Dataset Information

ROSET Model of TonB Action in Gram-Negative Bacterial Iron Acquisition.

ABSTRACT: The rotational surveillance and energy transfer (ROSET) model of TonB action suggests a mechanism by which the electrochemical proton gradient across the Gram-negative bacterial inner membrane (IM) promotes the transport of iron through ligand-gated porins (LGP) in the outer membrane (OM). TonB associates with the IM by an N-terminal hydrophobic helix that forms a complex with ExbBD. It also contains a central extended length of rigid polypeptide that spans the periplasm and a dimeric C-terminal-????-domain (CTD) with LysM motifs that binds the peptidoglycan (PG) layer beneath the OM bilayer. The TonB CTD forms a dimer with affinity for both PG- and TonB-independent OM proteins (e.g., OmpA), localizing it near the periplasmic interface of the OM bilayer. Porins and other OM proteins associate with PG, and this general affinity allows the TonB CTD dimer to survey the periplasmic surface of the OM bilayer. Energized rotational motion of the TonB N terminus in the fluid IM bilayer promotes the lateral movement of the TonB-ExbBD complex in the IM and of the TonB CTD dimer across the inner surface of the OM. When it encounters an accessible TonB box of a (ligand-bound) LGP, the monomeric form of the CTD binds and recruits it into a 4-stranded ?-sheet. Because the CTD is rotating, this binding reaction transfers kinetic energy, created by the electrochemical proton gradient across the IM, through the periplasm to the OM protein. The equilibration of the TonB C terminus between the dimeric and monomeric forms that engage in different binding reactions allows the identification of iron-loaded LGP and then the internalization of iron through their trans-outer membrane ?-barrels. Hence, the ROSET model postulates a mechanism for the transfer of energy from the IM to the OM, triggering iron uptake.

SUBMITTER: Klebba PE

PROVIDER: S-EPMC4800874 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ROSET Model of TonB Action in Gram-Negative Bacterial Iron Acquisition.

Klebba Phillip E PE

Journal of bacteriology 20160119 7

The rotational surveillance and energy transfer (ROSET) model of TonB action suggests a mechanism by which the electrochemical proton gradient across the Gram-negative bacterial inner membrane (IM) promotes the transport of iron through ligand-gated porins (LGP) in the outer membrane (OM). TonB associates with the IM by an N-terminal hydrophobic helix that forms a complex with ExbBD. It also contains a central extended length of rigid polypeptide that spans the periplasm and a dimeric C-terminal ...[more]

PMID: 26787763

Similar Datasets

Project description:BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Dataset Information

ROSET Model of TonB Action in Gram-Negative Bacterial Iron Acquisition.

Publications

ROSET Model of TonB Action in Gram-Negative Bacterial Iron Acquisition.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets