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PubMed2004

ORFDB: an information resource linking scientific content to a high-quality Open Reading Frame (ORF) collection.

The ORFDB (http://orf.invitrogen.com/) represents an ongoing effort at Invitrogen Corporation to integrate relevant scientific data with an evolving collection of human and mouse Open Reading Frame (ORF) clones (Ultimate ORF Clones). The ORFDB serves as a central data warehouse enabling researchers to search the ORF collection through its web portal ORFBrowser, allowing researchers to find the Ultimate ORF clones by blast, keyword, GenBank accession, gene symbol, clone ID, Unigene ID, LocusLink ID or through functional relationships by browsing the collection via the Gene Ontology (GO) Browser. As of October 2003, the ORFDB contains 6200 human and 2870 mouse Ultimate ORF clones. All Ultimate ORF clones have been fully sequenced with high quality, and are matched to public reference protein sequences. In addition, the cloned ORFs have been extensively annotated across six categories: Gene, ORF, Clone Format, Protein, SNP and Genomic links, with the information assembled in a format termed the ORFCard. The ORFCard represents an information repository that documents the sequence quality, alignment with respect to public protein sequences, and the latest publicly available information associated with each human and mouse gene represented in the collection.

Animals↗
PubMedDate not supplied

Protein detection methods in proteomics research.

In proteomics research chemical as well as physical methods are used to detect proteins subsequently to their separation. Physical methods are mostly applied after chromatography. They are either based on spectroscopy like light absorption at certain wavelengths or mass determination of peptides and their fragments with mass spectrometry. Chemical methods are used after two-dimensional electrophoresis and employ staining with organic dyes, metal chelates, fluorescent dyes, complexing with silver, or pre-labeling with fluorophores. In some cases autoradiography is still used. Since all of these techniques are very different in terms of sensitivity, their usefulness for quantitative determinations varies significantly. This review will describe the various protein detection methods applied to electrophoresis gels.

Blotting, Western↗
PubMed2001

Multiresolution image registration for two-dimensional gel electrophoresis.

In proteomic research, two-dimensional electrophoresis (2-D) is an important tool for investigating differential patterns of qualitative and quantitative protein expression. The strength of the technique is due to its unrivalled power of being able to separate simultaneously thousands of proteins. The key to the comparison of 2-D protein profiles, however, lies in the use of a fast and robust image matching process which is essential to the subsequent quantification procedure. To satisfy the growing demand for a robust and fully automatic method of matching 2-D gel protein separation profiles, we describe in this paper a novel registration technique based on image intensity distribution rather than selected features. The method uses a multiresolution representation of the gel profiles and exploits the fact that coarse approximations to the optimal matching can be extracted efficiently from low-resolution images. This permits the removal of misalignments at different scales in a systematic manner and the strength of the new method has been confirmed by a double blind trial of 111 2-D gel pairs. The proposed method requires neither landmarks nor an a priori image alignment, and takes about five seconds for processing a typical gel pair on a standard personal computer.

Algorithms↗
PubMed2004

Towards developing a protein infrared spectra databank (PISD) for proteomics research.

Fourier transform infrared (FTIR) spectroscopy is an attractive tool for proteomics research as it can be used to rapidly characterize protein secondary structure in aqueous solution. Recently, a number of secondary structure prediction methods based on reference sets of FTIR spectra from proteins with known structure from X-ray crystallography have been suggested. These prediction methods, often referred to as pattern recognition based approaches, demonstrated good prediction accuracy using some error measure, e.g., the standard error of prediction (SEP). However, to avoid possible adverse effects from differences in recording, the analysis has been mostly based on reference sets of FTIR spectra from proteins recorded in one laboratory only. As a result, these studies were based on reference sets of FTIR spectra from a limited number of proteins. Pattern recognition based approaches, however, rely on reference sets of FTIR spectra from as many proteins as possible representing all possible band shape variation to be related to the diversity of protein structural classes. Hence, if we want to build reliable pattern recognition based systems to support proteomics research, which are capable of making good predictions from spectral data of any unknown protein, one common goal should be to build a comprehensive protein infrared spectra databank (PISD) containing FTIR spectra of proteins of known structure. We have started the process of developing a comprehensive PISD composed of spectra recorded in different laboratories. As part of this work, here we investigate possible effects on prediction accuracy achieved by a neural network analysis when using reference sets composed of FTIR spectra from different laboratories. Surprisingly low magnitude of difference in SEPs throughout all our experiments suggests that FTIR spectra recorded in different laboratories may be safely combined into one reference set with only minor deterioration of prediction accuracy in the worst case.

Algorithms↗
PubMed2004

Proteome study of human cerebrospinal fluid following traumatic brain injury indicates fibrin(ogen) degradation products as trauma-associated markers.

Traumatic brain injury (TBI), like other central nervous system pathologies, causes changes in the composition of cerebrospinal fluid (CSF). Consequently analysis of the CSF components is important to better understand the pathological processes involved in such diseases. The aim of this work was to identify specific markers of severe TBI. Proteomic analysis including two-dimensional gel electrophoresis combined with mass spectrometry analysis was used to compare the CSF protein profile of severe TBI patients and controls. Proteins (alpha 1 antitrypsin, haptoglobin 1 alpha1, alpha2, and beta) belonging to the acute phase response showed an increased expression in severe TBI patients. Two other proteins, identified as proteolytic degradation products of the carboxyl-terminal portion of the fibrinogen beta, were present only in TBI patients. The presence of these markers could correlate with a post-traumatic local increase in fibrinolysis as well as to an inflammatory event following CNS tissue injury.

Adult↗
PubMed2002

Integrating cancer genomics and proteomics in the post-genome era.

The dawn of the post-genome era is leading to extraordinary opportunities in biomedicine. Our group has embarked on a major effort to integrate genomics, transcriptomics and proteomics for the profiling of tumor tissues, an approach we refer to as operomics. Our major goals are the molecular classification of tumors and the identification of markers for the early detection of cancer. Molecular analyses of tumors rely on microdissected tissues, which are simultaneously investigated for genomic, transcriptomic and proteomic changes. Genomic alterations in tumor cells being investigated include deletions, amplifications and methylation changes across the entire genome as well as point mutations in specific genes. Expression analysis at the RNA level is being undertaken using oligonucleotide and cDNA based microarrays. An important aspect of our approach is the large-scale identification and quantitative analysis of tumor proteins in whole cell lysates as well as in protein compartments. Protein separation strategies include two-dimensional polyacrylamide gel electrophoresis and liquid chromatography. Specific protein subsets, of interest include membrane proteins, secreted proteins and antigenic proteins as sources of biomarkers for early detection of cancer. Our current approach is illustrated with findings stemming from our studies of human gliomas.

Brain Neoplasms↗
PubMed2005

Clinical bioinformatics.

Clinical bioinformatics provides biological and medical information to allow for individualized healthcare. In this review, we describe the uses of clinical bioinformatics. After the analysis of the complete human genome sequences, clinical bioinformatics enables researchers to search online biological databases and use the biological information in their medical practices. The data obtained from using microarray is extremely complicated. In clinical bioinformatics, selecting appropriate software to analyze the microarray data for medical decision making is crucial. Proteomics strategy tools usually focus on similarity searches, structure prediction, and protein modeling. In clinical bioinformatics, the proteomic data only have meaning if they are integrated with clinical data. In pharmacogenomics, clinical bioinformatics includes elaborate studies of bioinformatics tools and various facets of proteomics related to drug target identification and clinical validation. Using clinical bioinformatics, researchers apply computational and high-throughput experimental techniques to cancer research and systems biology. Meanwhile, researchers of bioinformatics and medical information have incorporated clinical bioinformatics to improve health care, using biological and medical information. Using the high volume of biological information from clinical bioinformatics will contribute to changes in practice standards in the healthcare system. We believe that clinical bioinformatics provides benefits of improving healthcare, disease prevention and health maintenance as we move toward the era of personalized medicine.

Computational Biology↗
PubMed2004

Effect of collagen substrates on proteomic modulation of breast cancer cells.

We have previously described the occurrence, in breast and colon cancer extra-cellular matrix, of an oncofoetal form of collagen, OF/LB, able to induce an increase in cell proliferation and motility in the breast cancer cell line 8701-BC. It also caused an increased amount of type V collagen which appears to exert an anti-proliferative effect on the same cells. The aim of the present study was to investigate, at the proteomic level, the effect of OF/LB and type V collagens used as substrates for neoplastic cell growth. Due to the complexity of a whole proteomic profile, a subset of significant protein classes was used to assess variations in protein expression levels. For this study we adopted a multivariate statistical procedure that allows a global view of the variations induced by different growth conditions, when several variables have to be analyzed simultaneously. The results of this research indicate that in response to different growth substrates, chaperons and heat shock proteins contributed most to the dissimilarity in levels of expression of the selected protein spots. Moreover, we observed that different isoforms of the same protein showed independent levels of expression from one another in relation to the different collagen treatments.

Breast Neoplasms↗
PubMed2003

A novel experimental design for comparative two-dimensional gel analysis: two-dimensional difference gel electrophoresis incorporating a pooled internal standard.

The comparison of two-dimensional (2-D) gel images from different samples is an established method used to study differences in protein expression. Conventional methods rely on comparing images from at least 2 different gels. Due to the high variation between gels, detection and quantification of protein differences can be problematic. Two-dimensional difference gel electrophoresis (Ettan trade mark DIGE) is an emerging technique for comparative proteomics, which improves the reproducibility and reliability of differential protein expression analysis between samples. In the application of DIGE different samples are labelled with mass and charge matched spectrally resolvable fluorescent dyes and are then separated on the same 2-D gel. Using an Escherichia coli lysate "spiked" with varying amounts of four different known proteins, we have tested a novel experimental design that exploits the sample multiplexing capabilities of DIGE, by including a standard sample in each gel. The standard sample comprises equal amounts of each sample to be compared and was found to improve the accuracy of protein quantification between samples from different gels allowing accurate detection of small differences in protein levels between samples.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2002

[Comparative genomics and proteomics of Drosophila, Brenner's nematode, and Arabidopsis: identification of functionally similar genes and proteins of meiotic chromosome synapsis].

The published principles of computer analysis of genomes and protein sets in taxonomically distant eukaryotes are expounded. The authors developed a search strategy to identify in genomes of such organisms genes and proteins nonhomologous in primary structure but having similar functions in cells dividing by meiosis. This strategy based on the combined principles of genomics, proteomics, and morphometric analysis of subcellular structures was applied to a computer search for genes encoding the proteins of synaptonemal complexes in genomes of Drosophila melanogaster, the nematode Caenorhabditis elegans, and the plant Arabidopsis thaliana. These proteins proved to be functionally similar to their counterparts in yeast Saccharomyces cerevisiae (protein Zip1p) and mammals (protein SCP1).

Animals↗
PubMed2006

High-accuracy peak picking of proteomics data using wavelet techniques.

A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS.

Algorithms↗
PubMed2005

Peroxisomal proteomics, a new tool for risk assessment of peroxisome proliferating pollutants in the marine environment.

In an attempt to improve the detection of peroxisome proliferation as a biomarker in environmental pollution assessment, we have applied a novel approach based on peroxisomal proteomics. Peroxisomal proteins from digestive glands of mussels Mytilus galloprovincialis were analyzed using 2-DE and MS. We have generated a reference 2-DE map from samples obtained in a well-studied reference area and compared this with peroxisomal proteomes from other sequenced genomes. In addition, by comparing 2-DE maps from control samples with samples obtained in a polluted area, we have characterized the peroxisome proliferation expression pattern associated with exposure to a polluted environment. Over 100 spots were reproducibly resolved per 2-DE map; 55 differentially expressed spots were quantitatively detected and analyzed, and 14 of these showed an increase in protein expression of more than fourfold. Epoxide hydrolase, peroxisomal antioxidant enzyme, and sarcosine oxidase (SOX) have been identified by ESI MS/MS, and acyl-CoA oxidase, multifunctional protein, and Cu,Zn-superoxide dismutase were immunolocalized by Western blotting. Our results indicate that a peroxisomal protein pattern associated to marine pollutant exposure can be generated, and this approach may have a greater potential as biomarker than traditional, single-protein markers.

Animals↗
PubMed2005

Proteomic analysis of livers of patients with primary hepatolithiasis.

Primary hepatolithiasis or intrahepatic calculi (IHC), which is characterized by the formation of gallstones in the intrahepatic bile duct, is an intractable liver disease and suspected to be one of the causes of cholangiocellular carcinoma. To obtain an insight into the disease, we performed proteomic analysis of liver tissue specimens of paired affected and unaffected hepatic segments from patients with primary hepatolithiasis by two-dimensional gel electrophoresis followed by identification of proteins. For the specimens from the unaffected segments, 125 spots out of 613 spots were identified, defining 83 unique protein names. For the specimens from the affected segments, 102 spots out of 671 spots were identified, defining 74 unique protein names. To further precisely compare, we used two-dimensional fluorescence difference gel electrophoresis. Consequently we identified 12 up-regulated proteins and 21 down-regulated proteins. The up-regulated proteins contained the proteins related to liver fibrosis and to cellular oxidoreduction. The down-regulated proteins contained RAF kinase inhibitor protein, chaperonin and proteins related to principal liver function.

Chaperonins↗
PubMed2006

BioNetBuilder: automatic integration of biological networks.

UNLABELLED: BioNetBuilder is an open-source client-server Cytoscape plugin that offers a user-friendly interface to create biological networks integrated from several databases. Users can create networks for approximately 1500 organisms, including common model organisms and human. Currently supported databases include: DIP, BIND, Prolinks, KEGG, HPRD, The BioGrid and GO, among others. The BioNetBuilder plugin client is available as a Java Webstart, providing a platform-independent network interface to these public databases. AVAILABILITY: http://err.bio.nyu.edu/cytoscape/bionetbuilder/

Algorithms↗
PubMed2004

Use of proteomic analysis to monitor responses to biological therapies.

Proteomics has the potential to revolutionise diagnosis and disease management. Serum protein pattern profiling by surface-enhanced laser desorption/ionisation time of flight (SELDI-TOF) mass spectrometry is emerging as a novel approach to discover protein patterns capable of distinguishing disease and disease-free states with high sensitivity and specificity. This method has shown great promise for early diagnosis of ovarian cancer and is being applied to a range of pathological states. Protein microarray technology is being evaluated as a new means to track biological responses to therapy. Through the measurement of key protein phosphorylation sites at different stages of disease progression or before and after treatment, protein signal pathways can be mapped and thus become the starting point for individualised therapy. Laser capture microdissection (LCM) coupled with immunostaining of protein microarrays allows isolation of pure cell populations and relative quantitation of phosphorylated and non-phosphorylated forms of the cell's key signalling proteins. This technology is currently in use at the National Institutes of Health in Phase II clinical trials of metastatic breast and ovarian cancer. Cell survival and apoptotic protein pathways are monitored as biological markers of disease progression in these clinical trials. Proteomic technologies, such as serum protein pattern profiling, combined with protein microarray technologies, constitute a new paradigm for detecting disease and monitoring disease response to therapy. Ultimately, proteomics and genomics will become integrated into cancer patient management through the design and tracking of individualised therapy.

Animals↗
PubMed2005

Comparative secretome analyses of three Bacillus anthracis strains with variant plasmid contents.

Bacillus anthracis, the causative agent of anthrax, secretes numerous proteins into the extracellular environment during infection. A comparative proteomic approach was employed to elucidate the differences among the extracellular proteomes (secretomes) of three isogenic strains of B. anthracis that differed solely in their plasmid contents. The strains utilized were the wild-type virulent B. anthracis RA3 (pXO1(+) pXO2(+)) and its two nonpathogenic derivative strains: the toxigenic, nonencapsulated RA3R (pXO1(+) pXO2(-)) and the totally cured, nontoxigenic, nonencapsulated RA3:00 (pXO1(-) pXO2(-)). Comparative proteomics using two-dimensional gel electrophoresis followed by computer-assisted gel image analysis was performed to reveal unique, up-regulated, or down-regulated secretome proteins among the strains. In total, 57 protein spots, representing 26 different proteins encoded on the chromosome or pXO1, were identified by peptide mass fingerprinting. S-layer-derived proteins, such as Sap and EA1, were most frequently observed. Many sporulation-associated enzymes were found to be overexpressed in strains containing pXO1(+). This study also provides evidence that pXO2 is necessary for the maximal expression of the pXO1-encoded toxins lethal factor (LF), edema factor (EF), and protective antigen (PA). Several newly identified putative virulence factors were observed; these include enolase, a high-affinity zinc uptake transporter, the peroxide stress-related alkyl hydroperoxide reductase, isocitrate lyase, and the cell surface protein A.

Bacillus anthracis↗
PubMed2004

Integrated analysis of genetic, genomic and proteomic data.

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.

Animals↗
PubMed2006

Finding diagnostic biomarkers in proteomic spectra.

In seeking to find diagnostic biomarkers in proteomic spectra, two significant problems arise. First, not only is there noise in the measured intensity at each m/z value, but there is also noise in the measured m/z value itself. Second, the potential for overfitting is severe: it is easy to find features in the spectra that accurately discriminate disease states but have no biological meaning. We address these problems by developing and testing a series of steps for pre-processing proteomic spectra and extracting putatively meaningful features before presentation to feature selection and classification algorithms. These steps include an HMM-based latent spectrum extraction algorithm for fusing the information from multiple replicate spectra obtained from a single tissue sample, a simple algorithm for baseline correction based on a segmented convex hull, a peak identification and quantification algorithm, and a peak registration algorithm to align peaks from multiple tissue samples into common peak registers. We apply these steps to MALDI spectral data collected from normal and tumor lung tissue samples, and then compare the performance of feature selection with FDR followed by classification with an SVM, versus joint feature selection and classification with Bayesian sparse multinomial logistic regression (SMLR). The SMLR approach outperformed FDR+SVM, but both were effective in achieving good diagnostic accuracy with a small number of features. Some of the selected features have previously been investigated as clinical markers for lung cancer diagnosis; some of the remaining features are excellent candidates for further research.

Algorithms↗