GSE46225: De Novo Transcriptomic Analysis of Hydrogen Production in the Green Alga Chlamydomonas moewusii through RNA-Seq
Summary: Background: Microalgae can make a significant contribution towards meeting global renewable energy needs in both lipid-based liquid biofuel and hydrogen biofuel. The development of energy-related products and chemicals from algae could be accelerated with improvements in systems biology tools, and recent advances in sequencing technology provide a platform for enhanced transcriptomic analyses. However, these techniques are still heavily reliant upon available genomic sequence data.  We have developed a de novo sequencing, annotation, and quantitation pipeline that can be applied to unsequenced organisms for effective quantitative gene expression profiling. Chlamydomonas moewusii is a unicellular green alga capable of evolving molecular hydrogen (H2) under both dark and light anaerobic conditions, and has high hydrogenase activity that can be rapidly induced. However, to date, there is no systematic investigation of transcriptomic profiling during induction of hydrogen photoproduction in this organism. Results: In this work, we measured rates of hydrogen production and extracted RNA from samples of C. moewusii following various lengths of dark anaerobic incubation. RNA-Seq was applied to investigate transcriptomic profiles during the dark anaerobic induction of hydrogen photoproduction. One hundred fifty six million reads generated from seven samples were then used for de novo assembly after data trimming. The BlastX results against NCBI database and Blast2GO results were used to interpret the functions of the assembled 39,136 contigs, which were then used as the reference transcripts for RNA-Seq analysis. Nearly 98% transcripts had Blast hits, although more than one-third were annotated as hypothetical proteins. The expression value of RNA-Seq results was imported into statistical software for data quality control, normalization, and subsequent statistical analyses such as One-way ANOVA and K-means Clustering. Our results indicated that more transcripts were differentially expressed during the period of early and higher hydrogen photoproduction, and fewer transcripts were differentially expressed when rates of hydrogen photoproduction decreased. Conclusions: Herein, we have described a workflow to analyze RNA-Seq data without reference genome sequence information, which can be applied to the rapid development of other unsequenced microorganisms (both prokaryotic and eukaryotic) with the potential for development as fuel production strains.  This study provided the first transcriptomic RNA-Seq dataset, as well as biological insights into the metabolic changes that occur concomitant with induction of hydrogen photoevolution in C. moewusii, which can help further development of this organism as a hydrogen photoproduction strain. Overall Design: Examine time course gene differential expression post anaerobic induction for hydrogen production