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. 2024 Feb 13:14:1282859.
doi: 10.3389/fimmu.2023.1282859. eCollection 2023.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Anna Niarakis  1   2 , Marek Ostaszewski  3 , Alexander Mazein  3 , Inna Kuperstein  4   5   6 , Martina Kutmon  7 , Marc E Gillespie  8   9 , Akira Funahashi  10 , Marcio Luis Acencio  3 , Ahmed Hemedan  3 , Michael Aichem  11 , Karsten Klein  11 , Tobias Czauderna  12 , Felicia Burtscher  3 , Takahiro G Yamada  10 , Yusuke Hiki  13 , Noriko F Hiroi  14   15 , Finterly Hu  7   16 , Nhung Pham  7   16 , Friederike Ehrhart  16 , Egon L Willighagen  16 , Alberto Valdeolivas  17 , Aurelien Dugourd  17 , Francesco Messina  18 , Marina Esteban-Medina  19   20 , Maria Peña-Chilet  19   20   21 , Kinza Rian  19 , Sylvain Soliman  2 , Sara Sadat Aghamiri  22 , Bhanwar Lal Puniya  22 , Aurélien Naldi  2 , Tomáš Helikar  22 , Vidisha Singh  1 , Marco Fariñas Fernández  23 , Viviam Bermudez  23 , Eirini Tsirvouli  23 , Arnau Montagud  24 , Vincent Noël  4   5   6 , Miguel Ponce-de-Leon  24 , Dieter Maier  25 , Angela Bauch  25 , Benjamin M Gyori  26 , John A Bachman  26 , Augustin Luna  27   28 , Janet Piñero  29   30 , Laura I Furlong  29   30 , Irina Balaur  3 , Adrien Rougny  31   32 , Yohan Jarosz  3 , Rupert W Overall  33 , Robert Phair  34 , Livia Perfetto  35 , Lisa Matthews  36 , Devasahayam Arokia Balaya Rex  37 , Marija Orlic-Milacic  8 , Luis Cristobal Monraz Gomez  4   5   6 , Bertrand De Meulder  38 , Jean Marie Ravel  4   5   6 , Bijay Jassal  8 , Venkata Satagopam  3   39 , Guanming Wu  40 , Martin Golebiewski  41 , Piotr Gawron  3 , Laurence Calzone  4   5   6 , Jacques S Beckmann  42 , Chris T Evelo  16 , Peter D'Eustachio  36 , Falk Schreiber  11   43 , Julio Saez-Rodriguez  17 , Joaquin Dopazo  19   20   21   44 , Martin Kuiper  23 , Alfonso Valencia  24   45 , Olaf Wolkenhauer  46   47 , Hiroaki Kitano  48 , Emmanuel Barillot  4   5   6 , Charles Auffray  38 , Rudi Balling  49 , Reinhard Schneider  3 ; COVID-19 Disease Map Community
Affiliations

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Anna Niarakis et al. Front Immunol. .

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: SARS-CoV-2; disease maps; dynamic models; large-scale community effort; mechanistic models; systems biology; systems medicine.

PubMed Disclaimer

Conflict of interest statement

AN collaborates with SANOFI-AVENTIS R&D via a public–private partnership grant CIFRE contract, n° 2020/0766. DM and AB are employed at Labvantage-Biomax GmbH and will be affected by any effect of this publication on the commercial version of the AILANI software. JB and BG received consulting fees from Two Six Labs, LLC. TH has served as a shareholder and has consulted for Discovery Collective, Inc. RB and RS are founders and shareholders of MEGENO SA and ITTM SA. JS-R reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer and Grunenthal. JP and LF are employees and shareholders of MedBioinformatics Solutions SL. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
The main workflow of the pipelines was developed to analyse the mechanistic content of the C19Dmap. We used it to suggest intervention points, drug repurposing and novel hypotheses for in vitro testing.
Figure 2
Figure 2
Multi-omics data analysis using available omics data to identify differentially expressed genes, active TFS, causal interactions and affected pathways in samples from cell lines and SARS-CoV-2 patients.
Figure 3
Figure 3
25 genes differentially expressed in both cell lines are linked to 19 pathways. C19DMap pathways are represented as grey diamonds, and shared DEGs are coloured as rectangles following expression fold change.
Figure 4
Figure 4
Activation levels of significant C19DMap pathways in SARS-CoV-2-infected nasopharyngeal tissue; (A) Renin-Angiotensin pathway, and (B) Interferon-1 pathway. Activation levels were calculated using GSE152075 transcriptional data and the HiPathia mechanistic pathway analysis algorithm. Nodes represent genes (ellipses), metabolites/non-gene elements (circles), or functions (rectangles). Pathway-derived circuits connect receptor genes/metabolites to effector genes/functions, simplifying functional interactions into inhibitions or activations. Red arrows indicate circuits activated in infected cells. Node colours correspond to differential expression levels in SARS-CoV-2-infected vs. normal lung cells. Blue: down-regulated elements, red: upregulated elements, white: elements not differentially expressed. HiPathia calculates the overall circuit activation and can indicate deregulated interactions even if interacting elements are not individually differentially expressed.
Figure 5
Figure 5
Dynamical modelling workflow of the C19DMap pathways. In this section, we include pathway-level modelling, focused on the Type 1 IFN pathway of the C19DMap, cellular-level modelling, focusing on macrophages, and multicellular level modelling combining macrophages, T-cells and epithelial cells in an Agent-Based Model.
Figure 6
Figure 6
Stable states from the macrophage Boolean model specific for SARS-CoV-2 infection. Model stable states upon different inputs (virus infection, inflammatory conditions + virus infection, and inflammatory condition) are presented in the heatmap. Each input evolves into a unique stable state (rows, delimited by white horizontal lines), where node activity is shown in orange when active and blue when inactive. Nodes, listed at the bottom of the heatmap, are clustered (delimited with white vertical lines) by their relation with specific modules, with the activation of macrophage phenotypes, or with biological processes.
Figure 7
Figure 7
Multiscale simulation workflow. (A) Overview of the top-level interaction model that integrates virus infection, host respiratory epithelial cell demise, and the response of different immune cells. (B) The apoptosis model from C19DMap (https://fairdomhub.org/models/712). (C) The modified version of the apoptosis model was included in each respiratory epithelial cell type.
Figure 8
Figure 8
Diagram of the identified targets and the corresponding targeting entities (drugs, chemicals, mirRNAs, small molecules).
Figure 9
Figure 9
The CAP score estimates the likelihood of a particular gene carrying pharmacogenomic variants, while the DPR score estimates the likelihood of the response to a drug being affected by pharmacogenomic variants (50). The CAP score depends on the number of pharmacogenomic variants and their population frequency.
Figure 10
Figure 10
Hierarchical exploration of centrality values in the disease map using LMME-DM. The following pathways are detailed: Coagulation: yellow; Apoptosis: red; Interferon 1: blue; Interferon lambda: green; Renin-Angiotensin: orange. The aggregated centrality values are mapped to the node sizes in the detail view.
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