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Ovarian cancer proteins identified with microarrays
Next, arrays were constructed with tissue samples from patients with ovarian cancer and controls, and these arrays Patients with cancer often produce autoantibodies against were probed with antibodies against the candidate biomarkantigens that are aberrantly expressed during tumorigeners in immunohistochemistry (IHC) experiments. Lamin A/ esis. To discover autoantigens involved C, SSRP1, and RALBP1 staining was in ovarian cancer, Michael Snyder and more intense in ovarian cancer tissues colleagues at Yale University used prothan in tissues from control subjects. (a) tein microarrays. With this technology, In most cases, the staining for these they identified three candidate markers three proteins also was higher in canthat were specific for cancer. cerous ovarian tissue than in control Proteome microarrays with 5005 tissue from the same patient. Compurified human proteins were probed pared with staining for CA125 (the gold(b) with sera from 30 patients with ovaristandard biomarker for ovarian cancer an cancer and 30 controls. Many prodiagnostics), lamin A/C and SSRP1 teins on the microarrays were bound staining was much more intense. In a by autoantibodies from patients, conblinded IHC experiment, researchers trols, or both. After statistical analyses scored the staining of randomly sewere performed, the researchers genlected images. Neither lamin A/C nor Stained proteins. Antibodies against (a) lamin erated a list of 90 proteins that were SSRP1 had high sensitivity or speciA/C or (b) CA125 were used to probe tissue mi targeted by tumor-associated autoanficity when analyzed separately. When croarrays that include (top rows) cancerous and tibodies. the markers were combined, a sensi(bottom rows) noncancerous tissue. (Adapted with permission. Copyright 2007 National Four of the antigens that were more tivity of 95% and a specificity of 97.5% Academy of Sciences, U.S.A.) reactive to sera from patients with canwere obtained. cer than to sera from controls were choFinally, lamin A/C, SSRP1, and RALsen for further study. The researchers BP1 levels were assayed in other canprobed lysates from various cancer and normal tissues with cerous and normal tissues with IHC. These antibodies exhibcommercial antibodies against lamin A/C, SSRP1, RALBP1, ited more intense staining in the cancer samples from ovary, and ZNF265 on dot blots. Antibodies against lamin A/C and breast, and lung tissues than in normal samples. However, SSRP1 stained ovarian cancer lysates more intensely than the normal kidney samples stained more intensely than the control ovarian lysates. However, SSRP1 staining also was cancer samples from that tissue. Although the researchers observed for other cancerous tissues and some healthy tissay that these markers could be diagnostic for cancer biopsues. No signal was observed for the other antibodies. Westsies, they also say that the three antigens identified in this ern blots also were performed with antibodies against the four study must be further characterized to discover how they are candidates. The levels of lamin A/C and SSRP1 proteins again involved in cancer. (Proc. Natl. Acad. Sci. U.S.A. 2007, 104, were higher in cancer samples than in controls. 17,494–17,499)
Potential biomarker for neural progenitor cells Because neural progenitor cells (NPCs) can generate new neural cells in adults, it is tantalizing to think that NPCs could be used to fix damaged nerve tissue in humans. As a step toward that goal, Mirjana Maleti´c-Savati´c, Grigori Enikolopov, and colleagues at the State University of New York Stony Brook, Brookhaven National Laboratory, and Cold Spring Harbor Laboratory report the discovery of a metabolic biomarker for NPCs. With proton magnetic resonance spectroscopy (1H MRS), they can detect the marker—and, presumably, NPCs—in living rats and humans. To identify metabolites that are specific for NPCs, the researchers analyzed © 2008 American Chemical Society
in vitro samples with 1H NMR. Spectra of NPCs from embryonic mouse brain tissue were compared with those of cultured neuronal cells, embryonic stem cells, and other cell types. Only NPC spectra included a major peak at 1.28 ppm, and the amplitude of the peak was proportional to the number of NPCs present. In addition, the 1.28 ppm signal decreased over time as the neuronal cells differentiated into astrocytes, whereas the signal of a differentiation marker increased. Similarly, adult mouse brains with many differentiated cells had low levels of the proposed NPC marker, but embryonic brains with many NPCs had high levels. Compared with the cortex, where no new neuronal cells are formed, the hippocampus (the site of neurogen-
esis in adult brains) had high levels of the marker. Additional 1H-NMR studies suggest that the marker is a mixture of lipids. 1H NMR cannot be applied to living organisms, so the researchers used 1H MRS to see whether the candidate NPC marker could be detected in rats and humans in vivo. The signal was visible only after a sensitive signal-processing algorithm was applied to the data. This processing step revealed that the marker was present in the hippocampi of rats and humans. Also, the signal was stronger in preadolescents than in adult human subjects. The investigators say that the potential NPC marker could provide information about neurogenesis in patients with neurological and psychiatric disorders. (Science 2007, 318, 980–985)
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currents Percolator To improve the sensitivity of current da tabase search algorithms, Michael Mac Coss and co-workers at the Universi ty of Washington and NEC Laboratories developed Percolator, an automated al gorithm that uses a semisupervised ap proach. With Percolator, a support vec tor machine is trained to distinguish correct peptide–spectrum matches (PSMs) from incorrect ones after a da tabase search. In contrast with simi lar methods that apply a supervised ap proach to rerank PSMs generated by search engines, Percolator can take a variety of data features into account. A manually curated training set is not necessary with this algorithm. Instead, PSMs obtained from a decoy database search are considered negative exam ples, and high-scoring PSMs derived from the unmodified database are con sidered positive examples. The researchers applied Percolator to data from an unfractionated tryptic di gest of Saccharomyces cerevisiae pro teins in which the peptides were iden tified with Sequest. Percolator greatly increased the number of correctly iden tified peptides over Sequest alone or in combination with PeptideProphet, even when data from nontryptic digests were analyzed. The algorithm is available for free to nonprofit researchers at http:// noble.gs.washington.edu/proj/percola tor. (Nat. Methods 2007, 4, 923–925)
Enhanced ion charge states for ETD For bottom-up analyses, proteomics researchers typically digest proteins with trypsin and fragment the peptides with collision-induced dissociation. With this LC/ESI-MS/MS approach, precursor peptide ions usually have 2+ charges. Recently, the technique of electron transfer dissociation (ETD) has been developed to fragment ions and preserve posttranslational modifications, but the method is more efficient with ≥3+ ions. To implement ETD more effectively, Ole Jensen and colleagues at the University of Southern Denmark have come up with a simple method to increase the charge states of tryptic peptides. Building on previous work reported in the literature by other groups, Jensen’s team used m-nitrobenzyl alcohol (mNBA) to increase the charge states of
The biochemical network database Researchers often search several high ly focused databases to access detailed information about the proteins they have identified. Therefore, Jan Küntzer and colleagues at Saarland Universi ty and Eberhard Karls University (both in Germany) developed a one-stop shop called the biochemical network data base (BNDB) to integrate the informa tion from these protein databases in one location. The new collection gath ers information from various sequence, pathway, protein-interaction, and tran scription-factor databases. BNDB in formation can be accessed with a web interface, a network visualizer (called BiNA), or a programming interface. It is available for free at www.bndb.org. (BMC Bioinformatics 2007, 8, 367)
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tryptic peptide ions. Instead of putting the additive into the ESI solution as was done previously, Jensen and colleagues added m-NBA to both mobile phases of the LC separation. The addition of 0.1% m-NBA caused the most prevalent charge state in a digest of bovine serum albumin (BSA) to shift from 2+ to 3+. The average charge state increased from 2.2+ to 2.6+. As expected, the fragmentation efficiency was higher for the BSA peptides with shifted charge states. Also, the Mascot scores for 3+ precursors gave a confidence level >280× greater than that for 2+ forms. The team also examined the effect of m-NBA on phosphopeptides; the charge states and fragmentation were enhanced, but to a lesser extent than for nonphosphorylated peptides. Jensen’s group observed that m-NBA affected individual peptides differently
Calorimetry for proteomics
Jonathan Chaires and colleagues at the University of Louisville report that differential scanning calorimetry (DSC) can be used as a tool to study plasma and serum proteomes. DSC monitors temperature-induced protein denaturation, and for a mixture of proteins, a DSC thermogram is the sum of the denaturation behavior of all of the components. The scientists obtained thermograms from the plasma samples of 15 healthy individuals and found that the behavior of the samples was remarkably similar. By collecting and combining individual thermograms of each of the 16 most abundant plasma proteins, they also showed that the observed plasma thermogram was dominated by the behavior of this protein subset. The researchers then collected DSC thermograms of plasma from individuals with three diseases: rheumatoid arthritis, Lyme disease, and systemic lupus. Not only did the thermograms from people with these diseases differ significantly from those of the healthy individuals, but they showed marked differences from one another as well. To shed light on what may cause these differences, the scientists measured the concentrations of the major plasma proteins in each type of sample. They found that the samples from individuals with disease had essential-
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ly the same levels of these proteins as those from healthy subjects. They hypothesize that the contrast between the groups results from differences in the interaction of lower-concentration biomarkers with the abundant proteins in plasma. (Biophys. J. 2007, DOI 10.1529/biophysj.107.119453)
currents but could not find any correlation between the observed changes and the length, hydrophobicity, or pI of the peptides. The researchers note that their results contribute one more piece of information to the puzzle of how the ESI charging process works but conclude that more detailed studies are needed. (Anal. Chem. 2007, 79, 9243–9252)
Quantitative analysis of protein complexes Ruedi Aebersold and co-workers at ETH Zurich, the University of Zurich, the Institute for Systems Biology, and the Competence Center for Systems Physiology and Metabolic Diseases (Switzerland) combined several proteomics methods into a single workflow to quantify and characterize phosphorylated protein complexes. With their new strategy, changes in the identities, amounts, and phosphorylation status of complex components can be determined. In the method, a tagged protein and
its associated proteins are pulled down in an affinity purification procedure. A mock-purified control sample also undergoes the same steps, but without a tagged protein. (Alternatively, the state of a complex under two conditions can be analyzed.) Each sample is divided into two, and all four samples are labeled for isobaric tagging for relative and absolute quantitation (iTRAQ). For phosphorylation analysis, phosphatases are added to one of the mock samples and to one of the affinity-purified samples. The samples are mixed, then analyzed by LC/MS/MS with a MALDI-TOF/TOF mass spectrometer. The investigators applied the method to the analysis of model proteins, a yeast complex, and a complex purified from Drosophila melanogaster cells. The method was reproducible, and it correctly identified known complex components and phosphorylated sites. (Mol. Cell. Proteomics 2007, DOI 10.1074/mcp. M700282-MCP200)
Notice to professional tea tasters: soon, you may be sharing your duties with an 1H-NMR spectrometer! Because tea tasters can be inconsistent in their quality assessments, Eiichiro Fukusaki and colleagues at Nara Prefectural Small and Medium Sized Enterprises Support Corp., Osaka University, and the University of Tokushima (all in Japan) developed an 1H-NMR-based method to discover the metabolic profile of high-quality green teas. In contrast to other analytical techniques that have been considered for determination of tea quality, 1H-NMR techniques are rapid, require little sample preparation, and allow researchers to conduct global metabolomics studies in one run. With this method, compounds associated with good-tasting green teas were identified. Teas that had been judged by professional tea tasters in a 2005 contest in Japan were analyzed. To find out which metabolites can be detected in green tea, the scientists identified water-soluble compounds in the best tea by 1H NMR. Then, the spectra of the 25 highest-ranking teas were compared with the spectra of the 28 lowest-quality teas. Differences were
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Tea tasters replaced by NMR?
Toolbox Mass spectrum quality assessment To assess mass spectral quality, Keng Wah Choo and Wai Mun Tham at Nan yang Polytechnic (Singapore) have ap plied self-convolution, a method that is commonly associated with digital com munication and image processing. Un like other prefiltering methods, this one does not require knowledge of the charge or length of the fragmented ion. Self-convolution takes into account the symmetrical nature of b- and y-ions, which are the ions that peptide identifi cation algorithms typically search. Pro cessing time was reduced by using a fast Fourier transform and its inverse transform without the direct-current component. The self-convolution approach was validated with theoretical and experi mental tandem mass spectra. A sepa rate group of 60 tandem mass spectra was analyzed as a test set. When the researchers added randomly generated peaks to spectra, the quality score was not affected, but when they removed bor y-ions or changed the intensities of these ions, the quality score decreased. Therefore, the researchers say that the method is resistant to noise or contami nants that are present in samples. (BMC Bioinformatics 2007, 8, 352)
Getting the most out of MS2 and MS3 data Mmmm? 1H-NMR analysis allows re searchers to assess the quality of this tea without taking even a sip.
apparent in the middle-frequency regions of the spectra. For example, caffeine peaks were present in the spectra of high-quality teas but were absent or reduced in the spectra of low-ranked ones. Multivariate analyses were conducted on the data to generate a metabolic fingerprint of good green tea. With principal components analysis, Fukusaki and colleagues discovered that signals from six metabolites differentiated good teas from bad ones. Finally, they developed a model in which the quality of a green tea could be predicted on the basis of its metabolites. (J. Agric. Food Chem. 2007, 23, 9330– 9336)
Proteomics researchers sometimes per form MS 3 to obtain more information when a protein is identified by a sin gle peptide or when phosphopeptides are present in a sample. Alexey Nesvizh skii and co-workers at the Universi ty of Michigan, ETH Zurich, the Institute for Systems Biology, and the Univer sity of Zurich have developed a meth od to analyze consecutive MS2 and MS 3 spectra. In this method, the research ers initially identify peptides by search ing the two types of spectra separate ly with the same search engine, and the identifications are independently vali dated with PeptideProphet. The match ing consecutive MS2 and MS 3 scans are coupled, and the peptide probabilities are adjusted for the linked data. Final ly, a combined probability score is ob tained. (Mol. Cell. Proteomics 2007, DOI 10.1074/mcp.M700128-MCP200)
Journal of Proteome Research • Vol. 7, No. 1, 2008 21
