The progress of viral infection involves numerous transcriptional regulatory events. The identification of the newly synthesized transcripts helps us to understand the replication mechanisms and pathogenesis of the virus. Here, we utilized a time-resolved technique called metabolic RNA labeling approach called thiol(SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) to differentially elucidate the levels of steady-state and newly synthesized RNAs of BHK21 cell line in response to human coronavirus OC43 (HCoV-OC43) infection. Our results showed that the Wnt/β-catenin signaling pathway was significantly enriched with the newly synthesized transcripts of BHK21 cell line in response to HCoV-OC43 infection. Moreover, inhibition of the Wnt pathway promoted viral replication in the early stage of infection, but inhibited it in the later stage of infection. Furthermore, remdesivir inhibits the upregulation of the Wnt/β-catenin signaling pathway induced by early infection with HCoV-OC43. Collectively, our study showed the diverse roles of Wnt/β-catenin pathway at different stages of HCoV-OC43 infection, suggesting a potential target for the antiviral treatment. In addition, although infection with HCoV-OC43 induces cytopathic effects in BHK21 cells, inhibiting apoptosis does not affect the intracellular replication of the virus. Monitoring newly synthesized RNA based on such time-resolved approach is a highly promising method for studying the mechanism of viral infections.

Here, we present Scribe (https://github.com/aristoteleo/Scribe-py), a toolkit for detecting and visualizing causal regulatory interactions between genes and explore the potential for single-cell experiments to power network reconstruction. Scribe employs restricted directed information to determine causality by estimating the strength of information transferred from a potential regulator to its downstream target. We apply Scribe and other leading approaches for causal network reconstruction to several types of single-cell measurements and show that there is a dramatic drop in performance for \"pseudotime\"-ordered single-cell data compared with true time-series data. We demonstrate that performing causal inference requires temporal coupling between measurements. We show that methods such as \"RNA velocity\" restore some degree of coupling through an analysis of chromaffin cell fate commitment. These analyses highlight a shortcoming in experimental and computational methods for analyzing gene regulation at single-cell resolution and suggest ways of overcoming it.