Research article
Computational prediction of the osmoregulation network in Synechococcus sp. WH8102
1 Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
2 College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
3 The Institute for Genomic Research and the J. Craig Venter Institute, Rockville, MD 20850, USA
4 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093, USA
BMC Genomics 2010, 11:291 doi:10.1186/1471-2164-11-291
Published: 10 May 2010Abstract
Background
Osmotic stress is caused by sudden changes in the impermeable solute concentration around a cell, which induces instantaneous water flow in or out of the cell to balance the concentration. Very little is known about the detailed response mechanism to osmotic stress in marine Synechococcus, one of the major oxygenic phototrophic cyanobacterial genera that contribute greatly to the global CO2 fixation.
Results
We present here a computational study of the osmoregulation network in response to hyperosmotic stress of Synechococcus sp strain WH8102 using comparative genome analyses and computational prediction. In this study, we identified the key transporters, synthetases, signal sensor proteins and transcriptional regulator proteins, and found experimentally that of these proteins, 15 genes showed significantly changed expression levels under a mild hyperosmotic stress.
Conclusions
From the predicted network model, we have made a number of interesting observations about WH8102. Specifically, we found that (i) the organism likely uses glycine betaine as the major osmolyte, and others such as glucosylglycerol, glucosylglycerate, trehalose, sucrose and arginine as the minor osmolytes, making it efficient and adaptable to its changing environment; and (ii) σ38, one of the seven types of σ factors, probably serves as a global regulator coordinating the osmoregulation network and the other relevant networks.
