@article {805, title = {Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.}, journal = {Front Immunol}, volume = {14}, year = {2024}, month = {2023}, pages = {1282859}, 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.

}, keywords = {Computer Simulation, COVID-19, drug repositioning, Humans, SARS-CoV-2, Systems biology}, issn = {1664-3224}, doi = {10.3389/fimmu.2023.1282859}, author = {Niarakis, Anna and Ostaszewski, Marek and Mazein, Alexander and Kuperstein, Inna and Kutmon, Martina and Gillespie, Marc E and Funahashi, Akira and Acencio, Marcio Luis and Hemedan, Ahmed and Aichem, Michael and Klein, Karsten and Czauderna, Tobias and Burtscher, Felicia and Yamada, Takahiro G and Hiki, Yusuke and Hiroi, Noriko F and Hu, Finterly and Pham, Nhung and Ehrhart, Friederike and Willighagen, Egon L and Valdeolivas, Alberto and Dugourd, Aur{\'e}lien and Messina, Francesco and Esteban-Medina, Marina and Pe{\~n}a-Chilet, Maria and Rian, Kinza and Soliman, Sylvain and Aghamiri, Sara Sadat and Puniya, Bhanwar Lal and Naldi, Aur{\'e}lien and Helikar, Tom{\'a}{\v s} and Singh, Vidisha and Fern{\'a}ndez, Marco Fari{\~n}as and Bermudez, Viviam and Tsirvouli, Eirini and Montagud, Arnau and No{\"e}l, Vincent and Ponce-de-Leon, Miguel and Maier, Dieter and Bauch, Angela and Gyori, Benjamin M and Bachman, John A and Luna, Augustin and Pi{\~n}ero, Janet and Furlong, Laura I and Balaur, Irina and Rougny, Adrien and Jarosz, Yohan and Overall, Rupert W and Phair, Robert and Perfetto, Livia and Matthews, Lisa and Rex, Devasahayam Arokia Balaya and Orlic-Milacic, Marija and Gomez, Luis Cristobal Monraz and De Meulder, Bertrand and Ravel, Jean Marie and Jassal, Bijay and Satagopam, Venkata and Wu, Guanming and Golebiewski, Martin and Gawron, Piotr and Calzone, Laurence and Beckmann, Jacques S and Evelo, Chris T and D{\textquoteright}Eustachio, Peter and Schreiber, Falk and Saez-Rodriguez, Julio and Dopazo, Joaquin and Kuiper, Martin and Valencia, Alfonso and Wolkenhauer, Olaf and Kitano, Hiroaki and Barillot, Emmanuel and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {795, title = {Visualization of automatically combined disease maps and pathway diagrams for rare diseases.}, journal = {Front Bioinform}, volume = {3}, year = {2023}, month = {2023}, pages = {1101505}, abstract = {

Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.

}, issn = {2673-7647}, doi = {10.3389/fbinf.2023.1101505}, author = {Gawron, Piotr and Hoksza, David and Pi{\~n}ero, Janet and Pe{\~n}a-Chilet, Maria and Esteban-Medina, Marina and Fernandez-Rueda, Jose Luis and Colonna, Vincenza and Smula, Ewa and Heirendt, Laurent and Ancien, Fran{\c c}ois and Grou{\`e}s, Valentin and Satagopam, Venkata P and Schneider, Reinhard and Dopazo, Joaquin and Furlong, Laura I and Ostaszewski, Marek} } @article {689, title = {COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms.}, journal = {Sci Data}, volume = {7}, year = {2020}, month = {2020 05 05}, pages = {136}, keywords = {Betacoronavirus, Computational Biology, Coronavirus Infections, COVID-19, Databases, Factual, Host Microbial Interactions, Host-Pathogen Interactions, Humans, International Cooperation, Models, Biological, Pandemics, Pneumonia, Viral, SARS-CoV-2}, issn = {2052-4463}, doi = {10.1038/s41597-020-0477-8}, author = {Ostaszewski, Marek and Mazein, Alexander and Gillespie, Marc E and Kuperstein, Inna and Niarakis, Anna and Hermjakob, Henning and Pico, Alexander R and Willighagen, Egon L and Evelo, Chris T and Hasenauer, Jan and Schreiber, Falk and Dr{\"a}ger, Andreas and Demir, Emek and Wolkenhauer, Olaf and Furlong, Laura I and Barillot, Emmanuel and Dopazo, Joaquin and Orta-Resendiz, Aurelio and Messina, Francesco and Valencia, Alfonso and Funahashi, Akira and Kitano, Hiroaki and Auffray, Charles and Balling, Rudi and Schneider, Reinhard} } @article {729, title = {The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research.}, journal = {F1000Res}, volume = {9}, year = {2020}, month = {2020}, chapter = {1229}, abstract = {

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While "High-Throughput" sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR{\textquoteright}s recently established with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.

}, keywords = {Computational Biology, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans}, issn = {2046-1402}, doi = {10.12688/f1000research.24887.1}, author = {Salgado, David and Armean, Irina M and Baudis, Michael and Beltran, Sergi and Capella-Gut{\'\i}errez, Salvador and Carvalho-Silva, Denise and Dominguez Del Angel, Victoria and Dopazo, Joaquin and Furlong, Laura I and Gao, Bo and Garcia, Leyla and Gerloff, Dietlind and Gut, Ivo and Gyenesei, Attila and Habermann, Nina and Hancock, John M and Hanauer, Marc and Hovig, Eivind and Johansson, Lennart F and Keane, Thomas and Korbel, Jan and Lauer, Katharina B and Laurie, Steve and Lesko{\v s}ek, Brane and Lloyd, David and Marqu{\'e}s-Bonet, Tom{\'a}s and Mei, Hailiang and Monostory, Katalin and Pi{\~n}ero, Janet and Poterlowicz, Krzysztof and Rath, Ana and Samarakoon, Pubudu and Sanz, Ferran and Saunders, Gary and Sie, Daoud and Swertz, Morris A and Tsukanov, Kirill and Valencia, Alfonso and Vidak, Marko and Yenyxe Gonz{\'a}lez, Cristina and Ylstra, Bauke and B{\'e}roud, Christophe} }