A new algorithm is described for label-free quantitation of relative protein abundances across multiple complex proteomic samples. Q-MEND is based on the denoising and peak picking algorithm, MEND, previously developed in our laboratory. Q-MEND takes advantage of the high resolution and mass accuracy of the hybrid LTQFT MS mass spectrometer (or other high resolution mass spectrometers, such as a Q-TOF MS). The strategy, termed "cross-assignment", is introduced to increase substantially the number of quantitated proteins. In this approach, all MS/MS identifications for the set of analyzed samples are combined into a master ID list, and then each LC/ MS run is searched for the features that can be assigned to a specific identification from that master list. The reliability of quantitation is enhanced by quantitating separately all peptide charge states, along with a scoring procedure to filter out less reliable peptide abundance measurements. The effectiveness of Q-MEND is illustrated in the relative quantitative analysis of E.coli samples spiked with known amounts of non-E.coli protein digests. A mean quantitation accuracy of 7% and mean precision of 15% is demonstrated. Q-MEND can perform relative quantitation of a set of LC/MS datasets without manual intervention and can generate files compatible with the Guidelines for Proteomic Data Publication.

Recombinant tissue plasminogen (rt-PA) with 35 cysteine residues has been completely assigned by mapping the 17 disulfide linkages and the unpaired cysteine. The result is consistent with the prediction from homology except for the unassigned cysteine, which was identified at Cys83. This cysteine was found to be blocked and paired with either a glutathione or cysteine residue in a ~ 60 : 40 ratio, respectively. The analysis was conducted using a multi-fragmentation approach consisting of ETD and CID, in combination with a multi-enzyme digestion strategy (Lys-C, trypsin, and Glu-C). The disulfide-linked peptides, even those containing N or O-linked glycosylation, could be assigned since the disulfide bonds were still preferably cleaved over the glycosidic cleavages under ETD fragmentation. The use of a multiple and sequential enzymatic digestion strategy was important in producing fragment sizes suitable for analysis. For the analysis of complex intertwined disulfides, the use of CID MS 3 to target partially disulfide dissociated peptides from the ETD fragmentation was necessary for linkage assignment. The ability to identify the exact location and status of the unpaired cysteine (free or blocked with a glutathione or cysteine) could shed light on the activation of rt-PA, upon stimulation by either oxidative or ischemic stress.

In an attempt to develop high producing mammalian cell lines expressing glucagon-like-peptide-1-antibody fusion proteins (GLP-1), we have noted that the N-terminal GLP-1 portion of the fusion protein was susceptible to proteolytic degradation during cell culture, which resulted in an inactive product. The majority of the N-terminal clipped product appeared to be due to the removal of the entire biologically active peptide (30 amino acids) from the intact molecule. A number of parameters that influenced the degradative process were investigated. Additionally, protease inhibitors specific for each class of protease were tested. Results suggested that one or more serine-threonine class of protease(s) were involved in this process and inhibitors that are specific for this class of protease, including benzamidine hydrochloride could significantly inhibit the proteolytic degradation of the fusion proteins. Identification of the specific proteases involved in this process by shotgun proteomics methodology will pave the way for engineering the CHOK1SV cell line which will serve as a superior host for the production of future fusion protein products.

In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a model system for the evaluation of selective integration of subsets of transcriptomic and proteomic data. We assessed the oncogene status with RPKM values (Reads Per Kilobase per Million mapped reads1) for ERBB2 (14.4, 400 and 300 for SUM149, SUM 190 and SKBR3 respectively and for EGFR 60.1, not detected and 1.4 for the same 3 cell lines. We then used RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines (total 31) and interrogated the corresponding proteomics data sets for proteins with significant interaction values with these oncogenes. The number of observed interactors for each oncogene showed a significant range, e.g. 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a fraction of the total protein interactions in a given data set vs. total interactors for that oncogene in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D (Interologous Interaction Database, version 1.95). This approach allowed us to focus on 4 main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used the following bioinformatics sites, GeneGo, PathwayCommons and NCI receptor signaling networks to identify pathways which contained the four main oncogenes, had good coverage in the transcriptomic and proteomic data sets as well as significant number of oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-Seq values allowed the use of transcript ratios to correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for ERBB signaling, caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings: branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD), nucleolin (NCL) (high levels of EGFR transcript); transferrin receptor (TFRC), metadherin (MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 sub-pathway. Future studies of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to explore the significance of these observations.

The purpose of this discovery phase study was to identify candidate protein biomarkers for high-grade dysplastic cervical cells using mass spectrometry. Laser Capture Microdissection (LCM) was utilized to isolate high-grade dysplastic and normal cells from ThinPrep slides prepared from cervical cytological specimens. Following cell capture, samples were solubilized and proteins separated by gel electrophoresis in preparation for enzymatic digestion and liquid chromatography mass spectrometry analysis (LC-MS). Processed samples were subsequently analyzed using a linear ion trap coupled with a Fourier transform mass spectrometer (LTQ-FT MS). It was determined that both PreservCyt Solution and ThinPrep Pap Stain (Cytyc Corporation) were compatible with the sample processing and LC-MS analysis. In total, from 9 normal and 9 abnormal cervical cytological specimens, more than 1000 unique proteins were identified with high confidence, based on approximately 12,000 captured cells per specimen. Quantitative protein differences between HSIL (High-Grade Squamous Intraepithelial Lesion) and NILM (Negative for Intraepithelial Lesions or Malignancy) samples were determined by comparing the intensities of the representative (label-free) peptide ions. More than 200 proteins were found to exhibit a 3-fold difference in protein level. Interestingly, significant up-regulation of nuclear and mitochondrial proteins in HSIL specimens was noted. In several cases, the increased protein abundance observed in high-grade cells, as determined by quantitative LC-MS, was validated by immunocytochemical methods using ThinPrep cervical specimens. With the study of additional clinical specimens, the differential abundance of proteins in high-grade dysplastic cells versus morphologically normal cervical cells may lead to validated novel biomarkers for cervical disease.

This paper examines the retention behavior of recombinant DNA-derived human growth hormone (rhGH) in reversed-phase chromatography and its separation from the closely related N-methionyl variant (Met-hGH). It is first shown that retention for rhGH decreases with increasing column temperature when 1-propanol (1-PrOH) is used as organic modifier. On the other hand, retention increases with temperature when acetonitrile (CH3CN) is employed. The differences in behavior for the two organic modifiers could be related to conformational changes in the protein as determined by solution and adsorption intrinsic fluorescence spectroscopy. Specifically, desorption and elution of rhGH using 1-PrOH could be correlated with a solvent-induced conformational change, with retention decreasing with increasing temperature due to the increasing ease of structural alteration. On the other hand for CH3CN the increase in retention correlated with temperature rise was related to a partial structural change yielding a more hydrophobic species. In this case, a surface-driven process is suggested. The work then turned to the separation of rhGH and Met-hGH where it was found for both organic modifiers optimum separation occurred at 45 degrees C and pH 6.5. Separate studies revealed that during the conformational change Met-hGH appeared more hydrophobic than rhGH since protein-protein aggregation was observed at a lower 1-PrOH concentration. It is suggested that this hydrophobic difference, which was optimized under the conditions cited above, resulted in the separation. The study demonstrates the importance of conformational changes in retention behavior and separation of protein samples.

Human polyclonal IgG antibodies directly against the non-human sialic acid N-glycolylneuraminic acid (Neu5Gc) are potential biomarkers and mechanistic contributors to cancer and other diseases associated with chronic inflammation. Using a sialoglycan microarray, we screened the binding pattern of such antibodies (anti-Neu5Gc IgG) in several samples of clinically-approved human IVIG (IgG). These results were used to select an appropriate sample for a multi-step affinity purification of the xeno-autoantibody fraction. The sample was then analyzed via our multi-enzyme digestion procedure followed by nanoLC coupled to LTQ-FTMS. We used characteristic and unique peptide sequences to determine the IgG subclass distribution and thus provided direct evidence that all four IgG subclasses can be generated during a xeno-autoantibody immune response to carbohydrate Neu5Gc-antigens. Furthermore, we obtained a significant amount of sequence coverage of both the constant and variable regions. The approach described here, therefore, provides a way to characterize these clinically significant antibodies, helping to understand their origins and significance.

We have used powerful HPLC-mass spectrometric approaches to characterize the secreted form of epidermal growth factor receptor (sEGFR). We demonstrated that the amino acid sequence lacked the cytoplasmic domain and was consistent with the primary sequence reported for EGFR purified from a human plasma pool. One of the sEGFR forms, attributed to the alternative RNA splicing, was also confirmed by transcriptional analysis (RNA sequencing). Two unusual types of glycan structures were observed in sEGFR as compared with membrane-bound EGFR from the A431 cell line. The unusual glycan structures were di-sialylated glycans (sialic acid attached to sialic acid) at Asn-151 and N-acetylhexosamine attached to a branched fucosylated galactose with N-acetylglucosamine moieties (HexNAc-(Fuc)Gal-GlcNAc) at Asn-420. These unusual glycans at specific sites were either present at a much lower level or were not observable in membrane-bound EGFR present in the A431 cell lysate. The observation of these di-sialylated glycan structures was consistent with the observed expression of the corresponding ␣-N-acetylneuraminide ␣-2,8-sialyltransferase 2 (ST8SiA2) and ␣-N-acetylneuraminide ␣-2,8-sialyltransferase 4 (ST8SiA4), by quantitative real time RT-PCR. The connectivity present at the branched fucosylated galactose was also confirmed by methylation of the glycans followed by analysis with sequential fragmentation in mass spectrometry. We hypothesize that the presence of such glycan structures could promote secretion via anionic or steric repulsion mechanisms and thus facilitate the observation of these glycan forms in the secreted fractions. We plan to use this model system to facilitate the Cancers are disease-associated with considerable morbidity, such as disease recurrence, anxiety, and side effects of treatment and mortality (1). Early diagnosis often significantly improves survival rates compared with late stage cancer detection, such as for breast, lung, and colon cancers (2-4). Proteins in the blood hold enormous promise for early stage cancer diagnostic tests, but the complexity and dynamic range of blood have confounded the search for cancer biomarkers. Nevertheless, the pressing need for a clinical assay has prompted us to investigate a different approach toward discovering new breast cancer biomarkers circulating in blood (5-7). In addition, the use of a panel of cancer cell lines, representing cancers with different subtypes, could alleviate the difficulty of analyzing the plasma samples directly. Although there is no substitute for the direct study of clinical samples, genetic and molecular aberrations found in cell lines can be translated to similar dysregulations in tumors (8). Cell lines, through the proteins they secrete or shed, should be a complementary model system for the discovery of circulating blood markers. For cancer biomarkers, the change of gene or protein sequence, such as mutation, is often a preclusion for cancers. A similar argument could also be true for the change of protein glycan structures, which re...