ABSTRACT

   Recent studies suggest the enhancement of liver injury in COVID-19
   patients infected with Hepatitis C virus (HCV). Hepatocytes express low
   levels of angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 entry
   receptor, raising the possibility of HCV-SARS-CoV-2 coinfection in the
   liver. This work aimed to explore whether HCV and SARS-CoV-2 coinfect
   hepatocytes and the interplay between these viruses. We demonstrate
   that SARS-CoV-2 coinfects HCV-infected Huh7.5 (Huh7.5^HCV) cells. Both
   viruses replicated efficiently in the coinfected cells, with HCV
   replication enhanced in coinfected compared to HCV-mono-infected cells.
   Strikingly, Huh7.5^HCV cells were eight fold more susceptible to
   SARS-CoV-2 pseudoviruses than naive Huh7.5 cells, suggesting enhanced
   SARS-CoV-2 entry into HCV-preinfected hepatocytes. In addition, we
   observed increased binding of spike receptor-binding domain (RBD)
   protein to Huh7.5^HCV cells, as well as enhanced cell-to-cell fusion of
   Huh7.5^HCV cells with spike-expressing Huh7.5 cells. We explored the
   mechanism of enhanced SARS-CoV-2 entry and identified an increased ACE2
   mRNA and protein levels in Huh7.5^HCV cells, primary hepatocytes, and
   in data from infected liver biopsies obtained from database.
   Importantly, higher expression of ACE2 increased HCV infection by
   enhancing its binding to the host cell, underscoring its role in the
   HCV life cycle as well. Transcriptome analysis revealed that shared
   host signaling pathways were induced in HCV-SARS-CoV-2 coinfection.
   This study revealed complex interactions between HCV and SARS-CoV-2
   infections in hepatocytes, which may lead to the increased liver damage
   recently reported in HCV-positive COVID-19 patients.

   IMPORTANCE Here, we provide the first experimental evidence for the
   coexistence of SARS-CoV-2 infection with HCV, and the interplay between
   them. The study revealed a complex relationship of enhancement between
   the two viruses, where HCV infection increased the expression of the
   SARS-CoV-2 entry receptor ACE2, thus facilitating SARS-CoV-2 entry, and
   potentially, also HCV entry. Thereafter, SARS-CoV-2 infection enhanced
   HCV replication in hepatocytes. This study may explain the aggravation
   of liver damage that was recently reported in COVID-19 patients with
   HCV coinfection and suggests preinfection with HCV as a risk factor for
   severe COVID-19. Moreover, it highlights the possible importance of HCV
   treatment for coinfected patients. In a broader view, these findings
   emphasize the importance of identifying coinfecting pathogens that
   increase the risk of SARS-CoV-2 infection and that may accelerate
   COVID-19-related co-morbidities.

   KEYWORDS: hepatitis C virus, SARS-CoV-2, coinfection, liver

INTRODUCTION

   Coronavirus (CoV) disease 2019 (COVID-19) caused by severe acute
   respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to many deaths
   worldwide since its emergence in December 2019 in Wuhan, China. The
   disease symptoms range from mild to severe ([48]1), that can progress
   to acute respiratory distress syndrome (ARDS) and death ([49]2, [50]3).
   Epidemiological studies have associated factors such as older age, male
   sex, and comorbidities, including hypertension, heart disease,
   diabetes, and malignancy, with increased risk of severe COVID-19
   ([51]3). It is now evident that SARS-CoV-2 has broad organotropism,
   including the kidneys, liver, heart, and brain ([52]4). Thereby, it can
   also contribute to multiorgan dysfunction such as acute cardiac injury,
   acute renal insufficiency, and liver damage, and can aggravate
   preexisting conditions ([53]5). Understanding the effect of
   preinfection clinical status on SARS-CoV-2 infection outcomes is
   crucial for identification of the risk factors for severe illness,
   stratification of high-risk patients, prediction of clinical outcome
   and prioritizing and tailoring treatment regimens.

   SARS-CoV-2 is an enveloped, non-segmented, positive-sense RNA virus.
   Its genome contains at least 6 open reading frames (ORFs), encoding
   structural and nonstructural proteins. The Spike (S) gene encodes the
   main envelope glycoprotein ([54]6), which is a trimeric class I
   membrane fusion protein responsible for host-cell binding, fusion of
   the viral and host cell membranes and entry into the host cell. The
   receptor-binding domain (RBD) at the S1 subunit binds the
   angiotensin-converting enzyme 2 (ACE2) cellular receptor, triggering
   conformational changes of the S trimer ([55]7, [56]8). Then, the S
   protein is cleaved at the S1/S2 by the cellular serine protease
   transmembrane serine protease 2 (TMPRSS2) at the cell surface or by
   cathepsin L in the endosomal compartment, thereby allowing fusion of
   viral and cellular membranes by the S2 subunit ([57]9, [58]10).
   Therefore, expression of both ACE2 and TMPRSS2 determines the tissue
   tropism of SARS-CoV-2, and the course of the disease ([59]11). Indeed,
   a broad spectrum of ACE2 and TMPRSS2 expression profiles was identified
   in lung, intestine, kidney, heart, adipose, reproductive tissues,
   brain, and liver tissues and cell lines ([60]12, [61]13). It is highly
   important to identify factors affecting the expression of the
   SARS-CoV-2 entry determinants.

   Liver tissue was found to express low levels of ACE2 and TMPRSS2
   ([62]12[63]–[64]17). Experiments with pseudoviruses and the authentic
   virus in various liver-derived cell lines and cellular models
   demonstrated their permissiveness to SARS-CoV-2 entry and replication
   ([65]12, [66]14, [67]18). However, due to the clinical challenge of
   obtaining liver tissues during active SARS-CoV-2 infection, direct
   evidence for virus infection and replication in clinical samples from
   the liver is scarce. Recent studies identified SARS-CoV or SARS-CoV-2
   RNA and coronavirus-like particles in liver samples obtained postmortem
   from patients who had severe COVID‐19-associated lung disease;
   abnormalities and fibrosis in the liver tissue were observed
   ([68]19[69]–[70]24). Anomalies in liver biochemistry in COVID-19
   patients is commonly reported, including elevations of serum alanine
   aminotransferase (ALT), aspartate aminotransferase (AST), serum
   bilirubin levels, and liver injury ([71]25[72]–[73]30). Particularly,
   patients with preexisting liver disease showed higher risk of severe
   COVID-19, elevated liver enzymes levels and higher mortality rate,
   although the reports are inconsistent ([74]31[75]–[76]36). Still, it
   remains to be determined whether liver damage is contributed by direct
   SARS-CoV-2 infection or is an indirect consequence of pathological
   processes, such as inflammation, occurring during COVID-19 ([77]37).

   Hepatitis C virus (HCV) infects 1–2% of the world's population and can
   lead to chronic liver disease, liver cirrhosis and hepatocellular
   carcinoma, cumulatively posing a major global health burden ([78]38).
   HCV primarily infects hepatocytes ([79]39), and, therefore, HCV-
   SARS-CoV-2 coinfection in the liver may be possible. However, it is
   currently unknown whether preexisting infection with HCV impacts the
   susceptibility of the liver to SARS-CoV-2 infection. Only limited data
   are available on liver pathology and mortality rates in COVID-19
   patients with chronic HCV infection. Butt et al. reported on a higher
   percentage of overall hospitalization of HCV-infected COVID-19
   patients, but not on a higher mortality rate, compared to COVID-19
   patients without HCV ([80]40). Other studies showed higher prevalence
   of hepatic manifestations, liver damage, liver enzymes levels, and
   mortality rate among COVID-19-HBV and COVID-19-HCV coinfected patients
   compared to COVID-19 patients ([81]40, [82]41). Although the interplay
   between HCV and SARS-CoV-2 infections is not known, studies suggested
   that SARS infection in chronic HCV and HBV patients may cause severe
   hepatitis due to higher HBV/HCV replication ([83]42). Moreover,
   preinfection with HBV prolonged the clearance of SARS-CoV-2 infection
   ([84]43). Taken together, the data reported thus far suggest a complex
   interaction between chronic HCV infection and SARS-CoV-2 infection.
   Moreover they suggest that treatment of COVID-19-HCV coinfected
   patients with direct-acting antivirals (DAAs) for HCV cure may improve
   disease severity and clinical outcome of COVID-19. Here, we show for
   the first time that both viruses coinfect liver cells, and show
   enhanced replication and infection in coinfected hepatocytes.

RESULTS

HCV and SARS-CoV-2 coinfect and co-replicate in hepatocytes.

   Previous studies demonstrated that the human hepatoma cell line Huh7.5,
   which supports HCV replication in vitro, is susceptible to infection by
   SARS-CoV-2 and other coronaviruses. In addition, this cell line was
   recently employed to identify host pathways that are important for the
   SARS-CoV-2 life cycle ([85]14). Here, this cell line was used to
   explore the interplay between HCV and SARS-CoV-2 infections. First, we
   aimed to evaluate whether HCV and SARS-CoV-2 coinfection occurs in
   hepatocytes. Following a 48 h exposure of HCV-preinfected and
   noninfected control Huh7.5 cells to SARS-CoV-2, cells were
   co-immunostained to determine the coinfection rate. Indeed, cells
   staining for both HCV and SARS-CoV-2 were identified. Their morphology
   was similar to that of cells infected with HCV alone, and indicated no
   cytopathic effect up to 72 h postinfection ([86]Fig. 1A), which aligns
   with a previous report showing limited cell death in
   SARS-CoV-2-infected Huh7.5.1 cells ([87]14). Higher resolution images
   of the HCV-SARS-CoV-2-coinfected cells clearly showed regions of
   co-localization of proteins from both viruses, as well as regions with
   proteins from only one of the viruses ([88]Fig. 1A, lower panels).
   Higher number of SARS-CoV-2 infected cells were observed in the
   coinfected culture than in SARS-CoV-2 infection alone ([89]Fig. 1B).

FIG 1.

   [90]FIG 1
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   HCV and SARS-CoV-2 coinfect hepatocytes. (A) Evaluation of HCV and
   SARS-CoV-2 coinfection in Huh7.5 cells by immunostaining. Non-infected
   Huh7.5 (top panel) and Huh7.5^HCV cells (middle and bottom panels) were
   exposed to SARS-CoV-2 for 48 h and then fixed and co-stained with
   HCV-positive serum and rabbit anti-SARS-CoV-2 serum, followed by
   anti-human cy3, and Alexa Fluor 488 conjugated goat anti-rabbit
   secondary antibodies, respectively. Images were visualized by
   fluorescence microscopy. Scale bars: 50 μm/20 μm. (B) Quantification of
   the percent of HCV/SARS-CoV-2/HCV- SARS-CoV-2 coinfection, calculate
   from at least 6 images for each.

   To determine whether the two viruses co-replicate in hepatocytes, the
   dynamics of RNA levels of the two viruses were monitored over time
   after infection ([92]Fig. 2A). An overall increase in RNA levels of
   both viruses was detected over the 48 h after SARS-CoV-2 infection,
   suggesting efficient replication of both viruses in the coinfected
   cells ([93]Fig. 2B and [94]C). The increase in SARS-CoV-2 RNA levels
   was slightly smaller in the coinfected compared to SARS-CoV-2
   mono-infected cells; however, this effect was not statistically
   significant ([95]Fig. 2B). In parallel, while HCV RNA levels remained
   stable over time in the Huh7.5^HCV cells, they increased in the
   coinfected cells, suggesting that the presence of SARS-CoV-2 enhanced
   HCV RNA replication ([96]Fig. 2C). We then aimed to determine the level
   of SARS-CoV-2 RNA at early time points postinfection. Interestingly, 5
   h postinfection, higher levels of SARS-CoV-2 RNA were observed in the
   coinfected cells compared to control cells infected with SARS-CoV-2
   only ([97]Fig. 2D). Overall, these observations suggest efficient
   coinfection and co-replication of HCV and SARS-CoV-2 in human
   hepatocytes.

FIG 2.

   [98]FIG 2
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   HCV and SARS-CoV-2 co-replicate in hepatocytes. (A) Experimental setup
   for HCV-SARS-CoV-2 coinfection. Non-infected Huh7.5 and Huh7.5^HCV
   cells were co-infected with SARS-CoV-2 at MOI of 0.01, and harvested 0,
   1, 5, 24 and 48 h postinfection. RNA was extracted and qRT-PCR was
   performed to determine SARS-CoV-2 RNA level (B) and HCV RNA level (C)
   0, 24 and 48 h after SARS-CoV-2 infection, and SARS-CoV-2 RNA levels
   (D) at 0, 1, and 5 h postinfection. Differential expression was
   calculated, with GAPDH as an endogenous control. Fold change was
   calculated compared to Huh7.5 control cells. Mean fold changes are
   shown ± standard deviation (SD), from 3 independent experiments
   (*P = 0.0229, **P = 0.0026, ***P = 0.0006, t test).

HCV infection enhances SARS-CoV-2 entry into hepatocytes.

   At early time points postinfection, SARS-CoV-2 RNA levels may reflect
   the quantity of viruses that entered the cells. Therefore, the higher
   SARS-CoV-2 RNA level detected 5 h after infection of HCV-preinfected
   cells suggests enhanced entry of SARS-CoV-2 into these cells. To
   explore this possibility, we compared the uptake of GFP-reporter
   pseudoparticles bearing SARS-CoV-2-spike by Huh7.5^HCV versus Huh7.5
   cells. [100]Fig. 3A and [101]B show that HCV-preinfection increased
   cell susceptibility to SARS-CoV-2-spike pseudoparticle infection, by
   approximately eight fold. To further assess the dependence of the
   enhanced entry of SARS-CoV-2-spike pseudoparticles into Huh7.5^HCV on
   interaction between the SARS-CoV-2 spike and the ACE2 receptor, the
   experiment was repeated in the presence of an anti-ACE2 antibody. As
   expected, anti-ACE2 antibody treatment abolished entirely the entry of
   SARS-CoV-2-spike pseudoparticles to the cells ([102]Fig. 3B). We also
   validated the enhanced entry of SARS-CoV-2-spike pseudoparticles in
   HCV-infected primary human hepatocytes (PHH) (50% infected with HCV)
   ([103]Fig. 3C).

FIG 3.

   [104]FIG 3
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   HCV infection enhances SARS-CoV-2 entry into hepatocytes. (A)
   Non-infected Huh7.5 and Huh7.5^HCV cells were incubated overnight with
   SARS-CoV-2-spike pseudoparticles with or without an anti-ACE2 antibody.
   Samples were visualized by fluorescence microscopy. Scale bars: 50 μm.
   (B) After a 24 h of incubation, all cells and GFP-positive cells were
   counted by fluorescence microscopy, and fold change of the number of
   Huh7.5^HCV GFP-positive cells was calculated compared to the number of
   Huh7.5 GFP-positive cells (****P < 0.0001, t test). (C) Non-infected
   and HCV-infected PHH were incubated overnight with SARS-CoV-2-spike
   pseudoparticles. Samples were visualized by fluorescence microscopy.
   GFP-positive cells were counted by fluorescence microscopy, and fold
   change of the number of GFP-positive cells was calculated
   (**P = 0.0015, t test). (D) RBD was incubated with HEK293, non-infected
   Huh7.5, and Huh7.5^HCV cells and then anti-RBD 555 was added. Flow
   cytometry was performed to evaluate RBD binding to the cells. (E)
   Non-infected Huh7.5 and Huh7.5^HCV cells were transfected with a
   spike-encoding plasmid. Following a 24-h incubation, the cells were
   stained with the CellMask Deep Red Plasma Membrane Stain and DAPI
   nuclear stain. Samples were visualized by fluorescence microscopy.
   Scale bars: 100 μm. (F) Percentage of DAPI staining relative to the
   percentage of membrane staining was quantified for Huh7.5
   non-infected-spike cells compared to Huh7.5^HCV -spike cells
   (*P = 0.0119, t test).

   Next, we measured the effect of HCV infection on each of the two
   SARS-CoV-2 entry steps: spike RBD binding to the ACE2 receptor and
   spike cleavage by cellular proteases like TMPRSS2. Flow cytometry
   analyses found no significant RBD binding to HEK293 cells that do not
   express ACE2 (1.72% positive cells), and low levels of RBD binding to
   Huh7.5 cells expressing low levels of ACE2 (6.63% positive cells). In
   contrast, higher levels of RBD binding were detected (26.8% positive
   cells) in Huh7.5^HCV cells, suggesting that higher levels of ACE2 are
   expressed on HCV-infected cells, leading to increased ACE2-RBD binding
   events ([106]Fig. 3D). To further characterize these effects on
   spike-mediated membrane fusion, we performed fusion assay by
   overexpression of spike in Huh7.5^HCV and Huh7.5 cells. In this assay,
   cell-cell membrane fusion will only occur between cells that express
   spike and ACE2 receptor on their surfaces. While no cell-cell fusion
   was observed in Huh7.5 control cells ([107]Fig. 3E), cell-cell fusion
   events were detected in non-infected, spike-expressing Huh7.5, and
   significantly increased in spike-expressing Huh7.5^HCV cells
   ([108]Fig. 3E and [109]F). In addition, Huh7.5^HCV-spike cells fused
   into big syncytia, each containing multiple nuclei ([110]Fig. 3E).
   Taken together, these observations show that the two spike-mediated
   viral entry steps are enhanced in the presence of HCV.

HCV infection induces increased expression of SARS-Cov-2 entry factors in
hepatocytes.

   We next aimed to further explore the mechanism of HCV-mediated enhanced
   SARS-CoV-2 entry into hepatocytes. In line with the suggested higher
   ACE2 expression in Huh7.5^HCV cells ([111]Fig. 3), we found
   approximately five fold higher ACE2 receptor RNA levels in Huh7.5^HCV
   compared to non-infected Huh7.5 cells ([112]Fig. 4A). Importantly,
   increased ACE2 expression remains persistent also in HCV-cured cells
   following treatment with DAAs ([113]Fig. 4A). Increased ACE2 mRNA
   expression was also measured in HCV-infected PHH ([114]Fig. 4B), and in
   HCV-infected (low interferon-stimulated genes) liver samples compared
   to normal liver samples obtained from the NCBI Gene Expression Omnibus
   ([115]GSE84346) ([116]Fig. 4C). However, increased expression of ACE2
   mRNA was not recapitulated in a cell line expressing the HCV-N
   subgenomic replicons that include only HCV nonstructural proteins (NS3,
   NS4A, NS4B, NS5A, and NS5B) ([117]Fig. 4D), suggesting that the
   increased expression of ACE2 may be mediated by HCV structural
   proteins. ACE2 mRNA expression was also unchanged in HepG2 cells
   infected with HBV lentivirus expressing HBV proteins ([118]Fig. 4E),
   pointing to an HCV-specific effect. In addition, the RNA level of the
   cellular serine protease TMPRSS2 were elevated in Huh7.5^HCV cells
   ([119]Fig. 4F).

FIG 4.

   [120]FIG 4
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   HCV infection upregulates ACE2 expression in hepatocytes. (A) qRT-PCR
   quantification of ACE2 mRNA levels in non-infected Huh7.5 cells,
   Huh7.5^HCV cells, and HCV-cured cells. Fold change was calculated
   compared to Huh7.5 control cells. Mean fold changes are shown ± SD,
   from three independent experiments (*P = 0.0149, **P = 0.0015, t test).
   (B) Quantification of ACE2 mRNA levels in non-infected PHH cells
   compared to HCV-infected PHH cells, as described above in (A). Mean
   fold changes are shown ± SD, from 3 independent experiments
   (*P = 0.0104, t test). (C) Shown are distributions of normalized read
   counts of ACE2 in normal (n = 6) and HCV-infected (n = 15) human livers
   (low interferon-stimulated genes) (P < 0.021, t test). (D)
   Quantification of ACE2 mRNA levels in Huh7.5^HCV cells compared to
   replicons cells, performed as described in (A). Mean fold changes are
   shown ± SD, from 3 independent experiments (**P = 0.0072, t test). (E)
   Quantification of ACE2 mRNA levels in HepG2 cells compared to
   HBV-infected HepG2 cells, performed as described in (A). (F)
   Quantification of TMPRSS2 RNA levels in non-infected Huh7.5 cells
   compared to Huh7.5^HCV cells, performed as described in (A). Mean fold
   changes are shown ± SD, from three independent experiments
   (*P = 0.0131, t test). (G) Immunostaining for ACE2 protein expression
   in non-infected Huh7.5 cells and Huh7.5 cell culture sample 50%
   infected with HCV. The cells were stained with HCV-positive serum and
   anti-human 488 Alexa fluor antibodies to detect HCV infection, and with
   anti-ACE2 and anti-rabbit Cy3 antibodies to detect ACE2 protein. Scale
   bars: 10 μm. (H) Lysates of HEK293 cells, HEK293 cells stably
   expressing ACE2, non-infected Huh7.5 cells, and Huh7.5^HCV cells were
   analyzed by Western blot using antibodies for ACE2 and β-actin. (I)
   Lysates of non-infected PHH cells and HCV-infected PHH cells were
   analyzed by Western blot using antibodies for ACE2 and β-actin. (J)
   HEK293, non-infected Huh7.5 and Huh7.5^HCV-infected cells were
   incubated with anti-ACE2 488. Flow cytometry was performed to evaluate
   ACE2 level on the cells.

   To determine whether HCV infection increases ACE2 protein expression,
   non-infected Huh7.5 cells and a Huh7.5 cell cultures in which 50% were
   infected with HCV, were immunostained with specific antibodies for the
   detection of HCV proteins and ACE2. No staining was detected in control
   cells ([122]Fig. 4G, upper panel) and the non-infected cells
   neighboring HCV-infected cells ([123]Fig. 4G, lower panel), likely due
   to the low level of ACE2 expression in Huh7.5 cells. In contrast,
   co-immunostaining was observed in the HCV-infected Huh7.5 cells
   ([124]Fig. 4G, lower panel), suggesting that the control over increased
   ACE2 expression is cell autonomous and specific to HCV-infected cells.
   Western blot analysis supports these results, indicating that the ACE2
   protein levels are not detected in HEK293 cells, are high in
   HEK293-ACE2 cells (HEK293 stably expressing hACE2), low in Huh7.5
   cells, and significantly increased in Huh7.5^HCV cells ([125]Fig. 4H).
   Similar differences were observed in non-infected versus HCV-infected
   PHH ([126]Fig. 4I). We also validated that expression level of ACE2 is
   upregulated on Huh7.5^HCV cells by flow cytometry ([127]Fig. 4J).
   Collectively, these data indicate that HCV infection induces expression
   of ACE2 on hepatocytes, which may underlie enhanced SARS-CoV-2 entry
   into HCV-infected hepatocytes.

ACE2 expression affects HCV infection by enhancing viral entry into
hepatocytes.

   In light of the observed upregulation of ACE2 expression in
   HCV-infected cells, we postulated that ACE2 plays a role in the HCV
   life cycle. To examine the effect of ACE2 protein levels on HCV
   infection, ACE2 was either overexpressed or knocked down in Huh7.5
   cells (Huh7.5-ACE2/Huh7.5-siACE2), and then the cells were infected
   with HCV. Immunostaining for HCV proteins 72 h postinfection uncovered
   a higher percentage of HCV infection in Huh7.5-ACE2 cells
   ([128]Fig. 5A, lower panel) compared to control Huh7.5 cells
   ([129]Fig. 5A, upper panel). Infection rates were approximately
   five-fold higher and five-fold lower in Huh7.5-ACE2 ([130]Fig. 5B) and
   Huh7.5-siACE2 cells ([131]Fig. 5C), respectively. Western blot analysis
   showing a higher level of HCV core protein in Huh7.5-ACE2
   ([132]Fig. 5D), versus a lower level of HCV core protein in
   Huh7.5-siACE2 cells ([133]Fig. 5E) compared to control, verified these
   results.

FIG 5.

   [134]FIG 5
   [135]Open in a new tab

   ACE2 expression increases HCV infection. (A) Huh7.5 cells were
   transfected with an ACE2-encoding plasmid, and 48 h after transfection,
   ACE2-overexpressing cells and control cells were infected with HCV. At
   72 h postinfection, cells were fixated and stained with HCV-positive
   serum, anti-human 488 Alexa fluor antibodies, to detect HCV infection,
   and with DAPI nuclear stain. Scale bars: 20 μm. (B) Numbers of
   HCV-infected Huh7.5 and ACE2-overexpressing HCV-infected Huh7.5 cells
   were determined, and percentage of infection was calculated and is
   presented as fold change from control. Mean fold changes are shown ±
   SD, from three independent experiments (****P < 0.0001, t test). (C)
   Huh7.5 cells were transfected with ACE2 siRNA, and 48 h after, were
   infected with HCV. At 72 h postinfection, cells were fixated and
   stained with HCV-positive serum, anti-human 488 Alexa fluor antibodies,
   to detect HCV infection, and with DAPI. Number of HCV-infected cells
   were quantified, and percentage of infection was calculated and is
   presented as fold change from control. Mean fold changes are shown ±
   SD, from 3 independent experiments (****P < 0.0001, t test). (D) Huh7.5
   cells and Huh7.5 cells were transfected with an ACE2-encoding plasmid;
   48 h after transfection cells were infected with HCV. At 72 h
   postinfection, cell lysates were analyzed by Western blot using
   antibodies for Core, ACE2 and β-actin. (E) Huh7.5 cells were
   transfected with ACE2 siRNA for 24 h, and then infected with HCV. Cell
   lysates were analyzed by Western blot using antibodies for Core, ACE2
   and β-actin.

   To determine whether ACE2 impacts HCV infection via modulation of HCV
   entry, GFP-reporter pseudoparticles bearing HCV E1E2 envelope proteins
   (HCVpp) were incubated with Huh7.5 and Huh7.5-ACE2 cells. A more than
   3-fold increase in HCVpp uptake was observed in Huh7.5-ACE2 cells
   compared to control Huh7.5 ([136]Fig. 6A and [137]B), demonstrating
   that ACE2 plays a role in HCV entry. In parallel, incubation of the
   cells with HCV at 4°C for 1 h, which allows for virus binding only,
   without viral entry, resulted in approximately two-fold higher viral
   RNA in Huh7.5-ACE2 cells compared to Huh7.5 cells, suggesting a role
   for ACE2 in HCV binding to its host ([138]Fig. 6C).

FIG 6.

   [139]FIG 6
   [140]Open in a new tab

   ACE2 expression enhances HCV entry. (A) Huh7.5 cells were transfected
   with an ACE2-encoding plasmid; 48 h following transfection, HCVpp were
   added to the transfected and non-transfected cells and cells were
   monitored using IncuCyte. Scale bars: 300 μm. (B) Following 24 h from
   HCVpp addition, all cells and GFP-positive cells were counted by
   fluorescence microscopy, and fold change was calculated compared to the
   number of non-transfected GFP-positive cells. Mean fold changes are
   shown ± SD, from 3 independent experiments (*P = 0.0299, t test). (C)
   Huh7.5 cells and ACE2-overexpressing Huh7.5 cells were mixed with HCV
   for 1 h in 4°C. RNA levels of cell-bound HCV were determined by
   qRT-PCR. Fold change was calculated compared to non-infected Huh7.5
   cells. Mean fold changes are shown ± SD, from three independent
   experiments (**P = 0.0087, t test). (D) Huh7.5 cells were transfected
   with an ACE2-encoding plasmid; 48 h after transfection
   ACE2-overexpressing cells and control cells were infected with HCV.
   Thereafter (72 h), HCV RNA levels were quantified by qRT-PCR. Fold
   change was calculated compared to non-infected Huh7.5 cells. Mean fold
   changes are shown ± SD, from 3 independent experiments (***P = <0.0001,
   t test). (E) Huh7.5 cells were transfected with ACE2 siRNA; 48 h after
   transfection, siACE2-overexpressing cells and control cells were
   infected with HCV. At 72 h postinfection, HCV RNA levels were
   quantified by qRT-PCR, as described above. Mean fold changes are shown
   ± SD, from 3 independent experiments (*P = 0.0115, t test). (F)
   Replicon cells were transfected with ACE2 plasmid. At 48 h
   posttransfection, RNA was extracted, and replicon RNA levels were
   quantified by qRT-PCR in replicon and ACE2-overexpressing replicon
   cells and compared to control Huh7.5 cells. Mean fold changes are shown
   ± SD, from 3 independent experiments.

   To evaluate whether ACE2 expression also has an effect on HCV RNA
   replication, HCV RNA levels in Huh7.5-ACE2, Huh7.5-siACE2, and control
   Huh7.5 cells were quantitated 72 h postinfection, as well as in HCV
   replicon cells with or without overexpression of ACE2. While HCV RNA
   levels were higher in Huh7.5-ACE2 ([141]Fig. 6D) and lower in
   Huh7.5-siACE2 cells ([142]Fig. 6E) compared to control cells, they were
   similar in replicon cells with versus without ACE2 overexpression
   ([143]Fig. 6F). In conclusion, ACE2 increases viral entry into
   Huh7.5-ACE2 cells without affecting HCV RNA replication.

Transcriptome analysis reveals shared pathways in HCV/SARS-CoV-2 mono versus
coinfections.

   To further investigate the interplay between HCV and SARS-CoV-2
   coinfection and the host cell, we performed a transcriptome analysis of
   mono- and coinfected cells. Analysis of the differential peaks compared
   to non-infected Huh7.5 cells (using a threshold of adjusted P < 10e-5,
   Log2FC > 0.5 and Log2FC<-0.5) identified 2037 differentially expressed
   genes (DEGs) in Huh7.5^HCV cells (868 downregulated and 1169
   upregulated), 4335 DEGs in cells infected with SARS-CoV-2 only (1906
   downregulated and 2429 upregulated), and 2840 DEGs in HCV-SARS-CoV-2
   coinfected Huh7.5 cells (1686 downregulated and 1154 upregulated)
   ([144]Fig. 7A). Taken together, both viruses, either alone or in
   coinfection, massively alter the gene expression pattern of the host
   hepatocyte. Most of the downregulated DEGs in the Huh7.5^HCV cells
   (76%) were also downregulated in the coinfected cells ([145]Fig. 7B).
   In parallel, a substantial proportion (31%) of the DEGs upregulated in
   the HCV infection group were also upregulated in the coinfected group
   ([146]Fig. 7B). Lower preservation was observed for the genes
   dysregulated upon SARS-CoV-2 infection compared to HCV-SARS-CoV-2
   coinfection; of all the SARS-CoV-2 DEGs, 53% were downregulated and 24%
   were upregulated in the coinfected cells ([147]Fig. 7B). Among the DEGs
   identified in the coinfected cells, most (75% downregulated and 74%
   upregulated) were also observed in either HCV/SARS-CoV-2 mono-infected
   cells or in the coinfected cells, while the remaining DEGs were unique
   to the coinfection group ([148]Fig. 7B). Among the DEGs, we identified
   ACE2 as significantly upregulated in the coinfected cells in the
   RNA-seq data. Altogether, the shared DEGs analysis demonstrates partial
   preservation of DEGs between the HCV/SARS-CoV-2 mono-infections and
   HCV-SARS-CoV-2 coinfection, with more shared downregulated than
   upregulated genes.

FIG 7.

   [149]FIG 7
   [150]Open in a new tab

   Differentially expressed genes and pathway analysis for HCV/SARS-CoV-2
   mono- and coinfections. (A) Volcano plot showing upregulated (red) and
   downregulated (blue) genes in HCV-infected, SARS-CoV-2-infected and
   HCV-SARS-Cov-2 coinfected Huh7.5 cells compared to non-infected cells.
   Log2FC > 0.5 and Log2FC<-0.5, and adjusted P-value = 10e-5 used as
   cutoffs. (B) Venn diagrams represent the overlap between upregulated
   (right) and downregulated (left) genes in HCV-infected,
   SARS-CoV-2-infected and HCV-SARS-CoV-2-coinfected cells. The numbers in
   the green and blue circles refer to percentage of genes from total
   SARS-CoV-2 and HCV differentially expressed gene set, respectively,
   located in each area of the circle. At regions where HCV and SARS-CoV-2
   sets are overlapped, the green and blue percentages correspond to
   SARS-CoV-2 and HCV data, respectively. (C) Barplot of differentially
   enriched pathways, obtained from KEGG database, of HCV (left) and
   SARS-CoV-2 (right) data. Bars represent the significance level of
   enrichment in SARS-CoV-2-infected, HCV-infected, and
   HCV-SARS-CoV-2-coinfected cells (red, green and blue bars,
   respectively). P-value (adjusted) = 0.05 used as cutoff.

   A pathway analysis performed to gain deeper insights into the nature of
   the host environment that supports the replication of the 2 viruses in
   a coinfection state, found that the top pathways enriched in HCV
   monoinfection were also enriched to some degree in the coinfected cells
   ([151]Fig. 7C, left). Similarly, the most enriched pathways in
   SARS-CoV-2 moninfection were also enriched in coinfected cells
   ([152]Fig. 7C, right). The heatmap in [153]Fig. 8 presents the pathways
   that were enriched in at least one of the groups, and shows that many
   pathways overlapped between the three groups. Interestingly, many of
   the shared pathways were recently identified in genetic screens and
   transcriptomic and proteomic analyses as altered by SARS-CoV-2 and were
   shown to play a role in the viral life cycle and were also implicated
   in HCV infection and propagation. The most enriched pathway in all 3
   groups was “Protein processing in endoplasmic reticulum” (ER)
   ([154]Fig. 8). Both viruses require a tight interaction with the host
   cell endomembrane organelles. HCV recruits the ER to form a membranous
   web for the translation and replication of its RNA genome ([155]44).
   ER-derived membrane structures are indeed essential for replication,
   translation, post-translation protein modifications, and assembly of
   SARS-CoV-2 ([156]45). Enriched pathways also included cholesterol and
   lipid metabolism ([157]Fig. 8), which is essential for the life cycle
   of both viruses as well; cellular cholesterol is required for
   SARS-CoV-2 entry ([158]14) and lipid metabolism is essential throughout
   its life cycle ([159]46) and is dysregulated in COVID-19 patients
   ([160]47). Similarly, HCV alters the expression of genes involved in
   cholesterol and lipid metabolism, resulting in their accumulation,
   which facilitates HCV replication, assembly, and release ([161]48), and
   also results in hepatic steatosis in HCV patients ([162]49). Other
   pathways common to the two viruses and which have been implicated in
   the life cycle of both viruses, included amino acids metabolism and
   biosynthesis of amino acids, focal adhesion, HIF-1 signaling pathway,
   cell cycle, and lysosome ([163]Fig. 8) ([164]50[165]–[166]56). Overall,
   the transcriptome analysis suggested extensive convergence of host
   factors dependency of both viruses that are dysregulated also in the
   coinfected cells, which, most likely, enable efficient co-replication
   in the host hepatocytes.

FIG 8.

   [167]FIG 8
   [168]Open in a new tab

   Overall analysis of pathways enriched in at least one of the groups
   (SARS, HCV, and HCV-SARS-CoV-2). Heatmap of all the pathways enriched
   in at least one of the infected cell groups (HCV, SARS-CoV-2 and
   HCV-SARS-CoV-2). The dendrogram was made by hierarchical clustering
   applied on enrichment levels. Values represent log(-log[10][adjusted
   P-value]) from pathway analysis. All values below -2 and larger than 2
   were set to be -2 and 2, respectively.

DISCUSSION

   Identification of viral coinfections and understanding virus-virus
   interactions, and their influence on the host cell are emerging fields
   in virology. It is evident that preinfection with one virus may affect
   the infection cycle, disease course and outcomes of a second virus.
   Viral coinfections may result in competition, interference, and
   replication suppression or enhancement ([169]57). Most of the reported
   data describe the effect of coinfections of two viruses on the organism
   level. However, little is known about the interactions between
   coinfecting viruses at the cellular level ([170]57). Here, we provide
   the first experimental evidence for the coexistence of SARS-CoV-2 and
   HCV infections, and the interplay between both viruses. The study
   revealed a complex relationship of enhancement between the two viruses,
   where HCV infection increased the expression of the SARS-CoV-2 entry
   receptor ACE2, thus facilitating SARS-CoV-2 entry, and potentially, HCV
   entry. Thereafter, SARS-CoV-2 infection enhanced HCV replication in
   hepatocytes.

   Although interference of one virus with the replication of a
   coinfecting virus via activation of innate immune responses is the most
   common outcome of coinfections, enhancement as shown here, has also
   been acknowledged ([171]57). Relationship of enhancement was recently
   reported in coinfection of SARS-CoV-2 with influenza A virus (IAV) at
   the organism level. Increased expression of ACE2 triggered by
   preinfection with influenza A virus (IAV) significantly enhance
   SARS-CoV-2 infection ([172]58). Increased IAV viral load was also
   observed in SARS-CoV-2-IAV coinfection ([173]59). In addition, the
   SARS-CoV-2-IAV coinfection triggered severe disease and higher
   mortality compared to each infection alone ([174]59). Enhancement of
   HCV infection was observed both at the organism and cellular levels in
   coinfections with human immunodeficiency virus (HIV) and hepatitis B
   virus (HBV). A low level of HIV infection and replication was suggested
   in hepatocytes ([175]60), and an enhancing effect on HCV life cycle and
   disease outcome were reported ([176]61). The impact of HIV on HCV
   infection was suggested to be related to the HIV-mediated acquired
   immunodeficiency or direct virus-virus and virus-host interactions
   ([177]61). HIV infection was shown to directly increase HCV replication
   through enhancement of TGF-1 expression mediated by the HIV protein
   gp120 ([178]62). Moreover, accumulation of HCV RNA was observed in
   HIV-HCV coinfected cells and advanced liver fibrosis was observed in
   HIV–HCV coinfected patients ([179]63). HBV/HCV coinfection in the liver
   is rather common, with rates ranging between 1% and 15%, and is
   associated with increased liver disease severity and mortality compared
   to monoinfections ([180]64[181]–[182]66). Since both viruses primarily
   infect hepatocytes, direct interaction between the two viruses is
   possible, despite their entirely different life cycles. In vitro
   experiments suggest impaired HBV replication in HCV-infected
   hepatocytes, due to IFN signaling or direct virus-virus interactions,
   while HCV replication is not affected by HBV ([183]66, [184]67). Other
   studies suggest efficient co-replication of both viruses without
   interference ([185]68, [186]69). Here, since SARS-CoV-2 and HCV are two
   unrelated viruses, it is less probable that their coexistence in
   hepatocytes and the enhanced HCV replication in response to SARS-CoV-2
   is mediated by trans-complementation of viral proteins that directly
   facilitate their replication. It is more likely that the effects of
   these viruses on the host cell is indirect, by creating a supporting
   environment that has a synergistic effect on the replication of both
   viruses. Indeed, the changes in the host cell induced by both viruses
   converge to common signaling pathways that are hijacked by both viruses
   for their own propagation.

   Importantly, we showed enhanced SARS-CoV-2 entry in HCV-preinfected
   hepatocytes, which was associated with an HCV-mediated increase in ACE2
   expression. An intriguing question is whether the increase in ACE2
   expression is related to an HCV-specific mechanism or to a cellular
   antiviral response, which may be triggered in response to any virus
   that induces innate immune signaling. A recent study reported that ACE2
   is an interferon-stimulated gene (ISG) in airway epithelial cells and
   suggested that the interferon-mediated upregulation of ACE2 expression
   is exploited by SARS-CoV-2 to enhance infection ([187]70). However,
   another study demonstrated that not the full, but, rather, a truncated
   isoform of ACE2 that does not bind SARS-CoV-2 spike protein is an ISG
   ([188]71), and therefore, induction of IFN by a co-infecting virus is
   not a likely mechanism of enhanced cellular entry of SARS-CoV-2. In
   support of this conclusion, an IAV-mediated increase in ACE2 expression
   was shown to specifically enhance SARS-CoV-2 entry in IAV-preinfected
   cells, but not in cells infected with a panel of other viruses that
   cause common cold in humans ([189]58). Importantly, IFNα treatment did
   not promote SARS-CoV-2 infection, and did not significantly increase
   ACE2 expression. Moreover, the enhanced ACE2 expression and SARS-CoV-2
   infectivity were maintained in IAV-preinfected IFNAR–/– A549 cells
   lacking the IFN-α receptor ([190]58). In line with these observations,
   the present work showed that ACE2 expression was not enhanced by
   replication of subgenomic HCV RNA replicons or HBV, suggesting that the
   enhanced ACE2 expression in response to infection is virus-specific.

   Another important finding is the role of ACE2 in the HCV life cycle.
   Further investigation is needed to uncover the nature of ACE2-dependent
   enhancement of HCV entry. Specifically, it remains to be determined
   whether ACE2 expression levels affect HCV entry via direct interaction
   between the E1E2 envelope proteins and ACE2, rendering ACE2 an
   attachment factor, receptor, or co-receptor. Alternatively, the impact
   of ACE2 on HCV may be indirect.

   It is worth noting that most of the experiments in this study were
   performed using the Huh7.5 cancer cell line due to the limitation of
   low infection efficiency in primary hepatocytes. Further study is
   needed to confirm the results in more physiological conditions.

   In summary, this study provides the first experimental evidence for
   SARS-CoV-2-HCV coinfection of liver cells, and suggests that HCV
   infection is a risk factor for enhanced liver disease in coinfected
   patients. Although this outcome is supported by recent clinical
   findings ([191]40[192]–[193]42), the specific effects of enhanced
   SARS-CoV-2 infection and HCV replication in coinfection states on liver
   pathology remain to be evaluated in clinical settings. In a broader
   view, this study emphasized the importance of identifying coinfecting
   pathogens that increase the risk of SARS-CoV-2 infection and that may
   accelerate COVID-19-related co-morbidities.

MATERIALS AND METHODS

Cell lines.

   Huh-7.5 cells (a generous gift from Charles Rice, Rockefeller
   University), Huh7/FT3-7 cells (a generous gift from Stanley M. Lemon,
   University of North Carolina at Chapel Hill), HepG2 (ATCC), HEK293
   (ATCC), and HEK293 cells stably expressing hACE2 (HEK293-ACE2)
   (in-house preparation) were cultured in Dulbecco’s modified Eagle’s
   medium (DMEM) (Gibco), supplemented with 10% fetal bovine serum (FBS),
   1% l-glutamine, 1% penicillin-streptavidin, and 1% non-essential amino
   acids (NEAA). African green monkey kidney clone E6 cells (Vero E6, ATCC
   CRL-1586) were grown in DMEM containing 10% FBS, MEM NEAA, 2 mM
   l-glutamine, 100 U/mL penicillin, 0.1 mg/mL streptomycin, and 12.5 U/mL
   nystatin (P/S/N) (Biological Industries). Normal primary human
   hepatocytes (PHH) (Gibco) were cultured on collagen (Gibco) in
   William's Medium E (Gibco), supplemented with cocktail B (Gibco) and
   HEP-extend (Gibco). The HCV-N NS3-NS5B subgenomic replicon cell line
   was cultured as we previously described ([194]72). All cells were
   cultured at 37°C, 5% CO[2], with 95% humidity.

Preparation of HCV stock and infection.

   HJ3–5 is a H77C/JFH1 chimeric HCV containing core-NS2 proteins from
   genotype 1a and NS3-NS5B from genotype 2a (a generous gift from Stanley
   M. Lemon, University of North Carolina at Chapel Hill). Virus stocks
   were produced in Huh7/FT3-7 cells and viral titers were determined
   using the focus forming unit (FFU) assay in Huh-7.5 cells, as we
   described previously ([195]73). For all experiments with 100%
   HCV-infected Huh7.5, cells were infected with HJ3-5 at a multiplicity
   of infection (MOI) of 0.1, and passaged for at least 2 weeks until
   approximately 100% of the cells were HCV-positive, as determined by
   immunofluorescence, as we previously described ([196]72). PHH cells
   were infected with HJ3-5 at MOI 1; the HCV inoculum was incubated with
   PHH for 8 h. Then, hepatocytes were washed and incubated with fresh
   medium. Cell culture medium was replaced at 24 h intervals. HCV-cured
   cells were prepared as we previously described ([197]55). In brief,
   HCV-infected cells (~100% infected, generated as described above) were
   treated with 0.5 μM sofosbuvir, 0.5 μM daclatasvir, and 7.5 nM
   paritaprevir for 14 days. Following treatment, cells were immunostained
   with HCV-positive serum from an infected subject and then with
   anti-human 488 Alexa flour (Jackson) as secondary antibody to confirm
   absence of HCV infection. Cured cells were used for experiments 1 month
   following curing.

Preparation of SARS-CoV-2 stock and infection.

   SARS-CoV-2 (GISAID accession EPI_ISL_406862) was kindly provided by
   Bundeswehr Institute of Microbiology, Munich, Germany. Virus stocks
   were propagated (4 passages) and titered on Vero E6 cells. All
   SARS-CoV-2 virus handling was conducted in a biosafety level 3
   facility, in accordance with the biosafety guidelines of the Israel
   Institute for Biological Research.

   For HCV-SARS-CoV-2 coinfection experiments, 100% HCV-infected or
   non-infected Huh7.5 cells were seeded in 12- or 24-well plates, or on
   8-well chamber slides (Thermo) and grown overnight. The next day,
   SARS-CoV-2 stock was used to infect the cells at an MOI of 0.01 for 1,
   5, 24, or 48 h. Then, cells were lysed for RNA purification, or fixed
   for immunostaining for detection of infection.

Immunofluorescence staining.

   Cells were seeded in 8-well chamber slides (Thermo) and fixated with
   3–4% formaldehyde for 15–30 min at room temperature. To detect HCV
   infection, cells were immunostained with human serum derived from
   HCV-infected patient, followed by incubation with anti-human Cy3
   (Jackson) secondary antibody. To detect SARS-CoV-2 infection, the fixed
   cells were blocked with PBS containing 2% FBS and stained with
   hyperimmune rabbit serum from intravenous SARS-CoV-2-infected rabbits
   (in-house preparation), and then with Alexa Fluor 488-conjugated goat
   anti-rabbit IgG (Sigma). To detect ACE2 expression, cells were
   immunostained with mouse anti-ACE2 (Abcam) or goat anti-ACE2 (R &D
   systems), and then with anti-mouse 488 Alexa fluor IgG (Jackson) and
   anti-goat IgG (Invitrogen), respectively, and counterstained with DAPI.
   Images were obtained with a Zen Live Imaging (Time Lapse) microscope
   (Zeiss) or confocal microscope (LSM 780 + Chameleon Vision II).

Quantitative real-time RT-PCR.

   Total RNA was extracted from cells using a RNA kit (Qiagen). Equal
   amounts of the RNA isolated from treated or control cells were
   transcribed into cDNA, using the High-Capacity cDNA Reverse
   Transcriptase kit (Applied Biosystems) and analyzed by RT-PCR using the
   Power SYBR green Master Mix (Life Technologies). The thermal program
   included 40 cycles at 95˚ for 20 s and 60˚ for 20 s. Differential
   expression was calculated using the equation of 2^(-ΔΔCt), with GAPDH
   as an endogenous control. RT-PCR analysis was conducted using RNA from
   3 independent experiments.

   Real-time quantitative RT-PCR was carried out using primers for: the
   3-untranslated region (UTR) of HCV (FW GCCATGGCGTTAGTATGAGTGT; REV
   CCCTATCAGGCAGTACCACAA), ACE2 (FW AGGACACTGAGCTCGCTTCT; REV
   GAACTTGGGTTGGGCGCTAT), TMPRSS2 (FW AGGTGAAAGCGGGTGTGAGG; REV
   ATAGCTGGTGGTGACCCTGAG), SARS-CoV-2 spike (FW CAGATGCTGGCTTCATCAAA; REV
   GGTTGGCAATCAATTTTTGG) and HBV (FW CCACCGTGAACGCCCATC; REV
   TTGTGCCTACAGCCTCCTAATAC).

Preparation of SARS-CoV-2-spike pseudoparticles and transduction experiments.

   SARS-CoV-2-spike pseudoparticles were generated by co-transfection of
   Expi293F cells with pCMV delta R8.2, pLenti-GFP (Genecopoeia), and
   pCDNA3.1 spikeΔC19 according to manufacturer’s instructions
   (ThermoFisher Scientific) at a ratio of 1:2:1, respectively. The
   supernatant was harvested 72 h posttransfection, centrifuged at
   1500 × g for 10 min to remove cell debris, and passed through a 0.45 μm
   filter (LIFEGENE, Israel). Next, the pseudoparticles-containing
   supernatant were concentrated to 5% of their original volume using
   Amicon Ultra Centrifugal Filter Unit with 100 kDa cutoff, at 16°C
   (Merck Millipore).

   HCV-infected and non-infected cells were seeded in 96-well plates
   (Greiner) or 8-well chamber slides (Thermo). The following day,
   concentrated pseudoparticles were added in the pre-seeded wells and
   incubated for 48 h. Thereafter, the medium was replaced with fresh DMEM
   without Phenol Red, and the plates were imaged 24 h later with the
   IncuCyte ZOOM system (Essen BioScience), under a 10× objective. Default
   IncuCyte software settings were used to calculate number of
   GFP-positive cells in four GFP-channel images from each well.

   To inhibit infection, HCV-infected and non-infected cells were seeded
   on 8-well chamber slides (Thermo). Cells were incubated with 5 mg/mL
   anti-ACE2 antibody (R &D systems) for 1 h, and then overnight with
   pseudoparticles. Images of the cells were obtained with a Zen Live
   Imaging (Time Lapse) microscope (Zeiss) and quantified using Image Pro
   10 software.

Transfections.

   Huh7.5 cells were plated in a 24-well plate, 18 to 24 h prior to
   transfection. Fresh culture medium was added to each well 1 h before
   transfection. Thereafter, the cells were transfected with 0.5 μg
   pcDNA3.1+/C-(K)DYK-ACE2 (GenScript) diluted in 25 μL serum-free DMEM,
   or mock transfected, and PolyJet reagent (SignaGen Laboratories),
   according to the manufacturer’s instructions. Medium was replaced after
   6 h and 48 h posttransfection, both transfected and non-transfected
   cells were infected with HCV at an MOI of 0.1. Cells were harvested 72
   h postinfection for RNA isolation.

   For knockdown experiments, Huh7.5 cells were seeded in a 24-well plate
   and incubated until they reached 80% confluence, which was generally
   24 h after seeding. Cells were then transfected with ON-TARGETplus
   Human ACE2 siRNA (Dharmacon) or Non-targeting Pool (Dharmacon) using
   RNAiMAX (Invitrogen), incubated for 24 h and then infected with HCV at
   an MOI of 0.1. Cells were harvested 72 h postinfection for RNA
   isolation.

Expression and purification of the spike receptor binding domain.

   The receptor binding domain (RBD) (residues 319–541) of SASR-CoV-2 were
   subcloned into a pMT-BiP-His vector (Invitrogen) between the BamHI and
   NotI restriction sites, downstream to the BiP signal peptide and in
   frame with a C-terminal 2×StrepTag, cleavable by HRV 3C protease. The
   plasmid (pMT-RBD) was then co-transfected into S2 Drosophila cells with
   a puromycin resistance plasmid (pCo-Puro) at a 20:1 ratio,
   respectively. Cells were selected using puromycin until a stable cell
   line was obtained.

   S2 cells stably expressing the SARS-CoV-2 RBD were grown to in ESF921
   (Expression Systems) serum-free medium, at 27° C. When cells reached a
   density of 1 × 10^7 cells/mL, protein expression was induced with
   600 μM CuSO[4]. Seven days post-induction, cells were pelleted by
   centrifugation (500 × g/4°C/10 min). The medium was further clarified
   by centrifugation (14,000 × g/4°C/10 min), concentrated and then
   exchanged for 20 mM Tris pH 8.0, 150 mM NaCl using a tangential flow
   filtration (TFF) system (PALL). The concentrated sample was then loaded
   onto a StrepTactin column (GE Healthcare), which was then treated
   following the manufacturer protocols. Elution fractions were analyzed
   by SDS-PAGE, pooled, and placed on a size exclusion chromatography
   column (Superdex200, GE health care) pre-equilibrated with 20 mM Tris
   pH 8.0, 150 mM NaCl. Relevant fractions were concentrated to 2 mg/mL,
   aliquoted, flash-frozen in liquid nitrogen, and stored at −80°C until
   use.

Flow cytometry.

   To evaluate ACE2-RBD binding, 100% HCV-infected Huh7.5 cells,
   non-infected Huh7.5 cells, HEK293 and HEK293-ACE2 cells, were harvested
   and fixated with 4% paraformaldehyde. The fixed cells were then
   incubated for 1 h at room temperature with 1 μg/mL purified recombinant
   RBD diluted in 2.5% BSA. Cells were washed and then incubated for 1 h
   at room temperature with 555 fluorophore-conjugated anti-RBD antibody
   (Sino biological). Flow cytometry analysis was performed using a
   Gallios Flow Cytometer (Beckman Coulter) and FlowJo software (BD). To
   evaluate cell surface level of ACE2, the cells were treated as above,
   but incubated with rabbit anti-ACE2 antibody (Abcam) followed by 488
   conjugated goat anti-rabbit secondary antibody.

Cell-cell fusion assay.

   All 100% HCV-infected or non-infected Huh7.5 cells were seeded in
   24-well plates. The next day, cells were transfected with pCDNA3.1
   spikeΔC19 using PolyJet (Signagen Laboratories), according to
   manufacturer protocol. After a 4-day incubation, CellMask
   (Thermo-Fisher) membrane dye was applied according to the
   manufacturer’s instructions. Images were taken using a Zen Live Imaging
   (Time Lapse) microscope (Zeiss), and then cells were fixated with 4%
   paraformaldehyde and stained with DAPI. The fusion index was calculated
   by measuring the ratio between percent of area stained with DAPI
   (nuclear stain) and the percent of area stained with membrane dye using
   ImageJ.

Preparation of HBV lentiviruses and transduction experiment.

   HEK293T cells were seeded in 9 cm dishes and, the next day, were
   transfected with the lenti expression vector, PHR- HBV or PHR-GFP
   packaging vector (cytomegalovirus delta‐R8.9), and VSV‐G vector, using
   PEI (Sigma). Growth medium was replaced after 8 to 16 h and
   lentivirus-containing supernatant was harvested 24 and 48 h later.

   For HBV lentivirus infection, HepG2 cells at 50% – 70% confluence were
   transduced. The following day, viral medium was added to the cells with
   8 μg/mL Polybrene (Sigma-Aldrich). After 24 h, the cells were washed
   with PBS and used for experiments.

Western blot.

   Total cell lysates were separated by SDS-PAGE. The proteins were
   transferred to nitrocellulose membranes and incubated overnight with
   primary antibodies specific to ACE2 (Abcam), HCV Core (Invitrogene),
   and β actin (Millipore) and then with HRP-conjugated secondary antibody
   for 1 h. Reactive bands were visualized with ECL reagent (Pierce) using
   the Platinum Q9 gel documentation system. Protein levels were
   quantified and normalized to β actin levels using Image Studio Lite
   software.

Analysis of ACE2 expression in liver biopsies.

   RNA-seq data were obtained from the NCBI Gene Expression Omnibus
   ([198]GSE84346) using the R statistical software
   ([199]www.r-project.org). The data set included gene expression of
   liver biopsies from normal and HCV-infected patients. HCV-infected
   patients was sub-classified into low interferon-stimulated genes and
   high interferon-stimulated genes, and only patients with low
   interferon-stimulated genes were used for the analysis. Student's t
   test was used to compare the distributions of ACE2 expression.

Preparation of HCV pseudoparticles (HCVpp) and transduction experiments.

   HCVpp were generated by transfecting HEK293T cells with a plasmid
   encoding the HCV E1E2 gene (a generous gift from Prof. Mansun Law, The
   Scripps Research Institute), using PEI. Twenty-four hours after
   transfection, VSVΔG-G viruses (containing a GFP-reporter) were added to
   the cell cultures. Six hours later, plates were washed 6 times with
   PBSX1, and fresh medium was added. After 48 h, the supernatant was
   collected and HCVpp were concentrated x40 using Amicon Ultra with
   100 kDa cutoff. Huh7.5 cells were seeded in 24-well plates and
   incubated for 24 h. Cells were then transfected with a plasmid encoding
   the ACE2 gene (GenScript), using Lipofectamine 3000 (Invitrogen)
   according to the manufacturer’s instructions. Two days after
   transfection, concentrated HCVpp were added to the wells and the
   GFP-positive cells were monitored and counted using the IncuCyte ZOOM
   system (Essen BioScience).

Assessment of HCV binding to hepatocytes.

   Huh 7.5 cells were seeded 24 h before transfection to reach 90%
   confluence at the time of transfection. Cells were then transfected
   with a plasmid encoding the ACE2 gene (GenScript), using Lipofectamine
   3000 (Invitrogen) according to manufacturer protocol. The cells were
   collected 48 h after transfection, and incubated with HCV at an MOI of
   1 for 1 h, at 4°C. Thereafter, they were gently washed 3 times with PBS
   to remove unbound virus, harvested, RNA was isolated, and qRT-PCR was
   performed with HCV-specific primers.

RNA-seq analysis.

   We performed RNA-seq for Huh7.5HCV cells (100% HCV-infected), Huh7.5
   cells infected with SARS-CoV-2 for 48 h, Huh7.5HCV cells coinfected
   with SARS-CoV-2 for 48 h, and non-infected Huh7.5 cells as control.

(i) Library preparation.

   Total RNA from cells (generated as described above) was purified using
   the RNeasy minikit (Qiagene). Next, 1 μg of total RNA was treated with
   the NEBNext poly (A) mRNA Magnetic Isolation Module (NEB). RNA-seq
   libraries were produced using the NEBNext Ultra RNA Library Prep Kit
   for Illumina (NEB). For each tested group 3 biological replicates were
   performed. All libraries were sequenced by Illumina MiSeq platform with
   single-end reads of 50 bp, according to the manufacturer’s
   instructions.

(ii) Sequence alignment.

   RNA-seq analysis was performed on data from SARS-CoV-2-infected cells
   (n = 3) and HCV+SARS-CoV-2 coinfected cells (n = 3) obtained in this
   study, and on data from HCV-infected cells (n = 6) and uninfected cells
   obtained from Perez et al. ([200]55). Raw reads were aligned to the
   NCBI RefSeq genes from the GRCh37/hg19 human genome assembly using
   RNA-seq by expectation maximization pipeline (RSEM) ([201]74).

(iii) Differential expression analysis.

   Differential gene expression analysis was conducted using the DESeq2
   ([202]75) package (release 1.30.1) in R (version 4.0.5). Specifically,
   data from HCV-infected, SARS-CoV-2-infected, and
   HCV-SARS-CoV-2-coinfected cells were compared separately to data from
   non-infected cells, using P-value (adjusted by Benjamini–Hochberg
   method) of 10e-5 and the absolute fold change of 0.5 as cutoffs. To
   avoid biases resulting from different number of replicates, we
   iteratively sampled 3 out of 6 samples for the HCV and non-infected
   categories, and used the average value over iterations.

(iv) Pathway enrichment analysis.

   Pathway analysis of differentially expressed genes (both up- and
   downregulated genes) was performed with ‘KEGGprofile’ R package
   (release 1.32.0) using the find_enriched_pathway function with the
   following parameters: returned_adjpvalue = 0.05, download_latest = T.

(v) Data visualization.

   Volcano plots of differential gene expression were generated using the
   ‘EnhancedVolcano’ package (release 1.8.0) in R. Venn diagrams
   visualized intersection of differentially expressed genes between HCV,
   SARS-CoV-2, and SARS-CoV-2+HCV groups ‘BioVenn’ R package (release
   1.1.3).

   Barplots were prepared to present the significance of differentially
   enriched pathways for the different sets, using ggplot2 R package
   (release 3.3.5).

   A heatmap was prepared using ‘pheatmap’ R package (release 1.0.12), to
   visualize the levels of pathway enrichment for different sets. The
   dendrograms accompanying the map was prepared by a built-in
   hierarchical clustering approach. For the heatmap, enrichment level was
   set to be transformed, such as log(-log[10][adjusted P-value]),
   adjusted P-values from the pathway analysis. All the values below -2
   and greater than 2 were replaced by -2 and 2, respectively.

Data availability.

   The RNA-seq data generated during the current study have been deposited
   in GEO repository with accession code [203]GSE207151.

ACKNOWLEDGMENTS