Concerted action of the new Genomic Peptide Finder and AUGUSTUS allows for automated proteogenomic annotation of the Chlamydomonas reinhardtii genome

Chlorella vulgaris genome assembly and annotation reveals the molecular basis for metabolic acclimation to high light conditions

Chlorella vulgaris is a fast-growing fresh-water microalga cultivated on the industrial scale for applications ranging from food to biofuel production. To advance our understanding of its biology and to establish genetics tools for biotechnological manipulation, we sequenced the nuclear and organelle genomes of Chlorella vulgaris 211/11P by combining next generation sequencing and optical mapping of isolated DNA molecules. This hybrid approach allowed us to assemble the nuclear genome in 14 pseudo-molecules with an N50 of 2.8 Mb and 98.9% of scaffolded genome. The integration of RNA-seq data obtained at two different irradiances of growth (high light, HL versus low light, LL) enabled us to identify 10 724 nuclear genes, coding for 11 082 transcripts. Moreover, 121 and 48 genes, respectively, were found in the chloroplast and mitochondrial genome. Functional annotation and expression analysis of nuclear, chloroplast and mitochondrial genome sequences revealed particular features of Chlorella vulgaris. Evidence of horizontal gene transfers from chloroplast to mitochondrial genome was observed. Furthermore, comparative transcriptomic analyses of LL versus HL provided insights into the molecular basis for metabolic rearrangement under HL versus LL conditions leading to enhanced de novo fatty acid biosynthesis and triacylglycerol accumulation. The occurrence of a cytosolic fatty acid biosynthetic pathway could be predicted and its upregulation upon HL exposure was observed, consistent with the increased lipid amount under HL conditions. These data provide a rich genetic resource for future genome editing studies, and potential targets for biotechnological manipulation of Chlorella vulgaris or other microalgae species to improve biomass and lipid productivity.

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching.

Omics in Chlamydomonas for Biofuel Production

In response to demands for sustainable domestic fuel sources, research into biofuels has become increasingly important. Many challenges face biofuels in their effort to replace petroleum fuels, but rational strain engineering of algae and photosynthetic organisms offers a great deal of promise. For decades, mutations and stress responses in photosynthetic microbiota were seen to result in production of exciting high-energy fuel molecules, giving hope but minor capability for design. However, '-omics' techniques for visualizing entire cell processing has clarified biosynthesis and regulatory networks. Investigation into the promising production behaviors of the model organism C. reinhardtii and its mutants with these powerful techniques has improved predictability and understanding of the diverse, complex interactions within photosynthetic organisms. This new equipment has created an exciting new frontier for high-throughput, predictable engineering of photosynthetically produced carbon-neutral biofuels.

Dual use of peptide mass spectra: Protein atlas and genome annotation

One of the objectives of genome science is the discovery and accurate annotation of all protein-coding genes. Proteogenomics has emerged as a methodology that provides orthogonal information to traditional forms of evidence used for genome annotation. By this method, peptides that are identified via tandem mass spectrometry are used to refine protein-coding gene models. Namely, these peptides are used to confirm the translation of predicted protein-coding genes, as evidence of novel genes or for correction of current gene models. Proteogenomics requires deep and broad sampling of the proteome in order to generate sufficient numbers of unique peptides. Therefore, we propose that proteogenomic projects are designed so that the generated peptides can also be used to create a comprehensive protein atlas that quantitatively catalogues protein abundance changes during development and in response to environmental stimulus.

A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues

Tremendous progress in plant proteomics driven by mass spectrometry (MS) techniques has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. These achievements include the refinement of existing techniques and the search for new techniques to address food security, safety, and health issues. It is projected that in 2050, the world's population will reach 9-12 billion people demanding a food production increase of 34-70% (FAO, 2009) from today's food production. Provision of food in a sustainable and environmentally committed manner for such a demand without threatening natural resources, requires that agricultural production increases significantly and that postharvest handling and food manufacturing systems become more efficient requiring lower energy expenditure, a decrease in postharvest losses, less waste generation and food with longer shelf life. There is also a need to look for alternative protein sources to animal based (i.e., plant based) to be able to fulfill the increase in protein demands by 2050. Thus, plant biology has a critical role to play as a science capable of addressing such challenges. In this review, we discuss proteomics especially MS, as a platform, being utilized in plant biology research for the past 10 years having the potential to expedite the process of understanding plant biology for human benefits. The increasing application of proteomics technologies in food security, analysis, and safety is emphasized in this review. But, we are aware that no unique approach/technology is capable to address the global food issues. Proteomics-generated information/resources must be integrated and correlated with other omics-based approaches, information, and conventional programs to ensure sufficient food and resources for human development now and in the future.

Systemic cold stress adaptation of Chlamydomonas reinhardtii

Chlamydomonas reinhardtii is one of the most important model organisms nowadays phylogenetically situated between higher plants and animals (Merchant et al. 2007). Stress adaptation of this unicellular model algae is in the focus because of its relevance to biomass and biofuel production. Here, we have studied cold stress adaptation of C. reinhardtii hitherto not described for this algae whereas intensively studied in higher plants. Toward this goal, high throughput mass spectrometry was employed to integrate proteome, metabolome, physiological and cell-morphological changes during a time-course from 0 to 120 h. These data were complemented with RT-qPCR for target genes involved in central metabolism, signaling, and lipid biosynthesis. Using this approach dynamics in central metabolism were linked to cold-stress dependent sugar and autophagy pathways as well as novel genes in C. reinhardtii such as CKIN1, CKIN2 and a hitherto functionally not annotated protein named CKIN3. Cold stress affected extensively the physiology and the organization of the cell. Gluconeogenesis and starch biosynthesis pathways are activated leading to a pronounced starch and sugar accumulation. Quantitative lipid profiles indicate a sharp decrease in the lipophilic fraction and an increase in polyunsaturated fatty acids suggesting this as a mechanism of maintaining membrane fluidity. The proteome is completely remodeled during cold stress: specific candidates of the ribosome and the spliceosome indicate altered biosynthesis and degradation of proteins important for adaptation to low temperatures. Specific proteasome degradation may be mediated by the observed cold-specific changes in the ubiquitinylation system. Sparse partial least squares regression analysis was applied for protein correlation network analysis using proteins as predictors and Fv/Fm, FW, total lipids, and starch as responses. We applied also Granger causality analysis and revealed correlations between proteins and metabolites otherwise not detectable. Twenty percent of the proteins responsive to cold are uncharacterized proteins. This presents a considerable resource for new discoveries in cold stress biology in alga and plants.

Deciphering the cryptic genome: genome-wide analyses of the rice pathogen Fusarium fujikuroi reveal complex regulation of secondary metabolism and novel metabolites

The fungus Fusarium fujikuroi causes "bakanae" disease of rice due to its ability to produce gibberellins (GAs), but it is also known for producing harmful mycotoxins. However, the genetic capacity for the whole arsenal of natural compounds and their role in the fungus' interaction with rice remained unknown. Here, we present a high-quality genome sequence of F. fujikuroi that was assembled into 12 scaffolds corresponding to the 12 chromosomes described for the fungus. We used the genome sequence along with ChIP-seq, transcriptome, proteome, and HPLC-FTMS-based metabolome analyses to identify the potential secondary metabolite biosynthetic gene clusters and to examine their regulation in response to nitrogen availability and plant signals. The results indicate that expression of most but not all gene clusters correlate with proteome and ChIP-seq data. Comparison of the F. fujikuroi genome to those of six other fusaria revealed that only a small number of gene clusters are conserved among these species, thus providing new insights into the divergence of secondary metabolism in the genus Fusarium. Noteworthy, GA biosynthetic genes are present in some related species, but GA biosynthesis is limited to F. fujikuroi, suggesting that this provides a selective advantage during infection of the preferred host plant rice. Among the genome sequences analyzed, one cluster that includes a polyketide synthase gene (PKS19) and another that includes a non-ribosomal peptide synthetase gene (NRPS31) are unique to F. fujikuroi. The metabolites derived from these clusters were identified by HPLC-FTMS-based analyses of engineered F. fujikuroi strains overexpressing cluster genes. In planta expression studies suggest a specific role for the PKS19-derived product during rice infection. Thus, our results indicate that combined comparative genomics and genome-wide experimental analyses identified novel genes and secondary metabolites that contribute to the evolutionary success of F. fujikuroi as a rice pathogen.

Proteogenomics for environmental microbiology

Proteogenomics sensu stricto refers to the use of proteomic data to refine the annotation of genomes from model organisms. Because of the limitations of automatic annotation pipelines, a relatively high number of errors occur during the structural annotation of genes coding for proteins. Whether putative orphan sequences or short genes encoding low-molecular-weight proteins really exist is still frequently a mystery. Whether start codons are well defined is also an open debate. These problems are exacerbated for genomes of microorganisms belonging to poorly documented genera, as related sequences are not always available for homology-guided annotation. The functional annotation of a significant proportion of genes is also another well-known issue when annotating environmental microorganisms. High-throughput shotgun proteomics has recently greatly evolved, allowing the exploration of the proteome from any microorganism at an unprecedented depth. The structural and functional annotation process may be usefully complemented with experimental data. Indeed, proteogenomic mapping has been successfully performed for a wide variety of organisms. Specific approaches devoted to systematically establishing the N-termini of a large set of proteins are being developed. N-terminomics is giving rise to datasets of experimentally proven translational start codons as well as validated peptide signals for secreted proteins. By extension, combining genomic and proteomic data is becoming routine in many research projects. The proteomic analysis of organisms with unfinished genome sequences, the so-called composite proteomics, and the search for microbial biomarkers by bottom-up and top-down combined approaches are some examples of proteogenomic-flavored studies. They illustrate the advent of a new era of environmental microbiology where proteomics and genomics are intimately integrated to answer key biological questions.

Peppy: proteogenomic search software

Proteogenomic searching is a useful method for identifying novel proteins, annotating genes and detecting peptides unique to an individual genome. The approach, however, can be laborious, as it often requires search segmentation and the use of several unintegrated tools. Furthermore, many proteogenomic efforts have been limited to small genomes, as large genomes can prove impractical due to the required amount of computer memory and computation time. We present Peppy, a software tool designed to perform every necessary task of proteogenomic searches quickly, accurately and automatically. The software generates a peptide database from a genome, tracks peptide loci, matches peptides to MS/MS spectra and assigns confidence values to those matches. Peppy automatically performs a decoy database generation, search and analysis to return identifications at the desired false discovery rate threshold. Written in Java for cross-platform execution, the software is fully multithreaded for enhanced speed. The program can run on regular desktop computers, opening the doors of proteogenomic searching to a wider audience of proteomics and genomics researchers. Peppy is available at http://geneffects.com/peppy .

Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation

BACKGROUND

Biogeochemical elemental cycling is driven by primary production of biomass via phototrophic phytoplankton growth, with 40% of marine productivity being assigned to diatoms. Phytoplankton growth is widely limited by the availability of iron, an essential component of the photosynthetic apparatus. The oceanic diatom Thalassiosira oceanica shows a remarkable tolerance to low-iron conditions and was chosen as a model for deciphering the cellular response upon shortage of this essential micronutrient.

RESULTS

The combined efforts in genomics, transcriptomics and proteomics reveal an unexpected metabolic flexibility in response to iron availability for T. oceanica CCMP1005. The complex response comprises cellular retrenchment as well as remodeling of bioenergetic pathways, where the abundance of iron-rich photosynthetic proteins is lowered, whereas iron-rich mitochondrial proteins are preserved. As a consequence of iron deprivation, the photosynthetic machinery undergoes a remodeling to adjust the light energy utilization with the overall decrease in photosynthetic electron transfer complexes.

CONCLUSIONS

Beneficial adaptations to low-iron environments include strategies to lower the cellular iron requirements and to enhance iron uptake. A novel contribution enhancing iron economy of phototrophic growth is observed with the iron-regulated substitution of three metal-containing fructose-bisphosphate aldolases involved in metabolic conversion of carbohydrates for enzymes that do not contain metals. Further, our data identify candidate components of a high-affinity iron-uptake system, with several of the involved genes and domains originating from duplication events. A high genomic plasticity, as seen from the fraction of genes acquired through horizontal gene transfer, provides the platform for these complex adaptations to a low-iron world.

Genome and low-iron response of an oceanic diatom adapted to chronic iron limitation

BACKGROUND

Biogeochemical elemental cycling is driven by primary production of biomass via phototrophic phytoplankton growth, with 40% of marine productivity being assigned to diatoms. Phytoplankton growth is widely limited by the availability of iron, an essential component of the photosynthetic apparatus. The oceanic diatom Thalassiosira oceanica shows a remarkable tolerance to low-iron conditions and was chosen as a model for deciphering the cellular response upon shortage of this essential micronutrient.

RESULTS

The combined efforts in genomics, transcriptomics and proteomics reveal an unexpected metabolic flexibility in response to iron availability for T. oceanica CCMP1005. The complex response comprises cellular retrenchment as well as remodeling of bioenergetic pathways, where the abundance of iron-rich photosynthetic proteins is lowered, whereas iron-rich mitochondrial proteins are preserved. As a consequence of iron deprivation, the photosynthetic machinery undergoes a remodeling to adjust the light energy utilization with the overall decrease in photosynthetic electron transfer complexes.

CONCLUSIONS

Beneficial adaptations to low-iron environments include strategies to lower the cellular iron requirements and to enhance iron uptake. A novel contribution enhancing iron economy of phototrophic growth is observed with the iron-regulated substitution of three metal-containing fructose-bisphosphate aldolases involved in metabolic conversion of carbohydrates for enzymes that do not contain metals. Further, our data identify candidate components of a high-affinity iron-uptake system, with several of the involved genes and domains originating from duplication events. A high genomic plasticity, as seen from the fraction of genes acquired through horizontal gene transfer, provides the platform for these complex adaptations to a low-iron world.

Translational plant proteomics: a perspective

Translational proteomics is an emerging sub-discipline of the proteomics field in the biological sciences. Translational plant proteomics aims to integrate knowledge from basic sciences to translate it into field applications to solve issues related but not limited to the recreational and economic values of plants, food security and safety, and energy sustainability. In this review, we highlight the substantial progress reached in plant proteomics during the past decade which has paved the way for translational plant proteomics. Increasing proteomics knowledge in plants is not limited to model and non-model plants, proteogenomics, crop improvement, and food analysis, safety, and nutrition but to many more potential applications. Given the wealth of information generated and to some extent applied, there is the need for more efficient and broader channels to freely disseminate the information to the scientific community. This article is part of a Special Issue entitled: Translational Proteomics.

Protein phosphorylation is a key event of flagellar disassembly revealed by analysis of flagellar phosphoproteins during flagellar shortening in Chlamydomonas

Cilia are disassembled prior to cell division, which is proposed to regulate proper cell cycle progression. The signaling pathways that regulate cilia disassembly are not well-understood. Recent biochemical and genetic data demonstrate that protein phosphorylation plays important roles in cilia disassembly. Here, we analyzed the phosphoproteins in the membrane/matrix fraction of flagella undergoing shortening as well as flagella from steady state cells of Chlamydomonas. The phosphopeptides were enriched by a combination of IMAC and titanium dioxide chromatography with a strategy of sequential elution from IMAC (SIMAC) and analyzed by tandem mass spectrometry. A total of 224 phosphoproteins derived from 1296 spectral counts of phosphopeptides were identified. Among the identified phosphoproteins are flagellar motility proteins such as outer dynein arm, intraflagellar transport proteins as well as signaling molecules including protein kinases, phosphatases, G proteins, and ion channels. Eighty-nine of these phosphoproteins were only detected in shortening flagella, whereas 29 were solely in flagella of steady growing cells, indicating dramatic changes of protein phosphorylation during flagellar shortening. Our data indicates that protein phosphorylation is a key event in flagellar disassembly, and paves the way for further study of flagellar assembly and disassembly controlled by protein phosphorylation.