Current Microbiology

, Volume 68, Issue 3, pp 342–351 | Cite as

Type 2 Quorum Sensing Monitoring, Inhibition and Biofilm Formation in Marine Microrganisms

  • Iram Liaqat
  • Robert Thomas Bachmann
  • Robert G. J. Edyvean


The quorum sensing (QS) dependent behaviour of micro-organisms, in particular expression of virulence genes, biofilm formation and dispersal, have provided impetus for investigating practical approaches to interfere with microbial QS. This study tests Halomonas pacifica and Marinobacter hydrocarbonoclasticus, two halophilic marine micro-organism, for their AI-2 dependent QS signalling and the effect of two well-known quorum-sensing inhibitors (QSIs), patulin and penicillic acid, on biofilm formation. We report, for the first time, the successful amplification of a putative luxS gene in H. pacifica using degenerated primers and AI-2 dependent QS as well as inhibition using QSIs. Penicillic acid had a strong inhibitory effect on AI-2 induction of H. pacifica at non-growth inhibitory concentrations, while patulin has an adverse effect only at the highest concentration (25 μM). QSIs effect on biofilm forming capability was isolate specific, with maximum inhibition at 25 μM of patulin in H. pacifica. In M. hydrocarbonoclasticus, no adverse effects were noted at any tested concentration of either QSIs. Detection of bioluminescence and the presence of a putative luxS gene provide biochemical and genetic evidence for the production of a signalling molecule(s) which is the essential first step in characterizing H. pacifica QS. This study highlights the importance of AI-2 dependent QS in a marine setting, not previously reported. It further suggests that QSI compounds must be selected in the specific system in which they are to function, and they cannot easily be transferred from one QS system to another.


Crystal Violet Ectoine Patulin Halomonas Cell Free Culture Supernatant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Iram Liaqat
    • 1
  • Robert Thomas Bachmann
    • 2
  • Robert G. J. Edyvean
    • 3
  1. 1.Department of ZoologyGovernment College UniversityLahorePakistan
  2. 2.Malaysian Institute of Chemical and Bioengineering TechnologyUniversiti Kuala Lumpur, Lot 1988, Taboh NaningAlor GajahMalaysia
  3. 3.Department of Chemical and Biological EngineeringThe University of SheffieldSheffieldUK

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