Microbial Ecology

, Volume 65, Issue 3, pp 555–565 | Cite as

Subsurface Associations of Acaryochloris-Related Picocyanobacteria with Oil-Utilizing Bacteria in the Arabian Gulf Water Body: Promising Consortia in Oil Sediment Bioremediation

  • Dhia Al-Bader
  • Mohamed Eliyas
  • Rihab Rayan
  • Samir Radwan
Microbiology of Aquatic Systems


Two picocyanobacterial strains related to Acaryochloris were isolated from the Arabian Gulf, 3 m below the water surface, one from the north shore and the other from the south shore of Kuwait. Both strains were morphologically, ultrastructurally, and albeit to a less extend, phylogenetically similar to Acaryochloris. However, both isolates lacked chlorophyll d and produced instead chlorophyll a, as the major photosynthetic pigment. Both picocyanobacterial isolates were associated with oil-utilizing bacteria in the magnitude of 105 cells g−1. According to their 16S rRNA gene sequences, bacteria associated with the isolate from the north were affiliated to Paenibacillus sp., Bacillus pumilus, and Marinobacter aquaeolei, but those associated with the isolate from the south were affiliated to Bacillus asahii and Alcanivorax jadensis. These bacterial differences were probably due to environmental variations. In batch cultures, the bacterial consortia in the nonaxenic biomass as well as the pure bacterial isolates effectively consumed crude oil and pure aliphatic and aromatic hydrocarbons, including very high-molecular-weight compounds. Water and diethylether extracts from the phototrophic biomass enhanced growth of individual bacterial isolates and their hydrocarbon-consumption potential in batch cultures. It was concluded that these consortia could be promising in bioremediation of hydrocarbon pollutants, especially heavy sediments in the marine ecosystem.


Direct Microscopic Counting Triacontane Bright White Light Major Photosynthetic Pigment Arabian Gulf Water 
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.



The work was supported by the University of Kuwait, research grant SL 09/08. Thanks are also due to the SAF unit and GRF, Kuwait University, for providing GLC (GS 02/01), HPLC (GS 03/01), UV–vis (GS 01/01), and genetic analyzer (GS 01/02) facilities.


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Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Dhia Al-Bader
    • 1
  • Mohamed Eliyas
    • 1
  • Rihab Rayan
    • 1
  • Samir Radwan
    • 1
  1. 1.Department of Biological Sciences, Faculty of ScienceKuwait UniversitySafatKuwait

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