- Meeting abstract
- Open Access
Elucidating gene signatures that control the circadian rhythm in cyanobacteria using bioinformatics methods
© Nandu et al; licensee BioMed Central Ltd. 2012
- Published: 14 December 2012
- Expression Data
- Circadian Rhythm
- Gene Expression Data
- Diurnal Cycle
- Combinatorial Library
The circadian rhythm, or biological “clock,” allows the organism to anticipate and prepare for the changes in the physical environment. Studies have found that the internal clock consists of an array of genes and the protein products they encode, which regulate various physiological processes throughout the body. Cyanothece sp. ATCC 51142 is an organism that has both photosynthetic (producing oxygen) and nitrogen fixing ability. It has developed a temporal regulation in which N2 fixation and photosynthesis occur at different times throughout a diurnal cycle with very high levels of CO2 fixation during the light and high levels of N2 fixation in the dark. The mechanisms underlying the circadian rhythm and the signature genes elucidating this mechanism are addressed in this research.
The objective is to integrate gene expression data with data and knowledge from prior studies using bibliomics techniques, in the de novo construction of quasi-complete regulatory networks to identify gene signatures in functional motifs and elucidate their role in circadian rhythms in Cyanothece sp. ATCC 51142.
Signature genes expressed during the day.
psbD2, psbO*, psbA1, psbA3, psbF, psbE, psbY, psbA4*, psbA1*, psbA2*, petA*,
psbV, petB*, petJ
glcD, glcE, glcF, rbcL
thiC, thiE, thiL, thiOG
Fatty Acid Biosynthesis
accD, fabl, fabG
Amino acyl – tRNA biosynthesis
cysS1*, cysS2*, serS*, pheS*, pheT*, thrS1*, thrS2*, proS*
ligA*, polA*, rnhA*
Glyoxylate and dicarboxylate metabolism
purU, glcD, folD, glcE, glcF
pdhA, ilvN, ilvB, gabD
Signature genes expressed during the night
Amino acid Biosynthesis
ribA, ribC, ribD
The analyses show that most of the top ranked genes in the topological analysis was obtained from text mining. This shows that expression data alone is not a good measure to study the biochemical pathways and signature genes in an organism (specially less studied species).
The algorithms and methodology developed can be extrapolated to any organism, which is less studied to study their gene regulatory elements and also elucidate gene signatures that lead to specific biochemical pathways in a particular organism.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.