Article (Scientific journals)
Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles
Willamme, R.; Bogaert, K. A.; Remacle, Françoise et al.
2018In Chemical Physics
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Keywords :
Surprisal analysis transcriptomics day/night cycles isocitrate lyase Chlamydomonas
Abstract :
[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.
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Willamme, R.
Bogaert, K. A.
Remacle, Françoise  ;  Université de Liège - ULiège > Département de chimie (sciences) > Laboratoire de chimie physique théorique
Remacle, Claire  ;  Université de Liège - ULiège > Département des sciences de la vie > Génétique et physiologie des microalgues
Language :
English
Title :
Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles
Publication date :
2018
Journal title :
Chemical Physics
ISSN :
0301-0104
Publisher :
Elsevier, Netherlands
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
FP7 - 618024 - BAMBI - Bottom-up Approaches to Machines dedicated to Bayesian Inference
Funders :
CE - Commission Européenne [BE]
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since 07 September 2018

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