Project description: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. Examine time course gene differential expression post anaerobic induction for hydrogen production

Project description: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.

Project description:Algal photo-bio hydrogen production, a promising method for producing clean and renewable fuel in the form of hydrogen gas, has been studied extensively over the last few decades. In this study, microarray analyses were used to obtain a global expression profile of mRNA abundance in the green alga Chlamydomonas reinhardtii at five different time points before the onset and during the course of sulphur depleted hydrogen production. The present work confirms previous findings on the impacts of sulphur deprivation but also provides new insights into photosynthesis, sulphur assimilation and carbon metabolism under sulphur starvation towards hydrogen production. For instance, while a general trend towards repression of transcripts encoding photosynthetic genes was observed, the abundance of Lhcbm9 (encoding a major light harvesting polypeptide) and LhcSR1 (encoding a chlorophyll binding protein) was strongly elevated throughout the experiment, suggesting remodeling of the photosystem II light harvesting complex as well as an important function of Lhcbm9 under sulphur starvation. This study presents the first global transcriptional analysis of C. reinhardtii during hydrogen production using five major time points at Peak Oxygen, Mid Oxygen, Zero Oxygen, Mid Hydrogen and Peak Hydrogen. Keywords: Time course, sulfur deprivation, hydrogen production. Time course microarray analyses were used to analyze the global gene expression in sulfur depleted hydrogen producing C. reinhardtii. When depleted of sulfur, a sealed an illuminated C. reinhardtii culture slowly becomes anaerobic and produces hydrogen gas. Based on the changes in the dissolved O2 level and H2 production rate, the whole course of H2 production from the starting of S deprivation to the end of H2 production can be divided into five phases including an Aerobic Phase (I) in which the dissolved O2 level goes up (I), an O2 Consumption Phase (II), an Anaerobic Phase (III), a H2 Production Phase (IV) and a Termination phase (V). Samples were taken from three different bioreactors (biological replicates) at the following time points after the start of S depletion: 6 h, 16 h, 21 h, 37 h and 52 h corresponding to Peak O2, Mid O2, Zero O2, Mid H2 and Peak H2.

Project description:Background: For many years, increasing demands for fossil fuels have met with limited supply. As a potential substitute and renewable source of biofuel feedstock, microalgae have received significant attention. However, few of the current algal species produce high lipid yields to be commercially viable. To discover more high yielding strains, next-generation sequencing technology is used to elucidate lipid synthetic pathways and energy metabolism involved in lipid yield. When subjected to manipulation by genetic and metabolic engineering, enhancement of such pathways may further enhance lipid yield. Results: In this study, transcriptome profiling of a random insertional mutant with enhanced lipid production generated from a non-model marine microalga Dunaliella tertiolecta is presented. D9 mutant has a lipid yield that is 2-4 fold higher than that of wild type. Using novel Bag2D-workflow scripts developed and reported here, the non-redundant transcripts from de novo assembly were annotated based on the best hits in five model microalgae, namely Chlamydomonas reinhardtii, Coccomyxa subellipsoidea C-169, Ostreococcus lucimarinus, Volvox carteri, and Chlorella variabilis NC64A. The assembled contigs (~181 Mb) includes 481,381 transcripts, covering 10,185 genes. Pathway analysis showed that a pathway from inositol phosphate metabolism to fatty acid biosynthesis is the most significantly correlated with higher lipid yield in this mutant. Conclusion: Herein, we described a pipeline to analyze RNA-Seq data without pre-existing transcriptomic information. The draft transcriptome of D. tertiolecta was constructed and annotated, which offered useful information for characterizing high lipid-producing mutants. D. tertiolecta mutant was generated with an enhanced photosynthetic efficiency and lipid production. RNA-Seq data of the mutant and wild type were compared, providing biological insights into the expression patterns of contigs associated with energy metabolism and carbon flow pathways. Comparison of D. tertiolecta genes with homologs of five other green algae can facilitate the annotation of D. tertiolecta, and lead to a more complete annotation of its sequence database, thus laying the groundwork for optimization of lipid production pathways based on genetic manipulation. Examine two sets of RNA-Seq data from high lipid-producing mutant and wild-type sample Dt_v1*: the FASTA and annotation files used for D9_1, WT_1 samples Dt_v10*: the FASTA and annotation files used for D9_2, WT_2 samples Dt_v10-hit*: the FASTA and annotation files only including the contigs that can be annotated in Dt_v10 Additional file 1.xls: processed data for sample 1,2 Additional file 4.xls: processed data for sample 3-6

Project description:Background: For many years, increasing demands for fossil fuels have met with limited supply. As a potential substitute and renewable source of biofuel feedstock, microalgae have received significant attention. However, few of the current algal species produce high lipid yields to be commercially viable. To discover more high yielding strains, next-generation sequencing technology is used to elucidate lipid synthetic pathways and energy metabolism involved in lipid yield. When subjected to manipulation by genetic and metabolic engineering, enhancement of such pathways may further enhance lipid yield. Results: In this study, transcriptome profiling of a random insertional mutant with enhanced lipid production generated from a non-model marine microalga Dunaliella tertiolecta is presented. D9 mutant has a lipid yield that is 2-4 fold higher than that of wild type. Using novel Bag2D-workflow scripts developed and reported here, the non-redundant transcripts from de novo assembly were annotated based on the best hits in five model microalgae, namely Chlamydomonas reinhardtii, Coccomyxa subellipsoidea C-169, Ostreococcus lucimarinus, Volvox carteri, and Chlorella variabilis NC64A. The assembled contigs (~181 Mb) includes 481,381 transcripts, covering 10,185 genes. Pathway analysis showed that a pathway from inositol phosphate metabolism to fatty acid biosynthesis is the most significantly correlated with higher lipid yield in this mutant. Conclusion: Herein, we described a pipeline to analyze RNA-Seq data without pre-existing transcriptomic information. The draft transcriptome of D. tertiolecta was constructed and annotated, which offered useful information for characterizing high lipid-producing mutants. D. tertiolecta mutant was generated with an enhanced photosynthetic efficiency and lipid production. RNA-Seq data of the mutant and wild type were compared, providing biological insights into the expression patterns of contigs associated with energy metabolism and carbon flow pathways. Comparison of D. tertiolecta genes with homologs of five other green algae can facilitate the annotation of D. tertiolecta, and lead to a more complete annotation of its sequence database, thus laying the groundwork for optimization of lipid production pathways based on genetic manipulation.

Dataset Information

De novo transcriptomic analysis of hydrogen production in the green alga Chlamydomonas moewusii through RNA-Seq.

Publications

De novo transcriptomic analysis of hydrogen production in the green alga Chlamydomonas moewusii through RNA-Seq.

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