currents
Proteins that could slow the aging process
MENG-QUI DONG
expressed in daf-2 worms compared with WT. Proteins that We’re all searching for the fountain of youth—we want to live were down-regulated in daf-2 mutants were involved in translonger and look good doing it. Interestingly, researchers have lation elongation and lipid transport. Those that were up-regfound clues about prolonging life span in a worm. When the ulated were involved in functions such as amino acid biosynC. elegans gene DAF-2, a homologue of the thesis and carbohydrate metabolism. The human insulin-like growth factor (known researchers validated some of the protein as IGF-1) receptor, is mutated, life span levels with western blotting and selected doubles. Also, the mutant worms remain reaction monitoring, in which a certain preyouthful in their appearance during this loncursor ion is fragmented and the intensity ger life span. DAF-2 negatively regulates the of a particular fragment ion is determined. DAF-16 transcription factor, but evidence To test whether some of the differenfrom microarrays and genetic studies sugtially regulated proteins in daf-2 mutants gests that many other gene products are are involved in extending life span, the indownstream effectors. vestigators performed RNAi experiments To discover additional proteins regulatto turn down the expression of those proed by DAF-2, John Yates and colleagues at teins. When the researchers reduced the the Scripps Research Institute, the Salk Inexpression of five genes involved in carbostitute for Biological Studies, and the Unihydrate metabolism (including gluconeoversity of California San Diego undertook a genesis) by RNAi, the worms lived longer. quantitative proteomics study. They identiIn contrast, RNAi against two translation fied 86 proteins that were differentially exgenes shortened life. This result was the Fountain of youth. Investigators pressed in daf-2 mutants compared with opposite of what might have been expectdiscover several proteins that are rewild-type (WT) worms. In addition, they ed on the basis of the protein abundance sponsible for prolonging life in C. concluded that a compensatory mechastudy. There, the levels of the five carboelegans. nism springs into action when DAF-2 sighydrate metabolism proteins were high in naling decreases. long-lived daf-2 mutants, and the levels Lysates from WT, daf-16, and daf-2 worms were prepared. of the two translation proteins were low. Similar results also Each lysate was mixed 1:1 with that of a reference sample of were obtained with a protein phosphatase called calcineu15N-labeled WT worms. Proteins were digested and analyzed rin. Therefore, Yates and colleagues propose that a compenby MS. The relative abundance of each protein was assessed satory mechanism or mechanisms act to shorten life span a by RelEx software and spectral counting. bit in daf-2 mutants. If that mechanism is inhibited, then life The protein abundances and phenotypes of the WT and is prolonged even further when DAF-2 signaling is reduced. daf-16 worms were similar, but 86 proteins were differentially (Science 2007, 317, 660–663)
Detection of amino acids from complex mixtures Some conditions, such as inborn errors of metabolism (IEMs), result in the buildup of specific amino acids in bodily fluids, such as urine. 1H NMR can be used to diagnose these conditions, but with this method, many amino acid signals overlap those produced by other metabolites. 13C NMR is an alternative technique with a larger chemical shift range and decreased spectral complexity, but it is not very sensitive. To improve the detection of amino acids with 13C NMR, Daniel Raftery and co-workers at Purdue University and the Indiana University School of Medicine developed a chemical derivatization strategy that allows researchers to sensitively detect amino acids with 13C NMR without separation steps. © 2007 American Chemical Society
In the new method, amines are rapidly acetylated with isotopically labeled acetic anhydride. Unlike other derivatization techniques, this one can be performed at room temperature in aqueous solution. Also, the acetylation approach gives a high yield, and the reagents are available commercially. To test the method, Raftery and coworkers derivatized 20 standard amino acids. The 1D 13C-NMR peaks of the labeled amino acids were well resolved. With the method, sensitivity increased by ~100-fold compared with the detection of unlabeled amino acids. Inversedetected 1H-13C heteronuclear single quantum coherence (HSQC) further enhanced the sensitivity and resolution. Chemical shifts were identified from a 2D HSQC spectrum of the sample. Compared with HSQC of unlabeled amino ac-
ids, the data from the derivatized sample produced a much simpler spectrum and were more sensitive. Signals from most amino acids were highly reproducible. Finally, the researchers applied the method to the study of urine and serum samples. Amino acids that were undetectable in untreated urine samples were clearly observed after derivatization. Serum contains many proteins that could interfere with the measurements, so the investigators precipitated the proteins, then analyzed them either immediately or after derivatization. Again, signals that were undetectable in the underivatized sample were clearly observed in the labeled sample. In addition, the approach was successfully applied to urine samples from patients with IEMs. (Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 11,540–11,544)
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currents Toolbox Using LC data to improve peptide IDs William Stafford Noble and colleagues at the University of Washington have developed an approach to exploit chromatographic retention time data to increase the confidence of peptide identifications determined by MS/MS. Unlike previously described methods, this one is generalizable to various LC/MS/MS conditions and techniques and does not require additional experimental steps. In the method, a support vector regressor is dynamically trained and tested on data from a single chromatography run of a Saccharomyces cerevisiae lysate. The researchers then filter out false-positive peptide identifications by eliminating those identifications whose observed retention time deviates from the predicted value by a constant amount of time. This step increases the number of true-positive identifications at most false-discovery-rate thresholds. When the investigators varied several parameters, a large improvement was observed for peptides digested with enzymes other than trypsin. (Anal. Chem. 2007, 79, 6111–6118)
MASPECTRAS Proteomics data management and analysis are still major challenges for investigators. Therefore, Zlatko Trajanoski and co-workers at the Graz University of Technology, Austrian Research Centers GmbH–eHealth Systems, FH Joanneum University of Applied Sciences, and the Research Institute of Molecular Pathology (all in Austria) developed the MS analysis system (MASPECTRAS) platform. MASPECTRAS tools help researchers analyze and visualize data. For example, the module responsible for importing and parsing data from search engines accepts various formats and integrates data from five popular search engines. PeptideProphet, which aids in peptide validation, and ASAPRatio, which is used for quantification of peptides, are included. In addition, the platform can export data in several file formats, including the XML format preferred by the Proteomics Identification (known as PRIDE) database. MASPECTRAS is available for free at http://ge nome.tugraz.at/maspectras. (BMC Bioinformatics 2007, 8, 197)
Absolute quantitation with metals Few absolute quantitation methods exist for proteomics, and those that do typically are targeted validation strategies that measure peptide amounts. To perform absolute and relative quantitation for intact proteins and peptides, Michael Linscheid and colleagues at Humboldt University of Berlin, Thermo Fisher Scientific, the Institute for Analytical Sciences, and Proteome Factory AG (all in Germany) developed an approach they called metal-coded affinity tag (MeCAT). With this new method, the researchers tag proteins and peptides with metal-containing labels and perform absolute quantitation with inductively
Monolithic SCX column for shotgun proteomics
coupled plasma MS (ICPMS). Proteins or peptides are labeled at cysteine residues with a tag that consists of a reactive group and a macrocyclic metal chelate complex loaded with various lanthanides. One version of the tag also includes an affinity anchor for purification. To characterize the tags, the researchers conducted several tests. Substituting various metals in the tag did not change the elution times of labeled peptides. In addition, peptide labeling went to completion. Next, the investigators tested the tags on proteins. Intact proteins also were labeled to completion, but the MeCAT tag changed the mass and pI of the proteins, so this feature must be taken
Hanfa Zou and colleagues at the Chinese Academy of Sciences have developed a phosphate monolithic column for shotgun proteomic analyses. Although sulfonate groups typically are introduced in monolithic columns for strong cation exchange (SCX) chromatography, these groups are hypothesized to swell excessively in Separator. A phosphate monolithic column separates aqueous buffers. The peptides on the basis of SCX properties. phosphate monolith, however, is more stable and is more hydrophilic and biocompatible weeks with a constant back pressure. than the sulfonate columns. To test the column in a proteomics Ethylene glycol methacrylate phosworkflow, the researchers injected phate and the cross-linker bisacrylsamples into an LC/MS/MS setup with amide were polymerized in a 150 µm an automatic sample injection apparai.d. capillary to create the monolith. tus. A 7 cm long phosphate monolithThe researchers compared the perforic column was used as a trap column mance of the column with the perforso that large volumes could be loadmance of one packed with particulate ed quickly at a high flow rate before SCX material. The monolithic column entering a reversed-phase analytical was 15× more permeable than a parcolumn with a smaller inner diameter. ticle-packed column with 0.1% formic A small volume of a tryptic digest of acid in acetronitrile and was ~10× more yeast protein was loaded, and 460 dispermeable with 0.1% formic acid in watinct proteins were identified. When a ter. Although the monolithic polymer much larger volume of the digest was swelled and contracted slightly in difloaded, 1522 proteins were identified ferent buffers, it did not detach from the with a false-positive rate of 0.46%. The capillary walls. The binding capacity of results for the new column were reprothe monolithic column was higher than ducible and independent of the samthat of the conventional SCX column. ple loading rate. (Anal. Chem. 2007, 79, The new column could be used for 3–4 6599–6606)
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currents into account when they are analyzed by 2DE. Tagged proteins mixed in different amounts were detected by ICPMS in the expected ratios. The detection limit was 110 amol for bovine serum albumin and 670 amol for a-lactalbumin. Finally, two proteome samples of a pig eye lens were labeled with different
metal-containing tags and run on 2DE gels. Spots of interest were cut out and analyzed by ICPMS for quantitation and by MS/MS for structural information. Expected proteins were identified with high sensitivity with the MeCAT method. (Mol. Cell. Proteomics 2007, DOI 10.1074/mcp.M700152-MCP200)
Automated analysis of yeast images
The subcellular localization of a protein can tell you a lot about its possible function and interaction partners. Manual inspection of hundreds or thousands of samples is a time-consuming and laborious task, so Robert Murphy and co-workers at Carnegie Mellon University have applied their automation expertise to the analysis of yeast cells. They have developed an automated method that classifies the subcellular locations of fluorescently labeled proteins without requiring co-localization with a second marker. The method is as accurate as or, in some cases, more accurate than manual determinations. The method was applied to the University of California San Francisco (UCSF) yeast green fluorescent protein (GFP) fusion library, in which the GFP sequence has been added in frame with most of the known yeast open reading frames (ORFs; Nature 2004, 425, 686– 691). The localizations of these tagged proteins have been determined manually, and each protein is categorized as belonging to one of 21 categories. Automated calls were compared with these manual groupings. When Murphy and co-workers analyzed cells within the four largest classes on the level of an entire microscopic field, the accuracy was 92.7%. When the approach was extended to all 21 classes, the accuracy decreased. This result suggested that most of the images from the smallest classes, which included few members, were classified incorrectly. Next, the researchers used the automated method on individual cells. Again, the accuracy was high for the most populous classes but low for the ones consisting of only a few members. Overall, the accuracy was 81%. The accuracy was greatly improved by considering only high-confidence classifications after instituting a threshold. When the threshold was set at 100%,
Mislabeled? An automated image analysis method revealed that the localizations of some yeast proteins, such as ORF YGR130C, may have been incorrectly assigned by a large-scale manual analysis. The original images for the manual analysis were obtained from the UCSF yeast GFP fusion localization database (http://yeastgfp.ucsf.edu) created by the groups of Erin O’Shea and Jonathan Weissman. (Blue = estimated cell boundaries; red = nuclear staining; green = YGR130C staining.) (Adapted with permission. Copyright 2007 Oxford University Press.)
Toolbox Integrating proteomic and genomic data In systems biology approaches, researchers must form conclusions on the basis of information gathered from many types of experiments. The challenge, however, is to make sense of these disparate data sets. To integrate proteomic and genomic data, Ailís Fagan and co-workers at University College Dublin, the Harvard School of Public Health, and the Dana-Farber Cancer Institute applied a multivariate statistical method called co-inertia analysis (CIA), which identifies relationships between parallel data sets. Although the method has been applied to data sets generated from a single type of molecular biology experiment, it has not been applied to data from both genomics and proteomics studies until now. CIA sample plots show how the samples relate to each other. In addition, gene and protein plots, as well as Gene Ontology terms, can be superimposed onto the sample plots to show which genes and proteins are expressed in particular samples. The researchers successfully applied the method to the analysis of genomic and proteomic data from a lifecycle study of Plasmodium falciparum, which is a parasite that causes malaria, and a study of 60 cancer cell lines. (Proteomics 2007, 7, 2162–2171)
Reproducibility of relative quantitation methods
the precision was 94.7% and the recall was >80%. The automated assessments were based on the assumption that the manual calls were correct. Because some of these manual calls could be incorrect, the investigators looked more carefully at those classifications that did not match the UCSF analysis. Of the 501 images that did not match, 96 were grouped with 100% confidence by the automated method. The researchers say that additional analyses must be performed, but the preliminary findings suggest that some cells may have to be in a certain phase of the cell cycle to be properly called, whereas in other cases, the manual determination was probably incorrect. (Bioinformatics 2007, 23, i66–i71)
Tao He and colleagues at Celera have developed a way to assess the reproducibility of label-free (decoupled) and label-dependent (coupled) relative quantitation strategies. The method could be applied as a quality control measure to minimize false positives and negatives. The assessment method features LC/MS map alignment, intensity matrix production, and analysis of intensity ratios. Slightly different methods were developed for coupled and decoupled quantitation strategies. The method was applied to decoupled data and to data generated with the coupled techniques called stable-isotope labeling by amino acids in cell culture (SILAC) and ICAT. The researchers concluded that the SILAC technique was the most reproducible one. (Anal. Chem. 2007, 79, 5651–5658)
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