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Acidic proteome of growing and resting Lactococcus lactis metabolizing maltose.

The acidic proteome of Lactococcus lactis grown anaerobically was compared for three different growth conditions: cells growing on maltose, resting cells metabolizing maltose, and cells growing on glucose. In maltose metabolizing cells several proteins were up-regulated compared with glucose metabolizing cells, however only some of the up-regulated proteins had apparent relation to maltose metabolism. Cells growing on maltose produced formate, acetate and ethanol in addition to lactate, whereas resting cells metabolizing maltose and cells growing on glucose produced only lactate. Increased levels of alcohol-acetaldehyde dehydrogenase (ADH) and phosphate acetyltransferase (PTA) in maltose-growing cells compared with glucose-growing cells coincided with formation of mixed acids in maltose-growing cells. The resting cells did not grow due to lack of an amino acid source and fermented maltose with lactate as the sole product, although ADH and PTA were present at high levels. The maltose consumption rate was approximately three times lower in resting cells than in exponentially growing cells. However, the enzyme levels in resting and growing cells metabolizing maltose were similar, which indicates that the difference in product formation in this case is due to regulation at the enzyme level. The levels of 30S ribosomal proteins S1 and S2 increased with increasing growth rate for resting cells metabolizing maltose, maltose-growing cells and glucose-growing cells. A modified form of HPr was synthesized under amino acid starvation. This is suggested to be due to alanine misincorporation for valine, which L. lactis is auxotrophic for. L. lactis conserves the protein profile to a high extent, even after prolonged amino acid starvation, so that the protein expression profile of the bacterium remains almost invariant.

Acetates↗

Multimembrane dot-blotting: a cost-effective tool for proteome analysis.

The molecular profiles of protein expression from hundreds of cell lysates can be determined in a high-throughput manner by using fluorescent bead technologies, enzyme-linked immunosorbent assays (ELISAs), and protein microarrays. Although powerful, these tools are costly and technically challenging and thus have limited accessibility for many research groups. We propose a modification of traditional dot blotting that increases throughput of this approach and provides a simple and cost-effective technique for profiling multiple samples. In contrast to traditional blotting that uses a single membrane, we introduce blotting onto a stack of novel, thin, sieve-like membranes. These membranes have a high affinity for binding proteins, but have a lower capacity of protein binding compared to traditional (nitrocellulose) membranes. We compare the linear binding capacity and variability of these novel membranes with nitrocellulose membranes. Also, we describe the use of these membranes in a multilayer dot blot format for profiling mitogen-mediated signal transduction pathways in T cells.

Blood Proteins↗

Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets.

We present the first analysis of the human proteome with regard to interactions between proteins. We also compare the human interactome with the available interaction datasets from yeast (Saccharomyces cerevisiae), worm (Caenorhabditis elegans) and fly (Drosophila melanogaster). Of >70,000 binary interactions, only 42 were common to human, worm and fly, and only 16 were common to all four datasets. An additional 36 interactions were common to fly and worm but were not observed in humans, although a coimmunoprecipitation assay showed that 9 of the interactions do occur in humans. A re-examination of the connectivity of essential genes in yeast and humans indicated that the available data do not support the presumption that the number of interaction partners can accurately predict whether a gene is essential. Finally, we found that proteins encoded by genes mutated in inherited genetic disorders are likely to interact with proteins known to cause similar disorders, suggesting the existence of disease subnetworks. The human interaction map constructed from our analysis should facilitate an integrative systems biology approach to elucidating the cellular networks that contribute to health and disease states.

Animals↗

Comparative modelling: an essential methodology for protein structure prediction in the post-genomic era.

The gap between the number of protein sequences and protein structures is increasing rapidly, exacerbated by the completion of numerous genome projects now flooding into public databases. To fill this gap, comparative protein modelling is widely considered the most accurate technique for predicting the three-dimensional shape of proteins. High-throughput, automatic protein modelling should considerably increase our access to protein structures other than those determined by experimental techniques such as X-ray crystallography and NMR (nuclear magnetic resonance) spectroscopy. The uses for these complete three-dimensional models are growing rapidly, ranging from guiding site-directed mutagenesis experiments to protein-protein interaction predictions. In recognition of this, a number of very useful comparative modelling servers have begun to emerge on the Web. Molecular biologists now have a powerful web-based toolkit to construct models, assess their accuracy, and use them to explain and predict experiments. There is, however, still much to do by those engaged in algorithmic development if comparative modelling is to compete on an equal footing with experimental protein structure determination techniques.

Computational Biology↗

Evaluation of proteome reference maps for cross-species identification of proteins by peptide mass fingerprinting.

We tested whether proteome reference maps established for one species can be used for cross-species protein identification by comparing two-dimensional protein gel patterns and protein identification data of two closely related bacterial strains and four plant species. First, proteome profiles of two strains of the fully sequenced bacterium Sinorhizobium meliloti were compared as an example of close relatedness, high reproducibility and sequence availability. Secondly, the proteome profiles of three legumes (Medicago truncatula, Melilotus alba and Trifolium subterraneum), and the nonlegume rice (Oryza sativa) were analysed to test cross-species similarities. In general, we found stronger similarities in gel patterns of the arrayed proteins between the two bacterial strains and between the plant species than could be expected from the sequence similarities. However, protein identity could not be concluded from their gel position, not even when comparing strains of the same species. Surprisingly, in the bacterial strains peptide mass fingerprinting was more reliable for species-specific protein identification than N-terminal sequencing. While peptide masses were found to be unreliable for cross-species protein identification, we present useful criteria to determine confident matching against species-specific expressed sequence tag databases. In conclusion, we present evidence that cautions the use of proteome reference maps and peptide mass fingerprinting for cross-species protein identification.

Electrophoresis, Gel, Two-Dimensional↗

Proteomic analysis of soluble factors secreted by limbal fibroblasts.

PURPOSE: To identify soluble factors selectively secreted by limbal fibroblasts as possible regulators of limbal basal epithelium. METHODS: Limbal, corneal, and conjunctival fibroblasts were first expanded in vitro in Dulbecco's modified Eagle medium containing 10% fetal bovine serum, and then maintained in serum-free medium for two weeks. Proteomic analysis of culture supernatants was done to compare differences in secreted matricellular proteins. Real time PCR and western blots were done to confirm the expression of secreted protein acid and rich in cysteine (SPARC), a protein found in abundance in extracellular proteins secreted by limbal fibroblasts. Immunohistochemistry of SPARC was done in human limbal tissue to show the spatial distribution of the protein. An adhesion assay was designed to demonstrate the effects of SPARC on an SV40 immortalized human corneal epithelial cell line (HCEC). RESULTS: Proteomic analysis revealed several proteins selectively secreted by limbal fibroblasts. The particular spots were identified as SPARC, vimentin, serine protease, collagen alpha 2 precursor, tissue inhibitor of metalloproteinase 2 (TIMP-2), and 5,10-methlenetetrahdrofolate reductase (FADH2). The expression of SPARC was confirmed by western blot analysis, and mRNA expression was significantly higher in limbal fibroblasts compared to central corneal fibroblasts when analyzed by real time PCR. Immunohistochemistry revealed higher distribution of SPARC in the subepithelial stroma of the limbus compared to the central cornea. The addition of 10 microg/ml murine SPARC in HCEC significantly reduced cell spreading at three h. CONCLUSIONS: The matricellular protein SPARC is preferentially secreted by limbal fibroblasts, and may modulate intercellular adhesion of basal limbal epithelial cells.

Animals↗

Comprehensive genome analysis of 203 genomes provides structural genomics with new insights into protein family space.

We present an analysis of 203 completed genomes in the Gene3D resource (including 17 eukaryotes), which demonstrates that the number of protein families is continually expanding over time and that singleton-sequences appear to be an intrinsic part of the genomes. A significant proportion of the proteomes can be assigned to fewer than 6000 well-characterized domain families with the remaining domain-like regions belonging to a much larger number of small uncharacterized families that are largely species specific. Our comprehensive domain annotation of 203 genomes enables us to provide more accurate estimates of the number of multi-domain proteins found in the three kingdoms of life than previous calculations. We find that 67% of eukaryotic sequences are multi-domain compared with 56% of sequences in prokaryotes. By measuring the domain coverage of genome sequences, we show that the structural genomics initiatives should aim to provide structures for less than a thousand structurally uncharacterized Pfam families to achieve reasonable structural annotation of the genomes. However, in large families, additional structures should be determined as these would reveal more about the evolution of the family and enable a greater understanding of how function evolves.

Algorithms↗

Prediction of beta-barrel membrane proteins by searching for restricted domains.

BACKGROUND: The identification of beta-barrel membrane proteins out of a genomic/proteomic background is one of the rapidly developing fields in bioinformatics. Our main goal is the prediction of such proteins in genome/proteome wide analyses. RESULTS: For the prediction of beta-barrel membrane proteins within prokaryotic proteomes a set of parameters was developed. We have focused on a procedure with a low false positive rate beside a procedure with lowest false prediction rate to obtain a high certainty for the predicted sequences. We demonstrate that the discrimination between beta-barrel membrane proteins and other proteins is improved by analyzing a length limited region. The developed set of parameters is applied to the proteome of E. coli and the results are compared to four other described procedures. CONCLUSION: Analyzing the beta-barrel membrane proteins revealed the presence of a defined membrane inserted beta-barrel region. This information can now be used to refine other prediction programs as well. So far, all tested programs fail to predict outer membrane proteins in the proteome of the prokaryote E. coli with high reliability. However, the reliability of the prediction is improved significantly by a combinatory approach of several programs. The consequences and usability of the developed scores are discussed.

Algorithms↗

Proteins associated with disease and clinical course in pancreas cancer: a proteomic analysis of plasma in surgical patients.

New biomarkers for pancreas cancer are needed to improve its detection and management. We surveyed the plasma of patients undergoing surgical resection to identify proteins which change in abundance after complete resection of tumor. Using longitudinally collected specimens from surgical patients, we control for normal inter-individual variation which can confound cross-sectional analysis. Recent refinements in two-dimensional gel electrophoresis allowed us to quantify changes in low abundance plasma proteins with precision. To circumvent the traditional limitations of image analysis in comparing two-dimensional gels, we used fluorometric two-dimensional difference gel electrophoresis to resolve the proteins from pre- and post-surgical plasma from each patient on one physical gel. Furthermore, we increased the ability of our assay to detect low-abundance proteins by depleting the plasma of 12 high-abundance proteins with a multi-affinity column. Informative protein spots from 20 plasma samples across 10 patients were submitted for identification with mass-spectrometry. We identified a group of proteins which change consistently in plasma following complete resection of pancreas tumor. Furthermore, we identified proteins which correlate with post-surgical rapid recurrence of disease. With further identification and validation, the candidate biomarkers which we identify in this study may prove to be useful in the diagnosis, management and prognostication of patients with pancreas cancer.

Biomarkers↗

Comparative genomic analysis of 18 Pseudomonas aeruginosa bacteriophages.

A genomic analysis of 18 P. aeruginosa phages, including nine newly sequenced DNA genomes, indicates a tremendous reservoir of proteome diversity, with 55% of open reading frames (ORFs) being novel. Comparative sequence analysis and ORF map organization revealed that most of the phages analyzed displayed little relationship to each other.

DNA, Viral↗

Growth-induced changes in the proteome of Helicobacter pylori.

Helicobacter pylori is a major human pathogen that is responsible for a number of gastrointestinal infections. We have used 2-DE to characterise protein synthesis in bacteria grown either on solid agar-based media or in each of two broth culture media (Brucella and brain heart infusion (BHI) broth). Significant differences were observed in the proteomes of bacteria grown either on agar-based or in broth media. Major changes in protein abundance were identified using principal component analysis (PCA), which delineated the profiles derived for the three key growth conditions (i.e. agar plates, Brucella and BHI broth). Proteins detected across the gel series were identified by peptide mass mapping and Edman sequencing. A number of proteins associated with protein synthesis in general as well as specific amino acid synthesis were depressed in broth-grown bacteria compared to plate-grown bacteria. A similar reduction was also observed in the abundance of proteins involved in detoxification. Two of the most abundant spots, identified as UreB and GroEL, in plate-grown bacteria showed a >140-fold drop in abundance in bacteria grown in Brucella broth compared to bacteria grown on agar plates. Two protein spots induced in bacteria grown in broth culture were both identified as glyceraldehyde 3-phosphate dehydrogenase based on their N-terminal amino acid sequences derived by Edman degradation. The underlying causes of the changes in the proteins abundance were not clear, but it was likely that a significant proportion of the changes were due to the alkaline pH of the broth culture media.

Agar↗

Proteome analysis of salt stress response in the cyanobacterium Synechocystis sp. strain PCC 6803.

In the present study, changes in protein synthesis patterns after salt shock visualized by 35S-methionine labeling and the changed protein composition in salt-acclimated cells of the cyanobacterium Synechocystis sp. strain PCC 6803 were analyzed by a combination of 2-DE for protein separation and PMF for protein identification. As a basis for the differential analysis, a proteome map with 500 identified protein spots comprising 337 different protein species was established. Fifty-five proteins were found, which are induced by salt shock or accumulated after long-term salt acclimation. Some of the proteins are salt stress-specific, such as enzymes involved in the synthesis of the compatible solute glucosylglycerol, while most of them are involved in general stress acclimation. Particularly, heat-shock proteins and proteins acting against lesions by reactive oxygen species were found. Moreover, changes in enzymes involved in basic carbohydrate metabolism were detected. The dynamic of the proteome of salt-stressed Synechocystis cells was compared to previous data concerning transcriptome analysis revealing that 89% of the proteins induced shortly after salt shock were also found to be induced at the RNA level. However, 42% of the stably up-regulated proteins in salt-acclimated cells were not detected previously using DNA microarrays. The comparison of transcriptomic and proteomic analyses shows the significance of post-transcriptional regulatory mechanisms in acclimation of Synechocystis to high salt concentrations.

Bacterial Proteins↗

LCM-Enriched Proteomic Characterization of Antibody-Mediated Glomerular Damage and Complement Activation in Pre-Clinical Models.

Biologics, lipid nanoparticles, and other therapeutic modalities can result in adverse events, often detected as lesions during preclinical pathology assessments. Characterization of these lesions provides valuable information during drug development to contextualize mechanisms of injury and assess species translatability. Here, we investigated the utility of a laser capture microdissection (LCM)-enriched mass spectrometry proteomics approach to analyze two well-characterized preclinical models of regional (glomerular) injury: Passive Heyman Nephritis in rats and bovine gamma globulin-induced glomerular injury in nonhuman primates (NHPs). Using LCM-enriched proteomics, glomeruli were isolated from formalin-fixed paraffin-embedded kidney tissue in the rat model, enabling identification of 4,661 proteins and quantification of 3,410. Proteinuria measurements were compared with digital pathology metrics of glomerular morphology and proteomics results, with all modalities yielding concordant evidence of glomerular injury and proteomics confirming the role of complement activation. The same LCM- enriched proteomics workflow was applied to an NHP model of induced glomerular damage, identifying 4,623 proteins, quantifying 3,000, and confirming qualitative concordance with established features of complement-mediated glomerular injury. Together, these findings illustrate the applicability of LCM-enriched proteomics for region-specific characterization of antibody-mediated tissue injury and support its use as a hypothesis-generating platform in translational toxicologic pathology.

Animals↗

Genomic and proteomic profiling for biomarkers and signature profiles of toxicity.

Toxicity profiling measures and compares all gene expression changes among biological samples after toxicant exposure. Toxicity profiling with DNA microarrays to measure all mRNA transcripts (transcriptomics), or by global separation and identification of proteins (proteomics), has led to the discovery of better descriptors of toxicity, toxicant classification and exposure monitoring than current indicators. A shared goal in transcript and proteomic profiling is the development of biomarkers and signatures of chemical toxicity. In this review, biomarkers and signature profiles are described for specific chemical toxicants that affect target organs such as liver, kidney, neural tissues, gastrointestinal tract and skeletal muscle, for specific disease models such as cancer and inflammation, and for unique chemical-protein adducts underlying cell injury. The recent introduction of toxicogenomics databases support researchers in sharing, analyzing, visualizing and mining expression data, assist the integration of transcriptomics, proteomics and toxicology datasets, and eventually will permit in silico biomarker and signature pattern discovery.

Animals↗

Protein microarrays to detect protein-protein interactions using red and green fluorescent proteins.

Proteomics, the study of protein function on a global scale, will play an important role in furthering our understanding of gene functions, complex biological pathways, and discovery of novel drug targets. A number of techniques have been developed for proteomic studies to identify and analyze proteins, compare protein expression levels, and study protein-protein interactions. Recent developments have applied a DNA array-type approach to immobilize proteins on a surface for high-throughput analysis. Here we report the development and construction of protein chips using derivatized glass and nitrocellulose-coated slides and the employment of recombinant proteins fused with green and red fluorescent proteins for detection. Fluorescent signals were found to be proportional to the amount of arrayed proteins and could be readily detected with a conventional fluorescence slide scanner. This technique allows the investigation of protein-protein interactions without the need for additional labeling steps of probe proteins.

Antibodies↗

Microevolutionary genomics of bacteria.

The availability of multiple complete genome sequences from the same species can facilitate attempts to systematically address basic questions in genome evolution. We refer to such efforts as "microevolutionary genomics". We report the results of comparative analyses of complete intraspecific genome (and proteome) sequences from four bacterial species--Chlamydophila pneumoniae, Escherichia coli, Helicobacter pylori and Neisseria meningitidis. Comparisons of average synonymous (K(s)) and nonsynonymous (K(a)) substitution rates were used to assess the influence of various biological factors on the rate of protein evolution. For example, E. coli experiences the most intense purifying selection of the species analyzed, and this may be due to the relatively larger population size of this species. In addition, essential genes were shown to be more evolutionarily conserved than nonessential genes in E. coli and duplicated genes have higher rates of evolution than unique genes for all species studied except C. pneumoniae. Different functional categories of genes were shown to evolve at significantly different rates emphasizing the role of category-specific functional constraints in determining evolutionary rates. Finally, functionally characterized genes tend to be conserved between strains, while uncharacterized genes are over-represented among the unique, strain-specific genes. This suggests the possibility that nonessential genes are responsible for driving the evolutionary diversification between strains.

Bacteria↗

Advances in recombinant antibody microarrays.

Antibody microarrays, one emerging class of proteomic technologies, have broad applications in proteome analysis, disease diagnostics and quantitative analysis. Compared to DNA microarrays, protein targets have significantly more complex interactions with their ligands such as antibodies. To introduce antibody microarrays for clinical diagnostics and thus to complement or replace conventional immunoassays, several new developments are addressed. We discuss different microarray surfaces, immobilization techniques, detection systems and advantages and disadvantages of antibody microarrays compared to standard clinical techniques. Currently, the probes with highest specificity, well-characterized binding properties, and the possibility of large-scale production using display libraries are recombinant antibodies.

Animals↗

Rapid computational identification of the targets of protein kinase inhibitors.

We describe a method for rapidly computing the relative affinities of an inhibitor for all individual members of a family of homologous receptors. The approach, implemented in a new program, SCR, models inhibitor-receptor interactions in full atomic detail with an empirical energy function and includes an explicit account of flexibility in homology-modeled receptors through sampling of libraries of side chain rotamers. SCR's general utility was demonstrated by application to seven different protein kinase inhibitors: for each inhibitor, relative binding affinities with panels of approximately 20 protein kinases were computed and compared with experimental data. For five of the inhibitors (SB203580, purvalanol B, imatinib, H89, and hymenialdisine), SCR provided excellent reproduction of the experimental trends and, importantly, was capable of identifying the targets of inhibitors even when they belonged to different kinase families. The method's performance in a predictive setting was demonstrated by performing separate training and testing applications, and its key assumptions were tested by comparison with a number of alternative approaches employing the ligand-docking program AutoDock (Morris et al. J. Comput. Chem. 1998, 19, 1639-1662). These comparison tests included using AutoDock in nondocking and docking modes and performing energy minimizations of inhibitor-kinase complexes with the molecular mechanics code GROMACS (Berendsen et al. Comput. Phys. Commun. 1995, 91, 43-56). It was found that a surprisingly important aspect of SCR's approach is its assumption that the inhibitor be modeled in the same orientation for each kinase: although this assumption is in some respects unrealistic, calculations that used apparently more realistic approaches produced clearly inferior results. Finally, as a large-scale application of the method, SB203580, purvalanol B, and imatinib were screened against an almost full complement of 493 human protein kinases using SCR in order to identify potential new targets; the predicted targets of SB203580 were compared with those identified in recent proteomics-based experiments. These kinome-wide screens, performed within a day on a small cluster of PCs, indicate that explicit computation of inhibitor-receptor binding affinities has the potential to promote rapid discovery of new therapeutic targets for existing inhibitors.

Binding Sites↗