/NicheNet_Omnipath

Building and Training of the NicheNet Method exclusively using OmniPath resources. SARS-CoV-2 case study

GNU General Public License v3.0GPL-3.0

OmniPath-based generation of the NicheNet Method prior model: A SARS-CoV-2 case study

Overview

NicheNet is a recently developed method to prioritize ligand–target relationships between interacting cells by combining their expression data with prior knowledge on interaction networks. For this purpose, it explores the most consistent inter- and intra-cellular protein interactions in accordance with a given gene expression dataset. The authors collected different types of interactions from more than 20 databases to build a ligand-receptor network, a signaling network and a gene regulatory network. OmniPath provides a single-access point covering all the different types of interactions employed in the NicheNet method. Therefore, we here highlight the value of OmniPath by exclusively using its resources to create a ligand-target regulatory potential model as described in the NicheNet article (Browaeys, Saelens and Saeys, 2019).

Nowadays, COVID-19, caused by SARS-CoV-2, is spreading globally throughout the planet. WHO has reported approximately 10 million confirmed cases and 500 000 deaths to date (June 29, 2020). Against this background, we aim at using our Omnipath-based version of NicheNet to explore the autocrine signaling after SARS-CoV-2 infection. In particular, we explore the potential regulatory effect of over-expressed ligands after infection on the expression of inflammatory response related genes in the Calu3 cell line. The RNAseq expression data was taken from a recent study.

Content

This repository contains the vignettes to reproduce the SARS-CoV-2 case study presented in the publication:

Türei, D., Valdeolivas, A., Gul, L., Palacio-Escat, N., Ivanova, O., Modos, D., Korcsmáros T. & Saez-Rodriguez, J. Integration of intra- and intercellular signaling resources with OmniPath

Important Links

Omnipath:

http://omnipathdb.org/
https://github.com/saezlab/pypath
https://saezlab.github.io/OmnipathR/

NicheNet:

https://www.nature.com/articles/s41592-019-0667-5
https://github.com/saeyslab/nichenetr/

RNAseq Expression data

https://www.biorxiv.org/content/10.1101/2020.03.24.004655v1
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507

References

Türei, D., Korcsmáros, T., & Saez-Rodriguez, J. (2016). OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nature methods, 13(12), 966–967. 10.1038/nmeth.4077

Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nature Methods 17, 159–162 (2020). 10.1038/s41592-019-0667-5

Blanco-Melo, D., Nilsson-Payant, B.E., Liu, W.-C., Uhl, S., Hoagland, D., Møller, R., Jordan, T.X., Oishi, K., Panis, M., Sachs, D., Wang, T.T., Schwartz, R.E., Lim, J.K., Albrecht, R.A., tenOever, B.R., 2020. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell 181, 1036–1045.e9. 10.1016/j.cell.2020.04.026