Host Gene Expression of Macrophages in Response to Feline Coronavirus Infection

Abstract

Feline coronavirus is a highly contagious virus potentially resulting in feline infectious peritonitis (FIP), while the pathogenesis of FIP remains not well understood, particularly in the events leading to the disease. A predominant theory is that the pathogenic FIPV arises from a mutation, so that it could replicate not only in enterocytes of the intestines but also in monocytes, subsequently systemically transporting the virus. The immune status and genetics of affected cats certainly play an important role in the pathogenesis. Considering the importance of genetics and host immune responses in viral infections, the goal of this study was to elucidate host gene expression in macrophages using RNA sequencing. Macrophages from healthy male cats infected with FIPV 79-1146 ex vivo displayed a differential host gene expression. Despite the virus uptake, aligned viral reads did not increase from 2 to 17 h. The overlap of host gene expression among macrophages from different cats was limited, even though viral transcripts were detected in the cells. Interestingly, some of the downregulated genes in all macrophages were involved in immune signaling, while some upregulated genes common for all cats were found to be inhibiting immune activation. Our results highlight individual host responses playing an important role, consistent with the fact that few cats develop feline infectious peritonitis despite a common presence of enteric FCoV.

CRFK and Primary Macrophages Transcriptomes in Response to Feline Coronavirus Infection Differ Significantly

“…The transcriptome of macrophages infected with FIPV showed rather individual host responses with few common genes (120 + 100) that were differentially expressed in all of the macrophage samples analyzed ( Drechsler et al, 2020 ). In the current study, we analyzed all of the significantly differentially expressed genes for macrophage samples, to obtain a more complete picture of their responses to coronavirus.…”

“…Ex vivo studies on monocytes/macrophages host responses have been limited by the fact that the presence of the virus is extremely low, in only about 0.2–2% of infected cells ( Dewerchin et al, 2005 ). We previously demonstrated that at 2 h, the virus is taken up by the macrophages, as evidenced by the presence of viral RNA, but were not able to observe a significant increase of viral RNA at 17 h using RNA sequencing, while even limited uptake of the virus into CRFK cells led to several log fold of replication ( Drechsler et al, 2020 ). This discrepancy in viral amplification in the two cell types, combined with their different function, most likely accounts for the pronounced difference observed in gene expression in this analysis.…”

“…The generalized linear model (GLM) fitting method was applied followed by a one-way ANOVA-like test (2 h-vs.-Ctrl and 17 h-vs.-Ctrl) with a 0.01 FDR threshold. Datasets were submitted to a multidimensional scaling (MDS) analysis, with the plotMDS function from EdgeR, to identify distinct samples clustered in a two dimensions-reduction landscape prior to the start of DE analyses ( Drechsler et al, 2020 ) ( Supplementary Figure S1 ). Additionally, Kallisto-generated transcripts per million (TPM) normalized expression data for each sample was used to plot heatmaps for both cell types using the heatmap.2 function from the gplots R package, with the embedded Hierarchical Clustering assessment turned on for both rows (transcripts) and columns (samples).…”

Animal Coronaviruses Induced Apoptosis

“…In vitro studies in nonhaemopoetic cell line also revealed the induction of apoptosis. Transcriptional profiling of CRFK (Crandell-Rees Feline Kidney Cell cells) infected with FIPV revealed that the expression of genes from apoptosis cluster is altered and the induction of apoptosis was correlated with p53, p38 MAPK, VEGF, TNF, and chemokines/cytokines signaling pathways [59,60].…”

Table_1.XLSX

“…The generalized linear model (GLM) fitting method was applied followed by a one-way ANOVAlike test (2 h-vs.-Ctrl and 17 h-vs.-Ctrl) with a 0.01 FDR threshold. Datasets were submitted to a multidimensional scaling (MDS) analysis, with the plotMDS function from EdgeR, to identify distinct samples clustered in a two dimensions-reduction landscape prior to the start of DE analyses (Drechsler et al, 2020) (Supplementary Figure S1). Additionally, Kallisto-generated 1 www.bioinformatics.babraham.ac.uk/projects/fastqc transcripts per million (TPM) normalized expression data for each sample was used to plot heatmaps for both cell types using the heatmap.2 function from the gplots R package, with the embedded Hierarchical Clustering assessment turned on for both rows (transcripts) and columns (samples).…”