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3. The iPPI-DB initiative: a community-centered database of protein–protein interaction modulators

Author

Torchet, Rachel, Druart, Karen, Ruano, Luis Checa, Moine-Franel, Alexandra, Borges, Hélène, Doppelt-Azeroual, Olivia, Brancotte, Bryan, Mareuil, Fabien, Nilges, Michael, Ménager, Hervé, Sperandio, Olivier, Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Département de Biologie structurale et Chimie - Department of Structural Biology and Chemistry, Institut Pasteur [Paris] (IP), none declared, This work used the computational and storage services (TARS cluster, VMHosting) provided by the IT department at Institut Pasteur, Paris. The authorswish to acknowledge in particular the help and technical advice of Eric Deveaud,Emmanuel Guichard, Thomas Me ́nard and Youssef Ghorbal (IT Department,Institut Pasteur). They also want to acknowledge the technical help of Tru Huynh(Structural Bioinformatics Unit, Institut Pasteur). They thank Jon Ison, BenjaminBardiaux and Pascal Campagne for their proofreading of the paper.Marvin JS (20.5.0, 2020, http://www.chemaxon.com) is used for drawingand displaying chemical structures in both Query mode and Contributionmode of iPPI-DB. Pipeline Pilot (server 19.1) is used to prepare the DrugBankdatabase from a SDF file prior to chemical similarity search 202 (2020), Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris], Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Spectrométrie de Masse pour la Biologie – Mass Spectrometry for Biology (UTechS MSBio), Centre National de la Recherche Scientifique (CNRS)-Centre de Ressources et de Recherche Technologique - Center for Technological Resources and Research (C2RT), Institut Pasteur [Paris]-Institut Pasteur [Paris], Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Etude de la dynamique des protéomes (EDyP), BioSanté (UMR BioSanté), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Grenoble Alpes (UGA), Biologie Computationnelle (ex C3BI), Bioinformatique structurale - Structural Bioinformatics, and Druart, Karen

Subjects
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], AcademicSubjects/SCI01060, Databases and Ontologies, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB], [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Original Papers, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract

International audience; Abstract Motivation One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets. Results Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well. Availability and implementation The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files. Supplementary information Supplementary data are available at Bioinformatics online.

Published
2021
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4. Effect of arsenite and growth in biofilm conditions on the evolution of Thiomonas sp. CB2

Author

Freel, Kelle, Fouteau, Stéphanie, Roche, David, Farasin, Julien, Huber, Aline, Koechler, Sandrine, Peres, Martina, Chiboub, Olfa, Cruveiller, Stephane, Varet, Hugo, Proux, Caroline, Deschamps, Julien, Briandet, Romain, Torchet, Rachel, Cruveiller, Stéphane, Lièvremont, Didier, Coppée, Jean-Yves, Barbe, Valérie, Arsène-Ploetze, Florence, Génétique moléculaire, génomique, microbiologie (GMGM), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Génomique métabolique (UMR 8030), Genoscope - Centre national de séquençage [Evry] (GENOSCOPE), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université d'Évry-Val-d'Essonne (UEVE), Hub Bioinformatique et Biostatistique - Bioinformatics and Biostatistics HUB, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Transcriptome et Epigénome (PF2), Institut Pasteur [Paris], MICrobiologie de l'ALImentation au Service de la Santé (MICALIS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), This work was supported by the Université de Strasbourg, the Centre National de la Recherche Scientifique (CNRS) and the Region Alsace (J.F.). This study was also financed by THIOFILM (ANR-12-ADAP-0013) projects. K.C.F., O.C. and J.F. were supported by the Agence Nationale de la Recherche, ANR THIOFILM (ANR-12-ADAP-0013). The Transcriptome and EpiGenome Platform is a member of the France Génomique consortium (ANR10-NBS-09-08), ANR-12-ADAP-0013,THIOFILM,Rôle des biofilms dans l'adaptation et la variabilité génomique des bactéries du genre Thiomonas, impliqués dans les processus de remédiation naturelle dans les drainages miniers :(2012), ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Évry-Val-d'Essonne (UEVE)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Institut Pasteur [Paris] (IP), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Strasbourg (UNISTRA)

Subjects
[SDV]Life Sciences [q-bio], Bacterial genome size, adaptation, comparative genomics, Biology, genome evolution, genomic islands, Microbiology, 03 medical and health sciences, chemistry.chemical_compound, acid mine drainage (AMD), Genomic island, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], Extreme environment, Gene, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Arsenite, 0303 health sciences, 030306 microbiology, [SDV.BID.EVO]Life Sciences [q-bio]/Biodiversity/Populations and Evolution [q-bio.PE], Biofilm, arsenic, Thiomonas, General Medicine, biology.organism_classification, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, chemistry, Bacteria
Abstract

Thiomonasbacteria are ubiquitous at acid mine drainage sites and play key roles in the remediation of water at these locations by oxidizing arsenite to arsenate, favouring the sorption of arsenic by iron oxides and their coprecipitation. Understanding the adaptive capacities of these bacteria is crucial to revealing how they persist and remain active in such extreme conditions. Interestingly, it was previously observed that after exposure to arsenite, when grown in a biofilm, some strains ofThiomonasbacteria develop variants that are more resistant to arsenic. Here, we identified the mechanisms involved in the emergence of such variants in biofilms. We found that the percentage of variants generated increased in the presence of high concentrations of arsenite (5.33 mM), especially in the detached cells after growth under biofilm-forming conditions. Analysis of gene expression in the parent strain CB2 revealed that genes involved in DNA repair were upregulated in the conditions where variants were observed. Finally, we assessed the phenotypes and genomes of the subsequent variants generated to evaluate the number of mutations compared to the parent strain. We determined that multiple point mutations accumulated after exposure to arsenite when cells were grown under biofilm conditions. Some of these mutations were found in what is referred to as ICE19, a genomic island (GI) carrying arsenic-resistance genes, also harbouring characteristics of an integrative and conjugative element (ICE). The mutations likely favoured the excision and duplication of this GI. This research aids in understanding howThiomonasbacteria adapt to highly toxic environments, and, more generally, provides a window to bacterial genome evolution in extreme environments.

Published
2020
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6. AntiBody Sequence Database.

Author

Malesys S, Torchet R, Saunier B, and Maillet N

Abstract

Antibodies play a crucial role in the humoral immune response against health threats, such as viral infections. Although the theoretical number of human immunoglobulins is well over a trillion, the total number of unique antibody protein sequences accessible in databases is much lower than the number found in a single individual. Training AI (Artificial Intelligence) models, for example to assist in developing serodiagnoses or antibody-based therapies, requires building datasets according to strict criteria to include as many standardized antibody sequences as possible. However, the available sequences are scattered across partially redundant databases, making it difficult to compile them into single non-redundant datasets. Here, we introduce ABSD (AntiBody Sequence Database, https://absd.pasteur.cloud), which contains data from major publicly available resources, creating the largest standardized, automatically updated and non-redundant source of public antibody sequences. This user-friendly and open website enables users to generate lists of antibodies based on selected criteria and download the unique sequence pairs of their variable regions., (© The Author(s) 2024. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

Published
2024
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7. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability.

Author

Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, and Julienne H

Abstract

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10 -60 ). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing., Competing Interests: Declaration of interests M.H.C. has received grant support from Bayer, unrelated to the current work.

Published
2023
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8. The iPPI-DB initiative: a community-centered database of protein-protein interaction modulators.

Author

Torchet R, Druart K, Ruano LC, Moine-Franel A, Borges H, Doppelt-Azeroual O, Brancotte B, Mareuil F, Nilges M, Ménager H, and Sperandio O

Abstract

Motivation: One avenue to address the paucity of clinically testable targets is to reinvestigate the druggable genome by tackling complicated types of targets such as Protein-Protein Interactions (PPIs). Given the challenge to target those interfaces with small chemical compounds, it has become clear that learning from successful examples of PPI modulation is a powerful strategy. Freely accessible databases of PPI modulators that provide the community with tractable chemical and pharmacological data, as well as powerful tools to query them, are therefore essential to stimulate new drug discovery projects on PPI targets., Results: Here, we present the new version iPPI-DB, our manually curated database of PPI modulators. In this completely redesigned version of the database, we introduce a new web interface relying on crowdsourcing for the maintenance of the database. This interface was created to enable community contributions, whereby external experts can suggest new database entries. Moreover, the data model, the graphical interface, and the tools to query the database have been completely modernized and improved. We added new PPI modulators, new PPI targets and extended our focus to stabilizers of PPIs as well., Availability and Implementation: The iPPI-DB server is available at https://ippidb.pasteur.fr The source code for this server is available at https://gitlab.pasteur.fr/ippidb/ippidb-web/ and is distributed under GPL licence (http://www.gnu.org/licences/gpl). Queries can be shared through persistent links according to the FAIR data standards. Data can be downloaded from the website as csv files., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press.)

Published
2021
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9. Effect of arsenite and growth in biofilm conditions on the evolution of Thiomonas sp. CB2.

Author

Freel KC, Fouteau S, Roche D, Farasin J, Huber A, Koechler S, Peres M, Chiboub O, Varet H, Proux C, Deschamps J, Briandet R, Torchet R, Cruveiller S, Lièvremont D, Coppée JY, Barbe V, and Arsène-Ploetze F

Subjects
Adaptation, Physiological genetics, Arsenates metabolism, Arsenic metabolism, DNA Repair genetics, DNA Transposable Elements genetics, Evolution, Molecular, Gene Expression Profiling, Genetic Variation genetics, Genomic Islands genetics, Mining, Whole Genome Sequencing, Arsenites metabolism, Biofilms growth & development, Burkholderiales genetics, Burkholderiales growth & development, Burkholderiales metabolism, Genome, Bacterial genetics
Abstract

Thiomonas bacteria are ubiquitous at acid mine drainage sites and play key roles in the remediation of water at these locations by oxidizing arsenite to arsenate, favouring the sorption of arsenic by iron oxides and their coprecipitation. Understanding the adaptive capacities of these bacteria is crucial to revealing how they persist and remain active in such extreme conditions. Interestingly, it was previously observed that after exposure to arsenite, when grown in a biofilm, some strains of Thiomonas bacteria develop variants that are more resistant to arsenic. Here, we identified the mechanisms involved in the emergence of such variants in biofilms. We found that the percentage of variants generated increased in the presence of high concentrations of arsenite (5.33 mM), especially in the detached cells after growth under biofilm-forming conditions. Analysis of gene expression in the parent strain CB2 revealed that genes involved in DNA repair were upregulated in the conditions where variants were observed. Finally, we assessed the phenotypes and genomes of the subsequent variants generated to evaluate the number of mutations compared to the parent strain. We determined that multiple point mutations accumulated after exposure to arsenite when cells were grown under biofilm conditions. Some of these mutations were found in what is referred to as ICE19, a genomic island (GI) carrying arsenic-resistance genes, also harbouring characteristics of an integrative and conjugative element (ICE). The mutations likely favoured the excision and duplication of this GI. This research aids in understanding how Thiomonas bacteria adapt to highly toxic environments, and, more generally, provides a window to bacterial genome evolution in extreme environments.

Published
2020
Full Text
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