Project description:Photosynthetic microbes can produce the clean-burning fuel hydrogen using one of natureâ??s most plentiful resources, sunlight 1,2. Anoxygenic photosynthetic bacteria generate hydrogen and ammonia during a process known as biological nitrogen fixation. This reaction is catalyzed by the enzyme nitrogenase and consumes nitrogen gas, ATP and electrons 3. One bacterium, Rhodopseudomonas palustris, has a remarkable ability to obtain electrons from green plant-derived material 4,5 and to efficiently absorb both high and low intensity light energy to form ATP 6. Manipulating R. palustris or a similar organism to produce hydrogen commercially will require us to identify all its genes that contribute to hydrogen production and to understand how this process is regulated in cells. Here we describe mutant strains in which metabolism is redirected such that hydrogen production is uncoupled from nitrogen fixation. Our data indicate that three different single amino acid changes in the transcriptional regulator NifA each yielded strains that produced hydrogen even in the presence of the repressing nitrogen source ammonium and in the absence of specific inducing metabolic signals. We used the mutants to show that, in addition to nitrogenase genes, 18 genes outside of the nitrogenase gene cluster may contribute to hydrogen production. Some of these genes are likely involved in efficient ATP acquisition and in channeling electrons to nitrogenase for reduction of protons to molecular hydrogen. Our results demonstrate that photosynthetic bacteria can be genetically manipulated for sustained production of pure hydrogen in a variety of cultivation conditions in the absence of oxygen, nitrogen or other gases as long as light and an electron donor are supplied. Transcriptome profile of wild type (CGA009) growing photosynthetically in the presence of amonium an acetate was compare with that of 4 different mutants (CGA570, CGA571, CGA572 and CGA574). We did 2 biological replicates per strain.

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:Biogenic amine-producing bacteria are responsible for the production of basic nitrogenous compounds, such as histamine, cadaverine, tyramine and putrescine, after foods spoil due to microorganisms. In the present work, we applied a shotgun proteomics approach to quickly and easily characterize 15 different foodborne strains of biogenic amine-producing bacteria. A total of 10673 peptide spectrum matches (PSMs) belonging to 4081 nonredundant peptides and corresponding to 1811 annotated proteins were identified. With the results, relevant functional pathways were determined and the strains were differentiated into different Euclidean hierarchical clusters. Moreover, a predicted protein‒protein interaction network of biogenic amine foodborne strains was created. The whole confidence network contains 260 nodes and 1973 interactions. Most of the identified proteins were related to pathways and networks of energy, putrescine metabolism and host‒virus interaction. In addition, a total of 556 nonredundant peptides were identified as virulence factors, and most of these peptides corresponded to functions such as toxins, antimicrobial compound production, antimicrobial resistance, additional resistances and tolerances, host colonization and immune evasion, ABC transporters, phage proteins, and alternative virulence factors and proteins involved in horizontal transfer. Potential species-specific peptide biomarkers were screened. Thus, 77 species-specific peptide biomarkers belonging to 64 different proteins were proposed to identify 10 species (Enterobacter aerogenes, Enterobacter cloacae, Hafnia alvei, Klebsiella oxytoca, Morganella morganii, Proteus mirabilis, Proteus penneri, Proteus vulgaris, Raoutella planticola, Stenotrophomonas maltophilia). All of these results constitute the first major dataset of peptides and proteins of seafood biogenic amine-producing strains. This repository may be useful for further studies, for the development of new therapeutic treatments for food intoxication and for tracking microbial sources in foodstuffs.

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Clostrida for biogenic hydrogen production

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