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Manos, J., Arthur, J., Rose, B., Tingpej, P., Fung, C., Curtis, M., Webb, J.S., Hu, H., Kjelleberg, S., Gorrell, M.D., Bye, P. and Harbour, C. (2008) Transcriptome Analyses and Biofilm-Forming Characteristics of a Clonal Pseudomonas aeruginosa from the Cystic Fibrosis Lung. Journal of Medical Microbiology, 57, 1454-1465.
https://doi.org/10.1099/jmm.0.2008/005009-0

has been cited by the following article:

• TITLE: Building Quantitative Gene Regulatory Mechanism in Quorum Sensing in Pseudomonas aeruginosa Using Transcriptomic Data

  AUTHORS: Shaomin Yan, Guang Wu

  KEYWORDS: Gene Quantitative Regulatory Mechanism, Pseudomonas aeruginosa, Quorum Sensing, Transcriptomic Analysis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.13 No.2, February 28, 2020

  ABSTRACT: A large amount of transcriptomic data provides opportunities 1) to verify the gene regulatory mechanism, which is usually obtained from a single experiment, at population level; 2) to uncover the gene regulatory mechanism at population level; and 3) to build a quantitatively gene regulatory mechanism. One of the best studied regulatory mechanisms in bacteria is the quorum sensing (QS), which plays an important role in regulation of bacteria population behaviors such as antibiotic production, biofilm formation, bioluminescence, competence, conjugation, motility and sporulation. Pseudomonas aeruginosa is a Gram-negative bacterium causing diseases in plants, animals, humans, and its biofilm and drug-resistance become great concerns in clinics. P. aeruginosa has three QS systems including a specific one for Pseudomonas. In this study, the transcriptomic data of P. aeruginosa were combined from 104 publications and QS gene expressions were analyzed under different experimental conditions. The results demonstrate the quantitatively regulatory mechanisms of QS genes at population level including 1) to rank and group QS-related genes according to their activity; 2) to quantitatively define the role of a single global regulator; 3) to find out the probability that a global regulator impacts QS genes and the probability that a QS gene responds to global regulators; and 4) to search for overlapped genes under four types of experimental conditions. These results provide integrative information on understanding the regulation of QS genes at population level.