/or lipid rafts to support their propagation and to speculate on how some of these observations can be explored for the development of novel strategies that target plasma membrane lipids to control the spread of these viral- and bacterial-pathogens.

In depression, bacterial translocation may drive inflammatory responses, oxidative and nitrosative stress (O&NS) and autoimmune responses directed against O&NS- damaged neoepitopes

Objective: Depression is accompanied by activation of immuno-inflammatory and oxidative and nitrosative stress (IO&NS) pathways, and increased IgM/IgA responses to lipopolysaccharide (LPS) of gram-negative commensal bacteria. The latter suggests that bacterial translocation has caused IgM/IgA responses directed against LPS. Bacterial translocation may drive IO&NS responses.

Method: To examine the associations between IgM/IgA responses to LPS and IO&NS measurements, including plasma/serum interleukin-1 (IL-1), tumor necrosis factor (TNF)α, neopterin, lysozyme, oxidized LDL (oxLDL) antibodies, peroxides, and IgM (auto)immune responses against malondialdehyde (MDA), azelaic acid, phophatidyl inositol (Pi), NO-tryptophan and NO-tyrosine in depressed patients and controls.

Results: We found significant positive associations between IgM/IgA responses to LPS and oxLDL antibodies, IgM responses against MDA, azelaic acid, Pi, NO-tryptophan, and NO-tyrosine. The IgA responses to LPS were correlated with lysozyme. There were no significant positive correlations between the IgM/IgA responses to LPS and IL-1 and neopterin.

Conclusion: The findings show that in depression there is an association between increased bacterial translocation and lysozyme production, an antibacterial compound, O&NS processes, and autoimmune responses directed against O&NS generated neoantigenic determinants. It is suggested that bacterial translocation may drive IO&NS pathways in depression and thus play a role in its pathophysiology.

Bacterial biosensors for measuring availability of environmental pollutants

Biosensors are projected to find many applications due to their high selectivity and sensitivity, rapid reaction, economy and ease of handling in field measurements. Even though biosensors for a wide range of environmental pollutants have been extensively reported in the literature, the decision to develop a suitable biosensing system that can be approved by a regulatory perspective for environmental applications is fraught with technical issues. These issues mainly concern the biological recognition element, the physico-chemical transducer and the interfaces between the biological and the physical components, but also aspects of fluidics, electronics, and software for data processing. This article focused on the development of microbial-based biosensors for environmental applications especially metal contaminants such as arsenic.

Minimum inhibitory and bacterial concentrations of theaflavin and synergistic combinations with epicatechin and quercetin against clinical isolates of Stenotrophomonas Maltophilia

Stenotrophomonas maltophilia is an important nosocomial pathogen with intrinsic resistance to multiple antibiotics. Previous investigations have shown flavanols from black tea to possess antibacterial activity. This study describes the determination of minimum inhibitory concentrations and minimum bactericidal concentration for theaflavin independently and in formulations with the polyphenols epicatechin and quercetin against nine clinical isolates of Stenotrophomonas maltophilia and the control isolate NCTC 130141 via the microtitre assay. The results demonstrate that theaflavin has strong antibacterial activity and also shows significant synergism with epicatechin and quercetin. The minimum inhibitory concentrations of the isolates range between 200-400 g/mL for theaflavin and 100-200 g/mL for both theaflavin:epicatechin and theaflavin:quercetin combinations. The minimum bactericidal concentrations were discovered to be a 2 fold increase on those of the minimum inhibitory concentrations. The research highlights the potential use of polyphenols for the clinical treatment of highly antibiotic resistant bacteria.

Peptide mimics targeting bacterial membranes

In recent years, society has been increasingly concerned with bacteria that are no longer susceptible to commercial antibiotics. Faced with a lack of tools, medical practitioners today are forced to prescribe medicines that, although effective, cause as much harm to the patient as the principal infection. The purpose of this research project is to develop novel antibacterials that remain potent against bacterial infections without being toxic to the patient.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.