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Fabien Campagne

Publications and source records attributed to Fabien Campagne.

14 recordsLinked to original sources
PubMed2006

Gene expression profiling separates chromophobe renal cell carcinoma from oncocytoma and identifies vesicular transport and cell junction proteins as differentially expressed genes.

PURPOSE: To compare gene expression profiles of chromophobe renal cell carcinoma (RCC) and benign oncocytoma, aiming at identifying differentially expressed genes. EXPERIMENTAL DESIGN: Nine cases each of chromophobe RCC and oncocytoma were analyzed by oligonucleotide microarray. Candidate genes that showed consistent differential expression were validated by reverse transcription-PCR using 25 fresh-frozen and 15 formalin-fixed, paraffin-embedded tumor samples. Immunohistochemical analysis was also done for two selected gene products, claudin 8 and MAL2. RESULTS: Unsupervised hierarchical clustering separated the chromophobe RCC and oncocytoma into two distinct groups. By a combination of data analysis approaches, we identified 11 candidate genes showing consistent differential expression between chromophobe RCC and oncocytoma. Five of these genes, AP1M2, MAL2, PROM2, PRSS8, and FLJ20171, were shown to effectively separate these two tumor groups by quantitative reverse transcription-PCR using fresh tissue samples, with similar trends seen on formalin-fixed tissues. Immunohistochemical analysis revealed selective expression of MAL2 and claudin 8 in distal renal tubules, with MAL2 antibody showing differential expression between chromophobe RCC and oncocytoma. Functional analyses suggest that genes encoding tight junction proteins and vesicular membrane trafficking proteins, normally expressed in distal nephrons, are retained in chromophobe RCC and lost or consistently down-regulated in oncocytoma, indicating that these two tumor types, believed to be both derived from distal tubules, are likely distinctive in their histogenesis. CONCLUSIONS: We showed that chromophobe RCC and oncocytoma are distinguishable by mRNA expression profiles and a panel of gene products potentially useful as diagnostic markers were identified.

Adaptor Protein Complex 1↗
PubMed2006

Mining expressed sequence tags identifies cancer markers of clinical interest.

BACKGROUND: Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. RESULTS: We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. CONCLUSION: These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies.

Animals↗
PubMed2006

SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures.

Reversible addition of NO to Cys-sulfur in proteins, a modification termed S-nitrosylation, has emerged as a ubiquitous signaling mechanism for regulating diverse cellular processes. A key first-step toward elucidating the mechanism by which S-nitrosylation modulates a protein's function is specification of the targeted Cys (SNO-Cys) residue. To date, S-nitrosylation site specification has been laboriously tackled on a protein-by-protein basis. Here we describe a high-throughput proteomic approach that enables simultaneous identification of SNO-Cys sites and their cognate proteins in complex biological mixtures. The approach, termed SNOSID (SNO Site Identification), is a modification of the biotin-swap technique [Jaffrey, S. R., Erdjument-Bromage, H., Ferris, C. D., Tempst, P. & Snyder, S. H. (2001) Nat. Cell. Biol. 3, 193-197], comprising biotinylation of protein SNO-Cys residues, trypsinolysis, affinity purification of biotinylated-peptides, and amino acid sequencing by liquid chromatography tandem MS. With this approach, 68 SNO-Cys sites were specified on 56 distinct proteins in S-nitrosoglutathione-treated (2-10 microM) rat cerebellum lysates. In addition to enumerating these S-nitrosylation sites, the method revealed endogenous SNO-Cys modification sites on cerebellum proteins, including alpha-tubulin, beta-tubulin, GAPDH, and dihydropyrimidinase-related protein-2. Whereas these endogenous SNO proteins were previously recognized, we extend prior knowledge by specifying the SNO-Cys modification sites. Considering all 68 SNO-Cys sites identified, a machine learning approach failed to reveal a linear Cys-flanking motif that predicts stable transnitrosation by S-nitrosoglutathione under test conditions, suggesting that undefined 3D structural features determine S-nitrosylation specificity. SNOSID provides the first effective tool for unbiased elucidation of the SNO proteome, identifying Cys residues that undergo reversible S-nitrosylation.

Animals↗
PubMed2005

Crooked tail (Cd) model of human folate-responsive neural tube defects is mutated in Wnt coreceptor lipoprotein receptor-related protein 6.

A cranial neural tube defect in Crooked tail (Cd) mice is prevented with prenatal dietary folic acid Cd positional cloning reveals a missense mutation of a highly conserved amino acid in the low density lipoprotein receptor-related protein 6 (Lrp6), a coreceptor required for Wnt canonical signaling. Molecular modeling predicts that Lrp6(Cd) alters a hinge region of the second YWTD beta-propeller domain. Mutant LRP6 binds to Wnt and Dickkopf1 (Dkk1) but not Mesd1, and Dkk1 cannot antagonize Wnt in Cd/Cd cells, resulting in hyperactivity. NIH 3T3 cells transfected with a mutant Lrp6 plasmid resist Dkk1 antagonism much like Cd/+ cells, confirming the significance of the mutation. The Lrp6 mutation in Cd mice provides evidence for a functional connection between Wnt signaling and folate rescue of neural tube defects.

Amino Acid Sequence↗
PubMed2005

Building a protein name dictionary from full text: a machine learning term extraction approach.

BACKGROUND: The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. RESULTS: We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM) to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. CONCLUSION: This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

Abstracting and Indexing↗
PubMed2005

Critical evaluation of the JDO API for the persistence and portability requirements of complex biological databases.

BACKGROUND: Complex biological database systems have become key computational tools used daily by scientists and researchers. Many of these systems must be capable of executing on multiple different hardware and software configurations and are also often made available to users via the Internet. We have used the Java Data Object (JDO) persistence technology to develop the database layer of such a system known as the SigPath information management system. SigPath is an example of a complex biological database that needs to store various types of information connected by many relationships. RESULTS: Using this system as an example, we perform a critical evaluation of current JDO technology; discuss the suitability of the JDO standard to achieve portability, scalability and performance. We show that JDO supports portability of the SigPath system from a relational database backend to an object database backend and achieves acceptable scalability. To answer the performance question, we have created the SigPath JDO application benchmark that we distribute under the Gnu General Public License. This benchmark can be used as an example of using JDO technology to create a complex biological database and makes it possible for vendors and users of the technology to evaluate the performance of other JDO implementations for similar applications. CONCLUSIONS: The SigPath JDO benchmark and our discussion of JDO technology in the context of biological databases will be useful to bioinformaticians who design new complex biological databases and aim to create systems that can be ported easily to a variety of database backends.

Computational Biology↗
PubMed2005

Minimum information requested in the annotation of biochemical models (MIRIAM).

Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.

Biochemistry↗
PubMed2005

Pseudomonas aeruginosa SoxR does not conform to the archetypal paradigm for SoxR-dependent regulation of the bacterial oxidative stress adaptive response.

SoxR is a transcriptional regulator that controls an oxidative stress response in Escherichia coli. The regulator is primarily activated by superoxide anion-dependent oxidation. Activated SoxR turns on transcription of a single gene, soxS, which encodes a transcriptional regulator that activates a regulon that includes dozens of oxidative stress response genes. SoxR homologues have been identified in many bacterial species, including the opportunistic pathogen Pseudomonas aeruginosa. However, the expected SoxR partner, SoxS, has not been found in P. aeruginosa. Thus, the primary gene target(s) of P. aeruginosa SoxR is unknown and the involvement of this regulator in the oxidative stress response of the bacterium remains unclear. We utilized transcriptome profiling to identify the P. aeruginosa SoxR regulon and constructed and characterized an unmarked P. aeruginosa DeltasoxR mutant. We provide evidence indicating that P. aeruginosa SoxR activates a six-gene regulon in response to O(2)(.-)-induced stress. The regulon includes three transcriptional units: (i) the recently identified mexGHI-ompD four-gene operon, which encodes a multidrug efflux pump system involved in quorum-sensing signal homeostasis; (ii) gene PA3718, encoding a probable efflux pump; and (iii) gene PA2274, encoding a probable monooxygenase. We also demonstrate that P. aeruginosa SoxR is not a key regulatory player in the oxidative stress response. Finally, we show that P. aeruginosa SoxR is required for virulence in a mouse model of intrapulmonary infection. These results demonstrate that the E. coli-based SoxRS paradigm does not hold in P. aeruginosa and foster new hypotheses for the possible physiological role of P. aeruginosa SoxR.

Animals↗
PubMed2004

Quantitative information management for the biochemical computation of cellular networks.

Understanding complex protein networks within cells requires the ability to develop quantitative models and to numerically compute the properties and behavior of the networks. To carry out such computational analysis, it is necessary to use modeling tools and information management systems (IMSs) where the quantitative data, associated to its biological context, can be stored, curated, and reliably retrieved. We have focused on the biochemical computation of cellular interactions and developed an IMS that stores both quantitative information on the cellular components and their interactions, and the basic reactions governing those interactions. This information can be used to construct pathways and eventually large-scale networks. This system, SigPath, is available on the Internet (http://www.sigpath.org). Key features of the approach include (i) the use of background information (for example, names of molecules, aliases, and accession codes) to ease data submission and link this quantitative database with other qualitative databases, (ii) a strategy to allow refinement of information over time by multiple users, (iii) the development of a data representation that stores both qualitative and quantitative information, and (iv) features to assist contributors and users in assembling custom quantitative models from the information stored in the IMS. Currently, models assembled in SigPath can be automatically exported to several computing environments, such as Kinetikit/Genesis, Virtual Cell, Jarnac/JDesigner, and JSim. We anticipate that, when appropriately populated, such a system will be useful for large-scale quantitative studies of cell-signaling networks and other cellular networks. SigPath is distributed under the GNU General Public License.

Animals↗
PubMed2004

Characterization of the mouse adeno-associated virus AAVS1 ortholog.

The nonpathogenic human adeno-associated virus (AAV) has developed a mechanism to integrate its genome into human chromosome 19 at 19q13.4 (termed AAVS1), thereby establishing latency. Here, we provide evidence that the chromosomal signals required for site-specific integration are conserved in the mouse genome proximal to the recently identified Mbs85 gene. These sequence motifs can be specifically nicked by the viral Rep protein required for the initiation of site-specific AAV DNA integration. Furthermore, these signals can serve as a minimal origin for Rep-dependent DNA replication. In addition, we isolated the mouse Mbs85 proximal promoter and show transcriptional activity in three mouse cell lines.

Animals↗
PubMed2003

Batch mode generation of residue-based diagrams of proteins.

SUMMARY: Residue-based diagrams of proteins are graphical representations that can be used in protein information systems. These diagrams make it possible to visually integrate different types of biological information. The approach has been used successfully for membrane proteins. We developed the Residue-based diagram generator to (i) make it possible to generate residue-based diagrams of proteins in a batch mode that is compatible with the needs of information system curators, (ii) allow the generation of residue-based diagrams for families of soluble proteins or domains. AVAILABILITY: Licensed. Royalty free licenses are granted to non-profit institutions for educational and research purposes. http://icb.mssm.edu/crt/RbDg/index.xml

Amino Acid Sequence↗
PubMed2003

Building protein diagrams on the web with the residue-based diagram editor RbDe.

The residue-based diagram editor (RbDe) is web-based software that greatly simplifies the construction of schematic diagrams of proteins. Residue-based diagrams display the sequence of a given protein in the context of its secondary and tertiary structure. Such diagrams are frequently used to summarize mutations or sequence features, in the context of the overall topology of a protein. The initial version of RbDe was designed for transmembrane proteins and has enabled many users to create diagrams of large systems such as G protein-coupled receptors or transporters. We present an extended diagram editor that supports other families of proteins. Users can now import custom-diagram layouts, use them to render members of any protein family and generate high-quality output for publication purposes. RbDe is available free over the web, at http://icb.mssm.edu/crt/RbDe

Amino Acid Sequence↗
PubMed2003

GPCRDB information system for G protein-coupled receptors.

The GPCRDB is a molecular class-specific information system that collects, combines, validates and disseminates heterogeneous data on G protein-coupled receptors (GPCRs). The database stores data on sequences, ligand binding constants and mutations. The system also provides computationally derived data such as sequence alignments, homology models, and a series of query and visualization tools. The GPCRDB is updated automatically once every 4-5 months and is freely accessible at http://www.gpcr.org/7tm/.

Amino Acid Sequence↗