In:
The Journal of Immunology, The American Association of Immunologists, Vol. 208, No. 1_Supplement ( 2022-05-01), p. 172.19-172.19
Kurzfassung:
The PHENotype SIMulator, is a systems biology tool, which leverages available transcriptomic and proteomic databases to model of SARS-CoV-2 host-infection in silico. PHENSIM can determine the viral effects on cellular host-immune responses, which we recently applied to in silico drug repurposing for COVID-19. There is a clear importance of previously imprinted viral infections on the host-immune response against SARS-CoV2. In this study we explore the potential clinical applicability of PHENSIM in addressing co-infection of SARS-CoV2 (SCoV2) and human rhinovirus (HRV), or SCoV2 and Influenza A Virus (IAV). We leveraged PHENSIM to simulate HRV-infection of A549 lung alveolar cells at 24hours in silico, yielding comparable cellular transcriptomic signatures as previously published for in vitro HRV-infection, including antiviral and inflammatory gene transcriptomics. Next, we simulated viral co-infection of HRV/SCoV2 or IAV/SCoV2 using PHENSIM, and assessed co-infection effects in silico. Similar to recent in vitro studies, our in silico results indicated HRV-infection prior to SCoV2 exposure induced ISG responses and downregulated SCoV2 transcriptomic activity. Further analysis revealed several key SCoV2 affected pathways, including IFN Type-I & -III and MHC-II pathways, to be negatively affected by HRV-infection. In contrast, co-infection with IAV and SCoV2 resulted in overall increased SCoV2 transcriptomic activity. Our PHENSIM results confirm HRV-imprinted downmodulation of SARS-CoV2 activity and the importance of timely IFN pathway activation for effective immune response and viral clearance. Identified anti-correlating pathways are of interest for therapeutic targeting and effective drug development. N.I.M. was funded in part by a fellowship award from the Netherlands-Caribbean Foundation for Clinical Higher Education (NASKHO). S.A., A.F. and A.P. have been partially supported by the MIUR PON research project BILIGeCT “Liquid Biopsies for Cancer Clinical Management”. National Cancer Institute Physical Sciences-Oncology Center Grant U54 CA193313-01 (to B.M.), and US Army grant W911NF1810427 (to B.M.)
Materialart:
Online-Ressource
ISSN:
0022-1767
,
1550-6606
DOI:
10.4049/jimmunol.208.Supp.172.19
Sprache:
Englisch
Verlag:
The American Association of Immunologists
Publikationsdatum:
2022
ZDB Id:
1475085-5
