DataSheet_2_Tissue-specific pathway activities: A retrospective analysis in COVID-19 patients.xlsx

The ACE2 receptors essential for SARS-CoV-2 infections are expressed not only in the lung but also in many other tissues in the human body. To better understand the disease mechanisms and progression, it is essential to understand how the virus affects and alters molecular pathways in the different affected tissues. In this study, we mapped the proteomics data obtained from Nie X. et al. (2021) to the pathway models of the COVID-19 Disease Map project and WikiPathways. The differences in pathway activities between COVID-19 and non-COVID-19 patients were calculated using the Wilcoxon test. As a result, 46% (5,235) of the detected proteins were found to be present in at least one pathway. Only a few pathways were altered in multiple tissues. As an example, the Kinin-Kallikrein pathway, an important inflammation regulatory pathway, was found to be less active in the lung, spleen, testis, and thyroid. We can confirm previously reported changes in COVID-19 patients such as the change in cholesterol, linolenic acid, and arachidonic acid metabolism, complement, and coagulation pathways in most tissues. Of all the tissues, we found the thyroid to be the organ with the most changed pathways. In this tissue, lipid pathways, energy pathways, and many COVID-19 specific pathways such as RAS and bradykinin pathways, thrombosis, and anticoagulation have altered activities in COVID-19 patients. Concluding, our results highlight the systemic nature of COVID-19 and the effect on other tissues besides the lung.</p

Use of graphical annotations in the NAD+ metabolism pathway at WikiPathways (wikipathways:WP3644) [62].

Since one of the most common uses of pathway models is data visualization on nodes, nodes must be adequately sized to allow for efficient data visualization. Accounting for this, node size can then be optimized to the overall size and complexity of the pathway model, utilizing a smaller node size for models with many nodes and a larger node size for sparse models. A consistent representation of different molecular types in terms of size and shape is important, for example, using a specific shape for gene products/proteins and another for metabolites. Any nonstandard nodes or entities should be defined in a legend in the model, as well as in the textual description. NAD+, nicotinamide adenine dinucleotide.</p

The development of the complement activation pathway model at 3 time points.

(A) Revision number 63169 as of May 8, 2013 [73]. (B) Revision number 82136 as of September 9, 2015 [74]. (C) Revision number 106816 as of September 17, 2019 [75].</p

Level of detail/abstraction.

The citric acid cycle (TCA cycle or Krebs cycle) represented as the canonical version (panel A, wikipathways:WP78) [37] and as part of processes involved in metabolic reprogramming in colon cancer (panel B, wikipathways:WP4290) [38]. In the canonical model, each enzymatic step in the cycle is represented in detail, whereas when the same process is represented in the context of colon cancer, specific steps in the cycle are summarized and abstracted at a higher level. Pathway nodes are depicted in green, metabolites in blue, and genes and proteins in black. TCA, tricarboxylic acid.</p

Three visualizations of part of the “PKC-gamma calcium signaling pathway in ataxia” at WikiPathways (wikipathways:WP4760) [63].

Visualizations of part of the “PKC-gamma calcium signaling pathway in ataxia.” (A) Node fill color is used to describe genes with mutations relevant to ataxia; orange indicates an activating mutation, and gray indicates an inactivating mutation. (B) When experimental data are visualized as node fill color, the mutation information is lost. (C) Mutations are shown as an added graphic to nodes; an orange triangle indicates an activating mutation, and a gray hexagon indicates an inactivating mutation. (D) Data visualization does not interfere with the mutation information, and both data types are visualized. Pathways were visualized in Cytoscape [64]. PKC, protein kinase C.</p

DataSheet_1_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.xlsx

IntroductionThe 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. MethodsExtensive 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.ResultsResults 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. DiscussionThe 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.</p

DataSheet_2_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.pdf

IntroductionThe 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. MethodsExtensive 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.ResultsResults 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. DiscussionThe 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.</p