Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet

Mhaned Oubounyt*, Lorenz Adlung, Fabio Patroni, Nina Kerstin Wenke, Andreas Maier, Michael Hartung, Jan Baumbach, Maria L. Elkjaer

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Abstract

Motivation: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem. Results: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies. First, we identify the drivers of the mechanotype of a cytokine storm associated with increased mortality in patients with acute respiratory illness. Secondly, we find 20 drugs for eight potential pharmacological targets in cellular driver mechanisms in the intestinal stem cells of obese mice. Availability and implementation: SCANet is a free, open-source, and user-friendly Python package that can be seamlessly integrated into single-cell-based systems medicine research and mechanistic drug discovery.

OriginalsprogEngelsk
Artikelnummerbtad644
TidsskriftBioinformatics
Vol/bind39
Udgave nummer11
Antal sider10
ISSN1367-4803
DOI
StatusUdgivet - nov. 2023

Bibliografisk note

Funding Information:
This work was funded by the German Science Foundation (DFG) via the Collaborative Research Center [SFB924]. This work was developed as part of the NetMap project and is funded by the German Federal Ministry of Education and Research (BMBF) [031L0309B]. J.B. was partially funded by his VILLUM Young Investigator [13154]. M.L.E. was supported by Lundbeckfonden [R347-2020-2454]. L.A. was supported by Deutsche Forschungsgemeinschaft [SFB841]. F.P. was supported by BAYER Foundation 2022 Carl Duisberg Fellowships for Medical Sciences.

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