Proteogenomics to Discover Principles Regulating Metabolic Networks in Oleagenous Green Algae.
EMSL Project ID
50490
Abstract
During the last decade a variety of oleaginous green algae (Chlorophyta) have emerged as potential platforms for feedstock production in biofuels applications. For example, multiple strains of the species Scenedesmus (Tetradesmus or Acutodesmus) obliquus were identified that can be cultivated outdoors in ponds. Our strain DOE0152z, which emerged from the US DOE funded National Alliance for Advanced Biofuels and Bioproducts project, was demonstrated to be robust in mass culture. As part of a collaborative systems approach including members of several national laboratories and institutions, S. obliquus strain DOE0152z is currently being developed as a model strain for domestication. Recently, its genome had been sequenced. To discover the principles that regulate growth processes, based on our annotated genome, we created the first manually curated metabolic network for core carbon metabolism. However, major gaps in knowledge still exist for this species. To gain a better understanding of metabolism, we need to employ a systems biology approach. For predictive understanding regarding biofuels productivities, we need improved metabolic models that will enable targeted reengineering of algae. Our long-term goal is to design and reengineer superior lines of S. obliquus for biofuels and bioproduct applications. To achieve this goal, our objective is to close gaps in knowledge regarding metabolism and its regulation at the molecular level in S. obliquus using strain DOE0152z as a model. Our genome annotation is currently only based on ab-initio models and transcript verified gene models. Although we have close genome coverage with more than 30,000 predicted genes for this diploid strain, protein data could only be included for annotation purposes from other green algae that are not closely related. While manually creating our carbon core metabolic network, we determined that there exists an about 25% error rate in gene models. Common errors are exon/intron boundaries, start codon position, and gene fusions (due to overlapping 5' and 3' UTR regions). As functional annotation relies on comparative analysis using alignments with similarities to sequences in databases such as NCBI, errors in gene models lead to incorrect functional annotations, which negatively impacts genome-based metabolic network reconstruction. Most of these issues could be resolved by appropriate proteomics coverage. To verify and/or correct our gene models, we propose a proteogenomics approach, which is part of our proposed Aim 1: Use of mass spectrometry for deep coverage of the proteome of S. obliquus strain DOE0152z. We expect that deep proteomic coverage will allow for adjustments of gene models, which would allow for better functional annotation leading to more robust metabolic networks.
Our hypothesis is that the greatest predictor of grow response and accumulation of desired bioproducts in algal cultures is most accurately determined by gene expression differences at the protein level. To discover regulatory elements, we study how strain DOE0152z responds to cultivation in a pond environment. Our Aim 2 will provide quantitative gene expression data at the protein level for samples already acquired by cultivation of strain DOE0152z in climate-simulated ponds. Overlaying protein-level expression with existing transcriptomics expression data is expected to allow identification of specific regulatory targets for creation of improved strains regarding growth optimization of strain DOE0152z.
In summary, our dual qualitative and quantitative proteomics approach is expected to provide information for metabolism, which could not be gathered without the expertise at EMSL.
Project Details
Project type
Exploratory Research
Start Date
2018-10-21
End Date
2020-01-31
Status
Closed
Released Data Link
Team
Principal Investigator
Team Members