@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 {751, title = {Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.}, journal = {Hum Mol Genet}, year = {2022}, month = {2022 Jan 12}, abstract = {

Recent studies have demonstrated a relevant role of the host genetics in the COVID-19 prognosis. Most of the 7000 rare diseases described to date have a genetic component, typically highly penetrant. However, this vast spectrum of genetic variability remains yet unexplored with respect to possible interactions with COVID-19. Here, a mathematical mechanistic model of the COVID-19 molecular disease mechanism has been used to detect potential interactions between rare disease genes and the COVID-19 infection process and downstream consequences. Out of the 2518 disease genes analyzed, causative of 3854 rare diseases, a total of 254 genes have a direct effect on the COVID-19 molecular disease mechanism and 207 have an indirect effect revealed by a significant strong correlation. This remarkable potential of interaction occurs for more than 300 rare diseases. Mechanistic modeling of COVID-19 disease map has allowed a holistic systematic analysis of the potential interactions between the loss of function in known rare disease genes and the pathological consequences of COVID-19 infection. The results identify links between disease genes and COVID-19 hallmarks and demonstrate the usefulness of the proposed approach for future preventive measures in some rare diseases.

}, issn = {1460-2083}, doi = {10.1093/hmg/ddac007}, author = {L{\'o}pez-S{\'a}nchez, Macarena and Loucera, Carlos and Pe{\~n}a-Chilet, Maria and Dopazo, Joaquin} } @article {720, title = {A DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin.}, journal = {Mol Oncol}, volume = {15}, year = {2021}, month = {2021 12}, pages = {3691-3705}, abstract = {

Predictive biomarkers of trabectedin represent an unmet need in advanced soft-tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR-related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six-gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high-risk gene signature group showed a significantly worse progression-free survival compared with patients in the low-risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.

}, issn = {1878-0261}, doi = {10.1002/1878-0261.12996}, author = {Moura, David S and Pe{\~n}a-Chilet, Maria and Cordero Varela, Juan Antonio and Alvarez-Alegret, Ramiro and Agra-Pujol, Carolina and Izquierdo, Francisco and Ramos, Rafael and Ortega-Medina, Luis and Martin-Davila, Francisco and Castilla-Ramirez, Carolina and Hernandez-Leon, Carmen Nieves and Romagosa, Cleofe and Vaz Salgado, Maria Angeles and Lavernia, Javier and Bagu{\'e}, Silvia and Mayodormo-Aranda, Empar and Vicioso, Luis and Hern{\'a}ndez Barcel{\'o}, Jose Emilio and Rubio-Casadevall, Jordi and de Juan, Ana and Fia{\~n}o-Valverde, Maria Concepcion and Hindi, Nadia and Lopez-Alvarez, Maria and Lacerenza, Serena and Dopazo, Joaquin and Gutierrez, Antonio and Alvarez, Rosa and Valverde, Claudia and Martinez-Trufero, Javier and Martin-Broto, Javier} } @article {713, title = {Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection.}, journal = {Signal Transduct Target Ther}, volume = {5}, year = {2020}, month = {2020 12 11}, pages = {290}, keywords = {Computational Chemistry, COVID-19, drug repositioning, Humans, Machine Learning, Molecular Docking Simulation, Molecular Targeted Therapy, Proteins, SARS-CoV-2, Signal Transduction}, issn = {2059-3635}, doi = {10.1038/s41392-020-00417-y}, author = {Loucera, Carlos and Esteban-Medina, Marina and Rian, Kinza and Falco, Matias M and Dopazo, Joaquin and Pe{\~n}a-Chilet, Maria} }