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PubMed2002

Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry.

The adaptation of sequences of chemical reactions to a solid-phase format has been essential to the automation, reproducibility, and efficiency of a number of biotechnological processes including peptide and oligonucleotide synthesis and sequencing. Here we describe a method for the site-specific, stable isotopic labeling of cysteinyl peptides in complex peptide mixtures through a solid-phase capture and release process, and the concomitant isolation of the labeled peptides. The recovered peptides were analyzed by microcapillary liquid chromatography and tandem mass spectrometry (microLC-MS/MS) to determine their sequences and relative quantities. The method was used to detect galactose-induced changes in protein abundance in the yeast Saccharomyces cerevisiae. A side-by-side comparison with the isotope-coded affinity tag (ICAT) method demonstrated that the solid-phase method for stable isotope tagging of peptides is comparatively simpler, more efficient, and more sensitive.

Chromatography, Liquid↗
PubMed2004

Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics.

To study the global dynamics of phosphotyrosine-based signaling events in early growth factor stimulation, we developed a mass spectrometric method that converts temporal changes to differences in peptide isotopic abundance. The proteomes of three cell populations were metabolically encoded with different stable isotopic forms of arginine. Each population was stimulated by epidermal growth factor for a different length of time, and tyrosine-phosphorylated proteins and closely associated binders were affinity purified. Arginine-containing peptides occurred in three forms, which were quantified; we then combined two experiments to generate five-point dynamic profiles. We identified 81 signaling proteins, including virtually all known epidermal growth factor receptor substrates, 31 novel effectors and the time course of their activation upon epidermal growth factor stimulation. Global activation profiles provide an informative perspective on cell signaling and will be crucial to modeling signaling networks in a systems biology approach.

Epidermal Growth Factor↗
PubMed2004

Effect of voluntary exercise on genetically obese Cpefat/fat mice: quantitative proteomics of serum.

OBJECTIVE: To compare the effect of voluntary exercise on body weight, food consumption, and levels of serum proteins between wild-type and carboxypeptidase E-deficient (Cpefat/fat) mice. RESEARCH METHODS AND PROCEDURES: Study 1 consisted of three groups of female mice: Cpefat/fat mice with continuous access to exercise wheels for 3 weeks (n = 4); wild-type C57BKS mice with access to exercise wheels for 3 weeks (n = 4); and sedentary Cpefat/fat mice (n = 3). Activity, body weight, and food consumption were monitored for this period and a subsequent 9-week period without exercise wheels. Study 2 consisted of four groups of male mice (n = 6 to 7 each): Cpefat/fat mice with exercise wheels, wild-type mice with exercise wheels, and Cpefat/fat and wild-type mice without exercise wheels. Body weight and food consumption were measured over 4 weeks. Sera were collected, and the protein profile was determined by 2-dimensional gel electrophoresis and mass spectrometry. RESULTS: Cpefat/fat mice were moderately hyperphagic but lost weight during the initial exercise period because of greater energy expenditure. The effect of exercise was temporary, and the mice gained weight after the second week. Several serum proteins were found to be altered by exercise: haptoglobin was decreased by exercise in Cpefat/fat mice, and several kallikreins were increased by exercise in wild-type mice. DISCUSSION: The access to exercise wheels provided an initial weight loss in Cpefat/fat mice, but this effect was offset by elevated food consumption. The serum proteomics results indicated that Cpefat/fat and wild-type mice differed in their response to exercise.

Adipose Tissue↗
PubMed2026

Quantitative proteomics reveals coordinated changes in the proteome during replicative senescence.

Cellular senescence is a state of irreversible cell cycle arrest triggered by telomere erosion, persistent DNA damage or chronic stress. The accumulation of senescent cells disrupts tissue function and contributes to aging and disease. Here, we employ mass spectrometry-based proteomics to systematically interrogate dynamic proteome changes at multiple levels during the progression of replicative cellular senescence. We demonstrate that proteome changes during senescence occur in a coordinated manner, characterized by widespread protein depletion on chromatin. Moreover, components of the cytoplasmic translation machinery are depleted, while mitochondrial proteins display increased insolubility. Autophagic and proteasome activity is compromised in senescent cells along with remodeling of ubiquitin linkages and depletion of ubiquitin E3 ligases. Comparison of the senescent proteome with different pathophysiological cellular states reveals a distinctive senescent signature shaped by changes in the proteostasis network. Collectively, we provide a resource for the exploration of temporally resolved changes in the senescent proteome.

Cellular Senescence↗
PubMed2004

Quantitative proteomics of the thyroid hormone receptor-coregulator interactions.

The thyroid hormone receptor regulates a diverse set of genes that control processes from embryonic development to adult homeostasis. Upon binding of thyroid hormone, the thyroid receptor releases corepressor proteins and undergoes a conformational change that allows for the interaction of coactivating proteins necessary for gene transcription. This interaction is mediated by a conserved motif, termed the NR box, found in many coregulators. Recent work has demonstrated that differentially assembled coregulator complexes can elicit specific biological responses. However, the mechanism for the selective assembly of these coregulator complexes has yet to be elucidated. To further understand the principles underlying thyroid receptor-coregulator selectivity, we designed a high-throughput in vitro binding assay to measure the equilibrium affinity of thyroid receptor to a library of potential coregulators in the presence of different ligands including the endogenous thyroid hormone T3, synthetic thyroid receptor beta-selective agonist GC-1, and antagonist NH-3. Using this homogenous method several coregulator NR boxes capable of associating with thyroid receptor at physiologically relevant concentrations were identified including ones found in traditional coactivating proteins such as SRC1, SRC2, TRAP220, TRBP, p300, and ARA70; and those in coregulators known to repress gene activation including RIP140 and DAX-1. In addition, it was discovered that the thyroid receptor-coregulator binding patterns vary with ligand and that this differential binding can be used to predict biological responses. Finally, it is demonstrated that this is a general method that can be applied to other nuclear receptors and can be used to establish rules for nuclear receptor-coregulator selectivity.

Amino Acid Motifs↗
PubMed2013

The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Acute Kidney Injury↗
PubMed2006

Label-free quantitative proteomics using large peptide data sets generated by nanoflow liquid chromatography and mass spectrometry.

We developed an integrated platform consisting of machinery and software modules that can apply vast amounts of data generated by nanoflow LC-MS to differential protein expression analyses. Unlabeled protein samples were completely digested with modified trypsin and separated by low speed (200 nl/min) one-dimensional HPLC. Mass spectra were obtained every 1 s by using the survey mode of a hybrid Q-TOF mass spectrometer and displayed in a two-dimensional plane with m/z values along the x axis, and retention time was displayed along the y axis. The time jitter of nano-LC was adjusted using newly developed software based on a dynamic programming algorithm. The comprehensiveness (60,000-160,000 peaks above the predetermined threshold detectable in 60-microg cell protein samples), reproducibility (average coefficient of variance of 0.35-0.39 and correlation coefficient of over 0.92 between duplicates), and accurate quantification with a wide dynamic range (over 10(3)) of our platform warrant its application to various types of experimental and translational proteomics.

Actinin↗
PubMed2006

Quantitative proteomics reveals posttranslational control as a regulatory factor in primary hematopoietic stem cells.

The proteome is determined by rates of transcription, translation, and protein turnover. Definition of stem cell populations therefore requires a stem cell proteome signature. However, the limit to the number of primary cells available has restricted extensive proteomic analysis. We present a mass spectrometric method using an isobaric covalent modification of peptides for relative quantification (iTRAQ), which was employed to compare the proteomes of approximately 1 million long-term reconstituting hematopoietic stem cells (Lin(-)Sca(+)Kit(+); LSK(+)) and non-long-term reconstituting progenitor cells (Lin(-)Sca(+)Kit(-); LSK(-)), respectively. Extensive 2-dimensional liquid chromatography (LC) peptide separation prior to mass spectrometry (MS) enabled enhanced proteome coverage with relative quantification of 948 proteins. Of the 145 changes in the proteome, 54% were not seen in the transcriptome. Hypoxia-related changes in proteins controlling metabolism and oxidative protection were observed, indicating that LSK(+) cells are adapted for anaerobic environments. This approach can define proteomic changes in primary samples, thereby characterizing the molecular signature of stem cells and their progeny.

Animals↗
PubMed2001

Proteomic analysis of the brain in Alzheimer's disease: molecular phenotype of a complex disease process.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder accounting for about 50% of all dementias, yet its pathogenic mechanisms remain poorly understood. In order to provide a more complete picture of pathogenesis in AD, we analysed six human brain regions for alterations in their proteomes. Quantitative proteome analysis was used to compare signals corresponding to individual proteins between post mortem brain tissues from persons with AD, and those from age-matched nondemented control (NC) tissues. In severely injured brain regions, 76 proteins were differentially expressed in AD hippocampus compared with NC, 62 proteins were differentially expressed in temporal cortex, and 39 proteins were differentially expressed in entorhinal cortex. Significant differences were also present in relatively spared regions. Thus, 34 proteins were differentially expressed in AD cerebellum compared with NC, 125 proteins were differentially expressed in cingulate gyrus, and 75 proteins were differentially expressed in sensorimotor cortex. The identity of 37 of these proteins was determined, and the possible relevance of changes in key pathogenic pathways analysed. These studies provide a unique snapshot illustrating the complexity of interrelated disease mechanisms at work in a complex, multifactorial disease, and show that comparative proteome analysis is a method with the power to develop important new insights into pathogenic mechanisms in the dementias.

Alzheimer Disease↗
PubMed2003

Recent liquid chromatographic-(tandem) mass spectrometric applications in proteomics.

Conventional proteomics makes use of two-dimensional gel electrophoresis followed by mass spectrometric analysis of typtic fragments derived from in-gel digestion of proteins. Although being a very strong technique capable of separating and visualizing hundreds of proteins, 2D-gel electrophoresis has some well-documented disadvantages as well. More recently, liquid chromatographic-(tandem) mass spectrometric techniques have been developed to overcome some of the shortcomings of 2D-gel electrophoresis. In this review we have described several recent applications of liquid chromatography-(tandem) mass spectrometry in the field of proteomics and especially in the field of membrane proteomics, quantitative proteomics and in the analysis of post-translational modifications.

Chromatography, Liquid↗
PubMedDate not supplied

Implications of new proteomics strategies for biology and medicine.

Advances in proteomics have fundamentally changed the paradigm of discovery for drug targets and novel biomarkers. Proteomics methodologies currently used will be reviewed in this paper, including structural proteomics, quantitative proteomics, and functional proteomics. A strategy to identify differentially expressed cell surface proteins as monoclonal therapeutic targets in oncology will be discussed.

Biology↗
PubMed2006

[Advances in plant proteomics--I. Key techniques of proteome].

With the completion of genome sequences of the model plants, such as rice (Oryza sativa L.) and Arabidopsis thaliana, it has come into plant functional genomics era, which becomes a hard base of the appearance and development of plant proteomics. This review focuses on the background of proteomics, concept of proteomics and key techniques of proteomics. The key techniques of proteomics include separation, such as 2-DE (Two-Dimensional Electrophoresis), RP-HPLC (Reverse Phase High Performance Liquid Chromatography) and SELDI (Surface Enhanced Laser Desorption/ Ionization) protein chip, mass spectrometry, such as MALDI-TOF-MS (Matrix Assisted Laser Desorption/Ionization-Time Of Flight-Mass Spectrometry) and ESI-MS/MS (Electrospray Ionization Mass Spectrometry/Mass Spectrometry), databases related to proteomics, quantitative proteome, TAP (Tandem Affinity Purification) and yeast two-hybrid system. Challenges and prospects of proteomic techniques are discussed.

Computational Biology↗
PubMed2005

Mass spectrometry-based proteomics turns quantitative.

The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.

Base Sequence↗
PubMed2006

Quality control metrics for LC-MS feature detection tools demonstrated on Saccharomyces cerevisiae proteomic profiles.

Quantitative proteomic profiling using liquid chromatography-mass spectrometry is emerging as an important tool for biomarker discovery, prompting development of algorithms for high-throughput peptide feature detection in complex samples. However, neither annotated standard data sets nor quality control metrics currently exist for assessing the validity of feature detection algorithms. We propose a quality control metric, Mass Deviance, for assessing the accuracy of feature detection tools. Because the Mass Deviance metric is derived from the natural distribution of peptide masses, it is machine- and proteome-independent and enables assessment of feature detection tools in the absence of completely annotated data sets. We validate the use of Mass Deviance with a second, independent metric that is based on isotopic distributions, demonstrating that we can use Mass Deviance to identify aberrant features with high accuracy. We then demonstrate the use of independent metrics in tandem as a robust way to evaluate the performance of peptide feature detection algorithms. This work is done on complex LC-MS profiles of Saccharomyces cerevisiae which present a significant challenge to peptide feature detection algorithms.

Algorithms↗
PubMed2005

Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach.

Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can subsequently be tested using other techniques for use in early detection of cancers. Here we demonstrate the use of stable isotope labeling with amino acids in cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5-fold change), several of which were previously reported as either up-regulated (e.g. cathepsin D, macrophage colony stimulation factor, and fibronectin receptor) or down-regulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have not been correlated previously with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta1), and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins not described previously to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, apoE, and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly the protein expression data from SILAC were compared with mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and human pancreatic duct epithelial), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.

Biomarkers, Tumor↗
PubMed2004

Chemical probes and tandem mass spectrometry: a strategy for the quantitative analysis of proteomes and subproteomes.

Quantitative proteome profiling using mass spectrometry and stable isotope dilution is being widely applied for the functional analysis of biological systems and for the detection of clinical, diagnostic or prognostic marker proteins. Because of the enormous complexity of proteomes, their comprehensive analysis is unlikely to be routinely achieved in the near future. However, in recent years, significant progress has been achieved focusing quantitative proteomic analyses on specific protein classes or subproteomes that are rich in biologically or clinically important information. Such projects typically combine the use of chemical probes that are specific for a targeted group of proteins and may contain stable isotope signatures for accurate quantification with automated tandem mass spectrometry and bioinformatics tools for data analysis. In this review, we summarize technical and conceptual advances in quantitative subproteome profiling based on tandem mass spectrometry and chemical probes.

Amino Acids↗