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. 2018 Nov 18;10(11):649.
doi: 10.3390/v10110649.

Characterizing the Different Effects of Zika Virus Infection in Placenta and Microglia Cells

Maria Del Pilar Martinez Viedma  1 , Brett E Pickett  2
Affiliations

Characterizing the Different Effects of Zika Virus Infection in Placenta and Microglia Cells

Maria Del Pilar Martinez Viedma et al. Viruses. .

Abstract

Zika virus (ZIKV) is a neuropathic virus that causes serious neurological abnormalities such as Guillain-Barre syndrome in adults and congenital Zika syndrome (CZS) in fetuses, which makes it an important concern for global human health. A catalogue of cells that support ZIKV replication, pathogenesis, and/or the persistence of the virus still remains unknown. Here, we studied the behavior of the virus in human placenta (JEG-3) and human microglia (HMC3) cell lines in order to better understand how different host tissues respond during infection. We quantified the host transcriptional response to ZIKV infection in both types of cells at 24 and 72 h post-infection. A panel of 84 genes that are involved in the innate or adaptive immune responses was used to quantify differential expression in both cell lines. HMC3 cells showed a unique set of significant differentially expressed genes (DEGs) compared with JEG-3 cells at both time points. Subsequent analysis of these data using modern pathway analysis methods revealed that the TLR7/8 pathway was strongly inhibited in HMC3 cells, while it was activated in JEG-3 cells during virus infection. The disruption of these pathways was subsequently confirmed with specific small interfering RNA (siRNA) experiments that characterize their role in the viral life cycle, and may partially explain why ZIKV infection in placental tissue contributes to extreme neurological problems in a developing fetus.

Keywords: TLR7/8; microglia cells; placenta cells; siRNA; zika virus.

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Conflict of interest statement

The authors declare no conflict of interest

Figures

Figure 1
Figure 1
Quantification of Zika virus (ZIKV) titer, replication, and cytopathic effects (CPE) over time. (A) RT-qPCR standard curve to measure the number of virus genomes (upper panels); RT-qPCR to quantify ZIKV molecules in VERO, HMC3 and JEG-3 cells after one, two, and three days of infection. (B) CPE induced by ZIKV infection in HMC3 and JEG-3 cells at one, three, four, and five days post-infection, with the green stain representing healthy cells and the red stain indicating unhealthy and/or dying cells and the scale bar representing 100 micrometers. The lower panel shows the results from plaque assays performed in triplicate to quantify the ZIKV particles released to the media at each time point. Error bars represent standard deviation.
Figure 2
Figure 2
Results from an intracellular innate immune response RT-qPCR array. Each circle or square represents an individual gene that was either upregulated (black) or downregulated (blue). (A) Differentially expressed genes at one dpi versus three dpi in human microglia and placenta cells. (B) Differentially expressed genes of ZIKV-infected (I) samples versus time-matched mock-infected (UI) samples at 24 h and three days post infection with ZIKV. The numerical values on the Y-axis have been collapsed in regions that had no differentially expressed genes in order to improve visibility.
Figure 3
Figure 3
Viral and transcriptional effects of siRNAs targeting TLR7, TLR8, or TLR7 + TLR8 on ZIKV virus replication in different cell types over time. (A) Plaque-forming units (PFU) per mL of supernatant were measured to quantify infectious virus production at each time point in each cell type. (B) The number of ZIKV RNA molecules from the same samples was measured with RT-qPCR to quantify viral genome replication at both time points in each cell type (* p < 0.05). Error bars represent standard deviation.
Figure 4
Figure 4
Fold induction values for genes involved in the innate immune response of time-matched mock-infected (UI) versus ZIKV-infected (I) HMC3 and JEG-3 cells at 24 h post infection (hpi), 48 hpi, and three dpi. Values were determined by calculating the fold induction (FI) using the delta-delta cycle threshold (∆∆Ct) method for each gene, normalizing the values for each gene to the UI results.
Figure 5
Figure 5
A comparison of the effects of a panel of siRNAs including TLR7, TLR8, TLR7 + TLR8, or scramble (control) on the expression of selected innate immune response factors. (A) HMC3 cells transfected with siRNA and either time-matched mock-infected (UI) or infected with ZIKV (I) at one dpi and three dpi. (B) JEG3 cells transfected with siRNA and either time-matched mock-infected (UI) or infected with ZIKV (I) at one dpi and three dpi. The horizontal black line marks the two-fold induction (FI) threshold in each plot.
Figure 6
Figure 6
Role of STAT2 in ZIKV replication and in the intracellular response to infection. (A) STAT2 expression in time-matched mock-infected (UI) and ZIKV-infected (I) HMC3 and JEG-3 cells at one dpi and three dpi. (B) Effect of STAT2 knockdown on selected innate immune genes in time-matched mock-infected (UI) and infected (I) HMC3 and JEG-3 cells at one dpi and three dpi. The horizontal black line represents the two-fold induction (FI) threshold in each plot. (C) Number of ZIKV RNA molecules detected from the total RNA collected from cells treated with siRNAs against STAT2. (* p < 0.05).
Figure 7
Figure 7
Role of AXL in ZIKV infection. (A) AXL fold induction ZIKV-infected (I) relative to time-matched mock-infected (UI) HMC3 and JEG-3 cells at one dpi and three dpi. (B) Effect of siRNAs targeting TLR7, TLR8, TLR7 + TLR8, or STAT2 on AXL expression in time-matched mock-infected (UI) and ZIKV-infected (I) HMC3 and JEG-3 cells at one dpi and three dpi. (* p < 0.05). Error bars represent standard deviation.
Figure 8
Figure 8
A potential schematic molecular model depicting the differing relationships between the expression of STAT2, TLR7, TLR8, and AXL during ZIKV infection in human placenta (JEG-3) and microglia (HMC3) cells.
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