4.7 Article

Cross-species transcriptional network analysis reveals conservation and variation in response to metal stress in cyanobacteria

Journal

BMC GENOMICS
Volume 14, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/1471-2164-14-112

Keywords

Cross-species; Transcriptional network; Metal stress; Cyanobacteria

Funding

  1. National Basic Research Program of China (National 973 program) [2011CBA00803, 2012CB721101]
  2. National High-tech R&D Program (National 863 program) [2012AA02A707]
  3. Tianjin University
  4. 985 Project of Ministry of Education

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Background: As one of the most dominant bacterial groups on Earth, cyanobacteria play a pivotal role in the global carbon cycling and the Earth atmosphere composition. Understanding their molecular responses to environmental perturbations has important scientific and environmental values. Since important biological processes or networks are often evolutionarily conserved, the cross-species transcriptional network analysis offers a useful strategy to decipher conserved and species-specific transcriptional mechanisms that cells utilize to deal with various biotic and abiotic disturbances, and it will eventually lead to a better understanding of associated adaptation and regulatory networks. Results: In this study, the Weighted Gene Co-expression Network Analysis (WGCNA) approach was used to establish transcriptional networks for four important cyanobacteria species under metal stress, including iron depletion and high copper conditions. Cross-species network comparison led to discovery of several core response modules and genes possibly essential to metal stress, as well as species-specific hub genes for metal stresses in different cyanobacteria species, shedding light on survival strategies of cyanobacteria responding to different environmental perturbations. Conclusions: The WGCNA analysis demonstrated that the application of cross-species transcriptional network analysis will lead to novel insights to molecular response to environmental changes which will otherwise not be achieved by analyzing data from a single species.

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