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  • COVID-19 Disease Map, a computational knowledge … › content › 10.1101 › 2020.10.26.356014v1.full.pdfOct 26, 2020  · Bazin 75475 Paris 10. Institute of Experimental Genetics
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1 COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms Marek Ostaszewski 1 , Anna Niarakis 2,3 , Alexander Mazein 1 , Inna Kuperstein 4,5 , Robert Phair 6 , Aurelio Orta-Resendiz 7,8 , Vidisha Singh 2 , Sara Sadat Aghamiri 9 , Marcio Luis Acencio 1 , Enrico Glaab 1 , Andreas Ruepp 10 , Gisela Fobo 10 , Corinna Montrone 10 , Barbara Brauner 10 , Goar Frischman 10 , Luis Cristóbal Monraz Gómez 4,5 , Julia Somers 11 , Matti Hoch 12 , Shailendra Kumar Gupta 12 , Julia Scheel 12 , Hanna Borlinghaus 13 , Tobias Czauderna 14 , Falk Schreiber 13,14 , Arnau Montagud 15 , Miguel Ponce de Leon 15 , Akira Funahashi 16 , Yusuke Hiki 16 , Noriko Hiroi 16,17 , Takahiro G. Yamada 16 , Andreas Dräger 18,19,20 , Alina Renz 18 , Muhammad Naveez 12,21 , Zsolt Bocskei 22 , Francesco Messina 23,24 , Daniela Börnigen 25 , Liam Fergusson 26 , Marta Conti 27 , Marius Rameil 27 , Vanessa Nakonecnij 27 , Jakob Vanhoefer 27 , Leonard Schmiester 28,29 , Muying Wang 30 , Emily E. Ackerman 30 , Jason Shoemaker 30,31 , Jeremy Zucker 32 , Kristie Oxford 32 , Jeremy Teuton 32 , Ebru Kocakaya 33 , Gökçe Yağmur Summak 33 , Kristina Hanspers 34 , Martina Kutmon 35,36 , Susan Coort 35 , Lars Eijssen 35,37 , Friederike Ehrhart 35,37 , D. A. B. Rex 38 , Denise Slenter 35 , Marvin Martens 35 , Robin Haw 39 , Bijay Jassal 39 , Lisa Matthews 40 , Marija Orlic- Milacic 39 , Andrea Senff Ribeiro 39,41 , Karen Rothfels 39 , Veronica Shamovsky 42 , Ralf Stephan 39 , Cristoffer Sevilla 43 , Thawfeek Varusai 43 , Jean-Marie Ravel 44,45 , Rupsha Fraser 46 , Vera Ortseifen 47 , Silvia Marchesi 48 , Piotr Gawron 1,49 , Ewa Smula 1 , Laurent Heirendt 1 , Venkata Satagopam 1 , Guanming Wu 50 , Anders Riutta 34 , Martin Golebiewski 51 , Stuart Owen 52 , Carole Goble 52 , Xiaoming Hu 51 , Rupert W. Overall 53,54 , Dieter Maier 55 , Angela Bauch 55 , Benjamin M. Gyori 56 , John A. Bachman 56 , Carlos Vega 1 , Valentin Grouès 1 , Miguel Vazquez 15 , Pablo Porras 43 , Luana Licata 57 , Marta Iannuccelli 57 , Francesca Sacco 57 , Anastasia Nesterova 58 , Anton Yuryev 58 , Anita de Waard 59 , Denes Turei 60 , Augustin Luna 61,62 , Ozgun Babur 63 , Sylvain Soliman 3 , Alberto Valdeolivas 60 , Marina Esteban-Medina 64,65 , Maria Peña-Chilet 64,65,66 , Tomáš Helikar 67 , Bhanwar Lal Puniya 67 , Dezso Modos 68,69 , Agatha Treveil 68,69 , Marton Olbei 68,69 , Bertrand De Meulder 70 , Aurélien Dugourd 60,71 , Aurelien Naldi 3 , Vincent Noel 4 , Laurence Calzone 4,5 , Chris Sander 61,62 , Emek Demir 11 , Tamas Korcsmaros 68,69 , Tom C. Freeman 72 , Franck Augé 22 , Jacques S. Beckmann 73 , Jan Hasenauer 28,74 , Olaf Wolkenhauer 12 , Egon L. Wilighagen 35 , Alexander R. Pico 34 , Chris T. Evelo 35,36 , Marc E. Gillespie 43,75 , Lincoln D. Stein 39 , Henning Hermjakob 43 , Peter D'Eustachio 40 , Julio Saez-Rodriguez 60 , Joaquin Dopazo 64,65,66,76 , Alfonso Valencia 77 , Hiroaki Kitano 78,79 , Emmanuel Barillot 4,5 , Charles Auffray 70 , Rudi Balling 1 , Reinhard Schneider 1 , and the COVID-19 Disease Map Community 80 . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . CC-BY 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014 doi: bioRxiv preprint . 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    COVID-19DiseaseMap,acomputationalknowledgerepositoryofSARS-CoV-2virus-hostinteractionmechanismsMarekOstaszewski1,AnnaNiarakis2,3,AlexanderMazein1,InnaKuperstein4,5,RobertPhair6,AurelioOrta-Resendiz7,8,VidishaSingh2,SaraSadatAghamiri9,MarcioLuisAcencio1,EnricoGlaab1, Andreas Ruepp10, Gisela Fobo10, Corinna Montrone10, Barbara Brauner10, GoarFrischman10,LuisCristóbalMonrazGómez4,5,JuliaSomers11,MattiHoch12,ShailendraKumarGupta12,JuliaScheel12,HannaBorlinghaus13,TobiasCzauderna14,FalkSchreiber13,14,ArnauMontagud15,Miguel Ponce de Leon15, Akira Funahashi16, YusukeHiki16, NorikoHiroi16,17,Takahiro G. Yamada16, AndreasDräger18,19,20, Alina Renz18,MuhammadNaveez12,21, ZsoltBocskei22, Francesco Messina23,24, Daniela Börnigen25, Liam Fergusson26, Marta Conti27,MariusRameil27,VanessaNakonecnij27,JakobVanhoefer27,LeonardSchmiester28,29,MuyingWang30, Emily E. Ackerman30, Jason Shoemaker30,31, Jeremy Zucker32, Kristie Oxford32,JeremyTeuton32,EbruKocakaya33,GökçeYağmurSummak33,KristinaHanspers34,MartinaKutmon35,36,SusanCoort35,LarsEijssen35,37,FriederikeEhrhart35,37,D.A.B.Rex38,DeniseSlenter35, Marvin Martens35, Robin Haw39, Bijay Jassal39, Lisa Matthews40, Marija Orlic-Milacic39,AndreaSenffRibeiro39,41,KarenRothfels39,VeronicaShamovsky42,RalfStephan39,Cristoffer Sevilla43, Thawfeek Varusai43, Jean-Marie Ravel44,45, Rupsha Fraser46, VeraOrtseifen47, Silvia Marchesi48, Piotr Gawron1,49, Ewa Smula1, Laurent Heirendt1, VenkataSatagopam1,GuanmingWu50,AndersRiutta34,MartinGolebiewski51,StuartOwen52,CaroleGoble52,XiaomingHu51,RupertW.Overall53,54,DieterMaier55,AngelaBauch55,BenjaminM.Gyori56,JohnA.Bachman56,CarlosVega1,ValentinGrouès1,MiguelVazquez15,PabloPorras43,Luana Licata57, Marta Iannuccelli57, Francesca Sacco57, Anastasia Nesterova58, AntonYuryev58, Anita de Waard59, Denes Turei60, Augustin Luna61,62, Ozgun Babur63, SylvainSoliman3, Alberto Valdeolivas60, Marina Esteban-Medina64,65, Maria Peña-Chilet64,65,66,Tomáš Helikar67, Bhanwar Lal Puniya67, Dezso Modos68,69, Agatha Treveil68,69, MartonOlbei68,69, Bertrand De Meulder70, Aurélien Dugourd60,71, Aurelien Naldi3, Vincent Noel4,Laurence Calzone4,5, Chris Sander61,62, Emek Demir11, Tamas Korcsmaros68,69, Tom C.Freeman72,FranckAugé22,JacquesS.Beckmann73,JanHasenauer28,74,OlafWolkenhauer12,EgonL.Wilighagen35,AlexanderR.Pico34,ChrisT.Evelo35,36,MarcE.Gillespie43,75,LincolnD.Stein39, Henning Hermjakob43, Peter D'Eustachio40, Julio Saez-Rodriguez60, JoaquinDopazo64,65,66,76, Alfonso Valencia77, Hiroaki Kitano78,79, Emmanuel Barillot4,5, CharlesAuffray70, Rudi Balling1, Reinhard Schneider1, and the COVID-19 Disease MapCommunity80

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

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    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

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    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

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    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

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    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

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    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

    The copyright holder for this preprintthis version posted October 28, 2020. ; https://doi.org/10.1101/2020.10.26.356014doi: bioRxiv preprint

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  • 2

    1. LuxembourgCentreforSystemsBiomedicine,UniversityofLuxembourg,Esch-sur-Alzette,Luxembourg2. DepartmentofBiology,Univ.Evry,UniversityofParis-Saclay,GenHotel,Genopole,91025,Evry,France 3. LifewareGroup,InriaSaclay-IledeFrance,Palaiseau91120,France4. InstitutCurie,PSLResearchUniversity,Paris,France. INSERM,U900,Paris,France.5. MINESParisTech,PSLResearchUniversity,Paris,France.6. IntegrativeBioinformatics,Inc.,346PaulAve,MountainView,CA,USA7. InstitutPasteur,HIV,InflammationandPersistenceUnit,Paris,France 8. BioSorbonneParisCité,UniversitédeParis,Paris,France9. Inserm-Institutnationaldelasantéetdelarecherchemédicale.Saint-LouisHospital1avenueClaudeVellefauxPavillon

    Bazin75475Paris10. InstituteofExperimentalGenetics (IEG),HelmholtzZentrumMünchen-GermanResearchCenter forEnvironmental

    Health(GmbH),IngolstädterLandstraße1,D-85764Neuherberg,Germany11. OregonHealth&SciencesUniverity;DepartmentofMolecularandMedicalGenetics;3222SWResearchDrive,Portland,

    Oregon,U.S.A9723912. DepartmentofSystemsBiologyandBioinformatics,UniversityofRostock,18051Rostock,Germany13. DepartmentofComputerandInformationScience,UniversityofKonstanz,Konstanz,Germany14. Monash University, Faculty of Information Technology, Department of Human-Centred Computing,Wellington Rd,

    ClaytonVIC3800,Australia15. BarcelonaSupercomputingCenter(BSC),Barcelona,Spain16. KeioUniversity,DepartmentofBiosciencesandInformatics,3-14-1HiyoshiKouhoku-kuYokohamaJapan223-852217. Sanyo-OnodaCityUniversity,FacultyofPharmaceuticalSciences,UniversitySt.1-1-1,Yamaguchi,Japan756-088418. ComputationalSystemsBiologyofInfectionsandAntimicrobial-ResistantPathogens,InstituteforBioinformaticsand

    MedicalInformatics(IBMI),UniversityofTübingen,72076Tübingen,Germany19. DepartmentofComputerScience,UniversityofTübingen,72076Tübingen,Germany20. GermanCenterforInfectionResearch(DZIF),partnersiteTübingen,Germany21. RigaTechnicalUniversity,InstituteofAppliedComputerSystems,1KalkuStreet,LV-1658Riga,Latvia22. SanofiR&D,TranslationalSciences,1avPierreBrossolette91395Chilly-MazarinFrance23. DipartimentodiEpidemiologiaRicercaPre-ClinicaeDiagnosticaAvanzata,NationalInstituteforInfectiousDiseases

    'LazzaroSpallanzani'I.R.C.C.S.,Rome,Italy24. COVID19INMINetworkMedicineforIDsStudyGroup,NationalInstituteforInfectiousDiseases'LazzaroSpallanzani'

    I.R.C.C.S.,Rome,Italy25. BioinformaticsCoreFacility,UniversitätsklinikumHamburg-Eppendorf,Martinistraße52,20246Hamburg,Germany26. TheUniversityofEdinburgh,Royal(Dick)SchoolofVeterinaryMedicine,EasterBushCampus,Midlothian,EH259RG27. FacultyofMathematicsandNaturalSciences,UniversityofBonn,Bonn,Germany28. HelmholtzZentrumMünchen-GermanResearchCenterforEnvironmentalHealth,InstituteofComputationalBiology,

    85764Neuherberg,Germany29. TechnischeUniversitätMünchen,CenterforMathematics,ChairofMathematicalModelingofBiologicalSystems,85748

    Garching,Germany30. DepartmentofChemicalandPetroleumEngineering,UniversityofPittsburgh31. DepartmentofComputationalandSystemsBiology,UniversityofPittsburgh32. PacificNorthwestNationalLaboratory,902BattelleBoulevard,Richland,WA,US33. AnkaraUniversity,StemCellInstitute,CeyhunAtifKansuSt.No:16906520Cevizlidere,Ankara,Turkey34. InstituteofDataScienceandBiotechnology,GladstoneInstitutes,SanFrancisco,CA94158,US35. DepartmentofBioinformatics-BiGCaT,NUTRIM,MaastrichtUniversity,Maastricht,TheNetherlands 36. MaastrichtCentreforSystemsBiology(MaCSBio),MaastrichtUniversity,Maastricht,TheNetherlands37. MaastrichtUniversityMedicalCentre,Universiteitssingel50,6229ERMaastricht,TheNetherlands38. CenterforSystemsBiologyandMolecularMedicine,Yenepoya(DeemedtobeUniversity),Mangalore575018,India39. OntarioInstituteforCancerResearch,MaRSCentre,661UniversityAvenue,Suite510,Toronto,Ontario,CanadaM5G

    0A340. NYUGrossmanSchoolofMedicine,NewYorkNY10016,US41. UniversidadeFederaldoParaná,Brasil42. NYULangoneMedicalCenter,NewYork,USA43. EMBL-EBI,MolecularSystems,WellcomeGenomeCampus,Hinxton,Cambridgeshire,CB101SD

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  • 3

    44. UniversityofLorraine,INSERMUMR_S1256,Nutrition,Genetics,andEnvironmentalRiskExposure(NGERE),FacultyofMedicineofNancy,F-54000Nancy,France

    45. Laboratoiredegénétiquemédicale,CHRUNancy,Nancy,France46. TheUniversityofEdinburgh,Queen'sMedicalResearchInstitute.47LittleFranceCrescent,Edinburgh,EH164TJ47. Senior Research Group in Genome Research of Industrial Microorganisms, Center for Biotechnology, Bielefeld

    University,Universitätsstraße27,33615Bielefeld,Germany48. UppsalaUniversity,Sweden49. PoznanUniversityofTechnology,InstituteofComputingScience,ul.Piotrowo2,60-965Poznan,Poland50. DepartmentofMedical Informatics andClinicalEpidemiology,OregonHealth&ScienceUniversity,3181S.W. Sam

    JacksonParkRoad,Portland,OR97239-3098,USA51. HeidelbergInstituteforTheoreticalStudies(HITS),Schloss-Wolfsbrunnenweg35,D-69118Heidelberg,Germany52. TheUniversityofManchester,DepartmentofComputerScience,OxfordRoad,Manchester,M139PL,UK53. GermanCenterforNeurodegenerativeDiseases(DZNE)Dresden,Tatzberg41,01307Dresden,Germany54. Center for Regenerative Therapies Dresden (CRTD), Technische Universität Dresden, Fetscherstraße 105, 01307

    Dresden,Germany55. BiomaxInformaticsAG,Robert-Koch-Str.2,82152Planegg,Germany56. HarvardMedicalSchool,LaboratoryofSystemsPharmacology,200LongwoodAvenue,Boston,MA,US57. UniversityofRomeTorVergata,DepartmentofBiology,ViadellaRicercaScientifica1,00133Rome,Italy58. Elsevier,1600JohnFKennedyBlvd#1800,Philadelphia,PA19103,US59. Elsevier,ResearchCollaborationsUnit,71HanleyLane,Jericho,VT05465,US60. HeidelbergUnivarsity,InstituteforComputationalBiomedicine,BQ0053,ImNeuenheimerFeld267,69120Heidelberg,

    Germany61. cBioCenter,DivisionsofBiostatisticsandComputationalBiology,DepartmentofDataSciences,Dana-FarberCancer

    Institute,Boston,MA,02215,US 62. DepartmentofCellBiology,HarvardMedicalSchool,Boston,MA,02115,US63. UniversityofMassachusettsBoston,ComputerScienceDepartment,100WilliamT,MorrisseyBlvd,Boston,MA02125,

    US64. ClinicalBioinformaticsArea,FundaciónProgresoySalud(FPS),HospitalVirgendelRocio,Sevilla,41013,Spain65. ComputationalSystemsMedicinegroup, InstituteofBiomedicineofSeville (IBIS).HospitalVirgendelRocio,Sevilla

    41013,Spain66. BioinformaticsinRareDiseases(BiER).CentrodeInvestigaciónBiomédicaenReddeEnfermedadesRaras(CIBERER),

    FPS,HospitalVirgendelRocío,41013,Sevilla,Spain67. UniversityofNebraska-Lincoln,DepartmentofBiochemistry,1901VineSt.,Lincoln,NE,68588,US68. QuadramInstituteBioscience,RosalindFranklinRoad,NorwichResearchPark,Norwich,NR47UQ,UK69. EarlhamInstitute,NorwichResearchPark,Norwich,NR47UZ,UK70. EuropeanInstituteforSystemsBiologyandMedicine(EISBM),Vourles,France71. Institute of Experimental Medicine and Systems Biology, Faculty of Medicine, RWTH, Aachen University, Aachen,

    Germany72. TheRoslinInstitute,UniversityofEdinburghEH259RG73. UniversityofLausanne,Lausanne,Switzerland74. InterdisciplinaryResearchUnitMathematicsandLifeSciences,UniversityofBonn,Germany75. St.John’sUniversityCollegeofPharmacyandHealthSciences,Queens,NYUSA76. FPS/ELIXIR-es,HospitalVirgendelRocío,Sevilla,42013,Spain77. InstitucióCatalanadeRecercaiEstudisAvançats(ICREA),Barcelona,Spain78. SystemsBiologyInstitute,TokyoJapan79. OkinawaInstituteofScienceandTechnologyGraduateSchool80. FAIRDOMHub:https://fairdomhub.org/projects/190

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  • 4

    AbstractWeherebydescribealarge-scalecommunityefforttobuildanopen-access,interoperable,andcomputablerepositoryofCOVID-19molecularmechanisms-theCOVID-19DiseaseMap.Wediscussthetools,platforms,andguidelinesnecessaryforthedistributeddevelopmentofitscontentsbyamulti-facetedcommunityofbiocurators,domainexperts,bioinformaticians,andcomputationalbiologists.Wehighlighttheroleofrelevantdatabasesandtextminingapproaches in enrichment and validation of the curated mechanisms. We describe thecontentsofthemapandtheirrelevancetothemolecularpathophysiologyofCOVID-19andtheanalyticalandcomputationalmodellingapproachesthatcanbeappliedtothecontentsof the COVID-19 Disease Map for mechanistic data interpretation and predictions. Weconcludebydemonstratingconcreteapplicationsofourworkthroughseveralusecases.

    1.IntroductionThe coronavirus disease 2019 (COVID-19) pandemic due to severe acute respiratorysyndromecoronavirus2(SARS-CoV-2)[1]hasalreadyresultedintheinfectionofover40millionpeopleworldwide,ofwhomonemillionhavedied1.Themolecularpathophysiologythat links SARS-CoV-2 infection to the clinicalmanifestations and course of COVID-19 iscomplexand spansmultiplebiologicalpathways, cell typesandorgans [2,3].Togain theinsightsintothiscomplexnetwork,thebiomedicalresearchcommunityneedstoapproachit froma systemsperspective, collecting themechanisticknowledge scatteredacross thescientific literature and bioinformatic databases, and integrating it using formal systemsbiologystandards.

    Withthisgoal inmind,we initiatedacollaborativeeffort involvingover230biocurators,domainexperts,modelersanddataanalystsfrom120institutionsin30countriestodeveloptheCOVID-19DiseaseMap,anopen-access collectionof curatedcomputationaldiagramsandmodelsofmolecularmechanismsimplicatedinthedisease[4].

    To this end, we aligned the biocuration efforts of the Disease Maps Community [5,6],Reactome [7], and WikiPathways [8] and developed common guidelines utilisingstandardisedencodingandannotationschemes,basedoncommunity-developedsystemsbiology standards [9–11], and persistent identifier repositories [12]. Moreover, weintegratedrelevantknowledgefrompublicrepositories[13–16]andtextminingresources,providingameanstoupdateandrefinecontentsofthemap.ThefruitoftheseeffortswasaseriesofpathwaydiagramsdescribingkeyeventsintheCOVID-19infectiouscycleandhostresponse.

    1https://covid19.who.int/

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  • 5

    Weensured that this comprehensivediagrammaticdescriptionofdiseasemechanisms ismachine-readable and computable. This allows us to develop novel bioinformaticsworkflows,creatingexecutablenetworksforanalysisandprediction.Inthisway,themapisboth human and machine-readable, lowering the communication barrier betweenbiocurators, domain experts, and computational biologists significantly. Computationalmodelling,dataanalysis,andtheir informedinterpretationusingthecontentsof themaphave the potential to identify molecular signatures of disease predisposition anddevelopment,andtosuggestdrugrepositioningforimprovingcurrenttreatments.

    COVID-19DiseaseMapisacollectionof41diagramscontaining1836interactionsbetween5499 elements, supported by 617publications andpreprints. The summary of diagramsavailableintheCOVID-19DiseaseMapcanbefoundonline2inSupplementaryMaterial1.The map is a constantly evolving resource, refined and updated by ongoing efforts ofbiocuration,sharingandanalysis.Here,wereportitscurrentstatus.

    InSection2weexplainthesetupofourcommunityefforttoconstructtheinteroperablecontentoftheresource,involvingbiocurators,domainexpertsanddataanalysts.InSection3wedemonstratethatthescopeofthebiologicalmapsintheresourcereflectsthestate-of-the-artaboutthemolecularbiologyofCOVID-19.Next,weoutlineanalyticalworkflowsthatcanbeusedonthecontentsofthemap,includinginitial,preliminaryoutcomesoftwosuchworkflows,discussedindetailasusecasesinSection4.WeconcludeinSection5withanoutlooktofurtherdevelopmentoftheCOVID-19mapandtheutilityoftheentireresourceinfutureeffortstowardsbuildingandapplyingdisease-relevantcomputationalrepositories.

    2https://covid.pages.uni.lu/map_contents

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  • 6

    2.BuildingandsharingtheinteroperablecontentTheCOVID-19DiseaseMapprojectinvolvesthreemaingroups:(i)biocurators,(ii)domainexperts,and(iii)analystsandmodellers:

    i. Biocurators develop a collection of systems biology diagrams focused on themolecularmechanismsofSARS-CoV-2.

    ii. Domain experts refine the contents of the diagrams, supported by interactivevisualisationandannotations.

    iii. Analysts andmodellers develop computationalworkflows to generatehypothesesandpredictionsaboutthemechanismsencodedinthediagrams.

    All threegroupshavean importantrole in theprocessofbuilding themap,byprovidingcontent,refining it,anddefiningthedownstreamcomputationaluseof themap.Figure1illustratestheecosystemoftheCOVID-19DiseaseMapCommunity,highlightingtherolesofdifferentparticipants,available formatconversions, interoperable tools,anddownstreamuses. The information about the community members and their contributions aredisseminated via the FAIRDOMHub [17], so that content distributed across differentcollectionscanbeuniformlyreferenced.

    2.1 Creating and accessing the diagrams

    ThebiocuratorsoftheCOVID-19DiseaseMapdiagramsfollowtheguidelinesdevelopedbythe Community, and specific workflows of WikiPathways [8] and Reactome [7]. Thebiocurators build literature-based systems biology diagrams, representing themolecularprocesses implicated in theCOVID-19pathophysiology, their complex regulationand thephenotypic outcomes. These diagrams are main building blocks of the map, and arecomposedofbiochemicalreactionsandinteractions(furthercalledaltogetherinteractions)takingplacebetweendifferenttypesofmolecularentitiesinvariouscellularcompartments.Astherearemultipleteamsworkingonrelatedtopics,biocuratorscanprovideanexpertreviewacrosspathwaysandacrossplatforms.Thisispossible,asallplatformsofferintuitivevisualisation, interpretation, and analysis of pathway knowledge to support basic andclinicalresearch,genomeanalysis,modelling,systemsbiology,andeducation.Table1listsinformationaboutthecreatedcontent.FormoredetailsseeSupplementaryMaterial1.

    .CC-BY 4.0 International licenseavailable under a(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made

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  • 7

    Figure1:TheecosystemoftheCOVID-19DiseaseMapCommunity.ThemaingroupsofCOVID-19 Disease Map Community are biocurators, domain experts, analysts, and modellers;communicatingtorefine,interpretandapplyCOVID-19DiseaseMapdiagrams.Thesediagramsarecreated and maintained by biocurators, following pathway database workflows or standalonediagrameditors,andreviewedbydomainexperts.ThecontentissharedviapathwaydatabasesoraGitLab repository; all can be enriched by integrated resources of text mining and interactiondatabases.TheCOVID-19DiseaseMapdiagrams,availableinlayout-awaresystemsbiologyformatsand integrated with external repositories, are available in several formats allowing a range ofcomputationalanalyses,includingnetworkanalysisandBoolean,kineticormultiscalesimulations.

    Both interactions and interacting entities are annotated following a uniform, persistentidentification scheme,usingeitherMIRIAMor Identifiers.org [18], and theguidelines forannotationsofcomputationalmodels[19].Viralproteininteractionsareexplicitlyannotatedwiththeir taxonomyidentifiers tohighlight findings fromstrainsother thanSARS-CoV-2.Moreover, tools like ModelPolisher [20], SBMLsqueezer [21] or MEMOTE3 help toautomaticallycomplementtheannotationsintheSBMLformatandvalidatethemodel(seealsoSupplementaryMaterial2).

    3https://memote.io

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  • 8

    Table 1. COVID-19 Disease Map contents. The table summarises biocuration resources andcontent of the map across three main parts of the repository. All diagrams are listed inSupplementaryMaterial1.

    Source

    Individualdiagrams Reactome WikiPathways

    Diagramcontents

    21diagrams1334interactions

    4272molecularentities397publications

    1diagram101interactions

    489molecularentities227publications

    19diagrams401interactions

    738molecularentities61publications

    Access Gitlabgit-r3lab.uni.lu/covid/models

    SARS-CoVinfectionscollection

    reactome.org/PathwayBrowser/#/R-HSA-9679506

    COVIDpathwayscollectioncovid.wikipathways.org

    Exploration TheMINERVAPlatform[22]covid19map.elixir-luxembourg.org

    Guide:covid.pages.uni.lu/minerva-guide

    Nativewebinterface

    Guide:Linktoinstructions

    Nativewebinterface

    Guide:Linktoinstructions

    Biocurationguidelines

    Community4 Community5Platform-specific5

    Community5Platform-specific6

    DiagramEditors

    CellDesigner7Newt8SBGN-ED[23]yEd+ySBGN9

    Reactomepathwayeditor6 PathVisio[24]

    Formats CellDesigner SBML [25]SBGNML[26,27]

    Internal,SBMLandSBGNMLcompliant

    GPML[24]

    2.2 Enrichment using knowledge from databases and text mining

    TheknowledgeonCOVID-19mechanismsisrapidlyevolving,asdemonstratedbytherapidgrowth of the COVID-19 Open Research Dataset (CORD-19) dataset, a source scientific

    4https://docs.google.com/document/d/1DFfJZe2xjXrKMHorp_-7hlqWoNVSmx6sIzAaRzXgtQs5https://reactome.org/community/training6https://www.wikipathways.org/index.php/Help:Editing_Pathways7http://celldesigner.org8https://newteditor.org9https://github.com/sbgn/ySBGN

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  • 9

    manuscripttextandmetadataonCOVID-19andrelatedcoronavirusresearch[28].CORD-19currentlycontainsover130,000articlesandpreprints,overfourtimesmorethanwhenitwasintroduced10.Insuchaquicklyevolvingenvironment,biocurationeffortsneedtobesupported by other repositories of structured knowledge about molecular mechanismsrelevant for COVID-19, like molecular interaction databases, or text mining resources.ContentsofsuchrepositoriesmaysuggestimprovementsintheexistingCOVID-19DiseaseMap diagrams, or establish a starting point for developing new pathways (see Section“Biocurationofdatabaseandtextminingcontent”).

    InteractionandpathwaydatabasesInteractionandpathwaydatabasescontainstructuredandannotatedinformationonproteininteractionsorcausalrelationships.Whileinteractiondatabasesfocusonpairsofmolecules,offeringbroadcoverageofliterature-reportedfindings.Pathwaydatabasesprovidedetaileddescription of biochemical processes and their regulations of related interactions,supported by diagrams. Both types of resources can be a valuable input for COVID-19Disease Map biocurators, given the comparability of identifiers used for molecularannotations,andthereferencetopublicationsusedfordefininganinteractionorbuildingapathway.Table2summarisesopen-accessresourcessupportingthebiocurationofthemap.SeeSupplementaryMaterials[tools]fortheirdetaileddescription.

    Table 2. Resources supporting biocuration of the COVID-19 Disease Map. They include (i)collectionsofCOVID-19interactionsbytheIMExConsortium[14]andtheSIGNOR2.0[15],(ii)non-COVIDmechanisticinteractiondatabaseOmniPath[13]and(iii)theElsevierPathwayCollection,amanuallyreconstructedopen-accessdatasetofannotatedpathwaydiagramsforCOVID-1911.

    Resource Type Manuallycurated

    Directed Layout COVID-19specific

    IMExConsortiumdatabase[29] Interaction Yes No No Yes12[14]

    SIGNOR2.0database[15] Interaction Yes Yes Yes Yes13

    OmniPathdatabase[13] Interaction No Yes No No

    ElsevierPathwayCollection14 Pathway Yes Yes Yes Yes9

    10https://www.semanticscholar.org/cord19/download(accessedon20.10.2020)11https://data.mendeley.com/datasets/h9vs5s8fz2/draft?a=f40961bb-9798-4fd1-8025-e2a3ba47b02e12https://www.imexconsortium.org13https://signor.uniroma2.it/covid/14https://pathwaystudio.com

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  • 10

    TextminingresourcesText-miningapproachescanhelptosievethroughsuchrapidlyexpanding literaturewithnaturallanguageprocessing(NLP)algorithmsbasedonsemanticmodelling,ontologies,andlinguisticanalysistoautomaticallyextractandannotaterelevantsentences,biomolecules,andtheirinteractions.Thisscopewasrecentlyextendedtopathwayfiguremining:decodingpathway figures into their computable representations [30].Altogether, theseautomatedworkflowsleadtotheconstructionofknowledgegraphs:semanticnetworksincorporatingontology concepts, unique biomolecule references, and their interactions extracted fromabstractsorfull-textdocuments[31].

    TheCOVID-19DiseaseMapProject integrates open-access textmining resources, INDRA[32],BioKB15,AILANICOVID-1916,andPathwayStudio10.Allplatformsofferkeyword-basedsearchallowing interactiveexploration.Additionally, themapbenefits fromanextensiveprotein-proteininteractionnetwork(PPI)17generatedwithacustomtext-miningpipelineusingOpenNLP18andGNormPlus[33].ThispipelinewasappliedtotheCORD-19datasetandthe collection of MEDLINE abstracts associated with the genes in the SARS-CoV-2 PPInetwork [34] using the Entrez Gene Reference-Into-Function (GeneRIF). For detaileddescriptionsoftheresources,seeSupplementaryMaterial3.

    BiocurationusingdatabaseandtextminingcontentMolecular interactionsfromdatabasesandknowledgegraphsfromtextminingresourcesdiscussedabove(fromnowoncalledaltogether‘knowledgegraphs’)haveabroadcoverageat the cost of depth of mechanistic representation. This content can be used by thebiocuratorsintheprocessofbuildingandupdatingthesystemsbiologyfocuseddiagrams.Biocuratorscanusethiscontentinthreemainways:byvisualexploration,byprogrammaticcomparison,andbydirectincorporationofthecontent.

    First, the biocurators can visually explore the contents of the knowledge graphs usingavailable search interfaces to locate new knowledge and encode it in the diagrams.Moreover, solutions like COVIDminer project19, PathwayStudio andAILANI offer a visualrepresentation of a group of interactions for a better understanding of their biologicalcontext,allowingsearchbyinteractions,ratherthanjustisolatedkeywords.Finally,INDRA

    15https://biokb.lcsb.uni.lu16https://ailani.ai/cgi/login_bioxm_portal.cgi17https://git-r3lab.uni.lu/covid/models/-/tree/master/Resources/Text%20mining18https://opennlp.apache.org19https://rupertoverall.net/covidminer

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  • 11

    and AILANI offer assistant bots that respond to natural language queries and returnmeaningfulanswersextractedfromknowledgegraphs.

    Second, programmatic access and reproducible exploration of the knowledge graphs ispossibleviadataendpoints:SPARQLforBioKBandApplicationProgrammingInterfacesforINDRA,AILANI,andPathwayStudio.Userscanprogrammaticallysubmitkeywordqueriesandretrievefunctions,interactions,pathways,ordrugsassociatedwithsubmittedgenelists.Thisway,otherwise time-consumingtasks likeanassessmentofcompletenessofagivendiagram,orsearchfornewliteratureevidence,canbeautomatedtoalargeextent.

    Finally, biocurators candirectly incorporate the content of knowledge graphs into SBMLformat using BioKC [35]. Additionally, the contents of the Elsevier COVID-19 PathwayCollection can be translated to SBGNML20 preserving the layout of the diagrams. TheSBGNMLcontentcanthenbeconvertedintootherdiagramformatsusedbybiocurators(seeSection2.3below).

    2.3 Interoperability of the diagrams and annotations

    Thebiocurationof theCOVID-19DiseaseMap isdistributedacrossmultiple teams,usingvarying tools and associated systems biology representations. This requires a commonapproach to annotations of evidence, biochemical reactions,molecular entities and theirinteractions. Moreover, the interoperability of layout-aware formats is needed forcomparisonandintegrationofthediagramsinthemap.

    Layout-awareformatsformolecularmechanismsTheCOVID-19DiseaseMapdiagramsareencodedinoneofthreelayout-awareformatsforstandardisedrepresentationofmolecularinteractions:SBML21[36–38],SBGNML[27],andGPML[24].TheseXML-basedformatsfocustoavaryingdegreeonuser-friendlygraphicalrepresentation,standardisedvisualisation,andsupportofcomputationalworkflows.Forthedetaileddescriptionoftheformats,seeSupplementaryMaterial1.

    Each of these three languages has a different focus: SBML emphasizes standardisedrepresentation of the data model underlying molecular interactions, SBGNML providesstandardised graphical representation of molecular processes, while GPML allows for apartially standardisedrepresentationofuncertainbiologicalknowledge.Nevertheless,allthreeformatsarecenteredaroundmolecularinteractions,provideaconstrainedvocabularytoencodeelementandinteractiontypes,encodelayoutoftheirdiagramsandsupportstable

    20https://github.com/golovatenkop/rnef2sbgn21here,SBMLstandsfortwoformats:CellDesignerSBMLandSBMLwithlayoutandrenderpackages

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  • 12

    identifiers for diagram components. These shared properties, supported by a commonontology22 [39], allow cross-format mapping and enable translation of key propertiesbetweentheformats.Therefore,whendevelopingthecontentsofthemap,biocuratorsusethetoolstheyarefamiliarwith,facilitatingthisdistributedtask.

    FormatinteroperabilityTheCOVID-19DiseaseMapCommunity ecosystemof tools and resources (see Figure 1)ensures interoperability between the three layout-aware formats for molecularmechanisms:SBML,SBGNML,andGPML.Essentialelementsofthissetuparetoolscapableof providing cross-format translation functionality [40,41] and supporting harmonisedvisualisation processing. Another essential translation interface is a representation ofReactomepathwaysinWikiPathwaysGPML[42]andSBML.TheSBMLexportofReactomecontenthasbeenoptimisedinthecontextofthisprojectandfacilitatesintegrationwiththeotherCOVID-19DiseaseMapsoftwarecomponents.

    ThecontentsoftheCOVID-19DiseaseMapdiagramscanbedirectlytransformedintoinputsofcomputationalpipelinesanddatarepositories.BesidesthedirectuseofSBMLformatinkineticsimulations,CellDesignerSBMLfilescanbetransformedintoSBMLqual[43]usingCaSQ[44],enablingBooleanmodelling-basedsimulations(seealsoSupplementaryMaterial3). In parallel, CaSQ converts the diagrams to the SIF format23, supporting pathwaymodellingworkflowsusingsimplifiedinteractionnetworks.Notably,theGitLabrepositoryfeaturesanautomated translationof stableversionsofdiagrams intoSBMLqual.Finally,translationofthediagramsintoXGMMLformat(theeXtensibleGraphMarkupandModellingLanguage) using Cytoscape [45] or GINSim [46] allows for network analysis andinteroperabilitywithmolecularinteractionrepositories[47].

    3.StructureandscopeofthemapThanks to the community effort discussed above supported by a rich bioinformaticsframework,weconstructedtheCOVID-19DiseaseMap,focussingonthemechanismsknownfrom other coronaviruses [48] and suggested by early experimental investigations[PMID:32511329]. Then, we applied the analytical and modelling workflows to thecontributed diagrams and associated interaction databases to propose initialmap-basedinsightsintoCOVID-19molecularmechanisms.

    TheCOVID-19DiseaseMapisanevolvingrepositoryofpathwaysaffectedbySARS-CoV-2.Figure 2. It is currently centred on molecular processes involved in SARS-CoV-2 entry,

    22http://www.ebi.ac.uk/sbo/main/23http://www.cbmc.it/fastcent/doc/SifFormat.htm

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  • 13

    replication,andhost-pathogeninteractions.Asmechanismsofhostsusceptibility,immuneresponse,cellandorganspecificityemerge,thesewillbeincorporatedintothenextversionsofthemap.

    Figure2:Thestructureandcontentof theCOVID-19DiseaseMap.Theareasof focusof theCOVID-19Mapbiocuration.

    The COVID-19 Map represents the mechanisms in a “host cell”. This follows literaturereportsoncellspecificityofSARS-CoV-2[3,49–53].SomepathwaysincludedintheCOVID-19Mapmaybesharedamongdifferentcelltypes,asforexampletheIFN-1pathwayfoundincellssuchasdendritic,epithelial,andalveolarmacrophages[54–58].Whileatthisstage,wedonotaddresscellspecificityexplicitlyinourdiagrams,extensiveannotationsmayallowidentificationofpathwaysrelevanttothecelltypeofinterest.

    The SARS-CoV-2 infection process andCOVID-19progression follow a sequence of steps(Figure 3), starting from viral attachment and entry, which involve various dynamicprocesses on different time scales that are not captured in static representations ofpathways.Correlationofsymptomsandpotentialdrugssuggestedtodatehelpsdownstreamdataexplorationanddrugtargetinterpretationinthecontextoftherapeuticinterventions.

    Other tissues/organs/systems (cardiovascular, renal, gastrointestinal, nervous)

    Innate immune and inflammatory response

    T cellsAdaptive immune response

    Human host

    ILC1, ILC-2, ILC3

    Antigen-Presenting Cell

    Natural killer

    Systemic circulation

    Renin–angiotensin–aldosterone system

    (RAAS)

    Host cell (Type II Pneumocyte)

    Respiratory tract

    Antibodies

    production

    Coagulation and

    thrombosisCytokine release

    SARS-CoV-2

    B cells

    Red blood cells

    Th1

    Th2

    Macrophages

    Monocytes

    Viral replication cycle

    Endosome and

    uncoating

    Genome

    replication

    Transcription and

    translationVirion

    Target cell

    GranulocytesT-cell

    activation

    Specific T-cell

    response

    Alveoli

    Bronchial epithelium

    Nasal mucosa

    COVID-19Disease Map

    Vascular endothelial cell

    Cellular metabolism

    Viral protein interactions

    Golgi

    ER

    CD8+

    CD4+

    ACE2 TMPRSS2

    PAMP Signalling

    Integrative stress response

    Unfolded protein responseMitochondrial ETC

    Viral molecules (ssRNA, dsRNA)

    Mitochondria

    ApoptosisJNK PathwayAutophagy

    Endoplasmic

    Reticulum Stress

    Dendritic cells

    ACE2

    Viral shedding

    IFN-IIL-6

    IL-6

    Attachment and entry

  • 14

    Figure3:OverviewofthemapinthecontextofCOVID-19progression.Pathwaysandcelltypesinvolved in the sequential stages of COVID-19, including some of the most common clinicalmanifestationsandmedicalmanagementfromthemomentofinfectiontothediseaseresolution,areshown.Thedistributionoftheelementsisforillustrativereferenceanddoesnotnecessarilyindicateeitheraunique/staticinterplayoftheseelementsoranunvaryingprogression.Fortheliteratureonclinicalmanifestationssee[59–65].

    SupplementaryMaterial1summarisesthecontentsoftheCOVID-19DiseaseMapdiagrams,theircentralplatformofreference.TheonlineversionofthetableiscontinuouslyupdatedtoreflecttheevolvingcontentoftheCOVID-19DiseaseMap24.

    3.1 Virus replication cycle and subversion of host defence

    VirusattachmentandentryTransmission of SARS-CoV-2 primarily occurs through contact with respiratory drops,airborne transmission, and through contact with contaminated surfaces [66–68]. Uponcontactwiththerespiratoryepithelium,thevirusinfectscellsmostlybybindingthespikesurfaceglycoprotein(S)toangiotensin-convertingenzyme2(ACE2)withthehelpofserineprotease TMPRSS2 [69–72]. Importantly, recent results suggest viral entry using otherreceptorsof lungsand the immunesystem[73,74].Onceattached,SARS-CoV-2canentercellseitherbydirectfusionofthevirionandcellmembranesinthepresenceofproteases

    24https://covid.pages.uni.lu/map_contents

    Viral replication

    • Attachment and entry.

    • Replication cycle.• Viral proteins

    interactions.

    • Dendritic cells.• NK cells.• Monocytes and

    macrophages.

    • T cells, Th1 and Th2 response.

    • B cells, antibody production.

    Cytokine release and systemic inflammation

    Asymptomatic/Pre-symptomatic.

    Tissue damage

    Target cell (respiratory tract)

    Immune response and inflammation modulation

    Vaccine?Pre-exposure prophylaxis?

    Antivirals?

    SIRS, shock.Shortness of breath.

    Recovery

    Coagulation

    Anosmia, ageusia, cough, fever, diarrhea.

    Organ damage

    Multiple organ dysfunction

    ARDS, complications.

    Death

    Host response

    • Signaling.• Metabolism.

    • Cellular stress.• Apoptosis.

    Innate immune response

    Adaptive immune response

    Cellular mechanisms

    Systemic response

    Treatment of complications

    Systemic and ventilation supportOxygen therapy

    Host

    RAAS

    ARDS; Acute respiratory distress syndrome. RAAS; Renin-angiotensin-aldosterone system. SIRS; Systemic inflammatory response syndrome.

    Interventions, potential treatments

    SARS-CoV-2

    Pathophysiology

    Virus-host cell interactions and host response

    COVID-19Disease Map

    Severity and clinical manifestations

    SevereMild CriticalAsymptomatic

    (Lung, heart, kidney)(Nasal and respiratory epithelium, alveoli, vascular endothelial)

  • 15

    TMPRSS2andfurinorbyendocytosisintheirabsence.Regardlessoftheentrymechanism,the S protein has to be activated to initiate the plasma or endosomemembrane fusionprocess.While in thecellmembrane,Sprotein isactivatedbyTMPRSS2and furin, in theendosome S protein is activated by cathepsin B (CTSB) and cathepsin L (CTSL) [71,75].Activated S promotes the cell- or endosome-membrane fusion [76] with the virionmembrane,andthenthenucleocapsidisinjectedintothecytoplasm.Thesemechanismsarerepresentedinthecorrespondingdiagramsofthemap25.

    ReplicationandreleaseWithinthehostcell,SARS-CoV-2hijackstheroughendoplasmicreticulum(RER)-linkedhosttranslationalmachinery.Itthensynthesisesviralproteinsreplicasepolyprotein1a(pp1a)and replicase polyprotein 1ab (pp1ab) directly from the virus (+)genomic RNA (gRNA)[48,77].Throughacomplexcascadeofproteolyticcleavages,pp1aandpp1abgiveriseto16non-structuralproteins(Nsps)[78–80].MostoftheseNspscollectivelyformthereplicationtranscription complex (RTC) that is anchored to themembraneof thedouble-membranevesicle[78,81],acoronavirusreplicationorganelleinducedbyNsps3,4,and6[82].RTCisthought to play two main roles: 1) triggering the synthesis of both (+)gRNA and(+)subgenomicmessengerRNAs(sgmRNAs)vianegative-strandedtemplates[83–85];and2) protecting intermediatedouble-strandedRNAs from the cell innate immunity sensors[86]. The (+)sgmRNAs are translated by the RER-attached translation machinery intostructural(E,M,NandS)andaccessoryproteins(Orf3a,Orf6,Orf7a/b,Orf8,Orf9b,Orf10andOrf14).Thestructuralproteinsandthenewlygenerated(+)gRNAsareassembledintonew virions in the endoplasmic reticulum-Golgi intermediate compartment. These arereleasedtotheextracellularspaceviasmooth-walledvesicles[48,77].

    Endoplasmicreticulumstressandunfoldedproteinresponse

    Asdiscussedabove,thevirushijackstheERtoreplicate.Productionoflargeamountsofviralproteinsexceeds theprotein foldingcapacityof theER, creatinganoverloadofunfoldedproteins.Asaresult,theunfoldedproteinresponse(UPR)pathwaysaretriggeredtoassuretheERhomeostasis,using threemainsignallingroutesofUPRviaPERK, IRE1,andATF6[87].Theirrole istomitigatethemisfoldedproteinloadandreduceoxidativestress.Theresulting protein degradation is coordinated with a decrease in protein synthesis viaeIF2alpha phosphorylation and induction of protein folding genes via the transcriptionfactorXBP1[88].WhentheERisunabletorestoreitsfunction,itcantriggercellapoptosis[89,90].

    25https://covid19map.elixir-luxembourg.org/minerva/?search%3Dvirus%2520replication%2520cycle

  • 16

    The results are ER stress and activation of the UPR. The expression of some humancoronavirus(HCoV)proteinsduringinfection,inparticulartheSglycoprotein,mayinduceactivationoftheERstressinthehostcells[91].BasedonSARS-CoVresults,thismayleadtoactivationofthePERK[92],IRE1andinanindirectmanner,oftheATF6pathways[93].

    Autophagyandproteindegradation

    Processes of degrading malfunctioning proteins and damaged organelles, including theubiquitin-proteasomesystem (UPS)andautophagy [94] areessential tomaintainenergyhomeostasisandpreventcellularstress[95,96].Autophagyisalsoinvolvedincelldefence,includingdirectdestructionofthevirusesviavirophagy,presentationofviralantigens,andinhibitionofexcessiveinflammatoryreactions[97,98].

    SARS-CoV-2directlyaffectstheprocessofUPS-basedproteindegradation,asindicatedbythe host-virus interactome dataset published recently [34]. This mechanism may be adefenceagainstviralproteindegradation[99].Themapdescribesindetailthenatureofthisinteraction,namelytheimpactofOrf10virusproteinontheCul2ubiquitinligasecomplexanditspotentialsubstrates.

    Interactions between SARS-CoV-2 and host autophagy pathways are inferred based onresultsfromotherCoVs.AfindingthatCoVsusedouble-membranevesiclesandLC3-Iforreplication[100]maysuggestthatthevirusinducesautophagy,possiblyinATG5-dependentmanner [101], although some evidence points to the contrary [102]. Also, the CoVNsp6restricts autophagosome expansion, compromising the degradation of viral components[103].RecentlyrevealedmutationsinNsp6[104]indicateitsimportance,althoughtheexacteffectofthemutationsremainsunknown.Basedontheconnectionbetweenautophagyandthe endocytic pathway of the virus replication cycle [105], autophagy modulation wassuggested as a potential therapy strategy, either pharmacologically [96,105–107], or viafasting[108].

    Apoptosis

    Apoptosis,asynonymforprogrammedcelldeath,istriggeredbyvirus-hostinteractionuponinfection,astheearlydeathofthevirus-infectedcellsmaypreventviralreplication.Manyviruses block or delay cell death by expressing anti-apoptotic proteins to maximize theproduction of viral progeny [109]. In turn, apoptosis induction at the end of the viralreplication cycle might assist in viral dissemination while reducing an inflammatoryresponse.Forinstance,SARS-CoV-2[110]andMERS[111]areabletoinvokeapoptosisinlymphocytes,compromisingtheimmunesystem.

    Apoptosisfollowstwomajorpathways[112],calledextrinsicandintrinsic.Extrinsicsignalsaretransmittedbydeathligandsandtheirreceptors(e.g.,FasLandTNF-alpha).Activateddeath receptors recruit adaptors like FADD and TRADD, and initiator procaspases like

  • 17

    caspase-8,leadingtocelldeathwiththehelpofeffectorcaspases-3and7[113,114].Inturn,theintrinsicpathwayinvolvesmitochondria-relatedmembersoftheBcl-2proteinfamily.Cellular stress causes Bcl-2 proteins-mediated release of cytochrome c from themitochondria into the cytoplasm. Cytochrome c then forms a complex with Apaf1 andrecruitsinitiatorprocaspase-9toformtheapoptosome,leadingtotheproteolyticactivationofcaspase-9.Activatedcaspase-9cannowinitiatethecaspasecascadebyactivatingeffectorcaspases 3 and 7 [114]. The intrinsic pathway is modulated by SARS-CoV molecules[115,116].Asintrinsicapoptosisinvolvesmitochondria,itsactivitymayalsobeexacerbatedbySARS-CoV-2disruptionsoftheelectrontransportchain,mitochondrialtranslation,andtransmembrane transport [34]. The resulting mitochondrial dysfunction may lead toincreasedreleaseofreactiveoxygenspeciesandpro-apoptoticfactors.

    Another vital crosstalk is that of the intrinsic pathway with the PI3K-Akt pro-survivalpathway.ActivatedAkt canphosphorylate and inactivate variouspro-apoptotic proteins,includingBadandcaspase-9[117].SARS-CoVusesPI3K-Aktsignallingcascadetoenhanceinfection[118].Moreover,SARS-CoVcouldaffectapoptosis inacell-type-specificmanner[119,120].

    SARS-CoVstructuralproteinsS,E,M,N,andaccessoryproteins3a,3b,6,7a,8a,and9bhavebeenshowntoactascrucialeffectorsofapoptosisinvitro.Structuralproteinsseemtoaffectmainlytheintrinsicapoptoticpathway,withp38MAPKandPI3K/Aktpathwaysregulatingcell death. Accessory proteins can induce apoptosis via different cascades and in a cell-specificmanner [114]. SARS-CoV E and 7a proteinwere shown to activate the intrinsicpathwaybyblockinganti-apoptoticBcl-XLlocalizedtotheER[121].SARS-CoVMproteinandtheionchannelactivityofEand3awereshowntointerferewithpro-survivalsignallingcascades[114,122].

    3.2 Integrative stress response: endothelial damage, coagulation, and

    inflammation The viral replication and the consequent immune and inflammatory responses causedamagetotheepitheliumandpulmonarycapillaryvascularendotheliumandactivatethemain intracellular defence mechanisms, as well as the humoral and cellular immuneresponses.Resultingcellularstressandtissuedamage[123,124]impairrespiratorycapacityandleadtoacuterespiratorydistresssyndrome(ARDS)[61,125,126].Hyperinflammationisaknowncomplication,causingwidespreaddamage,organfailure,anddeath,followedbyanotyetcompletelyunderstoodrapid increaseofcytokine levels(cytokinestorm)[127–129],andacuteARDS[130].Otherreportedcomplications,suchascoagulationdisturbancesand thrombosis are associated with severe cases, but the specific mechanisms are still

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    unknown[64,131–133],althoughsomereportssuggestthatCOVID-19coagulopathyhasadistinctprofile[134].

    TheSARS-CoV-2infectiondisruptsthecoagulationcascadeandisfrequentlyassociatedwithhyperinflammation, renin-angiotensin system (RAS) imbalance and intravascularcoagulopathy [132,135–137]. Hyperinflammation leads in turn to detrimentalhypercoagulability and immunothrombosis, leading to microvascular thrombosis withfurtherorgandamage [138]. Importantly,RAS is influencedby risk factorsofdevelopingsevereformsofCOVID-19[139–141].

    ACE2,usedbySARS-CoV-2forhostcellentry,isaregulatorofRASandiswidelyexpressedin the affected organs [142]. The main function of ACE2 is the conversion of AngII toangiotensin1-7(Ang1-7),andthesetwoangiotensinstriggerthecounter-regulatoryarmsofRAS [143]. The signalling via AngII and its receptor AGTR1, elevated in the infected[142,144],inducesthecoagulationcascadeleadingtomicrovascularthrombosis[145],whileAng1-7anditsreceptorMAS1attenuatetheseeffects[143].

    3.4 Innate immune response

    PAMPsignalling

    The innate immune system detects specific pathogen-associated molecular patterns(PAMPs), through Pattern Recognition Receptors (PRRs). Detection of SARS-CoV-2 ismediatedthroughreceptorsthatrecognisedouble-strandedandsingle-strandedRNAintheendosome during endocytosis of the virus particle, or in the cytoplasm during the viralreplication. These receptorsmediate the activation of transcription factors such as AP1,NFkappaB, IRF3, and IRF7, responsible for the transcription of antiviral proteins, inparticular,interferon-alphaandbeta[146,147].

    SARS-CoV-2reducestheproductionoftypeIinterferonstoevadetheimmuneresponse[49].Thedetailedmechanism isnotclearyet;however,SARS-CoVMprotein inhibits the IRF3activation[148]andsuppressesNFkappaBandCOX2transcription.Atthesametime,SARS-CoVNproteinactivatesNFkappaB[149],sotheoverallimpactisunclear.ThesepathwaysarealsonegativelyregulatedbySARS-CoVnsp3papain-likeproteasedomain(PLPro)[150].

    Themapcontainstheinitialrecognitionprocessoftheviralparticlebytheinnateimmunesystemandtheviralmechanismstoevadetheimmuneresponse.Itprovidestheconnectionbetweenvirusentry(detectingtheviralendosomalpatterns),itsreplicationcycle(detectioncytoplasmic viral patterns), and the effector pathways of pro-inflammatory cytokines,especiallyoftheinterferontypeIclass.ThelatterseemstoplayacrucialbutcomplexroleinCOVID-19pathology:bothnegative[151,152]andpositiveeffects[3,153]ofinterferonsonvirusreplicationhavebeenreported.

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    InterferontypeIsignalling

    Interferons(IFNs)arecentralplayersintheantiviralimmuneresponseofthehostcell[55],specifically affected by SARS-CoV-2 [154–157]. Type I IFNs are induced upon viralrecognitionofPAMPsbyvarioushostPRRs [48]asdiscussedearlier.The IFN-Ipathwaydiagram represents the activation of TLR7 and IFNAR and the subsequent recruiting ofadaptor proteins and the downstream signalling cascades regulating key transcriptionfactorsincludingIRF3/7,NF-kappaB,AP-1,andISRE[48,158].Further,themapshowsIRF3-mediatedinductionofIFN-I,affectedbytheSARS-CoV-2proteins.SARS-CoVNsp3andOrf6interferewithIRF3signalling[159,160]andSARS-CoVM,N,Nsp1andNsp3actasinterferonantagonists[48,150,158,161,162].Moreover,coronavirusesORF3a,ORF6andnsp1proteinscan repress interferon expression and stimulate the degradation of IFNAR1 and STAT1duringtheUnfoldedProteinResponse(UPR)[163,164].

    AnothermechanismofviralRNArecognitionisRIG-likereceptorsignalling[58],leadingtoSTING activation [165], and via the recruitment of TRAF3, TBK1 and IKKepsilon tophosphorylationofIRF3[56].ThisinturninducesthetranscriptionofIFNsalpha,betaandlambda[166].SARS-CoVviralpapain-like-proteases,containedwithinthensp3andnsp16proteins,inhibitSTINGandthedownstreamIFNsecretion[167].Inlinewiththishypothesis,SARS-CoV-2 infectionresults inaunique inflammatoryresponsedefinedby low levelsofIFN-Iandhighexpressionofcytokines[58,168].TheIFNlambdadiagramdescribestheIFNLreceptor signaling cascade [169], including JAK-STAT signaling and the induction ofInterferon Stimulated Genes, which encode antiviral proteins [170]. The interactions ofSARS-CoV-2proteinswiththeIFNLpathwayarebasedontheliterature[171]orSARS-CoVhomology[58].

    Alteredhostmetabolism

    Metabolicpathwaysgoverntheimmunemicroenvironmentbymodulatingtheavailabilityofnutrientsandcriticalmetabolites[172].Infectiousentitiesreprogramhostmetabolismtocreatefavourableconditionsfortheirreproduction[173].SARS-CoV-2proteinsinteractwithavarietyofimmunometabolicpathways,severalofwhicharedescribedbelow.

    Hemecatabolismisawell-knownanti-inflammatorysysteminthecontextofinfectiousandautoimmune diseases [174,175]. The main effector of this pathway, heme oxygenase-1(HMOX1) was found to interact with SARS-CoV-2 Orf3a, although the nature of thisinteraction remains ambiguous [34,176]. HMOX1 cleaves heme into carbon monoxide,biliverdin (then reduced to bilirubin), and ferrous iron [PMID:31396090]. Biliverdin,bilirubin,andcarbonmonoxidepossesscytoprotectiveproperties,andhaveshownpromiseas immunomodulatory therapeutics [177,178]. Importantly, activation of HMOX1 alsoinhibits the NLRP3 inflammasome [178–180], which is a pro-inflammatory andprothromboticmultiproteinsystem[181]highlyactiveinCOVID-19[182–184].Itmediates

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    production of the pro-inflammatory cytokines IL-1B and IL-18 via caspase-1 [181]. TheSARS-CoVOrf3a,E,andOrf8aincitetheNLRP3inflammasome[185–188].Still,thepotentialoftheHMOX1pathwaytofightCOVID-19inflammationremainstobetested[176,189,190]despitepromisingresultsinothermodelsofinflammation[176,178,191–193].

    The tryptophan-kynurenine pathway is closely related to heme metabolism. The rate-limitingstepofthispathwayiscatalysedbytheindoleamine2,3dioxygenaseenzymes(IDO1andIDO2)indendriticcells,macrophages,andepithelialcellsinresponsetoinflammatorycytokineslikeIFN-gamma,IFN-1,TGF-beta,TNF-alpha,andIL-6[194–196].CrosstalkwiththeHMOX1pathwayalsoincreasestheexpressionofIDO1andHMOX1inafeed-forwardmanner. Metabolomics analyses from severe COVID-19 patients revealed enrichment ofkynurenines and depletion of tryptophan, indicating robust activation of IDO enzymes[197,198].Depletion of tryptophan [173,199,200] and kynurenines and their derivativesaffecttheproliferationandimmuneresponseofarangeofTcells[176,201–205].However,despitehighlevelsofkynureninesinCOVID-19,CD8+T-cellsandTh17cellsareenrichedinlungtissue,andT-regulatorycellsarediminished[206].ThisraisesthequestionofwhetherandhowtheimmuneresponseelicitedinCOVID-19evadessuppressionbythekynureninepathway.

    The SARS-CoV-2 protein Nsp14 interacts with three human proteins: GLA, SIRT5, andIMPDH2 [34]. The galactose metabolism pathway, including the GLA enzyme [207], isinterconnected with amino sugar and nucleotide sugar metabolism. SIRT5 is a NAD-dependent desuccinylase and demalonylase regulating serine catabolism, oxidativemetabolism and apoptosis initiation [208–210]. Moreover, nicotinamide metabolismregulated by SIRT5 occurs downstream of the tryptophan metabolism, linking it to thepathways discussed above. Finally, IMPDH2 is the rate-limiting enzyme in the de novosynthesis of GTP, allowing regulation of purine metabolism and downstream potentialantiviraltargets[211,212].

    Thepyrimidinesynthesispathway, tightly linkedtopurinemetabolism,affectsviralDNAandRNAsynthesis.Pyrimidinedeprivationisahosttargetedantiviraldefencemechanism,whichblocksviralreplicationininfectedcellsandcanberegulatedpharmacologically[213–215]. It appears that componentsof theDNAdamage response connect the inhibitionofpyrimidinebiosynthesistotheinterferonsignallingpathway,probablyviaSTING-inducedTBK1 activation that amplifies interferon response to viral infection, discussed above.InhibitionofdenovopyrimidinesynthesismayhavebeneficialeffectsontherecoveryfromCOVID-19[215];however,thismayhappenonlyinasmallgroupofpatients.

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    3.5 Biocuration roadmap

    COVID-19pathwaysfeaturedintheprevioussectioncovermechanismsreportedsofar.Still,certainaspectsof thediseasewerenotrepresented indetailbecauseof theircomplexity,namelycell-type-specific immuneresponse,andsusceptibilityfeatures.Theirmechanisticdescription isofgreat importance,assuggestedbyclinicalreportsonthe involvementofthesepathwaysinthemolecularpathophysiologyofthedisease.Themechanismsoutlinedbelowwillbethenexttargetsinourcurationroadmap.

    Celltype-specificimmuneresponseCOVID-19causesseriousdisbalanceinmultiplepopulationsofimmunecells.Somestudiesreport that COVID-19 patients have a significant decrease of peripheral CD4+ and CD8+cytotoxicTlymphocytes(CTLs),Bcells,NKcells,aswellashigherlevelsofabroadrangeofcytokinesandchemokines[128,216–219].ThediseasecausesfunctionalexhaustionofCD8+CTLs andNK cells, inducedby SARS-CoV-2 Sprotein andby excessivepro-inflammatorycytokineresponse[217,220].Moreover,theratioofnaïve-to-memoryhelperT-cells,aswellas thedecreaseofTregulatorycells,correlatewithCOVID-19severity[206].Conversely,high levelsofTh17and cytotoxicCD8+T-cellshavebeen found in the lung tissue [221].Pulmonaryrecruitmentoflymphocytesintotheairwaysmayexplainthelymphopeniaandtheincreasedneutrophil-lymphocyteratioinperipheralbloodfoundinCOVID-19patients[216,222,223].Inthisregard,anabnormalincreaseoftheTh17:Tregcellratiomaypromotethe release of pro-inflammatory cytokines and chemokines, increasing disease severity[224].

    SusceptibilityfeaturesofthehostSARS-CoV-2 infection isassociatedwith increasedmorbidityandmortality in individualswithunderlyingchronicdiseasesoracompromisedimmunesystem[225–228].Groupsofincreased risk are men, pregnant and postpartum women, and individuals with highoccupationalviralexposure[229–231].OthersusceptibilityfactorsincludetheABObloodgroups[232–240]andrespiratoryconditions[241–246].

    Importantly,age isoneof thekeyaspectscontributingtotheseverityof thedisease.Theelderly are at high risk of developing severe or critical disease [227,247]. Age-relatedelevated levels of pro-inflammatory cytokines (inflammation) [247–250],immunosenescenceandcellularstressofageingcells[125,227,247,251,252]maycontributetotherisk.Incontrast,childrenaregenerallylesslikelytodevelopseveredisease[253,254],withtheexceptionofinfants[125,255–257].However,somepreviouslyhealthychildrenandadolescents can develop a multisystem inflammatory syndrome following SARS-CoV-2infection[258–262].

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    Several genetic factorshavebeenproposedand identified to influence susceptibility andseverity,includingtheACE2gene,HLAlocus,errorsinfluencingtypeIIFNproduction,TLRpathways,myeloidcompartments,aswellascytokinepolymorphisms[156,235,263–269].

    Weaimtoconnectthesusceptibilityfeaturestospecificmolecularmechanismsandbetterunderstand the contributing factors. This can lead to a series of testable hypotheses,including the roleof vitaminD counteractingpro-inflammatory cytokine secretion [270–272] in an age-dependentmanner [247,273], andmodifying the severity of the disease.Anotherexampleofa testablehypothesismaybe that the immunephenotypeassociatedwithasthmainhibitspro-inflammatorycytokineproductionandmodifiesgeneexpressionintheairwayepithelium,protectingagainstsevereCOVID-19[245,246,274].

    4.BioinformaticsanalysisandcomputationalmodellingroadmapforhypothesisgenerationIn order to understand complex and often indirect dependencies between differentpathways and molecules, we need to combine computational and data-driven analyses.Standardised representation and programmatic access to the contents of the COVID-19DiseaseMapenablethedevelopmentofreproducibleanalyticalandmodellingworkflows.Here,wediscuss the range of possible approaches anddemonstrate preliminary results,focusingoninteroperability,reproducibility,andapplicabilityofthemethodsandtools.

    Our goal is to work on the computational challenges as a community, involving thebiocuratorsanddomainexperts intheanalysisoftheCOVID-19DiseaseMapandrelyontheirfeedbacktoevaluatetheoutcomes.Inthisway,weaimtoidentifyapproachestotacklethecomplexityandthesizeofthemap,proposingastate-of-the-artframeworkforrobustanalysis,reliablemodels,andusefulpredictions.

    4.1 Data integration and network analysis

    Visualisationofomicsdatacanhelpcontextualisethemapwithexperimentaldatacreatingdata-specificblueprints.Theseblueprintscouldbeusedtohighlightpartsofthemapthatareactiveinoneconditionversusanother(treatmentversuscontrol,patientversushealthy,normal versus infected cell, etc.). Combining information contained in multiple omicsplatformscanmakepatientstratificationmorepowerful,byreducingthenumberofsamplesneededorbyaugmenting theprecisionof thepatientgroups [275,276].Approaches thatintegratemultipledatatypeswithouttheaccompanyingmechanisticdiagrams[277–279]producepatientgroupingsthataredifficulttointerpret.Inturn,classicalpathwayanalysesoftenproducelonglistsmixinggenericandcell-specificpathways,makingitchallengingto

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    pinpoint relevant information. Using disease maps to interpret omics-based clustersaddressestheissuesrelatedtocontextualisedvisualdataanalytics.

    FootprintbasedanalysisFootprintsaresignaturesofamolecularregulatordeterminedbytheexpressionlevelsofitstargets[280].Forexample,afootprintcancontaintargetsofatranscriptionfactor(TF)orpeptides phosphorylated by a kinase. Combining multiple omics readouts and multiplemeasurements can increase the robustnessof such signatures.Nevertheless, anessentialcomponent is the mechanistic description of the targets of a given regulator, allowingcomputation of its footprint. With available SARS-CoV-2 related omics and interactiondatasets[281],itispossibletoinferwhichTFsandsignallingpathwaysareaffecteduponinfection[282].CombiningtheCOVID-19Diseasemapregulatoryinteractionswithcuratedcollections of TF-target interactions like DoRothEA [283] will provide a contextualisedevaluationoftheeffectofSARS-CoV-2infectionattheTFlevel.

    Viral–hostinteractomeThevirus–hostinteractomeisanetworkofvirus-humanprotein-proteininteractions(PPIs)thatcanhelpunderstandingthemechanismsofdisease[34,284–286].Itcanbeexpandedbymergingvirus-hostPPIdatawithhumanPPIandproteindata[287]todiscoverclustersofinteractionsindicatinghumanmechanismsandpathwaysaffectedbythevirus[288].TheseclustersfirstofallcanbeinterpretedatthemechanisticlevelbyvisualexplorationofCOVID-19 Disease Map diagrams. In addition, these clusters can potentially reveal additionalpathways to add to the COVID-19 Disease Map (e.g., E protein interactions or TGFBetadiagrams)orsuggestnewinteractionstointroduceintotheexistingdiagrams.

    4.2 Mechanistic and dynamic computational modelling

    Computationalmodellingisapowerfulapproachthatenablesinsilicoexperiments,producestestablehypotheses,helpselucidateregulationand,finally,cansuggestviapredictionsnoveltherapeutictargetsandcandidatesfordrugrepurposing.

    MechanisticpathwaymodellingMechanisticmodelsofpathwaysallowbridgingvariationsatthescaleofmolecularactivitytovariationsatthelevelofcellbehaviour.Thiscanbeachievedbycouplingthemolecularinteractions of a given pathway with its endpoint and by contextualising the molecularactivity using omics datasets. HiPathia is such a method, processing transcriptomic orgenomicdatatoestimatethefunctionalprofilesofapathwayconditionedbythedatastudiedand linkable to phenotypes such as disease symptoms or other endpoints of interest[289,290]. Moreover, such mechanistic modelling can be used to predict the effect of

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    interventionsas,forexample,theeffectoftargeteddrugs[291].HiPathiaintegratesdirectlywiththediagramsoftheCOVID-19MapusingtheSIFformatprovidedbyCaSQ(seeSection2.3),aswellaswiththeassociatedinteractiondatabases(seeSection2.2).

    Thedrawbackofapproaches likeHiPathia is theircomputationalcomplexity, limitingthesize of the diagrams they can process. An approach to large-scale mechanistic pathwaymodellingistotransformthemintocausalnetworks.CARNIVAL[292]combinesthecausalrepresentation of networks [13] with transcriptomics, phosphoproteomics, ormetabolomics data [280] to contextualise cellular networks and extract mechanistichypotheses. The algorithm identifies a set of coherent causal links connecting upstreamdrivers suchas stimulationsormutations todownstreamchanges in transcription factoractivities.

    DiscretecomputationalmodellingAnalysisofthedynamicsofmolecularnetworksisnecessarytounderstandtheirdynamicsanddeepenourunderstandingofcrucialregulatorsbehinddisease-relatedpathophysiology.Discretemodelling frameworkprovidesthispossibility.COVID-19DiseaseMapdiagrams,translated to SBML qual (see Section 2.3), can be directly imported by tools like CellCollective[293]orGINsim[46]foranalysis.Preservingannotationsandlayoutinformationensurestransparencyandreusabilityofthemodels.

    Importantly, Cell Collective is an online user-friendlymodelling platform26 that providesfeaturesforreal-timeinsilicosimulationsandanalysisofcomplexsignallingnetworks.Theplatformallowsuserswithoutcomputationalbackgroundtosimulateoranalysemodelstogenerate and prioritise new hypotheses. References and layout are used for modelvisualisation,supportingtheinterpretationoftheresults.Themathematicsandcodebehindeachmodel,however,remainaccessibletoallusers. Inturn,GINsimisa toolprovidingawiderangeofanalysismethods,includingefficientidentificationofthestatesofconvergenceofagivenmodel(attractors).Modelreductionfunctionalitycanalsobeemployedtofacilitatetheanalysisoflarge-scalemodels.

    Multiscaleandstochasticcomputationalmodelling

    Viralinfectionandimmuneresponsearecomplexprocessesthatspanmanydifferentscales,frommolecular interactions tomulticellular behaviour. Themodelling and simulation ofsuchcomplexscenariosrequireadedicatedmultiscalecomputationalarchitecture,wheremultiplemodelsruninparallelandcommunicateamongthemtocapturecellularbehaviourandintercellularcommunications.Multiscaleagent-basedmodelssimulateprocessestaking

    26https://cellcollective.org

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    placeatdifferenttimescales,e.g.,diffusion,cellmechanics,cellcycle,orsignaltransduction[294], proposed also for COVID-19 [295]. PhysiBoSS [296] allows such simulation ofintracellularprocessesbycombiningthecomputationalframeworkofPhysiCell[297]withMaBoSS[298]toolforstochasticsimulationoflogicalmodelstostudyoftransienteffectsand perturbations [299]. Implementation of detailed COVID-19 signalling models in thePysiBoSS framework may help to better understand complex dynamics of multi-scaleprocessesasinteractionsandcrosstalkbetweenimmunesystemcomponentsandthehostcellinCOVID-19.

    4.3 Case study: RNA-Seq-based analysis of transcription factor (TF) activity

    In this case study, we combine computational approaches discussed above and presentresults derived from omics data analysis on the COVID-19 Disease Maps diagrams. WemeasuredtheeffectofCOVID-19atthetranscriptionfactor(TF)activitylevelbyapplyingVIPER[300]combinedwithDoRothEAregulons[283]onRNA-seqdatasetsofthe SARS-CoV-2infectedcellline[168].Then,wemappedtheTFsnormalisedenrichmentscore(NES)ontheInterferontypeIsignallingpathwaydiagramoftheCOVID-19DiseaseMapusingtheSIFfilesgeneratedbyCaSQ(seeSection2.3).AshighlightedinFigure4,ourmanuallycuratedpathwayincludedsomeofthemostactiveTFsafterSARS-CoV-2infection,suchasSTAT1,STAT2,IRF9andNFKB1.Thesegenesarewellknowntobeinvolvedincytokinesignallingandfirstantiviralresponse[301,302].Interestingly,theyarelocateddownstreamofvariousviralproteins (E,S,Nsp1,Orf7aandOrf3a)andmembersof theMAPKpathway (MAPK8,MAPK14andMAP3K7).SARS-CoV-2infectionisknowntopromoteMAPKactivation,whichmediatesthecellularresponsetopathogenicinfectionandpromotestheproductionofpro-inflammatorycytokines[281].Altogether,weidentifiedsignalingeventsthatmaycapturethemechanisticresponseofthehumancellstotheviralinfection.

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    Figure 4: the Interferon type I signalling pathway diagram of the COVID-19 Disease MapintegratedwithTFactivityderivedfromtranscriptomicsdataafterSARS-CoV-2infection.AzoomwasappliedintheareacontainingthemostactiveTFs(rednodes)afterinfection.Nodeshapes:host genes (rectangles), host molecular complex (octagons), viral proteins (V shape), drugs(diamonds)andphenotypes(triangles).

    4.4 Case study: RNA-seq-based analysis of pathway signalling

    Inthisusecase,theHipathia[289]algorithmwasusedtocalculatethelevelofactivityofthesubpathways from the COVID-19 Apoptosis diagram, with the aim to evaluate whetherCOVID-19DiseaseMapdiagramscanbeusedforpathwaymodellingapproach.Tothisend,apublicRNA-seqdatasetfromhumanSARS-CoV-2infectedlungcells(GEOGSE147507)wasused.First,theRNA-seqgeneexpressiondatawasnormalizedwiththeTrimmedmeanofM

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    values (TMM)normalization [303], thenrescaled torange [0;1] for thecalculationof thesignalandnormalisedusingquantilenormalisation[304].Thenormalisedgeneexpressionvalueswereusedtocalculatethelevelofactivationofthesubpathways,thenacase/controlcontrastwithaWilcoxontestwasusedtoassessdifferencesinsignalingactivitybetweenthetwoconditions.

    Figure5.RepresentationoftheactivationlevelofApoptosispathwayinSARS-CoV-2infectedlung cell lines. The activation levels have been calculated using transcriptional data fromGSE147507 and Hipathiamechanistic pathway analysis algorithm. Each node represents a gene(ellipse),ametabolite(circle)ora function(square).Thepathwayiscomposedofcircuits fromareceptorgene/metabolitetoaneffectorgene/function,withinteractionssimplifiedtoinhibitionsoractivations(seeSection2.3,SIFformat).Significantlyderegulatedcircuitsarehighlightedbycolorarrows(red:activatedininfectedcells).Thecolorofthenodecorrespondstothelevelofdifferentialexpressionof eachnode in SARS-CoV-2 infected cells vsnormal lung cells.Blue: down-regulatedelements,red:up-regulatedelements,white:elementswithnotstatisticallysignificantdifferentialexpression.Hipathiacalculatestheoverallcircuitactivation,andcanindicatederegulatedinteractionevenifinteractingelementsarenotindividuallydifferentiallyexpressed.

    Results of the Apoptosis pathway analysis can be seen in Figure 5 and SupplementaryMaterial 5. Importantly, Hipathia calculates the overall activation of circuits (series ofcausally connected elements), and can indicatederegulated interactions resulting fromacumulativeeffect,evenifinteractingelementsarenotindividuallydifferentiallyexpressed.Whendiscussingdifferentialactivation,werefertothecircuits,whileindividualelements

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    arementionedasdifferentiallyexpressed.Theanalysisshowsanoveractivationofseveralcircuits,specificallytheoneendingintheeffectorproteinBAX.ThisoveractivationseemstobeledbytheoverexpressionoftheBADprotein, inhibitingBCL2-MCL1-BCL2L1complex,which in turn inhibits BAX. Indeed, SARS-CoV-2 infection can invoke caspase8-inducedapoptosis[305],whereBAXtogetherwiththeripoptosome/caspase-8complex,mayactasa pro-inflammatory checkpoint [306]. This result is supported by studies in SARS-CoV,showing BAX overexpression following in


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