%0 Journal Article %J Front Immunol %D 2024 %T Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. %A Niarakis, Anna %A Ostaszewski, Marek %A Mazein, Alexander %A Kuperstein, Inna %A Kutmon, Martina %A Gillespie, Marc E %A Funahashi, Akira %A Acencio, Marcio Luis %A Hemedan, Ahmed %A Aichem, Michael %A Klein, Karsten %A Czauderna, Tobias %A Burtscher, Felicia %A Yamada, Takahiro G %A Hiki, Yusuke %A Hiroi, Noriko F %A Hu, Finterly %A Pham, Nhung %A Ehrhart, Friederike %A Willighagen, Egon L %A Valdeolivas, Alberto %A Dugourd, Aurélien %A Messina, Francesco %A Esteban-Medina, Marina %A Peña-Chilet, Maria %A Rian, Kinza %A Soliman, Sylvain %A Aghamiri, Sara Sadat %A Puniya, Bhanwar Lal %A Naldi, Aurélien %A Helikar, Tomáš %A Singh, Vidisha %A Fernández, Marco Fariñas %A Bermudez, Viviam %A Tsirvouli, Eirini %A Montagud, Arnau %A Noël, Vincent %A Ponce-de-Leon, Miguel %A Maier, Dieter %A Bauch, Angela %A Gyori, Benjamin M %A Bachman, John A %A Luna, Augustin %A Piñero, Janet %A Furlong, Laura I %A Balaur, Irina %A Rougny, Adrien %A Jarosz, Yohan %A Overall, Rupert W %A Phair, Robert %A Perfetto, Livia %A Matthews, Lisa %A Rex, Devasahayam Arokia Balaya %A Orlic-Milacic, Marija %A Gomez, Luis Cristobal Monraz %A De Meulder, Bertrand %A Ravel, Jean Marie %A Jassal, Bijay %A Satagopam, Venkata %A Wu, Guanming %A Golebiewski, Martin %A Gawron, Piotr %A Calzone, Laurence %A Beckmann, Jacques S %A Evelo, Chris T %A D'Eustachio, Peter %A Schreiber, Falk %A Saez-Rodriguez, Julio %A Dopazo, Joaquin %A Kuiper, Martin %A Valencia, Alfonso %A Wolkenhauer, Olaf %A Kitano, Hiroaki %A Barillot, Emmanuel %A Auffray, Charles %A Balling, Rudi %A Schneider, Reinhard %K Computer Simulation %K COVID-19 %K drug repositioning %K Humans %K SARS-CoV-2 %K Systems biology %X

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.

%B Front Immunol %V 14 %P 1282859 %8 2023 %G eng %R 10.3389/fimmu.2023.1282859 %0 Journal Article %J Epidemiol Infect %D 2023 %T Evaluation of a combined detection of SARS-CoV-2 and its variants using real-time allele-specific PCR strategy: an advantage for clinical practice. %A Chaves-Blanco, Lucía %A de Salazar, Adolfo %A Fuentes, Ana %A Viñuela, Laura %A Perez-Florido, Javier %A Dopazo, Joaquin %A García, Federico %K Alleles %K COVID-19 %K COVID-19 Testing %K Humans %K Real-Time Polymerase Chain Reaction %K SARS-CoV-2 %K Sensitivity and Specificity %X

This study aimed to assess the ability of a real-time reverse transcription polymerase chain reaction (RT-PCR) with multiple targets to detect SARS-CoV-2 and its variants in a single test. Nasopharyngeal specimens were collected from patients in Granada, Spain, between January 2021 and December 2022. Five allele-specific RT-PCR kits were used sequentially, with each kit designed to detect a predominant variant at the time. When the Alpha variant was dominant, the kit included the HV69/70 deletion, E and N genes. When Delta replaced Alpha, the kit incorporated the L452R mutation in addition to E and N genes. When Omicron became dominant, L452R was replaced with the N679K mutation. Before incorporating each variant kit, a comparative analysis was carried out with SARS-CoV-2 whole genome sequencing (WGS). The results demonstrated that RT-PCR with multiple targets can provide rapid and effective detection of SARS-CoV-2 and its variants in a single test. A very high degree of agreement (96.2%) was obtained between the comparison of RT-PCR and WGS. Allele-specific RT-PCR assays make it easier to implement epidemiological surveillance systems for effective public health decision making.

%B Epidemiol Infect %V 151 %P e201 %8 2023 Nov 24 %G eng %R 10.1017/S095026882300184X %0 Journal Article %J Viruses %D 2022 %T Assessing the Impact of SARS-CoV-2 Lineages and Mutations on Patient Survival. %A Loucera, Carlos %A Perez-Florido, Javier %A Casimiro-Soriguer, Carlos S %A Ortuno, Francisco M %A Carmona, Rosario %A Bostelmann, Gerrit %A Martínez-González, L Javier %A Muñoyerro-Muñiz, Dolores %A Villegas, Román %A Rodríguez-Baño, Jesús %A Romero-Gómez, Manuel %A Lorusso, Nicola %A Garcia-León, Javier %A Navarro-Marí, Jose M %A Camacho-Martinez, Pedro %A Merino-Diaz, Laura %A Salazar, Adolfo de %A Viñuela, Laura %A Lepe, Jose A %A García, Federico %A Dopazo, Joaquin %K COVID-19 %K Genome, Viral %K Humans %K mutation %K Pandemics %K Phylogeny %K SARS-CoV-2 %X

OBJECTIVES: More than two years into the COVID-19 pandemic, SARS-CoV-2 still remains a global public health problem. Successive waves of infection have produced new SARS-CoV-2 variants with new mutations for which the impact on COVID-19 severity and patient survival is uncertain.

METHODS: A total of 764 SARS-CoV-2 genomes, sequenced from COVID-19 patients, hospitalized from 19th February 2020 to 30 April 2021, along with their clinical data, were used for survival analysis.

RESULTS: A significant association of B.1.1.7, the alpha lineage, with patient mortality (log hazard ratio (LHR) = 0.51, C.I. = [0.14,0.88]) was found upon adjustment by all the covariates known to affect COVID-19 prognosis. Moreover, survival analysis of mutations in the SARS-CoV-2 genome revealed 27 of them were significantly associated with higher mortality of patients. Most of these mutations were located in the genes coding for the S, ORF8, and N proteins.

CONCLUSIONS: This study illustrates how a combination of genomic and clinical data can provide solid evidence for the impact of viral lineage on patient survival.

%B Viruses %V 14 %8 2022 Aug 27 %G eng %N 9 %R 10.3390/v14091893 %0 Journal Article %J Mol Syst Biol %D 2021 %T COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. %A Ostaszewski, Marek %A Niarakis, Anna %A Mazein, Alexander %A Kuperstein, Inna %A Phair, Robert %A Orta-Resendiz, Aurelio %A Singh, Vidisha %A Aghamiri, Sara Sadat %A Acencio, Marcio Luis %A Glaab, Enrico %A Ruepp, Andreas %A Fobo, Gisela %A Montrone, Corinna %A Brauner, Barbara %A Frishman, Goar %A Monraz Gómez, Luis Cristóbal %A Somers, Julia %A Hoch, Matti %A Kumar Gupta, Shailendra %A Scheel, Julia %A Borlinghaus, Hanna %A Czauderna, Tobias %A Schreiber, Falk %A Montagud, Arnau %A Ponce de Leon, Miguel %A Funahashi, Akira %A Hiki, Yusuke %A Hiroi, Noriko %A Yamada, Takahiro G %A Dräger, Andreas %A Renz, Alina %A Naveez, Muhammad %A Bocskei, Zsolt %A Messina, Francesco %A Börnigen, Daniela %A Fergusson, Liam %A Conti, Marta %A Rameil, Marius %A Nakonecnij, Vanessa %A Vanhoefer, Jakob %A Schmiester, Leonard %A Wang, Muying %A Ackerman, Emily E %A Shoemaker, Jason E %A Zucker, Jeremy %A Oxford, Kristie %A Teuton, Jeremy %A Kocakaya, Ebru %A Summak, Gökçe Yağmur %A Hanspers, Kristina %A Kutmon, Martina %A Coort, Susan %A Eijssen, Lars %A Ehrhart, Friederike %A Rex, Devasahayam Arokia Balaya %A Slenter, Denise %A Martens, Marvin %A Pham, Nhung %A Haw, Robin %A Jassal, Bijay %A Matthews, Lisa %A Orlic-Milacic, Marija %A Senff Ribeiro, Andrea %A Rothfels, Karen %A Shamovsky, Veronica %A Stephan, Ralf %A Sevilla, Cristoffer %A Varusai, Thawfeek %A Ravel, Jean-Marie %A Fraser, Rupsha %A Ortseifen, Vera %A Marchesi, Silvia %A Gawron, Piotr %A Smula, Ewa %A Heirendt, Laurent %A Satagopam, Venkata %A Wu, Guanming %A Riutta, Anders %A Golebiewski, Martin %A Owen, Stuart %A Goble, Carole %A Hu, Xiaoming %A Overall, Rupert W %A Maier, Dieter %A Bauch, Angela %A Gyori, Benjamin M %A Bachman, John A %A Vega, Carlos %A Grouès, Valentin %A Vazquez, Miguel %A Porras, Pablo %A Licata, Luana %A Iannuccelli, Marta %A Sacco, Francesca %A Nesterova, Anastasia %A Yuryev, Anton %A de Waard, Anita %A Turei, Denes %A Luna, Augustin %A Babur, Ozgun %A Soliman, Sylvain %A Valdeolivas, Alberto %A Esteban-Medina, Marina %A Peña-Chilet, Maria %A Rian, Kinza %A Helikar, Tomáš %A Puniya, Bhanwar Lal %A Modos, Dezso %A Treveil, Agatha %A Olbei, Marton %A De Meulder, Bertrand %A Ballereau, Stephane %A Dugourd, Aurélien %A Naldi, Aurélien %A Noël, Vincent %A Calzone, Laurence %A Sander, Chris %A Demir, Emek %A Korcsmaros, Tamas %A Freeman, Tom C %A Augé, Franck %A Beckmann, Jacques S %A Hasenauer, Jan %A Wolkenhauer, Olaf %A Wilighagen, Egon L %A Pico, Alexander R %A Evelo, Chris T %A Gillespie, Marc E %A Stein, Lincoln D %A Hermjakob, Henning %A D'Eustachio, Peter %A Saez-Rodriguez, Julio %A Dopazo, Joaquin %A Valencia, Alfonso %A Kitano, Hiroaki %A Barillot, Emmanuel %A Auffray, Charles %A Balling, Rudi %A Schneider, Reinhard %K Antiviral Agents %K Computational Biology %K Computer Graphics %K COVID-19 %K Cytokines %K Data Mining %K Databases, Factual %K Gene Expression Regulation %K Host Microbial Interactions %K Humans %K Immunity, Cellular %K Immunity, Humoral %K Immunity, Innate %K Lymphocytes %K Metabolic Networks and Pathways %K Myeloid Cells %K Protein Interaction Mapping %K SARS-CoV-2 %K Signal Transduction %K Software %K Transcription Factors %K Viral Proteins %X

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.

%B Mol Syst Biol %V 17 %P e10387 %8 2021 10 %G eng %N 10 %1 https://www.ncbi.nlm.nih.gov/pubmed/34664389?dopt=Abstract %R 10.15252/msb.202110387 %0 Journal Article %J Gigascience %D 2021 %T Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. %A Ortuno, Francisco M %A Loucera, Carlos %A Casimiro-Soriguer, Carlos S %A Lepe, Jose A %A Camacho Martinez, Pedro %A Merino Diaz, Laura %A de Salazar, Adolfo %A Chueca, Natalia %A García, Federico %A Perez-Florido, Javier %A Dopazo, Joaquin %K Genome, Viral %K Phylogeny %K SARS-CoV-2 %K Whole Genome Sequencing %X

BACKGROUND: The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data.

RESULTS: We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (<20%).

CONCLUSIONS: Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.

%B Gigascience %V 10 %8 2021 12 02 %G eng %N 12 %1 https://www.ncbi.nlm.nih.gov/pubmed/34865008?dopt=Abstract %R 10.1093/gigascience/giab078 %0 Journal Article %J Sci Data %D 2020 %T COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. %A Ostaszewski, Marek %A Mazein, Alexander %A Gillespie, Marc E %A Kuperstein, Inna %A Niarakis, Anna %A Hermjakob, Henning %A Pico, Alexander R %A Willighagen, Egon L %A Evelo, Chris T %A Hasenauer, Jan %A Schreiber, Falk %A Dräger, Andreas %A Demir, Emek %A Wolkenhauer, Olaf %A Furlong, Laura I %A Barillot, Emmanuel %A Dopazo, Joaquin %A Orta-Resendiz, Aurelio %A Messina, Francesco %A Valencia, Alfonso %A Funahashi, Akira %A Kitano, Hiroaki %A Auffray, Charles %A Balling, Rudi %A Schneider, Reinhard %K Betacoronavirus %K Computational Biology %K Coronavirus Infections %K COVID-19 %K Databases, Factual %K Host Microbial Interactions %K Host-Pathogen Interactions %K Humans %K International Cooperation %K Models, Biological %K Pandemics %K Pneumonia, Viral %K SARS-CoV-2 %B Sci Data %V 7 %P 136 %8 2020 05 05 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/32371892?dopt=Abstract %R 10.1038/s41597-020-0477-8 %0 Journal Article %J Signal Transduct Target Ther %D 2020 %T Drug repurposing for COVID-19 using machine learning and mechanistic models of signal transduction circuits related to SARS-CoV-2 infection. %A Loucera, Carlos %A Esteban-Medina, Marina %A Rian, Kinza %A Falco, Matias M %A Dopazo, Joaquin %A Peña-Chilet, Maria %K Computational Chemistry %K COVID-19 %K drug repositioning %K Humans %K Machine Learning %K Molecular Docking Simulation %K Molecular Targeted Therapy %K Proteins %K SARS-CoV-2 %K Signal Transduction %B Signal Transduct Target Ther %V 5 %P 290 %8 2020 12 11 %G eng %N 1 %1 https://www.ncbi.nlm.nih.gov/pubmed/33311438?dopt=Abstract %R 10.1038/s41392-020-00417-y