Feline coronavirus influences the biogenesis and composition of extracellular vesicles derived from CRFK cells

Abstract

Introduction: Coronavirus (CoV) has become a public health crisis that causes numerous illnesses in humans and certain animals. Studies have identified the small, lipid-bound structures called extracellular vesicles (EVs) as the mechanism through which viruses can enter host cells, spread, and evade the host's immune defenses. EVs are able to package and carry numerous viral compounds, including proteins, genetic substances, lipids, and receptor proteins. We proposed that the coronavirus could alter EV production and content, as well as influence EV biogenesis and composition in host cells.

Methods: In the current research, Crandell-Rees feline kidney (CRFK) cells were infected with feline coronavirus (FCoV) in an exosome-free media at a multiplicity of infection (MOI) of 2,500 infectious units (IFU) at 48 h and 72 h time points. Cell viability was analyzed and found to be significantly decreased by 9% (48 h) and 15% (72 h) due to FCoV infection. EVs were isolated by ultracentrifugation, and the surface morphology of isolated EVs was analyzed via Scanning Electron Microscope (SEM).

Results: NanoSight particle tracking analysis (NTA) confirmed that the mean particle sizes of control EVs were 131.9 nm and 126.6 nm, while FCoV infected-derived EVs were 143.4 nm and 120.9 nm at 48 and 72 h, respectively. Total DNA, RNA, and protein levels were determined in isolated EVs at both incubation time points; however, total protein was significantly increased at 48 h. Expression of specific protein markers such as TMPRSS2, ACE2, Alix, TSG101, CDs (29, 47, 63), TLRs (3, 6, 7), TNF-α, and others were altered in infection-derived EVs when compared to control-derived EVs after FCoV infection.

Discussion: Our findings suggested that FCoV infection could alter the EV production and composition in host cells, which affects the infection progression and disease evolution. One purpose of studying EVs in various animal coronaviruses that are in close contact with humans is to provide significant information about disease development, transmission, and adaptation. Hence, this study suggests that EVs could provide diagnostic and therapeutic applications in animal CoVs, and such understanding could provide information to prevent future coronavirus outbreaks.

Keywords: CRFK cells; biogenesis; exosomes; extracellular vesicles; feline coronavirus; immunomodulation; pandemic.

Figure 1

The impact of FCoV on CRFK cell viability. (A) Bright-field microscopy images indicate the morphology of CRFK cells at 48 and 72 h. (B) CRFK cells were infected with FCoV in exosome-depleted media at a MOI of 2,500 IFU at 48 and 72 h time points. Post-infection enumeration of viable CRFK cells was subsequently incubated with MTT at 37°C for a duration of 3–4 h; absorbance was measured at 570 nm. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± standard deviation (SD) as stated: ∗p ≤ 0.05.

Figure 2

Morphological analysis of FCoV-infected CRFK-derived EVs. (A) Scanning electron microscopy (SEM) images demonstrating the surface morphology of the CRFK-derived control EVs at 72 h-time points at 5 μm. (B) NTA indicates the CRFK-derived EV’s mean particle size; (C) particle concentration after 48 h and 72 h FCoV infection. Statistical analysis of the acquired data points was conducted using a t-test.

Figure 3

The biological significance of FCoV-infected CRFK-derived EVs. Graphs showing the (A) total DNA, (B) total RNA, and (C) total protein content of CRFK-derived EVs after 48 h and 72 h FCoV infection. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± SD as stated: ∗∗∗∗p ≤ 0.0001.

Figure 4

The influence of FCoV infection on CRFK-derived EVs’ classical markers. Graphs indicate the quantitative dot blot analysis of (A) Alix at 48 and 72 h (Supplementary Figure S1A); (B) TSG101 at 48 and 72 h (Supplementary Figure S1B); and (C) CD63 at 48 and 72 h (Supplementary Figure S1C) in CRKF-derived control and FCoV-infected EVs. The dots displayed in the figure indicate the results obtained from six-fold dot blot experiments. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± SD as stated: ∗p ≤ 0.05, ∗∗p ≤ 0.01, and ∗∗∗p ≤ 0.001.

Figure 5

The presence of host receptors and host cell protease in response to FCoV infection. (A) Western analysis showing the expression of coronavirus host receptor, ACE2 at 48 h, and dot blot analysis showing the expression of ACE2 at 48 and 72 h in CRFK-derived control and FCoV infected EVs. (B) Western analysis showing the expression of CoV host cell protease, TMPRSS2 at 48 h, and dot blot analysis showing the expression of TMPRSS2 at 48 h and 72 h in CRFK-derived control and FCoV-infected EVs. Graphs indicate the quantitative dot blot analysis of (C) ACE2 at 48 and 72 h and (D) TMPRSS2 at 48 and 72 h in CRKF-derived control and FCoV-infected EVs. The dots displayed in the figure indicate the results obtained from six-fold dot blot experiments. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± SD as stated: ∗p ≤ 0.05 and ∗∗p ≤ 0.01.

Figure 6

The influence of FCoV infection on membrane trafficking proteins and adhesion molecules. Graphs indicate the quantitative dot blot analysis of (A) Flotillin-1 at 48 and 72 h (Supplementary Figure S1D); (B) Clathrin at 48 h and 72 h (Supplementary Figure S1E); (C) Cadherin at 48 and 72 h (Supplementary Figure S1F); and (D) CD29 at 48 and 72 h (Supplementary Figure S1G) in CRKF-derived control and FCoV-infected EVs. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± SD as stated: ∗p ≤ 0.05, ∗∗p ≤ 0.01, and ∗∗∗∗p ≤ 0.0001.

Figure 7

Initiation of pathogen recognition and proinflammatory responses after FCoV infection. Graphs indicate the quantitative dot blot analysis of (A) TLR3 at 48 and 72 h (Supplementary Figure S1H); (B) TLR6 at 48 and 72 h (Supplementary Figure S1I); and (C) TLR7 at 48 and 72 h (Supplementary Figure S1J) in CRKF-derived control and FCoV-infected EVs. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± SD as stated: ∗p ≤ 0.05 and ∗∗∗p ≤ 0.001.

Figure 8

The influence of FCoV infection on CRFK-derived EVs’ protein and inflammatory markers. Graphs indicate the quantitative dot blot analysis of (A) IRF4 at 48 and 72 h (Supplementary Figure S1K); (B) CD47 at 48 and 72 h (Supplementary Figure S1L); (C) TGF-β-3 at 48 and 72 h (Supplementary Figure S2A); (D) mCCL22 at 48 and 72 h (Supplementary Figure S2B); and (E) TNF-α at 48 and 72 h (Supplementary Figure S2C) in CRKF-derived control and FCoV-infected EVs. Statistical analysis of the acquired data points was conducted using a t-test. Statistical significance is implied through the mean ± SD as stated: ∗p ≤ 0.05 and ∗∗∗p ≤ 0.001.