Author(s): Jorge, Paula Alexandra Silva ; Pérez-Pérez, Martín ; Pérez Rodríguez, Gael ; Fdez Riverola, Florentino ; Pereira, Maria Olívia ; Lourenço, Anália
Date: 2016
Persistent ID: http://hdl.handle.net/1822/42166
Origin: RepositóriUM - Universidade do Minho
Publicado em "Biofilms7: microbial works of art: book of abstracts". ISBN 978-989-97478-7-6
The pervasive growth of antibiotic-resistant is pressing the development of novel strategies to control infectious diseases. Quorum sensing (QS) is a key communication mechanism that allows bacteria to regulate gene expression, and thus many physiological activities e.g. virulence, motility, and biofilm formation. Hence, QS inhibition or quorum quenching is being pursued as a promising strategy to control clinical pathogens.Most available information about drug interactions with QS genes and molecules is scattered in the vast and ever-growing biomedical bibliome. So, text mining and network mining are attractive solutions to identify relevant interactions and generate new hypothesis for antimicrobial research.Here, we describe the implementation of such an automated workflow that extracts key information on P. aeruginosa QS-focused antimicrobial strategies from PubMed records. The workflow produces an integrated network, capturing the effect of antimicrobial agents over QS genes, QS signals and virulence factors. Interactions are contextualised by information on the conducted experimental methods and details on the antimicrobials and QS entities retrieved. The public Web-based interface (http://pcquorum.org) enables users to navigate through the interactions and look for indirect, non-trivial antimicrobial-QS associations.Currently, the P. aeruginosa antimicrobial-QS network contains 439 interactions encompassing 170 different drugs and 72 different QS entities; but it is in continuous, semi-automated growth. It offers a comprehensive picture of emerging anti-QS findings and thus may help in gaining novel understanding and prioritising new antimicrobial experiments.
