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Author

Philippe Lamesch

Other affiliations: Université de Namur
Bio: Philippe Lamesch is an academic researcher from Harvard University. The author has contributed to research in topics: ORFeome & ORFS. The author has an hindex of 12, co-authored 13 publications receiving 6031 citations. Previous affiliations of Philippe Lamesch include Université de Namur.

Papers
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Journal ArticleDOI
20 Oct 2005-Nature
TL;DR: An initial version of a proteome-scale map of human binary protein–protein interactions is described, which increases by ∼70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins.
Abstract: Systematic mapping of protein-protein interactions, or 'interactome' mapping, was initiated in model organisms, starting with defined biological processes and then expanding to the scale of the proteome. Although far from complete, such maps have revealed global topological and dynamic features of interactome networks that relate to known biological properties, suggesting that a human interactome map will provide insight into development and disease mechanisms at a systems level. Here we describe an initial version of a proteome-scale map of human binary protein-protein interactions. Using a stringent, high-throughput yeast two-hybrid system, we tested pairwise interactions among the products of approximately 8,100 currently available Gateway-cloned open reading frames and detected approximately 2,800 interactions. This data set, called CCSB-HI1, has a verification rate of approximately 78% as revealed by an independent co-affinity purification assay, and correlates significantly with other biological attributes. The CCSB-HI1 data set increases by approximately 70% the set of available binary interactions within the tested space and reveals more than 300 new connections to over 100 disease-associated proteins. This work represents an important step towards a systematic and comprehensive human interactome project.

2,936 citations

Journal ArticleDOI
23 Jan 2004-Science
TL;DR: A large fraction of the Caenorhabditis elegans interactome network is mapped, starting with a subset of metazoan-specific proteins, and more than 4000 interactions were identified from high-throughput, yeast two-hybrid screens.
Abstract: To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains approximately 5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.

1,733 citations

Journal ArticleDOI
TL;DR: Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans successfully, and it is suggested that similar ORFeome projects will be valuable for other organisms, including humans.
Abstract: To verify the genome annotation and to create a resource to functionally characterize the proteome, we attempted to Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans. We successfully cloned approximately 12,000 ORFs (ORFeome 1.1), of which roughly 4,000 correspond to genes that are untouched by any cDNA or expressed-sequence tag (EST). More than 50% of predicted genes needed corrections in their intron-exon structures. Notably, approximately 11,000 C. elegans proteins can now be expressed under many conditions and characterized using various high-throughput strategies, including large-scale interactome mapping. We suggest that similar ORFeome projects will be valuable for other organisms, including humans.

406 citations

Journal ArticleDOI
01 Mar 2007-Genomics
TL;DR: This expansion of the original ORFeome resource greatly increases the potential experimental search space for large-scale proteomics studies, which will lead to the generation of more comprehensive datasets.

272 citations

Journal ArticleDOI
TL;DR: The generation of a first version of the human ORFeome is described using a newly improved Gateway recombinational cloning approach, and the use of hORFeome v1.1 for heterologous protein expression in two different expression systems at proteome scale is described.
Abstract: The advent of systems biology necessitates the cloning of nearly entire sets of protein-encoding open reading frames (ORFs), or ORFeomes, to allow functional studies of the corresponding proteomes. Here, we describe the generation of a first version of the human ORFeome using a newly improved Gateway recombinational cloning approach. Using the Mammalian Gene Collection (MGC) resource as a starting point, we report the successful cloning of 8076 human ORFs, representing at least 7263 human genes, as mini-pools of PCR-amplified products. These were assembled into the human ORFeome version 1.1 (hORFeome v1.1) collection. After assessing the overall quality of this version, we describe the use of hORFeome v1.1 for heterologous protein expression in two different expression systems at proteome scale. The hORFeome v1.1 represents a central resource for the cloning of large sets of human ORFs in various settings for functional proteomics of many types, and will serve as the foundation for subsequent improved versions of the human ORFeome.

230 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
23 Jan 2015-Science
TL;DR: In this paper, a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level.
Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

9,745 citations

Journal ArticleDOI
TL;DR: This work states that rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize the view of biology and disease pathologies in the twenty-first century.
Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

7,475 citations

Journal ArticleDOI
TL;DR: Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.
Abstract: Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

3,978 citations

Journal ArticleDOI
TL;DR: BioGRID is a freely accessible database of physical and genetic interactions that includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens.
Abstract: Access to unified datasets of protein and genetic interactions is critical for interrogation of gene/protein function and analysis of global network properties. BioGRID is a freely accessible database of physical and genetic interactions available at http://www.thebiogrid.org. BioGRID release version 2.0 includes >116 000 interactions from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens. Over 30 000 interactions have recently been added from 5778 sources through exhaustive curation of the Saccharomyces cerevisiae primary literature. An internally hyper-linked web interface allows for rapid search and retrieval of interaction data. Full or user-defined datasets are freely downloadable as tab-delimited text files and PSI-MI XML. Pre-computed graphical layouts of interactions are available in a variety of file formats. User-customized graphs with embedded protein, gene and interaction attributes can be constructed with a visualization system called Osprey that is dynamically linked to the BioGRID.

3,794 citations