Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles

Willamme, Rémi; Bogaert, Kenny A.; Remacle, Françoise; Remacle, Claire

doi:10.1016/j.chemphys.2018.04.015

Cited by 3 publications

(7 citation statements)

References 28 publications

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“…The λ 0 values for each sample are plotted in Figure 4a. As expected, for the balanced state [11,28], the values of λ 0 (s) (where s stands for sample index) are constant within a range (25 units) smaller than the error bars that reflects small variations from sample to sample.…”

Section: Resultssupporting

confidence: 70%

“…The values of the Lagrange multipliers and of the constraints were computed as described in [11,28] and in the Appendix. The λ 0 values for each sample are plotted in Figure 4a.…”

Section: Resultsmentioning

confidence: 99%

“…This categorization therefore allowed the identification of the gene sets that contribute most to the phenotype associated with a given constraint, α. From the Giα values computed for each transcript using surprisal analysis, we define weights of the pathways in each constraint and in the balanced state, Equations (2)–(4) [11,28], that quantifies the contribution to the phenotype of each gene set that defines a pathway. The pathways with the most negative weight ( N 0 ) dominate the phenotype of the balanced state (see 2.9 above), and those with the 10 most negative weights are listed in Table 2.…”

Section: Resultsmentioning

confidence: 99%

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Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green Microalga Chlamydomonas Grown on Different Acetate Concentrations

Bogaert

Perez

Rumin

et al. 2019

Cells

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Acetate can be efficiently metabolized by the green microalga Chlamydomonas reinhardtii. The regular concentration is 17 mM, although higher concentrations are reported to increase starch and fatty acid content. To understand the responses to higher acetate concentrations, Chlamydomonas cells were cultivated in batch mode in the light at 17, 31, 44, and 57 mM acetate. Metabolic analyses show that cells grown at 57 mM acetate possess increased contents of all components analyzed (starch, chlorophylls, fatty acids, and proteins), with a three-fold increased volumetric biomass yield compared to cells cultivated at 17 mM acetate at the entry of stationary phase. Physiological analyses highlight the importance of photosynthesis for the low-acetate and exponential-phase samples. The stationary phase is reached when acetate is depleted, except for the cells grown at 57 mM acetate, which still divide until ammonium exhaustion. Surprisal analysis of the transcriptomics data supports the biological significance of our experiments. This allows the establishment of a model for acetate assimilation, its transcriptional regulation and the identification of candidates for genetic engineering of this metabolic pathway. Altogether, our analyses suggest that growing at high-acetate concentrations could increase biomass productivities in low-light and CO2-limiting air-bubbled medium for biotechnology.

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Section: Resultssupporting

confidence: 70%

“…The values of the Lagrange multipliers and of the constraints were computed as described in [11,28] and in the Appendix. The λ 0 values for each sample are plotted in Figure 4a.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green Microalga Chlamydomonas Grown on Different Acetate Concentrations

Bogaert

Perez

Rumin

et al. 2019

Cells

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“…The experimental design has been described in the accompanying research paper (‘Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles’ by Willamme et al [1] ).…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“… The data presented in this article are associated to the research article “Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles” (Willamme et al, 2018) [1] . Here the RNA-seq data of the icl mutant, a null mutant of the isocitrate lyase gene, and its control are summarized and the FPKM values are listed.…”

mentioning

confidence: 99%

RNA-seq data and surprisal analysis of icl mutant and control strain of the green microalga Chlamydomonas reinhardtii during day/night cycles

et al. 2018

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The data presented in this article are associated to the research article “Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles” (Willamme et al., 2018) [1]. Here the RNA-seq data of the icl mutant, a null mutant of the isocitrate lyase gene, and its control are summarized and the FPKM values are listed. The data were analysed using surprisal analysis and the genes contributing the strongest to the mutant and wild type phenotype are listed. The raw data are accessible at BioProject PRJNA437393 with SRA accession number SRP136101 (experiments SRX3824204–SRX3824249). The raw data set and expression values used for surprisal analysis are made public to enable critical or extended analyses.

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Landscape of microalgae omics and metabolic engineering research for strain improvement: An overview

Stavridou,

Karapetsi,

Nteve

et al. 2024

Aquaculture

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Surprisal analysis of the transcriptomic response of the green microalga Chlamydomonas to the addition of acetate during day/night cycles

Cited by 3 publications

References 28 publications

Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green Microalga Chlamydomonas Grown on Different Acetate Concentrations

Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green Microalga Chlamydomonas Grown on Different Acetate Concentrations

RNA-seq data and surprisal analysis of icl mutant and control strain of the green microalga Chlamydomonas reinhardtii during day/night cycles

Landscape of microalgae omics and metabolic engineering research for strain improvement: An overview

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