Trade-Off between High Sensitivity and Increased Potential for False Positive Peptide Sequence Matches Using a Two-Dimensional Linear Ion Trap for Tandem Mass Spectrometry-Based Proteomics

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219

129

Obesity is a state of mild inflammation correlated with increased oxidative stress. In general, pro-oxidative conditions lead to production of reactive aldehydes such as trans-4-hydroxy-2-nonenal (4-HNE) and trans-4-oxo-2-nonenal implicated in the development of a variety of metabolic diseases. To investigate protein modification by 4-HNE as a consequence of obesity and its potential relationship to the development of insulin resistance, proteomics technologies were utilized to identify aldehydemodified proteins in adipose tissue. Adipose proteins from lean insulin-sensitive and obese insulin-resistant C57Bl/6J mice were incubated with biotin hydrazide and detected using horseradish peroxidase-conjugated streptavidin. High carbohydrate, high fat feeding of mice resulted in a ϳ2-3-fold increase in total adipose protein carbonylation. Consistent with an increase in oxidative stress in obesity, the abundance of glutathione S-transferase A4 (GSTA4), a key enzyme responsible for metabolizing 4-HNE, was decreased ϳ3-4-fold in adipose tissue of obese mice. To identify specific carbonylated proteins, biotin hydrazide-modified adipose proteins from obese mice were captured using avidin-Sepharose affinity chromatography, proteolytically digested, and subjected to LC-ESI MS/MS. Interestingly enzymes involved in cellular stress response, lipotoxicity, and insulin signaling such as glutathione S-transferase M1, peroxiredoxin 1, glutathione peroxidase 1, eukaryotic elongation factor 1␣-1 (eEF1␣1), and filamin A were identified. The adipocyte fatty acid-binding protein, a protein implicated in the regulation of insulin resistance, was found to be carbonylated in vivo with 4-HNE. In vitro modification of adipocyte fatty acid-binding protein with 4-HNE was mapped to Cys-117, occurred equivalently using either the R or S enantiomer of 4-HNE, and reduced the affinity of the protein for fatty acids ϳ10-fold. These results indicate that obesity is accompanied by an increase in the carbonylation of a number of adipose-regulatory proteins that may serve as a mechanistic link between increased oxidative stress and the development of insulin resistance. Molecular & Cellular Proteomics 6:624 -637, 2007. Reactive oxygen species (ROS)1 and reactive nitrogen species are generated as a result of normal metabolic processes in the cell, including the uncoupling of the electron transport chain in the mitochondria and the oxidation of excess NADPH by NADPH oxidase (1). Oxidative stress occurs due to increased pro-oxidative conditions or the decline of antioxidant systems and is highly correlated with a wide variety of inflammatory and metabolic disease states, including Alzheimer disease, macular degeneration, lung dysfunction, and obesity-linked insulin resistance (2-5). Although the precise role of oxidative stress in disease mechanisms is not completely understood, ROS are known to be highly reactive with proteins, DNA, carbohydrates, and lipids in the cell. The ROSinitiated peroxidation of polyunsaturated acyl chains of membrane phospholipid...

Section: Identification Of Carbonylated Proteins By Lc-esi Ms/ms-supporting

confidence: 91%

Carbonylation of Adipose Proteins in Obesity and Insulin Resistance

Grimsrud

Picklo

Griffin

et al. 2007

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219

129

“…7). An increased number of chimera spectra may explain why the ⌬CN method is less effective at recovering low XCorr hits in the LTQ dataset as also reported recently (33). The use of MAE PIC and IntFrag scoring will aid in determining the presence of chimera spectra to evaluate the prevalence of this problem.…”

Section: Mae Data Mining Functions Provide Detailed Analyses Of Ms/msmentioning

confidence: 74%

Improved Validation of Peptide MS/MS Assignments Using Spectral Intensity Prediction

Sun¹,

Meyer-Arendt

Eichelberger

et al. 2007

A major limitation in identifying peptides from complex mixtures by shotgun proteomics is the ability of search programs to accurately assign peptide sequences using mass spectrometric fragmentation spectra (MS/MS spectra). Manual analysis is used to assess borderline identifications; however, it is error-prone and time-consuming, and criteria for acceptance or rejection are not well defined. Here we report a Manual Analysis Emulator (MAE) program that evaluates results from search programs by implementing two commonly used criteria: 1) consistency of fragment ion intensities with predicted gas phase chemistry and 2) whether a high proportion of the ion intensity (proportion of ion current (PIC)) in the MS/MS spectra can be derived from the peptide sequence. Recent advances in genome sequencing, MS instrumentation, and chromatographic methods allow high throughput identification of peptide fragmentation spectra (MS/MS spectra)1 from samples as complex as whole cell extracts (1, 2). For very complex samples, the best results are obtained from ion trap mass spectrometers due to their fast scanning rate and ability to rapidly shift between MS and MS/MS modes during data collection. However, when operated for high data collection rate, mass accuracy and resolution are compromised. It was recognized early on that scores from search programs showed poor discrimination between correct and incorrect sequence assignments when using ion trap MS/MS spectral data to search large protein databases (3). Methods have been developed to specify thresholds for acceptance either by searching datasets against an inverted sequence database of similar size to identify false positive thresholds (4) or by statistical analysis of multiple scores and results from normal searches (4, 5). Using methods such as these, limits on search program scores or combinations of scores can be set to yield a low number of false positives (6), but they also will produce large false negative rates (4, 7). This problem is more acute when larger databases are used.To minimize false negatives, investigators often reduce the acceptance threshold to capture more information. Several methods have been developed to filter the resulting false positives based on agreement between sequence composition of the peptides and their behavior on ion exchange or reverse phase chromatography (7, 8), probability of missed cleavages (9), exact mass measurements (8), or differences in scores between the top ranking peptides and lower ranked candidates (10, 11). Methods have also utilized intensity in- 1 The abbreviations used are: MS/MS spectra, fragmentation spectra; ⌬CN, difference between first and second ranked assignments by XCorr; DTA, text file summary of MS/MS spectral information; sDTA, simplified DTA; IntFrag score, proportion of ion current assigned to internal fragment ions;

“…The steps of our three-step method are: 1) preparative IEF using free flow electrophoresis (FFE), 2) SCX chromatography, and 3) LC on line with ESI-MS/MS. We show that our method effectively fractionates complex peptide mixtures and increases significantly the number of proteins identified compared with our earlier described two-step method while retaining the benefits of peptide pI for high confidence protein identification (25,26,31). We identified over 1000 human proteins (each matched by at least two peptides) from the cells in whole saliva from OSCC patients, with a number of these being low abundance proteins having a previously described association in oral cancer progression, and identified for the first time directly from whole saliva.…”

mentioning

confidence: 87%

“…Our previous studies (11,23) have shown that the average pI of the FFE fraction approximates the pH of the fraction and is useful for filtering peptide sequence matches. Using this average pI assigned to each FFE fraction, only peptide matches (regardless of assigned p score) were kept for further consideration if their predicted pI was within Ϯ0.5 units, based on the FFE resolution (23), of the average pI value for the FFE fraction from which they were identified and if the peptide sequence was at least partially tryptic to maximize the high confidence matches (31). Those peptides passing this first phase of filtering were then further filtered using a two-step procedure similar to that we have described previously (11).…”

Section: Lc-ms/msmentioning

confidence: 99%

Proteomics Analysis of Cells in Whole Saliva from Oral Cancer Patients via Value-added Three-dimensional Peptide Fractionation and Tandem Mass Spectrometry

Xie

Onsongo

Popko

et al. 2008

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Whole human saliva possesses tremendous potential in clinical diagnostics, particularly for conditions within the oral cavity such as oral cancer. Although many have studied the soluble fraction of whole saliva, few have taken advantage of the diagnostic potential of the cells present in saliva, and none have taken advantage of proteomics capabilities for their study. We report on a novel proteomics method with which we characterized for the first time cells contained in whole saliva from patients diagnosed with oral squamous cell carcinoma. Our method uses three dimensions of peptide fractionation, combining the following steps: preparative IEF using free flow electrophoresis, strong cation exchange step gradient chromatography, and microcapillary reverse-phase liquid chromatography. We determined that the whole saliva samples contained enough cells, mostly exfoliated epithelial cells, providing adequate amounts of total protein for proteomics analysis. From a mixture of four oral cancer patient samples, the analysis resulted in a catalogue of over 1000 human proteins, each identified from at least two peptides, including numerous proteins with a role in oral squamous cell carcinoma signaling and tumorigenesis pathways. Additionally proteins from over 30 different bacteria were identified, some of which putatively contribute to cancer development. The combination of preparative IEF followed by strong cation exchange chromatography effectively fractionated the complex peptide mixtures despite the closely related physiochemical peptide properties of these separations (pI and solution phase charge, respectively). Furthermore compared with our two-step method combining preparative IEF and reversephase liquid chromatography, our three-step method identified significantly more cellular proteins while retaining higher confidence protein identification enabled by peptide pI information gained through IEF. Thus, for detecting salivary markers of oral cancer and possibly other conditions of the oral cavity, the results confirm both the potential of analyzing the cells in whole saliva and doing so with our proteomics method. Molecular & Cellular Proteomics 7:486 -498, 2008.Whole human saliva is easily collected in the clinic in a non-invasive, on-demand manner and in relatively large, easily stored quantities, making it an optimal bodily fluid for clinical diagnostics (1, 2). Diagnosis of oral cancer could benefit greatly from the development of whole saliva-based clinical tests given the physical proximity of the site of cancer development with the diagnostic fluid. In fact, oral cancer, usually diagnosed in the form of oral squamous cell carcinoma (OSCC), 1 has not seen a drop in its 50% mortality rate over the last 30 years (3), motivating the development of new and reliable, saliva-based clinical diagnostic tests for the early detection of OSCC. Such tests would lead to more informed treatment of patients and a reduction in the suffering and death caused by this cancer.To develop these tests, molecular markers that ar...