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Xia, XM, Vidyarathna NK, Palenik B, Lee P, Liu HB.  2015.  Comparison of the seasonal variations of synechococcus assemblage structures in estuarine waters and coastal waters of Hong Kong. Applied and Environmental Microbiology. 81:7644-7655.   10.1128/aem.01895-15   AbstractWebsite

Seasonal variation in the phylogenetic composition of Synechococcus assemblages in estuarine and coastal waters of Hong Kong was examined through pyrosequencing of the rpoC1 gene. Sixteen samples were collected in 2009 from two stations representing estuarine and ocean-influenced coastal waters, respectively. Synechococcus abundance in coastal waters gradually increased from 3.6 x 10(3) cells ml(-1) in March, reaching a peak value of 5.7 x 10(5) cells ml(-1) in July, and then gradually decreased to 9.3 x 10(3) cells ml(-1) in December. The changes in Synechococcus abundance in estuarine waters followed a pattern similar to that in coastal waters, whereas its composition shifted from being dominated by phycoerythrin-rich (PE-type) strains in winter to phycocyanin-only (PC-type) strains in summer owing to the increase in freshwater discharge from the Pearl River and higher water temperature. The high abundance of PC-type Synechococcus was composed of subcluster 5.2 marine Synechococcus, freshwater Synechococcus (F-PC), and Cyanobium. The Synechococcus assemblage in the coastal waters, on the other hand, was dominated by marine PE-type Synechococcus, with subcluster 5.1 clades II and VI as the major lineages from April to September, when the summer monsoon prevailed. Besides these two clades, clade III cooccurred with clade V at relatively high abundance in summer. During winter, the Synechococcus assemblage compositions at the two sites were similar and were dominated by subcluster 5.1 clades II and IX and an undescribed clade (represented by Synechococcus sp. strain miyav). Clade IX Synechococcus was a relatively ubiquitous PE-type Synechococcus found at both sites, and our study demonstrates that some strains of the clade have the ability to deal with large variation of salinity in subtropical estuarine environments. Our study suggests that changes in seawater temperature and salinity caused by the seasonal variation of monsoonal forcing are two major determinants of the community composition and abundance of Synechococcus assemblages in Hong Kong waters.

Su, ZC, Mao FL, Dam P, Wu HW, Olman V, Paulsen IT, Palenik B, Xu Y.  2006.  Computational inference and experimental validation of the nitrogen assimilation regulatory network in cyanobacterium Synechococcus sp WH 8102. Nucleic Acids Research. 34:1050-1065.   10.1093/nar/gkj496   AbstractWebsite

Deciphering the regulatory networks encoded in the genome of an organism represents one of the most interesting and challenging tasks in the post-genome sequencing era. As an example of this problem, we have predicted a detailed model for the nitrogen assimilation network in cyanobacterium Synechococcus sp. WH 8102 (WH8102) using a computational protocol based on comparative genomics analysis and mining experimental data from related organisms that are relatively well studied. This computational model is in excellent agreement with the microarray gene expression data collected under ammonium-rich versus nitrate-rich growth conditions, suggesting that our computational protocol is capable of predicting biological pathways/networks with high accuracy. We then refined the computational model using the microarray data, and proposed a new model for the nitrogen assimilation network in WH8102. An intriguing discovery from this study is that nitrogen assimilation affects the expression of many genes involved in photosynthesis, suggesting a tight coordination between nitrogen assimilation and photosynthesis processes. Moreover, for some of these genes, this coordination is probably mediated by NtcA through the canonical NtcA promoters in their regulatory regions.

Mao, X, Olman V, Stuart R, Paulsen IT, Palenik B, Xu Y.  2010.  Computational prediction of the osmoregulation network in Synechococcus sp. WH8102. Bmc Genomics. 11   10.1186/1471-2164-11-291   AbstractWebsite

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 CO(2) 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) sigma(38), one of the seven types of sigma factors, probably serves as a global regulator coordinating the osmoregulation network and the other relevant networks.

Keeling, PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, Armbrust VE, Archibald JM, Bharti AK, Bell CJ, Beszteri B, Bidle KD, Cameron CT, Campbell L, Caron DA, Cattolico RA, Collier JL, Coyne K, Davy SK, Deschamps P, Dyhrman ST, Edvardsen B, Gates RD, Gobler CJ, Greenwood SJ, Guida SM, Jacobi JL, Jakobsen KS, James ER, Jenkins B, John U, Johnson MD, Juhl AR, Kamp A, Katz LA, Kiene R, Kudryavtsev A, Leander BS, Lin S, Lovejoy C, Lynn D, Marchetti A, McManus G, Nedelcu AM, Menden-Deuer S, Miceli C, Mock T, Montresor M, Moran MA, Murray S, Nadathur G, Nagai S, Ngam PB, Palenik B, Pawlowski J, Petroni G, Piganeau G, Posewitz MC, Rengefors K, Romano G, Rumpho ME, Rynearson T, Schilling KB, Schroeder DC, Simpson AGB, Slamovits CH, Smith DR, Smith JG, Smith SR, Sosik HM, Stief P, Theriot E, Twary SN, Umale PE, Vaulot D, Wawrik B, Wheeler GL, Wilson WH, Xu Y, Zingone A, Worden AZ.  2014.  The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 12:e1001889.: Public Library of Science   10.1371/journal.pbio.1001889   AbstractWebsite

Current sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans.

Chen, X, Su Z, Dam P, Palenik B, Xu Y, Jiang T.  2004.  Operon prediction by comparative genomics: an application to the Synechococcus sp WH8102 genome. Nucleic Acids Research. 32:2147-2157.   10.1093/nar/gkh510   AbstractWebsite

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.