Fibronectin-binding proteins (FnBP), FnBPA and FnBPB, are purported to be involved in biofilm formation of Staphylococcus aureus. This study was performed to find which of three consecutive N subdomains of the A domain in the FnBP is the key domain in FnBP. A total of 465 clinical isolates of S. aureus were examined for the biofilm forming capacity and the presence of N subdomains of FnBP. In the biofilm-positive strains, N2 and N3 subdomains of FnBPA, and N1 and N3 subdomains of FnBPB were significantly more prevalent. Multivariate logistic regression analysis of 246 biofilm-positive and 123 biofilm-negative strains identified only the FnBPB-N3 subdomain as an independent risk determinant predictive for biofilm-positive strains of S. aureus (Odds ratio [OR], 13.174; P<0.001). We also attempted to delete each of the fnbA-N2 and -N3 and fnbB-N1 and -N3 from S. aureus strain 8325-4 and examined the biofilm forming capacity in the derivative mutants. In agreement with the results of the multivariate regression analysis, deletion of either the fnbA-N2 or -N3, or fnbB-N1 did not significantly diminish the capacity of strain 8325-4 to develop a biofilm, while deletion of the fnbB-N3 did. Therefore, it is suggested that the FnBPB-N3 subdomain of isotype I may be a key domain in FnBP which is responsible for the causing biofilm formation in S. aureus clinical isolates.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s12275-013-3319-yDOI Listing

Publication Analysis

Top Keywords

independent risk
8
risk determinant
8
determinant predictive
8
biofilm formation
8
formation staphylococcus
8
staphylococcus aureus
8
clinical isolates
8
domain fnbp
8
biofilm-positive strains
8
subdomain domain
4

Similar Publications

PSMA-PET/CT Findings in Patients With High-Risk Biochemically Recurrent Prostate Cancer With No Metastatic Disease by Conventional Imaging.

JAMA Netw Open

January 2025

Ahmanson Translational Theranostics Division, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles.

Importance: The phase 3 randomized EMBARK trial evaluated enzalutamide with or without leuprolide in high-risk nonmetastatic hormone-sensitive prostate cancer. Eligibility relied on conventional imaging, which underdetects metastatic disease compared with prostate-specific membrane antigen-positron emission tomography (PSMA-PET).

Objective: To describe the staging information obtained by PSMA-PET/computed tomography (PSMA-PET/CT) in a patient cohort eligible for the EMBARK trial.

View Article and Find Full Text PDF

Importance: People with kidney failure have a high risk of death and poor quality of life. Mortality risk prediction models may help them decide which form of treatment they prefer.

Objective: To systematically review the quality of existing mortality prediction models for people with kidney failure and assess whether they can be applied in clinical practice.

View Article and Find Full Text PDF

Background: Many studies have developed or validated predictive models to estimate the risk of sarcopenia in dialysis patients, but the quality of model development and the applicability of the models remain unclear.

Objective: To systematically review and critically evaluate currently available predictive models for sarcopenia in dialysis patients.

Methods: We systematically searched five databases until March 2024.

View Article and Find Full Text PDF

Background: Obesity is a chronic disease associated with other associated medical problems, including atherogenic dyslipidemia. Metabolic bariatric surgery (MBS) has been shown to reduce long-term cardiovascular risk (CVR). Anti-ApoA-1 antibodies (AAA1) are independently associated with cardiovascular disease, which remains a major cause of death in individuals with obesity.

View Article and Find Full Text PDF

Purpose: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!