Dataset Information

Deep Transcriptome Sequencing of Two Green Algae, Chara vulgaris and Chlamydomonas reinhardtii, Provides No Evidence of Organellar RNA Editing.

ABSTRACT: Nearly all land plants post-transcriptionally modify specific nucleotides within RNAs, a process known as RNA editing. This adaptation allows the correction of deleterious mutations within the asexually reproducing and presumably non-recombinant chloroplast and mitochondrial genomes. There are no reports of RNA editing in any of the green algae so this phenomenon is presumed to have originated in embryophytes either after the invasion of land or in the now extinct algal ancestor of all land plants. This was challenged when a recent in silico screen for RNA edit sites based on genomic sequence homology predicted edit sites in the green alga Chara vulgaris, a multicellular alga found within the Streptophyta clade and one of the closest extant algal relatives of land plants. In this study, the organelle transcriptomes of C. vulgaris and Chlamydomonas reinhardtii were deep sequenced for a comprehensive assessment of RNA editing. Initial analyses based solely on sequence comparisons suggested potential edit sites in both species, but subsequent high-resolution melt analysis, RNase H-dependent PCR (rhPCR), and Sanger sequencing of DNA and complementary DNAs (cDNAs) from each of the putative edit sites revealed them to be either single-nucleotide polymorphisms (SNPs) or spurious deep sequencing results. The lack of RNA editing in these two lineages is consistent with the current hypothesis that RNA editing evolved after embryophytes split from its ancestral algal lineage.

SUBMITTER: Cahoon AB

PROVIDER: S-EPMC5333069 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Deep Transcriptome Sequencing of Two Green Algae, Chara vulgaris and Chlamydomonas reinhardtii, Provides No Evidence of Organellar RNA Editing.

Cahoon A Bruce AB Nauss John A JA Stanley Conner D CD Qureshi Ali A

Genes 20170220 2

Nearly all land plants post-transcriptionally modify specific nucleotides within RNAs, a process known as RNA editing. This adaptation allows the correction of deleterious mutations within the asexually reproducing and presumably non-recombinant chloroplast and mitochondrial genomes. There are no reports of RNA editing in any of the green algae so this phenomenon is presumed to have originated in embryophytes either after the invasion of land or in the now extinct algal ancestor of all land plan ...[more]

PMID: 28230734

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Project description:BackgroundMicroalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data.ResultsWe have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield.ConclusionsAlgaGEM is a viable and comprehensive framework for in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels.

Dataset Information

Deep Transcriptome Sequencing of Two Green Algae, Chara vulgaris and Chlamydomonas reinhardtii, Provides No Evidence of Organellar RNA Editing.

Publications

Deep Transcriptome Sequencing of Two Green Algae, Chara vulgaris and Chlamydomonas reinhardtii, Provides No Evidence of Organellar RNA Editing.

Similar Datasets

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