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PubMed2026

Integration of metabolomics and proteomics reveals the toxicological mechanisms of environmentally relevant concentrations of cadmium on juvenile rockfish (Sebastes schlegelii).

As a highly toxic heavy metal, cadmium (Cd) is widely distributed in the coastal environments of the Bohai Sea, posing significant ecological and health risks. This is of particular concern for Sebastes schlegelii, a rockfish species commonly found along the Bohai coast and consumed by local populations. In this study, juvenile S. schlegelii were randomly assigned to three groups (control, 5 and 50 μg/L Cd) for a 14-day exposure period, followed by analysis of Cd bioaccumulation, as well as metabolomic and proteomic profiling. ICP-MS analysis indicated dose-dependent Cd bioaccumulation in the whole body, with 0.11 ± 0.07 μg/g dry weight in the 5 μg/L group and 0.38 ± 0.09 μg/g dry weight in the 50 μg/L group (9.5-fold higher than the control, p < 0.05). An iTRAQ-based proteomic analysis determined 168 differentially expressed proteins, while 1H NMR-based metabolomic profiling identified 34 metabolites with significant alterations. Integrated analysis of the proteomic and metabolomic data provided insights into the molecular responses of juvenile rockfish to Cd exposure. Specifically, metabolomic results indicated significant alterations in key metabolites, including lactate, phosphocholine, adenosine triphosphate, alanine, and inosine in the Cd-treated groups. Proteomic analysis further suggested that Cd exposure was associated with immune and oxidative stress responses, neurotoxicity, cellular damage, and disruptions in critical metabolic pathways, such as glycolysis, the tricarboxylic acid cycle, amino acid and lipid metabolism. Overall, this study demonstrates the utility of integrating proteomics and metabolomics to characterize molecular responses to Cd stress in juvenile S. schlegelii.

Animals↗

PubMed2006

Identification of developmentally-regulated proteins in Leishmania panamensis by proteome profiling of promastigotes and axenic amastigotes.

We have employed proteomics to identify proteins upregulated in the amastigote life-stage of Leishmaniapanamensis, using axenically-differentiated forms as models of authentic intracellular parasites. Resolution of the soluble proteomes of axenic amastigotes and promastigotes by two-dimensional electrophoresis (2DE) in the neutral pI range (5-7) revealed equivalent numbers of protein spots in both life-stages (644-682 using Coomassie Blue and 851-863 by silver staining). Although representing a relatively low proportion (8.1-10.8%) of the predicted 8000 gene products of Leishmania, these proteome maps enabled the reproducible detection of 75 differentially-regulated protein spots in amastigotes, comprising 24 spots "uniquely" expressed in this life-stage and 51 over-expressed by 1.2-5.7-fold compared to promastigotes. Of the 11 amastigote-specific spots analysed by mass spectrometry (MS), 5 yielded peptide sequences with no orthologues in Leishmania major, and the remaining 6 were identified as 7 distinct proteins (some of which were truncated isoforms) representing several functional classes: carbohydrate/energy metabolism (fructose 1,6-bisphosphate aldolase, glucose 6-phosphate dehydrogenase, pyruvate dehydrogenase), stress response (heat shock protein [HSP] 83), cell membrane/cytoskeleton (beta-tubulin), amino acid metabolism (cysteine synthase) and cell-cycle (ran-binding protein). Four additional over-expressed spots were tentatively identified as HSPs 60 and 70 and HSP 70-related proteins -1 and -4 by positional analogy with these landmark proteins in the Leishmania guyanensis proteome. Our data demonstrate the feasibility of proteomics as an approach to identify novel developmentally-regulated proteins linked to Leishmania differentiation and intracellular survival, while simultaneously pinpointing therapeutic targets. In particular, the amastigote-specific expression of cysteine synthase underlines the importance of de novo cysteine synthesis both as a potential parasite virulence factor and as a major metabolic difference from mammalian host cells.

Amino Acid Sequence↗

PubMed2004

The hydrophobic proteome of mitochondrial membranes from Arabidopsis cell suspensions.

The development of mitochondria and the integration of their function within a plant cell rely on the presence of a complex biochemical machinery located within their limiting membranes. The aim of the present work was: (1) to enhance our understanding of the biochemical machinery of mitochondrial membranes and (2) to test the versatility of the procedure developed for the identification of the hydrophobic proteome of the chloroplast envelope [Molecular and Cellular Proteomics 2 (2003) 325-345]. A proteomic analysis was performed, to provide the most exhaustive view of the protein repertoire of these membranes. For this purpose, highly purified mitochondria were prepared from Arabidopsis cultured cells and membrane proteins were extracted. To get a more exhaustive array of membrane proteins from Arabidopsis mitochondria, from the most to the less hydrophobic ones, various extraction procedures (chloroform/methanol extraction, alkaline or saline treatments) were applied. LC-MS/MS analyses were then performed on each membrane subfraction, leading to the identification of more than 110 proteins. The identification of these proteins is discussed with respect to their mitochondrial localization, their physicochemical properties and their implications in the metabolism of mitochondria. In order to provide a new overview of the biochemical machinery of the plant mitochondria, proteins identified during this work were compared to the lists of proteins identified during previous proteomic analyses performed on plant and algae mitochondria (Arabidopsis, pea, Chlamydomonas, rice, etc.). A total of 502 proteins are listed. About 40% of the 114 proteins identified during this work were not identified during previous proteomic studies performed on mitochondria.

Arabidopsis↗

PubMed2026

Temporal DIA-MS proteomics reveals coordinated metabolic reprogramming associated with oil accumulation in oil palm mesocarp.

Oil palm (Elaeis guineensis Jacq.) is the most productive oil-bearing crop globally, yet the molecular basis of mesocarp development and lipid accumulation remains poorly understood. Ultra-deep data-independent acquisition mass spectrometry (DIA-MS) was applied to characterize proteome dynamics in two contrasting genotypes, seedless (KS) and thin-shelled (TS), across five developmental stages (P1-P5) spanning fruit development to mature oil accumulation. Phenotypic analysis revealed higher mesocarp proportion and oil content in KS during late maturation. A total of 137,615 peptides corresponding to 12,163 protein groups were identified, providing a temporal proteomic landscape of mesocarp development. Multivariate analysis indicated that developmental progression was the primary contributor to proteomic variation, whereas genotype-associated differences increased during lipid accumulation. Differentially abundant proteins were mainly associated with carbohydrate metabolism, photosynthesis, proteolysis, antioxidant responses, and lipid biosynthesis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and KOG analyses suggested extensive remodeling of metabolic networks, including developmental changes in photosynthesis-associated proteins and increased representation of lipid-associated pathways during maturation. Weighted protein co-expression network analysis identified 17 modules associated with developmental progression and lipid accumulation, highlighting candidate proteins involved in carbon metabolism, energy production, and cellular protection. Genes encoding selected hub protein candidates were further examined by RT-qPCR. Biochemical analyses supported these proteomic patterns, showing increased acetyl-CoA availability, enhanced antioxidant enzyme activities (SOD, CAT, APX, and GR), improved GSH/GSSG balance, and reduced oxidative damage in KS. Together, these findings provide a temporal proteomic and biochemical framework for understanding genotype-associated differences in oil accumulation and identify candidate metabolic networks for functional studies.

Carbon metabolism↗

PubMed2004

Expression and functional proteomics studies in colorectal cancer.

Cell dysfunction results from multiple rather than from single gene interactions in the majority of colorectal cancers (CRC). Proteins, not mRNA, are the functional molecules in the cell, and the relationship between gene expression measured at the mRNA level and the corresponding protein level is not linear. Current proteomics tools allow for the determination of post-translational modifications, and hence the presence of protein isoforms--some of them being disease-relevant. Thus, proteomics approaches are a welcome complement to traditional genetic approaches. In CRC, expression proteomics studies were carried out with colorectal cell lines, whole tissue biopsies, and purified epithelial cells. For CRC, two-dimensional electrophoresis reference maps, protein, and membrane protein databases are available on the internet. Functional proteomics studies have been performed to better understand signaling pathways, to characterize the molecular targets of novel drugs, and to identify tumor-associated antigens in CRC. The increasing use of proteomics technologies, when addressing clinical problems, will accelerate the evolution towards personalized medicine in CRC.

Adenocarcinoma↗

PubMed2004

Application of proteomic technologies in the drug development process.

Proteins are the principal targets of drug discovery. Most large pharmaceutical companies now have a proteomics-oriented biotech or academic partner or have started their own proteomics division. Common applications of proteomics in the drug industry include target identification and validation, identification of efficacy and toxicity biomarkers from readily accessible biological fluids, and investigations into mechanisms of drug action or toxicity. Target identification and validation involves identifying proteins whose expression levels or activities change in disease states. These proteins may serve as potential therapeutic targets or may be used to classify patients for clinical trials. Proteomics technologies may also help identify protein-protein interactions that influence either the disease state or the proposed therapy. Efficacy biomarkers are used to assess whether target modulation has occurred. They are used for the characterization of disease models and to assess the effects and mechanism of action of lead candidates in animal models. Toxicity (safety) biomarkers are used to screen compounds in pre-clinical studies for target organ toxicities as well as later on in development during clinical trials. Complementary approaches such as metabolomics and genomics can be used in conjunction with proteomics throughout the drug development process to create more of a unified, systems biology approach.

Animals↗

PubMed2006

Methods for proteomics in neuroscience.

Proteomics reveals complex protein expression, function, interactions and localization in different phenotypes of neuron. As proteomics, regarded as a highly complex screening technology, moves from a theoretical approach to practical reality, neuroscientists have to determine the most-appropriate applications for this technology. Even though proteomics compliments genomics, it is in sheer contrast to the basically constant genome due to its dynamic nature. Neuroscientists have to surmount difficulties particular to the research in neuroscience; such as limited sample amounts, heterogeneous cellular compositions in samples and the fact that many proteins of interest are hydrophobic proteins. The necessity of exclusive technology, sophisticated software and skilled manpower tops the challenge. This review examines subcellular organelle isolation, protein fractionation and separation using two-dimensional gel electrophoresis (2-DGE) as well as multi-dimensional liquid chromatography (LC) followed by mass spectrometry (MS). The methods for quantifying relative gene product expression between samples (e.g., two-dimensional difference in gel electrophoresis (2D-DIGE), isotope-coded affinity tag (ICAT) and iTRAQ) are elaborated. An overview of the techniques used currently to assign post-translational modification status on a proteomics scale is also evaluated. The feasible coverage of the proteome, ability to detect unique cell components such as post-synaptic densities and membrane proteins, resource requirements and quantitative as well as qualitative reliability of different approaches is also discussed. While there are many challenges in neuroproteomics, this field promises many returns in the future.

Animals↗

PubMed2002

Use of proteomic patterns in serum to identify ovarian cancer.

BACKGROUND: New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. METHODS: Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. FINDINGS: The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99). INTERPRETATION: These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.

CA-125 Antigen↗

PubMed2006

Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.

BACKGROUND: We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. METHODS: We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. FINDINGS: Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93.5%, specificity 94.9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. INTERPRETATION: The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.

Adolescent↗

PubMed2003

[Proteomics and cardiovascular disease].

The description of the human genome has opened new venues for the study and understanding of pathophysiological phenomena. In the 20th century, individual cell components were studied. The 21st century began with a global analysis of cell components. Thanks to the development of new technologies such as DNA chips, or two-dimensional electrophoresis, we can now study the expression of thousands of genes, or the proteins they encode, in a few hours. Genomics has opened the way for proteomics. Improved knowledge of genes does not provide information about cell functions, because any cell expresses all genes simultaneously. Instead, there is selective gene expression depending on the cell type and the stimuli to which it is exposed. The result of this is the proteome, an ensemble of proteins that are responsible for cell functions at any given moment, which are the object of the study of proteomics. The description of the proteome of cardiac cells has begun and some new proteins have been found to be dysregulated in different cardiomyopathies. These proteins are involved either in energy production or in the stress response, or belong to the cell proteasome or cytoskeleton. They may be potential risk markers or new therapeutic targets in the future. In this sense, chemogenomics is a new methodology for the development of new drugs using genomic and proteomic data.

Animals↗

PubMed2001

Nucleocytoplasmic O-glycosylation: O-GlcNAc and functional proteomics.

The molecular complexity that defines different cell types and their biological responses occurs at the level of the cell's proteome. The recent increase in availability of genomic sequence information is a valuable tool for the field of proteomics. While most proteomic studies focus on differential expression levels, post-translational modifications such as phosphorylation, glycosylation, and acetylation, provide additional levels of functional complexity to the cell's proteome. The reversible post-translational modification O-linked beta-N-acetylglucosamine (O-GlcNAc) is found on serines and threonines of nuclear and cytoplasmic proteins. It appears to be as widespread as phosphorylation. While phosphorylation is recognized as a fundamental mechanism for controlling protein function, less is known about the specific roles of O-GlcNAc modification. However, evidence is building that O-GlcNAc may compete with phosphate at some sites of attachment. Aberrant O-GlcNAc modification has been linked to several disease states, including diabetes and Alzheimer's disease. Regulated enzymes catalyzing the addition (O-GlcNAc transferase, OGT) and removal (O-GlcNAcase) of the modification have been cloned and OGT is required for life at the single cell level. Here we review the properties of O-GlcNAc that suggest it is a regulatory modification analogous to phosphorylation. We also discuss the use of comparative functional proteomics to elucidate functions for this ubiquitous intracellular carbohydrate modification.

Acetylglucosamine↗

PubMed2002

Increased proteome coverage for quantitative peptide abundance measurements based upon high performance separations and DREAMS FTICR mass spectrometry.

A primary challenge in proteome measurements is to be able to detect, identify, and quantify the extremely complex mixtures of proteins. The relative abundances of interest span at least six orders of magnitude for mammalian proteomes, and this constitutes an intractable challenge for high throughput proteome studies. We have recently described a new approach, Dynamic Range Enhancement Applied to Mass Spectrometry (DREAMS), which is based upon the selective ejection of the most abundant species to expand the dynamic range of Fourier transform ion cyclotron resonanace (FTICR) measurements. The basis of our approach is on-the-fly data-dependent selective ejection of highly abundant species, followed by prolonged accumulation of remaining low-abundance species in a quadrupole external to the FTICR ion trap. Here we report the initial implementation of this approach with high efficiency capillary reverse phase LC separations and high magnetic field electrospray ionization FTICR mass spectrometry for obtaining enhanced coverage in quantitative measurements for mammalian proteomes. We describe the analysis of a sample derived from a tryptic digest of proteins from mouse B16 cells cultured in both natural isotopic abundance and 15N-labeled media. The FTICR mass spectrometric analysis allows the assignment of peptide pairs (corresponding to the two distinctive versions of each peptide), and thus provides the basis for quantiative measurements when one of the two proteomes in the mixture is perturbed or altered in some fashion. We show that implementation of the DREAMS approach allows assignment of approximately 80% more peptide pairs, thus providing quantitative information for approximately 18,000 peptide pairs in a single analysis.

Animals↗

PubMed2001

Application of proteomics to the study of cardiovascular biology.

Proteomics involves the integration of a number of technologies with the aim of analyzing the complete complement of proteins expressed by a biological system in response to various stimuli and/or under different physiological or pathophysiological conditions. Recent technical improvements to the methods employed for protein separation and protein identification have resulted in a dramatic increase in the number of proteomics-based research projects. More importantly, it has become readily apparent that examining changes in the proteome offers insight into understanding cellular and molecular mechanisms that cannot be obtained through genomic analysis. There are numerous examples of cardiovascular functions whose molecular pathways are mediated through post-translational processes such as phosphorylation. The use of proteomics offers the ability to simultaneously monitor the changes in protein expression and/or cell signaling pathways in response to such conditions as cardiac hypertrophy and heart failure. Together with complementary genomic data, proteomics-based research can greatly increase our understanding of cardiovascular biology.

Cardiovascular Diseases↗

PubMed2002

Proteome-wide analysis of protein function composition reveals the clustering and phylogenetic properties of organisms.

A 17-dimensional vector named the proteome vector is defined to represent an organism. The components of the vector reflect the relative contents of protein-encoding genes of the 17 cluster of orthologous groups of proteins (COGs) classes in the whole genome of the relevant organism. Based on the definition of this proteome vector, the fuzzy clustering of 36 completely sequenced organisms (8 archaea, 24 bacteria, and 4 eukarya) was performed and a proteome tree was constructed. Our results show that (1) the 36 organisms can be 100% correctly classified into three clusters corresponding to the three primary kingdoms, (2) our proteome tree is remarkably similar to that derived from 16S rRNA, and (3) the chromosomes and/or plasmids belonging to the same organism have very similar gene composition. Based on these results, we argue that the 17-dimensional proteome vector could be a good criterion for clustering approaches and to a large extent reveals the phylogenetic properties of organisms; the Three Primary Kingdoms Hypothesis is trustworthy although the existence of lateral gene transfer (LGT) brings controversy to the construction of the "universal tree of life."

Algorithms↗

PubMed2002

Proteomic analysis of striated muscle.

The techniques collectively known as proteomics are useful for characterizing the protein phenotype of a particular tissue or cell as well as quantitatively identifying differences in the levels of individual proteins following modulation of a tissue or cell. In the area of striated muscle research, proteomics has been a useful tool for identifying qualitative and quantitative changes in the striated muscle protein phenotype resulting from either disease or physiological modulation. Proteomics is useful for these investigations because many of the changes in the striated muscle phenotype resulting from either disease or changes in physiological state are qualitative and not quantitative changes. For example, modification of striated muscle proteins by phosphorylation and proteolytic cleavage are readily observed using proteomic technologies while these changes would not be identified using genomic technology. In this review, I will discuss the application of proteomic technology to striated muscle research, research designed to identify key protein changes that are either causal for or markers of a striated muscle disease or physiological condition.

Animals↗

PubMed2002

Combining proteomic and genetic studies in plants.

Plant proteomics is still in its infancy, although numerous experiments have been undertaken since the end of the 1970s. In this review we focus on the interactions between proteomics and genetics. A given genome can express various proteomes according to differentiation, development, tissues, cells and subcellular compartments, and proteomes are modified in function of biotic and abiotic environment. These different proteomes and the way they respond to environment can be compared between genotypes, allowing the characterization of mutants or lines, the study of mutation pleiotropic effects, the genetic mapping of expressed genes. These comparisons also permit to hypothesize for "candidate proteins" that might be involved in the genetic variation of traits of economic or agronomic interest.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2003

Proteome analysis reveals novel proteins associated with proliferation and differentiation of the colorectal cancer cell line Caco-2.

Here, we describe a proteomics approach to study protein expression changes in differentiating Caco-2 cells. Caco-2 is a colorectal carcinoma cell line, which upon differentiation loses its tumorigenic phenotype and displays characteristics of mature enterocytes, including brush borders with microvilli. Cells were grown in culture flasks and harvested at different stages of differentiation (days post-confluence: -3, 0, 3, 7, 10, 14, and 18). Two-dimensional gel electrophoresis was used to analyse proteome changes. Approximately 1400 protein spots were detected within the Caco-2 proteome, within the pH 4-7 range. Two-dimensional gel electrophoresis allowed for the detection of 18 proteins from which the levels of expression were found to be associated with differentiation. Of these proteins, 11 were identified by means of MALDI-TOF or NANO-ESI-MS/MS mass spectrometry and include liver fatty acid binding protein (FABL), three forms of alpha-enolase (ENOA), nucleoside diphosphate kinase A (NDKA), cofilin-1 (COF1), translationally controlled tumour protein (TCTP), mitochondrial 60-kDa heat shock protein (CH60), probable protein disulfide isomerase (ER60), creatine kinase B (KCRB), and glutathione S-transferase alpha (GTA1). Thus, proteomics revealed that the differentiation-related change in phenotype of Caco-2 involves changes in a variety of distinct biochemical pathways. Some of these proteins have not been shown before to be associated with Caco-2 differentiation (ER60; COF1; CH60; NDKA; TCTP and ENOA). Therefore, processes related to protein folding and disulfide bridge formation, cytoskeleton formation and maintenance, nucleotide metabolism, glycolysis as well as tumorigenesis-associated proteins may be involved in Caco-2 differentiation. Changes in the expression of CH60, TCTP, GTA1, NDKA, and FABL have also been reported to be associated with in vivo colon carcinogenesis. These findings illustrate that a combination of proteomics and cell culture is a useful approach to find markers for Caco-2 differentiation, which could contribute to the comprehension of the process of colon carcinogenesis.

Alkaline Phosphatase↗

PubMed2005

Making broad proteome protein measurements in 1-5 min using high-speed RPLC separations and high-accuracy mass measurements.

The throughput of proteomics measurements that provide broad protein coverage is limited by the quality and speed of both the separations as well as the subsequent mass spectrometric analysis; at present, analysis times can range anywhere from hours (high throughput) to days or longer (low throughput). We have explored the basis for proteomics analyses conducted on the order of minutes using high-speed capillary RPLC combined through on-line electrospray ionization interface with high-accuracy mass spectrometry (MS) measurements. Short 0.8-microm porous C18 particle-packed 50-microm-i.d. capillaries were used to speed the RPLC separations while still providing high-quality separations. Both time-of-flight (TOF) and Fourier transform ion cyclotron resonance (FTICR) MS were applied for identifying peptides using the accurate mass and time (AMT) tag approach. Peptide RPLC relative retention (elution) times that were generated by solvent gradients that differed by at least 25-fold were found to provide relative elution times that agreed to within 5%, which provides the basis for using peptide AMT tags for higher throughput proteomics measurements. For fast MS acquisition speeds (e.g., 0.2 s for TOF and either approximately 0.3 or approximately 0.6 s for FTICR), peptide mass measurement accuracies of better than +/-15 ppm were obtained with the high-speed RPLC separations. The ability to identify peptides and the overall proteome coverage was determined by factors that include the separation peak capacity, the sensitivity of the MS (with fast scanning), and the accuracy of both the mass measurements and the relative RPLC peptide elution times. The experimental RPLC relative elution time accuracies of 5% (using high-speed capillary RPLC) and mass measurement accuracies of better than +/-15 ppm allowed for the confident identification of >2800 peptides and >760 proteins from >13,000 different putative peptides detected from a Shewanellaoneidensis tryptic digest. Initial results for both RPLC-ESI-TOF and RPLC-ESI-FTICR MS were similar, with approximately 2000 different peptides from approximately 600 different proteins identified within 2-3 min. For <120-s proteomic analysis, TOF MS analyses were more effective, while FTICR MS was more effective for the >150-s analysis due to the improved mass accuracies attained using longer spectrum acquisition times.

Animals↗