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PubMed2024

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

OBJECTIVE: To sought comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. BACKGROUND: Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intrapancreatic microbial dysbiosis may drive malignant transformation. METHODS: Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n = 20; IPMN, n = 20; PDAC, n = 19) and pancreatic cyst fluid (IPMN, n = 30; serous cystadenomas, n = 10; mucinous cystic neoplasm, n = 10). Assessment of bacterial DNA by quantitative polymerase chain reaction and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium were analyzed to validate findings. RESULTS: Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade and high-grade dysplasia IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in high-grade dysplasia cyst fluid. Decontamination analysis using the Clinical Proteomic Tumor Analysis Consortium database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. CONCLUSIONS: Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Humans↗
PubMed2003

[Analytical chemical studies on high-performance recognition and detection of bio-molecules in life].

In order to understand the mechanism for maintaining life of animals based on the search of dynamics of biomolecules, I have developed several sensitive and selective methods for their quantification. Using the methods of derivatization with the developed benzofurazan fluorogenic reagents (4-fluoro-7-nitro-2,1,3-benzoxadiazole (NBD-F), ammonium 7-fluoro-2,1,3-benzoxadiazole 4-sulfonate (SBD-F) and etc.) followed by high-performance liquid chromatography (HPLC)--fluorescence detection, a certain kind of biological and clinical importance was demonstrated of chiral bio-molecules (D-amino acids, D-lactic acid and so on), peptides and proteins. The proposed method (derivatization with SBD-F, isolation of the fluorescent proteins by two-dimensional HPLC, enzymatic digestion and identification of the altered proteins by HPLC-mass spectrometry (MS)/MS with database-searching algorithm) for proteomics studies revealed the changed proteins in the islets of Langerhans of the dexamethazone-induced diabetic rats. An importance of catecholamine metabolism on the blood pressure regulation was also suggested by the method of HPLC-chemiluminescence detection of catecholamines and their 3-O-methylmetabolites. A new field of Analytical Chemistry, i.e., Bio-Analytical Chemistry, was also proposed.

Amino Acids↗
PubMed2002

Proteome analysis of human stomach tissue: separation of soluble proteins by two-dimensional polyacrylamide gel electrophoresis and identification by mass spectrometry.

Two-dimensional gel electrophoresis (2-DE) maps for human stomach tissue proteins have been prepared by displaying the protein components of the tissue by 2-DE and identifying them using mass spectrometry. This will enable us to present an overview of the proteins expressed in human stomach tissues and lays the basis for subsequent comparative proteome analysis studies with gastric diseases such as gastric cancer. In this study, 2-DE maps of soluble fraction proteins were prepared on two gel images with partially overlapping pH ranges of 4-7 and 6-9. On the gels covering pH 4-7 and pH 6-9, about 900 and 600 protein spots were detected by silver staining, respectively. For protein identification, proteins spots on micropreparative gels stained with colloidal Coomassie Brilliant Blue G-250 were excised, digested in-gel with trypsin, and analyzed by peptide mass fingerprinting with delayed extraction-matrix assisted laser desorption/ionization-mass spectrometry (DE-MALDI-MS). In all, 243 protein spots (168 spots in acidic map and 75 spots in basic map) corresponding to 136 different proteins were identified. Besides these principal maps, overview maps (displayed on pH 3-10 gels) for total homogenate and soluble fraction, are also presented with some identifications mapped on them. Based on the 2-DE maps presented in this study, a 2-DE database for human stomach tissue proteome has been constructed and is available at http://proteome.gsnu.ac.kr/DB/2DPAGE/Stomach/. The 2-DE maps and the database resulting from this study will serve important resources for subsequent proteomic studies for analyzing the normal protein variability in healthy tissues and specific protein variations in diseased tissues.

Databases, Protein↗
PubMed2004

The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology.

The Gene Ontology Annotation (GOA) database (http://www.ebi.ac.uk/GOA) aims to provide high-quality electronic and manual annotations to the UniProt Knowledgebase (Swiss-Prot, TrEMBL and PIR-PSD) using the standardized vocabulary of the Gene Ontology (GO). As a supplementary archive of GO annotation, GOA promotes a high level of integration of the knowledge represented in UniProt with other databases. This is achieved by converting UniProt annotation into a recognized computational format. GOA provides annotated entries for nearly 60,000 species (GOA-SPTr) and is the largest and most comprehensive open-source contributor of annotations to the GO Consortium annotation effort. By integrating GO annotations from other model organism groups, GOA consolidates specialized knowledge and expertise to ensure the data remain a key reference for up-to-date biological information. Furthermore, the GOA database fully endorses the Human Proteomics Initiative by prioritizing the annotation of proteins likely to benefit human health and disease. In addition to a non-redundant set of annotations to the human proteome (GOA-Human) and monthly releases of its GO annotation for all species (GOA-SPTr), a series of GO mapping files and specific cross-references in other databases are also regularly distributed. GOA can be queried through a simple user-friendly web interface or downloaded in a parsable format via the EBI and GO FTP websites. The GOA data set can be used to enhance the annotation of particular model organism or gene expression data sets, although increasingly it has been used to evaluate GO predictions generated from text mining or protein interaction experiments. In 2004, the GOA team will build on its success and will continue to supplement the functional annotation of UniProt and work towards enhancing the ability of scientists to access all available biological information. Researchers wishing to query or contribute to the GOA project are encouraged to email: goa@ebi.ac.uk.

Animals↗
PubMed2001

Hepatocellular carcinoma: from bedside to proteomics.

Hepatocellular carcinoma (HCC or hepatoma) is the most common primary cancer of the liver. It is responsible for approximately one million deaths each year, mainly in underdeveloped and developing countries. The aetiological factors identified in the development of HCC included persistent infection by hepatitis B and hepatitis C viruses, and exposure to aflatoxins. Although immunization can protect individuals from being infected by the hepatitis B virus, the early detection of HCC in those who have been infected by the virus remains a challenge. Thus most HCCs present late and are not suitable for curative treatment. Hence there is a tremendous interest and urgency to identify novel HCC diagnostic marker(s) for early detection, and tumour specific disease associated proteins as potential therapeutic targets in the treatment of HCC. Screening for these HCC proteins has been facilitated by proteomics, a key technology in the global analysis of protein expression and understanding gene function. Present and earlier proteome analyses of HCC have used predominantly experimental in vitro systems. The protein expression profiles of several hepatoma cell lines such as HepG2, Huh7, SK-Hep1, and Hep3B have been compared with normal liver, and nontransformed cell lines (Chang and WRL-68), while a comprehensive proteome analysis to create a protein database was carried out for the cell line HCC-M. In the future, proteome analyses utilizing tumour tissues, which reflect the pathological state of HCC more closely, will be undertaken. This work will complement the gene expression studies of HCC which are already underway. Efforts have also been directed at the proteome analysis of hepatic stellate cells, as these cells play an important role in liver fibrosis. Since liver fibrosis is reversible but not cirrhosis, it is of considerable importance to identify therapeutic targets that can slow its progression.

Amino Acid Sequence↗
PubMed2003

PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery.

Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.

Computational Biology↗
PubMedDate not supplied

Peptide end sequencing by orthogonal MALDI tandem mass spectrometry.

Highly sensitive peptide fragmentation and identification in sequence databases is a cornerstone of proteomics. Previously, a two-layered strategy consisting of MALDI peptide mass fingerprinting followed by electrospray tandem mass spectrometry of the unidentified proteins has been successfully employed. Here, we describe a high-sensitivity/high-throughput system based on orthogonal MALDI tandem mass spectrometry (o-MALDI) and the automated recognition of fragments corresponding to the N- and C-terminal amino acid residues. Robotic deposition of samples onto hydrophobic anchor substrates is employed, and peptide spectra are acquired automatically. The pulsing feature of the QSTAR o-MALDI mass spectrometer enhances the low mass region of the spectra by approximately 1 order of magnitude. Software has been developed to automatically recognize characteristic features in the low mass region (such as the y1 ion of tryptic peptides), maintaining high mass accuracy even with very low count events. Typically, the sum of the N-terminal two ions (b2 ion), the third N-terminal ion (b3 ion), and the two C-terminal fragments of the peptide (y1 and y2) can be determined. Given mass accuracy in the low ppm range, peptide end sequencing on one or two tryptic peptides is sufficient to uniquely identify a protein from gel samples in the low silver-stained range.

Calibration↗
PubMed2003

Proteome analysis of rice tissues by two-dimensional electrophoresis: an approach to the investigation of gibberellin regulated proteins.

Protein databases constructed using high-resolution two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) were used to explore the proteome expressed in various rice tissues. Proteins from leaf sheath, root, and cultured suspension cells were systematically analyzed using 2D-PAGE, mass spectrometry and Edman sequencing, followed by database searching. In all, 79 of the 431 spots detected by 2D-PAGE in the leaf sheath, 73 of the 508 spots in the root and 140 of the 962 spots in the cultured suspension cells could be identified. Protein lists were constructed for each tissue and used to investigate the effects of gibberellin (GA) treatment. In the leaf sheath, root and cultured suspension cells, 8, 21, and 14 of the identified proteins, respectively, were regulated by GA. These proteins included polypeptides involved in general metabolism, energy production, transcriptional regulation and signal transduction in the leaf sheath; in metabolism and defense in the root; and in metabolism, energy production, cell growth, defense and signal transduction in the cultured suspension cells. These results indicate that the proteome databases assembled in these studies will be useful for the rapid assessment of changes in protein content in specific tissues, and that proteins regulated by GA may play a significant role in tissue growth.

Cells, Cultured↗
PubMed2005

Proteomics in hematologic malignancies.

Basic science research in hematology has been determining the functions of gene products using classical approaches that typically involve studying one or a few genes at a time. Proteomics, defined as the study of protein properties on a large scale, provides tools to globally analyze malignant hematologic cells. A major challenge in cancer therapy is the identification of drugs that kill tumor cells while preserving normal cells. Differential display via proteomics enables analysis of direct as well as side-effects of drugs at a molecular level. Proteomics also allows a better understanding of cell signaling pathways involved during apoptosis in hematologic cells. Storing the information in a 2D electrophoresis database enhances the efficiency of proteome research on malignant cells. Finally, the work needed to be carried out on proteomic analysis prior to routine clinical adoption is discussed, and the necessity for multi-institutional collaborations is emphasized.

Animals↗
PubMed2004

Proteomics: a primer for otologists.

OBJECTIVE: On July 9, 2003, the National Institutes of Health (NIH) released a new program announcement entitled "Proteomics in Auditory and Developmental Disease Processes." This initiative makes it clear that proteomic analysis in otology is a multi-year research priority for the NIH. The goal of this article is to describe the mechanics of modern proteomic techniques and review their applications in otology to date. DATA SOURCES: General articles from the proteomic literature were used to construct a review of modern proteomic techniques. For literature on proteomics in otology, MEDLINE and CRISP databases were searched by various topics in otology and cross-referenced with principle proteomic technologies. STUDY SELECTION: The criterion for selection was any study in otology that employs proteomic technology. CONCLUSIONS: Incredible progress has been made in proteomic technology. However, modern proteomic techniques are currently underutilized in otologic research. The NIH proteomics initiative referenced above, in combination with an understanding of the basic tools of modern proteomic science, should help motivate otologists to discover innovative ways to apply modern proteomic techniques to specific problems in otology.

Biomedical Research↗
PubMed2004

TcruziDB: an integrated Trypanosoma cruzi genome resource.

TcruziDB (http://TcruziDB.org) is an integrated genome database for the parasitic organism Trypanosoma cruzi, the causative agent of Chagas' disease. The database currently incorporates all available sequence data (Genomic, BAC, EST) in a single user-friendly location. The database contains a variety of tools specifically designed for searching unannotated draft sequence via BLAST, keyword searches of pre-computed BLAST results, and protein motif searches. Release 1.0 of the database contains nearly 730 million bp of genome sequence from 1.1 million sequence reads generated by the TIGR-Karolinska-SBRI Trypanosoma cruzi Genome Consortium and 15 million bp of clustered EST and genomic sequence obtained from other sources. As annotation, microarray and proteomic data become available, the database will incorporate and integrate these data using the GUS (http://www.gusdb. org) relational framework.

Animals↗
PubMed2003

Protein database, human retinal pigment epithelium.

The retinal pigment epithelium (RPE) is a single cell layer adjacent to the rod and cone photoreceptors that plays key roles in retinal physiology and the biochemistry of vision. RPE cells were isolated from normal adult human donor eyes, subcellular fractions were prepared, and proteins were fractionated by electrophoresis. Following in-gel proteolysis, proteins were identified by peptide sequencing using liquid chromatography tandem electrospray mass spectrometry and/or by peptide mass mapping using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Preliminary analyses have identified 278 proteins and provide a starting point for building a database of the human RPE proteome.

Chromatography, Liquid↗
PubMed2003

An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms.

We sought to identify all genes in the Candida albicans genome database whose deduced proteins would likely be soluble secreted proteins (the secretome). While certain C. albicans secretory proteins have been studied in detail, more data on the entire secretome is needed. One approach to rapidly predict the functions of an entire proteome is to utilize genomic database information and prediction algorithms. Thus, we used a set of prediction algorithms to computationally define a potential C. albicans secretome. We first assembled a validation set of 47 C. albicans proteins that are known to be secreted and 47 that are known not to be secreted. The presence or absence of an N-terminal signal peptide was correctly predicted by SignalP version 2.0 in 47 of 47 known secreted proteins and in 47 of 47 known non-secreted proteins. When all 6165 C. albicans ORFs from CandidaDB were analysed with SignalP, 495 ORFs were predicted to encode proteins with N-terminal signal peptides. In the set of 495 deduced proteins with N-terminal signal peptides, 350 were predicted to have no transmembrane domains (or a single transmembrane domain at the extreme N-terminus) and 300 of these were predicted not to be GPI-anchored. TargetP was used to eliminate proteins with mitochondrial targeting signals, and the final computationally-predicted C. albicans secretome was estimated to consist of up to 283 ORFs. The C. albicans secretome database is available at http://info.med.yale.edu/intmed/infdis/candida/

Algorithms↗
PubMed2006

Proteomic analysis of log to stationary growth phase Lactobacillus plantarum cells and a 2-DE database.

Lactobacillus plantarum is part of the natural microbiota of many food fermentations as well as the human gastro-intestinal tract. The cytosolic fraction of the proteome of L. plantarum WCFS1, whose genome has been sequenced, was studied. 2-DE was used to investigate the proteins from the cytosolic fraction isolated from mid- and late-log, early- and late-stationary phase cells to generate reference maps of different growth conditions offering more knowledge of the metabolic behavior of this bacterium. From this fraction, a total of 200 protein spots were identified by MALDI-MS and a proteome production map was constructed to facilitate further studies such as detection of suitable biomarkers for specific growth conditions. More than half (57%) of the identified proteins were predicted to be involved in metabolic pathways of the bacterium. The protein profile changed during the growth of the bacteria such that 29% of the identified proteins involved in anabolic pathways were at least twofold up-regulated throughout the mid- and late-exponential and early-stationary phases. In the late-stationary phase, six proteins involved in stress or with a potential role for survival during starvation were up-regulated significantly.

Bacterial Proteins↗
PubMed2006

The alliance for cellular signaling plasmid collection: a flexible resource for protein localization studies and signaling pathway analysis.

Cellular responses to inputs that vary both temporally and spatially are determined by complex relationships between the components of cell signaling networks. Analysis of these relationships requires access to a wide range of experimental reagents and techniques, including the ability to express the protein components of the model cells in a variety of contexts. As part of the Alliance for Cellular Signaling, we developed a robust method for cloning large numbers of signaling ORFs into Gateway entry vectors, and we created a wide range of compatible expression platforms for proteomics applications. To date, we have generated over 3000 plasmids that are available to the scientific community via the American Type Culture Collection. We have established a website at www.signaling-gateway.org/data/plasmid/ that allows users to browse, search, and blast Alliance for Cellular Signaling plasmids. The collection primarily contains murine signaling ORFs with an emphasis on kinases and G protein signaling genes. Here we describe the cloning, databasing, and application of this proteomics resource for large scale subcellular localization screens in mammalian cell lines.

Animals↗
PubMed2004

Proteomics in postgenomic neuroscience: the end of the beginning.

Proteomics is complementary to genomic approaches anchored in DNA and RNA. Global characterization of proteins is providing new insights into general biological structures as well as synapses, receptor complexes and other neuronal and glial features. Current challenges for proteomics of the nervous system include problems relating to sample preparation, brain complexity, limited databases and informatics tools. The combination of proteomics with other global functional genomic approaches at the levels of genome and transcriptome, together with network biology, will provide important bridges between genes, physiology and pathology.

Animals↗
PubMed2003

The proteome of Saccharomyces cerevisiae mitochondria.

We performed a comprehensive approach to determine the proteome of Saccharomyces cerevisiae mitochondria. The proteins of highly pure yeast mitochondria were separated by several independent methods and analyzed by tandem MS. From >20 million MS spectra, 750 different proteins were identified, indicating an involvement of mitochondria in numerous cellular processes. All known components of the oxidative phosphorylation machinery, the tricarboxylic acid cycle, and the stable mitochondria-encoded proteins were found. Based on the mitochondrial proteins described in the literature so far, we calculate that the identified proteins represent approximately 90% of all mitochondrial proteins. The function of a quarter of the identified proteins is unknown. The mitochondrial proteome will provide an important database for the analysis of new mitochondrial and mitochondria-associated functions and the characterization of mitochondrial diseases.

Citric Acid Cycle↗