Publications by authors named "Charles Bettembourg"

7 Publications

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ABHD11, a new diacylglycerol lipase involved in weight gain regulation.

PLoS One 2020 24;15(6):e0234780. Epub 2020 Jun 24.

Sanofi Research and Development, Chilly-Mazarin, France.

Obesity epidemic continues to spread and obesity rates are increasing in the world. In addition to public health effort to reduce obesity, there is a need to better understand the underlying biology to enable more effective treatment and the discovery of new pharmacological agents. Abhydrolase domain-containing protein 11 (ABHD11) is a serine hydrolase enzyme, localized in mitochondria, that can synthesize the endocannabinoid 2-arachidonoyl glycerol (2AG) in vitro. In vivo preclinical studies demonstrated that knock-out ABHD11 mice have a similar 2AG level as WT mice and exhibit a lean metabolic phenotype. Such mice resist to weight gain in Diet Induced Obesity studies (DIO) and display normal biochemical plasma parameters. Metabolic and transcriptomic analyses on serum and tissues of ABHD11 KO mice from DIO studies show a modulation in bile salts associated with reduced fat intestinal absorption. These data suggest that modulating ABHD11 signaling pathway could be of therapeutic value for the treatment of metabolic disorders.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0234780PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313976PMC
September 2020

The RESOLUTE consortium: unlocking SLC transporters for drug discovery.

Authors:
Giulio Superti-Furga Daniel Lackner Tabea Wiedmer Alvaro Ingles-Prieto Barbara Barbosa Enrico Girardi Ulrich Goldmann Bettina Gürtl Kristaps Klavins Christoph Klimek Sabrina Lindinger Eva Liñeiro-Retes André C Müller Svenja Onstein Gregor Redinger Daniela Reil Vitaly Sedlyarov Gernot Wolf Matthew Crawford Robert Everley David Hepworth Shenping Liu Stephen Noell Mary Piotrowski Robert Stanton Hui Zhang Salvatore Corallino Andrea Faedo Maria Insidioso Giovanna Maresca Loredana Redaelli Francesca Sassone Lia Scarabottolo Michela Stucchi Paola Tarroni Sara Tremolada Helena Batoulis Andreas Becker Eckhard Bender Yung-Ning Chang Alexander Ehrmann Anke Müller-Fahrnow Vera Pütter Diana Zindel Bradford Hamilton Martin Lenter Diana Santacruz Coralie Viollet Charles Whitehurst Kai Johnsson Philipp Leippe Birgit Baumgarten Lena Chang Yvonne Ibig Martin Pfeifer Jürgen Reinhardt Julian Schönbett Paul Selzer Klaus Seuwen Charles Bettembourg Bruno Biton Jörg Czech Hélène de Foucauld Michel Didier Thomas Licher Vincent Mikol Antje Pommereau Frédéric Puech Veeranagouda Yaligara Aled Edwards Brandon J Bongers Laura H Heitman Ad P IJzerman Huub J Sijben Gerard J P van Westen Justine Grixti Douglas B Kell Farah Mughal Neil Swainston Marina Wright-Muelas Tina Bohstedt Nicola Burgess-Brown Liz Carpenter Katharina Dürr Jesper Hansen Andreea Scacioc Giulia Banci Claire Colas Daniela Digles Gerhard Ecker Barbara Füzi Viktoria Gamsjäger Melanie Grandits Riccardo Martini Florentina Troger Patrick Altermatt Cédric Doucerain Franz Dürrenberger Vania Manolova Anna-Lena Steck Hanna Sundström Maria Wilhelm Claire M Steppan

Nat Rev Drug Discov 2020 07;19(7):429-430

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http://dx.doi.org/10.1038/d41573-020-00056-6DOI Listing
July 2020

Optimal Threshold Determination for Interpreting Semantic Similarity and Particularity: Application to the Comparison of Gene Sets and Metabolic Pathways Using GO and ChEBI.

PLoS One 2015 31;10(7):e0133579. Epub 2015 Jul 31.

Université de Rennes 1, Rennes, France; IRISA, Campus de Beaulieu, Rennes, France; INRIA, Rennes, France.

Background: The analysis of gene annotations referencing back to Gene Ontology plays an important role in the interpretation of high-throughput experiments results. This analysis typically involves semantic similarity and particularity measures that quantify the importance of the Gene Ontology annotations. However, there is currently no sound method supporting the interpretation of the similarity and particularity values in order to determine whether two genes are similar or whether one gene has some significant particular function. Interpretation is frequently based either on an implicit threshold, or an arbitrary one (typically 0.5). Here we investigate a method for determining thresholds supporting the interpretation of the results of a semantic comparison.

Results: We propose a method for determining the optimal similarity threshold by minimizing the proportions of false-positive and false-negative similarity matches. We compared the distributions of the similarity values of pairs of similar genes and pairs of non-similar genes. These comparisons were performed separately for all three branches of the Gene Ontology. In all situations, we found overlap between the similar and the non-similar distributions, indicating that some similar genes had a similarity value lower than the similarity value of some non-similar genes. We then extend this method to the semantic particularity measure and to a similarity measure applied to the ChEBI ontology. Thresholds were evaluated over the whole HomoloGene database. For each group of homologous genes, we computed all the similarity and particularity values between pairs of genes. Finally, we focused on the PPAR multigene family to show that the similarity and particularity patterns obtained with our thresholds were better at discriminating orthologs and paralogs than those obtained using default thresholds.

Conclusion: We developed a method for determining optimal semantic similarity and particularity thresholds. We applied this method on the GO and ChEBI ontologies. Qualitative analysis using the thresholds on the PPAR multigene family yielded biologically-relevant patterns.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0133579PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4521860PMC
May 2016

Semantic particularity measure for functional characterization of gene sets using gene ontology.

PLoS One 2014 28;9(1):e86525. Epub 2014 Jan 28.

Université de Rennes 1, Rennes, France ; IRISA, Campus de Beaulieu, Rennes, France ; INRIA, Rennes, France.

Background: Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.

Results: We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term's distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure.

Conclusion: Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086525PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904913PMC
December 2014

Measuring the evolution of ontology complexity: the gene ontology case study.

PLoS One 2013 11;8(10):e75993. Epub 2013 Oct 11.

Université de Rennes, Rennes, France ; Institut de Recherche en Informatique et Systèmes Aléatoires, Rennes, France.

Ontologies support automatic sharing, combination and analysis of life sciences data. They undergo regular curation and enrichment. We studied the impact of an ontology evolution on its structural complexity. As a case study we used the sixty monthly releases between January 2008 and December 2012 of the Gene Ontology and its three independent branches, i.e. biological processes (BP), cellular components (CC) and molecular functions (MF). For each case, we measured complexity by computing metrics related to the size, the nodes connectivity and the hierarchical structure. The number of classes and relations increased monotonously for each branch, with different growth rates. BP and CC had similar connectivity, superior to that of MF. Connectivity increased monotonously for BP, decreased for CC and remained stable for MF, with a marked increase for the three branches in November and December 2012. Hierarchy-related measures showed that CC and MF had similar proportions of leaves, average depths and average heights. BP had a lower proportion of leaves, and a higher average depth and average height. For BP and MF, the late 2012 increase of connectivity resulted in an increase of the average depth and average height and a decrease of the proportion of leaves, indicating that a major enrichment effort of the intermediate-level hierarchy occurred. The variation of the number of classes and relations in an ontology does not provide enough information about the evolution of its complexity. However, connectivity and hierarchy-related metrics revealed different patterns of values as well as of evolution for the three branches of the Gene Ontology. CC was similar to BP in terms of connectivity, and similar to MF in terms of hierarchy. Overall, BP complexity increased, CC was refined with the addition of leaves providing a finer level of annotations but decreasing slightly its complexity, and MF complexity remained stable.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075993PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3795689PMC
July 2014

The duplicated genes database: identification and functional annotation of co-localised duplicated genes across genomes.

PLoS One 2012 28;7(11):e50653. Epub 2012 Nov 28.

INRA, UMR1348 PEGASE, Saint-Gilles, France.

Background: There has been a surge in studies linking genome structure and gene expression, with special focus on duplicated genes. Although initially duplicated from the same sequence, duplicated genes can diverge strongly over evolution and take on different functions or regulated expression. However, information on the function and expression of duplicated genes remains sparse. Identifying groups of duplicated genes in different genomes and characterizing their expression and function would therefore be of great interest to the research community. The 'Duplicated Genes Database' (DGD) was developed for this purpose.

Methodology: Nine species were included in the DGD. For each species, BLAST analyses were conducted on peptide sequences corresponding to the genes mapped on a same chromosome. Groups of duplicated genes were defined based on these pairwise BLAST comparisons and the genomic location of the genes. For each group, Pearson correlations between gene expression data and semantic similarities between functional GO annotations were also computed when the relevant information was available.

Conclusions: The Duplicated Gene Database provides a list of co-localised and duplicated genes for several species with the available gene co-expression level and semantic similarity value of functional annotation. Adding these data to the groups of duplicated genes provides biological information that can prove useful to gene expression analyses. The Duplicated Gene Database can be freely accessed through the DGD website at http://dgd.genouest.org.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0050653PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3508997PMC
May 2013

GO2PUB: Querying PubMed with semantic expansion of gene ontology terms.

J Biomed Semantics 2012 Sep 7;3(1). Epub 2012 Sep 7.

UMR936, INSERM, Université de Rennes 1, 2 av, Léon Bernard, F-35043 Rennes, France.

Background: With the development of high throughput methods of gene analyses, there is a growing need for mining tools to retrieve relevant articles in PubMed. As PubMed grows, literature searches become more complex and time-consuming. Automated search tools with good precision and recall are necessary. We developed GO2PUB to automatically enrich PubMed queries with gene names, symbols and synonyms annotated by a GO term of interest or one of its descendants.

Results: GO2PUB enriches PubMed queries based on selected GO terms and keywords. It processes the result and displays the PMID, title, authors, abstract and bibliographic references of the articles. Gene names, symbols and synonyms that have been generated as extra keywords from the GO terms are also highlighted. GO2PUB is based on a semantic expansion of PubMed queries using the semantic inheritance between terms through the GO graph. Two experts manually assessed the relevance of GO2PUB, GoPubMed and PubMed on three queries about lipid metabolism. Experts' agreement was high (kappa = 0.88). GO2PUB returned 69% of the relevant articles, GoPubMed: 40% and PubMed: 29%. GO2PUB and GoPubMed have 17% of their results in common, corresponding to 24% of the total number of relevant results. 70% of the articles returned by more than one tool were relevant. 36% of the relevant articles were returned only by GO2PUB, 17% only by GoPubMed and 14% only by PubMed. For determining whether these results can be generalized, we generated twenty queries based on random GO terms with a granularity similar to those of the first three queries and compared the proportions of GO2PUB and GoPubMed results. These were respectively of 77% and 40% for the first queries, and of 70% and 38% for the random queries. The two experts also assessed the relevance of seven of the twenty queries (the three related to lipid metabolism and four related to other domains). Expert agreement was high (0.93 and 0.8). GO2PUB and GoPubMed performances were similar to those of the first queries.

Conclusions: We demonstrated that the use of genes annotated by either GO terms of interest or a descendant of these GO terms yields some relevant articles ignored by other tools. The comparison of GO2PUB, based on semantic expansion, with GoPubMed, based on text mining techniques, showed that both tools are complementary. The analysis of the randomly-generated queries suggests that the results obtained about lipid metabolism can be generalized to other biological processes. GO2PUB is available at http://go2pub.genouest.org.
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http://dx.doi.org/10.1186/2041-1480-3-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3599846PMC
September 2012