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1. Graph-based machine learning model for weight prediction in protein–protein networks

Author

Hajer Akid, Kirsley Chennen, Gabriel Frey, Julie Thompson, Mounir Ben Ayed, and Nicolas Lachiche

Subjects
Protein–protein interactions, Weighted graphs, Machine learning, Link prediction, Computer applications to medicine. Medical informatics, R858-859.7, Biology (General), QH301-705.5
Abstract

Abstract Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein–protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The development of high-throughput experimental technologies allows for the generation of numerous data which permits increasing the sophistication of PPI models. However, despite significant progress, current PPI networks remain incomplete. Discovering missing interactions through experimental techniques can be costly, time-consuming, and challenging. Therefore, computational approaches have emerged as valuable tools for predicting missing interactions. In PPI networks, a graph is usually used to model the interactions between proteins. An edge between two proteins indicates a known interaction, while the absence of an edge means the interaction is not known or missed. However, this binary representation overlooks the reliability of known interactions when predicting new ones. To address this challenge, we propose a novel approach for link prediction in weighted protein–protein networks, where interaction weights denote confidence scores. By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. Our model significantly improves prediction accuracy, surpassing traditional approaches in terms of Mean Absolute Error, Mean Relative Absolute Error, and Root Mean Square Error. Our proposed approach holds the potential for improved accuracy in predicting PPIs, which is crucial for better understanding the underlying biological processes.

Published
2024
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2. CeGAL: Redefining a Widespread Fungal-Specific Transcription Factor Family Using an In Silico Error-Tracking Approach

Author

Claudine Mayer, Arthur Vogt, Tuba Uslu, Nicolas Scalzitti, Kirsley Chennen, Olivier Poch, and Julie D. Thompson

Subjects
fungal-specific transcription factors, genome annotation errors, large-scale biocomputing analysis, Biology (General), QH301-705.5
Abstract

In fungi, the most abundant transcription factor (TF) class contains a fungal-specific ‘GAL4-like’ Zn2C6 DNA binding domain (DBD), while the second class contains another fungal-specific domain, known as ‘fungal_trans’ or middle homology domain (MHD), whose function remains largely uncharacterized. Remarkably, almost a third of MHD-containing TFs in public sequence databases apparently lack DNA binding activity, since they are not predicted to contain a DBD. Here, we reassess the domain organization of these ‘MHD-only’ proteins using an in silico error-tracking approach. In a large-scale analysis of ~17,000 MHD-only TF sequences present in all fungal phyla except Microsporidia and Cryptomycota, we show that the vast majority (>90%) result from genome annotation errors and we are able to predict a new DBD sequence for 14,261 of them. Most of these sequences correspond to a Zn2C6 domain (82%), with a small proportion of C2H2 domains (4%) found only in Dikarya. Our results contradict previous findings that the MHD-only TF are widespread in fungi. In contrast, we show that they are exceptional cases, and that the fungal-specific Zn2C6–MHD domain pair represents the canonical domain signature defining the most predominant fungal TF family. We call this family CeGAL, after the highly characterized members: Cep3, whose 3D structure is determined, and GAL4, a eukaryotic TF archetype. We believe that this will not only improve the annotation and classification of the Zn2C6 TF but will also provide critical guidance for future fungal gene regulatory network analyses.

Published
2023
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3. MISTIC: A prediction tool to reveal disease-relevant deleterious missense variants.

Author

Kirsley Chennen, Thomas Weber, Xavière Lornage, Arnaud Kress, Johann Böhm, Julie Thompson, Jocelyn Laporte, and Olivier Poch

Subjects
Medicine, Science
Abstract

The diffusion of next-generation sequencing technologies has revolutionized research and diagnosis in the field of rare Mendelian disorders, notably via whole-exome sequencing (WES). However, one of the main issues hampering achievement of a diagnosis via WES analyses is the extended list of variants of unknown significance (VUS), mostly composed of missense variants. Hence, improved solutions are needed to address the challenges of identifying potentially deleterious variants and ranking them in a prioritized short list. We present MISTIC (MISsense deleTeriousness predICtor), a new prediction tool based on an original combination of two complementary machine learning algorithms using a soft voting system that integrates 113 missense features, ranging from multi-ethnic minor allele frequencies and evolutionary conservation, to physiochemical and biochemical properties of amino acids. Our approach also uses training sets with a wide spectrum of variant profiles, including both high-confidence positive (deleterious) and negative (benign) variants. Compared to recent state-of-the-art prediction tools in various benchmark tests and independent evaluation scenarios, MISTIC exhibits the best and most consistent performance, notably with the highest AUC value (> 0.95). Importantly, MISTIC maintains its high performance in the specific case of discriminating deleterious variants from benign variants that are rare or population-specific. In a clinical context, MISTIC drastically reduces the list of VUS (

Published
2020
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4. A mutation in VPS15 (PIK3R4) causes a ciliopathy and affects IFT20 release from the cis-Golgi

Author

Corinne Stoetzel, Séverine Bär, Johan-Owen De Craene, Sophie Scheidecker, Christelle Etard, Johana Chicher, Jennifer R. Reck, Isabelle Perrault, Véronique Geoffroy, Kirsley Chennen, Uwe Strähle, Philippe Hammann, Sylvie Friant, and Hélène Dollfus

Subjects
Science
Abstract

VPS15 is known as a VPS34-associated protein that functions in intracellular trafficking and autophagy. Here the authors identify a role for VPS15 in ciliopathy and ciliary phenotypes, and show that it interacts with GM130 and functions in IFT20-dependent cis-Golgi to cilium trafficking.

Published
2016
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5. Genetic Evidence Supporting the Role of the Calcium Channel, CACNA1S, in Tooth Cusp and Root Patterning

Author

Virginie Laugel-Haushalter, Supawich Morkmued, Corinne Stoetzel, Véronique Geoffroy, Jean Muller, Anne Boland, Jean-François Deleuze, Kirsley Chennen, Waranuch Pitiphat, Hélène Dollfus, Karen Niederreither, Agnès Bloch-Zupan, and Patimaporn Pungchanchaikul

Subjects
rare disease, dental anomalies, patterning, mutations, NGS, human, Physiology, QP1-981
Abstract

In this study, we report a unique dominantly inherited disorganized supernumerary cusp and single root phenotype presented by 11 affected individuals belonging to 5 north-eastern Thai families. Using whole exome sequencing (WES) we identified a common single missense mutation that segregates with the phenotype in exon 6 of CACNA1S (Cav1.1) (NM_000069.2: c.[865A > G];[=] p.[Ile289Val];[=]), the Calcium Channel, Voltage-Dependent, L Type, Alpha-1s Subunit, OMIM ∗ 114208), affecting a highly conserved amino-acid isoleucine residue within the pore forming subdomain of CACNA1S protein. This is a strong genetic evidence that a voltage-dependent calcium ion channel is likely to play a role in influencing tooth morphogenesis and patterning.

Published
2018
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6. IMPatienT: an integrated web application to digitize, process and explore multimodal patient data

Author

Corentin Meyer, Norma Beatriz Romero, Teresinha Evangelista, Brunot Cadot, Jocelyn Laporte, Anne Jeannin-Girardon, Pierre Collet, Kirsley Chennen, Olivier Poch, Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Institut de Myologie, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Association française contre les myopathies (AFM-Téléthon)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and MEYER, Corentin

Subjects
Patient data, OCR, free-text medical reports, exploratory data analysis, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], NLP, data formatting, image segmentation, data processing, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

Background Medical acts, such as imaging, generally lead to the production of several medical text reports that describe the relevant findings. Such processes induce multimodality in patient data by linking image data to free-text data and consequently, multimodal data have become central to drive research and improve diagnosis of patients. However, the exploitation of patient data is 2 challenging as the ecosystem of available analysis tools is fragmented depending on the type of data (images, text, genetic sequences), the task to be performed (digitization, processing, exploration) and the domain of interest (clinical phenotype, histology…). To address the challenges, the analysis tools need to be integrated in a simple, comprehensive, and flexible platform. Results Here, we present IMPatienT (dIgitize Multimodal PATIENt daTa), a free and open-source web application to digitize, process and explore multimodal patient data. IMPatienT has a modular architecture, including four components to: (i) create a standard vocabulary for a domain, (ii) digitize and process free-text data by mapping it to a set of standard terms, (iii) annotate images and perform image segmentation, and (iv) generate an automatic visualization dashboard to provide insight on the data and perform automatic diagnosis suggestions. Finally, we demonstrate the usefulness of IMPatienT on a corpus of 40 simulated muscle biopsy reports of congenital myopathy patients.ConclusionsIMPatienT is a platform to digitize, process and explore patient data that can handle image and free-text data. As it relies on a user-designed vocabulary, it can be adapted to fit any domain of research and can be used as a patient registry for exploratory data analysis (EDA). A demo instance of the application is available at https://impatient.lbgi.fr.

Published
2022

7. Novel IQCE variations confirm its role in postaxial polydactyly and cause ciliary defect phenotype in zebrafish

Author

Fouzia Studer, Corinne Stoetzel, Jean Muller, Amélie Piton, Aline Schneider, Olivier Poch, Christelle Etard, Damien Plassard, Clarisse Delvallée, Sabine Sigaudy, Hélène Dollfus, Alejandro Estrada-Cuzcano, Uwe Strähle, Elise Schaefer, Francesca Mattioli, Sophie Scheidecker, Kirsley Chennen, Véronique Geoffroy, Laboratoire de Génétique Médicale (LGM), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire de Génétique Médicale, Hôpital Jeanne de Flandre [Lille]-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Institut de génétique et biologie moléculaire et cellulaire (IGBMC), Université Louis Pasteur - Strasbourg I-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), univOAK, Archive ouverte, École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, and Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Fluorescent Antibody Technique, [SDV.GEN] Life Sciences [q-bio]/Genetics, Ciliopathies, Pronephric duct, Sciences du Vivant [q-bio]/Génétique, Fingers, Consanguinity, 03 medical and health sciences, Exome Sequencing, Sciences du Vivant [q-bio]/Biologie cellulaire, Genetics, medicine, Animals, Humans, Genetic Predisposition to Disease, Gene, Zebrafish, Genetic Association Studies, Genetics (clinical), 030304 developmental biology, 0303 health sciences, [SDV.GEN]Life Sciences [q-bio]/Genetics, Polydactyly, biology, Gene Expression Profiling, Cilium, Homozygote, 030305 genetics & heredity, Haplotype, Intracellular Signaling Peptides and Proteins, Genetic Variation, Membrane Proteins, Toes, medicine.disease, biology.organism_classification, Immunohistochemistry, Phenotype, Pedigree, 3. Good health, Transcriptome, Signal Transduction
Abstract

Polydactyly is one of the most frequent inherited defects of the limbs characterized by supernumerary digits and high-genetic heterogeneity. Among the many genes involved, either in isolated or syndromic forms, eight have been implicated in postaxial polydactyly (PAP). Among those, IQCE has been recently identified in a single consanguineous family. Using whole-exome sequencing in patients with uncharacterized ciliopathies, including PAP, we identified three families with biallelic pathogenic variations in IQCE. Interestingly, the c.895_904del (p.Val301Serfs*8) was found in all families without sharing a common haplotype, suggesting a recurrent mechanism. Moreover, in two families, the systemic phenotype could be explained by additional pathogenic variants in known genes (TULP1, ATP6V1B1). RNA expression analysis on patients' fibroblasts confirms that the dysfunction of IQCE leads to the dysregulation of genes associated with the hedgehog-signaling pathway, and zebrafish experiments demonstrate a full spectrum of phenotypes linked to defective cilia: Body curvature, kidney cysts, left-right asymmetry, misdirected cilia in the pronephric duct, and retinal defects. In conclusion, we identified three additional families confirming IQCE as a nonsyndromic PAP gene. Our data emphasize the importance of taking into account the complete set of variations of each individual, as each clinical presentation could finally be explained by multiple genes.

Published
2019
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8. INEX-MED: a Knowledge Graph to explore and link heterogeneous bio-medical data

Author

Maxime Folschette, Kirsley Chennen, Alban Gaignard, Richard Redon, Hala Skaf-Molli, Olivier Poch, Jocelyn Laporte, Julie Thompson, Institut Français de Bioinformatique - UMS CNRS 3601 (IFB-CORE), Institut National de la Recherche Agronomique (INRA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Unité de recherche de l'institut du thorax (ITX-lab), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Université de Nantes - Faculté des Sciences et des Techniques, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), and Université de Nantes (UN)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-WB]Computer Science [cs]/Web, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience; Health and life sciences nowadays face the massive availability of diverse biomedical data. Providing a unified and coherent access to these large-scale multi-source data is a major challenge. The INEX-MED project aims at gathering and representing multidisciplinary , multi-source and multi-modal data (clinical, genetic, imaging) as a Knowledge Graph, in the domain of intracranial aneurysms and congenital myopathies. The aim is to apply machine learning in order to make predictions, highlight new diagnosis variables and stratify patient populations. To this end, Linked Data principles are applied in order to integrate the data despite their initial heterogeneity, with the objective of ensuring "FAIR" data principles (Findability, Accessibility, Interoperability, Reusability) [1]. 2 Knowledge Graph Creation The acquired diverse data, come in different formats. For instance, clinical data are represented with tables (CSV format), while genetic (exome) data rely on the VCF format. Imaging data come in specific formats and are automatically processed to extract quantitative markers. All data are then represented in a directed labelled graph (RDF format). Many existing domain-specific ontologies were explored to find relevant concepts and relations. This knowledge graph can then be queried with SPARQL, a graph-pattern based query language designed to select nodes or edges, or assemble sub-graphs. The main advantage of this approach is that such queries can relate to all parts of the data at once (clinical, genetic, imaging) without the need for explicit joins. Our implementation thus offers convenient multi-source data access: it is now possible to return, for instance, clinical features (phenotype, diagnosis, ...) of individuals having a genetic variant on a specific set of genes, with a single SPARQL query. In addition, federated queries allow to pull data from external sources (for instance, Orphanet and Uniprot) and thus dynamically enrich the knowledge graph. 3 Demonstration scenario We propose to showcase our prototype in the form of a web application dedicated to biologists and a Jupyter Notebook dedicated to bioinformaticians. We will demonstrate how these interfaces directly interact with the knowledge graph in the form of template SPARQL queries and how they can be used to answer biological questions. We will show results as graphical plots, for monitoring purposes, or tables, for further bio-statistics analysis or machine learning based predictive modelling.

Published
2019

9. A mutation in VPS15 (PIK3R4) causes a ciliopathy and affects IFT20 release from the cis-Golgi

Author

Sophie Scheidecker, Hélène Dollfus, Philippe Hammann, Johan-Owen De Craene, Séverine Bär, Isabelle Perrault, Johana Chicher, Véronique Geoffroy, Christelle Etard, Kirsley Chennen, Corinne Stoetzel, Sylvie Friant, Jennifer R. Reck, Uwe Strähle, Institut de génétique et biologie moléculaire et cellulaire (IGBMC), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Louis Pasteur - Strasbourg I, Génétique moléculaire, génomique, microbiologie (GMGM), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), FRC 1589 Institut de Biologie Moléculaire et Cellulaire, and Centre National de la Recherche Scientifique (CNRS)

Subjects
0301 basic medicine, Male, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, General Physics and Astronomy, Golgi Apparatus, [SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC], Ciliopathies, Craniofacial Abnormalities, 0302 clinical medicine, Golgi, Renal Insufficiency, 10. No inequality, Child, Cells, Cultured, Zebrafish, Skin, Multidisciplinary, Learning Disabilities, Disease genetics, Cilium, Cell biology, Mechanisms of disease, Child, Preschool, symbols, Female, Hand Deformities, Congenital, Retinitis Pigmentosa, Life sciences, biology, Adolescent, Protein subunit, Science, Mutation, Missense, Saccharomyces cerevisiae, Biology, General Biochemistry, Genetics and Molecular Biology, Article, Vacuolar Sorting Protein VPS15, 03 medical and health sciences, symbols.namesake, Young Adult, Intraflagellar transport, ddc:570, medicine, Animals, Humans, Abnormalities, Multiple, Cilia, Ciliogenesis, Siblings, General Chemistry, Golgi apparatus, Fibroblasts, medicine.disease, Ciliopathy, 030104 developmental biology, Membrane protein, Case-Control Studies, Mutation, Carrier Proteins, 030217 neurology & neurosurgery
Abstract

Ciliopathies are a group of diseases that affect kidney and retina among other organs. Here, we identify a missense mutation in PIK3R4 (phosphoinositide 3-kinase regulatory subunit 4, named VPS15) in a family with a ciliopathy phenotype. Besides being required for trafficking and autophagy, we show that VPS15 regulates primary cilium length in human fibroblasts, as well as ciliary processes in zebrafish. Furthermore, we demonstrate its interaction with the golgin GM130 and its localization to the Golgi. The VPS15-R998Q patient mutation impairs Golgi trafficking functions in humanized yeast cells. Moreover, in VPS15-R998Q patient fibroblasts, the intraflagellar transport protein IFT20 is not localized to vesicles trafficking to the cilium but is restricted to the Golgi. Our findings suggest that at the Golgi, VPS15 and GM130 form a protein complex devoid of VPS34 to ensure the IFT20-dependent sorting and transport of membrane proteins from the cis-Golgi to the primary cilium., VPS15 is known as a VPS34-associated protein that functions in intracellular trafficking and autophagy. Here the authors identify a role for VPS15 in ciliopathy and ciliary phenotypes, and show that it interacts with GM130 and functions in IFT20-dependent cis-Golgi to cilium trafficking.

Published
2016
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10. Exome sequencing of Bardet-Biedl syndrome patient identifies a null mutation in the BBSome subunit BBIP1 (BBS18)

Author

Nathan W. Pierce, Elise Schaefer, Christelle Etard, Jean Muller, Maxence V. Nachury, Véronique Geoffroy, Valérie Pelletier, Hélène Dollfus, Vincent Marion, Sophie Scheidecker, Uwe Strähle, Corinne Stoetzel, Olivier Poch, Elisabeth Flori, and Kirsley Chennen

Subjects
Male, congenital, hereditary, and neonatal diseases and abnormalities, BBSome, Genetic Linkage, DNA Mutational Analysis, Biology, Polymorphism, Single Nucleotide, Article, Molecular Genetics, Diagnostics Tests, Consanguinity, Bardet–Biedl syndrome, Retinitis pigmentosa, Genetics, medicine, Animals, Humans, Exome, Bardet-Biedl Syndrome, Genetics (clinical), Exome sequencing, Genetic Association Studies, Zebrafish, Clinical Genetics, Base Sequence, Genetic heterogeneity, Genetic Screening, Molecular Sequence Annotation, Fibroblasts, Middle Aged, medicine.disease, Null allele, Pedigree, Ciliopathy, Ophthalmology, HEK293 Cells, Phenotype, Codon, Nonsense, Counselling, Carrier Proteins
Abstract

Background Bardet–Biedl syndrome (BBS) is a recessive and genetically heterogeneous ciliopathy characterised by retinitis pigmentosa, obesity, kidney dysfunction, postaxial polydactyly, behavioural dysfunction and hypogonadism. 7 of the 17 BBS gene products identified to date assemble together with the protein BBIP1/BBIP10 into the BBSome, a protein complex that ferries signalling receptors to and from cilia. Methods and results Exome sequencing performed on a sporadic BBS case revealed for the first time a homozygous stop mutation (NM_001195306: c.173T>G, p.Leu58*) in the BBIP1 gene. This mutation is pathogenic since no BBIP1 protein could be detected in fibroblasts from the patient, and BBIP1[Leu58*] is unable to associate with the BBSome subunit BBS4. Conclusions These findings identify BBIP1 as the 18th BBS gene ( BBS18 ) and suggest that BBSome assembly may represent a unifying pathomechanism for BBS.

Published
2013
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