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Nucleic Acids Res. 2016 Oct 14;44(18):8810-8825. Epub 2016 Aug 27.

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002.

Author information

1
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA.
2
Department of Biochemistry and Molecular Biology, The Pennsylvania State University, State College, PA 16802, USA.
3
Department of Biochemistry and Molecular Biology, The Pennsylvania State University, State College, PA 16802, USA Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT 59717, USA.
4
Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA alex.beliaev@pnnl.gov.

Abstract

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome and relatively few transcription factors exist. In this study, global transcript abundance data from the model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using Context-Likelihood of Relatedness (CLR). The resulting network, organized into 11 modules, provided insight into transcriptional network topology as well as grouping genes by function and linking their response to specific environmental variables. When used in conjunction with genome sequences, the network allowed identification and expansion of novel potential targets of both DNA binding proteins and sRNA regulators. These results offer a new perspective into the multi-level regulation that governs cellular adaptations of the fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also provides a methodological high-throughput approach to studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance gene grouping based on annotated function, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

PMID:
27568004
PMCID:
PMC5062996
DOI:
10.1093/nar/gkw737
[Indexed for MEDLINE]
Free PMC Article

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