COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

Ostaszewski, Marek; Niarakis, Anna; Mazein, Alexander; Kuperstein, Inna; Phair, Robert D.; Orta-Resendiz, Aurelio; Singh, Vidisha; Aghamiri, Sara Sadat; Acencio, Márcio Luís; Glaab, Enrico; Ruepp, Andreas; Fobo, Gisela; Montrone, Corinna; Brauner, Barbara; Frishman, Goar; Gómez, Luis Cristóbal Monraz; Somers, Julia; Hoch, Matti; Gupta, Satish; Scheel, Julia; Borlinghaus, Hanna; Czauderna, Tobias; Schreiber, Falk; Montagud, Arnau; León, Miguel Ponce de; Funahashi, Akira; Hiki, Yusuke; Hiroi, Noriko; Yamada, Takahiro; Dräger, Andreas; Renz, Alina; Naveez, Muhammad; Böcskei, Zsolt; Messina, Francesco; Börnigen, Daniela; Fergusson, Liam; Conti, Marta Zaffira; 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 L.; Eijssen, Lars; Ehrhart, Friederike; Rex, Devasahayam Arokia Balaya; Slenter, Denise; Martens, Marvin; Pham, Nhung; Haw, Robin; Jassal, Bijay; Matthews, Lisa; Orlic-Milacic, M; 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; Vázquez, Miguél; Porras, Pablo; Licata, Luana; Iannuccelli, Marta; Sacco, Francesca; Nesterova, Anastasia P.; Yuryev, Anton; Waard, Anita de; Türei, Dénes; Luna, Augustin; Babur, Özgün; Soliman, Sylvain; Valdeolivas, Alberto; Esteban-Medina, Marina; Peña‐Chilet, María; Rian, Kinza; Helikar, Tomáš; Puniya, Bhanwar Lal; Módos, Dezső; Treveil, Agatha; Ölbei, Márton; Meulder, Bertrand De; Dugourd, Aurélien; Naldi, Aurélien; Noël, Vincent; Calzone, Laurence; Sander, Chris; Demir, Emek; Korcsmáros, Tamás; Freeman, Tom C.; Augé, Franck; Beckmann, Jacques S.; Hasenauer, Jan; Wolkenhauer, Olaf; Willighagen, Egon; Pico, Alexander R.; Evelo, Chris T.; Gillespie, Marc; Stein, Lincoln; Hermjakob, Henning; D’Eustachio, Peter; Sáez-Rodríguez, Julio; Dopazo, Joaquı́n; Valencia, Alfonso; Kitano, Hiroaki; Barillot, Emmanuel; Auffray, Charles; Balling, Rudi; Schneider, Reinhard

doi:10.1101/2020.10.26.356014

Cited by 6 publications

(9 citation statements)

References 352 publications

(478 reference statements)

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“…The third article, by Ostaszewski et al [ 34 ], describes a large-scale community effort to build an open-access, interoperable, and computable repository of SARS-CoV-2 virus–host interaction mechanisms. They discuss the tools, platforms, and guidelines necessary for the distributed development of this Disease Map (a constantly evolving collection of machine-and-human-readable computational diagrams and models of molecular mechanisms implicated in the disease) by a community of biocurators, domain experts, bioinformaticians, and computational biologists.…”

Section: Resultsmentioning

confidence: 99%

“…Three papers were included in this application group: Chen et al [ 30 ], Reese et al [ 33 ], and Ostaszewski et al [ 34 ]. Chen et al [ 30 ] discussed four experiments in their paper: identifying experts on coronavirus topics for building collaborations, named entity recognition with BioBERT, co-occurrence frequency-based KG, and cosine similarity-based KG.…”

Section: Discussionmentioning

confidence: 99%

“…Ostaszewski et al [ 34 ] summarized the massive effort undertaken by the COVID-19 Disease Map community to create an open-access collection of curated computational diagrams and molecular mechanisms models implicated in COVID-19. The Disease Map is a collection of 41 such diagrams containing 1836 interactions between 5499 elements, supported by 617 articles.…”

Section: Discussionmentioning

confidence: 99%

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Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Chatterjee

Nardi

Oberije

et al. 2021

JPM

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Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Chatterjee

Nardi

Oberije

et al. 2021

JPM

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“…The resulting models are not conclusive static-snapshots of SARS-CoV-2 biology and their predictions should be challenged by confronting them with the results of ongoing projects aimed at revealing the molecular mechanisms underlying SARS-CoV-2 biology. This work has been done in synergy with the COVID-19 Disease Map project, a broad community-driven effort to build a knowledge repository of molecular mechanisms of COVID-19 [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

A Resource for the Network Representation of Cell Perturbations Caused by SARS-CoV-2 Infection

Perfetto

Micarelli

Iannuccelli

et al. 2021

Genes

Self Cite

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The coronavirus disease 2019 (COVID-19) pandemic has caused more than 2.3 million casualties worldwide and the lack of effective treatments is a major health concern. The development of targeted drugs is held back due to a limited understanding of the molecular mechanisms underlying the perturbation of cell physiology observed after viral infection. Recently, several approaches, aimed at identifying cellular proteins that may contribute to COVID-19 pathology, have been reported. Albeit valuable, this information offers limited mechanistic insight as these efforts have produced long lists of cellular proteins, the majority of which are not annotated to any cellular pathway. We have embarked in a project aimed at bridging this mechanistic gap by developing a new bioinformatic approach to estimate the functional distance between a subset of proteins and a list of pathways. A comprehensive literature search allowed us to annotate, in the SIGNOR 2.0 resource, causal information underlying the main molecular mechanisms through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and related coronaviruses affect the host–cell physiology. Next, we developed a new strategy that enabled us to link SARS-CoV-2 interacting proteins to cellular phenotypes via paths of causal relationships. Remarkably, the extensive information about inhibitors of signaling proteins annotated in SIGNOR 2.0 makes it possible to formulate new potential therapeutic strategies. The proposed approach, which is generally applicable, generated a literature-based causal network that can be used as a framework to formulate informed mechanistic hypotheses on COVID-19 etiology and pathology.

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“…The understanding of all mechanisms of SARS-CoV-2 infection also passes by overall visualization of biological reactions and pathways involved in COVID-19 and H-CoV infections. It can carry out on a disease map, such as COVID-19 Disease Maps, containing many diagrams about host molecular response during the infection (26)(27)(28).…”

Section: Pathogenesismentioning

confidence: 99%

Looking for pathways related to COVID-19 phenotypes: Confirmation of pathogenic mechanisms by SARS-CoV-2 - Host interactome

Messina

Giombini

Montaldo

et al. 2020

Preprint

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In the last months, many studies have clearly described several mechanisms of SARS−CoV−2 infection at cell and tissue level. Host conditions and comorbidities were identified as risk factors for severe and fatal disease courses, but the mechanisms of interaction between host and SARS−CoV−2 determining the grade of COVID−19 severity, are still unknown. We provide a network analysis on protein−protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS−CoV−2 and specific viral proteins. A host−virus interactome was inferred on published PPI, using an explorative algorithm (Random Walk with Restart) triggered by all the 28 proteins of SARS−CoV−2, or each single viral protein one−by−one. The functional analysis for all proteins, linked to many aspects of COVID−19 pathogenesis, allows to identify the subcellular districts, where SARS−CoV−2 proteins seem to be distributed, while in each interactome built around one single viral protein, a different response was described, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, an explorative network-based approach was applied to Bradykinin Storm, highlighting a possible direct action of ORF3a and NS7b to enhancing this condition. This network-based model for SARS−CoV−2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS−CoV−2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID−19 patients.

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COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

Cited by 6 publications

References 352 publications

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

A Resource for the Network Representation of Cell Perturbations Caused by SARS-CoV-2 Infection

Looking for pathways related to COVID-19 phenotypes: Confirmation of pathogenic mechanisms by SARS-CoV-2 - Host interactome

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