Review of the algal biology program within the National Alliance for Advanced Biofuels and Bioproducts

In 2010,when the National Alliance for Advanced Biofuels and Bioproducts (NAABB) consortiumbegan, littlewas known about themolecular basis of algal biomass or oil production. Very fewalgal genome sequenceswere available and efforts to identify the best-producing wild species through bioprospecting approaches had largely stalled after the U.S. Department of Energy\u27s Aquatic Species Program. This lack of knowledge included how reduced carbon was partitioned into storage products like triglycerides or starch and the role played bymetabolite remodeling in the accumulation of energy-dense storage products. Furthermore, genetic transformation and metabolic engineering approaches to improve algal biomass and oil yields were in their infancy. Genome sequencing and transcriptional profiling were becoming less expensive, however; and the tools to annotate gene expression profiles under various growth and engineered conditions were just starting to be developed for algae. It was in this context that an integrated algal biology program was introduced in the NAABB to address the greatest constraints limiting algal biomass yield. This review describes the NAABB algal biology program, including hypotheses, research objectives, and strategies to move algal biology research into the twenty-first century and to realize the greatest potential of algae biomass systems to produce biofuels

The Postgenomic Age: Characterization of Proteomes

Global analysis of biological systems is becoming increasingly feasible as technologies that facilitate genome-wide analyses of gene expression are developed. Proteomics is the global analysis of expressed proteins (including posttranslational modifications) and seeks to establish the relationship between genome sequence, expressed proteins, protein-protein interactions, and cell and tissue phenotype. While the relative abundance of transcripts can be quantified using gene expression microarrays, the identification and quantitation of expressed proteins is more challenging. Nevertheless, the potential payoff for global protein analyses is immense because identification of distinctive protein signatures associated with cell function may provide novel therapeutic targets, molecular markers of disease, and increased understanding of determinants of cell phenotype. The challenges and promises of applications of established and emerging proteome strategies to detect and quantify differentially expressed proteins in culture cells are discussed

Proteomics for Validation of Automated Gene Model Predictions

High-throughput liquid chromatography mass spectrometry (LC-MS)-based proteomic analysis has emerged as a powerful tool for functional annotation of genome sequences. These analyses complement the bioinformatic and experimental tools used for deriving, verifying, and functionally annotating models of genes and their transcripts. Furthermore, proteomics extends verification and functional annotation to the level of the translation product of the gene model

Genomes to Proteomes

Biologists are awash with genomic sequence data. In large part, this is due to the rapid acceleration in the generation of DNA sequence that occurred as public and private research institutes raced to sequence the human genome. In parallel with the large human genome effort, mostly smaller genomes of other important model organisms were sequenced. Projects following on these initial efforts have made use of technological advances and the DNA sequencing infrastructure that was built for the human and other organism genome projects. As a result, the genome sequences of many organisms are available in high quality draft form.While in many ways this is good news, there are limitations to the biological insights that can be gleaned from DNA sequences alone; genome sequences offer only a bird's eye view of the biological processes endemic to an organism or community. Fortunately, the genome sequences now being produced at such a high rate can serve as the foundation for other global experimental platforms such as proteomics. Proteomic methods offer a snapshot of the proteins present at a point in time for a given biological sample. Current global proteomics methods combine enzymatic digestion, separations, mass spectrometry and database searching for peptide identification. One key aspect of proteomics is the prediction of peptide sequences from mass spectrometry data. Global proteomic analysis uses computational matching of experimental mass spectra with predicted spectra based on databases of gene models that are often generated computationally. Thus, the quality of gene models predicted from a genome sequence is crucial in the generation of high quality peptide identifications. Once peptides are identified they can be assigned to their parent protein. Proteins identified as expressed in a given experiment are most useful when compared to other expressed proteins in a larger biological context or biochemical pathway. In this chapter we will discuss the automatic annotation and the generation of high quality gene models, the setup and execution of global proteomics experiments that are quantitative and statistically rigorous and finally add biological context to proteomics

Genomes to Proteomes

Biologists are awash with genomic sequence data. In large part, this is due to the rapid acceleration in the generation of DNA sequence that occurred as public and private research institutes raced to sequence the human genome. In parallel with the large human genome effort, mostly smaller genomes of other important model organisms were sequenced. Projects following on these initial efforts have made use of technological advances and the DNA sequencing infrastructure that was built for the human and other organism genome projects. As a result, the genome sequences of many organisms are available in high quality draft form.While in many ways this is good news, there are limitations to the biological insights that can be gleaned from DNA sequences alone; genome sequences offer only a bird's eye view of the biological processes endemic to an organism or community. Fortunately, the genome sequences now being produced at such a high rate can serve as the foundation for other global experimental platforms such as proteomics. Proteomic methods offer a snapshot of the proteins present at a point in time for a given biological sample. Current global proteomics methods combine enzymatic digestion, separations, mass spectrometry and database searching for peptide identification. One key aspect of proteomics is the prediction of peptide sequences from mass spectrometry data. Global proteomic analysis uses computational matching of experimental mass spectra with predicted spectra based on databases of gene models that are often generated computationally. Thus, the quality of gene models predicted from a genome sequence is crucial in the generation of high quality peptide identifications. Once peptides are identified they can be assigned to their parent protein. Proteins identified as expressed in a given experiment are most useful when compared to other expressed proteins in a larger biological context or biochemical pathway. In this chapter we will discuss the automatic annotation and the generation of high quality gene models, the setup and execution of global proteomics experiments that are quantitative and statistically rigorous and finally add biological context to proteomics

Phosphoprotein Isotope-Coded Affinity Tag Approach for Isolating and Quantitating Phosphopeptides in Proteome-Wide Analyses

A method has been developed that utilizes phosphoprotein isotope-coded affinity tags (PhIAT) that combines stable isotope and biotin labeling to enrich and quantitatively measure differences in the O-phosphorylation states of proteins. The PhIAT labeling approach involves hydroxide ion-mediated beta-elimination of the O-phosphate moiety and the addition of 1,2-ethanedithiol containing either four alkyl hydrogens (EDT-D0) or four alkyl deuteriums (EDT-D4) followed by biotinylation of the EDT-D0/D4 moiety using (+)-biotinyl-iodoacetamidyl-3,6-dioxaoctanediamine. The PhIAT reagent, which contains the nucleophilic sulfhydryl and isotopic label covalently linked to a biotin moiety, was synthesized and has the potential utility to reduce the O-phosphorylation derivatization into a one-step process. The PhIAT labeling approach was initially demonstrated using the model phosphoprotein beta-casein. After proteolytic digestion, the PhIAT-labeled peptides were affinity isolated using immobilized avidin and analyzed using capillary reversed-phase liquid chromatography-mass spectrometry. PhIAT-labeled beta-casein peptides corresponding to peptides containing known sites of O-phosphorylation were isolated and identified. The PhIAT labeling method was also applied to a yeast protein extract. The PhIAT labeling technique provides a reliable method for making quantitative measurements of differences in the O-phosphorylation state of proteins

Phosphoprotein Isotope-Coded Affinity Tags: Application to the Enrichment and Identification of Low-Abundance Phosphoproteins

The use of a phosphoprotein isotope-coded affinity tag (PhIAT), which employs differential isotopic labeling and biotinylation, has been shown capable of enriching and identifying mixtures of low-abundance phosphopeptides. A denatured solution of beta-casein was labeled using the PhIAT method, and after proteolytic digestion, the labeled peptides were isolated using immobilized avidin. The recovered peptides were separated by capillary reversed-phase liquid chromatography and identified by tandem mass spectrometry. PhIAT-labeled peptides corresponding to known O-phosphorylated peptides from beta-casein were identified along with the phosphorylated peptides from alphas1-casein and alphas2-casein, known low-level (\u3c5%) contaminants of commercially available beta-casein. All of the casein-phosphorylated residues identified by the present PhIAT approach correspond to previously documented sites of phosphorylation. The results illustrate the efficacy of the PhIAT-labeling strategy to not only enrich mixtures for phosphopeptides but also, more importantly, permit the detection and identification of low-level phosphopeptides. In addition, the differences in the phosphorylation state could be determined between phosphopeptides in comparative samples by stoichiometric conversion using the light and heavy isotopic versions of the PhIAT reagents. Overall, our results exemplify the application of the PhIAT approach and demonstrate its utility for proteome-wide phosphoprotein identification and quantitation