Integrative analysis of large scale expression profiles reveals core transcriptional response and coordination between multiple cellular processes in a cyanobacterium

Abstract

Background: Cyanobacteria are the only known prokaryotes capable of oxygenic photosynthesis. They play significant roles in global biogeochemical cycles and carbon sequestration, and have recently been recognized as potential vehicles for production of renewable biofuels. Synechocystis sp. PCC 6803 has been extensively used as a model organism for cyanobacterial studies. DNA microarray studies in Synechocystis have shown varying degrees of transcriptome reprogramming under altered environmental conditions. However, it is not clear from published work how transcriptome reprogramming affects pre-existing networks of fine-tuned cellular processes.

Results: We have integrated 163 transcriptome data sets generated in response to numerous environmental and genetic perturbations in Synechocystis. Our analyses show that a large number of genes, defined as the core transcriptional response (CTR), are commonly regulated under most perturbations. The CTR contains nearly 12% of Synechocystis genes found on its chromosome. The majority of genes in the CTR are involved in photosynthesis, translation, energy metabolism and stress protection. Our results indicate that a large number of differentially regulated genes identified in most reported studies in Synechocystis under different perturbations are associated with the general stress response. We also find that a majority of genes in the CTR are coregulated with 25 regulatory genes. Some of these regulatory genes have been implicated in cellular responses to oxidative stress, suggesting that reactive oxygen species are involved in the regulation of the CTR. A Bayesian network, based on the regulation of various KEGG pathways determined from the expression patterns of their associated genes, has revealed new insights into the coordination between different cellular processes.

Conclusion: We provide here the first integrative analysis of transcriptome data sets generated in a cyanobacterium. This compilation of data sets is a valuable resource to researchers for all cyanobacterial gene expression related queries. Importantly, our analysis provides a global description of transcriptional reprogramming under different perturbations and a basic framework to understand the strategies of cellular adaptations in Synechocystis.

Figure 1

Differential regulation of Synechocystis genes across multiple perturbations. Differential regulation of genes was assessed by using a statistical significance test. Only 68 data sets generated under various environmental and genetic perturbations had sufficient number of replicates to be considered for the determination of such differential regulation of genes. All but 5 genes present on chromosome (3259/3264) were identified as differentially regulated in at least one experimental data set. The complete data sets with their respective fold changes are provided in the additional file 2.

Figure 2

Coregulation of regulatory and CTR genes. A correlation matrix was generated for the 146 regulatory genes and 399 genes present in the CTR by using the Hamming distance. Based on previous experimental results, we used 60% agreement as a cut-off to identify coregulation. Using this criterion, we could identify 25 regulatory genes (red solid dots) that were coregulated with 85% of the genes present in the CTR (black solid dots). The relevant similarity measurement values are provided in the additional file 5.

Figure 3

Regulation of the functional category "Ribosome" determined from its associated genes. Distribution of log₂ (target/control) values of individual genes for the functional category "ribosome" are shown for (A) Singh_nitrogen_starvation', (B) 'KEGG_Hihara_hl_15 min' and (C) 'KEGG_Hihara_hl_15 h' (see additional file 6 for details). Based on the KS-test, "ribosomes" were either (A) downregulated, (B) not changed or (C) upregulated. Regulation of all KEGG pathways is provided in the additional file 6.

Figure 4

Bayesian network for 51 KEGG pathways derived using the GES algorithm. Based on regulation of their associated genes, 51 KEGG pathways were identified as significantly regulated across multiple perturbations. The Bayesian approach was used to generate the network. The color of the arrows represent the strength of the links, quantified using the True Link Strength Percent (see additional file 8).

Figure 5

Coregulation of ribosomal and ATP synthase genes. Data sets with the significant number of differentially regulated genes corresponding to functional categories "ribosome" and "ATP Synthase" were used to determine coregulation. The details of experimental conditions and differential regulation of genes corresponding of these two functional categories are provided in additional files 1 and 2, respectively. The various abbreviations used are HL = high light; DCMU = 3-(3,4-Dichlorophenyl)-1,1-dimethylurea; DBMIB = 2,5-dibromo-3-methyl-6-isopropyl-p-benzoquinone; BR = blue and red light; H₂O₂ = hydrogen peroxide; Cd = cadmium; LS = linear-stationary; and HT = high temperature.