Reference : Surprisal analysis of the transcriptomic response of the green microalga Chlamydomona...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/2268/227611
Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles
English
Willamme, R. [> >]
Bogaert, K. A. [> >]
Remacle, Françoise mailto [Université de Liège - ULiège > Département de chimie (sciences) > Laboratoire de chimie physique théorique >]
Remacle, Claire mailto [Université de Liège - ULiège > Département des sciences de la vie > Génétique et physiologie des microalgues >]
2018
Chemical Physics
Elsevier
Yes (verified by ORBi)
International
0301-0104
Netherlands
[en] Surprisal analysis transcriptomics day/night cycles isocitrate lyase Chlamydomonas
[en] Our study aims to find gene pathways that depend on acetate assimilation under diurnal conditions in the microalga Chlamydomonas. We compare the transcriptome of two strains, one control and one mutant deficient for the glyoxylate cycle essential in acetate metabolism, cultivated under day/night cycles with acetate. We apply surprisal analysis, an information-theoretic approach, to the RNA-seq data. Carrying out the analysis on groups of dark and light phase samples separately allows identifying constraints and gene pathways that discriminate between mutant and control samples. Carbon metabolism is the most important in the light phase for the control strain while the dark phase is enriched in cell division pathways. The mutant phenotype includes genes pathways of stress response and autophagy in the two phases. Cell division pathways are found in the light phase and catabolic pathways in the dark phase, highlighting a rewiring of the mutant transcriptome in these cyclic cultivation conditions.
Researchers
http://hdl.handle.net/2268/227611
10.1016/j.chemphys.2018.04.015
http://www.sciencedirect.com/science/article/pii/S0301010417310868
FP7 ; 618024 - BAMBI - Bottom-up Approaches to Machines dedicated to Bayesian Inference

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