key: cord-0962925-d7n5az69
authors: Chen, J; Neil, JA; Tan, JP; Rudraraju, R; Mohenska, M; Sun, YBY; Sun, G; Zhou, Y; Li, Y; Drew, D; Pymm, P; Tham, WH; Rossello, FJ; Nie, G; Liu, X; Subbarao, K; Polo, JM
title: An iTSC-derived placental model of SARS-CoV-2 infection reveals ACE2-dependent susceptibility in syncytiotrophoblasts
date: 2021-10-29
journal: bioRxiv
DOI: 10.1101/2021.10.27.465224
sha: 6777a981eda699a92c058dc13417e2a91a9023a0
doc_id: 962925
cord_uid: d7n5az69

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causing coronavirus disease 2019 (COVID-19) has caused a global health crisis. The primary site of infection is in the respiratory tract but the virus has been associated with a variety of complications involving the gastrointestinal and cardiovascular systems. Since the virus affects a variety of tissue types, there has been interest in understanding SARS-CoV-2 infection in early development and the placenta. ACE2 and TMPRSS2, two genes that are critical for SARS-CoV-2 virus entry are expressed in placenta-specific cell types including extravillous trophoblasts (EVTs) and especially, syncytiotrophoblasts (STs). The potential of SARS-CoV-2 to infect these placental cells and its effect on placental development and function is still unclear. Furthermore, it is crucial to understand the possible mechanism of vertical transmission of SARS-CoV-2 through the placenta. Here, we developed an in vitro model of SARS-CoV-2 infection of placental cell types using induced trophoblast stem cells (iTSCs). This model allowed us to show that STs but not EVTs are infected. Importantly, infected STs lack the expression of key differentiation genes, lack typically observed differentiated morphology and produce significantly lower human chorionic gonadotropin (HCG) compared to non-infected controls. We also show that an anti-ACE2 antibody prevents SARS-CoV-2 infection and restores normal ST differentiation and function. We highlight the establishment of a platform to study SARS-CoV-2 infection in early placental cell types, which will facilitate investigation of antiviral therapy to protect the placenta during early pregnancy and development.

In 2020, a novel coronavirus called severe acute respiratory syndrome coronavirus (SARS-CoV-2) emerged and spread worldwide, infecting millions of people and causing Coronavirus Infectious Disease 2019 . COVID-19 was declared a public health emergency by the World Health Organization in February 2020 (Zhu et al., 2020) . The infection is asymptomatic or mild in the majority of cases, however, patients can develop severe illness associated with acute respiratory distress syndrome (ARDS) and organ failure (Aguiar et al., 2020 , Brosnahan et al., 2020 , Long et al., 2020 . This disease can lead to death in 26% of critically ill patients (Grasselli et al., 2020) to as high as 61.5% in another report . This has led to an imperative to understand the extent of cells that can be infected, the mechanism of infection and develop adequate treatments.

The cellular receptor for SARS-CoV-2 is angiotensin-converting enzyme 2 (ACE2) that is present in many cell types such as lung alveolar epithelial cells, enterocytes, venous endothelial cells, and smooth muscle cells (Bhalla et al., 2020 , Hamming et al., 2004 , Jing et al., 2020 . Along with ACE2, transmembrane serine protease 2 (TMPRSS2) has been identified as an entry factor for SARS-CoV-2 infection. Cells that express both receptors are susceptible to infection (Hoffmann et al., 2020) .

Several reports have utilised in vitro derived systems to understand SARS-CoV-2 infection and screen for antiviral drugs or potential treatments. Conventionally, Vero cells, which have been derived from kidney epithelial cells of the African green monkey, have been used to study several viral infections in vitro (Govorkova et al., 1996 , Ksiazek et al., 2003 , and more recently one of the in vitro cell models used to replicate and isolate SARS-CoV-2 (Mantlo et al., 2020 , Takayama, 2020 , Zhou et al., 2020 . However, these cells fail to recapitulate many aspects of infection in human cells. Therefore, in order to facilitate a more accurate model relevant to human infectivity, pluripotent stem cells (PSCs)-derived in vitro models of cardiac, kidney, lung and intestinal cells have been used as platforms to study SARS-CoV-2 (Bailey et al., 2021 , Lamers et al., 2020 , Monteil et al., 2020 . These models are especially valuable as they not only better represent the nuances of different human cell types but are scalable and tractable.

Despite advances in understanding SARS-CoV-2 infection in several somatic cell types, little is known about the virus and its impact on early development of the embryo or placenta. There have been clinical reports on the effects of SARS-CoV-2 in the early and late pregnancy that show increased risk of complications in infected patients (Garrido-Pontnou et al., 2021 , Jang et al., 2021 . In the placenta, SARS-CoV-2 infection was observed within the villi or intervillous space (Best Rocha et al., 2020 , Hecht et al., 2020 , Morotti et al., 2021 , Patane et al., 2020 . These reports observed that villous syncytiotrophoblasts were the primary target of infection based on histopathological evidence (Hecht et al., 2020 , Morotti et al., 2021 , Patane et al., 2020 . In spite of the clinical evidence, the effect of SARS-CoV-2 infection on placental health and fetal development is still unclear. An in vitro-derived model is key to understanding the effects of SARS-CoV-2 infection in the placenta.

The derivation of trophoblast stem cells (TSCs) in vitro from first trimester placenta or blastocysts capable of differentiating into both main placental cell types: extravillous cytotrophoblasts (EVTs) and syncytiotrophoblasts (STs), facilitates the study of placenta biology and pathology in vitro (Okae et al., 2018) . Furthermore, we and others recently reported that fibroblasts can be reprogrammed into induced trophoblast stem cells (iTSCs) which are molecularly and functionally similar to TSCs (Castel et al., 2020 . Therefore, TSCs and iTSCs constitute a scalable and tractable model to study in vitro placental biology. Here we utilised iTSCs to generate an in vitro model of placenta infection by SARS-CoV-2. We found that STs were the only cell type that was productively infected, leading to phenotypic, transcriptomic and metabolic changes, with functional consequences. Importantly, we showed that infection and concomitant cellular changes in STs could be prevented using ACE2 antibodies.

We first verified the expression of ACE2 in the 1st trimester placenta by immunohistochemistry. Trophoblast cells within the placental villi, especially STs lining the villous surface (marked by HCG staining), were strongly positive for ACE2 ( Fig 1A) . In the maternal decidua, multiple cells were also faintly stained for ACE2, including EVTs which were identified by HLA-G positivity on serial sections ( Fig 1B) . This is consistent with previous reports that STs and EVTs express ACE2 (Pringle et al., 2011 , Valdes et al., 2006 . We have previously reported the in vitro generation of induced trophoblast stem cells (iTSCs) from fibroblasts, which could be used to model placental cell differentiation into EVTs and STs . These EVTs and STs expressed typical cell-specific markers HLA-G and HCG respectively ( Fig   1C) . We examined the expression of ACE2 in iTSCs before and after differentiation into EVTs and STs in two independent donor cell lines (32F and 55F) (Fig 1C, Fig   S1A-C) , and detected ACE2 mRNA and protein in EVTs and STs but not in iTSCs ( Fig 1C-D) . EVTs and STs also expressed higher levels of TMPRSS2 relative to iTSCs ( Fig 1D) . The expression of these 2 important factors, particularly ACE2, in EVTs and STs suggests a potential susceptibility to SARS-CoV-2 infection.

In order to test if these cells were susceptible to infection, we infected iTSCs, as well as EVTs and STs towards their terminal differentiation at day 6 and day 5 respectively (Fig 1E) . In iTSCs and EVTs, no significant increase in viral titres was observed in the supernatant over time and intracellular dsRNA was not detected at day 3 post infection, which would have been evidence of active viral replication within the cells (Valdespino-Vazquez et al., 2021) (Fig 1F-I) . In contrast, in STs viral titres increased in the supernatant 3 days after infection ( Fig 1J) ; infection was confirmed by identification of dsRNA within ST cells (Fig 1K) , which was similar to a report on SARS-CoV-2 infection of placental tissue (Hosier et al., 2020) . 

Since STs are the only cell type that was infected by SARS-CoV-2 in our model, we focused on this cell type for further study and dissected dynamics of SARS-CoV-2 infection in-depth. Placental development is critically dependent on trophoblast cell differentiation into EVTs and STs (Arnholdt et al., 1991) ; therefore, we wanted to understand how early during differentiation STs could be infected. We first analysed the expression of ACE2 during the differentiation of iTSCs into STs and found that cells begin to express ACE2 as early as day 2 of differentiation ( Fig 2A) . We verified this increase of expression by immunofluorescent staining for ACE2 in both cell lines ( Fig S1A and B ). To test whether the expression of ACE2 was sufficient to confer susceptibility to infection by SARS-CoV-2, we infected STs at days 2, 3, and 4 of differentiation ( Fig 2B) and found that cells could be infected as early as day 2 of differentiation ( Fig 2C) . Positive/productive infection was identified by the presence of dsRNA within the cells and presence of infectious virus in the cell supernatant in both cell lines ( Fig S2C) . In addition, we observed that the differentiation potential of STs was affected as infected (dsRNA+) cells appeared morphologically more immature (please see methods) than non-infected (dsRNA-) cells and had lower signal of HCG despite being multinucleated, suggesting that SARS-CoV-2 infection severely affects cellular differentiation ( Fig S2D) . We further assessed levels of HCG in cells and cellular morphology, to quantify proportions of differentiated and undifferentiated cells. We then categorized differentiated and undifferentiated cells as either dsRNA+ or dsRNA-to determine which cells were infected. We found that the ST cells that were dsRNA+ within a virus-infected culture (cells exposed to the virus and were indeed infected) were significantly less differentiated than dsRNA-cells (cells exposed to the virus but were not infected) within the same culture ( Fig   2D) . Importantly, dsRNA-cells within the virus-infected cultures had a similar differentiation potential as mock-infected cells. Taken together with the result seen in dsRNA+ cells, the data suggest that SARS-CoV-2 infection hinders the differentiation of STs.

Since STs produce human chorionic gonadotropin (HCG), the hormone that is vital for maintaining pregnancy, we analysed the metabolic activity in the cultures during differentiation (Nwabuobi et al., 2017) . We observed that HCG levels were significantly lower in infected cells throughout differentiation (day 2, 3 and 4) compared to mock controls ( Fig 2E) . We also analysed cell death using a lactate dehydrogenase (LDH) assay and found that infected cells released higher levels of LDH in the supernatant compared to mock controls, indicating significantly more cell death in the SARS-CoV-2infected cultures ( Fig 2F) . Taken together, SARS-CoV-2 infection of STs leads to an impairment of differentiation potential, lack of HCG production and increased cell death during differentiation. technical replicates of a representative experiment (E and F). *p<0.05, **p<0.01, ***p<0.005, ****p<0.001

To understand the effects of infection on the cells in greater depth, we analyzed the transcriptome of ST cells infected at day 3 of differentiation and 3 days post-infection to reach the theoretical fully differentiation time course (day 6), compared to mock-infected cells. Correspondence analysis (CoA) indicated that SARS-CoV-2infected cells were transcriptionally divergent from mock-infected cells ( Fig 3A) .

Differential gene expression (DGE) analysis identified 155 genes upregulated and 140 genes downregulated in infected cells (Table S1 ). Importantly, we identified that among the differentially expressed genes (DEGs), ST-specific genes such as CGA and PSG3 were significantly down regulated in SARS-CoV-2 infected cells compared to mock-infected cells, suggesting cultures were not differentiating into STs as efficiently as were mock-infected controls. Upregulated genes were related to interferon signaling (IFNL1, IFNB1, IFIH1) ( Fig 3B) . Other genes associated with TNFα signaling via NFKB such as MAPK4, STAT1, RELB and NFKBIA were also highly upregulated in infected cells compared to mock-infected cells, indicating that there was a strong innate response to viral infection (Mantlo et al., 2020) .

Furthermore, gene ontology analysis showed an enrichment of viral response, along with antiviral mechanism (IFN-stimulation) and response to type I interferon in the significantly upregulated genes in infected cells. In contrast, genes downregulated upon infection were enriched in cellular and metabolic processes such as Asparagine N-linked glycosylation, and response to endoplasmic reticulum stress ( Fig 3C) . Although not strongly enriched, we did identify 14 significantly up-regualted DEGs involved in positive regulation of cell death such as, EGR1, FOS, SNCA and PHLDA1. Moreover, expression levels of TSC, EVT and ST identity-specific genes, showed that while mock-infected cells have robust expression of ST-related differentiation genes, SARS-CoV-2 infected cultures failed to upregulate ST associated genes, and infected cultures also had higher levels of expression of TSC-related genes; suggesting that cells were less differentiated, consistent with our observations that infected cultures have impaired differentiation potential ( Fig 3D) .

Taken together, we show that SARS-CoV-2 infection of ST cultures elicits an NFKB-mediated inflammatory response and has a negative impact on the differentiation pathway of cells. 

Finally, as the high expression of ACE2 seemed to correlate well with the susceptibility of STs to SARS-CoV-2, we investigated whether ACE2 could be targeted to inhibit SARS-CoV-2 entry into STs using an anti-ACE2 antibody. We generated and characterised antibodies against recombinant human ACE2 from a phage library (Fig S3A) . We then validated the binding affinity of these clones and utilized them in our antibody blocking experiments ( Figure S3B -C). Among the clones validated, we selected WCSL141 and WCSL148 for the blocking experiments. We show that SARS-CoV-2 infection of STs was blocked by the addition of two anti-ACE2 antibodies (Virus αACE2 ) ( Fig 4A) . We did not detect infectious virus or viral genome copies in culture supernatants while both were detected in virus-treated control antibody conditions (Virus Ctrl ) demonstrating that ACE2 antibody inhibits virus entry ( Fig 4B) . We further analysed the transcriptomes of cells from these separate conditions. Hierarchical clustering of samples showed that cultures blocked with anti-ACE2 antibody (Virus aACE2 ) clustered closely with other mock-infected samples and were separate from infected cells treated with a control antibody (Virus Ctrl ) ( Fig 4C) . CoA also showed a divergent transcriptome of cells from the Virus Ctrl condition from the rest (Fig S4A) . In addition, we also verified SARS-CoV-2 expression in the samples. As expected, we observed high expression in Virus Ctrl , and neither in uninfected cells, nor, surprisingly, in Virus aACE2 . (Fig S4B,   S4C ). We further verified our results by performing unsupervised k-means clustering of all genes across the samples, and identified unique clusters of genes that were upregulated and downregulated in Virus Ctrl in contrast to the other samples (Fig 4D,   Table S1 ). Interestingly, we did not identify a specific signature in the treated and infected cells (Virus aACE2 ). We then performed functional enrichment analysis on clusters 1 and 2, which contained strongly upregulated or downregulated in infected samples (Virus Ctrl ). As before, we found an upregulation of host-defense response to virus and IFN signalling pathways, as well as downregulation of cellular and metabolic processes, such as transport of small molecules and nucleobase-small molecule metabolic process (Fig S4D) . We explored placental identity genes and found that Virus aACE2 cultures exhibited an increase in expression of ST identity genes, similar to mock-infected controls ( Fig 4E) . As the expression levels of differentiation-specific genes was similar to mock controls , we showed that anti-ACE2 antibody blocking rescued the differentiation potential of cells compared to SARS-CoV-2 infected cells, resulting in a similar number of cells with morphological features akin to mock-infected controls (Fig 4F) . We went on to assess whether the restoration of differentiation potential would also lead to a restoration of metabolic processes in STs. We observed that Virus aACE2 cultures showed comparable production of HCG to mock-infected controls ( Fig 4G) and lower levels of LDH compared to Virus Ctrl cells (Fig 4H) . Taken together, we demonstrated that SARS-CoV-2 infects ST cells and affects their differentiation and metabolic activity. Cells could differentiate and were metabolically active when infection was prevented by blocking ACE2. 

The use of in vitro models of different cell types to study SARS-CoV-2 infection allows us to understand inherent mechanisms. It also enables us to perform and test specific drug treatments for affected cell types (Han et al., 2020 , Pei et al., 2020 . In our in vitro model, SARS-CoV-2 is able to infect STs but not iTSCs or EVTs. While disparities between in vitro culture systems and clinical specimens have been observed in the lung with SARS-CoV-2 infected alveolar type 1 (AT1) cells observed in organoids but not isolated lung tissue (Pei et al., 2020 , Bradley et al., 2020 , our data are consistent with histopathological studies using clinical specimens. These indicate that STs in the intervillous space are the typical cells that harbor SARS-CoV-2 in infected placentas (Garrido-Pontnou et al., 2021 , Hecht et al., 2020 , Jang et al., 2021 , Morotti et al., 2021 , Patane et al., 2020 .

Although EVTs were not infected by SARS-CoV-2 in our model, others have reported that these cells are susceptible to viruses like adenovirus (Koi et al., 2001) . The reason for lack of SARS-CoV-2 infection in the in vitro generated EVTs despite evidence of ACE2 and TMPRSS2 expression is unclear and will require further investigations.

SARS-CoV-2 was able to infect STs during differentiation, which led to cell death and impaired differentiation. Since iTSC derived STs are a model of early placentation, these results support clinical evidence that the virus could affect the placenta in early development (Valdespino-Vazquez et al., 2021) . Furthermore, the sensitivity and tractability of our in vitro ST model enabled the assessment of functional impairments resulting in reduced levels of HCG production. Reduced HCG production may be associated with complications in pregnancy, including early miscarriage (Jing et al., 2020) . We were also able to determine the phenotypic effects of the virus on STs, which corresponded transcriptionally to the lack of expression of genes typical of ST differentiation and identity. Consistent with other reports of SARS-CoV-2 infection in a variety of cell types, we identified an upregulation of viral response and innate immunity genes. Importantly, SARS-CoV-2 infection elicited an IFN response in our model, similar to responses in other cell types such as lung and cardiac cells , Li et al., 2021 , Mulay et al., 2021 . Downregulation of genes involved in cellular processes and function were also observed (Daniloski et al., 2021 , Suryawanshi et al., 2021 . Although, as demonstrated by our results, this infection model can be of great utility, there are limitations. For example, the decidua contains 10-20% of macrophages but we cannot model their effects in this system (Kreis et al., 2020, Manaster and Mandelboim, 2010) . In the future, complex models that include immune cells could be used to enhance current models, as has been done with brain slice cultures and microglia from iPSC derived models (Grubman et al., 2021) . Recent studies have reported that host cell factors such as ACE2, TMPRSS2 or cathepsins are vital for SARS-CoV-2 entry and could be utilized as potential therapeutic targets against infection (Dong et al., 2020 , Hoffmann et al., 2020 . We also reported that blocking viral infection through ACE2 blockade restores the functional phenotype in STs, similar to the rescue of function in lung and cardiac cells through the inhibition of ACE2 or TMPRSS2 activity , Hoffmann et al., 2021 , Pei et al., 2020 , Bojkova et al., 2020 , Perez-Bermejo et al., 2021 . More importantly, this in vitro derived placental model allowed us to generate a quick and effective system.

We envision that our model will facilitate a deeper understanding of COVID-19 pathogenesis and will provide a platform for drug discovery and potential treatments. 

J.M.P. and X.L are inventors in a patent related to the generation of iTSCs filed by Monash University.

Ethics approval for the use of first trimester human placental tissues for research was obtained from the Human Ethics Committee at Monash Health 

Induced trophoblast stem cells (iTSCs) were maintained as previously , Okae et al., 2018 . Briefly, iTSCs were cultured in TSC medium consisted of DMEM/F-12, GlutaMAX (ThermoFisher) supplemented with 0.3% BSA (Sigma), 0.2% FBS (ThermoFisher), 1% ITS-X supplement (ThermoFisher), 0.1 mM 2-mercaptoethanol (ThermoFisher), 0.5% penicillin-streptomycin (ThermoFisher), 1.5 μg/ml l-ascorbic acid (Sigma), 5 μM Y27632 (ROCK inhibitor; Selleckchem), 2 μM CHIR99021 (Miltenyi Biotec), 0.5 μM A83-01 (Sigma), 1 μM SB431542, 50 ng/ml EGF (Peprotech) and 0.8 mM VPA (Sigma) onto 5 μg/ml collagen IV (Sigma-Aldrich)-coated plates. The iTSCs were routinely passaged every 4-5 days with medium replacement performed every other day.

Differentiation of iTSCs into STs and EVTs was performed and modified as previously described (Okae et al., 2018) . For the differentiation of iTSCs into STs, iTSCs were seeded at a density of 3.75 × 10 4 cells per well onto a 24-well plate pre-coated with 2.5 μg/ml collagen IV (Sigma) and cultured in 500 μl ST differentiation medium (DMEM/F-12, GlutaMAX (ThermoFisher) supplemented with 0.3% BSA (Sigma), 4% KSR (ThermoFisher), 1% ITS-X supplement (ThermoFisher), 0.1 mM 2-mercaptoethanol (ThermoFisher), 0.5% penicillin-streptomycin (ThermoFisher), 2.5 μM Y27632 (Selleckchem) and 2 μM forskolin (Selleckchem)).

Medium was replaced on day 3 of differentiation, and cells were analysed on day 6.

For the differentiation of iTSCs into EVTs, iTSCs were seeded at a density of 3.4 × 10 4 cells per well onto a 24-well plate pre-coated with 1 μg/ml collagen IV (Sigma) and cultured in 500 μl EVT differentiation medium (DMEM/F-12, GlutaMAX (ThermoFisher) supplemented with 0.3% BSA (Sigma), 4% KSR (ThermoFisher), 1% ITS-X supplement (ThermoFisher), 0.1 mM 2-mercaptoethanol (ThermoFisher), 0.5% penicillin-streptomycin (ThermoFisher), 2.5 μM Y27632 (Selleckchem), 100 ng/ml hNRG1 (Cell Signaling), 7.5 μM A83-01 (Sigma) and 2% Matrigel (Corning).

On day 3 of differentiation, the medium was replaced with EVT differentiation medium without hNRG1, and Matrigel (Corning) was added to a final concentration of 0.5%. On day 6 of differentiation, EVT differentiation medium was replaced without hNRG1 (Cell Signaling) and KSR (ThermoFisher), and Matrigel (Corning) was added to 0.5% final concentration. The cells were cultured for an additional 2 days before analyses were performed.

Placental cells (TSCs, terminally differentiated EVTs/STs and differentiating STs) in 24 well plates were infected in duplicate or triplicate with 10 4 tissue-culture infectious dose 50 (TCID 50 ) of SARS-CoV-2 (Australia/VIC01/2020) for 1h. Virus was removed and cells cultured in cell type-specific medium for 3 days. Supernatants were collected and medium replaced daily. Median TCID 50 in supernatants were determined by 10-fold serial dilution in Vero cells and calculated using the Reed and Muench method. RNA was extracted from supernatants using the QIAamp Viral RNA mini kit (Qiagen) and E-gene expression assessed using the SensiFAST Probe

No-Rox One

Step Kit (Bioline) and the following primers/probes: Fwd:

5'-ACAGGTACGTTAATAGTTAATAGCGT'-3, Rev: ATATTGCAGCAGTACGCACACA and Probe: FAM-ACACTAGCCATCCTTACTGCGCTTCG-BBQ. Viral genomes were interpolated using a standard curve generated by a plasmid containing the E-gene.

Each experiment was repeated independently at least twice.

Biopanning for anti-ACE2 human antibodies using the CSL human antibody phage library was performed as previously described (Panousis et al., 2016) . Phages 

Affinity determination measurements were performed on the Octet RED96e 

For ACE2 

Cell quantification was performed using the particle analysis option of the ImageJ software (http://rsb.info.nih.gov/ij/). Four fields of view taken at 10x magnification were scored first for DAPI-positive nuclei, followed by quantification of HCG and dsRNA positive cell bodies. All data were analyzed by one-way analysis of variance followed by Bonferroni's post-hoc-test to calculate p values. Data were quantified from a total of three independent experiments.

RNA was extracted from cells using RNeasy micro kit (74004, Qiagen) and QIAcube software.

Pre-processing RNA-seq Raw next generation RNA sequencing (RNA-seq) reads were obtained in FASTQ format, and prior to demultiplexing the forward read FASTQ was trimmed with trimmomatic to 18 nucleotides (nt) (the targeted read length as described above) with the following parameters: SE -phred33 CROP:18 MINLEN:18 (Bolger et al., 2014) . FASTQ files were then demultiplexed with sabre (Joshi, 2011) with the parameters pe -c -u -m 1 -l 10 -n for the barcode indexes as stated above. Following this, demultiplexed sample reads were filter-trimmed with trimmomatic to the targeted read length of 101 nt, with the parameters SE -phred33 CROP:101 MINLEN:10 (Bolger et al., 2014) . Sequencing reads were then mapped to a customised genome, composed of both GENCODE's GRCh38.p13 and human SARS-CoV2 (RefSeq -NC_045512.2; see "Custom genome for mapping" below for further details), with STAR v2.5.2b (Dobin et al., 2013) and the parameters:

--outSAMattributes All --alignIntronMax 1000000 --alignEndsType Local. Aligned BAM files were then sorted and indexed with sambamba (Tarasov et al., 2015) using default parameters; followed by deduplication by unique molecular identifiers (UMIs)

using Je's (v1.2) je markdupes function, with parameters: MM=0

REMOVE_DUPLICATES=true ASSUME_SORTED=true (Girardot et al., 2016) .

Read counts were then generated with Subread's (v1.5.2) featureCount function (Liao et al., 2013) , using default parameters.

For each set of analyses (STs infected with virus, STs infected with virus and treated aACE2), genes mapped to the hSARS-CoV2 were first removed, and following this genes with low counts were filtered out. Specifically, genes with less than 5 raw read counts across all samples were removed, and genes with at least 1 count per million (CPM) in a minimum of 2 samples were kept. Prior to library size normalisation, normalisation factors were calculated with EdgeR's (v3.32.1) calcNormFactors function (Robinson et al., 2010) . For differential gene expression analysis, normalization and transformation were performed with Limma's (v3.46.0) voom function (Ritchie et al., 2015 , Law et al., 2014 . Differential gene testing was performed with Limma's lmFit, makeContrasts, contrasts.fit, and eBayes functions.

For visualization purposes, this data was log2 CPM transformed using EdgeR's cpm function and parameters: prior = 1, log = TRUE, normalized.lib.sizes = TRUE.

Correspondence analyses were performed with MADE4 v1.64.0 (Culhane et al., 2005) . For all heatmap visualisations and where required, sample standardization was performed by normalization to the mean expression of each gene. K-means clustering was performed with R's (v4.0.2) base function kmeans with parameters: centers = 6, nstart = 25. K-means clustering was performed on the standardized log2CPM data (which was averaged between replicates prior to standardization).

Hierarchical clustering was performed utilizing base R's package stats (functions:

dist and hclust), with the distance measure canberra and linkage method Ward.D. A set seed of 123 was used. Dendrogram visualization was performed with dendexted v1.15.1 (parameter: k = 3) (Galili, 2015) ; 3D visualizations were performed with plotly v4.9.4.1 (Sievert, 2020) ; heatmap visualizations were performed with ComplexHeatmap v2.6.2 (Gu et al., 2016) ; all other visualizations were performed with ggplot2 v3.3.5 (Villanueva and Chen, 2019) and where required ggrepel v0.9.1 (Slowikowski et al., 2018) . Gene ontology and pathway analyses were performed with Metascape (http://metascape.org) (Zhou et al., 2019) .

To quantify the amount of expression of hSARS-CoV2 across all samples, the raw counts data was utilized which included genes from both the human and hSARS-CoV2 genes. The raw counts data was processed and visualised by the same procedures as stated above (Gene expression analyses of the human genome). Specifically, it filtered, normalization factors were calculated, log2 CPM counts and CPM (parameter: log = FALSE) counts were generated, as well as standardized expressions. For visualization purposes, the expression of hSARS-CoV2 genes across the respective genome were ordered by the genomic feature's starting base pair position.

As the libraries were generated with p(A) enrichment, to avoid multimapping of other genes with ACE2, we generated a custom GENCODE's GRCh38.p13 genomic reference files, where we removed the gene BMX. Additionally, we generated a custom hSARS-CoV2 (NC_045512v2) genomic reference files based on SwissProt precursor sequences (before cleavage) and Uniprot protein products (after cleavage) annotations. A custom genome combining these human and hSARS-CoV2 genomes was generated.

The protein products for annotation included: nsp1, nsp2, nsp3, nsp4, 3CL-PRO,

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CSL311, a novel, potent, therapeutic monoclonal antibody for the treatment of diseases mediated by the common beta chain of the IL-3, GM-CSF and IL-5 receptors

Vertical transmission of coronavirus disease 2019: severe acute respiratory syndrome coronavirus 2 RNA on the fetal side of the placenta in pregnancies with coronavirus disease 2019-positive mothers and neonates at birth

Host metabolism dysregulation and cell tropism identification in human airway and alveolar organoids upon SARS-CoV-2 infection

SARS-CoV-2 infection of human iPSC-derived cardiac cells reflects cytopathic features in hearts of patients with COVID-19

The expression and localization of the human placental prorenin/renin-angiotensin system throughout pregnancy: roles in trophoblast invasion and angiogenesis?

limma powers differential expression analyses for RNA-sequencing and microarray studies

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Interactive web-based data visualization with R, plotly, and shiny

Automatically position non-overlapping text labels with 'ggplot2

Dysregulation of Cell Signaling by SARS-CoV-2

In Vitro and Animal Models for SARS-CoV-2 research

Sambamba: fast processing of NGS alignment formats

Distribution of angiotensin-(1-7) and ACE2 in human placentas of normal and pathological pregnancies

Fetal and placental infection with SARS-CoV-2 in early pregnancy

ggplot2: elegant graphics for data analysis

Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in

China: a single-centered, retrospective, observational study

Recapitulation of SARS-CoV-2 infection and cholangiocyte damage with human liver ductal organoids

A pneumonia outbreak associated with a new coronavirus of probable bat origin

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

A Novel Coronavirus from Patients with Pneumonia in China