currents
An LC column for ultratrace detection of peptides
for two proteins. According to the researchers, this is a high Although conventional capillary LC columns can sepaloading capacity, given the column’s small i.d. of 10 µm. Next, rate small amounts of sample, they cannot handle minute they determined the detection limit by loading a 10-amol tryptic digest of bovine serum alamounts. So Barry Karger and co-workers at Northeastern bumin on the PLOT column University have developed a and running the separated long porous-layer open tubupeptides on an ion trap mass lar (PLOT) capillary column spectrometer. Four peptides with an i.d. of 10 µm. With were confidently identified. this column, the researchers The researchers say that the could analyze as little as 10 detection limit is in the attoamol of a tryptic digest. mole range (~10 amol), but Scientists have attempted because of the high S/N obfor decades to develop PLOT served for one of the pepcolumns, but several obstatides, the detection limit for cles prevented their success. some species could be even For example, appropriate delower. tectors and pumps have only Finally, representative prorecently become available. teomics experiments were Currently, the most difficult run with the column. To demchallenge is to coat the suronstrate the extended-range face of the column with a uniproteomic analysis (known as form porous layer. Karger and ERPA), the researchers loadco-workers have overcome ed a Lys-C digest of epiderthis challenge by polymerizmal growth factor receptor, a Going inside. Electron micrograph of the interior of a PLOT column. ing styrene and divinylbenprotein that is phosphorylatzene within the PLOT column ed and glycosylated. They obin a single step. By using scanning electron microscopy, they tained high sequence coverage for the protein. Also, a tryptic verified that the polymer adheres to the walls in a relatively digest of the proteome of Methanosarcina acetivorans was uniform layer of 0.5–1.0 µm. This thickness is within the range analyzed. To enrich the sample, it was run through a monoliththat is predicted to be optimal for PLOT columns. ic precolumn before being loaded on the PLOT column. A total Several parameters were assessed for the new columns. of 566 unique proteins were identified in a single analysis. The Column performance was reproducible over many runs researchers say that the column length and the specific polyand among columns that were made at different times. The mer used could be tailored for various types of proteomics exloading capacity of the column was in the femtomole range periments. (Anal. Chem. 2007, doi 10.1021/ac061411m)
Kinetic proteome study of yeast These days, scientists use whole-genome microarrays to study changes in mRNA levels of an organism on a routine basis. But similar studies of the proteome are not yet commonplace. As a proof of concept, Mark Flory and colleagues at Wesleyan University; ETH Zurich; New York University; the University of California, Los Angeles; Cedars-Sinai Medical Center; the University of Washington; the University of Zurich; and the Institute for Systems Biology performed such a study. They followed the levels of about half of the proteins in the yeast Saccharomyces cerevisiae during a cell cycle. Yeast cells were synchronized in G1 phase (before DNA replication), then © 2007 American Chemical Society
released. Samples were taken at G1 and at 4 time points spaced 30 min apart. Proteins were labeled with acid-cleavable ICAT reagents. These reagents incorporate 13C/12C labels that allow the labeled and unlabeled protein pairs to coelute from a reversed-phase (RP) LC column. In addition, the biotin moiety can be cleaved; this feature makes the tag less bulky so analyzing it by MS is easier. After labeling, the proteins were digested and run on a strong-cation exchange column. The peptides were separated by RPLC and analyzed by MS/ MS. Validation was performed with the PeptideProphet algorithm. Flory and colleagues detected 2754 proteins, which represent 48% of the predicted S. cerevisiae proteome and ~60%
of the expressed proteome. As expected, some proteins were observed at every time point, whereas others were only observed in certain samples. The researchers say that, in the future, increasing the number of samples examined could boost proteome coverage to ~95%. Many proteins that are differentially regulated throughout the cell cycle were identified. Proteins from several intracellular compartments also were detected. Protein levels did not necessarily correspond to mRNA levels measured at similar time points in a previous study (Mol. Biol. Cell 1998, 9, 3273–3297). This observation could be explained by posttranslational regulation mechanisms, say the researchers. (Proteomics 2006, 6, 6146–6157)
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Toolbox SpectConnect for metabolomics When analyzing GC/MS data from multiple conditions, metabolomics researchers typically compare the experimental spectra with those stored in a reference library. But some peaks cannot be easily matched to spectra of known metabolites. To bypass the identification step, Gregory Stephanopoulos and colleagues at the Massachusetts Institute of Technology developed SpectConnect. This algorithm tracks unidentified metabolites that are present in multiple samples by comparing all of the spectra for every sample. In addition, the algorithm determines which metabolites differentiate the samples. SpectConnect relies on the increase in S/N observed when many replicates are run, so several technical and biological replicates are a requirement for optimum performance. Stephanopoulos and colleagues tested the algorithm on standard mixtures and on the metabolomes of three E. coli strains. Almost all of the known compounds in the standards were detected; the only exception was that isoleucine and leucine could not be distinguished with their methods. When the researchers analyzed the E. coli samples with SpectConnect and conventional methods, the new algorithm detected more potential biomarkers. (Anal. Chem. 2007, doi 10.1021/ac0614846)
ProteomeCommons.org IO Framework Developed by Phil Andrews and colleagues at the University of Michigan, the ProteomeCommons.org Input and Output (IO) Framework helps researchers to analyze MS data. For example, the IO Framework can extract raw data from various file formats, including proprietary ones. With this feature, the data can be easily converted to other formats, such as mzData and mzXML. In addition, peptide and protein sequence files can be manipulated. Also, the tool can shuffle or reverse protein sequences so users can test for false positives. Another feature of the IO Framework is that it filters mass spectral data on the basis of intensity or m /z range. The tool is open-source and freely available at www.proteomecommons. org/current/531. (Bioinformatics 2007, doi 10.1093/bioinformatics/btl573)
Proteins that bind PrP C
PHOTODISC
currents PrPC, the cellular prion protein, is expressed with various glycosylation patterns in many tissues. Its normal physiological function, however, still is unknown. Researchers hypothesize that the protein is converted to its prion form, PrPSc, by binding to other molecules. Although studies have shown that PrPC can interact with several proteins, most experiments were performed with a nonglycoBaaad proteins. Researchers identify new proteins that insylated recombinant version teract with PrP C , the cellular prion protein. isolated from bacteria. So Theodoros Sklaviadis and Spyros Petrakis at the Aristotle Univeron the recPrPC columns, and fractions sity of Thessaloniki (Greece) sought prowere collected. One fraction from each teins that bound to recombinant PrPC sample was run on a 1DE gel. The most and to PrPC purified from normal brains. intensely stained bands were analyzed They identified 15 proteins, including by LC/MS/MS. Because the sheep gemany that were not known to bind PrPC. nome is not yet completely sequenced, In the first set of experiments, Hisovine proteins were identified by homoltagged recombinant PrPC (recPrPC) was ogy to proteins from other organisms. used to isolate interacting proteins from Nine interacting proteins were identihuman and sheep brain homogenates. fied, two of which were validated by The PrPC protein was a sheep form, western blotting. which is >90% homologous to the huIn the next set of experiments, the man form. Brain homogenates were run researchers isolated detergent-insoluble
Ion/ion reactions for top-down proteomics
The multiple charges on electrosprayed ions can result in a complicated mass spectrum with many overlapping peaks when mixtures of proteins are analyzed. One way to simplify such spectra is to perform ion/ion reactions. These reactions reduce the charge states of the ions; this process decreases the number of peaks and spreads them out so they can be studied more easily. To determine the best mass spectrometer for the job, Scott McLuckey and colleagues at Purdue University performed simulations and calculated the informing power of three types of mass spectrometers by analyzing intact proteins with and without ion/ion reactions. They found that when ion/ion reactions are performed, a TOF instrument can provide as much information as an FTICR mass spectrometer for top-down MS analyses. McLuckey and colleagues simulated the mass distribution generated by a mixture of 200 proteins that were randomly distributed from m/z 5000 to 50,000. A charge-state distribution was produced for each protein, and the ratio of separated peaks to the total number of peaks was calculated. MS data were simulated with and without ion/ion reactions for a quadrupole ion trap (QIT), a TOF, and an FTICR instrument. Ion/ion reactions substantially improved the peak separation for the QIT and the TOF, but only a modest improvement was observed for the FT ICR. Overall, charge reduction with a TOF instrument was the best MS approach. Because product ion peaks can cluster around the precursor ion in an MS/MS spectrum, the researchers also simulated this type of data for the six approaches. Ion/ion reactions improved the analyses for all of the instruments tested. For MS/MS data, therefore, charge reduction with an FTICR mass spectrometer was the most informative method. (Anal. Chem. 2007, doi 10.1021/ac061798t)
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currents membrane microdomains (which are known to contain PrPC) from normal human brain tissue. They cross-linked interacting proteins in these preparations, then isolated the complexes by immunoprecipitation of the natively expressed PrPC protein with an antibody against PrP. The complexes were run on a 1DE gel and transferred to nitrocellulose for western blotting. The researchers observed three PrPC bands that ran at high molecular weights, ~300, 83, and 35 kDa; they deduced that these bands included proteins that were cross-linked to PrPC. The proteins in the bands were
identified by LC/MS/MS. Two of the eight proteins also had been identified in the previous experiment with recPrPC. The interacting proteins are involved in several processes, including ion transport, signal transduction, protein synthesis, and trafficking. In addition, PrPC interacted with two proteins, neuron-specific enolase and 14-3-3, that previously have been proposed as surrogate markers for prion diseases. The researchers say that future studies will address whether any of these proteins convert PrPC to PrPSc. (Proteomics 2006, 6, 6476–6484)
Candidate biomarkers for the diagnosis of kidney cancer
Many patients with kidney cancer don’t know they have the disease until it is at a late stage. Currently, no adequate treatments or biofluid screening tests exist. So Robert Weiss and co-workers at the University of California, Davis, and the Department of Veterans Affairs Northern California Health Care System examined tumor tissue and urine for candidate biomarkers of renal cell carcinoma (RCC). Proteins from RCC tumor tissue and from adjacent nontumor tissue were run on 2DE gels. When the researchers compared the spots on tumor and nontumor gels, they observed that 46 were differentially regulated. They identified the proteins in these spots by performing MS/MS and database searches. Of the proteins that were identified, 31 were significantly up- or down-regulated in tumor tissue. Several proteins discovered in the analysis already had been implicated in RCC and other cancers. The researchers validated their findings for two of the identified proteins. By western blotting, they observed that phospho-Hsp27, a heat-shock protein with an anti-apoptotic role, was highly expressed in the tumor tissue but not expressed as much in nontumor tissue or in three RCC cell lines. Levels of PKM2, a protein that may be involved in oxygen deprivation, also were higher in tumor tissue than in nontumor samples on western blots. By performing network analyses, Weiss and co-workers observed that the levels of proteins involved in glycolysis, carbohydrate metabolism, and
Possible biomarkers. Proteins that are increased (red) or decreased (blue) in kidney cancer cells could be markers. (Adapted with permission. Copyright 2006 Perroud et al.; licensee Bio Med Central Ltd.)
Toolbox tYNA for comparative interactomics Several systems exist that allow researchers to visualize networks of genes or proteins, but so far, none of them provide complex analysis capabilities. Therefore, Mark Gerstein and co-workers at Yale University and the Dana-Farber Cancer Institute developed TopNet-like Yale Network Analyzer (tYNA), an interactive web-based system that compares many networks simultaneously. Although the system already includes the most widely used net work data sets, it also can accept new data in several popular formats. Statistics, such as the clustering coefficient and betweenness, are calculated by tYNA. To identify hubs and bottlenecks in networks, users can filter the data on the basis of a statistical cutoff value. In addition, the system identifies various motifs, such as chains, cycles, and feed-forward loops. tYNA can highlight deficiencies in some networks, including defective cliques in which edges may be missing. Analyses can be performed on several networks at once; these networks then can be merged in to one composite network. The results can be downloaded and saved in several formats. (Bioinformatics 2006, 22, 2968–2970)
IntNetDB for protein–protein interactions
amino acid metabolism were significantly altered in RCC tumors. To test whether the products of any of these altered metabolic processes could be assayed in urine, the scientists conducted a metabolic profiling study. The levels of sorbitol and other sugar alcohols were significantly higher in the urine of RCC patients than in healthy controls. The researchers say that a diagnostic assay based on the detection of these metabolites could help physicians screen patients for RCC. In the meantime, they plan to test whether the proteins and metabolites identified in the experiments are unique to RCC or are common to other cancers. (Mol. Cancer 2006, 5, 64)
To provide the scientific community with an up-to-date database of reliable protein–protein interactions (PPIs), Jing-Dong Han and co-workers at the Chinese Academy of Sciences have developed IntNetDB. The database is a compilation of PPIs generated by seven analytical and bioinformatics methods. A likelihood ratio is calculated to represent the probability that a PPI as generated by each method is correct. The likelihood ratios are integrated by the Naïve Bayes model into one score. The PPI network derived from these analyses can be viewed with an online visualization tool called intView. A tool to extract clusters is also available. Han and co-workers plan to update the database regularly as new PPI information is published. The web interface is freely available by contacting Han. (BMC Bioinformatics 2006, 7, 508)
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