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3. Reduced penetrance of an eastern French mutation in ATL1 autosomal-dominant inheritance (SPG3A): extended phenotypic spectrum coupled with brain 18F-FDG PET

Author

Hocquel, Armand, Ravel, Jean-Marie, Lambert, Laetitia, Bonnet, Céline, Banneau, Guillaume, Kol, Bophara, Tissier, Laurène, Hopes, Lucie, Meyer, Mylène, Dillier, Céline, Michaud, Maud, Lardin, Arnaud, Kaminsky, Anne-Laure, Schmitt, Emmanuelle, Liao, Liang, Zhu, François, Myriam, Bronner, Bossenmeyer-Pourié, Carine, Verger, Antoine, and Renaud, Mathilde

Published
2022
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6. Expanding the genetic and clinical spectrum of Tatton- Brown- Rahman syndrome in a series of 24 French patients.

Author

Thomas, Hortense, Alix, Tom, Renard, Émeline, Renaud, Mathilde, Wourms, Justine, Zuily, Stéphane, Leheup, Bruno, Geneviève, David, Dreumont, Natacha, Schmitt, Emmanuelle, Bronner, Myriam, Muller, Marc, Divoux, Marion, Wandzel, Marion, Ravel, Jean- Marie, Dexheimer, Mylène, Becker, Aurélie, Roth, Virginie, Willems, Marjolaine, and Coubes, Christine

Abstract

Background Tatton- Brown- Rahman syndrome (TBRS; OMIM 615879), also known as DNA methyltransferase 3 alpha (DNMT3A)- overgrowth syndrome (DOS), was first described by Tatton- Brown in 2014. This syndrome is characterised by overgrowth, intellectual disability and distinctive facial features and is the consequence of germline loss- of- function variants in DNMT3A, which encodes a DNA methyltransferase involved in epigenetic regulation. Somatic variants of DNMT3A are frequently observed in haematological malignancies, including acute myeloid leukaemia (AML). To date, 100 individuals with TBRS with de novo germline variants have been described. We aimed to further characterise this disorder clinically and at the molecular level in a nationwide series of 24 French patients and to investigate the correlation between the severity of intellectual disability and the type of variant. Methods We collected genetic and medical information from 24 individuals with TBRS using a questionnaire released through the French National AnDDI- Rares Network. Results Here, we describe the first nationwide French cohort of 24 individuals with germline likely pathogenic/ pathogenic variants in DNMT3A, including 17 novel variants. We confirmed that the main phenotypic features were intellectual disability (100% of individuals), distinctive facial features (96%) and overgrowth (87%). We highlighted novel clinical features, such as hypertrichosis, and further described the neurological features and EEG results. Conclusion This study of a nationwide cohort of individuals with TBRS confirms previously published data and provides additional information and clarifies clinical features to facilitate diagnosis and improve care. This study adds value to the growing body of knowledge on TBRS and broadens its clinical and molecular spectrum. [ABSTRACT FROM AUTHOR]

Published
2024
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8. Expanding the clinical spectrum of STIP1 homology and U-box containing protein 1-associated ataxia

Author

Ravel, Jean-Marie, Benkirane, Mehdi, Calmels, Nadège, Marelli, Cecilia, Ory-Magne, Fabienne, Ewenczyk, Claire, Halleb, Yosra, Tison, François, Lecocq, Claire, Pische, Guillaume, Casenave, Philippe, Chaussenot, Annabelle, Frismand, Solène, Tyvaert, Louise, Larrieu, Lise, Pointaux, Morgane, Drouot, Nathalie, Bossenmeyer-Pourié, Carine, Oussalah, Abderrahim, Guéant, Jean-Louis, Leheup, Bruno, Bonnet, Céline, Anheim, Mathieu, Tranchant, Christine, Lambert, Laëtitia, Chelly, Jamel, Koenig, Michel, and Renaud, Mathilde

Published
2021
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12. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Author

Sanofi, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), German Research Foundation, Ministero della Salute, European Commission, Generalitat de Catalunya, National Institutes of Health (US), Klaus Tschira Foundation, National Library of Medicine (US), Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, María, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Lal Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Fariñas Fernández, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce de León, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina BalaurIrina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert W., Phair, Robert, Perfetto, Livia, Matthews, Lisa, Balaya Rex, Devasahayam Arokia, Orlic-Milacic, Marija, Monraz Gómez, Luis Cristóbal, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, COVID- Disease Map Community the COVID-19 Disease Map Community, Sanofi, Instituto de Salud Carlos III, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), German Research Foundation, Ministero della Salute, European Commission, Generalitat de Catalunya, National Institutes of Health (US), Klaus Tschira Foundation, National Library of Medicine (US), Niarakis, Anna, Ostaszewski, Marek, Mazein, Alexander, Kuperstein, Inna, Kutmon, Martina, Gillespie, Marc E., Funahashi, Akira, Acencio, Marcio Luis, Hemedan, Ahmed, Aichem, Michael, Klein, Karsten, Czauderna, Tobias, Burtscher, Felicia, Yamada, Takahiro G., Hiki, Yusuke, Hiroi, Noriko F., Hu, Finterly, Pham, Nhung, Ehrhart, Friederike, Willighagen, Egon L., Valdeolivas, Alberto, Dugourd, Aurelien, Messina, Francesco, Esteban-Medina, Marina, Peña-Chilet, María, Rian, Kinza, Soliman, Sylvain, Aghamiri, Sara Sadat, Lal Puniya, Bhanwar, Naldi, Aurelien, Helikar, Tomas, Singh, Vidisha, Fariñas Fernández, Marco, Bermudez, Viviam, Tsirvouli, Eirini, Montagud, Arnau, Noël, Vincent, Ponce de León, Miguel, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Luna, Augustin, Piñero, Janet, Furlong, Laura I., Balaur, Irina BalaurIrina, Rougny, Adrien, Jarosz, Yohan, Overall, Rupert W., Phair, Robert, Perfetto, Livia, Matthews, Lisa, Balaya Rex, Devasahayam Arokia, Orlic-Milacic, Marija, Monraz Gómez, Luis Cristóbal, De Meulder, Bertrand, Ravel, Jean Marie, Jassal, Bijay, Satagopam, Venkata, Wu, Guanming, Golebiewski, Martin, Gawron, Piotr, Calzone, Laurence, Beckmann, Jacques S., Evelo, Chris T., D’Eustachio, Peter, Schreiber, Falk, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Kuiper, Martin, Valencia, Alfonso, Wolkenhauer, Olaf, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, and COVID- Disease Map Community the COVID-19 Disease Map Community

Abstract

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing., Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors., Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19., Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.

Published
2023

13. Additional file 1 of Clinical utility of periodic reinterpretation of CNVs of uncertain significance: an 8-year retrospective study

Author

Ravel, Jean-Marie, Renaud, Mathilde, Muller, Jean, Becker, Aurélie, Renard, Émeline, Remen, Thomas, Lefort, Geneviève, Dexheimer, Mylène, Jonveaux, Philippe, Leheup, Bruno, Bonnet, Céline, and Lambert, Laëtitia

Abstract

Additional file 1: FigureS1. Array-CGH interpretation workflow. Figure S2. Flowchart indicating all samples included in our study. aCGH: array comparative genomic hybridization; CNV: copy number variation; VUS:variant of uncertain significance. AnnotSV was applied to the whole VUS cohort. We then compare the automatic ACMG classification from AnnotSV to our own manual classification. Missing data correspond to patient for whome definitive CNV classification was not stated on the first biologist report or that the conclusion was not reported on our database. Table S1. Characteristics of the cohort composed of the 259 patients with a VUS identified on array-CGH. Only 180k array-CGH platform were used for this study. Table S2. Characteristics of CNV first reported as VUS. B: benign, LB: likely benign; VUS: variantof uncertain significance; LP: likely pathogenic; P: pathogenic. Table S3. AnnotSV performance. Contingency table of classification proposed by AnnotSV versus our classification for the372 CNVs primarily reported as VUS.

Published
2023
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14. First report of a short in‐frame biallelic deletion removing part of the EGF‐like domain calcium‐binding motif in LTBP4 and causing autosomal recessive cutis laxa type 1C

Author

Ravel, Jean‐Marie, primary, Comel, Margot, additional, Wandzel, Marion, additional, Bronner, Myriam, additional, Tatopoulos, Aurélie, additional, Renaud, Mathilde, additional, Lambert, Laëtitia, additional, Bursztejn, Anne‐Claire, additional, and Bonnet, Céline, additional

Published
2022
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16. High rate of hypomorphic variants as the cause of inherited ataxia and related diseases: study of a cohort of 366 families

Author

Benkirane, Mehdi, primary, Marelli, Cecilia, additional, Guissart, Claire, additional, Roubertie, Agathe, additional, Ollagnon, Elizabeth, additional, Choumert, Ariane, additional, Fluchère, Frédérique, additional, Magne, Fabienne Ory, additional, Halleb, Yosra, additional, Renaud, Mathilde, additional, Larrieu, Lise, additional, Baux, David, additional, Patat, Olivier, additional, Bousquet, Idriss, additional, Ravel, Jean-Marie, additional, Cuntz-Shadfar, Danielle, additional, Sarret, Catherine, additional, Ayrignac, Xavier, additional, Rolland, Anne, additional, Morales, Raoul, additional, Pointaux, Morgane, additional, Lieutard-Haag, Cathy, additional, Laurens, Brice, additional, Tillikete, Caroline, additional, Bernard, Emilien, additional, Mallaret, Martial, additional, Carra-Dallière, Clarisse, additional, Tranchant, Christine, additional, Meyer, Pierre, additional, Damaj, Lena, additional, Pasquier, Laurent, additional, Acquaviva, Cecile, additional, Chaussenot, Annabelle, additional, Isidor, Bertrand, additional, Nguyen, Karine, additional, Camu, William, additional, Eusebio, Alexandre, additional, Carrière, Nicolas, additional, Riquet, Audrey, additional, Thouvenot, Eric, additional, Gonzales, Victoria, additional, Carme, Emilie, additional, Attarian, Shahram, additional, Odent, Sylvie, additional, Castrioto, Anna, additional, Ewenczyk, Claire, additional, Charles, Perrine, additional, Kremer, Laurent, additional, Sissaoui, Samira, additional, Bahi-buisson, Nadia, additional, Kaphan, Elsa, additional, Degardin, Adrian, additional, Doray, Bérénice, additional, Julia, Sophie, additional, Remerand, Ganaëlle, additional, Fraix, Valerie, additional, Haidar, Lydia Abou, additional, Lazaro, Leila, additional, Laugel, Vincent, additional, Villega, Frederic, additional, Charlin, Cyril, additional, Frismand, Solène, additional, Moreira, Marinha Costa, additional, Witjas, Tatiana, additional, Francannet, Christine, additional, Walther-Louvier, Ulrike, additional, Fradin, Mélanie, additional, Chabrol, Brigitte, additional, Fluss, Joel, additional, Bieth, Eric, additional, Castelnovo, Giovanni, additional, Vergnet, Sylvain, additional, Meunier, Isabelle, additional, Verloes, Alain, additional, Brischoux-Boucher, Elise, additional, Coubes, Christine, additional, Geneviève, David, additional, Lebouc, Nicolas, additional, Azulay, Jean Phillipe, additional, Anheim, Mathieu, additional, Goizet, Cyril, additional, Rivier, François, additional, Labauge, Pierre, additional, Calvas, Patrick, additional, and Koenig, Michel, additional

Published
2021
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17. An interactive online atlas of interconnected network maps based on the NaviCell platform

Author

Saez-Rodriguez, Julio, Hernansaiz, Rosa, Noël, Vincent, Kuperstein, Inna, Ravel, Jean-Marie, and Zinovyev, Andrei

Subjects
Data visualization, Maps, Network, Web
Abstract

This is the accepted iPC deliverable D4.2 „An interactive online atlas of interconnected network maps based on the NaviCell platform“. With the development of the NaviCell 3.0 web server, there is a complete and automated web-based infrastructure for hosting molecular maps, patient similarity network maps, and multi-omics datasets for the project. The NaviCell platform supports molecular map navigation and exploration using the Google maps™ engine. The logic of navigation is taken from Google maps. This NaviCell 3.0 web-server is freely available and several step-by-step tutorials are accessible.

Published
2021
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18. A bi‐allelic loss‐of‐function SARS1 variant in children with neurodevelopmental delay, deafness, cardiomyopathy, and decompensation during fever

Author

Ravel, Jean‐Marie, primary, Dreumont, Natacha, additional, Mosca, Pauline, additional, Smith, Desiree E. C., additional, Mendes, Marisa I., additional, Wiedemann, Arnaud, additional, Coelho, David, additional, Schmitt, Emmanuelle, additional, Rivière, Jean‐Baptiste, additional, Tran Mau‐Them, Frédéric, additional, Thevenon, Julien, additional, Kuentz, Paul, additional, Polivka, Marc, additional, Fuchs, Sabine A., additional, Kok, Gautam, additional, Thauvin‐Robinet, Christel, additional, Guéant, Jean‐Louis, additional, Salomons, Gajja S., additional, Faivre, Laurence, additional, and Feillet, François, additional

Published
2021
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20. Two Different PRKN Compound Heterozygous Variants Combinations in the Same Family.

Author

Biehler, Margaux, Ravel, Jean‐Marie, Tir, Mélissa, Calmels, Nadège, and Schalk, Audrey

Subjects
*GENETIC variation, *MOVEMENT disorders, *INFORMED consent (Medical law), *PARKINSON'S disease, *SINGLE nucleotide polymorphisms, *GAIT disorders
Abstract

Bi-allelic I PRKN i variants are involved in 34% to 45% of familial recessive early-onset Parkinson's diseases,[[1]] also called PARK-Parkin (MIM #600116).[3] PARK-Parkin differs from idiopathic Parkinson's disease (PD) in the age onset before 45 years, dystonia at presentation, less frequent dementia, slower progression, better levodopa-responsivity, and a limited dopaminergic neuron depletion.[[2], [4]] A vast mutational spectrum in I PRKN i has already been noticed, including all types of CNV (copy number variant) and SNV (single nucleotide variant).[5] Here, we report four affected members of a family carrying two combinations of bi-allelic I PRKN i pathogenic variants. Finally, proband II.2 and siblings II.1 and II.4 carry two different I PRKN i deletions in I trans i , whereas III.1 is compound heterozygous for paternal exon 2 deletion and maternal splicing variant. Observation of asymmetrical parkinsonism around age 55 suggested instead degenerative parkinsonism, which was then confirmed by a severe dopaminergic depletion in the Datscan. [Extracted from the article]

Published
2023
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21. Reduced penetrance of an eastern French mutation in ATL1 autosomal-dominant inheritance (SPG3A): extended phenotypic spectrum coupled with brain 18F-FDG PET.

Author

Hocquel, Armand, Ravel, Jean-Marie, Lambert, Laetitia, Bonnet, Céline, Banneau, Guillaume, Kol, Bophara, Tissier, Laurène, Hopes, Lucie, Meyer, Mylène, Dillier, Céline, Michaud, Maud, Lardin, Arnaud, Kaminsky, Anne-Laure, Schmitt, Emmanuelle, Liao, Liang, Zhu, François, Myriam, Bronner, Bossenmeyer-Pourié, Carine, Verger, Antoine, and Renaud, Mathilde

Subjects
FAMILIAL spastic paraplegia, POSITRON emission tomography, MAGNETIC resonance imaging, NEUROPSYCHOLOGICAL tests, SPINAL cord, SYMPTOMS
Abstract

ATL1-related spastic paraplegia SPG3A is a pure form of hereditary spastic paraplegia. Rare complex phenotypes have been described, but few data concerning cognitive evaluation or molecular imaging of these patients are available. We relate a retrospective collection of patients with SPG3A from the Neurology Department of Nancy University Hospital, France. For each patient were carried out a 18F-FDG PET (positron emission tomography), a electromyography (EMG), a sudoscan®, a cerebral and spinal cord MRI (magnetic resonance imaging) with measurement of cervical and thoracic surfaces, a neuropsychological assessment. The present report outlines standardised clinical and paraclinical data of five patients from two east-France families carrying the same missense pathogenic variation, NM_015915.4(ATL1): c.1483C > T p.(Arg495Trp) in ATL1. Mean age at onset was 14 ± 15.01 years. Semi-quantitatively and in comparison to healthy age-matched subjects, PET scans showed a significant cerebellar and upper or mild temporal hypometabolism in all four adult patients and hypometabolism of the prefrontal cortex or precuneus in three of them. Sudoscan® showed signs of small fibre neuropathy in three patients. Cervical and thoracic patients' spinal cords were significantly thinner than matched-control, respectively 71 ± 6.59mm2 (p = 0.01) and 35.64 ± 4.35mm2 (p = 0.015). Two patients presented with a dysexecutive syndrome. While adding new clinical and paraclinical signs associated with ATL1 pathogenic variations, we insist here on the variable penetrance and expressivity. We report small fibre neuropathy, cerebellar hypometabolism and dysexecutive syndromes associated with SPG3A. These cognitive impairments and PET findings may be related to a cortico-cerebellar bundle axonopathy described in the cerebellar cognitive affective syndrome (CCAS). [ABSTRACT FROM AUTHOR]

Published
2022
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22. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

Author

Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Gomez, Luis Cristobal Monraz, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, de Leon, Miguel Ponce, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Draeger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Boernigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gokce Yagmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Ribeiro, Andrea Senff, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, de Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Pena-Chilet, Maria, Rian, Kinza, Helikar, Tomas, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noel, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Gomez, Luis Cristobal Monraz, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, de Leon, Miguel Ponce, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Draeger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Boernigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gokce Yagmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Ribeiro, Andrea Senff, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, de Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Pena-Chilet, Maria, Rian, Kinza, Helikar, Tomas, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noel, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Published
2021
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23. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Author

Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Fonds National de la Recherche - FnR [sponsor], Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Kumar Gupta, Shailendra, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, Ponce de Leon, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yağmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Senff Ribeiro, Andrea, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, de Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Peña-Chilet, Maria, Rian, Kinza, Helikar, Tomáš, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurélien, Naldi, Aurélien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Augé, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) [research center], Fonds National de la Recherche - FnR [sponsor], Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Kumar Gupta, Shailendra, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, Ponce de Leon, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yağmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Senff Ribeiro, Andrea, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, de Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Peña-Chilet, Maria, Rian, Kinza, Helikar, Tomáš, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurélien, Naldi, Aurélien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Augé, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Published
2021

24. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

Author

Fonds National de la Recherche Luxembourg, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Science, Research and Art Baden-Württemberg, German Center for Infection Research, Netherlands Organisation for Health Research and Development, National Institutes of Health (US), European Molecular Biology Laboratory, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Schreiber, Falk, Montagud, Arnau, Ponce de León, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Orlic-Milacic, Marija, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Senff Ribeiro, Andrea, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yagmu, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Rothfels, Karen, Eijssen, Lars, Ehrhart, Friederike, Arokia Balaya Rex, Devasahayam, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Shamovsky, Veronic, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Soliman, Sylvain, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Valdeolivas, Alberto, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vázquez, Miguel, Porras, Pablo, Esteban-Medina, Marina, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, Waard, Anita de, Turei, Denes, Luna, Augustín, Babur, Ozgun, Peña-Chilet, María, Rian, Kinza, Helikar, Tomas, Lal Puniya, Bhanwar, Modos, Dezso, Treveil, Agatha, Olbe, Marton, Fobo, Gisela, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Montrone, Corinna, Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L ., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, Brauner, Barbara, D’Eustachio, Peter, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Fonds National de la Recherche Luxembourg, European Commission, Federal Ministry of Education and Research (Germany), Ministry of Science, Research and Art Baden-Württemberg, German Center for Infection Research, Netherlands Organisation for Health Research and Development, National Institutes of Health (US), European Molecular Biology Laboratory, Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Schreiber, Falk, Montagud, Arnau, Ponce de León, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G., Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Orlic-Milacic, Marija, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Senff Ribeiro, Andrea, Ackerman, Emily E., Shoemaker, Jason E., Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yagmu, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Rothfels, Karen, Eijssen, Lars, Ehrhart, Friederike, Arokia Balaya Rex, Devasahayam, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Shamovsky, Veronic, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Soliman, Sylvain, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Valdeolivas, Alberto, Hu, Xiaoming, Overall, Rupert W., Maier, Dieter, Bauch, Angela, Gyori, Benjamin M., Bachman, John A., Vega, Carlos, Groues, Valentin, Vázquez, Miguel, Porras, Pablo, Esteban-Medina, Marina, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, Waard, Anita de, Turei, Denes, Luna, Augustín, Babur, Ozgun, Peña-Chilet, María, Rian, Kinza, Helikar, Tomas, Lal Puniya, Bhanwar, Modos, Dezso, Treveil, Agatha, Olbe, Marton, Fobo, Gisela, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurelien, Naldi, Aurelien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C., Montrone, Corinna, Auge, Franck, Beckmann, Jacques S., Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L ., Pico, Alexander R., Evelo, Chris T., Gillespie, Marc E., Stein, Lincoln D., Hermjakob, Henning, Brauner, Barbara, D’Eustachio, Peter, Sáez-Rodríguez, Julio, Dopazo, Joaquín, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Gupta, Shailendra Kumar, Scheel, Julia, Borlinghaus, Hanna, and Czauderna, Tobias

Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

Published
2021

25. A bi-allelic loss-of-function SARS1 variant in children with neurodevelopmental delay, deafness, cardiomyopathy, and decompensation during fever

Author

Metabole ziekten patientenzorg, Child Health, Regenerative Medicine and Stem Cells, Ravel, Jean-Marie, Dreumont, Natacha, Mosca, Pauline, Smith, Desiree E C, Mendes, Marisa I, Wiedemann, Arnaud, Coelho, David, Schmitt, Emmanuelle, Rivière, Jean-Baptiste, Tran Mau-Them, Frédéric, Thevenon, Julien, Kuentz, Paul, Polivka, Marc, Fuchs, Sabine A, Kok, Gautam, Thauvin-Robinet, Christel, Guéant, Jean-Louis, Salomons, Gajja S, Faivre, Laurence, Feillet, François, Metabole ziekten patientenzorg, Child Health, Regenerative Medicine and Stem Cells, Ravel, Jean-Marie, Dreumont, Natacha, Mosca, Pauline, Smith, Desiree E C, Mendes, Marisa I, Wiedemann, Arnaud, Coelho, David, Schmitt, Emmanuelle, Rivière, Jean-Baptiste, Tran Mau-Them, Frédéric, Thevenon, Julien, Kuentz, Paul, Polivka, Marc, Fuchs, Sabine A, Kok, Gautam, Thauvin-Robinet, Christel, Guéant, Jean-Louis, Salomons, Gajja S, Faivre, Laurence, and Feillet, François

Published
2021

28. Pleine conscience : un facteur d’adaptation au stress des étudiants ?

Author

Martin-Krumm, C, Ferrer, Marie-Hélène, Roynard, Fabien, Ravel, Jean-Marie, Cyril, Tarquinio, Trousselard, Marion, Religion, Culture et Société, Institut Catholique de Paris (ICP), Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université de Lorraine (UL), Institut de Recherche Biomédicale des Armées (IRBA), Charles Martin-Krumm, Cyril Tarquinio, Cognitions Humaine et ARTificielle (CHART), Université Paris 8 Vincennes-Saint-Denis (UP8)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Paris Nanterre (UPN)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), and École de Psychologues Praticiens (EPP)

Subjects
[SHS.PSY]Humanities and Social Sciences/Psychology, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

33. Application of Atlas of Cancer Signalling Network in preclinical studies.

Author

Gomez, L Cristobal Monraz, Kondratova, Maria, Ravel, Jean-Marie, Barillot, Emmanuel, Zinovyev, Andrei, and Kuperstein, Inna

Subjects
DRUG design, DATA modeling, CANCER, ATLASES, WEB services
Abstract

Cancer initiation and progression are associated with multiple molecular mechanisms. The knowledge of these mechanisms is expanding and should be converted into guidelines for tackling the disease. Here, we discuss the formalization of biological knowledge into a comprehensive resource: the Atlas of Cancer Signalling Network (ACSN) and the Google Maps-based tool NaviCell, which supports map navigation. The application of ACSN for omics data visualization, in the context of signalling maps, is possible via the NaviCell Web Service module and through the NaviCom tool. It allows generation of network-based molecular portraits of cancer using multilevel omics data. We review how these resources and tools are applied for cancer preclinical studies. Structural analysis of the maps together with omics data helps to rationalize the synergistic effects of drugs and allows design of complex disease stage-specific druggable interventions. The use of ACSN modules and maps as signatures of biological functions can help in cancer data analysis and interpretation. In addition, they empowered finding of associations between perturbations in particular molecular mechanisms and the risk to develop a specific type of cancer. These approaches are helpful, among others, to study the interplay between molecular mechanisms of cancer. It opens an opportunity to decipher how gene interactions govern the hallmarks of cancer in specific contexts. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other diseases. [ABSTRACT FROM AUTHOR]

Published
2019
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34. HLA-DPB1 and HLA class I confer risk of and protection from narcolepsy

Author

Ollila, Hanna M, Ravel, Jean-Marie, Han, Fang, Faraco, Juliette, Lin, Ling, Zheng, Xiuwen, Plazzi, Giuseppe, Dauvilliers, Yves, Pizza, Fabio, Hong, Seung-Chul, Jennum, Poul, Knudsen, Stine, Kornum, Birgitte R, Dong, Xiao Song, Yan, Han, Hong, Heeseung, Coquillard, Cristin, Mahlios, Joshua, Jolanki, Otto, Einen, Mali, Arnulf, Isabelle, Lavault, Sophie, Högl, Birgit, Frauscher, Birgit, Crowe, Catherine, Partinen, Markku, Huang, Yu Shu, Bourgin, Patrice, Vaarala, Outi, Désautels, Alex, Montplaisir, Jacques, Mack, Steven J, Mindrinos, Michael, Fernandez-Vina, Marcelo, Mignot, Emmanuel, Ollila, Hanna M, Ravel, Jean-Marie, Han, Fang, Faraco, Juliette, Lin, Ling, Zheng, Xiuwen, Plazzi, Giuseppe, Dauvilliers, Yves, Pizza, Fabio, Hong, Seung-Chul, Jennum, Poul, Knudsen, Stine, Kornum, Birgitte R, Dong, Xiao Song, Yan, Han, Hong, Heeseung, Coquillard, Cristin, Mahlios, Joshua, Jolanki, Otto, Einen, Mali, Arnulf, Isabelle, Lavault, Sophie, Högl, Birgit, Frauscher, Birgit, Crowe, Catherine, Partinen, Markku, Huang, Yu Shu, Bourgin, Patrice, Vaarala, Outi, Désautels, Alex, Montplaisir, Jacques, Mack, Steven J, Mindrinos, Michael, Fernandez-Vina, Marcelo, and Mignot, Emmanuel

Abstract

Type 1 narcolepsy, a disorder caused by a lack of hypocretin (orexin), is so strongly associated with human leukocyte antigen (HLA) class II HLA-DQA1(∗)01:02-DQB1(∗)06:02 (DQ0602) that very few non-DQ0602 cases have been reported. A known triggering factor for narcolepsy is pandemic 2009 influenza H1N1, suggesting autoimmunity triggered by upper-airway infections. Additional effects of other HLA-DQ alleles have been reported consistently across multiple ethnic groups. Using over 3,000 case and 10,000 control individuals of European and Chinese background, we examined the effects of other HLA loci. After careful matching of HLA-DR and HLA-DQ in case and control individuals, we found strong protective effects of HLA-DPA1(∗)01:03-DPB1(∗)04:02 (DP0402; odds ratio [OR] = 0.51 [0.38-0.67], p = 1.01 × 10(-6)) and HLA-DPA1(∗)01:03-DPB1(∗)04:01 (DP0401; OR = 0.61 [0.47-0.80], p = 2.07 × 10(-4)) and predisposing effects of HLA-DPB1(∗)05:01 in Asians (OR = 1.76 [1.34-2.31], p = 4.71 × 10(-05)). Similar effects were found by conditional analysis controlling for HLA-DR and HLA-DQ with DP0402 (OR = 0.45 [0.38-0.55] p = 8.99 × 10(-17)) and DP0501 (OR = 1.38 [1.18-1.61], p = 7.11 × 10(-5)). HLA-class-II-independent associations with HLA-A(∗)11:01 (OR = 1.32 [1.13-1.54], p = 4.92 × 10(-4)), HLA-B(∗)35:03 (OR = 1.96 [1.41-2.70], p = 5.14 × 10(-5)), and HLA-B(∗)51:01 (OR = 1.49 [1.25-1.78], p = 1.09 × 10(-5)) were also seen across ethnic groups in the HLA class I region. These effects might reflect modulation of autoimmunity or indirect effects of HLA class I and HLA-DP alleles on response to viral infections such as that of influenza.

Published
2015

35. HLA-DPB1 and HLA Class I Confer Risk of and Protection from Narcolepsy

Author

Ollila, Hanna M., primary, Ravel, Jean-Marie, additional, Han, Fang, additional, Faraco, Juliette, additional, Lin, Ling, additional, Zheng, Xiuwen, additional, Plazzi, Giuseppe, additional, Dauvilliers, Yves, additional, Pizza, Fabio, additional, Hong, Seung-Chul, additional, Jennum, Poul, additional, Knudsen, Stine, additional, Kornum, Birgitte R., additional, Dong, Xiao Song, additional, Yan, Han, additional, Hong, Heeseung, additional, Coquillard, Cristin, additional, Mahlios, Joshua, additional, Jolanki, Otto, additional, Einen, Mali, additional, Arnulf, Isabelle, additional, Högl, Birgit, additional, Frauscher, Birgit, additional, Crowe, Catherine, additional, Partinen, Markku, additional, Huang, Yu Shu, additional, Bourgin, Patrice, additional, Vaarala, Outi, additional, Désautels, Alex, additional, Montplaisir, Jacques, additional, Mack, Steven J., additional, Mindrinos, Michael, additional, Fernandez-Vina, Marcelo, additional, and Mignot, Emmanuel, additional

Published
2015
Full Text
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36. COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms

Author

Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta‐Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Monraz Gómez, Luis Cristóbal, Somers, Julia, Hoch, Matti, Kumar Gupta, Shailendra, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, Ponce De Leon, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G, Dräger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, Börnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E, Shoemaker, Jason E, Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, Gökçe Yağmur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic‐Milacic, Marija, Senff Ribeiro, Andrea, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean‐Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M, Bachman, John A, Vega, Carlos, Grouès, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, De Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban‐Medina, Marina, Peña‐Chilet, Maria, Rian, Kinza, Helikar, Tomáš, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aurélien, Naldi, Aurélien, Noël, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C, Augé, Franck, Beckmann, Jacques S, Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L, Pico, Alexander R, Evelo, Chris T, Gillespie, Marc E, Stein, Lincoln D, Hermjakob, Henning, D'Eustachio, Peter, Saez‐Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, Schneider, Reinhard, and Community, The COVID‐19 Disease Map

Subjects
systems biomedicine, open access community effort, large‐scale biocuration, omics data analysis, Articles, computable knowledge repository, EMBO10, EMBO23, Article, 3. Good health
Abstract

Funder: Bundesministerium für Bildung und Forschung (BMBF); Id: http://dx.doi.org/10.13039/501100002347, We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS‐CoV‐2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large‐scale community effort to build an open access, interoperable and computable repository of COVID‐19 molecular mechanisms. The COVID‐19 Disease Map (C19DMap) is a graphical, interactive representation of disease‐relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph‐based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS‐CoV‐2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID‐19 or similar pandemics in the long‐term perspective.

37. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms

Author

Ostaszewski, Marek, Niarakis, Anna, Mazein, Alexander, Kuperstein, Inna, Phair, Robert, Orta-Resendiz, Aurelio, Singh, Vidisha, Aghamiri, Sara Sadat, Acencio, Marcio Luis, Glaab, Enrico, Ruepp, Andreas, Fobo, Gisela, Montrone, Corinna, Brauner, Barbara, Frishman, Goar, Monraz G��mez, Luis Crist��bal, Somers, Julia, Hoch, Matti, Kumar Gupta, Shailendra, Scheel, Julia, Borlinghaus, Hanna, Czauderna, Tobias, Schreiber, Falk, Montagud, Arnau, Ponce De Leon, Miguel, Funahashi, Akira, Hiki, Yusuke, Hiroi, Noriko, Yamada, Takahiro G, Dr��ger, Andreas, Renz, Alina, Naveez, Muhammad, Bocskei, Zsolt, Messina, Francesco, B��rnigen, Daniela, Fergusson, Liam, Conti, Marta, Rameil, Marius, Nakonecnij, Vanessa, Vanhoefer, Jakob, Schmiester, Leonard, Wang, Muying, Ackerman, Emily E, Shoemaker, Jason E, Zucker, Jeremy, Oxford, Kristie, Teuton, Jeremy, Kocakaya, Ebru, Summak, G��k��e Ya��mur, Hanspers, Kristina, Kutmon, Martina, Coort, Susan, Eijssen, Lars, Ehrhart, Friederike, Rex, Devasahayam Arokia Balaya, Slenter, Denise, Martens, Marvin, Pham, Nhung, Haw, Robin, Jassal, Bijay, Matthews, Lisa, Orlic-Milacic, Marija, Senff Ribeiro, Andrea, Rothfels, Karen, Shamovsky, Veronica, Stephan, Ralf, Sevilla, Cristoffer, Varusai, Thawfeek, Ravel, Jean-Marie, Fraser, Rupsha, Ortseifen, Vera, Marchesi, Silvia, Gawron, Piotr, Smula, Ewa, Heirendt, Laurent, Satagopam, Venkata, Wu, Guanming, Riutta, Anders, Golebiewski, Martin, Owen, Stuart, Goble, Carole, Hu, Xiaoming, Overall, Rupert W, Maier, Dieter, Bauch, Angela, Gyori, Benjamin M, Bachman, John A, Vega, Carlos, Grou��s, Valentin, Vazquez, Miguel, Porras, Pablo, Licata, Luana, Iannuccelli, Marta, Sacco, Francesca, Nesterova, Anastasia, Yuryev, Anton, De Waard, Anita, Turei, Denes, Luna, Augustin, Babur, Ozgun, Soliman, Sylvain, Valdeolivas, Alberto, Esteban-Medina, Marina, Pe��a-Chilet, Maria, Rian, Kinza, Helikar, Tom����, Puniya, Bhanwar Lal, Modos, Dezso, Treveil, Agatha, Olbei, Marton, De Meulder, Bertrand, Ballereau, Stephane, Dugourd, Aur��lien, Naldi, Aur��lien, No��l, Vincent, Calzone, Laurence, Sander, Chris, Demir, Emek, Korcsmaros, Tamas, Freeman, Tom C, Aug��, Franck, Beckmann, Jacques S, Hasenauer, Jan, Wolkenhauer, Olaf, Wilighagen, Egon L, Pico, Alexander R, Evelo, Chris T, Gillespie, Marc E, Stein, Lincoln D, Hermjakob, Henning, D'Eustachio, Peter, Saez-Rodriguez, Julio, Dopazo, Joaquin, Valencia, Alfonso, Kitano, Hiroaki, Barillot, Emmanuel, Auffray, Charles, Balling, Rudi, and Schneider, Reinhard

Subjects
Computable Knowledge Repository, Large-scale Biocuration, Open Access Community Effort, Systems Biomedicine, Omics Data Analysis, 3. Good health
Abstract

Funder: Bundesministerium f��r Bildung und Forschung, Funder: Bundesministerium f��r Bildung und Forschung (BMBF), We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.

38. Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Author

Niarakis A, Ostaszewski M, Mazein A, Kuperstein I, Kutmon M, Gillespie ME, Funahashi A, Acencio ML, Hemedan A, Aichem M, Klein K, Czauderna T, Burtscher F, Yamada TG, Hiki Y, Hiroi NF, Hu F, Pham N, Ehrhart F, Willighagen EL, Valdeolivas A, Dugourd A, Messina F, Esteban-Medina M, Peña-Chilet M, Rian K, Soliman S, Aghamiri SS, Puniya BL, Naldi A, Helikar T, Singh V, Fernández MF, Bermudez V, Tsirvouli E, Montagud A, Noël V, Ponce-de-Leon M, Maier D, Bauch A, Gyori BM, Bachman JA, Luna A, Piñero J, Furlong LI, Balaur I, Rougny A, Jarosz Y, Overall RW, Phair R, Perfetto L, Matthews L, Rex DAB, Orlic-Milacic M, Gomez LCM, De Meulder B, Ravel JM, Jassal B, Satagopam V, Wu G, Golebiewski M, Gawron P, Calzone L, Beckmann JS, Evelo CT, D'Eustachio P, Schreiber F, Saez-Rodriguez J, Dopazo J, Kuiper M, Valencia A, Wolkenhauer O, Kitano H, Barillot E, Auffray C, Balling R, and Schneider R

Subjects
Humans, SARS-CoV-2, Drug Repositioning, Systems Biology, Computer Simulation, COVID-19
Abstract

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing., Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors., Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19., Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies., Competing Interests: AN collaborates with SANOFI-AVENTIS R&D via a public–private partnership grant CIFRE contract, n° 2020/0766. DM and AB are employed at Labvantage-Biomax GmbH and will be affected by any effect of this publication on the commercial version of the AILANI software. JB and BG received consulting fees from Two Six Labs, LLC. TH has served as a shareholder and has consulted for Discovery Collective, Inc. RB and RS are founders and shareholders of MEGENO SA and ITTM SA. JS-R reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer and Grunenthal. JP and LF are employees and shareholders of MedBioinformatics Solutions SL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Niarakis, Ostaszewski, Mazein, Kuperstein, Kutmon, Gillespie, Funahashi, Acencio, Hemedan, Aichem, Klein, Czauderna, Burtscher, Yamada, Hiki, Hiroi, Hu, Pham, Ehrhart, Willighagen, Valdeolivas, Dugourd, Messina, Esteban-Medina, Peña-Chilet, Rian, Soliman, Aghamiri, Puniya, Naldi, Helikar, Singh, Fernández, Bermudez, Tsirvouli, Montagud, Noël, Ponce-de-Leon, Maier, Bauch, Gyori, Bachman, Luna, Piñero, Furlong, Balaur, Rougny, Jarosz, Overall, Phair, Perfetto, Matthews, Rex, Orlic-Milacic, Gomez, De Meulder, Ravel, Jassal, Satagopam, Wu, Golebiewski, Gawron, Calzone, Beckmann, Evelo, D’Eustachio, Schreiber, Saez-Rodriguez, Dopazo, Kuiper, Valencia, Wolkenhauer, Kitano, Barillot, Auffray, Balling, Schneider and the COVID-19 Disease Map Community.)

Published
2024
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39. COVID-19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Author

Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta-Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frishman G, Monraz Gómez LC, Somers J, Hoch M, Kumar Gupta S, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Ponce de Leon M, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker JE, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex DAB, Slenter D, Martens M, Pham N, Haw R, Jassal B, Matthews L, Orlic-Milacic M, Senff-Ribeiro A, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel JM, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, de Waard A, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban-Medina M, Peña-Chilet M, Rian K, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, De Meulder B, Ballereau S, Dugourd A, Naldi A, Noël V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Willighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D'Eustachio P, Saez-Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, and Schneider R

Published
2021
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40. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Author

Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta-Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frishman G, Monraz Gómez LC, Somers J, Hoch M, Kumar Gupta S, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Ponce de Leon M, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker JE, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex DAB, Slenter D, Martens M, Pham N, Haw R, Jassal B, Matthews L, Orlic-Milacic M, Senff Ribeiro A, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel JM, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, de Waard A, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban-Medina M, Peña-Chilet M, Rian K, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, De Meulder B, Ballereau S, Dugourd A, Naldi A, Noël V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Wilighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D'Eustachio P, Saez-Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, and Schneider R

Subjects
Antiviral Agents therapeutic use, COVID-19 genetics, COVID-19 virology, Computer Graphics, Cytokines genetics, Cytokines immunology, Data Mining statistics & numerical data, Gene Expression Regulation, Host Microbial Interactions genetics, Host Microbial Interactions immunology, Humans, Immunity, Cellular drug effects, Immunity, Humoral drug effects, Immunity, Innate drug effects, Lymphocytes drug effects, Lymphocytes immunology, Lymphocytes virology, Metabolic Networks and Pathways genetics, Metabolic Networks and Pathways immunology, Myeloid Cells drug effects, Myeloid Cells immunology, Myeloid Cells virology, Protein Interaction Mapping, SARS-CoV-2 drug effects, SARS-CoV-2 genetics, SARS-CoV-2 pathogenicity, Signal Transduction, Transcription Factors genetics, Transcription Factors immunology, Viral Proteins genetics, Viral Proteins immunology, COVID-19 Drug Treatment, COVID-19 immunology, Computational Biology methods, Databases, Factual, SARS-CoV-2 immunology, Software
Abstract

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

Published
2021
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41. Application of Atlas of Cancer Signalling Network in preclinical studies.

Author

Monraz Gomez LC, Kondratova M, Ravel JM, Barillot E, Zinovyev A, and Kuperstein I

Subjects
Computational Biology methods, Humans, Neoplasms genetics, Atlases as Topic, Neoplasms metabolism, Signal Transduction
Abstract

Cancer initiation and progression are associated with multiple molecular mechanisms. The knowledge of these mechanisms is expanding and should be converted into guidelines for tackling the disease. Here, we discuss the formalization of biological knowledge into a comprehensive resource: the Atlas of Cancer Signalling Network (ACSN) and the Google Maps-based tool NaviCell, which supports map navigation. The application of ACSN for omics data visualization, in the context of signalling maps, is possible via the NaviCell Web Service module and through the NaviCom tool. It allows generation of network-based molecular portraits of cancer using multilevel omics data. We review how these resources and tools are applied for cancer preclinical studies. Structural analysis of the maps together with omics data helps to rationalize the synergistic effects of drugs and allows design of complex disease stage-specific druggable interventions. The use of ACSN modules and maps as signatures of biological functions can help in cancer data analysis and interpretation. In addition, they empowered finding of associations between perturbations in particular molecular mechanisms and the risk to develop a specific type of cancer. These approaches are helpful, among others, to study the interplay between molecular mechanisms of cancer. It opens an opportunity to decipher how gene interactions govern the hallmarks of cancer in specific contexts. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other diseases., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Published
2019
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42. [Narcolepsy: From the discovery of a wake promoting peptide to autoimmune T cell biology and molecular mimicry with flu epitopes].

Author

Ravel JM and Mignot EJM

Subjects
Animals, Autoimmunity immunology, Biomedical Research history, Dogs, Epitopes immunology, History, 20th Century, History, 21st Century, Humans, Influenza, Human immunology, Neurology history, Neuropeptides isolation & purification, Wakefulness physiology, Epitopes chemistry, Molecular Mimicry, Narcolepsy etiology, Narcolepsy history, Narcolepsy immunology, Orthomyxoviridae immunology, T-Lymphocytes immunology, Wakefulness-Promoting Agents isolation & purification
Abstract

Narcolepsy-cataplexy was first described in the late 19th century in Germany and France. Prevalence was established to be 0.05 % and a canine model was discovered in the 1970s. In 1983, a Japanese study found that all patients carried HLA-DR2, suggesting autoimmunity as the cause of the disease. Studies in the canine model established that dopaminergic stimulation underlies anti-narcoleptic action of psychostimulants, while antidepressants were found to suppress cataplexy through adrenergic reuptake inhibition. No HLA association was found in canines. A linkage study initiated in 1988 revealed in hypocretin (orexin) receptor two mutations as the cause of canine narcolepsy in 1999. In 1992, studies on African Americans showed that DQ0602 was a better marker than DR2 across all ethnic groups. In 2000, hypocretin-1/orexin A levels were measured in the cerebrospinal fluid (CSF) and found to be undetectable in most patients, establishing hypocretin deficiency as the cause of narcolepsy. Decreased CSF hypocretin-1 was then found to be secondary to the loss of the 70,000 neurons producing hypocretin in the hypothalamus, suggesting immune destruction of these cells as the cause of the disease. Additional genetic studies, notably genome wide associations (GWAS), found multiple genetic predisposing factors for narcolepsy. These were almost all involved in other autoimmune diseases, although a strong and unique association with T cell receptor (TCR) alpha and beta loci were observed. Nonetheless, all attempts to demonstrate presence of autoantibodies against hypocretin cells in narcolepsy failed, and the presumed autoimmune cause remained unproven. In 2009, association with strep throat infections were found, and narcolepsy onsets were found to occur more frequently in spring and summer, suggesting upper away infections as triggers. Following reports that narcolepsy cases were triggered by vaccinations and infections against influenza A 2009 pH1N1, a new pandemic strain that erupted in 2009, molecular mimicry with influenza A virus was suggested in 2010. This hypothesis was later confirmed by peptide screening showing higher activity of CD4 + T cell reactivity to a specific post-translationally amidated segment of hypocretin (HCRT- NH2 ) and cross-reactivity of specific TCRs with a pH1N1-specific segment of hemagglutinin that shares homology with HCRT- NH2 . Strikingly, the most frequent TCR recognizing these antigens was found to carry sequences containing TRAJ24 or TRVB4-2, segments modulated by narcolepsy-associated genetic polymorphisms. Cross-reactive CD4 + T cells with these cross-reactive TCRs likely subsequently recruit CD8 + T cells that are then involved in hypocretin cell destruction. Additional flu mimics are also likely to be discovered since narcolepsy existed prior to 2009. The work that has been conducted over the years on narcolepsy offers a unique perspective on the conduct of research on the etiopathogeny of a specific disease., (© Société de Biologie, 2019.)

Published
2019
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