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

Anna Niarakis; Marek Ostaszewski; Alexander Mazein; Inna Kuperstein; Martina Kutmon; Marc E Gillespie; Akira Funahashi; Marcio Luis Acencio; Ahmed Hemedan; Michael Aichem; Karsten Klein; Tobias Czauderna; Felicia Burtscher; Takahiro G Yamada; Yusuke Hiki; Noriko F Hiroi; Finterly Hu; Nhung Pham; Friederike Ehrhart; Egon L Willighagen; Alberto Valdeolivas; Aurelien Dugourd; Francesco Messina; Marina Esteban-Medina; Maria Peña-Chilet; Kinza Rian; Sylvain Soliman; Sara Sadat Aghamiri; Bhanwar Lal Puniya; Aurélien Naldi; Tomáš Helikar; Vidisha Singh; Marco Fariñas Fernández; Viviam Bermudez; Eirini Tsirvouli; Arnau Montagud; Vincent Noël; Miguel Ponce-de-Leon; Dieter Maier; Angela Bauch; Benjamin M Gyori; John A Bachman; Augustin Luna; Janet Piñero; Laura I Furlong; Irina Balaur; Adrien Rougny; Yohan Jarosz; Rupert W Overall; Robert Phair; Chris Evelo; COVID-19 Disease Map Community

doi:10.3389/fimmu.2023.1282859

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

Anna Niarakis^*, Marek Ostaszewski, Alexander Mazein, Inna Kuperstein, Martina Kutmon, Marc E Gillespie, Akira Funahashi, Marcio Luis Acencio, Ahmed Hemedan, Michael Aichem, Karsten Klein, Tobias Czauderna, Felicia Burtscher, Takahiro G Yamada, Yusuke Hiki, Noriko F Hiroi, Finterly Hu, Nhung Pham, Friederike Ehrhart, Egon L WillighagenAlberto Valdeolivas, Aurelien Dugourd, Francesco Messina, Marina Esteban-Medina, Maria Peña-Chilet, Kinza Rian, Sylvain Soliman, Sara Sadat Aghamiri, Bhanwar Lal Puniya, Aurélien Naldi, Tomáš Helikar, Vidisha Singh, Marco Fariñas Fernández, Viviam Bermudez, Eirini Tsirvouli, Arnau Montagud, Vincent Noël, Miguel Ponce-de-Leon, Dieter Maier, Angela Bauch, Benjamin M Gyori, John A Bachman, Augustin Luna, Janet Piñero, Laura I Furlong, Irina Balaur, Adrien Rougny, Yohan Jarosz, Rupert W Overall, Robert Phair, Chris Evelo, COVID-19 Disease Map Community

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	1282859
Journal	Frontiers in Immunology
Volume	14
DOIs	https://doi.org/10.3389/fimmu.2023.1282859
Publication status	Published - 2023

Keywords

SARS-CoV-2
disease maps
dynamic models
large-scale community effort
mechanistic models
systems biology
systems medicine
Humans
COVID-19
Drug Repositioning
Systems Biology
Computer Simulation

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Niarakis, A., Ostaszewski, M., Mazein, A., Kuperstein, I., Kutmon, M., Gillespie, M. E., Funahashi, A., Acencio, M. L., Hemedan, A., Aichem, M., Klein, K., Czauderna, T., Burtscher, F., Yamada, T. G., Hiki, Y., Hiroi, N. F., Hu, F., Pham, N., Ehrhart, F., ... COVID-19 Disease Map Community (2023). Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches. Frontiers in Immunology, 14, Article 1282859. https://doi.org/10.3389/fimmu.2023.1282859

@article{c538f9dee3014503821219fbc0fce92b,

title = "Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches",

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, Humans, COVID-19, Drug Repositioning, Systems Biology, Computer Simulation",

author = "Anna Niarakis and Marek Ostaszewski and Alexander Mazein and Inna Kuperstein and Martina Kutmon and Gillespie, \{Marc E\} and Akira Funahashi and Acencio, \{Marcio Luis\} and Ahmed Hemedan and Michael Aichem and Karsten Klein and Tobias Czauderna and Felicia Burtscher and Yamada, \{Takahiro G\} and Yusuke Hiki and Hiroi, \{Noriko F\} and Finterly Hu and Nhung Pham and Friederike Ehrhart and Willighagen, \{Egon L\} and Alberto Valdeolivas and Aurelien Dugourd and Francesco Messina and Marina Esteban-Medina and Maria Pe{\~n}a-Chilet and Kinza Rian and Sylvain Soliman and Aghamiri, \{Sara Sadat\} and Puniya, \{Bhanwar Lal\} and Aur{\'e}lien Naldi and Tom{\'a}{\v s} Helikar and Vidisha Singh and Fern{\'a}ndez, \{Marco Fari{\~n}as\} and Viviam Bermudez and Eirini Tsirvouli and Arnau Montagud and Vincent No{\"e}l and Miguel Ponce-de-Leon and Dieter Maier and Angela Bauch and Gyori, \{Benjamin M\} and Bachman, \{John A\} and Augustin Luna and Janet Pi{\~n}ero and Furlong, \{Laura I\} and Irina Balaur and Adrien Rougny and Yohan Jarosz and Overall, \{Rupert W\} and Robert Phair and Chris Evelo and \{COVID-19 Disease Map Community\}",

year = "2023",

doi = "10.3389/fimmu.2023.1282859",

language = "English",

volume = "14",

journal = "Frontiers in Immunology",

issn = "1664-3224",

publisher = "Frontiers Media S.A.",

}

Niarakis, A, Ostaszewski, M, Mazein, A, Kuperstein, I, Kutmon, M, Gillespie, ME, Funahashi, A, Acencio, ML, Hemedan, A, Aichem, M, Klein, K, Czauderna, T, Burtscher, F, Yamada, TG, Hiki, Y, Hiroi, NF, Hu, F, Pham, N, Ehrhart, F , Willighagen, EL, Valdeolivas, A, Dugourd, A, Messina, F, Esteban-Medina, M, Peña-Chilet, M, Rian, K, Soliman, S, Aghamiri, SS, Puniya, BL, Naldi, A, Helikar, T, Singh, V, Fernández, MF, Bermudez, V, Tsirvouli, E, Montagud, A, Noël, V, Ponce-de-Leon, M, Maier, D, Bauch, A, Gyori, BM, Bachman, JA, Luna, A, Piñero, J, Furlong, LI, Balaur, I, Rougny, A, Jarosz, Y, Overall, RW, Phair, R, Evelo, C & COVID-19 Disease Map Community 2023, 'Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches', Frontiers in Immunology, vol. 14, 1282859. https://doi.org/10.3389/fimmu.2023.1282859

TY - JOUR

T1 - Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

AU - Niarakis, Anna

AU - Ostaszewski, Marek

AU - Mazein, Alexander

AU - Kuperstein, Inna

AU - Kutmon, Martina

AU - Gillespie, Marc E

AU - Funahashi, Akira

AU - Acencio, Marcio Luis

AU - Hemedan, Ahmed

AU - Aichem, Michael

AU - Klein, Karsten

AU - Czauderna, Tobias

AU - Burtscher, Felicia

AU - Yamada, Takahiro G

AU - Hiki, Yusuke

AU - Hiroi, Noriko F

AU - Hu, Finterly

AU - Pham, Nhung

AU - Ehrhart, Friederike

AU - Willighagen, Egon L

AU - Valdeolivas, Alberto

AU - Dugourd, Aurelien

AU - Messina, Francesco

AU - Esteban-Medina, Marina

AU - Peña-Chilet, Maria

AU - Rian, Kinza

AU - Soliman, Sylvain

AU - Aghamiri, Sara Sadat

AU - Puniya, Bhanwar Lal

AU - Naldi, Aurélien

AU - Helikar, Tomáš

AU - Singh, Vidisha

AU - Fernández, Marco Fariñas

AU - Bermudez, Viviam

AU - Tsirvouli, Eirini

AU - Montagud, Arnau

AU - Noël, Vincent

AU - Ponce-de-Leon, Miguel

AU - Maier, Dieter

AU - Bauch, Angela

AU - Gyori, Benjamin M

AU - Bachman, John A

AU - Luna, Augustin

AU - Piñero, Janet

AU - Furlong, Laura I

AU - Balaur, Irina

AU - Rougny, Adrien

AU - Jarosz, Yohan

AU - Overall, Rupert W

AU - Phair, Robert

AU - Evelo, Chris

AU - COVID-19 Disease Map Community

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - SARS-CoV-2

KW - disease maps

KW - dynamic models

KW - large-scale community effort

KW - mechanistic models

KW - systems biology

KW - systems medicine

KW - Humans

KW - COVID-19

KW - Drug Repositioning

KW - Systems Biology

KW - Computer Simulation

U2 - 10.3389/fimmu.2023.1282859

DO - 10.3389/fimmu.2023.1282859

M3 - Article

SN - 1664-3224

VL - 14

JO - Frontiers in Immunology

JF - Frontiers in Immunology

M1 - 1282859

ER -

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

Abstract

Keywords

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