Quantitative Proteomic Profiling Reveals Differentially Regulated Proteins in Cystic Fibrosis Cells

The most prevalent cause of cystic fibrosis (CF) is the deletion of a phenylalanine residue at position 508 in CFTR (ΔF508-CFTR) protein. The mutated protein fails to fold properly, is retained in the endoplasmic reticulum via the action of molecular chaperones, and is tagged for degradation. In this study, the differences in protein expression levels in CF cell models were assessed using a systems biology approach aided by the sensitivity of MudPIT proteomics. Analysis of the differential proteome modulation without a priori hypotheses has the potential to identify markers that have not yet been documented. These may also serve as the basis for developing new diagnostic and treatment modalities for CF. Several novel differentially expressed proteins observed in our study are likely to play important roles in the pathogenesis of CF and may serve as a useful resource for the CF scientific community

Behavioral and Proteomic Analysis of Stress Response in Zebrafish (<i>Danio rerio</i>)

The purpose of this study is to determine the behavioral and proteomic consequences of shock-induced stress in zebrafish (<i>Danio rerio</i>) as a vertebrate model. Here we describe the behavioral effects of exposure to predictable and unpredictable electric shock, together with quantitative tandem mass tag isobaric labeling workflow to detect altered protein candidates in response to shock exposure. Behavioral results demonstrate a hyperactivity response to electric shock and a suppression of activity to a stimulus predicting shock. On the basis of the quantitative changes in protein abundance following shock exposure, eight proteins were significantly up-regulated (HADHB, hspa8, hspa5, actb1, mych4, atp2a1, zgc:86709, and zgc:86725). These proteins contribute crucially in catalytic activities, stress response, cation transport, and motor activities. This behavioral proteomic driven study clearly showed that besides the rapid induction of heat shock proteins, other catalytic enzymes and cation transporters were rapidly elevated as a mechanism to counteract oxidative stress conditions resulting from elevated fear/anxiety levels

Behavioral and Proteomic Analysis of Stress Response in Zebrafish (<i>Danio rerio</i>)

The purpose of this study is to determine the behavioral and proteomic consequences of shock-induced stress in zebrafish (<i>Danio rerio</i>) as a vertebrate model. Here we describe the behavioral effects of exposure to predictable and unpredictable electric shock, together with quantitative tandem mass tag isobaric labeling workflow to detect altered protein candidates in response to shock exposure. Behavioral results demonstrate a hyperactivity response to electric shock and a suppression of activity to a stimulus predicting shock. On the basis of the quantitative changes in protein abundance following shock exposure, eight proteins were significantly up-regulated (HADHB, hspa8, hspa5, actb1, mych4, atp2a1, zgc:86709, and zgc:86725). These proteins contribute crucially in catalytic activities, stress response, cation transport, and motor activities. This behavioral proteomic driven study clearly showed that besides the rapid induction of heat shock proteins, other catalytic enzymes and cation transporters were rapidly elevated as a mechanism to counteract oxidative stress conditions resulting from elevated fear/anxiety levels

Comparison of Protein Expression Ratios Observed by Sixplex and Duplex TMT Labeling Method

Stable isotope labeling via isobaric derivatization of peptides is a universally applicable approach that enables concurrent identification and quantification of proteins in different samples using tandem mass spectrometry. In this study, we evaluated the performance of amine-reactive isobaric tandem mass tag (TMT), available as duplex and sixplex sets, with regard to their ability to elucidate protein expression changes. Using rat brain tissue from two different developmental time points, postnatal day 1 (p1) and 45 (p45), as a model system, we compared the protein expression ratios (p45/p1) observed using duplex TMT tags in triplicate measurements versus sixplex tag in a single LC–MS/MS analysis. A correlation of 0.79 in relative protein abundance was observed in the proteins quantified by these two sets of reagents. However, more proteins passed the criteria for significant fold change (−1.0 ≤ log₂ ratio (p45/p1) ≥ +1.0 and <i>p</i> < 0.05) in the sixplex analysis. Nevertheless, in both methods most proteins showing significant fold change were identified by multiple spectra, increasing their quantification precision. Additionally, the fold change in p45 rats against p1, observed in TMT experiments, was corroborated by a metabolic labeling strategy where relative quantification of differentially expressed proteins was obtained using ¹⁵N-labeled p45 rats as an internal standard

Modified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast

A modified multidimensional protein identification technology (MudPIT) separation was coupled to an LTQ Orbitrap Velos mass spectrometer and used to rapidly identify the near-complete yeast proteome from a whole cell tryptic digest. This modified online two-dimensional liquid chromatography separation consists of 39 strong cation exchange steps followed by a short 18.5 min reversed-phase (RP) gradient. A total of 4269 protein identifications were made from 4189 distinguishable protein families from yeast during log phase growth. The “Micro” MudPIT separation performed as well as a standard MudPIT separation in 40% less gradient time. The majority of the yeast proteome can now be routinely covered in less than a days’ time with high reproducibility and sensitivity. The newly devised separation method was used to detect changes in protein expression during cellular quiescence in yeast. An enrichment in the GO annotations “oxidation reduction”, “catabolic processing” and “cellular response to oxidative stress” was seen in the quiescent cellular fraction, consistent with their long-lived stress resistant phenotypes. Heterogeneity was observed in the stationary phase fraction with a less dense cell population showing reductions in KEGG pathway categories of “Ribosome” and “Proteasome”, further defining the complex nature of yeast populations present during stationary phase growth. In total, 4488 distinguishable protein families were identified in all cellular conditions tested

Modified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast

A modified multidimensional protein identification technology (MudPIT) separation was coupled to an LTQ Orbitrap Velos mass spectrometer and used to rapidly identify the near-complete yeast proteome from a whole cell tryptic digest. This modified online two-dimensional liquid chromatography separation consists of 39 strong cation exchange steps followed by a short 18.5 min reversed-phase (RP) gradient. A total of 4269 protein identifications were made from 4189 distinguishable protein families from yeast during log phase growth. The “Micro” MudPIT separation performed as well as a standard MudPIT separation in 40% less gradient time. The majority of the yeast proteome can now be routinely covered in less than a days’ time with high reproducibility and sensitivity. The newly devised separation method was used to detect changes in protein expression during cellular quiescence in yeast. An enrichment in the GO annotations “oxidation reduction”, “catabolic processing” and “cellular response to oxidative stress” was seen in the quiescent cellular fraction, consistent with their long-lived stress resistant phenotypes. Heterogeneity was observed in the stationary phase fraction with a less dense cell population showing reductions in KEGG pathway categories of “Ribosome” and “Proteasome”, further defining the complex nature of yeast populations present during stationary phase growth. In total, 4488 distinguishable protein families were identified in all cellular conditions tested

Quantitative Proteomic Profiling Reveals Differentially Regulated Proteins in Cystic Fibrosis Cells

The most prevalent cause of cystic fibrosis (CF) is the deletion of a phenylalanine residue at position 508 in CFTR (ΔF508-CFTR) protein. The mutated protein fails to fold properly, is retained in the endoplasmic reticulum via the action of molecular chaperones, and is tagged for degradation. In this study, the differences in protein expression levels in CF cell models were assessed using a systems biology approach aided by the sensitivity of MudPIT proteomics. Analysis of the differential proteome modulation without a priori hypotheses has the potential to identify markers that have not yet been documented. These may also serve as the basis for developing new diagnostic and treatment modalities for CF. Several novel differentially expressed proteins observed in our study are likely to play important roles in the pathogenesis of CF and may serve as a useful resource for the CF scientific community

Off-Line Multidimensional Liquid Chromatography and Auto Sampling Result in Sample Loss in LC/LC–MS/MS

Large-scale proteomics often employs two orthogonal separation methods to fractionate complex peptide mixtures. Fractionation can involve ion exchange separation coupled to reversed-phase separation or, more recently, two reversed-phase separations performed at different pH values. When multidimensional separations are combined with tandem mass spectrometry for protein identification, the strategy is often referred to as multidimensional protein identification technology (MudPIT). MudPIT has been used in either an automated (online) or manual (offline) format. In this study, we evaluated the performance of different MudPIT strategies by both label-free and tandem mass tag (TMT) isobaric tagging. Our findings revealed that online MudPIT provided more peptide/protein identifications and higher sequence coverage than offline platforms. When employing an off-line fractionation method with direct loading of samples onto the column from an eppendorf tube via a high-pressure device, a 5.3% loss in protein identifications is observed. When off-line fractionated samples are loaded via an autosampler, a 44.5% loss in protein identifications is observed compared with direct loading of samples onto a triphasic capillary column. Moreover, peptide recovery was significantly lower after offline fractionation than in online fractionation. Signal-to-noise (S/N) ratio, however, was not significantly altered between experimental groups. It is likely that offline sample collection results in stochastic peptide loss due to noncovalent adsorption to solid surfaces. Therefore, the use of the offline approaches should be considered carefully when processing minute quantities of valuable samples

PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights

Modified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast

A modified multidimensional protein identification technology (MudPIT) separation was coupled to an LTQ Orbitrap Velos mass spectrometer and used to rapidly identify the near-complete yeast proteome from a whole cell tryptic digest. This modified online two-dimensional liquid chromatography separation consists of 39 strong cation exchange steps followed by a short 18.5 min reversed-phase (RP) gradient. A total of 4269 protein identifications were made from 4189 distinguishable protein families from yeast during log phase growth. The “Micro” MudPIT separation performed as well as a standard MudPIT separation in 40% less gradient time. The majority of the yeast proteome can now be routinely covered in less than a days’ time with high reproducibility and sensitivity. The newly devised separation method was used to detect changes in protein expression during cellular quiescence in yeast. An enrichment in the GO annotations “oxidation reduction”, “catabolic processing” and “cellular response to oxidative stress” was seen in the quiescent cellular fraction, consistent with their long-lived stress resistant phenotypes. Heterogeneity was observed in the stationary phase fraction with a less dense cell population showing reductions in KEGG pathway categories of “Ribosome” and “Proteasome”, further defining the complex nature of yeast populations present during stationary phase growth. In total, 4488 distinguishable protein families were identified in all cellular conditions tested