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Transcriptomics de novo sequencing data of Messastrum gracile SE-MC4 under exponential and stationary growth stages.

ABSTRACT: Messastrum gracile SE-MC4 is a non-model microalga exhibiting superior oil-accumulating abilities. However, biomass production in M. gracile SE-MC4 is limited due to low cell proliferation especially after prolonged cultivation under oil-inducing culture conditions. Present data consist of next generation RNA sequencing data of M. gracile SE-MC4 under exponential and stationary growth stages. RNA of six samples were extracted and sequenced with insert size of 100 bp paired-end strategy using BGISEQ-500 platform to produce a total of 59.64 Gb data with 314 million reads. Sequences were filtered and de novo assembled to form 53,307 number of gene sequences. Sequencing data were deposited in National Center for Biotechnology Information (NCBI) and can be accessed via BioProject ID PRJNA552165. This information can be used to enhance biomass production in M. gracile SE-MC4 and other microalgae aimed towards improving biodiesel development.

SUBMITTER: Afifudeen CLW

PROVIDER: S-EPMC8626828 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Transcriptomics de novo sequencing data of Messastrum gracile SE-MC4 under exponential and stationary growth stages.

Afifudeen C L Wan CLW Loh Saw Hong SH Wong Li Lian LL Aziz Ahmad A Takahashi Kazutaka K Wahid Mohd Effendy Abd MEA Cha Thye San TS

Data in brief 20211119

Messastrum gracile SE-MC4 is a non-model microalga exhibiting superior oil-accumulating abilities. However, biomass production in M. gracile SE-MC4 is limited due to low cell proliferation especially after prolonged cultivation under oil-inducing culture conditions. Present data consist of next generation RNA sequencing data of M. gracile SE-MC4 under exponential and stationary growth stages. RNA of six samples were extracted and sequenced with insert size of 100 bp paired-e ...[more]

PMID: 34869809

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Project description:The yeast Saccharomyces cerevisiae is a widely-used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimizing biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates, and standardized sample preparation procedures ensured reproducibility and accuracy. In addition, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimization of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared with the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step toward better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

Dataset Information

Transcriptomics de novo sequencing data of Messastrum gracile SE-MC4 under exponential and stationary growth stages.

Publications

Transcriptomics <i>de novo</i> sequencing data of <i>Messastrum gracile</i> SE-MC4 under exponential and stationary growth stages.

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