Network Activity Evaluation Reveals Significant Gene Regulatory Architectures during SARS-CoV-2 Viral Infection from Dynamic scRNA-seq Data.

Abstract

The key to understand COVID-19 caused by SARS-CoV-2, which has caused massive deaths worldwide, is to reveal the gene activities at molecular level. Single-cell RNA-sequencing (scRNA-seq) technology allows us to capture gene expression at high resolution, thereby delineating cell-specific gene regulatory network (GRN). Network activity refers to the degree of consistency between GRN architectures and gene expression profiles in a specific condition or cellular microenvironment. Currently, numerous experimentally determined molecular interactions, including regulatory relationships closely related to SARS-CoV-2 infection, are documented in knowledge-bases. However, GRN activity is closely related to the cell dynamic environment and the heterogeneity of cell clusters. Therefore, to evaluate the consistency of GRN with gene expression profiles, we propose a single-cell Network Activity Evaluation framework, called scNAE. First, scNAE performs ODE modeling of time-course gene expression data. Then, the loss function with regularization penalty terms is constructed for formulating GRN inference rules from transcriptomic data. Furthermore, we have devised a rapid-convergence alternating direction method of multipliers to solve the regularized and constrained programs. Finally, an empirical P-value is derived based on a permutation statistical testing procedure to quantify the likelihood significance of the network matching with the data. The efficiency and advantage of scNAE have also been demonstrated by extensive numerical experiments, which can clearly depict the dynamic responses underlying GRN architectures triggered by the infection of SARS-CoV-2 in cells. The code and data of scNAE are available at https://github.com/zpliulab/scNAE.