Tom P. J. Dunkley

Localization of Organelle Proteins by Isotope Tagging (LOPIT)

We describe a proteomics method for determining the subcellular localization of membrane proteins. Organelles are partially separated using centrifugation through self-generating density gradients. Proteins from each organelle co-fractionate and therefore exhibit similar distributions in the gradient. Protein distributions can be determined through a series of pair-wise comparisons of gradient fractions, using cleavable ICAT to enable relative quantitation of protein levels by MS. The localization of novel proteins is determined using multivariate data analysis techniques to match their distributions to those of proteins that are known to reside in specific organelles. Using this approach, we have simultaneously demonstrated the localization of membrane proteins in both the endoplasmic reticulum and the Golgi apparatus in Arabidopsis. Localization of organelle proteins by isotope tagging is a new tool for high-throughput protein localization, which is applicable to a wide range of research areas such as the study of organelle function and protein trafficking.

A systematic approach to modeling, capturing, and disseminating proteomics experimental data

Both the generation and the analysis of proteome data are becoming increasingly widespread, and the field of proteomics is moving incrementally toward high-throughput approaches. Techniques are also increasing in complexity as the relevant technologies evolve. A standard representation of both the methods used and the data generated in proteomics experiments, analogous to that of the MIAME (minimum information about a microarray experiment) guidelines for transcriptomics, and the associated MAGE (microarray gene expression) object model and XML (extensible markup language) implementation, has yet to emerge. This hinders the handling, exchange, and dissemination of proteomics data. Here, we present a UML (unified modeling language) approach to proteomics experimental data, describe XML and SQL (structured query language) implementations of that model, and discuss capture, storage, and dissemination strategies. These make explicit what data might be most usefully captured about proteomics experiments and provide complementary routes toward the implementation of a proteome repository.

Growth control of the eukaryote cell: a systems biology study in yeast

BACKGROUND Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. RESULTS Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae. Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. CONCLUSION This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

Putative Glycosyltransferases and Other Plant Golgi Apparatus Proteins Are Revealed by LOPIT Proteomics

The Golgi apparatus is the central organelle in the secretory pathway and plays key roles in glycosylation, protein sorting, and secretion in plants. Enzymes involved in the biosynthesis of complex polysaccharides, glycoproteins, and glycolipids are located in this organelle, but the majority of them remain uncharacterized. Here, we studied the Arabidopsis (Arabidopsis thaliana) membrane proteome with a focus on the Golgi apparatus using localization of organelle proteins by isotope tagging. By applying multivariate data analysis to a combined data set of two new and two previously published localization of organelle proteins by isotope tagging experiments, we identified the subcellular localization of 1,110 proteins with high confidence. These include 197 Golgi apparatus proteins, 79 of which have not been localized previously by a high-confidence method, as well as the localization of 304 endoplasmic reticulum and 208 plasma membrane proteins. Comparison of the hydrophobic domains of the localized proteins showed that the single-span transmembrane domains have unique properties in each organelle. Many of the novel Golgi-localized proteins belong to uncharacterized protein families. Structure-based homology analysis identified 12 putative Golgi glycosyltransferase (GT) families that have no functionally characterized members and, therefore, are not yet assigned to a Carbohydrate-Active Enzymes database GT family. The substantial numbers of these putative GTs lead us to estimate that the true number of plant Golgi GTs might be one-third above those currently annotated. Other newly identified proteins are likely to be involved in the transport and interconversion of nucleotide sugar substrates as well as polysaccharide and protein modification.

Quantitative proteomic approach to study subcellular localization of membrane proteins

As proteins within cells are spatially organized according to their role, knowledge about protein localization gives insight into protein function. Here, we describe the LOPIT technique (localization of organelle proteins by isotope tagging) developed for the simultaneous and confident determination of the steady-state distribution of hundreds of integral membrane proteins within organelles. The technique uses a partial membrane fractionation strategy in conjunction with quantitative proteomics. Localization of proteins is achieved by measuring their distribution pattern across the density gradient using amine-reactive isotope tagging and comparing these patterns with those of known organelle residents. LOPIT relies on the assumption that proteins belonging to the same organelle will co-fractionate. Multivariate statistical tools are then used to group proteins according to the similarities in their distributions, and hence localization without complete centrifugal separation is achieved. The protocol requires approximately 3 weeks to complete and can be applied in a high-throughput manner to material from many varied sources.

The use of isotope-coded affinity tags (ICAT) to study organelle proteomes in Arabidopsis thaliana

Organelle proteomics is the analysis of the protein contents of a subcellular compartment. Proteins identified in subcellular proteomic studies can only be assigned to an organelle if there are no contaminants present in the sample preparation. As a result, the majority of plant organelle proteomic studies have focused on the chloroplast and mitochondria, which can be isolated relatively easily. However, the isolation of components of the endomembrane system is far more difficult due to their similar sizes and densities. For this reason, quantitative proteomics methods are being developed to enable the assignment of proteins to a specific component of the endomembrane system without the need to obtain pure organelles.

Determination of Genuine Residents of Plant Endomembrane Organelles using Isotope Tagging and Multivariate Statistics

The knowledge of the localization of proteins to a particular subcellular structure or organelle is an important step towards assigning function to proteins predicted by genome-sequencing projects that have yet to be characterized. Moreover, the localization of novel proteins to organelles also enhances our understanding of the functions of organelles. Many organelles cannot be purified. In several cases where the degree of contamination by organelles with similar physical parameters to the organelle being studied has gone unchecked, this has lead to the mis-localization of proteins. Recently, several techniques have emerged, which depend on characterization of the distribution pattern of organelles partially separated using density centrifugation by quantitative proteomics approaches. Here, we discuss one of these approaches, the localization of organelle proteins by isotope tagging (LOPIT) where the distribution patterns of organelles are assessed by measuring the relative abundance of proteins between fractions along the length of density gradients using stable isotope-coded tags. The subcellular localizations of proteins can be determined by comparing their distributions to those of previously localized proteins by assuming that proteins that belong to the same organelle will cofractionate in density gradients. Analysis of distribution patterns can be achieved by employing multivariate statistical methods such as principal component analysis and partial least squares discriminate analysis. In this chapter, we focus on the use of the LOPIT technique in the assignment of membrane proteins to the plant Golgi apparatus and endoplasmic reticulum.