Hookeun Lee

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information.

A high-quality catalog of the Drosophila melanogaster proteome.

Understanding how proteins and their complex interaction networks convert the genomic information into a dynamic living organism is a fundamental challenge in biological sciences. As an important step towards understanding the systems biology of a complex eukaryote, we cataloged 63% of the predicted Drosophila melanogaster proteome by detecting 9,124 proteins from 498,000 redundant and 72,281 distinct peptide identifications. This unprecedented high proteome coverage for a complex eukaryote was achieved by combining sample diversity, multidimensional biochemical fractionation and analysis-driven experimentation feedback loops, whereby data collection is guided by statistical analysis of prior data. We show that high-quality proteomics data provide crucial information to amend genome annotation and to confirm many predicted gene models. We also present experimentally identified proteotypic peptides matching ∼50% of D. melanogaster gene models. This library of proteotypic peptides should enable fast, targeted and quantitative proteomic studies to elucidate the systems biology of this model organism.

Gene expression analyzed by high-resolution state array analysis and quantitative proteomics: response of yeast to mating pheromone.

The transcriptome provides the database from which a cell assembles its collection of proteins. Translation of individual mRNA species into their encoded proteins is regulated, producing discrepancies between mRNA and protein levels. Using a new modeling approach to data analysis, a striking diversity is revealed in association of the transcriptome with the translational machinery. Each mRNA has its own pattern of ribosome loading, a circumstance that provides an extraordinary dynamic range of regulation, above and beyond actual transcript levels. Using this approach together with quantitative proteomics, we explored the immediate changes in gene expression in response to activation of a mitogen-activated protein kinase pathway in yeast by mating pheromone. Interestingly, in 26% of those transcripts where the predicted protein synthesis rate changed by at least 3-fold, more than half of these changes resulted from altered translational efficiencies. These observations underscore that analysis of transcript level, albeit extremely important, is insufficient by itself to describe completely the phenotypes of cells under different conditions.

Approaching complete peroxisome characterization by gas-phase fractionation.

We examined the utility of gas‐phase fractionation (GPF) in the m/z dimension to increase proteome coverage and reproducibility of peptide ion selection by direct microliquid chromatography/electrospray ionization‐tandem mass spectrometry (νLC/ESI‐MS/MS) analysis of the peptides produced by proteolytic digestion of unfractionated proteins from a yeast whole‐cell lysate and in a peroxisomal membrane protein fraction derived from isolated yeast peroxisomes. We also investigated GPF in the relative ion intensity dimension and propose denoting the two types of GPF as GPFm/z and GPFRI. Comparison of results of direct νLC/ESI‐MS/MS analysis of the unfractionated mixture of peptides from proteolysis of a yeast whole cell lysate by DD ion selection from 400–1800 m/z in triplicate and GPFm/z from 400–800, 800–1200 and 1200–1800 produced the following results: (i) 1.3× more proteins were identified by GPFm/z for an equal amount of effort (i.e., 3 νLC/ESI‐MS/MS) and (ii) proteins identified by GPFm/z had a lower average codon bias value. Use of GPFRI identified more proteins per m/z unit scanned than GPFm/z or triplicate analysis over a wide m/z range. After tryptic digestion of all the proteins from a discontinuous Nycodenz gradient fraction known to be enriched with yeast peroxisomal membrane proteins we detected 93% (38/41) of known peroxisomal proteins using GPFm/z, but only 73% using a standard wide m/z range survey scan.

Gene expression in yeast responding to mating pheromone: Analysis by high-resolution translation state analysis and quantitative proteomics

The transcriptome provides the database from which a cell assembles its collection of proteins. Translation of individual mRNA species into their encoded proteins is regulated, producing discrepancies between mRNA and protein levels. Using a new modeling approach to data analysis, a striking diversity is revealed in association of the transcriptome with the translational machinery. Each mRNA has its own pattern of ribosome loading, a circumstance that provides an extraordinary dynamic range of regulation, above and beyond actual transcript levels. Using this approach together with quantitative proteomics, we explored the immediate changes in gene expression in response to activation of a mitogen-activated protein kinase pathway in yeast by mating pheromone. Interestingly, in 26% of those transcripts where the predicted protein synthesis rate changed by at least 3-fold, more than half of these changes resulted from altered translational efficiencies. These observations underscore that analysis of transcript level, albeit extremely important, is insufficient by itself to describe completely the phenotypes of cells under different conditions.

Proteome Analysis of Legionella Vacuoles Purified by Magnetic Immunoseparation Reveals Secretory and Endosomal GTPases

Legionella pneumophila, the causative agent of Legionnaires’ disease, replicates in macrophages and amoebae within ‘Legionella‐containing vacuoles’ (LCVs), which communicate with the early secretory pathway and the endoplasmic reticulum. Formation of LCVs requires the bacterial Icm/Dot type IV secretion system. The Icm/Dot‐translocated effector protein SidC selectively anchors to LCVs by binding the host lipid phosphatidylinositol‐4‐phosphate (PtdIns(4)P). Here, we describe a novel and simple approach to purify intact vacuoles formed by L. pneumophila within Dictyostelium discoideum by using magnetic immunoseparation with an antibody against SidC, followed by density gradient centrifugation. To monitor LCV purification by fluorescence microscopy, we used Dictyostelium producing the LCV marker calnexin‐GFP and L. pneumophila labeled with the red fluorescent protein DsRed. A proteome analysis of purified LCVs by liquid chromatography coupled to tandem mass spectrometry revealed 566 host proteins, including known LCV components, such as the small GTPases Arf1, Rab1 and Rab7. Rab8, an endosomal regulator of the late secretory pathway originating from the trans Golgi network, and the endosomal GTPase Rab14 were identified as novel LCV components, which were found to be present on vacuoles harboring wild‐type but not Icm/Dot‐deficient L. pneumophila. Thus, LCVs also communicate with the late secretory and endosomal pathways. Depletion of Rab8 or Arf1 by RNA interference reduced the amount of SidC on LCVs, indicating that the GTPases promote the recruitment of Legionella effectors by regulating the level of PtdIns(4)P.

Signal Maps for Mass Spectrometry-based Comparative Proteomics

Mass spectrometry-based proteomic experiments, in combination with liquid chromatography-based separation, can be used to compare complex biological samples across multiple conditions. These comparisons are usually performed on the level of protein lists generated from individual experiments. Unfortunately given the current technologies, these lists typically cover only a small fraction of the total protein content, making global comparisons extremely limited. Recently approaches have been suggested that are built on the comparison of computationally built feature lists instead of protein identifications. Although these approaches promise to capture a bigger spectrum of the proteins present in a complex mixture, their success is strongly dependent on the correctness of the identified features and the aligned retention times of these features across multiple experiments. In this experimental-computational study, we went one step further and performed the comparisons directly on the signal level. First signal maps were constructed that associate the experimental signals across multiple experiments. Then a feature detection algorithm used this integrated information to identify those features that are discriminating or common across multiple experiments. At the core of our approach is a score function that faithfully recognizes mass spectra from similar peptide mixtures and an algorithm that produces an optimal alignment (time warping) of the liquid chromatography experiments on the basis of raw MS signal, making minimal assumptions on the underlying data. We provide experimental evidence that suggests uniqueness and correctness of the resulting signal maps even on low accuracy mass spectrometers. These maps can be used for a variety of proteomic analyses. Here we illustrate the use of signal maps for the discovery of diagnostic biomarkers. An imple-mentation of our algorithm is available on our Web server.

Quantitative Proteomic Analysis of Protein Complexes Concurrent Identification of Interactors and Their State of Phosphorylation

Protein complexes have largely been studied by immunoaffinity purification and (mass spectrometric) analysis. Although this approach has been widely and successfully used it is limited because it has difficulties reliably discriminating true from false protein complex components, identifying post-translational modifications, and detecting quantitative changes in complex composition or state of modification of complex components. We have developed a protocol that enables us to determine, in a single LC-MALDI-TOF/TOF analysis, the true protein constituents of a complex, to detect changes in the complex composition, and to localize phosphorylation sites and estimate their respective stoichiometry. The method is based on the combination of fourplex iTRAQ (isobaric tags for relative and absolute quantification) isobaric labeling and protein phosphatase treatment of substrates. It was evaluated on model peptides and proteins and on the complex Ccl1-Kin28-Tfb3 isolated by tandem affinity purification from yeast cells. The two known phosphosites in Kin28 and Tfb3 could be reproducibly shown to be fully modified. The protocol was then applied to the analysis of samples immunopurified from Drosophila melanogaster cells expressing an epitope-tagged form of the insulin receptor substrate homologue Chico. These experiments allowed us to identify 14-3-3ε, 14-3-3ζ, and the insulin receptor as specific Chico interactors. In a further experiment, we compared the immunopurified materials obtained from tagged Chico-expressing cells that were either treated with insulin or left unstimulated. This analysis showed that hormone stimulation increases the association of 14-3-3 proteins with Chico and modulates several phosphorylation sites of the bait, some of which are located within predicted recognition motives of 14-3-3 proteins.

Toward a High-Throughput Approach to Quantitative Proteomic Analysis: Expression-Dependent Protein Identification by Mass Spectrometry

The isotope-coded affinity tag (ICAT) [1] technology enables the concurrent identification and comparative quantitative analysis of proteins present in biological samples such as cell and tissue extracts and biological fluids by mass spectrometry. The initial implementation of this technology was based on microcapillary chromatography coupled on-line with electrospray ionization tandem mass spectrometry. This implementation lacked the ability to select proteins for identification based on their relative abundance and therefore to focus on differentially expressed proteins. In order to improve the sample throughput of this technology, we have developed a two-step approach that is focused on those proteins for which the abundance changes between samples: First, a new software program for the automated quantification of ICAT reagent labeled peptides analyzed by microcapillary electrospray ionization time-of-flight mass spectrometry determines those peptides that differ in their abundance and second, these peptides are identified by tandem mass spectrometry using an electrospray quadrupole time-of flight mass spectrometer and sequence database searching. Results from the application of this approach to the analysis of differentially expressed proteins secreted from nontumorigenic human prostate epithelial cells and metastatic cancerous human prostate epithelial cells are shown.

UniPep - a database for human N-linked glycosites: a resource for biomarker discovery

There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep http://www.unipep.org - a database of human N-linked glycosites - as a resource for biomarker discovery.