Microbial Ecology

, Volume 61, Issue 4, pp 783–792 | Cite as

Characterization of Quorum Sensing Signals in Coral-Associated Bacteria

  • Karina Golberg
  • Evgeni Eltzov
  • Maya Shnit-Orland
  • Robert S. MarksEmail author
  • Ariel Kushmaro


Marine environment habitats, such as the coral mucus layer, are abundant in nutrients and rich with diverse populations of microorganisms. Since interactions among microorganisms found in coral mucus can be either mutualistic or competitive, understanding quorum sensing-based acyl homoserine lactone (AHL) language may shed light on the interaction between coral-associated microbial communities in the native host. More than 100 bacterial isolates obtained from different coral species were screened for their ability to produce AHL. When screening the isolated coral bacteria for AHL induction activity using the reporter strains Escherichia coli K802NR-pSB1075 and Agrobacterium tumefaciens KYC55, we found that approximately 30% of the isolates tested positive. Thin layer chromatography separation of supernatant extracts revealed different AHL profiles, with detection of at least one active compound in the supernatant of those bacterial extracts being able to induce AHL activity in the two different bioreporter strains. The active extract of bacterial isolate 3AT 1-10-4 was subjected to further analysis by preparative thin layer chromatography and liquid chromatography tandem mass spectrometry. One of the compounds was found to correspond with N-(3-hydroxydecanoyl)-l-homoserine lactone. 16S rRNA gene sequencing of the isolates with positive AHL activity affiliated them with the Vibrio genus. Understanding the ecological role of AHL in the coral environment and its regulatory circuits in the coral holobiont-associated microbial community will further expand our knowledge of such interactions.


Vibrio Quorum Sense Reporter Strain Acyl Homoserine Lactone Coral Mucus 
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.



This work was supported by the National Institute of Biotechnology in the Negev (NIBN), with financial support under grant number 8528620 “Bioactive compounds”, ISF Grant 1169/07, and Marie Curie Host Fellowships for the Transfer of Knowledge (TOK) Development Host Scheme (MEMBIOF) contract no. MTKD-CT-2005-029813. The authors also thank the IUI, Eilat, Israel, for use of their facilities, Prof Jun Zhu for strain KYC55, and Dr. Tamar Amir, Dr. Eitan Ben-Dov, Dr. Michal Shani Sekler, Luba Arotsker, and Nahshon Siboni for sample collection, technical support, and guidance.


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Karina Golberg
    • 1
  • Evgeni Eltzov
    • 2
  • Maya Shnit-Orland
    • 2
  • Robert S. Marks
    • 1
    • 3
    • 4
    Email author
  • Ariel Kushmaro
    • 1
    • 3
  1. 1.Department of Biotechnology EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Unit of Environmental Engineering, Faculty of Engineering ScienceBen-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.National Institute for Biotechnology in the NegevBen-Gurion University of the NegevBeer-ShevaIsrael
  4. 4.The Ilse Katz Center for Meso and Nanoscale Science and TechnologyBen-Gurion University of the NegevBeer-ShevaIsrael

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