Dataset Information

Metabolic responses to ethanol and butanol in Chlamydomonas reinhardtii.

ABSTRACT: BACKGROUND:Microalgae have been demonstrated to be among the most promising phototrophic species for producing renewable biofuels and chemicals. Ethanol and butanol are clean energy sources with good chemical and physical properties as alternatives to gasoline. However, biosynthesis of these two biofuels has not been achieved due to low tolerance of algal cells to ethanol or butanol. RESULTS:With an eye to circumventing these problems in the future and engineering the robust alcohol-producing microalgal hosts, we investigated the metabolic responses of the model green alga Chlamydomonas reinhardtii to ethanol and butanol. Using a quantitative proteomics approach with iTRAQ-LC-MS/MS technologies, we detected the levels of 3077 proteins; 827 and 730 of which were differentially regulated by ethanol and butanol, respectively, at three time points. In particular, 41 and 59 proteins were consistently regulated during at least two sampling times. Multiple metabolic processes were affected by ethanol or butanol, and various stress-related proteins, transporters, cytoskeletal proteins, and regulators were induced as the major protection mechanisms against toxicity of the organic solvents. The most highly upregulated butanol response protein was Cre.770 peroxidase. CONCLUSIONS:The study is the first comprehensive view of the metabolic mechanisms employed by C. reinhardtii to defend against ethanol or butanol toxicity. Moreover, the proteomic analysis provides a resource for investigating potential gene targets for engineering microalgae to achieve efficient biofuel production.

SUBMITTER: Jiang Y

PROVIDER: S-EPMC5646117 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Metabolic responses to ethanol and butanol in <i>Chlamydomonas reinhardtii</i>.

Jiang Yongguang Y Xiao Peng P Shao Qing Q Qin Huan H Hu Zhangli Z Lei Anping A Wang Jiangxin J

Biotechnology for biofuels 20171017

<h4>Background</h4>Microalgae have been demonstrated to be among the most promising phototrophic species for producing renewable biofuels and chemicals. Ethanol and butanol are clean energy sources with good chemical and physical properties as alternatives to gasoline. However, biosynthesis of these two biofuels has not been achieved due to low tolerance of algal cells to ethanol or butanol.<h4>Results</h4>With an eye to circumventing these problems in the future and engineering the robust alcoh ...[more]

PMID: 29075323

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Project description:BackgroundRecent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the C. reinhardtii genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale.ResultsWe assigned enzymatic functions to the translated JGI ORF models of C. reinhardtii by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST. In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate.ConclusionsWe functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.

Dataset Information

Metabolic responses to ethanol and butanol in Chlamydomonas reinhardtii.

Publications

Metabolic responses to ethanol and butanol in <i>Chlamydomonas reinhardtii</i>.

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