Mechanistic modeling of the SARS-CoV-2 disease map

Rian, Kinza; Esteban-Medina, Marina; Hidalgo, Marta R.; Çubuk, Cankut; Falco, Matías M.; Loucera, Carlos; Gunyel, Devrim; Ostaszewski, Marek; Peña‐Chilet, María; Dopazo, Joaquı́n

doi:10.21203/rs.3.rs-47964/v2

Cited by 5 publications

(11 citation statements)

References 19 publications

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“…The CoV-HiPathia (Rian et al, 2021) web tool was used to calculate the level of activity of the subpathways of the apoptosis diagram (https://fairdomhub.org/models/712) from the C19DMap. RNA-Seq transcriptomic profiles come from a public dataset of nasopharyngeal swabs from 430 individuals with SARS-CoV-2 and 54 negative controls, Gene Expression Omnibus reference GSE152075 Data ref: Lieberman et al, 2020).…”

Section: Rna-seq-based Analysis Of Pathway Signallingmentioning

confidence: 99%

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

Ostaszewski

Niarakis

Mazein

et al. 2021

Molecular Systems Biology

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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.

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Section: Rna-seq-based Analysis Of Pathway Signallingmentioning

confidence: 99%

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

Ostaszewski

Niarakis

Mazein

et al. 2021

Molecular Systems Biology

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“…A total of 2518 genes reported to be causative of a total of 3854 rare diseases were described in Orphanet repository (see Supplementary Material, Table S1 ). Among these, 359 genes were located within signaling circuits belonging to the COVID-19 disease map ( 13 , 14 ), being part of a total of 270 circuits. Table 1 shows the number of rare diseases, clustered by rare disease type, that have been found to interact with the COVID-19.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we use the version of the COVID-19 disease map already implemented and publicly available in the CoV-Hipathia tool ( 14 ). Briefly, the intersection of the SARS-CoV-2 virus-human interactome ( 62 ) with Kyoto encyclopedia of genes and genomes (KEGG) pathways ( 63 ) is used to define regions within the whole set of KEGG pathways that account for the molecular mechanism of the viral infection and the downstream consequences.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the large number of genes causative of rare disease genes along with the many human pathways affected by the infection suggest that the likelihood of potential interactions between a number of rare diseases and the SARS-CoV-2 infection process and their downstream pathologic consequences is not negligible. Here, a mechanistic model of the recently produced COVID-19 disease map ( 13 , 14 ) will be used to find rare disease genes whose LoF would have an effect over the different functional hallmarks of the coronavirus infection. This innovative strategy has allowed to detect, among the >7000 rare diseases described, a total of 365 that have a potential direct interaction with the COVID-19 molecular mechanism of infection and downstream consequences, 255 diseases with potential indirect interaction and 69 diseases with both direct and indirect.…”

Section: Introductionmentioning

confidence: 99%

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Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling

López-Sánchez

Loucera

Peña‐Chilet

et al. 2022

Human Molecular Genetics

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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.

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“…It is unrealistic to find therapeutic options for COVID-19 patients by experimental methods under such an urgent situation. Subsequently, systematic identification of anti-SARS-CoV-2 drugs by repositioning FDA-approved drugs offers an effective way to find possible pharmaceutical ingredients for COVID-19 [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

Shen

Liu

Huang

et al. 2022

Computers in Biology and Medicine

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Background A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. Methods In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. Results In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. Conclusions Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

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Mechanistic modeling of the SARS-CoV-2 disease map

Cited by 5 publications

References 19 publications

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

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

Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

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